diff --git a/CHANGELOG.md b/CHANGELOG.md index 7083cc1269..7e0ccc43a6 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -10,6 +10,35 @@ Code freeze date: YYYY-MM-DD ### Dependency Changes +### Added + +- Better type hints and overloads signatures for `ImpactFuncSet` [#1250](https://github.com/CLIMADA-project/climada_python/pull/1250) +- Add inter- and extrapolation options to `ImpactFreqCurve` with method `interpolate` [#1252](https://github.com/CLIMADA-project/climada_python/pull/1252) +- Adds `MeasureConfig` and related dataclasses for new `Measure` object retrocompatibility and (de)serialization capabilities [#1276](https://github.com/CLIMADA-project/climada_python/pull/1276) +- Adds new `Measure` class, implementing adaptation measures as closures. + +### Changed +- Updated Impact Calculation Tutorial (`doc.climada_engine_Impact.ipynb`) [#1095](https://github.com/CLIMADA-project/climada_python/pull/1095). +- Makes current `measure` module a legacy module, moving it to `_legacy_measure`, to retain compatibility with `CostBenefit` class and various tests. [#1274](https://github.com/CLIMADA-project/climada_python/pull/1274) + +### Fixed + +- Fixed asset count in impact logging message [#1195](https://github.com/CLIMADA-project/climada_python/pull/1195). +- `Hazard.from_raster_xarray` now returns a sparse matrix instead of a sparse array [#1261](https://github.com/CLIMADA-project/climada_python/pull/1261). + +### Deprecated +- `Impact.calc_freq_curve()` should not be given the parameter `return_per`. Use the parameter `return_periods` in `Impact.calc_freq_curve().interpolate()` instead. + +### Removed +- `climada.util.earth_engine.py` Google Earth Engine methods did not facilitate direct use of GEE data in CLIMADA. Code was relocated to [climada-snippets](https://github.com/CLIMADA-project/climada-snippets). [#1109](https://github.com/CLIMADA-project/climada_python/pull/1109) +- `doc.climada_util_earth_engine.ipynb` Tutorial about GEE not relevant to CLIMADA Core. Tutorial notebook was relocated to [climada-snippets](https://github.com/CLIMADA-project/climada-snippets). [#1109](https://github.com/CLIMADA-project/climada_python/pull/1109) + +## 6.1.0 + +Release date: 2025-09-30 + +### Dependency Changes + Added: - `bayesian-optimization` >=1.5,<2.0 @@ -135,9 +164,9 @@ Removed: - `climada.entity.impact_funcs.trop_cyclone.ImpfSetTropCyclone.get_impf_id_regions_per_countries` function [#1034](https://github.com/CLIMADA-project/climada_python/pull/1034) - `climada.hazard.tc_tracks.BasinBoundsStorm` Enum class `climada.hazard.tc_tracks.subset_by_basin` function [#1031](https://github.com/CLIMADA-project/climada_python/pull/1031) - `climada.hazard.tc_tracks.TCTracks.subset_years` function [#1023](https://github.com/CLIMADA-project/climada_python/pull/1023) --`climada.hazard.tc_tracks.compute_track_density` function, `climada.hazard.tc_tracks.compute_genesis_density` function, `climada.hazard.plot.plot_track_density` function +- `climada.hazard.tc_tracks.compute_track_density` function, `climada.hazard.tc_tracks.compute_genesis_density` function, `climada.hazard.plot.plot_track_density` function [#1003](https://github.com/CLIMADA-project/climada_python/pull/1003) --`climada.hazard.tc_tracks.TCTracks.from_FAST` function, add Australia basin (AU) [#993](https://github.com/CLIMADA-project/climada_python/pull/993) +- `climada.hazard.tc_tracks.TCTracks.from_FAST` function, add Australia basin (AU) [#993](https://github.com/CLIMADA-project/climada_python/pull/993) - Add `osm-flex` package to CLIMADA core [#981](https://github.com/CLIMADA-project/climada_python/pull/981) - `doc.tutorial.climada_entity_Exposures_osm.ipynb` tutorial explaining how to use `osm-flex` with CLIMADA - `climada.util.coordinates.bounding_box_global` function [#980](https://github.com/CLIMADA-project/climada_python/pull/980) diff --git a/climada/_version.py b/climada/_version.py index b0826edc01..e3dc32b910 100644 --- a/climada/_version.py +++ b/climada/_version.py @@ -1 +1 @@ -__version__ = "6.0.2-dev" +__version__ = "6.1.1-dev" diff --git a/climada/conf/climada.conf b/climada/conf/climada.conf index 5fd35b1c42..d0573ff7a6 100644 --- a/climada/conf/climada.conf +++ b/climada/conf/climada.conf @@ -69,5 +69,6 @@ "cache_dir": "{local_data.system}/.apicache", "supported_hazard_types": ["river_flood", "tropical_cyclone", "storm_europe", "relative_cropyield", "wildfire", "earthquake", "flood", "hail", "aqueduct_coastal_flood"], "supported_exposures_types": ["litpop", "crop_production", "ssp_population", "crops"] - } + }, + "trajectory_caching": true } diff --git a/climada/data/system/entity_template.xlsx b/climada/data/system/entity_template.xlsx index 7f1bbbee10..3c2daed503 100644 Binary files a/climada/data/system/entity_template.xlsx and b/climada/data/system/entity_template.xlsx differ diff --git a/climada/engine/impact.py b/climada/engine/impact.py index d8c944c7b9..e8042afef3 100644 --- a/climada/engine/impact.py +++ b/climada/engine/impact.py @@ -576,15 +576,6 @@ def local_exceedance_impact( self.frequency_unit ) - # check method - if method not in [ - "interpolate", - "extrapolate", - "extrapolate_constant", - "stepfunction", - ]: - raise ValueError(f"Unknown method: {method}") - # calculate local exceedance impact test_frequency = 1 / np.array(return_periods) @@ -732,15 +723,6 @@ def local_return_period( self.frequency_unit ) - # check method - if method not in [ - "interpolate", - "extrapolate", - "extrapolate_constant", - "stepfunction", - ]: - raise ValueError(f"Unknown method: {method}") - return_periods = np.full((self.imp_mat.shape[1], len(threshold_impact)), np.nan) nonzero_centroids = np.where(self.imp_mat.getnnz(axis=0) > 0)[0] @@ -804,6 +786,12 @@ def calc_freq_curve(self, return_per=None): ifc_impact = self.at_event[sort_idxs][::-1] if return_per is not None: + warnings.warn( + "Calculating the frequency curve on user-specified return periods is deprecated. " + "Use ImpactFreqCurve.calc_freq_curve().interpolate() instead.", + DeprecationWarning, + stacklevel=2, + ) interp_imp = np.interp(return_per, ifc_return_per, ifc_impact) ifc_return_per = return_per ifc_impact = interp_imp @@ -2299,15 +2287,108 @@ def plot(self, axis=None, log_frequency=False, **kwargs): ------- matplotlib.axes.Axes """ + # check frequency unit + return_period_unit = u_dt.convert_frequency_unit_to_time_unit( + self.frequency_unit + ) + if not axis: _, axis = plt.subplots(1, 1) axis.set_title(self.label) axis.set_ylabel("Impact (" + self.unit + ")") + if log_frequency: axis.set_xlabel(f"Exceedance frequency ({self.frequency_unit})") axis.set_xscale("log") axis.plot(self.return_per**-1, self.impact, **kwargs) + else: - axis.set_xlabel("Return period (year)") + axis.set_xlabel(f"Return period ({return_period_unit})") axis.plot(self.return_per, self.impact, **kwargs) + return axis + + def interpolate( + self, + return_periods, + *, + method="interpolate", + log_frequency=True, + log_impact=True, + min_impact=0, + bin_decimals=None, + y_asymptotic=0.0, + ): + """Interpolate and extrapolate impact frequency curve using different methods. + + Parameters + ---------- + return_periods : Iterable[float] + return periods for which to evaluate the impact frequency curve + + method : str, optional + Method to interpolate to new return periods. Currently available are "interpolate", + "extrapolate", "extrapolate_constant" and "stepfunction". If set to "interpolate", + return periods outside the range of the Impact object's observed return periods + will be assigned NaN. If set to "extrapolate_constant" or "stepfunction", + return periods larger than the Impact object's observed return periods will be + assigned the largest impact, and return periods smaller than the Impact object's + observed return periods will be assigned 0. If set to "extrapolate", + exceedance impacts will be extrapolated (and interpolated). The extrapolation to + large return periods uses the two highest impacts of the centroid and their return + periods and extends the interpolation between these points to the given return period + (similar for small return periods). Defauls to "interpolate". + min_impact : float, optional + Minimum threshold to filter the impact. Defaults to 0. + log_frequency : bool, optional + If set to True, (cummulative) frequency values are converted to log scale before + inter- and extrapolation. Defaults to True. + log_impact : bool, optional + If set to True, impact values are converted to log scale before + inter- and extrapolation. Defaults to True. + bin_decimals : int, optional + Number of decimals to group and bin impact values. Binning results in smoother (and + coarser) interpolation and more stable extrapolation. For more details and sensible + values for bin_decimals, see Notes. If None, values are not binned. Defaults to None. + y_asymptotic : float, optional + Has no effect if method is "interpolate". Else, if data size < 2 or if method + is set to "extrapolate_constant" or "stepfunction", it provides return value for + exceeded impact for return periods smaller than the data range. Defaults to 0. + + Returns + ------- + ImpactFreqCurve + impact frequency curve with inter- and extrapolated values + + See Also + -------- + util.interpolation.preprocess_and_interpolate_ev : + inter- and extrapolation method + """ + exceedance_frequency = 1 / np.array(return_periods) + + # sort return periods of ImpactFreqCurve + sorted_idxs = np.argsort(self.return_per) + impacts = np.squeeze(np.array(self.impact)[sorted_idxs]) + rps = np.asarray(self.return_per)[sorted_idxs] + frequency = np.diff(1 / np.array(rps)[::-1], prepend=0)[::-1] + impact_interpolated = u_interp.preprocess_and_interpolate_ev( + exceedance_frequency, + None, + frequency, + impacts, + log_frequency=log_frequency, + log_values=log_impact, + value_threshold=min_impact, + method=method, + y_asymptotic=y_asymptotic, + bin_decimals=bin_decimals, + ) + + return ImpactFreqCurve( + return_per=return_periods, + impact=impact_interpolated, + unit=self.unit, + frequency_unit=self.frequency_unit, + label=self.label, + ) diff --git a/climada/engine/impact_calc.py b/climada/engine/impact_calc.py index 0586166173..0f35bc904b 100644 --- a/climada/engine/impact_calc.py +++ b/climada/engine/impact_calc.py @@ -186,7 +186,7 @@ def impact( return self._return_empty(save_mat) LOGGER.info( "Calculating impact for %s assets (>0) and %s events.", - exp_gdf.size, + len(exp_gdf), self.n_events, ) imp_mat_gen = self.imp_mat_gen(exp_gdf, impf_col) diff --git a/climada/engine/option_appraisal/MCDM/DecisionMatrix.py b/climada/engine/option_appraisal/MCDM/DecisionMatrix.py new file mode 100644 index 0000000000..02befd05e2 --- /dev/null +++ b/climada/engine/option_appraisal/MCDM/DecisionMatrix.py @@ -0,0 +1,1251 @@ +import copy +from typing import Dict, List, Optional + +import matplotlib.cm as cm +import matplotlib.colors as mcolors +import matplotlib.lines as mlines +import matplotlib.pyplot as plt +import matplotlib.ticker as mtick +import numpy as np +import pandas as pd +from pyrepo_mcda import correlations as corrs +from pyrepo_mcda import distance_metrics as dists +from pyrepo_mcda import normalizations as norms +from pyrepo_mcda import weighting_methods as mcda_weights + +# Importing additional functions from pyrepo_mcda +from pyrepo_mcda.additions import rank_preferences +from pyrepo_mcda.compromise_rankings import ( + copeland, + dominance_directed_graph, + rank_position_method, +) + +# Importing various methods from pyrepo_mcda +from pyrepo_mcda.mcda_methods import ( + AHP, + ARAS, + COCOSO, + CODAS, + COPRAS, + CRADIS, + EDAS, + MABAC, + MARCOS, + MULTIMOORA, + MULTIMOORA_RS, + PROMETHEE_II, + PROSA_C, + SAW, + SPOTIS, + TOPSIS, + VIKOR, + VIKOR_SMAA, + VMCM, + WASPAS, +) + +# Utility functions +from .MCDMoutput import RanksOutput +from .utils import filter_dataframe + +""" +ToDO: + + - Make the plot methods as for the CalcRank results + - E.g., Distribution of criteria values + - Make the calc conditional criteria value-at-risk + - Create a color attribute for each alternative. A dictionary of colors. + +""" +# Define the MCDM ranking methods +MCDM_DEFAULT = {"Topsis": TOPSIS(), "Saw": SAW(), "Vikor": VIKOR()} #'AHP': AHP(), + +# Define the compromised ranking function of the rank matrices +COMP_DEFAULT = { + "copeland": copeland, +} + + +class DecisionMatrix: + def __init__( + self, + metrics_df: pd.DataFrame, + objectives: Dict[str, int], + alt_cols: List[str], + crit_cols: List[str], + weights: Optional[Dict[str, float]] = None, + group_cols: Optional[List[str]] = [], + group_weights: Optional[Dict[str, float]] = None, + unc_cols: Optional[List[str]] = [], + unc_var_prob_dist: Optional[Dict[str, object]] = None, + crit_cats: Optional[Dict[str, List[str]]] = None, + ): + """ + Initialize the DecisionMatrix object. + + Parameters: + - metrics_df : pd.DataFrame + DataFrame containing metrics data. + - objectives : Dict[str, int] + Dictionary mapping objectives to their values. + - alt_cols : List[str] + List of alternative columns. + - crit_cols : List[str] + List of criteria columns. + - weights : Dict[str, float], optional + Dictionary of criteria weights values. Defaults to an empty dictionary. + - group_cols : List[str], optional + List of group columns. Defaults to an empty list. + - group_weights : Dict[str, float], optional + Dictionary of weights for group columns. Defaults to an empty dictionary. + - unc_cols : List[str], optional + List of uncertainty columns. Defaults to an empty list. + - crit_cats : Dict[str, List[str]], optional + Dictionary of categorized criteria. Defaults to an empty dictionary. + """ + + # Assign input parameters to class attributes as copies + self.metrics_df = metrics_df.copy() + self.objectives = copy.deepcopy(objectives) + self.alt_cols = alt_cols.copy() + self.crit_cols = crit_cols.copy() + self.weights = weights.copy() if weights is not None else {} + self.group_cols = group_cols.copy() if group_cols is not None else [] + self.group_weights = group_weights.copy() if group_weights is not None else {} + self.unc_cols = unc_cols.copy() if unc_cols is not None else [] + # self.unc_var_prob_dist = unc_var_prob_dist.copy() if unc_var_prob_dist is not None else {} + self.crit_cats = crit_cats.copy() if crit_cats is not None else {} + + # Initialize other attributes as None + self.dm_df = None + self.alternatives_df = None + self.crit_df = None + self.cat_crit_df = None + self.groups_df = None + self.unc_smpls_df = None + + # Sort the dm_df based on alt_cols, group_cols, and unc_cols + self.metrics_df = self.metrics_df.sort_values( + by=alt_cols + group_cols + unc_cols + ) + + # Create internal group weights + if group_cols: + self.groups_df = ( + self.metrics_df[self.group_cols] + .drop_duplicates() + .reset_index(drop=True) + ) + self.groups_df.insert( + 0, + "Group ID", + ["G" + str(idx) for idx in range(1, len(self.groups_df) + 1)], + ) + + # Create internal group weights + for group_col in group_cols: + temp_df = pd.DataFrame(metrics_df[group_col].drop_duplicates()) + temp_df["Weight"] = ( + np.nan + ) # Initialize weight to NaN for each member of the group + + # Populate the group weights if given + for idx, member in temp_df.iterrows(): + member_name = member[group_col] + if ( + member_name != "ALL" + and group_weights + and group_col in group_weights + and isinstance(group_weights[group_col], dict) + and member_name in group_weights[group_col] + ): + temp_df.at[idx, "Weight"] = group_weights[group_col][ + member_name + ] + + # Exclude 'ALL' members from count + temp_df = temp_df[temp_df[group_col] != "ALL"] + + # Calculate the sum of defined weights and count of remaining NaN values + sum_defined_weights = temp_df["Weight"].sum() + remaining_members = temp_df[temp_df["Weight"].isna()] + remaining_count = len(remaining_members) + + # Distribute the remaining weight equally among the members whose weights are not defined + if remaining_count > 0: + remainder = 1 - sum_defined_weights + equal_weight = remainder / remaining_count + temp_df.loc[temp_df["Weight"].isna(), "Weight"] = equal_weight + print( + f"Remaining weights distributed equally among members of group column '{group_col}'." + ) + + self.group_weights[group_col] = temp_df + + # Get unique set of samples if unc_cols is provided and reset index + if self.unc_cols: + self.unc_smpls_df = ( + self.metrics_df[self.unc_cols].drop_duplicates().reset_index(drop=True) + ) + self.unc_smpls_df.insert( + 0, + "Sample ID", + ["S" + str(idx) for idx in range(1, len(self.unc_smpls_df) + 1)], + ) + # Calculate default likelihood distribution for uncertainty columns + # for unc_col in self.unc_cols: + # if unc_col not in self.unc_var_prob_dist.keys(): + # outcomes = list(self.unc_smpls_df[unc_col].drop_duplicates().values) + # probabilities = np.ones(len(outcomes)) / len(outcomes) + # self.unc_var_prob_dist[unc_col] = dict(zip(outcomes, probabilities)) + + # Calculate criteria weights if not provided + # Assumes equal weights if not provided + if not self.weights: + # Allow for max to decimals for weights + def custom_round(value): + decimal_count = ( + len(str(value).split(".")[1]) if "." in str(value) else 0 + ) + decimals = 2 if decimal_count >= 2 else 1 + return round(value, decimals) + + self.weights = { + crit: custom_round(1 / len(self.crit_cols)) for crit in self.crit_cols + } + # Check for duplicate rows + if self.metrics_df.duplicated( + subset=self.alt_cols + self.group_cols + self.unc_cols, keep=False + ).any(): + raise ValueError( + "Duplicated rows of alt_cols, group_cols, and sample. Some alternative IDs are counted more than once for some group ID and sample ID pairs." + ) + + # Create crit_df (DataFrame containing criteria data) + data = [] + if self.crit_cols: + for idx, criteria in enumerate(self.crit_cols): + data.append( + { + "Criteria ID": "C" + str(idx + 1), + "Criteria": criteria, + "Weight": self.weights[criteria], + "Objective": self.objectives[criteria], + } + ) + self.crit_df = pd.DataFrame(data) + + # Create cat_crit_df (DataFrame containing categorized criteria data) + if not self.crit_cats: + self.crit_cats = {crit: [crit] for crit in self.crit_cols} + + data = [] + for idx, cat_set in enumerate(self.crit_cats.items()): + for criteria in cat_set[1]: + data.append( + { + "Cat ID": "CAT" + str(idx + 1), + "Category": cat_set[0], + "Criteria": criteria, + } + ) + self.cat_crit_df = pd.DataFrame(data) + self.cat_crit_df = self.cat_crit_df.merge(self.crit_df, on="Criteria") + + # Create alternatives_df (DataFrame containing alternatives data) + if self.alt_cols: + self.alternatives_df = self.metrics_df[self.alt_cols].drop_duplicates() + self.alternatives_df.insert( + 0, + "Alternative ID", + ["A" + str(idx) for idx in range(1, len(self.alternatives_df) + 1)], + ) + else: + raise ValueError("No alternative column given.") + + # Merge alternatives_df with groups and sample to create dm_df (Initialized DataFrame for decision-making) + self.dm_df = self.alternatives_df.copy() + self.dm_df["_merge"] = 1 + + if isinstance(self.groups_df, pd.DataFrame): + self.groups_df["_merge"] = 1 + self.dm_df = self.dm_df.merge(self.groups_df, on="_merge") + self.groups_df = self.groups_df.drop("_merge", axis=1) + + if isinstance(self.unc_smpls_df, pd.DataFrame): + self.unc_smpls_df["_merge"] = 1 + self.dm_df = self.dm_df.merge(self.unc_smpls_df, on="_merge") + self.unc_smpls_df = self.unc_smpls_df.drop("_merge", axis=1) + + self.dm_df = self.dm_df.drop("_merge", axis=1) + self.dm_df = pd.merge( + self.dm_df, + self.metrics_df[ + self.alt_cols + self.group_cols + self.unc_cols + self.crit_cols + ], + on=self.alt_cols + self.group_cols + self.unc_cols, + how="left", + ) + + def pivot_and_reweight_criteria(self, piv_col): + """ + Pivot and reweight criteria based on a specified pivot column and group weights. + + Parameters: + - piv_col: str + The column name to pivot the criteria data. + + Returns: + - new_dm: DecisionMatrix + A new instance of DecisionMatrix with pivoted criteria. + """ + + # Define pivot and index columns for pivot + index_col = [ + col + for col in self.alt_cols + self.unc_cols + self.group_cols + self.crit_cols + if col not in self.crit_cols + [piv_col] + ] + + # Filter out rows where the specified column is ALL or nan + filt_dm_df = self.dm_df[ + self.alt_cols + self.unc_cols + self.group_cols + self.crit_cols + ] + filt_dm_df = filt_dm_df[ + ~filt_dm_df[piv_col].isin(["ALL"]) & filt_dm_df[piv_col].notna() + ] + + crit_piv_df = filt_dm_df.pivot( + index=index_col, columns=piv_col, values=self.crit_cols + ) + + crit_piv_df = crit_piv_df.reset_index() + crit_piv_df.columns = [ + f'{"_".join(col)}' if col[1] else f"{col[0]}" for col in crit_piv_df.columns + ] + + # Step 3: Remove duplicates and create a copy of weights + new_weights = copy.deepcopy(self.weights) + new_crit_cats = {key: [] for key in self.crit_cats.keys()} + + group_values = list(filt_dm_df[piv_col].dropna().drop_duplicates()) + new_objectives = copy.deepcopy( + self.objectives + ) # Initialize new objectives for pivoted criteria + + for crit_col in self.crit_cols: + new_crit_cols_temp = [ + crit_col + "_" + group_value for group_value in group_values + ] + temp_df = crit_piv_df[new_crit_cols_temp] + + cat_crit = self.cat_crit_df[self.cat_crit_df["Criteria"].isin([crit_col])][ + "Category" + ].values[0] + + # Step 4: Check if all columns have the same values + if temp_df.apply(lambda col: col.equals(temp_df.iloc[:, 0])).all(): + print( + f"{crit_col}: All columns have the same values. Retain the original name." + ) + crit_piv_df = crit_piv_df.rename( + columns={new_crit_cols_temp[0]: crit_col} + ) + if len(new_crit_cols_temp) > 1: + crit_piv_df = crit_piv_df.drop(columns=new_crit_cols_temp[1:]) + # Update cat crits + new_crit_cats[cat_crit].append(crit_col) + else: + print( + f"{crit_col}: Columns have different values. Reweight and introduce new criteria." + ) + for group_value in group_values: + idx = self.group_weights[piv_col][piv_col].isin([group_value]) + group_weight = self.group_weights[piv_col]["Weight"][idx].values[0] + new_weights.update( + { + crit_col + + "_" + + group_value: new_weights[crit_col] * group_weight + } + ) + new_objectives[crit_col + "_" + group_value] = self.objectives[ + crit_col + ] + + # Update cat crits + new_crit_cats[cat_crit].append(crit_col + "_" + group_value) + + del new_weights[crit_col] + del new_objectives[crit_col] + + # Step 4 (continued): Reorder the dictionary as per crit_piv_df columns + new_weights = { + key: new_weights[key] + for key in crit_piv_df.columns + if key in new_weights.keys() + } + + # Step 4 (continued): Reorder the dictionary as per crit_piv_df columns + new_objectives = { + key: new_objectives[key] + for key in crit_piv_df.columns + if key in new_objectives.keys() + } + + # Step 4 (continued): Reorder the dictionary as per crit_piv_df columns + new_group_cols = [col for col in self.group_cols if col != piv_col] + new_crit_cols = list(new_objectives.keys()) + + # Step 5: Remove the group key from the group weights + new_group_weights = { + key: value for key, value in self.group_weights.items() if key != piv_col + } + + # Create a new DecisionMatrix instance with modified attributes + new_self = DecisionMatrix( + metrics_df=crit_piv_df, + objectives=new_objectives, + alt_cols=self.alt_cols, + crit_cols=new_crit_cols, + weights=new_weights, + group_cols=new_group_cols, + unc_cols=self.unc_cols, + # unc_var_prob_dist=self.unc_var_prob_dist, + crit_cats=new_crit_cats, + group_weights=new_group_weights, + # Include other necessary attributes for initialization of the new instance + ) + + return new_self + + def mean_based_criteria(self, condition={}, derived_columns=None): + """ + Apply criteria based on mean values of uncertain variables to the given data + and generate a new instance of DecisionMatrix. + + Parameters: + - condition (dict): Dictionary of conditions to filter the data. + - derived_columns (dict): Dictionary of derived columns to be calculated. + + Returns: + - new_dm (DecisionMatrix): New instance of DecisionMatrix with updated criteria. + """ + + # Create a copy of the decision matrix DataFrame + dm_df = self.dm_df.copy() + + # Define base column + base_cols = list(self.alternatives_df.columns) + ["Group ID"] + if isinstance(self.groups_df, pd.DataFrame): + base_cols += list(self.groups_df.columns) + else: + dm_df["Group ID"] = "G1" + + # Remove duplicates in base_cols + base_cols = list(dict.fromkeys(base_cols)) + + # Create a dataframe to store the results + new_dm_df = pd.DataFrame(columns=base_cols + self.crit_cols) + + # Apply constraints per group and state combo + for _, alt_group_df in ( + dm_df[["Alternative ID", "Group ID"]].drop_duplicates().iterrows() + ): + # Filter the group and state + sg_df = dm_df[ + dm_df[["Alternative ID", "Group ID"]] + .isin(alt_group_df[["Alternative ID", "Group ID"]].values) + .all(axis=1) + ] + + # Filter the dataframe based on the condition + filt_sg_df, _ = filter_dataframe( + sg_df, filter_conditions=condition, derived_columns=derived_columns + ) + + # Print the alternative and group that are all filtered out + if filt_sg_df.empty: + print( + f"The alternative {alt_group_df['Alternative ID']} in group {alt_group_df['Group ID']} did not satisfy the condition and is filtered out." + ) + continue + + # Calculate the mean of the criteria + mean_crits_temp_df = filt_sg_df[self.crit_cols].mean() + + # Add the mean_crits_temp_df columns and results to the base dataframe + base_temp_df = sg_df[base_cols].drop_duplicates() + base_temp_df = base_temp_df.assign(**mean_crits_temp_df) + + # add the base_temp_df to the new_dm_df + if new_dm_df.empty: + new_dm_df = base_temp_df + else: + new_dm_df = pd.concat([new_dm_df, base_temp_df], ignore_index=True) + + # If only one group is present, remove the group column + if len(new_dm_df["Group ID"].unique()) == 1: + new_dm_df = new_dm_df.drop(columns=["Group ID"]) + + # Create a new DecisionMatrix instance with modified attributes + new_self = DecisionMatrix( + metrics_df=new_dm_df, + objectives=self.objectives, + alt_cols=self.alt_cols, + crit_cols=self.crit_cols, + weights=self.weights, + group_cols=self.group_cols, + crit_cats=self.crit_cats, + group_weights=self.group_weights, + ) + + return new_self + + # def mean_based_criteria( + # self, + # unc_var_mean_based: List[str] + # ) -> 'DecisionMatrix': + # """ + # Apply criteria based on mean values of uncertain variables to the given data + # and generate a new instance of DecisionMatrix. + + # Parameters: + # - unc_var_mean_based (list): List of uncertain variables based on their means. + + # Returns: + # - new_self (DecisionMatrix): New instance of DecisionMatrix with updated criteria. + # """ + # mean_dict = {} + # prob_dict = {} + + # # Create containers to store mean values and probability distributions + # matched_rows_df = pd.DataFrame() + + # # Iterate through each uncertain variable to determine means or probability distributions + # for unc_var in unc_var_mean_based: + # dist = self.unc_var_prob_dist[unc_var] + # if isinstance(dist, dict): + # prob_dict[unc_var] = dist # Store probability distributions + # else: + # mean_dict[unc_var] = self.unc_var_prob_dist[unc_var].mean() # Calculate mean values + # matched_rows_df[unc_var] = abs(self.dm_df[unc_var] - mean_dict[unc_var]) < 1e-6 # Find matched rows + + # # Filter the DataFrame based on matched rows or use the entire DataFrame + # if len(matched_rows_df) > 0: + # mean_matched_dm_df = self.dm_df[matched_rows_df.all(axis=1)] # Filtered DataFrame + # columns_to_drop = ['Alternative ID', 'Group ID', 'Sample ID'] + # columns_existing = list(set(columns_to_drop) & set(mean_matched_dm_df.columns)) + # metrics_df = mean_matched_dm_df.drop(columns_existing, axis=1) # Drop specific columns + # else: + # metrics_df = self.dm_df.drop(['Alternative ID', 'Group ID', 'Sample ID'], axis=1) # Use entire DataFrame + + # # Identify and update uncertain columns and their respective probability distributions + # new_unc_cols = [unc_var for unc_var in self.unc_cols if unc_var not in unc_var_mean_based] + # new_unc_var_prob_dist = {unc_var: dist for unc_var, dist in self.unc_var_prob_dist.items() if + # unc_var not in unc_var_mean_based} + + # # Update criteria based on probability distributions + # if prob_dict: + # for var, dist in prob_dict.items(): + # piv_col = var + + # # Define pivot and index columns for pivot + # index_col = [col for col in self.alt_cols + self.unc_cols + self.group_cols + self.crit_cols if + # col not in self.crit_cols + [piv_col] and col in metrics_df.columns] + + # # Filter out rows where the specified column is ALL or nan + # columns = [col for col in self.alt_cols + self.unc_cols + self.group_cols + self.crit_cols if + # col in metrics_df.columns] + # filt_dm_df = metrics_df[columns] + # filt_dm_df = filt_dm_df[~filt_dm_df[piv_col].isin(['ALL']) & filt_dm_df[piv_col].notna()] + + # crit_piv_df = filt_dm_df.pivot(index=index_col, + # columns=piv_col, + # values=self.crit_cols) + + # # Reduce the pivoted criteria columns to the expected criteria value + # crit_piv_df = crit_piv_df.reset_index() + # crit_piv_df.columns = [f'{"_".join(col)}' if col[1] else f'{col[0]}' for col in crit_piv_df.columns] + + # for crit_col in self.crit_cols: + # # Create column + # crit_piv_df[crit_col] = 0 + # # Calculate each probability weigthed contribution + # for event, prob in dist.items(): + # crit_piv_df[crit_col] += crit_piv_df[crit_col + '_' + event] * prob + # crit_piv_df = crit_piv_df.drop(crit_col + '_' + event, axis=1) + + # # Update the metrics df + # metrics_df = crit_piv_df + + # # Initialize a new DecisionMatrix instance with updated attributes + # new_self = DecisionMatrix( + # metrics_df=metrics_df, + # objectives=self.objectives, + # alt_cols=self.alt_cols, + # crit_cols=self.crit_cols, + # weights=self.weights, + # group_cols=self.group_cols, + # unc_cols=new_unc_cols, + # unc_var_prob_dist=new_unc_var_prob_dist, + # crit_cats=self.crit_cats, + # group_weights=self.group_weights, + # # Include other necessary attributes for initialization of the new instance + # ) + + # return new_self + + def plot_criteria(self, group_by_category=True): + """ + Plots the weights of criteria. + + Parameters: + - group_by_category (bool): If True, the criteria will be grouped by category and displayed as a stacked bar plot. + If False, the criteria will be displayed as individual bars. + + Returns: + None + """ + + # Get a list of unique criteria + criteria = self.crit_df["Criteria"].unique() + + # Generate a list of unique colors + colors = list(mcolors.CSS4_COLORS.keys()) + colors.remove("black") # Remove 'black' from the list of colors + colors = colors[0 : len(criteria)] + + # Make colors for each criteria and store in dictionary + criteria_colors = dict(zip(criteria, colors)) + + if group_by_category: + # Create a bar plot from the pivoted DataFrame + df = self.cat_crit_df.pivot( + index="Category", columns="Criteria", values="Weight" + ) + ax = df.plot( + kind="bar", + stacked=True, + figsize=(15, 8), + color=[criteria_colors[crit] for crit in df.columns], + ) + # Create an array to store the cumulative height of the bars + cumulative_height = np.zeros(len(df)) + # Iterate over each bar (patch) in the plot + for i, p in enumerate(ax.patches): + # Calculate the index of the current bar in its stack + bar_index = i % len(df) + # Update the cumulative height of the bars in the current stack + cumulative_height[bar_index] += p.get_height() + # Only annotate bars with a height greater than zero + if p.get_height() > 0: + # Annotate the height (weights value) of each bar on the plot + # The coordinates given are (x, y) where x is the bar's x coordinate and y is the cumulative height of the bars in the stack minus half the bar's height + ax.annotate( + str(round(p.get_height(), 2)), + ( + p.get_x() + p.get_width() / 2.0, + cumulative_height[bar_index] - p.get_height() / 2, + ), + ha="center", + va="center", + ) + + # Set the x-axis label + ax.set_xlabel("Criteria categories", fontsize=12) + # Set the x-axis labels to be truncated and tilted + ax.set_xticklabels([label[:10] for label in df.index], rotation=45) + # Set the legend + plt.legend( + bbox_to_anchor=(0.0, 1.02, 1.0, 0.102), + loc="lower left", + ncol=4, + mode="expand", + borderaxespad=0.0, + edgecolor="black", + title="Criteria", + fontsize=12, + ) + + else: + ax = self.crit_df.plot( + x="Criteria", + y="Weight", + kind="bar", + figsize=(15, 8), + color=[criteria_colors[crit] for crit in self.crit_df["Criteria"]], + title="Weight of criteria", + legend=False, + ) + for p in ax.patches: + # Only annotate bars with a height greater than zero + if p.get_height() > 0: + # Annotate the height (weights value) of each bar on the plot + # The coordinates given are (x, y) where x is the bar's x coordinate and y is half the bar's height + ax.annotate( + str(round(p.get_height(), 2)), + (p.get_x() + p.get_width() / 2.0, p.get_height() / 2), + ha="center", + va="center", + ) + ax.set_xlabel("Criteria", fontsize=12) + # Set the x-axis labels to be truncated and tilted + ax.set_xticklabels( + [label[:10] for label in self.crit_df["Criteria"]], rotation=45 + ) + + ax.set_ylabel("Weight", fontsize=12) + ax.set_axisbelow(True) + ax.grid(True, linestyle=":") + plt.tight_layout() + plt.show() + + return + + def calc_rankings( + self, + mcdm_methods=MCDM_DEFAULT, + comp_ranks=COMP_DEFAULT, + constraints={}, + rank_filt={}, + derived_columns=None, + ): + """ + Calculate rankings for a DecisionMatrix instance using specified Multi-Criteria Decision Making (MCDM) methods. + + Parameters: + - mcdm_methods: dict, optional + Dictionary of MCDM methods to use for ranking. Defaults to the MCDM_DEFAULT dictionary. + - comp_ranks: dict, optional + Dictionary of compromised ranking functions to use. Defaults to the COMP_DEFAULT dictionary. + - constraints: dict, optional + Dictionary of constraints to filter the data. Defaults to an empty dictionary. + - rank_filt: dict, optional + Dictionary of filters to apply to the ranking. Defaults to an empty dictionary. + - derived_columns: dict, optional + Dictionary of derived columns to calculate. Defaults to an empty dictionary. + + Returns: + - ranks_output: RanksOutput + An instance of the RanksOutput class containing the rankings. + """ + + # provide criteria weights in array numpy.darray. All weights must sum to 1. + weights = np.array([self.weights[crit_col] for crit_col in self.crit_cols]) + # provide criteria types in array numpy.darray. Profit criteria are represented by 1 and cost criteria by -1. + types = np.array([self.objectives[crit_col] for crit_col in self.crit_cols]) + + # Create a copy of the decision matrix DataFrame + red_dm_df = self.dm_df.copy() + # Check if both 'Group ID' and 'Sample ID' columns exist + if "Group ID" not in red_dm_df.columns: + red_dm_df["Group ID"] = "G1" + if "Sample ID" not in red_dm_df.columns: + red_dm_df["Sample ID"] = "S1" + # Pre-filter which sceanrio, groups and + red_dm_df, _ = filter_dataframe( + red_dm_df, filter_conditions=rank_filt, derived_columns=derived_columns + ) + + ## Create data frames to store data + # Define base column + base_cols = list(self.alternatives_df.columns) + if isinstance(self.groups_df, pd.DataFrame): + base_cols += list(self.groups_df.columns) + if isinstance(self.unc_smpls_df, pd.DataFrame): + base_cols += list(self.unc_smpls_df.columns) + # Alternatives not included + alt_exc_nan_df = pd.DataFrame( + columns=self.dm_df.columns + ) # To store nan alternatives + alt_exc_const_df = pd.DataFrame( + columns=base_cols + list(constraints.keys()) + ) # To store nan alternatives + # rank containers + ranks_crit_df = pd.DataFrame(columns=base_cols + self.crit_cols) + # ranks_mcdm_methods_df = pd.DataFrame(columns=base_cols + list(mcdm_methods.keys())) + # ranks_comp_df = pd.DataFrame(columns=base_cols + list(comp_ranks.keys())) + ranks_MCDM_df = pd.DataFrame( + columns=base_cols + list(mcdm_methods.keys()) + list(comp_ranks.keys()) + ) + + # Check if crit_cols contains any zero values + if red_dm_df[self.crit_cols].isin([0]).any().any(): + # Iterate through MCDM methods + for method_name, method_instance in mcdm_methods.items(): + if isinstance(method_instance, (ARAS, CODAS, CRADIS)): + print( + f"Warning: {method_name} is of type {type(method_instance)}, which may require special handling due to zero values in some criteria columns. Recmonedeation is to replace the zero values with negligaibel numbers.", + 3 * "...\n", + ) + + # Iterate through all pairs of 'Group ID' and 'Sample ID' + for _, group_scen_df in ( + red_dm_df[["Group ID", "Sample ID"]].drop_duplicates().iterrows() + ): + + # Check if both columns exist + sg_df = red_dm_df[ + red_dm_df[["Group ID", "Sample ID"]] + .isin(group_scen_df[["Group ID", "Sample ID"]].values) + .all(axis=1) + ] + + # Store all not included alternatives + # due to NaN values + nan_alt_rows = sg_df[self.crit_cols].isna().any(axis=1) + if nan_alt_rows.any(): + # Check if empty or all-NA rows + if alt_exc_nan_df.empty: + alt_exc_nan_df = sg_df[nan_alt_rows] + else: + alt_exc_nan_df = pd.concat( + [alt_exc_nan_df, sg_df[nan_alt_rows]], ignore_index=True + ) + sg_df.reset_index(drop=True, inplace=True) + nan_alt_rows.reset_index(drop=True, inplace=True) + sg_df = sg_df[~nan_alt_rows] + + # Store all not included alternatives + # due to NaN values + # nan_alt_rows = sg_df[self.crit_cols].isna().any(axis=1) + # if nan_alt_rows.any(): + # alt_exc_nan_df = pd.concat([alt_exc_nan_df, sg_df[nan_alt_rows]], ignore_index=True) + # sg_df.reset_index(drop=True, inplace=True) + # nan_alt_rows.reset_index(drop=True, inplace=True) + # sg_df = sg_df[~nan_alt_rows] + + if constraints: + sg_df, boolean_df = filter_dataframe( + sg_df, + filter_conditions=constraints, + derived_columns=derived_columns, + base_cols=base_cols, + ) + alt_exc_const_df = pd.concat( + [ + alt_exc_const_df, + boolean_df[ + ~(boolean_df[constraints.keys()] == True).all(axis=1) + ], + ], + ignore_index=True, + ) + + # Find the smallest negative number in each column and add its absolute value to the column + # matrix_df = sg_df[self.crit_cols].copy() + # matrix_df = matrix_df.apply(lambda col: col + abs(col.min()) if col.min() < 0 else col) + + # Convert the DataFrame back to a numpy array + # matrix = matrix_df.to_numpy() + # add a random small positive values to each element of the matrix + # matrix = matrix + np.random.rand(*matrix.shape) * 1e-9 + + # Find the smallest number in each column and add its absolute value plus one to the column + matrix_df = sg_df[self.crit_cols].copy() + matrix_df = matrix_df.apply(lambda col: col + abs(col.min()) + 1) + + # Convert the DataFrame back to a numpy array + matrix = matrix_df.to_numpy() + + # Add a random small positive value to each element of the matrix + matrix = matrix + np.random.rand(*matrix.shape) * 1e-4 + + if matrix.any(): + + # Temp container + temp_ranks_MCDM_df = sg_df[base_cols].copy() + + ## Calc ranking for each MCDM method + for pipe in mcdm_methods.keys(): + + # Calculate the preference values of alternatives + if not isinstance(mcdm_methods[pipe], SPOTIS): + pref = mcdm_methods[pipe](matrix, weights, types) + else: + # SPOTIS preferences must be sorted in ascending order + bounds_min = np.amin(matrix, axis=0) + bounds_max = np.amax(matrix, axis=0) + bounds = np.vstack((bounds_min, bounds_max)) + # Calculate the preference values of alternatives + pref = mcdm_methods[pipe](matrix, weights, types, bounds) + + # Generate ranking of alternatives by sorting alternatives descendingly according to the TOPSIS algorithm (reverse = True means sorting in descending order) according to preference values + if isinstance(mcdm_methods[pipe], (MULTIMOORA)): + temp_ranks_MCDM_df.loc[~nan_alt_rows, pipe] = mcdm_methods[ + pipe + ]( + matrix, weights, types + ) # Mu;timoora includes ranker + elif isinstance(mcdm_methods[pipe], (VIKOR, SPOTIS)): + temp_ranks_MCDM_df.loc[~nan_alt_rows, pipe] = rank_preferences( + pref, reverse=False + ) + else: + temp_ranks_MCDM_df.loc[~nan_alt_rows, pipe] = rank_preferences( + pref, reverse=True + ) + + # Calc compromised ranking + if comp_ranks: + for comp_rank in comp_ranks.keys(): + temp_ranks_MCDM_df.loc[~nan_alt_rows, comp_rank] = comp_ranks[ + comp_rank + ]( + temp_ranks_MCDM_df.loc[ + ~nan_alt_rows, mcdm_methods.keys() + ].to_numpy() + ) + + # Populate the containers + # Exclude empty or all-NA columns before concatenation + # Check if temp_ranks_MCDM_df is empty + temp_ranks_MCDM_df = temp_ranks_MCDM_df.dropna(how="all", axis=1) + if temp_ranks_MCDM_df.empty: + pass + elif ranks_MCDM_df.empty: + ranks_MCDM_df = temp_ranks_MCDM_df + else: + ranks_MCDM_df = pd.concat( + [ranks_MCDM_df, temp_ranks_MCDM_df], ignore_index=True + ) + ranks_crit_df = pd.concat( + [ + ranks_crit_df, + ranks_columns( + sg_df, columns=self.crit_cols, objectives=self.objectives + ), + ], + ignore_index=True, + ) # Calc criteria ranking + ranks_df = pd.merge(ranks_crit_df, ranks_MCDM_df) + + # Check if ranks_MCDM_df is empty + if ranks_MCDM_df.empty: + print("No alternatives to rank.") + return + # Store all not included alternatives with ranking zero + # Get all columns from list(mcdm_methods.keys()) that have a value of zero in ranks_MCDM_df + # zero = ranks_MCDM_df.columns[ranks_MCDM_df.isin([0]).any()].tolist() + + # TODO: Quick fix to add Group ID and Sample ID to the ranks_MCDM_df + if "Group ID" not in base_cols: + ranks_MCDM_df["Group ID"] = "G1" + if "Sample ID" not in base_cols: + ranks_MCDM_df["Sample ID"] = "S1" + + return RanksOutput( + ranks_df, + ranks_crit_df, + ranks_MCDM_df, + alt_exc_nan_df, + alt_exc_const_df, + list(mcdm_methods.keys()), + list(comp_ranks.keys()), + self, + ) + + def calc_imprt_sensitivity( + self, + mcdm_methods, + comp_ranks={}, + crit_cols_dict={}, + cat_crit_dict={}, + imp_tot=np.linspace(0, 1, 11), + crit_tag="Criteria", + alt_tag="Alternative ID", + **ranking_args, + ): + """ + Calculate the sensitivity of the rankings to the weights of the criteria. + + Parameters: + - mcdm_methods (dict): + A dictionary with MCDM method names as keys and ranking functions as values. + - comp_ranks (dict): + A dictionary specifying compromise ranking methods. + - crit_cols_dict (dict): + A dictionary specifying the criteria columns to use for the sensitivity analysis. + - cat_crit_dict (dict): + A dictionary specifying the category criteria to use for the sensitivity analysis. + - imp_tot (numpy.ndarray): + An array of total importance values to use for the sensitivity analysis. + - crit_tag (str): + The tag to use for the criteria column. + - alt_tag (str): + The tag to use for the alternative column. + - **ranking_args: + Additional keyword arguments for the ranking methods. + + Returns: + - ranks_imp_df (pd.DataFrame): + A DataFrame containing the rankings at the highest weights. + - imp_sens_df (pd.DataFrame): + A DataFrame containing the sensitivity of the weights values. + """ + + # Get the criteria dataframe from the decision matrix object + cat_crit_df = self.cat_crit_df + crit_df = self.crit_df + + # Check if a category criteria dictionary is provided + if cat_crit_dict: + # If so, create a group dataframe based on the category criteria + crit_group_df = cat_crit_df[ + cat_crit_df[cat_crit_dict.keys()] + .isin(cat_crit_dict.values()) + .all(axis=1) + ][[crit_tag, "Weight"]] + # Create a non-group dataframe for the remaining criteria + crit_non_group_df = cat_crit_df[ + ~cat_crit_df[cat_crit_dict.keys()] + .isin(cat_crit_dict.values()) + .all(axis=1) + ][[crit_tag, "Weight"]] + # store the items in xlabel as strings + xlabel = list(cat_crit_dict.items())[0][1] + else: + # If not, create a group dataframe based on the criteria columns dictionary + crit_group_df = crit_df[ + crit_df[crit_tag].isin(list(crit_cols_dict.items())[0][1]) + ][[crit_tag, "Weight"]] + # Create a non-group dataframe for the remaining criteria + crit_non_group_df = crit_df[ + ~crit_df[crit_tag].isin(list(crit_cols_dict.items())[0][1]) + ][[crit_tag, "Weight"]] + xlabel = list(crit_cols_dict.items())[0][0] + + # Create a dataframe to store the sensitivity of the weights values + imp_sens_df = pd.DataFrame( + index=imp_tot, + columns=list(crit_group_df[crit_tag].unique()) + + list(crit_non_group_df[crit_tag].unique()), + ) + + # Iterate over the total weights values + for imp in imp_tot: + # Create a new group weights dataframe + new_group_weights = crit_group_df.copy() + # Update the weights values based on the current total weights + new_group_weights["Weight"] = ( + imp * crit_group_df["Weight"] / crit_group_df["Weight"].sum() + ) + # Create a new non-group weights dataframe + new_non_group_weights = crit_non_group_df.copy() + # Update the weights values based on the current total weights + new_non_group_weights["Weight"] = ( + (1 - imp) + * crit_non_group_df["Weight"] + / crit_non_group_df["Weight"].sum() + ) + # Update the sensitivity dataframe with the new weights values + imp_sens_df.loc[imp, new_group_weights[crit_tag]] = new_group_weights[ + "Weight" + ].values + imp_sens_df.loc[imp, new_non_group_weights[crit_tag]] = ( + new_non_group_weights["Weight"].values + ) + + # Check if compromise ranks are provided + if comp_ranks: + # If so, get the ranking method name + rank_method_name = list(comp_ranks.keys())[0] + elif len(mcdm_methods) != 1: + # If not, check if only one MCDM method is provided + print( + "You need to specify a compromise ranking method or only one MCDM method" + ) + else: + # If only one MCDM method is provided, get the method name + rank_method_name = list(mcdm_methods.keys())[0] + + # Iterate over the rows in the weights sensitivity dataframe + for idx, row in enumerate(imp_sens_df.iterrows()): + # Get the new weights values from the current row + new_weights = row[1].to_dict() + # Create a temporary decision matrix with the new weights values + dm_temp = DecisionMatrix( + metrics_df=self.metrics_df, + objectives=self.objectives, + alt_cols=self.alt_cols, + crit_cols=list(self.objectives.keys()), + weights=new_weights, + ) + # Calculate the rankings with the temporary decision matrix + rank_obj_temp = dm_temp.calc_rankings( + mcdm_methods=mcdm_methods, comp_ranks=comp_ranks, **ranking_args + ) + + # Check if the rank object contains more than one group or sample + if ( + len(rank_obj_temp.ranks_df["Group ID"].unique()) != 1 + or len(rank_obj_temp.ranks_df["Sample ID"].unique()) != 1 + ): + raise ValueError( + "The rank object contains more than one group or sample" + ) + + # Store the rankings in a results dataframe + if idx == 0: + # If it's the first row, create a new dataframe + ranks_imp_df = rank_obj_temp.ranks_df[ + [alt_tag, rank_method_name] + ].copy() + ranks_imp_df["Weight"] = row[0] + else: + # If it's not the first row, create a temporary dataframe and append it to the results dataframe + ranks_imp_df_temp = rank_obj_temp.ranks_df[ + [alt_tag, rank_method_name] + ].copy() + ranks_imp_df_temp["Weight"] = row[0] + ranks_imp_df = pd.concat([ranks_imp_df, ranks_imp_df_temp], axis=0) + + # Plot the rankings at the highest weights + plot_rank_sens_weights( + ranks_imp_df, alt_tag, rank_method_name, xlabel, order_by="highest" + ) + # Plot the weights sensitivity dataframe + plot_crit_weights_sensitivity(imp_sens_df, xlabel) + # Plot the rankings at the lowest weights + # plot_rank_sens_weights(ranks_imp_df, alt_tag, rank_method_name, xlabel, order_by='lowest') + + # Return the results dataframe and the weights sensitivity dataframe + return ranks_imp_df, imp_sens_df + + +def ranks_columns(df, columns, objectives): + """ + Rank specified columns in a DataFrame according to provided ranking objectives. + + Parameters: + df (pandas.DataFrame): + The DataFrame containing the data to be ranked. + columns (list of str): + A list of column names to be ranked. + objectives (dict of {str: callable}): + A dictionary with column names as keys and ranking objective functions as values. + + Returns: + pandas.DataFrame: A new DataFrame with the specified columns ranked according to the objectives. + """ + + # Mapping for function selection + FUNCTION_MAP = {"1": False, "-1": True} + + # Copy the input DataFrame + ranked_df = df.copy() + + # Iterate over the columns to be ranked + for col in columns: + # Rank the column based on the specified objective + ranked_df[col] = ranked_df[col].rank( + method="min", ascending=FUNCTION_MAP[str(objectives[col])] + ) + # Convert the ranks to integers + ranked_df[col] = ranked_df[col].astype(int) + + return ranked_df + + +def plot_crit_weights_sensitivity(imp_sens_df, xlabel): + # Plot the DataFrame as a stacked bar plot + ax = imp_sens_df.plot(kind="bar", stacked=True, figsize=(12, 6)) + + # Set the x-axis label + ax.set_xlabel(f"Weight of {xlabel}", fontsize=14) + + # Set the y-axis label + ax.set_ylabel("Criteria Weight", fontsize=14) + + # Set x-axis ticks to be in percentage format with no decimals + ax.set_xticklabels( + [f"{int(tick*100)}%" for tick in imp_sens_df.index], rotation=0, fontsize=12 + ) + + # Format the y-axis labels to be in percentage format with no decimals + ax.yaxis.set_major_formatter(mtick.PercentFormatter(xmax=1.0, decimals=0)) + + # Place the legend on the right side of the plot and set its title to "Criteria" + ax.legend(loc="center left", bbox_to_anchor=(1, 0.5), title="Criteria", fontsize=12) + + # Show the plot + plt.tight_layout() + plt.show() + + +def plot_rank_sens_weights( + ranks_imp_df, alt_tag, rank_method_name, xlabel, order_by="highest" +): + # Pivot the DataFrame to make each alternative a column + plot_df = ranks_imp_df.pivot( + index="Weight", columns=alt_tag, values=rank_method_name + ) + + # Define a color map + color_map = cm.get_cmap("tab10", len(plot_df.columns)) + + # Create a dictionary that maps each column name to a specific color + color_dict = {col: color_map(i) for i, col in enumerate(plot_df.columns)} + + # Reorder the columns according to the rank at the highest or lowest weights + if order_by == "highest": + plot_df = plot_df[plot_df.iloc[-1].sort_values(ascending=False).index] + legend_loc = (1.05, 0.5) + elif order_by == "lowest": + plot_df = plot_df[plot_df.iloc[0].sort_values(ascending=False).index] + legend_loc = (-0.3, 0.5) + + # Plot the DataFrame with the color map + ax = plot_df.plot( + kind="line", + grid=True, + figsize=(12, 6), + color=[color_dict[col] for col in plot_df.columns], + ) + + # Set the x-axis label + ax.set_xlabel(f"Total weights of {xlabel}", fontsize=14) + + # Set the y-axis label + ax.set_ylabel("Rank", fontsize=14) + + # Format the x-axis labels to be in percentage format with no decimals + ax.xaxis.set_major_formatter(mtick.PercentFormatter(1.0, decimals=0)) + + # Set the y-axis limits + ax.set_ylim(0, plot_df.max().max() + 1) + + # set the x-axis limits + ax.set_xlim(plot_df.index[0], plot_df.index[-1]) + + # Set the y-ticks to be from 1 to the maximum rank number + ax.yaxis.set_ticks(range(1, int(plot_df.max().max() + 2))) + + # Enable the grid for each y-tick value + ax.yaxis.grid(True) + + # Create a custom legend for the rank at the highest or lowest weights + lines = [ + mlines.Line2D( + [], + [], + color=color_dict[col], + label=f'{col} ({int(plot_df.iloc[-1 if order_by == "highest" else 0, i])})', + ) + for i, col in enumerate(plot_df.columns) + ] + legend = plt.legend( + handles=lines, + bbox_to_anchor=legend_loc, + loc="center left", + borderaxespad=0.0, + edgecolor="black", + fontsize=14, + title=f'Rank at {int(plot_df.index[-1 if order_by == "highest" else 0]*100)}%', + ) + + # Show the plot + plt.tight_layout() + plt.show() diff --git a/climada/engine/option_appraisal/MCDM/MCDMoutput.py b/climada/engine/option_appraisal/MCDM/MCDMoutput.py new file mode 100644 index 0000000000..b8ab230f0d --- /dev/null +++ b/climada/engine/option_appraisal/MCDM/MCDMoutput.py @@ -0,0 +1,337 @@ +# I want to make a data frame container class that stores the following four data frames ranks_df, ranks_crit_df, ranks_MCDM_df, alt_exc_nan_df, alt_exc_const_df and has the following methods: + + +import matplotlib.pyplot as plt +import numpy as np + +# Importing the libraries +import pandas as pd +from tabulate import tabulate + +from .utils import filter_dataframe + + +# make a class +class RanksOutput: + def __init__( + self, + ranks_df, + ranks_crit_df, + ranks_MCDM_df, + alt_exc_nan_df, + alt_exc_const_df, + mcdm_cols, + comp_rank_cols, + dm, + ): + self.ranks_df = ranks_df + self.ranks_crit_df = ranks_crit_df + self.ranks_MCDM_df = ranks_MCDM_df + self.alt_exc_nan_df = alt_exc_nan_df + self.alt_exc_const_df = alt_exc_const_df + + self.mcdm_cols = mcdm_cols + self.comp_rank_cols = comp_rank_cols + + self.dm = dm + + # Check if self.dm.unc_smpls_df is not None + if isinstance(self.dm.unc_smpls_df, pd.DataFrame): + self.counts_rank_df, self.rel_counts_rank_df = calculate_counts( + self.dm.crit_cols, + mcdm_cols, + comp_rank_cols, + self.dm.unc_smpls_df, + ranks_df, + ) + else: + self.counts_rank_df = None + self.rel_counts_rank_df = None + + # make a method to plot the ranks + def plot_ranks( + self, + rank_type="MCDM", + alt_name_col="Alternative ID", + disp_rnk_cols=[], + sort_by_col=None, + transpose=False, + group_id="G1", + state_id="S1", + ): + + # Get the disp_rnk_cols + if disp_rnk_cols: + legend_title = "Rank columns" + df = self.ranks_df + elif rank_type == "criteria": + legend_title = "Criteria" + df = self.ranks_crit_df + disp_rnk_cols = self.dm.crit_cols + elif rank_type == "MCDM": + legend_title = "MCDM method" + df = self.ranks_MCDM_df + disp_rnk_cols = self.mcdm_cols + self.comp_rank_cols + + # Filter out based on group_id and state_id + df = df[df["Group ID"] == group_id] + df = df[df["Sample ID"] == state_id] + + # Filter out the columns + df = df[[alt_name_col] + disp_rnk_cols] + + # Store number of ranks + step = 1 + list_rank = np.arange(1, len(df) + 1, step) + + # Sort the columns + if sort_by_col: + df = df.sort_values(by=sort_by_col, ascending=True) + + # Check if transpose + if not transpose: + df = df.set_index(alt_name_col) + else: + df = df.set_index(alt_name_col).transpose() + # Rename the index + df.index.name = "Rank columns" + # Rename the legend title + legend_title = alt_name_col + + # Plot the dataframe + ax = df.plot( + kind="bar", width=0.8, stacked=False, edgecolor="black", figsize=(15, 8) + ) + ax.set_xlabel(df.index.name, fontsize=12) + ax.set_ylabel("Rank", fontsize=12) + ax.set_yticks(list_rank) + + # Make rotation of the labels tilted 45 degrees and truncate to the first 10 characters + ax.set_xticklabels([label[:10] for label in df.index], rotation=45) + ax.tick_params(axis="both", labelsize=12) + y_ticks = ax.yaxis.get_major_ticks() + ax.set_ylim(0, len(list_rank) + 1) + + # Legend + plt.legend( + bbox_to_anchor=(0.0, 1.02, 1.0, 0.102), + loc="lower left", + ncol=4, + mode="expand", + borderaxespad=0.0, + edgecolor="black", + fontsize=12, + title=legend_title, + ) + + ax.grid(True, linestyle=":") + ax.set_axisbelow(True) + plt.tight_layout() + plt.show() + + # make a print function + def print_rankings( + self, disp_filt={}, disp_rnk_cols=[], rank_type="MCDM", sort_by_col=None + ): + + # Filter the rank columns + filt_rank_df = filter_dataframe(self.ranks_df, disp_filt)[0] + + # Get the disp_rnk_cols + if disp_rnk_cols: + pass + elif rank_type == "criteria": + disp_rnk_cols = self.dm.crit_cols + elif rank_type == "MCDM": + disp_rnk_cols = self.mcdm_cols + self.comp_rank_cols + + # Define base column + base_cols = list(self.dm.alternatives_df.columns) + ["Group ID", "Sample ID"] + if isinstance(self.dm.groups_df, pd.DataFrame): + base_cols += list(self.dm.groups_df.columns) + if isinstance(self.dm.unc_smpls_df, pd.DataFrame): + base_cols += list(self.dm.unc_smpls_df.columns) + + # Remove duplicates in base_cols + base_cols = list(dict.fromkeys(base_cols)) + + # Ranking columns to print + filt_rank_df = filt_rank_df[base_cols + disp_rnk_cols] + + # Print the rankings per group and state combo + for _, group_scen_df in ( + filt_rank_df[["Group ID", "Sample ID"]].drop_duplicates().iterrows() + ): + # Print if there are more than one group and state + if ( + len(filt_rank_df[["Group ID"]].drop_duplicates()) > 1 + and len(filt_rank_df[["Sample ID"]].drop_duplicates()) > 1 + ): + group_id = group_scen_df["Group ID"] + scen_id = group_scen_df["Sample ID"] + print(f"Group: {group_id}, State: {scen_id}") + print("-----------------------------------") + elif len(filt_rank_df[["Group ID"]].drop_duplicates()) > 1: + group_id = group_scen_df["Group ID"] + print(f"Group: {group_id}") + print("-----------------------------------") + elif len(filt_rank_df[["Sample ID"]].drop_duplicates()) > 1: + scen_id = group_scen_df["Sample ID"] + print(f"State: {scen_id}") + print("-----------------------------------") + + # Filter the group and state + sg_df = filt_rank_df[ + filt_rank_df[["Group ID", "Sample ID"]] + .isin(group_scen_df[["Group ID", "Sample ID"]].values) + .all(axis=1) + ] + + # For the print exclude the group and state columns and the index column and sort by sort_by_col + if sort_by_col: + print_df = sg_df.drop(["Group ID", "Sample ID"], axis=1).sort_values( + by=sort_by_col, ascending=True + ) + else: + print_df = sg_df.drop(["Group ID", "Sample ID"], axis=1) + print_df = print_df.set_index("Alternative ID") + print(tabulate(print_df, headers="keys", tablefmt="psql")) + print("\n") + + def plot_rank_distribution( + self, disp_rnk_col, alt_name_col="Alternative ID", sort_by_perf=True + ): + # Assuming df is your DataFrame and it's already been prepared as needed + pivot_df = self.rel_counts_rank_df.pivot( + index=alt_name_col, columns="Rank_Count", values=disp_rnk_col + ) + + # Move column with 0 rank to the end + pivot_df = pivot_df[[col for col in pivot_df.columns if col != 0] + [0]] + # Rename the column to null + pivot_df.rename(columns={0: "null"}, inplace=True) + + # Normalize the data to get percentages and multiply by 100 + pivot_df = pivot_df.div(pivot_df.sum(axis=1), axis=0) * 100 + if sort_by_perf: + # Calculate the mean for each alternative based on multplying the column value with the cell calue for each row + # exclude the last column which is the 0 rank + pivot_df["mean"] = pivot_df.apply( + lambda row: np.mean(row[:-1] * pivot_df.columns[:-1]), axis=1 + ) + # Sort the pivoted data frame based on the sorted_cum_sum_df + sorted_pivot_df = pivot_df.sort_values(by="mean", ascending=True).drop( + "mean", axis=1 + ) + + # Create a colormap + cmap = plt.get_cmap("plasma") # Changed to a more contrasting colormap + colors = cmap(np.linspace(0, 1, len(pivot_df.columns))) + + # Plot the DataFrame + ax = sorted_pivot_df.plot( + kind="bar", stacked=True, figsize=(15, 10), color=colors + ) # Increased figure size + + plt.title("Distribution of Ranking Results", fontsize=20) + plt.xlabel(alt_name_col, fontsize=16) + plt.ylabel("Percentage of Total Samples", fontsize=16) + + # Move legend to the left side and increase its size + plt.legend( + loc="center left", bbox_to_anchor=(1, 0.5), prop={"size": 14}, title="Rank" + ) + + # Loop through the bars to annotate each segment with Rank_Count + for bar in ax.containers: + for rect in bar: + # Calculate height and width for the annotation position + height = rect.get_height() + width = rect.get_width() + x = rect.get_x() + y = rect.get_y() + + # The label is the Rank_Count, which corresponds to the column names in pivot_df + # We identify the Rank_Count based on the rectangle's position and size + label = bar.get_label() + + # Only annotate if there's enough space (height) in the bar segment + if height > 0: + ax.text( + x + width / 2, + y + height / 2, + str(label), + ha="center", + va="center", + color="white", + fontsize=12, + ) # Changed text color to white for better visibility + + # Tilt the x-axis labels + plt.xticks(rotation=45) + + plt.show() + + +def calculate_counts(crit_cols, mcdm_cols, comp_rank_cols, unc_smpls_df, ranks_df): + # Calculate max rank value and create base rank count DataFrame + max_rank_value = ranks_df[crit_cols + mcdm_cols + comp_rank_cols].max().max() + base_rank_count_df = pd.DataFrame( + {"Rank_Count": range(max_rank_value + 1), "merge_": 1} + ) + + # Calculate max rank value and create base rank count DataFrame + max_rank_value = ranks_df[crit_cols + mcdm_cols + comp_rank_cols].max().max() + base_rank_count_df = pd.DataFrame( + {"Rank_Count": range(max_rank_value + 1), "merge_": 1} + ) + + # Define columns + rank_cols = crit_cols + mcdm_cols + comp_rank_cols + base_cols = [ + col + for col in ranks_df.columns + if col not in rank_cols + list(unc_smpls_df.columns) + ] + + # Initialize result DataFrames + all_count_ranks_df, all_rel_counts_df = pd.DataFrame(), pd.DataFrame() + + # Iterate through all unique 'Group ID's + for _, group_df in ranks_df[["Group ID"]].drop_duplicates().iterrows(): + sg_df = ranks_df[ + ranks_df[["Group ID"]].isin(group_df[["Group ID"]].values).all(axis=1) + ] + + # Prepare counts DataFrame + counts_df = sg_df[base_cols].copy().drop_duplicates() + counts_df = pd.merge( + base_rank_count_df, counts_df.assign(merge_=1), on="merge_" + ).drop("merge_", axis=1) + counts_df[crit_cols + mcdm_cols + comp_rank_cols] = 0 + + # Count the relative number of ranks for each alternative and store in ranks_count_df + for rank_count in range(max_rank_value + 1): + for alt in sg_df["Alternative ID"].unique(): + for col in rank_cols: + count = ( + (sg_df[col] == rank_count) & (sg_df["Alternative ID"] == alt) + ).sum() + row_idx = (counts_df["Rank_Count"] == rank_count) & ( + counts_df["Alternative ID"] == alt + ) + counts_df.loc[row_idx, col] = count + + # Append counts_df to count_ranks_df + all_count_ranks_df = pd.concat([all_count_ranks_df, counts_df]) + + # Calculate the relative counts + rel_counts_df = counts_df.copy() + rel_counts_df[rank_cols] = rel_counts_df[rank_cols] / len( + sg_df["Sample ID"].unique() + ) + + # Append rel_counts_df to all_rel_counts_df + all_rel_counts_df = pd.concat([all_rel_counts_df, rel_counts_df]) + + return all_count_ranks_df, all_rel_counts_df diff --git a/climada/engine/option_appraisal/MCDM/__init__.py b/climada/engine/option_appraisal/MCDM/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/climada/engine/option_appraisal/MCDM/category.py b/climada/engine/option_appraisal/MCDM/category.py new file mode 100644 index 0000000000..2ee3eba5ef --- /dev/null +++ b/climada/engine/option_appraisal/MCDM/category.py @@ -0,0 +1,797 @@ +""" +This file is part of CLIMADA. + +Copyright (C) 2017 ETH Zurich, CLIMADA contributors listed in AUTHORS. + +CLIMADA is free software: you can redistribute it and/or modify it under the +terms of the GNU General Public License as published by the Free +Software Foundation, version 3. + +CLIMADA is distributed in the hope that it will be useful, but WITHOUT ANY +WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A +PARTICULAR PURPOSE. See the GNU General Public License for more details. + +You should have received a copy of the GNU General Public License along +with CLIMADA. If not, see . + +--- + +""" + +import logging +from collections.abc import Iterable +from typing import Any, Dict, List, Optional, Sequence, Set, Union, cast + +from climada.engine.option_appraisal.MCDM.constants import ( + DEFAULT_CATEGORY_WEIGHT, + IMPORTANCE_MATCH, +) +from climada.engine.option_appraisal.MCDM.weights import WeightedItem + +CategoryName = str +CategoryLike = Union[CategoryName, "CriteriaCategory"] + +LOGGER = logging.getLogger(__name__) + + +class CategorySpace: + """Manages a dedicated, isolated registry of CriteriaCategory objects.""" + + _default_space: Optional["CategorySpace"] = None + + def __init__(self): + self._registry: Dict[str, "CriteriaCategory"] = {} + + @classmethod + def get_default_space(cls) -> "CategorySpace": + """Returns the default CategorySpace instance, creating it if necessary.""" + if cls._default_space is None: + cls._default_space = CategorySpace() + return cls._default_space + + def reset_categories(self): + self._registry.clear() + + def get(self, name: CategoryName) -> Optional["CriteriaCategory"]: + """ + Retrieves a criteria category object by its unique name. + + Parameters + ---------- + name : CategoryName + The unique name of the criteria category. + + Returns + ------- + Optional[CriteriaCategory] + The CriteriaCategory object if found, otherwise None. + """ + return self._registry.get(name) + + def __contains__(self, item: object) -> bool: + """ + Allows checking if a category is in this space using 'category in space'. + + It checks if the item is a CriteriaCategory object and if it is + present in this space's internal registry. + """ + if not isinstance(item, CriteriaCategory): + return False + + return item.name in self._registry + + def remove(self, category: CategoryLike) -> None: + if not isinstance(category, str): + try: + category = category.name + except AttributeError as err: + err.add_note( + "category has to be the name of category or a Category object." + ) + raise + + cat = self._registry[category] + for parent in cat.parents: + parent._children.remove(cat) + parent._children.update(cat.children) + + for child in cat.children: + child._parents.remove(cat) + child._parents.update(cat.parents) + + del self._registry[category] + + def register(self, category: "CriteriaCategory") -> None: + if category.name in self._registry: + raise ValueError(f"Category '{category.name}' already exists in space.") + self._registry[category.name] = category + + def add_category( + self, + name: CategoryName, + parent_cats: Optional[Union[CategoryLike, Sequence[CategoryLike]]] = None, + category_type: Optional[str] = None, + weight: Optional[float] = None, + overwrite: bool = False, + ) -> "CriteriaCategory": + """Create and register a new category in this space. + + Parameters + ---------- + name : str + parent_cats : str or CriteriaCategory or list, optional + category_type : str, optional + weight : float or str, optional + overwrite : bool, optional + """ + return CriteriaCategory( + name, + parents=parent_cats, + category_type=category_type, + weight=weight, + overwrite=overwrite, + space=self, + ) + + def select_categories_by_type( + self, category_types: str | Iterable[str] + ) -> "list[CriteriaCategory]": + if isinstance(category_types, str): + category_types = [category_types] + + return [ + category + for category in self.all_categories + if category.category_type and category.category_type in category_types + ] + + def create_subset_by_type( + self, category_types: str | Iterable[str] + ) -> "CategorySpace": + if isinstance(category_types, str): + category_types = [category_types] + + subspace = CategorySpace() + selected_categories = self.select_categories_by_type(category_types) + for category in selected_categories: + subspace.register(category) + + return subspace + + def create_subspace( + self, selection: Union[CategoryLike, Sequence[CategoryLike]] + ) -> "CategorySpace": + if not isinstance(selection, Iterable): + selection = [selection] + + subspace = CategorySpace() + for category in selection: + if isinstance(category, str): + category = cast(CriteriaCategory, self.get(category)) + + subspace.register(category) + + return subspace + + @property + def effective_weights(self): + return {name: cat.effective_weight for name, cat in self._registry.items()} + + @property + def weights(self): + return {name: cat.weight for name, cat in self._registry.items()} + + @weights.setter + def weights(self, value: Dict[str, float]): + """Set weights from a dict. Missing keys keep their current value. + + Parameters + ---------- + value : dict[str, float] + Mapping of category name to weight. Values can be float or + importance strings (e.g. ``"high"``). + """ + if not isinstance(value, dict): + raise TypeError(f"weights must be a dict, got {type(value).__name__}.") + + unrecognized = [k for k in value if k not in self._registry] + unset = [k for k in self._registry if k not in value] + + if unrecognized: + LOGGER.warning( + "Weights given for unknown categories (ignored): %s", unrecognized + ) + if unset: + LOGGER.warning("No weight given for categories (unchanged): %s", unset) + + for name, w in value.items(): + if name in self._registry: + self._registry[name].weight = w # WeightedItem setter validates + + def reset_weights(self, weight=None) -> None: + """Reset all category weights to ``DEFAULT_WEIGHT``.""" + for cat in self._registry.values(): + cat.weight = weight if weight else DEFAULT_CATEGORY_WEIGHT + + def set_weight(self, category: CategoryLike, weight) -> None: + """Set the weight of a single category. + + Parameters + ---------- + category : str or CriteriaCategory + Target category name or object. + weight : float or str + New weight value. + """ + name = category if isinstance(category, str) else category.name + if name not in self._registry: + raise KeyError(f"Category '{name}' not in this space.") + self._registry[name].weight = weight + + @property + def all_categories(self) -> List["CriteriaCategory"]: + return list(self._registry.values()) + + @property + def category_types(self): + return list(set([cat.category_type for cat in self.all_categories])) + + def display(self) -> None: + """ + Prints the entire category hierarchy registered in the system using ASCII art. + It handles multiple roots and is robust against multiple inheritance. + """ + if not self._registry: + print("The category registry is empty.") + return + + # 1. Identify all root nodes (categories with no parents) + # Note: In a pure hierarchy, this is simple. With multiple inheritance (DAG), + # a category can be a root even if it has parents *not* in the registry, + # but here we assume all parents are created via the provided methods. + root_nodes = sorted( + [category for category in self._registry.values() if not category.parents], + key=lambda c: c.name, + ) # Sort by name for stable output + + if not root_nodes: + # This can happen in a pure graph/cyclic structure, or if only children were defined. + print("No category without a parent was found to act as a root.") + print(f"Categories present: {list(self._registry.keys())}") + return + + print("\n--- Criteria Category Hierarchy ---") + + # 2. Use a recursive helper function to print the tree starting from roots + def print_node_recursive( + node: CriteriaCategory, prefix: str = "", is_last: bool = True + ) -> None: + """Recursively prints the node and its children.""" + + # ASCII art characters + connector = "└── " if is_last else "├── " + + # Print the current node + print( + prefix + + connector + + str(node.category_type) + + ": " + + node.name + + " category weight: " + + str(self.weights[node.name]) + ) + + # Determine the prefix for the children + # If the current node is the last child of its parent, its children's prefix + # uses a space/indent. Otherwise, it uses the vertical line. + child_prefix = prefix + (" " if is_last else "│ ") + + # Sort children by name for predictable display order + sorted_children = sorted(list(node.children), key=lambda c: c.name) + + # Recursively call for children + for i, child in enumerate(sorted_children): + is_last_child = i == len(sorted_children) - 1 + print_node_recursive(child, child_prefix, is_last_child) + + # Print each root + for i, root in enumerate(root_nodes): + is_last_root = i == len(root_nodes) - 1 + # Root nodes use slightly different logic for the final block + print_node_recursive(root, "", is_last_root) + if not is_last_root: + # Add a blank line between separate root trees for clarity + print() + + +class CriteriaCategory(WeightedItem): + """ + Represents a criteria category in a dynamic, multiple-parent hierarchy. + + This class manages the structure and relationships of criteria, allowing + for runtime definition and retrieval of categories. It implements logic + to check for ancestral relationships, respecting multiple parent links. + + Attributes + ---------- + _registry : dict[CategoryName, CriteriaCategory] + A class-level dictionary serving as a global lookup for all defined + CriteriaCategory instances, keyed by their name. + name : CategoryName + The unique name of the criteria category. + parents : set[CriteriaCategory] + The set of direct parent categories this criteria inherits from. + children : set[CriteriaCategory] + The set of direct child categories that inherit from this criteria. + """ + + _registry: dict[CategoryName, "CriteriaCategory"] = {} + + def __init__( + self, + name: CategoryName, + parents: Optional[Union[CategoryLike, Sequence[CategoryLike]]] = None, + category_type: Optional[str] = None, + weight: Optional[float] = None, + space: Optional[CategorySpace] = None, + overwrite: Optional[bool] = False, + ) -> None: + """ + Initializes a new CriteriaCategory. + + Parameters + ---------- + name : CategoryName + The unique name of the category. + parents : Optional[Union[CategoryLike, Sequence[CategoryLike]]], optional + The parent criteria(s) this category inherits from. Can be a single + name/object or a list of names/objects. By default, None. + + Raises + ------ + ValueError + If a category with the given name already exists in the registry. + """ + WeightedItem.__init__(self, weight) + self._space = space if space is not None else CategorySpace.get_default_space() + self.name: CategoryName = name + self.category_type: str | None = category_type + self._parents: Set[CriteriaCategory] = set() + self._children: Set[CriteriaCategory] = set() + + if self in self.space: + existing = cast(CriteriaCategory, self.space.get(name)) + if not overwrite: + if not existing.has_parents_exactly(parents): + raise ValueError( + f"CriteriaCategory '{name}' with different parents ({existing.parents} != {parents})" + f" already exists in current category space ({self.space})." + " You can overwrite with `overwrite=True`." + ) + return + + self.space.register(self) + if parents: + self.add_parents(parents) + + @property + def effective_weight(self): + """Hierarchical weight: max of own weight and all ancestors' weights. + + Distinct from ``weight``, which is this category's direct assignment. + """ + if len(self.parents) > 0: + return max(self.weight, max([p.effective_weight for p in self.parents])) + else: + return self.weight + + @property + def space(self): + return self._space + + @property + def parents(self): + return self._parents + + @property + def children(self): + return self._children + + def __eq__(self, other: Any) -> bool: + """ + Defines equality based solely on the category name. + """ + # 1. Check if the other object is an instance of CriteriaCategory + if not isinstance(other, CriteriaCategory): + return NotImplemented # Defer to the other object's __eq__ + + # 2. Compare names + return self.name == other.name + + def __hash__(self) -> int: + """ + Defines the hash based solely on the category name. + Required for objects used in sets or as dictionary keys. + """ + return hash(self.name) + + @property + def _parents_names(self): + return [p.name for p in self.parents] + + def has_parents_exactly( + self, check_parents: Union[CategoryLike, Sequence[CategoryLike], None] + ) -> bool: + """ + Checks if the criteria's set of parents is exactly equal to the provided set of parents. + + This check is order-independent. + + Parameters + ---------- + check_parents : Union[CategoryLike, Sequence[CategoryLike]] + A single parent or a list of parent names or CriteriaCategory objects + to compare against the criteria's actual parents. + + Returns + ------- + bool + True if the provided list of parents (after resolution) is exactly + the same set as the criteria's actual parents, False otherwise. + + Raises + ------ + ValueError + If any parent name in `check_parents` cannot be found in tself.parents == resolved_check_parentshe registry. + """ + if check_parents is None and self.parents == set(): + return True + + if not isinstance(check_parents, list): + check_parents = [cast(CategoryLike, check_parents)] + + return check_parents == self._parents_names + + def add_parents( + self, parent_names: Union[CategoryLike, Sequence[CategoryLike]] + ) -> None: + """ + Internal helper to resolve and establish parent links. + + Parameters + ---------- + parent_names : Union[CategoryLike, Sequence[CategoryLike]] + The parent criteria(s) to link. + + Raises + ------ + ValueError + If any parent category specified by name is not found. + TypeError + If an item in the parent list is not a string or CriteriaCategory object. + """ + if not isinstance(parent_names, Sequence): + parent_names = [parent_names] + + for p_name in parent_names: + parent_obj: Optional[CriteriaCategory] = None + + if isinstance(p_name, str): + parent_obj = self.space.get(p_name) + if not parent_obj: + raise ValueError(f"Parent criteria '{p_name}' not found.") + elif isinstance(p_name, CriteriaCategory): + parent_obj = p_name + else: + raise TypeError( + "Parents must be a string (category name) or a CriteriaCategory object." + ) + + self.parents.add(parent_obj) + parent_obj.children.add(self) + + def is_a(self, other_category: CategoryLike | None) -> bool: + """ + Checks if this criteria is a subcategory (descendant) of or is + the target category. + + It performs a Breadth-First Search (BFS) up the parent hierarchy + to account for multiple parent links. + + Parameters + ---------- + other_category : CategoryLike + The target category to check against. Can be its name or object. + + Returns + ------- + bool + True if this category is a descendant of or is the target category, + False otherwise. + + Notes + ----- + Uses BFS with a visited set to handle cycles that might exist in + complex, manually defined DAGs, ensuring termination. + """ + if other_category is None: + return False + + if isinstance(other_category, str): + other_category = self.space.get(other_category) + if not other_category: + return False + + # BFS approach to traverse multiple parents + visited: Set[CriteriaCategory] = set() + to_visit: List[CriteriaCategory] = [self] + + while to_visit: + current = to_visit.pop(0) + + if current is other_category: + return True + + if current not in visited: + visited.add(current) + to_visit.extend(list(current.parents)) # Add all parents to the queue + + return False + + def __repr__(self, indent=0) -> str: + parent_names = sorted([p.name for p in self.parents]) + parent_str = f" Parents: {', '.join(parent_names)}" if parent_names else "none" + indent_space = " " * indent + return f"""{indent_space}name: {self.name} weight: {self.weight} type: {self.category_type}\n{indent_space}parents: {parent_str}""" + + +def create_criteria_category( + name: CategoryName, + parent_cats: Optional[Union[CategoryLike, Sequence[CategoryLike]]] = None, + category_type: Optional[str] = None, + space: Optional[CategorySpace] = None, + overwrite: bool = False, +) -> CriteriaCategory: + """ + Convenience function to simplify dynamic creation of CriteriaCategory objects. + + Parameters + ---------- + name : CategoryName + The unique name of the new criteria category. + parent_names : Optional[Union[CategoryName, List[CategoryName]]], optional + The name(s) of the parent criteria. By default, None. + + Returns + ------- + CriteriaCategory + The newly created criteria category object. + """ + return CriteriaCategory( + name, + parents=parent_cats, + category_type=category_type, + space=space, + overwrite=overwrite, + ) + + +def update_categories_from_dict( + hierarchy_dict: Dict[str, Any], space: Optional[CategorySpace] = None +) -> None: + """ + Updates or creates the internal hierarchy of CriteriaCategory objects from a nested dictionary. + + The keys of the dictionary become the new categories, and their immediate parents + are passed down through the recursion. + + Parameters + ---------- + hierarchy_dict : Dict[str, Any] + The dictionary representing the hierarchy. Keys are category names. + Values can be another nested dictionary (representing children) or None/Empty dict. + + Raises + ------ + TypeError + If a value in the dictionary is neither a dictionary nor None. + ValueError + If a category name is non-unique (already exists). + + Notes + ----- + The function modifies the global CriteriaCategory._registry as a side effect. + """ + space = CategorySpace.get_default_space() if space is None else space + return __categories_hierarchy_recursion(hierarchy_dict, space) + + +def __categories_hierarchy_recursion( + hierarchy_dict: Dict[str, Any], + space: CategorySpace, + current_parents: Optional[Union[CategoryLike, Sequence[CategoryLike]]] = None, +) -> None: + """ + Recursively creates a hierarchy of CriteriaCategory objects from a nested dictionary. + + The keys of the dictionary become the new categories, and their immediate parents + are passed down through the recursion. + + Parameters + ---------- + hierarchy_dict : Dict[str, Any] + The dictionary representing the hierarchy. Keys are category names. + Values can be another nested dictionary (representing children) or None/Empty dict. + current_parents : Optional[Union[str, List[str]]], optional + The name(s) of the categories that should be set as the parent(s) for the + current level's keys. Used internally for recursion. By default, None. + + Raises + ------ + TypeError + If a value in the dictionary is neither a dictionary nor None. + ValueError + If a category name is non-unique (already exists). + + Notes + ----- + The function modifies the global CriteriaCategory._registry as a side effect. + """ + + # Ensure current_parents is always a list for consistent handling + if current_parents is None: + parent_list = [] + elif isinstance(current_parents, str): + parent_list = [current_parents] + else: + parent_list = current_parents + + for category_name, value in hierarchy_dict.items(): + try: + create_criteria_category( + name=category_name, parent_cats=parent_list, space=space + ) + except ValueError as e: + if "different parents" in str(e): + space.get(category_name).add_parents(parent_list) # type: ignore + else: + print( + f"Warning: Category '{category_name}' skipped (likely duplicate). Error: {e}" + ) + continue + + # Recurse if there are children + if value is None: + continue + + if isinstance(value, dict): + new_parent_for_children: Union[str, List[str]] = category_name + __categories_hierarchy_recursion(value, space, new_parent_for_children) + + elif not isinstance(value, dict) and value is not None: + raise TypeError( + f"Value for category '{category_name}' must be a dictionary (for children) or None, " + f"but got {type(value).__name__}." + ) + + +class CategorizedObject: + """ + An object that uses composition to belong to one or more CriteriaCategories. + + The object maintains a set of direct criteria links. Checks against the + hierarchy are delegated to the CriteriaCategory system. + + Attributes + ---------- + name : str + The name or identifier of the object. + categories : Sequence[CriteriaCategory] + The set of CriteriaCategory objects this instance is directly assigned to. + """ + + def __init__( + self, + name: str, + categories: Optional[ + Union[CategoryLike, Sequence[CategoryLike], Sequence[CriteriaCategory]] + ] = None, + space: Optional[CategorySpace] = None, + ) -> None: + """ + Initializes the CategorizedObject. + + Parameters + ---------- + name : str + The name or identifier of the object. + categories : Optional[Union[CategoryName, List[CategoryName]]], optional + The name(s) of the initial categories to assign. By default, None. + """ + self.name: str = name + self._categories: Set[CriteriaCategory] = set() + self._space = space if space is not None else CategorySpace.get_default_space() + if categories: + self.add_categories(categories) + + @property + def space(self): + return self._space + + @property + def categories(self): + return self._categories + + @property + def category_space(self): + return self._space + + def add_categories( + self, + categories: Union[ + CategoryLike, Sequence[CategoryLike], Sequence[CriteriaCategory] + ], + ) -> None: + """ + Adds one or more criteria categories to the object by name. + + Parameters + ---------- + category_names : Union[CategoryName, List[CategoryName]] + The name(s) of the criteria categories to add. + + Raises + ------ + ValueError + If a category name does not exist in the CriteriaCategory registry. + """ + if not isinstance(categories, Sequence): + categories = [categories] + + for cat_to_add in categories: + if not isinstance(cat_to_add, CriteriaCategory): + cat = self.category_space.get(cat_to_add) + if not cat: + # Enforce that categories must be defined globally before being assigned to an object + raise ValueError( + f"CriteriaCategory '{cat_to_add}' is not defined. Create it first with `CriteriaCategory.create_criteria_category()`" + ) + self.categories.add(cat) + else: + self.categories.add(cat_to_add) + + def has_category(self, category_name: CategoryLike) -> bool: + """ + Checks if the object belongs to the specified category or any of its + subcategories in the hierarchy. + + Parameters + ---------- + category_name : CategoryName + The name of the criteria category to check against. + + Returns + ------- + bool + True if the object is directly or indirectly a member of the + target category, False otherwise. + """ + if isinstance(category_name, str): + target_category = self.category_space.get(category_name) + if not target_category: + return False + + elif isinstance(category_name, CriteriaCategory): + target_category = category_name + else: + raise ValueError(f"{category_name} is not a string or a CriteriaCategory") + + for category in self.categories: + if category.is_a(target_category): + return True + return False + + def __repr__(self) -> str: + cat_names = sorted([c.name for c in self.categories]) + return f"" diff --git a/climada/engine/option_appraisal/MCDM/constants.py b/climada/engine/option_appraisal/MCDM/constants.py new file mode 100644 index 0000000000..99a9cb6970 --- /dev/null +++ b/climada/engine/option_appraisal/MCDM/constants.py @@ -0,0 +1,20 @@ +NO_MEASURE_DEFAULT_NAME = "no_measure" + +OPTIONS_DEFAULT_COLNAME = "measure" +DATE_DEFAULT_COLNAME = "date" +PRESENT_DATE_DEFAULT_COLNAME = "present" +FUTURE_DATE_DEFAULT_COLNAME = "future" +CRITERION_DEFAULT_COLNAME = "criterion_name" + +DEFAULT_CATEGORY_WEIGHT = 0.0 +DEFAULT_CRITERION_BASE_WEIGHT = 0.0 +DEFAULT_ITEM_WEIGHT = 0.0 +IMPORTANCE_MATCH = { + "none": 0.0, + "very low": 0.2, + "low": 0.4, + "moderate": 0.5, + "high": 0.6, + "very high": 0.8, + "highest": 1.0, +} diff --git a/climada/engine/option_appraisal/MCDM/criterion.py b/climada/engine/option_appraisal/MCDM/criterion.py new file mode 100644 index 0000000000..edae837bad --- /dev/null +++ b/climada/engine/option_appraisal/MCDM/criterion.py @@ -0,0 +1,576 @@ +""" +This file is part of CLIMADA. + +Copyright (C) 2017 ETH Zurich, CLIMADA contributors listed in AUTHORS. + +CLIMADA is free software: you can redistribute it and/or modify it under the +terms of the GNU General Public License as published by the Free +Software Foundation, version 3. + +CLIMADA is distributed in the hope that it will be useful, but WITHOUT ANY +WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A +PARTICULAR PURPOSE. See the GNU General Public License for more details. + +You should have received a copy of the GNU General Public License along +with CLIMADA. If not, see . + +--- + +""" + +import logging +from dataclasses import dataclass, field +from typing import List, Optional, Sequence, Set, Union + +import numpy as np +import pandas as pd +from sklearn.preprocessing import MinMaxScaler + +from climada.engine.option_appraisal.MCDM.category import ( + CategorizedObject, + CategoryLike, + CategorySpace, + CriteriaCategory, +) +from climada.engine.option_appraisal.MCDM.constants import ( + CRITERION_DEFAULT_COLNAME, + DATE_DEFAULT_COLNAME, + DEFAULT_CATEGORY_WEIGHT, + DEFAULT_CRITERION_BASE_WEIGHT, + FUTURE_DATE_DEFAULT_COLNAME, + IMPORTANCE_MATCH, + NO_MEASURE_DEFAULT_NAME, + OPTIONS_DEFAULT_COLNAME, + PRESENT_DATE_DEFAULT_COLNAME, +) +from climada.engine.option_appraisal.MCDM.mcda_methods import APPROACH_FN, MCDAApproach +from climada.engine.option_appraisal.MCDM.weights import WeightedItem + +LOGGER = logging.getLogger(__name__) + + +class Criterion(CategorizedObject, WeightedItem): + def __init__( + self, + name: str, + categories: Optional[ + Union[CategoryLike, Sequence[CategoryLike], Set[CriteriaCategory]] + ] = None, + space: Optional[CategorySpace] = None, + data: pd.Series = None, + obj_maximize: bool = True, + base_weight: float = DEFAULT_CRITERION_BASE_WEIGHT, + ) -> None: + CategorizedObject.__init__(self, name, categories, space) + WeightedItem.__init__(self, base_weight) + self.data = data + self.data.name = name + self.obj_maximize = obj_maximize + + def __repr__(self, indent=4) -> str: + """ + Provides a custom, formatted, multi-line string representation + of the Criterion object for better readability. + """ + indent_space = " " * indent + # Format the Categories Set + # Use a list comprehension to format each category on a new line + formatted_categories = ",\n".join( + f"{cat.__repr__(indent=indent*2)}" + for cat in sorted( + list(self.categories), key=lambda c: c.name + ) # Sorting for consistency + ) + + return ( + f"Criterion(\n" + f"{indent_space}name='{self.name}',\n" + f"{indent_space}categories={{\n{formatted_categories}\n{indent_space}}},\n" + f"{indent_space}obj_maximise={self.obj_maximize}\n" + f"{indent_space}base_weight={self.weight}\n" + f"{indent_space}average_weight_from_categories={self.weight_from_category}\n" + f")" + ) + + @property + def category_weights(self): + return {cat.name: cat.weight for cat in self.categories} + + @property + def weight_from_category(self): + return np.array([cat.effective_weight for cat in self.categories]).prod() ** ( + 1 / len(self.categories) + ) + + +class CriteriaSet: + def __init__( + self, + criteria: list[Criterion], + category_weights: dict[str, float] | None = None, + criteria_weights: dict[str, float] | None = None, + ) -> None: + self.criteria = criteria + self._category_space = criteria[0].category_space + # self.criteria_base_weights = criteria_weights + self.category_weights = category_weights + + def display(self): + lines = [] + + total_weights = self.criteria_total_weights(active_only=False) + + active = [c for c in self.criteria if total_weights[c.name] > 0] + inactive = [c for c in self.criteria if total_weights[c.name] == 0] + + # Header + lines.append( + f"CriteriaSet {len(active)} active criteria | {len(self.category_space.all_categories)} categories" + ) + if inactive: + lines.append(f" {len(inactive)} inactive criteria (weight = 0)") + lines.append("=" * 60) + + # Category weights section + lines.append("\nCategories") + lines.append("-" * 60) + cat_types = self.category_space.category_types + for cat_type in sorted(t for t in cat_types if t is not None): + cats = self.category_space.select_categories_by_type(cat_type) + lines.append(f" [{cat_type}]") + for cat in sorted(cats, key=lambda c: c.name): + bar = _weight_bar(cat.weight) + lines.append(f" {cat.name:<30} {bar} {cat.weight:.3f}") + + # Criteria weights section + if len(active) > 0: + bar_width = len(active) if len(active) < 50 else 50 + max_len = max(len(crit.name) for crit in active) + total_sum = sum(total_weights[c.name] for c in active) + lines.append(f"\nCriteria{' ' * (max_len)}Weights") + lines.append("-" * 8 + " " * (max_len) + "-" * 9) + total_weights = self.criteria_total_weights() + for crit in sorted(active, key=lambda c: -total_weights[c.name]): + total = total_weights[crit.name] + effective = total / total_sum if total_sum > 0 else 0.0 + bar = _weight_bar(effective, width=len(active) * 2) + lines.append( + f" {crit.name:<{max_len+4}} " + f"base={crit.weight:.5f} total={total_weights[crit.name]:.5f} " + f"effective={effective:.5f} " + f"{bar}" + ) + + lines.append("") + print("\n".join(lines)) + + @classmethod + def from_risk_metrics( + cls, + risk_metrics: pd.DataFrame, + category_types: list[str], + criteria_cols: list[str], + options_colname: str = OPTIONS_DEFAULT_COLNAME, + excluded_value_cols=None, + criteria_min: Optional[list[str]] = None, + ) -> "CriteriaSet": + criteria_min = [] if criteria_min is None else criteria_min + if excluded_value_cols: + risk_metrics = risk_metrics[ + [col for col in risk_metrics.columns if col not in excluded_value_cols] + ].copy() + if ( + DATE_DEFAULT_COLNAME in risk_metrics.columns + and DATE_DEFAULT_COLNAME not in category_types + ): + if risk_metrics[DATE_DEFAULT_COLNAME].nunique() > 1: + LOGGER.info( + f"'{DATE_DEFAULT_COLNAME}' column with more than one value found in risk metric dataframe. Will apply default treatment: will define a category '{PRESENT_DATE_DEFAULT_COLNAME}' for earliest date and '{FUTURE_DATE_DEFAULT_COLNAME}' for latest one. You can make every date a category by explicitly including the '{DATE_DEFAULT_COLNAME}' column in the category_types." + ) + max_date = risk_metrics[DATE_DEFAULT_COLNAME].max() + min_date = risk_metrics[DATE_DEFAULT_COLNAME].min() + risk_metrics = risk_metrics.loc[ + risk_metrics[DATE_DEFAULT_COLNAME].isin([min_date, max_date]) + ] + risk_metrics[DATE_DEFAULT_COLNAME] = risk_metrics[ + DATE_DEFAULT_COLNAME + ].map( + { + min_date: PRESENT_DATE_DEFAULT_COLNAME, + max_date: FUTURE_DATE_DEFAULT_COLNAME, + } + ) + category_types.append(DATE_DEFAULT_COLNAME) + + cols_not_used = [ + col + for col in risk_metrics.columns + if col + not in category_types + + criteria_cols + + [options_colname] + + excluded_value_cols + ] + for col in cols_not_used: + if risk_metrics[col].nunique() > 1: + raise ValueError( + f"Column {col} is not defined as a category type or a criteria nor is excluded. As it has more that one unique value, I don't know how to handle it. Either add it to excluded_value_cols if it is a criterion values column you do not want or conversely to criteria_cols. If it is an identifier column either add it to category_type or subselect the dataframe to have a unique value." + ) + + risk_metrics = risk_metrics[ + category_types + criteria_cols + [options_colname] + ].copy() + risk_metrics = risk_metrics.loc[ + risk_metrics[OPTIONS_DEFAULT_COLNAME] != NO_MEASURE_DEFAULT_NAME + ] + risk_metrics = risk_metrics.melt( + id_vars=category_types + [options_colname], + value_vars=criteria_cols, + var_name=CRITERION_DEFAULT_COLNAME, + ) + groups = risk_metrics.set_index(options_colname).groupby( + category_types + [CRITERION_DEFAULT_COLNAME], as_index=False, observed=False + )["value"] + for gr in category_types: + LOGGER.info( + f"Categories found in type '{gr}': {risk_metrics[gr].astype(str).unique()}" + ) + + LOGGER.info(f"Total number of possible criteria: {len(groups)}") + + cat_space = CategorySpace() + crits = [] + for group_name, group in groups: + cats = [ + (str(col), str(val)) + for col, val in zip( + category_types + [CRITERION_DEFAULT_COLNAME], group_name + ) + ] + for cat in cats: + cat_space.add_category(name=cat[1], category_type=cat[0]) + crit_fullname = f"{'-'.join(['_'.join(c) for c in cats])}" + crits.append( + Criterion( + crit_fullname, + categories=[cat[1] for cat in cats], + data=group, + space=cat_space, + obj_maximize=(all(c not in crit_fullname for c in criteria_min)), + ) + ) + + return cls(criteria=crits) + + @property + def category_space(self): + return self._category_space + + @property + def criteria(self): + return self._criteria + + @criteria.setter + def criteria(self, value, /): + self._check_consistency(value) + self._criteria = [crit for crit in value] + + @property + def criteria_names(self): + return [crit.name for crit in self.criteria] + + @property + def criteria_matrix(self): + return pd.concat([crit.data for crit in self.criteria], axis=1) + + @property + def criteria_types(self): + return np.array([1 if crit.obj_maximize else -1 for crit in self.criteria]) + + @property + def criteria_with_weight(self): + return list(self.criteria_total_weights().keys()) + + def add_criteria(self, criteria: Criterion | list[Criterion]): + if not isinstance(criteria, list): + criteria = [criteria] + + # TODO: Warn duplicates + # Overwrite? + criteria = [c for c in criteria if c.name not in self.criteria_names] + self.criteria = self.criteria + criteria + + @staticmethod + def _check_consistency(criteria): + if not isinstance(criteria, list): + raise ValueError("Criteria must be a list of Criterion.") + + if not all([isinstance(criterion, Criterion) for criterion in criteria]): + raise ValueError("Criteria must be a list of Criterion.") + + first_index = criteria[0].data.index.sort_values() + mismatched_indices_info = [] + + # Iterate through the criteria starting from the second element (index 1) + for i, criterion in enumerate(criteria[1:]): + current_index = criterion.data.index.sort_values() + + if not first_index.equals(current_index): + # Find the specific differences (elements in one index but not the other) + diff_1 = first_index.difference(current_index) + diff_2 = current_index.difference(first_index) + + mismatched_indices_info.append( + f"Criterion {i + 1} (Name: {criterion.name if hasattr(criterion, 'name') else 'N/A'}) " + f"has an index mismatch with Criterion 0." + f"\n -> Unique to Criterion 0: {list(diff_1)}" + f"\n -> Unique to Criterion {i + 1}: {list(diff_2)}" + ) + + if mismatched_indices_info: + infos = "\n".join(mismatched_indices_info) + raise ValueError( + "All criteria must have the same index (options) to be combined." + f"\n\nDetails of Mismatches:\n\n{infos}" + ) + + if not all([criteria[0].space is criterion.space for criterion in criteria]): + raise ValueError("Criteria must share the same space of categories.") + + def criteria_total_weights( + self, + categories_influence: float = 0.5, + base_weight_influence: float = 0.5, + active_only: bool = True, + ) -> dict[str, float]: + """Compute weighted combination of base weight and category-derived weight. + + Parameters + ---------- + categories_influence : float + Weight given to category-derived score. Must sum to 1 with + ``base_weight_influence``. + base_weight_influence : float + Weight given to the criterion's base weight. + active_only : bool + If True (default), only criteria with non-zero total weight are returned. + + Returns + ------- + dict[str, float] + Mapping of criterion name to total weight. + """ + if categories_influence + base_weight_influence != 1.0: + raise ValueError( + "categories_influence and base_weight_influence must sum to 1." + ) + + weights = { + crit.name: crit.weight * base_weight_influence + + crit.weight_from_category * categories_influence + for crit in self.criteria + } + + if active_only: + return {k: v for k, v in weights.items() if v > 0} + return weights + + weights = { + crit.name: crit.weight * base_weight_influence + + crit.weight_from_category * categories_influence + for crit in self.criteria + } + + if active_only: + return {k: v for k, v in weights.items() if v > 0} + return weights + + @property + def criteria_base_weights(self): + return {v.name: v.weight for v in self.criteria} + + def get_criteria(self, name): + for crit in self.criteria: + if crit.name == name: + return crit + + @property + def category_effective_weights(self): + return self.category_space.effective_weights + + def set_criterion_weight(self, name: str, weight) -> None: + """Set the base weight of a single criterion. + + Parameters + ---------- + name : str + Criterion name. + weight : float or str + New weight value. + """ + crit = self.get_criteria(name) + if crit is None: + raise KeyError(f"Criterion '{name}' not found.") + crit.weight = weight + + def reset_category_weights(self) -> None: + self.category_space.reset_weights() + + def all_equal_category_weights(self) -> None: + self.category_space.reset_weights(weight=1.0) + + def update_category_weights(self, weights: dict[str, float]) -> None: + """Set the weight of a single category. + + Parameters + ---------- + name : str + Category name. + weight : float or str + New weight value. + """ + for name, weight in weights.items(): + self.category_space.set_weight(name, weight) + + def get_criteria_by_category( + self, + categories: Union[ + "CriteriaCategory", str, List[Union["CriteriaCategory", str]] + ], + ) -> List["Criterion"]: + """ + Retrieves all Criterion objects that are linked to any of the + given categories or any of their subcategories. + + :param categories: A single CriteriaCategory/string or a list of them + to filter by. + :return: A list of matching Criterion objects. + """ + + # Handle the case where a single string is passed (not a list) + if not isinstance(categories, list): + categories = [categories] + + matching_criteria = [ + criterion + for criterion in self.criteria + # Check if the criterion matches ANY category in the filter list + if any( + # We rely on the criterion's method which uses the recursive + # is_descendant_of logic. + criterion.has_category(cat) + for cat in categories + ) + ] + + return matching_criteria + + @property + def active_criteria_matrix(self) -> pd.DataFrame: + """Criteria matrix sub-selected for criteria with non-zero total weight. + + Returns + ------- + pd.DataFrame + Columns are active criterion names, index is options. + """ + active_names = self.criteria_with_weight + return self.criteria_matrix[active_names] + + def normalized_criteria_matrix( + self, + scaler=None, + ) -> pd.DataFrame: + """Criteria matrix normalized using a scikit-learn-compatible scaler. + + Only active criteria (non-zero total weight) are included. + + Parameters + ---------- + scaler : sklearn-compatible transformer, optional + Must implement ``fit_transform(X)``. Defaults to + ``sklearn.preprocessing.MinMaxScaler()``. + + Returns + ------- + pd.DataFrame + Normalized criteria matrix with same index and columns as + ``active_criteria_matrix``. + """ + if scaler is None: + scaler = MinMaxScaler() + + matrix = self.active_criteria_matrix + return pd.DataFrame( + scaler.fit_transform(matrix), + index=matrix.index, + columns=matrix.columns, + ) + + def score_matrix( + self, + approach: MCDAApproach | str = MCDAApproach.SAW, + scaler=None, + ) -> pd.Series: + """Score options using a MCDA approach. + + Parameters + ---------- + approach : MCDAApproach or str + Scoring method. One of ``MCDAApproach.SAW`` or ``MCDAApproach.TOPSIS``. + Strings ``"saw"`` and ``"topsis"`` are also accepted. + scaler : sklearn-compatible transformer, optional + Scaler passed to ``normalized_criteria_matrix``. + Defaults to ``MinMaxScaler()``. + + Returns + ------- + pd.Series + Scores indexed by option, sorted descending. + """ + if isinstance(approach, str): + approach = MCDAApproach(approach.lower()) + + fn = APPROACH_FN[approach] + + matrix = self.normalized_criteria_matrix(scaler=scaler) + active_criteria = [c for c in self.criteria if c.name in matrix.columns] + + total_weights = self.criteria_total_weights() + raw_weights = np.array([total_weights[c.name] for c in active_criteria]) + weights = raw_weights / raw_weights.sum() # normalise to sum=1 + + criteria_types = np.array( + [1 if c.obj_maximize else -1 for c in active_criteria] + ) + + scores = fn(matrix.values, weights, criteria_types) + return pd.Series(scores, index=matrix.index, name=approach.value).sort_values( + ascending=False + ) + + def display_space(self) -> None: + """ + Prints the entire category hierarchy registered in the system using ASCII art. + It handles multiple roots and is robust against multiple inheritance. + """ + self.category_space.display() + + +def _weight_bar(weight: float, width: int = 8) -> str: + """ASCII progress bar for a weight in [0, 1]. + + Parameters + ---------- + weight : float + Value in [0, 1]. + width : int + Total bar characters. + + Returns + ------- + str + e.g. ``[██░░░░░░]`` + """ + filled = round(weight * width) + return "[" + "█" * filled + "░" * (width - filled) + "]" diff --git a/climada/engine/option_appraisal/MCDM/mca_calc.py b/climada/engine/option_appraisal/MCDM/mca_calc.py new file mode 100644 index 0000000000..ab3c149c0e --- /dev/null +++ b/climada/engine/option_appraisal/MCDM/mca_calc.py @@ -0,0 +1,507 @@ +""" +This file is part of CLIMADA. + +Copyright (C) 2017 ETH Zurich, CLIMADA contributors listed in AUTHORS. + +CLIMADA is free software: you can redistribute it and/or modify it under the +terms of the GNU General Public License as published by the Free +Software Foundation, version 3. + +CLIMADA is distributed in the hope that it will be useful, but WITHOUT ANY +WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A +PARTICULAR PURPOSE. See the GNU General Public License for more details. + +You should have received a copy of the GNU General Public License along +with CLIMADA. If not, see . + +--- + +""" + +import bisect +import functools +from typing import Any, Iterable + +import matplotlib.pyplot as plt +import numpy as np +import pandas as pd +from pyrepo_mcda.additions import rank_preferences +from pyrepo_mcda.compromise_rankings import copeland +from pyrepo_mcda.mcda_methods import MULTIMOORA, SAW, SPOTIS, TOPSIS, VIKOR +from pyrepo_mcda.sensitivity_analysis_weights_values import ( + Sensitivity_analysis_weights_values, +) + +from climada.engine.option_appraisal.MCDM.criterion import CriteriaSet, Criterion + +MCDM_DEFAULT = {"Topsis": TOPSIS(), "Saw": SAW(), "Vikor": VIKOR()} #'AHP': AHP(), +"""Default MCDM ranking method""" + +COMPROMISE_DEFAULT = { + "copeland": copeland, +} +"""Default Compromise approach""" + + +class MCACalc: + def __init__( + self, + criteria_set: CriteriaSet, + ): + self._whole_criteria_set = criteria_set + self._current_criteria_set = criteria_set + + @property + def criteria_set(self): + return self._current_criteria_set + + def add_criteria(self, criteria: Criterion | list[Criterion]): + self.criteria_set.add_criteria(criteria) + + def help(self): ... + + def select_criteria(self, criteria: Criterion | list[Criterion]): ... + + def apply_constraint(self): ... + + def resolve_dominance(self, mcdm_methods: list[str]): ... + + def calc_ranking(self, mcdm_method): ... + + +class MCA_Calc: + def __init__( + self, + risk_metrics: pd.DataFrame, + criteria: list[Criterion], + criteria_weights: dict[str, float] | None = None, + metrics_col: str | None = None, + metrics_weights: dict[str, float] | None = None, + constraints: list[str] | None = None, + groups_col: str | None = None, + groups_weights: dict[str, float] | None = None, + options_col: str = "measure", + mcdm_methods: dict | None = None, + compromise_method: dict | None = None, + ) -> None: + self._criteria_weights = None + self._groups_weights = None + self._metrics_weights = None + self._sensitivity_analyser = Sensitivity_analysis_weights_values() + + self._norm_criteria_weights = None + self._norm_groups_weights = None + self._norm_metrics_weights = None + self.risk_metrics = risk_metrics.copy() + self.criteria = criteria + self.options_col = options_col + self.options_names = self.risk_metrics[options_col].unique() + self.metrics_col = metrics_col + self.metrics_names = self.risk_metrics[metrics_col].unique() + self.groups_col = groups_col + self.groups_names = self.risk_metrics[groups_col].unique() + + self.criteria_weights = ( + criteria_weights + if criteria_weights + else pd.Series( + [1 / len(criteria)] * len(criteria), + index=self.criteria_cols, + name="criteria weights", + ) + ) + self.groups_weights = groups_weights + self.metrics_weights = metrics_weights + self.constraints = constraints + self.mcdm_methods = mcdm_methods if mcdm_methods else MCDM_DEFAULT + self.compromise_method = ( + compromise_method if compromise_method else COMPROMISE_DEFAULT + ) + + @property + def criteria(self) -> list[Criterion]: + return self._criteria + + @criteria.setter + def criteria(self, value: Any, /): + if not isinstance(value, Iterable) and not all( + isinstance(c, Criterion) for c in value + ): + raise ValueError("Criterias should be a list of Criterion objects") + + fail_crit = [ + c + for c in value + if not self.criteria_col_in_risk_metrics(c, self.risk_metrics) + ] + if len(fail_crit) > 0: + raise ValueError(f"{fail_crit} not found in risk metric dataframe") + + self._criteria = sorted(value, key=lambda x: x.column_name) + + @property + def criteria_names(self) -> list[str]: + return [c.name for c in self.criteria] + + @property + def criteria_cols(self) -> list[str]: + return [c.column_name for c in self.criteria] + + @property + def criteria_type(self) -> list[bool]: + return pd.Series( + [c.obj_maximise for c in self.criteria], index=self.criteria_cols + ) + + def add_criterion( + self, criterion: Criterion, criterion_values: pd.Series, weight=None + ): + tmp = self.risk_metrics.copy() + tmp[criterion.column_name] = tmp[self.options_col].map(criterion_values) + self.risk_metrics = tmp + bisect.insort(self._criteria, criterion, key=lambda x: x.column_name) + if not weight: + weight = 1 / (len(self.criteria_weights) + 1) + + tmp = self.criteria_weights.copy() + tmp[criterion.column_name] = weight + self.criteria_weights = tmp + + @property + def criteria_weights(self) -> pd.Series: + return self._criteria_weights + + @criteria_weights.setter + def criteria_weights(self, value: Any, /): + if self.criteria_cols != list(value.keys()): + fail_weights = [k for k, _ in value.items() if k not in self.criteria_cols] + fail_crit = [k for k in self.criteria_cols if k not in value.keys()] + if len(fail_weights) > 0: + raise ValueError(f"{fail_weights} not found in criteria columns") + if len(fail_crit) > 0: + raise ValueError(f"{fail_crit} not found in weights") + + self._criteria_weights = pd.Series(value, name="criteria weights").sort_index() + self._norm_criteria_weights = self.normalize_weights(self._criteria_weights) + self._update_norm_weights() + + @property + def groups_weights(self) -> pd.Series: + return self._groups_weights + + @groups_weights.setter + def groups_weights(self, value: Any, /): + if value is None and len(self.groups_names) == 0: + self._groups_weights = None + self._norm_groups_weights = None + + if value is None: + self._groups_weights = pd.Series( + [1 / len(self.groups_names)] * len(self.groups_names), + index=self.groups_names, + name="group weights", + ) + + if value: + if self.groups_col != list(value.keys()): + fail_weights = [ + k for k, _ in value.items() if k not in self.groups_names + ] + fail_group = [k for k in self.groups_names if k not in value.keys()] + if len(fail_weights) > 0: + raise ValueError(f"{fail_weights} not found in criteria columns") + if len(fail_group) > 0: + raise ValueError(f"{fail_group} not found in weights") + + self._groups_weights = pd.Series(value, name="group weights").sort_index() + + self._norm_groups_weights = self.normalize_weights(self._groups_weights) + self._update_norm_weights() + + @property + def metrics_weights(self) -> pd.Series: + return self._metrics_weights + + @metrics_weights.setter + def metrics_weights(self, value: Any, /): + if value is None: + self._metrics_weights = pd.Series( + [1 / len(self.metrics_names)] * len(self.metrics_names), + index=self.metrics_names, + name="metric weights", + ) + + if value: + if list(self.metrics_names) != list(value.keys()): + fail_weights = [ + k for k, _ in value.items() if k not in self.metrics_names + ] + fail_metrics = [k for k in self.metrics_names if k not in value.keys()] + if len(fail_weights) > 0: + raise ValueError(f"{fail_weights} not found in criteria columns") + if len(fail_metrics) > 0: + raise ValueError(f"{fail_metrics} not found in weights") + + self._metrics_weights = pd.Series(value, name="metric weights").sort_index() + + self._norm_metrics_weights = self.normalize_weights(self._metrics_weights) + self._update_norm_weights() + + @property + def weights(self): + return self._normalized_weights + + def _update_norm_weights(self): + if ( + self.criteria_weights is not None + and self.metrics_weights is not None + and self.groups_weights is not None + ): + index = self.pivoted_risk_metrics().columns + values = [ + self._norm_groups_weights[idx[0]] + * self._norm_metrics_weights[idx[1]] + * self._norm_criteria_weights[idx[2]] + for idx in index + ] + self._normalized_weights = pd.Series(values, index=index) + else: + self._normalized_weights = None + + @staticmethod + def sub_select_df(df, sub_selection): + return df[ + functools.reduce( + lambda x, y: x & y, + ( + df[col].isin([val] if not isinstance(val, list) else val) + for col, val in sub_selection.items() + ), + ) + ] + + def pivoted_risk_metrics(self, sub_selection=None): + df = self.risk_metrics.copy().loc[ + :, + [self.options_col] + + self.criteria_cols + + [self.metrics_col] + + [self.groups_col], + ] + if sub_selection is not None: + df = self.sub_select_df(df, sub_selection) + + index = self.options_col + columns = [self.metrics_col, self.groups_col] + df = df.pivot_table(index=index, columns=columns) + # df.columns.name = [self.groups_col,self.metrics_col,"criteria"] + df.columns = df.columns.reorder_levels( + order=[self.groups_col, self.metrics_col, "criteria"] + ) + df = df.sort_index(axis=1) + return df + + def individual_rank(self, sub_selection=None): + weights = self.weights.reset_index(name="weight") + + if sub_selection is not None: + weights = self.sub_select_df(weights, sub_selection) + + reps = len(weights) / len(self.criteria_type) + types = np.tile(np.where(self.criteria_type, 1, -1), reps=int(reps)) + risk_metrics = self.pivoted_risk_metrics(sub_selection).copy() * types + return risk_metrics.rank(axis=0, ascending=False, method="max") + + def _mapped_criteria_types(self, weights): + reps = len(weights) / len(self.criteria_type[weights["criteria"]]) + return np.tile( + np.where(self.criteria_type[weights["criteria"]], 1, -1), reps=int(reps) + ) + + def calc_rankings( + self, + mcdm_methods=None, + compromise_method=None, + constraints=None, + sub_selection=None, + ): + """ + Calculate rankings for a DecisionMatrix instance using specified Multi-Criteria Decision Making (MCDM) methods. + + Parameters: + - mcdm_methods: dict, optional + Dictionary of MCDM methods to use for ranking. Defaults to the MCDM_DEFAULT dictionary. + - comp_ranks: dict, optional + Dictionary of compromised ranking functions to use. Defaults to the COMP_DEFAULT dictionary. + - constraints: list, optional + List of constraints (pandas query strings) to filter the data. Defaults to an empty list. + - sub_selection: dict, optional + Dictionary of groups, metrics to sub-select + + Returns: + - ranks_output: RanksOutput + An instance of the RanksOutput class containing the rankings. + """ + mcdm_methods = mcdm_methods if mcdm_methods else self.mcdm_methods + compromise_method = ( + compromise_method if compromise_method else self.compromise_method + ) + constraints = constraints if constraints else self.constraints + weights = self.weights.reset_index(name="weight") + if sub_selection is not None: + weights = self.sub_select_df(weights, sub_selection) + + types = self._mapped_criteria_types(weights) + risk_metrics = self.pivoted_risk_metrics(sub_selection).copy() + risk_metrics = risk_metrics.replace(0, np.finfo(float).eps) + if constraints: + for constraint in constraints: + # TODO update types if constraint + risk_metrics = risk_metrics.query(constraint) + + # TODO + mca_df = [] # self.risk_metrics.loc[:, self.options_col].copy() + for method_name, method in mcdm_methods.items(): + mca_df.append( + pd.Series( + self.rank_matrix( + risk_metrics, + weights=weights["weight"].to_numpy(), + types=types, + method=method, + ), + name=method_name, + ) + ) + + mca_df = pd.concat(mca_df, axis=1) + + if compromise_method: + compromise = [] + for comp_method_name, comp_method in compromise_method.items(): + compromise.append( + pd.Series( + comp_method(mca_df[list(mcdm_methods.keys())]), + name=comp_method_name, + index=risk_metrics.index, + ) + ) + mca_df = pd.concat([mca_df, pd.concat(compromise, axis=0)], axis=1) + + return mca_df + + @staticmethod + def rank_matrix(df, weights, types, method): + matrix = df.to_numpy() + if isinstance(method, SPOTIS): + bounds_min = np.amin(matrix, axis=0) + bounds_max = np.amax(matrix, axis=0) + bounds = np.vstack((bounds_min, bounds_max)) + # Calculate the preference values of alternatives + prefs = method(matrix, weights, types, bounds) + else: + prefs = method(matrix, weights, types) + if isinstance(method, MULTIMOORA): + return pd.Series(prefs, index=df.index) + elif isinstance(method, (VIKOR, SPOTIS)): + prefs = rank_preferences(prefs, reverse=False) + return pd.Series(prefs, index=df.index) + else: + prefs = rank_preferences(prefs, reverse=True) + return pd.Series(prefs, index=df.index) + + def calc_sensitivity(self, crit_col, method=TOPSIS()): + # TODO Can only work for one selected group and metric, decide on how to implement for more than that + sub_selection = {"group": "All", "metric": "aai"} + matrix = self.pivoted_risk_metrics(sub_selection) + weights = self.sub_select_df( + self.weights.reset_index(name="weight"), sub_selection + ) + weights_values = np.arange(0.05, 0.95, 0.1) + types = self._mapped_criteria_types(weights) + data_sens = self._sensitivity_analyser( + matrix.values, + weights_values, + types, + method, + self.criteria_cols.index(crit_col), + ) + data_sens.index = self.options_names + return data_sens + + def plot_weight_sensitivity(self, crit_cols=None, method=TOPSIS()): + crit_cols = crit_cols if crit_cols else self.criteria_cols + for j, name in enumerate(crit_cols): + data_sens = self.calc_sensitivity(name, method) + plot_lineplot_sensitivity( + data_sens, "TOPSIS", name, "Weight value", "value" + ) + + @staticmethod + def criteria_col_in_risk_metrics( + crit: Criterion, risk_metrics: pd.DataFrame + ) -> bool: + return crit.column_name in risk_metrics.columns + + @staticmethod + def normalize_weights(weights: pd.Series) -> pd.Series: + return weights / weights.sum() + + +def plot_lineplot_sensitivity( + data_sens, method_name, criterion_name, x_title, filename="" +): + """ + Visualization method to display line chart of alternatives rankings obtained with + modification of weight of given criterion. + + Parameters + ---------- + df_plot : DataFrame + DataFrame containing rankings of alternatives obtained with different weight of + selected criterion. The particular rankings are contained in subsequent columns of + DataFrame. + + method_name : str + Name of chosen MCDA method, i.e. `TOPSIS`, `VIKOR`, `CODAS`, `WASPAS`, `MULTIMOORA`, `MABAC`, `EDAS`, `SPOTIS` + + criterion_name : str + Name of chosen criterion whose weight is modified + + x_title : str + Title of x axis + + filename : str + Name of file to save this chart + + Examples + ---------- + >>> plot_lineplot_sensitivity(df_plot, method_name, criterion_name, x_title, filename) + """ + plt.figure(figsize=(8, 4)) + for j in range(data_sens.shape[0]): + + plt.plot(data_sens.iloc[j, :], linewidth=2) + ax = plt.gca() + y_min, y_max = ax.get_ylim() + x_min, x_max = ax.get_xlim() + plt.annotate( + " " + data_sens.index[j], + (x_max, data_sens.iloc[j, -1]), + fontsize=12, + style="italic", + horizontalalignment="left", + ) + + plt.xlabel(x_title, fontsize=12) + plt.ylabel("Rank", fontsize=12) + plt.yticks(fontsize=12) + plt.xticks(fontsize=12) + plt.title(method_name + ", modification of " + criterion_name + " weight") + plt.grid(True, linestyle=":") + plt.tight_layout() + criterion_name = criterion_name.replace("$", "") + criterion_name = criterion_name.replace("{", "") + criterion_name = criterion_name.replace("}", "") + # plt.savefig('./results/' + 'sensitivity_' + 'lineplot_' + method_name + '_' + criterion_name + '_' + filename + '.eps') + plt.show() diff --git a/climada/engine/option_appraisal/MCDM/mcda_methods.py b/climada/engine/option_appraisal/MCDM/mcda_methods.py new file mode 100644 index 0000000000..6a1c8d2d5d --- /dev/null +++ b/climada/engine/option_appraisal/MCDM/mcda_methods.py @@ -0,0 +1,78 @@ +from enum import Enum + +import numpy as np +import pandas as pd +from sklearn.preprocessing import MinMaxScaler + + +class MCDAApproach(Enum): + SAW = "saw" + TOPSIS = "topsis" + + +def _saw( + matrix: np.ndarray, + weights: np.ndarray, + criteria_types: np.ndarray, +) -> np.ndarray: + """Simple Additive Weighting. + + Parameters + ---------- + matrix : np.ndarray, shape (n_options, n_criteria) + Normalized criteria values. + weights : np.ndarray, shape (n_criteria,) + Normalized weights summing to 1. + criteria_types : np.ndarray, shape (n_criteria,) + +1 for maximization, -1 for minimization criteria. + + Returns + ------- + np.ndarray, shape (n_options,) + SAW scores. + """ + signed = matrix * criteria_types # flip minimization criteria + return signed @ weights + + +def _topsis( + matrix: np.ndarray, + weights: np.ndarray, + criteria_types: np.ndarray, +) -> np.ndarray: + """Technique for Order of Preference by Similarity to Ideal Solution. + + Parameters + ---------- + matrix : np.ndarray, shape (n_options, n_criteria) + Normalized criteria values. + weights : np.ndarray, shape (n_criteria,) + Normalized weights summing to 1. + criteria_types : np.ndarray, shape (n_criteria,) + +1 for maximization, -1 for minimization criteria. + + Returns + ------- + np.ndarray, shape (n_options,) + TOPSIS closeness scores in [0, 1]. + """ + weighted = matrix * weights + + # Ideal best/worst depend on criterion direction + ideal_best = np.where( + criteria_types == 1, weighted.max(axis=0), weighted.min(axis=0) + ) + ideal_worst = np.where( + criteria_types == 1, weighted.min(axis=0), weighted.max(axis=0) + ) + + d_best = np.linalg.norm(weighted - ideal_best, axis=1) + d_worst = np.linalg.norm(weighted - ideal_worst, axis=1) + + return d_worst / (d_best + d_worst) + + +APPROACH_FN = { + MCDAApproach.SAW: _saw, + MCDAApproach.TOPSIS: _topsis, +} diff --git a/climada/engine/option_appraisal/MCDM/utils.py b/climada/engine/option_appraisal/MCDM/utils.py new file mode 100644 index 0000000000..341ef08bcb --- /dev/null +++ b/climada/engine/option_appraisal/MCDM/utils.py @@ -0,0 +1,620 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +Created on Fri Feb 9 14:58:15 2024 + +@author: vwattin +""" + +import copy +import itertools +import os +from functools import partial + +import numpy as np +import pandas as pd +import scipy as sp +from IPython.display import clear_output + +from climada.engine import CostBenefit +from climada.engine.cost_benefit import risk_aai_agg, risk_rp_100, risk_rp_250 +from climada.engine.unsequa import CalcCostBenefit, InputVar, UncOutput +from climada.entity import Entity +from climada.util.api_client import Client + +# import functions as fcn + +# Constants +CURRENT_YEAR = 2018 +FUTURE_YEAR = 2040 +# Define the risk functions dictionary +RISK_FNCS_DICT = {"aai": risk_aai_agg, "rp250": risk_rp_250} + + +import inspect + + +def filter_dataframe(df, filter_conditions=None, derived_columns=None, base_cols=None): + """ + This function filters a DataFrame based on provided conditions and calculates derived columns. + + Parameters: + - df (pandas.DataFrame): The input DataFrame. + - filter_conditions (dict): A dictionary specifying filtering conditions for columns. + - derived_columns (dict): A dictionary specifying derived columns and their functions. + + Returns: + - filtered_df (pandas.DataFrame): The filtered DataFrame based on conditions and derived columns. + - boolean_df (pandas.DataFrame): A boolean DataFrame indicating whether values satisfy conditions. + """ + + # Create a copy of the input DataFrame + filtered_df = df.copy() + unfiltered_df = df.copy() + + # If conditions or derived columns are not provided, initialize them as empty dictionaries + if filter_conditions is None: + filter_conditions = {} + + if derived_columns is None: + derived_columns = {} + + # Calculate and add derived columns to the filtered DataFrame + if derived_columns: + for new_col, function in derived_columns.items(): + filtered_df[new_col] = function(df) + unfiltered_df[new_col] = function(df) + + # Create a boolean DataFrame to track conditions satisfaction + if base_cols: + boolean_df = df[base_cols].copy() + else: + boolean_df = df.copy() + + # Apply filtering conditions and update boolean DataFrame accordingly + if filter_conditions: + for col, cond in filter_conditions.items(): + + if isinstance(cond, list): + # Filter data based on whether column values are equal to the provided value + filtered_df = filtered_df[filtered_df[col] == cond["equal"]] + boolean_df[col] = unfiltered_df[col] == cond["equal"] + elif "equal" in cond: + # Filter data based on whether column values are equal to the provided value + filtered_df = filtered_df[filtered_df[col].isin(cond["equal"])] + boolean_df[col] = unfiltered_df[col].isin(cond["equal"]) + elif "in" in cond: + # Filter data based on whether column values are in the provided list + filtered_df = filtered_df[filtered_df[col].isin(cond["in"])] + boolean_df[col] = unfiltered_df[col].isin(cond["in"]) + elif "greater" in cond: + # Filter data based on whether column values are greater than the provided value + filtered_df = filtered_df[filtered_df[col] > cond["greater"]] + boolean_df[col] = unfiltered_df[col] > cond["greater"] + elif "less" in cond: + # Filter data based on whether column values are less than the provided value + filtered_df = filtered_df[filtered_df[col] < cond["less"]] + boolean_df[col] = unfiltered_df[col] < cond["less"] + elif "range" in cond: + # Filter data based on whether column values are within the provided range + lower, upper = cond["range"] + filtered_df = filtered_df[ + (filtered_df[col] >= lower) & (filtered_df[col] <= upper) + ] + boolean_df[col] = (unfiltered_df[col] >= lower) & ( + unfiltered_df[col] <= upper + ) + + # Drop derived columns from the final filtered DataFrame + if derived_columns: + filtered_df = filtered_df.drop(derived_columns.keys(), axis=1) + + return filtered_df, boolean_df + + +def generate_unique_sets(items): + """ + Generate all possible sets of unique items, including an empty set. + + Parameters: + items (list): List of items to generate sets from. + + Returns: + list of lists: List of lists representing all unique sets. + """ + all_sets = [[]] # Start with an empty set + + for r in range(1, len(items) + 1): + item_combinations = itertools.combinations(items, r) + all_sets.extend(item_combinations) + + # Convert sets to lists + all_sets_as_lists = [list(item_set) for item_set in all_sets] + + return all_sets_as_lists + + +def expand_dataframe(original_df, values_list, new_column_name): + # Create a list to hold the expanded rows + expanded_data = [] + + # Iterate through the original DataFrame + for _, row in original_df.iterrows(): + # For each row in the original DataFrame, create a new row for each value in values_list + for value in values_list: + new_row = row.copy() + new_row[new_column_name] = value + expanded_data.append(new_row) + + # Create the expanded DataFrame + expanded_df = pd.DataFrame(expanded_data) + + return expanded_df + + +def generate_metrics( + haz_dict, + ent_dict, + unc_func_dist_dict={}, + groups=[], + risk_fncs_dict=RISK_FNCS_DICT, + n_samples=1, + imp_time_depend=1.2, + future_year=FUTURE_YEAR, + current_year=CURRENT_YEAR, + file_output=None, +): + + # Initialize a flag variable + first_iteration = True + aggr_sets = generate_unique_sets(groups) + + unc_var_dist = {} + mean_dict = {} + unc_vars = [] + + # Add years to the entity objects + for ent_key in ent_dict.keys(): + ent_dict[ent_key]["today"].exposures.ref_year = current_year + ent_dict[ent_key]["future"].exposures.ref_year = future_year + + # Check if uncertainty variables are given and get them + if unc_func_dist_dict: + for unc_var in unc_func_dist_dict.keys(): + if ( + unc_func_dist_dict[unc_var]["func"] + and unc_func_dist_dict[unc_var]["distr"] + ): + for var in unc_func_dist_dict[unc_var]["distr"].keys(): + # Add the variable to the dictionary + unc_vars += [var] + unc_var_dist[var] = unc_func_dist_dict[unc_var]["distr"][var] + # Check if the variable is continuous or discrete + mean_dict[var] = unc_func_dist_dict[unc_var]["distr"][var].mean() + # If discrete, you will need all the variables # TODO: Add this later + + # Create container + # Build decision matrix + base_col = ["entity", "hazard", "haz_type"] + groups + # crit_col = ['cost'] + ['tot_' + fcn.__name__ for fcn in risk_fncs] + ['ben_' + fcn.__name__ for fcn in risk_fncs] + ['ben_cost_ratio_' + fcn.__name__ for fcn in risk_fncs] + # crit_col = ['crit_' + col for col in crit_col] + metrics_df = pd.DataFrame( + columns=base_col + ) # Decision matrix (contains the criteria values we will use to base a decision) + + for ent_key, haz_key, aggr_set in itertools.product( + ent_dict.keys(), haz_dict.keys(), aggr_sets + ): + # Clear the output + clear_output() + print(ent_key, haz_key, aggr_set) + + # Get the possible group aggregation combinations + if aggr_set: + aggr_combos_df = ( + ent_dict[ent_key]["today"].exposures.gdf[aggr_set].drop_duplicates() + ) + else: + aggr_combos_df = pd.DataFrame(["dummy"]) + + ## Get the entity objects (only exposure effected) at each aggregation combination + for index, aggr_combo in aggr_combos_df.iterrows(): + + # Get the entities + ent_today = copy.deepcopy(ent_dict[ent_key]["today"]) + ent_fut = copy.deepcopy(ent_dict[ent_key]["future"]) + + # Get the valuue unit + + # Filter the exposures to the aggregation combination + if aggr_set: + # Get the index of the exposures that match the aggregation combination + # Enitiy today + idx_combo = ( + ent_today.exposures.gdf[aggr_set] + .isin(aggr_combo.values) + .all(axis=1) + ) + ent_today.exposures.gdf = ent_today.exposures.gdf[idx_combo] + # Entity future + idx_combo = ( + ent_fut.exposures.gdf[aggr_set].isin(aggr_combo.values).all(axis=1) + ) + ent_fut.exposures.gdf = ent_fut.exposures.gdf[idx_combo] + + # Get the hazards + # To speed up calculations – reduce the hazard extent to the extent of the exposures + haz_today = haz_dict[haz_key]["today"].select( + extent=( + ent_today.exposures.gdf.longitude.min(), + ent_today.exposures.gdf.longitude.max(), + ent_today.exposures.gdf.latitude.min(), + ent_today.exposures.gdf.latitude.max(), + ) + ) + haz_fut = haz_dict[haz_key]["future"].select( + extent=( + ent_fut.exposures.gdf.longitude.min(), + ent_fut.exposures.gdf.longitude.max(), + ent_fut.exposures.gdf.latitude.min(), + ent_fut.exposures.gdf.latitude.max(), + ) + ) + + if not haz_today or not haz_fut: + print(f"No hazard data for {ent_key} {haz_key} {aggr_set}") + haz_today = copy.deepcopy(haz_dict[haz_key]["today"]) + haz_fut = copy.deepcopy(haz_dict[haz_key]["future"]) + if not haz_today or not haz_fut: + print(f"No hazard data for {ent_key} {haz_key} {aggr_set}") + continue + + # Get all measure names + measure_dict = ent_today.measures.get_measure()[haz_today.haz_type] + measure_names = ["no measure"] + list(measure_dict.keys()) + + ## Calculate the criteria values, using ether cost-benefit or Unsequa module, depending if uncertainty var included + if not unc_func_dist_dict: + # Create a temporary dict to later use to populate the data frame + t_dict_to_df = { + "entity": [ent_key for meas in measure_names], + "hazard": [haz_key for meas in measure_names], + "haz_type": [haz_today.haz_type for meas in measure_names], + "measure": measure_names, + } + + # Add aggregation columns + if aggr_set: + t_dict_to_df.update( + { + col_name: [col_value for meas in measure_names] + for col_name, col_value in aggr_combo.items() + } + ) + for group in groups: + if group not in aggr_set: + t_dict_to_df[group] = ["ALL" for meas in measure_names] + + # Calc criteria – Costs + t_dict_to_df["crit_cost"] = [ + measure_dict[meas].cost if meas != "no measure" else 0 + for meas in measure_names + ] + + # Calc criteria – Averted risk and cost benefit + for name, risk_fcn in risk_fncs_dict.items(): + costbenefit_disc = CostBenefit() + costbenefit_disc.calc( + hazard=haz_today, + entity=ent_today, + haz_future=haz_fut, + ent_future=ent_fut, + risk_func=risk_fcn, + imp_time_depen=imp_time_depend, + save_imp=True, + ) + + # Save benefit results in tmep dictionary + t_dict_to_df["crit_ben_" + name] = [ + costbenefit_disc.benefit[meas] if meas != "no measure" else 0 + for meas in measure_names + ] + t_dict_to_df["crit_bcr_" + name] = [ + ( + 1 / costbenefit_disc.cost_ben_ratio[meas] + if meas != "no measure" + else 0 + ) + for meas in measure_names + ] + t_dict_to_df["crit_npv_" + name] = [ + ( + costbenefit_disc.tot_climate_risk + - costbenefit_disc.benefit[meas] + if meas != "no measure" + else costbenefit_disc.tot_climate_risk + ) + for meas in measure_names + ] + + # Populate decision matrix + metrics_df = pd.concat( + [metrics_df, pd.DataFrame(t_dict_to_df)], ignore_index=True + ) + + else: + + # Define the input uncertainty variables + # Recode, generalize, later so that you can define and call other defined uncertainty variables that deterine + # the four objects haz_input_var, ent_input_var, haz_fut_input_var, ent_fut_input_var + # Entity today + base_dict = { + "ent_today_base": ent_today, + "ent_fut_base": ent_fut, + "haz_today_base": haz_today, + "haz_fut_base": haz_fut, + } + keys = ["ent_today", "ent_fut", "haz_today", "haz_fut"] + iv_dict = {} + + for key in keys: + if ( + unc_func_dist_dict[key]["func"] + and unc_func_dist_dict[key]["distr"] + ): + iv_dict[key + "_iv"] = InputVar( + partial(unc_func_dist_dict[key]["func"], **base_dict), + unc_func_dist_dict[key]["distr"], + ) + else: + iv_dict[key + "_iv"] = locals()[key] + + # Define the uncertainty cost-benefit object + unc_cb = CalcCostBenefit( + haz_input_var=iv_dict["haz_today_iv"], + ent_input_var=iv_dict["ent_today_iv"], + haz_fut_input_var=iv_dict["haz_fut_iv"], + ent_fut_input_var=iv_dict["ent_fut_iv"], + ) + + # Make samples (only first iteration) + if first_iteration: + df_samples = unc_cb.make_sample( + N=n_samples, sampling_kwargs={"calc_second_order": False} + ).get_samples_df() + # Add the mean + new_mean_row = {var: [mean_dict[var]] for var in unc_var_dist} + # TODO: Add all the varibles for the discrete case + # Append the new row to the DataFrame + df_samples = pd.concat( + [df_samples, pd.DataFrame(new_mean_row)], ignore_index=True + ) + nbr_of_samples = len(df_samples) + first_iteration = False + + # Create empty data frame to consectively add criteria columns to + # Later to be used to concatenate with metrics_df + t_pop_df = expand_dataframe(df_samples, measure_names, "measure") + keys = list(t_pop_df.columns) + + # Calc each criteria value for each uncertainty var combo + for name, risk_fcn in risk_fncs_dict.items(): + # Calculate criteria values with pool + output_cb = unc_cb.uncertainty( + UncOutput(df_samples), + risk_func=risk_fcn, + imp_time_depen=imp_time_depend, + future_year=future_year, + ) + df_results = output_cb.get_uncertainty( + metric_list=["benefit", "cost_ben_ratio", "tot_climate_risk"] + ) + + # Make basic empty data frame containing + t_crit_df = pd.DataFrame( + columns=keys + + ["crit_ben_" + name] + + ["crit_bcr_" + name] + + ["crit_npv_" + name] + ) + t_meas_crit_df = copy.deepcopy(df_samples) + + # Get the criteria values for each measure under each uncertainty variable set + for meas in measure_names: + if meas == "no measure": + t_meas_crit_df["measure"] = meas + t_meas_crit_df["crit_ben_" + name] = 0 + t_meas_crit_df["crit_bcr_" + name] = 0 + t_meas_crit_df["crit_npv_" + name] = df_results[ + "tot_climate_risk" + ] + t_crit_df = pd.concat( + [t_crit_df, t_meas_crit_df], ignore_index=True + ) + else: + t_meas_crit_df["measure"] = meas + t_meas_crit_df["crit_ben_" + name] = df_results[ + meas + " Benef" + ] + t_meas_crit_df["crit_bcr_" + name] = ( + 1 / df_results[meas + " CostBen"] + ) + t_meas_crit_df["crit_npv_" + name] = ( + df_results["tot_climate_risk"] + - df_results[meas + " Benef"] + ) + t_crit_df = pd.concat( + [t_crit_df, t_meas_crit_df], ignore_index=True + ) + + # Right join with t_pop_df + t_pop_df = pd.merge(t_pop_df, t_crit_df, on=keys, how="right") + + # Calc criteria – Costs + t_pop_df["crit_cost"] = [ + measure_dict[meas].cost if meas != "no measure" else 0 + for meas in t_pop_df.measure + ] + t_pop_df["entity"] = ent_key + t_pop_df["hazard"] = haz_key + t_pop_df["haz_type"] = haz_today.haz_type + + # Add aggregation columns + if aggr_set: + for group in groups: + if group not in aggr_set: + t_pop_df[group] = "ALL" + else: + for col_name, col_value in aggr_combo.items(): + t_pop_df[col_name] = col_value + else: + for group in groups: + t_pop_df[group] = "ALL" + + # Populate decision matrix + metrics_df = pd.concat([metrics_df, t_pop_df], ignore_index=True) + + # Make a backup if it fails + # backup_metrics_df = metrics_df.copy() + + # %% + + cols = [["measure"], ["entity"], ["hazard", "haz_type"], unc_vars, groups] + + for idx, col in enumerate(cols): + + # Get unique values from each column + df_temp = pd.DataFrame(metrics_df[col].drop_duplicates(), columns=col) + # Add a common column to each DataFrame + df_temp["_merge"] = 1 + + # Merge data frames + if idx == 0: + df_base = df_temp + else: + df_base = df_base.merge(df_temp, on="_merge") + + # Drop the common column + df_base = df_base.drop("_merge", axis=1) + + if unc_vars: + df_base = df_base.astype(metrics_df[df_base.columns].dtypes) + + # Drop duplicates + df_base = df_base.drop_duplicates() + + # %% + # Join criteria values + metrics_df = pd.merge(df_base, metrics_df, how="left") + + # #%% Update so certain metrics are the same for both provinces, e.g., cost + + # Get non + col = "crit_cost" + meas_cost_df = ( + metrics_df[["measure", col]] + .dropna() + .drop_duplicates() + .rename(columns={col: "clean"}) + ) + # # Merge with metrics_df + metrics_df = metrics_df.merge(meas_cost_df, how="left") + metrics_df[col] = metrics_df["clean"] + metrics_df = metrics_df.drop(columns=["clean"]) + + # %% Pivot the DataFrame to add exposure to column + + # # Define pivot and index columns for pivot + base_col = [ + col for col in metrics_df.columns if "crit" not in col and "entity" not in col + ] + piv_col = ["entity"] + val_col = [col for col in metrics_df.columns if "crit" in col] + + metrics_df = metrics_df.pivot(index=base_col, columns=piv_col, values=val_col) + + metrics_df = metrics_df.reset_index() + + # # Flatten the MultiIndex columns and add suffixes + metrics_df.columns = [ + f'{"_".join(col)}' if col[1] else f"{col[0]}" for col in metrics_df.columns + ] + + # %% Drop and rename columns + + # metrics_df = metrics_df.rename(columns={'crit_cost_Assets' :'crit_cost_USD'}) + # metrics_df = metrics_df.drop(columns=['crit_cost_People']) + + # %%% Add additional criteria + + # Average annual values + # for col in metrics_df.columns: + # if 'crit_npv_' in col: + # risk_fun = col.replace('crit_npv_', '') + # metrics_df['crit_avg_' + risk_fun] = metrics_df[col]/(future_year-current_year) + + # # # Break-even = cost/(benefit/year) (only for assets) + # # #metrics_df['crit_breakeven'] = metrics_df['crit_cost']/(metrics_df['crit_ben_aai_Assets']/(future_year-CURRENT_YEAR)) + + # %% Add feasibility and popularity criteria + + # Updated data including "Insurance" + # data = { + # 'measure': [ + # 'no measure', 'Retention Reservoirs', 'Swales', 'Waste Management', + # 'Rehabilitation Drainage', 'Flood Awareness', 'Spillways', 'Rain collection', + # 'Mobile flood embankments', 'Flood Wall', 'Storage + Sandbags', 'Green Roofs', + # 'Green Spaces', 'Insurance' # Added "Insurance" + # ], + # 'crit_approv': [ + # 3, 4, 4, 3, 3, 1, 4, 4, 1, 2, 4, 2, 3, 4 # Approval rating for "Insurance" + # ], + # 'crit_feas': [ + # 0.044420, 0.594257, 0.837318, 0.882075, 0.634717, 0.116425, 0.959374, + # 0.019499, 0.390462, 0.847239, 0.415067, 0.717244, 0.630990, 0.887256 # Feasibility for "Insurance" + # ] + # } + + # Create DataFrame + temp_df = pd.DataFrame(metrics_df["measure"].unique(), columns=["measure"]) + temp_df["crit_approv"] = [np.random.randint(1, 5) for _ in range(len(temp_df))] + temp_df["crit_feas"] = np.random.rand(len(temp_df)) + metrics_df = pd.merge(metrics_df, temp_df, on="measure") + + # %% Drop the criteria prefix + metrics_df.columns = [ + col.replace("crit_", "") if "crit" in col else col for col in metrics_df.columns + ] + + # %% Save the metrics + if file_output: + + # Path + path = os.path.join(os.getcwd(), "Data/Metrics") + # Make country directory + try: + # Create the directory + os.mkdir(path) + except: + pass + + # Save the file as a csv with suffix groups + file_output_csv = os.path.join(path, file_output + ".csv") + metrics_df.to_csv(file_output_csv, index=False) + + return metrics_df + + +def generate_unc_func_dist_dict(func_dict, unc_var_dist_dict): + # Generate unc_func_dist_dict + unc_func_dist_dict = {} + for func_name, func in func_dict.items(): + # Get the argument names of the function + arg_names = inspect.getfullargspec(func).args + # Map the argument names to their distributions + distr = { + arg: unc_var_dist_dict[arg] for arg in arg_names if arg in unc_var_dist_dict + } + # Add to unc_func_dist_dict + unc_func_dist_dict[func_name] = {"func": func, "distr": distr} + return unc_func_dist_dict diff --git a/climada/engine/option_appraisal/MCDM/weights.py b/climada/engine/option_appraisal/MCDM/weights.py new file mode 100644 index 0000000000..b16f1493a1 --- /dev/null +++ b/climada/engine/option_appraisal/MCDM/weights.py @@ -0,0 +1,60 @@ +""" +This file is part of CLIMADA. + +Copyright (C) 2017 ETH Zurich, CLIMADA contributors listed in AUTHORS. + +CLIMADA is free software: you can redistribute it and/or modify it under the +terms of the GNU General Public License as published by the Free +Software Foundation, version 3. + +CLIMADA is distributed in the hope that it will be useful, but WITHOUT ANY +WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A +PARTICULAR PURPOSE. See the GNU General Public License for more details. + +You should have received a copy of the GNU General Public License along +with CLIMADA. If not, see . + +--- + +""" + +from climada.engine.option_appraisal.MCDM.constants import ( + DEFAULT_ITEM_WEIGHT, + IMPORTANCE_MATCH, +) + + +class WeightedItem: + """Mixin providing a validated weight attribute. + + Parameters + ---------- + weight : float or str or None + Initial weight. Strings are resolved via ``IMPORTANCE_MATCH``. + ``None`` defaults to ``DEFAULT_WEIGHT``. + """ + + def __init__(self, weight=None) -> None: + self.weight = weight # use the public setter from the start + + @property + def weight(self) -> float: + """Direct weight of this item, in [0, 1].""" + return self._weight + + @weight.setter + def weight(self, value): + if value is None: + value = DEFAULT_ITEM_WEIGHT + if isinstance(value, str): + try: + value = IMPORTANCE_MATCH[value] + except KeyError as err: + err.add_note( + f"Importance '{value}' is not defined. " + f"Must be one of {list(IMPORTANCE_MATCH.keys())}" + ) + raise + if not 0.0 <= value <= 1.0: + raise ValueError(f"Weight must be in [0, 1], got {value}.") + self._weight = value diff --git a/climada/engine/option_appraisal/appraiser.py b/climada/engine/option_appraisal/appraiser.py new file mode 100644 index 0000000000..19a1c21c18 --- /dev/null +++ b/climada/engine/option_appraisal/appraiser.py @@ -0,0 +1,281 @@ +""" +This file is part of CLIMADA. + +Copyright (C) 2017 ETH Zurich, CLIMADA contributors listed in AUTHORS. + +CLIMADA is free software: you can redistribute it and/or modify it under the +terms of the GNU General Public License as published by the Free +Software Foundation, version 3. + +CLIMADA is distributed in the hope that it will be useful, but WITHOUT ANY +WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A +PARTICULAR PURPOSE. See the GNU General Public License for more details. + +You should have received a copy of the GNU General Public License along +with CLIMADA. If not, see . + +--- + +""" + +import copy +import logging +from typing import Iterable + +import pandas as pd +from tqdm import tqdm + +from climada.engine.option_appraisal.constants import ( + AVERTED_RISK_NAME, + MEASURE_IMPL_COST_NAME, + REFERENCE_RISK_NAME, +) +from climada.entity.disc_rates.base import DiscRates +from climada.entity.measures.measure_set import MeasureSet +from climada.trajectories.calc_risk_metrics import CalcRiskMetricsPoints +from climada.trajectories.constants import ( + COORD_ID_COL_NAME, + DATE_COL_NAME, + GROUP_COL_NAME, + MEASURE_COL_NAME, + METRIC_COL_NAME, + NO_MEASURE_VALUE, + RISK_COL_NAME, +) +from climada.trajectories.impact_calc_strat import ImpactComputationStrategy +from climada.trajectories.snapshot import Snapshot +from climada.trajectories.static_trajectory import StaticRiskTrajectory +from climada.trajectories.trajectory import DEFAULT_DF_COLUMN_PRIORITY, DEFAULT_RP +from climada.util import log_level +from climada.util.config import CONFIG +from climada.util.dataframe_handling import reorder_dataframe_columns + +tqdm.pandas() + +LOGGER = logging.getLogger(__name__) + + +class StaticAppraiser(StaticRiskTrajectory): + def __init__( + self, + snapshots_list: Iterable[Snapshot], + *, + measure_set: MeasureSet, + return_periods: Iterable[int] = DEFAULT_RP, + risk_disc_rates: DiscRates | None = None, + cost_disc_rates: DiscRates | None = None, + impact_computation_strategy: ImpactComputationStrategy | None = None, + ): + """Initialize a new `StaticAppraiser`. + + Parameters + ---------- + snapshots_list : list[Snapshot] + The list of `Snapshot` object to compute risk from. + measure_set: MeasureSet + The set of adaptation measures to appraise. + return_periods: list[int], optional + The return periods to use when computing the `return_periods_metric`. + Defaults to `DEFAULT_RP` ([20, 50, 100]). + all_groups_name: str, optional + The string that should be used to define "all exposure points" subgroup. + Defaults to `DEFAULT_ALLGROUP_NAME` ("All"). + risk_disc_rates: DiscRates, optional + The discount rate to apply to future risk. Defaults to None. + cost_disc_rates: DiscRates, optional + The discount rate to apply to future costs (of adaptation measures). + Defaults to None. + impact_computation_strategy: ImpactComputationStrategy, optional + The method used to calculate the impact from the (Haz,Exp,Vul) + of the two snapshots. Defaults to :class:`ImpactCalcComputation`. + + """ + + self._cost_disc_rates = cost_disc_rates + self.measure_set = copy.deepcopy(measure_set) + super().__init__( + snapshots_list, + return_periods=return_periods, + risk_disc_rates=risk_disc_rates, + impact_computation_strategy=impact_computation_strategy, + ) + self._risk_metrics_calculators = self._add_adaptation_metrics_calculators( + self._risk_metrics_calculators, measure_set + ) + + @staticmethod + def _add_adaptation_metrics_calculators( + risk_metrics_calculators, measure_set: MeasureSet + ) -> list[CalcRiskMetricsPoints]: + """Adds the risk metric calculators for the different adaptation options.""" + calculators = [risk_metrics_calculators] + [ + risk_metrics_calculators.apply_measure(meas) + for _, meas in measure_set.measures().items() + ] + return calculators + + @property + def cost_disc_rates(self) -> DiscRates | None: + """The discount rate applied to compute net present values of costs. + None means no discount rate. + + Notes + ----- + + Changing its value resets the metrics. + """ + return self._cost_disc_rates + + @cost_disc_rates.setter + def cost_disc_rates(self, value, /): + if value is not None and not isinstance(value, DiscRates): + raise ValueError("Risk discount needs to be a `DiscRates` object.") + + self._reset_metrics() + self._cost_disc_rates = value + + def _generic_metrics( + self, + metric_name: str | None = None, + metric_meth: str | None = None, + **kwargs, + ) -> pd.DataFrame: + """Generic method to compute metrics based on the provided metric name and method. + + This method calls the appropriate method from each calculators (corresponding to + each adaptation) to return the results for the given metric, + in a tidy formatted dataframe. + + It first checks whether the requested metric is a valid one. + Then looks for a possible cached value and otherwised asks the + calculators (`self._risk_metric_calculators`) to run the computation. + The results are then regrouped in a nice and tidy DataFrame. + If a `risk_disc_rates` was set, values are converted to net present values. + Results are then cached within `self.__metrics` and returned. + + Parameters + ---------- + metric_name : str, optional + The name of the metric to return results for. + metric_meth : str, optional + The name of the specific method of the calculator to call. + + Returns + ------- + pd.DataFrame + A tidy formatted dataframe of the risk metric computed for the + different snapshots. + + Raises + ------ + NotImplementedError + If the requested metric is not part of `POSSIBLE_METRICS`. + ValueError + If either of the arguments are not provided. + + """ + + if metric_name is None or metric_meth is None: + raise ValueError("Both metric_name and metric_meth must be provided.") + + if metric_name not in self.POSSIBLE_METRICS: + raise NotImplementedError( + f"{metric_name} not implemented ({self.POSSIBLE_METRICS})." + ) + + # Construct the attribute name for storing the metric results + attr_name = f"_{metric_name}_metrics" + + if getattr(self, attr_name) is not None: + LOGGER.debug("Returning cached %s", attr_name) + return getattr(self, attr_name) + + LOGGER.debug("Computing %s", attr_name) + with log_level(level="WARNING", name_prefix="climada"): + tmp = [ + getattr(calc_period, metric_meth)(**kwargs) + for calc_period in self._risk_metrics_calculators + ] + + try: + tmp = pd.concat(tmp) + except ValueError as exc: + if str(exc) == "All objects passed were None": + return pd.DataFrame() + raise exc + + if len(tmp) == 0: + return pd.DataFrame() + + tmp = self._metric_post_treatment(tmp, metric_name) + + if CONFIG.trajectory_caching.bool(): + LOGGER.debug("All computing done, caching value.") + setattr(self, attr_name, tmp) + return getattr(self, attr_name) + + return tmp + + def _metric_post_treatment( + self, metric_df: pd.DataFrame, metric_name: str + ) -> pd.DataFrame: + # Notably for per_group_aai being None: + def meas_impl_cost(measure_name: str) -> float: + if measure_name == NO_MEASURE_VALUE: + return 0.0 + + return self.measure_set.measures()[measure_name].cost_income.init_cost + + metric_df = self._handle_group_categories(metric_df) + if self._risk_disc_rates: + LOGGER.debug("Found risk discount rate. Computing NPV.") + metric_df = self.npv_transform(metric_df, self._risk_disc_rates) + + LOGGER.debug("Computing averted risk for: %s.", metric_name) + metric_df = self._calc_averted(metric_df) + metric_df[MEASURE_IMPL_COST_NAME] = metric_df[MEASURE_COL_NAME].map( + meas_impl_cost + ) + metric_df = reorder_dataframe_columns(metric_df, DEFAULT_DF_COLUMN_PRIORITY) + return metric_df + + def _handle_group_categories(self, metric_df: pd.DataFrame) -> pd.DataFrame: + if self._all_groups_name not in metric_df[GROUP_COL_NAME].cat.categories: + metric_df[GROUP_COL_NAME] = metric_df[GROUP_COL_NAME].cat.add_categories( + [self._all_groups_name] + ) + metric_df[GROUP_COL_NAME] = metric_df[GROUP_COL_NAME].fillna( + self._all_groups_name + ) + + return metric_df + + @staticmethod + def _calc_averted(base_metrics: pd.DataFrame) -> pd.DataFrame: + def subtract_no_measure(group, no_measure, merger): + # Merge with no_measure to get the corresponding NO_MEASURE_VALUE value + merged = group.merge( + no_measure, on=merger, suffixes=("", "_" + NO_MEASURE_VALUE) + ) + # Subtract the NO_MEASURE_VALUE risk from the current risk + merged[REFERENCE_RISK_NAME] = merged[RISK_COL_NAME + "_" + NO_MEASURE_VALUE] + merged[AVERTED_RISK_NAME] = ( + merged[RISK_COL_NAME + "_" + NO_MEASURE_VALUE] - merged[RISK_COL_NAME] + ) + return merged[ + list(group.columns) + [REFERENCE_RISK_NAME, AVERTED_RISK_NAME] + ] + + no_measures_metrics = base_metrics[ + base_metrics[MEASURE_COL_NAME] == NO_MEASURE_VALUE + ].copy() + merger = [GROUP_COL_NAME, METRIC_COL_NAME, DATE_COL_NAME] + if COORD_ID_COL_NAME in base_metrics.columns: + merger.append(COORD_ID_COL_NAME) + + return base_metrics.groupby( + [GROUP_COL_NAME, METRIC_COL_NAME, DATE_COL_NAME], + group_keys=False, + dropna=False, + observed=False, + ).apply(subtract_no_measure, no_measure=no_measures_metrics, merger=merger) diff --git a/climada/engine/option_appraisal/constants.py b/climada/engine/option_appraisal/constants.py new file mode 100644 index 0000000000..acbbe191de --- /dev/null +++ b/climada/engine/option_appraisal/constants.py @@ -0,0 +1,30 @@ +""" +This file is part of CLIMADA. + +Copyright (C) 2017 ETH Zurich, CLIMADA contributors listed in AUTHORS. + +CLIMADA is free software: you can redistribute it and/or modify it under the +terms of the GNU General Public License as published by the Free +Software Foundation, version 3. + +CLIMADA is distributed in the hope that it will be useful, but WITHOUT ANY +WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A +PARTICULAR PURPOSE. See the GNU General Public License for more details. + +You should have received a copy of the GNU General Public License along +with CLIMADA. If not, see . + +--- + +Define constants for option appraisal module. +""" + +REFERENCE_RISK_NAME = "reference risk" +AVERTED_RISK_NAME = "averted risk" +RESIDUAL_RISK_NAME = "residual risk" + +MEASURE_IMPL_COST_NAME = "measure implementation cost" +MEASURE_NET_COST_NAME = "measure net cost" +CUMULATED_COST_NAME = "cumulated measure cost" +CUMULATED_BENEFIT_NAME = "cumulated measure benefit" +CB_RATIO_NAME = "cost/benefit ratio" diff --git a/climada/engine/test/test_cost_benefit.py b/climada/engine/test/test_cost_benefit.py index e48afec110..8b0276c665 100644 --- a/climada/engine/test/test_cost_benefit.py +++ b/climada/engine/test/test_cost_benefit.py @@ -33,10 +33,10 @@ risk_rp_100, risk_rp_250, ) +from climada.entity._legacy_measures import Measure +from climada.entity._legacy_measures.base import LOGGER as ILOG from climada.entity.disc_rates import DiscRates from climada.entity.entity_def import Entity -from climada.entity.measures import Measure -from climada.entity.measures.base import LOGGER as ILOG from climada.hazard.base import Hazard from climada.test import get_test_file from climada.util.api_client import Client diff --git a/climada/engine/test/test_impact.py b/climada/engine/test/test_impact.py index 38b3def3d8..c79a264a22 100644 --- a/climada/engine/test/test_impact.py +++ b/climada/engine/test/test_impact.py @@ -321,6 +321,61 @@ def test_ref_value_rp_pass(self): self.assertEqual("USD", ifc.unit) self.assertEqual("1/week", ifc.frequency_unit) + def test_interpolate_freq_curve(self): + """Test inter- and extrapolate method of freq curve""" + imp = Impact() + imp.frequency = np.array([0.2, 0.1, 0.1, 0.1]) + imp.at_event = np.array([0.0, 100.0, 50.0, 110.0]) + imp.unit = "USD" + imp.frequency_unit = "1/year" + + ifc = imp.calc_freq_curve() + # the ifc has values + # impacts [0, 50, 100, 110] + # exceedance frequencies [.5, .3, .2, .1] + # return periods [2, 3.3, 5, 10] + + # stepfunction assigns zero return periods below data and max(impact) for those above + npt.assert_array_almost_equal( + ifc.interpolate([1, 5, 20], method="stepfunction").impact, + [0.0, 100.0, 110.0], + ) + + # interpolate assigns nan to return periods outside of data + npt.assert_array_almost_equal( + ifc.interpolate([1, 5, 20], method="interpolate").impact, + [np.nan, 100.0, np.nan], + ) + + # extrapolate_constant assigns zero return periods below data and max(impact) for those above + npt.assert_array_almost_equal( + ifc.interpolate([1, 5, 20], method="extrapolate_constant").impact, + [0.0, 100.0, 110.0], + ) + + # by binning the last two digits, 100 and 110 are rounded to 100 + npt.assert_array_almost_equal( + ifc.interpolate( + [1, 5, 20], method="extrapolate_constant", bin_decimals=-2 + ).impact, + [0.0, 100.0, 100.0], + ) + + # extrapolation is done by neglecting 0 impacts (min_impact=0) + # rp=1: extrapolate impacts [50, 100] and ex_freqs [.3, .2] to ex_freq=1 --> 0 + # rp=2.5: extrapolate impacts [50, 100] and ex_freqs [.3, .2] to ex_freq=0.4 --> 0 + # rp=4: extrapolate impacts [50, 100] and ex_freqs [.3, .2] to ex_freq=0.25 --> 75 + # rp=1: extrapolate impacts [100, 110] and ex_freqs [.2, .1] to ex_freq=0.05 --> 115 + npt.assert_array_almost_equal( + ifc.interpolate( + [1.0, 2.5, 4, 20], + method="extrapolate", + log_frequency=False, + log_impact=False, + ).impact, + [-300.0, 0.0, 75.0, 115.0], + ) + class TestImpactPerYear(unittest.TestCase): """Test calc_impact_year_set method""" diff --git a/climada/engine/test/test_impact_calc.py b/climada/engine/test/test_impact_calc.py index bd606c6e19..9e7b2e2cb2 100644 --- a/climada/engine/test/test_impact_calc.py +++ b/climada/engine/test/test_impact_calc.py @@ -441,7 +441,7 @@ def test_calc_insured_impact_no_insurance(self): self.assertEqual( logs.output, [ - "INFO:climada.engine.impact_calc:Calculating impact for 150 assets (>0) and 14450 events." + "INFO:climada.engine.impact_calc:Calculating impact for 50 assets (>0) and 14450 events." ], ) self.assertEqual(icalc.n_events, len(impact.at_event)) diff --git a/climada/engine/unsequa/input_var.py b/climada/engine/unsequa/input_var.py index 56a47fe845..9abdd8d3fa 100644 --- a/climada/engine/unsequa/input_var.py +++ b/climada/engine/unsequa/input_var.py @@ -246,9 +246,8 @@ def haz(haz_list, n_ev=None, bounds_int=None, bounds_frac=None, bounds_freq=None The frequency of all events is multiplied by a number sampled uniformly from a distribution with (min, max) = bounds_freq HL: sample uniformly from hazard list - From the provided list of hazard is elements are uniformly - sampled. For example, Hazards outputs from dynamical models - for different input factors. + For each sample, one element is drawn uniformly from the provided list of hazards. + For example, Hazards outputs from dynamical models for different input factors. If a bounds is None, this parameter is assumed to have no uncertainty. @@ -310,8 +309,8 @@ def exp(exp_list, bounds_totval=None, bounds_noise=None): with (min, max) = bounds_noise. EN is the value of the seed for the uniform random number generator. EL: sample uniformly from exposure list - From the provided list of exposure is elements are uniformly - sampled. For example, LitPop instances with different exponents. + For each sample, one element is drawn uniformly from the provided list of exposures. + For example, LitPop instances with different exponents. If a bounds is None, this parameter is assumed to have no uncertainty. @@ -376,9 +375,8 @@ def impfset( sampled uniformly from a distribution with (min, max) = bounds_int IL: sample uniformly from impact function set list - From the provided list of impact function sets elements are uniformly - sampled. For example, impact functions obtained from different - calibration methods. + For each sample, one element is drawn uniformly from the provided list of impact function sets. + For example, impact functions obtained from different calibration methods. If a bounds is None, this parameter is assumed to have no uncertainty. @@ -468,8 +466,8 @@ def ent( with (min, max) = bounds_noise. EN is the value of the seed for the uniform random number generator. EL: sample uniformly from exposure list - From the provided list of exposure is elements are uniformly - sampled. For example, LitPop instances with different exponents. + For each sample, one element is drawn uniformly from the provided list of exposures. + For example, LitPop instances with different exponents. MDD: scale the mdd (homogeneously) The value of mdd at each intensity is multiplied by a number sampled uniformly from a distribution with @@ -483,9 +481,8 @@ def ent( sampled uniformly from a distribution with (min, max) = bounds_int IL: sample uniformly from impact function set list - From the provided list of impact function sets elements are uniformly - sampled. For example, impact functions obtained from different - calibration methods. + For each sample, one element is drawn uniformly from the provided list of impact function sets. + For example, impact functions obtained from different calibration methods. If a bounds is None, this parameter is assumed to have no uncertainty. @@ -521,7 +518,7 @@ def ent( exp_list : [climada.entity.exposures.base.Exposure] The list of base exposure. Can be one or many to uniformly sample from. - meas_set : climada.entity.measures.measure_set.MeasureSet + meas_set : climada.entity._legacy_measures.measure_set.MeasureSet The base measures. haz_id_dict : dict Dictionary of the impact functions affected by uncertainty. @@ -566,7 +563,7 @@ def ent( bounds_noise=bounds_noise, exp_list=exp_list, meas_set=meas_set, - **kwargs + **kwargs, ), _ent_unc_dict( bounds_totval=bounds_totval, @@ -616,8 +613,8 @@ def entfut( with (min, max) = bounds_noise. EN is the value of the seed for the uniform random number generator. EL: sample uniformly from exposure list - From the provided list of exposure is elements are uniformly - sampled. For example, LitPop instances with different exponents. + For each sample, one element is drawn uniformly from the provided list of exposures. + For example, LitPop instances with different exponents. MDD: scale the mdd (homogeneously) The value of mdd at each intensity is multiplied by a number sampled uniformly from a distribution with @@ -631,9 +628,8 @@ def entfut( sampled uniformly from a distribution with (min, max) = bounds_impfi IL: sample uniformly from impact function set list - From the provided list of impact function sets elements are uniformly - sampled. For example, impact functions obtained from different - calibration methods. + For each sample, one element is drawn uniformly from the provided list of impact function sets. + For example, impact functions obtained from different calibration methods. If a bounds is None, this parameter is assumed to have no uncertainty. @@ -664,7 +660,7 @@ def entfut( exp_list : [climada.entity.exposures.base.Exposure] The list of base exposure. Can be one or many to uniformly sample from. - meas_set : climada.entity.measures.measure_set.MeasureSet + meas_set : climada.entity._legacy_measures.measure_set.MeasureSet The base measures. haz_id_dict : dict Dictionary of the impact functions affected by uncertainty. @@ -706,7 +702,7 @@ def entfut( impf_set_list=impf_set_list, exp_list=exp_list, meas_set=meas_set, - **kwargs + **kwargs, ), _entfut_unc_dict( bounds_eg=bounds_eg, diff --git a/climada/engine/unsequa/test/test_unsequa.py b/climada/engine/unsequa/test/test_unsequa.py index 1e7b965ed4..ad14cd4022 100755 --- a/climada/engine/unsequa/test/test_unsequa.py +++ b/climada/engine/unsequa/test/test_unsequa.py @@ -41,22 +41,18 @@ from climada.entity import Exposures, ImpactFunc, ImpactFuncSet from climada.entity.entity_def import Entity from climada.hazard import Hazard -from climada.util.api_client import Client +from climada.test import get_test_file from climada.util.constants import ( ENT_DEMO_FUTURE, ENT_DEMO_TODAY, - EXP_DEMO_H5, HAZ_DEMO_H5, TEST_UNC_OUTPUT_COSTBEN, TEST_UNC_OUTPUT_IMPACT, ) -test_unc_output_impact = Client().get_dataset_file( - name=TEST_UNC_OUTPUT_IMPACT, status="test_dataset" -) -test_unc_output_costben = Client().get_dataset_file( - name=TEST_UNC_OUTPUT_COSTBEN, status="test_dataset" -) +EXP_DEMO_H5 = get_test_file("exp_demo_today", file_format="hdf5") +test_unc_output_impact = get_test_file(TEST_UNC_OUTPUT_IMPACT) +test_unc_output_costben = get_test_file(TEST_UNC_OUTPUT_COSTBEN) def impf_dem(x_paa=1, x_mdd=1): @@ -578,7 +574,7 @@ def test_calc_sensitivity_all_pass(self): "sensitivity_kwargs": {"S": 10, "seed": 12345}, "test_param_name": ["x_exp", 0], "test_si_name": ["CV", 16], - "test_si_value": [0.25000, 2], + "test_si_value": [0.250000, 2], }, "hdmr": { "sampling_method": "saltelli", @@ -587,7 +583,7 @@ def test_calc_sensitivity_all_pass(self): "sensitivity_kwargs": {}, "test_param_name": ["x_exp", 2], "test_si_name": ["Sa", 4], - "test_si_value": [0.004658, 3], + "test_si_value": [0.004649, 3], }, "ff": { "sampling_method": "ff", @@ -618,7 +614,7 @@ def test_calc_sensitivity_all_pass(self): }, "test_param_name": ["x_exp", 0], "test_si_name": ["dgsm", 8], - "test_si_value": [1.697516e-01, 9], + "test_si_value": [0.1697516, 9], }, "fast": { "sampling_method": "fast_sampler", @@ -627,7 +623,7 @@ def test_calc_sensitivity_all_pass(self): "sensitivity_kwargs": {"M": 4, "seed": 12345}, "test_param_name": ["x_exp", 0], "test_si_name": ["S1_conf", 8], - "test_si_value": [0.671396, 1], + "test_si_value": [0.671546, 1], }, "rbd_fast": { "sampling_method": "saltelli", @@ -636,7 +632,7 @@ def test_calc_sensitivity_all_pass(self): "sensitivity_kwargs": {"M": 4, "seed": 12345}, "test_param_name": ["x_exp", 0], "test_si_name": ["S1_conf", 4], - "test_si_value": [0.152609, 4], + "test_si_value": [0.129919, 4], }, "morris": { "sampling_method": "morris", @@ -645,7 +641,7 @@ def test_calc_sensitivity_all_pass(self): "sensitivity_kwargs": {}, "test_param_name": ["x_exp", 0], "test_si_name": ["mu", 1], - "test_si_value": [5066460029.63911, 8], + "test_si_value": [7935400297.813827, 8], }, } @@ -700,7 +696,7 @@ def test_sensitivity_method( haz_unc, sensitivity_method, method_params, - places=2 if sensitivity_method == "rbd_fast" else 5, + places=5, ) diff --git a/climada/entity/__init__.py b/climada/entity/__init__.py index 7b830c2b70..ceb24ee065 100755 --- a/climada/entity/__init__.py +++ b/climada/entity/__init__.py @@ -19,8 +19,8 @@ init entity """ +from ._legacy_measures import * from .disc_rates import * from .entity_def import * from .exposures import * from .impact_funcs import * -from .measures import * diff --git a/climada/entity/_legacy_measures/__init__.py b/climada/entity/_legacy_measures/__init__.py new file mode 100755 index 0000000000..36d9250459 --- /dev/null +++ b/climada/entity/_legacy_measures/__init__.py @@ -0,0 +1,23 @@ +""" +This file is part of CLIMADA. + +Copyright (C) 2017 ETH Zurich, CLIMADA contributors listed in AUTHORS. + +CLIMADA is free software: you can redistribute it and/or modify it under the +terms of the GNU General Public License as published by the Free +Software Foundation, version 3. + +CLIMADA is distributed in the hope that it will be useful, but WITHOUT ANY +WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A +PARTICULAR PURPOSE. See the GNU General Public License for more details. + +You should have received a copy of the GNU General Public License along +with CLIMADA. If not, see . + +--- + +init measures +""" + +from .base import * +from .measure_set import * diff --git a/climada/entity/_legacy_measures/base.py b/climada/entity/_legacy_measures/base.py new file mode 100755 index 0000000000..4e539f9986 --- /dev/null +++ b/climada/entity/_legacy_measures/base.py @@ -0,0 +1,570 @@ +""" +This file is part of CLIMADA. + +Copyright (C) 2017 ETH Zurich, CLIMADA contributors listed in AUTHORS. + +CLIMADA is free software: you can redistribute it and/or modify it under the +terms of the GNU General Public License as published by the Free +Software Foundation, version 3. + +CLIMADA is distributed in the hope that it will be useful, but WITHOUT ANY +WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A +PARTICULAR PURPOSE. See the GNU General Public License for more details. + +You should have received a copy of the GNU General Public License along +with CLIMADA. If not, see . + +--- + +Define Measure class. +""" + +__all__ = ["Measure"] + +import copy +import logging +from pathlib import Path +from typing import Optional, Tuple + +import numpy as np +import pandas as pd +from geopandas import GeoDataFrame + +import climada.util.checker as u_check +from climada.entity.exposures.base import INDICATOR_CENTR, INDICATOR_IMPF, Exposures +from climada.hazard.base import Hazard + +LOGGER = logging.getLogger(__name__) + +IMPF_ID_FACT = 1000 +"""Factor internally used as id for impact functions when region selected.""" + +NULL_STR = "nil" +"""String considered as no path in measures exposures_set and hazard_set or +no string in imp_fun_map""" + + +class Measure: + """ + Contains the definition of one measure. + + Attributes + ---------- + name : str + name of the measure + haz_type : str + related hazard type (peril), e.g. TC + color_rgb : np.array + integer array of size 3. Color code of this measure in RGB + cost : float + discounted cost (in same units as assets) + hazard_set : str + file name of hazard to use (in h5 format) + hazard_freq_cutoff : float + hazard frequency cutoff + exposures_set : str or climada.entity.Exposure + file name of exposure to use (in h5 format) or Exposure instance + imp_fun_map : str + change of impact function id of exposures, e.g. '1to3' + hazard_inten_imp : tuple(float, float) + parameter a and b of hazard intensity change + mdd_impact : tuple(float, float) + parameter a and b of the impact over the mean damage degree + paa_impact : tuple(float, float) + parameter a and b of the impact over the percentage of affected assets + exp_region_id : int + region id of the selected exposures to consider ALL the previous + parameters + risk_transf_attach : float + risk transfer attachment + risk_transf_cover : float + risk transfer cover + risk_transf_cost_factor : float + factor to multiply to resulting insurance layer to get the total + cost of risk transfer + """ + + def __init__( + self, + name: str = "", + haz_type: str = "", + cost: float = 0, + hazard_set: str = NULL_STR, + hazard_freq_cutoff: float = 0, + exposures_set: str = NULL_STR, + imp_fun_map: str = NULL_STR, + hazard_inten_imp: Tuple[float, float] = (1, 0), + mdd_impact: Tuple[float, float] = (1, 0), + paa_impact: Tuple[float, float] = (1, 0), + exp_region_id: Optional[list] = None, + risk_transf_attach: float = 0, + risk_transf_cover: float = 0, + risk_transf_cost_factor: float = 1, + color_rgb: Optional[np.ndarray] = None, + ): + """Initialize a Measure object with given values. + + Parameters + ---------- + name : str, optional + name of the measure + haz_type : str, optional + related hazard type (peril), e.g. TC + cost : float, optional + discounted cost (in same units as assets) + hazard_set : str, optional + file name of hazard to use (in h5 format) + hazard_freq_cutoff : float, optional + hazard frequency cutoff + exposures_set : str or climada.entity.Exposure, optional + file name of exposure to use (in h5 format) or Exposure instance + imp_fun_map : str, optional + change of impact function id of exposures, e.g. '1to3' + hazard_inten_imp : tuple(float, float), optional + parameter a and b of hazard intensity change + mdd_impact : tuple(float, float), optional + parameter a and b of the impact over the mean damage degree + paa_impact : tuple(float, float), optional + parameter a and b of the impact over the percentage of affected assets + exp_region_id : int, optional + region id of the selected exposures to consider ALL the previous + parameters + risk_transf_attach : float, optional + risk transfer attachment + risk_transf_cover : float, optional + risk transfer cover + risk_transf_cost_factor : float, optional + factor to multiply to resulting insurance layer to get the total + cost of risk transfer + color_rgb : np.array, optional + integer array of size 3. Color code of this measure in RGB. + Default is None (corresponds to black). + """ + self.name = name + self.haz_type = haz_type + self.color_rgb = np.array([0, 0, 0]) if color_rgb is None else color_rgb + self.cost = cost + + # related to change in hazard + self.hazard_set = hazard_set + self.hazard_freq_cutoff = hazard_freq_cutoff + + # related to change in exposures + self.exposures_set = exposures_set + self.imp_fun_map = imp_fun_map + + # related to change in impact functions + self.hazard_inten_imp = hazard_inten_imp + self.mdd_impact = mdd_impact + self.paa_impact = paa_impact + + # related to change in region + self.exp_region_id = [] if exp_region_id is None else exp_region_id + + # risk transfer + self.risk_transf_attach = risk_transf_attach + self.risk_transf_cover = risk_transf_cover + self.risk_transf_cost_factor = risk_transf_cost_factor + + def check(self): + """ + Check consistent instance data. + + Raises + ------ + ValueError + """ + u_check.size([3, 4], self.color_rgb, "Measure.color_rgb") + u_check.size(2, self.hazard_inten_imp, "Measure.hazard_inten_imp") + u_check.size(2, self.mdd_impact, "Measure.mdd_impact") + u_check.size(2, self.paa_impact, "Measure.paa_impact") + + def calc_impact(self, exposures, imp_fun_set, hazard): + """ + Apply measure and compute impact and risk transfer of measure + implemented over inputs. + + Parameters + ---------- + exposures : climada.entity.Exposures + exposures instance + imp_fun_set : climada.entity.ImpactFuncSet + impact function set instance + hazard : climada.hazard.Hazard + hazard instance + + Returns + ------- + climada.engine.Impact + resulting impact and risk transfer of measure + """ + + new_exp, new_impfs, new_haz = self.apply(exposures, imp_fun_set, hazard) + # assign centroids if missing + if new_haz.centr_exp_col not in new_exp.gdf.columns: + LOGGER.warning( + "No assigned hazard centroids in exposure object after the " + "application of the measure. The centroids will be assigned during impact " + "calculation. This is potentiall costly. To silence this warning, make sure " + "that centroids are assigned to all exposures." + ) + new_exp.assign_centroids(new_haz) + + return self._calc_impact(new_exp, new_impfs, new_haz) + + def apply(self, exposures, imp_fun_set, hazard): + """ + Implement measure with all its defined parameters. + + Parameters + ---------- + exposures : climada.entity.Exposures + exposures instance + imp_fun_set : climada.entity.ImpactFuncSet + impact function set instance + hazard : climada.hazard.Hazard + hazard instance + + Returns + ------- + new_exp : climada.entity.Exposure + Exposure with implemented measure with all defined parameters + new_ifs : climada.entity.ImpactFuncSet + Impact function set with implemented measure with all defined parameters + new_haz : climada.hazard.Hazard + Hazard with implemented measure with all defined parameters + """ + # change hazard + new_haz = self._change_all_hazard(hazard) + # change exposures + new_exp = self._change_all_exposures(exposures) + new_exp = self._change_exposures_impf(new_exp) + # change impact functions + new_impfs = self._change_imp_func(imp_fun_set) + # cutoff events whose damage happen with high frequency (in region impf specified) + new_haz = self._cutoff_hazard_damage(new_exp, new_impfs, new_haz) + # apply all previous changes only to the selected exposures + new_exp, new_impfs, new_haz = self._filter_exposures( + exposures, imp_fun_set, hazard, new_exp, new_impfs, new_haz + ) + + return new_exp, new_impfs, new_haz + + def _calc_impact(self, new_exp, new_impfs, new_haz): + """Compute impact and risk transfer of measure implemented over inputs. + + Parameters + ---------- + new_exp : climada.entity.Exposures + exposures once measure applied + new_ifs : climada.entity.ImpactFuncSet + impact function set once measure applied + new_haz : climada.hazard.Hazard + hazard once measure applied + + Returns + ------- + climada.engine.Impact + """ + from climada.engine.impact_calc import ( + ImpactCalc, # pylint: disable=import-outside-toplevel + ) + + imp = ImpactCalc(new_exp, new_impfs, new_haz).impact( + save_mat=False, assign_centroids=False + ) + return imp.calc_risk_transfer(self.risk_transf_attach, self.risk_transf_cover) + + def _change_all_hazard(self, hazard): + """ + Change hazard to provided hazard_set. + + Parameters + ---------- + hazard : climada.hazard.Hazard + hazard instance + + Returns + ------- + new_haz : climada.hazard.Hazard + Hazard + """ + if self.hazard_set == NULL_STR: + return hazard + + LOGGER.debug("Setting new hazard %s", self.hazard_set) + new_haz = Hazard.from_hdf5(self.hazard_set) + new_haz.check() + return new_haz + + def _change_all_exposures(self, exposures): + """ + Change exposures to provided exposures_set. + + Parameters + ---------- + exposures : climada.entity.Exposures + exposures instance + + Returns + ------- + new_exp : climada.entity.Exposures() + Exposures + """ + if isinstance(self.exposures_set, str) and self.exposures_set == NULL_STR: + return exposures + + if isinstance(self.exposures_set, (str, Path)): + LOGGER.debug("Setting new exposures %s", self.exposures_set) + new_exp = Exposures.from_hdf5(self.exposures_set) + new_exp.check() + elif isinstance(self.exposures_set, Exposures): + LOGGER.debug("Setting new exposures. ") + new_exp = self.exposures_set.copy(deep=True) + new_exp.check() + else: + raise ValueError( + f"{self.exposures_set} is neither a string nor an Exposures object" + ) + + if not np.array_equal( + np.unique(exposures.latitude), np.unique(new_exp.latitude) + ) or not np.array_equal( + np.unique(exposures.longitude), np.unique(new_exp.longitude) + ): + LOGGER.warning("Exposures locations have changed.") + + return new_exp + + def _change_exposures_impf(self, exposures): + """Change exposures impact functions ids according to imp_fun_map. + + Parameters + ---------- + exposures : climada.entity.Exposures + exposures instance + + Returns + ------- + new_exp : climada.entity.Exposure + Exposure with updated impact functions ids accordgin to + impf_fun_map + """ + if self.imp_fun_map == NULL_STR: + return exposures + + LOGGER.debug("Setting new exposures impact functions%s", self.imp_fun_map) + new_exp = exposures.copy(deep=True) + from_id = int(self.imp_fun_map[0 : self.imp_fun_map.find("to")]) + to_id = int(self.imp_fun_map[self.imp_fun_map.find("to") + 2 :]) + try: + exp_change = np.argwhere( + new_exp.gdf[INDICATOR_IMPF + self.haz_type].values == from_id + ).reshape(-1) + new_exp.gdf[INDICATOR_IMPF + self.haz_type].values[exp_change] = to_id + except KeyError: + exp_change = np.argwhere( + new_exp.gdf[INDICATOR_IMPF].values == from_id + ).reshape(-1) + new_exp.gdf[INDICATOR_IMPF].values[exp_change] = to_id + return new_exp + + def _change_imp_func(self, imp_set): + """ + Apply measure to impact functions of the same hazard type. + + Parameters + ---------- + imp_set : climada.entity.ImpactFuncSet + impact function set instance to be modified + + Returns + ------- + new_imp_set : climada.entity.ImpactFuncSet + ImpactFuncSet with measure applied to each impact function + according to the defined hazard type + """ + if ( + self.hazard_inten_imp == (1, 0) + and self.mdd_impact == (1, 0) + and self.paa_impact == (1, 0) + ): + return imp_set + + new_imp_set = copy.deepcopy(imp_set) + for imp_fun in new_imp_set.get_func(self.haz_type): + LOGGER.debug("Transforming impact functions.") + imp_fun.intensity = np.maximum( + imp_fun.intensity * self.hazard_inten_imp[0] - self.hazard_inten_imp[1], + 0.0, + ) + imp_fun.mdd = np.maximum( + imp_fun.mdd * self.mdd_impact[0] + self.mdd_impact[1], 0.0 + ) + imp_fun.paa = np.maximum( + imp_fun.paa * self.paa_impact[0] + self.paa_impact[1], 0.0 + ) + + if not new_imp_set.size(): + LOGGER.info("No impact function of hazard %s found.", self.haz_type) + + return new_imp_set + + def _cutoff_hazard_damage(self, exposures, impf_set, hazard): + """Cutoff of hazard events which generate damage with a frequency higher + than hazard_freq_cutoff. + + Parameters + ---------- + exposures : climada.entity.Exposures + exposures instance + imp_set : climada.entity.ImpactFuncSet + impact function set instance + hazard : climada.hazard.Hazard + hazard instance + + Returns + ------- + new_haz : climada.hazard.Hazard + Hazard without events which generate damage with a frequency + higher than hazard_freq_cutoff + """ + if self.hazard_freq_cutoff == 0: + return hazard + + if self.exp_region_id: + # compute impact only in selected region + in_reg = np.logical_or.reduce( + [exposures.region_id == reg for reg in self.exp_region_id] + ) + exp_imp = Exposures(exposures.gdf[in_reg], crs=exposures.crs) + else: + exp_imp = exposures + + from climada.engine.impact_calc import ( + ImpactCalc, # pylint: disable=import-outside-toplevel + ) + + imp = ImpactCalc(exp_imp, impf_set, hazard).impact( + assign_centroids=hazard.centr_exp_col not in exp_imp.gdf + ) + + LOGGER.debug( + "Cutting events whose damage have a frequency > %s.", + self.hazard_freq_cutoff, + ) + new_haz = copy.deepcopy(hazard) + sort_idxs = np.argsort(imp.at_event)[::-1] + exceed_freq = np.cumsum(imp.frequency[sort_idxs]) + cutoff = exceed_freq > self.hazard_freq_cutoff + sel_haz = sort_idxs[cutoff] + for row in sel_haz: + new_haz.intensity.data[ + new_haz.intensity.indptr[row] : new_haz.intensity.indptr[row + 1] + ] = 0 + new_haz.intensity.eliminate_zeros() + return new_haz + + def _filter_exposures( + self, exposures, imp_set, hazard, new_exp, new_impfs, new_haz + ): + """ + Incorporate changes of new elements to previous ones only for the + selected exp_region_id. If exp_region_id is [], all new changes + will be accepted. + + Parameters + ---------- + exposures : climada.entity.Exposures + old exposures instance + imp_set :climada.entity.ImpactFuncSet + old impact function set instance + hazard : climada.hazard.Hazard + old hazard instance + new_exp : climada.entity.Exposures + new exposures instance + new_ifs : climada.entity.ImpactFuncSet + new impact functions instance + new_haz : climada.hazard.Hazard + new hazard instance + + Returns + ------- + new_exp,new_ifs, new_haz : climada.entity.Exposures, + climada.entity.ImpactFuncSet, + climada.hazard.Hazard + Exposures, ImpactFuncSet, Hazard with incoporated elements + for the selected exp_region_id. + """ + if not self.exp_region_id: + return new_exp, new_impfs, new_haz + + if exposures is new_exp: + new_exp = exposures.copy(deep=True) + + if imp_set is not new_impfs: + # provide new impact functions ids to changed impact functions + fun_ids = list(new_impfs.get_func()[self.haz_type].keys()) + for key in fun_ids: + new_impfs.get_func()[self.haz_type][key].id = key + IMPF_ID_FACT + new_impfs.get_func()[self.haz_type][ + key + IMPF_ID_FACT + ] = new_impfs.get_func()[self.haz_type][key] + try: + new_exp.gdf[INDICATOR_IMPF + self.haz_type] += IMPF_ID_FACT + except KeyError: + new_exp.gdf[INDICATOR_IMPF] += IMPF_ID_FACT + # collect old impact functions as well (used by exposures) + new_impfs.get_func()[self.haz_type].update( + imp_set.get_func()[self.haz_type] + ) + + # get the indices for changing and inert regions + chg_reg = exposures.gdf["region_id"].isin(self.exp_region_id) + no_chg_reg = ~chg_reg + + LOGGER.debug("Number of changed exposures: %s", chg_reg.sum()) + + # concatenate previous and new exposures + new_exp.set_gdf( + GeoDataFrame( + pd.concat( + [ + exposures.gdf[no_chg_reg], # old values for inert regions + new_exp.gdf[chg_reg], # new values for changing regions + ] + ).loc[ + exposures.gdf.index, : + ], # re-establish old order + ), + crs=exposures.crs, + ) + + # set missing values of centr_ + if ( + INDICATOR_CENTR + self.haz_type in new_exp.gdf.columns + and np.isnan(new_exp.gdf[INDICATOR_CENTR + self.haz_type].values).any() + ): + new_exp.gdf.drop(columns=INDICATOR_CENTR + self.haz_type, inplace=True) + elif ( + INDICATOR_CENTR in new_exp.gdf.columns + and np.isnan(new_exp.gdf[INDICATOR_CENTR].values).any() + ): + new_exp.gdf.drop(columns=INDICATOR_CENTR, inplace=True) + + # put hazard intensities outside region to previous intensities + if hazard is not new_haz: + if INDICATOR_CENTR + self.haz_type in exposures.gdf.columns: + centr = exposures.gdf[INDICATOR_CENTR + self.haz_type].values[chg_reg] + elif INDICATOR_CENTR in exposures.gdf.columns: + centr = exposures.gdf[INDICATOR_CENTR].values[chg_reg] + else: + exposures.assign_centroids(hazard) + centr = exposures.gdf[INDICATOR_CENTR + self.haz_type].values[chg_reg] + + centr = np.delete(np.arange(hazard.intensity.shape[1]), np.unique(centr)) + new_haz_inten = new_haz.intensity.tolil() + new_haz_inten[:, centr] = hazard.intensity[:, centr] + new_haz.intensity = new_haz_inten.tocsr() + + return new_exp, new_impfs, new_haz diff --git a/climada/entity/_legacy_measures/measure_set.py b/climada/entity/_legacy_measures/measure_set.py new file mode 100755 index 0000000000..228788ba15 --- /dev/null +++ b/climada/entity/_legacy_measures/measure_set.py @@ -0,0 +1,629 @@ +""" +This file is part of CLIMADA. + +Copyright (C) 2017 ETH Zurich, CLIMADA contributors listed in AUTHORS. + +CLIMADA is free software: you can redistribute it and/or modify it under the +terms of the GNU General Public License as published by the Free +Software Foundation, version 3. + +CLIMADA is distributed in the hope that it will be useful, but WITHOUT ANY +WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A +PARTICULAR PURPOSE. See the GNU General Public License for more details. + +You should have received a copy of the GNU General Public License along +with CLIMADA. If not, see . + +--- + +Define MeasureSet class. +""" + +__all__ = ["MeasureSet"] + +import ast +import copy +import logging +from typing import List, Optional + +import numpy as np +import pandas as pd +import xlsxwriter +from matplotlib import colormaps as cm + +import climada.util.hdf5_handler as u_hdf5 + +from .base import Measure + +LOGGER = logging.getLogger(__name__) + +DEF_VAR_MAT = { + "sup_field_name": "entity", + "field_name": "measures", + "var_name": { + "name": "name", + "color": "color", + "cost": "cost", + "haz_int_a": "hazard_intensity_impact_a", + "haz_int_b": "hazard_intensity_impact_b", + "haz_frq": "hazard_high_frequency_cutoff", + "haz_set": "hazard_event_set", + "mdd_a": "MDD_impact_a", + "mdd_b": "MDD_impact_b", + "paa_a": "PAA_impact_a", + "paa_b": "PAA_impact_b", + "fun_map": "damagefunctions_map", + "exp_set": "assets_file", + "exp_reg": "Region_ID", + "risk_att": "risk_transfer_attachement", + "risk_cov": "risk_transfer_cover", + "haz": "peril_ID", + }, +} +"""MATLAB variable names""" + +DEF_VAR_EXCEL = { + "sheet_name": "measures", + "col_name": { + "name": "name", + "color": "color", + "cost": "cost", + "haz_int_a": "hazard intensity impact a", + "haz_int_b": "hazard intensity impact b", + "haz_frq": "hazard high frequency cutoff", + "haz_set": "hazard event set", + "mdd_a": "MDD impact a", + "mdd_b": "MDD impact b", + "paa_a": "PAA impact a", + "paa_b": "PAA impact b", + "fun_map": "damagefunctions map", + "exp_set": "assets file", + "exp_reg": "Region_ID", + "risk_att": "risk transfer attachement", + "risk_cov": "risk transfer cover", + "risk_fact": "risk transfer cost factor", + "haz": "peril_ID", + }, +} +"""Excel variable names""" + + +class MeasureSet: + """Contains measures of type Measure. Loads from + files with format defined in FILE_EXT. + + Attributes + ---------- + _data : dict + Contains Measure objects. This attribute is not suppossed to be accessed directly. + Use the available methods instead. + """ + + def __init__(self, measure_list: Optional[List[Measure]] = None): + """Initialize a new MeasureSet object with specified data. + + Parameters + ---------- + measure_list : list of Measure objects, optional + The measures to include in the MeasureSet + + Examples + -------- + Fill MeasureSet with values and check consistency data: + + >>> act_1 = Measure( + ... name='Seawall', + ... color_rgb=np.array([0.1529, 0.2510, 0.5451]), + ... hazard_intensity=(1, 0), + ... mdd_impact=(1, 0), + ... paa_impact=(1, 0), + ... ) + >>> meas = MeasureSet([act_1]) + >>> meas.check() + + Read measures from file and checks consistency data: + + >>> meas = MeasureSet.from_excel(ENT_TEMPLATE_XLS) + """ + self.clear() + if measure_list is not None: + for meas in measure_list: + self.append(meas) + + def clear(self, _data: Optional[dict] = None): + """Reinitialize attributes. + + Parameters + ---------- + _data : dict, optional + A dict containing the Measure objects. For internal use only: It's not suppossed to be + set directly. Use the class methods instead. + """ + self._data = ( + _data if _data is not None else dict() + ) # {hazard_type : {name: Measure()}} + + def append(self, meas): + """Append an Measure. Override if same name and haz_type. + + Parameters + ---------- + meas : Measure + Measure instance + + Raises + ------ + ValueError + """ + if not isinstance(meas, Measure): + raise ValueError("Input value is not of type Measure.") + if not meas.haz_type: + LOGGER.warning("Input Measure's hazard type not set.") + if not meas.name: + LOGGER.warning("Input Measure's name not set.") + if meas.haz_type not in self._data: + self._data[meas.haz_type] = dict() + self._data[meas.haz_type][meas.name] = meas + + def remove_measure(self, haz_type=None, name=None): + """Remove impact function(s) with provided hazard type and/or id. + If no input provided, all impact functions are removed. + + Parameters + ---------- + haz_type : str, optional + all impact functions with this hazard + name : str, optional + measure name + """ + if (haz_type is not None) and (name is not None): + try: + del self._data[haz_type][name] + except KeyError: + LOGGER.info("No Measure with hazard %s and id %s.", haz_type, name) + elif haz_type is not None: + try: + del self._data[haz_type] + except KeyError: + LOGGER.info("No Measure with hazard %s.", haz_type) + elif name is not None: + haz_remove = self.get_hazard_types(name) + if not haz_remove: + LOGGER.info("No Measure with name %s.", name) + for haz in haz_remove: + del self._data[haz][name] + else: + self._data = dict() + + def get_measure(self, haz_type=None, name=None): + """Get ImpactFunc(s) of input hazard type and/or id. + If no input provided, all impact functions are returned. + + Parameters + ---------- + haz_type : str, optional + hazard type + name : str, optional + measure name + + Returns + ------- + Measure (if haz_type and name), + list(Measure) (if haz_type or name), + {Measure.haz_type : {Measure.name : Measure}} (if None) + """ + if (haz_type is not None) and (name is not None): + try: + return self._data[haz_type][name] + except KeyError: + LOGGER.info("No Measure with hazard %s and id %s.", haz_type, name) + return list() + elif haz_type is not None: + try: + return list(self._data[haz_type].values()) + except KeyError: + LOGGER.info("No Measure with hazard %s.", haz_type) + return list() + elif name is not None: + haz_return = self.get_hazard_types(name) + if not haz_return: + LOGGER.info("No Measure with name %s.", name) + meas_return = [] + for haz in haz_return: + meas_return.append(self._data[haz][name]) + return meas_return + else: + return self._data + + def get_hazard_types(self, meas=None): + """Get measures hazard types contained for the name provided. + Return all hazard types if no input name. + + Parameters + ---------- + name : str, optional + measure name + + Returns + ------- + list(str) + """ + if meas is None: + return list(self._data.keys()) + + haz_return = [] + for haz, haz_dict in self._data.items(): + if meas in haz_dict: + haz_return.append(haz) + return haz_return + + def get_names(self, haz_type=None): + """Get measures names contained for the hazard type provided. + Return all names for each hazard type if no input hazard type. + + Parameters + ---------- + haz_type : str, optional + hazard type from which to obtain the names + + Returns + ------- + list(Measure.name) (if haz_type provided), + {Measure.haz_type : list(Measure.name)} (if no haz_type) + """ + if haz_type is None: + out_dict = dict() + for haz, haz_dict in self._data.items(): + out_dict[haz] = list(haz_dict.keys()) + return out_dict + + try: + return list(self._data[haz_type].keys()) + except KeyError: + LOGGER.info("No Measure with hazard %s.", haz_type) + return list() + + def size(self, haz_type=None, name=None): + """Get number of measures contained with input hazard type and + /or id. If no input provided, get total number of impact functions. + + Parameters + ---------- + haz_type : str, optional + hazard type + name : str, optional + measure name + + Returns + ------- + int + """ + if ( + (haz_type is not None) + and (name is not None) + and (isinstance(self.get_measure(haz_type, name), Measure)) + ): + return 1 + if (haz_type is not None) or (name is not None): + return len(self.get_measure(haz_type, name)) + return sum(len(meas_list) for meas_list in self.get_names().values()) + + def check(self): + """Check instance attributes. + + Raises + ------ + ValueError + """ + for key_haz, meas_dict in self._data.items(): + def_color = cm.get_cmap("Greys").resampled(len(meas_dict)) + for i_meas, (name, meas) in enumerate(meas_dict.items()): + if (name != meas.name) | (name == ""): + raise ValueError( + "Wrong Measure.name: %s != %s." % (name, meas.name) + ) + if key_haz != meas.haz_type: + raise ValueError( + "Wrong Measure.haz_type: %s != %s." % (key_haz, meas.haz_type) + ) + # set default color if not set + if np.array_equal(meas.color_rgb, np.zeros(3)): + meas.color_rgb = def_color(i_meas) + meas.check() + + def extend(self, meas_set): + """Extend measures of input MeasureSet to current + MeasureSet. Overwrite Measure if same name and haz_type. + + Parameters + ---------- + impact_funcs : MeasureSet + ImpactFuncSet instance to extend + + Raises + ------ + ValueError + """ + meas_set.check() + if self.size() == 0: + self.__dict__ = copy.deepcopy(meas_set.__dict__) + return + + new_func = meas_set.get_measure() + for _, meas_dict in new_func.items(): + for _, meas in meas_dict.items(): + self.append(meas) + + @classmethod + def from_mat(cls, file_name, var_names=None): + """Read MATLAB file generated with previous MATLAB CLIMADA version. + + Parameters + ---------- + file_name : str + absolute file name + description : str, optional + description of the data + var_names : dict, optional + name of the variables in the file + + Returns + ------- + meas_set: climada.entity.MeasureSet() + Measure Set from matlab file + """ + if var_names is None: + var_names = DEF_VAR_MAT + + def read_att_mat(measures, data, file_name, var_names): + """Read MATLAB measures attributes""" + num_mes = len(data[var_names["var_name"]["name"]]) + for idx in range(0, num_mes): + color_str = u_hdf5.get_str_from_ref( + file_name, data[var_names["var_name"]["color"]][idx][0] + ) + + try: + hazard_inten_imp = ( + data[var_names["var_name"]["haz_int_a"]][idx][0], + data[var_names["var_name"]["haz_int_b"]][0][idx], + ) + except KeyError: + hazard_inten_imp = ( + data[var_names["var_name"]["haz_int_a"][:-2]][idx][0], + 0, + ) + + meas_kwargs = dict( + name=u_hdf5.get_str_from_ref( + file_name, data[var_names["var_name"]["name"]][idx][0] + ), + color_rgb=np.fromstring(color_str, dtype=float, sep=" "), + cost=data[var_names["var_name"]["cost"]][idx][0], + haz_type=u_hdf5.get_str_from_ref( + file_name, data[var_names["var_name"]["haz"]][idx][0] + ), + hazard_freq_cutoff=data[var_names["var_name"]["haz_frq"]][idx][0], + hazard_set=u_hdf5.get_str_from_ref( + file_name, data[var_names["var_name"]["haz_set"]][idx][0] + ), + hazard_inten_imp=hazard_inten_imp, + # different convention of signs followed in MATLAB! + mdd_impact=( + data[var_names["var_name"]["mdd_a"]][idx][0], + data[var_names["var_name"]["mdd_b"]][idx][0], + ), + paa_impact=( + data[var_names["var_name"]["paa_a"]][idx][0], + data[var_names["var_name"]["paa_b"]][idx][0], + ), + imp_fun_map=u_hdf5.get_str_from_ref( + file_name, data[var_names["var_name"]["fun_map"]][idx][0] + ), + exposures_set=u_hdf5.get_str_from_ref( + file_name, data[var_names["var_name"]["exp_set"]][idx][0] + ), + risk_transf_attach=data[var_names["var_name"]["risk_att"]][idx][0], + risk_transf_cover=data[var_names["var_name"]["risk_cov"]][idx][0], + ) + + exp_region_id = data[var_names["var_name"]["exp_reg"]][idx][0] + if exp_region_id: + meas_kwargs["exp_region_id"] = [exp_region_id] + + measures.append(Measure(**meas_kwargs)) + + data = u_hdf5.read(file_name) + meas_set = cls() + try: + data = data[var_names["sup_field_name"]] + except KeyError: + pass + + try: + data = data[var_names["field_name"]] + read_att_mat(meas_set, data, file_name, var_names) + except KeyError as var_err: + raise KeyError("Variable not in MAT file: " + str(var_err)) from var_err + + return meas_set + + def read_mat(self, *args, **kwargs): + """This function is deprecated, use MeasureSet.from_mat instead.""" + LOGGER.warning( + "The use of MeasureSet.read_mat is deprecated." + "Use MeasureSet.from_mat instead." + ) + self.__dict__ = MeasureSet.from_mat(*args, **kwargs).__dict__ + + @classmethod + def from_excel(cls, file_name, var_names=None): + """Read excel file following template and store variables. + + Parameters + ---------- + file_name : str + absolute file name + description : str, optional + description of the data + var_names : dict, optional + name of the variables in the file + + Returns + ------- + meas_set : climada.entity.MeasureSet + Measures set from Excel + """ + if var_names is None: + var_names = DEF_VAR_EXCEL + + def read_att_excel(measures, dfr, var_names): + """Read Excel measures attributes""" + num_mes = len(dfr.index) + for idx in range(0, num_mes): + # Search for (a, b) values, put a=1 otherwise + try: + hazard_inten_imp = ( + dfr[var_names["col_name"]["haz_int_a"]][idx], + dfr[var_names["col_name"]["haz_int_b"]][idx], + ) + except KeyError: + hazard_inten_imp = (1, dfr["hazard intensity impact"][idx]) + + meas_kwargs = dict( + name=dfr[var_names["col_name"]["name"]][idx], + cost=dfr[var_names["col_name"]["cost"]][idx], + hazard_freq_cutoff=dfr[var_names["col_name"]["haz_frq"]][idx], + hazard_set=dfr[var_names["col_name"]["haz_set"]][idx], + hazard_inten_imp=hazard_inten_imp, + mdd_impact=( + dfr[var_names["col_name"]["mdd_a"]][idx], + dfr[var_names["col_name"]["mdd_b"]][idx], + ), + paa_impact=( + dfr[var_names["col_name"]["paa_a"]][idx], + dfr[var_names["col_name"]["paa_b"]][idx], + ), + imp_fun_map=dfr[var_names["col_name"]["fun_map"]][idx], + risk_transf_attach=dfr[var_names["col_name"]["risk_att"]][idx], + risk_transf_cover=dfr[var_names["col_name"]["risk_cov"]][idx], + color_rgb=np.fromstring( + dfr[var_names["col_name"]["color"]][idx], dtype=float, sep=" " + ), + ) + + try: + meas_kwargs["haz_type"] = dfr[var_names["col_name"]["haz"]][idx] + except KeyError: + pass + + try: + meas_kwargs["exposures_set"] = dfr[ + var_names["col_name"]["exp_set"] + ][idx] + except KeyError: + pass + + try: + meas_kwargs["exp_region_id"] = ast.literal_eval( + dfr[var_names["col_name"]["exp_reg"]][idx] + ) + except KeyError: + pass + except ValueError: + meas_kwargs["exp_region_id"] = dfr[ + var_names["col_name"]["exp_reg"] + ][idx] + + try: + meas_kwargs["risk_transf_cost_factor"] = dfr[ + var_names["col_name"]["risk_fact"] + ][idx] + except KeyError: + pass + + measures.append(Measure(**meas_kwargs)) + + dfr = pd.read_excel(file_name, var_names["sheet_name"]) + dfr = dfr.fillna("") + meas_set = cls() + try: + read_att_excel(meas_set, dfr, var_names) + except KeyError as var_err: + raise KeyError("Variable not in Excel file: " + str(var_err)) from var_err + + return meas_set + + def read_excel(self, *args, **kwargs): + """This function is deprecated, use MeasureSet.from_excel instead.""" + LOGGER.warning( + "The use ofMeasureSet.read_excel is deprecated." + "Use MeasureSet.from_excel instead." + ) + self.__dict__ = MeasureSet.from_excel(*args, **kwargs).__dict__ + + def write_excel(self, file_name, var_names=None): + """Write excel file following template. + + Parameters + ---------- + file_name : str + absolute file name to write + var_names : dict, optional + name of the variables in the file + """ + if var_names is None: + var_names = DEF_VAR_EXCEL + + def write_meas(row_ini, imp_ws, xls_data): + """Write one measure""" + for icol, col_dat in enumerate(xls_data): + imp_ws.write(row_ini, icol, col_dat) + + meas_wb = xlsxwriter.Workbook(file_name) + mead_ws = meas_wb.add_worksheet(var_names["sheet_name"]) + + header = [ + var_names["col_name"]["name"], + var_names["col_name"]["color"], + var_names["col_name"]["cost"], + var_names["col_name"]["haz_int_a"], + var_names["col_name"]["haz_int_b"], + var_names["col_name"]["haz_frq"], + var_names["col_name"]["haz_set"], + var_names["col_name"]["mdd_a"], + var_names["col_name"]["mdd_b"], + var_names["col_name"]["paa_a"], + var_names["col_name"]["paa_b"], + var_names["col_name"]["fun_map"], + var_names["col_name"]["exp_set"], + var_names["col_name"]["exp_reg"], + var_names["col_name"]["risk_att"], + var_names["col_name"]["risk_cov"], + var_names["col_name"]["haz"], + ] + for icol, head_dat in enumerate(header): + mead_ws.write(0, icol, head_dat) + for row_ini, (_, haz_dict) in enumerate(self._data.items(), 1): + for meas_name, meas in haz_dict.items(): + xls_data = [ + meas_name, + " ".join(list(map(str, meas.color_rgb))), + meas.cost, + meas.hazard_inten_imp[0], + meas.hazard_inten_imp[1], + meas.hazard_freq_cutoff, + meas.hazard_set, + meas.mdd_impact[0], + meas.mdd_impact[1], + meas.paa_impact[0], + meas.paa_impact[1], + meas.imp_fun_map, + meas.exposures_set, + str(meas.exp_region_id), + meas.risk_transf_attach, + meas.risk_transf_cover, + meas.haz_type, + ] + write_meas(row_ini, mead_ws, xls_data) + meas_wb.close() diff --git a/climada/entity/measures/test/__init__.py b/climada/entity/_legacy_measures/test/__init__.py similarity index 100% rename from climada/entity/measures/test/__init__.py rename to climada/entity/_legacy_measures/test/__init__.py diff --git a/climada/entity/measures/test/data/.gitignore b/climada/entity/_legacy_measures/test/data/.gitignore similarity index 100% rename from climada/entity/measures/test/data/.gitignore rename to climada/entity/_legacy_measures/test/data/.gitignore diff --git a/climada/entity/_legacy_measures/test/test_base.py b/climada/entity/_legacy_measures/test/test_base.py new file mode 100644 index 0000000000..430ab7d44b --- /dev/null +++ b/climada/entity/_legacy_measures/test/test_base.py @@ -0,0 +1,672 @@ +""" +This file is part of CLIMADA. + +Copyright (C) 2017 ETH Zurich, CLIMADA contributors listed in AUTHORS. + +CLIMADA is free software: you can redistribute it and/or modify it under the +terms of the GNU General Public License as published by the Free +Software Foundation, version 3. + +CLIMADA is distributed in the hope that it will be useful, but WITHOUT ANY +WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A +PARTICULAR PURPOSE. See the GNU General Public License for more details. + +You should have received a copy of the GNU General Public License along +with CLIMADA. If not, see . + +--- + +Test MeasureSet and Measure classes. +""" + +import copy +import unittest +from pathlib import Path + +import numpy as np + +import climada.entity.exposures.test as exposures_test +import climada.util.coordinates as u_coord +from climada import CONFIG +from climada.entity._legacy_measures.base import IMPF_ID_FACT, Measure +from climada.entity._legacy_measures.measure_set import MeasureSet +from climada.entity.entity_def import Entity +from climada.entity.exposures.base import Exposures +from climada.entity.impact_funcs.base import ImpactFunc +from climada.entity.impact_funcs.impact_func_set import ImpactFuncSet +from climada.hazard.base import Hazard +from climada.test import get_test_file +from climada.util.constants import HAZ_DEMO_H5 + +DATA_DIR = CONFIG.measures.test_data.dir() + +EXP_DEMO_H5 = get_test_file("exp_demo_today", file_format="hdf5") + +HAZ_TEST_TC: Path = get_test_file("test_tc_florida", file_format="hdf5") +""" +Hazard test file from Data API: Hurricanes from 1851 to 2011 over Florida with 100 centroids. +Fraction is empty. Format: HDF5. +""" +ENT_TEST_MAT = Path(exposures_test.__file__).parent / "data" / "demo_today.mat" + + +class TestApply(unittest.TestCase): + """Test implement measures functions.""" + + def test_change_imp_func_pass(self): + """Test _change_imp_func""" + meas = MeasureSet.from_mat(ENT_TEST_MAT) + act_1 = meas.get_measure(name="Mangroves")[0] + + haz_type = "XX" + idx = 1 + intensity = np.arange(10, 100, 10) + intensity[0] = 0.0 + intensity[-1] = 100.0 + mdd = np.array( + [ + 0.0, + 0.0, + 0.021857142857143, + 0.035887500000000, + 0.053977415307403, + 0.103534246575342, + 0.180414000000000, + 0.410796000000000, + 0.410796000000000, + ] + ) + paa = np.array( + [ + 0, + 0.005000000000000, + 0.042000000000000, + 0.160000000000000, + 0.398500000000000, + 0.657000000000000, + 1.000000000000000, + 1.000000000000000, + 1.000000000000000, + ] + ) + imp_tc = ImpactFunc(haz_type, idx, intensity, mdd, paa) + imp_set = ImpactFuncSet([imp_tc]) + new_imp = act_1._change_imp_func(imp_set).get_func("XX")[0] + + self.assertTrue( + np.array_equal( + new_imp.intensity, + np.array([4.0, 24.0, 34.0, 44.0, 54.0, 64.0, 74.0, 84.0, 104.0]), + ) + ) + self.assertTrue( + np.array_equal( + new_imp.mdd, + np.array( + [ + 0, + 0, + 0.021857142857143, + 0.035887500000000, + 0.053977415307403, + 0.103534246575342, + 0.180414000000000, + 0.410796000000000, + 0.410796000000000, + ] + ), + ) + ) + self.assertTrue( + np.array_equal( + new_imp.paa, + np.array( + [ + 0, + 0.005000000000000, + 0.042000000000000, + 0.160000000000000, + 0.398500000000000, + 0.657000000000000, + 1.000000000000000, + 1.000000000000000, + 1.000000000000000, + ] + ), + ) + ) + self.assertFalse(id(new_imp) == id(imp_tc)) + + def test_cutoff_hazard_pass(self): + """Test _cutoff_hazard_damage""" + meas = MeasureSet.from_mat(ENT_TEST_MAT) + act_1 = meas.get_measure(name="Seawall")[0] + + haz = Hazard.from_hdf5(HAZ_TEST_TC) + exp = Exposures.from_mat(ENT_TEST_MAT) + exp.gdf.rename(columns={"impf": "impf_TC"}, inplace=True) + exp.check() + exp.assign_centroids(haz) + + imp_set = ImpactFuncSet.from_mat(ENT_TEST_MAT) + + new_haz = act_1._cutoff_hazard_damage(exp, imp_set, haz) + + self.assertFalse(id(new_haz) == id(haz)) + # fmt: off + pos_no_null = np.array( + [ + 6249, 7697, 9134, 13500, 13199, 5944, 9052, 9050, 2429, 5139, + 9053, 7102, 4096, 1070, 5948, 1076, 5947, 7432, 5949, 11694, + 5484, 6246, 12147, 778, 3326, 7199, 12498, 11698, 6245, 5327, + 4819, 8677, 5970, 7101, 779, 3894, 9051, 5976, 3329, 5978, + 4282, 11697, 7193, 5351, 7310, 7478, 5489, 5526, 7194, 4283, + 7191, 5328, 4812, 5528, 5527, 5488, 7475, 5529, 776, 5758, + 4811, 6223, 7479, 7470, 5480, 5325, 7477, 7318, 7317, 11696, + 7313, 13165, 6221, + ] + ) + # fmt: on + all_haz = np.arange(haz.intensity.shape[0]) + all_haz[pos_no_null] = -1 + pos_null = np.argwhere(all_haz > 0).reshape(-1) + for i_ev in pos_null: + self.assertEqual(new_haz.intensity[i_ev, :].max(), 0) + + def test_cutoff_hazard_region_pass(self): + """Test _cutoff_hazard_damage in specific region""" + meas = MeasureSet.from_mat(ENT_TEST_MAT) + act_1 = meas.get_measure(name="Seawall")[0] + act_1.exp_region_id = [1] + + haz = Hazard.from_hdf5(HAZ_TEST_TC) + exp = Exposures.from_mat(ENT_TEST_MAT) + exp.gdf["region_id"] = np.zeros(exp.gdf.shape[0]) + exp.gdf["region_id"].values[10:] = 1 + exp.check() + exp.assign_centroids(haz) + + imp_set = ImpactFuncSet.from_mat(ENT_TEST_MAT) + + new_haz = act_1._cutoff_hazard_damage(exp, imp_set, haz) + + self.assertFalse(id(new_haz) == id(haz)) + + # fmt: off + pos_no_null = np.array( + [ + 6249, 7697, 9134, 13500, 13199, 5944, 9052, 9050, 2429, 5139, + 9053, 7102, 4096, 1070, 5948, 1076, 5947, 7432, 5949, 11694, + 5484, 6246, 12147, 778, 3326, 7199, 12498, 11698, 6245, 5327, + 4819, 8677, 5970, 7101, 779, 3894, 9051, 5976, 3329, 5978, + 4282, 11697, 7193, 5351, 7310, 7478, 5489, 5526, 7194, 4283, + 7191, 5328, 4812, 5528, 5527, 5488, 7475, 5529, 776, 5758, + 4811, 6223, 7479, 7470, 5480, 5325, 7477, 7318, 7317, 11696, + 7313, 13165, 6221, + ] + ) + # fmt: on + all_haz = np.arange(haz.intensity.shape[0]) + all_haz[pos_no_null] = -1 + pos_null = np.argwhere(all_haz > 0).reshape(-1) + centr_null = np.unique(exp.gdf["centr_"][exp.gdf["region_id"] == 0]) + for i_ev in pos_null: + self.assertEqual(new_haz.intensity[i_ev, centr_null].max(), 0) + + def test_change_exposures_impf_pass(self): + """Test _change_exposures_impf""" + meas = Measure( + imp_fun_map="1to3", + haz_type="TC", + ) + + imp_set = ImpactFuncSet() + + intensity = np.arange(10, 100, 10) + mdd = np.arange(10, 100, 10) + paa = np.arange(10, 100, 10) + imp_tc = ImpactFunc("TC", 1, intensity, mdd, paa) + imp_set.append(imp_tc) + + mdd = np.arange(10, 100, 10) * 2 + paa = np.arange(10, 100, 10) * 2 + imp_tc = ImpactFunc("TC", 3, intensity, mdd, paa) + + exp = Exposures.from_hdf5(EXP_DEMO_H5) + new_exp = meas._change_exposures_impf(exp) + + self.assertEqual(new_exp.ref_year, exp.ref_year) + self.assertEqual(new_exp.value_unit, exp.value_unit) + self.assertEqual(new_exp.description, exp.description) + self.assertTrue(np.array_equal(new_exp.value, exp.value)) + self.assertTrue(np.array_equal(new_exp.latitude, exp.latitude)) + self.assertTrue(np.array_equal(new_exp.longitude, exp.longitude)) + self.assertTrue( + np.array_equal(exp.hazard_impf("TC"), np.ones(new_exp.gdf.shape[0])) + ) + self.assertTrue( + np.array_equal(new_exp.hazard_impf("TC"), np.ones(new_exp.gdf.shape[0]) * 3) + ) + + def test_change_all_hazard_pass(self): + """Test _change_all_hazard method""" + meas = Measure(hazard_set=HAZ_DEMO_H5) + + ref_haz = Hazard.from_hdf5(HAZ_DEMO_H5) + + hazard = Hazard("TC") + new_haz = meas._change_all_hazard(hazard) + + self.assertEqual(new_haz.haz_type, ref_haz.haz_type) + self.assertTrue(np.array_equal(new_haz.frequency, ref_haz.frequency)) + self.assertTrue(np.array_equal(new_haz.date, ref_haz.date)) + self.assertTrue(np.array_equal(new_haz.orig, ref_haz.orig)) + self.assertTrue( + np.array_equal(new_haz.centroids.coord, ref_haz.centroids.coord) + ) + self.assertTrue(np.array_equal(new_haz.intensity.data, ref_haz.intensity.data)) + self.assertTrue(np.array_equal(new_haz.fraction.data, ref_haz.fraction.data)) + + def test_change_all_exposures_pass(self): + """Test _change_all_exposures method""" + meas = Measure(exposures_set=EXP_DEMO_H5) + + ref_exp = Exposures.from_hdf5(EXP_DEMO_H5) + + exposures = Exposures() + exposures.gdf["latitude"] = np.ones(10) + exposures.gdf["longitude"] = np.ones(10) + new_exp = meas._change_all_exposures(exposures) + + self.assertEqual(new_exp.ref_year, ref_exp.ref_year) + self.assertEqual(new_exp.value_unit, ref_exp.value_unit) + self.assertEqual(new_exp.description, ref_exp.description) + self.assertTrue(np.array_equal(new_exp.value, ref_exp.value)) + self.assertTrue(np.array_equal(new_exp.latitude, ref_exp.latitude)) + self.assertTrue(np.array_equal(new_exp.longitude, ref_exp.longitude)) + + def test_not_filter_exposures_pass(self): + """Test _filter_exposures method with []""" + meas = Measure(exp_region_id=[]) + + exp = Exposures() + imp_set = ImpactFuncSet() + haz = Hazard("TC") + + new_exp = Exposures() + new_impfs = ImpactFuncSet() + new_haz = Hazard("TC") + + res_exp, res_ifs, res_haz = meas._filter_exposures( + exp, imp_set, haz, new_exp, new_impfs, new_haz + ) + + self.assertTrue(res_exp is new_exp) + self.assertTrue(res_ifs is new_impfs) + self.assertTrue(res_haz is new_haz) + + self.assertTrue(res_exp is not exp) + self.assertTrue(res_ifs is not imp_set) + self.assertTrue(res_haz is not haz) + + def test_filter_exposures_pass(self): + """Test _filter_exposures method with two values""" + meas = Measure( + exp_region_id=[3, 4], + haz_type="TC", + ) + + exp = Exposures.from_mat(ENT_TEST_MAT) + exp.gdf.rename(columns={"impf_": "impf_TC", "centr_": "centr_TC"}, inplace=True) + exp.gdf["region_id"] = np.ones(exp.gdf.shape[0]) + exp.gdf["region_id"].values[: exp.gdf.shape[0] // 2] = 3 + exp.gdf["region_id"][0] = 4 + exp.check() + + imp_set = ImpactFuncSet.from_mat(ENT_TEST_MAT) + + haz = Hazard.from_hdf5(HAZ_TEST_TC) + exp.assign_centroids(haz) + + new_exp = copy.deepcopy(exp) + new_exp.gdf["value"] *= 3 + new_exp.gdf["impf_TC"].values[:20] = 2 + new_exp.gdf["impf_TC"].values[20:40] = 3 + new_exp.gdf["impf_TC"].values[40:] = 1 + + new_ifs = copy.deepcopy(imp_set) + new_ifs.get_func("TC")[1].intensity += 1 + ref_ifs = copy.deepcopy(new_ifs) + + new_haz = copy.deepcopy(haz) + new_haz.intensity *= 4 + + res_exp, res_ifs, res_haz = meas._filter_exposures( + exp, imp_set, haz, new_exp.copy(deep=True), new_ifs, new_haz + ) + + # unchanged meta data + self.assertEqual(res_exp.ref_year, exp.ref_year) + self.assertEqual(res_exp.value_unit, exp.value_unit) + self.assertEqual(res_exp.description, exp.description) + self.assertTrue(u_coord.equal_crs(res_exp.crs, exp.crs)) + + # regions (that is just input data, no need for testing, but it makes the changed and unchanged parts obious) + self.assertTrue(np.array_equal(res_exp.region_id[0], 4)) + self.assertTrue(np.array_equal(res_exp.region_id[1:25], np.ones(24) * 3)) + self.assertTrue(np.array_equal(res_exp.region_id[25:], np.ones(25))) + + # changed exposures + self.assertTrue( + np.array_equal( + res_exp.gdf["value"].values[:25], new_exp.gdf["value"].values[:25] + ) + ) + self.assertTrue( + np.all( + np.not_equal( + res_exp.gdf["value"].values[:25], exp.gdf["value"].values[:25] + ) + ) + ) + self.assertTrue( + np.all( + np.not_equal( + res_exp.gdf["impf_TC"].values[:25], + new_exp.gdf["impf_TC"].values[:25], + ) + ) + ) + self.assertTrue(np.array_equal(res_exp.latitude[:25], new_exp.latitude[:25])) + self.assertTrue(np.array_equal(res_exp.longitude[:25], new_exp.longitude[:25])) + + # unchanged exposures + self.assertTrue( + np.array_equal( + res_exp.gdf["value"].values[25:], exp.gdf["value"].values[25:] + ) + ) + self.assertTrue( + np.all( + np.not_equal( + res_exp.gdf["value"].values[25:], new_exp.gdf["value"].values[25:] + ) + ) + ) + self.assertTrue( + np.array_equal( + res_exp.gdf["impf_TC"].values[25:], exp.gdf["impf_TC"].values[25:] + ) + ) + self.assertTrue(np.array_equal(res_exp.latitude[25:], exp.latitude[25:])) + self.assertTrue(np.array_equal(res_exp.longitude[25:], exp.longitude[25:])) + + # unchanged impact functions + self.assertEqual(list(res_ifs.get_func().keys()), [meas.haz_type]) + self.assertEqual( + res_ifs.get_func()[meas.haz_type][1].id, + imp_set.get_func()[meas.haz_type][1].id, + ) + self.assertTrue( + np.array_equal( + res_ifs.get_func()[meas.haz_type][1].intensity, + imp_set.get_func()[meas.haz_type][1].intensity, + ) + ) + self.assertEqual( + res_ifs.get_func()[meas.haz_type][3].id, + imp_set.get_func()[meas.haz_type][3].id, + ) + self.assertTrue( + np.array_equal( + res_ifs.get_func()[meas.haz_type][3].intensity, + imp_set.get_func()[meas.haz_type][3].intensity, + ) + ) + + # changed impact functions + self.assertTrue( + np.array_equal( + res_ifs.get_func()[meas.haz_type][1 + IMPF_ID_FACT].intensity, + ref_ifs.get_func()[meas.haz_type][1].intensity, + ) + ) + self.assertTrue( + np.array_equal( + res_ifs.get_func()[meas.haz_type][1 + IMPF_ID_FACT].paa, + ref_ifs.get_func()[meas.haz_type][1].paa, + ) + ) + self.assertTrue( + np.array_equal( + res_ifs.get_func()[meas.haz_type][1 + IMPF_ID_FACT].mdd, + ref_ifs.get_func()[meas.haz_type][1].mdd, + ) + ) + self.assertTrue( + np.array_equal( + res_ifs.get_func()[meas.haz_type][3 + IMPF_ID_FACT].intensity, + ref_ifs.get_func()[meas.haz_type][3].intensity, + ) + ) + self.assertTrue( + np.array_equal( + res_ifs.get_func()[meas.haz_type][3 + IMPF_ID_FACT].paa, + ref_ifs.get_func()[meas.haz_type][3].paa, + ) + ) + self.assertTrue( + np.array_equal( + res_ifs.get_func()[meas.haz_type][3 + IMPF_ID_FACT].mdd, + ref_ifs.get_func()[meas.haz_type][3].mdd, + ) + ) + + # unchanged hazard + self.assertTrue( + np.array_equal( + res_haz.intensity[:, :36].toarray(), haz.intensity[:, :36].toarray() + ) + ) + self.assertTrue( + np.array_equal( + res_haz.intensity[:, 37:46].toarray(), haz.intensity[:, 37:46].toarray() + ) + ) + self.assertTrue( + np.array_equal( + res_haz.intensity[:, 47:].toarray(), haz.intensity[:, 47:].toarray() + ) + ) + + # changed hazard + self.assertTrue( + np.array_equal( + res_haz.intensity[[36, 46]].toarray(), + new_haz.intensity[[36, 46]].toarray(), + ) + ) + + def test_apply_ref_pass(self): + """Test apply method: apply all measures but insurance""" + hazard = Hazard.from_hdf5(HAZ_TEST_TC) + + entity = Entity.from_mat(ENT_TEST_MAT) + entity.measures._data["TC"] = entity.measures._data.pop("XX") + for meas in entity.measures.get_measure("TC"): + meas.haz_type = "TC" + entity.check() + + new_exp, new_ifs, new_haz = entity.measures.get_measure( + "TC", "Mangroves" + ).apply(entity.exposures, entity.impact_funcs, hazard) + + self.assertTrue(new_exp is entity.exposures) + self.assertTrue(new_haz is hazard) + self.assertFalse(new_ifs is entity.impact_funcs) + + new_imp = new_ifs.get_func("TC")[0] + self.assertTrue( + np.array_equal( + new_imp.intensity, + np.array([4.0, 24.0, 34.0, 44.0, 54.0, 64.0, 74.0, 84.0, 104.0]), + ) + ) + self.assertTrue( + np.allclose( + new_imp.mdd, + np.array( + [ + 0, + 0, + 0.021857142857143, + 0.035887500000000, + 0.053977415307403, + 0.103534246575342, + 0.180414000000000, + 0.410796000000000, + 0.410796000000000, + ] + ), + ) + ) + self.assertTrue( + np.allclose( + new_imp.paa, + np.array( + [ + 0, + 0.005000000000000, + 0.042000000000000, + 0.160000000000000, + 0.398500000000000, + 0.657000000000000, + 1.000000000000000, + 1.000000000000000, + 1.000000000000000, + ] + ), + ) + ) + + new_imp = new_ifs.get_func("TC")[1] + self.assertTrue( + np.array_equal( + new_imp.intensity, + np.array([4.0, 24.0, 34.0, 44.0, 54.0, 64.0, 74.0, 84.0, 104.0]), + ) + ) + self.assertTrue( + np.allclose( + new_imp.mdd, + np.array( + [ + 0, + 0, + 0, + 0.025000000000000, + 0.054054054054054, + 0.104615384615385, + 0.211764705882353, + 0.400000000000000, + 0.400000000000000, + ] + ), + ) + ) + self.assertTrue( + np.allclose( + new_imp.paa, + np.array( + [ + 0, + 0.004000000000000, + 0, + 0.160000000000000, + 0.370000000000000, + 0.650000000000000, + 0.850000000000000, + 1.000000000000000, + 1.000000000000000, + ] + ), + ) + ) + + def test_calc_impact_pass(self): + """Test calc_impact method: apply all measures but insurance""" + + hazard = Hazard.from_hdf5(HAZ_TEST_TC) + + entity = Entity.from_mat(ENT_TEST_MAT) + entity.exposures.gdf.rename(columns={"impf": "impf_TC"}, inplace=True) + entity.measures._data["TC"] = entity.measures._data.pop("XX") + entity.measures.get_measure(name="Mangroves", haz_type="TC").haz_type = "TC" + for meas in entity.measures.get_measure("TC"): + meas.haz_type = "TC" + entity.check() + + imp, risk_transf = entity.measures.get_measure("TC", "Mangroves").calc_impact( + entity.exposures, entity.impact_funcs, hazard + ) + + self.assertAlmostEqual(imp.aai_agg, 4.850407096284983e09, delta=1) + self.assertAlmostEqual(imp.at_event[0], 0) + self.assertAlmostEqual(imp.at_event[12], 1.470194187501225e07) + self.assertAlmostEqual(imp.at_event[41], 4.7226357936631286e08) + self.assertAlmostEqual(imp.at_event[11890], 1.742110428135755e07) + self.assertTrue(np.array_equal(imp.coord_exp[:, 0], entity.exposures.latitude)) + self.assertTrue(np.array_equal(imp.coord_exp[:, 1], entity.exposures.longitude)) + self.assertAlmostEqual(imp.eai_exp[0], 1.15677655725858e08) + self.assertAlmostEqual(imp.eai_exp[-1], 7.528669956120645e07) + self.assertAlmostEqual(imp.tot_value, 6.570532945599105e11) + self.assertEqual(imp.unit, "USD") + self.assertEqual(imp.imp_mat.shape, (0, 0)) + self.assertTrue(np.array_equal(imp.event_id, hazard.event_id)) + self.assertTrue(np.array_equal(imp.date, hazard.date)) + self.assertEqual(imp.event_name, hazard.event_name) + self.assertEqual(risk_transf.aai_agg, 0) + + def test_calc_impact_transf_pass(self): + """Test calc_impact method: apply all measures and insurance""" + + hazard = Hazard.from_hdf5(HAZ_TEST_TC) + + entity = Entity.from_mat(ENT_TEST_MAT) + entity.exposures.gdf.rename(columns={"impf": "impf_TC"}, inplace=True) + entity.measures._data["TC"] = entity.measures._data.pop("XX") + for meas in entity.measures.get_measure("TC"): + meas.haz_type = "TC" + meas = entity.measures.get_measure(name="Beach nourishment", haz_type="TC") + meas.haz_type = "TC" + meas.hazard_inten_imp = (1, 0) + meas.mdd_impact = (1, 0) + meas.paa_impact = (1, 0) + meas.risk_transf_attach = 5.0e8 + meas.risk_transf_cover = 1.0e9 + entity.check() + + imp, risk_transf = entity.measures.get_measure( + name="Beach nourishment", haz_type="TC" + ).calc_impact(entity.exposures, entity.impact_funcs, hazard) + + self.assertAlmostEqual(imp.aai_agg, 6.280804242609713e09) + self.assertAlmostEqual(imp.at_event[0], 0) + self.assertAlmostEqual(imp.at_event[12], 8.648764833437817e07) + self.assertAlmostEqual(imp.at_event[41], 500000000) + self.assertAlmostEqual(imp.at_event[11890], 6.498096646836635e07) + self.assertTrue(np.array_equal(imp.coord_exp, np.array([]))) + self.assertTrue(np.array_equal(imp.eai_exp, np.array([]))) + self.assertAlmostEqual(imp.tot_value, 6.570532945599105e11) + self.assertEqual(imp.unit, "USD") + self.assertEqual(imp.imp_mat.shape, (0, 0)) + self.assertTrue(np.array_equal(imp.event_id, hazard.event_id)) + self.assertTrue(np.array_equal(imp.date, hazard.date)) + self.assertEqual(imp.event_name, hazard.event_name) + self.assertEqual(risk_transf.aai_agg, 2.3139691495470852e08) + + +# Execute Tests +if __name__ == "__main__": + TESTS = unittest.TestLoader().loadTestsFromTestCase(TestApply) + unittest.TextTestRunner(verbosity=2).run(TESTS) diff --git a/climada/entity/measures/test/test_meas_set.py b/climada/entity/_legacy_measures/test/test_meas_set.py similarity index 98% rename from climada/entity/measures/test/test_meas_set.py rename to climada/entity/_legacy_measures/test/test_meas_set.py index a2cbdc3f16..868510fbe8 100644 --- a/climada/entity/measures/test/test_meas_set.py +++ b/climada/entity/_legacy_measures/test/test_meas_set.py @@ -24,8 +24,8 @@ import numpy as np from climada import CONFIG -from climada.entity.measures.base import Measure -from climada.entity.measures.measure_set import MeasureSet +from climada.entity._legacy_measures.base import Measure +from climada.entity._legacy_measures.measure_set import MeasureSet from climada.util.constants import ENT_DEMO_TODAY, ENT_TEMPLATE_XLS DATA_DIR = CONFIG.measures.test_data.dir() @@ -58,7 +58,7 @@ def test_add_wrong_error(self): """Test error is raised when wrong ImpactFunc provided.""" meas = MeasureSet() with self.assertLogs( - "climada.entity.measures.measure_set", level="WARNING" + "climada.entity._legacy_measures.measure_set", level="WARNING" ) as cm: meas.append(Measure()) self.assertIn("Input Measure's hazard type not set.", cm.output[0]) @@ -76,7 +76,9 @@ def test_remove_measure_pass(self): def test_remove_wrong_error(self): """Test error is raised when invalid inputs.""" meas = MeasureSet(measure_list=[Measure(name="Mangrove", haz_type="FL")]) - with self.assertLogs("climada.entity.measures.measure_set", level="INFO") as cm: + with self.assertLogs( + "climada.entity._legacy_measures.measure_set", level="INFO" + ) as cm: meas.remove_measure(name="Seawall") self.assertIn("No Measure with name Seawall.", cm.output[0]) diff --git a/climada/entity/entity_def.py b/climada/entity/entity_def.py index d58af9efed..c1bc3b2550 100755 --- a/climada/entity/entity_def.py +++ b/climada/entity/entity_def.py @@ -26,10 +26,10 @@ import pandas as pd +from climada.entity._legacy_measures.measure_set import Measure, MeasureSet from climada.entity.disc_rates.base import DiscRates from climada.entity.exposures.base import Exposures from climada.entity.impact_funcs.impact_func_set import ImpactFuncSet -from climada.entity.measures.measure_set import MeasureSet LOGGER = logging.getLogger(__name__) diff --git a/climada/entity/exposures/base.py b/climada/entity/exposures/base.py index bc084628eb..1989e0fd80 100644 --- a/climada/entity/exposures/base.py +++ b/climada/entity/exposures/base.py @@ -403,6 +403,9 @@ def __init__( self.description = self._consolidate(meta, "description", description) self.ref_year = self._consolidate(meta, "ref_year", ref_year, DEF_REF_YEAR) + + if geodata.shape[0] > 0: + value_unit = self._consolidate(geodata.iloc[0], "value_unit", value_unit) self.value_unit = self._consolidate( meta, "value_unit", value_unit, DEF_VALUE_UNIT ) @@ -644,15 +647,17 @@ def assign_centroids( Caution: nearest neighbourg matching can introduce serious artefacts such as: - - exposure and hazard centroids with shifted grids can lead - to systematically wrong assignements. - - hazard centroids covering larger areas than exposures may lead - to sub-optimal matching if the threshold is too large - - projected crs often diverge at the anti-meridian and close points - on either side will be at a large distance. For proper handling - of the anti-meridian please use degree coordinates in EPSG:4326. - This might be relevant for countries like the Fidji or the US that - cross the anti-meridian. + + - exposure and hazard centroids with shifted grids can lead + to systematically wrong assignements. + - hazard centroids covering larger areas than exposures may lead + to sub-optimal matching if the threshold is too large + - projected crs often diverge at the anti-meridian and close points + on either side will be at a large distance. For proper handling + of the anti-meridian please use degree coordinates in EPSG:4326. + This might be relevant for countries like the Fidji or the US that + cross the anti-meridian. + Users are free to implement their own matching alrogithm and save the matching centroid index in the appropriate column ``centr_[hazard.HAZ_TYPE]``. diff --git a/climada/entity/exposures/test/test_base.py b/climada/entity/exposures/test/test_base.py index a8d25224ce..1ac9062ecf 100644 --- a/climada/entity/exposures/test/test_base.py +++ b/climada/entity/exposures/test/test_base.py @@ -182,7 +182,7 @@ def test__init__mda_in_kwargs(self): def test_read_raster_pass(self): """from_raster""" exp = Exposures.from_raster( - HAZ_DEMO_FL, window=Window(10, 20, 50, 60), attrs={"value_unit": "USD"} + HAZ_DEMO_FL, window=Window(10, 20, 50, 60), attrs={"value_unit": "PKR"} ) exp.check() self.assertTrue(u_coord.equal_crs(exp.crs, DEF_CRS)) @@ -204,7 +204,7 @@ def test_read_raster_pass(self): self.assertAlmostEqual( exp.gdf["value"].values.reshape((60, 50))[25, 12], 0.056825936 ) - self.assertEqual(exp.value_unit, "USD") + self.assertEqual(exp.value_unit, "PKR") def test_assign_raster_pass(self): """Test assign_centroids with raster hazard""" @@ -429,16 +429,23 @@ def test_read_template_pass(self): exp_df = Exposures(df) # set metadata exp_df.ref_year = 2020 - exp_df.value_unit = "XSD" + exp_df.value_unit = "PAK" exp_df.check() + def test_handling_unit_conflicts_pass(self): + """Check that the value_unit is correctly set when there are conflicting value_unit definitions in the data frame and the meta attribute.""" + df = pd.read_excel(ENT_TEMPLATE_XLS) + exp_df = Exposures(df, meta={"value_unit": "XSD"}, value_unit="XSD") + exp_df.check() + self.assertEqual(exp_df.value_unit, "XSD") + with self.assertRaises(ValueError) as cm: + exp_df = Exposures(df, meta={"value_unit": "XSD"}, value_unit="PAK") + def test_io_hdf5_pass(self): """write and read hdf5""" - exp = Exposures(pd.read_excel(ENT_TEMPLATE_XLS), crs="epsg:32632") - - # set metadata - exp.ref_year = 2020 - exp.value_unit = "XSD" + exp = Exposures( + pd.read_excel(ENT_TEMPLATE_XLS), crs="epsg:32632", ref_year=2020 + ) # add another geometry column exp.data["geocol2"] = exp.data.geometry.copy(deep=True) diff --git a/climada/entity/impact_funcs/base.py b/climada/entity/impact_funcs/base.py index c51540d573..3642eed7a8 100644 --- a/climada/entity/impact_funcs/base.py +++ b/climada/entity/impact_funcs/base.py @@ -189,7 +189,7 @@ def from_step_impf( haz_type: str, mdd: tuple[float, float] = (0, 1), paa: tuple[float, float] = (1, 1), - impf_id: int = 1, + impf_id: int | str = 1, **kwargs, ): """Step function type impact function. @@ -207,7 +207,7 @@ def from_step_impf( (min, max) mdd values. The default is (0, 1) paa: tuple(float, float) (min, max) paa values. The default is (1, 1) - impf_id : int, optional, default=1 + impf_id : int|str, optional, default=1 impact function id kwargs : keyword arguments passed to ImpactFunc() @@ -250,7 +250,7 @@ def from_sigmoid_impf( k: float, x0: float, haz_type: str, - impf_id: int = 1, + impf_id: int | str = 1, **kwargs, ): r"""Sigmoid type impact function hinging on three parameter. @@ -320,7 +320,7 @@ def from_poly_s_shape( scale: float, exponent: float, haz_type: str, - impf_id: int = 1, + impf_id: int | str = 1, **kwargs, ): r"""S-shape polynomial impact function hinging on four parameter. diff --git a/climada/entity/impact_funcs/impact_func_set.py b/climada/entity/impact_funcs/impact_func_set.py index 030f73f2be..b6f4cf73d7 100755 --- a/climada/entity/impact_funcs/impact_func_set.py +++ b/climada/entity/impact_funcs/impact_func_set.py @@ -24,7 +24,7 @@ import copy import logging from itertools import repeat -from typing import Iterable, Optional +from typing import Iterable, Optional, Union, overload import matplotlib.pyplot as plt import numpy as np @@ -119,7 +119,7 @@ def clear(self): """Reinitialize attributes.""" self._data = dict() # {hazard_type : {id:ImpactFunc}} - def append(self, func): + def append(self, func: ImpactFunc): """Append a ImpactFunc. Overwrite existing if same id and haz_type. Parameters @@ -141,7 +141,9 @@ def append(self, func): self._data[func.haz_type] = dict() self._data[func.haz_type][func.id] = func - def remove_func(self, haz_type=None, fun_id=None): + def remove_func( + self, haz_type: Optional[str] = None, fun_id: Optional[str | int] = None + ): """Remove impact function(s) with provided hazard type and/or id. If no input provided, all impact functions are removed. @@ -173,7 +175,29 @@ def remove_func(self, haz_type=None, fun_id=None): else: self._data = dict() - def get_func(self, haz_type=None, fun_id=None): + @overload + def get_func( + self, haz_type: None = None, fun_id: None = None + ) -> dict[str, dict[Union[int, str], ImpactFunc]]: ... + + @overload + def get_func( + self, haz_type: None = ..., fun_id: int | str = ... + ) -> list[ImpactFunc]: ... + + @overload + def get_func( + self, haz_type: str = ..., fun_id: None = None + ) -> list[ImpactFunc]: ... + + @overload + def get_func(self, haz_type: str = ..., fun_id: int | str = ...) -> ImpactFunc: ... + + def get_func( + self, haz_type: Optional[str] = None, fun_id: Optional[int | str] = None + ) -> Union[ + ImpactFunc, list[ImpactFunc], dict[str, dict[Union[int, str], ImpactFunc]] + ]: """Get ImpactFunc(s) of input hazard type and/or id. If no input provided, all impact functions are returned. @@ -209,7 +233,7 @@ def get_func(self, haz_type=None, fun_id=None): else: return self._data - def get_hazard_types(self, fun_id=None): + def get_hazard_types(self, fun_id: Optional[str | int] = None) -> list[str]: """Get impact functions hazard types contained for the id provided. Return all hazard types if no input id. @@ -231,7 +255,15 @@ def get_hazard_types(self, fun_id=None): haz_types.append(vul_haz) return haz_types - def get_ids(self, haz_type=None): + @overload + def get_ids(self, haz_type: None = None) -> dict[str, list[str | int]]: ... + + @overload + def get_ids(self, haz_type: str) -> list[int | str]: ... + + def get_ids( + self, haz_type: Optional[str] = None + ) -> dict[str, list[str | int]] | list[int | str]: """Get impact functions ids contained for the hazard type provided. Return all ids for each hazard type if no input hazard type. @@ -256,7 +288,9 @@ def get_ids(self, haz_type=None): except KeyError: return list() - def size(self, haz_type=None, fun_id=None): + def size( + self, haz_type: Optional[str] = None, fun_id: Optional[str | int] = None + ) -> int: """Get number of impact functions contained with input hazard type and /or id. If no input provided, get total number of impact functions. @@ -279,6 +313,7 @@ def size(self, haz_type=None, fun_id=None): return 1 if (haz_type is not None) or (fun_id is not None): return len(self.get_func(haz_type, fun_id)) + return sum(len(vul_list) for vul_list in self.get_ids().values()) def check(self): @@ -300,7 +335,7 @@ def check(self): ) vul.check() - def extend(self, impact_funcs): + def extend(self, impact_funcs: "ImpactFuncSet"): """Append impact functions of input ImpactFuncSet to current ImpactFuncSet. Overwrite ImpactFunc if same id and haz_type. @@ -323,7 +358,13 @@ def extend(self, impact_funcs): for _, vul in vul_dict.items(): self.append(vul) - def plot(self, haz_type=None, fun_id=None, axis=None, **kwargs): + def plot( + self, + haz_type: Optional[str] = None, + fun_id: Optional[str | int] = None, + axis=None, + **kwargs, + ): """Plot impact functions of selected hazard (all if not provided) and selected function id (all if not provided). diff --git a/climada/entity/impact_funcs/test/test_tc.py b/climada/entity/impact_funcs/test/test_tc.py index ebcccebdce..7b8c6ace5a 100644 --- a/climada/entity/impact_funcs/test/test_tc.py +++ b/climada/entity/impact_funcs/test/test_tc.py @@ -24,7 +24,11 @@ import numpy as np import pandas as pd -from climada.entity.impact_funcs.trop_cyclone import ImpfSetTropCyclone, ImpfTropCyclone +from climada.entity.impact_funcs.trop_cyclone import ( + CountryCode, + ImpfSetTropCyclone, + ImpfTropCyclone, +) class TestEmanuelFormula(unittest.TestCase): @@ -168,6 +172,16 @@ def test_get_countries_per_region(self): self.assertListEqual(out[2], [124, 840]) self.assertListEqual(out[3], ["CAN", "USA"]) + def test_get_countries_per_region_all_or_none(self): + ifs = ImpfSetTropCyclone() + out = ifs.get_countries_per_region() + out2 = ifs.get_countries_per_region("all") + self.assertEqual(out, out2) + for reg in CountryCode.REGION_NAME.value: + out_reg = ifs.get_countries_per_region(reg) + for i in range(4): + self.assertEqual(out[i][reg], out_reg[i]) + def test_get_imf_id_regions_per_countries(self): """Test get_impf_id_regions_per_countries()""" ifs = ImpfSetTropCyclone() diff --git a/climada/entity/impact_funcs/trop_cyclone.py b/climada/entity/impact_funcs/trop_cyclone.py index d4a867ceab..ac578fde1e 100644 --- a/climada/entity/impact_funcs/trop_cyclone.py +++ b/climada/entity/impact_funcs/trop_cyclone.py @@ -23,6 +23,7 @@ import logging from enum import Enum +from typing import Literal, overload import numpy as np import pandas as pd @@ -92,7 +93,7 @@ class CountryCode(Enum): "NAM", "NER", "NGA", "NLD", "NOR", "POL", "PRK", "PRT", "PSE", "REU", "ROU", "RUS", "RWA", "SDN", "SEN", "SGP", "SGS", "SJM", "SLE", "SMR", "SPM", "SRB", "SSD", "STP", "SVK", "SVN", "SWE", "SYC", "TCD", "TGO", "TUN", "TUR", "UKR", - "UMI", "VAT", "XKX", "ZMB", + "UMI", "VAT", "XKO", "ZMB", ], } @@ -363,10 +364,47 @@ def calibrated_regional_vhalf( reg_v_half[regions_short[-1]] = np.round(df_reg["v_half"].values[0], 5) return reg_v_half + @overload + @staticmethod + def get_countries_per_region( + region: Literal["all"] = "all", + ) -> tuple[ + dict[str, str], # region_name + dict[str, int], # impf_id + dict[str, list[int]], # numeric + dict[str, list[str]], # alpha3 + ]: ... + + @overload + @staticmethod + def get_countries_per_region( + region: None, + ) -> tuple[ + dict[str, str], dict[str, int], dict[str, list[int]], dict[str, list[str]] + ]: ... + + @overload + @staticmethod + def get_countries_per_region( + region: str, + ) -> tuple[ + str, int, list[int], list[str] # region_name # impf_id # numeric # alpha3 + ]: ... + @staticmethod def get_countries_per_region(region=None): - """Returns dictionaries with numerical (numeric) and alphabetical (alpha3) ISO3 codes - of all countries associated to a calibration region. + """Returns countries within a TC calibration region and associated impact functions. + + This method returns a tuple with numerical (numeric) and alphabetical (alpha3) + ISO3 codes of all countries associated to a calibration region. + + If no region or "all" is provided as argument, the method return a tuple of + dictionaries with short name of the tropical cyclone calibration regions as + keys and the values for each of those. + + Notes + ----- + Only contains countries that were affected by tropical cyclones between 1980 and 2017 according to EM-DAT. @@ -395,9 +433,12 @@ def get_countries_per_region(region=None): return ( CountryCode.REGION_NAME.value, CountryCode.IMPF_ID.value, - coordinates.country_to_iso( - CountryCode.ALPHA3.value, representation="numeric" - ), + { + reg: coordinates.country_to_iso( + CountryCode.ALPHA3.value[reg], representation="numeric" + ) + for reg in CountryCode.REGION_NAME.value + }, CountryCode.ALPHA3.value, ) diff --git a/climada/entity/measures/__init__.py b/climada/entity/measures/__init__.py index 36d9250459..65702e109f 100755 --- a/climada/entity/measures/__init__.py +++ b/climada/entity/measures/__init__.py @@ -19,5 +19,8 @@ init measures """ -from .base import * -from .measure_set import * +from .base import Measure +from .measure_config import MeasureConfig +from .measure_set import MeasureSet + +__all__ = ["Measure", "MeasureSet", "MeasureConfig"] diff --git a/climada/entity/measures/base.py b/climada/entity/measures/base.py index 4e539f9986..4d5a9f3d2a 100755 --- a/climada/entity/measures/base.py +++ b/climada/entity/measures/base.py @@ -19,552 +19,523 @@ Define Measure class. """ +from __future__ import annotations + +import dataclasses + +from pandas.tseries.offsets import BaseOffset + +from climada.entity.measures.helper import ( + helper_exposure, + helper_hazard, + helper_impfset, + identity_function, +) + __all__ = ["Measure"] import copy +import inspect import logging -from pathlib import Path -from typing import Optional, Tuple +from functools import wraps +from typing import TYPE_CHECKING, Any, Optional, Tuple, TypeVar -import numpy as np import pandas as pd -from geopandas import GeoDataFrame -import climada.util.checker as u_check -from climada.entity.exposures.base import INDICATOR_CENTR, INDICATOR_IMPF, Exposures -from climada.hazard.base import Hazard +from climada.entity.measures.measure_config import ( + ExposuresModifierConfig, + HazardModifierConfig, + ImpfsetModifierConfig, + MeasureConfig, +) -LOGGER = logging.getLogger(__name__) +from .cost_income import CostIncome -IMPF_ID_FACT = 1000 -"""Factor internally used as id for impact functions when region selected.""" +if TYPE_CHECKING: + from climada.entity.exposures.base import Exposures + from climada.entity.impact_funcs.impact_func_set import ImpactFuncSet + from climada.entity.measures.types import ( + ExposuresChange, + HazardChange, + ImpfsetChange, + ) + from climada.hazard.base import Hazard -NULL_STR = "nil" -"""String considered as no path in measures exposures_set and hazard_set or -no string in imp_fun_map""" + T = TypeVar("T", Exposures, ImpactFuncSet, Hazard) +LOGGER = logging.getLogger(__name__) -class Measure: - """ - Contains the definition of one measure. +# TODO: risk transfer? - Attributes - ---------- - name : str - name of the measure - haz_type : str - related hazard type (peril), e.g. TC - color_rgb : np.array - integer array of size 3. Color code of this measure in RGB - cost : float - discounted cost (in same units as assets) - hazard_set : str - file name of hazard to use (in h5 format) - hazard_freq_cutoff : float - hazard frequency cutoff - exposures_set : str or climada.entity.Exposure - file name of exposure to use (in h5 format) or Exposure instance - imp_fun_map : str - change of impact function id of exposures, e.g. '1to3' - hazard_inten_imp : tuple(float, float) - parameter a and b of hazard intensity change - mdd_impact : tuple(float, float) - parameter a and b of the impact over the mean damage degree - paa_impact : tuple(float, float) - parameter a and b of the impact over the percentage of affected assets - exp_region_id : int - region id of the selected exposures to consider ALL the previous - parameters - risk_transf_attach : float - risk transfer attachment - risk_transf_cover : float - risk transfer cover - risk_transf_cost_factor : float - factor to multiply to resulting insurance layer to get the total - cost of risk transfer + +def allow_kwargs(func): """ + Decorator that allows a function to accept (and silently ignore) keyword arguments. - def __init__( - self, - name: str = "", - haz_type: str = "", - cost: float = 0, - hazard_set: str = NULL_STR, - hazard_freq_cutoff: float = 0, - exposures_set: str = NULL_STR, - imp_fun_map: str = NULL_STR, - hazard_inten_imp: Tuple[float, float] = (1, 0), - mdd_impact: Tuple[float, float] = (1, 0), - paa_impact: Tuple[float, float] = (1, 0), - exp_region_id: Optional[list] = None, - risk_transf_attach: float = 0, - risk_transf_cover: float = 0, - risk_transf_cost_factor: float = 1, - color_rgb: Optional[np.ndarray] = None, - ): - """Initialize a Measure object with given values. + If the wrapped function already accepts ``**kwargs``, it is called unchanged. + Otherwise, any keyword arguments not present in the function's signature are + filtered out before the call, preventing ``TypeError`` from unexpected keywords. - Parameters - ---------- - name : str, optional - name of the measure - haz_type : str, optional - related hazard type (peril), e.g. TC - cost : float, optional - discounted cost (in same units as assets) - hazard_set : str, optional - file name of hazard to use (in h5 format) - hazard_freq_cutoff : float, optional - hazard frequency cutoff - exposures_set : str or climada.entity.Exposure, optional - file name of exposure to use (in h5 format) or Exposure instance - imp_fun_map : str, optional - change of impact function id of exposures, e.g. '1to3' - hazard_inten_imp : tuple(float, float), optional - parameter a and b of hazard intensity change - mdd_impact : tuple(float, float), optional - parameter a and b of the impact over the mean damage degree - paa_impact : tuple(float, float), optional - parameter a and b of the impact over the percentage of affected assets - exp_region_id : int, optional - region id of the selected exposures to consider ALL the previous - parameters - risk_transf_attach : float, optional - risk transfer attachment - risk_transf_cover : float, optional - risk transfer cover - risk_transf_cost_factor : float, optional - factor to multiply to resulting insurance layer to get the total - cost of risk transfer - color_rgb : np.array, optional - integer array of size 3. Color code of this measure in RGB. - Default is None (corresponds to black). - """ - self.name = name - self.haz_type = haz_type - self.color_rgb = np.array([0, 0, 0]) if color_rgb is None else color_rgb - self.cost = cost + The functions used by `Measure` objects to apply changes on ``Exposures``, ``Hazard``, + and ``ImpactFuncSet`` always receive ``base_exposure, base_hazard, base_impfset`` as + keyword arguments. This decorator is applied to users-defined functions and prevent + them from not accepting these kwargs and raising a ``TypeError``. - # related to change in hazard - self.hazard_set = hazard_set - self.hazard_freq_cutoff = hazard_freq_cutoff + Parameters + ---------- + func : callable + The function to wrap. + + Returns + ------- + callable + A wrapped version of `func` that accepts keyword arguments. + + Examples + -------- + >>> @allow_kwargs + ... def greet(name, greeting="Hello"): + ... return f"{greeting}, {name}!" + >>> greet("Alice", greeting="Hi", unused_param="ignored") + 'Hi, Alice!' + + >>> @allow_kwargs + ... def add(a, b): + ... return a + b + >>> add(1, 2, extra=99) + 3 + """ - # related to change in exposures - self.exposures_set = exposures_set - self.imp_fun_map = imp_fun_map + @wraps(func) + def wrapper(*args, **kwargs): + # Get the names of arguments the original function accepts + params = inspect.signature(func).parameters - # related to change in impact functions - self.hazard_inten_imp = hazard_inten_imp - self.mdd_impact = mdd_impact - self.paa_impact = paa_impact + # Filter kwargs to only include what the function can handle + # (Unless the function already has **kwargs in its signature) + if any(p.kind == p.VAR_KEYWORD for p in params.values()): + return func(*args, **kwargs) - # related to change in region - self.exp_region_id = [] if exp_region_id is None else exp_region_id + filtered_kwargs = {k: v for k, v in kwargs.items() if k in params} + return func(*args, **filtered_kwargs) - # risk transfer - self.risk_transf_attach = risk_transf_attach - self.risk_transf_cover = risk_transf_cover - self.risk_transf_cost_factor = risk_transf_cost_factor + return wrapper - def check(self): - """ - Check consistent instance data. - Raises - ------ - ValueError - """ - u_check.size([3, 4], self.color_rgb, "Measure.color_rgb") - u_check.size(2, self.hazard_inten_imp, "Measure.hazard_inten_imp") - u_check.size(2, self.mdd_impact, "Measure.mdd_impact") - u_check.size(2, self.paa_impact, "Measure.paa_impact") +class Measure: + """ + Contains a measure to be applied to a set of exposures, impact functions, + and hazard. - def calc_impact(self, exposures, imp_fun_set, hazard): - """ - Apply measure and compute impact and risk transfer of measure - implemented over inputs. + A ``Measure`` represents a single adaptation or risk-reduction action. It + holds three (optional) transformation functions, one each for + :class:`Exposures`, :class:`ImpactFuncSet`, and :class:`Hazard`, that are + can be applied to a triplet of ``(Exposures, ImpactFuncSet, Hazard)``, to + reflect the effect of the measure. - Parameters - ---------- - exposures : climada.entity.Exposures - exposures instance - imp_fun_set : climada.entity.ImpactFuncSet - impact function set instance - hazard : climada.hazard.Hazard - hazard instance + It also holds a `CostIncome` object to define the financial aspects of the + measure (see :class:`CostIncome` and :ref:`cost-income-tutorial`). - Returns - ------- - climada.engine.Impact - resulting impact and risk transfer of measure - """ + Finally it holds an `implementation_duration` attribute, in the form of a + pandas ``DateOffset``, which is used when the time dimension is considered. - new_exp, new_impfs, new_haz = self.apply(exposures, imp_fun_set, hazard) - # assign centroids if missing - if new_haz.centr_exp_col not in new_exp.gdf.columns: - LOGGER.warning( - "No assigned hazard centroids in exposure object after the " - "application of the measure. The centroids will be assigned during impact " - "calculation. This is potentiall costly. To silence this warning, make sure " - "that centroids are assigned to all exposures." - ) - new_exp.assign_centroids(new_haz) + Notes + ----- - return self._calc_impact(new_exp, new_impfs, new_haz) + The only requirement for each function is to return an object of the same + class (e.g. :class:`Hazard` for ``hazard_change``). Functions can accept + keyword arguments to enable advanced effect (depending on a year of + application for instance). These arguments can be passed when the + :class:`Measure` is applied (see :py:meth:`~Measure.apply`). Note that for + convenience, each functions receive the by default "base" ``(Exposures, + ImpactFuncSet, Hazard)`` triplet as keyword arguments (`base_exposure`, + `base_impfset`, `base_hazard`). - def apply(self, exposures, imp_fun_set, hazard): - """ - Implement measure with all its defined parameters. + If the ``Measure`` was defined from a ``MeasureConfig`` object, the + configuration is stored and the measure can be serialized to a file. + (see :ref:`measure-config-tutorial` and :ref:`measure-tutorial`). - Parameters - ---------- - exposures : climada.entity.Exposures - exposures instance - imp_fun_set : climada.entity.ImpactFuncSet - impact function set instance - hazard : climada.hazard.Hazard - hazard instance + Attributes + ---------- + name : str + Name of the measure. + exposures_change : ExposuresChange + Function to change exposures. + impfset_change : ImpfsetChange + Function to change impact function set. + hazard_change : HazardChange + Function to change hazard. + sub_measures : list of str, optional + List of measure names that this measure is a combination of. + cost_income : climada.entity.measures.cost_income.CostIncome + Cost and income object associated with the measure. + implementation_duration : pd.DateOffset, optional + Duration of implementation before the measure is fully functional. + """ - Returns - ------- - new_exp : climada.entity.Exposure - Exposure with implemented measure with all defined parameters - new_ifs : climada.entity.ImpactFuncSet - Impact function set with implemented measure with all defined parameters - new_haz : climada.hazard.Hazard - Hazard with implemented measure with all defined parameters + def __init__( + self, + name: str, + *, + exposures_changes: ExposuresChange = identity_function, + impfset_changes: ImpfsetChange = identity_function, + hazard_changes: HazardChange = identity_function, + sub_measures: Optional[list[str]] = None, + cost_income: Optional[CostIncome] = None, + implementation_duration: Optional[BaseOffset] = None, + color_rgb: Optional[Tuple[float, float, float]] = None, + _config: Optional[MeasureConfig] = None, + ): """ - # change hazard - new_haz = self._change_all_hazard(hazard) - # change exposures - new_exp = self._change_all_exposures(exposures) - new_exp = self._change_exposures_impf(new_exp) - # change impact functions - new_impfs = self._change_imp_func(imp_fun_set) - # cutoff events whose damage happen with high frequency (in region impf specified) - new_haz = self._cutoff_hazard_damage(new_exp, new_impfs, new_haz) - # apply all previous changes only to the selected exposures - new_exp, new_impfs, new_haz = self._filter_exposures( - exposures, imp_fun_set, hazard, new_exp, new_impfs, new_haz - ) - - return new_exp, new_impfs, new_haz - - def _calc_impact(self, new_exp, new_impfs, new_haz): - """Compute impact and risk transfer of measure implemented over inputs. + Initialize a new Measure object. Parameters ---------- - new_exp : climada.entity.Exposures - exposures once measure applied - new_ifs : climada.entity.ImpactFuncSet - impact function set once measure applied - new_haz : climada.hazard.Hazard - hazard once measure applied - - Returns - ------- - climada.engine.Impact + name : str + Name of the measure. + exposures_change : callable, optional + Transformation function for Exposures. Defaults to identity. + impfset_change : callable, optional + Transformation function for ImpactFuncSet. Defaults to identity. + hazard_change : callable, optional + Transformation function for Hazard. Defaults to identity. + sub_measures : list of str, optional + Names of component measures. + cost_income : CostIncome, optional + Financial data. If None, an empty CostIncome is initialized. + implementation_duration : pd.DateOffset, optional + Time offset for full implementation. """ - from climada.engine.impact_calc import ( - ImpactCalc, # pylint: disable=import-outside-toplevel - ) - imp = ImpactCalc(new_exp, new_impfs, new_haz).impact( - save_mat=False, assign_centroids=False + self.name = name + self.exposures_changes = allow_kwargs(exposures_changes) + self.hazard_changes = allow_kwargs(hazard_changes) + self.impfset_changes = allow_kwargs(impfset_changes) + self.sub_measures = sub_measures + self.cost_income = cost_income if cost_income is not None else CostIncome() + self.implementation_duration = implementation_duration + self.color_rgb = (0, 0, 0) if color_rgb is None else color_rgb + self._config = _config + + # DONE always provide exp, impfset and hazard as kwargs by default + # Have a precedence system (if users provide their own it takes over) + # TODO Check that it works + + @property + def is_serializable(self) -> bool: + return self._config is not None + + @classmethod + def from_config(cls, config: MeasureConfig) -> "Measure": + impfset_change = helper_impfset(config.impfset_modifier) + exp_change = helper_exposure(config.exposures_modifier) + haz_change = helper_hazard(config.hazard_modifier) + return cls( + name=config.name, + exposures_changes=exp_change, + impfset_changes=impfset_change, + hazard_changes=haz_change, + cost_income=CostIncome.from_config(config.cost_income), + implementation_duration=( + pd.tseries.frequencies.to_offset(config.implementation_duration) + if config.implementation_duration is not None + else None + ), + color_rgb=config.color_rgb, + _config=config, ) - return imp.calc_risk_transfer(self.risk_transf_attach, self.risk_transf_cover) - def _change_all_hazard(self, hazard): - """ - Change hazard to provided hazard_set. + @classmethod + def from_dict(cls, d: dict) -> "Measure": + return cls.from_config(MeasureConfig.from_dict(d)) - Parameters - ---------- - hazard : climada.hazard.Hazard - hazard instance + @classmethod + def from_yaml(cls, path: str) -> "Measure": + import yaml - Returns - ------- - new_haz : climada.hazard.Hazard - Hazard - """ - if self.hazard_set == NULL_STR: - return hazard + with open(path) as f: + return cls.from_dict(yaml.safe_load(f)["measures"][0]) - LOGGER.debug("Setting new hazard %s", self.hazard_set) - new_haz = Hazard.from_hdf5(self.hazard_set) - new_haz.check() - return new_haz + def apply_exposures_changes( + self, exposures: Exposures, enforce_copy: bool = True, **kwargs + ) -> Exposures: + """Apply the changes from the measure to the given :class:`Exposures` object. - def _change_all_exposures(self, exposures): - """ - Change exposures to provided exposures_set. + This method applies the `exposures_changes` function of the measure to + the provided :class:`Exposures` object. If ``enforce_copy`` is True (default), a + deep copy of the exposures is created before modification to ensure + immutability of the original object. + + Additional keyword arguments to the function can be passed directly. Parameters ---------- - exposures : climada.entity.Exposures - exposures instance + exposures : Exposures + The input exposures object to be transformed. + enforce_copy : bool, optional + If True (default), creates a deep copy of `exposures` before applying + changes, provided the transformation function is not the identity function. + If False, the original object may be modified in-place depending on the + behavior of `exposures_changes`. + **kwargs : dict, optional + Additional keyword arguments passed directly to the `exposures_changes` + function. Returns ------- - new_exp : climada.entity.Exposures() - Exposures + Exposures + The resulting :class:`Exposures` object after the transformation has been applied. + If `enforce_copy` was True, this is a new object. + + Notes + ----- + The deep copy operation is skipped if `enforce_copy` is False or if + `self.exposures_changes` is the identity function, optimizing performance + when no actual changes are expected or when in-place modification is desired. """ - if isinstance(self.exposures_set, str) and self.exposures_set == NULL_STR: - return exposures - - if isinstance(self.exposures_set, (str, Path)): - LOGGER.debug("Setting new exposures %s", self.exposures_set) - new_exp = Exposures.from_hdf5(self.exposures_set) - new_exp.check() - elif isinstance(self.exposures_set, Exposures): - LOGGER.debug("Setting new exposures. ") - new_exp = self.exposures_set.copy(deep=True) - new_exp.check() - else: - raise ValueError( - f"{self.exposures_set} is neither a string nor an Exposures object" - ) - if not np.array_equal( - np.unique(exposures.latitude), np.unique(new_exp.latitude) - ) or not np.array_equal( - np.unique(exposures.longitude), np.unique(new_exp.longitude) - ): - LOGGER.warning("Exposures locations have changed.") - - return new_exp + changed_exp = ( + copy.deepcopy(exposures) + if enforce_copy and self.exposures_changes is not identity_function + else exposures + ) + try: + return self.exposures_changes(changed_exp, **kwargs) + except TypeError as exc: + # Check if it's a missing argument error + if "missing" in str(exc) and "required positional argument" in str(exc): + raise TypeError( + f"The function to apply to the exposures requires additional arguments\ + that were not provided.\n" + f"Please check the function signature or the helper used and provide the\ + required arguments " + "via kwargs_exposures.\n" + f"Original error: {exc}" + ) from exc + raise + + def apply_impfset_changes( + self, impfset: ImpactFuncSet, enforce_copy: bool = True, **kwargs + ) -> ImpactFuncSet: + """ + Apply the changes from the measure to the given :class:`ImpactFuncSet` object. - def _change_exposures_impf(self, exposures): - """Change exposures impact functions ids according to imp_fun_map. + This method applies the `impfset_changes` function of the measure to + the provided :class:`ImpactFuncSet` object. If `enforce_copy` is True + (default), a deep copy of the impfset is created before modification to + ensure immutability of the original object. Parameters ---------- - exposures : climada.entity.Exposures - exposures instance + impfset : ImpactFuncSet + The input impfset object to be transformed. + enforce_copy : bool, optional + If True (default), creates a deep copy of `impfset` before applying + changes, provided the transformation function is not the identity + function. If False, the original object may be modified in-place + depending on the behavior of `impfset_changes`. + **kwargs : dict, optional + Additional keyword arguments passed directly to the `impfset_changes` + function. Returns ------- - new_exp : climada.entity.Exposure - Exposure with updated impact functions ids accordgin to - impf_fun_map + ImpactFuncSet + The resulting :class:`ImpactFuncSet` after the transformation has + been applied. If `enforce_copy` was True, this is a new object. + + Notes + ----- + The deep copy operation is skipped if `enforce_copy` is False or if + `self.impfset_changes` is the identity function, optimizing performance + when no actual changes are expected or when in-place modification is + desired. """ - if self.imp_fun_map == NULL_STR: - return exposures - LOGGER.debug("Setting new exposures impact functions%s", self.imp_fun_map) - new_exp = exposures.copy(deep=True) - from_id = int(self.imp_fun_map[0 : self.imp_fun_map.find("to")]) - to_id = int(self.imp_fun_map[self.imp_fun_map.find("to") + 2 :]) + changed_impfset = ( + copy.deepcopy(impfset) + if enforce_copy and self.impfset_changes is not identity_function + else impfset + ) try: - exp_change = np.argwhere( - new_exp.gdf[INDICATOR_IMPF + self.haz_type].values == from_id - ).reshape(-1) - new_exp.gdf[INDICATOR_IMPF + self.haz_type].values[exp_change] = to_id - except KeyError: - exp_change = np.argwhere( - new_exp.gdf[INDICATOR_IMPF].values == from_id - ).reshape(-1) - new_exp.gdf[INDICATOR_IMPF].values[exp_change] = to_id - return new_exp - - def _change_imp_func(self, imp_set): + return self.impfset_changes(changed_impfset, **kwargs) + except TypeError as exc: + # Check if it's a missing argument error + if "missing" in str(exc) and "required positional argument" in str(exc): + raise TypeError( + f"The function to apply to the impact function set requires\ + additional arguments that were not provided.\n" + f"Please check the function signature or the helper used\ + and provide the required arguments via kwargs_impfset.\n" + f"Original error: {exc}" + ) from exc + raise + + def apply_hazard_changes( + self, hazard: Hazard, enforce_copy: bool = True, **kwargs + ) -> Hazard: """ - Apply measure to impact functions of the same hazard type. + Apply the changes from the measure to the given :class:`Hazard` object. + + This method applies the `hazard_changes` function of the measure to the + provided :class:`Hazard` object. If `enforce_copy` is True (default), a + deep copy of the hazard is created before modification to ensure + immutability of the original object. Parameters ---------- - imp_set : climada.entity.ImpactFuncSet - impact function set instance to be modified + hazard : Hazard + The input hazard object to be transformed. + enforce_copy : bool, optional + If True (default), creates a deep copy of `hazard` before applying + changes, provided the transformation function is not the identity + function. If False, the original object may be modified in-place + depending on the behavior of `hazard_changes`. + **kwargs : dict, optional + Additional keyword arguments passed directly to the `hazard_changes` + function. Returns ------- - new_imp_set : climada.entity.ImpactFuncSet - ImpactFuncSet with measure applied to each impact function - according to the defined hazard type + Hazard + The resulting hazard object after the transformation has been + applied. If `enforce_copy` was True, this is a new object. + + Notes + ----- + The deep copy operation is skipped if `enforce_copy` is False or if + `self.hazard_changes` is the identity function, optimizing performance + when no actual changes are expected or when in-place modification is + desired. """ - if ( - self.hazard_inten_imp == (1, 0) - and self.mdd_impact == (1, 0) - and self.paa_impact == (1, 0) - ): - return imp_set - - new_imp_set = copy.deepcopy(imp_set) - for imp_fun in new_imp_set.get_func(self.haz_type): - LOGGER.debug("Transforming impact functions.") - imp_fun.intensity = np.maximum( - imp_fun.intensity * self.hazard_inten_imp[0] - self.hazard_inten_imp[1], - 0.0, - ) - imp_fun.mdd = np.maximum( - imp_fun.mdd * self.mdd_impact[0] + self.mdd_impact[1], 0.0 - ) - imp_fun.paa = np.maximum( - imp_fun.paa * self.paa_impact[0] + self.paa_impact[1], 0.0 - ) - - if not new_imp_set.size(): - LOGGER.info("No impact function of hazard %s found.", self.haz_type) - - return new_imp_set - def _cutoff_hazard_damage(self, exposures, impf_set, hazard): - """Cutoff of hazard events which generate damage with a frequency higher - than hazard_freq_cutoff. + changed_hazard = ( + copy.deepcopy(hazard) + if enforce_copy and self.hazard_changes is not identity_function + else hazard + ) + try: + return self.hazard_changes(changed_hazard, **kwargs) + except TypeError as exc: + # Check if it's a missing argument error + if "missing" in str(exc) and "required positional argument" in str(exc): + raise TypeError( + f"The function to apply to the hazard requires\ + additional arguments that were not provided.\n" + f"Please check the function signature or the helper used\ + and provide the required arguments via kwargs_hazard.\n" + f"Original error: {exc}" + ) from exc + raise + + def apply( + self, + exposures: Exposures, + impfset: ImpactFuncSet, + hazard: Hazard, + enforce_copy: bool = True, + **kwargs, + ) -> Tuple[Exposures, ImpactFuncSet, Hazard]: + """Apply all measure transformations to the provided triplet of + :class:`Exposures`, :class:`ImpactFuncSet`, :class:`Hazard`. + + This method applies the measure changes across all three + risk parts: exposures, impact function set, and hazard data. + + The method implements a flexible keyword arguments merging strategy + where the original triplet is provided as default context to each + transformation, which can then be overridden by entity-specific kwargs + dictionaries. This enables transformation requiring the information + from other risk components (for instance, removing events based on impact + threshold) or additional information (for instance, effect depending on + year of implementation). + + Refer to :ref:`measure-tutorial` for more details. Parameters ---------- - exposures : climada.entity.Exposures - exposures instance - imp_set : climada.entity.ImpactFuncSet - impact function set instance - hazard : climada.hazard.Hazard - hazard instance + exposures : Exposures + The input exposures object to be transformed. + impfset : ImpactFuncSet + The impact function set to be transformed. + hazard : Hazard + The hazard data to be transformed. + enforce_copy : bool, optional + If True (default), creates deep copies of entities before applying + changes, provided the transformation functions are not identity functions. + If False, entities may be modified in-place depending on the behavior + of the underlying transformation methods. + **kwargs : dict, optional + Additional keyword arguments for configuring transformations. Supports + nested dictionaries for entity-specific customization: + + * ``kwargs_exposures``: Dict of kwargs passed to `apply_exposures_changes` + * ``kwargs_impfset``: Dict of kwargs passed to `apply_impfset_changes` + * ``kwargs_hazard``: Dict of kwargs passed to `apply_hazard_changes` + + Each nested dict is merged with the default triplet context (exposures, + impfset, hazard), allowing transformations to access related entities + while permitting entity-specific overrides. Returns ------- - new_haz : climada.hazard.Hazard - Hazard without events which generate damage with a frequency - higher than hazard_freq_cutoff + Tuple[Exposures, ImpactFuncSet, Hazard] + A tuple containing the transformed entities in the order: + (changed_exposures, changed_impfset, changed_hazard). If `enforce_copy` + was True, these are new objects; otherwise, they may reference the + original inputs or modified versions thereof. + + Notes + ----- + The kwargs merging follows this priority order: + + 1. Default context: The original triplet (exposures, impfset, hazard) + 2. Entity-specific overrides: Values from ``kwargs_exposures``, + ``kwargs_impfset``, or ``kwargs_hazard`` respectively + + This ensures that each transformation receives full context about all entities + while allowing fine-grained control over individual transformations. + + The transformation order is: exposures → hazard → impact function set. + Each transformation is independent, so changes to one entity do not affect + the others during processing. """ - if self.hazard_freq_cutoff == 0: - return hazard - if self.exp_region_id: - # compute impact only in selected region - in_reg = np.logical_or.reduce( - [exposures.region_id == reg for reg in self.exp_region_id] - ) - exp_imp = Exposures(exposures.gdf[in_reg], crs=exposures.crs) - else: - exp_imp = exposures - - from climada.engine.impact_calc import ( - ImpactCalc, # pylint: disable=import-outside-toplevel + default_kwargs = { + "base_exposures": exposures, + "base_impfset": impfset, + "base_hazard": hazard, + } + # Always provide the triplet by default, and overwrite by custom kwargs. + kwargs_exp = default_kwargs | kwargs.get("kwargs_exposures", {}) + kwargs_impfset = default_kwargs | kwargs.get("kwargs_impfset", {}) + kwargs_hazard = default_kwargs | kwargs.get("kwargs_hazard", {}) + changed_exposures = self.apply_exposures_changes( + exposures, enforce_copy, **kwargs_exp ) - - imp = ImpactCalc(exp_imp, impf_set, hazard).impact( - assign_centroids=hazard.centr_exp_col not in exp_imp.gdf + changed_hazard = self.apply_hazard_changes( + hazard, enforce_copy, **kwargs_hazard ) - - LOGGER.debug( - "Cutting events whose damage have a frequency > %s.", - self.hazard_freq_cutoff, + changed_impfset = self.apply_impfset_changes( + impfset, enforce_copy, **kwargs_impfset ) - new_haz = copy.deepcopy(hazard) - sort_idxs = np.argsort(imp.at_event)[::-1] - exceed_freq = np.cumsum(imp.frequency[sort_idxs]) - cutoff = exceed_freq > self.hazard_freq_cutoff - sel_haz = sort_idxs[cutoff] - for row in sel_haz: - new_haz.intensity.data[ - new_haz.intensity.indptr[row] : new_haz.intensity.indptr[row + 1] - ] = 0 - new_haz.intensity.eliminate_zeros() - return new_haz - - def _filter_exposures( - self, exposures, imp_set, hazard, new_exp, new_impfs, new_haz - ): - """ - Incorporate changes of new elements to previous ones only for the - selected exp_region_id. If exp_region_id is [], all new changes - will be accepted. + return changed_exposures, changed_impfset, changed_hazard - Parameters - ---------- - exposures : climada.entity.Exposures - old exposures instance - imp_set :climada.entity.ImpactFuncSet - old impact function set instance - hazard : climada.hazard.Hazard - old hazard instance - new_exp : climada.entity.Exposures - new exposures instance - new_ifs : climada.entity.ImpactFuncSet - new impact functions instance - new_haz : climada.hazard.Hazard - new hazard instance + def calc_impact(self, exposures, impfset, hazard): + from climada.engine.impact_calc import ( + ImpactCalc, # pylint: disable=import-outside-toplevel + ) - Returns - ------- - new_exp,new_ifs, new_haz : climada.entity.Exposures, - climada.entity.ImpactFuncSet, - climada.hazard.Hazard - Exposures, ImpactFuncSet, Hazard with incoporated elements - for the selected exp_region_id. - """ - if not self.exp_region_id: - return new_exp, new_impfs, new_haz - - if exposures is new_exp: - new_exp = exposures.copy(deep=True) - - if imp_set is not new_impfs: - # provide new impact functions ids to changed impact functions - fun_ids = list(new_impfs.get_func()[self.haz_type].keys()) - for key in fun_ids: - new_impfs.get_func()[self.haz_type][key].id = key + IMPF_ID_FACT - new_impfs.get_func()[self.haz_type][ - key + IMPF_ID_FACT - ] = new_impfs.get_func()[self.haz_type][key] - try: - new_exp.gdf[INDICATOR_IMPF + self.haz_type] += IMPF_ID_FACT - except KeyError: - new_exp.gdf[INDICATOR_IMPF] += IMPF_ID_FACT - # collect old impact functions as well (used by exposures) - new_impfs.get_func()[self.haz_type].update( - imp_set.get_func()[self.haz_type] + new_exp, new_impfs, new_haz = self.apply(exposures, impfset, hazard) + if new_haz.centr_exp_col not in new_exp.gdf.columns: + LOGGER.warning( + "No assigned hazard centroids in exposure object after the " + "application of the measure. The centroids will be assigned during impact " + "calculation. This is potentiall costly. To silence this warning, make sure " + "that centroids are assigned to all exposures." ) - - # get the indices for changing and inert regions - chg_reg = exposures.gdf["region_id"].isin(self.exp_region_id) - no_chg_reg = ~chg_reg - - LOGGER.debug("Number of changed exposures: %s", chg_reg.sum()) - - # concatenate previous and new exposures - new_exp.set_gdf( - GeoDataFrame( - pd.concat( - [ - exposures.gdf[no_chg_reg], # old values for inert regions - new_exp.gdf[chg_reg], # new values for changing regions - ] - ).loc[ - exposures.gdf.index, : - ], # re-establish old order - ), - crs=exposures.crs, + new_exp.assign_centroids(new_haz) + imp = ImpactCalc(new_exp, new_impfs, new_haz).impact( + save_mat=False, assign_centroids=False ) - - # set missing values of centr_ - if ( - INDICATOR_CENTR + self.haz_type in new_exp.gdf.columns - and np.isnan(new_exp.gdf[INDICATOR_CENTR + self.haz_type].values).any() - ): - new_exp.gdf.drop(columns=INDICATOR_CENTR + self.haz_type, inplace=True) - elif ( - INDICATOR_CENTR in new_exp.gdf.columns - and np.isnan(new_exp.gdf[INDICATOR_CENTR].values).any() - ): - new_exp.gdf.drop(columns=INDICATOR_CENTR, inplace=True) - - # put hazard intensities outside region to previous intensities - if hazard is not new_haz: - if INDICATOR_CENTR + self.haz_type in exposures.gdf.columns: - centr = exposures.gdf[INDICATOR_CENTR + self.haz_type].values[chg_reg] - elif INDICATOR_CENTR in exposures.gdf.columns: - centr = exposures.gdf[INDICATOR_CENTR].values[chg_reg] - else: - exposures.assign_centroids(hazard) - centr = exposures.gdf[INDICATOR_CENTR + self.haz_type].values[chg_reg] - - centr = np.delete(np.arange(hazard.intensity.shape[1]), np.unique(centr)) - new_haz_inten = new_haz.intensity.tolil() - new_haz_inten[:, centr] = hazard.intensity[:, centr] - new_haz.intensity = new_haz_inten.tocsr() - - return new_exp, new_impfs, new_haz + return imp.calc_risk_transfer(0, 0) diff --git a/climada/entity/measures/cost_income.py b/climada/entity/measures/cost_income.py new file mode 100644 index 0000000000..01d5afe5d5 --- /dev/null +++ b/climada/entity/measures/cost_income.py @@ -0,0 +1,621 @@ +""" +This file is part of CLIMADA. + +Copyright (C) 2017 ETH Zurich, CLIMADA contributors listed in AUTHORS. + +CLIMADA is free software: you can redistribute it and/or modify it under the +terms of the GNU General Public License as published by the Free +Software Foundation, version 3. + +CLIMADA is distributed in the hope that it will be useful, but WITHOUT ANY +WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A +PARTICULAR PURPOSE. See the GNU General Public License for more details. + +You should have received a copy of the GNU General Public License along +with CLIMADA. If not, see . + +--- + +Define the CostIncome class to handle the cash flow of measures. +""" + +from datetime import datetime +from typing import Any, Optional, Tuple, cast + +import matplotlib.pyplot as plt +import numpy as np +import pandas as pd +import yaml + +from climada.entity.measures.measure_config import CostIncomeConfig + + +class CostIncome: + """ + Manages costs and incomes related to a measure over time. + + Income are stored a positive numbers and costs as negative + ones. + + Attributes + ---------- + mkt_price_year : datetime, default to today's year. + The reference year for market prices. + init_cost : float + Initial implementation cost (stored as negative). + periodic_cost : float + Recurring cost per period (stored as negative). + periodic_income : float + Recurring income per period. + cost_yearly_growth_rate : float + Yearly growth rate of costs. + income_yearly_growth_rate : float + Yearly growth rate of income. + custom_cash_flows : pd.DataFrame, optional + User-defined cash flows indexed by date. + freq : str + Frequency of the cash flows (e.g., 'Y', '3M', '7D'). + + """ + + def __init__( + self, + *, + mkt_price_year: Optional[int] = None, + init_cost: float = 0.0, + periodic_cost: float = 0.0, + periodic_income: float = 0.0, + cost_yearly_growth_rate: float = 0.0, + income_yearly_growth_rate: float = 0.0, + custom_cash_flows: Optional[pd.DataFrame] = None, + freq: str = "Y", + ): + """Initialize CostIncome with parameters. + + Parameters + ---------- + mkt_price_year : datetime, default to today's year. + The reference year for market prices. + init_cost : float + Initial implementation cost (stored as negative). + periodic_cost : float + Recurring cost per period (stored as negative). + periodic_income : float + Recurring income per period. + cost_yearly_growth_rate : float + Yearly growth rate of costs. + income_yearly_growth_rate : float + Yearly growth rate of income. + custom_cash_flows : pd.DataFrame, optional + User-defined cash flows indexed by date. + freq : str + Frequency of the cash flows (e.g., 'Y', '3M', '7D'). + """ + + self.freq = freq + self.mkt_price_year = datetime(mkt_price_year or datetime.today().year, 1, 1) + self.cost_growth_rate = cost_yearly_growth_rate + + self.init_cost = -abs(init_cost) + self.periodic_cost = -abs(periodic_cost) + self.periodic_income = abs(periodic_income) + + self.income_growth_rate = income_yearly_growth_rate + self.custom_cash_flows = custom_cash_flows + + def __repr__(self) -> str: + lines = [ + "CostIncome(", + f" mkt_price_year = {self.mkt_price_year.year}", + f" freq = {self.freq!r}", + f" init_cost = {self.init_cost:,.2f}", + f" periodic_cost = {self.periodic_cost:,.2f}", + f" periodic_income = {self.periodic_income:,.2f}", + f" cost_yearly_growth_rate = {self.cost_growth_rate:.2%}", + f" income_yearly_growth_rate = {self.income_growth_rate:.2%}", + " custom_cash_flows = " + f"{None if self.custom_cash_flows is None else f'DataFrame({len(self.custom_cash_flows)} rows)'}", + ")", + ] + return "\n".join(lines) + + @property + def custom_cash_flows(self) -> pd.DataFrame | None: + """:obj:`pd.DataFrame` : Get or set the optional user-defined cash + flows. + + Input cash flow have to contain a "date" column as well as at least one + of "cost" and "income". The custom cash flow is coerced to the internal + period frequency. + """ + return self._custom_cash_flows + + @custom_cash_flows.setter + def custom_cash_flows(self, value, /): + if value is None: + self._custom_cash_flows = None + + else: + if not isinstance(value, pd.DataFrame): + raise ValueError("Custom cash flows only accept pandas DataFrame.") + + self._custom_cash_flows = self._prepare_custom_flows(value) + + def _prepare_custom_flows(self, df: pd.DataFrame) -> pd.DataFrame: + """Process and resample custom cash flow dataframe + + Enforce costs as negative numbers and date to the correct frequency. + + Parameters + ---------- + df : pd.DataFrame + Custom cashflow + + Returns + ------- + pd.DataFrame + Processed custom cashflow + """ + + if "date" not in df.columns: + raise ValueError("No 'date' column found in custom cash flow DataFrame.") + + if "cost" not in df.columns and "income" not in df.columns: + raise ValueError( + "No 'cost' or 'income' column found in custom cash flow DataFrame." + ) + + df = df.copy() + if "cost" in df.columns: + df["cost"] = -df["cost"].abs() + + if "date" in df.columns: + df["date"] = pd.to_datetime(df["date"]) + df = df.set_index("date") + + freq = self._make_offset_compat(self.freq) + return df.resample(freq).sum() + + @staticmethod + def _make_offset_compat(freq: str, start=True) -> str: + suffix = "S" if start else "E" + match freq: + case "Y": + return "Y" + suffix + case "M": + return "M" + suffix + case "Q": + return "Q" + suffix + case _: + return freq + + @classmethod + def from_config(cls, config: CostIncomeConfig) -> "CostIncome": + """Create a `CostIncome` from a `CostIncomeConfig`. + + Parameters + ---------- + config : CostIncomeConfig + + Returns + ------- + CostIncome + """ + + df = None + if config.custom_cash_flows is not None: + df = pd.DataFrame(config.custom_cash_flows) + df["date"] = pd.to_datetime(df["date"]) + return cls( + mkt_price_year=config.mkt_price_year, + init_cost=config.init_cost, + periodic_cost=config.periodic_cost, + periodic_income=config.periodic_income, + cost_yearly_growth_rate=config.cost_yearly_growth_rate, + income_yearly_growth_rate=config.income_yearly_growth_rate, + custom_cash_flows=df, + freq=config.freq, + ) + + @classmethod + def from_dict(cls, args_dict: dict) -> "CostIncome": + """Create a `CostIncome` from a dictionary. + + Parameters + ---------- + args_dict : dict + + Returns + ------- + CostIncome + """ + + return cls.from_config( + CostIncomeConfig( + mkt_price_year=args_dict.get("mkt_price_year"), + init_cost=args_dict.get("init_cost", 0.0), + periodic_cost=args_dict.get("periodic_cost", 0.0), + periodic_income=args_dict.get("periodic_income", 0.0), + cost_yearly_growth_rate=args_dict.get("cost_yearly_growth_rate", 0.0), + income_yearly_growth_rate=args_dict.get( + "income_yearly_growth_rate", 0.0 + ), + freq=args_dict.get("freq", "Y"), + custom_cash_flows=args_dict.get("custom_cash_flows"), + ) + ) + + @classmethod + def from_yaml(cls, path: str) -> "CostIncome": + """Create a `CostIncome` from a yaml file. + + Parameters + ---------- + path : str + Path to the yaml file. + + Returns + ------- + CostIncome + """ + + with open(path) as f: + return cls.from_dict(yaml.safe_load(f)["cost_income"]) + + @classmethod + def _freq_to_days(cls, freq: str) -> str: + """ + Convert a frequency string to the equivalent number of days. + + Parameters: + ----------- + freq : str + A frequency string (e.g., 'D' for daily, 'M' for monthly, 'Y' for yearly). + + Returns: + -------- + float + The equivalent number of days for the given frequency string. + """ + + try: + # Convert the frequency string to a DateOffset object + freq = cls._make_offset_compat(freq, start=False) + offset = pd.tseries.frequencies.to_offset(freq) + + # Calculate the number of days by applying the offset to a base date + base_date = pd.Timestamp("2000-01-01") + end_date = base_date + offset + + # Return the difference in days + return f"{(end_date - base_date).days}d" + except ValueError as exc: + raise ValueError(f"Invalid frequency string: {freq}") from exc + + def _get_width_days(self) -> float: + """Return the number of days in the current frequency.""" + + ref = pd.Timestamp("2000-01-01") + freq = self._make_offset_compat(self.freq, start=False) + offset = pd.tseries.frequencies.to_offset(freq) + return float(((ref + offset) - ref).days) + + def calc_at_date( + self, impl_date: pd.Timestamp, curr_date: pd.Timestamp + ) -> Tuple[float, float, float]: + r"""Calculate cash flows for a single timestamp. + + Computes the total cash flow, total cost, and total income for a given + evaluation date, accounting for growth rates applied to base costs and + incomes, as well as the custom cash flow if provided. + + The calculation applies compound growth to both costs and incomes based + on the number of years elapsed since the market price reference date + (`self.mkt_price_year`). + + Parameters + ---------- + impl_date : pd.Timestamp + The implementation date that determines which cost/income regime + applies. Dates before this use have no cost or income; "at the date" uses + the implementation cost, and dates after use the initialized or + periodic amounts respectively. + curr_date : pd.Timestamp + The evaluation date for which cash flows are being calculated. This + is compared against `impl_date` to determine the applicable base + amounts and is also used to index into `custom_cash_flows` if present. + + Returns + ------- + Tuple[float, float, float] + A tuple containing: + + * total_cash_flow : float + Net cash flow for the period, calculated as `total_income + total_cost`. + Note: Costs are typically negative values in financial contexts, + so this represents the net position. + * total_cost : float + Total cost amount for the period, including both standard and + custom cost components. + * total_income : float + Total income amount for the period, including both standard and + custom income components. + + Notes + ----- + Growth calculations use compound interest formula: + + .. math:: + factor = (1 + rate)^{years\_passed} + + where `years_passed` is computed as `(curr_date - mkt_price_year).days / 365.0`. + + Cost and income regimes: + + - **Before impl_date**: Both base cost and income are zero + - **At impl_date**: Uses `init_cost` for cost + - **After impl_date**: Uses `periodic_cost` for cost and `periodic_income` for income + + Custom cash flows (if `self.custom_cash_flows` is not None) are added + on top of the calculated standard amounts. Missing dates in the custom + cash flow DataFrame will raise a KeyError. + """ + # Calculate growth factor based on years from market price reference + years_passed = (curr_date - self.mkt_price_year).days / 365.0 + + cost_factor = (1 + self.cost_growth_rate) ** years_passed + inc_factor = (1 + self.income_growth_rate) ** years_passed + + if curr_date < impl_date: + cost, income = 0.0, 0.0 + elif curr_date == impl_date: + cost = self.init_cost * cost_factor + income = 0.0 + else: + cost = self.periodic_cost * cost_factor + income = self.periodic_income * inc_factor + + if ( + self.custom_cash_flows is not None + and curr_date in self.custom_cash_flows.index + ): + c_cost = cast(float, self.custom_cash_flows.loc[curr_date, "cost"]) + c_inc = cast(float, self.custom_cash_flows.loc[curr_date, "income"]) + else: + c_cost, c_inc = 0.0, 0.0 + + total_cost = cost + c_cost + total_inc = income + c_inc + return (total_inc + total_cost), total_cost, total_inc + + def calc_cash_flows( + self, impl_date, start_date, end_date + ) -> Tuple[np.ndarray, np.ndarray, np.ndarray]: + """Calculate net cash flows, costs, and incomes over a period. + + Computes cash flow metrics across a specified date range by iterating + through each period. + + The method creates a period range based on the configured frequency + (`self.freq`) and evaluates cash flows at the start time of each period. + Results are returned as NumPy arrays for efficient downstream processing. + + Parameters + ---------- + impl_date : + The implementation date that determines which cost/income regime + applies. + start_date : + The beginning of the calculation period. + end_date : + The end of the calculation period. + + Returns + ------- + Tuple[np.ndarray, np.ndarray, np.ndarray] + A tuple containing three NumPy arrays of equal length: + + * net : np.ndarray + Net cash flow for each period (income + cost). + * costs : np.ndarray + Total costs for each period. + * incomes : np.ndarray + Total incomes for each period. + """ + + # 'Trick' to make sure that e.g., impl_date "2020-01-05" falls in + # period "2020-01" if freq is "M" + impl_ts = pd.Timestamp(str(impl_date)).to_period(self.freq).start_time + periods = pd.period_range(start=start_date, end=end_date, freq=self.freq) + + results = [self.calc_at_date(impl_ts, p.start_time) for p in periods] + net, costs, incs = map(np.array, zip(*results)) + return net, costs, incs + + def calc_total(self, impl_date, start_date, end_date) -> Tuple[float, float, float]: + """ + Calculate the total value of the cash flows over a given period. + + Parameters: + ----------- + impl_date: + The date the measure is implemented. + start_year: + The start date of the period. + end_year: int + The end year of the period. + + Returns: + -------- + Tuple[float, float, float] + the total net, cost, and income values over the given period. + """ + + net_cash_flows, costs, incomes = self.calc_cash_flows( + impl_date, start_date, end_date + ) + return np.sum(net_cash_flows), np.sum(costs), np.sum(incomes) + + def plot_cash_flows( + self, + impl_date: Any, + start_date: Any, + end_date: Any, + figsize: Tuple[int, int] = (12, 7), + title: Optional[str] = None, + ): + """Plot periodic and cumulative cash flows over a given period. + + Displays a two-panel figure: + - Top panel: stacked bar chart of costs and incomes per period, + with a net cash flow line overlay. + - Bottom panel: cumulative net cash flow over time. + + Parameters + ---------- + impl_date : + The date the measure is implemented. + start_date : + Start of the evaluation period. + end_date : + End of the evaluation period. + figsize : tuple, optional + Figure size as (width, height). Default is (12, 7). + title : str, optional + Overall figure title. Defaults to 'Cash Flow Analysis'. + + Returns + ------- + plt.Figure + """ + net, costs, incomes = self.calc_cash_flows(impl_date, start_date, end_date) + periods = pd.period_range(start=start_date, end=end_date, freq=self.freq) + dates = [p.start_time for p in periods] + cumulative_net = np.cumsum(net) + + width = pd.Timedelta(days=self._get_width_days() * 0.6) + + fig, (ax_bar, ax_cum) = plt.subplots( + 2, + 1, + figsize=figsize, + sharex=True, + gridspec_kw={"height_ratios": [3, 1], "hspace": 0.08}, + ) + + # --- top panel: stacked bars + net line --- + ax_bar.bar( + dates, incomes, width=width, color="#4C9BE8", label="Income", zorder=2 + ) + ax_bar.bar(dates, costs, width=width, color="#E8604C", label="Cost", zorder=2) + ax_bar.plot( + dates, + net, + color="black", + linewidth=1.5, + marker="o", + markersize=4, + label="Net", + zorder=3, + ) + ax_bar.axhline(0, color="black", linewidth=0.6, linestyle="--", zorder=1) + + ax_bar.set_ylabel("Cash flow") + ax_bar.legend(frameon=False, fontsize=9) + ax_bar.spines[["top", "right"]].set_visible(False) + ax_bar.tick_params(axis="x", which="both", bottom=False) + ax_bar.yaxis.set_major_formatter(plt.FuncFormatter(lambda x, _: f"{x:,.0f}")) + + # --- bottom panel: cumulative net --- + # dates = dates.append() + positive = np.maximum(cumulative_net, 0) + negative = np.minimum(cumulative_net, 0) + ax_cum.fill_between(dates, positive, alpha=0.25, color="#4C9BE8", step="mid") + ax_cum.fill_between(dates, negative, alpha=0.25, color="#E8604C", step="mid") + # ax_cum.plot(dates, cumulative_net, color="black", linewidth=1.5, zorder=3) + ax_cum.axhline(0, color="black", linewidth=0.6, linestyle="--", zorder=1) + + ax_cum.set_ylabel("Cumulative net") + ax_cum.spines[["top", "right"]].set_visible(False) + ax_cum.yaxis.set_major_formatter(plt.FuncFormatter(lambda x, _: f"{x:,.0f}")) + + fig.autofmt_xdate(rotation=30, ha="right") + fig.suptitle(title or "Cash Flow Analysis", fontsize=13, y=1.01) + return (ax_bar, ax_cum) + + def to_dataframe(self, impl_date, start_date, end_date) -> pd.DataFrame: + """Return cash flows as a formatted DataFrame.""" + net, costs, incs = self.calc_cash_flows(impl_date, start_date, end_date) + periods = pd.period_range(start=start_date, end=end_date, freq=self.freq) + + return pd.DataFrame( + {"date": periods, "net": net, "cost": costs, "income": incs} + ) + + @staticmethod + def comb_cost_income(cost_incomes: list["CostIncome"]) -> "CostIncome": + """Combine multiple CostIncomes together. + + Combination sums the costs and incomes from all provided CostIncome + objects. + """ + + first_ci = cost_incomes[0] + + if not all( + ( + first_ci.mkt_price_year.year == c.mkt_price_year.year + for c in cost_incomes + ) + ): + raise ValueError( + "Measure cost incomes have different market price years," + " combination is not possible." + ) + + if not all( + first_ci.cost_growth_rate == c.cost_growth_rate for c in cost_incomes + ): + raise ValueError( + "Measure cost incomes have different cost_growth_rate," + " combination is not possible." + ) + + if not all( + first_ci.income_growth_rate == c.income_growth_rate for c in cost_incomes + ): + raise ValueError( + "Measure cost incomes have different income_growth_rate," + " combination is not possible." + ) + + if not all(first_ci.freq == c.freq for c in cost_incomes): + raise ValueError( + "Measure cost incomes have different period frequencies," + " combination is not possible." + ) + + try: + custom_cash_flows = cast( + pd.DataFrame, + pd.concat([c.custom_cash_flows for c in cost_incomes]) # type: ignore + .groupby(level=0) + .sum() + .reset_index(), + ) + except ValueError as err: + if str(err) == "All objects passed were None": + custom_cash_flows = None + else: + raise err + + return CostIncome( + mkt_price_year=first_ci.mkt_price_year.year, + init_cost=sum(c.init_cost for c in cost_incomes), + periodic_cost=sum(c.periodic_cost for c in cost_incomes), + periodic_income=sum(c.periodic_income for c in cost_incomes), + cost_yearly_growth_rate=first_ci.cost_growth_rate, + income_yearly_growth_rate=first_ci.income_growth_rate, + freq=first_ci.freq, + custom_cash_flows=custom_cash_flows, + ) diff --git a/climada/entity/measures/helper.py b/climada/entity/measures/helper.py new file mode 100644 index 0000000000..6d378772e4 --- /dev/null +++ b/climada/entity/measures/helper.py @@ -0,0 +1,443 @@ +""" +This file is part of CLIMADA. + +Copyright (C) 2017 ETH Zurich, CLIMADA contributors listed in AUTHORS. + +CLIMADA is free software: you can redistribute it and/or modify it under the +terms of the GNU General Public License as published by the Free +Software Foundation, version 3. + +CLIMADA is distributed in the hope that it will be useful, but WITHOUT ANY +WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A +PARTICULAR PURPOSE. See the GNU General Public License for more details. + +You should have received a copy of the GNU General Public License along +with CLIMADA. If not, see . + +--- + +Define Measure class. +""" + +from __future__ import annotations + +import logging +from functools import reduce +from typing import TYPE_CHECKING, Any, Callable, Optional, TypeVar, cast + +import numpy as np +import pandas as pd + +from climada.entity.exposures.base import Exposures +from climada.entity.impact_funcs.base import ImpactFunc +from climada.entity.impact_funcs.impact_func_set import ImpactFuncSet +from climada.entity.measures.measure_config import ( + ExposuresModifierConfig, + HazardModifierConfig, + ImpfsetModifierConfig, +) +from climada.hazard.base import Hazard + +if TYPE_CHECKING: + from climada.entity.impact_funcs.impact_func_set import ImpactFuncSet + from climada.entity.measures.types import ( + ExposuresChange, + HazardChange, + ImpfsetChange, + ) + from climada.hazard.base import Hazard + + T = TypeVar("T", Exposures, ImpactFuncSet, Hazard) + +LOGGER = logging.getLogger(__name__) + + +def identity_function(x: T, **_kwargs: Any) -> T: + """Return the input object unchanged. + + Parameters + ---------- + x : T + Object to return. + **_kwargs : Any + Accepted but ignored. + + Returns + ------- + T + The unchanged input object. + """ + + return x + + +def composite_fun(*funcs: Callable[..., T]) -> Callable[..., T]: + """Compose multiple functions right-to-left into a single callable. + + Given functions ``f, g, h``, returns a function equivalent to + ``lambda x, **kw: f(g(h(x, **kw), **kw), **kw)``. + If no functions are provided, returns :func:`identity_function`. + + Parameters + ---------- + *funcs : Callable[..., T] + Functions to compose, applied from right to left. + Each must accept an object of type ``T`` as its first positional + argument and forward ``**kwargs``. + + Returns + ------- + Callable[..., T] + A single callable equivalent to the right-to-left composition of + all provided functions. + + Examples + -------- + >>> composed = composite_fun(f, g, h) + >>> result = composed(x, year=2030) # equivalent to f(g(h(x, year=2030), year=2030), year=2030) + """ + + def compose(f: Callable[..., T], g: Callable[..., T]) -> Callable[..., T]: + def composed(x: T, **kwargs: Any) -> T: + return f(g(x, **kwargs), **kwargs) + + return composed + + return reduce(compose, funcs, identity_function) + + +def replace_hazard(new_hazard: Hazard) -> HazardChange: + """Return a change function that unconditionally replaces the hazard. + + The returned function ignores its input and always returns ``new_hazard``. + Note that ``new_hazard`` is a shared reference; callers should ensure the + object is not mutated after being passed here. + + Parameters + ---------- + new_hazard : Hazard + The hazard object to substitute in place of the original. + + Returns + ------- + HazardChange + A callable with signature ``(hazard: Hazard) -> Hazard`` + that discards its input and returns ``new_hazard``. + """ + + def hazard_change(_: Hazard) -> Hazard: + return new_hazard + + return hazard_change + + +def impact_intensity_rp_cutoff_helper( + cut_off_rp: float, +) -> HazardChange: + """Return a change function that zeros out low-impact events based on a + return period threshold. + + The returned function computes impacts on the *base* triplet + (``base_exposures``, ``base_impfset``, ``base_hazard``) and identifies + events whose cumulative exceedance frequency does not exceed ``1 / + cut_off_rp``. The intensity rows of those events are set to zero in the + hazard being transformed. + + Parameters + ---------- + cut_off_rp : float + Return period threshold in years. Events whose cumulative exceedance + frequency is at or below ``1 / cut_off_rp`` are zeroed out. + + Returns + ------- + HazardChange + A callable with the following signature: + + .. code-block:: python + + def hazard_change( + hazard: Hazard, + base_exposures: Exposures, + base_impfset: ImpactFuncSet, + base_hazard: Hazard, + exposures_region_id: Optional[list[int]] = None, + ) -> Hazard + + Parameters of the returned callable: + + hazard : Hazard + The hazard object to modify in-place (intensity rows zeroed). + base_exposures : Exposures + Exposures used for the reference impact computation. + base_impfset : ImpactFuncSet + Impact function set used for the reference impact computation. + base_hazard : Hazard + Original hazard used for the reference impact computation. + exposures_region_id : list of int, optional + If provided, the impact computation is restricted to exposure + points whose ``region_id`` is in this list. + + Notes + ----- + The exceedance frequency is computed on the *base* hazard, not on the + already-modified hazard. This ensures the cutoff decision is always + relative to the unmodified risk landscape. + """ + + from climada.engine.impact_calc import ImpactCalc + + def hazard_change( + hazard: Hazard, + base_exposures: Exposures, + base_impfset: ImpactFuncSet, + base_hazard: Hazard, + exposures_region_id: Optional[list[int]] = None, + **_kwargs, + ) -> Hazard: + exp_imp = base_exposures + if exposures_region_id: + # Narrowing the type for the LSP via boolean indexing + in_reg = base_exposures.gdf["region_id"].isin(exposures_region_id) + exp_imp = Exposures(base_exposures.gdf[in_reg], crs=base_exposures.crs) + + imp = ImpactCalc(exp_imp, base_impfset, base_hazard).impact(save_mat=False) + + # Calculate exceedance frequencies + sort_idxs = np.argsort(imp.at_event)[::-1] + exceed_freq = np.cumsum(imp.frequency[sort_idxs]) + events_below_cutoff = sort_idxs[exceed_freq >= (1 / cut_off_rp)] + # Modify sparse data structure + intensity_modified = hazard.intensity.copy() + for event in events_below_cutoff: + start, end = ( + intensity_modified.indptr[event], + intensity_modified.indptr[event + 1], + ) + intensity_modified.data[start:end] = 0 + + hazard.intensity = intensity_modified + hazard.intensity.eliminate_zeros() + return hazard + + return hazard_change + + +def helper_hazard(hazard_modifier: HazardModifierConfig) -> HazardChange: + """Return a change function that scales, shifts, and optionally + replaces hazard intensities. + + Constructs a :class:`HazardChange` from a :class:`HazardModifierConfig`. + The returned function optionally loads a new hazard from disk, then applies + a linear transformation to all stored (non-zero) intensity values. + + If :attr:`~HazardModifierConfig.impact_rp_cutoff` is set, the returned + function is further composed (via :func:`composite_fun`) with + :func:`impact_intensity_rp_cutoff_helper` so that low-return-period events + are zeroed out after the linear transformation. + + Parameters + ---------- + hazard_modifier : HazardModifierConfig + Configuration object specifying: + + - ``new_hazard_path`` : path to an HDF5 hazard file to load, or + ``None`` to transform the input hazard in-place. + - ``haz_int_mult`` : multiplicative factor applied to intensity data. + - ``haz_int_add`` : additive shift applied to intensity data. + - ``impact_rp_cutoff`` : return period threshold passed to + :func:`impact_intensity_rp_cutoff_helper`, or ``None`` to skip. + + Returns + ------- + HazardChange + A callable with signature ``(hazard: Hazard, **kwargs) -> Hazard``. + + Notes + ----- + The transformation is applied directly to the sparse matrix's ``.data`` + array, so only explicitly stored (non-zero) entries are affected. + Structural zeros remain zero. + """ + + def hazard_change(hazard: Hazard, **_kwargs) -> Hazard: + changed_hazard = ( + Hazard.from_hdf5(hazard_modifier.new_hazard_path) + if hazard_modifier.new_hazard_path is not None + else hazard + ) + data = cast(np.ndarray, changed_hazard.intensity.data) + data *= hazard_modifier.haz_int_mult + data += hazard_modifier.haz_int_add + data[data < 0] = 0 + changed_hazard.intensity.eliminate_zeros() + return changed_hazard + + if hazard_modifier.impact_rp_cutoff is not None: + hazard_change = composite_fun( + impact_intensity_rp_cutoff_helper(hazard_modifier.impact_rp_cutoff), + hazard_change, + ) + + return hazard_change + + +def helper_impfset(impfset_modifier: ImpfsetModifierConfig) -> ImpfsetChange: + """Return a change function that applies linear modifications to selected + impact functions. + + Constructs an :class:`ImpfsetChange` from an :class:`ImpfsetModifierConfig`. + The returned function optionally loads a new :class:`ImpactFuncSet` from + disk, then applies independent linear transformations to the ``intensity``, + ``mdd``, and ``paa`` arrays of each targeted impact function. + + Parameters + ---------- + impfset_modifier : ImpfsetModifierConfig + Configuration object specifying: + + - ``new_impfset_path`` : path to an Excel file to load as the new + :class:`ImpactFuncSet`, or ``None`` to modify the input in-place. + - ``haz_type`` : hazard type string used to look up functions. + - ``impf_ids`` : IDs of functions to modify. Accepts ``None`` or + ``"all"`` to target every function of ``haz_type``, a single + ``int`` or ``str``, or a ``list`` of IDs. Raises :class:`ValueError` + for any other type. + - ``impf_int_mult``, ``impf_int_add`` : scale and shift for intensity. + - ``impf_mdd_mult``, ``impf_mdd_add`` : scale and shift for MDD. + - ``impf_paa_mult``, ``impf_paa_add`` : scale and shift for PAA. + + Returns + ------- + ImpfsetChange + A callable with signature ``(impfset: ImpactFuncSet, **kwargs) -> ImpactFuncSet``. + + Raises + ------ + ValueError + If ``impfset_modifier.impf_ids`` is not ``None``, ``"all"``, an + ``int``, a ``str``, or a ``list``. + """ + + def impfset_change(impfset: ImpactFuncSet, **_kwargs) -> ImpactFuncSet: + changed_impfset = ( + impfset.from_excel(impfset_modifier.new_impfset_path) + if impfset_modifier.new_impfset_path is not None + else impfset + ) + if impfset_modifier.impf_ids is None or impfset_modifier.impf_ids == "all": + ids_to_change = impfset.get_ids(haz_type=impfset_modifier.haz_type) + elif isinstance(impfset_modifier.impf_ids, list): + ids_to_change = impfset_modifier.impf_ids + elif isinstance(impfset_modifier.impf_ids, (str, int)): + ids_to_change = [impfset_modifier.impf_ids] + else: + raise ValueError( + f"Impact function ids to changes are invalid: {impfset_modifier.impf_ids}" + ) + + funcs = changed_impfset.get_func(haz_type=impfset_modifier.haz_type) + funcs = [funcs] if isinstance(funcs, ImpactFunc) else funcs + + for impf in funcs: + # Apply Intensity Mod + if impf.id in ids_to_change: + mult, add = ( + impfset_modifier.impf_int_mult, + impfset_modifier.impf_int_add, + ) + impf.intensity = impf.intensity * mult + add + + mult, add = ( + impfset_modifier.impf_mdd_mult, + impfset_modifier.impf_mdd_add, + ) + impf.mdd = impf.mdd * mult + add + + mult, add = ( + impfset_modifier.impf_paa_mult, + impfset_modifier.impf_paa_add, + ) + impf.paa = impf.paa * mult + add + + return changed_impfset + + return impfset_change + + +def change_impfset(new_impfsets: ImpactFuncSet) -> ImpfsetChange: + """Return a change function that unconditionally replaces the impact function set. + + The returned function ignores its input and always returns ``new_impfsets``. + Note that ``new_impfsets`` is a shared reference; callers should ensure the + object is not mutated after being passed here. + + Parameters + ---------- + new_impfsets : ImpactFuncSet + The :class:`ImpactFuncSet` to substitute in place of the original. + + Returns + ------- + ImpfsetChange + A callable with signature ``(impfset: ImpactFuncSet, **kwargs) -> ImpactFuncSet`` + that discards its input and returns ``new_impfsets``. + """ + + def impfset_change(_: ImpactFuncSet) -> ImpactFuncSet: + return new_impfsets + + return impfset_change + + +def helper_exposure(exposures_modifier: ExposuresModifierConfig) -> ExposuresChange: + """Return a change function that reassigns impact function IDs and zeros + selected exposure values. + + Constructs an :class:`ExposuresChange` from an + :class:`ExposuresModifierConfig`. The returned function optionally loads a + new :class:`Exposures` from disk, then applies two optional modifications + to its underlying GeoDataFrame: + + 1. **Impact function ID remapping**: replaces values in + ``impf_`` columns according to a provided mapping dict. + 2. **Value zeroing**: sets ``value`` to ``0`` for rows matching a boolean + mask or index. + + Parameters + ---------- + exposures_modifier : ExposuresModifierConfig + Configuration object specifying: + + - ``new_exposures_path`` : path to an HDF5 file to load as the new + :class:`Exposures`, or ``None`` to modify the input in-place. + - ``reassign_impf_id`` : a ``dict[haz_type, {old_id: new_id}]`` + mapping used to replace impact function IDs in the GeoDataFrame, + or ``None`` to skip. + - ``set_to_zero`` : a boolean array, index, or label accepted by + ``DataFrame.loc`` identifying rows whose ``value`` should be set + to ``0``, or ``None`` to skip. + + Returns + ------- + ExposuresChange + A callable with signature ``(exposures: Exposures, **kwargs) -> Exposures``. + """ + + def exposures_change(exposures: Exposures, **_kwargs) -> Exposures: + changed_exposures = ( + exposures + if exposures_modifier.new_exposures_path is None + else Exposures.from_hdf5(exposures_modifier.new_exposures_path) + ) + gdf = cast(pd.DataFrame, changed_exposures.gdf) + if exposures_modifier.reassign_impf_id is not None: + for haz_type, mapping in exposures_modifier.reassign_impf_id.items(): + gdf[f"impf_{haz_type}"] = gdf[f"impf_{haz_type}"].replace(mapping) + + if exposures_modifier.set_to_zero is not None: + gdf.loc[exposures_modifier.set_to_zero, "value"] = 0 + + return changed_exposures + + return exposures_change diff --git a/climada/entity/measures/measure_config.py b/climada/entity/measures/measure_config.py new file mode 100644 index 0000000000..e1cb0aedcb --- /dev/null +++ b/climada/entity/measures/measure_config.py @@ -0,0 +1,649 @@ +""" +This file is part of CLIMADA. + +Copyright (C) 2017 ETH Zurich, CLIMADA contributors listed in AUTHORS. + +CLIMADA is free software: you can redistribute it and/or modify it under the +terms of the GNU General Public License as published by the Free +Software Foundation, version 3. + +CLIMADA is distributed in the hope that it will be useful, but WITHOUT ANY +WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A +PARTICULAR PURPOSE. See the GNU General Public License for more details. + +You should have received a copy of the GNU General Public License along +with CLIMADA. If not, see . + +--- + +Define configuration dataclasses for Measure reading and writing. +""" + +from __future__ import annotations + +import dataclasses +import logging +import warnings +from dataclasses import MISSING, dataclass, field, fields +from datetime import datetime +from typing import Any, Dict, Optional, Tuple + +import numpy as np +import pandas as pd +import yaml + +LOGGER = logging.getLogger(__name__) + +__all__ = [ + "HazardModifierConfig", + "ExposuresModifierConfig", + "ImpfsetModifierConfig", + "CostIncomeConfig", + "MeasureConfig", +] + + +@dataclass +class ModifierConfig: + """ + Abstract base class for all modifier configuration dataclasses. + + Provides shared serialization, deserialization, and representation + logic for all concrete modifier config subclasses. Not intended to + be instantiated directly. + """ + + def _filter_out_default_fields(self) -> tuple[dict[str, Any], dict[str, Any]]: + """ + Partition the instance's fields into non-default and default groups. + + The ``haz_type`` field is always excluded from the output, as it + is managed at the ``MeasureConfig`` level. + + Returns + ------- + non_defaults : dict + Fields whose current value differs from the dataclass default. + defaults : dict + Fields whose current value equals the dataclass default. + """ + + non_defaults = {} + defaults = {} + for defined_field in fields(self): + val = getattr(self, defined_field.name) + default = defined_field.default + if defined_field.default_factory is not MISSING: + default = defined_field.default_factory() + + if (default is None and val is not None) or val != default: + non_defaults[defined_field.name] = val + else: + defaults[defined_field.name] = val + + if "haz_type" in non_defaults: + non_defaults.pop("haz_type") + return non_defaults, defaults + + def to_dict(self, omit_default: bool = True) -> dict[str, Any]: + """ + Serialize the config to a flat dictionary, omitting default values. + + The ``haz_type`` field is always excluded from the output, as it + is managed at the ``MeasureConfig`` level. + + Returns + ------- + dict + Dictionary containing only fields whose values differ from + their dataclass defaults. + """ + non_defaults, defaults = self._filter_out_default_fields() + if omit_default: + return non_defaults + + return defaults | non_defaults + + @classmethod + def from_dict(cls, kwargs_dict: dict): + """ + Instantiate a config from a dictionary, ignoring unknown keys. + + Parameters + ---------- + kwargs_dict : dict + Input dictionary. Keys not matching any dataclass field are + silently discarded. + + Returns + ------- + _ModifierConfig + A new instance of the calling subclass. + """ + + filtered = cls._filter_dict_to_fields(kwargs_dict) + return cls(**filtered) + + @classmethod + def _filter_dict_to_fields(cls, to_filter: dict): + """ + Filter a dictionary to only the keys matching the dataclass fields. + + Parameters + ---------- + to_filter : dict + Input dictionary, potentially containing extra keys. + + Returns + ------- + dict + A copy of ``to_filter`` restricted to keys that correspond to declared + dataclass fields on this class. + """ + + field_names = [f.name for f in fields(cls)] + return {key: val for key, val in to_filter.items() if key in field_names} + + def __repr__(self) -> str: + """ + Return a human-readable representation highlighting non-default fields. + + Non-default fields are shown prominently; default fields are shown + below them. This makes it easy to see at a glance what has been + configured on an instance. + + Returns + ------- + str + A formatted string representation of the instance. + """ + + non_defaults, defaults = self._filter_out_default_fields() + ndf_fields_str = ( + "\n\t\t\t".join(f"{k}={v!r}" for k, v in non_defaults.items()) + if non_defaults + else None + ) + _ = ( + "\n\t\t\t".join(f"{k}={v!r}" for k, v in defaults.items()) + if defaults + else None + ) + ndf_fields = ( + "(" "\n\t\tNon default fields:" f"\n\t\t\t{ndf_fields_str}" "\n)" + if ndf_fields_str + else "()" + ) + return f"{self.__class__.__name__}{ndf_fields}" + + +@dataclass(repr=False) +class ImpfsetModifierConfig(ModifierConfig): + """ + Configuration for modifications to an impact function set. + + Supports scaling or shifting MDD, PAA, and intensity curves, as well + as replacement of the impact function set, loaded from a file path. If + both a new file path and modifier values are provided, modifiers are + applied after the replacement (and a warning is issued). + + Parameters + ---------- + haz_type : str + Hazard type identifier (e.g. ``"TC"``) that this modifier targets. + impf_ids : int or str or list of int or str, optional + Impact function ID(s) to which modifications are applied. + If ``None``, all impact functions are affected. + impf_mdd_mult : float, optional + Multiplicative factor applied to the mean damage degree (MDD) curve. + Default is ``1.0`` (no change). + impf_mdd_add : float, optional + Additive offset applied to the MDD curve after multiplication. + Default is ``0.0``. + impf_paa_mult : float, optional + Multiplicative factor applied to the percentage of affected assets + (PAA) curve. Default is ``1.0``. + impf_paa_add : float, optional + Additive offset applied to the PAA curve after multiplication. + Default is ``0.0``. + impf_int_mult : float, optional + Multiplicative factor applied to the intensity axis. + Default is ``1.0``. + impf_int_add : float, optional + Additive offset applied to the intensity axis after multiplication. + Default is ``0.0``. + new_impfset_path : str, optional + Path to an Excel file containing a replacement impact function set. + If provided alongside modifier values, a warning is issued and + modifiers are applied after loading the new set. + + Warns + ----- + UserWarning + If ``new_impfset_path`` is set alongside any non-default modifier + values. + """ + + haz_type: str + impf_ids: int | str | list[int | str] | None = None + impf_mdd_mult: float = 1.0 + impf_mdd_add: float = 0.0 + impf_paa_mult: float = 1.0 + impf_paa_add: float = 0.0 + impf_int_mult: float = 1.0 + impf_int_add: float = 0.0 + new_impfset_path: str | None = None + + def __post_init__(self): + config = self.to_dict() + if "new_impfset_path" in config and any( + key in config + for key in [ + "impf_mdd_add", + "impf_mdd_mult", + "impf_paa_add", + "impf_paa_mult", + "impf_int_add", + "impf_int_mult", + ] + ): + warnings.warn( + "Both new impfset object and impfset modifiers are provided, " + "modifiers will be applied after changing the impfset." + ) + + +@dataclass(repr=False) +class HazardModifierConfig(ModifierConfig): + """ + Configuration for modifications to a hazard. + + Supports scaling or shifting hazard intensity, applying a return-period + frequency cutoff, and replacement of the hazard, loaded from a file path. + If both a new file path and modifier values are provided, modifiers are + applied after the replacement. + + Parameters + ---------- + haz_type : str + Hazard type identifier (e.g. ``"TC"``) that this modifier targets. + haz_int_mult : float, optional + Multiplicative factor applied to hazard intensity. + Default is ``1.0`` (no change). + haz_int_add : float, optional + Additive offset applied to hazard intensity after multiplication. + Default is ``0.0``. + new_hazard_path : str, optional + Path to an HDF5 file containing a replacement hazard. + If provided alongside modifier values, a warning is issued and + modifiers are applied after loading the new hazard. + impact_rp_cutoff : float, optional + Return period (in years) below which hazard events are discarded. + If ``None``, no cutoff is applied. + + Warns + ----- + UserWarning + If ``new_hazard_path`` is set alongside any non-default modifier + values or a non-``None`` ``impact_rp_cutoff``. + """ + + haz_type: str + haz_int_mult: Optional[float] = 1.0 + haz_int_add: Optional[float] = 0.0 + haz_freq_mult: Optional[float] = 1.0 + haz_freq_add: Optional[float] = 0.0 + new_hazard_path: Optional[str] = None + impact_rp_cutoff: Optional[float] = None + + def __post_init__(self): + config = self.to_dict() + if "new_hazard_path" in config and any( + key in config + for key in [ + "haz_int_mult", + "haz_int_add", + "haz_freq_mult", + "haz_freq_add", + "impact_rp_cutoff", + ] + ): + warnings.warn( + "Both new hazard object and hazard modifiers are provided, " + "modifiers will be applied after changing the hazard." + ) + + +@dataclass(repr=False) +class ExposuresModifierConfig(ModifierConfig): + """ + Configuration for modifications to an exposures object. + + Supports remapping impact function IDs, zeroing out selected regions, + and replacement of the exposures from a new file. If both a new + file path and modifier values are provided, modifiers are applied after + the replacement. + + Parameters + ---------- + reassign_impf_id : dict of {str: dict of {int or str: int or str}}, optional + Nested mapping ``{haz_type: {old_id: new_id}}`` used to reassign + impact function IDs in the exposures. If ``None``, no remapping + is performed. + set_to_zero : list of int, optional + Region IDs for which exposure values are set to zero. + If ``None``, no zeroing is applied. + new_exposures_path : str, optional + Path to an HDF5 file containing replacement exposures. + If provided alongside modifier values, a warning is issued and + modifiers are applied after loading the new exposures. + + Warns + ----- + UserWarning + If ``new_exposures_path`` is set alongside any non-``None`` + modifier values. + """ + + reassign_impf_id: Optional[Dict[str, Dict[int | str, int | str]]] = None + set_to_zero: Optional[list[int]] = None + new_exposures_path: Optional[str] = None + + def __post_init__(self): + config = self.to_dict() + if "new_exposures_path" in config and any( + key in config for key in ["reassign_impf_id", "set_to_zero"] + ): + warnings.warn( + "Both new exposures object and exposures modifiers are provided, " + "modifiers will be applied after changing the exposures." + ) + + +@dataclass(repr=False) +class CostIncomeConfig(ModifierConfig): + """ + Serializable configuration for a ``CostIncome`` object. + + Encodes all parameters required to construct a ``CostIncome`` instance, + including optional custom cash flow schedules. + + Parameters + ---------- + mkt_price_year : int, optional + Reference year for market prices. Defaults to the current year. + init_cost : float, optional + One-time initial investment cost (positive value). Default is ``0.0``. + periodic_cost : float, optional + Recurring cost per period (positive value). Default is ``0.0``. + periodic_income : float, optional + Recurring income per period. Default is ``0.0``. + cost_yearly_growth_rate : float, optional + Annual growth rate applied to periodic costs. Default is ``0.0``. + income_yearly_growth_rate : float, optional + Annual growth rate applied to periodic income. Default is ``0.0``. + freq : str, optional + Pandas period alias defining the period length (e.g. ``"Y"`` for + yearly, ``"M"`` for monthly). Default is ``"Y"``. + See [pandas documentation](https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#period-aliases). + custom_cash_flows : list of dict, optional + Explicit cash flow schedule as a list of records with at minimum + a ``"date"`` key (ISO 8601 string) and a value key. If provided, + overrides the periodic cost/income logic. + """ # noqa + + mkt_price_year: Optional[int] = field(default_factory=lambda: datetime.today().year) + init_cost: float = 0.0 + periodic_cost: float = 0.0 + periodic_income: float = 0.0 + cost_yearly_growth_rate: float = 0.0 + income_yearly_growth_rate: float = 0.0 + freq: str = "Y" + custom_cash_flows: Optional[list[dict]] = None + + @classmethod + def from_cost_income(cls, cost_income: "CostIncome") -> "CostIncomeConfig": + """ + Construct a :class:`CostIncomeConfig` from a live + :class:`CostIncome` object. + + Parameters + ---------- + cost_income : CostIncome + The live ``CostIncome`` instance to serialise. + + Returns + ------- + CostIncomeConfig + The config instance equivalent to the ``CostIncome``. + """ + + custom = None + if cost_income.custom_cash_flows is not None: + custom = ( + cost_income.custom_cash_flows.reset_index() + .rename(columns={"index": "date"}) + .assign(date=lambda df: df["date"].dt.strftime("%Y-%m-%d")) + .to_dict(orient="records") + ) + return cls( + mkt_price_year=cost_income.mkt_price_year.year, # datetime → int + init_cost=abs(cost_income.init_cost), # stored negative → positive + periodic_cost=abs(cost_income.periodic_cost), + periodic_income=cost_income.periodic_income, + cost_yearly_growth_rate=cost_income.cost_growth_rate, + income_yearly_growth_rate=cost_income.income_growth_rate, + freq=cost_income.freq, + custom_cash_flows=custom, + ) + + +@dataclass(repr=False) +class MeasureConfig(ModifierConfig): + """ + Top-level serializable configuration for a single adaptation measure. + + Aggregates all modifier sub-configs (hazard, impact functions, exposures, + cost/income) into a single object that can be round-tripped through dict, + YAML, or a legacy Excel row. + + This class is the primary entry point for defining measures in a + declarative, file-based workflow and serves as the serialization + counterpart to :class:`~climada.entity.measures.base.Measure`. + + Parameters + ---------- + name : str + Unique name identifying this measure. + haz_type : str + Hazard type identifier (e.g. ``"TC"``) this measure is designed for. + impfset_modifier : ImpfsetModifierConfig + Configuration describing modifications to the impact function set. + hazard_modifier : HazardModifierConfig + Configuration describing modifications to the hazard. + exposures_modifier : ExposuresModifierConfig + Configuration describing modifications to the exposures. + cost_income : CostIncomeConfig + Financial parameters associated with implementing this measure. + implementation_duration : str, optional + Pandas period alias (e.g. ``"2Y"``) representing the time before + the measure is fully operational. If ``None``, the measure takes + effect immediately. + color_rgb : tuple of float, optional + RGB colour triple in the range ``[0, 1]`` used for visualisation. + If ``None``, defaults to black ``(0, 0, 0)``. + """ + + name: str + haz_type: str + impfset_modifier: ImpfsetModifierConfig + hazard_modifier: HazardModifierConfig + exposures_modifier: ExposuresModifierConfig + cost_income: CostIncomeConfig + implementation_duration: Optional[str] = None + color_rgb: Optional[Tuple[float, float, float]] = None + + def __repr__(self) -> str: + """ + Return a detailed string representation of the measure configuration. + + All fields are shown, including sub-configs, with each on its own + indented line. + + Returns + ------- + str + A formatted multi-line string representation. + """ + + fields_str = "\n\t".join(f"{k}={v!r}" for k, v in self.__dict__.items()) + return f"{self.__class__.__name__}(\n\t{fields_str})" + + def to_dict(self, omit_default: bool = True) -> dict: + """ + Serialize the measure configuration to a flat dictionary. + + Sub-config dictionaries are merged into the top-level dict (i.e. + their keys are inlined, not nested). ``haz_type`` is always included + at the top level. Fields with ``None`` values are preserved. + + Returns + ------- + dict + Flat dictionary representation suitable for YAML or Excel + serialization. + """ + + return { + "name": self.name, + "haz_type": self.haz_type, + **self.impfset_modifier.to_dict(omit_default), + **self.hazard_modifier.to_dict(omit_default), + **self.exposures_modifier.to_dict(omit_default), + **self.cost_income.to_dict(omit_default), + "implementation_duration": self.implementation_duration, + "color_rgb": list(self.color_rgb) if self.color_rgb is not None else None, + } + + @classmethod + def from_dict(cls, kwargs_dict: dict) -> "MeasureConfig": + """ + Instantiate a :class:`MeasureConfig` from a flat dictionary. + + Delegates sub-config construction to the respective + ``from_dict`` classmethods. Unknown keys are silently discarded + by each sub-config parser. + + Parameters + ---------- + kwargs_dict : dict + Flat dictionary, as produced by :meth:`to_dict` or read from + a legacy Excel row. Must contain at minimum ``"name"`` and + ``"haz_type"``. + + Returns + ------- + MeasureConfig + A fully populated configuration instance. + """ + + return cls( + name=kwargs_dict["name"], + haz_type=kwargs_dict["haz_type"], + impfset_modifier=ImpfsetModifierConfig.from_dict(kwargs_dict), + hazard_modifier=HazardModifierConfig.from_dict(kwargs_dict), + exposures_modifier=ExposuresModifierConfig.from_dict(kwargs_dict), + cost_income=CostIncomeConfig.from_dict(kwargs_dict), + implementation_duration=kwargs_dict.get("implementation_duration"), + color_rgb=cls._normalize_color(kwargs_dict.get("color_rgb")), + ) + + @staticmethod + def _normalize_color(color_rgb): + # 1. Handle None and NaN (np.nan, pd.NA, float('nan')) + if color_rgb is None or pd.isna(color_rgb) is True: + return None + + # 2. Convert sequence types (list, np.array, tuple) to a standard tuple + try: + # Flatten in case it's a nested numpy array, then convert to tuple + result = tuple(np.array(color_rgb).flatten().tolist()) + + # 3. Enforce the length of three + if len(result) != 3: + raise ValueError(f"Expected 3 digits, got {len(result)}") + + return result + + except (TypeError, ValueError) as err: + # Handle cases where input isn't iterable or wrong length + raise ValueError(f"Invalid color format: {color_rgb}.") from err + + def to_yaml(self, path: str) -> None: + """ + Write this configuration to a YAML file. + + The file is structured as ``{"measures": []}``, + matching the expected format for :meth:`from_yaml`. + + Parameters + ---------- + path : str + Destination file path. Will be created or overwritten. + """ + + with open(path, "w") as opened_file: + yaml.dump( + {"measures": [self.to_dict()]}, + opened_file, + default_flow_style=False, + sort_keys=False, + ) + + @classmethod + def from_yaml(cls, path: str) -> "MeasureConfig": + """ + Load a :class:`MeasureConfig` from a YAML file. + + Expects the file to contain a top-level ``"measures"`` list; reads + only the first entry. + + Parameters + ---------- + path : str + Path to the YAML file to read. + + Returns + ------- + MeasureConfig + The configuration parsed from the first entry in + ``measures``. + """ + + with open(path) as opened_file: + return cls.from_dict(yaml.safe_load(opened_file)["measures"][0]) + + @classmethod + def from_row(cls, row: pd.Series) -> "MeasureConfig": + """ + Construct a :class:`MeasureConfig` from a legacy Excel row. + + Converts the row to a dictionary and delegates to :meth:`from_dict`. + This is the primary migration path for measures currently stored in + the legacy Excel-based ``MeasureSet`` format. + + Parameters + ---------- + row : pd.Series + A single row from a legacy measures Excel sheet, with column + names matching the flat dictionary keys expected by + :meth:`from_dict`. + + Returns + ------- + MeasureConfig + A configuration instance populated from the row data. + """ + + row_dict = row.to_dict() + return cls.from_dict(row_dict) diff --git a/climada/entity/measures/measure_set.py b/climada/entity/measures/measure_set.py index 90a2bb43c2..375299979f 100755 --- a/climada/entity/measures/measure_set.py +++ b/climada/entity/measures/measure_set.py @@ -21,18 +21,24 @@ __all__ = ["MeasureSet"] -import ast -import copy import logging -from typing import List, Optional +from functools import reduce +from typing import Any, Callable, Dict, Iterable, List, Optional, TypeVar, cast import numpy as np import pandas as pd -import xlsxwriter -from matplotlib import colormaps as cm import climada.util.hdf5_handler as u_hdf5 +from climada.entity.exposures.base import Exposures +from climada.entity.impact_funcs import ImpactFunc, ImpactFuncSet from climada.entity.measures.base import Measure +from climada.entity.measures.cost_income import CostIncome +from climada.entity.measures.helper import composite_fun +from climada.entity.measures.measure_config import ImpfsetModifierConfig, MeasureConfig +from climada.hazard.base import Hazard +from climada.util.string_parsers import parse_mapping_string, parse_range + +T = TypeVar("T", Exposures, ImpactFuncSet, Hazard) LOGGER = logging.getLogger(__name__) @@ -65,198 +71,106 @@ "sheet_name": "measures", "col_name": { "name": "name", - "color": "color", - "cost": "cost", - "haz_int_a": "hazard intensity impact a", - "haz_int_b": "hazard intensity impact b", - "haz_frq": "hazard high frequency cutoff", - "haz_set": "hazard event set", - "mdd_a": "MDD impact a", - "mdd_b": "MDD impact b", - "paa_a": "PAA impact a", - "paa_b": "PAA impact b", - "fun_map": "damagefunctions map", - "exp_set": "assets file", - "exp_reg": "Region_ID", - "risk_att": "risk transfer attachement", - "risk_cov": "risk transfer cover", - "risk_fact": "risk transfer cost factor", - "haz": "peril_ID", + "color": "color_rgb", + "implementation duration": "implementation_duration", + "cost": "init_cost", + "periodic cost": "periodic_cost", + "periodic income": "periodic_income", + "income growth rate (yearly)": "income_yearly_growth_rate", + "cost growth rate (yearly)": "cost_yearly_growth_rate", + "impact function id": "impf_id", + "hazard intensity impact a": "haz_int_mult", + "hazard intensity impact b": "haz_int_add", + "hazard event set": "haz_set", + "MDD impact a": "impf_mdd_mult", + "MDD impact b": "impf_mdd_add", + "PAA impact a": "impf_paa_mult", + "PAA impact b": "imfp_paa_add", + "damagefunctions map": "fun_map", + "assets file": "exp_set", + "Region_ID": "exp_reg", + "peril_ID": "haz_type", + "Impact RP cutoff": "impact_rp_cutoff", + "assets zeroing": "assets_to_zero", }, } """Excel variable names""" class MeasureSet: - """Contains measures of type Measure. Loads from - files with format defined in FILE_EXT. + """Contains measures of type Measure. Attributes ---------- _data : dict - Contains Measure objects. This attribute is not suppossed to be accessed directly. - Use the available methods instead. + Contains Measure objects keyed by their name. """ - def __init__(self, measure_list: Optional[List[Measure]] = None): - """Initialize a new MeasureSet object with specified data. + def __init__(self, measures: Iterable[Measure]): + """Initialize a new MeasureSet object. Parameters ---------- - measure_list : list of Measure objects, optional - The measures to include in the MeasureSet - - Examples - -------- - Fill MeasureSet with values and check consistency data: - - >>> act_1 = Measure( - ... name='Seawall', - ... color_rgb=np.array([0.1529, 0.2510, 0.5451]), - ... hazard_intensity=(1, 0), - ... mdd_impact=(1, 0), - ... paa_impact=(1, 0), - ... ) - >>> meas = MeasureSet([act_1]) - >>> meas.check() - - Read measures from file and checks consistency data: - - >>> meas = MeasureSet.from_excel(ENT_TEMPLATE_XLS) - """ - self.clear() - if measure_list is not None: - for meas in measure_list: - self.append(meas) - - def clear(self, _data: Optional[dict] = None): - """Reinitialize attributes. + measures : Iterable[Measure]. + The measures to include in the MeasureSet. - Parameters - ---------- - _data : dict, optional - A dict containing the Measure objects. For internal use only: It's not suppossed to be - set directly. Use the class methods instead. """ - self._data = ( - _data if _data is not None else dict() - ) # {hazard_type : {name: Measure()}} + self._data: Dict[str, Measure] = {meas.name: meas for meas in measures} - def append(self, meas): - """Append an Measure. Override if same name and haz_type. + def append(self, measure: Measure): + """ + Append a Measure. Overwrites if a measure with the same name exists. Parameters ---------- - meas : Measure - Measure instance + measure : Measure + The Measure instance to add. Raises ------ - ValueError - """ - if not isinstance(meas, Measure): - raise ValueError("Input value is not of type Measure.") - if not meas.haz_type: - LOGGER.warning("Input Measure's hazard type not set.") - if not meas.name: - LOGGER.warning("Input Measure's name not set.") - if meas.haz_type not in self._data: - self._data[meas.haz_type] = dict() - self._data[meas.haz_type][meas.name] = meas - - def remove_measure(self, haz_type=None, name=None): - """Remove impact function(s) with provided hazard type and/or id. - If no input provided, all impact functions are removed. + TypeError + If input is not an instance of Measure. - Parameters - ---------- - haz_type : str, optional - all impact functions with this hazard - name : str, optional - measure name """ - if (haz_type is not None) and (name is not None): - try: - del self._data[haz_type][name] - except KeyError: - LOGGER.info("No Measure with hazard %s and id %s.", haz_type, name) - elif haz_type is not None: - try: - del self._data[haz_type] - except KeyError: - LOGGER.info("No Measure with hazard %s.", haz_type) - elif name is not None: - haz_remove = self.get_hazard_types(name) - if not haz_remove: - LOGGER.info("No Measure with name %s.", name) - for haz in haz_remove: - del self._data[haz][name] - else: - self._data = dict() + if not isinstance(measure, Measure): + raise TypeError(f"Expected Measure, got {type(measure).__name__}") - def get_measure(self, haz_type=None, name=None): - """Get ImpactFunc(s) of input hazard type and/or id. - If no input provided, all impact functions are returned. + self._data[measure.name] = measure - Parameters - ---------- - haz_type : str, optional - hazard type - name : str, optional - measure name - - Returns - ------- - Measure (if haz_type and name), - list(Measure) (if haz_type or name), - {Measure.haz_type : {Measure.name : Measure}} (if None) + def measures(self, names: Optional[Iterable[str]] = None) -> Dict[str, Measure]: """ - if (haz_type is not None) and (name is not None): - try: - return self._data[haz_type][name] - except KeyError: - LOGGER.info("No Measure with hazard %s and id %s.", haz_type, name) - return list() - elif haz_type is not None: - try: - return list(self._data[haz_type].values()) - except KeyError: - LOGGER.info("No Measure with hazard %s.", haz_type) - return list() - elif name is not None: - haz_return = self.get_hazard_types(name) - if not haz_return: - LOGGER.info("No Measure with name %s.", name) - meas_return = [] - for haz in haz_return: - meas_return.append(self._data[haz][name]) - return meas_return - else: - return self._data - - def get_hazard_types(self, meas=None): - """Get measures hazard types contained for the name provided. - Return all hazard types if no input name. + Get a dictionary of measures. Parameters ---------- - name : str, optional - measure name + names : Iterable[str], optional + Filter by these measure names. If None, returns all. Returns ------- - list(str) - """ - if meas is None: - return list(self._data.keys()) + Dict[str, Measure] + Dictionary of measure names and objects. - haz_return = [] - for haz, haz_dict in self._data.items(): - if meas in haz_dict: - haz_return.append(haz) - return haz_return + """ + if names is None: + return self._data + return {name: self._data[name] for name in names if name in self._data} - def get_names(self, haz_type=None): + def get_measure(self, haz_type=None, name=None): + """This function is deprecated, use Entity.from_mat instead.""" + LOGGER.warning( + "The use of MeasureSet.get_measure() is deprecated." + "Use MeasureSet.measures().values() instead." + ) + if haz_type is not None: + LOGGER.warning( + "Selection per hazard type has been deprecated (as measures are no longer" + "considered specific to a hazard)" + ) + return self.measures(names=name).values() + + @property + def names(self): """Get measures names contained for the hazard type provided. Return all names for each hazard type if no input hazard type. @@ -270,359 +184,477 @@ def get_names(self, haz_type=None): list(Measure.name) (if haz_type provided), {Measure.haz_type : list(Measure.name)} (if no haz_type) """ - if haz_type is None: - out_dict = dict() - for haz, haz_dict in self._data.items(): - out_dict[haz] = list(haz_dict.keys()) - return out_dict + return list(self._data.keys()) - try: - return list(self._data[haz_type].keys()) - except KeyError: - LOGGER.info("No Measure with hazard %s.", haz_type) - return list() + @property + def size(self) -> int: + """ + Number of measures in the set. + + Returns + ------- + int + """ + return len(self._data) + + def __contains__(self, item: str) -> bool: + """Check if a measure name exists in the set.""" + return item in self._data + + def compose(self, names: List[str], combo_name: Optional[str] = None) -> Measure: + """ + Compose multiple measures into a single meta-Measure. + + This method creates a new Measure where the transformation functions + (hazard, exposures, impfset) are mathematically composed and financial + values are aggregated. + + Execution Order: + The composition follows a right-to-left nesting. For a list of + measures [m1, m2, m3], the resulting transformation is: + m1(m2(m3(x))) + This means m3 is applied first, then m2, then m1. - def size(self, haz_type=None, name=None): - """Get number of measures contained with input hazard type and - /or id. If no input provided, get total number of impact functions. + Financial Aggregation: + - Costs and incomes are summed across all selected measures. + - Growth rates and market price years are inherited from the + first measure in the list. Parameters ---------- - haz_type : str, optional - hazard type - name : str, optional - measure name + names : list of str + Ordered list of measure names to combine. + combo_name : str, optional + The name for the resulting Measure. If None, a name is + generated by joining names with an underscore and + appending 'composed'. Returns ------- - int - """ - if ( - (haz_type is not None) - and (name is not None) - and (isinstance(self.get_measure(haz_type, name), Measure)) - ): - return 1 - if (haz_type is not None) or (name is not None): - return len(self.get_measure(haz_type, name)) - return sum(len(meas_list) for meas_list in self.get_names().values()) - - def check(self): - """Check instance attributes. + Measure + A new Measure object representing the sequential application + of the component measures. Raises ------ ValueError + If the provided names do not match any measures in the set, + or if the MeasureSet is empty. + """ - for key_haz, meas_dict in self._data.items(): - def_color = cm.get_cmap("Greys").resampled(len(meas_dict)) - for i_meas, (name, meas) in enumerate(meas_dict.items()): - if (name != meas.name) | (name == ""): - raise ValueError( - "Wrong Measure.name: %s != %s." % (name, meas.name) - ) - if key_haz != meas.haz_type: - raise ValueError( - "Wrong Measure.haz_type: %s != %s." % (key_haz, meas.haz_type) + meas_list = list(self.measures(names).values()) + + if not meas_list: + raise ValueError("No measures found to compose.") + + def exposure_changes(exp: Exposures, **kwargs) -> Exposures: + return composite_fun(*[meas.exposures_changes for meas in meas_list])( + exp, **kwargs + ) + + def impfset_changes(impfset: ImpactFuncSet, **kwargs) -> ImpactFuncSet: + return composite_fun(*[meas.impfset_changes for meas in meas_list])( + impfset, **kwargs + ) + + def hazard_changes(haz: Hazard, **kwargs) -> Hazard: + return composite_fun(*[meas.hazard_changes for meas in meas_list])( + haz, **kwargs + ) + + return Measure( + name=combo_name or "_".join(names) + "composed", + exposures_changes=exposure_changes, + impfset_changes=impfset_changes, + hazard_changes=hazard_changes, + sub_measures=names, + cost_income=CostIncome.comb_cost_income([m.cost_income for m in meas_list]), + ) + + @staticmethod + def _combine_hazards(modified_hazards: list[Hazard]) -> Hazard: + """Finds the maximum effect (minimum intensity/freq) across hazards.""" + intensities = [h.intensity for h in modified_hazards] + fractions = [h.fraction for h in modified_hazards] + frequencies = [h.frequency for h in modified_hazards] + + hazard_mod = modified_hazards[0] + hazard_mod.intensity = reduce(lambda a, b: a.minimum(b), intensities) + hazard_mod.fraction = reduce(lambda a, b: a.minimum(b), fractions) + hazard_mod.frequency = np.minimum.reduce(frequencies) + return hazard_mod + + @staticmethod + def _combine_impfsets( + base_set: ImpactFuncSet, modified_sets: list[ImpactFuncSet] + ) -> ImpactFuncSet: + """Merges impact functions by taking the safest (minimum) damage parameters.""" + combined = ImpactFuncSet() + for haz_dict in base_set.get_func().values(): + for impf in haz_dict.values(): + versions = [ + s.get_func(haz_type=impf.haz_type, fun_id=impf.id) + for s in modified_sets + ] + + combined.append( + ImpactFunc( + impf.haz_type, + impf.id, + intensity=np.maximum.reduce([v.intensity for v in versions]), + mdd=np.minimum.reduce([v.mdd for v in versions]), + paa=np.minimum.reduce([v.paa for v in versions]), + intensity_unit=impf.intensity_unit, + name=impf.name, ) - # set default color if not set - if np.array_equal(meas.color_rgb, np.zeros(3)): - meas.color_rgb = def_color(i_meas) - meas.check() + ) + return combined + + @staticmethod + def _combine_exposures( + base_exp: Exposures, modified_exps: list[Exposures] + ) -> Exposures: + """Merges exposure changes, raising ValueError if two measures touch the same cell.""" + new_exps_gdfs = [exp.gdf for exp in modified_exps] + if not all( + set(new_gdf.columns) == set(base_exp.gdf.columns) + for new_gdf in new_exps_gdfs + ): + raise ValueError( + "All change DataFrames must have identical column structure and order." + ) + + # Align all changes into a single MultiIndexed DataFrame + # This stacks all change-sets on top of each other + stack = pd.concat( + new_exps_gdfs, + keys=range(len(new_exps_gdfs)), + names=["change_idx", "row_idx"], + ) - def extend(self, meas_set): - """Extend measures of input MeasureSet to current - MeasureSet. Overwrite Measure if same name and haz_type. + # Create a broadcasted baseline to match the stack's shape + # We use take() to repeat baseline rows for every change-set + baseline_repeated = base_exp.gdf.iloc[ + np.tile(np.arange(len(base_exp.gdf)), len(base_exp.gdf)) + ] + baseline_repeated.index = stack.index # Align indices for direct comparison + + # Identify changes: Mask is True where a cell differs from baseline + diff_mask = stack != baseline_repeated + + # Check for Conflicts: + # Sum the True values across the 'change_idx' level for every (row, col) + # If any cell has > 1 change, it's a conflict. + change_counts = diff_mask.groupby(level="row_idx").sum() + if (change_counts > 1).any().any(): + # Identify exactly where the conflict is for the error message + conflicting_cells = ( + change_counts[change_counts > 1] + .dropna(how="all") + .dropna(axis=1, how="all") + ) + raise ValueError( + f"Conflict: Multiple measures change the same cells:\n{conflicting_cells}" + ) + + # Merge: + # We take the baseline and update it with the sum of differences + # Only works if the data is numeric. For general objects (like 'if_' IDs): + result = base_exp.gdf.copy() + + # Efficiently collapse the stack: + # Since only one change exists per cell (checked in step 5), + # we can 'first' or 'max' to get the non-baseline value. + updates = stack.where(diff_mask).groupby(level="row_idx").first() + + exp_mod = Exposures( + updates.combine_first(result), + crs=base_exp.crs, + description=base_exp.description, + ref_year=base_exp.ref_year, + value_unit=base_exp.value_unit, + ) + return exp_mod + + def combine( + self, names: Optional[list[str]] = None, combo_name: Optional[str] = None + ) -> Measure: + """Combine multiple measures into a single composite Measure object. + + This method creates a new Measure that applies all specified sub-measures + sequentially to exposures, impact function sets, and hazard data. Each + sub-measure's transformation is applied individually, then the results are + aggregated through specialized combination methods that implement "best-of-all" + principles (taking minimum damage parameters, maximum effects). + + The combined Measure preserves the cost/income information from all sub-measures. Parameters ---------- - impact_funcs : MeasureSet - ImpactFuncSet instance to extend + names : list[str], optional + List of measure names to subset before combining. If None, uses all + measures. Each name must correspond to an existing measure in the + collection. + combo_name : str, optional + Custom name for the combined measure. If None, defaults to joining the + sub-measure names with underscores (e.g., "measure1_measure2_measure3"). + + Returns + ------- + Measure + A new Measure object containing: + + * Combined transformation functions for exposures, impact function sets, + and hazard data + * Aggregated cost/income information from all sub-measures + * Reference to all sub-measure names for traceability Raises ------ ValueError + If no measures are found to combine (empty `names` list or no valid + measures). Also raised if: + + * Multiple measures attempt to modify the same exposure cells (conflict + detected during exposure combination) + * Cost/income objects have mismatched market price years, cost growth + rates, or income growth rates + + Notes + ----- + Combination Logic by Entity Type: + + **Hazards**: Maximum effect is taken across all modified hazards, implemented + as minimum intensity, minimum fraction, and minimum frequency values. This + represents the most conservative (highest impact) scenario. + + **Impact Functions**: Merged by taking the safest (minimum) damage parameters + (MDD and PAA) across all modified impact functions, while preserving the + maximum intensity range. This ensures conservative damage estimation. + + **Exposures**: Changes are merged with strict conflict detection. If multiple + measures attempt to modify the same exposure cell, a ValueError is raised. + All change DataFrames must have identical column structure and order. + + **Cost/Income**: Values are summed across all sub-measures. Validation ensures + all cost/income objects share the same market price year, cost growth rate, + and income growth rate before aggregation. + + The wrapper functions preserve `**kwargs` support, allowing entity-specific + configuration to be passed through to individual sub-measure transformations. + This enables fine-grained control over how each sub-measure behaves during + the combined operation. """ - meas_set.check() - if self.size() == 0: - self.__dict__ = copy.deepcopy(meas_set.__dict__) - return + names = self.names if names is None else names + meas_list = list(self.measures(names).values()) + + if not meas_list: + raise ValueError("No measures found to combine.") + + def combined_exposure_changes(base_exp: Exposures, **kwargs) -> Exposures: + # 1. Apply all measures individually + mod_exps = [ + m.apply_exposures_changes(base_exp, **kwargs) for m in meas_list + ] + + # 2. Delegate combination to specialized methods + return self._combine_exposures(base_exp, mod_exps) + + def combined_impfset_changes(base_impfs: ImpactFuncSet, **kwargs): + # 1. Apply all measures individually + mod_impfs = [ + m.apply_impfset_changes(base_impfs, **kwargs) for m in meas_list + ] + + # 2. Delegate combination to specialized methods + return self._combine_impfsets(base_impfs, mod_impfs) + + def combined_hazard_changes(base_haz: Hazard, **kwargs): + # 1. Apply all measures individually + mod_haz = [m.apply_hazard_changes(base_haz, **kwargs) for m in meas_list] + + # 2. Delegate combination to specialized methods + return self._combine_hazards(mod_haz) + + return Measure( + name=combo_name or "_".join(names), + sub_measures=names, + exposures_changes=combined_exposure_changes, + impfset_changes=combined_impfset_changes, + hazard_changes=combined_hazard_changes, + cost_income=CostIncome.comb_cost_income([m.cost_income for m in meas_list]), + ) - new_func = meas_set.get_measure() - for _, meas_dict in new_func.items(): - for _, meas in meas_dict.items(): - self.append(meas) + @classmethod + def from_excel(cls, file_name: str, var_names: Optional[dict] = None): + """Read excel file following template and return a MeasureSet.""" + if var_names is None: + var_names = DEF_VAR_EXCEL + + df = pd.read_excel(file_name, sheet_name=var_names["sheet_name"]) + # inv_map = {v: k for k, v in var_names["col_name"].items()} + df = df.rename(columns=var_names["col_name"]) + + # Extract row processing to reduce locals in main method + measures = [cls._process_excel_row(row) for _, row in df.iterrows()] + return MeasureSet(measures) @classmethod - def from_mat(cls, file_name, var_names=None): + def from_mat(cls, file_name: str, var_names: Optional[dict] = None) -> "MeasureSet": """Read MATLAB file generated with previous MATLAB CLIMADA version. Parameters ---------- file_name : str - absolute file name - description : str, optional - description of the data + Absolute path to the MATLAB file. var_names : dict, optional - name of the variables in the file + Name of the variables in the file. Defaults to DEF_VAR_MAT. Returns ------- - meas_set: climada.entity.MeasureSet() - Measure Set from matlab file + MeasureSet """ if var_names is None: var_names = DEF_VAR_MAT - def read_att_mat(measures, data, file_name, var_names): - """Read MATLAB measures attributes""" - num_mes = len(data[var_names["var_name"]["name"]]) - for idx in range(0, num_mes): - color_str = u_hdf5.get_str_from_ref( - file_name, data[var_names["var_name"]["color"]][idx][0] - ) + def _parse_measure(idx: int, data: dict) -> MeasureConfig: + vn = var_names["var_name"] - try: - hazard_inten_imp = ( - data[var_names["var_name"]["haz_int_a"]][idx][0], - data[var_names["var_name"]["haz_int_b"]][0][idx], - ) - except KeyError: - hazard_inten_imp = ( - data[var_names["var_name"]["haz_int_a"][:-2]][idx][0], - 0, - ) + haz_type = u_hdf5.get_str_from_ref(file_name, data[vn["haz"]][idx][0]) + impf_id = 1 # MATLAB format has no explicit impf_id - meas_kwargs = dict( - name=u_hdf5.get_str_from_ref( - file_name, data[var_names["var_name"]["name"]][idx][0] - ), - color_rgb=np.fromstring(color_str, dtype=float, sep=" "), - cost=data[var_names["var_name"]["cost"]][idx][0], - haz_type=u_hdf5.get_str_from_ref( - file_name, data[var_names["var_name"]["haz"]][idx][0] - ), - hazard_freq_cutoff=data[var_names["var_name"]["haz_frq"]][idx][0], - hazard_set=u_hdf5.get_str_from_ref( - file_name, data[var_names["var_name"]["haz_set"]][idx][0] - ), - hazard_inten_imp=hazard_inten_imp, - # different convention of signs followed in MATLAB! - mdd_impact=( - data[var_names["var_name"]["mdd_a"]][idx][0], - data[var_names["var_name"]["mdd_b"]][idx][0], - ), - paa_impact=( - data[var_names["var_name"]["paa_a"]][idx][0], - data[var_names["var_name"]["paa_b"]][idx][0], - ), - imp_fun_map=u_hdf5.get_str_from_ref( - file_name, data[var_names["var_name"]["fun_map"]][idx][0] - ), - exposures_set=u_hdf5.get_str_from_ref( - file_name, data[var_names["var_name"]["exp_set"]][idx][0] - ), - risk_transf_attach=data[var_names["var_name"]["risk_att"]][idx][0], - risk_transf_cover=data[var_names["var_name"]["risk_cov"]][idx][0], + # hazard intensity: old files may lack the _a/_b suffix + try: + haz_int_a = data[vn["haz_int_a"]][idx][0] + haz_int_b = data[vn["haz_int_b"]][0][idx] + except KeyError: + haz_int_a = data[vn["haz_int_a"][:-2]][idx][0] + haz_int_b = 0.0 + + # optional fields that may be empty strings in legacy files + haz_set = ( + u_hdf5.get_str_from_ref(file_name, data[vn["haz_set"]][idx][0]) or None + ) + exp_set = ( + u_hdf5.get_str_from_ref(file_name, data[vn["exp_set"]][idx][0]) or None + ) + fun_map = ( + u_hdf5.get_str_from_ref(file_name, data[vn["fun_map"]][idx][0]) or None + ) + color_str = u_hdf5.get_str_from_ref(file_name, data[vn["color"]][idx][0]) + + if data[vn["exp_reg"]][idx][0]: + LOGGER.warning( + "Measure '%s' has exp_region_id set, which is no longer supported " + "and will be ignored. It will be reimplemented in a future version.", + u_hdf5.get_str_from_ref(file_name, data[vn["name"]][idx][0]), + ) + if data[vn["risk_att"]][idx][0] or data[vn["risk_cov"]][idx][0]: + LOGGER.warning( + "Measure '%s' has risk_transf_attach/cover set, which is no longer " + "supported and will be ignored. It will be reimplemented in a future version.", + u_hdf5.get_str_from_ref(file_name, data[vn["name"]][idx][0]), ) - exp_region_id = data[var_names["var_name"]["exp_reg"]][idx][0] - if exp_region_id: - meas_kwargs["exp_region_id"] = [exp_region_id] - - measures.append(Measure(**meas_kwargs)) + return MeasureConfig.from_dict( + dict( + name=u_hdf5.get_str_from_ref(file_name, data[vn["name"]][idx][0]), + haz_type=haz_type, + impf_id=impf_id, + impf_mdd_mult=data[vn["mdd_a"]][idx][0], + impf_mdd_add=data[vn["mdd_b"]][idx][0], + impf_paa_mult=data[vn["paa_a"]][idx][0], + impf_paa_add=data[vn["paa_b"]][idx][0], + intensity_multiplier=float(haz_int_a), + intensity_add=float(haz_int_b), + new_hazard_path=haz_set if haz_set != "nil" else None, + impact_rp_cutoff=float(data[vn["haz_frq"]][idx][0]) or None, + reassign_impf_id=( + parse_mapping_string(fun_map) + if fun_map and fun_map != "nil" + else None + ), + new_exposures_path=exp_set if exp_set != "nil" else None, + init_cost=float(data[vn["cost"]][idx][0]), + color_rgb=tuple(np.fromstring(color_str, dtype=float, sep=" ")), + ) + ) data = u_hdf5.read(file_name) - meas_set = cls() try: data = data[var_names["sup_field_name"]] except KeyError: pass - try: data = data[var_names["field_name"]] - read_att_mat(meas_set, data, file_name, var_names) - except KeyError as var_err: - raise KeyError("Variable not in MAT file: " + str(var_err)) from var_err + except KeyError as err: + raise KeyError("Variable not in MAT file: " + str(err)) from err - return meas_set + num_measures = len(data[var_names["var_name"]["name"]]) + measures = [ + Measure.from_config(_parse_measure(idx, data)) + for idx in range(num_measures) + ] + return cls(measures) - def read_mat(self, *args, **kwargs): - """This function is deprecated, use MeasureSet.from_mat instead.""" - LOGGER.warning( - "The use of MeasureSet.read_mat is deprecated." - "Use MeasureSet.from_mat instead." - ) - self.__dict__ = MeasureSet.from_mat(*args, **kwargs).__dict__ + @classmethod + def _load_dataset(cls, path: Optional[str], loader_func) -> Optional[Any]: + """Load dataset from path if valid, otherwise return None.""" + if path and path != "nil": + return loader_func(path) + return None @classmethod - def from_excel(cls, file_name, var_names=None): - """Read excel file following template and store variables. + def _process_excel_row(cls, row: pd.Series) -> Measure: + """Process a single Excel row into a Measure object.""" + return Measure.from_config(MeasureConfig.from_row(row)) + + def to_dict(self) -> dict: + """Serialize all serializable measures to a dict. Skips function-only measures.""" + serializable = { + name: measure._config.to_dict() + for name, measure in self._data.items() + if measure.is_serializable + } + skipped = [ + name for name, measure in self._data.items() if not measure.is_serializable + ] + if skipped: + LOGGER.warning( + "The following measures are not serializable and will be skipped: %s", + skipped, + ) + return {"measures": list(serializable.values())} - Parameters - ---------- - file_name : str - absolute file name - description : str, optional - description of the data - var_names : dict, optional - name of the variables in the file + @classmethod + def from_dict(cls, d: dict) -> "MeasureSet": + measures = [ + Measure.from_config(MeasureConfig.from_dict(m)) for m in d["measures"] + ] + return cls(measures) - Returns - ------- - meas_set : climada.entity.MeasureSet - Measures set from Excel - """ - if var_names is None: - var_names = DEF_VAR_EXCEL + def to_yaml(self, path: str) -> None: + import yaml - def read_att_excel(measures, dfr, var_names): - """Read Excel measures attributes""" - num_mes = len(dfr.index) - for idx in range(0, num_mes): - # Search for (a, b) values, put a=1 otherwise - try: - hazard_inten_imp = ( - dfr[var_names["col_name"]["haz_int_a"]][idx], - dfr[var_names["col_name"]["haz_int_b"]][idx], - ) - except KeyError: - hazard_inten_imp = (1, dfr["hazard intensity impact"][idx]) - - meas_kwargs = dict( - name=dfr[var_names["col_name"]["name"]][idx], - cost=dfr[var_names["col_name"]["cost"]][idx], - hazard_freq_cutoff=dfr[var_names["col_name"]["haz_frq"]][idx], - hazard_set=dfr[var_names["col_name"]["haz_set"]][idx], - hazard_inten_imp=hazard_inten_imp, - mdd_impact=( - dfr[var_names["col_name"]["mdd_a"]][idx], - dfr[var_names["col_name"]["mdd_b"]][idx], - ), - paa_impact=( - dfr[var_names["col_name"]["paa_a"]][idx], - dfr[var_names["col_name"]["paa_b"]][idx], - ), - imp_fun_map=dfr[var_names["col_name"]["fun_map"]][idx], - risk_transf_attach=dfr[var_names["col_name"]["risk_att"]][idx], - risk_transf_cover=dfr[var_names["col_name"]["risk_cov"]][idx], - color_rgb=np.fromstring( - dfr[var_names["col_name"]["color"]][idx], dtype=float, sep=" " - ), - ) + with open(path, "w") as f: + yaml.dump(self.to_dict(), f, default_flow_style=False, sort_keys=False) - try: - meas_kwargs["haz_type"] = dfr[var_names["col_name"]["haz"]][idx] - except KeyError: - pass - - try: - meas_kwargs["exposures_set"] = dfr[ - var_names["col_name"]["exp_set"] - ][idx] - except KeyError: - pass - - try: - meas_kwargs["exp_region_id"] = ast.literal_eval( - dfr[var_names["col_name"]["exp_reg"]][idx] - ) - except KeyError: - pass - except ValueError: - meas_kwargs["exp_region_id"] = dfr[ - var_names["col_name"]["exp_reg"] - ][idx] - - try: - meas_kwargs["risk_transf_cost_factor"] = dfr[ - var_names["col_name"]["risk_fact"] - ][idx] - except KeyError: - pass - - measures.append(Measure(**meas_kwargs)) - - dfr = pd.read_excel(file_name, var_names["sheet_name"]) - dfr = dfr.fillna("") - meas_set = cls() - try: - read_att_excel(meas_set, dfr, var_names) - except KeyError as var_err: - raise KeyError("Variable not in Excel file: " + str(var_err)) from var_err + @classmethod + def from_yaml(cls, path: str) -> "MeasureSet": + import yaml - return meas_set + with open(path) as f: + return cls.from_dict(yaml.safe_load(f)) - def read_excel(self, *args, **kwargs): - """This function is deprecated, use MeasureSet.from_excel instead.""" - LOGGER.warning( - "The use ofMeasureSet.read_excel is deprecated." - "Use MeasureSet.from_excel instead." - ) - self.__dict__ = MeasureSet.from_excel(*args, **kwargs).__dict__ + def to_json(self, path: str) -> None: + import json - def write_excel(self, file_name, var_names=None): - """Write excel file following template. + with open(path, "w") as f: + json.dump(self.to_dict(), f, indent=2) - Parameters - ---------- - file_name : str - absolute file name to write - var_names : dict, optional - name of the variables in the file - """ - if var_names is None: - var_names = DEF_VAR_EXCEL + @classmethod + def from_json(cls, path: str) -> "MeasureSet": + import json - def write_meas(row_ini, imp_ws, xls_data): - """Write one measure""" - for icol, col_dat in enumerate(xls_data): - imp_ws.write(row_ini, icol, col_dat) - - meas_wb = xlsxwriter.Workbook(file_name) - mead_ws = meas_wb.add_worksheet(var_names["sheet_name"]) - - header = [ - var_names["col_name"]["name"], - var_names["col_name"]["color"], - var_names["col_name"]["cost"], - var_names["col_name"]["haz_int_a"], - var_names["col_name"]["haz_int_b"], - var_names["col_name"]["haz_frq"], - var_names["col_name"]["haz_set"], - var_names["col_name"]["mdd_a"], - var_names["col_name"]["mdd_b"], - var_names["col_name"]["paa_a"], - var_names["col_name"]["paa_b"], - var_names["col_name"]["fun_map"], - var_names["col_name"]["exp_set"], - var_names["col_name"]["exp_reg"], - var_names["col_name"]["risk_att"], - var_names["col_name"]["risk_cov"], - var_names["col_name"]["haz"], - ] - for icol, head_dat in enumerate(header): - mead_ws.write(0, icol, head_dat) - for row_ini, (_, haz_dict) in enumerate(self._data.items(), 1): - for meas_name, meas in haz_dict.items(): - xls_data = [ - meas_name, - " ".join(list(map(str, meas.color_rgb))), - meas.cost, - meas.hazard_inten_imp[0], - meas.hazard_inten_imp[1], - meas.hazard_freq_cutoff, - meas.hazard_set, - meas.mdd_impact[0], - meas.mdd_impact[1], - meas.paa_impact[0], - meas.paa_impact[1], - meas.imp_fun_map, - meas.exposures_set, - str(meas.exp_region_id), - meas.risk_transf_attach, - meas.risk_transf_cover, - meas.haz_type, - ] - write_meas(row_ini, mead_ws, xls_data) - meas_wb.close() + with open(path) as f: + return cls.from_dict(json.load(f)) diff --git a/climada/entity/measures/test/conftest.py b/climada/entity/measures/test/conftest.py new file mode 100644 index 0000000000..e7cd59a718 --- /dev/null +++ b/climada/entity/measures/test/conftest.py @@ -0,0 +1,307 @@ +""" +This file is part of CLIMADA. + +Copyright (C) 2017 ETH Zurich, CLIMADA contributors listed in AUTHORS. + +CLIMADA is free software: you can redistribute it and/or modify it under the +terms of the GNU General Public License as published by the Free +Software Foundation, version 3. + +CLIMADA is distributed in the hope that it will be useful, but WITHOUT ANY +WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A +PARTICULAR PURPOSE. See the GNU General Public License for more details. + +You should have received a copy of the GNU General Public License along +with CLIMADA. If not, see . +--- + +A set of reusable fixtures for testing purpose. + +The objective of this file is to provide minimalistic, understandable and consistent +default objects for unit and integration testing. + +Values are chosen such that: + - Exposure value of the first points is 0. (First location should always have 0 impacts) + - Category / Group id of all points is 1, except for third point, valued at 2000 (Impacts on that category are always a share of 2000) + - Hazard centroids are the exposure centroids shifted by `HAZARD_JITTER` on both lon and lat. + - There are 4 events, with frequencies == 0.03, 0.01, 0.006, 0.004, 0, + such that impacts for RP250, 100 and 50 and 20 are at_event, + (freq sorted cumulate to 1/250, 1/100, 1/50 and 1/20). + - Hazard intensity is: + * Event 1: zero everywhere (always no impact) + * Event 2: max intensity at first centroid (also always no impact (first centroid is 0)) + * Event 3: half max intensity at second centroid (impact == half second centroid) + * Event 4: quarter max intensity everywhere (impact == 1/4 total value) + * Event 5: max intensity everywhere (but zero frequency) + With max intensity set at 100 + - Impact function is the "identity function", x intensity is x% damages + - Impact values should be: + * AAI = 18 = 1000*1/2*0.006+(1000+2000+3000+4000+5000)*0.25*0.004 + * RP20 = event1 = 0 + * RP50 = event2 = 0 + * RP100 = event3 = 500 = 1000*1/2 + * RP250 = event4 = 3750 = (1000+2000+3000+4000+5000)*0.25 + +""" + +import geopandas as gpd +import numpy as np +import pytest +from scipy.sparse import csr_matrix + +from climada.entity import Exposures, ImpactFunc, ImpactFuncSet +from climada.hazard import Centroids, Hazard + +# --------------------------------------------------------------------------- +# Coordinate system and metadata +# --------------------------------------------------------------------------- +CRS_WGS84 = "EPSG:4326" + +# --------------------------------------------------------------------------- +# Exposure attributes +# --------------------------------------------------------------------------- +EXP_DESC = "Test exposure dataset" +EXPOSURE_REF_YEAR = 2020 +EXPOSURE_VALUE_UNIT = "USD" +VALUES = np.array([0, 1000, 2000, 3000, 4000, 5000]) +CATEGORIES = np.array([1, 1, 2, 1, 1, 3]) + +# Exposure coordinates +EXP_LONS = np.array([4, 4.25, 4.5, 4, 4.25, 4.5]) +EXP_LATS = np.array([33, 33, 33, 33.25, 33.25, 33.25]) + +# --------------------------------------------------------------------------- +# Hazard definition +# --------------------------------------------------------------------------- +HAZARD_TYPE = "TEST_HAZARD_TYPE" +HAZARD_UNIT = "TEST_HAZARD_UNIT" + +# Hazard centroid positions +HAZ_JITTER = 0.1 # To test centroid matching +HAZ_LONS = EXP_LONS + HAZ_JITTER +HAZ_LATS = EXP_LATS + HAZ_JITTER + +# Hazard events +EVENT_IDS = np.array([1, 2, 3, 4, 5]) +EVENT_NAMES = ["ev1", "ev2", "ev3", "ev4", "ev5"] +DATES = np.array([1, 2, 3, 4, 5]) + +# Frequency are choosen so that they cumulate nicely +# to correspond to 250, 100, 50, and 20y return periods (for impacts) +FREQUENCY = np.array([0.03, 0.01, 0.006, 0.004, 0.0]) +FREQUENCY_UNIT = "1/year" + +# Hazard maximum intensity +# 100 to match 0 to 100% idea +# also in line with linear 1:1 impact function +# for easy mental calculus +HAZARD_MAX_INTENSITY = 100 + +# --------------------------------------------------------------------------- +# Impact function +# --------------------------------------------------------------------------- +IMPF_ID = 1 +IMPF_NAME = "IMPF_1" + + +@pytest.fixture +def exposures_factory(): + def _make_exposures( + values=VALUES, + exp_lons=EXP_LONS, + exp_lats=EXP_LATS, + value_factor=1.0, + ref_year=EXPOSURE_REF_YEAR, + hazard_type=HAZARD_TYPE, + group_id=None, + crs=CRS_WGS84, + impf_id=IMPF_ID, + description=EXP_DESC, + value_unit=EXPOSURE_VALUE_UNIT, + categories=None, + ): + gdf = gpd.GeoDataFrame( + { + "value": values * value_factor, + f"impf_{hazard_type}": impf_id, + "category": categories, + "geometry": gpd.points_from_xy(exp_lons, exp_lats, crs=crs), + }, + crs=crs, + ) + if group_id is not None: + gdf["group_id"] = group_id + + return Exposures( + data=gdf, + description=description, + ref_year=ref_year, + value_unit=value_unit, + ) + + return _make_exposures + + +@pytest.fixture +def exposures(exposures_factory): + return exposures_factory() + + +def hazard_frequency_factory(base=FREQUENCY): + def _make_frequency(scale=1.0): + return base * scale + + return _make_frequency + + +def hazard_frequency(): + return hazard_frequency_factory() + + +def hazard_intensity(max_intensity=HAZARD_MAX_INTENSITY, scale=1.0): + """ + Intensity matrix designed for analytical expectations: + - Event 1: zero + - Event 2: max intensity at first centroid + - Event 3: half max intensity at second centroid + - Event 4: quarter max intensity everywhere + """ + base = csr_matrix( + [ + [0, 0, 0, 0, 0, 0], + [max_intensity, 0, 0, 0, 0, 0], + [0, max_intensity / 2, 0, 0, 0, 0], + [ + max_intensity / 4, + max_intensity / 4, + max_intensity / 4, + max_intensity / 4, + max_intensity / 4, + max_intensity / 4, + ], + [ + max_intensity, + max_intensity, + max_intensity, + max_intensity, + max_intensity, + max_intensity, + ], + ] + ) + + return base * scale + + +@pytest.fixture +def centroids(): + return Centroids(lat=HAZ_LATS, lon=HAZ_LONS, crs=CRS_WGS84) + + +@pytest.fixture +def hazard_factory(): + def _make_hazard( + intensity_matrix=None, + frequency_array=FREQUENCY, + max_intensity=HAZARD_MAX_INTENSITY, + centroids=None, + intensity_scale=1.0, + frequency_scale=1.0, + hazard_type=HAZARD_TYPE, + hazard_unit=HAZARD_UNIT, + lat=HAZ_LATS, + lon=HAZ_LONS, + crs=CRS_WGS84, + event_id=EVENT_IDS, + event_name=EVENT_NAMES, + date=DATES, + frequency_unit=FREQUENCY_UNIT, + ): + if intensity_matrix is None: + intensity_matrix = hazard_intensity(max_intensity, intensity_scale) + + if centroids is None: + centroids = Centroids(lat=lat, lon=lon, crs=crs) + + return Hazard( + haz_type=hazard_type, + units=hazard_unit, + centroids=centroids, + event_id=event_id, + event_name=event_name, + date=date, + frequency=frequency_array * frequency_scale, + frequency_unit=frequency_unit, + intensity=intensity_matrix, + ) + + return _make_hazard + + +@pytest.fixture +def hazard(hazard_factory): + return hazard_factory() + + +@pytest.fixture +def impf_factory(): + def _make_impf( + paa_scale=1.0, + max_intensity=HAZARD_MAX_INTENSITY, + hazard_type=HAZARD_TYPE, + hazard_unit=HAZARD_UNIT, + impf_id=IMPF_ID, + negative_intensities=False, + ): + intensity = np.array([0, max_intensity / 2, max_intensity]) + mdd = np.array([0, 0.5, 1]) + if negative_intensities: + intensity = np.flip(intensity) * -1 + mdd = np.flip(mdd) + return ImpactFunc( + haz_type=hazard_type, + intensity_unit=hazard_unit, + name=IMPF_NAME, + intensity=intensity, + mdd=mdd, + paa=np.array([1, 1, 1]) * paa_scale, + id=impf_id, + ) + + return _make_impf + + +@pytest.fixture +def linear_impact_function(impf_factory): + return impf_factory() + + +@pytest.fixture +def impfset_factory(impf_factory): + def _make_impfset( + paa_scale=1.0, + max_intensity=HAZARD_MAX_INTENSITY, + hazard_type=HAZARD_TYPE, + hazard_unit=HAZARD_UNIT, + impf_id=IMPF_ID, + negative_intensities=False, + ): + return ImpactFuncSet( + [ + impf_factory( + paa_scale, + max_intensity, + hazard_type, + hazard_unit, + impf_id, + negative_intensities, + ) + ] + ) + + return _make_impfset + + +@pytest.fixture +def impfset(impfset_factory): + return impfset_factory() diff --git a/climada/entity/measures/test/test_base.py b/climada/entity/measures/test/test_base.py index d8688e4bf1..2020f83aa2 100644 --- a/climada/entity/measures/test/test_base.py +++ b/climada/entity/measures/test/test_base.py @@ -16,655 +16,353 @@ --- -Test MeasureSet and Measure classes. +Unit tests for the Measure class. """ import copy -import unittest -from pathlib import Path - -import numpy as np - -import climada.entity.exposures.test as exposures_test -import climada.util.coordinates as u_coord -from climada import CONFIG -from climada.entity.entity_def import Entity -from climada.entity.exposures.base import Exposures -from climada.entity.impact_funcs.base import ImpactFunc -from climada.entity.impact_funcs.impact_func_set import ImpactFuncSet -from climada.entity.measures.base import IMPF_ID_FACT, Measure -from climada.entity.measures.measure_set import MeasureSet -from climada.hazard.base import Hazard -from climada.test import get_test_file -from climada.util.constants import EXP_DEMO_H5, HAZ_DEMO_H5 - -DATA_DIR = CONFIG.measures.test_data.dir() - -HAZ_TEST_TC: Path = get_test_file("test_tc_florida", file_format="hdf5") -""" -Hazard test file from Data API: Hurricanes from 1851 to 2011 over Florida with 100 centroids. -Fraction is empty. Format: HDF5. -""" -ENT_TEST_MAT = Path(exposures_test.__file__).parent / "data" / "demo_today.mat" - - -class TestApply(unittest.TestCase): - """Test implement measures functions.""" - - def test_change_imp_func_pass(self): - """Test _change_imp_func""" - meas = MeasureSet.from_mat(ENT_TEST_MAT) - act_1 = meas.get_measure(name="Mangroves")[0] - - haz_type = "XX" - idx = 1 - intensity = np.arange(10, 100, 10) - intensity[0] = 0.0 - intensity[-1] = 100.0 - mdd = np.array( - [ - 0.0, - 0.0, - 0.021857142857143, - 0.035887500000000, - 0.053977415307403, - 0.103534246575342, - 0.180414000000000, - 0.410796000000000, - 0.410796000000000, - ] - ) - paa = np.array( - [ - 0, - 0.005000000000000, - 0.042000000000000, - 0.160000000000000, - 0.398500000000000, - 0.657000000000000, - 1.000000000000000, - 1.000000000000000, - 1.000000000000000, - ] - ) - imp_tc = ImpactFunc(haz_type, idx, intensity, mdd, paa) - imp_set = ImpactFuncSet([imp_tc]) - new_imp = act_1._change_imp_func(imp_set).get_func("XX")[0] - - self.assertTrue( - np.array_equal( - new_imp.intensity, - np.array([4.0, 24.0, 34.0, 44.0, 54.0, 64.0, 74.0, 84.0, 104.0]), - ) - ) - self.assertTrue( - np.array_equal( - new_imp.mdd, - np.array( - [ - 0, - 0, - 0.021857142857143, - 0.035887500000000, - 0.053977415307403, - 0.103534246575342, - 0.180414000000000, - 0.410796000000000, - 0.410796000000000, - ] - ), - ) - ) - self.assertTrue( - np.array_equal( - new_imp.paa, - np.array( - [ - 0, - 0.005000000000000, - 0.042000000000000, - 0.160000000000000, - 0.398500000000000, - 0.657000000000000, - 1.000000000000000, - 1.000000000000000, - 1.000000000000000, - ] - ), - ) - ) - self.assertFalse(id(new_imp) == id(imp_tc)) - - def test_cutoff_hazard_pass(self): - """Test _cutoff_hazard_damage""" - meas = MeasureSet.from_mat(ENT_TEST_MAT) - act_1 = meas.get_measure(name="Seawall")[0] - - haz = Hazard.from_hdf5(HAZ_TEST_TC) - exp = Exposures.from_mat(ENT_TEST_MAT) - exp.gdf.rename(columns={"impf": "impf_TC"}, inplace=True) - exp.check() - exp.assign_centroids(haz) - - imp_set = ImpactFuncSet.from_mat(ENT_TEST_MAT) - - new_haz = act_1._cutoff_hazard_damage(exp, imp_set, haz) - - self.assertFalse(id(new_haz) == id(haz)) - # fmt: off - pos_no_null = np.array( - [ - 6249, 7697, 9134, 13500, 13199, 5944, 9052, 9050, 2429, 5139, - 9053, 7102, 4096, 1070, 5948, 1076, 5947, 7432, 5949, 11694, - 5484, 6246, 12147, 778, 3326, 7199, 12498, 11698, 6245, 5327, - 4819, 8677, 5970, 7101, 779, 3894, 9051, 5976, 3329, 5978, - 4282, 11697, 7193, 5351, 7310, 7478, 5489, 5526, 7194, 4283, - 7191, 5328, 4812, 5528, 5527, 5488, 7475, 5529, 776, 5758, - 4811, 6223, 7479, 7470, 5480, 5325, 7477, 7318, 7317, 11696, - 7313, 13165, 6221, - ] - ) - # fmt: on - all_haz = np.arange(haz.intensity.shape[0]) - all_haz[pos_no_null] = -1 - pos_null = np.argwhere(all_haz > 0).reshape(-1) - for i_ev in pos_null: - self.assertEqual(new_haz.intensity[i_ev, :].max(), 0) - - def test_cutoff_hazard_region_pass(self): - """Test _cutoff_hazard_damage in specific region""" - meas = MeasureSet.from_mat(ENT_TEST_MAT) - act_1 = meas.get_measure(name="Seawall")[0] - act_1.exp_region_id = [1] - - haz = Hazard.from_hdf5(HAZ_TEST_TC) - exp = Exposures.from_mat(ENT_TEST_MAT) - exp.gdf["region_id"] = np.zeros(exp.gdf.shape[0]) - exp.gdf["region_id"].values[10:] = 1 - exp.check() - exp.assign_centroids(haz) - - imp_set = ImpactFuncSet.from_mat(ENT_TEST_MAT) - - new_haz = act_1._cutoff_hazard_damage(exp, imp_set, haz) - - self.assertFalse(id(new_haz) == id(haz)) - - # fmt: off - pos_no_null = np.array( - [ - 6249, 7697, 9134, 13500, 13199, 5944, 9052, 9050, 2429, 5139, - 9053, 7102, 4096, 1070, 5948, 1076, 5947, 7432, 5949, 11694, - 5484, 6246, 12147, 778, 3326, 7199, 12498, 11698, 6245, 5327, - 4819, 8677, 5970, 7101, 779, 3894, 9051, 5976, 3329, 5978, - 4282, 11697, 7193, 5351, 7310, 7478, 5489, 5526, 7194, 4283, - 7191, 5328, 4812, 5528, 5527, 5488, 7475, 5529, 776, 5758, - 4811, 6223, 7479, 7470, 5480, 5325, 7477, 7318, 7317, 11696, - 7313, 13165, 6221, - ] - ) - # fmt: on - all_haz = np.arange(haz.intensity.shape[0]) - all_haz[pos_no_null] = -1 - pos_null = np.argwhere(all_haz > 0).reshape(-1) - centr_null = np.unique(exp.gdf["centr_"][exp.gdf["region_id"] == 0]) - for i_ev in pos_null: - self.assertEqual(new_haz.intensity[i_ev, centr_null].max(), 0) - - def test_change_exposures_impf_pass(self): - """Test _change_exposures_impf""" - meas = Measure( - imp_fun_map="1to3", - haz_type="TC", - ) +from unittest.mock import MagicMock, patch - imp_set = ImpactFuncSet() - - intensity = np.arange(10, 100, 10) - mdd = np.arange(10, 100, 10) - paa = np.arange(10, 100, 10) - imp_tc = ImpactFunc("TC", 1, intensity, mdd, paa) - imp_set.append(imp_tc) - - mdd = np.arange(10, 100, 10) * 2 - paa = np.arange(10, 100, 10) * 2 - imp_tc = ImpactFunc("TC", 3, intensity, mdd, paa) - - exp = Exposures.from_hdf5(EXP_DEMO_H5) - new_exp = meas._change_exposures_impf(exp) - - self.assertEqual(new_exp.ref_year, exp.ref_year) - self.assertEqual(new_exp.value_unit, exp.value_unit) - self.assertEqual(new_exp.description, exp.description) - self.assertTrue(np.array_equal(new_exp.value, exp.value)) - self.assertTrue(np.array_equal(new_exp.latitude, exp.latitude)) - self.assertTrue(np.array_equal(new_exp.longitude, exp.longitude)) - self.assertTrue( - np.array_equal(exp.hazard_impf("TC"), np.ones(new_exp.gdf.shape[0])) - ) - self.assertTrue( - np.array_equal(new_exp.hazard_impf("TC"), np.ones(new_exp.gdf.shape[0]) * 3) - ) +import pytest - def test_change_all_hazard_pass(self): - """Test _change_all_hazard method""" - meas = Measure(hazard_set=HAZ_DEMO_H5) +from climada.entity.measures.base import Measure, allow_kwargs +from climada.entity.measures.cost_income import CostIncome - ref_haz = Hazard.from_hdf5(HAZ_DEMO_H5) - hazard = Hazard("TC") - new_haz = meas._change_all_hazard(hazard) +def _make_mock(): + """Return a simple MagicMock to stand in for Exposures / ImpactFuncSet / Hazard.""" + m = MagicMock() + m.__deepcopy__ = lambda self: copy.copy(self) # survive deepcopy + return m - self.assertEqual(new_haz.haz_type, ref_haz.haz_type) - self.assertTrue(np.array_equal(new_haz.frequency, ref_haz.frequency)) - self.assertTrue(np.array_equal(new_haz.date, ref_haz.date)) - self.assertTrue(np.array_equal(new_haz.orig, ref_haz.orig)) - self.assertTrue( - np.array_equal(new_haz.centroids.coord, ref_haz.centroids.coord) - ) - self.assertTrue(np.array_equal(new_haz.intensity.data, ref_haz.intensity.data)) - self.assertTrue(np.array_equal(new_haz.fraction.data, ref_haz.fraction.data)) - def test_change_all_exposures_pass(self): - """Test _change_all_exposures method""" - meas = Measure(exposures_set=EXP_DEMO_H5) +class TestAllowKwargs: + def test_unknown_kwargs_are_filtered(self): + @allow_kwargs + def add(a, b): + return a + b - ref_exp = Exposures.from_hdf5(EXP_DEMO_H5) + assert add(1, 2, extra=99) == 3 - exposures = Exposures() - exposures.gdf["latitude"] = np.ones(10) - exposures.gdf["longitude"] = np.ones(10) - new_exp = meas._change_all_exposures(exposures) + def test_known_kwargs_are_passed(self): + @allow_kwargs + def greet(name, greeting="Hello"): + return f"{greeting}, {name}!" - self.assertEqual(new_exp.ref_year, ref_exp.ref_year) - self.assertEqual(new_exp.value_unit, ref_exp.value_unit) - self.assertEqual(new_exp.description, ref_exp.description) - self.assertTrue(np.array_equal(new_exp.value, ref_exp.value)) - self.assertTrue(np.array_equal(new_exp.latitude, ref_exp.latitude)) - self.assertTrue(np.array_equal(new_exp.longitude, ref_exp.longitude)) + assert greet("Alice", greeting="Hi", unused="x") == "Hi, Alice!" - def test_not_filter_exposures_pass(self): - """Test _filter_exposures method with []""" - meas = Measure(exp_region_id=[]) + def test_func_with_var_keyword_receives_all(self): + received = {} - exp = Exposures() - imp_set = ImpactFuncSet() - haz = Hazard("TC") + @allow_kwargs + def sink(**kwargs): + received.update(kwargs) - new_exp = Exposures() - new_impfs = ImpactFuncSet() - new_haz = Hazard("TC") + sink(a=1, b=2) + assert received == {"a": 1, "b": 2} - res_exp, res_ifs, res_haz = meas._filter_exposures( - exp, imp_set, haz, new_exp, new_impfs, new_haz - ) + def test_positional_args_still_work(self): + @allow_kwargs + def mul(x, y): + return x * y - self.assertTrue(res_exp is new_exp) - self.assertTrue(res_ifs is new_impfs) - self.assertTrue(res_haz is new_haz) + assert mul(3, 4) == 12 - self.assertTrue(res_exp is not exp) - self.assertTrue(res_ifs is not imp_set) - self.assertTrue(res_haz is not haz) + def test_wraps_preserves_name(self): + def my_func(x): + return x - def test_filter_exposures_pass(self): - """Test _filter_exposures method with two values""" - meas = Measure( - exp_region_id=[3, 4], - haz_type="TC", - ) + wrapped = allow_kwargs(my_func) + assert wrapped.__name__ == "my_func" - exp = Exposures.from_mat(ENT_TEST_MAT) - exp.gdf.rename(columns={"impf_": "impf_TC", "centr_": "centr_TC"}, inplace=True) - exp.gdf["region_id"] = np.ones(exp.gdf.shape[0]) - exp.gdf["region_id"].values[: exp.gdf.shape[0] // 2] = 3 - exp.gdf["region_id"][0] = 4 - exp.check() - imp_set = ImpactFuncSet.from_mat(ENT_TEST_MAT) +class TestMeasureInit: + def test_name_is_set(self): + m = Measure("flood_barrier") + assert m.name == "flood_barrier" - haz = Hazard.from_hdf5(HAZ_TEST_TC) - exp.assign_centroids(haz) + def test_default_cost_income_created(self): + m = Measure("test") + assert isinstance(m.cost_income, CostIncome) - new_exp = copy.deepcopy(exp) - new_exp.gdf["value"] *= 3 - new_exp.gdf["impf_TC"].values[:20] = 2 - new_exp.gdf["impf_TC"].values[20:40] = 3 - new_exp.gdf["impf_TC"].values[40:] = 1 + def test_provided_cost_income_used(self): + ci = CostIncome(init_cost=500) + m = Measure("test", cost_income=ci) + assert m.cost_income is ci - new_ifs = copy.deepcopy(imp_set) - new_ifs.get_func("TC")[1].intensity += 1 - ref_ifs = copy.deepcopy(new_ifs) + def test_default_color_rgb(self): + m = Measure("test") + assert m.color_rgb == (0, 0, 0) - new_haz = copy.deepcopy(haz) - new_haz.intensity *= 4 + def test_custom_color_rgb(self): + m = Measure("test", color_rgb=(1.0, 0.5, 0.0)) + assert m.color_rgb == (1.0, 0.5, 0.0) - res_exp, res_ifs, res_haz = meas._filter_exposures( - exp, imp_set, haz, new_exp.copy(deep=True), new_ifs, new_haz - ) + def test_sub_measures_default_none(self): + m = Measure("test") + assert m.sub_measures is None - # unchanged meta data - self.assertEqual(res_exp.ref_year, exp.ref_year) - self.assertEqual(res_exp.value_unit, exp.value_unit) - self.assertEqual(res_exp.description, exp.description) - self.assertTrue(u_coord.equal_crs(res_exp.crs, exp.crs)) - - # regions (that is just input data, no need for testing, but it makes the changed and unchanged parts obious) - self.assertTrue(np.array_equal(res_exp.region_id[0], 4)) - self.assertTrue(np.array_equal(res_exp.region_id[1:25], np.ones(24) * 3)) - self.assertTrue(np.array_equal(res_exp.region_id[25:], np.ones(25))) - - # changed exposures - self.assertTrue( - np.array_equal( - res_exp.gdf["value"].values[:25], new_exp.gdf["value"].values[:25] - ) - ) - self.assertTrue( - np.all( - np.not_equal( - res_exp.gdf["value"].values[:25], exp.gdf["value"].values[:25] - ) - ) - ) - self.assertTrue( - np.all( - np.not_equal( - res_exp.gdf["impf_TC"].values[:25], - new_exp.gdf["impf_TC"].values[:25], - ) - ) - ) - self.assertTrue(np.array_equal(res_exp.latitude[:25], new_exp.latitude[:25])) - self.assertTrue(np.array_equal(res_exp.longitude[:25], new_exp.longitude[:25])) - - # unchanged exposures - self.assertTrue( - np.array_equal( - res_exp.gdf["value"].values[25:], exp.gdf["value"].values[25:] - ) - ) - self.assertTrue( - np.all( - np.not_equal( - res_exp.gdf["value"].values[25:], new_exp.gdf["value"].values[25:] - ) - ) - ) - self.assertTrue( - np.array_equal( - res_exp.gdf["impf_TC"].values[25:], exp.gdf["impf_TC"].values[25:] - ) - ) - self.assertTrue(np.array_equal(res_exp.latitude[25:], exp.latitude[25:])) - self.assertTrue(np.array_equal(res_exp.longitude[25:], exp.longitude[25:])) - - # unchanged impact functions - self.assertEqual(list(res_ifs.get_func().keys()), [meas.haz_type]) - self.assertEqual( - res_ifs.get_func()[meas.haz_type][1].id, - imp_set.get_func()[meas.haz_type][1].id, - ) - self.assertTrue( - np.array_equal( - res_ifs.get_func()[meas.haz_type][1].intensity, - imp_set.get_func()[meas.haz_type][1].intensity, - ) - ) - self.assertEqual( - res_ifs.get_func()[meas.haz_type][3].id, - imp_set.get_func()[meas.haz_type][3].id, - ) - self.assertTrue( - np.array_equal( - res_ifs.get_func()[meas.haz_type][3].intensity, - imp_set.get_func()[meas.haz_type][3].intensity, - ) - ) + def test_sub_measures_stored(self): + m = Measure("combo", sub_measures=["a", "b"]) + assert m.sub_measures == ["a", "b"] - # changed impact functions - self.assertTrue( - np.array_equal( - res_ifs.get_func()[meas.haz_type][1 + IMPF_ID_FACT].intensity, - ref_ifs.get_func()[meas.haz_type][1].intensity, - ) - ) - self.assertTrue( - np.array_equal( - res_ifs.get_func()[meas.haz_type][1 + IMPF_ID_FACT].paa, - ref_ifs.get_func()[meas.haz_type][1].paa, - ) - ) - self.assertTrue( - np.array_equal( - res_ifs.get_func()[meas.haz_type][1 + IMPF_ID_FACT].mdd, - ref_ifs.get_func()[meas.haz_type][1].mdd, - ) - ) - self.assertTrue( - np.array_equal( - res_ifs.get_func()[meas.haz_type][3 + IMPF_ID_FACT].intensity, - ref_ifs.get_func()[meas.haz_type][3].intensity, - ) - ) - self.assertTrue( - np.array_equal( - res_ifs.get_func()[meas.haz_type][3 + IMPF_ID_FACT].paa, - ref_ifs.get_func()[meas.haz_type][3].paa, - ) - ) - self.assertTrue( - np.array_equal( - res_ifs.get_func()[meas.haz_type][3 + IMPF_ID_FACT].mdd, - ref_ifs.get_func()[meas.haz_type][3].mdd, - ) - ) + def test_implementation_duration_stored(self): + from pandas.tseries.offsets import DateOffset - # unchanged hazard - self.assertTrue( - np.array_equal( - res_haz.intensity[:, :36].toarray(), haz.intensity[:, :36].toarray() - ) - ) - self.assertTrue( - np.array_equal( - res_haz.intensity[:, 37:46].toarray(), haz.intensity[:, 37:46].toarray() - ) - ) - self.assertTrue( - np.array_equal( - res_haz.intensity[:, 47:].toarray(), haz.intensity[:, 47:].toarray() - ) - ) + offset = DateOffset(years=2) + m = Measure("test", implementation_duration=offset) + assert m.implementation_duration == offset - # changed hazard - self.assertTrue( - np.array_equal( - res_haz.intensity[[36, 46]].toarray(), - new_haz.intensity[[36, 46]].toarray(), - ) - ) + def test_change_functions_wrapped_with_allow_kwargs(self): + def my_fn(obj): + return obj - def test_apply_ref_pass(self): - """Test apply method: apply all measures but insurance""" - hazard = Hazard.from_hdf5(HAZ_TEST_TC) - - entity = Entity.from_mat(ENT_TEST_MAT) - entity.measures._data["TC"] = entity.measures._data.pop("XX") - for meas in entity.measures.get_measure("TC"): - meas.haz_type = "TC" - entity.check() - - new_exp, new_ifs, new_haz = entity.measures.get_measure( - "TC", "Mangroves" - ).apply(entity.exposures, entity.impact_funcs, hazard) - - self.assertTrue(new_exp is entity.exposures) - self.assertTrue(new_haz is hazard) - self.assertFalse(new_ifs is entity.impact_funcs) - - new_imp = new_ifs.get_func("TC")[0] - self.assertTrue( - np.array_equal( - new_imp.intensity, - np.array([4.0, 24.0, 34.0, 44.0, 54.0, 64.0, 74.0, 84.0, 104.0]), - ) - ) - self.assertTrue( - np.allclose( - new_imp.mdd, - np.array( - [ - 0, - 0, - 0.021857142857143, - 0.035887500000000, - 0.053977415307403, - 0.103534246575342, - 0.180414000000000, - 0.410796000000000, - 0.410796000000000, - ] - ), - ) - ) - self.assertTrue( - np.allclose( - new_imp.paa, - np.array( - [ - 0, - 0.005000000000000, - 0.042000000000000, - 0.160000000000000, - 0.398500000000000, - 0.657000000000000, - 1.000000000000000, - 1.000000000000000, - 1.000000000000000, - ] - ), - ) - ) + m = Measure("test", exposures_changes=my_fn) + # The wrapped function should not raise on extra kwargs + stub = _make_mock() + result = m.exposures_changes(stub, unexpected_kwarg=42) + assert result is stub - new_imp = new_ifs.get_func("TC")[1] - self.assertTrue( - np.array_equal( - new_imp.intensity, - np.array([4.0, 24.0, 34.0, 44.0, 54.0, 64.0, 74.0, 84.0, 104.0]), - ) - ) - self.assertTrue( - np.allclose( - new_imp.mdd, - np.array( - [ - 0, - 0, - 0, - 0.025000000000000, - 0.054054054054054, - 0.104615384615385, - 0.211764705882353, - 0.400000000000000, - 0.400000000000000, - ] - ), - ) - ) - self.assertTrue( - np.allclose( - new_imp.paa, - np.array( - [ - 0, - 0.004000000000000, - 0, - 0.160000000000000, - 0.370000000000000, - 0.650000000000000, - 0.850000000000000, - 1.000000000000000, - 1.000000000000000, - ] - ), - ) - ) - def test_calc_impact_pass(self): - """Test calc_impact method: apply all measures but insurance""" +class TestIsSerializable: + def test_false_without_config(self): + m = Measure("test") + assert m.is_serializable is False - hazard = Hazard.from_hdf5(HAZ_TEST_TC) + def test_true_with_config(self): + mock_config = MagicMock() + m = Measure("test", _config=mock_config) + assert m.is_serializable is True - entity = Entity.from_mat(ENT_TEST_MAT) - entity.exposures.gdf.rename(columns={"impf": "impf_TC"}, inplace=True) - entity.measures._data["TC"] = entity.measures._data.pop("XX") - entity.measures.get_measure(name="Mangroves", haz_type="TC").haz_type = "TC" - for meas in entity.measures.get_measure("TC"): - meas.haz_type = "TC" - entity.check() - imp, risk_transf = entity.measures.get_measure("TC", "Mangroves").calc_impact( - entity.exposures, entity.impact_funcs, hazard - ) +class TestApplyExposuresChanges: + def test_identity_does_not_deepcopy(self): + m = Measure("test") # defaults to identity_function + exp = _make_mock() + result = m.apply_exposures_changes(exp, enforce_copy=True) + assert result is exp # identity: no copy + + def test_custom_fn_deepcopies_when_enforce_copy(self): + copied = _make_mock() + original = _make_mock() + + def fn(obj, **kw): + return obj + + with patch( + "climada.entity.measures.base.copy.deepcopy", return_value=copied + ) as mock_dc: + m = Measure("test", exposures_changes=fn) + result = m.apply_exposures_changes(original, enforce_copy=True) + mock_dc.assert_called_once() + assert result is copied + + def test_no_deepcopy_when_enforce_copy_false(self): + original = _make_mock() + + def fn(obj, **kw): + return obj + + with patch("climada.entity.measures.base.copy.deepcopy") as mock_dc: + m = Measure("test", exposures_changes=fn) + m.apply_exposures_changes(original, enforce_copy=False) + mock_dc.assert_not_called() + + def test_kwargs_forwarded(self): + received = {} + + def fn(obj, **kwargs): + received.update(kwargs) + return obj + + m = Measure("test", exposures_changes=fn) + exp = _make_mock() + m.apply_exposures_changes(exp, enforce_copy=False, year=2030) + assert received.get("year") == 2030 + + def test_missing_required_arg_raises_type_error_with_message(self): + def fn(obj, required_arg): + return obj + + m = Measure("test", exposures_changes=fn) + with pytest.raises(TypeError, match="required positional argument"): + m.apply_exposures_changes(_make_mock(), enforce_copy=False) + + +class TestApplyImpfsetChanges: + def test_identity_returns_same_object(self): + m = Measure("test") + impfset = _make_mock() + result = m.apply_impfset_changes(impfset, enforce_copy=True) + assert result is impfset + + def test_custom_fn_deepcopies_when_enforce_copy(self): + copied = _make_mock() + + def fn(obj, **kw): + return obj + + with patch("climada.entity.measures.base.copy.deepcopy", return_value=copied): + m = Measure("test", impfset_changes=fn) + result = m.apply_impfset_changes(_make_mock(), enforce_copy=True) + assert result is copied + + def test_kwargs_forwarded(self): + received = {} + + def fn(obj, **kwargs): + received.update(kwargs) + return obj - self.assertAlmostEqual(imp.aai_agg, 4.850407096284983e09, delta=1) - self.assertAlmostEqual(imp.at_event[0], 0) - self.assertAlmostEqual(imp.at_event[12], 1.470194187501225e07) - self.assertAlmostEqual(imp.at_event[41], 4.7226357936631286e08) - self.assertAlmostEqual(imp.at_event[11890], 1.742110428135755e07) - self.assertTrue(np.array_equal(imp.coord_exp[:, 0], entity.exposures.latitude)) - self.assertTrue(np.array_equal(imp.coord_exp[:, 1], entity.exposures.longitude)) - self.assertAlmostEqual(imp.eai_exp[0], 1.15677655725858e08) - self.assertAlmostEqual(imp.eai_exp[-1], 7.528669956120645e07) - self.assertAlmostEqual(imp.tot_value, 6.570532945599105e11) - self.assertEqual(imp.unit, "USD") - self.assertEqual(imp.imp_mat.shape, (0, 0)) - self.assertTrue(np.array_equal(imp.event_id, hazard.event_id)) - self.assertTrue(np.array_equal(imp.date, hazard.date)) - self.assertEqual(imp.event_name, hazard.event_name) - self.assertEqual(risk_transf.aai_agg, 0) - - def test_calc_impact_transf_pass(self): - """Test calc_impact method: apply all measures and insurance""" - - hazard = Hazard.from_hdf5(HAZ_TEST_TC) - - entity = Entity.from_mat(ENT_TEST_MAT) - entity.exposures.gdf.rename(columns={"impf": "impf_TC"}, inplace=True) - entity.measures._data["TC"] = entity.measures._data.pop("XX") - for meas in entity.measures.get_measure("TC"): - meas.haz_type = "TC" - meas = entity.measures.get_measure(name="Beach nourishment", haz_type="TC") - meas.haz_type = "TC" - meas.hazard_inten_imp = (1, 0) - meas.mdd_impact = (1, 0) - meas.paa_impact = (1, 0) - meas.risk_transf_attach = 5.0e8 - meas.risk_transf_cover = 1.0e9 - entity.check() - - imp, risk_transf = entity.measures.get_measure( - name="Beach nourishment", haz_type="TC" - ).calc_impact(entity.exposures, entity.impact_funcs, hazard) - - self.assertAlmostEqual(imp.aai_agg, 6.280804242609713e09) - self.assertAlmostEqual(imp.at_event[0], 0) - self.assertAlmostEqual(imp.at_event[12], 8.648764833437817e07) - self.assertAlmostEqual(imp.at_event[41], 500000000) - self.assertAlmostEqual(imp.at_event[11890], 6.498096646836635e07) - self.assertTrue(np.array_equal(imp.coord_exp, np.array([]))) - self.assertTrue(np.array_equal(imp.eai_exp, np.array([]))) - self.assertAlmostEqual(imp.tot_value, 6.570532945599105e11) - self.assertEqual(imp.unit, "USD") - self.assertEqual(imp.imp_mat.shape, (0, 0)) - self.assertTrue(np.array_equal(imp.event_id, hazard.event_id)) - self.assertTrue(np.array_equal(imp.date, hazard.date)) - self.assertEqual(imp.event_name, hazard.event_name) - self.assertEqual(risk_transf.aai_agg, 2.3139691495470852e08) - - -# Execute Tests -if __name__ == "__main__": - TESTS = unittest.TestLoader().loadTestsFromTestCase(TestApply) - unittest.TextTestRunner(verbosity=2).run(TESTS) + m = Measure("test", impfset_changes=fn) + m.apply_impfset_changes(_make_mock(), enforce_copy=False, scenario="rcp85") + assert received.get("scenario") == "rcp85" + + def test_missing_required_arg_raises_type_error_with_message(self): + def fn(obj, required_arg): + return obj + + m = Measure("test", impfset_changes=fn) + with pytest.raises(TypeError, match="required positional argument"): + m.apply_impfset_changes(_make_mock(), enforce_copy=False) + + +class TestApplyHazardChanges: + def test_identity_returns_same_object(self): + m = Measure("test") + hazard = _make_mock() + result = m.apply_hazard_changes(hazard, enforce_copy=True) + assert result is hazard + + def test_custom_fn_deepcopies_when_enforce_copy(self): + copied = _make_mock() + + def fn(obj, **kw): + return obj + + with patch("climada.entity.measures.base.copy.deepcopy", return_value=copied): + m = Measure("test", hazard_changes=fn) + result = m.apply_hazard_changes(_make_mock(), enforce_copy=True) + assert result is copied + + def test_no_deepcopy_when_enforce_copy_false(self): + def fn(obj, **kw): + return obj + + with patch("climada.entity.measures.base.copy.deepcopy") as mock_dc: + m = Measure("test", hazard_changes=fn) + m.apply_hazard_changes(_make_mock(), enforce_copy=False) + mock_dc.assert_not_called() + + def test_kwargs_forwarded(self): + received = {} + + def fn(obj, **kwargs): + received.update(kwargs) + return obj + + m = Measure("test", hazard_changes=fn) + m.apply_hazard_changes(_make_mock(), enforce_copy=False, intensity_scale=0.8) + assert received.get("intensity_scale") == 0.8 + + def test_missing_required_arg_raises_type_error_with_message(self): + def fn(obj, required_arg): + return obj + + m = Measure("test", hazard_changes=fn) + with pytest.raises(TypeError, match="required positional argument"): + m.apply_hazard_changes(_make_mock(), enforce_copy=False) + + +class TestApply: + def _make_measure_with_trackers(self): + """Return a Measure whose change functions record received kwargs.""" + exp_kwargs, haz_kwargs, impfset_kwargs = {}, {}, {} + + def track_exp(obj, **kw): + exp_kwargs.update(kw) + return obj + + def track_haz(obj, **kw): + haz_kwargs.update(kw) + return obj + + def track_impfset(obj, **kw): + impfset_kwargs.update(kw) + return obj + + m = Measure( + "tracker", + exposures_changes=track_exp, + hazard_changes=track_haz, + impfset_changes=track_impfset, + ) + return m, exp_kwargs, haz_kwargs, impfset_kwargs + + def test_returns_three_objects(self): + m = Measure("test") + exp, impfset, haz = _make_mock(), _make_mock(), _make_mock() + result = m.apply(exp, impfset, haz) + assert len(result) == 3 + + def test_default_triplet_context_passed_as_kwargs(self): + m, exp_kw, haz_kw, impfset_kw = self._make_measure_with_trackers() + exp, impfset, haz = _make_mock(), _make_mock(), _make_mock() + m.apply(exp, impfset, haz, enforce_copy=False) + + assert exp_kw["base_exposures"] is exp + assert exp_kw["base_impfset"] is impfset + assert exp_kw["base_hazard"] is haz + + def test_entity_specific_kwargs_override_defaults(self): + m, exp_kw, _, _ = self._make_measure_with_trackers() + exp, impfset, haz = _make_mock(), _make_mock(), _make_mock() + custom = _make_mock() + m.apply( + exp, + impfset, + haz, + enforce_copy=False, + kwargs_exposures={"base_exposures": custom}, + ) + assert exp_kw["base_exposures"] is custom + + def test_entity_specific_kwargs_not_leaked_to_other_functions(self): + m, exp_kw, haz_kw, _ = self._make_measure_with_trackers() + exp, impfset, haz = _make_mock(), _make_mock(), _make_mock() + m.apply( + exp, + impfset, + haz, + enforce_copy=False, + kwargs_exposures={"exp_only_param": 42}, + ) + assert "exp_only_param" not in haz_kw + assert "exp_only_param" in exp_kw + + def test_apply_with_all_identities_returns_originals(self): + m = Measure("test") # all identity functions + exp, impfset, haz = _make_mock(), _make_mock(), _make_mock() + new_exp, new_impfset, new_haz = m.apply(exp, impfset, haz) + assert new_exp is exp + assert new_impfset is impfset + assert new_haz is haz + + def test_apply_order_is_exposures_hazard_impfset(self): + """Verify the transformation order documented in the docstring.""" + call_order = [] + + def track(name): + def fn(obj, **kw): + call_order.append(name) + return obj + + return fn + + m = Measure( + "order_test", + exposures_changes=track("exposures"), + hazard_changes=track("hazard"), + impfset_changes=track("impfset"), + ) + m.apply(_make_mock(), _make_mock(), _make_mock(), enforce_copy=False) + assert call_order == ["exposures", "hazard", "impfset"] diff --git a/climada/entity/measures/test/test_base2.py b/climada/entity/measures/test/test_base2.py new file mode 100644 index 0000000000..5f7a323e71 --- /dev/null +++ b/climada/entity/measures/test/test_base2.py @@ -0,0 +1,99 @@ +""" +This file is part of CLIMADA. + +Copyright (C) 2017 ETH Zurich, CLIMADA contributors listed in AUTHORS. + +CLIMADA is free software: you can redistribute it and/or modify it under the +terms of the GNU General Public License as published by the Free +Software Foundation, version 3. + +CLIMADA is distributed in the hope that it will be useful, but WITHOUT ANY +WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A +PARTICULAR PURPOSE. See the GNU General Public License for more details. + +You should have received a copy of the GNU General Public License along +with CLIMADA. If not, see . + +--- + +Test Measure classes. +""" + +from unittest.mock import MagicMock + +import numpy as np +import pandas as pd +import pytest +from scipy import sparse + +from climada.entity.measures.base import Measure +from climada.entity.measures.helper import ( + helper_exposure, + helper_hazard, + helper_impfset, +) + + +def test_measure_init(hazard, exposures, impfset): + """Test if Measure initializes with default identity functions.""" + meas = Measure(name="test_meas") + assert meas.name == "test_meas" + assert meas.measure_effects(exposures, impfset, hazard) == ( + exposures, + impfset, + hazard, + ) + + +def test_apply_enforce_copy(hazard, exposures, impfset): + """Verify that the original object is not modified when enforce_copy=True.""" + + def double_intensity(haz): + haz.intensity *= 2 + return haz + + def double_mdd(impfset): + impfset.get_func(haz_type="TEST_HAZARD_TYPE", fun_id=1).mdd *= 2 + return impfset + + def double_value(exp): + exp.gdf["value"] *= 2 + return exp + + meas = Measure( + "scale", + hazard_change=double_intensity, + impfset_change=double_mdd, + exposures_change=double_value, + ) + original_haz = hazard.intensity.toarray().copy() + original_impf = impfset.get_func(haz_type="TEST_HAZARD_TYPE", fun_id=1).mdd.copy() + original_exp = exposures.gdf["value"].to_numpy().copy() + + transformed_haz = meas.apply_to_hazard(hazard, enforce_copy=True) + transformed_impfset = meas.apply_to_impfset(impfset, enforce_copy=True) + transformed_exp = meas.apply_to_exposures(exposures, enforce_copy=True) + + assert np.array_equal(hazard.intensity.toarray(), original_haz) + assert np.array_equal(transformed_haz.intensity.toarray(), original_haz * 2) + + assert np.array_equal( + impfset.get_func(haz_type="TEST_HAZARD_TYPE", fun_id=1).mdd, original_impf + ) + assert np.array_equal( + transformed_impfset.get_func(haz_type="TEST_HAZARD_TYPE", fun_id=1).mdd, + original_impf * 2, + ) + + assert np.array_equal(exposures.gdf["value"].to_numpy(), original_exp) + assert np.array_equal(transformed_exp.gdf["value"], original_exp * 2) + + +def test_apply_to_all(exposures, impfset, hazard): + """Test the bulk apply method.""" + meas = Measure("identity") + result = meas.apply(exposures, impfset, hazard) + + assert "exposure" in result + assert "hazard" in result + assert "impfset" in result diff --git a/climada/entity/measures/test/test_cost_income.py b/climada/entity/measures/test/test_cost_income.py new file mode 100644 index 0000000000..66ebdc5afc --- /dev/null +++ b/climada/entity/measures/test/test_cost_income.py @@ -0,0 +1,423 @@ +""" +This file is part of CLIMADA. + +Copyright (C) 2017 ETH Zurich, CLIMADA contributors listed in AUTHORS. + +CLIMADA is free software: you can redistribute it and/or modify it under the +terms of the GNU General Public License as published by the Free +Software Foundation, version 3. + +CLIMADA is distributed in the hope that it will be useful, but WITHOUT ANY +WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A +PARTICULAR PURPOSE. See the GNU General Public License for more details. + +You should have received a copy of the GNU General Public License along +with CLIMADA. If not, see . + +--- + +Unit tests for the CostIncome class. +""" + +from datetime import datetime + +import numpy as np +import pandas as pd +import pytest + +from climada.entity.measures.cost_income import CostIncome + + +class TestInit: + def test_defaults(self): + ci = CostIncome() + assert ci.init_cost == 0.0 + assert ci.periodic_cost == 0.0 + assert ci.periodic_income == 0.0 + assert ci.cost_growth_rate == 0.0 + assert ci.income_growth_rate == 0.0 + assert ci.freq == "Y" + assert ci.custom_cash_flows is None + + def test_costs_stored_negative(self): + ci = CostIncome(init_cost=100, periodic_cost=50) + assert ci.init_cost == -100.0 + assert ci.periodic_cost == -50.0 + + def test_costs_already_negative_stay_negative(self): + ci = CostIncome(init_cost=-200, periodic_cost=-30) + assert ci.init_cost == -200.0 + assert ci.periodic_cost == -30.0 + + def test_income_stored_positive(self): + ci = CostIncome(periodic_income=-80) + assert ci.periodic_income == 80.0 + + def test_mkt_price_year_default_is_current_year(self): + ci = CostIncome() + assert ci.mkt_price_year.year == datetime.today().year + + def test_mkt_price_year_custom(self): + ci = CostIncome(mkt_price_year=2015) + assert ci.mkt_price_year.year == 2015 + + def test_custom_cash_flows_processed(self): + df = pd.DataFrame( + { + "date": ["2020-01-01", "2020-06-01"], + "cost": [100, 200], + "income": [50, 60], + } + ) + ci = CostIncome(custom_cash_flows=df, freq="Y") + # After resampling to yearly, should have one row per year + assert isinstance(ci.custom_cash_flows, pd.DataFrame) + assert "cost" in ci.custom_cash_flows.columns + # Costs should be negative after processing + assert (ci.custom_cash_flows["cost"] <= 0).all() + + +# --------------------------------------------------------------------------- +# from_dict / from_config / from_yaml +# --------------------------------------------------------------------------- + + +class TestFromDict: + def test_basic(self): + d = { + "mkt_price_year": 2020, + "init_cost": 500, + "periodic_cost": 100, + "periodic_income": 200, + "cost_yearly_growth_rate": 0.02, + "income_yearly_growth_rate": 0.03, + "freq": "Y", + } + ci = CostIncome.from_dict(d) + assert ci.init_cost == -500.0 + assert ci.periodic_income == 200.0 + assert ci.cost_growth_rate == 0.02 + + def test_defaults_for_missing_keys(self): + ci = CostIncome.from_dict({}) + assert ci.init_cost == 0.0 + assert ci.freq == "Y" + + def test_with_custom_cash_flows(self): + d = { + "freq": "Y", + "custom_cash_flows": [ + {"date": "2021-01-01", "cost": 100, "income": 50}, + ], + } + ci = CostIncome.from_dict(d) + assert ci.custom_cash_flows is not None + + +class TestFromYaml: + def test_from_yaml(self, tmp_path): + yaml_content = """ +cost_income: + mkt_price_year: 2020 + init_cost: 1000 + periodic_cost: 200 + periodic_income: 300 + cost_yearly_growth_rate: 0.01 + income_yearly_growth_rate: 0.02 + freq: Y +""" + p = tmp_path / "ci.yaml" + p.write_text(yaml_content) + ci = CostIncome.from_yaml(str(p)) + assert ci.init_cost == -1000.0 + assert ci.periodic_income == 300.0 + + +# --------------------------------------------------------------------------- +# _freq_to_days +# --------------------------------------------------------------------------- + + +class TestFreqToDays: + def test_yearly(self): + result = CostIncome._freq_to_days("Y") + assert result == "365d" + + def test_monthly(self): + result = CostIncome._freq_to_days("M") + assert result == "30d" + + def test_daily(self): + result = CostIncome._freq_to_days("D") + assert result == "1d" + + def test_invalid(self): + with pytest.raises(ValueError): + CostIncome._freq_to_days("INVALID_FREQ_XYZ") + + +# --------------------------------------------------------------------------- +# _get_width_days +# --------------------------------------------------------------------------- + + +class TestGetWidthDays: + def test_yearly(self): + ci = CostIncome(freq="Y") + assert ci._get_width_days() == 365.0 + + def test_3yearly(self): + ci = CostIncome(freq="3Y") + assert ci._get_width_days() == 3 * 365.0 + + def test_monthly(self): + ci = CostIncome(freq="M") + assert ci._get_width_days() == 30.0 + + def test_daily(self): + ci = CostIncome(freq="D") + assert ci._get_width_days() == 1.0 + + +# --------------------------------------------------------------------------- +# _calc_at_date +# --------------------------------------------------------------------------- + + +class TestCalcAtDate: + def test_before_impl_date_is_zero(self): + ci = CostIncome(mkt_price_year=2020, init_cost=1000, periodic_income=500) + impl = pd.Timestamp("2021-01-01") + curr = pd.Timestamp("2020-01-01") + net, cost, inc = ci.calc_at_date(impl, curr) + assert net == 0.0 + assert cost == 0.0 + assert inc == 0.0 + + def test_at_impl_date_uses_init_cost(self): + ci = CostIncome(mkt_price_year=2021, init_cost=1000, periodic_income=0) + impl = pd.Timestamp("2021-01-01") + net, cost, inc = ci.calc_at_date(impl, impl) + assert cost == pytest.approx(-1000.0, rel=1e-3) + assert inc == pytest.approx(0.0) + + def test_after_impl_date_uses_periodic_cost(self): + ci = CostIncome(mkt_price_year=2020, periodic_cost=200, periodic_income=0) + impl = pd.Timestamp("2021-01-01") + curr = pd.Timestamp("2022-01-01") + net, cost, inc = ci.calc_at_date(impl, curr) + assert cost < 0 + assert abs(cost) == 200 + + def test_income_growth_applied(self): + ci = CostIncome( + mkt_price_year=2020, periodic_income=100, income_yearly_growth_rate=0.10 + ) + impl = pd.Timestamp("2020-01-01") + curr = pd.Timestamp("2021-01-01") + _, _, inc = ci.calc_at_date(impl, curr) + expected = 100 * (1.10**1.0) + assert inc == pytest.approx(expected, rel=1e-2) + + def test_net_equals_income_plus_cost(self): + ci = CostIncome(mkt_price_year=2020, periodic_cost=100, periodic_income=150) + impl = pd.Timestamp("2020-01-01") + curr = pd.Timestamp("2021-01-01") + net, cost, inc = ci.calc_at_date(impl, curr) + assert net == pytest.approx(cost + inc) + + def test_custom_cash_flows_added(self): + df = pd.DataFrame( + { + "date": ["2021-01-01"], + "cost": [500.0], + "income": [200.0], + } + ) + ci = CostIncome(mkt_price_year=2021, custom_cash_flows=df, freq="Y") + impl = pd.Timestamp("2021-01-01") + curr = pd.Timestamp("2021-01-01") + net, cost, inc = ci.calc_at_date(impl, curr) + assert inc == pytest.approx(200.0, rel=1e-3) + assert cost == pytest.approx(-500.0, rel=1e-3) + + +# --------------------------------------------------------------------------- +# calc_cash_flows +# --------------------------------------------------------------------------- + + +class TestCalcCashFlows: + def test_returns_three_arrays(self): + ci = CostIncome(mkt_price_year=2020, periodic_income=100) + net, costs, incs = ci.calc_cash_flows("2020-01-01", "2020-01-01", "2025-01-01") + assert isinstance(net, np.ndarray) + assert isinstance(costs, np.ndarray) + assert isinstance(incs, np.ndarray) + + def test_length_matches_periods(self): + ci = CostIncome(freq="Y") + net, costs, incs = ci.calc_cash_flows("2020-01-01", "2020-01-01", "2024-01-01") + periods = pd.period_range("2020-01-01", "2024-01-01", freq="Y") + assert len(net) == len(periods) + + def test_zero_cost_income(self): + ci = CostIncome() + net, costs, incs = ci.calc_cash_flows("2020-01-01", "2020-01-01", "2023-01-01") + np.testing.assert_array_equal(net, 0.0) + + def test_nonzero_cost_income(self): + ci = CostIncome( + mkt_price_year=2020, init_cost=5000, periodic_cost=200, periodic_income=1000 + ) + net, cost, income = ci.calc_cash_flows( + impl_date="2020-01-01", start_date="2019-01-01", end_date="2025-01-01" + ) + np.testing.assert_array_equal( + net, [0.0, -5000.0, 800.0, 800.0, 800.0, 800.0, 800.0] + ) + np.testing.assert_array_equal( + cost, [0.0, -5000.0, -200.0, -200.0, -200.0, -200.0, -200.0] + ) + np.testing.assert_array_equal( + income, [0.0, 0.0, 1000.0, 1000.0, 1000.0, 1000.0, 1000.0] + ) + + +# --------------------------------------------------------------------------- +# calc_total +# --------------------------------------------------------------------------- + + +class TestCalcTotal: + def test_total_is_sum_of_cash_flows(self): + ci = CostIncome(mkt_price_year=2020, periodic_income=100, periodic_cost=50) + net_arr, cost_arr, inc_arr = ci.calc_cash_flows( + "2020-01-01", "2020-01-01", "2024-01-01" + ) + total_net, total_cost, total_inc = ci.calc_total( + "2020-01-01", "2020-01-01", "2024-01-01" + ) + assert total_net == pytest.approx(float(np.sum(net_arr))) + assert total_cost == pytest.approx(float(np.sum(cost_arr))) + assert total_inc == pytest.approx(float(np.sum(inc_arr))) + + def test_returns_floats(self): + ci = CostIncome() + result = ci.calc_total("2020-01-01", "2020-01-01", "2022-01-01") + assert all(isinstance(v, (float, np.floating)) for v in result) + + +# --------------------------------------------------------------------------- +# to_dataframe +# --------------------------------------------------------------------------- + + +class TestToDataframe: + def test_columns(self): + ci = CostIncome(periodic_income=100) + df = ci.to_dataframe("2020-01-01", "2020-01-01", "2023-01-01") + assert set(df.columns) == {"date", "net", "cost", "income"} + + def test_row_count(self): + ci = CostIncome(freq="Y") + df = ci.to_dataframe("2020-01-01", "2020-01-01", "2022-01-01") + expected = len(pd.period_range("2020-01-01", "2022-01-01", freq="Y")) + assert len(df) == expected + + +# --------------------------------------------------------------------------- +# comb_cost_income +# --------------------------------------------------------------------------- + + +class TestCombCostIncome: + def test_costs_are_summed(self): + ci1 = CostIncome(mkt_price_year=2020, init_cost=100, periodic_cost=50) + ci2 = CostIncome(mkt_price_year=2020, init_cost=200, periodic_cost=30) + combined = CostIncome.comb_cost_income([ci1, ci2]) + assert combined.init_cost == -300.0 + assert combined.periodic_cost == -80.0 + + def test_incomes_are_summed(self): + ci1 = CostIncome(mkt_price_year=2020, periodic_income=100) + ci2 = CostIncome(mkt_price_year=2020, periodic_income=200) + combined = CostIncome.comb_cost_income([ci1, ci2]) + assert combined.periodic_income == 300.0 + + def test_mismatched_mkt_price_year_raises(self): + ci1 = CostIncome(mkt_price_year=2020) + ci2 = CostIncome(mkt_price_year=2021) + with pytest.raises(ValueError, match="market price years"): + CostIncome.comb_cost_income([ci1, ci2]) + + def test_mismatched_cost_growth_rate_raises(self): + ci1 = CostIncome(mkt_price_year=2020, cost_yearly_growth_rate=0.02) + ci2 = CostIncome(mkt_price_year=2020, cost_yearly_growth_rate=0.05) + with pytest.raises(ValueError, match="cost_growth_rate"): + CostIncome.comb_cost_income([ci1, ci2]) + + def test_mismatched_income_growth_rate_raises(self): + ci1 = CostIncome(mkt_price_year=2020, income_yearly_growth_rate=0.01) + ci2 = CostIncome(mkt_price_year=2020, income_yearly_growth_rate=0.03) + with pytest.raises(ValueError, match="income_growth_rate"): + CostIncome.comb_cost_income([ci1, ci2]) + + def test_single_element_list(self): + ci = CostIncome(mkt_price_year=2020, init_cost=500, periodic_income=100) + combined = CostIncome.comb_cost_income([ci]) + assert combined.init_cost == -500.0 + assert combined.periodic_income == 100.0 + + def test_preserves_growth_rates(self): + ci1 = CostIncome( + mkt_price_year=2020, + cost_yearly_growth_rate=0.02, + income_yearly_growth_rate=0.03, + ) + ci2 = CostIncome( + mkt_price_year=2020, + cost_yearly_growth_rate=0.02, + income_yearly_growth_rate=0.03, + ) + combined = CostIncome.comb_cost_income([ci1, ci2]) + assert combined.cost_growth_rate == 0.02 + assert combined.income_growth_rate == 0.03 + + def test_merges_custom_cash_flows(self): + df1 = pd.DataFrame( + { + "date": ["2020-01-01", "2020-03-01"], + "cost": [100, 200], + "income": [50, 60], + } + ) + df2 = pd.DataFrame( + { + "date": ["2020-01-01", "2020-03-01", "2020-04-01"], + "cost": [100, 200, 300], + "income": [50, 60, 70], + } + ) + + expected = pd.DataFrame( + { + "date": ["2020-01", "2020-02", "2020-03", "2020-04"], + "cost": [-200, 0, -400, -300], + "income": [100, 0, 120, 70], + } + ) + + expected["date"] = pd.to_datetime(expected["date"]) + expected = expected.set_index("date") + expected = expected.resample("MS").sum() + + ci1 = CostIncome( + mkt_price_year=2020, periodic_income=100, freq="M", custom_cash_flows=df1 + ) + ci2 = CostIncome( + mkt_price_year=2020, periodic_cost=50, freq="M", custom_cash_flows=df2 + ) + + combined = CostIncome.comb_cost_income([ci1, ci2]) + pd.testing.assert_frame_equal(combined.custom_cash_flows, expected) diff --git a/climada/entity/measures/test/test_helper.py b/climada/entity/measures/test/test_helper.py new file mode 100644 index 0000000000..226e15207e --- /dev/null +++ b/climada/entity/measures/test/test_helper.py @@ -0,0 +1,446 @@ +""" +This file is part of CLIMADA. + +Copyright (C) 2017 ETH Zurich, CLIMADA contributors listed in AUTHORS. + +CLIMADA is free software: you can redistribute it and/or modify it under the +terms of the GNU General Public License as published by the Free +Software Foundation, version 3. + +CLIMADA is distributed in the hope that it will be useful, but WITHOUT ANY +WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A +PARTICULAR PURPOSE. See the GNU General Public License for more details. + +You should have received a copy of the GNU General Public License along +with CLIMADA. If not, see . + +--- + + +Unit tests for the helper functions. +""" + +from unittest.mock import MagicMock + +import numpy as np +import pytest + +from climada.entity.measures.helper import ( + change_impfset, + composite_fun, + helper_exposure, + helper_hazard, + helper_impfset, + impact_intensity_rp_cutoff_helper, + replace_hazard, +) +from climada.entity.measures.measure_config import ( + ExposuresModifierConfig, + HazardModifierConfig, + ImpfsetModifierConfig, +) +from climada.entity.measures.test.conftest import ( + HAZARD_MAX_INTENSITY, + HAZARD_TYPE, + IMPF_ID, +) + + +class TestCompositeFun: + def test_single_function_applied(self): + double = lambda x, **kw: x * 2 + composed = composite_fun(double) + assert composed(3) == 6 + + def test_two_functions_applied_right_to_left(self): + add1 = lambda x, **kw: x + 1 + double = lambda x, **kw: x * 2 + # composite_fun(add1, double) => add1(double(x)) => 2*x + 1 + composed = composite_fun(add1, double) + assert composed(3) == 7 + + def test_three_functions_order(self): + f = lambda x, **kw: x + 10 + g = lambda x, **kw: x * 2 + h = lambda x, **kw: x - 1 + # f(g(h(x))) = f(g(x-1)) = f(2*(x-1)) = 2*(x-1) + 10 + composed = composite_fun(f, g, h) + assert composed(5) == 18 + + def test_no_functions_returns_identity(self): + composed = composite_fun() + obj = object() + assert composed(obj) is obj + + def test_kwargs_forwarded_to_all_functions(self): + received_by_f, received_by_g = {}, {} + + def f(x, **kw): + received_by_f.update(kw) + return x + + def g(x, **kw): + received_by_g.update(kw) + return x + + composite_fun(f, g)(42, year=2030) + assert received_by_f.get("year") == 2030 + assert received_by_g.get("year") == 2030 + + +# =========================================================================== +# replace_hazard +# =========================================================================== + + +class TestReplaceHazard: + def test_returns_new_hazard_ignoring_input(self): + new_haz = MagicMock() + original_haz = MagicMock() + fn = replace_hazard(new_haz) + result = fn(original_haz) + assert result is new_haz + + def test_different_inputs_always_return_same_object(self): + new_haz = MagicMock() + fn = replace_hazard(new_haz) + assert fn(MagicMock()) is new_haz + assert fn(MagicMock()) is new_haz + + +# =========================================================================== +# helper_hazard (no new hazard path, no rp cutoff) +# =========================================================================== + + +class TestHelperHazard: + def _config(self, mult=1.0, add=0.0, path=None, rp=None): + return HazardModifierConfig( + haz_type="TEST", + haz_int_mult=mult, + haz_int_add=add, + new_hazard_path=path, + impact_rp_cutoff=rp, + ) + + def test_identity_transform_leaves_data_unchanged(self, hazard_factory): + haz = hazard_factory() + original_data = haz.intensity.data.copy() + fn = helper_hazard(self._config(mult=1.0, add=0.0)) + result = fn(haz) + np.testing.assert_allclose(result.intensity.data, original_data) + + def test_multiplicative_scaling(self, hazard_factory): + haz = hazard_factory() + original_data = haz.intensity.data.copy() + fn = helper_hazard(self._config(mult=0.5)) + result = fn(haz) + np.testing.assert_allclose(result.intensity.data, original_data * 0.5) + + def test_additive_shift_on_nonzero_entries(self, hazard_factory): + haz = hazard_factory() + original_data = haz.intensity.data.copy() + fn = helper_hazard(self._config(add=10.0)) + result = fn(haz) + np.testing.assert_allclose(result.intensity.data, original_data + 10.0) + + def test_negative_results_clipped_to_zero(self, hazard_factory): + haz = hazard_factory() + fn = helper_hazard(self._config(mult=-1.0)) + result = fn(haz) + assert result.intensity.nnz == 0 + + def test_negative_after_add_clipped_and_eliminated(self, hazard_factory): + haz = hazard_factory() + fn = helper_hazard(self._config(add=-HAZARD_MAX_INTENSITY - 1)) + result = fn(haz) + assert (result.intensity.data >= 0).all() + + def test_combined_mult_and_add(self, hazard_factory): + haz = hazard_factory() + original_data = haz.intensity.data.copy() + fn = helper_hazard(self._config(mult=2.0, add=5.0)) + result = fn(haz) + np.testing.assert_allclose(result.intensity.data, original_data * 2.0 + 5.0) + + def test_returns_hazard_object(self, hazard_factory): + from climada.hazard.base import Hazard + + haz = hazard_factory() + fn = helper_hazard(self._config()) + assert isinstance(fn(haz), Hazard) + + +# =========================================================================== +# helper_hazard (with rp_cutoff — exercises impact_intensity_rp_cutoff_helper) +# =========================================================================== + + +class TestHelperHazardRpCutoff: + """ + Integration of helper_hazard + impact_intensity_rp_cutoff_helper. + + Using the conftest fixture setup: + - Event 1 (freq 0.03): zero impact everywhere → should be zeroed for any RP + - Event 2 (freq 0.01): zero impact (hits centroid 0, value=0) → zeroed for any RP + - Event 3 (freq 0.006): impact = 500 → RP ~167y + - Event 4 (freq 0.004): impact = 3750 → RP = 250y + - Event 5 (freq 0.0): zero frequency → never contributes + """ + + def _config_with_rp(self, rp): + return HazardModifierConfig( + haz_type=HAZARD_TYPE, + impact_rp_cutoff=rp, + ) + + def test_very_large_rp_zeros_all_events(self, hazard_factory, exposures, impfset): + """RP larger than any event → all intensities zeroed.""" + # Avoid the 0 freq of the default hazard_factory settings + haz = hazard_factory(frequency_array=np.array([0.1, 0.2, 0.3, 0.4, 0.5])) + fn = helper_hazard(self._config_with_rp(10_000)) + result = fn( + haz, + base_exposures=exposures, + base_impfset=impfset, + base_hazard=haz, + ) + assert result.intensity.nnz == 0 + + def test_very_small_rp_keeps_all_events(self, hazard_factory, exposures, impfset): + """RP smaller than all events → nothing is zeroed.""" + haz = hazard_factory() + original_nnz = haz.intensity.nnz + fn = helper_hazard(self._config_with_rp(1)) + result = fn( + haz, + base_exposures=exposures, + base_impfset=impfset, + base_hazard=haz, + ) + assert result.intensity.nnz == original_nnz + + +# =========================================================================== +# impact_intensity_rp_cutoff_helper (directly) +# =========================================================================== + + +class TestImpactIntensityRpCutoffHelper: + def test_returns_callable(self): + fn = impact_intensity_rp_cutoff_helper(100) + assert callable(fn) + + def test_region_id_filter_restricts_impact_computation( + self, hazard_factory, exposures_factory, impfset + ): + """Passing exposures_region_id should not raise and should return a Hazard.""" + from climada.hazard.base import Hazard + + exp = exposures_factory() + exp.gdf["region_id"] = [1, 1, 2, 2, 1, 1] + haz = hazard_factory() + + fn = impact_intensity_rp_cutoff_helper(50) + result = fn( + haz, + base_exposures=exp, + base_impfset=impfset, + base_hazard=haz, + exposures_region_id=[1], + ) + assert isinstance(result, Hazard) + + +# =========================================================================== +# change_impfset +# =========================================================================== + + +class TestChangeImpfset: + def test_returns_new_impfset_ignoring_input(self, impfset_factory): + new_ifs = impfset_factory() + original_ifs = impfset_factory() + fn = change_impfset(new_ifs) + assert fn(original_ifs) is new_ifs + + def test_repeated_calls_return_same_object(self, impfset_factory): + new_ifs = impfset_factory() + fn = change_impfset(new_ifs) + assert fn(impfset_factory()) is new_ifs + assert fn(impfset_factory()) is new_ifs + + +# =========================================================================== +# helper_impfset +# =========================================================================== + + +class TestHelperImpfset: + def _config( + self, + impf_ids=None, + int_mult=1.0, + int_add=0.0, + mdd_mult=1.0, + mdd_add=0.0, + paa_mult=1.0, + paa_add=0.0, + path=None, + ): + return ImpfsetModifierConfig( + haz_type=HAZARD_TYPE, + impf_ids=impf_ids, + impf_int_mult=int_mult, + impf_int_add=int_add, + impf_mdd_mult=mdd_mult, + impf_mdd_add=mdd_add, + impf_paa_mult=paa_mult, + impf_paa_add=paa_add, + new_impfset_path=path, + ) + + def test_identity_transform_leaves_functions_unchanged(self, impfset_factory): + ifs = impfset_factory() + original_mdd = ifs.get_func(haz_type=HAZARD_TYPE)[0].mdd.copy() + fn = helper_impfset(self._config()) + result = fn(ifs) + np.testing.assert_allclose( + result.get_func(haz_type=HAZARD_TYPE)[0].mdd, original_mdd + ) + + def test_mdd_multiplicative_scaling(self, impfset_factory): + ifs = impfset_factory() + original_mdd = ifs.get_func(haz_type=HAZARD_TYPE)[0].mdd.copy() + fn = helper_impfset(self._config(mdd_mult=0.5)) + result = fn(ifs) + np.testing.assert_allclose( + result.get_func(haz_type=HAZARD_TYPE)[0].mdd, original_mdd * 0.5 + ) + + def test_paa_additive_shift(self, impfset_factory): + ifs = impfset_factory() + original_paa = ifs.get_func(haz_type=HAZARD_TYPE)[0].paa.copy() + fn = helper_impfset(self._config(paa_add=0.1)) + result = fn(ifs) + np.testing.assert_allclose( + result.get_func(haz_type=HAZARD_TYPE)[0].paa, original_paa + 0.1 + ) + + def test_intensity_linear_transform(self, impfset_factory): + ifs = impfset_factory() + original_int = ifs.get_func(haz_type=HAZARD_TYPE)[0].intensity.copy() + fn = helper_impfset(self._config(int_mult=2.0, int_add=5.0)) + result = fn(ifs) + np.testing.assert_allclose( + result.get_func(haz_type=HAZARD_TYPE)[0].intensity, + original_int * 2.0 + 5.0, + ) + + def test_specific_impf_id_targeted(self, impfset_factory): + ifs = impfset_factory() + original_mdd = ifs.get_func(haz_type=HAZARD_TYPE)[0].mdd.copy() + fn = helper_impfset(self._config(impf_ids=IMPF_ID, mdd_mult=0.0)) + result = fn(ifs) + np.testing.assert_allclose( + result.get_func(haz_type=HAZARD_TYPE)[0].mdd, original_mdd * 0.0 + ) + + def test_non_matching_impf_id_not_modified(self, impfset_factory): + ifs = impfset_factory() + original_mdd = ifs.get_func(haz_type=HAZARD_TYPE)[0].mdd.copy() + fn = helper_impfset(self._config(impf_ids=IMPF_ID + 99, mdd_mult=0.0)) + result = fn(ifs) + np.testing.assert_allclose( + result.get_func(haz_type=HAZARD_TYPE)[0].mdd, original_mdd + ) + + def test_all_keyword_targets_every_function(self, impfset_factory): + ifs = impfset_factory() + fn = helper_impfset(self._config(impf_ids="all", paa_mult=0.0)) + result = fn(ifs) + for impf in result.get_func(haz_type=HAZARD_TYPE): + np.testing.assert_allclose(impf.paa, 0.0) + + def test_invalid_impf_ids_raises_value_error(self, impfset_factory): + ifs = impfset_factory() + fn = helper_impfset(self._config(impf_ids={"invalid": "dict"})) + with pytest.raises(ValueError, match="invalid"): + fn(ifs) + + def test_list_of_ids(self, impfset_factory): + ifs = impfset_factory() + fn = helper_impfset(self._config(impf_ids=[IMPF_ID], mdd_add=1.0)) + original_mdd = ifs.get_func(haz_type=HAZARD_TYPE)[0].mdd.copy() + result = fn(ifs) + np.testing.assert_allclose( + result.get_func(haz_type=HAZARD_TYPE)[0].mdd, original_mdd + 1.0 + ) + + +# =========================================================================== +# helper_exposure +# =========================================================================== + + +class TestHelperExposure: + def _config(self, reassign=None, set_to_zero=None, path=None): + return ExposuresModifierConfig( + new_exposures_path=path, + reassign_impf_id=reassign, + set_to_zero=set_to_zero, + ) + + def test_identity_config_leaves_exposure_unchanged(self, exposures_factory): + exp = exposures_factory() + original_values = exp.gdf["value"].copy() + fn = helper_exposure(self._config()) + result = fn(exp) + np.testing.assert_array_equal( + result.gdf["value"].values, original_values.values + ) + + def test_set_to_zero_by_boolean_mask(self, exposures_factory): + exp = exposures_factory() + mask = exp.gdf["value"] > 2000 + fn = helper_exposure(self._config(set_to_zero=mask)) + result = fn(exp) + assert (result.gdf.loc[mask, "value"] == 0).all() + + def test_set_to_zero_does_not_affect_other_rows(self, exposures_factory): + exp = exposures_factory() + mask = exp.gdf["value"] > 4000 + original_below = exp.gdf.loc[~mask, "value"].copy() + fn = helper_exposure(self._config(set_to_zero=mask)) + result = fn(exp) + np.testing.assert_array_equal( + result.gdf.loc[~mask, "value"].values, original_below.values + ) + + def test_reassign_impf_id_remaps_correctly(self, exposures_factory): + exp = exposures_factory(impf_id=1) + col = f"impf_{HAZARD_TYPE}" + fn = helper_exposure(self._config(reassign={HAZARD_TYPE: {1: 2}})) + result = fn(exp) + assert (result.gdf[col] == 2).all() + + def test_reassign_impf_id_unknown_value_left_unchanged(self, exposures_factory): + exp = exposures_factory(impf_id=1) + col = f"impf_{HAZARD_TYPE}" + fn = helper_exposure(self._config(reassign={HAZARD_TYPE: {99: 2}})) + result = fn(exp) + assert (result.gdf[col] == 1).all() + + def test_combined_set_to_zero_and_reassign(self, exposures_factory): + exp = exposures_factory(impf_id=1) + col = f"impf_{HAZARD_TYPE}" + mask = exp.gdf["value"] > 3000 + fn = helper_exposure( + self._config( + set_to_zero=mask, + reassign={HAZARD_TYPE: {1: 3}}, + ) + ) + result = fn(exp) + assert (result.gdf.loc[mask, "value"] == 0).all() + assert (result.gdf[col] == 3).all() diff --git a/climada/entity/measures/test/test_measure_config.py b/climada/entity/measures/test/test_measure_config.py new file mode 100644 index 0000000000..2fcd45113f --- /dev/null +++ b/climada/entity/measures/test/test_measure_config.py @@ -0,0 +1,425 @@ +""" +This file is part of CLIMADA. + +Copyright (C) 2017 ETH Zurich, CLIMADA contributors listed in AUTHORS. + +CLIMADA is free software: you can redistribute it and/or modify it under the +terms of the GNU General Public License as published by the Free +Software Foundation, version 3. + +CLIMADA is distributed in the hope that it will be useful, but WITHOUT ANY +WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A +PARTICULAR PURPOSE. See the GNU General Public License for more details. + +You should have received a copy of the GNU General Public License along +with CLIMADA. If not, see . + +--- + +Tests for MeasureConfig and related dataclasses. +""" + +# tests/entity/measures/test_measure_config.py + +import warnings +from datetime import datetime + +import pandas as pd +import pytest + +from climada.entity.measures.measure_config import ( + CostIncomeConfig, + ExposuresModifierConfig, + HazardModifierConfig, + ImpfsetModifierConfig, + MeasureConfig, +) + + +@pytest.fixture +def minimal_measure_dict(): + return {"name": "seawall", "haz_type": "TC"} + + +@pytest.fixture +def full_measure_dict(): + return { + "name": "seawall", + "haz_type": "TC", + "haz_int_mult": 0.8, + "haz_int_add": -0.1, + "impf_mdd_mult": 0.9, + "impf_paa_mult": 0.95, + "impf_ids": [1, 2], + "reassign_impf_id": {"TC": {1: 3}}, + "set_to_zero": [10, 20], + "init_cost": 1000.0, + "periodic_cost": 50.0, + "color_rgb": [0.1, 0.5, 0.9], + "implementation_duration": "2Y", + } + + +class TestModifierConfig: + + def test_to_dict_omits_defaults(self): + config = ImpfsetModifierConfig(haz_type="TC") + result = config.to_dict() + assert result == {} + + def test_to_dict_includes_non_defaults_no_omit(self): + config = ImpfsetModifierConfig( + haz_type="TC", impf_mdd_mult=0.5, impf_paa_add=0.1 + ) + result = config.to_dict(omit_default=False) + assert sorted(list(result.keys())) != sorted( + ["haz_type", "impf_mdd_mult", "impf_paa_add"] + ) + assert result["impf_mdd_mult"] == 0.5 + assert result["impf_paa_add"] == 0.1 + + def test_to_dict_includes_non_defaults(self): + config = ImpfsetModifierConfig( + haz_type="TC", impf_mdd_mult=0.5, impf_paa_add=0.1 + ) + result = config.to_dict() + assert sorted(list(result.keys())) == sorted(["impf_mdd_mult", "impf_paa_add"]) + assert result["impf_mdd_mult"] == 0.5 + assert result["impf_paa_add"] == 0.1 + + def test_from_dict_ignores_unknown_keys(self): + d = {"haz_type": "TC", "unknown_field": 99, "another_unknown": "foo"} + config = ImpfsetModifierConfig.from_dict(d) + assert config.haz_type == "TC" + assert not hasattr(config, "unknown_field") + + def test_from_dict_roundtrip(self): + config = ImpfsetModifierConfig( + haz_type="TC", impf_mdd_mult=0.5, impf_paa_add=0.1 + ) + d = {**config.to_dict(), "haz_type": "TC"} + recovered = ImpfsetModifierConfig.from_dict(d) + assert recovered.impf_mdd_mult == config.impf_mdd_mult + assert recovered.impf_paa_add == config.impf_paa_add + + def test_repr_shows_non_defaults_prominently(self): + config = ImpfsetModifierConfig(haz_type="TC", impf_mdd_mult=0.5) + r = repr(config) + assert "Non default fields" in r + assert "impf_mdd_mult" in r + + def test_repr_empty_when_all_defaults(self): + config = ImpfsetModifierConfig(haz_type="TC") + r = repr(config) + assert "Non default fields" not in r + + +class TestImpfsetModifierConfig: + + def test_config_defaults(self): + config = ImpfsetModifierConfig(haz_type="TC") + assert config.impf_ids is None + assert config.impf_mdd_mult == 1.0 + assert config.impf_mdd_add == 0.0 + assert config.impf_paa_mult == 1.0 + assert config.impf_paa_add == 0.0 + assert config.impf_int_mult == 1.0 + assert config.impf_int_add == 0.0 + assert config.new_impfset_path is None + + def test_config_from_dict_roundtrip(self): + config = ImpfsetModifierConfig( + haz_type="TC", impf_mdd_mult=0.8, impf_ids=[1, 2] + ) + d = {**config.to_dict(), "haz_type": "TC"} + recovered = ImpfsetModifierConfig.from_dict(d) + assert recovered.impf_mdd_mult == config.impf_mdd_mult + assert recovered.impf_ids == config.impf_ids + + def test_config_to_dict_roundtrip(self): + d = {"haz_type": "TC", "impf_mdd_mult": 0.8, "impf_paa_add": 0.05} + config = ImpfsetModifierConfig.from_dict(d) + result = {**config.to_dict(), "haz_type": "TC"} + assert result["impf_mdd_mult"] == d["impf_mdd_mult"] + assert result["impf_paa_add"] == d["impf_paa_add"] + + def test_config_warns_when_path_and_modifiers_combined(self): + with pytest.warns(UserWarning): + ImpfsetModifierConfig( + haz_type="TC", + new_impfset_path="path/to/file.xlsx", + impf_mdd_mult=0.5, + ) + + def test_config_no_warning_when_only_path(self): + with warnings.catch_warnings(): + warnings.simplefilter("error") + ImpfsetModifierConfig(haz_type="TC", new_impfset_path="path/to/file.xlsx") + + def test_config_no_warning_when_only_modifiers(self): + with warnings.catch_warnings(): + warnings.simplefilter("error") + ImpfsetModifierConfig(haz_type="TC", impf_mdd_mult=0.5) + + def test_config_impf_ids_accepts_int(self): + config = ImpfsetModifierConfig(haz_type="TC", impf_ids=1) + assert config.impf_ids == 1 + + def test_config_impf_ids_accepts_str(self): + config = ImpfsetModifierConfig(haz_type="TC", impf_ids="1") + assert config.impf_ids == "1" + + def test_config_impf_ids_accepts_list(self): + config = ImpfsetModifierConfig(haz_type="TC", impf_ids=[1, 2, "3"]) + assert config.impf_ids == [1, 2, "3"] + + def test_config_impf_ids_accepts_none(self): + config = ImpfsetModifierConfig(haz_type="TC", impf_ids=None) + assert config.impf_ids is None + + +class TestHazardModifierConfig: + + def test_config_defaults(self): + config = HazardModifierConfig(haz_type="TC") + assert config.haz_int_mult == 1.0 + assert config.haz_int_add == 0.0 + assert config.new_hazard_path is None + assert config.impact_rp_cutoff is None + + def test_config_from_dict_roundtrip(self): + config = HazardModifierConfig(haz_type="TC", haz_int_mult=0.8, haz_int_add=-0.1) + d = {**config.to_dict(), "haz_type": "TC"} + recovered = HazardModifierConfig.from_dict(d) + assert recovered.haz_int_mult == config.haz_int_mult + assert recovered.haz_int_add == config.haz_int_add + + def test_config_to_dict_roundtrip(self): + d = {"haz_type": "TC", "haz_int_mult": 0.7, "haz_int_add": -0.2} + config = HazardModifierConfig.from_dict(d) + result = {**config.to_dict(), "haz_type": "TC"} + assert result["haz_int_mult"] == d["haz_int_mult"] + assert result["haz_int_add"] == d["haz_int_add"] + + def test_config_warns_when_path_and_modifiers_combined(self): + with pytest.warns(UserWarning): + HazardModifierConfig( + haz_type="TC", + new_hazard_path="path/to/hazard.h5", + haz_int_mult=0.5, + ) + + def test_config_warns_when_path_and_rp_cutoff_combined(self): + with pytest.warns(UserWarning): + HazardModifierConfig( + haz_type="TC", + new_hazard_path="path/to/hazard.h5", + impact_rp_cutoff=100.0, + ) + + def test_config_no_warning_when_only_path(self): + with warnings.catch_warnings(): + warnings.simplefilter("error") + HazardModifierConfig(haz_type="TC", new_hazard_path="path/to/hazard.h5") + + def test_config_no_warning_when_only_modifiers(self): + with warnings.catch_warnings(): + warnings.simplefilter("error") + HazardModifierConfig(haz_type="TC", haz_int_mult=0.5) + + +class TestExposuresModifierConfig: + + def test_config_defaults(self): + config = ExposuresModifierConfig() + assert config.reassign_impf_id is None + assert config.set_to_zero is None + assert config.new_exposures_path is None + + def test_config_from_dict_roundtrip(self): + config = ExposuresModifierConfig( + reassign_impf_id={"TC": {1: 2}}, + set_to_zero=[10, 20], + ) + d = config.to_dict() + recovered = ExposuresModifierConfig.from_dict(d) + assert recovered.reassign_impf_id == config.reassign_impf_id + assert recovered.set_to_zero == config.set_to_zero + + def test_config_to_dict_roundtrip(self): + d = {"reassign_impf_id": {"TC": {1: 2}}, "set_to_zero": [5, 6]} + config = ExposuresModifierConfig.from_dict(d) + result = config.to_dict() + assert result["reassign_impf_id"] == d["reassign_impf_id"] + assert result["set_to_zero"] == d["set_to_zero"] + + def test_config_warns_when_path_and_modifiers_combined(self): + with pytest.warns(UserWarning): + ExposuresModifierConfig( + new_exposures_path="path/to/exp.h5", + reassign_impf_id={"TC": {1: 2}}, + ) + + def test_config_no_warning_when_only_path(self): + with warnings.catch_warnings(): + warnings.simplefilter("error") + ExposuresModifierConfig(new_exposures_path="path/to/exp.h5") + + def test_config_no_warning_when_only_modifiers(self): + with warnings.catch_warnings(): + warnings.simplefilter("error") + ExposuresModifierConfig(reassign_impf_id={"TC": {1: 2}}) + + def test_config_reassign_impf_id_accepts_int_keys(self): + config = ExposuresModifierConfig(reassign_impf_id={"TC": {1: 2}}) + assert config.reassign_impf_id == {"TC": {1: 2}} + + def test_config_reassign_impf_id_accepts_str_keys(self): + config = ExposuresModifierConfig(reassign_impf_id={"TC": {"1": "2"}}) + assert config.reassign_impf_id == {"TC": {"1": "2"}} + + def test_config_set_to_zero_accepts_none(self): + config = ExposuresModifierConfig(set_to_zero=None) + assert config.set_to_zero is None + + def test_config_set_to_zero_accepts_list(self): + config = ExposuresModifierConfig(set_to_zero=[1, 2, 3]) + assert config.set_to_zero == [1, 2, 3] + + +class TestCostIncomeConfig: + + def test_config_defaults(self): + config = CostIncomeConfig() + assert config.init_cost == 0.0 + assert config.periodic_cost == 0.0 + assert config.periodic_income == 0.0 + assert config.cost_yearly_growth_rate == 0.0 + assert config.income_yearly_growth_rate == 0.0 + assert config.freq == "Y" + assert config.custom_cash_flows is None + + def test_config_default_mkt_price_year_is_current_year(self): + config = CostIncomeConfig() + assert config.mkt_price_year == datetime.today().year + + def test_config_from_dict_roundtrip(self): + config = CostIncomeConfig(init_cost=1000.0, periodic_cost=50.0, freq="M") + d = config.to_dict() + recovered = CostIncomeConfig.from_dict(d) + assert recovered.init_cost == config.init_cost + assert recovered.periodic_cost == config.periodic_cost + assert recovered.freq == config.freq + + def test_config_to_dict_roundtrip(self): + d = {"init_cost": 500.0, "periodic_income": 20.0, "freq": "M"} + config = CostIncomeConfig.from_dict(d) + result = config.to_dict() + assert result["init_cost"] == d["init_cost"] + assert result["periodic_income"] == d["periodic_income"] + assert result["freq"] == d["freq"] + + +class TestMeasureConfig: + + def test_from_dict_minimal(self, minimal_measure_dict): + config = MeasureConfig.from_dict(minimal_measure_dict) + assert config.name == "seawall" + assert config.haz_type == "TC" + assert config.impfset_modifier == ImpfsetModifierConfig(haz_type="TC") + assert config.hazard_modifier == HazardModifierConfig(haz_type="TC") + assert config.exposures_modifier == ExposuresModifierConfig() + assert config.cost_income == CostIncomeConfig() + + def test_from_dict_full(self, full_measure_dict): + config = MeasureConfig.from_dict(full_measure_dict) + assert config.hazard_modifier.haz_int_mult == full_measure_dict["haz_int_mult"] + assert ( + config.impfset_modifier.impf_mdd_mult == full_measure_dict["impf_mdd_mult"] + ) + assert config.exposures_modifier.set_to_zero == full_measure_dict["set_to_zero"] + assert config.cost_income.init_cost == full_measure_dict["init_cost"] + assert config.color_rgb == tuple(full_measure_dict["color_rgb"]) + assert ( + config.implementation_duration + == full_measure_dict["implementation_duration"] + ) + + def test_from_dict_ignores_unknown_keys(self, minimal_measure_dict): + d = {**minimal_measure_dict, "completely_unknown": 42} + config = MeasureConfig.from_dict(d) + assert config.name == "seawall" + assert not hasattr(config, "completely_unknown") + + def test_to_dict_roundtrip(self, full_measure_dict): + config = MeasureConfig.from_dict(full_measure_dict) + recovered = MeasureConfig.from_dict(config.to_dict()) + assert recovered.name == config.name + assert recovered.haz_type == config.haz_type + assert recovered.hazard_modifier == config.hazard_modifier + assert recovered.impfset_modifier == config.impfset_modifier + assert recovered.exposures_modifier == config.exposures_modifier + assert recovered.color_rgb == config.color_rgb + assert recovered.implementation_duration == config.implementation_duration + + def test_to_dict_color_rgb_none(self, minimal_measure_dict): + config = MeasureConfig.from_dict(minimal_measure_dict) + result = config.to_dict() + assert result["color_rgb"] is None + + def test_to_dict_color_rgb_set(self, minimal_measure_dict): + config = MeasureConfig.from_dict( + {**minimal_measure_dict, "color_rgb": [0.1, 0.5, 0.9]} + ) + result = config.to_dict() + assert result["color_rgb"] == [0.1, 0.5, 0.9] + + def test_to_yaml_roundtrip(self, tmp_path, full_measure_dict): + path = str(tmp_path / "measure.yaml") + config = MeasureConfig.from_dict(full_measure_dict) + config.to_yaml(path) + recovered = MeasureConfig.from_yaml(path) + assert recovered.name == config.name + assert recovered.haz_type == config.haz_type + assert recovered.hazard_modifier == config.hazard_modifier + assert recovered.impfset_modifier == config.impfset_modifier + assert recovered.color_rgb == config.color_rgb + + def test_from_yaml_reads_first_entry(self, tmp_path, full_measure_dict): + import yaml + + second = {**full_measure_dict, "name": "second_measure"} + path = str(tmp_path / "measures.yaml") + with open(path, "w") as f: + yaml.dump({"measures": [full_measure_dict, second]}, f) + config = MeasureConfig.from_yaml(path) + assert config.name == full_measure_dict["name"] + + def test_from_row_roundtrip(self, full_measure_dict): + config = MeasureConfig.from_dict(full_measure_dict) + row = pd.Series(config.to_dict()) + recovered = MeasureConfig.from_row(row) + assert recovered.name == config.name + assert recovered.hazard_modifier == config.hazard_modifier + assert recovered.impfset_modifier == config.impfset_modifier + + def test_from_row_ignores_extra_columns(self, full_measure_dict): + config = MeasureConfig.from_dict(full_measure_dict) + d = {**config.to_dict(), "extra_column": "garbage"} + row = pd.Series(d) + recovered = MeasureConfig.from_row(row) + assert recovered.name == config.name + + def test_sub_configs_correctly_dispatched(self, full_measure_dict): + config = MeasureConfig.from_dict(full_measure_dict) + assert config.hazard_modifier.haz_int_mult == full_measure_dict["haz_int_mult"] + assert ( + config.impfset_modifier.impf_mdd_mult == full_measure_dict["impf_mdd_mult"] + ) + assert ( + config.exposures_modifier.reassign_impf_id + == full_measure_dict["reassign_impf_id"] + ) + assert config.cost_income.init_cost == full_measure_dict["init_cost"] + assert not hasattr(config.hazard_modifier, "impf_mdd_mult") + assert not hasattr(config.impfset_modifier, "haz_int_mult") diff --git a/climada/entity/measures/types.py b/climada/entity/measures/types.py new file mode 100644 index 0000000000..1ad90986b1 --- /dev/null +++ b/climada/entity/measures/types.py @@ -0,0 +1,10 @@ +from collections.abc import Callable +from typing import Concatenate + +from climada.entity.exposures.base import Exposures +from climada.entity.impact_funcs.impact_func_set import ImpactFuncSet +from climada.hazard.base import Hazard + +HazardChange = Callable[Concatenate[Hazard, ...], Hazard] +ImpfsetChange = Callable[Concatenate[ImpactFuncSet, ...], ImpactFuncSet] +ExposuresChange = Callable[Concatenate[Exposures, ...], Exposures] diff --git a/climada/entity/test/test_entity.py b/climada/entity/test/test_entity.py index 7805a24e70..4a88fc531a 100644 --- a/climada/entity/test/test_entity.py +++ b/climada/entity/test/test_entity.py @@ -24,11 +24,11 @@ import numpy as np from climada import CONFIG +from climada.entity._legacy_measures.measure_set import MeasureSet from climada.entity.disc_rates.base import DiscRates from climada.entity.entity_def import Entity from climada.entity.exposures.base import Exposures from climada.entity.impact_funcs.impact_func_set import ImpactFuncSet -from climada.entity.measures.measure_set import MeasureSet from climada.util.constants import ENT_TEMPLATE_XLS ENT_TEST_MAT = CONFIG.exposures.test_data.dir().joinpath("demo_today.mat") diff --git a/climada/hazard/base.py b/climada/hazard/base.py index abcbae2e83..b06c22e989 100644 --- a/climada/hazard/base.py +++ b/climada/hazard/base.py @@ -554,15 +554,6 @@ def local_exceedance_intensity( self.frequency_unit ) - # check method - if method not in [ - "interpolate", - "extrapolate", - "extrapolate_constant", - "stepfunction", - ]: - raise ValueError(f"Unknown method: {method}") - # calculate local exceedance intensity test_frequency = 1 / np.array(return_periods) @@ -707,15 +698,6 @@ def local_return_period( self.frequency_unit ) - # check method - if method not in [ - "interpolate", - "extrapolate", - "extrapolate_constant", - "stepfunction", - ]: - raise ValueError(f"Unknown method: {method}") - return_periods = np.full( (self.intensity.shape[1], len(threshold_intensities)), np.nan ) diff --git a/climada/hazard/tc_tracks.py b/climada/hazard/tc_tracks.py index a8027cd714..a37431d833 100644 --- a/climada/hazard/tc_tracks.py +++ b/climada/hazard/tc_tracks.py @@ -2281,7 +2281,9 @@ def compute_track_density( wind_min: float = None, wind_max: float = None, ) -> tuple[np.ndarray, np.ndarray, np.ndarray]: - """Compute tropical cyclone track density. Before using this function, + """Compute tropical cyclone track density. + + Before using this function, apply the same temporal resolution to all tracks by calling :py:meth:`equal_timestep` on the TCTrack object. Due to the computational cost of the this function, it is not recommended to use a grid resolution higher tha 0.1°. Also note that the time step (in hours) @@ -2291,7 +2293,7 @@ def compute_track_density( it returns the absolute count per bin. To plot the output of this function, use :py:meth:`plot_track_density`. - Parameters: + Parameters ---------- tc_track: TCTracks object track object containing a list of all tracks @@ -2310,7 +2312,8 @@ def compute_track_density( wind_max: float (optional), default: None Maximal wind speed below which to select tracks (exclusive if wind_min is also provided, otherwise inclusive). - Returns: + + Returns ------- hist_count: np.ndarray 2D matrix containing the absolute count per grid cell of track point. @@ -2319,8 +2322,8 @@ def compute_track_density( lon_bins: np.ndarray longitude bins in which the point were counted - Example: - -------- + Example + ------- >>> tc_tracks = TCTrack.from_ibtracs_netcdf("path_to_file") >>> tc_tracks.equal_timestep(time_step_h = 1) >>> hist_count, _, _ = compute_track_density(tc_track = tc_tracks, res = 2) diff --git a/climada/hazard/tc_tracks_synth.py b/climada/hazard/tc_tracks_synth.py index 52388fbfaf..a397473fc0 100644 --- a/climada/hazard/tc_tracks_synth.py +++ b/climada/hazard/tc_tracks_synth.py @@ -1049,7 +1049,7 @@ def _apply_decay_coeffs(track, v_rel, p_rel, land_geom, s_rel): # if there is no further landfall, correct until the end of # the track end_cor = track["time"].size - rndn = 0.1 * float(np.abs(np.random.normal(size=1) * 5) + 6) + rndn = 0.1 * float(np.abs(np.random.default_rng().normal() * 5) + 6) r_diff = ( track["central_pressure"][land_sea].values - track["central_pressure"][land_sea - 1].values diff --git a/climada/hazard/test/test_xarray.py b/climada/hazard/test/test_xarray.py index cf481e3c44..3e3e0799c4 100644 --- a/climada/hazard/test/test_xarray.py +++ b/climada/hazard/test/test_xarray.py @@ -149,8 +149,11 @@ def _load_and_assert(**kwargs): def test_type_error(self): """Calling 'from_xarray_raster' with wrong data type should throw""" # Passing a DataArray - with xr.open_dataset(self.netcdf_path) as dset, self.assertRaisesRegex( - TypeError, "This method only supports passing xr.Dataset" + with ( + xr.open_dataset(self.netcdf_path) as dset, + self.assertRaisesRegex( + TypeError, "This method only supports passing xr.Dataset" + ), ): Hazard.from_xarray_raster(dset["intensity"], "", "") diff --git a/climada/hazard/xarray.py b/climada/hazard/xarray.py index df7fc9bf67..95b20eee5e 100644 --- a/climada/hazard/xarray.py +++ b/climada/hazard/xarray.py @@ -58,7 +58,9 @@ def _to_csr_matrix(array: xr.DataArray) -> sparse.csr_matrix: output_dtypes=[array.dtype], ) sparse_coo = array.compute().data # Load into memory - return sparse_coo.tocsr() # Convert sparse.COO to scipy.sparse.csr_matrix + return sparse.csr_matrix( + sparse_coo.tocsr() + ) # Convert sparse.COO to scipy.sparse.csr_matrix # Define accessors for xarray DataArrays diff --git a/climada/test/conftest.py b/climada/test/conftest.py new file mode 100644 index 0000000000..727215b2f2 --- /dev/null +++ b/climada/test/conftest.py @@ -0,0 +1,308 @@ +""" +This file is part of CLIMADA. + +Copyright (C) 2017 ETH Zurich, CLIMADA contributors listed in AUTHORS. + +CLIMADA is free software: you can redistribute it and/or modify it under the +terms of the GNU General Public License as published by the Free +Software Foundation, version 3. + +CLIMADA is distributed in the hope that it will be useful, but WITHOUT ANY +WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A +PARTICULAR PURPOSE. See the GNU General Public License for more details. + +You should have received a copy of the GNU General Public License along +with CLIMADA. If not, see . +--- + +A set of reusable fixtures for testing purpose. + +The objective of this file is to provide minimalistic, understandable and consistent +default objects for unit and integration testing. + +Values are chosen such that: + - Exposure value of the first points is 0. (First location should always have 0 impacts) + - Category / Group id of all points is 1, except for third point, valued at 2000 (Impacts on that category are always a share of 2000) + - Hazard centroids are the exposure centroids shifted by `HAZARD_JITTER` on both lon and lat. + - There are 4 events, with frequencies == 0.03, 0.01, 0.006, 0.004, 0, + such that impacts for RP250, 100 and 50 and 20 are at_event, + (freq sorted cumulate to 1/250, 1/100, 1/50 and 1/20). + - Hazard intensity is: + * Event 1: zero everywhere (always no impact) + * Event 2: max intensity at first centroid (also always no impact (first centroid is 0)) + * Event 3: half max intensity at second centroid (impact == half second centroid) + * Event 4: quarter max intensity everywhere (impact == 1/4 total value) + * Event 5: max intensity everywhere (but zero frequency) + With max intensity set at 100 + - Impact function is the "identity function", x intensity is x% damages + - Impact values should be: + * AAI = 18 = 1000*1/2*0.006+(1000+2000+3000+4000+5000)*0.25*0.004 + * RP20 = event1 = 0 + * RP50 = event2 = 0 + * RP100 = event3 = 500 = 1000*1/2 + * RP250 = event4 = 3750 = (1000+2000+3000+4000+5000)*0.25 + +""" + +import geopandas as gpd +import numpy as np +import pytest +from scipy.sparse import csr_matrix +from shapely.geometry import Point + +from climada.entity import Exposures, ImpactFunc, ImpactFuncSet +from climada.hazard import Centroids, Hazard + +# --------------------------------------------------------------------------- +# Coordinate system and metadata +# --------------------------------------------------------------------------- +CRS_WGS84 = "EPSG:4326" + +# --------------------------------------------------------------------------- +# Exposure attributes +# --------------------------------------------------------------------------- +EXP_DESC = "Test exposure dataset" +EXPOSURE_REF_YEAR = 2020 +EXPOSURE_VALUE_UNIT = "USD" +VALUES = np.array([0, 1000, 2000, 3000, 4000, 5000]) +CATEGORIES = np.array([1, 1, 2, 1, 1, 3]) + +# Exposure coordinates +EXP_LONS = np.array([4, 4.25, 4.5, 4, 4.25, 4.5]) +EXP_LATS = np.array([33, 33, 33, 33.25, 33.25, 33.25]) + +# --------------------------------------------------------------------------- +# Hazard definition +# --------------------------------------------------------------------------- +HAZARD_TYPE = "TEST_HAZARD_TYPE" +HAZARD_UNIT = "TEST_HAZARD_UNIT" + +# Hazard centroid positions +HAZ_JITTER = 0.1 # To test centroid matching +HAZ_LONS = EXP_LONS + HAZ_JITTER +HAZ_LATS = EXP_LATS + HAZ_JITTER + +# Hazard events +EVENT_IDS = np.array([1, 2, 3, 4, 5]) +EVENT_NAMES = ["ev1", "ev2", "ev3", "ev4", "ev5"] +DATES = np.array([1, 2, 3, 4, 5]) + +# Frequency are choosen so that they cumulate nicely +# to correspond to 250, 100, 50, and 20y return periods (for impacts) +FREQUENCY = np.array([0.03, 0.01, 0.006, 0.004, 0.0]) +FREQUENCY_UNIT = "1/year" + +# Hazard maximum intensity +# 100 to match 0 to 100% idea +# also in line with linear 1:1 impact function +# for easy mental calculus +HAZARD_MAX_INTENSITY = 100 + +# --------------------------------------------------------------------------- +# Impact function +# --------------------------------------------------------------------------- +IMPF_ID = 1 +IMPF_NAME = "IMPF_1" + + +@pytest.fixture +def exposures_factory(): + def _make_exposures( + values=VALUES, + exp_lons=EXP_LONS, + exp_lats=EXP_LATS, + value_factor=1.0, + ref_year=EXPOSURE_REF_YEAR, + hazard_type=HAZARD_TYPE, + group_id=None, + crs=CRS_WGS84, + impf_id=IMPF_ID, + description=EXP_DESC, + value_unit=EXPOSURE_VALUE_UNIT, + categories=None, + ): + gdf = gpd.GeoDataFrame( + { + "value": values * value_factor, + f"impf_{hazard_type}": impf_id, + "category": categories, + "geometry": gpd.points_from_xy(exp_lons, exp_lats, crs=crs), + }, + crs=crs, + ) + if group_id is not None: + gdf["group_id"] = group_id + + return Exposures( + data=gdf, + description=description, + ref_year=ref_year, + value_unit=value_unit, + ) + + return _make_exposures + + +@pytest.fixture +def exposures(exposures_factory): + return exposures_factory() + + +def hazard_frequency_factory(base=FREQUENCY): + def _make_frequency(scale=1.0): + return base * scale + + return _make_frequency + + +def hazard_frequency(): + return hazard_frequency_factory() + + +def hazard_intensity(max_intensity=HAZARD_MAX_INTENSITY, scale=1.0): + """ + Intensity matrix designed for analytical expectations: + - Event 1: zero + - Event 2: max intensity at first centroid + - Event 3: half max intensity at second centroid + - Event 4: quarter max intensity everywhere + """ + base = csr_matrix( + [ + [0, 0, 0, 0, 0, 0], + [max_intensity, 0, 0, 0, 0, 0], + [0, max_intensity / 2, 0, 0, 0, 0], + [ + max_intensity / 4, + max_intensity / 4, + max_intensity / 4, + max_intensity / 4, + max_intensity / 4, + max_intensity / 4, + ], + [ + max_intensity, + max_intensity, + max_intensity, + max_intensity, + max_intensity, + max_intensity, + ], + ] + ) + + return base * scale + + +@pytest.fixture +def centroids(): + return Centroids(lat=HAZ_LATS, lon=HAZ_LONS, crs=CRS_WGS84) + + +@pytest.fixture +def hazard_factory(): + def _make_hazard( + intensity_matrix=None, + frequency_array=FREQUENCY, + max_intensity=HAZARD_MAX_INTENSITY, + centroids=None, + intensity_scale=1.0, + frequency_scale=1.0, + hazard_type=HAZARD_TYPE, + hazard_unit=HAZARD_UNIT, + lat=HAZ_LATS, + lon=HAZ_LONS, + crs=CRS_WGS84, + event_id=EVENT_IDS, + event_name=EVENT_NAMES, + date=DATES, + frequency_unit=FREQUENCY_UNIT, + ): + if intensity_matrix is None: + intensity_matrix = hazard_intensity(max_intensity, intensity_scale) + + if centroids is None: + centroids = Centroids(lat=lat, lon=lon, crs=crs) + + return Hazard( + haz_type=hazard_type, + units=hazard_unit, + centroids=centroids, + event_id=event_id, + event_name=event_name, + date=date, + frequency=frequency_array * frequency_scale, + frequency_unit=frequency_unit, + intensity=intensity_matrix, + ) + + return _make_hazard + + +@pytest.fixture +def hazard(hazard_factory): + return hazard_factory() + + +@pytest.fixture +def impf_factory(): + def _make_impf( + paa_scale=1.0, + max_intensity=HAZARD_MAX_INTENSITY, + hazard_type=HAZARD_TYPE, + hazard_unit=HAZARD_UNIT, + impf_id=IMPF_ID, + negative_intensities=False, + ): + intensity = np.array([0, max_intensity / 2, max_intensity]) + mdd = np.array([0, 0.5, 1]) + if negative_intensities: + intensity = np.flip(intensity) * -1 + mdd = np.flip(mdd) + return ImpactFunc( + haz_type=hazard_type, + intensity_unit=hazard_unit, + name=IMPF_NAME, + intensity=intensity, + mdd=mdd, + paa=np.array([1, 1, 1]) * paa_scale, + id=impf_id, + ) + + return _make_impf + + +@pytest.fixture +def linear_impact_function(impf_factory): + return impf_factory() + + +@pytest.fixture +def impfset_factory(impf_factory): + def _make_impfset( + paa_scale=1.0, + max_intensity=HAZARD_MAX_INTENSITY, + hazard_type=HAZARD_TYPE, + hazard_unit=HAZARD_UNIT, + impf_id=IMPF_ID, + negative_intensities=False, + ): + return ImpactFuncSet( + [ + impf_factory( + paa_scale, + max_intensity, + hazard_type, + hazard_unit, + impf_id, + negative_intensities, + ) + ] + ) + + return _make_impfset + + +@pytest.fixture +def impfset(impfset_factory): + return impfset_factory() diff --git a/climada/test/test_api_client.py b/climada/test/test_api_client.py index 26ce163fdb..fd77984d48 100644 --- a/climada/test/test_api_client.py +++ b/climada/test/test_api_client.py @@ -177,14 +177,14 @@ def test_get_exposures(self): "fin_mode": "pop", "exponents": "(0,1)", }, - version="v1", + version="v3", dump_dir=DATA_DIR, ) self.assertEqual(len(exposures.gdf), 5782) self.assertEqual(np.unique(exposures.region_id), 40) self.assertEqual( exposures.description, - "LitPop Exposure for ['AUT'] at 150 as, year: 2018, financial mode: pop, exp: [0, 1], admin1_calc: False", + "LitPop Exposure for ['AUT'] at 150 as, year: 2018, financial mode: pop, exp: (0, 1), admin1_calc: False", ) def test_get_exposures_fails(self): @@ -264,12 +264,12 @@ def test_get_hazard_fails(self): def test_get_litpop(self): client = Client() - litpop = client.get_litpop(country="LUX", version="v1", dump_dir=DATA_DIR) + litpop = client.get_litpop(country="LUX", version="v3", dump_dir=DATA_DIR) self.assertEqual(len(litpop.gdf), 188) self.assertEqual(np.unique(litpop.region_id), 442) self.assertEqual( litpop.description, - "LitPop Exposure for ['LUX'] at 150 as, year: 2018, financial mode: pc, exp: [1, 1], admin1_calc: False", + "LitPop Exposure for ['LUX'] at 150 as, year: 2018, financial mode: pc, exp: (1, 1), admin1_calc: False", ) def test_get_litpop_fail(self): diff --git a/climada/test/test_engine.py b/climada/test/test_engine.py index 7b7256ad0f..82bf1e1106 100644 --- a/climada/test/test_engine.py +++ b/climada/test/test_engine.py @@ -34,14 +34,15 @@ from climada.entity import Exposures, ImpactFunc, ImpactFuncSet from climada.entity.entity_def import Entity from climada.hazard import Hazard +from climada.test import get_test_file from climada.util.constants import ( ENT_DEMO_FUTURE, ENT_DEMO_TODAY, - EXP_DEMO_H5, HAZ_DEMO_H5, ) DATA_DIR = CONFIG.engine.test_data.dir() +EXP_DEMO_H5 = get_test_file("exp_demo_today", file_format="hdf5") EMDAT_TEST_CSV = DATA_DIR.joinpath("emdat_testdata_BGD_USA_1970-2017.csv") diff --git a/climada/test/test_measures.py b/climada/test/test_measures.py new file mode 100644 index 0000000000..d5f6c22649 --- /dev/null +++ b/climada/test/test_measures.py @@ -0,0 +1,306 @@ +""" +Integration tests for the Measure class. + +These tests use real CLIMADA objects (Exposures, ImpactFuncSet, Hazard) built +from the shared conftest fixtures. They verify that Measure transformations +produce analytically expected results rather than mocking internals. + +Expected impact values (from conftest docstring): + AAI = 18 + RP20 = 0 (event 1, freq 0.03) + RP50 = 0 (event 2, freq 0.01) + RP100 = 500 (event 3, freq 0.006 → 1000 * 0.5) + RP250 = 3750 (event 4, freq 0.004 → 15000 * 0.25) +""" + +import numpy as np +import pytest +from scipy.sparse import csr_matrix + +from climada.entity.measures.base import Measure +from climada.entity.measures.cost_income import CostIncome +from climada.test.conftest import HAZARD_TYPE + +# =========================================================================== +# apply() — structural / immutability tests +# =========================================================================== + + +class TestApplyStructural: + """Verify that apply() correctly transforms the risk triplet.""" + + def test_identity_measure_returns_equal_objects(self, exposures, impfset, hazard): + """An identity measure should return objects equal to the originals.""" + m = Measure("identity") + new_exp, new_impfset, new_haz = m.apply(exposures, impfset, hazard) + + assert new_exp.gdf["value"].tolist() == exposures.gdf["value"].tolist() + assert new_haz.intensity.nnz == hazard.intensity.nnz + assert list(new_impfset.get_func(haz_type=HAZARD_TYPE)) == list( + impfset.get_func(haz_type=HAZARD_TYPE) + ) + + def test_enforce_copy_true_does_not_mutate_originals( + self, exposures, impfset, hazard + ): + """With enforce_copy=True, the originals must not be mutated.""" + original_values = exposures.gdf["value"].copy() + + def double_values(exp, **kw): + exp.gdf["value"] *= 2 + return exp + + m = Measure("doubler", exposures_changes=double_values) + m.apply(exposures, impfset, hazard, enforce_copy=True) + + np.testing.assert_array_equal( + exposures.gdf["value"].values, original_values.values + ) + + def test_enforce_copy_false_mutates_original(self, exposures, impfset, hazard): + """With enforce_copy=False the change function receives the original object.""" + received_ids = [] + + def record_id(exp, **kw): + received_ids.append(id(exp)) + return exp + + m = Measure("recorder", exposures_changes=record_id) + m.apply(exposures, impfset, hazard, enforce_copy=False) + + assert received_ids[0] == id(exposures) + + def test_base_triplet_available_as_kwargs(self, exposures, impfset, hazard): + """Each change function receives base_exposures/impfset/hazard as kwargs.""" + received = {} + + def capture(**kwargs): + def fn(obj, **kw): + received.update(kw) + return obj + + return fn + + m = Measure( + "capture", + exposures_changes=capture(), + ) + m.apply(exposures, impfset, hazard, enforce_copy=False) + + assert "base_exposures" in received + assert "base_impfset" in received + assert "base_hazard" in received + + +# =========================================================================== +# Exposures transformations +# =========================================================================== + + +class TestExposuresTransformations: + """Measures that modify Exposures.""" + + def test_scale_values(self, exposures, impfset, hazard): + """Scaling exposure values by 0.5 should halve every entry.""" + + def scale_half(exp, **kw): + exp.gdf["value"] *= 0.5 + return exp + + m = Measure("scale_half", exposures_changes=scale_half) + new_exp, _, _ = m.apply(exposures, impfset, hazard) + + np.testing.assert_allclose( + new_exp.gdf["value"].values, exposures.gdf["value"].values * 0.5 + ) + + def test_zero_all_values(self, exposures, impfset, hazard): + """Zeroing all exposures should produce zero impacts downstream.""" + + def zero_exp(exp, **kw): + exp.gdf["value"] = 0.0 + return exp + + m = Measure("zero_exp", exposures_changes=zero_exp) + new_exp, _, _ = m.apply(exposures, impfset, hazard) + + assert new_exp.gdf["value"].sum() == 0.0 + + def test_exposures_change_uses_base_hazard_kwarg( + self, exposures, impfset, hazard_factory + ): + """Change function can inspect base_hazard via the default kwargs.""" + captured_haz = {} + + def fn(exp, base_hazard=None, **kw): + captured_haz["haz"] = base_hazard + return exp + + haz = hazard_factory() + m = Measure("haz_aware", exposures_changes=fn) + m.apply(exposures, impfset, haz, enforce_copy=False) + + assert captured_haz["haz"] is haz + + +# =========================================================================== +# Hazard transformations +# =========================================================================== + + +class TestHazardTransformations: + """Measures that modify the Hazard.""" + + def test_zero_intensity_eliminates_impacts( + self, exposures, impfset, hazard_factory + ): + """Setting hazard intensity to zero should yield zero impacts.""" + + def zero_intensity(haz, **kw): + haz.intensity = csr_matrix(haz.intensity.shape) + return haz + + haz = hazard_factory() + m = Measure("zero_haz", hazard_changes=zero_intensity) + new_exp, new_impfset, new_haz = m.apply(exposures, impfset, haz) + + assert new_haz.intensity.nnz == 0 + + def test_scale_intensity(self, exposures, impfset, hazard_factory): + """Halving hazard intensity should halve non-zero entries.""" + original_data = hazard_factory().intensity.data.copy() + + def half_intensity(haz, **kw): + haz.intensity = haz.intensity * 0.5 + return haz + + m = Measure("half_haz", hazard_changes=half_intensity) + _, _, new_haz = m.apply(exposures, impfset, hazard_factory()) + + np.testing.assert_allclose(new_haz.intensity.data, original_data * 0.5) + + +# =========================================================================== +# ImpactFuncSet transformations +# =========================================================================== + + +class TestImpfsetTransformations: + """Measures that modify the ImpactFuncSet.""" + + def test_scale_paa_to_zero_eliminates_impacts( + self, exposures, impfset_factory, hazard + ): + """Setting PAA to 0 should make all MDD-based impacts vanish.""" + + def zero_paa(ifs, **kw): + for func in ifs.get_func(haz_type=HAZARD_TYPE): + func.paa = np.zeros_like(func.paa) + return ifs + + impfset = impfset_factory() + m = Measure("zero_paa", impfset_changes=zero_paa) + _, new_impfset, _ = m.apply(exposures, impfset, hazard) + + for func in new_impfset.get_func(haz_type=HAZARD_TYPE): + np.testing.assert_array_equal(func.paa, 0) + + +# =========================================================================== +# Combined / multi-component transformations +# =========================================================================== + + +class TestCombinedTransformations: + """Measures that simultaneously affect more than one risk component.""" + + def test_combined_exposure_and_hazard_change( + self, exposures, impfset, hazard_factory + ): + """Both exposure and hazard transforms should be applied independently.""" + + def double_values(exp, **kw): + exp.gdf["value"] *= 2 + return exp + + def half_intensity(haz, **kw): + haz.intensity = haz.intensity * 0.5 + return haz + + haz = hazard_factory() + original_intensity_data = haz.intensity.data.copy() + + m = Measure( + "combined", + exposures_changes=double_values, + hazard_changes=half_intensity, + ) + new_exp, _, new_haz = m.apply(exposures, impfset, haz) + + np.testing.assert_allclose( + new_exp.gdf["value"].values, exposures.gdf["value"].values * 2 + ) + np.testing.assert_allclose( + new_haz.intensity.data, original_intensity_data * 0.5 + ) + + def test_kwargs_exposures_and_kwargs_hazard_routed_correctly( + self, exposures, impfset, hazard + ): + """kwargs_exposures and kwargs_hazard should reach the right function only.""" + exp_extra, haz_extra = {}, {} + + def track_exp(exp, my_exp_param=None, **kw): + exp_extra["val"] = my_exp_param + return exp + + def track_haz(haz, my_haz_param=None, **kw): + haz_extra["val"] = my_haz_param + return haz + + m = Measure("routed", exposures_changes=track_exp, hazard_changes=track_haz) + m.apply( + exposures, + impfset, + hazard, + enforce_copy=False, + kwargs_exposures={"my_exp_param": "exp_value"}, + kwargs_hazard={"my_haz_param": "haz_value"}, + ) + + assert exp_extra["val"] == "exp_value" + assert haz_extra["val"] == "haz_value" + + +# =========================================================================== +# CostIncome integration +# =========================================================================== + + +class TestCostIncomeIntegration: + """Verify that CostIncome financial data survives through Measure construction.""" + + def test_cost_income_attached(self, exposures, impfset, hazard): + ci = CostIncome(mkt_price_year=2020, init_cost=10_000, periodic_income=500) + m = Measure("with_ci", cost_income=ci) + assert m.cost_income is ci + + def test_total_cost_calculable_after_apply(self, exposures, impfset, hazard): + ci = CostIncome( + mkt_price_year=2020, + init_cost=10_000, + periodic_cost=1_000, + periodic_income=2_000, + ) + m = Measure("ci_measure", cost_income=ci) + m.apply(exposures, impfset, hazard) + + total_net, total_cost, total_inc = m.cost_income.calc_total( + impl_date="2020-01-01", + start_date="2020-01-01", + end_date="2030-01-01", + ) + # Over 10 years: init_cost once + 9 periodic_costs + 10 periodic_incomes + assert total_cost < 0 + assert total_inc > 0 + assert isinstance(total_net, float) diff --git a/climada/test/test_trajectories.py b/climada/test/test_trajectories.py new file mode 100644 index 0000000000..c782e26ba0 --- /dev/null +++ b/climada/test/test_trajectories.py @@ -0,0 +1,942 @@ +""" +This file is part of CLIMADA. + +Copyright (C) 2017 ETH Zurich, CLIMADA contributors listed in AUTHORS. + +CLIMADA is free software: you can redistribute it and/or modify it under the +terms of the GNU General Public License as published by the Free +Software Foundation, version 3. + +CLIMADA is distributed in the hope that it will be useful, but WITHOUT ANY +WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A +PARTICULAR PURPOSE. See the GNU General Public License for more details. + +You should have received a copy of the GNU General Public License along +with CLIMADA. If not, see . +--- + +Test trajectories. + +""" + +import numpy as np +import pandas as pd +import pytest + +from climada.engine.impact_calc import ImpactCalc +from climada.entity.disc_rates.base import DiscRates +from climada.entity.impact_funcs.base import ImpactFunc +from climada.entity.impact_funcs.impact_func_set import ImpactFuncSet +from climada.test.conftest import ( + CATEGORIES, + EXPOSURE_REF_YEAR, + hazard_intensity_factory, +) +from climada.trajectories import InterpolatedRiskTrajectory, StaticRiskTrajectory +from climada.trajectories.constants import ( + AAI_METRIC_NAME, + CONTRIBUTION_BASE_RISK_NAME, + CONTRIBUTION_EXPOSURE_NAME, + CONTRIBUTION_HAZARD_NAME, + CONTRIBUTION_INTERACTION_TERM_NAME, + CONTRIBUTION_VULNERABILITY_NAME, + DATE_COL_NAME, + GROUP_COL_NAME, + MEASURE_COL_NAME, + METRIC_COL_NAME, + NO_MEASURE_VALUE, + PERIOD_COL_NAME, + RISK_COL_NAME, + UNIT_COL_NAME, +) +from climada.trajectories.snapshot import Snapshot +from climada.trajectories.trajectory import DEFAULT_RP + +EXPOSURE_FUTURE_YEAR = 2040 + + +@pytest.fixture(scope="session") +def snapshot_factory( + exposures_factory, + hazard_factory, + impfset_factory, +): + """ + Factory for Snapshot objects. + + Allows controlled construction of baseline / future / counterfactual + scenarios by scaling exposure values, hazard intensity, and impact function. + """ + + def _make_snapshot( + *, + date=EXPOSURE_REF_YEAR, + exposure_value_factor=1.0, + hazard_intensity_factor=1.0, + hazard_frequency_factor=1.0, + paa_scale=1.0, + group_id=None, + negative_intensities=False, + ): + exposures = exposures_factory( + value_factor=exposure_value_factor, ref_year=date, group_id=group_id + ) + + hazard = hazard_factory( + intensity_scale=hazard_intensity_factor, + frequency_scale=hazard_frequency_factor, + ) + + impfset = impfset_factory( + paa_scale=paa_scale, + negative_intensities=negative_intensities, + ) + + return Snapshot( + exposure=exposures, + hazard=hazard, + impfset=impfset, + date=str(date), + ) + + return _make_snapshot + + +@pytest.fixture(scope="session") +def snapshot_base(snapshot_factory): + return snapshot_factory() + + +@pytest.fixture(scope="session") +def snapshot_future(snapshot_factory): + return snapshot_factory( + date=2040, + exposure_value_factor=2.0, + hazard_intensity_factor=2.0, + ) + + +def expected_static_metrics_from_snapshots( + snapshots, return_periods=DEFAULT_RP, disc_rates=None +): + rows = [] + if disc_rates is not None: + discount_factor = pd.Series(index=disc_rates.years, data=1 + disc_rates.rates) + discount_factor = 1 / ((discount_factor.shift(1, fill_value=1)).cumprod()) + else: + discount_factor = None + + for snap in snapshots: + imp = ImpactCalc(**snap.impact_calc_kwargs).impact() + curve = imp.calc_freq_curve(return_periods) + if discount_factor is not None: + discount = discount_factor.loc[pd.Timestamp(str(snap.date)).year] + else: + discount = 1 + rows.append( + [ + pd.Timestamp(str(snap.date)), + "All", + NO_MEASURE_VALUE, + "aai", + "USD", + imp.aai_agg * discount, + ] + ) + + rows.extend( + [ + [ + pd.Timestamp(str(snap.date)), + "All", + NO_MEASURE_VALUE, + f"rp_{rp}", + "USD", + val * discount, + ] + for rp, val in zip(curve.return_per, curve.impact) + ] + ) + if "group_id" in snap.exposure.gdf.columns: + aai_per_group = [ + [ + pd.Timestamp(str(snap.date)), + group, + NO_MEASURE_VALUE, + "aai", + "USD", + val * discount, + ] + for group, val in zip(snap.exposure.gdf["group_id"], imp.eai_exp) + ] + rows.extend(aai_per_group) + + res = pd.DataFrame( + rows, + columns=[ + DATE_COL_NAME, + GROUP_COL_NAME, + MEASURE_COL_NAME, + METRIC_COL_NAME, + UNIT_COL_NAME, + RISK_COL_NAME, + ], + ) + + res = res.groupby( + [ + DATE_COL_NAME, + GROUP_COL_NAME, + MEASURE_COL_NAME, + METRIC_COL_NAME, + UNIT_COL_NAME, + ], + as_index=False, + ).sum() + + return res.set_index( + [ + DATE_COL_NAME, + GROUP_COL_NAME, + MEASURE_COL_NAME, + METRIC_COL_NAME, + UNIT_COL_NAME, + ] + ).sort_index() + + +def test_static_trajectory(snapshot_factory): + present_date = 2020 + future_date = 2040 + + hazard_intensity_factor = 2.0 + exposure_value_factor = 10.0 + + snapshot_base = snapshot_factory( + date=present_date, + ) + + snapshot_fut = snapshot_factory( + date=future_date, + hazard_intensity_factor=hazard_intensity_factor, + exposure_value_factor=exposure_value_factor, + ) + + expected_static_metrics = expected_static_metrics_from_snapshots( + [snapshot_base, snapshot_fut] + ) + static_traj = StaticRiskTrajectory([snapshot_base, snapshot_fut]) + result = ( + static_traj.per_date_risk_metrics() + .set_index( + [ + DATE_COL_NAME, + GROUP_COL_NAME, + MEASURE_COL_NAME, + METRIC_COL_NAME, + UNIT_COL_NAME, + ] + ) + .sort_index() + ) + + # --- Assertion ---------------------------------------------------------- + pd.testing.assert_frame_equal( + result, + expected_static_metrics, + check_index_type=False, + check_categorical=False, + check_like=False, + ) + + +def test_static_trajectory_one_snap(snapshot_factory): + present_date = 2020 + + snapshot_base = snapshot_factory( + date=present_date, + ) + + expected_static_metrics = expected_static_metrics_from_snapshots([snapshot_base]) + static_traj = StaticRiskTrajectory([snapshot_base]) + result = ( + static_traj.per_date_risk_metrics() + .set_index( + [ + DATE_COL_NAME, + GROUP_COL_NAME, + MEASURE_COL_NAME, + METRIC_COL_NAME, + UNIT_COL_NAME, + ] + ) + .sort_index() + ) + + # --- Assertion ---------------------------------------------------------- + pd.testing.assert_frame_equal( + result, + expected_static_metrics, + check_index_type=False, + check_categorical=False, + check_like=False, + ) + + +def test_static_trajectory_with_group(snapshot_factory): + present_date = 2020 + future_date = 2040 + + hazard_intensity_factor = 2.0 + exposure_value_factor = 10.0 + + snapshot_base = snapshot_factory(date=present_date, group_id=CATEGORIES) + + snapshot_fut = snapshot_factory( + date=future_date, + hazard_intensity_factor=hazard_intensity_factor, + exposure_value_factor=exposure_value_factor, + group_id=CATEGORIES, + ) + + expected_static_metrics = expected_static_metrics_from_snapshots( + [snapshot_base, snapshot_fut] + ) + static_traj = StaticRiskTrajectory([snapshot_base, snapshot_fut]) + result = ( + static_traj.per_date_risk_metrics() + .set_index( + [ + DATE_COL_NAME, + GROUP_COL_NAME, + MEASURE_COL_NAME, + METRIC_COL_NAME, + UNIT_COL_NAME, + ] + ) + .sort_index() + ) + + # --- Assertion ---------------------------------------------------------- + pd.testing.assert_frame_equal( + result, + expected_static_metrics, + check_index_type=False, + check_categorical=False, + check_like=False, + ) + + +def test_static_trajectory_change_rp(snapshot_factory): + present_date = 2020 + future_date = 2040 + + hazard_intensity_factor = 2.0 + exposure_value_factor = 10.0 + + snapshot_base = snapshot_factory(date=present_date, group_id=CATEGORIES) + + snapshot_fut = snapshot_factory( + date=future_date, + hazard_intensity_factor=hazard_intensity_factor, + exposure_value_factor=exposure_value_factor, + group_id=CATEGORIES, + ) + + expected_static_metrics = expected_static_metrics_from_snapshots( + [snapshot_base, snapshot_fut], return_periods=[10, 60, 1000] + ) + static_traj = StaticRiskTrajectory( + [snapshot_base, snapshot_fut], return_periods=[10, 60, 1000] + ) + result = ( + static_traj.per_date_risk_metrics() + .set_index( + [ + DATE_COL_NAME, + GROUP_COL_NAME, + MEASURE_COL_NAME, + METRIC_COL_NAME, + UNIT_COL_NAME, + ] + ) + .sort_index() + ) + + # --- Assertion ---------------------------------------------------------- + pd.testing.assert_frame_equal( + result, + expected_static_metrics, + check_index_type=False, + check_categorical=False, + check_like=False, + ) + + # Also check change to other return period + static_traj.return_periods = DEFAULT_RP + expected_static_metrics = expected_static_metrics_from_snapshots( + [snapshot_base, snapshot_fut], return_periods=DEFAULT_RP + ) + result = ( + static_traj.per_date_risk_metrics() + .set_index( + [ + DATE_COL_NAME, + GROUP_COL_NAME, + MEASURE_COL_NAME, + METRIC_COL_NAME, + UNIT_COL_NAME, + ] + ) + .sort_index() + ) + pd.testing.assert_frame_equal( + result, + expected_static_metrics, + check_index_type=False, + check_categorical=False, + check_like=False, + ) + + +def test_static_trajectory_risk_disc_rate(snapshot_base, snapshot_future): + risk_disc_rate = DiscRates( + years=np.array(range(EXPOSURE_REF_YEAR, EXPOSURE_FUTURE_YEAR + 1)), + rates=np.ones(EXPOSURE_FUTURE_YEAR - EXPOSURE_REF_YEAR + 1) * 0.01, + ) + static_traj = StaticRiskTrajectory( + [snapshot_base, snapshot_future], risk_disc_rates=risk_disc_rate + ) + expected_static_metrics = expected_static_metrics_from_snapshots( + [snapshot_base, snapshot_future], disc_rates=risk_disc_rate + ) + + result = ( + static_traj.per_date_risk_metrics() + .set_index( + [ + DATE_COL_NAME, + GROUP_COL_NAME, + MEASURE_COL_NAME, + METRIC_COL_NAME, + UNIT_COL_NAME, + ] + ) + .sort_index() + ) + pd.testing.assert_frame_equal( + result, + expected_static_metrics, + check_index_type=False, + check_categorical=False, + check_like=False, + ) + + # Also check change to other disc_rate + expected_static_metrics = expected_static_metrics_from_snapshots( + [snapshot_base, snapshot_future] + ) + + static_traj.risk_disc_rates = None + result = ( + static_traj.per_date_risk_metrics() + .set_index( + [ + DATE_COL_NAME, + GROUP_COL_NAME, + MEASURE_COL_NAME, + METRIC_COL_NAME, + UNIT_COL_NAME, + ] + ) + .sort_index() + ) + pd.testing.assert_frame_equal( + result, + expected_static_metrics, + check_index_type=False, + check_categorical=False, + check_like=False, + ) + + +# ----------- INTERPOLATED TRAJ ---------------- + + +@pytest.fixture(scope="session") +def snapshot_future_interp(snapshot_factory): + return snapshot_factory( + date=2022, # Closer date for less rows + exposure_value_factor=6.0, + hazard_intensity_factor=2.0, # Different factor for contributors + ) + + +@pytest.fixture(scope="session") +def snapshot_future_interp_vulchange(snapshot_factory): + return snapshot_factory( + date=2022, # Closer date for less rows + exposure_value_factor=6.0, + hazard_intensity_factor=2.0, # Different factor for contributors + paa_scale=0.5, + ) + + +@pytest.fixture(scope="session") +def snapshot_base_neg(snapshot_factory): + return snapshot_factory( + hazard_intensity_factor=-1.0, + negative_intensities=True, + ) + + +@pytest.fixture(scope="session") +def snapshot_future_interp_neg(snapshot_factory): + return snapshot_factory( + date=2022, + exposure_value_factor=6.0, + hazard_intensity_factor=-2.0, + negative_intensities=True, + ) + + +@pytest.fixture(scope="session") +def expected_interp_metrics(): + # fmt: off + return pd.DataFrame.from_dict( + {'index': [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11], + 'columns': [DATE_COL_NAME, GROUP_COL_NAME, MEASURE_COL_NAME, METRIC_COL_NAME, UNIT_COL_NAME, RISK_COL_NAME], + 'data': [[ pd.Period("2020"), 'All',NO_MEASURE_VALUE, 'aai', 'USD', 18.0], + [ pd.Period("2021"), 'All',NO_MEASURE_VALUE, 'aai', 'USD', 94.5], + # Above should indeed not be 216+18 / 2 and slightly + # because as we interpolate each contributor separately, + # the interaction term grows slower. + [ pd.Period("2022"), 'All',NO_MEASURE_VALUE, 'aai', 'USD', 216.0], + [ pd.Period("2020"), 'All',NO_MEASURE_VALUE, 'rp_50', 'USD', 0.0], + [ pd.Period("2021"), 'All',NO_MEASURE_VALUE, 'rp_50', 'USD', 0.0], + [ pd.Period("2022"), 'All',NO_MEASURE_VALUE, 'rp_50', 'USD', 0.0], + [ pd.Period("2020"), 'All',NO_MEASURE_VALUE, 'rp_100', 'USD', 500.0], + [ pd.Period("2021"), 'All',NO_MEASURE_VALUE, 'rp_100', 'USD', 2625.0], + [ pd.Period("2022"), 'All',NO_MEASURE_VALUE, 'rp_100', 'USD', 6000.0], + [ pd.Period("2020"), 'All',NO_MEASURE_VALUE, 'rp_250', 'USD', 3750.0], + [ pd.Period("2021"), 'All',NO_MEASURE_VALUE, 'rp_250', 'USD', 19687.5], + [ pd.Period("2022"), 'All',NO_MEASURE_VALUE, 'rp_250', 'USD', 45000.0]], + 'index_names': [None], + 'column_names': [None] + }, + orient="tight" + ) + # fmt: on + + +@pytest.fixture(scope="session") +def expected_interp_metrics_wgroup(expected_interp_metrics): + return pd.concat( + [ + expected_interp_metrics, + # fmt: off + pd.DataFrame.from_dict( + { + "index": [0, 1, 2, 3, 4, 5, 6, 7, 8], + "columns": [DATE_COL_NAME, GROUP_COL_NAME, MEASURE_COL_NAME, METRIC_COL_NAME, UNIT_COL_NAME, RISK_COL_NAME,], + "data": [ + [pd.Period("2020"), 1, NO_MEASURE_VALUE, AAI_METRIC_NAME, "USD", 11.0,], + [pd.Period("2020"), 2, NO_MEASURE_VALUE, AAI_METRIC_NAME, "USD", 2.0,], + [pd.Period("2020"), 3, NO_MEASURE_VALUE, AAI_METRIC_NAME, "USD", 5.0,], + [pd.Period("2021"), 1, NO_MEASURE_VALUE, AAI_METRIC_NAME, "USD", 57.75,], + [pd.Period("2021"), 2, NO_MEASURE_VALUE, AAI_METRIC_NAME, "USD", 10.50,], + [pd.Period("2021"), 3, NO_MEASURE_VALUE, AAI_METRIC_NAME, "USD", 26.25,], + [pd.Period("2022"), 1, NO_MEASURE_VALUE, AAI_METRIC_NAME, "USD", 132.0,], + [pd.Period("2022"), 2, NO_MEASURE_VALUE, AAI_METRIC_NAME, "USD", 24.0,], + [pd.Period("2022"), 3, NO_MEASURE_VALUE, AAI_METRIC_NAME, "USD", 60.0,], + ], + "index_names": [None], + "column_names": [None], + }, + orient="tight", + ), + # fmt: on + ], + ignore_index=True, + ) + + +@pytest.fixture(scope="session") +def expected_period_metrics(): + # fmt: off + return pd.DataFrame.from_dict( + {'index': [0, 1, 2, 3], + 'columns': [PERIOD_COL_NAME, GROUP_COL_NAME, MEASURE_COL_NAME, METRIC_COL_NAME, UNIT_COL_NAME, RISK_COL_NAME], + 'data': [[f"{EXPOSURE_REF_YEAR} to 2022", 'All', NO_MEASURE_VALUE, 'aai', 'USD', 328.5/3], + [f"{EXPOSURE_REF_YEAR} to 2022", 'All', NO_MEASURE_VALUE, 'rp_100', 'USD', 9125/3], + [f"{EXPOSURE_REF_YEAR} to 2022", 'All', NO_MEASURE_VALUE, 'rp_250', 'USD', 68437.5/3], + [f"{EXPOSURE_REF_YEAR} to 2022", 'All', NO_MEASURE_VALUE, 'rp_50', 'USD', 0.0], + ], + 'index_names': [None], + 'column_names': [None]}, + orient="tight" + ) + # fmt: on + + +@pytest.fixture(scope="session") +def expected_interp_period_wgroup(expected_period_metrics): + return pd.concat( + [ + # fmt: off + pd.DataFrame.from_dict( + {'index': [0, 1, 2], + 'columns': [PERIOD_COL_NAME, GROUP_COL_NAME, MEASURE_COL_NAME, METRIC_COL_NAME, UNIT_COL_NAME, RISK_COL_NAME], + 'data': [ + [f"{EXPOSURE_REF_YEAR} to 2022", 1, NO_MEASURE_VALUE, 'aai', 'USD', 66.91666666666667], + [f"{EXPOSURE_REF_YEAR} to 2022", 2, NO_MEASURE_VALUE, 'aai', 'USD', 12.166666666666666], + [f"{EXPOSURE_REF_YEAR} to 2022", 3, NO_MEASURE_VALUE, 'aai', 'USD', 30.416666666666668], + ], + 'index_names': [None], + 'column_names': [None]}, + orient="tight" + ), + expected_period_metrics + # fmt: on + ], + ignore_index=True, + ) + + +@pytest.fixture(scope="session") +def expected_interp_metrics_rpchange(): + # fmt: off + return pd.DataFrame.from_dict( + {'index': [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11], + 'columns': [DATE_COL_NAME, GROUP_COL_NAME, MEASURE_COL_NAME, METRIC_COL_NAME, UNIT_COL_NAME, RISK_COL_NAME], + 'data': [[ pd.Period("2020"), 'All',NO_MEASURE_VALUE, 'aai', 'USD', 18.0], + [ pd.Period("2021"), 'All',NO_MEASURE_VALUE, 'aai', 'USD', 94.5], + # Above should indeed not be 216+18 / 2 and slightly + # because as we interpolate each contributor separately, + # the interaction term grows slower. + [ pd.Period("2022"), 'All',NO_MEASURE_VALUE, 'aai', 'USD', 216.0], + [ pd.Period("2020"), 'All',NO_MEASURE_VALUE, 'rp_20', 'USD', 0.0], + [ pd.Period("2021"), 'All',NO_MEASURE_VALUE, 'rp_20', 'USD', 0.0], + [ pd.Period("2022"), 'All',NO_MEASURE_VALUE, 'rp_20', 'USD', 0.0], + [ pd.Period("2020"), 'All',NO_MEASURE_VALUE, 'rp_50', 'USD', 0.0], + [ pd.Period("2021"), 'All',NO_MEASURE_VALUE, 'rp_50', 'USD', 0.0], + [ pd.Period("2022"), 'All',NO_MEASURE_VALUE, 'rp_50', 'USD', 0.0], + [ pd.Period("2020"), 'All',NO_MEASURE_VALUE, 'rp_500', 'USD', 3750.0], + [ pd.Period("2021"), 'All',NO_MEASURE_VALUE, 'rp_500', 'USD', 19687.5], + [ pd.Period("2022"), 'All',NO_MEASURE_VALUE, 'rp_500', 'USD', 45000.0]], + 'index_names': [None], + 'column_names': [None] + }, + orient="tight" + ) + # fmt: on + + +@pytest.fixture(scope="session") +def expected_period_metrics_rpchange(): + # fmt: off + return pd.DataFrame.from_dict( + {'index': [0, 1, 2, 3], + 'columns': [PERIOD_COL_NAME, GROUP_COL_NAME, MEASURE_COL_NAME, METRIC_COL_NAME, UNIT_COL_NAME, RISK_COL_NAME], + 'data': [[f"{EXPOSURE_REF_YEAR} to 2022", 'All', NO_MEASURE_VALUE, 'aai', 'USD', 328.5/3], + [f"{EXPOSURE_REF_YEAR} to 2022", 'All', NO_MEASURE_VALUE, 'rp_20', 'USD', 0.], + [f"{EXPOSURE_REF_YEAR} to 2022", 'All', NO_MEASURE_VALUE, 'rp_50', 'USD', 0.0], + [f"{EXPOSURE_REF_YEAR} to 2022", 'All', NO_MEASURE_VALUE, 'rp_500', 'USD', 22812.5], + ], + 'index_names': [None], + 'column_names': [None]}, + orient="tight" + ) + # fmt: on + + +@pytest.fixture(scope="session") +def expected_interp_metrics_ratechange(): + # fmt: off + return pd.DataFrame.from_dict( + {'index': [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11], + 'columns': [DATE_COL_NAME, GROUP_COL_NAME, MEASURE_COL_NAME, METRIC_COL_NAME, UNIT_COL_NAME, RISK_COL_NAME], + 'data': [[ pd.Period("2020"), 'All',NO_MEASURE_VALUE, 'aai', 'USD', 18.0], + [ pd.Period("2021"), 'All',NO_MEASURE_VALUE, 'aai', 'USD', 90.0], + # Above should indeed not be 216+18 / 2 and slightly + # because as we interpolate each contributor separately, + # the interaction term grows slower. + [ pd.Period("2022"), 'All',NO_MEASURE_VALUE, 'aai', 'USD', 195.9183673469], + [ pd.Period("2020"), 'All',NO_MEASURE_VALUE, 'rp_20', 'USD', 0.0], + [ pd.Period("2021"), 'All',NO_MEASURE_VALUE, 'rp_20', 'USD', 0.0], + [ pd.Period("2022"), 'All',NO_MEASURE_VALUE, 'rp_20', 'USD', 0.0], + [ pd.Period("2020"), 'All',NO_MEASURE_VALUE, 'rp_50', 'USD', 0.0], + [ pd.Period("2021"), 'All',NO_MEASURE_VALUE, 'rp_50', 'USD', 0.0], + [ pd.Period("2022"), 'All',NO_MEASURE_VALUE, 'rp_50', 'USD', 0.0], + [ pd.Period("2020"), 'All',NO_MEASURE_VALUE, 'rp_100', 'USD', 500.0], + [ pd.Period("2021"), 'All',NO_MEASURE_VALUE, 'rp_100', 'USD', 2500.0], + [ pd.Period("2022"), 'All',NO_MEASURE_VALUE, 'rp_100', 'USD', 5442.176870]], + 'index_names': [None], + 'column_names': [None] + }, + orient="tight" + ) + # fmt: on + + +@pytest.fixture(scope="session") +def expected_period_metrics_ratechange(): + # fmt: off + return pd.DataFrame.from_dict( + {'index': [0, 1, 2, 3], + 'columns': [PERIOD_COL_NAME, GROUP_COL_NAME, MEASURE_COL_NAME, METRIC_COL_NAME, UNIT_COL_NAME, RISK_COL_NAME], + 'data': [[f"{EXPOSURE_REF_YEAR} to 2022", 'All', NO_MEASURE_VALUE, 'aai', 'USD', 101.3061224489], + [f"{EXPOSURE_REF_YEAR} to 2022", 'All', NO_MEASURE_VALUE, 'rp_100', 'USD', 2814.0589], + [f"{EXPOSURE_REF_YEAR} to 2022", 'All', NO_MEASURE_VALUE, 'rp_20', 'USD', 0.0], + [f"{EXPOSURE_REF_YEAR} to 2022", 'All', NO_MEASURE_VALUE, 'rp_50', 'USD', 0.], + ], + 'index_names': [None], + 'column_names': [None]}, + orient="tight" + ) + # fmt: on + + +@pytest.fixture(scope="session") +def expected_interp_metrics_contributions(): + return pd.DataFrame.from_dict( + # fmt: off + {'index': [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14], + 'columns': [DATE_COL_NAME, GROUP_COL_NAME, MEASURE_COL_NAME, METRIC_COL_NAME, UNIT_COL_NAME, RISK_COL_NAME,], + 'data': [ + [pd.Period(str(2020)), 'All', NO_MEASURE_VALUE, CONTRIBUTION_BASE_RISK_NAME, 'USD', 18.0], + [pd.Period(str(2021)), 'All', NO_MEASURE_VALUE, CONTRIBUTION_BASE_RISK_NAME, 'USD', 18.0], + [pd.Period(str(2022)), 'All', NO_MEASURE_VALUE, CONTRIBUTION_BASE_RISK_NAME, 'USD', 18.0], + [pd.Period(str(2020)), 'All', NO_MEASURE_VALUE, CONTRIBUTION_EXPOSURE_NAME, 'USD', 0.0], + [pd.Period(str(2021)), 'All', NO_MEASURE_VALUE, CONTRIBUTION_EXPOSURE_NAME, 'USD', 45.0], + [pd.Period(str(2022)), 'All', NO_MEASURE_VALUE, CONTRIBUTION_EXPOSURE_NAME, 'USD', 90.0], + [pd.Period(str(2020)), 'All', NO_MEASURE_VALUE, CONTRIBUTION_HAZARD_NAME, 'USD', 0.0], + [pd.Period(str(2021)), 'All', NO_MEASURE_VALUE, CONTRIBUTION_HAZARD_NAME, 'USD', 9.0], + [pd.Period(str(2022)), 'All', NO_MEASURE_VALUE, CONTRIBUTION_HAZARD_NAME, 'USD', 18.0], + [pd.Period(str(2020)), 'All', NO_MEASURE_VALUE, CONTRIBUTION_VULNERABILITY_NAME, 'USD', 0.0], + [pd.Period(str(2021)), 'All', NO_MEASURE_VALUE, CONTRIBUTION_VULNERABILITY_NAME, 'USD', 0.0], + [pd.Period(str(2022)), 'All', NO_MEASURE_VALUE, CONTRIBUTION_VULNERABILITY_NAME, 'USD', 0.0], + [pd.Period(str(2020)), 'All', NO_MEASURE_VALUE, CONTRIBUTION_INTERACTION_TERM_NAME, 'USD', 0.0], + [pd.Period(str(2021)), 'All', NO_MEASURE_VALUE, CONTRIBUTION_INTERACTION_TERM_NAME, 'USD', 22.5], + [pd.Period(str(2022)), 'All', NO_MEASURE_VALUE, CONTRIBUTION_INTERACTION_TERM_NAME, 'USD', 90.0]], + 'index_names': [None], + 'column_names': [None]}, + # fmt: on + orient="tight", + ) + + +@pytest.fixture(scope="session") +def expected_interp_metrics_contributions_vulchange(): + return pd.DataFrame.from_dict( + # fmt: off + {'index': [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14], + 'columns': [DATE_COL_NAME, GROUP_COL_NAME, MEASURE_COL_NAME, METRIC_COL_NAME, UNIT_COL_NAME, RISK_COL_NAME,], + 'data': [ + [pd.Period(str(2020)), 'All', NO_MEASURE_VALUE, CONTRIBUTION_BASE_RISK_NAME, 'USD', 18.0], + [pd.Period(str(2021)), 'All', NO_MEASURE_VALUE, CONTRIBUTION_BASE_RISK_NAME, 'USD', 18.0], + [pd.Period(str(2022)), 'All', NO_MEASURE_VALUE, CONTRIBUTION_BASE_RISK_NAME, 'USD', 18.0], + [pd.Period(str(2020)), 'All', NO_MEASURE_VALUE, CONTRIBUTION_EXPOSURE_NAME, 'USD', 0.0], + [pd.Period(str(2021)), 'All', NO_MEASURE_VALUE, CONTRIBUTION_EXPOSURE_NAME, 'USD', 45.0], + [pd.Period(str(2022)), 'All', NO_MEASURE_VALUE, CONTRIBUTION_EXPOSURE_NAME, 'USD', 90.0], + [pd.Period(str(2020)), 'All', NO_MEASURE_VALUE, CONTRIBUTION_HAZARD_NAME, 'USD', 0.0], + [pd.Period(str(2021)), 'All', NO_MEASURE_VALUE, CONTRIBUTION_HAZARD_NAME, 'USD', 9.0], + [pd.Period(str(2022)), 'All', NO_MEASURE_VALUE, CONTRIBUTION_HAZARD_NAME, 'USD', 18.0], + [pd.Period(str(2020)), 'All', NO_MEASURE_VALUE, CONTRIBUTION_VULNERABILITY_NAME, 'USD', 0.0], + [pd.Period(str(2021)), 'All', NO_MEASURE_VALUE, CONTRIBUTION_VULNERABILITY_NAME, 'USD', -4.5], + [pd.Period(str(2022)), 'All', NO_MEASURE_VALUE, CONTRIBUTION_VULNERABILITY_NAME, 'USD', -9.0], + [pd.Period(str(2020)), 'All', NO_MEASURE_VALUE, CONTRIBUTION_INTERACTION_TERM_NAME, 'USD', 0.0], + [pd.Period(str(2021)), 'All', NO_MEASURE_VALUE, CONTRIBUTION_INTERACTION_TERM_NAME, 'USD', 3.375], + [pd.Period(str(2022)), 'All', NO_MEASURE_VALUE, CONTRIBUTION_INTERACTION_TERM_NAME, 'USD', -9.0]], + 'index_names': [None], + 'column_names': [None]}, + # fmt: on + orient="tight", + ) + + +def test_interpolated_trajectory( + snapshot_base, + snapshot_future_interp, + expected_interp_metrics, + expected_period_metrics, +): + interp_traj = InterpolatedRiskTrajectory( + [snapshot_base, snapshot_future_interp], return_periods=[50, 100, 250] + ) + pd.testing.assert_frame_equal( + interp_traj.per_date_risk_metrics(), + expected_interp_metrics, + check_dtype=False, + check_categorical=False, + ) + pd.testing.assert_frame_equal( + interp_traj.per_period_risk_metrics(), + expected_period_metrics, + check_dtype=False, + check_categorical=False, + ) + + +def test_interpolated_trajectory_negative_intensities( + snapshot_base_neg, + snapshot_future_interp_neg, + expected_interp_metrics, + expected_period_metrics, +): + interp_traj = InterpolatedRiskTrajectory( + [snapshot_base_neg, snapshot_future_interp_neg], return_periods=[50, 100, 250] + ) + pd.testing.assert_frame_equal( + interp_traj.per_date_risk_metrics(), + expected_interp_metrics, + check_dtype=False, + check_categorical=False, + ) + pd.testing.assert_frame_equal( + interp_traj.per_period_risk_metrics(), + expected_period_metrics, + check_dtype=False, + check_categorical=False, + ) + + +def test_interp_trajectory_with_group( + snapshot_factory, expected_interp_metrics_wgroup, expected_interp_period_wgroup +): + snapshot_base = snapshot_factory( + group_id=CATEGORIES, + ) + snapshot_future = snapshot_factory( + date=2022, + exposure_value_factor=6.0, + hazard_intensity_factor=2.0, + group_id=CATEGORIES, + ) + interp_traj = InterpolatedRiskTrajectory( + [snapshot_base, snapshot_future], return_periods=[50, 100, 250] + ) + pd.testing.assert_frame_equal( + interp_traj.per_date_risk_metrics(), + expected_interp_metrics_wgroup, + check_dtype=False, + check_categorical=False, + ) + pd.testing.assert_frame_equal( + interp_traj.per_period_risk_metrics(), + expected_interp_period_wgroup, + check_dtype=False, + check_categorical=False, + ) + + +def test_interp_trajectory_change_rp( + snapshot_base, + snapshot_future_interp, + expected_interp_metrics, + expected_interp_metrics_rpchange, + expected_period_metrics, + expected_period_metrics_rpchange, +): + interp_traj = InterpolatedRiskTrajectory( + [snapshot_base, snapshot_future_interp], return_periods=[20, 50, 500] + ) + pd.testing.assert_frame_equal( + interp_traj.per_date_risk_metrics(), + expected_interp_metrics_rpchange, + check_dtype=False, + check_categorical=False, + ) + pd.testing.assert_frame_equal( + interp_traj.per_period_risk_metrics(), + expected_period_metrics_rpchange, + check_dtype=False, + check_categorical=False, + ) + + # Also check change to other return period + interp_traj.return_periods = [50, 100, 250] + pd.testing.assert_frame_equal( + interp_traj.per_date_risk_metrics(), + expected_interp_metrics, + check_dtype=False, + check_categorical=False, + ) + pd.testing.assert_frame_equal( + interp_traj.per_period_risk_metrics(), + expected_period_metrics, + check_dtype=False, + check_categorical=False, + ) + + +def test_interp_trajectory_risk_disc_rate( + snapshot_base, + snapshot_future_interp, + expected_interp_metrics, + expected_interp_metrics_ratechange, + expected_period_metrics, + expected_period_metrics_ratechange, +): + risk_disc_rate = DiscRates( + years=np.array(range(2020, 2023)), rates=np.ones(3) * 0.05 + ) + interp_traj = InterpolatedRiskTrajectory( + [snapshot_base, snapshot_future_interp], risk_disc_rates=risk_disc_rate + ) + pd.testing.assert_frame_equal( + interp_traj.per_date_risk_metrics(), + expected_interp_metrics_ratechange, + check_dtype=False, + check_categorical=False, + ) + pd.testing.assert_frame_equal( + interp_traj.per_period_risk_metrics(), + expected_period_metrics_ratechange, + check_dtype=False, + check_categorical=False, + ) + + # Also check change to other return period + interp_traj.return_periods = [50, 100, 250] + interp_traj.risk_disc_rates = None + pd.testing.assert_frame_equal( + interp_traj.per_date_risk_metrics(), + expected_interp_metrics, + check_dtype=False, + check_categorical=False, + ) + pd.testing.assert_frame_equal( + interp_traj.per_period_risk_metrics(), + expected_period_metrics, + check_dtype=False, + check_categorical=False, + ) + + +def test_interp_trajectory_risk_contributions( + snapshot_base, snapshot_future_interp, expected_interp_metrics_contributions +): + interp_traj = InterpolatedRiskTrajectory([snapshot_base, snapshot_future_interp]) + pd.testing.assert_frame_equal( + interp_traj.risk_contributions_metrics(), + expected_interp_metrics_contributions, + check_dtype=False, + check_categorical=False, + ) + + +def test_interp_trajectory_risk_contributions_vulchange( + snapshot_base, + snapshot_future_interp_vulchange, + expected_interp_metrics_contributions_vulchange, +): + interp_traj = InterpolatedRiskTrajectory( + [snapshot_base, snapshot_future_interp_vulchange] + ) + pd.testing.assert_frame_equal( + interp_traj.risk_contributions_metrics(), + expected_interp_metrics_contributions_vulchange, + check_dtype=False, + check_categorical=False, + ) diff --git a/climada/trajectories/__init__.py b/climada/trajectories/__init__.py new file mode 100644 index 0000000000..f5d065005f --- /dev/null +++ b/climada/trajectories/__init__.py @@ -0,0 +1,35 @@ +""" +This file is part of CLIMADA. + +Copyright (C) 2017 ETH Zurich, CLIMADA contributors listed in AUTHORS. + +CLIMADA is free software: you can redistribute it and/or modify it under the +terms of the GNU General Public License as published by the Free +Software Foundation, version 3. + +CLIMADA is distributed in the hope that it will be useful, but WITHOUT ANY +WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A +PARTICULAR PURPOSE. See the GNU General Public License for more details. + +You should have received a copy of the GNU General Public License along +with CLIMADA. If not, see . + +--- + +This module implements risk trajectory objects which enable computation and +possibly interpolation of risk metrics over multiple dates. + +""" + +from .interpolated_trajectory import InterpolatedRiskTrajectory +from .interpolation import AllLinearStrategy, ExponentialExposureStrategy +from .snapshot import Snapshot +from .static_trajectory import StaticRiskTrajectory + +__all__ = [ + "AllLinearStrategy", + "ExponentialExposureStrategy", + "Snapshot", + "StaticRiskTrajectory", + "InterpolatedRiskTrajectory", +] diff --git a/climada/trajectories/calc_risk_metrics.py b/climada/trajectories/calc_risk_metrics.py new file mode 100644 index 0000000000..6fe9b53113 --- /dev/null +++ b/climada/trajectories/calc_risk_metrics.py @@ -0,0 +1,1196 @@ +""" +This file is part of CLIMADA. + +Copyright (C) 2017 ETH Zurich, CLIMADA contributors listed in AUTHORS. + +CLIMADA is free software: you can redistribute it and/or modify it under the +terms of the GNU General Public License as published by the Free +Software Foundation, version 3. + +CLIMADA is distributed in the hope that it will be useful, but WITHOUT ANY +WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A +PARTICULAR PURPOSE. See the GNU General Public License for more details. + +You should have received a copy of the GNU General Public License along +with CLIMADA. If not, see . + +--- + +This modules implements the CalcRiskMetrics classes. + +CalcRiskMetrics are used to compute risk metrics (and intermediate requirements) +in between two snapshots. + +As these computations are not always required and can become "heavy", a so called "lazy" +approach is used: computation is only done when required, and then stored. + +""" + +import datetime +import itertools +import logging +import re + +import numpy as np +import pandas as pd +from scipy.sparse import csr_matrix + +from climada.engine.impact import Impact, ImpactFreqCurve +from climada.engine.impact_calc import ImpactCalc +from climada.entity.measures.base import Measure +from climada.trajectories.constants import ( + AAI_METRIC_NAME, + CONTRIBUTION_BASE_RISK_NAME, + CONTRIBUTION_EXPOSURE_NAME, + CONTRIBUTION_HAZARD_NAME, + CONTRIBUTION_INTERACTION_TERM_NAME, + CONTRIBUTION_TOTAL_RISK_NAME, + CONTRIBUTION_VULNERABILITY_NAME, + COORD_ID_COL_NAME, + DATE_COL_NAME, + DEFAULT_PERIOD_INDEX_NAME, + EAI_METRIC_NAME, + GROUP_COL_NAME, + GROUP_ID_COL_NAME, + MEASURE_COL_NAME, + METRIC_COL_NAME, + NO_MEASURE_VALUE, + RISK_COL_NAME, + RP_VALUE_PREFIX, + UNIT_COL_NAME, +) +from climada.trajectories.impact_calc_strat import ImpactComputationStrategy +from climada.trajectories.interpolation import ( + ImpactInterpolationStrategy, + linear_convex_combination, +) +from climada.trajectories.snapshot import Snapshot +from climada.util.config import CONFIG + +LOGGER = logging.getLogger(__name__) + +__all__ = [ + "CalcRiskMetricsPoints", + "CalcRiskMetricsPeriod", + "calc_per_date_aais", + "calc_per_date_eais", + "calc_per_date_rps", + "calc_freq_curve", +] + + +def lazy_property(method): + """ + Decorator that converts a method into a cached, lazy-evaluated property. + + This decorator is intended for properties that require heavy computation. + The result is calculated only when first accessed and then stored in a + corresponding private attribute (e.g., a method named `impact` will + cache its result in `_impact`). + + Parameters + ---------- + method : callable + The method to be converted into a lazy property. + + Returns + ------- + property + A property object that handles the caching logic and attribute access. + + Notes + ----- + The caching behavior can be globally toggled via the + `_CACHE_SETTINGS["ENABLE_LAZY_CACHE"]` flag. If disabled, the + method will be re-evaluated on every access. + + """ + attr_name = f"_{method.__name__}" + + @property + def _lazy(self): + if not CONFIG.trajectory_caching.bool(): + return method(self) + + if getattr(self, attr_name) is None: + setattr(self, attr_name, method(self)) + + return getattr(self, attr_name) + + return _lazy + + +class CalcRiskMetricsPoints: + """This class handles the computation of impacts for a list of `Snapshot`. + + Note that most attribute like members are properties with their own docstring. + + Attributes + ---------- + + impact_computation_strategy: ImpactComputationStrategy, optional + The method used to calculate the impact from the (Haz,Exp,Vul) of the snapshots. + Defaults to ImpactCalc + measure: Measure, optional + The measure applied to snapshots. Defaults to None. + + Notes + ----- + + This class is intended for internal computation. + """ + + def __init__( + self, + snapshots: list[Snapshot], + impact_computation_strategy: ImpactComputationStrategy, + ) -> None: + """Initialize a new `CalcRiskMetricsPoints` + + This initializes and instantiate a new `CalcRiskMetricsPoints` object. + No computation is done at initialisation and only done "just in time". + + Parameters + ---------- + snapshots : List[Snapshot] + The `Snapshot` list to compute risk for. + impact_computation_strategy: ImpactComputationStrategy, optional + The method used to calculate the impact from the (Haz,Exp,Vul) of the two snapshots. + Defaults to ImpactCalc + + """ + + self._init_impact_data() + self.snapshots = snapshots + self.impact_computation_strategy = impact_computation_strategy + self._date_idx = pd.DatetimeIndex( + [snap.date for snap in self.snapshots], + name=DATE_COL_NAME, + ) + self.measure = None + try: + self._group_id = np.unique( + np.concatenate( + [ + snap.exposure.gdf[GROUP_ID_COL_NAME] + for snap in self.snapshots + if GROUP_ID_COL_NAME in snap.exposure.gdf.columns + ] + ) + ) + except ValueError as exc: + error_message = str(exc).lower() + if "need at least one array to concatenate" in error_message: + self._group_id = np.array([]) + else: + raise + + def _init_impact_data(self): + """Util method that resets computed data, for instance when + changing the computation strategy. + + """ + self._impacts = None + self._eai_gdf = None + self._per_date_eai = None + self._per_date_aai = None + + _reset_impact_data = _init_impact_data + + @property + def impact_computation_strategy(self) -> ImpactComputationStrategy: + """The method used to calculate the impact from the (Haz,Exp,Vul) + of the snapshots. + + """ + return self._impact_computation_strategy + + @impact_computation_strategy.setter + def impact_computation_strategy(self, value, /): + if not isinstance(value, ImpactComputationStrategy): + raise ValueError( + "The provided value is not an ImpactComputationStrategy object. " + "See the trajectory module documentation for more information on " + "how to define your own impact computation strategies." + ) + + self._impact_computation_strategy = value + self._reset_impact_data() + + @lazy_property + def impacts(self) -> list[Impact]: + """Return Impact object for the different snapshots.""" + + return [ + self.impact_computation_strategy.compute_impacts( + snap.exposure, snap.hazard, snap.impfset + ) + for snap in self.snapshots + ] + + @lazy_property + def per_date_eai(self) -> np.ndarray: + """Expected annual impacts per snapshot.""" + + return np.array([imp.eai_exp for imp in self.impacts]) + + @lazy_property + def per_date_aai(self) -> np.ndarray: + """Average annual impacts per snapshot.""" + + return np.array([imp.aai_agg for imp in self.impacts]) + + def calc_eai_gdf(self) -> pd.DataFrame: + """Convenience function returning a DataFrame + from `per_date_eai`. + + This can easily be merged with the GeoDataFrame of + the exposure object of one of the `Snapshot`. + + Notes + ----- + + The DataFrame from the first snapshot of the list is used + as a basis (notably for `value` and `group_id`). + + """ + + metric_df = pd.DataFrame(self.per_date_eai, index=self._date_idx) + metric_df = metric_df.reset_index().melt( + id_vars=DATE_COL_NAME, var_name=COORD_ID_COL_NAME, value_name=RISK_COL_NAME + ) + eai_gdf = pd.concat( + [ + snap.exposure.gdf.reset_index(names=[COORD_ID_COL_NAME]).assign( + date=snap.date.as_unit(self._date_idx.unit) + ) + for snap in self.snapshots + ] + ) + if GROUP_ID_COL_NAME in eai_gdf.columns: + eai_gdf = eai_gdf[[DATE_COL_NAME, COORD_ID_COL_NAME, GROUP_ID_COL_NAME]] + else: + eai_gdf[[GROUP_ID_COL_NAME]] = pd.NA + eai_gdf = eai_gdf[[DATE_COL_NAME, COORD_ID_COL_NAME, GROUP_ID_COL_NAME]] + + eai_gdf = eai_gdf.merge(metric_df, on=[DATE_COL_NAME, COORD_ID_COL_NAME]) + eai_gdf = eai_gdf.rename(columns={GROUP_ID_COL_NAME: GROUP_COL_NAME}) + eai_gdf[GROUP_COL_NAME] = pd.Categorical( + eai_gdf[GROUP_COL_NAME], categories=self._group_id + ) + eai_gdf[METRIC_COL_NAME] = EAI_METRIC_NAME + eai_gdf[MEASURE_COL_NAME] = ( + self.measure.name if self.measure else NO_MEASURE_VALUE + ) + eai_gdf[UNIT_COL_NAME] = self.snapshots[0].exposure.value_unit + return eai_gdf + + def calc_aai_metric(self) -> pd.DataFrame: + """Compute a DataFrame of the AAI for each snapshot.""" + + aai_df = pd.DataFrame( + index=self._date_idx, columns=[RISK_COL_NAME], data=self.per_date_aai + ) + aai_df[GROUP_COL_NAME] = pd.Categorical( + [pd.NA] * len(aai_df), categories=self._group_id + ) + aai_df[METRIC_COL_NAME] = AAI_METRIC_NAME + aai_df[MEASURE_COL_NAME] = ( + self.measure.name if self.measure else NO_MEASURE_VALUE + ) + aai_df[UNIT_COL_NAME] = self.snapshots[0].exposure.value_unit + aai_df.reset_index(inplace=True) + return aai_df + + def calc_aai_per_group_metric(self) -> pd.DataFrame | None: + """Compute a DataFrame of the AAI distinguised per group id + in the exposures, for each snapshot. + + """ + + if len(self._group_id) < 1: + LOGGER.warning( + "No group id defined in the Exposures object. Per group aai will be empty." + ) + return None + + eai_pres_groups = self.calc_eai_gdf()[ + [DATE_COL_NAME, COORD_ID_COL_NAME, GROUP_COL_NAME, RISK_COL_NAME] + ].copy() + aai_per_group_df = eai_pres_groups.groupby( + [DATE_COL_NAME, GROUP_COL_NAME], as_index=False, observed=True + ).agg({RISK_COL_NAME: "sum"}) + aai_per_group_df[METRIC_COL_NAME] = AAI_METRIC_NAME + aai_per_group_df[MEASURE_COL_NAME] = ( + self.measure.name if self.measure else NO_MEASURE_VALUE + ) + aai_per_group_df[UNIT_COL_NAME] = self.snapshots[0].exposure.value_unit + return aai_per_group_df + + def calc_return_periods_metric(self, return_periods: list[int]) -> pd.DataFrame: + """Compute a DataFrame of the estimated impacts for a list + of return periods, for each snapshot. + + Parameters + ---------- + + return_periods : list of int + The return periods to estimate impacts for. + """ + + per_date_rp = np.array( + [ + imp.calc_freq_curve(return_per=return_periods).impact + for imp in self.impacts + ] + ) + rp_df = pd.DataFrame( + index=self._date_idx, columns=return_periods, data=per_date_rp + ).melt(value_name=RISK_COL_NAME, var_name="rp", ignore_index=False) + rp_df.reset_index(inplace=True) + rp_df[GROUP_COL_NAME] = pd.Categorical( + [pd.NA] * len(rp_df), categories=self._group_id + ) + rp_df[METRIC_COL_NAME] = RP_VALUE_PREFIX + "_" + rp_df["rp"].astype(str) + rp_df = rp_df.drop("rp", axis=1) + rp_df[MEASURE_COL_NAME] = ( + self.measure.name if self.measure else NO_MEASURE_VALUE + ) + rp_df[UNIT_COL_NAME] = self.snapshots[0].exposure.value_unit + return rp_df + + def apply_measure(self, measure: Measure) -> "CalcRiskMetricsPoints": + """Creates a new `CalcRiskMetricsPoints` object by applying the effects + of the given measure. + + The effects of the measure are applied to all the snapshots contained + in the initial `CalcRiskMetricsPoints` and a new `CalcRiskMetricsPoints` + containing the modified snapshots is returned. + + Parameters + ---------- + measure : Measure + The measure to apply. + + Returns + ------- + + CalcRiskMetricsPoints + The risk period with given measure applied. + + """ + snapshots = [snap.apply_measure(measure) for snap in self.snapshots] + risk_period = CalcRiskMetricsPoints( + snapshots, + self.impact_computation_strategy, + ) + + risk_period.measure = measure + return risk_period + + +class CalcRiskMetricsPeriod: + """This class handles the computation of impacts for a risk period. + + This object handles the interpolations and computations of risk metrics in + between two given snapshots, along a DateTimeIndex build from either a + `time_resolution` (which must be a valid "freq" string to build a DateTimeIndex) + and defaults to "Y" (start of the year) or `time_points` integer argument, in which case + the DateTimeIndex will have that many periods. + + Note that most attribute like members are properties with their own docstring. + + Attributes + ---------- + + date_idx: pd.PeriodIndex + The date index for the different interpolated points between the two snapshots + interpolation_strategy: InterpolationStrategy, optional + The approach used to interpolate impact matrices in between the two snapshots, + linear by default. + impact_computation_strategy: ImpactComputationStrategy, optional + The method used to calculate the impact from the (Haz,Exp,Vul) of the two snapshots. + Defaults to ImpactCalc + measure: Measure, optional + The measure to apply to both snapshots. Defaults to None. + + Notes + ----- + + This class is intended for internal computation. + """ + + def __init__( + self, + snapshot_start: Snapshot, + snapshot_end: Snapshot, + *, + time_resolution: str, + interpolation_strategy: ImpactInterpolationStrategy, + impact_computation_strategy: ImpactComputationStrategy, + ): + """Initialize a new `CalcRiskMetricsPeriod` + + This initializes and instantiate a new `CalcRiskMetricsPeriod` object. + No computation is done at initialisation and only done "just in time". + + Parameters + ---------- + snapshot0 : Snapshot + The `Snapshot` at the start of the risk period. + snapshot1 : Snapshot + The `Snapshot` at the end of the risk period. + time_resolution : str, optional + One of pandas date offset strings or corresponding objects. + See :func:`pandas.period_range`. + time_points : int, optional + Number of periods to generate for the PeriodIndex. + interpolation_strategy: InterpolationStrategy, optional + The approach used to interpolate impact matrices in + between the two snapshots, linear by default. + impact_computation_strategy: ImpactComputationStrategy, optional + The method used to calculate the impact from the (Haz,Exp,Vul) + of the two snapshots. + Defaults to ImpactCalc + + """ + + LOGGER.debug("Instantiating new CalcRiskPeriod.") + self._snapshot_start = snapshot_start + self._snapshot_end = snapshot_end + self.date_idx = self._set_date_idx( + date1=snapshot_start.date, + date2=snapshot_end.date, + freq=time_resolution, + name=DEFAULT_PERIOD_INDEX_NAME, + ) + self.interpolation_strategy = interpolation_strategy + self.impact_computation_strategy = impact_computation_strategy + self.measure = None # Only possible to set with apply_measure() + + self._group_id_E0 = ( + np.array(self.snapshot_start.exposure.gdf[GROUP_ID_COL_NAME].values) + if GROUP_ID_COL_NAME in self.snapshot_start.exposure.gdf.columns + else np.array([]) + ) + self._group_id_E1 = ( + np.array(self.snapshot_end.exposure.gdf[GROUP_ID_COL_NAME].values) + if GROUP_ID_COL_NAME in self.snapshot_end.exposure.gdf.columns + else np.array([]) + ) + self._groups_id = np.unique( + np.concatenate([self._group_id_E0, self._group_id_E1]) + ) + + def _reset_impact_data(self): + """Util method that resets computed data, for instance when changing the time resolution.""" + for fut in list(itertools.product([0, 1], repeat=3)): + setattr(self, f"_E{fut[0]}H{fut[1]}V{fut[2]}", None) + + self._per_date_eai = None + self._per_date_aai = None + + @staticmethod + def _set_date_idx( + date1: str | pd.Timestamp | datetime.date, + date2: str | pd.Timestamp | datetime.date, + freq: str | None = None, + name: str | None = None, + ) -> pd.PeriodIndex: + """Generate a date range index based on the provided parameters. + + Parameters + ---------- + date1 : str or pd.Timestamp or datetime.date + The start date of the period range. + date2 : str or pd.Timestamp or datetime.date + The end date of the period range. + freq : str, optional + Frequency string for the period range. + See `here `_. + name : str, optional + Name of the resulting period range index. + + Returns + ------- + pd.PeriodIndex + A PeriodIndex representing the date range. + + Raises + ------ + ValueError + If the number of periods and frequency given to period_range are inconsistent. + """ + ret = pd.period_range( + date1, + date2, + freq=freq, # type: ignore + name=name, + ) + return ret + + @property + def snapshot_start(self) -> Snapshot: + """The `Snapshot` at the start of the risk period.""" + return self._snapshot_start + + @property + def snapshot_end(self) -> Snapshot: + """The `Snapshot` at the end of the risk period.""" + return self._snapshot_end + + @property + def date_idx(self) -> pd.PeriodIndex: + """The pandas PeriodIndex representing the time dimension of the risk period.""" + return self._date_idx + + @date_idx.setter + def date_idx(self, value, /): + if not isinstance(value, pd.PeriodIndex): + raise ValueError("Not a PeriodIndex") + + self._date_idx = value # Avoids weird hourly data + self._time_resolution = self.date_idx.freq + self._reset_impact_data() + + @property + def time_points(self) -> int: + """The numbers of different time points (periods) in the risk period.""" + return len(self.date_idx) + + @property + def time_resolution(self) -> str: + """The time resolution of the risk periods, expressed as + a pandas period frequency string. + + """ + return self._time_resolution # type: ignore + + @time_resolution.setter + def time_resolution(self, value, /): + self.date_idx = pd.period_range( + self.snapshot_start.date, + self.snapshot_end.date, + freq=value, + name=DEFAULT_PERIOD_INDEX_NAME, + ) + + @property + def interpolation_strategy(self) -> ImpactInterpolationStrategy: + """The approach used to interpolate impact matrices in between the two snapshots.""" + return self._interpolation_strategy + + @interpolation_strategy.setter + def interpolation_strategy(self, value, /): + if not isinstance(value, ImpactInterpolationStrategy): + raise ValueError("Not an interpolation strategy") + + self._interpolation_strategy = value + self._reset_impact_data() + + @property + def impact_computation_strategy(self) -> ImpactComputationStrategy: + """The method used to calculate the impact from the (Haz,Exp,Vul) of the two snapshots.""" + return self._impact_computation_strategy + + @impact_computation_strategy.setter + def impact_computation_strategy(self, value, /): + if not isinstance(value, ImpactComputationStrategy): + raise ValueError("Not an impact computation strategy") + + self._impact_computation_strategy = value + self._reset_impact_data() + + def apply_measure(self, measure: Measure) -> "CalcRiskMetricsPeriod": + """Creates a new `CalcRiskMetricsPeriod` object with a measure. + + The given measure is applied to both snapshot of the risk period. + + Parameters + ---------- + measure : Measure + The measure to apply. + + Returns + ------- + + CalcRiskPeriod + The risk period with given measure applied. + + """ + snap0 = self.snapshot_start.apply_measure(measure) + snap1 = self.snapshot_end.apply_measure(measure) + + risk_period = CalcRiskMetricsPeriod( + snap0, + snap1, + time_resolution=self.time_resolution, + interpolation_strategy=self.interpolation_strategy, + impact_computation_strategy=self.impact_computation_strategy, + ) + + risk_period.measure = measure + return risk_period + + ################################################### + ##### Impact objects cube / Risk Cube corners ##### + + @lazy_property + def E0H0V0(self) -> Impact: + """Impact object corresponding to starting exposure, + starting hazard and starting vulnerability. + + """ + return self.impact_computation_strategy.compute_impacts( + self.snapshot_start.exposure, + self.snapshot_start.hazard, + self.snapshot_start.impfset, + ) + + @lazy_property + def E1H0V0(self) -> Impact: + """Impact object corresponding to future exposure, + starting hazard and starting vulnerability. + + """ + return self.impact_computation_strategy.compute_impacts( + self.snapshot_end.exposure, + self.snapshot_start.hazard, + self.snapshot_start.impfset, + ) + + @lazy_property + def E0H1V0(self) -> Impact: + """Impact object corresponding to starting exposure, + future hazard and starting vulnerability. + + """ + return self.impact_computation_strategy.compute_impacts( + self.snapshot_start.exposure, + self.snapshot_end.hazard, + self.snapshot_start.impfset, + ) + + @lazy_property + def E1H1V0(self) -> Impact: + """Impact object corresponding to future exposure, + future hazard and starting vulnerability. + + """ + return self.impact_computation_strategy.compute_impacts( + self.snapshot_end.exposure, + self.snapshot_end.hazard, + self.snapshot_start.impfset, + ) + + @lazy_property + def E0H0V1(self) -> Impact: + """Impact object corresponding to starting exposure, + starting hazard and future vulnerability. + + """ + return self.impact_computation_strategy.compute_impacts( + self.snapshot_start.exposure, + self.snapshot_start.hazard, + self.snapshot_end.impfset, + ) + + @lazy_property + def E1H0V1(self) -> Impact: + """Impact object corresponding to future exposure, + starting hazard and future vulnerability. + + """ + return self.impact_computation_strategy.compute_impacts( + self.snapshot_end.exposure, + self.snapshot_start.hazard, + self.snapshot_end.impfset, + ) + + @lazy_property + def E0H1V1(self) -> Impact: + """Impact object corresponding to starting exposure, + future hazard and future vulnerability. + + """ + return self.impact_computation_strategy.compute_impacts( + self.snapshot_start.exposure, + self.snapshot_end.hazard, + self.snapshot_end.impfset, + ) + + @lazy_property + def E1H1V1(self) -> Impact: + """Impact object corresponding to future exposure, + future hazard and future vulnerability. + + """ + return self.impact_computation_strategy.compute_impacts( + self.snapshot_end.exposure, + self.snapshot_end.hazard, + self.snapshot_end.impfset, + ) + + ############################### + + ################################################# + ### Impact Matrices arrays / Risk Cube edges #### + + def _interp_mats(self, start_attr, end_attr) -> list: + """Helper to reduce repetition in impact matrix interpolation.""" + start = getattr(self, start_attr).imp_mat + end = getattr(self, end_attr).imp_mat + return self.interpolation_strategy.interp_over_exposure_dim( + start, end, self.time_points + ) + + def _imp_mats(self, invariant: str) -> list: + """List of `time_points` impact matrices with changing + exposure, and invariant hazard and vulnerability. + + """ + if re.match(r"H[01]V[01]", invariant): + return self._interp_mats(f"E0{invariant}", f"E1{invariant}") + + if re.match(r"E[01]H[01]V[01]", invariant): + return [getattr(self, invariant).imp_mat] * self.time_points + + raise ValueError( + f"Unrecognised invariant format ({invariant}), should be H[01]V[01] | E[01]H[01]V[01]" + ) + + ############################### + + ############################### + ########## Core EAI ########### + + def _per_date_eais_interp(self, invariant: str) -> np.ndarray: + """Expected annual impacts for changing exposure, and fixed + hazard and vulnerability. + + """ + return calc_per_date_eais( + self._imp_mats(invariant=invariant), + ( + self.snapshot_start.hazard.frequency + if "H0" in invariant + else self.snapshot_end.hazard.frequency + ), + ) + + ############################## + ######### Core AAIs ########## + + # Not required for final AAIs computation (we use final EAIs instead), + # but could be useful in the future? + + def _per_date_aais_interp(self, invariant: str) -> np.ndarray: + """Average periodic impacts for specified invariant.""" + return calc_per_date_aais(self._per_date_eais_interp(invariant=invariant)) + + ############################# + ######### Core RPs ######### + + def _per_date_return_periods( + self, invariant: str, return_periods: list[int] + ) -> np.ndarray: + return calc_per_date_rps( + self._imp_mats(invariant=invariant), + ( + self.snapshot_start.hazard.frequency + if "H0" in invariant + else self.snapshot_end.hazard.frequency + ), + self.date_idx.freqstr[0], + return_periods, + ) + + ################################## + ##### Interpolation of metrics ### + + # Actual results + + def _calc_eai(self) -> np.ndarray: + """Compute the EAIs at each date of the risk period + (including changes in exposure, hazard and vulnerability). + + """ + + per_date_eai_H0V0, per_date_eai_H1V0, per_date_eai_H0V1, per_date_eai_H1V1 = ( + self._per_date_eais_interp("H0V0"), + self._per_date_eais_interp("H1V0"), + self._per_date_eais_interp("H0V1"), + self._per_date_eais_interp("H1V1"), + ) + per_date_eai_V0 = self.interpolation_strategy.interp_over_hazard_dim( + per_date_eai_H0V0, per_date_eai_H1V0 + ) + per_date_eai_V1 = self.interpolation_strategy.interp_over_hazard_dim( + per_date_eai_H0V1, per_date_eai_H1V1 + ) + per_date_eai = self.interpolation_strategy.interp_over_vulnerability_dim( + per_date_eai_V0, per_date_eai_V1 + ) + return per_date_eai + + ### Fully interpolated metrics ### + + @lazy_property + def per_date_eai(self) -> np.ndarray: + """Expected annual impacts per date with changing + exposure, changing hazard and changing vulnerability. + + """ + return self._calc_eai() + + @lazy_property + def per_date_aai(self) -> np.ndarray: + """Average annual impacts per date with changing + exposure, changing hazard and changing vulnerability. + + """ + return calc_per_date_aais(self.per_date_eai) + + #################################### + ######## Tidying results ########### + + def calc_eai_gdf(self) -> pd.DataFrame: + """Convenience function returning a DataFrame from `per_date_eai`. + + This dataframe can easily be merged with one of the snapshot exposure geodataframe. + + Notes + ----- + + The DataFrame from the starting snapshot is used as a basis + (notably for `value` and `group_id`). + + """ + metric_df = pd.DataFrame(self.per_date_eai, index=self.date_idx) + metric_df = metric_df.reset_index().melt( + id_vars=DEFAULT_PERIOD_INDEX_NAME, + var_name=COORD_ID_COL_NAME, + value_name=RISK_COL_NAME, + ) + if GROUP_ID_COL_NAME in self.snapshot_start.exposure.gdf: + eai_gdf = self.snapshot_start.exposure.gdf[[GROUP_ID_COL_NAME]] + eai_gdf[COORD_ID_COL_NAME] = eai_gdf.index + eai_gdf = eai_gdf.merge(metric_df, on=COORD_ID_COL_NAME) + eai_gdf = eai_gdf.rename(columns={GROUP_ID_COL_NAME: GROUP_COL_NAME}) + else: + eai_gdf = metric_df + eai_gdf[GROUP_COL_NAME] = pd.NA + + eai_gdf[GROUP_COL_NAME] = pd.Categorical( + eai_gdf[GROUP_COL_NAME], categories=self._groups_id + ) + eai_gdf[METRIC_COL_NAME] = EAI_METRIC_NAME + eai_gdf[MEASURE_COL_NAME] = ( + self.measure.name if self.measure else NO_MEASURE_VALUE + ) + eai_gdf[UNIT_COL_NAME] = self.snapshot_start.exposure.value_unit + return eai_gdf + + def calc_aai_metric(self) -> pd.DataFrame: + """Compute a DataFrame of the AAI at each dates of the risk period + (including changes in exposure, hazard and vulnerability). + + """ + aai_df = pd.DataFrame( + index=self.date_idx, columns=[RISK_COL_NAME], data=self.per_date_aai + ) + aai_df[GROUP_COL_NAME] = pd.Categorical( + [pd.NA] * len(aai_df), categories=self._groups_id + ) + aai_df[METRIC_COL_NAME] = AAI_METRIC_NAME + aai_df[MEASURE_COL_NAME] = ( + self.measure.name if self.measure else NO_MEASURE_VALUE + ) + aai_df[UNIT_COL_NAME] = self.snapshot_start.exposure.value_unit + aai_df.reset_index(inplace=True) + return aai_df + + def calc_aai_per_group_metric(self) -> pd.DataFrame | None: + """Compute a DataFrame of the AAI distinguised per group id in the exposures, + at each dates of the risk period (including changes in exposure, hazard and vulnerability). + + Notes + ----- + + If group ids changes between starting and ending snapshots of the risk period, + the AAIs are linearly interpolated (with a warning for transparency). + + """ + if len(self._group_id_E0) < 1 or len(self._group_id_E1) < 1: + LOGGER.warning( + "No group id defined in at least one of the Exposures object. " + "Per group aai will be empty." + ) + return None + + eai_gdf = self.calc_eai_gdf() + eai_pres_groups = eai_gdf[ + [ + DEFAULT_PERIOD_INDEX_NAME, + COORD_ID_COL_NAME, + GROUP_COL_NAME, + RISK_COL_NAME, + ] + ].copy() + aai_per_group_df = eai_pres_groups.groupby( + [DEFAULT_PERIOD_INDEX_NAME, GROUP_COL_NAME], as_index=False, observed=True + ).agg({RISK_COL_NAME: "sum"}) + if not np.array_equal(self._group_id_E0, self._group_id_E1): + LOGGER.warning( + "Group id are changing between present and future snapshot." + " Per group AAI will be linearly interpolated." + ) + eai_fut_groups = eai_gdf.copy() + eai_fut_groups[GROUP_COL_NAME] = pd.Categorical( + np.tile(self._group_id_E1, len(self.date_idx)), + categories=self._groups_id, + ) + aai_fut_groups = eai_fut_groups.groupby( + [DEFAULT_PERIOD_INDEX_NAME, GROUP_COL_NAME], + as_index=False, + observed=False, + ).agg({RISK_COL_NAME: "sum"}) + aai_per_group_df[RISK_COL_NAME] = linear_convex_combination( + aai_per_group_df[RISK_COL_NAME].to_numpy(), + aai_fut_groups[RISK_COL_NAME].to_numpy(), + ) + + aai_per_group_df[METRIC_COL_NAME] = AAI_METRIC_NAME + aai_per_group_df[MEASURE_COL_NAME] = ( + self.measure.name if self.measure else NO_MEASURE_VALUE + ) + aai_per_group_df[UNIT_COL_NAME] = self.snapshot_start.exposure.value_unit + return aai_per_group_df + + def calc_return_periods_metric(self, return_periods: list[int]) -> pd.DataFrame: + """Compute a DataFrame of the estimated impacts for a list of return + periods, at each dates of the risk period (including changes in exposure, + hazard and vulnerability). + + Parameters + ---------- + + return_periods : list of int + The return periods to estimate impacts for. + + """ + + # currently mathematicaly wrong, but approximatively correct, + # to be reworked when concatenating the impact matrices for the interpolation + per_date_rp_H0V0, per_date_rp_H1V0, per_date_rp_H0V1, per_date_rp_H1V1 = ( + self._per_date_return_periods("H0V0", return_periods), + self._per_date_return_periods("H1V0", return_periods), + self._per_date_return_periods("H0V1", return_periods), + self._per_date_return_periods("H1V1", return_periods), + ) + per_date_rp_V0 = self.interpolation_strategy.interp_over_hazard_dim( + per_date_rp_H0V0, per_date_rp_H1V0 + ) + per_date_rp_V1 = self.interpolation_strategy.interp_over_hazard_dim( + per_date_rp_H0V1, per_date_rp_H1V1 + ) + per_date_rp = self.interpolation_strategy.interp_over_vulnerability_dim( + per_date_rp_V0, per_date_rp_V1 + ) + rp_df = pd.DataFrame( + index=self.date_idx, columns=return_periods, data=per_date_rp + ).melt(value_name=RISK_COL_NAME, var_name="rp", ignore_index=False) + rp_df.reset_index(inplace=True) + rp_df[GROUP_COL_NAME] = pd.Categorical( + [pd.NA] * len(rp_df), categories=self._groups_id + ) + rp_df[METRIC_COL_NAME] = RP_VALUE_PREFIX + "_" + rp_df["rp"].astype(str) + rp_df = rp_df.drop("rp", axis=1) + rp_df[MEASURE_COL_NAME] = ( + self.measure.name if self.measure else NO_MEASURE_VALUE + ) + rp_df[UNIT_COL_NAME] = self.snapshot_start.exposure.value_unit + return rp_df + + def calc_risk_contributions_metric(self) -> pd.DataFrame: + """Compute a DataFrame of the individual contributions of risk (impact), + at each dates of the risk period (including changes in exposure, + hazard and vulnerability). + + """ + aai_changes_hazard_only = self.interpolation_strategy.interp_over_hazard_dim( + self._per_date_aais_interp("E0H0V0"), self._per_date_aais_interp("E0H1V0") + ) + aai_changes_vulnerability_only = ( + self.interpolation_strategy.interp_over_vulnerability_dim( + self._per_date_aais_interp("E0H0V0"), + self._per_date_aais_interp("E0H0V1"), + ) + ) + metric_df = pd.DataFrame( + { + CONTRIBUTION_TOTAL_RISK_NAME: self.per_date_aai, + CONTRIBUTION_BASE_RISK_NAME: self.per_date_aai[0], + CONTRIBUTION_EXPOSURE_NAME: self._per_date_aais_interp("H0V0") + - self.per_date_aai[0], + CONTRIBUTION_HAZARD_NAME: aai_changes_hazard_only + - self.per_date_aai[0], + CONTRIBUTION_VULNERABILITY_NAME: aai_changes_vulnerability_only + - self.per_date_aai[0], + }, + index=self.date_idx, + ) + metric_df[CONTRIBUTION_INTERACTION_TERM_NAME] = metric_df[ + CONTRIBUTION_TOTAL_RISK_NAME + ] - ( + metric_df[CONTRIBUTION_BASE_RISK_NAME] + + metric_df[CONTRIBUTION_EXPOSURE_NAME] + + metric_df[CONTRIBUTION_HAZARD_NAME] + + metric_df[CONTRIBUTION_VULNERABILITY_NAME] + ) + metric_df = metric_df.melt( + value_vars=[ + CONTRIBUTION_BASE_RISK_NAME, + CONTRIBUTION_EXPOSURE_NAME, + CONTRIBUTION_HAZARD_NAME, + CONTRIBUTION_VULNERABILITY_NAME, + CONTRIBUTION_INTERACTION_TERM_NAME, + ], + var_name=METRIC_COL_NAME, + value_name=RISK_COL_NAME, + ignore_index=False, + ) + metric_df.reset_index(inplace=True) + metric_df[GROUP_COL_NAME] = pd.Categorical( + [pd.NA] * len(metric_df), categories=self._groups_id + ) + metric_df[MEASURE_COL_NAME] = ( + self.measure.name if self.measure else NO_MEASURE_VALUE + ) + metric_df[UNIT_COL_NAME] = self.snapshot_start.exposure.value_unit + return metric_df + + +#################################### +### Metrics from impact matrices ### + + +def calc_per_date_eais(imp_mats: list[csr_matrix], frequency: np.ndarray) -> np.ndarray: + """Calculate expected average impact (EAI) values from a list of impact matrices + corresponding to impacts at different dates (with possible changes along + exposure, hazard and vulnerability). + + Parameters + ---------- + imp_mats : list of np.ndarray + List of impact matrices. + frequency : np.ndarray + Hazard frequency values. + + Returns + ------- + np.ndarray + 2D array of EAI (1D) for each dates. + + """ + return np.array( + [ImpactCalc.eai_exp_from_mat(imp_mat, frequency) for imp_mat in imp_mats] + ) + + +def calc_per_date_aais(per_date_eai_exp: np.ndarray) -> np.ndarray: + """Calculate per_date aggregate annual impact (AAI) values + resulting from a list arrays corresponding to EAI at different + dates (with possible changes along exposure, hazard and vulnerability). + + Parameters + ---------- + per_date_eai_exp: np.ndarray + EAIs arrays. + + Returns + ------- + np.ndarray + 1D array of AAI (0D) for each dates. + """ + return np.array( + [ImpactCalc.aai_agg_from_eai_exp(eai_exp) for eai_exp in per_date_eai_exp] + ) + + +def calc_per_date_rps( + imp_mats: list[csr_matrix], + frequency: np.ndarray, + frequency_unit: str, + return_periods: list[int], +) -> np.ndarray: + """Calculate per date return period impact values from a + list of impact matrices corresponding to impacts at different + dates (with possible changes along exposure, hazard and vulnerability). + + Parameters + ---------- + imp_mats: list of scipy.crs_matrix + List of impact matrices. + frequency: np.ndarray + Frequency values. + return_periods : list of int + Return periods to calculate impact values for. + + Returns + ------- + np.ndarray + 2D array of impacts per return periods (1D) for each dates. + + """ + return np.array( + [ + calc_freq_curve(imp_mat, frequency, frequency_unit, return_periods).impact + for imp_mat in imp_mats + ] + ) + + +def calc_freq_curve( + imp_mat_intrpl, frequency, frequency_unit, return_per=None +) -> ImpactFreqCurve: + """Calculate the estimated impacts for given return periods. + + Parameters + ---------- + + imp_mat_intrpl: scipy.csr_matrix + An impact matrix. + frequency: np.ndarray + The frequency of the hazard. + return_per: np.ndarray + The return periods to compute impacts for. + + Returns + ------- + np.ndarray + The estimated impacts for the different return periods. + + """ + + at_event = np.sum(imp_mat_intrpl, axis=1).A1 + + # Sort descendingly the impacts per events + sort_idxs = np.argsort(at_event)[::-1] + # Calculate exceedence frequency + exceed_freq = np.cumsum(frequency[sort_idxs]) + # Set return period and impact exceeding frequency + ifc_return_per = 1 / exceed_freq[::-1] + ifc_impact = at_event[sort_idxs][::-1] + + if return_per is not None: + interp_imp = np.interp(return_per, ifc_return_per, ifc_impact) + ifc_return_per = return_per + ifc_impact = interp_imp + + return ImpactFreqCurve( + return_per=ifc_return_per, + impact=ifc_impact, + frequency_unit=frequency_unit, + label="Exceedance frequency curve", + ) diff --git a/climada/trajectories/constants.py b/climada/trajectories/constants.py new file mode 100644 index 0000000000..969e585531 --- /dev/null +++ b/climada/trajectories/constants.py @@ -0,0 +1,55 @@ +""" +This file is part of CLIMADA. + +Copyright (C) 2017 ETH Zurich, CLIMADA contributors listed in AUTHORS. + +CLIMADA is free software: you can redistribute it and/or modify it under the +terms of the GNU General Public License as published by the Free +Software Foundation, version 3. + +CLIMADA is distributed in the hope that it will be useful, but WITHOUT ANY +WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A +PARTICULAR PURPOSE. See the GNU General Public License for more details. + +You should have received a copy of the GNU General Public License along +with CLIMADA. If not, see . + +--- + +Define constants for trajectories module. +""" + +DEFAULT_TIME_RESOLUTION = "Y" +DATE_COL_NAME = "date" +PERIOD_COL_NAME = "period" +GROUP_COL_NAME = "group" +GROUP_ID_COL_NAME = "group_id" +MEASURE_COL_NAME = "measure" +NO_MEASURE_VALUE = "no_measure" +METRIC_COL_NAME = "metric" +UNIT_COL_NAME = "unit" +RISK_COL_NAME = "risk" +COORD_ID_COL_NAME = "coord_id" + +DEFAULT_PERIOD_INDEX_NAME = "date" + +DEFAULT_RP = (20, 50, 100) +"""Default return periods to use when computing return period impact estimates.""" + +DEFAULT_ALLGROUP_NAME = "All" +"""Default string to use to define the exposure subgroup containing all exposure points.""" + +EAI_METRIC_NAME = "eai" +AAI_METRIC_NAME = "aai" +AAI_PER_GROUP_METRIC_NAME = "aai_per_group" +CONTRIBUTIONS_METRIC_NAME = "risk_contributions" +RETURN_PERIOD_METRIC_NAME = "return_periods" +RP_VALUE_PREFIX = "rp" + + +CONTRIBUTION_BASE_RISK_NAME = "base risk" +CONTRIBUTION_TOTAL_RISK_NAME = "total risk" +CONTRIBUTION_EXPOSURE_NAME = "exposure contribution" +CONTRIBUTION_HAZARD_NAME = "hazard contribution" +CONTRIBUTION_VULNERABILITY_NAME = "vulnerability contribution" +CONTRIBUTION_INTERACTION_TERM_NAME = "interaction contribution" diff --git a/climada/trajectories/impact_calc_strat.py b/climada/trajectories/impact_calc_strat.py new file mode 100644 index 0000000000..b1bb6eebd3 --- /dev/null +++ b/climada/trajectories/impact_calc_strat.py @@ -0,0 +1,114 @@ +""" +This file is part of CLIMADA. + +Copyright (C) 2017 ETH Zurich, CLIMADA contributors listed in AUTHORS. + +CLIMADA is free software: you can redistribute it and/or modify it under the +terms of the GNU General Public License as published by the Free +Software Foundation, version 3. + +CLIMADA is distributed in the hope that it will be useful, but WITHOUT ANY +WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A +PARTICULAR PURPOSE. See the GNU General Public License for more details. + +You should have received a copy of the GNU General Public License along +with CLIMADA. If not, see . + +--- + +This modules implements the impact computation strategy objects for risk +trajectories. + +""" + +from abc import ABC, abstractmethod + +from climada.engine.impact import Impact +from climada.engine.impact_calc import ImpactCalc +from climada.entity.exposures.base import Exposures +from climada.entity.impact_funcs.impact_func_set import ImpactFuncSet +from climada.hazard.base import Hazard + +__all__ = ["ImpactCalcComputation"] + + +# The following is acceptable. +# We design a pattern, and currently it requires only to +# define the compute_impacts method. +# pylint: disable=too-few-public-methods +class ImpactComputationStrategy(ABC): + """ + Interface for impact computation strategies. + + This abstract class defines the contract for all concrete strategies + responsible for calculating and optionally modifying with a risk transfer, + the impact computation, based on a set of inputs (exposure, hazard, vulnerability). + + It revolves around a `compute_impacts()` method that takes as arguments + the three dimensions of risk (exposure, hazard, vulnerability) and return an + Impact object. + """ + + @abstractmethod + def compute_impacts( + self, + exp: Exposures, + haz: Hazard, + vul: ImpactFuncSet, + ) -> Impact: + """ + Calculates the total impact, including optional risk transfer application. + + Parameters + ---------- + exp : Exposures + The exposure data. + haz : Hazard + The hazard data (e.g., event intensity). + vul : ImpactFuncSet + The set of vulnerability functions. + + Returns + ------- + Impact + An object containing the computed total impact matrix and metrics. + + See Also + -------- + ImpactCalcComputation : The default implementation of this interface. + """ + + +class ImpactCalcComputation(ImpactComputationStrategy): + r""" + Default impact computation strategy using the core engine of climada. + + This strategy first calculates the raw impact using the standard + :class:`ImpactCalc` logic. + + """ + + def compute_impacts( + self, + exp: Exposures, + haz: Hazard, + vul: ImpactFuncSet, + ) -> Impact: + """ + Calculates the impact. + + Parameters + ---------- + exp : Exposures + The exposure data. + haz : Hazard + The hazard data. + vul : ImpactFuncSet + The set of vulnerability functions. + + Returns + ------- + Impact + The final impact object. + """ + return ImpactCalc(exposures=exp, impfset=vul, hazard=haz).impact() diff --git a/climada/trajectories/interpolated_trajectory.py b/climada/trajectories/interpolated_trajectory.py new file mode 100644 index 0000000000..f718c0abe5 --- /dev/null +++ b/climada/trajectories/interpolated_trajectory.py @@ -0,0 +1,920 @@ +""" +This file is part of CLIMADA. + +Copyright (C) 2017 ETH Zurich, CLIMADA contributors listed in AUTHORS. + +CLIMADA is free software: you can redistribute it and/or modify it under the +terms of the GNU General Public License as published by the Free +Software Foundation, version 3. + +CLIMADA is distributed in the hope that it will be useful, but WITHOUT ANY +WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A +PARTICULAR PURPOSE. See the GNU General Public License for more details. + +You should have received a copy of the GNU General Public License along +with CLIMADA. If not, see . + +--- + +This file implements interpolated risk trajectory objects, to allow a better evaluation +of risk in between points in time (snapshots). + +""" + +import datetime +import itertools +import logging +from typing import Iterable, cast + +import matplotlib as mpl +import matplotlib.dates as mdates +import matplotlib.pyplot as plt +import matplotlib.ticker as mticker +import pandas as pd + +from climada.entity.disc_rates.base import DiscRates +from climada.trajectories.calc_risk_metrics import CalcRiskMetricsPeriod +from climada.trajectories.constants import ( + AAI_METRIC_NAME, + AAI_PER_GROUP_METRIC_NAME, + CONTRIBUTION_BASE_RISK_NAME, + CONTRIBUTION_EXPOSURE_NAME, + CONTRIBUTION_HAZARD_NAME, + CONTRIBUTION_INTERACTION_TERM_NAME, + CONTRIBUTION_VULNERABILITY_NAME, + CONTRIBUTIONS_METRIC_NAME, + COORD_ID_COL_NAME, + DATE_COL_NAME, + DEFAULT_TIME_RESOLUTION, + EAI_METRIC_NAME, + GROUP_COL_NAME, + MEASURE_COL_NAME, + METRIC_COL_NAME, + PERIOD_COL_NAME, + RETURN_PERIOD_METRIC_NAME, + RISK_COL_NAME, + UNIT_COL_NAME, +) +from climada.trajectories.impact_calc_strat import ( + ImpactCalcComputation, + ImpactComputationStrategy, +) +from climada.trajectories.interpolation import ( + AllLinearStrategy, + ImpactInterpolationStrategy, +) +from climada.trajectories.snapshot import Snapshot +from climada.trajectories.trajectory import ( + DEFAULT_DF_COLUMN_PRIORITY, + DEFAULT_RP, + INDEXING_COLUMNS, + RiskTrajectory, +) +from climada.util import log_level +from climada.util.config import CONFIG +from climada.util.dataframe_handling import reorder_dataframe_columns + +LOGGER = logging.getLogger(__name__) + +__all__ = ["InterpolatedRiskTrajectory"] + + +class InterpolatedRiskTrajectory(RiskTrajectory): + """This class implements interpolated risk trajectories, objects that + regroup impacts computations for multiple dates, and interpolate risk + metrics in between. + + This class computes risk metrics over a series of snapshots, + optionally applying risk discounting. It interpolate risk + between each pair of snapshots and provides dataframes of risk metric on a + given time resolution. + + """ + + _grouper = [MEASURE_COL_NAME, METRIC_COL_NAME] + """Results dataframe grouper""" + + POSSIBLE_METRICS = [ + EAI_METRIC_NAME, + AAI_METRIC_NAME, + RETURN_PERIOD_METRIC_NAME, + CONTRIBUTIONS_METRIC_NAME, + AAI_PER_GROUP_METRIC_NAME, + ] + """Class variable listing the risk metrics that can be computed. + + Currently: + + - eai, expected impact (per exposure point within a period of 1/frequency + unit of the hazard object) + - aai, average annual impact (aggregated eai over the whole exposure) + - aai_per_group, average annual impact per exposure subgroup (defined from + the exposure geodataframe) + - return_periods, estimated impacts aggregated over the whole exposure for + different return periods + - risk_contributions, estimated contribution part of, respectively exposure, + hazard, vulnerability and their interaction to the change in risk over the + considered period + + """ + + _DEFAULT_ALL_METRICS = [ + AAI_METRIC_NAME, + RETURN_PERIOD_METRIC_NAME, + AAI_PER_GROUP_METRIC_NAME, + ] + + def __init__( + self, + snapshots_list: Iterable[Snapshot], + *, + return_periods: Iterable[int] = DEFAULT_RP, + time_resolution: str = DEFAULT_TIME_RESOLUTION, + risk_disc_rates: DiscRates | None = None, + interpolation_strategy: ImpactInterpolationStrategy | None = None, + impact_computation_strategy: ImpactComputationStrategy | None = None, + ): + """Initialize a new `InterpolatedRiskTrajectory`. + + Parameters + ---------- + snapshot_list : list[Snapshot] + The list of `Snapshot` object to compute risk from. + return_periods: list[int], optional + The return periods to use when computing the `return_periods_metric`. + Defaults to `DEFAULT_RP` ([20, 50, 100]). + time_resolution: str, optional + The time resolution to use for interpolation. + It must be a valid pandas string used to define periods, + e.g., "Y" for years, "M" for months, "3M" for trimester, etc. + Defaults to `DEFAULT_TIME_RESOLUTION` ("Y"). + risk_disc_rates: DiscRates, optional + The discount rate to apply to future risk. Defaults to None. + interpolation_strategy: ImpactInterpolationStrategy, optional + The interpolation strategy to use when interpolating. + Defaults to :class:`AllLinearStrategy` + impact_computation_strategy: ImpactComputationStrategy, optional + The method used to calculate the impact from the (Haz,Exp,Vul) + of the two snapshots. Defaults to :class:`ImpactCalcComputation`. + + """ + super().__init__( + snapshots_list, + return_periods=return_periods, + risk_disc_rates=risk_disc_rates, + ) + self.start_date = min((snapshot.date for snapshot in snapshots_list)) + self.end_date = max((snapshot.date for snapshot in snapshots_list)) + self._risk_metrics_calculators = self._reset_risk_metrics_calculators( + self._snapshots, + time_resolution, + interpolation_strategy or AllLinearStrategy(), + impact_computation_strategy or ImpactCalcComputation(), + ) + + @property + def interpolation_strategy(self) -> ImpactInterpolationStrategy: + """The approach used to interpolate impact matrices in between the two snapshots.""" + return self._risk_metrics_calculators[0].interpolation_strategy + + @interpolation_strategy.setter + def interpolation_strategy(self, value, /): + if not isinstance(value, ImpactInterpolationStrategy): + raise ValueError("Not an interpolation strategy") + + self._reset_metrics() + for rmcalc in self._risk_metrics_calculators: + rmcalc.interpolation_strategy = value + + @property + def impact_computation_strategy(self) -> ImpactComputationStrategy: + """The method used to calculate the impact from the (Haz,Exp,Vul) triplets.""" + return self._risk_metrics_calculators[0].impact_computation_strategy + + @impact_computation_strategy.setter + def impact_computation_strategy(self, value, /): + if not isinstance(value, ImpactComputationStrategy): + raise ValueError("Not an interpolation strategy") + + self._reset_metrics() + for rmcalc in self._risk_metrics_calculators: + rmcalc.impact_computation_strategy = value + + @property + def time_resolution(self) -> str: + """The time resolution to use when interpolating. + + It must be a valid pandas string used to define periods, + e.g., "Y" for years, "M" for months, "3M" for trimester, etc. + + See `here `_ + + Notes + ----- + + Changing its value resets the corresponding metric. + """ + return self._risk_metrics_calculators[0].time_resolution + + @time_resolution.setter + def time_resolution(self, value, /): + if not isinstance(value, str): + raise ValueError( + "time_resolution should be a valid pandas Period" + ' frequency string (e.g., `"Y"`, `"M"`, `"D"`).' + ) + self._reset_metrics() + for rmcalc in self._risk_metrics_calculators: + rmcalc.time_resolution = value + + @staticmethod + def _reset_risk_metrics_calculators( + snapshots: list[Snapshot], + time_resolution, + interpolation_strategy, + impact_computation_strategy, + ) -> list[CalcRiskMetricsPeriod]: + """Initialize or reset the internal risk metrics calculators. + + Notes + ----- + + This methods sorts the snapshots per date. + """ + + def pairwise(container: list): + """ + Generate pairs of successive elements from an iterable. + + Parameters + ---------- + iterable : iterable + An iterable sequence from which successive pairs of elements are generated. + + Returns + ------- + zip + A zip object containing tuples of successive pairs from the input iterable. + + Example + ------- + >>> list(pairwise([1, 2, 3, 4])) + [(1, 2), (2, 3), (3, 4)] + """ + first, second = itertools.tee(container) + next(second, None) + return zip(first, second) + + return [ + CalcRiskMetricsPeriod( + start_snapshot, + end_snapshot, + time_resolution=time_resolution, + interpolation_strategy=interpolation_strategy, + impact_computation_strategy=impact_computation_strategy, + ) + for start_snapshot, end_snapshot in pairwise( + sorted(snapshots, key=lambda snap: snap.date) + ) + ] + + def _generic_metrics( + self, + /, + metric_name: str | None = None, + metric_meth: str | None = None, + **kwargs, + ) -> pd.DataFrame: + """Generic method to compute metrics based on the provided metric name and method. + + This method calls the appropriate method from the calculator to return + the results for the given metric, in a tidy formatted dataframe. + + It first checks whether the requested metric is a valid one. + Then looks for a possible cached value and otherwised asks the + calculators (`self._risk_metric_calculators`) to run the computations. + The results are then regrouped in a nice and tidy DataFrame. + If a `risk_disc_rates` was set, values are converted to net present values. + Results are then cached within `self.__metrics` and returned. + + Parameters + ---------- + metric_name : str, optional + The name of the metric to return results for. + metric_meth : str, optional + The name of the specific method of the calculator to call. + + Returns + ------- + pd.DataFrame + A tidy formatted dataframe of the risk metric computed for the + different snapshots. + + Raises + ------ + NotImplementedError + If the requested metric is not part of `POSSIBLE_METRICS`. + ValueError + If either of the arguments are not provided. + + """ + + if metric_name is None or metric_meth is None: + raise ValueError("Both metric_name and metric_meth must be provided.") + + if metric_name not in self.POSSIBLE_METRICS: + raise NotImplementedError( + f"{metric_name} not implemented ({self.POSSIBLE_METRICS})." + ) + + attr_name = f"_{metric_name}_metrics" + + if getattr(self, attr_name) is not None: + LOGGER.debug("Returning cached %s, ", attr_name) + return getattr(self, attr_name) + + LOGGER.debug("Computing %s", attr_name) + with log_level(level="WARNING", name_prefix="climada"): + tmp = [ + getattr(calc_period, metric_meth)(**kwargs) + for calc_period in self._risk_metrics_calculators + ] + + try: + tmp = pd.concat(tmp) + except ValueError as exc: + if str(exc) == "All objects passed were None": + return pd.DataFrame() + raise exc + + if len(tmp) == 0: + return pd.DataFrame() + + tmp = self._metric_post_treatment(tmp, metric_name) + + if CONFIG.trajectory_caching.bool(): + LOGGER.debug("All computing done, caching value.") + setattr(self, attr_name, tmp) + return getattr(self, attr_name) + + return tmp + + def _metric_post_treatment( + self, metric_df: pd.DataFrame, metric_name: str + ) -> pd.DataFrame: + # Notably for per_group_aai being None: + metric_df = self._avoid_duplicates(metric_df) + metric_df = self._handle_group_categories(metric_df) + if metric_name == CONTRIBUTIONS_METRIC_NAME and len(self._snapshots) > 2: + # If there is more than one Snapshot, we need to update the + # contributions from previous periods for continuity + # and to set the base risk from the first period + # This is not elegant, but we need the concatenated metrics from each period, + # so we can't do it in the calculators, and we need + # to do it before caching in the private attribute + metric_df = self._risk_contributions_post_treatment(metric_df) + + if self._risk_disc_rates: + LOGGER.debug("Found risk discount rate. Computing NPV.") + metric_df = self.npv_transform(metric_df, self._risk_disc_rates) + + metric_df = reorder_dataframe_columns(metric_df, DEFAULT_DF_COLUMN_PRIORITY) + return metric_df + + def _avoid_duplicates(self, metric_df: pd.DataFrame) -> pd.DataFrame: + metric_df = metric_df.set_index(INDEXING_COLUMNS) + if COORD_ID_COL_NAME in metric_df.columns: + metric_df = metric_df.set_index([COORD_ID_COL_NAME], append=True) + + # When more than 2 snapshots, there are duplicated rows, we need to remove them. + metric_df = metric_df[~metric_df.index.duplicated(keep="first")] + metric_df = metric_df.reset_index() + return metric_df + + def _handle_group_categories(self, metric_df: pd.DataFrame) -> pd.DataFrame: + if self._all_groups_name not in metric_df[GROUP_COL_NAME].cat.categories: + metric_df[GROUP_COL_NAME] = metric_df[GROUP_COL_NAME].cat.add_categories( + [self._all_groups_name] + ) + metric_df[GROUP_COL_NAME] = metric_df[GROUP_COL_NAME].fillna( + self._all_groups_name + ) + + return metric_df + + def _compute_period_metrics( + self, metric_name: str, metric_meth: str, **kwargs + ) -> pd.DataFrame: + """Helper method to compute total metrics per period + (i.e. whole ranges between pairs of consecutive snapshots). + + """ + metric_df = self._generic_metrics( + metric_name=metric_name, metric_meth=metric_meth, **kwargs + ) + return self._date_to_period_agg(metric_df, grouper=self._grouper) + + def eai_metrics(self, **kwargs) -> pd.DataFrame: + """Return the estimated annual impacts at each exposure point for each date. + + This method computes and return a `DataFrame` with eai metric + (for each exposure point) for each date. + + Notes + ----- + + This computation may become quite expensive for big areas with high resolution. + + """ + df = self._compute_metrics( + metric_name=EAI_METRIC_NAME, metric_meth="calc_eai_gdf", **kwargs + ) + return df + + def aai_metrics(self, **kwargs) -> pd.DataFrame: + """Return the average annual impacts for each date. + + This method computes and return a `DataFrame` with aai metric for each date. + + """ + + return self._compute_metrics( + metric_name=AAI_METRIC_NAME, metric_meth="calc_aai_metric", **kwargs + ) + + def return_periods_metrics(self, **kwargs) -> pd.DataFrame: + """Return the estimated impacts for different return periods. + + Return periods to estimate impacts for are defined by `self.return_periods`. + + """ + + return self._compute_metrics( + metric_name=RETURN_PERIOD_METRIC_NAME, + metric_meth="calc_return_periods_metric", + return_periods=self.return_periods, + **kwargs, + ) + + def aai_per_group_metrics(self, **kwargs) -> pd.DataFrame: + """Return the average annual impacts for each exposure group ID. + + This method computes and return a `DataFrame` with aai metric for each + of the exposure group defined by a group id, for each date. + + """ + + return self._compute_metrics( + metric_name=AAI_PER_GROUP_METRIC_NAME, + metric_meth="calc_aai_per_group_metric", + **kwargs, + ) + + def risk_contributions_metrics(self, **kwargs) -> pd.DataFrame: + """Return the "contributions" of change in future risk (Exposure and Hazard) + + This method returns the contributions of the change in risk at each date: + + - The 'base risk', i.e., the risk without change in hazard or exposure, + compared to trajectory's earliest date. + - The 'exposure contribution', i.e., the additional risks due to change + in exposure (only) + - The 'hazard contribution', i.e., the additional risks due to change + in hazard (only) + - The 'vulnerability contribution', i.e., the additional risks due to + change in vulnerability (only) + - The 'interaction contribution', i.e., the additional risks due to the + interaction term + + + """ + + return self._compute_metrics( + metric_name=CONTRIBUTIONS_METRIC_NAME, + metric_meth="calc_risk_contributions_metric", + **kwargs, + ) + + def _risk_contributions_post_treatment(self, df: pd.DataFrame) -> pd.DataFrame: + """Post treat the risk contributions metrics. + + When more than two snapshots are provided, the total risk of the previous pair + (period) becomes the base risk for the subsequent one. + This method straightens this by resetting the base risk to the risk from + the first snapshot of the list and correcting the different contributions + by cumulating the contributions from the previous periods. + + """ + + df.set_index(INDEXING_COLUMNS, inplace=True) + start_dates = [snap.date for snap in self._snapshots[:-1]] + end_dates = [snap.date for snap in self._snapshots[1:]] + periods_dates = list(zip(start_dates, end_dates)) + df.loc[pd.IndexSlice[:, :, :, CONTRIBUTION_BASE_RISK_NAME]] = df.loc[ + pd.IndexSlice[ + pd.to_datetime(self.start_date).to_period(self.time_resolution), + :, + :, + CONTRIBUTION_BASE_RISK_NAME, + ] # type: ignore + ].values + for p2 in periods_dates[1:]: + for metric in [ + CONTRIBUTION_EXPOSURE_NAME, + CONTRIBUTION_HAZARD_NAME, + CONTRIBUTION_VULNERABILITY_NAME, + CONTRIBUTION_INTERACTION_TERM_NAME, + ]: + mask_last_previous = ( + df.index.get_level_values(0) + == pd.to_datetime(p2[0]).to_period(self.time_resolution) + ) & (df.index.get_level_values(3) == metric) + mask_to_update = ( + ( + df.index.get_level_values(0) + > pd.to_datetime(p2[0]).to_period(self.time_resolution) + ) + & ( + df.index.get_level_values(0) + <= pd.to_datetime(p2[1]).to_period(self.time_resolution) + ) + & (df.index.get_level_values(3) == metric) + ) + + df.loc[mask_to_update, RISK_COL_NAME] += df.loc[ + mask_last_previous, RISK_COL_NAME + ].iloc[0] + + return df.reset_index() + + def per_date_risk_metrics( + self, + metrics: Iterable[str] | None = None, + ) -> pd.DataFrame: + """Returns a DataFrame of risk metrics for each dates + + This methods collects (and if needed computes) the `metrics` + (Defaulting to AAI_METRIC_NAME, RETURN_PERIOD_METRIC_NAME and AAI_PER_GROUP_METRIC_NAME). + + Parameters + ---------- + metrics : list[str], optional + The list of metrics to return (defaults to + [AAI_METRIC_NAME,RETURN_PERIOD_METRIC_NAME,AAI_PER_GROUP_METRIC_NAME]) + return_periods : list[int], optional + The return periods to consider for the return periods metric + (default to the value of the `.default_rp` attribute) + + Returns + ------- + pd.DataFrame | pd.Series + A tidy DataFrame with metrics value for all possible dates. + + """ + + metrics = self._DEFAULT_ALL_METRICS if metrics is None else metrics + return pd.concat( + [getattr(self, f"{metric}_metrics")() for metric in metrics], + ignore_index=True, + ) + + @staticmethod + def _get_risk_periods( + risk_periods: list[CalcRiskMetricsPeriod], + start_date: datetime.date, + end_date: datetime.date, + strict: bool = True, + ): + """Returns risk periods from the given list that are within `start_date` and `end_date`. + + Either using a strict inclusion (period is stricly within start and end) or extending + to overlap inclusion, i.e., start or end is within the period. + + Parameters + ---------- + risk_periods : list[CalcRiskPeriod] + The list of risk periods to look through + start_date : datetime.date + end_date : datetime.date + strict: bool, default True + If true, only returns periods stricly within start and end dates. Else, + additionaly returns periods that have an overlap within start and end. + """ + if strict: + return [ + period + for period in risk_periods + if ( + start_date <= period.snapshot_start.date + and end_date >= period.snapshot_end.date + ) + ] + + return [ + period + for period in risk_periods + if not ( + start_date >= period.snapshot_end.date + or end_date <= period.snapshot_start.date + ) + ] + + @staticmethod + def _identify_continuous_periods(group, time_unit): + """Calculate the difference between consecutive dates.""" + + if time_unit == "year": + group["date_diff"] = group[DATE_COL_NAME].dt.year.diff() + if time_unit == "month": + group["date_diff"] = group[DATE_COL_NAME].dt.month.diff() + if time_unit == "day": + group["date_diff"] = group[DATE_COL_NAME].dt.day.diff() + if time_unit == "hour": + group["date_diff"] = group[DATE_COL_NAME].dt.hour.diff() + # Identify breaks in continuity + group["period_id"] = (group["date_diff"] != 1).cumsum() + return group + + @classmethod + def _date_to_period_agg( + cls, + metric_df: pd.DataFrame, + grouper: list[str], + time_unit: str = "year", + colname: str | list[str] = RISK_COL_NAME, + ) -> pd.DataFrame: + """Group per date risk metric to periods.""" + + df_sorted = metric_df.sort_values(by=grouper + [DATE_COL_NAME]) + + if GROUP_COL_NAME in metric_df.columns and GROUP_COL_NAME not in grouper: + grouper = [GROUP_COL_NAME] + grouper + + # Apply the function to identify continuous periods + df_periods = df_sorted.groupby( + grouper, dropna=False, group_keys=False, observed=True + )[df_sorted.columns].apply(cls._identify_continuous_periods, time_unit) + + if isinstance(colname, str): + colname = [colname] + agg_dict = { + "start_date": pd.NamedAgg(column=DATE_COL_NAME, aggfunc="min"), + "end_date": pd.NamedAgg(column=DATE_COL_NAME, aggfunc="max"), + } + df_periods_dates = ( + df_periods.groupby(grouper + ["period_id"], dropna=False, observed=True) + .agg(func=None, **agg_dict) # type: ignore + .reset_index() + ) + + df_periods_dates[PERIOD_COL_NAME] = ( + df_periods_dates["start_date"].astype(str) + + " to " + + df_periods_dates["end_date"].astype(str) + ) + df_periods = ( + df_periods.groupby(grouper + ["period_id"], dropna=False, observed=True)[ + colname + ] + .mean() + .reset_index() + ) + df_periods = pd.merge( + df_periods_dates[grouper + [PERIOD_COL_NAME, "period_id"]], + df_periods, + on=grouper + ["period_id"], + ) + df_periods = df_periods.drop(["period_id"], axis=1) + return df_periods[ + [PERIOD_COL_NAME] + + [col for col in df_periods.columns if col != PERIOD_COL_NAME] + ] + + def per_period_risk_metrics( + self, + metrics: Iterable[str] = ( + AAI_METRIC_NAME, + RETURN_PERIOD_METRIC_NAME, + AAI_PER_GROUP_METRIC_NAME, + ), + **kwargs, + ) -> pd.DataFrame: + """Return a tidy dataframe of the risk metrics with the total + for each different period (pair of snapshots). + + """ + + metric_df = self.per_date_risk_metrics(metrics=metrics, **kwargs) + return self._date_to_period_agg( + metric_df, grouper=self._grouper + [UNIT_COL_NAME], **kwargs + ) + + def _calc_waterfall_plot_data( + self, + start_date: datetime.date | None = None, + end_date: datetime.date | None = None, + ): + """Compute the required data for the waterfall plot between `start_date` and `end_date`.""" + start_date = self.start_date if start_date is None else start_date + end_date = self.end_date if end_date is None else end_date + risk_contributions = self.risk_contributions_metrics() + risk_contributions = risk_contributions.loc[ + (risk_contributions[DATE_COL_NAME] >= str(start_date)) + & (risk_contributions[DATE_COL_NAME] <= str(end_date)) + ] + risk_contributions = risk_contributions.set_index( + [DATE_COL_NAME, METRIC_COL_NAME] + )[RISK_COL_NAME].unstack() + return risk_contributions + + # Acceptable given it is a plotting function + # pylint: disable=too-many-locals + def plot_time_waterfall( + self, + ax=None, + figsize=(12, 6), + ): + """Plot a waterfall chart of risk contributions over a specified date range. + + This method generates a stacked bar chart to visualize the + risk contributions. + + Parameters + ---------- + ax : matplotlib.axes.Axes, optional + The matplotlib axes on which to plot. If None, a new figure and axes are created. + + Returns + ------- + matplotlib.axes.Axes + The matplotlib axes with the plotted waterfall chart. + + """ + if ax is None: + fig, ax = plt.subplots(figsize=figsize) + else: + fig = ax.figure # get parent figure from the axis + + risk_contribution = self._calc_waterfall_plot_data( + start_date=self.start_date, end_date=self.end_date + ) + risk_contribution = risk_contribution[ + [ + CONTRIBUTION_EXPOSURE_NAME, + CONTRIBUTION_HAZARD_NAME, + CONTRIBUTION_VULNERABILITY_NAME, + CONTRIBUTION_INTERACTION_TERM_NAME, + ] + ] + positive_contrib = ( + risk_contribution[risk_contribution > 0].dropna(how="all", axis=1).fillna(0) + ) # + base_risk.iloc[0] + negative_contrib = ( + risk_contribution[risk_contribution < 0].dropna(how="all", axis=1).fillna(0) + ) # + base_risk.iloc[0] + + color_index = { + CONTRIBUTION_EXPOSURE_NAME: 1, + CONTRIBUTION_HAZARD_NAME: 2, + CONTRIBUTION_VULNERABILITY_NAME: 3, + CONTRIBUTION_INTERACTION_TERM_NAME: 4, + } + csequence = mpl.color_sequences["tab10"] + ax.stackplot( + positive_contrib.index.to_timestamp(), # type: ignore + [positive_contrib[col] for col in positive_contrib.columns], + labels=positive_contrib.columns, + colors=[csequence[color_index[col]] for col in positive_contrib.columns], + ) + if not negative_contrib.empty: + ax.stackplot( + negative_contrib.index.to_timestamp(), # type: ignore + [negative_contrib[col] for col in negative_contrib.columns], + labels=negative_contrib.columns, + colors=[ + csequence[color_index[col]] for col in negative_contrib.columns + ], + ) + handles, labels = plt.gca().get_legend_handles_labels() + newLabels, newHandles = [], [] + for handle, label in zip(handles, labels): + if label not in newLabels: + newLabels.append(label) + newHandles.append(handle) + + ax.legend(newHandles, newLabels) + value_label = "Deviation from base risk" + title_label = f"Contributions to change in risk between {self.start_date} and {self.end_date} (Average)" + + locator = mdates.AutoDateLocator() + formatter = mdates.ConciseDateFormatter(locator) + + ax.axhline(y=0, linestyle="--", color="black", linewidth=2) + ax.xaxis.set_major_locator(locator) + ax.xaxis.set_major_formatter(formatter) + ax.yaxis.set_major_formatter(mticker.EngFormatter()) + ax.set_title(title_label) + ax.set_ylabel(value_label) + ax.set_ylim(top=1.1 * ax.get_ylim()[1]) + return fig, ax + + def plot_waterfall( + self, + ax=None, + ): + """Plot a waterfall chart of risk contributions between two dates. + + This method generates a waterfall plot to visualize the changes in risk contributions. + + Parameters + ---------- + ax : matplotlib.axes.Axes, optional + The matplotlib axes on which to plot. If None, a new figure and axes are created. + + Returns + ------- + matplotlib.axes.Axes + The matplotlib axes with the plotted waterfall chart. + + """ + start_date_p = pd.to_datetime(self.start_date).to_period(self.time_resolution) + end_date_p = pd.to_datetime(self.end_date).to_period(self.time_resolution) + risk_contribution = self._calc_waterfall_plot_data( + start_date=self.start_date, end_date=self.end_date + ) + if ax is None: + _, ax = plt.subplots(figsize=(8, 5)) + + risk_contribution = risk_contribution.loc[ + (risk_contribution.index == str(self.end_date)) + ].squeeze() + risk_contribution = cast(pd.Series, risk_contribution) + + labels = [ + f"Risk {start_date_p}", + f"Exposure contribution {end_date_p}", + f"Hazard contribution {end_date_p}", + f"Vulnerability contribution {end_date_p}", + f"Interaction contribution {end_date_p}", + f"Total Risk {end_date_p}", + ] + values = [ + risk_contribution[CONTRIBUTION_BASE_RISK_NAME], + risk_contribution[CONTRIBUTION_EXPOSURE_NAME], + risk_contribution[CONTRIBUTION_HAZARD_NAME], + risk_contribution[CONTRIBUTION_VULNERABILITY_NAME], + risk_contribution[CONTRIBUTION_INTERACTION_TERM_NAME], + risk_contribution.sum(), + ] + bottoms = [ + 0.0, + risk_contribution[CONTRIBUTION_BASE_RISK_NAME], + risk_contribution[CONTRIBUTION_BASE_RISK_NAME] + + risk_contribution[CONTRIBUTION_EXPOSURE_NAME], + risk_contribution[CONTRIBUTION_BASE_RISK_NAME] + + risk_contribution[CONTRIBUTION_EXPOSURE_NAME] + + risk_contribution[CONTRIBUTION_HAZARD_NAME], + risk_contribution[CONTRIBUTION_BASE_RISK_NAME] + + risk_contribution[CONTRIBUTION_EXPOSURE_NAME] + + risk_contribution[CONTRIBUTION_HAZARD_NAME] + + risk_contribution[CONTRIBUTION_VULNERABILITY_NAME], + 0.0, + ] + + ax.bar( + labels, + values, + bottom=bottoms, + edgecolor="black", + color=[ + "tab:cyan", + "tab:orange", + "tab:green", + "tab:red", + "tab:purple", + "tab:blue", + ], + ) + for i, val in enumerate(values): + ax.text( + labels[i], # type: ignore + val + bottoms[i], + f"{val:.0e}", + ha="center", + va="bottom", + color="black", + ) + + # Construct y-axis label and title based on parameters + value_label = "USD" + title_label = f"Evolution of the contributions of risk between {start_date_p} and {end_date_p} (Average impact)" + ax.yaxis.set_major_formatter(mticker.EngFormatter()) + ax.set_title(title_label) + ax.set_ylabel(value_label) + ax.set_ylim(0.0, 1.1 * ax.get_ylim()[1]) + ax.tick_params( + axis="x", + labelrotation=90, + ) + + return ax diff --git a/climada/trajectories/interpolation.py b/climada/trajectories/interpolation.py new file mode 100644 index 0000000000..45fc18f575 --- /dev/null +++ b/climada/trajectories/interpolation.py @@ -0,0 +1,473 @@ +""" +This file is part of CLIMADA. + +Copyright (C) 2017 ETH Zurich, CLIMADA contributors listed in AUTHORS. + +CLIMADA is free software: you can redistribute it and/or modify it under the +terms of the GNU General Public License as published by the Free +Software Foundation, version 3. + +CLIMADA is distributed in the hope that it will be useful, but WITHOUT ANY +WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A +PARTICULAR PURPOSE. See the GNU General Public License for more details. + +You should have received a copy of the GNU General Public License along +with CLIMADA. If not, see . + +--- + +This modules implements different sparce matrices and numpy arrays +interpolation approaches. + +""" + +import logging +from abc import ABC +from collections.abc import Callable +from typing import Any, Dict, List, Optional + +import numpy as np +from scipy import sparse + +LOGGER = logging.getLogger(__name__) + +__all__ = [ + "AllLinearStrategy", + "ExponentialExposureStrategy", + "linear_convex_combination", + "linear_interp_matrix_elemwise", + "exponential_convex_combination", + "exponential_interp_matrix_elemwise", +] + + +def linear_interp_matrix_elemwise( + mat_start: sparse.csr_matrix, + mat_end: sparse.csr_matrix, + number_of_interpolation_points: int, +) -> List[sparse.csr_matrix]: + r""" + Linearly interpolates between two sparse impact matrices. + + Creates a sequence of matrices representing a linear transition from a starting + matrix to an ending matrix. The interpolation includes both the start and end + points. + + Parameters + ---------- + mat_start : scipy.sparse.csr_matrix + The starting impact matrix. Must have a shape compatible with `mat_end` + for arithmetic operations. + mat_end : scipy.sparse.csr_matrix + The ending impact matrix. Must have a shape compatible with `mat_start` + for arithmetic operations. + number_of_interpolation_points : int + The total number of matrices to return, including the start and end points. + Must be $\ge 2$. + + Returns + ------- + list of scipy.sparse.csr_matrix + A list of matrices, where the first element is `mat_start` and the last + element is `mat_end`. The total length of the list is + `number_of_interpolation_points`. + + Notes + ----- + The formula used for interpolation at proportion $p$ is: + $$M_p = M_{start} \cdot (1 - p) + M_{end} \cdot p$$ + The proportions $p$ range from 0 to 1, inclusive. + """ + + return [ + mat_start + prop * (mat_end - mat_start) + for prop in np.linspace(0, 1, number_of_interpolation_points) + ] + + +def exponential_interp_matrix_elemwise( + mat_start: sparse.csr_matrix, + mat_end: sparse.csr_matrix, + number_of_interpolation_points: int, +) -> List[sparse.csr_matrix]: + r""" + Exponentially interpolates between two "impact matrices". + + This function performs interpolation in a logarithmic space, effectively + achieving an exponential-like transition between `mat_start` and `mat_end`. + It is designed for objects that wrap NumPy arrays and expose them via a + `.data` attribute. + + Parameters + ---------- + mat_start : object + The starting matrix object. Must have a `.data` attribute that is a + NumPy array of positive values. + mat_end : object + The ending matrix object. Must have a `.data` attribute that is a + NumPy array of positive values and have a compatible shape with `mat_start`. + number_of_interpolation_points : int + The total number of matrix objects to return, including the start and + end points. Must be $\ge 2$. + + Returns + ------- + list of object + A list of interpolated matrix objects. The first element corresponds to + `mat_start` and the last to `mat_end` (after the conversion/reversion). + The list length is `number_of_interpolation_points`. + + Notes + ----- + The interpolation is achieved by: + + 1. Mapping the matrix data to a transformed logarithmic space: + $$M'_{i} = \ln(M_{i})}$$ + (where $\ln$ is the natural logarithm, and $\epsilon$ is added to $M_{i}$ + to prevent $\ln(0)$). + 2. Performing standard linear interpolation on the transformed matrices + $M'_{start}$ and $M'_{end}$ to get $M'_{interp}$: + $$M'_{interp} = M'_{start} \cdot (1 - \text{ratio}) + M'_{end} \cdot \text{ratio}$$ + 3. Mapping the result back to the original domain: + $$M_{interp} = \exp(M'_{interp}$$ + """ + + mat_start = mat_start.copy() + mat_end = mat_end.copy() + mat_start.data = np.log(mat_start.data + np.finfo(float).eps) + mat_end.data = np.log(mat_end.data + np.finfo(float).eps) + + # Perform linear interpolation in the logarithmic domain + res = [] + num_points = number_of_interpolation_points + for point in range(num_points): + ratio = point / (num_points - 1) + mat_interpolated = mat_start * (1 - ratio) + ratio * mat_end + mat_interpolated.data = np.exp(mat_interpolated.data) + res.append(mat_interpolated) + return res + + +def linear_convex_combination(arr_start: np.ndarray, arr_end: np.ndarray) -> np.ndarray: + r""" + Performs a linear convex combination between two n x m NumPy arrays over their + first dimension (n rows). + + This function interpolates each metric (column) linearly across the time steps + (rows), including both the start and end states. + + Parameters + ---------- + arr_start : numpy.ndarray + The starting array of metrics. The first dimension (rows) is assumed to + represent the interpolation steps (e.g., dates/time points). + arr_end : numpy.ndarray + The ending array of metrics. Must have the exact same shape as `arr_start`. + + Returns + ------- + numpy.ndarray + An array with the same shape as `arr_start` and `arr_end`. The values + in the first dimension transition linearly from those in `arr_start` + to those in `arr_end`. + + Raises + ------ + ValueError + If `arr_start` and `arr_end` do not have the same shape. + + Example + -------- + >>> arr_start = [ [ 1, 1], [1, 2], [10, 20] ] + >>> arr_end = [ [2, 2], [5, 6], [10, 30] ] + >>> linear_interp_arrays(arr_start, arr_end) + >>> [[1, 1], [3, 4], [10, 30]] + + Notes + ----- + The interpolation is performed element-wise along the first dimension + (axis 0). For each row $i$ and proportion $p_i$, the result $R_i$ is calculated as: + + $$R_i = arr\_start_i \cdot (1 - p_i) + arr\_end_i \cdot p_i$$ + + where $p_i$ is generated by $\text{np.linspace}(0, 1, n)$ and $n$ is the + size of the first dimension ($\text{arr\_start.shape}[0]$). + """ + if arr_start.shape != arr_end.shape: + raise ValueError( + f"Cannot interpolate arrays of different shapes: {arr_start.shape} and {arr_end.shape}." + ) + interpolation_range = arr_start.shape[0] + prop1 = np.linspace(0, 1, interpolation_range) + prop0 = 1 - prop1 + if arr_start.ndim > 1: + prop0, prop1 = prop0.reshape(-1, 1), prop1.reshape(-1, 1) + + return np.multiply(arr_start, prop0) + np.multiply(arr_end, prop1) + + +def exponential_convex_combination( + arr_start: np.ndarray, arr_end: np.ndarray +) -> np.ndarray: + r""" + Performs exponential convex combination between two NumPy arrays over their first dimension. + + This function achieves an exponential-like transition by performing linear + interpolation in the logarithmic space. + + Parameters + ---------- + arr_start : numpy.ndarray + The starting array of metrics. Values must be positive. + arr_end : numpy.ndarray + The ending array of metrics. Must have the exact same shape as `arr_start`. + + Returns + ------- + numpy.ndarray + An array with the same shape as `arr_start` and `arr_end`. The values + in the first dimension transition exponentially from those in `arr_start` + to those in `arr_end`. + + Raises + ------ + ValueError + If `arr_start` and `arr_end` do not have the same shape. + + See Also + --------- + linear_interp_arrays: linear version of the interpolation. + + Notes + ----- + The interpolation is performed by transforming the arrays to a logarithmic + domain, linearly interpolating, and then transforming back. + + The formula for the interpolated result $R$ at proportion $\text{prop}$ is: + $$ + R = \exp \left( + \ln(A_{start}) \cdot (1 - \text{prop}) + + \ln(A_{end}) \cdot \text{prop} + \right) + $$ + where $A_{start}$ and $A_{end}$ are the input arrays (with $\epsilon$ added + to prevent $\ln(0)$) and $\text{prop}$ ranges from 0 to 1. + """ + if arr_start.shape != arr_end.shape: + raise ValueError( + f"Cannot interpolate arrays of different shapes: {arr_start.shape} and {arr_end.shape}." + ) + interpolation_range = arr_start.shape[0] + + prop1 = np.linspace(0, 1, interpolation_range) + prop0 = 1 - prop1 + if arr_start.ndim > 1: + prop0, prop1 = prop0.reshape(-1, 1), prop1.reshape(-1, 1) + + # Perform log transformation, linear interpolation, and exponential back-transformation + log_arr_start = np.log(arr_start + np.finfo(float).eps) + log_arr_end = np.log(arr_end + np.finfo(float).eps) + + interpolated_log_arr = np.multiply(log_arr_start, prop0) + np.multiply( + log_arr_end, prop1 + ) + + return np.exp(interpolated_log_arr) + + +class ImpactInterpolationStrategy(ABC): + r""" + Base abstract class for defining a set of interpolation strategies for impact outputs. + + This class serves as a blueprint for implementing specific interpolation + methods (e.g., 'Linear', 'Exponential') describing how impact outputs + should evolve between two points in time. + + Impacts result from three dimensions—Exposure, Hazard, and Vulnerability— + each of which may change differently over time. Consequently, a distinct + interpolation strategy is defined for each dimension. + + Exposure interpolation differs from Hazard and Vulnerability interpolation. + Changes in exposure do not alter the shape of the impact matrices, which + allows direct interpolation of the matrices themselves. For the Exposure + dimension, interpolation therefore consists of generating intermediate + impact matrices between the two time points, with exposure evolving while + hazard and vulnerability remain fixed (to either the first or second point). + + In contrast, changes in Hazard may alter the + set of events between the two time points, making direct interpolation of + impact matrices impossible. Instead, impacts are first aggregated over the + event dimension (i.e. the EAI metric). The evolution of impacts is then + interpolated as a convex combination of metric sequences computed from two + scenarios: one with hazard fixed at the initial time point and one with + hazard fixed at the final time point. + + The same aggregation-based interpolation approach is applied to the + Vulnerability dimension. + + Attributes + ---------- + exposure_interp : Callable + The function used to interpolate sparse impact matrices over time + with changing exposure dimension. + Signature: (mat_start, mat_end, num_points, **kwargs) -> list[sparse.csr_matrix]. + hazard_interp : Callable + The function used to interpolate NumPy arrays of metrics over time + with changing hazard dimension. + Signature: (arr_start, arr_end, **kwargs) -> np.ndarray. + vulnerability_interp : Callable + The function used to interpolate NumPy arrays of metrics over time + with changing vulnerability dimension. + Signature: (arr_start, arr_end, **kwargs) -> np.ndarray. + """ + + exposure_interp: Callable + hazard_interp: Callable + vulnerability_interp: Callable + + def interp_over_exposure_dim( + self, + imp_E0: sparse.csr_matrix, + imp_E1: sparse.csr_matrix, + interpolation_range: int, + /, + **kwargs: Optional[Dict[str, Any]], + ) -> List[sparse.csr_matrix]: + """ + Interpolates between two impact matrices using the defined strategy for the exposure + dimension. + + This method calls the function assigned to :attr:`exposure_interp` to generate + a sequence of impact matrices of length "interpolation_range". + + Parameters + ---------- + imp_E0 : scipy.sparse.csr_matrix + A sparse matrix of the impacts at the start of the range. + imp_E1 : scipy.sparse.csr_matrix + A sparse matrix of the impacts at the end of the range. + interpolation_range : int + The total number of time points to interpolate, including the start and end. + **kwargs : Optional[Dict[str, Any]] + Keyword arguments to pass to the underlying :attr:`exposure_interp` function. + + Returns + ------- + list of scipy.sparse.csr_matrix + A list of ``interpolation_range`` interpolated impact matrices. + + Raises + ------ + ValueError + If the underlying interpolation function raises a ``ValueError`` + indicating incompatible matrix shapes. + """ + try: + res = self.exposure_interp(imp_E0, imp_E1, interpolation_range, **kwargs) + except ValueError as err: + if str(err) == "inconsistent shapes": + raise ValueError( + "Tried to interpolate impact matrices of different shapes. " + "A possible reason could be Exposures of different shapes." + ) from err + + raise err + + return res + + def interp_over_hazard_dim( + self, + metric_0: np.ndarray, + metric_1: np.ndarray, + /, + **kwargs: Optional[Dict[str, Any]], + ) -> np.ndarray: + """ + Generates the convex combination between two arrays of metrics using + the defined interpolation strategy for the hazard dimension. + + This method calls the function assigned to :attr:`hazard_interp`. + + Parameters + ---------- + metric_0 : numpy.ndarray + The starting array of metrics. + metric_1 : numpy.ndarray + The ending array of metrics. Must have the same shape as ``metric_0``. + **kwargs : Optional [Dict[str, Any]] + Keyword arguments to pass to the underlying :attr:`hazard_interp` function. + + Returns + ------- + numpy.ndarray + The resulting interpolated array. + """ + return self.hazard_interp(metric_0, metric_1, **kwargs) + + def interp_over_vulnerability_dim( + self, + metric_0: np.ndarray, + metric_1: np.ndarray, + /, + **kwargs: Optional[Dict[str, Any]], + ) -> np.ndarray: + """ + Generates the convex combination between two arrays of metrics using + the defined interpolation strategy for the hazard dimension. + + This method calls the function assigned to :attr:`vulnerability_interp`. + + Parameters + ---------- + metric_0 : numpy.ndarray + The starting array of metrics. + metric_1 : numpy.ndarray + The ending array of metrics. Must have the same shape as ``metric_0``. + **kwargs : Optional[Dict[str, Any]] + Keyword arguments to pass to the underlying :attr:`vulnerability_interp` function. + + Returns + ------- + numpy.ndarray + The resulting interpolated array. + """ + # Note: Assuming the Callable takes the exact positional arguments + return self.vulnerability_interp(metric_0, metric_1, **kwargs) + + +class CustomImpactInterpolationStrategy(ImpactInterpolationStrategy): + r"""Interface for interpolation strategies. + + This is the class to use to define custom interpolation strategies. + """ + + def __init__( + self, + exposure_interp: Callable, + hazard_interp: Callable, + vulnerability_interp: Callable, + ) -> None: + super().__init__() + self.exposure_interp = exposure_interp + self.hazard_interp = hazard_interp + self.vulnerability_interp = vulnerability_interp + + +class AllLinearStrategy(ImpactInterpolationStrategy): + r"""Linear interpolation strategy over all dimensions.""" + + def __init__(self) -> None: + super().__init__() + self.exposure_interp = linear_interp_matrix_elemwise + self.hazard_interp = linear_convex_combination + self.vulnerability_interp = linear_convex_combination + + +class ExponentialExposureStrategy(ImpactInterpolationStrategy): + r"""Exponential interpolation strategy for exposure and linear for Hazard and Vulnerability.""" + + def __init__(self) -> None: + super().__init__() + self.exposure_interp = exponential_interp_matrix_elemwise + self.hazard_interp = linear_convex_combination + self.vulnerability_interp = linear_convex_combination diff --git a/climada/trajectories/snapshot.py b/climada/trajectories/snapshot.py new file mode 100644 index 0000000000..1d5f778135 --- /dev/null +++ b/climada/trajectories/snapshot.py @@ -0,0 +1,202 @@ +""" +This file is part of CLIMADA. + +Copyright (C) 2017 ETH Zurich, CLIMADA contributors listed in AUTHORS. + +CLIMADA is free software: you can redistribute it and/or modify it under the +terms of the GNU General Public License as published by the Free +Software Foundation, version 3. + +CLIMADA is distributed in the hope that it will be useful, but WITHOUT ANY +WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A +PARTICULAR PURPOSE. See the GNU General Public License for more details. + +You should have received a copy of the GNU General Public License along +with CLIMADA. If not, see . + +--- + +This modules implements the Snapshot class. + +Snapshot are used to store a snapshot of Exposure, Hazard and Vulnerability +at a specific date. + +""" + +import copy +import datetime +import logging +from typing import cast + +import numpy as np +import pandas as pd + +from climada.entity.exposures import Exposures +from climada.entity.impact_funcs import ImpactFuncSet +from climada.entity.measures.base import Measure +from climada.hazard import Hazard + +LOGGER = logging.getLogger(__name__) + +__all__ = ["Snapshot"] + + +class Snapshot: + """ + A snapshot of exposure, hazard, and impact function at a specific date. + + Parameters + ---------- + exposure : Exposures + hazard : Hazard + impfset : ImpactFuncSet + date : datetime.date | str | pd.Timestamp + The date of the Snapshot, it can be an string representing a year, + a datetime object or a string representation of a datetime object. + measure : Measure | None, default None. + Measure associated with the Snapshot. The measure object is *not* applied + to the other parameters of the object (Exposure, Hazard, Impfset). + To create a `Snapshot` with a measure use `apply_measure()` instead (see notes). + The use of anything but None should be reserved for advanced users. + ref_only : bool, default False + Should the `Snapshot` contain deep copies of the Exposures, Hazard and Impfset (False) + or references only (True). + + Attributes + ---------- + date : datetime + Date of the snapshot. + measure: Measure | None + A possible measure associated with the snapshot. + + Notes + ----- + + Providing a measure to the init assumes that the (Exposure, Hazard, Impfset) triplet + already corresponds to the triplet once the measure is applied. Measure objects + contain "the changes to apply". Creating a consistent Snapshot with a measure should + be done by first creating a Snapshot with the "baseline" (Exposure, Hazard, Impfset) triplet + and calling `.apply_measure()`, which returns a new Snapshot object + with the measure applied. + + Instantiating a Snapshot with a measure directly does not garantee the + consistency between the triplet and the measure, and should be avoided. + + If `ref_only` is True (default) the object creates deep copies of the + exposure, hazard, and impact function set. + + Also note that exposure, hazard and impfset are read-only properties. + Consider snapshots as immutable objects. + + """ + + def __init__( + self, + *, + exposure: Exposures, + hazard: Hazard, + impfset: ImpactFuncSet, + date: datetime.date | str | pd.Timestamp, + measure: Measure | None = None, + ref_only: bool = False, + ) -> None: + self._exposure = exposure if ref_only else copy.deepcopy(exposure) + self._hazard = hazard if ref_only else copy.deepcopy(hazard) + self._impfset = impfset if ref_only else copy.deepcopy(impfset) + self._measure = measure if ref_only else copy.deepcopy(measure) + self._date = self._convert_to_timestamp(date) + + @property + def exposure(self) -> Exposures: + """Exposure data for the snapshot.""" + return self._exposure + + @property + def hazard(self) -> Hazard: + """Hazard data for the snapshot.""" + return self._hazard + + @property + def impfset(self) -> ImpactFuncSet: + """Impact function set data for the snapshot.""" + return self._impfset + + @property + def measure(self) -> Measure | None: + """(Adaptation) Measure data for the snapshot.""" + return self._measure + + @property + def date(self) -> pd.Timestamp: + """Date of the snapshot.""" + return self._date + + @property + def impact_calc_kwargs(self) -> dict: + """Convenience function for ImpactCalc class.""" + return { + "exposures": self.exposure, + "hazard": self.hazard, + "impfset": self.impfset, + } + + @staticmethod + def _convert_to_timestamp( + date_arg: str | datetime.date | pd.Timestamp | np.datetime64, + ) -> pd.Timestamp: + """ + Convert date argument of type str, datetime.date, + np.datetime64, or pandas Timestamp to a pandas Timestamp object. + """ + if isinstance(date_arg, str): + try: + date = pd.Timestamp(date_arg) + except (ValueError, TypeError) as exc: + raise ValueError( + "String must be in a valid date format (e.g., 'YYYY-MM-DD')" + ) from exc + + elif isinstance(date_arg, (datetime.date, pd.Timestamp, np.datetime64)): + date = pd.Timestamp(date_arg) + + else: + raise TypeError( + f"Unsupported type: {type(date_arg)}. Must be str, date, Timestamp, or datetime64." + ) + + # Final check for NaT (Not-a-Time) + if date is pd.NaT: + raise ValueError( + f"Could not resolve '{date_arg}' to a valid Pandas Timestamp." + ) + + return cast(pd.Timestamp, date) + + def apply_measure(self, measure: Measure) -> "Snapshot": + """Create a new snapshot by applying a Measure object. + + This method creates a new `Snapshot` object by applying a measure on + the current one. + + Parameters + ---------- + measure : Measure + The measure to be applied to the snapshot. + + Returns + ------- + The Snapshot with the measure applied. + + """ + + LOGGER.debug("Applying measure %s on snapshot %s", measure.name, id(self)) + exp, impfset, haz = measure.apply(self.exposure, self.impfset, self.hazard) + snap = Snapshot( + exposure=exp, + hazard=haz, + impfset=impfset, + date=self.date, + measure=measure, + ref_only=True, # Avoid unecessary copies of new objects + ) + return snap diff --git a/climada/trajectories/static_trajectory.py b/climada/trajectories/static_trajectory.py new file mode 100644 index 0000000000..b81ead7b1c --- /dev/null +++ b/climada/trajectories/static_trajectory.py @@ -0,0 +1,338 @@ +""" +This file is part of CLIMADA. + +Copyright (C) 2017 ETH Zurich, CLIMADA contributors listed in AUTHORS. + +CLIMADA is free software: you can redistribute it and/or modify it under the +terms of the GNU General Public License as published by the Free +Software Foundation, version 3. + +CLIMADA is distributed in the hope that it will be useful, but WITHOUT ANY +WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A +PARTICULAR PURPOSE. See the GNU General Public License for more details. + +You should have received a copy of the GNU General Public License along +with CLIMADA. If not, see . + +--- + +This file implements \"static\" risk trajectory objects, for an easier evaluation +of risk at multiple points in time (snapshots). + +""" + +import logging +from typing import Iterable + +import pandas as pd + +from climada.entity.disc_rates.base import DiscRates +from climada.trajectories.calc_risk_metrics import CalcRiskMetricsPoints +from climada.trajectories.constants import ( + AAI_METRIC_NAME, + AAI_PER_GROUP_METRIC_NAME, + COORD_ID_COL_NAME, + DATE_COL_NAME, + EAI_METRIC_NAME, + GROUP_COL_NAME, + MEASURE_COL_NAME, + METRIC_COL_NAME, + RETURN_PERIOD_METRIC_NAME, +) +from climada.trajectories.impact_calc_strat import ( + ImpactCalcComputation, + ImpactComputationStrategy, +) +from climada.trajectories.snapshot import Snapshot +from climada.trajectories.trajectory import ( + DEFAULT_ALLGROUP_NAME, + DEFAULT_DF_COLUMN_PRIORITY, + DEFAULT_RP, + RiskTrajectory, +) +from climada.util import log_level +from climada.util.dataframe_handling import reorder_dataframe_columns + +LOGGER = logging.getLogger(__name__) + +__all__ = ["StaticRiskTrajectory"] + + +class StaticRiskTrajectory(RiskTrajectory): + """This class implements static risk trajectories: objects that + regroup impacts computations for multiple dates. + + This class computes risk metrics over a series of `Snapshot` objects, + optionally applying risk discounting, and offers access to the results + in tidy formatted pandas DataFrames. + + Contrary to InterpolatedRiskTrajectories, it does not interpolate risk + between the snapshot and only provides results at each snapshot specific + date. + + """ + + POSSIBLE_METRICS = [ + EAI_METRIC_NAME, + AAI_METRIC_NAME, + RETURN_PERIOD_METRIC_NAME, + AAI_PER_GROUP_METRIC_NAME, + ] + """Class variable listing the risk metrics that can be computed. + + Currently: + + - eai, expected impact (per exposure point within a period of 1/frequency + unit of the hazard object) + - aai, average annual impact (aggregated eai over the whole exposure) + - aai_per_group, average annual impact per exposure subgroup (defined from + the exposure geodataframe) + - return_periods, estimated impacts aggregated over the whole exposure for + different return periods + + """ + + _DEFAULT_ALL_METRICS = [ + AAI_METRIC_NAME, + RETURN_PERIOD_METRIC_NAME, + AAI_PER_GROUP_METRIC_NAME, + ] + + def __init__( + self, + snapshots_list: Iterable[Snapshot], + *, + return_periods: Iterable[int] = DEFAULT_RP, + risk_disc_rates: DiscRates | None = None, + impact_computation_strategy: ImpactComputationStrategy | None = None, + ): + """Initialize a new `StaticRiskTrajectory`. + + Parameters + ---------- + snapshots_list : list[Snapshot] + The list of `Snapshot` object to compute risk from. + return_periods: list[int], optional + The return periods to use when computing the `return_periods_metric`. + Defaults to `DEFAULT_RP` ([20, 50, 100]). + risk_disc_rates: DiscRates, optional + The discount rate to apply to future risk. Defaults to None. + impact_computation_strategy: ImpactComputationStrategy, optional + The method used to calculate the impact from the (Haz,Exp,Vul) + for each snapshot. Defaults to :class:`ImpactCalcComputation`. + + """ + super().__init__( + snapshots_list, + return_periods=return_periods, + risk_disc_rates=risk_disc_rates, + ) + self._risk_metrics_calculators = CalcRiskMetricsPoints( + self._snapshots, + impact_computation_strategy=impact_computation_strategy + or ImpactCalcComputation(), + ) + + @property + def impact_computation_strategy(self) -> ImpactComputationStrategy: + """The approach or strategy used to calculate the impact from the snapshots.""" + return self._risk_metrics_calculators.impact_computation_strategy + + @impact_computation_strategy.setter + def impact_computation_strategy(self, value, /): + if not isinstance(value, ImpactComputationStrategy): + raise ValueError( + "The provided impact computation strategy is not an ImpactComputationStrategy, " + "please refer to the documentation to define your own strategies or stick to the " + "default one" + ) + + self._reset_metrics() + self._risk_metrics_calculators.impact_computation_strategy = value + + def _generic_metrics( + self, + /, + metric_name: str | None = None, + metric_meth: str | None = None, + **kwargs, + ) -> pd.DataFrame: + """Generic method to compute metrics based on the provided metric name and method. + + This method calls the appropriate method from the calculator to return + the results for the given metric, in a tidy formatted dataframe. + + It first checks whether the requested metric is a valid one. + Then looks for a possible cached value and otherwised asks the + calculators (`self._risk_metric_calculators`) to run the computation. + The results are then regrouped in a nice and tidy DataFrame. + If a `risk_disc_rates` was set, values are converted to net present values. + Results are then cached within `self.__metrics` and returned. + + Parameters + ---------- + metric_name : str, optional + The name of the metric to return results for. + metric_meth : str, optional + The name of the specific method of the calculator to call. + + Returns + ------- + pd.DataFrame + A tidy formatted dataframe of the risk metric computed for the + different snapshots. + + Notes + ----- + + The computation checks that there are no duplicated rows of results + for the same tuples (Date, Group, Measure, Metric, + [Coordinates for metrics on that level]) and takes the first row in + this case. + + + Raises + ------ + NotImplementedError + If the requested metric is not part of `POSSIBLE_METRICS`. + ValueError + If either of the arguments are not provided. + + """ + if metric_name is None or metric_meth is None: + raise ValueError("Both metric_name and metric_meth must be provided.") + + if metric_name not in self.POSSIBLE_METRICS: + raise NotImplementedError( + f"{metric_name} not implemented ({self.POSSIBLE_METRICS})." + ) + + # Construct the attribute name for storing the metric results + attr_name = f"_{metric_name}_metrics" + + if getattr(self, attr_name) is not None: + LOGGER.debug("Returning cached %s", attr_name) + return getattr(self, attr_name) + + with log_level(level="WARNING", name_prefix="climada"): + tmp = getattr(self._risk_metrics_calculators, metric_meth)(**kwargs) + if tmp is None: + return tmp + + tmp = tmp.set_index( + [DATE_COL_NAME, GROUP_COL_NAME, MEASURE_COL_NAME, METRIC_COL_NAME] + ) + if COORD_ID_COL_NAME in tmp.columns: + tmp = tmp.set_index([COORD_ID_COL_NAME], append=True) + + # When more than 2 snapshots, there might be duplicated rows, we need to remove them. + # Should not be the case in static trajectory, but in any case we really don't want + # duplicated rows, which would mess up some dataframe manipulation down the road. + if tmp.index.duplicated().any(): + LOGGER.warning( + "Duplicated rows were found in the results. Will keep the first one." + ) + tmp = tmp[~tmp.index.duplicated(keep="first")] + + tmp = tmp.reset_index() + if self._all_groups_name not in tmp[GROUP_COL_NAME].cat.categories: + tmp[GROUP_COL_NAME] = tmp[GROUP_COL_NAME].cat.add_categories( + [self._all_groups_name] + ) + tmp[GROUP_COL_NAME] = tmp[GROUP_COL_NAME].fillna(self._all_groups_name) + + if self._risk_disc_rates: + tmp = self.npv_transform(tmp, self._risk_disc_rates) + + tmp = reorder_dataframe_columns(tmp, DEFAULT_DF_COLUMN_PRIORITY) + + setattr(self, attr_name, tmp) + return getattr(self, attr_name) + + def eai_metrics(self, **kwargs) -> pd.DataFrame: + """Return the estimated annual impacts at each exposure point for each date. + + This method computes and return a `DataFrame` with eai metric + (for each exposure point) for each date. + + Notes + ----- + + This computation may become quite expensive for exposures with many points + (e.g., big areas with high resolution). + + """ + metric_df = self._compute_metrics( + metric_name=EAI_METRIC_NAME, metric_meth="calc_eai_gdf", **kwargs + ) + return metric_df + + def aai_metrics(self, **kwargs) -> pd.DataFrame: + """Return the average annual impacts for each date. + + This method computes and return a `DataFrame` with aai metric for each date. + + """ + + return self._compute_metrics( + metric_name=AAI_METRIC_NAME, metric_meth="calc_aai_metric", **kwargs + ) + + def return_periods_metrics(self, **kwargs) -> pd.DataFrame: + """Return the estimated impacts for different return periods. + + Return periods to estimate impacts for are defined by `self.return_periods`. + + """ + return self._compute_metrics( + metric_name=RETURN_PERIOD_METRIC_NAME, + metric_meth="calc_return_periods_metric", + return_periods=self.return_periods, + **kwargs, + ) + + def aai_per_group_metrics(self, **kwargs) -> pd.DataFrame: + """Return the average annual impacts for each exposure group ID. + + This method computes and return a `DataFrame` with aai metric for each + of the exposure group defined by a group id, for each date. + + """ + + return self._compute_metrics( + metric_name=AAI_PER_GROUP_METRIC_NAME, + metric_meth="calc_aai_per_group_metric", + **kwargs, + ) + + def per_date_risk_metrics( + self, + metrics: list[str] | None = None, + ) -> pd.DataFrame | pd.Series: + """Returns a DataFrame of risk metrics for each dates. + + This methods collects (and if needed computes) the `metrics` + (Defaulting to AAI_METRIC_NAME, RETURN_PERIOD_METRIC_NAME and AAI_PER_GROUP_METRIC_NAME). + + Parameters + ---------- + metrics : list[str], optional + The list of metrics to return (defaults to + [AAI_METRIC_NAME,RETURN_PERIOD_METRIC_NAME,AAI_PER_GROUP_METRIC_NAME]) + + Returns + ------- + pd.DataFrame | pd.Series + A tidy DataFrame with metric values for all possible dates. + + """ + + metrics = ( + [AAI_METRIC_NAME, RETURN_PERIOD_METRIC_NAME, AAI_PER_GROUP_METRIC_NAME] + if metrics is None + else metrics + ) + return pd.concat( + [getattr(self, f"{metric}_metrics")() for metric in metrics], + ignore_index=True, + ) diff --git a/climada/trajectories/test/conftest.py b/climada/trajectories/test/conftest.py new file mode 100644 index 0000000000..57a1d3a274 --- /dev/null +++ b/climada/trajectories/test/conftest.py @@ -0,0 +1,346 @@ +""" +This file is part of CLIMADA. + +Copyright (C) 2017 ETH Zurich, CLIMADA contributors listed in AUTHORS. + +CLIMADA is free software: you can redistribute it and/or modify it under the +terms of the GNU General Public License as published by the Free +Software Foundation, version 3. + +CLIMADA is distributed in the hope that it will be useful, but WITHOUT ANY +WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A +PARTICULAR PURPOSE. See the GNU General Public License for more details. + +You should have received a copy of the GNU General Public License along +with CLIMADA. If not, see . +--- + +A set of reusable fixtures for testing purpose. +""" + +import geopandas as gpd +import numpy as np +import pytest +from scipy.sparse import csr_matrix +from shapely.geometry import Point + +from climada.entity import Exposures, ImpactFunc, ImpactFuncSet +from climada.hazard import Centroids, Hazard +from climada.trajectories import Snapshot + +# --------------------------------------------------------------------------- +# Coordinate system and metadata +# --------------------------------------------------------------------------- +CRS_WGS84 = "EPSG:4326" + +# --------------------------------------------------------------------------- +# Exposure attributes +# --------------------------------------------------------------------------- +EXP_DESC = "Test exposure dataset" +EXPOSURE_REF_YEAR = 2020 +EXPOSURE_VALUE_UNIT = "USD" +VALUES = np.array([0, 1000, 2000, 3000, 4000, 5000]) +CATEGORIES = np.array([1, 1, 2, 1, 1, 3]) + +# Exposure coordinates +EXP_LONS = np.array([4, 4.25, 4.5, 4, 4.25, 4.5]) +EXP_LATS = np.array([45, 45, 45, 45.25, 45.25, 45.25]) + +# --------------------------------------------------------------------------- +# Hazard definition +# --------------------------------------------------------------------------- +HAZARD_TYPE = "TEST_HAZARD_TYPE" +HAZARD_UNIT = "TEST_HAZARD_UNIT" + +# Hazard centroid positions +HAZ_JITTER = 0.1 # To test centroid matching +HAZ_LONS = EXP_LONS + HAZ_JITTER +HAZ_LATS = EXP_LATS + HAZ_JITTER + +# Hazard events +EVENT_IDS = np.array([1, 2, 3, 4, 5]) +EVENT_NAMES = ["ev1", "ev2", "ev3", "ev4", "ev5"] +DATES = np.array([1, 2, 3, 4, 5]) + +# Frequency are choosen so that they cumulate nicely +# to correspond to 250, 100, 50, and 20y return periods (for impacts) +FREQUENCY = np.array([0.03, 0.01, 0.006, 0.004, 0.0]) +FREQUENCY_UNIT = "1/year" + +# Hazard maximum intensity +# 100 to match 0 to 100% idea +# also in line with linear 1:1 impact function +# for easy mental calculus +HAZARD_MAX_INTENSITY = 100 + +# --------------------------------------------------------------------------- +# Impact function +# --------------------------------------------------------------------------- +IMPF_ID = 1 +IMPF_NAME = "IMPF_1" + +# Sanity checks +for const in [VALUES, CATEGORIES, EXP_LONS, EXP_LATS]: + assert len(const) == len( + VALUES + ), "VALUES, REGIONS, CATEGORIES, EXP_LONS, EXP_LATS should all have the same lengths." + +for const in [EVENT_IDS, EVENT_NAMES, DATES, FREQUENCY]: + assert len(const) == len( + EVENT_IDS + ), "EVENT_IDS, EVENT_NAMES, DATES, FREQUENCY should all have the same lengths." + + +@pytest.fixture(scope="session") +def exposure_values(): + return VALUES.copy() + + +@pytest.fixture(scope="session") +def categories(): + return CATEGORIES.copy() + + +@pytest.fixture(scope="session") +def exposure_geometry(): + return [Point(lon, lat) for lon, lat in zip(EXP_LONS, EXP_LATS)] + + +@pytest.fixture(scope="session") +def exposures_factory( + exposure_values, + exposure_geometry, +): + def _make_exposures( + value_factor=1.0, + ref_year=EXPOSURE_REF_YEAR, + hazard_type=HAZARD_TYPE, + group_id=None, + ): + gdf = gpd.GeoDataFrame( + { + "value": exposure_values * value_factor, + f"impf_{hazard_type}": IMPF_ID, + "geometry": exposure_geometry, + }, + crs=CRS_WGS84, + ) + if group_id is not None: + gdf["group_id"] = group_id + + return Exposures( + data=gdf, + description=EXP_DESC, + ref_year=ref_year, + value_unit=EXPOSURE_VALUE_UNIT, + ) + + return _make_exposures + + +@pytest.fixture(scope="session") +def exposures(exposures_factory): + return exposures_factory() + + +@pytest.fixture(scope="session") +def hazard_frequency_factory(): + base = FREQUENCY + + def _make_frequency(scale=1.0): + return base * scale + + return _make_frequency + + +@pytest.fixture(scope="session") +def hazard_frequency(): + return hazard_frequency_factory() + + +@pytest.fixture(scope="session") +def hazard_intensity_factory(): + """ + Intensity matrix designed for analytical expectations: + - Event 1: zero + - Event 2: max intensity at first centroid + - Event 3: half max intensity at second centroid + - Event 4: quarter max intensity everywhere + """ + base = csr_matrix( + [ + [0, 0, 0, 0, 0, 0], + [HAZARD_MAX_INTENSITY, 0, 0, 0, 0, 0], + [0, HAZARD_MAX_INTENSITY / 2, 0, 0, 0, 0], + [ + HAZARD_MAX_INTENSITY / 4, + HAZARD_MAX_INTENSITY / 4, + HAZARD_MAX_INTENSITY / 4, + HAZARD_MAX_INTENSITY / 4, + HAZARD_MAX_INTENSITY / 4, + HAZARD_MAX_INTENSITY / 4, + ], + [ + HAZARD_MAX_INTENSITY, + HAZARD_MAX_INTENSITY, + HAZARD_MAX_INTENSITY, + HAZARD_MAX_INTENSITY, + HAZARD_MAX_INTENSITY, + HAZARD_MAX_INTENSITY, + ], + ] + ) + + def _make_intensity(scale=1.0): + return base * scale + + return _make_intensity + + +@pytest.fixture(scope="session") +def hazard_intensity_matrix(hazard_intensity_factory): + return hazard_intensity_factory() + + +@pytest.fixture(scope="session") +def centroids(): + return Centroids(lat=HAZ_LATS, lon=HAZ_LONS, crs=CRS_WGS84) + + +@pytest.fixture(scope="session") +def hazard_factory( + hazard_intensity_factory, + hazard_frequency_factory, + centroids, +): + def _make_hazard( + intensity_scale=1.0, + frequency_scale=1.0, + hazard_type=HAZARD_TYPE, + hazard_unit=HAZARD_UNIT, + ): + return Hazard( + haz_type=hazard_type, + units=hazard_unit, + centroids=centroids, + event_id=EVENT_IDS, + event_name=EVENT_NAMES, + date=DATES, + frequency=hazard_frequency_factory(scale=frequency_scale), + frequency_unit=FREQUENCY_UNIT, + intensity=hazard_intensity_factory(scale=intensity_scale), + ) + + return _make_hazard + + +@pytest.fixture(scope="session") +def hazard(hazard_factory): + return hazard_factory() + + +@pytest.fixture(scope="session") +def impf_factory(): + def _make_impf( + paa_scale=1.0, + max_intensity=HAZARD_MAX_INTENSITY, + hazard_type=HAZARD_TYPE, + hazard_unit=HAZARD_UNIT, + impf_id=IMPF_ID, + ): + return ImpactFunc( + haz_type=hazard_type, + intensity_unit=hazard_unit, + name=IMPF_NAME, + intensity=np.array([0, max_intensity / 2, max_intensity]), + mdd=np.array([0, 0.5, 1]), + paa=np.array([1, 1, 1]) * paa_scale, + id=impf_id, + ) + + return _make_impf + + +@pytest.fixture(scope="session") +def linear_impact_function(impf_factory): + return impf_factory() + + +@pytest.fixture(scope="session") +def impfset_factory(impf_factory): + def _make_impfset( + paa_scale=1.0, + max_intensity=HAZARD_MAX_INTENSITY, + hazard_type=HAZARD_TYPE, + hazard_unit=HAZARD_UNIT, + impf_id=IMPF_ID, + ): + return ImpactFuncSet( + [impf_factory(paa_scale, max_intensity, hazard_type, hazard_unit, impf_id)] + ) + + return _make_impfset + + +@pytest.fixture(scope="session") +def impfset(impfset_factory): + return impfset_factory() + + +@pytest.fixture(scope="session") +def snapshot_factory( + exposures_factory, + hazard_factory, + impfset_factory, +): + """ + Factory for Snapshot objects. + + Allows controlled construction of baseline / future / counterfactual + scenarios by scaling exposure values, hazard intensity, and impact function. + """ + + def _make_snapshot( + *, + date=EXPOSURE_REF_YEAR, + exposure_value_factor=1.0, + hazard_intensity_factor=1.0, + hazard_frequency_factor=1.0, + paa_scale=1.0, + group_id=None, + ): + exposures = exposures_factory( + value_factor=exposure_value_factor, ref_year=date, group_id=group_id + ) + + hazard = hazard_factory( + intensity_scale=hazard_intensity_factor, + frequency_scale=hazard_frequency_factor, + ) + + impfset = impfset_factory( + paa_scale=paa_scale, + ) + + return Snapshot( + exposure=exposures, + hazard=hazard, + impfset=impfset, + date=str(date), + ) + + return _make_snapshot + + +@pytest.fixture(scope="session") +def snapshot_base(snapshot_factory): + return snapshot_factory() + + +@pytest.fixture(scope="session") +def snapshot_future(snapshot_factory): + return snapshot_factory( + date=2040, + exposure_value_factor=2.0, + hazard_intensity_factor=2.0, + ) diff --git a/climada/trajectories/test/test_calc_risk_metrics.py b/climada/trajectories/test/test_calc_risk_metrics.py new file mode 100644 index 0000000000..3bf3d30cfc --- /dev/null +++ b/climada/trajectories/test/test_calc_risk_metrics.py @@ -0,0 +1,206 @@ +""" +This file is part of CLIMADA. + +Copyright (C) 2017 ETH Zurich, CLIMADA contributors listed in AUTHORS. + +CLIMADA is free software: you can redistribute it and/or modify it under the +terms of the GNU General Public License as published by the Free +Software Foundation, version 3. + +CLIMADA is distributed in the hope that it will be useful, but WITHOUT ANY +WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A +PARTICULAR PURPOSE. See the GNU General Public License for more details. + +You should have received a copy of the GNU General Public License along +with CLIMADA. If not, see . + +--- + +Unit tests for `calc_risk_metrics.py` . + +""" + +from unittest.mock import MagicMock, call, patch + +import numpy as np +import pandas as pd +import pytest + +from climada.entity.measures.base import Measure +from climada.trajectories.calc_risk_metrics import CalcRiskMetricsPoints +from climada.trajectories.constants import ( + AAI_METRIC_NAME, + COORD_ID_COL_NAME, + DATE_COL_NAME, + EAI_METRIC_NAME, + GROUP_COL_NAME, + MEASURE_COL_NAME, + METRIC_COL_NAME, + NO_MEASURE_VALUE, + RISK_COL_NAME, + UNIT_COL_NAME, +) +from climada.trajectories.impact_calc_strat import ( + ImpactCalcComputation, + ImpactComputationStrategy, +) +from climada.trajectories.snapshot import Snapshot +from climada.trajectories.test.conftest import CATEGORIES + + +@pytest.fixture(scope="module") +def sample_data(snapshot_factory): + """Fixture to manage expensive data loading and setup once for the module.""" + snap1 = snapshot_factory(date=2020, group_id=CATEGORIES) + snap2 = snapshot_factory(date=2022, hazard_intensity_factor=2, group_id=CATEGORIES) + snap3 = snapshot_factory( + date=2025, + hazard_intensity_factor=2, + exposure_value_factor=3, + group_id=CATEGORIES, + ) + return { + "snapshots": [snap1, snap2, snap3], + "expected_eai": np.array( + [ + [0.0, 4.0, 2.0, 3.0, 4.0, 5.0], + [0.0, 8.0, 4.0, 6.0, 8.0, 10.0], + [0.0, 24.0, 12.0, 18.0, 24.0, 30.0], + ] + ), + "expected_aai": np.array([18.0, 36.0, 108.0]), + "expected_aai_per_group": np.array( + [11.0, 2.0, 5.0, 22.0, 4.0, 10.0, 66.0, 12.0, 30.0] + ), + "expected_rp": np.array([0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 500.0, 1000.0, 3000.0]), + } + + +class TestCalcRiskMetricsPoints: + + @pytest.fixture(autouse=True) + def setup_calc(self, sample_data): + self.snapshots = sample_data["snapshots"] + self.calc = CalcRiskMetricsPoints( + self.snapshots, + impact_computation_strategy=ImpactCalcComputation(), + ) + self.expected = sample_data + + def test_reset_impact_data(self): + self.calc._impacts = "A" + self.calc._eai_gdf = "B" + self.calc._per_date_eai = "C" + self.calc._per_date_aai = "D" + + self.calc._reset_impact_data() + + assert self.calc._impacts is None + assert self.calc._eai_gdf is None + assert self.calc._per_date_aai is None + assert self.calc._per_date_eai is None + + def test_set_impact_computation_strategy(self): + mock_strat = MagicMock(spec=ImpactComputationStrategy) + self.calc.impact_computation_strategy = mock_strat + assert self.calc.impact_computation_strategy == mock_strat + + def test_set_impact_computation_strategy_wtype(self): + with pytest.raises( + ValueError, + match="The provided value is not an ImpactComputationStrategy object", + ): + self.calc.impact_computation_strategy = "NotAStrategy" + + @patch.object(CalcRiskMetricsPoints, "impact_computation_strategy") + def test_impacts_arrays(self, mock_impact_compute): + mock_impact_compute.compute_impacts.side_effect = ["A", "B", "C"] + results = self.calc.impacts + + expected_calls = [call(s.exposure, s.hazard, s.impfset) for s in self.snapshots] + mock_impact_compute.compute_impacts.assert_has_calls(expected_calls) + assert results == ["A", "B", "C"] + + def test_per_date_eai(self): + np.testing.assert_allclose( + self.calc.per_date_eai, self.expected["expected_eai"] + ) + + def test_per_date_aai(self): + np.testing.assert_allclose( + self.calc.per_date_aai, self.expected["expected_aai"] + ) + + def test_eai_gdf(self): + result_gdf = self.calc.calc_eai_gdf() + assert isinstance(result_gdf, pd.DataFrame) + assert result_gdf.shape[0] == sum(len(s.exposure.gdf) for s in self.snapshots) + + expected_cols = { + DATE_COL_NAME, + COORD_ID_COL_NAME, + GROUP_COL_NAME, + RISK_COL_NAME, + METRIC_COL_NAME, + MEASURE_COL_NAME, + UNIT_COL_NAME, + } + assert expected_cols.issubset(result_gdf.columns) + + np.testing.assert_allclose( + result_gdf[RISK_COL_NAME].values, self.expected["expected_eai"].flatten() + ) + assert (result_gdf[METRIC_COL_NAME] == EAI_METRIC_NAME).all() + assert result_gdf[MEASURE_COL_NAME].iloc[0] == NO_MEASURE_VALUE + assert ( + result_gdf[UNIT_COL_NAME].iloc[0] == self.snapshots[0].exposure.value_unit + ) + assert result_gdf[GROUP_COL_NAME].dtype.name == "category" + + def test_calc_aai_metric(self): + result_df = self.calc.calc_aai_metric() + assert len(result_df) == len(self.snapshots) + np.testing.assert_allclose( + result_df[RISK_COL_NAME].values, self.expected["expected_aai"] + ) + assert (result_df[METRIC_COL_NAME] == AAI_METRIC_NAME).all() + + def test_calc_aai_per_group_metric(self): + result_df = self.calc.calc_aai_per_group_metric() + assert len(result_df) == len(self.snapshots) * len(self.calc._group_id) + np.testing.assert_allclose( + result_df[RISK_COL_NAME].values, self.expected["expected_aai_per_group"] + ) + + def test_calc_return_periods_metric(self): + rps = [20, 50, 100] + result_df = self.calc.calc_return_periods_metric(rps) + assert len(result_df) == len(self.snapshots) * len(rps) + np.testing.assert_allclose( + result_df[RISK_COL_NAME].values, self.expected["expected_rp"] + ) + + unique_metrics = result_df[METRIC_COL_NAME].unique() + for rp in rps: + assert f"rp_{rp}" in unique_metrics + + @patch.object(Snapshot, "apply_measure") + @patch("climada.trajectories.calc_risk_metrics.CalcRiskMetricsPoints") + def test_apply_measure(self, mock_calc_class, mock_snap_apply): + mock_measure = MagicMock(spec=Measure) + mock_snap_apply.return_value = "MockedSnapshot" + + # We need the class mock to return a mock instance that has a .measure attribute + mock_instance = MagicMock(spec=CalcRiskMetricsPoints) + mock_calc_class.return_value = mock_instance + + result = self.calc.apply_measure(mock_measure) + + assert mock_snap_apply.call_count == len(self.snapshots) + mock_calc_class.assert_called_with( + ["MockedSnapshot", "MockedSnapshot", "MockedSnapshot"], + self.calc.impact_computation_strategy, + ) + # Note: In the original test, result.measure was checked. + # Since we mocked the return of CalcRiskMetricsPoints, we check the mock instance. + assert result == mock_instance diff --git a/climada/trajectories/test/test_impact_calc_strat.py b/climada/trajectories/test/test_impact_calc_strat.py new file mode 100644 index 0000000000..eb5a53a2c0 --- /dev/null +++ b/climada/trajectories/test/test_impact_calc_strat.py @@ -0,0 +1,97 @@ +""" +This file is part of CLIMADA. + +Copyright (C) 2017 ETH Zurich, CLIMADA contributors listed in AUTHORS. + +CLIMADA is free software: you can redistribute it and/or modify it under the +terms of the GNU General Public License as published by the Free +Software Foundation, version 3. + +CLIMADA is distributed in the hope that it will be useful, but WITHOUT ANY +WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A +PARTICULAR PURPOSE. See the GNU General Public License for more details. + +You should have received a copy of the GNU General Public License along +with CLIMADA. If not, see . + +--- + +Tests for impact_calc_strat + +""" + +from unittest.mock import MagicMock, patch + +import pytest + +from climada.engine import Impact +from climada.entity import ImpactFuncSet +from climada.entity.exposures import Exposures +from climada.hazard import Hazard +from climada.trajectories import Snapshot +from climada.trajectories.impact_calc_strat import ( + ImpactCalcComputation, + ImpactComputationStrategy, +) + +# --- Fixtures --- + + +@pytest.fixture +def mock_snapshot(): + """Provides a snapshot with mocked exposure, hazard, and impact functions.""" + snap = MagicMock(spec=Snapshot) + snap.exposure = MagicMock(spec=Exposures) + snap.hazard = MagicMock(spec=Hazard) + snap.impfset = MagicMock(spec=ImpactFuncSet) + return snap + + +@pytest.fixture +def strategy(): + """Provides an instance of the ImpactCalcComputation strategy.""" + return ImpactCalcComputation() + + +# --- Tests --- +def test_interface_compliance(strategy): + """Ensure the class correctly inherits from the Abstract Base Class.""" + assert isinstance(strategy, ImpactComputationStrategy) + assert isinstance(strategy, ImpactCalcComputation) + + +def test_compute_impacts(strategy, mock_snapshot): + """Test that compute_impacts calls the pre-transfer method correctly.""" + mock_impacts = MagicMock(spec=Impact) + + # We patch the ImpactCalc within trajectories + with patch("climada.trajectories.impact_calc_strat.ImpactCalc") as mock_ImpactCalc: + mock_ImpactCalc.return_value.impact.return_value = mock_impacts + result = strategy.compute_impacts( + exp=mock_snapshot.exposure, + haz=mock_snapshot.hazard, + vul=mock_snapshot.impfset, + ) + mock_ImpactCalc.assert_called_once_with( + exposures=mock_snapshot.exposure, + impfset=mock_snapshot.impfset, + hazard=mock_snapshot.hazard, + ) + mock_ImpactCalc.return_value.impact.assert_called_once() + assert result == mock_impacts + + +def test_cannot_instantiate_abstract_base_class(): + """Ensure ImpactComputationStrategy cannot be instantiated directly.""" + with pytest.raises(TypeError, match="Can't instantiate abstract class"): + ImpactComputationStrategy() # type: ignore + + +@pytest.mark.parametrize("invalid_input", [None, 123, "string"]) +def test_compute_impacts_type_errors(strategy, invalid_input): + """ + Smoke test: Ensure that if ImpactCalc raises errors due to bad input, + the strategy correctly propagates them. + """ + with pytest.raises(AttributeError): + strategy.compute_impacts(invalid_input, invalid_input, invalid_input) diff --git a/climada/trajectories/test/test_interpolated_risk_trajectory.py b/climada/trajectories/test/test_interpolated_risk_trajectory.py new file mode 100644 index 0000000000..5ed9992095 --- /dev/null +++ b/climada/trajectories/test/test_interpolated_risk_trajectory.py @@ -0,0 +1,1419 @@ +""" +This file is part of CLIMADA. + +Copyright (C) 2017 ETH Zurich, CLIMADA contributors listed in AUTHORS. + +CLIMADA is free software: you can redistribute it and/or modify it under the +terms of the GNU General Public License as published by the Free +Software Foundation, version 3. + +CLIMADA is distributed in the hope that it will be useful, but WITHOUT ANY +WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A +PARTICULAR PURPOSE. See the GNU General Public License for more details. + +You should have received a copy of the GNU General Public License along +with CLIMADA. If not, see . + +--- + +unit tests for interpolated_risk_trajectory + +""" + +import datetime +import unittest +from itertools import product +from unittest.mock import MagicMock, Mock, call, patch + +import numpy as np # For potential NaN/NA comparisons +import pandas as pd + +from climada.entity.disc_rates.base import DiscRates +from climada.trajectories.calc_risk_metrics import CalcRiskMetricsPeriod +from climada.trajectories.constants import ( + AAI_METRIC_NAME, + AAI_PER_GROUP_METRIC_NAME, + CONTRIBUTION_BASE_RISK_NAME, + CONTRIBUTION_EXPOSURE_NAME, + CONTRIBUTION_HAZARD_NAME, + CONTRIBUTION_INTERACTION_TERM_NAME, + CONTRIBUTION_VULNERABILITY_NAME, + CONTRIBUTIONS_METRIC_NAME, + COORD_ID_COL_NAME, + DATE_COL_NAME, + EAI_METRIC_NAME, + GROUP_COL_NAME, + MEASURE_COL_NAME, + METRIC_COL_NAME, + PERIOD_COL_NAME, + RETURN_PERIOD_METRIC_NAME, + RISK_COL_NAME, + UNIT_COL_NAME, +) +from climada.trajectories.impact_calc_strat import ( + ImpactCalcComputation, + ImpactComputationStrategy, +) +from climada.trajectories.interpolated_trajectory import ( + INDEXING_COLUMNS, + InterpolatedRiskTrajectory, +) +from climada.trajectories.interpolation import ( + AllLinearStrategy, + ExponentialExposureStrategy, + ImpactInterpolationStrategy, +) +from climada.trajectories.snapshot import Snapshot + + +class TestInterpolatedRiskTrajectory(unittest.TestCase): + def setUp(self): + # Common setup for all tests + self.dates1 = [ + pd.Period("2023-01-01", freq="Y"), + pd.Period("2024-01-01", freq="Y"), + ] + self.dates2 = [ + pd.Period("2025-01-01", freq="Y"), + pd.Period("2026-01-01", freq="Y"), + ] + self.groups = ["GroupA", "GroupB", pd.NA] + self.measures = ["MEAS1", "MEAS2"] + self.metrics = [AAI_METRIC_NAME] + self.aai_dates1 = pd.DataFrame( + product(self.dates1, self.groups, self.measures, self.metrics), + columns=INDEXING_COLUMNS, + ) + self.aai_dates1[RISK_COL_NAME] = np.arange(12) * 100 + self.aai_dates1[GROUP_COL_NAME] = self.aai_dates1[GROUP_COL_NAME].astype( + "category" + ) + + self.aai_dates2 = pd.DataFrame( + product(self.dates2, self.groups, self.measures, self.metrics), + columns=INDEXING_COLUMNS, + ) + self.aai_dates2[RISK_COL_NAME] = np.arange(12) * 100 + 1200 + self.aai_dates2[GROUP_COL_NAME] = self.aai_dates2[GROUP_COL_NAME].astype( + "category" + ) + + self.aai_alldates = pd.DataFrame( + product( + self.dates1 + self.dates2, self.groups, self.measures, self.metrics + ), + columns=INDEXING_COLUMNS, + ) + self.aai_alldates[RISK_COL_NAME] = np.arange(24) * 100 + self.aai_alldates[GROUP_COL_NAME] = self.aai_alldates[GROUP_COL_NAME].astype( + "category" + ) + self.aai_alldates[GROUP_COL_NAME] = self.aai_alldates[ + GROUP_COL_NAME + ].cat.add_categories(["All"]) + self.aai_alldates[GROUP_COL_NAME] = self.aai_alldates[GROUP_COL_NAME].fillna( + "All" + ) + self.expected_pre_npv_aai = self.aai_alldates + self.expected_pre_npv_aai = self.expected_pre_npv_aai[ + [ + DATE_COL_NAME, + GROUP_COL_NAME, + MEASURE_COL_NAME, + METRIC_COL_NAME, + RISK_COL_NAME, + ] + ] + + self.expected_npv_aai = pd.DataFrame( + product( + self.dates1 + self.dates2, self.groups, self.measures, self.metrics + ), + columns=INDEXING_COLUMNS, + ) + self.expected_npv_aai[RISK_COL_NAME] = np.arange(24) * 90 + self.expected_npv_aai[GROUP_COL_NAME] = self.expected_npv_aai[ + GROUP_COL_NAME + ].astype("category") + self.expected_npv_aai[GROUP_COL_NAME] = self.expected_npv_aai[ + GROUP_COL_NAME + ].cat.add_categories(["All"]) + self.expected_npv_aai[GROUP_COL_NAME] = self.expected_npv_aai[ + GROUP_COL_NAME + ].fillna("All") + expected_npv_df = self.expected_npv_aai + expected_npv_df = expected_npv_df[ + [ + GROUP_COL_NAME, + DATE_COL_NAME, + MEASURE_COL_NAME, + METRIC_COL_NAME, + RISK_COL_NAME, + ] + ] + self.mock_snapshot1 = MagicMock(spec=Snapshot) + self.mock_snapshot1.date = datetime.date(2023, 1, 1) + + self.mock_snapshot2 = MagicMock(spec=Snapshot) + self.mock_snapshot2.date = datetime.date(2024, 1, 1) + + self.mock_snapshot3 = MagicMock(spec=Snapshot) + self.mock_snapshot3.date = datetime.date(2025, 1, 1) + + self.snapshots_list: list[Snapshot] = [ + self.mock_snapshot1, + self.mock_snapshot2, + self.mock_snapshot3, + ] + # self.snapshots_list = cast(list[Snapshot], self.snapshots_list) + + # Mock interpolation strategy and impact computation strategy + self.mock_interpolation_strategy = MagicMock(spec=AllLinearStrategy) + self.mock_impact_computation_strategy = MagicMock(spec=ImpactCalcComputation) + + # Mock DiscRates if needed for NPV tests + self.mock_disc_rates = MagicMock(spec=DiscRates) + self.mock_disc_rates.years = [2023, 2024, 2025] + self.mock_disc_rates.rates = [0.01, 0.02, 0.03] # Example rates + + self.mock_risk_period_calc1 = MagicMock(spec=CalcRiskMetricsPeriod) + self.mock_risk_period_calc2 = MagicMock(spec=CalcRiskMetricsPeriod) + # Mock npv_transform return value + self.mock_risk_period_calc1.calc_aai_metric.return_value = self.aai_dates1 + self.mock_risk_period_calc2.calc_aai_metric.return_value = self.aai_dates2 + self.mock_risk_metric_calculators = [ + self.mock_risk_period_calc1, + self.mock_risk_period_calc2, + ] + + self.mock_interpolated_risk_traj = MagicMock(spec=InterpolatedRiskTrajectory) + self.mock_interpolated_risk_traj._risk_metrics_calcultators = ( + self.mock_risk_metric_calculators + ) + self.mock_interpolated_risk_traj._risk_disc_rates = ( + self.mock_disc_rates + ) # For NPV transform check + + # --- Test Initialization and Properties --- + # These tests focus on the __init__ method and property getters/setters. + + ## Test `__init__` method + @patch.object( + InterpolatedRiskTrajectory, "_reset_risk_metrics_calculators", return_value=1 + ) + def test_init_basic(self, mock_reset_metrics_calculators): + # Test basic initialization with defaults + rt = InterpolatedRiskTrajectory( + self.snapshots_list, + interpolation_strategy=self.mock_interpolation_strategy, + impact_computation_strategy=self.mock_impact_computation_strategy, + ) + self.assertEqual(rt.start_date, self.mock_snapshot1.date) + self.assertEqual(rt.end_date, self.mock_snapshot3.date) + self.assertIsNone(rt._risk_disc_rates) + mock_reset_metrics_calculators.assert_called_once_with( + self.snapshots_list, + "Y", + self.mock_interpolation_strategy, + self.mock_impact_computation_strategy, + ) + self.assertEqual(rt._risk_metrics_calculators, 1) + # Check that metrics are reset (initially None) + for metric in InterpolatedRiskTrajectory.POSSIBLE_METRICS: + self.assertIsNone(getattr(rt, "_" + metric + "_metrics")) + + @patch.object(InterpolatedRiskTrajectory, "_reset_risk_metrics_calculators") + def test_init_with_custom_params(self, mock_reset_calculators): + # Test initialization with custom parameters + mock_disc = Mock(spec=DiscRates) + mock_interp = Mock(spec=ImpactInterpolationStrategy) + mock_impact_compute = Mock(spec=ImpactComputationStrategy) + rt = InterpolatedRiskTrajectory( + self.snapshots_list, + time_resolution="MS", + risk_disc_rates=mock_disc, + interpolation_strategy=mock_interp, + impact_computation_strategy=mock_impact_compute, + ) + + mock_reset_calculators.assert_has_calls( + [call(self.snapshots_list, "MS", mock_interp, mock_impact_compute)] + ) + self.assertEqual(rt._risk_disc_rates, mock_disc) + + @patch.object(InterpolatedRiskTrajectory, "_reset_risk_metrics_calculators") + @patch.object(InterpolatedRiskTrajectory, "_reset_metrics", new_callable=Mock) + @patch( + "climada.trajectories.interpolated_trajectory.CalcRiskMetricsPeriod", + autospec=True, + ) + def test_set_impact_computation_strategy( + self, + mock_calc_risk_metrics, + mock_reset_metrics, + mock_reset_risk_metrics_calculators, + ): + mock_reset_risk_metrics_calculators.return_value = ( + self.mock_risk_metric_calculators + ) + rt = InterpolatedRiskTrajectory( + self.snapshots_list, + interpolation_strategy=self.mock_interpolation_strategy, + impact_computation_strategy=self.mock_impact_computation_strategy, + ) + mock_reset_metrics.assert_called_once() # Called during init + with self.assertRaises(ValueError): + rt.impact_computation_strategy = "A" + + # There is only one possibility at the moment so we just check against a new object + new_impact_calc = ImpactCalcComputation() + rt.impact_computation_strategy = new_impact_calc + self.assertEqual(rt.impact_computation_strategy, new_impact_calc) + mock_reset_metrics.assert_has_calls([call(), call()]) + for rp in self.mock_risk_metric_calculators: + self.assertEqual(rp.impact_computation_strategy, new_impact_calc) + + @patch.object(InterpolatedRiskTrajectory, "_reset_risk_metrics_calculators") + @patch.object(InterpolatedRiskTrajectory, "_reset_metrics", new_callable=Mock) + @patch( + "climada.trajectories.interpolated_trajectory.CalcRiskMetricsPeriod", + autospec=True, + ) + def test_set_interpolation_strategy( + self, + mock_calc_risk_metrics, + mock_reset_metrics, + mock_reset_risk_metrics_calculators, + ): + mock_reset_risk_metrics_calculators.return_value = ( + self.mock_risk_metric_calculators + ) + rt = InterpolatedRiskTrajectory( + self.snapshots_list, + interpolation_strategy=self.mock_interpolation_strategy, + impact_computation_strategy=self.mock_impact_computation_strategy, + ) + mock_reset_metrics.assert_called_once() # Called during init + with self.assertRaises(ValueError): + rt.interpolation_strategy = "A" + + # There is only one possibility at the moment so we just check against a new object + new_interp = ExponentialExposureStrategy() + rt.interpolation_strategy = new_interp + self.assertEqual(rt.interpolation_strategy, new_interp) + mock_reset_metrics.assert_has_calls([call(), call()]) + for rp in self.mock_risk_metric_calculators: + self.assertEqual(rp.interpolation_strategy, new_interp) + + @patch( + "climada.trajectories.interpolated_trajectory.CalcRiskMetricsPeriod", + autospec=True, + ) + def test_risk_periods_lazy_computation(self, MockCalcRiskPeriod): + # Test that _calc_risk_periods is called only once, lazily + rt = InterpolatedRiskTrajectory( + self.snapshots_list, + interpolation_strategy=self.mock_interpolation_strategy, + impact_computation_strategy=self.mock_impact_computation_strategy, + ) + + # First access should trigger calculation + risk_periods = rt._risk_metrics_calculators + MockCalcRiskPeriod.assert_has_calls( + [ + call( + self.mock_snapshot1, + self.mock_snapshot2, + time_resolution="Y", + interpolation_strategy=self.mock_interpolation_strategy, + impact_computation_strategy=self.mock_impact_computation_strategy, + ), + call( + self.mock_snapshot2, + self.mock_snapshot3, + time_resolution="Y", + interpolation_strategy=self.mock_interpolation_strategy, + impact_computation_strategy=self.mock_impact_computation_strategy, + ), + ] + ) + self.assertEqual(MockCalcRiskPeriod.call_count, 2) + self.assertIsInstance(risk_periods, list) + self.assertEqual(len(risk_periods), 2) # N-1 periods for N snapshots + + @patch( + "climada.trajectories.interpolated_trajectory.CalcRiskMetricsPeriod", + autospec=True, + ) + def test_calc_risk_periods_sorting(self, MockCalcRiskPeriod): + # Test that snapshots are sorted by date before pairing + unsorted_snapshots: list[Snapshot] = [ + self.mock_snapshot3, + self.mock_snapshot1, + self.mock_snapshot2, + ] + _ = InterpolatedRiskTrajectory(unsorted_snapshots) + # Access the property to trigger calculation + MockCalcRiskPeriod.assert_has_calls( + [ + call( + self.mock_snapshot1, + self.mock_snapshot2, + **MockCalcRiskPeriod.call_args[1], + ), + call( + self.mock_snapshot2, + self.mock_snapshot3, + **MockCalcRiskPeriod.call_args[1], + ), + ] + ) + self.assertEqual(MockCalcRiskPeriod.call_count, 2) + + @patch.object(InterpolatedRiskTrajectory, "_reset_metrics", new_callable=Mock) + @patch( + "climada.trajectories.interpolated_trajectory.CalcRiskMetricsPeriod", + autospec=True, + ) + def test_set_time_resolution( + self, mock_calc_risk_metrics_points, mock_reset_metrics + ): + rt = InterpolatedRiskTrajectory( + self.snapshots_list, + impact_computation_strategy=self.mock_impact_computation_strategy, + ) + mock_reset_metrics.assert_called_once() # Called during init + with self.assertRaises(ValueError): + rt.time_resolution = 75 + + # There is only one possibility at the moment so we just check against a new object + rt.time_resolution = "5M" + self.assertEqual(rt.time_resolution, "5M") + mock_reset_metrics.assert_has_calls([call(), call()]) + + # --- Test Generic Metric Computation (`_generic_metrics`) --- + # This is a core internal method and deserves thorough testing. + + @patch.object( + InterpolatedRiskTrajectory, "_reset_risk_metrics_calculators", new_callable=Mock + ) + @patch.object(InterpolatedRiskTrajectory, "npv_transform", new_callable=Mock) + def test_generic_metrics_basic_flow( + self, mock_npv_transform, mock_risk_metrics_calculators + ): + mock_risk_metrics_calculators.return_value = self.mock_risk_metric_calculators + mock_npv_transform.return_value = self.expected_npv_aai + rt = InterpolatedRiskTrajectory(self.snapshots_list) + rt._risk_disc_rates = self.mock_disc_rates + result = rt._generic_metrics( + metric_name=AAI_METRIC_NAME, metric_meth="calc_aai_metric" + ) + # Assertions + self.mock_risk_period_calc1.calc_aai_metric.assert_called_once() + self.mock_risk_period_calc2.calc_aai_metric.assert_called_once() + + # Check concatenated DataFrame before NPV + # We need to manually recreate the expected intermediate DataFrame before NPV for assertion + # npv_transform should be called with the correctly formatted (concatenated and ordered) DataFrame + # and the risk_disc_rates attribute + mock_npv_transform.assert_called_once() + pd.testing.assert_frame_equal( + mock_npv_transform.call_args[0][0].reset_index(drop=True), + self.expected_pre_npv_aai.reset_index(drop=True), + ) + self.assertEqual(mock_npv_transform.call_args[0][1], self.mock_disc_rates) + + pd.testing.assert_frame_equal( + result, self.expected_npv_aai + ) # Final result is from NPV transform + + # Check internal storage + stored_df = getattr(rt, "_aai_metrics") + # Assert that the stored DF is the one *before* NPV transformation + pd.testing.assert_frame_equal( + stored_df.reset_index(drop=True), + self.expected_npv_aai.reset_index(drop=True), + ) + + result2 = rt._generic_metrics( + metric_name=AAI_METRIC_NAME, metric_meth="calc_aai_metric" + ) + # Check no new calls + self.mock_risk_period_calc1.calc_aai_metric.assert_called_once() + self.mock_risk_period_calc2.calc_aai_metric.assert_called_once() + pd.testing.assert_frame_equal( + result2, + self.expected_npv_aai.reset_index(drop=True), + ) + + @patch.object( + InterpolatedRiskTrajectory, "_reset_risk_metrics_calculators", new_callable=Mock + ) + def test_generic_metrics_not_implemented_error( + self, mock_reset_risk_metrics_calculators + ): + rt = InterpolatedRiskTrajectory(self.snapshots_list) + with self.assertRaises(NotImplementedError): + rt._generic_metrics(metric_name="non_existent", metric_meth="some_method") + + @patch.object( + InterpolatedRiskTrajectory, "_reset_risk_metrics_calculators", new_callable=Mock + ) + def test_generic_metrics_value_error_no_name_or_method( + self, mock_reset_risk_metrics_calculators + ): + rt = InterpolatedRiskTrajectory(self.snapshots_list) + with self.assertRaises(ValueError): + rt._generic_metrics(metric_name=None, metric_meth="some_method") + with self.assertRaises(ValueError): + rt._generic_metrics(metric_name=AAI_METRIC_NAME, metric_meth=None) + + @patch.object( + InterpolatedRiskTrajectory, "_reset_risk_metrics_calculators", new_callable=Mock + ) + # @patch.object(InterpolatedRiskTrajectory, "npv_transform", new_callable=Mock) + def test_generic_metrics_None_concat_returns_empty( + self, mock_reset_risk_metrics_calculators + ): + self.mock_risk_period_calc1.calc_aai_per_group_metric.return_value = None + self.mock_risk_period_calc2.calc_aai_per_group_metric.return_value = None + mock_reset_risk_metrics_calculators.return_value = ( + self.mock_risk_metric_calculators + ) + rt = InterpolatedRiskTrajectory(self.snapshots_list) + # rt = self.mock_interpolated_risk_traj + # Mock CalcRiskPeriod instances return None, mimicking `calc_aai_per_group_metric` possibly + + result = rt._generic_metrics( + metric_name=AAI_PER_GROUP_METRIC_NAME, + metric_meth="calc_aai_per_group_metric", + ) + pd.testing.assert_frame_equal(result, pd.DataFrame()) + + @patch.object( + InterpolatedRiskTrajectory, "_reset_risk_metrics_calculators", new_callable=Mock + ) + # @patch.object(InterpolatedRiskTrajectory, "npv_transform", new_callable=Mock) + def test_generic_metrics_empty_df_concat_returns_empty( + self, mock_reset_risk_metrics_calculators + ): + self.mock_risk_period_calc1.calc_aai_per_group_metric.return_value = ( + pd.DataFrame() + ) + self.mock_risk_period_calc2.calc_aai_per_group_metric.return_value = ( + pd.DataFrame() + ) + mock_reset_risk_metrics_calculators.return_value = ( + self.mock_risk_metric_calculators + ) + rt = InterpolatedRiskTrajectory(self.snapshots_list) + # rt = self.mock_interpolated_risk_traj + # Mock CalcRiskPeriod instances return None, mimicking `calc_aai_per_group_metric` possibly + + result = rt._generic_metrics( + metric_name=AAI_PER_GROUP_METRIC_NAME, + metric_meth="calc_aai_per_group_metric", + ) + pd.testing.assert_frame_equal(result, pd.DataFrame()) + + @patch.object( + InterpolatedRiskTrajectory, "_reset_risk_metrics_calculators", new_callable=Mock + ) + @patch.object( + InterpolatedRiskTrajectory, + "_risk_contributions_post_treatment", + new_callable=Mock, + ) + def test_generic_metrics_risk_contribution_treatment( + self, + mock_risk_contributions_post_treatment, + mock_reset_risk_metrics_calculators, + ): + mock_risk_contributions_post_treatment.return_value = pd.DataFrame([42]) + self.mock_risk_period_calc1.calc_risk_contributions_metric.return_value = ( + self.aai_dates1 + ) + self.mock_risk_period_calc2.calc_risk_contributions_metric.return_value = ( + self.aai_dates2 + ) + mock_reset_risk_metrics_calculators.return_value = ( + self.mock_risk_metric_calculators + ) + rt = InterpolatedRiskTrajectory(self.snapshots_list) + # rt = self.mock_interpolated_risk_traj + # Mock CalcRiskPeriod instances return None, mimicking `calc_aai_per_group_metric` possibly + result = rt._generic_metrics( + metric_name=CONTRIBUTIONS_METRIC_NAME, + metric_meth="calc_risk_contributions_metric", + ) + mock_risk_contributions_post_treatment.assert_called_once() + pd.testing.assert_frame_equal(result, pd.DataFrame([42])) + + @patch.object( + InterpolatedRiskTrajectory, "_reset_risk_metrics_calculators", new_callable=Mock + ) + @patch.object(InterpolatedRiskTrajectory, "npv_transform", new_callable=Mock) + def test_generic_metrics_coord_id_handling( + self, mock_npv_transform, mock_risk_metric_calc + ): + mock_risk_metric_calc.return_value = self.mock_risk_metric_calculators + self.mock_risk_period_calc1.calc_eai_gdf.return_value = pd.DataFrame( + { + DATE_COL_NAME: [pd.Timestamp("2023-01-01"), pd.Timestamp("2023-01-01")], + GROUP_COL_NAME: pd.Categorical([pd.NA, pd.NA]), + MEASURE_COL_NAME: ["MEAS1", "MEAS1"], + METRIC_COL_NAME: [EAI_METRIC_NAME, EAI_METRIC_NAME], + COORD_ID_COL_NAME: [1, 2], + RISK_COL_NAME: [10.0, 20.0], + } + ) + self.mock_risk_period_calc2.calc_eai_gdf.return_value = pd.DataFrame() + rt = InterpolatedRiskTrajectory(self.snapshots_list) + result = rt._generic_metrics( + metric_name=EAI_METRIC_NAME, metric_meth="calc_eai_gdf" + ) + + expected_df = pd.DataFrame( + { + GROUP_COL_NAME: pd.Categorical(["All", "All"]), + DATE_COL_NAME: [pd.Timestamp("2023-01-01"), pd.Timestamp("2023-01-01")], + MEASURE_COL_NAME: ["MEAS1", "MEAS1"], + METRIC_COL_NAME: [EAI_METRIC_NAME, EAI_METRIC_NAME], + RISK_COL_NAME: [10.0, 20.0], + COORD_ID_COL_NAME: [ + 1, + 2, + ], # This column should remain and be placed at the end before risk if not in front_columns + } + ) + # The internal logic reorders columns, ensure it matches + cols_order = [ + DATE_COL_NAME, + GROUP_COL_NAME, + MEASURE_COL_NAME, + METRIC_COL_NAME, + COORD_ID_COL_NAME, + RISK_COL_NAME, + ] + pd.testing.assert_frame_equal(result[cols_order], expected_df[cols_order]) + + # --- Test Specific Metric Methods (e.g., `eai_metrics`, `aai_metrics`) --- + # These are mostly thin wrappers around _compute_metrics/_generic_metrics. + # Focus on ensuring they call _compute_metrics with the correct arguments. + + @patch.object(InterpolatedRiskTrajectory, "_compute_metrics") + def test_eai_metrics(self, mock_compute_metrics): + rt = InterpolatedRiskTrajectory(self.snapshots_list) + rt.eai_metrics(npv=True, some_arg="test") + mock_compute_metrics.assert_called_once_with( + npv=True, + metric_name=EAI_METRIC_NAME, + metric_meth="calc_eai_gdf", + some_arg="test", + ) + + @patch.object(InterpolatedRiskTrajectory, "_compute_metrics") + def test_aai_metrics(self, mock_compute_metrics): + rt = InterpolatedRiskTrajectory(self.snapshots_list) + rt.aai_metrics(other_arg=123) + mock_compute_metrics.assert_called_once_with( + metric_name=AAI_METRIC_NAME, metric_meth="calc_aai_metric", other_arg=123 + ) + + @patch.object(InterpolatedRiskTrajectory, "_compute_metrics") + def test_return_periods_metrics(self, mock_compute_metrics): + rt = InterpolatedRiskTrajectory(self.snapshots_list) + rt.return_periods_metrics(npv=True, rp_arg="xyz") + mock_compute_metrics.assert_called_once_with( + npv=True, + metric_name=RETURN_PERIOD_METRIC_NAME, + metric_meth="calc_return_periods_metric", + return_periods=rt.return_periods, + rp_arg="xyz", + ) + + @patch.object(InterpolatedRiskTrajectory, "_compute_metrics") + def test_aai_per_group_metrics(self, mock_compute_metrics): + rt = InterpolatedRiskTrajectory(self.snapshots_list) + rt.aai_per_group_metrics() + mock_compute_metrics.assert_called_once_with( + metric_name=AAI_PER_GROUP_METRIC_NAME, + metric_meth="calc_aai_per_group_metric", + ) + + @patch.object(InterpolatedRiskTrajectory, "_compute_metrics") + def test_risk_components_metrics(self, mock_compute_metrics): + rt = InterpolatedRiskTrajectory(self.snapshots_list) + rt.risk_contributions_metrics() + mock_compute_metrics.assert_called_once_with( + metric_name=CONTRIBUTIONS_METRIC_NAME, + metric_meth="calc_risk_contributions_metric", + ) + + ## Test `npv_transform` (class method) + def test_npv_transform_no_group_col(self): + df_input = pd.DataFrame( + { + DATE_COL_NAME: pd.to_datetime(["2023-01-01", "2024-01-01"] * 2), + MEASURE_COL_NAME: ["m1", "m1", "m2", "m2"], + METRIC_COL_NAME: [ + AAI_METRIC_NAME, + AAI_METRIC_NAME, + AAI_METRIC_NAME, + AAI_METRIC_NAME, + ], + RISK_COL_NAME: [100.0, 200.0, 80.0, 180.0], + } + ) + # Mock the internal calc_npv_cash_flows + with patch( + "climada.trajectories.trajectory.RiskTrajectory._calc_npv_cash_flows" + ) as mock_calc_npv: + # For each group, it will be called + mock_calc_npv.side_effect = [ + pd.Series( + [100.0 * (1 / (1 + 0.01)) ** 0, 200.0 * (1 / (1 + 0.02)) ** 1], + index=[pd.Timestamp("2023-01-01"), pd.Timestamp("2024-01-01")], + ), + pd.Series( + [80.0 * (1 / (1 + 0.01)) ** 0, 180.0 * (1 / (1 + 0.02)) ** 1], + index=[pd.Timestamp("2023-01-01"), pd.Timestamp("2024-01-01")], + ), + ] + result_df = InterpolatedRiskTrajectory.npv_transform( + df_input.copy(), self.mock_disc_rates + ) + # Assertions for mock calls + # Grouping by 'measure', 'metric' (default _grouper) + pd.testing.assert_series_equal( + mock_calc_npv.mock_calls[0].args[0], + pd.Series( + [100.0, 200.0], + index=pd.Index( + [ + pd.Timestamp("2023-01-01"), + pd.Timestamp("2024-01-01"), + ], + name=DATE_COL_NAME, + ), + name=("m1", AAI_METRIC_NAME), + ), + ) + assert mock_calc_npv.mock_calls[0].args[1] == pd.Timestamp("2023-01-01") + assert mock_calc_npv.mock_calls[0].args[2] == self.mock_disc_rates + pd.testing.assert_series_equal( + mock_calc_npv.mock_calls[1].args[0], + pd.Series( + [80.0, 180.0], + index=pd.Index( + [ + pd.Timestamp("2023-01-01"), + pd.Timestamp("2024-01-01"), + ], + name=DATE_COL_NAME, + ), + name=("m2", AAI_METRIC_NAME), + ), + ) + assert mock_calc_npv.mock_calls[1].args[1] == pd.Timestamp("2023-01-01") + assert mock_calc_npv.mock_calls[1].args[2] == self.mock_disc_rates + + expected_df = pd.DataFrame( + { + DATE_COL_NAME: pd.to_datetime(["2023-01-01", "2024-01-01"] * 2), + MEASURE_COL_NAME: ["m1", "m1", "m2", "m2"], + METRIC_COL_NAME: [ + AAI_METRIC_NAME, + AAI_METRIC_NAME, + AAI_METRIC_NAME, + AAI_METRIC_NAME, + ], + RISK_COL_NAME: [ + 100.0 * (1 / (1 + 0.01)) ** 0, + 200.0 * (1 / (1 + 0.02)) ** 1, + 80.0 * (1 / (1 + 0.01)) ** 0, + 180.0 * (1 / (1 + 0.02)) ** 1, + ], + } + ) + pd.testing.assert_frame_equal( + result_df.sort_values(DATE_COL_NAME).reset_index(drop=True), + expected_df.sort_values(DATE_COL_NAME).reset_index(drop=True), + rtol=1e-6, + ) + + def test_npv_transform_with_group_col(self): + df_input = pd.DataFrame( + { + DATE_COL_NAME: pd.to_datetime( + ["2023-01-01", "2024-01-01", "2023-01-01"] + ), + GROUP_COL_NAME: ["G1", "G1", "G2"], + MEASURE_COL_NAME: ["m1", "m1", "m1"], + METRIC_COL_NAME: [AAI_METRIC_NAME, AAI_METRIC_NAME, AAI_METRIC_NAME], + RISK_COL_NAME: [100.0, 200.0, 150.0], + } + ) + with patch( + "climada.trajectories.trajectory.RiskTrajectory._calc_npv_cash_flows" + ) as mock_calc_npv: + mock_calc_npv.side_effect = [ + # First group G1, m1, aai + pd.Series( + [100.0 * (1 / (1 + 0.01)) ** 0, 200.0 * (1 / (1 + 0.02)) ** 1], + index=[pd.Timestamp("2023-01-01"), pd.Timestamp("2024-01-01")], + ), + # Second group G2, m1, aai + pd.Series( + [150.0 * (1 / (1 + 0.01)) ** 0], index=[pd.Timestamp("2023-01-01")] + ), + ] + result_df = InterpolatedRiskTrajectory.npv_transform( + df_input.copy(), self.mock_disc_rates + ) + + expected_df = pd.DataFrame( + { + DATE_COL_NAME: pd.to_datetime( + ["2023-01-01", "2024-01-01", "2023-01-01"] + ), + GROUP_COL_NAME: ["G1", "G1", "G2"], + MEASURE_COL_NAME: ["m1", "m1", "m1"], + METRIC_COL_NAME: [ + AAI_METRIC_NAME, + AAI_METRIC_NAME, + AAI_METRIC_NAME, + ], + RISK_COL_NAME: [ + 100.0 * (1 / (1 + 0.01)) ** 0, + 200.0 * (1 / (1 + 0.02)) ** 1, + 150.0 * (1 / (1 + 0.01)) ** 0, + ], + } + ) + pd.testing.assert_frame_equal( + result_df.sort_values([GROUP_COL_NAME, DATE_COL_NAME]).reset_index( + drop=True + ), + expected_df.sort_values([GROUP_COL_NAME, DATE_COL_NAME]).reset_index( + drop=True + ), + rtol=1e-6, + ) + + @patch.object(InterpolatedRiskTrajectory, "_generic_metrics") + @patch.object(InterpolatedRiskTrajectory, "_date_to_period_agg") + def test_compute_period_metrics(self, mock_date_to_period, mock_generic_metrics): + mock_date_to_period.return_value = 42 + mock_generic_metrics.return_value = 46 + rt = InterpolatedRiskTrajectory(self.snapshots_list) + result = rt._compute_period_metrics("name", "method", other_args=5) + mock_generic_metrics.assert_called_once_with( + metric_name="name", metric_meth="method", other_args=5 + ) + mock_date_to_period.assert_called_once_with(46, grouper=rt._grouper) + self.assertEqual(result, 42) + + def test_risk_contributions_post_treatment(self): + # Create a sample DataFrame + data = { + GROUP_COL_NAME: ["All"] * 15, + DATE_COL_NAME: [ + pd.Period("2023-01-01", freq="Y"), + pd.Period("2024-01-02", freq="Y"), + pd.Period("2025-01-02", freq="Y"), + ] + * 5, + MEASURE_COL_NAME: ["measure1"] * 15, + METRIC_COL_NAME: [ + CONTRIBUTION_BASE_RISK_NAME, + CONTRIBUTION_BASE_RISK_NAME, + CONTRIBUTION_BASE_RISK_NAME, + CONTRIBUTION_EXPOSURE_NAME, + CONTRIBUTION_EXPOSURE_NAME, + CONTRIBUTION_EXPOSURE_NAME, + CONTRIBUTION_HAZARD_NAME, + CONTRIBUTION_HAZARD_NAME, + CONTRIBUTION_HAZARD_NAME, + CONTRIBUTION_VULNERABILITY_NAME, + CONTRIBUTION_VULNERABILITY_NAME, + CONTRIBUTION_VULNERABILITY_NAME, + CONTRIBUTION_INTERACTION_TERM_NAME, + CONTRIBUTION_INTERACTION_TERM_NAME, + CONTRIBUTION_INTERACTION_TERM_NAME, + ], + RISK_COL_NAME: [100, 100, 195, 0, 50, 100, 0, 10, 20, 0, 5, 10, 0, 30, 60], + } + df = pd.DataFrame(data) + + # Call the method + rt = InterpolatedRiskTrajectory(self.snapshots_list) + result_df = rt._risk_contributions_post_treatment(df) + + # Expected output + expected_data = { + DATE_COL_NAME: [ + pd.Period("2023-01-01", freq="Y"), + pd.Period("2024-01-02", freq="Y"), + pd.Period("2025-01-02", freq="Y"), + ] + * 5, + GROUP_COL_NAME: ["All"] * 15, + MEASURE_COL_NAME: ["measure1"] * 15, + METRIC_COL_NAME: [ + CONTRIBUTION_BASE_RISK_NAME, + CONTRIBUTION_BASE_RISK_NAME, + CONTRIBUTION_BASE_RISK_NAME, + CONTRIBUTION_EXPOSURE_NAME, + CONTRIBUTION_EXPOSURE_NAME, + CONTRIBUTION_EXPOSURE_NAME, + CONTRIBUTION_HAZARD_NAME, + CONTRIBUTION_HAZARD_NAME, + CONTRIBUTION_HAZARD_NAME, + CONTRIBUTION_VULNERABILITY_NAME, + CONTRIBUTION_VULNERABILITY_NAME, + CONTRIBUTION_VULNERABILITY_NAME, + CONTRIBUTION_INTERACTION_TERM_NAME, + CONTRIBUTION_INTERACTION_TERM_NAME, + CONTRIBUTION_INTERACTION_TERM_NAME, + ], + RISK_COL_NAME: [100, 100, 100, 0, 50, 150, 0, 10, 30, 0, 5, 15, 0, 30, 90], + } + expected_df = pd.DataFrame(expected_data) + + # Assert the result + pd.testing.assert_frame_equal( + result_df.reset_index(drop=True), expected_df.reset_index(drop=True) + ) + + # --- Test Per Period Risk Aggregation (`_per_period_risk`) --- + def test_per_period_risk_basic(self): + df_input = pd.DataFrame( + { + DATE_COL_NAME: pd.to_datetime( + ["2023-01-01", "2024-01-01", "2025-01-01", "2023-01-01"] + ), + GROUP_COL_NAME: ["All", "All", "All", "GroupB"], + MEASURE_COL_NAME: ["m1", "m1", "m1", "m1"], + METRIC_COL_NAME: [ + AAI_METRIC_NAME, + AAI_METRIC_NAME, + AAI_METRIC_NAME, + AAI_METRIC_NAME, + ], + RISK_COL_NAME: [100.0, 200.0, 300.0, 50.0], + } + ) + result_df = InterpolatedRiskTrajectory._date_to_period_agg( + df_input, grouper=InterpolatedRiskTrajectory._grouper + ) + + expected_df = pd.DataFrame( + { + PERIOD_COL_NAME: [ + "2023-01-01 to 2025-01-01", + "2023-01-01 to 2023-01-01", + ], + GROUP_COL_NAME: ["All", "GroupB"], + MEASURE_COL_NAME: ["m1", "m1"], + METRIC_COL_NAME: [AAI_METRIC_NAME, AAI_METRIC_NAME], + RISK_COL_NAME: [200.0, 50.0], # 100+200+300 for 'All', 50 for 'GroupB' + } + ) + # Sorting for comparison consistency + pd.testing.assert_frame_equal( + result_df.sort_values([GROUP_COL_NAME, PERIOD_COL_NAME]).reset_index( + drop=True + ), + expected_df.sort_values([GROUP_COL_NAME, PERIOD_COL_NAME]).reset_index( + drop=True + ), + ) + + def test_per_period_risk_multiple_risk_cols(self): + df_input = pd.DataFrame( + { + DATE_COL_NAME: pd.to_datetime(["2023-01-01", "2024-01-01"]), + GROUP_COL_NAME: ["All", "All"], + MEASURE_COL_NAME: ["m1", "m1"], + METRIC_COL_NAME: ["risk_components", "risk_components"], + CONTRIBUTION_BASE_RISK_NAME: [10.0, 20.0], + CONTRIBUTION_EXPOSURE_NAME: [5.0, 8.0], + } + ) + result_df = InterpolatedRiskTrajectory._date_to_period_agg( + df_input, + grouper=InterpolatedRiskTrajectory._grouper, + colname=[CONTRIBUTION_BASE_RISK_NAME, CONTRIBUTION_EXPOSURE_NAME], + ) + + expected_df = pd.DataFrame( + { + PERIOD_COL_NAME: ["2023-01-01 to 2024-01-01"], + GROUP_COL_NAME: ["All"], + MEASURE_COL_NAME: ["m1"], + METRIC_COL_NAME: ["risk_components"], + CONTRIBUTION_BASE_RISK_NAME: [15.0], + CONTRIBUTION_EXPOSURE_NAME: [6.5], + } + ) + pd.testing.assert_frame_equal(result_df, expected_df) + + def test_per_period_risk_non_yearly_intervals(self): + df_input = pd.DataFrame( + { + DATE_COL_NAME: pd.to_datetime( + ["2023-01-01", "2023-02-01", "2023-03-01"] + ), + GROUP_COL_NAME: ["All", "All", "All"], + MEASURE_COL_NAME: ["m1", "m1", "m1"], + METRIC_COL_NAME: [AAI_METRIC_NAME, AAI_METRIC_NAME, AAI_METRIC_NAME], + RISK_COL_NAME: [10.0, 20.0, 30.0], + } + ) + # Test with 'month' time_unit + result_df_month = InterpolatedRiskTrajectory._date_to_period_agg( + df_input, grouper=InterpolatedRiskTrajectory._grouper, time_unit="month" + ) + expected_df_month = pd.DataFrame( + { + PERIOD_COL_NAME: ["2023-01-01 to 2023-03-01"], + GROUP_COL_NAME: ["All"], + MEASURE_COL_NAME: ["m1"], + METRIC_COL_NAME: [AAI_METRIC_NAME], + RISK_COL_NAME: [20.0], + } + ) + pd.testing.assert_frame_equal(result_df_month, expected_df_month) + + # Introduce a gap for 'month' time_unit + df_gap = pd.DataFrame( + { + DATE_COL_NAME: pd.to_datetime( + ["2023-01-01", "2023-02-01", "2023-04-01"] + ), # Gap in March + GROUP_COL_NAME: ["All", "All", "All"], + MEASURE_COL_NAME: ["m1", "m1", "m1"], + METRIC_COL_NAME: [AAI_METRIC_NAME, AAI_METRIC_NAME, AAI_METRIC_NAME], + RISK_COL_NAME: [10.0, 20.0, 40.0], + } + ) + result_df_gap = InterpolatedRiskTrajectory._date_to_period_agg( + df_gap, grouper=InterpolatedRiskTrajectory._grouper, time_unit="month" + ) + expected_df_gap = pd.DataFrame( + { + PERIOD_COL_NAME: [ + "2023-01-01 to 2023-02-01", + "2023-04-01 to 2023-04-01", + ], + GROUP_COL_NAME: ["All", "All"], + MEASURE_COL_NAME: ["m1", "m1"], + METRIC_COL_NAME: [AAI_METRIC_NAME, AAI_METRIC_NAME], + RISK_COL_NAME: [15.0, 40.0], + } + ) + pd.testing.assert_frame_equal( + result_df_gap.sort_values(PERIOD_COL_NAME).reset_index(drop=True), + expected_df_gap.sort_values(PERIOD_COL_NAME).reset_index(drop=True), + ) + + # --- Test Combined Metrics (`per_date_risk_metrics`, `per_period_risk_metrics`) --- + + @patch.object(InterpolatedRiskTrajectory, "aai_metrics") + @patch.object(InterpolatedRiskTrajectory, "return_periods_metrics") + @patch.object(InterpolatedRiskTrajectory, "aai_per_group_metrics") + def test_per_date_risk_metrics_defaults( + self, mock_aai_per_group, mock_return_periods, mock_aai + ): + rt = InterpolatedRiskTrajectory(self.snapshots_list) + # Set up mock return values for each method + mock_aai.return_value = pd.DataFrame( + {METRIC_COL_NAME: [AAI_METRIC_NAME], RISK_COL_NAME: [100]} + ) + mock_return_periods.return_value = pd.DataFrame( + {METRIC_COL_NAME: ["rp"], RISK_COL_NAME: [50]} + ) + mock_aai_per_group.return_value = pd.DataFrame( + {METRIC_COL_NAME: ["aai_grp"], RISK_COL_NAME: [10]} + ) + + result = rt.per_date_risk_metrics() + + # Assert calls with default arguments + mock_aai.assert_called_once_with() + mock_return_periods.assert_called_once_with() + mock_aai_per_group.assert_called_once_with() + + # Assert concatenation + expected_df = pd.concat( + [ + mock_aai.return_value, + mock_return_periods.return_value, + mock_aai_per_group.return_value, + ] + ) + pd.testing.assert_frame_equal( + result.reset_index(drop=True), expected_df.reset_index(drop=True) + ) + + @patch.object(InterpolatedRiskTrajectory, "aai_metrics") + @patch.object(InterpolatedRiskTrajectory, "return_periods_metrics") + @patch.object(InterpolatedRiskTrajectory, "aai_per_group_metrics") + def test_per_date_risk_metrics_custom_metrics_and_rps( + self, mock_aai_per_group, mock_return_periods, mock_aai + ): + rt = InterpolatedRiskTrajectory(self.snapshots_list) + mock_aai.return_value = pd.DataFrame( + {METRIC_COL_NAME: [AAI_METRIC_NAME], RISK_COL_NAME: [100]} + ) + mock_return_periods.return_value = pd.DataFrame( + {METRIC_COL_NAME: ["rp"], RISK_COL_NAME: [50]} + ) + + custom_metrics = [AAI_METRIC_NAME, RETURN_PERIOD_METRIC_NAME] + result = rt.per_date_risk_metrics(metrics=custom_metrics) + + mock_aai.assert_called_once_with() + mock_return_periods.assert_called_once_with() + mock_aai_per_group.assert_not_called() # Not in custom_metrics + + expected_df = pd.concat( + [mock_aai.return_value, mock_return_periods.return_value] + ) + pd.testing.assert_frame_equal( + result.reset_index(drop=True), expected_df.reset_index(drop=True) + ) + + @patch.object(InterpolatedRiskTrajectory, "per_date_risk_metrics") + @patch.object(InterpolatedRiskTrajectory, "_date_to_period_agg") + def test_per_period_risk_metrics( + self, mock_per_period_risk, mock_per_date_risk_metrics + ): + rt = InterpolatedRiskTrajectory(self.snapshots_list) + mock_date_df = pd.DataFrame( + {METRIC_COL_NAME: [AAI_METRIC_NAME], RISK_COL_NAME: [100]} + ) + mock_per_date_risk_metrics.return_value = mock_date_df + mock_per_period_risk.return_value = pd.DataFrame( + {PERIOD_COL_NAME: ["P1"], RISK_COL_NAME: [200]} + ) + + test_metrics = [AAI_METRIC_NAME] + result = rt.per_period_risk_metrics(metrics=test_metrics, time_unit="month") + + mock_per_date_risk_metrics.assert_called_once_with( + metrics=test_metrics, time_unit="month" + ) + mock_per_period_risk.assert_called_once_with( + mock_date_df, grouper=rt._grouper + [UNIT_COL_NAME], time_unit="month" + ) + pd.testing.assert_frame_equal(result, mock_per_period_risk.return_value) + + # --- Test Plotting Related Methods --- + # These methods primarily generate data for plotting or call plotting functions. + # The actual plotting logic (matplotlib.pyplot calls) should be mocked. + + @patch.object(InterpolatedRiskTrajectory, "risk_contributions_metrics") + def test_calc_waterfall_plot_data(self, mock_risk_contributions_metrics): + rt = InterpolatedRiskTrajectory(self.snapshots_list) + rt.start_date = datetime.date(2023, 1, 1) + rt.end_date = datetime.date(2025, 1, 1) + + # Mock the return of risk_components_metrics + mock_risk_contributions_metrics.return_value = pd.DataFrame( + { + DATE_COL_NAME: pd.to_datetime( + ["2023-01-01"] * 5 + + ["2024-01-01"] * 5 + + ["2025-01-01"] * 5 + + ["2026-01-01"] * 5 + ), + METRIC_COL_NAME: [ + CONTRIBUTION_BASE_RISK_NAME, + CONTRIBUTION_EXPOSURE_NAME, + CONTRIBUTION_HAZARD_NAME, + CONTRIBUTION_VULNERABILITY_NAME, + CONTRIBUTION_INTERACTION_TERM_NAME, + ] + * 4, + RISK_COL_NAME: np.arange(20) + * 1.0, # Dummy data for different components and dates + } + ) # .pivot_table(index=DATE_COL_NAME, columns=METRIC_COL_NAME, values=RISK_COL_NAME) + # Flattened for simplicity, in reality it's more structured + + result = rt._calc_waterfall_plot_data( + start_date=datetime.date(2024, 1, 1), + end_date=datetime.date(2025, 1, 1), + ) + + mock_risk_contributions_metrics.assert_called_once_with() + + # Expected output should be filtered by date and unstacked + expected_df = pd.DataFrame( + { + DATE_COL_NAME: pd.to_datetime(["2024-01-01"] * 5 + ["2025-01-01"] * 5), + METRIC_COL_NAME: [ + CONTRIBUTION_BASE_RISK_NAME, + CONTRIBUTION_EXPOSURE_NAME, + CONTRIBUTION_HAZARD_NAME, + CONTRIBUTION_VULNERABILITY_NAME, + CONTRIBUTION_INTERACTION_TERM_NAME, + ] + * 2, + RISK_COL_NAME: np.array([5.0, 6, 7, 8, 9, 10, 11, 12, 13, 14]), + } + ).pivot_table( + index=DATE_COL_NAME, columns=METRIC_COL_NAME, values=RISK_COL_NAME + ) + pd.testing.assert_frame_equal( + result.sort_index(axis=1), expected_df.sort_index(axis=1) + ) # Sort columns for stable comparison + + @patch("matplotlib.pyplot.subplots") + @patch("matplotlib.dates.AutoDateLocator") + @patch("matplotlib.dates.ConciseDateFormatter") + @patch.object(InterpolatedRiskTrajectory, "_calc_waterfall_plot_data") + def test_plot_per_date_waterfall( + self, mock_calc_data, mock_formatter, mock_locator, mock_subplots + ): + rt = InterpolatedRiskTrajectory(self.snapshots_list) + rt.start_date = datetime.date(2023, 1, 1) + rt.end_date = datetime.date(2023, 1, 2) + + # Mock matplotlib objects + mock_ax = Mock() + mock_fig = Mock() + mock_subplots.return_value = (mock_fig, mock_ax) + mock_ax.get_ylim.return_value = (0, 100) # For ylim scaling + + # Mock data returned by _calc_waterfall_plot_data + mock_df_data = pd.DataFrame( + { + CONTRIBUTION_BASE_RISK_NAME: [10, 10], + CONTRIBUTION_EXPOSURE_NAME: [2, 3], + CONTRIBUTION_HAZARD_NAME: [5, 6], + CONTRIBUTION_VULNERABILITY_NAME: [1, 2], + CONTRIBUTION_INTERACTION_TERM_NAME: [0.5, 0.7], + }, + index=pd.period_range(start="2023-01-01", end="2023-01-02", freq="D"), + ) + mock_calc_data.return_value = mock_df_data + + # Call the method + fig, ax = rt.plot_time_waterfall() + + # Assertions + mock_calc_data.assert_called_once_with( + start_date=datetime.date(2023, 1, 1), + end_date=datetime.date(2023, 1, 2), + ) + mock_ax.stackplot.assert_called_once() + self.assertEqual( + mock_ax.stackplot.call_args[0][0].tolist(), + mock_df_data.index.to_timestamp().tolist(), # type: ignore + ) # Check x-axis data + self.assertEqual( + mock_ax.stackplot.call_args[0][1][0].tolist(), + mock_df_data[CONTRIBUTION_EXPOSURE_NAME].tolist(), + ) # Check first stacked data + mock_ax.set_title.assert_called_once_with( + "Contributions to change in risk between 2023-01-01 and 2023-01-02 (Average)" + ) + mock_ax.set_ylabel.assert_called_once_with("Deviation from base risk") + mock_ax.set_ylim.assert_called_once() # Check ylim was set + mock_ax.xaxis.set_major_locator.assert_called_once() + mock_ax.xaxis.set_major_formatter.assert_called_once() + self.assertEqual(fig, mock_fig) + self.assertEqual(ax, mock_ax) + + @patch("matplotlib.pyplot.subplots") + @patch.object(InterpolatedRiskTrajectory, "_calc_waterfall_plot_data") + def test_plot_waterfall(self, mock_calc_data, mock_subplots): + rt = InterpolatedRiskTrajectory(self.snapshots_list) + rt.start_date = datetime.date(2023, 1, 1) + rt.end_date = datetime.date(2024, 1, 1) + + mock_ax = Mock() + mock_fig = Mock() + mock_subplots.return_value = (mock_fig, mock_ax) + mock_ax.get_ylim.return_value = (0, 100) + + # Mock _calc_waterfall_plot_data to return a DataFrame for two dates, + # where the second date (end_date) is relevant for plot_waterfall + start_date = "2023-01-01" + end_date = "2024-01-01" + mock_data = pd.DataFrame( + { + DATE_COL_NAME: pd.to_datetime([start_date] * 5 + [end_date] * 5), + METRIC_COL_NAME: [ + CONTRIBUTION_BASE_RISK_NAME, + CONTRIBUTION_EXPOSURE_NAME, + CONTRIBUTION_HAZARD_NAME, + CONTRIBUTION_VULNERABILITY_NAME, + CONTRIBUTION_INTERACTION_TERM_NAME, + ] + * 2, + RISK_COL_NAME: [ + 10, + 2, + 5, + 1, + 0.5, + 15, + 3, + 7, + 2, + 1, + ], # values for 2023-01-01 and 2024-01-01 + } + ).pivot_table( + index=DATE_COL_NAME, columns=METRIC_COL_NAME, values=RISK_COL_NAME + ) + mock_calc_data.return_value = mock_data + # Call the method + ax = rt.plot_waterfall() + + # Assertions + mock_calc_data.assert_called_once_with( + start_date=datetime.date.fromisoformat(start_date), + end_date=datetime.date.fromisoformat(end_date), + ) + mock_ax.bar.assert_called_once() + # Verify the bar arguments are correct for the end_date data + end_date_data = mock_data.loc[pd.Timestamp(end_date)] + expected_values = [ + end_date_data[CONTRIBUTION_BASE_RISK_NAME], + end_date_data[CONTRIBUTION_EXPOSURE_NAME], + end_date_data[CONTRIBUTION_HAZARD_NAME], + end_date_data[CONTRIBUTION_VULNERABILITY_NAME], + end_date_data[CONTRIBUTION_INTERACTION_TERM_NAME], + end_date_data.sum(), + ] + # Compare values passed to bar + np.testing.assert_allclose(mock_ax.bar.call_args[0][1], expected_values) + start_date_p = pd.to_datetime(start_date).to_period(rt.time_resolution) + end_date_p = pd.to_datetime(end_date).to_period(rt.time_resolution) + mock_ax.set_title.assert_called_once_with( + f"Evolution of the contributions of risk between {start_date_p} and {end_date_p} (Average impact)" + ) + mock_ax.set_ylabel.assert_called_once_with("USD") + mock_ax.set_ylim.assert_called_once() + mock_ax.tick_params.assert_called_once_with(axis="x", labelrotation=90) + self.assertEqual(ax, mock_ax) + + # --- Test Private Helper Methods (`_reset_metrics`, `_get_risk_periods`) --- + + def test_reset_metrics(self): + rt = InterpolatedRiskTrajectory(self.snapshots_list) + # Set some metrics to non-None values + rt._eai_metrics = "dummy_eai" # type:ignore + rt._aai_metrics = "dummy_aai" # type:ignore + rt._reset_metrics() + + for metric in rt.POSSIBLE_METRICS: + self.assertIsNone(getattr(rt, "_" + metric + "_metrics")) + + def test_get_risk_periods(self): + # Create dummy CalcRiskPeriod mocks with specific dates + mock_rp1 = Mock() + mock_rp1.snapshot_start.date = datetime.date(2020, 1, 1) + mock_rp1.snapshot_end.date = datetime.date(2021, 1, 1) + + mock_rp2 = Mock() + mock_rp2.snapshot_start.date = datetime.date(2021, 1, 1) + mock_rp2.snapshot_end.date = datetime.date(2022, 1, 1) + + mock_rp3 = Mock() + mock_rp3.snapshot_start.date = datetime.date(2022, 1, 1) + mock_rp3.snapshot_end.date = datetime.date(2023, 1, 1) + + all_risk_periods: list[CalcRiskMetricsPeriod] = [mock_rp1, mock_rp2, mock_rp3] + + # Strict case + + # Test case 1: Full range, all periods included + result = InterpolatedRiskTrajectory._get_risk_periods( + all_risk_periods, datetime.date(2020, 1, 1), datetime.date(2023, 1, 1) + ) + self.assertEqual(len(result), 3) + self.assertListEqual(result, all_risk_periods) + + # Test case 1b: More than full range, all periods included + result = InterpolatedRiskTrajectory._get_risk_periods( + all_risk_periods, datetime.date(2018, 1, 1), datetime.date(2024, 1, 1) + ) + self.assertEqual(len(result), 3) + self.assertListEqual(result, all_risk_periods) + + # Test case 2: Range including some period + result = InterpolatedRiskTrajectory._get_risk_periods( + all_risk_periods, datetime.date(2021, 1, 1), datetime.date(2023, 1, 1) + ) + self.assertEqual(len(result), 2) + self.assertListEqual(result, all_risk_periods[1:]) + + # Test case 2: Range including no period + result = InterpolatedRiskTrajectory._get_risk_periods( + all_risk_periods, datetime.date(2021, 6, 1), datetime.date(2022, 6, 1) + ) + self.assertEqual(len(result), 0) + self.assertListEqual(result, []) + + # Overlap case + + # Test case 1: Full range, all periods included (should still work) + result = InterpolatedRiskTrajectory._get_risk_periods( + all_risk_periods, + datetime.date(2020, 1, 1), + datetime.date(2023, 1, 1), + strict=False, + ) + self.assertEqual(len(result), 3) + self.assertListEqual(result, all_risk_periods) + + # Test case 1b: More than full range, all periods included + result = InterpolatedRiskTrajectory._get_risk_periods( + all_risk_periods, + datetime.date(2018, 1, 1), + datetime.date(2024, 1, 1), + strict=False, + ) + self.assertEqual(len(result), 3) + self.assertListEqual(result, all_risk_periods) + + # Test case 2: Range including some period + result = InterpolatedRiskTrajectory._get_risk_periods( + all_risk_periods, + datetime.date(2021, 1, 1), + datetime.date(2023, 1, 1), + strict=False, + ) + self.assertEqual(len(result), 2) + self.assertListEqual(result, all_risk_periods[1:]) + + # Test case 2: Range including no period but overlap + result = InterpolatedRiskTrajectory._get_risk_periods( + all_risk_periods, + datetime.date(2021, 6, 1), + datetime.date(2022, 6, 1), + strict=False, + ) + self.assertEqual(len(result), 2) + self.assertListEqual(result, all_risk_periods[1:]) + + # Test case 2: Range including no period at all + result = InterpolatedRiskTrajectory._get_risk_periods( + all_risk_periods, + datetime.date(2024, 6, 1), + datetime.date(2026, 6, 1), + strict=False, + ) + self.assertEqual(len(result), 0) + self.assertListEqual(result, []) + + +if __name__ == "__main__": + TESTS = unittest.TestLoader().loadTestsFromTestCase(TestInterpolatedRiskTrajectory) + unittest.TextTestRunner(verbosity=2).run(TESTS) diff --git a/climada/trajectories/test/test_interpolation.py b/climada/trajectories/test/test_interpolation.py new file mode 100644 index 0000000000..da6f614fe1 --- /dev/null +++ b/climada/trajectories/test/test_interpolation.py @@ -0,0 +1,217 @@ +""" +This file is part of CLIMADA. + +Copyright (C) 2017 ETH Zurich, CLIMADA contributors listed in AUTHORS. + +CLIMADA is free software: you can redistribute it and/or modify it under the +terms of the GNU General Public License as published by the Free +Software Foundation, version 3. + +CLIMADA is distributed in the hope that it will be useful, but WITHOUT ANY +WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A +PARTICULAR PURPOSE. See the GNU General Public License for more details. + +You should have received a copy of the GNU General Public License along +with CLIMADA. If not, see . + +--- + +Tests for interpolation + +""" + +from unittest.mock import MagicMock + +import numpy as np +import pytest +from scipy.sparse import csr_matrix + +from climada.trajectories.interpolation import ( + AllLinearStrategy, + CustomImpactInterpolationStrategy, + ExponentialExposureStrategy, + exponential_convex_combination, + exponential_interp_matrix_elemwise, + linear_convex_combination, + linear_interp_matrix_elemwise, +) + +# --- Fixtures --- + + +@pytest.fixture +def interpolation_data(): + """Provides common matrices and constants for interpolation tests.""" + return { + "imp_mat0": csr_matrix(np.array([[1, 2], [3, 4]])), + "imp_mat1": csr_matrix(np.array([[5, 6], [7, 8]])), + "imp_mat2": csr_matrix(np.array([[5, 6, 7], [8, 9, 10]])), + "time_points": 5, + "rtol": 1e-5, + "atol": 1e-8, + "dummy_metric_0": np.array([10, 20, 30]), + "dummy_metric_1": np.array([100, 200, 300]), + "dummy_matrix_0": csr_matrix([[1, 2], [3, 4]]), + "dummy_matrix_1": csr_matrix([[10, 20], [30, 40]]), + } + + +# --- Tests for Interpolation Functions --- + + +def test_linear_interp_arrays(interpolation_data): + arr_start = np.array([10, 50, 100]) + arr_end = np.array([20, 100, 200]) + expected = np.array([10.0, 75.0, 200.0]) + result = linear_convex_combination(arr_start, arr_end) + np.testing.assert_allclose( + result, + expected, + rtol=interpolation_data["rtol"], + atol=interpolation_data["atol"], + ) + + +@pytest.mark.parametrize( + "func", [linear_convex_combination, exponential_convex_combination] +) +def test_convex_combination_shape_error(func): + arr_start = np.array([10, 100, 5]) + arr_end = np.array([20, 200]) + with pytest.raises(ValueError, match="different shapes"): + func(arr_start, arr_end) + + +def test_exponential_convex_combination_2d(interpolation_data): + arr_start = np.array([[1, 10, 100]] * 3) + arr_end = np.array([[2, 20, 200]] * 3) + expected = np.array( + [[1.0, 10.0, 100.0], [1.4142136, 14.142136, 141.42136], [2, 20, 200]] + ) + result = exponential_convex_combination(arr_start, arr_end) + np.testing.assert_allclose( + result, + expected, + rtol=interpolation_data["rtol"], + atol=interpolation_data["atol"], + ) + + +@pytest.mark.parametrize( + "func", [linear_convex_combination, exponential_convex_combination] +) +def test_convex_combinations_start_equals_end(interpolation_data, func): + """Test that if start and end are identical, the result is the same array.""" + arr = np.array([5.0, 5.0]) + result = func(arr, arr) + np.testing.assert_allclose(result, arr, rtol=interpolation_data["rtol"]) + + +@pytest.mark.parametrize( + "func,expected", + [ + ( + linear_interp_matrix_elemwise, + np.array( + [ + [[1.0, 2.0], [3.0, 4.0]], + [[2.0, 3.0], [4.0, 5.0]], + [[3.0, 4.0], [5.0, 6.0]], + [[4.0, 5.0], [6.0, 7.0]], + [[5.0, 6.0], [7.0, 8.0]], + ] + ), + ), + ( + exponential_interp_matrix_elemwise, + np.array( + [ + [[1.0, 2.0], [3.0, 4.0]], + [[1.49534878, 2.63214803], [3.70779275, 4.75682846]], + [[2.23606798, 3.46410162], [4.58257569, 5.65685425]], + [[3.34370152, 4.55901411], [5.66374698, 6.72717132]], + [[5.0, 6.0], [7.0, 8.0]], + ] + ), + ), + ], +) +def test_impmat_interpolate(interpolation_data, func, expected): + data = interpolation_data + result = func(data["imp_mat0"], data["imp_mat1"], data["time_points"]) + + assert len(result) == data["time_points"] + assert all(isinstance(mat, csr_matrix) for mat in result) + + dense = np.array([r.todense() for r in result]) + np.testing.assert_array_almost_equal(dense, expected) + + +# --- Tests for Interpolation Strategies --- + + +def test_custom_strategy_init(): + mock_func = lambda a, b, r: a + b + strategy = CustomImpactInterpolationStrategy(mock_func, mock_func, mock_func) + assert strategy.exposure_interp == mock_func + assert strategy.hazard_interp == mock_func + assert strategy.vulnerability_interp == mock_func + + +def test_custom_strategy_exposure_dim_error(interpolation_data): + mock_exposure = MagicMock(side_effect=ValueError("inconsistent shapes")) + strategy = CustomImpactInterpolationStrategy( + mock_exposure, linear_convex_combination, linear_convex_combination + ) + + with pytest.raises( + ValueError, match="Tried to interpolate impact matrices of different shape" + ): + strategy.interp_over_exposure_dim( + interpolation_data["dummy_matrix_0"], csr_matrix(np.array([[1]])), 3 + ) + + +# --- Tests for Concrete Strategies --- + + +def test_all_linear_strategy(interpolation_data): + data = interpolation_data + strategy = AllLinearStrategy() + + # Test property assignment + assert strategy.exposure_interp == linear_interp_matrix_elemwise + + # Test Hazard dim + result_haz = strategy.interp_over_hazard_dim( + data["dummy_metric_0"], data["dummy_metric_1"] + ) + expected_haz = linear_convex_combination( + data["dummy_metric_0"], data["dummy_metric_1"] + ) + np.testing.assert_allclose(result_haz, expected_haz) + + # Test Exposure dim + result_exp = strategy.interp_over_exposure_dim( + data["dummy_matrix_0"], data["dummy_matrix_1"], 3 + ) + assert len(result_exp) == 3 + # Check midpoint (index 1) manually + expected_mid = csr_matrix([[5.5, 11], [16.5, 22]]) + np.testing.assert_allclose(result_exp[1].data, expected_mid.data) + + +def test_exponential_exposure_strategy(interpolation_data): + data = interpolation_data + strategy = ExponentialExposureStrategy() + + result_exp = strategy.interp_over_exposure_dim( + data["dummy_matrix_0"], data["dummy_matrix_1"], 3 + ) + + # Midpoint should be geometric mean for exponential strategy + # sqrt(1*10) = 3.162278 + expected_mid_data = np.array([3.162278, 6.324555, 9.486833, 12.649111]) + np.testing.assert_allclose( + result_exp[1].data, expected_mid_data, rtol=data["rtol"], atol=data["atol"] + ) diff --git a/climada/trajectories/test/test_snapshot.py b/climada/trajectories/test/test_snapshot.py new file mode 100644 index 0000000000..77830d3b54 --- /dev/null +++ b/climada/trajectories/test/test_snapshot.py @@ -0,0 +1,162 @@ +import datetime +from unittest.mock import MagicMock + +import numpy as np +import pandas as pd +import pytest + +from climada.entity.exposures import Exposures +from climada.entity.impact_funcs import ImpactFunc, ImpactFuncSet +from climada.entity.measures.base import Measure +from climada.hazard import Hazard +from climada.trajectories.snapshot import Snapshot +from climada.util.constants import EXP_DEMO_H5, HAZ_DEMO_H5 + +# --- Fixtures --- + + +@pytest.fixture(scope="module") +def shared_data(): + """Load heavy HDF5 data once per module to speed up tests.""" + exposure = Exposures.from_hdf5(EXP_DEMO_H5) + hazard = Hazard.from_hdf5(HAZ_DEMO_H5) + impfset = ImpactFuncSet( + [ + ImpactFunc( + "TC", + 3, + intensity=np.array([0, 20]), + mdd=np.array([0, 0.5]), + paa=np.array([0, 1]), + ) + ] + ) + return exposure, hazard, impfset + + +@pytest.fixture +def mock_context(shared_data): + """Provides the exposure/hazard/impfset and a pre-configured mock measure.""" + exp, haz, impf = shared_data + + # Setup Mock Measure + mock_measure = MagicMock(spec=Measure) + mock_measure.name = "Test Measure" + + modified_exp = MagicMock(spec=Exposures) + modified_haz = MagicMock(spec=Hazard) + modified_imp = MagicMock(spec=ImpactFuncSet) + + mock_measure.apply.return_value = (modified_exp, modified_imp, modified_haz) + + return { + "exp": exp, + "haz": haz, + "imp": impf, + "measure": mock_measure, + "mod_exp": modified_exp, + "mod_haz": modified_haz, + "mod_imp": modified_imp, + "date": pd.Timestamp("2023"), + } + + +# --- Tests --- + + +@pytest.mark.parametrize( + "input_date,expected", + [ + ("2023", pd.Timestamp(2023, 1, 1)), + ("2023-01-01", pd.Timestamp(2023, 1, 1)), + (np.datetime64("2023-01-01"), pd.Timestamp(2023, 1, 1)), + (datetime.date(2023, 1, 1), pd.Timestamp(2023, 1, 1)), + (pd.Timestamp(2023, 1, 1), pd.Timestamp(2023, 1, 1)), + ], +) +def test_init_valid_dates(mock_context, input_date, expected): + """Test various valid date input formats using parametrization.""" + snapshot = Snapshot( + exposure=mock_context["exp"], + hazard=mock_context["haz"], + impfset=mock_context["imp"], + date=input_date, + ) + assert snapshot.date == expected + + +def test_init_invalid_date_format(mock_context): + with pytest.raises(ValueError, match=r"String must be in a valid date format"): + Snapshot( + exposure=mock_context["exp"], + hazard=mock_context["haz"], + impfset=mock_context["imp"], + date="invalid-date", + ) + + +def test_init_invalid_date_type(mock_context): + with pytest.raises( + TypeError, + match=r"Unsupported type", + ): + Snapshot( + exposure=mock_context["exp"], + hazard=mock_context["haz"], + impfset=mock_context["imp"], + date=2023.5, # type: ignore + ) + + +def test_properties(mock_context): + snapshot = Snapshot( + exposure=mock_context["exp"], + hazard=mock_context["haz"], + impfset=mock_context["imp"], + date=mock_context["date"], + ) + + # Check that it's a deep copy (new reference) + assert snapshot.exposure is not mock_context["exp"] + assert snapshot.hazard is not mock_context["haz"] + + assert snapshot.measure is None + + # Check data equality + pd.testing.assert_frame_equal(snapshot.exposure.gdf, mock_context["exp"].gdf) + assert snapshot.hazard.haz_type == mock_context["haz"].haz_type + assert snapshot.impfset == mock_context["imp"] + assert snapshot.date == mock_context["date"] + + +def test_reference(mock_context): + snapshot = Snapshot( + exposure=mock_context["exp"], + hazard=mock_context["haz"], + impfset=mock_context["imp"], + date=mock_context["date"], + ref_only=True, + ) + + # Check that it is a reference + assert snapshot.exposure is mock_context["exp"] + assert snapshot.hazard is mock_context["haz"] + assert snapshot.impfset is mock_context["imp"] + assert snapshot.measure is None + + +def test_apply_measure(mock_context): + snapshot = Snapshot( + exposure=mock_context["exp"], + hazard=mock_context["haz"], + impfset=mock_context["imp"], + date=mock_context["date"], + ) + new_snapshot = snapshot.apply_measure(mock_context["measure"]) + + assert new_snapshot.measure is not None + assert new_snapshot.measure.name == "Test Measure" + assert new_snapshot.exposure == mock_context["mod_exp"] + assert new_snapshot.hazard == mock_context["mod_haz"] + assert new_snapshot.impfset == mock_context["mod_imp"] + assert new_snapshot.date == mock_context["date"] diff --git a/climada/trajectories/test/test_static_risk_trajectory.py b/climada/trajectories/test/test_static_risk_trajectory.py new file mode 100644 index 0000000000..167edef0c8 --- /dev/null +++ b/climada/trajectories/test/test_static_risk_trajectory.py @@ -0,0 +1,371 @@ +""" +This file is part of CLIMADA. + +Copyright (C) 2017 ETH Zurich, CLIMADA contributors listed in AUTHORS. + +CLIMADA is free software: you can redistribute it and/or modify it under the +terms of the GNU General Public License as published by the Free +Software Foundation, version 3. + +CLIMADA is distributed in the hope that it will be useful, but WITHOUT ANY +WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A +PARTICULAR PURPOSE. See the GNU General Public License for more details. + +You should have received a copy of the GNU General Public License along +with CLIMADA. If not, see . + +--- + +unit tests for static_risk_trajectory + +""" + +import datetime +from itertools import product +from unittest.mock import MagicMock, patch + +import numpy as np +import pandas as pd +import pytest + +from climada.entity.disc_rates.base import DiscRates +from climada.trajectories.constants import ( + AAI_METRIC_NAME, + AAI_PER_GROUP_METRIC_NAME, + DATE_COL_NAME, + EAI_METRIC_NAME, + GROUP_COL_NAME, + MEASURE_COL_NAME, + METRIC_COL_NAME, + RISK_COL_NAME, +) +from climada.trajectories.impact_calc_strat import ImpactCalcComputation +from climada.trajectories.snapshot import Snapshot +from climada.trajectories.static_trajectory import ( + DEFAULT_ALLGROUP_NAME, + DEFAULT_RP, + StaticRiskTrajectory, +) +from climada.trajectories.trajectory import RiskTrajectory + +# --- Fixtures --- + + +@pytest.fixture +def mock_snapshots(): + """Provides a list of mock Snapshot objects with sequential dates.""" + snaps = [] + for year in [2023, 2024, 2025]: + m = MagicMock(spec=Snapshot) + m.date = datetime.date(year, 1, 1) + snaps.append(m) + return snaps + + +@pytest.fixture +def mock_disc_rates(): + """Provides a mock DiscRates object.""" + dr = MagicMock(spec=DiscRates) + dr.years = [2023, 2024, 2025] + dr.rates = [0.01, 0.02, 0.03] + return dr + + +@pytest.fixture +def rt_basic(mock_snapshots): + """A basic StaticRiskTrajectory instance.""" + return StaticRiskTrajectory(mock_snapshots) + + +@pytest.fixture +def trajectory_metadata(): + """Common metadata for DataFrame generation.""" + return { + "dates1": [pd.Timestamp("2023-01-01"), pd.Timestamp("2024-01-01")], + "dates2": [pd.Timestamp("2026-01-01")], + "groups": ["GroupA", "GroupB", pd.NA], + "measures": ["MEAS1", "MEAS2"], + "metrics": [AAI_METRIC_NAME], + } + + +@pytest.fixture +def expected_aai_data(trajectory_metadata): + """Generates the expected AAI DataFrames used for comparison.""" + meta = trajectory_metadata + all_dates = meta["dates1"] + meta["dates2"] + + df = pd.DataFrame( + product(meta["groups"], all_dates, meta["measures"], meta["metrics"]), + columns=[GROUP_COL_NAME, DATE_COL_NAME, MEASURE_COL_NAME, METRIC_COL_NAME], + ) + df[RISK_COL_NAME] = np.arange(len(df)) * 100.0 + + # Handle Categories and Nulls + df[GROUP_COL_NAME] = df[GROUP_COL_NAME].astype("category") + df[GROUP_COL_NAME] = df[GROUP_COL_NAME].cat.add_categories([DEFAULT_ALLGROUP_NAME]) + df[GROUP_COL_NAME] = df[GROUP_COL_NAME].fillna(DEFAULT_ALLGROUP_NAME) + + cols = [ + DATE_COL_NAME, + GROUP_COL_NAME, + MEASURE_COL_NAME, + METRIC_COL_NAME, + RISK_COL_NAME, + ] + return df[cols] + + +@pytest.fixture +def mock_components(): + """Provides standard CLIMADA mock objects.""" + snaps = [ + MagicMock(spec=Snapshot, date=datetime.date(2023 + i, 1, 1)) for i in range(3) + ] + strat = MagicMock(spec=ImpactCalcComputation) + dr = MagicMock( + spec=DiscRates, years=[2023, 2024, 2025, 2026], rates=[0.01, 0.02, 0.03, 0.04] + ) + return {"snaps": snaps, "strat": strat, "disc_rates": dr} + + +# --- Pure RiskTrajectory Tests --- + + +def test_init_basic(rt_basic, mock_snapshots): + assert rt_basic.start_date == mock_snapshots[0].date + assert rt_basic.end_date == mock_snapshots[-1].date + assert rt_basic._risk_disc_rates is None + assert rt_basic._all_groups_name == DEFAULT_ALLGROUP_NAME + assert rt_basic._return_periods == DEFAULT_RP + + for metric in StaticRiskTrajectory.POSSIBLE_METRICS: + assert getattr(rt_basic, f"_{metric}_metrics") is None + + +def test_init_args(mock_snapshots, mock_disc_rates): + custom_rp = [10, 20] + rt = StaticRiskTrajectory( + mock_snapshots, + return_periods=custom_rp, + risk_disc_rates=mock_disc_rates, + ) + assert rt._risk_disc_rates == mock_disc_rates + assert rt.return_periods == custom_rp + + +# --- Property & Setter Tests --- + + +def test_set_return_periods(rt_basic): + with pytest.raises(ValueError): + rt_basic.return_periods = "A" + + rt_basic.return_periods = [1, 2] + assert rt_basic.return_periods == [1, 2] + + +def test_set_disc_rates(rt_basic, mock_disc_rates): + # Mock the reset_metrics method on the instance + with patch.object(rt_basic, "_reset_metrics", wraps=rt_basic._reset_metrics) as spy: + with pytest.raises(ValueError): + rt_basic.risk_disc_rates = "A" + + rt_basic.risk_disc_rates = mock_disc_rates + # Once in __init__, once in setter + assert spy.call_count == 1 + assert rt_basic.risk_disc_rates == mock_disc_rates + + +# --- NPV Transformation Tests --- + + +def test_npv_transform_no_group_col(mock_disc_rates): + df_input = pd.DataFrame( + { + "date": pd.to_datetime(["2023-01-01", "2024-01-01"] * 2), + "measure": ["m1", "m1", "m2", "m2"], + "metric": [AAI_METRIC_NAME] * 4, + "risk": [100.0, 200.0, 80.0, 180.0], + } + ) + + with patch( + "climada.trajectories.trajectory.RiskTrajectory._calc_npv_cash_flows" + ) as mock_calc: + # Side effects to simulate discounted values + mock_calc.side_effect = [ + pd.Series( + [99.0, 196.0], index=pd.to_datetime(["2023-01-01", "2024-01-01"]) + ), + pd.Series( + [79.2, 176.4], index=pd.to_datetime(["2023-01-01", "2024-01-01"]) + ), + ] + + _ = RiskTrajectory.npv_transform(df_input.copy(), mock_disc_rates) + + # Check calls: Grouping should happen by (measure, metric) + assert mock_calc.call_count == 2 + # Verify first group args + args, _ = mock_calc.call_args_list[0] + assert args[1] == pd.Timestamp("2023-01-01") + assert args[2] == mock_disc_rates + + +def test_calc_npv_cash_flows_logic(mock_disc_rates): + """Standalone test for the math inside _calc_npv_cash_flows.""" + cash_flows = pd.Series( + [100, 200, 300], + index=pd.to_datetime(["2023-01-01", "2024-01-01", "2025-01-01"]), + ) + start_date = datetime.date(2023, 1, 1) + + # NPV Factor: Product[ (1 / (1 + rate_i))] + # For a constant rate or 0.01 + # 2023: (1/1.01)^0 = 1.0 -> 100 + # 2024: (1/1.01)^1 = 0.99099... -> 198.019... + # 2025: (1/1.01)^2 = 0.98029... -> 294.088... + + result = RiskTrajectory._calc_npv_cash_flows( + cash_flows, start_date, mock_disc_rates + ) + assert result.iloc[0] == pytest.approx(100.0) + assert result.iloc[1] == pytest.approx(200 / (1.02)) + assert result.iloc[2] == pytest.approx(300 * (1 / 1.02) * (1 / 1.03)) + + +def test_calc_npv_cash_flows_invalid_index(mock_disc_rates): + cash_flows = pd.Series([100, 200]) # No datetime index + with pytest.raises(ValueError, match="PeriodIndex or DatetimeIndex"): + RiskTrajectory._calc_npv_cash_flows( + cash_flows, datetime.date(2023, 1, 1), mock_disc_rates + ) + + +# ---- StaticRiskTrajectory tests --- + +# --- Metric Computation Tests --- + + +def test_compute_metrics(rt_basic): + with patch.object( + StaticRiskTrajectory, "_generic_metrics", return_value="42" + ) as mock_generic: + result = rt_basic._compute_metrics( + metric_name="dummy", metric_meth="meth", arg1="A", arg2=12 + ) + + mock_generic.assert_called_once_with( + metric_name="dummy", metric_meth="meth", arg1="A", arg2=12 + ) + assert result == "42" + + +def test_init_basic_static(mock_components): + # Patch the calculator class used inside __init__ + with patch( + "climada.trajectories.static_trajectory.CalcRiskMetricsPoints", autospec=True + ) as mock_calc_cls: + rt = StaticRiskTrajectory( + mock_components["snaps"], + impact_computation_strategy=mock_components["strat"], + ) + + mock_calc_cls.assert_called_once_with( + mock_components["snaps"], + impact_computation_strategy=mock_components["strat"], + ) + assert rt.start_date == mock_components["snaps"][0].date + + +def test_set_impact_strategy_resets(mock_components): + rt = StaticRiskTrajectory(mock_components["snaps"]) + with patch.object(rt, "_reset_metrics", wraps=rt._reset_metrics) as spy_reset: + new_strat = ImpactCalcComputation() + rt.impact_computation_strategy = new_strat + + assert rt.impact_computation_strategy == new_strat + # Called once in init, once in setter + assert spy_reset.call_count == 1 + + +# --- Logic & Metric Tests --- + + +def test_generic_metrics_caching_and_npv(mock_components, expected_aai_data): + """Tests the complex logic of _generic_metrics including NPV transform and caching.""" + rt = StaticRiskTrajectory( + mock_components["snaps"], risk_disc_rates=mock_components["disc_rates"] + ) + + # Mock the internal calculator's method + mock_calc = MagicMock() + mock_calc.calc_aai_metric.return_value = expected_aai_data + rt._risk_metrics_calculators = mock_calc + + # Mock NPV transform to return a modified version + npv_data = expected_aai_data.copy() + npv_data[RISK_COL_NAME] *= 0.9 + with patch.object(rt, "npv_transform", return_value=npv_data) as mock_npv: + + # First call + result = rt._generic_metrics(AAI_METRIC_NAME, "calc_aai_metric") + + mock_calc.calc_aai_metric.assert_called_once() + mock_npv.assert_called_once() + pd.testing.assert_frame_equal(result, npv_data) + + # Verify Internal Cache + assert rt._aai_metrics is not None # type: ignore + + # Second call (should be cached) + result2 = rt._generic_metrics(AAI_METRIC_NAME, "calc_aai_metric") + assert mock_calc.calc_aai_metric.call_count == 1 # No new call + pd.testing.assert_frame_equal(result2, npv_data) + + +@pytest.mark.parametrize( + "metric_name, method_name, attr_name", + [ + (EAI_METRIC_NAME, "calc_eai_gdf", "eai_metrics"), + (AAI_METRIC_NAME, "calc_aai_metric", "aai_metrics"), + ( + AAI_PER_GROUP_METRIC_NAME, + "calc_aai_per_group_metric", + "aai_per_group_metrics", + ), + ], +) +def test_metric_wrappers(mock_components, metric_name, method_name, attr_name): + """Uses parametrization to test all simple metric wrapper methods at once.""" + rt = StaticRiskTrajectory(mock_components["snaps"]) + with patch.object(rt, "_compute_metrics") as mock_compute: + wrapper_func = getattr(rt, attr_name) + wrapper_func(test_arg="val") + mock_compute.assert_called_once_with( + metric_name=metric_name, metric_meth=method_name, test_arg="val" + ) + + +def test_per_date_risk_metrics_aggregation(mock_components): + rt = StaticRiskTrajectory(mock_components["snaps"]) + + # Setup mock returns for the constituent parts + df_aai = pd.DataFrame({METRIC_COL_NAME: ["aai"], RISK_COL_NAME: [100]}) + df_rp = pd.DataFrame({METRIC_COL_NAME: ["rp"], RISK_COL_NAME: [50]}) + df_grp = pd.DataFrame({METRIC_COL_NAME: ["grp"], RISK_COL_NAME: [10]}) + + with ( + patch.object(rt, "aai_metrics", return_value=df_aai) as m1, + patch.object(rt, "return_periods_metrics", return_value=df_rp) as m2, + patch.object(rt, "aai_per_group_metrics", return_value=df_grp) as m3, + ): + + result = rt.per_date_risk_metrics() + assert len(result) == 3 + assert list(result[METRIC_COL_NAME]) == ["aai", "rp", "grp"] + # Verify it called all three internal methods + m1.assert_called_once() + m2.assert_called_once() + m3.assert_called_once() diff --git a/climada/trajectories/test/test_trajectory.py b/climada/trajectories/test/test_trajectory.py new file mode 100644 index 0000000000..4e0259483b --- /dev/null +++ b/climada/trajectories/test/test_trajectory.py @@ -0,0 +1,52 @@ +""" +This file is part of CLIMADA. + +Copyright (C) 2017 ETH Zurich, CLIMADA contributors listed in AUTHORS. + +CLIMADA is free software: you can redistribute it and/or modify it under the +terms of the GNU General Public License as published by the Free +Software Foundation, version 3. + +CLIMADA is distributed in the hope that it will be useful, but WITHOUT ANY +WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A +PARTICULAR PURPOSE. See the GNU General Public License for more details. + +You should have received a copy of the GNU General Public License along +with CLIMADA. If not, see . + +--- + +unit tests for RiskTrajectory (Being an abstract ) + +""" + +import datetime +from unittest.mock import MagicMock, call + +import pandas as pd +import pytest + +from climada.entity.disc_rates.base import DiscRates +from climada.trajectories.constants import AAI_METRIC_NAME +from climada.trajectories.snapshot import Snapshot +from climada.trajectories.trajectory import ( + DEFAULT_ALLGROUP_NAME, + DEFAULT_RP, + RiskTrajectory, +) + + +@pytest.fixture +def mock_snapshots(): + """Provides a list of mock Snapshot objects with sequential dates.""" + snaps = [] + for year in [2023, 2024, 2025]: + m = MagicMock(spec=Snapshot) + m.date = datetime.date(year, 1, 1) + snaps.append(m) + return snaps + + +def test_abstract(): + with pytest.raises(TypeError, match="abstract class"): + RiskTrajectory(mock_snapshots) # type: ignore diff --git a/climada/trajectories/trajectory.py b/climada/trajectories/trajectory.py new file mode 100644 index 0000000000..a8cb3779dc --- /dev/null +++ b/climada/trajectories/trajectory.py @@ -0,0 +1,262 @@ +""" +This file is part of CLIMADA. + +Copyright (C) 2017 ETH Zurich, CLIMADA contributors listed in AUTHORS. + +CLIMADA is free software: you can redistribute it and/or modify it under the +terms of the GNU General Public License as published by the Free +Software Foundation, version 3. + +CLIMADA is distributed in the hope that it will be useful, but WITHOUT ANY +WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A +PARTICULAR PURPOSE. See the GNU General Public License for more details. + +You should have received a copy of the GNU General Public License along +with CLIMADA. If not, see . + +--- + +This file implements abstract trajectory objects, to factorise the code common to +interpolated and static trajectories. + +""" + +import datetime +import logging +from abc import ABC, abstractmethod +from typing import Iterable + +import pandas as pd + +from climada.entity.disc_rates.base import DiscRates +from climada.trajectories.constants import ( + DATE_COL_NAME, + DEFAULT_ALLGROUP_NAME, + DEFAULT_RP, + GROUP_COL_NAME, + MEASURE_COL_NAME, + METRIC_COL_NAME, + PERIOD_COL_NAME, + RISK_COL_NAME, + UNIT_COL_NAME, +) +from climada.trajectories.snapshot import Snapshot + +LOGGER = logging.getLogger(__name__) + +__all__ = ["RiskTrajectory"] + +DEFAULT_DF_COLUMN_PRIORITY = [ + DATE_COL_NAME, + PERIOD_COL_NAME, + GROUP_COL_NAME, + MEASURE_COL_NAME, + METRIC_COL_NAME, + UNIT_COL_NAME, +] +INDEXING_COLUMNS = [DATE_COL_NAME, GROUP_COL_NAME, MEASURE_COL_NAME, METRIC_COL_NAME] + + +class RiskTrajectory(ABC): + """Base abstract class for risk trajectory objects. + + See concrete implementation :class:`StaticRiskTrajectory` and + :class:`InterpolatedRiskTrajectory` for more details. + + """ + + _grouper = [MEASURE_COL_NAME, METRIC_COL_NAME] + """Results dataframe grouper used in most `groupby()` calls.""" + + POSSIBLE_METRICS = [] + """Class variable listing the risk metrics that can be computed.""" + + def __init__( + self, + snapshots_list: Iterable[Snapshot], + *, + return_periods: Iterable[int] = DEFAULT_RP, + risk_disc_rates: DiscRates | None = None, + ): + self._reset_metrics() + self._snapshots = sorted(snapshots_list, key=lambda snap: snap.date) + self._all_groups_name = DEFAULT_ALLGROUP_NAME + self._return_periods = return_periods + self.start_date = min((snapshot.date for snapshot in snapshots_list)) + self.end_date = max((snapshot.date for snapshot in snapshots_list)) + self._risk_disc_rates = risk_disc_rates + + def _reset_metrics(self) -> None: + """Resets the computed metrics to None. + + This method is called to inititialize the `POSSIBLE_METRICS` to `None` during + the initialisation. + + It is also called when properties that would change the results of + computed metrics (for instance changing the time resolution in + :class:`InterpolatedRiskMetrics`) + + """ + for metric in self.POSSIBLE_METRICS: + setattr(self, "_" + metric + "_metrics", None) + + @abstractmethod + def _generic_metrics( + self, /, metric_name: str, metric_meth: str, **kwargs + ) -> pd.DataFrame: + """Main method to return the results of a specific metric. + + This method should call the `_generic_metrics()` of its parent and + define the part of the computation and treatment that + is specific to a child class of :class:`RiskTrajectory`. + + See also + -------- + + - :method:`_compute_metrics` + + """ + raise NotImplementedError( + f"'_generic_metrics' must be implemented by subclasses of {self.__class__.__name__}" + ) + + def _compute_metrics( + self, /, metric_name: str, metric_meth: str, **kwargs + ) -> pd.DataFrame: + """Helper method to compute metrics. + + Notes + ----- + + This method exists for the sake of the children classes for option appraisal, for which + `_generic_metrics` can have a different signature and extend on its + parent method. This method can stay the same (same signature) for all classes. + """ + return self._generic_metrics( + metric_name=metric_name, metric_meth=metric_meth, **kwargs + ) + + @property + def return_periods(self) -> Iterable[int]: + """The return period values to use when computing risk period metrics. + + Notes + ----- + + Changing its value resets the corresponding metric. + """ + return self._return_periods + + @return_periods.setter + def return_periods(self, value, /): + if not isinstance(value, Iterable): + raise ValueError("Return periods need to be a list of int.") + if any(not isinstance(i, int) for i in value): + raise ValueError("Return periods need to be a list of int.") + self._return_periods_metrics = None + self._return_periods = value + + @property + def risk_disc_rates(self) -> DiscRates | None: + """The discount rate applied to compute net present values. + None means no discount rate. + + Notes + ----- + + Changing its value resets all the metrics. + """ + return self._risk_disc_rates + + @risk_disc_rates.setter + def risk_disc_rates(self, value, /): + if value is not None and not isinstance(value, (DiscRates)): + raise ValueError( + "The discount rate applied to risk values needs to be a `DiscRates` object." + ) + + self._reset_metrics() + self._risk_disc_rates = value + + @classmethod + def npv_transform( + cls, metric_df: pd.DataFrame, risk_disc_rates: DiscRates + ) -> pd.DataFrame: + """Apply provided discount rate to the provided metric `DataFrame`. + + Parameters + ---------- + metric_df : pd.DataFrame + The `DataFrame` of the metric to discount. + risk_disc_rates : DiscRate + The discount rate to apply. + + Returns + ------- + pd.DataFrame + The discounted risk metric. + + """ + + def _npv_group(group, disc): + start_date = group.index.get_level_values(DATE_COL_NAME).min() + return cls._calc_npv_cash_flows(group, start_date, disc) + + metric_df = metric_df.set_index(DATE_COL_NAME) + grouper = cls._grouper + if GROUP_COL_NAME in metric_df.columns: + grouper = [GROUP_COL_NAME] + grouper + + metric_df[RISK_COL_NAME] = metric_df.groupby( + grouper, + dropna=False, + as_index=False, + group_keys=False, + observed=True, + )[RISK_COL_NAME].transform(_npv_group, risk_disc_rates) + metric_df = metric_df.reset_index() + return metric_df + + @staticmethod + def _calc_npv_cash_flows( + cash_flows: pd.Series, + start_date: datetime.date, + disc_rates: DiscRates | None = None, + ) -> pd.Series: + """Apply discount rate to cash flows. + + If it is defined, applies a discount rate `disc` to a given cash flow + `cash_flows` using `start_date` as the reference year. + + Parameters + ---------- + cash_flows : pd.DataFrame + The cash flow to apply the discount rate to. + start_date : datetime.date + The date representing the present. + disc : DiscRates, optional + The discount rates to apply. + + Returns + ------- + + A Series (copy) of `cash_flows` where values are discounted according to `disc`. + + """ + + if disc_rates is None: + return cash_flows + + if not isinstance(cash_flows.index, (pd.PeriodIndex, pd.DatetimeIndex)): + raise ValueError( + "cash_flows must be a pandas Series with a PeriodIndex or DatetimeIndex" + ) + + growth_factors = ( + pd.Series(disc_rates.rates, index=disc_rates.years) + .loc[lambda x: x.index > start_date.year] + .add(1) + .cumprod() + ) + discount_factors = 1 / cash_flows.index.year.map(growth_factors).fillna(1.0) + return cash_flows.multiply(discount_factors, axis=0) diff --git a/climada/util/coordinates.py b/climada/util/coordinates.py index 1743150275..f76fc11b74 100644 --- a/climada/util/coordinates.py +++ b/climada/util/coordinates.py @@ -1136,8 +1136,7 @@ def estimate_matching_threshold(coords_to_assign): def degree_to_km(degree): - r""" - Convert an angle from degrees to kilometers. + r"""Convert an angle from degrees to kilometers. This function converts a given angle in degrees to its equivalent distance in kilometers on the Earth's surface. It assumes a spherical Earth with a constant @@ -1160,9 +1159,11 @@ def degree_to_km(degree): Notes ----- The conversion is based on the formula: + .. math:: - distance = angle_{radians} \\times R - where R is the Earth's radius in km. + d = a \times R + + where d is the distance in km, a is the angle in radians, and R is the Earth's radius in km. Examples -------- @@ -1173,38 +1174,39 @@ def degree_to_km(degree): def km_to_degree(km): - r""" - Convert a distance from kilometers to degrees. + r"""Convert a distance from kilometers to degrees. - This function converts a given distance in kilometers on the Earth's surface - to its equivalent angle in degrees. It assumes a spherical Earth with a - constant radius. + This function converts a given distance in kilometers on the Earth's surface + to its equivalent angle in degrees. It assumes a spherical Earth with a + constant radius. - Parameters - ---------- - km : float or array_like - The distance(s) in kilometers to convert. + Parameters + ---------- + km : float or array_like + The distance(s) in kilometers to convert. - Returns - ------- - float or ndarray - The equivalent angle(s) in degrees. + Returns + ------- + float or ndarray + The equivalent angle(s) in degrees. - See Also - -------- - degree_to_km : The inverse function to convert degrees to kilometers. + See Also + -------- + degree_to_km : The inverse function to convert degrees to kilometers. + + Notes + ----- + The conversion is based on the formula: - Notes - ----- - The conversion is based on the formula: .. math:: - angle_{radians} = distance / R - where R is the Earth's radius in km. + a = d / R - Examples - -------- - >>> km_to_degree(111.195) - 1.0000030589140416 + where a is the angle in radians, d is the distance in km, and R is the Earth's radius in km. + + Examples + -------- + >>> km_to_degree(111.195) + 1.0000030589140416 """ return np.rad2deg(km / EARTH_RADIUS_KM) @@ -1412,15 +1414,17 @@ def match_centroids( Caution: nearest neighbourg matching can introduce serious artefacts such as: - - coordinates centroids with shifted grids can lead - to systematically wrong assignements. - - centroids covering larger areas than coordinates may lead - to sub-optimal matching if the threshold is too large - - projected crs often diverge at the anti-meridian and close points - on either side will be at a large distance. For proper handling - of the anti-meridian please use degree coordinates in EPSG:4326. - This might be relevant for countries like the Fidji or the US that - cross the anti-meridian. + + - coordinates centroids with shifted grids can lead + to systematically wrong assignements. + - centroids covering larger areas than coordinates may lead + to sub-optimal matching if the threshold is too large + - projected crs often diverge at the anti-meridian and close points + on either side will be at a large distance. For proper handling + of the anti-meridian please use degree coordinates in EPSG:4326. + This might be relevant for countries like the Fidji or the US that + cross the anti-meridian. + """ try: diff --git a/climada/util/dataframe_handling.py b/climada/util/dataframe_handling.py new file mode 100644 index 0000000000..15cb9fbf8d --- /dev/null +++ b/climada/util/dataframe_handling.py @@ -0,0 +1,65 @@ +""" +This file is part of CLIMADA. + +Copyright (C) 2017 ETH Zurich, CLIMADA contributors listed in AUTHORS. + +CLIMADA is free software: you can redistribute it and/or modify it under the +terms of the GNU General Public License as published by the Free +Software Foundation, version 3. + +CLIMADA is distributed in the hope that it will be useful, but WITHOUT ANY +WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A +PARTICULAR PURPOSE. See the GNU General Public License for more details. + +You should have received a copy of the GNU General Public License along +with CLIMADA. If not, see . + +--- + +Define functions to handle dataframes +""" + +import pandas as pd + + +def reorder_dataframe_columns( + dataframe: pd.DataFrame, priority_order: list[str], keep_remaining: bool = True +) -> pd.DataFrame: + """ + Applies a column priority list to a DataFrame to reorder its columns. + + This function is robust to cases where: + 1. Columns in 'priority_order' are not in the DataFrame (they are ignored). + 2. Columns in the DataFrame are not in 'priority_order'. + + Parameters + ---------- + dataframe: pd.DataFrame + The input DataFrame. + priority_order: list[str] + A list of strings defining the desired column + order. Columns listed first have higher priority. + keep_remaining: bool + If True, any columns in the DataFrame but NOT in + 'priority_order' will be appended to the end in their + original relative order. If False, these columns + are dropped. + + Returns: + pd.DataFrame: The DataFrame with columns reordered according to the priority list. + """ + + present_priority_columns = [ + col for col in priority_order if col in dataframe.columns + ] + + new_column_order = present_priority_columns + + if keep_remaining: + remaining_columns = [ + col for col in dataframe.columns if col not in present_priority_columns + ] + + new_column_order.extend(remaining_columns) + + return dataframe[new_column_order] diff --git a/climada/util/earth_engine.py b/climada/util/earth_engine.py deleted file mode 100644 index 92fa96cadb..0000000000 --- a/climada/util/earth_engine.py +++ /dev/null @@ -1,184 +0,0 @@ -""" -This file is part of CLIMADA. - -Copyright (C) 2017 ETH Zurich, CLIMADA contributors listed in AUTHORS. - -CLIMADA is free software: you can redistribute it and/or modify it under the -terms of the GNU General Public License as published by the Free -Software Foundation, version 3. - -CLIMADA is distributed in the hope that it will be useful, but WITHOUT ANY -WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A -PARTICULAR PURPOSE. See the GNU General Public License for more details. - -You should have received a copy of the GNU General Public License along -with CLIMADA. If not, see . - ---- - -Regroup methods to obtain images from Google Earth Engine API -""" - -import logging -import webbrowser - -# This module works only if you have a Google Earth Engine account. -# That's why `earthengine-api` is not in the CLIMADA requirements. -# See tutorial: climada_util_earth_engine.ipynb -# pylint: disable=import-error -LOGGER = logging.getLogger(__name__) - -try: - import ee - - LOGGER.info("Google Earth Engine API successfully imported.") - EE_AVAILABLE = True -except ImportError: - LOGGER.error( - "Google Earth Engine API not found. Please install it using 'pip install earthengine-api'." - ) - EE_AVAILABLE = False - -if not EE_AVAILABLE: - LOGGER.error( - "Google Earth Engine API not found. Skipping the init of `earth_engine.py`." - ) -else: - ee.Initialize() - - def obtain_image_landsat_composite(landsat_collection, time_range, area): - """Selection of Landsat cloud-free composites in the Earth Engine library - See also: https://developers.google.com/earth-engine/landsat - - Parameters - ---------- - collection : - name of the collection - time_range : ['YYYY-MT-DY','YYYY-MT-DY'] - must be inside the available data - area : ee.geometry.Geometry - area of interest - - Returns - ------- - image_composite : ee.image.Image - """ - collection = ee.ImageCollection(landsat_collection) - - # Filter by time range and location - collection_time = collection.filterDate(time_range[0], time_range[1]) - image_area = collection_time.filterBounds(area) - image_composite = ee.Algorithms.Landsat.simpleComposite(image_area, 75, 3) - return image_composite - - def obtain_image_median(collection, time_range, area): - """Selection of median from a collection of images in the Earth Engine library - See also: https://developers.google.com/earth-engine/reducers_image_collection - - Parameters - ---------- - collection : - name of the collection - time_range : ['YYYY-MT-DY','YYYY-MT-DY'] - must be inside the available data - area : ee.geometry.Geometry - area of interest - - Returns - ------- - image_median : ee.image.Image - """ - collection = ee.ImageCollection(collection) - - # Filter by time range and location - collection_time = collection.filterDate(time_range[0], time_range[1]) - image_area = collection_time.filterBounds(area) - image_median = image_area.median() - return image_median - - def obtain_image_sentinel(sentinel_collection, time_range, area): - """Selection of median, cloud-free image from a collection of images in the Sentinel 2 - dataset. - See also: https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S2 - - Parameters - ---------- - collection : - name of the collection - time_range : ['YYYY-MT-DY','YYYY-MT-DY'] - must be inside the available data - area : ee.geometry.Geometry - area of interest - - Returns - ------- - sentinel_median : ee.image.Image - """ - - # First, method to remove cloud from the image - def maskclouds(image): - band_qa = image.select("QA60") - cloud_mask = ee.Number(2).pow(10).int() - cirrus_mask = ee.Number(2).pow(11).int() - mask = band_qa.bitwiseAnd(cloud_mask).eq(0) and ( - band_qa.bitwiseAnd(cirrus_mask).eq(0) - ) - return image.updateMask(mask).divide(10000) - - sentinel_filtered = ( - ee.ImageCollection(sentinel_collection) - .filterBounds(area) - .filterDate(time_range[0], time_range[1]) - .filter(ee.Filter.lt("CLOUDY_PIXEL_PERCENTAGE", 20)) - .map(maskclouds) - ) - - sentinel_median = sentinel_filtered.median() - return sentinel_median - - def get_region(geom): - """Get the region of a given geometry, needed for exporting tasks. - - Parameters - ---------- - geom : ee.Geometry, ee.Feature, ee.Image - region of interest - - Returns - ------- - region : list - """ - if isinstance(geom, ee.Geometry): - return geom.getInfo()["coordinates"] - if isinstance(geom, (ee.Feature, ee.Image)): - return geom.geometry().getInfo()["coordinates"] - raise ValueError( - "parameter must be one of `ee.Geometry`, `ee.Feature`, `ee.Image`" - ) - - def get_url(name, image, scale, region): - """It will open and download automatically a zip folder containing Geotiff data of 'image'. - If additional parameters are needed, see also: - https://github.com/google/earthengine-api/blob/master/python/ee/image.py - - Parameters - ---------- - name : str - name of the created folder - image : ee.image.Image - image to export - scale : int - resolution of export in meters (e.g: 30 for Landsat) - region : list - region of interest - - Returns - ------- - path : str - """ - path = image.getDownloadURL( - {"name": (name), "scale": scale, "region": (region)} - ) - - webbrowser.open_new_tab(path) - return path diff --git a/climada/util/hdf5_handler.py b/climada/util/hdf5_handler.py index 8408972bd0..16c183e930 100644 --- a/climada/util/hdf5_handler.py +++ b/climada/util/hdf5_handler.py @@ -89,7 +89,7 @@ def get_string(array): ------- string """ - return "".join(chr(int(c)) for c in array) + return "".join(chr(c.item()) for c in array) def get_str_from_ref(file_name, var): diff --git a/climada/util/interpolation.py b/climada/util/interpolation.py index 67bcc3ee81..bf64f37cd7 100644 --- a/climada/util/interpolation.py +++ b/climada/util/interpolation.py @@ -29,7 +29,7 @@ def preprocess_and_interpolate_ev( - test_frequency, + exceedance_frequency, test_values, frequency, values, @@ -46,12 +46,12 @@ def preprocess_and_interpolate_ev( Parameters ---------- - test_frequency : array_like - 1-D array of test frequencies for which values (e.g., intensities or impacts) should be + exceedance_frequency : array_like + 1-D array of test exceedance frequencies for which values (e.g., intensities or impacts) should be assigned. If given, test_values must be None. test_values : array_like - 1-D array of test values (e.g., intensities or impacts) for which frequencies should be - assigned. If given, test_frequency must be None. + 1-D array of test values (e.g., intensities or impacts) for which exceedance frequencies should be + assigned. If given, exceedance_frequency must be None. frequency : array_like 1-D array of frequencies to be interpolated. values : array_like @@ -106,13 +106,22 @@ def preprocess_and_interpolate_ev( could use bin_decimals=5. """ + # check method + if method not in [ + "interpolate", + "extrapolate", + "extrapolate_constant", + "stepfunction", + ]: + raise ValueError(f"Unknown method: {method}") + # check that only test frequencies or only test values are given - if test_frequency is not None and test_values is not None: + if exceedance_frequency is not None and test_values is not None: raise ValueError( "Both test frequencies and test values are given. This method only handles one of " "the two. To use this method, please only use one of them." ) - if test_frequency is None and test_values is None: + if exceedance_frequency is None and test_values is None: raise ValueError("No test values or test frequencies are given.") # sort values and frequencies @@ -128,10 +137,10 @@ def preprocess_and_interpolate_ev( frequency = np.cumsum(frequency[::-1])[::-1] # if test frequencies are provided - if test_frequency is not None: + if exceedance_frequency is not None: if method == "stepfunction": return _stepfunction_ev( - test_frequency, + exceedance_frequency, frequency[::-1], values[::-1], y_threshold=value_threshold, @@ -139,7 +148,7 @@ def preprocess_and_interpolate_ev( ) extrapolation = None if method == "interpolate" else method return _interpolate_ev( - test_frequency, + exceedance_frequency, frequency[::-1], values[::-1], logx=log_frequency, diff --git a/climada/util/string_parsers.py b/climada/util/string_parsers.py new file mode 100644 index 0000000000..1184b5c89e --- /dev/null +++ b/climada/util/string_parsers.py @@ -0,0 +1,68 @@ +""" +This file is part of CLIMADA. + +Copyright (C) 2017 ETH Zurich, CLIMADA contributors listed in AUTHORS. + +CLIMADA is free software: you can redistribute it and/or modify it under the +terms of the GNU General Public License as published by the Free +Software Foundation, version 3. + +CLIMADA is distributed in the hope that it will be useful, but WITHOUT ANY +WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A +PARTICULAR PURPOSE. See the GNU General Public License for more details. + +You should have received a copy of the GNU General Public License along +with CLIMADA. If not, see . + +--- + +Define functions to parse strings +""" + +from itertools import chain + +import pandas as pd + + +def parse_mapping_string(map_str: str | None) -> dict[int, int] | None: + """ + Parses strings like "1to8, 5to2" into {1: 8, 5: 2}. + """ + if map_str is None or map_str == "" or map_str == "nil": + return None + # 1. Split by comma to get individual pairs: ['1to8', '5to2'] + pairs = (p.strip() for p in map_str.split(",") if p.strip()) + + # 2. Split each pair by 'to' and convert to integers + def split_pair(p): + k, v = p.split("to") + return int(k), int(v) + + # 3. Construct the dictionary + return dict(split_pair(p) for p in pairs) + + +def parse_range(s: str | None) -> list | None: + """ + Parses strings like "1,4-6,10,12-14" into [1,4,5,6,10,12,13,14]. + """ + if s is None or s == "nil" or s == "": + return None + try: + return list( + chain.from_iterable( + range(r[0], r[-1] + 1) + for r in [[int(i) for i in part.split("-")] for part in s.split(",")] + ) + ) + except ValueError as exc: + raise ValueError( + f"Invalid string format for zeroing assets: {s} (Ex: '1,4-6,10')" + ) from exc + + +def parse_color(color_value: str | None) -> tuple | None: + """Parse RGB color string to tuple.""" + if color_value is not None and not pd.isna(color_value): + return tuple(color_value.split(" ")) + return None diff --git a/climada/util/test/test_dataframe_handling.py b/climada/util/test/test_dataframe_handling.py new file mode 100644 index 0000000000..1595e53c40 --- /dev/null +++ b/climada/util/test/test_dataframe_handling.py @@ -0,0 +1,66 @@ +""" +This file is part of CLIMADA. + +Copyright (C) 2017 ETH Zurich, CLIMADA contributors listed in AUTHORS. + +CLIMADA is free software: you can redistribute it and/or modify it under the +terms of the GNU General Public License as published by the Free +Software Foundation, version 3. + +CLIMADA is distributed in the hope that it will be useful, but WITHOUT ANY +WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A +PARTICULAR PURPOSE. See the GNU General Public License for more details. + +You should have received a copy of the GNU General Public License along +with CLIMADA. If not, see . + +--- + +Test coordinates module. +""" + +import pandas as pd + +from climada.util.dataframe_handling import reorder_dataframe_columns + + +def test_reorder_dataframe_columns(): + # Setup: Create a sample DataFrame + df = pd.DataFrame( + {"A": [1, 2], "B": [3, 4], "C": [5, 6], "D": [7, 8], "E": [9, 10]} + ) + + # Test Case 1: Standard reordering with keep_remaining=True + # Priority: C, A (should move C and A to front, keep B, D, E in original order) + priority = ["C", "A", "Z"] # 'Z' is not in df, should be ignored + result = reorder_dataframe_columns(df, priority, keep_remaining=True) + + expected_cols = ["C", "A", "B", "D", "E"] + assert ( + list(result.columns) == expected_cols + ), f"Expected {expected_cols}, got {list(result.columns)}" + + # Test Case 2: Dropping remaining columns (keep_remaining=False) + # Priority: B, D (should only keep B and D) + priority = ["B", "D"] + result = reorder_dataframe_columns(df, priority, keep_remaining=False) + + expected_cols = ["B", "D"] + assert ( + list(result.columns) == expected_cols + ), f"Expected {expected_cols}, got {list(result.columns)}" + + # Test Case 3: All priority columns missing + priority = ["X", "Y"] + result = reorder_dataframe_columns(df, priority, keep_remaining=True) + + # Should return original order since no priority matches + expected_cols = ["A", "B", "C", "D", "E"] + assert list(result.columns) == expected_cols + + # Test Case 4: Empty priority list + priority = [] + result = reorder_dataframe_columns(df, priority, keep_remaining=True) + + expected_cols = ["A", "B", "C", "D", "E"] + assert list(result.columns) == expected_cols diff --git a/doc/api/climada/climada.entity._legacy_measures.rst b/doc/api/climada/climada.entity._legacy_measures.rst new file mode 100644 index 0000000000..19e622c1ef --- /dev/null +++ b/doc/api/climada/climada.entity._legacy_measures.rst @@ -0,0 +1,22 @@ +climada\.entity\._legacy_measures package +========================================= + +.. note:: + This package implements the legacy way of defining measures + and is retained for compatibility. + +climada\.entity\._legacy_measures\.base module +---------------------------------------------- + +.. automodule:: climada.entity._legacy_measures.base + :members: + :undoc-members: + :show-inheritance: + +climada\.entity\._legacy_measures\.measure\_set module +------------------------------------------------------ + +.. automodule:: climada.entity._legacy_measures.measure_set + :members: + :undoc-members: + :show-inheritance: diff --git a/doc/api/climada/climada.entity.measures.rst b/doc/api/climada/climada.entity.measures.rst index 8e63a2082b..5f97f72bd4 100644 --- a/doc/api/climada/climada.entity.measures.rst +++ b/doc/api/climada/climada.entity.measures.rst @@ -1,6 +1,10 @@ climada\.entity\.measures package ================================= +.. note:: + This package implements the new way of defining measures. + For the previous way, see :ref:`climada.entity._legacy_measures` + climada\.entity\.measures\.base module -------------------------------------- @@ -9,10 +13,27 @@ climada\.entity\.measures\.base module :undoc-members: :show-inheritance: -climada\.entity\.measures\.measure\_set module ----------------------------------------------- -.. automodule:: climada.entity.measures.measure_set +climada\.entity\.measures\.measure_config module +------------------------------------------------ + +.. automodule:: climada.entity.measures.measure_config + :members: + :undoc-members: + :show-inheritance: + +climada\.entity\.measures\.cost_income module +--------------------------------------------- + +.. automodule:: climada.entity.measures.cost_income + :members: + :undoc-members: + :show-inheritance: + +climada\.entity\.measures\.types module +--------------------------------------- + +.. automodule:: climada.entity.measures.types :members: :undoc-members: :show-inheritance: diff --git a/doc/api/climada/climada.entity.rst b/doc/api/climada/climada.entity.rst index f7eac11700..f4f4df0d98 100644 --- a/doc/api/climada/climada.entity.rst +++ b/doc/api/climada/climada.entity.rst @@ -7,6 +7,7 @@ climada\.entity package climada.entity.exposures climada.entity.impact_funcs climada.entity.measures + climada.entity._legacy_measures climada\.entity\.entity\_def module ----------------------------------- diff --git a/doc/api/climada/climada.rst b/doc/api/climada/climada.rst index 557532912f..2e8d053946 100644 --- a/doc/api/climada/climada.rst +++ b/doc/api/climada/climada.rst @@ -7,4 +7,5 @@ Software documentation per package climada.engine climada.entity climada.hazard + climada.trajectories climada.util diff --git a/doc/api/climada/climada.trajectories.impact_calc_strat.rst b/doc/api/climada/climada.trajectories.impact_calc_strat.rst new file mode 100644 index 0000000000..1bf211b4c0 --- /dev/null +++ b/doc/api/climada/climada.trajectories.impact_calc_strat.rst @@ -0,0 +1,7 @@ +climada\.trajectories\.impact_calc_strat module +---------------------------------------- + +.. automodule:: climada.trajectories.impact_calc_strat + :members: + :undoc-members: + :show-inheritance: diff --git a/doc/api/climada/climada.trajectories.interpolation.rst b/doc/api/climada/climada.trajectories.interpolation.rst new file mode 100644 index 0000000000..98e1ec7b32 --- /dev/null +++ b/doc/api/climada/climada.trajectories.interpolation.rst @@ -0,0 +1,7 @@ +climada\.trajectories\.interpolation module +---------------------------------------- + +.. automodule:: climada.trajectories.interpolation + :members: + :undoc-members: + :show-inheritance: diff --git a/doc/api/climada/climada.trajectories.rst b/doc/api/climada/climada.trajectories.rst new file mode 100644 index 0000000000..371f2477d4 --- /dev/null +++ b/doc/api/climada/climada.trajectories.rst @@ -0,0 +1,10 @@ + +climada\.trajectories module +============================ + +.. toctree:: + + climada.trajectories.snapshot + climada.trajectories.trajectories + climada.trajectories.impact_calc_strat + climada.trajectories.interpolation diff --git a/doc/api/climada/climada.trajectories.snapshot.rst b/doc/api/climada/climada.trajectories.snapshot.rst new file mode 100644 index 0000000000..ba0faf57ac --- /dev/null +++ b/doc/api/climada/climada.trajectories.snapshot.rst @@ -0,0 +1,7 @@ +climada\.trajectories\.snapshot module +---------------------------------------- + +.. automodule:: climada.trajectories.snapshot + :members: + :undoc-members: + :show-inheritance: diff --git a/doc/api/climada/climada.trajectories.trajectories.rst b/doc/api/climada/climada.trajectories.trajectories.rst new file mode 100644 index 0000000000..35583b89d8 --- /dev/null +++ b/doc/api/climada/climada.trajectories.trajectories.rst @@ -0,0 +1,23 @@ +climada\.trajectories\.static_trajectory module +---------------------------------------- + +.. automodule:: climada.trajectories.static_trajectory + :members: + :undoc-members: + :show-inheritance: + +climada\.trajectories\.static_trajectory module +---------------------------------------- + +.. automodule:: climada.trajectories.interpolated_trajectory + :members: + :undoc-members: + :show-inheritance: + +climada\.trajectories\.trajectory module +---------------------------------------- + +.. automodule:: climada.trajectories.trajectory + :members: + :undoc-members: + :show-inheritance: diff --git a/doc/api/climada/climada.util.rst b/doc/api/climada/climada.util.rst index 8d98734d92..9e4c037da4 100644 --- a/doc/api/climada/climada.util.rst +++ b/doc/api/climada/climada.util.rst @@ -65,14 +65,6 @@ climada\.util\.dwd\_icon\_loader module :undoc-members: :show-inheritance: -climada\.util\.earth\_engine module ------------------------------------ - -.. automodule:: climada.util.earth_engine - :members: - :undoc-members: - :show-inheritance: - climada\.util\.files\_handler module ------------------------------------ @@ -97,14 +89,14 @@ climada\.util\.hdf5\_handler module :undoc-members: :show-inheritance: -climada\.util\.interpolation module ------------------------------------ - -.. automodule:: climada.util.interpolation - :members: - :undoc-members: - :show-inheritance: - +climada\.util\.interpolation module +----------------------------------- + +.. automodule:: climada.util.interpolation + :members: + :undoc-members: + :show-inheritance: + climada\.util\.lines\_polys\_handler module ------------------------------------------- diff --git a/doc/conf.py b/doc/conf.py index 82e0abfa97..775d8290f3 100644 --- a/doc/conf.py +++ b/doc/conf.py @@ -44,7 +44,6 @@ "sphinx_mdinclude", "myst_nb", "sphinx_markdown_tables", - "readthedocs_ext.readthedocs", ] # read the docs version used for links diff --git a/doc/development/Guide_Euler.ipynb b/doc/development/Guide_Euler.ipynb index 1798f11b10..7d91f270d3 100644 --- a/doc/development/Guide_Euler.ipynb +++ b/doc/development/Guide_Euler.ipynb @@ -106,15 +106,23 @@ "\n", "```bash\n", "pip install \\\n", - " dask[dataframe] \\\n", - " fiona==1.9 \\\n", - " gdal==3.6 \\\n", - " netcdf4==1.6.2 \\\n", - " rasterio==1.4 \\\n", - " pyproj==3.7 \\\n", - " geopandas==1.0 \\\n", - " xarray==2024.9 \\\n", - " sparse==0.15\n", + " --no-binary fiona \\\n", + " --no-binary rasterio \\\n", + " pyproj==3.7.1 \\\n", + " fiona \\\n", + " rasterio\n", + "\n", + "pip install \\\n", + " 'numpy<2' \\\n", + " netcdf4 \\\n", + " gdal==3.6.3 \\\n", + " geopandas \\\n", + " xarray \\\n", + " cartopy \\\n", + " sparse \\\n", + " salib \\\n", + " pyarrow \\\n", + " xlrd\n", "```" ] }, diff --git a/doc/development/Guide_Testing.ipynb b/doc/development/Guide_Testing.ipynb index 12f59efb3f..cd7aada118 100644 --- a/doc/development/Guide_Testing.ipynb +++ b/doc/development/Guide_Testing.ipynb @@ -20,7 +20,9 @@ "Writing tests is work. As a matter of facts, it can be a _lot_ of work, depending on the program often more than writing the original code.\\\n", "Luckily, it essentially follows always the same basic procedure and a there are a lot of tools and frameworks available to facilitate this work.\n", "\n", - "In CLIMADA we use the Python in-built _test runner_ [pytest](https://docs.pytest.org/en/7.1.x/index.html) for execution of the tests." + "In CLIMADA we use the Python in-built _test runner_ [pytest](https://docs.pytest.org/en/7.1.x/index.html) for execution of the tests.\n", + "\n", + "We now leverage `pytest` fixtures for defining the data and context used by tests. Please read [how to use fixtures for testing in CLIMADA](fixture-tutorial)." ] }, { @@ -53,7 +55,6 @@ }, { "cell_type": "markdown", - "id": "5819e8c6", "metadata": {}, "source": [ "### Basic Test Procedure\n", @@ -277,7 +278,9 @@ "source": [ "### Test Configuration\n", "\n", - "Use the configuration file `climada.config` in the installation directory to define file paths and external resources used during tests (see the [Constants and Configuration Guide](./Guide_Configuration.ipynb))." + "Integration tests should make use of the fixtures defined in `climada/test/conftest.py`. Learn how to do that in [how to use fixtures for testing in CLIMADA](fixture-tutorial).\n", + "\n", + "Test data can also use paths defined in the configuration file `climada.config` (in the installation directory) which define file paths and external resources to be used during tests (see the [Constants and Configuration Guide](./Guide_Configuration.ipynb))." ] }, { @@ -334,9 +337,9 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "Python [conda env:climada_env_dev]", "language": "python", - "name": "python3" + "name": "conda-env-climada_env_dev-py" }, "language_info": { "codemirror_mode": { @@ -348,7 +351,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.6" + "version": "3.11.15" }, "vscode": { "interpreter": { diff --git a/doc/development/Guide_test_fixtures.ipynb b/doc/development/Guide_test_fixtures.ipynb new file mode 100644 index 0000000000..88e58f5099 --- /dev/null +++ b/doc/development/Guide_test_fixtures.ipynb @@ -0,0 +1,193 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "359d0d32-68c5-4e8d-8b34-2c5b2a0cad21", + "metadata": {}, + "source": [ + "(fixture-tutorial)=\n", + "# Testing with pytest Fixtures\n", + "\n", + "**Fixtures** are pytest's way of setting up the data and objects that tests need. Think of them as reusable \"preparation steps\" that run before your tests.\n", + "\n", + "Instead of copy-pasting setup code into every test function, you define a fixture once and let pytest inject it automatically in tests:\n", + "```python\n", + "import pytest\n", + "\n", + "@pytest.fixture\n", + "def sample_user():\n", + " return {\"name\": \"Alice\", \"role\": \"admin\"}\n", + "\n", + "def test_user_has_name(sample_user):\n", + " assert sample_user[\"name\"] == \"Alice\"\n", + "\n", + "def test_user_has_role(sample_user):\n", + " assert sample_user[\"role\"] == \"admin\"\n", + "```\n", + "\n", + "Fixtures can also handle **teardown** (cleanup after a test), be **scoped** to run once per module or session, and be **parametrized** to run the same test against multiple inputs. They aim at keeping the test suite easy to maintain.\n", + "\n", + "## Usefull references\n", + "- [pytest fixtures — official docs](https://docs.pytest.org/en/stable/reference/fixtures.html)\n", + "- [How to use fixtures — pytest how-to guide](https://docs.pytest.org/en/stable/how-to/fixtures.html)\n", + "- [A Complete Guide to Pytest Fixtures — Better Stack](https://betterstack.com/community/guides/testing/pytest-fixtures-guide/#step-6-parametrizing-fixtures) *(includes parametrizing fixtures)*" + ] + }, + { + "cell_type": "markdown", + "id": "62247b28-05e5-46a5-8aca-c35b94d64216", + "metadata": {}, + "source": [ + "## The `conftest.py` File\n", + "\n", + "Pytest has a special file called `conftest.py` that acts as a **shared fixture library**. Any fixture defined there is automatically available to every test in the same directory and all its subdirectories.\n", + "\n", + "### How it works\n", + "```\n", + "climada/\n", + "├── test/\n", + "| ├── conftest.py ← fixtures here are available to all tests in climada/test/\n", + "| ├── test_engine.py\n", + "| └── ...\n", + "├── entity/exposures/test/\n", + " |── conftest.py ← fixtures here are available only within climada/entity/exposures/test/\n", + " └── test_exposures.py\n", + "...\n", + "```\n", + "\n", + "Define your fixture in the corresponding `conftest.py` or in your test file directly depending on its specificity:\n", + "```python\n", + "import pytest\n", + "from climada.entity.exposures import Exposure\n", + "\n", + "@pytest.fixture\n", + "def empty_exposures():\n", + " return Exposure()\n", + "```\n", + "\n", + "Then just use it by name in any test that requires it.\n", + "```python\n", + "def test_empty_exposures(empty_exposures): # pytest injects the fixture automatically\n", + " assert empty_exposures.gdf.empty()\n", + "```\n", + "\n", + "```{attention} Never import fixtures directly. \n", + "pytest's injection mechanism won't work properly with imported fixtures, and you may get confusing errors. Just use the fixture name as a function argument and let pytest handle the rest.\n", + "```\n", + "\n", + "### When to use `conftest.py`\n", + "\n", + "| Put fixtures in `conftest.py` when... | Keep fixtures in the test file when... |\n", + "|---|---|\n", + "| Multiple test files need them | Only one test file uses them |\n", + "| They set up shared resources | They are very specific to one test scenario |" + ] + }, + { + "cell_type": "markdown", + "id": "550e12a9-2ab0-4f27-9276-ebc412a78c0b", + "metadata": {}, + "source": [ + "## How to use `climada/test/conftest.py` for integration tests.\n", + "\n", + "Our integration test `conftest.py` defines a set of **ready-made CLIMADA objects** (exposures, hazard, impact functions) with values chosen so that expected results are rather easy to compute by hand and minimalistic. All fixtures are `session`-scoped, meaning they are created once and shared across the entire test session.\n", + "\n", + "Here is what is available at a glance:\n", + "\n", + "| Fixture | What it gives you |\n", + "|---|---|\n", + "| `exposures` | An `Exposures` object with 6 points and values `[0, 1000, 2000, 3000, 4000, 5000]` |\n", + "| `hazard` | A `Hazard` with 5 events and \"helpful\" intensities and frequencies (see below) |\n", + "| `linear_impact_function` | An `ImpactFunc` where intensity % == damage % (identity) |\n", + "| `impfset` | An `ImpactFuncSet` wrapping the linear impact function |\n", + "| `centroids` | Hazard centroids (slightly offset from exposure points (`+0.1°`)) |\n", + "\n", + "### Using fixtures directly for simple assertions\n", + "\n", + "When you just need a standard object to test against, request the fixture by name:\n", + "```python\n", + "def test_impact_aai(exposures, hazard, impfset):\n", + " impact = ImpactCalc(exposures, impfset, hazard).impact()\n", + " assert impact.aai_agg == pytest.approx(18) # analytically known value\n", + "```\n", + "\n", + "Values of the defaults fixtures were chosen such that impacts are rather easy to compute by hand. \n", + "This is documented at the top of `conftest.py`, but here are some key design choices:\n", + "\n", + "- There are 4 events, with frequencies == 0.03, 0.01, 0.006, 0.004, 0,\n", + " such that impacts for RP250, 100 and 50 and 20 correspond to `at_event`,\n", + " (sorted frequencies cumulate to 1/250, 1/100, 1/50 and 1/20).\n", + "- Hazard intensity is:\n", + " * Event 1: zero everywhere (always no impact)\n", + " * Event 2: max intensity (100) at first centroid (also always no impact (first centroid is 0))\n", + " * Event 3: half max intensity at second centroid (impact == half second centroid)\n", + " * Event 4: quarter max intensity everywhere (impact == 1/4 total value)\n", + " * Event 5: max intensity everywhere (but zero frequency)\n", + "\n", + "This results in the following expected values:\n", + "\n", + "| Metric | Expected value | Why |\n", + "|---|---|---|\n", + "| AAI | `18` | Events 3 & 4 weighted by frequency |\n", + "| RP 20 & 50 | `0` | Events 1 & 2 produce zero impact |\n", + "| RP 100 | `500` | Event 3: half intensity on second point (value 1000) |\n", + "| RP 250 | `3750` | Event 4: quarter intensity on all points |\n", + "\n", + "```{note}\n", + "The overview above reflects the `conftest.py` file at the time of writing. If you notice any discrepancy, the docstring at the top of `conftest.py` is the authoritative source.\n", + "```\n", + "\n", + "### Using factories for custom scenarios\n", + "\n", + "When your test needs a variation (e.g. scaled intensity, a different hazard type, group IDs), you can make use of the `_factory` fixtures. Each factory is a callable that accepts keyword arguments:\n", + "\n", + "```python\n", + "def test_scaled_hazard(hazard_factory):\n", + " stronger_hazard = hazard_factory(intensity_scale=1.5)\n", + " # intensities are scaled by 1.5\n", + "\n", + "def test_grouped_exposure(exposures_factory):\n", + " exp = exposures_factory(group_id=np.array([1, 1, 2, 1, 1, 3]))\n", + " # exposure with group_id column populated\n", + "\n", + "def test_custom_impf(impf_factory):\n", + " impf = impf_factory(paa_scale=0.5)\n", + " # PAA halved\n", + "```\n", + "\n", + "```{tip}\n", + "Prefer the direct fixtures (`exposures`, `hazard`, …) when the default setup is sufficient. Reach for factories only when your test specifically targets behaviour that depends on a variation.\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f605aee8-563a-421a-a3bb-b0bec8a15be5", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python [conda env:climada_env_dev]", + "language": "python", + "name": "conda-env-climada_env_dev-py" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.15" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/doc/development/index.rst b/doc/development/index.rst index a5e5f90c68..0249d35eba 100644 --- a/doc/development/index.rst +++ b/doc/development/index.rst @@ -25,6 +25,7 @@ If you are interested in contributing to CLIMADA, we recommand you to start with CLIMADA Configuration convention Documenting your code Writing tests for your code + Using fixtures for writing tests Guide_Review Guide_Euler Authors <../misc/AUTHORS> diff --git a/doc/getting-started/install.rst b/doc/getting-started/install.rst index 6035d481ed..96588b3fdd 100644 --- a/doc/getting-started/install.rst +++ b/doc/getting-started/install.rst @@ -264,7 +264,7 @@ However, if you want to develop CLIMADA, we strongly recommend you install them. With the ``climada_env`` activated, enter the workspace directory and then the CLIMADA repository as above. Then, add the ``test`` extra specification to the ``pip install`` command (**mind the quotation marks**, - and see also `pip install examples `_): +and see also `pip install examples `_): .. code-block:: shell diff --git a/doc/user-guide/adaptation.rst b/doc/user-guide/adaptation.rst new file mode 100644 index 0000000000..193e9c6b87 --- /dev/null +++ b/doc/user-guide/adaptation.rst @@ -0,0 +1,16 @@ +.. _adaptation-guides: + +========================== +Adapation appraisal guides +========================== + +These guides show everything you need to know in order to evaluate adaptation options with CLIMADA. + +.. toctree:: + :maxdepth: 1 + + .. Adaptation measures in CLIMADA + Using measure configurations + Defining measure cash flows + .. Cost benefit evaluation + .. Adapation planning evaluation diff --git a/doc/user-guide/climada_cost_income.ipynb b/doc/user-guide/climada_cost_income.ipynb new file mode 100644 index 0000000000..fd53c2585e --- /dev/null +++ b/doc/user-guide/climada_cost_income.ipynb @@ -0,0 +1,1297 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "2147f042", + "metadata": {}, + "source": [ + "(cost-income-tutorial)=\n", + "# Measure cash flows with `CostIncome`\n", + "\n", + "This notebook introduces the `CostIncome` class from CLIMADA, which is used to model the financial cash flows of adaptation or risk-reduction measures over time." + ] + }, + { + "cell_type": "markdown", + "id": "a97e1043", + "metadata": {}, + "source": [ + "## Quickstart\n", + "\n", + "A `CostIncome` object tracks:\n", + "- **Initial (one-off) costs** — the upfront implementation cost\n", + "- **Periodic costs** — recurring expenses (e.g., maintenance)\n", + "- **Periodic income** — recurring revenues (e.g., insurance savings, avoided losses)\n", + "- **Growth rates** — how costs and incomes evolve over time\n", + "- **Custom cash flows** — arbitrary user-defined flows (layered on top)" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "eca21610-32a8-477d-999e-54255557a3cc", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
datenetcostincome
020200.0000000.0000000.000000
120210.0000000.0000000.000000
22022-9705.636889-9705.6368890.000000
320232940.807977-4901.3466297842.154606
420242970.216057-4950.3600957920.576152
520253000.000000-5000.0000008000.000000
620263030.000000-5050.0000008080.000000
720273060.300000-5100.5000008160.800000
820283090.903000-5151.5050008242.408000
920293121.897135-5203.1618928325.059028
1020303153.116107-5255.1935118408.309618
\n", + "
" + ], + "text/plain": [ + " date net cost income\n", + "0 2020 0.000000 0.000000 0.000000\n", + "1 2021 0.000000 0.000000 0.000000\n", + "2 2022 -9705.636889 -9705.636889 0.000000\n", + "3 2023 2940.807977 -4901.346629 7842.154606\n", + "4 2024 2970.216057 -4950.360095 7920.576152\n", + "5 2025 3000.000000 -5000.000000 8000.000000\n", + "6 2026 3030.000000 -5050.000000 8080.000000\n", + "7 2027 3060.300000 -5100.500000 8160.800000\n", + "8 2028 3090.903000 -5151.505000 8242.408000\n", + "9 2029 3121.897135 -5203.161892 8325.059028\n", + "10 2030 3153.116107 -5255.193511 8408.309618" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "(, )" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAABAgAAAJyCAYAAABJ8PKHAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjgsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvwVt1zgAAAAlwSFlzAAAPYQAAD2EBqD+naQAAhCtJREFUeJzt3Qd4FFXXwPGz6bSE3jsiXVBAmhQLTQGRDkpRUBELVYqCFF9pKqIi+CIqigiogIIgAp90UBFp0qSEHoi0JLTU/Z5zcfdNJwnZ7G72/3ueeWZndmb37t1hyT1z77kWq9VqFQAAAAAA4NG8nF0AAAAAAADgfAQIAAAAAAAAAQIAAAAAAECAAAAAAAAAECAAAAAAAAAECAAAAAAAgEGSQgAAAAAAQIAAAAAAAAAQIAAAAAAAAAQIAABwfevXrxeLxSJz587Nkvdr1qyZlC1bNkvey90cP37cfBfjxo1z6Pvoe/Tp08eh7wEAQGLkIAAAIA1u3rwpH374oTRt2lQKFCggvr6+UrhwYWnZsqV88sknEhkZ6fL1qI3OlBZHN3gzU8OGDWlAAwDgAD6OeFEAALLbXePHHntM9u/fLw899JCMGDFCChUqJBcvXjR39/v37y/bt2+X2bNni6u755575NVXX012vzs4cOCAbNu2TSpWrCjffvutfPDBBxIYGCjZzY0bN8Tb29vZxQAAeBgCBAAA3KbnQJs2beTQoUPyzTffSOfOnRM8P2zYMPnrr79k9erVblGPxYoVk6eeekrc1aeffiq5cuWS+fPny/333y8LFy6U5557TrKbgIAAZxcBAOCBGGIAAMBtGqT79u2TIUOGJAkO2FSvXt08b/P777+b8eN333235MyZU/LkySONGjWSpUuXJjn31KlT0rdvXylTpoz4+/ub4Qt169Y1wxaSM2fOHKlatao5Vs+ZOnVqln1/W7ZskVatWknevHklR44cUrNmTTPswmq12o/56quvTPd/7VlhExsbK0FBQWb/r7/+at+vwzK0fnr16pWm94+OjpZ58+ZJp06dTB1pgEC/n9TyKJw+fVq6dOki+fLlM4EFHRLy999/Jzg2IiJCRo8eLfXq1ZOCBQuaur3rrrtk5MiRcv369VTLdP78efHz85Mnn3wy2edfeeUV87lt73np0iVzrVSoUMEEAbRc2nvjrbfeum0OghUrVpghLtp7Rc8tXry4tGvXzlyfAABkBnoQAACQCu3Grp5//vk015MGArRB2L17dylZsqQZivDFF19Ihw4dzJ3vHj16mONiYmKkefPmcubMGXnhhRekUqVKEh4ebnokbNy4UZ599tkErztr1iwJDQ2Vfv36mQa3NsZ1uIO+h+0109LIvnDhQoJ9Xl5ekj9//lTPW7lypTz++OOmAT1o0CDTsF28eLFpAO/du9c+vOLhhx826//7v/8zjXSlwy/0c+n76P769eub/TpUQLvS67CNtFi+fLn5/L179zbbun7xxRdNfWmQJrFr166ZBnWDBg1k4sSJEhwcLO+//775HHqOrQu/1r8GGjQApA193b9hwwYTfNm5c6f8/PPPKZapSJEi5vWWLFkily9fNvUSPwCi33eTJk1MsEjpe+h3q9eTBlj08+u1ogGV119/PcX30fJoMKBGjRomcKFBmpCQEFm3bp05v1q1ammqQwAAUmUFAAApyp8/vzVPnjzpqqGrV68m2Xft2jXr3Xffba1SpYp93+7du/XWu3Xq1Kmpvt66devMccWKFbNevnw5wWsWLFjQWr9+/TSVS18juSUoKCjBcU2bNrWWKVPGvh0TE2O2tR5OnTqVYH+rVq3Ma2zZssW+v1KlStYGDRrYt9966y1rYGCgtV27dtYHH3zQvn/MmDHm3BMnTqSp/I8++qi1bNmy1ri4OLN96dIlq7+/v3Xw4MFJjtXPoK89ZcqUBPu1rnX/qlWr7PsiIyOt0dHRSV5j9OjR5tjffvvNvi84ONjsGzt2rH3f6tWrzb4PP/wwwfkLFy40+7/88kuzfeXKFbM9YMCA235WPa537972bf2Mui80NPS25wIAkFEMMQAAIBV65zu9SfC0K7uNdlHXHgS61jvlmmRPX1NpLwD1yy+/mK7qt/P000+bO8c22j1f78YfPnw4zWWrU6eOrFmzJsHyww8/pHrOn3/+KSdOnDBd3rW3go3eaX/ttdfMY72DbqOfU3sNaNd92+fT3gQtWrSQrVu3mrwOtv3alb906dK3Lbfe5dc7+TocQbvfK71br3fVddhBVFRUknO0x4L2cIjP1lshfp3pEAEfHx97rw7tCaC9LB555BGz77fffku1bHpc+fLlkwx30G39jnVIhNJhGTo0QIdZaOLL9LB979qjRcsIAIAjECAAACAVGhywNXTTSrvBa+I87X6uwQLtlq/jxj/++GPz/JUrV8xacwi88cYbJsGhjie/7777zAwD8cfpx6eN0MQ0Z4EGINJKj9cGbfxFu+Gn5tixY2adXDd27fIe/xhbI1wbsdqVXrvZa1BA9+mi25rLQLv/a64G25CE25k7d67JZaBTHB45csS+aOBBG/PLli1Lco7WaeJkf/r5VeI6mzlzpskFoPkHdLiFfl+2IRIaMEiNBix0OMiuXbtMMEVpQEWHU+iQBQ0M2AIROsRBcwaUK1fO5JJ46aWXTJDmdvS42rVrmyEVWr7WrVub10pLYAkAgLQiQAAAQCq0Aax3/I8ePZqmeoqLizN5BTTngN7tXrRokaxatco0Am15AvQYm/Hjx5uGrk7Xp+PUP//8czNmPvGdb+Wsae/iJyFMiwcffNA0mrWBbMszoIGAKlWqmFkUdP+mTZtMPoS05B/Q99d6UZokUac4tC3aYFbJJStMrb7if6Z3333XvI6W7b///a9JBqjflwYlEn9fqfXu8PX1NUkklZZXz9N8EfFp4Eh7D+hxmmhR81Voz4onnngi1ffRoIAGVDQXwcCBA02PlKFDh5prJn5CSAAA7gRJCgEASIV2D9dGmc4qMHny5NvWlSbs27Nnj+kZoI3/+GyNx8T0brI2UHXRO+ya9E5nBxg8eLB5ztk0475KLlu+JvuLf4ztLr3ejddAgPag0J4UtiSCGhDQ/Roc0CCCBhNuRxvAGqDRoEnjxo2TPK/JEnUKSp2xIP4QiLTSZI8648FPP/1khiXYaGAnrfQz6nCHr7/+Wt5++20TXNAeIffee2+SY4sWLWpmrtBFgwLa++Czzz4z11lq9aFl04SHuigdrqK9CsaOHWvOBQDgTtGDAACAVGgjTu98613m+OPsEwcF9Pn4d60T33XXhnTiaQ7DwsJMQzk+7eJu68qvU+K5Am3o6nAI7RWhuQBstHE7adIk81jvgMengQCtl++++y5BLwF9vGPHDjMkQHtnaFf+29HeAdo41nwHGrBJvOisCloW2x3/9NLvTIMV8b8zHSKRloBQ4t4B+p3279/fDDFI3HtA7/onnjZRP1etWrVu+30nnnlCae8BnULTVa4TAID7owcBAACp0PHjP/74ozz22GPSsWNHM2Zfu4RrXgEdx653bnUKQNuUhBpM0Aa+TpGnjUGdulCnodOu63oX3TZGXekUddqo1Ne1NfZ0HLseq3fgbQ1HZ9MGtI7R154N2i1ep+jTBIEaMNHPr59dcwPEp4GA9957Tw4ePCjDhg2z79ehBppLQOtEG/a3o/katIfAAw88YO7SJ+f+++83PQf0LrxOFWhLYphWGmQYNWqUGdevU1HqkBLtCaBDBtJDh5ZongjtkaDXTeKpJ/Uza74HDaboNaI9LbR+dPpKzZdgS4qYHK1j7SGh154Ga7SniSYs1HwXmrcCAIDMQIAAAIDb0Eaf3vXWYQZ6R1zvmmviQm0k69113f/UU0/ZG9M6hl0bxXrHXZPxaWBAH+/evTtBgKBmzZqmQaqN7Pnz55uGc6lSpcy52uhzVs6B5Dz66KMmoPHmm2/KtGnTTANVcwBooryXX345yfHaENaZAfROfPweBNq41frUpIZpyT+gDXWd9cA2E0ByNCCg9ah5HLSMaXnd+LSutfeA9lTQ8f06BKBr164mr4AmEkwrLYf2GtCeDp07d7bPUmGj3+0zzzxjyqgzR+jn0sCA5qoYOXJkkuPj69mzp+khodfRP//8Y5JnVq5c2dRP9+7d0/V5AQBIiUXnOkzxWQAAAKTZO++8YwIOOoNDcvkSAABwZQQIAAAAMoH2ltAhJTq1YnIJHQEAcHUMMQAAALgDwcHBZjpHHTagQye02z8AAO6IAAEAAMAd0BwSmq9AE1fq9JbkBAAAuCuGGAAAAAAAAPGiDgAAAAAAAAECAAAAAABAgAAAAAAAABAgAAAAAAAABAgAAAAAAAABAgAAAAAAYJCkEAAAAAAAECAAAAAAAAAECAAAAAAAAAECAAAAAABAgAAAAAAAABgkKQQAAAAAAAQIAAAAAAAAAQIAAAAAAECAAAAAAAAAECAAAAAAAAAGSQoBAAAAAAABAgAAAAAAQIAAAAAAAAAQIAAAAAAAAAQIAAAAAACAQZJCAAAAAABAgAAAAAAAABAgAAAAAAAABAgAAAAAAAABAgAAAAAAYJCkEAAAAAAAECAAAAAAAAAECAAAAAAAAAECAAAAAABAgAAAAAAAABgkKQQAAAAAAAQIAAAAAAAAAQIAAAAAAECAAAAAAAAAECAAAAAAAAAGSQoBAAAAAAABAgAAAAAAQIAAAAAAAAAQIAAAAAAAAAQIAAAAAACAQZJCAAAAAABAgAAAAAAAABAgAAAAAAAABAgAAAAAAAABAgAAAAAAYJCkEAAAAAAAECAAAAAAAAAECAAAAAAAAAECAAAAAABAgAAAAAAAABgkKQQAAAAAAAQIAAAAAAAAAQIAAAAAAECAAAAAAAAAECAAAAAAAAAGSQoBAAAAAAABAgAAAAAAQIAAAAAAAAAQIAAAAAAAAAQIAAAAAACAQZJCAAAAAABAgAAAAAAAABAgAAAAAAAABAgAAAAAAAABAgAAAAAAYJCkEAAAAAAAECAAAAAAAAAECAAAAAAAAAECAAAAAABAgMAJrFarhIeHmzUAAAAAAK6CJIVZLCIiQoKCgswaAAAAAABXQYAAAAAAAAAQIAAAAAAAAAQIAAAAAAAAAQIAAAAAAECAAAAAAAAAGCQpBAAAAAAABAgAAAAAAAABAgAAAAAAQIAAAAAAAAAQIAAAAAAAAAZJCgEAAAAAAAECAAAAAABAgADp1KxZM/H29pY9e/bY9125ckUsFoscP348TedPnz6degcAAAAAF8MQg2xkyZIlUrNmTcmRI4dZ67Yj5MuXT0aNGuWQ1wYAAAAAOAcBAhdltVrl2rVraV6+/vpr6dixo+zdu1du3rxp1rqt+9Nyvr5fWg0YMEC2bt0qGzduTPb5hQsXyj333CN58+aVunXrmmPV0KFDZdOmTTJixAjJnTu3tG7dOtPqCwAAAABwZyzW9LQMccfCw8MlKChIwsLCJDAwMMXjtNGujeiscvXqVcmVK1eahgi0b99ebty4IcuXLzeNfx1ioL0KgoODZf/+/fLcc8/JsmXLpFatWvL999/Ls88+K3///bcUKFDAfv6gQYOy5HMBAAAAcG1t55wXd7a8XxHJLnycXQC4J23gz5gxwwQAtNFv89FHH8mrr74q9913n9nu0KGDvPvuu7Jy5Urp2bOnE0sMAAAAOAYNXGQXBAhcVM6cOc1d/bSqX7++7Nu3L8FQAU0cWL16ddm2bVua3i89NM/B2LFj5bXXXjPDBmw0UaHu0+dsoqOj5cyZM+l6fQAAAABA1iJA4KK0cZ+WLv8248ePNzkH9DwNEtjWuj89r5Meffv2lWnTpskXX3xh31eqVCl5+eWXpX///sme4+VF2gsAAABXwx1wAIoAQTahXfkXL14sEyZMkEOHDkmlSpXMXfwnnnjCYe+p0x2+9dZb8vzzz9v3vfTSSzJw4ECTnFCHGWiuAs1TULlyZSlZsqQUKVJEjh496rAyAQAAz0QDFwDuHLdzs1mQYNeuXaZRrmtHBgdstNfCXXfdZd9u06aNTJ482SQm1MSF5cqVk/fff1/i4uLsuQvWrl1rZjjQYwEAAAAAroEeBEiX9evXJ9n366+/Jtju3LmzWZJTr149OXDgALUOAEAyuAsOAHAmAgQAACDT0MAFAMB9ESAAACAeGrgAAMBTkYMAAAAAAADQgwAAshvugAMAACAj6EEAAAAAAADcL0BQtmxZsVgsSZYXX3wxxaz7yR1/8ODBBMctXrxYqlatKv7+/ma9dOnSJK81c+ZMM21fQECA1K5dWzZt2uSwzwkAAAAAQFZyuwDB9u3bJSQkxL6sWbPG7E9pWj2bQ4cOJTivYsWK9ue2bdsmXbt2lZ49e8ru3bvNukuXLvLbb7/Zj1m0aJEMGjRIXn/9ddm5c6c0btxYWrduLSdPnnTgpwUAAAAAIGu4XYCgUKFCUrRoUfvy448/SoUKFaRp06apnle4cOEE53l7e9ufmz59ujRv3lxGjRollStXNuuHH37Y7LeZNm2a9O3bV/r16ydVqlQxz5UqVUpmzZrl0M8LAAAAAEBWcLsAQXxRUVHy1VdfyTPPPGOGDaTm3nvvlWLFipmG/7p16xI8pz0IWrRokWBfy5YtZevWrfb32bFjR5JjdNt2TEoiIyMlPDw8wQIAAAAAgKvxETf2/fffy5UrV6RPnz4pHqNBgdmzZ5ucAdpYnzdvngkSaG6CJk2amGPOnTsnRYoUSXCebut+deHCBYmNjU31mJRMmjRJxo8f79LZyJf3S/i5AAAAAACex60DBJ9++qnJA1C8ePEUj6lUqZJZbBo0aCCnTp2Sd955xx4gUIl7IFit1iT70nJMYjpcYciQIfZt7UGgQxPcVbNmzaR9+/YmHwMAAAAAIPtw2wDBiRMnZO3atbJkyZJ0n1u/fn0zNMFGcxIk7gkQGhpq7zFQsGBBk7MgtWNSorMi6AIAAAAAgCtz2xwEn3/+uUk8+Nhjj6X7XJ2FQIcexO9VYJsNwWb16tXSsGFD89jPz88MUUh8jG7bjvE0OkQjb968MmfOHNMjokCBAjJ8+PAk9VOvXj1znNa3Drew0QCNJnvU5x544AHzncTvpTBixAgzFCRXrlwmoHPmzBkZN26cSVJZsmTJBNNQak+ODz74wCSY1NfT8w8cOJBFNQEAAAAA2YNbBgji4uJMgKB3797i45N6JwidbUBzFRw+fFj27dtnuvwvXrxYXnrpJfsxAwcONAGBKVOmyMGDB81aeyfE70avwwS0MfzZZ5+ZxufgwYPNFIf9+/cXTxURESF79+41dbt582b56KOPTOBAaYP/8ccfN0GDf/75x9Trgw8+aJ7btGmTvPDCC/Lf//7XPNepUyeTFDIsLMz+2vPnz5f3339fLl68aIIEOq1kUFCQmaJy7Nix8uyzz0p0dLQ5VmeS0OEmy5cvN/kiOnToIG3btjXJJQEAAAAA2ThAoI13bZzr7AWJ6V3msmXL2re1kThs2DC55557TCNTG7IrVqwwjUgb7QWwcOFCE3TQ4+bOnSuLFi0yd79tunbtaoINEyZMkFq1asnGjRtl5cqVUqZMGfFUeudeewUEBASY3gBajzrbg9LEkN26dZOOHTuKr6+vadxrTwD15ZdfylNPPWVyQOhzGojJly+f+V5s9Pnq1aub19bXuHHjhgnKaEDoySefNIEDHWaiNDCh30vFihXN86+88oo5/rfffnNSzQAAAACA+3HLHAQ6vaA2TpNz/Phx08XcRu9gJ+76nhy9i61LagYMGGAW3BIYGCg5c+a0V4fe6ddeBUob7xqQSc7p06cTfEeqXLlyZn/8vBA2+h7xcz3Y3vPq1av271wDCponIn5gKP7rAQAAAACyYYAgNRs2bDB39+Fc2rPiyJEjyT6nOQS0UR+fbuv+jNAcCNq7o1WrVhk6HwAAAADgpkMMUhMcHOzW0whmF5ojYMGCBSaZYExMjMkv8Ouvv5rn9G6/5hjYsmWLee7DDz80QwYeffTRDL3Xiy++KG+88YYcOnTIPpXkDz/8YO/NAAAAAADwwB4E2c3yfqlPo+iq7rvvPpMMcsyYMSaZZO7cuU0ySM1D0LRpUxMU6Nu3r0k6qLkGfvrpJzMDQUZowkkdXqB5JU6dOiV58uQxMyM89NBDmf65AAAAACC7IkCAdLHNUqCuXLmS4DmdLSK+1q1bmyU5GjTQ5Xbvofr06WOW+OLnoLBYLOSHAAAAAIA7lO2GGAAAAAAAgPQjQAAAAAAAAAgQAAAAAAAAAgQAAAAAAIAAAQAAAAAAIEAAAAAAAAAMkhQCAAAAAAACBAAAAAAAQMSHSnBt559u6/D3KPL5coe/BwAAAADAtTHEABmyefNmad26teTLl0/y5s0rNWvWlKlTp0pUVFSGa9RisciuXbv4RgAAAADACQgQIN1+/PFHExxo2bKlHD58WK5cuSKLFi2S/fv3S0hICDUKAAAAAG6IAAHSxWq1yiuvvCIjRoyQQYMGScGCBc3+ypUry9y5c6VMmTLyxx9/SKNGjUzPgqpVq8qCBQvs5//5559Sv359CQwMNOe2bXtrCMX9999v1g0bNpTcuXPLxIkT+WYAAAAAIAuRgwDpoj0GgoODpXv37sk+r70JWrVqJWPHjpX+/fvL1q1b5bHHHpPSpUuboMFLL71kggK6Pzo6Wn777Tdz3u+//26GGOj+WrVq8a0AAAAAQBajBwHS5Z9//jHrEiVKJPv8ihUrpFChQvLyyy+Lr6+vNG3aVHr06CFffPGFeV73nThxQs6ePSv+/v7SpEkTvgEAAAAAcAEECJAutiEFZ86cSfb506dPS9myZRPsK1++vNmvPvvsM7l586bUrl3bDEuYMWMG3wAAAAAAuAACBEiXu+++2wQAFi5cmOzzJUuWlOPHjyfYp0MSdL+qUKGCfPnll3Lu3DmZM2eODBs2THbs2GGe0yEGAAAAAADnIECAdNFG/IcffiiTJ08264sXL5r9f//9t/Tt21ceeOABCQ0NlZkzZ0pMTIxs2rRJvv76a+nVq5c5ToMD58+fN6+jUyR6eXmJj8+tVBhFihSRo0eP8o0AAAAAgBOQpNDFFfl8ubiaNm3ayE8//ST/+c9/ZMyYMWafJiHs2bOnFCtWzDynMxyMGjVKihcvLrNmzTKBA7V27VoZPny4XL161QQE3n77balZs6Z57s033zQzJPTr18/MkjBy5Einfk4AAAAA8CQECJAh2uBftWpVss/plIU6G0FytAdBSjQwoAsAAAAAIOsxxAAAAAAAABAgAAAAAAAABAgAAAAAAAABAgAAAAAAQIAAAAAAAAAYJCkEAAAAAAAECAAAAAAAAAECAAAAAABAgAAAAAAAALhlgGDcuHFisVgSLEWLFk31nA0bNkjt2rUlICBAypcvLx9//HGSYxYvXixVq1YVf39/s166dGmSY2bOnCnlypUzr6Ovt2nTpkz9bAAAAAAAOIvbBQhUtWrVJCQkxL7s3bs3xWODg4Pl0UcflcaNG8vOnTvltddek1deecUEBGy2bdsmXbt2lZ49e8ru3bvNukuXLvLbb7/Zj1m0aJEMGjRIXn/9dfM6+nqtW7eWkydPOvzzAgAAAADgaG4ZIPDx8TG9BmxLoUKFUjxWewuULl1apk+fLlWqVJF+/frJM888I++88479GH2uefPmMmrUKKlcubJZP/zww2a/zbRp06Rv377mfH0dfa5UqVIya9Ysh39eAAAAAAAczS0DBIcPH5bixYub7v7dunWTY8eOpXis9g5o0aJFgn0tW7aUP/74Q6Kjo1M9ZuvWreZxVFSU7NixI8kxum07JiWRkZESHh6eYAEAAAAAwNW4XYCgXr168uWXX8rPP/8sn3zyiZw7d04aNmwoFy9eTPZ4fb5IkSIJ9ul2TEyMXLhwIdVjdL/S42JjY1M9JiWTJk2SoKAg+6K9DgAAAAAAcDVuFyDQcf8dO3aUGjVqyCOPPCIrVqww+7/44osUz9FEhvFZrdYk+5M7JvG+tByTmA5XCAsLsy+nTp267WcEAAAAACCr+Yiby5UrlwkW6LCD5GiOgsR3+UNDQ00egwIFCqR6jK3HQMGCBcXb2zvVY1KisyLoAgAAAACAK3O7HgTJjfE/cOCAFCtWLNnnGzRoIGvWrEmwb/Xq1VKnTh3x9fVN9RgduqD8/PzMtIaJj9Ft2zEAAAAAALgzt+tBMGzYMGnbtq2ZmUDv4P/nP/8xif969+6d7PH9+/eXGTNmyJAhQ+TZZ581CQk//fRTWbBggf2YgQMHSpMmTWTKlCny+OOPyw8//CBr166VzZs324/R83X6Qw0saEBh9uzZZopDfX0AAAAAANyd2wUITp8+Ld27dzeJA3V6w/r168uvv/4qZcqUMc+PGzdO5s6dK8ePHzfbOtPBypUrZfDgwfLRRx+Z2Q8++OADk8fARnsBLFy4UEaPHi1jxoyRChUqyKJFi0xCRJuuXbuaRIgTJkyQkJAQqV69unld2/sCAAAAAODO3C5AoA351GhgoFmzZgn2NW3aVP78889Uz+vUqZNZUjNgwACzAAAAAACQ3bhdgOB2NmzYIBs3bnR2MQAAAAAAcCvZLkAQHBzs7CIAAAAAAOB23H4WAwAAAAAAcOcIEAAAAAAAAAIEAAAAAACAAAEAAAAAACBAAAAAAAAACBAAAAAAAACDJIUAAAAAAIAAAQAAAAAAIEAAAAAAAAAIEAAAAAAAAAIEAAAAAADAIEkhAAAAAAAgQAAAAAAAAAgQAAAAAAAAAgQAAAAAAIAAAQAAAAAAMEhSCAAAAAAACBAAAAAAAAACBAAAAAAAgAABAAAAAABQPlQDAAAAALiekD9XyN/L35Vr545KrqIV5O62Q6XYfY85u1hug/pLPwIEAAAAQApoYNwZ6u/O6m7HrL4iFouI1SoRZw6Y7dovfGqCBFar1ey3WuPireP+XUu8bWu8/db/bcf9+7xYJTj4usTFxZlFn0/u8Z1up/bcmd8uixbalNE8F3drO+7fffqB/t2f5HMk+Px6zq3jw88clJDt39vrM3H9IXkECAAAgMujkUH9uWIDLa0SNMjiN4JuteLM/itX/NPUsEprgyu9x4X+dTFB2W6VO2Ej8lYDLX6j9H8Ns6SN0jgJO7lXTm2ab6+HiNP7Tf2VaNBZ8hSvlKAxl7BO9H2tKTYYE5bn1v7+fwRkWn1l5DWO/BOVqJH+v3qyNVwTN9IT77edY9uOuXnNdgElWMe/JjNL+ZGS/Wl9WSymRwYBgpQRIAAApAkNtDtD/Tm/kZaWxltUVFSqjYL0NtrS+/jSkYuJGmT/u3uWuCF1q+xJG52J77pdObZTjq/7NEkjrXTjnhJYqqr9tU09JGp03SpLwjuU/2vUJN3/6qGcDmnUpuXxnrOR/9ZBwjpLqbyJG3FJ98fJzSvnbRdJgvWO/z4nvgG5U74zm2h/WuQbKB7jzLZvM/01/7tePMudBAcsXmLx0tnuLWKxeIl4eUmAj0W8vLzsi8WS8nZGn0vt2L3nYsy2KZuWSR+atW3bksL61uvYtm2fSz/Tqc0LbgWxEtWbDtdAyizWW/8bIIuEh4dLUFCQhIWFSWBgIPUOINO1nfPvH7QObKDZ1o7opre8X5EMNcwy65yBS/6J94d+0gZDso22eHezkmu0XT66Q4LXfHzrjxY97t91mQefkbxlaiS9U2a/g/a/10u2UZPMnbdutXKmu3GVmQ3d7Sdv3KofSa6hlEyXV/tnsCbz2W/tv/7PSbHGRiW5VizePuKXp2Ciu5gJ78Ddbn9m3oEDMltqDaq0NrzSes7xS7GmUWUaW9pw9EqpcWZJw/5br3Nu16qkDTRl8ZKS9TslaMyZ88z7Jn19WwPQtj/xe+vyVJ3ATG3Mpve5cT+HJWp0/7v+ty5tj237EzdybQ3i/21bZPuMXv82ZuP9TlkskrtoRWkwbPGt8+PXX6LG8v+247+ffr/J/9+b3f522TD+IRNQTvA7b7FIYImq0mTs/2Xqey13cv1lJnoQAABuS7vjJejO+O/6z9n9JSBv0aR34hI0nCXpHcl4DUJ7A/ffcy3PZucGmzXB+sS6z+REJr/Dm8vFY1hjYyTyyjmnvX9GGmW3e3wuwpqmxljiu2W2Bontcfz9/xzYmGIjrei9rRPedbM//rexkloDLpn9HWvmyfQ6Sevjt9eFJVNXiRtMad1/a/3nnAFyPfR4wgaaWEyyuLoDPk90J/bf+ohfj+l4/+/7FkvwuVJqyGWPBloVqfXMB5n6Xm84uYE280zm11/lJ0YlG5yv1H6k+AcWyvT3y240oWNy9Vex7VBnF82lESAAANyWuYORzJ1Wa2y03Lh4ymVq0NZYSK4BkdbGxsUbt+7vJ9cF89Yf/ulstFkscunIb8nfqbZ4SeHqD6XQdfJ2d+mSuStk8ZI21XKl+jnT0+jKyLHvb4pItuFouyOW5M6ZSnBs0jttu+cOMr0IkjTSipST+577b7y7nYnupsW/Cxr/fRI1AuM32hb1LpKmz+yoxltWN9LqvPC/oQeZYaoTG2nzr2Z+3VXpODrZBkblJ16T3MUqZup7+fn5SXZDA+3OaA897amXcBaDYVLsvkcz6RvK3qi/jCFAAAC4Lf2jJOL0gSQNtJyFy8q9fT9K1DUypS6TiRu2yXeD/OqpwhluwLpjA+3+V77K1Pea4eS7aEtiHdBI6/RG8o20DqMlqHSNTH2vvHnzSnZDIy3jaGDcGeovc+qQhHrUX1YiQAAASHsDw+bfBlqVjmMkX/n7MrUGixTJPuP4bGig3RkaGdSfM9FAo/4AT0KAAABwW0VqthSfgDwSczNCLN6+pmst3RzTjgbunaORRv0BABzv38F/7mPSpElSt25dyZMnjxQuXFjat28vhw4dSvWc9evX28cLxl8OHjyY4LjFixdL1apVxd/f36yXLl2a5LVmzpwp5cqVk4CAAKldu7Zs2rQp0z8jALiaf/avN8EBv9z5pfWMY9J07C+MgcxAA1fr7dFZJ6g/AADgktwuQLBhwwZ58cUX5ddff5U1a9ZITEyMtGjRQq5du3bbczWQEBISYl8qVvxfcplt27ZJ165dpWfPnrJ7926z7tKli/z222/2YxYtWiSDBg2S119/XXbu3CmNGzeW1q1by8mTmjgJALL/nNXF739CvHx8nV0cAAAAOIDbDTFYtWpVgu3PP//c9CTYsWOHNGnSJNVz9biUkg9Nnz5dmjdvLqNGjTLbutZghO5fsGCB2Tdt2jTp27ev9OvXz37Ozz//LLNmzTI9GwAgO4q+Hi7ndv1sHpds0NnZxQEAAICDuF0PgsTCwsLMOn/+/Lc99t5775VixYrJww8/LOvWrUvwnPYg0J4I8bVs2VK2bt1qHkdFRZkgROJjdNt2THIiIyMlPDw8wQIA7iRkx48SF33T5B0IKlPT2cUBAACAg7h1gMBqtcqQIUPkgQcekOrVq6d4nAYFZs+ebXIMLFmyRCpVqmSCBBs3brQfc+7cuSSZs3Vb96sLFy5IbGxsqsckR3sWBAUF2ZdSpUrdwScGgKx3+tdvzLpk/c4Om/sdAAAAzud2Qwzie+mll2TPnj2yefPmVI/TgIAuNg0aNJBTp07JO++8k2BYQuI/fDUAkXhfWo6JT4cqaBDDRnsQECQA4C6uXzgpl/7+1UxrWKJeB2cXBwAAAA7ktj0IXn75ZVm2bJkZKlCyZMl0n1+/fn05fPiwfbto0aJJegKEhobaewwULFhQvL29Uz0mOTojQmBgYIIFANzFmV8Xm3WBSo0kR4H0/9YCAADAfbhdgEDv2GvPAR0q8Msvv5gpBzNCZyHQoQfxexXorAjxrV69Who2bGge+/n5mWkNEx+j27ZjACA70d/b0//OXlCyfidnFwcAAAAO5nZDDHSKw6+//lp++OEHyZMnj/2Ovo7vz5EjR7Ln6GwDZcuWlWrVqplkg1999ZXJR6CLzcCBA81wgylTpsjjjz9uXn/t2rUJhi/oUAGd/rBOnTomoKB5DXSKw/79+2fBJweArHUleKdcCz0mXn45pFjtNlQ/AABANud2AQKdUlA1a9YsyXSHffr0MY/HjRsnc+fOlePHj5ttDQoMGzZMzpw5Y4IIGihYsWKFPProo/bztRfAwoULZfTo0TJmzBipUKGCLFq0SOrVq2c/pmvXrnLx4kWZMGGChISEmMSIK1eulDJlymTRpweArHN6263khMXufVR8AnJT9QAAANmcjzt2eb0dDQzEDyAMHz7cLLfTqVMns6RmwIABZgGA7CwuJkrObv/BPC7B8AIAAACP4HYBgrTYsGFDgikMAQDpE7r3/yT62mXxDyoiBas0pvoAAAA8QLYMEAQHBzu7CADg1mzJCXVqQy/vbPlfBQAAANx9FgMAgGNFXbss5/fcmrGlZIPOVDcAAICHIEAAAEhAcw9YY6MlsGQ1CSxZldoBAADwEAQIAADJDi+g9wAAAIBnIUAAALC7ev6YXDm2Q8TiJcXvf4KaAQAA8CAECAAAdmd+vdV7oFC1phKQtwg1AwAA4EEIEAAADGtcnJze9p15XLJ+F2oFAADAwxAgAAAYl478LjcunhKfgNxStFZLagUAAMDDECAAABin/x1eUKx2G/H2z0mtAAAAeBgCBAAAiY26ISF/LDM1UaJ+J2oEAADAAxEgAADI+d2rJeZGhATkLyEF7m5IjQAAAHggAgQAADm97dbwgpL1O4nFi/8aAAAAPBF/BQKAh4sM/0f+2bfOHiAAAACAZyJAAAAe7szv34s1LlaCytaS3MUqOrs4AAAAcJcAwZNPPimzZ8+Wv//+2zElAgBkqTO24QUNOlPzAAAAHizdAYLcuXPLtGnTpHLlylK8eHHp3r27fPzxx3Lw4EHHlBAA4DARZw5K2Mk9YvH2kRJ121PTAAAAHizdAYL//ve/Jhhw9uxZEygICgqS999/X6pVqybFihVzTCkBAA5x+tfvzLpw9YfFL08BahkAAMCDZTgHQZ48eSRfvnxmyZs3r/j4+EjRokUzt3QAAIfRvANnfl1sHjO8AAAAAOkOEIwYMULq168vBQsWlNGjR0tUVJSMGjVKzp8/Lzt37qRGAcBNXDy0VW5eCRHfnEFS+J7mzi4OAAAAnMwnvSe8/fbbUqhQIRk7dqw8/vjjUqVKFceUDADgUKe3fWPWxeo8Lt6+/tQ2AACAh0t3gEB7CWzYsEHWr18v7777rnh7e0vTpk2lWbNmZiFgAACuLybymoT8ucI8ZngBAAAAMhQgqFmzplleeeUVs717926ZPn262Y6Li5PY2FhqFgBc3LmdP0ls5HXJWais5KtQx9nFAQAAgDsGCGy9CLQHgS6bNm2S8PBwqVWrljz44IOZX0IAQKY7ve1bsy5Zv5NYLBZqGAAAAOkPEOisBVevXjW9CHRIwbPPPitNmjSRwMBAqhMA3MCNyyFy4cBG87hEg87OLg4AAADcNUAwb948AgIA4MbO/r5ExGqVfHfdL7kKlXF2cQAAAOCuAYI2bdrYH58+fdp0TS1RokRmlwsA4ABWq/V/wwvoPQAAAIB4vCSdNBHhhAkTJCgoSMqUKSOlS5eWvHnzyptvvmmeAwC4rvBT+yTizEHx8vGXYrXbOrs4AAAAcOceBK+//rp8+umnMnnyZGnUqJG5G7VlyxYZN26c3Lx5U9566y3HlBQAcMdsvQeK1GwufrnyUqMAAADIeIDgiy++kDlz5ki7du3s+zRhoQ4zGDBgAAECAHBRcbExt/IPmOEFXZxdHAAAALj7EINLly5J5cqVk+zXffocAMA1Xdi/QSLD/xG/3PmlUDWmpQUAAMAdBgi0t8CMGTOS7Nd9+hwAwLWHFxS//wnx8vF1dnEAAADg7kMMpk6dKo899pisXbtWGjRoYGYx2Lp1q5w6dUpWrlwpnmDmzJny9ttvS0hIiFSrVk2mT58ujRs3dnaxACBF0dfD5dyuVeYxsxcAAAAgU3oQNG3aVP7++2954okn5MqVK2ZYQYcOHeTQoUMe0UhetGiRDBo0yCRr3Llzp/nMrVu3lpMnTzq7aG5jyZIlprdJjhw5zFq3Qf3BsUL+/FHiom9K7qIVJagMvb0AAACQCT0IVPHixT02GeG0adOkb9++0q9fP7OtvQd+/vlnmTVrlkyaNMnZxXN5Ggzo2LGj6XmiM2Ds3bvXbL/77rvyyCOPOLt4Lk977gwdOjRJ/S1evNgE6oDbDS/Q3gN6/QAAAAAZChDs2bNH0uqee+6R7CoqKkp27NghI0eOTLC/RYsWZphFciIjI81iEx4eLp5s/Pjx9satsq210Yu0i19/Wp8TJkwgQIAUXb9wUi79vU3EYpES9QgkAQAA4A4CBLVq1UrQqEuJHhMbGyvZ1YULF8znK1KkSIL9un3u3Llkz9FeBdooTqxu3bri7e0t3bt3lxdffFEeeOAB+3P79++XwYMHm54JavTo0XLXXXdJnz59zHaFChVk+fLl0rZtWzl69KjZN3fuXDly5Ij85z//MdstW7aU9957T6pWrWp/3c2bN8tHH30kCxYsMNv6vnqcbcrKfPnyyZYtW6R3796yfft2e48JNWTIEHu5darLRo0ayeXLl82+ZcuWmbLqa6vUPpPe8U7pOtJ61GErMTExZjsoKMjU99WrV822v7+/5MmTx3wPNgUKFJDr16/LjRs3zHbOnDnNcbayeXl5Sf78+SUsLEyio6PNvsDAwATBGl9fX/NeOlwmLi7OXhca2NHXVjocQl/74sWL9vcuWLCgRERE2ANAuXPnNt+pvpfy8fGRvHnzZupnSu7fl9an1u+qVasy7XtyhWtvXb0K4s46/H3J4f+eUvqeEtfd9N2H5RcRaVQkv8w/OE5cXdVpR7n27kCjz5137elvxJyC4ta+ztndqf/nztlyq/7dVr/lXHsZxLXnvtee/kbM2XKrd7G7+v2eMU5ta7j7b1/vTfmddu2l53vS17wdi/V2rX4ROXHiRJorp0yZMpJdnT17VkqUKGF6C2iCRhsdbjFv3jw5ePBgmnoQlCpVyjQibQ1VT6I5BxIHCTSwpD1Pdu3a5dSyuQNPqr/zT7cVd1bk8+UuUXd6rTywdL0cDb8m0xvVlG4VS4mrc2bdKa496o/rz/1/+9wRv33UH9ef+yri5L9dsjxJoSYk1MasNv41+lGoUCHzOLklO9M7xnqHOHFvgdDQ0CS9Cmz0zq/WXfzFk40dO9beLV7ZeqbofqS//hT1h9TsvHDFBAdyeHtJm7LFqCwAAADcWYDgwIEDcu3aNfNYu8vbukd7Gj8/P6ldu7asWbMmwX7dbtiwodPK5U40kZ4m1NM73gEBAWatiQs1CIX01Z8OYVAasGvTpg3Vh2R9e/S0WbcuU1Ry+2YoLy0AAAA8RJpzEDz99NNmTITerXznnXfMeOvkvPHGG5Kd6ViSnj17Sp06dcwwg9mzZ5spDvv37+/sorlVI5eM+3def5qjoGzZsqYHy8KFC811CcQXFRsn3wefNY87VyhJ5QAAAODOAwSa7EC7Nv/444+ma/NPP/1kv3sZnz6X3QMEXbt2NYnqNGt8SEiIVK9eXVauXJnth1fA9WjiRE1cMmrUKJMM88knnzRJGQGbX86EyuXIaCmSw18aF3PzzHEAAABwjQBBpUqVzB1KpQ2Q//u//5PChQuLpxowYIBZAGd74YUXZPLkyWYY0A8//MBQDSQ7vOCJ8iXEh+ARAAAAbiPdtxt1GjhPDg4ArkSnTXzppZfM44kTJ952KlJ4jsuRUbL61HnzuAvDCwAAAJAG9EcG3NzAgQPNcIM//vhD1q5d6+ziwEUsCw6R6DirVMsXKFXze/bsKQAAAEgbAgSAm9NZDJ577jl7LwIg/vCCThVKUCEAAABIEwIEQDYwdOhQ8fX1lfXr18vWrVudXRw42bHwq/LHP5fFyyLSoTwBAgAAAKQNAQIgGyhVqpT06tXLPNYZDeDZvjt6xqybFi8kRXIGOLs4AAAAyO4BgqioKDl9+rScPHkywQLAOUaMGGFmGdHpSHfv3s3X4KE0kaxteEFnkhMCAADAkQGCw4cPS+PGjU1StDJlyki5cuXMUrZsWbMG4BwVK1aUzp07m8c69SE805YtW+TU1RuSy8dbWpUu6uziAAAAwI34pPeEPn36iI+Pj7lLWaxYMbFYLI4pGYB0GzVqlCxatEi++eYbmTBhggkawLPMmzfPrNuWLS45fbydXRwAAABk5wDBrl27ZMeOHVK5cmXHlAhAhtWsWVMee+wxWbFihUydOlU++eQTatOD3LhxwwSHFLMXAAAAwOFDDKpWrSoXLlxI9xsByBqvvfaaWX/xxRcmTwg8x/LlyyUsLExK5AqQhkULOLs4AAAAyI4BgvDwcPsyZcoUGT58uJlO7eLFiwme0wWAczVs2FCaNm0q0dHR8u677/J1eODwgk4VSooXw78AAADgiCEGefPmTZBrwGq1ysMPP5zgGN2nx8TGxqa3DAAc0Itgw4YNMnv2bPO4UKFC1HE2FxoaKj/99JN53Kl8SWcXBwAAANk1QLBu3TrHlwRApmnevLnUrl3b5Av54IMP5M0336R2s7mFCxeaAG3dunWlYt7czi4OAAAAsmuAQLsrA3Af2ptHew507NhRPvzwQ3n11VclMDDQ2cWCA3355Zdm3bNnT5E/V1PXAAAAcHySwlWrVsnmzZvt2x999JHUqlVLevToIZcvX05/CQA4RPv27c1sI5q0bubMmdRyNrZ//37TW0SnoO3WrZuziwMAAABPCRDonUhbMsK9e/fKkCFD5NFHH5Vjx46ZxwBcg5eXl4waNco8njZtmly/ft3ZRYKDkxPqbzH5JgAAAJBlAYLg4GAz1aFavHixtG3bViZOnGjuUNoSZAFwDd27d5cyZcrIP//8I5999pmziwMH0LwDX3311f+GFwAAAABZFSDw8/Oz34lcu3attGjRwjzOnz8/0xwCLsbX19dMS6qmTp0qUVFRzi4SMplOOXv69Gkz20ybNm2oXwAAAGRdgOCBBx4wQwk0K/rvv/8ujz32mNn/999/S8mSTK0FuJqnn35aihQpIqdOnZKvv/7a2cWBg4YXdO3aVQICAqhfAAAAZF2AYMaMGSYR1nfffSezZs2SEiVKmP06vKBVq1YZLwkAh8iRI4c9P8jkyZNNl3RkD9euXTO/xYrhBQAAAMiSaQ7jK126tPz4449J9r/33nt3XBgAjtG/f3+ZNGmSHDp0SJYuXSqdOnWiqrMB/S41SFC+fHlp2LChs4sDAAAAT+tBEN+NGzdM3oH4CwDXExgYKC+//LJ5rElFrVars4uETBxe0KtXL7FYLNQpAAAAsjZAoHerXnrpJSlcuLDkzp1b8uXLl2AB4JpeeeUVyZkzp+zcuVN+/vlnZxcHd+jMmTMmUax66qmnqE8AAABkfYBAM6L/8ssvZlpDf39/mTNnjowfP16KFy8uX3755Z2XCIBDFCxYUJ5//nl7LwK4N004GRcXJ40aNZIKFSo4uzgAAADwxADB8uXLTXBAxzBrssLGjRvL6NGjTYNj/vz5jiklgEwxdOhQM1Xppk2bzAL3pENEbAFZHV4AAAAAOCVAcOnSJSlXrpx9XLNu26Y/3LhxY6YUCoBj6Kwjffr0MY81aSHc0+7du+Wvv/4ywZ7OnTs7uzgAAADw1ACBZss+fvy4eVy1alX55ptv7D0L8ubNm/klBJCpdJiQl5eXmZpU8xHAfZMTtmvXjtwvAAAAcF6A4OmnnzZ3r9SoUaPsuQgGDx4sr776auaVDIBD6Hj1bt26mcf0InA/MTEx9uFcDC8AAABAZvJJ7wkaCLB58MEH5eDBg/LHH3+YRkfNmjUztXAAHGPkyJEmyd13330nhw4dkkqVKlHVbmLNmjVy/vx5k3SyVatWzi4OAAAAPLkHQWKlS5eWDh06EBwA3EiNGjVM93RNdjdlyhRnFwcZGF7QvXt38fX1pe4AAACQ9QECndpQcw6Eh4cneS4sLEyqVatGVnTAjegQIVuD8+TJk84uDtJAf3+XLl1qHjO8AAAAAE4LEEyfPl2effZZM3NBYkFBQWZ+9WnTpmV2+QA4SP369eWhhx4yY9rfeecd6tkN6JCQmzdvSuXKlaV27drOLg4AAAA8NUCgiQlTG+/aokUL2bFjhzhSdHS0jBgxwnSPzpUrlxQvXtzcRTt79myq582dO1csFkuSRf/Qjk8TLuoUjgEBAeaP78TzxGt37HHjxpn3zZEjhzRr1kz27dvnkM8KZIXXXnvNrD/55BMJDQ2l0t1keIH+7ulvGAAAAOCUAIEmxUptvKuPj4/8888/4kjXr1+XP//8U8aMGWPWS5Yskb///tuMpb4d7fkQEhKSYNFAgM2iRYtk0KBB8vrrr5up3xo3biytW7dO0PV66tSpppfEjBkzZPv27VK0aFFp3ry5REREOOwzA46kPQjuv/9+EyzTXkJwXSdOnJD169ebx08++aSziwMAAABPDhCUKFFC9u7dm+Lze/bskWLFiokj6VAGzeDdpUsXk3Vdu0h/+OGHpufC7cZQ6902bdDHX+LThn/fvn2lX79+UqVKFdNYKlWqlMyaNcvee0D3aQBBkzJWr15dvvjiCxO00GzwgDvSfxe2XgQfffSRXLlyxdlFQgq++uor++wxmhwWAAAAcFqA4NFHH5U33ngjSbd8dePGDRk7dqy0adNGspomSNRGTt68eVM97urVq1KmTBkpWbKkKaf2ErCJiooyQQYdJhGfbm/dutU8Dg4OlnPnziU4xt/fX5o2bWo/JjmRkZEmsVj8BXAlbdu2NUlG9drUYTZwPRqgjD+8AAAAAHBqgGD06NFy6dIlufvuu01X+x9++EGWLVtmpkjTu/n6nN5dz0oarND53Hv06JFs8kQbTeileQi0vAsWLDBDCxo1aiSHDx82z1+4cEFiY2OlSJEiCc7TbQ0KKNs6tWOSM2nSJNPzwbZorwTAlXh5edlnNHjvvfdMrxi4Fh3SdOjQIZP7pGPHjs4uDgAAADw9QKANYb1Trl3rtTHxxBNPSPv27U33ZN23ZcuWJI3nOzV//nzJnTu3fYmfNFATFnbr1k3i4uJue9dThyI89dRTUrNmTZNb4JtvvjGBDh2eEF/ipF961y7xvrQcE5/WlfZysC2nTp1K02cHslLXrl1Ngk4Nls2ZM4fKdzFffvmlWevvbp48eZxdHAAAAGRTPuk5WLvor1y5Ui5fvixHjhwxjeOKFStKvnz5HFI4TT5Yr169BHkQbMEBzUOg3f5/+eWXVHsPpHTHtG7duvYeBAULFhRvb+8kPQE0q7st6GHLWaDHxM+1EP+Y5OgwBF0AV6ZJRnWGkP79+8vbb79t1n5+fs4uFv4dArVw4UJTFwwvAAAAgEv0IIhPAwLawNbs544KDii9U3bXXXfZF+1eawsOaON+7dq1UqBAgXS/rgY2du3aZW/oa0NIpzXUBIjx6XbDhg3NY727qkGC+MfoH+4bNmywHwO4s969e5t/E6dPn7YnxIPz/fTTT3Lx4kXz+/Pwww87uzgAAADIxjIUIHCWmJgY6dSpk/zxxx9m+IHmDdA7+rpoYz0l48ePl59//lmOHTtmAgM6W4Gu9S6pzZAhQ0zX6s8++0wOHDgggwcPNjMj2I7RYQQ6DeLEiRNl6dKl8tdff0mfPn0kZ86cJgcC4O40N8fQoUPN48mTJ5t/X3A+W3JCndpQe3oAAAAAjuJWf23qnU1NNKhq1aqV4Ll169ZJs2bNzGNtuB8/ftw+Z7hO3fbcc8+ZQIImCrz33ntl48aNpgdE/DHYepduwoQJEhISYvIq6HAKHVZhM3z4cDNjw4ABA8wwCx3+sHr1asYEI9t4/vnn5a233jI9dBYvXmx668B5NPnr8uXLzWOGFwAAAMDR3KoHQdmyZc3wgOQWW3BAaXAg/rZmZj9x4oSZclBzBmhvggYNGiR5fW3467l6nE572KRJkwTPay+CcePGmQCCzqCgwws0kABkF5oMdODAgeax9pbRf1twHk2oqr2j7rnnHrMAAAAAjuRWAYK0iIiIkKNHj8qwYcOcXRTALb388suSK1cu2b17txn/DucPL6D3AAAAALJCtgsQaGJDnUpQ74QCSL/8+fPLCy+8YB7rcAN6ETiHzhSjU8vqrCvkOQEAAEBWyHYBAgB3TpN26uwe2kDdtGkTVerE3gPNmzdPMLUqAAAA4CgECAAkoQ3SZ555xp6LAFlLe20wvAAAAABZjQABgGS9+uqr4u3tbZJ6atJOZJ0tW7ZIcHCwGSrVvn17qh4AAABZggABgGSVL19eunfvbh5PmjSJWspCX375pVl36tRJcubMSd0DAAAgSxAgAJCikSNHmvWSJUvkwIED1FQW0ClUdXpDxewFAAAAyEoECACkqFq1aqaLu46JnzJlCjWVBZYvXy5hYWFSqlQpadq0KXUOAACALEOAAECqRo0aZdZfffWVHD9+nNrKouEFTz31lJniEAAAAMgq/PUJIFX333+/PPLIIxIbGyvvvPMOteVAoaGhsmrVKvO4Z8+e1DUAAACyFAECALf12muvmfWcOXPk3Llz1JiDLFy4UGJiYqRu3bpSpUoV6hkAAABZigABgNtq1qyZ1K9fXyIjI2X69OnUmIPMmzfPrOk9AAAAAGcgQADgtiwWi70XwcyZM+Xy5cvUWibbv3+//PHHH+Lj4yPdunWjfgEAAJDlCBAASJPHHntMatSoIREREfLRRx9Raw7qPdC6dWspVKgQ9QsAAIAsR4AAQNp+LLy87DMa6DCDq1evUnOZJC4uzswSoXr16kW9AgAAwCkIEABIs86dO0uFChXk4sWL8sknn1BzmWT9+vVy+vRpyZs3r7Rp04Z6BQAAgFMQIACQZjo+fsSIEeaxTnmoSQtx57788kuz7tKliwQEBFClAAAAcAoCBADSRbvAFy9eXM6ePWtv2CLjrl27JosXL7bXLQAAAOAsBAgApIu/v78MGzbMPJ4yZYrExMRQg3fg+++/N/kcypcvLw0bNqQuAQAA4DQECACk27PPPisFChSQo0ePyrfffksN3gFbL4yePXua6SQBAAAAZyFAACDdcufOLQMHDjSPJ06caLLwI/10mMbatWvtAQIAAADAmQgQAMiQl156yQQK/vrrL1mxYgW1mAFff/21Ca40atTIzA4BAAAAOBMBAgAZki9fPhkwYIB5/NZbb4nVaqUm72B4AQAAAOBsBAgAZNjgwYNN0sLffvtN1q9fT02mw+7du2Xv3r3i5+dnpjcEAAAAnI0AAYAMK1q0qPTt29eeiwDp7z3Qrl070xsDAAAAcDYCBADuyKuvvire3t4m2d7vv/9ObaaBTg2p+QcUwwsAAADgKggQALgjZcuWlSeffNI8njRpErWZBhpMOXfunBQsWFBatWpFnQEAAMAlECAAcMdGjhwpFotFvv/+e9m3bx81msbhBd27dzc5CAAAAABXQIAAwB2rUqWKPPHEE+bx5MmTqdFUhIeHm0CKYngBAAAAXAkBAgCZYtSoUWa9YMECOXbsGLWagsWLF8uNGzekcuXKUqdOHeoJAAAALoMAAYBMoY3dFi1aSGxsrLz99tvU6m2GF/Tq1csMywAAAABcBQECAJnmtddeM+vPPvtMQkJCqNlETpw4IevXrzePbYkdAQAAAFfhdgGCPn36mLtu8Zf69eunqVtv1apVxd/f36yXLl2a5JiZM2dKuXLlJCAgQGrXri2bNm1K8LzVapVx48ZJ8eLFJUeOHNKsWTMSsgHxNGnSRBo2bChRUVEybdo06iaR+fPnm/WDDz4opUuXpn4AAADgUtwuQKB0WjC9O2lbVq5cmerx27Ztk65du5qEYLt37zbrLl26yG+//WY/ZtGiRTJo0CB5/fXXZefOndK4cWNp3bq1nDx50n7M1KlTTaNnxowZsn37dilatKg0b95cIiIiHPp5AXehATv9N6RmzZolly5dcnaRXIYGGOMPLwAAAABcjY+4Ie0FoI3ztJo+fbppyNuSqOl6w4YNZr8mVFPa8O/bt6/069fPfs7PP/9sGjk6t7v+ca/7tPHToUMHc8wXX3whRYoUka+//lqef/55h3xWwN1oYK1mzZomGPfhhx/K2LFjnV0kl/DHH3/IoUOHTO+jjh07OvS9iny+3KGvDwAAgOzJLQMEOoa3cOHCkjdvXmnatKm89dZbZju1HgSDBw9OsK9ly5amwa+0O/SOHTvMXO7xacK1rVu3msfBwcFy7tw5sy9+oELfX49JKUAQGRlplvhTnAHZvReB5iLQXjvvv/++DBkyRPLkySOeztZ7QKeDpD5cGwEWAADgqXzc8e5k586dpUyZMqbRPmbMGHnooYdMA18b7MnRhr3e6Y9Pt3W/unDhgsm8ntoxtnVyx2jisZRo74Px48dn8NMC7knvkFesWFEOHz4ss2fPlqFDh4on0yCkrbeSDnECsjMCLAAAuC8fV0/oFf/O/E8//WTuStpUr17dTK2mwYIVK1bYu/4nJ/F0YjpkIPG+zDomPh3OoHdQ4/cgKFWqVIrHA9mBt7e36ZGjw3beffddefHFF03yT0+1atUquXjxohka9cgjjzi7OABcGAEWAIAzuXSAoF27dlKvXj37dokSJZIcU6xYMRMg0DuVKdE/ym09AGxCQ0PtvQEKFixoGjSpHWPLeaDH6Hsmd0xytFdDSj0bgOzsqaeeMvkHTp8+bfJ1eHKeDtvwAp3a0MfHpX92AcBtEVwBgDvn0n+p6jjd243V1btyp06dStBoT6xBgwayZs2aBHkIVq9ebaZjU35+fmZaQz1Gxwfb6Pbjjz9uHuv0hxok0H333nuvvduwJjucMmXKHX9WILvRf1evvvqqDBw40Pwb0d4Entg4vnz5sixffitpIMMLAACuigALAOVWf61fvXpVxo0bZ8Y3a0Dg+PHjJhma9gCI37BPTBsoOj+7NlK0wf/DDz/I2rVrZfPmzfZjdBiA/vGuQxY0oKDjpnWKw/79+5vndRiBToM4ceJEM7ZaF32cM2dO6dGjR5Z8fsDd6Kwgb775pskXolOJ6h10T/PNN9+YYOI999xjZncAAADZDwEW6i+78BI3osMA9u7daxr5d999t/Tu3dusdZaC+D0N+vTpI82aNbNva0+BhQsXyueff27+SJ87d65prMQfvqC5DXRWgwkTJkitWrVk48aNsnLlSjN8wWb48OEmSDBgwAATSDhz5ozpiUBGciB5GkCz9dzRhJ1xcXEeO7ygV69ezi4KAAAAkCqLVbPsZTMaHNBFexu4Gk1SGBQUJGFhYRIYGOjs4gAOd+XKFRNo02v/+++/tw/buZ3zT7cVd7+TcPToUbnrrrvEy8vL5GJIbSgUAAAA4Gxu1YMgLSIiIswf5cOGDXN2UQCISN68ec0sBkqH5WTDmGSK5s2bZ9bNmzcnOAAAAACXl+0CBNrdX5MW5s6d29lFAfAvHZqj0xz+/vvv8ssvv3hEvWggxBYgYHgBAAAA3EG2CxAAcD2FCxeWZ5991t6LwBNs3bpVjh07ZoKV7du3d3ZxAAAAgNsiQAAgS+iwH53mUHsQ/Prrrx6TnLBTp04mWSMAAADg6ggQAMgSpUuXNlOJ2mY0yM5uxsSamVIUwwsAAADgLggQAMgyI0aMEIvFIsuWLTNTlmZXa06fNzOVlCpVSpo2bers4gAAAABpQoAAQJapVKmS6XKvJk+enG1r/tujZ8z6qaeeMlMcAgAAAO6Av1wBZKlRo0aZ9cKFC82UpNnNhZuR8svpUPPYNqQCAAAAcAcECABkqXvvvVdat24tcXFxMnXq1GxX+98Hn5UYq1Xq1KkjVapUcXZxAAAAgDQjQAAgy7322mtmPXfuXDlz5lZ3/Ozi2yOnzZrkhAAAAHA3BAgAZLkHHnhAGjduLFFRUTJt2rRs8w38fSVCdl8MEx+LRbp16+bs4gAAAADpQoAAgFN7EXz88cdy8eLFbPEtfPdvcsKHShaWQoUKObs4AAAAQLoQIADgFC1btjT5CK5fvy4ffPCB238LcVarLD52a3hB5wolnF0cAAAAIN0IEABwCovFYu9FoAGCiIgIt/4mtp67KGeu3ZQgP19pXrKIs4sDAAAApBsBAgBO88QTT0ilSpXkypUrZqiBO/v26K3eA+3KFpMAH29nFwcAAABINwIEAJzG29tbRo4caR6/++67cuPGDbf8Nq7HxMqPx0PM484VSjq7OAAAAECGECAA4FRPPvmklC5dWs6fPy+ff/65W34bq06ek2sxsVImT06pWzifs4sDAAAAZAgBAgBO5evrK6+++qp5PHXqVImOjna7b+TbI7eGF3QqX8LkVgAAAADcEQECAE7Xt29fKVy4sJw4cUIWLFgg7uT89ZuyIeQf85jhBQAAAHBnBAgAOF2OHDlk8ODB5vGkSZMkLi5O3MWSY2ckzipmaEHZwFzOLg4AAACQYQQIALiEF154QYKCguTgwYPy/fffi7vNXkDvAQAAALg7AgQAXIIGB1566SXzeOLEiWK1WsXV7bsULvsvR4ifl5eZ3hAAAABwZwQIALiMgQMHmuEGO3bskA1nL4i79B5oXqqw5PX3c3ZxAAAAgDtCgACAyyhUqJA899xz5vH7ew6LK4uJizP5B1SXCiWdXRwAAADgjhEgAOBShg4daqY+3Hb+kvx+/pK4qk0hFyT0RqTk9/eTB0sUdnZxAAAAgDtGgACASylVqpT06tXLPP5g7xFx9eEF7csVFz9vfkoBAADg/virFoDLGTFihHhZRNaeDpW/LoaJq7kaHSM/nThnHne+i+EFAAAAyB4IEABwORUrVpS2ZYqbxx/uPSqu5sfjIXIjNk4qBuWWWgWCnF0cAAAAIFMQIADgkl655y6zXnb8rBwNuyquOLygU4USYrFYnF0cAAAAIFMQIADgkqrlD5RHShYWq4h89Jfr9CI4ffWGbDl30TzuWJ7hBQAAAMg+CBAAcFkD/+1FoHfsz1y7Ia7ANrVho6IFpGTuHM4uDgAAAJBpCBAAcFl1C+eXBkXyS3ScVT7+65iziyNWqzXe8AJ6DwAAACB7cbsAgY73TW55++23Uzxn7ty5yZ5z8+bNBMfNnDlTypUrJwEBAVK7dm3ZtGlTksbBuHHjpHjx4pIjRw5p1qyZ7Nu3z2GfFYD2IqhoquGrv0/IhZuRTq2SXRfD5HDYVcnh7SVtyhTl6wEAAEC24nYBgpCQkATLZ599Zhr7HTt2TPW8wMDAJOdqIMBm0aJFMmjQIHn99ddl586d0rhxY2ndurWcPHnSfszUqVNl2rRpMmPGDNm+fbsULVpUmjdvLhEREQ79zIAna1q8oNxTIMjMGjBnf7BTy/Ldv70HWpcpKnn8fJ1aFgAAAEA8PUCgjfL4yw8//CAPPviglC9fPtXzNIiQ+Nz4tOHft29f6devn1SpUkWmT58upUqVklmzZtl7D+g+DSB06NBBqlevLl988YVcv35dvv76a4d+ZsCT6b9dWy6Czw4cl/CoaKeUIyo2TpYeO2sedyI5IQAAALIhtwsQxHf+/HlZsWKFadjfztWrV6VMmTJSsmRJadOmjeklYBMVFSU7duyQFi1aJDhHt7du3WoeBwcHy7lz5xIc4+/vL02bNrUfk5zIyEgJDw9PsABIn9ali0rFoNwSHh0jXxw84ZTqW3cmVC5FRknhHP7SpHhBp5QBAAAAcCS3DhDoHfw8efKYO/qpqVy5sslDsGzZMlmwYIEZWtCoUSM5fPiwef7ChQsSGxsrRYoUSXCebmtQQNnWqR2TnEmTJklQUJB90V4JANLHy2KRl2vc6kXw8f5jcj0mNsur8Nujt2Yv6FC+hPh4ufVPJwAAAJAsl/4rd/78+ZI7d277kjhpoOYfePLJJxPkEkhO/fr15amnnpKaNWua3ALffPON3H333fLhhx8m6cocnw4rSLwvLcfEN2rUKAkLC7Mvp06duu3nBpDUE+WLS8lcOeTizShZcPh/uUGywpXIKFl96rx53JnZCwAAAJBN+YgLa9eundSrV8++XaJECftjDRYcOnTIJBdMLy8vL6lbt669B0HBggXF29s7SU+A0NBQe48BW84CPaZYsWLJHpMcHYagC4A74+vlJS/WqCCjfv1LZv51VHreXUb8vLMmxrn8eIhExcVJ1Xx5pFr+wCx5TwAAACCruXQPAh0+cNddd9kXnVrQ5tNPPzVTEWqvgPTSu/67du2yN/T9/PzMa61ZsybBcbrdsGFD81inP9QgQfxjNHfBhg0b7McAcKxud5WSQgH+cubaTVly7FaX/6zw7b+zF3Si9wAAAACyMZcOEKREE/19++23ZsaBtBg/frz8/PPPcuzYMRMY0KSGuu7fv7/9mCFDhsicOXPMsIUDBw7I4MGDzRSHtmN0GIFOgzhx4kRZunSp/PXXX9KnTx/JmTOn9OjRw2GfFcD/5PDxluerlTOPZ+w9IrFxVodXz/Hwa/J76GXxstzKPwAAAABkVy49xCAlCxcuNL0Aunfvnuzz2nA/fvy4rF+/3mxfuXJFnnvuOTM8QBMF3nvvvbJx40a5//777ed07dpVLl68KBMmTJCQkBAzjeHKlSvNzAc2w4cPlxs3bsiAAQPk8uXLZvjD6tWrTU8HAFmjd6Uy8uHeo3Ik/JqsPBkibcsWd+j7ffdvT4WmxQpJ0Zyp5zsBAAAA3JnFqi3tbKZZs2ZmGTdunLhi7wcNUmjCwsBAxjIDKTn/dNsUn5uy85C8t/uw1MgfKKvbNk41Ueid0J/H+kvWyYmI6/JR41rSMR1DDIp8vtwhZQIAAAAcxS2HGKQmIiJCjh49KsOGDXN2UQA4yLNVyklOH2/Zeylc1p35x2H1vD30sgkO5PLxllalbyUqBQAAALKrbBcg0O7+OpWgTosIIHvKH+AnPSvdGv7zwd4jDnuf7/5NTtimbDHJ5euWI7IAAAAAzw0QAPAM/auWEz8vL/n1/CX59fzFTH/9mzGx8sPxEPO4M7MXAAAAwAMQIADglorlyiFd77qVE+CDPZnfi2DN6fMSFhUtJXIFSMOiBTL99QEAAABXQ4AAgNt6sXoFM/3gL2f+kb0XwzL1tb87emv2go7lS4qXg5IgAgAAAK6EAAEAt1U2MJe0L1c803MRXLgZKf93OtQ87lShRKa9LgAAAODKCBAAcGsv17jLrH88HiJHwq5mymt+H3xWYqxWqVkgSO7OmydTXhMAAABwdQQIALi1KvkCpWWpImIVkRl7j2bq7AWd/81xAAAAAHgCAgQA3N4r//Yi0Ib96as37ui1Dl+5KrsuhImPxWIfvgAAAAB4AgIEANxe7cL55IFiBcywgFn77qwXwXfHbvUeeKhkYSkY4J9JJQQAAABcHwECANnCwBoVzXr+3yflnxuRGXqNOKv1f8MLSE4IAAAAD0OAAEC2oD0I7i2YV27Gxskn+4Mz9Bpbz12UM9duSqCvjzQvWSTTywgAAAC4MgIEALIFi8UiA++5lYvg84PHJSwyOt2v8d3RM2b9eLniEuDjnellBAAAAFwZAQIA2UaLUkWkUt48EhEdI3MPHU/XuddjYmX58bPmcecKzF4AAAAAz0OAAEC24WWxyCs1KpjH/90XbBr9abXq5Dm5FhMrZfLklLqF8zmwlAAAAIBrIkAAIFvR4QGlc+eUS5FRMv/vE2k+79t/kxN2Kl/CDFcAAAAAPA0BAgDZio+Xl7z0by+CmX8dk6jYuNuec/76Tdlw9h/zuBPDCwAAAOChCBAAyHa6VCgpRXL4S8j1m/ZpC1Oz5NgZibOKGVpQLjBXlpQRAAAAcDUECABkOzoDQf9q5c3jD/cekVht/adh9gKSEwIAAMCTESAAkC31qlRG8vr5SnDEdVl+IiTF4/ZdCpd9l8PFz8tL2pUtlqVlBAAAAFwJAQIA2VIuXx/pV7WcefzBnsNitVpTTU7YvFRhyevvl6VlBAAAAFwJAQIA2VbfKmUlp4+37L8cIWtPhyZ5PiYuzuQfsOUtAAAAADwZAQIA2VY+fz/pXamMefz+niNJehFsCrkgoTciJb+/rzxYorCTSgkAAAC4BgIEALI1TVao+QX++OeybDt/KdnkhO3LlRA/b34OAQAA4Nn4ixhAtlYkZ4B0q1jKPP5gzxH7/qvRMbLy3+SFne9ieAEAAABAgABAtvdi9fLibbHI+rP/yK4LV8y+H4+HyI3YOLkrMJfUKhDk7CICAAAATkeAAEC2VyZPLnmiXHHz+MO9t3oRfHfstL33gMVicWr5AAAAAFdAgACAR3ipxl1mveLEOVl3JlS2hFw02x3LM7wAAAAAUAQIAHiEyvnySOvSRczjJ9f8LjqfgU6BuPvirSEHAAAAgKcjQADAY9xXMJ9Zx/27fT0mVvqu2yEr/k1WCAAAAHgyAgQAPMbS4FvTGsan2Qfe3fW3U8oDAAAAuBKL1WrVnrbIIuHh4RIUFCRhYWESGBhIvQNZKEeOHHLz5s0k+wMCAuTGjRt8FwAAAPBoLtWDYMmSJdKyZUspWLCgySq+a9euJMdERkbKyy+/bI7JlSuXtGvXTk6fvpWNPDUzZ86UcuXKmYZA7dq1ZdOmTQme1zjJuHHjpHjx4qYR0axZM9m3b1+mvDcA13D33XcnmbFAtytVquS0MgEAAACuwqUCBNeuXZNGjRrJ5MmTUzxm0KBBsnTpUlm4cKFs3rxZrl69Km3atJHY2NgUz1m0aJE57/XXX5edO3dK48aNpXXr1nLy5En7MVOnTpVp06bJjBkzZPv27VK0aFFp3ry5RERE3NF7A3AdY8eONcFAW5BA17qt+wEAAABP55JDDI4fP27u9mtjvlatWvb92i2/UKFCMm/ePOnatavZd/bsWSlVqpSsXLnS9D5ITr169eS+++6TWbNm2fdVqVJF2rdvL5MmTTINBO05oAGAESNG2HsLFClSRKZMmSLPP/98ht87MYYYAM7vqTRhwgQ5dOiQ6TmgwYEnnniCrwUAAAAez6V6ENzOjh07JDo6Wlq0aGHfpw376tWry9atW5M9JyoqypwX/xyl27ZzgoOD5dy5cwmO8ff3l6ZNm9qPych72wINGhSIvwBwng4dOpjhS5pzQNcEBwAAAAA3DBBoI97Pz0/y5bs1VZmN3unX55Jz4cIFMwRAj0npHNv6dsek972V9lDQpIS2RXscAAAAAADgapwWIJg/f77kzp3bviROGpge8ccUpyTx88mdk5Zj0vveo0aNMsMTbMupU6dSfT0AAAAAAJzBxynvKmJmANDcADYlSpS47TmaOFCHDFy+fDnBnfzQ0FBp2LBhsufojAPe3t5J7vLrObYeA/q6So8pVqxYisek971tQxV0AQAAAADAlTmtB0GePHnkrrvusi86teDt6PSEvr6+smbNGvu+kJAQ+euvv1JspOuwAD0v/jlKt23naEJEDQDEP0aDARs2bLAfk5H3BgAAAADAXTitB0FyLl26ZKYe1NkBlGYZV9p410XH8Pft21eGDh0qBQoUkPz588uwYcOkRo0a8sgjj6T4ukOGDJGePXtKnTp1pEGDBjJ79mzzPv379zfP6xABncFg4sSJUrFiRbPo45w5c0qPHj3MMRl9bwAAAAAA3IFLBQiWLVsmTz/9tH27W7duZq3TkI0bN848fu+998THx0e6dOlispA//PDDMnfuXDOMwKZZs2ZStmxZs1/ptIQXL140U5vpXX+deUCnJixTpoz9nOHDh5vXGzBggBlGoMMfVq9ebXo62KTlvQEAAAAAcEcWq2bZy2Y0OKABhT59+oir0WkOtTeCJiwMDAx0dnEAAAAAAHC/aQ7T4uDBg+auf69evZxdFAAAAAAA3Ea27EHgyuhBAAAAAABwRdmuBwEAAAAAAHDzJIWewNZhQ3sSAAAAAACQVXQ4vs7ilxICBFksIiLCrEuVKpXVbw0AAAAA8GBht0mWTw6CLBYXFydnz569beQGAAAAAIDMdLt2KAECAAAAAABAkkIAAAAAAECAAAAAAAAAECAAAAAAAAAECAAAAAAAgOF1awUAAAAAADwZAQIAAAAAAECAAAAAAAAAECAAAAAAAAAECAAAAAAAAAECAAAAAABgkKQQAAAAAAAQIAAAAAAAAAQIAAAAAAAAAQIAAAAAAECAwAmsVquEh4ebNQAAAAAAroIkhVksIiJCgoKCzBoAAAAAAFdBgAAAAAAAABAgAAAAAAAABAgAAAAAAICI+FALAAAAAICMioyxSmyc6yZh9/ayiL+PxdnFyJ4BgpMnT0qpUqXEYklYwZqV/9SpU1K6dOnMLB8AAAAAwIWDA7vPRMqNGNcNEOTwsUjNEv4ECRwRIChXrpyEhIRI4cKFE+y/dOmSeS42Nja9LwkAAAAAcEPac0CDA75eFvHzdr279FGxt8p3q4eD65XP7QME2lMgce8BdfXqVQkICMiscgEAAAAA3IQGB1y1G3+0Cw9/cNsAwZAhQ8xagwNjxoyRnDlz2p/TXgO//fab1KpVyzGlBAAAAAAArhEg2Llzp70Hwd69e8XPz8/+nD6uWbOmDBs2zDGlBAAAAAAArhEgWLdunVk//fTT8v7770tgYKAjywUAAAAAALKQV3pP+Pzzz01w4MiRI/Lzzz/LjRs37D0LAAAAAACAhwQIdLaChx9+WO6++2559NFHzYwGql+/fjJ06FBHlBEAAAAAALhagGDQoEHi6+srJ0+eTJCosGvXrrJq1arMLh8AAAAAAHDFaQ5Xr15thhaULFkywf6KFSvKiRMnMrNsAAAAAADAVXsQXLt2LUHPAZsLFy6Iv79/ZpULAAAAAAC4coCgSZMm8uWXX9q3LRaLxMXFydtvvy0PPvhgZpcPAAAAAAC44hADDQQ0a9ZM/vjjD4mKipLhw4fLvn37TPLCLVu2OKaUAAAAAADAtQIEVatWlT179sisWbPE29vbDDno0KGDvPjii1KsWDHHlBIAAAAAHCQyxiqxcUzbntG6Q/ZhsVqtfKNZKDw8XIKCgiQsLEwCAwOz8q0BAAAAJNPA3X0mUm7Q0M2wm9FWKZTLW3y9LS75/V6LjpO6pfwlp1+6R9h7nHT3IFBXrlyR33//XUJDQ03+gfh69eqVWWUDAAAAAIfSngMaHPD1soifCzZw3UEeP3HJ4ADSL90hlOXLl0vp0qWldevW8tJLL8nAgQPty6BBg+RObdy4Udq2bSvFixc3CRC///77BM9rh4dx48aZ53PkyGHyIWgOhNtZvHixGR6hMy3oeunSpUmOmTlzppQrV04CAgKkdu3asmnTpkx5bwAAAACuTYMD/j4sGakDggMeHCAYOnSoPPPMMxIREWF6Ely+fNm+aKLCO6U5DWrWrCkzZsxI9vmpU6fKtGnTzPPbt2+XokWLSvPmzU15UrJt2zbp2rWr9OzZU3bv3m3WXbp0kd9++81+zKJFi0yA4/XXX5edO3dK48aNTRDk5MmTd/TeAAAAAABkyxwEuXLlkr1790r58uXF0bQHgd7pb9++vdnWourde23IjxgxwuyLjIyUIkWKyJQpU+T5559P9nU0OKBj/3/66Sf7vlatWkm+fPlkwYIFZrtevXpy3333meSLNlWqVDHvPWnSpAy/d2LkIAAAAABcx/WoONl+KlJy+XqZu+HIXshB4OAeBC1btjRTHDpDcHCwnDt3Tlq0aGHfp0MGmjZtKlu3bk21B0H8c2yfw3aOTte4Y8eOJMfotu2YjL63BhE0KBB/AQAAAADA7ZMUPvbYY/Lqq6/K/v37pUaNGuLr65vg+Xbt2omjaANd6V37+HT7xIkTqZ6X3Dm217tw4YLExsamekxG31t7H4wfPz6NnxAAAAAAADcJEDz77LNmPWHChGSHBGhD29H0feLT7v+J92XknMw6Jr5Ro0bJkCFD7Nvag6BUqVKplhUAAAAAAJcPECSe1jAraVJA2938YsWK2ffrdIuJ7+wnPs/WAyC5cwoWLCje3t6pHpPR99ZhCLoAAAAAAJCtchA4k05BqA31NWvW2Pdp/oANGzZIw4YNUzyvQYMGCc5Rq1evtp/j5+dnpjVMfIxu247J6HsDAAAAAJAtexA42tWrV+XIkSP2bU0OuGvXLsmfP7+ULl3azCIwceJEqVixoln0cc6cOaVHjx4pvubAgQOlSZMmZraBxx9/XH744QdZu3atbN682X6MDgPQ6Q/r1KljAgqzZ882Uxz279/fPK/DCDLy3gAAAAAAuAOXCxDoDAkPPvigfds2fr93794yd+5cGT58uNy4cUMGDBggly9fNtMTam+APHny2M/p06ePHD9+XNavX2+29Q7/woULZfTo0TJmzBipUKGCLFq0yJwbfyrEixcvmtwKISEhUr16dVm5cqWUKVPGfkxa3hsAAAAAAHdksWqWvWymWbNmZhk3bpy4Gk1SGBQUJGFhYRIYGOjs4gAAACCbzPUeG5ft/qzPsrrbExIluXy9xN8n9cTncM/v91p0nNQt5S85/dxqhL1TuFwPgjsVEREhR48elR9//NHZRQEAAACypAG0+0yk3IghQJBRN6OtkscvU78WwHMCBNoA//zzz836/fffl8KFC8uqVavM9H3VqlUTZ9Lu/qdOnXJqGQAAAICsoj0HNDjg62URP2/ugGeEBgd8qTsg/QECzdrfunVradSokWzcuFHeeustEyDYs2ePzJkzR7777juqFQAAAMhiGhygizyAO5HuQRgjR46U//znP2a6P50e0EYTC27btu2OCgMAAAAAANwkQLB371554oknkuwvVKiQmQUAAAAAAAB4QIAgb968ZhrAxHbu3CklSpTIrHIBAAAAAABXDhD06NFDRowYIefOnROLxSJxcXGyZcsWGTZsmPTq1csxpQQAAAAAAK4VINCkhKVLlza9Ba5evSpVq1aVJk2aSMOGDWX06NGOKSUAAAAAAHAoi9VqzdCEqTrFoQ4r0B4E9957r1SsWDHzS5cNhYeHS1BQkISFhUlgYKCziwMAAAA3dz0qTrafipRcvl7MYgAkEhljlWvRcVK3lL/k9Ev3/XGPk6FpDps2bSoVKlQwCwAAAAAAcH/pDqE0b97cDDHQ6Q7/+usvx5QKAAAAAAC4doDg7NmzMnz4cNm0aZPcc889Zpk6daqcPn3aMSUEAAAAAACum4NABQcHy9dffy0LFiyQgwcPmmSFv/zyS+aWMJshBwEAAEDKY4Vj4zL8p6lH19uekChyEAAp/PsgB0EWBQhUbGys/PTTTzJmzBjZs2eP2UbKCBAAAAAk/0f87jORciOGAEFG3Iy2SqFc3uLrbeHyAhL9thAgcGCSQpstW7bI/Pnz5bvvvpObN29Ku3btZOLEiRl9OQAAAHgw7TmgwQFfL4v40chNtzx+QnAAQNYHCF577TUzpEBzETzyyCMyffp0ad++veTMmfPOSwMAAACPpsEBfx/uggOAWwQI1q9fL8OGDZOuXbtKwYIFHVMqAAAAAADg2gGCrVu3OqYkAAAAAADAtQMEy5Ytk9atW4uvr695nBrNRQAAAAAAALLhLAZeXl5y7tw5KVy4sHmc4otZLMxicBvMYgAAAJDU9ag42X4qkqn6AGQqZjFwQA+CuLi4ZB8DAAAAAIDsIeXuACn48ssvJTIyMsn+qKgo8xwAAAAAAPCAAMHTTz8tYWFhSfZHRESY5wAAAAAAgAcECDRlgeYaSOz06dMSFBSUWeUCAAAAAACuOM3hvffeawIDujz88MPi4/O/U2NjYyU4OFhatWrlqHICAAAAAABXCBC0b9/erHft2iUtW7aU3Llz25/z8/OTsmXLSseOHR1TSgAAADfJlh0bd9sJopBC3QEA3CRAMHbsWLPWQEDXrl0lICDAkeUCAABwuwbu7jORcoOGbobdjLZKHr/M/FYAAA4JENj07t07vacAAABke9pzQIMDvl4W8fNOmq8Jt6fBAV/qDgDcJ0Cg+Qbee+89+eabb+TkyZNmesP4Ll26lJnlAwAAcCsaHPD3IUAAAPCAWQzGjx8v06ZNky5dupjpDocMGSIdOnQQLy8vGTdunGNKCQAAAAAAXCtAMH/+fPnkk09k2LBhZiaD7t27y5w5c+SNN96QX3/91TGlBAAAAAAArhUgOHfunNSoUcM81pkMtBeBatOmjaxYsSLzSwgAAAAAAFwvQFCyZEkJCQkxj++66y5ZvXq1ebx9+3bx9/cXTzBz5kwpV66cmcmhdu3asmnTJmcXCQAAAACArA0QPPHEE/J///d/5vHAgQNlzJgxUrFiRenVq5c888wzkt0tWrRIBg0aJK+//rrs3LlTGjduLK1btzYJGwEAAAAAcFcWq9VqvZMX0LwDW7duNb0J2rVrJ9ldvXr15L777pNZs2bZ91WpUkXat28vkyZNuu354eHhEhQUZIZmBAYGOri0AAAgq1yPipPtpyIll68XsxgAgIuIjLHKteg4qVvKX3L6pfv+uMdJ9zSHidWvX98snkCndNyxY4eMHDkywf4WLVqYIElyIiMjzRI/QIBbIi6FS1S8ukE24+Ul4usZw44AQEXFWMX72jWJsVrFi2kOAcAlxMZaxctLm738XZppAYJly5ZJWmXnXgQXLlyQ2NhYKVKkSIL9uq3JG5OjvQp0asjE6tatK97e3mYWiBdffFEeeOAB+3P79++XwYMHy88//2y2R48ebXpo9OnTx2xXqFBBli9fLm3btpWjR4+afXPnzpUjR47If/7zH7PdsmVLee+996Rq1ar21928ebN89NFHsmDBArOt76vH2b6zfPnyyZYtW6R3794mp4TSKS2VTmdpK/cXX3whjRo1ksuXL9uvDy2rvrZKy2ca/upwyRntJa9NvDU1ZqkSJWXWlOnywohBcurMabNv4mvj5OSZU/LxF5+a7Ub3N5BRrwyVNk91sr/uVzM/la+XfCMr19563e4dusgD9zeQF0cONtuBeQLl61mfyci33pC/Duw3+0a8fOuzTPnw1merXqWqTH59gvR44RkJj7gVwPlo8nuy+fdtsmDJN2b70UdaSo8OXeSpAX3t7/3jV9/JpA/elS2/bzPb/Xv3ldIlSvGZbN/Tkx1FrHGmbr5+532Zv/wHWbHhF7P9ZJvH5YHadeWF8aNvfU+588jCdz+QEe9Olr1/HzL7Rj77gllP/uRWb50ad1eSKUNHSrehr0j41Qizb9bY/8jmHdtl/o8/mO3Hmj4kT7Z9XHoMG2j/nlb+93OZOHumOU690O0pKVO8hIycNsVsly5WXD4e95b0H/e6nAw5e+s9h4yQE2fPyKyFX5ltLetrzw2QR59/2v66fCa+J649/j0l9xthtVjEKl4ye/on8t2yb2X1L7dyNXV6vJPUr9NAho0Zarbz5Mkjc97/VMZNGScHDt36/2lQ/0FmPf3j6WZdpVJVGTdinPQb2FciIm797r3z5rvy6x/b5LsfvjPbLR5qIZ3adZbnBj1r/31a9Nk3Mm3mNPntj1uzSz395DNSqkQpmTD11t8jJYqXkGn/eU+GjB4sZ86eMfveGD5WTp05JZ/P/8xs16tTX4YMGCJdn+lif10+E98T1x7/ntz1N0LbGj+vWS1tO3Z22/bT6ExoE+prZsoQAy+9E5gGFovFNKCzq7Nnz0qJEiVMb4EGDRrY97/11lsyb948OXjwYJp6EJQqVYohBvQgyN6iIiXuxGERbx8RXz9nlwYAskZ0lHjFxYp/+YoiftypAgBX4e3nLzmC8ji7GNmnB0Fc3K27gJ6uYMGC5q5/4t4CoaGhSXoV2OjMDp4yu0N65clPDobsKu7GdYk85yVeATnEwvUPuBVrdLT+x+/sYrglq7e3WGPixD9/PvHKkdPZxQEAIOtzEHgSPz8/M63hmjVrzGwONrr9+OOPO7VsAABkRnAg9tI/YgkIoDIzyOKfQyze3tQfAMAzAgQTJkxI9fk33nhDsjMdS9KzZ0+pU6eOGWYwe/ZsM8Vh//79nV00AADuTFycCQ74Vb5HLHSRzxANDlB3AACPCRAsXbo0wXZ0dLQEBweLj4+PSZSQ3QMEXbt2lYsXL5pASUhIiFSvXl1WrlwpZcqUcXbRAADIFNrApYs8AACeJ01JCm9HE+9pNkXtdq9315F6XQUFBZGkENk/B8Ge7eIVkIscBIAbsUZGStzNa+J/T10CBAAAeKC0TU9wG4GBgeaO+pgxYzLj5QAAAAAAgDsGCNSVK1fMXXEAAAAAAOABOQg++OCDBNs6QkHH4s+bN09atWqVmWUDAAAAAACuGiB47733Emx7eXlJoUKFpHfv3jJq1KjMLBsAAAAAAHDVAIHOWAAAAAAAALKXTMtBAAAAAAAAPKgHwc2bN+XDDz+UdevWSWhoqMTFxSV4/s8//8zM8gEAAAAAAFcMEDzzzDOyZs0a6dSpk9x///1isVgcUzIAAAAAAOC6AYIVK1bIypUrpVGjRo4pEQAAd8AaHS2SqHcb0lh3MVFUFQAAHizdAYISJUpInjx5HFMaAADuMDgQe+kfsQQEUI8ZZPHPIRZvb+oPAAAPlO4AwbvvvisjRoyQjz/+WMqUKeOYUgEAkBFxcSY44Ff5HrH4+VOHGaDBAeoOAADPlO4AQZ06dUyiwvLly0vOnDnF19c3wfOXLl3KzPIBAJBu2sD1ypGTmgMAAHBkgKB79+5y5swZmThxohQpUoQkhQAAAAAAeGKAYOvWrbJt2zapWbOmY0oEAAAAAACynFd6T6hcubLcuHHDMaUBAAAAAADuESCYPHmyDB06VNavXy8XL16U8PDwBAsAAAAAAPCAIQatWrUy64cffjjBfqvVavIRxMbGZl7pAAAAAACAawYI1q1b55iSAAAAAAAA9wkQNG3a1DElAQAAAAAA7hMg2LhxY6rPN2nS5E7KAwAAAAAA3CFA0KxZsyT7NPeADTkIAAAAAADwgADB5cuXE2xHR0fLzp07ZcyYMfLWW29lZtkAwGNZo6NF4uKcXQy3Y42JcnYRAAAAPCdAEBQUlGRf8+bNxd/fXwYPHiw7duzIrLIBgMcGB2Iv/SOWgABnF8UtWfxziMXb29nFAAAAyP4BgpQUKlRIDh06lFkvBwCeKy7OBAf8Kt8jFj9/Z5fG7WhwgHoDAADIggDBnj17EmxbrVYJCQmRyZMnS82aNTNQBABAcrSR65UjJ5UDAAAA1wwQ1KpVyyQl1MBAfPXr15fPPvssM8sGAAAAAABcNUAQHBycYNvLy8sMLwhgrCwAAAAAAJ4TIChTpoxjSgIAAAAAAJzGK60H/vLLL1K1alUJDw9P8lxYWJhUq1ZNNm3alNnlAwAAAAAArhQgmD59ujz77LMSGBiY7NSHzz//vEybNi2zywcAAAAAAFwpQLB7925p1apVis+3aNFCduzYkVnlAgAAAAAArhggOH/+vPj6+qb4vI+Pj/zzzz+ZVS4AAAAAAOCKAYISJUrI3r17U3x+z549UqxYsTsqzJIlS6Rly5ZSsGBBM5Xirl27khwTGRkpL7/8sjkmV65c0q5dOzl9+vRtX3vmzJlSrlw5M9tC7dq1k+RL0Gkbx40bJ8WLF5ccOXJIs2bNZN++fZny3gAAAAAAZJsAwaOPPipvvPGG3Lx5M8lzN27ckLFjx0qbNm3uqDDXrl2TRo0ayeTJk1M8ZtCgQbJ06VJZuHChbN68Wa5evWreNzY2NsVzFi1aZM57/fXXZefOndK4cWNp3bq1nDx50n7M1KlTTQ6FGTNmyPbt26Vo0aLSvHlziYiIuKP3BjyZNSZKrJGRLOmtg5goZ391AAAA8EAWq946T+MQg/vuu0+8vb3lpZdekkqVKpm7/AcOHJCPPvrINJL//PNPKVKkyB0X6vjx4+Zuvzbma9WqlWC2hEKFCsm8efOka9euZt/Zs2elVKlSsnLlStP7IDn16tUzZZ81a5Z9X5UqVaR9+/YyadIk03tAew5oAGDEiBH23gL6WaZMmWISMGb0vfV1dLHRWSD0HH295BI+AtmBNSpSIvfvFmvkDWcXxW1Z/HOIf9WaYvHzd3ZRAAAA4CF80nqgNpa3bt0qL7zwgowaNco0qpUGCbRxrF34MyM4kBpNghgdHW0SItpow7569eqmbMk10qOiosx5I0eOTLBfX0PPUcHBwXLu3LkEr+vv7y9NmzY1x2iAICPvrTQAMX78+Ez5/IC70EatNm6t9K7JeB16exMcAAAAgGsGCFSZMmXM3fLLly/LkSNHTJCgYsWKki9fPskK2oj38/NL8n4amNDnknPhwgXTuyFx8CL+ObZ1csecOHEiw++tNJgyZMiQJD0IAE8IElicXQgAAAAAmZ+DID5tJNetW1fuv//+DAcH5s+fL7lz57YviZMGpocGKrQnQ2oSP5/cOWk5Jr3vrT0RdChB/AUAAAAAALfuQZCZdAYAzQ0Qf5aE29HEgTpkQHswxA9MhIaGSsOGDZM9R2cc0LwJie/y6zm2HgP6ukqPiT8TQ+Jj0vveybENzdCeBAAAAAAAZJU8efKkfhPc6oKCg4O1FW3duXNngv1Xrlyx+vr6WhctWmTfd/bsWauXl5d11apVKb7e/fffb33hhRcS7KtSpYp15MiR5nFcXJy1aNGi1ilTptifj4yMtAYFBVk//vjjO3rvxE6dOmU+Gwt1wDXANcA1wDXANcA1wDXANcA1wDXANcA1IFlYB2FhYam2V53WgyA5ly5dMlMP6uwA6tChQ/a797oEBQVJ3759ZejQoVKgQAHJnz+/DBs2TGrUqCGPPPJIiq+rOQB69uwpderUkQYNGsjs2bPN+/Tv3988rxEUncFg4sSJJqeCLvo4Z86c0qNHD3NMRt87MU1seOrUqdtHbrI5Wy4GrQuGXYDrD56C3z5w/cET8dsHrj/Xoe3Q1LhUgGDZsmXy9NNP27e7detm1mPHjpVx48aZx++99574+PhIly5d5MaNG/Lwww/L3LlzzTACm2bNmknZsmXNfqXTEl68eFEmTJggISEhZuYBTbaoSRdthg8fbl5vwIABZhiBDn9YvXp1ggpMy3vfjpeXl5QsWfIOayr7IC8DuP7gifjtA9cfPBG/feD6c30W7UYg2YwGBzSg0KdPH2cXBalEkrVXRlhYGD0IkOW4/uAsXHtwJq4/cO3BE/HblwWzGLiygwcPmrv+vXr1cnZRAAAAAABwGy41xCAzVK5cWfbu3evsYuA2dPpHHTqiayCrcf3BWbj24Excf+Dagyfity99suUQAwAAAAAA4OFDDAAAAAAAQPoRIAAAAAAAAAQIAAAAAAAAAQIAAAAAAECAAAAAAAAAECCAwzA5BpyFaw8AAMAz8Hdf5iNJITJdaGioRERE2Lf5h4usEhYWJrGxsVx7cIojR47ImjVrqH1kub///lv69+8vmzZtovaR5U6dOiU7duyQs2fPUvvIUrQ5HIMAATJNTEyM9O3bV+6//3555JFH5Mknn5QLFy6IxWKhluFQ0dHR8uKLL8qjjz5qljfffNMECrj2kFX27Nkjd999t3Tv3l1OnDhBxSNLxMXFyeDBg6VWrVpy7dq1BMF5ICv+733++eflvvvuk2eeeUZq1qwpW7ZsoeLhcLQ5HIsAATLtH2qfPn1k//798sUXX5g/kvUP5g4dOsiBAweoZTiM3rGtWrWq7Nu3T1599VUpVaqUzJ8/X8aNG2eepwcLskJUVJS0bNlSfH19ZerUqVQ6ssRPP/0k27dvN+t58+aZAKkNv31wpKtXr0qnTp3k8OHDsnr1avnmm29MoGDMmDFcf3Ao2hyOR4AAmSIkJER+//13cxe3adOm5o6GNtyOHTsms2bNkvPnz1PTyHTh4eHmjxJtmOn11r59e3O9devWzfzRfP36dXoRIEv8+eefki9fPhOcmj17tvk9BBxtzpw5pveA/r+7YcMG0zibO3eunDx5kt8+OJTeENIbQHrN3XvvvVKpUiXp3Lmz5MmTx/RsoQcfHIU2h+MRIECmuHjxopw+fVrq169vtiMjI6Vo0aIyatQoE1neuHEjNY1Mp8MIHnjgAenXr5+5c6t3zPz8/OTmzZty48YNyZkzJ3fR4DDx79D6+/tLmTJl5KGHHpK6devK+PHj7UEswBHXng4n0GF8Dz/8sPznP/8xgdG9e/fKG2+8Ya7D5cuXU/Fw6PACzbuiv31Kr8WPPvpIihcvLp999pn5PxhwBNocjkeAAOmmd8c++eSTBI3+ihUrmoDAV199devC8rp1aWmPAo0ma/dHDRoAmXHt6Z0ypXdse/XqZe6gKb1rYUtWWL58efOYuxhw1PWn15btmtMeBNrlVmkvglWrVknr1q1N75aDBw/yJSDTrz39v1UbadqLQJMULlmyRL777juTA6NChQqmkca1B0f93deoUSNp1qyZPP300+a3rkiRIubvQA3S682h3r17m4AVcCd0yN5rr71meofaaG8Vvd5ocziQFUijr7/+2lq4cGFrgwYNrLVq1bIWKlTI+tZbb5nnwsLCrMOHD7fefffd1vPnz5t9N27cMOsvvvjCmjdvXvs2kJnXXkxMjP24uLg4s65Xr551zpw5CfYBmXn9TZw40TwXGRlp1t26dbOuXbvWPP7kk0+sOXLksPr6+lq/++47Kh4OufbUp59+arVYLOb/3tDQUPv+jRs3WosVK2bdunUrtQ+H/N+rrl69aj18+LC1YcOG1nfeece+f+fOndby5ctbv/nmG2ofGTJ//nxzvem117lzZ/N/6nPPPWeeCw8Pp83hYAQIkOZ/qDVr1rR+/PHHZvvMmTPWGTNmWHPlymWCA2rNmjXWunXrWgcMGJCgYbZu3TrzH8zu3bupbWTqtaf/SSQWHBxs/lM5ePCgfd/Ro0fNOjY2lm8ADrn+evfube3Zs6f5DdTr780337Tmy5cvwR/NQGb/v/vXX39ZmzVrZq1atao1JCTEfq4G5HPnzm399ttvqXQ49Lfvzz//tFaqVMkEqGx/92ngnt8/ZJQG2TUg9d///tdsR0dHm+tRgwQRERFmH20Ox2KIAW7Xw8SstRtjvXr1THdupWPMtFt3iRIlTKIapWPBe/ToYWYxWLp0qTlH6ZQ3mmW+Ro0a1DYy9dpLboYM7dqtMxloF7SdO3eaczU3hma9tQ19ATLz+tOxtpprYOXKlWaaV73uRo8eLSNGjDAzaxw/fpwKh0N++ypXriyDBg2So0ePyscffyxnzpwx+5ctW2b+z23SpAk1D4f+36u5fnQmg1OnTtmH9Gn+i3LlyplcGEBa2Ybs6XU3bNgwMzua8vHxkUuXLsmzzz4rAQEBZl/Dhg1Nm0OTstLmcAAHByDgpnbs2GG9fPmyffvKlSsJunKrXbt2WYsWLWq9dOmSfZ+t20+ePHmsTZs2tXcL+uijj8zzdPeGo64927X18ssvWzt16mQdPHiw1cvLy9q3b1/rzZs3qXg49Lfv999/t+7bty/BcXrdTZ06lZ4rcOi1pz744ANr8eLFzZ3cJ554wtzljd8VHHDU9Xfx4kVr9+7drTlz5rT279/f2qtXL/M34BtvvMHffEjzb59eb/HFby+MHDnS6uPjY61cubLpWTBv3jzTqyAqKso6bNgw2hwO4OOIoAPc1+LFi83dCM1Kq9FjTTKjiQY1GYgtume7C/vLL7+YREiaKE7nANfENJo0acqUKSaL919//WWmN9TkXXqXQ5EwDo669mwJ4/TOmU7xpdN+aYIk7b0COOq3T5Ov6jn6m5eY7tceBIAj/99VL7/8sum9ov/f6p3cyZMny913303Fw+HXX/78+eXTTz81PfdCQ0PNcX/88QfXH9J97WmPgRdeeMEku7RdewsXLpRff/1VFixYYPbrzGh6TFBQkLRt21befvtt0+OANkcmc0TUAe5p+/btJjo3ffp0ky9g5syZZiztCy+8YCLEtjHcGrVTepfixRdfdHKpkR1k1rWnEehJkyZZf/755yz/DHBf/PaBaw+eKLN/+2zHAZlx7SnNt5L4uipdurR1/PjxVLIDMSAX9vFmGvHVabp0ypp77rnHROjGjh1rxtPOnDnTHKNRZF30nD179pipbZROsdS9e3dz5wJw1rWnEeWRI0dKixYt+BKQ5dcf4KzfPsAVrj8dKw5k1rWntGey7brSczWnj+a9KFiwIBXtQAQIYO/2HxwcbLqExf+B1+4+tWvXlp9++kn27dt366Lx8pLt27ebf6D33Xef6R6k/7gvXrwohQsXpkbhlGuvUKFC1DzShd8+OAvXHpyJ6w/ucu3FH5p85coVM6RAh788/vjjTii95yBA4IHWrFkjr7zyirz//vvy+++/2/c3atRItm7dKufOnTPbsbGxkitXLvOPUP+B6rgfG83WreN9NFO8vp7OVKDP6zgiwBnXni2zLcBvH1wN/++C6w+e6E5/+zTXxaJFi0w+n2rVqsmOHTtk1qxZZjYNOA4BAg8SEhJiEno89dRTZroQTSqjXbFt/2D1cdmyZU2SwfhRu+bNm5s7t0eOHLG/lq+vr+neo9OLaJRPI34A1x5cEb994NqDJ+K3D+5+7emwAr0BdOjQIRNk0ISFVapU4Yt1NEcmOIDruHbtmrV3797Wrl27Wo8dO2bfX7duXWufPn3MY53O5ssvvzRTw23ZsiXB+U8++aS1WbNm9u3Q0NAsLD3cGdceuP7gifjtA9cfPFFm//ZpokxkLXoQeAgds63d/3V8T7ly5SQmJsbsb9OmjRw4cMA89vb2li5dupjuPf369ZMNGzaYyJ12/zl8+LCJAtow3htce3AH/PaBaw+eiN8+ZJdrzzbNJrKORaMEWfh+cCKdY1SHBij92rU7T8+ePSVHjhwye/Zs+76bN2+aLLX79++XWrVqmfHepUuXlm+++cbMcwtw7cGd8NsHrj14In77wLWHjCBA4OGaNGkizzzzjInyaYAgLi7ORPXOnz9vprPRjPE6RqhHjx7OLiqyGa49cP3BE/HbB64/eCJ++9wHAQIPduzYMWnYsKGsWLHCnmRQs4X6+fk5u2jI5rj2wPUHT8RvH7j+4In47XMvDOrwQLZRJZs3b5bcuXPbgwPjx4+XgQMHSmhoqJNLiOyKaw9cf/BE/PaB6w+eiN8+9+Tj7AIg69mmEtGpRjp27GjmKH3uuefk+vXrMm/ePClcuDBfC7j2kO3w2weuPXgifvvAtYf0YIiBh9JEhDVq1JCjR4+aIQXae2DEiBHOLhY8ANceuP7gifjtA9cfPBG/fe6HAIEHa968uVSsWFGmTZsmAQEBzi4OPAjXHrj+4In47QPXHzwRv33uhQCBB4uNjTUzFgBce/Ak/PaBaw+eiN8+cO0hLQgQAAAAAAAAZjEAAAAAAAAECAAAAAAAAAECAAAAAABAgAAAAAAAABhet1YAAAAAAMCTESAAAAAAAAAECAAAAAAAAAECAAAAAABAgAAAAAAAABAgAAAAAAAAov4fRbiMidQcsgsAAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from climada.entity.measures.cost_income import CostIncome\n", + "\n", + "ci = CostIncome(\n", + " mkt_price_year=2025,\n", + " init_cost=10_000, # 10 000 upfront\n", + " periodic_cost=5_000, # 5 000 / year maintenance\n", + " periodic_income=8_000, # 8 000 / year in avoided losses\n", + " cost_yearly_growth_rate=0.01,\n", + " income_yearly_growth_rate=0.01,\n", + " freq=\"Y\", # annual cash flows\n", + ")\n", + "\n", + "impl_date = \"2022-01-01\"\n", + "start_date = \"2020-01-01\"\n", + "end_date = \"2030-01-01\"\n", + "\n", + "\n", + "display(ci.to_dataframe(impl_date, start_date, end_date))\n", + "ci.plot_cash_flows(impl_date, start_date, end_date)" + ] + }, + { + "cell_type": "markdown", + "id": "e75b7d54-6785-48af-9134-6dc4b564a76d", + "metadata": {}, + "source": [ + "## Defining a `CostIncome`\n", + "\n", + "The simplest way to create a `CostIncome` is by passing keyword arguments directly.\n", + "\n", + "| Parameter | Meaning |\n", + "|---|---|\n", + "| `init_cost` | One-off implementation cost |\n", + "| `periodic_cost` | Recurring cost each period |\n", + "| `periodic_income` | Recurring income each period |\n", + "| `mkt_price_year` | Reference year for cost/income growth rates |\n", + "| `cost_yearly_growth_rate` | Growth rate for costs |\n", + "| `income_yearly_growth_rate` | Growth rate for costs |\n", + "| `freq` | Period frequency (e.g. `'Y'` for yearly, `'M'` for monthly, `'Q'` for quaterly)
see more [here](https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#period-aliases))\n", + "\n", + "```{note} **Sign convention**\n", + "`CostIncome` stores costs as negative numbers internally.\n", + "```\n", + "\n", + "```{note}\n", + "Financial values in `CostIncome` are currently unitless (no currency is specified)\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "41147b9d-9fef-4e58-9070-b384a5377f59", + "metadata": {}, + "outputs": [], + "source": [ + "from climada.entity.measures.cost_income import CostIncome\n", + "\n", + "ci = CostIncome(\n", + " mkt_price_year=2025,\n", + " init_cost=10_000, # 10 000 upfront\n", + " periodic_cost=5_000, # 5 000 / year maintenance\n", + " periodic_income=8_000, # 8 000 / year in avoided losses\n", + " cost_yearly_growth_rate=0.01,\n", + " income_yearly_growth_rate=0.01,\n", + " freq=\"Y\", # annual cash flows\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "3c9a8398", + "metadata": {}, + "source": [ + "## Calculating cash flows\n", + "\n", + "Three methods are available to calculate cash flows:\n", + "\n", + "| Method | Returns |\n", + "|---|---|\n", + "| `calc_cash_flows(impl_date, start_date, end_date)` | Three `np.ndarray`: net, costs, incomes |\n", + "| `calc_total(impl_date, start_date, end_date)` | Three scalars: summed net, cost, income |\n", + "| `to_dataframe(impl_date, start_date, end_date)` | A tidy `pd.DataFrame` |\n", + "\n", + "The `impl_date` is when the measure is *deployed*. Cash flows before this date are zero; the initial cost lands on `impl_date`; periodic flows start the following period.\n", + "\n", + "```{note}\n", + "Dates should follow the standard format \"yyyy-mm-dd\" or be an integer representing the year.\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "25bf1e1f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Period | Net | Cost | Income\n", + "---------------------------------------------\n", + "2020 | 0 | 0 | 0\n", + "2021 | 0 | 0 | 0\n", + "2022 | -9706 | -9706 | 0\n", + "2023 | 2941 | -4901 | 7842\n", + "2024 | 2970 | -4950 | 7921\n", + "2025 | 3000 | -5000 | 8000\n", + "2026 | 3030 | -5050 | 8080\n", + "2027 | 3060 | -5100 | 8161\n", + "2028 | 3091 | -5152 | 8242\n", + "2029 | 3122 | -5203 | 8325\n", + "2030 | 3153 | -5255 | 8408\n", + "---------------------------------------------\n", + "Total net : 14662\n", + "Total cost : -50318\n", + "Total income : 64979\n", + "---------------------------------------------\n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "\n", + "impl_date = \"2022-01-01\"\n", + "start_date = \"2020-01-01\"\n", + "end_date = \"2030-01-01\"\n", + "\n", + "net, costs, incomes = ci.calc_cash_flows(impl_date, start_date, end_date)\n", + "total_net, total_cost, total_income = ci.calc_total(impl_date, start_date, end_date)\n", + "\n", + "print(\"Period | Net | Cost | Income\")\n", + "print(\"-\" * 45)\n", + "periods = pd.period_range(start=start_date, end=end_date, freq=\"Y\")\n", + "for p, n, c, i in zip(periods, net, costs, incomes):\n", + " print(f\"{p} | {n:>9.0f} | {c:>9.0f} | {i:>6.0f}\")\n", + "print(\"-\" * 45)\n", + "print(f\"Total net : {total_net:>10.0f}\")\n", + "print(f\"Total cost : {total_cost:>10.0f}\")\n", + "print(f\"Total income : {total_income:>10.0f}\")\n", + "print(\"-\" * 45)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "f3fd0c35", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
datenetcostincome
020200.0000000.0000000.000000
120210.0000000.0000000.000000
22022-9705.636889-9705.6368890.000000
320232940.807977-4901.3466297842.154606
420242970.216057-4950.3600957920.576152
520253000.000000-5000.0000008000.000000
620263030.000000-5050.0000008080.000000
720273060.300000-5100.5000008160.800000
820283090.903000-5151.5050008242.408000
920293121.897135-5203.1618928325.059028
1020303153.116107-5255.1935118408.309618
\n", + "
" + ], + "text/plain": [ + " date net cost income\n", + "0 2020 0.000000 0.000000 0.000000\n", + "1 2021 0.000000 0.000000 0.000000\n", + "2 2022 -9705.636889 -9705.636889 0.000000\n", + "3 2023 2940.807977 -4901.346629 7842.154606\n", + "4 2024 2970.216057 -4950.360095 7920.576152\n", + "5 2025 3000.000000 -5000.000000 8000.000000\n", + "6 2026 3030.000000 -5050.000000 8080.000000\n", + "7 2027 3060.300000 -5100.500000 8160.800000\n", + "8 2028 3090.903000 -5151.505000 8242.408000\n", + "9 2029 3121.897135 -5203.161892 8325.059028\n", + "10 2030 3153.116107 -5255.193511 8408.309618" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ci.to_dataframe(impl_date, start_date, end_date)" + ] + }, + { + "cell_type": "markdown", + "id": "612c0b66", + "metadata": {}, + "source": [ + "## Visualising cash flows\n", + "\n", + "`plot_cash_flows` draws a bar chart of the cash flows. The top panel of the plot shows costs, incomes and net values for each period, while the bottom panel shows the cumulated net value.\n", + "\n", + "Figure size and title can be customized directly in the method. The method returns a tuple with the two matplotlib axes objects." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "315fc0ac", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(, )" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ci.plot_cash_flows(\n", + " impl_date,\n", + " start_date,\n", + " end_date,\n", + " figsize=(16, 7),\n", + " title=\"Custom title for cash flow plot\",\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "4bb73614", + "metadata": {}, + "source": [ + "## Growth rates\n", + "\n", + "Costs and incomes can grow year-over-year using compound-interest factors anchored to the `mkt_price_year` attribute.\n", + "\n", + "$$\\text{value}(t) = \\text{base} \\times (1 + r)^{\\frac{t - t_0}{365}}$$\n", + "\n", + "Pass `cost_yearly_growth_rate` and / or `income_yearly_growth_rate` (as decimals, e.g. `0.02` for 2 %)." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "b69f1ae7", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(, )" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ci_growth = CostIncome(\n", + " mkt_price_year=2025,\n", + " init_cost=10_000,\n", + " periodic_cost=5_000,\n", + " periodic_income=8_000,\n", + " cost_yearly_growth_rate=0.15,\n", + " income_yearly_growth_rate=0.10,\n", + " freq=\"Y\",\n", + ")\n", + "\n", + "ci_growth.plot_cash_flows(impl_date, start_date, end_date)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "067c07aa", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " No growth With growth\n", + "Net 14662 17138\n", + "Cost -50318 -58474\n", + "Income 64979 75612\n" + ] + } + ], + "source": [ + "# Compare totals with and without growth\n", + "no_growth = ci.calc_total(impl_date, start_date, end_date)\n", + "with_growth = ci_growth.calc_total(impl_date, start_date, end_date)\n", + "\n", + "labels = [\"Net\", \"Cost\", \"Income\"]\n", + "print(f\"{'':15s} {'No growth':>12s} {'With growth':>12s}\")\n", + "for label, ng, wg in zip(labels, no_growth, with_growth):\n", + " print(f\"{label:15s} {ng:>12.0f} {wg:>12.0f}\")" + ] + }, + { + "cell_type": "markdown", + "id": "1408d2db", + "metadata": {}, + "source": [ + "## Custom cash flows\n", + "\n", + "For irregular or one-off flows, pass a `pd.DataFrame` with columns `date`, `cost`, and/or `income`.\n", + "\n", + "These are **added on top** of any periodic amounts; dates not present in the DataFrame simply contribute zero." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "c469839c-e2e2-43e9-ac33-93de7f04a6d9", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
datenetcostincome
020200.00.00.0
120210.00.00.0
22022-10000.0-10000.00.0
320233000.0-5000.08000.0
42024-7000.0-15000.08000.0
520253000.0-5000.08000.0
6202618000.0-5000.023000.0
720273000.0-5000.08000.0
82028-17000.0-25000.08000.0
920293000.0-5000.08000.0
1020303000.0-5000.08000.0
\n", + "
" + ], + "text/plain": [ + " date net cost income\n", + "0 2020 0.0 0.0 0.0\n", + "1 2021 0.0 0.0 0.0\n", + "2 2022 -10000.0 -10000.0 0.0\n", + "3 2023 3000.0 -5000.0 8000.0\n", + "4 2024 -7000.0 -15000.0 8000.0\n", + "5 2025 3000.0 -5000.0 8000.0\n", + "6 2026 18000.0 -5000.0 23000.0\n", + "7 2027 3000.0 -5000.0 8000.0\n", + "8 2028 -17000.0 -25000.0 8000.0\n", + "9 2029 3000.0 -5000.0 8000.0\n", + "10 2030 3000.0 -5000.0 8000.0" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "custom_flows = pd.DataFrame(\n", + " {\n", + " \"date\": [\"2024-01-01\", \"2026-01-01\", \"2028-01-01\"],\n", + " \"cost\": [10_000, 0, 20_000], # extra one-off costs\n", + " \"income\": [0, 15_000, 0], # extra one-off income\n", + " }\n", + ")\n", + "\n", + "ci_custom = CostIncome(\n", + " mkt_price_year=2025,\n", + " init_cost=10_000,\n", + " periodic_cost=5_000,\n", + " periodic_income=8_000,\n", + " custom_cash_flows=custom_flows,\n", + " freq=\"Y\",\n", + ")\n", + "ci_custom.to_dataframe(impl_date, start_date, end_date)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "11f21296", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(, )" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ci_custom.plot_cash_flows(impl_date, start_date, end_date)" + ] + }, + { + "cell_type": "markdown", + "id": "f1f011b6", + "metadata": {}, + "source": [ + "## Sub-annual frequencies\n", + "\n", + "`freq` accepts any [pandas-compatible period aliases string](https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#period-aliases).\n", + "\n", + "Common options:\n", + "\n", + "| `freq` | Meaning |\n", + "|---|---|\n", + "| `\"Y\"` | Annual |\n", + "| `\"Q\"` | Quarterly |\n", + "| `\"M\"` | Monthly |\n", + "| `\"7D\"` | Every 7 days |\n", + "\n", + "```{note}\n", + "The periodic amounts are interpreted as **per-period** values. A periodic cost of 500 with a monthly frequency (\"M\"), means a cost of 500 per month. The growth rates however are always considered to be yearly.\n", + "```\n", + "\n", + "```{note}\n", + "The implementation, starting and ending dates are coerced to the period frequency. For instance `\"2022-01-05\"` with monthly frequency is considered as `\"2022-01\"`.\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "277e6948", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
datenetcostincome
02022-01-5000.0-5000.00.0
12022-02200.0-500.0700.0
22022-03200.0-500.0700.0
32022-04200.0-500.0700.0
42022-05200.0-500.0700.0
52022-06200.0-500.0700.0
62022-07200.0-500.0700.0
72022-08200.0-500.0700.0
82022-09200.0-500.0700.0
92022-10200.0-500.0700.0
102022-11200.0-500.0700.0
112022-12200.0-500.0700.0
\n", + "
" + ], + "text/plain": [ + " date net cost income\n", + "0 2022-01 -5000.0 -5000.0 0.0\n", + "1 2022-02 200.0 -500.0 700.0\n", + "2 2022-03 200.0 -500.0 700.0\n", + "3 2022-04 200.0 -500.0 700.0\n", + "4 2022-05 200.0 -500.0 700.0\n", + "5 2022-06 200.0 -500.0 700.0\n", + "6 2022-07 200.0 -500.0 700.0\n", + "7 2022-08 200.0 -500.0 700.0\n", + "8 2022-09 200.0 -500.0 700.0\n", + "9 2022-10 200.0 -500.0 700.0\n", + "10 2022-11 200.0 -500.0 700.0\n", + "11 2022-12 200.0 -500.0 700.0" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ci_monthly = CostIncome(\n", + " mkt_price_year=2022,\n", + " init_cost=5_000,\n", + " periodic_cost=500,\n", + " periodic_income=700,\n", + " freq=\"M\",\n", + ")\n", + "\n", + "df_monthly = ci_monthly.to_dataframe(\n", + " impl_date=\"2022-01-05\",\n", + " start_date=\"2022-01\",\n", + " end_date=\"2022-12\",\n", + ")\n", + "df_monthly" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "03a80747", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(, )" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ci_monthly.plot_cash_flows(\n", + " \"2022-01\",\n", + " \"2022-01\",\n", + " \"2022-12\",\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "10191dfd", + "metadata": {}, + "source": [ + "## Combining multiple CostIncome objects\n", + "\n", + "`CostIncome.comb_cost_income()` aggregates a list of `CostIncome` objects into a single one by summing costs and incomes.\n", + "\n", + "```{warning}\n", + "All objects must share the same `mkt_price_year`, `cost_growth_rate`, and `income_growth_rate`.\n", + "```\n", + "\n", + "```{note}\n", + "`custom_cash_flows` are merged by summing them together.\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "39c99bd5-3a54-4d5c-bf0d-ffbb713ce1a8", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
costincome
date
2022-01-01-20000
2023-01-0100
2024-01-01-100000
2025-01-0100
2026-01-01020000
2027-01-0100
2028-01-01-200000
2029-01-01-100000
\n", + "
" + ], + "text/plain": [ + " cost income\n", + "date \n", + "2022-01-01 -2000 0\n", + "2023-01-01 0 0\n", + "2024-01-01 -10000 0\n", + "2025-01-01 0 0\n", + "2026-01-01 0 20000\n", + "2027-01-01 0 0\n", + "2028-01-01 -20000 0\n", + "2029-01-01 -10000 0" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import pandas as pd\n", + "\n", + "custom_flows_a = pd.DataFrame(\n", + " {\n", + " \"date\": [\"2024\", \"2026\", \"2028\"],\n", + " \"cost\": [10_000, 0, 20_000], # extra one-off costs\n", + " \"income\": [0, 15_000, 0], # extra one-off income\n", + " }\n", + ")\n", + "\n", + "custom_flows_b = pd.DataFrame(\n", + " {\n", + " \"date\": [\"2022\", \"2026\", \"2029\"],\n", + " \"cost\": [2_000, 0, 10_000], # extra one-off costs\n", + " \"income\": [0, 5_000, 0], # extra one-off income\n", + " }\n", + ")\n", + "\n", + "ci_a = CostIncome(\n", + " mkt_price_year=2020,\n", + " init_cost=30_000,\n", + " periodic_cost=2_000,\n", + " periodic_income=4_000,\n", + " custom_cash_flows=custom_flows_a,\n", + " freq=\"Y\",\n", + ")\n", + "\n", + "ci_b = CostIncome(\n", + " mkt_price_year=2020,\n", + " init_cost=20_000,\n", + " periodic_cost=3_000,\n", + " periodic_income=4_000,\n", + " custom_cash_flows=custom_flows_b,\n", + " freq=\"Y\",\n", + ")\n", + "\n", + "ci_combined = CostIncome.comb_cost_income([ci_a, ci_b])\n", + "ci_combined.plot_cash_flows(\n", + " \"2025\",\n", + " \"2020\",\n", + " \"2030\",\n", + ")\n", + "\n", + "ci_combined.custom_cash_flows" + ] + }, + { + "cell_type": "markdown", + "id": "8a15baaf", + "metadata": {}, + "source": [ + "## Loading from dict / YAML\n", + "\n", + "### From a Python dictionary" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "eeac2088", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CostIncome(\n", + " mkt_price_year = 2020\n", + " freq = 'Y'\n", + " init_cost = -50,000.00\n", + " periodic_cost = -5,000.00\n", + " periodic_income = 8,000.00\n", + " cost_yearly_growth_rate = 2.00%\n", + " income_yearly_growth_rate = 3.00%\n", + " custom_cash_flows = None\n", + ")\n" + ] + } + ], + "source": [ + "config_dict = {\n", + " \"mkt_price_year\": 2020,\n", + " \"init_cost\": 50_000,\n", + " \"periodic_cost\": 5_000,\n", + " \"periodic_income\": 8_000,\n", + " \"cost_yearly_growth_rate\": 0.02,\n", + " \"income_yearly_growth_rate\": 0.03,\n", + " \"freq\": \"Y\",\n", + "}\n", + "\n", + "ci_from_dict = CostIncome.from_dict(config_dict)\n", + "print(ci_from_dict)" + ] + }, + { + "cell_type": "markdown", + "id": "0b2a868e", + "metadata": {}, + "source": [ + "### From a YAML file\n", + "\n", + "Create a YAML file structured as follows, then load it with `CostIncome.from_yaml()`.\n", + "\n", + "```yaml\n", + "# measure_cost.yaml\n", + "cost_income:\n", + " mkt_price_year: 2020\n", + " init_cost: 50000\n", + " periodic_cost: 5000\n", + " periodic_income: 8000\n", + " cost_yearly_growth_rate: 0.02\n", + " income_yearly_growth_rate: 0.03\n", + " freq: \"Y\"\n", + " # Optional custom flows:\n", + " # custom_cash_flows:\n", + " # - date: \"2024-01-01\"\n", + " # cost: 10000\n", + " # income: 0\n", + "```\n", + "\n", + "---\n", + "\n", + "```python\n", + "# Inside Notebook or script\n", + "ci_from_yaml = CostIncome.from_yaml(\"measure_cost.yaml\")\n", + "```" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python [conda env:climada_env_dev]", + "language": "python", + "name": "conda-env-climada_env_dev-py" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.15" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/doc/user-guide/climada_engine_CostBenefit.ipynb b/doc/user-guide/climada_engine_CostBenefit.ipynb index de98c79260..a7e4e28f8c 100644 --- a/doc/user-guide/climada_engine_CostBenefit.ipynb +++ b/doc/user-guide/climada_engine_CostBenefit.ipynb @@ -2,14 +2,25 @@ "cells": [ { "cell_type": "markdown", + "id": "b4f2192d", "metadata": {}, "source": [ "# END-TO-END COST BENEFIT CALCULATION" ] }, { - "attachments": {}, "cell_type": "markdown", + "id": "d16b53e4", + "metadata": {}, + "source": [ + "```{attention}\n", + "Adapation measures and cost-benefit evaluation are being completely revamped. Associated tutorials are under their own menu [Adaptation appraisal guides](adaptation-guides).\n", + "```" + ] + }, + { + "cell_type": "markdown", + "id": "dd997383", "metadata": {}, "source": [ "## Introduction" @@ -17,12 +28,8 @@ }, { "cell_type": "markdown", - "metadata": { - "ExecuteTime": { - "end_time": "2020-10-20T07:58:34.931387Z", - "start_time": "2020-10-20T07:58:34.855569Z" - } - }, + "id": "01a52976", + "metadata": {}, "source": [ "The goal of this tutorial is to show a full end-to-end cost-benefit calculation. Note that this tutorial shows the work flow and some data exploration, but does not explore all possible features.\n", "\n", @@ -30,11 +37,12 @@ "\n", "If you just need to see the code in action, you can skip these first parts, which are mostly for reference.\n", "\n", - "The tutorial assumes that you're already familiar with CLIMADA's **[Hazard](climada_hazard_Hazard.ipynb)**, **[Exposures](climada_entity_Exposures.ipynb)**, **[impact functions](climada_entity_ImpactFuncSet.ipynb)**, **[Impact](climada_engine_Impact.ipynb)** and **[adaptation measure](climada_entity_MeasureSet.ipynb)** functionality. The cost-benefit calculation is often the last part of an analyses, and it brings all the previous components together. \n" + "The tutorial assumes that you're already familiar with CLIMADA's **[Hazard](climada_hazard_Hazard.ipynb)**, **[Exposures](climada_entity_Exposures.ipynb)**, **[impact functions](climada_entity_ImpactFuncSet.ipynb)**, **[Impact](climada_engine_Impact.ipynb)** and **[adaptation measure](climada_entity_MeasureSet.ipynb)** functionality. The cost-benefit calculation is often the last part of an analyses, and it brings all the previous components together.\n" ] }, { "cell_type": "markdown", + "id": "da439104", "metadata": {}, "source": [ "## What is a cost-benefit?" @@ -42,11 +50,12 @@ }, { "cell_type": "markdown", + "id": "0043be98", "metadata": {}, "source": [ "A cost-benefit analysis in CLIMADA lets you compare the effectiveness of different hazard adaptation options.\n", "\n", - "The cost-benefit ratio describes how much loss you can prevent per dollar of expenditure (or whatever currency you're using) over a period of time. When a cost-benefit ratio is less than 1, the cost is less than the benefit and CLIMADA is predicting a worthwhile investment. Smaller ratios therefore represent better investments. When a cost-benefit is greater than 1, the cost is more than the benefit and the offset losses are less than the cost of the adaptation measure: based on the financials alone, the measure may not be worth it. Of course, users may have factors beyond just cost-benefits that influence decisions. \n", + "The cost-benefit ratio describes how much loss you can prevent per dollar of expenditure (or whatever currency you're using) over a period of time. When a cost-benefit ratio is less than 1, the cost is less than the benefit and CLIMADA is predicting a worthwhile investment. Smaller ratios therefore represent better investments. When a cost-benefit is greater than 1, the cost is more than the benefit and the offset losses are less than the cost of the adaptation measure: based on the financials alone, the measure may not be worth it. Of course, users may have factors beyond just cost-benefits that influence decisions.\n", "\n", "CLIMADA doesn't limit cost-benefits to just financial exposures. The cost-benefit ratio could represent hospitalisations avoided per Euro spent, or additional tons of crop yield per Swiss Franc.\n", "\n", @@ -129,6 +138,7 @@ }, { "cell_type": "markdown", + "id": "32032c3f", "metadata": {}, "source": [ "## CostBenefit class data structure\n", @@ -140,12 +150,8 @@ }, { "cell_type": "markdown", - "metadata": { - "ExecuteTime": { - "end_time": "2020-10-20T09:19:16.170229Z", - "start_time": "2020-10-20T09:19:16.143316Z" - } - }, + "id": "0f958f51", + "metadata": {}, "source": [ "| Attributes created in `CostBenefit.calc` | Data Type | Description|\n", "| :- | :- | :- |\n", @@ -161,6 +167,7 @@ }, { "cell_type": "markdown", + "id": "d51d3f03", "metadata": {}, "source": [ "Each dictionary stored in the attributes `imp_meas_future` and `imp_meas_present` has entries:" @@ -168,6 +175,7 @@ }, { "cell_type": "markdown", + "id": "a80dabe6", "metadata": {}, "source": [ "| Key | Data Type | Description |\n", @@ -181,6 +189,7 @@ }, { "cell_type": "markdown", + "id": "fa093057", "metadata": {}, "source": [ "The dictionary will also include a 'no measure' entry with the same structure, giving the impact analysis when no measures are implemented." @@ -188,6 +197,7 @@ }, { "cell_type": "markdown", + "id": "8cd602cb", "metadata": {}, "source": [ "### The `calc` calculation\n", @@ -200,14 +210,14 @@ "\n", "These are:\n", "- `hazard` (Hazard object): the present-day or baseline hazard event set\n", - "- `entity` (Entity object): the present-day or baseline Entity object. `Entity` is the container class containing \n", + "- `entity` (Entity object): the present-day or baseline Entity object. `Entity` is the container class containing\n", " - `exposure` (Exposures object): the present-day or baseline exposure\n", " - `disc_rates` (DiscRates object): the discount rates to be applied in the cost-benefit calculation. Only discount rates from `entity` and not `ent_future` are used.\n", " - `impact_funcs` (ImpactFuncSet object): the impact functions required to calculate impacts from the present-day hazards and exposures\n", " - `measures` (MeasureSet object): the set of measures to implement in the analysis. This will almost always be the same as the measures in the `ent_future` Entity (if set).\n", "- `haz_future` (Hazard object, optional): the future hazard event set, if different from present.\n", "- `ent_future` (Entity object, optional): the future Entity, if different from present. Note that the same adaptation measures must be present in both `entity` and `ent_future`.\n", - "- `future_year` (int): the year of the future scenario. This is only used if the Entity's `exposures.ref_year` isn't set, or no future entity is provided. \n", + "- `future_year` (int): the year of the future scenario. This is only used if the Entity's `exposures.ref_year` isn't set, or no future entity is provided.\n", "- `risk_func` (function): this is the risk function used to describe the annual impacts used to describe benefits. The default is `risk_aai_agg`, the average annual impact on the Exposures (defined in the CostBenefit module). This function can be replaces with any function that takes an Impact object as input and returns a number. The CostBenefit module provides two others functions `risk_rp_100` and `risk_rp_250`, the 100-year and 250-year return period impacts respectively.\n", "- `imp_time_depen` (float): This describes how hazard and exposure evolve over time in the calculation. In the descriptions above this is the parameter $k$ defining $\\alpha_k(t)$. When > 1 change is superlinear and occurs nearer the start of the analysis. When < 1 change is sublinear and occurs nearer the end.\n", "- `save_imp` (boolean): whether to save the hazard- and location-specific impact data. This is used in a lot of follow-on calculations, but is very large if you don't need it.\n" @@ -215,6 +225,7 @@ }, { "cell_type": "markdown", + "id": "4f318d1e", "metadata": {}, "source": [ "## Detailed CostBenefit calculation: LitPop + TropCyclone" @@ -222,6 +233,7 @@ }, { "cell_type": "markdown", + "id": "032035ed", "metadata": {}, "source": [ "We present a detailed example for the hazard __[Tropical Cyclones](climada_hazard_TropCyclone.ipynb)__ and the exposures from __[LitPop](climada_entity_LitPop.ipynb)__ .\n", @@ -231,6 +243,7 @@ }, { "cell_type": "markdown", + "id": "1f6ca136", "metadata": {}, "source": [ "### Download hazard\n", @@ -240,18 +253,10 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, + "id": "b3b5aba4", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2022-03-03 05:35:22,192 - climada.hazard.base - INFO - Reading /Users/chrisfairless/climada/data/hazard/tropical_cyclone/tropical_cyclone_10synth_tracks_150arcsec_HTI_1980_2020/v1/tropical_cyclone_10synth_tracks_150arcsec_HTI_1980_2020.hdf5\n", - "2022-03-03 05:35:28,402 - climada.hazard.base - INFO - Reading /Users/chrisfairless/climada/data/hazard/tropical_cyclone/tropical_cyclone_10synth_tracks_150arcsec_rcp85_HTI_2080/v1/tropical_cyclone_10synth_tracks_150arcsec_rcp85_HTI_2080.hdf5\n" - ] - } - ], + "outputs": [], "source": [ "from climada.util.api_client import Client\n", "\n", @@ -278,6 +283,7 @@ }, { "cell_type": "markdown", + "id": "f7a38aba", "metadata": {}, "source": [ "We can plot the hazards and show how they are forecast to intensify. For example, showing the strength of a 50-year return period wind in present and future climates:" @@ -285,66 +291,10 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, + "id": "149fba65", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2022-03-03 05:35:28,479 - climada.hazard.base - INFO - Computing exceedance intenstiy map for return periods: [50]\n", - "2022-03-03 05:35:36,986 - climada.hazard.base - INFO - Computing exceedance intenstiy map for return periods: [50]\n" - ] - }, - { - "data": { - "text/plain": [ - "(,\n", - " array([[41.84896948, 41.98439726, 41.62016887, ..., 49.52344953,\n", - " 51.35294266, 51.51945831]]))" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/chrisfairless/opt/anaconda3/envs/climada_env/lib/python3.8/site-packages/cartopy/crs.py:825: ShapelyDeprecationWarning: __len__ for multi-part geometries is deprecated and will be removed in Shapely 2.0. Check the length of the `geoms` property instead to get the number of parts of a multi-part geometry.\n", - " if len(multi_line_string) > 1:\n", - "/Users/chrisfairless/opt/anaconda3/envs/climada_env/lib/python3.8/site-packages/cartopy/crs.py:877: ShapelyDeprecationWarning: Iteration over multi-part geometries is deprecated and will be removed in Shapely 2.0. Use the `geoms` property to access the constituent parts of a multi-part geometry.\n", - " for line in multi_line_string:\n", - "/Users/chrisfairless/opt/anaconda3/envs/climada_env/lib/python3.8/site-packages/cartopy/crs.py:944: ShapelyDeprecationWarning: __len__ for multi-part geometries is deprecated and will be removed in Shapely 2.0. Check the length of the `geoms` property instead to get the number of parts of a multi-part geometry.\n", - " if len(p_mline) > 0:\n" - ] - }, - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Plot the hazards, showing 50-year return period hazard\n", "haz_present.plot_rp_intensity(return_periods=(50,), smooth=False, vmin=32, vmax=50)\n", @@ -353,6 +303,7 @@ }, { "cell_type": "markdown", + "id": "8dc9c2af", "metadata": {}, "source": [ "### Download LitPop economic exposure data\n", @@ -362,23 +313,17 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, + "id": "98c98d8f", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2022-03-03 05:35:52,700 - climada.entity.exposures.base - INFO - Reading /Users/chrisfairless/climada/data/exposures/litpop/LitPop_150arcsec_HTI/v1/LitPop_150arcsec_HTI.hdf5\n" - ] - } - ], + "outputs": [], "source": [ "exp_present = client.get_litpop(country=\"Haiti\")" ] }, { "cell_type": "markdown", + "id": "70e2a7af", "metadata": {}, "source": [ "For 2080's economic exposure we will use a crude approximation, assuming the country will experience 2% economic growth annually:" @@ -386,7 +331,8 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, + "id": "f701934a", "metadata": {}, "outputs": [], "source": [ @@ -402,6 +348,7 @@ }, { "cell_type": "markdown", + "id": "5879163e", "metadata": {}, "source": [ "We can plot the current and future exposures. The default scale is logarithmic and we see how the values of exposures grow, though not by a full order of magnitude." @@ -409,91 +356,10 @@ }, { "cell_type": "code", - "execution_count": 5, - "metadata": { - "ExecuteTime": { - "end_time": "2020-10-20T15:09:07.000153Z", - "start_time": "2020-10-20T15:09:05.634377Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2022-03-03 05:35:52,895 - climada.util.coordinates - INFO - Raster from resolution 0.04166666666666785 to 0.04166666666666785.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/chrisfairless/opt/anaconda3/envs/climada_env/lib/python3.8/site-packages/pyproj/crs/crs.py:1256: UserWarning: You will likely lose important projection information when converting to a PROJ string from another format. See: https://proj.org/faq.html#what-is-the-best-format-for-describing-coordinate-reference-systems\n", - " return self._crs.to_proj4(version=version)\n", - "/Users/chrisfairless/opt/anaconda3/envs/climada_env/lib/python3.8/site-packages/cartopy/crs.py:825: ShapelyDeprecationWarning: __len__ for multi-part geometries is deprecated and will be removed in Shapely 2.0. Check the length of the `geoms` property instead to get the number of parts of a multi-part geometry.\n", - " if len(multi_line_string) > 1:\n", - "/Users/chrisfairless/opt/anaconda3/envs/climada_env/lib/python3.8/site-packages/cartopy/crs.py:877: ShapelyDeprecationWarning: Iteration over multi-part geometries is deprecated and will be removed in Shapely 2.0. Use the `geoms` property to access the constituent parts of a multi-part geometry.\n", - " for line in multi_line_string:\n", - "/Users/chrisfairless/opt/anaconda3/envs/climada_env/lib/python3.8/site-packages/cartopy/crs.py:944: ShapelyDeprecationWarning: __len__ for multi-part geometries is deprecated and will be removed in Shapely 2.0. Check the length of the `geoms` property instead to get the number of parts of a multi-part geometry.\n", - " if len(p_mline) > 0:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2022-03-03 05:35:59,536 - climada.util.coordinates - INFO - Raster from resolution 0.04166666666666785 to 0.04166666666666785.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/chrisfairless/opt/anaconda3/envs/climada_env/lib/python3.8/site-packages/pyproj/crs/crs.py:1256: UserWarning: You will likely lose important projection information when converting to a PROJ string from another format. See: https://proj.org/faq.html#what-is-the-best-format-for-describing-coordinate-reference-systems\n", - " return self._crs.to_proj4(version=version)\n", - "/Users/chrisfairless/opt/anaconda3/envs/climada_env/lib/python3.8/site-packages/cartopy/crs.py:825: ShapelyDeprecationWarning: __len__ for multi-part geometries is deprecated and will be removed in Shapely 2.0. Check the length of the `geoms` property instead to get the number of parts of a multi-part geometry.\n", - " if len(multi_line_string) > 1:\n", - "/Users/chrisfairless/opt/anaconda3/envs/climada_env/lib/python3.8/site-packages/cartopy/crs.py:877: ShapelyDeprecationWarning: Iteration over multi-part geometries is deprecated and will be removed in Shapely 2.0. Use the `geoms` property to access the constituent parts of a multi-part geometry.\n", - " for line in multi_line_string:\n", - "/Users/chrisfairless/opt/anaconda3/envs/climada_env/lib/python3.8/site-packages/cartopy/crs.py:944: ShapelyDeprecationWarning: __len__ for multi-part geometries is deprecated and will be removed in Shapely 2.0. Check the length of the `geoms` property instead to get the number of parts of a multi-part geometry.\n", - " if len(p_mline) > 0:\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "execution_count": null, + "id": "173e3b22", + "metadata": {}, + "outputs": [], "source": [ "exp_present.plot_raster(fill=False, vmin=4, vmax=11)\n", "exp_future.plot_raster(fill=False, vmin=4, vmax=11)" @@ -501,6 +367,7 @@ }, { "cell_type": "markdown", + "id": "8a3be1ac", "metadata": {}, "source": [ "We then need to map the exposure points to the hazard centroids. (Note: we could have done this earlier before we copied the exposure, but not all analyses will have present and future exposures and hazards on the same sets of points.)" @@ -508,20 +375,10 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, + "id": "94b4b5f4", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2022-03-03 05:36:15,405 - climada.entity.exposures.base - INFO - Matching 1329 exposures with 1329 centroids.\n", - "2022-03-03 05:36:15,421 - climada.util.coordinates - INFO - No exact centroid match found. Reprojecting coordinates to nearest neighbor closer than the threshold = 100\n", - "2022-03-03 05:36:16,026 - climada.entity.exposures.base - INFO - Matching 1329 exposures with 1329 centroids.\n", - "2022-03-03 05:36:16,042 - climada.util.coordinates - INFO - No exact centroid match found. Reprojecting coordinates to nearest neighbor closer than the threshold = 100\n" - ] - } - ], + "outputs": [], "source": [ "# This would be done automatically in Impact calculations\n", "# but it's better to do it explicitly before the calculation\n", @@ -531,53 +388,20 @@ }, { "cell_type": "markdown", + "id": "e223a604", "metadata": {}, "source": [ - "### Define impact function \n", + "### Define impact function\n", "\n", "In this analysis we'll use the popular sigmoid curve impact function from Emanuel (2011)." ] }, { "cell_type": "code", - "execution_count": 7, - "metadata": { - "ExecuteTime": { - "end_time": "2020-10-20T15:09:51.503229Z", - "start_time": "2020-10-20T15:09:51.499628Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2022-03-03 05:36:16,069 - climada.entity.impact_funcs.base - WARNING - For intensity = 0, mdd != 0 or paa != 0. Consider shifting the origin of the intensity scale. In impact.calc the impact is always null at intensity = 0.\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "execution_count": null, + "id": "930e7c1c", + "metadata": {}, + "outputs": [], "source": [ "from climada.entity import ImpactFuncSet, ImpfTropCyclone\n", "\n", @@ -591,7 +415,8 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, + "id": "bc539145", "metadata": {}, "outputs": [], "source": [ @@ -607,6 +432,7 @@ }, { "cell_type": "markdown", + "id": "b215e7f4", "metadata": {}, "source": [ "### Define adaptation measures\n", @@ -618,7 +444,8 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, + "id": "35a37be6", "metadata": {}, "outputs": [], "source": [ @@ -650,6 +477,7 @@ }, { "cell_type": "markdown", + "id": "b6e5bc4d", "metadata": {}, "source": [ "### Define discount rates\n", @@ -659,7 +487,8 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, + "id": "53cee32f", "metadata": {}, "outputs": [], "source": [ @@ -675,6 +504,7 @@ }, { "cell_type": "markdown", + "id": "18fa6c68", "metadata": {}, "source": [ "### Create Entity objects\n", @@ -686,7 +516,8 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, + "id": "43f08ade", "metadata": {}, "outputs": [], "source": [ @@ -708,6 +539,7 @@ }, { "cell_type": "markdown", + "id": "73559001", "metadata": {}, "source": [ "### Cost-benefit #1: adaptation measures, no climate change or economic growth\n", @@ -719,35 +551,10 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, + "id": "d60a3433", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2022-03-03 05:36:16,236 - climada.engine.impact - INFO - Exposures matching centroids found in centr_TC\n", - "2022-03-03 05:36:16,238 - climada.engine.impact - INFO - Calculating damage for 1329 assets (>0) and 42779 events.\n", - "2022-03-03 05:36:16,258 - climada.engine.impact - INFO - Exposures matching centroids found in centr_TC\n", - "2022-03-03 05:36:16,259 - climada.engine.impact - INFO - Calculating damage for 1329 assets (>0) and 42779 events.\n", - "2022-03-03 05:36:16,295 - climada.engine.impact - INFO - Exposures matching centroids found in centr_TC\n", - "2022-03-03 05:36:16,296 - climada.engine.impact - INFO - Calculating damage for 1329 assets (>0) and 42779 events.\n", - "2022-03-03 05:36:16,332 - climada.engine.cost_benefit - INFO - Computing cost benefit from years 2018 to 2080.\n", - "\n", - "Measure Cost (USD bn) Benefit (USD bn) Benefit/Cost\n", - "--------- --------------- ------------------ --------------\n", - "Measure A 5 4.74132 0.948265\n", - "Measure B 0.22 1.10613 5.02787\n", - "\n", - "-------------------- --------- --------\n", - "Total climate risk: 11.0613 (USD bn)\n", - "Average annual risk: 0.175576 (USD bn)\n", - "Residual risk: 5.21385 (USD bn)\n", - "-------------------- --------- --------\n", - "Net Present Values\n" - ] - } - ], + "outputs": [], "source": [ "from climada.engine import CostBenefit\n", "from climada.engine.cost_benefit import risk_aai_agg\n", @@ -767,6 +574,7 @@ }, { "cell_type": "markdown", + "id": "89bb6aa7", "metadata": {}, "source": [ "Let's take a moment to look through these results.\n", @@ -785,27 +593,10 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, + "id": "2272cfc0", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Measure Cost (USD bn) Benefit (USD bn) Benefit/Cost\n", - "----------------- --------------- ------------------ --------------\n", - "Combined measures 5.22 5.84344 1.11943\n", - "\n", - "-------------------- --------- --------\n", - "Total climate risk: 11.0613 (USD bn)\n", - "Average annual risk: 0.175576 (USD bn)\n", - "Residual risk: 5.21787 (USD bn)\n", - "-------------------- --------- --------\n", - "Net Present Values\n" - ] - } - ], + "outputs": [], "source": [ "combined_costben = costben_measures_only.combine_measures(\n", " [\"Measure A\", \"Measure B\"],\n", @@ -817,39 +608,29 @@ }, { "cell_type": "markdown", + "id": "7e22e0c6", "metadata": {}, "source": [ "Note: the method of combining measures is naive. The offset impacts are summed over the event set while not letting the impact of any single event drop below zero (it therefore doesn't work in analyses where impacts can go below zero).\n", "\n", - "#### Plotting benefits by return period \n", + "#### Plotting benefits by return period\n", "\n", "Finally, we can see how effective the adaptation measures are at different return periods. The `plot_event_view` plot shows the difference in losses at different return periods in the future scenario (here the same as the present scenario) with the losses offset by the adaptation measures shaded." ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, + "id": "d02af8e2", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "ax = costben_measures_only.plot_event_view((25, 50, 100, 250))" ] }, { "cell_type": "markdown", + "id": "27da73f2", "metadata": {}, "source": [ "We see that the Measure A, which reduces wind speeds by 5 m/s, is able to completely stop impacts at the 25 year return period, and that at 250 years – the strongest events – the measures have greatly reduced effectiveness.\n", @@ -861,46 +642,10 @@ }, { "cell_type": "code", - "execution_count": 15, - "metadata": { - "ExecuteTime": { - "end_time": "2020-10-20T09:59:49.351752Z", - "start_time": "2020-10-20T09:59:49.340451Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2022-03-03 05:36:16,478 - climada.engine.impact - INFO - Exposures matching centroids found in centr_TC\n", - "2022-03-03 05:36:16,480 - climada.engine.impact - INFO - Calculating damage for 1329 assets (>0) and 42779 events.\n", - "2022-03-03 05:36:16,498 - climada.engine.impact - INFO - Exposures matching centroids found in centr_TC\n", - "2022-03-03 05:36:16,500 - climada.engine.impact - INFO - Calculating damage for 1329 assets (>0) and 42779 events.\n", - "2022-03-03 05:36:16,534 - climada.engine.impact - INFO - Exposures matching centroids found in centr_TC\n", - "2022-03-03 05:36:16,535 - climada.engine.impact - INFO - Calculating damage for 1329 assets (>0) and 42779 events.\n", - "2022-03-03 05:36:16,572 - climada.engine.impact - INFO - Exposures matching centroids found in centr_TC\n", - "2022-03-03 05:36:16,574 - climada.engine.impact - INFO - Calculating damage for 1329 assets (>0) and 16808 events.\n", - "2022-03-03 05:36:16,592 - climada.engine.impact - INFO - Exposures matching centroids found in centr_TC\n", - "2022-03-03 05:36:16,593 - climada.engine.impact - INFO - Calculating damage for 1329 assets (>0) and 16808 events.\n", - "2022-03-03 05:36:16,615 - climada.engine.impact - INFO - Exposures matching centroids found in centr_TC\n", - "2022-03-03 05:36:16,616 - climada.engine.impact - INFO - Calculating damage for 1329 assets (>0) and 16808 events.\n", - "2022-03-03 05:36:16,637 - climada.engine.cost_benefit - INFO - Computing cost benefit from years 2018 to 2080.\n", - "\n", - "Measure Cost (USD bn) Benefit (USD bn) Benefit/Cost\n", - "--------- --------------- ------------------ --------------\n", - "Measure A 5 13.9728 2.79457\n", - "Measure B 0.22 3.65387 16.6085\n", - "\n", - "-------------------- --------- --------\n", - "Total climate risk: 36.5387 (USD bn)\n", - "Average annual risk: 0.984382 (USD bn)\n", - "Residual risk: 18.912 (USD bn)\n", - "-------------------- --------- --------\n", - "Net Present Values\n" - ] - } - ], + "execution_count": null, + "id": "3878a13c", + "metadata": {}, + "outputs": [], "source": [ "costben = CostBenefit()\n", "costben.calc(\n", @@ -917,12 +662,8 @@ }, { "cell_type": "markdown", - "metadata": { - "ExecuteTime": { - "end_time": "2020-10-20T15:09:51.515128Z", - "start_time": "2020-10-20T15:09:51.505127Z" - } - }, + "id": "db1fbefe", + "metadata": {}, "source": [ "What has changed by adding climate change and population growth?\n", "\n", @@ -932,44 +673,17 @@ "\n", "**Exercise**: try changing the value of the `imp_time_depen` parameter in the calculation above. Values < 1 front-load the changes over time, and values > 1 back-load the changes. How does it affect the values in the printout above? What changes? What doesn't?\n", "\n", - "#### Waterfall plots \n", + "#### Waterfall plots\n", "\n", "Now that there are more additional components in the analysis, we can use more of the CostBenefit class's visualisation methods. The waterfall plot is the clearest way to break down the components of risk:" ] }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, + "id": "357828b9", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2022-03-03 05:36:16,647 - climada.engine.impact - INFO - Exposures matching centroids found in centr_TC\n", - "2022-03-03 05:36:16,649 - climada.engine.impact - INFO - Calculating damage for 1329 assets (>0) and 42779 events.\n", - "2022-03-03 05:36:16,665 - climada.engine.impact - INFO - Exposures matching centroids found in centr_TC\n", - "2022-03-03 05:36:16,667 - climada.engine.impact - INFO - Calculating damage for 1329 assets (>0) and 16808 events.\n", - "2022-03-03 05:36:16,695 - climada.engine.cost_benefit - INFO - Risk at 2018: 1.756e+08\n", - "2022-03-03 05:36:16,696 - climada.engine.impact - INFO - Exposures matching centroids found in centr_TC\n", - "2022-03-03 05:36:16,699 - climada.engine.impact - INFO - Calculating damage for 1329 assets (>0) and 42779 events.\n", - "2022-03-03 05:36:16,714 - climada.engine.cost_benefit - INFO - Risk with development at 2080: 6.113e+08\n", - "2022-03-03 05:36:16,715 - climada.engine.cost_benefit - INFO - Risk with development and climate change at 2080: 9.844e+08\n" - ] - }, - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# define this as a function because we'll use it again later\n", "def waterfall():\n", @@ -983,6 +697,7 @@ }, { "cell_type": "markdown", + "id": "82004e46", "metadata": {}, "source": [ "The waterfall plot breaks down the average annual risk faced in 2080 (this is \\\\$0.984 bn, as printed out during the cost-benefit calculation).\n", @@ -996,37 +711,10 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, + "id": "1a000005", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2022-03-03 05:36:16,803 - climada.engine.impact - INFO - Exposures matching centroids found in centr_TC\n", - "2022-03-03 05:36:16,804 - climada.engine.impact - INFO - Calculating damage for 1329 assets (>0) and 42779 events.\n", - "2022-03-03 05:36:16,820 - climada.engine.impact - INFO - Exposures matching centroids found in centr_TC\n", - "2022-03-03 05:36:16,821 - climada.engine.impact - INFO - Calculating damage for 1329 assets (>0) and 16808 events.\n", - "2022-03-03 05:36:16,849 - climada.engine.cost_benefit - INFO - Risk at 2018: 1.756e+08\n", - "2022-03-03 05:36:16,850 - climada.engine.impact - INFO - Exposures matching centroids found in centr_TC\n", - "2022-03-03 05:36:16,851 - climada.engine.impact - INFO - Calculating damage for 1329 assets (>0) and 42779 events.\n", - "2022-03-03 05:36:16,866 - climada.engine.cost_benefit - INFO - Risk with development at 2080: 6.113e+08\n", - "2022-03-03 05:36:16,867 - climada.engine.cost_benefit - INFO - Risk with development and climate change at 2080: 9.844e+08\n" - ] - }, - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "costben.plot_arrow_averted(\n", " axis=waterfall(),\n", @@ -1041,6 +729,7 @@ }, { "cell_type": "markdown", + "id": "d5a88403", "metadata": {}, "source": [ "**Exercise**: In addition, the `plot_waterfall_accumulated` method is available to produce a waterfall plot from a different perspective. Instead of showing a breakdown of the impacts from the year of our future scenario, it accumulates the components of risk over the whole analysis period. That is, it sums the components over every year between 2018 (when the entire risk is the baseline risk) to 2080 (when the breakdown is the same as the plot above). The final plot has the same four components, but gives them different weightings. Look up the function in the `climada.engine.cost_benefit` module and try it out. Then try changing the value of the `imp_time_depen` parameter, and see how front-loading or back-loading the year-on-year changes gives different totals and different breakdowns of risk." @@ -1048,6 +737,7 @@ }, { "cell_type": "markdown", + "id": "1066d572", "metadata": {}, "source": [ "### Cost-benefit #3: Adding discount rates\n", @@ -1059,13 +749,9 @@ }, { "cell_type": "code", - "execution_count": 18, - "metadata": { - "ExecuteTime": { - "end_time": "2020-10-20T09:59:49.355854Z", - "start_time": "2020-10-20T09:59:49.353112Z" - } - }, + "execution_count": null, + "id": "d311ef8f", + "metadata": {}, "outputs": [], "source": [ "entity_present_disc = Entity(\n", @@ -1084,6 +770,7 @@ }, { "cell_type": "markdown", + "id": "34b99ff1", "metadata": {}, "source": [ "And then re-calculate the cost-benefits:" @@ -1091,47 +778,10 @@ }, { "cell_type": "code", - "execution_count": 19, - "metadata": { - "ExecuteTime": { - "end_time": "2020-10-20T09:59:50.233588Z", - "start_time": "2020-10-20T09:59:49.357688Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2022-03-03 05:36:16,969 - climada.engine.impact - INFO - Exposures matching centroids found in centr_TC\n", - "2022-03-03 05:36:16,971 - climada.engine.impact - INFO - Calculating damage for 1329 assets (>0) and 42779 events.\n", - "2022-03-03 05:36:16,988 - climada.engine.impact - INFO - Exposures matching centroids found in centr_TC\n", - "2022-03-03 05:36:16,989 - climada.engine.impact - INFO - Calculating damage for 1329 assets (>0) and 42779 events.\n", - "2022-03-03 05:36:17,024 - climada.engine.impact - INFO - Exposures matching centroids found in centr_TC\n", - "2022-03-03 05:36:17,026 - climada.engine.impact - INFO - Calculating damage for 1329 assets (>0) and 42779 events.\n", - "2022-03-03 05:36:17,062 - climada.engine.impact - INFO - Exposures matching centroids found in centr_TC\n", - "2022-03-03 05:36:17,064 - climada.engine.impact - INFO - Calculating damage for 1329 assets (>0) and 16808 events.\n", - "2022-03-03 05:36:17,079 - climada.engine.impact - INFO - Exposures matching centroids found in centr_TC\n", - "2022-03-03 05:36:17,081 - climada.engine.impact - INFO - Calculating damage for 1329 assets (>0) and 16808 events.\n", - "2022-03-03 05:36:17,100 - climada.engine.impact - INFO - Exposures matching centroids found in centr_TC\n", - "2022-03-03 05:36:17,101 - climada.engine.impact - INFO - Calculating damage for 1329 assets (>0) and 16808 events.\n", - "2022-03-03 05:36:17,123 - climada.engine.cost_benefit - INFO - Computing cost benefit from years 2018 to 2080.\n", - "\n", - "Measure Cost (USD bn) Benefit (USD bn) Benefit/Cost\n", - "--------- --------------- ------------------ --------------\n", - "Measure A 5 8.46661 1.69332\n", - "Measure B 0.22 2.20086 10.0039\n", - "\n", - "-------------------- --------- --------\n", - "Total climate risk: 22.0086 (USD bn)\n", - "Average annual risk: 0.984382 (USD bn)\n", - "Residual risk: 11.3412 (USD bn)\n", - "-------------------- --------- --------\n", - "Net Present Values\n", - "(0, 0)\n" - ] - } - ], + "execution_count": null, + "id": "25eba39e", + "metadata": {}, + "outputs": [], "source": [ "costben_disc = CostBenefit()\n", "costben_disc.calc(\n", @@ -1149,6 +799,7 @@ }, { "cell_type": "markdown", + "id": "5da0b14f", "metadata": {}, "source": [ "How has this changed the numbers?\n", @@ -1167,28 +818,17 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": null, + "id": "b91f907f", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "ax = costben_disc.plot_cost_benefit()" ] }, { "cell_type": "markdown", + "id": "09742bc3", "metadata": {}, "source": [ "The x-axis here is damage averted over the 2018-2080 analysis period. The y-axis is the Benefit/Cost ratio (so higher is better). This means that the area of each shape represents the total benefit of the measure. Furthermore, any measure which goes above 1 on the y-axis gives a larger benefit than the cost of its implementation.\n", @@ -1198,16 +838,18 @@ }, { "cell_type": "markdown", + "id": "641ab896", "metadata": {}, "source": [ "**Exercise**: How sensitive are cost benefit analyses to different parameters? Let's say an adaptation measure is a 'good investment' if the benefit is greater than the cost over the analysis period, and it's a 'bad investment' if the benefit is less than the cost.\n", - "- Using the hazards and exposures from this tutorial, can you design an impact measure that is a good investment when no discount rates are applied, and a bad investment when a 1.4% (or higher) discount rate is applied? \n", + "- Using the hazards and exposures from this tutorial, can you design an impact measure that is a good investment when no discount rates are applied, and a bad investment when a 1.4% (or higher) discount rate is applied?\n", "- Create hazard and exposure objects for the same growth and climate change scenarios as this tutorial, but for the year 2040. Can you design an impact measure that is a good investment when evaluated out to 2080, but a bad investment when evaluated to 2040?\n", "- Using the hazards and exposures from this tutorial, can you design an impact measure that is a good investment when `imp_time_depen` = 1/4 (change happens closer to 2018) and a bad investment when `imp_time_depen` = 4 (change happens closer to 2080)." ] }, { "cell_type": "markdown", + "id": "e10eacad", "metadata": {}, "source": [ "Finally we can use some of the functionality of the objects stored within the CostBenefit object. Remember that many impact calculations have been performed to get here, and if `imp_mat` was set to True, the data has been stored (or ... it will be. I found a bug that stops it being saved while writing the tutorial.)\n", @@ -1219,39 +861,10 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": null, + "id": "63e243c0", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Measure Cost (USD bn) Benefit (USD bn) Benefit/Cost\n", - "----------------- --------------- ------------------ --------------\n", - "Combined measures 5.22 10.6616 2.04245\n", - "\n", - "-------------------- --------- --------\n", - "Total climate risk: 22.0086 (USD bn)\n", - "Average annual risk: 0.984382 (USD bn)\n", - "Residual risk: 11.347 (USD bn)\n", - "-------------------- --------- --------\n", - "Net Present Values\n" - ] - }, - { - "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXgAAAEWCAYAAABsY4yMAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8/fFQqAAAACXBIWXMAAAsTAAALEwEAmpwYAABIgUlEQVR4nO3dd3hU1dbA4d9KgRBC74QSepESIAFpigJiFwXFgpeIotjQ+ym2q6Je9VqwXPVasGHvIIoNW0BUakB6B+mdkJCE1P39sU+SSUhCApmcmcl6n2eemTl1zUmysmefXcQYg1JKqcAT5HYASimlvEMTvFJKBShN8EopFaA0wSulVIDSBK+UUgFKE7xSSgUoTfCqQomIEZG2bseRS0QaicgcEUkWkWfcjkep8hTidgCqfInIFqARkO2xeKox5hZ3IvJ51wP7gZpGO4WoAKMJPjBdYIz5ye0g/ERLYFVxyV1EQowxWRUck9/R6+SbtIqmEhGRV0Tkc4/3T4rIz2IFi8h9IrLRqa5YLCLNne06isiPInJQRNaKyGUex6gqIpNFZKuI7BGRV0Wkmsf6iSKyS0R2isjYQvGcJyJLRCRJRLaJyEMe66Kc6pwxzrH3i8i/PNafULyFzj8VGAPcJSJHRGSIiDwkIp+LyPsikgTEiUgtEXnT+Rw7RORREQn2iGOyE98mEbnZiTvEWb9FRIZ4nPMhEXnf4/2pIvKHiCSKyF8iMshjXbyI/FtEfnc+4ywRqe+xfoDHvttEJE5EYp2fQ4jHdiNEZGkx16CaiDwjIn+LyGERmessGyQi2wttm/dZirhO94lImojU9di+h3NdQp33Y0VktYgcEpEfRKRlUTGpcmSM0UcAPYAtwJBi1oUD64A4YCC2aqKZs24isBzoAAjQHagHVAe2Addgv/H1dPY7xdnveeAroC5QA/ga+I+z7mxgD9DFOc6HgAHaOusHAV2xBY1uzrbDnXVRzravA9WceNKBTicTbxHXZCrwqMf7h4BMYLgTVzXgS+A159gNgQXADc7244E1QHPnGvzqxB1S1M/DOf77zutI4ABwrnOuoc77Bs76eGAj0N6JIx54wlnXAkgGrgBCnc8e7axbBZzjcc7pwB3FfP7/OceNBIKBfkBV52ezvbjfrWKu0y/AOI/tnwZedV4PBzYAnZyfy/3AH27/vQT6w/UAjgkI3gL2AitKse1pQAKQBYwstG4MsN55jHH7c1Xg9dsCHAESPR6ef3S9gYPA38AVHsvXAhcVcbxRwG+Flr0GTMIm1hSgjce6vsBmj5/lEx7r2uOR4Is41/PAc87rKGfbZh7rFwCXn2i8xZxzKscm+Dke7xth/7FU81h2BfCr8/oXYLzHurMofYK/G3ivUDw/5P6+YhPv/R7rbgK+d17fC0wv5jPdDXzgvK4LpAJNitguCEgDuhexbhDHT/BzCq2/DvjFeS3Yf7SnOe+/A64tdO5UoKXbfzOB/PDFOvipwEvAu6XYdiu2NHqn50Lna+IkIAb7x7ZYRL4yxhwq10h913BTTB28MWaBiGzClkQ/9VjVHFtaLKwl0EdEEj2WhQDvAQ2w3woWi0juOsGWBAGaAos99vvb88Ai0gd4AlvCr4ItOX5W6Py7PV6nAhEnEW9pbSt0vFBgl8dnDPLYpmmh7Qt8xuNoCVwqIhd4LAvFfgvIVdbPD/A+sFpEIoDLsP/wdhWxXX0grITjHM+2Qu8/B14UkaZAO+zf3m/OupbAf6VgSyXBfnMoyzVTZeBzCd4YM0dEojyXiUgb7FfJBthf8nHGmDXGmC3O+pxChxkG/GiMOeis/xFbXfCRd6P3fSJyMzaR7gTuAv7jrNoGtAFWFNplGzDbGDO0iGPllgBPMcbsKOJ0u7CJKFeLQus/xP4zP8cYc1REnscmndIoc7xl4HnDdRu2BF/fFH0T8XifMQX7TzBX40LHfs8YM+4EYtyG/TZ2DGPMDhH5E7gYuBp4pZhj7AeOYq/jXyXF7dxzaFD4VIXOmygis7D/VDoBHxmnuO7E+5gx5oPjfC5VjvzlJusU4FZjTC9saf3l42wfScHSxXZnWaUmIu2BR4HR2D/8u0Qk2ln9BvBvEWknVjcRqQfMBNqLyNUiEuo8YkWkkzEmB1tH/pyINHTOESkiw5xjfoq9SdlZRMKx36o81QAOOsm9N3BlGT5OmeMt4+UCwCn5zgKeEZGaIhIkIm1E5HSPzzhBRJqJSB3gnkKHWApc7sQRA4z0WPc+cIGIDHNu1oY5NzeblSK0D4AhInKZiISISD2PnyXYb8B3Ye9xTC/ms+Vgq9GeFZGmTgx9RaQq9l5NmNgb4aHYOvOqpYjrQ+AfwAjnda5XgXtF5BQAsTeuLy3F8dRJ8PkE73zN7Ad85rQEeA1ocrzdilhWmdo4fy22VUjuY7rTquJ94EljzF/GmPXAfcB7zh/0s9hkNQtIAt7E1jsnY+uVL8eW+ncDT5L/x3439ubZPKc1xU/YG58YY77D1qv/4mzzS6E4bwIeEZFk4EEKVhkdz4nGeyL+ga1CWgUcwlZF5P4Ovo6tN/8Lez9oWqF9H8CWkA8BD+OR9Iwx24CLsD+HfdhCyURK8XdpjNmKvTl7B/aeylLsjeZc07HVItONMSklHOpO7M3qhc5xngSCjDGHsT+fN4Ad2BL99uIO4uErbPXMHmNM3rcCY8x059gfO78nK4BzSnE8dRIk/xuU73CqaGYaY7qISE1grTGm2KQutrnbTGPM5877K4BBxpgbnPevAfHGmEpfRaO8y/nd3QyEFlOlU5GxbMS29tE+EZWUz5fgjTFJwObcr3PO1/Hux9ntB+AsEanjfG0+y1mmVKUgIiOw31oLf2tSlYjPJXgR+Qj4E+ggIttF5FrgKuBaEfkLWIn9WotTt7oduBR4TURWAjg3V/+N/dq5EHgk94arUoFOROKxN1ZvdurZVSXlk1U0SimlTp7PleCVUkqVD59qB1+/fn0TFRXldhhKKeU3Fi9evN8YU7iPAuBjCT4qKopFixa5HYZSSvkNESm2J7BW0SilVIDSBK+UUgFKE7xSSgUor9bBi8g/sUOIGmx36GuMMUfLcozMzEy2b9/O0aNl2k0FgLCwMJo1a0ZoaKjboSjll7yW4EUkEpgAdDbGpInIp9jxQaaW5Tjbt2+nRo0aREVF4TFcqwpwxhgOHDjA9u3badWqldvhKOWXvF1FEwJUcwa6CscO/lQmR48epV69eprcKxkRoV69evrNTamT4LUE74wPPhk7Kccu4LAxZlbh7UTkehFZJCKL9u3bV+SxNLlXTvpzV+rkeC3BO4N8XQS0ws56U11ERhfezhgzxRgTY4yJadCgyLb6SikVeEwOHF4NG9+CVU955RTevMk6BDs35z4AEZmGHdf9/RL38kHBwcF07dqVrKwsOnXqxDvvvEN4ePjxdyxH8fHxVKlShX79+lXoeZVS5STjEOyfD/vnwYF59nVmol1XrSl0uhOkfMvc3kzwW4FTnZl80oDBgF92U61WrRpLly4F4KqrruLVV1/l//7v//LWZ2dnExwcXMze5SM+Pp6IiAhN8Er5g5xsOLzSSeR/2qSetMaukyCo1QVaXgb1+0K9U6Fm+3JP7uDFBG+MmS8in2NnuckClmCn3vNrAwcOZNmyZcTHx/Pwww/TpEkTli5dyvLly7nnnnuIj48nPT2dm2++mRtuuIFdu3YxatQokpKSyMrK4pVXXmHgwIHMmjWLSZMmkZ6eTps2bXj77beJiIggKiqKMWPG8PXXX5OZmclnn31GWFgYr776KsHBwbz//vu8+OKLDBw40O1LoZTKdXQfHJifn8wPLICsI3Zd1fo2ibe62j7Xi4XQGhUSllfbwRtjJnHsPJwn7PbbwSlIl5voaHj++dJtm5WVxXfffcfZZ58NwIIFC1ixYgWtWrViypQp1KpVi4ULF5Kenk7//v0566yzmDZtGsOGDeNf//oX2dnZpKamsn//fh599FF++uknqlevzpNPPsmzzz7Lgw8+CED9+vVJSEjg5ZdfZvLkybzxxhuMHz+eiIgI7rzzzvK9AEqpssnJhMRlNpHvd0roRzbadRIMdaKh1Riof6otoUe0BpcaDPjUYGO+Ki0tjejoaMCW4K+99lr++OMPevfunddGe9asWSxbtozPP/8cgMOHD7N+/XpiY2MZO3YsmZmZDB8+nOjoaGbPns2qVavo378/ABkZGfTt2zfvfJdccgkAvXr1Ytq0wlN8KqUqVNqu/ES+fx4cXATZaXZdWGObxNveYBN63V4QUrH350riVwm+tCXt8uZZB++pevXqea+NMbz44osMGzbsmO3mzJnDN998w9VXX83EiROpU6cOQ4cO5aOPip4itmpVOz90cHAwWVmuTuupVOWSnQ6HlnpUtcyDFGewxqBQqNMzP5nXPxXCW7hWOi8Nv0rwvmzYsGG88sornHnmmYSGhrJu3ToiIyPZv38/kZGRjBs3jpSUFBISEvjXv/7FzTffzIYNG2jbti2pqals376d9u3bF3v8GjVqkJSUVIGfSKkAZwykbitY1XIoAXIy7Prw5rZ03uE2+1wnGoLDXA25rDTBl5PrrruOLVu20LNnT4wxNGjQgC+//JL4+HiefvppQkNDiYiI4N1336VBgwZMnTqVK664gvT0dAAeffTREhP8BRdcwMiRI5kxY4beZFXqRGSlwcHFBVu2pDmd64PDoG4MdJjgtGzpA+GR7sZbDnxqTtaYmBhTeMKP1atX06lTJ5ciUm7Tn786IcZAyuaCdeeHloJxqjwjWtsWLfX72qqWOt1tFYwfEpHFxpiYotZpCV4p5f8yj8DBhfnVLQfmwdG9dl1IdajXGzpNzK87D2vobrwVRBO8Usq/GAPJ6wrWnR9ebrv+A9TsAE3OyS+d1zoFgipnqqucn1op5T8yDtuOQ54tWzIO2XWhNW19ebP7bTKv1xuq1nM3Xh+iCV4p5TtyB+Da/2f+zdDDq7FzBoktjTcfkd+JqGZHr3TxDxSa4JVS7kk/YAfdyk3mBxZAptMcuEpdm8hbXA4N+kLdWKhSy914/YwmeKVUxcjJgsMrCrZsSV5n10kQ1O4GUVc5rVtOhRrtfLoTkT/QBF8KucMF5/ryyy+JiooqcttAGNb3oYceOuFxb5YuXcrOnTs599xzy7TfoEGDmDx5MjExRbb2Uv4obY9TMnceBxdCVopdF9bQVrG0vsbp4h8DoRHuxhuANMGXQnFDFRTlRIb1rYjhhivK0qVLWbRoUZkTvPJz2RmQ+FfB0nnKZrtOQqBOD2g9Nr9lS/UoLZ1XAL07cYKioqLYv38/AIsWLWLQoEFs2bKFV199leeee47o6Gh+++034uLi8gYgA4iIsKWU+Ph4zjjjDK688kq6du1KdnY2EydOJDY2lm7duvHaa68dN4bcYwF8/vnnxMXFARAXF8eECRPo168frVu3zjv/kSNHGDx4MD179qRr167MmDEjb//HHnuMDh06MGTIENauXZu3/PXXXyc2Npbu3bszYsQIUlNT884xfvx4Bg4cSPv27Zk5cyYZGRk8+OCDfPLJJ0RHR/PJJ5+QkpLC2LFjiY2NpUePHnnnTEtL4/LLL6dbt26MGjWKtLS0E/kxKLek7oCtX0DCnfDjAPi8FvzQGxZPgL2zoW5P6DEZhs6FS5Pg7AUQ8wJEXQERrTS5VxD/KsEvvt32RitPdaKh1/MlbuI5mmSrVq2YPn16kdtFRUUdM6zvm2++WexxSzPccO5olWW1a9cu5s6dy5o1a7jwwgsZOXIkYWFhTJ8+nZo1a7J//35OPfVULrzwQhISEvj4449ZsmQJWVlZ9OzZk169egF2ZMtx48YBcP/99/Pmm29y6623ArBlyxZmz57Nxo0bOeOMM9iwYQOPPPIIixYt4qWXXgLgvvvu48wzz+Stt94iMTGR3r17M2TIEF577TXCw8NZtmwZy5Yto2fPnif0OVUFyD4KB5cUbNmSut2uC6pqR1Bsd1N+y5bwZu7Gq/L4V4J3SVmqaMqiNMMNn2iCHz58OEFBQXTu3Jk9e/YAdsTL++67jzlz5hAUFMSOHTvYs2cPv/32GxdffHHeNIQXXnhh3nFWrFjB/fffT2JiIkeOHCkwWuZll11GUFAQ7dq1o3Xr1qxZs+aYOGbNmsVXX33F5MmTATh69Chbt25lzpw5TJgwAYBu3brRrVu3E/qcqpwZY0dPzJtW7k84tMSOgQ62aqXBgPxu/nW6Q3BVV0NWxfNagheRDsAnHotaAw8aY54/4YMep6RdkUJCQsjJsT3njh49WqrtjDFkZGTkrSvtcMPFEY+vuYVjyB1yOPfYAB988AH79u1j8eLFhIaGEhUVlbefFPOVOS4uji+//JLu3bszdepU4uPjizx/cccwxvDFF1/QoUOHEuNXLslKsQNwedadH91t1wVXs7MPdfw/J6H3gWpN3I1XlYnX6uCNMWuNMdHGmGigF5AKFF234YeioqJYvHgxAF988UXe8ho1apCcnFzkdjNmzCAzM7PI4+UON5y7ft26daSk2BYHHTt2LHKfRo0asXr1anJycoqtNvJ0+PBhGjZsSGhoKL/++it//23HuT7ttNOYPn06aWlpJCcn8/XXX+ftk5ycTJMmTcjMzOSDDz4ocLzPPvuMnJwcNm7cyKZNm+jQocMxn3/YsGG8+OKLef9klixZknfO3OOtWLGCZcuWHTd+dZKMgeQNsPk9WHgzfNcLPqsFP50OS++GxBXQeCjE/A/OToBLD8OQ2RD9BDQfrsndD1VUFc1gYKMx5u8KOp/XTZo0iWuvvZbHH3+cPn365C0vPKzvuHHjuOiii+jduzeDBw8uUGr3VNxww/v376e4ET+feOIJzj//fJo3b06XLl04cuRIiTFfddVVXHDBBcTExBAdHZ33j6Nnz56MGjWK6OhoWrZsWWAo4n//+9/06dOHli1b0rVr1wLJu0OHDpx++uns2bOHV199lbCwMM444wyeeOIJoqOjuffee3nggQe4/fbb6datG8YYoqKimDlzJjfeeCPXXHMN3bp1Izo6mt69e5f62qtSykx2uvjPy68/Tz9g14VE2C7+ne/JHx43rL678apyVyHDBYvIW0CCMealItZdD1wP0KJFi165pcpclX242JkzZ7Jp06a8+mpfERcXx/nnn8/IkSO9ep7K/vMvNZMDSWsLJvPEFdgu/kDNTvk3QeufCjU7Q1BgNM2t7FwdLlhEqgAXAvcWtd4YMwWYAnY8eG/H42/OP/98t0NQvijjEOxf4NGyZT5kJtp1obVtEm8+Ir/uvEptF4NVbqmIKppzsKX3PRVwLlVBpk6d6nYIlUdONhxe6dEr9E9IclosSRDU6gItL8tv2VKzvQ7ApYCKSfBXAEXPLq2UOtbRfXBgvsfwuAsgy7m/UrW+TeStrrbP9WIhtIa78Sqf5dUELyLhwFDgBm+eRym/lZMJicvzk/n+eXBkg10nwVC7O7Qak19/HtFae4GqUvNqgjfGpAI6+r5SudJ2eyTzP+HgIsh2hmkIa2yTeNtx9rluLwgJdzde5de0J6tS3pKdYXuBerZsSXFaiQWFQp2e0PaG/HlCw1to6VyVK03wpeDPwwXHx8czefJkZs6cWeZ9ExMT+fDDD7npppvKtN/JDDfst4yx47McmAf7nGR+MAFy0u368Oa2VN7hNlt3XrcHBIe5G7MKeJrgS6GyDhecmJjIyy+/XOYEXylkpdku/p4tW9J22nXBYXZ88w635nciCo90N15VKWlbqhPkC8MF33jjjcTExHDKKacwadKkvOXff/89HTt2ZMCAAUybNi1v+YIFC+jXrx89evSgX79+ecMCT506lYsuuoizzz6bDh068PDDDwNwzz33sHHjRqKjo5k4cSIATz/9dF6MnucsbrjhgGAMHNkEWz6ERbfC97HwWU34aSAsmWirYRoOgl4vwrCFdnjcob9Bj6eh+SWa3JVr/KoEf/v3t7N099JyPWZ042ieP/v5Erfx1eGCH3vsMerWrUt2djaDBw9m2bJltG/fnnHjxvHLL7/Qtm1bRo0albd9x44dmTNnDiEhIfz000/cd999eePo5MYSHh5ObGws5513Hk888QQrVqzI+/Yya9Ys1q9fz4IFCzDGcOGFFzJnzhyqV69e7HDDfi9lG8wdaZsqAoRUh3q9odPE/LrzsIbuxqhUMfwqwbvFV4cL/vTTT5kyZQpZWVns2rWLVatWkZOTQ6tWrWjXrh0Ao0ePZsqUKXnHHDNmDOvXr0dECgx8NnToUOrVsw2eLrnkEubOncvw4cMLnG/WrFnMmjWLHj16AHYCkfXr15OcnFzscMN+be9cmDvCVsf0fB4aDYJap0CQ/tko/+BXv6nHK2lXJLeHC968eTOTJ09m4cKF1KlTh7i4uOMO/fvAAw9wxhlnMH36dLZs2cKgQYPy1pV26N97772XG24o2K3h+eefD7yhf9e/BotvheqtYPBsqFX0iJ5K+TKtgz9Bbg8XnJSURPXq1alVqxZ79uzhu+++y9t28+bNbNy4EYCPPsrvRHz48GEiI219cOGhBn788UcOHjxIWloaX375Jf379y9y6N+33norb9TKHTt2sHfv3hKHG/Y72Rmw4EZYOB4aDYFh8zW5K7+lCf4ETZo0idtuu42BAwcWaAFzwQUXMH369LybrOPGjWP27Nn07t2b+fPnlzhccOfOnenZsyddunThhhtuICsrq9jhgrt3706PHj045ZRTGDt2LP379wcgLCyMKVOmcN555zFgwABatmyZt89dd93FvffeS//+/cnOzi5wvAEDBnD11VcTHR3NiBEjiImJoV69evTv358uXbowceJEzjrrLK688kr69u1L165dGTlyJMnJyQWGGx4xYkSB4Yb9Stoe+GUwbHjVDqN7+tc6SJfyaxUyXHBpxcTEmEWLFhVYVtmHi62I4YKnTp1aYB5VX1JhP/+Di2HOcDteep837eTQSvkBV4cLVidHhwuuAFs+hPnXQtWGMPR32wlJqQCgCV4RFxdHXFyc22FUvJxs+OseWD0ZGp4GAz7TJo8qoPhFgjfGBF4rDXVcXq0+zDgEv18Bu36AdjfZCd2DQr13PqVc4PMJPiwsjAMHDlCvXj1N8pWIMYYDBw4QFuaF8VoOr4LZF0Hq39B7ih29UakA5PMJvlmzZmzfvp19+/a5HYqqYGFhYTRr1qx8D7p9Bvwx2vZIHfwrNOhfvsdXyof4fIIPDQ0tsTenUqVicmDFo7B8kh0I7LTpEF7O/zyU8jFebQcvIrVF5HMRWSMiq0WkrzfPp1SRMo/A3Ettco+6GobM0eSuKgVvl+D/C3xvjBkpIlUAnZ5GVawjm2x9e9Iq6PksdLhdJ9VQlYbXEryI1AROA+IAjDEZQEZJ+yhVrnb/BHNHAQYGfQ9NhrodkVIVyptVNK2BfcDbIrJERN4QkWP66YvI9SKySEQW6Y1UVS6MgTXPwa/DoFoTO0a7JndVCXkzwYcAPYFXjDE9gBTgnsIbGWOmGGNijDExDRo08GI4qlLIPgrz4iDh/yDyIjjrT6jRxu2olHKFNxP8dmC7MWa+8/5zbMJXyjtSd8CPp8Hmd6HrwzDwcwit4XZUSrnGa3XwxpjdIrJNRDoYY9YCg4FV3jqfquT2/QG/jYCsIzBwOjQf7nZESrnO261obgU+cFrQbAKu8fL5VGW04Q1YdBOEt4Azf4Lap7gdkVI+wasJ3hizFChyGEulTlpOJiz+J6z/HzQ+C/p/BFXruh2VUj7D53uyKlWko/ts56W9s6HTndD9PzpXqlKF6F+E8j+HltrOS+l7oe/70OoqtyNSyifplH3Kv/z9CczqB+TA0Lma3FVAOJJxxCvH1RK88g852bDsflj1hB0BcsAXUK2R21EpVWaHjx5m8a7FLNyxkIU77SNIgth82+ZyP5cmeOX7MhLh9yth13fQ9gbo9QIEV3E7KqWOKy0zjaW7l+Yl8oU7FrL2wNq89a3rtKZvs77ENo0lx+QQJOVbqaIJXvm2w2tgzkV20LDYV6DdeLcjUqpIWTlZrNy7Mi+RL9y5kOV7l5OVkwVAk4gmxEbGMrrbaGKbxhLTNIZ64fW8GpMmeOW7dnwDf1wJQVVh8C/QcKDbESkFQI7JYcPBDQWqWZbsWkJaVhoAtcNqE9M0hon9JtI7sjexTWOJrBlZ4XFqgle+xxhY9R/4636o08NOzlG9hdtRqUrKGMOO5B0FkvnCHQs5nH4YgGoh1ejZpCc39LqB2MhYYpvG0rZuW5+YYlQTvPItWSkwbyxs/RRaXgl9XocQnUZAVZwDqQcKVLMs3LmQ3Ud2AxASFELXhl0ZdcooWzKPjKVzg86E+GgfDN+MSlVOR7bAnOFweDn0eBo63qGTcyivOpJxhIRdCXnJfMGOBWxOtK1ZBKFD/Q4MbT2U2KaxxEbGEt04mrAQL0wE7yWa4JVv2BMPc0fa5pCnfwtNh7kdkQow6VnpLNuzrEA1y+r9q8kxOQC0rNWS2MhYxseMJ7ZpLL2a9qJm1ZouR31yNMErdxkD616ChH9CjfZw2gyo2c7tqJSfy87JZs3+NSzYsSAvoS/bs4yMbDupXIPwBsRGxjKy88i80nnD6g1djrr8aYJX7slOh4U3waa3IPJC6PcehPp3iUlVPGMMmxM3F6gzX7xzMSmZKQDUqFKDmKYx3N7n9ryboC1qtfCJm6DepgleuSNtF8y5BA7Mgy4PQtdJUM6dPFRg2n1kNwt3LMwrnS/auYgDaQcAqBpclejG0VwTfU1eMu9Qv0O5dyDyFyUmeBEJA84HBgJNgTRgBfCNMWal98NTAWn/fPjtEsg8DAO/gOaXuB2R8lGJRxNZtHNRgdL59qTtAARJEF0admF4x+F51SxdGnahivZyzlNsgheRh4ALgHhgPrAXCAPaA084yf8OY8wy74epAsamqbDgBqgWaedLrd3V7YiUj0jNTGXJriUFboKuP7g+b33bum0Z2GIgsU1j6R3Zmx5NehAeqk1oS1JSCX6hMeahYtY9KyINgRJ7n4jIFiAZyAayjDE6+UdllZMFS+6Etf+FRoNhwCdQ1bvdtJXvyszOZMXeFQXam6/Yu4Jskw1AZI1IYiNjiYuOy+vWX6daHZej9j/FJnhjzDcl7WiM2Yst1R/PGcaY/WUNTAWQ9AMw9zLY8wt0+Cf0eEon56hEckwO6w6sK1DNsnT3Uo5mHQWgTlgdYiNjuaD9BXn15k1qNHE56sBwvDr4McBtQAdn0WrgBWPMu94OTAWIQ8ts56W0nXDqO9D6H25HpLzIGMO2pG0FOg4t3rWYpPQkAKqHVqdnk57cFHNTXjJvXad1pWjR4oaS6uD/AdwO/B+QAAjQE3haRChlkjfALBExwGvGmClFnOd64HqAFi10vJGAsvVz+HMMVKkNQ+ZA/d5uR6TK2b6Ufcd069+bYr/YhwaF0r1xd67qelXeTdBO9TsRHBTsctSVhxhjil4hMg+43BizpdDyKOBjY8ypxz24SFNjzE6nvv5H4FZjzJzito+JiTGLFi0qQ/jKZ214AxaMg/p9bUuZavqV298lpyfnTVSxYOcCFu5YyN+H/wZst/5ODTrZRO4k8+6NulM1pKrLUQc+EVlc3P3NkqpoahZO7gDGmC0iUqreKMaYnc7zXhGZDvQGik3wKkCkbIWE2+3N1EHfQLD+kfubo1lH+Wv3XwVatKzZvwaDLRC2qt2KPs36cEvvW4htGkvPJj2pUbWGy1GrwkpK8GknuA4AEakOBBljkp3XZwGPlDE+5W+Msb1TjYE+b2hy9wNZOVms3rc6r8584c6FLN+znMycTAAaVW9EbGQsl3e5PK90Xj+8vstRq9IoKcF3EpGi2rgL0LoUx24ETHdunoQAHxpjvi97iMqvbP0Udn4DPZ+FiCi3o1GFGGPYeGhjgTrzhF0JpGamAlCrai1imsZwR9878m6CNqvZTG+C+qkSE/zJHNgYswnofjLHUH4m/SAsngB1Y6H9BLejUcDO5J22VO4k9EU7F3Ho6CEAwkLC6NG4B9f1uC5vbPO2ddtW2m79gaikdvB/e74XkXrAacBWY8xibwem/NCSO22b9zNmgbaUqHAH0w4e061/Z/JOAIIlmK6NuhYYPfGUBqcQGhzqctTKm0pqJjkTuMcYs0JEmmCbSi4C2ojIFGPM8xUUo/IHu3+GTW9D53ugjn5x87aUjBQ7UYXHTdCNhzbmrW9frz1nRJ2RNx9odONoqoVWczFi5YaSqmhaGWNWOK+vAX40xvxDRGoAvwPPezs45SeyUu34MhFt7ciQqlxlZGewfM/yAu3NV+5bmTdRRfOazYmNjOW6ntflTVRRO6y2u0Ern1BSgs/0eD0YeB3AaRWT49WolH9Z/jAc2QiDf4EQLSWejByTw5r9awpUs/y1+y/Ss9MBqB9en9imsVzc8eK8m6CNIhq5HLXyVSUl+G0iciuwHduD9XsAEakGaMWdsg4ugTXPQJtrodEZbkfjV4wx/H347wLJfNHORRzJOAJARJUIejXpxa29b81L5lG1o7RFiyq1khL8tdh260OAUcaYRGf5qcDbXo5L+YOcLJh/HVStbyfJViXac2TPMd3696facfiqBFchunE0Y7qPybsJ2qFeB+3Wr05KSa1o9gLji1j+K/CrN4NSfmLtf+FQAgz4FKroUK6eDh89fEy3/m1J2wA7UUXnBp3t6IlOMu/WqJtOVKHKXUmtaL4GPAeqMcB+4FdjzPveDkz5uCObYNkDEHkBNB/pdjSuSstMY+nupQVatKw9sDZvfZs6bejfon/eOC09mvQgokqEixGryqKkKprJRSyrC4wWkS7GmHu8FJPydcbAgvEgIRD7MlSiOuGsnCxW7l2Z16U/d6KKrJwsAJpENCE2MpbR3UbTO7I3MU1jqFutrstRq8qqpCqa2UUtF5GvgMWAJvjKavN7sPtHiHkJwpu5HY3Xzd4ym+lrprNw50KW7FpCWpYdiql2WG1im8ZyV7+78m6CRtaMdDlapfKVeVodY0y23sWvxI7uhYR/2mGA293odjRelZyezB2z7uD1hNepFlKNnk16Mj5mfF69eZs6bbRFi/JpJdXBF/W9sg7wD2Cl1yJSvm3xPyErGXq/DgE8Zskvm39h7IyxbD28lYn9JvLIGY8QFhLmdlhKlUlJJfjF2BuruUWU3Jus8UBgF91U0XZ+B39/aHur1j7F7Wi8IiUjhbt/upv/Lfwf7eq2Y+7YufRr3s/tsJQ6ISXVwbeqyECUj8s8AgtvhJod4ZT73I7GK+ZunUvcl3FsPLSR2/rcxuODHyc8NNztsJQ6YcV+xxaRASXtKCI1RaRL+YekfNKyByDlb1s1E2CTeKRlpnHHD3dw2tunkWNyiB8Tz/NnP6/JXfm9kqpoRojIU9ghChYD+4AwoC1wBtASuMPrESr37V8A616wN1Ublvh/3+/M3z6fuBlxrNm/hvG9xvP0WU9rG3UVMEqqovmniNQBRgKXAk2wU/WtBl4zxswtzQlEJBg7zPAOY8z5Jx+yqlA5mbDgOghrDN3/43Y05SY9K52HZz/Mk78/SWSNSGaNnsXQNkPdDkupclViM0ljzCHsKJKvn8Q5bsP+UyjVRN3Kx6yeDInL4bQvoUott6MpFwm7Ehjz5RhW7F3B2OixPDvsWWqFBcZnU8qTV9u5iUgz4DzgDW+eR3lJ0jo7FHDzEdDsIrejOWmZ2Zk8FP8Qfd7ow4HUA8y8YiZvXvSmJncVsMrc0amMngfuAmoUt4GIXA9cD9CiRQsvh6NKzeTAgushOAxiXnQ7mpO2fM9yxnw5hiW7lzC622heOPsF6lTTAdJUYDtuCV5EjmkyUdSyIrY5H9h7vPlbjTFTjDExxpiYBg0aHO+wqqJsfAv2zrbDAFdr4nY0JywrJ4v//PYfek3pxY7kHUwfNZ33Ln5Pk7uqFEpTgv8TO+HH8ZYV1h+4UETOxba+qSki7xtjRpc9TFWh0nbBkonQ8DQ7kYefWr1vNXEz4liwYwGXdr6Ul897mfrh9d0OS6kKU9JQBY2BSKCaiPQgv0drTeC4DYSNMfcC9zrHGgTcqcndTyyaANlp0HuKXw5HkJ2TzfPznudfv/yLiCoRfDLyEy475TK3w1KqwpVUgh8GxAHNgGfIT/BJQGB2ZVSwfQZs+xy6PQo1O7gdTZltOLiBuC/j+H3b71zU4SJePf9VGkc0djsspVxRUjv4d4B3RGSEMeaLkzmJMSYeO4aN8mWZSbDwZqjdFTpNdDuaMskxOfxvwf+4+6e7qRpSlfcufo+rul6loz2qSq003797iUjt3DciUkdEHvVeSMo1S++FtJ3OcAT+M33clsQtDHl3CBO+n8CgqEGsuHEFo7uN1uSuKr3SJPhzPCbczu38dK7XIlLu2Pc7rH8ZOkyA+n3cjqZUjDFMWTyFrq90ZdHORbxxwRt8c+U3OumGUo7StKIJFpGqxph0ABGpBgTWaFOVXXY6zB8H4S1s3bsf2J60neu+uo4fNv7AkNZDePPCN2lRS/tRKOWpNAn+feBnEXkbOyb8WOAdr0alKtaqJyBpNQz6FkJ9e6AtYwzv/PUOt39/O1k5Wbx87suMjxmv1TFKFeG4Cd4Y85SILAcGY1vS/NsY84PXI1MV4/AqWPkYtLwCmp7jdjQl2pW8i+tnXs/MdTM5reVpvH3R27Su09rtsJTyWaUaqsAY8x3wnZdjURXN5NiqmZAa0Ot5t6MpljGGj1d8zM3f3kxaVhrPDXuOCX0mEOSHbfSVqkjHTfAicirwItAJqAIEAynGGB0d0t+tfxX2/wGnToWwhm5HU6S9KXu56Zub+GL1F5za7FTeGf4O7eu1dzsspfxCaUrwLwGXA58BMdhJt9t6MyhVAVK3w9J7oPEQaPUPt6Mp0hervuDGb27kcPphnhzyJHf0vYPgoGC3w1LKb5S2imaDiAQbY7KBt0XkDy/HpbzJGNuhyWRB7KvgYzcoD6Qe4NbvbuWjFR/Rq0kv3hn+Dqc0DMxJvpXyptIk+FQRqQIsdabw2wVU925Yyqu2fQE7voLop6BGG7ejKeDrtV9z/czrOZB6gH+f8W/u7n83ocGhboellF8qzV2qq53tbgFSgObACG8GpbzE5MDal2BeHNTpAR3/6XZEeRKPJhL3ZRwXfnwhDas3ZOG4hdx/2v2a3JU6CaVpJvm3U4KPAqYBa40xGd4OTJWzpHUw/1rYNxeaDIM+b0CQt+d7KZ0fNvzAdV9fx67kXdw/8H4eOP0BqvjRUAlK+arStKI5D3gV2IhtB99KRG5wmk4qX5eTDWufg2UPQFAYnPo2tBrjE/XuyenJ3DHrDl5PeJ3ODToz7bJpxEbGuh2WUgGjNEW4Z4AzjDEbAESkDfAN2i7e9yWuhPlj4cACiLwQYl+B8KZuRwXAr5t/5ZoZ17AtaRt39buLh894mLCQMLfDUiqglCbB781N7o5NwF4vxaPKQ04mrHoKVjwCoTWg30fQcpRPlNpTMlK456d7eGnhS7Sr246518ylb/O+boelVEAqTYJfKSLfAp9ix6K5FFgoIpcAGGOmeTE+VVaHlsK8a+xzi1EQ84LPdGL6fevvxM2IY8PBDdzW5zYeH/w44aHHnRxMKXWCSpPgw4A9wOnO+31AXeACbMIvMsGLSBgwBzvyZAjwuTFm0skGrIqRnQ4rHrUDh1WtDwOnQfOL3Y4KgLTMNB749QGe/fNZompHET8mntOjTj/+jkqpk1KaVjTXnOCx04EzjTFHRCQUmCsi3xlj5p3g8VRx9s+3de2HV9kbqD2fhap13Y4KgPnb5xM3I441+9dwY8yNPDX0KSKq+PaIlUoFitK0omkF3IptJpm3vTHmwpL2M8YY4IjzNtR5mBMNVBUhKw2WPwhrnoVqTe1wvz4yImR6VjoPz36YJ39/ksgakfx49Y8MaT3E7bCUqlRKU0XzJfAm8DWQU5aDi0gwsBg7ds3/jDHzi9jmeuB6gBYtdMKGUjEG9sbDghsgeT20vQF6PAWhvjH+W3ZONqM+H8WMtTO4tse1PHPWM9QKq+V2WEpVOqVJ8EeNMS+cyMGdsWuinTldp4tIF2PMikLbTAGmAMTExGgJvyjGwJFNsOeX/MfRvVC9FZz5MzQ+0+0IC7j7p7uZsXYG/z37v0zoM8HtcJSqtEqT4P8rIpOAWdh6dQCMMQmlPYkxJlFE4oGzgRXH2VwBpO4smNBT/rbLqzWBxkOh0Zm26WOIbw0LNGXxFJ758xluib1Fk7tSLitNgu+KHY/mTPKraIzzvlgi0gDIdJJ7NWAI8ORJxBr4slJtj9Od30LSGrusSh1odAZ0mgiNBkPNDj7Rnr0oP236iZu+uYlz2p7Dc2c/53Y4SlV6pUnwFwOtT2D8mSbAO049fBDwqTFmZlkDrFTWPm9vmDY5G9pcaxN6ne7gBzMXrd63mpGfjqRzg858PPJjQnxknBulKrPS/BX+BdSmjL1XjTHLgB4nEFPllJkMq5+BpufBIP/6P7gvZR/nfXgeYSFhzLxyJjWr+sbNXqUqu9Ik+EbAGhFZSME6+BKbSaoyWvc/yDgIXf2rL9jRrKMM/2Q4u47sYnbcbFrU0pZQSvmK0iR4/8o4/ijzCKyZDE3OgXr+M5qiMYZrv7qWP7b9wWeXfkbvyN5uh6SU8lCanqyzKyKQSm39y5B+ALo+6HYkZfLI7Ef4cPmHPH7m44zsPNLtcJRShRSb4EUkmaJ7ngq2o6pWtJaHrBRYPRkanwX1T3U7mlL7cPmHPDT7IeKi47hnwD1uh6OUKkKxCd4YU6MiA6m01r8C6fv8qu79962/c82Mazi95em8dv5riI8221SqsvP99neBLCsVVj8NjYdAg35uR1Mqmw5tYvgnw2lZqyVfXPaFTq2nlA/TBO+m9a/aIQe6+EfpPfFoIud9eB7ZOdl8c+U31Auv53ZISqkSaG8Ut2Slwuqn7JADDQe4Hc1xZWZnculnl7Lx4EZ+vPpH2tVr53ZISqnj0ATvlg1T4OgeGPCp25EclzGGW769hZ82/cTbF72tk3Uo5Se0isYNWWmw6kloOAganuZ2NMf13LznmJIwhXsH3EtcdJzb4SilSklL8G7Y+Doc3Q39P3I7kuOasWYGd866k5GdR/LomY+6HY5Sqgy0BF/Rso/a0nuDgdDQt6s6EnYlcOW0K4lpGsM7w98hyA8GPVNK5dMSvDdlJMLBBDvrUvJ6SF5n501N2wl93/XZYX8BdiTt4IKPLqB+eH2+uuIrwkPD3Q5JKVVGmuC9JTMZvu0Kqdvt++AwiGgDtbtBh9tt6xkfdSTjCBd8dAFJ6Un8PvZ3Gkc0djskpdQJ0ATvLWuetcm93wfQYACEN/OLcd2zc7K5atpV/LXnL76+4mu6NermdkhKqROkCd4bju6z48s0HwFRV7odTZnc/dPdfLX2K14850XObXeu2+EopU6C14qUItJcRH4VkdUislJEbvPWuXzOyschOxW6+VerE8/5VG/pfYvb4SilTpI3S/BZwB3GmAQRqQEsFpEfjTGrvHhO96X8bYf/bX0N1OrodjSlpvOpKhV4vFaCN8bsMsYkOK+TgdVApLfO5zOWPwSI34wvA/nzqXZq0EnnU1UqgFTIXT8RicLOzzq/iHXXi8giEVm0b9++igjHexJXwuZ3ocOtUL2529GUSoH5VK/Q+VSVCiReT/AiEgF8AdxujEkqvN4YM8UYE2OMiWnQoIG3wyl/WamQthuS1sPSeyAkAjr7xwQYnvOpfnXFV7Ss3dLtkJRS5cir38VFJBSb3D8wxkzz5rlcse5lWHRzwWXdHoWqvj+MrjGGsTPG6nyqSgUwryV4sdP8vAmsNsY8663zuGrXD7Z9e+d7IbQGVG0ITYa6HVWpPDL7ET5a8ZHOp6pUAPNmCb4/cDWwXESWOsvuM8Z868VzVqxDCXY8mfY3uR1JqRljeOevd3ho9kOM6T5G51NVKoB5LcEbY+ZiJ+gOTEf32p6qdXq6HUmpxW+JZ1L8JOb8PYfTW57OlAum6HyqSgUwbQ93og4m2Oe6vp/gZ2+ZzUOzHyJ+SzxNIprwwtkvMK7XOJ1PVSmXpKfD4cP5j8xM6Nu3/M+jCf5EHXISfJ1oV8MoyW9//8ak+En8uuVXGkc05r9n/5fre11PWEiY26Ep5beysiApCRITCybpsrw/erTgMRs1gt27yz9WTfAn6mCCHR2ySm23IznG71t/Z1L8JH7e/DONqjfiuWHPcUOvG6gWWs3t0JRyVU4OJCeXPSF7vk9JOf55wsOhdm2oVcs+6tSBVq3y39eqVXB93bre+bya4E/UwQSoF+N2FAX8se0PHop/iB83/UjD6g155qxnGB8zXsdyVwHBGJtcS5OMi9smOdkepyRVqx6bhJs2PTYpF36f+7pmTQgN9e61KC1N8Cci4xCkbIa217sdCQDzts9jUvwkZm2cRYPwBkweOpkbY2/UxK58hjG2WuJEqjNy3yclQXZ2yecJCTk2AbdpU3RCLup9rVoQFkA1mJrgT8TBJfbZ5RusC3YsYFL8JL7f8D31w+vz1JCnuCn2JqpXqe5qXCrwZGSceH1z7vvMzJLPIXJssm3eHLp0KX2CDg/36YnSKpwm+BORd4O1h9dPlZqZyo6kHWxP2s6OZPu8PWk7K/etJH5LPPXD6/PkkCe5KfYmIqpEeD0e5X+ys0tXfVHS+7S0458nIqJgwm3YENq3Lzkhe76PiIAg358Tx69ogj8RBxMgvDmEle/YOYlHE3k4/mHWHlibl8gPHT10zHa1w2rTvGZz/jP4P9zS+xZN7AEsJweOHDm5FhtHjhz/PNWqFUy6tWtDy5bF1zMXVe8cHOy1y6BOkCb4E3EoodyrZ4wxjJ85ns9XfU73xt1pVacVA1sMJLJmJM1qNqNZzWZE1ogksmakJnQ/YQykpp5ci42kpOPfFAwNPTYJN25ctnrnKtolIiBpgi+rzGRIWgcty3cqvveXvc8nKz/hsTMf476B95XrsdXJ2bULtm07sQR9vJuCQUHHJtySmtMV9T4sTOudVdE0wZfVoSWAgbq9yu2Qmw5t4uZvb2Zgi4Hc3f/ucjuuKhtjYMsWSEiwjyVL7POePcXvU7NmwYTbtCl07lz6BF29uiZn5T2a4Mtq53cgwVC/fPoVZ+VkMXraaIIkiPcufo/gIK3IrAjZ2bBuXcFkvmSJLXmDrU/u3BnOPht69IDWrfMTc+5zjRp6U1D5Nk3wZWEMbJsGDQdB1fLpevb4b4/z5/Y/+fCSD3XCDS/JyICVKwuWyv/6y9aPg+3Y0q0bjBplk3nPnrZpXjXt+Kv8nCb4skhaDcnroMNt5XK4P7f9ySOzH2F0t9Fc0fWKcjlmZZeSAsuWFUzmK1bkt8GOiLBJfNy4/GTesaPv9DxUqjxpgi+LbdPtc7PhJ32o5PRkRk8fTbOazXjpnJdO+niVUWJifhLPfV671jYtBKhXzybwf/7TPvfsaXs1arWKqiw0wZfFtmlQ71QIb3rSh5rw/QS2JG5hdtxsaoXVKofgAtuePcfe/Ny8OX99ZKRN4Jdemp/MmzXTG5iqcvPmlH1vAecDe40xXbx1ngqT8rdt/x791Ekf6tOVnzJ16VTuH3g/A1oMKIfgAocxsHXrscl81678bdq0gZgYW83Ss6etamnY0L2YlfJV3izBTwVeAt714jm8LyPRVs3s/Ma+b35xqXdNSk9iS+IWUjJSSMlMISUjhcPph7nt+9voHdmbB09/0Dsx+4mcHFi/vmAiT0iAQ07n3aAg6NQJhgzJry+PjrYtWJRSx+fNKfvmiEiUt45fIbIz4Nez4cB8kBBb916jbal2Xbp7KYPfHczBtIPHrKsTVocPLvmA0ODKc2cvMxNWrTq2JUtuN/oqVaBrVxgxIr+KpWtXO3iUUurEuF4HLyLXA9cDtGjRwuVoCvnrXpvc+74LLa+AoNJdrhV7VzDk3SFUD63Oy+e+TK2wWkRUiaB6aHWqV6lOk4gm1Khaw8vBu2vTJpg1K79Uvny5ba4ItnNPdDTExeUn806dtLu8UuXN9QRvjJkCTAGIiYk5zqgbFShxJax5FtrdBK2uLvVua/evZci7Q6gSXIVfxvxC27qlK/EHgq1b4dNP4ZNPYNEiu6xOHZvAJ0zIT+Zt2+rAVEpVBNcTvM9KWmWf295Q6l02HNzAme+eicFUmuS+axd89hl8/DH8+add1qsXPPUUXHyxvSGqLVmUcocm+OKk/G2fq5fcu9QYw5LdS/hq7Ve8nvA66VnpxMfF07F+xwoI0h379sEXX9ikPmeObfnSrRs89hhcdpktoSul3OfNZpIfAYOA+iKyHZhkjHnTW+crdylbIbQmVCm+ycZPm34i7ss4diTvQBD6Ne/HS+e+RJeG/t8qtLCDB2H6dFv98ssvdiyXjh3hwQdtF/9OndyOUClVmDdb0fh33/vUrRBe8k3ff/3yL0KCQph60VTObXcuDaqX7wQgbktKghkzbFKfNcu2hGndGu6+2yb1rl21+kUpX6ZVNMVJ+bvE6pkVe1ewYMcCnj3rWcZEj6nAwLwrJQVmzrTVL999B+np0KIF3HabTeq9emlSV8pfaIIvTsrWEocEfnvJ24QGhTK62+gKDMo70tJsMv/kE5vcU1OhSRO44Qa4/HLo00fHb1HKH2mCL0rmEcg4WGwJPiM7g3eXvcuFHS7022qZjAxb7fLJJ7YaJjkZGjSAMWNsSX3AAG3KqJS/0wRflNSt9rmYOviZ62ayP3U/Y3uMrcCgTtzBg3Zyi/Xr7WPtWpvcExNtO/XLLrNJ/YwzIER/I5QKGPrnXJQUJ8FXLzrBv7XkLSJrRDKszbAKDKpkSUn5CXz9+oIJ/aDHaAlBQdCyJVxwgU3qQ4dqD1KlApUm+KKU0AZ+R9IOvtvwHff0v8cr0+sdPGgna05NLflx5IgdLjc3iReeN7R5c2jXzg6f266dfbRvbyd0rlq13MNWSvkgTfBFSd1q510Na3LMqgd/fZAgCeLanteW2+mysuDrr+F//4Offy79fo0b26R9/vn5SbxdO9t7VAfpUkppgi8sMxl2/wLhzaBQCX3e9nm8tfQtJvabSOs6rU/6VHv2wBtvwGuvwbZtdoKKSZNsKTs8vORHtWpataKUKpkmeE85mfDTaZC4DGJfKbAqOyebm7+9maY1mvLAaQ+U6bDG2J6fmZn2sXy5La1//rl9P2QIvPCCLYnrTU6lVHnRdOJp09twaCn0/xhajgLgYNpBZqyZwbcbviVhVwIfjfioxKF+c3Jg3jw7ANe0aXYwrtwJnz3VqgU33QQ33ggdOnjp8yilKjVN8LkyDsHyh23nphaXAfDh8g+57fvb2J+6n9phtbkp5iZGnTIqb5fERNvc8MABe3N0xw746iv7XLUqDBsGV10FoaG2ZB4aah+NGtmRFqtXd+mzKqUqBU3wSevspB4bXoP0fTBwGojwx7Y/uGraVfSJ7MO3V35Lr6a9CJL87pwbNsA559jnXBERcOaZ8OSTthlizZoufB6llHJU7gS/9kVYPMG+DomAPm9C/T4AfLPuG0KCQph19SxqVi2Yqf/80yZwEfj+e+je3XYY0uaHSilfUnkTfHYGrHwcGgyE2JehZqcCrWZ+2PgDfZv1LZDcjx6F55+Hhx+2LV6++07HPldK+a7Km+D3/wlHd9vWMrULjt++L2UfCbsS+L8ej/D++7BmjR1l8csvYcsWuOgi27yxfn1XIldKqVKpvAl+7xxAoNHpx6z6efPPGAzP3nwWZrvt3l+9um3t8sYbMHhwxYerlFJl5dUELyJnA/8FgoE3jDFPePN8pWIMbPsCNrwKtbtBlToApGelsy1pGy/Nf5mPl32GHK1Dz8a9eOsbOOUUHVlRKeV/vDllXzDwP2AosB1YKCJfGWNWeeucx8jJhOQNcGQjJK4g/dBf7N31O3uTt7FbIvghfQQ/PXQpydn72Rn8BzmSAdmhsGkwEatu5fNpwURFVVi0SilVrrxZgu8NbDDGbAIQkY+Bi4ByT/Bd/x1BKhlkY8jxfIghG8g2kGngiPHc6wjwIhxuSVBqE6ruH0+d7I6c3vRchpzZkjMfRZO7UsqveTPBRwLbPN5vB/oU3khErgeuB2jRouQ5UIs9kTQik0yCTAhCMEESjJhgMFU4mlWPzJzaHM1qRBXTmBpBjagV0pBawY3o3bkJccNbEhqqc9AppQKPNxN8UVnTHLPAmCnAFICYmJhj1pfG9/dvPJHdlFIqoHlzps3tQHOP982AnV48n1JKKQ/eTPALgXYi0kpEqgCXA1958XxKKaU8eK2KxhiTJSK3AD9gm0m+ZYxZ6a3zKaWUKsir7eCNMd8C33rzHEoppYrmzSoapZRSLtIEr5RSAUoTvFJKBShN8EopFaDEmBPqW+QVIrIP+PsEd68P7C/HcPyZXouC9Hrk02tRUCBcj5bGmAZFrfCpBH8yRGSRMSbG7Th8gV6LgvR65NNrUVCgXw+tolFKqQClCV4ppQJUICX4KW4H4EP0WhSk1yOfXouCAvp6BEwdvFJKqYICqQSvlFLKgyZ4pZQKUH6f4EXkbBFZKyIbROQet+OpCCLylojsFZEVHsvqisiPIrLeea7jse5e5/qsFZFh7kTtHSLSXER+FZHVIrJSRG5zlle66yEiYSKyQET+cq7Fw87ySnctPIlIsIgsEZGZzvvKcz2MMX77wA5DvBFoDVQB/gI6ux1XBXzu04CewAqPZU8B9ziv7wGedF53dq5LVaCVc72C3f4M5XgtmgA9ndc1gHXOZ6501wM7i1qE8zoUmA+cWhmvRaHr8n/Ah8BM532luR7+XoLPm9jbGJMB5E7sHdCMMXOAg4UWXwS847x+BxjusfxjY0y6MWYzsAF73QKCMWaXMSbBeZ0MrMbOB1zproexjjhvQ52HoRJei1wi0gw4D3jDY3GluR7+nuCLmtg70qVY3NbIGLMLbNIDGjrLK801EpEooAe25Fopr4dTHbEU2Av8aIyptNfC8TxwF5DjsazSXA9/T/Clmti7kqsU10hEIoAvgNuNMUklbVrEsoC5HsaYbGNMNHYO5N4i0qWEzQP6WojI+cBeY8zi0u5SxDK/vh7+nuB1Yu98e0SkCYDzvNdZHvDXSERCscn9A2PMNGdxpb0eAMaYRCAeOJvKey36AxeKyBZs9e2ZIvI+leh6+HuC14m9830FjHFejwFmeCy/XESqikgroB2wwIX4vEJEBHgTWG2MedZjVaW7HiLSQERqO6+rAUOANVTCawFgjLnXGNPMGBOFzQ2/GGNGU5muh9t3eU/2AZyLbTmxEfiX2/FU0Gf+CNgFZGJLHdcC9YCfgfXOc12P7f/lXJ+1wDlux1/O12IA9mv0MmCp8zi3Ml4PoBuwxLkWK4AHneWV7loUcW0Gkd+KptJcDx2qQCmlApS/V9EopZQqhiZ4pZQKUJrglVIqQGmCV0qpAKUJXimlApQmeOVVIpItIktFZIWIfJ3bTruE7YeLSOcKCq/MRCRGRF4o4z5bRKR+EctFRH4RkZrlF2GR5/9YRNp58xzKN2mCV96WZoyJNsZ0wQ6QdvNxth+OHdWv1EQk5ARjKxMRCTHGLDLGTCinQ54L/GVKHlrhpIhIMPAKdjwWVcloglcV6U+cwZtEpI2IfC8ii0XkNxHpKCL9gAuBp51SfxsRiReRGGef+k63c0QkTkQ+E5GvgVnO+2nOMdeLyFNFBeCUpp90xk1fICJtneUNROQLEVnoPPo7yx8SkSkiMgt4V0QGFRpX/EsRWSYi80Skm7O8nojMcsYgf42ixzgBuAqnF6WI/Fucseyd94+JyATn9UQnpmXijPHuLP/SuX4rReR6j+VHROQREZkP9AV+A4ZU1D9C5UPc7mmlj8B+AEec52DgM+Bs5/3PQDvndR9sN3KAqcBIj/3jgRjndX1gi/M6DtuLt67H+01ALSAM+BtoXkQ8W3B6PAP/IL9344fAAOd1C+zQBwAPAYuBas77QR77vAhMcl6fCSx1Xr9Afi/S87A9besXEcvfQA3ndRSQ4LwOwvamrAechZ0YWpzlM4HTnO1yP3s1bM/Ves57A1xW6Fw/Ar3c/n3QR8U+9D+68rZqzvC1UdhE+aMz8mM/4DM7lAxgJ1koqx+NMZ7j4v9sjDkMICKrgJYUHP4110cez885r4cAnT3iqSkiNZzXXxlj0oo4zgBgBIAx5hen5F4LOyHLJc7yb0TkUDHx1zV2DHuMMVtE5ICI9AAaAUuMMQdE5Cxskl/i7BOBHSNlDjBBRC52ljd3lh8AsrGDr3naCzTF/gxUJaEJXnlbmjEm2kl8M7F18FOBRGOHtT2eLPKrEsMKrUsp9D7d43U2xf9+myJeBwF9CydyJ+EXPk/e6hKOXZoxQLJEJMgYkztW+RvYbyKNgbc8zvEfY8xrheIahP2n1NcYkyoi8eRfn6PGmOxC5woDivonpQKY1sGrCuGUrCcAd2ITzWYRuRTyWpN0dzZNxk69l2sL0Mt5PbKcwhnl8fyn83oWcEvuBiISXYrjzMHWo+cm3P3G3jD1XH4OUKeY/ddip5vMNR07vG8s8IOz7AdgrPOtBxGJFJGG2KqoQ05y74idmq8k7YGVpfhMKoBoglcVxhizBDvn5eXYBHitiPyFTTy5Uy1+DEx0blC2ASYDN4rIH9g6+PJQ1bkBeRvwT2fZBCDGuZG5ChhfiuM8lLsP8AT5Q9A+DJwmIgnY6pWtxez/DbZOHwBjp538Ffg0twRujJmFvT/wp4gsBz7H/gP8Hghxzv1vYF5xQYpII+w3qV2l+EwqgOhokqpScVrhxBhj9vtALE2Ad40xQ533QUACcKkxZn05nuefQJIx5s3yOqbyD1qCV8olTon6dRGp6XTu2oC9UVxuyd2RSP4k06oS0RK8UkoFKC3BK6VUgNIEr5RSAUoTvFJKBShN8EopFaA0wSulVID6fwqJ+kZNs8hTAAAAAElFTkSuQmCC", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "combined_costben_disc = costben_disc.combine_measures(\n", " [\"Measure A\", \"Measure B\"],\n", @@ -1274,6 +887,7 @@ }, { "cell_type": "markdown", + "id": "a00fe82b", "metadata": {}, "source": [ "## Conclusion\n", @@ -1286,54 +900,11 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "Python [conda env:climada_env_dev]", "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.10" - }, - "latex_envs": { - "LaTeX_envs_menu_present": true, - "autoclose": false, - "autocomplete": true, - "bibliofile": "biblio.bib", - "cite_by": "apalike", - "current_citInitial": 1, - "eqLabelWithNumbers": true, - "eqNumInitial": 1, - "hotkeys": { - "equation": "Ctrl-E", - "itemize": "Ctrl-I" - }, - "labels_anchors": false, - "latex_user_defs": false, - "report_style_numbering": false, - "user_envs_cfg": false - }, - "toc": { - "base_numbering": 1, - "nav_menu": {}, - "number_sections": true, - "sideBar": true, - "skip_h1_title": false, - "title_cell": "Table of Contents", - "title_sidebar": "Contents", - "toc_cell": false, - "toc_position": {}, - "toc_section_display": true, - "toc_window_display": false + "name": "conda-env-climada_env_dev-py" } }, "nbformat": 4, - "nbformat_minor": 4 + "nbformat_minor": 5 } diff --git a/doc/user-guide/climada_engine_Impact.ipynb b/doc/user-guide/climada_engine_Impact.ipynb index 150d76e0da..d1044a6724 100644 --- a/doc/user-guide/climada_engine_Impact.ipynb +++ b/doc/user-guide/climada_engine_Impact.ipynb @@ -170,22 +170,20 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Reminder: The exposures must be defined according to your problem either using CLIMADA exposures such as __[BlackMarble](https://climada-petals.readthedocs.io/en/stable/tutorial/climada_entity_BlackMarble.html)__, __[LitPop](climada_entity_LitPop.ipynb), [OSM](https://climada-petals.readthedocs.io/en/stable/tutorial/climada_exposures_openstreetmap.html)__, extracted from external sources (imported via csv, excel, api, ...) or directly user defined.\n", + "Exposures contain a geopandas dataframe with a geometry column, and a 'value' column of exposures (e.g. monetary value, population count etc.). They are either defined as a series of (latitude/longitude) points or as a raster of (latitude/longitude) points. Fundamentally, this changes nothing for the impact computations. Note that for larger number of points, consider using a raster which might be more efficient (computationally). For a low number of points, avoid using a raster if this adds a lot of exposures values equal to 0. \n", "\n", - "As a reminder, exposures are geopandas dataframes with at least columns 'latitude', 'longitude' and 'value' of exposures.\n", + "We shall here use a raster example.\n", "\n", - "For impact calculations, for each exposure values of the corresponding impact function to use (defined by the column `impf_`) and the associated hazard centroids must be defined. This is done after defining the impact function(s) and the hazard(s).\n", - "\n", - "See tutorials on __[Exposures](climada_entity_Exposures.ipynb)__ , __[Hazard](climada_hazard_Hazard.ipynb)__, __[ImpactFuncSet](climada_entity_ImpactFuncSet.ipynb)__ for more details." + "Reminder: The exposures must be defined according to your problem either using CLIMADA exposures such as __[BlackMarble](https://climada-petals.readthedocs.io/en/stable/tutorial/climada_entity_BlackMarble.html)__, __[LitPop](climada_entity_LitPop.ipynb), [OSM](https://climada-petals.readthedocs.io/en/stable/tutorial/climada_exposures_openstreetmap.html)__, extracted from external sources (imported via csv, excel, api, ...) or directly user defined." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Exposures are either defined as a series of (latitude/longitude) points or as a raster of (latitude/longitude) points. Fundamentally, this changes nothing for the impact computations. Note that for larger number of points, consider using a raster which might be more efficient (computationally). For a low number of points, avoid using a raster if this adds a lot of exposures values equal to 0. \n", + "For impact calculations, for each exposure values of the corresponding impact function to use (defined by the column `impf_`) and the associated hazard centroids must be defined. This is done after defining the impact function(s) and the hazard(s).\n", "\n", - "We shall here use a raster example." + "See tutorials on __[Exposures](climada_entity_Exposures.ipynb)__ , __[Hazard](climada_hazard_Hazard.ipynb)__, __[ImpactFuncSet](climada_entity_ImpactFuncSet.ipynb)__ for more details." ] }, { @@ -202,119 +200,121 @@ "name": "stdout", "output_type": "stream", "text": [ - "2023-01-26 11:57:14,980 - climada.entity.exposures.litpop.litpop - INFO - \n", + "2025-09-25 13:45:59,293 - climada.entity.exposures.litpop.litpop - INFO - \n", " LitPop: Init Exposure for country: CUB (192)...\n", "\n", - "2023-01-26 11:57:15,071 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2018. Using nearest available year for GPW data: 2020\n", - "2023-01-26 11:57:15,073 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n", - "2023-01-26 11:57:15,130 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2018. Using nearest available year for GPW data: 2020\n", - "2023-01-26 11:57:15,132 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n", - "2023-01-26 11:57:15,193 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2018. Using nearest available year for GPW data: 2020\n", - "2023-01-26 11:57:15,195 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n", - "2023-01-26 11:57:15,305 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2018. Using nearest available year for GPW data: 2020\n", - "2023-01-26 11:57:15,307 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n", - "2023-01-26 11:57:15,371 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2018. Using nearest available year for GPW data: 2020\n", - "2023-01-26 11:57:15,373 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n", - "2023-01-26 11:57:15,410 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n", - "2023-01-26 11:57:15,412 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2018. Using nearest available year for GPW data: 2020\n", - "2023-01-26 11:57:15,413 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n", - "2023-01-26 11:57:15,444 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n", - "2023-01-26 11:57:15,445 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2018. Using nearest available year for GPW data: 2020\n", - "2023-01-26 11:57:15,448 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n", - "2023-01-26 11:57:15,488 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n", - "2023-01-26 11:57:15,488 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2018. Using nearest available year for GPW data: 2020\n", - "2023-01-26 11:57:15,491 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n", - "2023-01-26 11:57:15,521 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n", - "2023-01-26 11:57:15,523 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2018. Using nearest available year for GPW data: 2020\n", - "2023-01-26 11:57:15,525 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n", - "2023-01-26 11:57:15,603 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2018. Using nearest available year for GPW data: 2020\n", - "2023-01-26 11:57:15,605 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n", - "2023-01-26 11:57:15,661 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2018. Using nearest available year for GPW data: 2020\n", - "2023-01-26 11:57:15,663 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n", - "2023-01-26 11:57:15,724 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2018. Using nearest available year for GPW data: 2020\n", - "2023-01-26 11:57:15,725 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n", - "2023-01-26 11:57:15,791 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2018. Using nearest available year for GPW data: 2020\n", - "2023-01-26 11:57:15,792 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n", - "2023-01-26 11:57:15,826 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n", - "2023-01-26 11:57:15,828 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2018. Using nearest available year for GPW data: 2020\n", - "2023-01-26 11:57:15,831 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n", - "2023-01-26 11:57:15,892 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2018. Using nearest available year for GPW data: 2020\n", - "2023-01-26 11:57:15,894 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n", - "2023-01-26 11:57:15,958 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2018. Using nearest available year for GPW data: 2020\n", - "2023-01-26 11:57:15,959 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n", - "2023-01-26 11:57:16,033 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2018. Using nearest available year for GPW data: 2020\n", - "2023-01-26 11:57:16,035 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n", - "2023-01-26 11:57:16,771 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2018. Using nearest available year for GPW data: 2020\n", - "2023-01-26 11:57:16,773 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n", - "2023-01-26 11:57:16,831 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2018. Using nearest available year for GPW data: 2020\n", - "2023-01-26 11:57:16,833 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n", - "2023-01-26 11:57:16,866 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n", - "2023-01-26 11:57:16,868 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2018. Using nearest available year for GPW data: 2020\n", - "2023-01-26 11:57:16,871 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n", - "2023-01-26 11:57:16,939 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2018. Using nearest available year for GPW data: 2020\n", - "2023-01-26 11:57:16,941 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n", - "2023-01-26 11:57:17,005 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2018. Using nearest available year for GPW data: 2020\n", - "2023-01-26 11:57:17,007 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n", - "2023-01-26 11:57:17,066 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2018. Using nearest available year for GPW data: 2020\n", - "2023-01-26 11:57:17,067 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n", - "2023-01-26 11:57:17,127 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2018. Using nearest available year for GPW data: 2020\n", - "2023-01-26 11:57:17,130 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n", - "2023-01-26 11:57:17,163 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n", - "2023-01-26 11:57:17,166 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2018. Using nearest available year for GPW data: 2020\n", - "2023-01-26 11:57:17,169 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n", - "2023-01-26 11:57:17,197 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n", - "2023-01-26 11:57:17,199 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2018. Using nearest available year for GPW data: 2020\n", - "2023-01-26 11:57:17,202 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n", - "2023-01-26 11:57:17,269 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2018. Using nearest available year for GPW data: 2020\n", - "2023-01-26 11:57:17,271 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n", - "2023-01-26 11:57:17,334 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2018. Using nearest available year for GPW data: 2020\n", - "2023-01-26 11:57:17,336 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n" + "2025-09-25 13:45:59,323 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2018. Using nearest available year for GPW data: 2020\n", + "2025-09-25 13:45:59,323 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n", + "2025-09-25 13:45:59,333 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2018. Using nearest available year for GPW data: 2020\n", + "2025-09-25 13:45:59,333 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n", + "2025-09-25 13:45:59,345 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2018. Using nearest available year for GPW data: 2020\n", + "2025-09-25 13:45:59,345 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n", + "2025-09-25 13:45:59,380 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2018. Using nearest available year for GPW data: 2020\n", + "2025-09-25 13:45:59,380 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n", + "2025-09-25 13:45:59,389 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2018. Using nearest available year for GPW data: 2020\n", + "2025-09-25 13:45:59,390 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n", + "2025-09-25 13:45:59,394 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n", + "2025-09-25 13:45:59,394 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2018. Using nearest available year for GPW data: 2020\n", + "2025-09-25 13:45:59,394 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n", + "2025-09-25 13:45:59,399 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n", + "2025-09-25 13:45:59,399 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2018. Using nearest available year for GPW data: 2020\n", + "2025-09-25 13:45:59,400 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n", + "2025-09-25 13:45:59,406 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n", + "2025-09-25 13:45:59,407 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2018. Using nearest available year for GPW data: 2020\n", + "2025-09-25 13:45:59,408 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n", + "2025-09-25 13:45:59,414 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n", + "2025-09-25 13:45:59,414 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2018. Using nearest available year for GPW data: 2020\n", + "2025-09-25 13:45:59,414 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n", + "2025-09-25 13:45:59,425 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2018. Using nearest available year for GPW data: 2020\n", + "2025-09-25 13:45:59,425 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n", + "2025-09-25 13:45:59,432 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2018. Using nearest available year for GPW data: 2020\n", + "2025-09-25 13:45:59,432 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n", + "2025-09-25 13:45:59,439 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2018. Using nearest available year for GPW data: 2020\n", + "2025-09-25 13:45:59,440 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n", + "2025-09-25 13:45:59,448 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2018. Using nearest available year for GPW data: 2020\n", + "2025-09-25 13:45:59,448 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n", + "2025-09-25 13:45:59,451 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n", + "2025-09-25 13:45:59,452 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2018. Using nearest available year for GPW data: 2020\n", + "2025-09-25 13:45:59,452 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n", + "2025-09-25 13:45:59,461 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2018. Using nearest available year for GPW data: 2020\n", + "2025-09-25 13:45:59,461 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n", + "2025-09-25 13:45:59,471 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2018. Using nearest available year for GPW data: 2020\n", + "2025-09-25 13:45:59,472 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n", + "2025-09-25 13:45:59,481 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2018. Using nearest available year for GPW data: 2020\n", + "2025-09-25 13:45:59,482 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n", + "2025-09-25 13:45:59,673 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2018. Using nearest available year for GPW data: 2020\n", + "2025-09-25 13:45:59,674 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n", + "2025-09-25 13:45:59,680 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2018. Using nearest available year for GPW data: 2020\n", + "2025-09-25 13:45:59,681 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n", + "2025-09-25 13:45:59,684 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n", + "2025-09-25 13:45:59,684 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2018. Using nearest available year for GPW data: 2020\n", + "2025-09-25 13:45:59,685 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n", + "2025-09-25 13:45:59,699 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2018. Using nearest available year for GPW data: 2020\n", + "2025-09-25 13:45:59,699 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n", + "2025-09-25 13:45:59,708 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2018. Using nearest available year for GPW data: 2020\n", + "2025-09-25 13:45:59,708 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n", + "2025-09-25 13:45:59,717 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2018. Using nearest available year for GPW data: 2020\n", + "2025-09-25 13:45:59,718 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n", + "2025-09-25 13:45:59,725 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2018. Using nearest available year for GPW data: 2020\n", + "2025-09-25 13:45:59,726 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n", + "2025-09-25 13:45:59,731 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n", + "2025-09-25 13:45:59,731 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2018. Using nearest available year for GPW data: 2020\n", + "2025-09-25 13:45:59,732 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n", + "2025-09-25 13:45:59,737 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n", + "2025-09-25 13:45:59,737 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2018. Using nearest available year for GPW data: 2020\n", + "2025-09-25 13:45:59,737 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n", + "2025-09-25 13:45:59,748 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2018. Using nearest available year for GPW data: 2020\n", + "2025-09-25 13:45:59,748 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n", + "2025-09-25 13:45:59,757 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2018. Using nearest available year for GPW data: 2020\n", + "2025-09-25 13:45:59,758 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n", + "2025-09-25 13:45:59,781 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2018. Using nearest available year for GPW data: 2020\n", + "2025-09-25 13:45:59,782 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n", + "2025-09-25 13:45:59,785 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n", + "2025-09-25 13:45:59,785 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2018. Using nearest available year for GPW data: 2020\n", + "2025-09-25 13:45:59,786 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n", + "2025-09-25 13:45:59,788 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n", + "2025-09-25 13:45:59,789 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2018. Using nearest available year for GPW data: 2020\n", + "2025-09-25 13:45:59,789 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n", + "2025-09-25 13:45:59,797 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2018. Using nearest available year for GPW data: 2020\n", + "2025-09-25 13:45:59,797 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n", + "2025-09-25 13:45:59,801 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n", + "2025-09-25 13:45:59,802 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2018. Using nearest available year for GPW data: 2020\n", + "2025-09-25 13:45:59,802 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n", + "2025-09-25 13:45:59,811 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2018. Using nearest available year for GPW data: 2020\n", + "2025-09-25 13:45:59,812 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n", + "2025-09-25 13:45:59,820 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2018. Using nearest available year for GPW data: 2020\n", + "2025-09-25 13:45:59,821 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n", + "2025-09-25 13:45:59,836 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2018. Using nearest available year for GPW data: 2020\n", + "2025-09-25 13:45:59,836 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n", + "2025-09-25 13:45:59,852 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2018. Using nearest available year for GPW data: 2020\n", + "2025-09-25 13:45:59,852 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n", + "2025-09-25 13:45:59,863 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2018. Using nearest available year for GPW data: 2020\n", + "2025-09-25 13:45:59,864 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n", + "2025-09-25 13:45:59,879 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2018. Using nearest available year for GPW data: 2020\n", + "2025-09-25 13:45:59,879 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n", + "2025-09-25 13:45:59,892 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2018. Using nearest available year for GPW data: 2020\n", + "2025-09-25 13:45:59,892 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n", + "2025-09-25 13:45:59,897 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n", + "2025-09-25 13:46:00,283 - climada.util.finance - INFO - GDP CUB 2018: 1.001e+11.\n", + "2025-09-25 13:46:00,313 - climada.util.finance - INFO - Income group CUB 2018: 3.\n", + "2025-09-25 13:46:00,320 - climada.entity.exposures.base - INFO - Hazard type not set in impf_\n", + "2025-09-25 13:46:00,321 - climada.entity.exposures.base - INFO - category_id not set.\n", + "2025-09-25 13:46:00,321 - climada.entity.exposures.base - INFO - cover not set.\n", + "2025-09-25 13:46:00,321 - climada.entity.exposures.base - INFO - deductible not set.\n", + "2025-09-25 13:46:00,321 - climada.entity.exposures.base - INFO - centr_ not set.\n", + "2025-09-25 13:46:00,322 - climada.entity.exposures.base - INFO - Hazard type not set in impf_\n", + "2025-09-25 13:46:00,322 - climada.entity.exposures.base - INFO - category_id not set.\n", + "2025-09-25 13:46:00,322 - climada.entity.exposures.base - INFO - cover not set.\n", + "2025-09-25 13:46:00,322 - climada.entity.exposures.base - INFO - deductible not set.\n", + "2025-09-25 13:46:00,322 - climada.entity.exposures.base - INFO - centr_ not set.\n" ] }, { - "name": "stdout", + "name": "stderr", "output_type": "stream", "text": [ - "2023-01-26 11:57:17,411 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2018. Using nearest available year for GPW data: 2020\n", - "2023-01-26 11:57:17,413 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n", - "2023-01-26 11:57:17,445 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n", - "2023-01-26 11:57:17,448 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2018. Using nearest available year for GPW data: 2020\n", - "2023-01-26 11:57:17,451 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n", - "2023-01-26 11:57:17,476 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n", - "2023-01-26 11:57:17,477 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2018. Using nearest available year for GPW data: 2020\n", - "2023-01-26 11:57:17,479 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n", - "2023-01-26 11:57:17,546 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2018. Using nearest available year for GPW data: 2020\n", - "2023-01-26 11:57:17,550 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n", - "2023-01-26 11:57:17,582 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n", - "2023-01-26 11:57:17,584 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2018. Using nearest available year for GPW data: 2020\n", - "2023-01-26 11:57:17,585 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n", - "2023-01-26 11:57:17,645 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2018. Using nearest available year for GPW data: 2020\n", - "2023-01-26 11:57:17,646 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n", - "2023-01-26 11:57:17,702 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2018. Using nearest available year for GPW data: 2020\n", - "2023-01-26 11:57:17,704 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n", - "2023-01-26 11:57:17,769 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2018. Using nearest available year for GPW data: 2020\n", - "2023-01-26 11:57:17,771 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n", - "2023-01-26 11:57:17,837 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2018. Using nearest available year for GPW data: 2020\n", - "2023-01-26 11:57:17,840 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n", - "2023-01-26 11:57:17,924 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2018. Using nearest available year for GPW data: 2020\n", - "2023-01-26 11:57:17,925 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n", - "2023-01-26 11:57:17,998 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2018. Using nearest available year for GPW data: 2020\n", - "2023-01-26 11:57:18,000 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n", - "2023-01-26 11:57:18,066 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2018. Using nearest available year for GPW data: 2020\n", - "2023-01-26 11:57:18,068 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n", - "2023-01-26 11:57:18,100 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n", - "2023-01-26 11:57:18,576 - climada.util.finance - INFO - GDP CUB 2018: 1.000e+11.\n", - "2023-01-26 11:57:18,671 - climada.util.finance - INFO - Income group CUB 2018: 3.\n", - "2023-01-26 11:57:18,692 - climada.entity.exposures.base - INFO - Hazard type not set in impf_\n", - "2023-01-26 11:57:18,694 - climada.entity.exposures.base - INFO - category_id not set.\n", - "2023-01-26 11:57:18,695 - climada.entity.exposures.base - INFO - cover not set.\n", - "2023-01-26 11:57:18,697 - climada.entity.exposures.base - INFO - deductible not set.\n", - "2023-01-26 11:57:18,699 - climada.entity.exposures.base - INFO - centr_ not set.\n", - "2023-01-26 11:57:18,700 - climada.entity.exposures.base - INFO - Hazard type not set in impf_\n", - "2023-01-26 11:57:18,702 - climada.entity.exposures.base - INFO - category_id not set.\n", - "2023-01-26 11:57:18,703 - climada.entity.exposures.base - INFO - cover not set.\n", - "2023-01-26 11:57:18,704 - climada.entity.exposures.base - INFO - deductible not set.\n", - "2023-01-26 11:57:18,706 - climada.entity.exposures.base - INFO - centr_ not set.\n" + "/Users/shuelsen/climada_install/climada_python/climada/util/finance.py:294: FutureWarning: Downcasting behavior in `replace` is deprecated and will be removed in a future version. To retain the old behavior, explicitly call `result.infer_objects(copy=False)`. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", + " dfr_wb = dfr_wb.replace(\n" ] } ], @@ -367,77 +367,58 @@ " \n", " \n", " value\n", - " geometry\n", - " latitude\n", - " longitude\n", " region_id\n", " impf_\n", + " geometry\n", " \n", " \n", " \n", " \n", " 0\n", " 1.077368e+05\n", - " POINT (-81.37500 21.70833)\n", - " 21.708333\n", - " -81.375000\n", " 192\n", " 1\n", + " POINT (-81.375 21.70833)\n", " \n", " \n", " 1\n", - " 1.671873e+06\n", - " POINT (-81.54167 21.62500)\n", - " 21.625000\n", - " -81.541667\n", + " 1.671874e+06\n", " 192\n", " 1\n", + " POINT (-81.54167 21.625)\n", " \n", " \n", " 2\n", - " 3.421208e+06\n", - " POINT (-82.95833 21.87500)\n", - " 21.875000\n", - " -82.958333\n", + " 3.421209e+06\n", " 192\n", " 1\n", + " POINT (-82.95833 21.875)\n", " \n", " \n", " 3\n", " 1.546590e+07\n", - " POINT (-82.87500 21.87500)\n", - " 21.875000\n", - " -82.875000\n", " 192\n", " 1\n", + " POINT (-82.875 21.875)\n", " \n", " \n", " 4\n", - " 7.168305e+07\n", - " POINT (-82.79167 21.87500)\n", - " 21.875000\n", - " -82.791667\n", + " 7.168308e+07\n", " 192\n", " 1\n", + " POINT (-82.79167 21.875)\n", " \n", " \n", "\n", "" ], "text/plain": [ - " value geometry latitude longitude region_id \\\n", - "0 1.077368e+05 POINT (-81.37500 21.70833) 21.708333 -81.375000 192 \n", - "1 1.671873e+06 POINT (-81.54167 21.62500) 21.625000 -81.541667 192 \n", - "2 3.421208e+06 POINT (-82.95833 21.87500) 21.875000 -82.958333 192 \n", - "3 1.546590e+07 POINT (-82.87500 21.87500) 21.875000 -82.875000 192 \n", - "4 7.168305e+07 POINT (-82.79167 21.87500) 21.875000 -82.791667 192 \n", - "\n", - " impf_ \n", - "0 1 \n", - "1 1 \n", - "2 1 \n", - "3 1 \n", - "4 1 " + " value region_id impf_ geometry\n", + "0 1.077368e+05 192 1 POINT (-81.375 21.70833)\n", + "1 1.671874e+06 192 1 POINT (-81.54167 21.625)\n", + "2 3.421209e+06 192 1 POINT (-82.95833 21.875)\n", + "3 1.546590e+07 192 1 POINT (-82.875 21.875)\n", + "4 7.168308e+07 192 1 POINT (-82.79167 21.875)" ] }, "execution_count": 2, @@ -513,27 +494,27 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "2023-01-26 11:57:25,332 - climada.hazard.tc_tracks - WARNING - `correct_pres` is deprecated. Use `estimate_missing` instead.\n", - "2023-01-26 11:57:26,701 - climada.hazard.tc_tracks - INFO - Progress: 10%\n", - "2023-01-26 11:57:26,905 - climada.hazard.tc_tracks - INFO - Progress: 20%\n", - "2023-01-26 11:57:27,075 - climada.hazard.tc_tracks - INFO - Progress: 30%\n", - "2023-01-26 11:57:27,229 - climada.hazard.tc_tracks - INFO - Progress: 40%\n", - "2023-01-26 11:57:27,390 - climada.hazard.tc_tracks - INFO - Progress: 50%\n", - "2023-01-26 11:57:27,542 - climada.hazard.tc_tracks - INFO - Progress: 60%\n", - "2023-01-26 11:57:27,713 - climada.hazard.tc_tracks - INFO - Progress: 70%\n", - "2023-01-26 11:57:27,876 - climada.hazard.tc_tracks - INFO - Progress: 80%\n", - "2023-01-26 11:57:28,032 - climada.hazard.tc_tracks - INFO - Progress: 90%\n", - "2023-01-26 11:57:28,197 - climada.hazard.tc_tracks - INFO - Progress: 100%\n", + "2025-09-25 13:46:15,137 - climada.hazard.tc_tracks - WARNING - `correct_pres` is deprecated. Use `estimate_missing` instead.\n", + "2025-09-25 13:46:16,551 - climada.hazard.tc_tracks - INFO - Progress: 10%\n", + "2025-09-25 13:46:16,581 - climada.hazard.tc_tracks - INFO - Progress: 20%\n", + "2025-09-25 13:46:16,608 - climada.hazard.tc_tracks - INFO - Progress: 30%\n", + "2025-09-25 13:46:16,636 - climada.hazard.tc_tracks - INFO - Progress: 40%\n", + "2025-09-25 13:46:16,664 - climada.hazard.tc_tracks - INFO - Progress: 50%\n", + "2025-09-25 13:46:16,692 - climada.hazard.tc_tracks - INFO - Progress: 60%\n", + "2025-09-25 13:46:16,720 - climada.hazard.tc_tracks - INFO - Progress: 70%\n", + "2025-09-25 13:46:16,748 - climada.hazard.tc_tracks - INFO - Progress: 80%\n", + "2025-09-25 13:46:16,779 - climada.hazard.tc_tracks - INFO - Progress: 90%\n", + "2025-09-25 13:46:16,807 - climada.hazard.tc_tracks - INFO - Progress: 100%\n", "num tracks hist: 60\n", - "2023-01-26 11:57:28,229 - climada.hazard.tc_tracks - INFO - Interpolating 60 tracks to 0.5h time steps.\n", - "2023-01-26 11:57:32,223 - climada.hazard.tc_tracks_synth - INFO - Computing 60 synthetic tracks.\n", + "2025-09-25 13:46:16,819 - climada.hazard.tc_tracks - INFO - Interpolating 60 tracks to 0.5h time steps.\n", + "2025-09-25 13:46:17,452 - climada.hazard.tc_tracks_synth - INFO - Computing 60 synthetic tracks.\n", "num tracks hist+syn: 120\n" ] } @@ -614,9 +595,16 @@ "Hint: computing the wind speeds in many locations for many tc tracks is a computationally costly operation. Thus, we should define centroids only where we also have an exposure." ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Hint: The operation of computing the windspeed in different location is in general computationally expensive. Hence, if you have a lot of tropical cyclone tracks, you should first make sure that all your tropical cyclones actually affect your exposure (remove those that don't). Then, be careful when defining the centroids. For a large country like China, there is no need for centroids 500km inland (no tropical cyclones gets so far)." + ] + }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 5, "metadata": { "ExecuteTime": { "end_time": "2020-10-20T15:09:42.732647Z", @@ -626,15 +614,12 @@ "outputs": [], "source": [ "# Define the centroids from the exposures position\n", - "lat = exp_lp.gdf[\"latitude\"].values\n", - "lon = exp_lp.gdf[\"longitude\"].values\n", - "centrs = Centroids.from_lat_lon(lat, lon)\n", - "centrs.check()" + "centrs = Centroids.from_exposures(exp_lp)" ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "metadata": { "ExecuteTime": { "end_time": "2020-10-20T15:09:51.496340Z", @@ -646,21 +631,18 @@ "name": "stdout", "output_type": "stream", "text": [ - "2023-01-26 11:58:43,439 - climada.hazard.centroids.centr - INFO - Convert centroids to GeoSeries of Point shapes.\n", - "2023-01-26 11:58:44,363 - climada.util.coordinates - INFO - dist_to_coast: UTM 32616 (1/3)\n", - "2023-01-26 11:58:44,606 - climada.util.coordinates - INFO - dist_to_coast: UTM 32617 (2/3)\n", - "2023-01-26 11:58:45,335 - climada.util.coordinates - INFO - dist_to_coast: UTM 32618 (3/3)\n", - "2023-01-26 11:58:45,767 - climada.hazard.trop_cyclone - INFO - Mapping 120 tracks to 1388 coastal centroids.\n", - "2023-01-26 11:58:46,030 - climada.hazard.trop_cyclone - INFO - Progress: 10%\n", - "2023-01-26 11:58:46,203 - climada.hazard.trop_cyclone - INFO - Progress: 20%\n", - "2023-01-26 11:58:46,499 - climada.hazard.trop_cyclone - INFO - Progress: 30%\n", - "2023-01-26 11:58:46,784 - climada.hazard.trop_cyclone - INFO - Progress: 40%\n", - "2023-01-26 11:58:47,231 - climada.hazard.trop_cyclone - INFO - Progress: 50%\n", - "2023-01-26 11:58:47,353 - climada.hazard.trop_cyclone - INFO - Progress: 60%\n", - "2023-01-26 11:58:47,546 - climada.hazard.trop_cyclone - INFO - Progress: 70%\n", - "2023-01-26 11:58:47,667 - climada.hazard.trop_cyclone - INFO - Progress: 80%\n", - "2023-01-26 11:58:48,102 - climada.hazard.trop_cyclone - INFO - Progress: 90%\n", - "2023-01-26 11:58:48,418 - climada.hazard.trop_cyclone - INFO - Progress: 100%\n" + "2025-09-25 13:46:31,179 - climada.util.coordinates - INFO - Sampling from /Users/shuelsen/climada/data/GMT_intermediate_coast_distance_01d.tif\n", + "2025-09-25 13:46:31,209 - climada.hazard.trop_cyclone.trop_cyclone - INFO - Mapping 120 tracks to 1388 coastal centroids.\n", + "2025-09-25 13:46:31,357 - climada.hazard.trop_cyclone.trop_cyclone - INFO - Progress: 10%\n", + "2025-09-25 13:46:31,430 - climada.hazard.trop_cyclone.trop_cyclone - INFO - Progress: 20%\n", + "2025-09-25 13:46:31,604 - climada.hazard.trop_cyclone.trop_cyclone - INFO - Progress: 30%\n", + "2025-09-25 13:46:31,742 - climada.hazard.trop_cyclone.trop_cyclone - INFO - Progress: 40%\n", + "2025-09-25 13:46:31,942 - climada.hazard.trop_cyclone.trop_cyclone - INFO - Progress: 50%\n", + "2025-09-25 13:46:32,010 - climada.hazard.trop_cyclone.trop_cyclone - INFO - Progress: 60%\n", + "2025-09-25 13:46:32,107 - climada.hazard.trop_cyclone.trop_cyclone - INFO - Progress: 70%\n", + "2025-09-25 13:46:32,178 - climada.hazard.trop_cyclone.trop_cyclone - INFO - Progress: 80%\n", + "2025-09-25 13:46:32,468 - climada.hazard.trop_cyclone.trop_cyclone - INFO - Progress: 90%\n", + "2025-09-25 13:46:32,639 - climada.hazard.trop_cyclone.trop_cyclone - INFO - Progress: 100%\n" ] } ], @@ -670,18 +652,6 @@ "tc.check()" ] }, - { - "cell_type": "markdown", - "metadata": { - "ExecuteTime": { - "end_time": "2020-10-16T12:28:38.751677Z", - "start_time": "2020-10-16T12:28:38.747250Z" - } - }, - "source": [ - "Hint: The operation of computing the windspeed in different location is in general computationally expensive. Hence, if you have a lot of tropical cyclone tracks, you should first make sure that all your tropical cyclones actually affect your exposure (remove those that don't). Then, be careful when defining the centroids. For a large country like China, there is no need for centroids 500km inland (no tropical cyclones gets so far)." - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -698,7 +668,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 7, "metadata": { "ExecuteTime": { "end_time": "2020-10-20T15:09:51.503229Z", @@ -726,7 +696,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 8, "metadata": { "ExecuteTime": { "end_time": "2020-10-20T09:59:49.321371Z", @@ -751,7 +721,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 9, "metadata": { "ExecuteTime": { "end_time": "2020-10-20T09:59:49.338296Z", @@ -763,10 +733,10 @@ "name": "stdout", "output_type": "stream", "text": [ - "2023-01-26 11:58:48,792 - climada.entity.exposures.base - INFO - category_id not set.\n", - "2023-01-26 11:58:48,792 - climada.entity.exposures.base - INFO - cover not set.\n", - "2023-01-26 11:58:48,792 - climada.entity.exposures.base - INFO - deductible not set.\n", - "2023-01-26 11:58:48,792 - climada.entity.exposures.base - INFO - centr_ not set.\n" + "2025-09-25 13:47:11,759 - climada.entity.exposures.base - INFO - category_id not set.\n", + "2025-09-25 13:47:11,760 - climada.entity.exposures.base - INFO - cover not set.\n", + "2025-09-25 13:47:11,760 - climada.entity.exposures.base - INFO - deductible not set.\n", + "2025-09-25 13:47:11,760 - climada.entity.exposures.base - INFO - centr_ not set.\n" ] }, { @@ -791,80 +761,61 @@ " \n", " \n", " value\n", - " geometry\n", - " latitude\n", - " longitude\n", " region_id\n", " impf_TC\n", + " geometry\n", " \n", " \n", " \n", " \n", " 0\n", " 1.077368e+05\n", - " POINT (-81.37500 21.70833)\n", - " 21.708333\n", - " -81.375000\n", " 192\n", " 1\n", + " POINT (-81.375 21.70833)\n", " \n", " \n", " 1\n", - " 1.671873e+06\n", - " POINT (-81.54167 21.62500)\n", - " 21.625000\n", - " -81.541667\n", + " 1.671874e+06\n", " 192\n", " 1\n", + " POINT (-81.54167 21.625)\n", " \n", " \n", " 2\n", - " 3.421208e+06\n", - " POINT (-82.95833 21.87500)\n", - " 21.875000\n", - " -82.958333\n", + " 3.421209e+06\n", " 192\n", " 1\n", + " POINT (-82.95833 21.875)\n", " \n", " \n", " 3\n", " 1.546590e+07\n", - " POINT (-82.87500 21.87500)\n", - " 21.875000\n", - " -82.875000\n", " 192\n", " 1\n", + " POINT (-82.875 21.875)\n", " \n", " \n", " 4\n", - " 7.168305e+07\n", - " POINT (-82.79167 21.87500)\n", - " 21.875000\n", - " -82.791667\n", + " 7.168308e+07\n", " 192\n", " 1\n", + " POINT (-82.79167 21.875)\n", " \n", " \n", "\n", "" ], "text/plain": [ - " value geometry latitude longitude region_id \\\n", - "0 1.077368e+05 POINT (-81.37500 21.70833) 21.708333 -81.375000 192 \n", - "1 1.671873e+06 POINT (-81.54167 21.62500) 21.625000 -81.541667 192 \n", - "2 3.421208e+06 POINT (-82.95833 21.87500) 21.875000 -82.958333 192 \n", - "3 1.546590e+07 POINT (-82.87500 21.87500) 21.875000 -82.875000 192 \n", - "4 7.168305e+07 POINT (-82.79167 21.87500) 21.875000 -82.791667 192 \n", - "\n", - " impf_TC \n", - "0 1 \n", - "1 1 \n", - "2 1 \n", - "3 1 \n", - "4 1 " + " value region_id impf_TC geometry\n", + "0 1.077368e+05 192 1 POINT (-81.375 21.70833)\n", + "1 1.671874e+06 192 1 POINT (-81.54167 21.625)\n", + "2 3.421209e+06 192 1 POINT (-82.95833 21.875)\n", + "3 1.546590e+07 192 1 POINT (-82.875 21.875)\n", + "4 7.168308e+07 192 1 POINT (-82.79167 21.875)" ] }, - "execution_count": 10, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -890,14 +841,54 @@ "source": [ "We are finally ready for the impact computation. This is the simplest step. Just give the exposure, impact function and hazard to the ImpactCalc.impact() method.\n", "\n", - "Note: we did not specifically assign centroids to the exposures. Hence, the default is used - each exposure is associated with the closest centroids. Since we defined the centroids from the exposures, this is a one-to-one mapping.\n", - "\n", "Note: we did not define an `Entity` in this impact calculations. Recall that `Entity` is a container class for __[Exposures](climada_entity_Exposures.ipynb)__, __[Impact Functions](climada_entity_ImpactFuncSet.ipynb)__, __[Discount Rates](climada_entity_DiscRates.ipynb)__ and __[Measures](climada_entity_MeasureSet.ipynb)__. Since we had only one Exposure and one Impact Function, the container would not have added any value, but for more complex projects, the Entity class is very useful." ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Assigning centroids manually" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It is best to create centroids from exposures directly as we have done above, to ensure `Hazard` and `Exposure` are matching well. However, sometimes you may have pre-defined `Hazard` and `Exposure` data. In those cases, it can make sense to manually match `Centroids` and `Exposure`.\n", + "\n", + "The matching could then look like this:\n", + "```\n", + "exp_lp.assign_centroids(tc, distance=\"euclidean\", threshold=0.1) # assign centroids manually\n", + "```\n", + "The `threshold` argument defines the maximum distance of the nearest neighbor in the units of the `Exposure` crs. Setting `threshold` to 0 disables nearest neighbor matching and enforces exact matching. By default twice the highest resolution of the hazard centroids is chosen.\n", + "\n", + "When assigning `Centroids` manually, make sure to pass `assign_centroids=False` to the impact calculation, so that the assignment is not lost:\n", + "```\n", + "imp = ImpactCalc(exp_lp, impf_set, tc).impact(\n", + " save_mat=False,\n", + " assign_centroids=False, # false, since we assign centroids manually\n", + ") \n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Assigning centroids automatically" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If we do not specifically assign centroids to the exposures. Hence, the default (i.e. assign_centroids=True) is used - each exposure is associated with the closest centroids. Since we defined the centroids from the exposures above, this is a one-to-one mapping." + ] + }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 10, "metadata": { "ExecuteTime": { "end_time": "2020-10-20T09:59:49.351752Z", @@ -909,8 +900,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "2023-01-26 11:58:48,877 - climada.entity.exposures.base - INFO - Matching 1388 exposures with 1388 centroids.\n", - "2023-01-26 11:58:48,877 - climada.engine.impact_calc - INFO - Calculating impact for 4164 assets (>0) and 120 events.\n" + "2025-09-25 13:47:28,593 - climada.entity.exposures.base - INFO - Matching 1388 exposures with 1388 centroids.\n", + "2025-09-25 13:47:28,596 - climada.engine.impact_calc - INFO - Calculating impact for 4164 assets (>0) and 120 events.\n" ] } ], @@ -925,7 +916,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 11, "metadata": { "ExecuteTime": { "end_time": "2020-10-20T15:09:51.515128Z", @@ -955,11 +946,9 @@ " \n", " \n", " value\n", - " geometry\n", - " latitude\n", - " longitude\n", " region_id\n", " impf_TC\n", + " geometry\n", " centr_TC\n", " \n", " \n", @@ -967,51 +956,41 @@ " \n", " 0\n", " 1.077368e+05\n", - " POINT (-81.37500 21.70833)\n", - " 21.708333\n", - " -81.375000\n", " 192\n", " 1\n", + " POINT (-81.375 21.70833)\n", " 0\n", " \n", " \n", " 1\n", - " 1.671873e+06\n", - " POINT (-81.54167 21.62500)\n", - " 21.625000\n", - " -81.541667\n", + " 1.671874e+06\n", " 192\n", " 1\n", + " POINT (-81.54167 21.625)\n", " 1\n", " \n", " \n", " 2\n", - " 3.421208e+06\n", - " POINT (-82.95833 21.87500)\n", - " 21.875000\n", - " -82.958333\n", + " 3.421209e+06\n", " 192\n", " 1\n", + " POINT (-82.95833 21.875)\n", " 2\n", " \n", " \n", " 3\n", " 1.546590e+07\n", - " POINT (-82.87500 21.87500)\n", - " 21.875000\n", - " -82.875000\n", " 192\n", " 1\n", + " POINT (-82.875 21.875)\n", " 3\n", " \n", " \n", " 4\n", - " 7.168305e+07\n", - " POINT (-82.79167 21.87500)\n", - " 21.875000\n", - " -82.791667\n", + " 7.168308e+07\n", " 192\n", " 1\n", + " POINT (-82.79167 21.875)\n", " 4\n", " \n", " \n", @@ -1021,95 +1000,70 @@ " ...\n", " ...\n", " ...\n", - " ...\n", - " ...\n", " \n", " \n", " 1383\n", - " 1.496797e+06\n", - " POINT (-78.62500 22.54167)\n", - " 22.541667\n", - " -78.625000\n", + " 1.496798e+06\n", " 192\n", " 1\n", + " POINT (-78.625 22.54167)\n", " 1383\n", " \n", " \n", " 1384\n", - " 5.387835e+07\n", - " POINT (-78.37500 22.54167)\n", - " 22.541667\n", - " -78.375000\n", + " 5.387837e+07\n", " 192\n", " 1\n", + " POINT (-78.375 22.54167)\n", " 1384\n", " \n", " \n", " 1385\n", - " 4.077093e+06\n", - " POINT (-78.45833 22.45833)\n", - " 22.458333\n", - " -78.458333\n", + " 4.077095e+06\n", " 192\n", " 1\n", + " POINT (-78.45833 22.45833)\n", " 1385\n", " \n", " \n", " 1386\n", " 2.249377e+07\n", - " POINT (-78.37500 22.45833)\n", - " 22.458333\n", - " -78.375000\n", " 192\n", " 1\n", + " POINT (-78.375 22.45833)\n", " 1386\n", " \n", " \n", " 1387\n", - " 6.191982e+06\n", - " POINT (-78.29167 22.45833)\n", - " 22.458333\n", - " -78.291667\n", + " 6.191984e+06\n", " 192\n", " 1\n", + " POINT (-78.29167 22.45833)\n", " 1387\n", " \n", " \n", "\n", - "

1388 rows × 7 columns

\n", + "

1388 rows × 5 columns

\n", "" ], "text/plain": [ - " value geometry latitude longitude \\\n", - "0 1.077368e+05 POINT (-81.37500 21.70833) 21.708333 -81.375000 \n", - "1 1.671873e+06 POINT (-81.54167 21.62500) 21.625000 -81.541667 \n", - "2 3.421208e+06 POINT (-82.95833 21.87500) 21.875000 -82.958333 \n", - "3 1.546590e+07 POINT (-82.87500 21.87500) 21.875000 -82.875000 \n", - "4 7.168305e+07 POINT (-82.79167 21.87500) 21.875000 -82.791667 \n", - "... ... ... ... ... \n", - "1383 1.496797e+06 POINT (-78.62500 22.54167) 22.541667 -78.625000 \n", - "1384 5.387835e+07 POINT (-78.37500 22.54167) 22.541667 -78.375000 \n", - "1385 4.077093e+06 POINT (-78.45833 22.45833) 22.458333 -78.458333 \n", - "1386 2.249377e+07 POINT (-78.37500 22.45833) 22.458333 -78.375000 \n", - "1387 6.191982e+06 POINT (-78.29167 22.45833) 22.458333 -78.291667 \n", + " value region_id impf_TC geometry centr_TC\n", + "0 1.077368e+05 192 1 POINT (-81.375 21.70833) 0\n", + "1 1.671874e+06 192 1 POINT (-81.54167 21.625) 1\n", + "2 3.421209e+06 192 1 POINT (-82.95833 21.875) 2\n", + "3 1.546590e+07 192 1 POINT (-82.875 21.875) 3\n", + "4 7.168308e+07 192 1 POINT (-82.79167 21.875) 4\n", + "... ... ... ... ... ...\n", + "1383 1.496798e+06 192 1 POINT (-78.625 22.54167) 1383\n", + "1384 5.387837e+07 192 1 POINT (-78.375 22.54167) 1384\n", + "1385 4.077095e+06 192 1 POINT (-78.45833 22.45833) 1385\n", + "1386 2.249377e+07 192 1 POINT (-78.375 22.45833) 1386\n", + "1387 6.191984e+06 192 1 POINT (-78.29167 22.45833) 1387\n", "\n", - " region_id impf_TC centr_TC \n", - "0 192 1 0 \n", - "1 192 1 1 \n", - "2 192 1 2 \n", - "3 192 1 3 \n", - "4 192 1 4 \n", - "... ... ... ... \n", - "1383 192 1 1383 \n", - "1384 192 1 1384 \n", - "1385 192 1 1385 \n", - "1386 192 1 1386 \n", - "1387 192 1 1387 \n", - "\n", - "[1388 rows x 7 columns]" + "[1388 rows x 5 columns]" ] }, - "execution_count": 12, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -1127,7 +1081,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 12, "metadata": { "ExecuteTime": { "end_time": "2020-10-20T09:59:49.355854Z", @@ -1139,7 +1093,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Aggregated average annual impact: 563366225.0 $\n" + "Aggregated average annual impact: 257456287.0 $\n" ] } ], @@ -1174,7 +1128,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 13, "metadata": { "ExecuteTime": { "end_time": "2020-10-20T10:01:57.068990Z", @@ -1184,7 +1138,7 @@ "outputs": [ { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "
" ] @@ -1218,7 +1172,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -1257,26 +1211,26 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "2023-01-26 11:58:52,364 - climada.entity.exposures.base - INFO - Exposures matching centroids already found for TC\n", - "2023-01-26 11:58:52,364 - climada.entity.exposures.base - INFO - Existing centroids will be overwritten for TC\n", - "2023-01-26 11:58:52,364 - climada.entity.exposures.base - INFO - Matching 1388 exposures with 1388 centroids.\n", - "2023-01-26 11:58:52,370 - climada.engine.impact_calc - INFO - Calculating impact for 4164 assets (>0) and 106 events.\n", - "2023-01-26 11:58:52,379 - climada.entity.exposures.base - INFO - Exposures matching centroids already found for TC\n", - "2023-01-26 11:58:52,379 - climada.entity.exposures.base - INFO - Existing centroids will be overwritten for TC\n", - "2023-01-26 11:58:52,382 - climada.entity.exposures.base - INFO - Matching 1388 exposures with 1388 centroids.\n", - "2023-01-26 11:58:52,388 - climada.engine.impact_calc - INFO - Calculating impact for 4164 assets (>0) and 14 events.\n" + "2025-09-25 13:48:30,543 - climada.entity.exposures.base - INFO - Exposures matching centroids already found for TC\n", + "2025-09-25 13:48:30,544 - climada.entity.exposures.base - INFO - Existing centroids will be overwritten for TC\n", + "2025-09-25 13:48:30,544 - climada.entity.exposures.base - INFO - Matching 1388 exposures with 1388 centroids.\n", + "2025-09-25 13:48:30,549 - climada.engine.impact_calc - INFO - Calculating impact for 4164 assets (>0) and 106 events.\n", + "2025-09-25 13:48:30,551 - climada.entity.exposures.base - INFO - Exposures matching centroids already found for TC\n", + "2025-09-25 13:48:30,551 - climada.entity.exposures.base - INFO - Existing centroids will be overwritten for TC\n", + "2025-09-25 13:48:30,552 - climada.entity.exposures.base - INFO - Matching 1388 exposures with 1388 centroids.\n", + "2025-09-25 13:48:30,553 - climada.engine.impact_calc - INFO - Calculating impact for 4164 assets (>0) and 14 events.\n" ] }, { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "
" ] @@ -1286,7 +1240,7 @@ }, { "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAkkAAAHFCAYAAADmGm0KAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8qNh9FAAAACXBIWXMAAA9hAAAPYQGoP6dpAABOWklEQVR4nO3dd3gVZd7G8fuknfQE0hsQIEovAuqiSC8CiouKCgro6uIiSnSVYkFQIYKvLFZcXZoi4O5KUWygFAsqvRcpAQJJSICQ3jPvHzFnc8wEEkhIAt/PdZ3LzDPPzPzmSfTcPmfOjMUwDEMAAACw41DTBQAAANRGhCQAAAAThCQAAAAThCQAAAAThCQAAAAThCQAAAAThCQAAAAThCQAAAAThCQAAAAThCSgFrJYLBV6rVu3TpJ06tQpTZgwQa1bt5anp6dcXV0VFRWlsWPH6uDBgxc83vPPP6+BAwcqLCxMFotFI0eOvORzGDly5Hlrv9rMnz9fFotFR48ePW+/NWvW6KGHHlKzZs3k4eGhsLAwDRo0SFu2bDHtv3XrVvXq1Uuenp7y9fXV4MGDdeTIkTL9Zs2apcGDBysyMlIWi0XdunUz3d+JEycUHR2trl27ytfXVxaLRfPnz6/k2QJXBqeaLgBAWT///LPd8ssvv6y1a9dqzZo1du0tWrTQxo0bNXDgQBmGoTFjxuhPf/qTXFxcdODAAS1cuFDXX3+9UlJSznu8f/zjH2rTpo1uv/12zZ07t8rOw83NrUzNOL/Zs2frzJkzGjt2rFq0aKHk5GS9/vrruvHGG/XNN9+oR48etr779+9Xt27d1K5dO/373/9WTk6OJk2apC5dumj79u0KCAiw9X3vvffk4eGhHj166PPPPy/3+IcOHdLHH3+sdu3aqX///lq8eHG1ni9QmxGSgFroxhtvtFsOCAiQg4NDmfa0tDQNGjRIrq6u2rBhg8LDw23runXrplGjRum///3vBY+Xnp4uB4fiieWPPvqoCs6gmFnNOL933nlHgYGBdm39+vVT06ZNNW3aNLuQNGnSJFmtVq1cuVLe3t6SpA4dOigqKkr/93//p+nTp9v67t271/Y7btWqVbnHv+WWW5ScnCxJ2rx5MyEJVzU+bgPqsA8++ECJiYmaMWOGXUAq7a677rrgfkrePGvCunXrZLFYtGjRIo0fP14hISHy9PTUbbfdplOnTik9PV1//etf5e/vL39/fz344IPKyMiw28c777yjW265RYGBgfLw8FDr1q01Y8YM5efn2/Xr1q2bWrVqpU2bNqlLly5yd3dX48aN9eqrr6qoqMjWr7yPxkpqLfmYs8S3336rnj17ytvbW+7u7rrpppv03XffXdR4/DEgSZKnp6datGihuLg4W1tBQYFWrlypO++80xaQJKlhw4bq3r27li1bZrePiv6Oa/JvAaht+LcBqMNWrVolR0dH3XbbbZfleCUhYfLkyRXepqCgoMyrdCAp8eyzzyopKUnz58/X66+/rnXr1um+++7TnXfeKR8fHy1evFjjxo3TRx99pGeffdZu28OHD2vo0KH66KOPtHLlSv3lL3/Ra6+9plGjRpU5TmJiooYNG6b7779fn332mW699VZNnDhRCxcurPR4SNLChQvVp08feXt7a8GCBfr3v/+t+vXrq2/fvhcdlP4oNTVVW7duVcuWLW1thw8fVnZ2ttq0aVOmf5s2bXTo0CHl5ORUyfGBqxUftwF12PHjxxUQECAPD4/LcjyLxSJHR8cKzzZkZmbK2dm5THvPnj317bff2rW1adNG8+bNsy3v379fs2bN0hNPPKHXXntNktS7d2/9/PPP+vjjj/Xmm2/a+s6cOdP2c1FRkbp06SI/Pz89+OCDev3111WvXj3b+jNnzujLL7/U9ddfL0nq1auX1q1bp0WLFmn48OEVOq8SWVlZGjt2rAYOHGg3c9O/f39dd911evbZZ/Xrr79Wap9mHnvsMWVmZuq5556zOw9Jql+/fpn+9evXl2EYSklJUUhIyCUfH7haEZIAVFjXrl1VUFBQ4f5ubm76/vvvy7SX/nioxMCBA+2WmzdvLkkaMGBAmfbly5crIyNDnp6ekqRt27bpxRdf1E8//aSzZ8/a9f/tt990ww032JaDg4NtAalEmzZttH379gqfV4kNGzbo7NmzGjFiRJlx6devn2bMmKHMzMxLCrEvvPCCPv74Y7311lvq0KFDmfXn+6bg1fgtQqAqEZKAOqxBgwY6ePDgJb8RVxcHBwd17NixQn3/OCPi4uJy3vacnBx5enrq+PHj6tKli6699lq98cYbatSokVxdXbVx40Y99thjys7Ottvez8+vzLGtVmuZfhVx6tQpSee/7uvs2bMX/buZMmWKXnnlFU2dOlVjxoyxW1dyHiUzSn88psVika+v70UdF0AxQhJQh/Xt21erVq3S559/rnvvvbemy6kRy5cvV2ZmppYuXaqGDRva2i9mZqiEq6urJCk3N9eu/fTp03bL/v7+kqS33nqr3G/xBQUFXVQNU6ZM0eTJkzV58uQy12BJUpMmTeTm5qZdu3aVWbdr1y41bdrUdh4ALg4XbgN12F/+8hcFBwdr3LhxOnnypGmfpUuXXuaqLq+Sj5SsVqutzTAMffDBBxe9z0aNGkmSdu7cadf+2Wef2S3fdNNN8vX11d69e9WxY0fTV8nMV2W8/PLLmjx5sp5//nm9+OKLpn2cnJx02223aenSpUpPT7e1Hz9+XGvXrtXgwYMrfVwA9phJAuowHx8frVixQgMHDlT79u3tbiZ58OBBLVy4UDt27LjgG+b69ett98YpLCzUsWPHbPdX6tq1q+2mhOvXr1fPnj01adIkTZo06YL1FRUV6ZdffjFd1759e7tgc7F69+4tFxcX3XfffRo3bpxycnI0e/bsC95A83w6deqka6+9Vk8//bQKCgpUr149LVu2TD/++KNdP09PT7311lsaMWKEzp49q7vuukuBgYFKTk7Wjh07lJycrNmzZ1fq2K+//romTZqkfv36acCAAWXGr/SM1ZQpU9SpUycNHDhQEyZMsN1M0t/fX3//+9/tttu8ebPtlgZpaWkyDMP2O+7UqZPdLFxJe8mduzdv3my7/qsit5QArhgGgFpvxIgRhoeHR7nrExMTjfHjxxstW7Y03N3dDavVajRt2tQYNWqUsWvXrgvuv2vXroYk09fatWtt/dauXWtIMl588cUK1VzePiUZBw8etNvnf/7zH7vt582bZ0gyNm3aZNf+4osvGpKM5ORkW9vnn39utG3b1nB1dTXCwsKMZ555xvjqq6/K1N+1a1ejZcuWprU2bNjQru23334z+vTpY3h7exsBAQHG448/bnzxxRdl9mkYhrF+/XpjwIABRv369Q1nZ2cjLCzMGDBggN05lZxPbGzsecftfL8Ls/9kb9682ejZs6fh7u5ueHt7G3fccYdx6NAh03Msb5/z5s2z61uZ4wNXMothGEb1RTAAAIC6iWuSAAAATBCSAAAATBCSAAAATBCSAAAATBCSAAAATBCSAAAATHAzSRXf8C4+Pl5eXl48EBIAgDrCMAylp6crNDRUDg5VP+9DSJIUHx+viIiImi4DAABchLi4OIWHh1f5fglJkry8vCQVD7K3t3cNVwMAACoiLS1NERERtvfxqkZI0v8ekOnt7U1IAgCgjqmuS2W4cBsAAMAEIQkAAMAEIQkAAMAEIQkAAMAEIQkAAMAEIQkAAMAEIQkAAMAEIQkAAMAEIQkAAMAEIQkAAMBEjYak77//XrfddptCQ0NlsVi0fPlyu/WGYWjy5MkKDQ2Vm5ubunXrpj179tj1yc3N1eOPPy5/f395eHjo9ttv14kTJy7jWQAAgCtRjYakzMxMtW3bVm+//bbp+hkzZmjmzJl6++23tWnTJgUHB6t3795KT0+39YmOjtayZcu0ZMkS/fjjj8rIyNDAgQNVWFh4uU4DAABcgSyGYRg1XYRU/HC6ZcuW6Y477pBUPIsUGhqq6OhojR8/XlLxrFFQUJCmT5+uUaNGKTU1VQEBAfroo490zz33SJLi4+MVERGhL7/8Un379q3QsdPS0uTj46PU+HgecAsAQB2RlpYmn9BQpaamVsv7t1OV77GKxMbGKjExUX369LG1Wa1Wde3aVRs2bNCoUaO0ZcsW5efn2/UJDQ1Vq1attGHDhnJDUm5urnJzc23LaWlpJRtXz8kAAIA6p9ZeuJ2YmChJCgoKsmsPCgqyrUtMTJSLi4vq1atXbh8zMTEx8vHxsb0iIiKquHoAAFDX1dqZpBIWi8Vu2TCMMm1/dKE+EydO1FNPPWVbTktLKw5K8fESH7cBAFA3pKVV66dAtTYkBQcHSyqeLQoJCbG1JyUl2WaXgoODlZeXp5SUFLvZpKSkJHXu3LncfVutVlmt1rIrPDyKXwAAoPar5i9p1dqP2yIjIxUcHKzVq1fb2vLy8rR+/XpbAOrQoYOcnZ3t+iQkJGj37t3nDUkAAAAXUqMzSRkZGTp06JBtOTY2Vtu3b1f9+vXVoEEDRUdHa9q0aYqKilJUVJSmTZsmd3d3DR06VJLk4+Ojv/zlL/r73/8uPz8/1a9fX08//bRat26tXr161dRpAQCAK0CNhqTNmzere/futuWS64RGjBih+fPna9y4ccrOztbo0aOVkpKiG264QatWrZKXl5dtm3/84x9ycnLSkCFDlJ2drZ49e2r+/PlydHS87OcDAACuHLXmPkk1yXafpGq6zwIAAKh61f3+XWuvSQIAAKhJhCQAAAAThCQAAAAThCQAAAAThCQAAAAThCQAAAAThCQAAAAThCQAAAAThCQAAAAThCQAAAAThCQAAAAThCQAAAAThCQAAAAThCQAAAAThCQAAAAThCQAAAAThCQAAAAThCQAAAAThCQAAAAThCQAAAAThCQAAAAThCQAAAAThCQAAAAThCQAAAAThCQAAAAThCQAAAAThCQAAAAThCQAAAAThCQAAAAThCQAAAAThCQAAAAThCQAAAAThCQAAAAThCQAAAAThCQAAAAThCQAAAAThCQAAAAThCQAAAAThCQAAAAThCQAAAAThCQAAAAThCQAAAAThCQAAAAThCQAAAAThCQAAAAThCQAAAAThCQAAAAThCQAAAAThCQAAAAThCQAAAAThCQAAAAThCQAAAAThCQAAAAThCQAAAAThCQAAAAThCQAAAATtTokFRQU6Pnnn1dkZKTc3NzUuHFjvfTSSyoqKrL1MQxDkydPVmhoqNzc3NStWzft2bOnBqsGAABXglodkqZPn6733ntPb7/9tvbt26cZM2botdde01tvvWXrM2PGDM2cOVNvv/22Nm3apODgYPXu3Vvp6ek1WDkAAKjranVI+vnnnzVo0CANGDBAjRo10l133aU+ffpo8+bNkopnkWbNmqXnnntOgwcPVqtWrbRgwQJlZWVp0aJFNVw9AACoy2p1SLr55pv13Xff6bfffpMk7dixQz/++KP69+8vSYqNjVViYqL69Olj28Zqtapr167asGFDufvNzc1VWlqa3QsAAKA0p5ou4HzGjx+v1NRUNWvWTI6OjiosLNTUqVN13333SZISExMlSUFBQXbbBQUF6dixY+XuNyYmRlOmTKm+wgEAQJ1Xq2eSPvnkEy1cuFCLFi3S1q1btWDBAv3f//2fFixYYNfPYrHYLRuGUaattIkTJyo1NdX2iouLq5b6AQBA3VWrZ5KeeeYZTZgwQffee68kqXXr1jp27JhiYmI0YsQIBQcHSyqeUQoJCbFtl5SUVGZ2qTSr1Sqr1Vq9xQMAgDqtVs8kZWVlycHBvkRHR0fbLQAiIyMVHBys1atX29bn5eVp/fr16ty582WtFQAAXFlq9UzSbbfdpqlTp6pBgwZq2bKltm3bppkzZ+qhhx6SVPwxW3R0tKZNm6aoqChFRUVp2rRpcnd319ChQ2u4egAAUJfV6pD01ltv6YUXXtDo0aOVlJSk0NBQjRo1SpMmTbL1GTdunLKzszV69GilpKTohhtu0KpVq+Tl5VWDlQMAgLrOYhiGUdNF1LS0tDT5+PgoNTVV3t7eNV0OAACogOp+/67V1yQBAADUFEISAACACUISAACACUISAACACUISAACACUISAACACUISAACACUISAACACUISAACACUISAACACUISAACACUISAACACUISAACACUISAACACUISAACACUISAACACUISAACACUISAACACUISAACACUISAACACUISAACACUISAACACUISAACACUISAACACUISAACACUISAACACUISAACACUISAACACUISAACACUISAACACUISAACACUISAACACUISAACACUISAACACUISAACACUISAACACUISAACACUISAACACUISAACACUISAACACUISAACACUISAACACUISAACACUISAACACUISAACACUISAACACUISAACACUISAACACUISAACACUISAACACUISAACACUISAACACUISAACACUISAACACUISAACACUISAACAiVofkk6ePKn7779ffn5+cnd3V7t27bRlyxbbesMwNHnyZIWGhsrNzU3dunXTnj17arBiAABwJXC6mI3i4uJ09OhRZWVlKSAgQC1btpTVaq3q2pSSkqKbbrpJ3bt311dffaXAwEAdPnxYvr6+tj4zZszQzJkzNX/+fF1zzTV65ZVX1Lt3bx04cEBeXl5VXhMAALg6WAzDMCrS8dixY3rvvfe0ePFixcXFqfRmLi4u6tKli/7617/qzjvvlIND1UxQTZgwQT/99JN++OEH0/WGYSg0NFTR0dEaP368JCk3N1dBQUGaPn26Ro0aVaHjpKWlycfHR6mpqfL29q6S2gEAQPWq7vfvCoWksWPHat68eerTp49uv/12XX/99QoLC5Obm5vOnj2r3bt364cfftDixYvl5OSkefPmqVOnTpdcXIsWLdS3b1+dOHFC69evV1hYmEaPHq1HHnlEknTkyBE1adJEW7duVfv27W3bDRo0SL6+vlqwYIHpfnNzc5Wbm2tbTktLU0REBCEJAFClCgsLlZ+fX9Nl1FnOzs5ydHQsd311h6QKfdzm4uKiw4cPKyAgoMy6wMBA9ejRQz169NCLL76oL7/8UseOHauSkHTkyBHNnj1bTz31lJ599llt3LhRTzzxhKxWq4YPH67ExERJUlBQkN12QUFBOnbsWLn7jYmJ0ZQpUy65PgAAzBiGocTERJ07d66mS6nzfH19FRwcLIvFctmPXeGP22qCi4uLOnbsqA0bNtjannjiCW3atEk///yzNmzYoJtuuknx8fEKCQmx9XnkkUcUFxenr7/+2nS/zCQBAKpTQkKCzp07p8DAQLm7u9fIG3xdZxiGsrKylJSUJF9fX7v3+RK1YiapPKdPn9avv/6qwsJCderUyfQELkVISIhatGhh19a8eXN9+umnkqTg4GBJUmJiot2xk5KSyswulWa1WqvlQnMAAAoLC20Byc/Pr6bLqdPc3NwkFb+vBwYGnvejt+pw0VdYf/rpp2ratKmmTJmiF198UU2aNNG8efOqsjbddNNNOnDggF3bb7/9poYNG0qSIiMjFRwcrNWrV9vW5+Xlaf369ercuXOV1gIAQEWUXIPk7u5ew5VcGUrGsSau7arwTFJGRoY8PT1ty1OmTNHGjRt1zTXXSJK++OILPfLII3rwwQerrLgnn3xSnTt31rRp0zRkyBBt3LhR77//vt5//31JksViUXR0tKZNm6aoqChFRUVp2rRpcnd319ChQ6usDgAAKouP2KpGTY5jhWeSOnTooBUrVtiWnZyclJSUZFs+deqUXFxcqrS4Tp06admyZVq8eLFatWqll19+WbNmzdKwYcNsfcaNG6fo6GiNHj1aHTt21MmTJ7Vq1SrukQQAAC5JhS/cPnr0qEaPHi2r1ap33nlHhw8f1r333qvCwkIVFBTIwcFB8+fPV//+/au75irHfZIAAFUlJydHsbGxioyMlKura02XU+edbzyr+/27wjNJjRo10pdffqm7775bXbt21Y4dO3To0CGtXr1a3377rY4fP14nAxIAACg2cuRIWSwWPfroo2XWjR49WhaLRSNHjrTra7FY5OzsrKCgIPXu3Vtz585VUVGR3baNGjWy9XVzc1OjRo00ZMgQrVmz5nKc1kWr9IXbQ4cO1caNG7Vt2zZ169ZNRUVFateuHWkZAIArQEREhJYsWaLs7GxbW05OjhYvXqwGDRrY9e3Xr58SEhJ09OhRffXVV+revbvGjh2rgQMHqqCgwK7vSy+9pISEBB04cEAffvihfH191atXL02dOvWynNfFqNQtAL766ivt3btXbdu21Zw5c7Ru3ToNHTpU/fv310svvWT7qh4AAKibrrvuOh05ckRLly61XQO8dOlSRUREqHHjxnZ9rVar7XY8YWFhuu6663TjjTeqZ8+emj9/vh5++GFbXy8vL1vfBg0a6JZbblFISIgmTZqku+66S9dee+1lOsOKq/BM0rhx4zRy5Eht2rRJo0aN0ssvv6xu3bpp27Ztslqtateunb766qvqrBUAgDrJMKTMzJp5Xcwtox988EG72/rMnTtXDz30UIW27dGjh9q2baulS5desO/YsWNlGIbdF8NqkwrPJM2dO1fffPONOnTooLNnz+rGG2/UCy+8IBcXF73yyiu67777NGrUKN16663VWS8AAHVOVpZU6i46l1VGhuThUbltHnjgAU2cOFFHjx6VxWLRTz/9pCVLlmjdunUV2r5Zs2bauXPnBfvVr19fgYGBOnr0aOUKvEwqHJLc3d0VGxurDh06KC4ursw1SC1bttSPP/5Y5QUCAIDLy9/fXwMGDNCCBQtkGIYGDBggf3//Cm9vGEaF729Umb6XW4VDUkxMjIYPH64nnnhCWVlZWrBgQXXWBQDAFcPdvXhGp6aOfTEeeughjRkzRpL0zjvvVGrbffv2KTIy8oL9zpw5o+Tk5Ar1rQkVDknDhg1Tv379dOTIEUVFRcnX17caywIA4MphsVT+I6+a1q9fP+Xl5UmS+vbtW+Ht1qxZo127dunJJ5+8YN833nhDDg4OuuOOOy62zGpVqW+3+fn58bA+AACuAo6Ojtq3b5/tZzO5ublKTExUYWGhTp06pa+//loxMTEaOHCghg8fbtc3PT1diYmJys/PV2xsrBYuXKh//etfiomJUdOmTav9fC5Ghb7d9uijjyouLq5CO/zkk0/08ccfX1JRAACg5nl7e5/3TtZff/21QkJC1KhRI/Xr109r167Vm2++qRUrVpQJVpMmTVJISIiaNm2qBx54QKmpqfruu+80fvz46j6Ni1ahmaSAgAC1atVKnTt31u23366OHTsqNDRUrq6uSklJ0d69e/Xjjz9qyZIlCgsLsz2AFgAA1B3z588/7/rly5fb9b1Q/xK19dtrF1KhkPTyyy/r8ccf15w5c/Tee+9p9+7dduu9vLzUq1cv/etf/1KfPn2qpVAAAIDLqcLXJAUGBmrixImaOHGizp07p2PHjik7O1v+/v5q0qRJrf36HgAAwMWo1IXbJXx9ffl2GwAAuKJV+gG3AAAAVwNCEgAAgAlCEgAAgAlCEgAAgIlKh6QePXro3LlzZdrT0tLUo0ePqqgJAACgxlU6JK1bt872LJfScnJy9MMPP1RJUQAAADWtwrcA2Llzp+3nvXv3KjEx0bZcWFior7/+WmFhYVVbHQAAQA2p8ExSu3bt1L59e1ksFvXo0UPt2rWzvTp06KBXXnlFkyZNqs5aAQBANRo5cqQsFossFoucnZ3VuHFjPf3008rMzLT1+etf/ypHR0ctWbLkvPvq06ePHB0d9csvv1R32dWmwjNJsbGxMgxDjRs31saNGxUQEGBb5+LiosDAwHKfEgwAAOqGfv36ad68ecrPz9cPP/yghx9+WJmZmZo9e7aysrL0ySef6JlnntGcOXN07733mu7j+PHj+vnnnzVmzBjNmTNHN95442U+i6pR4ZDUsGFDSVJRUVG1FQMAAGqW1WpVcHCwJGno0KFau3atli9frtmzZ+s///mPWrRooYkTJyokJERHjx5Vo0aNyuxj3rx5GjhwoP72t7/p+uuv16xZs+Th4XGZz+TSVfrC7ZiYGM2dO7dM+9y5czV9+vQqKQoAgCuKYUiZmTXzMoxLKt3NzU35+fmSpDlz5uj++++Xj4+P+vfvr3nz5pmcqqF58+bp/vvvV7NmzXTNNdfo3//+9yXVUFMqHZL++c9/qlmzZmXaW7Zsqffee69KigIA4IqSlSV5etbMKyvrosveuHGjFi1apJ49e+rgwYP65ZdfdM8990iS7r//fs2bN6/MJ0zffvutsrKy1LdvX1u/OXPmXPzY1aBKh6TExESFhISUaQ8ICFBCQkKVFAUAAGrGypUr5enpKVdXV/3pT3/SLbfcorfeektz5sxR37595e/vL0nq37+/MjMz9e2339ptP2fOHN1zzz1yciq+oue+++7Tr7/+qgMHDlz2c7lUFb4mqURERIR++uknRUZG2rX/9NNPCg0NrbLCAAC4Yri7SxkZNXfsSujevbtmz54tZ2dnhYaGytnZWYWFhfrwww+VmJhoCz9S8S2A5syZoz59+kiSzp49q+XLlys/P1+zZ8+261cXL8updEh6+OGHFR0drfz8fNsdtr/77juNGzdOf//736u8QAAA6jyLRaojFy57eHioadOmdm1ffvml0tPTtW3bNrtvsu/fv1/Dhg3TmTNn5Ofnp48//ljh4eFavny53fbfffedYmJiNHXqVLuQVdtVutJx48bp7NmzGj16tO3O266urho/frwmTpxY5QUCAICaNWfOHA0YMEBt27a1a2/ZsqWio6O1cOFCjR07VnPmzNFdd92lVq1a2fVr2LChxo8fry+++EKDBg26nKVfkkpfk2SxWDR9+nQlJyfrl19+0Y4dO3T27FluJAkAwBXo1KlT+uKLL3TnnXeWWWexWDR48GDNmTNHW7Zs0Y4dO0z7eXl5qU+fPnXuAm6LYVzidwOvAGlpafLx8VFqaqq8vb1ruhwAQB2Wk5Oj2NhYRUZGytXVtabLqfPON57V/f59UR8Mbtq0Sf/5z390/PjxMg+7Xbp0aZUUBgAAUJMq/XHbkiVLdNNNN2nv3r1atmyZ8vPztXfvXq1Zs0Y+Pj7VUSMAAMBlV+mQNG3aNP3jH//QypUr5eLiojfeeEP79u3TkCFD1KBBg+qoEQAA4LKrdEg6fPiwBgwYIKn4+S6ZmZmyWCx68skn9f7771d5gQAAADWh0iGpfv36Sk9PlySFhYVp9+7dkqRz584p6xJufQ4AAFCbVPrC7S5dumj16tVq3bq1hgwZorFjx2rNmjVavXq1evbsWR01AgAAXHaVDklvv/22cnJyJEkTJ06Us7OzfvzxRw0ePFgvvPBClRcIAABQEyodkurXr2/72cHBQePGjdO4ceOqtCgAAICadlH3SSosLNSyZcu0b98+WSwWNW/eXIMGDapTz2MBAAA4n0qnmt27d2vQoEFKTEzUtddeK0n67bffFBAQoM8++0ytW7eu8iIBAAAut0p/u+3hhx9Wy5YtdeLECW3dulVbt25VXFyc2rRpo7/+9a/VUSMAALgMRo4cKYvFokcffbTMutGjR8tisWjkyJF2fS0Wi5ydnRUUFKTevXtr7ty5Kioqstu2UaNGtr5ubm5q1qyZXnvtNdX2J6NVOiTt2LFDMTExqlevnq2tXr16mjp1qrZv316VtQEAgMssIiJCS5YsUXZ2tq0tJydHixcvLnPT6H79+ikhIUFHjx7VV199pe7du2vs2LEaOHCgCgoK7Pq+9NJLSkhI0L59+/T000/r2WefrfX3V6x0SLr22mt16tSpMu1JSUlq2rRplRQFAABqxnXXXacGDRrYPYt16dKlioiIUPv27e36Wq1WBQcHKywsTNddd52effZZrVixQl999ZXmz59v19fLy0vBwcFq1KiRHn74YbVp00arVq26HKd00S7qsSRPPPGE/vvf/+rEiRM6ceKE/vvf/yo6OlrTp09XWlqa7QUAAP4nMzOz3FfJ7XUq0rf0LM/5+l6sBx98UPPmzbMtz507Vw899FCFtu3Ro4fatm1b7gPvDcPQunXrtG/fPjk7O190jZdDpS/cHjhwoCRpyJAhslgskmT7TPG2226zLVssFhUWFlZVnQAA1Hmenp7lruvfv7+++OIL23JgYGC5T7Lo2rWr1q1bZ1tu1KiRTp8+XabfxV7z88ADD2jixIk6evSoLBaLfvrpJy1ZssTumOfTrFkz7dy5065t/Pjxev7555WXl6f8/Hy5urrqiSeeuKj6LpdKh6S1a9dWRx0AAKCW8Pf314ABA7RgwQIZhqEBAwbI39+/wtuXTJaU9swzz2jkyJFKTk7Wc889px49eqhz585VXXqVqnRI6tq1a3XUAQDAFS8jI6PcdY6OjnbLSUlJ5fZ1cLC/Wubo0aOXVJeZhx56SGPGjJEkvfPOO5Xadt++fYqMjLRr8/f3V9OmTdW0aVN9+umnatq0qW688Ub16tWrymquahd198ecnBzt3LlTSUlJZb7md/vtt1dJYQAAXGk8PDxqvG9F9evXT3l5eZKkvn37Vni7NWvWaNeuXXryySfL7VOvXj09/vjjevrpp7Vt27Yys061RaVD0tdff63hw4ebfvbJdUgAAFwZHB0dtW/fPtvPZnJzc5WYmKjCwkKdOnVKX3/9tWJiYjRw4EANHz78vPt/7LHHNH36dH366ae66667qrz+qlDpb7eNGTNGd999txISElRUVGT3IiABAHDl8Pb2lre3d7nrv/76a4WEhKhRo0bq16+f1q5dqzfffFMrVqwoN1iVCAgI0AMPPKDJkyeX+VSqtrAYlbz03dvbW9u2bVOTJk2qq6bLLi0tTT4+PkpNTT3vHwMAABeSk5Oj2NhYRUZGytXVtabLqfPON57V/f5d6Zmku+66q8JfAQQAAKirKn1N0ttvv627775bP/zwg1q3bl3mRlC1/Z4HAAAAFVHpkLRo0SJ98803cnNz07p16+yuSLdYLIQkAABwRaj0x23PP/+8XnrpJaWmpuro0aOKjY21vY4cOVIdNdrExMTIYrEoOjra1mYYhiZPnqzQ0FC5ubmpW7du2rNnT7XWAQAArnyVDkl5eXm65557ytzIqrpt2rRJ77//vtq0aWPXPmPGDM2cOVNvv/22Nm3apODgYPXu3Vvp6emXtT4AAHBlqXTSGTFihD755JPqqKVcGRkZGjZsmD744APVq1fP1m4YhmbNmqXnnntOgwcPVqtWrbRgwQJlZWVp0aJFl7VGAABKq61fa69ranIcK31NUmFhoWbMmKFvvvlGbdq0KXPh9syZM6usuBKPPfaYBgwYoF69eumVV16xtcfGxioxMVF9+vSxtVmtVnXt2lUbNmzQqFGjqrwWAADOx8XFRQ4ODoqPj1dAQIBcXFxq7R2lazPDMJSXl6fk5GQ5ODjIxcXlstdQ6ZC0a9cutW/fXpK0e/duu3XV8UewZMkSbdmyRZs3by6zLjExUZIUFBRk1x4UFKRjx46Vu8/c3Fzl5ubaltPS0qqoWgDA1c7BwUGRkZFKSEhQfHx8TZdT57m7u6tBgwaX/TIf6SJC0tq1a6ujDlNxcXEaO3asVq1add4bcv0xnJk9fbi0mJgYTZkypcrqBACgNBcXFzVo0EAFBQU8jeISODo6ysnJqcZm4ip9x+3Lafny5frzn/9sd2vzwsJCWSwWOTg46MCBA2ratKm2bt1qm92SpEGDBsnX11cLFiww3a/ZTFJERAR33AYAoA6p7jtuV3gmafDgwRXqt3Tp0osu5o969uypXbt22bU9+OCDatasmcaPH6/GjRsrODhYq1evtoWkvLw8rV+/XtOnTy93v1arVVartcrqBAAAV54KhyQfH5/qrMOUl5eXWrVqZdfm4eEhPz8/W3t0dLSmTZumqKgoRUVFadq0aXJ3d9fQoUMve70AAODKUeGQNG/evOqs46KNGzdO2dnZGj16tFJSUnTDDTdo1apV8vLyqunSAABAHVarr0m6XKr7M00AAFD1qvv9+/J/nw4AAKAOICQBAACYICQBAACYICQBAACYICQBAACYICQBAACYICQBAACYICQBAACYICQBAACYICQBAACYICQBAACYICQBAACYICQBAACYICQBAACYICQBAACYICQBAACYICQBAACYICQBAACYICQBAACYICQBAACYICQBAACYICQBAACYICQBAACYICQBAACYICQBAACYICQBAACYICQBAACYICQBAACYICQBAACYICQBAACYICQBAACYICQBAACYICQBAACYICQBAACYICQBAACYICQBAACYICQBAACYICQBAACYICQBAACYICQBAACYICQBAACYICQBAACYICQBAACYICQBAACYICQBAACYICQBAACYICQBAACYICQBAACYICQBAACYICQBAACYICQBAACYICQBAACYICQBAACYICQBAACYICQBAACYICQBAACYqNUhKSYmRp06dZKXl5cCAwN1xx136MCBA3Z9DMPQ5MmTFRoaKjc3N3Xr1k179uypoYoBAMCVolaHpPXr1+uxxx7TL7/8otWrV6ugoEB9+vRRZmamrc+MGTM0c+ZMvf3229q0aZOCg4PVu3dvpaen12DlAACgrrMYhmHUdBEVlZycrMDAQK1fv1633HKLDMNQaGiooqOjNX78eElSbm6ugoKCNH36dI0aNapC+01LS5OPj49SU1Pl7e1dnacAAACqSHW/f9fqmaQ/Sk1NlSTVr19fkhQbG6vExET16dPH1sdqtapr167asGFDjdQIAACuDE41XUBFGYahp556SjfffLNatWolSUpMTJQkBQUF2fUNCgrSsWPHyt1Xbm6ucnNzbctpaWnVUDEAAKjL6sxM0pgxY7Rz504tXry4zDqLxWK3bBhGmbbSYmJi5OPjY3tFRERUeb0AAKBuqxMh6fHHH9dnn32mtWvXKjw83NYeHBws6X8zSiWSkpLKzC6VNnHiRKWmptpecXFx1VM4AACos2p1SDIMQ2PGjNHSpUu1Zs0aRUZG2q2PjIxUcHCwVq9ebWvLy8vT+vXr1blz53L3a7Va5e3tbfcCAAAorVZfk/TYY49p0aJFWrFihby8vGwzRj4+PnJzc5PFYlF0dLSmTZumqKgoRUVFadq0aXJ3d9fQoUNruHoAAFCX1eqQNHv2bElSt27d7NrnzZunkSNHSpLGjRun7OxsjR49WikpKbrhhhu0atUqeXl5XeZqAQDAlaRO3SepunCfJAAA6h7ukwQAAFADCEkAAAAmCEkAAAAmCEkAAAAmCEkAAAAmCEkAAAAmCEkAAAAmCEkAAAAmCEkAAAAmCEkAAAAmCEkAAAAmCEkAAAAmCEkAAAAmCEkAAAAmCEkAAAAmCEkAAAAmCEkAAAAmCEkAAAAmCEkAAAAmCEkAAAAmCEkAAAAmCEkAAAAmCEkAAAAmCEkAAAAmCEkAAAAmCEkAAAAmCEkAAAAmCEkAAAAmCEkAAAAmCEkAAAAmnGq6AAAAcPUoKjKUkZGn1NQcpaXlKjMzX76+YcrJkXJypD17dujUqSRlZuYoMzNHWVnFr+zsXOXlFapTp7G2vtu2/bdaayUkAQBwlTMMKTdXOnkyRUlJ6Tp9OlMpKZlKTc1SWlq2MjKyVVDgrGbNBig7W8rKkr7/frZOnz6mnJws5eRkKzc3W3l5WcrLy5bF4qXIyH/b+p44MVB5eT/JMHIk5fzh6PUknS21/HdJ35VTqbM++2xsqeWfqnAUyiIkAQBQh+TmSrGxp5WYmKakpHQlJaXpzJl0nTuXodTUTDk4eKtp0zuVmSllZkpr1kxQauoJ5eZmKjc3U/n5xa/CwkxZLA1ltX6rzEypqEiSbpB0sJwjN5I0oNTyHElbyunrr+Tk0suZks6Ve04+PpKra/Hr3Lmmys09LUdHq5ycXOXk5Cpn5+J/urhY1aOHITc3i1xdpUOH+mjZsrkVG7iLQEgCAKCaFRQU6eTJNMXFndPJk+eUmJiqwkJ3BQd3Unq6lJYmffHFy0pLO6uMjDRlZ6crJydNubnpys9Pl6NjS1mti5WeLuXnS1ILScnlHK29pDtLLf9X0uFy+hrKyyu97CHJRRaLpxwc3OXg4C4nJ3c5OrrJzS1MN9wgubkVv3777T7l5d0iV1c3ubm5y8PDTe7ubvLwcJevr7e6dv1f3zNn/iUnp3x5e7vK19ft93+6ytPTRQ4Olj/U9F6FxzUt7VYtW1bh7pVGSAIA4AIKC4uDTEqKoV9/3a6EhHM6deqckpPP6ezZc0pJOafU1HNycWmikJAnlJoqnTsn7dkTpYKCZElpkow/7LWbpLWllt+QdKacCpyVmVl62VtSphwcvOTo6C0nJy+5uHjKxcVTPj5RuvlmycOj+LV37zOSMuTt7SkvLw/5+HjI17f4FRDgo7Zt/9fX1XWzrFbHCo7K3yvYT5KaVKJv7UFIAgBcNYqKDJ04kabTpwtUVOSnM2ekpKQCrVgxW6dPn9HZs2eUlnZGGRlnlJ19Rnl5Z2Sx3Kz8/I9K7aWTpMJyjtBd0hOlls9JSi217CYHB185OXnLy6uB2raVvL0lLy9p377RcnLKl5eXl3x8vOXr66V69bxUv76XwsIC1KFDcT9vb8nd/Tc5O1f0C+qjKjw+UkUD0tWBkAQAqLNSUnK0d+8pHTyYpMxMN7m5tVJyspSUlKfPP/+b0tPPKCvrjHJyzig//4wM46ykAkmDJX36+14cJT2p8oNPpO0nd3eL8vKay9GxSC4uvnJ19ZW7u6+8vHzl7e2r8PBr1bOn5Otb/EpK+lYBAW4KD/dVRISPvL2t5zmblypx5tzB53IgJAEAao2iIkPHjp3Tvn2ndPhwkrKyPOXpeZ1OnZLi43P05Zf3KSMjSTk5ScrPPyUpvdTWd6r4+htJcpb0oYoDUVkODukKDZX8/CQ/P4uOHBkhNzdn1avnJ39/PwUG+ikkxE/h4X6KigpVq1bFFxe7uEjSrkqcUdvKDwJqDUISAKDapaXlaufOBO3dG6/UVHdZre0UHy/FxWVr1ao/KzMzSbm5SSoqSpKUX2rL0jM+Vklf/GG9JLnI0TFIvr6+uv56KSCgOPjs3z9dvr7uCg72U1iYnyIi/BQZ6acmTfxUv77bH/Yxp3pOHHUaIQkAcNHy8gq1e/cpxccXqqgoQvHx0vHjufrvf0crJSVeGRnxys2Nl2GcLrVV6eDjKmmNygYfHzk7B8nfP1idO0uBgVJQkEVHjnygwEAvRUYGqkmTQDVvHqTwcG+Tb0hJ0lPVcMa4mhCSAACmsrOLFB/voOPHpSNH8rRo0XQlJsbr7Nl4paXFKycnXkVFiZKKJP1Z0tLft3SR9LGk3D/s0UVOTqHy8/PXLbdIYWFScLBFR49+pJAQbzVuHKhrrglUs2aB57l2Z0T1nCxggpAEAFchw5BOnSrSqlVbtXv3cR08eFzHj8fp1KnjOnfuuLKzj6uo6CbZX+MzVWWDjyQ5ymrNU5s2UmioFBZm0bFjrykw0FNNmoSqWbNQtW0bqsaN65cz43NPdZ0mcEkISQBwBTp7Nlu//npcO3Yc1/79xxUbe1zx8XEqLGwoJ6cXdfy4lJtrkdRFZR8TUeKY3NykBg2kBg0sOnXqSfn6uig8PFSNG4fq2mtD1aZNmJo1C5CLyx+/Ov549Z4gcBkQkgCgDsrJKdDGjXHatClWCQmSq2sPxcZKR44Y2rgxUkVFx8rZsqOkFyVJFotFzs6d5OKSr3r1GigoqIEaNmyga65poFatGqhjxwaKipIstsmfmMtwZkDtQUgCgFrIMKSkJP0efKSPPvo/HTmyX8nJsUpPj1VBwXH9774+HSVt+v1ni4q/BSZJnrJaG8rLq4ECAhooLKyBmje/VnfeWTw7FBYmubh8f7lPDagzCEkAUENOn87SDz8c0aZNh7V37xHFxh5RYmKsUlNjlZ8fqKKi0o+seF9lHzzqImfnRvLza6o77pAiI4tfjo4r1bp1fTVpUt41QAAqgpAEANXEMAwdPnxWP/xwWFu3HtaJE9ny9X1Ihw9Lhw9L8fFtVP6DR5NksUjh4cXBJzPzr/LyylRUVKTato3UDTc0Vrt2IXJyMrvzclQ1nhVw9SAkAcAlKCw0lJBg0eHD0qFD0uLFM/Xbb7/qzJnDys4+LMM4V6p3gKSHSi03lsVyRm5uTeTn10ShoZFq0iRSrVs3VqdOkerSpeQOz5L09OU6JQC/IyQBwAVkZuZrw4aj+uWXw9q585AOHz6skycP6dy5w8rPz5ZhxJbq/aWk7+y2d3AIkadnEwUENNEDDxTommuc1LSpFBb2mUJDXS/ruQCoOEISAEhKSsq0XR904MBJBQU9ZpsdOnr0Vv0x+JTm4JChyEhPNWkiFRX9RW5ut6plyya6/vqm6tKlsfz93cvZkoAE1GaEJABXBcOQzp6V7XqgTz9dqO3bv1Vy8mFlZBxWUVHCH7Z4UFJJuGksaYOs1iaqV6+JwsKaKCqqqdq0aaIbbmiim25yl9V2g+j7LtcpAahmhCQAV4y0tFz98ssxbd4cqz17jurw4VjFx8f+fn3QERnGSUklDzZdJ2mB3fYWSz25uzeVn18TDR6cqbZt3dWkiRQa+oYiI//JN8WAqwwhCUCdkZdXqC1bTmrjxljt2hWrgwdjFR4+UcePu+roUenEidGS5p5nD7EKDW2hJk0kF5c/y2KJVPPmTdShQ1N16dJEjRvXK2e7Pz4xHsDVgJAEoNYoKjK0f/9ppaTUU1yck2JjpW+++VC7di1Uenqs8vOPqezT4u+T1Oz3nyMluctqjZSPT6SCgyPVqFEjtWjRWNdf31TduzeVr2/JdgN+fwGAOUISgMuiqEhKTpZOnJC+/367fv75J8XFndCpUyeUknJCmZknlJ9/QsXPEdsv6drftzwmaXWpPTnJyamhvL0jFRAQqYEDXdShQ/G9hMLDxys09Dk+FgNQJQhJAC5ZYaG0f/9Zbdx4SPv2ndDhwycUF3dCSUnFASgr64Qsli+Vn18y47NS0gvl7s/P74SaN79WkZGSm9vtKiyMUOvWkbr++kh16BBm8jDVEs5VfWoArmKEJACm8vKkgwdTtHPnUR09mqy4uGTFxycpKSlZZ88mKzU1ScHB/6czZ6IUHy8VFr6r8wUfKU4WSzMFB0uenu2VkfFnBQSEKywsXJGR4WrWLFytW4fruuvC5O1tLbVd299fAHB5XTEh6d1339Vrr72mhIQEtWzZUrNmzVKXLl1quiygVjAMKSdHOnNG2r37hLZs2aMTJ5KVkJCspKQknTmTrLS0ZGVkJKlevblKTW2utDRJ+qekieXuNzHxcZU8AsNiaSgHh3C5uUXI1zdcAQHhCg8PV+PG4WrePFw339xK11wjOTtLXA8EoC64IkLSJ598oujoaL377ru66aab9M9//lO33nqr9u7dqwYNGtR0ecBFy8srVEJCuhIT05WYmKZTp9KUnJymM2fSFBnZWwUFvkpPl3bs+Fp79nyqrKx05eSkKScnTfn5aSooSFdhYZocHL5RYWHH3/f6saQJ5R4zI+OEpOaSJIslRBZLsFxcAuTmFiAvr0D5+gbI3z9AQUEB6tr1WrVrV/x8saCgB+Tk9EB1DwkAXDYWwzCMmi7iUt1www267rrrNHv2bFtb8+bNdccddygmJuaC26elpcnHx0fffLNLHh5eZdZ7eHiqXj0/SVJRUZHi4+PK3Zebm7v8/AIkFT/c8uTJ4+X2dXV1k79/oG35xIlj5fa1Wl0VEBBkW46Pj1NRUZFpXxcXFwUGhtiWExJOqLCw0LSvs7OzgoJCbcuJiSdVUFBg2tfJyUnBwWG25aSkBOXl5Zn2dXR0VEhIeKm+icrLyzXta7FYFBb2vzCbnHxKubk5tuU//omGhTWy65uVlWnXt3T3Bg0aS7L83jdRWVkZKioyVFRkKD+/UAUFhb//s0hNmrSR5KiiIunYsUNKSopXYWGR8vMLVVhYpIKC4v6FhUVq06a3HB1dVVgoHT68VXFx+5WdnaucnOJXdnaOcnNzlZubq3bt/i6Lpb5yc6X9+5fo2LFlys/PUX5+rgoLc1VQkKPCwlwVFeUqMHC5ioqilJsrpaZOUX7+ZNMxK7ZJUknweU3SuPP0/VZSTzk5SZ6e/1ZOzlRb6KlX73+hJywsQF263KxrrglUQIDk6ys5mD0/FQBqgZL379TUVHl7e1f5/uv8TFJeXp62bNmiCRPs/8+4T58+2rBhg+k2JW9eJdKKP1dQ376tyznKXyT96/efMyQ1Ok9F90pa/PvPhRfoe5ukz0otXyPJPHRIPVX8RleiraSUcvreKOnnPyyfLKdva0k7Sy33kPRbOX2bSDpUanmApG3l9A2WVPoOxndJ+qmcvl6S0kotD5e0qpy+jpJKh7hHJS0vp69U/E2pkutbnlbxLEp5UiT5/v7zdP3vd24mTlJJCPxQ0hvl9tywYYSk+r8v7ZL07/L3Gpdeaqn0vXmcZbH4yNHRW87O3nJx8VabNs4KD5e8vaWMjC5KTHxZPj7eqlfPW/Xre8vPz1v+/l4KCvJWs2YNFRgoeXlJFssQSUPOc24AAOkKCEmnT59WYWGhgoKC7NqDgoKUmJhouk1MTIymTJlissaqklmH0hwdnX+/jqL42o7c3PKft+Tg4Gx7aveF+7qUesK3lJPjKsn8f9vN+5rv22JxKfWIhOIaDKO8vtY/9LVWUV9Xu755eS4qKipvLFzl6nqhviW/F0e5l3oMVm6uqwoLPcrpK3l6SpbfF3Ny3JSf7/17fQ4qHmvH3392VEiIIScnydFRSkkJUWbmtZIcZLEU9yn9z2bNnOTqWtw3IeFanT7dU05OVjk5WeXsbJWzs6tcXKxycbGqc2cv+flJrq5SQsJAJSYGy83NVW5uVrm5WeXubpW7u6s8PKxq3TpK9etLVqtUUPCorNYHFRbm/YcLmc3c+PsLAFBV6vzHbfHx8QoLC9OGDRv0pz/9ydY+depUffTRR9q/f3+ZbcxmkiIiIqptug4AAFQ9Pm67AH9/fzk6OpaZNUpKSiozu1TCarXKar3Q/5kDAICrWZ2/JNPFxUUdOnTQ6tWr7dpXr16tzp0711BVAACgrqvzM0mS9NRTT+mBBx5Qx44d9ac//Unvv/++jh8/rkcffbSmSwMAAHXUFRGS7rnnHp05c0YvvfSSEhIS1KpVK3355Zdq2LBhTZcGAADqqDp/4XZVqO4LvwAAQNWr7vfvOn9NEgAAQHUgJAEAAJggJAEAAJggJAEAAJggJAEAAJggJAEAAJggJAEAAJggJAEAAJggJAEAAJi4Ih5LcqlKbjqelpZWw5UAAICKKnnfrq6HhxCSJJ05c0aSFBERUcOVAACAyjpz5ox8fHyqfL+EJEn169eXJB0/frxaBvlqkpaWpoiICMXFxfEcvEvAOFYdxrLqMJZVg3GsOqmpqWrQoIHtfbyqEZIkOTgUX5rl4+PDH2wV8fb2ZiyrAONYdRjLqsNYVg3GseqUvI9X+X6rZa8AAAB1HCEJAADABCFJktVq1Ysvviir1VrTpdR5jGXVYByrDmNZdRjLqsE4Vp3qHkuLUV3fmwMAAKjDmEkCAAAwQUgCAAAwQUgCAAAwQUgCAAAwcdWHpHfffVeRkZFydXVVhw4d9MMPP9R0SbVeTEyMOnXqJC8vLwUGBuqOO+7QgQMH7PoYhqHJkycrNDRUbm5u6tatm/bs2VNDFdcNMTExslgsio6OtrUxjhV38uRJ3X///fLz85O7u7vatWunLVu22NYzlhVTUFCg559/XpGRkXJzc1Pjxo310ksvqaioyNaHsSzr+++/12233abQ0FBZLBYtX77cbn1Fxiw3N1ePP/64/P395eHhodtvv10nTpy4jGdRO5xvLPPz8zV+/Hi1bt1aHh4eCg0N1fDhwxUfH2+3jyobS+MqtmTJEsPZ2dn44IMPjL179xpjx441PDw8jGPHjtV0abVa3759jXnz5hm7d+82tm/fbgwYMMBo0KCBkZGRYevz6quvGl5eXsann35q7Nq1y7jnnnuMkJAQIy0trQYrr702btxoNGrUyGjTpo0xduxYWzvjWDFnz541GjZsaIwcOdL49ddfjdjYWOPbb781Dh06ZOvDWFbMK6+8Yvj5+RkrV640YmNjjf/85z+Gp6enMWvWLFsfxrKsL7/80njuueeMTz/91JBkLFu2zG59Rcbs0UcfNcLCwozVq1cbW7duNbp37260bdvWKCgouMxnU7PON5bnzp0zevXqZXzyySfG/v37jZ9//tm44YYbjA4dOtjto6rG8qoOSddff73x6KOP2rU1a9bMmDBhQg1VVDclJSUZkoz169cbhmEYRUVFRnBwsPHqq6/a+uTk5Bg+Pj7Ge++9V1Nl1lrp6elGVFSUsXr1aqNr1662kMQ4Vtz48eONm2++udz1jGXFDRgwwHjooYfs2gYPHmzcf//9hmEwlhXxxzf2iozZuXPnDGdnZ2PJkiW2PidPnjQcHByMr7/++rLVXtuYBc4/2rhxoyHJNsFRlWN51X7clpeXpy1btqhPnz527X369NGGDRtqqKq6KTU1VdL/HhQcGxurxMREu7G1Wq3q2rUrY2viscce04ABA9SrVy+7dsax4j777DN17NhRd999twIDA9W+fXt98MEHtvWMZcXdfPPN+u677/Tbb79Jknbs2KEff/xR/fv3l8RYXoyKjNmWLVuUn59v1yc0NFStWrViXC8gNTVVFotFvr6+kqp2LK/aB9yePn1ahYWFCgoKsmsPCgpSYmJiDVVV9xiGoaeeeko333yzWrVqJUm28TMb22PHjl32GmuzJUuWaMuWLdq8eXOZdYxjxR05ckSzZ8/WU089pWeffVYbN27UE088IavVquHDhzOWlTB+/HilpqaqWbNmcnR0VGFhoaZOnar77rtPEn+XF6MiY5aYmCgXFxfVq1evTB/ek8qXk5OjCRMmaOjQobaHBVflWF61IamExWKxWzYMo0wbyjdmzBjt3LlTP/74Y5l1jO35xcXFaezYsVq1apVcXV3L7cc4XlhRUZE6duyoadOmSZLat2+vPXv2aPbs2Ro+fLitH2N5YZ988okWLlyoRYsWqWXLltq+fbuio6MVGhqqESNG2PoxlpV3MWPGuJYvPz9f9957r4qKivTuu+9esP/FjOVV+3Gbv7+/HB0dy6TKpKSkMmkf5h5//HF99tlnWrt2rcLDw23twcHBksTYXsCWLVuUlJSkDh06yMnJSU5OTlq/fr3efPNNOTk52caKcbywkJAQtWjRwq6tefPmOn78uCT+JivjmWee0YQJE3TvvfeqdevWeuCBB/Tkk08qJiZGEmN5MSoyZsHBwcrLy1NKSkq5ffA/+fn5GjJkiGJjY7V69WrbLJJUtWN51YYkFxcXdejQQatXr7ZrX716tTp37lxDVdUNhmFozJgxWrp0qdasWaPIyEi79ZGRkQoODrYb27y8PK1fv56xLaVnz57atWuXtm/fbnt17NhRw4YN0/bt29W4cWPGsYJuuummMreh+O2339SwYUNJ/E1WRlZWlhwc7N8aHB0dbbcAYCwrryJj1qFDBzk7O9v1SUhI0O7duxnXPygJSAcPHtS3334rPz8/u/VVOpaVusz7ClNyC4A5c+YYe/fuNaKjow0PDw/j6NGjNV1arfa3v/3N8PHxMdatW2ckJCTYXllZWbY+r776quHj42MsXbrU2LVrl3Hfffdd9V8RrojS324zDMaxojZu3Gg4OTkZU6dONQ4ePGh8/PHHhru7u7Fw4UJbH8ayYkaMGGGEhYXZbgGwdOlSw9/f3xg3bpytD2NZVnp6urFt2zZj27ZthiRj5syZxrZt22zfuKrImD366KNGeHi48e233xpbt241evTocVXeAuB8Y5mfn2/cfvvtRnh4uLF9+3a796Dc3FzbPqpqLK/qkGQYhvHOO+8YDRs2NFxcXIzrrrvO9jV2lE+S6WvevHm2PkVFRcaLL75oBAcHG1ar1bjllluMXbt21VzRdcQfQxLjWHGff/650apVK8NqtRrNmjUz3n//fbv1jGXFpKWlGWPHjjUaNGhguLq6Go0bNzaee+45uzcgxrKstWvXmv53ccSIEYZhVGzMsrOzjTFjxhj169c33NzcjIEDBxrHjx+vgbOpWecby9jY2HLfg9auXWvbR1WNpcUwDKNyc08AAABXvqv2miQAAIDzISQBAACYICQBAACYICQBAACYICQBAACYICQBAACYICQBAACYICQBuCIcPXpUFotF27dvr5b9z5kzR3369KmWfZdn5cqVat++ve2RIAAuL0ISgPMaOXKk7rjjjkptY7FYtHz58mqppzwRERFKSEhQq1atJEnr1q2TxWLRuXPnLnnfubm5mjRpkl544YVL3pckZWdny93dXfv37z9vv4EDB8pisWjRokVVclwAlUNIAnBFcHR0VHBwsJycnKp8359++qk8PT3VpUuXKtnf6tWrFRERoWbNml2w74MPPqi33nqrSo4LoHIISQAqpVu3bnriiSc0btw41a9fX8HBwZo8ebJtfaNGjSRJf/7zn2WxWGzLkvT555+rQ4cOcnV1VePGjTVlyhQVFBTY1lssFv3rX//Sn//8Z7m7uysqKkqfffaZbX1KSoqGDRumgIAAubm5KSoqSvPmzZNk/3Hb0aNH1b17d0lSvXr1ZLFYNHLkSH344Yfy8/NTbm6u3TndeeedGj58eLnnvGTJEt1+++12bSUzbNOmTVNQUJB8fX1t5/PMM8+ofv36Cg8P19y5c8vsb8WKFbb97dixQ927d5eXl5e8vb3VoUMHbd682db39ttv18aNG3XkyJFy6wNQPQhJACptwYIF8vDw0K+//qoZM2bopZde0urVqyVJmzZtkiTNmzdPCQkJtuVvvvlG999/v5544gnt3btX//znPzV//nxNnTrVbt9TpkzRkCFDtHPnTvXv31/Dhg3T2bNnJUkvvPCC9u7dq6+++kr79u3T7Nmz5e/vX6a+iIgIffrpp5KkAwcOKCEhQW+88YbuvvtuFRYW2gWv06dPa+XKlXrwwQfLPd8ffvhBHTt2LNO+Zs0axcfH6/vvv9fMmTM1efJkDRw4UPXq1dOvv/6qRx99VI8++qji4uJs2xQVFWnlypUaNGiQJGnYsGEKDw/Xpk2btGXLFk2YMEHOzs62/g0bNlRgYKB++OGH8/xGAFSLKntsL4Ar0ogRI4xBgwbZlrt27WrcfPPNdn06depkjB8/3rYsyVi2bJldny5duhjTpk2za/voo4+MkJAQu+2ef/5523JGRoZhsViMr776yjAMw7jtttuMBx980LTOkqeDb9u2zTCM/z1JPCUlxa7f3/72N+PWW2+1Lc+aNcto3LixUVRUZLrflJQUQ5Lx/fff27WPGDHCaNiwoVFYWGhru/baa40uXbrYlgsKCgwPDw9j8eLFtraffvrJ8Pf3t23n5eVlzJ8/3/TYJdq3b29Mnjz5vH0AVD1mkgBUWps2beyWQ0JClJSUdN5ttmzZopdeekmenp621yOPPKKEhARlZWWZ7tvDw0NeXl62ff/tb3/TkiVL1K5dO40bN04bNmyodO2PPPKIVq1apZMnT0oqnvEaOXKkLBaLaf/s7GxJkqura5l1LVu2lIPD//4zGhQUpNatW9uWHR0d5efnZzc2K1as0MCBA23bPfXUU3r44YfVq1cvvfrqqzp8+HCZ47i5udmNEYDLg5AEoNJKfxwkFV9LdKGvqRcVFWnKlCnavn277bVr1y4dPHjQLoCcb9+33nqrjh07pujoaMXHx6tnz556+umnK1V7+/bt1bZtW3344YfaunWrdu3apZEjR5bb38/PTxaLRSkpKWXWmdV6obH57LPPbB+1SdLkyZO1Z88eDRgwQGvWrFGLFi20bNkyu32cPXtWAQEBlTlNAFWAkASgyjk7O6uwsNCu7brrrtOBAwfUtGnTMq/SszEXEhAQoJEjR2rhwoWaNWuW3n//fdN+Li4uklSmDkl6+OGHNW/ePM2dO1e9evVSREREucdzcXFRixYttHfv3grXWJ6DBw/q6NGjZe63dM011+jJJ5/UqlWrNHjwYNvF6JKUk5Ojw4cPq3379pd8fACVQ0gCUOUaNWqk7777TomJibYZmEmTJunDDz+0zZzs27dPn3zyiZ5//vkK73fSpElasWKFDh06pD179mjlypVq3ry5ad+GDRvKYrFo5cqVSk5OVkZGhm3dsGHDdPLkSX3wwQd66KGHLnjcvn376scff6xwneVZsWKFevXqJXd3d0nFH+WNGTNG69at07Fjx/TTTz9p06ZNduf0yy+/yGq16k9/+tMlHx9A5RCSAFS5119/3XYvoJIZkL59+2rlypVavXq1OnXqpBtvvFEzZ85Uw4YNK7xfFxcXTZw4UW3atNEtt9wiR0dHLVmyxLRvWFiYpkyZogkTJigoKEhjxoyxrfP29tadd94pT0/PCt0o85FHHtGXX36p1NTUCtdqZsWKFXYftTk6OurMmTMaPny4rrnmGg0ZMkS33nqrpkyZYuuzePFiDRs2zBasAFw+FsMwjJouAgAut969e6t58+Z68803K9R/yJAhat++vSZOnHhRxzt9+rRCQkIUFxen4ODgCm2TnJysZs2aafPmzYqMjLyo4wK4eMwkAbiqnD17VkuWLNGaNWv02GOPVXi71157TZ6enpd03JkzZ1Y4IElSbGys3n33XQISUEOYSQJwVWnUqJFSUlL0wgsvVPqbcQCuLoQkAAAAE3zcBgAAYIKQBAAAYIKQBAAAYIKQBAAAYIKQBAAAYIKQBAAAYIKQBAAAYIKQBAAAYIKQBAAAYOL/AW4asnWl1I/oAAAAAElFTkSuQmCC", + "image/png": "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", "text/plain": [ "
" ] @@ -1327,22 +1281,31 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 16, "metadata": {}, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-09-25 13:48:34,805 - climada.engine.impact - WARNING - The Impact.tot_value attribute is deprecated.Use Exposures.affected_total_value to calculate the affected total exposure value based on a specific hazard intensity threshold\n", + "2025-09-25 13:48:34,806 - climada.engine.impact - WARNING - The Impact.tot_value attribute is deprecated.Use Exposures.affected_total_value to calculate the affected total exposure value based on a specific hazard intensity threshold\n", + "2025-09-25 13:48:34,807 - climada.engine.impact - WARNING - The Impact.tot_value attribute is deprecated.Use Exposures.affected_total_value to calculate the affected total exposure value based on a specific hazard intensity threshold\n" + ] + }, { "data": { "text/plain": [ "Text(0.5, 1.0, 'Expected annual impact: Total')" ] }, - "execution_count": 18, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "
" ] @@ -1352,7 +1315,7 @@ }, { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "
" ] @@ -1362,7 +1325,7 @@ }, { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "
" ] @@ -1403,7 +1366,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 33, "metadata": { "ExecuteTime": { "end_time": "2020-10-19T09:58:18.829168Z", @@ -1415,27 +1378,19 @@ "name": "stdout", "output_type": "stream", "text": [ - "2023-01-26 11:59:01,455 - climada.entity.exposures.base - INFO - Setting impf_ to default impact functions ids 1.\n", - "2023-01-26 11:59:01,457 - climada.entity.exposures.base - INFO - category_id not set.\n", - "2023-01-26 11:59:01,458 - climada.entity.exposures.base - INFO - cover not set.\n", - "2023-01-26 11:59:01,460 - climada.entity.exposures.base - INFO - deductible not set.\n", - "2023-01-26 11:59:01,463 - climada.entity.exposures.base - INFO - geometry not set.\n", - "2023-01-26 11:59:01,464 - climada.entity.exposures.base - INFO - region_id not set.\n", - "2023-01-26 11:59:01,466 - climada.entity.exposures.base - INFO - centr_ not set.\n", - "2023-01-26 11:59:03,801 - climada.hazard.tc_tracks - INFO - Progress: 100%\n", - "2023-01-26 11:59:03,846 - climada.hazard.centroids.centr - INFO - Convert centroids to GeoSeries of Point shapes.\n", - "2023-01-26 11:59:04,466 - climada.util.coordinates - INFO - dist_to_coast: UTM 32645 (1/1)\n", - "2023-01-26 11:59:04,580 - climada.hazard.trop_cyclone - INFO - Mapping 1 tracks to 7 coastal centroids.\n", - "2023-01-26 11:59:04,595 - climada.hazard.trop_cyclone - INFO - Progress: 100%\n", - "2023-01-26 11:59:05,457 - climada.entity.exposures.base - INFO - No specific impact function column found for hazard TC. Using the anonymous 'impf_' column.\n", - "2023-01-26 11:59:05,458 - climada.entity.exposures.base - INFO - Matching 7 exposures with 7 centroids.\n", - "2023-01-26 11:59:05,463 - climada.engine.impact_calc - INFO - Calculating impact for 21 assets (>0) and 1 events.\n", - "Nearest neighbor hazard.centroids indexes for each exposure: [0 1 2 3 4 5 6]\n" + "2025-11-05 18:03:53,989 - climada.hazard.tc_tracks - WARNING - The cached IBTrACS data set dates from 2024-12-09 21:15:55 (older than 180 days). Very likely, a more recent version is available. Consider manually removing the file /Users/lseverino/climada/data/IBTrACS.ALL.v04r01.nc and re-running this function, which will download the most recent version of the IBTrACS data set from the official URL.\n", + "2025-11-05 18:03:55,286 - climada.hazard.tc_tracks - INFO - Progress: 100%\n", + "2025-11-05 18:03:55,312 - climada.util.coordinates - INFO - Sampling from /Users/lseverino/climada/data/GMT_intermediate_coast_distance_01d.tif\n", + "2025-11-05 18:03:55,318 - climada.hazard.trop_cyclone.trop_cyclone - INFO - Mapping 1 tracks to 6 coastal centroids.\n", + "2025-11-05 18:03:55,328 - climada.hazard.trop_cyclone.trop_cyclone - INFO - Progress: 100%\n", + "2025-11-05 18:03:55,400 - climada.entity.exposures.base - INFO - Matching 6 exposures with 6 centroids.\n", + "2025-11-05 18:03:55,401 - climada.engine.impact_calc - INFO - Calculating impact for 18 assets (>0) and 1 events.\n", + "Nearest neighbor hazard.centroids indexes for each exposure: [0 1 2 3 4 5]\n" ] }, { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "
" ] @@ -1445,7 +1400,7 @@ }, { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "
" ] @@ -1455,7 +1410,7 @@ }, { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "
" ] @@ -1473,20 +1428,21 @@ "from climada.engine import ImpactCalc\n", "\n", "# Set Exposures in points\n", - "exp_pnt = Exposures(crs=\"epsg:4326\") # set coordinate system\n", - "exp_pnt.gdf[\"latitude\"] = np.array(\n", - " [21.899326, 21.960728, 22.220574, 22.298390, 21.787977, 21.787977, 21.981732]\n", - ")\n", - "exp_pnt.gdf[\"longitude\"] = np.array(\n", - " [88.307422, 88.565362, 88.378337, 87.806356, 88.348835, 88.348835, 89.246521]\n", + "latitude = np.array([21.899326, 21.960728, 22.220574, 22.298390, 21.787977, 21.981732])\n", + "longitude = np.array([88.307422, 88.565362, 88.378337, 87.806356, 88.348835, 89.246521])\n", + "\n", + "values = np.array([1.0e5, 1.2e5, 1.1e5, 2.0e5, 2.5e5, 0.5e5])\n", + "\n", + "exp_pnt = Exposures(\n", + " lat=latitude, lon=longitude, value=values, value_unit=\"USD\", crs=\"epsg:4326\"\n", ")\n", - "exp_pnt.gdf[\"value\"] = np.array([1.0e5, 1.2e5, 1.1e5, 1.1e5, 2.0e5, 2.5e5, 0.5e5])\n", - "exp_pnt.check()\n", + "\n", + "# plot exposures\n", "exp_pnt.plot_scatter(buffer=0.05)\n", "\n", "# Set Hazard in Exposures points\n", "# set centroids from exposures coordinates\n", - "centr_pnt = Centroids.from_lat_lon(exp_pnt.latitude, exp_pnt.longitude, exp_pnt.crs)\n", + "centr_pnt = Centroids.from_exposures(exp_pnt)\n", "# compute Hazard in that centroids\n", "tr_pnt = TCTracks.from_ibtracs_netcdf(storm_id=\"2007314N10093\")\n", "tc_pnt = TropCyclone.from_tracks(tr_pnt, centroids=centr_pnt)\n", @@ -1495,7 +1451,7 @@ " c=np.array(tc_pnt.intensity[0, :].todense()).squeeze()\n", ") # plot intensity per point\n", "ax_pnt.get_figure().colorbar(\n", - " ax_pnt.collections[0], fraction=0.0175, pad=0.02\n", + " ax_pnt.collections[2], fraction=0.0175, pad=0.08\n", ").set_label(\n", " \"Intensity (m/s)\"\n", ") # add colorbar\n", @@ -1509,9 +1465,9 @@ "[haz_type] = impf_set.get_hazard_types()\n", "[haz_id] = impf_set.get_ids()[haz_type]\n", "# Exposures: rename column and assign id\n", - "exp_lp.gdf.rename(columns={\"impf_\": \"impf_\" + haz_type}, inplace=True)\n", - "exp_lp.gdf[\"impf_\" + haz_type] = haz_id\n", - "exp_lp.gdf.head()\n", + "exp_pnt.gdf.rename(columns={\"impf_\": \"impf_\" + haz_type}, inplace=True)\n", + "exp_pnt.gdf[\"impf_\" + haz_type] = haz_id\n", + "exp_pnt.gdf.head()\n", "\n", "# Compute Impact\n", "imp_pnt = ImpactCalc(exp_pnt, impf_pnt, tc_pnt).impact()\n", @@ -1532,7 +1488,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 18, "metadata": { "ExecuteTime": { "end_time": "2020-10-19T10:08:03.171168Z", @@ -1544,132 +1500,102 @@ "name": "stdout", "output_type": "stream", "text": [ - "2023-01-26 11:59:11,285 - climada.entity.exposures.litpop.litpop - INFO - \n", + "2025-09-25 13:50:45,839 - climada.entity.exposures.litpop.litpop - INFO - \n", " LitPop: Init Exposure for country: VEN (862)...\n", "\n", - "2023-01-26 11:59:13,749 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2018. Using nearest available year for GPW data: 2020\n", - "2023-01-26 11:59:13,751 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n", - "2023-01-26 11:59:13,783 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n", - "2023-01-26 11:59:13,785 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2018. Using nearest available year for GPW data: 2020\n", - "2023-01-26 11:59:13,787 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n", - "2023-01-26 11:59:13,850 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2018. Using nearest available year for GPW data: 2020\n", - "2023-01-26 11:59:13,852 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n", - "2023-01-26 11:59:13,926 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2018. Using nearest available year for GPW data: 2020\n", - "2023-01-26 11:59:13,928 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n", - "2023-01-26 11:59:13,984 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2018. Using nearest available year for GPW data: 2020\n", - "2023-01-26 11:59:13,985 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n", - "2023-01-26 11:59:14,045 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2018. Using nearest available year for GPW data: 2020\n", - "2023-01-26 11:59:14,046 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n", - "2023-01-26 11:59:14,105 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2018. Using nearest available year for GPW data: 2020\n", - "2023-01-26 11:59:14,108 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n", - "2023-01-26 11:59:14,167 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2018. Using nearest available year for GPW data: 2020\n", - "2023-01-26 11:59:14,168 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n", - "2023-01-26 11:59:14,220 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2018. Using nearest available year for GPW data: 2020\n", - "2023-01-26 11:59:14,221 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n", - "2023-01-26 11:59:14,252 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n", - "2023-01-26 11:59:14,253 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2018. Using nearest available year for GPW data: 2020\n", - "2023-01-26 11:59:14,255 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n", - "2023-01-26 11:59:14,283 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n", - "2023-01-26 11:59:14,284 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2018. Using nearest available year for GPW data: 2020\n", - "2023-01-26 11:59:14,286 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n", - "2023-01-26 11:59:14,314 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n", - "2023-01-26 11:59:14,315 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2018. Using nearest available year for GPW data: 2020\n", - "2023-01-26 11:59:14,317 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n", - "2023-01-26 11:59:14,348 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n", - "2023-01-26 11:59:14,349 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2018. Using nearest available year for GPW data: 2020\n", - "2023-01-26 11:59:14,351 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n", - "2023-01-26 11:59:14,381 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n", - "2023-01-26 11:59:14,383 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2018. Using nearest available year for GPW data: 2020\n", - "2023-01-26 11:59:14,386 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n", - "2023-01-26 11:59:14,416 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n", - "2023-01-26 11:59:14,417 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2018. Using nearest available year for GPW data: 2020\n", - "2023-01-26 11:59:14,419 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n", - "2023-01-26 11:59:14,491 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2018. Using nearest available year for GPW data: 2020\n", - "2023-01-26 11:59:14,493 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n", - "2023-01-26 11:59:14,556 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2018. Using nearest available year for GPW data: 2020\n", - "2023-01-26 11:59:14,557 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n", - "2023-01-26 11:59:14,622 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2018. Using nearest available year for GPW data: 2020\n", - "2023-01-26 11:59:14,624 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n", - "2023-01-26 11:59:14,703 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2018. Using nearest available year for GPW data: 2020\n", - "2023-01-26 11:59:14,705 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n", - "2023-01-26 11:59:14,779 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2018. Using nearest available year for GPW data: 2020\n", - "2023-01-26 11:59:14,780 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n", - "2023-01-26 11:59:14,854 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2018. Using nearest available year for GPW data: 2020\n", - "2023-01-26 11:59:14,855 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n", - "2023-01-26 11:59:14,922 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2018. Using nearest available year for GPW data: 2020\n", - "2023-01-26 11:59:14,924 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n", - "2023-01-26 11:59:14,988 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2018. Using nearest available year for GPW data: 2020\n", - "2023-01-26 11:59:14,990 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n", - "2023-01-26 11:59:15,026 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n", - "2023-01-26 11:59:15,028 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2018. Using nearest available year for GPW data: 2020\n", - "2023-01-26 11:59:15,029 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n", - "2023-01-26 11:59:15,065 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n", - "2023-01-26 11:59:15,066 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2018. Using nearest available year for GPW data: 2020\n", - "2023-01-26 11:59:15,069 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n", - "2023-01-26 11:59:15,130 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2018. Using nearest available year for GPW data: 2020\n", - "2023-01-26 11:59:15,131 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n", - "2023-01-26 11:59:15,200 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2018. Using nearest available year for GPW data: 2020\n", - "2023-01-26 11:59:15,202 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n", - "2023-01-26 11:59:15,263 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2018. Using nearest available year for GPW data: 2020\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-01-26 11:59:15,265 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n", - "2023-01-26 11:59:15,289 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n", - "2023-01-26 11:59:15,833 - climada.util.finance - INFO - GDP VEN 2014: 4.824e+11.\n", - "2023-01-26 11:59:15,909 - climada.util.finance - INFO - Income group VEN 2018: 3.\n", - "2023-01-26 11:59:15,933 - climada.entity.exposures.base - INFO - Hazard type not set in impf_\n", - "2023-01-26 11:59:15,934 - climada.entity.exposures.base - INFO - category_id not set.\n", - "2023-01-26 11:59:15,936 - climada.entity.exposures.base - INFO - cover not set.\n", - "2023-01-26 11:59:15,937 - climada.entity.exposures.base - INFO - deductible not set.\n", - "2023-01-26 11:59:15,939 - climada.entity.exposures.base - INFO - centr_ not set.\n", - "2023-01-26 11:59:15,949 - climada.entity.exposures.base - INFO - Hazard type not set in impf_\n", - "2023-01-26 11:59:15,951 - climada.entity.exposures.base - INFO - category_id not set.\n", - "2023-01-26 11:59:15,952 - climada.entity.exposures.base - INFO - cover not set.\n", - "2023-01-26 11:59:15,953 - climada.entity.exposures.base - INFO - deductible not set.\n", - "2023-01-26 11:59:15,955 - climada.entity.exposures.base - INFO - centr_ not set.\n", - "2023-01-26 11:59:15,962 - climada.util.coordinates - INFO - Raster from resolution 0.08333332999999987 to 0.08333332999999987.\n", - "\n", - " Raster properties exposures: {'width': 163, 'height': 138, 'crs': \n", - "Name: WGS 84\n", - "Axis Info [ellipsoidal]:\n", - "- Lat[north]: Geodetic latitude (degree)\n", - "- Lon[east]: Geodetic longitude (degree)\n", - "Area of Use:\n", - "- undefined\n", - "Datum: World Geodetic System 1984\n", - "- Ellipsoid: WGS 84\n", - "- Prime Meridian: Greenwich\n", - ", 'transform': Affine(0.08333333000000209, 0.0, -73.41666666500001,\n", - " 0.0, -0.08333332999999987, 12.166666665)}\n", - "2023-01-26 11:59:23,374 - climada.util.coordinates - INFO - Reading C:\\Users\\F80840370\\climada\\demo\\data\\SC22000_VE__M1.grd.gz\n", - "2023-01-26 11:59:25,519 - climada.util.coordinates - INFO - Reading C:\\Users\\F80840370\\climada\\demo\\data\\SC22000_VE__M1.grd.gz\n", - "Raster properties centroids: {'driver': 'GSBG', 'dtype': 'float32', 'nodata': 1.701410009187828e+38, 'width': 163, 'height': 138, 'count': 1, 'crs': \n", - "Name: WGS 84\n", - "Axis Info [ellipsoidal]:\n", - "- Lat[north]: Geodetic latitude (degree)\n", - "- Lon[east]: Geodetic longitude (degree)\n", - "Area of Use:\n", - "- undefined\n", - "Datum: World Geodetic System 1984\n", - "- Ellipsoid: WGS 84\n", - "- Prime Meridian: Greenwich\n", - ", 'transform': Affine(0.08333333000000209, 0.0, -73.41666666500001,\n", - " 0.0, -0.08333332999999987, 12.166666665)}\n", - "2023-01-26 11:59:28,695 - climada.entity.exposures.base - INFO - No specific impact function column found for hazard FL. Using the anonymous 'impf_' column.\n", - "2023-01-26 11:59:28,695 - climada.entity.exposures.base - INFO - Matching 10772 exposures with 22494 centroids.\n", - "2023-01-26 11:59:28,704 - climada.engine.impact_calc - INFO - Calculating impact for 32310 assets (>0) and 1 events.\n", + "2025-09-25 13:50:46,558 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2018. Using nearest available year for GPW data: 2020\n", + "2025-09-25 13:50:46,560 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n", + "2025-09-25 13:50:46,565 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n", + "2025-09-25 13:50:46,565 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2018. Using nearest available year for GPW data: 2020\n", + "2025-09-25 13:50:46,566 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n", + "2025-09-25 13:50:46,576 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2018. Using nearest available year for GPW data: 2020\n", + "2025-09-25 13:50:46,576 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n", + "2025-09-25 13:50:46,595 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2018. Using nearest available year for GPW data: 2020\n", + "2025-09-25 13:50:46,596 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n", + "2025-09-25 13:50:46,604 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2018. Using nearest available year for GPW data: 2020\n", + "2025-09-25 13:50:46,604 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n", + "2025-09-25 13:50:46,613 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2018. Using nearest available year for GPW data: 2020\n", + "2025-09-25 13:50:46,613 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n", + "2025-09-25 13:50:46,621 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2018. Using nearest available year for GPW data: 2020\n", + "2025-09-25 13:50:46,622 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n", + "2025-09-25 13:50:46,631 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2018. Using nearest available year for GPW data: 2020\n", + "2025-09-25 13:50:46,631 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n", + "2025-09-25 13:50:46,642 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2018. Using nearest available year for GPW data: 2020\n", + "2025-09-25 13:50:46,643 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n", + "2025-09-25 13:50:46,646 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n", + "2025-09-25 13:50:46,647 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2018. Using nearest available year for GPW data: 2020\n", + "2025-09-25 13:50:46,647 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n", + "2025-09-25 13:50:46,650 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n", + "2025-09-25 13:50:46,650 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2018. Using nearest available year for GPW data: 2020\n", + "2025-09-25 13:50:46,651 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n", + "2025-09-25 13:50:46,653 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n", + "2025-09-25 13:50:46,653 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2018. Using nearest available year for GPW data: 2020\n", + "2025-09-25 13:50:46,654 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n", + "2025-09-25 13:50:46,657 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n", + "2025-09-25 13:50:46,657 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2018. Using nearest available year for GPW data: 2020\n", + "2025-09-25 13:50:46,657 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n", + "2025-09-25 13:50:46,660 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n", + "2025-09-25 13:50:46,660 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2018. Using nearest available year for GPW data: 2020\n", + "2025-09-25 13:50:46,660 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n", + "2025-09-25 13:50:46,663 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n", + "2025-09-25 13:50:46,663 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2018. Using nearest available year for GPW data: 2020\n", + "2025-09-25 13:50:46,664 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n", + "2025-09-25 13:50:46,684 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2018. Using nearest available year for GPW data: 2020\n", + "2025-09-25 13:50:46,685 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n", + "2025-09-25 13:50:46,695 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2018. Using nearest available year for GPW data: 2020\n", + "2025-09-25 13:50:46,695 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n", + "2025-09-25 13:50:46,713 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2018. Using nearest available year for GPW data: 2020\n", + "2025-09-25 13:50:46,713 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n", + "2025-09-25 13:50:46,735 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2018. Using nearest available year for GPW data: 2020\n", + "2025-09-25 13:50:46,735 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n", + "2025-09-25 13:50:46,749 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2018. Using nearest available year for GPW data: 2020\n", + "2025-09-25 13:50:46,750 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n", + "2025-09-25 13:50:46,768 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2018. Using nearest available year for GPW data: 2020\n", + "2025-09-25 13:50:46,770 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n", + "2025-09-25 13:50:46,786 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2018. Using nearest available year for GPW data: 2020\n", + "2025-09-25 13:50:46,787 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n", + "2025-09-25 13:50:46,798 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2018. Using nearest available year for GPW data: 2020\n", + "2025-09-25 13:50:46,799 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n", + "2025-09-25 13:50:46,806 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n", + "2025-09-25 13:50:46,806 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2018. Using nearest available year for GPW data: 2020\n", + "2025-09-25 13:50:46,806 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n", + "2025-09-25 13:50:46,812 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n", + "2025-09-25 13:50:46,813 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2018. Using nearest available year for GPW data: 2020\n", + "2025-09-25 13:50:46,813 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n", + "2025-09-25 13:50:46,823 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2018. Using nearest available year for GPW data: 2020\n", + "2025-09-25 13:50:46,823 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n", + "2025-09-25 13:50:46,839 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2018. Using nearest available year for GPW data: 2020\n", + "2025-09-25 13:50:46,839 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n", + "2025-09-25 13:50:46,848 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2018. Using nearest available year for GPW data: 2020\n", + "2025-09-25 13:50:46,849 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n", + "2025-09-25 13:50:46,853 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n", + "2025-09-25 13:50:47,320 - climada.util.finance - INFO - GDP VEN 2014: 4.824e+11.\n", + "2025-09-25 13:50:47,343 - climada.util.finance - INFO - Income group VEN 2018: 3.\n", + "2025-09-25 13:50:47,355 - climada.entity.exposures.base - INFO - Hazard type not set in impf_\n", + "2025-09-25 13:50:47,356 - climada.entity.exposures.base - INFO - category_id not set.\n", + "2025-09-25 13:50:47,356 - climada.entity.exposures.base - INFO - cover not set.\n", + "2025-09-25 13:50:47,356 - climada.entity.exposures.base - INFO - deductible not set.\n", + "2025-09-25 13:50:47,357 - climada.entity.exposures.base - INFO - centr_ not set.\n", + "2025-09-25 13:50:47,357 - climada.entity.exposures.base - INFO - Hazard type not set in impf_\n", + "2025-09-25 13:50:47,358 - climada.entity.exposures.base - INFO - category_id not set.\n", + "2025-09-25 13:50:47,358 - climada.entity.exposures.base - INFO - cover not set.\n", + "2025-09-25 13:50:47,358 - climada.entity.exposures.base - INFO - deductible not set.\n", + "2025-09-25 13:50:47,359 - climada.entity.exposures.base - INFO - centr_ not set.\n", + "2025-09-25 13:50:47,362 - climada.util.coordinates - INFO - Raster from resolution 0.08333332999999987 to 0.08333332999999987.\n", + "2025-09-25 13:50:48,604 - climada.util.coordinates - INFO - Reading /Users/shuelsen/climada/demo/data/SC22000_VE__M1.grd.gz\n", + "2025-09-25 13:50:50,098 - climada.util.coordinates - INFO - Reading /Users/shuelsen/climada/demo/data/SC22000_VE__M1.grd.gz\n", + "2025-09-25 13:50:52,861 - climada.entity.exposures.base - INFO - No specific impact function column found for hazard FL. Using the anonymous 'impf_' column.\n", + "2025-09-25 13:50:52,862 - climada.entity.exposures.base - INFO - Matching 10772 exposures with 22494 centroids.\n", + "2025-09-25 13:50:52,867 - climada.util.coordinates - INFO - No exact centroid match found. Reprojecting coordinates to nearest neighbor closer than the threshold = 100\n", + "2025-09-25 13:50:52,892 - climada.engine.impact_calc - INFO - Calculating impact for 32310 assets (>0) and 1 events.\n", "\n", " Nearest neighbor hazard.centroids indexes for each exposure: [ 39 40 41 ... 3387 3551 2721]\n", - "2023-01-26 11:59:28,714 - climada.util.coordinates - INFO - Raster from resolution 0.08333332999999987 to 0.08333332999999987.\n" + "2025-09-25 13:50:52,902 - climada.util.coordinates - INFO - Raster from resolution 0.08333332999999987 to 0.08333332999999987.\n" ] }, { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "
" ] @@ -1679,7 +1605,7 @@ }, { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "
" ] @@ -1689,7 +1615,7 @@ }, { "data": { - "image/png": "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", + "image/png": "iVBORw0KGgoAAAANSUhEUgAAA5AAAAKFCAYAAAC6IAa4AAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjYsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvq6yFwwAAAAlwSFlzAAAPYQAAD2EBqD+naQABAABJREFUeJzs3XecXXWd+P/XqbeX6SWTTiodQkIVIoQemop1ERZW8bfq6rqrq7tLgLWgq7u66hYXBf2u7oIKBIiC1NB7CBBISJsk0zL19nLq74+T3GSSTDJJptw783nyGDL3zrnnfm4797w/5f2WXNd1EQRBEARBEARBEIRDkMe7AYIgCIIgCIIgCEJlEAGkIAiCIAiCIAiCMCwigBQEQRAEQRAEQRCGRQSQgiAIgiAIgiAIwrCIAFIQBEEQBEEQBEEYFhFACoIgCIIgCIIgCMMiAkhBEARBEARBEARhWEQAKQiCIAiCIAiCIAyLCCAFQRAEQRAEQRCEYREBpCAIwhi7++67kSRpyJ+nn356vJs4LLlcjltvvXVU2vv0009X1HMxUna/N1pbW0dku0rwwgsvcOutt5JIJMa7KYIgCMIwqOPdAEEQhMnqrrvuYv78+ftdv3DhwnFozeHL5XLcdtttAJx33nnj25hJ5rLLLuPFF1+kqalpvJty1F544QVuu+02rr/+euLx+Hg3RxAEQTgEEUAKgiCMk+OOO45FixaNdzOEClRXV0ddXd14N0MQBEGYhMQUVkEQhDL1f//3f0iSxE9+8pNB169YsQJFUXjssccAaG1tRZIkvve97/Gtb32LadOm4ff7WbRoEU888cR++924cSOf+MQnqK+vx+fzsWDBAn7605/ut10ikeArX/kKs2bNwufzUV9fz6WXXsr69etpbW0tBTC33XZbafrt9ddff9j3s379ei6++GKCwSC1tbXcfPPNpNPpYT1HmzZt4oYbbmDOnDkEg0GmTJnC8uXLefvttwdtt3tK7P/+7//y93//9zQ3NxONRrngggvYsGHDoG3PO+88jjvuOF599VXOOeccgsEgs2bN4o477sBxnNJ2Q00jPdD028cee4wrr7ySlpYW/H4/xxxzDJ/97Gfp7e0d1uPc14Hue3e7X3zxRc4880wCgQAzZszgrrvuAmDVqlWccsopBINBjj/+eB555JFB+7z11luRJIk1a9ZwzTXXEI1GicVifOpTn6Knp2fQtvfccw8XXnghTU1NBAIBFixYwN/93d+RzWb3a+vLL7/M8uXLqampwe/3M3v2bL70pS+V7vNv//ZvAZg5c2bFTeMWBEGYjMQIpCAIwjixbRvLsgZdJ0kSiqIA8LGPfYzVq1fzla98hdNPP51Fixbx5JNP8s1vfpNvfOMbLFu2bNBtf/KTnzB9+nR++MMf4jgO3/ve97jkkktYvXo1Z5xxBgDvvvsuZ555JtOmTeMHP/gBjY2NPProo3zxi1+kt7eXFStWAJBOpzn77LNpbW3la1/7GkuWLCGTyfDMM8/Q2dnJmWeeySOPPMLFF1/MjTfeyE033QRQCiqHez87d+7k3HPPRdM0/v3f/52GhgZ+/etf8/nPf35Yz2FHRwc1NTXccccd1NXV0d/fzy9/+UuWLFnCmjVrmDdv3qDtv/GNb3DWWWdx5513kkql+NrXvsby5ct57733Ss87QFdXF5/85Cf5yle+wooVK7j//vv5+te/TnNzM9ddd92w2ra3zZs3c8YZZ3DTTTcRi8VobW3lX/7lXzj77LN5++230TTtsPd5IF1dXdxwww189atfpaWlhR//+Mf8+Z//OTt27OB3v/sd3/jGN4jFYtx+++1cddVVbNmyhebm5kH7uPrqq7n22mu5+eabWbduHf/4j//Iu+++y8svv1xq58aNG7n00kv50pe+RCgUYv369Xz3u9/llVde4cknnyzt69FHH2X58uUsWLCAf/mXf2HatGm0trbypz/9CYCbbrqJ/v5+fvzjH3PfffeVpuRWyjRuQRCESckVBEEQxtRdd93lAgf8URRl0LaFQsE9+eST3ZkzZ7rvvvuu29DQ4J577rmuZVmlbbZu3eoCbnNzs5vP50vXp1Ipt7q62r3gggtK11100UVuS0uLm0wmB93P5z//edfv97v9/f2u67ru7bff7gLuY489NuTj6OnpcQF3xYoV+/1tuPfzta99zZUkyX3zzTcHbbds2TIXcJ966qkh7/9ALMtyDcNw58yZ4375y18uXf/UU0+5gHvppZcO2v7ee+91AffFF18sXXfuuee6gPvyyy8P2nbhwoXuRRddVLq8+3XcunXroO1239dQbXccxzVN0922bZsLuCtXrjzkPvd1oO12t/u1114rXdfX1+cqiuIGAgG3vb29dP2bb77pAu6//du/la5bsWKFCwx63lzXdX/961+7gPs///M/B308q1evdgF37dq1pb/Nnj3bnT179qD35b7++Z//eViPWRAEQSgPYgqrIAjCOPnVr37Fq6++Oujn5ZdfHrSNz+fj3nvvpa+vj1NOOQXXdfnf//3fQaNlu11zzTX4/f7S5UgkwvLly3nmmWewbZtCocATTzzB1VdfTTAYxLKs0s+ll15KoVDgpZdeAuCPf/wjc+fO5YILLjjsx3U49/PUU09x7LHHcuKJJw7axyc+8Ylh3ZdlWXz7299m4cKF6LqOqqrous7GjRt577339tv+iiuuGHT5hBNOAGDbtm2Drm9sbGTx4sX7bbvvdsPV3d3NzTffzNSpU1FVFU3TmD59OsAB23mkmpqaOPXUU0uXq6urqa+v56STTho00rhgwQJg/8cN8MlPfnLQ5WuvvRZVVXnqqadK123ZsoVPfOITNDY2oigKmqZx7rnnDno877//Pps3b+bGG28c9L4UBEEQKpuYwioIgjBOFixYMKwkOscccwznnHMOq1at4nOf+9yQmTcbGxsPeJ1hGGQyGTKZDJZl8eMf/5gf//jHB9zH7jV5PT09TJs27TAezR59fX3Dvp++vj5mzpw5rMdyIH/913/NT3/6U772ta9x7rnnUlVVhSzL3HTTTeTz+f22r6mpGXTZ5/MB7Lftvtvt3vZA+zwUx3G48MIL6ejo4B//8R85/vjjCYVCOI7D6aeffkT7HEp1dfV+1+m6vt/1uq4DXrC/r32fe1VVqampoa+vD4BMJsM555yD3+/nm9/8JnPnziUYDLJjxw6uueaa0uPZvW6ypaXl6B+YIAiCUDZEACkIglDm7rzzTlatWsXixYv5yU9+wkc/+lGWLFmy33ZdXV0HvE7XdcLhMJqmoSgKf/Znf8Zf/uVfHvC+dgdzdXV1tLW1HVF7q6qqhn0/NTU1Q7Z7OP7nf/6H6667jm9/+9uDru/t7R31khC7R9WKxeJ+9723d955h7Vr13L33Xfz6U9/unT9pk2bRrV9R6qrq4spU6aULluWRV9fXymofvLJJ+no6ODpp58ujToC+9Vx3L0e9kjfR4IgCEJ5ElNYBUEQytjbb7/NF7/4Ra677jqeffZZTjjhBD760Y8yMDCw37b33XffoBGldDrNQw89xDnnnIOiKASDQZYuXcqaNWs44YQTWLRo0X4/u4OESy65hPfff39QQpR9DTV6dzj3s3TpUtatW8fatWsH7eM3v/nNsJ4fSZJK7dht1apVtLe3D+v2R2PGjBkAvPXWW4Ouf/DBBwddliQJYL92/td//dfoNe4o/PrXvx50+d5778WyrFKtz+E+nrlz5zJ79mx+8Ytf7Bdk722o95EgCIJQnsQIpCAIwjh555139svCCjB79mzq6urIZrNce+21zJw5k3//939H13XuvfdeTjnlFG644QYeeOCBQbdTFIVly5bx13/91ziOw3e/+11SqRS33XZbaZsf/ehHnH322Zxzzjl87nOfY8aMGaTTaTZt2sRDDz1UChi/9KUvcc8993DllVfyd3/3dyxevJh8Ps/q1au5/PLLWbp0KZFIhOnTp7Ny5UrOP/98qqurqa2tZcaMGYd1P7/4xS+47LLL+OY3v1nKwrp+/fphPYeXX345d999N/Pnz+eEE07g9ddf55//+Z/HZNrkaaedxrx58/ibv/kbLMuiqqqK+++/n+eee27QdvPnz2f27Nn83d/9Ha7rUl1dzUMPPVQqw1Ju7rvvPlRVZdmyZaUsrCeeeCLXXnstAGeeeSZVVVXcfPPNrFixAk3T+PWvf71fJwDAT3/6U5YvX87pp5/Ol7/8ZaZNm8b27dt59NFHS4Hq8ccfD3jvzU9/+tNomsa8efOIRCJj96AFQRCEYRMjkIIgCOPkhhtu4IwzztjvZ+XKlQDcfPPNbN++nd/+9reEQiEAZs2axZ133snKlSv54Q9/OGh/n//851m2bBlf/OIX+cQnPoFlWaxatYqzzjqrtM3ChQt54403OO644/iHf/gHLrzwQm688UZ+97vfcf7555e2i0QiPPfcc9x444387Gc/47LLLuMv/uIv2LBhw6BkLD//+c8JBoNcccUVnHbaadx6662HdT+NjY2sXr2ahQsX8rnPfY5PfepT+P3+/WpfDuVHP/oRn/rUp/jOd77D8uXLefDBB7nvvvuYPXv2Yb0WR0JRFB566CHmz5/PzTffzHXXXYfP59uv7Zqm8dBDDzF37lw++9nP8vGPf5zu7m4ef/zxUW/jkbjvvvtYv34911xzDbfccgvLly/nT3/6U2ndZE1NDatWrSIYDPKpT32KP//zPyccDnPPPffst6+LLrqIZ555hqamJr74xS9y8cUXc/vtt9PQ0FDa5rzzzuPrX/86Dz30EGeffTannXYar7/++pg9XkEQBOHwSK7ruuPdCEEQBOHItba2MnPmTP75n/+Zv/mbvxnv5ggV6tZbb+W2226jp6eH2tra8W6OIAiCUKbECKQgCIIgCIIgCIIwLGUfQO7cuZMHH3yQv//7v+eCCy4gFoshSRKSJJWmSh1KoVBg5cqVfOELX2DJkiVUV1ejaRo1NTWcccYZ3HrrrcPO+NfR0cH1119PXV0dwWCQc88996DTkFpbW0vtlSSJ008//ZD3ceutt5a2b21tHVa7BEEQBEEQBEEQRlvZT2Hdne3tQFasWHHIIPKtt97i7LPPJp1OH3S7aDTKf//3f5eSBBxIR0cHS5Ys2S8luSzL/PKXv+RTn/rUfrfZPbVsbw8//DCXXXbZkPezexoRwNatW0uZ/gRBEARBEARBEMZT2Y9A7m3WrFl84AMfOKzbpFKpUvB41lln8Z3vfIfHHnuMN954g0cffZTPfvazKIpCKpXiE5/4BH/84x+H3NeXv/xl2traOOOMM3jooYd47rnn+PKXv4zrutx8882lIsuHcssttxzWYxAEQRAEQRAEQSgHZV/G45ZbbmHJkiUsWbKEmpoann76aZYuXTrs28uyzLXXXsuKFStYuHDhfn+/8MILueSSS7j66quxbZsvfOELbNy4cb+Rz2KxyMqVK5k6dSqPPfZYKSPiWWedheM4/OhHP2LVqlVcd911Q7altraW3t5e3njjDe6//36uvvrqYT8OQRAEQRAEQRCE8Vb2AeTe9cuOxJlnnsmZZ5550G2uvPJKrrnmGn7/+9+zefNm1qxZwymnnDJom76+PorFIosXLy4Fj7udf/75/OhHPzpk4errr7+eX/3qV3R3d7NixQquuuqqg07RHa5CoYBhGEe9H0EQBEEQBEE4XLqu4/f7D7ldpZ2zDvdxTTZlH0COlaVLl/L73/8egM2bN+8XQFZVVaGqKq+99hq5XI5gMFj629NPPw149cwOJhQK8bWvfY2vfOUrvP322/z2t7896JrL4SgUCsQCVRgUjmo/giAIgiAIgnAkGhsb2bp160GDrUKhwMzpYbq67TFs2dEZzuOajEQAuUuxWCz9rijKfn8PBAJcdNFFrFq1igsvvJCvf/3rVFVV8cADD/DDH/6QYDDIpZdeesj7+dznPsf3v/99Ojs7ufXWW/nwhz+MLB/5UlTDMDAocDaXoqId8X4qTeOFUWSfRMdDyf3+JskSNdPj9G1L4DplnSMKSYWWq+I4RZf2AzyWcqXXqky9Mo6VtbFyDmbKJvlugWK3td+2B3o9IvN81J8doX1VkkKXOdbNP2KyLlF1coDYwgC4kO8yybYWSW0owq63Ws3pQeLHBul/M8vA6/lRaYfil5jxyRo6H08Rnq4TmeMn12HQ/VQau3Dw93wlfT4mA/F6lBfxepSXw309ak4LEp7tY9u9A+AM/tvUj1RR6DToeS47Sq2d+IZ6PSxMnuv6A4ZhHDTQMgyDrm6bba/PIBop/zQsqbTD9FNbD/m4JiMRQO6yevXq0u8LFiw44DY//OEPeemll3j++ee5/PLLS9dLksS//du/0dDQcMj7CQQCfP3rX+eLX/wi7733Hr/5zW8OmL31cKloqNLkCCCjC/xUzQ7T+WjygI9ZkiQ0WUeVNFypzE8AbEi+YjDlsjiJOoNi7/4BWDnSNRVd1+m4rx8zsbsnURr265F/38FeKDHlvCq2/3YA19nvZuXJhOQrJtl3bULTdULTdZrPDVFzrMXOp9IYAzbJl03kokHj6VX4dB+9L43CyYoBqqQRqvZDXkJBpffxJJKloh5iVvxRfT4kCE3TyW43SgGzcHQq6ng1CYjXo7wc7usRrAlATkJ1NdjnWBiI+iAto0qVM32y3Az5ehzmRyUakYlG9h+sESpH+Yf/Y2Dt2rWsWrUKgGOPPXbIAPKYY47hlVde4dprryUej+P3+znjjDN4+OGHufHGG4d9f5/5zGdoaWkB4Pbbb8e2K2cof7zJPom6s8Ik3smT2ToxvgQcwzvyShX0aVRD3oHfNY/8BKv7mTRaVKHqpOChNy4zVsYhua5Axx9S7HgggaxJTP1QFTWLg6hhmcQ7eQbezFF1UpDoglHotXQhu90gOt9PZnMRRZcJNOsjfz/7UPwStWeEiB0remIFQSg/+S4Tf6OGpA2OHvVqBUWXyW6bGOcNlc7BxamI/0Qn0lAm/QhksVjkpptuKgVx3/72tw+6/axZs7jnnnuO6j59Ph9///d/z+c+9zk2btzIr371K2644Yaj2udkEZqmI6sS/W/kxrspI6bq5CBm2qbQUxmjj0pIpvaMELkOAyt75EOHRr/NwNo8VacESW8uYiYrsyOl2G2x/XcDVJ8aJH58kOpTBifZGq2OgYG1OaZdU0XVKV4AbudGfxjXzrvkOgzixwdIviPWXQuCUF5cG2RFQvFJWKaLv14lNNNHZLYP23DIbC0eeieCIBzSpA8gP//5z/Paa68B8OlPf5orrrhiTO73xhtv5Lvf/S6tra380z/9E5/61KfQtMkxBfVISbIXbOU7jDE5WR4L1YuChGf46HgkWRFTAmVNYsolMXCh6/H0Ue+v/40skWN81J0dpmNV5awB3ZdrQ98rOQbW5gk0aciahF1wKPbZo/ZeLXZbZLcXic7xU+g2x2z6sxpUMDOVGewLgjAxybrElMtj+Ou986iaxSHsnFOa4ZLZWqTvlSxOsQK+aCcB23WwK+ClsCtmfc3Ym9QB5He+8x3uvPNOAE477TR++tOfjtl9a5rGP/7jP3LjjTeydetWfvGLX/DZz372iPcnydKIlAQpZ9WnBtBjCjueTCPJQz9W77ngoNuMN71KofbMEMEpOv2vZsltN8u6vbtVnxpEi8q0PZDEKbjDavNBXw8H+l/L0vDBKFpEOaoRzXLgmpDbPjgp0Gi+rl2Ppwk0Fil0D//9c7Sfj94XssSP8+Ov0yj2VcaoeTmrhOPVZCJej/Iy3Ncj0KSXgkeA6Fxvmr2Zsul8JIWxa62+eF2PzlCvh+RK+yUtEia2SRtA/td//Rff+MY3AJg/fz5/+MMf9qvvONquu+46vvOd77Bp0ya+9a1vcf311+Pz+Y5oXzXT42jy6K+BGi9qRKbpA1GSGwpEo1GIDr2tJEvEmyMgSWWXRU8JScQWBAhN17GyNgNr8sgDfmpnVMaasqaTIhR7LaJxF+LDu82hXg8ZiXAwwtQzVdIbxfSiIxFsHv62R/v5CM3QqVkUgrMhtbFAYu3oZJqdLMr5eDUZidejvBzO65F+1kENKzhFBzPtoMcU7KJENB4d9veVcHBDvR6mY8DW4e/HWwNZ/p+vSmjjeJmUAeT//u//8v/9f/8fANOnT+exxx6jtrZ2zNuhqiq33HIL1113HTt27OC///u/+fznP39E++rblpjQWVhrzwgy0OOy7ZH+Q2bslGQJXJfeMkvDHjlGp+70CEYxz84/9JLcUKyoHrtAs0Yehc7XU4eViGA4r4e2xca0bHpbMyPVXGEIR/v56N0Gva06kXk+Bl7Li1HIo1Sux6vJSrwe5UW8HuVlqNfDciunHJcwMiZdAPnggw9y3XXX4TgOTU1NPPHEE6WMqOPhk5/8JN/+9rdZv3493/72t7npppuOaD+u407olOPhWT7Smws41vAeo+vuek7K4AtH8UtEF/ipOS1EemOR7mfTuBV4zl2zOEi+0zyiJASHej3MlI0WUcri9ZoMjvbzkdlSJLNFjBaPlHI6Xgni9Sg34vUoLwd6PVz38F4bL8Np+auMVo6PCioccPSeeOIJrr32WizLoqamhscee4zZs2ePa5tkWebWW28FoLOzk//4j/8Y1/aUo/BsH2pIIdtaeem3axaHmHV9LTWLQiTeybPz6coMHmWfhL9eI7ludKYr2nkHxS/WpgiCIAiCIJS7SRNAvvDCC1x55ZUUi0Wi0SiPPvooxx577Hg3C4Brr72W448/HoA77riDbHYUCo9XqGCLRuP5EdIbC+TbK2uKhKxLxE8IkFpfYMv/66P3hWxFZFo9EC3m1X00RqnUhuuyX9FnQRAmp+BUjbpzwsRPCIx3UwRBEIQDmBRTWN98800uu+wystksoVCIP/zhD5x66qnj3awSSZK49dZb+dCHPkR3dzd33333eDdpXEmyN+oYW+An0KyT3W7Q9eTRl4wYa+FZPiQZel/J4hQqNHLcxV+n4touRv8oDZ86wATPIiyMPX+jSsuVcZyiS7HP8ka6gzKp9woiYVOZUoIyjRdEUXxe/3agWWPnk2kco7KPoYIg7GG7LvZhTnsdD5XQxvFS9gHkc889x6ZNm0qX169fX/r9zTff3C/Yuv766wdd3rx5MxdddBGJRAKAb37zm8RiMd55550h77OlpYV4PH60TT8sV199NSeffDJr1qyht7d3TO+7nOhVCo3nR/HVquTaDDofS3lr7iroM+yrU/HXq8SPC5BrNydEzcrQNB1JkQg0a+R2jMZIsIs0aeZDCGPFX68hSRKKXyI4ZU+Wal+NyPhbjiRNomlZFFzYfFcv/nqNxmURahaH6HlOJNgSBEEoF2UfQN5555388pe/PODfVq5cycqVKwddt28A+eyzz9Ld3V26/OUvf/mQ93nXXXftt5/RJkkSt99+O8uXLx/T+y0Xwak6wRaN2LEBzJTN9t8NjFlh9BEjQ91ZYeLH7pl21ftichwbNHIG3srjq9eIzvWPSgDpWiCpYgRSGFmJt/Ik3soj6xJaVEHWJcy0jb1vvVHJe/+55sj2VEkqaBHFm54tAQ4YA6MzDXwi8NUoBJo0Bt7O4RRdcjsMUu8WCM/20fPceLdOEISRIsp4VL6yDyAnk8svv5zFixfzyiuvjHdTxoykSTR+MEJ4pg8ra5Ncl6fv1WzFJZrx1ajUnhki0Kixc3Wa8Ewfsi6R3V55iX8OJN9uktlUILYwgG9tfsSDeyNhoQZkZL9U8dN9hfLjGO5B37MN50UIz/ax4/cDIxbgBVs0plwe3+/6jkeSFZkQbCzYOQfHcqk6Pkh4pg/HcNEiCna+8mdxCIIgTCRlH0DefffdR7Um8Prrrx/z0cS9zZgx47DSG7/88suj2JrxpUVlAlN0ZF1CViUUv0x4lo6kSXT8MXlYtQXHnQz+epVAs05omk6gUcNM27Q/nMQuODScG5lwZQ56X8oSaNKpOydM7/MZrLyDlR6ZE7tiv3fS7q/TyO2ooPeBUFlkUHQJx3Rxd8WJoZk60Xl+AKIL/fQ+PzJJzKy9pq73vJihsNNk6lVV+GpUEUAOwUw5bP1VH6HpOnpcQdIlJFkitaEw3k0TBGEEObjYFTC6J0Ygh1b2AaRQ+WRdovbMELH5AVzH9U7eLBfHcMluM+hfkxuxQGTUSOCrVQlO0Qg06wSaNGRNwi465DtNb1Rhm0GgWWP6R6u9m2gTa0qma0P38xmmXhln6jVVAGz9f31Y+04HPAJmwsZIWESO8YkAUhhxakSm+aIYvtoDf+UV+y181SpVxwcZWJMfkXXLRr/NjvsHqD8nQt0ZYcCrnSaCx4NzDFesTxUEQShzIoAURoUakmlYGsHfoOG6LhLQ/Uya1PuFipmeqtcoBJt1As0agWYNxSfjmC75LpP+17Pk2k1vWpzrPd7qU4NE5/qxCw6tv+mfkFkDC50mdsFB8e/JkDhSJ3vp94tUnRSg+1kq5j0ilCctplB9chAtrhBo1Ab9re+VLHbRof6cSOk6X7X3VZjeXBzRpFeFnRbbfzdAYIqGFlUwEzbFPvHmFgRhchNrICufCCCFEaNFZfz1Gv5GjcgcH47pMrA2B0ByXR47X94fRC2uEGzWCEzRCTZrKAEZx3Ip7DRJvJUn125Q6La8khO7KAGJqpODxBYGcO1d275TmJDB4249L2ZoXBoFRjbxTWpjgZrFIULTfWQ2ixEI4cj5alWi8/2Druv4Q3LQmuTU+gJ6XEWNyjhFr0SNPUrrb/PtZsXVsRUEQRCEoYgAUjgqerVCbIGf8CwfamhPsfnkewUG1niZ9MqVGpEJTtEJTNEINmuoIcULArstEu/mybebFHaapbVSSKCGZfS4gl6lEpzqjU66lsvAmhyJt/I4I5zFsRyl3y/SuNT7Pd8+ctPxrLRDvsskOlcEkMLRyWwusrmtF0mRhhxRdG0o9lkU+8a4cYIgCIJQ4UQAKRwZCWqWhKg6MYCdc0hvLpLbYVLoNss2aFRDMoFmrRQ0ahEF1/GyM6beL5JvN8h3meB6tRz1mEJwagg9pqDFFbSYgqx4I26O5ZLvNOl7KUvq/ULZPuZR4XpT/dSQjJk6uul+4Zk6/kYNx3Sx8w7pjQXqzgyj+KVRGw0SJgfvMyneQ4IgCOXGdl3sw0gwOV4qoY3jRQSQwmGTfV6x50CzRt/LWQbeyg+a1lkuFJ+Ev1nflfhGQ497b/dir0Vma9GbVtZp4hguSkAiNM1H4/kBgi06sibhui5W2sFI2uTbTZLr8phJGyNhY2XK8AGPISNhEZkdQlLYM0J7CFpUITLPR2p9AVmTiM7zET8+OGib9ocTAIRn+0iuE5kXBUEQBEEQyo0IIIXDotcoNF8UQ9Yk2h9Oku8ow3U9EsSPDxC7QEaSJYwBi1y7Sd8rWXIdZqnOoF6lEDs2QGi6jr/B+ygUdlpegpwdJkbCGnZwNJloUZmqE4KkNxUO+vz4G1VCU3UUv0ztzDDxoDfFufpkL2i0iw59r2RJbSxQfUqQ2IIAseMCZHcYROb4RQApCIIgCBOQQ1mOO+ynEto4XkQAKQxbeLaPhvMiGEmLtodSZVt6o/qUAJE5Plqf7Ce5fk9WRTUiE2jQCLRohKf70KIKjumS22Gw8+k0uW2GmDY5DHq1iqxJ9L68p16eEpDw12soQRklIBNo0ghN1bGyduk57XkujRpRcC2XXLvpTRfe9RbqXp0h32HSeH6U3pcy1J4eRospmEkRwQuCIAiCIJQTEUAKhyZBzeIQ1ScHSW8ssHN1umzLLOhVClUnB70kPm8WkH1e2yNzfWhhbwTMTNtktxlktxXJd5hilPEwGf0WruNSsyhEcl2eyBw/0QV+ZNWb9mvnXcyUTedjKTKbi0iyRO0MiWRrEdcZOkDfXf/RzjtYeYeqEwJ0P5sZq4clCIIgCMIYsHGxK2CNeiW0cbyIAFI4KFmXaLwgSrBFo+eFDIm38uPdpIOqWRLCyjjkNhQItmg0XhDBdSG9sUh2u4HRZ41I4fvJzEw57HwqTf25EaLz/Nh5h/43cqQ3FLByzhHnLbELLsU+i0CLzsCbOWoXh+h/M1e2I92CIAiCIAiTkQgghSHpVQpNF8dQfBIdf0iSayvD9Y578TdphGf46Ho8hd8JUbMkRKHXovOR1ISuyzge0huLZLYW8dWquA5oIRm7eOTBY2m/m4tUnxSg5/kMVScGqT4lSPdqMQopCIIgCBOF7Xo/5a4S2jhe5PFugFB+JAXiJwSYenUc13LZcd9A2QePALWnhyh0m2S2GCgBCV+NSnJdQQSPo8S1oNBlUXNqkKaLYhxzUx3Nl8VQI0d+WMlsKiDrMtF5fgbW5IjO86NFxWFKEARBEAShXIgzM2EPGWIL/cz4RDW1p4dIbyqy4/6Bo671NxbCs3QCDRq9L3mJXZSA99Y2Bsp0seYE0vlYily7t34xNFVn5idrqDsnjBo6/MOLmXKwsjZqSCb5bh4771B7RnjE2hqartPwwQjNl8aIzPGN2H4FQRAEQRgep4J+hAMTU1gFkCAyx0fNohBqRCa9qUj/a7nKyYApe2sfs9u9pDiSLO1K1iIhKdJ4t27Ccy1ofyjJ9I9Xoce8Q0r82ADxYwNenU3LJSD7KaBTdUoQK+fQ/Ux6yFqa+S6TyGwf/a/l6H4mQ/MlMapPCdL/Ru6A20uaRKBJQ1bBGLAxkzbuPrv21arULAkRmqpT6PVKuTSeH6XqJAvXcnFdyG4zSK3PY+fFiLUgCIIgCMJQRAA5yfnqVBqWRvBVq2S2Fun4YxZjoEICx11iC/xoUYXOR1Ol67zSERJaWKbYM35tm0zaH0rib9Dw16lUneTVekQC13GJzPEhT4lS7Lfw16u0XBVnYE3ugLUee1/IMuOT1UTm+ki+U6DvlSw1i0M4pkvi7T1JnCQF6s4ME53vH9RR4DouZtrBSts4poseV9CrVMy0TfsfkuS2e6OlwWk64Vk6uCBrEtWnBKlZFCS7zaDQa5F4K1e22YYFQRAEQRDGiwggJ7FAs0bzxVGMhM323w9Q7Km8s2VJk6heFCL9fhGjf0/g6xRczLSNv0kjs9UYxxZOfGpEJjzDR77TJLO5SGZzkXynSfMlMVzLpevRNM4WjURPkmK/RWiGTtOFUWpPD2MXXDKbi4P2p0UVJFnC2VU/sv+NHLJPou6sMMGpOgNrvZHIujPCaHGFvlezZLYUcQwXLa6gx1X0uIIalpFUiUK3Rc/zGXLt5qAkP7ntRimYBJD1DJE5PmILAoRn+dDjCt3PlG/JGkEQBEGoRA4SNuU/Q8ypgDaOFxFATlKhaTqNF0YpdJl0PJKs2JPkqhMDyJpE3yvZ/f6W7zIJNGrj0KrJpfmSGL5q71Cy86kUkiYTm+8HvBFuAMd0MRJegJ9tNdhydx8NSyM0LYuSnWfQ8YckyFB/TpjYggC5doNM657AsvfFLLkdBg1LI7QsjwNQ6DHZcf8ARt+ejgO7y6LQdWRvZsdwSa4rkFxXIHKMj/rzIgSaNDKbixT7LZyii6RKyJrk/at6HRiy6v0YCZvUBpG0SRAEQRCEiU0EkJOMXqVQdWKQyFwf2W0GXY+ncCtrxmqJEpSpOjFI4u38AWs7Fjq9tXSSSsUGyJWg67EU0z9aDUD9uREkea8euyFiKcdw6Xw0RXimTtNFMeo/EMa1XWILAnQ/myb5bmG/2+baTLb+uh8tLKMEZArd1lGXDRlKelORQo9F9SlBwrN8e6bk7m6/5eKaLo7l4piuN013vp/48QHaH05UROIpQRAEQRgPjuv9lLtKaON4EQHkJBKcptN0URQ779D7UtZbT1bBH46aU4O4tsvAmgMnVyl0WUiyhL9BI99e/mVIKl3XUylC03xoEZnOP6VQfDIoB79NZqtB3ytZYscFUHwSfa9mD7gussTxMrWORYBmJm12PpUGdo80gmOBa7kH/NyoEZkpl8aYsjxO2wOJA3ZqCIIgCIIgVDoRQE4SgWaNxg9GyLcZdD6a2i9LZaXRYgrRBX56X8oOOWXQSNjYBYdAkwggR40EdWeFMdM26Y1F0hv2TDvdnWV10IjkAfS/kaN/TQ5ZlXDM8uzRcE0X+xBvISvt0P5wkpar4rRcGafz8RTFbjH0LQiCIAh7sytkDWQltHG8iDqQk0B0gZ+WK+KYGZuuJ9MVHzwC1C4JYWUdku/kD7pdvtMk2KKPUasmFy2m0HRhlECzxs6n00dXMMmlbIPHw2FlHdoeTGAXHKZeHafx/AiBKRriO0gQBEEQhIlCjEBOcEpAomZRkPSmAl2Pp8e7OSPC36gSnuWj64lDj6Sm1hdoviRGoFkj3yFGIY+Uv1GjdnEQFAkr4yDJEJqhY+ccup5IixHevVhphx0PJIjN9xM/MUjLHD920UFSJApdJn2v5Sh0iedLEARBOHpaXME13YpaNiFGICufCCAnsPgJAapPDeLa0PvS/llKK1Xt6WEKPSbpjcVDbpvdZlDoNqleFKT9weQYtG5iUUMyVScHiR3rp9BtYQxYaGEFWZPofiZDekNhQoxojzgHku8WSL5bwFevEpyiocdVfLUqUy6Psf23A5jJCs1eJQiCIIwrvUrB36Bh5RymXBoDoG1lgnyn6JwUxoYIICcg2SfReEGUYItG8t0C/a9lsfOVPz0QvFGvQKNG+8OJYd+m/7UczZeKUcjDocUVqk4KEJ3jx7Fcel+s/KRL46XYbZXWQkoKTP9YNU0XRun8U0oEkYIgCMKw+BtUInP8Xodk1Z7T92K/ha9aRQmKVWnC2BEB5ATha1AJVfnR4gqh6TpqUKZjVZJc2wQKmGSoPT1EdodxWI8ru90bhaw5LUTbysTota8ChWfp1J8TwUjaZLcVcW0ITtUJtmjYOYfel7Mk3yvgToD1ieXAtaHjj0maLowy7SNVJNflGViTwy6I51cQBEHYnxqRafxglECThpmxyW4z6H8jR67dJDhFo9hvMf0j1TjFypkO5LgSjlv+00MroY3jRQSQE0TL5XE0TcPKOBT7LHY+kabYN7EyQMbm+9FiCp2PpQ77tn2v5ZhyaYzAFJGRdTe9WqHpQm/qSyAgo1cpSKpEvsOk++kM6Y1ieupoMPpttv8+QdUJAeInBIgtDJB4O8/Am7khMwoLgiAIk49eozDl0hiuDe1/SJLbYQyaCZRrM2g4LwKAIWa0CGNIBJATxPb7BiAt407Q44ekQvWiEOmNRYy+w3+Que0GhZ0mNYtCtLUnRr6B5UaGYLOGMWCXFtbLmoQakdHCCmpExle35+O/5Vd92DnHyxYqYphR55ou/a/nSLyTp+rEIPHjA0Tn+eh6UiQkEgRBmOwkBapPDVJ1YhBjwKZ9VWLQUiTFL9GwNIKvXkMNyOS7TKx05fT4iiQ6lU8EkBOEOWCjShN3/nvViUFkn0TfK0eeDKjvtRxTLosRbNEm1tTeXZSQTLBZI9iiE5ymowZkXMfFLrpICij6nveHa7uYGQcra9P+h6QXPIIIHseYU3TpeyVL4p08DUsjTLk0RudjKbKtxng3TRAEQRgH/kaVhnMjaFGlVCd57zJZSkCiaVmUQLNOvsuk59k0GfGdIYwxEUAKZU8JSFSdGCD5dr5UnP5I5HYY5Hea1CwOkWtPVHywJPskgs0agRadYPOeRfXFXovUhgKZTUV8tSpKQMJ1vBqFVtrGzDhewFjhj38isXMOHX9I0nRRlPpzwuzoTRzVe10QBEGoPFUnB6ldEiLfZdL5uwGMAW/GVaDJqyesxRRqTw+BC20PJSp2xoqNjF0Bpegn6KS+ESECSKHsVZ8awnXxeuGOUu+LGVquiFN1cpCBN45+f+MhNFOn+uQgvjoVSZIwEhb5dpO+V7PkOkycvRKyFHsn1jrYCc2FnucyTL06zoyPV5PZUiTfaVJ7Rpi+V7Mk3sqPdwsFQRCEUSBpEuEZOrVLQvS/kaPv1Sy4Xub56kVB/LVaadvU+wV6ns/gFEUvsDB+RAAplBcJJIlS8hYtphBb6Kf35eyIHCwLXRb9b+SoWRQk325Q2Fk5AZa/QaX2jDCBRo3sDoOdT3vr5cRI1cRhZRy23TNAdJ6f2LF+InP8gJd9OLW+IJLsCMI40KsU6s+NMLA2R3armCoojCwlIDH9o9Uofm8tY//rWapPCRI5xodepZJrM2h7MIG1a6mJmaj8cTG3QrKwuhXQxvEiAkhh3MmaRHiWTmSu35ty6ZMxkjZmykYNyVgZh+Q7Izf60v96jmCLTuP5Ubb/dgCnzEtUaDGFmsUhIrN9FHrNip62IhyaY7gk3s6TeDtPsEUjfnyAQLOO65T3+1QQJiI1JDNleRw1KKP4Qhj9tqjfKowoLaai+GVc20UNyzRfEiPQpJHeVGTn6gyFLvF9L5QfEUAKYy66wE9omlerUtIktIiCpEK+3aT/Da+Uga9GRYsq2HmHrhcyI5td1oWuJ1JM+0gV9R8I0/VEegR3PnIUv0T1qSFiC/1YOYeuJ1KkNxbHu1nCGMq1mfgbNfyNmlizKghjSJJBjSg0Lovg2i7tq5LUfyDMlMtjbLunH7dyJq8IZa6w02RgbY7IXD9aWEHRJdr/kBQdxUJZEwGkMKbqzgoRPz5Irs2g2G/hWC5WxiGzuTimUzGttEP36gyNF0SoN126n8sMynI2niQV4scHqTo5AC7e+re38xO2RItwcOmNRapP8dK591foul1BqCTVpwaILZORJMnLVL0qidFv0/HHJNOvrSbQrJPbLqayCiPEhd4Xs/S+mEWLK1hpe8J/34syHpVPBJDCmApO00msy9PzbGa8m0Jmc5GdCjScFyHQpNH7UpbstvE7KZB1icgxPqpOCaIGZBLr8t6IbEEMPU1mZtKmf02O6lODZLcbIjGSIByKDIpPwjFdQtN96FUKjuF6HZXZg/cU6tUK4Zl+tj2TIN9hku8yS2uPjX4bM20TnikCSGF0TIT1jcLkIAJIYczo1QpaWMHoK59skun3ixT7LOrOCNN8SYxcu0Hvi9kxPUn3N6jEjw8QmuFDkiGzpUjfK1nMVJkMiQrjrv/1HKFpOo3nR9j+u4EJ3zstCIdLDcnIPgk1pFB/bhgtrOCYLrLmjSLKukxsgZ9t9w4MOR1ckqHqpACu7TKwNo+d3/8YnHqvQNXJQXqez4hprIJwhGxXxnYroIyH6L8fkggghTGhRmSmXBajOODVKCwnRp9N+8NJgtN0ak8PMfVDcdLve0HcoXqrj4akSd7UxBMCGEmbvlezpDcWvRqNgrA3B3Y+mWbaR6qILQyQeLt8OmEEYbxVnRSg9vRw6XK+y6T/tRxKQCa9qYCVdgjP9tG0LIril7DzBz4rrFkcIjTTR99rQ2f9zm43qFkcwt+giTVqgiBMWiKAFMZE/TkRXBs6ViXLb/REhvqzwki6RHJdHi2qEJnrJzzLx84nU2RGOG277JOIzvNTdWIAWZfpfyPnrW0TPV3CQRgDNqkNBapOCZJ8Ly9GPwRhl8gxfrLbDfpfy+JYLkb//l8yVtq7rvqUEMl386UC7XvT4gr5DhNzx9CdeMVei0KvSc1pIdraEyP2GARhMnGQcCj/EUhHnJgNSQSQwqjT4gqhaTpdT6SG7PkdNzK0XBEn0KhR7LeIzPYB0PdKFl+tSuMFUXbcnxiRKa3+Jo3YQi8wlYD0piJ9r2ZFHUdh2PpfzxGd6yd+fJCBNSKhjiDIuoQWV0htKFDoHvo4Xei2SLydIzLPT/z4AKkNBXpezAxaYy7JEsPJmdH3UpYpl8cJzdDJtoq1kIIgTD4igBRGXWyhHyvvZVotO45XhxKg808prIxN4wVRak8Pk91hICkSgWbtiAJISZPQYwqBJo3oAj++ahUjYdH3Spb0hgK2SI4jHCYr45B8t0DVSQGS6/Kl5B6CMFnFjg0AkN506KURPc97mS4jc/3Unh4iPLOa/E4LK+ONRoam6XQ/k0YneND95NpMsjsMqhcFRQApCEdAZGGtfCKAFEaVpEJ0np/kuwXcMh1oa/9DkimXxph6VZz2h5MUukzCM3xeCY1XskOu2fTVqUSO8eFv1NAiCopvV++1BJK056Dj2i6Z1iI9z2XId4g1M8LR6V+TIzrfT/zEAP2vilFI4cgpAQl/g0agSSM4Vce1XDKtBukNhVFd/z1SJAVql4QwBqxhdaaoERlJkUitL5DdViQ6P4C/XsVXoyKpEn2vZkmtL1I7Y+gAMtCsUbM4RKBRA7wRUNGRIwjCZCMCSGFURef5kXWJ5Lvlm/TDzjq0rUwwZXmM+nPD7Ph9goE3D97e3fUsAVIbCuS2G9h5B3f3eYQLjuViJm3MhI1jihMMYWTYOYfEujxVx3vJdESZF+FIhGfp1J8bQfHJ2AWHzJYisiZRfXKQmkVBCjutXesJLXpfypblGm01rACgV6nMur6WfKeBXXRxDZfsdgMzY2NlHJyiS3BXFmNZleh/I8fA2twBp4FL8tAjDpE5PhrPj5YupzYUcKwyfGIEocxVThZW8fkeiggghVGj1yjUnh4mtd7LglfOHMMl12YSnesb1vapDUXixwexsjYDa3MHTNogCCMpMs9H1fFBlIBEvsNE1mUix/hIvlNeWY2F8qf4JRrOj5LbZtDzfAYr55QCRFnLED7GR3CKRuQYP6GpOv2v58pylM1M2my6swct6q2zDzTrqEEJrVEpTW0FcB0XSZa8Oqr9FlWnBInO99P1WOqg6yb3Fpqu0/DBCJmtRcy0TXabIbKwCoIwaYkAUhgVwRaNxguiGAmLnucz492cYdEiMsYwi/gWey22/KqPKZfHmH5tNQDGgMW2ewZGs4nCJBVd4Kfh3AjgTYmOzPF718/xk1xXKMvRIaF8BZo0ZEXCzNiEZ/vIbC2WOvkc0yX1XsH72VBkymUxAlM0siOcjXqkuBYY/TZGf37QzBElJKOGZLSQjBKUMQZs8p0muJBcl6fxgihNF0Vp/d/+Q2c0lqD29BC5NpPOR1Oj+4AEQRAqgAgghRElqVB7Rpj4sQGy2w26Hk9VTLkBM+UQaNaHvb2dc9jx+wEaPhghMtubqisIoyG3wyD1fgFfrYoWUZC8mXv4GzRarozT9ViqItasCeUh02ow8GaOqpOCuLZL7ekhstsNjH4LxwJZgepTQ3tuUIFvLTvrYGcdDpS6zUo7dD2eYsbHqmk8P0q+yySzxQuiZV0iNFNHnh4CJIwBC1mX0KtUup4UHYSCMBK8Mh7lf85UCW0cLyKAFEaODM0Xx/A3aHQ/m/ZGRipIvtOg+pQgepVywBphB6IEZcIzfWRai3SvTo9yC4XJyso47Hxyz/tL0iTUgIRerdJ0YZTqRUG6V1fGSL9QBlzofSlL32tZwFurHp7tIzLHj6RKuPaeIe2dq9Nkt5Xn6OPRsNIOva9kiR8bIDzTR90ZYbLbiwSn6ERjQXq2FnBtCM3QUXwyma1Fij0V0hsqCIIwykQAKYyY+rPD+Js0Oh5OelOFKky+w8QuOETn+b2kEcOgRRQkWaL/9Vz51bgUJqT4CQFiC/xIilQa9U6+V1mdNUJ52D07JLmusF+Hn+yXJnyCpsTaPIm1eWbfWIusSciaTO+LGdKOS/f6JK7jPX4lKOMUK3AYVigrkuolfpI1b2S7UmZnjQYHGZvyT6LjiPUhQxIBpDAi9BqF2MIA3c+mKzJ4BHBtSK0vEJ3vp++17LAO7oUuEzNtEz8+MGiESBBGg16lUHdmmPTGAmbKxnUgs7UokjgJI25CBY8yBKfoKAEJI2Fj9Fm4e31kOh5J4tre8VySpf3KeNg5ETwKw6eGZEIzdXw1KmpIQQ1763EV356AyXVcCt0WbSsTYg27UJFEACmMiJrTQhhJq+JHQhLv5IkdF6DqpCD9r+1J8e5v8mqlyao3fVBWJOy8Q7Hfy8YXmj78tZOCcKT0au+Q3f1spiyzYgpCOWpcGiklngJIvJ0flNxtUDZV2UsypKcUir2TeIhIALxgUNYlrKwz5DFXUqBmcYjwTB9qyKs16touxT4LK+uQbzexsl5JGTPr4NouzRfHkDVp0gaPooxH5RMBpHDU1LBMeIaPnU+lKjLZwt6sjEPirTxVJwXJbC6W1kLWnRHCX+8Vji72W7i2i+KXqY542UwyWw+UqkEQRtbueqKieLkgDJ8W947TdsFB8cs4xtBfVE0XR6lbGMZ3vIORtMlsLpJ4K4c9kUZkhWGpOjlI7ZI9yaRsw6spCpQCP9cFxSchqV69azNpY6UdCj3mkMtawsf4UIMyXY+LjL5C5RIBpHDUIsf4cEyX9JaJkWih/40soek6TRdG2fFAAqfo0vV4iuZLYziGS/vDydLJuxZXiBzjI/1+ZY+8CpVh94iIv04lk5kYnzdBGG3tDye9dcOqRGGnRW7HgT87SkAi1KKT2Vpg59spAo06sWP9xI7z0/5wkuIwa0ZWOklh0BTfyUBSoOGDUYItGgNv5lBDCvHjAgy8mSPTWixNRZV1ycvLued/AKQ3FzD6Dv2kSapXEiazpUi+ozKX+4wEBxlHrIGsaCKAFI5aZI6fbGsR15wYHzTXgs4/pZh6dZypV8Vp/0MSM+XQ9USaKZfFaLkizo77B3BtMBP2oKmugjCa7JyDmbHx1WtkyrQunyCUG6foDqoRORQ775LdUaTp2CgNNVFS7xdo/U0/U6+Oe+vcn5i469z9jSqR2T7Cs3yoIYX05gLZVgMj6U29xHFxXXAdvN/HOcCUdQk1ImOlnNLMjEORZAhO9Zab2EUXvUoh0KihBL01ir5dSwRql4RxLJeBtbm9EuqNTOdB7RlhlIBMz4sia7ZQ2UQAKRwVvVrBV6PS98rwspZWCjNps+P+BM2XRpl+bTUDa3Mk1uZp/0OSaddUEWjSyLVN3t5DYfwUdpoEp2j0jXdDBGEC6vxjGneHjn6cQ3imD8UvoYYVrNby6bDx1avEjw2QXJenMNSoqMTB19fJ4KvdFTTO9qGFFaysTXpzESvrEFsYIDLbP+TNrZyD0W9R7LMo9lsYfbaXWXSEA0stqlC9KIAVzSNrEq7rlVbx12qlbbb8sveQWdD9DSoNH4ygx/ac9rqOt07RTDkYBYv+N3LEFvox+m16nhv5AC+20E/82AA7n05jpSt8vY8w6R1RALlz505efvnl0s+rr75KKuXN5V6xYgW33nrrYe3vkUce4Wc/+xmvvPIKPT091NXVsXjxYj7zmc9w8cUXH0kTB5kxYwbbtm075HbTp0+ntbX1oNv8z//8D9///vdZv349dXV1fPSjH+WWW24hGo0ecPvrr7+eX/7yl6XLf/zjHw/5mCTJmxbx6U9/mrvvvvuQ7R5PkWP82AWH7BBTgiqZmbTZ8fsEVScHqTopSHCqTtdju9YsyKK4rDA+MlsMmpZF0WIKZnKSzTMThDFgZRy670sQP8GPXq2SfCdP/6t7Okljx/qRdYmBNYce1RxJkgLVi0JUnRhAkiX0GoXE2jzVpwaRZAnH8tbmK34vOUux3yKzuegFhWkHNSITOcZP5BgfepVXgsrKOWS2FElvKlLo2tMpmlibR1JBr1JR/DKSDMggyRKS4gV2vhqV0Awf8RMCSJKE67iYKRtjwMZM7fpJ7vo34xw0R4K/USM6z4esy+w6BUKNyPhqVMJxP051uLRtZksRar3f+984RAktyUtwU3VigEK3xbZH+7Hz3lpYM+PsN3Mqs2nk8xlImoS/XqXurDCJt3Ok1oslL7YrYbvlfx5VCW0cL0cUQDY2No7Inbuuy80338zPfvazQde3t7dz//33c//99/OZz3yG//zP/ywFVePp9ttvZ8WKFaXLbW1t/OAHP+DJJ5/k2WefJRQKHeTWnltuuWVEguJyEZnjI725WPHJc4biGC59r2SJzvNj9Fr467yPjMjOJ4yXbGsR23CIzPGJ6dOCMEq8Y/+BP1/150QAMAZssiM0MulvUKk/N0K+wySzpYhjuphJG1mT0KsUHBvqzwmjxxT6Xs1R6DZpWhal8fwoRtIm32l6SV7yLnbBAcnLJlu9KETt6WFcx/WCTNMls7VI4p08Rr/ljWAOEX+5FhR7Dv1dJ6lehmhftYperaDHVULTda9OsuKdu7mOi110ce1dP5ZXNiXXbhCcohM7NoAxYHmBpgvgjQ6m3iuQtlwGupM4toOsSgSaNMKzfHQ9kSK98eABn69GpfrkIHbRYWBNDlmTsHOUEuSNNiUoM/3aKhS/TK7NoOfFiTVbS5i8jnoK66xZs2hpaeGZZ5457Nv+wz/8Qyl4PPnkk/nqV7/K7Nmz2bx5M9/73vdYs2YNP/vZz6irq+Ob3/zm0TaVK6+88qD70fWhSzG8++673Hbbbfj9fv7+7/+eCy64gO3bt3PLLbewZs0a/umf/ok77rjjkG149dVXefDBB7niiiuO6DGUk0CzhhZRDnkAr3R6XEENyqQ3F9FiXjY/15oY6z2FyuPakN1iiABSEA5CDcvETwgwsOYQI1SHyVfvnTY5tkvTsiiJt/OkNhQOOyCRNQl/o4oS8EbZglN1fLuCsPhxAQBc2y0FYACFbpPtvxso3deWX/UhK9KQawCT6wrIeoZAo4YalrFyDrl2c8TzFbgWFLut/ZMMSd7roEUUtKiCEvRGMiVFQtElglO9wNEuOnQ/lyb5zv4jc5Isoc8IYqZs1KhM/PgAsYWBYbet2GuR3lQgcoyf5ktipevzXSaJt3LeWvJR/DqvOiEALuy4f8DreJ6gne2Hy0bGroAkOrZIojOkIwogb7nlFpYsWcKSJUuoqanh6aefZunSpYe1j02bNvG9730PgEWLFvHMM88QCHgHhdNOO40rrriCc889l9dee43vfve73HDDDcyePftImlsSj8c57rjjjui2v/3tb3Ech+9973t84QtfAOD000/nrLPOYu7cudx7772HDCBra2vp7e3llltuYfny5WUxqnrEJKg9I0Sh1xw07WUiCk3XvaK/PRZG0sYxXWpOC9LzvOhJFMZHamOB6Pw4/iaNQufE/vxVAsUvEZnrRwnIFPssb4qdOFEcV+FZPqpOCKJXqfS+kMFI2gd8TdSITGSOH0WXGHgrjzNEuQ5Zl/A3qNSeHsYYsNj+uwGqTgkS31U32C445Lu878N8p0mxx/ISzuy3Iy+oqD4liKwPPoHufjZNsd+m6oQAhZ0myN7Ux+S7BXAh17ZPsOOA4xz8BNcxXLLbx2mJiQtW2sFKOwfMOCrrErJPws46+z9XMqgBmWCz5j1Xs0wis3w4hkvPCxnMpD3sx9X1eJqdT6WRfd70Xl+NSnS+n6YLYxgJi+5nMqOWEdVXp5LrMCnsFLOWhInliALI22677ajv+F//9V+xLO8D9eMf/7gUPO4WDAb58Y9/zBlnnIFlWfzwhz/kxz/+8VHf75Fqb28H2C9QnjJlCvPnz+edd9455D6++tWv8tWvfpW1a9dy33338aEPfWhU2joW4scH8NWq7Lg/Md5NGVWKX6J6UYjsNgPXdLFNl75Xs9SdGSa9uUihS3wpCGMv32FS7LWoXRKi7YHEeDencsig6JJ34qrLu/7dc1nZ6zIS4HhZJ13X9U7WLZfcDmPQyaAWU5iyPIbil7FzDtUne6Ml/a/nSL1fmLSFwsdTaIZO7ZIQjuXir1eZ/tFqHNulsNMk8VaebKuBFleoPjlIZI5XhkqSpdJoWKwqSjglIUlgJG0kiVIdYGPAovNPKVwb+l/NMbAmh79BI9CkEWjUqD41tCvZi4uVdbAyDlbaxkx72ULDs72RxsTbeTKbi/ibNNLvF3BMb1onQOcE7RTyN2rUnhFCjyleOYxduQTsvIO56znCcfE3aaghudTJrrsKuqXQ90qWgbfyR9Q549peFms7B0a/TXpjEV+tSu2ZIVquiNO/JkffyyPfKaz4ZIyBifl6Hg3HlXHc8h+BdFxxAB/KuGRhdV2XlStXAjB//nxOP/30A253+umnM2/ePDZs2MADDzzAv/3bv43bqF19fT0Aq1evHjSK2dXVxYYNG4a1LvQv//Iv+cEPfsDOnTtZsWIFV199NbJc/h+gfQVbNGpPD5F4Oz/h62I5hrdeo9i/53Hunr4ame2n0CVScQvjwIWeFzO0LI8Tnu0js3liTyPfm16tEJyio4RkLzhzXW/0YneJAVwUn4wS2P0jef/6ZWR16O8Px3JxDBfHcLzPveOl/ZdkaVfyEJB1mZpFIcyMjdFvYSRswjN9uKZL6/392FkHvUqhelGIhqURqhcFybUZ3shUp4mZEsOSYyEy24eVd9j2m35QJHy1qpeMZaZO88Ux7IKD7JOwsg69L2ZJvpdHDXs1fSVAqfeT7MrjOi7+Bg0k6HoyRaHbwkwMnqrqWpBvN8m37woSZG/dna9GRY3IaGEFNSLjb9SQVAljwGLHfYnSOvohs6hOMFpMYcqlUYoDNgNv5rCLLk7RQVIk1IiCFvGmusqaTHpjETNpY+Uc7IxDNG7T25rEPcRo6+Eq9lq0P5hkzs11VJ8cZOCN3LBLggyXXXRQg5V3nicIhzIuAeTWrVtLI3rnnnvuQbc999xz2bBhA21tbbS2tjJz5syxaOJ+rrrqKr71rW/xt3/7t6RSKZYuXUpbWxsrVqwgm81y8803H3IfwWCQv/u7v+PLX/4y69at45577uHjH//4GLR+5OjVCo0XRsm1GfROgsXgsi7tWnTvnfjFjgtQf3aYxLq8qOMkjKt8u0mmtUjtkpBXh3WCJmSVVAhN1fHVaQSnafhrNRzTxcrZgDdKhMyuf70i33bRwc672HmHYreNlXdwii520dkVJA4OFm3DHfaoRmiGjr9ew1ejEJqqY/R7U+DsrLcDY8Cm67EU/W8oxBYECDRqROf7kSSJxDt5Bt7MYWUdMTI5irLbDCJz/IRn+0hvLFLo9AL45Dt5/A0qwRYdM2WT2VzEdbzjfGiqTrHXIrfdRJntJ9Ga3xVMHGamVcdLPDOc5DOThgSNH4xg5RzaH0qURlqHdVNZgviotQyA1v/rZ+o1ceo/EKZrhGt95tpNqk8OIilM2GP0kRBrICvfuASQ7733Xun3+fPnH3Tbvf/+3nvvHVUA+cwzz3DCCSewefNmXNeloaGBxYsX8/GPf5wrr7zyoKObixYt4q/+6q/40Y9+xDe+8Y1BfzvuuOO45ZZbhtWGm2++me9///u0t7dz2223ce2116IoyhE/prGkhGSaL41hJm06H0tPihMg1/W+wPaMOvrIbivS86wIHoXx1/tilunXVlF1cnDCJdSRFIgtDFB1chA1KGNlbfJdJv2v5ryyQeM0mJdtNYaVedPo21NLTtYlInN91J0ZLiVIMQYs2h5M4EyeweMxk95UJDi1QP15EQo9e0YN1bBMaLoP2eeNKkcXBnBtl6qTAqX6gFbOoboxTjQpk9th0vdqdtIFg7JPQosoqGEZ13axCy52zvE6Po5A1clBfHUqbQ8cXvA4VsyETd8rOerOCCE/kxnRUcjMliK1i0MEW3Sy2yZeuTNh8hqXAHLHjh2l31taWg667dSpUw94uyOxdevWQZdbW1tpbW3l3nvv5ayzzuKee+5hypQpQ97+hz/8IfPmzeMnP/kJGzdupKamho985CPcfvvtQ9aB3Jff7+cb3/gGf/mXf8mGDRv49a9/zXXXXXdUj2usNC6NgAsdf0yNeBa3chWa5mXmze9a8+QYDmpEEb2JQlkwkzb9b+SoXhSk0G2RG69kGcMg6xJqSEYJyqghGTUoe+sLLRfHcnFtb83x7oyN/gYvQ2VqQ4GBNbmKnv7pGC6p9QUKXRbReX7ixwfQq9QDJ1kRRkT36jTBFo34sQF6X8lSdVKAqhODuJaLmbF31Uv0pjXvnk7qmC6p9Xmc7SrJ3gzRBQGmfagKK2vvKUFheRk8U+sLFVuHVdYk1Oiu7KgRb4rt3r8rvgOPDJlpm1ybQa7NxBiwUIMyWlyh2G2VpuKqIRk1LGP0ewnnfLUqNacGGXgzV9bTdbPbitSfHSbQrI1ooGcmbIyERXCqCCCFiWVcAsh0es8UgXA4fJAtGVRbMZM5slEfXde54ooruPDCCznuuOOIxWIkEglefPFF/uM//oMdO3bw/PPPs2zZMl588UVisdiQ+/rc5z7H5z73uSNqx2433XQT3/3ud9m+fTu33347n/jEJ1DVcXkphk2NyARbdLqeSJWmc04G1acEybQWSz3Yva9kmXp1FVOvrmLn02lRD1IYd/2v5/DVqTSeH6F9VXJc1iXLuuSV9YkqKD4J2S+j+Ly1h2pIRg0pyNrgGR67a9XJ2p5kGq7tndybKYfMFoPEO/mKPUnfV8uVcfx1XiKWQo9J97MZnKJbeuzCyHIdKPbZqGGZ+nPChGf7SLyVp//17KBRMElmUCAvyRLyDD/J1iKJdQXCM3X0arWU9EXxS0Tn+6k6KUBmi0H/G1mMvjJ/j0pQdWKA6kWh/dYBO6aLmbaxMjb5nSbmRruU9MfK2Lses4wakQlM0byajQv2JD3cXWrESFggSei7Sl3ZeYf05iKROT6K/VbZz5DYPQVd1kf+82hlnFIJMMHjALZb/se+yXO2e/jGJWopFPbU+jlY7UUAn89X+j2fP8y1CLu88sorxOPx/a4/77zz+PznP8+HP/xh/vSnP/Hee+9x22238S//8i9HdD/Dpes6//AP/8BnPvMZNm/ezN13381NN900qvd5tCKzfaUCxJOFFlPQq9RBhX+NPpsd9w/QsDTC1Gvi9L2WY+CN8v5iFCa+nU+mab40RsvyOF1PpEasuPm+JM07gVZ83khioFEj2KLhq1VLRcrtguOtNdw17a3YY2Htmv7m/djYOWfwCL4MsirhGBN3ZoMaUjCSNp2PJMesiPlkl+8yqV3sdUL3vJghsXb/c4iDjgK7kNliwJbBnydJgchcP1UnBZn2oSr6X8sx8GaubEeUG86LEJ3n3+/6tocSe5L/DMnLJlvsY9dxJYsSkNCiCnbRxUzaBKfqXrkr06Wvx8JM2sQW+AlO0ci1mXSvTpftc1MySsvxas/wpq+KnAmTj2EY/L//9//47W9/y9q1a+nv70fTNKZMmcJZZ53FZz7zmSGTiFaCcQkg/f49BzLDOPiJTrG4J2DZt9THcB0oeNwtEolw7733Mnv2bPr6+vjZz37GHXfcccjA9mjdcMMN3HHHHWzZsoVvfvObXHfddUd1n5IsjVqGWjUkU3VS0AseHQmpzNc9e88FR92zr+yq0eXkB48SmAMObQ8kqT45QO3iEHbWJr1RTE0Zyki9HsLQXAs6ViWpXxqh+eIYhR6TzKYiTtH1ylHseup3HyL0sIK0fZ/XQ6K0rtlXrRKcpuGrV0ujiopfHlTUHLz1YvkOg9R7BXLtJlZmuGeJ+x9HXGtiv0cc08FMeaOrez9O8fkYPcm38uC4yJpE6t2C9xzv9T4/kGG9Hi6kNxRJv1+k5tQA1YuCxI4LYKZtlICMHlXoeTZN8r3x73Ct/0CoFDx2r06Ter+IJMOMT1YTbNIodFrET/BjJp1hT7F0ilDs8TpBJEki32aSbxsciO5bJ/lI399j9fnQo94IoZUduVkBkgJVJwYZeCNL8u3ChPiMD/V6SK50WMN1DjJOBSTROdI27tixg8suu4y333570PWGYfD+++/z/vvvc9ddd/HlL3+ZH/zgBxVZF35cAshIJFL6/VDTUrPZPQehQ013PVKxWIyPfexj/PSnPyWbzfLaa69x5plnjsp97aaqKrfccgvXX38927Zt4+c///lRTY2tmR5Hk0c+6JX9Eg3nRZCAbEea2hlVI34fI02SJeLNEZCko0r7rcVkotEoNdPBiBxgxKAPgnKIhvk6PvPIRscng5F6PYRDszdBMScTnh6hbln1oC9513W9E2fJO5ZWnxwkt80ASSI0Q0cLKxT6LLBd/PUatuFgDtiYaRs762IbXiZTx3S9jKaGg51zAQ0dDb0WqB2vR17+3DaNxiUhIqEo/a/nSkGM+HyMspT3T/OpYWpOCaKGlVKNxlybSXpTAaew53k/3NdDSoC9VaX65BDsVc3LalbQ8uPXsajFZJqW7VmO0/FIEr0YpHZ6EICgFkad7scvh2k8zzsn63khQ76jvGoWjtXnIzRDJxoNEY04hP0jdD8yRKNRzLCCNGP/EeBKNNTrYToGbD3IDScRy7IGBY8nnHACf/3Xf828efNIp9M899xz/OAHPyCbzfKv//qvNDU18bd/+7fj3OrDNy4B5N6Jc9ra2g667d6Jc/ZOqDPSFi5cWPp9d4mR0fapT32Kb3/727z//vt861vf4oYbbhg0Ons4+rYlUCVthFsIzZdFSWfStD2QOIzRhfElyRK4Lr3bEkf1heNvUAmlJAa6kkN+qUaLMv1bsyRaCwf8uzByr4cwTK3ACww50iIpEs2n1GLF84RavCUCO9encQoO8ROCoMH2+3aS2WpMikzLY6YVBrp0GpZG0Bfa7Hw8jb17DaT4fIy6aUvi9O9MkHqqAJJXqzF8jI9Ig0R6s0Ghw8Quuqg+Cer86HU2virvFKnYa5FvM7ypxy5ImjdK72vS8NeoSIrNQE+Cwk6TQpdJ6n1jXHMFhKbrxJdESaVSuK7L1rv798ssGki4FHpMLNMm0WuTbTMJzdfpfr+81vaP1efDd1KMrvfydG9Mjeh+g22QKxTpbZ0YS12Gej0s9/A6HmxXxnbLfwTySNq4cuXKUvB4xhln8Oyzzw6qtrBs2TKuuOIKzjjjDEzT5Dvf+Q5f/vKXyz4Xyr7GpbV7B2vr168/6LZ7/33BggWj1ibXHfsvbkVRWLFiBZ/85Cdpb2/nv/7rv/irv/qrI9qX67i40sg+Bn+9SnCKTscfk5ipylqz47q7npMj+MJRwzJ1Z4cJTdUxMzbFPvOA+1GCMrImYaRtceJ3CEfzeggjr9hj0ftqBpdd5Xhcb7qVEpRJrS+QayuvUYiJIr2xiJV1aLwgytQPx+l8LEWx2xafj1GkhmRqFofQYyo77h+gsHNPcNT7UpboQj/ReX6ic73OW9d18aGS3GST2ehNQQ00asSODwzKTmqmvbIy6fUF8l0mRn/5fEcaCYvM1iKp9YUhp6VaWRs1JGOmHVwXdj6VpuXyGI3LImz/3YA3/b1MjPbnQ4vKBOo1Oh9Njvh9GL0WerU6oT7bB3o9xuMculw9//zzpd+//vWvH7BU36mnnsrll1/O/fffz8DAAOvXr+e4444by2YetXEJIGfOnElzczMdHR2sXr36oNs+88wzAEyZMoUZM2aMWpvefffd0u/Nzc2jdj/7+tjHPsa3v/1t1q1bxx133MFf/MVfjNl9H4qvTsW13UmVelqLKTRfEkVSJHpezJB+vzhkYo9gy66Mil3iZFuoUHsNkrg2dD0+skW0hf3lO0y2/26ApgsitFwRp++lLIj8GiPKV6cSmqYTmKIRaNCwDZedq9ODgkfwMpAm1uZJrM0jySDpEq4FtdMcelszpRPkxFveEgXZtytb8K6yM+XKGPBqkEYX+Ikd60ePq0gK5NpMrwTJewWKfTaRY3wk380jq0EaPxih+9kMUy6P0XxJjPaHy7Nm42hQI94JvpEY+Re12GeVar8KezhIOJT/ur8jaePeuV1mzZo15HazZ88u/b53vpdKMS7jx5IkceWVVwLeCONLL710wO1eeuml0gjklVdeOWqLTJPJJPfccw8AwWCQRYsWjcr9HIgsy9x6660AdHV18e///u9jdt+HIslS+WdOGyGyLtFwfoQZH69G1iTaHkyQfKdw0KyQZtp7ckLTfUNuIwiCsC8759D2UJLEW3lqzwxTdVJgv/IKwvD56lSUgERops60D1cx7UNVxE8I4BRdel7I0PqbflLvHXyZgevgrYU8yHeeU/TW/5Zz8LjbzD+roWZRiNA0H1pUQQ0pROf5aTg3wpQrYlSfHESLKDRfEkdSJELTdVqujJPaWMBXrdB0YWyczhDHXrHbwjEcQjNG/ru8sNNECcj4aitreqJw5ObMmVP6fcuWLUNut3nzZsCLifa+TaUYt8PDl770pdJ83y984Qv7lejI5/N84QtfALyEM1/60pcOuJ/zzjsPSfIykLa2tu7390ceeeSg5T8ymQzXXnstfX19ANx4442DSoeMhQ996EOceOKJAHz3u98d0/s+GElhQk27GIoakZl6TZzQVJ3uZ9Jsu3cAK33oyLnQaZJ8N0/9uWFC00c3a68gCBOM602h7H0xQ2iGj6kfiaP4RRB5uKZ9xAsYZ326lprTvJId7asSbLm7j85HUyTXFXDNif89djBWzmHH/QOltY3B5j3fV11PpOh4JEluh4GsSVQdH8TKuwRbNBrPj1IBg0RHzbFcUCT89SMf5OU6TIykTe3poUNvPInsXgNZCT+H6+Mf/3gp6ed3v/tdbHv/Hqc1a9awatUqAD75yU8SjUaP7gkdB0f0aXnuuefYtGlT6fLe6xTffPNN7r777kHbX3/99fvtY+7cufzN3/wNd9xxB6+99hpnnXUWX/va15g9ezabN2/mu9/9LmvWrAHgb//2b484Or/jjjv45Cc/yTXXXMPZZ5/N7NmzCYfDJJNJXnjhBf7zP/+T7du3AzBv3rzSaOBYkiSJ2267jauuuore3t4xv/+hKEF5XBMBjAVZl2i+JAaSxI77BjBTh/d4u5/JoARkGs+PeIFnhSQaEgShPCTeLtDpJAmcIlF/XoSuJ9KTPuA5HGbKxlfjncr4qlX638iR2yGWFWz+RS/+Ro0pl8ZQg17d1rYHEzScF8FM2eTaDPKdZmk0NdtqUHd2mPBsH1pYxrUhPEOn9swQvfuU5JiI7JxDsGUUOoId6Hk2zZTL49QsCdH3SlYkJ6tAqdTg5Eo+n2/Iwab6+nruuusu/uzP/oznn3+e0047jS996UvMnTuXTCbD888/zw9+8AMMw+CUU07hBz/4wVg8hBF3RAHknXfeyS9/+csD/m3lypWsXLly0HUHCiABvvWtb9Hd3c0vfvEL1qxZw8c+9rH9trnxxhv55je/eSTNLOnv7+fOO+/kzjvvHHKbD3zgA/zmN7+hurr6qO7rSF155ZUsWrSI1157bVzu/0DUgIw1wQPIurPDqCGZHfclDjt43K3ryTQzPlZF1clBep4Vi5kEQTg8dtal55ksDR+MMOu6GnY+nSazufLWxIwJCapODoLrUui2kPaZ+pt8T5RU2i1+/J61d67p4hgunX8aOstoodskflyAxLo88WO928aPDUz8ANL1Spg0XxRDDclY2ZE978m1mfS+lKFmcYhAk0bnI0nsgogiK8m+VSBWrFhx0AGnD3/4wyxYsIAf/OAH3HXXXXz6058e9PeGhgZuv/12PvOZzxAMBkejyaNuXCdly7LMz3/+cz70oQ/xs5/9jFdffZXe3l5qa2s57bTT+OxnP8sll1xyVPfx/e9/nyeeeIIXX3yRDRs20NvbSyKRIBgM0tzczJIlS/j4xz/OhRdeOO6FPG+//XYuvfTScW3D3pSAjJ2fuAGkr1YlOtfPztVpzOSRL2pxTZfMliKhGT56ZA6rmK4gCAJAdptBrs0gPNNH07IoLIO+V7OkNhTEzIa9+Bs1ahfvPx0wu8Og7+XssJYfTAY1S0KEpur0vZolMsdH/PjAIbMrpzcWCTR5wWO+2yRQr+1XMH6i2p0ML9Cskd448p03A2/m8dWoROb40eIq9iRPvmcjY1fAItvdbdyxY8egaaaHWupmGAa/+c1vePjhhw/49507d/Kb3/yGOXPmcNlll41cg8fQEQWQd999937TVI/GpZdeesSB09NPP33Qvy9atGhMk+Ls63Ceq0suuaSsUiErQZli/8RNw1azJIQxYJFaf/Q1HFMbisQWBqj/QJjup8UopCBMRkqzROQzg08somoBn7LnONr6fQWz78An5fsm2qg5LUTNaSHSGwsk3snvl0V0Mip0eaM5VacEUfQ9J6Cu6ZZV/cLxltlS9EYRZRhYm6fh3AiKXzr4yJcL3asz5NpMGs+PYOUd+l6e4KOPu9h5Fytro1epwMgHkOHZPiJz/PQ8nxGZ2ytQNBod9jrFbDbLpZdeyjPPPIOiKHz1q1/lhhtuYNasWRQKBV5++WVuv/12nnvuOZYvX86//uu/HnEJv/Ek0kIJQ1In8AhksEUjNFWn45HkiKxHKPZa7FydpvGDUbLbDLJbJ0/pE0EQPHJUInixNui6Kr1ASNlzHN3xHwpm34Fv3/VEipblXlbMvUXm+InM8VPoNkm8nSezuTg5MmTvjg/3fqwuJN7eM5oDu0YfX50cgc6wSNCyPA5401glScJM28OeNpnZXGSgTiW2wE9qw9F3sFYCNSKjBORRq3kdmq6XPr8COK6E45b/6PaRtHHFihWlEoQ///nPB01f1XWdZcuWsXTpUi688EKeeuop/vqv/5qlS5dywgknjFi7x4IIIIUDk3dNYZ2gayBrloTId5lkW0cu0Eu/XyS20CQ61y8CSEEQDluhy2L77wZwbZdAk07d2WFkTdpV9F3BX6/ReL6Gc57LwJocyXV57Hz5zFoZCbIuEZ3nJzhNJzTVS2qS2VLE2VWT2OizqDsrjL9Bo+fFDLntBsZABdTVGEsu9K/JoccUjJSNU3BIHea0zMJOk+qTgsi6hFOs/PeYpHolt1zHxRiwvWUrux6WFpVpuiiGlXVIj9La40CzRmaTWNc80bmuy1133QV4yUL3Xfu4m6qq/NM//RNnn302juNw11138a//+q9j2dSjJgJI4YCUgNf1a02wkxOAyFwf/jqNHQ8MjPi+s9sMqk8OIKlMmiLMgiCMnN3BkJkqUOy1aL40imtD6v0CvmoVX62KrEjULApRfWqQ9MYiibfzFHsq84CjhmWQ8NYuStB0UZTgFJ1c+55OuPAsH0bCIrprxNEuOnT8IUm+U0wFHMrRTj1VQwqO7eKaLmpYJnKMDzPtkN1WrMjvtpYr4vjr98wOcG0X1/ECy90jtO2rkqOSAVnWJbSwQqFCP6OjwamQNZDOYbZx586d9Pf3A3DyyScfdNtTTz219Pve1SwqhQgghQNSdwWQE20EUovK1J0dJr2xQKFr5A/m6Y0Fqk8N0nJVnNR7BVLrCxVRdFoQhMMn+aD2rhCS4l12dQnDUQZt8/ZAMzlzT3kAn9WGxJ5jj2+hTP0tOjre2snMQxbJ/+f9vdhnseP+BPXnhInM9u03tRUg0KgRnesn32WSeCdPZkuxohJ5Tb06jhpSyHUY+Bs0JAnaViZKwaGkgF6lUuy10OIKetTbthKDmEriq1Uw+i1cB6Ysj6GGFGRVwso59L2cIbWhskbT1IhCYl2e/lez6NUqepWCJEs4lotTdMjuMEevfM6uGMS1J16HvDDY7vr2AJZ18IOUae7pANv7dpWi8losjAkluHsEsoLORA5BUqDxwih2zqH7mdFJdGNlvJ7x+AkB6s4KE1sQoPNPySMuESIIQhmTQJ0ulwJIx5UwncFBXsHSyOwVQGpI7B1iSn5QmyU0TQIJ5KrBt7cyDh1/TCHrEnq1ghqUkWQJJSiT3WZgpmxC03TixwdouiCKlbVJrCuQ3lAY8XIEo2l3cXsjYQ0aWXRtSslxzISNmRA9cmPBTNhE5vjxN2roMZWOPyQxEhbVi0I0LI0SnFake3UaxyjzoEiCujNDqAGZQqeJXXDJd5jkO8Zu9Lr0iS7zp2osOa6M41bACORhtrG6uppoNEoqleLFF1/Esqwhg8PVq1eXfp85c+ZRtXM8lP+rJ4wLdVcAOZGS6NSdGUaPq3Q+lsIZxULd+Q6TzkdSbP/9AJIGU66Io0bER00QhCPnGC6FLovMFoP0piKJt/KldVzZbQbtDyfZdk//rmn0QWb+WQ1TPxyn+rQgvvry7SsudFvYxp7vmd4XRDKccpBYVwAHGi+IAN5UTzPlsPPJNJ2PJglO0Wi+NLZXdFReJBWiC/xM/1g1sWMD7FydJj1OaxB3z0KStDJ9soQRI8tyqSxHR0cH3/rWtw643cDAAF/72tdKly+//PIxad9IKt9vFWFc+RtUr4THBIkf/Q1q6UvE6BubHmyjz6ZtZZKWK2K0XBFnxwMJ7AoaERAE4dDcggu7ppa6uODs0zlVdJH2HqXZt+/KAdfYdZIpAUcxNdMYsOl+JkPvS1mCU3VC03XixwaoOTWElXPIbTfIdRoYfTbGgFUW0+v1aoXMpiI9L2S850IcIsuCa7p0PJL0gkS8MhSZLd661MxWAyuXpOXKOFUnBRhYUx6ZRdWITGia974PNOtIipeAqetPeYp94zjnWQLHdNGiyqG3FSreLbfcwsqVK8nlctx66628/vrrfPrTny6V8XjppZf44Q9/yPbt2wE4//zzufDCC8e51YdPBJDCAYWm+0i9PwHSd0vgr1epXhTCzjuk3hvbx2TnHNoeSjLtmjh1Z4bpeiw1pvcvCMLocQvQdeHBp8NrJKje67K0T9BWWOvQ+dkiva0FXMcdkWlujuGS2Vwks7noHQMbVELTfYSm60Tne4loXMfFTNoU+y2KfTZGv0Wxz/KS2YwRX62KHlPpeS4r1jSWoXy7Sfr9ArEFASKz/XQ/kyllZC3stEi+WyB+3PgHkGpEpv6cCKFpOq7tku806XslS2ZrcUzfz/vSaxRqTw8TnKIhyRKamIlUYiNhl+vw9V6OpI3z589n5cqVfPzjH6e3t5eHHnqIhx566IDbfvCDH+S3v/3t0TZzXIgAUtiPr05F3bW+ppJF5/moWRJC8ctYOYedT6fHpR121qH3pSyN50dJrtPGdO2FIAij7BCjeIc8/XB37cNmdGZ8uF55kEKXRd/LWSQVfNUqeo2Kr1pBr1GpOkFH8e9atmA4GH27AsteqxRYjnSAJylQe2YII2kPyrgqHD1JpZQgJjrHjzFgUeix0KKKN+35MOQ7TWILAgBoYYVicc8bIb25SPy4AL56lWL3+PQAKCGZaddU4ZguXU+kyG4zymJdZuxYP3VnhTESNj3PZXAdKHSL7/7J4oILLmD9+vX8/Oc/549//CPr1q0jkUigqiqNjY2cdtppfOITn+CKK65Akso/kD4QEUAK+wlN17GLDoWuyj3YBadrxOdFSK7Pk3q34KXPHsfvlPTGIlUnWsSPD4gAUhCEceNa3rrDwj4n/EpI9sqE1Cj4alQCTRqxBX4k2Tu5sYsOvS9kvZkpQxxLI3N9xI8PoMdVrKztjWwOWNgFB6foeqNXMmgxhfixAdSQTNvDyQmzVKJcNJwXIXKMH8d0kfdZd5dYl6fn2cGj5nqNgh5TsDLOoPeF4peoPjUEeFMwzezg4LPQZWLnHcIzfGMaQEoKxI4LEJqu46/TcEyXzsdT4HrvLQBcryaflXFGto6lDGpoV5LBfUY3lYBEoFkntsBPsEVn4O0cvS9mxfv7ACZqEp291dTU8NWvfpWvfvWrI9ii8iECSGE/oek6ue1GxWYMkxSIHx+k691eup8enWyrRyL5Xp66M8MoAWnCFf8WBKGy2VmHXNYgt2PPdZICelyl6eIoWkShYWmEhqURir0Wfa9lyW4zkFUJf4NK9akhAk0ama1F0huzqGEvII0t8CP7ZGR1TyDjmC65NoP2VdnDHhETDi35boHIMf79gkeA6Fw/WkTBV6PQ+1IWWZeoPydS+nuu3aCw08JXqxKatid7cO9LGZzCPt9bLmS3G4Rm6PS9MjbJjyLH+Kg9w5tZlN1mYBcctIjCtGuqhrxNoduk9+Us+Xav81aLKUTm+AhO0bGyNkbSRlIkFL+E4pe90XjHxcq72AUHO++ghmR8tSq+arVUTscxXYykhZ130aIyesw7pc7vNOn4Y7LiZ3EJwsGIAFIYRAnJ+Os0EmvLY1H8kQg266h+mYHXyusxpDcVqTs7TGiaXnE1tARBmHxc26tF2b4qyYyP7VnJ6atVab44tmsbF0mRMAYs2h5MDDnDQlJA9snguqIDbZTlO0x2PDBA/QciWBlnUCAoa1LpcuP50dL1juEg6zLBKTrBKfqg/XU+lvLW0x5AenOR6Dw/4dk+ZFUitbEw5IhbeKaOGlVIbyhg7xuMDoNepdB4QRQr79D9TJrwbC8YTm0okN5YwM67uLhIuyeOK6DHFKIL/LQsj1Pst1BDMopPxjG82o9KQCbWpOFYXtZ5u+B4pWJkUAIyekxDCUhYeYdir0VqfcHr9JC8+qR6XEH2ecFs384chS6zosrnjBdv1n75T90U3VtDEwGkMEhomo7ruGR3VG7Pma9ewSo4mKny+ug7RZdin4W/SRMBpCAIFcNM2Gz5ZS/xE4KEpun4avacOvS+4o1EHqo+o2t7ScWEsVHosth+7wB6lUJoWvWgv+U7TQJN2qDruh5PE5iiEZ3vxyl6iWjyXSaZzcWDrinMbTfI7zRpOC+MrMloMeWAo5G1Z4WoOj4IQGiqTvvDycN+TPauIM5Xq9KwNIqRtA450lfstkhvLBI+xkegUcPKefvIdxhHva43t0MsRxEmLxFACoOEpukUusyRXTMwliQIzfRhjGfK7oPId5qEpvnGuxmCIAiHxc679L2cpe9lUaexkpiZPUF76//14xQd7LyLEpKZ/uEqlIC3xiswRaP3xay3Zu8wdT+dZvpHvSC16uQAyXX5QaNwwak6VccH6X427U2Nna4z9Zo4il8m126Q7zDJtZuH7GCwCy7bfz9AoEnDsdzDWneZ2VQkM051IIX9TYY1kBOdeGaEQYItOtntlTv6GFvgx1etklpfniVICp0mekwh2KKJosKCIAjCqHJNl/Qm7/vQV6WUpg/bWYeup/ZkJi/2HHmnqzFg41i7Op1daDg/Mujv1ad6I49egpkAru1NZ85uN/DXazSeH2XWdTVM+0gV8RMCyL6DfDe63hTd8cr6KgiCR4xACiWS7K2PqNj5+5L3RZV6v4AzoB16+3GQ7zRxXZcpl8cxMzZdj6codIkvQkEQBGF0dD2epuvx/ctY5XYYdD+bxi64Q65xHK7UhgKxhV7WXkUfPDbhOl5w6a9XKQ5Y+KpUOh5Jkm31OqsVv0SgWSM8y0ftkhA1i0Mk383T/1pO1AedoGxXxq6A0b1KaON4EQGksEeFf06CUzTUkEJyXZpIqDwDSDvv0rYyiSRDzaIgzZfE2HFfQmQiFARBEMaWC8l1IzNbJ99hEj82QHaHQf9rg6fBdj6SwnVhxser8UW8MhuyLhFs0XAdLz9AZqtBZouB4s8QWxig6qQA0Tl+uleXTyZ1QRD2EAGkULI7MYJxiGQI5Soy14+RsCj2WERC492aoe2ur9nxSIqpV8dpviTKjvsSZVH8WBAEQRAOV/XJ3jTV/teyFHZ6w4ZaVMYuuKXvNjNlowa9nurGD0b320f7wwlybSb9b+RIvpen/gMRmi6O4nbo9G4bGKNHIgjCcFT4mJMwkny1Kq7tZQqtNOFjfETn+km+W55rHw/EMVw6Hkmh+GUaL4hSARmtBUEQBGE/uQ6vJmPLlXHCM3W0uMKMT9Qw7cNVKAHvy81M2BT7LLLbDzxddsrlcYIt3uwhO+/S+WiK3pcyROf6aL40iuIXX5IThYuEUwE/rjgxG5IIIIUSWZO8hfAVtgQyMEWjcWmE1IYCibfKq/bjoZhJm87HUgRbNKpOCox3cwRBEAThsPW+kGXLL/vIbCnSsDRCeKZXS1INy0Tn+QHItRv4atRB39Pdz6bZdGcPiXXedVMu92blxI71I2kSibcKdD+bQa9SmfrhKvwNhzdxLtCsMefmOmb/RS0tV8QITtMPfSNBEA5JBJBCSbHfRvF5dZwqhV6j0HRRlFy7yc7V+ycJqAT5dpOBt/JULwrhqxOzygVBEIQK5MLO1RmsvEtkrh/XdSn2WOjV3vdars1LmiP75VJm2NrTw0iSRP9r2VKyndB0H/XnRJj+kSokxcsQu+P3Cay0Q8sV8VJAOhyO6e1TViT0WpUpl8ZouSouvmvH2e4kOpXwIxyYeGaEkvyuKSixY4d/cB5Psk9iyqUxzIRN55+SFTdyurf+V7MUeyyaL40hi/IegiAIQgVyTZfeFzL4qlQkScLfoGGmvLwKdt7FtV0UXaLvtRzgzXwKTNGw8y6tv+6n96UMmdYidtHByjmwKzWAnXdoeyhBan2BhqURQjOHN5JY7LHoeCSJY7nYOYfOx1NIKky9Ok7tmSEkTUKqnD5zQSgbogtGKHEtyGwtEmzWgfIvFl11QgBZl+n4faLiU31LqoQallEDMrJPKvWaCoIgCEIlyW4zyHea+OpUZFUit6u2tBKUkRQJu+hiJmyMpI0eU9CrVbKtBlbWYeDNPLBniqsk79Wh6kD3sxlkv0zDeRG29w5gZRyi8/2oYRkcKPZZFHaapXqXANlWg44/JGm6MEr9OWG6nkijxxVqTgsRnuVDC3sRZOdjKYq9lsiKPgYcV8Jxy7+zvBLaOF5EACkMolepmOnKOHiGZvjIbiti5yp46HEXSZHQwgrJ9/JYmcp/PBOVNUXHqd7rsOmA9m4OScT7giAIJb0vZ5h6VRXdz6UpdHs9vJHZPlzbLU1ldXdPLz3MWTfdq9NM/VAVU6+Kk3gnT+2SMABW3kENeBPr8jtNsluLZLYamEmbfIfJtnv6aTw/SuP5EdpWJshsLVJ/TqQUQDYt8zLD9r2apf/13NE/CYIwgYkAUijx1akEGjU6H0uNd1OGJd9lEp7pQ1LArYyYd0h2zqHYa+Fv0LwRyKKISMpR7opqchdVlS5LhkP9xzdAhb//BEEQRlKhyyLTWiR+fNDLju5AZI6P7Haj9P22e82jrB9eAOkYLm0PJmg4L1IKHot9FjvuH0D2yQSbNcIzfVSfGqL29DBW1qbYa1HYaZFYl6f5ohjVp4boeixFxx+SRBf4aTg3Utp/zWkh7IIzYjUyhf3ZyNgVsIquEto4XsQzI5RUnxLESFhkthw4xXa5SazNofglogsmRvbSzsdTqAGZluVxsQ5SEARBqGh9L2fRYwrhmT60uIK/XiP9/p6gzN012eZIvu/srEPHqiSpXfvz1ag0LfNKfaQ3Fun8U4otd/fS8UiS1PoCSBLxEwM0XxQD9tRjBki9V2D7fYPrTNadHSYyx3fY7RKEyUKMQAqAd/ANz/TR9USqtGi93Jkph9SGAjWnBcntMCp+3YKZsGl7MEHLVXFqTg/R82xmvJskCIIgCEfEGLAx0za+WpXQNB0775DdtR4S9hqBPIoO04G1OcKzfMiqRGi6j9B0Hx2PJMm2Gri2t/4x27rnPrW4Ai77nS8Uuy26nkjReL43jTXXZtLwwQiyLomRSEE4ADECKQBeb5uRsEhvqozRx916X8xiZR1arohVVPmRoRgDNumNRcIzRK0qQRAEoXLJPgk1JKNFFaLz/PS+kh283OQoRiB3M/rsQaOaADWLQ0NubybsITub0xuLpDZ6+0qtL5B4O0/9ORFiCysjM30l2Z1EpxJ+hAMTI5ACsWP9BJo0dqxMVMzo426O4dL+UIKWK+K0XBGj7cEkVrpyk9DImkR0np9sa2UF8pNFbTCLWrXX+6vgIlItCOWm/8ONFGcHS5frf7oNJVPZMzSEyqNFFCRZIjLbW/uYem9woLd7BJKjPEePLfSWsWy7t5/p11bjq1bRqxSMgcN/z+98Mk2x2yLfYZDZXAQJ6s4JYxdd77IgCIAYgZz01LBM7elhEuvyFDrNQ9+gDNl5l7aHkjiGy5TlMbRI5b6tZZ+ErEmk3hdfVOXIr5pE9MJeP+J1EsqPMStI/sTorp8Irip60YWxV+yzKPSYZFqLdD6a3O/v0q735ZEEerv5m7TS70a/Tfdz3tKPqR+qGuomB+dC4u18qQxI7/NZ0huLNC2LUn1a8BA3FobLQa6YH+HAxDMzydWfG8EuOPS9VP51Hw/Gzjm0PZTEtVwaL44iVfrYulthQ8GCIAiCsDcXdtyXoPOR1H6Z0iWVXTWnodh75IWcmy7wsqf2PO8FjmbKuyN5BDtNdj6ZpvelDNWnBGm+JHrYWWMFYSISAeQkFmzRCE3V6X42MyEK19s5h45HUmhhhaoTKjMzqxb11nFa+cqdhisIQjlwqbg1CcLEc6C3oASNH4yWLqY3HVmSGsUvoYa878zku/kj2sdwDbyZp2NVEn+DxtQPVaHXVH7OhfFku1LF/AgHVunjNMJRiM73U+y3yO2VFa3SmQmb/teyzFwWRQ3JmOnKWvcTaNawCw5GX2W1e7JI/6RA5r/3mbYqXqoJozg1QPtX5pQuR17op+7etnFs0ZGp++m2QdNWldSRj/AIwkirXRIiPMsrkdH5aBL3CN+ergP5TpPku/nSCKdTHL3O11ybyfbfD9B0YZSpV1fR9ViK7LaJc/4kCIdDBJCTlKxLhGb46Hu1sqeuHkhqfRHpIonq04LsfDI93s05LIFmjXyFrkWdDNwsuFkxqjNRuYqEFduzpsoJVOYkHSUrejWE8pXaWMDKemW4HOPIj6eO4dK2MrHfdaPJSju0PZCg+ZIYNYtD3pRZGdHpe5gqJcNpJbRxvFTmt6Nw1CJzfEiyl7Z6oonO83o2o3P9yL7K+vD7GzTyXSKAFARBECYmo88m8XZ+VII9u+jtczS/R10bEu/k8dWoTP9oNdM/Uk3LVXF8dSrIIPsllJCMFlOQxFm2MEGJEchJSI3IVJ8aIr25iJ2beGvt4ifvyZQWOzbAwBuVUWghOs+HrEgYfWK6WSWR4nt1Utjgpsd3hNLRJVzf4PU5csZCEgOnhyTZLmp6z+dPLky846MgTGR2zqH7ufSod45ntxlktxcJTfM6rGVNYuo1ceycU1qXCV4+g8TaHIl1BdwJkGtCEHYTAeQkE57to+7MEI7p0vtCZrybMyoymwpUN3i/1y4OkVib2y8DXLmR/RJ1Z0dIvpcn1yZGICuGD6ofjIHiBZHWRpvk9alxbVLyg3X0fqJl0HUz/+pt1AHxvjoU3448s764drybIQjCUUi+c2RJeQ6LC51/ShFbECDTWsTKOMQW+vHXaRS6c5gZB9dyicz2UXNaiOpFIbLbDBLvVG7JtJHkujKOW/7Ds24FtHG8iABykpBUaDw/Snimj8yWIt3PZUp1jiaa3hdzBOQMvmO9y8EpOtkyTxSkhRSv/uOGiTeleOKrrGnSgiAIwtFzLa9m5G7JdQWSDA5e8x0m/W/kCB/jBZKR2T46H0+R2SS+64XKJgLISaL2jDDBKRodjyTJtpZ3MHU0wrN0mi6MkXvdodBrIqsS2bbyf7zFAQsr7xCaoVMQayAFQRAEYUKwsg6JtXkUn0z1KUFCU/VJH0DaSNgV0PlaCW0cL2JsdhKQfRLRuX7638xP6OARINjiFSZuOC+Cv1ZDj6vM+Uwds/68Bi1WxnWbHCh0mvhrRZ+OIAiCIEw09q76zkpQpu6cMIFm7RC3EITyJc5WJ4H48QGQITXKhXbLQWZLkdjCQKl4sZmx0cIKii4z5bIYO+4fKNupu1bOIRAVXyiVJmvqpfebYx06AVLx6jhudFdnhuXi+03/QRPc5M6MYs7wly5Lhkv0dz1Dbi8ZMnJ6n0O7yAUjCIIwrrLbDeIn2mhhGS0iEz82QK7DoPuZDGaizBM1jDDHrYwSGU55ni6WBRFATnB6jUL1yUEG1uaxCxP/k5BrM8nuKBI9Nkpma5Gux1PoNSqKT6JpWZRZn65lYG2O3peypZP+cmEXnIorOyJI5CyN0jpI+9DTOoxLYjgtuzoKii6+/+uHg5w7FBZHyZ0TK12WMw6R3/UMObFGMiWk9D6j7RXwRS0IgjCRmUmb1v/pL10OTtWpOyvEtA9V0floUiTQEyqKCCAnMEmGxg9GMQZs+l7LjndzxszOJzIoAwG6XkrjOlDstogfH0DWvVP7qhODyLpE9+ryykLrFF0Uv5hVLgiCIAgTXW6HwfbfGTRdGKNxWZTtvx3AykyO6SJOhWRhrYQ2jhfxzExg1aeF0OMKXU+mJtUUNsdwByWi0aIydWeFB20TWxCg+bIYSqB8RmbsooOsSUiiW0cQBEEQJjzXgq7HUziGS/OlMWR/+ZyTCMLBiFPVCcrfqFJ1YoC+V7IY/ZNrbv2+lOCefpItv+wFwF+vUX9uhKaLYrQ/mMAF1JCMlR6/SLuw01s/F2zRJ3yyownDdJG+3rOnkkdu8Lxo67Qg5tWxQdf5/6cPKb3rM+mAdIi3nGXJmOaeQ7VsHvzz7Crg+PaZn32wRZaTWPenp2I2+PZc4Uq41p4TuNDbSaoe3zkOLRMEYbJwDJf2h5NMvSZO0wVRuh5PTfglRw4STgVkOK2ENo4XEUBOQJIKDUujFLotBtZO/MQ5h7I7MBt4M1dKoJPdZtDxSJKWK+I0Xx4j2Oxlb934n0MnJxltZtKm2G8RnukTAWSlcEB6Y+h07G6Ngn1KYNB1vn/vRT6MtS6uK+E4e77EpEOsZ3QlELNuhqcwO0RxRnDPFQ645p71o1rv5E61LwjC2DCTNv2v56g5LUTd2WG6Hk+Pd5ME4aDEacYEVHVSEDUos/OpdNklihkXLmy6s8dLnLOXYrdF+6okWnTPCeN4T2nN7TBEam9BEARBmGQSb+XpezVLeKZPLGURyp4IICcaGWIL/KQ2FDCTk3vq6t7cIaorFDpNtv92oHRZDY9vrUhJkXBF3mhBEARBmHTyHSaSIuGrmdgRpO1KFfMjHNjEfodOQuHpOmpIITkJaj6OFKfoku8wCDTrFHsOXcdvNChBmcgxPqLz/OK1q2QquLV7dULE5IOuoHAlsGr0g+7S9e+7j4N3MEiuWPJ4xCRAdgdfFgRBGAP+Jo36c8KH3lAQyoAIICeY6MIA+S7zsBLn+GpVapaEMAYsUu8VMAYm18ilpECh10If4x4/SYX4CUF8NSrBqRqyIpFpNeh7dfKUXJlo3Bka7n81li4rQIDB6x2lvQJAV5No/c7xuMpekYoL7LXmUQ8ZaPqejo1DJdGRbJCL+0Q+ohd1WCQZ5MBez7U2idJXC4IwrqpPDuKr9s5DJvp5mCjjUflEADmBaHGF0FSdrqdSw75NoEmj6ZIoVtohNDWIFlHofHT4t690erVC04VR9LhK6v3C2N2xDM2XxPA3aBS6TJLvFBh4M4djiKEjQRAEQZgsJMXLvu5vULELDp2PpcS5gFD2RAA5QUgqNJwbwUzZZDYNI3PgrrWStWeEKXSZdDySJLYgQM3pIWRdmhQHL3+TRvPFURzDxUhY+yXZGU11Z4QJNGq0PZQcVLNSEARBEISJz1er4qtTqTopiB5TMBIWHY+kMBMTe/QRdpXxqICZMaKMx9BEADlBNF8Ww1er0r4qgXuQY4/il6hZEiI8y4esS6TWF+h5NoPrgJV3kBUJJSDjGBP7AOavV5l6ZRwAZVcZOH+tSnb76JfPaFgaITrPT/czaRE8TjQZB1bnShftBh1ztn/wNidFcFu8qZGuKnnTS/edKSntsw7vML7DtN4Cobf7Bu/O3HMHriKROyZKJizh7robvTuHvvPI194aU/wYUwY/ztAbSSSrvDuiguvSaN17OtwkCSTJLT3fvq25IW4pCIJwZNSQTPOlsVKinOz2Ip2PJDEGbCQFIvN8aBGFbKtBsXd88jIIwqFMqgCyUCjw85//nN///ve89dZbpNNp6urqOOWUU7j++uu55pprDnp713X50Y9+xH/+53+ydetWpk6dyvXXX89Xv/pVdP3AiTDOO+88Vq9eDYAsy7z11lsce+yxQ95Ha2srM2fOBGDFihXceuutw3pssirRtjJx0IONFlOYcnkMWZNIvJUns6U4aJ690WfhmC5VJwXoXp0Z1v1WrAOckIdn+0Y9gNSishc8Ppsm+e4YTpkVxoTUZSPdvid4KyyLk/zL+KBtzE+3lAI3XAl3QB4cQMouKHsCL0lykfcKKOVDZMgJvjdA8L2BIf/uaDIDl06nu2iUVmNWPb4D/bG2g+73YDJL4gx8qGnPFa7LjM++jWKVd0dU7T3t490EQRAmCUmByBw/8eMCKH6JtocSFHaapSzxvjqVpgujqCEZx3SJnxCg7f7EhFwP6SJVxOieWwFtHC+TJoDcsGEDV1xxBe+///6g69vb22lvb+ehhx7iqquu4v/+7//w+XwH3MdNN93EL37xi9LlzZs384//+I88//zzPPzwwyjKwUtAOI7Drbfeym9/+9ujf0D7aHsggeIMXT8wOFWjYWkUp+iw/YEEVnb/5BDGgE3y3TzhmQd+/BNJYafFpp/14DoQbNGYcnmcyDE+r3bmKAq26Li2S+p9UaBcEARBECYDWZNouiRKsFkn32XS/VgaNaRQd1YYLaqQ7zSJzvPj2i4dj6Tw1arULg5Rf26EtgcS4918QdjPpEgv1NPTw7Jly0rB48c+9jFWrVrFG2+8wcqVK1m+fDkADzzwADfccMMB9/HHP/6RX/ziF1RVVfFv//ZvvPTSS/ziF7+gubmZRx55hP/+7/8eVlt+//vfs3bt2pF5YHvxN+uEZuiEZ/uIzPHx/7N333FSVefjxz/33ult+7IsHWuwglI0iiJ2jNEYjYlfSxISNZpYokZj/cY0E39G4zfGJLYUo8HYEDEYEBGxYEVUsICU3WVh68xOn7n3/P4YmN2hLLtsmdnd5+1rXnLv3HvPmZ1y73PPOc8J7O+i+CA3FV/2MuqsYkbMKibZmqbm2Z0Hj9uYMQvdMTTuuKitf4bk1vkyWz/s4+kzdPDv6yK+JY1KFXbXPiGEEEL0nO7UGPGVIjzVmZ5qsU1JKo70MfyEAK5hdnSHRtnhXmwenbbPEgw/MUDZYR4A0uHB1/ooBoch0QL5v//7v2zcuBGA22+/nZtuuin73MSJEzn99NO58sorueeee3jsscf49re/zQknnJBzjDlz5gDw17/+NRtwTp06lYkTJzJx4kTmzJnDJZdcsss6BAIB4vE4yWSSW265hWeffbZXX2P1iYEdutFaaUUqZJJqNal5o5VY7e7H2+lOHTMxtIKbdNgiviWFfx8Xja/3TSIdw6VReYwfV4WN2nnBPilDCCGEEAVCB/94J6WTvTiK2nuoBfZ3YyUVG55qIbEljW8vJ8NPsGOlFaWTPIQ+idPwWjjbtXUwstQASaIzAOqYL4M+gDRNk0cffRSAsWPH8tOf/nSn2/3617/mH//4B01NTfz617/eIYCsrc2MlZkxY0bO+kMPPZTS0tLs87tSUlLCrFmzuO+++5g7dy5vv/02hx9++J6+rB2se6wZQ9kyCXRMlW1d6y5lKgxn7hfGO9ZBYD8XzjIb9oBBcFWMLa+Edzef+YBRepgHZ7mNuhd6f/oSw6URmOCm5GA3AJv+GyK2SRLnDBWuN9uwf5E71rXjCUnZdDZeMQGlbz9vY/s/A//cgvfj1vb97QY11xzAtoG8zpoIlY9/kX0+NLWU1pntc1Fmdmo/vqZpVOqg9PZiunuOjO3ro+GbI7PL3g+CjLj5k5xt9Fh+75ynyhxsunSvnHWlz2/C915rfiokhBgyDLdG9alFuCpyhxZZSQvdrtHybpTElkyEGNjXiZVWGE4dK2kN+uBRDA6DPoD87LPPaG1tBeCEE05A13fea9flcnHsscfy5JNPsmTJEhobGykvL88+X1lZCcCSJUuYNWtWdv3KlStpbm7mwAMP3G1dbrzxRh566CHi8Ti33HIL8+fP78Ery2VGrUz2wB6KbEhSdriXkkPdhL9IUjbFi38vJ7G6JLFNKVJtJkVfcuOqsFMzrxUrPvCjSEexgbLA5um9Ht3OchvFB7nx7e0EBW2fxmlaHsEcBH8v0XV6yEQP7TqQsuy7/8wZm5M4Pm8PQk2PQXy0L5sIqmOGVQAzYCc+1rtdQR0CSAX08B6G5TFIjGsvw7siiGttYWUsVTZth7+D6R/0pzwhRAGoOMqXDR7TMYumNyIMm+Fny6thqo4LkNraNXXYcX68Y9rzTjS/FxsSwaOldCxV+KPoBkId82XQ/2Wam5uz/94WBO7KsGHDgEyr5bJly3KeO+OMMwC44IIL+MMf/sDy5cv561//yqmnngrA2Wefvdu6VFdXc/HFFwOZMZWvv/56l19Hf0lsSdPyfpTyaT7GfrMUd7WdTf8NUTM3yObFbdQ+F6R2fhBnuQ3v6J1nnh1oGt+IkNiSYtgMP9WnFu3xcWw+naID3Yw8o5jRXy/BXW2n+a0IX/y9iS2vhCV4FEIIIQY5V5UN/9bpm5KtaWqebkHb2hCZjmWuA3zjnIw4rYjAvi6UUkRrk9TOD9LyXmHdiBNiVwb97Vivt/0OdDDY+dizjs+vWrWKr371q9nls846izPOOINnnnmGyy+/PGe/mTNndjr+saMbbriBv/zlL0SjUW6++WYWLlzYpf36U+MbEcLrk6i0ItmS3uFumHu4HaVUl8ZUDgTpsEXN3CDVpwbwjnZSNMHV5Sk2nGU2vOMc+MY6cZbbUKYiWpui7j9BIuuTg6abr9gzaidzOGrbdy+3chOF79CdVNPIuQmq7aa/qcocc4cyt67K7r3dXJM5ZVg7znTT8XnFdmUU6Od8h791gdZTCDF4lBy8NQFOzGLjM5meWmWHewl9GmfkrMxN6sC+LtTW31BN0/CMcFD73NDJjyBjIAe+QR9A7r333thsNtLpNEuXLt3ldpZl8eqrr2aXN2zYkPO8pmk88cQT3HHHHTz00ENs3LiR6upqzj//fG666SZstq79KYcNG8Zll13Gb3/7WxYtWsQrr7zC9OnT9+zF9aF4J+P0wmsTFB/opuhAN01v9k3SmXzYtCBExZd9VE73U3q4l8j6BPHNaZLNadIxC6xMNjVHqQ33MDvesQ7sfgMzYRHZkKT5vSjRDUksybAqtoocU0LTd0bkrKu+7lPs9Zn5RvWUxV5Xv5vzfOsxw2g6fXR2ueGsvWj8aoexfBpoHbqksl2QVLx4C0VLG3JXdoz1fDaSdx+OZaWyq5u/WkXLCe3zOFY99Cnej9svZtLFdtb94qDssufjEHtd+n57ldKF95m3b0mw1w9z/7bbd/cVQojeZPPr+MZnuqQGP4phxRX2IgPDpRPdkMRdbcfm1Ql/niAdtUg0pak6LpDnWgvRfYM+gPR6vcycOZMFCxawYsUK5syZwznnnLPDdn/6059Yv359drmtbcf5AG02GzfeeCM33nhjj+p03XXX8cc//pFwOMzNN9/MkiVLenS8/pZoSNP2WRzfWMegCiCVCVteCRNcFce3lxPvaAeB/V1oO2nxSYVMIuuShNclMklx5LpU7ITSNZRzu5EC232e9GTuh2f7YEzZ9G4NNtBMhWbuOqCztpXXoQVS2fTcem6f1AewOjyvDA09Udgfek2BVuB1FEIMLtvm0Y5sSNLyXpSKo30UTXCRjlnoLg27zyC+JYVnlAPDlflNVZbii7835bPa/c5Cw9qhn0vhGQh1zJdBH0AC3HbbbSxatIh0Os3555/PmjVr+J//+R+qqqqoqanhwQcf5Ne//jV2u51UKtPyFov13ZyA5eXl/OhHP+KXv/wlr7zyCgsXLuT444/vs/L6QrQ2RdEEN4ZXx+xkXsmBKNGQJtGQpumNCJoBjmIbuktD0zJToySbTaxk4bW4CCGEECJ/QqviRGuSJJtNKo7KBI+Nr0dItJhUn5RpaXRVZgZEptpMNANsHoPSw7w0LBs82e3F4DckAshp06bx5z//mYsvvphkMslPf/rTHabzsNvt/OY3v+Gqq64CwO/392mdrrnmGv7whz8QDAa5+eabexxAarq205ayvhKvywTa3pF22j5L9lu5XZH5W2T+32MKki07ZtHslWMPEb36fgwwmrbjWMLd/S2232cnwyhzt6d7f1tN03Z/zO3quG2fPS1T7NpQ/n4UInk/CstAez+UCalWK5tTIfR5HPdwO+XTvNnXkAymMeOK2meD6HaN8d8uo/hAN8nmNKHViTy/gs7t6v3QlCY9sYaYIRFAAnz729/m0EMP5Re/+AULFiwgHA4DYBgGJ5xwAr/61a+y6yAzb2NfKikp4aqrruK2227jjTfe4IUXXuCUU07Z4+OVjSnGrvdvVlSX5WH4IU6cqcLKGqbpGsXV/kzyEUtu5+XbUH4/AspNyVu5N1iMY0aidWjB1lLbnYjH+nDa29O6aw6VE+1paYXng/bxibaWNIGxnf9eRQ4MoOyZiayVU6NId4Bqv9ntaEzgqGnvdeHzeHCMbe+yavptJHVXdtnlTVG+mzJF1wzl70chkvejsAzE98NdbafiQB/KUvgO8ZMKmTiLM5fb8aY0gVE2mt+NUD5WR7NBIJBpmQyZFo6xnnxWfbd29X6krCR80cmO25EkOgPfkAkgASZOnMi///1v0uk0mzZtIpFIMGLECNzuzCTvf/zjH7PbTpgwoc/rc9VVV3HPPffQ0tLCLbfc0qMAsml9KzbNvvsNe9PKBN69nDSua+nfcndD0zVQisb1rQPmhDOYDen3Y10LvJy76vPbDyA5rD0Ys7XYc7otWTaFSnW4C+02wd5+a1dPm4z/84fZmDIJNO6mGmsvGUW6OHODSdNgpEpSa8WzxZa+vYmyZ+qz24e22z9dZKcm2R4Ie0NR9AL73g9UQ/r7UYDk/SgsA+790GDMESW0NrUSrUvR8EqEYcf7cFl2IhsSeMc42fBkG+Ev2n9P7atMNJvGls8KPwvrrt6PtBocWflF1w2pAHIbm83GqFGjdljfMQvrlClT+rwegUCAa665hhtvvJG3336bZ599lkMOOWSPjqUshdL698c1UpOi+BAPNr9OKrjrydLzQamtf5OBcMIZAuT9aKfIHeay/V9k++d3un83/447K7Pjum3vzy73V2rH/eW97DXy/Sgs8n4UloH0fnhG2rH7M709Qh/HCezvxFVup/b5VqpmBmj7JE7bmvYbhP59nXhGONj039CAeH2w8/dDqe7VXVogB75u5PYb3CKRCM899xwA++23HwcffHC/lPujH/2I8vJyAG699dZufwnzKbYpiTIVnpH93PIphBBCCFFgPKMdWEkLM2aRCpkE9ncRXpvAVZkJLIOftM8xXXSgi2Ez/ARXxQivKeyxj0Jsb0i2QO7MHXfckZ264wc/+EG/levz+fjJT37Ctddey4oVK3jyySf7reyeUmmIb07hGeEg+FF89zsIISh7pg7Tk7lDrSnQklpO82B0QhFth5e2r0joaMkO9/rSOo3f7dCDwgIr1f68Z1UI/zu53Uv1mI7uyGxjpCxK36on1Rhi2/0q5xedj2PWoyaVj7YPcLE3F1biLCGEKATu4XbMhMLuNxj99RLSMYu2NXGGn1hEywdRElvSGC6NymP8+MY5afkgSuNrO06Hpjs1Avu6sFKK0OrBd30lLZAD35AJIDds2MDo0aN3+tycOXP45S9/CcDBBx/MpZde2p9V47LLLuP//b//R319PXfccUe/lt1T0doUxQe7M0k+Bk7jqRB543+rudPnTa9OaHJ7gho9pYHZfhJTuk7ouLJsYh1lalix9p9yPWHtEEBqSR09kQkg9ZiF9+1Wita1dLnLlJ60KF7a0KVthRBiqNIMjURjmvCaBGZS0fZZnOpTiki3mYTXJhl2rB/f3k5UWlH3nyCRdUmKDnBh29rtFUUmsc6+Loytc+8OxgBSDHxDJoA88MADOfLIIzn77LM54IADsNlsfPHFFzz++OM89dRTAFRUVPDYY49ht/dvl0y3283111/PlVdeSWPj7tJhFJZobZKyyV6cFTYSW9L5ro4QQgghRF7EN6VwV9vZtCCEZ6Sd0V8vwXDqmAmLUWcUk2ozaX47QnB1HCuuKDnUTfk0H8lg7vVT2+cJXBU2XJV23MPtxDYNriQ10gI58A2ZANKyLBYsWMCCBQt2+vwhhxzCo48+2i/ZV3fm4osv5re//S21tbV5KX9PxbeksZIWnhF2CSCFEEIIMWQ5K20kWkxGnlGMu6q9MSK+OU1kfQIzZmHzG5RN9uIoMXBX2Wl+L0rTmzt2Y7X5dUZ+tZiSQ92DLoAUA9+QCSAfeOABFixYwPLly6mvryccDlNZWckhhxzCOeecw7e+9S1stvz9OVwuFzfeeGO/jr/sFRZE6zLjIFvei+1+eyFEp/Skhj1kZJdLXtyA+5MOXVI1wNbe9TQ+ysvm88a3P7+TXqnDH/wYZeiZuR9dOlvO35/aaPs0HkXLNlP02pbefSFCCDGEOEoNXBV24vUx/OMzc/m2rYmDAs8oB97RmamUzKRFOpRJsrNlaZjQJzvvoppuswh+GKNssnfQtUIqwKLwW/dkZNauDZkA8txzz+Xcc8/t93JffvnlLm976aWX9vv4y94Qq01RNtWLZoAqrNk8hBh4FGgdvke2pgTO2l0nuTHt2/+M73hSdtS339yxvDaSw9wkUkb25JgucvSgwkIIMcRpUDndTzKYpumtMOmISeBLbvx7uUi1mbSujBGtSZJqNTHjXQ9LWj+I4dvLSeUxPtb/q0UiGlEwhkwAKfpOtDZJhc2Hq8pOrHbw3CETQgghhNidkkPcuIbZ2PTfENWnZrqvhr9IsHlxG/H6Pbsu0u0armE2HMU2lKUwXBpmTCJIURgkgBQ9lmw2SUctPCMcEkAKIYQQYshwlBiUTvYS+jTBsGP8WAnFxmda9zhwBCib4qFkogdN00g0pXGW2ag6PkDtc8FerHn+SBKdgU8CSNErYrVJPCPtNC3Pd02EGNiMcApHbXtCBctjEB/tyS5rFjhrtuvS2uGmtOm35Wy//fM4DRybY7gisexqTVMkxrrby9B3vMutOnSN1SMm9s0yF6QQQgw71k8qZOIdaSfZYlI3P4iV3POWwuKD3ZRO8tL0TgTDqVF8YOb3PNUqY4RE4ZAAUvSKaE2Kyr2d6A6tRz+cQgx1vhWN+Fa0T+ez6bt7UX/hmOyyEU0z/up3d5l+IDS1nNC0spx1WkrP2X/K/WswOswD2XxmFRt/vn92G4czjWFY2WWlwDTbj+F+p43KO9btycsTQohBw1Fi4BpmJ9GYRlmwaUHPgkfDpVE6yUM6YlI6aWsLZGMaZ7mN5vd3PRZ+oJEWyIFPAkjRK6J1STRNw11tJ7JOWiaEEEIIMbh5xzmxUhbOcht1/wn2eIxi5TF+NENDt2s0vh4hXp/CSivGnFNK8YFuks0mVsoiVp/GjFq7P6AQfUQCSNEr0m0WyaCJZ6RDAkghhBBCDHrOMhupkIWzTO9xDghHiYFvnBNlKZrfi9L6QXv27Jb3oxQf7EbTMy1i6ZjFun82o1IDs8eXtEAOfBJAil4Tq8mMgxRCZCSrXET3CeSuNBRa++hDSOaeoJx1UVzrwtllz6ogeqx97IumFMFjK7LLqQrXbuuhOo5p3Mn4RpSGSrfXw9R1lNm+rCUsfK82Z5cdG3Y+b5kQQgwlNq+OlVYoU2H1IJhzlBhUzyrCSinSUYuWd9u7q+oOjeCqOM3vRrFSisB+LoYd68fm1kgN0ABSDHwSQIpe07Y2QdEBbnzjHYTXSiukELG9/Gz51tjclU4LtMxJX1OgtebedClesikngCx6rZGi19rHRJoegzW/n9g+3aMCOt74VrDDXJBGh4sMY8d6KlNDpdqfSCkNrcMhbC1Jyv5Su9PXKIQQBU0D315OnKU2QBH8OE463DvdP+1FBsnmNGZi62+6DUoO9uAZ48DuNzBcGpquoZQivjlNsikzntFRZkOlFemIhWbTsPt00jEL3a6BpRj51WJs3sy4c8Oto+kaVloR35LCUWIj0ZwmFdr9a3CUGhQd4MZZbsNKKKyEhZnc+v+EwkoozISFlVCZ19GNOSp7QlogBz4JIEWvidWmCH+RoOLLPuKbW0lHpH++EEIIIfKnaqYf/94uUm0mul2j+CAPze9ECK9NtAdhWiaBjeHS0V169t+GS0czIL45Taw2iepwWeMaZsPm1klYkAqZOMoMqmYGcBQZxBvSJBpTKAWaAamQhc2t4xqeSbjTtiaBZmiZFsxUJpCM1SYoP8KP4dZIRy0i65NoBigLki1pbF4DV6WNaChJywe7T6jjGmZj5FeLMWMW0ZoUul3D8Og4SnR0p4bu1DAces4+iaY04TUJgh/H+i2YFAOTBJCiV215NcyoM4qpnlVEzdxWLPkBEqL3yU1RIYToEne1g+DqGFteDqPbNUqneCib4qV8mg8zbmWCR6e+w35KKay4QgFlh+uYSYvo+iTJFhN0KJrgJtmaRpkK9ygHo88qIRU0MVMKd5UdK5lp5UMD70iD0CdxNsxpyR5fd2QCSJvPwObT8e/twoxZaIaOo8TAM9KBbsv82CtL0fhGhPqFbV1+3RVf9pFoSrPx6VbY1f18jUwg6dRxVdjwjHJQMtFDySQPrStjNL8dQcnsIWInJIAUvcqMWNTOCzLyq8WMOLWI2ueCPRoXIITYCYUEkUII0QXhtQmKD3RjOHUaXw/TuCxC81tRyo/w4h3jJB02ibalMOMWmg6+cU4Ml44ZU8Q2JQENzQArqXCUGLir7aBpKGvruEcLQp/ESbamKTrAjRmx2PCv5pyMrNWnFuEZ7WD4iQHMmIVndKaL6zbKyox9TIct0mGTREOa1NZ/pyMWgX1dlB/hRdOhZUUsd27fnSib5sVVaWfjMy27Dh4BFFhxhRU3SQVN2j5P0PBamOKD3JQc6sE33knLu1Fim5Kk2nqvV5lSGmoAdA8dCHXMFwkgRa9LBU1qn29l5OnFDD85QN38oNzBEkOS/51m3J+Gcldudz7Stjsnd0yYA9B6URWxw9sT8SilYWtu/+l2bgxT8fhnndaj5poJpP2ZsZaWw2DTFXtRF49lr0Esn4HmaC+38uENuD9uv9OtWXITSPQtZUDdHfui7JmWINvmBFW//CLPtRKDQcOrYWL1KcqnehnzjVIiG5MkGtK4q+zYPDo2j46rMrOtGbeIbEjS9nkCzyg7rnI7Vlph8+rY/QZf/KMJm09n1FeLMeOQbDZxD7djuHV0uwtlKr54KhM8uqpslBzsIbIxiXu4Dd2uZ8c+RtYniW1KZQPEdMTqNChsaAxjpRRlU70UTXATrU1iJhQqqTCTikRDinhDGqzMeM/SQz00LAsTr093++9lJRTNb0dp+yxBxZFehs3wZ9abilhdEmuDBFVCAkjRR5JNJnUvBBkxq5iq4wNsejG02ztmQgw2etzEEe/Z3ROzyEa6ypFdVqaGtrb9BK4nFI6GxG4O0v5PpWmkS+ykLKv9K6mpnMDW1prCsWU3xxSil6WGOVH2rR/EtJwwRO8Jf54gsi7TEuke4chMiWFoRGqSOMts6AagazS9HSH4YSbLdHRDezJAR4nBmG+U4igxqDjSR6rNYuPTLah0JnHOsBl+/Hu50AwNK5G5K+iuduAb78Q33kmiJY2jWGP94817di2koGl5hPCaBEUHuHCW29AdmfkidaeObmtPsuMstRHZkKR1ZWz3x+1EKmhS90IIw63hKLVRPs2LZ6QD/34BElqUlg/2/PgWGtYA6EYzEOqYLxJAij4Tr0+z6cUQ1ScHGHasn82Lu953XwghhBCit6g0tLwfo+X9GJqR6VbqHtaeBTtak6TyKD++sU5im1Mok+wYxFhdJpj0jnHgqszsM+qMEtIxCzOeaUFMNKexFxmUTfVi8xp4RmS2a3itLTPO0e3q8Y30RFOaLa+Ec1dqmfko3dV2XMPsRDcmaXp790l2usqMKWK1KWqfCzLmGyVYNovyI33obp2mNyO9Vo4YWCSAFH0qujHJ5sVtDJvpx0xYNL4mPzZicLE6Jl9QCj3Zuy0nWkqhxdv7uWqWRnfbNPWkiZ7IjLfRdnJDVUsrNLNDvaXLqsgDPW5l5x/VkzuOt1J2DWXkzk+q9fFHVdk1lN6hzGTfl9nnDMCx3Q9BQnU+Vm6QUSbUPhcEMi2Iw08I4BvrxExYaAYU7Z9pTVQWaDoUfclFvDFFySEekkGTaG0SFNj9mUDRcOnZXhxFE9xgKbStwWfFkZkuoD1tEdz1i4FEY5pEYxroozLIjAHdsiRM6bkltL0Xp3SiB2Vmurt2+1gyjceAJwGk6HNtnyfQHRqV0/2ZvvXv9N6dMSHyyXJorL3vYNh6UetcH2PUrat7tYySP9ZScn/7HIyWx8aa30zKLtt8u7/qG/O/H2QvbpTXTuL3UzJzP24r47nNlD1X177DELqQFIVBM2HUxR91uk3Ld6sJzyjJLldd9zmO9fE+rdeWS0YTmVKcXR7141XYtwzweY6nudD+tzRnlfpRI3w8wF/XHlJpqPtPCM8IO5XH+nFV2lGAvvV3PR3NzM+YajEJrU5QeZSP4EcmKq0I7O/CjFi0rIih0io7/YfNb+Aa1n6JbSYsGpaFd1GDgSNakyK8No53tJOW96OUHe4lVpuirS61+53FoCIBpOgXwY/j6E6d8qlezIQi+GHf3SUTol/p7S0UasdM8D2mKXK7PW1rHezGjdGOx1Bqx+YTTe2YzEeI/ra7z6DSgA6tgf0yPEnPbfUcFLb/O25bN5SpTHBUO7cV7zgnKq2I1adQaRhzTgltaxP493LiLM9cNjtKDYr2d9O6MkrjG7lTXWgG7P29ipzDb1kyeIbwtHwQw324wjvGQWxzispj/YTnRKEb+XokC+vAJwGk6Dct70UxnBqVR/mwEhZtn0mSDiGEEEIUhlTIonVF7g3uyIYk7io7m/4bIrCfi3hjGmeFjcj6BA3LdhyW4yhtv7Re+9fGnOk8BgOVhk3z2xjx1SKstInNq1N6mAfezHfNRH/qg/vlQuxa4xsRgqtiDJvhxzvGsfsdhBBCiHwZXNf+Yg80vh4GBVUzA8Q3p9FtGs4S2y6zkGrt0zsOuuBxm1TIpG5+EEexjVTIpOgAd7f23zYGciA8xM5JC6Tod1teCaM7dIafGKD1wxjBVXFSrTJRpCh8kQMCJEZ7ssvK0FApI9t1x3Q5aZlZnX3eURfBuyrYq3XQUhalL27KdjnTUorg9A5l1oRxr82dezJ0xDAsZ+aqRjl1XIqt/QEzYvv6aJk1rEMh0PHKWY9bFC1s7NXXIUR3ud9rwwi1nyvSkz2Yh229cFXgerYFrZdPJZ53gtg6TJOjR/v/XBWbVYxytd/vN+qSOF/vwXi6mjQ8vl2XykY5B+9KKmSxfk4zFV/2ZYbhxC02LQgRq935uL9Ek4mVVpm5HQexREOaTS8GqT65iFibjIEcaiSAFP1PQf2iEGWHewl8yUXRAW7q5geJySBsUeDCh5UQPCZ3bIsWa7/dnHYZNJ4+Ortc9PrmXg8g9ZSi/JmN2eX42ACbvn9Ae5mv1u0QQLYcNxIzkGnx13SoQstpWYkd4Cd+iDe3oI7zQjanJIAUeed5I4TnjfbPdvD3YzDHOzMLaYVrXiuYvdvi41/WAst69ZDdFv1GOVZx+++M441wzwLIdWnUn0O7305kqXTm5nd8c5rI+kSnLYsqpVj7cCPaYBs7uxPRjSk2L2mj9ChXvqsi+pkEkCI/rMykuM3vRKg+pYhhx/lZ/1hzzkB0IYQQQoiCoCC0umtZf5UJqpdvZhSqtk8TKGc3MuggSXQGAxkDKfJKmbBlaRi7z8A71pnv6gghhBBCiG5o/aBvp9MRhUdaIEXepYIm6YiJo8TY/cZC9KPovgGCR7WPDXTWhBl+35rssrJpbP7O+OytOE0Hm739Tmz8cA91JXvlHLPysQ3YWnuvu7Z9S5TKf36SXU5Wudn0nX1ytkkXGyh7ZjyOppHJ8qC33x1XaR1S7fcTNaeJZrQ/b/kM6i7eO7vsWh+h9D+beu01iKEn+OVyIgcXZZc1S1H1wBdo3Wi18TzUgPJu/dwqIN2zFp/UMAct36xG93nZHA6ggKLnt+D6rO/mLrbG2kn9T0nOOvtjrehr2udkdNlSKEf7eDqbLbe1x3mCHeex9uxy9JEE6c+kO48oXGqAJKiRFshdkwBS5J1mgO7SMWODe8C5GHhSZU7Ch7ZPuO1ZHcT/Tkt22XJobDas7Dxxmq7Q3e0XbuZwO/Hi3Ox05U/V9GodjWga74fN2eVkRVVOnYGd9DXZ7kLbAmW2nyg1BzljIC27TnhS+0WusstJVfRMYpSb8GHtnynNVCj9i24lwbG/37uBnemzEZlSTExzEVGZyeS9r7fsdr+eUMUG6aN8Oets/8lNcGMzrJx5G3Ut9/trG2/gnNGe1Tw+N4kQQvQlCSBF3rmH29ENTZLoCCGEEEIMcgpQA2CI6ACoYt7IGEiRd/YiA2Uqki3S5UYIIYQQQohCJi2QIu/SYQvN0LAHdFIh6cYqCogBytHhHqSRez9SSynGXv9hhxWgdeheppS2wy1MW0vftrQHXm/A90Fut7uaKw/A9Ge6uBnxNNUPr8O+oRW19RZw6/ThBI8Znt2+8s8bcX/WPv2IWWRn4437Z5fjh3jZ+Pv25cALjRS9INN8iK4re66OkoWbs8tK19hwywEoW+a+tr0hzsi7Pu3XOjnXxxh15SoqRhWjb8x8P4xQ7njD0K9GYlW2jze0rYrhu7M+u+w/06Dowvbnm36TJPbars9r+scJ3BdtyFlnv9iP9uP2bq3qpnpo6/BDEs89XvSfCWIduq1azXIeFf1Ls2vQjVObhYZG4Q+FsAZAHfNFAkiRd9HaJOmYRclED1uW9GBuKyH62PbdWTQF9sbCGm9kxE2MeG5rvtbxetICoyWFvSmJsjKvyIimc16cEUxhb2h/XVpa5YyJVE6NtL99zJXllQRYonuMiIkRaf+cKkMjVeZE2TMBpJbu/yBISyvsjUlsvhT2xvbvR0dWuQ1rWHuAaNXnXjXrfg1bdYfxxO7OL0C1pEKrzw1SNa+GNrz98kxrMqGToFC1KVSbdLYT/UczwL+vC89IO64qO8phsvjBfNdK9CcJIEXeqTS0vBul/AgvLStipFqlK6sQQgghRKHRXRojv1aMo8ggviVN+PME4c2xfFdL9DMJIEVBCH4Uo/hgN2WTvdT/N5Tv6gghhBBCiA6cZTaqZvoJtYbY8ERLNndFWnVvaIZS2oCYImMg1DFfJIAUBUFZ0PJ+lIov+9CdGlZCuuOIAqByu39qvfCxjO3lw3Lo2eO5V4dyRlmkix2kKtqn/lA6qA5zNmqmwvNpe5p/02OQGN1hGgDFDn1tnevDOJyZMvWUIjHGR9RutmfBUxqeT1pz6hDdv32OPtNv5HZx1RUeR/sFg92QXgOih5TCsyqEsmW+Dfbm3XcNj4/xYTnbu0/bvdt1J21MY2zs3S7mto9jqA7dVo21iZzndzu/nQ28h7c/bwYV8VW5m6hPUzldxkn17IfH4bQ4cFoku9zaaGPtR+5O9hBi5yqO9WLGFRufCpKOyO9+vsViMV5//XXeeecd1q5dS319PZFIBLvdTnFxMaNHj+aAAw5g6tSp7LPPPrs/YDdIACkKRviLJJVHa3jHOGj7NLH7HYToa5aGltRzlntq8wXjSA7LXLxpKYu9r3wbOpyHIweW0TxrbHbZ9FhYHRL5GNE043/8bvb6Mlntofby9oQ2WlpDyx1SxZjb38EWzFz0Wh4bTb/6MvWJZDYmLPnvRqr/uDq7fd0l+xI5sMPk5hooq72STiNNVXGHngKuhKQ7Fz2iWTDi3s+6tU/DWeNJVnu2HgCK921G6/AVdc5vxXvfll6sJfh+t7nT5y000qo9qN0+mDR8MPr/jGyAGF2uWH9p7oW4+UDuPJA9VVye5lePrckuL3uhiJ99d1yvliGGBmexjej6ZI/n7baUhjYAWvc6vRmUJ+FwmCeeeILHHnuMpUuXkkx27SbZ6NGj+drXvsZ5553HpEmTelwPmcZDFAwzahGrT+Eb58x3VYQYZArvJCiEEGLg0HSw0grNIeeT7qqtreVnP/sZkydPpqKiApfLxahRozjqqKO4+eab+fDDD7t0jCuvvJKRI0cye/ZsFi5cSCKRQCmVfbhcLoYPH05xcTGapuU8t379eu6++24mT57MtGnTeOKJJ3r0mqQFUhSUyIYkJYdI1xohhBBCiEJRdJAbzaYRr+v5VFRK0T6EooD1Rh0feOABrr76atracnsW1NTUUFNTw7Jly2hra+Puu+/e6f6tra3cdttt/OlPfyKZTKKUwmazcdRRRzFt2jSmTJnCYYcdxrBhw3A42rOjK6UIBoN8+umnvPXWWyxfvpyXX36ZjRs3snz5cs4991xuv/12fvvb33LSSSd1+3VJACkKihm1MJzSMC4Kg2ZY4GnvDxo9NIBZ1GHKCktR8mJ9dmxkOmAj9OWK9ucVO3R7NX022Dam0QYts6pyxhcmRvpzuqwquwJbh2WXRnDGiOxyqtSJnmwvQ+nbzV2Zrci2FwWWTWFZKrs2up8fZWufBzI5zJUz7jJT8fYy0mmDtkR7TwGHacOBEP1L86bRApnvp2YpXE+2oFntXetsn8Rztk9O9mJ27OGSVriebsl+f61Sg9SJAZLFfuKtCqXA/loYY2PXL5iTK03aHmnvUpZel9vVz4pD48Pt61I1vX8VbZvpRK9uP4+mDDuP/X5YdnnDp65eL1MMfu7hdmKbUqRaZdqmrrr77ru56qqrgEwX0ksuuYRp06YRCASora3l008/5ZlnnkHXd33du88++9Dc3IxSiiOPPJJvfetbnHPOOZSXl3datqZpFBcXM2XKFKZMmcJll10GwNKlS/nnP//JE088wYcffsipp57KPffcw+WXX96t1yYBpMg7e0DHHjCwUgrvWAdWSmU6V8tcyCLf7ArN2z4+KXx4EW2Hto8N1FIWJQvrs2MY00UOGs8Y2b6/paGlcwNI5bSyPUqVTaPprOrcMpM6dEwiZbdyA0hdo+Wk0Tm76B2GQFhOC3P7AHK7HkfKpsj8lxE5wE/kQD+d6hAIp1MGoXj7Rag/LQGk6H+aL40WyHz4NVPhebQRrZOEM6kjfSROaE8ORdzC9UxL9v6KVW4jcX4ZSeUlodlRGhi1qe4FkO+bJN/fdXIRFYeGP/Ttyc1+qgvblPa5KpObLR75+vBO9hCic5pdw11lJ/hRDJ2eB5BDIQvrm2++yY9//GMATjvtNObMmYPb3d7D7rDDDgPgmmuuIZXa9W9MU1MTs2bN4uabb2bKlCl7XJ9tjj76aI4++mj+3//7f/zhD3/grrvuorm5udvHkQBS5JW9yGD0OSXoRvuXdPOSNgkehRBCCCH6mc2vU3KoB7tPB01DmQpHsYFmaIRWJyiukBbsrrj00kuxLIsxY8bw+OOP5wSP27Pb7bt87p133mHixIm9Xj+Px8O1117LD3/4Q9avX9/t/SWAFHlVdIALK26x7slWDJeGlVSkwxI9CiGEEEL0J89IO1UnBFBpRaLJRKUVmgGJxjSbFoZIRyyo2P1xhrrXX3+d9957D4Brr70Wr9e7x8fqi+CxI5fLxX777dft/SSAFHmj2cA33klkQxIzamFG810jIXais1H0CjL9QwdANoDepnbxbyHyQYHSdpNvuCuf053Mo9ozOztY4XfdE0OPb5yDYTMDxOqS1C9sw0ru+NnV9N757A72LqwdM5yeffbZ2X83NTXR3NxMRUUFxcXFPa1eXkkAKfLCcGlUHR/AcGi0vCeRoyhMvleb8b7d2mGNlnM9aNk1vrjjENS2k6oN9A5Jd+x2E6czd2xDJOzCMjtJFGVXYGs/hlJabiIeBVqHIVaWXZEubl+hpTT06HbH3/46wNJyuolrgOo4rEXbyUVvhyQ6zvciVP318/bNE9JrQPSuVJWDup/lTnyt2uyoWPsHteovn+DYHMs8Z2hsunwSytb+2XeUx3GNCmeXnX9rwv1IY8cjonX46BprEvjP+wLPmGL861tRSlHxYxveW9q77NVdmCC9qf37YTvEhu+X7eOH4/+OE384ll0+9PYIVce1D1JOtuq8NKuI3gwiU+cWk/pa0baXhHXPFoyfdZinVW7wiF3QbBDY30XR/m6c5Tba1sTZ/FIbatfDeEUXvPnmmwCMHz+eiooK/vSnP3HXXXfx6aefZrf50pe+xPe//31+8IMf5GRPHSgkgBT9rmyKh+KDPai0ou6FEKmQXHyKwqSnFKR2fSbVHDqmz9YeQOoKvWN0pyn07bOZbl3fqY7XltttutM9t7sW3d3heyyt0ENyhSH6jtI1LH/uJYqlbCijfZ0eszDazPbtPfacANLypVEdsiZrJp1+bjUTtDYLPazQ2yyUpdAdoBd3+IJtf+/HBlqH57XthofZfApHSccvZO+f75RLQwXaX6dKgApK1Ch2Q4fqU4twD7MT2ZCkaXmEyIauTUrfU5bS0AZAC6S1tY6hUChnvdPpxOnc9ZzlH3/8MQBjxozhvPPO47HHHtthm1WrVnHVVVfx1FNP8dxzz1FUVLTDNp2JRqO89dZbrFq1io0bNxIOh4nFYrjdbnw+H6NGjeJLX/oShx9+eI+60O6KBJCiX9n8OqWTvIQ+jdPwWhgrLic5IYQQQoj+VDbFi7vKTs3cVuL16d3vMISNGjUqZ/nWW2/ltttu2+m2lmURDAYBeO2110gkElRVVfHb3/6WWbNm4XK5eOutt/jJT37CG2+8wdKlS5k9e3ZOt9fOvPrqq/z2t7/lv//9L4lEYrfbO51Ojj/+eK655hqmT5/epTK6QgJI0a/MqEUyaOIst0mmVTHwKYURTKG2ZhHWdIUWb2/h0H0K5dp+Dg1yu6Tu5iasEUujdRyLYoERJtsUqbkhXdpxj+7f1dUSFkaHllbTZ6Ds23Wb7XC3uM9bOMWQp1kKI5h7Uau1gepw01Ezcz+ISlO585cmLbSWDi2Oe9DV2mpTmE25378cKVBN7SvVdiMyUm0aicb2ZstksG9aXTT5Uoou0h0aFUf7COzjouG1cF6CR6U6Ty9QKLbVcePGjQQCgez6zlofo9EoauuOiUQCj8fDyy+/nJOoZvr06bz00kscccQRrFixgn//+98sX76802k6kskks2fP5tFHH91at679AePxOM8//zzPP/883/rWt3jwwQd7pcusBJCiXykT6l4IMurMYiqO9LH55bZ8V0mIPaanFON/smKXz7fNLKXp+yNzV7Y40FNbLyI1MF1WTsyn2S1wtl+QVv5jPf7XW3ZZRmx/L3W3tI8V00wbpLebp2v7rkIaOWWWLNxE2fza7HLtD/chcnBxzi56vEMAmSr8rkdiYLPXJxl9yUdd30GDVECh7O0XVe5lIQJPfNGjejT9MkUTu56jLf1BmtbTW3f5/IpbvOz4C9G73x9DU6C3/2ZIMCl2xTPSTuWxfnS7Rv3CEG2f774FS0AgEMgJIDvjcuX2Y589e/ZOs5y63W5+8YtfcNpppwHw+OOPdxpAnn322cybNw+lFIZhcPzxx3PMMcew//77M2rUKLxeL06nk0QiQSQSYePGjaxevZolS5awcOFCTNPkn//8J8FgkLlz53bj1e+cBJCi36VaTVpXxCg+1A0v57s2QvQhuY4TYoiTmy2iMBRNcFFxtI9oTYotL7dlpuTIk0wLZOF/N/akldRms+FyuYjH4wCcdNJJu9x25syZ2Gw20uk0b7/99i63+9e//sVzzz2Hpml85Stf4b777mPEiBGd1uOwww4D4Prrr6empoYf/OAHzJs3j+eff545c+ZwzjnndP/FddBJKkAh+k6yNY3h0NEdhf8DIoQQQggxUNkDOpXT/QQ/jFP3fDCvweNQ0HHM5MiRI3e5ncvlory8HIAtW7bscrtHHnkEgBkzZvDMM8/sNnjc3siRI3nmmWc49thjUUrx0EMPdWv/nZEAUuTFtvmFdKcEkEIIIYQQPaHZMlOk6Q4NzQaaAYZbw1lhY9iMAGbCovHN8O4P1A+2zQM5EB57YsKECdl/m2bnGcu3PW+z7bpT6IoVK9A0jSuuuAJN27M66brOVVddlT1eT0kXVpEX2tYEHSotffzE4OX8PErJo5ty1qmwrT2JjgaWLfc7oOkKjPZ1zvUxOmPfkqTsn3XtK5I6JHLvDYaOqIJtJx2HhlvPJBvJlrLd+SiwrBH3p7njk7Vk+zHtjfFO6yREdykNmr4yMichle7KvfCyUjqqwxyqJQvrsQW3jk+0FGXPb0R1+Og7a4fGHMPaWzGMDi1KWs2ux2yKwcdebDDsGD/Ochu6fefBRSpsUvdCECXJVvvF9OnTefbZZwFYu3YtEydO3Ol2oVCIxsbM3LSdtSq2tGTyIFRXV/eoXtv2b21t7dFxQAJIkSfekQ5SIRMzJgGkGLwcG+I4NvRtsGVrTlE8b9ddXwA2Xn8Y6UAm65qmgVsjt//Jdtcc/nd3nbRHiD6ha7QcX4WyZz6Ymq6wFeUm+DBjdqwONzICbzZlA0hNQfHLuTdrhgr9wwR8KMlQhiLNBiNOK8JKKpqWR0i1mWg6oGugQJmKdNQi2ZyW4LEfnXnmmVxzzTUopXj66ac566yzdrrd008/nc2mevTRR+/yeFVVVWzYsIEPP/wwO7ZxT6xcuRKAYcOG7fExtpEurKL/aeAd76RtrZzwhBBCCCH2RPHBHgy3Tt0LQVpXxoisSxJemyT8eYLwmgSRdUkSWwoveFQD6LEnxo0bx9lnnw3AY489xqJFi3bYpr6+nptuugkAh8PBt7/97V0e7+ijj0YpxS9+8Ytsa2R3tbS08Itf/AJN0zoNVrtKAkjR79xVdmxunbAEkEIIIYQQ3eYoMyid6CH4UYx0myTFKTS/+c1vqKiowLIsTjvtNG644QaWLl3K22+/zX333cfkyZOpqakB4Pbbb++0C+sPfvADdF1nzZo1TJo0iSeeeIJ0umt3BdLpNHPmzOGwww5jzZo1aJrGZZdd1uPXl7curMceeyxLlizp1j6LFy/m2GOP7dY+t912G//7v//bK8f/5JNP+OlPf8qiRYswTZPp06fzs5/9bJfNyS+//DIzZszILn/jG9/g8ccf77QOF110EX/961+Brk8SOtA4SgyUpUhsKbBbYkIMUhX//BRl2zq2TIOKMQGoC2ZTlNsb5WaOyD+bL4W1tQurvSlJxe/X5zyvLA06XCfbG2QsrhiaNLtG9clFJFvTNC2P5Ls63daTBDX9qSd1HDNmDM8//zxnnnkmtbW1/PrXv+bXv/51zjaapvHTn/6U6667rtNjTZs2jVtuuYXbbruNDRs2cO655xIIBDjiiCPYf//9GTlyJD6fD4fDQTKZJBwOU1NTw+rVq3n99dcJhULZmOLmm29m2rRpe/y6thkwYyB1XWefffbZ/YZ9ZOXKlRx99NEEg8Hsuvnz57No0SLmz5/Pcccdt9tjzJkzhxtvvJGDDjqoL6ta8Ay3jhmTu2VC9BfXhvaEOJqu4VI2POvCKGtw3qQSA5Nms9C3XpXoZhr3R4WRMVKIQuMebsfuN6ibL4lxCtnkyZP58MMP+b//+z+efvpp1qxZQzwep7q6mmOOOYbLL7+8y2Mab7nlFqqrq7nuuutobW0lGAyyYMECFixY0Ol+2wLHoqIi7rjjDr7//e/3+HVBHgPIhx9+mEik87smH3/8Md/4xjeAzGSb3Z33ZHvbBo/uyrhx43b53OzZswkGg8yaNYsrrrgCh8PBQw89xN/+9jcuuugi1qxZg91u7/T4SiluvfVWnnrqqT2q/2BheHTSEkAKIYQQQnTbtpvwmq3wW/F2qicDDPtTL9SxuLiYm266KTvesSdmz57N2WefzZ///GeeffZZli9f3mlXVsMwmDJlCmeccQazZ8+mpKSkx3XYJm8BZGfB2jZ///vfs/++4IILelzmgQceuEf7rV+/nuXLlzN58mTmzp2Lrme62BxzzDEEg0GeffZZXn/9daZPn77LY5SXl9PY2MjTTz/Nu+++y6RJk/aoLoOB4dIl+6oQQgghxB5wV9lRlsKMy834oaaoqIhrr72Wa6+9llQqxWeffcbGjRtpa2sjHo/jcrnw+/2MHDmSfffdd7eNW3uqYLuwWpbFo48+CoDP5+NrX/ta3upSW1sLZOZ12RY8bjNz5kyeffbZ7Da7csUVV/Dzn/+cRCLBrbfeynPPPddn9S10No9OKtT5xKpCCCGGlnTIgeXYOo1HWC6MhdiVogPdtH2eGLjJcwbIGEgKvI52u50JEyYwYcKEfi+7YLOwLlq0KBuUff3rX8fj8eStLpWVlQC8+uqrWFbul3VbIqCqqqpOjzFy5Mhsv+N58+bx5ptv9kFNB4jC/j4KIYTIA6U0sLY+pJOKEDvQnRq+8Q4cRQZpucki8qhgA8i//e1v2X/3RvfVnth777054IADePPNNznzzDNZuHAhr7zyCt/5znd48sknqa6u5sgjj9ztcW644QbcbjeQGQw7VCWa0riH902TuhBCCCHEYDT8hADDTywisjFJ6BPJQizypyADyHA4zNNPPw3A6NGjuz11x66ccMIJlJWV4XA4qKys5Nhjj+XXv/51lybl/POf/4zb7Wbu3LmccMIJHHPMMTz88MM4HA4efPBBnE7nbo8xfPhwLr30UgBefPFFXn311R6/poEovCaBPWDgqizYHtRCCCGEEAXDN96BZ6SD2nmt1D0fJBUcuEOBlBo4j8EkGo2yYcMGNmzY0ONjFWQA+eSTT2YztJ5//vloWu/0eVy4cCHNzc2kUikaGhpYsmQJN9xwA+PHj+fZZ5/tdN8jjzySZcuWceqpp+Lz+fB4PBx//PEsWbKEk08+uct1uP766/F6vcDQbYWMbUphJizcIxz5rooQQohCoBT+d5vxv92E/+0mvB+25rtGQhQMZ5mNiqP8hNcliNak8l0dMUA98cQTjBs3jvHjx/f4WAXZBNTb3VcPOuggzjjjDKZMmUJ1dTWpVIpPPvmERx99lBdffJHW1lbOOussnnvuOU455ZRdHmfixIk8//zzPapLRUUFl19+OXfccQeLFy9m8eLFzJgxo0fHHHAUKBO0gvz0CSGE6G+aBVUPr813NYQoKP59nZRO8uAotpEKmWx5uW33Ow0AaoAk0RkIdewu1UvNqgXXAllTU8PLL78MwLRp09h33317dLwrr7ySDz74gJ/97GecdtppTJo0ialTp3LBBRewYMEC7r//fgBM02T27NnEYrGevoTduvbaa/H7/QDcfPPNfV5eoXFW2LB5dOL1MvutEEIIIcT2bD6dymP8JFtNap8Psv7xZsz4IOtTKQasggsg//GPf2QznV544YU9Pl5xcXGnz1988cXMnj0bgLq6Op566qkel7k7ZWVlXHnllQAsW7aMBQsW9HmZhcS/j5NU2CRak8x3VYQQQgghCo53rBMNqF8YIroxiRpMSVeVNnAeYqcKrhPh3//+dwCcTiff+MY3+qXMiy++mAceeADITMtx3nnn9XmZV199Nffeey+tra3ccsstnHTSST06nqZrvTZWtK85ig2SzelMffugypm/Reb/Iv/k/Sgs8n4UFnk/8kcZGo2nj4Ctf3tbc4LSJQ3dej/M4XaSpwSyy7aVUexv9X1PpqFiKH8/zLCFZmg4S+wkmgqjx9au3g9NaTCYAtwC853vfKdXjvP555/3ynGgwALIt99+m48//hiA0047jZKSkn4pt+MEnNvmnuxrxcXFXH311dxyyy0sX76cefPmcdppp+3x8crGFGPXB0ZSGq/djXeMA3NfO1ay97tjaLpGcbUfNA1lSXePfJP3o7DI+1FY5P3IH8uuETtpTDaAdNRFKV+f7tb7Ye7rJHZGZXbZ4QvhaAj2WZ2HmiH9/dDArXkYf4qbhlfD+a4NsOv3I2Ul4YuuH2egZDgtlDo+8sgjBddIVFABZMfkOb3RfbWremtAaXddeeWV3HPPPTQ1NXHLLbcwa9asPT5W0/pWbNrAmFuxpb6V0d8ogWEJGpdGev34mq6BUjSubx16J5wCJO9HYZH3o7DI+5E/ll2nNp1AbQ0gXdEY9nUt3Xo/0gEXEc2bXXYGQ7jW7X5qMNE1Q/37EftPhOEnBWB4gtCqBMnW/E7dsav3I60kM2x/yFe8sjMFE0CmUikef/xxIJOptLNsqL1tW6snQHV1db+V6/f7ufbaa7n++ut57733eOaZZ/b4WMpSKK1wPlidSUcVTcsjVB7tJxWxaH4nCr1cdaW2/k2G4AmnEMn7UVjk/Sgs8n7kh1IKRfvpR7H1fejG+6FU7jCpbccQvWcofz/CXyRoWRElsL+LogluNv03RGRdfvNH7Oz9KKTAZjAqKyujubmZE088kT/96U97fJx///vfXHvttb1Sp4IJIF944QUaGhoA+Na3voXN1n9V6/hmHHPMMf1WLsDll1/OXXfdxZYtW7j11luZOHFiv5afL8GP4hgundLDPHhHOwiuihP+IoElGcaEEEL0F1Ox7d6rtieNOwpIdzhvyTgw0csaX4/Q+GaE0V8vwTfemfcAsld0vHNTyAqkjlOnTmX+/PmsWrWKMWPG7PFxysvLe61OBRNA7sncj4888gjf/va3Abj11lu57bbbcp5fuXIlbrebvffee5fH+POf/8yDDz4IQFVVFWeeeWY3a94zXq+Xn/zkJ/z4xz9m5cqVbNq0qV/Lz6fmd6JEa5OUHe6l8mgfldN9pIImyRYTM2qhAE0HNEi2mCS2pIltkm4SQgghek5PWux19TvZ5T3pxGOsjBH4eof5K80CueIUg4sF6TYTzwg73jEOIusHQRApumxbAFlTU0N9fT1VVVX5rlJhBJAtLS3MmzcPgAMPPJBJkyb1ynHfeecdZs+ezYwZMzjllFM46KCDKCsrI51Os3r1ah599FFefPFFAAzD4E9/+hNer3c3R+19l156KXfeeSebNm2isbGx38vPp3h9mtp5QQy3hne0A2e5DXvAwDXMnukSYQE6+MY7MRw6rR/GCmYwuRBCiIFNT20X8HUz26emgD5IBifE9hpei1A53cfwkwKsn9NCKs/jIXtCKQ01AKbIKJQ6Tp06NfvvN998k69+9at5rE1GQQSQ//rXv0gkEkDXWx+7yjRNFi5cyMKFC3e5TVlZGQ8++CCnn356r5bdVW63m5/+9Kf88Ic/zEv5hcCMKUKfJOCTxC63KTrQReVRfuKbU7R9tuvthBBCCCEGk1TQpO75IGPPK6XkIDdblsrN9KFiypQp2a6ra9eu3c3Wu3booYdy66239kqdCiKA3Db3o2EYvToH46mnnsqDDz7I66+/znvvvcfmzZtpampCKUVpaSmHHHIIJ598MhdddBGBQGD3B+xD3/ve9/jNb37Dxo0b81qPQhb8MI6rwk7lMX5SIZP45sKYF0kIIcTQkCpzooztWiX0Dv+MpLG1yblJ9A1lQfDjOCUTPTS/HyXdNoAH3UrDfZcVFxfzxRfdmCdlFw455BAOOeSQXqhRgQSQy5Yt26P9LrroIi666KJdPl9ZWcl3vvOdXpuAs7uOPfbYLmemcjqdbNiwoY9rNPBtWdpGYD8X7uF2CSCFEEL0q03fn0CqzJldVnaF5W/vSlj6wibKn+if+aTF0NSyIkrJJA++sU5aV8byXR0xRBVEAClEV9mLDAAJHoUQQggx5BQd4EY3NJJBGQPZ1wZCHfNF3/0mQhQOR0nmnkeiSQJIIYQQQgwdRRNcVBzho/ndCNENkolV5I+0QIoBxdqa7U6za5L5TgghhBBDgw4lkzyEPonTtDya79r0jMwDOeBJACkGlFQo02XDWWIQjQzgweNCiF6ldEhdVgb2TJcjrdnE/khLnmsl9lQ6YKfp9JG5KxXQoUtZ6Qs12Fv6thUmeMQwEqPap/cqWlKHlu5w7tEz4yC3cW4c4Bf2omAZbh27z6BhrWRfFexRfhdN03C5XBQVFbH33nszdepUDjjggD0qXwJIMaCkWk3SERPPKAfRmlS+qyOEKBQamCf4wJkZmaHVpCSAHMAsj0HwyIqcdZql5bQIFC+phz4OIGP7BIgcXJZdLln0HvYmmUZK9D+Vznz4DZeMPhPwyCOPoGk9H6N56KGHcueddzJjxoxu7SefQjHgtK1N4N/HJZ9eIYQQQgwJVkIR2ZCk+BA3mrHj85oNPCPtlB7moegAF7qzkBPAaAPoUZhGjx7N6NGjKS8vRymVfTgcDoYNG8awYcNwOBzZ9QDl5eWMHDmSQCCQXf/ee+9x/PHH89BDD3WrfLkEFwNO8KM4hkuj6EuufFdFCCGEEKJfNC2PYPcbjDitGMPTfglv8+mMPruUEacVU3qYh4ojfYw9t5ThJwWoOt5P+RHeAg8oRXetW7eOJ598Ep/Ph9Pp5Mc//jHvv/8+kUiEuro66urqiEQivP/++1x99dU4HA68Xi///ve/aWlpoaamht/85jfZYPLSSy/t1lyT0oVVDDipVpO2zxKUbh1MriQhqxACaGjxo7Z2YbUHE1TnuT5iz9kbE4z5+crOt2mId/p85MAygse0j6MsfmkjnlXN3apH2dz1lPy3fV5HW6tkvhT5k2hMU/tcK8NPCjD6rGI2/TdEvD5N1XF+NB3WPd5MqtXEcGuUHu7FHjDQnRqeUQ7c1XbqF7WRai2A6T8kiU6P1dXVceqppxKJRHjllVeYPHnyDtvous7BBx/MnXfeyTnnnMPMmTOZNWsW7777LiNHjuSaa67hxBNP5MgjjyQWi3Hvvfdy1113dal8aYEUA1LT2xEMl07RAe58V0UIUSBSpkEqnXmkTTm9DWRaWuGsi3X60FOdX91ZbhupYZ7sw3J3/565vTWJc1M0+9DMAr6iFENCfEuaDf9uIRk0GfmVYkafXYK72sGWJe3BoRlTNCwNU/d8kNq5mYfh1BlzTgnl07yF3DNTdNGdd97Jli1buPbaa3caPG5vypQpXHPNNTQ2NvKb3/wmu/7ggw9m9uzZKKVYtGhRl8uXM6wYkNJtFsHVcUonetDt8kvYkWZkxkIIIYQQYvAxY4raeUFaP4xhuDTaPo93mlgw0ZRm/ePNNC2PUHywm5KJnn6s7U6oAfQoUPPmzUPTNE444YQu73PiiScCMH/+/Jz1J598MgAbNmzo8rHkMlMMWM3vRgns56L4EDfNbw+91OmaAY5iG7pLw+4zcA234a6y4yi2YaUU8YYUW5aESQUzdyQ1G7iG2YnXp1AF0INFCCGEEHvIgsbXIzS9GUF1YVYzZUHL+zHsxQb+vZ20vDv0rpsGk5qaGgBstq6HcoaRyb5UV1eXs76qqgqAWCzW5WNJACkGLDNi0boyRulED/H61JCY1sNRYuAb78RRbOAd40B3ZDoRKKVINplEa1I0vxfFcOoUH+xm7DdLSYVNVEph8xvoNg1lKWqeaSW+RQaPikFEgWttFGXL9EiwNclYtSFPA9UhW6WSzipiEOpK8LjjPnluWlPawPhCFnAd/X4/iUSC1157rUtdWAFee+217L4dbQscy8rKdthnVySAFANa01sRnOU2qmcVEfkiSfO7UZLNPWxe06F8qhe73wAdYnUpWj+IoRnkteVOd2iM+Eoxmi2TSKjl/RjRmiTpmIUZs3ZIJhT8OIZnpANnhQ2bWye5Kk60JknldD9VJwbY+GQLZqyA+2cI0Q2aBSN+/lm+qyEKiGWDtLv9N07Z5PdODG2GS8M3zknwo84TUInCN23aNJ577jl++ctfcuaZZzJ69OhOt1+/fj2/+tWv0DSNadOm5Tz38ccfAzBs2LAuly8BpBjYLKibHySwv4uSQ9yMOquY5uURCHZtd92lUXyAG92uYQ8YGF4dR5GB4dKJN6aw4oqKI30E9nXhKDVItpgoU5FsNbESipb3oyhT4d/XheHSsZIW0ZoU6YiJvciGzauj0orI+p61htiLDKpm+tEM2DCnhXR497ccVRoi65JE1uWWvenFEKPPKmb4CQFq5gVhD+5eCiGEEGLgsPl0hp8QQFnQulK6rw50P/rRj3juuedobGxkypQp/OxnP+Ob3/zmDq2LoVCIxx57jFtvvZWGhgY0TePKK6/M2WbbeMojjjiiy+VLACkGPgWhVXFCq+OUHu6hbKoPR8KNWRQjuilFoiG905ZDz2gH5VO8OMttmHGLRGOaZEsa3a4Rq0uxaWEILAh8yYVvvJPk2jSekQ7SUQvDpeMZaaP4IDeWqUCBGcusL5+2Y5eHhtfCtH7Q9b7l2+gujcB+LsoO95KOmtTOC3YpeOyMGbXY9N8QI79SzPATA5kAUwNNg9Dq+B51hxFCCCFEYfKOdVB1nB8zqaibH8x77yOlMo9CV8h1nDlzJjfeeCO/+MUvaGho4NJLL+Wyyy5jr732ory8HE3TaGhoYM2aNViWhdr6Ym688UZmzJiRPc7atWuZN28eSilOOeWULpcvAaQYPBQ0vxUlscVkzDGZLGNlU3UsU5HYkiZWnyLZnEZZ4Blpp+hLbqyUou2zOJuXtO1yPsnQqjihVTt29zBcGt6xTgyXRnBVHCuhQAdXpQ3dppGOWiRbM2m2PSMdOw0gvWMdOMu3bh+xMJMWmq5h8+k4y2x4RzkydfgkTsNr4V6b8zJen6Z+YYiyaT68oxxohra1Pk6a34kQ39yNgnSkFVMIUXAcm6IUvVrfvlzf/Zt4Qgx0hken6vgA0Y1JNr/UhrWb6W/EwHH77bez1157cd1119HY2Ihpmnz66ad89llmOIfqEAGXlZVx5513cuGFF+YcY/z48cTjmWvcbUl2ukICSDHoRDcmaXg1TOP6FhwlBq6qTHZS/z5O7D4PylJYacXmJW07DQy7yowrQqu329/KBGcdRdYnKJ/mY59LKrDSCjNuEVodR9M1Sid5UKYi1WZi8xpoOqBBOmqRCpk0vB4h/HkcM977P/jhL5KEv2hGs2W6u/rGOyib6mXUmSVE65K0vBvdaWIimy8z/6ZnhB1H6dbgN2aRaEjT8Fq4MCYpFkIMee4v2nB/0ZbvagiRV76xDjQNNi8uoOCxwKfIyBoAdbzooos499xzefrpp3nppZf46KOPaGlpAaCkpIQJEyZw3HHH8bWvfQ2Xy7XTY3QncNxGAkgxeClINKZJNKYJfpgJ9DQDdLuGmVD99sPQ8n6M+JY09qJMgOgaZqfscC9WWtH0VoTmd9rHImi2rRnV+rFFb1urZnhtkvDaJN6xDkoneRhxWjHxhhQt70YJf5FEt2uUTPRQfLAblVJEapK0fZ7ASihsfh3/Xk5GnVnMpgUhYnWDPyOuEEIIUahsfj1z83w/VyZvQ3IARENij7hcLr75zW/yzW9+s9/KlABSDCnKBNPs/x/RWF0qG1QFP4rTsDSMUmqHLqm91UW1J7Yl3nGPsFM6ycPwk4qwTIVuaFhpRcuKKC3vx1Db3cls/SDG8BMDVJ9SxManW3qeDVcIIYQQ3aLZNcomeyg+0I2mZ4antBRa0hyZxmPAkwBSiDwomG4knYjVpqitDeKstOGqsGHGLOKb06QjO28etZKKuv8EGXVGCcNPKmLjky1yx1MIIYToJ95xDiq+7MNw6jQtjxD8KI5m1zCjkqhgqEin0zldWG22vgn19D45qhBi0EhsSRP8KE54bXKXweM2Kg11C4IYTo2qmf5OtxVCCCFEz9n8OtWnBKg+qYhEY5r1/2qm5f0YVkoVZPCoqYHzGAhWr17Nj370IyZMmIDL5aKqqoqqqipcLhcTJkzgyiuvZPXq1b1apgSQQohelW6zqF8YwjPaQenhnnxXRwghhBiUdIdGyaFuxpxTiqPMRt1/gmz6T6jH032JgeOmm27ioIMO4g9/+AOrV6/OTtmhlMKyLFavXs29997LQQcdxC233NJr5UoXViFEr4vWpGhaHqF8qo9kk4yFFEIIIXpMB+9oB54RDtzD7TjKDFDQujJG01uRgsij0CWShbVXXH311dxzzz3Z6Tq+9KUvMXXqVKqqqlBKsXnzZpYvX87HH3+MaZr84he/IBwOc9ddd/W4bAkghRB9ouW9GK4KO5UzfMTekYHoQgghxJ5ylBgMPymAo9hGMpgmtilFy8oosdqUtDgOQa+99hp33303mqYxYcIE/vznP3PkkUfudNvXX3+dSy65hJUrV3LPPfdw9tlnc8QRR/SofOnCKoToM/WL20hHLCqO8KLbJIgUQgghPCPtVB3vZ/hJAXx7OXe7ve7UqJ5VhDJh/RPNrH+shS0vh2n7JCHB4xD1pz/9CYCxY8eybNmyXQaPAEcccQSvvPIK48ePB+D+++/vcfkSQAoh+oxKKeoXtKG7dCqP8+W7OkIIIUTeuYbb8e/twlFsMPyEAMNPClB6uIfhJweoONqHb+/coLLyaB+6XaNufnBwDAvZNo3HQHgUqFdeeQVN07j++uspKira7fZFRUVcd911KKVYunRpj8uXAFII0adSIZPmt6L4xjopmSRJdYQQQgxtwY/iWGlFcFWczS+3YS8yKD7QjW7TcA+3M/z4ACNOK8JVZadsqhf/3i62LA3vNhO6GDo2b94MwKRJk7q8z2GHHQZAfX19j8uXMZBCiD4X25QikoxTNtlDoiFNdGMy31USQggh8sIzwo5u07ASFqFPEoRWx3OfH2mn8hg/o84oRilF45thwp8n8lTbPiBJdHrM6XSSSCSIRCJd3mfbtk7n7rtN744EkEKIftH8TgxHmY2q4/1sfLKFVEjupAohhBhkdMACR6mBd4yDVMjCUWLgqrSRDlvoDg3feCfBVTFCn+w8KIzWpFj/WDM2v1GwczmK/Bo3bhwrVqxg3rx5TJ8+vUv7zJ07FyA7FrInJIAUQvSbzS+1MeqsYoafVMTGp1sGTspxIYQQohO+vZxUHuVDs2no9h3HzkU2JnFW2FBpaFkRo2l55y1HyoJUcBCMd9wZaYHssVNPPZX333+f3//+95x88skcd9xxnW6/aNEi7r33XjRN49RTT+1x+RJACiH6jZVUbPpPiFFfK2HYsX7qF7blu0pCCCFEj7iq7Aw7zk90Q5JobRKbxyC6MUmsPoXNo5OOWSCNiKIXXXnllfzf//0fbW1tnHzyyXzve9/jO9/5DhMnTkTXMyluLMvivffe48EHH+SBBx4gnU5TVFTElVde2ePyJYAUQvSrZIvJ5sUhhp9YRLwhTeuKWL6rJIQQQuwRe5FB9ckB4vUpNv03tEOgKIlvdkJaIHusvLycOXPmcPrpp5NMJrn//vu5//77cTgclJaWomkaTU1NJJOZnBNKKRwOB0888QRlZWU9Ll+ysAoh+l14bZLm96KUT/XiHmHPd3WEEEKIPVJxlA8zodi0YMfgUYi+dOKJJ/LGG29w+OGHo5RCKUUikWDTpk3U1dWRSCSy6ydPnsybb77J8ccf3ytlSwukECIvmpZHcJbbGH58gA1PtZBukzOvEEKIgcM9wo53lIO6/wSxkgXcXCUGrUMPPZTly5fz1ltvsXDhQj788EOam5sBKC0t5cADD+SEE07g8MMP79VyJYAUQuSHgvqFIUafVcLwEwPUPNOKGqT5AoQQQgw+xQe6iTemiKyTqam6RWmZR6EbCHXcavLkyUyePLnfypMurEKIvLESiroFQRzFNiqn+/NdHSGEEKJLDLeGd4yD0Mfx3W8sxCAjLZBCiLxKNplsWdJG1fEB4g0pgh/KyVgIIURhc1XZ0XSN8HppfewuTWUehW4g1DFfJIAUQuRd2+cJnJVRKo7wkWg0iden8l0lIYQQYpccJTbMuIUpWVZFH9qwYUOfHHf06NE92l8CSCFEQWh8I4KzzMbwEwNseLJFTspCCCEKlhmzMFw6vr2chNck8l2dgUWm8eiycePG9foxNU0jnU736BgyBlIIURgsqP9vCGUphp8YQJNfJyGEEAWq7dM4bWsSDD8hQNkUb76rIwapbdNw9Pajp6QFUghRMMx4Zi6tkV8tpuIoH1teCee7SkIIIcQOlJm56ZlocFM+zUd4TYJEU89adYTY3sMPP5zvKuyUBJBCiIKSaEjTsDTMsBl+4g1pQqskqY4QQojC1LIiRvHBHvz7OCWAFL3uwgsvzHcVdko6iQkhCk7okzitH8WoOMqHq1LucwkhhChQCsJrEwT2d2F45bJaDA3ySRdCFKSGZWESDWmqTgxguAfOZL5CCCGGlqa3Img6BPZx5rsqA4JG+1QeBf3I9x+qgEkAKYQoTBZsejGEpsHwEwLyayWEEKIwKVAKNF1CDjE0yCWZEKJgmVGLTf8N4Rpmp/wIyXInhBCi8BQf5EbTNUKfypj9LlHawHnk2dVXX83mzZv77PjPPPMMc+bM6fZ+EkAKIQpavD5Nw2thSg7KJCkQQgghComryk60Jkk6LPMXi9519913M27cOK644grWrVvXK8dMp9M88cQTHHrooZx11lmsXr2628eQAFIIUfCCH8UJrY5TeYwfZ7kk1RFCCFE47H6dVMjMdzUGDjWAHnl23nnnkUgk+L//+z/22msvvvzlL3PfffdRX1/freOYpskrr7zCJZdcQlVVFeeeey4ffPAB48aNY+bMmd2ul1yJCSEGhC1L23CUGgw/KcCGJ1uw4gXwyy6EEGLIM9w6ZkxaH0Xv+/vf/84Pf/hDbrrpJhYuXMjrr7/OG2+8wQ9/+ENGjRrF5MmTmThxIpWVlZSUlFBSUkIsFqOlpYWWlhY+++wz3nrrLVasWEEikQBAKUVFRQU333wzl1xyCTZb98NBCSCFEAOCMjNJdUadVcLw4wPUPh8siLuDg4272k7JoR5SbSbpNhMzrjBjFumIRaI5DXKNJIQQOVJBE0eZXFJ3WYG07u1WgdRxypQpvPjii7z11lvcfffdPPXUUyQSCTZs2MDGjRt56qmnOt1fqfYXcthhh/H973+fb33rW3i9e55bQj7tQogBIx22qP9viBGnFVE21UvTG5F8V2lQ8Y5xUHVigHTYxHBp2Mc7MdztIx2stCK+JUW8PkWsPk28PoWVLJAzrBBC5El0Y5Lig9yERtiJ1abyXR0xSE2ePJlHH32UUCjEs88+y+LFi1m6dClr1qzZ5T4ej4dp06Zx9NFHc8YZZ3DIIYf0Sl0kgBRCDCixuhSNb0SoONJHoiFNeE0i31Ua2DSoPqUIzwg7mqER/iLBpv+G2lsaNTLBpN/AVWXHXWUnsL+b0kk6SimSzSax+hTRDUki65N5fSlCCJEPLR/EcA23M2JWETVzW4nXp/NdpYK2bZ7FQleodQwEApx//vmcf/75ADQ0NFBTU0NDQwPNzc24XC4qKiqoqKhgr732wjCMXq+DBJBCiAGn9YMYrgobw471k2xJk2yW5AV7yjXMhne0g9YPY0TWJ4nWJHO77SgwYwozlia+JU3rBzEA7AE9E1AOt+OptlN8gJtoTZL6xW2YEennKoQYOqyEova5IKPPLqFsspfa54L5rpIYQrYFi/1pSGZhra2t5Wc/+xmTJ0+moqICl8vFqFGjOOqoo7j55pv58MMPd7qfUoq7776b/fffH6fTyd57783Pf/5zksld33U/9thj0TQNTdMwDIOPPvqo07qtW7cuu/1tt93Wk5cpxKC2eUkbqaDJ8JOK0B19OFdT/qeB6jp9a2BXacNZbsPw6Lusv6PUoPRwD8Nm+Em1mTQsCxPdmOzymI9UyKLt0wRbloRZ/68Wap8PYi82GPXVYmz+IXlqEUIMZQqSzemBdc4QYg8NuRbIBx54gKuvvpq2trac9TU1NdTU1LBs2TLa2tq4++67d9h39uzZPPTQQ9nlNWvWcPPNN7Ns2TLmzZu32yZiy7K47bbbeOKJJ3rltQgxlKk01C0IMvqsEqpm+ql7IdSrx/ft5aRssgdHsY10xMy0vq2MkWhMo+lgFkgWWM0A71gn/n2ceEc50IzcqxelVCYRTsTCSinQoWxUEcVJAzNuEa1N0fx2pMfJAqIbk9Q808qIrxQx+mslbF7SRqw+JdlyhRBDhj1gkGiS7qu7JUl0BrwhFUDefffdXHXVVQCMHj2aSy65hGnTphEIBKitreXTTz/lmWeeQdd3vHv+wgsv8NBDD1FSUsL//u//MmXKFD7++GNuuukm/vOf//CXv/yFSy65ZLd1ePLJJ1mxYkWvDWIVYihLt1nULwxRPauI0sM9NL8d7ZXjFh/spuJIH+EvErS8H8Pu1/GOcTLy9OLsNonmNOG1CdJtJkqRaQW1IB2ziNWlsBLdP/NoRiYLqneME3e1Hd2hodIqM8ZwY4pobTITkGngHm7Hv68T33gnhkMntjlF45sRks1p0hELzdAwPDo2j579v+7M1DGcTFC/MkSkJtGrWVXTYYuNT7dSdZyf6pOLsus/f6ABJddUQohByl5sYPfpuCrtRCWJjhgChkwA+eabb/LjH/8YgNNOO405c+bgdruzzx922GEAXHPNNaRSO37558yZA8Bf//pXvvKVrwAwdepUJk6cyMSJE5kzZ06nAWQgECAej5NMJrnlllt49tlne+21CTGURWtSNC2PUD7VR6IxTWTdnidyMVwaw44L4B3toPndKE3L27O8Nr0VxVFmYPca6E4Nz2gHxQe60ewauqFhpRWaAZqmoaxM0BdZlyTRlCbZYmJGcyM1w61RfJAbm89At2voTg1XhR3drpFqM4luSJKOW+gODU+1g6L9M79XVioTmOp2jWTQpPWDGG2fJUgFuzYOVNM1rLH2zFjHPhiqaMUVdS+EGHNuCY4iG6FP4hI8CiEGLd9eTqpm+jO/rSlFdIMkE9staYEc8IZMAHnppZdiWRZjxozh8ccfzwket2e323dYV1tbC8CMGTNy1h966KGUlpZmn9+VkpISZs2axX333cfcuXN5++23Ofzww/fglQghttfyXgxnuZ1hx/nZ+FQrqdZMMKU7NYomuPGOdmClLCIbUgQ/jO30GIZHZ+RXMuMp6/4T3GkgmmwySTZljt32WSb7q27XsEyVDcZsXh3PaAe+sQ7KpnjRbZkupfEtKVrejxL+Iomj2KD61ExZyaY0VirTxbR5Q4TI+iTJlu2DwQiGV8dVYcNeZICCeH2K+JbCjMxclTYcRTbqF4WyfychhBh0NCif5iWyPsmWpWGsuIWSHGJiCBgSAeTrr7/Oe++9B8C11167RxNnVlZWArBkyRJmzZqVXb9y5Uqam5s58MADd3uMG2+8kYceeoh4PM4tt9zC/Pnzu10PIcTObX65jVFnFjNiVhGNr4VxV2emm0CDyPoEdr9B5VE+DJe2Q1dXm09nxFeK0XSomRvscmsetLcIbpOOWIRWxQmtiqPpmWM7K+wUTXAx/MQilKnQDI1EY5qNz7R2OWOpGbGIRAbGnW2bLzMevCetwUIIUchsfp3SQz3YfDqbFkR36GUidk2m8Rj4hkQA2TFpzdlnn539d1NTE83NzVRUVFBcXNzpMc444wweffRRLrjggmwG11WrVnHTTTftcNxdqa6u5uKLL+aee+7hhRde4PXXX+eII47YsxclhMihUopNC0KMmFXE8JOKSEdMWj6IElwZyya8KTnUTfk0H+mwRWh1HAD3CDvDjvWDgpq5raTbeu8iQFmZbKWpUILwmgTOChuuYTZSQZPYptTg7dqpMn9vza5BSs7AQojBQXdpVBzpwzvageHSsdKKhlfDJBoH64+5EDs3JALIN998E4Dx48dTUVHBn/70J+666y4+/fTT7DZf+tKX+P73v88PfvADHA7HDsc466yzOOOMM3jmmWe4/PLLc56bOXNmlxLoANxwww385S9/IRqNcvPNN7Nw4cIevDIhREepoMn6Oc3YvMZOWxFb3o9h8xtUTvfhLLdh8+n4xjqJ1aeo/2+IdB/PX5hoSJNoGPwXGrHNaaykRelhHhqWhvNdHSGE6BWVR/vxjLDT+mGMREM6kzBNbpJ1n9Iyj0I3EOqYJ0Nisq6PP/4YgDFjxnDeeedxySWX5ASPAKtWreKqq67i+OOPJxjccQJYTdN44okn+PnPf8748eOx2+2MGTOGm266ieeffx6brWux+LBhw7jssssAWLRoEa+88koPX50QoiOVptMuqA2vhmn9KEbxgW6cZTY2/TdEzTOtfR48DiVmxKJxeYTiA9y4qobEfUohxCBn8+v4xjtofCNC89tRIuuTEjyKIWvQB5CWZWUDwtdee43HHnuMqqoq/v73v9Pc3Ew0GmXJkiVMmzYNgKVLlzJ79uydHstms3HjjTeyZs0akskk69at4/bbb8fpdHarTtdddx0+nw+Am2++uQevTgjRbQoal0X4/M8NrH+8mfAaSfLSF4IfxYnVp6iaGcBZLkGkEGJgKznEg5VUtH0ez3dVhMi7QR9ARqNR1NbxOIlEAo/Hw8svv8z//M//UFJSgtvtZvr06bz00kvZuRn//e9/s3z58j6rU3l5OT/60Y8AeOWVV6QbqxB5oCxQXc+VI7pLQf2iEGbcYtSZxdiLjXzXSAgh9oir0kbRAS6a344O3rHr/UkNoIfYqUEfQLpcrpzl2bNns99+++2wndvt5he/+EV2+fHHH+/Tel1zzTUUFWUm2pZWSCHEYJRus6h5phUzqQjs272eGkIIURB0qDzGT6IhTesupoESIp8WL17MBRdcwD777IPf78dms2WH722zdOlS7rvvPv7xj3/0SpmDvl+RzWbD5XIRj2e6HJx00km73HbmzJnYbDbS6TRvv/12n9arpKSEq666ittuu4033niDF154gVNOOWWPj6fpGpomg31h298i83+Rf/J+FJZ+fz8UJFvS2IsM+QzshHw/Cou8H4WlEN6PkoluHCUGNU8FM9dZQ/ijsav3Q1Nadi7kLh1HpvHoFdFolAsvvJCnnnoKINvjcmfxgM1m4/LLL0fTNKZOnco+++zTo7IHfQAJMGrUKD777DMARo4cucvtXC4X5eXl1NfXs2XLlj6v11VXXcU999xDS0sLt9xyS48CyLIxxdj1HbPHDkWarlFc7QdNQ1kF/u0fAuT9KCz9/X64qmyUjctMtG2Otfd5eQONfD8Ki7wfhSWf74fu0ig+wIVvnIvg6hj+gB9/oF+rUHB29X6krCR8kceKDVHnnHMOL7zwAkoppk6dytFHH82dd965022POOIIDj74YFauXMmTTz7J9ddf36Oyh0QAOWHChGwAaZqdD3ra9nxXs6r2RCAQ4JprruHGG2/k7bff5tlnn82Ow+yupvWt2DS5OIOtd8aUonF9q1wAFAB5PwpLf70f3jEOyqZ5cBQpGje0UP/fNsyYZLrdnnw/Cou8H4UlX++Hu9rO8GP9pKw4a55vIrhKkq3Brt+PtEp170ADZXxhAdfxySefZP78+WiaxoMPPsi3v/1tgF0GkABf+9rX+OCDD1iyZIkEkF0xffp0nn32WQDWrl3LxIkTd7pdKBSisbERgBEjRvRL3X70ox/xu9/9jsbGRm699VaefvrpPTqOshSq0Nva+5FSW/8mcgFQEOT9KCx9/X4E9nMybEaAyIYEmxe3Ea+XrBOdke9HYZH3o7Dk4/0IfMmF7tBZ81AjVlI+Bx3t7P3Y1nVS9J+//vWvaJrGBRdckA0ed+ewww4DMlMX9tSgT6IDcOaZZ2b7A3cWoD399NPZL8HRRx/dL3Xz+Xz85Cc/AWDFihU8+eST/VKuEEL0Be8YB5XH+Al+FKNufkiCRyHEgNP2WSZvhr1Iskf3CdU+DrKQH4XcArktV8s555zT5X2qqqoAaGho6HH5QyKAHDduHGeffTYAjz32GIsWLdphm/r6em666SYAHA5Hl6P53nDZZZdl39Q77rij38oVQoje5KywUXVCgPAXSba8Gs53dYQQYo9E1idJtqYZdqwf3TGEs+aIgtXU1AR0r8fktkYyy+r5cJIhEUAC/OY3v6GiogLLsjjttNO44YYbWLp0KW+//Tb33XcfkydPpqamBoDbb7+937qwQmYKkW19kbd1oRVCiIHEcGkMm+En2ZJm86JQQd+5FUKITimo+08Im1en+pQiNGmIFAVm21SAra2tXd5nzZo1QGY++p4aMgHkmDFjeP755xkxYgTxeJxf//rXTJ8+ncmTJ3PZZZdRU1ODpmnceOONXHfddf1ev4svvrhfg1YhhOgtNq/OyK8WYzg1Nr/UhpJcOUKIAS7ValI3P4iz3EbV8YEhPX1Hr1MD6FGg9t13XwDeeOONLu/zxBNPAOwyF0x3DJkAEmDy5Ml8+OGH3H777UyaNImioiKcTifjxo3joosu4q233uLnP/95Xurmcrm48cYb81K2EELsKXtAZ+QZxWg2jZpnW0m2dJ7pWgghBor4ljSb/hvKjO0+2pfv6giRNWvWLJRS3HfffUQikd1uP3fuXJ5++mk0TeMrX/lKj8sfUgEkQHFxMTfddBPvvPMOra2txONx1q5dy8MPP5zNTtSbXn75ZZRSrFu3brfbXnrppSilso/bbrut1+sjhBC9RbNrjDitGJVW1DzTSiokTY9CiMEluiHJ5pfbKJrgpvRwT76rMzjku1VxELRAXnbZZZSWlrJx40ZOO+00amtrd7pdW1sbt99+ezYXzKhRo7jooot6XP6QmMZDCCFE7yuf5sVw66yf00w6IsGjEGJwavs0geEOU3GEDzNmEfwonu8qiSEuEAjwr3/9i1NPPZVXXnmF8ePHM3Xq1OzzN9xwAy0tLSxfvpxUKoVSCpfLxZw5c7Dbez5v/JBrgRRCCNFz9oBO8QFuGt+MkG6T4FEIMbi1rojRsiJKxVE+fHs5812dAS3f03N0ayqPAjZz5kxeeuklRo0aRSqV4tVXX81OWzhv3jyWLVtGMplEKcWoUaNYvHgxU6ZM6ZWypQVSCCFEtwW+5MaMW4RWxfJdFSGE6BeNr0cw3DpVx/mpjVvEalP5rpIY4r785S/z2Wef8fjjjzN37lzeeecdtmzZgmmalJWVMXHiRE4//XQuvPBCHA5Hr5UrAaQQQohu84130rYmgZKcOUKIIWTzy20YLp3qkwLUzA2SaEznu0piiLPb7Zx//vmcf/75/VamdGEVQgjRLbpdw1FkEK+Xu+9CiCHGgk0vBkm2mFTPKsJeJJNEiqFHAkghhBDdYi/OXDDJlB1CiKFIpaH2hSBW3GLErCIMj1xOd0u+M6vmKQvrddddh6Zp2cfLL7/cuwX0I/nECyGE6JatY/RRZoFnGBBCiD5ixRW1zwfRDBhxahG6Q8t3lUQBW7FiBb/73e/yXY1eIwGkEEKIbsmGjXK9JIQYwtJhi9p5QWx+neEnBdCkN6vYCcuy+N73vkc6naaysrJXjmkYxh4/bLaep8CRAFIIIUS3pNsyXVdl7I8QYqhLtpjUvRDENczOsOMCcmOtC/I9NUd/T+Px+9//nrfeeov999+f7373u71yTKVUjx49JVlYhRBCdIsZU6QjJq5yG5EvkvmujhBC5FW8Pk39f0MMPylAxVE+GpaG810lUSA2btzIzTffDMAf//jHXhv3eOutt+52m0gkwieffMLChQuJx+NMnTqVk046qVfKlwBSCCFEtyWa0jjL5RTSXYlxpSh7e8utEZdMtkIMBpH1Sba8EmbYsX7MqEXzO9F8V6mwDZEh9D/4wQ8Ih8NceOGFHHvssf0aQG7T3NzMd7/7XZ577jm+8Y1vcMUVV/S4fDn7CyGE6LZUyMI3zoHu0LCSQ+RKoBdEp47BLHJllx2bQvDhpjzWSAjRW0Kr4xhunfKpXsyYRfDjeL6rJPJozpw5zJs3j9LSUn7729/mrR6lpaX8+9//Ztq0aVxzzTVMmTKFI444okfHlDGQQgghui34UQzNplF1vD/fVRFCiILR8l6U1pVRKo724RvnyHd1ClO+p+boh2k8Wltbsy19d9xxBxUVFXt+sF5gGAY/+tGPME2Tu+66q8fHkxZIIYQQ3ZZqM0m3WehOuQ8phBAdNSyLYLh1hh0fwHw+SKxOuqoPZKFQKGfZ6XTidDo73ee6666jvr6eI488stcS5/TUAQccAMBrr73W42PJmV8IIUS3lR7uxV5k0LBMkkUIIcT26l9qI74pxfCTAjjKJGN1R/nOrNrdLKyjRo2iqKgo+/jVr37V6et79dVXeeCBB7DZbNx///1oWmGk5g2HM+frpqamHh9LWiCFEEJ0m3+8k9CncRJb0vmuSsFIl7lpPXW/nHX+petwrmvNLgdeWIXS2y8mdNOCcm9/VVEI0V8sqFsQYuRXihhxahEbn2kl3Wblu1ZiD2zcuJFAIJBd7qz1MZlM8v3vfx+lFFdddRUHHXRQf1SxS/7xj38AUFVV1eNjSQAphBCiy+zFBlXH+bEHDMJrE/muTkFRhk66zJOzznLknmaNYG5SDU3XJIAUYpBSKUXd/CAjzyxmxGlF1DzdihmXpGMDTSAQyAkgO/PLX/6SVatWMXr06G5lSu1La9as4a677uKBBx5A0zROOeWUHh9TAkghhBBdYi82GPmVIsyEou4/QWK1Mq5HCCE6Y8YVtfOCjDqjmOpTi6h5LohKDfEgsocJavpNN+u4evXqbPfWe++9F6+3724Ojh8/frfbWJZFa2srbW1t2XXl5eXcdNNNPS5fAkghhBCdcpQYlB/pwzPSTrLFpHau3EXfFc3M7aKmqc7/TkoDZWiZhwYohbZdLzdl5I6f0czcYyoN6NAtFktlx+4IIfIv3WZROz/IyNOLqT4pQO38IEhv1kHnd7/7HclkkvHjxxONRnn88cd32ObDDz/M/vull16ivr4egK985SvdCjjXrVvX7fpNmTKFhx9+mBEjRnR73+1JACmEEGKnHKUG/r1dlBziJhUy2fJymLY1cZQMe9wp25YIlX94M3flbgLIZLWfpm8cSH04gQK879QRWLohZ5um8ydhee0AaPE0FQ++nfN89PCRRA8bmV0umr8ax4bWPX4dQojel2wy2bQgRPWpRVTN8FO/qG33Ow1SHRPUFLLu1jGRyAzrWLt2Ld/85jd3u/3tt9+e/fcXX3zRrQDywgsv3O02uq7j9/sZN24c06dPZ+LEiV0+/u5IACmEEGIH3rEOqmYG0HRoW5ug8bUwZmwAnPHzSAOwuvk32tZ6aGxNir6TbH1K11B65nlN30k2P639edjaIimEKDixuhT1i0IMPzFARdyiYVkk31USA9TDDz+c1/JlGg8hhBA5PKPsVJ9cRGRDkjUPN7J5UZsEj0II0QsiXyRpWBqm+CAPJRM9u99hMFID6NENjzzyCEqpTh8dE+ssXrw4u37s2LHdKyzPpAVSCCFElmaDyul+ojVJ6v8b2v0OQgghuiX4cRzDrVM+1YsZswitju9+JyEKiASQQgghsooOcGO4dWqfa813VQal6IQKlLP91GuWunBoW5PpwNZ+sLncH21GOTMTkWupnWfe0DreKpfGYiEKXvM7UQyPTuV0H2bMIrI+me8q9Z9BmoV1KJEAUgghRJZ7uJ3YphSpkKQI7AuRqSNJl7izy5oGAY2dBo7b+N7cuPsDd7jQkSGQQgwMDa+GMVw6VScEqJ0XJF4vUyOJXH/729/65LgXXHBBj/aXAFIIIUSWs9xG22eJfFdDCCEGPwWbF4WonlVE9SkBap5tJdls5rtWfW6wZmHtCxdddBHaTpKr9YSmaT0OICWJjhBCiKx02MIekFODEEL0B2XBpv+ESLWZjJhVhM3ff7+/hkfHN96BZ6RdIoJ+ctttt2UT5xx77LFd2md3iXn25NFT0gIphBAiK1abpOgAN5pdQ6UK4PbrION/ZR3KbmSXNR38wwPENoVQCuxNsW4f07mmCaOlfT+jSaYGEGIgsVKKuueDjDyjmBGnFVPzbCtmtO+GETgrbVQc4cM93J5d1/xOhKa3ohhuTbJuF5Avvvgi31XYKQkghRBCZAVXxSk+xEPxAS5a3u9+MCM651rTkrOs6RquuIVnXQuqu3NIbmVrimJrivZG9YQQeWLGFLXzgoz8ajEjZhVRM7cVK9H7gZx7uJ3qU4tItqapXxgiWpuk9DAvpYd58e/jwh4wsFKK9XOaSbf1URArSXS6bMyYMfmuwk5JACmEECIrHbaIrEvgG+eUAFIIIfpRus3KtESengki29YlcAQM7D4DZSmsFFhJi3SbRduaBKngrsdL+vZ2Yrh0zKhJZEMSlQb3iMwcv/HNKepeCKK27t60PIKVtHBW2GldGaNsqpeSgz00LAv30ysXA40EkEIIIXIkmk3cIxz5roYQQgw5yRaT2vlBKo/2UTHFh7IUqZBJstVEt2vY/TZ84w1KD/fQtiYBCgy3juHUQIPQ6jjKgmHH+FFKoWkaVkphxi1sPp1oTYpNC9qDRwArqWha3t6LQXdqlBzspumtCFayD5rhpAVywJMAUgghRA7docEedqcUQgjRM4mGNLXPB3FV2XAPc1A0wYU9ADXPZrq1agYUHejGv48TlQIzZpGMWGh2qDzaD0BwdYwtL4exB3TcIxy4ym0kmtMEP47vNjAKfhyndKKHwH4uWldKTxSxIwkghRBC5PDv7SS8bghNai2EEAXGSiii61NE16cIfRJn9NklBPZ30boihjKhdUWM1hU7BnfhNQmUBdGNmd/wVMgiFYoT6kbZZtSibW2CkkPdtK1J9HpCH5nGo3d9/vnnzJ07lxUrVtDU1EQsFus006qmaSxatKhHZUoAKYQQIsvm1bH7DKIbJIAUQohCkAqaRNYlMi2COwkaOwqv7Z3f7qY3IrjPLGbUGcVsWhgisSXdK8cVvSccDnPZZZfx6KOP7hAwbuu+vP06oFfmlZQAUgghRFY2E6jMCSaEEAUjtDrOiNOKcVba+iWYS0csap5tZfgJAUadWUzbJwma3uqlbM8yBrLHLMvijDPOYPHixSilqKioYMSIEbz//vtomsb06dNpaWlh9erVpFIpNE1jv/32o6qqqlfKl0sEIYQQWWZMkQyaFO3vyndVhBBCbBWtTZEKmwT267/f5nSbxcanW2l4NYx3jIMx55ZQdJAbet6AJXro8ccf56WXXgLgl7/8JZs2beJvf/tb9vmXX36ZFStW0NLSwu9//3t8Ph9NTU3cdtttLF68uMflSwAphBAiR+NrYTyjHZQe7sl3VYQQQgAoaPs0jn9vJ5rRv+UGP4qz7rFmgh/GKNrPhWeEvR8rIHbmscceA2D69Olcf/316Lq+066pHo+Hyy+/nMWLFxONRjnrrLPYuHFjj8uXAFIIIUSOyPokbZ8lKDrAne+qCCGE2Cr0SQLDqeMd5+z3sq2kys4NrDt61gS5LYnOQHgUqnfffRdN0/jud7/bpe0nTZrEZZddRnNzM/fcc0+Py5cAUgghRA7NBr5xTqLrJZGOEEIUilTQJLYp1a/dWDtyVWVaHhNN5m62FH2tqakJgHHjxmXX2e3tLcPR6I7jVU899VQA5s+f3+PyJYAUQgiRQzM0dLtGZH0i31URQgjRQWh1HM9IOzZf/1/Ce0fbSYdNUsEeBpBqAD0K1LZg0eNpH2oSCASy/66rq9thH6/XC0BNTU2Py5cAUgghRA6V3prq2yaZEoQQopC0rU2g0pn5evube5SdWH2q38sVOxo1ahQAmzdvzq6rqqrKBolvvvnmDvt8/PHHQCaDa09JACmEECKHZmQCR9W7c0cLIYToIZVSJBrTOEr7fyY+w6mTjvbCiSHfrYqDoAVy4sSJALz//vs564866iiUUtx9990kk+3DUEKhEL/5zW/QNI3999+/x+VLACmEECKHu3rbOBeZOFoIIQpNKmRiL+rPVKwZmqHJjcUCcfzxx6OU4vnnn89Zf8kllwCZJDsHHXQQ1157LZdffjkHHXRQtgXy/PPP73H5EkAKIYTI4RvvJNGUJtUqiRKEEKLQ2AMGVrJ/m8ccJQa6TSMd7vl5QRtAj0J15plnMnr0aDZs2MDatWuz67/61a9y4YUXopTis88+46677uKPf/xjduqOmTNn8sMf/rDH5fd/+7cQQoiC5hpmJ7JOEugIIUShcZbZcA+3U7cg2K/l+vd2YsYtElsKuF/nEFJcXMy6det2+txDDz3El7/8ZR588EE++ugjUqkU++yzD+effz5XXnklut7z9kMJIIUQQmRpNrAHdJLN0n1VCCEKigZl07yk2kwi6/p3miX3cDvRuhTKktCh0GmaxuzZs5k9e3aflSGfAiGEEFm6U0fTNNIxucsshBCFpHyaF88IO7XPB/s9wYvNZxDfHEfrjdChwBPUZA2EOuaJjIEUQgiRZcUtzKSFp9q++42FEEL0C/++TkoO8dDwWoRYbf9PpaHZNSyZwaNg/OMf/yASieStfAkghRBCZCkTQqvi+Pdz5bsqQgghAGeljcrpfoKrYgQ/jOWlDrpNy84R3FOaGjiPQnXBBRcwbNgwvvWtb/H8889jmv2b9E4CSCGEEDl0h4YyC/jMKYQQQ4Sz3Eb1yUUkGtM0LA3npQ6OUgPdrpGUzNwFJRqN8q9//YvTTz+dqqoqLrvsMpYtW9YvZUsAKYQQIodulwBSCCHyzTPKwcjTi0iHTTb9J5i3ORh945yYCYtoXS/1YVUD6FGgFi1axHe/+12KiopQStHU1MT999/P9OnTGTduHDfeeCMfffRRn5UvAaQQQogc9iKDWG9dKAghhOgW3aFROd3HiFlFROtS1MxtxYznL5qxFxkkm03IUwArdjRjxgz+8pe/sHnzZp5++mm+/vWv43Q6UUqxfv16fv3rX3PwwQdz6KGHcuedd1JTU9Or5UsAKYQQIofu0PN6sSKEEEOR7tQoOdTNmHNL8e3tZMsrbWz6TwiV51mVzKiF4dZ696D5blkcwK2PHdntdr761a8yZ84ctmzZwiOPPMIJJ5yArusopfjggw/4yU9+wtixYzn22GN54IEHaG1t7XG5BRlAXnfddWialn28/PLLe3ys2267LedYnT12V84nn3zCWWedRXFxMX6/n1mzZvHOO+/scvuXX3455/jnnnvubut70UUXZbcXQoj+Zrg0bF4dMyq3moUQoj/YiwwqjvIx7n/KKD3cS2R9gg3/aiH4cTzfVQPASil0R0GGDKIDn8/HBRdcwIIFC6itreWee+5hypQpKKWwLIulS5dy8cUXM3z48B6XVXCfhhUrVvC73/0u39XYwcqVK5k6dSpPPfUUwWCQcDjM/Pnz+fKXv8xLL73UpWPMmTOHlStX9nFNhRBiz5VN9qJMReizwrhwEUKIwcg7xkHVCQFGnF7EmHNL8I130vJ+lHX/aGLLkjDpSOHcxHOW20gFey+BTr4zqw6GLKy7U1lZyQ9/+EPeeOMNPv/8c372s59lx0smk8keH78XZgPtPZZl8b3vfY90Ok1lZSVbtmzp1ePvLngbN27cLp+bPXs2wWCQWbNmccUVV+BwOHjooYf429/+xkUXXcSaNWuw2zufN00pxa233spTTz21R/UXQoi+5tvLSfDDGJZ0YRVCiD5hD+gMPzFAojlNus1iy8th2j6PowowyamjxMA7xkHDsvxkgBU98+mnn/LPf/6Txx57jGAw2GvHLagA8ve//z1vvfUW+++/P2eeeSa/+tWvevX4Bx544B7tt379epYvX87kyZOZO3cuup5puD3mmGMIBoM8++yzvP7660yfPn2XxygvL6exsZGnn36ad999l0mTJu1RXYQQoq/oTg3DpZNoyvOAGyGEGMSKD3JjJhU1z7QWZNCYpcOwGX5SQZPQqjggw6sGgk2bNvH444/zz3/+k3fffRfINGIBOJ1OZs2a1eMyCqYL68aNG7n55psB+OMf/4jD4chzjdrV1tYCMH369GzwuM3MmTNzttmVK664AqfTCcCtt97aB7UUQoie0R2ZiwMzIa2PQgjRF3SHRmB/N8GPYoUdPAKlh3pwltuof6mtd6cQyXdynEGWSAcgFArx0EMPcfzxxzN69GiuueYa3nnnHZRSaJrGjBkzeOCBB9i8eTP//ve/e1xewQSQP/jBDwiHw1x44YUce+yx+a5OjsrKSgBeffVVLCv3G7RkyRIAqqqqOj3GyJEj+f73vw/AvHnzePPNN/ugpkIIsee2BZBWagCdNYUQYgDx7+sEHYIfxfJdlc5pUHyIm9aVMRIN0iulECWTSZ588knOOusshg0bxve+9+5fNUcAAGh6SURBVD0WL16MaZoopZg4cSJ33nknGzduZNGiRXznO98hEAj0StkF0YV1zpw5zJs3j9LSUn7729/muzo72HvvvTnggAN48803OfPMM/nhD3+Iw+HgkUce4cknn6S6upojjzxyt8e54YYbeOCBB4jFYtxyyy0sWLCgH2ovhBBd4yy3oZQi1Vrgt8WFEGKAcg93EN+cwowV9o06R6mB4dSJbOh5wpXtDZQENYVcx+985zs89dRTtLW1Ae1dVMePH883v/lNzjvvPPbff/8+Kz/vAWRraytXXHEFAHfccQcVFRV9VtYJJ5zAu+++S1tbG8XFxUyYMIGTTz6Ziy++mJKSkk73/fOf/8zxxx/P3LlzmTt3bna9w+HgwQcfzHZP7czw4cO59NJLueuuu3jxxRd59dVXOeqoo3r8uoQQojfY/QZm1MJKFvBZU/Q7pYHlyx1WosVN9JTcaBCiu9xVNkKfJvJdjV0qm+LBv7cLw62TCpvEN6fyXSWxE4888kj23xUVFZxzzjmcd955TJs2rV/Kz3sX1uuuu476+nqOPPJIvvvd7/ZpWQsXLqS5uZlUKkVDQwNLlizhhhtuYPz48Tz77LOd7nvkkUeybNkyTj31VHw+Hx6Ph+OPP54lS5Zw8sknd7kO119/PV6vF4BbbrmlR69HCCF6k0orNEOSJIhcltdB0wWH5zziEyrzXS0hBhz3CDs2r0F0Y++36vWGsikeSid5idYkaX4nwsanW1F90Xs13+MaB8EYSI/Hw3nnncf8+fOpq6vj3nvv7bfgEfLcAvnqq6/ywAMPYLPZuP/++9G0vrlwOeiggzjjjDOYMmUK1dXVpFIpPvnkEx599FFefPFFWltbOeuss3juuec45ZRTdnmciRMn8vzzz/eoLhUVFVx++eXccccdLF68mMWLFzNjxoweHVMIIXrK8OoUTXCTlO6rQgjR6wyXxrAZfmKbUsTqCq9VT3dqlE7y0vRWhOZ3ovmujtiNhoYG3G533srPWwtkMpnk+9//PkoprrrqKg466KA+KefKK6/kgw8+4Gc/+xmnnXYakyZNYurUqVxwwQUsWLCA+++/HwDTNJk9ezaxWN8Par722mvx+/0A2cyzQgiRT/69nRhenfpFoXxXRQghCopmA2elDXe1HZtP7/ZsFp5RDqpPK0IzNDYtLMzf2G1J1LZN42S4NPx7O9H6IFLYNgZyIDwKVT6DR8hjC+Qvf/lLVq1axejRo/t0Wovi4uJOn7/44ot5++23eeCBB6irq+Opp57ivPPO67P6AJSVlXHllVdy++23s2zZMhYsWMBJJ53Up2UKIURnzKiFbmg4S22Y0WTBp5cX/UdLWzjXNuWsM4Lxbh3D9DlIV/qyy7amaLePIUS+jPhKMe5h9uyyZSoSDWlCq+OEPo3D1gT9Nr+OZ6QDZ7kNu9/A7tex+Q10m0Zsc4q6F4KYkd6cD6P3pNssEo1pyqd5sRcZVByR+b6G1ybY9GIo2+RkuDX8ezuwLIh8kZBzxRCVlwBy9erV/OpXvwLg3nvvzY4JzJeLL76YBx54AMhMy9HXASTA1Vdfzb333ktrayu33HJLjwNITdf6rAvwQJP5W2T+L/JP3o/Csqv3I7w2SWxCkupTikhH/n979x0nVX3vDfxzzpnet7IsSxEbRVSqCipYsHeN9/ok8aIxMeaaWBITo49KNM1EY7y+fIyJNcarxmhAERugIAgiSlMElb67bJ/ezzm/549hB9YtbJm6+3m/XvNimDlz5jvz3ZlzvvNrGva87ONkOjlQDJ8PJaHB8/aXne/oQ8zqCBcCZx6V/r9j1U7YNjVkIryMKoZ8DCUFkQ8JMNhlqGENdYsCMLoUGF0yrDVGDJvjRNl0G5JBHYpFgsljgNAFEj4NyYCGSG0CalBHrFlFrFFNv6ZC1bAkiGGnO1BxkgOJgIbQlzGUTrNj7LwyQAK0uI7yGg8CgVQ1GW1IYt9bARhLTUDPy6HTIJOXAvKhhx5CIpHA2LFjEYlE8OKLL3ba5rPPPktfX7ZsGRoaUgeaCy+8MOMF54QJE9LX6+py8wnweDy49dZbcffdd2Pt2rVYtGgRLrjggn7vr2y0B0bZdOgNhwBJluCpdgKSBKHzBDjfmI/C0lM+4p8Boh6oPNkN+QwLAl+whSjbhsrnI1Htgs154Bhlq3LCEii8mSiHSj6KRaHkQ9tuQOUpTogpJoR3JoAwoG4DwvsE7GOtMJklICkQ/VxFvEmFnpSQOsU2wADAYQUcY/IWfp/ENgAJiw49ASiyBfoeQ2pyNVXA4pQhmg2IbABglDDs1FIM+0kZ4vE43vxzH56kwCeoSSuGGPMkLwVkPJ46aOzYsQNXXXXVIbe/77770td37tyZ8QKyfe2UXLv55pvx8MMPo7W1FXfffTfOP//8fu+rdbcPBsl46A2HAEmWACHQstvHE4ACwHwUlkPmYyeglcRhsMlo2eXPfYBDzFD5fMSMMgLBAzNPOhqCsO3y5jGirg2VfBSLgsnHLkAriUGySWjZ9Y0xjJvyElHONH994LokSygf7Unno2lrKxyHmaBKhTcpEGVX3teBLARbtmxJX6+urs7Z8zqdTtx22224/fbbsX79eixYsKDf+xK6gCjk0b45JsT+94QnAAWB+Sgsh8pHZG8Cw2Y7UXmqA43vB3Mc3dBTjJ+P6JEliNc40/93fNoIg7/7FkWlOQzbmt3p/xvqgwX7eosxH4NZrvIhKamuqpAl6AkBPaFDUiSYPArso82wDjchtCM+5P8uDs5HMqDBuzEKVfSxgGQLZNHLSwH5zDPPdFgAsyvz58/Hr371KwDAe++9hzlz5mQtnscffzx9ffbs2Vl7nq7ceOON+NOf/oSmpibcc889mDx5ck6fn4jomwJfxGCrMcJxuJkFJHUpdpgboRMO/OBq/drbYwFpaIvC0MZBUlR4TGUKPBOtcB5hhmzqespRLa7DvzWK1jXhHEdHVJiKugXymWeewTXXXAMAuOeeezB//vwO92/evBlWqxVHHHFEt/v461//iieffBIAUFVVhUsvvTRr8XbFbrfjF7/4BX76059i8+bN2LdvX06fn4ioK7JRQqyR3ZKIaPAyumSMvKwEWkSHd2MU0YYkhAYoJgmSAYAAEgENCa+WnmmVBq7Ql8hoVwwx5ktRF5CH8sknn+C6667DaaedhnPPPReTJk1CWVkZVFXF1q1b8fzzz+Odd94BACiKgscffzwvM8LecMMNeOCBB7Bv3z60tLTk/PmJiL4p3qLCM8kGg0OGGuKZExENPiVTbNCiOna/2MblKIj6YFAXkACgaRqWLFmCJUuWdLtNWVkZnnzySVx00UU5jOwAq9WKO+64Az/+8Y/z8vxERN/kXR+Fa7wVnmOsaGG3LfoGSSA1GIqoiJlLDYjs5bq3OccxkEVvUBeQ5513Hp588kmsXr0a69evR2NjI1pbWyGEQGlpKY477jicc845mDdvHlwuV15j/f73v48//OEP2Lt3b17jICICAKEJQBeAku9IqBC5l+2Ga/mB45WU5Bk4FR/FIkOLskog6quCLSDnz5/faUzjN82bNw/z5s3r9v7Kykpce+21uPbaazMbXC/NmTOn10uEmM1m7NmzJ8sRERH1jvsYKxSrDP/nXAuSOpM0AUlj0UjFTTZL0GLsok/UVwVbQBIRUf44jzAjtCOOpI9FAmWObJJgH2NKtfzEdMQaVST9/Buj3JONEhSzzAIyDyQhIBVBF/hiiDFfWEASEVEnRpeC0M7ul2Ug6itJAWou8sBcboCW0KGYZAhdIPBlDK0fR6CFeSJPueM53gqhCUTrONs0UV+xgCQiok4kRYLg+TxlkGOsGeZyA/a87EW8VYWkAK7xVpROscFWY0Ld6362RtKA2GqMcB1tgdGlQDJK0BMC8RYVsYYkFJsMW40JikWCpEgwlxnQujYMlT9c5B4n0Sl6LCCJiKiThFeFuZSHCMocc4UBCZ+KeKsKABAa4P8sitCOOEZc4EbNJR7UveZLrblH1BcyUHWaE84jLYi3qIi3qNDiOhSLDPsoE9wTLIAAovuS6R8pfJujCGzlGG+i/uDZARERdRJtSMJxmDnfYdBgI3W+SYvoqH3Nh5oLPaia68Lef3nZ+k29J6WKR/tYM/YtCSD0deeu94ol1aNCT7BJqRBIYv9SQAWuGGLMFznfARARUeGJ1iVhdCowOHiYoMyI1idhchtgH2PqdJ8eE2hYGoDJraBkii0P0dFAKHYZpjIFsqWLXwiyrOJkBxyHm9HQTfEIAFpMsHgkyiC2QBIRUScJb6qboalEgRpicxANXHhXAuHdcVSf40Z4bwLe9RFE6w9MYJJo0+D7PAr3RCvaPo0A/LMreJJRQs2FblgqjenbEj4Vwa/i8G+N9XtiJEkGTOUGmD0KrJVGGFpkJAMduzYb7DLKTrDDdZQFje8FEd6ZGNBroRziGMiixwKSiIg6SQZ0JPwaSo6zIbrPD6HmOyIaDOrfDMB5pBmeSVbUXORB2ydhtH4cSd8f2BZDybGpSXUie7opCCTA6JRh9BhgciswuhUoFgmQUmv6aVEdWkxAi+nQY3pqPFyMZ4LZ4BhjgqXSiMblQcSbVZg8CqwjjCg53obSqTaEdyXg+zzap5lOzRUGVJ3phMmdOkV1uRwwH6MjUptApC4BxSzDVGaArdoIXU21XAe/4ozRRLnEApKIiLrU/EEQw892Y8QFHtQt8rGIpIwIfhVH8Ks4So63ovxEByJ1yXRLZKJVQ6wpibLpNmgRHbJJgsmTKhKNbiV13alAUlJdJXVVIOnXoMV1QAeMbgMUiwzFIkM2HOhOGWtOIlKbRHhXHLFG/iEPhLnCAM+xVtiqjTDYFUT3JRH4IjUZTbxFRfDrOFo+DMN5lBnuiVbUXOhBwqfCvyWGwLYY9Hj3xXzJ8VaUTbcj3qpi77+9SHp1lI3REZNCcB5lQclxNugJgYRPQ/OqEAJfxSGS/HGg2HAMZPFjAUlERF2K1CZR+5oPIy50o/xEB5pXhvIdEg0i3g1ROI+ywDXO0qEra9MHIYy40I1RV5QAAIQukAxoSPo1hPckkPRpSPhT/++pe7VkABSrDGuVEbaRJriOtqB0sg2R+gQalga57mQ/KDYZNRe6oUZ0BLbFEW9OItxFS7GeFPB/HoP/8xgsVUZ4JlpQfoIdZTPsCH0dh29LFPGmA4W8qURBxSwHbDUmtK2PoPXjMKADkixBqALBXQkEtrGVkahQsIAkIqJuxZtVBLbF4BhjRvPKfEdDg01oZxyeidbU7Kz7f+2PN6vY9VwbDC4ZQgWSQa1f4yGFCqhBHcFgPN3F0T7ahIqTHai50I29C3zQ2bW1T8zlBsgmGXUveXu9fmKsIYmGhiSUD0NwjbPAPcEK1zgLEn4NaliDucQAxSpDDWuofd3Xp+6uRJQfLCCJiKhHoR0JlEyyoeR4K7wbovkOhwaR8M4EyqbaYR9j6jAJip4USLRmfj3I8O4E4m0+jLq8BOXT7Wj6gK3qfaGY9ncd7ke3US0q4F0fhXdDFPaRJlhHGGFwKPA3xBBrSCJcm+DESUMFJ9EpeiwgiYioR7F9Sfi/iMI1zsICkjIq3qIiUptA2XQ7wrsSOTlhU4M62j6NoPxEO9o2RKAGWbW0k80SDDYZik0GdECN6KnZT/fnxTrCiIRXHdiSGAII70l02fWViIoDC0giIjokPSlg8hggm6UeJ8Eg6quWtWGMuqwEziPMOZtN078lirJpNriOtKSWDBni7KNMKDvBDnNZ59NCXUtNVCQpEowOGd5N/BGJBoaT6BQ/FpBERHRIvs1ReCZa4TzCDP/nsXyHQ4NIvElFaFccpdPsCG6P56Qbo1CB0K4E7GNNQ76ANJcbUH1eam3OtvUBqEENWlSHEIDRlZr51lRiAERqPKp/CwtIoqGOBSQRER2SGtSR8GkweZR8h0KDUOvHYYy6ogQlx9ngXZ+bgi5al4TzCDNkkzSwLplFzlSa+kzve7vzeq9qUOekNpR5HANZ9FhAEhFRr+hJAdko5zsMGoQSrRq866Mom2GDGtYQ/DL7XVmjDUlIsgRLpQGR2vwUSZIClEy2weRRoEZ0xJtVhHcnclrQto8BNTgUJH2Zn7iIiAYfFpBERNQrelJAMkqH3pCoH1rXhqFYJVSd7oKlIoLm1eGsdmdN+jWoUR3WamOPBaSkAI7DzbBUGiEbJWhRHYGvYp1miTWVKDBXGiCSAkm/jnibesgWjMpTnHAcYUasMQlLlRElx9qgJ1KT/Pg2RSFy0J1XMuz/TLO1hXKI4wuLGwtIIiLqFTWkwVJpzHcYNIg1LQ8h3qyiYpYDphID6t/0Q2SxUSyyJwHHWDNa13bdbdY9wYLSaXYoFglqWIca0mGrMaLkeBtiTUmoIR2QAEuVEQZrx9Z5PaGjZW0Y/s+6HjNsHW6Ea5wFDcsC6RZXg12G53grymbY4RpvRcvqUGp22ixyjDUhGdKQ9LP1kYh6hwUkERH1SnhPAu7xVlhHGDkuirLGvyWGhE9D9bluVM11Yd9bgaw9V2BbDK6jPZ3+pg12GZWzHbDVmBDYFkPbJ2EkA/ubA2XAPtoE51gzZIu8P+YoovVJxBqTgADMFQY4j7Sg8mQnjC4FLavDnVr4LMMM0BN6h+66alhHy6owAltiKJ/pQPU5brSuC6NtXXbGhcomCc6jLGhbF87K/olocGIBSUREvRLemUB0XxIVMx3Y8y8vu7xR1kTrk9j3bgAjznPDM8kK3+bszPwZrU8iui+JqjNdaHwvCOiAZ5IVrgoZWkxH3SJf5+6teuqzEN7ZfctgrEFFrCGERFuqNdXoUNCwNNChNdVSZUS8tetWv4RXQ/0bfpQcb0X5iQ5IEtD6ceaLSEulAbIiIbSDazJSDgmRuhS6YogxTzgbAhER9VrzhyGYywxwT7DkOxQa5CJ7EvBuiqDsRDtMZdmb/bf+LT+0qI4R57kx4gI3bKOM8G2MYM9L3gFPruP/PIZ9bwdgG2nCyMtK4BhrgqQAtlEm2EeZEPy65yVxvBuiaF4dQulUOxxjTQOKpSva/jVdZTPHNhNR77EFkoiIei3erMK/NYrSaXYEvojlZJIPGrpa14RhqzZh+FwX6t7wp2cMzSQ9LrDnX14YHTJkkwyXR0PbjiiEnpnWh/DuBGoX+FB+kh3Dz3Knb4+3qb2abda3MQpLpQGVs52INXlT4y4zJNGmQg1rKJ1iy2pXYaKDSaI4JtEphhjzhS2QRETUJ94NURisMmyjMt8iQnQwoQP73g0AkoRRV5TAnq2/OR1IBnQk2rSszPwab1VRt8iPPa96UbfYj93/bMOef3qhJ3t3htq0PAQ9IVB1hgvIYGOh0IDWdRE4xpg5wzIR9RoLSCIi6pOkL7X8gamUnVgo+5J+DXtf8SJan0T1eW6UTbdltIjKpXiTisieRKpQ7QM9IdCwJADLMAPKZtgzG1OLCgCwDuPnmXJEFNGFusQCkoiI+kwNaTA6eAih3NATAvveDqBlTQglk20Ycb4biqVIq8h+ijWqaPkojNLJNgw/2wU5Q68/3qwi3qLCM8makf0R0eDHoz8REfVZ0q/BXMYWC8ot74Yo6hb5YSo1YNR/lKYmcxpCdaRvYxT1b/lhrTJi9LdKYB2RmXVZfZ9FYR9tzlhRStQTSS+eC3WNBSQREfVZeFcClmHG7I1JI+pGtD6JPf/yIrIngcpTnRhzVSlKp9hgGCIt4uFdCex+2YuEV8OI89wZKSIjdallPCyVmSlIiWhw48/HRETUZ8Gv43AcHsfwc1yI1CVhsMlQLBKiDSpCX8cQ2pXg+BHKGi2io/G9IHybo/BMsqJksg2l02zwfRZF69owhJrvCLNLi+ioe8OP6nPdGH6WC3v/7UPS17dxlQdTgzq0uA5zmYLIngwGSkSD0tD4uY6IiDKu4d0Amj8MQ2gCsYYkQjsSMDpkDD/bjeFzXfkOj4aAeIuKxveC2PH3VrSuDcM93ophpznzHVZuiNRnUA2l1rAc6JhQoQpIMruwUg7ke2IcTqIzYGyBJCKifhE64P8sCv9n0Q63O8aaMPwsNzzHWuHbFO3m0USZI5IC3g1RJHwaqs9xI96qwftpJN9hZZ2eFKh/04+Rl5Vg+Dlu1L3ug+hnQySLRyLqLbZAEhFRRoV2JODdEEH5CXYoNh5mKHfCuxJo/TiM8hl22McMjfG5akhH/Vt+mMsNGDanf62vRpcMxSoj1jzI+/5SQZBE8VyoazyyExFRxrV9GoEQgOsoc75DoSGm7ZMIgtvjqDrdCUvV0OhoFW9S0bgsAOeRFpROs/X58Y6xZgg91RWdiOhQWEASEVHG6QmB0I44XOMs+Q6FhqDG9wKItagYcaEHzqOHxo8YoR0JtHwUQtk0O2x9mB1ZsUoomWKD7/Mo9ASbXCgHhCieC3WJBSQREWVFYGsMJo8BliouDUC5JVSgbpEfwS9jqDrNhZLjrfkOKSe866MI702g8hTHoSfVkQHJAAw73QWhAW3rBv+YUSLKjKHRt4OIiHIuWp9Ewq/BPc7CrnGUezrQtDwENaKj/EQH4i0qIrWD/++waXkQIy8rQc0lHjQsCSLe0nlcY8lkK0qOtUGxyhCaQN1iP/Q4W1soN4plfGExxJgvLCCJiChrAttiKJ1sQ/OqEPQkj8aUe20fR+A4zAznkZYhUUCqIR21C30YPteFkZd7EPo6jtCuBNSgBsUqwzXeAscYMwLbYgjvSSDRpiLh7f8akkSySQIS+Y6CcokFJBERZU1wWwxl021wHG5GYGss3+HQEBVvUWF0Dp1RO0m/hj2veuEeZ4FnkhXOIw+MRU4GNNS/7Ud4J8/4KTMkQx8LyGJZY7EYYswTFpBERJQ1alhHZG8SrnEWFpCUPwKANMTWOdQB/5YY/FtiUCwSFJsMPSGghvR8R0aDjBbl39RQM3R+jiMiorwIbI3CWmWEqUTJdyg0RBnsMrTY0D3J1WICiTaNxSNlB1vqhhwWkERElFXhXQmoYQ3lJ9l51KGcM5UqsFYbEalll02iQtA+iU4xXKhrPJQTEVFWCR1ofD8IW40Jo64ogfsYS2rSBaIsk4wShs91IdGmsQs1EVGGsIAkIqKsi+xNYu+/fUj6NFSc5MDIy0ug2HkIouwqm26DwaFg37sBCE40SlQYhCieC3WJR28iIsqJeLOKfe8EsPulNkgyUHOBG7KZLZGUPe7xVng3RpD0s3okIsoUFpBERJRTyYCO+sV+mEoMsNWY8h0ODVKKVYJslBBvUfMdChEdJN/jGjkGcuBYQBIRUc6J/ZNBahHOCknZocUE9IQOk4ez/xIRZRLXgSQiopxzjDVB6AIJdi2kbBFArEWFuYKnOkQFRaA4lv4ohhjzhC2QRESUUwanjNIpdvg2R9kCSVkVb1ZhqTDmOwwiokGFBSQREeWODFSd7oIW09G6LpLvaGiQizWpMLoUyBZO1kRElCns10FERDlTMcsBc6UBdQt9EEn2D6LsijcnAQCWcgMitck8R0NEQPFMUFMMMeYLWyCJiCgnXOMs8Ey0ovmDEGJNnBmTsi8Z0KHFdJgr2Y2VKFtMHGfcwaefforf/va3OPfcczFy5EiYzWY4HA4cddRRuOaaa7By5cp8hzhgzDgREWWdZbgRFac44Ps8isDWWL7DoSEkGdBgdPD3cqJsqTjJDrzWhwfoInUpdP2Icfbs2VixYkWn2xOJBL766it89dVXeOaZZ/Dd734XTzzxBEym4lzKigUkERFlhWSUUHKsFbYaEyzDDIjuS6J5VSjfYdEQo0V1KFYWkETZYK40cKKqg9TV1QEAqqur8a1vfQunnHIKRo0aBU3TsHr1ajz44IOoq6vDc889B1VV8b//+795jrh/Bv03altbG/7xj3/gpptuwsknn4yxY8fC5XLBbDZj+PDhOOuss/Doo48iFOr5pEYIgT//+c8YN24czGYzjjjiCPz6179GIpHo9jFz5syBJEmQJAmKouDzzz/v8Tl27dqV3n7+/Pn9eblERAVBtkioudCNkuNtUMMamleGUPeGH+Ckq5RrssTp+ImyxDHaBC3Wxy92UUSXPho3bhxeeukl7NmzB3/+859x+eWXY/r06TjxxBNxyy23YMOGDTjqqKMAAC+88EKXrZXFYNC3QC5btgzf/e53u7yvoaEBDQ0NePfdd3H//ffjlVdewfTp07vc9rrrrsNTTz2V/v/27dtx1113YdWqVVi0aBEUpeeFinVdx/z58/Hyyy/3/8UQERUBg13GiAvckM0y9i7wItHKtR4pfwx2GdG67n/sJaL+kU0SnEdaEKgN5zuUgrFo0aIe7y8vL8eDDz6ICy+8EADwr3/9C6eeemouQsuoQd8CCQAjRozAVVddhUceeQQLFizA2rVrsWLFCvzjH//AueeeC0mSsHfvXpx11lmor6/v9Pg333wTTz31FEpKSvA///M/WLNmDZ566ilUV1fjrbfewt/+9rdexfHKK69g48aNmX55REQFw+hWUHOJB5IioXahj8Uj5Z3BLkMNs+mbKNMqT3FANkto2xjt0+MkHJiJtaAv2XnbMGfOnPT17du3Z+lZsmvQt0BecskluOKKK7q9/9vf/jb+53/+BzfddBN8Ph8eeOAB/OlPf+qwzT//+U8AwLPPPpv+xeCEE07A5MmTMXnyZPzzn//ED3/4w26fw+VyIRaLIZFI4O6778bChQsz8MqIiAqLucKA6vPc0KI66t7wQ+NJO+WZZAAUMwtIomww2GUkvBpUP38o7IuDh78dqgdjoRr0LZAGw6Fr5B/96EdwOBwA0GVf5PYBsaeddlqH248//niUlpam7+9OSUkJrrvuOgDAa6+9hnXr1vUqdiKiYmGtNqLmQjeSAQ21C30sHqkgyKbUaY4e5yBIokwzOBVYq4wwlfaxPUqI4rlkwfLly9PXx48fn5XnyLZBX0D2hsFggMViAQDE4/FO91dWVgLomHAA2Lx5M9ra2lBVVXXI57jzzjvTz3H33XcPNGQiooJhH2NC9XluRBtV1L3u48k6FQw9kfohQzJlqzMa0dAlyalJJhPewb2ubyAQ6HDpqlboLV3X8fvf/z79/29961uZCDHnWEACWLp0KVpaWgCkZk/6pksuuQQAcPXVV+PRRx/F2rVr8eyzz+K8884D0LvkV1dX4/rrrweQGlO5evXqDEVPRJQ/zqPNGH6WC+Hdcex70w8xuM8jqMgIFdBiOqzDBv2IHaKcso8xQbHIiNYnB/3s2iNHjoTb7U5ffve73/V7Xw899BDWrl0LALj00ksxbdq0TIWZU0P2GzUYDKK2thYvv/wyHnzwwfTtP/nJTzpte/nll+OSSy7BggULcOONN3a474wzzuhx/OPBfvnLX+Jvf/sbIpEI7rrrLixZsmRgL4KIKI88x1pRMdMB/5Yomj4IcakEKki+TVGUTLHBuykKNTjIz3SJckA2SRg224lIbQKN7wf7/Pj2SWoKXXuMe/fuhcvlSt9uNpv7tb/ly5fj9ttvB5Dq3fjYY48NOMZ8GVItkA888EB6nUWXy4UJEybgnnvuQSAQgKIo+POf/4xTTjml0+MkScLLL7+MX//61xg7diyMRiNGjx6N//t//y/eeOONXo2zBIBhw4bhv//7vwGkWj2Lde0XIqKyGTZUzHSg7dMImlaweKTC5dschRbVUX22G5KRXVmJBqr8RBsgA43vB6FFB/+Xv8vl6nDpTwH5+eef49JLL4WqqrBYLHj55ZcxbNiwLESbG0OqgOzOnDlzsGnTJtx0003dbmMwGHDnnXdi+/btSCQS2LVrF+67774+/xH9/Oc/T0/Yc9dddw0obiKinJNSU7eXTrGjeXUIrWu5/hcVNj0pUL/YD4NTRtXpznyHQ1TUFJsE51EWtK2L9L94FEV0yYCdO3firLPOgtfrhaIoePHFF4ty7ceDDakC8pprrsHmzZuxefNmfPTRR3j22Wdxxhln4P3338d//ud/4qOPPsp6DOXl5elusitWrGA3ViIqGpIMVJ3phGu8BY3vBeDr49pfRPmS8Gpoej8Ix2Fm2EYa8x0OUdGyjTBB6IB/ayzfoRSF+vp6nHnmmaivr4ckSXjqqadw8cUX5zusARtSBWRZWRmOOeYYHHPMMZgxYwauvvpqLFmyBL/73e+wefNmzJkzB++8807W4/jZz34Gt9sNgK2QRFQcJAMw/Fw37KPN2PdOAIFt/Z+FjigfQjsTiNQnUH6SI3srhBMNckaXjIRXhUj2v3lOEqJoLgPR0tKCuXPnYseOHQCARx55BFdfffWA9lkohuwkOge7/fbbsXDhQqxZswbf//73sX379l6Pa+yPkpIS3HLLLZg/fz7WrFmDN998E+eee+6A9inJqbGd1P5epP6l/GM+Ckt/8iGbJAw/1wVTiYJ9bwYQ3ZdkPjOEn4/cal0TwcjLPHCPtyCwtfOPIMxHYWE+Cks6D9/IiSSkQT8Ta1/5/X6cffbZ2LJlCwDg97//fXoelMGABeR+F110EdasWYM9e/Zg7dq1mDlzZlaf75ZbbsHDDz8Mr9eLu+++e8AFZNloD4yyKUPRFTdJluCpdgKSBKEP/sHdhY75KCx9zYdskVB5igOyWUbLqhDsZgfsY7If51DBz0fuyW0mjD2nGo2mAJKBjme9zEdhYT4KiyRLsDscMFsNiI050IkxqSeAnX3YkY7iKDj7GWMkEsH555+PTz/9FEBqLfhf/OIXGQws/1hA7ldeXp6+vnv37qwXkC6XCz/72c9w5513Yt26dVi4cCGOO+64fu+vdbcPBonjOoD9v4oJgZbdPh5wCgDzUVj6kg+jU0b1+W4EQ0HU/zOApF/LUZRDBz8fudda60XNJR4YxwONr/ihqwfed+ajsDAfhUWSJTjHm6F7YmjZ5U3fropkHqMqLIlEApdeeilWrVoFALjpppvw61//Os9RZR4LyP3q6urS19tnSc22n/zkJ3jooYfQ0tKCe+65B//+97/7vS+hC4hiWFQnR4TY/57wgFMQmI/C0pt8mMoUjDjfAz2uo+4NP9RQMfxcXJz4+cgtkQD2veXHqCtL4T42NZtkh/uZj4LCfBQWg1VCJKJ3yIcY4FjBweSqq65Kz6dy+umn43vf+x4+++yzbre32+047LDDchVexrCABKDrOl599dX0/ydOnJiT53U4HPjFL36B2267DRs3bsQrr7ySk+clIuqJpcqA6nPdSPo11C/2Q4vx5IAGl2RAh/+zKDzHWuHbHIUe5984UW+Yy43wblIHtI9MTFCTC/2J8eB6YtmyZTj22GN73H727Nl4//33+/w8+TboZ2F96qmnkEx237Su6zp+/vOfY/PmzQCAWbNmYezYsbkKD//93/+NqqoqAMD999+fs+clIuqKrcaIEed7EG9RUfc6i0cavLwbIpAkCSXH2/IdClFRMNhkGB0KIvXssjrUDfoWyFtvvRV33HEHrrjiCsycORNjxoyB3W6Hz+fD+vXr8eyzz2LDhg0AAKfTiUcffTSn8VmtVtx+++24+eab0dLSktPnJiI6mGySMOwMF6L7ktj3th+CQx5pENNiAr5NEXiOtcG3aQCLohMNEdYRqbIhOtACUuy/FLp+xDhUuvMO+gISABobG/Hoo4/2WBweffTReO655wY0kU1/XX/99fjjH//YYRwmEVGuuSdYIBskNL4fTBePkpIqLHlyTYORd2MU7mOsKJlsQ8uH4XyHQ1TQrFVGJHwq9ASPB0PdoC8g16xZgyVLluC9997Dl19+icbGRni9XthsNgwfPhyTJ0/GpZdeiksuuQQmU36WwbBYLLjzzjvxox/9KC/PT0QEALJZhhrR4RhjgnWECUaXDINNhsGuIPh1DE0rQjxxoEFFTwh4N0ZROsUG38Yofygh6oFkljMzrEGI1KXQFUOMeTLoC8hx48Zh3LhxuPHGG3P+3H0ZFHvDDTfghhtuyF4wRESHIEmAya2g8lQnog1JxJtURDUBPS5QMtmGMaNMCGyJIRnQYCo1ABIQ2ZtAeFci36ET9ZtvcxQlk6womWJDyyq2QhJ1RzYAQmVRRUOggCQiot5RbKl51QJfxdC4NNjhPv+WKDzH2uAeb4FslKBGdBidCjwTrahb5EOklpMqUHESSYG2DRGUz7DD+40lPYjoAMkgQdcGXkBKInUpdMUQY76wgCQiIgBA4/tBhHcnEN4V73SfFhVo/SiM1o/CkE1SuivriAvdqDjZgd0vejs9hqhYBLbFUH6CHfYxJqDznz8RAZANEpCBApKK36BfxoOIiHpJB0Lb44ecffXgcZCta8MweQywVPH3SCpeekwg2pCEbUx+5kIgKgapFsh8R0GFgAUkERH1W7xZhRACJjcLSCpuoe1x2EeaYHTz1IioK7JByszyTu2T6BTDhbrEb0kiIuo3oQNqWIfRxcMJFbfAFzGoYR2OseZ8h0JUkCSDxEl0CADHQBIR0QDFW1TYRpkQqUtC6ALJoA4trOc7LKI+ETogOL6LqFuSAgg9A5Po6KlLoSuGGPOFBSQREQ2Ib1MU1ee6UXORJ31bYFsMjcuDAA/AVCRkkwSTx4D4VjXfoRAVJC2qw+JkbxNiAUlERAMUrU9i53OtUCwSIEuwVRtRMcuBhFeFd0M03+ER9YpslgAgMwulEw1Coa/jKD21BIpNhhoawGDIYhlfWAwx5gl/RiAiogHTEwLJgI6kT4N/SwzBHXG4jrZA4s+UVCRkU6qAFEmeNBJ1xbcpBqEJlE625jsUyjMWkERElHG+zVEYHArGfLsM9sO4NAIVPoNdAQBoCfa7JuqKrgoEv4rDNc4CxSr1f0eiiC7UJRaQRESUcfEmFbtfakO8RcWwU50DO9kgygHXUWYkgxq0MM8aibqjxXVIigRzhTHfoVAesYAkIqKsUEM6GpcFIHSBmos8sFbzhIMKk6XSAOcRFrR9Es53KEQFLeFVkQioGD7X1e8fBiUhiuZCXWMBSUREWaNFBeoW+6GrAsPPdqXHmREVkvKTHIi3qAh+lch3KEQFLenTUfuKH5AAx+GWfIdDecICkoiIsirRqqF+ceqEo/ocF0wlSr5DIkqzjTLBOtyI5tUhjnki6gU9KaDHdQ5NGMJYQBIRUdZpUYF9bwag2BWM+lYJHIeb8x0SEQDAOdaMeJuKaF0y36EQFY1Yswr7yH5OkNa+jEcxXKhLLCCJiCgnovuS2P1SG8J7EiibYc93OEQAAMUmI+kbwJp2REOQ//MoLJVGWKo4tn0oYgFJRES5owO+jVGY3Aoq5zhgsPMwRPkVb1NhHWFExSkOlBxvhetoMxyHm2CuMADsoUfUpcjeJOJtKkqO68eakAKAXgQXNkB2i0s8ExFRTkX3JdHyUQieY22wjzKj7nUfEl62AFF++DZEYKk0wD7aBNkgwemxQB7tAgBoMR2RuiQitQlEahNQg92vESkpACRAaOCJJw0Jvs1RVJ7qgGKRgFi+o6FcYgFJREQ5510fReCLGEZc6MHISz0Ibo8jtD2OSC3HoVFuaTGButf8AABJllA+RqCt1gdjqQxbjQm2GhMqT3FAkiUkfCqSfg1aTAASICkSjE4ZBqcCxSRBUlJNlkIICA0QmoDQAT2uI+HTkPRpSAY16KqAUPffr6W21RMC8VaVxScVjXiTCkmSoDiUPhWQxbJERjHEmC8sIImIKC+0mEDtQh9KjrPCcbgZ7vFWhPcm0LwyhKSfLZKUP7oqEGtQEWtQ0bYuAtkkwVpthG2kCUaHDKNLgRAC0FNdYEO7EtAiOnRVQFJShagkpwpMSQEUiwyTR4H9MDOMDjldaH5TMqShdqGvx5ZOokKhRlN/pwYbhyIMNSwgiYgob/SEQOvHEbR+HIF9lAkVJzsw8lIPdr/YlmrlISoAekIgvCuB8K4MrRMppbq8SgYJsiJBMkgw2GXUXOSBfaQJ/i3sD0iFz1yWKiOE1sfvaoHimOG0CELMF/5kQEREBSG8J4Ha131QLDJn9qPBTQBCBfSYgBrWkfRr6fPpWLOa39iIesk+yggtriNaz6EHQw0LSCIiKhhqWIcW12EdwQKShhZLpQFaQkecBSQVieDXcShmGcNOd+Y7FMoxdmElIqLCoadaIu01JrQgnO9oiHJGNkgQSfaZo+IRa1Sx720/3Cea+/ZAIYqkC2sRxJgnLCCJiKhgWEcY4Rhrhm9TNN+hEOWU0ES3k+sQFarQzgT8eyP5DoNyjAUkEREVBEuVAdXnuRGtS6BtHVsfaWjRVQHJwAKSik+fW851AMXwp87JkLvFApKIiAqCe4IVakBD/VsBHrhpSJKK4aSaiIY8FpBERFQQtIgO2SxDAmdPp6HHPsqMeCsn0KHBTxICUhGMLyyGGPOFs7ASEVFB8G+NwWCT4RjbxwkZiIqcqUSBfZQJvs0c+0tEhY8tkEREVBCSPg2xxiTso00Ifh3PdzhEOeM5zopkSENwO//uaQjgLKxFjy2QRERUMKL7krBWG4tjggWiTJAA51gzAl/EOPaXiIoCC0giIioYwR1xGOwKbCOM+Q6FKCckGZBNMpIhLd+hEBH1CruwEhFRVhnsMtRw75pW4k0q4m0qPJOsiNQmsxwZUX7JZgmeiVYAQNLP5kcaItiFteixgCQioqywDDei6nQnjE4FCZ+K4FdxBLbGDllMtq0LY/hZboy8zIPAthgCX8QgeG5NRUo2SXAdbYFslgABCB0wlykwlxlg9CiADvi3RBFr4A8mRFQcWEASEVHGGN0K3MdYYHIrsFabEG9W0bo2DFuNCSXHWVEyxYam5UEEv+x+spDQjgTqFvngnmhFxSwHTB4DmleFcvgqiAbOYJfhOc4K93grIAF6XAckCZICJFpVRGoTiG/QEN4ThxZlSwcNIWyBLHosIImIKCMcY00YdroLelxHrFmFd30E3o0RCBUIfhVH00oJFTPtGHaaE0IDQj3MOBmpTSJSm0T5iXY4DjejeVUOXwjRANlHmTDsDCcgAO/GCPyfR1kkEtGgwQKSiIgGxOCUUXq8De6JVgS+iqFpeRCii/XQRVKgaXkIsklCxSwHwnsSEMmeT6rVsA7FwvneqHiUTrWhbLodoV1xNC4LQk+wcCTqQEdxzLTNoRPdYgFJRET95p5oQcUsB7S4QPOHIfg2HXoh9JbVYYz+TzNKJlnR9mmkx221hA7ZKKXmDOfBnAqYbJIw7DQnHIeZ0fpxGG2f9Py3TURUrFhAEhFRvziPNqPyFCd8myNo+SjcZatjV9SQjsC2GNwTLWjbEOmxMDRYZeiqANiIQwXM6FFQfbYLik1G/WI/wnsS+Q6JqGBJQkAqgvGFxRBjvrBfEBHRIOAab0HZDHtqpscccIw1YdhsJ/xbomhe1fvisZ1/SxQGuwL7aFO32xicMkom2xDaGWcBSQXLPsaEkZd5IASw91Ufi0ciGvTYAklEVORko4Rhs50AgGh9IuvrJ9pGGlF1hguh7XE0fdC/2VETrRqiDUm4J1gR3tn1CbfzcDMUs8wZWKkgSQagdIodpVNsCG6Po/H94CHH9BIROAvrIMACkoioyDmPNKevl82ww1wRR/DrONRg5gcNmssNGH6WG+G9CTS8FxxQy6B/SxRVp7tgKlGQ8Gqd7g/vTaBshkDpFBtaPgwPIGqiXpIAyzADKkfZYSo1QDbLkA2ptRuFDkAICB2QFAmKRYJskNDyUQje9Yce+0tENFiwgCQiKnKuCRaEdsbh2xxFyWQbSifbUDbDjpY1Yfg2Zu7EVjZJGH6WCwmvioZ3AwOe1Cb0dRyJqRrKT7Sj/s1Ap/sTrRpa1oRRMdMB6EDLGhaRlD1Gt4KquU5UjnWiZW8S0foktLhItSpKgCRLkGQAMiA0QE8IBL+OZeWHGiKiQsYCkoioiJkrDLCUG1G31o9ofRLRen+qa900OypOciDWpCK2LzNdWofNcUI2S6h9PQDRucGwz4QOtH4UwvCz3LCPMnU5dsy3KQpJkVB+gh2hXQnEGrLbPZeGJvtoE6rOcEKN6mh4P4D6dT4Ind3XiLJCF4BUBJ8vfgd0i5PoEBEVMfcEC5JBDZG9B4ovoQKta8KIt6goOdaamec5xgLHWDMa3wtmtMUltCOB8J44hp3mhMHe9SHJuyGCZFCDZ1JmXgvRwSQDUDnbiWhDEntf8SPRkoFfR4iIBjEWkERERUo2SnAeYUFga6zLsYiBL2OwjTRBNg1sZlZzhQEVJzng3RRBeFfmZ5hsWBaErglUnenq+qgkgNaPw3Aeboa5jB1nKLNcR1ugmCU0fRCCUNniQJR17ZPoFMOFusQCkoioSDmPNENSAP/WWJf3B7fHISmpZQb6SzZJGD7XhXirmrUxiHpMoOHdACyVBpRNt3e5TfCrONSwBtd4S1ZioKHLVGpAwqtxLCMRUS+xgCQiKkKSAniOtSK8OwEt3PWJrxbWEd2XhGtc/4uu9nGP+zIwaU5PYo0qWtaGUTrZBvuoLgpeAQS2xdNFM1GmKBYJWozFI1HuFEDLYq9aH9kC2R0WkERERah0qg0mjwHR+p4nlfFtjsJWbYJlWN+7frqPsWZl3GN3fBujCO2KY9jpThgcnQ9PgW0xKGYZ9jHmLh5N1D+KRYYWZQFJRNRbLCCJiIqQFkv9MloxywFPDxPlhHcmkPCq3XYN7Y51hBEVJ9mzNu6xO43vBaEnBarmdh4PmfRriDYk4TqaBSRljmKR058nIsqBfLcscgzkgLGAJCIqQr5NUXz1l2ZEG5IwV/Tcuti8KgRbjQnuib3ryuoYa0L1eW5E6pI5X3tRjwvsezcAS7kB5TM6F72BbTHYakxQupmxlagvJBkwuhQkg5x5lYiotzidHREVFYNThucYa7poEioQa0oi3qQi1pQcei0JAnAdaYEEILQjjtDOzq2FkdokfJ9FUX6SA3pcdDuW0VSmoPwEB+yjTAh+HUPDsmBWxz12J96konVdGGXT7PB9Hu3QfTa0PY6KWQ64jjTDuyGa++BoUDFXGiAbpUN2BSciogNYQBJRQVJsMiQJUCM6ZKMEU4kCz6TUmDw9LhCpTRVKskmCe6IVhmmpFqnwnjh8n8UQ6WJR+sHI91kUWlyHucIAx+FmbH+6FSLZuYhuWR2CbJZQdaYLcpsZMTmMZEiDYpZhGWaEfZQJ5nIDEn4N9W/5c9pttSu+zVF4jrWhdLINTStC6dv1hEBoZxyuoy0sIGnAbNUmaHEd8VY136EQDR16kUxQoxdBjHmS1wKysbERH330Ufry8ccfIxAIAADuuecezJ8/PyPPM3/+fPzqV7/q1bbvvfce5syZ0+3927Ztwx133IGlS5dC0zSceuqpuPfeezF16tQut3///fdx2mmnpf//H//xH3jxxRd7jGHevHl49tlnAQCC/a9pCJEUwD3BipLjrfsLyI7rFyb8GppXhRDYFoP4xvmewSnDNiLVTXPEeW4k/BoC22JIeFVoUR1CA9DDcoid7urt0omH3E7q8uqAnvMgWlSHb1MUtlEmlB5vw/C5Lux72596vQcRGtC4NIhEs4qRJ9tTay7up0Z0ROsTaF0XRnhPIi+tjt8kVMC3MYKy6Xa0fRqBGjoQVHBbDK4jPbBUGhBr4ok/9Z/RrSDh1YriXJaIqFDktYCsqqrK59P32ebNm3HKKafA7/enb1u8eDGWLl2KxYsX4/TTTz/kPv75z3/izjvvxKRJk7IZKlHRKJlig8Emw2CTYRlmgGKVEdgWQ3hPAkJLtTBCF1AjOmKNarcnempQR2BrDIGtMViGGeA+xorSyTbIxn5UZUVG6AKSnHqd9lEmOA4zI/h1vMttfZ/FYAgF4G30QzICIimgdrMMSL75Poui5DgbSo63oXnlgVbISF0SyaAG19EWxJpCPeyBqGd6UkA2DP7vCKKCIvTUpdAVQ4x5UjBdWMeOHYuamhqsWLEiq8+zefPmHu8/7LDDur3vuuuug9/vx/nnn4+bbroJJpMJTz31FP7+979j3rx52L59O4xGY4/7F0Lgnnvuwauvvtqv+IkGE+eRZpTPsCPeqkKL6QjtTMC3OYqkf2ATWsQaVcQag2hEELJFgsEq9651r4+tED12EOhvi8Yheh0cslOCQIfWuu5oUR0iXNjNLkIF/F9E4Z5gRfOHoQMtowIIfhmD+5jU7d9sbSXqLT0hUj9SERFRr+W1gLz77rtxwgkn4IQTTkBZWVmn7p7ZcMwxx/Trcbt378batWsxffp0vPbaa5Dl1Hir2bNnw+/3Y+HChVi9ejVOPfXUbvdRXl6OlpYW/Pvf/8ann36KKVOm9CsWosGife21fe8EBlw0dkePCSRirDCKVXB7HKVT7LBVGxGpPTDRSWBbDKVT7T22thL1CutHotwqliUyiiHGPMnrPOi/+tWvcN5556GsrCyfYfRKXV0dAODUU09NF4/tzjjjjA7bdOemm26C2Zxav+yee+7JQpRExSW5v6VMsfIMjrqWaNWQ8GtwjO249mMyoCPWkoR1RM+9Poh6Yqk0INHGcbRERH3BhbR6qbKyEgCwcuVK6HrH7mHLly8HcOgxnTU1NfjBD34AAFi0aBE++uijLERKVDzSc+TwRz7qQWhnHPbDzJ1aitSgDoONhzHqH1OJAluNCaEdbMEmyildFM+FusQjby8dccQRmDhxIj766CNceumlWLJkCVasWIFrr70Wr7zyCqqrqzFz5sxD7ueXv/wlrFYrgFQXXiICC0jqUWhHHAarDOtwtjZS5lTMciAZ1BD4igUkEVFfDLkCcu7cuSgrK4PJZEJlZSXmzJmD3//+9/B6vYd87F//+ldYrVa89tprmDt3LmbPno2nn34aJpMJTz75ZLp7ak+GDx+OG264AQDwzjvvYOXKlQN+TURFa3+LEutH6km8SYUa1WGt7lhACh2QFHZ/pr4rP9EOW40Jje8HC2LZGqIhpX0MZDFcqEtDroBcsmQJ2trakEwm0dzcjOXLl+OXv/wlxo4di4ULF/b42JkzZ2LVqlU477zz4HA4YLPZcOaZZ2L58uU455xzeh3D7bffDrvdDoCtkEREvZHwqjC5lY63takwVxggFcx84lToTCUKqs93o+R4G5pWBhGtSx76QURE1MGQKSAnTZqEu+66C6+//jo++eQTrFmzBs8++yzOOussAIDP58Pll1+ON998s8f9TJ48GW+88QaCwSDC4TDeffddnHjiiX2KpaKiAjfeeCMA4L333sN7773XvxdFVOw4BpJ6KenXYPR0LCAD22KQjRI8x1jzFBUVk7IZNoz6VgmMLhn1b/rh/yyW75CIiIrSkCggb775ZmzatAn33nsvLrjgAkyZMgUnnHACrr76arz99tv4y1/+AgDQNA3XXXcdotFo1mO67bbb4HQ6AQB33XVX1p+PqBBJnD+fekkN6TDYlU63+T6LonSqHQb7kDicUT9VzHKgdIodresi2P2SF+HdiXyHRDR0CeS/a2qvLvl+owrXkOj44/F4erz/+uuvx7p16/DEE0+gvr4er776Kr797W9nNaaysjLcfPPNuO+++7Bq1Sq8/fbbOPvss/u9P0mWIEk8GQfa34vUv5R/PeWj/TYJEvOVI8X6+VDMEvSE3ilu77oonEdYUDrVhuaV4TxF13/Fmo9CYCpV4J5ggR7XEalVEd3XdXfUypPtcE2womlFEIGt8dQPV9383sB8FBbmo7B0lw9JSBxLPMQMiQKyN66//no88cQTAFLLcmS7gASAW2+9FY888gh8Ph/uvvvuARWQZaM9MMqmDEZXvCRZgqfaCUgSBKdgzrue8mEqUeByuVA6UiDp5NEnF4r181E60gaDVUb5mM4nkrLXjOqpLoja4vsOLNZ85JMkA+5JVjiPMEOL6FCsMiRZQsOyABJtWno72SShdKoN1moj2j4JwxSzoXyM7RD7Zj4KCfNRWLrLR1JPADv7sKNimaCmGGLMExaQ+02YMCF9va6uLifP6fF4cOutt+Luu+/G2rVrsWjRIlxwwQX92lfrbh8MEqe4B/b/MiYEWnb7eMApAD3lwxw2wBYAWvf4Opz4UfYU6+fDOEGD7hVo2RXsdF8UJsgjXPA2+KDFiuc1AcWbj3wxuhUMO90BURrHznfa4Ps8BoNVxphvlyLgDSC0K9U11VplROVMB+KGCPa+HEJ4T++6rDIfhYX5KCzd5UMVnIxqqGEBuZ/I068MN998Mx5++GG0trbi7rvvxvnnn9+v/QhdQEj8cm0nxP73hAecgtBdPto/d8xVbhXj50OxSEj6tS5jNjhk6EkBNVKcrdjFmI9cMzhllE61wXWUBcmghr3/9iHeogIAPMdZocV0hPcmIHSBkik2lE2zIboviYZlQWjhvv1dMB+FhfkoLF3lo8/n0LqOoujzqhdBjHnCWQf227JlS/p6dXV1zp7X6XTitttuAwCsX78eCxYsyNlzE+VbujMizwvoEAxWGVq064O5wSkXbfFIhyABJVNsGPOfpbCPMqP5wzD2/NObLh4VqwT3eAu8GyPQE6nisXyGHW2fRFC3yN/n4pGIiA6NBeR+jz/+ePr67Nmzc/rcN954IyorKwEA99xzT95aQ4lyjvMiUC8plu4LyFh9Eia3grIZPY9vo+JTMcuBsmk2eDdGset/W+H/LApxUG93+ygzIAH+LTEYXTLKptnQ+kkYbZ9E+MMUUaHK++yqfbhQl4q+gHzmmWcgSakZSOfPn9/p/s2bN+Prr7/ucR9//etf8eSTTwIAqqqqcOmll2Yj1G7Z7Xb84he/AJCKd/HixTl9fqK8YQFJvSCbJEiK1G0BGdqZQPPqEEqn2OE5lmtCDhb20SZ4jrGi6YMQWteGIdTO2xg9CtSQDj0u4DnOBi0u4P00kvtgiYiGkLyOgVy5cmWH4m7r1q3p6xs2bMAzzzzTYft58+b1+Tk++eQTXHfddTjttNNw7rnnYtKkSSgrK4Oqqti6dSuef/55vPPOOwAARVHw+OOPw2639+v1DMQNN9yABx54APv27UNLS0vOn58on/gjH/VEsaZ+61Sj3f+h+DZGYXQqKJ1qg39LtMtig4pL6VQbInUJBL6IdbuNHk/Nwuo5zgr3OAtaPw53aKEkIqLMy2sB+cQTT+DZZ5/t8r6FCxdi4cKFHW7rTwEJAJqmYcmSJViyZEm325SVleHJJ5/ERRdd1K/nGCir1Yo77rgDP/7xj/Py/ER50b52KQtI6kF7AdldC2Q73+YoPMdYYasxIbyLC8UXM+sIIyyVRtS94e9xu+DXcbjGW1FxkgOR+gS8m6I5ipCI+q1YuocWQ4x5MuhnYT3vvPPw5JNPYvXq1Vi/fj0aGxvR2toKIQRKS0tx3HHH4ZxzzsG8efPgcrnyGuv3v/99/OEPf8DevXvzGgdRrrAHK/WGYk39pRyqgEz6NWhxHUa3kouwKEtMpQqGzXYi2pBEZG/PPwSoIR27X2qDbJCgJ3iyR0SUC3ktIJ955plO3VT7at68eT22TFZWVuLaa6/FtddeO6Dn6a85c+b0elIcs9mMPXv2ZDkiogLSXkHyVz7qgcEqQ2gCerznvxOjR4Fs6n6sJBU4GSidbEPpFBsSfg0NSwO9e5wOFo9ExUQXKIquR1w6pluDvgWSiAof60fqiWKVocUOXRSWTrZBDesIfR3PQVSUSeYyA4ad5oSpVEHb+gi8n0Qg+DsAEVFBYgFJRPnDPqzUC4pVhtbDBDrtDA4Z8WaVhUeBkQyA62gLHGPNiNQm4f88mm4xlGSgZKoNpZNtSLRp2PuqL73GIxENTkLoEEXwRV0MMeYLC0giyhuJBST1gmKRetUCGa1PouR4G2SLBD3GZu1CIBmAERd4YK0yItaYROlUG0qOtyKwLQahppbqMHkUtH0SQdv6CMDzNSKigscCkojyj+f61AOho1erFvu/iKJksg0lx1nR+hHXAsw3g11G1ZkumEsV7HnVi3iTCsUmw3OsFc4jzJAkIOHVsGdZAIlWrr1BNGQIURzjCzm+plssIIkof9KT6OQ1CipwelyHUtbF4UoCLBUG2EaaYBtpgqXSAEmW4DzKgoRPQ3Abx0Lmmv0wE1xHW2AuN8DoUKCGNdS94Ue8KdUtVYvoaF0TRuuacJ4jJSKi/mIBSUT5s78PK+tH6oka0WFwyICcmpE1VTAaYRthgmKRocV1RGqTaF4ZQqxZhXuCBVWnuWCwheBdz3UBc0ICymfaUTLJhui+JILbYkj4NIR3JzhDKhHRIMMCkojyj+eX1IPw7gTKT3DgyB9UAACELhBrVuH7LIrI3gRiTWqHv6Gm5SGoIR3lJzhgsClo/jDEv7EsMpUoKJ/pgG2EEU0fBOH/PJbvkIiokIkiWcaDXVi7xQKSiPJG4jqQ1AuJtlQ3SKNThhYXiNQmDrkmZNsnEagRHZWnOKDYZDQuC0BwmF3mSUDVXBcUs4T6NwOI7E3kOyIiIsoyFpBElH+sH+kQ+lOYBL6IQYvoqJrrQvX5bux7K8DulANgHW5EyfFWmEoNUKwyhCYgyRJko4TmVSEWj0TUO7oOSEUw5TKX8egWC0giyh8u40FZFt6dQN3rPlSf60bNxR7ULfZDC/OkoK8kBag60wktJhDaHoca1lMz4wog1qQi1pDMd4hERJQjLCCJKO8EAMipxcYtlQYYnQqi+5Jo+zTC1kkasFijir0LfBhxvhsjL/Gg7g0/kj72Z+0L13grFKuM2oVtSAZYgBPRAHAMZNFjAUlE+bO/BdI2woTSKTaYShQk2jRoMR1l0+2ABCR8GkxuBUaPAoNdhmKRoYZ0hLbHENqZmuFRMgBGpwKhA0n/gcJAUgDnURZIcmq9uWg9W0mGqqRPSxWR56WKyPo3/Yg1qvkOq2i4x1sQ3B5n8UhERCwgiSh/RDL1697wuS7EmpLY84oXiVYNkgKMnVeOsml2AKllHJJ+DcmghkSbBlOJgso5Tgw7rXMf2NDOOBqWBGB0Kag60wVTiQJJTm0XqU+gdW2E3e2GKC2so3ahD8PPcWHEhR40vBtAeDfH7fWGwSEj8CULbiIaOKHrEEUwBlJwDGS3WEASUd5EapPY8y8vhCaQ8GnpHi1CA3b9bytkkwwtqnc58Ylik2GtMkA2yhC6QDKowehSUDnbicOvK4ckSYi3qdjzLy8SbRpso0wom27DyEs88H0eRfMHoRy/WioEekKg/g0/hp3uwvCzXWj6IITAF1x2oieSAVDMMrQIT6aIiIgFJBHlWbyl61YNLSqgRbsfp6ZFdIR2dGw9ijWoiDWpKD3ehsi+BELb4xD7dx/Zk0BkTwKu8RZUnuqAnhRo/ShcFMMwKLOEBjQsCaBilgPDZjthsMlo+ySS77AKlsEmA0j1BCAiGjCOgSx6LCCJaFBJ+jQ0vh/s9v7AFzHIRgnlJ9lh8ijY91Ygh9FRwRBA88oQ1LCO8hPsMNhkNK0MFcU5DSTAMswAo0uBbJQgVKSW1DCkumongxqidZnrpm1wKACQmnmViIiGPBaQRDTk+DZFocV0VJ3ugtElc2KQIcy7PgItoqFythOKTUbDkgBEAU/Q6jzCjPKT7DDYU0Wd0AQkRep0PbQzjoalgXQL/EBYq4zQ4nqHCaqIiGjoYgFJRENSpDYJoQtUnOJEw9IA9FgxND1RNgS2xaFGBYbPdWHEBR7Uv+WHHi+svwdLlQFl0+yw1ZgQ3B6Db2MAsRYV0AFIqRmHAUCogH2MCVVnuDD8LBfq3wwMrFVVBhxjzakZjAvrLSGiYqULQCqCLxR2Ye2WnO8AiIjyQYvoaFgSgH2kCY7DzPkOh/IssieButd9MHkU1FzsgcFeGIdH+2gTai72YOQlJVBsMuoW+9HwbhCxpv3FIwCIVOHY3toY3pXAvrf9sNWkJo7qLeP+5XJk8/7ZjWWgYpYDphIFbes5RpSIiFLYAklEQ1b7BD5qiF3zCIg1qam1Ii9wo+ZSD+rf8CPhzc/fhm2kEeUnOWAuNSC6L4n6N/19WnIkUptE2ycRlE6xwf9FDGqw+27ail1G9dkuWCqN6dt0TQCagGyS0fRBEPEmLuFBRBkiBA78AlbA2ALZLRaQRDRkaXEBLaqjYqYD3k1RBLbFiuKYRtmT9GvY+28fRpznRs0lHtS/FUBsX27XDS2dZkPZNDsitQnsXeFFrKF/xZt3YwSe46xwHmmB99OuWxAlA1B9tguKVUb9m35oCQGDTYbBJkNSgPDuRN6KaCIiKkyF0UeHiCgP9LhA7Ws+JAMaKk9xYMR57nyHRAVAi+iofc2HeLOKEee7YR9jytlzl01PFY8tH4VQt8jf7+IRSHVpjTepMJd1/1vxsDlOmEoMqH8r1cIZ25dEaHscvs1ReDdEWTwSUcYJXRTNZSD27NmDn/3sZxg/fjzsdjtKS0sxY8YMPPDAA4hEintYAAtIIhrSEl4NjctDUEM6bDWm9GQkNLTpCYH6xX6Ed8Ux/CwX3BMsWX9O5xFmlE61o2VNCN710Yzss31pj66UTrHBeYQFDcsCSLSyUCQiypQ33ngDxx57LB588EFs3boVkUgEXq8XH3/8MW677TZMmTIFO3bsyHeY/cYCkoiGPM8xFhhdCppXhQp6CQfKLaEDDUuC8H0WReWpTpRO6/2ENH1lqTSgco4TgW0xeDdkpng0OGRYq4wI74p3us86woiyGXa0rA0jvLP3YyuJiAZM6MVz6YeNGzfiyiuvhN/vh8PhwG9+8xt8+OGHWLp0Kb7//e8DALZt24bzzz8foVAok+9sznAMJBENeZJBQsKvwrc5MyfuNLi0fBiGFtFRfqIDBruMpg9CGR0ra6kywjnOhXiziqYVwYzt13OcFboqENrVuUAsP9GO6L5kt2MjiYiof26++WZEIhEYDAa88847OOmkk9L3nX766TjyyCPx85//HFu3bsWf/vQn3H333XmMtn/YAklEQ168WYXJbTiwfAHRN3g3RNGwLADXURaMvMQDo7t/fZ0lA+Aab0HlqQ5UzXWi5nI3Kk92INaoou4NX8ZawB1jTSiZZEPr2jBEsuM4HnO5AZYKI9pYPBIRZdTHH3+M999/HwDwve99r0Px2O6nP/0pxo8fDwD485//jGQytxO1ZQILSCIa8mLNqYlKeppshCj4ZRx7F/ogmySMuqIE7omWTmNmFbsMx+FmWEcYIRs7/iAhmyWMurwElac4YK4wQDbJSLRoaF4dwr43A+l1HAfKYJdROceJ4Ncx+DZ1blU3elJBRxuK76SFiIpfvifGyeYkOgsWLEhfv+aaa7rcRpZlXH311QAAr9ebLjiLCc+WiGjIM9j4Wxr1TrxJxZ5/eVF+kgOVpzhRMdOBeIsKLS6gWCVYKg6spagldDSvDCH4ZRyySUL1uW7IFhl7XvamZzeVZAnlY4zdPV2/lM90QCQFmlZ0M7Zm/0mRrAAaa0giooz54IMPAAB2ux1Tp07tdrvZs2enr69cuRJz587NemyZxAKSiIqaqUyBZ5IV5jID1LCO6L5kaixjL8eoGRwyhp/jQqwliVgjz6bp0IQKNH8Qgv/zKKzDjbAMM0KxyEj6NPg2RhGuTUBWJJSdYEfV6S6UTddgdCrQEzpqF/mzujSGwSnDebgZDcsC0BOdfz2XjRJKJtuQ8KvQYlwkm4jyQOgoikWX+zGJzhdffAEAOOKII2AwdF9mjRs3rtNjigkLyEFCRRLguQAAQBISknoCqkhCCL4p+ZatfBjcCtzjzHBPtEIN6Qjvi0K2ynBNNsJYBTQs6V2XQIPdCA0qfNvDUPUkBvufDD8fmaO2JhFpjQGfdX1/3dI42rYaYRthRMKrIVKXhBbpeEKS+XzISCQSiAbiUEXnH0QqTrBDWDTsecMPlVMOd8LPR2FhPgpLd/lQ0bcfX4vlnLX9dQUCgQ63m81mmM3mTtvHYjG0tLQAAGpqanrcd0lJCex2O8LhMPbu3ZuhiHOHBWSRM5lMqKqqwsqGxfkOpXDoAHbmOwhKy1Y+/AA+2n8ZiAYATw48nKLBz0du1e2/dCfT+Qig57/nD/ZfqGv8fBQW5qOw9JCPqqoqmEymHh9ejOesDocDI0eO7HDbPffcg/nz53faNhg8MIu2w+E45L7bC8hiXMqDBWSRs1gs2LlzJxIJruNFRERERLlnMplgsVh63KYYz1mFEJCkjhOiddX6CKRaINsdqpg+eD/RaPEtIcYCchCwWCyH/NASEREREeXTYD5nPfh19aZIjsfjAACr1Zq1mLKFUw8SERERERENgNPpTF/vTbfUcDgMoHfdXQsNC0giIiIiIqIBsFgsKC8vBwDU1tb2uK3X600XkN8cY1kMWEASEREREREN0Pjx4wEAX3/9NVS1+6ngt27d2ukxxYQFJBERERER0QCdfPLJAFLdUz/55JNut1u+fHn6+qxZs7IeV6axgCQiIiIiIhqgSy65JH396aef7nIbXdfx97//HQDg8Xhw2mmn5SK0jGIBSURERERENEAzZszAKaecAgB48sknsXr16k7bPPjgg/jiiy8AADfddBOMRmNOY8wEFpCUEXPmzIEkSX26vP/++x32EYvFsHDhQvz4xz/GCSecgNLSUhiNRpSVleGkk07C/Pnz0dDQ0Kt46uvrMW/ePFRUVMBms2H27NlYsmRJl9tOmDABkiRh2LBhh9zvkUcemY7/D3/4Q4/bLly4ML3tAw880Ku4MyUT+TjYW2+9hcsuuww1NTUwm82oqanBZZddhrfeeqtX8Qz1fHSlrq4O9957L6ZPn46KigpYLBaMHDkSJ598Mu666y589tln3T529+7duP322zF16lR4PB4YjUaUlpZi5syZuO+++9Dc3HzI52dOOhpIPtq9//77uPbaa3HkkUfC4XDA5XLhyCOPxGWXXYb/9//+X4+z8jEfHWUiH+0ikQjGjh2bfm1jxow55GOYjwP6kwsez7NnoJ8NHs+z7+GHH4bVaoWqqjjrrLPwu9/9DmvWrMF7772H66+/Hj//+c8BAEcddRR++tOf5jnafhJEGTB79mwBoNcXWZZFbW1t+vEbN24UTqfzkI9zuVzipZde6jGWuro6UVNT0+VzPvfcc522v/7669PbbN26tdv9NjQ0dNjfBRdc0GMcP/3pT9Pbrlmz5hDvYGYNNB/tdF0XP/jBD3p87A9+8AOh63q3sTAfnf3tb3875N/7TTfd1OVjn3/+eWGz2Xp8bFlZmVi6dGm3z8+cdDSQfAghRCAQEFdeeeUhP2fr16/v8vHMR0cDzcc3Hfy6AIjRo0f3uD3zcUB/csHjefYM5LPB43luvfbaa8LlcnX7Xh911FHiq6++yneY/cYCkjJix44dYvPmzT1eXnrppfQHZ+7cuR0e/8EHH6TvmzVrlvjd734n3n33XfHpp5+Kt99+W1x//fVCURQBQCiKIhYvXtxtLO0ncieddJJ4/fXXxcqVK8Utt9wiJEkSdrtdtLS0dNj++eefTz/33/72t273+/LLL6efH4AoKSnp8Yt2xowZAoCw2+0imUz28p3MjIHmo90dd9yR3mby5MnihRdeEGvXrhUvvPCCmDx5cvq+O++8s9tYmI+OHnroofTrGzVqlPjtb38rli1bJtatWycWLlwo/vjHP4pZs2aJW265pdNjP/zww/TrlWVZXHPNNWLBggVi7dq14l//+pe48MIL0/u22+1i586dXcbAnBwwkHwIIUQoFBKzZs1K7+Occ84Rzz77rFi9erVYtWqV+N///V9xyy23iJqamm4LSObjgIHm45s+/fRToSiKsFgs6RPvQxWQzEdKf3PB43l2DPSzweN57u3atUvccsst4qijjhI2m014PB4xbdo0cf/994twOJzv8AaEBSTlzM9//vP0F8k3f6latWqVuPLKK8Xnn3/e7eMXLFggJEkSAMThhx/e5ZdLLBYTZrNZjBw5UoRCoQ733XTTTQKAePbZZzvcvmfPnnRc3/3ud7t9/p/85CcCgLj00kuF2WwWAMSmTZu63DYcDguDwSAAiDPPPLPbfeZTT/kQQoivvvoq/RqmTZsmIpFIh/vD4bCYNm2aACAMBoP4+uuvO+2D+ehozZo1Qpbl9C+s33xPD5ZIJDrddsEFF6Tfm0cffbTLx916663pbX784x93up85OWCg+RBCiBtuuCFd0D/99NPdPl7X9S5PdJiPAzKRj4OpqiqmTp0qAIh7771XjB49+pAFJPORMpBc8HieeQP9bPB4TpnGMZCUE7qu4/nnnwcAOBwOXHbZZR3unzlzJl566SVMmDCh231cfPHF6cdt374d69ev77RNa2sr4vE4ZsyYAbvd3uG+M844A0Bq/MDBRo4cmR4T88EHH3T7/O33nXHGGZg6dWqP269evTq9/k/7YOpCcqh8AMBDDz2Ufg2PPPIIrFZrh/ttNhseeeQRAICqqvjzn//caR/MR0c33HADdF3H6NGj8eKLL3Z6Tw/W1aD6VatWAQDKysrwox/9qMvH3X333enrH374Yaf7mZMDBpqPDRs24C9/+QsA4NZbb8W8efO6fbwkSTAYDJ1uZz4OGGg+vunhhx/GJ598gqOPPhq/+MUvehUD85EykFzweJ55A/1s8HhOmcYCknJi6dKl6S+WK664AjabrV/7OXiq4+3bt3e6v6SkBAaDAevWrUMkEulwX/skMVVVVZ0e1/4ltGvXLtTW1na6PxAIYNOmTelt29f56e4L7uDbTz311J5eUl4cKh9CCCxcuBAAMG7cOJx44old7ufEE0/E0UcfDQBYsGABhBAd7mc+Dli9enX6JOm2227rdADujUQiAQA47LDDut3G7XajvLwcABCPxzvdz5ykZCIfjz32GIQQMJvNuP322/sVB/ORkol8HGz37t3pH1Mee+wxmEymXj2O+ch8LrrD43nvDDQfPJ5TVuS1/ZOGjO985zvpbgzLli3r934efPDB9H5eeeWVLrc5//zz02MvFi1aJFatWiVuu+02IcuysNlsoqGhodNj/va3v6X3+/zzz3e6/8033xQAhNvtFpqmiYULFwoAYsSIEV3GcPrppwsAwmQy9djVJF8OlY/t27en77/++ut73NfBg/J37NjR6X7mI+WWW25Jv6bGxsb07S0tLeLLL78UXq/3kPs4/vjjBZCaJKc7fr8//TyXXXZZl9swJwPPh67rorS0VAAQ5557bvr2ZDIpdu/eLXbt2iVisVivYmE+MvP5ONh5553Xqdtcb7qwCsF8ZDoX3eHxvHcGmg8ezykbWEBS1gWDQWG32wWQGvjd08DoQ7nooovSX0RbtmzpcpuvvvpKlJWVpbdrv0iSJJ544okuH7N169b0dj/84Q873X/nnXd2OFFsaWlJj9/45pdsMplMz5I5a9asfr/WbOlNPhYtWpR+Px566KEe9/enP/0pve0bb7zR6X7mI2XmzJkCgBg7dqzQdV385S9/EUcddVSH92T8+PHioYceEvF4vMt9PProo+ltH3vssS63+dnPfpbe5t133+1yG+Zk4Pn48ssv09vde++9orm5WfzgBz/oMEOi2WwWZ599tlixYkWPsTAfmfl8tHvhhRcEkJqI4+AT7t4WkEM9H5nMRU94PO+dgeaDx3PKBhaQlHXPPPNM+sujp9m9DmXDhg3pGbomTpzY47bbt28XV155pfB4PMJisYiTTjqpyy/Dgw0bNqzbfZ966qkCgPjtb3+bvm38+PEC6DxofM2aNenXe/vtt/fhFeZGb/Lx2GOPpbd5+eWXe9xf++xpAMRf/vKXLrdhPoTweDwCgDjttNPEVVdd1ekAfPDllFNOET6fr9M+ksmk+M///E8BpCZtue6668Rrr70mPv74Y/HKK6+ISy+9tNeftaGek4HmY8GCBen7f/WrX4nhw4d3+3hJksQf//jHHuNhPgb++RBCiLa2tvT78vjjj3e4r7cFpBBDOx+ZykVPeDzvvYHmg8dzygYWkJR17d0NAIht27b1ax+xWCw9QxgAsXDhwgxHKcTll1+ePtk7eCrqeDwuLBaLANChJeG6664TAMR1113XYT9//OMf03H2ND15vvQmH3/4wx/S27z55ps97m/x4sXpbR944IGMxTmY8qFpWvoX1fYZ5qqqqsRzzz0n2traRCQSEcuXLxcnnnhiOtYrrriiy33pui5eeOEFcdxxx3V5AnHaaaeJd955JyuvY7DkJBP5eOqpp9L3te/jnHPOEWvXrhWxWEw0NTWJxx57TLjd7vR2r7/+ekZfB/PR2fe+9z0BpJYZ+Gbvir4UkP0xGPKRyVx0h8fz3stEPng8p2xgAUlZtXfv3vTU0yeeeGK/99P+ZQJA/Nd//VfmAjzIww8/nH6OBQsWpG9fuXKlAFL97aPRaPr29pa8o48+usN+2rvlyLIs/H5/VmLtr97m4957702/Fz0tSC+EEEuXLk1ve99992Us1sGUj2Aw2KHIs9lsXS6qHIlEOhSGH330UadtvvjiC3HRRRelf73/5sVisYhvf/vbor6+PuOvY7DkJBP5eOSRRzrsY+7cuUJV1U77+OCDD9KfuYkTJw6oC/83MR8dPx/Lly8XkiQJg8EgNm7c2Onx2S4gB0M+Mvld1R0ez3svE/ng8ZyygbOwUlb94x//gK7rAID/+q//6tc+fve73+GJJ54AAEyfPh2PPvpoxuI72MHTQR88y1f79enTp8NisaRvb58pbNu2bWhqagIACCHSSy0cd9xxcLlcWYm1v3qbj4NfZ/vMn905eKbPnqYW76vBlI+D4wSA6667Lj3b3cGsVit+85vfpP//4osvdrj/gw8+wEknnYTXXnsNNTU1eO6559DQ0IBEIoG9e/fi0UcfhdVqxfPPP48ZM2bgiy++yOjrGCw5yUQ+vrmP+++/H4qidNrHySefnF6u4PPPP8fmzZsHFPvBmI8D+YjH4/jBD34AIQRuuukmHHvssdkLuBuDIR+Z+q7qDo/nfZPp7yoezylTWEBSVj333HMAALPZjP/4j//o8+Mff/xx3HHHHQBS008vXrw4a1OKH3fccXC73QC6/oJr/0Jrd/jhh6enrF65ciUAYMuWLWhtbQVQmNNL9zYfTqczfT0UCvW4z3A4nL7ucDgGGOEBgykfBoOhw8Hx7LPP7nbbM844I71e4Lp169K3x+NxXHXVVfD5fKiqqsKaNWvwne98B8OGDYPRaERNTQ1+9KMf4YMPPoDFYkFtbS2uvvrqjL6OwZKTTOTj4M9IRUUFJk+e3O0+Dt7/wfsYKObjwHv5m9/8Btu2bcPIkSMxf/78rMXak8GQj0zkojs8nvddpr+reDynTGEBSVmzbt06bNmyBQBwwQUXoKSkpE+Pf+GFF9KLpY8ePRrvvvtuen27bJBlGTNnzgQAfPrppwiHw9B1Pb0Y+ze/4ABg1qxZAA58CRby+kR9yUdNTU36elfrNh1s79696esjR44cYJQHDLZ8HPzeHPz+fpPFYkn/nbf/8goAb731Vnrtzh//+MddrrcFABMnTsR3vvMdAKmcb9y4ccCxtxtMORloPnr7+G9ue/A+Bor5OPBe3n///QCAM888E4sWLcKLL77Y6dJ+chwOh9O3LVu2LGOvYbDkY6C56AqP5/030HzweE7ZwAKSsubvf/97+npfu6++9tpruPrqq6HrOoYPH46lS5ce8iQtE9q/lFRVxerVq/HZZ5/B5/NBkqT0l9nB2r/0VqxY0eFfoGOXjULQl3xMmDAhfX3r1q09bnvw/ePHj+9ndF0bTPk4+D3VNK3Hbdvvb/81GUCH7qhTpkzp8fFTp05NXz9U/vpqsORkoPnoz+O/uY9MYD5S2rvmPf3007jqqqu6vLS0tAAAWlpa0rfde++9GX0dgyEfA83FN/F4PjCZ/K7i8ZwyhQUkZUUymUz3wa+oqMC5557b68cuXboUV155JVRVRVlZGd59910cfvjh2Qq1g2/202//xWvixIldtti1f+lt3LgRwWAw3dVi3LhxqKioyEHEvdPXfBx22GGorq4GACxfvrzHbdu/1EeMGIExY8YMPNiDDKZ8HPyL6Y4dO7rdLhAIpE90R4wYkb794BMCVVV7fK5kMtnl4zJhsORkoPnweDyYNGkSAGDXrl3pscVd2b59e/r6wfvIBOajsAyGfGQyFzyeD9xA88HjOWUDC0jKijfffBPNzc0AgP/zf/5Pr09iP/zwQ1x88cWIx+NwuVx4++23MXHixGyG2sH06dPTA8dXrFjRbf/8dpMnT4bdboemaXj++efT3T8KrXtFX/MhSRIuvvhiAKlfJNesWdPldmvWrEn/YnnxxRdDkqQMRj248nHppZem359///vf3W7373//G0IIAB0PuIcddlj6+sFdebpy8EnCwY/LhMGSk4HmA0B6cpxAINBjV8hXX301fT3Tv5wzHykiNat8j5fRo0cDSHWhbL/t/fffz+jrGAz5yMRnA+DxPFMGmg8ezykrcjfhKw0l7Wv+ABCffPJJrx6zfv369IK5drtdrFy5MstRdm327NkCgLBaraKqqkoAEM8//3y327evqzh27Nj0a/7HP/6Rw4gPrT/52LZtmzAYDAKAmDZtmohEIh3uj0Qi6bW8DAaD+PLLL7MR+qDKx5VXXpmegnzJkiWd7t+3b5+oqalJT2teW1ubvs/r9QqbzSYACKfTKTZt2tTlcyxevDi9bMSIESOEpmkZfx2DJScDyYcQQjQ1NQmn0ykAiEmTJnU5rfxzzz2Xfs3nn39+Vl4H89E72V7Go91gyMdAc8HjeWYNNB88nlOmsYCkjGtra0sveHvMMcf06jFff/21qKysTH9BPPTQQ2Lz5s09Xrxeb1biv+uuuzqsuwRA7N69O2Pb51p/8tHu9ttvT7+myZMnixdffFF8/PHH4sUXXxSTJ09O3/fLX/4yS9EPrnzs2rVLVFRUCCC1XuPtt98uVqxYIT7++GPx6KOPpk8AAIj777+/0+MPXs/L4XCIX/7yl2LZsmVi/fr14q233hI33HBD+iQBgHjuueey8joGS04Gmg8hhHj00UfT2xx99NHiqaeeEuvWrRPLli0TN954Y3q9TpfLlbWTMuajd3JVQA6GfAwkFzyeZ14mPhs8nlMmsYCkjHvsscfSH/Q//OEPvXrM008/3elL4lCXp59+Oivxv/POOx2eZ+TIkT1u//bbb3fYPtsnJ33Vn3y00zRNXHvttT3m4Xvf+15WWrnaDbZ8rF27VowYMaLb91OSJHHnnXd2+Vhd18XNN98sJEnqMSdGo1H88Y9/zNprGEw5GUg+2t13333pQrGrS0VFhVi1alXWXgPz0Tu5KiAHSz76mwsez7NjoJ8NHs8pk1hAUsbNnDlTABCKooi6urpePaaQDjihUKhDK85VV13V4/aBQKDDyeN3v/vdrMTVX/3Jxze98cYb4uKLLxbV1dXCZDKJ6upqcfHFF4vFixdnONrOBls+hEh1R73vvvvElClThNvtFmazWRx22GFi3rx5Yt26dYd8/Lp168QPf/hDccwxxwin0ykURRFut1tMnTpV3HrrrWLbtm1ZjX+w5WSg+RBCiI8//lhcc801YsyYMcJsNguXyyWmTp0qfvWrX2WtdaUd89E7uSogB1M++pMLHs+zJxOfDR7PKRMkIfaPuCUiIiIiIiLqAWdhJSIiIiIiol5hAUlERERERES9wgKSiIiIiIiIeoUFJBEREREREfUKC0giIiIiIiLqFRaQRERERERE1CssIImIiIiIiKhXWEASERERERFRr7CAJCIiIiIiol5hAUlERERERES9wgKSiIiIiIiIeoUFJBEREREREfUKC0giIiIiIiLqFRaQRERERERE1Cv/H5pkqQnAWrnOAAAAAElFTkSuQmCC", "text/plain": [ "
" ] @@ -1713,7 +1639,6 @@ "exp_ras.gdf.reset_index()\n", "exp_ras.check()\n", "exp_ras.plot_raster()\n", - "print(\"\\n Raster properties exposures:\", exp_ras.meta)\n", "\n", "# Initialize hazard object with haz_type = 'FL' (for Flood)\n", "hazard_type = \"FL\"\n", @@ -1732,7 +1657,6 @@ "haz_ras.intensity[haz_ras.intensity == -9999] = 0 # correct no data values\n", "haz_ras.check()\n", "haz_ras.plot_intensity(1)\n", - "print(\"Raster properties centroids:\", haz_ras.centroids.meta)\n", "\n", "# Set dummy impact function\n", "intensity = np.linspace(0, 10, 100)\n", @@ -1757,275 +1681,11 @@ ")\n", "imp_ras.plot_raster_eai_exposure();" ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Visualization\n", - " " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Making plots " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The expected annual impact per exposure can be visualized through different methods: `plot_hexbin_eai_exposure()`, `plot_scatter_eai_exposur()`, `plot_raster_eai_exposure()` and `plot_basemap_eai_exposure()` (similarly as with `Exposures`)." - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": { - "ExecuteTime": { - "end_time": "2020-10-16T09:22:39.209869Z", - "start_time": "2020-10-16T09:22:36.580905Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-01-26 11:59:40,355 - climada.util.coordinates - INFO - Setting geometry points.\n", - "2023-01-26 11:59:40,364 - climada.entity.exposures.base - INFO - Setting latitude and longitude attributes.\n", - "2023-01-26 11:59:43,294 - climada.entity.exposures.base - INFO - Setting latitude and longitude attributes.\n" - ] - }, - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "imp_pnt.plot_basemap_eai_exposure(buffer=5000);" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Making videos\n", - "\n", - "Given a fixed exposure and impact functions, a sequence of hazards can be visualized hitting the exposures." - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-01-26 11:59:44,414 - climada.util.finance - INFO - GDP CUB 2016: 9.137e+10.\n", - "2023-01-26 11:59:44,483 - climada.util.finance - INFO - Income group CUB 2016: 3.\n", - "2023-01-26 11:59:44,483 - climada_petals.entity.exposures.black_marble - INFO - Nightlights from NASA's earth observatory for year 2016.\n", - "2023-01-26 11:59:48,126 - climada_petals.entity.exposures.black_marble - INFO - Processing country Cuba.\n", - "2023-01-26 11:59:48,987 - climada_petals.entity.exposures.black_marble - INFO - Generating resolution of approx 2.5 km.\n", - "2023-01-26 11:59:49,129 - climada.entity.exposures.base - INFO - Hazard type not set in impf_\n", - "2023-01-26 11:59:49,137 - climada.entity.exposures.base - INFO - category_id not set.\n", - "2023-01-26 11:59:49,137 - climada.entity.exposures.base - INFO - cover not set.\n", - "2023-01-26 11:59:49,137 - climada.entity.exposures.base - INFO - deductible not set.\n", - "2023-01-26 11:59:49,137 - climada.entity.exposures.base - INFO - geometry not set.\n", - "2023-01-26 11:59:49,137 - climada.entity.exposures.base - INFO - centr_ not set.\n", - "2023-01-26 11:59:49,159 - climada.entity.exposures.base - INFO - Hazard type not set in impf_\n", - "2023-01-26 11:59:49,159 - climada.entity.exposures.base - INFO - category_id not set.\n", - "2023-01-26 11:59:49,159 - climada.entity.exposures.base - INFO - cover not set.\n", - "2023-01-26 11:59:49,167 - climada.entity.exposures.base - INFO - deductible not set.\n", - "2023-01-26 11:59:49,167 - climada.entity.exposures.base - INFO - geometry not set.\n", - "2023-01-26 11:59:49,167 - climada.entity.exposures.base - INFO - centr_ not set.\n", - "2023-01-26 11:59:49,167 - climada.entity.exposures.base - INFO - Adding sea at 5 km resolution and 100 km distance from coast.\n", - "2023-01-26 11:59:50,567 - climada.hazard.tc_tracks - INFO - Progress: 100%\n", - "2023-01-26 11:59:50,589 - climada.hazard.centroids.centr - INFO - Convert centroids to GeoSeries of Point shapes.\n", - "2023-01-26 12:00:02,901 - climada.util.coordinates - INFO - dist_to_coast: UTM 32616 (1/3)\n", - "2023-01-26 12:00:09,800 - climada.util.coordinates - INFO - dist_to_coast: UTM 32617 (2/3)\n", - "2023-01-26 12:00:32,928 - climada.util.coordinates - INFO - dist_to_coast: UTM 32618 (3/3)\n", - "2023-01-26 12:00:42,661 - climada.hazard.trop_cyclone - INFO - Mapping 1 tracks to 23083 coastal centroids.\n", - "2023-01-26 12:00:42,684 - climada.hazard.trop_cyclone - INFO - Progress: 100%\n", - "2023-01-26 12:00:42,702 - climada.hazard.trop_cyclone - INFO - Mapping 1 tracks to 26071 coastal centroids.\n", - "2023-01-26 12:00:42,733 - climada.hazard.trop_cyclone - INFO - Progress: 100%\n", - "2023-01-26 12:00:42,750 - climada.hazard.trop_cyclone - INFO - Mapping 1 tracks to 29579 coastal centroids.\n", - "2023-01-26 12:00:42,781 - climada.hazard.trop_cyclone - INFO - Progress: 100%\n", - "2023-01-26 12:00:42,818 - climada.hazard.trop_cyclone - INFO - Mapping 1 tracks to 32525 coastal centroids.\n", - "2023-01-26 12:00:42,849 - climada.hazard.trop_cyclone - INFO - Progress: 100%\n", - "2023-01-26 12:00:42,865 - climada.hazard.trop_cyclone - INFO - Mapping 1 tracks to 35241 coastal centroids.\n", - "2023-01-26 12:00:42,902 - climada.hazard.trop_cyclone - INFO - Progress: 100%\n", - "2023-01-26 12:00:42,934 - climada.hazard.trop_cyclone - INFO - Mapping 1 tracks to 37535 coastal centroids.\n", - "2023-01-26 12:00:42,981 - climada.hazard.trop_cyclone - INFO - Progress: 100%\n", - "2023-01-26 12:00:43,002 - climada.hazard.trop_cyclone - INFO - Mapping 1 tracks to 39661 coastal centroids.\n", - "2023-01-26 12:00:43,034 - climada.hazard.trop_cyclone - INFO - Progress: 100%\n", - "2023-01-26 12:00:43,065 - climada.hazard.trop_cyclone - INFO - Mapping 1 tracks to 42459 coastal centroids.\n", - "2023-01-26 12:00:43,103 - climada.hazard.trop_cyclone - INFO - Progress: 100%\n", - "2023-01-26 12:00:43,118 - climada.hazard.trop_cyclone - INFO - Mapping 1 tracks to 43981 coastal centroids.\n", - "2023-01-26 12:00:43,181 - climada.hazard.trop_cyclone - INFO - Progress: 100%\n", - "2023-01-26 12:00:43,203 - climada.hazard.trop_cyclone - INFO - Mapping 1 tracks to 45805 coastal centroids.\n", - "2023-01-26 12:00:43,250 - climada.hazard.trop_cyclone - INFO - Progress: 100%\n", - "2023-01-26 12:00:43,266 - climada.hazard.trop_cyclone - INFO - Mapping 1 tracks to 47236 coastal centroids.\n", - "2023-01-26 12:00:43,319 - climada.hazard.trop_cyclone - INFO - Progress: 100%\n", - "2023-01-26 12:00:43,334 - climada.hazard.trop_cyclone - INFO - Mapping 1 tracks to 48530 coastal centroids.\n", - "2023-01-26 12:00:43,381 - climada.hazard.trop_cyclone - INFO - Progress: 100%\n", - "2023-01-26 12:00:43,404 - climada.hazard.trop_cyclone - INFO - Mapping 1 tracks to 48252 coastal centroids.\n", - "2023-01-26 12:00:43,435 - climada.hazard.trop_cyclone - INFO - Progress: 100%\n", - "2023-01-26 12:00:43,466 - climada.hazard.trop_cyclone - INFO - Mapping 1 tracks to 47331 coastal centroids.\n", - "2023-01-26 12:00:43,504 - climada.hazard.trop_cyclone - INFO - Progress: 100%\n", - "2023-01-26 12:00:43,535 - climada.hazard.trop_cyclone - INFO - Mapping 1 tracks to 45981 coastal centroids.\n", - "2023-01-26 12:00:43,582 - climada.hazard.trop_cyclone - INFO - Progress: 100%\n", - "2023-01-26 12:00:43,604 - climada.hazard.trop_cyclone - INFO - Mapping 1 tracks to 44618 coastal centroids.\n", - "2023-01-26 12:00:43,635 - climada.hazard.trop_cyclone - INFO - Progress: 100%\n", - "2023-01-26 12:00:43,667 - climada.hazard.trop_cyclone - INFO - Mapping 1 tracks to 43393 coastal centroids.\n", - "2023-01-26 12:00:43,704 - climada.hazard.trop_cyclone - INFO - Progress: 100%\n", - "2023-01-26 12:00:43,720 - climada.hazard.trop_cyclone - INFO - Mapping 1 tracks to 42463 coastal centroids.\n", - "2023-01-26 12:00:43,767 - climada.hazard.trop_cyclone - INFO - Progress: 100%\n", - "2023-01-26 12:00:43,782 - climada.hazard.trop_cyclone - INFO - Mapping 1 tracks to 41702 coastal centroids.\n", - "2023-01-26 12:00:43,820 - climada.hazard.trop_cyclone - INFO - Progress: 100%\n", - "2023-01-26 12:00:43,836 - climada.hazard.trop_cyclone - INFO - Mapping 1 tracks to 41057 coastal centroids.\n", - "2023-01-26 12:00:43,869 - climada.hazard.trop_cyclone - INFO - Progress: 100%\n", - "2023-01-26 12:00:43,900 - climada.hazard.trop_cyclone - INFO - Mapping 1 tracks to 40802 coastal centroids.\n", - "2023-01-26 12:00:43,936 - climada.hazard.trop_cyclone - INFO - Progress: 100%\n", - "2023-01-26 12:00:43,951 - climada.entity.exposures.base - INFO - Matching 21923 exposures with 49817 centroids.\n", - "2023-01-26 12:00:43,951 - climada.util.coordinates - INFO - No exact centroid match found. Reprojecting coordinates to nearest neighbor closer than the threshold = 100\n", - "2023-01-26 12:00:44,005 - climada.engine.impact - WARNING - The use of Impact().calc() is deprecated. Use ImpactCalc().impact() instead.\n", - "2023-01-26 12:00:44,005 - climada.entity.exposures.base - INFO - No specific impact function column found for hazard TC. Using the anonymous 'impf_' column.\n", - "2023-01-26 12:00:44,021 - climada.engine.impact_calc - INFO - Calculating impact for 43962 assets (>0) and 1 events.\n", - "2023-01-26 12:00:44,021 - climada.engine.impact - WARNING - The use of Impact().calc() is deprecated. Use ImpactCalc().impact() instead.\n", - "2023-01-26 12:00:44,021 - climada.entity.exposures.base - INFO - No specific impact function column found for hazard TC. Using the anonymous 'impf_' column.\n", - "2023-01-26 12:00:44,036 - climada.engine.impact_calc - INFO - Calculating impact for 43962 assets (>0) and 1 events.\n", - "2023-01-26 12:00:44,052 - climada.engine.impact - WARNING - The use of Impact().calc() is deprecated. Use ImpactCalc().impact() instead.\n", - "2023-01-26 12:00:44,052 - climada.entity.exposures.base - INFO - No specific impact function column found for hazard TC. Using the anonymous 'impf_' column.\n", - "2023-01-26 12:00:44,052 - climada.engine.impact_calc - INFO - Calculating impact for 43962 assets (>0) and 1 events.\n", - "2023-01-26 12:00:44,067 - climada.engine.impact - WARNING - The use of Impact().calc() is deprecated. Use ImpactCalc().impact() instead.\n", - "2023-01-26 12:00:44,067 - climada.entity.exposures.base - INFO - No specific impact function column found for hazard TC. Using the anonymous 'impf_' column.\n", - "2023-01-26 12:00:44,067 - climada.engine.impact_calc - INFO - Calculating impact for 43962 assets (>0) and 1 events.\n", - "2023-01-26 12:00:44,084 - climada.engine.impact - WARNING - The use of Impact().calc() is deprecated. Use ImpactCalc().impact() instead.\n", - "2023-01-26 12:00:44,084 - climada.entity.exposures.base - INFO - No specific impact function column found for hazard TC. Using the anonymous 'impf_' column.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-01-26 12:00:44,084 - climada.engine.impact_calc - INFO - Calculating impact for 43962 assets (>0) and 1 events.\n", - "2023-01-26 12:00:44,105 - climada.engine.impact - WARNING - The use of Impact().calc() is deprecated. Use ImpactCalc().impact() instead.\n", - "2023-01-26 12:00:44,105 - climada.entity.exposures.base - INFO - No specific impact function column found for hazard TC. Using the anonymous 'impf_' column.\n", - "2023-01-26 12:00:44,105 - climada.engine.impact_calc - INFO - Calculating impact for 43962 assets (>0) and 1 events.\n", - "2023-01-26 12:00:44,121 - climada.engine.impact - WARNING - The use of Impact().calc() is deprecated. Use ImpactCalc().impact() instead.\n", - "2023-01-26 12:00:44,121 - climada.entity.exposures.base - INFO - No specific impact function column found for hazard TC. Using the anonymous 'impf_' column.\n", - "2023-01-26 12:00:44,136 - climada.engine.impact_calc - INFO - Calculating impact for 43962 assets (>0) and 1 events.\n", - "2023-01-26 12:00:44,136 - climada.engine.impact - WARNING - The use of Impact().calc() is deprecated. Use ImpactCalc().impact() instead.\n", - "2023-01-26 12:00:44,152 - climada.entity.exposures.base - INFO - No specific impact function column found for hazard TC. Using the anonymous 'impf_' column.\n", - "2023-01-26 12:00:44,152 - climada.engine.impact_calc - INFO - Calculating impact for 43962 assets (>0) and 1 events.\n", - "2023-01-26 12:00:44,167 - climada.engine.impact - WARNING - The use of Impact().calc() is deprecated. Use ImpactCalc().impact() instead.\n", - "2023-01-26 12:00:44,167 - climada.entity.exposures.base - INFO - No specific impact function column found for hazard TC. Using the anonymous 'impf_' column.\n", - "2023-01-26 12:00:44,167 - climada.engine.impact_calc - INFO - Calculating impact for 43962 assets (>0) and 1 events.\n", - "2023-01-26 12:00:44,183 - climada.engine.impact - WARNING - The use of Impact().calc() is deprecated. Use ImpactCalc().impact() instead.\n", - "2023-01-26 12:00:44,183 - climada.entity.exposures.base - INFO - No specific impact function column found for hazard TC. Using the anonymous 'impf_' column.\n", - "2023-01-26 12:00:44,199 - climada.engine.impact_calc - INFO - Calculating impact for 43962 assets (>0) and 1 events.\n", - "2023-01-26 12:00:44,205 - climada.engine.impact - WARNING - The use of Impact().calc() is deprecated. Use ImpactCalc().impact() instead.\n", - "2023-01-26 12:00:44,205 - climada.entity.exposures.base - INFO - No specific impact function column found for hazard TC. Using the anonymous 'impf_' column.\n", - "2023-01-26 12:00:44,205 - climada.engine.impact_calc - INFO - Calculating impact for 43962 assets (>0) and 1 events.\n", - "2023-01-26 12:00:44,221 - climada.engine.impact - WARNING - The use of Impact().calc() is deprecated. Use ImpactCalc().impact() instead.\n", - "2023-01-26 12:00:44,221 - climada.entity.exposures.base - INFO - No specific impact function column found for hazard TC. Using the anonymous 'impf_' column.\n", - "2023-01-26 12:00:44,221 - climada.engine.impact_calc - INFO - Calculating impact for 43962 assets (>0) and 1 events.\n", - "2023-01-26 12:00:44,237 - climada.engine.impact - WARNING - The use of Impact().calc() is deprecated. Use ImpactCalc().impact() instead.\n", - "2023-01-26 12:00:44,237 - climada.entity.exposures.base - INFO - No specific impact function column found for hazard TC. Using the anonymous 'impf_' column.\n", - "2023-01-26 12:00:44,237 - climada.engine.impact_calc - INFO - Calculating impact for 43962 assets (>0) and 1 events.\n", - "2023-01-26 12:00:44,252 - climada.engine.impact - WARNING - The use of Impact().calc() is deprecated. Use ImpactCalc().impact() instead.\n", - "2023-01-26 12:00:44,252 - climada.entity.exposures.base - INFO - No specific impact function column found for hazard TC. Using the anonymous 'impf_' column.\n", - "2023-01-26 12:00:44,252 - climada.engine.impact_calc - INFO - Calculating impact for 43962 assets (>0) and 1 events.\n", - "2023-01-26 12:00:44,268 - climada.engine.impact - WARNING - The use of Impact().calc() is deprecated. Use ImpactCalc().impact() instead.\n", - "2023-01-26 12:00:44,268 - climada.entity.exposures.base - INFO - No specific impact function column found for hazard TC. Using the anonymous 'impf_' column.\n", - "2023-01-26 12:00:44,268 - climada.engine.impact_calc - INFO - Calculating impact for 43962 assets (>0) and 1 events.\n", - "2023-01-26 12:00:44,283 - climada.engine.impact - WARNING - The use of Impact().calc() is deprecated. Use ImpactCalc().impact() instead.\n", - "2023-01-26 12:00:44,283 - climada.entity.exposures.base - INFO - No specific impact function column found for hazard TC. Using the anonymous 'impf_' column.\n", - "2023-01-26 12:00:44,283 - climada.engine.impact_calc - INFO - Calculating impact for 43962 assets (>0) and 1 events.\n", - "2023-01-26 12:00:44,299 - climada.engine.impact - WARNING - The use of Impact().calc() is deprecated. Use ImpactCalc().impact() instead.\n", - "2023-01-26 12:00:44,306 - climada.entity.exposures.base - INFO - No specific impact function column found for hazard TC. Using the anonymous 'impf_' column.\n", - "2023-01-26 12:00:44,306 - climada.engine.impact_calc - INFO - Calculating impact for 43962 assets (>0) and 1 events.\n", - "2023-01-26 12:00:44,321 - climada.engine.impact - WARNING - The use of Impact().calc() is deprecated. Use ImpactCalc().impact() instead.\n", - "2023-01-26 12:00:44,321 - climada.entity.exposures.base - INFO - No specific impact function column found for hazard TC. Using the anonymous 'impf_' column.\n", - "2023-01-26 12:00:44,321 - climada.engine.impact_calc - INFO - Calculating impact for 43962 assets (>0) and 1 events.\n", - "2023-01-26 12:00:44,337 - climada.engine.impact - WARNING - The use of Impact().calc() is deprecated. Use ImpactCalc().impact() instead.\n", - "2023-01-26 12:00:44,337 - climada.entity.exposures.base - INFO - No specific impact function column found for hazard TC. Using the anonymous 'impf_' column.\n", - "2023-01-26 12:00:44,337 - climada.engine.impact_calc - INFO - Calculating impact for 43962 assets (>0) and 1 events.\n", - "2023-01-26 12:00:44,352 - climada.engine.impact - WARNING - The use of Impact().calc() is deprecated. Use ImpactCalc().impact() instead.\n", - "2023-01-26 12:00:44,352 - climada.entity.exposures.base - INFO - No specific impact function column found for hazard TC. Using the anonymous 'impf_' column.\n", - "2023-01-26 12:00:44,352 - climada.engine.impact_calc - INFO - Calculating impact for 43962 assets (>0) and 1 events.\n", - "2023-01-26 12:00:44,368 - climada.engine.impact - WARNING - The use of Impact().calc() is deprecated. Use ImpactCalc().impact() instead.\n", - "2023-01-26 12:00:44,368 - climada.entity.exposures.base - INFO - No specific impact function column found for hazard TC. Using the anonymous 'impf_' column.\n", - "2023-01-26 12:00:44,368 - climada.engine.impact_calc - INFO - Calculating impact for 43962 assets (>0) and 1 events.\n", - "2023-01-26 12:00:44,384 - climada.engine.impact - INFO - Generating video ./results/irma_imp_fl.gif\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "22it [09:44, 26.57s/it] \n" - ] - }, - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# exposure\n", - "from climada.entity import add_sea\n", - "from climada_petals.entity import BlackMarble\n", - "\n", - "exp_video = BlackMarble()\n", - "exp_video.set_countries([\"Cuba\"], 2016, res_km=2.5)\n", - "exp_video.check()\n", - "\n", - "# impact function\n", - "impf_def = ImpfTropCyclone.from_emanuel_usa()\n", - "impfs_video = ImpactFuncSet([impf_def])\n", - "impfs_video.check()\n", - "\n", - "# compute sequence of hazards using TropCyclone video_intensity method\n", - "exp_sea = add_sea(exp_video, (100, 5))\n", - "centr_video = Centroids.from_lat_lon(exp_sea.latitude, exp_sea.longitude)\n", - "centr_video.check()\n", - "\n", - "track_name = \"2017242N16333\"\n", - "tr_irma = TCTracks.from_ibtracs_netcdf(provider=\"usa\", storm_id=track_name) # IRMA 2017\n", - "\n", - "tc_video = TropCyclone()\n", - "tc_list, _ = tc_video.video_intensity(\n", - " track_name, tr_irma, centr_video\n", - ") # empty file name to not to write the video\n", - "\n", - "# generate video of impacts\n", - "file_name = \"./results/irma_imp_fl.gif\"\n", - "imp_video = Impact()\n", - "imp_list = imp_video.video_direct_impact(exp_video, impfs_video, tc_list, file_name)" - ] } ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "climada_env", "language": "python", "name": "python3" }, @@ -2039,7 +1699,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.6" + "version": "3.11.11" }, "latex_envs": { "LaTeX_envs_menu_present": true, @@ -2071,11 +1731,6 @@ "toc_position": {}, "toc_section_display": true, "toc_window_display": false - }, - "vscode": { - "interpreter": { - "hash": "4aebf7f26d9a9d4c9696d8ddcd034589cd11abb7fe515057c687f2f3cec840ea" - } } }, "nbformat": 4, diff --git a/doc/user-guide/climada_engine_impact_data.ipynb b/doc/user-guide/climada_engine_impact_data.ipynb index 6f6972f3b3..07a22d3d0d 100644 --- a/doc/user-guide/climada_engine_impact_data.ipynb +++ b/doc/user-guide/climada_engine_impact_data.ipynb @@ -35,7 +35,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -70,7 +70,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 2, "metadata": {}, "outputs": [ { @@ -241,13 +241,14 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ + "2025-11-26 10:27:26,085 - climada.engine.impact - WARNING - The Impact.tot_value attribute is deprecated.Use Exposures.affected_total_value to calculate the affected total exposure value based on a specific hazard intensity threshold\n", "Number of TC events in EM-DAT in the Philipppines, 2013: 8\n", "\n", "People affected by TC events in the Philippines in 2013 (per event):\n", @@ -255,29 +256,18 @@ " 3.596000e+03 3.957300e+05 2.628840e+05]\n", "\n", "People affected by TC events in the Philippines in 2013 (total):\n", - "17944571\n" + "17944571\n", + "2025-11-26 10:27:26,187 - climada.engine.impact - WARNING - The Impact.tot_value attribute is deprecated.Use Exposures.affected_total_value to calculate the affected total exposure value based on a specific hazard intensity threshold\n" ] }, { "data": { + "image/png": "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", "text/plain": [ - "Text(0, 0.5, 'People Affected')" + "
" ] }, - "execution_count": 31, "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, "output_type": "display_data" } ], @@ -310,9 +300,9 @@ "ax = plt.scatter(impact_emdat_PHL_USD.at_event, impact_emdat_PHL.at_event)\n", "plt.title(\"Typhoon impacts in the Philippines, 2013\")\n", "plt.xlabel(\"Total Damage [USD]\")\n", - "plt.ylabel(\"People Affected\");\n", - "# plt.xscale('log')\n", - "# plt.yscale('log')" + "plt.ylabel(\"People Affected\")\n", + "plt.xscale(\"log\")\n", + "plt.yscale(\"log\")" ] }, { @@ -341,7 +331,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -364,7 +354,7 @@ }, { "data": { - "image/png": "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\n", + "image/png": "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", "text/plain": [ "
" ] diff --git a/doc/user-guide/climada_engine_unsequa.ipynb b/doc/user-guide/climada_engine_unsequa.ipynb index d1f60722fe..8c13638c9c 100644 --- a/doc/user-guide/climada_engine_unsequa.ipynb +++ b/doc/user-guide/climada_engine_unsequa.ipynb @@ -12,7 +12,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "This is a tutorial for the unsequa module in CLIMADA. A detailled description can be found in [Kropf (2021)](https://eartharxiv.org/repository/view/3123/)." + "This is a tutorial for the unsequa module in CLIMADA. A detailled description can be found in [Kropf et al. (2022)](https://doi.org/10.5194/gmd-15-7177-2022)." ] }, { @@ -31,7 +31,7 @@ "\n", "In this module, it is possible to perform global uncertainty analysis, as well as a sensitivity analysis. The word global is meant as opposition to the 'one-factor-at-a-time' (OAT) strategy. The OAT strategy, which consists in analyzing the effect of varying one model input factor at a time while keeping all other fixed, is popular among modellers, but has major shortcomings [Saltelli (2010)](https://www.sciencedirect.com/science/article/abs/pii/S1364815210001180), [Saltelli(2019)](http://www.sciencedirect.com/science/article/pii/S1364815218302822) and should not be used.\n", "\n", - "A rough schemata of how to perform uncertainty and sensitivity analysis (taken from [Kropf(2021)](https://eartharxiv.org/repository/view/3123/))" + "A rough schemata of how to perform uncertainty and sensitivity analysis (taken from [Kropf et al. (2022)](https://doi.org/10.5194/gmd-15-7177-2022)." ] }, { @@ -50,7 +50,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "1. [Kropf, C.M. et al. Uncertainty and sensitivity analysis for global probabilistic weather and climate risk modelling: an implementation in the CLIMADA platform (2021)](https://eartharxiv.org/repository/view/3123/)\n", + "1. [Kropf, C.M. et al. Uncertainty and sensitivity analysis for probabilistic weather and climate-risk modelling: an implementation in CLIMADA v.3.1.0. Geoscientific Model Development, 15, 7177–7201 (2022)](https://doi.org/10.5194/gmd-15-7177-2022).\n", "2. [Pianosi, F. et al. Sensitivity analysis of environmental models: A systematic review with practical workflow. Environmental Modelling & Software 79, 214–232 (2016)](https://www.sciencedirect.com/science/article/pii/S1364815216300287).\n", "3.[Douglas-Smith, D., Iwanaga, T., Croke, B. F. W. & Jakeman, A. J. Certain trends in uncertainty and sensitivity analysis: An overview of software tools and techniques. Environmental Modelling & Software 124, 104588 (2020)](https://doi.org/10.1007/978-1-4899-7547-8_5)\n", "4. [Knüsel, B. Epistemological Issues in Data-Driven Modeling in Climate Research. (ETH Zurich, 2020)](https://www.research-collection.ethz.ch/handle/20.500.11850/399735)\n", @@ -542,12 +542,12 @@ "source": [ "| Attribute | Type | Description |\n", "| --- | --- | --- |\n", - "| sampling_method | str | The sampling method as defined in [SALib](https://salib.readthedocs.io/en/latest/api.html). Possible choices: 'saltelli', 'fast_sampler', 'latin', 'morris', 'dgsm', 'ff'|\n", + "| sampling_method | str | The sampling method as defined in [SALib](https://salib.readthedocs.io/en/latest/api.html). Possible choices: 'saltelli', 'fast_sampler', 'latin', 'morris', 'dgsm', 'ff', 'finite_diff'|\n", "| sampling_kwargs | dict | Keyword arguments for the sampling_method. |\n", "| n_samples | int | Effective number of samples (number of rows of samples_df)|\n", "| param_labels | list(str) | Name of all the uncertainty input parameters|\n", "| problem_sa | dict | The description of the uncertainty variables and their distribution as used in [SALib](https://salib.readthedocs.io/en/latest/basics.html). |\n", - "| sensitivity_method | str | Sensitivity analysis method from [SALib.analyse](https://salib.readthedocs.io/en/latest/api.html) Possible choices: 'fast', 'rbd_fact', 'morris', 'sobol', 'delta', 'ff'. Note that in Salib, sampling methods and sensitivity analysis methods should be used in specific pairs.|\n", + "| sensitivity_method | str | Sensitivity analysis method from [SALib.analyse](https://salib.readthedocs.io/en/latest/api.html) Possible choices: 'sobol', 'fast', 'rbd_fast', 'morris', 'dgsm', 'ff', 'pawn', 'rhdm', 'rsa', 'discrepancy', 'hdmr'. Note that in Salib, sampling methods and sensitivity analysis methods should be used in specific pairs.|\n", "| sensitivity_kwargs | dict | Keyword arguments for sensitivity_method. |\n", "| unit | str | Unit of the exposures value |" ] @@ -2466,7 +2466,7 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -2475,10 +2475,10 @@ "haz.basin = [\"NA\"] * haz.size\n", "\n", "# apply climate change factors\n", - "haz_26 = haz.apply_climate_scenario_knu(ref_year=2050, rcp_scenario=26)\n", - "haz_45 = haz.apply_climate_scenario_knu(ref_year=2050, rcp_scenario=45)\n", - "haz_60 = haz.apply_climate_scenario_knu(ref_year=2050, rcp_scenario=60)\n", - "haz_85 = haz.apply_climate_scenario_knu(ref_year=2050, rcp_scenario=85)\n", + "haz_26 = haz.apply_climate_scenario_knu(target_year=2050, scenario=\"2.6\")\n", + "haz_45 = haz.apply_climate_scenario_knu(target_year=2050, scenario=\"4.5\")\n", + "haz_60 = haz.apply_climate_scenario_knu(target_year=2050, scenario=\"6.0\")\n", + "haz_85 = haz.apply_climate_scenario_knu(target_year=2050, scenario=\"8.5\")\n", "\n", "# pack future hazard sets into dictionary - we want to sample from this dictionary later\n", "haz_fut_list = [haz_26, haz_45, haz_60, haz_85]\n", @@ -2489,7 +2489,7 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -2501,7 +2501,7 @@ "\n", "def exp_base_func(x_exp, exp_base):\n", " exp = exp_base.copy()\n", - " exp.gdf[\"value\"] *= x_exp\n", + " exp.data[\"value\"] *= x_exp\n", " return exp\n", "\n", "\n", @@ -2821,7 +2821,7 @@ }, { "cell_type": "code", - "execution_count": 61, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2023-08-03T12:00:12.180767Z", @@ -2844,7 +2844,7 @@ "\n", " entity = Entity.from_excel(ENT_DEMO_TODAY)\n", " entity.exposures.ref_year = 2018\n", - " entity.exposures.gdf[\"value\"] *= x_ent\n", + " entity.exposures.data[\"value\"] *= x_ent\n", " return entity\n", "\n", "\n", @@ -2954,7 +2954,7 @@ }, { "cell_type": "code", - "execution_count": 64, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2023-08-03T12:00:12.959984Z", @@ -3070,7 +3070,7 @@ ], "source": [ "ent_avg = ent_today_iv.evaluate()\n", - "ent_avg.exposures.gdf.head()" + "ent_avg.exposures.data.head()" ] }, { @@ -5320,7 +5320,7 @@ }, { "cell_type": "code", - "execution_count": 77, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -5335,7 +5335,7 @@ "\n", "def exp_func(cnt, x_exp, exp_list=exp_list):\n", " exp = exp_list[int(cnt)].copy()\n", - " exp.gdf[\"value\"] *= x_exp\n", + " exp.data[\"value\"] *= x_exp\n", " return exp\n", "\n", "\n", @@ -5523,7 +5523,7 @@ "source": [ "Loading Hazards or Exposures from file is a rather lengthy operation. Thus, we want to minimize the reading operations, ideally reading each file only once. Simultaneously, Hazard and Exposures can be large in memory, and thus we would like to have at most one of each loaded at a time. Thus, we do not want to use the list capacity from the helper method InputVar.exposures and InputVar.hazard.\n", "\n", - "For demonstration purposes, we will use below as exposures files the litpop for three countries, and for tha hazard files the winter storms for the same three countries. Note that this does not make a lot of sense for an uncertainty analysis. For your use case, please replace the set of exposures and/or hazard files with meaningful sets, for instance sets of exposures for different resolutions or hazards for different model runs.\n" + "For demonstration purposes, we will use below as exposures files the litpop for three countries, and for the hazard files the winter storms for the same three countries. Note that this does not make a lot of sense for an uncertainty analysis. For your use case, please replace the set of exposures and/or hazard files with meaningful sets, for instance sets of exposures for different resolutions or hazards for different model runs.\n" ] }, { @@ -5600,17 +5600,18 @@ "def exp_func(f_exp, x_exp, filename_list=f_exp_list):\n", " filename = filename_list[int(f_exp)]\n", " global exp_base\n", - " if \"exp_base\" in globals():\n", - " if isinstance(exp_base, Exposures):\n", - " if exp_base.gdf[\"filename\"] != str(filename):\n", - " exp_base = Exposures.from_hdf5(filename)\n", - " exp_base.gdf[\"filename\"] = str(filename)\n", + " if (\n", + " \"exp_base\" in globals()\n", + " and isinstance(exp_base, Exposures)\n", + " and exp_base.description == str(filename)\n", + " ):\n", + " pass # if correct file is already loaded in memory, we do not need to reload it\n", " else:\n", " exp_base = Exposures.from_hdf5(filename)\n", - " exp_base.gdf[\"filename\"] = str(filename)\n", + " exp_base.description = str(filename)\n", "\n", " exp = exp_base.copy()\n", - " exp.gdf[\"value\"] *= x_exp\n", + " exp.data[\"value\"] *= x_exp\n", " return exp\n", "\n", "\n", @@ -5624,14 +5625,16 @@ "def haz_func(f_haz, i_haz, filename_list=f_haz_list):\n", " filename = filename_list[int(f_haz)]\n", " global haz_base\n", - " if \"haz_base\" in globals():\n", - " if isinstance(haz_base, Hazard):\n", - " if haz_base.filename != str(filename):\n", - " haz_base = Hazard.from_hdf5(filename)\n", - " haz_base.filename = str(filename)\n", + " if (\n", + " \"haz_base\" in globals()\n", + " and isinstance(haz_base, Hazard)\n", + " and hasattr(haz_base, \"description\")\n", + " and haz_base.description == str(filename)\n", + " ):\n", + " pass\n", " else:\n", " haz_base = Hazard.from_hdf5(filename)\n", - " haz_base.filename = str(filename)\n", + " setattr(haz_base, \"description\", str(filename))\n", "\n", " haz = copy.deepcopy(haz_base)\n", " haz.intensity *= i_haz\n", @@ -5707,7 +5710,7 @@ "source": [ "# Ordering of the samples by hazard first and exposures second\n", "output_imp = calc_imp.make_sample(N=2**2, sampling_kwargs={\"skip_values\": 2**3})\n", - "output_imp.order_samples(by=[\"f_haz\", \"f_exp\"])" + "output_imp.order_samples(by_parameters=[\"f_haz\", \"f_exp\"])" ] }, { diff --git a/doc/user-guide/climada_engine_unsequa_helper.ipynb b/doc/user-guide/climada_engine_unsequa_helper.ipynb index adad223232..f246b539d1 100644 --- a/doc/user-guide/climada_engine_unsequa_helper.ipynb +++ b/doc/user-guide/climada_engine_unsequa_helper.ipynb @@ -67,8 +67,7 @@ "- EN: mutliplicative noise (inhomogeneous)\n", "> The value of each exposure point is independently multiplied by a random number sampled uniformly from a distribution with (min, max) = bounds_noise. EN is the value of the seed for the uniform random number generator.\n", "- EL: sample uniformly from exposure list\n", - "> From the provided list of exposure is elements are uniformly sampled. For example, LitPop instances with different exponents.\n", - "\n", + "> For each sample, one element is drawn uniformly from the provided list of exposures. For example, LitPop instances with different exponents.\n", "\n", "If a bounds is None, this parameter is assumed to have no uncertainty." ] @@ -201,7 +200,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "id": "802ac379-39a0-476d-b068-36520d03a459", "metadata": { "ExecuteTime": { @@ -226,8 +225,8 @@ " print(\"\\n Computing litpop for m=%d, n=%d \\n\" % (m, n))\n", " litpop_kwargs[\"exponents\"] = (m, n)\n", " exp = LitPop.from_countries(**litpop_kwargs)\n", - " exp.gdf[\"impf_\" + haz.haz_type] = impf_id\n", - " exp.gdf.drop(\"impf_\", axis=1, inplace=True)\n", + " exp.data[\"impf_\" + haz.haz_type] = impf_id\n", + " exp.data.drop(\"impf_\", axis=1, inplace=True)\n", " if value_unit is not None:\n", " exp.value_unit = value_unit\n", " exp.assign_centroids(haz, **assign_centr_kwargs)\n", @@ -897,7 +896,7 @@ "- HF: scale the frequency of all events (homogeneously)\n", "> The frequency of all events is multiplied by a number sampled uniformly from a distribution with (min, max) = bounds_freq\n", "- HL: sample uniformly from hazard list\n", - "> From the provided list of hazard is elements are uniformly sampled. For example, Hazards outputs from dynamical models for different input factors.\n", + "> Uniformly sample one element from the provided list of hazards. For example, Hazards outputs from dynamical models for different input factors.\n", "\n", "If a bounds is None, this parameter is assumed to have no uncertainty." ] @@ -1146,15 +1145,14 @@ }, "source": [ "The following types of uncertainties can be added:\n", - "- MDD: scale the mdd (homogeneously)\n", + "- MDD: scale the Mean Damage Degree MDD (homogeneously)\n", "> The value of mdd at each intensity is multiplied by a number sampled uniformly from a distribution with (min, max) = bounds_mdd\n", - "- PAA: scale the paa (homogeneously)\n", + "- PAA: scale the Percentage of Affected Assets PAA (homogeneously)\n", "> The value of paa at each intensity is multiplied by a number sampled uniformly from a distribution with (min, max) = bounds_paa\n", "- IFi: shift the intensity (homogeneously)\n", "> The value intensity are all summed with a random number sampled uniformly from a distribution with (min, max) = bounds_int\n", "- IL: sample uniformly from impact function set list\n", - "> From the provided list of impact function sets elements are uniformly sampled. For example, impact functions obtained from different calibration methods.\n", - "\n", + "> For each sample, one element is drawn uniformly from the provided list of impact function sets. For example, impact functions obtained from different calibration methods.\n", "\n", "If a bounds is None, this parameter is assumed to have no uncertainty." ] @@ -1268,13 +1266,15 @@ "- EN: mutliplicative noise (inhomogeneous)\n", "> The value of each exposure point is independently multiplied by a random number sampled uniformly from a distribution with (min, max) = bounds_noise. EN is the value of the seed for the uniform random number generator.\n", "- EL: sample uniformly from exposure list\n", - "> From the provided list of exposure is elements are uniformly sampled. For example, LitPop instances with different exponents.\n", + "> For each sample, one element is drawn uniformly from the provided list of exposures. For example, LitPop instances with different exponents.\n", "- MDD: scale the mdd (homogeneously)\n", "> The value of mdd at each intensity is multiplied by a number sampled uniformly from a distribution with (min, max) = bounds_mdd\n", "- PAA: scale the paa (homogeneously)\n", "> The value of paa at each intensity is multiplied by a number sampled uniformly from a distribution with (min, max) = bounds_paa\n", "- IFi: shift the intensity (homogeneously)\n", "> The value intensity are all summed with a random number sampled uniformly from a distribution with (min, max) = bounds_int\n", + "- IL: sample uniformly from impact function set list\n", + "> For each sample, one element is drawn uniformly from the provided list of impact function sets. For example, impact functions obtained from different calibration methods.\n", "\n", "\n", "If a bounds is None, this parameter is assumed to have no uncertainty." @@ -1819,7 +1819,7 @@ "- EN: mutliplicative noise (inhomogeneous)\n", "> The value of each exposure point is independently multiplied by a random number sampled uniformly from a distribution with (min, max) = bounds_noise. EN is the value of the seed for the uniform random number generator.\n", "- EL: sample uniformly from exposure list\n", - "> From the provided list of exposure is elements are uniformly sampled. For example, LitPop instances with different exponents.\n", + "> For each sample, one element is drawn uniformly from the provided list of exposures. For example, LitPop instances with different exponents.\n", "- MDD: scale the mdd (homogeneously)\n", "> The value of mdd at each intensity is multiplied by a number sampled uniformly from a distribution with (min, max) = bounds_mdd\n", "- PAA: scale the paa (homogeneously)\n", @@ -1827,8 +1827,7 @@ "- IFi: shift the impact function intensity (homogeneously)\n", "> The value intensity are all summed with a random number sampled uniformly from a distribution with (min, max) = bounds_impfi\n", "- IL: sample uniformly from impact function set list\n", - "> From the provided list of impact function sets elements are uniformly sampled. For example, impact functions obtained from different calibration methods.\n", - "\n", + "> For each sample, one element is drawn uniformly from the provided list of impact function sets. For example, impact functions obtained from different calibration methods.\n", "\n", "If a bounds is None, this parameter is assumed to have no uncertainty." ] diff --git a/doc/user-guide/climada_entity_Exposures.ipynb b/doc/user-guide/climada_entity_Exposures.ipynb index aa1b39fd38..90a0c81ebe 100644 --- a/doc/user-guide/climada_entity_Exposures.ipynb +++ b/doc/user-guide/climada_entity_Exposures.ipynb @@ -4,6 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ + "(exposure-tutorial)=\n", "# Exposures class" ] }, diff --git a/doc/user-guide/climada_entity_ImpactFuncSet.ipynb b/doc/user-guide/climada_entity_ImpactFuncSet.ipynb index fd349487cd..ad1841a750 100644 --- a/doc/user-guide/climada_entity_ImpactFuncSet.ipynb +++ b/doc/user-guide/climada_entity_ImpactFuncSet.ipynb @@ -4,6 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ + "(impact-functions-tutorial)=\n", "# Impact Functions" ] }, diff --git a/doc/user-guide/climada_entity_MeasureSet.ipynb b/doc/user-guide/climada_entity_MeasureSet.ipynb index 0af0b37d70..7c82117648 100644 --- a/doc/user-guide/climada_entity_MeasureSet.ipynb +++ b/doc/user-guide/climada_entity_MeasureSet.ipynb @@ -2,15 +2,21 @@ "cells": [ { "cell_type": "markdown", + "id": "6461c33a", "metadata": {}, "source": [ "# Adaptation Measures\n", "\n", - "Adaptation measures are defined by parameters that alter the exposures, hazard or impact functions. Risk transfer options are also considered. Single measures are defined in the `Measure` class, which can be aggregated to a `MeasureSet`." + "Adaptation measures are defined by parameters that alter the exposures, hazard or impact functions. Risk transfer options are also considered. Single measures are defined in the `Measure` class, which can be aggregated to a `MeasureSet`.\n", + "\n", + "```{attention}\n", + "Adapation measures and cost-benefit evaluation are being completely revamped. Associated tutorials are under their own menu [Adaptation appraisal guides](adaptation-guides).\n", + "```" ] }, { "cell_type": "markdown", + "id": "4e45076c", "metadata": {}, "source": [ "## Measure class\n", @@ -22,7 +28,7 @@ " * haz_type (str): related hazard type (peril), e.g. TC\n", " * color_rgb (np.array): integer array of size 3. Gives color code of this measure in RGB\n", " * cost (float): discounted cost (in same units as assets). Needs to be provided by the user. See the example provided in `climada_python/climada/data/system/entity_template.xlsx` sheets `_measures_details` and `_discounting_sheet` to see how the discounting is done.\n", - " \n", + "\n", "Related to a measure's impact:\n", " * hazard_set (str): file name of hazard to use\n", " * hazard_freq_cutoff (float): hazard frequency cutoff\n", @@ -39,12 +45,12 @@ "\n", "`hazard_set` and `exposures_set` provide the file names in h5 format (generated by CLIMADA) of the hazard and exposures to use as a result of the implementation of the measure. These might be further modified when applying the other parameters.\n", "\n", - "`hazard_inten_imp`, `mdd_impact` and `paa_impact` transform the impact functions linearly as follows: \n", - " \n", + "`hazard_inten_imp`, `mdd_impact` and `paa_impact` transform the impact functions linearly as follows:\n", + "\n", " intensity = intensity*hazard_inten_imp[0] + hazard_inten_imp[1]\n", " mdd = mdd*mdd_impact[0] + mdd_impact[1]\n", " paa = paa*paa_impact[0] + paa_impact[1]\n", - " \n", + "\n", "`hazard_freq_cutoff` modifies the hazard by putting 0 intensities to the events whose impact exceedance frequency are greater than `hazard_freq_cutoff`.\n", "\n", "`imp_fun_map` indicates the ids of the impact function to replace and its replacement. The `impf_XX` variable of `Exposures` with the affected impact function id will be correspondingly modified (`XX` refers to the `haz_type` of the measure).\n", @@ -56,17 +62,19 @@ }, { "cell_type": "markdown", + "id": "4f5b9bb0", "metadata": {}, "source": [ "Methods description:\n", "\n", - "The method `check()` validates the attibutes. `apply()` applies the measure to a given exposure, impact function and hazard, returning their modified values. The parameters related to insurability (risk_transf_attach and risk_transf_cover) affect the resulting impact and are therefore not applied in the `apply()` method yet. \n", + "The method `check()` validates the attibutes. `apply()` applies the measure to a given exposure, impact function and hazard, returning their modified values. The parameters related to insurability (risk_transf_attach and risk_transf_cover) affect the resulting impact and are therefore not applied in the `apply()` method yet.\n", "\n", "`calc_impact()` calls to `apply()`, applies the insurance parameters and returns the final impact and risk transfer of the measure. This method is called from the `CostBenefit` class." ] }, { "cell_type": "markdown", + "id": "123d60bf", "metadata": {}, "source": [ "The method `apply()` allows to visualize the effect of a measure. Here are some examples:" @@ -74,49 +82,10 @@ }, { "cell_type": "code", - "execution_count": 1, - "metadata": { - "ExecuteTime": { - "end_time": "2021-03-05T12:21:38.016347Z", - "start_time": "2021-03-05T12:21:37.436104Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0.5, 1.0, 'TC: Modified impact function')" - ] - }, - "execution_count": 1, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "execution_count": null, + "id": "d2841a6d", + "metadata": {}, + "outputs": [], "source": [ "# effect of mdd_impact, paa_impact, hazard_inten_imp\n", "%matplotlib inline\n", @@ -153,69 +122,10 @@ }, { "cell_type": "code", - "execution_count": 2, - "metadata": { - "ExecuteTime": { - "end_time": "2021-03-05T12:21:59.949353Z", - "start_time": "2021-03-05T12:21:55.818484Z" - } - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "$CONDA_PREFIX/lib/python3.8/site-packages/pyproj/crs/crs.py:68: FutureWarning: '+init=:' syntax is deprecated. ':' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n", - " return _prepare_from_string(\" \".join(pjargs))\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "execution_count": null, + "id": "9b34ea69", + "metadata": {}, + "outputs": [], "source": [ "# effect of hazard_freq_cutoff\n", "import numpy as np\n", @@ -264,93 +174,10 @@ }, { "cell_type": "code", - "execution_count": 3, - "metadata": { - "ExecuteTime": { - "end_time": "2021-03-05T12:22:17.076339Z", - "start_time": "2021-03-05T12:22:11.117433Z" - } - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "$CONDA_PREFIX/lib/python3.8/site-packages/pyproj/crs/crs.py:68: FutureWarning: '+init=:' syntax is deprecated. ':' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n", - " return _prepare_from_string(\" \".join(pjargs))\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAsoAAAJyCAYAAADdD+ZYAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/Z1A+gAAAACXBIWXMAAAsTAAALEwEAmpwYAAEAAElEQVR4nOz9ebgtS3YXBv4iM/c+83Tn4U31ang1SVWlEpKYJCEwg5rBzWfEYEAyuM1nPquZ2mDTbls2QwMNuMUn3CAkg8Q8CIyhm0EWgywMJTSUVKp6NbyqN9x5vmc+e+/MjP4jY0WuWLl2nNzn3vvOPffG7/vOl/tkREZGZkZGZv7Wb61lrLVISEhISEhISEhISAiRHXcHEhISEhISEhISEp5GpBflhISEhISEhISEBAXpRTkhISEhISEhISFBQXpRTkhISEhISEhISFCQXpQTEhISEhISEhISFKQX5YSEhISEhISEhAQF6UU5ISHhqYQx5p8YY779uPvBYYz5C8aY/8dx9+NJwhhjjTHvexf288yfy4SEhJMPk+IoJyQ8WzDG7LB/FwGMAFTu/98F4B8B+B8A/HoApwDcBPCPAfxRa+3dd7Grjw3GmH8F4K9Za7/vXdznN7t9vvCE2rcA3m+tfeNJtP+07TcGY8xfAXDVWvvfHHdfEhISni8kRjkh4RmDtXaZ/gC8A+DXsP//LoAfAfARAL8SwCqAXwDgHoCvO64+JyQkJCQkPI1IL8oJCc8XfjuAlwD8n621n7PW1tba29baP2Kt/f9pGzhT/O82xnzJGLNtjPkjxpj3GmP+rTFmyxjzd4wxQ1d3wxjzj40xd4wxD9zvF1zZKWPMVWPMr3H/Lxtj3jDG/PYp+/1Xxpj/1P3+DmPMjxlj/rRr901jzK9yZX8MwC8G8D3GmB1jzPe49R80xvywMea+MeYLxphvY23/FWPMnzfG/H/dMX3KGPNeV2aMMf+jMea2MWbTGPOzxpiPsu3+qDFmCcA/AXDJ7XPHGHPJGLNnjDnN9vNJdy4GyvF9nTuHD40xN4wx38PO44+6aj/j2v6NU87R7zDGvO7OyT8zxrzMyr7bGHPFXaOfNMb8YlaWG2P+sDHmy+74f9IY8yJr+pe56/3AnSczZf/fZYz5e8aYv+3a+SljzMdY+YfcdXxojPmsMebXimvwR93vb3Zj4w+4837DGPOfuLL/DMB/DOAPunPxj7S+JCQkJDwJpBflhITnC78MwD+11u4cWjPErwTwSQDfAOAPAvheNC8vLwL4KIDf7OplAP4ygJfRvJDvA/geALDW3gfwOwD8JWPMOQD/I4BPW2t/sGcfvh7AFwCcAfCnAHy/McZYa//vAP53AP+FY87/C/ci+8MA/gaAc65//5Mx5iOsvd8M4L8HsAHgDQB/zK3/5QC+EcAHAKwD+I1oGHcPa+0ugF8F4Dpj8K8D+FcAvo1V/a0A/pa1dqIcTwXg97nj+fkAfimA3+3a/0ZX52Ou7b8tNzbG/IcA/jAaCc1Zdw7+Jqvy7wF8HI285m8A+LvGmHlX9vvd8X8rGqvC7wCwx7b91QB+HoCPueP5FUr/Cb8OjaWC9vO/GGMG7uPgHwH452iuwXcC+OvGmNemtHMBwBqAywB+J4A/b4zZsNZ+L4C/DuBPuXPxayJ9SUhISHisSC/KCQnPF04DuHGE7f6ktXbLWvtZAD8H4J9ba79ird1Ew6x+AgCstfestT9krd2z1m6jefn8JmrEWvvP0co//k9oNNN98ba19i9ZaysAPwDgIoDzU+r+agBvWWv/srW2tNb+FIAfAvAfsTp/31r749baEs2L2Mfd+gmAFQAfROPH8bq1tu85+wE0L8cwxuRoXkb/qlbRWvuT1tp/5/r3FoC/CHaueuB3Afh/uv6VAP44gI8Tq2yt/WvuepTW2j8DYA4AvaT+pwD+G2vtF2yDn7HW8o+BP2GtfWitfQfAv0R7bjT8pLX277mPgT8LYB7NB9U3AFh2bY2ttf8CjRb+N09pZwLgf7DWTpx1Y4f1NyEhIeFYkF6UExKeL9xD84I5K26x3/vK/8sAYIxZNMb8RWPM28aYLQA/CmDdvTQSvhcNC/2XxcvZYbhJP6y1xH4uT6n7MoCvdyb/h8aYh2gY8Atae2jY1GXX9r9Aw4L/eQC3jDHfa4xZ7dnHfwjgw8aYVwH8BwA2rbU/rlU0xnzANNKUm+5c/XE07HJfvAzgu9nx3Qdg0DCycDKG15185CEatpbafxHAlyNtq+dmCq7QD2ttDeAqgEvu74pbR3ib+qfgnnvh77vfhISEhCeO9KKckPB84X8D8CucNOFJ4A+gYQG/3lq7ikbCADQvcMSy/kUAPwjgPzePLwyZDN9zBcC/ttaus79la+1/3qsxa/+ctfaTaJwePwDgv+yxT1hrDwD8HTQv5b8NU9hkh/8PgM+jiTCxikZGoWqBp+AKgN8ljnHBWvt/OD3yH0Ijm9iw1q4D2GTtXwHw3hn2FYPXNhtjMgAvALju/l506wgvAbh2hH2k8EwJCQnHgvSinJDwfOGvonlJ+iHTOLtlxpjTzrHrWx9D+ytoGOaHxphTAP47Uf6H3fJ3APjTAH5QsM1HxS0Ar7L//zGADxhjfhvpZY0xP88Y86HDGnL1vt5pbHcBHKANryf3edoYsybW/yCA7wDwawH8tciuVgBsAdgxxnwQgHyJl8ck8RcA/NekuzbGrBljfgNruwRwB0BhjPlv0WiRCd8H4I8YY95vGnw1d0KcEZ80xvx6Y0wB4PeiCUf47wB8Cs35+4Pu/H8zgF8D4G8dYR+HnYuEhISEJ4L0opyQ8BzBWjtC49D3eTTOblsAfhyNSf5Tj2EX/28ACwDuonlZ+qdUYIz5JBonst/udMZ/Eg1T+F89hv1+N4D/yEVp+HNOH/3LAfwmNMzmTbe/uR5trQL4SwAeoJEK3EPzUh/AWvt5NM5zX3Hyh0tu/b8BUAP4Kac9nob/G4DfAmDb7U867H0XgB9wbX+bKIO19h+4Y/pbTrrxc2gcDAHgn6HRjn/RHcMBmEQCjZb476BxtNsC8P1orttR8A/RODw+QMOi/3qnMx6j+Vj4VWjGw/+E5tp//gj7+H40kpaHxpj/5Yj9TEhISJgZKeFIQkJCwmOGMeZfAPgb72YClOOAMea7ALzPWvtbj7svCQkJCU8CxXF3ICEhIeFZgjHm5wH4GjRh0xISEhISTjCS9CIhISHhMcEY8wNoHCZ/r5N/JCQkJCScYCTpRUJCQkJCQkJCQoKCxCgnJCQkJCQkJCQkKDjRGuVTZ16zk8lut8BFCs0yi0FewsCirHPUNoMxFrXNUGvhSpVVNlImt7Mmso6V2Uf9PNH6OUv0Vd6UNChoBgar7Naty2BQWxtsZ0SdoEy0H/yv1G/rRSwfsxhFtH76MqWhWQ0uVH/K9RgMK9TWoCrFIDAm3J22vXrdDTID1DZSLzI21MPTxmykzaOOvVnQGaeAPr6O2L68T7V7WS1zyLLmPlC3i3VOO7DO+W/ruGEC47YzrE1al9GSbUfr2lPWbkjDntbx26B2k1XtDixIHUIHW9OJ4WWuTzU6Zeq19IWi+gwX1M9FssnYHKfNbRJHNbr2eZ5oz4zI9r3m/th20fHWHWdUZpQxSD3lYzAHUMOysdQW+jFETdVsQz8we4wlDbNeoz7jKnK/x69NZFCZsEoAbUeRsWusWOeWoxtX/5m19lcqe8Cv+CVL9t59LdLl8eInf3Y0tc9PA070i/JkvItP/oLv9P/bzA203N2Q7l1krhhjfWkHS8MDTOoci4MRvrJ5EQfVXFDPumiufLxSm74Of78x4bp60G7omkY1dGVDdMpmfsEQ+7Ms+qzN/Jtrp+1pN1RQ5iatjN1DptKXAJC5/FnnBgPc2Z+EZVVYx5R8OxuU0f/qOt5P9yZotEmzDuuHL9/hdkGbFZXZsB2+P/+gZxv6/dhuGdX344UNmMxgcfkApy5s48qb58JxlmduGY63YF3OXm58+wan1ga4tz1hZVQ/rBuAXsy1udltV7PZgcY2rasHrL720JdlmF4nChrWfFyW4bpgfFVHe6uh46sG9D8v05dAe67OLA1wezxBPWj3bwv3ckrnP2N9E+ssKzNuO5M3gykr2rK8aA66cMtB0Z6YOfd7ftCMhYWiHROL7je9rIzrdvIYV83viVtX1e2A2R03E9fBqDno8agdFPXItXHQLLNxe3GN+527ZdZ2BdnY1XH3SjjWw3XafNtWDv89NzfAnQO3IzY/ZHIeY+OF1mXKC31nPlHmzV5jn5XRabfuNPI5nO4tX8bPi3xGBefMhmW57WwHWsfKQOPKj7P2pNHvnJZZW1a4+n7Jys6bIe5n+5i4MXUwacfL2P2eTNw4G7cHbydNR824WapjadQsjfKep12jzkumRlZpL8P0PNfOp79+4t4G2vvbXw92T8v7veKDgo6LPjxZl6uwLBi7ZXhv0Tj/wnf9/qnZPe/dr/Dj/+ylacXHhvzil2bJSPqu40S/KCMD6rl2pHZfapv/9zCPnck8MGleNjbqbby6fgO3JuvYLBexj/CFuS66LyRQJm7/8eu3a8tqelF2S/6iTNv5ibvvs70Ot7N8wpDHHnlR1lhcemiZ4CbVl3w7XxdKmbI/o/VBbEd9zzQGIcZ4i5fpsA/0Msz64l+wbfg/2HmgdRk/QmLn3OTF+0KTpaf+2FYZsLs/hwtz95ENKlR15PYzypN3RrQPV2U80/8ao6V8+PkXZO0h3uNl2I/52INMQewlJTaeeyF2iiMvTHx/RyUbu/vjA8WNL8/AsQe1W1e7l1meHbr93jPBUltX9/xKpxckjcGOMe1GzJcqwaC8SMZePGWdThXT7ie4Wz0Z4JasL/6F11dWytSP7On9i8GIvlitL5GXPv9x0Z2OoveD1iatMwr72469o81DNG7oZRoAysq9dCsf/jWxy/7Dkb0oS2Io9sHCIJ9b6rmm/9l2dEv5l1ttfqGXW+Xe9OeVd4qeC27+NPyDRZDohvXGQnQ05zdLOE/ECPeER8PJflE+Ih6UKzjYH+D0YBvvX7iO7XoBV0ZnMcHjSBCWkBCBNdjdXsDKyj4ebq4cd28SEhISEp4TWAB1eqWeGSf6Rdkag3KOfya69d50zL5YhclqK1vAFhZgqjN4Ib+PDy++g7fMWdzDcsAMd+QYmjmMLCaMNabfJL0IzK7i8zcwp/TQYamsgsbcTNte+0Im4lQxS8Z0hqZ25THWOEK7BX0RrEDwQS7PSz29Uc4Mexbeii9zoMMkG262F0x0IH0UbDEva83Jmlyi+b29s4D19R082GIZhYUp8FDwPhj2P9iYdfuuA4lO2JewzbBOYCERpuKoHl9p0++fs459LCoxK0iMbY41SX14bHSwa8vqbHOHKeSFntVTOhhhAX0RZ40FM6gxyhpkGTez56R3dvOX4TciUW7SisIPQZmPPCMcGze0C42919q2gpQXZby72vlUx4IYJ5xFfmRdvrZfadVTWGMrx77SVsAC9/HtUBnscAzyNlvrghsbgbbZwhjrp7OcPdCIXSZrSMCqurnXKhKRVnqm0MfyPtIsosoz1d+bWfg/wE67mMubde7YfQOsTDDewakX86ZmhdSmJc/258p1lBand8NZ5DnFiX5RfhywyPCOPYN7dhnvzW9hGQd4E2ePu1sJzzDGowEGw6fPoSIhISEh4VmGRWUTozwrTvaLcgZUc139pcYox5xEbA5sYR6fzl7Ax0dXsFHM485gxe9D2z74TYwyc/Kp5jSqiDYMvwADlo1+KGNZOrIFX8qSZVHYiBjD69usuuuiumDLWGVRFttO6pGDLpvut3VMtxzTPXd0yBqjTExywEyK7dh+pfbX1N0xyMITsEab33MLEwyHJRYWD7B/MO+2646F7sFMLwodA0MmOXT6NKKf3TY0Zz5pPYk6WSn91DTKXk9KY67HeAnaEKz/1DZEv1RdoxhDUW5GY/NmhT+G7omxog+Wjy+vTW4qcTY4pieVrLHGMOcK9UbsMi0z7nToftfekZiXhcdljXKja3rkCNvvq2nXL2sqWIU1NnLMxq6fxvA+TsuDQHDK6R6j/Snze5z9Vaxmvo6oO7WiaEoZJ7Polvllp7GT0Zji1jY/Z7l1gUOiDepoFoFMYY+1Z5qEZJYBdCxOYZting5OJ7HhyvOL/Fm6h9A2kYfMctA/JSINOWOSJSk78mSUcBhO9ovyY0Zlcnx+eAEfGV1Hhhq3BmvH3aWEZxBllaGqDS6ce4A337l43N1JSEhISHgOYNGE70uYDSnhiMBuNo+fWXwBr4zu44Xxg+PuTsIziL29BXz5jcsYDEocnZJMSEhISEhIeNI40YyyNcxZDsx8E4tRabplMqblTjHET8y/iK+//zZurC1jlA9UZz7vJEUmSCa98GZILYC637EzKXHTakReIUMLaXKHVr6glB0ioei0Kepw+LaqxrSlmbzaura7nWaeJ1O85sxCdRQTfgcReQa3XUnJRWDCFzGW+XihTmhxYL1pzsdDVkzOGVAjw3hSYG5hgoPRHDPlKRIMzfGOupIZWGP8GAa6jqu8D1EJktHrBL8j5vKZnfno9EsJBtAdzzFzcl9Exl4MqjPeYfvg2/ky7eC728WkS94k7sZZzeYOklNQHGRNXuHNtOwGKoSeiW+XCccto0gv2hixrJ9kLtfCJGoOTdSm5qgsoZ0fIa/QpFLRc63835mreoxv4JC+x9DDmS/m4BmXvAl5hrIucNgTjcRCxxk2ljJjkRmrxlWgsSSd+gB04wxzCo/iElOcYR6fWMYe1qQXkeeJ5pDdkVwo40x7ztadc9y9gBTSNQwBJ2rz45N1ApWZO/959/rFkKJezI7EKE/BOC9wc34F79+6i6JOjlcJjx87ewtYWdo77m4kJCQkJCQkTMHJZ5TnGaMisx5x9lewxmoIOLH80vA0PvDwLr7u3jv4sRdfQp1lIbOoff12Otn9vPdkErGOnP0Qwc4Dlk18IauMcsQBL8bORUO5RepndeNIEWbKm95mh00yyle3xjhJBz9eKDLzhX0P6UA1BFxdB/1u+i5oxJqzLbXb3IU54uwAXVOFhWrLmuX+eIi15d2mLjHJnllhbUbYd5uhYai1rH0+rFJ3u9YaorQpnZ/YcahOgHL897QEdJi+gJl3PzSHKmkh0VhAsd9wpdLNIypguCOpsfFmgmOXNJLmZOWHIBt7IlxhEB6OWGZiltmF6Tj8cCcrcdI4o9w681m5GbOQaCdU1IlZIGLzER9nskw7n/S/Eh4Ocrxp3X0aOJGjMt89rBNW3U5jm8NVvElpqQiYTDEG5NgKypglIyOWWXHmgwgPZ1hWOxkCTs0s+4gEKn9ExZIr0fCiey0W+9SyM+od9fz++LGLCxBYNt2COpXSQDwxnOgX5SeNKsvxubPn8LU3ruH0/j7uLC0dd5cSniFMyiJIQZyQkJCQkPCkYGFR9dKQJXA8fy/KM+oorQHuz89jbXSAO8tLej3JcPHCCENLyT0yJeGIZzYiAdSD/dEXdZ+v5yjtNdt21rhD5aSCOC886QatVLLJdtq3EZZNpZoUESqlEfUlwfkhi4BjhpUQXZ5JDphXSrRATAdnc0245MduwjrjeoBBUTZ1ZHi4gLEVbDOrZ7PpjK4VS/lbosMkK6yxGn4wcv90+sRXyWNVtuuVwloLTSjrav1UutlLMzztnraiTNDoWpIWvaNhnYBh6idD7GBcNXTTfN5MNoVCsxFTyFlAChlHS5UFlIlHmk4DUMYN+60mgogc36Epni2rw++JiOWiF0sds3jE+h1h09X/6Xork2NUhyyZYc2Ep1jGIKwTgWWMfC1EOEJevyILBrN+1dagtiaa9KbVNrddobGU5c3+Ks4oi7TWgYWMrMDu2ciTKxkxzgLdurRa9pgXZZ9lX+R7gK57ljsGrEgPbwPrpbhu/PrRb/+cTS/ATwrP34vyEbAzHOLSzvZxdyPhGUNVZ7DWYFhMMOZpHRMSEhISEp4AUni42ZGc+Xrg3uIiNg4OsDQeH3dXEp4pGOzsL2BlMTn0JSQkJCQkPI14phnlqKmzJ4xtEpF84cwZfPzGDXzqxRcwKRp7C8/I1uyQbSeyjamW1dKEdQFkEypDtyzSppdlVN2yWoj8Y6HVrOYQoDlnyXLNWSefVrldGSamCstCSYG03TNnCHL2UHrns+d581T3IHxIKsVsbrzZPPB+CtoOwq91ytApA3P4e3iwjAtr93Fnf911RkgwWBtWcdiLhq6KQcosgG5YOE2yoWwXy6bWJ2ufuk7KKxSZETm2BfdBLBQUjQHlnMnsjkGbkTJuuj80g5smoYiZ57V+xqQJogNV3eVBSFZBEgy+jpbcXF4IZz4K7QUApQ8P57bj0gvheBV1DEW3LCqr0eZSOv+ahILaVPbnd9dDcqM6osYQmzdjY175v4/DnjI1dvvO5QfkFOe90Ni8SfOQFsKU5jjvNMrKnOyilVmw/VFfKEycohOs3DrD5Rw0h5O0gD0TO87P2nymSC/oWepXKXPBzI6oPeYsdTsh7+PPfN+VSPi79hwczhRbAFVilGdGYpR74trqKm4tL+Prrl7D/GRy3N1JeEawO5pHkVWYH4yOuysJCQkJCQkJAiefUX4cH0eiDY0xggXe2DiFSZ7j579zFe+sreHq2ipGRXsK9TzziheTAznxGe7MFwl3I0NlaWyzZLIBIBdfnmreA1oXUrxBWYy0DBjQXgx299O69WFxjiCszDs8KpQMsR4yzE5T5hsVK9DSc8pXvv9a951i+xNMslXLwv+1smb/Bnd213Fu5SHe3rzAHOr4dkpbjO2oC9PbYU+yJJqTVcyqoIaOE3W0660mipH9VAhXjUHrJMeJMG+aVUkNhdiDsesTtksNAafBXweyWLCizsErm0eusWf4qvZCSnaZO+wtFpOpZZljGSlMXKYmHHHLwAHLHYESQtNbZyI0TezaWvF/02ZYX51zYmM3cm39XMCbmsGCo4Z31OYc2WctaUp0R0qbvu9kIWPVPdPaZY3JEc479bE2pVOeDFFYsQvLQxTSvmksSQsIAJSFS5ZTse1oXAmnvqYxt1+3NArb7OdPfo9pIVYJ8rmnWPfUuU7OPZpTnr/v+aBwx1W1tTu/XFs8NJ533qM+JWe+J4aT/6L8bsIYvL2+jnsLC3hpcxO/6O13cH1lBZ8/eyY0zSckzID7eys4t/IARVZiYtMtmZCQkJDwZJCc+WbHiX4qH+XVVNMn9mqIfS3uDObwuTPn8MWN0/iqO7fxdVeu4c31DTyYX0BFocbcl18n3BvfrVunBklXgqXLtoyyXVZ2bwIr9FsmwgYG21GRxkT7tt1Xduzei96X3S9rySw366xYx/R0gp4J09cSjeH2FlCvtAgZ6WY7wVL3DAEn00ZHy/x1yLA5XsLa4i7uHKw3x8sT4mhhkTijnIf1YxpX2o4IxloLtaQwyrFA+52wa5HrrRLKGgso1sWYm7As3LmWKla1IHi9s2IBiuxP7dc0BGNPsEFTG42Ds3KUEnhC2lHG/noWz7HMGessMcjELPMyCiPnGWXT3U5LYd0mIVHMUZHEHzL0ZWxMdMJv2SllEXT0yxobqzGLszx8NKtGjzCeVtOjanE15bniemKa2+i8KCHuOlploD2Bnh3NWFGzksLD5ZxRBoWHc/tgF3Dgx5LtlBFonFVMC1+7Z6pnUHmKZzoG71+hWOJoTPHDE9sHzwwqUywQHYuVNs6kxQsto98ObNamt+a6eV4zu5HWP0jEIqw1PTTKCUfDiX5RPm6UeY6fPn8Bl3a28crmA3zs9k2M8xy3FpfxxlpimRP648FoBZeX7uLOwRqO9gmYkJCQkJAwHRZICUeOgPSi/KgwBtdXVnF9ZRWmtliaTPDa/bt4dfMevrx+5rh7l3BCsDOZB2CxMtjH9mTxuLuTkJCQkJCQgGf9RfmopjIOEtEf0j4AmNpgL5/Dl1dP48P3b+Mrq1Cd6/pIKKLyCipjToBZZcPtuKOLcKhRQ5ZFnEV8U5rpX8sMN7WBwxDaxLVwbd70FJj7hCwjCAFHZj73L8+UJ8y8WiYlmU2P/+4VAk4pU7fLgVujDZxffIDNvUUfEonXC6UXrUnbFgZ1oehiqO/8PpDyilnDw8l9cNhuWce6GpE09A2HJcPCafKkNpogM9MKZ8/gEGLyCtGnKCGjZZPsA03K0mPOqgKTeLMBZc/LWUdrEcoyyL5XOVkFQgkG0IbworKcnWzjpRd0I7G+eOkF/Q9WRp1yVSKZFdVTOGW8zCSDkYhJ8mIm+Fkxy5Dgc7gc61qWOU3OQYV0Pfj+aT5S5IF+HhNh4pr6ofSMjy2bN32l6WnIdIVeqiPCEXJM8mYSKpnEwGeApDmRlVkKsarNkZVYF7m22hziM0gq1yHmOKlCyCt4ozSXG2Vm8uddkZ14eeCMznx9kvcmhEjh4Z4AxnmOQa2IkhMSIngwWUZhKizlB8fdlYSEhISEhASccEbZmkO+7CMMlcoe0lc7D9fWpyPCOWh+VGJscphSdxaQjnpaeLhOuDewcHK+ju2WycDmrF99Ql9prIz/zg1Y6nY/09h6f1piJJt6/RxTwR2O3LJ2ZdzhCKVgPfh1J/ZWYwCIaa/lF33bB89Ias5InpXosrlhCDhRprJsBoDBw3oJy4N9bFULbZnmqMIYEWsQMsOifTU5iNaHTt8xFdo1V0lmcW9kyviKhWvrFcpN7WC3T9JpM7jaM9wjU8vslLInAKscnwRPHJITixfplEw8wn979pjtkJyyMhEmzu3ILenCdy0kkqlvCqcsGbQxYcW5n5Xx1ZKStE5Syv5moeVifZnV0qlYiWTYvCMjElPPsrBkNTHJ5DTKrBp1lqFC5plkPpYkk6w5lNJYCthmcW0Ch7YinMN5mRVzvxqCUZvHhFVWe1fQ2OMuHxxD96HYcfgD2KSsXGQfns92NpsGC5sSjhwBiVF+Ariwv437c0lnmjA7tqoFrOW7x92NhISEhISEBJxwRhkIw1vJL8CYtkgNO0TsbV+2QH46WmC5HOHS3hb+3cYryCb9GGW+P8kkZxrb7ELABWHlfID+kDWjfqlLVr/9OkUHhzIWBtoHclufbyjZmdinWpAaNWyUB7n3YXKU0F5+Oy0kFaWppi96NUC8Oy+MSrMizXTArmbTy2I6eSrbsot42dzBYn6AXTvflHXCyYWsr81FmLdIoP0OI9VTG6vWn1ZHYfbU+0CsU1NRK/drV/fMWCtx7wa6Ut/k9Htk1hTWvO9SJ9uxYuHJQobb0tg8Qs7SB0smOTO8rDmBhRIertUodxllr1EWiUea377xZsnnMTlelJMWSzfdSes7K/iQkOE0+bySd9d1mtKeIzPoz9W03xHSUe2L3E9fBpvaorBkXDfr5klKClKzzlRVhtJkXsues2OQTLIWHk5Dm9BGubjOqkpMMvfVoOelZ/+1uY7+5c9Zcb+q7LGiX/ZpqvVdRCAuoJaMRGnNP7ek/vkZgzHmNQB/m616FcB/C+AH3fpXALwF4NustQ+eRB8So/yYsZMPcZAXmK9SmuuE2WFhcKPcwOX8/nF3JSEhISHhWYJtvi2etr9ol639grX249bajwP4JIA9AP8AwH8F4Eeste8H8CPu/yeC9KL8uGEMHgwWsVKOjrsnCScUd6tVLGRjLJnk1JeQkJCQkODwSwF82Vr7NoBfB+AH3PofAPAfPqmdnmzphYGaRSwa7ktrRjjCqaaWHlYN2u/yZIyhqXAjX/OZvmIm55j0InTms6JOT2NuVHqhrJNl0/6ftjvx+RWYCUUkqWiTgYQiXKddW6uZSOnCaabSmszDXWc+b+rSMmFJJz5NXqFlJNPWKbAwuF5v4HJxH1+oL08J69f2xYr7gH7XMtxb0HfRX1lv2nZUFjkGNdxXj7EXy8IWc/SLhQULszS6db4qkxFI2U7sHonNK+p2bnzxEFvogajsQDj5oB3rWSTzmZYDScoqAmc+hOu4ZKNwpvCyUrKpmTCbmuE7FuM5kMfIcJqRealz3S3U7fzzQJE9dNbxNmkKcPeR5oiq/e/7FZNEyP1riNybvcen3E8wH4kTyTrjJSwkk+DOmE6OQU593Jmvygwqm6FyHa7qtjM+O6QyzjqOfmyc5UU4WfE2rXPmI1oyuN99Zj4xlzNoGXOlI7DVyuiU8ee6CHs4s9aKdlgp6/z1MJ0yH/q0h5TF4qkND3fGGPMT7P/vtdZ+r1LvNwH4m+73eWvtDQCw1t4wxpx7Up072S/KTym+tHAWr+3dwsf3ruJevozbxTImGBx3txJOEO7YNVzKHmAZ+9jGwuEbJCQkJCQknEzctdZ+bayCMWYI4NcC+K/fnS61ONEvyhahE5NaYcq6jEuIheNINHD4YR0CcIA5/MzgRZyrt7FR7uHiZBOfKV7AxBTB/jLp1BeUuS9kLTxcPf3LsXVE4BTHDCL/PsyyVh5zGNPYHS0EmXSw0Jhhx6D1TUYinQANT+TR+cHbdPV9sha2nXCuC5KYiGPvyxh1tnes8qXsPj6fXXb7Yd2jbbNmfc3uZM8oF22dznZa/yKOftHtqEhj86TDnmZZibCA7b1pu2V92EOlL5JZ5vXaZCbTHf3Ufjo2s3eos1nYpghLzZ1a6Z5XHXLF9dLCxBWm7pQVbpIqvHMWY/r8OmIB2TXy9024bH6L+0dzoIuwsVMthoxRjjrSHbaOmpSMHWd1I5aLI3ttRqw28n4IWHjprBbMm3rb6o64hdJbQbp98Q7O5OjH5sa6NqhhUDpHP56ghpjnPO9eHG+xcPUHRfvg82O8EMcEluyEwsSxMHZ+HqRnKt+hfOb3vGaSSTZ8bqV1ynhRmWtf6Op7iyM/QHcdlIQx/jz4JCaHdh+AQTVzTMKnBr8KwE9Za2+5/28ZYy46NvkigNtPasdJo/ykYAxu56v44uACbmWr+Gh5DYUtD98uIcHhjl3FkhlhAUnvnpCQkJDwXOM3o5VdAMD/CuDb3e9vB/APn9SOTzSjPE2jrFalL0ehRwbw2Bll2dYNnEJha3x0ch2fxWXUrtNtmDjGxMj01gEDF372xr6CZ2a2iKllX5vRsHAzsCZqqCZFny0/29QiEWSd96XVIzP2SmhP+XY+8QQxKYwZoXNN50MN85Z1v+Q5uyIxK7tskeFWvY7L+QO8gQuqRtnmCqNMhosejLLaP4Xpi4aVo1VK+nR5b01lY0XZTBrlgM1zjLDSQU+uaayc3I+W0lZhn/g6Y0O2K5ryWjuuTkdprLM2j0hXavpjXybaLFjcSWLsSMc8YGWUbjhzGmUeHs6QNpnW8Xnas8y+c75I6paDISiTKYlzzcPzBUfUI4FOLJGHrBO0+QQEn2oIOBpnfgUrkz8ilhk+T3jWmJ4rPARmOwiD7ZvfblxSmDiewto2jDKxxxNm8qX5gawSBYt9Skwy6d5r8DZDOpbvr85CLTyUhCPeF4Wxzeq8An2dUYr8ELTdenKe6TQidycsF4H5RzyHNLaZ1vVLONKejpMEY8wigP8AwO9iq/8EgL9jjPmdAN4B8Bue1P5P9ovyCcIVnEYGi6/CFbyBC9jFHAwsbCL1EyK4ZdfwMbyFISY4SDr3hISEhITnDNbaPQCnxbp7aKJgPHGkF+V3DQZv4yx2MI8P4joMLExu8U59Bnewip4Co4TnDBVy3MEqLuEhvoKzx92dhISEhISE5won/kV5VpmEaq6NZAiLWjqjDhKiLdf2faxgyzbprQd1ifcVN7GYjfHO5AwAozg/MXNmH5NJH+cNrb6yj0e20PRwCtP8DL3ZlYf6kbIA5RppGbs6DjmK+dRLMAKblHAe5JKKXDgjqaZjRbJBVkiyCDInwDoP13FJ0bV8Ax+v3sbb5hRKkwf7qV1WPi08nAzDxRHLzOfVKmpYOds5LkMmzT7mzJ4SCmka1cr8vaHJHZROeDmG0r9olsAeMpBOeDKgNW3bbj87WSRVfcaTQ9+saOTEV2ThEmAm9Lzr6Ff6zHxiCXinJaOMz44jFB+DQpbGYV25HyeakxX9rzjCRZ2LtbmjYy5n+5NzXLe7/ULV9URMNtSGzBRjEV0pUTD7kbyCwsMp47odw2z+qw0sDGrTlWVUbk6cVM0ExR39hnkVthlBzerUTvbj1zGHROsd4sP5mv9WQxQq0h5fJoq0Z5Tq/KnU7+xPgyzTsvaZaZV1nGBnvmNDsvsfAyrkqJBjhCE+X17GohnhheLecXcr4SnF2BS4b5ZxsXp43F1JSEhISEh4rnCyGWU7hYWKIRamSgkU3jdsDDCFcVDbtL6sRo43JhfxoYWrGNVD3CtXXZkNt+d4Eh+EERYw6pTk+tMnIUvfLsTCMbV94VSM+7L24XkYw+GcOzoMHsASjrjtuAOJp6ndOoX9lWGu+DrPQuXdMs4G+zZdUP2OI57r3zWzgY9OruJasdEkc+Dsk0HIoEm2ZEaWrGXvOQsotu95X3SY4WlsrCwTYeGi23FLgGwrIGAEA6OMoU7iEXV/bZHsV9yJljsT0bjEU4daocI8s8wO3oeM88lI2oOhUHG1WDYV3bET08cdsCrBNnPLhZxTObPsngWdhCVAa1mhdvhwkYmINIZXcSyUbWn9VDHDPKnNqZojY+ye7DDmfOz6zeics+vQ2bHWQbdgbdbWNOyuu4A8KRaNicqdtIqNs9Ldw3NF4+AXs3hYwWADwIQY8IKVuQRdNp8+ztqkJKx9mnvI8T+wBLl1MYucZhGVFgv+PhANHRebcMMBolnRJCwSo3wUJEb5KUCFHF86uIjLw3tYylLa4oQu9s0Q29k8zldbx92VhISEhISE5wYnmlE20Jnh2IdVLPGBxlA9KqPcFrIiwUyZGhhjiCsHZ/DS/G18fucFNfW1b0rT083CTGlsRI92pmq3xTa9tHYKa9xhrhVGWdMhe80chZ1S+tYJwQPG/gpmmVekPtURZjjQKIu00ap+WeiRgS6THDLRzfLKYAMfHN/EjWIt6IM1gn3vMMOsrT6MMjFLRyQe+qaiNqJO39Bx0tpilLLWLMG6IjWjkf2FCU7EvaiNfaePDW4tEVYuds+E7Hb4o6+e+N2AplGmJU87TOxku2SNeP2xu1+1eyQyh2jh07w+WeiRObR7uaNpVsaL9r+0egVVH/WZoXbeLRQGuzuWur+1+68zLrXYeIqFxYoQjJaHa6sMapvBuBNbswtfOXa5cuOEp76WIQpJs6yBM8qU2KQqXJsVG2hkuaBxxudbwSRrjLI/d6wr8lZUH6WR8JOdMH+svgofQc+d62CycwtqrO43YdeaqSIhisQoP0V4UC6jthnODBNrmNDFdraAPTPEhWrzuLuSkJCQkJDwXCC9KD9VMLgzXsVqsX/cHUl4SvH24DRenDxAZmM0REJCQkJCQsLjwImWXgC6hMKv00KxaPKKjvRi+nYxqBYNxcTdlV60jY8xwNzc2DlBGN1Bg0xBgYzg8P7NHDpObqeAfMkC05zcXDNZ8gbkhpq5XYSF07IeWVGnqRc6cnCznc++l3XNixAhvXQJRegQwutpYYe8E6CynZRc2ClyiZ18Htv5HC6Um7g23PAXwGpjQelDx6TNBrYm1WgLlXWPCmEWViUbsXBtMedb6bgHZipGWIdXVEPAyflhyjEYq/fFj0HNcy92Xr25lo3ZJ3IhpiNmpi2E9II789G6KnMDmjvzkSmcJgM1DKFYoiudCcze7vx7uYsoA9p7K3CyErsJx4uAJlOScp7Hicc5T3s5AD+hTkIhZW18O62MTPwV7dYEZdYa1E4CwR0EaSyR5IJLL7y8SHn2ShlGIL0oXFtufzWT/5CDNMipj2cQ7DiUsvbF/R4+T8Ky4PFF6zQZjzavUJu+w90yD3cKMrbHmsLDkTNmcuZ7YkiM8lOGvWoOtc3w4tId5FF32ITnFW8PTuPFcWKVExISEhISnjRONqNsoTvlqQ574ddzX2c+X2dGIqdl7kz4P9CK8NUEJwZf3rmIS3P38eH1d7A1XsDuZB4PD5ZReUpEMGPgjIHWGeHgxLcTX7rBYfZwEtGgBbD3m2VhHa2bWrgw/1M7TuGMwhOHWPF1HyYcIVrI8ipu34YXqSHgrAgTB/DQcQjqNGViOyVJiN+O9UUy5rvZHDarBVwuH+LtuVPNeoU11ljtTsKQiFUiuEYajTcLNNZ4Sp2m3vT7tb3ero5iHdKYPp88QUk80pkDNGZYs3Tw7awYs+JYY8l81BBU6ILYtEdllvskduCIMssiKQnQhgIztC5rB5e0eAQOtn0c6Oh/Xs/V9deKcQy1wiRL+DYjrLF2r8zsZ6l1XlZRL3y4XTBeIm115mJlXEeZZG07eY/xcJy1af5onuGOft6y0vxfsbLczaElDYoIs1xZ7+GGMSUvcUlvqoLN/RSarqC+MHab2GblXaEWRhD+OuD96Kvudp1kWMrzMvZsjDLLyjU2InScjcYlpJoGVeJHZ0Y6Y08hKpvj6u5ZvP7gReyMF7BYjPDaqasosvLwjROeC7w9dxovjB4ir5PVISEhISEh4UnhZDPKEF90sfTPgr3VGeWQxVLRk0Fov/UEi3VIG7xfpS1wf7SG+6M1XFy6iwtLD3B151wvNvBdg2V/06B86MYC5/uvbk0rHklhrbHqraY51CoDYNpToU8Fv260Y65vC49BDc2mhIeTmmFNv6yFzdPCAe6aIe4Xi3hx9BC7SxfiiUM0HXKk77EwaC2rynSzkkFRWCs9zNvh+5PscbCdxlKT5YhYf1Yk70ktJNuhaarF/oL68j7oM1c8Yp1AZi1SC3PWOMYI1+7M+CWrm7kDLLLmg6xkA4bKZJg4/jvL6XpwK4/77cMQ8ntEsGMRFrejHWVafW1+1+acqEWN6nQ368xpqrZZmadjGuxoCLjHhJhVIzoW6+4BeoY/oPabi0BMMk9hTeOqJo0yGxMT973vx5CSupwwl7eE0aRoJk4fJo6Fh6tLtz/PHncPnp4xvJ+ZHF/KI8MP3Vl5CsVa1MciADmGwcY4HXLPvqTwcLMjMconBHf313FqfhsLxei4u5LwlODt+VO4vP8QRWKVExISEhISnghOPKP8vGB1uIv9cohRNTjuriQ8JdjPh7g7XMbFgy1cGy4fd3cSEhISEp5iWKSoF0fBM/Wi3JpiuxKKWEipXpIL35CyTnHQiFk3ZGap0MEs7CeVbZWL2MA2PnzqbXxh+zJG9TDqEPLEcVRfItln6ZHDm9YyG9EKblqVMgJ2Qq0PAefq8tBQ8lwH3SLTPZmClT73cJ6YFfUg7Bvfnxbm7c3sFL5lfA/DYh7jvBD9C+tqZcH5F2ZvM2GbkWlSkx9EZE0dOVREMqOGetTkFRE5h/8tJBi8yMgVvH0/F3BpiVg3RZIyLTzcE4no5ho32v1DJm4uoXBltI5LKEpnCvemcUUm5h322EUaOjkGbZcrznwkz+DSC//bS5C4NMiIMnbIkRCRNmv+VIdLYUpXHS41kzhCqGUxGRyNYaUsmr1UGzfC9hsLRRqrF5wzf65nHKDUhiKPIfmRl/0Ec3Hzm5z4siCTXHOAVoxhAICTpQ0UncPAOfgNimY5KVsP6WpQu/26trn0guZ8J8uwrGn6zZ2tO6A6R3wE9JJbQBmDWlndlQ4mPF4k6cUJwbge4EtbL2BzvIiVASUksVjK93F6uIlzcw9xcf4eLs3fRWG405/FfJbkGs8qRvkAd4ZLeGXn/nF3JSEhISEh4ZnDM8Uoe/RxyFFCwPX9yusWxsrM1LYls8zbmvY1e69cwatLt3BmbguFqTCxOfaqeVTWoLYZMmPxodUr2J4s4qAaYKk4wOpgHzdGG9ir53B6sIW3RudRItxBX4ZiKmb8mo3tT2OFpGOf4exq59qyMmJwlID7JsY2+y4pTkVGX8rfj4Ka35lamDeGa/NreN+D23hnaQMHhSLNiTn6BWwe1emysRDhkEIHOlEWY4ZjYRm5w17EAiRZZvXeFMwyPx6/xnbpmRhrHJ1X3G91DEnnoFnBHXhiE5Houu7oF/4PMJbZMW9l3c4N5MSn/U/Mc1F32eaBqzd2CUeMknBEd+Zz/dOS5SiJHAim1NcHdbTrIMaQMhXH72nFKugPR8w9Qb1YUp8I86059nagldXdsui4VB9Oso7Wz8aRzyjHXntG2SUjCYYWZdSg8HDthoV4FmZsh5nrBIWOKwctSUT7sRVZPnjfQ5Y5DNVJlhXaRwvqs7QaNIU4HEd9PmhjXsyt/Rz6Dapj8/w/uXg2X5SfUWSosZCP8fr2ZeTGokSOiSVbPZmHgZsHG1gf7GCQVdicLOHtg3P4wNI1rNtdFKZEYUqUUbtSwklCmee4srSOV3fu4XPrF467OwkJCQkJCc8MnskX5Rh7dVQdT6+g7gFzF9uAGu3Wje1nY7CDFxbu4YWFe/jMzsuY1LpjX2Vz3BuvtW1mBp/deRnWAK8tXMPAVDjQ+iwhGQNNH+W0mUdmUjWmXSuToaG0firh4eS6gJl2DKNkloPtaBezfoRr+uAe0LSLnuDTUlG78rfX1vELbryN5eoAW8W8ayNiNZFaSaAbfivQh7oPMZfyNdCIe/YW3TK5LpZyXmVxbVgHzBpEVBhnjfuMa9lvDoUlk+M/KOPbWdFmhImOJR54VKiyV01/7OC1yYq2WYaSqtmgkIlGBizVMKWz9sucpRZ2rGFNoRpZyEaf3l1hTjXLTwd0jfiqPtdB207O0wzyLKoJlDT2ONJmh92OMd+aFUu58LJf+rzZ1SrL7kUTnHCtcW3cn5tTedpooRXWEl9YN0EXA6Z3dzvPlJNsRBlPd1063bL1iU7YuKZ5jFJZF6yfQgodWATEvBlNUDMr/PtAjzpgz6ge5D/fvE6K25mRztgJwn49xKgusFvNqQ+8OAwAgwflMl4c3sVStn/oFgknB2WW40vrZ/DBh7cfwcafkJCQkJCQwPFMMsrPKnarBXx25xU1PW9fbFWLuDR3DxcHD/DGaOGx9i/heHFjcQUv7Gzi8u4Wri2vHb5BQkJCQsJzhRQebnY8Wy/KMT8XzcPlce02Mu6iYYCOaqrpY2KZsr/M1LAweHt8bsadiqaeAGkpz5VmxvaXLxIeLnTmmy6vaL01wjpqW5qsRru2fcaChh5l9UA/6bawqN2J+dzZs/jaG9cwGma4PVjuti1CcgXng8yQJIkIHKlCkzj38ZISir5h3jqSBkXOEcuUp5c5qYYP78cQy9on+9B7f22ZseH0ItuUTk9hWz1vKG/ytcGyL7TQcVKWwa1VJLWIZfMaCsc9gGXrc5ILLTycDBMHdMdl6HwrD6b92QnPp8klNAmZXBdoL8JV6imI7K/dcWS7WP8iEpEYwhBwYR+ic6omG4pBOWftdXD3WM0y5VViYmEdJflHkR++Y02CoYFCxlG2vjpv+2KF7CcIUUhRNmsxz/PfytzfyXra97kuz6Mm7dLmJSlPSu+/TwzP1otywqHYq+dxY7KBD81fwdvjc3hol467SwmPEdtzc/jMufP46J3buL2UkpAkJCQkJDSwNkW9OApO/oty8NUdesiEX8+2u+5xdyXr90kXZZl7NSCWvFGFOZdftrfKDezW83h1eBNz1TpulRtH7MgTgvZFLpkRzkLRD8WZT67TQsdJZrmp55iGnOqaznaxEFZ1Tg4y3TLfZt4t00LBdZIUAF0mg/0/qGuM8xwZatRZ9thC1k1FHzZWYZtlWDgtiYkWzjHG1LZOciGzHHRLSUYiGbQjOfpxRpO36Zd8f+Ex9zZ0iXrcQUmyy2EIOFoqjk3EJGvOfArLPA1aeDjPLLNkJCWxeX7M8xMa3jdqeDgtkYcJ74NgnNF80sM6EVgEZPW+rLFkCLWyWFM9mO/g+EQilr5JsSAtR1oyJjovmpej5pHq7gHrnOX4mKQEJ965jjv6mXBc8usgx+WQmbPIec9bRfLu2CVmuWb7q0tXn5z4KtZP6h+xzRXrp5i71dB/EbaZwstpToBaqMGOc6o2lvqEDEx4JKRPi+cUO/UCrk9OY8mkZCTPGm4uL2N7OMTPv3IFy6N0fRMSEhISEo6Kk80o22kMlVtqYaqm/H+k3csvOaswIxqEprYv4xfTsKHzcc+YJu1L1dW1xjy5L9Gjsvfi/ADdY1dDJ0lmha8jloBrJSUDrYSOI9RBQPpwO5Vtzrvb0WdpLeoAbYIRWlplf1NTmwom0xqDz5y/gBd3HuIjd27j375y2Y9HU4XHpafzVSj9mBZTsl3TGFexfYeB1ixAxMAE23l6NPyf//Y6ZH5HKLrlqf3kDLY7d95i1T0vpM1UU3RH7tcY29xL/8pBp8UdJ2dBrGuzjajH2ebpjHLZg0vxDB8bsz61sGOSx+y+I91y7co4C+g1oxQmjt8HNBampbeeNo9JVk6bN5VnhmcBI4yrrMt3pM27nS5qY6KeUnfa/uQ4EZrhTv1pfVDStrda+u6TJcpgux1z1ljOxXVQ1vymJCFcHkDrJlUzGLhG2Y+9yJsMHUNZsTYL6p9rq2SMshurnm1W3iNmZZQpWW4mE5bwftK4Uc6nP9UxJron7Tl7xKyExCg/x1jIxhjbk/2tlDAd76yvYq6ssDyeHHdXEhISEhISTiTSW9JzjMVshAM7wIrZxxZSqLhnDsbg9vIiXni4hc+fP3PcvUlISEhIOEZY6IleEuI40S/KBoBhIvxupi9WJrLtBO3MIBE4qmOUlklJc+qzEcmGN78IM6oGzaQuzXBvTs7hxcFdfHDuGn58/L74AcwC2S+ln95qNMVBrdMkldE9rpjwNQlFx0lOcYLRMvp1DoHLJIRZWHW80xz2TKSsmF7WdoaZKlWRTdhWXVh86cIGftEbV3FjfQkPF+fbbRQ/nJhUIHaPzBLKLSZN0Jz5NBlV1HnQm61DCQag3Ls135DkFegiIi0Jymqo58yK/7WycH9GFHZrkTk5cJaida5+qOYInaUqLTycIr2Igczew8zZlev2cULZ+gonweCZ+TIKD5eHy6bQ9UkLX0hyGhvWBdBx5ouGcuOmdFmFm8sjTnKdjHfaPBYzocsVrE1NydcZgxHpYN85dWrntP1ZpZqWupBdA0BIBXwmOXcdeUY/IYHhY5CkF6XL6GgqPjk2iEkwvPSiYNILChlXCac+IHDsa/rJDsH12T8zYqH4yrbMS8i0OSsyXjoepUp21pjKM+Hx4ES/KCc8GsZ2gPv1cvDSlfBsYVzk+NzF0/jqq3fwY++7DIvuQyYhISEh4XlACg93FJz4F2X+ZZ2JrzYtgUGMho2xZu3XImP1hLBfb9TV0cIcefaj6wwWDfHTg/EJEx+EDDQ/zt16Hi8XdzCoS4wxmI2BeARo/mKSDVIdFzQWXrDMQeg4EUoqCOHnHb9iHXVVmcORZH37OuzF2OaOE58gacSqdi05zbA7uXaOKnDLm2eWcHF7Fx+4+wCfv3C6qV+FzEiz8vCLH3VMk3W0+sq4jCYqqQXVBGZF8g5/3Krkzoc/P+iUQUk4Ih3oDDsXMcacs4BGlkWdFRGi57eqZI371A26oIWHE0xyySarAiF1SeG4mt8NZVa4iZc7CVGoOFoOWEgvSkySkVMfu2EpJJcRYeKA9j4jv8CZmbTukOg68WXdMpVVFfvWnh3RDKoaMyzvAy05Urh52L7p1o0+T6goYl1SnzlHjbEqzIicUa69g59z5mP3dOn2lylMsgQPHUdvNzQuJyz5yaRs2qqK5mRZ5uhHjHIbJq4tklbEYO6n612FS3Udv7by2kSee8F1l8/CxHc9MaRPi+ccIwxwo9rAa8V15IjoUxJOND77wmlcur+D9d2D4+5KQkJCQkLCicHJZpRtnIUKvuhq8bnVQ3MJdHVHUSjMsPpFL5hkq31dRrR2WtejbDjpqrrkHIy1uFlvYJBVeK24jtfry6i9eHd6mzHMnEThCAjOmWBngnMmroOW3lo/n0KnxpkmwST3ZY17JRwROkXev2Cd+6cumn7wMGjEJNuC2DlgXGT43Mun8NVX7+B//9AlWNPsnGvsfOg4lQENy/qyxrOFSFOEkBrzJtYF10qw1BwUcqylEfuxxt1+KmWinWA3yjmT50VLfd0HfSNSUrXKzQW5olEmk6yWcISYZK9HBmOL6WSzMqrfJhxpO1o4JrnUNMo+PBwt2TXyLOOME9MsbGrPa3Rk9o6205LlQJTxzUQCECjjLBpWTmEk+5zGqJZeiqp7tx0yts1vN/bcc5qHcpPp2rkun0LFlW47Hjpu3lk8aDxP8nbCnXTSW7cThvVh4aifrOd+Du+eUMkaaxbtmEa5tXqy/YmpKphfRHjZPhYWC7TP94TeSGcsAQDwTn0GB3aAD2bXcNpsYREjJFvOs4Wbp5awtTDEa9cfHHdXEhISEhISTgRONqOc8Bhh8JX6PM5mm9jALl7K7uG2XcU1nD7ujiU8Rnz2xdP4xa9fw83VZTxcmj98g4SEhISEZwZVD1+UhBAn/kU5lF6EkoswY5fYsKcuwEibVcSh44mAO63F9ieVJVqIHtVsx2UZBrexjttYx8CW+Lh5C9exETjb9EG0nzGnvBnKNBOUdPYJ+tRHDhJY/sPrHoaHC9epsgy6sxRnJG07CDNa9LyglVXYwgK5heVhwgbNYDdFV4synsvw2ZdO4auv3MGPffASatMeGIXkIgnG1DBofMl+q2bFiIRCOvGp9zItrVImM9ixetoY7GTW08zXWj8jEgr/u3bb9DxnrTlfyEFYPW921YQ4VrkR/LF0Hf6ssIXz3clwcPx/CuVGMossMHvXwXLA2sjcvgsvveiGh6N1FZNe+N8kvWA3s5dKKZn5kDV/apZNCW1+ishj5Njg9fo8A7TMfH2cYjn8FK71JeLoJx38jCJZ05y8QY5sMe2FNnZl27wrXjnT3Y7kDT78GnO6pvBwGckyZrR0koPfgDmiDpz0Qjr1Aa0cAz5EIZOICNldPqtbz4zqodjp99ddkWwkPF6c+BflhCeDCQpYGOSok6bpGcPNU0u4eH8PH7jxAJ+/kBKRJCQkJDwPsDAp4cgRcLJflK1w0hNskMZQyboqNDbPf8krlHKvsu6+jWSHlG4FbIFkHXmTMUceYjAjrIlcFi4CRom8/WKdwnIexqqoweofFdo5oK99VuZzIVCdvlGOxFiKhQhSv+SZs11nOxP+H7ShJVrQzj8xygMLW1hYNlBMYYPtAriiz71wGr/o89dwc5lJMALrAqAxaGqZYJI1xrUT7k1pM8biQtmuvaj9RlUsmU+n7eAemc5EB/eN1Y8h7hypUJNyPuH3Dw1uYlyDmy88D/xwiQk+anKCOkKBkTMfT5ROIeOIieYOWOToR0keKua41UkAUTLWkRL9KA7Sdd7cT1p4xRjU6/0uIBoCc0bEko909hvbXcwpL+hn+FAMrBU1gKq1gWjDuk0GxMpE2MIgtKGrzsdJH5A1gzrDQxQOXNlAOPUBrWMfhQQNnU1dn/wzgDmbyuRWSnIQPz4PsRj6NgUDrU51041LCY8JJ/tFOeGJYR5jHGCAI8/eCU81xkWOz73gEpG8/zLqLLEMCQkJCc866qTRmBkn+kXZQLBXQpusapQls8zAQxG1K11bWnwdyWxp5E6EUbayLmtC7aUgCtXxrmigZPpnLRQVrXuxvodlHGAH8xij6B9OqO/79KN+9SrsaieMEyc4BHOTKQyHtqKj/9LCvCnMlmQH4umtbbdMsBLNuu5J4xplmwMm615UWhek7TbtCby5towLD3bxgZsP8PmLpxm7dghzCsFiRS05Yqky0YoQr8f+OsfE24qgj65U1SH7/zm7zc4V/Yl+xsLDaSy8ldchsCrZoA7XL/tbXwnbRetizHLf1NWE3LfptMpsh5nY31zeho7zKYWd9pSnt65EeDgElhwa8+7/qt2fqdFYt5T7R85N0bE0I1St8qPOk5oFSVZRtLHRprVHmywL5glap9CVtEp7WtXUDmmN2RynZXzxhdOtNqRRtj3uba6Fp/FMY5CPb0qtXriQceoYJMscS35CDLIRcznQzrPeV4afT3e9tHTonWurzSFaQhxRPxHKTw7p0yLBY9nu4zw2sYlFvIj7OIXd4+5SwhPGZy+ewaXNHWzs7h93VxISEhISEp46nGhGOeHxIbM1Xrb3kKPGCAXewHm8D7cwQIlJGiYnHh966wFWd8f40oV13Ftd8OsnRY6fu3QGH7t6B//m1RdQsqD8CQkJCQnPDiyQnPmOgBP/BqRl39NMwGQ+iZnKVBOGNBMpTj6m7tqSrNyRZk4R7TRFJigLnC/IsSzTCqf0VynTstrBWKyV+xgjx3txCz+dv4zP2suY+EBPkTalaVOYlY+aoU8LcddxrmS718pk34JLJU3qmumRrN+KhCKWPVGDD2NHJj0lrJyXWXDTnLZu4MbeoIbJ6uAkk7mTmz3rvOnnx9+8izcvrODN0+vNSS0z3F5ZxrntfXzo5l185tJ5VQ4gpROqvEKRGXWyZUbDw3GpVHi/9nfYowZmqKsWci3EdCdHfq6MDXfbmYci51PL+ua3y7tlavgtYe7mDnQyq1kQrm0GLyA+GxRu8qXtB6g69WR2NP6bTOo8Y1rpTOCV08xzeRLppmSILqB5DtgMUWe+Izs7xeQxR3XhUOaOXooNbQzSqlhmvh59CTaUckI+cboToUUoNLWBqQ3aTKFMHkPbqeeTnPialdy5LnPrrJcddcdSNpheFpMUkcNfwaUXzsHPO5ZySabIGBnI5+gc0Sr2btKRY0Qc96LSoMgYTt5ETw7p0yIhgAVwH0v4aHUNOWrYGWMoJzyd2F4coCwy/B8fvoBL9/bwtV+5hYsPdpBXzaz8+oXT2Ngf4cLWzjH3NCEhISHhScDCoLJP39/TjpPNKNtpDJVjT5gIX4aH05xgNLQsM33umell7IJLZ7CYM1/IZAr2kO2PGMlOeCxWNqtDK9UvTY6fNi/hbLWNbTOHPczhQv0Q94rlKIvRcWDifVd3GGlLImBV3SrNGUK0GZDGgrkxXWIECvnhz62v0leRIJkizcmH6hSMjfBOg122WQsZ55OJOEZfZQ8Zo7yzPMDKlS0cLBT4tx89j0s393D5/g4+unMXn710Btc3VvAzl87hk1du4uGLCxgNipBFkmNOYUdVx7tIUhFvkSEnXN6mZ2WeIhcVhQnr3NdamcbQi/MZsGudEHBsOxDz5thjft0dXUXXnTPFOa2jJbrjJcYsU/0w4Yhjkt0FnDDqeyGfuMNzjnts8NK6yq0bV+12PgGED9HFBj2dD7o3eejFykxllNVEOO8C1KQ3kbnRRup0E2YpDdAzgD8TOw0p2/WxRkbqB+Hh3D1gZMIS3ucYK16ZThkxwoYOjA0JGaxHs1zQMgw5Fx4Yd+bz94h06gP8fGTLkFkGgNo1QUlIgvlaczKlsh6Wp87zD2jP/6NaNxIOxcl+UU54bDDW4hOTdwAAByjwpeI8NrB3zL1KeFzYXSgwPyqRVTXqPMOVM6u4cmYVL97axtntfVzfWMHmwjzePrWGr7p1Cz9x+RLSzJuQkJDwbCElEJsdJ/5FWdU1RsJUtV/B/LONhKjExClss6LH6nzI8a89vz/Sc3XZOc9I8y/PTtv809qEZXy8yzSmMzPLBp8dXMQHJrdxM1+DNSbct8S7/Q4V0Sh3NOYKq+c3i+g9tTBvmvKkV6rtHgjCP8lQc0HqXleUR64Hh9IHmxnsLgywsj/B5vKcP1nreyNszw+bzSzw5ql1nN3ew/vuPcCbK6fbJsWYjYaH05hThUWK6Z4710QzBWhafXnwkTBhGqy2P7FbKMfnNco9j70zB2jxDiPprRUD15GTiRCmeCM0ZT0EvnnANlPIuOlsNSWAKJhempi9zFlM6rIts5SEpAq1ykCT2Kcu+qUWVnXBvQTCsyGa5rqHlj6mVY1eDu770NEad/vgWUvN+qXonv2zjCxXCAobjTLNXzYsC9Ypz0t9/qSKmavKQsA5nXvpNM0Fo5gr7xTSLLhVY+KsGBSisGbWM5mEpGbvA16v7K0bzIpMbHPWHZ9WsMxaeD/fjnJto882ZZpIeLw48S/KCY8PD/JlfCpbBgDktsKSHSGzKYX1s4LN5QFObx00L8rW4mu/fBvnN/fxv33wJV/HGoNPn7+Ab7h6FTv5HO4sLh9jjxMSEhISHhesZR8QCb2RzliCitP1Dg7MIJq6NuFk4cqlZbx8cxvGWpzePsD5zX381IvnMB6E38vjosDPXLiAD9+/jcXJ+Jh6m5CQkJCQcPw42YyycJ5pHWS6dgrKpiXDr/H6Pm87l3P4da5JZhL3jjW+P9xEGpqZQmdCEy6YGUY6nQUff53E76xMfPIYpSutWR/T4bY7U+3iqjnVmNI0EyLvyizv0keUJvjNI44LMZ8Gb7xTHCDVvmkSD9kv5Rp1nP4UU1kvaCc7V+ym3ulw+oklBxlbGSyMK2SlxantEb58bg03V1eYg147rjbn5vGltdP4+J3r+NT5F2F9/LoexxWRGKgmY81m6J0bI1obBd78GdMhmPD+O6TJFuR8qKVMs+yPEHVkpKUy5yhW6z5os/V1YcSJ5x/APlOeG8h8riKzM5VpzlKjunD/tzcCrRvTkjnsUci4wp2QUdU+hqgemdLrgpvZxZhnMRttAdjctqHj2JMtF2b9UEbg1ilznJx/ZnXO1+rLuSOoIuRlgXQwdo+JdZrDl5o9Vp4X7Z6m88L7IvQqgYxR3gPBCaX7J9KmMbxq0E+jZCgleUTpJBfjrB1nJPehUIgTNgZJckHOoxl7MHjnVjdwDHMo9X2g6ZBn7fMh47rH59eR4zKXXmhzKu0vIt+RZUcOf5hwKBKjnKBiF0Os2+TM9yyhLDKUmUFR1RhUNUZFPJTH9eU13J9bxIcf3H6XepiQkJCQ8ORgUD+Ff087TjSjbABkQQg490P7shZfpZyB87+ISeEh2VxjklkGWnbZh5rTvsjVL3nFE0d2RiPSfIgYYsC1DZV/Y23KLriyZTvCAywhq+JMjGcQMqUshlnvDcG2qJYEWQcKm6dcIx9ajycwoOOJOJlo2/WSf2njM8IiUii4rKg7ZXWZNY5/FRuz0nHVMXG7wwHeOreCD1zZxO5giMVROSWUmwGshaksvrR2Bt9w6x2cOdjBvbll3WEvdlwRyJBXwTl295tnN7mDre2uk9AY5di4jCUqiYV6NIwlM1awgDJUmTJmNbZZhjs8KiSLPA0yfBZ3LJRMMn+ojerw8cEZZc8kO+86YpEBoHBOfKU7QQvFpN2ubLajJCQVZ5Qde+jDdTHm21rHKLsyy+4H6mZGKiKVclfW9Sl7RGhJlVRHPzFewkamt9lxAqyVetr4rMQ6/k0tHdFkAivL5gBONvv7qPtAaZOlKPd2j3FcOraY3/aeUXbz34RlqCEmmu6RIu+eWGKiS85k0/sDjUuWGIXmWQrxycegfFYEbPpMlkb2W1geTkA44hOLE/2inPDksIwDvIELx92NhMeMNy6u45t/7hruXFzEe+5uYmcwxLW11cCzGwA+cfs6Ng72cX1xDVVm8LGHN/Cvz74XNhmhEhISEk4kLJIz31Fwsl+UrdXTVHuNcltEzI//GuYsoNAtc82VD5wumWXWvpZwxNNCFCydf4X7UGC2s1nLjmoiNkxfJxS6vZmKKchhUcE0zR7xS3WWdMJAXHPlf8bCtUWY2hirp7UtJaQaQ9+mpGbb9U1McgjsgGnmHEOhMcoAYEwdsGvUac9asMFQmQxfPr+G81t7+PFXLuBD1+/jg7fuYW84wG4xxN5wgAMzwOrBAR4MFrAyPsBmMY+VyRgfe3ANn15+0bXJO+v6oZ5jcZK1UGeme8Hba9Klcb1vAFmTFCZaD8klyiKMdNTfYJrlqEb0vKgpwdVQlnT9NIFiSD+FyUjc8ohiRY01lkkbxtX0R0bAKLt68y7xSMEOcM6xyyNfp21zNGjqk4aUpzKuXRZJr5Pm+tDMACzhSJCMxIbrTEtgd60E7zYrxx8Z0iiolEnrDaDcd4org5+ztO20aSX2LiUsJTJ5R4Dgfgg7oaYEV02Gkb4IlDz1tbNYVKSzZxrlXKSuHjCNMo0vStLDk5FQiEKvlw9CwLnnudAqA+1c7JeaX5JfwQ5IXNtgJhCWrkQoPzmc7BflhCcECwMbdRBLOLm4vzyPl+5sY3thDj/x0iXkVY2l8RjL+yWWJmOc29sBjEEBi83hAs4d7OCthXW8sv8Qq+U+toqF4z6EhISEhIQjoEpWwZmRXpQTOshgHauUXpSfRewPC8yVLaVR5Rm2Fuaxl1l85PYuViYjfHHjDG4PVgBjsFyOvOb3tb3b+ImVl5DGRkJCQkLC84BDX5SNMS8C+EEAF9AYab7XWvvdxpiPA/gLAOYBlAB+t7X2x5Xt3wKwjUb0UFprv9atvwTgr7my/9hau2OM+S4AfxDAK9ba267ejrVWz3rQCG7afcnMfKxMOtAFZWQRykMJRrMLYf7kJm4j6qsm2XC/QZk3AbPtvOWpa2uhsFRRiZHm6CTfaRQZAf8/h0syMsu70KzWXs1hqc/+YiZSrc2I+ds35U2IrIzOMZnP+OWjEFRk5mUZ86ysHzsvmkndOSOZot0wdzKMgkkveLOZsciYdwg5PcGH02r3Y2qgNBnyukZWWn+gg6rCKw8eIrMW/+bCK6izDJnzvzrIByiRo4LBxOS4PHqIG9lG26Z0NNLuA1G3+UdUCWRNUhfTPVed8IzK/mJZ0dQyxcTZMWlrEgqgm5lPyioUx8lOaD6gzRBaK3NOZDw9ama+dvdd6cW4nq4poux7PPOZl1w4x725rHXmG7gTUjg92kE18GUHTpYxdnKMScE+6MiZj6QpRdtPWzWOfK0zX9s/ql/LcQroztZPCbQMgrSOh7mMyctkZr0gC6yu5Aqa0FQ8vg2ljO4Bq+jnOvNsrRzgo3qwMrSh30IpRbOu+U1ZIXOeHdItx3ke1AVaGVzHqQ/wjn3SqQ9AG6JTcwAnOYY2Pmm/cjpE95QlZ74nhz6McgngD1hrf8oYswLgJ40xPwzgTwH47621/8QY863u/2+e0sYvsdbeFev+rwC+E8CrAH4rmpduALgL4A8A+EMzHUnCY8MQFcpknnl2YQz2BwMsjSfYLXKs7+3j665cwzjP8RMXL6EWebtXxwe4vbSCg7zAncEyLo43cWNu/fG9mSUkJCQkPHFYmOBDOKEfDn1RttbeAHDD/d42xrwO4DKa75hVV20NwPUZ952jdYHhV+5/BvAdxpg/aa29f1gjPDi+ZJKNGsS/S0n6kDR+wcsgyti+5Tqe4IS+lr3jl+KMRP9zRz/hKGa0Hcboj9iXeevt090fc/o4hR08xFJnt7GwQ8FulHP1JCGTtKiOd1pZNOyXW5WFS6DLJAfsQHG0o27bapY8AD4Fvg+cSqh/uUEG2zIdQBs+kOoEjHLz+8HCPE7tHGB3dR7ndnbxlVMb+MrqmWZ/RP5ZYG28j4GtsJXPw1jgYb6IF+wDLNcj7GTzvY+vPZ+MBTShU0/gaEShldqbi7cWrDPsoto6cv5jznuynzG2OZhXWD+tYIMEE60lkOiw8VDOR82PLxzQhp0Xf72P+CDUwsNVzqmupCQMbH8UfotCwQ0Zazznk4o0k9tAcebLXNvEPje/m7ZGRVPnoGwfUTT+a5cAouKMXdEkG/HEN3+yEQnousCdbzPl2jx2zHo5IlYQNQSmtJYpFh0r6wAsJNt0aOxx97kn9mfRjnnFkOrHc4RtVkNn+uNj85noTOBXI+hw7uRKDDKNYV5GjqcDZw2pCuZQStYJSkLC5xtvzaC5nN23IixckIxEOvpx1l+E6dMsXVr41oTHi5k0ysaYVwB8AsCnAPxeAP/MGPOn0YgHfsGUzSyAf26akfgXrbXf69Z/D4C/CmATwG9h9XfQvCz/HgD/3Sz9S3g82MAO3sS54+5GwhPE/cUFnNvZxZXVdSxOJri5rKub3rNzD28tnYI1BgYWtQFuDVdxvtzCzrD/i3JCQkJCwvEjOfPNjt4vysaYZQA/BOD3Wmu3jDF/FMDvs9b+kDHm2wB8P4Bfpmz6C621140x5wD8sDHm89baH7XWvg3gG6fs7s8B+LQx5s/E+pQZg1Nrw7aPFHamkp+gjDFQ9YmkHyIBWFtiZZg2noyE6ikhqaJpjv1uI5+CyjqpTQ7YOcF8Bt/SEY2ylX03wGkzh1t2CXOUDlQ5Z/R7Y64ArOgbSSs1fW8WrtO2k3U4NH22ZD1UnSh9mXPmTgTOD0K7Rfopw8JxLVo91CiRKeAhrGgoKGlTC3cd5pgkonIdK5HjFIaoWBmNWevSunJmgxjlfH0VL+3s42ZeYGlpGRcGQ2DQaEXdZlixBzg3N8CNjdM4X1lsHMxjdWkBpZ3Da7s3sDOfoTI5MkrL6gjFjJ1X41O2huxx0xfqMDplMoRUcK1qURaEVnNLdNG5Isq4tMpY9/epZl1wOtnV+Rymtv7/pszVdxJcLvOt3ToKWcZZzkpYFwKto7eeuHHCqCma/3K3LmNsV+bq56Blt4x6nrN5s3DxNAvX5hxLHDKwIfM2YBc+r5rfQ7eOh4ebrxoGuXYntK7aD668biKqDOpmXl/J2hNzUDi22Z2skp1Qiwynsxxmzp0LNjcaN/4zd20Y8Q3jHh+ZI7XVhBzaPCTLInO/ZsXy90XsftCSUigMeCbnsci4DhKHCKONNse1/jusjMZs3i07lRcwtfEW1GC+JquqYmXtnuMupWykmBfd5zpnhklbTPcBH4OZCzU4cGL2gj0YiG1ecRPhftZq6MduPE4GzUHzkIgUY96Q7xKbw+lR6sdg26SfN2mZ8/E5of5iKmYNw5owO3q9KBtjBmhekv+6tfbvu9Xfjob1BYC/C+D7tG2ttdfd8rYx5h8A+DoAPxrbn7X2oTHmbwD43bF6dW3x4MHY/995UQ5kGXSTdl9q/brIi3I7cURelLXsdHJ7rWzWF2U/0URelBX5QWf/rA3e9p7ZwV1bosrCtw5tAkYG3N2boA4mxGbhn2OxCVgp6/WirPUl9uJUhUv5G4CeYU95OfLOfDRvM7lF5R8GPV6UzfQXoIx5I9HLyjwLAEtB4yfuyXfbtmU0eVt3UXiGKFO6yRwG793fx87eHq5WFU7dv4/bZ5oXFpqUL929iZ+bX8atcYlTe3u4OSpxa9AULoxybBxcw+vzF/2Lh1w2+3PrvByKlfV5UZYfOmjv5dZZrvvgVJ2QZvhgDD/gwnuEjxN6Ma4L4O7OBPWg+6Ls3vmCl+HaHQ+9MNfsvFTuhJA0pQ7GiTt2+qDiD396UXbjhcsk6AWZpBA5N0PTi7LrOndski/BPMPeMA9fhrn0QjrxcemFzZs5m16Ud9But+lehh66F+39ur3we+T8h+akTViwZBrrt9x2pmyvQzYxYglWFi75ODvqy3CMJJHzUPR+0OYx5X7o9aKsjF05r6svytqYFx9+QczqDLg9nrQfefw+onW0P6618rGZNbaD5BH0ccju93bP7n/2ooxQLjRgJ43G/FD7yHPjn5jXXbZdO/ZcjGb+sUbZ/twcm03Yi/KYlt2PNT/23DLXxierLzHLi7JF+HKf0A+HnjHTfB59P4DXrbV/lhVdB/BN7ve3APiSsu2ScwCEMWYJwC8H8HM9+/ZnAfwupBB27zqMDw+X8Czj/tI8Tu3v4+rqKk7v7WFQtQ+EzNbYGO/j5sIKAGB9so+HgzZ+8nY2h6GNzN4JCQkJCQnPAPq8hP5CAL8NwGeMMZ926/4wgP8LgO82xhQADgD8ZwAo7Nv3WWu/FcB5AP/AmSIKAH/DWvtP+3TMWnvXMdC/b1odAyArFS+MiAmezD2BAxCxeeQExZ2fiCEkRxduEhIh43ib/ktcid1Cv3y2P+WlVOMj22Og/3kt0YbCKKuif5FBMDdlN2xaj3dmXqWzPbTCw9tU0WP74IOZ6sdCGinsjtxcdQKk4+MsDZnJY/1z4d54CDgjnUrY9p0ytKxHabKARekLY4H7Cws4v7WHq6tr2B8MMF+WKLMcqIHV0Qg7xdBHwFif7OOdpQ0gA5bKEV6d3MVnFy7CZl1GKmSRmkVN5kiN/e1aVFsnOc+WsMKyvYPCSuynkp0zZlnp1OGWo9h2tl0aC9VcrmXf64SHC9hDN9doZXSS6JwF/XQMdN290anNNqIJt90364h15s580kNey9qnlbV1HLsWhIdzTlKe6Wv7QubxeefMFzoWNr8p+xrP2lfVBshsG66LlVEYOXL05BYW6WTFQfOCenfJIaiV9UHEMhZAzGOqRKTP7pTHl1bWcaBT+uLrB9Ye1zfNGgiSJLjroIRE9M8qbgWjDLZSK8KOwQgGHGivKbHbFXuuk7ysoude0Bc3vtySs83k2Ndx6uPHoEmmRLY+Pt6IlCZDThDaUFhnNWa5j48/q+1lewn90SfqxY9h+iX4pFL/OoBvdb+/AuBjfTtjrf0u8f/vB/D7+26f8Oh4xdzBTazj6G+yCScFN1aX8YHb97E4GeMgLzBfldhBI/JcLkfYHjS/jbVYKQ+wOZjHpd2HeHX/Hr44dw5becrQl5CQkJDwbONkyxqsDcOzOLRhcmxYF4yt4Y4EgjYMQi4JdiZkY8XmPZORyA9Aq1AIMZZZMsvNOtGqplHW9MsMK2YPyzjAz9Yvyw5223yCiDLZil5algXnenqehC4JL5iRqXX9+SQdHtfahTq8oAnHJFO4t2zAHLE8axz+D7QhjHjAfOeLgsw06caNJsqNwRrUJsetpWVc2N7BKC8wPylh5przX9Q1SpMDFlgZj7CfD7A2PsAr+/fw6dXL2AWLeEHjishOds798XhWlTFFQmMcsGREMBGDxhgmr72tlLFOrCHdf4ruudM5MFZ81jHOxxz9UZGwPKiJSiLWL42JluHhgrHumWinTWcbEqNPbCyf+L32UzlnxPoRu1ZnXepVi8tKTHLhBJdBwhHH0JGmumCMHYWK09hpSmgyqUkfyljjysCYurXSsIRSxCR7R0tF+0v68SByJg0vxZFqFmubiu7la6duzRhC/yhzv2R41blHY4jllDHFUtKpKywlQRPEKGsHSMwwhTZkz0vf51p5Blfhs1Q7Zxqs2B9nsOU6PpaI2qVxzccihSj0joKMNa58WLiQWW7WmaCMW639AZEFQ/GH0ayX3gnwUcdiwqE42S/KCY8VK9jHjl3wTjYJzz5uLS3htfv3cGdxEXMVc8aqa5RMdlGZDB/auonPrVzE9mA+cIhKSEhISHj6YZGc+Y6Ck/2ibNGk4Z1arrHGIbPc/EMl0zWPXr8ciMpcWxReju06loxEZjRTPfQjLLNklgF0dcuB9k183SvswIId4UK+ic/VlztlkunotPG40KXaW0SZA1dFY76VL/E+kLpl7Su/XXLmwI0JYn9ZWeb0k4WLHMETiHiGQtEj0++cX/As82UGNkizWntN3+F4OL+A+XKCymRYKcd+o9zWGGcFjAXWx/vYy4eokOHBYBGwIXvR0VYGDK8rUthRyTJryTpoPPOp3eudKQkG0+11mOQg/XNYFosMo6HP84V75LfMGZXxxhCWadEPlO1kcoIwoYNjwIgBZYXEFhNxxvWaxtcjPXFLoUpWTUtvrf0/yCnRSLjkv8kysJjziC3TL0RJyU8qxygzHXI9yJDDwjhTCw/TJ/XqgT7UnYc+rw4qs+yWUTZPmcfUhFJUXbEydPTEytjtxSjyx5C4X63ST42JpuFRi7qAY5S1yCF8A28142X0bLOdQj8Ha1ZdP/d0aXhpWeF6fmKQad4Mjc8mWMfZZhovPvkNS7Fek4VLaJUBxiQ7ZplHucnpfFI0Vh59iZqnZw2/ftStiI9NwuPByX5RTnhsOJ9t4la9hn2nUU14PmCNwa2lZcyVE5wa7SGra1hkyG2Nyj1YFqoJtubmMaxTlIuEhISEk4zkzDc7EgefgBwVTmfbuF2vHXdXEo4Bb65t4OLuDh4O5/G+rXsAgMJaL72AfTIGhISEhISEhKcdJ59RVpz5CEaJbeOlE4F5XkoomNnbm+vcBlxM7609XXNRa37pfr2pYdN8oagT7SfbTEoEZvhoPJNv46FdxASFau57ZGeBmIQiVjbj/joSDN5GHwmGckFk5icA3TBo3PRI4QedzCJjIeByZ6Yr3HI4YOZoZ8/0SeAU6QVftz/JfP3OaeqcY8VxhJUdFENcW17D/GSCi3tbeGvxFPK6bgLu186xxhrAGt1ULKUX3ARIJuYpu5/ab3/eyfTPJCxiA14mZQvc3NqakW3wf9gHd2/FZBmR0HFBNSmrsN0yLQQchCTFRspUqkMJc0lyFVJh1cyc7H0iKWwb05jkYg7VpBezxlynTICLGSUe6bd96UJzTZz0YsKOoawy5KiQOZu25Y5+NBf7BEGmU2a1C2mmLKHLMHxZZL7sJIfQ5GxKWTSz7KPO09q8J9oKZH5URzkv3qFVkQa13pHunLOEWTJMaegY75ZensGbFGNdeV6SXCLI1kjh3dySZzalDIB9xiWXvJFjX8epD2gdv+mNi59PekdQnlExZz46RT7HVWRM+vasSRrlIyCdsece1sku1o+7IwnHiDfXNnBqtI/N4TzOH2w76QVl8WuQWOWEhISEhOcNJ55RDpxnvCNPj0d6kC/efanS11rAGAnmhn8EG1Gff4BmyrrOdpGyR4TqeCeXAFbNPuazCVbrfeza+YCBexJh4aRzSNC0dByJMMNqcHwt/J04BpVtpiV32KOlcod0EpQw5s4Hys8ojTRz9vAsRlM2zNv06+T8pLHH5PQ0qtrOkDNWVWWokbVJJngfNEcxCrUkjqEyOa4vrGKxnODi/hbm6hJj01gYjLXNkrephNKLhTpT+yKd1ZRkHe2K9qd06gmYKSJniNkPnHRov6GzTrOux5wRuw9Mt9y3KZz6+G/VmU86+vFzTU497jry0IReKeOdFtnc2OYNnnosLavabudZ48jERGUliwu4Ww6n1vcoDgAAy/nB4XXRhocbu/2UjBmr6gxDW6Bw3k88YYW/tK5+HXhuhWxeEB6uCtcFWc0jof/atqky3zAss92hq6JNca9UpuuuOY3G2pRzo1amHJ8R66woM5bVVxzOtTJ/XMT6aw71bsPQQTC8OGriGFelKtkVpKk04nZRUCjEICRimLAndK5zYRYzZcDIRCOcGS7C3fDwmtRPn/eMd1CmF+953avEKM+MdMaec+zVQ4xt7s2hCc8vbi+sYG28j9VyhLm6wkHWzNIGwOnJLnbzHi8/CQkJCQkJzxBONKNsrA0Cy5uo/lShDGaA1baTekbGbHUSGJjuF7KeNEMwP7076DbXAtJHQDrBm9V60E7T2GxdkH2ZmW0R9WPhkaL75QlAPIMjrpXWPy3Qe+Q8qqmvieGgJAcshBUxfoOiCYc1LJQkDAqjTExykGCBMX01TLAfn3jCs8dMmyf0bLzvu/kcdgdzGFcVVqsRsro5X8ZabOXzWKgm0XBmkmlqOijqaGHQelxv7fJrY7wNraWwxt7y0B2QPkSdD602fT8x/bKWWrgXa6ydT7pG3C/C65Zt8H+zsv+cERhkxGZ92GOgGzKuZnMcsct9mOXlomWUY+wyaSup7TLQKOeYq0sUThdas2Q+MtyXGkKMnoSaptbdpoHUXzC7wdiFWKeUqYy+3E4ZZ5o1ksanPxtcqxqzCor77qja5o51T7Nmgo1x0z1p3hqsUKctS2yCzZtqNlxnu/eDlfsFY5eVdwZvWXFjouCMsv/t0r6zm4f0yrWSiMqnVvdmz+7x+VTrWpnC3suQcSbRnk8M6dQ+xxigxHsGt/CVyXlU2oyR8NzhzeXTmHNh4M6OdwA0jycLYBL9Ek1ISEhIeJph0XzgPm1/TzvSi/JzjFPFNjarJWzXi8fdlYSnBFuDeWwXTSztD+/e9HRFaTJkSPKchISEhITnCydaegELmLpr67RKSDaCz7B3ZAmG8luVUNCya/JX5RhTEDVHa2WqzEG3ra3me7hTrR3ah0dFNOScopLQnES8mQ/Ty6TMAmDmL8p4F0hLaLw0/9b8s9GbzzqH0znH3PztZQ8+JFFbj8LDDdxyLm+lF8M8zFZ2EDjuOfNw4JgWOiHxUGDwMoKu9MLHAvNhmLrH9dbCaaxODjBEjaKsYGAxQYFhvd/KAbiDmTTB9nRai5p+O05PrJCujdg/h5rdTNi9NcdO7wSofBNEQ8FZZZuY9CImSaFz7P2GmHTGSc3IYSjIGJmFY46HPGvvN1dHmSg0Zz5rZVkL6egXSDZItTCDBAMAFp2DqybBqFz7I+f9NGZeT5Mqx1xdYE6EBOPHQCo9XkYrrTLO6F70909EcqOKXhTpRecMa/IK+lcry8P/ASa3U7rg71O1cHo/e5F806R1TH5h5Hqgk6GP/7aK9NDLjWgQ8/uBmqZLxOUc0lGTj2tyhu7DFbK3JOngFzrzufFC2Vn5uwloDqdjaEu85IJCFbL5miRBfjOuAqExS8+vXsork5z5joB0xp5DDMwEa9kuFrMxNhObnCCwU8xja7AAAPjq3WswFhhnOQZ1j0CdCQkJCQkJzxBONqMMBM589Ill6OstwixzPPIHFrE7Cmusss6CZe4bzqdtnD6RFcZIsFh837SbD85dw1xW4ov7l1BDOBccxaFDOnBM6ZO6H5UB75b1cTjR6rQOXNr2IWscjAO/bvrFUa8bvUu6sZcxh708by4KscfzCqNMTPJhiR1onbWmCaUUOIA41rAWFx4Kk6k529TAm/OnsT7Zw2o9xgg5qjrDQj1pGE1j1O1iiTVURpmqx8ac5rwk7mvNh81bjoKKso7Wzy57GAsdx49rOqNMDBNn/acs0T2PGfs+cbk2WCIkvh1ZCWjDqd0OGGKNSZbg402ui4aQE8wy0J9dBkJmmZiwUdFllMd1jrlq4BnlqlbuFbLM8OtZhQx0xllHwSSH7L0r0uZNMX+pjn7if7fzAMHppEOdVf1EfSArhWKJU+8/2XfNMqM9vyyC8HBR52leSNfEn3LFKlgrZ02u4vcYXbfQ/84VuflhFmYZ6Dj48edDTut8ApG2LxUlOaIkJOzNi8ZjTYlwlLmgFmHiAHQd/HocgkXcWTdBR2KUnztYDE2Jn9p9FVuJTU6Ygt18Dg/zZnyMsgLztkRtMszbyTH3LCEhISEh4d3DyWaUrdU1yu6TzLDvAK99ijjuq+yvTDQS0xUrrLEWCi76QRcJ8dRhTAOBVKi7VCVpln2A28xHM3gcUPWeMY2eomHr6JdjO4yxzZrWTvnqrh2x1eqYWfP05R/RKMd0iV6nxtKYkjaZwsLNM7ZZfuVrmtCQBWyX1oj6QgsYhhCjdaIuW0fLt4ansb6/h61sAavlPrbNHFaqEUYYRvW2akioGHul3XcCs6YeV1MS024kE4NW2+hvW84+eYY+3D7YtWJV6YTiY21m9JuWGmvs1lmlL1Zp0/r6RuwEPvGNZ0JZP61ghDlrRVYMX0exasgwcSpYEbHL+9Wg2V/kTufx3Vccu+w1ykX7+BrXBRZQYtGdSB5KsfZMsusn14AOqJIJ6gDtufXnTNXUUkPdMo2NldXD6yDa5nOPSAkdtYLyvsh6M6qnjkw+Ksfe8UlQ5vA24QgrM6KM94nuTX892i2lxc8qbDPpinnZLLrlnI1Pmt/1udt111uCuPNK+EDhVqLW0hFuz+t7y1PPh3lv9jzBI52x5wwWzY1SzDpjJjx32MvncL9YwgAVzlS72MnmsGz7ZVFLSEhISEh4FpBelJ87GOxVc1jqmTY24fnG28PT2Kj2UBmDAjVW6tFxdykhISEhIeFdw8mWXggLh5dXaPKILFLniKHipPkt7pTUNfu0ppOuzUTNciY257CiYuAMIfazWS41MZTLpe5+ghhkRmwf7LBdyj5GnKbslDrqdkqWMy3skOyfZlamTgT+I5S9izIcMZmEdwxVZBmH9btps1mZMXsY/SZzXcEOkMzXtC5n2aByZ4szqvdgD2gmTumsM2W7fTPEg2wR69UeFusxluyocU6b1SarOUzSbiLSC9U5VeCwY5i6LmhT2Mt7Ok35ez/rHpuUanCnwNbxrtsXklyQcoLLTjoyDlZGJlsfzkwJHddmcGQ7JKkHSROYnqNtquk7lzTQupIOkEtL3E2pZZoMMp0BvRMOkAxjrdgHAEzYiZnUORbtAMtZV3rRmr27+5m4+FtejqFt5+VD2gBtFmqIQookpsj6/XSmObtRnZ73beee6ns/CBmH5qx4ZPS4p7WVRjj1AWChNumCsGebPyGmux11Qc2+R0vRNkPtswUyWQZJIagPTN5U5BQyrivByItQUxKMRQoLR/KfgheFehx+WWSkuT6PBwuTnPmOgMQoP4e4O1nBWrGLxSyxygmH4+3hKQxR4VS9hxoGSzaxygkJCQkJzwdONqMMhExtJ7yOwhrHnCF82Db2xSpCuQUfY1n3K7bdTqnfAzEmuW2cKivb+SoKe+UWK/kBxvUAo2rYMlKHscZyH2ydsYKokE4pijOL5hAXDQEnHUEUJkZ1AoydR2pj6BjbvGW6TIRR7mOAyFxbRdFqwYklLvyyLaOv/MINTM66jXrIyYNA+/6HESvQPbk9rvfIDHE7X8H5ahs1DC7XD/DF7OLhnQJjq4i1VxyiNIfXzj2lJCrx/0ausTZmvWNU1i2KZ+5R+snW2ewQ5jvCGgdzlyuTTn28zz7hCC+TDCH3/CGm1HXCsoOv/cnNXDts7nCNVq5+xVlxcoBUwud5Jtmdx0Kh6DVmq3adJ4ej2vJ+EjsdMstA4yC4aAdYdiev5M58YjDt2jY8XTUgRz+REAIsyp63rLF7TCb8Ye3Tdc6UkGz+pzLMOqu0sUv/apYZWhexohiFNbZi3GiI3ZuybOozT6zXE2YRs8/GmS/qPlBkMhI+N9s21lx3h2ISt8p9S/cIv48onGrp77v2BqR7RXPq81MrjRfOihfhMYfJtJyVR3X/bOBtRD2tYHXiR2dGOmPPIeazMbbLhXTDJPTGO/lpAMA8SpyudzGXwsQlJCQkJDwHOOGMstFTUdO6nu+BHY0k386XKRoouWtNh6zUtSG5Iz6DQzogpjvqQ5YGTbpljroJ3m8tjGQdrbKh1Cp36sX3F+t8VGus7O+oIeeiIcjcjvJh+0lOjLBnljnLRnWy7ic8sQgDka4aaMPBeUY5olHmms5MGQRcam/YfptOGH5YooNTluy3ZtUYZQPczFdxodpCBot1u4dbZk3ZAVSto5Z6t8OuRe6t4DZXrnO3UeV/ebk4w+tZWLciU3Y4pW9HQScJCe+b1xq7rvDz6fXEYZ2mYsgyc20laeaJZQuT6zrmzDWqaZRp/Fc8yZGr58cnT6/r1nnLyBHPGU+3O3AHSywzDyu3Wuxjvh5ixTT3GE9wQiAGe8LKSkfb16XrJ38iigQlPiEEELW2dcIk9p2rRB3VGhK5V0xoGAg3616iuHW1DyLXdNbEUPL4ArZZpLyOJiMJHsHhmA8SjsiTzO4V2h+x1LUWVk7R87fPiGYdn/tlwh5btAdYOSrYhypUX1ymWzW8rLsHo2xtmwo+oT8SpfgcYiEf4aAedNbPmxGymVM/JTwvuJKf8r/fV9+Oax4SEhISEhKeAaQX5ecOFsv5AXbKhU7J++du4JPzX8EApbJdwvOOUTbAjWwNYydsP2O3j7lHCQkJCQkJTxYnXHohoDjjETph4RRHP6s68x2+26jj3lFDz/nGp7N2JmL70pzkUANz+QSVzVDWhTPZtxt8ZXQBH1q4io/NvQVjgK9MzuFuvaqbEJnJUTrzdcyRirIk5ninZW6KmTql/1VvU2Lk0gwGTiZBEozAwalZknOelllsSFn48vajg6QXi0Wj7x0wZz5vDqu6TD9B2w9l5usbAs5Yfaltp+FqvoEzdfOC/Fp9Cw+yJdTuxbkjs+DrhONQs0OxLuYwpJi25VgK+q6MPcV/qrOdNI0fumUfUj1WRzh+Nf+4zepwCaATHi6QV3gHVNqOzWNOQ+H9oYLQi87ErITDkibmioXRouvQ3gf8ZD+atUFz9KvEoOBOepmxyIzFatFE8inZIKS2xk5yMS7bx97E3af1oKlT8XmFzOQ+nRov850AAJiJpilS/o8MpU4RryPvn5gEUGlTUWUo0sHp7UTxuNo5bDezZu1zHelIMNCOeZ/RlrdKEiafybGbIpEc90pWRuE/SR5hmTSvdfCjDrQtyjBv4dAIH2bBfUGqEdp++qNjehsJvZAY5ecMy8U+dqp5tWyvHqKyBlcmZ1BZgxeKe3i5uJMY5gSPsRngTraC+2YRAPCh6nqSYCQkJCQkPLM4+Yxy4HgX8QASjgvRUFQKfP2s26bGGke/1qUThcI4aIRMZx0X9stDDxKOtN4TS8UBdiYLXbbRAqeLHezW87hdruN2uY48q3CxuI+vGbyJ1yeXsWUXu7u2zV+we8kIczbQiDKFhFK3E+tUtlll9AVVqNZR1jnMORY450waOf8Rk8Yd71xnFgYNazzPGOXFYtyU5U3ZUt7GI96t5qZ3QgEPmN8wJT0ZrVmgWAJggKvFKXxi/DbumyWcsrs4l23hVrYWTVzg1/EyeU0iLJnm3KNZHjqOVKHXWrAbzZox9X+0lqYgzFs0Nt30ok7ViDMfD+klk5BwJroSiUOC0HG5CdrWEjrQOp98Ay1z1km0wMrIIS5jDkpmBtaKOxfFWGMfOk4sm7ImkQLdiytFGyOenPcOquZxNxq0EVvGru9VFYaJAxQnK7Y/b80YuzHRZ74G4tY2aezU7gfNeimfJ9r+YnX6INKX3pDHrDHt2q4FoR/OBWQFIVqVPffEDo3yINIiaHYY6ODYXdhCurdYSFGy2rRMctfRTw0dJzpRB89uosqzoE5T5uprBzEFTcKRxI/OinTGnitYLA/2sVPqjPJyvo/VfB8fX/gKvnbxDWSmxka+CwBYy3axhAOY5Oz33GNsCtzJVjFvJ5ggx8vVPSykJCQJCQkJCc8gTj6jrCQH6aSr5vUUjbLXJuf0RdhuZkVSESUKl6oR64TAURhQmeKW/9Y0pLEvxk7CkUBLaLBQjHB25SHKOse4HLQf9a7eACVG9QD79RAL2Rj3y2WM6wJ3ylXMZRPMocR78tsYoMSb1Xk8sEt+v9M0yppGtpYMIz+fMfZRal3VEH7dNqMgBqAiLRpjtlxHiY2q2QcCscY+/Sk7wDMLzYcF6ZAXi/YFkpjkRbfcKdvEB/tOm+x1lBVLz1t111H/bGWaMcb63sb0ahY8JaqV6VLZd48nG3OxAmysuuXb2QbO728ih8WtfAWvVTfx6eJF1CaLapRjjHI0GUnP+8eKMo1t9uwtPz5RB8p2XhPImNo27axBXZiAYe6EcNOOQWHFfft0rtj+6P7JnKGCR67KSjeOhVYZaMe4BtumLGj+58lIZLKbiArLsPGZuXB0lPgjsLr4BA4ufXTADAtNJhswhQ8P1xzLROiQ63rotcmL2bjtctGsK13bJduHv4/UubXpn3b56ppYdNo/Oz4K21XQ/+12hs6f8lzorJqVsY2xxkeUpar3Q8Ty1wtUX5vDtepyXlfuzZb9DZ97TZl8GLf7prk70C/T9nQ+Y3p+zZ+pDu8nXo+skDx0nNfC2/B/3lGrqMzlmr6p4KXVJuFwnPwX5YRDUWQl3n/mGgDgyvZZyFlpaCZ4/+J1bJZLuDo+jZ1qHhVyIANulKfYy47BstnHB/Nr+Mn61eAhNocJ1rCHAwywiUUkPNuYZAWuD9ZwqtzDRrWH2hi8OrmDN4bnj7trCQkJCQkJjw1JevEc4NWNGwAaneHDg+VO+UI+Qm5qzGdjFKaKZuzbsQvYtXNYQZs+9hLu46vwDlawj/fhFs7Zzcd/EAlPHa4ONjC0Jd4anMbAVjhb7eBMmULGJSQkJDyNsGj1/E/T39OOk80oG4SOdN6cr4jwI+F12u3E9mzdrLKM1vlMeFNAMWdpdj9vh1HarJX6mRhsbh/DYoJ557xyf3+lMRXzftbA1ngZn6mWsFbs4cxwExcHD3BjfAr36uXm4EQ/a2TIYGEsMGcnOI+H+Fn7MiYosIo9vIS7uIM1v50qy9DMhBFTfNTxS5Nj9IE7oUZIMIA2JJB38lEcEkmOcWphz5etDhsnoqW8Mf0uMekFmYN3nOPeftVKLw6E9IJnDyPJRclM29S/uspgswyWxbXyJsO8ewF8djFNak7OYEK+wOtz+cIIBa7Xa1iuD3BnsIyBrfCe8i5uL6z4zcg63kcyEwsdpzlGxcLDxSQUqvQitp10JOXOdc6CWhdNeCZTd+9zMu8aZX+q+Zr6ojjzdcPDsTJaV5pOmT+f6ML3oSATsO2UkcSk4g5KQpbBwxdmJg/KSsXhlVCzuVE+NPn/A3cCyXTMs+8VWYXSZpgzzVw3l7UakQV3L664Czeu2sceSUOqOjZ5OAkG79vAyVRcm0G/I5n5/KNGSjBYPa8wiMgylM3alUpmPhUxKZ84hmB/h0ktrCI9BFunOdSLSr2d7TtzFZNC5HTfGWV/4XOZO/p1nPj4s0Y6vgaZKt06r4WYnrWPS5GGAxoM3dcxf4tZeg5xnZnbTdDyswljzDqA7wPwUTRH/jsAfAHA3wbwCoC3AHybtfbBk9j/s3xuEwAsDEb+YXB/f3VqPYsMD8tlfOngMt4encXZwSa+euEtvDi4g/VsB2fyLbyvuIEVs4c5M8HIuYIv4QCbWMTE3eTbmMcCxsis9iaW8KzhytwGzk52cGu4irVyH3m67gkJCQkJjxffDeCfWms/COBjAF4H8F8B+BFr7fsB/Ij7/4ngZDPKgOqw10kuwn+rSUXCL85QSy+/LhW2WbK5vA33BagmyNAQc+arIp/yoozCQVlrsDeew72dNYyc85jhjLQ/Ly1XsVsv4ouTRQyKMU4VO3ipuOsZmtvlGuZQ+hflwlqMmFONRYY900gzHjqHvyitoTDDqlNeJHGF3C5g/eU1jSVCYI0SY0sB5TNWRg4Zy3MNe7U6bFnjtUEjSVl2od+WWQi4rarJhkhM8gFLLkKs8YFLhsCTIpSC3QaAqnS/K9P0mx0XOe+pDm3EQjgyO0hm4ZlWE/4PxmQKBmeMHFcn6zhbbePqwjpe3H/o2+aIMsrolsn6GhurhQr0rJUPlcbalE6AvEw6BsacB3k/3X7qvGGVjXI+yZGHl2UVzQsK9SaOizO3/nbVGOU8rG+0JCaaU5+fq9ycwUNsUTgsUFKRFpXgWYKpkfrg2XTONodUGGfXPEPn5irO1BYZOfM1++UJexbzCearEvOOPZ7L2hBw5PRHzrRrw1Y25h38Iowy9YE7+3onTsXy5yN6CSctdshdZhno0sbKVCUuVbNOs9wRulHFpiIYL3ID1RQR7l8FPy8xWk5jkmeBZgWj5zo5twZkbPe55zdD2BdjtO3c/+wGJEc/hVD266y3ynbNpT50HD8sGnuaRU3cf/3C2Z+88HDGmFUA3wjgOwDAWjsGMDbG/DoA3+yq/QCAfwXgDz2JPpysM5YwMw7GQywORtgd6SHhYhjZIW5MNlCYClcmp/ETo1exbRdgYP2NblB3vG1vYRWX8PBxdD/hBODK4jrOjHZwe34Fu8UAH9lKSUgSEhISEh4LXgVwB8BfNsb8tDHm+4wxSwDOW2tvAIBbnntSHTjRjLKFAXLOfoivvVhyA1UrKdhjtp3GNsfCw7VNK4UR1tjIdKlcezrDuwdpVidVgXFVYGl4gJ3JPAATfCJ3NJJBeCsDwKC0OR6WS4xdMhjaEhMU2MMc1nAPV1jf7tlVvIj7WLIH2DXzwSeypr+ThWriCnmuNf2l307ZYQS2e9k9e0RsEmfiluac5tExyauDlqHaKBq98rJLeMBjVo+cNlKGggPaZAhjv2zLSJtcKymJkTV/Jg/okmZBrCM/LvqhMK481TkgmMwI21zXOa4sr+OF/ftYrCZYLce4Xu/i3nzXcTSWKCG43nQ8VEfVDFNht8yz6ZxxpXXKsSPCKEs9cXBe3G9bNIxyprHUFNKNh3nzLLNCB4p5QQtxRxLc4PjcOj+NlWy8yHBYCiOpMZlWnOuAHfXH2tVdkiY5o2QkvIyOmc4L1zaLiYF/hBduh8QsL2WttWbOTDA0pWeS5w1jlJ1Vh5jlkrHNy86HQE2Z7cb6JA/TXANASX4N9ARl9GGbCKJZct16e1zueNk6I0Lvme4tra+QzWtza4RZzmQIQKUPwemR41Ps21g2TrTHn6b9lfUekVkG2H1KjfPngp8flAlGzCuB1c2E6yy7SJT6ne4Lfm2lbpmHFKXfNNYLnsREavb5P/78k04evdA3jNy7jDPGmJ9g/3+vtfZ73e8CwNcA+E5r7aeMMd+NJyiz0JAY5ecAe6N5LM3t44Pnr+CF9dvoZ4hrsVPPYzlrM13dqtbx3uImzmcPMYcSC2YctGlhcBurOGtTBITnBe8sr+PMeB9fXjkNAFgbHxyyRUJCQkJCAgDgrrX2a9nf97KyqwCuWms/5f7/e2henG8ZYy4CgFveflKdSy/KzwHu76zi1NI2BnmF1fk9vGf9JooskjkggMXAVMFX6PXyFB7WS5gzE8ybMd6uurGZRxhgGMtOkPBMocpyvLO0jlX3gnxxP30kJSQkJDxNsLaJ3vK0/cX7bG8CuGKMec2t+qUAPgfgfwXw7W7dtwP4h0/qvJ1o6QUMk1u4/wH4UG6aM59VnfnEkss5IiHn5DrVzCsdNDgiIeD0zHyHM8FtFJq2o5Nxgbtba7iw8QB5ZlHVGd5/6hqubZ7BZrmkHgv16/zwIQrU2JwseXO9hcHNagMAsFsPcddOgu0MapzHJm5jtemPYsrVzMq+jnI+pWRGd/wKTbq8rTZbo9Km8r83hzkHuqXF1sy75CUXzXKdSy8GTWa+PRcCbo95tu3XjeSCnPgOWJgqkmWMSwoF1x6Ed+YrWcgt79RVw2Q1oEgvFD+VbsgkLufwch+3Gd9dRJZB+3v71Bp+0ZW3cXNxGRf2dpDnJSaUuky7znSI7rqF0gvRd00OAMVJRzr1sAsunfiC8SXPmSr1cP8rIdkoPBzPoued1dyy7k5H/jqojlTqHOAWynXI/PkUS8Bn1KszcnDibbrzTx3kIe6ysIybfml/ZPLlY9Y78fkQjCxrnzuIzCiDwu2anOsCSYRrYtFJLngIuLmsxDArveRiPnDma8bgyK0r87YvC7Vbpzj1TUSoRh5Crnbh4YhA4GG7vLLEO2IxaYmYdILz6ZZegqFJ1qQPGqsm68g2gp3InYs2O2M+0mRfZ+1uBtXZLJuHtj+tiszeh3ZcQynz84omrZRSpFrZLuuOidZ/1XS6bVxb8p4Bwgx+sjMVBQqANqE9c/hOAH/dGDME8BUA/wma0fx3jDG/E8A7AH7Dk9r5yX5RTuiNe1trqJDh8sY9rC/s4urDM7i8dhf5boX7B3rYOIMaF+bu47N7L3U8bKfBoMZruI4RBriN6eHoEp49VFmGt9fWsbHffDi8Z/MBvrhx9ph7lZCQkJBAOGlRLwDAWvtpAF+rFP3Sd2P/J/9FOWDLQiY5CNsmncD4dlTPb9/dTcyZb1Z0WONaoQfIiU/5Ku3U1fbB2iT2/OHOKsqywMtnb+GF9bt4+/45XFq7h0lZYLta6rSxPtjBbjmPshp0crtwByyb0/8WH8QNTFDgS+Z8ez0U9pf82FhkuXZd0S2TDl+WMahUn1ge1VHTs5bsfA6a37n7as8HLcUycEHg67obimq+aMrm825yg4Gj+AZuHS/bd2XD3K0rW7bZh6DyDBVneruOVBk7fmMsMuaZ5lkOqsJzP8iBHDifCUZRKyO2mc8crF9vnVvFy288xF5R4OXth/jy2XWMi6JlpmplOy10HF0v5R6Tznyas1sbVo6NExHGMda2FlYuxijbArADgBGn3uGu0rajEH7ufAaOftQG3Ssa005VAgfBcD9hPx1rVRKby7cjRosaZWOPGO+8O9l0rUPamO2OZ2Jv6doM84g3GYNMVKKB2OO9cs6v264ah9ot51i7W7VlZMlZcU59PIkJ9ZOY5MCZ1v0uiSkMWH9iw6lQYyuNO6YWVM1PUQobq1nbaAz4zXhfxFTAEXPijD5bItZAWPY3rU1x/zbdFPe71rb2fjcDKR3c08TsCmaZl7V9Y2U2vB84gUQMsvFLVuYde12YRcXq2RLt3YnJhyMM5s+Q1TaDWm6W8Jhw8j4tEh4JOweLeOPOJQDAy6du4/buOl5cu4M5F3+UY2O4g/vjlc56DUNM8BpuwMDiS+Z8+Fad8NygyjK8eWodo6J54fjIrTvH3KOEhISEhISj4xlglBUdskwSArAU1NPZZjXcW48XvpktGfTFKUNSsd+6RpnWRT6jlbhDRrAKB6M5XL1/Bi+cuotT89u4vb2O961fAwBc2zmDh6Pm5figHGIxG+F+SV/IjAFgXVjCAT6I67hp1nDVbHTSfQcsfK4vAc4MI9i+qRfqWG3BGWVXNmwOnphi3lFiDrgushg2VEzhGOXhoGW2iD0iBnrItGJDR+FQalyuh/SMslg2v52u0WsXpzNwgTZTYWdoCGRF3WWUM20gOwjxvFVY45aRZv2rwkFktbHglm9dWMEr9x/g4cIQ53d3cWa0jdvLy8Hum99iYDK2XzJnaqgsurZBmQkPM/AbaCrWNOtplqPOj24/g3BaRL4PgKpuWWSAEYl037LZVqaitqys9ZUIl7JfgM6myzBx/LdRfBGkFjOYGzNPzXfanEVjysczMbR0L9ZsEKq6ZYHc1clZ3YnNkdscO87ngu9vq2wS/ey4RD/juj3ZlCCILD8TNmCIUaZQjVyjLFNec7dlfzR0r3Bts2fvu7QxDf+OPh/sVqTLp1hmtCdVZy8xhlfDNNZ4yv/GNn8quy32Fz7GSLurWC7kc4SPXZmOPva41ixPtF+jVKQ5kvfSBjXC3VE//XVnc6RPepK5Om2rWRZeQG3arhWfEu8vQs/CHveOhVFDISbEkRjl5xSbe0soqwxFXqGqM3zuzsu4t7eKxaJ1WrNWjy8awuI95jbeMmdwJTsdZCpKeD5RZxm+fH4DZd6MhY9cu4ui6mdeT0hISEhIeJqQ3mqeU1hk2B0tYH88h9NLWwAMyjrHMJ9goTjAxtwWzsxv4UFEepG56BYAcMf0k2gkPB+4cnoFS6MJRkWO/WGBD9y+f9xdSkhISHjuUcM8dX9PO0689CKWfS8MAWeCOgFRKqUaSui4R80UxJ2K2tBVwo4DdJz4Ake/iFbfyLa4mUmaW90+dvbmsbK0h2FRYs6Msbm3jKXhAV5YvotJleOtrQsYT4YwJIVgbWa2xlfNvYPMrOPn6gtAwc4ZmYyFCTkoEzIL/rsehDILAD78mfe14dKLAUkumiV3yiuGZXDoGUudRlKLhUEjnQhMq25M0DrvgAdg3v0mKQWXVwyd3XviYqsNTJdJrb2zj2HrQslFmAFNMbvRubUGFqY13wHeBGeVjGDe5KvJDigEGO2PZSO0dH0V6QXTQrgON0P1y5fW8J6bWziYy3F5cwdfeHEDFbM4+PbJGYVLKKQ5MjDhhvUD0yg5ASpeTG2oR7E8BFZIpQKrO8mM8uZPDQGnmJop9J4fHkqcMOm416wMl7HQeIFERBx7zeY4I88Lu+5tRjeaP4OJJdivZSebxnHlT0g3c127C2aGdu0X5PQUMSdPglBu8yirOew5h70JO3m7zrGPMmGusXCOyy5r36KTUQXOfE5nTyHjKjYhxWK/ToSTleVzuAl/BNIncspTwvt5hy//7GA7lHN+d5qQaoKgrVhIRK8eikgwOvIKq9+bse1aJz7TLewOoc52fjj3VwM128lzDoCy6dIcZPg86EOydWUZHWdfPkXKc8znd5rPMkXeRNWVZ4ApXH3qZ6I9nxhO/ItywtGxtbeE8xsPMC4HWJnfw73ddbyzed6XyygBHGfyLezbIe7U53CAydR6Cc8vrp5dxvuuPcTq3hh3V+Zx8cEOrp5KIQMTEhISjgMWfeSUCRIn+0XZQHfmo2WufForziydhCOBw4pYN2M0B80pr+OwV9np9bnzk/ysD77k5ac0+/KsBD3gv4Yz3HxwCpdO34XZX3LsNft6pi9U1ieDGi/O38VysYc3xhcxj+nQnJHIj0Yum9+ODaTwcNy5i8oc20xf0wB8qJ7cOecNmFPe3NCFcKMQcIxRni8ck2xD5yIAyIkRc+t4CKvCUXW05KzxnEt4MHaeW9ysVCFkjXVnPncu6kwpY9eUwinVBtaE1hN/jEpIr04iCGU4Vy5xhGVsv9+3wihbsY6zHm9fXMb7r2zhSy+t4YXbu7h6cbltVDLXnMmkrnsPJ+7EEjLm/BA8qRYJLxdFpL5nbNm8QuHW6hyohTOf7JOaxESEiQPQcQ5WHaK0/yXbzNqUIeOCMvpN7HHVnQPIKlWzpCLtvaQ4YHXGMZvIMgoP1/xbslhZlJiE7sWhYpGhBCAP68VgfTWZx7Zzqyu5U55gkpeLNsLPWtGsW8yadZOinazIsa8US6C1NHnnWzbmKzc3Ve482kIZVDSsA2ubYJk19jfiAC6aVhH0JGKd6BMeTnfK09s+tEyw4dGEUrwvgtlVw09qEGVh6DjRNg8pSnMWhflDd86ynTh/7X3nLTNB2FfXFoVNVI6T2syYtdQ7qHtGeUY6PaE3TvaLcsIjY3N3GVu7S6i1yVzBpbn7GJgSnx29hBpZ9EU5IeHexjxevb6NtZ0x1na6IQgTEhISEhKeZpz4F2U1JXEkqUir/5qNbYbQOMt6AEToK1raTpkMD6cxw347/rluRR31a12yxwyqxtkAMO2XshKHi8oG+RinBlt4fecl2CJzW6KTptpvTuwAZ5RJ7+w1yow1Jo0y6Y/Z1zMxyhQKJ2Ph2jLH4BCTPDdkemKXHGRh0LykLQ/alzWZtrZkIi/JMhfsAhZCmxxqlKdHd5BJRTSNsmdl+YaCzeX1PHOqDBNi/HLObEQYZc+o03WIscaavrruHtf+qQzGWrxwZwfDymJQlKhzYicFk6yFoyPWmOs8/b0bMstAO+xrTa8X0Wd360bKOGvsu25hCys0yibYrRbez2s5IyHg4kkblP4pZT4cndR0otVgWsE6A2Cp301nO3/9fBm3noRhsLJgLIUTUcbTW7sTQvckv+/oft2a6J/nppzDlrs4fLvlwQEAYMlF9FnNW43yWr4HoA3xeMDi9JHOuXRLHlZOptiu6u79YJX7gU4yjW/Vf4OuVey6R5hTjTWOJ+5R2pFsMz+Ew8aeZeM6whprIR+tks6+cy8Gz2DRQcW8pD0nO+eD3yt1uIqzxl63LJhlAO1cJSyxQPd+D/TnsetA5HRBiUo4S+3uLUPMcr+EIycxM99xI52xhN44NdzGg8myf3AkJBwGmxlsrQ1x71TjULW0l/TsCQkJCQknByeeUU54dzDMJjg73MSXdi8fd1cSThgerg0xGDVsx8U7e9heGR6yRUJCQkLCY4dNCUeOgpP/oqyGgOvKJOTY0Mpimfm0MHEdC4YioZByCYBJLry8AkpZpM3HiM6xB5KU5vfa3A5eXrqNq6PT2McckCHIlmQz6GH6vBMGa1KY5LTwVt2GuoWhM5mQLWimNmVyODXXmF3J2Sd0AAplGXtl69m2VzYvelt5YwJeZOm/F7PGvLtZNdnANl1WMADYdqGrqK0D1mZM2tAezxTHkcwETlZdKyRzAHGFmZBgNLVcWcQpxCh2TMqQlhnFDI0MmxsDvPKVHQDAe67u4Cuvrjj5hTNDU4Y25RjUlFv+3hDOT2DnTbO3xp4Ps9xa7B6xZStZqmFh+P1TiPtbMUO3Zd1rGwvbFZNe6JIst3DGoCAs49Cd/4FyU5KEjKQC6tzoJAZsLvbJIWn37KaU2SdjYRL5PZm5NoopJubMWDWcnL+/3ZKHjjtwei9qe8LkFSP3m5wHeT9JwuVDx/Hse+To5+6DSa3cy/5+Z9uJsZBpKi4/T7erciH9V+WBXpPEK7oi5bJ3bpXY/SHGp7H6/C7HrO05rrvPhUhfOMQYNFyiFWtDFIZKCJIphRIMvk6VPsWcAOk5qQxrI5yZQyfAoEh9xiU8HiTpRUIURVZifbiDzFhM7Mn/rkp497G1NsDKTolrF5uPhvN39g/ZIiEhISHhccMiJRw5Ck7+mw8/x52EAsoFkMlFeL0Zr5cRX4DREHCRr+cgGYl31LOdsicCyVBwQiwv8ZFTb/v/X5q/gwf7y0F9zyZzRplYKyUEXBvBnpZd1tg7UwRsBDl3uZVZt6xlr5hTkUi6oZmdTs81bCdPNkChocjxYcclLQCArUnzm5KQLOSt7pbCTG2WTeiqh5OWUd5x2+1PGhZrzJyYJqVgrSJMDIetM1hkQXg4YmaJ/bXsZHliSWGUfR13/ou8S2nFWIvMB8xn598AyIH9xRyjxaYfL17bxa3LrUNW7VluznzTj+meOMTA2ICxI8eyCKMs2Hu+rhdYODN/H8B6h75OmyJBira/0GmXxjqVKX2Iss3dMURlPgTjkFkSCnFPafurwz7xdX68BValpqJ1IQpD50+3O4VZpHqVd7BtCykxiXdeitKCXRBDPApY4+ZezF3bvKx15mv6suycATliyUho7glCx9F1qMV8BnSYVu6QSpYKcsrkYQg7lk3tFu3DCCtDNxYNVbWU0P9amTQOqXOccl76PJe1aYKcU6lMcbbXpzNP0Ybbo51z+oSOCzoumWH+zPcee+E+xNEgqASgdgdEa6oZQ9cm9MfJf1FOeCTMDcZYW9rFzd0NSJfhdfcCCTSygTcn5zvbJyT0wcP1AUwNlLnB3KjCytYEm0tzh2+YkJCQkPDYkDTKs+OEvygbNU31lKquTriUv5u6plvW5ytdidGlhofz64SmCWi/emtBL7A2Wvb3aAOe65AHwxJn1zaxX89h82AZeV7h5dVbyLMaCy4w/+3xGq6OT6MqMn8eKMSXzRoGuVYYZb/UQv0QiR8kVHH1fUPdslarxVnEkLXSWM+YeYfYpNNzW36dDA01l/EU1g2DTAyxxihvOT3yDgtlJbXJxCLz3zKRQfBbY4WoiLOqpPmlIPWseidVLG+KWGa/37asyEOaQzvHtH0QwshRYDunBzh/7QB7SzkmwwwvXt3F9oeb8+DZMsNZFnTWyX37IdGXUZbbK0wmlLLO9lzDTeyyqYGiCs5rV4+qaCRVdpsoRfp/+nYa2nT2XcbcM7wDfgy0v8PnzyAxg9MtE5Nc84QxFDIuC68V0L1f+b0ptckxixqFfgQattk0tpXmf+X603401pjCwo1Ylh2yMK0UB1PbjCUjoaWaPKhWxhmFjvOXj113CgVG25vOZipkl/smFRGkqlo2NaycNm9r9bUyxSXB87PKsG5zWLt/tTTX1ABPwETzhMbemvAHDwFnBOvbO3Scq+cTRXE/B3ofEMwy3w9bw3678YXpqa8THg+SRvk5x+6okQZcXL0HwOKVtVswxmJvMoeH4yUAwLnhJl5buHaMvUw46XhweoCVhxNsrQ0wms9w5vYIxaRf3M+EhISEhITjwglnlBMeFbXNcH9nBaeWt/HC2h08HC3hzMImTi9s48s7F3B17wyKYdkwLSl8csIRUQ0y3Lo8j7m9CnMHNe6dncOl6/t45+Wl4+5aQkJCwnMBiyS9OApO9ouygcqJq2HeNCe+KdvphcruI2atjomUi/eFk44J5BWhA4Ge0a/rDRFNtuPNUl2zHQxwb3cV60s7qJDh/OID7JVzyE2NEYYYZwMcYACYVm4BhPIKmwM8IEZHesHLOjIXfnxuFZnYuCnYm2upjmKbIwsydxwSF1wz81bKoDg/aGQYZJpdyNoYTCSrGDo5xua4ddjz65wTHw8rN6qaE0FOfONJe2Kqisy07hpV2vEpA9TajrnTCierIKRQPC4SgFY6wZ0AyWQsJRhhV7qyB8oKaEyNm++dw8f+9UMYC1z5wCJe/ewurr9nHnlup26nQU703DxMZm7vyKhmP0SnTEpJQqlAWFaV7GajEHCokFnh/Cj2E45n+qHIauT4D6RZYgwE2wlzvpaukRz3lPtOdbKKzomusKLNujI4ctQ0zMmxG86x3a6sDjdykgSCO8MO8woWhjn61Z36JMvgmckOeJw8ABMmk1h1Gf0yhJk4OWTouea3lF4oY5DuUX7OLPWv+T8wpVMYQsXpunONtFtHkTt0poK+708xhY5FkKlVlW5o6xTJBW9z2mYeNGf1Ga9AJ+xaICmiOn4du0YU3k25NaOh47w3bSjBaOrTmBASDKCXgx+FGDSROTPh0XCyX5QTHgtG5RAP9pcxX4xxZecsTs9v4c7+Gg7qlBgi4fFhPJ/j/vk5rDwsMRjVKAcGG3fH2Do/OHzjhISEhIRHRmKUZ8ez9aLcegCF/0NjMrnjSVgUG0ehc4L4glPK9GQD4SeyqaxS5ulRpRfddd6BQGXYBZPMmWH3NXtt5wxOL2zh/OIDDLISS4MD2LHBjclG4LjXbtcu66xlj4E2HFzLLLOvZ3+NxJKX+XBaXRaqk8iFlcUSjsQmBy3xAeHi4AEAYDFb9Ovms8Z5j5z4uDPftnPeO6ic417V3mIHLizcyDHJFWPPiKW0KqM8/bha9oYzU3RgtII1JQe7RlK77TLGHhNLQuc4z7uMXQy5Cx138/1zOP1jI1y8so+77xnixWu7ePPFRn4RMsqHM9cE7brJpBbNOlcf4Xjh9fqwzVXe3d/QZpjDJNxObh8w36IPSj81Rrllpacz0ep4kZdIcxD0KzAdkfFilBCF3gmQT7du7HjHPc4i0zyiJNugcVbWNvgfALLaoqwz32bB2DVil+WS94GSimwM9trt3EnLzXRGmSxOEzY5egc/zyy3B+8Tk1iq27bVJhzpOvT680f3IXcGY/6kgO5AF3UCVYaSKAoQtQqK/cbCXAbW0kxUDz32wnXsAL1jrTYuZXXF4Vzri3SaDx3+un0QvVRDx3lLkOI4Kx38Zg4dl957nziSM18CgOYF6u7+KhaLMQpT453ds1jKD/ChhatYNN34oQkJR8HBSo7tUwUWtytsnyqwfL/CcC859SUkJCQ8aVg0Kayftr+nHSeeUQ6/rEPm03Lmp0vczIQYaRZNKqKlqZZMcqws2BEtp7PHvoyzxkW4jsnpxLkyuDFax/nhQ7yweA+fGb2IjXwPHxhcx46dxxvZeVSO8rGMNbZFmFREJhzhbHOMUaaD9/0bMEaTtKCFY4WKtix39QaDRh88P2zDRi0MGrZ3aSDyvKJlpAbEGLGcsQPTtDF0LBKxyABwYJvfxCaNsvbgN90BbY2b0HF7k1a+cuCY5MnE6RpH7YmxxDRRqmDOcFAYLt55RrBbmHBgZ20Z/z+AZ++1MncdeEpiqu6sA5wNl8lLckZlyjJjgKvvXcSH723hwhdHuHdpiFNvTnDlQws+FXbTdxNsp4b863EzB/plhUmehlhYOq6fpj4YF54s3CBknzib73XgnsHmDGjYdxuEFwvnDC1pivU6SIWV0+J+eXZbEYNKRjgI/YcQnOF192dOS26BcNYC2h1P/+zPe0bh4dABMb0yGUlljWdzMzbJtcywu9/ZzVW7kV25OgNGZecKAz0NfOxKC8vCcCKrYw9dWRvNWi2z3JaRDtWPeYUB1fJcCCNdcM189ccpbbXsT7Ytjku1kMkQqEB7rLF5jJpWnid03/F7mqysfn5h7xFkOPDnVWWr2/u+WxbsPjgGPweY7k2mMt+SMA+u33R2O+HxIjHKCQFujE9jr57HIKtwefAAd6tVfLp6BRMUeA03jrt7Cc8Atk8NsLuSY3mrhLHA2XdG+odhQkJCQsJjxXGnqz6JKazTi3JCAAuDt/bPwVrgfLGJlWwPFhners9iGQfINfFgQsKMuPa+RZgKWH5YYnctx6kbXcY/ISEhISHhuHGipRfW6E4NWii4XpKLSGob1ZziC2nJzJLSHKaGgOvaoKQcI3BoE7KK0MwU9p1LL7wznpBg8DIukzjAEF8Zn8d7527hg3PX8Rn7InYxjx3MYTE7wGa25OvXebMtDwHXtmV9m20/bbfv1GcKIUVLJr3IvOTCmVEH7Qt7kTe/F+acc92gNXWS5ILModxcTybZwkkuuLMO/SYJBi+bN037I9M45/HQUHtlY1KlUHB74zaiw4H7XY6dfGXCTkxJJlVnhtO+R0z3t7GNiV0zwfssUmoMPqV9At1HPGQSnb8qDL/m/gnW1dwsrEgvAOD2qXm8ZHaRTSwOVguce3OM25faMHuZ2C4mvdCcCWPh4fwxRUhszZmvj9QjJtmI1TfKtaXj4s490mE1lGW4zbUwUdJErYWqU/rpj4fGWyQEFT+GvGgGMEkueFjBruNku12bxY6yjbFGXdnE5GHf0JyrymbegS5w9BNyjEwxl1fumZGDS0Ssa2s2DT0d37Kbe8pI7M4D0434MlHC5lmRxTU4Z3QfuP8NU3p4eYUmdZOVjgohHZwaHk5KGSKyDM3RT5NgdHxhg3kpbDzIdCi21yhDPwfz+Uz0ySpOea1zJCsTfQmy/3rJBZ20bj/9+WSPDCPais1nCY+GxCgnqHhQreAr5TkAwFeZK5jHGNtmAWt2/5h7lvBMwBhcfc8SqsJgZbPE8KDG0mZ5+HYJCQkJCUeDxbE77iVnvmNAcI61hBoSMlTazDvsssaeIeZfwbHEIYJtNjwEXJTmEnUyxiZl4QEF/3snvnAJtExwXVBZu9ntbA2mtnhPdgcfMlfxmcEL+Nj4Kq7mGygLx4oOgLoSDoIUFs6HkJvOcNkBY8sca2yE4x7QMsiFWzcctC9VA8deEZO8MpwtSod38lEZ5elsMyUr2KmYwx4lFSmd417JEhE4Jrl2S4zYdyo57MXIK04CcvbBIAhu32FzOCPirS6iMoN3BmPsTMskU0Pd7TKfXETzBusezvVzi3j5izuoigzjYYZzb47wxY82TpB9GGXPYAfMjeLRJI9PYexikAxo2Id2aabV8T/YWFccjDr1HYIwfZLdZg6o8niMyq65ujJxybS+++NTLBGiPmebiUHOveOedv26+2ud1VyINV7o2swo7Bqb/0oXHq515mOMsnNA9iwz8zymsTN0y8Apz52Q3Dn/5aqZp0HFjm+5GAVtl4pjodw/0IaRo9CBJU9Y4ef+LPwfzIKjnGPvg0zzLZtyvOPcEX3CplpXbbftoL5cauv4IzESWg1yXPZ9rtP5oEuj3iuKda9WqzRt0YBWHPh9qDnNAa9Db/M21W43ZcJymGjPJ4cT/6Kc8GRxC+sYZzmW7Aj72RB38yW8WN7Hl+fOHnfXEk44bGZw5eVlnLmzj8W9Got7I3zltRrlIM34CQkJCY8bFinhyFFw8l+UOTsqvio1DW8nPJmsNwWxEHBamWSLgzTVPhW1wkQfFcTe+tBx7LxkYhkkBzHBukC/7EbHncEK7mAFc9kE+/kAlycP8UZx1m9ni7BNn2CENMr8vYdOktchs/NCjBGFlGIa5cKxxsQkzxVMMyxCwHHWZhaPWq5FbMPDuZBzpnU2u2NXAAD7LnMhJRcBgIMyTFNdsjS79cSdCBcWzkwYE+o0yj5da3DOaMkpipapNcYErHGbllUwMWAshNfFQYFjalkIOM/sKlQKlVWeidba7B6DzSyuXFjCy29uYzRfYGm3xNkrI1x5eYlFQoxpYjVtcmTfArNq+rQU3Tyi2mHNhdNRWFs/FmLV23U0RiuZsOTQ7dw11VirSMdj51Nux8PmZYJJDvTEbtknCRCYBpty8ND9PalCpra2ptUos6vhmWRn7akZCy/7kPPtQEzy9IQjFF5utThgbTotNSUeCZw0aDthGQCzFtTEGrf7864rRKqyCSKj7bT4cGR8nHRKuiHjItYCjmjyErE+mLJi28l1WhtauDZv8rDdZuTw4qclp2evmdolNe039YGYZS2Mnbf2KK36+Z3Nn/IHO2mebFasWoae2Yddj4RHxsl/UU5413Cq3MWr43vH3Y2EZwhVkeHa5SUs75RY2SnxwtU9XHlpcbY33oSEhISEXkiM8uxINs6E3hhlBcYmx2fnLx53VxKeIVx5YQnrD0fYXB1gcb/CqfspVFxCQkJCwtOBk80oGymvaBatvOKIsgxeJB3oAnmFWMdztNeyjG3nyx6/rUTKLJrfRiy79WWYOIA7+jXLO8USLpVDLGKEulhu6rhrEEgvZB/y9jhlhj1eRlILCi1VMHnF0DnzzReNFGKBZdpbLLqZrx4V5NQz76QX95zcAgD2nORiz0kuuPRiXIVOfBUPAUdSBiezyEbM/FaHJkQtO1oQ3sjHgrJADZ+9D2DDyrfFtpNDj5vyqFrWLbMdhrfrmKapXDqSAmP8zknms58PcfPsEoqqwhomuPzOPu6uL4Tb92lbrzZ1uz5h2zhi9Zu8fGF5H3mFvu9mKR0a+e9MoThi22kh0QizyJNmz5A4/VxrYf1km7xtukcq083aV2aNI58WHo5Cv9cZSRSYvEzo3kLJBmXms52yQdbMC6t5EwWIO0hPnPNehYj0wh1XyaUl5KQ4VOQArn5FfWfPmtpS/8g8z6VBdMzumNhUKSUXmqOf6niHcJ18BBtMkQPIdTOXUUetUuaW3NEvDzsYOJX7cdWVKbVznSJloTVa6DghZwvCw3nJBUk+eGNyN933FqP0xcvoSILR6WXC48LJflFOeHdhDF5fvICv2b6CrdEc7g+XjrtHCScMw3GFX/rvr6PMDX7qQ6dxf2MeAPDWC8v4+k/fhgVw+v4B5g9KHMyn6SkhISHhccHiZIRje9pw8p9EmlOexhDPwDZrrLEMBRess+H/wTrhuKfWV76QTSzOlHdgYJvxOFVA4JQnnfj6OvppZaO8wOsr5/GJzWsAgLfmXsXtLA+9NrxjIf3Py2yw5KGvjHMAojBTPEnBwDngDB2jPMxZwpEsZIW0pCIEYqQBYGvSvKQt5C5RSbboyxazhrFeyppQT/fL9qPgwaT5vTlpWM+t8Zwv8858jlGuS3aRiEl2XecscEv+muD/4B/F2cZmDaUfhIeLBaL34yv8v9m3+6E4zXQIZW2uVdZJJnqUG7x1fhmv3NrBJ16/hwcrc7h2fgn3V+dwf20eK7tjzE1qXLq6jzfes+Y7qju7KUxPL9a2u13X6Sz2MFFYVWQNg6g5REWairHilVJHOuplSlnrpMXnuOnnsU+YvD5OgFqSFm172i6PJC+JOR9qsNb4P6B1pgVaJlnfEVVq7tuCOfoVbjIcuPBwEzY5Usg4Ypa5cx21mSlz+FLezCcTF16TJyNpwxCGy3Ad1WWN0ro25Uhb5M5jju5cYP0zzS0jZdMc9DptGve80Ma+fM7OWkb/amysZgUTDC84se9OoGd9gxCawgoiD5Ah4/ELpfGMb+b7p1jfxAkNQupJayK/38kRke7tE5AK+qTi5L8oJ7zreDhYxLWFVVzc38J7tu7jjdUUKi6hPz73ygaKymJtd4wqM7h8exef+ELoJPreK9v48surUKzWCQkJCQlHRN/48QktTvyLcsCOal+jnbLpbHOHbWO/Y+HhOrF7eFtqmVgXtCm+LvnxUSpq0hprrLGSplpqkzWNMrQy8bXOX1pe3ziPy/tbWKwmMKhD5rBz0lhRJtb1ZNt8AgNHJxSMVhg6FkjTWhJLs1g4hjhvtc3bZcMEPxg3TDJnkygUFIWHu1u2GuX7jlF+OGoY5b1Jm3BkPGluqdJpk23JTqgLB2cEs9xUdMsuKdSyzAFb2Y4dk0Fo5Yhu7J5/z6D47Rmr7esr7OERGWWdiTb42fecxqX7u3j/tU0MJxV25wosjZpz/aXLq3j/tS2cuXOA22cXwn4r+3mcATJmdRugfVcmQ2XzI4da07I3aGHe/DrSOqr1u9tJPXBfvXSMGW7rTN8uFr6uItYy0s9Z4ROO8ElVhN9UNdnuvhszJ41uIqL2PvfziptPgtCSkf4Ru03h5Pj5pN+kVdZCx9FhlfzwBHtbMx990i3X2v1OSUGUZxtibLOmI6bNCqAe6GUdC6VRykz4f1DPt8k3JDq9y7h21gWWzbDRYH4Rod/EE9k12X2PkJbioEz+UOZ3X0Vjm0230HYSsCSV8pPCiX9RTjg+3J1bRFIpJxwJxuD6mWVcP72EYVljPMyxvD/GN/7MDZzeaszT77265V+UExISEhIeHbM47yY0SOHhEo6Mz62fxyTLMFeVh1dOSNBgDMbDhn3fWRzgRz92EYsHJT79vlP4ufeeOubOJSQkJCQ87zjZjLIxcYc9UTcoU5xuOqHgWJkaHk7kdA9kGVVYP16mONgI5zwASpg3TZaBYAm04d308HARWUauL+n3KC9wLVvF+zfv4tNLbWzlWCi+9rBmMxORSZYc90JnvjBjFnesWRw07OSyM5EuFaPOdtvOqY8kGEBrbiVnnQeTtmxz3LCcu05yMSrb22jinfhcHyZtX8h5z3hnPuVAFScY79ARmCNdW7AwVrRF15Ic1ILP4cMd/fpLKA5fF5VsyCGeATvzQ7z+8gY+/NZ9vHN+BbvDAcoim9Kn0HntkeDVUKGJW4WyvzrLGpO51Sp2JTCdJrX7QblXgrCAsozqZ90y2SPNQTAa/i4aGm+2+r4Prk7GnHGJ7fIh2XrOE1Wdoa6NDx1n+XbeYe/wdkrTTnITZ0Pfd5KL2raEQCUcBOeYVxfNGRndsEyW0c5jzQ27Mmgz+pFshELGceavdeLrSlq8zEU5nlp6VnOJlpBV2MChLdyvlnVWlV4MgLpqy9TLJyR9wTrtPpfXTZFsqPuTqkfu8EyyBbrcmjyNZBaKLKOT1RBs+lT6YqXMSJFlaLKT9iGqyTRFn3owxdamhCNHQWKUEx4JN5aXsT46wOI4JYlIeDy4cXoJ/+4jF7CyP8GH335w3N1JSEhISHiOcaIZZQsIUbz7EXNG6vEx1duZT4R3C0PAhZ+znkX2He/WAQBLmQQUhz25jge5l+tqzdGvTwg45Wu9zpUyt64qMtxZXMSZgz28veCcXeS55k4UM3zMas5PxCirznyug0uDfV+27Jz4KDHASt4yOPMu+j6xQdvlvC+7P27U1wO3P/ofAHZKxySL5CIAUDom2Tvx8RBwxGjQUmMHVGcPUQdg4ZAMTCXCw9F4UpxRYo5+UBxUuh2k/zXqR1nVg1HuhKUDsDsc4udeOo1v+sxV4JXTzPmGd1frqKSweg44yZLNyLrYLIOtJKMs+qBYCfz2EUY5KIuxxpLlioSVizHYKiIOe+3/SpMRy0N73ZnVxYRjN8YoV2z/lTVNwhGX1EcNPRdhlsn5r2SZXCiRkE9+wk7QUJysinFOZHvKvVdXW5a7nZPFqmYnaMVZu8gipjona2HeLPXB/R9s4OpTV/gT3zvzTZ+PpMMfr6ddGiuc+dTLR4899dkd2S7c3PUv3IBbWTuOflrCEc3Rz+/HWeT4wYvQrNEkLbyfYn7RLOF+VTQZCetfx2KYnPmeFBKjnPDIuLO0hPPbO/qTMiHhiBgPcuzOD3D+wf7hlRMSEhISDgWPOf60/D3tONGMMiC/zGKMmNyQFWnaZIh1Xg+k6P7oC5B/Cfov8i5rLJlky/PRCtY4pkOGwijrrLEJ1vVOOBLVKFu/3Z2VJbz//j2c2dvFnZUlXwbPeuGxgzNNlIxkwTHEqwVnlBsGeY0Y5awtG7jQb16PzIS+W6Zhlx+6pCI7ZZtUZH/SME0jkVwEaFNWWxcCLkgq4prPFI2yTH8aDcYPtOMxa8Yfb4uurzquveTN1eFtaixzpxPdf6PzXIyJlgwo12C7zb5waQMfeecebq0uMt3+9CZnMVmo33UKo6wlZ2nru/OYGxjJKEsrgdZNLcGJZ6voerC+UDIEn2yg23ktzF88FXjYF842Rx9iPR5wWqITOfZqPv25ZV2LFWjDtRFrLPXZlc1Qubm0DlI8m2AZJp4QHWZ+yeQHoTHKlWxTAVms+Lwi02HzeYwsW6RbjoWOiyV3KZl1yQ7cAfrt2nqWfCYU/WtHmxzQv7KMFWXiORG5x8KkSqKtCOdilHmifT6zeqLNoC9+fClssw+75rZT56zuPSYtJaquO6wa9s9bi5TN3DU1yolpZ5Cn/4XzpOLEvygnHD+sMfj8+dP44K17uLu8mAxACY8Nd1cXsD9X4KU723j73OpxdychISHhBCOlsD4KHpv0whjzojHmXxpjXjfGfNYY83vc+r9tjPm0+3vLGPNpts3/yxjzE8aYb3L/v2KMscaY72R1vscY8x2Pq58JTwZ3lhZxMCjw4oOt4+5KwjOGL17awHtubSVpT0JCQkLCu47HySiXAP6AtfanjDErAH7SGPPD1trfSBWMMX8GwKb7/UG3+hsB/BUA/9r9fxvA7zHG/EVrbTyUgkHcrs+tRd1IPdPRQ4zf/O7x4FbNxUJyEcu+F8gkpCyDtZiJOlqYt1gIuFjWPsVxoS2zQGZhc4PXL5zG1719A9fOLKPMs/acR0NfsVW6ZV1tg2fCIjPmggsBt8iy7y1mLixcNgqWADBxni1+mbPQUO7E3B01kouDqr1VpBNfxbLvWTILk1mTmwKF5CKIaiezQSnnJXAc8RKKZh8sOpVy3dgY8uY929mPb1T7fJYXRZNsqJh+UdvMj10Pl3ZcGGwO51FnBqe3Rri/vNBpqy9B0lGBBI2IxiJmU71xNN5Usc4ojnex86OpMqzmmETVRFjAMORVKL0IpBC+AdMpk+irJzSi89Z0r62XZfDd0ZxBx8KkSzTdkqOeDcaLhbUGldtfHpivw/qVqvVw/7LOZE6+kTkpWVnnbLNmHclBRkzqQesmTj+SKdeqIwcBfN9JQlYNmPQC3fqyLQI/LzIxLA+dKUPAcfiQcf5+4PqKsA5HVVhUxh7yDNUmg+6qznZqoVgqGf2sJinzGWm1edAtCju1az6MISv1kreIFLOTgZXvW0g3AlA/I1l1+5pyT4Im+GnDY2OUrbU3rLU/5X5vA3gdwGUqN8YYAN8G4G+6VTmaoWQRDuE7AH4EwLc/rr4lvDvYnp/D7eVFvPf2g0PrLu9NMJhogYQTEgSMwdVTK3jh3s5x9yQhISEh4TnDE9EoG2NeAfAJAJ9iq38xgFvW2i8BgLX2s8aYRQA/BuC/FE38CQD/xBjzPx+2r16Oez2hhoDzTgJd9qOtP50xtcqXbpuD3tWJfF0GX54xR79eDnsxJlrsVzsGpQzGlbuT8cXzG/jFb1zFO6dXsecyroWB5Zt6H//8Xazsl/jCe1bx1ksrTZFw/AnYHeEEE8B9arVJCtpPeXKkyURYJqAN30T1edvEHo1dtpZxxULAOdaYlnXFDpAYMJ9chLFe5OCnMA6+W8Q6a9YQ7dpkgMkBwxhlyUoHbflxZcL/tf0oVE6f8aybBKbfI2pYOlH/2uoyvunmVQxGNSZFHmz/WPgRyfj0ZYb99s2NEA9rZYL/1P3LNoHgAI1g34PrJ0JlheHowjLNQantSuRaHdZn3/Vwg4DBluOSzVWVm0szazvb1W5ys7bL1GZomLK6FhQxAOPmy3a+ZvekJWawy9jSb0oAwvdHyUEKhRmeuPpF3g3zRpYqWnKGl+YmcuqrwPuSdfYj+0nQGEM1dBzNTYoVpX2k2U6Zd2DVboPcop5yE8SdYiNl09rhm/vnGBtn3qncreBlInGPBhtOM2r3eHg/ctz3jpABoyyuiXI/RO8xxeLbtSodPhNapIQjR8Fjf1E2xiwD+CEAv9daywWrvxktmwwAsNZ+JxRYa980xvw4gN8S21dmgNOrA6UTSptSeqGYVumFNzCXk0m86pZ14ifz7WQc5eClKBzgVol57CN4BJEt3GRZhC/FQBs3meJk8jjKPjOf267m21GZW1ez00m/5bL53RzDaffS4ielYoDNF87j6x7u4/W1U82hBHGUm9/XP3QJX/OFe/jY3RrLSxVun1n0MUbppXaeHeCie/gsuRfW5bIAJsDCzxgMrq2h+pYDLJxtyob10G9XZE30iixropvarI2HnNXNukHV1Jmr2jjKi5Om3mrZLKtqwZcNbNP+gsvYNTbtbWQz97ugl2IWQ9U9mOjSBO8qsexTkQ8VmwGnhkXvD5yYma/zsaS9KNOP2MedOg9HXr40k6Pc92CAvQtn8cntA7x5dj2ynyPiEV+UT7vrHn1Rjh2fuh+xVNbpRIHyohwp657HR39R7kYmmW6L1z4A6aNV8/LP0P2wzSywgSGby9sbqqAXazcX53w7hHHZCzaJD2zzepm7+X3AtFI+Q2jdyCSGrIx+z1dU1n7FGjfh5m6iLtn8QB/6Q/fVa9k8lrnY7cZF3xlU7WQ85+oduDbH7LE+cZkGayfzs0yT4qMkxF5ctf8jL8qn88grha+vPaCn7FdB7EUZsRdlda47fIdaNAn/4ck+8uS7hVHOWVuZ9bPdURexF+VO35sG3lSaSXg0PNYXZWPMAM1L8l+31v59tr4A8OsBfHKG5v44gL8H4EenVagtcHd30u5fvGyEg4pWduv6l2FijdmLbOxF2dePviiHdWX7gHxRNuGyYC+89DLstGs1L6OX4ALdsoGoo70M03asa/6w/OExdsezQRa3yknAQt3eGOKbP3sb41MFHq7MwSgR/u8sWeBsgdfe2sTFz+7gi99wAUNT4eydA9x6cQ5VkXmtHgAcOK1xWR9g+BAYbI8x98UMu0OgGAHV6T0Ugz0AwMKgNdHbvFlX+GUbHi6v3Etz1bww70/aqAoP3APsbtY8aO6xmD1bbtLbcy++47abwNiNiQPHQo3a65AfuBdlJ6FmculOOlkZcqkp1Ndl88Bd3gkT1gnmaLku9qLMYEWbwQMp8qLceZFTX/qsXldgc3Eev+jNK3i4OI9xwT5Oer7T9qrX40Gt6okLg9vlZKaXx8P6pELqLXu+KFtxrrV+6clBRP0YvTatXuf/sH/8Q1qGsQs+JrPwBTljlrwsq1EBuGtGnbLcvSjTi27OjomsSnkW1gHa8HD+JZql2qaXYXoJnsvbl2GatyZu3Zxp701ikkduwtXCw9WOWeYvyrV7MbbuRXkyaT/q97Nm3V7W1N9DO8GP3O+J21+gz/Zicfd/EHIOIfoyypnFrXysD+zOxygrM+GPXrpk3kYd/hs04ucXPhkofeixP2PCG88ox6mti7dP7wqxDxd3DBEfqz7f3glHw2N7UXYa5O8H8Lq19s+K4l8G4PPW2qt927PWft4Y8zkAvxrAjz+ufiY8edRZhi9c2sCH3rkPY4Gf/cAp7C50mf83X1jG/LjEy9d38Uv+7U3UpnkPuH9+CPKdO/fWCGu3JyhPAXObNVbuVJisAGbVYHLeIt8E6q8uU+qc5wCjIsfeYIDVgzHuLqfIlgkJCQkzwSofQQmH4nG+XvxCAL8NwLewcHDf6sp+E4Tsoif+GIAXHlcHE949XDu1jKwG1nfH+Mafvoml/Um3kjH4/PvWcOViw+hurwzwYH2IF9/ew8JeiRc+v4/LX9zH7noO1MD2pRw3fkWOg/MGxXWD4p5BeQ6wX6W0nfDMYFhW+MTVm/glX3obBhb3F+cP3yghISEhIeEx4LHRMtbaH8M0daK139GzjbcAfJT9/zOIvcybcI9cszsTpEyiK6llppqjfY5p5m+trOOwp+pSQ3lGU1+WaW2GdXn7vcLDaRpLugaKk8EbL6zik1+8CwD42Bfv4d9+4nybdYgc7zLg8x9cxxc+sIZv+Pd38NZ7l/HKO9v4+h/bx+2Xh3jz44vYOjfAQjHG6TdKnPvhCvuXDLZ+TQXrlBODyHWXDnsDMGc+Z68j54YDpkkZCSe+CXPmq5zzHi3BMmF5Jz7KpMSd+YTsRw0dpxyDd9SbojXOCoCpVDoZnlTphRaGLiLL8KZwulX4Mce0zT2kF+T4ZbR6BlgaTXBhexf3F+bxzvoabJ015k3SuKIfvPpKk07McFurVe20gviG6n5jB1SJeUGVUmiaSvEj5lzJIJ3yep+oqJwjXAaRx0SIumB8+uxyboO8vYEsMtTIvHOVNU3DK1slVncnWNwpMX9QAabpk/O9hMkan42DjQz76xmqdXjdqdcP06KqYUrADo135suEUx8ATMS6ism2SHoxcDIOLssg2cdc1v3w95kA3XNBczr05yJq+meSJZIrqPKBSFsxUa2pm7SKqnzAXZuY7CEqP1DWye04KKRarmjhjwive9bmSH/OhJSCQaoswrLwPSQs1DpzSGenoO49YyYQkv0y4YnhzvoCDgYZ5ic11nYnWNqbYG9Zcb5E8zL/zktLeOnKLn72k+tY3i5RnreAMRgc1Hjvvz9AOWdw61tyTNYMVoeJRX5e8HBhAf/y1VdwbncX77n/EBv7B3j9/Nnj7lbCU4yl7Qne//oOFndLjBZzTIYG1dCgmFgMJjWqgcHOqQLlApBPLJbvVjj7pTGGexYHaxkOThlM1g2qeQDzFtYAZ37aYvgA2L9scfAaMDl33EeZkJDwbuDEvyhzVrVdOf1TS3Pa6YSF45vXkc+2o2p95KctP4YOO8dZ45BNCphoyQxz1liyiFrCEY1tJsKU1mnB3HMLa237pQ3A5PQlD/ybj13Ah95+gOGkxsV7+/jKatNYpgSBv3VpHhdu7+OFd/bxznuXsGgar7f1mxNUA+DtXziHlWGzrrbTDQ2V8sVM7HHOaNzKHdCBbV7eiUUG2vBPY3fwJQsBR78pGQJPipBRCDglqYhMOGJ4WWwMRkhDm6GbcEQydsp1M9pYEOHhwlBEoi+c6RP905NLdLeTjLBmPSGMswJXV9Zwc3EZ3/TWW3hj4xRKlyBmZt+ZoxZGyFhY01gRZvSoUfveo4l2N/xCzLCfyLnWo2VEdhJhp3Xm290jmajM6vkxFMxxbrwojF2W16iRwViLb/zXt73PY5kbZKVFbpprMxkYjBYLDEY1zn95hK1zBa59ZB733t/UH1QV5h/WWHhQY/FuhfzAohhbZGOLnfdluPWNBstvWax+qobNgL2fl6G8ECYjGbg5htZx50EZAo6Hq5x3TDIxynyOk6xxUHZEhrAW7K3GHmuPUqs9gBwMKmS2UscwbXdktroPy6w4JJpoQqTZ4CNg0DMu4i1slCQtVqvrzwdEJQU8tKHcvscwsDjM4pCg4cS/KCc83RgPc3zmQ6exvjnCR770AF95z/LUujYz+OJrq/ian7yPK+9Z9Os3zw1w4e0DvPipEXY+BpSr6UZ/XlHmOR7Oz+PU/j5uL08fSwnPJ7La4sGpIUZzGe6em8f900NQ9MaColg4yUZWWrzw5X186F/tYPNSgXJoYDILmwHjpQx3XilQzRsf7YKiX2x/wGD0wRpzV4G1f2MwfhEoPwlAN5YlJCSccJzoF2VrwpjABDU8i+38aIt8fY0ucwvSc2lMTC3+B3xyAf9VyTWdQovEGTjLNXYQrJ5k5XiTQpsctBnRpcbY5k6az2l9ydo6vMx/yRuLh2sD5JXF8u4Eu0x+IeOr7i0V2F3NceH2PrZedqmilzK89S1zOP3FEhd+pMTNX5WhmOtm9duvmvBIc4xe3TYhc8MZnG0XN3mzbF7Kd13oJQDYmTS/KV31WNEo1y51NSUSAdBNJqKEIfTpqq1SpujkY+ts1miYA42y1JZHYh6ryWfoUJX+ddKminq8bf5bY6m7SSnQgU8gwPZxd2ERp/f2cHtxeep2j4yebVo+B9SHbKiRsY+w76auEvJK9k1U6+xE7C8am1ntw/QD46nIO23F5tTO9u26Nh0z67PNUBmDg2yAn/yqMz7tNADkLpV0CfGwMMA7ry3i1otzOH1rjLy0MKaxzqxcrXDp0yVGKxl2L2bYO5fBrhjM37VY/0KFuQdNaM3yjEFxCxj+4wF2vqVCvdpqlEm/nLGQbHMsxNxhWM4P/G+fRImmNuXJrabFrsNEJVYpIwQEKOmrSRfOnl9eP04hyzjLadwzT+qR2XbR0GzKczrKgMqxx3YoGdcnAROcQjofym79Mct3DcWKqLDN6nmhd4wZ+5wwO070i3LCCYIxuHl2AS9d3cXnP7AG+cziuHN5Dqdujf2LMgDYwuDuhwdYuVdi+BDA2hPvccJTinuLi3jp5s3j7kbCM4LRYo47720+jHP2IlugwuLdGms3Jzj36QmGuxbjdYP/P3t/HmRJkuf3YR+P41358mVmZd1nX9V39/QcOz3HYnf2xh4CFoABIiTCABAyUKJEggJNZpDIP2CkGY2QGUkjIYkABAECREAiFtzFHtidnd3ZnZ3Z3Tm2Z3p6+j6qu7q77srK891xuP6I8AgPD39RL7Oruiqr4mtWFS/DIzw8Ijw8PL6/7+/323zUYXoiiYO+dDFi4VWIO7D4ZZf+j0Vw9E6dSY0aN4OoM/PtAfVEucbHhvdPdnnmjXU++93rXDi5wM6iT7/nlliB/pLH6beG1jrihkBMbyNFUOOuR99v4MqYdhAw8mt7d43bA+kIBoddpunEV0844omYyIPhkzA+DQd+B8LDku7vuQRfiIgfmJ85rlGjxt2NfT9Rji1nUOWwpyQKemguZd7NHLGsWfssdWYm9HSiV7AFGmW6SchwoiiI/jMTdfpDDwFn+FDML8uwL63rCs4zxeWs8GQIaZhI7TbgScPju59Y5ei1EQdvjHng/T6B5/DmYz2WtgNunPJ57OUd8BLP9HYcEDWSfTOd4FTitnO9IORSC2Xy3Aw6mHDMtI3AepqZT22/FeRpqvtBmhY2SCZi00Bz9AtSJzKL9CILD6eWNgmFLTzcHNIL3WFPl144wS6kF3PIMlTYqMKzZTrzlX2zrH3PPE7hNpjb64+P2t7mFCREIr8YDbngL+3atCpsJs5Soe24ls3V0qHgnKlXZZcyVDWwYr95KhKWEnMzi2Qjc/S0XgJtHLM42s2C1ZnP2L/YztmNyMfEtEyTV0gpkY5DbIaQg8x6JS2eaepwmcOdIUfQUWDi0jqnkQtt2PqJkKXfEQyfhM63Gkz6MZM00Kmefa+UpFQ7nJul2k6WLS10XCfNTKre2E5UPhfl1Kc7Foa+UyiLtHNQqb1tDKOSl4VhmgI7rrjZhgOdtEkHsEgoKp7bWXUksGm0Kuqs7tgVjZijg1vqtKW8luYv/fSM8cjmBGgNm6ekLzdvZbEtNc+0a9T5zGp8vBACN5IcvzbmT55b5cqxNs++vMHxS0Oe/9oNFrZDVi8FBA0Hf2TmJE8y8YU9e9U17h9stlosjcc337DGLYMfRvzkW+d5eG2jftsaiHqw83lovwWDnwppvOXgf9svT4xr1Kix77CvGWUpIPbKX292Rjn5o+SUBFlyiNxxT2N/o+KXXIEFNI5TcCJUx7OwV9m6WLXJ8nlpY5UytkWUzmGvbHMp+L+Fic6TVGgXVHPwkxJrCJ4sHJDhmKEsmGcuDHjv8S6XzrRBSo5tDdle9fnslzdoDWPoxTRT1qjhRLgDiRASZ0EWHPY8FYMtrTfQTn47TBz2XAujvBEoRjlhkhWLDDAO05BxKaOimBWAOCgyyYWkIulhVJOs1glb/zSaV9gvPVUntExORLLeCbRr7BT7s60vVFoXLH09Y5ctTK/pJKp3L+UYaH3uso0s7ZTGVtoBhUwcWYTRjpvCtOTY2G0bGzQHgSpcgYhEZR6PqvB3xR3KrNPsbXdXJm3nWXV+2sreaMrI9zjYH9IKQl47chCEqCTerGHsjFXWREaW7YU5HunPj5tUFKcbOdoMNcr4oGSd0MxfiimNlDN04f4ZPJL251SFf0zbO40TiYZ/NMZ7zWPjp2H5Gw7e7zeZfknmb9piUwpss8kou9oA4aeDwKIzLpVl55JeyMDTxqqMZU6TJHn5SZjOfIH292SaNFhdH1nFKJvQWeU5n8/52OaKOiuY5PyVuotzKLRNa8Fcz+Rsljnr3TarW+YcqR/cYJlvwfdpHR5u96gZ5RofOy4cTSaoJy4PtVFMsHmkQewJzn+2xZs/1kEaH0HNG5JwlfkmEDXuWTTCkIc2N9hstW++cY1bht5kwka7xXdPHmN5NObE1s6dbtJM+EHEU29v8OQ7Gx/rcbc+JWhcBm8dRj8ZgivxfqsJo4+1GTVq1LiF2NeMMgKkfgalMCtakfoyywiqsi5Y5ZRwdIYw02YqhrisncoSSNj0qBV6abseVRTXiVKRpjMts0JWjbLxIV6pUbakorbm9tC1hwnFp1VaXBYvtSB2BJEjiFzB8mTCcDG5iU0vYU0mZwBiWm7OGne8gIUNkIciGk6cs8jkGuVMZxjlnUIlEdmw6JaVNnmYhpUbhrlj2DhMw8Kl2uQo0C6C6ihGumrQ2GVbeLiouK7AaBosp84eO2FxqUOKZL1bSGFdvG92RtlilVDbp+20PVsZs2zr6zYGWzHJBrOs2q63ifIjqUVTyq/xciq5uLTQu7mO0MLaW5O6mLA8WlmVellahxNZ7o/xDBe1uOW68jKhbzI/E202Sm+K2YZdMtFCSo5u93lvZYUIl5ePHOEzFy9xdaGbJX6ZG/rYYRyulDJbh+qXqi/p2ljiNFyWACn5xJvrBJ7Dcn/Kq8qXQB1P8/tQrKqjrIh6+Dal1U8POFOjrMOFzecDlr4JW3/GY/pF6L4k8X+jzfAnA1hJt0ufEV/mxwvSMHYqhGVgCQ2kkpF0nEmpLGOUtaxRal3oFcPE6ecepJrmkeaHkYWTU0mV4ooHQocQhW13zV7ulUmuSpYyBxmrdztTVWRPw75Lajfr62ULWek4Bc23hWX+CChpyGvMhZpRrnFHEPiJBMOiiJgJ7wZEB2tt5P2O650uI8/j1PbmnW7K/QEpeebqVQLX5Wo3sQbtNJtstZoc2+nf4cYZkJKT1/s0g5gPj3QZN3Y5ib8FmB4TBIeg9QYgYPqpiOnTEZ3f8hHX6ldujRr7DfubUa6xbzFpuixtB0xac7444sScOVmtJ8r3O6QQXOwucWTY54M73Zj7AI+vreFHES8eO1ag1949sMKzV65ydKfPxd4iV7tdIvf2TwSdOObo9oBD/RHtIMSJJQJJr91m1O8jheC7jx/k0OaYzW7z5hXeBvSfgdXfFYwfl9CE4NGYuCNp/26T8AtTeMhiHqpRo8Zdif09URbFzHx5Dvni38nKdKnMy7q5PAtdlWxU8FswHPWs5vIsk5nucFThOGQ6/+n7lczDupOWsbTJK4zMfkllM5aWdbY6rU5hpvOe1ZxTtnm5fmJWXD/aYHEnwOlIGqlNtemGIGUSFskRdDwtPNJ2CB2PhYXE5Kg78/lmXC6tT6isWMocub6zQPxSi/gHTXb+3QicXHIx1qQXSnKRhUcK85OXhuRCP7wwnPgcvcy4t8Iiy1CSC92Mn63T5BX5juAGpjOfKiv3hbwPWWQ1SjXiWhybTDOmRZaRhXTTi5Sl0ebMJ41tZHm/WfbSHb/Bw9PJzHLbNbY+i7uAapPFbxWhrCMWiZUtvCLGqqoQcsL2TJYqyPUvNvnCXp2Q3Djm1NYWm60WZ2/c4Hp7gfVOGk6x0eYPT53hyKDPsX6fJ66vsdbpcLnbZW1hgU4QEDoOE8+z3ychEFJybGeHw4MB1xcWuLzYJXIcGmHIsZ0+rTBk5HuMfB9HSg4Ohhwe9NlpNbm0vMCw4RMJgfTgYKPFFdll0PQQHhyfDpHkUgCZZZkrPyt5+DPtOU+vh5tqMEznt2TrZJtQu3ZTXKYdyQEi5FAQNtJQaydA/FRA86sNoi2BfCYg0AYPx0nGGl06UTqeLMozIM/gF1GWXqhQcWF6oqF2DuP09a/GvVgri6NiRj6rM59llXREYtrfM59hq7Tib0sGwNJ2VYNJ+RV862B95mRFmdpP2n7eMtQJR3aP/T1RrrFvceGhDifeG/DEN3Z4/Ye72Zt89a2QI69ETJdh/BRMHky2dzYF0YqsSug3E3Ig4O0GrLmEl9xEW+xTC4/2MULHwa1DlAHJZPbHL5wjBl5dPcLl7q2Lnxg5Dn948gzdYEo3mPLkjesMt3xePHoMmU7KrywucmVxET+KONLv88DmJs9ducLE83DjmMhx2Gy22Gk2GPgNBo0GY88jdgRPXrvGQhBwcXGRw/0+j69dZ+T5tMKQawsLDBo+3cmUw4MBsRDc6HQ4d+gko4aP1OIo40nars8gcwqQXDjU5QuvXeGdU71Mo/txQX04Na4AEfgXBd4lh+lPRIx/bkrrjzycd12iRwXiUIRoxsglEHX+nBo17jrs64myFBDrA4v5sWZx5KkK85Y5ZVXtZ2F/7eHhinXbnfnmcRCs+JLfJWtczUQXtwHyiaQwT2aXmPEBu32gwcGrU9w4dxLrH3U5+E7A1nMOB16IcEYwejJlVf38+K7WlhKjrCF8t0HwRwuIgxHygp+wRC1J9DNjpnHCjCnHvTDKT179VsxKIbyf6YxXlaCmokxY+llVMpJC3gL15IrknhWd8oqeW9Ywgo6xrb4u/RrRrTXlUIHcOlgcV29OvCRsZKlLGs+3jT22JQ+yVD/PqrwsSp9Z23Ne9bza/q5i0y11xTi8313mVH+LZ25c5eBwyDtLq0y8fHDcVfISY9uJ02DcarDWgm2vxSevX6IzCRk2GoXtQuFysbvExe4SThwTO0nsyHYYsDwe0w2mnBjvsBBMaYUhjpT0/QbfOX6SyHG41F3CiyI6YUC/0SByjE6m+q5LKblLdqnTjimJGfgNri51OHO5z7kTS8TK0Vbk9aqwljnjrnUYp/hDD3OpEpOEaRv1kKKOkODCjR+NWXhT4r/lEbeSZ9b7vsf4szHiZ6a4b7iIN5vwggORIH50gv+lQeZc5zh6nUm7FFvsau1spaYmNQ7q1jb1e+IkA4ZupVMss3JydDRHxizcp6W/iXneAx/Via/COc/qeGdx6iuPDaL8U87aeHbTgPlCzVVdpsqxx2Zau3Wo+YXdY19PlGvsb6wfTCbKzUHMaCl5AUyWHKbL4O1INn8KVn4TJqcgXpb4r6YmvTnHjslLbYKXOzR/dofpH3dwPj1iehzoxtAGprfpxGrcdsj5XlX3Dd7uHWTiejy8fYPFYMLz1z7gwsIy5xdXkgnrLUAnmPLs2mVeWT3C0KumPrNjCsHIbzDyG+WNLDOf0HXZdt2bzw+kpBUEdKYBXixxZcRhv0FrOmar02RjKTnexkKT5VE5QsTHgekhwfSQyLKKijEc/LJDcErCCXAvuTASsBzDSoT3/PCOtLNGjRrV2N8TZQFRS/vbJD7nTUU9z34m66zXUaGJtgUKN5OQVKY5LpQVXyxVYd6qtMaFGYaZurqCba5mxGwnUWgukGsBw8hhbaXFo/RZfW/Ku8908dMwU2uf8Dj+tZBrZxyGpyX+u4LhJzx8RxB90CA+FdPVZrmKUVEB+10ZM3hhkfDNNos/v87wm4vQAOcTYwLZSk5a5no9FS4n0i6oWidt7KPxFrdq4Sv6hC18YZYQx7ZferiomR9Xsb3ShbApCLU02pWMstlPbKHjbIxyOlLoTLZ5XpkO2WbNmGdWaznnmZuKGc+Wem4sCV+qnq1dETe2Z0syU6Nsfe6M/W0JCGxts1nLVMmHnRW2vDZPb15mo9GhE0z5wpX3Obe4ypX2IvMJle3XwpMRT65f44PuCtebi4n23rzfWv27YrBnS0eTH1LSCkMWgwmL0wm96ZilSTL5HTR8AscldAXdjkMwCXjw+hbr3RbvHFnh+PqAKysdZCgyJlnvEpElRF0O5WhQWKRNTvbLwstpnSlOB41pbAjFGhAciJAbLuGxmPjpmM7XXIKfnUADXMeBmIxJj7VWxVKtS4+rseJKJ62SkOjJlRQT7afOEl0/z2ap6lch7nQNdhgl28e+0gBX3FC9LBAQi8yPY26tctWYmq2cXaU15KPZ5krt73wmpFIeldvC/patl1Vt2S3q8HC7x/6eKNfY15i0XYYLDkc/mHD+yYVs/XTZYee0w+KrMd4ODI4CAkbPSBa+7zM5Wc0Qjc+1GL/RYelnb7D1eyu4B0L44VHynqydze8JJIxybUM0sd1o8Z2Dp/nE+iVGns+rK0d5ZHuNU8NN3uodYquxtyQtT25cY+x6vN9dvrUNNuDGMd1gwuJ0Snc6YTGY0J1OiRxBv9Fkp9HgUrfH6webjBpeThp4kiOez5W4ixvHPHXlOl98+yKXlxb48ODibW3zbiA0h9z4kCR+IML7nSbhzyST5Ro1atx9qCfKNe4ozj3b5ZlvbtPdCIm0d/jWYy6nfjvGCWD9i9AEpqdh4bsgtgQcttcnY+j/0RKLP7LF1lcO4D84ofVDfQbxnQkTVeP2IGGU73Qr7k6EjsuLqyd4ZuMyp/sbfG/1BIfGfZ7euMJWo8WbvUME7vxD/4nBJsvTEd86fBopPpqMoxGFLE/GLE9GLE3GNCIt6oOUNOKIvt9gx2/SbzS4utCl32gSuG52w6uiiEDCyP7gdD5ASCcGKVkYBQSuQzsK2O7emVnp8ClY/BYMHwU8iJ4PcP+wgf9vWsjnJZwM67dyjdsGiagZ5T1gXz+S0oFIm/+UMt5ZJBTYJA0VYbsqy+Zy2Jstr7CZ5zOnENvxKuzRWUioKgeuCnmFmS3Lejjb4YX2z9hOWHQEqpkqRM3mUpP1gw1C4RCm78xJ6DFpwmQlpH1N4l10mD6ctvdgiLPu0DySUzMqW5UjY4YftnHbMWIIXi+k/dl+ejwVHsmStUqZNbUBJMtMZQmPlMt4yrqCUj+p6i8V0MeyuFGW2mQh3NzEPBq1ymZvW7bFSomO4cynh4CzSi4MWM/Z6F+3aoyOETjIstzBfF4t4fmsz90ujl2QQmShJcvSCzNj4cznxygrWYxvsr15vOSnw8u94zyxfZVP3rjIS8vHWTvY5eGdNZ7duMyLKyfzMcOQiKiqG1HIqeEGx0Y7vLB6khCv6ERntsUxiqSkE05ZniYT4+XpGD+K2Gq22Gq0OddbZaJN2KUQjNyUJTb7i8wPlFkSCg59AoTIEuvpsoWzV7d45MpGQTrxm587nWyX3sBqpUBcbAtA5sSX/OlqjnChJYxcttsRSXNV0vi6z/DTEmdJEnwhwr0oab3Ygt9z2FmMcFYipgcCGmdHuMtR5tinHPbcwkOW1m3xXFXOfEoaUhzjknZO/VR6oQ0w6hwiFSbOci5qwhWMtXsYJbILEVk66DwPf8XYOKdyqFxX1r/3OPjoz3tp8L6Vmghjqf+2KUPq+e7Hhn09Ua5xb+DVzywB0KYYKNgbSdY+43DgpYgbJyBuCQhA+rNH053XenSe6ON2o0SXWOOehZqM7W6ae/9ACsFrvSOc7V/nUxsX+f7ycd5ePMQzW5d5dOcaFzrLdMIpgfDY9FvZm9eRMWcGG5wcbnK1tch3Vk8nsZBvAieOWZqOWZqOWZ4mjHHgumw2Wmw225xfXGHgNzI3zI+L2OpMAt47tMTD17cA+PYTM8xRHxO2noelV2DptwQiaiC7knhRwpEQHpI0GgE0JfK6y86/WcU7NsX74Q3cxdnRfWrUqHH7sL8nysLOKFeG5rI57Jn7WRiqjFm2sdRGJJ3CuoqvRCsTrZJYVDgdZtXYPmYtLCLGOqsDl43xm8cJifT9avvqNpllyMMxpX9HGlOrWAwVHmm86MDQYeu0YOlPIra+KHHWHPzDQeacAjnLEo4cRh+0OfZj66x/dZXWsXEWTilQjLLOmqS/lRNLrLUlY5JL3huUv/wr7m0la2mrM0WkWYaVc56eh0B3uIt8iGJ9e/sSyPuAWy4rMcqWoNWVyTNsz50ykNzEXF6qq+ynVyhrhSFjxwWE1cpjfV5NB789SjcK4akUoxxRCllmskHWSI+261J1jYy6rNaJwjrBO+1DPCDW+fT6BV5aOsHrC0f4xPYlnt64zNBt0I4DHCnZ8DsIJAenAzb9Nt9ZOsPUTZKFqIhj+jn4hBwaD+iFY7rhhIVwSt9rstlocam1xOu9I6UJduF+VJyvNXlN9iMdI/UxR0och4zJdLSL8PahFT7/7iUuriywMAm5sdDOKOS4ol+a7gw6a5wZ/hS5XcEi65jiggP9TwX0PwWtOMDZETh9cAYSseMwvdIGCf4DE1p/aodozWPtlw7TfX6b9jPrCFF0HlTOe4plbmqZidR1UAy7zhpnjHI63urOh4Fy8EutWYVkJOn1H4+TyCdSC6spFJtcRdFXfCGVxgtboXVH23Fu8vdN2oLxHiv8sdfj2WBanS3v/KqERLtFrVjbPfb3RLnGPQMnkjRGMdN2Puhe+5TPA1+e8O7PNVn+nQj/GklatKb9Ud9+o0f3wQH9V7uEWz6rP3OdzXjBum2N/YvudMLjm9fYbrRuvnENEILznVUC4fLJrQu8uniU7y2dysulpBeOWQwTJ9n3l1YYu9Ua3pXpkCd3rrDpt9lodrjU6bHjNzMpw92EUcPne6eP8Myl67x6cvXusln7EB+QxAeg6QVIoOVOia96cNFl+lIH0YtY+cU1tn9/hctvtzjwhRt0TtSxLWvU+LiwryfK0oFYmzSV2d+yrtSq/a1keI1lhebRrlE2/rZsb0s8YdcozwELe1VKKjKnVtLcv8h6aY0XkuLJp4uUPdYD5yt2OV8m61c2pjzzvS1efr7H5EiadrUtGB9w6KzFhIck/lTi9GLEGy6NTwWMznUYvNGleQbiwOHGd1dpLAeE19us/PwaofAyRjlMl7FFh5eFh9NYkzyQvWL49b5UZP1tyWvs7EVFWVq9YnAK7LFKs2thlKWTaIljrQ2ZntjCKJtMchWjrHfMyhCDJUtO+bmrCnM0D/RnZGkyIhAury8dma1RrrAO5cl89tYWfbfMNaABIqTa+mJjjS2MUeU8roLQuhmjdclfZipdnt6+zJbX5t3WQcauj5CCvmjT99vZfo5Bp0oHkJKVaMjhaZ/V6YDXFo+y2ejkfUharuk8TJiurVbnZ+svRt8rHkqAFNr4rPVBAVvNNt84m34chHqnUDKQdAywpOu0B8pJ01qrD4MKVYTuPKViKmd+EdYQcAKORDSPTfAfHTP45QM0fjpg9c9dI3qzwbXfPsL2yjInfuQKrdUJDsW01kvuKKszcIrjXqQdT42Nk3TAmMb5dCDTKKfLqXZ+04liktWgpd2kOPlbzBEervL5s3Zwm0mmoo4qzPXslx+2fBy0VGDzR5rjeLmVu3zNSiEmLc3bnYMFtTPfHrCvJ8o17h2MW8mgfeqdIe8c6Wbr+8cduhcj4kVwd8D9sQHhbyxy+ZUTOL4kWG/w4fkOnUMj4omL2x7R+4UbODNY5xr7H16cZF0Lnb0kNL+/sdZYZN1f4NRkg0/3P+Bc6yBXG0vVO0nJUjDmzPgGTRlxudnjveXVRJZR47YiON/EPZpIKYQDvad3WHxih+lrHd75nx/g4T9/ns7hO5NQpUaN+wX1SFfjrsCok4TLX74R4k5jokbCZPRPuKy+FjJ9QuLsCEQvxvsL26wGA8bvt9n64wN0Dg+Z9n2O/9wlug/32Y46d/ZkatxW+DImvAtN/PsFsXB4v7XKNX+RZwcX6UZTLjd6DJ1Gic5uxgFnR9doxwEftla43Orl0TJq3DbIGMbf7hK80aLzixuFMuHCwU+sI1zJxa8dY/Uvbd+hVtbYl6g5pF1jf0+UhSRu6jZnwySuJzWvcpLL1hnb6L8tYeUqHf2qzKBzOA/O48xXZXIpmNQ9+xI0M30FqhyrrNtX2NSEsU3mlOLBxiGf5bWAw+enXH60lZgpu7DziMPya5LglMR3kvTTC0tj+i/0OPil6yy2RnSPDRl0fULc3KT4dotg3MB9Ypo7CEb5CSuHlcAIhQQQq9/KhGjLyGi57xj3zdYnbJcny4BlkVkoa3DBX0jbLjakF9l9VjILi/MZ1uPI4jbzynCM89ETgUijkt3KHWzPgRdHjD3Pfo3nCOe4Z1mT2s9iihVxKumocNirylhonXtWPd9zO/PNXjemwYvtU5yZrvPs4BIRgr7bJBAuAuhGY9pxwIXGMq92jicT5FhrlmPUbbsuFedl60uVTpyzD0eWGVGN81oNoipTpXquVRg0WT6yZVVet5KQuXlrTMc+x80rUI5w5hJyGUYYu8gLHnzQoP0XNxALcVaWSygclp7Y4vIfH2a03aTZCypDx8VKnqGtW3STLH0jN1nbcfPScWotmHiJ8ERl75sbu5VcGOHk5n4ePmow9Xmc+QpaK+NH1ThY1bQKWajtnZ/1fZsUadbfNW4Z9vdEucY9hRvHGrQHEYffnTBadOGhZP36Uy7NA1NMv7xo6LH2tUNMTu8wuNKm9akB3kIuphu/2iG+3MR5rHZ8uZfgx3Etu7hFCByPc83DnGtIuvGEdhzgyxAXydXmIcaOz9Tx6pfwbYQYQ+slB3faIPpkAAfS9Z0YZ2H219zoWhtikX/U16hR47Zgf0+UHaCdT4ykwf7pSSKyeD6KcbAxvIo6KFBwaZkZWkqrK4tUVsUo69gFo2x1OozLzSwdQptHxClRoJjG2LdsbzrRoDu0qYr0dmq0kNEQdd13ax1fO9HkxLkR/YMup14dIt+H4UnB5ICAYxHNV1wmryzS+NQQHoCV59fpv7HIyqlthtdaXPgXp+g8PoAnA2jGeI9OCC83mb7UYfpkmVGepL+DMA2FpDPKYcowpX2q6MQpjKVeVlxSwTbbHPbmCdtWKBfpvzkTzGQkiYVVVSZ1lfTGxl5kq6z9cncWmV2hwCjHhNKxPpOVzpRqm5vUP3M/9dyZiTXS4860IlWwY1Vss7WZxn2Yl1G++XgkGNBi4LSKzDDJeZvHBa0PWQ6bbWcZUmdto29nqzPbbxaDrd2DgsNSNq6XKxVprPXsXPRxM/OGTcv014nKEqhCN2rHk0arbYmMtI1Z/lce05Mx7gUP+g6TX4hx3mriHQoyRztleVPh3nwRceF3j7H6hRuIpZhx7GeOiIE2UMSyOAgvao5+wzh5gFRSEk8Ludlw0zI3ZZQ1PXrgpuOllzwQcagPIrYroJfl18qZaqxqVYjoSqe19Prv8UOu6rkwk3gVfirrm60ua2cvjqmO5b1g3d9kqQvjxN4G09qZb/fY3xPlGvcUpCN4/bM9nvjeNmHTIVqO8TclC+/F+OMGsifxzo4Y/foy77LMA/+7d2mfGLPojFl5fIvGcyO2v9dj8KvLyKnAWYyRhyLEyz7OqZh48U6fYY1bAS+OCZ2aRatxD0DA6JkYdxvipZi4F+N+uYWYgPOnxpW7xlOHzpnhx9TQGjXuX+zribJwJG47D95jsoDFPMCKZU6LLGmHzZBgye90qZhFC1smqxhlW8IKY/+qUHVV6bSrWLACo1zBJJtf6YUwVer8VF0WFl7K5Jru1bdK1zO7TkzYFbz9pQ4rH4Ycf3NMsAQbzzscON6HviD4egvnaMDCiSFT4SFiaInEKzxuC7pf3CH+XIicCMLXmgxPubjvuLiv+PR/yGEU5hdhEhYZ5YIJU93vUDHKZfZjtyHIsn5m0Q6bodyK6arTdfrTqnXx2AOpx7ByytuYbbCG38oY07LOMyPxbJbgqnB5pmZ7tySIpa97MiKSTm7JsVz/ypCNZt1Vx6X8vBVZ+PRHbLk2JvNpe0aqxLi2IrNsXkZ5F8fVNblmQpQCm24w7BVyzcqyKo2yqUOG2ex0IUplxf1Dy9iZtT1LMKRpm9Pf2aNi0RorPbIeAtPNruPsG6n2D2OXwROw/DsghuCdd5l+PiR8IKbtednBFaOs9MeuaBBLwVj6+HGSdUuFfjNZZIBuqkf2NepWJSZR65paTMCWm5QprbLvagmevDTBUzpexl7e8aWUSN0zwdYXJ7Z36ez3ZFmLW97IquGdA3OFqLNYe9QZijk7fcmQoN2i3CI929ptq7NyoKhAlea+hh37eqJc4x6FI9g44yMemdI9Jzn0tRjHbcFU4D03xP/kkJ4/qqxCNCX+J8fIaYfooYjWV3y2Pqbm17iNkJJGHGXxYWvU2O+QDdj4+UT6sPT/cwlPxnO9mYUDMtzbZKlGjRrzo54o17h74Qr6jwr6j0iOhCNYkCy2d29qlEsSd0D9KX0PoBeOmToegePtPmpFjRp3O5xUEz7Hpo0jEwavL7D4heFdlWywxt0LSa1R3gv29UTZdWI67TyigemUpZvSTclFwdEv20j9KEsvbFmIzHW2F3eVg2BVeKtSODs0k8wcjlEF071NcmFUmjXP4kRmd5BRS5H+m28SmplUlVNDwWSZmhdTTweVxQoHktDIInNU0R1WglSToNYNo/yEh2Filmy6MJr4jHXpRZDsF6p+E2j9JZXxOEp6oTE3mRwjtVQWHP0MWYZ+j6ySC4rrsm10xz0lnbFILwBwi7KMrC5LmLcqB82SB5UuT1LLCgmRtV8afXzPIdnS5aHxgDV/IbnONnmSebwqM+YcxyvUb9MDiHwb0yFJmI++zdvNZt7dlW6hXDaXg9+8bVGw3Dc9I58J0+HOdrh5rN7SsrIyhJxFUqTG/myctvUJ2/mpqtzyO0P9jtOBLNbK4lS65KY16OOmGRZuqsVnjBFIB6ahi4zBEflDnUkvlFNfLGl9oc/Ol1d475+dYfHZHRae65fOQUkuGulgpWRqUJZctN28bBQlKcxbqTPf2M1lGdPUmqOWoSZJEbFM5HQ2Gc8k6TA2WaFNxpaVlV6hNu8665/GfuU6K2F5HmT6vrJJPUrtdGwnY6lTvQsrxrPKh6bmf247ao+YGvcFpLOHSVqNuw7tOGDgNu90M2rUuC2Im+DdmI/xcxZien/+Bv6BgHhSv8przAGJRm7dRf/ucux7Rnmpk2tVp6lz1iRlCBXDDLnTRfZ1b/kKs5kk4oydTlmFQl57Y50lKUV+PFkqM8NqFdepY+RFlY5RhiOAdGd/Zlr7pbCUGesK7Ogev2Kzj20z4QjlEEg6wpR5UctAY2KUXlWFUhoVGGWf1T+McAOYTDwmWvwn5YySMclh/rIxnfg0P5fst7ofTiF0XPojPYVC7gHzuheYinSZNk9njzNGWZfl6t3Qg9gWXqnintocQqs+JCodSI3+aHVAvUWMMjKpcxajXGqnfn7MLsu2mYMVtxFaTpT2A1ud6j7oToAm21/1rpj3PbIbRtlSvy0EnBEhrbopFpbMUlRNkhkrrWS6ZTxCpvfH1icyJjlleAuJexSdV263UBRf5rStMcMpy6ySmehJRpy4yP7qj63abpqOQ7F2hrEUbD8e033TZXCqODbmdakxMmmUHAsml1p0f2ozs6gpFhk01lgoxz3NYS9d13GTFNjDuJGVtd3EUjtOx9KWpzHKaVjNaaSc+vIzFFGMEHHuzjfVvcrTdVnITaHtRxEVZKwVFYV7noZZEy+l7/y0gcVwbcX9qhMMaSeoQgxa+nVN7twdqD9Da9zzUNbGqHn3f7nWqIZE4NRa8xp3EIuDKas3qkO37QmRZPF1yeTULvbxEplD3K+dW2vUuF3Y14yyIyQLfq5R9tLPL9dJv3jd/HPMTCuqI5MRW6jWjHWMy7rnEttsS3NsE+kZGtC4wBobdVkSnFjZQHUSVWF25kFBo1ykdaSuucqYMJn8szCnItUYC12HnN4TtfS0yOteGoJID0WUNcugyfS/FROidHVKlwxJgpGrzzmc+L2QeOgSaPxOGKSsjmKSbTpkI3RP8hujrNTcYig3ta6CUVa/FZNc0BxbEo6YbHRB02yyeTbriaWeqhTbVRrXShbXrNP2rbILsaovIwIzFqHlsZtLg6hXM4+m1tDsV7bXrMTczNAzzpOgqLBqBqtqO0ahzHL9Swyv5Ryy9lZp2nf7HWphjW8WAk4/nK3Q1s5MSi3LY6oam9Tzo3ctWRqLk+XJKwMeuNrnwpEObz7WI3Yd9BFLjU2hxWpmQk9Asvq9iOmiYOthAZEoWNacjElWdeb+G83n+2x9fYVDv3gNyFnn5LcKJ1dkuZPfcWF7PfW1arNKQtIoJCMJ02Vq3dPGa+FJHBkT7THl9a1ISHSrIS3jhE0nb4nKV0bVQFj1/MxxfvNeu5pn2D1qRrnGPY+g57B2usHJV6tDytW4u+HKmF44Zttr3+mm1LiP0W/7rC03ERKe/+5alsnyo6CxHbN4LmbzGZfKEBYSxPsu0bfahL+3QPyBh2hL4p2aUa5R43ahnijXuC9w+dEW3fWQpY3pzTeucVdCAgJJWA9bNe4gLhzu0usHDFsejUlV7uX5MV0UbD7tcOhbs+tzrwjcX2vhfN+HVow81yD67UXG31ik/aXtW9KOGvcB5F347y7HvpZeCIpmLWHYHnQzU7uZOCzYzGCxYefTJRhKchGlZrdppDuDGeHorHG/ynXGpklPa1LJ3GfJLpgRGJocIDutqGxerITpPKOHv1PzESWd0M2SqbOgcJNwQEKTUDhppiYn3cbTJDAqq1PDIrNopiY9FZKo4+WT2gVvUljq6EctAEZpHDw95FIQ5aGMNg836GxFrC8ZGRwNJ5OkMF1kzhelw5acNwAyfxjbw2/UYe0uFkcQW7Y+/b6VHIfN49y8KcXtLF1vHmc+LM5uWV0VofHmlR1EaueGTMJy2cy1RjsrnfJs8hElq7G0oeoaRD5EDXud2TlXOffYQkLNgcI9Mvav6hNWCYatnWqVzWJsXI9CKDBjE2tdlraYkhKbOXtm37XdT3IZRrZOH8cMzYYohIBL16lxIXW6ihE0wpizH2zzh585QiTdgjetkus56ZiowsWlf1han+DaY/DoqxOmoQOOyN9VUtJ5w6P1imDyuZDoTEzbi6A1gliw8MgQWnJG6Mzk9zgdG3V5hVo3TnVeE5k7QU9TDZgKGRdqY+rUS8dUw6kPwI1iXOJsbNXD5pFJFNNLoX0TOOrGWzKa3jFUjCHq/azLCs3nQJSL8g5eES620jHxbrgu9yH29US5Ro3doNWPGB6ou/x+RTsKmDou8V7zpdeocQugolp8/bmjjDq3cDxxRDJ/KkzMJL3vS5qXBVs/K2ksaV9UjyaEgvDq2VONeSHqhCN7wD05a1AsZUdz9FMspS0smYKNEQ5TSkMxybpToApRNg3LoX5UZ8wCzGtf1nmouvS4Wp3Zx2Vc3k8a+9kC4GMu50TGBlnC3ylGWXfmEyr8nJPYThxtsHZTRlk553kaa+w7RSa55eVB7jvpb7VccPP719GC4QP0o9xhT4WDG4QJnauz/uoehZFDeydip93QnDCLzPLcoXgy58ZkGTUsm8zx7qocryyMcpGRlPk6R9rLbJWpOqsPaWmDjaZMYbCwhUiIRts/CqPcCacM/EZ2ve1OrcY6S5k1ucE8bLONYVJFXprYR3cUM6ual6ndI3ZTVVWIyEq22XbACqbdZLmtderX2myDjZWzQJBeS8tFqLSGGAeWmt44d6xOd0/HiQjBb37uTLJNpDq/9vGmhuBsvM7Hv+wdYWGWnZFEuiTSolgQCoelV2OalyXrPyWQTYGTPlChzhqndXlidjKmcfayyY83ThnkYTqWBlqb2k5xvA3cvM5xyiSbTn0ArhvhERF76ftLT/qVvhdQ466nvy+LN06LYvexo9TXbeOE+rPADMvi9haT3DyO0oXjKwfiijGrxu3HPTlRrlHDhtgVt8TxpsadwUI4pe/XyUZq3Htwp5LT35pw4yE/m7SLQLL4Rszln3Xxm7dGC12jRj3B3j3uqYmyCjXWTZnIhsZktlJGMg+vk/cWx6ASY00YpxjlLOGFVqaYy2mWDEMLHZd+FkayrF/Odc9FhhhyxiELR1fBROvsdokl3eWXZ5bau6DTLa4r6LFUKk83xnFiXE/TqaX3wdQjAzS8IpPcsTLKZT2yCrA/SEO/TbSMHIpRHoYqPFyutVMa5TBymDRdGiPJsJFetyxNuNIpmldFWyfKtFdVavC5QqzNyyJmDLZdBCqNslLosSr+WGfsSjpWWS5zDNZEr0pYrqNKkV6llqjon0JjZxaiCWvthZIOvDLd9LzM8C4YZVt7Yy8N7WfZz8qimwmFKkJQFWAyprb7YNmt3IDZ6yrZZltb1CZWTebs/W1seqnt9i5frjNlk63prY1jVybW0YNHKCZZhYrUHBXya5SOt/rJq6RBxjsgaUr6PGSbJ2XHXx0xWnS5/EQTN/2Y710KGR90GLU8FCmtQqAG2kmohEt+Sn2PtYEpe7dZ+pkKq6kY6KYzm8YNtHMYu6kviKFVBvDCGF9EWjjVvJ3ZuyxKr0GkjS+Gv05B734XfSOU+pdt7FGnUvX8Wp7bzHKrddhyWmxttzmStNwrEEKcB3ZIVOyhlPIzQogDwP8EPACcB/6SlHLjdhy/FvvVuG8waTq0bpGXeo2PH93plL5v0bnUqLHP4U1jwlZxRu/vSCZLtZ60Ro0UPyalfE5K+Zn0778DfFVKeRb4avr3bUE9Ua5x32DSqifK+xVCSjphwKCeKNe4B3HhqTa9yyEH38ktbN5IErbriXKNWwiZWI/vtn97xJ8F/ln6+58Bv3grLpEN+156oWcM6qbhxJTMQoUbS7ZLfvsWO46SYbgWW6dycFDmKT1MjjJHqVA6urzClGrosozQkGMU91PSCVHYVl+XyQn0OlPJhVrGFWGIdGSWICWz0KQeVZkHHTcPAefIGEcz/ZuSi4Ymy2hkod9s0otEatF1y7GOlfOekllMNenFOJNepOGONOnFJMydMEcNj2Y/ys6nFAKudNQc+rMcNQyPDusO5VV7De1TFV4sceYrlpnjjl14sUsvMqMNtmxV0rNcF5tsZNYhbA5f6cksTKaMPS9l3Yr2z2oJRdmur8oq98NSZjObKh+p1JmvID8wpQlxabcc+rBU4dBmZqCrlAvpqNi+MmOkpQmz2mKzJldXkBaV/eDy5lrkHLbLYm5kqjIK0H2sjPttC4+pwo7qsrRMhaHM5Zbwn9lSK1Onmg2lyr+t5XLpbIsDlwKupeO7O4DxYYcgcjNZYfY+0eWB6bsmSN9tjuW1Hjvl90GQjqEdZ5o2pXzjlWykrTlTj9LfplMfJE7aDRERpg5+sZdf4CxknJsex83vjPms6G3J/IHvIo7DNhbY5Bjm9lWyJlso0qwPqhWRpWxeJ/S7GweFEC9of/8jKeU/0v6WwFdE8jD+w7TsiJTyMoCU8rIQ4vDtaty+nyjXqDEvdno+D1/u3+lm1NgDupMp/UbNJte4dzFccjn98gh3KokaAn8oCTq10bfGLcbdqWVe0yQVNnxRSnkpnQz/jhDijY+rYXAPTJRb2tdsxiSn7LHunKDYZeUUpjvzucY6V/tEixyDUbaE5ZlkjLLmXJd+AwYWJ0DFAMQW1jhbp5whtC9rtU45bwQ6u52uC92bD6y2MHjqiz7UQqspRiR3zCgzKp4X4RNlLDJA00uvv58sPe16tjwVUih16tOYCsVaeKmVQLHHkF9jxSSPtbJxmAbMzwLga9dFMcqhw/pCk08MNzi0PqI1jfjg0GJyLm6ZEjPHEunykVFiTK0ZPWZj1ubSlUi/wivPwoDmjN1sys4WpihzzrM5YFU5HdpYceOUpfWP5Ec3HLPT9okbcXZwLR9DCVb212SSY9v2ZY+4EgNtKZM+xPGM/SoSsVRelwpnIKuj0F4xD6M8L3Ndtf2s/W3WEEvinVK/NMsEdqfRedpZcW/NJBPJHwZFWHE/CiE+M9YwtdKlncofxxx5Z4I/lXSuRWweb+BOYNJwCKWTjf3ZUuhjf5rgJC6/zrN3mnL0k/k2vuG8F1sunrLA6iHn1HtVWXN1Z23PkXhOnL0PIi08XOyl1lIVeUiLQJQ9IhZWFeOa3VIGdbd92DY2zlGn2Welq3c0w6QpyuOncpTWr0vGsAvj73sQUspL6fKaEOJXgM8CV4UQx1I2+Rhw7XYdv/5crXHfQDqCzcUGn3l9jafP3Rbn2Bq3CYf6I25023e6GTVq3HIs3gh45htbRL7g+z/ZY/N4YjmJXXDu4clPjTsFcRf+q2itEAtCiEX1G/hp4BXg14C/mm72V4Ff3eMFuSn2NaMcS5HppNTfkLOqOhurGEkVLqfIKMtCmQ61napb/7LOtGEVsa9sOuSszNJBzO30v7O6KNepCA71de865XOxtcE8v1BLR6r0aWEWck5nlJP9Om5MV0wy9hhy9l7p6vRrrdhlL7sPeTvVfbBpxavOYR4oDfXWqs/BrUna9glBwyUK0/4SavdR6enCj/YtaQ2Zpf4upD+d47yEffvYlcRh+X6b6YALjbAxYaYI1MIMl8LE6TAS1IDGgOgJUszjmQ3WV0k4tt6nHQTcWG0gRayxqkVm2dyvVGcWkm02MyVMplDbr0qjHHmpfFAryyY5Nv2zqZe2lVUwynOF1KvA3AzxHhm0uY6noKeUNpm3KhbeqD/yCyRlab/qJCvlHUvp5LV+nelDU2ZQD53pGM+GHsZTjYVq/OzdCDj7/T5vf6LLzpHEStYQESKW+BNJkEbCMBllPS22eg/5aScqhjdV1sd0vK3oOLb3UTNNPKKnvlZjtrL86eN7ww1ouUHu82IZ/7IU1rpmX52P8odx87aoxCQqCclt0eRWscBV29kup+o3lv5iGzelOQbo1yyzRsjiNpCPS7tNmLX/cAT4ldSi4AH/Ukr5ZSHEnwD/SgjxN4APgL94uxqwryfKNWrsFmurLR5+t8+o5bK8NeX6oZqlvJvRHU157r01/vjxY3mc5ho17gH01gMe+/4Ob3xqke0DfkYUALR2YqYdUcheV6PGLcHdqVGeCSnlu8AnLOtvAD/xcbShnijXuK+w0/P53Z84xgPv7bBST5TveowaHqOGR3sSsrVQZ+Wrsb/hT2IOXpmwMAg5eHnCm5/ssn2gnLmovR0xXroFjhE1atT4yNjXE+UYUcjCpsLOKBNSIWOQYSaymaAci/lNmZlscgDlPKac8QrZ/qpMXIYco0qWUZ3RLy9rZCHZirIHW/3FrISGtERzhFPnJS3tVNKOLg0Cd1wI8+ZZZB9ZmSG5cAttUbKMsknPdDQpSFKMayUL7Uzq8JpJ+5TJc3DU4dEX+3zwWIfQK15XwC7H2AtsjmZZVsOylCKXDMx/CEcIRJxLX7L6lcnZYsozM2HZICzSC8eoW4cyIdokOrlDjrb9HOcqG4LXH1rmkQ+3uHa0Vap/9o5qqUso1Lmnf0flerK6i+kyC3XZpCyxGxM7sRb3iyybmlVKYcg/bNILhSoJxl5DDhaqqpA0zNxWx15JT3WpK6QX1uyQM9ob+5K44oJUhlm0tk8W26cPBU7xBhSe5bTObDzS+tljL20Qeg7byz7f/fwC4YLI72XaCYSQNIYxk4XZ74XQ5jiu3kNamZOOaU7q7O1UeIHqY6py2FMst6+JpX1DvqiP/a0oZKI5aBfGafXeaqj2auenwtepEJO+9hylmRHVUn/GTMyVzdK2ibVf707DZG5ekNapW+LObqA0xycoOByXjmFKuu5d6cUdR+3MV+O+RH/Zp9/zOfPOgFsbQqDGrUbkioIms0aN/YiD18a0BxGvPdvjwoMdJu3ZjHHsQUVG6Ro19g55F/67y7GvGWUpRcbqQv71qhz8dGZTMbwZo2xhVW0wGWjbtqHFma9qe9NpwuqwZ2FxTea04ZUd6FTIHhuraz33tJ05C6+Fh6tg7lQdK0S43jBj7G+2n2qfYig8LaaNn7ZZOY74hbLktx4Wbhb08H5eo8h+CO3cLz3b5Oy3+nTHAe8922HSyvuSCisXRrO/JaumblYfj/TYnkrWYnO4VM46FkfNWWgi6IhJ6Tiqfp2BzawSUfk4s9qr16UYYd1ZVNUfZYkWtCQ56TIj83SGUDFulmPrbW7uhEQ++K2wUCaq2EP1/BQY3nSduqe2GHfWxhSZngJjrtY5ktiRhYQAwpgHWZ0qd8soG06YczPKFaxaZcIR4wAf1anPtr3VYU8l8thFndKTxFYTjrGd7fzmYdM1NjCz2lids5T5BBZ3Ah59exsnljSmMa88s0yIi5N6Hc6K5hn5Am9Hlt4f9iRVKVus2F+tMynnc8UkVzHKLSdngn0jQZduIVWWv4U0MZRe1vICQi9PGBUVEmYVz6EwLoVpoVroz61y7MvCy1kabxlLqp6N6n5sPoD6jhUVmWOIPoarvlOVeEltE+vji/H8WXbLrpXFQlbj1mBfT5Rr1PgoCNoOr//IIk9+bYfD70/48JH6cbgb4UWSsHZqqrEPEbmClc0p5x7qcv5MF+aUHUe+wJ3uA6qtxv6CZHdfn/cAhBAngX8H+FPAcWBEEl7u3wK/JaX106uAfT0ziCWMg5xhnIrkdFwn13opeKK8TsH8arexzTZts6nv1WGGRrMyy3Owxja2uZVqwlqenqJbJfBQGuWc2iqz6VpItnRdpI7nlSmOqrByCxFIf1wq1+t0C/chZZSNEENJO2dfM3UOavtpXH7jqHvsNy3sqqVOFWpJOJL4sGShoTEh6XUI5kwFPgu+xryqfqmWvoWFr0pdPus+9GKQ3iBfp0IJWVOy5ym99SXk9ytrrx5C0ejPtvCD1jpV0pr0fGyMclUCHCkFLRlAA1rNoLSdWaeCYrV1i0Cm7U/jUsUWa4FNN22GoStopPXwTW5c0KOa2uYCmZedYHoOsYWZSmEP71dq+sz9rajSGttCpe2RLa6CtB2vSod8E/pcutKeKt1gjXetR7Vtb6a3jsvjtHBi+i2f7z17gGdf3eDIlTE7PZ+dRY/BksvOkl/ou/o4HTfADWZbPavChhbClaZ9sCr0ZlMEhePr2+e+JPnz3nUnhTbpY7gbtQm9SalNejujzPclb4NINdSZTld//oznVlo0yla/hapbWtE/zfGk0ieisGnR2lNIXqPqsPbPikrNYdbaFtUHZzfzfoUQ4p8CJ4DfAP4eSVKSFvAo8KeB/1QI8XeklF+vqmdfT5Rr1LgVuP6Uz4nvTPDOJnF0vSmsH/UZHqgfj7sBbiiJaka5xj7FjdUWX/vhoywMQ5b6Uxa3A45eHeGFktd/aJHxQvmjP/ZErVGucVtwn7nk/NdSylcs618BflkI0QBO36ySeiZQ477HZNHh8nMN2tdiYhcOvxMw9Z16onyXoJZe1NjvkI6g3/UZ9Dw4Dp4bcfrdAQ++PuD1z/RK2zuRxGI0q1Gjxi5gmyQLIVaAU1LKH0gpp8A7N6tnX88EpBSMg/wURGYWLpueq7K9mWatgjnMcAKzyQGq5By27HSmFEI3T5kZmHRnCGWqV9n3GprJS0kuWmloHpu8wpQvAIUg97MQVdhRfdmg4YR2+YllN1NyoTvsNfdIoahjt/2yCbFKehFLQfs9ydJ3k3XXP+MRtiH6EIYPQ9uf0t2GYEHkmaOM+k2Y/UTdK8ilIWqdZ0mlpCQXRWed8jrVrzwnZimMaXn90jmra2xz/FHSlVCTSZhOqXr7zP5c1XZbnWqdHn5QwZ5pMv/dliGTtsNia1LYz/bcqf3UcXQ5iZJh2GQZpnm1qJIwnG81eYUyC4s4QsioGLrKDMFnc9C0yTlS5FIP/eBGnVWo2sbmQDePJGEeZ8B5YXGkq8zkqKlcrHBi8CpyPmc+dhapR3b8Oem23TiDaXC0d9SVMy1O/8GQZhARNp3sveU6MV4cI71c8mA+dzZn7Sy7qjbDdlMHMTWG61IyNd5mbSo870XH6o6Ty9JM6O9W4YZErl0iZXPiUwhCaWyj7aekF+nfhf2NSy1tz5gFpvSiaiyxwVZ3HlrSJrW6eZ1WzPOxZHlUahQhhPga8GdI5rzfB64LIf5ASvm359m/Dg9X475G3IagB9e+5HLwxZCTXw259lmPqClYvBBx9isjDr1uH/hrfDxwglp6UePeQ+Q7rJ1scOKdUanMCZMQcTVq3HLIu/Df7ceSlHIb+PPAP5VSfhr4yXl33tePopSCqYVRVkkRbOGtYotzVhWLIDJGmVKd2dd+Gu5LD61mhrDSWUhVVytlQPUvecUgRxYWMRLFttucDm1OblUh7rJELJbeaoaxK9Sptg+b7FCcSKr9bMcLVVg4xTRqMbRUKCPFUAzCPBPbIGoAMAzVUnPiTK97Veg/mzOm58RMV6GxBVEDth9yOPBqPIb1TQABAABJREFUzMLFGP9GwPK5kEufaXDwjYDVcwGbD3jEHgwPugwO5+3W+4/uYGker+w0qieFcQrb69de7df1c0Z1IQ3B5ImIBQcWGtulem3OpuoaK9ZJd/zJ2pDurzP8qi5lAfAtjJZiq9Qx9OOM0rB+eng/G5Oc75f3/1YcIRci3HaR1apykFX9w5ZAJ1Rss+6ARZHtqnJgVaED9Tr82KVBUHTqyhIIlJ2Xql4OOTNl/F3Yb3fOS+U4fRU7Wqq2jpGisKhE5f4Whz1z3NXXzWLlPBw8Wf6orWQPRXFdJVFcwTrqoR7Nd4VellkDU+Z7+2nBQ18ZM3oY5GpqtRExC3GM14xZbo6zdcn+YeFvyPtq9oxqFsOGEebNZrUzneySdel7KH3n6FbFyLA86VablojxRYTvlY/XMEKY7jj5+B42Zie3ioznRx8bS9YeC6M8TzjJKue+Kma6yGAX215wSKxg0z8qPp555r6HJ4Q4Bvwl4D/d9c63vj01auwfSF+w+Yzg2Jcj3BCufdqluS4RUczFLzYYrHhsnXI58nLA6tsh20cdVt4JGa46rJ/1GR528UaSsEX1aFtjT2hek/hbkqFfX9sa9x6iluDKJ31O/vGUSz/lEbUE3pak91rM4Jk73boa9yTus/BwKf5z4LeBP5RS/okQ4iHg7Xl33tcTZSkh1FIMC5Md0EKxiFTsk+mcbGkwK5gUYfwNOZPspmFrIk3zOA2L4W70utWX/8hLWC/FLOt1ZqG2tE5dpY0dh8mttGodDVZV17c1jNTeOkymwraNGzbo0yytL+xXYHGL+lxdL+3HxfTWI419VEyySiYzCbXkIClrOY+OvMCmp/F0wkdjpIQD34/pnY8RkWS66DA9KJIEEo7g+hM+Gw96HH41wAugMZSceGHK8JBD70LE4IzgxmcdWoZGUtfrVievSS0e0i2V9dLwez0vD8PXToP9d9wpTSGIm1v5dcxSgKs68zYotnccz07c4maMspaAQETWpQ5V90SrWx1nO2oDMLQkjMm0lRoTHcQu3lsOzT9Otu92Riw0FatdZLNtzLxpgYDc8qCYZZsFKKtHe2Bdg6HXkxxN037YjjwWxLTIUhtWIRvDVMVSWtntObSOVfrJKqbWZFd17Jk1nmN7Vx+nKxjlqnCcAG0pWCCxutjSqNusglkbduHDou9nu0dmKEX9ujQzX5J06YVwFqYjOP2VKZOHoHkOxp+O8M9GqKdFMbM2vwPTEqS312SSq/xA9D4fZdYep7DUEaXKTX0saIiQSDtG0/Jbsdy6/8bU4rugYPpt2ELj2f6OjP1s97HqnWpDlZXV1GBXseK3ElVtut8hhPjLwFeklL8E/JJaL6V8F/gL89ZTa5Rr1ACmhwRxA9aedxmccTB91eKGYNpz2HgomRi1tiXnf6xB0IbNpx2a1yTLP4hqO9gtgnNJ0Piex/jz6Yv25gkZa9TYtxg8C9s/IREBDH8kInikHkhq3B4Ieff9u404A/ySEOIbQoi/K4R4Xojdm373NaNco8atwvSAYOtxh2O/G4KED3+qYd1ucCSNGrEgWH0z5MC5iNFhgfRh4TwsXIDgEIQrMD4FdD6+c7hn0Be0vu4z/tEgizYiG/XEoca9jWgFBs9LvKo0xzVq1JgbUsr/CvivhBCLJM57/x7wD4QQrwNfBn5bSnn1ZvXs74myFESBxalKmRAtJr3MrGnJhGU1hxoOJ45bdqJwVfYxPSxW2q5o6haOodc59VInwGZ+Dk0/DdmTtj3W2qnWKUtXUDAdp22ymGGUeUkPP6SgHD+qzKZVTnLNyGM4g+6zSy/cdJlKL+JYKys6Fg6jfLKqJBdKYhJoTotK5lJlWrWZQaP02KFy9HwiYnDSQToQtGebxm887rJ9yuHB30mkCc2NmOlJ8C5BtBLTOu8QbUgWXhZMHgsZPxuDmzu2WU2ARjuX/NwTfjGVXKx4w2xdJ82O1RIBXuTR8DezMl8kfchmTq6SXKi+o0ypLaFLL1JTcSrHcEspo2Aqk3szjHMpzk7cSs4hGqdl+T3NTKNZ6KpUunG5zeS0YPWBAdO3m4T4HFzoI3zVFuVQODsUWD9KjtvXpBfKOXSahhMIC85LxeemKpzjWJdepG1uTxwCd1Tol7m5tWyKtfVHBXN7XX5V5TA0qx4bCqEzLeE0s3ZWhkKcfexSPRXyhYKDtJItWEJvVkkvpBT0Ypg65SyhVfIrmxxt1v5V8rTCOsOZWa+75RblBwXpmSFH02ULqs/HlCV5prTALfTdomOuTTK1W5jjhP7KaTgR8Qx5hyn/0M9vUhHiIzalF5Zsf+Y2UJZsVI67u3RmN49fbEv5ebeF/bxV2FWdkvvS6iml3AF+Jf2HEOJJ4GeBfw78zM32398T5Ro1bjHCxXTQqUgHuvZMMmPbfDIEVxA9FLH8FXAmAvGBw/DPhDRedJFIvGuw+GWX/pciaH8MJ3APIFr3cFaTl7DcTCee9UhVo0aNGjX2CCHEs8AD5G+T96SUN50kw35//UiQmjNf/mGVMgi6CUsUigxGWRTLbMdKA7fr+dSz8E9uyvxoznwZk6zaF2jHUwy0nzKZgcZEN5Jb4jVSVlAPO5T+VOGp5g0/o8ITZYyya2OUS7uVWC/Xwro0Y59B3LAycFm7dQc6LVEGFMMcNdyiZF4PAaeY5GnK5k21EF3KEaQqhJ+tTJn1bWHKTIZJ30+xQsGn0mD83pTpjwm8N1288w6d92Oi5wM47+G/0CB6OGDpt1xGvzBFdnJ2Qb9Oqg0rjQEAyxqjvJQyyQfcQbau46SMshNA5NPy8vBwC2mZT5GF0lGZRCbdr6GxTy3FKGfOfOWnZJA64+1orLFilDedBQDGMr+nyvlPMdGBdJExvHfhAId+6hqtxoTL6y1GwPGCs2KRHXMtyU92UufBHbeVret7CaPcj4rMso6cPdSekcxJSjmZ5uenHE4b+LjesBCeUf22MVrzsJSqTxQSuBgsdWE/455WMU36c2cyrVXM27yYZ3sb45oxvBbHNNM5tcDmIVgKBcIblMrKdWpWLMtxzOPZLDNmGMfIdj8MJzuAdpqMwxZm0atwmM2PYwkbmj7fyspjG/+qnHDzesrnoNbpZVV1NZ0QnOq488oqpTPKekjJWcifC+09ayRZ0dsZVqQ2dEoOwfP176pnSjkjRwbLnZTdTka5djW7GYQQ/wR4FniVnAaTwC/Ps//+nijXqHGXID4smR4O8a778KGH+3IymYpXY+JHI2RP0vyGz/in6+QlVdh5p4vXiWgeTSb78bjO41vjFiAkedvVAQJq3NcQ92t4uM9JKZ/c6877e6IsQWpsbJ72VNGHFkZZscD6B2S6Lvuo1NlmRzHJZc0wUVrmWnTPKs1tmC61djrpOhkoRyXtCzlKWZN0G7eRf7UrjXKmU7Sw4tK4BABC6f5SVtwJ85MP3DTJh6OYV13fmx43075ZkkxIl6njza1BjKRihVK2U0sNbTJpYy0EnMkk6+GEAkOjrDPfkRCFsgLbkl4/13HK+0nFJiVLxSJDnlREpQvXmZHWv5MwWsHLLYggfjZM+I9jIP/ZIu2XBJNPhun55vd9IdUcqxBwS5oeWTHJy26+btFNGOeWCIjcDgtunsJ6IdUWt7IwcewJLZ1FTxMPNNMhwxflCexWrHTTY22/IvtbZJTTeyrd7O/+Wo/DD9/gsL8DwMU0x8oBLUV3zmilek8bsyXUvck/TEwmS9dFmglVCul8KfZ/Xec9StlzgU/T3ymw1CMjFJ49tXoMQWrtSXc1WbLYwpJV6TVn/a2jwOIa7LbOtu2WSTbrrNxGXXNNp5uzzOl9KFyzIqMcGee+4IDn5VYXBW9Dwq8tQEfi/PwAv5c/r4otdmxsLLNZR9UXbAyvgmJ69TJTX6+zsiZTWzy/4jnrY4dqg2thzLP+bLGUmKhiJl2h6Y6NUy2yzTdnlFVbqtJiV8F2XYKMzS2HwqzSKGfXrKK/VmmVI4ufQ3ZcjfkOslCRZSvDblCzx3vGN4UQT0opX9vLzvt7olyjxl0K9+mUEZVuYuj5ZguakvjER3emuZchpShIpg48tsVkq5zi956ABO8FD/8Vn+jhkOhH9zZxqHETOIAvEccj4j/oIH9hu84NVOP+xX3ozAf8M5LJ8hVgQvK5J6WUz86zcz1RrlHjduN1H972if7cCBYl1KzATEz7Pq2lPFX3sc9du4Otub1o/oGHdz6NhHLVof6Euk1YieHTE+SLzcTK804D92z9UVKjxn2EfwL8FeBlKl317djnE2WB0J3klI3ZlGCQy3KyIkt4uOzy6WyDkmUoyUasSRNcI1yRJaxcVmSTgWRLvazoiBhrDoLSuL0F+YhyRFQSDK3IUSZAdYKa1VxGxYxBvjf7da2boExTlc15Ri0bmmzBdOZraGZXVYeSXIyCclY1JbPQnfnUNVKh+3SnSjPTl54FLEzlBI4lbJ4riuuk4Tikt1e/LkE6CdbN5uI0tK5I4hfbDD4vM8eOhibZmMaJ85mSA+iygImfmPD1sGsHU/Nezx1D3CSOc6e1KD2vaVq/a3acGVCOd+NUEtHSzK2tTM6RrFPOhMX9k/0247a2rhjqoyXyCUqU9kc58Vj/sMfWtUX6lxd49MfPs3G9y/qbyyw+n7B/NnNyQ8wOebWThofb0a5LP0x+T1L5R1MLf1cpvUh/B+n56bKYVpzUEbsdGt6wEP7Oy6QvaV/SnhmxLgjP5/dT9J0sDKCCGfoK8v4VG6H1oOxQZjPT2hzUzHba4H5EGmpeU7OfOfkqyUBZBpKFzaNo9m7HDVx/WNg2q+MZmE47DF9eIP52mwMPbuG0pFVOZrbZJltQ62xSCAVVpl87M2ummdWucC20MjOUom7yNyUeNvlIleOreQwdZqi7m0G4LSJNImarM2vbHuUHNiiHYP09pKRdtntk9sd5+7fp8FiQwGRhLtO26M+m89HIkXmcACOL07YV9yej/IGU8tf2uvM+nyjXqHH3Q7Zg/NmI7q94DJ8DWjfb4/7BdOTx7X/xLO3emObSlKf/3FsEI593f+MMk80mi89v37ySfYjoD5NMNP7PbRNf9xAHaj75dqPzqR3G77SJ+y4731pi6Uubd7pJNWrU+HjwhhDiXwK/DmQsj5TyPoh6IUGE2pdWxuKWnfkyctnisCcMJln/eMs+ziNjCaCc+FJmuXA89buC3c6Wuj9i+ltnkk1UJSnIzk87B3V6Thas3rK/W/5izdjlio9ZScJGF5wHDSZZOb9BHo7JFuBfJXKYKMc9zWFvNElYQMUWRzqjnPYBxysyy3pb1DlEukXA2MbR7l+W+MBwoIQyo6yH71LnVSoTED0U0/1yTLgM0hdMH3cJV4vXYDN1ABtrjmCKXR56OVsZZA5wO7hRh47G3E5Fsv1COh5UsSWbcZ468Hq4mOyfMcplRzgVem7RyXXDqn5V13ZU/hJQjog6M+WKmLV3V1g8NOCTf/bNbL/X/83DTDabCC9mJBslBsRk42yMsko4sqO1RV3HLPmCxYnMtyRLUAxRfs3zvreUnlfojWl6I5pxvn/TSe6XLTnLZr+b1PXbi3S/sEX34X5pG4WCxSILgzXbicwWDlDB5rS2myQUt5IFNEOs6evMsGZQ7cwH0JA+LT9x5pvF4i787EXO/3/PMHqty4M/eaHEwtnuVWYZ0xhfW9sVTBbVtVhDfAtjbsIXZafD/Bjl8HD5dcmPn4eMSy14lnttC203DztpbbvTBrdfatO8dVbWbYHJDCsWGfLzUuuK1peiA2Shzop7a+5vWjUAAlFksqF6DDb7sW1bqxPnXkO43J+McptkgvzT2ro6PFyNGncb1p9xGR53aE5jFt6LaVyiNFG+3zAZ+HSWipKDhUNDNt9buqdDeTUeHBNcaBJvefS/scTC0/3awexjQPPglMf+5tvE09pPoEaN+wVSyr/+Ufbf3xNlCSLSqcx0odJVx7PfPAWNstq++Kd9P/14sbF0ynVmH5x6U9Rvk3WGPKycbZag2qneqLbTs+isMxI9O0x5R9u1yg6jGBn9MMbmOkPVNJhkPbSaZ3zB60lFFIMcZkvt6zllZsM0kUusMcrqmim2WA85p3TkwpKmVxj9JdKaFmX65TLjkGkk43JYOcVS5umHnUJZfxWmboQMBL3zEeGTFPqNYqB1VlaFHNMTXejsZjPssRhuZWWL7riwjY0x3IwS9nct7GXr1oJuYT897J1Kmb2Ypghe0nSICqYeGXIm2c20sVoIvlhw9e2DHP/MVabSzTTY09DHWw5AyEIacxOmrliHSiqia73NNL620FwKOmtc0qQXNMNJ/YvOmMgd0nTy/uyn7LJN43rwxxMGWUogBtebzaDp7JpisPLQV7ub8Jm6W7NdN8O8TN+tgu3e2phJlxjXaYKXhBU0LRfJupSR7jjQKe5vYxjV9m7WzzSGNy2rYklt18rU19ug6vTR9ctqDE7PoSKJUJUuWGeUqxhJUxOtw9QrF55pd4zrDq37R5lF86N9pLgFrXiF5jp7VlKGt4KF323YvPxcdEvjzc/PDCs4L6q06Xk76y9tE0KI/wz4f0gp12eU/zjQkVL+RlU9+3uiXKPGPsXgtGD5DWhcg+nRO92aO4Pr768QTFwOPVIcw448f51wCRpHJkx1z9N7EELAPX6KNWrUuFsgud8SjrwM/LoQYgx8D7hO4iV0FngO+F3gv7xZJfVEuUaNOwDpCoJD0Lxyf06Ug4nL6994kEc/90Gmy1dwGzHdpxLGVY9uUqNGjRo1aswLKeWvAr8qhDgLfBE4BmwD/yPwN6WUcwXp39cTZSGLSYCyDyXHIk0wnfkKFQl9UQ3L/spqo3+oybQNuVOfXlZcV5BzmJZGUf4jO44lHF32p/63Ch1nlXOkZalsIbJcsyzcmyV0nEiP1dAc9pqG5EJlsNPRDxLTuHLggzz7nspiFGiTpChMTWXpUmoaQ3X9MqdKzTFRnZfKTqhfgix0XCaPya9nnDn4pXXrZjs32WHiJO3zNOdBZaZV/pJ6mTJZhuk2G085HP2tmJ0HBGEvdf6zZPranqaSBM25UW0Xxi6doEvfX87KApmYnxfTrIuOZp5U8oi1IHHcuxEsZGXr04VC3fp9UxkD+27qJOfl0hDT+USXZSjTaL6UjPoNvv6vn2Px6IDDj99gI04kH3r4u2EqndDDrc2C6egEuUNoU5P9mJILm6OYNcNX7M4sy5xsEEQ4heyJyhmyymHIXnZzeUOVmbcy5Jmlbtv1mwe7kWHMkkvMKpvnGI5pgg8bkGao1M/JPGddtmA6stn2UzIJm5yjCjanLL9CclG1v5lhz9flDqWQZWJmWRVsjmm2EHcKtnCCsTPGd0ZWZ1NTGlLldFoF/X6a96HKEa7g6FdqS4UMpdBfDLnDnH1pHticKs022Jwjq/azYY+P+76GlPJt4O297l97NNSocYcQdwQ7Twh6r91fI9effPlJjj10g6d+6lyJTa5Ro0aNGjXuJuxrRtkMD6dY1CzJh43FzTae8ZtqCY/1a0zRsHqYt4zdLteZfRRmIeds8egq2iDMH5STl1iY6Oz4tvNzLIxyiqmlTS0/Ycs8N8Z3okKoLZVERC115mEYJgzhVLF0cc6SBkZSkUgLuyZVw1QCGK2hYpr+VlXpjLLJMrt5WyTFayUsDL1ilhWLnPxOWAxhS1SSrlOh9XTCI0yDzouU5Q4dh/4JwZF3whKTrIecU+u2NAc/ffte2GFrkjvlZQyon7BrOqukmGTFHm9Mc6+mzWmrcLyOlzPKQy9heBf9lFmOcva3mzr6LXk3t2JNpcuNKz0+8TNvMpJNkDCIy+zxKP090sLkzQPV15Tjnqc77KX90XfKiUpUkhUzFBxobI7hSKf/DqVLIN0C45SFxLOwj67hKFY4hz06zO01dNtumOF5E0/MgyoWb542mOcbuWMa7iDdJi/L2PfdhnKjmKTDr3Aiqwr/VZXIpcoByy+ElZOF4/jabq5xXpHWlsg4dFzYzmSitTJxc0bZti4QEU0nzOqKtHdUHuKsWPdHQe4oW7w++u+MPSZPdqTaoHwgqphvHbGY3ZdmtW23qOqnVc6RczP09xcvc0tQ8zk1atxBBIuJfMgd3j+jVzhxaTTnN0HXqFGjRo0ae4UQ4sBH2X9/M8pAIXqQweIWPszSL0b1cW/9kLcxi8b+xXXG7hprXdJL62xzFsLNKpi+6WFtm+a6ZUs1pn7Z8kfWEu2AKvOxSn4SacxpoKVojnAK7G+mn02XXkl4bYfS9WZ1Sb3MMBdo1Eh2GVWacT3xi7rfapWe8lxp01VSEQtDr1hnnaFSX/KOLXGLLIehU3BSxlyxHypV9+BQTPdNSdAFN4jZfNgtPJnS0gn0UGWxFNbACZmm9hYwN+XA/vkRFbucpy4vawj1hAdx7DByfKZGAo8CU6vCAcqbO/PZNHqKidZD3KnQbWqdXqbY5YmFUc5Y47is+XY1bXMgXcYyZ8CV9WRW+uCt9xZZOr2D494klXIF46vruudJnjEPbOnJ50li8lG2nwWdcVf3WaUgNzXDTrgMQWIF0cOgLbqjdH8LQ2joPHUdq9ITV4WHm8ryK/SjJmVZdtKQitr4UqVR3kmtLx2n7AtSxd6aOuJQCn77f3yexZUhncUJ7YUJpx+7QmdxMrdGeRQ38KJW9hzo1zwLcVYR/m63yO6RLDLLNthYYzPNtY4q/bIZeg5gHN/c+pWHZ5yvj1Qlk1F9VvXh3SQOug/xbSHE94F/CvyWlJaXdwVuyigLIU4JIX5fCPG6EOJVIcTfStf/XSHERSHE99N/Pzdj//NCiJfTbV7Q1h8XQvyeEOJXhRBdrc6hEOKwtt3slFU1atwDuP6Ujz+QtG/E9N6N6Z3/eGPVfpyIY0CKOrkGcO5XH+CdX3vgTjejRo0Ma5dWGPWb+I2Q9984yjd+9bk73aQaNW4FHgX+EfBXgHeEEP+lEOLReXeeR3oRAv+JlPIJ4HPA/14I8WRa9t9KKZ9L//1mRR0/lm7zGW3dfwT8h8A/Bv5dbf0a8J/MewI1aux3THsOV5/3CBYETiAZHLt3FVE3zi/TO1JnoQN48q+9SaM75f2vnrjTTalRAykFnh/yI3/u+zz1uXcZDRo8+8V37nSzatT4yJAJfkdK+ZeB/w3wV4HvCCH+QAjx+Zvtf1PphZTyMnA5/b0jhHgduBUju0tiQI8pqgH+CfDXhBB/b1Y2lbxxhnpBGsuylT0rK+xXkitUvMUrCHupZ4RTpntH/W2RZVRl2LO0pEpekdcvittq29nlJqlUQBXqjnDp+cSxWmpmtDgN7SQEUSwymQUUHdFuhoI5TJm64uJxk8akC9v9y2QZsrBtUkkqr7BlacwcH23XrFimm9+E0qS45c6gZB/C9KIhv0dxkJjodCfA1rpk5Y0p7/50k6DjWPuZfq1y6YUglgKhXX9lmh7GZVPcPFKGKqiwaxMtrJ8KwZY51BScnpSpOCIYurz6Ow/z0I99yCBuZrKBSWqy1E2XSgKhH2cWbKZRdX3GmjOgci61SS88R8kjkvYGWh8uSS/07G1qe+kxif0srJ3erpnh6BbBPR4y3fBZD7vFshkwTba69GInKjpjVsFmolbZF23h2pSj5W4zAdowj4nYyRzo8m3Vealz1vuLK2KawRKBU77Wy3EiZVBZJW3nZ5P/mNn6rFkCZdnBU2/TbqDap+6xLi0xM/MNZe74up2GfFThCHtOMSU83MRBLS3buLZIa3HK0G8w3FwkmPosnB6wE7esTpzq/HS50SBcgjDI7k0xfJqSCCTP3YIm8anqE6aMR5fumKH7bGNPdnxtnMhlaZase3OEgFOSi6l237N+mV6PKnmFLStoZYg6i/RCPQ/qWa7K9qjjfgwPJ4RYJSFk/wpwlYSk/TWSpCO/BDxYtf+uRj0hxAPAJ4Fvp6v+D0KIHwgh/okQYmXGbhL4ihDiu0KIv6mt/78B/xD435IEf1bok0yW/9Zu2lajxn7G4KDDaMVh6fy9qzP78I+Os3p2k9VHtmZuI0OI1va968Tc6D2+w8HPV/MBNWp8HNi4vMjysSQGu4wFEvjWv36WjSuLd7ZhNWp8dHwT6AG/KKX8eSnlL0spQynlC8A/uNnOc7+RUh3x/wz8x1LKbSHE/wD8FyQT4f8C+K+Bf8+y6xellJdS3fHvCCHekFJ+XUr5PvAjMw733wPfF0L811VtcoTgiKcJ6E3mdF7zrvmFZdtvnq+wKuc6ZpfZ2F97/bLYlAqHxEKd2eeQLXScsX1B456GQZPJ5M2Lta/1KFm3iE8oXFra12xHJGF4uqmzWkNqTlOhbyxzZqSZruukbKLOTMv0JKRIvuCFxnxnbLhyyqv4/NO/pqXlVwmKpNZiJynnP5FeD91aIAxmQ2c/POXoZwkr5yBZ/9QiD744oPO2x9ZD5UdTZwpbKVO6GLp0ozbxNA8P50YJwyTcaWk/N2V6mun+vTBnJEXKhqrt2yJ3DvJlHprOhJuyeOp4kZczRVM3YbcG9Bh9eJLjf/48w+FxAMYpAxOlbKUbtQj+oANXffxfHIOAVnTzhCP6+ZlJdWzsqkgZ8Ehn/FJWR91LTysTKWvkpP3R0xKxuGldo6DLOOxYj6eOE2oskmewjVUsctFpTTmLJttPtPsXZiH1kqXFqGGFSgzkpYzyyCszkpOoGNYRIJwj97ZjcYY1z72wfXb9k2sVamEnZfpgT9K+O9LCCU5jl3a4wGSaTOoa2n5B2gfHKYO54ObnZzpshTYnznTs0RlK9ZyrMil1C4TJgOb7mazhosaqumm7HINZhvyaDWRyvwea5UKFV2w7yfO3qTn1LYjivdQZSfNZ2b56gsXDY/rDk0SOgy9WaC4IXv7tz/GJv/BmPm6l7ZpQtgQF44OEYS9zctRZXC/dT7VTzxbmGYzyPFYRyNnYZvr+EYVnxQgfqFs9jXtkY5bV9bGxuKHVAbmRrkuuRz8uj+G5pUQbX9Lf6vrYEoOp66j3M3XNsnOe15nv/kphrfCfSSn/lb5CCPEXpZS/JKX8ezfbea6JshDCJ5kk/wsp5S8DSCmvauX/T+A3bPtKKS+ly2tCiF8BPgt8vep4UspNIcS/BP6Dqu1iKbk20Tx97+aJckXZrifKVedXNVE299eQPX/a5I10IuioibLmpax++yJmTYxpa4PeRJlw0wmTnuFtSDKY9FPTYT/K6xykD/o4HYD1l5aSfcg0coQI8wHDSSNZqJjJc0+UhbHSdh+y66kNbE5xsqJHv1CyDDMjHeQvO6tEQUhowfanJY98Y4uo0WDrdPHx1F8e7fSaynRSE/uaz2s6YRXp9df3U5OovpMuyT9iNlOphtp+ot23hjt7EG6kEgaZHs/x85ezn0YcaIzGjMNt6K1lvSiIk8n3JJUMDIZTpu96iKURw0ZyDkORTwhmoSBJmWOirPqjPmHznLw/Q3Himr0U00li6GrxWPW+7W9bj+enz5RnMbcqVEe9KL/8VfvGTv4Bo2QfAyf9AJnzhRil5+CmfanhDUvbjCMlk8n7pE1uYEJN7PWJcpWZ3ckiTKR9UZPHKNnHOO3DusxFtWvkJ4xoqEc0SeN7t1T0CzeP922atG3RTtRSnyibmRzjwkS5eF2qJsotvS3qd5bdUZ9MJddDpjKLSIuprp4jP52AxnrUEseMba5NlPWIFJHg0lun+OT/6jyyOcVpwqP/iz9h6fgOL/3rx3nve01O/9BlvGaUtUumY3ikfbCEcYOgucE0nTzrE2WpJEiWdso9TpSz/dL3j6A8Ac3+1l8Mpb5rmSin20vLRDlSHwKFibJ6RpLrYYuCoZ7f2JIVVJ2LTd6Ux6DW2qLkOOo866gXVfg7wL8y1v2fSWQXN8VNJ8pCCAH8v4DXpZT/jbb+WKpfBvhzwCuWfRcAJ9U2LwA/Dfzn8zQM+G+AP6lqo5DYw8PZJpKGRrkAQ/eqP6O70fPo/dtMfqLDdGSyTpStrK8oFtkm35ZjZOHSqlJ7pxdBFtJppxNQpVXWWNxs4ip3F7/cTKgRRpr+S9WpwrzpuuJMa5w226JNF7YP64oBV2QfDhWDctZvdCYmLqyL9cE5XZfdf1m+Zo6aOGnXM1IJX3zBe08JTr014vrx5AWoM89ZXSpgfuwSxC5BpOs108lJlA7KWttH6eA9TScWVbq4gu48nP31MRXFsHf6JFNNDt1mRDj2GARN0s3ppy97tRxfaOMA8eE40wPbUnorzLPOto1KBa5P/h2DSfY0RjJLPZ62xRH5kJSFk5u22ZH2l705CS+uKyflMWFLq6y21xOybIfJdRymLPMwzMvM69DyyhPQKmQTUe146qPLdo3NvqCfg1dxzpmeWz0jms5eHSf72NOsDePIR4Yt1mJ1jPxaD/zkevT95Pr03HySuegq3bLSnuYTH6WdN1OY623PP6LKE+WqyZ5KCe9YJt9qnR6OTqWeV8/KjjZRVuEZleZ+UWPMO+mkdNE1J8xFhnX73S6NlSnhosNOOvEWRyP6cZtTP32R975yig//4VGOfGqNQz+0hnDJttM/WGSwyEjIrL/oH0WdzMJVfqbVdjZm17yOer9RulylCy5qf4ua3XnTP5vp4fUxUjHlNk370PiY1BMoTQx2udBOp2h91MuqQj6aH5VzaeIlu3th73MIIX4W+DnghBDiv9eKesB8om7mY5S/SCKAfjmNQwfwfwH+shDiOZLLfh7499OGHQf+sZTy54AjwK8kc2084F9KKb88T8OklGspA/1/nPdkatS4F7B1yOOhl2KaOxGTxY/mfHe3wPEk7dMjLv36cU784iXrNuJCcq7ywYB7IMR7jbsQMoZo4uB27t0QjLvFdNvn4tePsnJ2i/O/fZL+hQWiwKG1PKV7bMDC8SGnf+QSwoEP/uA4m7/0IGd+5iIs3emW16hxU1wCXgD+DPBdbf0Ou5hbzhP14g+xG/mt4eBSqcXPpb/fBT4xb2OklH/X+PtvA3973v1r1Lgn4Ai2jvr0roVcv0cmygALT2+z+XsHrWUyBPFGysCcjGCOaBc1asyFETARxI7L+JeXmLRiDv+1yzfd7X5AFAhe/6dJONkbr61w5FNrHP3sNdxmxHi9xfBSmxuvrTDZajDd8WmvjpluNnjjn5/l4I9dY/GpnTt8BjVqzIaU8iXgJSHEv5BSzs0gm9j3byOb9MIqezCn+pr5IbPk2EKP7aYtluPN49Q3736lU7CV2TTRqXQiO54tg6ByFtAtPaHS/qYmKM07KFah0aRAIqzZ47JttTIluVDmzELmJhUWLipKMPTfWZg32/0Ly2V5BZZ1SspScW/skh0lO7FU6qZh89JtCtc6/Z2FdtPMtaYcY6vn01sPGIcevgpdptWlZCrT2CWQTialAHAipQstt0+ZzlXYNJUdUG+Xqns0Z5g/JQ3xMi2urkfNzYnbLy2z8Px2ZjYeKq3pxId/njhhjX5uShw3svYpmYRVZlERFm4eWcZYm4xn0gvLOah1ahv9WmfXLGixNVN6YaszKqzTpR65rrd8/xwxW3rRD5T0wi8sAUZBcq29VG4SylxnbcKWhU1JdvTjTdPrp66jfn3Na+YUpBdFnXWVVly/Lqb0Yhjq0gsPL/LZlMVtl98N8b6XapvpJOf+xQmXJgkd2vMSc3k39auwabDVUi8LMzmGU/gbtMykWvZSBSU/sMlOTNN5X5NXqGdmkMpqdNmJktoo7f2m287Kul5yvLazABRlGY6IkQIO/vg1iAVLj2/j+JIAlwAXjsV0j+/QZYdJ7BOHgsmVJvHbi4xe7bL2B4dYP7dM/MMT6KR+F9MF+tq42NY0/KakQdcvV4Vyy/bPpE9lvbsKjWcLu6aua1VoPFtZfv/z+25KdAKtTwwzZ1r1rJSlF9O4KFODvP+rcV5/7rOwlcjC38l5JeuCLGvjnFaS+0t68a+klH8JeFEU0+UKQEopn52nnn0/Ua5R415Ef9njxLmREYVkn0PA+FybxokJ7qI2qZyIpOxLAfHhe+h8a9xxTHqCracEIoaGGxI8ELNwpHZ6UhAO9FJW2PZhpsPxJO2TY+TxGP/YlO2vL8PIgV9ZgB8ew5k9E3Y1atwuqDDDv/BRKtnfE2WZO3cBJTbWxtTu1plv7sgZu91WP15VVXqUhvSjd7dRL6qibChfBBUxAl+7oOlvkbKkrlf+Yg0ihwCXqZuXjVImS4Vo0p0AFfsUpkxhEGoB/iPFXKfba46FImWLzWXht+VCzmMdsLL3xo66k6Oau2bt1AOFeGn7XKNTAbGKlpEtteOlDFyY7jdtuEwaQx783pB3nuoSew6+pzl5pMx6ELt4cZMhOfvU8VJHOK/Mxo4NFnCksY62aBzzwGRMHQtDGEvBwk9tMnihx42vHKTzi5sZ4zJoNuCvTBOGMH3XKjbUxlYq2KwYVVEvqqweihUP4yITqp+fWmeNbBF5DCl6uVczym6xTAu9aLLGVRMYPaGKySQrFhny/hKn7Jh+fuZxbKHqxoYlAnK2Xw8Zp6Dug5MxYXmdcTowe2qA1p6DzKqkogNo91M5XoUWJ6tQOoTSyawhqqy/6iCPJm3o+RHg0rREtlAOcXpyCZXsJmONdUc/NY5ZGOUs1Jxy3tWutbKiVDlv5udribZgi35gSa6jYB5Hf7bNfmV77lVfmGgRHPpRCx6W0BwRf7VNeCrG+WaL+P2Y+DmPqetpdWoRWyr6cVXSDZPtLURNMYhg3flN/W5U+GzZmGSFVhpJQ49qpK51lkhJcwhW2w/TiEJ6QiM11ilmWWeGTYde/fyyxEuKudbfNYopT5+jeRL5wN4t5vsRWsCJNWAkpYzT1NWPA781bz0fPc1SjRo1bj2E4KXnVpCO4NPf3KC7Fdx8n7sdAuRYIPz7aKSuUeNexbEIfmGIc8WBNjiXHBZ+16XxqrOLeAI1anws+DrQEkKcAL4K/HXg/z3vzvubUabIKJeYZAurav2aUuvmYYTnZI0rWV+jDcJWZNNLKwZa3bV5tLX6b1WZHk7SS0OkpZMX9TfkrKiTMsm63tYMWaYzw1MnDY/jqDSmeWMUEzO1Mcrpb6k0ylpIsoxJjtRSO730d9YXKuZhtjjKtv5iXlydNMuOk94HqWt5lY7bEtNZGoxy4TM1pZcdN87+DKTkwsEuW90Jz7ywyfayR7/ns7Pks3XEAyEIYpe29NmRelzV2UyfYpCnYfnRb3kpg2LRlVbB1NnqYdAUA7O11SH+agfZlsgfGxPcaBN/uw2xIHocguNFzfDYSJVdCLOX/rYyyllZuZ0mW6UzaiKNWaf6td6/g7jIuNpYr2bkMZK+tZ2KdfI1FsmTRSbZs4QAnOc+6BrzQcogj9N7HNnStqfQ73+eFGT28UxLBOSMaZUePGPhtWuWXUcVdlJPSpHFmU3W6X3YDCumJyQK44RNjjPGtXwfFPsbaHUqza9u+cjqNONn6+ESjXVmW/Rz16+nqr8fzo4PbrNcKKbbpsHPrl96GJ1ZNq0DjiW8mIJN46q20fXZw0jTqy/C9M9G+O8Kmq+4iAm0X3Bpv+ASNyThIw7RqgRPMm440JLI1WR809sSiOJYVRVy0Bp2z0L5qf6i6tYZV/NcdSbaTCHe0hIvqX6pWOZCW9KfzZRZbmq5BZQ2XcVW1o9vapNt8cnVuej3IdMvS8v7pAr3J08hpJRDIcTfAP6+lPL/KoR4cd6d9/1EuUaNexEilnz2pess7+ROV61xTL8HZ84NcN6RrB1tMll08FYad/WTLAeC+N8uIB4JiJ8LYCwQv9khfizGf8mhJSE4fn+O3jVq7Hu4EJyVBI+EiKuS0Y0Y/zI0Ljo0XssnfXLRBQfiqInzpSGcuINtrnG/QQghPg/8r4G/ka6b+615F79ea9S4f3H86hAvjLl2oMnBjTTD4Sjm6rEW1w83WZgE9DYjehcnHDsH7x8MuPBkyx7C4w5CjgThby4iHg1wnpsQxQ7iay04GyAGHtHxmP4X7q4216hRYw8QEB2A8WGYPhkzIMYLY9zrAu8DB/99B3kkwnlgSvy7HaJfmOKu1o6VHzvuT07ib5Fk4vsVKeWrQoiHgN+fd+d9P1G2OfPZnNay+cMcncQqhZjjXb7bcHTS0qYsu5wsl2VyDOXUVzDrV7UllQGkH/exphHNpRepyV9z2FOSC9ctm6O9dJ0nZcFJCXJZhW8xmyuTr5JcxJp5OFbh6GwOe4bkwtHKHFMPZ7meVkc/4x5Zfb2Uv5F+ik6xLbEu/1FZBW3yCnOdZvpUzpSx4yBiyUMfbPPSIwfZWGzSm4x5/gfX2Vhq8Nx3NoldQb/r8YPnVnCdmOtBgyMvXeX0C2PefGYxc3qKLKZxdW8U9Ox0wjAV2xy+bLA5qwEwgelv9ojOREwfEbjvdXDfchERDB+WdH/DZe0XYeK6EBbN+plEJ1SpYstOeXYHv+I288JJ742rJANaxsjM56zC9DuNXSbCK8gIVBtU9snAza+9kmO4oiy9mEdykaUZ1yQU80gubFDX2rz/OjInXE1iEFkyrCnkEoryNVPnmjnu6XIHMdt+bMoO9GsdS0EshbVNWRbQdKmHlQvT0JdKAmOTNJgyC1udelnpOaiA3pYq5OdedmTM2pz5Rub3T8kwlFPdyJIJMP+7fN/VfsNCRkYju6cs34dYvWw8CI/B5JjE+6GA5nddxHdbRE+HjH57CefPDhAtaZW+mO1ybdKnmMK6QJvS+OmLQUkTYq1vqax9tnNWMgwljyiEnDNkGbGFmFBlC1qK7kGW3rqcQdB0mHSlJROrRVehZBjKqW/e6HD3I6SUXyfRKau/3wX+o3n3r535atS4y7C6M2biu2wspjFTuw1eefQAvX7A5lKD9850aQT5qBj6glc/vYQXxDz+0jYivjsoA+ebTeKjMbIh6fxSA/91l/CIZPCTEY33HManQc43T6hRo8Z+hwuTz0ZEj4c477uIUyHx19pY5oU1atxSCCEeFUL8IyHEV4QQv6f+zbv/vmaUhaTwFVVyyrIxvBbHvV2ST7P3q3DcsznlWQ+ryioc07KPUcs5ZH4ABbYyWcYWhz3lxCe8ouMe5I5lim3ztBBwimV2ZDwznNhUscZaeLhJuk6VRZozn3KKy5hkPTycYpQzp778ONnh06X1WlfMHQ1Cpgh1XS3svepvQmfF03bl90HfT1E+6X3Q2QgVTs6FgxtjrvU6mTNjDFzudTjT3GZ1fcLB9Qnf/uQhwsBFuoIIl7Hj8f1nD/DMDzZ56DtDXnt2ibCpEqNYnM+8MqNiJt3QUeXolTGE6cnuhE3EGHofumz+Wei84iKPCjZ/RBAKB/+6pPua5OKP+wSByMOMaYyy6h+qTFYwyrrjnszWlc/ZDPlnKzOd+opl6hjl401jn7GIjHB06fVRSWi0GYFimT2LtWZeJ0ooOuVVMclZuDY5e/BR139oOb5yaLOF27My+1kyn7RPWRw8s35jYeWsDlspFIur3+NQOkxjlzgNtxDpzHfGJCeMXeg4hf309tmYWqvzoOHEVwh/Z4TG08+lZJXQTtNkl6uS7NicFc1t0iMCxUQxeYl6pm8+U7UlW8mvi+aQnYbpy89TC2OnWO0nJe0rImlb6BB/t034mZx9TdqkOY6nfVYZDvXQatlx4yILDLlTnGJvIye/LirMns/sZB2KWS44AabtaoikvVPN8c6teG6bqUPgxCkzysrBL6MtC9bLdJWyElrOIWf2b857Cnl/hYfT8EvAPwD+MbBrvU/NKNeocZdhdWfEWq9VXCkELz55kMuH2sRIHvhwh5XNSWHWJh3By88uIyScOj+8fQ2UEmKJCCXOxDLqBtD9qsvkMYlsweATgITO6+D2JavfkGx8ThD0am1yjRr3HQSMvhgi3neRD4eIdzx4pTyRr1HjFiKUUv4PUsrvSCm/q/7Nu/O+ZpTB/nWUkSYWRnmv7HF1I9SBLUUVGtkqBrSKCRWWL09FCmRJSTQpaqZDVqylHgJOhX5TYcncvMxR4eFUyCyNUVYhbTwZ3TTQua6LDTImOU1jGuTfajJQbLHSiWrnF9mXAKnczHqtS8z8HFrlZGXxTxujnF1jrU4l+c3Shtt05DaNeZqoRMaSZhgxEQ1EoJizZJuJ3+DlBw/x5smIEzf6PPHWJo6UDE8d5Nphh8hLtv/gWJcH39/h3QeTtNA6W6mSlqhztjGZ9tTJkuYVycoLMf5O+hyJ5Pre+HnwNqH5XvKy237UISJm7WmXMHRBgLsc4e7A8h9IbjzqsXHYYxIUk8/o/UQxrmpZGR7OwvDuFhnbnO6ua2WFsY3tGAEOU9xiWVqlYrvCqKzxVyyzjcWvQlXCRlv4utI2OsupfqhQYFH5tWBjj9U6xWTrz5HMWPiy9UStq0r2YGunQmRhlGOZhEqcGmHGINduZyywxiira6UYZV1rXNJEa8fLGEzFiltY46rzserd05+KWbbqkC1/l8pEuQ9mmnZL2vZ5UAybNztFt9IoZ9sWsskkC8+JoAXjHw1p/V6D8ZcCmt9sEPVd5GcDEMUkH4pJVsy3bjkJcfM6KbK6jixaLArtTJ9vtSymvi46vej9VIW0U0ZSXYdcBddRfT7ZXk+LXWKS9cciXaeSl0QWbjO7t/Gc07nbMgm66/HrQoj/APgVILtpUsr1eXbe9xPlGjXuNThSVo5lU9/l/PFFzh/r8uCVHT59qc81fC4cX8CJJKcuDrh2qH1rGxVJDnwnZutTgvFJcBzofUfSOQer/zbZZON5wfILkpXtmMlhkcycJHgDSe9cTLggmPYEG2fLk5kaNWrcX4gPS6ZPRzRe8Jj86QmN32/A1xpEPzKFeoiocWvxV9Pl/0lbJ4GH5tm5nijXqHGX4cZCmyNbQz442KveUAhOXhmA06Q1iXj8nU0O3Rizvejz4YnOrWtQLGldhagN41Oa+SRllRVWvi3Z+oRg6SWJ35dsPyVBSo7+fkjUFjih5MpnGnddCLsadxe6V0IaA8m0Ixj1XIK2qPvMPYrwqQj3qsB/xWf6M1OaX/fxvtIk/tEJdO5PMe1tx314WaWUD36U/ff9RNkqW7CMqSU5hkWWURlWrqrMrKdw4NnrKkPA2faraEtVODqUaVTJMyzhzNQ2MtLNU6nJSpmp9DhomZOBQ4RT6SilO9bEptm0cO7FtqCZ2FTbhUWukv2eR8qiy1XmcPDLoFm8svB86f4FC7K5TreCZuvKsgxlNZM+XFro8cjaDS50eyAEsQpVFuUNjlPz8TcfP8aT/Zh40Gfkurz48CJbvQYEINLtHVciYol0BKGfmBiDMFnqcgcVKs53I5xAcvT1CQs3IjqbyUW79FyDrUk+ZGw8A8eCKf4gZuuUx4nvTll6SXLui20e/qMRx389ZtqWXH+wyaF3prz9xS7j2IU0h4oyoao2hFpbokj1OUNmoa1TN0la7vdunfnUfcqfd02eZOznaBkqM/kBDpF0DOlFamZ3y7IK0+lQl3qo+qvkFR8V+tiVhd+ySCgUVFsiizShCkqaUMyCOHv7xiDi5HemrB/3WbgYcXx7inTgyiMtrp1JPrKUCV0WpBCCqXQJLFSkuraN9PwitxziTjkkmw6C+jqbvEI5ORYyOabPXXbuln5Xtc4mfVKocubLnHG1cdp0tM3TF1Ufpwp52LxyVsJG6lSZHV934nSKbQ+V1OfzsPxvHeQSDH4kpvF9F//ftJl+LiR6QJeGKdmDJq9Iz0E5trnaOant1VhZcPBM+0S2tDjJKZmDLncwZU02KYTNMTCQxTa1tKx9JcmFtrvqQ113DMAwzh0+h1ESFamjMuHGdQihWRBCdIC/DZyWUv5NIcRZ4DEp5W/Ms/++nyjXqHGvYa3T4ez6DQ73h1xbXKjcNvBcrhxoc/WA/ijnL4v2OOTJ9zZY3Rzz+sMrrB/xmTZvbtdsb0asvhfw7g+3Wboc4gaS9Qe90mvh8qfywbl/0OWx3x7hTSRvfanDQ384ZOO4T+zCziGPca+2p9aoxoFLAZtHfT74RG4RaW+FPPi9ISKSXH04cXJdvjLlxFtjQl9w6ZE2GwfqScJ+hWzC4CdCOl/ziK8Kxp+NiE9HNL/hEb/vMHk+hNbN66lRowL/FPgu8IX07wskkTDug4mypBhKRRQWRbayinFVsLCApf3mZKLnSThiczAziYYCQeUY6yzMjJWlzsLJKSZTY3fUl3Tm3KBXlixip+iYo1WVHyIuf1kr6ElFsubF5YtWYostrHi2Tr/vFfuZznxWRtliiSjdPp2FN5z4bOx2dlq6w56xTmeUlS9JwugLzi2t8tjVNXZEm3HLLR6X/B5KB/CcYmFadnJ9m8cvrnPu+BJvH1vm0Q83efrtDS4e7HDxWIedBR83jNnpJc5OEzfmyOURD7w3ZLjg4sRwpd3hyiMiufeTajbQiWM2DgRsiQZDx+dBRpw73WNlbUp7OmZn3LTup/pHgfFJnT2zBC56xzRZ48IzY1s3c1Vpt/ymakXGQxkXnp90HSIJz2RxLJQZM6mVqQQz6TaFx06Un7e9QGdHzbpsjLJiXm3HtYXdm4dRjix1muH2dNxYafDo+30mgQNCIARMuy6vf8bjmT/awhlDaxizsBXyzhOLeIHkoe/32elN2X6sSdiuGodS9lhjXJWzqHK2soX3U6yzsDCZOfLnz3SUdaqsE5Y6qxK/lBxZyVlON2NQy6y4E9+crd4tVN36NZuELmPplfoUgKccVy1JdsKew/DnYOkFh4Vfdpg8Jtn+yZjW6w7tX20w/EzM9ME4cfQrsMbpfTNC8iVlytEydQLUrktuLRWF/ZN1jnWpbx9ZnAAVZoVLhdzBUH8vKHZZlTnay20ii+HkdOfBsaNY5mRcHcv5pnP3aXi4h6WU/0shxF8GkFKOhJhfz7W/J8o1atyjuN5eYKE75bOXP+R7x4/Rb9onmQBISWcS0ApCHClxkCwPxhzf7POHTxxntJA85n/yxBHOXN3mqfMbnFjLw8e9d6bLsOOy1J+yujbl7We69DYCbhxrZFkI58HZHwxYWQ9ZXt9m2HVZO9rEiSSn3x1w9XhNCdW4OfpLSV/tboX0l/OQYZOOy6uf7bF6ZcrWAZ+3n+4ydZNt1w43Of7BkCd/sM3mYz47y3WosX0JDwafl4yegs5r0PtNl+EXYqZnYjrfduFtwfBLEVQMhTVqzMBUCNEmpTKEEA+jRb+4Gfb9RNmWwtpGdFi1ycZ+1hSQ5n5V+1vY2Cw5hU03S7msxBbbWGqTWdbrzJY6A5euVMyrpkOWxjXTP7KyBBmmfpP8S14IiRCywPiZ+lCdKZQmM2zTKFcwtaUU3/rvqoQjc5RVki26akDpzYy/C3UZ9x/K97bAKKdPooo9LzzBh+0DTPD5oYuXeG35MGsLC3SDCUuTMYvBhGYU4cchRxoNNkcjRr5HLASxEAwbPt8+c4KR9GGUN/D95SW2z7YYtl0ObY3YWfQ4sj5i6XpA0HT55jOHCdoOl9spszVK25P1gdkXqb0VMWi7tMcRb53pEXkOz317g/Mnu3xwojtzWJJmJ4Q8nbnqO1bdudpGLyv2vXnZk/zelBnlzOCQ3bdy55OukzCWVnZb6UT152B2AzNG0Sm3pfok5tjGch+zU69gsqsSuVTBFjjSuUkovEsnOxw7N+G1TxQ/rqYdl62Hmrmfg5Z46fyZRQ5HU06/O+AHz61Y2XQVnU9PT670yqpNtsQ2Zj3mb3PbquQ1Cjl7rK0zrv+84duyZBRKb6s9ECaD/FGtFDbouvVW7DHFsyaaMZOzeFo7s/B8woEFGH9W4j8oWflDl+GjcONnYfF70Pqax85P2Vjjok5bL1Na6EaBNU6Z5FuoX86QNsGmVbZB9Y9WmpREr1IlW8nTaeeh6xadZHAep+lN59Yo35+M8t8FvgycEkL8C+CLwF+fd+d9P1GuUeNexrX2ImPP46mNqzy7fpmh32Cr0WKr2WTieUx8lwudDhe0F5Iy61vD1ArBRreFdCUfHlpEeDFb3YSiUVkZxR5H0j/6/BF++JtXcSREnsMzr23w5iM9rhy5hRE4atzzuHiyzefOD1nYCRkszveKag9CVm8E/N4zN4kUU2PfIDgkWPvTkkO/AeOHYPApWP3XIIYg6yGlxi4gpfyKEOK7wOdIuIG/JaVcm3f/eqJco8Zdjq1mmz8++gA4MkvckCc9kTRdF+L52IvbBSeSdAYBXhizdqDJs69t8IMnVlg/UNtJa+wOsSf48IEOp98b8PqzS3Pt0x5FjFoOk1btMHovIW4Lxqclrfdg/BTELXCmENUT5b1B3p8aZSHEV6WUPwH8W8u6m2LfT5Rt0gtlKLE5180ly7BsI21W0HlkEhZHsbzS2e20mu4NyUVBskFxne44VHJa0x2jlPkyczDLyxzDBGhzShEkmlhXM0eHUdE5S29LbDhn6ZKNkqyiYFIvLgv33ZBVCMt+lWUW6UVJQmGTbFTUaetv2f2zOI0qi1qagKnouJf+jl1RWicdgRsL3FAvS6+tYpYL+ym7oFpqJscsS6Ols5vObhpak5Avff8yjky6Vncn5DuPH2G704Dx7P0qNQUqlGFUvpDC6MdVsqZ5yfHcBG4MIoWN0qUeHk79lg7EjlVHJVGOiZbG2MYj5VhmkYEIx6hjt1m2LPIK06WlcAzzcLcgq1ccV9SRFl04ssAXz11DTkSWcbIq9N96r0nr/IAjH465dLRdOCnTZ0d/XNUYpcY2/XTNo1RJLwqIoDmOcKUkdgWx69AaxXT6Ie1RSOwKNg43GPQ8630wJRg6bM54piOb0LITmvKN2yG9KITUixwCbfCKNNmJa8hb9LBynuH4qEs2dh6MOfCtmO3HHZyRZNwS2XvDyRzu1P56OLn03tqumZJe3EJHv3mQhbjTZCdu9nJJgvdlEgxyyYUKiadLL/z05b2YDrJjp9bmmxBCtIAOcFAIsUL+WPeA4/PWs+8nyjVq1Liz8KMYR8Kw6XJ1pcN7x3qMm/XQUmPvCBoOm0sNVm9MuHbk5lkmY9fhtceWeeJPLrCx1GDU+Xj7X3McsbgVsHp9ysFrE0JPIB2BE0ucWDJpuQy6HuOOgxdInnxhm+0Vj6unW/SXXCJ/dxOu+wnTVfD6IIKElJD1fPCj4f5ilP994D8mmRR/l3yivA383+etZH+/zaSdUbb+vQu22cYaC2MbdXzrcW372Zy6LO0ssY16mcEk29iPjPnWSaHMWaq8Y5aEJGWWhe4PYDj+FMIjZYxyco66rrUUTqvgzFcM+1UMAac2KjOFVaHjSszuvGXxzcsyxz0bo5wyhLZ2Sst9z60EolTmKNbYxig7xW30cukmfoZuYClTxynsl67zZLks276KUS7+vTCe8pl3rwPw9uEVLq4uJtdnzO5hSxJiud8lJtlG1Fa9DKxmpdk7lJ9X3QNLMwuFjsE2q/ZmO2p1zK5SKnYzGwP0Dqa23+PbLj2QsNRpa8udxHqvSW8j4PJqGku8gs2WEgIcQlcwaPgQWfqSbff0Gc4jaJYZ3nxd+f4t9APOvrNDb2vK1lKD9QNN3nm+x2RGrHLF1r/zoOTkhwNOvjVicTvg+uEWbzzbKxzX5sxXdd9tZeY5z+sguBvo4eEC6TKVbnacSCtzY+Ucl7KkGttsJn4pJOCRMTAlHrqErZBpnE9bzDB0esg5xSTbzlkxyIqJjgqscVo2h6NfUGDvby5/M53zCkgP44rc61mFjlPssR56zlzX29Oge29DSvnfAf+dEOI/lFL+/b3Ws78nyjVq1LhjaAQhz5+7TL+VUDxXlquTo9SosRtsLTZ45P3tubc/sDnm2oHWxzbTP35xyCPndnj3oS4/eGYll0bNMReNXcHGSoPuIGShH3L1WB0+cRa8IYRtcMeSqH2XfMXV2FeQUv59IcQXgAfQ5r1Syn8+z/77eqIsKDLKJuFQGC/nYJurmOEqtnkubbM+eFoSTmRV2nTLZpliQitY6qI2VhaPZ2Gpq7S4VckGZLqPLaxSFpVOLzNZY435EVnqanUQvYxCme3jvUq7ja1MzC4racVtrLHStcVlds6ewloU26lnBA9TdjrTF2v7KbZZe1qlxjw7DrjT8vbmUm+XTDXNsV6m0mgbYQELv4UEKWlGIU9fvk4rjIgnghdOHiMOvURrvdt32RwTi6IOuTghsWrE56i7SDQV702hxHy29LB+jmJoBSIUxY1tjLBZZvvb2K9olSiyzXudE0obo3yXzUE2mm0W+zeIxs5NBvMEizsj3u62iacGm1t5Xsa9sd7/soWlPQl45J0dvvnsYYZtHwKSf/p+Mne+VXBkzLG1Iacv9WkEMR8e7/LaDx0g8B2YpAyoqUO3teUmbZ61/W5vcVWoQBsmeIzwczl/QaMsC+t0vxZTo6yzzf52wGTBJd5ymbQopshW+6UDtP6u8cz06doAmqUCV+HlbIzyHPplna22JR8xoY4XaMlBVNv91Kw71TXeRug/nYlWoekyHfO8mor7S3oBgBDi/wM8DHyf3IAkgXt/olyjRo2PH09fuc7h/oBGFLPebvG9U0cJ3DraQI1bi9BTmfQkkXvzKV4riBEtByeWhex0twN+mExOOuMIR6Zqo6abxWU+daXP4+9tcvlghysHO4Su4NDGmFNX++ws+Jw73eP6gVaeGbXGTDT6kmlH0LkRMVyttdw19oTPAE9KKff0mVBPlGvUqDE3/DDiyM6AD1Z6rA5G/MnpY5mWr0aNW4nWJESKm+jNNXx4uMvD5y7z+AcbvHlqmQuHF29b23a6DV55ZIVH39/EjSVCQiOIuHRoganvcOpKnxeePMTKzoSHL2zjxJKtboPvPHOIQce/69j7uxWt7YjDb07ZPOWx8kHExsM+9yUlegtxP4aHA14BjgKX97Lzvp8oi4LZ2zAZ28zsxqaF7SukF3PJMirqLFj1DdmAVYIRl8uy/ZT53Nbhbe11iktpcziyJRiKlQmquEyOraQXghhBpMkklPODWsZ6JkAVFi5dJyzyCmXBspnUHYt1y5TFWK9ZtqK8vzUUn7mJ3pdUtkVTgqEfz1Jn6diWc3GD1ISoZzq0yE0y62MMjp/EFs22n0d6ka4TepnKhmchh6UAJ445s7ONF8cc3+zzrRMnIXAx58l7jSBWOYBr55478ZUfrip/mlL9Vc+PvlmF9EI57wkhcAJhlzXNE3LO0lCZOd7px7Y4Wu4GlWPcLuu+HS/c9Nh+GPH5N67w9tEVwsi3p/gzjn+j1eK1x0/RHU35oXevEsYeV5a75RtfcX62vitnXJfL3UUuP5FPxptByJlrO7RGEd987BjDls+NVod3DhkVjslD8FneGRE3vw+VzpxV8pEqzCE7ghnvnRQBbiIrUF1ee9coSYnjppn5XC1rn5JJqG2EZOl6wEMvjhj2XFbeD7n8VJOdno8flTtDFsJU7xRxsSzWTiZ7p6UvFP3dNo+jn3Li0+UWNjmGCSXx0EPo+amOTq3bifIoL8pRTx1Hl16oUHFudu53Npb+XY6DwGtCiO+g5YiVUv6ZeXbe9xPlGjVq3H6c3Vjj9M5m5rX94tGjTL16+Khxe/DQtS2uLXU4f3hp1/v22w1eOn2IZz5c48rSwsfG3k58j7dOrHw8B7vH0RhGPPJinzc/0+Xo+2MunGixfaYeb2rsGX/3o+y8v3ueNBikKkZkDrbZ6hizG7bZwiZlddqs0zaHNHM728dpxRe9LaRUHqrM4hyUJaVImTGbI4nyhSiwxin7KAUSUXT0M5KJxFE5PBym4562TrHMum+EGXatItJWEerc1WlZ2Obsetic+VSRzloqJ0eDWdbrt7Uz27+KHUuX7lS7f5aEIyILAZcwyoXwcGl7YiOZTFKH0U63oiw93GIwpjUNiBEEjsP3V08wFM3MgbDkEHoLJiZV7K8Zus9K4Nh8nm4Ro2xz0HSFwJ0Ke/IgW/OqrpHZhwpOu8Y4tlv2t2J8uJv0AMfWB7x44ghiPL+sR3gCESbbb/htQkdweH3E9cU5orFUkP7We1XF0FbdXPMdZesvljG4ZNWYty1VltQqVL1LK0/dI47jfH997FGscao9l14+EEapWUox30c/HHPtSIsbi20ONALcEQRR2dSlGFo/ZYZ1NlcxvCp5ia3MdOqzrdMd/UKSNnjp8dTfAEG6TrG/NmZZMdd6WDnl2KcssDprnDk3UmSWAVyRRBtyjKQkNcqQUv7BR9l/f0+Ua9SocVvx0M4NHhys862DpznXO8jUcWtNco3bju1Wk6M7A7bbewybJgTnDyzzwPrWfBPlGncVepsBV48n937tWINHX+xz/dFGQeZWY4+4j+bTQogdZlMiUkrZm6ee/T9RtjB9Vbq/SrY5+1urfjdss4UdsLKOJlNUwSwXwt+Z8xNLO0tpkvXjZH/L8m9zCXkqY3UMnTVODx7FgkgIQo01zjTKkZFcJNkxqVtplAvh4Yxl1QNtu9aWomydui4W9rcycYxZkf7bYJahrFEuNNlkkuccsNKY8wU9vkoQE7sCJ7RrlHXW2WyDrZ+Y25+YbPLgaJ01f4GRbCY6UZnqxAusqtHg2/Eusz3ntn5iylEryirzFdtgPGOQP3eOU7wHhe13Ubf+hz3E4C7qrDyebpJRmmhbWwx8TC/ZLb/FYzdu8M7Sat7WKuJUgtNIWH2Fa41FHpvcYGlrSr/ZrL5muywzw77NS+ZVWgQMbXplE/ZqrZg3vJzZvnnvuyuSxDtqP+0dIj31zlCprLXDZaHjkmV3O+Stsw2iyGGr12TUGdG7EHLjZLNwONN/xtecWByD1db1y2Y4OT1KiqlfDrUHXtXhZQlS8jrVulAUWWfImeA4ZaB1jfJO1ErrKqfhViHfstTX+jlk68qMeY0EUspb4tFbU0M1atQooRtOODu6zobX5pWFY3e6OTXuJ0jJkUGfHxw6sgutgKUaIfiwt8SZ7c1b17Yatx1eEOOFknHbpdMPQUpuHG6ydCO8002rcZ+inijXqFEDAFdGLIVDAIauz58snual7omPNFmpUWO3ODIY4ErJ5W73I9d1YXGJw8MBjbCeZO0XNIKYadPh1AdDPvetNdqDCMSt8X247yETo8Ld9u9ux/6WXpjOfAoWk+euZBkFk+xsL4+SLEPfxpRVWMynZri3wvbGtsX2Fo9fqKPKpGcJBZe1RYW50qUXlnUKegi4EJc4ziuVhjOf1MPDZWHhCk1LfkfFZQFVZuEKRxfzlhZCbc1Tt6VSaXSXwgENOUel457eTmlf6r8dzcFPOfaJSOIEMgsrB7ksI8sEaAsPpzvzSckBOeCJMAkx+Q15FnAYOU0co/9apT3G9bM7P5XL5hogLdfDlL5UZeabW5ahNpnDBG9z2HMccMcU+lCVE2AVTMmF7Vpbx4VSRZZ11jak/WSe52De48wDy3GcOObM5hbnFw7gTmec5IzjOQjccbHSGI+rrS6nN7Z5t7c6VxvmKtsjKh1fs3fN7Au6a/lIhWajsn/u8dyF5+AETibh0x2Qle5Npp1Xd5ITaqDxJFHs0JjEnDk/IPAE8dTBGcPEcwlCt1Cnr+RvlgvjGZMDXZpghpPTQ5gqGYaSXOj7qTpVmX6MecLK+el5DqOGtV0ww5lPyTK0zm9KNebOzFdj16gZ5Ro17nMcibezSfILjTN3uDU17ke0wymfvfYBP3rlXWIBa61b54D34cIKJ4dbONLGqtS42zBqe5w/0+Xlp5YREsZtl8Y0JmjUlPItgbwL/93l2N+M8k1gZYxs2xmFRdarSB/qjhYlAtriRZZtY/UwszZ7NkwWooId149XChmnX5jMiS8tsgWIV1/fTv6iUSHg4jhJOKKHjlPh4DImWWtMyYlP+5LPQsYZjGFSxk3LhKWstK6qA1Q8sAWnN9VP1HEtLHVWpWW/khOhsd1s5AdywvQ+uwInAkezLGdh3mS5Qy+4Y55xPuS96BBXWAIpGMsG58VBLjlLyNjJ/TktVhDTaVTfzkz8Uvhjr++4intqhonTf1sPZ2OnZx123vYqRtlLnfnmYvVuchzzsbE4D+6a4jDuqY0Vvy2JYmxNsRxnZTwmwOXrqw8nY2w4+xRnHc/qUAmMRYMdt8nJ7W0utpdv2pbyAefYpgKFMcC0fllZXFHab9fh60p1qr/lzKJ5QxuWG5D/dEicj9V70tFmGFlo0Cgdu/RkYel2Ubru/UNdPvPKGh8c6zIWHoubAVcOLzIJPLxYS/KRssvqXeVr76EwXee55XBtipl1nbIjnMkWF8LKpayxk3qHzxtWLktUko7JNqe8nD3WXwyqveW2mE58rtW8XuNW4J6eKNeoUSOBIOaks85BsQ3Ag1ynT4sdWmyLNtu0aw1gjTuGSAhcGVsjPtwKnFs4yHNbF1lvdBi5jZvvUOO2wYljnvvwGq0wZNTw6Ld8Bh2PQcunv+ARuQ5Pv73BxlKTt870OHJ9iB/GbC3V9+2WYB8wuHcb9vdEeTcawAqmyew3VdsIbWuZbpkxjPoOljTAe4LtK9/SwLn0ZraQYE6RZhOWMvW1ruuQsxA6sUOEk4WCg1zvpRgEXaMsTI2yJalIiTHUyyzpnEt65yqG0TZIVJoZUtjC+ykmTmc7DVJc7wc2SXq50nlHMcXMS0QsEZHOlohCu2JiPtM4hyNgLe7SZcIYj2HcLEQKzCzTNoZRWRyqGGXzVMzfe4GNUTbKbDpkK9us9v+IDGiB8FGMsk+SgMVmycl2rDiI1QKU/q1v91HDw1WNHfOw/7fhJduOpjw6vM6b7SO4k5tvb4WczSgDjGhyvnmAp7au8OLCSaTYBSW/x2ttpouH8rWu+i4ohCGsep/M0a+sVkgjBbw9BGqVma3cQR0EzlRkx9HIX9K8GjSQHNoZ8drRg4SuoDsJOLw+ZmG8w8I0IBaCScPlxYcO4w1jnnhrixcfPUQQJhXoGuXIK7KqkauFckvZYi99D/maRVQlNnFlmanNdMsWJlq997K02BVh5fS02FnKa5UYpYpRtpSpB7/AKCtLr+Ucatxa7O+Jco0aNW4KAWzEXabSIxAe1+MltkSb2+KtVKPGLnAw2OHs+DrvNVfZ8G5vYpDL/hIr4ZCHJjc41zp0W49VYzYmvsfLxw7z+LU1Xjl+iLcPrUCSZA7pRrSCiKDpEDuChz/c4vJqh41eM5Mf1KjxcaOeKNeocY/AFRFdd0zHn7AedhmRBOeXCM6FR5PfigGp58g17jAOBjs8Mr7Oy53j9N09ZuDbDYTgrfYRPt3/gB23yTV/rqRcNW4DrvS6TH2HJ66s8ei1dS4c6HJpucvEcxg3vMyaeWBnwisPrdzh1t47EOzeolbjXpgoz2kmtMojZlQxr7+X6ehX9KArr9oVLCbu6tBC9m0K6yzZ90wnPqGZpzLzknLc00MLSSWvEEiENQQclux7mcNeJsHQZBnzyCsqymzympIJvnADuTkqr6dl+1jiExIIv1SkzKeZwkGrM/PvU04w89q4JUghOehtcaqzltU5CJqMZbPUTJn22czPT7/+Rh8Stv5lMf1nIQaNbUu/jf12Ex6uykHTKr2okuHstS9UwG0k0ou5HaIqrstcz/I8MokKVLVzd+KfvePUZJN3vCOMohauLYwiu7tFrpK/mCgMyy6v+cd5anSRxemUi94yU3GLX4Pp8xBbqjWfsUKZ0S8L+6vn1TLmlCQT5deQ/R1lHtciL1PjkfVZVa8TzZHY8Yt/63XmYUOTv9cbC/zRqQ4HRiOO97c5e+UC7x/o8faRFaSfbOtGkjB2kVMnew9JTXqhHMcdNw3JpskDlaNemMrSQq2Teen2blyWNChZhnrf6U5yuTNfuo2erVaFh0v3C7Xe6xlZ/vSwcll4NyNknV6W/12+EWb2vhq3Hvt/olyjxl2EjpzyqekHbDht3vCPEYlbJVa3o+1MOLtwjamzA8DV8RLXp0tMpX/rdPI1atxieDKiI6dsOm068YSRaNw2Rz4TQ6fJD5qnOBFu8qnJ+7zrHeKaV7PLdwRCsN7pcKPX5s0w5FMXrjBZd3n/SHI/3DjO4hPXuEWoGeVd496cKFcxTbM3zyAsNIaco8wazsxKRVsaYdZpJhDR1s3FNFUdQmeUMye+ouMe5EyyoiyEdvKKUY6lIJaiwExiJBrRGeW5mGFLGXJ2WSXDWOXoZ6Lq2jnl36bzG8DAaXLJXeJ4tMUnJx/weuMYA6dV2M7mmGM6+hWC8QtwiVh2hiw5Aw64fQLp4CBoOCG+s8CFSY8b0x6juImI1aFyF9QMhrObLGQ2Uessu2V9IP27IjxcAR+VUVbbViQVqWSbtRBUVf3ko0b8EEGS+MVeWF5V5ehXmjDOa82YA7u9R7vBbq7nohwzoMmZyTon2eADcYALwpIQZM7jQRKKzMooG5ACAnzOc4irYoknwos0o5BLrFR711XVqTvsqfakzGpsGfutR6mweKixRjnEFZ4/4zoUnjEjFKU1TKkFWfKnihem2kZnkAVGmMQCo5wus/dCXhZHEOLz+qFDfOrSZS70logdgRNL4shDTJ0srFzsaWOjm1rIUpY5drX3lxE6Tnc4D1NGWYWOc/VEWyrEanqxXM3KGknF/qYh4HRnPsUkpyy1p+1nMsmhnsTEcPArJkYpssSuZdB0akb5tuPenCjXqHEH8a53iMV4TITDk9PLfL95imBP5l3JcbHBMW8TX0QM4iYLThIWYBR7dN0JH0wOIsUqN8Z1it4a+wPLcsCyHNJjxJbogIQjcnvXE+VbgZFo8AqneFJexEXy4R1oQ40c260WI8/nwHDEWreDK2UWg7jGLYCsNcp7wb01Ua74Iq/UKFuIN6PKm4SMUysslIqNHZhRD5SZ5KpUwZWh42w5kG26OINR1r+6szIbw6g0ylHyW2eU1e/sA7fAKBevi40ptN4/k0mu0Chb97Mcb9fpYIGmDEBKxqLBEbmFJ2OueV2mSpMsEkbwTf8on5heYNNp81hwhYvOEk+FV3jTP8J1NzErVjHLQkCLgNPcSNZLwYIz4Uq0zIfxanJNg+R8Vh2noNtTFyA/V40tyX6I8jUwmcWCPr7Yh+yaWlsHM+qs0mZWodCdiw/zvBrlchjBKkptzpezSiAQSNzpjPosdVWHjpPFbSosR7c2TNztn5AsiwEPutcYSZ+p8DguN5ji0hQh7UnAlLK2fx4IKXF9iTuZfU9nnV+Ex+uc4BnvAzYmuRPsPFDPsN6VMgY5fbtayf8KC5cwt0FjY5V+WWew1e+93j7bc6RgPjOUmWSdGXYoMsrC0k6lvS6E1VR+MBFcby1wZGvIemOBUDj4Y4h9B6nYYi0UJl7KMqfjn9AYZZNlVjpmyLXF6n3npmWtYcSjr+3gRpJrDzRZO9nE1d5trpMywykTrafONpnkUBtAPYOBrk5wouuXUw11epN0OsTUK9eM8u1DncK6Ro15ISWfjs/z6fB9OvGYh8NrLMgJn5x8SCeecDq4QTceAzByGpz3VmnLxBa8LEcAPBZczSd6N8GYBt/kLN+KzvJmfAyAD+JV4vqxrbFPsShGbMoOb0QniHCY4tEQEWvxIk96F2iz10DKHw0hHn3ZoiXm0G7UuK241ulyZNDHjWOmrkcz+nisZf4k4tkXNtg86PPB2Q4n3xpx6o1h9Qf1foS8C//d5ajfuDVqzAlPo7Efiy4jEaw7CwycBsejTc5E6zwzuQhxDFJyxe0xET4xgtVowPf8U4yED0iQkmemH/JgeH2uY2+xwJ8EDyPrR7bGPoNHxKPuRQ6IHVziNB6uoC9bbMZJ7OSL0QEuRKs84l25Y+0U2v817hyGfoP1dpszm5sMGj5Lo4/n4+nsa32uHWtx8aEOmwcbvPLFHr0bIU9+o8/h9yazfRBq3PPY99ILq7XBapKVxXU2M5zF4aHki2dxRsrKbM+RzbRWIQOZte282+/16yyTS5T9vrRfmrwiMx2K1Pxk2TG9kFbnOkOCoW83T/a9wt0zHQRtpnhLprZSD6h6R0o4wxrbtOkxokNIhOB4uEmPMTeEwwifNgGfmbxPm5BvtM/yduMwnx2/x1A08Yl5ofkAAKtRnyU55propR4wxeYVziEti7RJsnIccZBIYUh0ZLHX2kyqUpVZ71v5QagMHWdUMHeItN3A0k+qTMb5/ZaldVVZ+6wHnANOMNuRTFobWHFkU35VuJ4WycxeYJVezNHOqssyo08c8bboijG+iJhKj/cnh3GR+CJiJ+rwwuRhQDAUieOUO2NCctNzjnI5gC2sZqUlR4BPSDhvqJgqCYypurO8M0r1aDvk7S0VzfnS0PYz21clRapykNblFcrRL73WhfBwblJubW/6W/nNSV0GZ0jqzvUO8tkrHzB1XBamARcXljInPv29oBzOM6c+T9fApM9KqoXRQ5jGKhxqul8cC6QEZwLh2CV2BaHj8L1Pr7C6OeHIxTFPv73Dq5/rMe66WVg53blOOf2pZTGLXuo86BSXkMsqPMt+6rctXKgpvVBOgTVuPfb9RLlGjY8LKwxokA9GLpKp8HjZPUkgXEIcVuSAqXBppmqyxIlP0JRhIbPUDWeBbzYfuu3h42rUuDOQ9JwRR/0NfBHyxvgkE9nISh1iFt0RHwYHaYkpY9kgkg6OiFl2+2xG3Y+9vS0RMNLaWOPOYeT5/NHxBzg22OHgeJB+8N5etv/1x5Z44s1tPv9H17l4ssPFB1pEnsPmoQabhxqceHfI6beGvPWpxdvajtuOmhjfNfb1RFnIWYyyhUkzSVELu5B9yevMaXawj9DQWahgR6UR1qfw21ySX4dsP0sij8whpBDKLQ9IBoDmDGGyhgVfRZPeqWLTK9jAec8v+61YEAvDUekgmLEns0eJSmcmCS9xhh9y3+W6XOSQSOIWh9JlR7az41xzl5JmOumxHbjoLbPmdekLLfuYEIS6N5/B1NoYKqGzZGqZHsMWLkooh0urWaN8rlkotWx/zaaQtW/2Ncqfn3LbPzJsfSgrk6UyYfSJwn5V48MuoepwIokTVlcyNwts9gHtmptOlXuFLXHFR64z3b/lTHl04SJT6XF9usyNYBEQmj1Ecrq1xlawgB9FPNG9wE7Y5u3hcd4ZHOfhzmUIBdvRgt2Z2dZ0CY7tXVDYaHZRzxkxwSdwy89kFZRTnf4om+sKoeOMLmINqWe5Nxkz7JS3MdfZkgEpFEhH23hJcZ1ijQvWPdOZT98/TPbJxjGtqORbXMU2S4hxudha5sPuMiLSkjJpx3OMd1shaYpyGkyZZKmFchOGY6B0BSEu33vsIN3xlIff3+GT39rkpWcOMOomDqbXF9usXt5iMvWykHN66Di1TjG9eqIS19je125ExkDHRWYZ7CHjsjJRLIs/spmpxizs64lyjXsbnoyIEEjxcehyJR5xZnp1iYhw6YkhJ5wbRMJhRQwB6DImkoJz4gg3uDnzdb5xUB2iRo17HpPYI8bhw/EhBlG7UOYQc7ZzkRjBe6Oj9LwRk9ij64441lzncGOTfthmyR+wHS18bG0+4PS5HvdqiXINBgs+P3jyAKeu9Pmh763x6pNL3DjYYtxyaY32v7yhDg+3e+z7ibKIK+66hXG1bmam4NU/+53ip/HcH20l1muXZfOwsbrGyyluIyvq1BOAZJt5BrNMmRUQs56w0vlY6ARjW2EszXYemu7w1DBx7HmjdYTrTq+4n4XhsDEkihWt1KUKeMi/yqrbZzPq0HamNEXIheAAJ711Ypl0g1jCWtxjTS6yQxvppLYHS58oX02sTJGtLTCD8ZOlzZLQfI4obJ8nE1Hb6oyk0U4t1FLGHln6ek5qVjDyt4GlzCu3VG3TnZussVV3aWGUjeNUJc+w7edEJIyybTyqyCw2T4hCu3b4I2qVq4wne64zp0Qv9ld5cOEK72yfYBrnId98J6LjTrgwOMTjnQsEscu18TJHWxsEoYvblAgkW9OFdGy3OIyoP/V7ZPY9/ZKZxi+LVcQlTnT/urFtjrBrWcgzp7zOxiib7bOxv9bnSME2Phj7WdNcqzGySoesM7VG6DfdgpetU/vpGmUv1ShXjInSZIH1dWmd+mNU3k8bz5T+WOmR9T6htlPssf4cZkxyysbqVtbUMvT+wR5bjSaffP0GV1envP7wMm4oiUYOUarQCR0tnKpqb6ZVzhtjrgucvFP4WdKTNKxcrIeHKz77Nv2yGXquxq3Hvp8o17g3ccNf4Pvtkzw3usDj46tc79z6FLMdMeHJ5oe8Fx6mJQIuBgcIcBnIJgecPm1nwnbU5kbcZUt2shivsh6QatSoxNa0i+9EPNy7xHs7RxlHSWziIPa4Nl5mpbnD+/3DDMI23caQceQnUWQmXd4fHvnYmd112eWw2OIaSx/vgWvc1djsNfnGp4/wxe9d5eKRgFHbY2EYMmjs46lTzSjvGvv4bte4lxELhx23yZrbYSUa3bJ6D7ubdJwJgfQ47m8A8JB/DYAFZ0IoHXbiNteiJdajLmH6iFSyPDVq1ChhbbxEJB0e7l3ig/5hdoJESnF5WMx+N4oatN0pR1sbXJss34GWwobscspZY5ERO7RvvsM9AiEljShk4u4t0cv9gMhzuHqwzcGNMdcPtDh1acAbSz2rn0eNexP7e6Isi2afKpjWy4JZ2ZBlFB2jlAm3/FDMNXmqkBhURiuyOTyYDnuW7bMwb7ojlvoRlc9BGD/0OL1xlDoZKBOivmOFM9884buqTOlq++VwxMFoyEutEyUnvoIpMAv7pS6CvU5BzOnmWmF8e2t0nJ47oOFEXA2WGcRNdDrLqjjIru0cjm26aVU1z5T6WKq0Oe7NkrJIUTQnK1O9zaFGQUkuCmbTyNjScnpV+jaryf5WvUwsD0tVBs4qh708HJacXVY4zs0pGBHEONNZ+q7UzGu7npWVVsgrrPqP+a/1XrJS3rxO/UYklWyFXYKpx5nlq1wbBNwYLZWOHUcub66fZKk5ZH3cw0Ef48p6o2yss52v5dG0SYnMOmMhuCgPcNK5wWucSArU8+PO3j+TO1gy0GWyDN0/0GyLxSmv6t7YzmUeyYUpl4BkbHxi6yrHxjt8a+UMYxql/WwOe8Ici/Uy05mvIIWY3V5TcuHYZBlu8e/kd3Kg2CLDyiL9WfpENgard6Imk8gzAOZ/jzyfzjjkrdPLPP/aVR57dYdXH14pzKBUdlsnlWOEuvRCOfilGQAjN3+BmRn9dAdB1/BQtYWcU5KLeJ4HV7JvGWUhhAu8AFyUUv6CEOIA8D8BDwDngb8kpdy4HceuebIady02vAW+3j3Llte5JfVJBO9PDnFudJRXh6f4bv9hdqIOF4JDvDs5yiBuUXvz1KhxazEMWryzfpzVzjanetdoOEFpmzD2uDHqcSefv+uyR4uANvdPdr5uOGHdb3NsvH2nm3JXozec8sDVHc5e2OKdE0s0g4hn316vZrtq3Gr8LeB17e+/A3xVSnkW+Gr6923B/meUq5z5KmBLtJCxgBbHVunYPuWzyvbWBgsjJs11FkasqixnlrXjZExyymxZGmwL7aW2U1U5FqcUqRKO2K5LKdp9uZ2VTLQlDJDJZiS/i45bJmuS1ylYD3Ktswp1Ns/t08fDvFmzO4DVmc+s0xKJKrtvFh8m25gs4nQDvQnGZdevR8YkZ2xQXmgm6dhtt7Zuf8uc+SoY5cJ2RlkVa2wJK2cLjZfBFkYr3c4JYpzANE2pjSztrEBuHVD0WtU2hcbs4hgVhXu0AhRDGqq2Jyuj0OPdq8c5uLjF2QMX2RotcG1nmTD2CsfLxlkza4fWrJnOcTc7/ar7kXrlDmjSJmAkmv9/9v401pJty/fCfjMiVr/7NvvMk6c/99xbt6+iqh48WiGMQbKMZWHQs80Xy4jGYNN+AFnCeggZGzeSXTLIYEAC0QhkgQ16qFyv3ru3qm5/zr3n3NNln7tv1t6rXxEx/WFFxJpzxtiRa+fJPJk7T/ylrbVXzCZmxJoRMeM//mOMfHOzXfKbSOHhispyt0SJUS6ir6RjdJ0cixI1OZa4g8oct/qHnHgN27qUJhMRnAA9h0m2QnVWtGWZshzv0vNRwChnVlOJURbKXAba2l+SfGQaHm7aTnnptvTEGCfRd647T/PF6hIHjQbVKOLt+8eMAp+57pDVvRF7ywmRk4ZpS5OZmIxywiSnToNRNB1MyjKHaTIS32CUle0EaDLMcWpxSibTqxweTil1DfjvAf8a8M8km/9B4C8n//+7wB8D//zz2P/FXiiXKFGiRIkLgVh77J4ss99bYH2uzZsbjzjqzbHXXSaKpdXvRC51pXXAKK6wN1h67mMcEVAlz3i/qrjTXGE3mKPvVYvf6r/m6DSqdBpVtK+5c2mBb94/YKUz5PpuZ7pQviB4ScPDrSmlfmJ8/yOt9R8Z3/+PwD8HmNleNrXWWwBa6y2l1MbzGtyFXigrnADqBZgG70836FyZkqgGl+GbmeG1GehnaaERU2C78lLze3YMeYoxHVcuYQlME1ZkZQYD9zxfXh12wdwmssYOUygnHNH5slnEWq6IG4ktNvuxqXnrPLlz8LwPJpOYT5mpJNmIpFksColYdOjnvZHmNP7W3Eh/gBk6mnVMrhVJskpkZTOyxm5CGulaPmsfTKwaKjzHD1pwASlX0yz4R6ShGguT5JhwWErLcpTrw7g/nEOcZ4drS8W/Kbs23Ucc+ey0V9jvLLAxf8xb6w846C2w110kNjJVKmJuL25R90ccDBcMRjLPEKZp3N3032mZte3MMs0CPXbVppUWXkryEafJLCTW2NEtS+dQZI+T+6uYbKoAqf9Msf9GvmzCGisGuoaKsBnlGRKOpNusEJNGGvHJsZjscrot+f3Mc5b2n54znS9LP63QcenlGgjtUqY1QChMP/Ma5XSCaOcZPi1RfHhthc3DHpeO+syfjjltVqdh+oT1QJz8qOlvG5vMcDrO9LwY+4v91KobW3Vhyi6n1+9MGuWXF/ta6+9LBUqpvx/Y1Vr/VCn1l7/SUSW40AvlEiVKlChxMRHGAY/ba+z2lthsHfHO+gMO+/P0whoVP2K1fkI3rNMPa4RnMM7PBpoKETfZJ8ajQ/3JTUp8rRF7HjtLTRb6I75x74Afv3PpRQ9pdrycjHIR/gD4B5RSfx9QBxaUUv8+sKOUupywyZeB3ec1gNKZr0SJEiVKvDCMowoPTzb4/OAKGsVyvUPNH7PVXeVBd4PACxk/44WyIuaad8D73n2+733B73CPMT4fqmvP2VxW4lVBu1nltFHBjzVXDrovejivLLTW/6LW+prW+hbwPwb+O631PwL8l8BfSar9FeC/eF5juNiMstaZI5cI0eydmDcEZ6nUZmKZjt0sc5b55ilvqF/yja4ozNGX3v/L+raZM6kXlAmQnFlmwnRy5DYq5dbJm90sWYJTX/TLcR3OjIqWLMaUXihsp6BUMpPZJfNlJI4uhIbzS2rTnGEuibKO1MwuNThvkhYn1J/suKfzZTkJ0vnkFZKjX9ZXLJRl80qjQlei4cgjRElKwXkRQ6Slv21qBDYlZAV9ZR65Z1cpwkxJdixnN+c+a0mDbOlE+n1Aja3+xIkuM8v7EPgRY+VnZmhJ0qC9ifk9DoRz7foHepq31NYkCg7r9KkyVh4oZYVyc8O1WWWBvc1y5nOcDqWsmbIjd9rwyTc0M1xpLoueJJM4I5SbWeZF+XaeJOdwnaedjH629CL/f3pfspzykt82u2WZ5yUNs5fuA6NM22UIjpPTzK3CjTpTV0hlwu9hSDZ2Flu8tXXMr6+v8O6DQw4XagxqU+dUs1kmp8pkhUYY1hkuzmloUUE2mamcZrvHvqQa5afBXwX+Y6XUPwbcB/6h57Wji71QLlGiRIkSrzQi7U3S9H7JB/yC6tFkyILq4aH5SF8x/Da+/DhLfL0wqAZ8cmWJ29sn3NtY4Duf7/PjdzbLzK3PEVrrP2YS3QKt9QHwd34V+73YC2VNjsWxYLLGbjgYYzK7PUjET86pT2j33KOzzNC/FJGtuMEMu3USWLxIFL4NS05WKcsphEObbYfSQafnI+9UlLGIyQ8gOVVKCUeKwuaJTkiGw4/peGS2lZyCpixHyopLjlsFjmYznD+TYcrmYZHlJ21XxPCacJ3xTJbMZeNmZI1Ftvis/UksdRhP/sQYfqnDkLAtc9wTHPa0W1eyEpiORmf3dS4UNffPLrTDw9nbzMWDyyRb89p12FPQiRss1045GdUZ68rUISqw+4x9NXXqMu/9Gubp8zrbHDDPkWqxy8LkPBmWGcC2zLjMsPG0zDnzScxpVmbOs+S6O6cj73TOJsyrwdrmWGOBGRZZY8dhT2KUpfumyzJbZaHGG2vRoThvSTAO0LlvSuE4Mydq4RLLyszfXXhmZ7vLZa/J15Gd5tP7+mTrvfV5ru93OJqrsdwd8vaDYz6+tZzfX9oudcK1nguT0Rcxy152XqYnNEqu8zR0XPQKh4d70Sg1yiVKlChR4qXF/miBUVzhG40HvF7boq7OnxBkQAWFZosldliyMpCWKPHUUIrdxQYbx31+eXuNy0ddNg57L3pUxdAv4d9LjovNKCOk3cVgE0wmJkuiIOiOPLu+/Qbp6JUEPNcXuaK+JQ22gOn5eIYzMmOZNUrpFzrX3cMSA+5noYymZbP9bhmtN92SsiUuC2zWFsjHnG5Z0lGSL8sYGLPYZJnPZJTzc93VUFtJT2KHZTExyw9cwOjnBw4ue6skZrggyYfkN3AujbFRRWSLU+S0zXmKXoUxaiwL4JVId6WMaV5rnLsfmc2cOJV26DG7ry/NLD8Bbtgzm1GWdcj2tnw/LsusFYT43B+v8yBeZcNv8271Ib8Or09i/yaIg+lfbixas6a7ECuU59yrDEa5qYcsxH18Yk68Bsd+I+vb/ARyYeGsY/Dt38Y6T9lNIx14gfXGunfYTHIha1zENgv6ZbFPJ/SbFVbOYZLNsHJEE7+htL75u2dsqp8vy+5HyfVk6m1dJtkzmf1k31KysOy6k06xfRnZemL39iDcw83v99fm+cOPHvNwfY6fv77O9z7d5ceNCt1mkG9XdGvNMcuQHn12Xkxtupdu08n3MhD280L5Wl2iRIkSJV56xHhsR8vsxousebOlXG7oIe9Hj9iM2/zau8oIn0DbLzRzus+3wvu8Hz5kNe5Q1RFvj7a5PD5+DkdR4lXDoBrw26vL/M6dfdrNGh/fWOb3frPNze1TvKfMHPzc8KKZ45JRLlGiRIkSJZ4vDvUcr/s7PNBr1nZPx9yI91nW3UkyKmI8NI/UMi0GvB8/JEjYuh/xBnXGXNMHLOo+Y3w8NHU9phWPaHsNLoUnbFWWvvoDLHHh8GB1jstHXW5vt/n82iInrSpvPzji9uMTPru6wMNLrZfDyafEU+FCL5SVxnbmc0yWdvq2yYd2qpiFqdOFlaEvb6WdCc/VoWaWsiJZhuif5tiizgvrXLvn/3znYhaFiOQ0NZVZGKb4zPSo82XJ50wSDMtJxHEM1fn5IpnasuNKnfqk/Xr5sqwLQbWgUulF3qpvOPMJEiRhDK4zn3Wuin6TWRz8JElEJl3JyyzOI6+wMzGefcHmpBPi/iTbqF1fCdIL4ggVRXK7zJlvasDLJBfuPWtSOPlITNSOimCyLc5fr7nfzyzLYkglG56hLVG7oeCMsUhZ2KZyB1ueYfYhZthLtnVVjSpjlBcTJydwgR7v6FMeKc2nwSaRUsQoBqpCS4+4PjrkTrDG5ajNgdci9uC98SN2vAUiPBYY8NPKLYZBQDMeshGe8rC2iA6YOggWOvPln0NiWXrvkO5VudBxhpndDQGXy7Anl2WOekJmvryjX/6+6YmSDW3v13H080JpTk7/z90/wZBA5Od1mnEunxEVVMUepxF1LSdxs+U4WLDkc5mOQ7q/u/el6QP3V7fW+MPfPGZ3ucFps8pP3tlk8XTIu/eO2Dzq8/O31ogrtlOfdezZAQuh44T7RJyMxUtlkAUynuw4Oe/TuASU0osSJUqUKHGBoFH0qTLHAIBL+og34x0eeqt8WrlEx6vT82oMvCooRU9VeRQssxmdsO0v8NBbAWCoAq7Ex1SI+KBylZGarH57Xo271TVOEo1yiRKzYFAN+PjaMt/64oAgSWffnq/xZ+9tEnmK1x/NJhcq8fLhQjPKaG07C7kEZkE4JptSthtqIe+7iC/7alZExD1L3Y7ggJDD84xCrmY8nwmyF/rCOvnfTwqtlnO4M6fLLK+JgkOVzlkuZuhH6tocy/N4zXfZOQxCRGKbfYfaEKwSmbXFDCuXhAzLCBiTmUqvKfc3+ooghmubBYWJSgr6lOJa+fmy6d3oy1qeZmyfc7wT2gnMW+ZwlSb7EBzvCh32UlZV6LOINS4MKxfAvp5nXZ1w7DdZDTt8FmwQBA10OLb2CxOG8F6wwn29kmyYfPzSv0ZVh4xUJRtD7E8kHNdGx1zrH3GvscK9xnK+TzdRYN4gIEM5zx+hnYpsxz2gOJRbEdtcFB4uZ22T2tnssTWWSLNQ66JizemgCSi82HGYthyJ03tp3uqThj+Lk42e4OnsMsuQny+SM19WJllZI2dMxg7kpDDpP+k9T1tlD1fmWOiN+MMPt7hzeZ52q8bxXJWPbi7zhx9sc+/yHKOqbzDn07FkTrjGMFN2OUqtZ8Y4p+FJ82WFuACa4JcNJaNcokSJEiUuFHZZYEn3uBQfM1QB83owLdSaRjyiHo+LO1Fqskg24OuIb/UeMRcP+fXcJW72D6nE503n+fXBeuuYm6u7rM21X/RQXg4oxUc3V/nw1irz/THf+mKfH3y8S+wpHq81S1b5guJiM8rY+qjcm6NIZAr6ZTdWjBlWrmjnT/tm5uqI81JqQ7OV31+mLxVCgomhx1J2JtUw+cIOn6NwSdTPCmRsIZM8QzuROXD7ts5LniU7q502Ei2k/8dCaKjc+RQYo2wsQnrrjMwVxiROZz35s3W6zn6MvuJsRylzYzRz01tHedYj1SObjOQ0FF7CbJhhn2L7UwzDlM5dIQ33VL8s6QTzFiDlpp2VdHsCOzNNLZtnitKBuslazONCeRMNclHYSXPy+ersMredeT7TbSnDax6e5/RlaX/tC0dkPYXrQTtMssTwSu0yDa+kQ3aYZC2Ms4htjn2I8fnAu8pr4T4+MfeCFVbUmMvxEVdHbRp6skj+8fwthv5kMeyGRgSyc7QQ9bkyarM66LJTnefTxjpxRbE7nuN254DfLqw7x27rQqUEIlPDTH6+TEMVTosyJnns1EHQIQsMr5xuWm4/aafPbOeGhVOGlcgMD7d7ssyt1W0uLR7RPm1BVLGSCyn7Tp1uxPknK/NCm1mGKXPqpcyyca1ksv90bggpuiVtswtlXn/p9S6sFTJ9teR/k916NHsLDfaX6iit+cbdQ965f8zH15f5ww8e06sG3L08n103UybZ/j7py+7cuu+mczBbtjzHh/jXHBd+oVyiRIkSJb5+6Hs1flO9yqXwmO8MH1CrzPMo0nzc2OS9/hanfn1aWWtWxj1ujA5pRUMAAjQ/nbvO+rjDxviUB7VlPmusM/amuoovWmt86+Qxv793l7955WbmPFhigu6wwf7JIuuLba6t7XE6vP6ih/RSQSvFo7UWbz84ZlT1+fF7m3zn033qo4iPby7NLp16hviq5W+vAsqrvkSJEiVKXFg09YiRCrhTWeNRdYlqHFLTEV/U1xh6FRrRiN/pPuL9/mMWowFj5TNWAQMVgNZsjk74yfwNHtWXrEUyQKg8TitVQuVNrTElLOweL3HSa9KqD7mxucNCs/uih/RSIfQ9quGEhu81Kvz5exustge8ff+YQn+HEi8NLjajrG2TEK5JTzD5Z01906YneS99OaSmW9fk+UQUOd45pkPRdC84aWXhu0Rzq139mRImRcdcJPn4smUGiuQcrsOQbI6efFhZudzMYpa8wulL6DMXBgqmc1eQ3EiWSst0q89QEtldToocKYnpbJNKSbzMzGeaHJPPKJUmGP2nUoZUCmGOPQ1dlGUJFAb2lckynP1KWQI957v5vxQeLu3UV8k5FWQZQmgnndqMpTJXJmHeq1w5hnkImRxDKMvmY8FcP6/DnjPHLWc3JyycJL0Qrzu3T+v4nM+kny+q61yJj7gdHrE07DHwAobKp6bGrI9OuTpoc6+xAiPYr7bo+lUi5THwAt7u7fK4sciw4XrnQT0a8357i6EfcFRv8u39x2y35tiaXzCO1ZZgTP63b+LW9edIL5Qhb3Id9ZSQYW8akk3oU3L0O1cIOKMstOtL4eHS0KxKKx7trhFc2qZSVVxb3+PelqI3aDhyMXte2o5wtuTClG9lod/Sfsz7UlIvvT9ICiYpIqIbIdJSi7nhTa3r1r3nmBKRdIvKSha7Q67vdzhYqE925inGQcCfv7vBDz/aJVZtPr2xNGmfSjAER7/pAPL/T0NNls58zwsXe6FcokSJEiW+1tBK8aC2wqBR4UT18NA09Ii3urt0gho/XbwOGm71D7jvLfN2d5cgjkAptuoL3Gst5/pcHXR572SHe61lutUKb7b3+XRllXcP9hgEFQ7my9BxJrT2eLCzSaUyZDAY48+6aHuFsXrS5ztf7CXZ+passnHF5y/e3eAv/XKLRxstevWK3EmJlwIXe6GsQUXGq65LH4qssQA1fQOU9vEkCNFuckOStomso/OiqoQ3yKIkKJKjXy5uusjgCDt8SjFTkVPdTCyz9P0c7cQXcoGhciElRZiWTTekzKvIbs/AJE87PXN3drUsUH++rUrYZMmxU+zbOQ+xcVyZs0w23vyJnDoxGUWpg4swhdyxi0z0C2Kb7cHY47SdaJM+c0590/6156F9X2abHQc8a5tkcSpy2Ctgm/OOcGezv0hh1xwLi1UmMdiOBc+cS67VRSqTkuvkrlNhf6ZTX1YWgK8ifu/kLn0voKJjPly8xGGtRRBFvN/e4lFzkcvjNvfnlnjcWCBSHjqwz1k1GnO7fch6v8cv1i9zXG/wxtE+W615dubn0B68c7jH31i4Dspyu5oeQubgl79/5iwzBmucMqeZM58UAs51BjTqSQ57rlOezTbb17IdAk5bdcx2OMmb0mkexx4Hx4scHU1eInJPVddwa/7uyUokc9wzxpKxzClzKh27cAPN2GKHPTahhLFI26aFdufW3E1+92oUcmv3hBsHJ/zitXX2FxqTe5TGuGkpRpWArdUWl/b7fHG1Khq2VcECRLvrnVkf1+U7zLlxsRfKJUqUKFGiRIKx8vjRyi3wFUEccatzwLVem536PHdby/zB3l0+Wtwg8qxAy3xn9zHLwz4xiofzi/zo8g3G/qROPQo5rDcB2G22uH14xGany8783As4whIvJbRms93j+uEJy70hO4tN/vTdKwyqxUusvaU6tx+f8MXVxa9ooCWeBhd6oazQqNB4T8w0dhN6wNJApYyGE25l5n0VsLgS3ID+T51QomiHM+qXXQbUZnAc/fKM7GgOUp+OJs3cdxHTXqQ1nqW9xKCKKOrL0U9KIeCmYxGYuxQCjSEGsp9huBJrrNX0zx2fOGddtl9iCBHKMrZYYhZtZskiVTPGNf1uaoYLmOhnxTab58VpJ8UYlBlzZz/myU7Pma8mOmWBbc6YU88086SfebbZ1RPb14/b59ntrN+zYD7nE4CczQzbv3tBn8k9J2WSbf1yQZ/uvCxglN0+x4FP36/wWv+AQMdc6p+y25jjVyuXmAtHfPfoEbvNFoOWTzoZtDd5TrTCEb7WfHhpg62F+bRXAHwVM66pJIW14rebq3xza5f9uTpRspi2EsekuvyUNTbnvBsCTkpFHdrfgWzOFqWinjLK0/3lE47kNcpywhF7mwqti9MuMxPpRHrSXyYQzp8Xd+4DkPQvhXLz3BuaEMpNSkaSIvUvMVnxdFhK2GF2boU4s5keOA2xmnT02v4xNw/bfHxplV/e2GAc+OBp/DBmpTNkfjhkoTeiOQwJopjQV5y0qhws1FnqjgjGMWElndimnWKGh+8sDw+jbhn14vy40AvlEiVKlChRIsVHSxtc7x7TD6r8aOMmm/0O3zrcYr/e4ovFFfYaLbFd6sA19vNLrdZoRLcy1ZAeNptsL7T4zsMdfnL9MrLGrMTXCVuLc9w+OGYU+JNFMrB53OUbDw/oVQParSr7C3V69Qpj36MSRyx1htzaOcWLNYvdIQdLpe79ZUW5UC5RokSJEq8EOpU6Hy1dypjpW51DPli9zFGtmYtUYX47rVYB8LTm+nGbxwvzRJ7H5ZNTALq1qtXm441V/uDOQ9a6ffbnms/xiF4FnIfyvJgYVCr84toG33y0R2+/AmhawzE/e22D41Yd7ac666SB0hzN17lzeTFjmEu8vLjYC2WNlYpHuel5pMn3JR33nqkQXjD9F3bvyisEc/TLaFURw/QJx66c+kVOeZK5VgkmvdSUPq1TIJOQJCKSLa/gvp+TVViyAOfXETQRRY+Up5bvFMhpREcx55yBYZZ1HUMx5BhZHcFLMTsw4/w789gMVfelZRmZ+dRsl2yS7LuOs6EW5lAu6x+GLEOpiRTBODGZ85/nTtC8zMHOvueMoSjDniS9EJ0A7fqi9CKtI8gkxLByrgTJaBfnMvoZfbph3qT5mdY1/0/rB/Z3mJjXtT/9aerxiJunxyg0YaCIK2SLFeuaTrb9/PomG50eb+wdUoljPGJOa1Xe2dvjJzeuJPI1czSK7YU5VnpPXiircHpQrsOd5UDnSC4kpzXZKS/9zDveuZIL23nQkVBYsgxHjmFdm+ln3ktOaY3SOssCqzzN0sIplzaPuPdog16/YdwS8g6Qhdd7KmUxFUzpsafXmhWmLznOMDfM6a1OnV1Gdq6M3y9zeE7GYhzDQavFX3+9wXq3S+h5HM/VCAMfYuM2mHoWGveC0POTtYykF5sdetYHxMu4SHjJcbEXyiVKlChRooSDH+w8pBZH9IIK3Ur1yQ2UYne+xe58i29s7VGNIr79eIefXbvESb0mNulVAjYHvWc88lcPsVbEMVzdPODh9hr9Yf3JjS4oYs9jezFx8syH5i5xQXHxF8px/otKnGZsAs9+25KCnU83mPVym/JlIsObvOGmb56C41bhm53AnM4CyVEse1uPHZYA4w1cCqeVvQZrq8qkqIj6sVkvO+Sc45hhMmmuc08Be1VUJjPYKcNl/O65pAgShZ1WNoqc382aSy4DXdDO/v1tNsEOT5YUCQlmUueMXOg5s47ZlXP+lHQeBbZkmsgmP5aslpTsJpuPeTYvd/0Yc++Zsc0Cazxllk32KZ3j6fVqdOpeI8bxZRaLQKEDz/7hHEdGOVybwCgXJuRwtpmnOrWgCWzzdLz5Mtc5rjCpiDBfYqmdwxrHQuhFKcxbUahHl1G2kgAljHL6U/380iWunZyw2evyzf1tHs8l4d2UEi1HKeqDiEsnHY4bdR7Pz3NcaxrOXXbdSPkEUTxhjC32d1LRdaQz/3c/Ic8222HeispcZlgocxKI2PW19Wn9X8BuSoku4sAjDjxrvpz0W6zHbTr9Bmsrbe7tJlpcs2spvtuXhHtPtJwj0/GmZUL7bJ5ZG5PfVkoOEtj3B+sensxd18/dqC4+92ZOIuK0K6xWMsrnxsVfKJcoUaJEiRIG2vUG7XqDj+OYjW6XGydtbreP+Nmly/T9s5M7vHFwyEGryWq3xwebm4X76AcBzfH4WQ/9lUOsPfaOlliY61KrjKlXhwxGMktfosTLiGe2UFZKXQf+PeASk5ewP9Ja/1tG+f8a+DeAda31frLt3wD+duCf1Vr//5RSt4A7wD+ptf4/J3X+L8BPtNb/z9xOtUYZGmWdvhdmEdAFtrIgXXVR2K5pSJw8a1IEiZF2t1mM2LN/sc6zgBYDl5Ylb8EFKTnNt9spUagmb9W2aHXyKTCMGQMnsGVF7JX7q0lpo7PUpkKfUyYzzxpLqajdOhIznDI3hTpmsythfp1d6QkTwTndEktQZAWR+snHrxcY9tj5bc0dSWxz1oWy61rt8r8bbtHTss0mg+3Of/M6SJk6h+W2+pBY+4w1TlJYS2nlHfbYLkutGtK8lBjl5FPSGhcwyUUa5YyZFcLK5a4NJZQ5lhkwWGY3OY85zty1OWXx3HFbfZ6hUY4DMqtZ2meIx+PKPI8X5/h7P/ucufGIfkNYKCvwk0V1r1Lh3vISofItdtZF169SDSOqo4jQeJTm9MRWIg+nTkFZYSpqI1zbNAV1+qnz7QRrYi4VtZkgxbFcmBbE9BmRXSuOJU57+fbHvRYbK0e0e3OsLbV5cLgpX+9f8vlnW5eSbcJ167LLdjg6e5sn9Jml3JbuZ4G7wbiXCrf37F9Jz58f+rRd7lzNSBWXjPK5IT3inxYhkwXvu8DvAf+4Uuo9yBbRfzdwP62slHon+fdvBf5xo59d4J9SSs0gLCtRokSJEiWejEfz87y3t8dyr58r8+KYv+fjO9SiiCCOubuy9OQOlSL2FF5R1tcSAGjtsX+6SKM6oFUbUAtGL3pIJUrMjGe2UNZab2mtf5b8fwp8BFxNiv8PwD+H/S7jM3lp09jvlnvAXwP+yrMaW4kSJUqU+BpDKT64tMmvN9b5zqMtvNimGWPP489vXOaj9TV+fPMakffkR2MjkV0M/dJraxYcnC6itYdGsTbfftHD+doitUC+TH8vO56LRjmRUHwH+DOl1D8APNJa/9IKzaX1r5VSTeBPgf+N08VfBf5rpdS/88SdWeHhkr5nuMlJYbvELDip5CI16RaVSWYY6btrOjaKBEv/06GoA1OSEtuf1hnITmPecSFz9NLJn+CwlwvbZkIw5bpSCNNsfh5HP8t8l5mF8yd7ahZ2NAduH2dBMu055128Ceh8WT7SkmG2S6UlZ8wvFSNOoi99AzLPgWtmNc27zpAkSZIoDcnOg3D9OO1MJ8yndgJ0zqNUlsveZ/YpKRoyR7hEemFWysI9JXXMMs/ZZkkaHAmE5HgnyDmKJUR2n3YIOKesKAul5LAn9emGfhNkVNPjM8fi9CVIPVKpiA6Ma8SfXONuOEgXB/N1vC35CX0w1+Qwi/Sms3NmhnczMTcY0/MrqMiTHe8c5zzzf1cuYdZ3nfom9bXYflLfLrOzStqSC8thz5VcnDMsmRbCEGbXwRmyoa2TFW6vbbHY6LLTXWYcVex655VeuPc6Swthz+tZJBhg3Iec+wzk1VfmpRK74SMD43mZrjFi6Xq39RiWE7V7Qow5m1ORPhfdZgl4DgtlpdQc8J8C/zQTOca/DPw9Ul2t9T9xxvY7Sqk/B/7hwn15iqX1urlh8hkkUS+MBbNOt2XxPY0bfpAupjzrO0DseoQbWsLY2RYLCzvxZlKQihXnYSfqBYvaCQ+76Th1rl32sEkXktYxpNuSaCLWYnjy/4qX6v2M85k9/JVTAipIt6lkd9PS9OdSyfg8Y5xeMlNVsjv7QZGUOQ8TmN4c3QcNmA/6s/WeRRplSW/m3qtmXiini4AsJfK0LE7n6hnvf8uNoDAWrQhhDOLLklM/24X5QHLPh4CiPou029Oy/EJ5OhZhnAXt5MgwzjiF47OO2cHiXN5FXj3lQjmnFRaiUBRqhqUB5lJfT4tyMY+luSQtlAvuObmFbsFC2dUam+MUF8qZVllbZatBMH35OeMaaA1H1ObnuKZ8BkGQH5/5+2VRHeTO/GCBtX6Hd+IxR7VpLOXs3pTes8T7UfLd/LFCp8y8x6VzKV0om+mmswWvcAxuumlr0f7khXJGdlgv6XZ9c74szqc3aIFYUgBVqAyZrw94PRiy22nafQinOusjfc6az+f0pSndVBAfXLxW0nkd5MtiQQufiwEuPWezSDjGQeTSd5v7c569wjhz/Vjb7K/38zVKfEk804WyUqrCZJH8H2it/zOl1DeB14CUTb4G/Ewp9UOt9fYTuvvfAf8J8CdnVdCx5nhnMN2QLoaTT4SFclxJF8xmWXIBpg8MczFcsbfFVhlWmXzRpO2NcRcseF0mRmznOspIZcJDKw7yF3Dm/OIumGHqtKHSz3xMMI3Hjh4hPQmnobZMlm0yMC8NoWQG409ka97Y/rS2hQVlwmI45+gS2Q/Xyafwll/EcMywwEshPmCEsnQMOSco8gsZa1zJ50F3ekLONXahrLCOW5fpw/i8ZhD3/M36UlFc31kYWAtXp46wUC4si/PHadY7ao/tsnSuuYtiMBbP6XdhAeoL89Jlf6UFtvS7O/cTc8EbO/cMMRlJAfubhXeTXuqd/Zv/u855MDUQZg57gmOTTk66pZ5QGq01O/HkOtC+lhqifFgPxyxub3P32qaRMCY5BHMhmdybpo53JkM4+fyz6gLf2H7Ip6uvZWW5MG+mw56z0C0KHSc67DkJRMz+3TBxk/+dbVZ4OPeNM/9CJiJblAokkK84PB0XhkQ87rd4Z+2QURiy35nL1Ttz/+m8qcz4suY6m0qWEunF0Vkgxxax5NQRFtjaIabM+tPFsPQcEsgqX7rJJfXdczSLCVFz7vt0iWcb9UIB/zbwkdb63wTQWn8AbBh17gLfT6NeFEFr/bFS6jfA3w/8+bMaZ4kSJUqU+HpCe4putcJx49kkvQjiKMvSVmI2hHHAr3dvlkqBEhcGz5JR/gPgHwU+UEr9Itn2L2mt/6sv0ee/Bvz8zFJtsFkwNbsVRBHPGAQz5kuqGyrQVhbqJ4vKjLGeWWbCNefPyOoVbpsFBusy3eYM1DyGNK0vGqW0fQ6ycG35Lqch3ASbnmuGLjAByxrldL/5t3Unn4fw5QmQWESR7TybNc7LF/I7UIIpPnfO3K4clqBo6hQmPXnKuSPKB7L9FTAiWaWCsRSUZVXy9l2DMTIYtPQ0JtYMKQRcdn8oKLPGkjB02lPEvhJDbGXDltjfInmFa/Gwjktgj512cpjEPLtWmHCkyFLllhlDcSVk4lgky5izHyupSCodC+zvaf1Up5wfTFYLgLHvUYmjCQud6kLF+1/yGU/rKK1ZHXRZGA5ZGfZYGg/4eH6jOBX1rBrlglTUrrzM2l/KJKfz2/QfcJjkohBwFhzW2ERhGE9/wioX/e4ohcYX51Ju/9J+nzPcBDPWZZz+fim7XTAm04KU/kbZ2TfPq5vISEp2JLDF7q5FHytxYLNVKzHFM1soa63/FHF6W3VuPaH8LvC+8f2XPNsQdiVKlChR4muM03qV9c7TpZ5+52iX9UGXajxZMbWDOq939unM1+gEr25q5hIlvs4oM/OVKFGiRImvDY6bNd7cPXqqtgvjAX4c8/ncKvfmliFWvHG6x+q4Vy6US7z0UMwmZS5h44IvlJ/wi5smjGlKnWRDnvwuklK4dcS9ixIMx6wyY7uvTHKRtS8w2GfnxTAlGboDNx+9dqUvVtqj1PQ7NVln7WZxzHDNy5PBnI3C85jKR/ImyGm4Iee72U6SpGTSnvzucuGJBDmBmBkuG4ssU1FnOGjM6oyXq184/8+eH7Ng5igg51HFSGZaP+3TmF+ZgsX5/cyySAlltizGOoZMRqAmTsFmVBX3WM1xFnrk22WSc13m2GR51hf0WeC8JDkcZ2WZIzC5OkV95qJlOOZ56dPaT+p4LMoy0jLjBAd64viUShKsqBD2BOmrGpUopt6PGQV2tBJb0pD83qnsIYat+gKHi016qgbRRB7RCMfsBfUzJReFmflmDAGXSS7GqYSCHKb3SOM+rey5a87P6Y7PlvFkfUuyIXHuJn/KrivVl+ZSUeQNaWx5R9T880TMeCc9R4T+zyordJguKDu3lDOnTyNfmMlkyhXw80IpayhRokSJEl8fKMVxvc5Sf1BYrTUe8nff/5RaOI0o86C1TDeoWfUa8ZiBJ6TFLlGixCuBC84oI7LGhQlEEkgMoRapKbt+zonqCWVFEBOO5AnoM/ssZLeLILGV4tvs2SyzNl6ptVb2C68zdlXE/krtRCZNrmOWFfnmzWRusphhm7mRYvUWhyc721FGbOc4mdoh9YT6RlxOFWt7fAKLd+a+i6wgBfXOy1ZLKIpHfS5IDkPCUKbJSxKmMJbK8oydcpMEGMy+NpzytIc9CYvOQ46Zkiwr+TmUS/JRYJGZ1ZmviC5xHfasUFlueEvhmhSvZYdJthz2UiY569u8KO06udBZiuk51/mDV0ZIyd1mi9cOjul6NRaGA9Z7PWpRmOUhCXTM/HgIQKgDvNBgf7XOrk8VQk9VmRsP6eqJ9MJli2VnPp0r81xnPsFhL61T+HwQrBNFF5kUyi0rK7BOyPWVzOqSn5+Sk2puHwLEuVTkpCpZPNw5LzH0wtgKHRkL7rtFKHTudwcjLVHS29Os+y2J53Pj4i+US5QoUaJEiXPg4fwCc6MRP9h6SKdSZac1zyAIUBEsD/tc7Z4w8AJ+u7ievQwtjXu80d2jGkd83lhjt7aA0jGLUZ+HwcoLPqISXwfM90dcOe7w2cYSUXDOFXmJp0a5UC5RokSJEl8vKMXHa+v8dnUdpTUr/T6b3Q4bvQ79oMoXCys8ri+glUdlHHGtf8zVXpvPWuusjzq819uh51dZHfXoeHW6Xu3J+yxR4kvinccHrHf67M81OFh4OufRopCdJWS8mgvlnOMe+djKduBfq7kU+zbbNGM7nfvnfMh6FvqcyVQj9Sk4a2USCiG7Wi62qJYGk/xvWtFSZ5vYqON0Wmgqk0xJruNQkaPS2RY9+/iyTbZZEwTJhZRCucghI3OiyY9TRJbFMPkuyS2sHRmfMU581PSzSHr0lPKKgmMuROG5mqG91dBpN+M5zl0/xtzLfu9s8uUHmkl8IuUWTePHStePNJYCE64bY1l2oBOunyJZRoEjVRGy/Tn7AHLZ8wozn1mZJtNt2urH3CbFwcZpZ0JFKrsWwJFKOQ653tiWx6z1u3z7cAsN/GjtFgN/ojdWY821wTG3eod0/BqHQYs3unt0/Mmi+PunDzhVNT6pbsryCse5b1Jm32sKnfmMzHyu8540t0SlnDNf7BugI+0pkFcUOfrZA0vqFsjniu79s6DIEVWSBuViepvDPa/sq+DeU+TEdy5IuQyc/Z7WK9P9pNdIGc7iueHVXCiXKFGiRIkSM2C/Mcdf37jFt462WB90eNBaZnHU562TPQB6foX5aMCgMsevWlfp+TU2Bie8Ptzns+oGPa8mRqEoUeJZ4zdX1/g8jBhVAp6KhdNP1+zrjou/UDaZtKKMfJmDnwDt1LEquWyqwSblsvw93avkrE55T8skF2IGp6zpS63KFxb0oxz2ePK/XWY7MaWUj+Bw5LxRiwxH0Vu+hHR3Upas2D7ZheHhhD7lrIRnMzjZ+YvcDUZFc5PhLKW0PfbsHBeE+st6flrWuGDezOzod47LRcxAJzJhT+50yiwLjmL5aZkPC2dmfszY6SQzn2WtKTpJ9jglh708G5i3mhQydpLj3XlvUQ4rJznsxULAB5fhs7LopeNKGWXrGJJ6aR3PbOdck5E5mPQzf4BTK01SZoaOS663KPa521jld04ec713TD0O+bixwbXhMT2vxq9bV4i0DxpUCPtqgVoQcXnc5vOgbjPDufBwBjOcy8xnlKUMdMYs59nfp4Ywl2ZxKD3r+1mYWFbk/RWG/5zl8LLxzthnOvckJjmtk36X7kuzPGuKOhWsS1oom5pQ888Vd9nRqwf0mCySM8fjjFkWxlTimeDiL5RLlChRokSJL4FLgxPe7OwDUI9DYqAVjxh4FT6tbYBSuRdAD01crk5KXDCUCo3z42u3UM5YpNh8K3W2mfq9NExYGlJKetsT95PUkQrPM1GfdlJr4X9Ja/yU+0u1zVorOayeCfOk5Vhjg1FxtJmWvtfdJpS5+s3JNm2VWeSAw+pI4cJcZlk8LNOq4TAAyhPmmcDg5Bqa+0uZNOuZPD0gFctjL2SBnpI1npWBnqWda4eRSJ1i/WT6Pc+SzYLY+G2y/C2x8xuZ/6cWJDOhQ3qufdCBfWmp+OzB5H4b4fjc5CJmu1kSh5hlsRDCbRZkrLGQHCSnibbKtF3HYrcd3aWpOc4YbG3XNZCdV/P8RmqymI2dOpDTLZtzUUWam70jbvUOiPH4ZfMKrWhIXYdcGbbx0fxu9y53gzX2vblsonhhzGZ4wuds4o15piHgpnNwth/rXMl5LCvd2RauIjb2SWOZhErMWwXPEx7uSfs4q08kljrzSzLK3FMsXn8FfUpsM3ZZ0T1L8oHI1TXrZdeDUeRcK+UK+Pnha7dQLlGiRIkSJQDmwiG3ewcAdP0KN4eHBDqiHo/ZChaJleLG+Igb4SEb6pTH/iIBMZeiNl1V5ZTGCz6CEiVKPG+UC+USJUqUKPG1xGmlzn+3/iZekowkZZ2DOOJ3eg8B2Pda/LZ6ic3whJvhAWPls+fNs6/m5ZTQJUq8zCiJ53Pj4i+UfcMW4WYYsmz3Wv6EaUiozElL5ZoVCfSLcrQX5XYXJ6y7H8k6L4Rdywn6BTWAdAz5cUoOMqk0xWhn2qzOMjGnzgmm+dQxJYkZyQSzVi4EXFEoqiLTnHnOtPw5aeA4Dkm/lSDLyEluTGcdQy6R79SRxUgyCwvT8+dKL/KeKmc3t/by1NKLJ995pXauvEKZc8G5hLUw16d1pp2fJ8wUUgaxtPM4P+Bsf0KWRu0lGckkB0HRPPtkWUaRw17ad+wbx1AkhSjKUlaAzCmvmnwajntpRr0sm55gMp7u7+zfSIV5E3wmtZLM85l8wZDAxAqlVNaX7Vxnbyt0vEs+Y3x+E1zhSnTMY28ZYo8dltjxl85op/N9ZqEljYPNMnfa38G4RhwJGswogSjKrCfWx96Pdd/Mj2EWaF9N/kRpUEHfTyu9cLM8FoRLFOVp6aPNmNe5a8zKHJls8+3vZrucxM4YSyahEJ5R2TPRdHwNbIc961maOvMln54QNrHEs8HFXyiXKFGiRIkSzxhDr8Idbx2vDP12MaA1jXhMX1Vm1ne/KCitqY9D+lUhZMxz3/dXvssLjwu+UFa2I483eTXLtklMlxRCLmMP845UbnIJMfyTmyzCqJ85DRhjyTm/FU3cgrFIzLCU5GMaxi7f/Swh51LHGKtOdqoUOrKPJ3vrFZiKKX2bsgoGo5KyR47Dn9VXQZgqKUzO1CFKeJO3i0R2p9BhL60b59tJDnvpfFF+/nxmfaTjtR7OxdSwirXFaElOYOdBEWs8c+g3t8+ixUb2mwqWB1VwLNL8mgHZdSDsbuoAJJhyCixAsT/5E61R7k7M5tI257hsZtE5HxKDJjjzFTnlFQVuSOunjJvFvKVMcpGjYHYhmftzToxhkcoYYSmcncvom4xyNClOk4mIrHFsfzfrZXV0vqyoneiUJzgN5sqEOu68tFnH2a/pmVlgx+JRFMptVsTeZM6ISW/cBCBPHR7O2OZBoENeD3ep6pCGHtP3Kvyqfo04tTZLFkrnlioyw8quY5Wl1hST/S1yLHSc8da6PX54d5sv1hb5+Oqq1ZcKpjdLlW4TrqMpkzyp7wmOryWeDcrYNiVKlChRokSJC4nN+ASA+8EqP27eZqAqvDXcQemX1xTgac1xo8bmSZeNdver3bl+Cf9eclxsRlkBvvmK5byWmtdJ+nIppre22U2b/XX6shjehL3IJR4hxzJLc0Esc5lMlSuSN6QMhcQ0uX1K5Gg2XuPYY7fMHHz6oVCRso9d0I66ZXKa6vStW1l1JtvsdhYb4dYxQ+i4bIIVSsx+y5e0p0U6ZIllzYV2skLjJceVhoEy9aVue5PRjN1a07mnVcKmmU5Fos55dswSAk5kiIsYZaEsmwOCQ1T2u7lB9THnxfmY8xyTXNDOZtJcS1P+AkrDYolhGWdhkoXjE8PDOSyXyBoXaZQlbaXAQOf6rDrssdkuDQVXqIc1rwO3TKieppkWUm0rSaMcgadUzikPMBJ52O0hzzZbYd4cJllKN53TIwOuKUFiqdM6Rckw7HtV0myWsG2Sb05BveLkNe6gnoAk4chM4eHM3/ZpNcoenFJjc3xCN6gSB4rf+pu809/hdzt3aQcNFJo7zVV6Sepxc3+i1thlkqVrM5nzpn55ypTn2V8rkY7W3N4/Zqk/JAa+8Wifg8UqUXXSgRdMJ5qXsMtTt6v875kyyV6pEXpuKBnlEiVKlChRosSFxLHf4sBv8Y3hY9CaWHn8pnmZX85fY+z5rI+/Ysb2CXj38QEr3SEwWYD1qwHvPjz6anauJy9uL9vfy45yoVyiRIkSJUqUuJjQmp1ggbl4xHw0yDb3/Cr36isMlc/6qPMCB2ijXw3YXWgwDDx2FxpsrbRYO+2zdtx/0UMrcQYutvQCRDOT6PKUOozM8vpimu1Sc4orXzC2iY5+uQEYfTrWfNEJUBqXK50Q5QDJh3AMWWYxr8A0Z5kJHQmEIL0gNTcLTjeSBGNq1nIkCkaZEkyBWR3JFOge36ym6sTslmXxslQZ9g8hhY6bOuQYhem21HnD6NSVVWjDAc918DMdBEUHP9O5I9aibKTIibMIs8gqnkmYuBnMrZkjqVk3M9nm51BuSJbTrr1fS2aUbJSmel6yYczZdFtqcpauybSudI1kZYKZXTD95h2whHFKzlKOidk0e7sOfnY77bQzzllaL5AukgK4xy5lG0t+91RKYTWP8g57KgbPl2US6owQcNY2qZ0T+k105kuzes4gV7LqFc1ZyfksJz0r0mwIG6XxOXOoMJTbjNKI2FOTkIWOVETaj1U2W/cTmOfFhx9271LXkx/n9eE+x3GDg0qLtldn6Af8xcoNvn38CBTcaa2AUrm5bksoHFlM0fUX6HyZ68xu9ulr7lxe4K63AMDvfbTFyULAh60V3r9zwN/8/iZUp336fiq9yEvQvGxbKr24ANTsBcXFXyiXKFGiRIkSJb6WeFhZxlcxY+Uz9j3moiFv9XapdCM+ba2zW5/n50tX+c7xIxSaL1qrnEsU/Ryx0hny1v02ka/otCq89+kxH787P43Y8TxQrqfPjYu9UFY4FE76SpdnmlL2KGP4zHZuohEhlJuUOKQo4UhhopGsc+dzxjLRKa+A3XaZVpMx1A4ba4a3wnVKMENf+UaZcgkS+00XoUwM++UwHJaxwNkmhRaS2uUYAMkxo6AsOwVCeLh0TqkoX5Z9WqyJPb+UcfCug59FvMbCeTTOn9LO+JRdpzA026wocFAqbnd2Ua4P0RRks/CAEzrPYHAwGGghBNyUSU6KzvmsfNt7xLLq8WfxG8ZvmY4hTThijMvtQNifOIaC8FRuO4uVcxlCs8xhkmPJKc8N92aWGYxYBseZLxf27UlIfivRUTP7raaFLutrzesYVDBloAsZZdPaFsmfk/0lTJ1Ull7zRfNbuO5y1p2C9hIbK903z2sxytrlGGXjfuRaJ2a8VnTmzCeMzXE2RbiHz7QPx1LyMFia9u3DDvN8zhoL0YB329usjTv8dnGDn25c47v7j/B7MZ+srBJ5/tRiYjoWzuScKozbuQ60wCgrM6mI1pw2Khyu1JjrjRnXFNVRzA9/us8vfrBMWPGoJI596c/uGTfNjEkuV77PHaVGuUSJEiVmwBdsAvC73mcod7VeokSJlwonlTp/sXqD0PP57sFDQPPT9av4OuYPH93jxsmRLZv7qqEUf/3bl/ni5gKf31xgc3fAr99b4nC1xjd/dmyTH89ql/DCHfcuojPfxWaU81RmsrngVVBKx+w5ZcZrostEizrkTLNqMq7OkCT2VxxfQZlbR+e3ZSyGxMZKzHc25vRt2OzSZnz0s9RAFbHp0v7ctK6Sfi9hfORUug6daPRp6X3ddhLx7c4h63dIv6Q6yjwDh6SXd0PHWTo8Wz9r7kcxYbzM/ags7JNkjshvclHEpBTd1LJzLKRxPjec0FVSOlhRr+laCUwqwGXQzqkLHhPwM32L76q7fM+7w1+o1w1GORmXcE3KiTic/RnI9JLSsTvbzOObppR2+oFCTWaRDtm1mpnJQdL5ld0fZvyx3YQhZgrr9P+UPZaTfDj9JNtUCCphlD0hzJsbJs7qo+A+VAjn9mmiKGnHLIsDi+HNsb/5/cgWmYIdONfPswgPF/vJ3CpIKjJTiDsBbkIda9sZDHaMx0er67zePuR7+4+4u7hEp1bl/vICt4+OuHX/iI831thampv2mR3z2c+F6Wf+XpeFtDTH4jsPvHBaGAEnlRqnrQrrW0M+ub3A7/zqiJuf9Lj7Tss6B5JGOesnPucJLTEzSka5RIkSJWbEiAo/5yZ7LLzooZQoUWIWKMXnS6tstea4cXLM/GjIt3Z2+HR1lV9cucS7u/v48Yu3EO2sNVg5HoJS/ObdJa5s9Wl0wyc3PC+0fvn+XnKUC+USJUqUOAcGqspd1vhd/RmXOGI2M1CJEiVeJO4sr/Djqzf4YOMSn62u8oNHj1gYDjlsNnh75+DFyjCAXiOg2Z8sjMdVj0dXGlx+NHhCqxJfBS629EKB9qZr/aKJrh05hlU3M6WnJiijLDVnSJKNzCyprCrmF6ksk3MU2Z5ckydMTTtSmSurkEzAaTdWmDH7H21lf0uaC9mxZjLhp46QYlw5oX6qSJgldJxpNc+Zw6Q+8+1y5swZ2+XCwmnhnEl291lCxwlygkxWIYWESvZpOw45JvFzWuSUK3ORUPCTSqGrCiUbwviyOVdwDJLJuEhC4UoupHZF+7OgFMe6yWvsM1IBsb9C7MvXZG4fUv/md8Mx6axxis58szjsOY571jbBYS+7hiWJghNWURW9MBhm4VRe4UowwHDYS+sITnmZdMKY814IXmBINgpCwFnhP4skYGmRIL/KthXIa8T5qa2PQhTKhiyZhCNTmhUFMp7zSC+s/QaTeVgYvlCSNxUgm89CaMNcxknrOtLWNrPd4+U5OvWA37v3iL/+xnXe3Tng9+8+5FfX1jmZq1rjs6V89n1ayiybbTKdjB3HeG1OwkSGcRpUafVDwpEHSnE4V+fW41PC0Bi49dxLji+5tuJSevHccLEXyiVKlCjxgvBbdQWfiAiP1Rc9mBIlSpwLtTBi7HvMD0Z8trHE+mmP3//8EcfNGo+W59habhEGEkv0fDCo+owCn0v7fbbXm5y2Ksw9B+nFRXCee9lwoRfKWikIpq+sOtUZFcmNMichk96xmUEzJFFGGOTJw1xCB0vXL9TPDaWA/Z1pLkvsldDn1BFnlj6NY/edUUisiZr8KbGiwJwXsGzpQNPfRgrzJrc74xOBLS5sp/JlTvi8ybbUIcMadrKjlFUQGHqXlTPH6TrsWeHeUqvEGbMi0qgw71SSMtBFPIPIQrkZccwxuEyaWU0IyzgLQys6aArM+ll9zswoFziEzswkOwg9P2unPeeUOT+XfK7zY4kdlsxKhuDUjyVGuchhT2KUs3BdyTY//+PqzJHOtIIkZdnlLhxgGgLOcryz2WIlMcppmbFOyJVZ4dqSv1mcAHX++GZaPMzIquaY5Kck+sSEIxJL7Vh+Zp7DLkstWDWysoL27tyNfXNOFYyr6HyaCXFcS4mUHKTgHjI9FtOKotldavCRv8q1oxMqUUw1itDAUndINYp4Z+uQg4U6j1dbLHaGxL7HqOJxPFej3apM7nPS7UlwAlROeLjYODHZMyLy+eDWKt/9bI9qP+bupXm8WON1YVzLL9jTpGIpo6xLRvm54UIvlEuUKFGiRIkSJc4LrRSPl+d5vDyfvUh6ccytgxNe2z6h3apRG8d897N9OrWAMPA4aVW5sdOhNorYX6rzxZWFqVTjGeB4rs6dy/Osngy5tznPaavC0smIvfXGs9mBZkYWroSJC79QTpk4ANTkrUul9EU044xww8lZmlObFZU0iFP22GQq7PqF4eGEYUqkXlG4qRw7bWkJrSEVExxSWtGnfVEtGK8cXseueN7kIFKZq19+2j7NkU0Zb0GjnFkZbGYZTHY5ZXqNY0/ZMTexAOR0cS5UrOWYm1LCA6cPJX3xhIN2x2Al80k7dz6N/Yk697SOpLEsZIbPqGNtyx9DUWi13Lk9p6YaZfyl9Zz5X8QoW7cVNwyWFAKuKE31OXXI2TUvlaUWEjdcHwYzLKQZz3T8KaMsaIZFHXJs17GYYUd/bCcHATWeJhyx2sX2zVSdU6NcBMnykUvV/LQu8wUstaj9Fdq54xTrCddDdjoktrno2vSxNMqiBU9Azl/EWJmkcz6u2FYRc3xTa6TQd1rHmvP2XFfpd+DOtXnuX25xdb/LanvArzeWOFyo8bsf7jGqety5Pke3EbB0OuL7v93jdK7Ch28tM6r61liU8WzLbkdCtqM4uVHr5GC2l1psHvb5u376kN2lBu98ccxhs86o4jsPqeQjZZJLRvm54cIvlEuUKFGiRIkSJZ4VIt/jwaU5Hlyayxb4f/y9y2we9bm032OxM8KPNZ1mhcXTEe//9pCfvb+WJ92eAv1ahR+/e5n37h3Qr/v06gE//GiXH7+3Seh/ec30M8nU+jVDuVAuUaJEiRIlSpQoQBR4bG002dpogoLqKGKpM2LtcMDK8ZDGIKLfeHZLqsP5Ot+4f8Cvbq/SGEa88ajNxzdWnln/JWbHhV8oa98ID5eaXxIThmn6IHX0O+8bXyadSCUYRlns1hEcXby8qaXQ0a9IluFYk0VroeS4lzSQ/NKKO3sy0hSUdvi7pMtUfiJkL8qFdDP/f4ZlOROg2C4dlOAQVyDnmEowzB1Ozm5mFjb35ziciBIRSa4iyjGwIDojCRMs241oik3Mu5m53RyDM/mMHYoOd2dAyjYmmn5nkFdM6+bPS5HpVyxzMeMhFToppnKHov4lU7EjoSgK82aXPZ3DXuaM7JihzcGLEqT0EBIJhhmSDcdhzzOz7zmZ9SRZhuu4Z5WdIb3wIkN6EeWPQZJZzCK9EK8txzG0KLTaea4PE4XOdc9QelEcStHev1jfaBcHZzifCpCOL5aySqaSi8Bx3LPGIsjTsrHnJUXZXPfycz7bJtzrlJqWRYHisFXlcHOqUfaJjGec0Q4b2p8+hXV2smPr+/Zak0Hg890v9ni0Msf13VPuLS3Sr1Um9R1HWTWr9KLUKJ8bZcKREiVKlChRokSJlwxH83V+fnud13faVKOYdx8fvughfS1xsRllhePM5zCDRlpKlbwTpPXNRCVIIXdSOMlIzPztGaMiMcQOiyGG0zpzwxnbZqkjkKNuMhIxKYLARGdOOqkzjHl+YuMzVmcwtQUeiQ4rCyZTa3+3+hKctHLhwoShfGnMyIxk/2bn1WAVXLZYTCiQzDPjKFJnDcv5z2h7ljOfmFjDhZDURQdCO8eZSyxzwpqZxyOGoHJ/NzHMW/73LmKE3TBRhU5I5MvOjbSdPzluLZXNwoqb9xWHVdMCo5wxbpIVRPq9lX0fsxKApGxaFm7KHFjKDCf1LYY3ZbRs9hjIOeq5odzOKitMDmIPyXGwTbZL1pq0DkK7bFDuBqGOiYK5+2XDw4nXq7OfczPK6aeQrKPQuTW9hfv5MvE6dJ35CsZl+aW5DntiaEOBNc7dU/PXgxZY4/T3nrLH+fttmsBDtojmx6Ld+kXPDCMkoo48a5syrS+xIo4mJ79fCbjU7rF8NOK4WTfCvgoPvhLPFCWjXKJEiRIlSpQo8RKi3azxX33jdf709jUAvv1oh8IEDU9AKpd8mf5edlxwRllZGuXszTFLjWrSbslbacpQmVrJlJXWTnumb9mZRtl6hXQ+zaG5ZKrINuf7LGonRJaZDe44zbGkx+rSLmBon9K6Z9A0sc3+ZoxPyt4XMN9FumCbRXQYzSLGooi1tBhUbZcJ2rfC9LXCEKa0eDKXjIPXzkk2NWXu3C0KcQc4zJ6eprnGOEbp6nZD8FnhxZT9WZAmV2SYBMtMTq8p/jYzsMYFZYWscQEVILYT6hVdbuYllSYdOXMsQqdSYgaXSRaTLwT23LX7Fhit7P9knhmMVnpdp/sRtY4Ze2x06SYOMZiwIta4KHFITr9ssdv2sYiWnBkgaWPPi/wcNK9lu+9Zw8OJlpV0N9ouK9Lzi32LyTrO3m82heJ8WVF6+TiAOCoeS5aa3dTJB/a2VJcMTEO5Cc8Md67a13Qy14VrTHmJL4knXPHZRZ1cF8JzT4umhxmed2lVM3FPZn2xP82xqEgREfBofp6rp6dcPuqy05qflAlJfUo8W5SMcokSJUqUKFGixEuOg0aTsefx1sHB07HKmkm7l+3vJUe5UC5RokSJEiVKlHjJ0asE9IIK9TCkEYZPblDimeBCSy+0InM8gqnZruj9ZGpWzssdJMwihciFH4Jplj4ho5/r/Ffo6CfBMUHOiqn439zo9CnJTqL0nBmjy2LNqbwzX1am7e/mDtNjlxwEJdPqDKb4bNN5pSlPi3TskVAmSlnsEIXW8bm/6awmfEjeyo3v/mTHU0lDvuE0A5ZpMp78n5lGTXlSahpV+bJZHI0KHQsLftNCxybhXOVM4kJzEQVyjln60KkzX4FZf+ZwX46Z3HLASiUXRdKLgv3lpFYYModQ+I0yE7wtszD/dyUYdp/J7osy7FlyDqe9JL3IKjv/K+NeIPx+2SVZJP8xi1Jp3FNKg6Tf1n3WiFIdYc67zx9xLglzYRoqcPJphhMsaudmc5XuR26WyHRbbD1fDHmF4+xrSy+S/9N5LYVrk+4FsX1CrENR9vMnlVuYFXXRBZTtw7gPZteP0C69RrT93d6YfLXCJZ7dzr0eKmPQKB7ML3H1+JQ7C6vTa2TGm91F0AS/bLjQC+USJUrYWFlts75xgtbQ7rTYP15kFFb46t4eSpQoUaLE88CVzgl7jRah5zE/HL7o4XxtcOEXyrERHi5N/JA5Ngk0bsY4mG9VrhOfpZmxFxiWc5bLwlq7swX2lvOTM7yisHLP/eXPYcVN5JhvkykwnHtUfAZrIlIcdpl0qsWcMEXrvCJnlgLmp7CdC5NBzc5DemLMDuwTaYW/yzYm883K0mKzzEqaghLT5CQCGY0nwej3DhdpNEbcvrrFSbfF4/21pH7SLgsBNx2fyyRbIZpybHN+fFKCBZeRKnS8k6aLEGKrkDUuYsmKUDSWGfrSnsAou+MscHI09+E67FkOWAnTljGDkjOsBCe8ojROJTjsTZ2Jkk+J7RKY4cxhr4hRlhKOOEyyWVbkzIdK6goMcRpWMXaT5rh9OHCZ5OKkN/ky0eKRfnr2d7PeLHPXskK6TnlSghqB/c3+lyybnr1NsnhM7yXThnGgic37mtXOYYsFRjlLBGI67GWMcv4hlTLCWgyR5txMpfMqPPcylll4Pis3vKKV1Cwtc+pC7kc1HfamzvL5duZ10BoPWRn0+O38BpuDU7x4kmBHuv4KUTLK50apUS5R4hVC57TJnQeXqFVCqsEYpTSxhtWFNvPNLl7pGl2iRIkSFw7Xum0eNheJPK9c637FuNiMsrLZq7waTcB5PSwdrXHhcCStcUYhGG/IDptqs9T226yte047sD6s3Zw3hFyO1TZPT/o26+obEXR0AitelOBE2l9Op2s2k1idGcrOBYsVck+ywGakFgwzOUim655hf9aadcqaKC9mfnlAtR7ROa7T79UmZeZc8O2DNRm0wbDG490a2lcoFbMw1+Hq2iFh5KGUpjts0A1r9Mc1hjogjAJiPLSv8FXEYr1LRYU8DleyEzDVF2pqaozyNH1dnRx9QYKFWcLDFf3eImvsMrQzMoQ5zMpuzzC/cql7hXEWs/DTIpdJFlNRB8LFVcRyZlWS+lIe+4xZNkpm0SEXhYBz9MjSNuvdzWGUpVTUZzG8XqTF1PESk2y2s/oqsFyYOFf4Qml/BdfKTBYM4YsUAs4NMWhZNtPdpVZP4T4taaLddOjWvPY1WuvpNjPJR/p/OneNMi8zLyQfBnuc/u+JjHLyGXu5Y9BumMOCR7+lVS6wsubqhNODz64HSaPsWGXFMIsGs6x0TDMc0wuSe38EC8MhnzfX8EZApPCjGG/0FIxyiXPjYi+US1x8aM1ib8RCf0S7XuO0MVl8uXVawzGNcEyrH7Le7+LHmjtzKxzVmi9k2M8FSrO41mP5cpfRIKBaC2ktDbj7mw1menoK0NrjuLuA58Hy3CnVIKRV67PQ6OXqjmMfjWYUV5kLBhx25xjEtenw0LxZeUzDGwFwFLdox02UgpYackiLDo2nGmeJEiVKlAC05ncOt1gb9vj14ibbzQUAKjoiSlirnl/lRnR07q4VlM58T4FyoVziq4PW1IcRrcFkwdsajFk9GeDHmuO5Gre3T6iNQ9rNGu1GDRXB3HDMcn/A2PfoVSoMvAoP5xbwQ3jveIeDWpPPm2uEnkCVXCDU50Zce+cQgPHIo9YY0zupsf94gaddJE+hmW/0OOrM4wcRYewTJzfchXqXhVqfMPZQaCpeTNUbcDJuMIirVi8Lfo9Fv087aqCVYtM/YU4NGRKwqHpUCOnocqFcokSJEk+LK70TAh3z52vX+ebRFld7bT6bW2enNs9rvQM+nL9Mz6/SiMcJhf5lnw8lnoQLv1A2w1vhmt1My0cWpi0xF5mmj3SbJJOIndcvs52Xq27s0P567hBwBRDlFa5sQTKjSd8dn4TCsRk7zM6LnrQVTVfJP63emBu7p1w96BIrRbce0KlV6NYrfHhjjaNWDZRCaaiEEUvdIYu9IbHyeNis88HVdcZ64qTmjSf78UawX2/yxskBf8vhXY4rDU68OiMvIIx8Ripg6AXEyk8OM8YnNvzuVOYw4inQxnedbGsEQ+bqfTrDJniacRwQxf70nGVmVPO8JDKELMuZcVoc8xs++JWIa2/tU6lONh7vt+h3qvQ6deLUnJhepVb2L5V9as8Tw7xVGyMa1RF4mrnGgLnGAICdzhK7g2UA9uJFgn5IqH0qQcRSpcNetEDEtE+PGM/TdKlyJ1rHJybEZ6R9VlSXQEX0qPFQrUysjQXm5MJseLOYxoU+ZzWXF2IWE3wRvGRckpk+NXsrYZtjxjYrZteYEGYqu3+Z0jP3niM5Gn3ZDHtSKLcimZEgP5heN8mnypdlIciK4rwZ0GoisXDDGEpjcduZYxEdZqU54fY543zJZe0z6xY45eU7EjYJ8oqpHENb+zD7mDqxC1KBTNZhOuwl/7iOpUxkQho9nW+S7M6V9DGVTky/56UXuiCLXiqzsJ9DzhgKpRTG/dNxqrMy5TnXj+Ww5zqiWpIit920yA3vtjLo0xyPqYQxjSikEYV8/+gBf2PxNu8Ot3n3ZJuPG5cYqoCl0YDTxIo3k/TigiT4eNlw4RfKJV4+zA1GXGp3aY5HzPXH1McRD9da/I33LtGvTVjKacQQ+6kyDnz2FprsLTRRI0P/Nc7vJ/J8fru0wd3GCiujPgujAXPjEdUopKojajpkrHzG+LT0kAgvuTkneuCUMK1OtZvme8YoqtAf1bi6uDdhW/2Iw+48uwdLiE/dmaAJKiHVRsja1ROq9ckqY/vuEqfHTePB+/QsQeCHXFo9pFkfMAormf6vM6xzOJjnxuIeu8MlouQpGiari5GusDtaNh58mter2yx6PTSKoa7wm/jaRM+sYFcvcpeYqhrTp4pWisv6iOW4yxxDAmKI4IG3zP1gzTkNGp+YSF1sS0CJEiVKPEt8tLDBxrDDOyc72ba9SotAR/y6eZn3e495e7DDg+oyrw33+VX12pd6XpR4Mi70QlkrZTs1pf+mzglSmKPsLTPvXDetY7xxpc4ks7yECXVEptZ9W5cc2op2KznCOYelhPquw9+Z/c+wP4tS0QrQeLHmrd0DrrY7PFqe42Cuzv3VedoL1SkDWhAc3Q2NJjJNxm5TjP2AncY8e8E8MAmZM+lATxbOOqQTNYiVRyKvndQLE0Y5tL+bfWR1Io3vRVxb3uP2lS12jlfodRJHCyvkks0caE9TbwxZWO7RaI6oVEOi0MsY5L3HCxzvz03aBdMDtZnXvDNSOu9jX6EDlYRJ1KwtttlcPk4PH9+PiLRPFHtopbgyf8Dj7grjwLf6mYyVpM/0u8eICj1dY5slbqsddKCJzbp4jL3JebgSH/Ga3meMT0BMT1WI8ej5VSOcGayHp7w52sFHc6eyyoPqSu74sjGl2wqSiogMtlv3SXD6Eh3hipyBvLMZQDfRgrUtZcuMspRJVtJ9zEz0MyktGFTe0pGxTgZLljnepWyZGcrNcdjzBEe/jEkz7yszxFMqDMUnlT3BmU8bDpXn/d1FZ1N3WxFrLB2DO26EuVBkfRFCskl9unVi0/kzbefMNzDusxn7WzCXTEbZd7aZTG+S7CjrKxJOWkaSGP27N32zWRYCruChIRAv2mHKxetXaJdji83nrMsom9aUGVhjMTlIehqz68ljz1vgYH6OG4Mjbg0PWR93WRn3uF9Z4TfVK7w/fAzxAE9r3h1u84W/TpjzJpZRapTPjwu9UC7x8mB+MORbj3bpVwL+5O1rjAMfHUzuClL8y68EStHzavSo4c1ilnoCotjn3sEmi7Ue19d2+G3nOjKznLDG1ZB6Y8jSapfDvTna+y3GwwpaK9781iOG/QCloFofMxxVhX7OQt4GXQnGvHXtEQDj0GervcrJuInWHrEPnopoVQYMdI1RXJn5yr8fr/KD4HNeY5eP9ZVCBjhl5R/7S2z5i4TKF09PRUfEKD6qXeat4Q6h8tiqLNGKhnyr/5DPahvsVeZnG2CJEiVKvIKIlce92irblQVuD/ZZCzu8Nj5gMeqzG8zz5miXO8EalTjiO+F99pljX5X3zeeBi71QVrY+Kks44gRSB+FNTtCOZnVNJiZd5KVMrRCuSGKNi1JfPzWc9dHMzHCBtm8WiNonpUBrrh+1uXF4xPxoxCfrKzxcmp+wGJr82741poLBFOkLpRhyEvs0S9859kqir5JvWZHipN/ist7Hr8SEUUC1MmJhrksliKgFI6rVkDhWDEcVhoMKD+6uMxpVJkP3Jn199tvLNJojGs0hV28fsL21QrfTmLJJJtObjGu+1WVhvkujPiLwI8LIZzCu0Gq1aHT7AHy+d5lu1LD6iAOICWjruck2f3LdKDTVYIRHTITHQE0y+KVJfHwv4jY7KAUnNGj7TVHLmZ6bB8EKD1gpZskUPPSX8FXE26Nt2kGD6+Mjxr7Ha8ND9itz3B7t0a1U6AT1aUdFaaqFsGv53+3sOlJ9iXUpZCnV2fsuCgEnaZRd64ukdUznpxYOJsuvIGkknXBv5rYps0yuTAwB5zLKxu6KWP+cH4VEOgphFnOWJmeexb6hn5XfX88sk1Km53TkJgosEE+yPEifUl9i4hBfmphOncB88DnjM+eZNE9w6qcQwrxl7LRZN4jBi6cstZmIyknVbFlSk05ESV46vzLLmnEMTgIQm8V1nj+SxddNH23uWzg/UybZvp6selI7V+NfsDZx9zemwifBZQ5UhzfGO6zEPVqjEV2qDOMK2/EyuyyyHp/yutrlT/OHWeJL4mIvlEu8MARxxK32ESse/GJpiYOFOpEnvKG8glBoPC8mjHwCP+TGlR2OT+bo9usc91qMRhXiqMjTE6LIp3PaoHPa4LTd5OqNfR4/WqXfr4v1fS/i8sYhO/vL7B4sM4oCKkFItR6ysjRgZ1TnweFmUtltrQlURMMfUa+MaHgjGv6QujdmpH0iPALiiVOerjEkwFeaefocMEekFVve0jM5dynu1VbZqi7yzd4jIuVxdXiMBu7VVziMmnyr+5ieVyVSioe15VcrDGCJEiVKnBMH/hxt3eBafMRS3KPBiJvxPuuc0qVKT9fY01ee3NGr/Xh+LigXyiXOheZ4xBvtA1YHXY7qDT6+fpU9rWWP5FcUjeqQ4XgS73mh2aPTbbJ3uAyANz6/xmMwqLL1aJUrVw94+GCd4TAvw1haPOW026B9OjfZ4ME4rDDqVznuBDQq7axuxQuZq/WoV8Y0giH1YIQGBlGNXlylEzXYiRYZxFUif0r5eF5EUw2p+BFR7PG5tzGJbkGFN/U2n7PBIXPnPr6zMPICPmxe4dromN1gjvWww/c69zn2m9yrr7A67rIa9tiqTuKIBnHEyrjHXq2VhbcrUaJEia8LIuVzz1/jnj+xbjcY0YpHtBiy6bVpMuTPXvQgX0Fc7IWyQnRGkkyPbpmYES4tssT7dn3z+eyarOTMfHl7n2R6OhOWnuPJ1SUXn8LQbznZiV3m6Zj1QYdqHNEKR6wPutybW+LDtU0iz2PdD1DjseismH2VnAAL7JOpOdkKb5Wa5maQWYhJlkSHHOlsOfWc/Xgq5vraDnunS1SDMYtzHQ6PFwxzaWqSSyeMMT9z45yWdUcNtndXuHZ9n3uPNxiNp4vlemPIytIpd7YuE6dh4DJnPjgNm6zVt3l94xG+F+OpmJNRk76u0R416Y5qhNoH1NThx2ciAzGun8gPaBPkzMIPWOGIBu9Gj/m5V2ekAtnpaYbse27ZwK/wWWUdgJN6g7t6lc3hCYthn4Ef8Bet63SCOtqDN7u7LI4HXBqd8IulK1lotCLHMXEsElzZRJHjj2S6LwoPZ2Q1PGubtu4rqXdPskE0Q9vfwbikBDM0jsnYkxz2MqdWo6xAepHdZyP7u3U8kjwGu0zaJl6vrjOfKZNgIj2QQvG545UgymP8s8ty9wfx3i/sqOhaccvM+eJm2JOy1EmhBrPfQZhn2XNB6Ms5f8qUXiT9T29fjixD6alk0XSuSx2dU9mPWeboaoodzvLPUoQ+i57POSmSJFMSJUw8scwNqWi1k+RUroJFWH/IDoKKITVGcY0j5m1ZaAFKZ77z42IvlEvI0BqFniw4jcVYNR6zHPVBQaQUMR6h5xEphdaKWjSmRkg9GlOPQurxmGY4pl2t0wuqDIKAv3npJqHnE39NCT0NjMIKlxYPCed82t0WJ91nIwvodJvsHGhuXt2h02tw1J44Zlzb2OPR3hqjsCK2i2Kfj/Zu0qgMCWOfAROtcRpTNvalFcn5cOo12NaL3Ar3+KRy+Uv3dxYi5fG4vsQjtWQXaM3msMODxhLX+8csjfscl3KMEiVKlCjxnHGhF8oaiCt5ykEMBu443omMskQsuky0+RacvMHJYWds+sNitx0N6axOeYGOaDCiQw2tPHwdEeFZi+Gr4yNuRfsZKztUAUMVUNMhgY44CprESuHrGB+Nr2M8JgvroRfQ9ysM/IDTap1BENAPqozSUGIGi2UxWrF9rqdOSHkWo5DVcw/eYgPt8ynGlU99PQoYTXG3EtvpIg3bhs8Xe1cAnbzBqyzaw6SPpF5wzsVpUr3dm+f0YZPlhVMubxyglGavvURn2HSSikw+40AR+xAF3iQrnjJCZBmsc9YuY5SV9d38X2TXFNz3Vvje6B4L9DgOpovUWZKKFJUVOtxldRS/WL7C5f4JAJeHJxw2m3bdGfss9CMtYpILUBgeTmCN8ZxtAtsshoBL70ei81Lazq4DeUZYCvNWyCgL4bCUw6CZyDnlmder+5JddG0WJABx7yv6Cc58hUyvxFK714N5reT9ygp2ePZ+Zg0ZiDOHdC4kG9M5ZbHUNvsrRiCS2PecqdH4N6NTne+A8uJJ3PaUILasIZ7VzgoP5zLJOl8mOuXlQrIJ1pdZEoAIlpIiZ75ZHPaKrC9F6w+rz2zdkS/z0vWHdHxnQQNSmL0ShbjQC+VXBYoYxSQmrQtPxzQZskifK/ExI3wUmhPV4JI+4VA1+Si4AkrRiEdci474SfUWA6+C0pq6HlNTY4aqQi+oTBZyVtzf5FMyWX5NWePZ8OVZ2rMQxz777SX220vPe1fnQqw8vqis8fpoj58H17+cTlhrlsc9lsZ9PDQPGksMfZkxT3FQa3FQa/F5NBKjPZQoUaJEiRLPGhd7oawgMp6tKj77rXS6LWWBBbZZCt3ippM1y5w0xSYbkWpJM1bPYL4nXWpW/A6blWMa3sTZ6jRu8EW8SYjPpm6zrtrU9STrWYcaH/hX6asaS3GXJd1jiM+K7rEU9zhWLa5GRzz2FxmqSrIfRV9V6XtVY79noEB/eW44jEPh/i4ILPYqG7vDdOS/FEO5c2LapZmaeMoiTbel8ysOQFeUNb9iR89oSi9craPENp8VfgtgL5hjSff4Xu8e29VFBl5AR9Xo+RV0EvVETlOtWR91JoemNTd6RyhgrzbH0qjHjf4xf7zxerb4zrGGRp/dStXuu5CRPlt/KYaAcytJhVKRr+XQXWCEYstbHrK06Waf2T1HGGcRE+ampxaYKTcUHEyZZFGv6VjilHBPLfQbKLIkFGnaObtMmhPaS66F6Ml9FkFklJX93ewzLphLMzHKgp64SNOeWR7MueYykuYQnMeWZb1M55J4fpwDM1M8O/d3O728RxwbYn3TkprNz+TTSkbifhrWEPf4Cq4Ha87n0k0bZU8IyfbEMuHaLNQTOzp+6RqTjs9NPlSUxETNyhRfsGfvy4CLvVC+oGh4A27U91FoHo1XaNPEQ/NaZYc3/G0Umkh53NPrHHsN3Gxtx16LE93gcnQMwGvRHnsMWY07/Kx68wUdVYmvBZTi0/omy1GXpajPatjlxvCIejzmqNLk0+Y6gyDPDC+GA755sg1A16/wWWudg2oy73uH7FXLSBYlSpQoUeLlQ7lQ/sqgafkDViunLAZdHo1W2Y/mATVJAAHMeUMex8tc9/b5pb7JiMqZbEisPH4R3GQ9PmU57tKKh/yycoOxKn/SEs8fR5UWR5UWMGG7fB1xbdjm26cP+dnidUaePQ/blQa/nt/kar9NMxrx3uk2Pb9KN6jysLHItX4bP46IvAlt1whHXOqfMqgEbDfmc4voxnjEoFKxIocAVKOQa6dtloYDQs9jZ26O/XqDK51T1ntdAD5ZXeO0Vntep6ZEiRIlSrxCuNirKjU1QQMox84kmcPE/O1Fpp3MQVBwnnFMLZb0ojKVXlRUyBvNLQIVczie59fdG0T4mckr3V1AyG68QEsN+K53lx/Hb4gSkXRTX1W5769y31+dybxYGCbuvDDNrQr5ZD+lY9Qsdc8d4uYZmpumEoikU9NxL8xVz0P6rTzbamCZgL1UZmGYP42IFrGvpk5MTB2aCh32hKxjRZnkcmZvR+ce4nOnskLkKb7XfsBWfYGBPx2URnFcbbDTmMRErsQhrWhMMxzRjEYcVJtoT2X9z42HXOm36UQ1Xj854P7CEg/mFwl9H4j5wwf36PsBv9y8RLs+SdISRBF/8PgeR/U69WhM16/yO7vb3F9Y4MbJCb+4tMn8cMT7ezv86MaNJ2SzLJjPkvRCcWZ4uHSyWqZmKcOkW5T+INI9Jw3zJjjlFWcUSz6NeZrJMIQhZf9Kp0OSJLgQ5nNhBjpBVuHuz5UopGU6gDg9FkE2NMs9Unv5/8WQbI7cpFB9ZX532xU4eEoOe5K8J3cvNJ040yGk+ytykhOQSYSkdu5OAB346Gj6g4qZ8lyJEMKct/Zn92XLFs6WV+RCuQmOqKJkw5VeaKFMkl6411iBTMJef+iCMrdvw3GyyFmxAGV4uPPjYi+ULwB8FfFm8xEH4Tzbo2XOvlsr2nGLN/xtWmrI5/FGQd0SJV5O3G8uc1ytsz7ssjgeZNs9HfNGZ58v5lZ5XF9g7AUc+wHH1YatcdWapVGf9X4XX2t+vnGVudGQm6dH/KVHdzmsNxj5PiPP5+O1Nb67/Zg/vX6Tse8Teh5Ka5YHA3ZbLZYGA4a+z2GjwY2TE97aP6AZhny+vPxCzk2JEiVKlLh4uNALZa0gNp35it6QXacUU/juvjlab4nKaWeUpdFuUgcSgxmZsH+aW41d2lGT7dGKMc7p+M393RltsF5ps68XOPSfXQa0Z4anXbc/izfYQk/EF4McswyogpjFxeHn7DpmIhDTcS/ry59uiwOITGfRWRz2JFbOYYsLkyEUMITtoEG73kg2Tqu1wiHvtne41j9muz7PSaXO2JuEN2yGI+bDAZv9ziSW8vw8n6yuElc0J5UqH8xtUoki1no9KlHEveVFOvUaa/0et06O+GR9DVD8dn2NII65s7KEYuI4CMAOdGpVmmHIw8WFwiQlZ6LQmY9JWEShbDpLjHmSc/DLW45EJixl4yTWyk0OIjFaaQg4M6SbMwQxQVD61Ty+AudPF1Y3DpMsWjyEPnOMsuv4Z4boE9pJZblxSklhJEbaCbM2a8jBXD2L+U4qSiED0/8FZ7fCQaTzJe1b5eeZZLkQhjeF80w0d6s0qFBNmV6RGbbnsLRvJSQOkRjX/HM932ehc10RE+3sw9yPFK6tkFHOha49+xjs9YfTLhLKpGMvwkyZzkqYuNAL5ZcJ85Uei/Uu7XGTk/FEuznnD2h4I+4MNmfqI8JnK155csUSJS4gukGNn6xcZ3nUZ33Y5XbnAD9JjtP3K5zUavxy9QqdStWI4jG9qY99n635eevB/OnaCn947z6PFhfoVqs8WFqcFKhJyyhZGPz8yiXe2D9ka36OahSy0u6z12oyCia3wOZoxPcebfGTa1foV4rD1JUoUaJEia8PLvZC2WGUi7TJ+TdP6c06374wPEvyEF6odLne3GV3vMSVxgHr9Tb74QKblWN2R4vop6GvhLdLN/yPxNI8SxSyH65G+RnimWqovoKXZytsG/Zb/sxEeDKXpqHdjD4rdhnYSUV0MM3CZ/WRapUtHWQ6ZvvT+v8MDehZZW4dIfO48V1xVG9yVJ8mLMmOJcgzaFMW0Cgz+hz6Pp+vLfPu7h4/uXE559w3aaqpxCFj32Ot22NhOKQ1GvPx+ip3V5dAa97b3aM1HrPZ6XB3dakwBbL4m3pnh4fLToLVztUtCxau5B41cwKQonTTLrtWJMEWddZJHZP9Tf85g+G1mgusatEcFOO6K2GbgSyNuFFX7LNIayxphiV2203ccU42XfzupJk200Zns+Rp72exMAfdZ0xB+mcTrp7Yel7i4Y2nJ7FIMyztz2VszW1FYQ8lXX62bQbNcGF4uAIGW9I2u2sGcX9WnzOwzVKfrnV8xrlRapTPj4u9UH4BuBwccqkyiQHrKc0X/U0uVw950F/nOJ5jd7TIt+fusBD0eThcZW+8+KKHXKLEK437Swusdvr8rZ/f55P1FfbmmmgUke/hRzHfebTNercPJKnblWK31eTh0iRFuK81q70+J7Uq7+wdsNbt8fNrm4njYIkSJUqU+DqjXCifAx4xm5VjfjO4zkgHfK/5BbcbO+yP5mmHTfBA4/Fh9wYRHjHlg7ZEieeN2PP46Y3LrHR7vLV3yPvbe3ix5vHiPAuDIe1GjZNald9urBL6HtVxxN5cM6NOI8/j441Vbh8c89nqMiu9Pj+8/5gPL21w0ijDyJUoUeIVgeYrsbI+Syil6sCfADUma9b/RGv9ryilVoD/CLgF3AX+R1rro+cxhgu9UNbKdnByJ4BoQZRCsJxlatGalh7hE+PHMUuqy4lu0PcnmcH+ov86PvEkE1GgsnZj8hrH527ucBxyJAvb2RsMzOgj8lzwlDvKZQ17gTeCVIZx3iGYznmTz7xznhUCzgj9Fvt2hsqcw54QfuusMG9WmWC+Fr876gFdOPmMokw+MqlkOSsGthm6EMkPf7DQ4EcLVwG4fNzltYNj7qwu0m7U+eG9x+zPTZP3uGO7u7rEYbPBraNjlvoDdudb/C33HvL/fec2bsIf6xgMk31urM5Fb+/aPmlm6LhCp6AieUWada+gneiQ6EhmJKlAXCRpkJzdXCdA8iiUXhRJg87Amc58vj2XbHlFuk3n27nbrDmfr5+1y8krhKNPz7kgvVDO9SR1+tTPE9Gsr6zvVv+zhFM1+vRQeCMlluUc4URHP6FdQUi23LYCxzuvwNFPdG4tlF7MIK+wznWBU5526ohrk7OlF0T5dq8YhsDfobXuKKUqwJ8qpf5r4H8A/DWt9V9VSv0LwL8A/PPPYwAXeqH8vODpmGscskGbEJ8xPrHyiFE8DFeNmmoSD/mFjbREiRISthbn2FqcRI5Z7E/C1FWimHFwtpXnpFHjV81NRr7P4mDIL65uniHYLVGiRIkSXwW01hroJF8ryZ8G/kHgLyfb/13gjykXygIURIZltJBRzDnzGWWxJiAGDZHy+cbwMYu6z76a4xP/8qR+ytIYZyx9hrphWqQxiGWCA0nmGHaOIPkSCl8uC5x1irZJjjVi2dMidWZJOldCmTTQjNUTWRr70wpl5DrpWCxU8pkxKoID0NmHMNP5MJ0AtcMkx7lQg4hhtOKEUT6rDChkjUV2TTofrjOfxDZ7BQcvOY85DnuSMx8zMcrCNiP5ULtV49HyPL977xF31hYZVAKGvk+/Usl0yOb94ONLa1Y/y70B650erdGYyFPcX1rkuDlJcmLeT/IOUMk8TueQGJ5KWd/tY3A+jf9zVhTzf6md44wn3h8KfvdCK0OBg+csEC0eQmhDNyRbrp9Ao+N8ndxcN8OuuXPHYvOS6y7dr/n7es5vat5Xsn+Ee5ZzH9IFnq+Sw7k0l8RQbE47CS5rXOh4JyS+khzTPAXeGNlyW8Aoz8QaFzm7uf2Y+5bY7cLkIM7zvIhNn5X9zfYzA2ssnpe8uadonGdBMWXDXzKsKaV+Ynz/I631H6VflFI+8FPgDeD/qrX+M6XUptZ6C0BrvaWU2nheg7vYC+UviblowEZ0ylrUwdMxHpqOqlHVk1n92F+aafKVKFHi5cZvN5dpN6psnvSojSOqYURjPOao2eA3l9bODAm33unxzce77M63qI9DWqMxV9sdfnZ1k52FlzDWeYkSJUpcPOxrrb9/VqHWOgK+rZRaAv5zpdT7X9nIuOALZa0grkoF+U3Tt1nN8rjHzdERjXjMVnWBXzau0PNreJFmOeyiYjjyW2ilpno/J7mIve3Jb/TmW1yWoCRjT/J61KdlZ4owE+s7K9ts9HlmGDmBoTrXi0fBWGZlhovGUhymKtWlpj/gbJT5LIc3TbQw7TN2dMh2whG7ndWHBwSgjRBi0xBzQrsZWEA5HFZ+W4aU5ErvJibzV8R2ZCyntj6BXMreWfV3uRB1RrvtlRbbK62skhfF3Dw44ffvPOSz9WXurizaKeOBke9TiWMunXTZXmhx1Kzz2mGbbz/a4Wd47DdbyQ7VJNGCcHyy1lidWeZiZtbY2Sax/iIzPMM1IpbNkIRmFhSGhxMSgJwFy7JiTsHMspLMsxksQlYf2aQyirRwknOdnb1tes/KM7UZW2z07c4PMaW0ZC11+zb7cPZTFCJNZFWFsbmMshzqTBiTy1IXhYcT2VhhbO6284ZyK2SUBfY3l0LeeOa7+5PGmaannnV/af9O30/ErIlJXkJorY+VUn8M/L3AjlLqcsImXwZ2n9d+L/RC+Tzw44jVcY8bvSMUmge1ZXar85O4q9k9T3FYmZPNoCVKlHilEHsed9aX2Flo8a0He1xqd/ngyjqguHZ8QiWKubuyyF978xaxUsT+ZKVWiWJqYcQ3t3f55aVNDpvN4h2VKFGiRImnglJqHRgni+QG8HcB/zrwXwJ/Bfiryed/8bzG8MoulNeHp9zsHVGNIwIdo7TmpFLnTnOFg0orxxyVKFHi64lercKPb13htYM2v3vvMQA7cy20grd3D/jZ9ctW/TvLy/zg4SNCz+N7j7f4+eVLsLT0AkZeokSJEufDS6pRLsJl4N9NdMoe8B9rrf/fSqkfAf+xUuofA+4D/9DzGsDFXigrOzOf0pobnSN8rbnabfObpU26QZWx5xEpb2IelcwprrDfNFUnJu3Ud0MZ5j/lyjKKzD6RyhVm5n3JNC4dbpG17wnyiLM7LSgrqvu0sWjSPgpM8dNCQ5LiWj9nHPcsx55mczOzbWXWzGcYCtuV2pwV7s3dr5tpzx1f7Duh1WaRXjj9ADnzupiZT4ITKksXOUsVtLOcDoPJRZllJzM6muU+r3P/TPtQOm/ixFfc2VikU6sSKcVRs0E1DPlLnz+Y7NCwPHVrVf76rZt8Y2eXoBfzjZ09wnEEns9RvUHkOSc5zbBnWqpcByXJiiVJiWbBLJkVlVD/vNKLovlyHgc/4brDkWBM+jr7h1d60o+4vxkkF7n9w9TRMrsdabmiNBam9xW5UtqPUCSFZJtFCqEL2rl1jXpFUo2i56UcHg784Rl9F/RZ5GxfHHbtjL6tbZIj3Nl9Fjrz5Y7dkFe4YxHKsvaR1E6QgaRjj53v0n4u3gJ4JmitfwV8R9h+APydX8UYnoMS9sWgNR7yg70HrAx7XO22uTu/zEG9xSCoEHk+ZZinEiVKPAn7c02OWg0ARkHAKPCZH45y9SLf47cba3haszPXYhgE3Gof87fdv8M39nbw4wssBCxRokSJEhkuPKOsKzG32kfcbB/z6coaj+bmCXRMqDyZ9RRC4bhMsuSwl75SKMNpKovulfSlpTdP4XmZMQ1fMqySiBlYqIoOuTk4ZG3cwUPT9WscVRscVRqceHVi5QxIcKDTioRpK97XE8d53jozMOcWK1RU5jJiRc5PTwkrBJwT8kpKKiIyyhK7Zjjzxb7NNrvOfEVOeWIkt3OGJswxi2a7SmyVye10rk7GJAu0jsp2NMvg8v9riSlyNqXOdkfNOiu9Aad1Iw5lUmnoB/zo2nW+sbfLkufz88UVBl7AD3Ye8pc7d/izS9fpVmoya+UmDpHW1QW/kcQsaocZlpzrisK8iXPd2bdkgZBDDeqzy1wIiTxkh1LnBzcHE0/qSs6f57qGpTnlMrYgJwVx6ouXj9OukOGVQrIVMcpSaLWz2ov7E/o8L6OswBshn4MZWGqRNXaOy3Jam+W8CMeec64TEo7Mtr8CRjkS2onh4QpY4xy7bezPCSs38/3w1SSenysu9EK5EY75/Yf36VcCfnz1OoNgosMI1TO0l79q0Jr3e4859ev8fO46ka+YC4csRn1e7x7QOhnSCWocVZucVms0wxEL4wGh59Gu1zmp1GiFY26eHtEajWB5mQdxzHGrRqwUjfGYRhjSGI/RnuKwVaM9V2VY8amNI+rjiPoopD6OpjeNdGhKcdyqsj/fYFB9uqmptKaS6NJ9rdGxoq8q8FSr+RJfdxw2G2ycdrm3siiW9ysVfnL5Ct8ajnhrb5fGeEyvUmFxNOT3t++z1Zxju7HAYa1JOQdLlChR4uLhQi+UQ8/jV1c2addrs0srpHAwaaim9I3O1CE74dpM+WGmX04+pdjxUhKMXDgzE0Vve2798z53FSxGAzyt+bSxPhmIB0fVgAN/EubKUzGL4z5Loz5Xem26QZWd5jxBHDM/GnClc8LID/jtyhrH9Tq3fAjbbTZPuwD0KgGdWoW9+QaeilnpDNl4fEw1jBkFHoNqQL/q061ViE22FQhizUa7z7sPjxj5HgdzTbYXWhzM1bODVVrTHI2Y749pjse0whGNcUg1jqhEYWJN8AmVR6wUlTgmQrHvzbHvzzOgSi4lsat/Nr6YjPB5IKWPznTIaXIRiSEWwgPmGGKnnq5M49Fb+5a0v0Xsr8vwzoocMyxQtSmzbF5bDu2khHbpNi3lxRaGqV2dphV+y2Vn8rrnaRrnyT8H83Xe2TlIOpRo8clgd5rz7F6uZzrkK902bx/tMVIBt08OeUPv84vFq4y9yY/vuYyywWi5DLJ5CN/oPWYt7HLq1wiVR4RHqLzJnPc8dqvz9IJJzMyZNcpFbPM55ouYbtpNES31Jc0X6R7pOmkYk16nbHJkV/lScPdXwKpakNhbc6Bn1DlPSLbzsr+z6IKt8GkFbCyOkch6lvpJeLhzMrz58HB55nQW5tvu073ejXaRXV/anxSSLX/OZmONXZZ5Vq2xGzLOblfARJ8JzauqZX6euNAL5VHg027UX/QwLhQa8YjToH7mi0XkeRzWWhzWWtMHWiYZmLJqqYnzpF5h11+2HbjSDGuBZnt5zo6PWwCl4C4L6Fiz0B+xfjjk3a0D/DimU60yNxpTH4f0g4BeUKVbqXJYbzKqBYw8n7H2GSsfz3Cc9EaauXjIxqjDe6PHaBT7ao4DNU+fKiXLV6IIg2pA5Cne3Tng7vIi/aqcmMTF49YiXqS51Tnkw6XLrIx6fPf4ET9ZujbxmXhKHAYt1sIu89HQ2q6B7eoC3z15wE8Xr9P3pQDzJUqUKFHivLjQC+US54evJwzrl0E1CnnzYJ9PVlaZpF1/xlCKk2aN06DBF+uLLPWG1AcRnVqVgaqilUKNp8fgJ75Wpn7c7Kvj1+lV6twNVlkYD1nVHd6JHxPjsccC2yyhywVziTPw0+uXudo+5ffvPuTT9RUeLCzN1O5Ra4mBH/DN4y3alTqtaMQ7nV1+vXBZrH9zeMDN8WH2/cetWww9+/raqi2yVV3AJybQk7+KjrgyPGZ53CNSahLhp0SJEiUEPG3Aqq8zLvZCWUFcndG7PDXlJtmztGSGEUI1pfWVY7oEco5+cuaf5HNWs50LwcxU6JAj9ZEeu45ZC7ts1Ram7c7pHAfgac3Vzimh53Ew18jvtMAUWIRcNQUoxfFcHVVNfof09zDbpY6WBabjiYVccerVOaXOXb3KQjTgGoeseqd8pi8x9KYsnE7NYE/p1JfWL3LYM0O6uebvIlmGtc2b/rn7LnLOKnKW0ue9K7ime2uciakx6dsL4ly7qRTJMDk6d3NLeuFMSMsaGToHaJk/nWvfugek5nznO9CtVvlkfZUHc4v84PEj4tjj0eL0GrIHY389qM/xY7/JxvCUWhSxOeywM+hwGMxZ9bWCjl+F8eT757U1hl6A6yyrk2si9HxC/Oycn9aqrI667NTns8QooryiQI4jOnEWONfl5GUzZhfM/j3vu2ls/0Zu5jplhPDLQqydF5L8ILuXFzjXmSgqm8Xh7rwSg6K+3XYF8grJubVQeiH06Qfgj8/oM3cMkvxAGGeRM98Z7aWxi055RTKJdFtRKDexnS2JsPYtZvRzJBfWWiG2+hKlF8L+SjxbXOyFcomZsBj1WQtPWQs7nPgN9ipzX6q/MBFqb/S6HDyLAb4IKEVHNfhYX+GSbvMN9ZD7ao19fcYCqMTXHv1qhZ9eucLvPnzIQbPBoDKbNSXyPLYai2xXFvjOyUNa0YhD4c67H8zxYf0yp36NkVc510Jy7AVsNRafXLFEiRJfb5Qa5XPjYi+UlUbX8q9RRX59KcOXMpNgMJLpW5tvMBUJE5YxTIZ5XznMnRj+qeANuRCp75NRN3uBLHDIsd/gNa+N91iJu2xXFvlV8yo9vzZTGCgRSR1Pa0aeRzWK8JI3Xosxctm8Wa/LpBOJYcw2ZoxYvkxiUnOsv8mkxZO2O3qJExq85W8xr/vcjddzTnySU14hZmCGRRbY0YVL7az6KaNslp2HTDPOcVzTuXGd2cycw+mYKykjPS1UiV7dS5z5gkqUa5eyx4K7njGX8pMoLYtCYcDptWIm+skSjjg7AUgtFQKjbF7Lfa/K47kFbh61+WR1bVpHz2DSVIqfL163+rSd1hQH/txMST6kOXQu1rggyYeJorFMK+XHklm/0qlgXk/p752WGTfszOkv+41s1thEjuH1FF4qySpiCItg9JljTCWroARnP8+E4T1HWZHj3bPosyg8nKqCCouZ2tw+hL7O78z3ZGZYZJQdZ7nZ+xRYY5cRtkK5OXNeZNNt9tjaVri/cuH7vFGK2V5RVHTIN0cPqeuQnzVv8KC6Qs+vPbnhDPC1xtOa+BVK4tKnxofRDQIivuE/5Gawx7zqvehhlXgJcW9hkaunJ1QiKZ1eiRIlSpR4lXCxGWUPvNrZDytX5wgQp+yTb7wjpLrlhE3QpiYpYUJSBshkTTxHvywGNJcSChRp+Zx20rtitk1apypYiPq8M95mO1jgfrAyTXByRv0zv+fYpJT5iwm05sH8PLHnQRQ5zE/CLKbHUqSDNlmoHJNsME2OnlViY5UQGsrVWYupe5NxRp7HJ1xmWXdpMOKtyhY/1a8n+zhba1x0XCJrXJRaWjhXYn2TBVTnZJGN/aQsMpiMcH7WTfWa6bWS7ytLO12ZtvcSBjmoJp/+9HrN8u4kncdCuDYtHFjGJEdPPmiLdUzrSwxaOleTa9ozLUdO+MiRqrLVXOD93V1+tXZ5cn2dN8hEgU9BYUg2Z14VatOLUkMLfWZQGi+OaY3GnNSrKChMx+zODYDWcMjl0w6e1vQqFQ6aDXrVanJYqQVI0mTav5GoCz6D4fUUmZNvoeZUQgGrOhOjLPQ9k464iKktYKKt/Th1ZtUTu30XMt8i++uwpIAXavyxLm4nMa4ZwyuMsyjM2zkY5afVGkuJQ+R2M7DGEgucMeb5zGV5/bIwAc6jUdZPsIaUEPHEhbJS6jrw7wGXmPwUf6S1/reUUv8G8N8HRsDnwP9Ma30stL8LnDKJcBlqrb+fbL8C/PtJ2f9Ea91RSv2rwD8H3NJa7yb1OlrrLyeqfcWhdMxa1GEp7lPVIXPxkE9qmxwlsZGfNXqVCh+trfFoYYGV57KHFwnFEXOEus/weUT0KPFK4NOlVb69v8V7hzv8emXzRQ/nqaG05tJJh0oUcfOozZ/fuApK887OASu9HpU4Bg29aoWTeo2DZoNHS/OFC+fN0w7v7e7xaGGeseexNBjw+uEhnWqVj9fX6DSejWWrRIkSJb4KzMIoh8A/q7X+mVJqHvipUuq/Bf5b4F/UWodKqX8d+BeBf/6MPv52rfW+s+2fBP4J4DbwjwD/t2T7PvDPFvRVIoGnYy6Hba6GR3S9God+i6FqceI1CL9ErNYnQinuLy09v/5fAiyqHsfx83nRKHHxoZXHL1cv8539x/ze9n2ChUU2+gP6foV7c8tE3sVQta11e3xza4fI86jEMUprfufxDsv9AT+9eonWeMzdlUVawxELgxFX26fcOmqzO9dk7Hv4saYSxYx9jxiP5mjMWq/HT65e4bRWMxg3zQ8ePeIP7z/gr73+GuOgzJ5aosQLQalpPjeeuFDWWm8BW8n/p0qpj4CrWuv/xqj2Y+B/eM59+0wY6hjb4PzvAP9TpdS/rrU+FFsmUEpTqQnBcwWyI5VhpI4/seEAlIZTIkrCKpkm3aR7KTNfnIUls02z5v+ZM1mB2c76nlqmJUtL8r9PzOXwmKvjY9p+g1/Xr9JV52Bpzmmml2QLlnlYIdsZtVvZgJfZooxxpduEcTrZv5TlAGR/WmFkXUmDIC2RnNcm0gKdSS4KJRQFTlCiA16RlKLIgcvc5k/rneV85yYWM/9PJRexIZMgcYxVUoKY1CqYyRAEWUzquBdM5RWp814lsD9hKrnIdlEgvTAlGOPwKRdZmXk37/DlSi5MZ9+cY25W5PHzlassjAZcqfocxR4b/Q5vH+/ym5VLOTO5NYeKxulKLySnPGnuefY1YkmDsr7sPY+qHh4Qo/l4Y4V+PaDdqKLQvL+9S+j7VKOQjy+vctqq8mh5jvVOj4XBiGoYEXkeg0pAEE3iOfdqAX9z41qWgTATUWnFvcUlVvrbNEZjxp6fSTCse4Ajc5GlCfL91lNq+vvNKjFwURCWTCpz+5ZQ5LQmhWR7+qx/+bHlQtxJ8oqsriQVEMZZcAzeWOONdbG0RNi/63B3bulFFn5NKHMlERjHKoZdO2O/Ul/nlVdIZW62vhlDwOXqS2HzSjwTnEujrJS6BXwH+DOn6H8O/EdnNNPAf6MmK9X/u9b6j5Lt/xfg/wW0gX/YqN9hslj+p4B/5Tzje+WhNatxl9vjPU68Oh/Ur9LzkgVyeY08UzQY06bxoodR4iWHVop2rUGtXmHXq7Jbn+N3d++z0T9lrzr/oof3RBw36/zJ69cZBj6hP1lZn9RrNEchv765xncfbFM1nRaVYm++xd58K//Cb96DhIXq7twcv11d5dvb2/zF1av0a6W0qUSJrxzlWuHcmHmhrJSaA/5T4J/WWp8Y2/9lJrzrf3BG0z/QWj9WSm0A/61S6mOt9Z9ore8Bf+sZbf5PwC+UUv/7ojH5Cq5VpoeQOe8Jr9spCxQm7HEUTKmYMGGodObUNy3LGLSUhRKSkaROLJLDnhLK3DdyiVHOMSNac3N0yHw84F7rBh2vTgvIiQMyCscYim9vE53ICpyD4owB1bmy1UqaFCF/ziWnotwYjDLlMMq6YrB6yQ+YnXPDuS5VmXgps2/M6vT/NMGZNzbKMicP+3tVh2xqD6ijo4o9XozzmfStz3uuhbKsjsR8u2VO+UolEG9+uQQ1QJSGgEvCtenatDB1vMt+E5M1Tnfuq1xZ5uSZOPP5lWmfQRKOsZ6c/4rpFJkRISp3fKkGNt1vaLDIYTJZ06grpswhTv/Pxpun2zMm0wwFllqMkuOzfN3SL25IN+P/FS/A80EHsLN5g++f7vLz1hKx5xUymKITpmtBMM9LxhbrXHv3WjZ3rN1t5n7rweQ+kmwbX1rhwaUV6sCuf4Xbu8dce3TAYatOt1qlW63Qq1bQaajNAvY26zSp093c4KTZ5O84POLXmxv0KhUx1KBkpUuPS/D1RXmwEgRTJ0zJylLESAqnJduf5JRcoKwpNNgVhFY7a0zWNvJlxe0cJlJgsGdxRCxieM2ypVowucemdfJdy6x4EYPtsMUSw+v2M6k3A2uMxBon9aP8icmzuEKZdEJdq0Qc5wtnYY2lYy/DxD13zLRQVkpVmCyS/wOt9X9mbP8rwN8P/J1ay7+S1vpx8rmrlPrPgR8Cf1K0P631sVLqPwT+l0X1YjR7amCMU1uf9jFMPsdJ2rDQuNOFyYolTp4mhQtl0yQb2mXiQlkoy2XtM8uSG322UI4ArXlnuMMwDvlZ/Qp66JGl8HJRtFAWTLJFMoIsg1xaZkRDMB/Ku+NxzqRr1zFtc+kw8xe3yuQYSZEZyzaRymTn3Pgd0sVvGkPVTGU9LbM/wTj/6UI50swz4Kbe4guW2KIK0aSBtVBOF8jppynHmeVcS6bxtE6RROSMhTLA7ig/H8SFcnKO4+TgzZjHXhKRIr1+TLmDzrKiFSyUk1AYgfE2WUm0Sw01GV/Vm5al/WcLZctSmWxLPsdqerLSazitExknMkqv3XR8UT66jXvdTrYl5yAU5lBk15EWrijYHY6T38ijFiqubt/lg6XLmQxBwkwLZVH2o+39g/DSO+NC2dmvWbbTrPDrm2ss9oasnfaZP+lwbTjCizUfXFnnYK4pLva8WLN52qEaxtxfWrQOdK9WY69e580v7vDTq1foVAzZWLb4Eu6p7sJMWEjuDce5sllkBOLCtYjQKFgTFS2Ui2IQzzROd/8FfU/a6YKygr5mWShL5yWGw5NxbjEtjV2UNEjH7sQ6nrndl5VXiDGWz7GoNeGOxVwoP6284ikXyqpcUJ8bs0S9UMC/DXyktf43je1/LxOHu79Nay0GnFVKtQAv0Ta3gL8H+N/OOLZ/E/iLojFqFJ6Xv0tLDtmpHrJWCZPv+VVKlIapMpMwZLRCnkKY6gXTG4f5BE2aZW+QxvhS4m5s6yKB7GjT6krBtcExVR3yYf0KsTqDoSq4kSr3OhIeIrPo4cTrS0/+zDFlmlXhJTg3JqGsENI9yNUon7GQsT61ZoE+a1GHahzS1GMCHRGj+CJY55CJ2VwJi9opw558GjM021bAKOeYP6N+4aJYYgg1xL62tcZOdfvlJ9mY1FfGy4+Xaoz9yeyLjUWmThfPKdNrnuSU5Pft9gBB8r+ftPesaytp5x6nMfZhMobIuLbShXWUlMVCUhE5hbXdt5g0JZ27Qpeq4LfRKtmefP94cYNb3UN+cHCfD5Yv066dQ8ZTGALOeZkU9Pja0SqbSO9R9jE4F6NZpicb2rUG7VojO2frp12+9WiPfiXguFEn9Dw0CqU1jXHIerfLMAioRBF9r8J+s2Xtbqs5j449vv/wMT+6fIOxHyTjM/eLzLieoV9WvvEiPONCuXBOzFA2yyKzcGFu1i0IHTdtf/aN8/zHYHcys+7Z0eda4eEijRfq8y+UU0RCuxyDbRTmtL/S/gr6zJ7P08JzLYalh6K4MC9o59aPhElfpF/O+ikXwM8LszDKfwD8o8AHSqlfJNv+JSbyiBoTOQXAj7XW/4sk7Nv/Q2v99wGbwH+elAfAf6i1/v/MMjCt9X7CQP+vznE8rxwqccSN4RG/mLtGHAtPvlcZWtMYhfixRkc+sVLEkUfsKbT20EoRRBEr3QELwyFjfIZ+QHUcUYsiavGYrlejE9SII5+qDmmORzT0mNWoS4xiN5in49W459WJ8Bnhg1LTzGklSjwNlOLO/ConlTq/c7TFF/MrPGwuym/xFxB78y3+pNVgpTdgqT/A1zEqnrxAndRr3Fta5IcPH1GNYyLPoxaGLA4HLA0GLA6HLAwHaKWoxDHNcEzbD1Bas9bvsjgasF9r4WsNGtqV+lRSU6JEiRJfMWaJevGnyNak/+qM+o+Bvy/5/wvgd2YdjNb6X3W+/zPAPzNr+1cRNwcH7Fbm6PlVBPL8lcXlgy5vPGxTiWJGSRgqT+vJZ6zx9US8EStFu16nXasxH4asDnqEeIy8gG5QY3405NLglEocMVIBfSoMvAq/rW5y6tUnCxfJI7xEiWeAg3qLv1i7xrcOt1gYDfh4aYNYsE5dRMSex/5ck4NWM9kwfUxcOjmlGsfstFp8a2cbT2vatTrtep07i0u0qzVunrRZ73V563AP7Xm0xiO6QYV2rcH39h/SC6qEyqMVjrjfWuL+3PIrc+5KlHhhKKUX58aFzsynI0V/ME2JVavaoeJMrbJrPvVN8/AMac0MJee0T9cMajoHxc62KF82dbrJayWVgmY4YmPc4c+Xb6K96fxWknkRZ5tpxnYX2DPqE7MuM6cWwTaeSC9sxyhtD69I6mGVTfrYPOzy9v0jfnVznYO5yWI2y7iVakjjiZlXG+0yKYsh1y3SKLumacu86DgOWTrkVJtckGFPyrSX0yhLob2k3+YJ9XWgiQWNuBgeLtW2pjIJ4+3LT0K3pb+fef2kMof0ePxqPsPeLPdfs8/0cKaW3OlAR9HkpKah4MLI0CE7IR61EepRj+0fVVkZIwtkGemmRH4SGz+cO86zsidqH/H37gVV/vzSdd473OH3du/xYH6Jk2qdRjhmr9HKok247bL7iyDRSU9jPRyz3B9QjSK0B0PfZ1ANGFQChoE/lci4JnhzUjjHo4XLXDpnrqzMvD+MdIX784u0q3U+XVhj4FWs+9y7Bztc7Z2wU59jZdhjv97i45UNesHkfr5dWaDnVwBFPRrzZmeP7+895FcLVxinHrrOWDx/em+c1VHMdeKbWRNdFPHDaS9tU6J5Xu5b7H9WOYcrWzAtZU6f8jHkZQvF0otJdj5RBlAkuZhBoyzKK3LtDAmFFMLNHUuqFZ5VXlEU5i23D0EmUSTLiAv6LNpP1u5rxKR9xbjQC+VXHW+c7nO/scz4eSYPeQmxcdzj/sY8B/PFuk53EVCixMuK2PP4cO0yi8M+N06P2ex1iDyPWydHfLi2SadSY73fQTOxkAyDRLOrNT94+IjmeMxeq8XdpUU8rVnuD7jU6dAajThsNhgGAQrNahhSCyPqYUg1jBgFPoNgsmgeVAKGfpAlCgnimG61ytbiHPXxmKvtU/qVgINWg0Hly4VuO643OK43ZG0sMAgCxglb/OnCGpf7p7ze2eeDxcugVLZgRsPAr/DBwmVe6x7ynfZDfrpwnehrdk8s8ezRmBsyOA3OYCdeUWhmS3VdwsLFXijHilFvyiinjFQlcdhTgh7QTxg0z2Dg0iQI2UuiP22ntfM2a3jtK+cfbTnzpdEBkq8G65V630vzNR3zaqdHKxzywfKl/HWcDmWc33bmdwyWWyKTChyVsofcGWUqdtjOnDefeV6cbdaL9eTLo9V5vvP5LhE+J40qoe9xGtgJVUyPfuXuryAZiRglQHC8y3w943yZyyRbx+44WYllQsSCwpBgqQOexVZO2UYdaDnqSLrJjH6QMKZpUhEzuUh6TaQsc2xSmckkkK6f1EEvdbKLTeuC4MTnIi3rh9PbkcskR2aCIJdJHk3LsmgoLhNqbhO89XNzwXByTNllyekwu8z9xNIg/UYGjoI6R61LSQXNlc4p3919xNj3iZTHyPd573AHrRSntWqW9e5HN69x47jNd7a2iZTitFbj85VlDuaaUwdLZ39erKkmi+b6KPkchzRGIZFShJ7Huzt7HFfqNMKQN/eP6AcBb+8e8tPLlzmt16edCfKkXEQfIQTm9GTYZfcaq9yrrSTfJ74C3zt+yKVOh73qXHZip/0o7ldWqYUhb3T2+bS+mevTC6b3ROuWOQujLMmv3HbS8Un32cKQZfJ+xbIC5tXe39l9TrflmeFCRrnA0S/nAGeWhRp1ljNfbn9FTG1+LDOxxta5dhha81wTc+2NA/bvz9Hebs7OGhf0WYRzRa8ogjVOh0kuJRXPDRd7ofyKwo8j3mrv8dniGvprqMk7XKjzszc2uLFzyuWjDivdIX/z9hWOm/UnNy5R4qJAKR7PL7DdmmN5MOC4Xp/EhFaaehjSCkcAHLQmi+FP1tf4ZG1t0jZ9JhaQYVophpWAYSWgXXMWtQnqUcRmt8O9pWV+cuUK39zZ4YvlZb63tcVv1tfZa7XsCCfPEoZFaDEcMB8NuTJs8053h8NKk88a64yUzWx/UV/jh6f3eBCPGagyYUmJp4RWaA1rNzq0dxpfG6ukQpfh4Z4CF3uhHANdI75qKjdqpMyyqaPM6DVgyl7ClEEz0+u68ATd5rTz5NMoykJXpayXud71FY3hmFv7p/hRzEm1zsFck7Hn0YxDvrG7x3Gjwfb8nElgT19CBTbDZVKKLgWLSXOOQQyqn76wCqyx0kl/AmmcdS28PBeVaeBwrs5RfSK9uHLU4ZuP9vjVtXX6QZVR4DuJCFKGNmXWjP6LGF4nN4UcTispkxjl9JilOLdZmcHmOvXtFMPariMxymdplH3QhtZYuUym0Vcauk0JodxS3X7KGptR12pJjGU33BvkGeXISkVtL7Kk8HDD8eQ2FBrh6NL/pZTzOSZ5bOiJM0ZZEhJPPtxUyJNtzvkXQuplumUhtJr29YSFFjTKT0KEx361Od3gQb8S0Lduz2esjKWY8Vmsa2Obk1QnPdCd+hyvtw+5P7fCYaXF0A8YqAofrF7izcN9vrG7y25zjt36HIf1pnPO7M/ZGVe7vtKwMByyH7Sox2M+rm+yFPZ5s7vHR9Ur1inQ+Bz4c6yNOjz2l60+VWTEny9ihiV2VGJxs20CW1mkUS5kat2x6Fw9MeJOUZ9ZnTyLm9MTzzpOR2M8c0rpKMYLY1mL6+5XOHZ3H+IxSGxs4f4ExjaGrY8WufJem2owZtzzz64/a1zjFJ5w73H1x7MuVotCwLlMchke7rnhYi+ULyiq45C3Hx5TG0ZsLbdYOh3wxv4RXqwZBgFb83PcnVvhVQkl9WXxeKlFYxDy/qN9mqOQrcUWH22slSGjSpT4kjhsNPne7mPePtzluNYgiGP2GxMW+c8aN6iHYzZ7HW6fHPL+4Tb79RY7jXkOaw0K09OdE/tBixuDQ0ZeQM+rcjs64NSriXX7qkJNh2JZiRKzot+ucu8ny0Qjn68NpQylROMpUC6Uv2IsdIb84YfbDAOPD66vsbvY4sGCobHMIjeUi+QMSvH5xjKfbyzjjzXvb+3xl754wBerS2wtzJWOPSVKPCW0UtxZWGZ10OPGaZtfrV2ypBaDoMK9hWXuzy1TC8ds9rrcOj3kneOQ3yxd4rh6jkQqBRj4VX688Bo6hoqOidQk0fhC1KfnVdFApD1QioGqsBp1Jg98pbgaHtHQI6J4jf2kjos53WdODxlQoU0rV17i64nJIrlEiWJc6IWyisHvTheZafau1PSr69MbZqU2YSDStMjaCkqcpEdO7D+mBCO956YmYzk9dmKaNRKCpM5IcWKqjkIfFWuuHXXYX6zxF9/YSEzIsRMhzQ7DZZnN07S+BT4GWXg58mXZeM0vjhnUMjk726RMZrl+zCp5i2W+vn3w9kZBBhJVFL+8scHK6YCbh23e3j1kb67Jo8V5DmpNUEp02Muk3kKoraJMeaLD3iyZ01wJhtm/K7OwxlAgszAc76Yh3EB5MUrnpRfTT6OrZD6mn4GVRc+WV9SNPM6VpKySaIF84/rJSS+M6yCVK6RlnvGDD8KJxtQNBQfFIeBcyYU3Nl80sSCbqPN63XRxmIU2lLL2CRKYbJ4EyXUmhKR8llDStZGWhfZx2amFHec447x8vrDG5/Oa5nhMr1K1M4UacpUxFR7VlnhUW2J12OH9wy1+vnSNAdVcn7n9FJRNx+nh6YkU5Vf+da5Hh9we79HQ4+zYT6lzX63ha82t0SEP1Cpzesg6p/iRohbGfMYl574C3/QeotTkN9qOl3gQrU5OYpEswx3njBKKM8+Beaxin2dLGnJ9miiSQmT3d0E+MsvYs32YZWfLHYqd+VLpxNnHkJO7mGWzZLwrkkmI+zunvKIodFy6WChy8HtaNlcKAVcyw18ZLvRC+SJh8WTI+58cMaj5/Pr28pMblCjEYavBYatBJYy4ctzhzb1Dvhnu8nh+nseNRXrV6pM7eckwNxix3u3SbtQ4nK+X0psSXx2UoleZ/Zo5qM1xpxXx3eOHfNpcZ68yh7h6f0po5XE/WOO+sRYIoohV3eEd/Zg2DdY5YU2f0KNGBHzBJa7zGTVGDLGP5c/iN7jCEde8Q9a8E/q6yn688MzGW6LEhUG5wD43LvZCWYM/sChXgInnOBCrKUMVJkyRH0zeyLTBeqWOfVl4LOM1OGXOZCbZruP5UyY6dRAchz7zx2O++es2H7+9wO5GnTj28IlIa1sOT8lYpi+zZ4e3shiHAqbCdRCTWM7MgcRwJMnWaZJzSVoWK4jVlOky95NlpzAYh4TZ0oJzUtqHlpwh3P0mBziueNxfXeT+6iLzvRFX2qf8YOch/SDg8fwCO7V5Qs8vDA8nJgdxHe8K2F8x5JzDHpt95NhjINAhv3vvEdtLTa4/PuF4vs6vbq1OfgTBkTQL7+ZplIrwMRllO4SbGcrNz5jkyY9aNeZsyiSnjLDJGivHohIIjHKYXlOmtSbdlhx7yiJDnkmODGe+jElOtpmJRFLmNHXcM1nkogQZuToS3FCDJtIhmEWmEUTzTNaKuWOwxmd/WuHFMqe6PGNeyGRSUJZuS50BjT53/EV6zRpv93e4PDjhbmWVjlcDpYoZZbfvonGaBFrss88ix3qOG2ofX8WMCFhRXSKt2IhOCfEIxz6e4Ci2o5epV8bU1Zjr/gHHozni5GKc6XxIzmfCOM9sb/RRzGAXMK9uXROx0K5wf04nYqKTPBNdFK7NCzXeOD6DxXX7FOp8Wda4gIk2IbHGXhDjBRAOPLusMDnIOYMSp8mFzkuCZI71pqd6OqbYrlPimeNiL5S/QtR6EWFFEVXOPxtf+6zDp2/Os7v5bPR8JWR0ajU+2ajx2dIqq/0eV09Pee3oiJ+uX2WoXm6GuR5GhJ7HhzfX8OKYP/x4i6sHXR6tzb3ooZUocSZOgzo/a9zg8rjNe8MtNHDiN+ipKj1VpZ9cdyHeNKPel0SIzxd6k8fxCguqR19X+EbwiDXvlB+Nr6ILVgwVFdKOJhFG5r0+7ajUK5eYYOnWiMWbI+78tVfY0qApE448BS78Qtl8s/ZGCVsZJLrIwNAv+3ZMLzMsVqorjVNm2WTulKY6iPj+nxxzvFzhlz9Yyt4GMyZZ5VmolKkLwpj505DffjPItM9hIhpNWe3I0EBlOkgpKUKU1kmO0yzzrWY2ChjlVI+YbRNCSuX6MeBFkz87DJjDFFgJQFLWw2WWp4OYpvg+m5WQkP2OgWJvvsXefIubB22+ebjNX6zfmNSR0k1LIdmK2GZXh3xG2LZcu/T/TOs6PZhuLSCIYxrxiEEt4JdvrfCDX+9xvBTQbyVZ2s7QGlfwqRq0asr6+qkVxZjrqca4GtiaY4BaMJkMKdPbGU6jDrhh3szv6f7SbaFxQtJto4Q1HhmpqNNkItnneFo21SYncyI09uduE4wuSrBYpJtE0plp44QAAGLQSURBVNixVIgoYECVx8Q/wLSGPDmSW+F+pO9TzenZrLGYcrmI4XXZUcFSJYUuMxnsLW+J7coiTT1iXg9oRiMWdZuGHtNgzL6a47f+5SfuL58W2xynzW6OdcABC1QIsyfZKh2qKqSvqxyE81RUhK9jhroKGrbiVd5sPOJoPMeS6nIaGmH5wGb6ithYyaqXjdNuL5dJx+4wwpY2Nl9/pnEW6ntnGCf5OoVJPuIYFZ3BKM+SyMOpK9Y/r34567OApZ40nnxGzoPvWYRk852XN3OenYdd9qTjcxjwEs8cF36h/NyhNa991uXhzQZLByNW90YcbMhhi1LUuhGrD0dUhpqNeyN2rtcY1Uvv2heBh/OLvH58yPxowGn1HAlLtMbTOpPxPG9opdhdanL5sMedywuctqpsrTd54+EJH7y98pWMoUSJLwWl6KkaPWrTlzoNq/Ep70TbxNE2n/qXnvlu1/wT9sN5vHCZujpmpAM2K8fU1YjNShsNPBiuc7V6wK+6t9gdLrFea+Oh2RstMohfbmtTia8Gpw8qKD9m+Y0RR5+9unOiTDhyfpSqliIki+T5kzH3Xm8Re4pLD/tPfHNb3hqzfm/Exr0R3QWfL94vzXsvClopPl5Z51uHW9TCsVjncu+Ea91j6uEYL46pRBHfOtjmb3/0Od/ffsil7ilBbLMMq70ut46OqIbPLp7r7lKD1eNB9n3se/Rr5btsiYuNU9XgC2+dJd1jXvefWb8+EZv+ERtBm8fhCsdRi/vjDbbDFe4ON7hcPcZTmt3xErfqu1S8iAW/x85oiWFcYawD3mg9puENn9mYSlxcxBEsXI1Yuj2mOn928rESXz+8Uk/hzPSYxiA2zR2BpjEc8+6DQz67ukhnaXroytegNc1RRGMQcbJUoTKOeeuTExqDkJ99d5Wx57Gz2eDagy7f/xuH7G7WGcx7DBs+YWuy71oYUe3HbH4x5O63m4Q1RW/Rp6JitGHO8RxTdWymQPPtEDPaKku2xfYnZJbYqWlUMCunFnEz/FNmhpbCTaX9Fzj1qTjZp/XukMgqUgcwhLJUghHlTdWSDORcr3ROVrWtxTlqYcj3Dh7x24V1ToMaY89nddjl9dMD5scjDmoNbnWOqMQRMYrjep0/vvEaa4MelzonvHeww2GjwWcrK1zqdrh82mG/1eT3H0ziOfcqlewYTmtVevX0u86PK5FeWOHefE1YUfikTiUxC4MR2xsNgurkxJsZ91LZT+DHVHVAI5i+BPgqdUCdfK8YDntVP0w+EwmGIb2oJ2WjRKZkyitORxMrSuZcqkNqnZjBoketF7N8b8z2uxNHLjM8nOuwZ4WAS7NXulksAZJtKnHik6QX0w35/7NwibF0ISQfkq+cZAXNmaHzcg4Vq8lcFjJGis55M5A65w0vVixbcPoqkl5YzmDyfs12kqOf2WdIwK5eQqO4He7xsb5KiF8oPyiUuSRlVypHLPsdPu1dJYwq+DXwxpPCATU+ia5wqXbE5eoRAFu9FXr9Bh6KB6cbvLHwiH5Y483WYz5vX6Ef1azBZKEli6QsWWVdUCadF52rm+0nksryModpn7PLKwrDtRVl0ZP2JznXhTEqNKQX53XYk76fJ/verKHcXMmEjgnmE2fmISzfHrHz04pc96w+XUjWSOlB6xU8hItQJC0p8UzxSi2UG+GIS70O9XhE7CnGFUXoeYS+hz7V3No5oTUMiT3FSbdCYxDS7Ic0hyH1QcSo6tMYRgxqHl6seXitxW/eXyROFq/3b7Z4eLPBYnvM2u6Q9e0htX5MbRgRe4qwOvm7936Tk83KE0Zb4qvE/YVlxp7Pa6eHNMMRvtb4yVPg52tXJtnIBK3x1vw8W/Pz+Drmmzs7/P7Dh9xfXODH168xrAQ8WBxwo91mtZswZQqW+gNO6jXurC6yt+BoIAugUdZDoDqOGVVfTqPPyv0xV381JKwpvFAzWWMrtt95dU2WJb4c9ligzpjfUXfZ1ss8YpmckPsc2BkvsVLpsF5p8zhazZV3ogaf92pcqh6zVjsh0gpfRYQ6YBRX+OzkCm8sbKGA6/O7fHJ8/ekPrsSFx/BIMTxRxGPwG6/worOUXpwbr8xCeWHU51tHW2w35zmqN/C0xvNigjimOQ5R45hPLi1zsFjj7UfH1PoxnVqV3YUm/aZPvx5ABd799IiThQoHKzWGtYT1CKchtrTncbxY43ixlgu15ZuMn7ZZvXowpXFHSdi6NEFKXJk+LMLUyS1l5azQaiRlk8/YeMiYTkWTSsbJcdhmk9jKXmILmLScU5/ZdewwUNb+8g5709BQeUes6ZiSc22+YafHnp7jGbM5ZE55vubx4jzbzYlHsxfH1HRI6HkMK4HdN0yd/pL2oVL88uoGQRwzCpL6StNu1figtWHtz4tjNjtdvv1whz9+9wbjwM/GAExDu/lTVkL5GuXHoMALYvwgpt/0WeqO6G1O2pvh2tJQboEXU41DGpUpo+wmBTFDuaWsccok1/1pu1pSNo7P1tOfDCc679qxZuvNGvs3qpO7iIZbP+3z5n/X4+Nvz9Ofn5yjlEEO0xBwBqOcWlIyq4kRHo6UNRac+VLogneI7FoxoylljIs06Z7u4ZE2S51a7fHZDLYUdu3M72Z9iZGUWOOC0GozJdZI+zTH4LKpAgMqJjhx9pee+0esss8CN4M9vudNmN6xDjiI5jkI5wl1UNCnzYrG2ufjk2tcbezzbusBe9FtvLFzTHjsDpdp91pcnjvkUuOIKPZ4cLJBb1Tn04OrVP2QMPbxYm3fG9PfU2L6vmxykFiokzLJsc6VZXM3dr4XjUnYHwLbLDrXOff0mdnfSEMYny/Zx3n3d17W2HXAk/pKyrZ/5NPc1Az2FYTCgy9jrgtCR/jC/VM92Zkve97NzI3Y1udSSPv88EoslFcGXb7R3uHXK5sTZjCYTGbTRyNuTCa9DjS/em0NFRiLtzSCAJqP3lzO4tCWeHURex4973zsZ+x5jGZw7os9j62lOS63O2ye9Hi4Mp+vpDWX97tc3+2wfDrgo9vLdBqBtUh5cL3Jt39xxPbNembVeFmw81qNt/9mh6ii2Hu9Ckrx8N06b/24+7RrzhJfEwyp8El4mWocoYG6GrPmn/J+/T5jHTDWPmHsE+JxMFykH5/tPB1pn/u9TW41d9hsHXFIi5o/5trcHhUv4mAwz253mWFU5W77EqB5e+UhlUR/Fmmfflg6WpeYIB4pOg/M4OivGnTJKD8FLvxCuRqPef94m1+sXuG44cQpNtmWNO1t8gapzbfS5CykYdo8476Zvgjq5JXcZEeL5lvK9vrCxZaG4RJT/kaT/cSp6Diatk+3ZckfzFBNqU5QSNaRaZIl9lgIGYdQDQTmGCDRKFvtU9Yj218+5FL224hjSY7TXJNm+bAFJnqG6z5lFmNzxqfMtZimOmW18+OcbtO5sux/T7O13OLqUYcHGxNnTmWEhVs+GfDO/SM+em2Jj28v8sMPd/nw7WUUGs/XBH7MYNnnZKnCtUddtm43rOQgZkrpRuwTVkZZWcoWSzrkajIZGgmTXDME6w1/0scwOUlmspv0/3Red6jx4R8s8Povuyxsh3z8w3kWDiK6cwH0PMBjWPcJnVTUlg45tZ5EKWtsapSTU5WxesI5zr6bDFOqgU+KzBCK2bazrRIzLfKLmOECTa3UR2GZpI0tCgFXlFTEZYuL2hVom4v0yybL+eT0z4ooYY/7ccADGjxgnYYaEngRgY5p+ENu1nf5pH3NGWf+pD1ur/JOs8e7y/cZRRWUhruHl3h99RFHJ/NEyUW+UO+iY8VJr2klSXJvL5ONtq+FhCLWWKyTWekcptdoN503xvjS54DANn9Z1rgwXNssyTpMxBH///bePNaW7c7r+6yqPe995uGec+f3np/t13bbbux2u910E+ikBSJAQkQEClEriUKCWhBQhIAERco/EYIMihQpBEGiREEkJEwJM6EDpG233e3Gbr/Xfvab7njmac9TVa38UbVqr1q1Tt197rvn3XfuXV/pnNq71lCrxr3q+/v+fj8Rhnb9chETndMMz8lgF7HGUcE4C/chstd90roPm0k1TUR1QWo4tSK7CfBl4cqT9SvjIafVOu2KS+bh8PHCwWKDld6IkjLhSclaZ0hjNKUSRLRbFfbXG3SbFQ7W69za7ef6uP+JJjfeH+IFH7+H4Ljp8xs/uUBQEdx9a8DB9Sq9xRKf/EGHL//yEa++26U6ct7jDvNCMIyqdIMGZ9MWB+Nlqt6UhfKApUoPr0ADFkqf3f4q9063ECKiURkzDsp0xw22Fk9oVEZUSxO2Fk/Y665i15o5OLzgkMQT6o/b38ccV55RXh/1Oa668GsOHz+EvsfBcoOv/HCfe5sLSB/eeHAKwKTsc7A6i+t8tFrjjXfOGNWzZuD+QomzjQqvvNXn4Rdq2YQWHwcIwXufb/H5f3pGYzvk/TdalNuS2/f7bO6PCPB4/65FeuLg8ASE0mdvuMJm/ZSF8ohDf4mdft5pT8cwqLLbXeO11V0+sf6Yve4Kr6zu06qOKPsBZ8MmS7UemwunTIIyJ/0FBtMLxFd3cHB46XDlJ8pr4wGPmsuxKSp1dlNZq7Q3FeUwZJgE48/ZzDZ6MxWSSyZ1MhYhlSEvsdepzHugOaSpEFGarcy/iIhTnxeVE/lHmDUvAwiVQj7MLuP9ievbzAdqXy8sy9aPozRMiLbwbmmZIZ3ImDrVYIxtQOoYpZpLS9a+QkclZWnT2xmZ9fRwbZjbEZKF0YR+tZKed6v0Qm0u6eu7n1hjoz3izn6XhcGEt15dpbdQoj4KOFmr4CUHbloXVKcRgwWfkh9mrqXHn63y2q8O+NH/t8NgzWe07HF2vcS04SXXlcxkk1Sf1TIjoSAroQjRyxJ5RHJAdGfRVHphLAFGUYkPXmtx890h3/l8nUHZ5we3yvzWnV3Wj8a8c30ZGWYz7QGps6e5zHwuui5TKaFFQlEoTRDZ75nOnoxMO2W+9smFSZwve5vxXe9TZr/r6+bJvlfYp2WcRdubySzycoWLjmWeEHDqQXs8XuKYJZrlIduLx3x27QMG0xplL2CnvUZ/Uk/79wLwAsk0kQ4dtJe5uXLEYWeRzrDFUr3HcrPLUXeZdr9FrTzh9soBB+0VeuM6q60Oo2mF9rBJfD2oZ3cyJIssw8aGzRceLruf1nY2FGz3MlGYpMKUSTxpbLZQbmbZ3CHgCjLlzdOXgi63KJJ6pPUt6z6sH0kqR5z1LRPHwHmShMh5JRvOBevCuNIT5Xo4pSwjVsZDJ71wuFS8etjm9f1Tpr7HWzfW2V+e04ohBIfLdQ6X66nu2fMjBvVS5uXp7v0eAGcreQfDsOLxw59sstwfUz+LqJ9GvP6LAzpbJc4+5UPrKXZoAgwETHwoS6hKZB14yqiGRxtVPvF2j8/9+gmjconVdpzEIfiYOSE6XE30p3XePb6JL0JalWEc0m3lkB/s30QaFEDJD5kGPr1xnfcOrrOxcMb1lSMmQZn3928wDeOLfDCu0x/VeeXaDlIK2oMWa60Oa60OO2drTIIyN1aO8LyIR6cbBNI5/Tk4vIy40hNlAXzQWKXt1xEBaUKONIJYxhkpWVmyhJkyPkjNuc5MyBH5s9cx5ZwVqqXWToWMUwSaHtorSCZI0zRklubMF2SZNz06RzomFV4smu2LyTILi0Migfo+a2f6AXh68rqCl9iUTZLxX+aFt4BpwjieOiKTqdWbGeG+9H2fxYsyrAb655Qhzo8FCzudXjpJ/WlZ0KuVGVZKfPH+Pn9/9TZRktAmsy/K0VKN08tfL56KsjK7UMGD9lKJ9naJcinMOO5VEudPuS4ZrAsG+Bz+aImV9wJu/n8Tmq0yp35AUBMENQF1COvgex7+UOIPBf4A/KHEG5YpDQFZJmrAtCYRUxATGEyAEGQl/quVPaJSfOzXwwgvgo1giBdJRDRARDJOvb1W43C9hogkm0dj3rte5b2tJU4WawxKZZgwSxyiy0wNZjfLKOfXnQsbFWdjjYuuy4t0b2NjveTeKWCUixz35mZ4z+n7idubg/21JbUwx1Iccs7Cjqo6uuOxUd+adMMa6gwkHt1p/JK6XOux2Wxz2FmBSCJCiQgk46BMf1Tj9uo+jw832DteM/Zr1uk4LPPeznWkFARhCYRkudXj7voeQkjOei0iSryyvst7B9dTy2LqCHzR98A5kkKkzyit89TBWTn1WZynZxaWWZEwCFe9z9TqmV5nFstMalG1MLVmeDKIPeF9f1ZHZ0eN7ehEQb7PAhbY4ryb1hcWZngepz6djY2Mg2ZzRvcs41RlKrlYJrxpmG1nOZ6z02eZo6jzb9mHi4eVc7gorvREGeKJ8vaowyAsM/av/O44fEzxaG0BP5K0RhOGZR8/khSEG74YhOCtLywBWflOEaKK4PiNMnufrLLdqTAcTSiNJaWRpNKX1I7BkxFhXRDWYboFYcNjUvMI6lCtBSAElUyM5SmEMB37iAn0+yXEFPCgF1aRHvSCMtITjMIykQelQLK2O+b19zscrdQ4Xazy+v0OAsmvfHoznig7OFwC9k5XubOxB8Dh2bJWInh8tM7Wyil3ru3zwd52MvmQvLK1R706Zu9klZPuAiCYBuVM27PeAu1eC98P48mzJ6iWpqw0epz0Fz+6HXRwuATMI+NwyOJKzyyFlPzU8QdUZcivLd1kEibB6tWLpFbXU2GjUh2y5a3Npm1ON6aWGkutUhGruM2a/jIsx693pXI88Qm1ZApeMjlJkzBMtRmX0muWz6ehhPkWDaluVphJHLSxR0rnO7W8kVuYLW8WcSw3FKmzF9IYphHSK/NCbmiTMzprYwy229mWSGLWWbaOjrS+nt7aYJJt4eHSY+fDB1vxj2SaMMQm7FYv9wlrLDTds6dJL4CMrlhNkNOQblqCGpWeWiULAQg8pZkvEy4KThdnsWZVwhE9dXXaLrF6DMaVXJ2aH08YJsKHKnSYOTn1k/rjaXyPqcQQlOB0q8G7WyuxxUIIHiwu88pBm+v7A45vxLoQlTAkwxAbJ1hnHT1Vfy721yYST0pst/I8fdraFbCqnrggo1zEUpv1C0LO2cZSyCibocQs9e0svJ3hzdS37EMudNm8ZTZ22zhmASXu7Wzz6o0der0aIizjaeE0D46WubV9wEqzw1m3xas3dqlWAibTEqsLHYKpT3dgZM7UMjCFURzXXErJaFKm7AUzRjdNO0x2eVFoD8f0UWxhhnOMq35czCraYFIfmbRzrWFkrMs8iw22Wd9BM6ycfmJ8Ef8Wqd9Saxi1KDcUk822ss1F2lo19kh/iEdGWUH7TF8qlqgl5JypEdedq00zqS1UXWQ5ZklIWEpPZl6kzRSbWmKdzO2ycKXJ+oFfYVCqMPDKdErOc9nB4bkieVAHvsf99QVW+mNeOzh9zoNyeJERRj6n3QVWF7uWUsHhyTLryx0+decRw3H8MnnaaeF5kkkwL08kWWwM6I/cb4zDC4DnHQrOhYf7aNEMJ4y8Et9b2La/aTk4ODwXTMolvvn6Fj/xw9g0/v7q6nMekcOLipP2Iq/dfIwUk1zZaFzlnQc3EEIipcf+yQqN6pjJtMR4Ml9mzoX6ECmFmyg7OLykuNIT5UB4/KC5BSTWHMNkpVvWZ9YbkS9TFhY/+x1gcThmeTiiW6vQqVUJfd0+lSwT83pU1kxeiQlISSD88qxTlWEvTKQXUpNlkJr1s8sMlFVSN1epzypknSYDSc1DKjuh7iSiTGwqS5rluKQOfhZzn4jyUodcOKWMs1tSpqxMNnO0csTSsyBmN5H12TB9AHXrfiq5kNkx6WOxOfUZ7TL7kDrqJedIk1ek0e8SWYanSy+SAfqJ9EJ38JxJLuJlVZNZKMlFRnqh6USqYZlyNNMtTBPxdGSxByuHU7UcTGeThWEi9wjCuL2SW4AmuZgkfWtyITlNzlfiiCqS7xMq/MrNG3z54WO8ieCD5VW7c57NOqv8YayZwfLrzu3TKgc4v2/bmHLbs0ghPOXMZ3G8u7D0okhekSvTb1hjO+dbved2oEvlMNZ9MOpbjpnVQdCQV1iz0xXsw6wfTWZxuMzdO6ecHDSZTrO6+NixVuB7Ic3KEISkWplybeWU49NFwshHqJyrFpnY+tIZR2dL2YeHGnvybH1aTkwW2HQzfZphNTPPP0PmYJGySNvxtEk8FExZRkZ9YMgyMo7VIr4ZVB39gKpshEraIG0XRb4olXHY0rnmHPW0nY8MzYVFlWHdYOocZ0gw9HrzhOmzOUCmk40Cyca8MCQXcp4IQ5K5HEodsrjSE2WPhLa/RDb5J+4/pmTcDH//jVdfaD1QJQxYCEb0ZY2Jd6UvEYfnjHGpxLduxZNlgHsLxQkjHByeBt1eg15vwp3b+3Q6TQ4Ol8m+3UpuXT8AoFabIAQsLfRYXuwynZYpV6aMxxUe7m0SRR4gUxa65Ifpy6ODg8PLhys9CyrJiFIUEXgqKHdSYHWGiBdp5B39JVGxqVGWXQX4xVdfZXk0ohyGlKOQqefPmDNVLWFvI50tSwT6imWOwtkGFbusnP88PVGJEVbMszDKipG+e7/H3Yc9gpJgWC0xqvoMKz6jSolhucSo4jOslpj6fjxYCzuQOjUmw6tPJ/zU0X1OK3Va0zEnlQYPyyv0SjV8zbIp9Q+RQQiYTLKFjbWWmeSAVqjYZdu7sPnKYnPKS31ZChz2suPMtssy+1m239NCBnqGJUBnjX21zsIol5J1FQt7rD43SrMTkGGUZUjVm9VX19XU8uOeMsrJjukhDceJhUNNCqYaa5xjkiezdopBTh3w9KQiU8GUMt9evckXjx8hpoL7rViGURR2LW0/T9IOG56WUS7aThFzGsYJLwqTdRQwykXJQTJJPkwHwSLG3brveRY3ZZkNZvnJ+/BkRjllEedul2VFM85gTyDC2ict2mc+128dc33riN3HSarqSCBERL0+IQg8RqMK43GZ46NFgsinWp0SBD7ra202V045Ol7i7p09pITj0yUq5ZA7W/u8fe+2NlBlgZP61xiKZbZxKQWZNc362Wsi+/zT2cMZa2+wzswY3dlx1QuNdhlWNe0gjwInQFnykCVvdo4150oVvtXGRKfjszG06lmcbChzTZiOenqYN2HsX6h7CxvtijBv6DiTibaRaWbIOn0shWMwtgGzBCOe8b0QspgFd7DiSk+Ux8JPJ8mXBSkEp/X6xeNlXjak5OZun0oQUQliB6qdaw2qo5DF/oTN0ZD6JKQ2CQg9j5NWlbN6ndNmja5fPVfTPfTLHFcbICXvtDZYm/T5Uu8hb9c3ORRLH/FOOrwomPglvr12ky8ePQJIJ8sODs8SUeTx+ME6128dcf3mCTuP4smyejmdTn0e3L+W1pdCMBrFTn4Hh8u88soepVJIFAmqlYCVxR6P9tYZT2ZyjlZjwPpyh2ngs3e6GoeQc3BweGFxpe/wiVeyalUVMlo7U76lz6/T13RhVCJ9s067LqKx9AQgacprQ89FSrLY9ccFkGmIOwES3ru9yI+8e4aQkpOlKo83m5kkK2rDtX7ESnfMSmfCzZMOjUnAWb3GcavGSa1Bu1aNWQBiNv0769tcG/a40evgyYiOX2VYLmfD7SW6ZS+M/2za31lCj3yZVTNnsFYXfTkx9ch6H7YyU4ec3QfFDBt1QQsPlxRpfaYh4BK2uGRhm1X4Nr/g/GfSR6u005kEOrN1kRSFeuRAY43D5BqNUq2y1qdal2jmFYsMIJOEIYpJFjqjrCwj6pqYakxRQnR7AYSU+G7rJl/oPqIUSO7VVhNLR37/52Gbi8rmSWdu09w/LaPsBfH+zx2uDbNsPtY4nzb6fPY3sw+5PvPtLswom6moC1jjjJbaZIsL2eb5n5Eikul2du+vsXXzhO3rJ+w9WsUvhQSBR6USIGSUPie1aHDI0GN3d43btw6QEvYOVjhrt5BiZkFcXeqwstRl/3iFamXCq9u7PD5cpz/WMsMqFj19/mm/C+ZtWvCMs6XMzoXPA6Shk7adh8tkm7O/wSIeT85xZNbHLGmKNIvsYeWMMHRW/bLqKyygiH3tR99MUGIL5WaDTbdslqV6cgvbrPq+aLwxW58my/xxI/NeIFzpifLLCi+SfOGtYyqTiF/53DrtxWpGSpKBEAxrZYa1Mo+X4zurPIxY7Y9Y6w/57O4BtSBkr9nig5UVxlSIPI/d5iL71ThusIqnnMna5+DwFJh4Jb6zcJPP9HZphmPebm4RXu0olQ4fQ0gp2Hu0yiuf3KNWnyCEZDouEYQ+65ttDveXre0GgxoPH20QhR7DSTVTVq+NuLZ+yrv3rzMJy3QHDYbjGjc3D3lwsMlw7KJiOFwBOOnFheF+oa4gXr3fJfIE3/yxTdqL1Sc3MBD4PgeLTb6/vc7XXr3NL71yi3HJ5yceP6I5GV/CiB0cZognyzeYCp+faN/j0/09NiZdfDlfVkIHh3kgpeBof5GN7TP8UkgYeezvLNNsjri2fUprYUCjOcSk4Pv9OsNR/rm6vXECwNbGSdqmP6pzdLbEcrN/2bvj4ODwnHC1GWWJ1Xxa9L5kNU+mZh+jjl5lpr3Ib1B91Rsqc1jq3JWXZcxE+HlbrqofGCGJ7u51ubnT5xuf2SIYz06fckD0KmHSXpcfKHOPWmZNgaNKiffW1kAIbgw6/KC2kd130wFPX5ecg4yFzctWlzYpi94+3T9ze7p5MdveKvWwSChykgvd8cTPHpeMFMaQjwhdQpG08xMnzKz0QjnqyVyZb5TpznzK8a6XJEWYavmxlXQiKHm5dYOgjB+W6Wk7PUn6mqiQblr4QSXZkKkEQ5NeJE6qaoketlBJLlQoOE1e4U0M6YXuM5OsUxKMmZnW493KNR6WpqwGfbbHHT45OOCDyjp75aUPL72wlM0jobD1WVimnhnleB+zMokiacI5fet92qQQubKicRZIKIqkF4Uh5/IDLnLKS83sc8orcvust0vvSbuTlAhl1nkM6B7XWVgasn3zlL2dFaLA4+H7G6xudllcHFCuhIwXBuztrua7TrUM8eL+w2tIKbh144AbG0fsHq4hI4/BoMbq1gG+XCaSXtpJOhL9eM4Twktt3vaMS8ekO8IZ7fQvpu+Z5TykEgz92aikKXPIMvTfL1kSGWc+9HdfU46RkTskReoZbpFezK6l/EWfrhK+VmTUDy0aJkuW25wcwybFsIa4M+QRBY6b2WgCRl+Z+zZ7jp4JHKN8YVztifJLhPI05HPvHlOdRnz9M1sMa+XiycNTIBLiWXfp4FCIsVdmt7zMbnmZejThM6MdlsIhR36LnldlIkr2iZGDw1wQ7HywhvAkUfKWHEUeR3tLcakveeX1PUpHAcG0+OdQafgfPt5g69opd2/s8Whvk/GkTG9Q59WbO+wcrjPQ9coODg5XHld/omxjaUxnMr1aAaNclIAgfaG3/WanL5K60D7LDOtOdsJkN/U38vSNOuu88akHJ0y8Er/22nrMSJsWw+qT49zMnEv07SlWADb7Pd5fWp0xs17W4cUaI10kf5ZwU1ZnPoO9Lwq4n92gGpPRt15mOu5BnknOsP7JOVLMslaWniNFDmiJQ0wm2bOEgJsxy1qZSjhiKVNMr2KDB1rWMKtTXrKTo2mZWlRhoDE+iklWTnmhxgzPQiEmS73MSByis0EqUYhy1FMscrwuqRNkv0MSMg1mCURslpwEYyp8t3yb6+Ep29M2LTkmQnDoLXDkLTAQ58iMbKxsEWtcwOZanw+FfSbnO5R4gbSGXcu1t43Bwv6ayT7iPk02tmicBYyyzrzOMRab0+HTOuVZmet0ewarlvEgVpWSa9fKLOdXIePrPbX46SG2EAx6VZqNMZ2zkko7ojaU/E+eL+qewWN3b5WV5S63r+9x/9EW+0dr9BsDbmwe0ek3ODhZRmVVyvRoEJlzI91nNfD8+bNCsZq268WwSljHmT9kuWd4xpqYOvORb2iy0/o1UZDEJGVVU4e9PONqo8xzDoK2sYTWnUjaGb8dMGOCrWHeLA535yFL32f7ylzzxj6XPiRpILEz5A6FuPoT5ZcEa70R3757LX4IzRP78SngRRLfmWUcniNC4fGwtBb/bklJU05Yj7r8SLCDR0RP1DjwFjnyFp73UB1eEIxHZaq1i3oqC07PFvE8yc3tQ+7vXCOMPO492mJz7ZRXb+yyc7TuHPwcHF4AXPmJcoa1MV6eyRfNJpk6WWlmlbRpw877rq3TtZmK/POUjksPR2dkkpaalikle7WdqE4DykHEgCreSKRvyFHlgjPmlJW1s9tvbW/wxYe7ICR7rYWZdMoSIi0dexIJKBMJx3zJJ99ulvnl3GHaiZL0mOVLC0PAmYw0ectDJhW1YpuTMl9LCmMyyX6BRtnXmArFICv2xNcu3ig5J35y4Uw09niQxHDNaI2TQU+mJcayzEi79oIkKYjSGks9EY66HpNwb3pykDRhSKiWs2YiyK7LsMZTYxlo7RRhk9MoFyM+RIIhVR5S5SFrlAlZkCPuhIcQwgkLWl0DT8kMz1Pfxgx7AfhTo8952F+b1rigXS5xSBFLXaA1tqe+zjNaueNSqFG2sMZpghN9e9ntCNvLuW1d+uy2MHceiChC2MJ2pZphU+gLIhL02zVuvnbEdOLRbTcJQx8hJJGUzExmM2YZZuzy8dEiJT/ktds7+F7EeFLG9yIkcGvzgLNei8PTlXTbKqmVNfX1HGTh7DfKZi6do71+jgy2OUOqFiUxUfVTaa3G0JcEsiSsrHFeo6wNzEwJnUk3nZxTMxQcIM2yC4eVs4xTsczpb9ScbLOpSS4MM2cxz6b3tM6KZ9dltNvqmnLStEvHlZ8ovwzwpHy2Yv5z0K7X+NXrN/jC3h7LwxHvLK6nMUQdHJ4rhGBKiRNaRFJwi+N0ouzg8GEwHZd59ME6qxtd1jb3mE5KlCsBwdTnYH8Zz5eUywGVakDJD5kGPu1OK0lUItjfX6XdmxBFgkZ9xDQosb1xzMP9TdaWO7x6Y4fDsyW6/eZH8hx3cDgfkvNjyTqcBzdRvgIYVspMSh5LozHt+uWa8rrVKt+4eZPPHh7wlb2H9MoV7jVW6ZcvHobOweEycEaDT7GD4KN5gXR48TEZl9l7tAoe1OsTphOf+sKY9c02YeAzmZQYjyr0k6QlN64fMRpV2D9YodUasrbaoVwO6XQbLC6cArC5esrh6TKeJ1lb6rC9fkx/VKM3bNAZNQmjy80q6+Dg8Gxw5SfKGXORWmd8txUKzTw8c8bL92/KMawyAgtSyYUyT2nPxDR8mk1+YJrBkn4Oay1unPTorjaIqhfUERfNJQxHPSEgKPl8Z2uL9e6IxfGIz7b3+Ob67YyJR2iyjJxDnuEfob/Azsx12Tr6uiKHSxuKQsDlnQAtdmwVkk8r85JwcJ4lzFs+BFzemU/JKjLOfCpbX1JW0sqU+TJM1nma9EKFjhuEs4tIhXULQ48JPtPpbHyRyqIX5qUXKqybKbMA3SnPIr1Q51RJL7T7J+fMp59vs/6FpBcaDBv1pFSmGYzxhGSRAX1ZoyPr6PqrFa/HsuhzL7hm7avIsfCijndeIPGmWWe+izjJZbY9j/TCyIpn2459H54srxA2k3Eqocivszr6Fckrihz2zDFZoJ5DGWNXJBBhZL++lIxKtc8+xeOy1FEPhv3YkbZ72qR72tS2O2t1erLA6lqHu3f2KJUiPri/RaUyZXP9jPGkhBDQrI+pVg45OF3h/t4WvhfSbAxp1YdsLp+yc7xGd9yy9v9E5NVUF25oPoOLZBnWMJ62Z7gniHxhlWXMMvLlB56GUTXDH4KWGdEioUjCwVnDyqkMiZawazk5hu48aMoxbLKhogy9CjZjbJoR0CK9sMlAzLHbnGlTZ/Y5LwTnh3RhXPmJ8suCB4vLfPXxAx61luhUK09u8GEhBCe1BifVOtf6PdbHfU681pPbOTh8BDiIFrntH1EWIRUCJrJERQQMZYUQjxpTEJKaCLgfbDrm2eGZQkrB8dESnU6ThYUB40mF8aRCt9dkcbHP9WvHQOxDsNzqcdZdIIx82v0W7X6LWnXM7Y0Dyt2Ik94CcwmUHRwcngteuolyypIVOOVlopKZHyxlaX3diclwhMvGFzfoaSsrlF0GlHhvcZU3Tg74VuNG/PaZSUaSMCLSYAK0AcrU4UEvM8apO+x58SD3Ggusj/scN1tGWbIP3jmEr4XdMRl6a1D9eX4z9DpGKLcsa2ysy7RLVlnK0pf8uULAzbanmGTlbCcmEJWz5yZKOo+0nVf1VSi4TFnyWXfmSx32ph6B5xNpjHLqqGewx4AW5i35nnHmU3WyS/2zWQe0Q6sYZWs7CyNZhALSQ0g4YJlWZcSCP8QTcBTU2ZusUPOmeCIiiEoMowo/1nyPZjhmEMV6Un282e0VsLjmmG1GiWnCKF+Qibay2wZbbEsSUeiQWOgkl5RZWDJbghMrW2y0uyhrbGX4zHbnfQdEmuxBYyQFEIII1cVrcWA17mm9nulnFn9JmqcJlLTtJQdmOilxcryYYQ8r5SlHx4u0ey2C0OMTdx5TIoilFsmzfzSucm9vi1ubBzQqI3aO1wiV2bHg+Wd1dLY9S8/twFLJdNzTN5AmvNC6KGCbpS+QvtBCz1mOtZnMhNlzQrGiurP2LNyo4UiHhWXOMMMGy/zUYeUs17WCjW0ugo1lNhOU6OdI7bul3VMlI5G48HBPAeepdYXwqLWEH0mud7sf2TaFlGwPOhxVm0+u7JCi2g/55Ld6fOHvdVh/36UFf/YQfDDZ4v5kk4PpIjuTNQJK9KI6nbDJMJkYD6MqbzQfURXTpFXErdohr9T3uFU7YLnUw8+kD3Nw+HAIAp9yOWAyLRNFPt1Bg6XFXq7eJCjz/t42USR4/cYjNhZOKXxDdHBweC648oxyhgEtYCRNVqjocWR7P5urfkZXZTScgzXWP9sC0wsEb69s8vmjHQ4rLaSvhQtTb6PJukxaUaU5tSSSyKV/1rXUJfjKgwf4SA5azYwuNc0ZUIr/MsdXadhsjHJp1k7/rm9b+bjIksy1U2mnpa8fUGOpb89YZ5VxFWjUrdVTTXO+LJJw7YMxN344YtDyCX3o+RX640rKQJcS/bMeOk4lE5kGSfrpYHYiQjO1NBAl5TIQRL4Po1lfKilIGuZNt3SYjLIt3bRFh5yyzFG+nakrzPSZMMme0T6DOeYGdkmg4DRY5FTG0S88PeyXhJIIaPpjBkEFGQg8KbleP6bClNPRAmUvYK3c4U5tn3FYoTetEUofKQVSxkz+NCrRCzTtszHeVKMcxvs6b5rqS2GNDRbQ2qft+KeaaAvTO08It3lYYxvbnFYW55fZxms5oEK1TRm4c+ows6zp9az6ZfM4WqxRKdusjbPXrrOx1qbkBYShz8npIrev79Pr1ZkEiWxOhYeLPHaO1ym3p1xfO2ZhY5dHxxtMw3J2e0ZSpSy7nR/eebCxzqmF03ot2RqoZZ7ell7CKKtHlf6baFozMhaBLMusP0NS7W16s2ljMcPQzRtWzmCZrdYQz6irlxn64AysoQ2fbClJi4ywh8mGiutdBE6jfGFc+Ynyy4ZOpcZBo8XrZ8e81dy49O31KhVGXuWcGaaDifVHE67dH7N/s8bmzojvfWmZ7kr5eQ/rpUQgS/ywc53Vapc3Fh/SmTZYLA94++wWQfLmdThaRiBp+CNa5RGeiBBC4iERQtIodRhWqzzqXP695vBiIAx9Op0GK0tdjk6WGU8qHByvcOfmPsNRld2D2PqhYxqUuX9wjdWFDq9e22G/vcrpoMV8018HB4fLhJsoX0G8s7zGT+3e5+Fwgc4lh4trTqeMPgrnwUtCdRpw46THemfIb9xapd98xvsiJX4gCcsCEUlu/HCEP5WsHkz47leWGdXdLfY80Q/r9Ad1dkXAUrlPN6ink2QFiaAf1OkH9Zye2BMRb6w9oOovMQ6v7n3g8NGi16uxvtHh6EQCgna3RafXYHPtjO3NYx4eXLO0Epx0l+hPGtxcPWSl2eVsEMcNr5anHPWWCKQLKefg8FHjav+KC0MqYJrZLaGatKY5zCOvKDRa6DIJMwNgkebfYsotkpGEns87S+t8Zu+QX75xE4SYZXqaKmcIzakhlV7kHSVMlXpGylKSPFhZ5FMHx1RkwEm5wWG9FZt8VBd+LIvIhICzhPhRMCUX+vkzJRfZkHoyO/b0uEpun3TYWW4SlHxKYURQ8kBKfvbNh1SCMK0amZmTCmKCzeQV58sskJLXv9NjbXdCZ63E8lGsVdi7U+WD15uEZY/pdLYTkvizl/SpJBgwc9QLEklFqMssjHBvccXknE4FoiTwxuefb6uEwpJhL3XGs2TRS+vbHP3S85037+fbPZ3Zb17JRj6sXLwiwudsvBiPxdbQlECkN6lgv7PCKyu7nPUX6I1rDMe11ES/UB0QRR5eUMKbyuIxFTrz5eUOs7pPKa8ocsrLhAKLkJGYndMimcRF5RVF0g1VtdB5Ld9OqnGac0YpZ5n5TEc/mMksLurod05GP31dNkSmYNCrEa12ef3VR0yDEpNJmZPTBQ6Ol/n0aw8piylBWDLkFfGX8ajMezvbLDQGLNSHlEohzdqI487S7PdBd3ZTGdoowByyNGu2vwKHUNVp5rT7xA59Mn+szd82S7S2mWwv007JJPIDT8PKKQdB7flSGFbOkGMUyitsZaqdLWWhWqffx0qiUyRhsmr/5tEOqn7OL7Juz2FuXO2J8osEKdkedjmtNBj7Tz4tu80Frg/a3Op0eLi0dGnDery8yGG1xa1Om8+d7PHLm7fpVT4+yUc2OwM+++iY1mjKb9xc4+e+d5/9pQaBJ6gGIRPf43CxzvdvrzAulz68IVNKmu2ASc1j7XBMoxvy1lcX+dGvdQD43m9epL9cIgydn+yLgpPBIqNphcXagK2lE6qlKce9JQ47y9xe30cIOOrVn/cwLwS/FFJrTllYHdJcGhMGHu39Bu2Dxmwi6vAhIHj0cBPPCynVIhq1MTe2jxCepNurE4RPYoYF3WGT7rBJyQ94bXsnntS5OY6Dw0eOKz9RtlmiZsxIUUO9QbLI+ybk+y7oKhNFJqUx8w1t7JiQks+c7RMIj++uXue0mv/hTd/EBSAEb69t8KXdx5zVanS9ePIaetm3aGCWVCJlVrROTacXnVFO3ronVZ/dpRavnp3Sq5WJvFmzNOFIxolCtc/vp8kk68mpZo562e+Zz0Ky1hty46zH8mBMKYzo1sr4UYT04PFKkxun/fhYCHjz9iq7q63ZvtscgWwOGSJbpljgT7zVZeV4gh9ISlPJ2UaZT3+rywdvNNi9W8NTY5d5lkexxIEK96Yd+yhUDntJO41RVp9VuDfIssYi8qyMsjWUmxnmTU8cEmbX2cK8zZz6ZL5dymRq7ZJ6qaPgBUMTFcb1n5O9vUhfuYQeCUbTKqN+FVih7AVc3zjijev36Q3qDEZVtjdPOdw34uGaLG5Bko/sWAy2WGd/TbY4w8oZnSXf6wtjGgtj/FJEqRxRrgV4vmTcKzNoVzh4d4FyNWTlRp9bnxmw//Yik0Epux2jz8znIuatIHmJYscyz9Sc1SePlMvVJ/SejB84UeplNasv9AenwaSaLLOFiU5ZZmEps7DNM3ZZEIU+k4HPZFBm2K9w+84+zcaIO9f32dlfQ3iSheaQybRMf1gjijztWRV/CKMSR+0l7m7s8t7ezZjFzBgLpDEWrewizECeaLd3cI7VBrTfg9kB0qol4zTDy0HqVDdz6tPKDJbZGlbOYKuBfFg5vV1RWDmTNc7sb5YtLmSIvfy6lIHOtDMYaAvHUui4p47r+TX0yo5Rfgpc+YnyiwIpPPZqC1SjKZ893WW3vsj7i6tE4nxmslep8vbaBl99/BCAe0tL/MbN9UsZ36BSYa/R4sf3HvH9tU163uVqo23woojPPT5kYTThrFmlXy2zOBwzLvn84PoqAN+5u8FZs0p1GvJorUW//mwd6aqjkKAsCEsCiBi2fN770SaTuprxu4fQi44gLPFg7xq+FxEmL6HXNnrcvbHHaWeB0TjWMpf9gFIppOzH0Q/OzprIXBrLZ4NyJWBpvc9kGD/Sp6MSYSioNSasXu/ROWgwGZYIJx7B2Gc68kD70Z4MS+z/cInW+oitNzo8/vUlwqnTwz5LLC4O6A/q7B/Gaa9fubWH50WMJhXC0GN744hOv8lJe4HxNKuHP+4ssrV6ys21A8qlgM6owVF3CekivDo4XDqu9kRZZLVhwjAZ2rIV22AW6W9muTBvloqzFKCWTlUdPUzOOb8/D5vL/Eh7j1/euM0b7QO+fPiQ76xfZ1Qq5weW4Kxao1cu05pOudtu88PNNSLPs7IDqa5NDx1nMMpCY3Skoat689omnz4+4rNHe3z9+l0gZoTN8HAzJiY/XtVnZGONzRBwaYpvyWuHp7x6eIYUgl6twnp3yM5Kix9uL9NplhPWQIKAe1uLue0WhYWb1ckzDrPsovH3e59qUh2HTKsew5Yfp2vV+okKKJw04UiiOc5I6JPwcEpjLjRG2Qz3BpCEBcYLBL7MapRzzLCuUTZYZi8Tys2oow3QU2GULGHeVFnKoGb0yzJTf15G+SJMcmH4tYJ21j5s2l+jvR7oX+Wh2d9bIQgEi60Ba0ttpBQEgU8w9QkCn0Z9yNJCj8cP1wiCUn57Bamh9QHUGxMWVoeUqwGeF0fm8HyJ50cMzqpUFqdEoaC8FuIl99HBuwuMusnEK8OEzR5ca6/0CUaCSb9EMBas3Opz9E7zCXppgzUuYteK1lk0w9YyBZveU4pYn1oQHi7tUh+KYhLnCSeX0QWrMvU902v838vWPjttsrl1xiu3dzk6WuL9D7ap1icMBrH10CtFrCx2ub19wGRS5ri9SG9YBySby20AwkhwfLLK+lKbW6sHPDi+pmmU88x3OqILas7M9znb8922Ik1hnV4SujXEYJIzImWjLKM1NljmzFiyLHNhWLkMiZtldrOh49JKue3l2OY5NcrnWXsysFo2zz9x6XNIHdd53mslM6uLw9y42hPlFwydcpVyGDuf/frqNp/oHPPJs0O+t7ZtNb1c73b45MkRJ7U6remUndZCPEm+JLQmE7Z6Pd7c3Ly0bZhYGI356fceATAu+YxKPg9XF9hZbWoZDj86FnewWGIoHNPmYELQ7TbpdRqzVZnLUrK62uXuqwf0ejUGvRqDXjXjtHk+JK2lEcubPfxSROeoQe+0ShQKiARRKJiO/bxUY04Tq18JWdwaMe77NNbiifao7X4anjWm0zKPH21QKgfcuHHE8nKfMBJ462cMBjWOzpY4Ol3m6GyJxdaArfUTzrpx1IuNlTZSwt7JGpH0uH9c4+76HtvLx+y215jX8O7g4HBxuKfhxwlCcFRrcmPQ5oPFNT5YWOWzp3v8xP4DHiwsc1atJRpkyZ3uGWvDAb+yfZNKGLA16PP2+uXILgBW+0M+v7vP22vrHDWaGW3rZSISgl6lzGmzxkmzxuPVVvyWXcAYOTh8/CA4OV6g027QWhiysDhgc/uU6aTEoFdlPKgQTD3C0MMTEX4polwJqbfG1JsTphOfs4MW/bMkFbdF9/y0sp9w4tHeqdFcm+BXQ8KxR2tjgl+WdHcrmUQ3Dh8ewbTEvftb1GoThCeRUrC60mVtpc3B0Sog6PSa9EdV7lzfp1oOCEKP/ZMVopTqFTw4vsarGzust9oc9Zaf4x45XCk4jfKFcaUnyhc63QX6CpOQtMjs55JgFGXfk3nLnJUEuLewypeOHnFWrXNabfDdtW3WRgOuDzrc6Z7iJRd5r1Llm9dvEvg+fSr8w9c+EXegfjh10lMYS9vYcwMHgaQ5nnLruMv1To/vXbvGcSNmzHTHO+kbEpg0bV9+c6ajnrQ586m+PEm/Xuaff+rWzMHBi03GmfBI5u94EcOsn3clr1CmXJGv5qUOHZp5UYUisqVOSxAm5jC9XepMpNZpUgqmWYc9XWaRSi80Zz7dUc8T4E+0sZth3nR5RZHDXroub8ZU5zQt0+UcBfIKdYhm18QFNFDn4LwQcIV1nrCdwox3ZjtLmRdGeIGZ/tOQVUhJNBV0hg06soGSUjQWxiws9fFL8QQ5igRR4DEd+wxOq5zcbxFM/GRc9m1Yx14U5s1od/JendN3ayAk5WpIuRnR2Jhy80sjDr/fYHTsn9/nXJnICsy9+g1sOvNlbkp131gkBl48jnnCwz0TR7/kcJgSjHid8YNgUZYIIRgPKmnZqFqhWp/E8hEVDXJa4t7DLdZX27QaQ9rd5kx2EkokgvsH13h1a5dJUKIzaiVbzUsaZuoD/flu2WdVpg6jkYHQhoxsSMR/VmfAVO2g5E36M1X1ZTm3hhwjEzrOyPKXKQuN42+OU+/blh0yHYomyzCd8YrkFUXt9IuiSN5kwiZXsZU5PFNc6Ynyi4hBqcKbK1t85mSf41qDHy6vc1xvclxv5h9alyQ5qExD7h61uX3UZep5HLSafP3uTSbCZZhzcHh2EIz6FUb9yjmTzaTWR/kDKAXToc906DM4KlNbDtj8zICj79cYHLn7/zJwbeuEVmvIo0f57I9R5BMGfqJVzs9og7DE/eNr3F3fIzgpMZh89E7WDg4vOq72RNlw5suHrdGqmiHjbLSx5av5/md1iihgiG3I+RYY7PZppcE3Nm/zeueIHz94xLfXbzD1S7M33PRtP/8WnPYdGZ3CLASOPk7tbbY+nvLqXofrZz12l1t8/e5NhpVyym7mmANAipjdFZGFOVAklMi3s4aqU0idDvPr0gDzRSHurOchaac5D84Y5ShZzsq8ZJ1aTgMtcUiOUdY2rUivxDkvkzhEscTqWE20nUgY5ZTxtYSC05OD6Myw8M9JHGJJDmKyzF4gzy3LhIBLywyWVKuXMsqaI44Zzuyp3+2KWGAdRcRljlW1bccyTqOdNbxjIBFBVDzOedlfc7vzOgUVMrxFYzm/3ehIsPcvamz92JDdX4Fp13JzWeNAGn3pDHPqW6AeFFr75L5J68z7khDKeBtqO7qFKx3m+Y57Zt0n1U/DkhnMsj5k9Uy0JQdJw4UJWFwYcO/9a0yDMgI5u4aSvuu1Me1uM7nu1HNwtsXxqMKjow1urR9w72CbcTh7oRHaduJ9yf+6WQhzcs51etGTrDWS/HNe6yrnfKhVk37eEc5kYW1hWHOMtL4PZpg4vUFK8eudGvsUFbSzhIC7MGusOa3Hm7dsL21ouf+8gjIbHPN8YTjx2ccUoefz9vI1DmtNPneyd6msUms44fP3DvjNb+8Qeh7//JM3eevGOsOKY5AcHF52TDo+J+9U2Pz86NKsWC8zut06C4sDa5kQkkZ9zHBUnOSpP6pz0F7h9voevhcW1nVwcLgYrjajjJ1RtkpH83KxXJk0vsOMubxoeJ3ztqHDKns2XnDfW1jj8yc7fLJ9yA+W42gTs7BpWrOUSc72HX/JbkkKQEqWh2NeOzpjaTDm3voSv/HGBoGiSML8b6KVvVeatEzqV2bbMQeTK9N0XJ5RXw9j5yv218I2F5zclDU222tlXrLO08biJ+vUMtLaTRKWWIV0y4SVS5YqgYjUtMZSrUv1yFoIuDQldfY7aMk69OQgWig3z2SUjaQi9hTWyT5b2WaZb6dYY0s7UpY5z8amDM9FmN4nIWc5sjB+hayXpb45vgxTq/osYCKjCBFG1v0s1AzPk7hgTra5cHsma1ykX7boyHsPBK1rgtb2lN4j/4n1z+1bH7OVpkkKI0vhk2gdKe3HRel6VTWdIZyDXb4Iswyaxc9CVqbPv3RMgtOjBW7ePSQKPUqlECmgfdakXA1ZW+swGFQIAz9mm1XqZT3JR9Jnu92iIqbcWdvj3t5WHLNb7YR/fjtb4hdphMe0Ql1Kxu9CJoGg7dmfftWejeZNnenTsIRaWGMzPXZcz2huSYplSwiWp6n1gWXLMmHzTB2yvktGSMO5U1+rEst9ZEbCKkxKoo2y8H51sOLKT5RfeAjBmytb/PjRI7588IAouRm65SqPFxbpVKrzm1yA1njCVrfLdruPkJIPNpf4F3c2Z2HlPqJoFg4ODlcJgrP3Sqx/dkrvkZasxOFDYzIuc7i3RKM5Jgh81je6rG90mU59jo8W6XYbcx/uw7NlSqWQu9f2eXi4QRC5n3gHhw8LdxddAYSez7c2brEwHccrPFgeD/nc0S6lKCISHlJAhGDqe7y5scmwUqYahlSDgGoU0JpMuNbrUQ4jdpdafPfmBu16FUruB8/BweHJGJ0IhCcpNSCwKwUcnhLddpNuuwlIKrUpvU6dTrdJOkOeO3S7YOd4jY2lNq9d3+GwvcRZb4Fw/g4cXmRIkEURaBysuPoTZX2ep0wryrycVwPYHXhUHZufSuokYGyDc8xLZqEpB9Hq20xCZp8zxz+PdqWejqFdqXNvcYVKFCKkTBQQktXxgC/vPMaXkrHvMy6VGJVKDMsl3rq2wVm9ljpMCAnSMMPYHJVSk6NFfyK9+E/fv9SyaZVeGM4l5MtSxz3dP9B0/tPlDmZ2wUxZ0sy3OOyl5sWsBCP+HNf3kwMSak4wXtKpCgEXWczEyolPSTCAWda9iXLOs8krkjJdZjHNLuN6yTIEr2w4+hkZ9jzduc7IyKc73s2kGnmzYurEZyz1emmZRbbw4Z355pBLnFMvNxYLCp3q5pBHiDBKnPnmbG9u7ym3m60/h7zCmrEwytc/p89JW1BphgRd/aE1x0m1OVKlz1SbGdpycs37zLztdOlFpl22/rwOewrzSDayGeGSdZZwdDk5hr7ryfh2H6xrZTJbzXwO6v2nsRjh6HSJbr/O+nKbjRtt2v0mw3GV4aTKJCgBIs3CKrV2uZ0ugs3hLzkHM4c9S5+WZsLYYDacalbKkAkBl14u+WNtzgf0DZq/vXoYz9mzzZBS6H1YZCezZ1u+XSrHsfVpHpBMn4ZUw3Z9X8Ci7PB0uPoT5ZcZQjDx41Oo7vNBtcyjhcX4Bk5uoPRm9uf4QXNwcHA4B+MzQW1NMth73iNxeBLGkwqPDzcpl6YstvosNAZsrpzheyEnnUUOuis4CY2Dw5NxtSfKwmAdjbc9mzNfIWwva2aR5UUwt32tmrA4C5hkQiZYvZetY0uMkmO5IX0LjVkPEb+J5l6MLW+l0fkPyhzrqzO1+tuvp7EStnZ6nyYjbGMxLM58s88Jw5JJOKKYYfV9dmC85OXAS1njWZmftrMwysm61KlPO2aqXpSsC6c6u5a8nCROfFIrE0k4OM8W7k2xzYrVzZRZ1mmh37ypkXDECP1mTThic9hLHfWSdlrouBxrnGGpE7beYI/1dnaHtPyqeVAYIs3ERZ0In5bhTcPDzdnnPOHhrOxvwb6brLH+YCly2JvneCbreg/h+s9EtN+BcGTc6JG2PS9vZUkhjOeQ1YplWSfVxaucnoxK0TmMclqe7zvPFlueOcphLMOKJ/e3RdFQ5ASYnm9FYth+vyyscVpkGWauvsh2GkxKHJ8upWXV8oTXbu4wDX1Ou4tWQnLGNufLcg7ZBYfcGnXNYmksfBSYzLWF4ZWGUx+QZ5kzZcZS79OorzvezRITiex3vXvL/CN13rT9sBeEnyxMVGIetHnfeZwz34XhwsM5ODg4OMyFYCDofCBY+1Gnc7yqCEKfdq/B9bUT7m7tsrrQoeQ7L24Hh/NwtRnlp8BFNZK5lzYLS23rsuhl2yzLauaSdba3bvVWms/SmtOwSRsVrRhXfYNGqtJCvbW+SmN9pS+LNcdFrLFFSC4Nhjgz9jThSJ5R9gt0yCU/m0AE8oyyL/LthLHMlKnta2VKmyyDfAg4kYaAy2uUTSbZyihbwrx5QfI3ldYyMDTKSr8cKfZY71Nal3ofdkbZYEX1U3oR9lfHhRKHzMG8PqHvuRJ5FPUdhDC1x6+9aJKPp04NXahDNs+Rre+Cg67Vb78Nt38neF5IND2nvtlXEcNsbW9Zl0tCkovpp+27zgwbDz6L84Mw62BnhE2k4eH0Z1VR6muR7TPz2JTZ6tkwi0mZRfc8S1ihKpMvEwKQ+H6IJyKCJIHScFilXh6zsX3GNIinA5Npmb3TFYKwNOvK1/YhHVPySdOYC5mM1eKLYtUtzwo5v5BsX/q+R9njmGluXi8ZFl6dh+S7fuuaY8g8C9TJUVVtbHO6JleWY50znSV15tQ9pyUXfWe9xJwMLypeuomyg4ODg8PTQ0Yw6UBlEUbHz3s0Dk+CEJJrGycsLfSJpCCKPCrl+I358GQ5mbxLmvUxwpM0qiM+ceMxQegznpbp9Ju0Ry3mt+07OLxYcBNlBwcHB4cLYdqDspsoXwmsrnQol0LevXeDUPoIEfH63UcMhzV0E19/FEdV6g0a7J+tUC1PqVYmrC92aNTH7J6uP7d9cHhGkLLYcuRgxZWeKEsBUtsD0/IobL4sltBxhbBIIHJVbOJ905KX9wOxluVC6Nic+SxhjlLTkc3CaWbm001CvmkusjQ3TIKZ8XkyNgfa9sH23ZBjyIKyjMlTyR0ssozUCU9Z03SZhMq6l0gulAQDwEuOhynB0PuYhrGJMtQch9KwcMqhRytLs+8p6UVG2qAc9kS+LHXuUh3NyqxOpqYFXg+ZxPnHuDCSUHrck/2yqQ+KsqoV3Xeq7IJmv7lCo+nOg6YMpAhPm/HOUl9EESKK5nPA09d92LBtRX0WySsuKlcxHPCG+9C8Ad33z2+SaWdz9DOd+s5bl47d+G4LkZZq157S/Ua/ltSqC4SQe1L9mQzH4ixnZhDM9hqXJX3rmVBzmQCNZmU/YDioQCjZWDllZaWL70lOz1qxfMIML5eMYRqWmYzL9Ht1Xru1w5k3YjSpzgbo58+ViDD61Pc+++z+0Nlu9SFL47sxLnN7OfljoY+cLjuxSGbSsmStr8Yi82U2GFKNQgdBixOgdJ5ml44rPVF2cHBwcPjoMTyAtS8871E4zIPTkwVu3zlgdbVLt1tPoh9JBsNqru7m2gnN+oiHu5sALC/1WGr18UREqRRSCgOmLtvf1YbTKF8YV/6Kl1psYMXwqZftzP2s/A7SyvmyubaXf8m3wgz1Y2X1rA5wxtKzlMl8WcomFFpV8sxBGlbHllQk3U5+R6XO4gpznEZ9C5tuc/QTuTKNqTBYYz0EnCqbhYCbtfNV4hDl8CfyZbNQcLM+J4ljS5gMONLC6KkEI1HCEOvh4dKkIilrrDG9uWQf5MqKmP25r9MLMDU2K4HN6VMqxxgbW2I62ejnNA32L7N1rIPRzncRK2qwvhmm12RM57Qy5rY3LzOsEEaxQ19R/UxItuj8ujmHvTnY48x2LKxx0VjSKlG+LDnvwmBooyEgBa3bkv4jkGbQhCKWS41vHgc/W6KSlKXWx5mU2xz9lMWncEyqn/z2cswyFLPLc7DGaV3ts8lEZ65rI5ycLYHVzHFP61PAZFRi9/Eq40mZYFrirDaJnY5DEVuftPr16phadcrrdx8Thh7tbpPBsMpCa8Dm8imlUkgQ+hyfLdLut2jURgwmVXwvolqZ0vAjxv6ECIGUAkn8F0lBILOpz/Vweya7nAkrV/B7WRh+sgDSfFTp5z11tM2PLXVITC8K/Rwli7SdZcC2YZohBm11rBYuSz2HS8GVnyg7ODg4OHz02P+aZPnTgrUvxBKMk193v9wfV/T79fTzeFSJP9gmntq63f1VuoMG25vHnLYXODxZAST1+pjN1TPWljvUKlM6gzrN2ojhpMpia0pV9BFCIoTES5ZKDtYdN9jrrBA4VtrhCuFqX63C0OeopBvJG5o1Fr9qaknCYA2DZm7yor8FBrOc7SypUsQ2FzEHellReDezvYVFlDbBmDm+89hHQZaltqSZNtvl2OMnjFMYGmVbchDPYIhhpkkuJWVlf3biU0Y5OZDjcHY7KB2y0iYHWipqZbmIEh0yOmuchoVLrBuZEHCKZSaztK3LpI9Wy4z+TltexCKShhi0UFOmVlFjWaS6gdJ7TNsvY0xSH5ARaN+m1cuHVeIJjKnaBwtzaqbRLmJqdczDYBf1E4YQnBOLtkhjfJHtQt4RZ07W2MoWn9tOF5knmnvLNTE6gt5DwcGvSO78Lp+Tt6KUWRYmi2tj12zMsskaz6Obhni/zk2iovb9AsyyjqLU16mp0nIfqa+2dnPpl/Pmy4umxZ7ta0E7rf6DDzZpNkdUa1M21s5YXuzRbre4du2EybhEr99gOKhxf3CNuzfj9IyLjSEfPL7GcFxjZbXKydmidedL5ZDVZofXNx5zNmhxOlhgHFRyQ9bHlt0vG0MrM3WeVvec2Z66xm1WF/N32ZJO2zqG9LqeYyzk+7T95hfqnov6d858F4aTgTs4ODg4XBjlJdj8CZ+bP+czPJB5+YXDFYSg369zcrLIvQ+2GI6qXLt2QqkUcX3rhNfuPk7rPdi5RhQJ+sMqi80BT3pjD8ISB51V3j/cJpKCu+t7XFs8eWI7B4fnjavNKDs4ODg4PBfUVgW9BxGn34+Ydp73aByePQTHx0scHy9SrU2pVKbc2D7mjU/cZzQu4/sRnX6D/eMVbm8d8Prtx1SqS9T8Cb1hnc6gae11ElQ46Kxy3F/i1soBr6zvsXO2lrLLDpcJWWylcbDiyk+UpS6EV+FSlGncYqpOKXTdaqdWhbmiXPunhVWyUSSFsJjFipz5UrO3LXRcru+8XasoE2D63TYW9TnzXZkJs99tY7GvM8yMWl9KcpEpS53xkqUWAs4XypkvkV5oqehUu0kiuciEgEt2XjnxRXrZNL7ooiS7FXr2PdOZL5jtqMqwZ5NeeKnJOrvMfNatu8YhtYUum11DeRNukZwmvcyskqDELGyxRc1Cs+krTa+ZjO0wWVqcl4oc9tKyKFMHNDmGLfPd02a8M9vb2oVR/DevhKJQ7mCOZY7xMqe8wmZ2NcNoaXVETgoxO/HTrsfi6x7TU8tYkvstJ8GwwTYmm6Pfk+QYUpNfWJzychKMJ40rHV++rnlc5nb0Ux+sGf2MujZnPlPyQV6OYZUt5AbAhWQZk2GZ6ciH7fj7oF8jlB7HJ4tI4XHv0RblcsArdyasLPRYavbp3qujh+lT8q3UFzP0uL9/jdWFLnfX9+LU2v0W7UEzG1lDnXb1u/AM53k5pz60I5vIywq3p8ubcvoR7XNhXM6iAT5dM4dniys/UXZwcHBw+OgxOpB4JaiuCcbH7hf9RYeUHg/ub3Lr9gH1+hgElPyQ/aNVbt/Yp14bUyot8979awzGtTl7FZx0FznpL9Ksjlhq9PjE9hnDSYWzQYvOsEnkFKLPDpJiXw0HK672RFmQCQ+XOkEYThHxuniZssc6AaD1B4Cmtcu9JD6DayzlBPIv8naWWdU34/TrzYz6mWGqsshSV7EQyXHMMIVmCLeM41eyTMIy2cOMmTS+DeezzSKzPZlZlyFGjKQiepg3xTIrJz69bGwwyaG2E8qZTznxhbozX8ISp6HgLA57iiH2tGtJkdm2MmEkwskwyrYkOXK2LnepmISklRk2V8w2oELBCUuYsPTeslxDtrByZlD8jAOjeVNabi5hY0IVM2VLLhIaDn42Z76nTeRx3neAKIwd+my4aJIPYwxSZ7fnCR1XsD2rI0+hE2D2IWWynGdvwurnfHZ/Mcie+Eidd4NZhryDny3hyEUd/cx1+m56Zv2C0HG2PhVCS1mRo5+NWTaPp61dzvypbdbyo2G2E7btPi3bbFg4h/0KP3z7VlzFj3jl1V0a9RHjSZl33r3J0lKVUW+aPpNtz55cOEniw9If1OgPa+yeRCzUhyw1e2wvH9MetNg/WyFU5uOnJGdtKLT0qrEVdaA7Nc8zN7jgJPVZJGW56hBC3AL+F2CL+Ob9i1LK/1YIsQr878Bd4B7wb0opTy9jDO5VzcHBwcHhqdB9P8SvC6pr7hf9ZUMUedy/d42DgxV2dtYTedqHvw6k9OgMmjw8vMY7j2+ChE9sP6ZZHX74QTvERMHH7a8YAfAfSynfAL4C/IIQ4keAPwX8Eynl68A/Sb5fCq44oyyR5TzLGSlGzBKyR0mm9Pj5iu1KdaI626xYv6IXQcWk2eoUtCuS3JnpqvXtpOHvbGVevixd52eX8WeDSdalfWmfFoZDzBgDKciwJ8KorycHMZ+jokC/7OnpppNx+rZU1Mm6IDQjt2tIEohEGgMwnJaTdioE3Ky9WhepsvGsLJrEn8UkLvN0jbIalmJ59GdAlF1mzpHaZ6Vd1Jp5Rh3QCK0w7kffLy+lm8kugUjdB4qZzzDXRpke+shcp5On5nb0fZ4jkUrKh2nsoWKLZdJQkGcWZao51capPtg0ymb8JlsCEEta3rTX8IkP9CwuEobJFsrNUqZVym+jQGtcaAor0mcbjGtGv+x5IKH/IKRxXTA+tuxveq1r7ebRLaftC/TLZjKS80Ll2ZKJpPUsuuV0O/OML183vZ4teuKiPnOj0+6x9N63MN8iZ07UtcYFbHPKMuev+VyoOdtvjYBg4hNMSyoNByKK/6xhUU1Lgs1CaTDfYeize7zGcFxhvdlmMKhZz6M1EcuHxIdmiJ+lwuElfg+VUu4Cu8nnrhDi+8AN4PcA/1JS7X8G/inwJy9jDI5RdnBwcHB4aowOI8coO1wqesM69eqYZzX79ETI+sIZjcrwmfXp8KGwLoT4Ve3vD9kqCSHuAj8GfBO4lkyi1WR687IGd7UZZQcHB4ePMSqLkms/GROX4RhO3oTx8fMe1bNFNJZ4ZTdRdrg8BIk/SckLCS7A7/leiO9FTINSmqCjVRtwY/mIwaTGSqPLaFplEpSolGLzcSQFk6BEb9RgNKk++515jpCA/Hg68x1JKb9UVEEI0QL+OvDHpJSdnCXlEnG1J8oCqM5Mc8oJSSqHKu1AponIlGOUFrZLGLKDTNp3tW4OCUbGWCuzy6L6luhdVlNS+tGQWcQDzLbLmPV9cylzZVicL3JyDM1sl5Z5sckzY9IzJBe2MG+mc56OVFagh3kzJBd6Zj7dQQ+eIMHI1IsHM57EEoyMw14qvVCOe7M+Z5KL8+UVaZg33bpomGkz58+IdCYs16DN6TMCIj8rp0nlFepa1wYx80HKOuBlBhvays5ZAgQ2O2sWFiO0VmiRQhiSC6kfSJV9zxw3IJP66T5bnfksTl342Tr6rkTpgcyP/QnyinACpQYM9uMJ8vZPw9nbcPZ9JZ1I7jubTs8mH5nHUa8wE+D547X/gMb7LtIHUj7cVziWeKVy1pkxvdCS+roKxObgp1A0Bypy9It7xnSWSwaaHYN1G+qG1QrTUHMFYzKa6/ULHe/mcPTTMU/WvovLMpI6lmx/Rb9DOTmGLgmTsfTCGt60UM5hbMimkpECH0m92mNjqU2lPKU/rLN3usI0LJu1WV/qsL7YJow8fC9i/3SZenVCszbi0dE6g3Ed3wvZXD5FRtDp1wGBJyIq5YA763scnC1z2lu0DCa/anYMzi+7KPKZAJ9d31cJQogy8ST5r0gp/0ayel8IsS2l3BVCbAMHl7X9qz1RdnBwcPgYIxwJ7v8dydrnYOVH4nUrPwIIOPuN5zq0Z4ZoCp7LFeFwyTjrtrizvYeUgt2TVYbjKisLXe5c22fvdJVmdcRyq8ckKFHyIoLI5929GwRhiWp5zCe2dgD4/oPbRAlLEUY+uyfr1u2d9lq8tr3DWb+FtAWOv4qQch7nuY8VRPy295eB70sp/2ut6P8Cfh74s8nyb1/WGK70RFl4ElGZsRipM1DCCugx4NMXVoujn84um1C3R8rq6SG95nlzlMZSgzDroLGMtjdIsw8L+5tz+tDLlOOexZlvxlJbHCxsDKhK/CESNll3ZjGYZKEx2J7WTq9j2y09cUgpDe+WOPUJnW0+/0QUscuqTCUVCSazulKxqurET/JJRVRZNjlIdp1eljL6tmvBZGptznJY1om430zinVxfGcpHH3o2XNsFHP08fVDqKaIITQuxmPadCbGVNbtkL/X0ziMzYEAk61L2OHNAk7KUNNbapYx5Qegx5cynO+4ZDlEZmIydhWGOJnD8XWjdjr/3d2Dhbnyc2t9XlS7msHdR9nguc2sh25wca916k6yLxhFeReQd/fSx68fa4uCXtksd/fIOZrNKBrOs919U/6Kh49LxXpD5Vte4EUIONMtKkaNfAbOchkbMhHVU12xS5aJsc8pS5zdnY5tzLLO5OSmtZTmWuZClFrmyo5MlxuMK/XGVMHl2H58ucm3ljDubB0QSTtqLdAdNokgwCspJY8k4rPDoMJ4QR7bzacF0WmY0qbDa7AJQLgWcdheYTM95K5wjdmyhhdl2/I2Hv8wWviz4KeDfBr4nhPhOsu4/IZ4g/zUhxL8HPAB+32UN4EpPlB0cHByuAqIp7PwzWP4kVJYgGMK097xH9WwgAwhHklJLEPQ+lvpHhxcCgm6/YUwoBY/21/H9iEZtxFF7iSiyx1tu91sX3mK732S52Wc0qRCGHne39zg4WeGst/DUe/G88THVKJ8LKeUvcf5rwc9+FGO40hNlISTl6oziDUTCEKbsqHZsE8ZBBpmvGUS2c2G8BWeaBUbVouvPVqbWeZZ1RW+LRdtRRE5BCLiMRlnXGptjKUg4YoaAs5UpJllnhmc68ISZ1MNGGWW+RaPsp+mq82VFsDHLoZFMRGrMq0xCvqVM6kRjJgtY4zQymyUEnFknA5v21yjTfyD0MHLSg0zGV0NSmyFnUmZYlWlsv2KCc2mnmZ3fxPoS6Sl0DSZaZ5tl7tN8NEhKOglDc4zGJJvsMczCyKWsnNZOHRc/qWNjjVMGVBunOi5q3VNaLsdHkv2j5EuRNtlkkrUdzDHJthB36dd82dP+SComWWpsXMouRx6Tk4jmTZ/229NYKhwpq5LBLINVt5wOM5f62qI1TgeglUWRcSzmYJZtYyjSL2dWXSTEXb5/m345zwhb2GaFgsQoVicnC105sz5aWGqjUiY0ZVHK7FAaViOtr3OSmGR2wfaYMPfdYJu7vSYAZ+2FpM9kf57BfPCsvcBZezYpbvea3L2+x3BUYTytzL+NOerZnoyS7LEu8llyuDy8IMIbBwcHB4fnhdPvjln8VJkbv71OqeV+uR1eTEymZQ5OlrmxeYRwYeVeGriJsoODg8PHAMKPI2SUlwTlK2bZHR9FPPxbA3r3A27+zgbNO/NFnnFwuGo467aYBCXWV9rPeyhPh+edhe/imfmeO6609MITknJZ9xyKFyrKV6iH+jGcBCLNfGO+LWQkGKb52mIuSkPH2ayENvO3sp4WOTVgKZsDOSkFzBy9DMe9zDovXyaNdbYQcAgJQmZCuZmSC73dTHIhM9/jdYm8osBhT8ksyt7svFuz+50DXYKhnPhyjnvokgvlMKZ1EplLXZYRL9NjPq8cx7i+bDKLTIY9tUrE59wq8bDJOdRnU2YBRMqpzhINTZll06x/uoOgYS+1yTKw7EMuk58lE5mwPEPT28ZT8hhdlpF1/hO6yV85U6r9y8SBNEzhGdO24Vh4nnZGCMNp7XyZRCq50ELAbf10ifrWrH3Qj9j5fyZM23lnvkKHPZvM4kP+GNmc+XLrIo/2mxOGuwFbv62O8Cb0PkjCy9mOiy3DnpKk2ELIFTn4qfU258E5nKysIQrN7WXCp832Oa775E0AeUc/8tubT5ah43yHvdz1rGfbzIWcs3Rtk2UY9TOO8Ul4OKNqUqY+qL617RjPPXvI1OT4FElSbMfnWRo4ZNxhGPiJ7s2yuXmuN2vX+YHOrpJ8n8rRz0kwLh9CPuVJ/ThACPGrz3sMDg4ODg4ODg4fExxJKX+7rUAI8Q8Aezy854tzx/xxwJWeKDs4ODg4ODg4ODhcFpxG2cHBwcHBwcHBwcECN1F2cHBwcHBwcHBwsMBNlB0cHBwcHBwcHBwscBNlBwcHBwcHBwcHBwvcRPklhhDijwsh3hJCvCmE+KtCiJoQ4s8LId4WQvy6EOJvCiGWz2l7TwjxPSHEd/ToI0KI60KIXxRC/G0hREsIsSyEOBZJfCEhxE8KIaQQ4mbyfUkIcSKEcNdignPOy+9L1kVCiC8Z9f+8EOJXhRC/Jfn+N4UQ/5pW/gMhxJ/Rvv91IcTv/ch26GMMIcSnkmtY/XWEEH9MCPF5IcQ3kmv8/xZCLJ7T3t0Hl4CC8/IFIcQvq+MthPiy1sbdB0+J8453UvZHkmP3lhDiz53T3t0HDi8s3MX4kkIIcQP4o8CXpJSfJY4y+/uBfwx8Vkr5OeCHwJ8u6Oa3Sim/IKXUJ25/FPgjwF8C/qCU8gzYA95Iyr8K/ItkCfAV4JvSmsv35UPBeXkT+L3APzfqfzr5+DPALySfv05yfIUQa0AP+Emt2U8mdV56SCl/kFzDXwC+CAyAv0l8/f4pKeWPJt//REE37j54xig4L38O+M+T9f9Z8t3dBx8S5x1vIcRvBX4P8Dkp5WeA/7KgG3cfOLyQcBPllxsloC6EKAENYEdK+Y+klCqFyi8DNy/Yp0+cmSFiFgL9a8wehF8F/hvju/uxysJ2Xr4vpfyBpa463pLzj/ffATZEjFeAoZRy71L34GriZ4H3pJT3gU8xeyn5x8C/ccG+3H3w7KCfFwkodn8J2Ek+u/vg2UE/3n8Y+LNSyjGAlPLggn25+8DhysNNlF9SSCkfE7MDD4BdoC2l/EdGtX8X+PvndQH8IyHEt4UQf0hb/98B/wPwHwL/a7IuZXaAV4H/A1Csw1eJH5wOzH1e9PpvEU+mfwn475PV3wY+K4SoEB/fbwA/IGZx3PE+H78f+KvJ5zeB3518/n3ArXPauPvg8qGflz8G/HkhxEPi++RPg7sPnjH04/1J4KeFEN8UQvwzIcSPn9PG3QcOLyzcRPklhRBihdik9gpwHWgKIf6gVv6fAgHwV87p4qeklL8J+B3ALwghfgZASnlfSvkzUsrfJaXsJnW/Bnw1YXHuSSlH8SZEi9jM961L2MUriSedFxuklH9ESvlFKeUvJt/HwFvAbyIxZRJPEr6KY2ysSCZTv5v4Rxvil8RfEEJ8G1gAJuc0dffBJcJyXv4w8MellLeAPw78ZVXX3QcfHpbjXQJWiI/fnwD+mtIXG3D3gcMLCzdRfnnxLwMfSCkPpZRT4G8w0/P9PPCvAv+WPCd1o5RyJ1keEGsHv2yrl9R5h/hh+7uIf6ggZnv+nWQMvWeyRy8Gzj0vF8TXifWaC1LKU2IZjZogOMYmj98B/JqUch9ASvm2lPLnpJRfJGbX3rM1cvfBpSNzXoCfJ74nIJ7MnXu8E7j74GIwj/cj4G/IGN8illDkUiC7+8DhRYabKL+8eAB8RQjRSBiCnwW+L4T47cCfBH63lHJgayiEaAohFtRn4OeITdVF+AbwHzF7MH6D2IzqWJ0srOflKfr5GvAfAN9Nvv86MSt0m5hlc8jiDzAzNyOE2EyWHvBngL9gNnD3wUeCzHkh1iT/luTzbwPeeUJ7dx9cDObx/lvExxkhxCeBCnCkN3D3gcOLDjdRfkkhpfwm8H8CvwZ8j/ha+IvEmrIF4B8noX7+AqRhfv5e0vwa8EtCiO8Sm8n+rpTyHzxhk18j1nmq0EHfINanuQejhvPOixDiXxdCPCL21P+7Qoh/+ISuvk58fL+R9BsAB8CvOo/yLIQQDeBfYcZUAvwBIcQPgbeJJ2f/U1LX3QcfEc45L/8+8F8lx/y/AP6Qra0Gdx/MiXOO9/8IvCqEeBP434Cfl1JKdx84vEwQ51jWHRwcHBwcHBwcHF5qOEbZwcHBwcHBwcHBwQI3UXZwcHBwcHBwcHCwwE2UHRwcHBwcHBwcHCxwE2UHBwcHBwcHBwcHC9xE2cHBwcHBwcHBwcECN1F2cHBwcHBwcHBwsMBNlB0cHBwcHBwcHBws+P8BJBp6Gr3fKusAAAAASUVORK5CYII=", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "execution_count": null, + "id": "03888cca", + "metadata": {}, + "outputs": [], "source": [ "# effect of exp_region_id\n", "import numpy as np\n", @@ -403,42 +230,10 @@ }, { "cell_type": "code", - "execution_count": 5, - "metadata": { - "ExecuteTime": { - "end_time": "2021-03-05T12:22:25.447960Z", - "start_time": "2021-03-05T12:22:25.134337Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2022-03-30 20:10:29,899 - climada.hazard.base - INFO - Reading /home/yuyue/climada/demo/data/tc_fl_1990_2004.h5\n", - "2022-03-30 20:10:30,001 - climada.entity.exposures.base - INFO - Reading /home/yuyue/climada/demo/data/exp_demo_today.h5\n", - "2022-03-30 20:10:30,030 - climada.entity.exposures.base - INFO - centr_ not set.\n", - "2022-03-30 20:10:30,034 - climada.entity.exposures.base - INFO - Matching 50 exposures with 2500 centroids.\n", - "2022-03-30 20:10:30,035 - climada.util.coordinates - INFO - No exact centroid match found. Reprojecting coordinates to nearest neighbor closer than the threshold = 100\n", - "2022-03-30 20:10:30,047 - climada.engine.impact - INFO - Calculating damage for 50 assets (>0) and 216 events.\n", - "2022-03-30 20:10:30,084 - climada.engine.impact - INFO - Exposures matching centroids found in centr_TC\n", - "2022-03-30 20:10:30,087 - climada.engine.impact - INFO - Calculating damage for 50 assets (>0) and 216 events.\n", - "risk_transfer 2.7e+07\n" - ] - }, - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "execution_count": null, + "id": "a1454057", + "metadata": {}, + "outputs": [], "source": [ "# effect of risk_transf_attach and risk_transf_cover\n", "import numpy as np\n", @@ -483,33 +278,22 @@ }, { "cell_type": "markdown", + "id": "b89c65c0", "metadata": {}, "source": [ "## MeasureSet class\n", "\n", - "Similarly to the `ImpactFuncSet`, `MeasureSet` is a container which handles `Measure` instances through the methods `append()`, `extend()`, `remove_measure()`and `get_measure()`. Use the `check()` method to make sure all the measures have been properly set. \n", + "Similarly to the `ImpactFuncSet`, `MeasureSet` is a container which handles `Measure` instances through the methods `append()`, `extend()`, `remove_measure()`and `get_measure()`. Use the `check()` method to make sure all the measures have been properly set.\n", "\n", "For a complete class documentation, refer to the Python modules docs: {py:class}`climada.entity.measures.measure_set.MeasureSet`" ] }, { "cell_type": "code", - "execution_count": 6, - "metadata": { - "ExecuteTime": { - "end_time": "2021-03-05T12:22:34.616104Z", - "start_time": "2021-03-05T12:22:34.608011Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Sandbags 22000000\n" - ] - } - ], + "execution_count": null, + "id": "a47f76d9", + "metadata": {}, + "outputs": [], "source": [ "# build measures\n", "import numpy as np\n", @@ -551,6 +335,7 @@ }, { "cell_type": "markdown", + "id": "a4f5557a", "metadata": {}, "source": [ "## Read/write measure sets to/from Excel files\n", @@ -561,12 +346,8 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "ExecuteTime": { - "end_time": "2021-03-05T12:22:41.336852Z", - "start_time": "2021-03-05T12:22:41.266438Z" - } - }, + "id": "e7e36c1c", + "metadata": {}, "outputs": [], "source": [ "from climada.entity.measures import MeasureSet\n", @@ -579,56 +360,12 @@ } ], "metadata": { - "hide_input": false, "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.12" - }, - "latex_envs": { - "LaTeX_envs_menu_present": true, - "autoclose": false, - "autocomplete": true, - "bibliofile": "biblio.bib", - "cite_by": "apalike", - "current_citInitial": 1, - "eqLabelWithNumbers": true, - "eqNumInitial": 1, - "hotkeys": { - "equation": "Ctrl-E", - "itemize": "Ctrl-I" - }, - "labels_anchors": false, - "latex_user_defs": false, - "report_style_numbering": false, - "user_envs_cfg": false - }, - "toc": { - "base_numbering": 1, - "nav_menu": {}, - "number_sections": true, - "sideBar": true, - "skip_h1_title": false, - "title_cell": "Table of Contents", - "title_sidebar": "Contents", - "toc_cell": false, - "toc_position": {}, - "toc_section_display": true, - "toc_window_display": false } }, "nbformat": 4, - "nbformat_minor": 4 + "nbformat_minor": 5 } diff --git a/doc/user-guide/climada_hazard_Hazard.ipynb b/doc/user-guide/climada_hazard_Hazard.ipynb index 412346d041..0b6bd40373 100644 --- a/doc/user-guide/climada_hazard_Hazard.ipynb +++ b/doc/user-guide/climada_hazard_Hazard.ipynb @@ -4,6 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ + "(hazard-tutorial)=\n", "# Hazard class\n", "\n", "## What is a hazard?\n", diff --git a/doc/user-guide/climada_hazard_TropCyclone.ipynb b/doc/user-guide/climada_hazard_TropCyclone.ipynb index c58cc4a300..8e50c233eb 100644 --- a/doc/user-guide/climada_hazard_TropCyclone.ipynb +++ b/doc/user-guide/climada_hazard_TropCyclone.ipynb @@ -17,7 +17,7 @@ "\n", "`TCTracks` reads and handles historical tropical cyclone tracks of the [IBTrACS](https://www.ncdc.noaa.gov/ibtracs/) repository or synthetic tropical cyclone tracks simulated using fully statistical or coupled statistical-dynamical modeling approaches. It also generates synthetic tracks from the historical ones using Wiener processes.\n", "\n", - "The tracks are stored in the attribute `data`, which is a list of `xarray`'s `Dataset` (see [xarray.Dataset](http://xarray.pydata.org/en/stable/generated/xarray.Dataset.html)). Each `Dataset` contains the following variables:\n", + "The tracks are stored in the attribute `data`, which is a list of `xarray`'s `Dataset` (see [xarray.Dataset](http://xarray.pydata.org/en/stable/generated/xarray.Dataset.html)), one for every track. Each `Dataset` contains the following variables:\n", "\n", "| Coordinates|\n", "| :- |\n", @@ -1776,7 +1776,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2021-01-14T08:56:49.670099Z", @@ -1802,7 +1802,7 @@ " \n", "### d) Load TC tracks from other sources\n", "\n", - "In addition to the [historical records of TCs (IBTrACS)](#Part1.a), the [probabilistic extension](#Part1.b) of these tracks, and the [ECMWF Forecast tracks](#Part1.c), CLIMADA also features functions to read in synthetic TC tracks from other sources. These include synthetic storm tracks from Kerry Emanuel's coupled statistical-dynamical model (Emanuel et al., 2006 as used in Geiger et al., 2016), from an open source derivative of Kerry Emanuel's model [FAST](https://github.com/linjonathan/tropical_cyclone_risk?tab=readme-ov-file), synthetic storm tracks from a second coupled statistical-dynamical model (CHAZ) (as described in Lee et al., 2018), and synthetic storm tracks from a fully statistical model (STORM) Bloemendaal et al., 2020). However, these functions are partly under development and/or targeted at advanced users of CLIMADA in the context of very specific use cases. They are thus not covered in this tutorial." + "In addition to the [historical records of TCs (IBTrACS)](#Part1.a), the [probabilistic extension](#Part1.b) of these tracks, and the [ECMWF Forecast tracks](#Part1.c), CLIMADA also features functions to read in synthetic TC tracks from other sources. These include synthetic storm tracks from Kerry Emanuel's coupled statistical-dynamical model (Emanuel et al., 2006 as used in Geiger et al., 2016), from an open source derivative of Kerry Emanuel's model [FAST](https://github.com/linjonathan/tropical_cyclone_risk?tab=readme-ov-file), synthetic storm tracks from a second coupled statistical-dynamical model (CHAZ) (as described in Lee et al., 2018), and synthetic storm tracks from a fully statistical model (STORM) Bloemendaal et al., 2020). However, these functions are not covered in this tutorial." ] }, { @@ -2017,82 +2017,108 @@ "cell_type": "markdown", "metadata": {}, "source": [ - " \n", - "### d) Making videos\n", - "\n", - "Videos of a tropical cyclone hitting specific centroids can be created with the method `video_intensity()`.\n", - "\n", - "**WARNING:**
\n", - "Creating an animated gif file may consume a lot of memory, up to the point where the os starts swapping or even an 'out-of-memory' exception is thrown." + "# Track density" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Track density is measure of the spatial and temporal recureence of storms. It informs on how many tracks crossed a particular region over a particular period of time. The short section below will present an example analysis that can be performed with the `compute_track_density()` and `plot_track_density()` functions." ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "2022-04-08 10:01:29,114 - climada.hazard.centroids.centr - INFO - Convert centroids to GeoSeries of Point shapes.\n", - "2022-04-08 10:01:31,696 - climada.util.coordinates - INFO - dist_to_coast: UTM 32617 (1/1)\n", - "2022-04-08 10:01:38,120 - climada.hazard.trop_cyclone - INFO - Mapping 1 tracks to 11374 coastal centroids.\n", - "2022-04-08 10:01:38,135 - climada.hazard.trop_cyclone - INFO - Progress: 100%\n", - "2022-04-08 10:01:38,144 - climada.hazard.trop_cyclone - INFO - Mapping 1 tracks to 12032 coastal centroids.\n", - "2022-04-08 10:01:38,158 - climada.hazard.trop_cyclone - INFO - Progress: 100%\n", - "2022-04-08 10:01:38,170 - climada.hazard.trop_cyclone - INFO - Mapping 1 tracks to 12314 coastal centroids.\n", - "2022-04-08 10:01:38,184 - climada.hazard.trop_cyclone - INFO - Progress: 100%\n", - "2022-04-08 10:01:38,194 - climada.hazard.trop_cyclone - INFO - Mapping 1 tracks to 12314 coastal centroids.\n", - "2022-04-08 10:01:38,207 - climada.hazard.trop_cyclone - INFO - Progress: 100%\n", - "2022-04-08 10:01:38,217 - climada.hazard.trop_cyclone - INFO - Mapping 1 tracks to 12314 coastal centroids.\n", - "2022-04-08 10:01:38,232 - climada.hazard.trop_cyclone - INFO - Progress: 100%\n", - "2022-04-08 10:01:38,241 - climada.hazard.trop_cyclone - INFO - Mapping 1 tracks to 12314 coastal centroids.\n", - "2022-04-08 10:01:38,256 - climada.hazard.trop_cyclone - INFO - Progress: 100%\n", - "2022-04-08 10:01:38,267 - climada.hazard.trop_cyclone - INFO - Mapping 1 tracks to 12314 coastal centroids.\n", - "2022-04-08 10:01:38,285 - climada.hazard.trop_cyclone - INFO - Progress: 100%\n", - "2022-04-08 10:01:38,296 - climada.hazard.trop_cyclone - INFO - Mapping 1 tracks to 12314 coastal centroids.\n", - "2022-04-08 10:01:38,313 - climada.hazard.trop_cyclone - INFO - Progress: 100%\n", - "2022-04-08 10:01:38,323 - climada.hazard.trop_cyclone - INFO - Mapping 1 tracks to 12314 coastal centroids.\n", - "2022-04-08 10:01:38,338 - climada.hazard.trop_cyclone - INFO - Progress: 100%\n", - "2022-04-08 10:01:38,348 - climada.hazard.trop_cyclone - INFO - Mapping 1 tracks to 12314 coastal centroids.\n", - "2022-04-08 10:01:38,364 - climada.hazard.trop_cyclone - INFO - Progress: 100%\n", - "2022-04-08 10:01:38,374 - climada.hazard.trop_cyclone - INFO - Mapping 1 tracks to 12314 coastal centroids.\n", - "2022-04-08 10:01:38,391 - climada.hazard.trop_cyclone - INFO - Progress: 100%\n", - "2022-04-08 10:01:38,400 - climada.hazard.trop_cyclone - INFO - Mapping 1 tracks to 12314 coastal centroids.\n", - "2022-04-08 10:01:38,416 - climada.hazard.trop_cyclone - INFO - Progress: 100%\n", - "2022-04-08 10:01:38,427 - climada.hazard.trop_cyclone - INFO - Mapping 1 tracks to 12314 coastal centroids.\n", - "2022-04-08 10:01:38,441 - climada.hazard.trop_cyclone - INFO - Progress: 100%\n", - "2022-04-08 10:01:38,452 - climada.hazard.trop_cyclone - INFO - Mapping 1 tracks to 12314 coastal centroids.\n", - "2022-04-08 10:01:38,470 - climada.hazard.trop_cyclone - INFO - Progress: 100%\n", - "2022-04-08 10:01:38,479 - climada.hazard.trop_cyclone - INFO - Generating video irma_tc_fl.gif\n" + "2025-11-19 15:14:20,115 - climada.hazard.tc_tracks - WARNING - The cached IBTrACS data set dates from 2024-11-18 20:14:56 (older than 180 days). Very likely, a more recent version is available. Consider manually removing the file /Users/ncolombi/climada/data/IBTrACS.ALL.v04r01.nc and re-running this function, which will download the most recent version of the IBTrACS data set from the official URL.\n", + "2025-11-19 15:14:24,509 - climada.hazard.tc_tracks - WARNING - 2045 storm events are discarded because no valid wind/pressure values have been found: 1950004S11059, 1950012S14150, 1950013S14139, 1950022S16161, 1950026S19069, ...\n", + "2025-11-19 15:14:24,536 - climada.hazard.tc_tracks - WARNING - 85 storm events are discarded because only one valid timestep has been found: 1950058S20114, 1951263N19267, 1952024S11159, 1952330N23300, 1953259N23270, ...\n", + "2025-11-19 15:14:25,984 - climada.hazard.tc_tracks - INFO - Progress: 10%\n", + "2025-11-19 15:14:27,343 - climada.hazard.tc_tracks - INFO - Progress: 20%\n", + "2025-11-19 15:14:28,711 - climada.hazard.tc_tracks - INFO - Progress: 30%\n", + "2025-11-19 15:14:30,072 - climada.hazard.tc_tracks - INFO - Progress: 40%\n", + "2025-11-19 15:14:31,535 - climada.hazard.tc_tracks - INFO - Progress: 50%\n", + "2025-11-19 15:14:32,917 - climada.hazard.tc_tracks - INFO - Progress: 60%\n", + "2025-11-19 15:14:34,349 - climada.hazard.tc_tracks - INFO - Progress: 70%\n", + "2025-11-19 15:14:35,743 - climada.hazard.tc_tracks - INFO - Progress: 80%\n", + "2025-11-19 15:14:37,109 - climada.hazard.tc_tracks - INFO - Progress: 90%\n", + "2025-11-19 15:14:38,526 - climada.hazard.tc_tracks - INFO - Progress: 100%\n" ] + } + ], + "source": [ + "from climada.hazard import TCTracks, tc_tracks\n", + "import cartopy.crs as ccrs\n", + "\n", + "# select 50 years of tracks 1950-2000\n", + "ibtracks = TCTracks.from_ibtracs_netcdf(year_range=(1950, 2000))" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# plot tracks (optional)\n", + "ibtracks.plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "From the above graph, can you determine where tropical cylones occours the most ? Not really... that is why we are going to compute the tracks density of the selected tracks at a 1° resolution (roughly 100km)." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "15it [00:53, 3.60s/it] \n" + "Processing Tracks: 100%|██████████| 3690/3690 [00:00<00:00, 6429.60it/s]\n" ] } ], "source": [ - "# Note: execution of this cell will fail unless there is enough memory available (> 10G)\n", + "res = 1 # 1° ~ 100km at the equator\n", + "genesis = False # do not consider only the strating points (genesis) of the tracks\n", + "filter_tracks = True # allow a track to cross a grid cell only once\n", "\n", - "from climada.hazard import Centroids, TropCyclone, TCTracks\n", - "\n", - "track_name = \"2017242N16333\" #'2016273N13300' #'1992230N11325'\n", - "\n", - "tr_irma = TCTracks.from_ibtracs_netcdf(provider=\"usa\", storm_id=\"2017242N16333\")\n", - "\n", - "lon_min, lat_min, lon_max, lat_max = -83.5, 24.4, -79.8, 29.6\n", - "centr_video = Centroids.from_pnt_bounds((lon_min, lat_min, lon_max, lat_max), 0.04)\n", - "centr_video.check()\n", - "\n", - "tc_video = TropCyclone()\n", - "\n", - "tc_list, tr_coord = tc_video.video_intensity(\n", - " track_name, tr_irma, centr_video, file_name=\"results/irma_tc_fl.gif\"\n", + "# compute track density: resolution 1° x 1°\n", + "hist_ibtracks, *_ = ibtracks.compute_track_density(\n", + " res=res, genesis=genesis, filter_tracks=filter_tracks\n", ")" ] }, @@ -2100,85 +2126,290 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "`tc_list` contains a list with TropCyclone instances plotted at each time step
\n", - "`tr_coord` contains a list with the track path coordinates plotted at each time step" + "If the previous calculation yielded the following warning message: \n", + "UserWarning: The time step is too big for the current resolution. For the desired resolution, \n", + "apply a time step equal or lower than \"x\" h, it means that for the given resolution, the time step is to large and it might result in the tracks jumping (skipping) one grid cell. To avoid it, you can homogenizie the tracks time steps to a lower value than \"x\" with the following code:" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-11-19 15:18:04,753 - climada.hazard.tc_tracks - INFO - Interpolating 3690 tracks to 0.8h time steps.\n" + ] + } + ], + "source": [ + "# apply an equal timestep of 1h to all tracks\n", + "ibtracks.equal_timestep(time_step_h=0.8)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "#### Saving disk space with mp4\n", - "Animated gif images occupy a lot of space. Using mp4 as output format makes the video sequences much smaller! However this requires the package _ffmpeg_ to be installed, which is not part of the ordinary climada environment. It can be installed by executing the following command in a console:\n", - "```bash\n", - "conda install ffmpeg\n", - "```\n", + "Now it is time to plot the results. The function \"compute_track_density()\" returned the absolute count of the the tracks that crossed every grid cell, to convert this number to a frequency, simply divide it by the number of years in the dataset, 50 years in this example." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Divide by number of years\n", + "hist_ibtracks = hist_ibtracks / 50\n", "\n", - "Creating the same videa as above in mp4 format can be done in this way then:" + "# plot track density\n", + "tc_tracks.plot_track_density(\n", + " hist=hist_ibtracks,\n", + " title=\"IBTrACKS Track Density \\n 1950-2000\",\n", + " projection=ccrs.Robinson(),\n", + " cmap=\"turbo\",\n", + " cbar_kwargs={\n", + " \"orientation\": \"horizontal\",\n", + " \"pad\": 0.05,\n", + " \"shrink\": 0.4,\n", + " \"label\": f\"n° tracks per {res}° x {res}° grid cell per year\",\n", + " \"extend\": \"max\",\n", + " },\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let's compare two times periods, 1980-1990 and 1990-2000" ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "2022-04-08 10:03:27,161 - climada.hazard.trop_cyclone - INFO - Mapping 1 tracks to 11374 coastal centroids.\n", - "2022-04-08 10:03:27,182 - climada.hazard.trop_cyclone - INFO - Progress: 100%\n", - "2022-04-08 10:03:27,192 - climada.hazard.trop_cyclone - INFO - Mapping 1 tracks to 12032 coastal centroids.\n", - "2022-04-08 10:03:27,207 - climada.hazard.trop_cyclone - INFO - Progress: 100%\n", - "2022-04-08 10:03:27,218 - climada.hazard.trop_cyclone - INFO - Mapping 1 tracks to 12314 coastal centroids.\n", - "2022-04-08 10:03:27,235 - climada.hazard.trop_cyclone - INFO - Progress: 100%\n", - "2022-04-08 10:03:27,247 - climada.hazard.trop_cyclone - INFO - Mapping 1 tracks to 12314 coastal centroids.\n", - "2022-04-08 10:03:27,263 - climada.hazard.trop_cyclone - INFO - Progress: 100%\n", - "2022-04-08 10:03:27,275 - climada.hazard.trop_cyclone - INFO - Mapping 1 tracks to 12314 coastal centroids.\n", - "2022-04-08 10:03:27,294 - climada.hazard.trop_cyclone - INFO - Progress: 100%\n", - "2022-04-08 10:03:27,304 - climada.hazard.trop_cyclone - INFO - Mapping 1 tracks to 12314 coastal centroids.\n", - "2022-04-08 10:03:27,319 - climada.hazard.trop_cyclone - INFO - Progress: 100%\n", - "2022-04-08 10:03:27,333 - climada.hazard.trop_cyclone - INFO - Mapping 1 tracks to 12314 coastal centroids.\n", - "2022-04-08 10:03:27,350 - climada.hazard.trop_cyclone - INFO - Progress: 100%\n", - "2022-04-08 10:03:27,363 - climada.hazard.trop_cyclone - INFO - Mapping 1 tracks to 12314 coastal centroids.\n", - "2022-04-08 10:03:27,382 - climada.hazard.trop_cyclone - INFO - Progress: 100%\n", - "2022-04-08 10:03:27,393 - climada.hazard.trop_cyclone - INFO - Mapping 1 tracks to 12314 coastal centroids.\n", - "2022-04-08 10:03:27,412 - climada.hazard.trop_cyclone - INFO - Progress: 100%\n", - "2022-04-08 10:03:27,422 - climada.hazard.trop_cyclone - INFO - Mapping 1 tracks to 12314 coastal centroids.\n", - "2022-04-08 10:03:27,442 - climada.hazard.trop_cyclone - INFO - Progress: 100%\n", - "2022-04-08 10:03:27,453 - climada.hazard.trop_cyclone - INFO - Mapping 1 tracks to 12314 coastal centroids.\n", - "2022-04-08 10:03:27,471 - climada.hazard.trop_cyclone - INFO - Progress: 100%\n", - "2022-04-08 10:03:27,480 - climada.hazard.trop_cyclone - INFO - Mapping 1 tracks to 12314 coastal centroids.\n", - "2022-04-08 10:03:27,496 - climada.hazard.trop_cyclone - INFO - Progress: 100%\n", - "2022-04-08 10:03:27,507 - climada.hazard.trop_cyclone - INFO - Mapping 1 tracks to 12314 coastal centroids.\n", - "2022-04-08 10:03:27,523 - climada.hazard.trop_cyclone - INFO - Progress: 100%\n", - "2022-04-08 10:03:27,533 - climada.hazard.trop_cyclone - INFO - Mapping 1 tracks to 12314 coastal centroids.\n", - "2022-04-08 10:03:27,548 - climada.hazard.trop_cyclone - INFO - Progress: 100%\n", - "2022-04-08 10:03:27,559 - climada.hazard.trop_cyclone - INFO - Generating video irma_tc_fl.mp4\n" + "2025-11-19 15:20:18,909 - climada.hazard.tc_tracks - WARNING - The cached IBTrACS data set dates from 2024-11-18 20:14:56 (older than 180 days). Very likely, a more recent version is available. Consider manually removing the file /Users/ncolombi/climada/data/IBTrACS.ALL.v04r01.nc and re-running this function, which will download the most recent version of the IBTrACS data set from the official URL.\n", + "2025-11-19 15:20:21,492 - climada.hazard.tc_tracks - WARNING - 396 storm events are discarded because no valid wind/pressure values have been found: 1980015S18060, 1980032S14071, 1980056S15059, 1980068S13068, 1980075S11072, ...\n", + "2025-11-19 15:20:21,497 - climada.hazard.tc_tracks - WARNING - 11 storm events are discarded because only one valid timestep has been found: 1980002S15081, 1980005S11059, 1980009S14066, 1980010S20043, 1980010S22048, ...\n", + "2025-11-19 15:20:21,846 - climada.hazard.tc_tracks - INFO - Progress: 10%\n", + "2025-11-19 15:20:22,170 - climada.hazard.tc_tracks - INFO - Progress: 20%\n", + "2025-11-19 15:20:22,487 - climada.hazard.tc_tracks - INFO - Progress: 30%\n", + "2025-11-19 15:20:22,806 - climada.hazard.tc_tracks - INFO - Progress: 40%\n", + "2025-11-19 15:20:23,123 - climada.hazard.tc_tracks - INFO - Progress: 50%\n", + "2025-11-19 15:20:23,439 - climada.hazard.tc_tracks - INFO - Progress: 60%\n", + "2025-11-19 15:20:23,756 - climada.hazard.tc_tracks - INFO - Progress: 70%\n", + "2025-11-19 15:20:24,084 - climada.hazard.tc_tracks - INFO - Progress: 80%\n", + "2025-11-19 15:20:24,400 - climada.hazard.tc_tracks - INFO - Progress: 90%\n", + "2025-11-19 15:20:24,685 - climada.hazard.tc_tracks - INFO - Progress: 100%\n", + "2025-11-19 15:20:24,807 - climada.hazard.tc_tracks - WARNING - The cached IBTrACS data set dates from 2024-11-18 20:14:56 (older than 180 days). Very likely, a more recent version is available. Consider manually removing the file /Users/ncolombi/climada/data/IBTrACS.ALL.v04r01.nc and re-running this function, which will download the most recent version of the IBTrACS data set from the official URL.\n", + "2025-11-19 15:20:27,256 - climada.hazard.tc_tracks - WARNING - 149 storm events are discarded because no valid wind/pressure values have been found: 1990108N09087, 1990145N19276, 1990181N10259, 1990205N13231, 1990222N11219, ...\n", + "2025-11-19 15:20:27,261 - climada.hazard.tc_tracks - WARNING - 2 storm events are discarded because only one valid timestep has been found: 1995269N21088, 1996318N33146.\n", + "2025-11-19 15:20:27,688 - climada.hazard.tc_tracks - INFO - Progress: 10%\n", + "2025-11-19 15:20:28,098 - climada.hazard.tc_tracks - INFO - Progress: 20%\n", + "2025-11-19 15:20:28,519 - climada.hazard.tc_tracks - INFO - Progress: 30%\n", + "2025-11-19 15:20:28,921 - climada.hazard.tc_tracks - INFO - Progress: 40%\n", + "2025-11-19 15:20:29,321 - climada.hazard.tc_tracks - INFO - Progress: 50%\n", + "2025-11-19 15:20:29,726 - climada.hazard.tc_tracks - INFO - Progress: 60%\n", + "2025-11-19 15:20:30,124 - climada.hazard.tc_tracks - INFO - Progress: 70%\n", + "2025-11-19 15:20:30,527 - climada.hazard.tc_tracks - INFO - Progress: 80%\n", + "2025-11-19 15:20:31,062 - climada.hazard.tc_tracks - INFO - Progress: 90%\n", + "2025-11-19 15:20:31,438 - climada.hazard.tc_tracks - INFO - Progress: 100%\n" ] - }, + } + ], + "source": [ + "# select 2 peridos of 10 years\n", + "ibtracks_1980_1990 = TCTracks.from_ibtracs_netcdf(year_range=(1980, 1990))\n", + "ibtracks_1990_2000 = TCTracks.from_ibtracs_netcdf(year_range=(1990, 2000))" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "15it [00:55, 3.71s/it] \n" + "Processing Tracks: 100%|██████████| 841/841 [00:00<00:00, 6119.08it/s]\n", + "Processing Tracks: 100%|██████████| 1051/1051 [00:00<00:00, 7142.76it/s]\n" ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ - "# Note: execution of this cell will fail unless there is enough memory available (> 12G) and ffmpeg is installed\n", + "# compute track density for every scenario\n", + "res = 1\n", + "genesis = False\n", + "\n", + "hist_ibtracks_1980_1990, *_ = ibtracks_1980_1990.compute_track_density(\n", + " res=res, genesis=genesis, filter_tracks=True\n", + ")\n", + "hist_ibtracks_1990_2000, *_ = ibtracks_1990_2000.compute_track_density(\n", + " res=res, genesis=genesis, filter_tracks=True\n", + ")\n", "\n", - "import shutil\n", - "from matplotlib import animation\n", - "from matplotlib.pyplot import rcParams\n", + "# compute the difference and plot\n", + "diff = (hist_ibtracks_1990_2000 - hist_ibtracks_1980_1990) / 10\n", "\n", - "rcParams[\"animation.ffmpeg_path\"] = shutil.which(\"ffmpeg\")\n", - "writer = animation.FFMpegWriter(bitrate=500)\n", - "tc_list, tr_coord = tc_video.video_intensity(\n", - " track_name, tr_irma, centr_video, file_name=\"results/irma_tc_fl.mp4\", writer=writer\n", + "# plot track density\n", + "tc_tracks.plot_track_density(\n", + " hist=diff,\n", + " title=\"Difference in Track Density IBTrACKS \\n 1990-2000 and 1980-1990\",\n", + " projection=ccrs.Robinson(),\n", + " cmap=\"RdBu\",\n", + " div_cmap=True,\n", + " cbar_kwargs={\n", + " \"orientation\": \"horizontal\",\n", + " \"pad\": 0.05,\n", + " \"shrink\": 0.4,\n", + " \"label\": f\"n° tracks per {res}° x {res}° grid cell per year\",\n", + " \"extend\": \"both\",\n", + " },\n", ")" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Subset by basin\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here below you can see the current Tropical cyclone bounds supported by CLIMADA, as defined by STORM (Bloemendaal et al., 2020)" + ] + }, + { + "attachments": { + "image.png": { + "image/png": "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" + } + }, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![image.png](attachment:image.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If you wish to access these basin boundaries you can simply do so using the example code below." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "NA_basin_polygon = tc_tracks.BasinBoundsStorm[\"NA\"].value\n", + "NA_basin_polygon" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If you wish to subset an ensemble of tracks into the respective basins, you can do so as follow:" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "defaultdict(list,\n", + " {'SI': ,\n", + " 'SP': ,\n", + " 'WP': ,\n", + " 'NA': ,\n", + " 'EP': ,\n", + " 'NI': })" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "subset, tracks_outside = ibtracks.subset_by_basin()\n", + "subset" + ] + }, { "cell_type": "markdown", "metadata": {}, diff --git a/doc/user-guide/climada_measure_config.ipynb b/doc/user-guide/climada_measure_config.ipynb new file mode 100644 index 0000000000..8fa4739bac --- /dev/null +++ b/doc/user-guide/climada_measure_config.ipynb @@ -0,0 +1,570 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "659605a5-d601-47d3-89f7-b606e3e39c93", + "metadata": {}, + "source": [ + "(measure-config-tutorial)=\n", + "\n", + "# Defining Adaptation Measures with configurations" + ] + }, + { + "cell_type": "markdown", + "id": "c68c6cf1-d0a1-40ae-ae45-838741988ac6", + "metadata": {}, + "source": [ + "## Introduction\n", + "\n", + "CLIMADA uses `Measure` objects to model the effects of adaptation measures. `Measure` objects were formerly defined declaratively (via for instance, a shifting or scaling of the hazard intensity or a change of impact function), and are now defined as python functions to enable more flexibility on the possible changes (see the [tutorial on measure objects](measure-tutorial)'). \n", + "\n", + "The caveat of defining measure effects as python functions is that it cannot be serialized (written to a file), and also makes reading from a file a challenge.\n", + "\n", + "In order to retain close that gap, the `measure` module now ships `MeasureConfig` objects, which handle the reading, writing and \"declarative\" defining of `Measure` objects.\n", + "\n", + "`Measure` objects can be instantiated from `MeasureConfig` objects using `Measure.from_config()`.\n", + "\n", + "### Summary of `Measure` vs `MeasureConfig`\n", + "\n", + "| `Measure` | `MeasureConfig` |\n", + "|-----------|--------------------|\n", + "| Is used for the actual computation | Is transformed into a `Measure` for actual computation |\n", + "| Uses python function to define what change to apply to the `Exposures`, `ImpactFuncSet`, `Hazard` objects | Define the changes (functions) to apply via the former way (scaling/shifting effect, alternate file loading, etc.) |\n", + "| Accepts any possible effect as long as it can be defined as a python function | Is restricted to a set of defined effects |\n", + "| Cannot be written to a file (unless it was created by a `MeasureConfig`) | Can easily be read from/written to a file (`.xlsx` or `.yaml`) |" + ] + }, + { + "cell_type": "markdown", + "id": "6d786faa-5b8c-4ee6-83cd-5fdafc1b2c29", + "metadata": {}, + "source": [ + "### Configuration classes\n", + "\n", + "The definition of measures via `MeasureConfig` is organized into a hierarchy of specialized classes:\n", + "\n", + "- `MeasureConfig`: The top-level container for a single measure.\n", + "- `HazardModifierConfig`: Defines how the hazard is changed (e.g., shifting intensity).\n", + "- `ImpfsetModifierConfig`: Adjusts impact functions (e.g., scaling vulnerability curves).\n", + "- `ExposuresModifierConfig`: Modifies exposure data (e.g., reassigning IDs or zeroing regions).\n", + "- `CostIncomeConfig`: Handles the financial aspects, including initial costs and recurring income.\n", + "\n", + "Note that everything can be defined and accessed directly from the `MeasureConfig` container, the underlying ones are there to keep things organized.\n", + "\n", + "In the following we present each of these subclasses and the possibilities they offer." + ] + }, + { + "cell_type": "markdown", + "id": "4887d2a6-8295-4fda-8442-cbcbd3b16fea", + "metadata": {}, + "source": [ + "## Quickstart" + ] + }, + { + "cell_type": "markdown", + "id": "5c40640d-50a4-4102-8e45-0dc8b9a770f2", + "metadata": {}, + "source": [ + "You can directly define a `MeasureConfig` object with a dictionary, using `MeasureConfig.from_dict()`.\n", + "\n", + "Below are the possible parameters:\n", + "\n", + "| Scope | Parameter | Type | Description |\n", + "| :--- | :--- | :--- | :--- |\n", + "| **Top-Level** | `name` (required) | `str` | Unique identifier for the measure. |\n", + "| | `haz_type` (required) | `str` | The hazard type this measure targets (e.g., \"TC\", \"FL\"). |\n", + "| | `implementation_duration` | `str` | Pandas offset alias (e.g., \"2Y\") for implementation time. |\n", + "| | `color_rgb` | `tuple` | RGB triple (0-1 range) for plotting and visualization. |\n", + "| **Hazard** | `haz_int_mult` | `float` | Multiplier for hazard intensity (default: 1.0). |\n", + "| | `haz_int_add` | `float` | Additive offset for hazard intensity (default: 0.0). |\n", + "| | `new_hazard_path` | | Path to an HDF5 file to replace the current hazard. |\n", + "| | `impact_rp_cutoff` | `float` | Return period (years) threshold; events below this are ignored. |\n", + "| **Impact Function**| `impf_ids` | `list` | Specific impact function IDs to modify (None = all). |\n", + "| | `impf_mdd_mult` / `_add` | `float` | Scale or shift the Mean Damage Degree curve. |\n", + "| | `impf_paa_mult` / `_add` | `float` | Scale or shift the Percentage of Assets Affected curve. |\n", + "| | `impf_int_mult` / `_add` | `float` | Scale or shift the intensity axis of the function. |\n", + "| | `new_impfset_path` | | Path to an Excel file to replace the impact function set. |\n", + "| **Exposures** | `reassign_impf_id` | `dict` | Mapping `{haz_type: {old_id: new_id}}` for reclassification. |\n", + "| | `set_to_zero` | `list` | List of Region IDs where exposure value is set to 0. |\n", + "| | `new_exposures_path` | | Path to an HDF5 file to replace the current exposures. |\n", + "| **Cost & Income** | `init_cost` | `float` | One-time investment cost (absolute value). |\n", + "| | `periodic_cost` | `float` | Recurring maintenance/operational costs. |\n", + "| | `periodic_income` | `float` | Recurring income generated by the measure. |\n", + "| | `mkt_price_year` | `int` | Reference year for pricing (default: current year). |\n", + "| | `freq` | `str` | Frequency of cash flows (e.g., \"Y\" for yearly). |\n", + "| | `custom_cash_flows` | `list[dict]`| Explicit list of dates and values for complex cash flows. (See the [cost income tutorial](cost-income-tutorial)) |" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "62cf6502-7765-452c-be32-eb49a363b4a8", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MeasureConfig(\n", + "\tname='Tutorial measure'\n", + "\thaz_type='TC'\n", + "\timpfset_modifier=ImpfsetModifierConfig(\n", + "\t\tNon default fields:\n", + "\t\t\timpf_ids=[1, 2]\n", + "\t\t\timpf_mdd_mult=0.8\n", + ")\n", + "\thazard_modifier=HazardModifierConfig(\n", + "\t\tNon default fields:\n", + "\t\t\tnew_hazard_path='path/to/new_hazard.h5'\n", + ")\n", + "\texposures_modifier=ExposuresModifierConfig(\n", + "\t\tNon default fields:\n", + "\t\t\treassign_impf_id={'TC': {1: 2}}\n", + ")\n", + "\tcost_income=CostIncomeConfig(\n", + "\t\tNon default fields:\n", + "\t\t\tinit_cost=10000\n", + "\t\t\tperiodic_cost=500\n", + ")\n", + "\timplementation_duration=None\n", + "\tcolor_rgb=(0.1, 0.5, 0.3))\n" + ] + } + ], + "source": [ + "from climada.entity.measures.measure_config import MeasureConfig\n", + "\n", + "measure_dict = {\n", + " \"name\": \"Tutorial measure\",\n", + " \"haz_type\": \"TC\",\n", + " \"impf_ids\": [1, 2],\n", + " \"impf_mdd_mult\": 0.8,\n", + " \"new_hazard_path\": \"path/to/new_hazard.h5\",\n", + " \"reassign_impf_id\": {\"TC\": {1: 2}},\n", + " \"color_rgb\": [0.1, 0.5, 0.3],\n", + " \"init_cost\": 10000,\n", + " \"periodic_cost\": 500,\n", + "}\n", + "\n", + "meas_config = MeasureConfig.from_dict(measure_dict)\n", + "\n", + "print(meas_config)" + ] + }, + { + "cell_type": "markdown", + "id": "ac98393f-575f-4580-ac4a-dae578638916", + "metadata": {}, + "source": [ + "## Modifying Impact Functions: `ImpfsetModifierConfig`\n", + "\n", + "The `ImpfsetModifierConfig` is used to define how an adaptation measure changes the vulnerability (refer to the [impact functions tutorial](impact-functions-tutorial)).\n", + "\n", + "When \"translated\" to a `Measure` object the `ImpfsetModifierConfig` populates the `impfset_change` attribute with a function that takes an `ImpactFuncSet` and returns a modified one, according to the specifications.\n", + "\n", + "```{note}\n", + "Modifications are always applied to a specific hazard type (`haz_type` parameter).\n", + "```\n", + "\n", + "`ImpfsetModifierConfig` allows you to modify the main components of an impact function set, as well as to replace it entirely:\n", + "\n", + "- The MDD (Mean Damage Degree) array: via `impf_mdd_mult` to scale it and `impf_mdd_add` to shift it.\n", + "- The PAA (Percentage of Assets Affected) array: via `impf_paa_mult` to scale it and `impf_paa_add` to shift it.\n", + "- The intensity array: via `impf_int_mult` to scale it and `impf_int_add` to shift it.\n", + "- Replacing the set: via providing the `new_impfset_path` parameter. It needs to be a valid `.xlsx` file readable by `ImpactFuncSet.from_excel()`\n", + "\n", + "See below for code examples.\n", + "\n", + "```{warning}\n", + "If you provide a new_impfset_path and other modifiers, CLIMADA will load the new file first and then apply the modifiers to it. (A warning will be issued to ensure this sequence is intended).\n", + "```\n", + "\n", + "```{note}\n", + "By default the changes are applied to all the impact functions in the set, but you can provide the `impf_ids` parameter to apply the changes to a selection of impact function ids.\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "5ffb447b-1b8f-4e40-9d1c-7db33a11255e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "--- Scaling Config ---\n", + "ImpfsetModifierConfig(\n", + "\t\tNon default fields:\n", + "\t\t\timpf_ids=[1, 2]\n", + "\t\t\timpf_mdd_mult=0.8\n", + "\t\t\timpf_int_add=5.0\n", + ")\n", + "\n", + "--- Replacement Config ---\n", + "ImpfsetModifierConfig(\n", + "\t\tNon default fields:\n", + "\t\t\tnew_impfset_path='path/to/new_impact_functions.xlsx'\n", + ")\n" + ] + } + ], + "source": [ + "from climada.entity.measures.measure_config import ImpfsetModifierConfig\n", + "\n", + "# 1. Scaling existing Impact Functions\n", + "# Let's say we want to simulate a 20% reduction in MDD\n", + "# and a slight shift in the intensity threshold for Hazard 'TC'.\n", + "impf_mod_scaling = ImpfsetModifierConfig(\n", + " haz_type=\"TC\",\n", + " impf_ids=[1, 2], # Apply only to specific function IDs\n", + " impf_mdd_mult=0.8, # Reduce Mean Damage Degree by 20%\n", + " impf_int_add=5.0, # Shift intensity axis by 5 units (e.g., higher resistance)\n", + ")\n", + "\n", + "print(\"--- Scaling Config ---\")\n", + "print(impf_mod_scaling)\n", + "\n", + "# 2. Replacing the Impact Function Set from a file\n", + "# Useful for measures that implement completely new building standards.\n", + "impf_mod_replace = ImpfsetModifierConfig(\n", + " haz_type=\"TC\", new_impfset_path=\"path/to/new_impact_functions.xlsx\"\n", + ")\n", + "\n", + "print(\"\\n--- Replacement Config ---\")\n", + "print(impf_mod_replace)" + ] + }, + { + "cell_type": "markdown", + "id": "234ebc89-83b0-42b1-8b97-734016306b84", + "metadata": {}, + "source": [ + "## Modifying Hazards: `HazardModifierConfig`\n", + "\n", + "The `HazardModifierConfig` is used to define how an adaptation measure changes the hazard (refer to the [hazard tutorial](hazard-tutorial)).\n", + "\n", + "When \"translated\" to a `Measure` object the `HazardModifierConfig` populates the `hazard_change` attribute with a function that takes a `Hazard` (possibly additional arguments, see below) and returns a modified one, according to the specifications.\n", + "\n", + "```{note}\n", + "Modifications are always applied to a specific hazard type (`haz_type` parameter).\n", + "```\n", + "\n", + "`HazardModifierConfig` allows you to modify the intensity and frequency of the hazard, to apply a cutoff on the return period of impacts, as well as to replace it entirely:\n", + "\n", + "- The intensity matrix: via `haz_int_mult` to scale it and `haz_int_add` to shift it.\n", + "- The frequency array: via `haz_freq_mult` to scale it and `haz_freq_add` to shift it.\n", + "- Replacing the hazard: via providing the `new_hazard_path` parameter. It needs to be a valid hazard HDF5 file readable by `Hazard.from_hdf5()`\n", + "- Applying a cutoff on frequency based on impacts: via `impact_rp_cutoff` (see the note).\n", + "\n", + "```{note}\n", + "Providing a value for `impact_rp_cutoff` \"removes\" (it sets their intensity to 0.) events from the hazard, for which the exceedance frequency (inverse of return period) of impacts is below the given threshold.\n", + "\n", + "For instance providing 1/20, would remove all events whose impacts have a return period below 20 years.\n", + "\n", + "In that case the function changing the hazard (`Measure.hazard_change`) will be a function with the following signature:\n", + "\n", + " f(hazard: Hazard, # The hazard to apply on\n", + " exposures: Exposures, # The exposure for the impact computation\n", + " impfset: ImpactFuncSet, # The impfset for the impact computation\n", + " base_hazard: Hazard, # The hazard for the impact computation\n", + " exposures_region_id: Optional[list[int]] = None, # Region id to filter to\n", + " ) -> Hazard\n", + "```\n", + "\n", + "```{warning}\n", + "If you provide a new_hazard_path and other modifiers, CLIMADA will load the new file first and then apply the modifiers to it. (A warning will be issued to ensure this sequence is intended).\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "f6061c1c-b21f-4aef-a394-c172784a25ab", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "--- Scaling Config ---\n", + "HazardModifierConfig(\n", + "\t\tNon default fields:\n", + "\t\t\thaz_int_add=-10\n", + "\t\t\thaz_freq_mult=0.8\n", + ")\n", + "\n", + "--- Replacement Config ---\n", + "HazardModifierConfig(\n", + "\t\tNon default fields:\n", + "\t\t\tnew_hazard_path='path/to/new_floods.h5'\n", + ")\n", + "\n", + "--- Cutoff Config ---\n", + "HazardModifierConfig(\n", + "\t\tNon default fields:\n", + "\t\t\timpact_rp_cutoff=0.05\n", + ")\n" + ] + } + ], + "source": [ + "from climada.entity.measures.measure_config import HazardModifierConfig\n", + "\n", + "# 1. Scaling existing hazard\n", + "# Let's say we want to simulate a 20% reduction in frequency\n", + "# and a reduction by 10m/s in the intensity for our tropical cyclones.\n", + "haz_mod = HazardModifierConfig(\n", + " haz_type=\"TC\",\n", + " haz_int_add=-10, # Reduce hazard intensity by 10 units\n", + " haz_freq_mult=0.8, # Scale hazard frequency by 20%\n", + ")\n", + "\n", + "print(\"--- Scaling Config ---\")\n", + "print(haz_mod)\n", + "\n", + "# 2. Replacing the hazard from a file\n", + "# Useful for measures that correspond to a different hazard modelling.\n", + "# E.g., a dike leading to a change in (physical) flood modelling.\n", + "haz_mod_new = HazardModifierConfig(\n", + " haz_type=\"FL\", new_hazard_path=\"path/to/new_floods.h5\"\n", + ")\n", + "\n", + "print(\"\\n--- Replacement Config ---\")\n", + "print(haz_mod_new)\n", + "\n", + "# 3. Applying a cutoff on the return period of the impacts\n", + "# Useful when measures are defined to avoid damage for a specific RP (exceedance frequency).\n", + "# Note that it looks a the distribution of the impacts, not the hazard intensity!\n", + "haz_mod_cutoff = HazardModifierConfig(\n", + " haz_type=\"TC\",\n", + " impact_rp_cutoff=1\n", + " / 20, # Set intensity to 0 for events with impacts with a return period below 20 years\n", + ")\n", + "\n", + "print(\"\\n--- Cutoff Config ---\")\n", + "print(haz_mod_cutoff)" + ] + }, + { + "cell_type": "markdown", + "id": "c7499c1a-2491-42c4-bdbb-d224090b85fb", + "metadata": {}, + "source": [ + "## Modifying Exposures: `ExposuresModifierConfig`\n", + "\n", + "The `ExposuresModifierConfig` is used to define how an adaptation measure changes the exposure (refer to the [exposure tutorial](exposure-tutorial)).\n", + "\n", + "When \"translated\" to a `Measure` object the `ExposuresModifierConfig` populates the `exposures_change` attribute with a function that takes an `Exposures` and returns a modified one, according to the specifications.\n", + "\n", + "`ExposuresModifierConfig` allows you to modify the impact function assigned to different hazard, to set a list of points to 0 value, or to load a different Exposures:\n", + "\n", + "- Remapping the impact function: via `reassign_impf_id` with a dictionary of the form `{haz_type: {old_id: new_id}}`.\n", + "- Setting values to zero: via `set_to_zero` with a list of indices of the exposure GeoDataFrame.\n", + "- Replacing the exposure: via providing the `new_exposures_path` parameter. It need to be a valid HDF5 exposure file readable by `Exposures.from_hdf5()`\n", + "\n", + "```{warning}\n", + "If you provide a new_exposures_path and other modifiers, CLIMADA will load the new file first and then apply the modifiers to it. (A warning will be issued to ensure this sequence is intended).\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "5237930d-a18c-4498-afe5-373c5dadf882", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "--- First Config ---\n", + "ExposuresModifierConfig(\n", + "\t\tNon default fields:\n", + "\t\t\treassign_impf_id={'TC': {1: 2}}\n", + "\t\t\tset_to_zero=[0, 25, 78]\n", + ")\n", + "\n", + "--- Replacement Config ---\n", + "ExposuresModifierConfig(\n", + "\t\tNon default fields:\n", + "\t\t\tnew_exposures_path='path/to/exposures.h5'\n", + ")\n" + ] + } + ], + "source": [ + "from climada.entity.measures.measure_config import ExposuresModifierConfig\n", + "\n", + "# 1. Changing existing Exposures\n", + "exp_mod = ExposuresModifierConfig(\n", + " reassign_impf_id={\"TC\": {1: 2}}, # Remaps exposures points with impf_TC == 1 to 2.\n", + " set_to_zero=[\n", + " 0,\n", + " 25,\n", + " 78,\n", + " ], # Sets the value of exposure points with index 0, 25 and 78 to 0.\n", + ")\n", + "\n", + "print(\"--- First Config ---\")\n", + "print(exp_mod)\n", + "\n", + "# 2. Replacing the expoosure from a file\n", + "exp_mod_new = ExposuresModifierConfig(new_exposures_path=\"path/to/exposures.h5\")\n", + "\n", + "print(\"\\n--- Replacement Config ---\")\n", + "print(exp_mod_new)" + ] + }, + { + "cell_type": "markdown", + "id": "2c2d4488-28e5-4ced-b9cf-e4d5c0cade3e", + "metadata": {}, + "source": [ + "## Defining the financial aspects of the measure\n", + "\n", + "For in depth description of CostIncome objects, refer to the [related tutorial](cost-income-tutorial).\n", + "\n", + "```{note}\n", + "The default for mkt_price_year if not provided is the current year.\n", + "```\n", + "\n", + "You can easily define the CostIncome object to be associated with the measure using `CostIncomeConfig`:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "7c107fc5-606b-4904-8b8e-059f846c2e39", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "--- Growth & Income Config ---\n", + "CostIncomeConfig(\n", + "\t\tNon default fields:\n", + "\t\t\tinit_cost=500000.0\n", + "\t\t\tperiodic_cost=20000.0\n", + "\t\t\tperiodic_income=100000.0\n", + "\t\t\tcost_yearly_growth_rate=0.02\n", + "\t\t\tincome_yearly_growth_rate=0.03\n", + ")\n", + "\n", + "--- Custom Schedule Config ---\n", + "CostIncomeConfig(\n", + "\t\tNon default fields:\n", + "\t\t\tcustom_cash_flows=[{'date': '2024-01-01', 'value': -1000000}, {'date': '2029-01-01', 'value': -200000}, {'date': '2034-01-01', 'value': 500000}]\n", + ")\n" + ] + } + ], + "source": [ + "from climada.entity.measures.measure_config import CostIncomeConfig\n", + "\n", + "# This models a measure where costs increase by 2% annually,\n", + "# but it generates 100k in yearly income which grows by 3%.\n", + "growth_finance = CostIncomeConfig(\n", + " init_cost=500_000.0,\n", + " periodic_cost=20_000.0,\n", + " cost_yearly_growth_rate=0.02,\n", + " periodic_income=100_000.0,\n", + " income_yearly_growth_rate=0.03,\n", + " freq=\"Y\",\n", + ")\n", + "\n", + "print(\"\\n--- Growth & Income Config ---\")\n", + "print(growth_finance)\n", + "\n", + "\n", + "# Custom Cash Flow\n", + "# If the investment isn't linear (e.g., a major retrofit in year 5),\n", + "# you can define a list of specific events.\n", + "custom_schedule = [\n", + " {\"date\": \"2024-01-01\", \"value\": -1000000}, # Initial cost\n", + " {\"date\": \"2029-01-01\", \"value\": -200000}, # Mid-term overhaul\n", + " {\"date\": \"2034-01-01\", \"value\": 500000}, # Terminal value\n", + "]\n", + "\n", + "custom_finance = CostIncomeConfig(custom_cash_flows=custom_schedule)\n", + "\n", + "print(\"\\n--- Custom Schedule Config ---\")\n", + "print(custom_finance)" + ] + }, + { + "cell_type": "markdown", + "id": "ab4216dd-fd0b-4939-844d-56bd5ea49504", + "metadata": {}, + "source": [ + "## Reading from and writing to\n", + "\n", + "You can easily write/read measure configurations from YAML, as well as from pandas Series.\n", + "\n", + "You can also create `Measures`/`MeasureSet` directly, using the same methods (these methods first load the file as a `MeasureConfig` and convert it directly to a `Measure`)\n", + "Similarly you can still create `MeasureSet` from legacy Excel or matlab files using `MeasureSet.from_excel()` which takes care of remapping the legacy parameter names to the new ones.\n", + "See the [measure tutorial](measure-tutorial) for more details on that." + ] + }, + { + "cell_type": "markdown", + "id": "63132690-dd6f-4f45-96a9-5519fa2dec07", + "metadata": {}, + "source": [ + "\n", + "```python\n", + "import pandas as pd\n", + "from climada.entity.measures.measure_config import MeasureConfig\n", + "\n", + "# 1. Exporting to YAML\n", + "# Assuming 'my_measure_config' is a MeasureConfig object created previously\n", + "my_measure_config.to_yaml(\"seawall_config.yaml\")\n", + "\n", + "# 2. Loading from YAML\n", + "loaded_measure_config = MeasureConfig.from_yaml(\"seawall_config.yaml\")\n", + "\n", + "# 3. Loading from Pandas\n", + "row_data = pd.Series({\n", + " \"name\": \"Mangrove_Restoration\",\n", + " \"haz_type\": \"TC\",\n", + " \"impf_mdd_mult\": 0.7,\n", + " \"init_cost\": 250000,\n", + " \"color_rgb\": (0.1, 0.8, 0.1)\n", + "})\n", + "\n", + "pandas_measure_config = MeasureConfig.from_row(row_data)\n", + "\n", + "# 4. Measure object directly\n", + "measure = Measure.from_yaml(\"seawall_config.yaml\")\n", + "```" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python [conda env:climada_env_dev]", + "language": "python", + "name": "conda-env-climada_env_dev-py" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.15" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/doc/user-guide/climada_trajectories.ipynb b/doc/user-guide/climada_trajectories.ipynb new file mode 100644 index 0000000000..2e2b381f07 --- /dev/null +++ b/doc/user-guide/climada_trajectories.ipynb @@ -0,0 +1,3172 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "0ec4f4ee-6c4f-45db-a4e5-9ad77554757a", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "Trajectories Module\n", + "===================" + ] + }, + { + "cell_type": "markdown", + "id": "856ac388-9edb-497e-a2ff-a325f2a22562", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "# Important disclaimers" + ] + }, + { + "cell_type": "markdown", + "id": "f7d4fdab-8662-4848-bb87-9b6045447957", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "### Interpolation of risk can be... risky" + ] + }, + { + "cell_type": "markdown", + "id": "8f9531a7-9a1a-400f-8c82-3a51fdc6671a", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "One purpose of this module is to improve the evaluation of risk in between two \"known\" points in time.\n", + "\n", + "This part relies on interpolation (linear by default) of impacts and risk metrics in between the different specified points, \n", + "which may lead to incoherent results in cases where this simplification drifts too far from reality.\n", + "\n", + "For instance if you are using different historical events as you points in time, a static comparison of the different risk\n", + "estimates may be interesting, but interpolating in between makes very little sense.\n", + "\n", + "As always users should carefully consider if the tool fits the purpose and if the limitations \n", + "remain acceptable, even more so when used to design Disaster Risk Reduction or Climate Change Adaptation measures." + ] + }, + { + "cell_type": "markdown", + "id": "c588329e-f5a5-4945-aad1-900b7bb675e3", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "### Memory and computation requirements\n", + "\n", + "This module adds a new dimension (time) to the risk, as such, it **multiplies** the memory and computation requirement along that dimension (although we avoid running a full-fledge impact computation for each \"interpolated\" point, we still have to define an impact matrix for each of those). \n", + "\n", + "This can of course (very) quickly increase the memory and computation requirements for bigger data. We encourage you to first try on small examples before running big computations.\n" + ] + }, + { + "cell_type": "markdown", + "id": "b53b1da2-7be1-4507-96bb-2efd8dd3e910", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "## Using the `trajectories` module" + ] + }, + { + "cell_type": "markdown", + "id": "4e0f3261-f443-4cc6-b85b-c6a3d90b73e3", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "The fundamental idea behing the `trajectories` module is to enable a better assessment of the evolution of risk over time, both by facilitating point by point comparison, and the \"evolution\" or risk.\n", + "\n", + "This module aims at facilitating answering questions such as:\n", + "\n", + "- How does future hazards (probabilistic event set), exposure and vulnerability change impacts with respect to present?\n", + "- How would the impacts compare if a past event were to happen again with present / future exposure?\n", + "- How will risk evolve in the future under different assumptions on the evolution of hazard, exposure, vulnerability and discount rate?\n", + "- *etc*.\n", + "\n", + "To achieve this, this module introduces two concepts:\n", + "\n", + "- Snapshots of risk, a fixed representation of risk (via its three components Exposure, Hazard and Vulnerability) for a given date. This concept is intended to be generic, as such the given date can be something else than a year, a month or a day for instance, but keep in mind that we will not check that the data you provide makes sense for it!\n", + "- Trajectories of risk, a collection of snapshots,for which risk metrics can be computed and regrouped to ease their evaluation." + ] + }, + { + "cell_type": "markdown", + "id": "6396ab9f-7b09-49a7-81a5-a45e7a99a4ff", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "### `Snapshot`: A snapshot of risk at a specific year" + ] + }, + { + "cell_type": "markdown", + "id": "274a342f-54c0-4590-9110-5e297010955e", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "We use `Snapshot` objects to define a point in time for risk. This object acts as a wrapper of the classic risk framework composed of Exposure, Hazard and Vulnerability. As such it is defined for a specific date (usually a year), and contains an `Exposures`, a `Hazard`, and an `ImpactFuncSet` object.\n", + "\n", + "Instantiating such a `Snapshot` is done simply with:\n", + "\n", + "```python\n", + "snap = Snapshot(\n", + " exposure=your_exposure,\n", + " hazard=your_hazard,\n", + " impfset=your_impfset,\n", + " date=your_date\n", + " )\n", + "```\n", + "\n", + "Note that to avoid any ambiguity, you need to write explicitly `exposure=your_exposure`.\n", + "\n", + "Think of `Snapshot` as a representation of risk at, or around, a specific date. Your hazard should be a probabilistic set of events that are representative for the designated date.\n", + "\n", + "To be consistent with the intuitive idea of a snapshot, by default `Snapshot` objects make a \"deep copy\" of the risk triplet and are immutable.\n", + "This means that they do not change once created (notably even if you change one of the component, e.g. the Hazard object, outside of the `Snapshot`).\n", + "If you want a `Snapshot` with a different `Hazard`, you need to create a new one.\n", + "\n", + "In that spirit, you cannot directly instantiate a Snapshot with an adaptation measure. To include adaptation, you need to first create the snapshot without adaptation, and then use `apply_measure()`, which\n", + "will return a new `Snapshot`, with the changed (Exposure, Hazard, ImpactFuncSet) according to the given measure.\n", + "\n", + "You can supply a measure object to `Snapshot(<...>, measure=measure)`, but **it will not be applied** to the triplet (Exposure, Hazard, ImpactFuncSet) and assume the triplet already include the change.\n", + "Only advanced users with a good understanding of what they are doing should supply a measure parameter directly to the `Snapshot` constructor, else, stick to the `apply_measure()` method.\n", + "\n", + "Below is an concrete example of how to create a Snapshot using data from the data API for tropical cyclones in Haiti:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "ac3a397b-9a47-4fd1-ba14-0913ca707e29", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2026-02-26 10:48:49,071 - climada.entity.exposures.base - INFO - Reading /home/sjuhel/climada/data/exposures/litpop/LitPop_150arcsec_HTI/v3/LitPop_150arcsec_HTI.hdf5\n", + "2026-02-26 10:48:54,125 - climada.hazard.io - INFO - Reading /home/sjuhel/climada/data/hazard/tropical_cyclone/tropical_cyclone_10synth_tracks_150arcsec_HTI_1980_2020/v2/tropical_cyclone_10synth_tracks_150arcsec_HTI_1980_2020.hdf5\n" + ] + } + ], + "source": [ + "from climada.util.api_client import Client\n", + "from climada.entity import ImpactFuncSet, ImpfTropCyclone\n", + "from climada.trajectories.snapshot import Snapshot\n", + "\n", + "client = Client()\n", + "\n", + "exp_present = client.get_litpop(country=\"Haiti\")\n", + "\n", + "haz_present = client.get_hazard(\n", + " \"tropical_cyclone\",\n", + " properties={\n", + " \"country_name\": \"Haiti\",\n", + " \"climate_scenario\": \"historical\",\n", + " \"nb_synth_tracks\": \"10\",\n", + " },\n", + ")\n", + "\n", + "impf_set = ImpactFuncSet([ImpfTropCyclone.from_emanuel_usa()])\n", + "exp_present.gdf.rename(columns={\"impf_\": \"impf_TC\"}, inplace=True)\n", + "exp_present.gdf[\"impf_TC\"] = 1\n", + "\n", + "snap1 = Snapshot(\n", + " exposure=exp_present, hazard=haz_present, impfset=impf_set, date=\"2018\"\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "044e2b4f-506a-492f-9627-471f46ad7c3a", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "All risk dimensions are freely accessible from the snapshot:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "aa0becca-d334-40b4-86c0-1959c750f6d5", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2026-02-26 10:48:54,165 - climada.util.coordinates - INFO - Raster from resolution 0.04166665999999708 to 0.04166665999999708.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/sjuhel/Repos/climada_python/climada/util/coordinates.py:3134: FutureWarning: The `drop` keyword argument is deprecated and in future the only supported behaviour will match drop=False. To silence this warning and adopt the future behaviour, stop providing `drop` as a keyword to `set_geometry`. To replicate the `drop=True` behaviour you should update your code to\n", + "`geo_col_name = gdf.active_geometry_name; gdf.set_geometry(new_geo_col).drop(columns=geo_col_name).rename_geometry(geo_col_name)`.\n", + " df_poly.set_geometry(\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "snap1.exposure.plot_raster()\n", + "snap1.hazard.plot_intensity(0)\n", + "snap1.impfset.plot()" + ] + }, + { + "cell_type": "markdown", + "id": "d2e6daae-6345-41ac-a560-71040942db39", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "### Evaluating risk from multiple snapshots using trajectories" + ] + }, + { + "cell_type": "markdown", + "id": "8e8458c3-a3f9-4210-9de0-15293167f2f9", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "Trajectories facilitate the evaluation of risk of multiple snapshot. The module implements two kinds of trajectories:\n", + "\n", + "- `StaticRiskTrajectory`: which estimate the risk at each snaphot only, and regroups the results nicely.\n", + "- `InterpolatedRiskTrajectory`: which also includes the evolution of risk in between the snapshots using interpolation.\n", + "\n", + "So first, let us define `Snapshot` for a future point in time. We will increase the value of the exposure following a certain growth rate, and use future tropical\n", + "cyclone data for the hazard, we will also change the vulnerability to be slightly lower in the future:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "c516c861-c5c1-475b-82e2-c867c5c08ec9", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2026-02-26 10:49:01,309 - climada.hazard.io - INFO - Reading /home/sjuhel/climada/data/hazard/tropical_cyclone/tropical_cyclone_10synth_tracks_150arcsec_rcp60_HTI_2040/v2/tropical_cyclone_10synth_tracks_150arcsec_rcp60_HTI_2040.hdf5\n" + ] + } + ], + "source": [ + "import copy\n", + "\n", + "future_year = 2040\n", + "exp_future = copy.deepcopy(exp_present)\n", + "exp_future.ref_year = future_year\n", + "n_years = exp_future.ref_year - exp_present.ref_year + 1\n", + "growth_rate = 1.02\n", + "growth = growth_rate**n_years\n", + "exp_future.gdf[\"value\"] = exp_future.gdf[\"value\"] * growth\n", + "\n", + "haz_future = client.get_hazard(\n", + " \"tropical_cyclone\",\n", + " properties={\n", + " \"country_name\": \"Haiti\",\n", + " \"climate_scenario\": \"rcp60\",\n", + " \"ref_year\": str(future_year),\n", + " \"nb_synth_tracks\": \"10\",\n", + " },\n", + ")\n", + "\n", + "impf_set = ImpactFuncSet(\n", + " [\n", + " ImpfTropCyclone.from_emanuel_usa(v_half=78.0),\n", + " ]\n", + ")\n", + "exp_future.gdf.rename(columns={\"impf_\": \"impf_TC\"}, inplace=True)\n", + "exp_future.gdf[\"impf_TC\"] = 1\n", + "snap2 = Snapshot(\n", + " exposure=exp_future, hazard=haz_future, impfset=impf_set, date=str(future_year)\n", + ")\n", + "\n", + "### Now we can define a list of two snapshots, present and future:\n", + "snapcol = [snap1, snap2]" + ] + }, + { + "cell_type": "markdown", + "id": "27ca72b1-b1fa-4cd2-8f74-a69dc6eb3c9c", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "Based on such a list of snapshots, we can then evaluate a risk trajectory using a `StaticRiskTrajectory` or a `InterpolatedRiskTrajectory` object." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "e782ab8b", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "from climada.trajectories import StaticRiskTrajectory, InterpolatedRiskTrajectory\n", + "\n", + "static_risk_traj = StaticRiskTrajectory(snapcol)\n", + "interpolated_risk_traj = InterpolatedRiskTrajectory(snapcol)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "483767e7-9089-4b5e-a307-514ac302e773", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "remove-input" + ] + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%%html\n", + "" + ] + }, + { + "cell_type": "markdown", + "id": "2d7e8653-4ef9-40f5-8f8a-ef0e8b3b8a8c", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "#### Tidy format\n", + "\n", + "We use the \"tidy\" format to output most of the results.\n", + "\n", + "A **tidy data** format is a standardized way to structure datasets, making them easier to analyze and visualize. It's based on three main principles:\n", + "\n", + "1. **Each variable forms a column.**\n", + "2. **Each observation forms a row.**\n", + "3. **Each type of observational unit forms a table.**\n", + "\n", + "Example:\n", + "\n", + "| group | date | metric | risk |\n", + "| :---: | :---: | :---: | :---: |\n", + "| All | 2018-01-01 | aai | $1.840432 \\times 10^{8}$ |\n", + "| All | 2040-01-01 | aai | $6.946753 \\times 10^{8}$ |\n", + "| All | 2018-01-01 | rp\\_20 | $1.420589 \\times 10^{8}$ |\n", + "\n", + "In this example, every descriptive quality (variable) of the risk evaluation is placed in its own column:\n", + "\n", + "* **`group`**: The exposure subgroup for the risk evalution point.\n", + "* **`date`**: The date for the risk evalution point.\n", + "* **`metric`**: The specific risk measure (e.g., 'aai', 'rp\\_20', 'rp\\_100').\n", + "* **`unit`**: The unit of the risk evaluation.\n", + "* **`risk`**: The actual value being measured.\n", + "\n", + "Each row represents a single, complete observation. For example, the very first row is a measurement of the **'aai' metric** for **group 'All'** on **'2018-01-01'**, with the resulting **risk** value of **$1.840432 \\times 10^{8}$ USD**." + ] + }, + { + "cell_type": "markdown", + "id": "ca8951cc-4a0a-4f3d-9c21-96dd6a835810", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "#### Static and Interpolated trajectories" + ] + }, + { + "cell_type": "markdown", + "id": "dc76cb91", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "`StaticRiskTrajectory` will compute and hold risk metrics for all the given snapshots without interpolation:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "14453563", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2026-02-26 10:49:01,429 - climada.trajectories.calc_risk_metrics - WARNING - No group id defined in the Exposures object. Per group aai will be empty.\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
dategroupmeasuremetricunitrisk
02018-01-01Allno_measureaaiUSD1.840432e+08
12040-01-01Allno_measureaaiUSD2.749295e+08
22018-01-01Allno_measurerp_20USD1.420589e+08
32040-01-01Allno_measurerp_20USD2.357976e+08
42018-01-01Allno_measurerp_50USD3.059112e+09
52040-01-01Allno_measurerp_50USD4.580720e+09
62018-01-01Allno_measurerp_100USD5.719050e+09
72040-01-01Allno_measurerp_100USD8.477125e+09
\n", + "
" + ], + "text/plain": [ + " date group measure metric unit risk\n", + "0 2018-01-01 All no_measure aai USD 1.840432e+08\n", + "1 2040-01-01 All no_measure aai USD 2.749295e+08\n", + "2 2018-01-01 All no_measure rp_20 USD 1.420589e+08\n", + "3 2040-01-01 All no_measure rp_20 USD 2.357976e+08\n", + "4 2018-01-01 All no_measure rp_50 USD 3.059112e+09\n", + "5 2040-01-01 All no_measure rp_50 USD 4.580720e+09\n", + "6 2018-01-01 All no_measure rp_100 USD 5.719050e+09\n", + "7 2040-01-01 All no_measure rp_100 USD 8.477125e+09" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "static_risk_traj.per_date_risk_metrics()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "cd169d1b-741c-471c-b402-391096e20613", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + " The `InterpolatedRiskTrajectory` object goes further and computes the metrics for all the dates between the different snapshots in the given collection for a given time resolution (one year by default). In this example, from the snapshot in 2018 to the one in 2040. \n", + "\n", + "Note that this can require a bit of computation and memory, especially for large regions or extended range of time with high time resolution.\n", + "Also note, that most computations are only run and stored when needed, not at instantiation.\n", + "\n", + "From this object you can access different risk metrics:\n", + "\n", + "* Average Annual Impact (aai) both for all exposure points (group == \"All\") and specific groups of exposure points (defined by a \"group_id\" in the exposure).\n", + "* Estimated impact for different return periods (20, 50 and 100 by default)\n", + "\n", + "Both as average over the whole period:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "9c485dc4-c009-46fb-aa4a-603bc9dcf5b4", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2026-02-26 10:49:01,923 - climada.trajectories.calc_risk_metrics - WARNING - No group id defined in at least one of the Exposures object. Per group aai will be empty.\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
periodgroupmeasuremetricunitrisk
02018 to 2040Allno_measureaaiUSD2.309016e+08
12018 to 2040Allno_measurerp_100USD7.148372e+09
22018 to 2040Allno_measurerp_20USD1.896739e+08
32018 to 2040Allno_measurerp_50USD3.847129e+09
\n", + "
" + ], + "text/plain": [ + " period group measure metric unit risk\n", + "0 2018 to 2040 All no_measure aai USD 2.309016e+08\n", + "1 2018 to 2040 All no_measure rp_100 USD 7.148372e+09\n", + "2 2018 to 2040 All no_measure rp_20 USD 1.896739e+08\n", + "3 2018 to 2040 All no_measure rp_50 USD 3.847129e+09" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "interpolated_risk_traj.per_period_risk_metrics()" + ] + }, + { + "cell_type": "markdown", + "id": "af53286d-ee62-44a5-907b-84103302663d", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "Or on a per-date basis:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "6b73a589-9ee4-41e8-90e0-910bfe4dd8fc", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2026-02-26 10:49:01,941 - climada.trajectories.calc_risk_metrics - WARNING - No group id defined in at least one of the Exposures object. Per group aai will be empty.\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
dategroupmeasuremetricunitrisk
02018Allno_measureaaiUSD1.840432e+08
12019Allno_measureaaiUSD1.885312e+08
22020Allno_measureaaiUSD1.929908e+08
32021Allno_measureaaiUSD1.974211e+08
42022Allno_measureaaiUSD2.018214e+08
.....................
872036Allno_measurerp_100USD8.025179e+09
882037Allno_measurerp_100USD8.140512e+09
892038Allno_measurerp_100USD8.254300e+09
902039Allno_measurerp_100USD8.366514e+09
912040Allno_measurerp_100USD8.477125e+09
\n", + "

92 rows × 6 columns

\n", + "
" + ], + "text/plain": [ + " date group measure metric unit risk\n", + "0 2018 All no_measure aai USD 1.840432e+08\n", + "1 2019 All no_measure aai USD 1.885312e+08\n", + "2 2020 All no_measure aai USD 1.929908e+08\n", + "3 2021 All no_measure aai USD 1.974211e+08\n", + "4 2022 All no_measure aai USD 2.018214e+08\n", + ".. ... ... ... ... ... ...\n", + "87 2036 All no_measure rp_100 USD 8.025179e+09\n", + "88 2037 All no_measure rp_100 USD 8.140512e+09\n", + "89 2038 All no_measure rp_100 USD 8.254300e+09\n", + "90 2039 All no_measure rp_100 USD 8.366514e+09\n", + "91 2040 All no_measure rp_100 USD 8.477125e+09\n", + "\n", + "[92 rows x 6 columns]" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "interpolated_risk_traj.per_date_risk_metrics()" + ] + }, + { + "cell_type": "markdown", + "id": "00e0a09b-9dd6-4378-81a1-cda5290f9aa4", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "You can also plot the \"contribution\" or \"components\" of the change in risk (Average ) via a waterfall graph:\n", + "\n", + " - The 'base risk', i.e., the risk without change in hazard or exposure, compared to trajectory's earliest date.\n", + " - The 'exposure contribution', i.e., the additional risks due to change in exposure (only)\n", + " - The 'hazard contribution', i.e., the additional risks due to change in hazard (only)\n", + " - The 'vulnerability contribution', i.e., the additional risks due to change in vulnerability (only)\n", + " - The 'interaction contribution', i.e., the additional risks due to the interaction term (between exposure, hazard and vulnerability)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "08c226a4-944b-4301-acfa-602adde980a5", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "interpolated_risk_traj.plot_waterfall()" + ] + }, + { + "cell_type": "markdown", + "id": "7896af66-b0aa-4418-b22e-c64fd4d2cfe1", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "And as well on a per date basis (keep in mind this is an interpolation, thus should be interpreted with caution):" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "6a15775f-af9e-4940-b18d-eb16bd0c8c85", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(
,\n", + " )" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "interpolated_risk_traj.plot_time_waterfall()" + ] + }, + { + "cell_type": "markdown", + "id": "501e455b-e7c6-4672-9191-d5fefe38d424", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "#### DiscRates" + ] + }, + { + "cell_type": "markdown", + "id": "0dba0218-55fe-423d-a520-61d3cb2a991c", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "To correctly assess the future risk, you may also want to apply a discount rate, in order to express future costs in net present value.\n", + "\n", + "This can easily be done providing an instance of the already existing `DiscRates` class when instantiating the trajectory.\n", + "\n", + "The discount rate is applied by assuming the year of the date of the first Snapshot is the baseline (no discounting).\n", + "\n", + "Note that when interpolating on a sub-yearly basis, the discount rate remains on a yearly basis: All dates " + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "651e31cb-5a55-4a22-a7c3-b5f79b3a20ef", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "from climada.entity import DiscRates\n", + "import numpy as np\n", + "\n", + "year_range = np.arange(exp_present.ref_year, exp_future.ref_year + 1)\n", + "annual_discount_stern = np.ones(n_years) * 0.014\n", + "discount_stern = DiscRates(year_range, annual_discount_stern)\n", + "discounted_risk_traj = InterpolatedRiskTrajectory(\n", + " snapcol, risk_disc_rates=discount_stern\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "d86bedbb-6c0a-4f7d-a63e-5012510339d3", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "You can easily notice the difference with the previously defined trajectory without discount rate." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "ee3b0217-fe14-44a9-98f5-e1fc7f45e613", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ax = interpolated_risk_traj.aai_metrics().plot(\n", + " x=\"date\", y=\"risk\", label=\"No discount rate\"\n", + ")\n", + "discounted_risk_traj.aai_metrics().plot(\n", + " x=\"date\", y=\"risk\", label=\"Stern discount rate\", ax=ax\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "0152e9fa-55fa-4cf2-b187-59e6228af563", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "## Advanced usage\n", + "\n", + "In this section we present some more advanced features and use of this module." + ] + }, + { + "cell_type": "markdown", + "id": "dbf4b23d-d502-4c06-8e0d-eb832af8ebe4", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "### Exposure sub groups" + ] + }, + { + "cell_type": "markdown", + "id": "86a58a22-63a5-42a9-9afb-cf8962156e36", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "It is often useful to look at sub-groups of your exposure (social groups of different social vulnerability, buildings of different type, etc.)\n", + "\n", + "The `trajectory` module facilitate looking at risk specifically for sub-groups of exposure points. In order to do so, you need to set a column \"group_id\" in the `GeoDataFrame` of your exposure.\n", + "\n", + "Here we create dummy groups for exposure points above and below the mean exposure value:" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "566f0c34-b19b-403a-a905-92c51093c182", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "exp_present.gdf[\"group_id\"] = (\n", + " exp_present.gdf[\"value\"] > exp_present.gdf[\"value\"].mean()\n", + ") * 1\n", + "exp_future.gdf[\"group_id\"] = (\n", + " exp_future.gdf[\"value\"] > exp_future.gdf[\"value\"].mean()\n", + ") * 1\n", + "\n", + "snap1 = Snapshot(\n", + " exposure=exp_present, hazard=haz_present, impfset=impf_set, date=\"2018\"\n", + ")\n", + "snap2 = Snapshot(exposure=exp_future, hazard=haz_future, impfset=impf_set, date=\"2040\")\n", + "static_risk_traj = StaticRiskTrajectory([snap1, snap2])\n", + "interpolated_risk_traj = InterpolatedRiskTrajectory([snap1, snap2])" + ] + }, + { + "cell_type": "markdown", + "id": "e0123f8f-ec7f-47ac-9f60-0f59808b9670", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "You can now access the `aii_per_group` metric, which will give you the average impact (for the frequency unit of you hazard) restricted to the exposure points of the corresponding group." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "344a84d7-275c-426d-80d1-5375696f5cc3", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
dategroupmeasuremetricunitrisk
02018-01-010no_measureaaiUSD6.094866e+06
12018-01-011no_measureaaiUSD1.508360e+08
22040-01-010no_measureaaiUSD1.063804e+07
32040-01-011no_measureaaiUSD2.642915e+08
\n", + "
" + ], + "text/plain": [ + " date group measure metric unit risk\n", + "0 2018-01-01 0 no_measure aai USD 6.094866e+06\n", + "1 2018-01-01 1 no_measure aai USD 1.508360e+08\n", + "2 2040-01-01 0 no_measure aai USD 1.063804e+07\n", + "3 2040-01-01 1 no_measure aai USD 2.642915e+08" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "static_risk_traj.aai_per_group_metrics()" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "43cff641-6288-48d6-81bb-40d9755c24d1", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
dategroupmeasuremetricunitrisk
020180no_measureaaiUSD6.094866e+06
120181no_measureaaiUSD1.508360e+08
220190no_measureaaiUSD6.285071e+06
320191no_measureaaiUSD1.555734e+08
420200no_measureaaiUSD6.476829e+06
\n", + "
" + ], + "text/plain": [ + " date group measure metric unit risk\n", + "0 2018 0 no_measure aai USD 6.094866e+06\n", + "1 2018 1 no_measure aai USD 1.508360e+08\n", + "2 2019 0 no_measure aai USD 6.285071e+06\n", + "3 2019 1 no_measure aai USD 1.555734e+08\n", + "4 2020 0 no_measure aai USD 6.476829e+06" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "interpolated_risk_traj.aai_per_group_metrics().head()" + ] + }, + { + "cell_type": "markdown", + "id": "4fcc943d-e5c6-4667-8ec6-8f7d2f0b3ce4", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "### Results caching" + ] + }, + { + "cell_type": "markdown", + "id": "b3f326db-458b-4238-a30b-fcc9215f8f36", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "Trajectory objects regroup a large number of computations, especially for the interpolated ones. The module makes use of both a caching process to avoid recomputing the same metric over and over, and a \"lazy\" flow, which means computations are run only when needed.\n", + "\n", + "As such, the first time you call any metric can take a bit of time, but the subsequent ones should be much faster.\n", + "\n", + "Modifying attributes that would change the results (e.g. the time resolution or the impact computation strategy), will reset the cache.\n", + "\n", + "However this caching process can also get memory expensive. So you can deactivate it by setting \"trajectory_caching\" to false in CLIMADA's configuration (see __[Configuration](../development/Guide_Configuration.ipynb)__)." + ] + }, + { + "cell_type": "markdown", + "id": "42c9daed-6488-488b-b01a-fd6dfc5d0274", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "### Higher number of snapshots" + ] + }, + { + "cell_type": "markdown", + "id": "6db14802-fa35-4e33-91ef-7dddd4d43da7", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "You can of course use the module to evaluate more that two snapshots. With the `StaticRiskTrajectory` you will get a collection of results for each snapshot.\n", + "\n", + "For the `InterpolatedRiskTrajectory` the interpolation will be done between each pair of consecutive snapshots and all results will be collected together, this is usefull if you want to explore a trajectory for which you have clear \"intermediate points\", for instance if you are evaluating the risk in an area for which you know some specific development projects will start at a certain date.\n", + "\n", + "Below is an example featuring three snapshots:" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "d93eb82b-65d2-48fe-a195-6cb12f23bf47", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2026-02-26 10:49:04,288 - climada.entity.exposures.base - INFO - Reading /home/sjuhel/climada/data/exposures/litpop/LitPop_150arcsec_HTI/v3/LitPop_150arcsec_HTI.hdf5\n", + "2026-02-26 10:49:09,406 - climada.hazard.io - INFO - Reading /home/sjuhel/climada/data/hazard/tropical_cyclone/tropical_cyclone_10synth_tracks_150arcsec_HTI_1980_2020/v2/tropical_cyclone_10synth_tracks_150arcsec_HTI_1980_2020.hdf5\n", + "2026-02-26 10:49:14,468 - climada.hazard.io - INFO - Reading /home/sjuhel/climada/data/hazard/tropical_cyclone/tropical_cyclone_10synth_tracks_150arcsec_rcp60_HTI_2040/v2/tropical_cyclone_10synth_tracks_150arcsec_rcp60_HTI_2040.hdf5\n", + "2026-02-26 10:49:19,501 - climada.hazard.io - INFO - Reading /home/sjuhel/climada/data/hazard/tropical_cyclone/tropical_cyclone_10synth_tracks_150arcsec_rcp60_HTI_2060/v2/tropical_cyclone_10synth_tracks_150arcsec_rcp60_HTI_2060.hdf5\n", + "2026-02-26 10:49:24,689 - climada.hazard.io - INFO - Reading /home/sjuhel/climada/data/hazard/tropical_cyclone/tropical_cyclone_10synth_tracks_150arcsec_rcp60_HTI_2080/v2/tropical_cyclone_10synth_tracks_150arcsec_rcp60_HTI_2080.hdf5\n" + ] + } + ], + "source": [ + "from climada.engine.impact_calc import ImpactCalc\n", + "from climada.util.api_client import Client\n", + "from climada.entity import ImpactFuncSet, ImpfTropCyclone\n", + "from climada.trajectories.snapshot import Snapshot\n", + "from climada.trajectories import InterpolatedRiskTrajectory\n", + "import copy\n", + "\n", + "client = Client()\n", + "\n", + "future_years = [2040, 2060, 2080]\n", + "\n", + "exp_present = client.get_litpop(country=\"Haiti\")\n", + "haz_present = client.get_hazard(\n", + " \"tropical_cyclone\",\n", + " properties={\n", + " \"country_name\": \"Haiti\",\n", + " \"climate_scenario\": \"historical\",\n", + " \"nb_synth_tracks\": \"10\",\n", + " },\n", + ")\n", + "\n", + "impf_set = ImpactFuncSet([ImpfTropCyclone.from_emanuel_usa()])\n", + "exp_present.gdf.rename(columns={\"impf_\": \"impf_TC\"}, inplace=True)\n", + "exp_present.gdf[\"impf_TC\"] = 1\n", + "exp_present.gdf[\"group_id\"] = (exp_present.gdf[\"value\"] > 500000) * 1\n", + "\n", + "snapcol = [\n", + " Snapshot(exposure=exp_present, hazard=haz_present, impfset=impf_set, date=\"2018\")\n", + "]\n", + "\n", + "for year in future_years:\n", + " exp_future = copy.deepcopy(exp_present)\n", + " exp_future.ref_year = year\n", + " n_years = exp_future.ref_year - exp_present.ref_year + 1\n", + " growth_rate = 1.02\n", + " growth = growth_rate**n_years\n", + " exp_future.gdf[\"value\"] = exp_future.gdf[\"value\"] * growth\n", + "\n", + " haz_future = client.get_hazard(\n", + " \"tropical_cyclone\",\n", + " properties={\n", + " \"country_name\": \"Haiti\",\n", + " \"climate_scenario\": \"rcp60\",\n", + " \"ref_year\": str(year),\n", + " \"nb_synth_tracks\": \"10\",\n", + " },\n", + " )\n", + " impf_set = ImpactFuncSet(\n", + " [\n", + " ImpfTropCyclone.from_emanuel_usa(v_half=78.0),\n", + " ]\n", + " )\n", + " exp_future.gdf.rename(columns={\"impf_\": \"impf_TC\"}, inplace=True)\n", + " exp_future.gdf[\"impf_TC\"] = 1\n", + " snapcol.append(\n", + " Snapshot(\n", + " exposure=exp_future, hazard=haz_future, impfset=impf_set, date=str(year)\n", + " )\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "b85d5b95-4316-481a-9eed-86977647b791", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "risk_traj = InterpolatedRiskTrajectory(snapcol)" + ] + }, + { + "cell_type": "markdown", + "id": "537a9dd8-96e9-4ef4-a137-358990c658d2", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "The \"static\" waterfall plot shows the evolution of risk between the earliest and latest snapshot." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "1c5aeb4b-6320-479d-82a6-9b2c3901868e", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAt0AAAKKCAYAAADhvOe9AAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjgsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvwVt1zgAAAAlwSFlzAAAPYQAAD2EBqD+naQAAjvFJREFUeJzt3QeYFFX29/FDzjmDZAkiqARFMICSBUFRUZEg4CqLIggIIuqCARQlKLq6KhJEgi5gVoIICogiigQRE0EURJScQ7/P7/63++3uCcyM1NRM9/fzPA091TU91XWrqk/dOnVulkAgEDAAAAAAnsnq3VsDAAAAIOgGAAAA0gE93QAAAIDHCLoBAAAAjxF0AwAAAB4j6AYAAAA8RtANAAAAeIygGwAAAPAYQTcAAADgsbgNuidPnmxZsmRJ8rF48WLP/nalSpXs1ltvTdPvTp8+3caPH5/oa1ru4cOHW0b20UcfWYMGDSxfvnxued98881E5/vtt9/cZ1m9enWC17Tu8ufPb7Hq3//+t9s+U0Pba/R268V6Wr58uWuXPXv2JHitadOm7pHZpXQbTW17pPTY0K5du1T/vb/7u0kZOXJkmj5/ZrFo0SLr2bOn1axZ07V3uXLlrEOHDrZq1apE5//qq6+sefPmbr8qXLiwdezY0X7++ecE8+kYrdcqV67stoPk9ouPP/7YWrRoYSVLlnTve95559kzzzxjJ0+etIwkJd8v33//vQ0aNMjq16/v1k/RokXtkksusf/+97+Jzr9z5053nCpevLjlzZvXGjVq5Pa/aEePHrUnn3zSateu7dqpVKlS1qZNG3c8inb8+HEbMWKE2x9y5crl2nbChAmp+qwPP/yw1apVy06dOpXgtV27drn31fr48ssvU/W+8RJXbd682TK75cl8111++eXWv3//tL1xIE5NmjQpoI+v/z/77LMEj71793r2tytWrBjo3r17mn63bdu27vcTo+X+5ZdfAhnVqVOnAkWLFg1cfPHFgYULF7rl/euvvxKdd+XKlaH2iaZ1ly9fvkCsOvfccwNNmjRJ1e9oe43ebr1YT08++aRrl02bNiV4bf369e6RmaVmG01te6SE9m3t42nxd343Kdp+0nqsygyuv/76wBVXXBH497//HVi8eHHgjTfecG2fPXv2wEcffRQx74YNGwIFChQIXHbZZYH33nsvMHv2bLevli1bNrBz586IeWvUqBGoV69eoGfPnoESJUokuT8vWLAgkDVr1kDTpk0Db775pvu5b9++bh+7++67AxmJlulf//pXsvNMmDAhULNmzcBjjz0WmD9/fuD99993249+d8SIERHzHjlyJFC7du3AWWedFZg2bZqbv0OHDm7dqy3Cde3a1a2nYcOGuXZRO9WvX9/N+/nnn0fMe9tttwVy5coVGD16dODjjz8O3HfffYEsWbK4ZUqJX3/91W33+huJGTt2rPs8evTu3TtF7xkvtB/ouKe2zeyeTOa7Tttnjhw5At99912q3zfug24Fd+nNq6A7o9u2bZvbiJ944onTzkvQnbKg+9ixY4Hjx48n+lp6B92xIDXbaGrbIyUIutPX77//nmDa/v37A6VKlQo0a9YsYvoNN9wQKF68eMSJ1ObNm92X7+DBgyPmPXnyZIpOom+55RYXIB44cCBiesuWLQMFCxYMZLag+48//nAnrol9b+XNmzciGHvuuefcey5fvjw0TftOrVq1AhdddFFomn4nW7ZsgS5dukS852+//Zbg5GTdunUuwB45cmTEvP/4xz8CefLkCfz555+n/Zxqy3LlykW0YTidKJQsWTJw4YUXBgoVKhQ4dOhQIL0dPHgw3f9mvHnyNN912g60XaVW3KaXpFTdunXtsssuSzBdl/50KVKXEIP++usv69Onj5ueM2dOq1Klig0bNsxdGkvLJZnoS9S6RPnee+/Zli1bIlJhkrv8t27dOne5tEiRIpY7d2674IILbMqUKYn+nRkzZrjlLVu2rBUsWNBdRt24cWOK1tPSpUutWbNmVqBAAXeZsHHjxm5Zg7RcZ511lns+ZMgQ9/d0+S8xWp4LL7zQPe/Ro0foc0Z/th9//NGuuuoqd0m2fPnyNnDgwATr+tixY/boo4+6S4y6JFiiRAn3nn/88UeKPtfnn39uV199tRUrVsytv6pVqya4rHS6zx7exrqU/M9//tNdTtV7avtRKk2Q1sn69ettyZIloc8dXE/Bdnr11VfdZ9V2ps+k9ZBcOoPeT8uny7L6/HfddZcdOnQo9Lq2O/1uYikt4etd/997773uefCyefT2GX0ZPaX7hN5Hy6XPds4557j1eP7559u7774bMZ/a7fbbb3ftHWxPXb5euHBhum6jaWkPpSHcdNNNbv/SfLpEruVJLIUqOt0oe/bs9q9//ctSYu7cuS5FQdur1rdSFaLt27fPpQGoHdUuWnZt1wcPHgzNo+XXzzpeBNta7avf1fLocn/4JfesWbNaoUKF7MSJE6Hpd999t2uj/4vZ/o/aSp9bxxi1g9ovsZSCH374wTp37uzSLrS+tF0899xzZ/TYpfeOpuOJUgt++eWX0DR9Jm2L1113nXv/oIoVK9oVV1zh1nk4rYuUyJEjh1v/efLkiZiu1Ay13+ksWLDAHd+13Wr+s88+2+644w7XHuG0bWs96Vhw8803u3bS9qfUmr1790bMq/b9xz/+4Y5PWhetW7d2aSMpoeNa+HdS0EUXXeSOOToeBGmd1ahRw6WUBGm76tKli33xxRf266+/htZlcNsKp3bQ9PD1pFQobWs6xofTz4cPH7YPP/ww2eXX98XEiRPddpdYG+r7QN+pXbt2detI62727Nmh17UP6TirdRjtxhtvdOtc6S9Bs2bNcp9fv6N13apVK/v6668jfi+YJrh27Vpr2bKlO35p/0lN+8tbb73ljgval3RcePrpp0PbRTitPx1zFCtou1TscP311yeaRpWSWEbHDKUFffbZZ+6Yq/fUcXXSpEnudR2D69Wr544FderUSdBGwWXUetH3pdpd24K2k+jvca1PraMyZcq4v6Njxn333RdxXEvJd/vpvutE24DSfffv33/a9RK9guM6vWTFihXu7Dr8ceLEidB8Tz/9tJvv+++/j/h9XTbT9Lffftv9fPjw4cB5553nehafeuopd6nswQcfdJe/rrrqqmR7uoPLEn1GpUtjmq7/RZfuL7nkkkDp0qUjUmGS6onQpQ9dDq1atWpg6tSp7pLozTffnKAnL/h3KlWq5HpeNN+MGTMCFSpUCFSrVi1ifSQmeKlFl/tmzZrlLpOqp0Y9DjNnznTzKO1lzpw57u/o8qmW+6uvvkr0/dSTFFwnDzzwQOhzBlNntO5y5swZOOecc9y6VhrAQw895P5e+CVM9VS0bt3atYmm69Ltyy+/7Hox1Jtyuh6KDz/80H0utevkyZMDixYtCrzyyiuBm266KVWfPbyNq1Sp4j7/vHnz3LIUKVLEXd4O0jrRPHXr1g197uB6CraTll+XxbXtvfvuu673JnpbCV9Pasfg5d7hw4e7bbJdu3ah+bTdJZXKE75Naf0HL32rLaNTsdSbF96jl5p9Irj9qYfr9ddfd/uXLrlr3p9++ik0X6tWrdzl+hdffNGte61vtX34uk6PbTQt7aGUg7PPPjvw6quvBpYsWeLSEwYOHBgxT3hPt3oM9bqWO7G2iabf1bKovbWdah1qf9ZyqNcmvJfsggsucL22ulSu/UfHOfXaXXnllaGeSn1+9Q6qrYJtHUwfUgqG1l+Q1mHu3Lnd+ly2bFlouvbRTp06hX7WZ9c811xzjVvX77zzjtsW1ZOp5QjS39Hy1KlTxx27tO1oXSjFQNvwmTp2JWbPnj3ub1977bURx1L9HfXORhs0aJD7TNreE5NcT7e+f9TTfeedd7q0ht27d7vPqzbXPnM6zz//fGDUqFFu29M2NWXKlMD555/vtjVddQnSPqzl13TtLzoWqu31t3v06BGaT22v45GmB48Z+l0dk1LS050U7cvab8PbQ99junoQTfuQ/paOkUH9+vUL5M+fPzB37lx3vNExS99lOn7+8MMPofl0bNbfiaYrCXrPoUOHJrucn3zyiZtP+05i1LOp17V97tu3z/Xe67MFffPNN+71l156KeL31K5apwMGDAhN0/rVdqMUJH1m7Q+NGjVyx8vwND0dx7U9aBtXWyu9JrhuUtr+H3zwQSiNSetQqTMNGzZ07xkdBuoz6u9pf9N34PTp013KkK7+7NixI9n1l1gso22/WLFibpkmTpzoll37fDDlSPu49lmtcx1XtJ60L0Rvuzq+3Xvvve73te1qPel7MvxzPvLII4Fx48a544CO+S+88EKgcuXKEd+xKfluP913nSitKTwGTKm4D7oTe+gLIGjXrl0ucLn//vsjVpy+SLQRBi8lq3H1uwoYwim41XQdvP5u0H269JLog6I2IG3AW7dujZivTZs27mChL5fwvxMdCOmzaHp4YJ8Y7Si63KbLskE6uAbz9YJf4sHgLjwASGt6SWLrWsuvHTtIO7LmU3CT2HsrjzM5OlnRI6kv09R89mAb9+nTJ+L3lXeo6du3bz/tl3SwnS6//PIkX4sOujVNAVU4Hew1fenSpakKuk93yS066E7NPqGftT/piyxIB3h9UehLJUhfvP379w+klhfbaGraQ8cR/Tx+/Phk3zMYdOuE8LrrrnPBX3gwerrf1Zf46tWrI6a3aNHCpSoEL0lrfWq9RqfW/fe//00QcCSV062TYQXkwXQB5dHqBFdfYsETX31x6v10giT6+8qXv/rqqyPeSyfHChTCUwp0cqV2ic6Jv+uuu1xwH8yz/7vHrsQoeNfJ3pdffhmaphMJvZ+OKdGUyqDXlO6Qlns09N7KCw///tFxIbW0Dev7aMuWLe593nrrrQSBS/T76nik9Rnc/hWcJXfMSEvQrQA0sfdU0HPHHXckmF/pJppfwV74Z9PJgrbb4HrSidXXX3+dYFsP/w4Ip+/x22+/PdllDR6bEgsutf1qP9KxJEj7hva5H3/8MTRNufyNGzeO+F191+h9165d637Wd7K2MQV24XR80slI+Ilq8DiuoDCt7a9UmPLlyweOHj0a8bcUDIcH3dpf9POYMWMi3ltBqPb36DSqlAbdmha+P/35559uO9d7hgfYOnZp3meeeSbBtnvPPfdE/K3XXnvNTdf9AMmtD52MaD6dEKXmu/106SUK9tX2Q4YMCaRG3KeXTJ061VauXBnx0GWHIF160CUIXWIN3sm8e/dud6mmW7du7nJY8C54XSLSZZhwwSoliV0+9ZqWSZehdCk+epl0qU+Xe8K1b98+4mddihKlsyRFl220vvS5wytlZMuWzV1+2bZtW4pTVFJDl3rULtHLG76suhysy7SaT5eHgw9dNitdunSylSV0OfWnn36yXr16JXmZNy2fPS3rOJoucafGLbfcEvGzLp2KUl28lNp9Qpfpdek0SJdidfk/fN3oMrUuYSplaMWKFRGXav3aRlPSHqrioMuXSskYO3asu1SaWGUE+fPPP+3KK690l9iDKTEpde6557q0nOj21uVuVd4I7he63Kv9IHy/0KXtlFZc0TLpcn2weoRSRlSBQ2kduuQdnCaaJppX6QXdu3eP+LtaD0ph0LFXbXXkyBG3bVx77bXuknP4vEon0+tq+zO9X8mDDz5or732mo0bN85V4IiWWOpESl5Liqqk6HPqb73zzjtunxk6dKg98MAD9sgjj5z291X9o3fv3u4Yr+8ipaso5UU2bNiQYP7E1pPWp94n/JiQ1DEjtT744AO788473b7Xt2/fNK/Pxx57zJ566il32V/LqO9fpaZom4tOx/g7baRUP82jNJlor7/+utuPlJITpOfqMwimSgRTWbSthx9T9LpSJrXfybx589z2rBgifPvWd02TJk0S3QcTO86kpP21T6nKyjXXXONSmYJ0LIz+DtWxQZ9fqRvhy6XvSx1X0lrVTeke4ftT0aJF3bFdxyClhAUpHSSp/TZ6m+zUqZP7zOHfY0qB0baq5dXxXetD6zN8faTkuz0l9N6KL4JpUCn1fxFjHFMjqzxYcrRjKW9LXyb6YlL+oHJSw8v+6YtSDR29U2vD0oah19Ob/qY29mjBjTx6mXSCEU65X6Iv16ToBEQHndT8nTNBX8bRO4yWV18gQb///rsr9xN+oAmXWN5bUDBXLJjje6Y+e1rWcbTE/l5StO1F/01tp4kt25mW2n0iejmD6yd83ShnTwH3yy+/7AIkfXEoaBk9enToc6X3NpqS9tA6UCCpUmRaVuWA64tHXyQKKMJPNvSloGVWzmjwSzqlElsH0e2t/UJ55/rSSO1+EaTcTO2DCqz1ha8cTgVAOoFRebYDBw6415Q7qpzI4N+V6JOwcArKlUurL3q9T1Kl3qKX8UzsVyozp21L7aH7CxJ7/8S2Ey2z2ldfwKmlgFQnl8pvVpAQPPnUOlCAqe1D6zAxOllR/qoCRe0LyofVSa6mX3zxxYl+9tOtJ32+5I4ZqaHAUjm42i50IhN9HNDfSGp9ivaPYLD00EMPuf1G9yEEqWSgcu8HDBgQCrz0nondI6HAU/nawfdMitaD9otgW4RTrre+c3SCGCwjp5MW5SerI0Dbj35Pbabl1LRRo0bZt99+604olScdFNwXgvcuRYvOJ9e+Fn4vQWraP3j803YWLXqaliupeSWpbfF0ElvvOXPmTDA9+F0d/j2e1DYY3E6D25COObr/Tm2k/bh69epuveneDG2HwfWRku/2lNLfSs0xxi333/6rcUCBtr6cdbaq5/q/YcOGbocPUuOrN00bbPjBRWei+gJJ7Mw5KBg8Rt9clpIvv+RombZv355gevDGveSWKaV0k4UOEF7/nbQI3qyY1M0z4YFONN38JQoiMtpnT02PmrY9HZTCv0R37Njh/g9OS2r7+7tB+d/ZJ5Ki31ENZD22bt1qb7/9trtRRu+ZVDt73U4pbQ/1QOmLOxhYq+dMgZWCgRdeeCE0n26suuGGG1xPjDz//PMpvjEv2LaJTQu2tz6rbjJ65ZVXEn2PlKwLfTleeumlLrDWl5e+EPWlH/xSVo+YTjLC64YH31eBtIKCxOjLXttG8CqEgtLEBAP5M0UBk9pCj/vvvz/B67pKoXWmm9miaZpuYEtLr5kCRN3YGB3kKRhT8KSAM6lARzf0ffPNNy6409WDIJ1QpZW2keSOGakJuNWzql5GdVgl1vGh7SWp9SnBE059Rh1DogNUBcfqfdVN5+HvOXPmTLe84UFa9HsmRduo9kcF6Qpgg7S/6qqTVKhQIcnPrCsxOt7o5kZdRVfwp3hB24baOfzviOqXB3umU3uMSWn7a3n0+8FAP1x0uwZvhP30009DJ2ThEpuWXnbs2OFu+A6K3k51lUjHcx17gr3bEl1nOyXf7SmlE5rUfnfEfXpJSgS/AHRntDZGXaoJv8QUvNyqM63ogSS04wVfT0qwQsKaNWsipiugOF3PX3L0N4MbYvQy6QwwqS++1NCBSScgc+bMiVgufWFMmzbNfSHrjDO10tJTFU1f+NopVWlGVzOiH7o8mRQts75oFZgkVX3Gy8/+dz53NPUyhdMd1xKsNKJAR18K0dufLuEmtmySkuX7O/tESujLTz2S6kkLpk6kZzv9Hfp7SiFQkJDYsutLVMGDvrB1CTqlA6WoOoW+iKPbWyeYqhAQ3C90eVVfVontF+EVW5LbFpU2ovQIBVXBFBKtax1XFFjruBOcLqpSot5g9fwl9nf1UHCmY5N6e5U2oJ7ExOZL7KpIWimNQ8G22iOpCjHqVdOleG1D4dUKdOKnXtbwKlapoc4cfZ9Et28w9S+53rhgIBYdCP3nP/+xtNJ6T+6YkRLz5893AbdOyrTvJxWo6QrVd999F5HOqUBK+6T21+BVqOD/0SlFOi5r3wlfRwp2tV6iK3QpMNVJk3qpk6MqV6L9I1zwZPmll15y7R3+eP/9990JQPhJrFJMtP3rNX0efdbwKyHqvNM2pb+T1L5wOiltf+2Tej+1hU4ognRsjq4OpWODTnCUMpHYMul45ZfXorZJdVpoewl+j6V0faTkuz38fZI6/ql91SMf3vmaEnHf062zxfASV0FqlOAZkSjIfuKJJ1y+kHZelf8Jpy9GlbPSl6UutWrj1JmxRnTT2W/4l080ncErANQlKS2Lzkx1uTF4Zh1O76sDv3q/lCOlHrCkdlB9gWin0oFUl+d0KUcbrkr06FJddAmmtNIlNAU++jv6DPri1KU0rVul4qQl1zHYs6TlVQqQ0gh08A3P/zodlWfT72v99+vXz+UD6+CoM1wdLHWA1sEwKWpPfdEqiLjnnntckKcvWfVoBA8AXnz2YG+NUinUy6WAOK0HOy3PmDFj3AFW25lyDdX7okuz+lKUYA6fDkJa7+o9Uj5xYl+0weVQuSlt61qf2nYTu2rwd/aJxKg8l9az9kF9Oepv6rKterhPF/R40U6poRManSCoB7tatWru7+uEWNPVU58YpWEoANX/OvBrOZNKlQrS/qG8XQWRSnvRF77S4nTs0nuJymIpUNaoatquFdjqBETbtgImpb4o6BG1mXqOlG+s99M6D56s6qRJwaJ6tMODHLWrjj1ap8pND9I+rGBc24NSCPS5lGqky706UdD/Oq4Fty9tn7pcrBKbOhFQsKtevGDu85mgfUPHRgVjbdu2TRDYhXdMqDdc+5ACE7WZvnD1u+rp0joLp0A6WDZNecAKZIKjMuo9gr2bWv8qq6jjjEq9qY20PrVcWo/R+fnhtA9of9Wy6P11fNe6CebUp4XSFbRdDB482PX26rtl2bJlrixmSmj/VsCtXmZdMYhO9VCAEkyT0Heqjg/aJx5//HG3LWifVC50eAlQbQdaZ9qmdS+Slk/HAm1LmzZtilg23dOgK0Ta/tRZpt/TNv3iiy+6497p0kuCAZy2g+B9AfpOVkeBvoduu+22RH9P7adOMm3Dihu0HnUyoHKp6qGNLmGo7VmpZipzqTxkbX/63ldvtI69CpS1vSUnNe2vv6XtW8G+vgu13+r+Eu2T4WUcdWKskqxaXm3DWtdaFl0lVNvqeKD90Q9z5sxxJyo6jqtzQSk12j+U2x1MedM6VI672l/fTfqeju6ESOl3++m+64LHiuCJaooF4lTwLtukHtElf0R3JOs13d2eGN2RqxGqypQp4+5MVjUBlSiKHp0pscFxVJIwOCCCSh7prmaVvYmuSKG79lWerHDhwu7O2fAmTOzuct0trWoBqoKgu7dVJSC6SkWwAkD0CFzJVbWI9umnn7pyY6p2oDuSdYe3yoEl9n4pqQwhqhSgUkW6yz38syU16EvwLudwuntZpbf0uXWXvqpf6D1113x4qamk6G5uVXvR+lMlGN3xHH0XdUo+e3B7i64YkVjVEQ24oW1B5R6DpZLC501spLSkqpdomdasWeNKRWnZVD3in//8Z4LBOFQlQhUoVEFEv6NtRsuR2DalbVrVFoKVBIJ/M7p6SWr2Cb2PyqZFC99X9Dt6L1XI0H6iz6NKBVq+lAwWcaa30dS0hwZhufXWW922p7+v7VCfQ+WtwsuoJTY4jt5D86s6SHJlLoO/qyokqpih/V0lwVReK5raXxVItP40X7A8n7bt8MoNqiagMqWqdqTPE96+qg6gsoOaHl6BIFjpQ1UcEqNqAlpObYvat1XmUD8ndvxROTW9rvl0XNQx+NFHHz1jx65gZYWkHtFUgUGD5mh9aBtU6cPwyhXRFScSe0Qvk6orXXrppW5dattQ26n0WfQ+mphvv/3WVezQsULl81SCT5Uxovfb4LFRg9ecrtqEqlppves7Rp9T7x8smXi66iXBv5PUI/z4JNrWunXr5rYFHZ+1T6qcYTQtk0ajVAlKLZMqEemYllhpP1WV0HKouom27erVq0dUwzgdjTgaXg1H5UVPV3lIJeiiq36o4pmmqWpIUgPt6L1Vzk7bkr5ftA/r+z28YlFyg5yltP1FpQK1jwfLyD7++ONuYCH9XjRVSlFJweCxUt97aqfwCiSpqV6ibTqlA4FZ1HdBcJtatWqV+17SsVCfVyUjowe3UuUblV3UNqLjhb7TVPY1sf0uJd/tSX3XBUdJ1fpMrSz/+5AAAABxTVeBdCVbFTTCc4hjjSo/qXqIPqOuBmRUw4cPd73+uorg1/1h0XT1SlcVVeVIN7ynBjndAAAAZi5VTWkpSkmLJUq7UdqibjwNjtyoG3WVSoTUUbCtlJTotKGUiPucbgAAANG9CLphUjnautchpZWDMjrdE6H7WdRjrPxk3VitGz1Te28NzN2XoJtzg+O0pAbpJQAAAIDHYuMUDgAAAMjACLoBAAAAjxF0AwAAAB7jRsozQDdbaHQiFU33epANAAAApJ6qZOumUpX88+MmWYLuM0ABd/ny5c/EWwEAAMBDv/zyixs1NL0RdJ8BwWFB1YjBIW4BAACQcezbt891kgbjtvRG0H0GBFNKFHATdAMAAGRcWXxKBeZGSgAAAMBjBN0AAACAxwi6AQAAAI8RdAMAAAAeI+gGAAAAPEbQDQAAAHiMoBsAAADwGEE3AAAA4DGCbgAAAMBjBN0AAACAxwi6AQAAAI8RdAMAAAAeI+gGAAAAPEbQDQAAAHiMoBsAAADwGEE3AAAA4DGCbgAAAMBjBN0AAACAxwi6AQAAAI8RdAOIe/v377f+/ftbxYoVLU+ePNa4cWNbuXKlp+tl9uzZVqtWLcuVK5f7f+7cuRGvV6pUybJkyZLgceedd8Z9ewFAZkTQDSDu3XbbbbZgwQJ79dVXbe3atdayZUtr3ry5/frrr2laN5MnT7amTZsm+fpnn31mN954o3Xt2tW++eYb93+nTp3s888/D82joH/79u2hh5ZPbrjhhrhvL3jj+eeft/POO88KFizoHo0aNbIPPvjA09XNyaf5frJ9ujY4ceKEPfDAA1a5cmXXKVGlShV7+OGH7dSpU54tU8wK4G/bu3dvQKtS/wPIXA4dOhTIli1b4N13342Yfv755weGDRvmnh89ejRw7733BsqWLRvImzdv4KKLLgp8/PHHSb7npEmTAk2aNEny9U6dOgVat24dMa1Vq1aBm266Kcnf6devX6Bq1aqBU6dOpeLTASn39ttvB957773Axo0b3eP+++8P5MiRI7Bu3bo0rcbT7QfLly93+97IkSMDGzZscP9nz549sGLFitA8O3fuDGzfvj30WLBggfu+TW7/y8zO9Oc9E23w6KOPBooVK+aOkZs2bQq88cYbgfz58wfGjx8fyGz2+hyv0dMNIK6pF+fkyZOWO3fuiOnq0Vm6dKl73qNHD1u2bJnNnDnT1qxZ43qbW7dubT/88EOa/qZ6utWbHq5Vq1a2fPnyROc/duyYTZs2zXr27Ol6vQAvXH311XbVVVdZ9erV3eOxxx6z/Pnz24oVK0Lb4eDBg61cuXKWL18+a9iwoS1evDjNf2/8+PHWokULGzp0qNWsWdP936xZMzc9qESJEla6dOnQ491337WqVatakyZNzshnzmhO93n9aAMdrzp06GBt27Z1PfHXX3+9O359+eWXZ+QzxxOCbgBxrUCBAu4y+iOPPGK//fabC8AV4CrVQ2kdP/30k82YMcPeeOMNu+yyy9wX4KBBg+zSSy+1SZMmpelv7tixw0qVKhUxTT9remLefPNN27Nnj916661p+ntAamk/0EnmwYMH3f4hnHymr8ROtv1oAx3rPvroI/v+++/dz0qJU4eETtCQOtlTOT8AxBzlcuuLTb1H2bJls3r16lnnzp3tq6++co9AIOB6/sIdPXrUihUr5p5v3brV5UKG954fP37c9RIGdenSxV544YXQz9E91vobSfViT5w40dq0aWNly5Y9Y58ZSIzuaVCQfeTIEbf9Kr9X23bw5HPbtm2h7VAnnx9++KE7+Rw5cmSqVygnn8mLPtn2qw2GDBlie/fudT3hOj7qhExXQW6++eZU/714R9ANIO6p93rJkiWuV2/fvn1WpkwZd6OjbhzSzUL6olm1apX7P1wwqNYX4OrVq0PT58yZ425Oeu2110LTdGNakC4bR/dq79y5M8GXn2zZssUWLlzo3hPwWo0aNdy2rGBP23D37t3dvrF+/XpOPtNZ9Mm2Xx0As2bNcj3u06dPt3PPPddtH6r2pOXS9oGUI+gGgP9RjqQeu3fvtnnz5tno0aOtbt26rmdHQbHSSxI9kGbPbmeffXbo55IlS7qc8PBp4dSTqGok99xzT2ja/PnzXanCaOrB0vspnxLwWs6cOUPbbYMGDVwVnaefftquvPJKTj7TUWIn2351ANx7771233332U033eR+rlOnjlu+UaNGEXSnEkE3gLinAFu9O+rl+/HHH92XjJ4rfzJHjhx2yy23WLdu3WzMmDEuCN+1a5ctWrTIffmkJa+xX79+dvnll9sTTzzhblB666233Bds8MbN8C9ZBd3qTVJgD6Q37RfqSeXkM30ldrLtVxscOnTIsmaNvAVQQT8lA9PAl5opMcbvEjQA/p5Zs2YFqlSpEsiZM2egdOnSgTvvvDOwZ8+e0OvHjh0LPPTQQ4FKlSq5Emqa59prrw2sWbMmTWW6RGW3atSo4d6vZs2agdmzZyeYZ968ee7YovJtgNeGDh0a+OSTT1xZOG3bKhmYNWvWwPz5893rt9xyi9sHtK3+/PPPgS+++CLw+OOPuzKDadkPli1b5srV6T1Urk7/R5erk5MnTwYqVKgQGDJkSCAeJPd5/WiD7t27B8qVKxcqGThnzpxA8eLFA4MHDw5kNnt9jtcIumOgEQEA+Lt69uwZqFixojv5LFGiRKBZs2ahgFs4+UwfyZ1s+9EG+/btc+ME6EQgd+7croNCYxho/ILMZq/P8VoW/ZOWHnL8f7rxqlChQu7u3vBcKQAAAGQMfsdrJAkCyNR0t75yrJE6xYsXtwoVKtAGMdIGADI+gm4AmTrgrlGzhh05fMTvRcl0cufJbRu/2/i3gz61wTk1atihI7RBauXNnds2bPz7bRBsB04+U4+TT/8Vj6OTT4JuAJmWggwF3GfdfpblKpvL78XJNI7+dtS2vbjNrb+/+2Wn91DA/USZMlY1599vg08PHrBCWbNZqezZ7VggYO/u32efHTxoE8qdZYWiyqSl1Kw9u+2PEyfsruIlEn19md5/1x92W9FiVjN3Lluwf799dOCAjStbzkr8r2rM7pMnIn7n68OH7fk//7Rny5azUjlypHqZfjp21IZs335G2kABd82a59jhw4f+1vvEozx58tp33204I21Qo+Y5doQ2SLXcefLaxjPQBpkBQTeATE8Bd55KefxejLimgLtW7tx/+32i3+OSfPnsoh9/iHht/8mT9tQfO11gfDQQsNq5c9uQEiWtZhJ/X4Hz8UAgyeUb8fsOu65QYbu7xP8F5S0LFLR2m362VYcP2YASJRP9nRl79thFefPaFQUKmN8UuCvg7n7lUCtdOPYDlzNlx56tNmXRqDN28qmAu1i7gZajWPkztoyx7vifv9if7445I22QGRB0AwAyJPV0v753jxXImtVq5vq/XnTd+//PX7e53vAXzjrL8mfNZq/v2WM9t/1i71euYoVT2Ruuv/HtkSP2j6L/N6JfUOO8+Wz14cOJ/s6uEyfskwMHbGSZMpaRKOAuXyJytEKkLwXcuUonXhMbIOgGAGQoiw8csIG//WpHAgHXS/3yWeWtyP/SPD4/dMi+P3rUllY923L+b8COwSVL2kcH9tv8/futU+HCqfpbe06esJNmVix7ZLCun3cd1CsJvbV3r+XNmtVa5Pe/lxtA5kHQDQDwxTv79trwsCGo/3NWeWuQN69L25hTqbLtOXnS3ti7xwZs/81mVqhoxbJnt/VHj9ihU6es0f9SToKUZvLL8WPu+ZeHDtkd234JvabUEpm3f39o2u3FitkdxYqHfs5iWSLeT7+RJXJSyJx9e61dwYKWK2qUPgBIDkE3AMAXV+bPb+dVqhz6WTdPinqRK+bMaRXN7Pw8eaz1zz/Z7L17XaCs+Fm935PLJ8z/LPi/IFg53grag6bt3m07TxyPyM8O3pRZOFt2y/a/lJFwf504acUSSVVRQL/p2DEbU6bsGVkHAOIHQTcAwBf5smazfDlPn4OtXudjgVPuuW6GVICcPYtZuRw5E50/9/+C9qBC2bLawVOR04JyZsni3nP5oYPWPOymSP2sk4Joc/busXNz5U7ypk0ASApBNwAgQ1DayH/+/NMFu8WzZ7e9J0/ajD277fcTJ6xVgf8bPa5R3ryu9/uuX3+1gSVKWOWcOW2nu7HxoDUrkN9q5059FZtbixS1Idt/s3Nz57YLcudxKS3bjx+3GwsXiZjvwMmTLkXl3pKJVzQBgOT4mpBWqVIly5IlS4LHnXfeGZpHd6oPHz7cypYta3ny5LGmTZva+vXrI97n6NGj1rdvX1dgPV++fNa+fXvbtm1bsn/71ltvdX+rd+/eCV7r06ePe03zAADSh/q8Nx07av1++9Wu2vSz/XPbNvvr5El7tXwFq/a/6iU6Nv/nrLOsQZ689sCOHdbm559t4G+/2a/Hj1uxbGnrR2pTsKANLVnKnt+1yzpu2exSSJRfXi6q/vb7+/e7Xve2/zsBAIBM09O9cuVKO3ny/98dvm7dOmvRooXdcMMNoWmjR4+2sWPH2uTJk6169er26KOPunk2btxoBf53KbB///72zjvv2MyZM61YsWI2cOBAa9euna1atcqyJVM+qnz58u53xo0b5wJ6OXLkiM2YMSMu6kUCQEaiGxOfKXdWitJShpUq5R4pkdSgOOFuLlLEPZKjyiiprY4CABmip7tEiRJWunTp0OPdd9+1qlWrWpMmTUK93OPHj7dhw4ZZx44drXbt2jZlyhQ7dOiQTZ8+3c2zd+9emzhxoo0ZM8aaN29udevWtWnTptnatWtt4cKFyf79evXqueB6zpw5oWl6rmBc7wMAAADEVE73sWPHXLA8YMAAd/lQNm3aZDt27LCWLVuG5suVK5cLypcvX2533HGH680+fvx4xDxKRVGArnlatWqV7N/t0aOHTZo0yW655Rb38yuvvGI9e/a0xYsXJ/k7SmfRI2jfvn1/67MDQGanYc3h7/rSCItgfSHjyjBB95tvvml79uyJyKNWwC2loi4h6uctW7aE5smZM6cVibosqHmCv5+crl272tChQ23z5s0u2F+2bJlLOUku6B41apSNGDEi1Z8RAGKN7qXJq2HYt2/3e1EyHa03rb+/S++RJ09eN6Q5Ukfr7Uy0AZCpgm6liLRp08b1UkcL9nwHKe0kelq0lMwj2tnatm3r0lb0O3p+uh1QQbp65MN7upWSAgDxRil6GzZutF27dvm9KJmOvmvOxP1Deo/vvttAG/jYBkCmCbrVa6386/DcalGet6jHukyZMqHpO3fuDPV+ax6lpuzevTuit1vzNG7cOEV/X+kkd911l3v+3HPPnXZ+pbjoAQD4v6CPwMVftAGQ8WWIMWyVU12yZEnXyxyucuXKLqhesGBBaJoC7CVLloQC6vr161uOHDki5tm+fburhJLSoLt169buffU4XQ44AAAAkOl6uk+dOuWC7u7du1v2/w0BHKT0EJUDHDlypFWrVs099Dxv3rzWuXNnN0+hQoWsV69erkygygUWLVrUBg0aZHXq1HHVTFJCZQU3bNgQeg4AAADEVNCttJKtW7e6FI/EDB482A4fPuwGrFEKScOGDW3+/PmhGt2iOtsK2Dt16uTmbdasmavrnZoAumBBBjsAAABAjAbdKvWnGxiTot5ujUipR1Jy585tEyZMcI+UUlB+umoqAAAAQMzkdAMAAACxjKAbAAAA8BhBNwAAAOAxgm4AAADAYwTdAAAAgMcIugEAAACPEXQDAAAAHiPoBgAAADxG0A0AAAB4jKAbAAAA8BhBN+Cj4cOHW5YsWSIepUuX9vRvzp4922rVqmW5cuVy/8+dOzfi9RMnTtgDDzxglStXtjx58liVKlXs4YcftlOnTnm6XAAAxDKCbsBn5557rm3fvj30WLt2bZrfa/Lkyda0adMkX//ss8/sxhtvtK5du9o333zj/u/UqZN9/vnnoXmeeOIJe+GFF+zZZ5+1DRs22OjRo+3JJ5+0CRMmpHm5AACIdwTdgM+yZ8/uereDjxIlSoReO3bsmA0ePNjKlStn+fLls4YNG9rixYvT/LfGjx9vLVq0sKFDh1rNmjXd/82aNXPTwwPzDh06WNu2ba1SpUp2/fXXW8uWLe3LL7/8258VAIB4RdAN+OyHH36wsmXLunSOm266yX7++efQaz169LBly5bZzJkzbc2aNXbDDTdY69at3e+khQJqBdDhWrVqZcuXLw/9fOmll9pHH31k33//vftZPeJLly61q666Ks2fEQCAeJfd7wUA4pl6rqdOnWrVq1e333//3R599FFr3LixrV+/3vbs2WMzZsywbdu2uaBcBg0aZB9++KFNmjTJRo4cmeq/t2PHDitVqlTENP2s6UFDhgyxvXv3up7wbNmy2cmTJ+2xxx6zm2+++Qx8YgAA4hNBN+CjNm3ahJ7XqVPHGjVqZFWrVrUpU6ZY+fLlLRAIuIA83NGjR61YsWLu+datW93NkOE3QR4/ftzy588fmtalSxeXox2kmzXD6W+ET5s1a5ZNmzbNpk+f7vLNV69ebf3793eBf/fu3c/wGgAAID4QdAMZiPK2FXwrfUR53OppXrVqlfs/XDCoViCsoDhozpw5rjrJa6+9FppWsGDB0HPljIf3asvOnTsjer/vvfdeu++++1yqi2h5tmzZYqNGjSLoBgAgjQi6gQxEvdiqGHLZZZdZ3bp1XWqHgmL9nNRNmGeffXbo55IlS7oyf+HTwqknfcGCBXbPPfeEps2fP9+ltAQdOnTIsmaNvN1DQT8lAwEASDuCbsBHytG++uqrrUKFCi64Vk73vn37XI9yxYoV7ZZbbrFu3brZmDFjXBC+a9cuW7Roket9TsuNjf369bPLL7/clQVUhZK33nrLFi5c6G6UDNLyKIdby6T0kq+//trGjh1rPXv2PMOfHgCA+EHQDfhIN0nqBkUF0yoVePHFF9uKFStcwC26YVKB+MCBA+3XX391udzqrU5rJRH1aKsSiga/efDBB13+uHK4dUNnkOpx67U+ffq4EwGlsNxxxx320EMPnbHPDQBAvMkS0F1U+FvUM1moUCFX8SE8fxaAt7766iurX7++VR1e1fJUysPqTqHDmw/bT8N/cvcL1KtXj/UGnKFjUenu4y1X6cTT+5DQ0R0/2o4p/dPtWOR3vEadbgAAAMBjpJcAf4NK9ik1BClXvHhxly9+Jh397ShNwPoCgAyNoBv4GwH3OTVr2KHDR1iHqZA3T27b8N3GMxJ4K4DPnSe3bXtxG22QSlpvWn8AgPRB0A2kkXq4FXBPuzaPnVOCTK2U2PDHKesy97Bbd2ci6NZ7bPxuI1cbMsgVBwBA0gi6gb9JAXe9MpGD1yD9KHAkeAQAZHR0zwEAAAAeI+gGAAAAPEbQDQAAAHiMoBsAAADwGEE3AAAA4DGCbgAAAMBjBN0AAACAxwi6AQAAAI8RdAMAAAAeI+gGAAAAPEbQDQAAAMR60P3rr79aly5drFixYpY3b1674IILbNWqVaHXA4GADR8+3MqWLWt58uSxpk2b2vr16yPe4+jRo9a3b18rXry45cuXz9q3b2/btm1L9u/eeuutliVLFuvdu3eC1/r06eNe0zwAAABApg66d+/ebZdcconlyJHDPvjgA/v2229tzJgxVrhw4dA8o0ePtrFjx9qzzz5rK1eutNKlS1uLFi1s//79oXn69+9vc+fOtZkzZ9rSpUvtwIED1q5dOzt58mSyf798+fLudw4fPhyaduTIEZsxY4ZVqFDBo08NAACAeJPdzz/+xBNPuMB30qRJoWmVKlWK6OUeP368DRs2zDp27OimTZkyxUqVKmXTp0+3O+64w/bu3WsTJ060V1991Zo3b+7mmTZtmnvfhQsXWqtWrZL8+/Xq1bOff/7Z5syZY7fccoubpuf63SpVqnj4yQEAABBPfO3pfvvtt61BgwZ2ww03WMmSJa1u3br20ksvhV7ftGmT7dixw1q2bBmalitXLmvSpIktX77c/axUlOPHj0fMo1SU2rVrh+ZJTo8ePSKC/ldeecV69uyZ7O8onWXfvn0RDwAAACBDBt3qZX7++eetWrVqNm/ePJdffffdd9vUqVPd6wq4RT3b4fRz8DX9nzNnTitSpEiS8ySna9euLiVl8+bNtmXLFlu2bJnLMU/OqFGjrFChQqGHesYBAACADJlecurUKdfTPXLkSPezerp1k6QC8W7duoXm002N4ZR2Ej0tWkrmEd182bZtW5e2ot/Rc01LztChQ23AgAGhn9XTTeANAACADNnTXaZMGatVq1bEtHPOOce2bt3qnuumSYnusd65c2eo91vzHDt2zN2UmdQ8p6N0ksmTJ7vA+3SpJcEUl4IFC0Y8AAAAgAwZdKtyycaNGyOmff/991axYkX3vHLlyi6oXrBgQeh1BdhLliyxxo0bu5/r16/vqp+Ez7N9+3Zbt25daJ7Tad26tXtfPZK78RIAAADIdOkl99xzjwuMlV7SqVMn++KLL+zFF190D1F6iMoB6nXlfeuh56rn3blzZzePcqp79eplAwcOdLW+ixYtaoMGDbI6deqEqpmcTrZs2WzDhg2h5wAAAEDMBN0XXnihq6+tHOmHH37Y9WyrRGCwfJ8MHjzY1dHWgDVKIWnYsKHNnz/fChQoEJpn3Lhxlj17dhe4a95mzZq5dJHUBNCkiAAAACAmg27RIDZ6JEW93RqRUo+k5M6d2yZMmOAeKaWgPDlvvvlmit8LAAAAyNDDwAMAAACxjqAbAAAA8BhBNwAAAOAxgm4AAADAYwTdAAAAgMcIugEAAACPEXQDAAAAHiPoBgAAADxG0A0AAAB4jKAbAAAA8BhBNwAAAOAxgm4AAADAYwTdAAAAgMcIugEAAACPEXQDAAAAHiPoBgAAADxG0A0AAAB4jKAbAAAA8BhBNwAAAOAxgm4AAADAYwTdAAAAgMcIugEAAACPEXQDAAAAHiPoBgAAADxG0A0AAAB4jKAbAAAA8BhBNwAAAOAxgm4AAADAYwTdAAAAgMcIugEAAACPEXQDAAAAHiPoBgAAADxG0A0AAAB4jKAbAAAA8BhBNwAAAOAxgm4AAADAYwTdAAAAQCwH3cOHD7csWbJEPEqXLh0xTyAQcPOVLVvW8uTJY02bNrX169dHzHP06FHr27evFS9e3PLly2ft27e3bdu2Jfu3b731Vvf3evfuneC1Pn36uNc0DwAAAJDpe7rPPfdc2759e+ixdu3aiNdHjx5tY8eOtWeffdZWrlzpgvIWLVrY/v37Q/P079/f5s6dazNnzrSlS5fagQMHrF27dnby5Mlk/3b58uXd7xw+fDg07ciRIzZjxgyrUKGCB58WAAAA8cj3oDt79uwukA4+SpQoEdHLPX78eBs2bJh17NjRateubVOmTLFDhw7Z9OnT3Tx79+61iRMn2pgxY6x58+ZWt25dmzZtmgveFy5cmOzfrlevnguu58yZE5qm5wrG9T4AAABATATdP/zwg0sdqVy5st100032888/h17btGmT7dixw1q2bBmalitXLmvSpIktX77c/bxq1So7fvx4xDx6PwXowXmS06NHD5s0aVLo51deecV69uyZ7O8onWXfvn0RDwAAACBDBt0NGza0qVOn2rx58+yll15yAXbjxo3tzz//dK/rZylVqlTE7+nn4Gv6P2fOnFakSJEk50lO165dXUrK5s2bbcuWLbZs2TLr0qVLsr8zatQoK1SoUOihnnEAAAAgKdnNR23atAk9r1OnjjVq1MiqVq3qUkgGDBgQek03NYZT2kn0tGgpmUd082Xbtm3d39Tv6LmmJWfo0KERy6eebgJvAAAAZMigO5oqjyj4VsqJBCuZqMe6TJkyofl27twZ6v3WPMeOHbPdu3dH9HZrHvWap4TSSe666y73/Lnnnjvt/Epx0QMAAADIFDnd0bnSGzZsCAXYyvNWUL1gwYLQPAqwlyxZEgqo69evbzly5IiYR1VQ1q1bl+Kgu3Xr1u599WjVqtUZ/1wAAACIb772dA8aNMiuvvpqV0FEPdOPPvqoS9Xo3r27e13pISoHOHLkSKtWrZp76HnevHmtc+fObh7lVPfq1csGDhxoxYoVs6JFi7r3VY+5qpmkRLZs2VywH3wOAAAAxEzQrQFsbr75Ztu1a5crFXjxxRfbihUrrGLFiqF5Bg8e7Opoa8AapZDo5sv58+dbgQIFQvOMGzfOlR7s1KmTm7dZs2Y2efLkVAXQBQsWPOOfDwAAAPA96NbANKej3m6NSKlHUnLnzm0TJkxwj5RSUJ6cN998M8XvBQAAAGSanG4AAAAgFhF0AwAAAB4j6AYAAAA8RtANAAAAeIygGwAAAPAYQTcAAADgMYJuAAAAwGME3QAAAIDHCLoBAAAAjxF0AwAAAB4j6AYAAAA8RtANAAAAeIygGwAAAPAYQTcAAADgMYJuAAAAwGME3QAAAIDHCLoBAAAAjxF0AwAAAB4j6AYAAAA8RtANAAAAeIygGwAAAPAYQTcAAADgMYJuAAAAwGME3QAAAIDHCLoBAAAAjxF0AwAAAB4j6AYAAAA8RtANAAAAeIygGwAAAPAYQTcAAADgMYJuAAAAwGPZU/sLp06dssmTJ9ucOXNs8+bNliVLFqtcubJdf/311rVrV/czAAAAgDT2dAcCAWvfvr3ddttt9uuvv1qdOnXs3HPPtS1bttitt95q1157bWreDgAAAIgLqerpVg/3J598Yh999JFdccUVEa8tWrTIrrnmGps6dap169btTC8nAAAAEB893TNmzLD7778/QcAtV155pd1333322muvncnlAwAAAOIr6F6zZo21bt06ydfbtGlj33zzzZlYLgAAACA+g+6//vrLSpUqleTrem337t1nYrkAAACA+Ay6T548admzJ50Gni1bNjtx4kSaFmTUqFGu8kn//v0T3Lw5fPhwK1u2rOXJk8eaNm1q69evj5jn6NGj1rdvXytevLjly5fP3ey5bdu2ZP+ebvzU3+vdu3eC1/r06eNe0zwAAABAut5IqQBYgWiuXLkSfV3Bb1qsXLnSXnzxRTvvvPMSvDZ69GgbO3asu4mzevXq9uijj1qLFi1s48aNVqBAATePAvV33nnHZs6cacWKFbOBAwdau3btbNWqVe5EICnly5d3vzNu3DgX0MuRI0dc7nqFChXS9FkAAACAv9XT3b17dytZsqQVKlQo0YdeS23lkgMHDtgtt9xiL730khUpUiRBkD9+/HgbNmyYdezY0WrXrm1TpkyxQ4cO2fTp0908e/futYkTJ9qYMWOsefPmVrduXZs2bZqtXbvWFi5cmOzfrlevnguuVXM8SM8VjOt9AAAAgHTv6Z40aZKdaXfeeae1bdvWBczqxQ63adMm27Fjh7Vs2TI0Tb3sTZo0seXLl9sdd9zherOPHz8eMY9SURSga55WrVol+/d79OjhPpcCf3nllVesZ8+etnjx4iR/Rz364b36+/btS9NnBwAAQHw4I8PAa3Ccb7/91o1WmRpK7fjqq69cPndiFHBL9M2b+jn4mv7PmTNngl7y8HmSo1E0ly5d6kbX1OdYtmyZdenSJdnf0fKG9/CrZxwAAAA4I0G3UjuU7hHu9ttvtypVqrjRKdW7/Msvv6TovTRfv379XCpI7ty5k503emh5pZ2cbrj5lMwjuvlSPe36bOrx1nNNS87QoUNdWkvwkdLPDAAAgPiUqqD7hRdecD27QR9++KELVDUKpW6GLFy4sI0YMSJF76W0kJ07d1r9+vVdRRQ9lixZYs8884x7rkoppUuXdvNG91jr94K935rn2LFjCUoVhs9zOkon0Y2aCrz1/HSU4lKwYMGIBwAAAHBGgu7vv//eGjRoEPr5rbfecuX5lA+tmxJHjhzphohPiWbNmrmbHVevXh166L31XnquqiOVK1d2QfWCBQtCv6cAW8F548aN3c8K2nPkyBExz/bt223dunWheU5HA/7offU4XQ44AAAA4OmNlIcPH47o1dWNiuE9w0ozSUketajcn9JRwqnGtkr+BacH63YrmK9WrZp76HnevHmtc+fObh71vPfq1cuVCdTvFi1a1AYNGuTSXXRzZkoowN+wYUPoOQAAAOBb0F2xYkWXFqL/d+3a5QapufTSS0OvK+AOTz85EwYPHuyCfQ1YoxSShg0b2vz580M1ukV1tpWS0qlTJzevetGVLpKaAJoUEQAAAGSIoFs1uFXiT8H2okWLrGbNmi69I7znO7r3OjUSK9On3m6NSKlHUnQj5oQJE9wjpRSUJ+fNN99M8XsBAAAAZyzoHjJkiBuYRgPIKNf6jTfeiHhd5fZuvvnm1LwlAAAAEPNSFXRnzZrVHnnkEfdITHQQDgAAACCV1UsUdCtPOvqhgWkuvvjiiOHUAQAAAKShp3vu3LmJTt+zZ4998cUXbiRH1bq+4YYbUvO2AAAAQExLVdDdoUOHJF/r3r271apVy5566imCbgAAACCt6SWn07JlSzeADgAAAACPgm7VyFb5PgAAAAAeBd0vvfSS1a1b90y+JQAAABBfOd0DBgxIdPrevXvtyy+/tJ9++sk+/fTTM7VsAAAAQPwF3V9//XWSQ6i3bt3aDdWuIeIBAAAApDHo/vjjj1MzOwAAAIAzndMNAAAAICGCbgAAAMBjBN0AAACAxwi6AQAAAI8RdAMAAAAeI+gGAAAAPEbQDQAAAHiMoBsAAADwGEE3AAAA4DGCbgAAAMBjBN0AAACAxwi6AQAAAI8RdAMAAAAeI+gGAAAAPEbQDQAAAHiMoBsAAADwGEE3AAAA4DGCbgAAAMBjBN0AAACAxwi6AQAAAI8RdAMAAAAeI+gGAAAAPEbQDQAAAHiMoBsAAADwGEE3AAAA4DGCbgAAACCWg+7nn3/ezjvvPCtYsKB7NGrUyD744IOIeQKBgA0fPtzKli1refLksaZNm9r69esj5jl69Kj17dvXihcvbvny5bP27dvbtm3bkv3bt956q2XJksV69+6d4LU+ffq41zQPAAAAkKmD7rPOOssef/xx+/LLL93jyiuvtA4dOkQE1aNHj7axY8fas88+aytXrrTSpUtbixYtbP/+/aF5+vfvb3PnzrWZM2fa0qVL7cCBA9auXTs7efJksn+/fPny7ncOHz4cmnbkyBGbMWOGVahQwaNPDQAAgHjja9B99dVX21VXXWXVq1d3j8cee8zy589vK1asCPVyjx8/3oYNG2YdO3a02rVr25QpU+zQoUM2ffp0N8/evXtt4sSJNmbMGGvevLnVrVvXpk2bZmvXrrWFCxcm+/fr1avngus5c+aEpum5gnG9DwAAABBTOd3qlVav88GDB12aiWzatMl27NhhLVu2DM2XK1cua9KkiS1fvtz9vGrVKjt+/HjEPEpFUYAenCc5PXr0sEmTJoV+fuWVV6xnz57J/o7SWfbt2xfxAAAAADJs0K0eafVuK5hWfrXSRGrVquVeU8AtpUqVivgd/Rx8Tf/nzJnTihQpkuQ8yenatatLSdm8ebNt2bLFli1bZl26dEn2d0aNGmWFChUKPdQzDgAAACQlu/msRo0atnr1atuzZ4/Nnj3bunfvbkuWLAkF3qKbGsMp7SR6WrSUzCO6+bJt27YubUW/o+ealpyhQ4fagAEDQj+rp5vAGwAAABk26FYv9dlnn+2eN2jQwN0s+fTTT9t//vMfd9OkqMe6TJkyod/ZuXNnqPdb8xw7dsx2794d0duteRo3bpyiZVA6yV133eWeP/fcc6edX73yegAAAACZIr0kmnqblTMtlStXdkH1ggULQq8rwFZPeDCgrl+/vuXIkSNinu3bt9u6detSHHS3bt3ava8erVq1OuOfCQAAAPHN157u+++/39q0aeNSM1QCUDdSLl682D788EP3utJDVA5w5MiRVq1aNffQ87x581rnzp3dPMqp7tWrlw0cONCKFStmRYsWtUGDBlmdOnVcNZOUyJYtm23YsCH0HAAAAIiZoPv33393NzKqZ1rBswbKUcCtOtxBgwcPdnW0NWCNUkgaNmxo8+fPtwIFCoTmGTdunGXPnt06derk5m3WrJlNnjw5VQG0BucBAAAAYi7oVn3t01Fvt0ak1CMpuXPntgkTJrhHSikoT86bb76Z4vcCAAAAMlVONwAAABBrCLoBAAAAjxF0AwAAAB4j6AYAAAA8RtANAAAAeIygGwAAAPAYQTcAAADgMYJuAAAAwGME3QAAAIDHCLoBAAAAjxF0AwAAAB4j6AYAAAA8RtANAAAAeIygGwAAAPAYQTcAAADgMYJuAAAAwGME3QAAAIDHCLoBAAAAjxF0AwAAAB4j6AYAAAA8RtANAAAAeIygGwAAAPAYQTcAAADgMYJuAAAAwGME3QAAAIDHCLoBAAAAjxF0AwAAAB4j6AYAAAA8RtANAAAAeIygGwAAAPAYQTcAAADgMYJuAAAAwGME3QAAAIDHCLoBAAAAjxF0AwAAAB4j6AYAAAA8RtANAAAAeIygO46NGjXKLrzwQitQoICVLFnSrrnmGtu4caPnf3f27NlWq1Yty5Url/t/7ty5Ea+fOHHCHnjgAatcubLlyZPHqlSpYg8//LCdOnXK82UDAACIuaA7JUFfIBCw4cOHW9myZV0A1rRpU1u/fn3EPEePHrW+ffta8eLFLV++fNa+fXvbtm1bsn/71ltvtSxZsljv3r0TvNanTx/3muaJZUuWLLE777zTVqxYYQsWLHDBbsuWLe3gwYNpfs/Jkye7NkrKZ599ZjfeeKN17drVvvnmG/d/p06d7PPPPw/N88QTT9gLL7xgzz77rG3YsMFGjx5tTz75pE2YMCHNywUAABC3QXdKgj4FXGPHjnUB2MqVK6106dLWokUL279/f2ie/v37u97SmTNn2tKlS+3AgQPWrl07O3nyZLJ/v3z58u53Dh8+HJp25MgRmzFjhlWoUMFi3YcffuhOLM4991w7//zzbdKkSbZ161ZbtWpVaJ5jx47Z4MGDrVy5cu6EpmHDhrZ48eI0/83x48e79hs6dKjVrFnT/d+sWTM3PTww79Chg7Vt29YqVapk119/vdsuvvzyy7/9mQEAAOIu6D5d0KdebgVjw4YNs44dO1rt2rVtypQpdujQIZs+fbqbZ+/evTZx4kQbM2aMNW/e3OrWrWvTpk2ztWvX2sKFC5P9+/Xq1XPB9Zw5c0LT9FzBuN4n3mhdStGiRUPTevToYcuWLXMnJ2vWrLEbbrjBWrdubT/88EOa/oYCagXQ4Vq1amXLly8P/XzppZfaRx99ZN9//737WT3iOpm66qqr0vjJAAAA/JU1Iwd9mzZtsh07dkQEacoDbtKkSShIU4B+/PjxiHmUiqIAPTyQS4qCSgX7Qa+88or17Nkz2d9ROsu+ffsiHpmdTnAGDBjgAl6tO/npp59cr/8bb7xhl112mVWtWtUGDRrk5glfZ6mh9ixVqlTENP2s6UFDhgyxm2++2fWE58iRw50A6WqGpgEAAGRG2S0DB33BQCyxIG3Lli2heXLmzGlFihRJNpBLinKKleKwefNml8cd7NVNLoVCuegjRoywWHLXXXe5nmz1KAd99dVXrl2qV6+e4KSjWLFi7rmuTOhmyCClCOkkKH/+/KFpXbp0cTnaQVrP4fQ3wqfNmjXLXa3Q1QxdBVm9erULunUy1b179zP8yQEAAOIo6E4s6EtpkJaYlMwjuvlSucNKW9Hv6LmmJUdBuk4QgtTTrZSUzEo3ob799tv2ySef2FlnnRWarmoh2bJlc1cT9H+4YFCtQFhBcXh6jqqTvPbaa6FpBQsWDD1XTn70ydDOnTsjTqzuvfdeu+++++ymm25yP9epU8edZOlkh6AbAABkRtkzctCnAE0UpJUpUybRIE3z6Ga/3bt3R/R2a57GjRun6O8rnURBvzz33HOnnV8pLnpkdjrJ0LrXTajq2VeJvnBK69DNqFqXSi9JTPbs2e3ss88O/awqNKoyEz4tXKNGjdxNs/fcc09o2vz58yPaSjn7WbNGZj4p6KdkIAAAyKyy+h30KdhV7+iiRYsSBH36WUG1grQgBdiqehIM0urXr+/yfsPn2b59u61bty7FQbduDNT76qGb+uKFKscE0zhUtlEnN3oEq7koreSWW26xbt26uTZSjr0qyKik3/vvv5+mv9mvXz8XZOs9vvvuO/e/bnhV+kjQ1VdfbY899pi99957Lu1HJwWqYHPttdeesc8OAAAQNz3dCvoU8L311luhoE8KFSrkekuVHqJgbOTIkVatWjX30PO8efNa586dQ/P26tXLBg4c6PKMdROmbvZTSoKqmaSEelFVDzr4PF48//zz7v/outq6STJYo1zPH330Ubd+f/31V7eO1Vud1koiOhFSzrwGv3nwwQfdzZnK4VYpwiDV49ZrqpeuXnalsNxxxx320EMP/a3PCwAAEJdBd0qCPtWIVs+rAjClkCg4U0+pgvSgcePGuTQHDbKieVX3WYO0pCaADs87jhe60nA6uoqgm0ZTeuOo2u10gwqp7rYeSVHbqlRkeO1uAACAzCx7Rg/61NutESn1SEru3Lld72hqRixUUJ6cN9980zIyVQ3ZtWuX34uR6egm2TM98NGGPxiennUFAEAmuJESqQ+4a5xzjh05dIhVl0q58+a1jRs2nJHAWwF83jy5rcvc/z+iKU5P6+x0FYIAAIg1BN2ZkHq4FXAXvP8xy14h8uZTJO3E1k22b+Qwt/7ORNCt99jw3UauOGSAqw0AAGR0BN2ZmALuHNXP8Xsx4pqCRwJIAACQqYaBBwAAAGIRQTcAAADgMYJuAAAAwGME3QAAAIDHCLoBAAAAjxF0AwAAAB4j6AYAAAA8RtANAAAAeIygGwAAAPAYQTcAAADgMYJuAAAAwGME3QAAAIDHCLoBAAAAjxF0AwAAAB4j6AYAAAA8RtANAAAAeIygGwAAAPAYQTcAAADgMYJuAAAAwGME3QAAAIDHCLoBAAAAjxF0AwAAAB4j6AYAAAA8RtANAAAAeIygGwAAAPAYQTcAAADgMYJuAAAAwGME3QAAAIDHCLoBAAAAjxF0AwAAAB4j6AYAAAA8RtANAAAAeIygGwAAAPAYQTcAAAAQy0H3J598YldffbWVLVvWsmTJYm+++WaCeQKBgA0fPtzNkydPHmvatKmtX78+Yp6jR49a3759rXjx4pYvXz5r3769bdu2Ldm/feutt7q/2bt37wSv9enTx72meQAAAIBMHXQfPHjQzj//fHv22WeTnGf06NE2duxYN8/KlSutdOnS1qJFC9u/f39onv79+9vcuXNt5syZtnTpUjtw4IC1a9fOTp48mezfL1++vPudw4cPh6YdOXLEZsyYYRUqVDhDnxIAAADxztegu02bNvboo49ax44dE31dvdzjx4+3YcOGuXlq165tU6ZMsUOHDtn06dPdPHv37rWJEyfamDFjrHnz5la3bl2bNm2arV271hYuXJjs369Xr54LrufMmROapucKxvU+AAAAQMzndG/atMl27NhhLVu2DE3LlSuXNWnSxJYvX+5+XrVqlR0/fjxiHqWiKEAPzpOcHj162KRJk0I/v/LKK9azZ89kf0fpLPv27Yt4AAAAAJky6FbALaVKlYqYrp+Dr+n/nDlzWpEiRZKcJzldu3Z1KSmbN2+2LVu22LJly6xLly7J/s6oUaOsUKFCoYd6xgEAAICkZLdMQDc1RqedRE+LlpJ5RDdftm3b1qWt6Hf0XNOSM3ToUBswYEDoZ/V0E3gDAAAgUwbdumlS1GNdpkyZ0PSdO3eGer81z7Fjx2z37t0Rvd2ap3Hjxin6O0onueuuu9zz55577rTzK8VFDwAAACDTp5dUrlzZBdULFiwITVOAvWTJklBAXb9+fcuRI0fEPNu3b7d169alOOhu3bq1e189WrVq5cEnAQAAQDzztadbpf1+/PHHiBsnV69ebUWLFnVVRZQeonKAI0eOtGrVqrmHnufNm9c6d+7sfkc51b169bKBAwdasWLF3O8OGjTI6tSp46qZpES2bNlsw4YNoecAAABAzATdX375pV1xxRWhn4N50t27d7fJkye754MHD3Z1tDVgjVJIGjZsaPPnz7cCBQqEfm/cuHGWPXt269Spk5u3WbNm7vdTE0AXLFjwjH42AAAAIEME3RpdUjcvJke93RqRUo+k5M6d2yZMmOAeKRUM6pOS2OiYAAAAQMzldAMAAACxgKAbAAAA8BhBNwAAAOAxgm4AAADAYwTdAAAAgMcIugEAAACPEXQDAAAAHiPoBgAAADxG0A0AAAB4jKAbAAAA8BhBNwAAAOAxgm4AAADAYwTdAAAAgMcIugEAAACPEXQDAAAAHiPoBgAAADxG0A0AAAAQdAMAAACZGz3dAAAAgMcIugEAAACPEXQDAAAAHiPoBgAAADxG0A0AAAB4jKAbAAAA8BhBNwAAAOAxgm4AAADAYwTdAAAAgMcIugEAAACPEXQDAAAAHiPoBgAAADxG0A0AAAB4jKAbAAAA8BhBNwAAAOAxgm4AAADAYwTdAAAAgMcIugEAAACPEXQDAAAAHiPoDvPvf//bKleubLlz57b69evbp59+6vX6BwAAQBwg6P6fWbNmWf/+/W3YsGH29ddf22WXXWZt2rSxrVu3+ttCAAAAyPQIuv9n7Nix1qtXL7vtttvsnHPOsfHjx1v58uXt+eef97eFAAAAkOll93sBMoJjx47ZqlWr7L777ouY3rJlS1u+fHmC+Y8ePeoeQXv37nX/79u3Lx2W1uzAgQPu/+M/bLDA4UPp8jdjwYltW0LrL73aCgAQ+4Lfy0d3/Ginjh3xe3EyjeN/bUvX7+Xg3wgEAuYHgm4z27Vrl508edJKlSoVsXL0844dOxKstFGjRtmIESMSTFfPeHraP+aRdP17saJJkyZ+LwIAIAbtnves34uQKTVJ5+/l/fv3W6FChSy9EXSHyZIlS8TK0ZlQ9DQZOnSoDRgwIPTzqVOn7K+//rJixYolOn880VmkTj5++eUXK1iwoN+LE7doB//RBv6jDfxHG/iPNoiM6xRwly1b1vxA0G1mxYsXt2zZsiXo1d65c2eC3m/JlSuXe4QrXLiw122VqSjgJuj2H+3gP9rAf7SB/2gD/9EG/8ePHu4gbqQ0s5w5c7oSgQsWLIhYOfq5cePGfrUNAAAAYgQ93f+jdJGuXbtagwYNrFGjRvbiiy+6coG9e/f2t4UAAACQ6RF0/8+NN95of/75pz388MO2fft2q127tr3//vtWsWJFf1sok1Hazb/+9a8E6TegHeIN+4L/aAP/0Qb+ow0yjiwBv+qmAAAAAHGCnG4AAADAYwTdAAAAgMcIugEAAACPEXQDAAAAHiPoBgAAADxGyUCk2TvvvGNffvmltW7d2tU2X7RokT311FN26tQp69ixo91+++2s3XRw8OBBmz59ui1fvtyNqpolSxY3kuoll1xiN998s+XLl4928Ni2bdvs+eefT9AGGlxLtf7Lly9PG6SDH374IdE2qFatGus/HbAf+I82yNgoGYg0eeGFF6xv3752/vnnuy+6f//73/bPf/7T1TvPli2bTZ061UaNGmX9+vVjDXvo22+/tRYtWtihQ4esSZMmLshQFdCdO3fakiVLXMA9f/58q1WrFu3gkaVLl1qbNm1cYN2yZcuINtCotr/88ot98MEH7iQI3ti7d69169bNdQRoiOeSJUu6Nvjjjz9s3759dvXVV7tjkobBhjfYD/xHG2QCqtMNpNY555wTePHFF93zRYsWBXLnzh147rnnQq9PmjTJzQNvNW3aNHDTTTcFjh49muA1Tbv55pvdPPBOgwYNAv3790/ydb2meeCdrl27BurUqRNYsWJFgtc07bzzzgt069aNJvAQ+4H/aIOMj55upEnevHntu+++swoVKrifc+bMaV999ZUbyVM2b95s5557rkt9gLftoBSfpHqy161bZxdddJHrCYc38uTJY6tXr7YaNWok+rr2k7p169rhw4dpAo8ULlzY5s2bZw0bNkz09RUrVrg0uD179tAGHmE/8B9tkPFxIyXSpFixYrZlyxb3/LfffrMTJ07Y1q1bQ6/rtaJFi7J2PVakSBGX3pOUH3/80c0D75QpU8blESfls88+c/PAW8rhTstrODPYD/xHG2R83EiJNOnQoYP16tXLunfvbm+//bbLpxw4cKBlzZrVfcHde++9Lr8V3vrHP/7h2uCBBx5wud3KJ9b6141kyiceOXKk9e/fn2bw0KBBg9zNkqtWrUq0DV5++WUbP348beAh5WxrX5g4caI1aNAg4jVdCVL7tG/fnjbwEPuB/2iDTMDv/BZkTgcOHAjcdtttgdq1awd69+4dOHbsWODJJ58M5MyZM5AlSxaXR/z777/7vZhx4fHHHw+UKVPGrfesWbO6h55r2hNPPOH34sWFmTNnBho2bBjInj27W/d66LmmzZo1y+/Fi3m7d+8OtG7d2q33IkWKBGrUqBGoWbOme679oU2bNm4eeIv9wH+0QcZGTjfOqCNHjtjx48etQIECrNl0tmnTJte7KqVLl7bKlSvTBulM2/6uXbvc8+LFi1uOHDlog3S0YcMGl78dvh+onGnNmjVph3TEfuA/2iBjIugGAI+obB35xIh37Af+ow0yBm6khCdUm7hnz56s3XQwYcIEl9f9+uuvu59fffVVV81EvXv333+/u8kV3jl69Ki7n0F10p988kk37dFHH3U10vPnz2+dO3d2taLhrW+++cYmTZrkrvjI+vXrrU+fPi6fW5VN4C32A//RBpmA3/ktiE2rV692uZTw1sMPPxwoUKBA4LrrrguULl3a5XcXK1Ys8OijjwZGjhwZKFGiROChhx6iGTx0zz33BMqWLRsYOHCgq01/5513BipUqBCYNm1aYPr06YGzzz470LdvX9rAQ//9738D2bJlc9u+9oeFCxcGChcuHGjevHmgVatW7rXXXnuNNvAQ+4H/aIOMj6AbafLWW28l+xg3bhxBdzqoUqVKYPbs2aETHQUXCvaC5syZ44I+eKd8+fKBBQsWuOc//fST2+7ffPPN0Ovz588PVKxYkSbwUL169dyJpsyYMcMF3DohDXrqqacCF1xwAW3gIfYD/9EGGR853UiTYGlAnbglRa+fPHmSNZzOgxR9/fXXbmCiYL10pZowSJF/bcBAUd5TGo8GgqpUqZI7JuXKlcuVcKxTp457/eeff7bzzz/f9u/fnw5LE5/YD/xHG2R85HQjzUX4Z8+ebadOnUr0odEp4T1VZ/j222/dcw2So5Oc4M/BvNaSJUvSFB5SsK0BcGTlypXuZPOLL74Ivf75559buXLlaAMPqVrSn3/+6Z5r1EndxxD8WfRcgTm8w37gP9og42NwHKRJ/fr1XWB9zTXXJPr66XrBcWboJj0NTKTBij766CMbMmSIGyBBQYba4LHHHrPrr7+e1e0h3ah36623ukFw1Ls6ZswYdwOrer91Rej55593N1rCO82bN7c777zT+vbta7NmzbJWrVrZ0KFD3Y2VwcG6Lr30UprAQ+wH/qMNMj7SS5Amn376qUtZaN26daKv6zWNBKeKDvCOerYff/xxV5tYQYWC7pkzZ9rgwYPt0KFDbqS+Z5991lXSgHdee+21UBvceOONtnjxYnvooYdCbfDggw+6ABze+P33361Lly6uDS677DK3DwwbNsyee+45F3RXrVrVPvjgA/c/vMN+4D/aIGMj6AYAxCTlcuvER+Uzs2fnwi4AfxF0A8AZvPKgESnVu1qsWDHLli0b6xZxh/3Af7RBxsT1TqSZbhq75ZZb3HDjefLkcXdO67mmKbUE6YN28N/cuXPtkksucftA2bJl3Y3Geq5pb775pt+LFxeU0vbSSy9Zjx49rE2bNnbVVVe558q1p3pP+mA/8B9tkLHR0400USDRqVMna9asmbtpqVSpUu7GyZ07d9r8+fPdTX0aIVE3+ME7tIP//vOf/9jdd9/tRmCN3hc0EqJu5tOoof/4xz/8XtSYpYo9LVq0cKkkuo8kvA2WLFni7mnQcUnlM+EN9gP/0QaZgN+FwpE5nXvuuYFRo0Yl+bpGRqxVq1a6LlM8oh38V7Vq1cDLL7+c5OsTJ050gxjBO02bNg3cdNNNgaNHjyZ4TdNuvvlmNw+8w37gP9og46OnG2mSO3duW7NmjVWvXj3R1zdu3OgGozhy5Ahr2EO0g/+UWrV69WqrUaNGoq+rdGDdunXt8OHD6b5s8UKpPEppS6onWwPnXHTRRa4nHN5gP/AfbZDxkdONNFHpreRyVd966y2rUqUKa9djtIP/NPLkiy++mOTryjMOjk4JbxQpUsQNDpWUH3/80c0D77Af+I82yPiooYQ0efjhh+2mm25y+ZItW7Z0OZSq2LBjxw5bsGCBy59UrVx4i3bwnwbDadu2rX344YeJ7gtbtmyx999/3+/FjGnKl+/evbs98MADLrc7ug1Gjhxp/fv393sxYxr7gf9og4yP9BKkmYa+fvrpp93/+nILDkveqFEj69evn/sf3qMd/Ld582Y38qQGZ4neFzRKXKVKlfxexJj3xBNPuOOR1r8CbtHNlGoHBdwaMAreYj/wH22QsRF0AwBixqZNmyJOfFTGFAAyAoJuAAAAwGPcSIk0++abb+zRRx+1f//7324UvnD79u1zdYvhPdrBf++9957ddtttLoVhw4YNEa/t3r3brrzySt+WLV6oOszSpUtdze5oqqI0depUX5YrnrAf+I82yOD8rlmIzGnevHmBnDlzujrRFSpUCBQvXjywaNGi0Os7duwIZM2a1ddljAe0g/9ee+21QLZs2QJt27YNXHrppYHcuXMHpk2bFnqdfcF7GzduDFSsWDGQJUsWd9xp0qRJ4LfffqMN0hH7gf9og4yPoBtp0qhRo8D999/vnp86dSowevToQP78+QMffPCBm0agkT5oB//VrVs38Mwzz4R+fuONN9y+EBwwh33Be9dcc02gXbt2gT/++CPwww8/BK6++upA5cqVA1u2bKEN0gn7gf9og4yPoBtpUrBgwcCPP/4YMW369OmBfPnyBd5++20CjXRCO/hP2/zPP/8cMe3jjz8OFChQIPD888+zL6SDkiVLBtasWRMxrU+fPu4q3E8//UQbpAP2A//RBhkfdbqRJrly5bI9e/ZETLv55psta9asrn636oXCe7SD/woWLGi///57RJWMpk2b2jvvvGPt2rWzbdu2+bp88ZLPnT175NfZc889545HTZo0senTp/u2bPGC/cB/tEHGR9CNNLngggvs448/tvr160dMv/HGG+3UqVNuoAp4j3bwn4YX/+CDD+ziiy+OmK5gLxh4w1s1a9Z0w8Cfc845EdMnTJjganW3b9+eJvAY+4H/aIOMj+olSJN//vOf9uuvvyb6mnq8p0yZYpdffjlr12O0g//uuecey507d6Kvqcf73XfftW7duqX7csWTa6+91mbMmJHoa88++6w7Jin4hnfYD/xHG2R81OkGAAAAPEZPNwAAAOAxgm4AAADAYwTdAAAAgMcIugEAAACPEXTjb1mzZk2Sr7355pusXQAA0tFHH32U5Guq5gP/UL0Ef0uZMmVs2bJlVqVKlYjps2fPdmXSDh48yBpOBydPnrTJkye7g+3OnTtdrfRwixYtoh08pm398ccfT7INfv75Z9ogHXz//fe2ePHiRNvgoYceog08xn7gv8KFC9uCBQvswgsvjJg+fvx4tw/s27fPt2WLdwyOg79dJ7pZs2a2fPlyF4DLrFmzrGfPni4IRPro16+fW99t27a12rVrW5YsWVj16ey2226zJUuWWNeuXd2+QBukv5deeskdk4oXL26lS5eOaAM9J+j2HvuB/8aNG2dXXXWVOx7VqlXLTXvqqafskUcesffee8/vxYtr9HTjjAR8CxcutE8//dQ+/PBDd9B99dVX7brrrmPtphMFGVOnTnUHWvjXu6QvtEsuuYQm8EnFihWtT58+NmTIENrAJ+wHGYOCbPVsL1261HWEjRw50o2c27hxY78XLa7R042/7emnn3a9exoGW6NUamS4Dh06sGbTUc6cOe3ss89mnfuoSJEiVrRoUdrAR7t377YbbriBNvAR+0HGMGjQIPvzzz+tQYMGLv1w/vz51rBhQ78XK+7R041Ue/vttxNMO378uBuCtmXLlta+ffvQ9PDn8M6YMWNczrBukiGtwR/Tpk2zt956y6ZMmWJ58+b1aSniW69evVwea+/evf1elLjFfuCPZ555Jske78svv9wuuuii0LS77747HZcM4Qi6kWpZs6as6I2CP51hw3vXXnutffzxx66n9dxzz7UcOXJEvD5nzhyawWN169a1n376yQKBgFWqVClBG3z11Ve0gcdGjRplY8eOdfc21KlTJ0EbEGx4j/3AH5UrV07x9zI3dfuH9BKkWnRFAGSMPEoF3vDPNddcw+r32Ysvvmj58+d3N5DpER1sEHR7j/3AH5s2bfLpLyM16OnGGbdnzx4XBAIAAH/pivPatWvdjcbKuYd/GBwHf8sTTzzh7owO0k1MSnEoV66cffPNN6zddPbHH3+4u9VVO13Pkf5WrVrl8lpfe+01+/rrr2kCnyjNRw/4g/3AP/3797eJEyeGAm7ldNerV8/Kly/vatjDRwHgb6hcuXJg2bJl7vn8+fMDhQsXDsybNy/Qq1evQIsWLVi36eTAgQOBHj16BLJlyxbIkiWLe2TPnj3Qs2fPwMGDB2mHdPD7778HrrjiCrfuixQp4vYFPb/yyisDO3fupA3SyZQpUwK1a9cO5MqVyz3q1KkTmDp1Kus/nbAf+K9cuXKBlStXuudz584NlC1bNrBx48bAsGHDAo0bN/Z78eIaPd34W7Zv3+7OnuXdd9+1Tp06uQomgwcPtpUrV7J208mAAQNcDus777zj0nv0UCUNTRs4cCDtkA769u3rRnpbv369/fXXX6583bp169w0conTh26i1OA4qlf/+uuvu6twrVu3dtVMNGAIvMd+4L9du3a5waHk/fffd1egq1ev7qr7KM0EPvI76kfmVqZMmVBPd/Xq1QOvv/66e/7dd98FChQo4PPSxY9ixYoFPv744wTTFy1aFChevLgvyxRvChYsGPjiiy8STP/8888DhQoV8mWZ4k2lSpVcT3e0yZMnu9fgPfYD/1WoUMFdcT5x4kSgfPnygXfeecdNX7dunbsCB/9QvQR/S8eOHa1z585WrVo1V4i/TZs2bvrq1asZrCUdHTp0yEqVKpVgesmSJd1rSJ+qPtEl6kTTqPiTflfeEhtxT9P0GrzHfuC/Hj16uKvOZcqUcVV7WrRo4aZ//vnnVrNmTb8XL66RXoK/RZds77rrLqtVq5YtWLDAlesSfcFpOGakj0aNGtm//vUvO3LkSGja4cOHbcSIEe41eO/KK6+0fv362W+//RaaphFaNWhUs2bNaIJ0oFFZlVYSTWkm6hiA99gP/Dd8+HB7+eWX7fbbb3c31efKlctNz5Ytm913331+L15co2QgEAOUO6zcVQXd559/vuvd0NWG3Llz27x589yAOfDWL7/8Yh06dHBtofsc1AZbt251g7Qov/6ss86iCTw2e/Zsu/HGG6158+Z2ySWXuDZQNZ+PPvrIBePUsvce+wGQNIJupGkYeKWR6LJ5YkPCh2MY+PSjnm2Vqvvuu+9cqTRdfbjlllssT5486bgU0BWf8DZQAIj0LVWnK3AbNmwItYFuJtZIiUg/7AfpPwy8erbV0ZLUkPBB3NjtH4JupGkY+B07drh84eSGhGcYeAAA0mcY+C+//NKKFSuW7JDwDAPvL4JuIJPiioP/6F3yn0oyFixYMPQ8OcH5cGaxHwApQ9ANz+gmMo1MCW9wxcF/9C75TzeH6cbt4JU39eRFU5oJV968w36QeWj8jAsvvNDvxYhbBN0445R68thjj7m7p5VnDABe0QBQumkye/bs7nlymjRpQkMg5h04cMCdjIbfz6Mb6x988EE3WI6Ghoc/KBmINNGIh7pJr0SJEla2bFl3eVH1WR966CGrUqWKrVixwl555RXWbjqZOnWqHT16NMH0Y8eOudfgvYcffjjRmug68dRr8IYCaQXcwR7Xyy+/3E0Lf2hacnmuOHPYD/yzbds2dwJaqFAh99BIxTomdevWzfVuq3SgqvnAP/R0I01Ug1tDjqs814cffugqBbRq1cqVrFO9aHqU/LvEHk4DFmkaPRu0QTxgP/AfbeCfLl26uGHe//GPf7jymZ988oldcMEFroyserk58fQfI1IiTd577z2bNGmSK4emAFyDUlSvXt3Gjx/PGvVBMGc1sZ4P9XjAvzb45ptvrGjRojSBj22gy+0qpQb/2oD9wHsff/yxq0ev3u7rr7/eXYW+4YYbGBAnAyHoRppo1D3VvxWlk+gL7bbbbmNtpjPVHtYXnB4a9TB4mV3Uu71p0yY3aA68U6RIkVAb6MQzPOBQGyjg6927N03gIV1GF6179ejlzZs3og00/LV6/OAd9oOMcT9V1apV3fPSpUu7nG4N2IWMg6AbaaL8bQ2OE35JMV++fKzNdHbNNdeEbpJRek/+/PlDr+XMmdMqVapk1113He3iIV3dUe9ez549bcSIERFXFoJt0KhRI9rAQ19//bX7X+2gy+ta7+FtoMvrgwYNog08xH6QMei7OEjVfLjCk7GQ04000c6sUSl1Y4Yov/vKK69MEHjPmTOHNZwOpkyZ4vLrOcD6R5UzGjduHHEyivTVo0cPe/rpp6nH7SP2A3+/l2vXrh264rlmzRqrWbNmxEmofPXVVz4tIQi6keYvt5RQ3jcQD7Zu3Zrs6xUqVEi3ZQH8wn7gH11pSwkVO4A/CLqBGJDUoCBBVC+hDeKBrrYlZ9GiRem2LPGKYxGQNHK6gRigNJ7woPv48eMuz1VpJynt/cCZySuOboOxY8e6waLgPeVuR7eB7ndYt26dde/enSZIB+wHQNLo6QZi2PTp023WrFn21ltv+b0ocV1e88knn7TFixf7vShxa/jw4a6KzFNPPeX3osQt9gOAoBuIaT/99JOdd955dvDgQb8XJW798MMPrlwdbeCfH3/80S666CL766+/fFyK+MZ+AJBeAsQsDT8+YcIEO+uss/xelLiwb9++iJ9Vvk6jhKqXtVq1ar4tF8w+++wzKvukE/YDIGnkdAMxNDBFeMC3f/9+N0jItGnTfF22eFG4cOEEN7OqHcqXL28zZ870bbniSceOHRM98fnyyy/doDnwHvsBkDRyuoEYoBsmoysIlChRwho2bOgCcqRPfeLE2uDss8+OGCkU6VfKNNgGqmrSsmVLVn06YD/wxzPPPJPiee+++25PlwVJI+gGAADIxCpXrpyi+XQ17ueff/Z8eZA4gm4gRuzevdsmTpxoGzZscAfWc845x/X8FS1a1O9FixsbN250efTBNtBocHfddZf7H+lH6STh+0H9+vVZ/emI/QBIXNYkpgPIZJd0K1Wq5C4xKvhWlQY9V+9H9OVeeOO///2vG4J51apVrl60qsZouOU6derYG2+8wWpPB9u2bbPLLrvMVSrp16+fu4x+4YUX2qWXXmq//PILbZAO2A+ApNHTDcQABXuNGze2559/3rJlyxYahbJPnz62bNkyNzgIvFWlShXr0qWLPfzwwwmGXH711Ve5pJsOlLet6hm6x6FGjRqhXteePXtavnz5bP78+emxGHGN/SDjnIC+/fbbtnXrVjt27FjEaxqwC/4g6AZiQJ48edzIe8FAI0gBh2pEq3wgvKVKMWvWrHE3TkbXJ1bP96FDh2iCdNgPli9fbnXr1o2YrisOl1xyCftBOmA/8N9HH31k7du3d1c69R2gTpnNmze7aj716tWzRYsW+b2IcYv0EiAG6ECqHNZomqagG95r2rSpffrppwmmL1261KU8wHsVKlRwQ79HO3HihJUrV44mSAfsB/4bOnSoDRw40F3hzJ07t82ePdulVzVp0sRuuOEGvxcvrlHHCsik1KsapNxV5bBq5L2LL77YTVuxYoU999xz9vjjj/u4lLFNl2+D1LM0ZMgQl9Md3gbK5x4xYoSPSxk/Ro8ebX379nXbvW6e1I2UuqlS+wZDwHuH/SBjUWfLjBkz3HOVK9WVzvz587vUtw4dOtg///lPvxcxbpFeAmRSqkGsoEKXDJOjeZTfDW/aICVog/QbGOrgwYOuZztYGz34XDndDAPvDfaDjKV06dIuhaRWrVp27rnn2qhRo1ynwDfffOPSrA4cOOD3IsYterqBTGrTpk1+L0LcO3XqVNyvA7+NHz/e70WIe+wHGYuutOkGegXdbdu2dakma9eutTlz5oSuwsEf9HQDAADECA1+o95slS3VDdyDBg1y95boJu9x48ZZxYoV/V7EuEXQDWTiPMo2bdpYjhw5InIqE6NLizjzVAv99ttvdzcrnW4YZoZe9oZKBBYsWDD0PDnB+XBmsR8AKUPQDWTiPModO3ZYyZIlk82pJJ/YOyrJpRv1ihUrluwwzAy97B3Vpd++fXtoPwjP7w7SfQ/sB95hP8h4tdJXrlzpjkvh9uzZ4ypdMQy8f8jpBmIgj5KcSv/z6smx94duGCtatKh7/vHHH/u0FPGN/SBjUU3uxG6eP3r0qP3666++LBP+D0E3kMmpLrFG4vvPf/5j1atX93tx4rYNNDDRu+++625eQvpR7eFglZLFixe70SfLly9PE/iA/cBf4WmG8+bNs0KFCoV+VhCuQXMqVark09JBCLqBTE453RoEIbHL6ki/NlAvEm3gH5UFVC3u7t27+7gU8Y39wF/XXHON+1/Hoej9QG2jgHvMmDE+LR2EESmBGNCtWzebOHGi34sR1zQoyxNPPOF6XOGPZs2aud5u+If9wD9KM9RDI7Pu3Lkz9LMe6hTQkPDt2rXzcQlBTzcQA44dO2Yvv/yyLViwwBo0aOAGAgk3duxY35YtXnz++efu8u38+fOtTp06CdpANXLhLVXz0RDYuvKjESmj24AqPt5jP/Af95dkXFQvAWLAFVdckezr3GDmvR49eiT7+qRJk9JhKeIbVXz8x36QMSxZssSlW2lIeKWbnHPOOXbvvffaZZdd5veixTWCbgAAgBgxbdo0d/LTsWNHN+y7SmYuX77c5s6da5MnT7bOnTv7vYhxi5xuIAaoYsP+/fsTTD948KB7Dd678sorXR3caBqwRa/Be1OnTnW5q4mlX+k1eI/9wH+PPfaYjR492mbNmuUG5erXr597/vjjj9sjjzzi9+LFNXq6gRgbICTcrl27rHTp0tzcl86DFYXTDU3lypVz5dTgz37w559/ummJ1S7GmcV+4L9cuXLZ+vXr3bDv4X788UerXbu2HTlyxLdli3fcSAlkYupF1aVDPdTTreHIgxRgvP/++wkCEJxZa9asCT3/9ttvXeAd3gYffvihC7rhveDIk9G2bdsWUbMYZx77QcahOvW6qTs66NY0atj7i6AbyMQKFy7sggw9EhsYR9NHjBjhy7LFiwsuuCDUBomlkeTJk8cmTJjgy7LFi7p164baQGUDVbM7/MRH1Rxat27t6zLGOvYD/ymV8Omnn7aBAwe6tJLVq1db48aN3X6xdOlSl8+t1+Ef0kuATH6Hunr3FOzNnj07NBy25MyZ0ypWrGhly5b1dRlj3ZYtW1wbVKlSxb744gsrUaJERBvoSoPSHuCd4Iml/lfAkT9//og20KAg1113nXsOb7AfZKz0Kt00qYFwVL1EgtVLOnTo4PdixjWCbiBGvvB02TC5kmlArJsyZYrdeOONEWlWQLzn0yPjIOgGYoQqZ6inNTgSWfSIlfDe999/70ZETKwNHnroIZognahaSWJtoJH64D32A/+C7t9//z3iahsyFoJuIAa88847dsstt7gSgQUKFIi4mUzP//rrL1+XLx689NJL9s9//tOKFy/uKsZEt8FXX33l6/LFgx9++MHltaomcWI3WFK9xHvsB/4G3bphOLGbicPxfeAfgm4gBugmyquuuspGjhxpefPm9Xtx4pLy5/v06WNDhgzxe1HilgYC0U2U9913n5UpUyZB8HH++ef7tmzxgv3A36B7/Pjxp63U071793RbJkQi6AZiQL58+Wzt2rXuZj74o2DBgq5aAG3g736watUqq1mzpo9LEd/YD/xDTnfGx11XQAxo1aqVffnll34vRly74YYbbP78+X4vRlyrVauWGxAK/mE/8M/p0krgP+p0AzGgbdu2rhyUBmepU6eO5ciRI+L19u3b+7Zs8UIDUTz44IO2YsWKRNtAdXPhrSeeeMIGDx7s0qwSawP1wsJb7Af+0b0LyNhILwFiQHKlArmBLH1Urlw52Tb4+eef02lJ4ldwP4ju8eNGyvTDfgAkjaAbABAzg0Ulp0mTJum2LAAQjaAbADy6zEuOJeIZ+wEQiRspgRjq5bv66qtdTmW1atVcHvenn37q92LFlalTp7pc4jx58rjHeeedZ6+++qrfixV3g0Rp+OvbbrvN/vGPf9i4ceNs7969fi9WXGE/ABJH0A3EgGnTplnz5s1djW7dsHfXXXe5oK9Zs2Y2ffp0vxcvLowdO9YNjqN66a+//rrNmjXLWrdubb1793aBH7ynCj5Vq1Z161sDgKiSidpF0xicKH2wHwBJI70EiAHnnHOO3X777XbPPfck+ALUCHEbNmzwbdni6QayESNGWLdu3SKmT5kyxYYPH26bNm3ybdnixWWXXeau9Gib1yA5cuLECdfrrRtZP/nkE78XMeaxHwBJI+gGYkCuXLls/fr1LuAI9+OPP1rt2rXtyJEjvi1bvMidO7etW7cuQRtoaHKlnNAG3tPVna+//jrB4DgqpdmgQQM7dOhQOixFfGM/AJJGegkQA8qXL28fffRRgumaptfgPQXbSiuJpjQT5djDe6rDvXXr1gTTf/nlFytQoABNkA7YD4CkMTgOEAMGDhzocrk1DHnjxo1d1YylS5fa5MmT7emnn/Z78eKCUktuvPFGl8JwySWXhNpAJz6JBeM487T+e/XqZU899VTEfqCBo26++WZWeTpgPwCSRnoJECPmzp3rqjYE87eV561go0OHDn4vWtxYtWqVu4lPbaByaRqWXCdEdevW9XvR4sKxY8fcNv/CCy+4XG7RqJS6wfXxxx93aVjwHvsBkDiCbgBATFHu9k8//eROfJTuoKo+AOA3gm4gBqxcudJOnTplDRs2jJj++eefW7Zs2dxNZPDW+++/79Z1q1atIqbPmzfPtU2bNm1oAo+pHvfJkyetaNGiEdNVPlDVTJTzDW+xHwBJ40ZKIAbceeed7maxaL/++qt7Dd677777XMAXTb2teg3eu+mmm2zmzJkJpiunXq/Be+wHQNIIuoEYoJJo9erVSzBducR6Dd5TaUDlcEdT+TqVboT3dGXniiuuSDC9adOm7jV4j/0ASBpBNxADdIPY77//nmD69u3bQ4OEwFuFChVyA7BEU8CdL18+Vn86OHr0aOgGynDHjx+3w4cP0wbpgP0ASBpBNxADWrRoYUOHDnU5rUF79uyx+++/370G77Vv39769+/vbuALD7hVvUSvwXsXXnihvfjiiwmmq5pJ/fr1aYJ0wH4AJI0bKYEYoNztyy+/3P78889QeTrV7C5VqpQtWLCAAXLSgU54WrdubV9++aWdddZZbtq2bdvc0ORz5syxwoULp8dixLVly5ZZ8+bNXfDdrFkzN0110nWj8fz5811bwFvsB0DSCLqBGHHw4EF77bXX7JtvvnHDYZ933nluQBDVKUb60E2TOskJbwOdDCH96GTzySefdP8H20BXgRgVNP2wHwCJI+gGAAAAPEZONwAAAOAxgm4AAADAYwTdAAAAgMcIugEAAACPMWoGECNUl/u///2vqxN97733WtGiRe2rr75yZQPLlSvn9+LFpH379qV43oIFC3q6LDCbPHmyderUyfLmzcvq8NGpU6dcjfqdO3e65+Go5oN4RvUSIAasWbPG1SfWaHCbN2+2jRs3WpUqVezBBx+0LVu22NSpU/1exJiUNWtWy5IlS4rmPXnypOfLE+/KlCnjSmfecMMN1qtXL2vcuLHfixR3VqxYYZ07d3bHHZUODKd9hf0A8Yz0EiAGDBgwwG699Vb74YcfLHfu3KHpbdq0sU8++cTXZYtlH3/8sS1atMg9XnnlFStZsqQNHjzY5s6d6x56risNeg3e02BE06ZNs927d9sVV1xhNWvWtCeeeMJ27NjB6k8nvXv3tgYNGti6devsr7/+cm0RfOhnIJ7R0w3EAPVwK5WkatWqVqBAATc4i3q61dtUo0YNO3LkiN+LGPM0AuJtt93mBiQKN336dDc0+eLFi31btnik1AYF4Eo5+e6779xooer9vvrqq90VCngjX7587vhz9tlns4qBKBx5gBig3u3E8ouVZlKiRAlflinefPbZZ66HL5qmffHFF74sUzzTVYdLLrnEGjVq5ILstWvXuqtBOjHlBMg7DRs2dPncABIi6AZiQIcOHezhhx+248ePh3Int27davfdd59dd911fi9eXChfvry98MILCab/5z//ca8hffz+++/21FNP2bnnnmtNmzZ1J6Pvvvuubdq0yX777Tfr2LGjde/enebwSN++fW3gwIHuCsOqVavc/SbhDyCekV4CxAAFFldddZWtX7/e9u/fb2XLlnV5rOrle//9990lX3hL61knOOpJvfjii0M3lamazOzZs137wFtKHZk3b55Vr17dpfp069bNVfEJp8D7rLPOSlBVA2dGYqk76gTQTZXcSIl4R9ANxBDd0KfcbgUU9erVcxVNkL438j3//PO2YcMGF2TUqlXL3VhGT3f6UM62gm2dbCZF7aKrQBUrVkynpYovuo8kOax3xDOCbiCTO3HihMvpXr16tdWuXdvvxYlLSutp2bKlSyVRLyv8odKYN954o+XKlSti+rFjx2zmzJmu5xsA/EJON5DJZc+e3fUeUf/WPzly5HAl0lJasxve6NGjh+3duzfBdKVc6TWkD6VUKbdbV9patGhhd999t5sGxDuCbiAGPPDAAzZ06FDq4PpIvagTJ070cxHiXjBvOLG0H5XVhPeUU6+0KlXsOe+889zVt88//9zd2LpgwQKaAHGN9BIgBtStW9eV6VKag3q9o2+cVJ43vKWePaU3qD6xygRGt8HYsWNpAg+3fwXbqg+t4E5Xf4J0BUiVS1Sn+/XXX6cN0qEtWrVqZY8//njEdFVSmj9/PscixLX/f2QCkGldc801fi9C3FN6iW5ele+//z5ifZB2kj7bv+5rUMCXP3/+0Gs5c+a0SpUqUToznegm4sRObnr27Gnjx49Pr8UAMiSCbiAG/Otf//J7EeKehoSHv9u/gmvdSKkbi+EPDcalk59q1apFTNc0DVgExDOCbgBATGDQG//94x//sNtvv91+/vlna9y4sbvKs3TpUnviiSfcoDlAPCOnG4iRASmSS2Ggskn6WLlypb3xxhuuDrTK1IWbM2dOOi1FfNHgN0rnKV68uBUpUiTZ/eCvv/5K12WL15tZlUYyZswYNxCRaLCue++911UxIdUK8YyebiAGzJ07N+Jn3VD59ddf25QpU2zEiBG+LVc8CdaBVr1uVWnQ/z/88IMbGfTaa6/1e/Fi1rhx46xAgQKh5wR1/tL6v+eee9xDpRol2D5AvKOnG4hh06dPt1mzZtlbb73l96LEPJVHu+OOO+zOO+90QYYqaVSuXNlNK1OmDCc/ABDnCLqBGKYBKRQMHjx40O9FiXkqEbh+/Xp3M59SHXRjZZ06dVw1hyuvvNK2b9/u9yLGpH379qV43oIFC3q6LPFKVXs++ugjl94TLN+YFMqXIp6RXgLEqMOHD9uECRPsrLPO8ntR4ia3OHg5vVy5cq6EoILuPXv22KFDh/xevJhVuHDh06aUBAfN4d4Gb3To0MFy5coVek6KD5A4gm4gBkTfQKYgQwFg3rx5bdq0ab4uW7y47LLLXC63Au1OnTpZv379bNGiRW5as2bN/F68mEWpxoxVsnT48OG+LguQkZFeAsSAyZMnRwTdqmaierkNGzZ0ATm8p8oYR44ccZUaTp06ZU899ZQrlaYRKh988EHaAXGhSpUqropPsWLFIqbrio/SUFRKEIhXBN0AgExrzZo1Vrt2bXeiqefJ0f0N8JbaQRV7ogfC+f333618+fIJSmkC8YT0EiAGfPjhh27o60svvdT9/Nxzz9lLL71ktWrVcs/p7faebpZs0qRJgtFBd+/e7YYgV6oJzrwLLrggFOTpua74KL0qGjnd3nr77bdDz+fNm2eFChUK/axcet1oqWo+QDyjpxuIAcoj1ohvV111la1du9YaNGjgRn9ToHfOOefYpEmT/F7EuOjh0yX1Sy65xF577TVXzSTYw6eUE27i88aWLVusQoUKLqjW8+RUrFjRo6WAtn9J7KQnR44crqqPBsxp164dKwtxi6AbiAHq5Va1DH2x6UYmPf/vf//rynMpEFdPILwPOjQgkepyq0TjO++849qDoBvxRL3ZyulW2UwAkf7v1BRAppYzZ85QWbqFCxe60RCDZexSU8cYf48GwVmyZInLHb7wwgtt8eLFrNJ0tnHjRrvrrrtcxZjmzZu755qG9LFp0yYCbiAJBN1ADFAu94ABA+yRRx6xL774wtq2beumf//999TpTifB6jGqV6z0EpUMbN26tf373/9Or0WIe7q6o5sqV61aZeeff747+dHVHk1744034n79pIe7777bnnnmmQTTn332Wevfvz9tgLhGegkQA7Zu3Wp9+vSxX375xX3p9erVy02/5557XC5xYl+C8L5qw+zZs6179+5uoCJyutOnXF2XLl3s4Ycfjpium1tfffVVytWlAw0MpZsq69evHzFdJz/t27e3bdu2pcdiABkSQTcAnOEb+sIpv149rwq+4S0NBqWygaqNHu6HH35wPd+MDOq93Llzu20+ug1+/PFHd8VBteyBeEXJQCBGqCf1zTfftA0bNrjAT1VLNCRztmzZ/F60uJBUZQwFGnrAe02bNrVPP/00QcCnQYo0Yii8p3WvEqbKpQ/3wQcfuCsRQDwj6AZigHqRVKXk119/tRo1ariSXcrn1mAU7733nlWtWtXvRYwLqtqg3GGl+0QPAjJnzhzflite6kMrfWHIkCHuysLFF1/spq1YscK1yYgRI3xcyvihe0sUcP/xxx+udr2oRrfKBY4fP97vxQN8RXoJEAMUcCvQ1g18qlgif/75p8tvVa6xAm94a+bMmdatWzdXOWbBggXuf6U1KM/72muvpVa6x/WhT4fBcdLP888/b4899pj99ttv7udgKVPtH0A8I+gGYoAGYlGPngbJCffNN9+4wVoOHDjg27LFC1XKUI3uO++80woUKODWvWoWa5pKCdLTinij3u48efK4cQQAUDIQiAkqU7d///4E0xVsq4Y3vPfTTz+FSjWqPTRAjnpXVUHmxRdfpAkQd0qUKEHADYQhpxuIARpa+fbbb7eJEyfaRRdd5KZ9/vnn1rt3b5fnCu8prSd44qOyaargoCsPe/bsoWpGOtLJjgYoSiyvXuU0kT710l9//fVE20ClA4F4RdANxADV4VZJukaNGlmOHDnctBMnTriA++mnn/Z78eKCqmMol1uBdqdOndzgOIsWLXLTNDoivPf111+7+xtUGlDBt06Edu3a5UoJqn46QXf6HIuGDRvmjkdvvfWW9ejRw10F0k3GSr0C4hk53UAM0Y17KhkotWrVSlA6Dd7566+/XA3ismXL2qlTp+ypp55yperUBg8++KAVKVKE1Z8OJQOrV6/ubuQrXLiwy6vXSahuKNZJUMeOHWkDj9WsWdMNRnTzzTeH7m1QqcCHHnrI7SMamRKIVwTdQIxRFROJHqQFiHUKtJVWpbKZev7ZZ5+5evWapp7X7777zu9FjHm6qqATf9Wt19UFXenRwETqEFAZR1VVAuJVymotAcjwlM+tQVg0Ipweev7yyy/7vVgxb9++fSl6wHvq1Q6ebJYqVcrlFEuhQoVCz+Gt0qVLhwJrBd6qqiSbNm0KdQgA8YqcbiAGKH1h3Lhx1rdvX5fXLerlU+WMzZs326OPPur3IsYs9agmd1VBgQY1otNH3bp17csvv3QpJldccYVLaVBO96uvvpqgnCa8oQFx3nnnHatXr5716tXLHYN0Y6XahfQexDvSS4AYULx4cZswYYLLoww3Y8YMF4gr8IA3VCkjPMDWjXy6wqAKJuGaNGlCE3hMgZ0qyCjgVo1opZQE8+onTZrk0hzgLd3PoEf27P/Xp6cqJsE2UDUlSpginhF0AzFAN+l98cUXVq1atYjpGgpeJQRVtg7pI/zmMaQfnfAohUR5xBqQBelPFZM0EmXPnj2tfPnyNAEQhZxuIAaoOoMqNkTToCy33HKLL8sEpHfQrZPObdu2seJ9ot7tJ5980k6ePEkbAIkgpxuIoRsp58+f7yoEiG5g+uWXX6xbt242YMCA0Hxjx471cSkBb2TNmtUF3bqJL/qKD9JP8+bNbfHixXbrrbey2oEoBN1ADNDoh7pxSTQQRXAIZj30WhBlBNMH69kfo0ePtnvvvddd9VH1HqS/Nm3a2NChQ91xp379+pYvX76I1xkhF/GMnG4A+BuiKzKocoMqOEQHG3PmzGE9p8O9DRqNUrnFumEvOrdbg7PA+ysOSaGKD+IdPd1ADPj9999dXeLErFmzxs4777x0X6Z4oRrQ0fn18Mf48eNZ9T5T5RIAiaOnG4gBqtigMnXRl241FLlqeB8+fNi3ZQMQn44cOeIG6gLwf6heAsSAIUOG2I033ujq4CrA/vXXX12KgyoJzJo1y+/FA9KN7ml44IEHXM36nTt3umkffvihrV+/nlZIB6pc8sgjj7g69fnz57eff/7ZTdfJv272BuIZQTcQAwYOHOiqlSxbtsylkuihfFallnDjEuJpoCKNPPn555+7HPoDBw646doP/vWvf/m9eHFBdbonT57sbmoNHwhH7aKrcUA8I+gGYoQGYzn33HPdsO/79u2zTp06JZnnDcSi++67zx599FFbsGBBRMCnESo/++wzX5ctXkydOjU0PkC2bNlC09UR8N133/m6bIDfCLqBGBDs4f7xxx9dr55Kpmn4dwXeu3fv9nvxgHSxdu1au/baaxNMV+lM1e+G95TapiHfE7vB8vjx4zQB4hpBNxADlL+tnG715p1zzjl222232ddff+1G59NlXSAeFC5c2LZv355guvYF5RjDe7ra9umnnyaY/sYbb1jdunVpAsQ1SgYCMUAjUTZp0iRiWtWqVW3p0qUuxxKIB507d3Y3FSvAU01o9a7qKtCgQYPcyKzwnnLnu3bt6nq8tf6VW79x40aXdvLuu+/SBIhrlAwEAMQEpS9o+PGZM2daIBCw7Nmzu2oaCsZ1c194jjG8M2/ePBs5cqStWrXKBd4aLfehhx6yli1bstoR1wi6gUzsqquushkzZoQGaFGv9p133ukus4vyWC+77DL79ttvfV5SIH3LBiqlRAGfUhqqVavG6gfgO4JuIBNTz51yWDU4jhQsWNBWr17tKpkER6osW7as6+0DAK/p2LNy5UorVqxYxPQ9e/a4Hu9g3W4gHpHTDWRiuoSe3M9APNHJpdJIPvroIzcwTvSQ5IsWLfJt2eKFSpYmdpJ/9OhRl+cNxDOCbgBATOjXr58Lutu2bWu1a9d2N1Mifbz99tsROd3BlDdREK4ToUqVKtEciGsE3UAmpqAiOrAg0EC80g2Ur7/+urvXAenrmmuuCR1/unfvHvFajhw5XMA9ZswYmgVxjaAbyMSUTqJqDbly5XI/HzlyxHr37m358uULXdIF4oVGoUxsYBZ4L5jKU7lyZZfTXbx4cVY7EIUbKYFMrEePHimab9KkSZ4vC+A39aTqRr1nn32WKz4AMhyCbgBATNAQ8B9//LEVLVrUjYyotIZwGqgF3lP+dlI3s77yyis0AeIW6SUAgJig+vQKvOGfESNG2MMPP2wNGjSwMmXKcMUBCENPNwAAOCMUaI8ePdoNBQ8gUtaonwEAANLk2LFj1rhxY9YekAh6ugEAmZaGeU9pmcyvvvrK8+WJd0OGDLH8+fPbgw8+6PeiABkOOd0AgExfHxoZg8qWvvjii7Zw4UI777zzEtzMOnbsWN+WDfAbPd0AAOCMuOKKK5IOOLJksUWLFrGmEbcIugEAAACPkV4CAIgJWbNmTTa/++TJk+m6PAAQjqAbABAT5s6dG/Hz8ePH7euvv7YpU6a4+tHwTseOHVM0HwMUIZ4RdAMAYkKHDh0STLv++uvd6JSzZs2yXr16+bJc8aBQoUJ+LwKQ4ZHTDQCIaT/99JOrpHHw4EG/FwVAHGNwHABAzDp8+LBNmDDBzjrrLL8XBUCcI70EABATihQpEnEjZSAQsP3791vevHlt2rRpvi4bAJBeAgDI1FavXm0XXHCBu2EyuppJiRIlrGHDhi4gBwA/EXQDADI1BdcaDv62226zzp07c1MfgAyJnG4AQKa2bNkyq1evnt13331WpkwZ69q1q3388cd+LxYARKCnGwAQMzdNvv766zZp0iT79NNPrVKlStazZ0/r3r07N1IC8B1BNwAgJssEKvieOnWqbd++3Vq0aGHvv/++34sFII4RdAMAYtKBAwfstddes/vvv9/27NnDMPAAfEXJQABATFmyZIm98sorNnv2bMuWLZt16tSJ0SgB+I6ebgBApvfLL7/Y5MmT3WPTpk3WuHFjF2gr4M6XL5/fiwcA9HQDADI35WurWolqcnfr1s3dPFmjRg2/FwsAIpBeAgDI1PLkyeNSSdq1a+fSSQAgIyK9BAAAAPAYg+MAAAAAHiPoBgAAADxG0A0AAAB4jKAbAAAA8BhBNwAAAEDQDQAAAGRu9HQDAAAA5q3/BxJGcezBFkyjAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "risk_traj.plot_waterfall()" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "16faf81c-8760-4c02-a575-ae033bcb637d", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(
,\n", + " )" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAA/4AAAIOCAYAAAABedZgAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjgsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvwVt1zgAAAAlwSFlzAAAPYQAAD2EBqD+naQAAthFJREFUeJzs3QV4FNf6BvA3RkhCEqw4tFiRAkmhvW2pIG2pu9663Bp1b2//1VsXoEK9hQItVdpCBXd3DxZDYkBCXHf3/7yHO3s3IbIborvv73kWsrOzM2dmbb4j3/FzOBwOiIiIiIiIiIhX8q/vAoiIiIiIiIhI7VHgLyIiIiIiIuLFFPiLiIiIiIiIeDEF/iIiIiIiIiJeTIG/iIiIiIiIiBdT4C8iIiIiIiLixRT4i4iIiIiIiHgxBf4iIiIiIiIiXkyBv4iIiIiIiIgXU+Avbtu4cSNuv/12dO3aFU2bNkWzZs0wcOBAvP3220hPT6+1M5mUlISXXnoJ69ev9+h5t912G4477rhSy/z8/PDAAw/UaPk+/vhjjB8//ojlCQkJZn/lPdYQVFTuujB06FD069cP3oznlq8/3wd1gfvi58TTzwg/x9VxNM+tyHfffYcxY8bAW+3YsQNPPPEEBg0ahObNm6Nly5Y4/fTT8fPPP5e7flpamjnPrVu3RmhoKE477TTMmTPniPX++OMP3HLLLejfvz+CgoLMe6Eiu3btws0334wuXbogJCQE3bt3x2OPPYaDBw+6fRy1Ua7KfPjhh+jduzeCg4PN78/LL7+M4uLiUuvs3bsXjzzyCIYMGWLObXW+e+Pi4nDllVea5/O9fe6552Lt2rXlrvv9998jOjra/BZ26NDB7DsnJ8fn91UX+D3nznvpyy+/xOWXX26uA/he79GjB+677z4kJycf1Wu6bt06s12uw/c/35uvvPIK8vLyjliX759zzjnHvJ/4vuL7i+8zT7hTruzsbDz11FMYMWIEjjnmmGr9HnjyGZgwYQKuv/569OrVC/7+/kdca9XU59qT7xtf3NfZZ5+Ne++9t9zHpk6dat4HrVq1QmFhIXwRzzt/47z5usJjDhE3fP75547AwEDHCSec4Bg7dqxj3rx5jpkzZzpef/11R9euXR2XX355rZ3HVatWOfhWHTdunEfP27Vrl2Pt2rWllnE7999/f42Wj+dkyJAhRywvKChwLFu2zJGWluZoiCoqd13gfrl/b8bXna8/3wd1gfvas2ePR8+59dZbHWFhYdXa39E8tyIXXXSR49hjj3V4qw8//NDRu3dvx2uvvWa+P//66y9zHvm99PLLL5dal++bfv36OTp16uSYNGmSWf+yyy4z38Pz588vte4dd9zh6Nmzp+Paa691DBo0yGyvovdkq1atzHf2+PHjHXPnznW89957jmbNmjmio6MdNputymOojXJV5tVXX3X4+fk5nn32WfO78/bbbzuaNGniuOuuu0qtx8dat27tOOeccxz//Oc/Pf7N4Lnp0KGD+V765ZdfHH/++afjjDPOcISHhzu2bdtWal0eN7f/r3/9y5zDTz/91BEZGek499xzfXpfdeXFF190673E477xxhsd3377rXlvfvbZZ+Z92759e0dKSkq1jn3Lli2Opk2bOqKiohw//PCDY86cOaY8AQEBjksvvbTUujExMeY8n3nmmea88/zzdWC53L0ucLdc8fHxZvlZZ51l1uVzWC5PePJe4eeM3wM33XSTo0ePHh5/b7v7ufbk+8bX9vXbb785goODHXv37i33cb4f+T7g7fvvv3f4Kv7WtWjRwnHgwIH6LkqDoMBfqrR06VLzo3b++eeXG8QUFhY6fv/99wYT+Ofm5lb4WF0G/g2dAv/Gz263O/Ly8qr9fAX+dWv//v3mNSuvwiM0NLTU9ysrWPl9xe9fS3FxsaNv376Of/zjH6We7xqw8/utoqDoiy++MI/Nnj271HJW4HJ52YrS8tRGuSrCCzUGWXfffXep5aw44cU1g7Dy9lWdyuInn3zSERQU5EhISHAuy8zMNJUJrLiwlJSUmMBxxIgRpZ7P4JL7ZGWOr+6roQX+qampRyyz3hv/+c9/qnXszz33nFnGhgVXfI9yeXp6unPZNddcY84zz7eFrwNfj6eeeqrK8ntSLn6vWN8t/J6pTuDv7nul7OfN0wpbTz7Xnnzf+NK+iOtdf/315T6WnJxsKhGGDx9uylRflXdHc31SUxijtGzZ0rwOosBf3HDxxRebL5Ddu3e7db74g/DWW285evXqZWo6jznmGMfNN998RGuk1eq7cuVKU6scEhJiWqLeeOMN548Ka0ytGkvXm/WDZgUuGzduNF9sbLk69dRTnY+V/TGyAn/WmrMliuXr06ePY/LkyW5dWPBCkstZu07cftmyWfvkOuVdfC5atMh8GbOsPObTTjvN8ccff5S7H9bw33vvvaaVjl9cV1xxhWPfvn2l1mWLA88lH+cXfOfOnR1XXnllpRUglZWbEhMTTUsJXzueI7ZSvvvuu261CFoXJnwd+NrwxtaRL7/80qPXnvLz8x2PPfaYeX5ERISpteV2WdNdlvXaTpgwwZSX2xwwYIBj2rRpR6zL5/fv398cG/c7ZsyYcl9zXkjxB5r757lt3ry546qrrnLExsZWeQ7Kvlc8Oe6KWMf4ySefmGPkRRr/th5zvdDj6//44487jjvuONMqwHPHVtfvvvuu0sB/8eLF5v3Gi7mcnJwKy2I9d/Pmzeb9zMCVF4gsX9n3njvnkeemvM86nXTSSY4LL7yw1DbZWsLHeS4tbKXiMn4fWHbs2GFagF3fyx999NERx8MLXOt88byy5evhhx8+4hx48j5zF1v7ud2kpKRSLWr8Di3LCtIrauWpLMBmywcfY/Dj6uOPPzbLt27dWmVZa6NcVbV2sjeLK54nLq/oQq46gT9bLc8777wjlvMinq8xL8qtzwe3XfY3o6ioyHynl23Z85V91dZ3NX8buU1+dvnZfOedd9wO/MvD7yI2ZLgGZ54c+0svvWTWZXDtioG8v7+/8/uC55XHdc899xxRBgbyvP6oSnVfk+oG/u6+V8ryNPD35HNd3e8bb98XK2m5HntllOfNN980j/MzdcMNN5j3pmuFDnt48RqkvMom/vbxWtM1cGZFmXVNz9/522677YheK3wP8L3A32Fun9cdTz/9tHmMv7ns+cLfYV4r8PebcQLfy2U/nzxPXbp0Mc/nNQt7Q/D6oGwDm7u/2XTfffeZ8tnLqXj3NWrxl0rxS4Af0lNOOcXtM2XVfD/wwAOO6dOnmyCbH3YGpK4/lvwQM8DgDyDXmTVrlmPkyJHmud98843zg20FUP/3f/9nvlBduzQz+OAHnh98Bk8MgmfMmFFp4M9ysFaVP6ZTp041PRm4/KeffvI48OeXb7du3Rwnnniis2xWq1l5gT+7cLG8/DJjN0FeFPEigLXBrl2xrP1w2w8++KA5JgbOvJgaNmyYcz3uw6rN5ba4fQbdrGjJyMio8DWqrNz8Mu/YsaN5zfi68DXka8ny8MuzKs8//7xZl5UPPKf80h41apRZ7slrT4cOHTI/MBMnTjSVICzLE088YX7EXNezXlu+D1gL/uOPP5rWkKFDh5pKK9cA8++//zbP52O//vqrKSPf33xu2decF1Z8vfjjwn0zaOaFatu2bY/oKupu4O/OcVeE6/G14UUyy8JzwsDbesz1Qo8XnPzs8tyzAo0X0LwYYHfzigJ/vif5Y8vXmZ/9yvC5vAjgD7TVdZ0XxTzfrCz09DyyNeT00093tGvXzvmetC6gnnnmGXOha10k8Dk8Xl6Mul5MsdzcpoXbZPdXVvIwyGAZWQa+/iyrhRUVvFDhBQ3PF1vE33//ffNcVmq4Xiy4+z7zBJ/Pz5vrOed5YIthWXwdWQbre86TAJufJ75e7A7M9012drZjwYIFZtkll1ziVllro1wV4evO55R3IcfXihU6NRH4s1WK38Fs8SyLF6zc1vbt2819fm5537X1zsIKKlbkuuK6rhes3rKvsmrju5qfQwbpDFCmTJlivqtPPvlk836tbuBvNSbw823x5Nj5fc6Ky6uvvtqUNSsrywRX/K7gb7WFXeO5TVZ4lsXzwteFlSWVqe5rUp3A35P3ytEG/p58rj35vrGu2/gae9O+yvPKK6+Yzwa/w8tz/PHHm94i/E3h54jbdP3N4/ufy1gx7oqfRS7ntTGxQYLXyLxOYAU1r1l4LcrrEF5Hu7bo8z3AffLa8uuvvzbnxqqYf/TRR00jBb8X+P0wevRoc05uv/32UvvnEAnun3EE12UvNX7euV3X7zZPfrOta5uyjQK+SoG/VMq6wK6oO1FZHNPG9RnMuFqxYoVZ/u9///uIFj4+5opfJq61zpVdxFnjY/klU95j5QX+DBZcgzZ+MTIIYW23p4F/ZV3mywv82QLSpk2bUl/W3L811sv6srL2U/Y8crwYl7MbF/3888/m/vr16x2eqqjc1o9X2deFQRUvDCr68ae4uDjzY8TeApVx97Uvi+eKrQ533nmnqbRwxe0x6OOFmIWvMy88WSlk4YUjK39Yi23h68GA3PU1Z9DJ+xwD7YqVTnwPVdVVs6LAvzrH7XqM/GFz7U7q+pjrhR7fU1Xl3nAN/FkpwNeOtfDusD57rhfQxECcy9la5el5rOgC0rpwWbhwobNlheNO+flwrQhjhQpbNyw8p/xcuXa1JVZkscLMOo98f/B9UrYl3Pp8uXapdfd95i6r+33Z88iKkvJaC9k1lOu79tzwJMBm6xMDBtdeFbwAdTcXRW2VqzysMGJFVEUXtmW7QFc38GcvKq5f3uvH43Htkmu9v63vYFcsD8vlip8pXoh6276qUhPf1ayQZSuea4DM57B3W3UCfz6XPfz4/e/6G+zpsfM6h9cMrp+hhx56qFSwsWTJknJb611bdV17+JSnuq9JdQJ/T94rRxv4e/K59uT7hoEpPwOuY+S9YV/lueCCC8x7sDz8neQ2eC1HfF+yZ6FrizeHJbDi3vWanDikg59Nq3cH37/cFlvxy/uOZW8xC7fPc1LZNaJVmcDtszKe61u/w/yf5++6664rtb51DeF6verJbzbt3LnTLP/kvz0kfZmy+kuNmjdvnvmfmUpd/eMf/0CfPn2OyFjarl0785irAQMGIDEx0aP9XnXVVR5lQW3btq3zfkBAAK677jqT7ZqZoWtLbm4uVqxYgauvvrpUNnTun1m2ue/t27eXes6ll156xLkh6/ww02+TJk1w991345tvvvE4W3B55s6di759+x7xuvA15TUbH6/IrFmzYLPZcP/991e5H3df+59++slkPuc5CwwMNNnBv/rqK8TExByxzWHDhiE8PNx5n69zmzZtnNvka7B69WqTkZnnzcJtX3LJJUdkJGdG3JtuugklJSXOG8sdFRWF+fPnV3mMR3PcFRk+fDhatGhR5Xrcx99//41nnnnGlDU/P7/c9fia3nPPPXjxxRdNVn1mhvbEjTfeWOr+DTfcUOq7oCbOI19/ZrSePXu2833GmSHOP/98LF261GTT3rNnD3bu3GkyaFNBQYH5vrniiitMxmTXfV944YXm8eXLlzvLyFkm+HlyXe+8884zZS9bxqreZ+7i68PPCr8THnzwwSMeryxzeXUy5GdkZOCyyy5DVlYWvv32WyxcuNDM7rF48WLzXcNjtt4TrufBWl5b5Sq7r8OxYe3sqzKe7Kuidcsu5/GUl6m7se+rPDX9Xb1q1SqTYZ6ffQufU/a72h38vHNb3D7LWd6MJO4cO2dp4f6ZKZ2zcSxYsMDMbMQZJP71r3+5vU3Xx/ib6fr+t9vtHpfLXdy26764b0/LWxNq4j1Z9rEXXnjBHBNn9vCmfVU02xU/L+XhZ47uuOMO57Z4/cb3vvX9wPcv38e8brTeb/x9+P33381MLPz8Wr+NnOGB67q+b/hbyd/wsr+NvJY5/vjjy50Jg78x3C+vefndwP3w/cfZboi/x5x94Nprry313FNPPfWIWSM8/c22ztW+ffvg6xT4S6WsaUbi4+PdOlPWlFDt27c/4jFOQ1N2yih+CZTFKVAqClLKw/JFRES4vT6/rCpa5smUVp7ilyovaCs6N+Xtv+z54bkh6/xwmhIGQ/xSYwDB+7y9//771S4ny+BJGV3t37/f/N+pU6cq9+POaz9lyhTzI9CxY0dMmjQJy5YtMxeD/EHjhZyn27ReA9eKH0vZZampqc51+SPleuMP1IEDB6o8xuoed2XKe23K88EHH+Dpp5/Gb7/9Zi6yOXUcKzwYHLsqKirCDz/8gBNOOAEXXHABPMGLg7LHU/azVBPnkRf+DCiswJ8XL5xmisE/LxwWLVpkKgPICvy5f14IcGqlsvtl4E/WvllGTldadj0GGSx72TLWxPfWjBkzTCDC42AQXvZCj/so77NmTZ3K19NTb731lpkWleeKFTRnnnmmmd6M+585c6b5n3gxWPZc1Ga5yu6L+7f2xc95edOkcX/V2Vd5WJHG8+/OcVmvfUXrVlUmb91XbXxXMyCp7PfaXQwmWAHICi5OcXbKKaeUWxZ3jp0Vqaw44+eXDQ5nnXUWnnzySTNd2Ndff20qAtzZJl8XBlTE32zX9z+nBvS0XO7itl33xX17+l45Wp58ro/2+8Zb98XPiWuFmOu0jqzYYsU/p3U8dOiQufH9z9fXqhQgfjYZCFu/nZMnTzafFdeGO/428vlsKCn7PZ2SknLEb2N51ye7d+82vzXcF69N+XvN74axY8c6j4Ws8+Hu9Zknv9nWucr34DfaWx2u0hGpAGvm2ELOlim2SFcV0Fk/VJwnt+y6rKFkRUJN87QWml9WFS2zym99SfBL0Aq2qbrBnvXDyvluy5tDmOeGqnN++IXKGwMgtmYz0OE8v/yi5Dy7nuI5qG4Z+UNDfK907twZR4sXkJwHl4Gp6+tc3TlprYsb/mhU9b7gcXJd/ki5vgcs5S2rC+6+38PCwsz8wbzxeK3Wf9bcb9u2rdRxsHWeNeUMmqdPn+5WjwJiYM0fa9eL+LKfpZo6j/weYivLypUrzfuLATN/5E8++WRz4cL3J1sarPcdj8HqTVNRDxS+t6wycp5vXriXp6a/txg0sBKGrUW//PJLqd4nlv79+2PTpk1HLLeWsbXDUwz6GZiVvTjjOaTNmzeb//ke4YVZeWqjXGX3Zb0u3Je1bddgzbrgrM6+ymPN8V7RcfHxbt26HVEm9oxy/Szwc/XPf/7TJ/dVW9/Vlf1eu4P752eN33FszeT3SFmeHDs/Q1yH368VfYb4uWZAzfNb0bnn62JdZ0ybNq3UebIq2Y/2NSkPewdefPHFR3z/evJeOVqefK6P9vvGW/fF3ySrksAVg3dWPPB3srzf8V9//dVUqvEx/ubzvTZu3DjzN/9nuV3fa9wPf8t5XVAe1147FV2fsPGBPXhYOXjssceW+iy5sq4ZKro+c2319/Q32zpXrWshBmls1OIvVXr22WdNDdpdd91lWgfLKi4uNj9cVjdk6yKg7IUdu/uV96NblbKt3EeLrYWuXywMmHmxwh9qq7LC+oJhjaIr6zjLls+dsvFCgV+q/PJzXZ+tGjxf3Hd5XaTcxSCH27dqUdeuXVvp+hWVm6/R1q1bj3j+hAkTzJc6W48rMmLECFOOTz75BDWB+2NQ5Ppjwh8AXsBVB1+Dk046yfwQub6Xc3JyTNcxV7w44vuetdR8Ttmb9SPfGLASiLX4vFDkcJKyLRInnniiaaliQM1W9LS0NLe3bbUSWzhcgLgdT89jZZ8lVkrwgvf55583n5XevXs7l7MnAIegWK39Vk8gvlfZxZDdD8vbt3WhwTLGxsaa++WtV7ab4dFgyzoDkTPOOMO8Dyuq+GALDS/uOTzIwuPndwU/51Zw4Ak+h69x2e6ObJ0l6/uvvPNQm+Wq6HXhUA4GR+xG7Yr3+Z3A81hTeFx8D3HIiGvrGb+v2UXV6vrKY2TFSdkysds3v0fYi8MX91Ub39VstWQ5XXsMsOzl/Q5X1tLP42cFG4Ob8nhy7Hx/b9myxSyv7DPE88oKNJafZXZt/WQlhOs2+R3o+v63PkM18fqXxW1X9P3r7nvlaHnyuT7a7xtv3Rd//8ob2skWfQbjvM7l+8z19s4775jPhPWbbVWM83eIFfNsOLKGB1j428jKfV4nl/fb2KtXL1TF+k5w/a3jNcEXX3xRaj0eN9fh9bgr9gwsO4zO099s61z1danU8Fn1nWRAGofPP//cZNxlwjBmqWVCE2b3ZLI5JsVzTSLGbJxMAvfII4+YzKSfffaZSWjHhDpMKFJ2arOqkvIxeyeTgDHjN7OEMpmHNaVdZfOQe5rV3zWrPpOBMYEQs4Ez8zuz9nL6MSZIKZuwjfthQhI+nxlMrayhlWX1Z9IiZij+/fffTQKyirL6l01cYmUktjLJMlEJE3Nxmi5mSmVCE2YbdicrbEXltrL6M+ssX3duh4mLWMayyQYry+rPcjAhDBOzffDBB44XXnjB49eeSRut2QQ4YwOPs3v37iaJW9mvL95nArGyuD1ut6Ks/kwGw9eD6/EYXfG9zMz4zHTM9wDPMWdNYHlck9p4Op1fVcddkYqO0XrMNZkTM2Yz8y9ne2DmdmaIZgJD10zQZT8/zFLN9zin7Sk7/aYnWf2ZeKg659FKqsllTIDo+v5nQiDOasHHXTMB89isBFvM/O2K2bD5HJ4Lvh783PAzzyzArkkBmYWZCciYCJBJCPndxvc9E+/x87V8+XKP32fl4VSe/C5jRnOeA9fZC3hzTULIZHt8r/D7iueKZeIUSzy/rgmliNM08fuEN9dZSnhzPYerV692TmHKTOssAz+b/H5mQqeyU5SVpzbKVZlXX33VfC6ZhIrb53Ru/N4qbyoza9tMUGm9TtayqvB7j5mjre98fpdy9gMmkWQyN1fMXG9lnuZ7it+TzPRe3lzZZRPuecu+yqqN72p+p/C7mln9re9qKzmrO5evnF2E6z333HNHfNbKZsp399j5m833IxP1MlM4j5Xff5x1hNcVrkljeX65nOeb553fT7yGYsLCslOhVcST14T74Hvdei343WW9/yub3rc67xWeP2vbnKWIs5JY98ubhaC6n2tPvm/KS7jnDfsqDxPjlZ1pYdOmTc7PYHk4Kw6v65gN38Ln8zn87eNvE2fnKJukk7/nvB7mMfP6idd0/Hzzs+r6m2tN51cW3zv83eE1l/U54PvX+m5wnRnByurPxIfM6s8ZBHiO+L6s7m82cR2+XhmVzHblKxT4i9uYOZ4fdF7o80PMgIEfPAZ0rj9ivEDnhRezmDLI5XQbN9100xGBhCdBEIN0a95y1wCnOoE/LzgYWPCihNvjdvnFWxaD4cGDB5vtMxDmPvklVDaY48Uts7Xyx5GPWfssL/C3Lv55ccbt8ouWFxBl5y92N/DnBQx/LLhP/rgwsON5taZiqUxF5abExESTHZ3b4zliIMgfMHfmmrd+lHiBxszpvPDh+8T1PHjy2jPbvDUXPQMWfrGXN+uCJwEZL2p4cWMFrtwHKzcYJJbFiyhWDFivF983t9xyiwmiGnLgz4y+nPKJx8Rzxyl2OKWOa+VbeZ8fzh/MzwTPeWXT01nPZYURf9B5bnhxwIuO8qYzcuc8MqsvK4x4YcuLp7KvMd/rXOb6eeXFDLfJAKG8H3We/zvuuMN8hvle5gUqP9e8QHPFMnPKUGuuYmsaQJ4z11lAjibwt963Fd1cL4CI++U54nnlZ4nfFbzAqei9Vt6tbJk4bSfPIy+YrPfFv/71L8fu3bsrLXttl6synPGAvyfW55Xnsez8z1TZuXXHrl27TCU256FnRdXZZ5/tWLNmTbnrMus2p9VkmXgxze+P8qbWKpuN2lv2VZ7a+K7mb5lVHuu7uqJZdzx5P5R3ntw9dlaY8beT6/C7jO9NThPq+t1q4fcbzzfPO88/Xwe+Hp5wt1w8fxUdr+vvUGXcfa9U9l3m7mwC7n6u3f2+KW+KPW/YV3lYScxrKza+WdjYVtUsT9asTa6vKX8Puayi2ZiYgf/dd991REVFOa/peI3A4JzZ8qsK/InXuNbz+VvMRgBWIpQ9h5x1gL/N/H3i+eP7nlMc8rn83arObzadeeaZbk9Z6+1M81Z99zoQEalPHK7C7LAc/8yu2CIiIiINFWeCYZd+Dj2p6RlOGhImF+fQBs489O9//9vj53NIQM+ePU1unXPPPRe+ToG/iPicO++80/wAcPwkx6F++umnZow7g37XceIiIiIiDQ1zVTEvFMf1c0pYb7BhwwaToHDw4MFmti7mJOJ0mZxJg4kzy8v4X5Xbb7/d5LaxZi/wdcrqLyI+hwmLnnjiCTP9IKeAGThwIP766y8F/SIiItLgMQhmoj5m6fcWTOrJJIOszOA0gpGRkSZR8GuvvVatoJ8JE5m4m0nK5TC1+IuIiIiIiIh4MU3nJyIiIiIiIuLFFPiLiIiIiIiIeDEF/iIiIiIiIiJeTMn9aoDdbkdSUhLCw8O9ekoNERERERERaRgcDodJWt2hQwf4+1fepq/AvwYw6O/cuXNNbEpERERERETEbXv27EGnTp0qXUeBfw1gS791wjnvpIiIiIiIiEhtysrKMg3QVjxaGQX+NcDq3s+gX4G/iIiIiIiI1BV3hpsruZ+IiIiIiIiIF1PgLyIiIiIiIuLFFPiLiIiIiIiIeDGN8a9DNpsNxcXFdblLEZ8XFBSEgIAAnz8PIiIiIuK7FPjX0fyKKSkpOHToUF3sTkTKaN68Odq1a+dW4hMREREREW+jwL8OWEF/mzZtEBoaquBDpA4r3fLy8pCWlmbut2/fXudeRERERHyOAv866N5vBf2tWrWq7d2JSBkhISHmfwb//Byq27+IiIiI+Bol96tl1ph+tvSLSP2wPn/KsSEiIiIivkiBfx3R2GKR+qPPn4iIiIj4MgX+IiIiIiIiIl5Mgb9II/XSSy8hOjraef+2227D5ZdfXif7EhERERGRxkPJ/erTS5F1vL/Mut2feGzo0KEmwB4zZkyV6z7xxBN48MEHa6Vb/K+//lqqEqG29iUiIiIiIrVPgb/4HCZ4CwoKQmOeoo6zRTRr1szc6kJd7ktERERERGqWuvpLpQHm22+/jW7dupkp0aKiovDzzz87HzvnnHNw/vnnm7+J0xZ26dIFzz33nLk/f/5803r8559/muc2bdoUp5xyCjZt2lRqP7/88gtOOOEEBAcH47jjjsN7771X6vGPP/4YPXv2NM9v27Ytrr76audjXL9s6zhbzNk13cIyfPrpp7jssssQFhaGV1991SyfNm0aBg0aZLbLY3z55ZdRUlJS6Tvi66+/dpaVc8I/8MADzsd2795t9sEAOSIiAtdeey1SU1OP6C4/ceJEU+7IyEhcf/31yM7OdnbVX7BgAd5//31TZt4SEhKc53HGjBk46aSTzL4XLVpUYfd7HgenrWMZ7rnnHhQVFbl9vvg4XXHFFWaf1v2y+7Lb7XjllVfQqVMnUx4+Nn36dOfjLDefP2XKFAwbNsxk1ed7YNmyZZWeXxERERERqXkK/KVC//d//4dx48bhk08+wZYtW/Doo4/ipptuMsEpg7pvvvkGK1euxAcffGDWv/fee01g7hp005NPPol3330Xq1atMgHppZde6pxWbc2aNSZAZgDMCgE+9/nnn8f48ePN46tXr8ZDDz1kgszt27eb4PKss87y+FV78cUXTVDOfdxxxx0miOaxcNtbt27FZ599Zvb52muvVbgNnof7778fd999t9nO1KlT0aNHD/MYKz/YNT49Pd2cn1mzZiE2NhbXXXddqW1w2W+//YY//vjD3Ljum2++aR5jwH/aaafhrrvuQnJysrl17tzZ+dynnnoKb7zxBmJiYjBgwIByyzhnzhzz+Lx58zB58mTTZZ8VAe7ia0R83bl/635ZLCsraPi6bty4Eeedd555XXfu3FlqPVYCcZjA+vXrcfzxx+Of//xnlZUrIiIiIiJSs9TVX8qVm5uLUaNGYe7cuSYYJbaKL1682ATJQ4YMQceOHc3fN998s2nZZgv6unXrjuhGz6D73HPPNX+zsoCtxAxIGfBzH2effbYJ9onBIQPxd955x7SAsxWdrfQXX3wxwsPDceyxx+LEE0/0+FW74YYbTMBvYZmfeeYZ3Hrrrc5j+89//mOCa5a3POwp8Pjjj+Phhx92Ljv55JPN/7NnzzYBcHx8vDNYZ8s+ewcweLbWY0s5Kxh4LFY5GKyzwoE9AJo0aWJax9u1a3fE/ln5YZ3HivD57JXAbXDffA4rXnhs/v5V1/Mdc8wx5v/mzZuXWwYLA/6nn37aVNjQW2+9ZSob2Jtg7NixzvUY9F900UXmb1ZAsEy7du1C7969qyyLiIiIiIjUDLX4S7kYfBcUFJhA0xrfzduECRNMq7XlmmuuwZVXXmlaotkCzMC9LKvigFq2bIlevXqZVmni/6effnqp9XmfLcccx879M9hnYM4g+dtvv0VeXp7Hrxq7yLtiTwMGxa7HZrW0l7f9tLQ0JCUlmUqK8vA4GPC7ttD37dvXBNDWsRK7zltBP3G4ALddnWMoD7vTM+h3Pfc5OTnYs2cPakpWVpY5F+W9bq7HSq49E3is5O7xioiIiIhIzVCLv5SLLdPE8fls2XfFMd0WBskMogMCAo7o5l0ZDhWwushbf1usnAHEIHnt2rVmnPvMmTPxwgsvmOEAbEVnUM1WbNf1yRpG4Iq9BsoeH1ugWWlRFsf8l8UcB5Up7zjKW162NwQfs851VcoegyesMrh7vjzZZmXnwPV4rcfcPV4REREREakZavGXcrG1mgE+u9pzHLvrzbVVm13fGUz+/fffZqw/hwaUtXz5cuffGRkZ2LFjh7OrN/fD4QOuli5danoOsDKBAgMDTSJBJhpkd3omjrP2w67pbKV3bY1md/uqDBw40OQMKHtsvJXXJZ4VEGytZ7f8is4Xz5Vryzp7TWRmZqJPnz5wF7vqs6dDdW3YsAH5+fmlzj17M3B4hbvni8F6ZWVg0sAOHTqU+7p5cqwiIiIiIlI31OIv5WKgy/HZTOjHFtozzjjDBIkM7hhIcmw8ewNwPDkztTOQtsbMMzhv0aKFc1vsUt+qVSuT+I/J3lq3bu2cI54VBxz/zjHoTITHbX300Ucmkz8xAV5cXJxJ6Mdt/vXXX6Y8HC5Aw4cPN2PmL7nkEvM4cwVYFQaVYc8B5g1gJQaHKzDYZ7mZtM/K+l8WexowgSETFF5wwQUmG/+SJUvM/PasmGC39htvvNGMc2cCu5EjR5pcCO500bewcmHFihWmcoPnmUMjPMEM/nfeeadJzJiYmGjyFXDmAasyw53zZVVwsOs+K39cX0sL8wZw2927dzcZ/ZkMkAn8OBRDREREREQaFrX4S4UYjDNA5vh9tuQyczsT+HXt2hX79+83ASaDYQb9xECQLcEMjl0xaz0T4nHqPLY2Mxs+W7aJz/3xxx/x/fffo1+/fmZ/rChgYj9id35OCceAlWXgtHzMVs8kcfTss8+aSgEG8RdeeKGpUGAwWhUeCysVmH2fFQ+nnnqqSTTIfAIVYaUGg3pWSnD/3Kc1vIHd2Jmtn0Eyy8OKAOYl+OGHHzx6h7GyhYE4exCwdZ69CDzBHASc+pBlYPJEBviusyy4c76Yq4HnhZUiFSVS5GwIrLThrX///ma2Bb6u3LeIiIiIiDQsfo6yA37FY2wJZ0Z2dutmN2hXTJDHrtQMlssbO+7NOC6fc7izez8DeJH64sufQxERERHxvTi0LLX4i4iIiIiIiHgxBf4iIiIiIiIiXkzJ/aTWDB069Iip40RERERERKRuqcVfRERERERExIsp8BcRERERERHxYgr8RURERERERLyYAn8RERERERERL6bAX0RERERERMSL1Xvgv2/fPtx0001o1aoVQkNDER0djTVr1jgfZ1b4l156CR06dEBISIjJFL9ly5ZS2ygsLMSDDz6I1q1bIywsDJdeein27t1b6X5vu+02+Pn54d577z3isZEjR5rHuI6IiIiIiIhIY1avgX9GRgZOP/10BAUF4e+//8bWrVvx3nvvoXnz5s513n77bYwaNQofffQRVq1ahXbt2uHcc89Fdna2c51HHnkEv/76K77//nssXrwYOTk5uPjii2Gz2Srdf+fOnc1z8vPzncsKCgowefJkdOnSBb6OlSw8t43FcccdhzFjxqCxYIUWK7osrGi6/PLL62RfIiIiIiLiOwLrc+dvvfWWCb7HjRtXKnhzbe1nIPfcc8/hyiuvNMu++eYbtG3bFt999x3uueceZGZm4quvvsLEiRNxzjnnmHUmTZpktjt79mycd955Fe5/4MCBiIuLw5QpU3DjjTeaZfybz+3WrRtqW/9v+qMubbp1U53uz1crSxhgu1MB8cQTT5ieKjWNvVVYEeZaiVBb+xIRERERkYavXlv8p06dipNOOgnXXHMN2rRpgxNPPBFffPGF8/H4+HikpKRgxIgRzmXBwcEYMmQIli5dau5zWEBxcXGpdTgsoF+/fs51KnP77beXqnj4+uuvcccdd9TgUUpNYi8Ou93eqE8qK7RKSkrQrFkzM8SlLtTlvkREREREpGGp18Cfre2ffPIJevbsiRkzZpjx9g899BAmTJhgHmfQT2zhd8X71mP8v0mTJmjRokWF61Tm5ptvNsMDEhISkJiYiCVLlpicA5VhToGsrKxSN2/FIPupp55Cy5YtzTALdhl3xWEY/fv3N7kV2FOC+RE41MK1BZwt0GVvPN/uPH/8+PFm6Mcff/yBvn37moofvk5paWm45JJLTN6Hrl274ttvv3XreFixc8IJJ5jttG/fHg888IDzsd27d+Oyyy4zQXJERASuvfZapKamHtFdnr1L2DMlMjIS119/vXPYCbvqL1iwAO+//36p45w/f775m+9xVnRx34sWLaqw+/3LL79sKsJYBvZqKSoqqnQ4A7dhvS5Wj5krrrjC7NO6X3ZffF1feeUVdOrUyZSHj02fPt35OMvN57MHzLBhw0z+jaioKCxbtsyt8ywiIiIiIg1HvQb+DD7Y3f711183rf0Mcu666y5TGeCKAUjZFtOyy8pyZx1iQsCLLrrIDCFgyz//5rLKvPHGGybos24MWL0VzwuD8hUrVph8CwwWZ82a5Xzc398fH3zwATZv3mzWnTt3rqkosDBwTE5Odt44ZKNXr17Oypyqnk95eXnmnH/55ZcmsSODYgbZDE65/s8//4yPP/7YVAZUhu+r+++/H3fffTc2bdpkepz06NHD+X5h1/j09HQTvPMYY2Njcd1115XaBpf99ttvpiKCN6775ptvmscY8J922mnmPWwdr+t7g8fF44iJicGAAQPKLeOcOXPM4/PmzTO5JthlnxUB7mIeDOJ7mfu37pfFsjKfxrvvvouNGzeaITFMirlz585S63GYDYcJrF+/Hscffzz++c9/mt4KIiIiIiLSeNTrGH+2uLIV11WfPn3wyy+/mL/Zwkxsuee6FgZ4VuDIddgiykSBrq3+XGfw4MFulYNd+62W37Fjx1a5/rPPPovHHnvMeZ8t/t4a/DNAffHFF83f7JnBJIsMTplgkVyT/7Hl/T//+Q/uu+8+E4gTewpYRo8ebQJ1ViKwpd6d5xOHcvA+W5xpx44dJhnk8uXLccopp5hlzPPA905lXn31VTz++ON4+OGHnctOPvlk8z/zQTAA5vAS67Vkyz57BzB4ttZjZRV7IYSHhzt7jPB8vPbaa6YSiL1P2DpuvXddsdLEOm8V4fPZK4Hb4L75nCeffNKcF1aSVOWYY44x/7OXRHllsDDgf/rpp02PBSvfBisb2JvA9TPAoJ+VYcQKCJZp165d6N27d5VlERERERGRhqFeW/yZ0X/79u2lljGoO/bYY52BIIMX1xZmBvlsZbWC+kGDBplZAVzXYUsnW5DdDfzPP/98s13eKksGaGHXaHbDdr15q7It06yAcW1ZZ7DIYLZjx44mGL7llltw8OBB5ObmlnoeA/VnnnkGP/zwg2k59uT5DIZdy8EW8cDAQNNt3sJA1HU2iLJY5qSkJJx99tnlPs5tMuB3rcBhpRS3yccs7DpvBf3lnY/KuJa3IqzcYNBvYQ8CDn3Ys2cPagorqngu+Plzxfuux0qu592qfHP3eEVEREREpGGo18D/0UcfNa227OrPVkRm6v/8889Nd2xiV322CPNxdnlmMM8u3gyMbrjhBrMOW1nvvPNO05LLltd169aZMfocN25l+a9KQECACXh449/yP6xUccXXxEqux7H2F154oUmkyF4aTLRotRazld7CaRrZsswu8a5JGN19PnsHuA7bYLd8qyzusnoYeDo0pOzyys5HVThkorqsMrDV3zp+i+u5qs42KzsHrsdrPdbYkyuKiIiIiPiaeg382X2aAT3HMjP4Y3dmdjW2ptazxkUz+GfSN7aY7tu3DzNnzizV6sou5ByfzWRsbLVkxcC0adM8CuK9veW+NqxevdqM9+ZY8VNPPdW05LMl2RVb75mEj2P7WdHj6fPLwy79fB6fb2HPkUOHDlX4HL5f2FrPyqHysHWfyf1cW9ZZYcHpIqsaQuCKvRM480B1bdiwAfn5+c77rBhjskEm4bO68rNHi2vrPYcnlA3WKysD3+ec+YJJLV1xFgxPjlVERERERBqHeh3jTxdffLG5VYStjMxIXjabvKumTZviww8/NDd3cZx2ZZjATSrXvXt3E4DzvDO454wIn376aal1GPCztZ2vn+ssCwxg3Xl+eZgckMMzmESPPUTY7Z+VQ1W16rMMnDmCyQEvuOACk42f++T89uwdwm7trHRi5RPLxcomTh3pThd9CysXmMOAiQcZsLvmOHAHh5uwB8v//d//mR4RzK/A/BPW+P7hw4eb9y7PF3NaPP/880dUcFkVHKwE47CUsjNeEPMGcNt8DZjRn8kAmcDP3dkRRERERESk8ajXFn9p3Bgwcjo+JoZjjw0Gjcxa72rhwoUmEz+DUY4Rt25sWXfn+RVhoMrx+AzMWbnATP0M6Ctz6623mqCeiQKZpI4VTlYWe1YwsbKHQfJZZ51lKgK6detmchJ4gsnwGIizBwErN9iLwBPMQcAkiiwDe7AwwHet9GJiST7GsnOYBHu6MHh3xR4UzHnB88PZMsrDaTM5PIY3DovhVH6c5YD7FhERERER7+LnKDtgWDzG7tbMNcBu4WWHCxQUFJiu2ExUyJ4JIlL39DkUEREREV+KQ8tSi7+IiIiIiIiIF1PgLyIiIiIiIuLFFPiLiIiIiIiIeDEF/iIiIiIiIiJeTIG/iIiIiIiIiBdT4C8iIiIiIiLixRT4i4iIiIiIiHgxBf4iIiIiIiIiXkyBv4iIiIiIiIgXU+AvFRo6dCgeeeQRrztDt912Gy6//HL4Gj8/P/z222/m74SEBHN//fr1tb4vERERERGpX4H1vH+fNvbeuXW6v/s/He7R+lOmTEFQUJDb6zOY7Nq1K9atW4fo6GjUt4rK8/7778PhcKCxmz9/PoYNG4aMjAw0b968yvWTk5PRokWLGi3DSy+9ZAL8shUItbEvERERERGpHgX+UqGWLVvW29kpLi72qNLBE5GRkfAlRUVFaNKkCdq1a1dn+6zLfYmIiIiI1KTiQhuCggO86qSqq7+43dX/uOOOw+uvv4477rgD4eHh6NKlCz7//HPn42xdpxNPPNF09ebzLePGjUOfPn3QtGlT9O7dGx9//LHzMavb+Y8//miew3UmTZqEgwcP4p///Cc6deqE0NBQ9O/fH5MnTy5VRrvdjrfeegs9evRAcHCwKdNrr71WaXnKdvUvLCzEQw89hDZt2ph9n3HGGVi1alWplnU+f86cOTjppJNMWQYPHozt27dX+u7Zu3cvrr/+elOBEhYWZp67YsUK5+OffPIJunfvboLyXr16YeLEiaWez31++eWXuOKKK8w+e/bsialTpzrPGVv7iS3rXJfHZb1uDzzwAB577DG0bt0a5557boXd77dt22aOhcd9wgknmGO1jB8//oieBHw+t2M9/vLLL2PDhg1mGW9cVt6+Nm3ahOHDhyMkJAStWrXC3XffjZycHOfj1mvy7rvvon379mad+++/31QAiYiIiIjUlbTELPzw2kqv6CHsSoG/eOS9994zASy7z48cORL33XefCR5p5cqV5v/Zs2ebrt4cKkBffPEFnnvuOROQx8TEmMqD559/Ht98802pbT/99NMmAOc65513HgoKCjBo0CD88ccf2Lx5swkWb7755lLB87PPPmsCf25v69at+O6779C2bdtKy1PWU089hV9++cWUZ+3ataYSgftPT08vtR6Pgce/evVqBAYGmgqQijCoHTJkCJKSkkywzuCY+2FFBf366694+OGH8fjjj5tju+eee3D77bdj3rx5pbbDwPraa6/Fxo0bceGFF+LGG2805ercubMpM7ECgsfHIQwWHgvLuGTJEnz22WcVlvPJJ580ZeDryQqASy+91FS4uOO6664zz2WFAffPG5eVlZeXh/PPP99UULBC5aeffjKvCSsnXPHYY2Njzf8sPysRrIoEEREREZHa5LA7sGZ6An55ew0y0/K97mSrq794hMEnA34rUB89erRpJWYr/jHHHGOWs7XWtav3f/7zHxMwX3nllc6WeAbpDEhvvfVW53rsXWCtY3niiSecfz/44IOYPn26CRxPOeUUZGdnm2D3o48+cm6HLehssaeKyuMqNzfXtLwzwLzgggucFRWzZs3CV199ZQJjCysuGMzTM888g4suushUTrC1vCxWQOzfv98EutaQCVYoWNiyzVZu61yydX758uVmudWST1yHvR6IFSYffvihqdBgIG1tlz0VyrbMc19vv/02qsLg+6qrrjJ/8zzw/PK4WUlRFbbeN2vWzFQwVNa1/9tvv0V+fj4mTJhgej4QX7NLLrnEVNpYFTWsGODygIAA837i+WUvi7vuuqvKsoiIiIiIVFdORgFmj9uKfTsOee1JVIu/eGTAgAHOv9mdmwFfWlpahesz+N2zZw/uvPNOEyRat1dffdW07rpiTwJXNpvNBNvcJ4N3Pm/mzJnYvXu3eZw9A9hN/+yzz672q8gysDv56aef7lzG3AL/+Mc/zPYrOnZ2R6eKjp3J7jjEoKI8Cdy26z6J9yvbJ4NmDrGo7HxXdC4rctpppzn/ZgDP55Utw9Hi9qKiopxBv3Ws7P3gOlyCPQcY9LueY3eOVURERESkumLXpeH7V1d6ddBPavEXj5RNuMfg3+q+Xh7rMbais5XelWuQR66BIbGXAHsUjBkzxozv5+PsFcBkdVaL89Gyxu5Y49Zdl5dd5nrs1mMVHbs7ZfN0n+6c74rOpSesMvj7+x8xtqk6Y+7LO66y+zqaYxURERERqU4Cv8U/7sDWJck+cfLU4i81hknqrJZ6C7txd+zYEXFxcab7uevNSr5XkUWLFuGyyy7DTTfdZFqMu3Xrhp07dzofZ7I7BtjsDu5uecpiObje4sWLSwW3HMfPZITVxZZ6tvqXzRNg4bZd90lLly71aJ/uHF9VOLzAUlJSgjVr1phu9tZQCQ6n4HAIS9lp+1iGqvbft29f8zzX7TD3ACsWjj/++GqXXURERESkugn8fnx9lc8E/aTAX2oMx5ozEOc48dTUVGRmZjrnen/jjTfMePwdO3aYDO/M8j9q1KhKt8egnGPtGRCzuzgT4KWkpDgf59h65hngeHSOH2e3fQayHKNeWXnKtowzQSHH8nM95h7gmHImpOPwhOriuHwOg2Cmega5rPhgMr5ly5aZx7k/5hX49NNPTWUGzwWTD7rmNKjKsccea1rFmfyQQypcs+S7a+zYsSbRIBM0Mot+RkaGM2khe2hwNoF///vf2LVrl8lbUDbZHmd6iI+PN4H9gQMHzNCLspiQkK8V8zAwkSGT9zFfAxM1WuP7RURERERqm8PhwNoZiSaB36HUPJ864Qr8pcZwjPgHH3xgkvZ16NDBtNbTv/71LzMtHYNGdtlngjz+XVWLPzP1Dxw40GTY5xR1ViBddh1mln/hhRdMazmzylvjwisqT1lvvvmmSXDHQJT7Y5A7Y8YMk2yuutgSznwErHxgQkQeN/djDW/gcbAi5J133jFj21lGVoa4ToFYFfakYNZ/JhpkAF02S747WCYm2GOPCvaw+P33380UgMT8BJxW8a+//nJOpchKHFc8b0w0yISE7CFQdrpFYuUBzyd7P5x88sm4+uqrTV4GJvITEREREakLORmF+H3Meiz7NRZ2m3dN1ecOP4e3TVBYD7KyshAZGWlalCMiIko9xqzvbBFlkFte9ncRqX36HIqIiIj4rrh1+zF3UgwKc0vcfs7IT4ZVmKeqMcShZSm5n4iIiIiIiHid4iIbFv+0E1sXJcHXKfAXERERERERr0vgN+vrrT43lr8iCvxFRERERETEKzjsDqydmYiV0+J9cix/RRT4i4iIiIiISKOXnV6A2eO2ImnnofouSoOjwF9EREREREQatZ2rU7Hgu+0ozHM/gZ8vUeAvIiIiIiIijVJRQQkWfr8D25en1HdRGjQF/iIiIiIiItLopMRlYtbXW5B1oKBGt+vnhwY/lZ+nFPiLiIiIiIhIo2G3O7D6z3is/jvRJPOrSc06BGJRtx/hcAzzquBfgb+IiIiIiIg0Cpn78zF73BakxGXV6HaDQgKQOWAnPvf/CHabDd7Gv74LIN5t6NCheOSRR9AQzJ8/39TaHTpUcZbP8ePHo3nz5s77L730EqKjo533b7vtNlx++eXwdnV53GX3JSIiIiJSnm3LkvHDaytrPOgP61uCXwa+jQn+78MO7wv6SS3+9Simd5863V+fbTF1ur/G6LrrrsOFF15Y4ePvv/8+HA5HqYoNBq1jxoxBQ+dJWZ944gk8+OCDNV4GVrz8+uuvpSoRamtfIiIiIuIdCnKLTcb+XWvSanS7YccEYV2vvzHXNg2ww6sp8JdGr6ioCE2aNKmRbYWEhJhbRSIjI+HNWKlhs9nQrFkzc6sLdbkvEREREWlc9m5Lx5xvYpCTUVhj2wwI8kdxdDI+ChyFQlvNJgZsqNTVX8r12WefoWPHjrDbS1d9XXrppbj11lsr7P7Nbv1sWa7Icccdh9dffx133HEHwsPD0aVLF3z++eel1tm3b59peW/RogVatWqFyy67DAkJCc7Hrf2+8cYb6NChA44//nizfNKkSTjppJPMdtu1a4cbbrgBaWlH1gouWbIEUVFRaNq0KU455RRs2rSpwq7+ZbkeM/9esGCB6QXAlmze4uPj0aNHD7z77rulnrd582b4+/sjNja2wm1//fXXOOGEExAcHIz27dvjgQcecD62e/ducx4YIEdERODaa69FamrqEd3lJ06caM4xKyiuv/56ZGdnV1hWnlNr+MOMGTPMueO+Fy1aVGH3+5dffhlt2rQxZbjnnntMpYvra1u2NwG3wW1Zj9MVV1xh9mndL7svvudeeeUVdOrUyZSHj02fPt35OMvN50+ZMgXDhg1DaGioeT2XLVtW4bkVERERkcbFVmLHkp934vf319do0B/eww8zTvkEnwe+jkL4RtBPCvylXNdccw0OHDiAefPmOZdlZGSYAPHGG288qrP23nvvmSBz3bp1GDlyJO677z5s27bNPJaXl2eCOQa4CxcuxOLFi83f559/fqkgc86cOYiJicGsWbPwxx9/mGV8/D//+Q82bNiA3377zQThDHjLevLJJ01gvmrVKhPEsjKjuLjY4+NgEH3aaafhrrvuQnJysrmxIoOVGuPGjTsiqD/zzDPRvXv3crf1ySef4P7778fdd99tKiKmTp1qKhCsVnhWNqSnp5vgncfMCgRWjrjiMh43zwdvXPfNN9+ssKydO3d2Pvepp54yFSk8pwMGDCi3jNY553ti8uTJpss+KwLcxfNNPDfcv3W/vPPK9whfo40bN+K8884zr9HOnTtLrffcc8+ZYQLr1683lT///Oc/UVJS4nZ5RERERKRhOpiUg5/eWI31s/cANZS0P6R5IPadvgLvHPMQdtm2wteoq7+Uq2XLlibY/u6773D22WebZT/99JNZbt2vLo6hZ8BPTz/9NEaPHm1annv37o3vv//etIx/+eWXzukzGCiyFZ7rjBgxwiwLCwsz67h28WfAbenWrRs++OAD/OMf/0BOTk6pruQvvvgizj33XPP3N998Y1qWGcSyFd0TbFXn/tnizB4Glttvvx0vvPACVq5cafbPSgX2RnjnnXcq3Narr76Kxx9/HA8//LBz2cknn2z+nz17tgmAWZFhBets2WfvAAbP1npsKWePBfZ4oJtvvtkE66+99lqFZbWwhd06JxXh81mBwW1w33wOK1FY2cLXrCrHHHOM+Z+vZXllsDDg5/uCPRborbfeMpUN7E0wduxY53oM+i+66CLzNysgWKZdu3aZ95GIiIiIND5s8No4dy+W/RYLW3HNDLr3D/CDf9QhfNn0XWTbazYpYGOiFn+pEFv2f/nlFxQWHu5a8+2335pgLCAg4KjOmmuLMoN7BoFWl/w1a9aY4I3BqzX2m5UNBQUFpbrJ9+/f/4hx/exBwO7wxx57rHm+NeSA3eRdseXbwm336tXLtGTXFHbTZ0DKIJnY+s7ysxdFeXjsSUlJFVaosGwM+F1b6Pv27WsCaNdys+u8FfRb5ShvqEN52AOjKuxOz6Df9TyyUmXPnj2oKVlZWeZcnH766aWW837Z18j1fcRjJXePV0REREQaltzMQkz7cAMW/7SzxoL+8GMDsOS0Sfgo+AVkO3w36Ce1+EuFLrnkEtOK/Oeff5pWZY79HjVqlPNxtvK6Zrgnd7rMBwUFlbrP4N/KJcD/Bw0aZCoZKmoxtlr8XeXm5preALyxdZ3rMuBnN3HXIQIVsXoX1JR//etfpsWdvRnYY4Hd8l2DZleVJRMknuPyyld2eWXntSplz6cnrDJU9/1Q2TYrOweux2s95u7xioiIiEjDEbsuDfMnbTfZ+2tCcLNApPTfgE/xObx0dj6PKfCXCjEgvfLKK00QzlZ4jqNmUG5hcM2kda443rpsAOqJgQMH4ocffnAmkHMXcwQwJwHHtFst46tXry533eXLl5ux+Fbegh07dlS7ezh7HTALfnnDGRhMc+z+33//bfIVVISt9GytZ7d85jcoi637rMRgy7p1bFu3bkVmZib69Olz1GV1F3Mn5OfnOysqeB7ZI4NDJaz3A8fuu7bec3iCK743KisDX3MmbGRuh7POOsu5fOnSpWbYhIiIiIh4j6KCEiz+cSdilv7vGvJosC0ouF8+JoaPwkGHeoK6Uld/qbK7P1v82W39pptuKvXY8OHDTXA9YcIEk3iNY+fLVgRUZ3+tW7c2XfbZw4CBI5PUcez73r17K3weA3kGth9++CHi4uJMcjyOPS8Px6YzyGZZmfyP+ys7O4G7GLCvWLHCZJpnxYPV4szhENz2s88+a5L0uQ4vKA8z2zOhHfMS8FyuXbvWHAudc845pls7zw2XM3fALbfcgiFDhrjVRb+qsrqLPSfuvPNOU+nAygy+3px5wBrfz/cDcw/wdeO55ewPZYeFWBUcKSkpptKlPMwbwHH9rADavn07nnnmGVOh5Jr/QEREREQat5S4TPzw2qoaC/qbtQ/EhtOnYkyzZxT0l0OBv1SKwRzHwTMA4/R4rtiN/vnnnzcZ4TkUgFPHMSA9GuwOz9ZxBvLsbcAWbSbtY0tzZT0A2NrMxHZMQMgWcrb8l51Sz8LHGESy9wJbqFlJUDZfgLuYYI7BLfdpDS+wMEhmsOyadLAiDJKZvO7jjz82SeouvvhiZxZ7dmNntn5Ob8hWcFYEMHkhA+OaKqs7mIOgZ8+epgxMhMihINZUfcRKDj7GsrPHAytTys5iwMoNzkrAngsnnnhiuft56KGHTKJD3pjLgVP58TXivkVERESkcbPb7Fj5Rzx+fXctsvbnH/X2gpoGIP+UBIw57mEstc2pkTJ6Iz9H2UG54jF2aWbWdHa9LhucMqkbW627du1q5o0X37FkyRKTYJA9Fdq2bVvfxfFp+hyKiIiI1L9DqXmYNW4r0hJqJtFeWG8bfmz1IfbYSw8vrQkbb9lY43nA6jIOLUtj/EVqGGdB4Hh89oZgy7iCfhERERHxdZsX7sOSn3eipOjokzGHtQ7Cht4zMNv2O6Dczm5R4C9SwyZPnmy6+UdHR5sx7yIiIiIiviovqwhzJ8QgcfPBo95WQJA/iqOS8VHQKBTaCmqkfL5Cgb9IDWNSP95ERERERHxZ3Lr9mPftNhTkHP00feE9/PBru0+wy7a1RsrmaxT4i4iIiIiISI1O07fohx3YtizlqLcV0jwQsScsw1T7JKD6M1P7PAX+dUQ5FEXqjz5/IiIiInUjadchzBm/FVkHjq4rvr+/HwKiMvFlyDvIttdMMkBfpsC/lgUFBZn/8/LyEBISUtu7E5Fy8PPn+nkUERERkZpl4zR9U+OxbmYijnbeuPAuAZjZZQI22FYCmoOuRijwr2WcN7158+ZIS0tzzlPf0KeFEPGmln4G/fz88XPIz6OIiIiI1Kz0pFzMGrcFB/bkHNV2gsMCkTpgEz5zfAaHTRF/TVLgXwfatWtn/reCfxGpWwz6rc+hiIiIiNRcI8vGuXux7LdY2IqPYl49PyCkXwEmRYzGfnuKuS81S4F/HWALf/v27dGmTRsUFx99RksRcR+796ulX0RERKRm5WQUYM43Mdi7LeOottOsfSCWdp+CpbY5wFHUHUgDDvxfeuklvPzyy6WWtW3bFikpKaVqkbjO559/joyMDJxyyikYO3YsTjjhBOc6hYWFeOKJJ8z86fn5+Tj77LPx8ccfo1OnThXum9OtffPNN7jnnnvw6aeflnps5MiR+OSTT3Drrbdi/PjxNXa8DD4UgIiIiIiISGO2Y2UKFn6/A4V5JdXeRlBTf+REJeBL//dRYqv+dsQ9/qhnDOCTk5Odt02bNpV6/O2338aoUaPw0UcfYdWqVaa77rnnnovs7GznOo888gh+/fVXfP/991i8eDFycnJw8cUXw2arfL6Hzp07m+ewssBSUFBgKhC6dOlSC0crIiIiIiLSOBXkFmPGl5sx6+utRxX0h/W24fdBozHO/z2UQEG/T3T1DwwMrHDsLVv7x4wZg+eeew5XXnmlWcZWevYK+O6770xrfWZmJr766itMnDgR55xzjlln0qRJJqifPXs2zjvvvAr3PXDgQMTFxWHKlCm48cYbzTL+zed269atVo5XRERERESksdm99SDmTtiG3EOF1d5GWOsgbOo9CzNtv6pbv6+1+O/cuRMdOnRA165dcf3115tA3BIfH2+6/Y8YMcK5LDg4GEOGDMHSpUvN/TVr1phx867rcHv9+vVzrlOZ22+/HePGjXPe//rrr3HHHXdU+hwOLcjKyip1ExERERER8TYlRTbTrX/ahxuqHfQHBPnDcVIaxvZ8/HDQL74V+HO8/oQJEzBjxgx88cUXJsgfPHgwDh48aB63xvqzhb+iPAD8v0mTJmjRokWF61Tm5ptvNsMDEhISkJiYiCVLluCmm26q9DlvvPEGIiMjnTf2EBAREREREfEmaYlZ+OG1Vdg0fy9Qzdn1wrv7YdY/PsVnQa+hAP8bYi0+1NX/ggsucP7dv39/nHbaaejevbvpzv/YY485Hys77z2HAJRdVpY761Dr1q1x0UUXmX3yOfybyyrz7LPPliofW/wV/IuIiIiIiDew2x1Y83cCVv+ZYP6ujpDmgYg9YRmm2iepW38DUO9j/F2FhYWZCgB2/ydr7D9b7jkdniUtLc3ZC4DrFBUVmYz/rq3+XIe9B9zBrv0PPPCA+ZszBlSFww14ExERERER8SaHUvMwe/xWpMZXbzizn78fAqIO4cuQd5Ft15DohqLex/iXHTsfExPjDPI57p+B/axZs5zrMMhfsGCBM6gfNGiQmafbdR3ODrB582a3A//zzz/fbJe3ypIBioiIiIiIeKvNC/bih9dWVjvoDz82AEsHT8JHTV9AtkNBf0NSry3+TzzxBC655BIzdR5b6F999VXTbf7WW281j7OrPqfqe/3119GzZ09z49+hoaG44YYbzDocY3/nnXfi8ccfR6tWrdCyZUuzXfYcsLL8VyUgIMBUOFh/i4iIiIiI+IrczEKTsX/3lsO51jwV3CwQKf034FN8DlQ+o7r4YuC/d+9e/POf/8SBAwdwzDHH4NRTT8Xy5ctx7LHHOtd56qmnkJ+fj5EjR5ru/EwIOHPmTISHhzvXGT16tJkW8NprrzXrnn322Rg/frxHQXxERESNH5+IiIiIiEhDtmtNGhZ8tx0FucUeP5cp1YL75WNi+CgcdKTVSvmkZvg5mNFOjgp7KbDnQWZmpioQRERERESkwWOgz2n6dq5Krdbzm3UIxJJuv2CZbS680cZbNrqVLL6xxKENKrmfiIiIiIiI1K49W9MxZ0IMcg8VevzcoJAAZA3YhS/9P0KJraRWyic1T4G/iIiIiIiIDygusmHpL7uweeE+oBr9vsP6lOD7lqOwz55YG8WTWqTAX0RERERExMulxGWaafoy0/I9fm7YMUFY32s65timAvZaKZ7UMgX+IiIiIiIiXspms2PVtHisnbkbDrtnzfwBQf4ojkrGR0GjUGgrqLUySu1T4C8iIiIiIuKFDiblYPa4rTiwJ8fj54b38MOv7T7BLtvWWimb1C0F/iIiIiIiIl6ELfvr5+zBit/jYCvxrG9+SPNAxJ6wFFPt3wK2Wiui1DEF/iIiIiIiIl4i60A+5nwTg6Sdhzx6nr+/HwKiMvFlyDvItmfVWvmkfijwFxERERER8QJblyRh8U87UVzgWVN9eJcAzOwyARtsK6uV7V8aPgX+IiIiIiIijVhuZiHmT9qGhE0HPXpecFggUgdsxGeOz+GwKeL3Zgr8RUREREREGqmdq1OxcPIOFOQWu/8kP6BpvwJ8GzEa++0p5r54NwX+IiIiIiIijQwD/YWTt2Pn6jSPntesfSCWdp+CpbY5gGd5/6QRU+AvIiIiIiLSiCRsOoB5k7YhL7PI7ecENfVHTlQCvvR/HyW2klotnzQ8CvxFREREREQagaKCEiz5aSe2Lkn26HlhvW34qdX72G2Pq7WyScOmwF9ERERERKSB27cjA3MnxCDrQIHbzwlrHYSNvWZilv03dev3cQr8RUREREREGqiSYhuW/xaHDXP3uD3VXkCQP0qiUjA2aBQK7Pm1XURpBBT4i4iIiIiINEBpiVmYPW4rMlLy3H5OeHc/TG3/GbbbNtdq2aRxUeAvIiIiIiLSgNhsdqz+KwFr/06E3e5eM39IZCDi+y3Hp/aJgK3WiyiNjAJ/ERERERGRBuLgvhzMHr8VB/bkuLW+v78fAqIy8VXIe8iyH6r18knjpMBfRERERESknrFlf+2MRKz6Mx72Evda+cM7B2LWsROx3rbc7fH/4psU+IuIiIiIiNSjjJRczB4fg7SELLfWDw4LQNqALfjM8SkcNkX8UjUF/iIiIiIiIvXAYXdg/Zw9WDE1DrZie9VP8ANCTijEt5GjkWZPNvdF3KHAX0REREREpI5l7s/DnG9ikLwr0631m7ULxPIev2OxbSbgRh2BiCsF/iIiIiIiInXE4XBg84J9WPprLEoKq06/Hxjsj9zoRHzpPwYltpI6KaOv69GsM/z8vKs7hQJ/ERERERGROpB1MB9zJ2zDvu0Zbq0f1suOn1p/gN322Fovm8AE/PcWBWLEpoXAlQ7Ai4J/Bf4iIiIiIiK1bMuifVjyyy4UF1Tdyh/WKgib+8zBDNsv6tZfB7o364R7i4IwYvMi+Du8cxyFAn8REREREZFakpNRiHmTYrB7S3qV6wYE+cMWlYqxQe+hwJav16SWdTMBfxOct3mh1wb8FgX+IiIiIiIitWDbsmQs/mknCvOqHpsf3t0P09p/gW22jXotalnXsI64tyQY5/tAwG9R4C8iIiIiIlKDcjMLMf/b7UjYeKDKdUMiAxHfbwU+tU8Aqh4FIEfhOBPwN8UFWxb4TMBvUeAvIiIiIiJSQ3asTMHCH3agMLfyVn5/fz/4D8jEV6HvIct+SOe/Fh0X1gH3lITiwi3zfS7gtyjwFxEREREROUp5WUVYMHk74tbtr3Ld8C4BmNllAjbYVgIOnfraciwDfhsD/gUIcPh2dwoF/iIiIiIiIkdh15o0E/QX5BRXul5wWCBSB2zEZ47P4bAp4q/NgP9uWyguUsDvpMBfRERERESkGhjoM+Bn4F8pP6Bpv3x8GzEG++0p5r7UvC6h7XG3vRku3jLf51v4y1LgLyIiIiIi4qG49fsx/7vtyM8qqnS9Zu0DsbT7FCy1zQF8c3h5resc2g5328Nx8db5CPTxLv0VUeAvIiIiIiLipoLcYiz6YQd2rEytdL2gpgHIjorFl/4fosRW9XR+4rlODPgd4bgkZgEC7TrHlVHgLyIiIiIi4oaETQcwb9I25GVW3sof1tuGH1uNwR57vM5rLegY2hb3OCJxScx8BfxuUuAvIiIiIiJSicK8Yiz+cSe2LU+p9DyFtQ7Cht4zMNv2u7r111LAf7cjEpcq4PeYAn8REREREZFKWvnnT9qG3Epa+QOC/FEclYKxQaNQYMvXuayFgP8uMOBfgCB75TMnSPkU+IuIiIiIiFSzlT+8hx9+b/spdti36BzWsA4hbXCXXwtcFjNfAX9DCvwdDgf8/DQ3hYiIiIiINPJW/m+3I/dQYYXrhEQGIq7fMnxqn6Ru/TWsfcgx+JdfS1yhgL/G+Hv6hDfeeKPc5TabDTfccENNlElERERERKReWvnnfLMVf47dWGHQ7+/vh6ATs/DVCc/hdwb9UmPahRyD58N648/tm3Dtlllq5a/PFv8xY8agVatWuPvuu0sF/ddffz02b95ck2UTERERERFpMK384V0CMLPLBGywrQQcemFqStuQ1rjLvzWuZAu/rfIZE6SOAv+//voL55xzDpo3b45rr70WxcXFuO6667Bt2zbMmzevmsUQERERERGpe4X5JVj84w5sW1bxWP7gsECkDtiEzxyfwWFTxF9T2jRtjX8FHIOrYuajia3iCheph8B/0KBB+PXXX3HZZZchODgYX331FWJjY03Q37Zt2xookoiIiIiISO1L3HwQ85ixv6JWfj8gpF8BJkWMxn57irkvR69N01a4M7ANrt6qgL9BJ/cbOnQoJk6ciKuuugp9+vTBggUL0Lp165ovnYiIiIiISG208v+0E9uWJle4TrP2gVjafQqW2uYoeV8NBvx3BLTB1dsWILhkXU1tVmoq8L/yyivLXX7MMceYLv+u4/2nTJniziZFREREREQa3Fj+oKb+yIlKwJf+76PEVlLn5fNGCvgbSeAfGRlZ7vLzzjuvpssjIiIiIiJS4wpyi00r//blFY/lD+ttw0+t3sdue5xegRpwTNOWuDOgrVr4G0vgP27cuNoviYiIiIiISC2I38hW/m3Iyyw/Y3xY6yBs6j0LM22/qlt/DQX8dwS2xTUx7NK/viY2KXU9xj8/Px8OhwOhoaHmfmJiokn217dvX4wYMeJoyyMiIiIiIlJjrfyLftiBHStTy308IMgftqhUjA16DwW2fJ31o6SA34sCf2bz55j/e++9F4cOHcI//vEPNGnSBAcOHMCoUaNw33331U5JRURERERE3BS3bj/mT96O/KzyW/nDu/thWvsvsM22Uef0KCngb/j8PX3C2rVrceaZZ5q/f/75Z7Rr1860+k+YMAEffPBBbZRRRERERETELfk5RZjx5Wb8/dmmcoP+kMhApA5eg3faPKSg/yi1Dm6Jp5r1xV+7tuGmTTMQXFKgd6m3tPjn5eUhPDzc/D1z5kzT+u/v749TTz3VVACIiIiIiIjUh11r0rDw++3Izy4+4jF/fz8EDMjC16HvIdORUS/l86aA/46gdrhm2wI0LdYYfq8M/Hv06IHffvsNV1xxBWbMmIFHH33ULE9LS0NERERtlFFERERERKRC+dlFWDB5O2LX7i/38fDOgZh97CSssy0DHDqR1dUquAXuCGqPaxXwe3/g/8ILL+CGG24wAf/ZZ5+N0047zdn6f+KJJ9ZGGUVERERERMq1c3UqFn6/AwU5R7byB4cFYP+ArfjM8QkcNkX81dWSAX8TBvwLEVK0Qe9EXxjjf/XVV2P37t1YvXo1pk+f7lzOSoDRo0dXuyBvvPEG/Pz88Mgjj5RazhkEXnrpJXTo0AEhISEYOnQotmzZUmqdwsJCPPjgg2jdujXCwsJw6aWXYu/evZXu77bbbjP7Y5LCskaOHGke4zoiIiIiItLw5GYW4q9PNmLml1uODPr9gJB+hZgc/Rq+w8dw+Cnor46Wwc3xePgJmB63E7dunI6QoryaefGk4Qf+xIR+bN3n2H4Ls/v37t27WoVYtWoVPv/8cwwYMOCIx95++20zW8BHH31k1uO+zz33XGRnZzvXYWUBpxT8/vvvsXjxYuTk5ODiiy+GzWardL+dO3c2z+EUhZaCggJMnjwZXbp0qdaxiIiIiIhI7YpZmoTJL69A/IYDRzzWrF0Qtpz+F0aHP4U0e7JeimoG/I+Fn4C/42Jx28a/FfD7Sld/JvAbP368GcPPvyszZcoUjwrAIP3GG2/EF198gVdfffWI1v4xY8bgueeec+73m2++Qdu2bfHdd9/hnnvuQWZmJr766itMnDgR55xzjlln0qRJJqifPXs2zjvvvAr3PXDgQMTFxZkyswxW+fncbt26eXQcIiIiIiJSu7LTCzB/0jbs3pp+xGOBwf7Ii0rE+wEfoNhW/hR+UrkWTSJxa3An/HPbQoQWaZpDn2vxj4yMNF3frb8ru3nq/vvvx0UXXeQM2l3Fx8cjJSUFI0aMcC4LDg7GkCFDsHTpUnN/zZo1KC4uLrUOhwX069fPuU5lbr/9dowbN855/+uvv8Ydd9xR6XM4tCArK6vUTUREREREagcbBDfN34vJr6woN+hv1suBaSe9j68D3kUxFPR7qnmTSDwc0Q/TE+Jx58a/EVqUW0OvnDSqFn8rMLbG2x9zzDEIDQ096p2zm/3atWtNF/7yMOgntvC74n1r6kCu06RJE7Ro0eKIdaznV+bmm2/Gs88+i4SEBFO5sWTJElOu+fPnV5qP4OWXX3brGEVEREREpPoOpeVh3sRtSNp56IjHQlsFYWvvuZhu/xmw6yxXJ+C/tWln3MAW/sJNOoFezKOs/gz8e/bsaZLr8f+jsWfPHjz88MNmNoCmTZtWuq7V28C1HGWXlVfWqtYhJgRkjwMOIeBz+DeXVYYVBY899pjzPlv8OTxARERERERqhsPuwPo5e7ByahxKiktH9QGBfrBF7cenwaOQZ1frtKcim0Tg1qZdcMO2RQhTwO8TPAr8mcyPAf/BgwePOvBnF/20tDQMGjTIuYzJ+BYuXGgS+bE7PRP5EVvu27dv71yPz7N6AXCdoqIiZGRklGr15zqDBw92qyzs2v/AAw+Yv8eOHVvl+hxuwJuIiIiIiNS89KRczJ0Yg9T4I4fUhnfzx7QOX2CbbSOgZP0eiWgSjlubHosbTcC/ueZeMPG+rP7Msv/kk09i8+aje6Nw+r9NmzZh/fr1zttJJ51kkuzx74CAAHTt2tUE9rNmzXI+j0H+ggULnEE9Kw6CgoJKrZOcnGzK527gf/7555vt8lZZMkAREREREak9dpsdq/+Kxw+vrzwi6A+JDETK4DV4p+2Dh4N+8SjgfyCiH2Yk7sHdG/5CWOH/ZkgT3+BRiz/ddNNNyMvLQ1RUlBlbHxISUurx9PQjk22UJzw83CTgcxUWFoZWrVo5l7OrPqfqe/31100PA974N/ML3HDDDWYdJhS888478fjjj5vntmzZEk888QT69+9fbsLA8rCSISYmxvm3iIiIiIjUrf17sjF3QgwO7Mkptdzf3w8BA7Lwdeh7yHRk6GXxQHhQM9wcchxu2r4E4QVbdO58mMeBP6fXq0tPPfUU8vPzMXLkSNOd/5RTTjF5AVhxYBk9ejQCAwNx7bXXmnXZm4DTD3oSxHOqQhERERERqVu2YjtW/hmP9TN3w24v3Xc/vEsAZnf5Futsy9St39OAP/Q43LSNAf/Wmn7JpBHyczCjnRwVJvdjz4PMzExVIIiIiIiIuCk5NhPzJsYgIyWv1PLgsECkDdiE7x2fweGncMWTgP+m0K64adtiRBRk6n14NF48xC7oXhOHetziLyIiIiIicjSKC21Y/lssNs3fi1LNkH5A034F+DZiNPbbU8x9qVqzoDDcFNoNN29fjIh8tfDLkRT4i4iIiIhIndkTk475325D1oGCUsubtQ/E0u5TsNQ2Byg9e59UEvDfaAL+JYjMP5yzTKQ8CvxFRERERKTWFeaXYMnPOxGzJLnU8qCmAciOisOX/h+gxFaiV8INYYGhuCGsO27dsRSReQr4pWoK/EVEREREpFbFrd+PhZO3IzezqNTysD42/NhyDPbY4/UKeBzwb9M5k9oP/Hft2oXY2FicddZZZko/5gjk9HsiIiIiIiKUn12EhT/swK7VaaVOSFjrIGzoPQOzbb+rW78bQhnwN+uOW7cvQ3MF/FIXgf/Bgwdx3XXXYe7cuSbQ37lzJ7p164Z//etfaN68Od57773qlENERERERLzIjpUpWPTjThTkFDuXBQT5oyQqBWODRqHAll+v5WssAf/1Yd1x287laJGrFn6pPn9Pn/Doo48iMDAQu3fvRmhoqHM5KwOmT59+FEUREREREZHGLiejEH+O3YBZX28tFfSHd/fD7FM+w2dBr6EACvorExIYgtub98f0pP14dP2faJF7sA5eOfFmHrf4z5w5EzNmzECnTp1KLe/ZsycSExNrsmwiIiIiItJIcOjvlkVJWDZlF4oKbM7lIZGBiO+3HJ/aJwL/WywVBPzXN+uB23asQMvc7TpHUn+Bf25ubqmWfsuBAwcQHBxcU+USEREREZFG4lBqHuZN2oaknYecy/z9/RAQlYmvQt5Dlv1/y+VICvilwQX+TOY3YcIE/Oc//zH3Oc7fbrfjnXfewbBhw2qjjCIiIiIi0gDZbXasm7Ubq/5MgK3Y7lwe3jkQs46diPW25YCjXovYoCnglwYb+DPAHzp0KFavXo2ioiI89dRT2LJlC9LT07FkyZLaKaWIiIiIiDQo+3dnY+7EGBzYk+NcFhwWgLQBW/CZ41M4bIr4KxIS0BTXhffE7erSLw018O/bty82btyITz75BAEBAabr/5VXXon7778f7du3r51SioiIiIhIg1BSbMOqPxKwftZu2O3/De79gJATCvFt5Gik2ZPNfak44L9t50q02vWnTpHUGT8Hs3DIUcnKykJkZCQyMzMRERGhsykiIiIiXilpZwbmTdpuxvRbmrULxPIev2OxbWa9lq2hB/zXsoWfAX/O/voujrjjxUMc1w5viUM9bvHnlH3NmjXDGWecYe6PHTsWX3zxhekJwL9btGhR/ZKLiIiIiEiDU5RfgqVTdmHL4iTnmP3AYH/kRifiS/8xKLGV1HcRG3DAfzxu37lCLfxSr/w9fcKTTz5pahZo06ZNeOyxx3DhhRciLi7O/C0iIiIiIt4jYeMBTH5lhZmqzwr6m/W2Y+pJYzDO/12UQEF/WU0DgnFL8wH4K+UQnlj3h1r5pd553OIfHx9vWvfpl19+wSWXXILXX38da9euNRUAIiIiIiLS+OVnF2HRjzuxc1Wqc1loqyBs6T0bM+xTgP8l8ReXgP+a8F64Y+dKtN71h86LNN7Av0mTJsjLOzymZ/bs2bjlllvM3y1btnT2BBARERERkcZr27JkLPl5Fwpyi839gEA/2KL249PgUciz59Z38Rqc4P8G/HfuWqWAX7wj8OfYfnbpP/3007Fy5Ur88MMPZvmOHTvQqVOn2iijiIiIiIjUgawD+Zj/3Xbs2ZruXBbezR/TOnyBbbaNzq7+8r+A/2oG/LGrcYxa+MWbAv+PPvoII0eOxM8//2ym9OvYsaNZ/vfff+P888+vjTKKiIiIiEgt4rR8G+fuwYpp8SgptJllTSMCsbv/Snxq/wY4vEj+q4l/E1wd2Rt3xq5BGwX80ghoOr8aoOn8RERERKSxOrA3B/MmxiAtMdvc9/MHggbk4JvQd5HpyKjv4jW4gP+qiN64M24t2mYm1XdxpDa96OPT+bnKz89HcfHhcT8WzWMvIiIiItLwlRTbsOrPBKyfudu0+FN450DMOfZbrLUtVbf+MgH/FZG98K+49WgXq6R90vh4HPjn5ubi6aefxo8//oiDBw8e8bjNpn5AIiIiIiIN2b4dGZj/7XYcSj2ctLtJaAAODojBZ/gYDpsG8luC/INwZURv/CueAf+f9fiKidRx4P/UU09h3rx5+Pjjj01G/7Fjx2Lfvn347LPP8Oabbx5lcUREREREpLYU5pdg6S+7sHVJ0uEWfT8gpG8Rvoscg1THPp14l4D/isjeuCtugwJ+8c3Af9q0aZgwYQKGDh2KO+64A2eeeSZ69OiBY489Ft9++y1uvPHG2impiIiIiIhUW9z6/Vg4eTtyM4vM/WZtg7Cy51QstE1Xt/7/CvQPxBWRfXBX/Ea0Vwu/+HLgn56ejq5duzrH8/O+Nc3ffffdV/MlFBERERGRass9VIiFP+xA3Lr95n5gsD/yo3bj/YD3UWw7XAng6xjwX24C/k3ooIBfvJDHgX+3bt2QkJBgWvj79u1rxvr/4x//MD0BmjdvXjulFBERERERjzgcDmxZuA/LfotDUX6JWdaslwM/tX4fifZdOpv/Dfgvi+yDuxXwi5fzOPC//fbbsWHDBgwZMgTPPvssLrroInz44YcoKSnBqFGjaqeUIiIiIiLitvSkXMz/dhuSYzPN/dCWQYjpMw9/238C7DqRgX6BuKx5H9yVsAkd1cIvPsDPwarAo5CYmIg1a9age/fuiIqKgi/yZP5EEREREZHaYiu2Y/XfCVg7MxH2Egf8A/3giDqACU1GIRc5Pn/iGfBf0rwP7k7YhE7pu33+fEglXjwE+PnBW+JQj1v8y2KXf95ERERERKT+JO08PEVfRsrhKfrCu/rjz45fYattvc+/LAz4L2bAn7gZneM0LZ/4nmoF/nPmzMHo0aMRExMDPz8/9O7dG4888gjOOeecmi+hiIiIiIhUqDCv+PAUfUuTTXb+phGB2N1vFT51jAdsvn3iAvwCcHHzvrgncYsCfvFpHgf+H330ER599FFcffXVePjhh82y5cuX48ILLzRj/B944IHaKKeIiIiIiJSxc3UqFv+4E3lZRfDzBwKjsjEu9D1kOjLg6wH/Rc374J7dMeiiFn4Rz8f4d+zY0ST1Kxvgjx07Fq+99hqSkpJ87rRqjL+IiIiI1KXs9AIsnLwdCZsOmvvhnQMx59hvsda2FL4e8F/IFv7dMTj2QFx9F0casxd9fIw/N37++ecfsXzEiBF4+umnPd2ciIiIiIi4yWF3YOO8vVgxNQ7FhTYEhwUgrf8WfIZP4bAdVc7uRh/wX8AW/j3bcZxa+EWOPvC/9NJL8euvv+LJJ58stfz333/HJZdc4unmRERERETEDfv3ZGP+pG1IS8wG/ICQfoX4NnI00uzJPnv+/P38cX7zvrh3z3Z0jfurvosj0rgD/w8++MD5d58+fUyX/vnz5+O0005zjvFfsmQJHn/88dorqYiIiIiIDyousmHltHhsnLMHdrsDzdoHYVn3KVhimw3Y4bMB/3kM+PfuQDcF/CI1M8a/a9eu7m3Mzw9xcb43lkZj/EVERESkNiRuPogFk7cj+2ABgpr6IycqARP930cJSnzyhJcK+NN21XdxxJu96INj/OPj42uqbCIiIiIiUgVm6V/84w7sXJ1m7of1tuGnVu9jt933GtlILfwidTzGX0REREREagc7425dnIRlv8aiMK8EYa2CsKnPLMy0/eqT3fr/F/DvVJd+kaOgwF9EREREpAHISMnFvEnbkLwrEwFB/sBJ+zE26D0U2PLhaxTwi9QsBf4iIiIiIvXIVmzH6ukJWDsjEfYSB8K7+2Fa+y+wzbbR514XBfwitUOBv4iIiIhIPdm3IwPzv92OQ6l5CIkMRPwJK/CpYwJg872Af0TzPrh3byy6K0u/SI1T4C8iIiIiUscKcoux9JddiFmWbBKHB0Vn4+vQ95DpyPCp18IPfhjRoi/uMwH/3/VdHBGvVa3Av6CgABs3bkRaWhrs9tJZRi699NKaKpuIiIiIiNfZvjwZS37ZhfzsYoR3DsScY7/FWttSoMpJtr0r4D+XAf++OPSIV8Av0uAC/+nTp+OWW27BgQMHjnjMz88PNpuP9UsSEREREXEDu/MvmLwde7dlIDgsANmn7cBnjo/hsDl8KuA/578Bf08F/CINN/B/4IEHcM011+CFF15A27Zta6dUIiIiIiJewlZiN4n71kxPNH+H9CvEdxHvI9Wxj5GwzwT8Z7fog3uTEtFLAb9Iww/82b3/scceU9AvIiIiIlKFpJ2Hk/dlpOShWbsgrOjxBxbZZvhMt34G/MOb98F9ybvRK356fRdHxGd5HPhfffXVmD9/Prp37147JRIRERER8aLkfYFN/FH0jz14P+ADFNuK4CuGs0t/8m70XqeAX6TRBf4fffSR6eq/aNEi9O/fH0FBQaUef+ihh2qyfCIiIiIijTZ5X7NedvzU+kMk2nfBVwxt3hcjU3ajz1oF/CKNNvD/7rvvMGPGDISEhJiWfyb0s/BvBf4iIiIi4uvJ+0JbBSH+jIWYYfsFKD0Jltcayi79qXvRVy38Io0/8P+///s/vPLKK3jmmWfg7+9fO6USEREREWlsyfv+ToQZvD/oAD4NHoU8Wy58wRAT8O/DCetm1HdRRKSmAv+ioiJcd911CvpFRERExOft25GBBd8dTt4X3tUff3T8CjG2DT6RvO+s5n0wMi1JAb+INwb+t956K3744Qf8+9//rp0SiYiIiIg0cPk5RVj68y5sW56CphGBSBu8Dp86xgM2eL0zm/fGyLRk9FMLv4j3Bv42mw1vv/22Gec/YMCAI5L7jRo1qibLJyIiIiLSYDgcDsQsTcayKbEozC9Gk+gcfBM6ChmOg/B2ZzDg35+C/utm1ndRRMRDHg/S37RpE0488UTT1X/z5s1Yt26d87Z+/XqPtvXJJ5+YyoOIiAhzO+200/D3338f8eX60ksvoUOHDiah4NChQ7Fly5ZS6xQWFuLBBx9E69atERYWhksvvRR79+6tdN+33XabSUZ47733HvHYyJEjzWNcR0RERESE0pNy8et7azFv4jYEtXBg9eCf8EHIc14f9J/evDe+LW6BT9bNRP+9G+u7OCJSFy3+8+bNQ03p1KkT3nzzTfTo0cPc/+abb3DZZZeZSoQTTjjBLGPvAvYiGD9+PI4//ni8+uqrOPfcc7F9+3aEh4ebdR555BFMmzYN33//PVq1aoXHH38cF198MdasWYOAgIAK99+5c2fznNGjR5tKBSooKMDkyZPRpUuXGjtOEREREWm8SopsWPVnAtbP3o3AYH9kn7oDn+FjOGzePZB/cPNeGHkgDVFq4RfxvcDfFVvV2TLesWPHaj3/kksuKXX/tddeM70Ali9fbgJ/tvaPGTMGzz33HK688kpn5UDbtm3NtIL33HMPMjMz8dVXX2HixIk455xzzDqTJk0yQf3s2bNx3nnnVbj/gQMHIi4uDlOmTMGNN95olvFvPrdbt27VOiYRERER8R6Jmw9i4ffbkXWwAKF9izC5+ftItlfes7SxO80E/PsRvW5WfRdFROqrq7/dbjfT+UVGRuLYY481LePNmzfHf/7zH/NYdTF3AFvfc3NzTZd/io+PR0pKCkaMGOFcLzg4GEOGDMHSpUvNfbbqFxcXl1qHwwL69evnXKcyt99+O8aNG+e8//XXX+OOO+6o9DkcWpCVlVXqJiIiIiLeI/dQIaZ/vgl/fLQB9gAbtp7+N0ZFPOnVQf+pzY/HhJJW+HzdLETv8WwIr4h4WYs/W9/Zws4u+qeffrpplV+yZIkZh89u8my19zRnAAN9PrdZs2b49ddf0bdvX/MYg35iC78r3k9MTHSu06RJE7Ro0eKIdaznV+bmm2/Gs88+i4SEBNN7gcfCCoj58+dX+Jw33ngDL7/8skfHKSIiIiINn8PuwKYFe7Hi9zjY7Q4U/2Mf3g8Yg2JbEbzVKc2Px8iD6Ri4bnZ9F0VEGkrgz672X375pUmgZ4mKijLd/ZkUz9PAv1evXiYp4KFDh/DLL7+Y6QIXLFjgDP6JAbkrVjaUXVaWO+sQEwJedNFF5rj4HP7NZZVhRcFjjz3mvM8Wfw4PEBEREZHGKy0xCwu+2460xGw0O96BX9qMRbxtJ7zVPyKPx8j0DAxSwC/i9TwO/NPT09G7d+8jlnMZH/MUW+ut5H4nnXQSVq1ahffffx+fffYZ2rVrZ5az5b59+/bO56SlpTl7AXCdoqIiZGRklGr15zqDBw92qwzs2v/AAw+Yv8eOHVvl+hxuwJuIiIiINH6F+SVY8VssNi/ch5AWQdh9xhL8ZfsRsMErnRzZE/dlZOLk9WrhF/EVHo/xZ+v+Rx99dMRyLuNjR4ut7hxDT127djWB/axZ/0sswiCfPQKsoH7QoEEICgoqtU5ycrKZatDdwP/888832+WtsmSAIiIiIuJddqxMwXcvLseWJUnwG5iOz3o9dTjo90InRfbE1/Y2+Hr9HJycuLq+iyMiDbnFn9PrsTs8M+ZzbD670zOJ3p49e/DXX395tK1///vfuOCCC0w3+ezsbOfY+unTp5vHuW1O1ff666+jZ8+e5sa/Q0NDccMNN5h1mGTwzjvvNFP4cSq/li1b4oknnkD//v2dWf6rwin/YmJinH+LiIiIiHfLSMnFwu93YO+2DIR39cf8AV9jq807E9oNiuyJkYey8I/1c+q7KCLSWAJ/ZtTfsWOH6RK/bds200LPqfY4vp/Z9D2RmppqkuuxhZ4B/IABA0zQf+655zrXeeqpp5Cfn2+2z+78p5xyCmbOnInw8HDnOqNHj0ZgYCCuvfZas+7ZZ5+N8ePHexTER0REeFR2EREREWl8SopsWP13AtbN2o0moQHYP3g9PnWM88pu/QMje5iA/xQF/CI+z8/ByN1N1rR5HH9//PHH+/zJc03ux4qLzMxMVSCIiIiINFAJmw5g0Q87kJ1egKD+OZgYNgoZjoPw2oA/Qd35RartxUPsgu41cahHLf4cS8+x8+5kyxcRERERaQhyMgqw6MediFu3H+GdA7Fq8I9YbVsCuN381XgC/vsys3Hq+rn1XRQRaexd/W+55RZ89dVXePPNN2unRCIiIiIiNcBus2PDnL1Y9Wc8/AKA7FN34DN8DIfNuyJ+BfwiUuOBPzPff/nllyaLPqffCwsLK/X4qFGjPN2kiIiIiEiNStp1CAsnb8fBpFyEnFCI7yLeR6pjn1ed5RNNC38OTlMLv4jUdODPrv4DBw40fzPJnysNARARERGR+mIrsWPXmjRsXrAPKXGZaNYuEJtP/xOLbTO9qlu/An4RqZXAf+PGjejXrx/8/f0xb948j3ciIiIiIlJbsg7mY8vCJMQsTUJ+djFCWwah8JTd+Mr/QxTbirzmxEdHdMfIrFy18ItI7QT+J554oplyr02bNujWrRtWrVqFVq1aeb43EREREZEawImpdm9Jx+YFe5G4+SCCQgLg3z0HmyPmYrF9pled4ygT8Odh8AY1wIlILQb+zZs3R3x8vAn8ExISYLfbq7k7EREREZHqK8gpxtalSdiyKAk56QUI7e5A0mmrMcPxMwqQD3jRZeoABvzZeThdAb+I1EXgf9VVV2HIkCFo3769GcfPpH4BAQHlrhsXF3e0ZRIRERERKSUlPtOM3ecY/tC2/kjttgMzev58OGGfF43fpwER3XBfTiHOUMAvInUZ+H/++ee48sorsWvXLjz00EO46667EB4eXlNlEBERERE5QnGRDTtXpZqAPze7APndUrDglKnYalt/eAUvC/j7RXTFyJwinLlhfn0XRUR8Nav/+eefb/5fs2YNHn74YQX+IiIiIlIrMlJyTbAfuyEN9g7ZWNtlHhaWTIfDzwHYvO+kM+C/L6cYZ21YUN9FEREv5fF0fuPGjaudkoiIiIiIz7LZ7IhffwBbFu9DZkkGYtusxfTePyPPkXs42PeD1+kbfhxG5pZgiAJ+EWlogb+IiIiISE3JySgwifri45KwL3InZrT8Ecn2vV6VpK+sPgz48+wYunFhfRdFRHyEAn8RERERqfOp+PbEpGPr2j1IKIjFwqbTsKnFmsMPenXAfyzuy3dgmAJ+EaljCvxFREREpE4U5BZj66q92Lw3BquxCPNL/oQjyDvH7bvqbQJ+YPjGRfVdFBHxUQr8RURERKRW7d2ZjrWxm7Dm4CrMKPkFucg5/IAXjtt31csZ8C+Gn7dNQSAi3h/479ixA/Pnz0daWhrs9tL9sV544YWaKpuIiIiINFJ5WUVYu3UrVuxbgVl507DPnghfcXyzLhhZ4KeAX0Qab+D/xRdf4L777kPr1q3Rrl07+Pn9r6qWfyvwFxEREfFNDrsDO2J3Y1HiIsxLnYONJavhS3o264L7Cv1wzia18ItIIw/8X331Vbz22mt4+umna6dEIiIiItKopB/KxPxdCzE3cR4W5cyF3dsH7ZfRo1ln3FcYgHM3LVKXfhHxjsA/IyMD11xzTe2URkREREQaBZvNhqWJyzE9dgbmJs9GjiMbvoYB/71FARihgF9EvC3wZ9A/c+ZM3HvvvbVTIhERERFpsHZk7MAfu/7AX7F/IbUwFb6IAf89hYE4b9NCtfCLiHcG/j169MDzzz+P5cuXo3///ggKCir1+EMPPVST5RMRERGRerY/bz/+iv8L02KnYXvGdviq7s064d6iJhixeSH8HaUTXIuINGR+DofDo7lFunbtWvHG/PwQFxcHX5OVlYXIyEhkZmYiIiKivosjIiIictTyivMwZ/cc/BH3B1Ykr4DN4Vvj9l11Y8Bf3ATnbVfAL+IzXjzEABfeEod63OIfHx9/NGUTERERkQbK7rCbIJ/B/uzE2cgryYMv6xrWEfeWNMX5mxeohV9EGjWPA39XVmcB1yn9RERERKQRjtuP/QN/xv+JtLw0+LrjwjrinpKmuHCLAn4R8eHAf8KECXjnnXewc+dOc//444/Hk08+iZtvvrmmyyciIiIitUDj9o90XFgH3F0Sggu3LESADw9tEBHv43HgP2rUKJPc74EHHsDpp59uWv2XLFlisvwfOHAAjz76aO2UVERERESOSn5J/uFx+7F/YHnycp8et39EwG8LNS38CvhFxBt5HPh/+OGH+OSTT3DLLbc4l1122WU44YQT8NJLLynwFxEREWlg4/ZXpqw0Gfk1br+0Y8M64B4F/CLiAzwO/JOTkzF48OAjlnMZHxMRERGR+hd7KBZTY6fiz7g/kZqXWt/FaXABP1v4L1ILv4j4CI8D/x49euDHH3/Ev//971LLf/jhB/Ts2bMmyyYiIiIiHjiYfxB/xf9lWvdj0mN07sroEtoe99ib4aIt89WlX0R8iseB/8svv4zrrrsOCxcuNGP8mdF/8eLFmDNnjqkQEBEREZG6U2grxLzd8zAtbhqW7luKEkeJTn85Af/d9ma4eKsCfhHxTR4H/ldddRVWrFiB0aNH47fffjPJ/fr27YuVK1fixBNPrJ1SioiIiIgTr79Wp642LfuzEmchpzhHZ6ccnUPb4W57uAn4A5XIUER8WLWm8xs0aBAmTZpU86URERERkQrFZcaZjPwct5+Um6QzVY5A/0CcFNENlxQ4cGHMAgTa1QNCRMStwD8rKwsRERHOvytjrSciIiIiRy+jIMOM22fAv/ngZp3ScoQGhuL08K4YnleAsxLXIiI2TudJRMTTwL9FixYmY3+bNm3QvHlzM66/vC5nXG6zaT5YERERkaNRZCvCvD3zTLC/OGkxStRqfYSWwS0wLLQThmdm4NS4NWhi26Y3nYjI0QT+c+fORcuWLc3f8+bNc+cpIiIiIuIBNqKsTVtrxu3PTJyJ7KJsnb9yxuwPb3IMhh9IQnTCBvg7NugciYjUVOA/ZMgQ599du3ZF586dj2j154/Vnj173NmciIiIiPxXYlaiCfb/iPsD+3L26byU0Tf8OAz3a4bhKbHoGb9S50dEpC6S+zHwt7r9u0pPTzePqau/iIiISOUyCzPxd/zfZgq+jfs36nS5Xpz6BWJQRDcML/HH8L1b0S5+oc6PiEhdB/7WWP6ycnJy0LRp06Mtj4iIiIhXKrYVY+HehZgaOxWL9i1Csb24vovUYIQEhhxOzpdfhLMS1iIyTsn5RETqJfB/7LHHzP8M+p9//nmEhoY6H2Mr/4oVKxAdHV2jhRMRERFp7NanrTdd+WckzjAt/XJYy+DmGBLaGcOzDuG0+DUILtmuUyMiUt+B/7p165wt/ps2bUKTJk2cj/HvqKgoPPHEE7VTShEREZFGZE/2HjNmn1n5d2fvru/iNBidrOR8B5Nx4vb18HdomIOISIMK/K1s/rfffjvef/99RERE1Ga5RERERBoVtubPSJhhAv51aYcbTAToE34shvmFY3hKHHopOZ+ISOMY4z9u3LjaKYmIiIhIYxy3v2+hadnn+P0iexF8HZPzDTTJ+QIwfN9WtI9fVN9FEhHxeR4H/rRq1Sr89NNP2L17N4qKSv/ATZkyxedPqoiIiHi3Dfs3HB63nzADhwoPwdeFBDTF4PBuGF5QhCGJ6xAZN7u+iyQiIkcT+H///fe45ZZbMGLECMyaNcv8v3PnTqSkpOCKK67wdHMiIiIijWrc/p9xfyIxKxG+rkWTSAwJ63I4OV/iWjQt3lHfRRIRkZoK/F9//XWMHj0a999/P8LDw814/65du+Kee+5B+/btPd2ciIiISIOVVZSF6fHTNW7/vzqGtsWwJm0wPD0VA3esQ4BjU/2+QCIiUjuBf2xsLC666CLzd3BwMHJzc80Uf48++iiGDx+Ol19+2dNNioiIiDQYxfZiLNq7yAT7C/Ys8Plx+6WT862q75dHRETqIvBv2bIlsrOzzd8dO3bE5s2b0b9/fxw6dAh5eXnVKYOIiIhIvdu4f6Nz3H5GYQZ8OTnfoIhuGGYLwPC9Ss4nIuKTgf+ZZ55pxvYz2L/22mvx8MMPY+7cuWbZ2WefXTulFBEREakFSTlJJthn635CVoLPnuOQwBCcHt4Vw/OLcFbCWkTGxdV3kUREpD4D/48++ggFBQXm72effRZBQUFYvHgxrrzySjz//PM1WTYRERGRGpdTlIOZiTMxNXYq1qauhQMOnzzLLYObY0ho58PJ+eLXILhke30XSUREaomfw+HwzV+7GpSVlYXIyEhkZmYiIiKivosjIiIiZZTYS7A0aalp3Z+3Zx4KbYU+eY46h7bDsCbH4OyDyYjesx7+Dnt9F0lEpGF68RDg5wdviUMD3d2gtSH+XRkFviIiItJQbDm4xQT7f8f/jfSCdPiiPuHHYbhfMwxPicXx8SvruzgiIlIP3Ar8W7RogeTkZLRp0wbNmzc3WfzLYscBLrfZbLVRThERERG3pOSmmDH7DPjjMuN8Ojnf2Xu2oF38wvoukoiI1DN/d1Zi8j5m87f+Lu82b948878n3njjDZx88skIDw83lQqXX345tm/ffkSFwksvvYQOHTogJCQEQ4cOxZYtW0qtU1hYiAcffBCtW7dGWFgYLr30Uuzdu7fSfd92222mouLee+894rGRI0eax7iOiIiINHy5xbn4deevuHPGnTjvl/Pw/tr3fSroZ3K+c1qcgNeb9sD85HR8uX42btw0A+0OVX49JCIivqFex/iff/75uP76603wX1JSgueeew6bNm3C1q1bTQBPb731Fl577TWMHz8exx9/PF599VUsXLjQVBCwwoDuu+8+TJs2zazTqlUrPP7440hPT8eaNWsQEBBQ7r4Z1LOigkMX2JuBlQrExIXt27c3QxaGDRtmtlkVjfEXERGpeza77fC4/bhpmLd7Hgpsh5MP+4oWTSIxNKzL4eR8CUzO51vHLyJSq170wTH+rrp164Ybb7wRN910E3r16nU05cT06dNL3R83bpxp+WfAftZZZ5nW/jFjxpgKAc4aQN988w3atm2L7777Dvfcc485yK+++goTJ07EOeecY9aZNGkSOnfujNmzZ+O8886rcP8DBw5EXFwcpkyZYo6J+Defy+MUERGRhmdb+jbTjf+v+L9wIP8AfEnH0LYY3qQNhh9MwYk71iPAsam+iyQiIt7S1d/VAw88YAL2Pn36YNCgQSYwZ4t5TWAQT9awgvj4eKSkpGDEiBHOdYKDgzFkyBAsXbrU3GclQXFxcal1OCygX79+znUqc/vtt5sKB8vXX3+NO+64o0aOR0RERGpGWl4avt78Na74/QpcM+0aTNg6wWeC/j7hx2JkRD/8nBeK6VtW4al1f+Kk3WsQ4FBeJRERcY/HLf6PPfaYue3YsQPffvstPvnkEzz55JOmWzx7Adxyyy2oDrbuc7tnnHGGCdqJQT+xhd8V7ycmJjrXadKkiUlAWHYd6/mVufnmm/Hss88iISHBjOtfsmQJvv/+e8yfP7/C5zCnAG+WqmY6EBEREc/lFedhzu45mBo7FStTVsLuI1PPBfgFYGBENwy3BWL43hh0iF9U30USERFfC/wtHG//8ssvm9vy5cvNOHu2nlc38GdPgo0bN2Lx4sVHPFZ2FgFrBoHKuLMOMSHgRRddZIYQ8Dn8m8uqSkrI4xYREZGaxeB+efJy/BH7B2bvno38knyfOMUhAU1xGoP9/GIMSVyH5nFz6rtIIiLiRaod+NPKlSvNWPsffvjBdNO/+uqrq7UdZuSfOnWqSdrXqVMn5/J27dqZ/9lyz4R7lrS0NGcvAK5TVFSEjIyMUq3+XGfw4MFu7Z9d+1nxQGPHjq1yffYQYO8E1xZ/5gUQERGR6tmZsdOM2/8z/k/Trd9XkvOdFdYZw7OzMDhhDZoW76jvIomIiJfyOPC3uvgz4Gf3eHbxf/PNN03yPSvLvrvYws6g/9dffzVd67t27Vrqcd5nYD9r1iyceOKJZhmD/AULFphs/8Q8A0FBQWada6+91ixjzoHNmzfj7bffdnt2AW6XKksG6JpngDcRERGpPo7R/zPuT/wR94dJ2OcryfmGNWmL4enJGKjkfCIi0lAD/969e+Okk07C/fffb6bis1rlq4PbYAXC77//bioNrDH5nJKA0+uxq/4jjzyC119/HT179jQ3/h0aGoobbrjBue6dd95ppvDjVH5MDPjEE0+gf//+ziz/VeGUfzExMc6/RUREpHaw6z7H7bMrP7v023wgQV2v8C4Y7heB4akJ6B2/qr6LIyIiPsjjwH/btm1mfH9NYGJAGjp0aKnlzLJ/2223mb+feuop5OfnY+TIkaY7/ymnnIKZM2eW6l0wevRoBAYGmhZ/rnv22Wdj/PjxHgXxVc17KCIiItUft8/kfOzKPztxNvJK8rw+Od+JJjlfEIbv24qO8UfmLxIREalLfg72t/fQoUOH8PPPPyM2NtZk9Gcr+9q1a824+44dO8LXcIw/ex4wz4EqEERERFzG7cdNM935vX3c/hHJ+fLS67tIIiJyNF48xCzzXhOHetziz8z7bFFv3ry5GeN/1113mcCf4/Q5xd6ECROOpuwiIiLSiPnSuH0l5xMRkcbC48D/0UcfNdP2MXGea3f7Cy64wDnuXkRERHxr3P7c3XNN6/7yJO8et8/kfMObtMHw9BScqOR8IiLirYH/6tWr8fnnnx+xnF38reR8IiIi4v3j9lelrMLU2KkmWV9ucS68VZ/wYzHMPwLDk2PRS8n5RETEFwL/pk2bmrEEZW3fvh3HHHNMTZVLREREGqDYQ7EmSd+f8X8iJdc7K/wD/QIxkOP1SwJMcr728Yvqu0giIiJ1G/hfdtlleOWVV/Djjz+a+5xyb/fu3XjmmWdw1VVXHV1pREREpME5mH8Qf8f/bVr3Y9IPT3/rjcn5BpvkfEUmOV9k3Oz6LpKIiEj9Bf7vvvsuLrzwQrRp08ZMnTdkyBDTxf+0007Da6+9VnMlExERkXpTaCvEvN3zTLC/LGkZShwlXvdqKDmfiIj4Co8Df04TsHjxYsydO9dM4We32zFw4ECcc845tVNCERERqROc4Xd16mqTkX9WwixkF2d7ZXK+Yf9NzjdQyflERMRHeBz4W4YPH25uIiIi0rjFZ8YfHrcf9yeScpPquzg1rld4F5ztH4nhKXFKziciIj7Jo8Cfrfvjx4/HlClTkJCQYMb3d+3aFVdffTVuvvlmc19EREQavoyCDDNun637mw5sgjcJ8AvAiRyvbwsyyfk6xi+u7yKJiIg0jsCf3f8uvfRS/PXXX4iKikL//v3NspiYGNx2222mMuC3336r3dKKiIhItRXZijB/z3xMi5uGxfsWo8TuPeP2mwYE47Tw7hheUIyhievQPG5OfRdJRESk8QX+bOlfuHAh5syZg2HDhpV6jOP9L7/8ckyYMAG33HJLbZRTREREqmlt6loT7M9ImIHsIu8Zt9+8SSTOCuuM4dlZGJy4FiFFO+u7SCIiIo078J88eTL+/e9/HxH0E8f6czq/b7/9VoG/iIhIA7A7a7cJ9v+I/QN7c/bC65LzZaRh4I61CHB41zAFERGReg38N27ciLfffrvCxy+44AJ88MEHNVUuERER8VBmYSamx0/H1Lip2Lh/o9ecv17hx2K4XziGpyagd/yq+i6OiIiI9wb+6enpaNu2bYWP87GMjIyaKpeIiIi4odhWjIV7F5rWff5fbC/2iuR80S7J+TrFL6rvIomIiPhG4G+z2RAYWPHqAQEBKCnxniRBIiIiDdn6tPUmI//0hOmmpd8bkvOdGt4NwwtLMDRxPVooOZ+IiEj9ZPVn9v7g4OByHy8sLKy5UomIiMgR9mbvNS37f8b9icSsxEZ/hiKbRGBIWBcMz87G4MQ1Ss4nIiJS34H/rbfeWuU6yugvIiJSs7KKskw2fibpW5e2Dg44GvUp7hDSBsOC25rkfINMcr7N9V0kERERr+d24D9u3LjaLYmIiIgYHKe/aO8i05V/wZ4FKLIXNeoz0yu8C4b7RWB4GpPzra7v4oiIiPgctwN/ERERqV3MxD8tdppp4c8ozGjUyflOdEnO1zF+cX0XSURExKcp8BcREanncfts2ee4/YSshEadnO+0iO4Ynl+MoYnr0FzJ+URERBoMBf4iIiJ1zFvG7TdvEomzwjpjeHYWBieuVXI+ERGRBkqBv4iISB2N21+8d7HJyt+Yx+13DG2LYU3aYHh6KgbuWIcAx6b6LpKIiIhUQYG/iIhILdq0fxOmxk5t1OP2e4Ufi+F+4RieyuR8q+q7OCIiIuIhBf4iIiI1LCknyYzbZ6K+xjhun8n5ol2S83WKX1TfRRIREZGjoMBfRESkBuQU5WBm4kwT7K9JXdPoxu0zOd+p4d0wvLAEQxPXo4WS84mIiHgNBf4iIiLVVGIvwdKkpSbYn79nPgpsBY3qXEY2icCQsC4Ynp2NwYlrlJxPRETESynwFxER8dDWg1tNsP9X/F9IL0hvVOevQ0gbDAtui+EZaRi4cx0C7Zvru0giIiJSyxT4i4iIuCElN8WM2+cUfLGZsY3qnPVs1hnDA5pjeGo8+savru/iiIiISB1T4C8iIlKBvOI8M26fwf6q1FWwO+yN4lz5+/n/NzlfEwxPikHn+CX1XSQRERGpRwr8RUREXNjsNixLXma68s/bMw/5JfmN4vwEBwTjNJOcz4YhievRMm5ufRdJREREGggF/iIiIgC2pW9zjts/kH+gUZyTiCbhGBJ2HIZnZ2Fw4lqEFu2s7yKJiIhIA6TAX0REfFZaXhr+jPsT0+KmYWdG4wia24Ucg2HB7TD8UBpOMsn5ttR3kURERKSBU+AvIiI+N25/zu45pnV/RcqKRjFuv4eVnC8tASfEr6nv4oiIiEgjo8BfRES8HoP75cnLTZK+2btnN/hx+87kfPYmGL5vm5LziYiIyFFR4C8iIl6L3ffZsv9n/J+mW39DpuR8IiIiUlsU+IuIiFdhYj6O2/8j7g+TsK8hi2wSgbPCumB4draS84mIiEitUeAvIiKNXkFJAebunoupcVOxPGk5bA4bGqoOIW0OJ+fLSMVAk5xvc30XSURERLycAn8REWm04/ZXpawyXfk5bj+3OBcN1fHNumB4QCSGp8ajT/zq+i6OiIiI+BgF/iIi0qjsythlpt9jd/7UvFQ0RAF+AYiK6IrhtiYYnhSDzvGL67tIIiIi4sMU+IuISKMYt/9X3F9m3H5MegwaIiXnExERkYZKgb+IiDRIjWHcfkRQOIY0O1bJ+URERKRBU+AvIiINgsPhwI6MHViWtAzLkpdhbepaFNgK0NC0DznGmZxv0K71CLRvqe8iiYiIiFRKgb+IiNSblNwUZ6C/InkF0gvSG+Sr0aNZZwwPaI6z0xLQN35NfRdHRERExCMK/EVEpM4w8/7K5JUm0GfAn5CV0CDPvr+fP6IjumG4vQmG79uGzvFL6rtIIiIiItWmwF9ERGpNib0Emw5sMkH+8uTl2LR/E0ocJQ02Od8p4d1wdqENQ3ZvQKu4ufVdJBEREZEaocBfRERqVFxmnDPQX52yGjnFOQ32DIcHNcNZzY7D8NwcnJGwFqGFO+u7SCIiIiI1ToG/iIgclYP5B02Qb904br8haxvSGsOC22P4of04eddaBNq31neRRERERGqVAn8REfF4mj1m3LfG6TMTvwOOBn0WezTrhGEBLXB2WiJOiF9b38URERERqVMK/EVEpFJ2hx0xB2NMoL88aTnWpa1Dkb2oQZ81JueLCu+K4Y5gDE/aji7xS+u7SCIiIiL1RoG/iIgcYW/2XmegvzJlJQ4VHmrwZ6mJfxOcGtEdw4rsGJq4Aa3j5tV3kUREREQaBAX+IiKCzMJME+BbSfn2ZO9pFGfFmZwvJwdnJDI53676LpKIiIhIg6PAX0TEBxXbirF+/3oT6PO2NX2r6dLfGCg5n4iIiIhnFPiLiPgAh8NhkvCxNZ9d+JmcL78kH41Fj2adMTygOYanJSg5n4iIiIiH/FGPFi5ciEsuuQQdOnSAn58ffvvtt3IvVl966SWzTkhICIYOHYotW7aUWqewsBAPPvggWrdujbCwMFx66aXYu3dvpfu+7bbbzD7vvffeIx4bOXKkeYzriIg0Vqm5qfht1294euHTGPbjMFw97Wq8u/pdLNm3pMEH/U0DgnFyZE88Ed4Xf2X549dNS/Dg+j9xQlLp738RERERaeAt/rm5uYiKisLtt9+Oq666qtx13n77bYwaNQrjx4/H8ccfj1dffRXnnnsutm/fjvDwcLPOI488gmnTpuH7779Hq1at8Pjjj+Piiy/GmjVrEBAQUOH+O3fubJ4zevRoU6lABQUFmDx5Mrp06VJLRy0iUjtyi3OxMnmls1U/PjO+0Zxqdt+PbtoW0cV2RKcnoVfyNgTZd9Z3sURERES8Qr0G/hdccIG5VYSt/WPGjMFzzz2HK6+80iz75ptv0LZtW3z33Xe45557kJmZia+++goTJ07EOeecY9aZNGmSCepnz56N8847r8LtDxw4EHFxcZgyZQpuvPFGs4x/87ndunWr8eMVEalJJfYSbDqwyTlOf/OBzShxlDT4kxzoF4he4Z0R7d8M0Xk5iE7dhXbxa+u7WCIiIiJeq0GP8Y+Pj0dKSgpGjBjhXBYcHIwhQ4Zg6dKlJvBnq35xcXGpdTgsoF+/fmadygJ/Ym+DcePGOQP/r7/+GnfccQfmz59fi0cmIlI9cZlxhzPvJy3HqtRVppW/oWvRJBJRoR0QZfNH9KE09EveiqbFcfVdLBERERGf0aADfwb9xBZ+V7yfmJjoXKdJkyZo0aLFEetYz6/MzTffjGeffRYJCQlmXP+SJUtM9//KAn/mFODNkpWV5fGxiYi442D+wcNd9/87zV5qXmqDPnH+fv7oFtYRUUHNEZ1fgOj98TjuwCYAvImIiIhIfWjQgb+FAXnZIQBll5XlzjrEhIAXXXSRGULA5/BvLqvMG2+8gZdfftnN0ouIuK+gpABrUtcc7r6fvAw7M3bCAUeDPYVhgaHo36wToh3BiM46gAFJ2xBekFDfxRIRERGRxhL4t2vXzvzPlvv27ds7l6elpTl7AXCdoqIiZGRklGr15zqDBw92az/s2v/AAw+Yv8eOHVvl+uwh8Nhjj5Vq8WdeABERT9kddsQcjDFBPrvvr0tbhyJ7UYM9kZ1D2yE6uDWiC4sRdXAveqZuh79jW30XS0REREQaa+DftWtXE9jPmjULJ554olnGIH/BggV46623zP1BgwYhKCjIrHPttdeaZcnJydi8ebOZEcAd559/vtkuVZUTwMozwJuISHXszd5rAn226q9MWYnMwswGeSKDA4LRN6wTov1DEZWTiejk7WiVs7K+iyUiIiIijSnwz8nJwa5du0ol81u/fj1atmxpptNjV31O1ff666+jZ8+e5sa/Q0NDccMNN5jnREZG4s477zRT+HEqPz73iSeeQP/+/Z1Z/qvCKf9iYmKcf4uI1CQG9gzw2aLPgH9P9p4GeYLbNG2FqJB2iC52IDojCX12b0OQTVPqiYiIiDR29Rr4r169GsOGDXPet7rP33rrrRg/frz5+6mnnkJ+fj5GjhxpuvOfcsopmDlzJsLDw53PGz16NAIDA02LP9c9++yzzfM9CeIjIiJq9NhExHcV24qxfv96Z0K+rQe3wuawoaFNqdeTY/MDwhGdn4vo1Fh0yFhX38USERERkVrg52BGOzkqHOPPngeZmZmqQBDxUTsydjhb9JmcL78kHw1Jc2tKPTun1NtvptQLKcqr72KJiIiINEwvHmKWeXhLHNqgx/iLiDRUaXlppabZO5B/AA2FH/zQvRmn1GuB6IJCRO2PR9f9mlJPRERExFcp8BcRcUNecR5Wp64+PM1e0jLEZsY2mPPWLCgM/cM6IcrRxGVKvcT6LpaIiIiINBAK/EVEymGz27DpwCZnq/7GAxtRYi9pEOeqS2h7M6VeVGHR4Sn1Ejil3uEEpSIiIiIiZSnwFxH5r/jMeGegvzplNbKLs+v93DTllHrNOiPKLwTRnFIvaRta5q6o72KJiIiISCOiwF9EfFZ6QbozId+K5BVIzk2u7yKhTdPWiA5pe3hKvfR96J28DUF2TaknIiIiItWnwF9EfEZBSYHJuG8l5GMmfgcc9TqlXq/wzoj2b4bovFxEpcWifcbaeiuPiIiIiHgnBf4i4rXsDjtiDsaYFn0G++vT1qPIXlTvU+pF2/0RdSgN/ZJjEFIUV2/lERERERHfoMBfRLzK3uy9zkB/ZcpKZBZm1ks5NKWeiIiIiDQUCvxFpFFjYM8A35pmb2/O3nopR1hgKPqFdUI0ghGdnY4BSVsRka8p9URERESk/inwF5FGpdhWjPX71zsD/a3pW02X/rrWKbSdmVIvuqgE0Qf2oGcqp9TbVuflEBERERGpigJ/EWnwmITPBPrJy7A2dS3yS/LrdP/BnFKPrfn+oYjKyUJU8na0zllZp2UQEREREakuBf4i0uCk5aU5A31Os3cg/0Cd7r9N01aICmmHKE6pdygZfXfHIMimKfVEREREpHFS4C8i9S6vOA+rUlaZQH950nLEZsbW6ZR6xzfrhOiAcETn5yIqNRYdMtbV2f5FRERERGqbAn8RqXM2uw2bDmzC8uTlpmV/44GNKLGX1MOUevvRL3mrptQTEREREa+mwF9E6kRCZoIz0GfrfnZxdp1PqRe9Px7H7d8EgDcREREREd+gwF9EakVGQYYZn8/u+wz2k3OT62RKvf7stu8IRnTWQfRPjtGUeiIiIiLi8xT4i0iNKLQVmoz71jj9benb4ICjVs9uZ5cp9aI0pZ6IiIiISLkU+ItItTgcDsSkxzi7769LW2eC/7qZUi8T0cnb0UpT6omIiIiIVEmBv4i4LSknyTnN3srklcgozKj1KfWiOaVeRhL67N6mKfVERERERKpBgb+IVCirKMsE+Far/u7s3bU2pV5Plyn1ojWlnoiIiIhIjVHgLyJOxbZirN+/3gT5DPa3HtwKm8NW42coskkEokI7IsoegOjM/ei3bytCi+L0SoiIiIiI1AIF/iI+bkfGDpOMj93316SuQX5Jfo1PqdfNZUq9qP3x6Lp/C/ywuUb3IyIiIiIi5VPgL+Jj0vLSnF33+f+B/AM1uv1QTqkX1glRCEZ0djoGJMUgMj+xRvchIiIiIiLuU+Av4uXyivOwOnX14aR8ScsQmxlbo9vvGNoW0cHHmCn1og/sRc/U7QhwbKvRfYiIiIiISPUp8BfxMja7DZsPbnYG+hsPbESJvaQWptTLQlTydrTOWVUj2xYRERERkdqhwF/ECyRkJji7769KWYXs4uwa2W6bpq0RFdL2v1PqJaPP7hhNqSciIiIi0sgo8BdphDIKMkygbwX7ybnJNTKlXq/wzojyb4bovFxEp8WifcbaGimviIiIiIjUHwX+Io1Aoa3QZNxn9n0G+9vSt8EBx1Fts3mTSESFdkC03R9Rh9LQLzkGIZpST0RERETE6yjwF2mAHA4HYtJjnJn316WtM8H/0Uyp171ZJ0QFNUdUQQGi9yeg6/5NAHgTERERERFvpsBfpIFIyklyBvorklcgozCj2tsKCwxFv2adEO0IRnTWQQxIjkGEptQTEREREfFJCvxF6kl2UTZWJq/EsuTDwX5iVvXnuu8U2g7Rwa3NlHpRmlJPRERERERcKPAXqSPF9mJs3L/x8DR7ycuw5cAW2By26k2p16wzovxCEO2cUm9lrZRZREREREQaPwX+IrVoV8YuZ4v+6pTVyCvJ83gbxzRtieiQdogqhplSr6+m1BMREREREQ8o8BepQfvz9jun2OP/+/P3ezylXk+OzQ8IR3R+HqJSY9ExY71eIxERERERqTYF/iJHIa84D6tTVzsD/V2Hdnn0/MgmERhgptQLRHTmfvTbtxWhmlJPRERERERqkAJ/EQ/Y7DZsOrDJ2aq/8cBGlNhL3J5Sr2uzjogOanF4Sr0DiegavwV+2KzXQEREREREao0Cf5EqJGQmOAP9VSmrkF2c7dY5Cw0MRf+wTohCMKKy0xGVFINITaknIiIiIiJ1TIG/SBkZBRlYkbzCJOVjsJ+cm+z2lHpR/51SL/rgXvRM2Y4AxzadXxERERERqVcK/MXnFdoKsTZ17eHs+0nLsS19GxxwVD2lXlgnRPuHIkpT6omIiIiISAOmwF98jsPhMMG91aK/Lm2dCf4r06ZpK0SZKfUcmlJPREREREQaFQX+4hOSc5KdgT678WcUZrg5pV4uolNj0SFjXZ2WV0REREREpKYo8BevlF2UjZUpK52BfkJWQqVT6kWFdkS0PQBRmlJPRERERES8jAJ/8QrF9mJs3L/RBPrMwL/5wGbYHLbyp9QL64DoJi0RXVCIqAMJmlJPRERERES8mgJ/abRiD8WaQJ9d+FenrEZeSV6lU+pFZ6djgKbUExERERERH6PAXxqNA/kHnC36zL6flp92xDodQ9siOvgYRP13Sr3jNaWeiIiIiIj4OAX+0mDlFedhTeoaZ1K+XYd2lXq8iX8T9GnW2UypF52TheiUHWidvareyisiIiIiItIQKfCXBsNmt2HLwS2mRZ+B/ob9G8zYfUvr4JaIDm2H6BIgKiMFfZNi0MRWujJARERERERESlPgL/UqMSvRdNtnqz6z8DMbPwX4BZgp9aICIxCdl4fotFh0Sl+vV0tERERERMRDCvylTmUUZJjp9RjoM+BPyk0yyyOahB+eUq9pIKKyDmDAvq0ILYzXqyMiIiIiInKUFPhLrSq0FZpx+lZCvm3p28xyTql3apNWiLKHIfpAIrrGb4UftujVEBERERERqWEK/KVGORwOxKTHOLPvr0tbZ7rtc0q9MxGMB/07aEo9ERERERGROqTAX45aUk6SM9BnN/7QgCZmSr2zi0rweG4QempKPRERERERkXqjwF88xgR8TMTHYH9t6ho0cwBRfiE4PycLTycno3VOms6qiIiIiIhIA6HA38XHH3+Md955B8nJyTjhhBMwZswYnHnmmfB1nFJv4/6NJtCPPbAFgfmH0K/EgYszkvB0UgyCbEX1XUQRERERERGpgAL///rhhx/wyCOPmOD/9NNPx2effYYLLrgAW7duRZcuXeBrYg/FYmXychzcvw0lmXvQM/cQrkiNRceM3fVdNBEREREREfGAn4PZ2ASnnHIKBg4ciE8++cR5Nvr06YPLL78cb7zxRqVnKCsrC5GRkcjMzERERESjPJsH8g9ga9JK5KVuQsGB7eiQvhv9krYitCi3vosmIiIiIiJSt148BPj5Neiz7kkcqhZ/AEVFRVizZg2eeeaZUidnxIgRWLp0KbxRQXE+DiatRv6+VShIXo/Q1C04My0WflA9kIiIiIiIiDdR4M/W7gMHYLPZ0LZt21Inh/dTUlKOOGmFhYXm5lrTQr1794a/v3+lJ5y9CqZOnVpq2aWXXoq1a9dW+WI99thj5mbJzs42vRLc8fvvv2PQoEFwFBUgf+di/PHDN3j0o99g4nx2+nA4zH+H//mfsKBALL3jglLLXlqwAb9uq7rL/znd2uO9c08qtezcSbOQlltQ5XNfOGsArupzrPP+rvQsXPXTAjeOFJh54zlo2yzEeX/Cxli8t2xrlc/r3iIcU64dWmrZvX8ux7K9+6t87k39u+HJwSeUWhb12TS3yvvxhafg9M5tnPeX7EnDyL9WuPXcDfdcUur+O0u3YNKmuCqfd1qnY/DpRaeWWnblj/MRm5Fd5XMfP60vbhnQ3Xk/NScfI76d7VZ5f7lmCHq0/F9t5C8xiXhl4cYqn9cmrClm3XRu6XLMWo3ZcclVPveK3l3w0pCoUssGf/03cotLqnzuO+cMwojuHZz3N6Sm45bflsAdS24/H82aBDnvf7J6Oz5ds6PK5w1o0wITrzij1LKbf12MjWkZVT733kHH476Tejnv5xQV4/Rx090q74TLT0dU25bO+zNjk/Dk7DVVPu//27vz2Djqs4Hjz87sru04jiEkIQkxhyhHIYFSgWiqFqoWCgjaoETlaAQSoLwFiVJR9Y8iUaVppKZ/oPJPW7UVLVJVoNwIvUAljpAXmnA0NJwNCYESGhw7vvc+59Xzm53Zw2t7Ekxiz34/YpjZubL2Pp6d5ze/g2sE14hGXCO4RnCNqLkuc43gGsF9xKy9j7Du6Wl67Ouvvy5LlizxX//xj3+UX/ziF1P+m6eeeqq88MILdevWrl0rW7ZMneOsW7dO1q9fX7du2bJlUi6XJSgS/xqRhqoc2gqicZ3Sqv8bNmwYt147BZxKT8/4ADpw4IDs27dvymO9Aoba9xfkOK9Wg4rE22XOmRdJZMWwfDr81ymP6+rslI41t0s5mZRiMinlRFJGtv1XepPvT3ls34cHZOB/65O6/YMJ6StOnXAd2P6xDOwZrb7O5aQ3mZnyOLPvs++JHasmXP1DQ4GOnZMvjXu/fZ8MSm966mP7d34qA0OlunWB3+/WD2Sgs1rAdCCVDHxs4/vt7+8LdGzf3sFxx/b2jUhvJU4m07/jExnYW20CcqBQCP6zbn5fjmprq74eGQl0bDlbGP/Z7DsQ7GfdvV8GEvV/x72jKUkFuFD2v/ahDPx7oPp+M5ngn80z70jWtqvvYyDY+11UdMb/3Xw6JL3Zqb/E+t75rwzsrxZKJkul4J/N/+2WgY5qgVl/YizQsZ2WNf6z2b8/2GfDNSLYZ8M1YsrfEdcIrhHj/m64jwh0feE+gvuIuu9l7iOkTrJ5nqUPjOt2SyYD5WRaJX/c3+DAQKBjtSp/o6B5oIfEX0QWLFggtm2Pe7rf398/rhaAuuOOO+qevGtCrgm9lvxM9cR/4cKFTdcdd9xxU35Yje02tFAiyHEqHo/Xve7o6Ah07Ny5c2Xuyvonw0vfflOO+/jjyQ90RHouukhO3LDRLzAoJZOy+Jb/EWtoyBRaiFMWKXu1DRwRTcQqtQ/iXUdLORoVq1gwp4tGRI6NBgtXu6GwZo5lBTp2QZN9jrLtQMfOtaoJnifo+403vF99HfTYZu8jyLH6czX7+ZMBkmH9fTb+voO+X/0ca7VbwY5dYI/fpzvgZzOvyWezKBqVdICftT1S/7PGDuJnbSwzDPrZzG/ys+q6Q4lDfQ9B36/+bI0/e5BjG+PB+50HOVY/wyCfdTMaO7W4RgTDNWI8rhFcIxpxH8F9BNcIV6vfR0Sb5IFK88bGfClIXtUsr9Q8NMixzQoN9Dh94h/k4bOic7+azv20Krz26u8544wzZNWqVS3Rud9MUy6VJZcuSjGVllIyJeVUyhQclJJps+y+dufldNrsV9YpnZJSKiNOKilWclis0QGJZJIys7vlAAAAADCTnP7v95rW/p5J6NzvEOgT/Ouuu07OPfdcWblypWmrsXfvXrn55pun/xPClCzbko6uuIhOctRB/8ZKpbLk00VTeFBIZaWU1gKEtJm7BQS6nHGXMxl3OZ2RUkZf5ySSToiVGhY7MSzW2KBEEsN0fAgAAABgVqKqf8XVV18tg4ODpmMGrS6xfPlyefrpp+WEE6qdPmD2sCsFB6bwQOZoZenAx2qzg0KuZAoN3Kkg+XReSulspXAgK6VMRspmnpNyNmuWy1ldzonk0mJnE2JnEmKlR8VODLmFB2MDftMFAAAAADhcqOo/Dajqj9pCg2KhLLmUW2DgFRzoPJsqSDGTdwsKcjrPSUnn3pQviFXISrSUETufkmg2KVZmTOz0qNtsgaYLAAAAwGFxOlX9AUxE2wHF4raZ5h5d7b0+cE2DbEmyWlBQKTjI1hQgpFK6viD5TMEUEpTzRSkVCuIUSiKlosQiBYmWdcpKtFgpPMinxMokTOGBnR4xTRZsU/tgiKYLAAAAQIugqj8wgwoN4h1RM8kxB3dssVDyaxVooYHOzXJlnRYaaCGCu85dXyqUTPKvIx/Go47ErJJEI0WJOVp4kBO7lJVoPi12Pil2Lil2esxtuqA1D0zhwaBYherQcQAAAABmJhJ/IASiMVui3bZ0dh9cLYNSoWxqGDQWGLiv3cKChM6T9etKRXcoPDsWkXibXSk8EInZJbcAQdzCg2gpJ9GiFh6kJJpL+rUPTLMF7fuAURcAAACAzx2JP9DC7JhlCgsOpsDA9GOQL9cVEmjBgFsoUJBMpZAgm6wUJCTzki0XJV8sisR0INLKVGFZEYm1WRJvi0jMFB6UK7UPCm7tg1JW7GJGooW0RHMpsbLa78GY2KlhiSRHTO0Da3SQpgsAAADABEj8ARx8Pwb6lL/Nlq757Qc1xKKpVeAXDFQLDkxhgRYQmO15Sfg1DIoi0ll/IktE5lamY6ur3cIDy9Q+iEUdiVeaLkSdgsS8pgsFt/aBndWmC6M0XQAAAEBLIPEHcNiGWJwzL26moMpaWFDpp8AtHKgWGGQSebOcqRQieNtTY1pYUHuJ06lj/Mk7KlNNfwpe04V4TGsfOG7ThYhb+6Cx6YKdS4ntj7owIpHkkNha8yA9JpHP+LsCAAAAphOJP4AZy7It6eiKm+nogMdo/wPZhsIALSTwmyBUls3rynK55LjHFhzJFIqSqX8XIqJNIZo0h2jSdCFiidvvQTwi8Vil6YJdNv0exJy8abpgmi9ox4naaaI2XcgkTOGB6ftgbEAsHXXBcftRAAAAAD4rEn8AoWJHD67fAm8YRbcwIC/ZRKG63FBA4BUg6P4Tnq8sksuUJJdpVkoQa950obMyNTZdiFsSi9c3XWDUBQAAABwsEn8ALa12GMXuhU2aBEwwfGImUV+bwK9V4L+urstnapsfBFPIlc1U79CaLlRHXSi7hQd1TRcylaYLSdN0wdKOE5PaceKQ2FrzIDVK0wUAAIBZjsQfAA72whnTjg2Dd26owyZqDYLawgAz915X1qXH3HkxN3GNgoPxuTVdsLQAwWu6oAUImfqmC2bUhZHKqAs0XQAAADjSSPwB4DAMmzj36HYzBVHIleoKBtJe4UBNQUF6rFqI4JTdPgo+D4fUdGG6Rl3IjLlvAMCMFhHH9EtiSdnMI6LLjr/sbzMTAOBIIPEHgBnGHS6xQ+YtmLrpgSb9OvKBqS1QU0jgvq7UIqhZV8wfmUT6kJsuADhkVjQisbgt0ZglUZ2byV2OVeZmm6nVY5u5btdlvQ55+3tDuGptp6gW4FWWLTsilhWRiEU6DwAzHYk/AMxiesPdPjdmpnFP3yeoTeAXEox5BQSVub7W5VF3PlknhgAO7e/VTcIt0xGpJtZaI8isi1lia2Jd2aaJuV1J0M16P3l3j482rqscW31tmZFRAABQJP4A0EL0SZ12YhikI8Ni3i0k8AoD6goIGiYtUABmskjEHfVDE21Lk+7Ksh2N+Ou9uW6zapbd9RE/Aff3M8l6JYn3E/pqMq9P3L2E3OxHIg4AOEJI/AEAzb8g4rZpbhCkyYFXk6BaMJCTVGU55RUaaE2CsbyUirTbD42IuNW9bU1q3SrfduW1Jr1mvc51vSbTuq0y1yTYfe3t557Dqp1XknL/PJW5dy5zjJfMT/S6kpjrewAAoFWR+AMADltNAsep9kngTjm3QGC05nWlsECHSzxkEfOfeczrzr117obquup281KTw8p+OqKBe4qIeVps9tVt5n/uE2T9n2nj7C/X76fnqy43znWbe4xZ9vb111f29be567z1mtia89S0s66dm+2Vc5pE3K5f77fPNkm4rrPqt3v7eIm8v1yzjmQaAIBZgcQfAHDYaKLa3hkz0/wlnVN2XFg7XkHd89rKC5OEAwAAYFIk/gCAGck8rT7SbwIAACAE6O4VAAAAAIAQI/EHAAAAACDESPwBAAAAAAgxEn8AAAAAAEKMxB8AAAAAgBAj8QcAAAAAIMRI/AEAAAAACDESfwAAAAAAQozEHwAAAACAECPxBwAAAAAgxEj8AQAAAAAIMRJ/AAAAAABCjMQfAAAAAIAQI/EHAAAAACDESPwBAAAAAAgxEn8AAAAAAEIseqTfQBg4jmPmY2NjR/qtAAAAAABawFgl//Ty0cmQ+E+DRCJh5j09PdNxOgAAAAAAAuej3d3dk+4TcYIUD2BS5XJZPv30U+nq6pJIJMJvq6YESgtDPvnkE5k3bx6/F9QhPjAVYgTEBw4V1w8QI2iFa4jjOCbpX7p0qVjW5K34eeI/DfSXvGzZsuk4VSjpH8tM/oPBkUV8gBgB1xDwHYMjhfsQzPb4mOpJv4fO/QAAAAAACDESfwAAAAAAQozEH5+btrY2Wb9+vZkDxAe4hoDvGBwu3IOAGAHXkHp07gcAAAAAQIjxxB8AAAAAgBAj8QcAAAAAIMRI/AEAAAAACDESfwAAAAAAQozEHxPatGmTnHfeedLV1SWLFi2SK6+8Ut5///26fRzHkZ///OeydOlS6ejokG984xvy7rvv+tuHhobkhz/8oZx22mkyZ84cOf744+W2226T0dHRuvMMDw/LddddJ93d3WbS5ZGRET6dkMeH+sEPfiAnn3yy2b5w4UJZtWqV7Ny5s24f4qO1Y6R238suu0wikYg88cQTdduIkdaND12nMVE7XXPNNXX7EB+z03ReQ7Zt2ybf/OY3pbOzU4466iizXyaT8bcTI60ZH//5z3/GXT+86eGHH/b3Iz5a9/qxf/9+k5csXrzYXD++/OUvyyOPPFK3z6yJDweYwCWXXOLce++9zjvvvOPs2LHDufzyy53jjz/eSSaT/j6/+tWvnK6uLufRRx913n77befqq692lixZ4oyNjZntum716tXOk08+6XzwwQfO888/75xyyinOmjVr6v6tSy+91Fm+fLmzdetWM+nyFVdcwWcT8vhQf/jDH5wtW7Y4H330kbN9+3bnO9/5jtPT0+MUi0V/H+KjtWPE8+tf/9q57LLLHP3qevzxx+u2ESOtGx8XXnihs27dOqe3t9efRkZG6v4t4qO1Y0TvK+bNm+ds2rTJnGvXrl3Oww8/7GSzWX8fYmT2mY740HuN2muHThs2bHA6OzudRCLhn4f4aN3rx0UXXeScd955zquvvurs2bPH2bhxo2NZlvPGG2/Muvgg8Udg/f395oZbkzRVLpedxYsXmz8aj36Jdnd3O7///e8nPM9DDz3kxONxp1AomNfvvfeeOe8rr7zi77Nt2zazbufOnXxCLRYfb775pjmPFhQp4iM8PkuM6Jf2smXLzE1ZY+JPjLR2fGji/6Mf/WjC8xIf4XGoMXL++ec7d95554TnJUbCYbruQ770pS85N954o/+a+Gjt+Ojs7HT+8pe/1J1r/vz5zj333DPr4oOq/gjMq54/f/58M//oo49M9Zdvf/vb/j5tbW1y4YUXytatWyc9z7x58yQajfrV77RazPnnn+/v85WvfMWsm+w8CF98pFIpuffee+Wkk06Snp4es474CI9DjZF0Oi3XXnut/OY3vzFV7RoRI+HwWa4h9913nyxYsEDOPPNM+clPfiKJRMLfRny0doz09/fLq6++aqr6fvWrX5Vjjz3WbH/55Zf9Y4iRcJiO+5Dt27fLjh075KabbvLXER+tHR9f+9rX5MEHHzTNl8vlsvztb3+TXC5nmgXMtvgg8UcgWjvkxz/+sQn+5cuXm3X6x6L0S7SWvva2NRocHJSNGzeadt0e3Ve/kBvpuonOg3DFx+9+9zuZO3eumf7+97/Ls88+K/F43D8P8dHaMXL77bebG3bt/6EZYqS142Pt2rXywAMPyIsvvig/+9nP5NFHH5XVq1f724mP1o6RDz/80My1He+6devMd4y20f3Wt74lu3fv9s/D98zsNl33qX/605/ki1/8ovnO8RAfrR0fDz74oBSLRTnmmGNMwYDmMI8//rjpn2q2XT/cR67AFG699VZ566236krIPdoBSuMfV+M6NTY2JpdffrmcccYZsn79+knPMdl5EL740Bv3iy++WHp7e+Wuu+6Sq666Sv7xj39Ie3t703NMdB6EL0aefPJJeeGFF+Rf//rXpOcnRlr3GqLJnEdv6E455RQ599xz5Y033jAJXrNzNDsPwhkj+oRO6c36DTfcYJbPOeccef755+XPf/6z6QCs2Tkaz4Pw36dqZ4/333+/KUCc6hyTnQfhio8777zTdN733HPPmZpl2rnw9773PXnppZdkxYoVTc/R7DwzAU/8MSXtlV9vvjdv3izLli3z13tVbhtLs7RaXWPpmVa7vPTSS80TXS0li8Videfp6+sb9+8eOHBg3HkQzvjQ6lB6s37BBReYnlK1V3+NE+88xEfrxogm/Xv27DG9cGvzIK+J0Jo1a/xqdsTI7DYd15Bamuzrd4z3NJf4aO0YWbJkiZnrQ4da+lR37969/nn4npm9pusaovcf2rTs+uuvr1tPfLRufOzZs8c0M9RCQq0ldPbZZ5uHl1q4/Nvf/nbWXT9I/DEhLanSErLHHnvM3Hxru+ta+lqDXatle/L5vGzZsqWuipQ+6df2M1p1W//wvKe4npUrV5p2N6+99pq/Ttvj6bra8yCc8THRubX9lCI+WjtGfvrTn5pSem1z6U3q7rvvNv1BKGJkdvq8riE6FFOhUPATPuKjtWPkxBNPNEN1NQ7jtWvXLjnhhBPMMjEyO033NUSr+X/3u981QwvXIj5aNz7S6bSZW1Z9ymzbtl+baFbFx5HuXRAz1y233GJ6tnzxxRfrhjlJp9P+PtoTpu7z2GOPmWEwrr322rphMHSuvemuWLHC9NJee57G4drOOuss0wumTrr/TBwGA9MbHzosyi9/+Uvnn//8p/Pxxx+bIVBWrVplekvt6+vzz0N8tG6MNDPRcH5cQ1ovPvR7RYfeev31182QoE899ZRz+umnO+eccw7fMSEwXdeQu+++2wznp0P47d692/Tw397e7o8eo7iGtPZ3jMZFJBJxnnnmmab/FvHRmvGRz+edL3zhC87Xv/51M5yfXjPuuusuEyv6fTPb4oPEHxMHh0jTScfE9OhQGOvXrzfDYbS1tTkXXHCB+cPxbN68ecLz6E2aZ3Bw0Fm7dq0ZS1MnXR4eHubTCXl87Nu3z4zLvmjRIicWi5nh2r7//e+PG/6E+GjdGAma+BMjrRkfe/fuNeu0sFCHiT355JOd2267zcRDLeJjdprOa8imTZvMd8ycOXOclStXOi+99FLddmKktePjjjvuMPFRKpWa/lvER+vGx65du5zVq1ebe1W9fmiC3zi832yJj4j+70jXOgAAAAAAAJ8P2vgDAAAAABBiJP4AAAAAAIQYiT8AAAAAACFG4g8AAAAAQIiR+AMAAAAAEGIk/gAAAAAAhBiJPwAAAAAAIUbiDwAAAABAiJH4AwAAAAAQYiT+AAAAAACEGIk/AAAAAAAhRuIPAAAAAICE1/8DM8C8eBSOP2UAAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "risk_traj.plot_time_waterfall()" + ] + }, + { + "cell_type": "markdown", + "id": "fed22016-ab8f-4761-892a-c893d18357b7", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "### Non-default return periods" + ] + }, + { + "cell_type": "markdown", + "id": "fcaed625-82a8-4cc4-82de-e36b67601dcb", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "You can easily change the default return periods computed, either at initialisation time, or via the property `return_periods`.\n", + "Note that estimates of impacts for specific return periods are highly dependant on the data you provided.\n", + "\n", + "**We cannot check if the event set you provide is fit for computing impacts for a specific return period.** " + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "0ade93f9-c43a-4e8a-8225-9343bbbb3615", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
dategroupmeasuremetricunitrisk
02018Allno_measurerp_10USD1.225210e+07
12019Allno_measurerp_10USD1.277500e+07
22020Allno_measurerp_10USD1.330821e+07
32021Allno_measurerp_10USD1.385172e+07
42022Allno_measurerp_10USD1.440553e+07
.....................
872036Allno_measurerp_30USD8.662373e+08
882037Allno_measurerp_30USD8.894772e+08
892038Allno_measurerp_30USD9.129904e+08
902039Allno_measurerp_30USD9.367770e+08
912040Allno_measurerp_30USD9.608368e+08
\n", + "

92 rows × 6 columns

\n", + "
" + ], + "text/plain": [ + " date group measure metric unit risk\n", + "0 2018 All no_measure rp_10 USD 1.225210e+07\n", + "1 2019 All no_measure rp_10 USD 1.277500e+07\n", + "2 2020 All no_measure rp_10 USD 1.330821e+07\n", + "3 2021 All no_measure rp_10 USD 1.385172e+07\n", + "4 2022 All no_measure rp_10 USD 1.440553e+07\n", + ".. ... ... ... ... ... ...\n", + "87 2036 All no_measure rp_30 USD 8.662373e+08\n", + "88 2037 All no_measure rp_30 USD 8.894772e+08\n", + "89 2038 All no_measure rp_30 USD 9.129904e+08\n", + "90 2039 All no_measure rp_30 USD 9.367770e+08\n", + "91 2040 All no_measure rp_30 USD 9.608368e+08\n", + "\n", + "[92 rows x 6 columns]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
dategroupmeasuremetricunitrisk
02018Allno_measurerp_150USD8.436864e+09
12019Allno_measurerp_150USD8.697801e+09
22020Allno_measurerp_150USD8.960766e+09
32021Allno_measurerp_150USD9.225760e+09
42022Allno_measurerp_150USD9.492784e+09
.....................
642036Allno_measurerp_500USD2.643662e+10
652037Allno_measurerp_500USD2.698681e+10
662038Allno_measurerp_500USD2.753977e+10
672039Allno_measurerp_500USD2.809551e+10
682040Allno_measurerp_500USD2.865402e+10
\n", + "

69 rows × 6 columns

\n", + "
" + ], + "text/plain": [ + " date group measure metric unit risk\n", + "0 2018 All no_measure rp_150 USD 8.436864e+09\n", + "1 2019 All no_measure rp_150 USD 8.697801e+09\n", + "2 2020 All no_measure rp_150 USD 8.960766e+09\n", + "3 2021 All no_measure rp_150 USD 9.225760e+09\n", + "4 2022 All no_measure rp_150 USD 9.492784e+09\n", + ".. ... ... ... ... ... ...\n", + "64 2036 All no_measure rp_500 USD 2.643662e+10\n", + "65 2037 All no_measure rp_500 USD 2.698681e+10\n", + "66 2038 All no_measure rp_500 USD 2.753977e+10\n", + "67 2039 All no_measure rp_500 USD 2.809551e+10\n", + "68 2040 All no_measure rp_500 USD 2.865402e+10\n", + "\n", + "[69 rows x 6 columns]" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "snapcol = [snap1, snap2]\n", + "risk_traj = InterpolatedRiskTrajectory(snapcol, return_periods=[10, 15, 20, 30])\n", + "display(risk_traj.return_periods_metrics())\n", + "\n", + "risk_traj.return_periods = [150, 250, 500]\n", + "display(risk_traj.return_periods_metrics())" + ] + }, + { + "cell_type": "markdown", + "id": "39059ec5-9125-4cfc-b8c6-e6327d8b98cc", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "### Non-yearly date index" + ] + }, + { + "cell_type": "markdown", + "id": "4f8f83d6-a45d-4d3b-b25d-d3294e6e1955", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "You can use any valid pandas [frequency string for periods](https://pandas.pydata.org/docs/user_guide/timeseries.html#period-aliases) for the time resolution,\n", + "for instance \"5Y\" for every five years. This reduces the resolution of the interpolation, which can reduce the required computations at the cost of \"precision\".\n", + "Conversely you can also increase the time resolution to a monthly base for instance.\n", + "\n", + "Same as for the return periods, you can change that at initialisation or afterward via the property.\n", + "\n", + "Keep in mind that risk metrics are still computed the same way, so if you initialy had hazards with annual frequency values, you would still have \"Average Annual Impacts\" values for every months and not average monthly ones!\n", + "\n", + "Also note that `InterpolatedRiskTrajectory` uses `PeriodIndex` for the time dimension. These indexes are defined with the dates of the first and last snapshot, and the given time resolution.\n", + "\n", + "This means that an `InterpolatedRiskTrajectory` for a 2020 `Snapshot` and 2040 `Snapshot` with a yearly time resolution will include all years from 2020 to 2040 included (11 years in total).\n", + "\n", + "However, a trajectory with the same snapshots with a monthly resolution will have January 2040 as a last period if you only provided year 2040 for the last date. If you want to include the whole 2040 year, you need to explicitly give the date \"2040-12-31\" to the last snapshot." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "128fac77-e077-4241-a003-a60c4afcad74", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
dategroupmeasuremetricunitrisk
02018Allno_measureaaiUSD1.569309e+08
12023Allno_measureaaiUSD1.845465e+08
22028Allno_measureaaiUSD2.134182e+08
32033Allno_measureaaiUSD2.435459e+08
42038Allno_measureaaiUSD2.749295e+08
\n", + "
" + ], + "text/plain": [ + " date group measure metric unit risk\n", + "0 2018 All no_measure aai USD 1.569309e+08\n", + "1 2023 All no_measure aai USD 1.845465e+08\n", + "2 2028 All no_measure aai USD 2.134182e+08\n", + "3 2033 All no_measure aai USD 2.435459e+08\n", + "4 2038 All no_measure aai USD 2.749295e+08" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "snapcol = [snap1, snap2]\n", + "risk_traj = InterpolatedRiskTrajectory(snapcol, time_resolution=\"5Y\")\n", + "risk_traj.per_date_risk_metrics().head()" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "c1e66906-63e3-4a29-8a0b-0e706e6a2a09", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
dategroupmeasuremetricunitrisk
02018-01Allno_measureaaiUSD1.569309e+08
12018-02Allno_measureaaiUSD1.573399e+08
22018-03Allno_measureaaiUSD1.577492e+08
32018-04Allno_measureaaiUSD1.581589e+08
42018-05Allno_measureaaiUSD1.585688e+08
.....................
15852039-111no_measureaaiUSD2.633593e+08
15862039-120no_measureaaiUSD1.061942e+07
15872039-121no_measureaaiUSD2.638252e+08
15882040-010no_measureaaiUSD1.063804e+07
15892040-011no_measureaaiUSD2.642915e+08
\n", + "

1590 rows × 6 columns

\n", + "
" + ], + "text/plain": [ + " date group measure metric unit risk\n", + "0 2018-01 All no_measure aai USD 1.569309e+08\n", + "1 2018-02 All no_measure aai USD 1.573399e+08\n", + "2 2018-03 All no_measure aai USD 1.577492e+08\n", + "3 2018-04 All no_measure aai USD 1.581589e+08\n", + "4 2018-05 All no_measure aai USD 1.585688e+08\n", + "... ... ... ... ... ... ...\n", + "1585 2039-11 1 no_measure aai USD 2.633593e+08\n", + "1586 2039-12 0 no_measure aai USD 1.061942e+07\n", + "1587 2039-12 1 no_measure aai USD 2.638252e+08\n", + "1588 2040-01 0 no_measure aai USD 1.063804e+07\n", + "1589 2040-01 1 no_measure aai USD 2.642915e+08\n", + "\n", + "[1590 rows x 6 columns]" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "## snapcol = [snap, snap2]\n", + "\n", + "## Here we use \"1MS\" to get a monthly basis\n", + "risk_traj.time_resolution = \"1M\"\n", + "\n", + "## We would have to divide results by 12 to get \"average monthly impacts\"\n", + "risk_traj.per_date_risk_metrics()" + ] + }, + { + "cell_type": "markdown", + "id": "f5d6b725-41ee-495b-bc72-5806db4cfdba", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "### Non-linear interpolation" + ] + }, + { + "cell_type": "markdown", + "id": "a8065729-5d0b-4250-8324-2ce82cb0d644", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "The module allows you to define your own interpolation strategy. Thus you can decide how to interpolate along each dimension of risk (Exposure, Hazard and Vulnerability).\n", + "This is done via `InterpolationStrategy` objects, which simply require three functions stating how to interpolate along each dimensions.\n", + "\n", + "For convenience the module provides an `AllLinearStrategy` (the risk is linearly interpolated along all dimensions) and a `ExponentialExposureStrategy` (uses exponential interpolation along exposure, and linear for the two other dimensions).\n", + "\n", + "This can prove helpfull if you are interpolating between two distant dates with an exponential growth factor for the exposure value. On the example below, we show the difference in risk estimates using an the two different interpolation strategies for the exposure dimension:" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "c97e768e-bd4c-47d7-bace-96645f8b3bc4", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2026-02-26 10:49:36,137 - climada.hazard.io - INFO - Reading /home/sjuhel/climada/data/hazard/tropical_cyclone/tropical_cyclone_10synth_tracks_150arcsec_rcp60_HTI_2080/v2/tropical_cyclone_10synth_tracks_150arcsec_rcp60_HTI_2080.hdf5\n" + ] + }, + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'Comparison of average annual impact estimate for different interpolation approaches')" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from climada.trajectories import StaticRiskTrajectory, InterpolatedRiskTrajectory\n", + "from climada.trajectories import ExponentialExposureStrategy\n", + "import seaborn as sns\n", + "\n", + "future_year = 2100\n", + "exp_future = copy.deepcopy(exp_present)\n", + "exp_future.ref_year = future_year\n", + "n_years = exp_future.ref_year - exp_present.ref_year + 1\n", + "growth_rate = 1.04\n", + "growth = growth_rate**n_years\n", + "exp_future.gdf[\"value\"] = exp_future.gdf[\"value\"] * growth\n", + "\n", + "haz_future = client.get_hazard(\n", + " \"tropical_cyclone\",\n", + " properties={\n", + " \"country_name\": \"Haiti\",\n", + " \"climate_scenario\": \"rcp60\",\n", + " \"ref_year\": \"2080\",\n", + " \"nb_synth_tracks\": \"10\",\n", + " },\n", + ")\n", + "impf_set = ImpactFuncSet(\n", + " [\n", + " ImpfTropCyclone.from_emanuel_usa(v_half=60.0),\n", + " ]\n", + ")\n", + "exp_future.gdf.rename(columns={\"impf_\": \"impf_TC\"}, inplace=True)\n", + "exp_future.gdf[\"impf_TC\"] = 1\n", + "\n", + "snap2 = Snapshot(exposure=exp_future, hazard=haz_future, impfset=impf_set, date=\"2100\")\n", + "snapcol = [snap1, snap2]\n", + "\n", + "exp_interp = ExponentialExposureStrategy()\n", + "risk_traj = InterpolatedRiskTrajectory(snapcol)\n", + "risk_traj_exp = InterpolatedRiskTrajectory(snapcol, interpolation_strategy=exp_interp)\n", + "ax = risk_traj.aai_metrics().plot(\n", + " x=\"date\", y=\"risk\", label=\"Linear interpolation for exposure\"\n", + ")\n", + "risk_traj_exp.aai_metrics().plot(\n", + " x=\"date\", y=\"risk\", label=\"Exponential interpolation for exposure\", ax=ax\n", + ")\n", + "\n", + "ax.set_title(\n", + " \"Comparison of average annual impact estimate for different interpolation approaches\"\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "4a5991b8-659e-4b0a-81cc-bc0d085ff1e7", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "### Spatial mapping" + ] + }, + { + "cell_type": "markdown", + "id": "d47bcc7e-defe-4058-b7a3-4dafd4374f35", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "You can access a DataFrame with the estimated annual impacts at each coordinates through \"eai_metrics\" which can easily be merged to the exposure GeoDataFrame:" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "431d26f1-c19f-4654-814b-20e8a243848e", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
dategroupmeasuremetricunitcoord_idrisk
020180no_measureeaiUSD02993.678321
120190no_measureeaiUSD03994.537003
220200no_measureeaiUSD05038.235198
320210no_measureeaiUSD06125.062545
420220no_measureeaiUSD07255.308683
........................
11030220960no_measureeaiUSD132899978.314476
11030320970no_measureeaiUSD1328102320.813007
11030420980no_measureeaiUSD1328104694.867359
11030520990no_measureeaiUSD1328107100.640151
11030621000no_measureeaiUSD1328109538.294005
\n", + "

110307 rows × 7 columns

\n", + "
" + ], + "text/plain": [ + " date group measure metric unit coord_id risk\n", + "0 2018 0 no_measure eai USD 0 2993.678321\n", + "1 2019 0 no_measure eai USD 0 3994.537003\n", + "2 2020 0 no_measure eai USD 0 5038.235198\n", + "3 2021 0 no_measure eai USD 0 6125.062545\n", + "4 2022 0 no_measure eai USD 0 7255.308683\n", + "... ... ... ... ... ... ... ...\n", + "110302 2096 0 no_measure eai USD 1328 99978.314476\n", + "110303 2097 0 no_measure eai USD 1328 102320.813007\n", + "110304 2098 0 no_measure eai USD 1328 104694.867359\n", + "110305 2099 0 no_measure eai USD 1328 107100.640151\n", + "110306 2100 0 no_measure eai USD 1328 109538.294005\n", + "\n", + "[110307 rows x 7 columns]" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = risk_traj.eai_metrics()\n", + "df" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "61abb90f-42f8-446c-aa27-8a5b5eaa3729", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "gdf = snap1.exposure.gdf\n", + "gdf[\"coord_id\"] = gdf.index\n", + "gdf = gdf.merge(df, on=\"coord_id\")\n", + "\n", + "fig, axs = plt.subplots(1, 3, figsize=(24, 5))\n", + "\n", + "gdf.loc[gdf[\"date\"] == \"2018-01-01\"].plot(\n", + " column=\"risk\",\n", + " legend=True,\n", + " vmin=gdf[\"risk\"].min(),\n", + " vmax=gdf[\"risk\"].max(),\n", + " ax=axs[0],\n", + ")\n", + "gdf.loc[gdf[\"date\"] == \"2050-01-01\"].plot(\n", + " column=\"risk\",\n", + " legend=True,\n", + " vmin=gdf[\"risk\"].min(),\n", + " vmax=gdf[\"risk\"].max(),\n", + " ax=axs[1],\n", + ")\n", + "gdf.loc[gdf[\"date\"] == \"2100-01-01\"].plot(\n", + " column=\"risk\",\n", + " legend=True,\n", + " vmin=gdf[\"risk\"].min(),\n", + " vmax=gdf[\"risk\"].max(),\n", + " ax=axs[2],\n", + ")\n", + "\n", + "axs[0].set_title(\"Average Annual Risk in 2018\")\n", + "axs[1].set_title(\"Average Annual Risk in 2050\")\n", + "axs[2].set_title(\"Average Annual Risk in 2100\")\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "98159b83-677e-4c23-a926-d03da8c80f3b", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "### Custom Impact Computation strategy" + ] + }, + { + "cell_type": "markdown", + "id": "825b9b95-3343-4250-8e1c-e89120359482", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "By default, trajectory objects use `ImpactCalc().impact()` to compute the `Impact` object and the resulting metric, but you can customize this behaviour via the `impact_computation_strategy` argument.\n", + "\n", + "The value has to be a class derived from `ImpactComputationStrategy`, and should at the very least implement a `compute_impacts()` method, taking `Exposures`, `Hazard` and `ImpactFuncSet` arguments and returning an `Impact` object.\n", + "\n", + "For instance, if you don't want the matching of the exposure and hazard centroids to be done internally you can do the following:" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "f3b8d931-e4e5-40bf-b702-31183c6c7ec3", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "from climada.trajectories.impact_calc_strat import ImpactComputationStrategy\n", + "\n", + "\n", + "class ImpactCalcNoAssign(ImpactComputationStrategy):\n", + " def compute_impacts(\n", + " self,\n", + " exp,\n", + " haz,\n", + " vul,\n", + " ):\n", + " return ImpactCalc(exposures=exp, impfset=vul, hazard=haz).impact(\n", + " assign_centroids=False\n", + " )" + ] + }, + { + "cell_type": "markdown", + "id": "998fa84d-12e7-4e18-aa96-41ca4bac3ed7", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "Note that you now have to assign the centroids before running the computations or else they will fail:" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "8d8d3b88-2c17-471e-acc3-afd8391a469d", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2026-02-26 10:49:39,233 - climada.entity.exposures.base - INFO - Reading /home/sjuhel/climada/data/exposures/litpop/LitPop_150arcsec_HTI/v3/LitPop_150arcsec_HTI.hdf5\n", + "2026-02-26 10:49:39,258 - climada.entity.exposures.base - INFO - Matching 1329 exposures with 1332 centroids.\n", + "2026-02-26 10:49:39,261 - climada.util.coordinates - INFO - No exact centroid match found. Reprojecting coordinates to nearest neighbor closer than the threshold = 0.08333333333331439 degree\n", + "2026-02-26 10:49:39,264 - climada.entity.exposures.base - INFO - Matching 1329 exposures with 1332 centroids.\n", + "2026-02-26 10:49:39,267 - climada.util.coordinates - INFO - No exact centroid match found. Reprojecting coordinates to nearest neighbor closer than the threshold = 0.08333333333331439 degree\n" + ] + } + ], + "source": [ + "exp_present = client.get_litpop(country=\"Haiti\")\n", + "exp_present.gdf.rename(columns={\"impf_\": \"impf_TC\"}, inplace=True)\n", + "exp_present.gdf[\"impf_TC\"] = 1\n", + "\n", + "\n", + "exp_future = copy.deepcopy(exp_present)\n", + "exp_future.gdf[\"value\"] = exp_future.gdf[\"value\"] * growth\n", + "\n", + "exp_present.assign_centroids(haz_present)\n", + "exp_future.assign_centroids(haz_future)\n", + "\n", + "snap1 = Snapshot(\n", + " exposure=exp_present, hazard=haz_present, impfset=impf_set, date=\"2018\"\n", + ")\n", + "snap2 = Snapshot(exposure=exp_future, hazard=haz_future, impfset=impf_set, date=\"2040\")\n", + "\n", + "impact_calc_no_assign = ImpactCalcNoAssign()\n", + "\n", + "static_risk_traj = StaticRiskTrajectory(\n", + " [snap1, snap2], impact_computation_strategy=impact_calc_no_assign\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "a94d99b5-2c7b-418e-88e9-a9dff39ab21e", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2026-02-26 10:49:39,321 - climada.trajectories.calc_risk_metrics - WARNING - No group id defined in the Exposures object. Per group aai will be empty.\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
dategroupmeasuremetricunitrisk
02018-01-01Allno_measureaaiUSD4.027184e+08
12040-01-01Allno_measureaaiUSD1.330047e+10
22018-01-01Allno_measurerp_20USD4.096584e+08
32040-01-01Allno_measurerp_20USD2.003911e+10
42018-01-01Allno_measurerp_50USD7.738183e+09
52040-01-01Allno_measurerp_50USD2.719459e+11
62018-01-01Allno_measurerp_100USD1.351303e+10
72040-01-01Allno_measurerp_100USD4.438845e+11
\n", + "
" + ], + "text/plain": [ + " date group measure metric unit risk\n", + "0 2018-01-01 All no_measure aai USD 4.027184e+08\n", + "1 2040-01-01 All no_measure aai USD 1.330047e+10\n", + "2 2018-01-01 All no_measure rp_20 USD 4.096584e+08\n", + "3 2040-01-01 All no_measure rp_20 USD 2.003911e+10\n", + "4 2018-01-01 All no_measure rp_50 USD 7.738183e+09\n", + "5 2040-01-01 All no_measure rp_50 USD 2.719459e+11\n", + "6 2018-01-01 All no_measure rp_100 USD 1.351303e+10\n", + "7 2040-01-01 All no_measure rp_100 USD 4.438845e+11" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "static_risk_traj.per_date_risk_metrics()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python [conda env:climada_env_dev]", + "language": "python", + "name": "conda-env-climada_env_dev-py" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.14" + }, + "toc": { + "base_numbering": 1 + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/doc/user-guide/climada_util_earth_engine.ipynb b/doc/user-guide/climada_util_earth_engine.ipynb deleted file mode 100644 index bf773ef7d4..0000000000 --- a/doc/user-guide/climada_util_earth_engine.ipynb +++ /dev/null @@ -1,566 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Google Earth Engine (GEE) and Image Analysis\n", - "\n", - "This tutorial explains how to use the module ***climada.util.earth_engine***. It queries data from the Google Earth Engine Python API (https://earthengine.google.com/). A few basic methods of image processing will also be presented using algorythms from Scikit-image (https://scikit-image.org/). A lot of complementary information can be found on this page https://developers.google.com/earth-engine/ (concerns mostly the GEE Java API, but concept and methods are well detailed). GEE is a multi-petabyte catalog of satellite imagery and geospatial datasets. The data are also available on the website of providers, GEE is just more user-friendly as all datasets are available through the same platform. \n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Connect to Google Earth Engine API\n", - "To access the data, you have to create an account on https://signup.earthengine.google.com/#!/, this step might take some time. Then, install and connect your Python to the API using the terminal. Be sure that climada_env is activated." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In Terminal or Anaconda prompt\n", - "\n", - " $ source activate climada_env \n", - "\n", - "$ conda install -c conda-forge earthengine-api \n", - "\n", - "Then, when the installation is finished, type\n", - "\n", - "$ earthengine authenticate\n", - "\n", - "This will open a web page where you have to enter your account information and a code is provided. Paste it in the terminal. \n", - "\n", - "Then, check in Python if it has worked with the lines below. Import also webbrowser for further steps." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'type': 'Image', 'bands': [{'id': 'elevation', 'data_type': {'type': 'PixelType', 'precision': 'int', 'min': -32768, 'max': 32767}, 'dimensions': [432000, 144000], 'crs': 'EPSG:4326', 'crs_transform': [0.000833333333333, 0, -180, 0, -0.000833333333333, 60]}], 'version': 1494271934303000.0, 'id': 'srtm90_v4', 'properties': {'system:time_start': 950227200000, 'system:time_end': 951177600000, 'system:asset_size': 18827626666}}\n" - ] - } - ], - "source": [ - "import webbrowser\n", - "\n", - "import ee\n", - "\n", - "ee.Initialize()\n", - "image = ee.Image(\"srtm90_v4\")\n", - "print(image.getInfo())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Obtain images\n", - "The module ***climada.util.earth_engine*** enables to select images from some collections of GEE and download them as Geotiff data.\n", - "\n", - "In GEE, you can either access directly one **image** or a **collection**. All products available are detailed on this page https://developers.google.com/earth-engine/datasets/. " - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "# Access a specific image\n", - "image = ee.Image(\"LANDSAT/LC08/C01/T1_TOA/LC08_044034_20140318\")\n", - "# Landsat 8 image, with Top of Atmosphere processing, on 2014/03/18\n", - "\n", - "# Access a collection\n", - "collection = \"LANDSAT/LE07/C01/T1\" # Landsat 7 raw images collection" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If you have a collection, specification of the time range and area of interest. Then, use methods of the series **obtain_image_type(collection,time_range,area)** depending the type of product needed.\n", - "### Time range\n", - "It depends on the image acquisition period of the targeted satellite and type of images desired (without clouds, from a specific period...) \n", - "\n", - "### Area\n", - "GEE needs a special format for defining an area of interest. It has to be a GeoJSON Polygon and the coordinates should be first defined in a list and then converted using ee.Geometry. It is possible to use data obtained via Exposure layer. Some examples are given below." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "\n", - "\n" - ] - } - ], - "source": [ - "# Landsat_composite in Dresden area\n", - "area_dresden = list(\n", - " [(13.6, 50.96), (13.9, 50.96), (13.9, 51.12), (13.6, 51.12), (13.6, 50.96)]\n", - ")\n", - "area_dresden = ee.Geometry.Polygon(area_dresden)\n", - "time_range_dresden = [\"2002-07-28\", \"2002-08-05\"]\n", - "\n", - "collection_dresden = \"LANDSAT/LE07/C01/T1\"\n", - "print(type(area_dresden))\n", - "\n", - "# Population density in Switzerland\n", - "list_swiss = list(\n", - " [(6.72, 47.88), (6.72, 46.55), (9.72, 46.55), (9.72, 47.88), (6.72, 47.88)]\n", - ")\n", - "area_swiss = ee.Geometry.Polygon(list_swiss)\n", - "time_range_swiss = [\"2002-01-01\", \"2005-12-30\"]\n", - "\n", - "collection_swiss = ee.ImageCollection(\"CIESIN/GPWv4/population-density\")\n", - "print(type(collection_swiss))\n", - "\n", - "# Sentinel 2 cloud-free image in Zürich\n", - "collection_zurich = \"COPERNICUS/S2\"\n", - "list_zurich = list(\n", - " [(8.53, 47.355), (8.55, 47.355), (8.55, 47.376), (8.53, 47.376), (8.53, 47.355)]\n", - ")\n", - "area_zurich = ee.Geometry.Polygon(list_swiss)\n", - "time_range_zurich = [\"2018-05-01\", \"2018-07-30\"]\n", - "\n", - "\n", - "# Landcover in Europe with CORINE dataset\n", - "dataset_landcover = ee.Image(\"COPERNICUS/CORINE/V18_5_1/100m/2012\")\n", - "landCover_layer = dataset_landcover.select(\"landcover\")\n", - "print(type(landCover_layer))" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "# Methods from climada.util.earth_engine module\n", - "def obtain_image_landsat_composite(collection, time_range, area):\n", - " \"\"\"Selection of Landsat cloud-free composites in the Earth Engine library\n", - " See also: https://developers.google.com/earth-engine/landsat\n", - "\n", - " Parameters:\n", - " collection (): name of the collection\n", - " time_range (['YYYY-MT-DY','YYYY-MT-DY']): must be inside the available data\n", - " area (ee.geometry.Geometry): area of interest\n", - "\n", - " Returns:\n", - " image_composite (ee.image.Image)\n", - " \"\"\"\n", - " collection = ee.ImageCollection(collection)\n", - "\n", - " ## Filter by time range and location\n", - " collection_time = collection.filterDate(time_range[0], time_range[1])\n", - " image_area = collection_time.filterBounds(area)\n", - " image_composite = ee.Algorithms.Landsat.simpleComposite(image_area, 75, 3)\n", - " return image_composite\n", - "\n", - "\n", - "def obtain_image_median(collection, time_range, area):\n", - " \"\"\"Selection of median from a collection of images in the Earth Engine library\n", - " See also: https://developers.google.com/earth-engine/reducers_image_collection\n", - "\n", - " Parameters:\n", - " collection (): name of the collection\n", - " time_range (['YYYY-MT-DY','YYYY-MT-DY']): must be inside the available data\n", - " area (ee.geometry.Geometry): area of interest\n", - "\n", - " Returns:\n", - " image_median (ee.image.Image)\n", - " \"\"\"\n", - " collection = ee.ImageCollection(collection)\n", - "\n", - " ## Filter by time range and location\n", - " collection_time = collection.filterDate(time_range[0], time_range[1])\n", - " image_area = collection_time.filterBounds(area)\n", - " image_median = image_area.median()\n", - " return image_median\n", - "\n", - "\n", - "def obtain_image_sentinel(collection, time_range, area):\n", - " \"\"\"Selection of median, cloud-free image from a collection of images in the Sentinel 2 dataset\n", - " See also: https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S2\n", - "\n", - " Parameters:\n", - " collection (): name of the collection\n", - " time_range (['YYYY-MT-DY','YYYY-MT-DY']): must be inside the available data\n", - " area (ee.geometry.Geometry): area of interest\n", - "\n", - " Returns:\n", - " sentinel_median (ee.image.Image)\n", - " \"\"\"\n", - "\n", - " # First, method to remove cloud from the image\n", - " def maskclouds(image):\n", - " band_qa = image.select(\"QA60\")\n", - " cloud_mask = ee.Number(2).pow(10).int()\n", - " cirrus_mask = ee.Number(2).pow(11).int()\n", - " mask = band_qa.bitwiseAnd(cloud_mask).eq(0) and (\n", - " band_qa.bitwiseAnd(cirrus_mask).eq(0)\n", - " )\n", - " return image.updateMask(mask).divide(10000)\n", - "\n", - " sentinel_filtered = (\n", - " ee.ImageCollection(collection)\n", - " .filterBounds(area)\n", - " .filterDate(time_range[0], time_range[1])\n", - " .filter(ee.Filter.lt(\"CLOUDY_PIXEL_PERCENTAGE\", 20))\n", - " .map(maskclouds)\n", - " )\n", - "\n", - " sentinel_median = sentinel_filtered.median()\n", - " return sentinel_median" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'type': 'Image', 'bands': [{'id': 'B1', 'data_type': {'type': 'PixelType', 'precision': 'int', 'min': 0, 'max': 255}, 'crs': 'EPSG:4326', 'crs_transform': [1, 0, 0, 0, 1, 0]}, {'id': 'B2', 'data_type': {'type': 'PixelType', 'precision': 'int', 'min': 0, 'max': 255}, 'crs': 'EPSG:4326', 'crs_transform': [1, 0, 0, 0, 1, 0]}, {'id': 'B3', 'data_type': {'type': 'PixelType', 'precision': 'int', 'min': 0, 'max': 255}, 'crs': 'EPSG:4326', 'crs_transform': [1, 0, 0, 0, 1, 0]}, {'id': 'B4', 'data_type': {'type': 'PixelType', 'precision': 'int', 'min': 0, 'max': 255}, 'crs': 'EPSG:4326', 'crs_transform': [1, 0, 0, 0, 1, 0]}, {'id': 'B5', 'data_type': {'type': 'PixelType', 'precision': 'int', 'min': 0, 'max': 255}, 'crs': 'EPSG:4326', 'crs_transform': [1, 0, 0, 0, 1, 0]}, {'id': 'B6_VCID_1', 'data_type': {'type': 'PixelType', 'precision': 'int', 'min': 0, 'max': 255}, 'crs': 'EPSG:4326', 'crs_transform': [1, 0, 0, 0, 1, 0]}, {'id': 'B6_VCID_2', 'data_type': {'type': 'PixelType', 'precision': 'int', 'min': 0, 'max': 255}, 'crs': 'EPSG:4326', 'crs_transform': [1, 0, 0, 0, 1, 0]}, {'id': 'B7', 'data_type': {'type': 'PixelType', 'precision': 'int', 'min': 0, 'max': 255}, 'crs': 'EPSG:4326', 'crs_transform': [1, 0, 0, 0, 1, 0]}, {'id': 'B8', 'data_type': {'type': 'PixelType', 'precision': 'int', 'min': 0, 'max': 255}, 'crs': 'EPSG:4326', 'crs_transform': [1, 0, 0, 0, 1, 0]}]}\n", - "\n", - "\n" - ] - } - ], - "source": [ - "# Application to examples\n", - "composite_dresden = obtain_image_landsat_composite(\n", - " collection_dresden, time_range_dresden, area_dresden\n", - ")\n", - "median_swiss = obtain_image_median(collection_swiss, time_range_swiss, area_swiss)\n", - "zurich_median = obtain_image_sentinel(collection_zurich, time_range_zurich, area_zurich)\n", - "\n", - "# Selection of specific bands from an image\n", - "zurich_band = zurich_median.select([\"B4\", \"B3\", \"B2\"])\n", - "\n", - "\n", - "print(composite_dresden.getInfo())\n", - "print(type(median_swiss))\n", - "print(type(zurich_band))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Download images\n", - "\n", - "To visualize and work on images, it is easier to download them (in Geotiff), using the **get_url(name, image, scale, region)** method. The image will be downloaded regarding a region and a scale. 'region' is obtained from the area, but the format has to be adjusted using **get_region(geom)** method." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "def get_region(geom):\n", - " \"\"\"Get the region of a given geometry, needed for exporting tasks.\n", - "\n", - " Parameters:\n", - " geom (ee.Geometry, ee.Feature, ee.Image): region of interest\n", - "\n", - " Returns:\n", - " region (list)\n", - " \"\"\"\n", - " if isinstance(geom, ee.Geometry):\n", - " region = geom.getInfo()[\"coordinates\"]\n", - " elif isinstance(geom, (ee.Feature, ee.Image)):\n", - " region = geom.geometry().getInfo()[\"coordinates\"]\n", - " return region\n", - "\n", - "\n", - "region_dresden = get_region(area_dresden)\n", - "region_swiss = get_region(area_swiss)\n", - "region_zurich = get_region(area_zurich)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "# If you want to apply this function to a list of regions:\n", - "region_list = [get_region(geom) for geom in [area_dresden, area_zurich]]" - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "https://earthengine.googleapis.com/api/download?docid=6b6d96f567d6a055188c8c17dd24bcb8&token=00a796601efe425c821777a284bff361\n", - "https://earthengine.googleapis.com/api/download?docid=15182f82ba65ce24f62305e4465ac21c&token=5da59a20bb84d79bcf7ce958855fe848\n", - "https://earthengine.googleapis.com/api/download?docid=07c14e22d96a33fc72a7ba16c2178a6a&token=0cfa0cd6537257e96600d10647375ff4\n" - ] - } - ], - "source": [ - "def get_url(name, image, scale, region):\n", - " \"\"\"It will open and download automatically a zip folder containing Geotiff data of 'image'.\n", - " If additional parameters are needed, see also:\n", - " https://github.com/google/earthengine-api/blob/master/python/ee/image.py\n", - "\n", - " Parameters:\n", - " name (str): name of the created folder\n", - " image (ee.image.Image): image to export\n", - " scale (int): resolution of export in meters (e.g: 30 for Landsat)\n", - " region (list): region of interest\n", - "\n", - " Returns:\n", - " path (str)\n", - " \"\"\"\n", - " path = image.getDownloadURL({\"name\": (name), \"scale\": scale, \"region\": (region)})\n", - "\n", - " webbrowser.open_new_tab(path)\n", - " return path\n", - "\n", - "\n", - "url_swiss = get_url(\"swiss_pop\", median_swiss, 900, region_swiss)\n", - "url_dresden = get_url(\"dresden\", composite_dresden, 30, region_dresden)\n", - "url_landcover = get_url(\"landcover_swiss\", landCover_layer, 100, region_swiss)\n", - "\n", - "# For the example of Zürich, due to size, it doesn't work on Jupyter Notebook but it works on Python\n", - "# url_zurich = get_url('sentinel', zurich_band, 10, region_zurich)\n", - "\n", - "print(url_swiss)\n", - "print(url_dresden)\n", - "print(url_landcover)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Image Visualization and Processing\n", - "\n", - "In this section, basics methods of image processing will be presented as well as tools to visualize the image. The images downloaded before are used as examples but these methods works with all tif data. Scikit-image (https://scikit-image.org/) needs to be imported." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "First, bands can be combined, for example to obtain an RGB image from the Red, Blue and Green bands. It is done with gdal_merge.py (see: https://gdal.org/programs/gdal_merge.html). It is better if bands are named just as B1, B2, B3 ... in the folder containing the image data.\n", - "\n", - "If you don't have any bands that you want to combine, you don't have to execute the following codes for this tutorial.\n", - "\n", - "In Terminal or Anaconda prompt (be sure that climada_env is activated):\n", - "\n", - " $ cd '/your/path/to/image_downloaded_folder'\n", - "\n", - " $ gdal_merge.py -separate -co PHOTOMETRIC=RGB -o merged.tif B_red.tif B_blue.tif B_green.tif\n", - "\n", - "The RGB image will be merged.tif" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "from skimage import data\n", - "import matplotlib.pyplot as plt\n", - "from skimage.color import rgb2gray\n", - "\n", - "from skimage.io import imread\n", - "from skimage import exposure\n", - "from skimage.filters import try_all_threshold\n", - "from skimage.filters import threshold_otsu, threshold_local\n", - "from skimage import measure\n", - "from skimage import feature" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "from climada.util import DEMO_DIR\n", - "\n", - "swiss_pop = DEMO_DIR.joinpath(\"earth_engine\", \"population-density_median.tif\")\n", - "dresden = DEMO_DIR.joinpath(\"earth_engine\", \"dresden.tif\") # B4 of Dresden example" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAlYAAAFiCAYAAAA0ppY5AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjAsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+17YcXAAAgAElEQVR4nOy9yY+k2XUvdmKeh5yzsrOmrp5IdnNoNswWIVKQAAFP2hCUAMPyXm9laWv9Cd5r9xaGtKINCRIgQA+wABOSSFEtcWpWN5vsqbKGrKzIKTIypow5vPj0O/E757tR3bbZfkUjDlDIrIwv7neHc8894+8m5vO5rGhFK1rRila0ohWt6P89Jf9bd2BFK1rRila0ohWt6P8vtFKsVrSiFa1oRSta0Yp+RbRSrFa0ohWtaEUrWtGKfkW0UqxWtKIVrWhFK1rRin5FtFKsVrSiFa1oRSta0Yp+RbRSrFa0ohWtaEUrWtGKfkX0mSlWiUTiPyUSifcTicRHiUTizz6r96xoRSta0YpWtKIVPSuU+CxwrBKJREpEPhCR3xWRQxH5oYj80Xw+f+9X/rIVrWhFK1rRila0omeEPiuP1X8nIh/N5/N78/l8JCL/m4h86zN614pWtKIVrWhFK1rRM0Hpz6jd50TkEf3/UES+tuzhUqk0X1tbk9lsJiIi3ouWTCb1b5PJREREMpmMJBIJERFJp9OSTi+GMh6P9blUKqVt4G/LvHTz+dx8hv6Mx2MREUkkEtqe79d4PJZkMmnan81mMp1O9bv8Gfcdv3vC+3kcs9lM/859Afn3hD7j981mM8lkMmYOMN5Q+/P5XOcRc4A28Tv3kQnt4nt4nucR85VMJs37Q+2lUinzztDYp9OpWQ88w+/k8Y9GI/17KpXS96JfPC94Nz7Ds37d8JzvG/clxPv8Pf4sk8nEeI3JzxU/w/zH8xtqJ5VK6b5KJBL6DH5eXV1Jr9fT5yeTSXB8oFwuJ2tra/p//v3q6kpERIbDoWkDf+e1TaVSumbMa4lEQn/nuUsmk7pG0+lUx42xpVKp2L70vDSbzYL8ynzvxxzaD9g7XhZNp9PYevv+eL7yaxZa51A7fh19v/268fMhvvO/f5J8wrxns1kRsfPk3x+SxyKRLBkMBvp/tMnngP+M28TfuM1lc80yiZ8NyXv/Hu67X3NeC5GIJ1jOePLzuexZjCOVSkmhUHhq3xKJhORyOTO+0WhkZPVoNAruhxD/+OeexpOhfj+tzdA5ief5PAkR73seq3+/fxf4BPKGn/n444/P5vP5Vuh9n5ViFdIUzKwmEon/LCL/WUSkXq/Ln/7pn8poNBKRaNC7u7v67GQykb/6q78SEZFvfOMbIiJSLpfNZioWi/p8s9mUJ0+eiIjI1lY07vX1dXnw4IG27wXdf/RJGSqdTuvGbbVaIiJSrVbN4mQyGW2n1+vp81jsVqsV2/DdbtdMyv7+vmxuboqISLvd1nex0BiNRkYIDYdDERHJ5/PaH4wDi44DaTKZSD6f1++KiJRKJalUKiIicnJyIvv7++Zd6HupVBKRiCk3NjZ0nKenp/o8nhkMBlIul0VE5PLyUi4uLsy4R6ORti0SrR+eR9/Pzs50nLu7u1KtVk2/vMKXz+d1ntrttn6XBWk+n9fx9/v92DiHw6G88cYb+vfDw0P58pe/LCIiP/3pT+Wjjz4ybQ6HQ1Um0Aba5zUO8XKlUjGKCG9utFEul5VHMC8QoNjcd+7c0d8bjYaOjeeflUVWCnj8LIBDQrpUKsne3p6IiFy7dk3W19dFZHEgfvTRR/Jv//Zv+vzdu3e1P6PRyCgiGOOrr76qz//RH/2RvPnmm9pHtHl2dqZj+vDDD0VE5OjoSEQiWdHtdnXPbG1tKQ8MBgPle8yniEihUJBOpyMi0dpg3Hhma2tLarWaiIjyLeYDc5rNZqVer+vYwEubm5tmbtGXfr8v29vbIhKtI9b0+PhYRET7w98DXzPv4P0ilq8rlYq20Ww29XsYUz6f13USWewZPmyz2ax+F593u10jo9hordfr+l084w+s6XSqbYKKxaKOaTab6b7e2dnRZ7a3t6Xdbuvcgfd5/2LuRETu3bsn5+fn+sxrr70mIiLPPfeczgnWEvOAswDtzWYzPTeSyaT2sVgsGlkB3rxx44auRzqd1nVn+SWy2JPdble/2+/3tU3w6Gw2MwrM6empzgHzFOa/UqmY5/EsP8+K0u7urly/fl1ERM8M9AVzUCqVdA7A34PBQMcgInJ+fi7Xrl0TkcX++PDDD80Zg/eDF55m8PH5u4xms1lMAWdjwhux+Xze7BUQ9nqr1TL8xkoaZGUymdR3Yn5u3rwpImLOIjzzh3/4hw+W9f+zUqwOReQ6/X9fRI74gfl8/l9E5L+IiDz//PPz/f19FZ7b29sqOEUiBvrWt6JIIjbt6empKgciooqUSGQZ12o1M1FnZ2e6ASAcRCzD5fN5cyDhM1jWe3t7RhGbzWaqZORyOWVobKBsNquCFn29vLwUEdEDttFo6Hvm87lRqED1el3fWywWlXkzmYxuNP89Pli8NQ5lSCTaFI8fP469EwqeSGQdYtP1+30zRxBWnU5H57rZbOqYeAOxoB+NRjoX7NUDA7OijO9i86LtVqtl5hdjZsVvOBzquFl442e325X3339fn/+TP/kTbfNf/uVfjLAFYe3QH7+hp9OprtdsNtM2+PDM5XL6nslkom3U63WdD6yBFyYXFxc6l+C1dDptDjvmB/AlHwaJRELniy14zAnGicP7tddek+eff15EFrxVrVbN2G/duiVvv/22iIi8++67Zp4x/p/85Cf6/1arpQL8d37nd0RE5KWXXlIF7uTkROeAD7p0Oq1/r9fryjO8f3iPs9I4mUz0QAbvVioVbR9rC35nJQPfE1nsodFoZMYI+VQul+Xhw4ciEu15tI/9ijUBL4/H49hhxF4ONvRERGq1mho7aJv3WrfbNbIK88Eyczgc6uGIdSwUCjGPEctI7APwy2AwMLJmMBjofHB/2PuPcbByOZlMjPGI5weDge6fR48WQZBer6djyWaz+jv4YjabqbIMnsda4mc2m9Vxs3HJ+4S/X6/XjVcH/cJcYG7Aj6PRSM8lViwxfm/wsOEZolQqpevFlEwmjbEA5RLKULFYNO/Z2dnRPZZMJuXw8FBERL73ve/p/Ny4cUOff+6553StcBa+8sor8vHHH4tItMdZ3oWI388e3GVeppCSIyJm7vj7fCaB2FHCES5+ng1AXg98Dp7kcfG+WkaflWL1QxF5MZFI3BaRxyLyP4jI/7js4Ww2Kzdu3NDOd7td1f5FookFs7LQhwVTLBZNWAHP8qI1m00VeslkUpkOAlQk2vRg7kajoYLxc5/7nD7jvSF8yItECwBGKBaLujAQxPgJAe4PtBCDsNWYz+d1XBhPPp9XrwjahRCaz+faHwjXdru9lKE5NIH2i8WiUUREFsyF952dnRn3Oitvfi7QBxwImHMOPfE7/P+xucbjsQrGfD6vfec2rq6utC+tVkv7D/5qtVrGi/Tnf/7nyj/379/XeYJgGQwGOg4WPhiTSHRosrcPBGtPxFqW2WxWnzs5OdF5wXgwXvaIgU/wt8lkos+xJ5P7wJ5PFtKz2cw8jzllK42FJ+Zkf3/fKJy7u7u6r0A/+tGPzDry4fHBBx/IX/7lX4qIqHL/7W9/W3l5NBrpAcBrNBwOdT0ajYZRFmAc8bxzmD6TyejaoO/ZbFbXDm35EOR4PNbPqtWqjomVqnQ6rV6q0Whkwn4Q8FhT9ixhTHgG78nn82pgnp2dGYMB702lUrp29Xpd+WI+nxtllL16rFxD5rIhxTSZTJTHCoWC9Pt92dzc1Pay2axZ39PTU30eHoLT01Odc/a48DubzaauR61WU96bzWaq0LJSUalUdK7ZO4d+bW5uKq9hrtBPzOlkMlEZt729bdpjGcJKJB/obBjxuzGu4+Nj/Rv4kUOlGN/Tfvfhv1AaRjqd1nPs1q1bqlCC1tfXzXiq1apRXNEOFKyTkxMTMUqn03p2wLiaTCbat8ePH+v+ZGWZw9shr7kfc0iZ5Wc43BdSzIbDoTlDOG3Fy8SQAhjqH84L8CYrxk+jz0Sxms/nk0Qi8T+JyP8hIikR+V/n8/nPn9qRdFqFDNz/oOvXr+sAvQUA4onCIhcKBRMegUfs+vXrwRi7yGIT/vKXv9TPwKisGIDYchWxYaBut6vCEQIaQhN9ERHjacF3WbGsVqu6cXq9nh7IeHY8HuvCo10onexShVuYlaZMJmMYDnN3dXWljMaeC28x4fnpdGpc7PidPW0817y5MQ72nKTTaSNEWHhh/GyF8CZmb+d0OlWhxlY8vBXFYtEoEB988IHyANY7n88blzOEDn+P54K9a+gn+g4aDofy4osvioj1xuD3wWCga445CeX4sLeCP2cFHYdZMplUPuHwFNoLWYhQBt9//32dV7bQ+dlyuSyvvPJKrK/wYvE40Wd4rL773e9qmwj37+/vy61bt2Jzx965TqcTU9A5RCwS8WzIWoVRVCgUzF7C/IhEYVe8P5RDx3KIeZwVK/aEe+8x+plKpVSWsDcLh/R4PDZzjfZ2dnaUX6+uroyix0oMh8+5/3gn+nV5ebn04AAvQK6B+Pl8Pq8KFTzSqVRKx4R+ekqn07qOpVJJ20cI9uTkRF544QV9vlAo6P5gRYjXg719IguDAXPU6/WMRxrPsxe2Wq2asfLcYdyQ1ZC9UAQHg0HMOPSKBHsDMV72ouA5/ukpnU6rwrO1tWVkrkgkD72Sjf9zusrt27dFJJpb7ufFxYWeHTB0Dg8Pde4ymYwJqYL4LFw2B/7vT8uT4hxZEKczeOL8ZZ/rynuD87ZB/rxj7+d/S4+VzOfz/yoi//XTPDsYDOTw8FDzWd577z0VqCIRw/KhBWKtnheQ3X9g/o2NDY3Ds0eLF7nT6WgIo9lsyuc//3kRsWEFn08BKxKC3h+2EDQnJyciElmVV1dX8tJLL4lItInRd7YOWRAx8xeLxWDyJ/4GhQEHFp7JZrPKJLu7uzovo9HIeAOghHB4KpVKKaP5cCoLSQg6nlcoIZznImI9EJizdDqth3cikTBriudLpZLx0qA/7PXxyaroY7fb1T6iXz6fYHt7Wzcj+subeTweG0+e/xzjR/vZbFYPRz/PaD+TyZg+ok3vIfI5Xfw3DhXl83njseFDHYKcP/fzBaF7eXmpB26z2ZT79++LyEK4cqgK78X8gu8mk4mu77vvvmvmmnOJ4B3+i7/4C3nvvQiV5Vvf+pZ6BVlZ3tzc1DVoNBoxXry4uDCGyWAw0DngsCgXhGDfYDzoG8ZxfHysPM0Hv/cgsBeB5xVeNMgmzAnmstPpxBS3ZDJp9h23x7mQ3Ac8M51OjTce5K14zB23zbKn3++bPFIReyh53tnc3NT2wcus+HD+lA+h4+8cLuQwXEi2Yi587iob6lhDKFQwztmTNhgMlKeYd5jK5bLOKStikO2cA8b9EAknmrOBwOMXsUng6P+yUGGhUNA16Xa7sZwpNmJFoj2DsbJHDXOxs7Nj1tinFuB7kBMcamTZlM1m9XycTqexYrAQeY8Uz0sikYjNoy9qY37k/D/WBVg++sIiH6oHgXd8isrScXyqp1a0ohWtaEUrWtGKVvSJ9Jl5rP7v0Hg8lrOzM9XI9/f3TQb/5eVlTJP0uTicZ+G9XSKRtu/L5GezmfEi3L9/Xy2I/f39WFjD5yaxqxMWdaFQ0Gd2dnY0yXGZK5yrCQuFgvYR1VgikZUHrwcny3KOCHJU0JbP1fIuzJDnT2RRVXP//n21EF966SXjyRNZhDTxPCd/Ir+r0WioFceWDOaALQiRyFuEfo3HY03ORf8xRz7nCJ/DU8bjHAwG6qUJlTOPRiOT65HP59Xty/AGIdgO9r5wQiZbouyRZN6B51Ik8lagX+hrqVSKVQVyhRrGxt47nuOQVciJ9OPx2ISH8Pky6na78stf/lJEFrzJ+wyEMcIT8/rrr+ve2N3dlbfeekufhQes2+3q9/r9vj5zdnYmv/d7v6ftgM7Ozsz+8CGYZrNpLGwOB2PMhUJB5+709NTkHYks+BveC+RviFjPo+eBUBUh5/NhXbD2kAu5XC7mmRyNRrFQEQjPXl1dmapRyIR+v2/ywjjPDvmobH0jzNdqtWIFJyxnRKJ59lW/oHw+r9VwnIeFtSmVSrG5FrGhHPaGcHGG98Bhj/FaQ2bl83n9LuQlQpOY31wup+t17949nSvOUZpMJuo17PV6KufAf81m06RdMIUgANhrmc/njRdqWUK398KAfLUj+gi5gflqNpvGC3d2dmbyK7m/ItHe4DOK5Yx/NyjkweX1Ojk5+VQhtNAchHLPlkUnPIU8YP59DMXii0rAl1zx7/OHQ/RMKFa5XE62t7eD1XQii43R6XR04OVyWQXE1dWVcRND4HMSPFf0YLJzuZxh1Gq1quXfYIpEImGEGytInBCHTTkYDEzIC3/ncCKPjQUQlzyzYsQKRrvd1vY5lIFxgMGhmHIODtqcTCb6e6/XM0zMFT347uPHj3UOOKlUZHFAHB8fxwRYuVw2+UV8IKVSKRVO6DOHdGezmZnHUH7bYDBQ4cwhMd7AvV5PBdna2pr2B6En7+Lmij5+N/hhMpno2Jnnzs7OzIEBfmPFmZ9PpVImGTlUVYO+Qvh7oVMsFs2hyvzA4+IwOrvMvfLtE6x5ftHfZDKpIfvNzU1zOI9GI+0j/r6+vq7j3t7e1vJlEZGf/zxKu7x7964qWZyE/+6778q7774r29vbqtThe1//+tdFRORLX/qShl0ZToSVZYZhAB+zQsK8xUnetVpNP8tkMspjhUJBeZYNiuFwqM+sr6+b0nyRBVQEvxOftVotnTOEljKZjK5Rp9MxVYOcI4p3MhwCxgEFiMMwjG3kD3GP4ZTJZPTgxn49PDzUfnlFjCtTMb/tdlt5gBUr3t9cXVgqlbRNrFetVjPvSafTMcicdDodK7jgz331ZL1e1zPk/Pxc+8NGPedLDgYDPU9Y+fUJ6iDGwQNxoQgbY097hvNIK5WK8jb6ee3aNRNGxZwx7MUnVRyORiNVGJLJpKkUv7i4kB/+8Idy+/ZtU/mNNnu9XlDZYGgNkXhyP59by/rExVr4DhPj7HniQgFvhHN/GYcQz4N3eO5B7MRZRs+EYoUJgELUarXUQyKymEwup+ZycX5GZMFQh4eHuig3b96MeXVE7ITt7u6q4oSDsd/vG+Hqc1fwfxxerEDA8kmn00aIzGYzg12FDY+Dtl6vG22f853y+by2j59s+aBdzA3jPIEGg4EyNyucGxsbJkeEy5wh9HkzMfX7/Vj5cblcVkHkE53H47F0Oh2zcfr9vjK893CFksHH43EMnJMFnUh0gKIteNeSyaQWSECZwOFxfn6u42AAT8w/b2C2bDOZjHovcrmcyfMLWYf9fl/7WS6XYwf1YDDQz33+CNYin88rX52fnyufbm5uBr1XnOfAyqNP/ORn0A7WO5fLyd27d0UkUhSRK+iJsb0YlgKVVyKi3719+7aWer/77ruGV0Uir9Df//3fm79hj7///vtqDCGB1xckXFxc6N5iTwjWkteFlZdCoWA8PVzqDR7kuWPFcn19PZi4DD7iwxrU7/eNwsfCP5/PG6OOq3OxvuyV54OZKZVKxfJVWZllT6kfH+aO8+a8x5dzIRnOw+esceUs3oO5YSw3zj9lJZ4VfPSxVCqpgs6FGhxx4Dmp1+sqq0qlkqmsA7Fs3d/fNxhVGK9XWjy+FRNjPo1GI3OeLKtU83ArmFcYFFyswQnxXt5wmx4zjc85NhxErJKO8XNxTS6XM5WTIJZbjJfF4+W56/f7Ojds7ISAn0Mgv/g/QySEcq8mk4kaeJPJxEA1+YiHNz4/jddN5BlRrAaDgZycnJjDm5NVwShcOcKUyWRMpQq8Ee12W774xS+KSHQIoH2UlXY6nVgpOBQBrh7D97zVCMuC3bupVEoPaQip4XAYew+sUmYsaMLlcjmG8cKuVhZY+BsYGoyPtvBzb2/PuDe9NScSeaYg3Gq1mqmQwib1BwOPCcQJzJi7VCql4SF8hjnlaiMIaS73xVyKWMbnpP0Q7AD+DwgAfO/w8FCfqdVqMSWdhbmIBUut1WoxOAT0i5UpjLtYLEqxWJThcGiEaDKZNMoH+B0/OSnZe9WgVHDCfr1e1zXy42GgRBb63tvnqzTT6XQshDaZTPTwfOedd4zA2tvbU89DqJjDCyV4QMrlsiqU3/ve9+QHP/iBiCx4mYsU/Bz867/+q7zzzjsisoC/4GpCkUhxAw9w4jLG8fjxY90nLPzZu8N7AEaBSJwHGBaGgTtFovlnPhVZWO9Qvtk7ysCtpVLJKPLo13Q6VX4cDocxHC+GYhCxBw9+cohyPp+bA5GBSJdV3LERiL4wf0+n009EROc0gG63qzzLOHXeeEa/eH9Dgb5582ZsH9++fVtarZb2t9VqxXAM0TZoc3PTFAlxYQfafloIHcSKLu/BZTctoM1sNmsMPJFFmB08yxWoIpH88Xud17RcLgfDjSFjW2Sxh3d2duTg4EBEIn5l4yIUsuaihXa7rXsLBhBHRUSi/e6NSH/LAveXf2Ltbt++rfNzcHBgqu9BzJssWxnCBp5M9mCLxDH/ltEzoViNRiP56U9/qhN/7do1I5B58jkHhyvYROJlvJVKxSAhe+TbEJ6TVyCq1aoedssQebkP6JtIpDQg78fH0tniYzwqtOWtIF9SPpvNdC5YcHlPTwidt9frBfNqstmsMn29XjfAliKLEAV/jyt/MCbPjCLR3HE4FIQwMAiKLaxRj/Y9nU7N774qxG/wzc3NGHjgaDRS4Xr9+nU5PT3VQ/7i4kLnjAUzh18xZgYszGQysSokEctHvBYcJuAQA/rPYWrMWwiVPwR9wPlVlUpFldRGo2H6FsrDE4nzkH8W/W42myZEh79vb28bwbcMn4bhMRh0FAoSvFi/+MUvYgKXw6KwdO/duycikWHF/bp165apwhUReeGFF3Su79+/r3twmUGTTCY1JM+hWRywIhZPbzAY6CEEHubqLI+8jrY5jMuYaZlMRmUAVzvlcjldm8vLy1g+HqNpi0T7C/sNz3C4ularxRT5Tqcjm5ubuqYXFxd6YI3HY+N5Ym8qXw2Fcn2eO5ZxDCrMqQqYl3w+r4CUIhEsiu/nZDLROdrZ2VElmqsPb9y4Yaqq2fMOHvOKEtp89OhRDLsqlKMUIu9ZBy3zUvHfea1FFriKXt5yexzl8CG5dDqtfIu5qFar2j5DmIhYSCH0JZvNGjksEoc18XLEXxeztrZm8CczmYxGlbyHLTRHvk8gDtOCfKSJZbvPR61WqxrdwJnHc9xqtYyTIETPhGIFVzFbxx5HQySaKAz4yZMnOlgcigzIJxIJFXZdikRCBBu4Xq+bg4bzcUJaqUewhWZ/dnZmDlU+MEulkmlXJPLGQHA8evTIuGNFIkHAC8f4S+xy5eRvWLMe1wOH+dHRkcFD8d4JtMPhJ0aFRtverY7Ne+vWLYNyj7b5sGbmxlgZ42d3dzeGKA6lAAK73W4bj4rfvBz7x/ewOThkwejEmUxGrztqtVoK/AqhPp1OVbmezWbGiwFiK4hDQpxozzw1GAxU4WM4BPQxn8/r55zsKmLvKmRAW1ZOQHyoFAoFfRfns2BdGP4B4+C8Rx4P3s95YuBZVqYYJoA9NkwMhFmtVrUdzH+tVtPwo0gk7DiUjvdgLt58800jP37wgx9oagEMh9dff11efvllnRfwCOeW8BUxGxsbOpeMos78l8vl9LMnT56YZGAQKx7oN/9E0jnml6+fAZ2fn+vBWK/XjTfR5255BPmLi4vYoco5Jz5XhqETwFdnZ2fKKz4HBvKnWq0aQ0wk4g/0Y29vz4yJvSqcLM/eI/6d4Rf4ah/O3wIfseztdrtBDKNluHbdbtecISEFyeNMhWAzmDgBO5SY7dtkqAp+jtcacoMVPr4yjA0TPgNZHmBPPf/88zHwVpHI6GNPD84/lqUeIJRDlHgXv5Pnem9vTz8D77Ch/ElzJSIxhww+Y89gPp83stqHzTlyAl4EzyxLVfH0TChWmUxG7ty5o50/Pz8PJvaur6+bpEZYtnD7w2JFO2dnZzFB1uv1jHudBUkul1OhwzF+ruBhC5WvswHx1RSz2Uza7bZBt8XfuWKFc1BE4onak8lE38vhwhCwJL/He07Y1Y3N4ittcFD5A1TEMicsDX+XEnt02OJnRRCfiVhg0XK5rAeJx8zBoVkul82VL94lLGLzoDgMy6jjoAcPHsjZ2ZkJH2NeMDfz+Vzb9ECnIA4Hc74Th3zZM8c5hZzojrENBoOYJcWAiHgnW9A+8RJtg8f52h9OVGWQV/8+9iKEiNcU1VFszbbb7Rhfe8+ex3/i3BGRSOB+9atf1fd897vf1YrdX/ziFyIS7X2M+5//+Z9NH5PJpH4GBavRaChe1uc//3lNBGbeYKT+9fV1TSGAp3J/f98UlvAccHUehDQrwtw3/sm8tba2pnPgPXYwpKbTqdkr2JdQKjD3jIEGGcLGGvMDyw1WqLFH2OPLyegiCyWn0+nou9fW1mR9fV1OTk6M8sRK/2w2i12ZxIqUjy5cu3YtlkOVSqWMhw7z7q834nb8XDAOFIivf8Hacy7b0zxV+B4bsmh/Op2qQsfnnR8T1sDvf+Ydzz/+uimeS4yB87fG47Ex7JlPsWb1el1l0nPPPafzi885dQDjAyGNp1gsmsp+VlA4HIy2Dw4OtO/Lbgdgg5+VyJAnD+NgsFguVhCJ9jAMbchsgKeGrrgJ0TOhWCHuD8bhsMPp6anJR+CQDDxZEJCwNtlLwgneItYK5aQ7EDM9foLp2bsisqjKqFQqJszmL+nNZrNm8cbjsfZ1OBzGkN1TqVSMKbym7OETQsizfNkm2kG/0D7nMrGSxRuLx+FzNvjKDzzLAo+9LGx98GGL+eXrd6CoQTEJKY5AkF9fXzfuXx5HuVyO5bMVCgVT6s+J9yCu7lxfXzfeK5GIL/0N8GzV+ARkr5D5EBnawLhbrVbsEtl2u23CbHzJKwObMo8OBgOjLGNtGAHch6VAx8fHscRZ9rx5RQwK4vr6urvGs/EAACAASURBVB7C4/HYJIqKxMNUXI3D4TI+SNiDu7e3p3wLGIb33ntPD4z79++b8nIOAYPvj46O9Jn79++rogZQYJEoRAlFiT2+CGtVKhVjaK2trcWSgjnPJJlMmrsE8TnmFXOBNWLjiffO2tqa9uXy8tIkV+Mn2vehq1qtFkMg54uXGTRURIyiD7mWSqWC/cZn3E+M4+TkRAaDgfH6sWFYr9djXryzszNd09lsFsvR9GDFGxsb2p8f//jHsYR59IWNQMw1zxN4VyTaV5jrZrOpvMTeok/yXrBixXueDVX27vMzuVxOZY+voubnMG7vnROJ9hQ/y2cJ1pcr/hjEVWSR93njxg313j1+/NjwjEi0R7yMwxozCDQr9F4G8jmN//MlyWyMgVi59bnMInEliBVnvmCawUwhe3B2IPyKOfLeWE/PhGIlYg8VHjiH81hRyGazsWq6EC4StGO2ptma5EnngwLMx1Vd7KZH30Rs6W8mk1FG5Gs3mBGOjo70MINyMJvN1HoqlUrmPbwxOGmWhRoEmj/s+I4qCGcIOI7Di9j7/M7OzmKJ/CyUsNkhPLEGrVZLD/hcLmcEbahMlVGxJ5OJCi4f0sQ6drtdU4GJZ/A5556J2OosjKfX65l1H41GxrrGM5i73d1dVX7RBluJIC5agFLEV9Tw2vjLtEUivkUbDN/gMX5Y6eciD7zLV3LxHZlcNCASrQkXXojYRGp8j8MbfGm4r7YRiZQc7hfa5et0+P98SHIpNniNva0i9poRWJR7e3sqJxqNhlGskG/14YcfGsRpDqM2Gg0TNhaJlDYoUaPRSHkAvNJsNk2ODoeAQYlEQr/HOaDov/fecS5TMplUGcJYRQxHgwvneS4Y7Z9R1tH30GXEvL7M17zXWPnFM5hn8CT6UqlUzEE9m83MzQcedZz3LRS+RqNheIfzcUAnJycm7wdyvtfrxTCI8DuUYb6TkFMbWFl+5ZVXNN8T4V3vTWaPTegnz+kyJSyVShlFi/OR7t+/L6+++mrM+8hhel/o0mg0lAe884Bz7LjSkGFh+CxgryXnBjPmHuaB55qx31jJQb/YIBaxnnaMh28G4Xn08AqcY+zn38NBLAvNspxk+CSRhdGC9WP8uhA9M4oVV+kw1Wo1FW4iC8HbaDRiXgHv+en3+7ESW/Yo8aEiYm+0hhDkGLfPEQFTnJ6e6oR7cDWRaDH4uoPz83MVMBjDw4cP9Xe+DFok2gA+92g6nWp/+/2+sSZB6XTabFZO5BeJNhgrVszYjDGCZ1i4+7wcCKN+v6+WZb1ejyXdY474SgEOp3jvibfOyuVyLNel2Wwazwpbwu12W/L5vLRaLePV4hLecrkcAwdkULxSqaQCBQrDxx9/bBKkuY/T6TSWrD4ejw2v5fP5mEHAYU7OP+C8gV6vZxQx8DWP2VdvseXOJfMitvLMA+hyeI6vwmEe44MCbfZ6PYMvg7/j3j0fsvQX+eKdXHrN+46FpzcMRCIhyIc2LP2XX35Z1+yjjz6KYRJ1u10D83JyciKvvvqqiIi8+uqrsby5YrFocKxOT09j90xmMhndm4lEInbfIsrvWcZwWIcvpPbzLBLxhs8xhaEmYtdQZJF3cn5+bvKFWOn3yiLmnD1pPh/KK4rj8ViNC3iUYMyhP+xZZs8qpynwvPChDY9/s9nUuW40GqZ6K4SR5IswvBc1m80aBW4ymcTyfVgRZfIFNGzsgbjKzedC8j2taIurVZflcOFz9E3Epl5UKpVgRIPlE4fswdN+nzI49WQyUV7mOzd57k5PT1WeskeT83hDeJVeEeScw9Dl9rPZzHzHIwfM53Oz7r5g7PLyUmq1muFdyGa0C0XK4zguo2dCsWLhK2I9G3wnUz4fv8W80+mYMncmTr5ljZbLzENVSqPRAin96urKVIt4IDU8z5U83krk5EKRaMN5BPnbt28bQRfCA+E54jg4e1dCBx3ahNLCjO1zNzi3zGMr3blzRxVEHFA+QZTDBIlEwiS+8/jRTqVS0fnkxHQf+uR+grnZ+4JD0gOE8vqjLRYio9FIKpWKKndYj0KhoMra6empWnl4N+ckYI4YyRvPeC8qaDwexyzKVqulQjybzeqB6XG5WNB4UE9GFPffxXeYJpOJHnz+IICyzMmeHD7xeQtcKMF4czgwOLkd70b7UCD40PDFAfw759fheVa4+dDA2q2vr6uR9tJLLylMw89+9jP9Hr+n2WzKT37yE+3rl7/8ZRGx8okVO1/NJWLvzmOPoQcs5FBGaA64X5ykzqXz2DusqPnKXvAsKv1AWOvBYBC8v46rejmPhS/yFlko57VaTfckvscFMZVKRXOqQDi82AhGu7VazVThsmeRvSGsQPgLrz1x8QX2Ua1WM96Ne/fuKf8yvACvl99T4ANWJtB+v9/XtWGDklMy2u12rFIclzMzsdGJPkCx4bON0xSYONJSLBaVHzw/+bGJRHPNMDkgziPd2trS94IHLi8vdZ0zmYzpF+Qhh6szmYzOU6/XMxXOTFjjyWSi/UJffR4x3/vJIXUuKgBhjr2yFgKsZnomFCtU1yAPiW8xH41G8sEHH+jvIBz2HBrim+BF7IHArlLOb+DEc66WYQXOT6KvbkgkEiq42FvF2i0rVpVKJeZmZaTi2WxmDmJWrEK5GGwF8AZlSIqTkxMTq8Y42e1dqVTUKwXm5GqRzc1NfQ8jqotYi4lvTseG4sOo3++r0EPVJKherxtYC7TPCMwQnlzFBn7wuRuh8AXmUiQ6bBm7B/POYz0+PtZDAm0fHByYkPVwODTVYphfvrCYD1Ou0OMcMPADe0J4jbhMPWRxZzIZ4/XzuXIcZsJ4wTPoj/fyeiBDtM97p9frGbR1VuhYoRVZ8Dz2O3tDeJ+yksLCG8qzr4JjYcnClD1wWI/9/X3Nm0AO2FtvvWVwb1B8IiLy9ttvy9tvvy1vvPGG/MZv/Ia+E5AKItEa+Ku3WMlhZcbzIod3fN4Se08xf1hv3itsAHKFE88rI/Vjj9dqNeMdDGEFsleL19Zfug35d3Z2FvOAlsvlYLEJ2gef4yeDhrbb7WBBUyaTUVkSqk4Mea5YOeDravBOL+9DwJZ4npXlZe/h8D0rYqECIRHrqcM6fvTRRwZXUGSB38V7hz2IvPdD1bjseWMPOfOCiFXOORetVquZSEyhUDBYa+n0AmSaiymwp1qtVjBVhM/Lzc1Nc0H6xcWFZLNZI3v4cubj42N9J0N1gNdFIhxDyCTIvNPTU3O2QSbi3fg/+N0bBZ6eXs6wohWtaEUrWtGKVrSiT03PhMcqmUxKtVqNhdBEIg0R3oIHDx7EKpI8CKXIwqswn8+15J/dm5xTwa5LBpJkDCeGOGDiZHBYTdx3WBgXFxcxhFdOjheJtGuOZbPFzXFtxr3hCklY/2xdDQYDtcBns5nijcBCn81mpgLm6uoqBmHRbDbV7crlxhgnxs3WDmNUwbJg62kwGKjLmq0J9uKFoAPwGZ5nhGAAQE4mEy2VFVlYov1+31SZwaINQVtgHJjLra0tnWuAFE4m9uJvxjBiC48tI/883g9r6PDwUHmMvX1MbOFxflToAln0AbwPPLTNzU2TTwjifBqRhfveJ97jOfbCcmiOPasMW4H1wty/+OKLIhLxGvdRxCaqhjDA8BnnZfiKJP+7z/HB33/rt35LRCIe/od/+Af93uHhobaJsb711lu6l998803znocPH6qVjHV6+PChCXWDmBeurq503SaTiUnwFonn9JTLZbPPvReS5YSHcuG1C2Gs8fsnk8UtBgwnwh71ZVADV1dXylvYg1/96leN95L55+zsTNbW1qTVaul+YNiO4+PjYJ5QKOFbZDH/3ovkK2ZF7J2b6XQ6lqfqvUfegxsKmTHsDveLf2cP3Hg81jWqVCoGwV8kyinzcAOA/8B87e3tGfkB7yigLkL9ZTkLuCLPM0/Dd2Jg4lwuF/My+zOZx+AjM/wseMYXOeBz3j+cU3lwcKBtwqNXr9eD6y4ixguIZ7a2ttRTBf67e/euvPHGG8Gc4RA9E4oV0E8xYCAui0QKB7umfTiLGdjnsWCRE4nE0gonDpuAEev1uh4CjLPk2+U7qnxobD6fqwLAAKL4DIIPm4sr9Pr9vukX5zoxpgfmIJPJyHA4lFKpFEvc5rwLn4fVbDZjeSzoP+AIPFwBlyqDOHR6fn6u4YB6va7t86FSKpXUNcylvT45WERMhaHI4poRDrly5d/R0ZERtpy7wxVnfE0R5lNE5Gtf+5p+F+8sFouqXELJL5fLJheDDyrM+e7urhF0LFSQ68MwDFwc0Ol09HkWCh43B5/hYL59+7a56PjJkye6fhA+169fV1RqdquH8nlELF5WLpdbiuWC/zNPIY+PoQggFDnnioEE8TcOefp74vAP8+srzJg46RptAl9OZGEUfOUrXzH79B//8R81l5BDdci7EhH57d/+bf0dStXJyYk+P51OTYUefud9WiwWDc+H8mt4/Jzsz3scxKEkhubgtnd2doyx6a/WwTxyPh0+SyQSupYMaCuykAv5fF4VNF5nGHoffvihGTMnj3MiPIeq2NgMYa+FEopTqZSRVRwm5XxKHMKdTic2B5z3iJ8M/xLKzWEZxPPCYTbeJ2dnZzomDnFzwY2v3uUcWxELH8RAu5PJRFML2JDmVALO7fN5rZzPjDXg8Dr6yqFItB8ihoXhHCufSoE2+AwTiYc1j4+PDWAtCAUOjCsoEuWh8UXzIhZwtlqtGpxJ0Ouvv65j+rWoCuz3+9rRW7duGUFxcHBgDqxl+C8iC4Hu83r6/b5RKtjCY48LW6cMAAfhwUKD38PJg+jfcDhUIeYrYAqFgrEE0UfGqmGNHO8tFAomSRQMiJ+cHCoSCUlWAvkCWpGImZAgLRJtHIb6F7G5MyiZ5jlAv3jN4AUcj8dLKz3Rr9PT01iFF3/PlynzBcw4yLa2tnTcvPkxRpFoTbn6De/EOkBJ4jvYsFn7/b5Uq1W5d++e4TdWlNgTimf4Oh3OBcS48d6Ql8ZXGIG8ku/L9UXE4P2wl5Wxbtij5O8iw5zwQRIqE2feERHD7x5RnIWbBwplIF/+yXl2LKSTyaSZbzzjqxl9gioLSa465ARs5E+JRJ7dv/3bvxURkR/96Ec6Hrz37t27Rhn+5je/KSIRL0BxXQYoy32vVqumNNwTf45xsYKIvvP1TxgTlAK/j2azmfZxa2tL/84GHeeHDQaDGCYR4yD5qmROQObqNxQMtFqtIG9y7gx7Pq9duybXrl2LGWrD4dDgcXnjBmMNEQMN87h5rnn/4e/Xr19Xrzgbp8sgALhwBs9sbW0ZA+by8lIVupOTE2PY+374cfFdeqwoeBmH373i4skbWPx//xnjT3pML/z+4YcfakRlPB7rGjPEA4+jVCqZv+N5ngM2NtnJgKgMX9PkcQpDfeScV97jHoUfc+ojKZ6eCcUqkUhoEqjHxuHrYM7Pz03Jq0g0IZhMeAEwoez+5GR0vowYxCWYXNbJihgY34PuZbNZPUAZ34WFGYd1gPjOGE6MWM6YVtxPxl8CTADaR79YMLPCl8vlNAzE1jRbGMViMWbh8vU6fLcWb062yovFoio8mCcfCm21Wpqg2uv1YuCbLMR9+JX7jg3HSZW1Ws0cdnzIYk5TqVQs7IXPINA4NM0YN+yt8dAbHqixXC4bpZi9KoyxwwovvKbgl+FwaOaaeZarAkMAqiAPgsj8Oh6P9XPGpWHID76EmtvzFahsXPA1PSKRVenRz2FRYv3v3LmjIej9/X3Tfuig3N7eNn30FcBYK7a8Q4cNv4OV5Wq1Kt/61rfM83fv3jUo3f/0T/+kz4Mfv/a1rxnsO4wv5IXGezF3lUolBj3BIWWM3/d/MBgYK9oXGPgDwV8IHlJIWB6wrGAF1hub+KxUKuneZdwv/J5Op2PYXCL2mjH2LnEIl59nedPtdtVg4QuweU+w8cJ4cIyJx8QQOFAg+MJzDoH797BHjMNlIlEYnKsy7927p4ngjHHI8nZZeBN0eXkZBHn2ihUrJbyWzNciYtbKE3s7se9CIMgikVKM3xmx/erqypxXmBuuLOdIFit1zKecEsHnCkMCcYpEv99X/mE5yJGsUGoNK3CfBAr7TChWrOk2m01zkHBlGw69brerE7O+vh67Uw0/8QxfMMlVCbDYRKJJw/OlUsl4xNjVKRLP72JNGm0z+OhsNjP5QIxS7t3U+DwE38+gbPV6XfOU+CZ4ZuxSqWTuEcNPXxoNWltb07mEy54te65c9FAOOHjr9bo+wyjfXNl2fn6ubXK1HnuUfJmrBw4tlUq6uYbDoQoLzpnjfK90Om0ECsNv8FxgHAwgyRubXfQ+Vl8oFCSXy5nwVEi4MbHlyldygDqdjplrtuI9cCie9wqPV7o457DT6eh3vVeIq045X8R7bEWs0cN9ZPe+h1nAekPBOjk50StqXn75Zc334wvBReyhGvJYL7uuiTHOOBy0rKR9Npvp4febv/mbIhKFALhykPcp8rPy+bzeQ3hwcKD7HeEmD+swm82Ub9PptBqEnNsSOtxEFnvfgyBibfhg6Pf7KoeSycWtCBwW9dWUrKCgTTZKsIZYE4Sh9/f3Y9AS/X7f3CXIfMp3jnIlt8e28uvHCgxDT7Cx6RULUMjA7nQ6wfAizwVymzC/IWBexiPLZDL6GUP98JryPal4D58Z6AMTvo81TqfTBmSbYXWWEbeJ8CR7NUUWczYYDIKQDOw19XhzItazzXx3cHAgu7u7qkiGchEzmYwBshWxTgiRiE+wltg77HUajUbGqPH5riLR/mHex77i86Rer+t+9956T8+EYpVMJnUApVLJxKePjo40LhwCQSwWi+qx4jCOyILx1tbW9LsXFxfKMBw+4Vg5C0tOeIaA9PAOPOEQuh54k4VIyN3PsA5eqQBj93o9nZutrS1VFrFZvTVSq9X0s1C+F+cmiUQb+fz8XNbX103IDZYwK6UsoFi5TaVSmuDOHiXecKw4cfIjNgfndqBd9J8tV86TCiW2NhoNXVO+rLbX66kA+tKXviQiEnP9vv/++6ZoAOPA7wx+KrJwTe/v7+vvjLifyWSMEEPbDL2Ry+WUlzn8inw3kUjowkJngY3+37t3z/RrZ2fHJB17SqVSMdRmtAdPJYdnRBZeZQ+Ey/mPPizC9xRCyDF2kEi0/uC1k5MTLUh47bXXtJ3t7W1dI+4v72UcTP4mB/b08MHMbXA7bBwBmuH3f//3VYF6+PBhcE2///3v67vr9brKL8gIHwZir/jR0ZHKB6wzH9IgHzrLZDLGWMCastd+MBgYA4LzxnyoHePhBG/e43gW84PP+OBF3iiSoh88eKDt53I5E2aBp3Jvb88o4Fg7DxsRKpbhsWMfeQzB8/Nzc+CKWL7gOfKEZx4/fmwiGvl8XrrdbswLyHnAjM0lEmGnMVTHxsaGnjU4O87Pz2MpI3iniJhIgoiFmgmBAaOdkDHP/8ecY35Y8QnlV4b2EuYAf+fv4e+4fw/E3mc4PWCw8rjz+bw5Q9vtdlDGYe7Ozs5iuHZXV1eytramc8P3Bu/s7Oha8B2Ls9lMFapfi+R1kYXQabVaRkvc3t42d33hJyZkc3NTJwFeEe8G5xwORsBma65UKumicOiK8S98QiHjZWHhOKmSw0ogroRg4cZucp83w1WMeOb09NQkY4PY23d5eanJ2+gDV6J4AcI5RaHrZzKZjAosPuzT6bTxEqL/8EQ0Gg2zuRlNH8KAvSgMWsko4/hMxCbGiyxCTr7SCLzh8wpwISiEOIQUX3niXdB8CXi32zXKMg4URvOv1+t6IPjCArQ5Ho9NNSgDp4pYSxZzygor5ojv3fMWqg8dsILO7Xi8Hii6vV7P5G3hb6xwZbPZWCUT/o62fR4RFDOMlYFzGRMKV4rg72y0YA3Yg+iTcNEvtmLZ4gZxMjx/j8M9X/nKV/Tvf/M3f2OSgUEPHz6Uf//3fxeRCLEdPInCh/X19ZgnCQYZV4syr7Nh0u12tS3wC+cfcajEW+pofzqdxnICRay34PDwUNf95s2bymOskPm58qH7+/fv612uqVRK15pzczAnaJvlmUcUR//w92azqXOAcWQyGZ0L71lmD3wIT2qZUiVib5tgpUzEyhf0IZQXxW35Cjr8H2PjGx840ZxD+88995zBEsP8c1I5y49lSlUo78or8/CC8d890HEIP44T4weDBcJ+yKOMnxgHe8UhV/hOWd935hNWvvgMxjx2Oh2Vg7u7u6ZSnfkH9O6772q/GL8rRM+EYjWbzQyCNOcKrK+vG9BLkUjQYVFYCWMhwpOfSqVMbpIHuRSxKOHD4TB4ubC/qgBKBhiBhSy/nxmL74jjZEuujPJoxoxezgm/nJwuEs/l4jJ5aOMcq+eSaH7PdDrVQ69QKJiye2ZEEFd3TCaTGIAnCwWRSOFDf8CofNUDW5Ccq8J95HuuptOpcbGD6vW6yWPA+rFH8O233xaRBSgtklLZO4UNt7OzYxCqWTCwUA+VWedyOdnf348dspxMmkgkVBnH57dv3zZXOiWTSfnwww91zvBOrorhww1Wf6vVCoZPuMzY5/HwPXFQODm/ipVYhEHRJocY8T30lxUW/snhIQ4zsRB77733dF8cHR0ZIFV4Z2A8QICDV9fX12M5hHxI8D4VEXNIcTgVXs7Dw8OYR0gk4kEoE4VCwSgT6KcHh+Rx41kGfmRl9eLiIuaNuLq6MldcgU99NSHWdD6fx7yKXjm4deuWehVC11Oxp80bgVjTg4MDk5TMILJYn/39/VhVKH5H+1hDPIf2z8/PDQwCP+PbA3EKgSeOXIjE7/0TsTlZIO+tabfbQcWGc0T9e9EfzNEXv/hFvRz8nXfeUUPVG3SQQ+12W/nxxo0bwYpZrirmsfJZ6BPUfT4Rj71QKCxV1kJAzaxYoX8ecX8ymRgEeTyL/rCzQyTiU/AD9j4b+Ht7eyafjyv3OVoCLxnfK8zn1tHRUQw8eRk9E4pVMpk04RNeOE5Sxua6c+eOhg+uXbu2lAFCbst0Oh3UhsfjsR5qnU5H34lDgO/88tqsv65GxFrWg8HAHEjYpKPRSAXgfD7X/sB6wyLi0Nza2jIXXqJtzBcfRiLRJgKjIRkvk8mYpF6fKCuyQD/HOLgCBMTfYwHV7XZjN3/v7+/H5trDBIiELSgWyixELy8vjcckpMyw8uUVUP8eeEVwCLIHDYJrOBzqWpfL5VjoDMTeEJ9nAn5izxiX/kJo4LlHjx4Zg+Hx48f6Xty9l8lkTH4a9wvjZsOB8504xM25Q+w1ZG8Vh2t5TSuVivbT535gTvgyVbyb5zebzRqkehAfagcHB6p0zudzzXfBnD958kT3hs8zunXrlkmkFrG5dJwszcQo/Bxefu211+S1116T73znOyJiPWvow89//nP5+te/LiKLHKvz83Ozf3Z3d2PXcXFuHCcIo88gGHci9iBjPgaNRqPYhcHcV8wBiAtXms2myioo+lz96T3KXAiE+ecL6tkw44pszp9lHC0OEYssZON8Pg8qTzyeUFL5aDQy+w6yxKdphKAUOHy+zLvDzgJ8xrKJKztFIvkGfuWqYj4rQmvnQ9FcOCMSrQM/n8vlglAJbPj7/EjQsupAboNlP3tcGRMLPOWrDln+ePgiLlQRiVdB81VjmC82PNkI5uIAvnuQc+fQb68UQ/Y8jedEnhHFiqlSqZhO+6RjEVs5yM/yc1xNwIob2vTfTSaTpmwXz0Mhubq60oXiJHr+WSwWdYFv3bqlzIrFELEKCcfVm82mvp/7gXZFbDI6bzho95xTJWJBKzl5nN3rrBRy3hMsVVwjwP3h6jg8wzg53tW7u7trkv16vV4scXQ6naqg86XlIM4JYwU1nU4HcW0Y7G80GsUsdHb5MjWbTb3dnS1JhmzI5/Mm/o73nJ2d6XvYg8jrhc9ErPCC0u+T1XlMDFiLQ2o2m6kCnUwmzUHJlU989QbnGYIPOAzEkBT5fD6Wz5fL5cyFq4z/w95ezEs2m9XfmS94LlKplDnIQ8KLKyHX1tZ03FijRqMRy/3CnlxfX9d38ZUwvhqK34Wf2DP1el0PD/T/d3/3d0VkYWCdnJyYfE0IbChW+XzeKMsfffSRqTgTiWQD5Jcv7Ub+TjqdNpeyg7zXEsR5nCLxC9QZuBh9YG8Q7qHkECtjdIksAHQxRxsbGypLK5WKjo9DMxxe4sPz6OhI5w5r6kNwXmnyxJV1eJeIveg+lUqZnKmQp4Xfw+FK7BHG9sJ7Ic9YCfmkA5nfdXJyYqpYQwUM7Xbb4IUhggN+ffjwYbDIhvvux8nJ8CIWtJd/iljIBFYWmc8ODg6UP9lI855ln1PM1Gw2zTiZT6Hctdtt493nVA5W5BkqA2vEKTpcYMHvGY/HTy0EYHpmFCtsNGYUEcvoPPHM/LDa/K3kXIrO7j8QW9y1Ws14Jnz8dzKZxCAVEPoLleQC3oD7jfHh+UQioQm6a2tr8v7774vIwtrzFXgsjEHr6+umr75aL5+PLq5mFzra90mx7JmCMsUXZIPhOD8DbcKixCbJ5/M6141Gwwj+arUaQ5dmGg6HMQBC9CtU5ppOp2N5RxgPJ+H73ApOqgSvNJtNk2jOYclOp2MUdN50HAZjXvMgn6xoiNh8QYZ+YIuN54CrrfCTk4g9cB1XR7Fy58vjRWzOA+d+LQtv8rt6vZ7BfvPv56RZ/Dw9PZWtrS2TnMpeQi/Qfduh8MDp6akqWXDpwxLmHCfeJ2xo+SopfI6/Q+HmseHeMHgLGo2G4R+8H+u4s7NjFAuGemEIF3hzWfnDXGMuGJaD9zhkDnuEe72eQUPnuzZFIq8Ag8vyPHGeGh+84BGEp5F7ib2xvb2t7afTaVP9yONnGY/5wgEYMn5Ynviw2nw+V57pdDpGbrIs8yDHItFcF4tFXQ/mPfC+Bx31beNZb8ixd4er3dFn8AzmiMPtDKTMMu7atWsmkR8KbQh2P6O7XAAAIABJREFUQSSSU4ysj/aZX30VscekY89uSAkVsevLRhvn5noQ1WVJ8BgzxjadTg1fcwie80XBp61Wy9zGwcYA+oX7ijFHIjbHTSRS7j/pYm8d/1M//f+I0um0Ki0e6RwhtZOTE3Mw4vfDw0NVcFgbZjiCra0t1Y45Ds8J2tVqVUMrPJkc+sCieSGIdgaDgb6HK234YPQJwmDA7e1t9YR5xoZFyfFsCKvd3V2TC8Nzx5uf8zX4QmF+npPhUaUjEs+98HhZXD0Ez1Q6ndb35PN5rQwSidYDGwPfu3btmgEIBXH/OGeM84TwHJcK+/FzDhuI0cB5jTgPTySeACxic8k4F4QPYUZL9q5t9nyBx/iiT87B8qFxkXgyfAgGg9/LtwNweAS8xe59kWgN0D5X6rJyzXNdKpViZdOci8JhIP4eX9cymUyCwtsTl/3zgYB3evgVCHPM76uvvqrPN5tN431gS5sVLs598t5G9lKLRHlgbNk2Gg1jrLzxxhtGxvT7fa0QwwFwcnISw6AC8QXPrPxhvOCZUqkU85BwRTN4hi+c5ZxVhm/hcC3a/OUvf6nAqfBU4TPkBrF3lAss2AvHJfsMjsneKT8O9J0PZ+yj6XRqwqjLcJ/YI8sV5EwsF3j/cOgd7wxdqcNzwjeAFItFk0KRz+flyZMn8tprr5lQHCsb4HteoxdeeEEV+sPDQ/Xw4Uzk2zJEbNFPs9lUlHGGF/IKnM9/89h6bDyB2GBkPgqthTd4MV7+G/MAV1LjXXiGb9LgtAJ/e4WIPf9SqZSeSZivq6src14zQr8/x2NjeuqnK1rRila0ohWtaEUr+tT0THisRGy+BVvo7AplEDv2EoXcsuwO51AYcqb4GgcQLAVOpGSASfzur3UB3kyv11NvVrvdjrl2+TsikZbMCXuVSkXOz88NHAP6LxJp5rA4QxWPIuGkOvb0pNNpnWvcaQZLkhPKQ1fBcLjW44X4HJHhcGiSGnncDMIHzf/i4sKgDGPd2eX73HPPKUZWu92OQTKwZ0HEep4YVZvDDmzN4Tu8Xqenp6YSke+cZGJ8Lc6D89WKvrKK54YvyuX8LSbM+/n5uT7DuTH+3jn2hvF9ix6PzINj9no9g36M3C5OOPe4PR5rjIsp8vm8CVmAOClXZLGXmDyOFn/fo0NzKBbrDSsWocsXXnjBrCOX8LN3iHmH8Zmwf9A+PGIABb179656aNlDDi9CNps1OR/sqeNKSZ5HrmriMbM3wVcWesgU3kvr6+taZQYvDSdLi4Q9RZPJRMfxve99T+eU99He3p7Kgfl8rp6D3d1d9Vb5sBGvXyhvzHuIQOBv9uxymC2TycRydDFHnFcFz5eHZ+E8HZ6Lp13uLBKtMaeIgNCeT+HAWvEF2+xB5ud5jfj6omKxqHzK6R68NswznGfJYT/vscJ68JkTQoHnOeCQos8T/TQU8mJh3BsbG0bO8zzxemEOMCfgFYbDYbxEeP4gGxhmRSQOffM0eiYUq/F4rMJvPp+bBULSoq/W4KTc6XQay49gTKBMJhMT6j5xmzdOKEHt8vIydtEjx8LRd6688lVPIhZniG8Lh3Dn60ZwoOD/7XZbFTEIAk6W5k0mYhVFDkkxIr1IPFmw2WzGsLREFkn7vhJnOBxqWwhhofJGJArzeeBBkWjOsUZXV1d62PAly9w3zjn44IMPjFtdxJYeow8MEoc2GFjUK8eZTEZOTk60naurKzMXIcXVH0AQOkje9iX9TGwwcCUVDqPnnnvOYI/hIEulUgqcmclkdF58gjLe7ZVMzhnC2Fj41et15T+GusBhxLyO+YWii4O/3W7rXORyuWBoz4O7Yp/zQcPEeD+sCII3OZyHfQHBytcIcU5NqCyeXf0MN7G7uxsDUPQI09/85jeVx7FeyWRSf793756iyotEPHHjxg1pt9vy1a9+VecTYVrO2RJZHAw8p9Pp1MgQERsawXuwD7lSiuUEh+hYPkIJ63a72q/JZHG5L6dhMNhioVDQZ6DEhniUK7vRx9Clxpz2ILI4Hzh0PJ1OjbzxFWToIxty/pqmL3zhCzpekWjNuMwfeZicd+VlANriwhrGUeMcX6zTRx99pPzK1ZIcTvWQBuj7/v5+LOF8MpmYajdOF+EwMsulZeCX4Du+s5bn3YfyuAqYUzjwbKiS0rcD4lspGKhaJK4wgcAb/ooeXmsYFZPJRPcneKRYLJr3cMXjr0VV4Hw+11vkK5WKsVrBuFwtyFgYWAxf/p5IJEyOAhYWTFapVGJVHCDGJOKcBGxurnC7efOmHtrD4dBY1yGYAo7Fc2UKCEi+GKf/ub6+LpeXl8aq4Of5GgRYqFyRJWITzEUWCiLnAYEZOf+B++jzPryS0263jYXBmxt/v7y8VCufqwkHg8V9jmx1e6A/9JGLGjyOFf7PHgG0yW1jnfE8Do9ut6sbjZVln3jJuUfgpdDm44MN7/Pgh5PJRIXr+fm5Uaw4j4k9m8wjIe9FIpEwcAje8vK5XLlcLpagycmufBcXj4eTztvttrn7EM/zGvE8cgUmK/++OIMPG6/kTCYTVQLQNtYSc3F4eGjK0vF3PrD4PTyffCBAUKN/mL9vfOMbKhP+7u/+TttGHz/++GO9IoeJk9RZsWs2mwYjjz2qnFTucZnY+yMSHU5oF2CsV1dXuh/r9brhgclkYvaBSFQIgHnZ3NzU9eX+5nI5c+UYzx2wgbyBESqmYH7xFWP+EOUq1nQ6ba7+4nxR7GXOEx2NRsq/bKTx//mSZPTdo5jz3GWzWeU/RpvHnppMJsZZwAo4vPKcf8g5tR7Hij3xrKDiPSyrG42GzgErvOBXVpT4bPV/Z0iMZQoRe8ZCz4SS+5cRG4aPHj1aesUTiB0ljN0mspAVfNNCMrnAqoMc3tzcNIbd8fFxsPo8RM+EYpXL5XSxO52OgSfA5K+vr+skhPAuvJeJwyObm5uxKoZHjx4ZBmWm543AB1aoIo03KN/XdevWLdMOqNvtGnczI3Pjp78nEJYSxsCWKnupOOTHz6+vrxvIAI9PgqomaO+np6dGoVxbW5NOpxMEy8N8+YTTfr+v4Qv2MojYUBGqkNhL1u/31SJkJTadTuvahzY0h27xPPiKy3A5ERvKBm/q999/XwUmlzCz4uovMg0pU5y06hUx9mZiPsH3k8lEDY1yuWwUCyjLbCycnZ3FvKk+pHZ6eqq8XKlUzJ2IInb+0Wd/DxyPzSf4430MJbC2tmYUK/zdK8ihKk3erx7k0l8/xEo8wwFgH8ErijVgw+DGjRtBT1qz2dR99eabb8bAOkOeLgYzhNKO7/X7ff385OTEyDjMEePKcfLvshsSuHqVvXDMax70la/hwnswfm+YlMtlPeSx7zxCN97JYUYu5uBKTy5S8AYl/s+8jAOOwW35HSJiDngOH4EfAeYqEvGGv14Iz3PSM54VsVVvmEt4L3kuPOBqKEKQTC4urWYjhcfPRVJM/moyUDab1b38xS9+0RQP4SfvWwYOhVxjxPZ2ux27n1VkOejq0yAkloVR8T1fEPM05Ws6nWp/ut2uCaUzyCfLYYYFQRsiC0NrOByq17Ber0u1WjV99cVQwFIUiRvWnp4JxUrEhsuYmbl6CQzE14aw4sOHXS6Xi22Ser1unmE3eShPqN1u60RymSpvcr6HiA8BdreyZTKfzw3qMwOz8Xj5dy+4s9msycEJ3ULe7/d1/KlUSpW7+XyuzwOrCl4jPtTQR642YywfpnQ6rcwK7wpXkqXTaRPnx3xdv37dgLJy3zF+HieDxLHF6wU3CAcg+iMSHWroIyvOfCE3W5P5fF6tSc7XYKGay+XM/7kqTsRuQi7lRfsikduaAfXw3V6vZ3iWc9+YODwksphPLs3nS7ER4sT3vABmYt5EH3u9nplvvCeXy2nffY4P2g/lvKAPXkHlXAmM31fu5fP52P1vTAzUKxIpWiEQSqaLiwsV6rdu3VJBDt58++239WCCYsQeR/CYhz8RiXiQDcGQdwFCXiRSWmq1muZysRKNcZTL5ZgV7bGPeKwcxgx5i9Dfl19+Wd555x2zdnh+d3c3djGziPXQs8XP7+Z56ff7qmiyd5YVJb+3WRkUsZGO27dv69rwPB8cHKiCyHeUiixkJ7yd3iOWTCbV8GQjlWVcKHTJYTA2wPwe4PGFYAcwF/1+PyZ7OMR979492d/fD+JSiUSKlZeh6fTiKhre4/y92czenuEBUv3v/tqfUE7WMo8VOzgYZJXlPMs/7L96va7zPhwOdX3BozASQqkVkFXsYfVj4grTT8q1emYUK15EjiPzvUicrOtzMCqVivEMseUKJjg7O9OJyeVyJiG00WjoxGNz+XJjr3iJRAcKA0Jio6MNLxSSyaQyRSKR0OchFBibh6+EYWJ0er5jkL0UfD1IKpVSxaZQKOimRp6Hjz2LLCwjMN5gMNBxhiAjNjY2zB100+lUPQUMTiqyUCpGo5HGrflaFiYW2Fw6zHyxbHPPZjM9ECE4j46OTFJlyOJmJaPX6+mBxvkfPNec1D4YDIzAwnu8EPEYTayUl8tlAzzIViqjBoeQ/b3yj/fs7e2Zq4xELHCtv49uNBqZ/ojYPKFMJmPmgAFi+fJm8DoOo+3tbSOgGYWcrzXCumcyGcMD7Xbb5Et6VHJ+Ftb73t6eNJtNM3d8/Q1fxwRKJBLKE2+99VYMLfrRo0fy4osvioi9HNbPHR9woaIY/p6IDYH6ew3xfU5wx++7u7va32VQA4lEIlYQwxfOcjgEc4P2cDiNx2NVWtbW1pYqZfg/J6bjnY1GwyjL7XZbeRIH6M7OjsnR4fxDkTjIbK1W09DW7u5uEPOJPfpc9MOGAHgEYwePJ5OLeyRxbozHY3MbRwjbKJlM6rnE67y3txe8n6/Vaj0VfgB9Z4LMhWJ/7969pXAxpVJJ90UoYb3RaCgmGYeWOSzGShYrT6xsDIfDYJ+fhv/F+Wp+zPy5yILH+RmfW+c9VN4Y9ZAQIvYqIn+2YP329/e1CG4ZPTOKFYe2RCRogbKiBGbC82tra8YrwlVKqNABtotIXFuG5cYXM3LFnEg4kX4wGBitOmT9LhsrNh27xi8vL2NWjUfwZQbi+8/YM+LvfsLmYnR2HC5olxNQuaISP9EmgxHyu/L5vEl45tAPCxUoxa1Wy2DPcKgGP/2Fu3w/o4i92fzy8tIcDMViUecVlmqz2dSDge9s47wVRvnlpG5ed1bK+UoZJkZ2BrFFJSImSZ9d4uwhwXw1Gg3jtWRXO9aAlXYRMeCb3pvDVjtjkYlYC53bxpp+4QtfMMoo8JdC1Zr8OysdItHBxcnQIPTF71POqxKJ8HqKxaI5PFgmgPr9vrnZgDHbMAf+Ch7w0sHBgXo0IWNqtZoqprjKBms2mUzk29/+tvz4xz82NyuwgsRW8+uvv66/Y43q9XrM6+I9GWxsIkcun88HgVXRpvcickh9NBoZ5RqYPZlMxtzBxtfl8GHLY+DwvM8Zffz4seFFVkqwV/wtAJhb/ETfQFzReXZ2pjzAd4Vev37dyCTIDfbIYmz+LsZKpaJj5DQBEAPkoi3MF/rF+E+Q/eAP7JnHjx+rF99X3YqE8fs8/8LgEIkURAblxBpUq1U1DF555RUd2/e//31jPIGurq70eY/BJmJzRfFdf9MC5kPEOj4YXZ5/+jzOEPE+Dd3SIiLmfPCE9eWQOmQz3/Aisljvz33uc5o+s4yeCcUqkUjEkvR8yIU3faVSiR1QXMIvEk2Uv2iVFR9/QzYsk3q9rooYeyJSqZQuIAtxvvdua2srZl1yDgMI7bDXhZG/+dDG5/wzlUqZ60mgPYPxRaIEXSiWDAHAzOXL0iE8U6lUDNZhb2/PxKz5YGDoBFaQuRSc55tzDsDYzMR8dYGHdfAhrmQyafLTWPkZj8eqPLL1z7lny8Jf3Ec8E7K20C8IQ1bKOPTmCevN1Ts+vCFiD5hOp6NjXV9fN3vDXyXB1X54Bu1ylQzCwQcHB8ai63a78ujRI9nc3NT19dcfca4jxr+xsaGHU6FQCOYvsQDmBFL0r9PpmNwKf2BxJe3HH38st27dMt44vJPDu1ziz/KGhbFPRAY/djodc1m4SCR3GGiRCR6BF198UQUwA5H6gpCQMZZOp7Ud8DAXBYhEawxZAiWvWq2acnJflu4NV77Qnt8hYq+4goxhby4ONV9VhnehPe9pC3mm/Vz0ej3lo1deeSXmAYQxynlP7IHBIcgGEyexA9CUx8kGLoOB4jMf2i+VSjGvF4ghYCCT+Iz66U9/avrEF73z2oTSHNjYuLy8NFWEIhEvcJ4U8xcbpD//+c+1X+B19uCyR+3Bgwf63tdffz2Wd+m9OwxnBGOb4TzYQPJo/Bj/w4cPtY+NRiN2DZqIRdnnHOdyuazXmHlFNZRu4ys6RaI15JsIULG7v7//61EVmE6nzUWdIguFyV/cCsKVBHwYecHvy485VOe9LtgsjNvEyMXY5P6SW2a+09NTteI5vsuCZDqdxhCoLy8vTbWB17q9K93PBf7OApIh/ENVfO12O1bhEHKjcuiNMXn4OhcOW7Ey5ROk/Zpy3o+v5kO//Vx7K4jLzH0CNldr8RUfXMEG4jVifJNQyCaZTJp154IAzBOvUbFYDOYPcJvn5+fmQOQkfRAn41erVVMZC/7kMAX/5KRYrNf169dNfh4LbPAo5+UMBgM1QM7OzkwokXNNWPkDceiA15q9RFx1xZ5SLkufzWYasm82m3pzPR9GvhRfJDrgOaEafIR7K0ulUqzwg6/7YG8xzw/mRUS04ICFLsJTd+/eNWGTv/7rvxYRkT/4gz+IwTaAuL8ii5AsfnK+E7wM7HEPeac8htHV1ZUJ27HhxcYIh4V8Hil7yfCeEIQDK6C8NlzcgfdsbGxowQl7rXk/cAXqgwcPtD/Xrl3T93OJfLValc9//vM6NhieUGpmswVsCvrHV56I2MKhfD5vbgzhMXGIKXQHHg57PIdDO8RXvF+8V4YPf85NxTPs0cfzGCvav3//vlEEMQf8PVYQW62WyieunmT+ZTwuNqj5rPK4kSCW/2jjF7/4hRYe+Bsn0F8uqMH3/J3BTKEk+Xw+r0bEzZs39QYBHisXZyyjZ0KxYmgEb/mEwNKYICAZ8I3/zt9lt6kHRAQcwGQyMQmZWBT8rVgsGiuIS5LH43EsqY9vQsfzXoDyPXYMQsaehX6/b8bHHj2MA1aQSMT8HFbwOWuM8cTJmHimWCyaA40tkqdp6wxOCKUMwo1drCLRpudSd2Zc/I0P3n6/HwwX4j31ej12CbVIpLh6t7NIJGihHPChurGxoe/hS5E5BML8dXJyYqrfRKIDjo0Cb3GjfQjjfD5vQo5Ya+5vJpMxXk6sF/rFgsVbYpz7xW0gn+Lhw4fBO/6YT1gRxbyBx6BEbW5uGgPAHyreu8FeAs5N89Yu6NGjR8pPsPaPjo4M9pFP4PfzyPlpHIZgfueCj1B4rVar6dzCM+Yr7l544YUYrhq3gT5yBRd7Ff1+w5xwsjwOfigH0+lUD7179+4Z7/fGxoYcHx9LsVg0hUAYmz8s4JWuVqvaL5ZBIM7xE7FVYOwNYi87V+u1Wi1dU4zj1q1bJmGbq2hB3W7X8CHLVX+xr5/Ts7Mz433EODisJLJQLrjYgWUFY9axcslKdKiggq9VEVkoWtvb2ybkJBLJr1DYH8/7MXOOmYchYmKnBJ4vFAq6xh47Eso7404izMgVqnivxyPLZrN6bRyHbj0oLSuICP0Wi0U16nD9k0gke9no8d51gBuLxHO40um07nmkmFy7ds1gs4UKHDhvcBk9E4oVg3li4/gLj7kaIlRtkEqlYu5Y3IsUshhms5k5hPEeZqxkMmmSXEUsfpCIvQAXbnF+Vz6fN4KU8ZdYWEMwTafTYIUgIzEz9AQrc7yxe72e8Z75zcXMAcGCsaLt3d3dmIDEOz3+EJ7hefRrCGZn5ZaBYRuNhql0qlarxuuRTqd1s0OZ7XQ6RlkM3QnFpbpcio1ExdPT05jSirnY3d1dWnXJBOsafMI5F8yDXmHyXgkRWyHoMdBwaLLSwDhksMJ92PXo6Ej7iBDT8fGxjpMxutAHEXsheSaTie0lH3LhvKt2u617ld34fAhzzhXzO+aF87pEFsL9xo0buo7vvPOOznWtVotZrZgH8MD5+XkwhOPvP/OWNhsxFxcXyms+kZbBWtnji4OP+YgNIBHr2fPeJf9/L8PQb1acufCBFWSWB/AActEEv+f09NQYQD6c6McUqphKJpPKI9vb22ZtqtWqKsm+WAVjwrzAKygSeakYk49hZzBONhZarZYBBfUGrje2RWykAt9DG/1+37QH8k6AZUnoTNgfnBKAebxz5465OJjnrtvtqieQCyywZ7/whS/EQuL4Hnui2YOJd3uZjzHy/lkGw3B8fByLvHz5y1823ij2lC4jrnpFWJ09mPfu3dPxecwu9A8GEufNiizW9Pnnn1f+q1ar2sYyz9SvjWLFB4a/8gPMsrW1pUIQwtvDJbC7msFBubQdjMC4MiKLSS6VSib0BgsKDHdycmIOe06aByPylSw+/yKfz5tKEpHIAkXfQ2CQ6G+IicHs3pJkhY/xOkJ5Qh66wh8QIvamex6riBglz182zOSFCitRHJZkoDZUV/oxYe04zDabzYzCh2c4pNvtdnUOML8+qZfzXy4vL2PhO69YlkqlmNeCkZi56obnwMM6YA2Xge6xK71YLKpyif5ubGwoL/rrlzKZjDQaDXMBcbfb1bX3eX2cTwh3OBKkMWciNpkb/eIQBnsDMAaeg/X19Vju13Q61bVpNptmT0ApvHnzpqm8xfyfnp7qweOVcvydIVJ47zLPjkYj3fvoCwpNuK/8O+YMXsAXXnhBwwqbm5smLAWaTqcqI55//nndV3iW59zvKS5Kgcdjd3fX5Ihw5SZ7TUOwJaFKVRHrfWB+RHvwcHO/ODcJ3+PEZS8X8RwrluwlgvL33nvvxforEgd7BPH4Oc/TX64rYiFEfLUj5D/fwMFV0KzgemWXDaxlxJWe8JjwGcLygMeNfvE1L+1220RpAKcjYnOs4Mxg+A/GIfQOBIZ1QMqDB6Vl8hWcOzs7uif5FguveIYUUfbQ87nNILWj0eLKN/aWsZdZZLEOnH8LOcBKm1eeOC/zk3Ksnv7pila0ohWtaEUrWtGKPjU9Ex4rEdGSabj6vZs2n8/H7gTymEXstWG4BjyfSqVMQnUIiJTdf3w32jJPzHQ6jXkj2DLiCjt8H++ClZlMLlBiGfCNwxTVatVYorBUYPmVSiXjeuWKN07M5TubOBlRZGG5YczValXb7HQ6atVks1m1+NnjsCyswncZ8jwNBos7w/huPs4nYD7A+DlpWmRhWfP9XPx8LpczXkufiFyr1UwJcafTMfhXeB5jrtfrsXwc9gzhPbBm+aoJf5cjPBqh3DDMAYghE4rFovYZHh0uiebKGpHFehweHpp+Yd5LpZKZV7yXq3uePHmi1jrmB54uzoXkfenvu+SkVvwfe56hJ9AXLtdnajabOmfVatUkXY/HY6nVamZ/D4dDkzeHPY51rFQqxkPLALt8CTe/33t4vafh6OhI54krotji7na7ajkzVhDLIx9a4+ti8Pxbb70lIlGIEn+bz+fBPclAipPJRNcG8ohzXECcy+rvGhWJX+fC4RSMmUOmzGseRgLPI4zJhRL+ih5+HsSFJP4+S4zVe1xFLIYR+uIxznq9nhnrspBQ6AJpng8Q3sc3Kvh7JkUWeXUeIBSwEyyTGe6C+879zefzZu/iHAEvcl6XiAVH5ijB0whjQ+Xx/v6+8qaPHoFYVjLPcJUjywP2lqbT6VjFPRdsYF+CrxCaZngK7nc6nTY8+WnGDHomFCtUdoWQXRlvBpsCCgFX4fEGEQkLhWRygXzr3dc+DIDnfViBFSOReOWESFywhNCHj4+P9dDhhD1OjvSw/WD+brdrABdFIkHB/eL5WFtbU8HESLPoO96HMXLlGcfV/UEiEq0PDidGMuecN1+FxyB9/tZ6j3obOhjy+bzZFJz3w89zlQ7j7XBYiucXdPPmTSNYcckx5z74MBP4FQc8J6CzEOHk29u3b+uaNZvNGJgm58uAeF35ZnYQ+oHnwEMIlXG4o1wu6+ccchJZKBxbW1sahjk7O9Nx450+NBvKu+EkVE5uxxjQZ+BBNZtN5VPOcROxIRPO++EwPP7Ge9PDtfhiGb7CBvPBaPIi0R4IFcVwHtve3p7O3YMHD1TZeumll+Ttt98WEatcs9xjA4vXnYFH+X3JZHRZ99HRkSbftlotA/jLYW3sjcFgoOvHWF++4pYPOJa5GB8jzTPv8Jpx6IkVq1BCt8jiAH38+LEBJ152rRKIITQwv74A5/bt28rLzBusKGItsAc5J4n7x/P1NOKqtdAhjX2BRPVyuaw8DmWKc3p8zi5XH7JBylWMy0Jr+Pva2pqpnBeJzgk2NMbjsTk70RaHiH2qB2QF8pd477fbbU3/Yd5hecBhQcagY572eYFYH1TF12o1oyiJLMLDkJE+1MrtLVOmluXKgZ4JxSqRSEg6nTaXBYewc2BRgMkZMiGRSJh4OieR++sTROI4PzgQOTm91+vpInKeBW9WXzYsYu+N8gcWBMrV1ZUqB8Vi0SReQoHi0lsG5huNRjHLazqdxqAJsGmr1apUq1Xp9/s6r4zejjmHtYINwcmZxWLR3C8G6na7OjdXV1cqqFG2y+X1mFfGDeJCBB4r5tPn0aEvocpBXzKPtRsOh8b685AErVbLHPapVEr5jnMoOMfAA5Fi/dBfvuSbE+a5isfjDHnPH2O0gPAeLrJgyw8HBw5ab3A8fvzYJI1ifQeDgfat0WjoHDHsAPMAGwXcByafQI+58J6Ovb09+f73v68ek8FgASjLhRr8nkajofx9cnKibbKgZw82F3+wAsOeLs4XQbI65hVj5X3HsCwgVgJ57e7cuaPv9BY6LGjGzmLMOD+XZZgbAAAgAElEQVTHoVwdrClfKt3v940nFPsklUoZowpryWsusphLRkrnPYh18bhUwD8TWXhirq6uTAI4FzlgPI8fP1YPK8NGwADLZDJmPLw/GBIDP+/fv2/memtrKzaH0+lUx53L5WL3FIL4PZ90qKJveN7TbDbTfYQqOTx3cnISuwuTPSdc1MDj4Daurq40f4qBpEUWSkU6nTY3e4CH0Z+joyP52c9+pm3zORxSfESsolksFqXVasmrr75qxg057HOcQWwA8VnIxS9sSDKU0Gy2QITHM5yHyHnX6APaYE+lN2D+n9AzoVglkxHibaVSMZdxisQFd0iIikRKkE+2E4kjC3tvBf6PSWeBwZYiJ9Oy14EPCT6kILDZNS8SDpexwGalg5n58vJSBQBX1bCV5t3P/sJOvh4k5M4GE6PyhuELWPP3FhCjGXOZr4hNUvS4Ysnk4lJSLsVG31j5E7GJsKFNwThXIhZ3BAnFLLB5vZh38vm8zn3oChuPUo7S4WazaQ4vtH/z5k1dOxaE7AESWfAgH/C+0pXD1P7iZRGLx8JzwN5IKF/tdlvX3BdyYB455LW/v29CFjweTvoNKQJcOcqHFrxUBwcHxmPCygELOChT+XzepA14qANfZg75IhJ5XY6Pj2VjY8Mo0LyvK5WK4UP85BAoe5BB7E2+urrS9vv9vvzxH/+xfOc734mFXPF/b4Thb756yhcmnJ2dqSzZ2dkxCOR8CCHsDPBXkUhpwt/5qiAedyqVikFF+NA58yArTTA8Gf/Jl+Zjn9y/f1/HFLoeJoRluOwzjIN5zWNgoS+cSI/55/SNer1urrbxqQifRH4sHo9OZCF7T09PVblhjywno3OVH/qSTqdV5obC2DCa8T52UCQSCcXCYrBRNgIZ8Bl84cOgLHvhpXrttdeCmGL1ej0Y3uUzJJvN6jr5ojYQg+2iirzb7RpjHr9j3nwE5datW2ZeQgrfp0lYN89/6ic/Q5rP5zIej02M24Pi+Ri+iMX0yGazavGK2IoEjrH6UlZ/cHU6HW2TS6U5NMSMsL6+rt4BWAn3799XgeKrKyB00+m0PlMqlUzJvEjEpB988IHpFx8eHqF+bW3NeNL4efbq4flSqaQHOeNlHR4eGlybENSAV0K4GoWRxNFXL7DRH84n4dvgQ0jYPFZeD5GFNeUZn7GTOOfNI/hyJSjmSSTiDRwSXBHm38V5ABgTA1JubW0FczH4rkQueWZPl3/ehypKpZK59w3kcbtYMLFXEQaAB8fEXhqNRipsNzY2TNiNiSETQrAAXHWEQ11EzJ5F/zlfpNVqmcMRYx0OhyZfA3g6rIR4zw76BT46OTkxeUccemdsJsbPwZ4djUbaDh+cPicM68sAqp53OEQXqh5dBqSIw3g2m5mUADYKOL+Uy/F5z0JucQiaaX19PRYCrtfregCtra19IqQAj9tfncMK+NOw8jx4MuP/idh9698pElfS0RfOKUMbXCW7s7OjczKfz828L6ts87hpnvw6s8fx+PhY7ty5E6wCn0wmZp+DRxuNhhoak8lE+dSH6nlPsAJxfn4u2Ww2BnQMqlarsbQE9u7MZjOt2BWJYB5E7F7OZrPKp7PZzHjkQJxfCfR0fJefAXG1rz/b8Tk7NkQWSjP+fuPGjVhVq58rz4+faSgwkUjcF5GOiExFZDKfz99IJBLrIvK/i8gtEbkvIv/9fD6/WNbGf7SjiWec/yGyEIJgFLaUOLkcGD0ikWbK8VtsltFopO1icvF9DpH5C1e95csHvkegFomEK+Or8CJw+AAMxTfTcwmoRyOHht3r9fQzKFMbGxumX5VKJXjXIANrhjZboVAwljuUL547PkhYuBUKhdgdeK1WKwgEx4SrB/zvItb6BbEyNxgMYuOAIoC+sOcOOS9ra2smb8RfHQNUa/YI4p2FQsEIS1ZaQnfQffjhh4qszO9JJpNL71MELdvAWJe1tTUjCEJXlvB7e72efndjY0P5n42FTqejCk+pVNL5ZHe5zy/ivRLC4MI4PZAi+GR/f9/AfICXoQQiQZ+BJLGWk8lEhTr2KgB/QZzfwXAQfDD6kD0Q9NlbwF4qxosCVatV3Se4ckdkYcH7w5694Wxp84GLv/l9xPmffCch+J29VdeuXYtdXo2+4/34GxfJgKBoMI9yHtWykBcfjMsUK8554bDrJ+UwhT7nGwT8LRn5fF55BvN1eHholHF/y4VIdJ4gSZw95Ny2L6QC8R2oIDZwvXcEcoXlP88tY1SJREqBSJQ/xnzic2qxn72xHWpfJOJFPhcSiYSudygZPpvNBpWTRqOhf/d3DMJrxrzAa9DtdmNn8OXlpZnfnZ2dmPHfbreDffVnH1/azYprKK2B2/ftBp996qefjn57Pp+zZvFnIvJ/zufz/yWRSPzZf/z/f35aA7CghsOhbnJ/xQ0YZD6fG0GPCeHDkw81zsFJpVLmmhMRMZYb/h9SqJYpBQwgx54fELtQ8ZxIJAy5moFztUT+L/be7Uey7DrzW3HLjIyMvF8q69LV3cXqC7ubTYqSqJmxBtJAgDEQDXgGAxvzItiAgflD/ORXP2oePPA8eGBjAMmCIImmNNBomhQlk1RTlLqri93N6rpkZeU1MuOSkZGREX4Ifjt+a50d3fQINkpA7ZfMjDxxzj77sva6fOtbnsDNbDL52kTi79nb23Nu/4hTiqGlTqeT+ptjcddzNAfkjWq328nlT4FGQcZaWIyTa+4ifkR/12q1QqFptYipMJvMqfpFZY71Fs08J4v6LOv89PTUWd9U4BT+2tnZcR4l9Xdtbc3hSrR2KMh3dnbSgbG3t2ff+ta3bGlpye7evZu+d3h4mNYti0PT+5PDnrFREJA7S018QPQi6ZndbjcJWlro9PDxPajA5p6j+1NImU3Wn56zv7+fBQBT2TKbKse5g8DMl6qg1UqcGg80eqt1DQX94uKiW3uxDJXekR6AmChgNpEHWifLy8sFRWlnZyeFP808CXG/37f333/fbt265UoKqUUvh55DT8HHH3+cxpS18dgXfqfX69nOzo4dHR2lfkSi4FqtlvZKzkgivon9pKLP9cIDkP+j93U4HKb5o0IfAeBxHfJg7vV6TrbduXMncTpxXjjXNHTUSD5arVYL3t0varmwZvRKkwhTfSdrfbxOfdE129vbLjyvs4Kgb7PpOfrJJ5+kvaX3WV1ddUZB9LTFWntMzFpbW3N7WNGWO3fupDN3OBy6+qJmRQeCmWfrl6yQYRWTx2q1mqu8oTEk835unzabzbQHuH5pbFFOUaf4eXB2/1+EAv9rM/v1n/3+v5rZn9oXKFZmk8W3tLTkFCIzn0pvNvFMkfGYZTDUWDD44OAgTbjCgrTYoquRGCtSLTCuzMWtA2lubi7FcKm9n52dOUVEk1upVByZozYfQYzc3J1OJ1nhetednZ0Ujz84OHBEiqRfYEaLwjDMDGJ6/09/+tP03O3t7fQ/CTkynau/uv7o6MgVwdRYRPBgZHqnxcCiuxSSsZQE8R8sPWI29YJQYOp/eudHjx6lfr/00kvOvU6vD3FdUsriIa+563a7aXPv7Oykw1H9fvDgQYGhmRiJGNJcXFwslGmKocDz83MXAmG9PTXO1/z8vKPZyGVJNZvNdM+lpaWshR5rPMZDn+WHTk9Pk8W8vLxsX/nKV9J9tGYPDw/Tel1ZWUnXK8SnNaH5a7VaLlQnCzuXiakxjRQAN27ccPipnKVaq9WcPNA4EWfHEGKtVkvjW61OS9SQJoOyajAYOKyU7sdxjUI84hiPj4+d11T18IjfOz4+zspKjUWlUkn3k4JPCIVCxvSyM+OPfSQ2NQcuz5UwMpsogtwrMZsuhzeMobThcOiUJoWkzCbM33o3ZhLzHlpnkbVfkYGLi4sC9c7nedZyFUFYeiqOWS5zkF5CKhXD4dCFJbknY4Zk9CK//PLLBU/02tpaelbs82AwsPPz84LHlR5xyhCeA+rLgwcPCsD84XDoPHD1et1Fber1uj179iybkW7mE8zokaWhpbXLMSdNEJOXZoWjqQBGHSDX/q6K1djM/q9SqTQ2s98ej8f/2syujcfjp2Zm4/H4aalU2v7cO9hEqO/t7bkabTE7kHw7uTRgCre1tbW04La2tgpWCAeYIEYz72rVZqI3qVqtFrISzLxwYTya6aVskbU3ZgydnZ058KDZ1NqQwBwOhy5Ozckm8yz7wgyvWMFdfdFmZEkZbcyHDx8mF6ruqftvbW0VSr0wi0P9I9OxmQc10ttHa5PWKT2VFxcXDtBtNhUmUrh4TyrqDIPRi8CMVP1Oz1C/33feEGaw6d2Ymq/xIheYmQ8JSLGjdUylUf2K9a5ozZKeIiYy6HqmSK+srLgwFMdaFu/q6mqhZAvxSxqTyC13fn6e7nl2duaUGR5aCuGPRqO0DpjAIE8i8YNmk8OJ2LZWq2Wrq6sutB+5aUjXwTFUH7l/WHpD65dgdHrZI/5TY8o+MGzG9Xh4eOiAy2aTuchx6dBTQL4jcv9sbm4mmcA5HY1GzvOTC2VoXmL4fTAYpP0h5W97e9vtY65TAsD1+dzcXHqm5i0eTjl6ETNvJND7k3sHeky3trbs61//evqfSpxFGgrSlqhvlPkMb9ML9/PSLaipX6VSKZsNzb+jFyd3kNPwJL6VilVUXsmWrzOThqTen+vIzFcCiHADM0/HYDaV3WTEZ2hYP3d2dpwBMBqNCiV6CE2IbTAYpH1DVne9L/kcYyRJ3rP9/f1CuZtcy/HQzbz2C6/4/PZfjMfj3Z8pT98ulUr3ft4vlkqlf2Vm/8psilnhguBAsgZfp9NJCgc9CszaevLkSVr0d+/eTZ4kKSY8sOl2/Fm/7ODgwG7cuOE0XW3oaHFK0J6enjryTeIyzIogYhY27ff7Tts384qi+qeFIc9Vr9dL43Tt2jWniOl5rVbLhcYonNV3bqCdnR13SEqQSgnKhTalFAgPQ86rVquVhGsOu2E2Edbq78XFRYFGwMx74Ej6yrT/WCuMhZE1Tpqj1157zdFMEFBNpV6HzObmplMmmKbPTFMqNponPfvw8LBweGvsNEf0XMUDq1qd1kLkGqRFT4JS9i9nCTebTXvzzTet3++7LKhr166lLKHRaFRQlunx1TzoJ5M2yI+m8aJndWNjwwljHtS6v5RSriezyaGpZ8U1qvdnduXp6WkhtHRwcJDmNBZc1e+lUsmFt7XGNZ4sAqt+SrEql8sp3KBr4tqnV4v4PAL/o4wy87xnNKrK5XKhXAzfh31nyyk0WnulUskWFhasUqmk96dcYZF39nN+ft7Jv6gIxczSarXqSIo1LrmyMfpe/KxcnhKqsqC92dS4ZRp/xHJpTck7refn6HNmAfa/CH9DSErk+2J2bfSaxXvTGOn1eun6HL+bvs91EkP2rO0pEL0aEyJopEWlRfOnOXvw4IHj46JiZ+bB6uoLQ25mHj+9v79fyJqOYU4a/uVy2WX88T6cR8lqGlS8njQmBOTPan8nxWo8Hu/+7Od+qVT6HTP7hpk9K5VK13/mrbpuZsUCWZPv/Gsz+9c/e5mx2UQzlYCmkOx2u24R54Tej3/84/T/SqWSQnQ8tPUZrelcbSGzSXFHLSBqulT+zKaTX6vVnDITM2ykLOjn5uamA3irL3TzcwyYVkoFSgrYzs5OAWdmNjk8qKTS0xFj5mYT/iMWCo6HKvurpvGRZU9Xd6vVKrhxyTtlNjmU9V1lh5r5jUArejQaFQ7yubm5wpgzo1SHXa4wdczcVLu6unIs16R4WF1dTcoCvZ1kYNdYk0MrkmPG9XF1dZXWEbOR1E+5x9Ui3kACLcevZuZd2urL6upqAueqXzEUb5ZX5tSiclAqlVwxczWGq6l09/v9LLFjxOfJKq1UKoXsXCYPRAVGIfNut+vCpRTSDEnwcypkWlPRo6FGL/q1a9eSJ41WbjyE9W7icCJ5cb/fd15stehhI15pVsgvHqR6J/aFLYbPeVDTC3rt2jU3Fww76b1/9KMfOa8sx4F/S7axsgQPO+6lnAJpZk7BYL+0vj755BNnfLFG58rKin322WdO9s5Ks5+Fs8nhQvn5jRs30npUP8i5ZzaRXcyajrg6PZ+eqegdjZl9uk730XfluSHou9FoOIWTMI54XpgVMWL0TspoZV1deuboWaV807xvb2+7xJaYraf/EYqjddTpdAoed30vlwAlJS8mCQwGA6cUzlp7av/ZilWpVFo0s/J4PG7/7Pf/0sz+RzP7PTP778zsf/rZz//zi+4luoUcW7eZZ8SlxXDz5k1bX1+3g4MD96IsZZBLD2+322kB6HCUQJbnazweOy9KLgZu5t3BJJBUf7WxdKDT8tDhQRyPFvDCwoJT6C4uLtI7aRG88sorKWRDDInZVEh//PHHjqZBC47FfyPDLjEEeha9QTH9Xf+TNUulpV6vF0J/vV7PFXXe2Nhw4RamtKs9e/bMCfWIUaCiSCJVs4nHSPOqzfH06dM0l91uN1seg+Gme/fuFZQgvY/uzRBxp9MphFQINlWfuZHNJmuIxK456/fy8tJ5snJCNweYnfW/wWDgQm3RSxWt3RwWiXNr5on7zKZjROu03W6nuWaIvV6fFirXXDBrV2Ogdbe9vW07OzvWarXS8xkCZ2N5oydPnqT7vfTSS1lyz1arlfbHtWvXsqSh3D+NRiPdn0TGDLuT+JdJJ5JhP/nJT+zXfu3XzMwfmLNAvoeHh8l40mdksNZ3c0BytUjiyqbDsNvtpjm9fv26O3g0dgzrE6+ytbXlyozknkOFXorHG2+8MTNxiMXu2Qj94KGtDM2/+Zu/SUb2a6+9VihcHoHY9JCbTeV8rupGbPTIan2/9tpraSzEr2c2WRvky2LL0TowilEue24/tYgr1NgTx0SsGPFxnF/tx4uLC5cEQuPMzFLBdt3//fffdxn48RylMWQ2kfNS1qikkestl8XIdyMOmGcYo0DVatVlTtNAV6PxRC63CNHJtb+Lx+qamf3Oz4R01cz+t/F4/EelUun/NrP/o1Qq/Q9m9tDM/pu/wzNetBftRXvRXrQX7UV70f7etP9sxWo8Hn9qZl/NfH5kZr/x/+Zeo9HIzs/PXSybmiPBbczekWUSw1XD4bRWEnEn9FDE7Clpo6xZR7wMyQUZSiF4l5iBmDHELAgzDxSkVUEvGbFPzPJgeIruX+Fi9I5mk1AKrWtp2yRIpeXVbreTR2o0GjkvjPpNC5Isv4qFxwK48obQK8Qin6+88kry7HU6HReWUjs6OkphpNXV1eSp0/vv7e0l64UWttmU3HN7eztdz9pisXizvre2tuaSG2KYWvdSv+bn51PfP/zwQ9vb2yvE6unxpFUsLwZpHWJabw7TUS6XC2EvvZPa1dWVs5yJkTDzIFj1QY0UGfQakypD/+e96bVdXV11a0otgpIZwqnXJ0WsY1ZTJMY1m+yx3d1dR+pJi9RsakHHygrMjO10Omnt0POj8SDPlNZ8BDDX6/WEqyJVhN4/9otzqne9f/9+ypyk140eBGYlttttxz+ka2P2YY4qIZcBRV4nM3MhXc5RjgcvZjHG8D8/Y6ak7q9nEkxMDwmzdzX2H374oSMZ1Vr/5JNPHAxC81Gv15NXZHt721EpmBXxrXt7e27P5/ilYiQjtyd1js0KJV2/ft1ViyC2LgcYZyO3H39GclmegfpJvBczQjl2pODheysyEr2uGlNWNJibm0uVU+ilYtidHiyNFzMRK5VKgRdMjbQ/OnOq1WrhzIvPIZ6NazpXJcWsCIXJteeCed1sCkTUomAYhaGliN8x87gUM78oCWonAZ4GmSVb2JjOTfcjD+DLy8tC9XOzyUKJRU4lWBXPZho3DyctslqtlhVGjH1TgWP2A9/79ddfT4KeipU2zcnJiQsNMfYufq3NzU3HMs3DRHgjM3OKjX7f29srFO808xu6Uqk47ATpNNRIrXF2dlYAvkbXtZkVEgIODg6SgLhx44ZTAhje1EGysrLiFEtSQmg8zXwZiJjFSkxCDLGVSqUk4EkQy9BPTrk2m67D8Xjs1oN+RmyH/uaYKJy4tLT0hTgShqSYARTB61p3+/v7rvYff+e99VxWNKBypP+z0LfZZFx13e7ubsIyMZzHBAOGlzWmLLMSsxw1phz/TqfjMh3NJkDZyLQeMR2j0cgdHrNAyerDgwcP0gHFdPXIBUWlk5xeum/E53H9NBoNl00YAfP8qTYrnBhlb67kSbvddjQqvD85BGOLXEI0sjU3V1dXSX4cHx+77C3KFSbPUEHTODLbnG11dTV9N8erFikIuP8YwiMdgN6d58729naSG1TAeY84J1RayCCv94pQAv0tGUceQCZmDYdDR1dDhwPxfPpcOCoZmTr3rl27VsBr9nq9dN3y8rJTQrmWNdZzc3OOq4pYYmZJk34lnklzc3NOmeV+IFUFSYdjUgqV9y9qz4ViJUuI6a5kTt7a2nJp7zF1lGmcZj7dUu38/Dzdm1YVaRzMpnHwg4MDpwSxmKhatD4J0r68vLRms+kKXfL73W43EfjlyAjn5uacwkOBRs8JPVM50k9agY1Gw4EyzSYbK3praK2bTTYT6ytRsbq8vCxkuLDVarU0Lhy7paUlBzDUZl1cXLSDgwNX5NbML3Sm5FLQ5Ji1zczNOxMMtFlZRy7WV2TqfMRW6Duap7Ozs6QoyXO1v7+fsB3RM0a+tU8//TT1md4fWo0aD5K0rq6uFqgOxBqvdn5+7oQBFSozn+Wn+0RMB0HZ3D/05FJAbWxspOsPDw+ztb6azWa6nh5MWoTE7ZlNMYtSmuTVqlarLnPn4uLCrR+y5nNsIjGt+idFjh5Zs6lSIGU6rnlRx5hNZQot5V6vl9aI2dTCp8d1d3fXPvzwQzObrJFcRhjT69fW1hwLu1mRtJOZyrOU6Nyc87kkUSXlCRuzHI+PjwvlrOglJIkwv2/mZTujCpTr+v3ll192mWRUOHPyYWdnx4GoScqpxnldXV1N64TZxdGg4ThG5ZTkp6PRtORNZLLPcehRsY4KbU7p1nolPxMjHrq/3kPyo1ye1lbc399PnruTkxNXqFvGU7fbLeA7I5B8c3MznQ/aF8+ePXNZ3TQ6yuUpL6KUr3J5SkS6vr7uzjAmQEhZ3tzcLCjpsehzo9FIa6DZbLoEiXhvNfUrkpzm2nOhWJVKpYL3hkJRxGRmk8NAWqUG8rPPPptZkJNWEYs+alClMBGsqp9arOPxOAteJ5cMUzAJoNWiUV9fe+219N2c9UP6ACpKalxUi4uLzpJin6mIxXCema9yzoVNUDQJUrUoo1fg8PAwKSm6pt/vp02Rmwv1TxuN3qboqlZbWFhwaex6b1Ij6P/xfQnGl7DqdrvpUIuCUeOxvLyc5v3y8rJw0Oi+EcTORoZflmoxm6wTHdxaOxsbG2kOzs/P3dzoeeSFymUIRgLCnFJFIGm00CnAaRCQeycKfTMv6En2y73BrMCrqyuXnavDlF5k7U99JsVXB5ys7H6/b61WKyli5+fnzjPEuoIM8UdQr+ZSihsJCNfW1pLSSs60SD8SFfBKpZLeg0koZuYOHYbIvvvd75rZNLx+586dQvhI77e6ulpQiiLI18wK0AMq0Pw9Xq81SA8flYdZ2YSLi4uFMlj0xMc1RIUjF/Li//muUqr1TK0R0QVordCjTg9fVAqiUc5sUBo9Wkfj8djtUzZ6QnV9p9NJfWTFg729vbQeGLJWI12O+snMNirpscV1Ts+m1hgV0Tt37qTvkhKBtBmxAofZdKzptRVhbU6BrdfrBY4q7Ut6MvXd5eVl50UmL2PO+8vxp7dzc3OzULqOSjlB77p/Tomd1Z4LxWo8HtvV1ZVz/cWQGzerBkulXZidoKYB0mBcXFy4dO4cN0yn03FcMlrkl5eXaZNGK5g4HQl13fPq6ioJai16kmXqf/RS6bsk8DQrelLMPK8RywbwvekBiXFjjSeVKd4r5+4fjUbuIJFnpt/vJxJBYuUuLy8LYRaNHbNFYl1DZniZeW+P6BOazWbaZP1+P42RPGO5qvLaoMz04QG8v7+fxo7kdvqO2fTQJYmj2eQQpqWm8ZFSXSqVnNAT7mc0GiXrsNlspnG6uLjIEpE2Go0kyJkBw4OO786ws+aUbv9Zhxh/MntIGbx6llpM42dhbdWy5HgeHh6mZ5NlXuuPB3nkqtFYsNYYM/Ta7bY7tHL4MO43fcYKA2beKtdnNPQajUaBjDUKXnp8T05O3DjxwGZISmvjo48+MrPJeuI8dbtd91zuAzPP/ad3ncWSr9+jV8NsclCSiyp6Ks38+hkOh0nG0cDRM1utVlKuhZPUfuLBzsOOWamzZBINDXkEdS89j+nyDJvlmODjcyJX38HBQYqqRMyN5qVSqTg5qPWzvLzs1qAaIy88A2cpfJTFqsnKUGikEzGbGmfEk7HOZsQ36XPtP2L1KpVKAb8cITU0ZjXf3KfECppZVtFvtVqu/BgdDvIsc30zSkPMHuU84Spa748fP85yPppN1uisIuW59lwoVsLtkMuIMXByuUQWXLM8FkCNjLEUrjkwIDcIXfCceIbtKLza7XYBeMk+6pDR4a/JZBHjRqORfl9cXHRem5yyVi6X0wEu5UjjRM4Y9f+zzz6zcnlCDKr+jMdj54HiIcCDlIs8Z6lUq9UkwGq1msNJRfe++q4+b25uFnjFCNg0yxNCck4bjUZS8khSaVbkL1FjgoMacX7sjzwwrD+mtcOwqzZfbm0uLi66w4Z4HlqzGgsJDfVT15NO5OzsrAAgr9Vq2ZIU6oNZUUjz7xhC0k+umVyjop8rPnt1deXev1qtpkN1fn6+4I6v1WpJoEvgRaFGepD19XXnptd6XFpaytai5KGte8SQINcyAeg0umiYaN/Nzc0lYG9U/rgGNcfNZjMB1n/wgx84qhD1h16EwWCQ3vXo6Ch58RVej8XhibfRz0giq/2Sq52nvpIMN2cwxTBypOGgt6lW3CkAACAASURBVE6ymTghKVe5uqskQtUY6Pl8p+hRicbnwsJCmg/KCO51rpNGo+FKdJlNDmaWkopkxrqPWqvVSv0gbo5KLL3+5CnLUQqoz7nGz7U2tc+03t555530PurD8fFxwVNt5pWM7e3t1AdRhZyenjooB597fHyc6gbSANTau3//fpb3i3tzNJrWc5T3Sv3Rc9vtdlLqGo2G8/zpPWOLHGL7+/vJgUJZpvGTURexvLn2XChW1WrVNjc302KPhwIXcbfbtd3dXRemGQwGjp31s88+S0qSFurGxoYLEzDTh01Fchmqq1artrKyYnNzc26ztNttly1CBUktWhskJB0MBqngr5oUpYuLiyw5p9l0oy0sLBSyGRSOogckZmStr6+nGmyPHz8u4AnUB4bB1O9YuoBjpIVIJZNkb+RKevDgQVIy5+bmkhCUUtRoNFxojZltemdy5iwtLTnsCv+vPvZ6vXRAxzIvaltbW2k+njx5kgQElSlt0sh0fHV1VeBgifenkORa0nvTUyqrUcqa5oAHNet7qY/0KJl5xY0HPD1zuRZDLwy76j0i+Sh56KI3iIWTdR9mYEmo6ufe3p4DcfNeDE3I4iagnWN+dXWVxuj4+Dgd7jdv3nSeFDPPk2U2OShynj2GXTnXUmwqlUpSrPr9fvIoMQzF+6yurtoPfvADM/NGxU9/+lMzm8z566+/nr7H99ZeZnYTcUT6PqEQ6pfW3azwRr1eT56/4XDo+H5yns6Ig4rce81mMx2+sXg670G2bIaxcsrErHC09oP6L/nP/tBDRg8nZW+5XE57kVxR4my6deuWS37RWltfX0/z2Ol0XD83NzcLtUMjZlfXk52d92BCCXGldAgwm85s6tlUH3u9nlMsJPsZdmYYjUTV7EvMpNR+ury8dHJaz1a/u91uoXSZGomnWS2B78Pse/VxeXnZJQHoZzQmqcSqzZKFlUrFbt68aY8fP85Gj2J7LhQrs4mAnqWNDwaDLGUC3akC/5pNBp9eBLPJwOig3tvbyyL8b9++naqfHx8fJ0GkAzCm0BLQOxpNM74k3KhZqxEDMRxOimhqglkYcjic1oDjQUprmWEjbRSWD9Bz1GTVlsvlNBbPnj2byWasQ4KWbaVScfekoKWCEZWPOH5LS0spbPbw4cP0LB7GvJ6hYvZRh83bb79dwG7cvHnTWWFM82X2Cd+PxKWHh4dpPEk6qLEjOZ/+ZzY5JLleNRf7+/tO+dN8PH78OIGVW61WIeFBz5EwoRGytrZWAKhGzAszhgjyJRCZ+y0X+mF4hLQD0QOkRgU8F0bQOMUU8YODg2Q1ttvtZDBpTOg1MptgQZh1lPMak6KE+DzdhyFyzaH+HgwGSWDrnsfHx84rwfVDDBmFNK+nZ1y/b29vp334zjvvFJTSR48eFWSi9ox+7u7uOm8R917MIjTzhJQsohszsundZ2iY9879nlsb9IwxccHMHDky6VT4TjHzTe9BvJX6QPoXNmIIid3JZYmZ+f0T4Qx6D5JG0utDnCGTaPRszmm73U5/r62tFag9YpIWz4jcuF9cXBTqlZpN1ou8nQwnEirBNcp9OxwO03uwnJr2huAgzMB/+PChKyze6/XSeF2/ft2NAatlSFkbDodpb8TEITlC6GhpNpsFJUrvqkbcmO5NXHMM73Kc/95kBcoNy1BA1Nw1yF/96oQ66/Hjx8lSI4jbzJIlNzc3lwaPpVW4UGhx93o9+9GPfmRmHhjIw5UWRq/XczgbLTgJsIjRMSuCCOlmLpVKqY/cwA8fPkzjcffu3bSoeGDETJuIkeEzGQp99dVXC3UW9VMYgvX1dQcuz5VpuLy8TJ8zBp5L5zabWBjEgxwfH9vW1pYLOVKx6vf7BcvnzTfftC9/+ctm5gVjrMene56cnCSBeXBwkGUQ73Q6aSxv3bpVOHAXFxcLjN4Rg8P6ifTS7O/v22//9m+n72lD/9t/+29d33m9mc8G1POkYDAMxlAPD/u5ubmCd40WdPQC5IDxLA2RwzLouTyA1Ef9XFhYcFxrg8EgrTft76WlJafwynBhnTdeT7xM7j3VLyql5PCJfWWShv5mdrDZ5KCWd4PzZWZuveiZ4/E47Y3BYFIaI2JAyX/1ve99z37pl34p9V2faa2bTZQvjb28EFRC6IlQf3Qo6J4nJyfu+lw7Pz9P6/DmzZvZEPLneQL03vIWkS09KlSSQwxPnZ6eFpRMs8k6yWUj5w5pGgq6JuIjY2YsnyWjeRYfXTSemQjFcY3GyLNnzwpYLu4tGRhaXzHTs9PpJPmr/VCv111EQ2tDa0vPiTQW+jwXyeHnNEJ0Fm5ubjpuOP5PczscDh3Fgu5PELnZdN+RXkfnvNkUp6X3zRVe5pgyw5XK0sOHD12oXNeSPoY6hc60V155JcmB3/3d37VZ7blQrNQI7uPEv/rqq8ntyk2rF2dhZjMvFKn158DSs9JdyWPFIq9xQWriZB30+9OCyuVyuZDZF0HzBO1Wq1XHZ6TW7/ddPwl2j+noatyA5EXS9+gB49hpoX7pS19Kh97Dhw9T6YX5+Xln/Si8x7Aow2zM6oogcRLmmU2UOSow9KTlwqVvv/12Ep5UFKKg03rY3NxM88dsrOhB0PiurKykudf4kvMpjjlLMDCrTO3111938/Knf/qnZjYRHPSUROwKw1L8m9f0ej0HFudzWKBVAi3yGuUOjeihoNdOY8h5YWkpHl66B7NkzXxChg6Ara2tREPy5MmTLP9VvV5PhzAVdnLNMFzNlG5+zn5x3ZlND/mrq6tCNh7TuaMXjiByzpPWQxT+mpdKpeI8T9///vddH3/1V3/VvvOd77g+kMxR48Kwd84Lub+/n8adHr5KpZK8z1TINjY20rwfHR2l99O+j3ug358WKOd+ZAajxlhhTipU+ql539/fT7KEBiCVEK4BGsTa6ywWHfsc16jukZOfVFoGg4ErR8U1oOc+ffrUQSVilGR5ebmAGdOakbxl8sTnNc3RrVu3ktKfy3wz81nFUZkTR2BMfCAmTddLtnO8Ii0Ks+45dsS2MWTN8ZBh3+/30/6NBgDJZXPhbPV1d3c3edPMJrJFSimTtXSPl19+2fWLTplZJK1sz4ViVSqVbGNjI234e/fuOSuEsWpt+kajkbTWo6Mj97JanNxkLNpaLpfThBP3Q3Zts6IngjFts8lgS0GTZj4ej7Pu8lhfTs85Ojpymr+exQVGbAXvW6lUbH9/38Wgo+AejYr8Nc1m04UDciHYZrOZwlblcjkpOQcHB/bJJ58kr47eS5l6GqcocFdXV5OiZmaJ0NFs6vWih7FcLjtMxHg8Tu8oxSqGTnM8Pdz0zITTe15eXrqxPjo6clxj7L/Z5KCNIbeIo6N1xLE9OTmx3/qt3zIzj/eo1WrJojw9PXVjajZVsrX2meEiRYH1DmNWoAyVmBkWs8LUuJbId6M1TmuUljCVTnqI2Q9ymXU6nXQIMAlESQivv/56WjM0NEajUZIVSigwm8xdBCybeYzZ1dVVGl9mHFJZ1LuZTetacr0y9TqGZ4gnY0gmek3U6KHm+t7c3EwYLTOz7373u8laN5t47iNFCUNlvLfZ1Oo/ODjIsvDX6/VsfUkmB5CmhhnWERoQlZWlpSX72te+ZmaTg19g5kipQg8ia5nSqGHTHiuXyw5ToxBZnNPcAapGb1BUlIhjJaaJoVWeG1LkWq2Wm3fdk+9Bpf3s7CzL9zYL/8Z7ak5LpVIyTG7fvp3FQ8UkgKisEDgev7u5uVnwdFOJV1+pJOta7g16xKTw7OzspHOGEYr9/f1CxQWGLzVeMROw3++n+8XizYyO0FDWXN+5c8c5ArR/dnd3nYdtVvtiQoYX7UV70V60F+1Fe9FetBft52rPhcdKGARiSBgy+Ou//utCDaJms5nAiS+99JL9+Mc/TtdLe481BmWdLS4u2sXFRSFtcmNjI2nv4/HYURyY+dCI+hLj+AsLC8naLZVKyfLK0fCbTbTlXKo9PRrkuCFgUWNE8kZ9j2VBovVAsHJ8J3pfZJ2ur68nYtMf/vCH7n30k0DJarWa3lE4gdPTU4fNqlQqBZZrehiZPWTmyUrJ5pzL4ogxdlqZzNjRcxhyJDEdedU4PtGqlPWksYslf/hdtcvLS5cUkbNKdV/1j670HLmnnht5ydSiG5sWG605rdmFhQVHcaE9SM8vaVG+/OUvJw+U5v3BgwfO+0AvBUN6TF1XY4Ykx4fZgqQTGY/H6XPO2Xg8TvuClQA0vxcXF24N8n9q6+vraa8TfxHnTSEskrsSY6X7ag1rn5IpXh6Hw8NDVz6E1Si+//3vJ082vSLETP3t3/5tul6hpXa7ncaUFQfMplxxtNTpSSNFSs5boc/lVdCYf+1rX0vp/eXylFhU3mmFBAlw1z0qlUryBkUslWQPWc3pIcnxu+kayVPh54jPizyK9MKSuZxE0Oq3/qf+al1zf2lPseIEv2fmk0xmQRzUSqVSit7cvXvXhbBy9AlRbmrNM/M2guSJoZLnlOWd+M76zGx6PjDaE+ll1Og5Y0ms+fn5FD2Rh5zA9viueg6Tvm7fvu30AXmguGf0fmbm4DFsg8EgRUw+rz0XipXZRFhqQbBYrtlkck5OTqzf76fN+tZbbznFiHQLyjZbWlpKAnNpacmBi80mi4ChCRJ+MvRAhSDWjtPG5IFNjFeuRhtd53Q5d7vdtNCoWJLjiwWOtfAoBKLg1rgwQ5Hhi8gXRdwCBar6pWfOKkKrFmvIPX361G34yH6sz1iElG0WIJFZNxFLpkYBSy4ojQtThXmYa60xNEFGXmawmPlsH2askvsmjo/6G7PZLi4uklAmqanGK4JDzXzmWU5ZZgiE4zkYDJzin8tMrVQqSTDp2Z9++ql7p3q9nv4mQaoUgvF47IQbBbzuGUPILKXB50iB+uSTT9I129vbtr29bQ8fPnQCm6SgrLWpz7a2tgp0H/q+wpIbGxtp/GOtRM1Tq9VK41gqldKeHQym5UliKSaNF7P29L3bt2/bBx98kN6Dh8if//mfJwXsn/7Tf5rGU+N3dnbm5obcbsTD6J1InhkNA4bCyCavPlGGkSNKc72zs5Oe2el0HLegxpjlV05PTx39CBMF1MjLxf3zeXQMxPVIpuk5w+HQEbTGwvRmVkja0RqYRWlwdXWVQtm9Xs8Rh+p7kdtPLZeJOQtr1Ww2U3Ycx5pKb+QayxG8UjbMSkggVo73juTMxN5qLIhVU4tcdxwD7b319fWE6420K3QExHk2m8qhubk5J3ukoO3v77uQY46OJvaLyvCs9lwoVvIC0CLhi2hwjo6OUrHH+fl5R1OQ8wwcHx+ng4pgUi28p0+fOoHd7xeLxVYqlXS/WGaAShk9VyR0lNCNhKdSTFZXVxPYk2VGmLa9tLSUvj8cDrPp35FAUQubXpWY6WJWtM7UeO3Z2Vkal2g96VBTuQ+zaUHOfr+fDgnWjFLTPbT5Wq1WOiwigzUtUY3X4eFh2iDCYrRarUJaeg47QaWRwm17ezttTI6h3omHkeZW/aQwJnZPa4m4n3q97oQ6cWB6/9iY8Rbfg8DaWenAVKzoFRYmLnr/6IUsl6dFZF999dU0LpFkUgqo1sXBwUH6fXl52aW+axxpxMzPz2exelQgR6NRUtbOz8/d3qrX63b37l1XJqPdbjvCT42j3ufg4CDdQ+Me0+uZgUUcJYX40dGR86izcHls8cDjnOmwWV9fT31kuSp979/8m39jr7/+evJc7ezspP40m003LjqY7t+/n8bi8PAw9TEWueZ4qq+ffPJJ+p3Fw3Op7eqz2cQ7oL3farUSdoxZWcRC0nPPe1NZjtxDlKdm+b3OdH99rj3barXSOpyVrs976Rn8LM4pqVPUoiHKeed+42ezvqsz56tf/aq9++67qU8s1ZVbXwSSE4xOLxkb9zlrLBJjGjHEsaoIx4cKH+lnCPCnYb+5uWn9ft+2t7dnGtzEF2pt7OzsOKOUc0qaF73H7u5uGjuthVgTdG5urpDMkmvPhWJlNpkwDgIVCwpjehcU/lteXnaLTgPLcMvi4mJaQMwwoGJ1dnbmANZmPq03kmOSpI9hTG1WZmNFdnMJ262trfQ7rUoeWGSBf/DgQSGMsLm5md5JCzN6sxqNhlMguJnIK5NzNzNcGb1MEsgkXSWQf5Z2T8tYc3B+fp4E5MrKihszPX80mpZD+Oijjxxrs35GMCYVK3qV4mdxnHgA0Ts1SxFl7TCGMnJjGkkRY9o7yf30U/PebrdTCI3XcN1RiaOVJ4F2cXFRSHOPFnG73XbjrmdJsY+JD51OJz2XmVjMDMqtwdXVVcfQLaFGvhuC5Le2tlKWMOuESrEmZ53ZxKjRu1G5l4LO0iMxpKQ1vbKy4kgJc6ndg8EgjSkNOQLmJY/oqYpNCsT29nYyND744AM31kxF/7M/+zMzM/vN3/xNt985n7/wC7+QricMgHuV91Vj+E9hVK6/eNhzP+k9Hj586Bj0SZyqtrS0VCDLVHvy5EmhsC6zZ3OZcywbE7Nsmamndffpp5+6fZZjAyeEgrAF1pfle5P+giFBKmcMjVNWzKo9yIiJlKl33303yyXG50fFKpdgxcw/zg2ZzKmkcv9GD5I8WLlkMN47Rm+oCMa9SPLTGOIcDodJURenZbVadcZoTg7Pzc05Mtq4dul11X0i7U+uPReKVbVataWlJWeREF+jBXzr1i0X4yfxolmRTbfT6ThCMWaCmU0WMrEU7Xa7gK04Pz9Pi2p5edktlOFwmPrJjUOXM/vCRk8ai+6a+Xi/vqsFVyqVCmn3rVYrTTy9P2ZT5evk5CQdsFQOqajW6/UsVQV5gHS/WA6m3W6n96B1o/48e/bMLUgyROuZlUolKbZxHth3Fp4WdkTlQGKWEhuxA7omkmNScaMLnoqVNl8kMM0xkxNPMCtsSqWIJR3IX2PmWezNJgoGU/T1bufn526OmY4vBeXGjRtp/2htRmZu3mM4HNr+/r6trq6m60ajkTvsms1moYTE6uqqE4a5Aqq8x9nZWVJ4GHblNfPz8+meKysrjj19Z2fHzs7OXN/pqaNHmNnDcb1FfM7x8bGz6IkHUuPeZNgshkfNpmPObDbdU+tqY2MjCfZWq+XK9TDcI8b2t956y/Ff8SBRH77+9a/b+++/b2beotd4MvszviuVVSkEy8vL2ULheiezyZxS+YjhRLOJPNHnpVKpEP7++OOPnVLcbDadUqD5lgxlNmHEROrzk5OTdAiz3FK5XE77kKSZpVLJkahKtmle4n6l14yVDyhvokzIhfvYF8pr7f1er1fw/vGnWVFhjrgz9p9r3WyyTgi50Pe1NmPJJo3P8fGxuz/3pOYiZobyHT6Pk5F0EerD+vq6ffDBB0mpu3//flp3d+7cKcAQdM+ckRTJdCUPtre3Z7Kzu/5/4RX/PzQW2DXz9er0f7MJDksTsbGxUUi/jbHiZ8+epUFbWVlxpTL0HDZOsgQkNyjxTWZTQs+VlRUXZoycOGZFAaxGjIIEx+npaQGDQ96rdrttm5ubaaESVKnnaGwIcM+lEEcrmO5z3lPzoc2s/+knK7zLi8Vi1+PxuFC7zGwyRxIW29vbaY6Oj4+zVlv0enz22WdmNsWSRdAhWcK1iaKnhY24H3ryyD8mYDbX6Hg8dkI0Ck9iDHItFjUmNotlk8ym7nO5yHU9STRzFvfV1VVSrOjRiIzQFDBRqA2H07Im9OaaeQwZXfoUUjkPJhMsiP8Yj8fpXeI6JemoPJ46tBlGiO+hvS++Ho2rxp8JLmzn5+fpmaVSKY1p5NeiAhcTJba3tx3mUH02m4TSI/HscDhMY3r37l0XgqAc0jt961vfSsBihXajh/nll1/OhnwIfYglRnSdZIrmRd/jfXKFzbnfOC6cIxJI0rAUznF5ebmwdvR9JvpI2aHXPxKMSmbs7e0VyneZ+TA+vTGnp6fJOCRpZfSQ58KFVKxmyR56vLV/K5VKun5tbc3tt1zBYHoM6f1mnxj2p1GR4wXTfXQ9SYhZHidHoRHxU1pXs0LphMJ0u92Cc4D7K3qUpdzu7e05KiMmIpF/kUaOvnt6eprmnglSHJtICDyrPReKldlkUWlANjY2Ch4eMw8G73a7hQUaBQa5KnSoHx0dOXcfNwg9DawCTkxWLnMjZljw83ho6TmcHFmIkUVdrd1uF4rEPnv2zHGXUOFk37gp9XupVEoLmzgxsym2i+GTwWBgzWbTer1ewfLne0QvFgkkyYxuNg3XHhwc2EcffWRmE8JP4UK2trbc9VKyyPXCZymzqNlsOuETMQf8XC1iz2KpGs4jM0fjBsthN2hZ8cAimLVWqxWy/Jh5puuYvWjmsUMke6SCw7VEjxLd7uSq0hio7zHTsVarJaVjfX3dKbJU1gjW1vgfHR1lQxZLS0tpfolJ4bXMiKOgbzabLlGi2+0mdnO1tbU1591V05jTkFHYUnxMmj9ybp2fnyflmlACsuzXarW0FzUHvB+hA2aTQ2U0GtnS0pIjNdY43rhxwx0M4v5hiPb+/fv2R3/0R2Zm9s/+2T9L42PmQ83CyA2HwzQ3kh80kNTnz8NgRoOByQi5PcY1FZVfyk3JRH0vGtInJydZ/iUpJCSLVT90gCoUuby8nIw6KlM8dAn2Pzs7S4pVznuSCzVFD1T8Hv+eVdxc4/LKK684r4u+G8NcuXAu52g0mlZu4H7TmEcF//DwsAChMJvODYH/OU5ENckQKkWxr5ozFvym/I4eVp3rkg88KxlapfPBzDMGaF0MBoO0NyTXolf/3r17Wd0ktudCsbq6urK9vT1HwshNR9exDg0SikarWtezzAdB6bGOoFpO8NVqtbQILy4unCDV85iBwwNR5HrxAKb7l0qWBNrKyorzOLTb7bSgJACiFSKFJLq/WfaBIZNcRmGlUnEafgSbs9+RaZfsvFQQ9U6rq6vuwKeSIGC4maVQhixljbE2PUvU0HOh1PK1tTV766230v1oIefwBGb5ki4EtUfyOR2YxAFeXV1lcSqfJ2j4vKjUjMfjdNjpObpGxsDh4aELDaiPtOA577du3XIHbQydR2WQh7b2zPLychJK8cBlPULN9f37911iRU4pppJ3dXVVqMnZ7/cL31NpnFKplJSlbrdr/X6/MM7xfnqmxiW3rvX+zCLVvLdaLcdwrsbw+eLiYlqztHqZkax3M8unrrNA+87OTgp3s1HhHI1G9ud//udmNjWwfu3Xfs3MpntyfX3d7YPl5WVXmJqJFGaTMeVhqvchtjICtM0ma4mwDWKTIubJbLI2qIBGnGO5XHb9GAwGacy0Pxjqj5QR6pPZtCwKcZyzSmIxpEpPT1RU+JOeJ+3JXMH3WZ7zmOwQa0LmSmrRk8Tf9XMWGJ9YTeKBua57vV76mzJenx0eHmaJTaM3kzAHvQfHWvhI9sssXy0iRhV0ztRqNZeJnwuLMqv4+PjYYSf1ecz2lwIX6YlmtedCsRJQTRNLy4QW5ng8zi7yiInRws55FwhsFdiVTUKYm4NhODZmrrDcB5WyeF/dm8pBjPFeXFy4UCC/K2Vybm4uLaDcwSBFhx4zbrTIAK++6/NGo5EOaI3rwcFB4TCl+z729fHjx8kDQFZu9rPT6bjUefVH1nl0yTJsxDI5wuU8evTIWZyrq6sFvIbuw3dTo5Ckx44ekhzGql6vuxT86EKOwi3+zfCK/i9vopSnuFb7/WmNu42NjTTWnKPl5WVXlkfPjNxo/B7DiznBn6O44OfM2vvOd77jMCi09pQi/iu/8ivpoCZflloUZouLi2lO9c5HR0d2dHSUFOnotZOHiV5CtVu3bqXDjKEM9oMcXJ1OJ4WIIj5M+4EeEyYMqEUmae5HfY8HyenpqUvikYG3u7vreKzkUXnvvffMbBoiF48Us/z0kwYolSyzydoQjlFjTu+3WZ7yI2KJ2LiXOHYan1ghIHc/nhcszE1POLGI6jfvSUZ87nfuMyoHGxsbaS/FOnNmRUM959XiZ7nMXzMfCjQr1sWLihMNUOJx1aLMjuNo5uVk9HApS5NjzrV5eHiYzqL4zrmwqN5tfn5+Jg4sR6ExHE6rRcTsYybgSMbE6ge8tz4fj8fpu8xQlOEfowDDoa++Mqs9F4qVmc9sYyydrljiWx4/fpwUMCk1mjhp9kdHR2mT0PKUMIseGYbktCCIxYh1w7RAYiqnlB8Sr1GI0IvFA5bZgdxY1N7pkWLoiVgNNbr6t7a20mLZ2dkpxP71HtognU7Hpayb+XIrrHFmNl3o9L4cHR25wszM7NIcRTCz+ktL3WwqVMrlsjucFxYWbGNjI11H8LmZr//IdZRL/dX9NaY5JYvvzvXT7XaddydaUicnJ+5dG41GNpQd8RFsWrcEXesgZTHvWYkPx8fH6b7r6+tWLpcLdcPUD7PJ3GqsWfqEVmsOt9fpdFJdu+9+97tp3cXQj971zp07ibNmYWGhQF+xtrbmjJpGo1HwtpVKJavX67a4uGhbW1sutDMYTEsMSWb0er1sskw8CHJGmJ65ubnp9ikLeHMuqRzwUOFP0jxIoS6Xy07Rk9K2vr7usgu1Z3jAKtwlD5YMFZaBovWv/Vgul50n9uLiIvVf4ce1tbV0v0qlkk1j397etnv37pmZDx/zmVQAyuVykjcLCwsuuUdjFxv3gX5qj+3v76exk0I6GAxsZWUly+k1iweJAPRqtZqUNPLaxf2jJqOc965Wq65u5ayEFjZFI/ScWKamXq+7M5OUCGZFLyQxiJIZxJIx5MZrBoOBS1xhVmbO2KTsZSSHezOH5zSbnIGXl5e2sLCQ7i38pPpo5os8q4+EL+SU14uLC+dZ5jqcJaOU5GDmw4iz2nOhWImDQi9L1zIFOWv5iS8qJ8CoEMQQy8rKShIci4uLDregRjdnpVJxFbdzGJBKpZIOhsvLywIWhqn4eg9meigjiHQQtE7Jr8XsPB6Aup7fIzdYt9t1BVQXFhbs9AUudQAAIABJREFU6dOnzjs4Ho+TANzc3HR1mzTmdMuaTS03jf1oNLI7d+6Ymc9se/LkiRM86sva2lpKnd/d3XXWutk041P339zcdPg3PYebkgdDo9Fwh4Z+Eps0y7LMbXoJ2nK5XFCsSBya40GKANKYFUdPgfBCZlNvnD7X9awR2e/3k9HBOaXXaXd3t5CCTktV48bDnB5Q/c3DjtYwnyOG/larZf/4H/9jM5se8mpSCO7fv5/WAwHxOTxU/K72ow7vy8tL6/V6zotyenpq6+vrdnZ25urrkdsqF57S+Jh59nRmBEbmaCli8/Pz7uAx8+FiyYOYZcuDl3PNg/Hhw4dJQSN3FcOgerfvfve7ZjY1ZL75zW+m+aaHht+LiRc6oHQgPn36NL3HzZs33UGpNhgM0jNPTk4c+NfMh+jNJnKEyrM46oiTih7dqJweHh4642AWNoxGdu69uU+pAJLPSZ7Ag4ODAk6V2EGziZLAdTILS8VGOR/hKfT2m03mQwa1xnwwGLgqBDGcGxNV6OmJY61xibVWqWTp+RFXp+9GHsVc43e1FobDaZY9FdHoDSU5NI18zSXlweHhYcGrbzaldBkMBklR1U9WIYljmWvPjWJFoDMPmMXFRSfclU3DjBviM/j9brfrJshsolRIYLfbbTfAdMdLQaFGOxpNWLmjkGBKbqfTSfdUf+r1ulO2qtWqS7MW7ksLKGJCqCiqjwTHRm4ttfhcHhL6P60zAlWZ/k08Viz8GsOvpEDY2tpKC7PZbCYcFNmBLy8vHT5MmzWCK6nAyWKgZ4YeRr5TrVaz3d1de/3119O7MVPQLM/qnlO2+Ht0c6+srDjhqb5prRE4bubxM8QQsl85a3ZpaSm9Pw92Kl/s+9ramsu4ixZZp9NJ70SqAL3j/Py842Q6PT11WayzXPkUulKoaLWbTZXne/fuJcHM8iQ6JGKyR61WKxBZ0pqlZW02TdCoVqvOMCOOJ647vZ/2YrvddnxZZhMDiEroaDRK+0Bjwb25urrqUv3NpgoiAfj0AhM8r9bpdFK4+9VXX03j9Jd/+Zeu3BDH+D/8h/9gZhPv96//+q+7a+bm5lK4dn9/38lEycGTk5OkKFxeXiacX7/fdzg/ZgTr89XV1UJZpuXlZfccKoV8Zyqx3NcXFxcFCAUPvIuLi0IGmZmPEBDvJVxl3B/EbVWr1bSWSN3Akmdm0wNah/He3p6DZDDjL6dkcU/RY0aOMo2P2WTMI450b2/P0ZzEMCUNYTNz1Ajlcjkb6qTyyb3OOY9jTaUoB/KfFS7W5yxFx8iB3l3rSrKLSU/E4bG/pVIp7R8mK9HTpzFmpMTMR7M+rxXTGF60F+1Fe9FetBftRXvRXrT/rPZceKzMvMszZk6oXV5eZmsFyaqMNAW1Wi39T5rzwcFB+u7e3p6zBKX1M5RDV3DkfuLn0vBXV1eTxUKul+iFootUrkeSK+aypxYXF5P2Tl6o69evp/tHz5jazZs3k1UcOajYCGCO+LTxeFwoPptzzcrLRBc5LQbhe/R9fffmzZsJQCgKBjUyyqvfTJWmF46eHnkU9vb2Eos1wbG5jMDPazEkqEbru9PpJPJZ4vk4p/1+3+EbzMzRWTQajeSFoteD4RJmRRJPwL7Mzc0lrwPrRdIzE8N9JNE0m8y7rFI+k9lA6rPenxZ6tFRjGGJ/f99+9KMfmZnZN77xjZSgQCLYWdwxMS3fbOKV4T6QV4jebcqKs7MzFwZRo7e61WqlZ+m7Z2dnBQb9yL3TbDYdD56axjqWoorjFD1QuqegBzdv3ky4H93jD/7gDwrfU0j9W9/6VrLClT3b7/dTf+/cuePWBr0kWl8Mx7fbbfv4448TpIJrSXKQJcfkGWPGpVrci8QGDQYDB0BmqJ71JEmQHKtR6D66J0OHxI7GM0GeDK4TefFJPK19LA985BY0KwLWidGL2eJmPpQf9ynHSc8i+7vuR/Jms8n6iTg7JuXwPDOb7q0nT54kuUkqI92He91sIs/o0dV8cN7iWU/smpnntGRhc85RtVpNe4uwmNPT0wIRr5mPCKm+qJ5lZo6uJYaTlZTyRV6r50Kxuri4sFarVcB/qDHGy5p8Zh7nQcyR2WRBceGoMWYeJ8hschjHQqRmU2C4hNonn3ySnkNG71g0dDQaOUFOl6pSsOlijJghfT4ej134QsLz/Py8gN3QOJJ5PbrOiVnT5+zDzs6OwxX0er0CLoj8RmYT5ZclIPjOant7ey4jSX1mYoD6qEOAiq7mTER/Ozs7SZhdXFy45zKsws1KPqdc6jT7wPnIhQXN/CGg5xD7wAQDNV0nbNR4PC6sZTOfgdTtdh3fjA4EKR/z8/OFAsTMmGHShN4hCm6FcvXzxo0bCTdH7pnd3V0X3tT6evr0aRJWZ2dnhQM0Kq/8mwB/HZL1et2FTC4uLtI9qVjpmf1+32Eh+bvGi2HXXq+XqgRQ6BLnZpavOce+8xAkDo1ltdSUNSniS/2PYU+G8nkwkJqANQS1V9555x37m7/5G9cPrd8HDx7Yn/zJn7jrd3Z2nFLN9ca6mXxv9Zflisymhky9Pi39sby87Co6qB+xvFFUrKi0dzqdbBFk4n7UT/2MPG36Hg1eNd2DIWW9l/7H0i45HqhYpohKTi7k9XnAdVIAaD/nFE+1qNBvbm46dvOouGvc6Exg+JgyVGfn3t5e+v309LSAX5KTQC3ikWN4sdlsuvOK/dRYDgYDVztU+1NGstlkDeq5xNcy+537Z3NzMyn9t27dKoQ3GXKMzp6IYZzVngvFilglM68c8KAg14gUgrm5uYIVELkrWMPs/PzcHdJcnASq0QOiwzHGYrUI/uqv/spZxQ8fPrSFhYX0zDgJzEqThn1xcZHeg/iAWY18SupXs9l0Vl273U4Li5tY11cqlUJmDrFoekfNT7vdTmMUs3aYgk8+pBxgsd/vpwNjMBi4kjZmHmfCchK6Pn725MmT9MwbN264sSPJpgQ9syLL5bLV6/XsHDFjSI0ktFRgnj59WlA2t7e30/gweUDjpPlmRpjeKVJlqF1eXqY5Jku2nj0ejx23EUt/zPKKRMVKZYWUBTY3N5cSDCjcu92uY4UnrujrX/+6mU3mWveJz6bRI8WNHlMq7bS46XHVvrtx40Zas7u7u87rovFlaZVut1ugCFlZWSkAl+kRjGVuzDzOjyBuKmRSPmLGnRr7TqOOFCVcg8y20nupv2YTxUrrTpl5PBDee+89pyT8w3/4Dx29Ce8tD1On03F4Vb1fxOowBZ9JJppXlgcTXlZ9j/yCo9HIYc9y1B+1Wi0rK4lfoveSVBGj0ciBvc08TYOuoSfnjTfecDVgue51TsRzbDgczqRWYOM1lNe6n9Z6VKJotEnG3L5923E1xVq16pfeu9PppPc4OzsrZOabTdYdubT0OTndiAcmSLxWq1mpVHLVS6LHl4krOaqb7e3tbDLH8fFx6sN4PC5walHu6nPyNWpsuC4iuaxaXOez2nOhWDEdmkBIMw82nJ+fTwNPBtwYqouNysObb77pSgHwGoJT6XaNtAyRhO6NN95I3hNNarfbdV4Reiu4KCV0d3Z20iJ7+PChE8B6/srKSgoPLS0tFUDyzO7R5xJWtHwI6ItkdPKeULjLA7e6upr+z/eh4FhaWiqQH8bGTctrNPe5rAu969bWVuqbxujhw4euDqEOaTYWbW21Wg68yJ85S4TKHPmp5LE0myh5Yu3Vej08PEyHx+7ubvZApnJEuomcJ1XX670Z6lYIdTgcOiB7rVZzadlKWMiFQsnTxLE4PDxMJK4Mt1CIViqVtE5IrXH//v2U+v+1r33NzCYga7Y333zTkbrqfhrzmLnJfcW1rPfe29tz3k8abjy09b7ydm5vbxdkAo2Evb09J5/I62PmOajYV4ZpaICpbW9vuxR6KZGXl5fZkEOpVHKcd5Hs94033rBvfOMbZjaVk7FwbK/XS4D2hYWF5AGIQGcW31XfaQTGckuSJ1dXV6lfm5ubtru7a5ubm2keWX/OLM8LxfHMUZOYeS6kfr/v5KauoWJ1cXGR3uny8rIAhxiNRgVAeVQeI0g7Go+6TiS2TI7IZYaq8bl85ne+8x37yle+UqBgkQwj8J+hU44dx5eRFMpq0k00Go20ZliVgIkt0fDrdrtO9pCpngaTZNbc3JzjjlpeXk77SWttcXEx7RXSxbAx3E6aHM1trNJyfn6epdnIZYRzPPnz74ViZeYxU7kFpv/JalK66/n5eZoMWfIx9fT09NRpujqYuSGq1aqzMrUo5X0yMyfoeQ2J5ugup1UTN1GsPl+pVNzzqcxJ8JdKJUeip8kn9xTfKVdaRdeZ+Sw/NfWTmCYJ+pWVlTSmPLgajUbCU52fn2fDWdwQ5Aar1+tJGGpemMGnMYqUCcSFlMvltIFZz8tsGrZdWVlx3iYKST6HByuzgeR907js7++7tfnqq6+6kKnZNPNL/ZcniI3eGKaIU3El/xfD1+VyOSnm9NLysKBFTQESPzObjn+08B4+fOiY/Klw8TkSgO+++25aV9evX7dvfvOb9od/+Ifpe++++66ZTUNjX/rSl9J7E59H4RqzANWojHNccgVXFxcXXchaiqB+1mq1QnkWHY4U1rpH9I6WSqWULUdLnKFuKrlqu7u7aW1sbW0VsDmj0cjNyXg8TuuDBXM5hpIZv/Irv2JmZt/+9rfNbLo2eTB+5zvfSYrVP/pH/6iQlWY2Wb85LyfXOJuySc2mIXuu7zhHpPGQQhb5lyJm1GyioGq8njx54gwfjR09iQwrMxNb8iXCI0iTQu98DmvJ/g0Gg6T4MEpCozKGNqWUkpSaRe4lq3T2qbVaraQ8kvNQSk673XYKv+QJ8WP7+/vpGslyhuDMPJ6s1WoV5p6VBdQXvaPOrfn5+XSOzqo1GmtCyqg7OztLz+cY3L59250XkSU+KpaMktAwohIVDRrd84sUKrXnQrHi4mTRSbNi6qmAehrIxcXFQlgqAkprtVpaoJ1OJ01ytEx0z6Ojo3QPWq2vvfaau55YLn2Xlqc2Srlcds8iKFwC4gc/+IHzEvEdRE62s7OTrifGipuQApvhC7MpnoPaOg8sgrp5YKi/FH6xwDQPAy1KFoSlJUNelWazmcZMG2h1dTWFJnS46n96n62trfSca9eupYPk6dOnLhQmoUo6j9FoVPAcRJBixFopiUECrNvtus1NhYO1rbR+Y0Fp1lPUQcKDnRgoCisWXuZBRSUkUiDo/pHFPraozPIglWCiV5T7lCnaH3zwgZtfswmWIRK16r3W19cLeJNer+doKOjFpOcx4nDMpp4xhtfie9Pjy3d4/fXX09/k9iG/Wkx/V6MBpT1DpXBpacmx2qsdHR05IlJSQpgVPSKkHiC9CL2vWm9SGsVyL+wVrfLDw0P71re+ZWbeW/zOO+84bJT+RyVLY6x9ob5fXFyk99ZYfPzxx46KIPLaRYqIg4MDx+FE8mMa4rqepMlaI6VSKckGNYaKyJ+m+3Kvzs/PF8Kx9OpRTsyiHqFixwgBje2I1+Tz1WIdWMnN8/PzRPeiZ/F8iNhBfZ8e2l6vl2SIFEIquGaeOzEm4phN1g7HejAYpPXAkLqM3Xq97gx0GSX1et1RKHGvSF6zukaj0XBh0Wg0MiKmvsdwKpOLjo+PnQFv5ueeYzKrPReKlZmvbccFurm5mQQ2OY00wMvLy24TmU0FKbPS9Pt4PC6EdcwmlrWUn48//jj1QeBqbmA1fZ8Ck0JXz1xYWMiCond2dtIEb2xspPdQiEeeHIW21tbWkmW1v7+fxoNszvR6EX+ge9frU8LVVqvlFJ4IkNVPvfezZ8+S0KEFfXFx4fBpUUDOzc058D6LDpP0TZufjO1qEYd3enrquEWYmcNwscaQ2Al66nRwalwJ3qfF8/jxY+t2u+n6l19+2Vnc/X4/rVPyJjEziUKS+CoJzMPDw/R8gu6jZagDhgqtvkciRzPPRB/51/SeaprTf/7P/7n9x//4H50i9EWgTa7v/f19+/73v29mUyXnK1/5SkE5ZqgmYisWFxfTdZGIU9fwPWu1WvJeSXDH/cq1XK1WC/XoBoOB83YqO5F95VjEsFmr1bKVlRU7PDxMSnej0Uiyod1up+sJ7H3ppZcKOEPdT8+OVRA0V8fHx8mQoDxSUfIIBmZYRW0wGNj9+/fNzOz3f//30+fNZjMpmmTZJ/+TmsaeYbdo3R8cHKT3Xl9fT95AteFwaPV63f7BP/gHZjY5aJVAEfFv2mskT2ahXa2NlZWVbHhW4yXlitxZMYtQjbKU2ZLRYFC/aBxxvnOgdcq76AU1m5wPEQ+md5Vs3djYcJ46ebNrtZorlk4ePK2xy8vLZCwzmYbjdXR0lM4LkUzT+00Wej1XMk8OgdXV1bT2PvvsMze+6uPGxoYzGHX/ZrOZNYQZgchltB4fHzv5QYwicWva+wcHB6m/0VsZk1NmtedCsaJXZW9vzx3aXwRYZGkKKgnXrl1zAGwNeKlUcgWT1agZUzOnwNdhqMXCSuoUamaTRZ/LHDGbentKpVJShGgpC5Oi51BI654nJyep/zHjhmOXwz7oe7VazfVN77e9vV0on6Lrc+nt9EAxU4qeiRjKoQDQeJCgUnOqBS4hQWJRKh9xLMy8JyRmeEXm61y6PSkx1DQXsawBQ5FSfA4PD92aoYVKjyjXQ6xlF0Hyo9GoQPRqVsRh8Xd9X4rNl770pfRukVlardlsZkNCs6hQYnhRB6y8rV/96ldTEWEpinS97+/v22g0cvuOQi2HmVpaWkoeEBKwRowYf87KfFJ/osDUGHBscinffC+G7mhBczy5H2KJD92fTPg8SF566aXkGWi1Wnbjxg07ODhwxovWSIQx6D7vv/9+GlPiTER7oWv/5b/8l2Y28XgRg7O5uWm9Xi89R/eSUcGwJ6MRzJDkIah5efPNN9MBS2xqv98veAjNPI7HrOiVnZubS147M28Eqg0GgzTWhHCYeYWOxgWL20tZj9m4vL/em1GVWR4rGnUK0d6+fbvgEdd6k/xoNptuzLXu6K038wWFdU273S6UCJIc47gzQ9LMn8/VarVQeoqGqp6tc7pSqTiFT2v61q1bjlw1gtBpSJtN5kxnZrVaTbJZc7S+vu6u39zcTO+ksXj48GE6H1577bXkEdOcUr7/PO25UKxy3iA1LjJWsI6hFTMfnmL6aLfbTYrFK6+8kg4tbkimijIkxrTPaGnEIsgsjaG+m00mKldt/fj4OH1eq9UK92cFeTNfjiEnaCKTPDcUD09q/XK/6vt6Hy1KZgIS18Q2i+Jfi/nZs2dOEVlfX0/PEg6MGVBkEpbwkFdH8350dOQOaGY6qhEvMRwO3cHDTDEzK2RPrq6uFkJe3W7XFQuOmSlmkzWh+apUKun5zWbTuaOplGssSJ9AfAMFMAU/AaE5j6TZRPnQvVixXUKTXDNUNHK1DD+vRYVL602KVb1et69+9atmZi6r0WwqrMhTx2ysWOJJQo+p9kw40XtpPTGxIuLHeODQG6U+RK8L+xVpWbgPcgq6PGmPHj0qYNuoaOuQ1z3ozY7P+vKXv5yupSEjhZPFmjUGGq/vfe97ZlYs8aH2ox/9KI37N7/5zeSV0hgyWqCfup7eEL3H8vKym1/KfB2wZKff3t5OcuNv//Zv7eTkxGGIzCahf63vpaUlFxJSP2KYLe5rhtBqtVphH8TMZ1K0RM+UGs+tRqORsmqZQce1J34kNSlUMrhHo1FSHmKITrKk1WqluSEYPWbIxvp6fFez6TkaIw8MyUfqEbMiZqparSa5SFyyxppeabOp15OKD/tPBZf7h/VOKXsIa4lnlL6v8dre3k7XkGdR95CnTWP39wK83u/30+Btb2+7OHTM1jPzNbQ42TyAovUgJejg4CAJmygIuUEi2LDRaBTCU+oXM+/Ur7t376bJqdVqhcK4Zh4TUK/XC1gQpmmbTTZCJN0kVcD8/PzMOnFcZKxQTmFA8KueQ+4QKhuM2RMXRxCxxm4wGLgNWK/XCx4Q3m88HhewXpHYkUrOT3/60xQOoQW0vb2dBFm1WnVKcixkqutkwdy6dSsdhJqXDz/80BWPzgHdedh0u90kBKNCylqR5FrRIaH+VatVZ3GT54gAfCkrEY/D+dVzHjx4kA7y8XicvhNBvhJ0n3766c8N2tQz4/X9fj8pwuq/xoAHF61/9f1LX/pSus+NGzdcGJdJI8TXEEMkYcg9Ti8WPW30jEUgM++l3ymrtDYpPxgu5MFFy5dYPLOppc3EEyohn376aVKUpHhcXFykA/HVV19N78TiscSL3r59Ox1Uf/Znf+bWm1qn00m1BpvNpv3Gb/yGmRUzt9kPtUajkUpYMeVeezAa0hqb/f391IfHjx+n+dB6igkgFxcXjnZGY0DSXYYnOX/aa8SalkqlLAUPFXdGQKLnXo1rbXl5ORkDpDRgBiMJTefn51OGMZNZuDbNpuuN0BbJr7hu2bQeLy8vs9moMUkrGna8x2g05fmi7DWb0rWcn5+nsaEyJaobNcpT4uByoVaOO5/L6wnlmIVh0zWbm5vJ0ZLLUNW1f6/oFsymHolbt265F2dWGjloCDqLB7kaQzl0kZ6cnDgCOTPvMSJJnATYjRs3Coejvq+N22630wa6fv16muBnz545QaoNwYP00aNH6bsS1tEFbeaxUmYTwaIDamFhodDHGNKkoIlkmmqtVsuxeKtfulen0ynU6ZMCQQwbw00kdGNIk55HjT8JIDUWp6entrS05FLj1cjVxDXAdPjLy8u0uahQa+1EL9DR0VG6F0Mm5OGh8k7FjNggzS8pK8y8Bae+7+3tuVRks8laobdmOBw6LIDurQMrel2UOdjv91MYkxk9Ozs7qW/kFTKbZpTV6/WEwfkiYjw1CSyNtRIZzKZCX2uMYSoq6GrRWCCPXK4WmML2MXRPRnj2M2dcmE3W8vz8vFuP/X7fzcesFHnNATGEUv5Zi1TX87CLBlws4kvBT4zgL/zCL6Qx+qu/+qvC+6j/ZpN9IgXp61//eqLAiGFnrf0//uM/Tu/3T/7JP0nPVItp6PV63X7xF3/R7t27l+av1+sl/E65XHbGFEMySlw5PDwsYMvifDOBY2trK+0JZh8S6MyqFlofMf2eMknyf35+PhkelUrFrZlcCn6j0XDeUXq49B6RUoWKwE9+8pM0n/q/xpHkoRy7VqvlHA6R485ssl5lYDHLjs/Phf3jd9UuLi5cUgHfSUowqTVoVMcxotKpRmyf3jPu2VnUF7pPLrs0vhuVPOoL8ZpZmYuxPReK1dzcXNrk0UXKg5qCS4v/9PQ0LTS6iWPGUAShjUYjp9VfXl66DRJj4q1Wy2VQmE0Hlx4oXb+2tuaUH4awdMg0Gg2HpYku5+jNOTk5Sc8iUzBLTUSrRs9hrJuWfXRHm002gsb08PDQlesxm1in9KLwYCDugh6fKIgiyz7voTHT+6mdn5+nOSBrL8eRoZnhcOg8M1J+qCjGMHNUfIjhe+edd9J7l8tl5+HRGv34448d+DOm18eSQOvr665gM7MFzSbCX8Jd34v3ZHhwbm4uC5Rl0Vtatp1OJx3y5OXqdrspnPTuu+8mj4QUrBgKzYXQ1tfX7Z133jGzyX5gAXH2nwcsM1wZVlI7OTlxCnvMYNR3uS94zSwgs945Vy2Aa5BeKnr72IjHZAaf+shDz8xDGOQBGA6HaQ0wzd1sslbjGC4vL6fnPHnyJBvGpYLGQ/j27dvp8/fffz9dT4/24eGh/fEf/3Hqo5nZL//yLxcwTSwubzYxlKXELy0tOdJmHk5a4xsbG0kBrVar6dCORg5DkOq7qkt0u93kqYvZn41GI82nxp3zSyoFvpfZ1JBhyGuWoREZ4WNmYZS95OwSrpTRCOKXeCaZmTMgSCWkPRv7QchHDJ32er1kYJEbSu+q3/VzeXk5PWdubi55KTUGscW54DUslUVZFT1WHBddo/Elto7nORU+Qkfo8SaRclxn8blf5LH6fHrvF+1Fe9FetBftRXvRXrQX7eduz4XHqlqtOvAmrQRmhsji7Xa7KSREwCK1y1arlT4nBkXavUIkatevXy9gQG7fvp0sqf39/dQPafzqs7TXZrOZrPxarZZcoTFdXqG4nZ2ddH2pVEqeFL0btWl9L2baRE6iXFiD2K/hcOisE14vTxqzSwaDQYGpPpa9GA6HyZNzdHSUsF8KPR0dHRWwHtHS7/V6BQspvk+pVHLeQc3vxsZGmjO6qkejUZoregg0LysrK8k6jFgrUjPE9cifaurz22+/nUIkjx49St4rJQnoebI+NzY20vqgp0n9vbq6Kjw/enDJy7KwsJAt5kp+MXIItVqtNGZ8fr/fT56D9fX1RPmhMX///fcddsWsuBabzWbyRNNjGD2x2kf0GNEzROu0Uqk43Av5jHQvZYDqviRgjeVGer1e+p3lgtTn+F6j0cj1m+uNmEqGDjVPs2pnlstl5wUzs5TKrufErMAY4qtUKinrkySm9IrMzc05kDY9eW+99ZbL7jUz+973vuewRPr9vffeM7OJh0HhKvFkxSywV155xXmXKIdp9eu96d1eWFhI3hPJoFhovNFopLGTbCiVSul+DHeZTdaCQul6n83NzeQZI5mnmQf7q+8nJyfpc+6pSMxL70mEdayvrxcwvvqbTPKMsPBzvYvGSWPBNZ/jQWNftra2nBww86Sp7JuZD4HqHsTdPn782K03hv+IF2aJularlTxejHrkuPKIJYvnTy7zVo2YWl1zfHzs1nS1WnVJMbofE9wiBc/ntedCsTKbDtqs8jF7e3uONIxAc/0eQWfEzUQXZjwExuNxCl3pOUwPPjo6SkI0umQlxN5+++3Ul/v37xfqN5lNlCkJiSdPniSFZDAY2KNHj+yll15yseHY14iJqlQqKQS6srLiJpwcSjrgV1ZWkiBaXV11m1s4MIL9FhcXCwSELJdg5nEkul+r1bLlS5gOAAAgAElEQVQPP/zQzDwOQveP2USVSsWFyXLvvri46PivomA5ODiYmdEzGo3SIU8BFUsL6Xpl4DCNn1l9W1tbzo3NQ0rzQZoJja3Wit5vb28vXaPvMdssAsGvrq7SmpQQPT09Tfc4Pz93gGopS0xa2NraKpANPnnyxGGZ9B0zjyeQIdBoNBJXldlEAMWwyNnZWRKiPEgZxtY4mU3WNsO0Ohz5PhELqXsQjD4ajQqlacwmc0RKFbPJAZRba2bmlAD1hYrVwsKCq1+q66msRbyIWkzQiaSj1WrVJarEsIq+T648kpmStFGN+C0SyRLvJ9C0+ivwOt9DcvaHP/xh+oyZgvE9tO+Gw2Gh9Jfejbg7rmWF32aVmVlcXHRKsplXVCPNyeHhoS0uLjpDo16vpzmNsAWN7+LiYtozrVYrGUqEPcTDVlgxYu7ie6uR3oQAaSYusR6e+k1c7erqqlPiaUyzEeOkZzLcFxMR9D8Wg9Z7l8vlZGA9evTIKTZyEPCcoYLIkD7HMobrY6LRYDBw5wl/zylkMYswx0HFRC8a0/HeufqtufZcKFYEhZn5CtU68G7cuJE2JbE2Wmxk+jWbHB6xJImZJ5rkYLNEDMsIMFWb3i6z6SEporu1tbXEA7O/v58WE4XSxsaGK1FCL8HFxYX95Cc/SYqCxoTAdy16km8yRT+WjjHzZXwWFhbSmDLl32xqAXW73bTIWRuOAHGOHXEMeue1tbUkgJlsoHGL2W8bGxvOyqZgVCPAktQTJFaNBbyJrSMTvq6X4FT/NZca0wcPHqTnrK+vp+efn5+n7y4tLTmLiUVD1Qh81ZiYTRQrPYubmBQIzMIiuzcVfSq0uYKvGxsbDhMWaTiWlpYKJK70VEppYGq7Ci2bTda7BKysvPX1dYdJiNbkeDx2eKH19fUsxxw9cDGlPXL79Pv9dEgRv6ef8t4S26i1GakYSDOS87RE3Iw8fO12O41lqVQqeLbpZdH9o0xhPb56ve4yI0X+2W63C7U+dY9cCaDz83OXkcn1YDaRM1w7b7/9dhr7v/iLvzCziWJC764oGzQ+mg/RC9ATQ6M5MmFLWaCn5/LyMn1H70P8lVkx4UJNcx+rWFA+ad8x+5NYWzYa7ay1SUqJaPTG+oyxCDuvH41GhUzD1dXV9Ny9vb2EgeQ48h5nZ2eu4kE0FPQcKtQ6LzSOi4uL6XuaU2IAzXw5tsPDw2S0X15eurONXkXJwhw9BfGm6iPXh/CDxAfyPCFOVU0GlllRCWLGORVKRadi1EyNnrQc3RPbc6FYcSAbjYYbVIZw2CKLdOR1ooXBtFIC1Dh4tH6pnGhSGo1G+i4VHXpRnj17lp5DcDWVkKOjo0LZC91HQkw/9T2GXOLBvLW15UI5dEfr92azmTbcyspKgStJ36fVSKZ6eRFYIJhKA612Ag1zVr7ZtDxGp9Nx75zLRuFhTI6SmBZt5vmv1HduYr0TPZJqcQx0n42NjSTcWI09gol179XV1XSosr+RJ4uNSQN6jsZ6aWmpsAeo6JiZK267trbmDlVdd+vWrdRHHnZ/8id/YmaexkDvIUGXe//j42OniL355puJc4chv0iXwT6ZeZD63bt3U9+Pjo7SO+XGTPfPjW9kBo+1Qa+urtLar9frjpsmR6FBhYN9iYqirqEhQ9Jb/j9azQTr8l5mk4ODyjU9B7l7HB0dZTPCFhYWHHO0DiPt042NjcLe+9Vf/VUzmx7m3/72tx3Tu2TlD37wg3Rf/nzzzTcd0DtX7+7Zs2fOw6a5FOUH3yHWaZ1F7cEQ1ywPBdnu6R2kskqZIZnHRAz2JwKkdRZIgR0Oh05uRt61mLx0dnaWwOB8RyYOEXTNZA7t2dFo5J7DBI52u53eg2HvSCMUMxqPjo6y5XuYQc4WMyAjsS5DhmZFxc3ME55GB0Kv10vjSlgQk8tyFAsEvfM9WPEgwgAIOfi89lwoVqVSyWmc3HR0/2miyUkkDXl3d9d5f5aWlhx/zerqqsNE5AZbhw3T5dn0TLrXiUUheztTNvluZBxutVpuc0eGZVkROogU6ouCRAshpmVrvOhmPj09dVW/1W+zqfBst9vpnoz7k/9KoTIzX+hUY3ZxceHcpsSi6NB55ZVXnJuXIRaSorKxDInuo3m8vLx0Y83DjMzzauPxuBAajpYKyxVtbm6mEABDE8wkOjw8TIqw+n52duaEmb5j5isEsNGDwXVNIUR6Ctaji5xhZn58iZGT8tTpdNyBQn447geSdkYDKApD4r0oLLlGl5aWUlHmN954I42Txi5Wpi+VSlkcIUPqCjVHLzXfj3QWap1Ox40dFVGum6joxrBBpVIp4CPZ57Ozs0J2lJ5FrzghB8wMJbWGvAWkFhG+jAXqzTxhcKlUSpEBUgDw0CZPmBSsjY0N+73f+z0zm4TLqaiZWVIEiFsTDouQAIXJ9D4yJFi5QGPH9c/zQPfRO9LDSGoYcl/pmqurqywhdKlUypKlzs/PJ282PWw6YFl8Xu+qsWEhYnmxdCapMVNXc7q/v5+uv3v3boELS43VASIembg2syL/YKR3IbYvhuzUut2uwznmmN/J89fpdNJ7DAaDRKEhmRXLRtGoI8yHcoP76ejoKJ01zFBkjdsc31jkt4oernjWXlxcOBqVz2vPhWJFrbpWqznXI608CkOBgOnd4cvu7u4WvA8UovW6LwxMgaoFT7coiTLp7uz1evbjH//YzCYbWpYltXtOHlO06/VpwdVut2v9ft/FydU/hhXUV1o1Aj8TgMdGpXQ8HhcUxqjtn52dFWrpNZvN9Nnc3Jzzoh0fH6d7aGHTY/jo0SPHR0OcFDc2BVCpVHLEcmaTzcqEAwkMbSBSYpjl2elZCsfMh4k0VmbeCnv77bfNbCLwpARRqeA7lcvlgnf04uKiYOGSbVzjzhATLSO+A7lsiPHT2ri8vHThJ3rndGBRyChh4d69e6kum9nEuNHhp3FmujH5gMx8TS+1fr/vQL46PGmYvPzyy+maXBr0/v5+gTOGFBk5Wo5ZmCmG1sglR69azqhiAkG1Wk1rICYy8LtRUS+VSmmsHz586PrLEEOkrTCbQCMIj5DheevWrfTsZ8+eOWoRfZ/vQSJMyh49h3U2zbw3RPv9tddes9/6rd8yM7Pf+Z3fsXv37qVr9S5m02LP/X4/rf2vfe1rST5xblhe6Pr164XIAMPFNIz1TlIGdW7Q8KZyaDYNJRLHyFAg8Udm5tYvMYJRaYr8V6VSqRAabLVajpw6p2w/evQojfVwOEyYN1JVRIcAz5oY9Yi0D4RHUHHIYY9JcGrmCbqpDLHQPRVaen64vuMzI76QIVN6sSQHGL5nn4fDoeMczIXzeD35GMljpUbyYL13LgqVa8+FYmVWJBRUx2XJUfmiAMhVrjfzmjQxU4wf073ebrfT4a/JqVQq6RoWeaU79/j4OG0Wema4UaPbNAes1XfIZxItYylQCwsLDvNC9lg18hlR6cxZ+/qfrAoCtincNDfLy8uFDLKIJ9jf30/3b7VaTpkhaWfkneLY6f3UmN3BuSH3VAQL5+on0tsUwc8aF43T3t6eC73kuJUODw8dJk3KpQTkyclJth9mno2a3ho98+nTp866J6g9zl/sF9+r3++nvbS3t5fWMN3l8SCQd45eDmZuUhAxBERQOgV2rvRRv99Phsn6+noymAh4ZpiGZIQnJyeFEB3HWQJf803DTPPVaDSc8aBGLpt6vZ6ew3BzTMDQ84jB0T4dj8fJc1KpVNyc8YDRvQaDQVr79KyaeY8VQx+6pxS4yOXHZI3RaFqjU4oai9PqvSMJ8nA4TB7GRqNh//7f/3szs0RIqr4ziYc8U7/8y7/s3tnMK+WUT6+99pqZTWSJZB8LC2vMnj17ZrVazZUkoedEhpHZVFbQizU/P18o46L+Ec9Inje1XGhV94zvdHl56Q5+NnJTSZnb2dlxGcC5jORWq+UY5FmLUc/mniC2mMB8fZYLmzLsajaZAyV6EfRNnDKJPRuNRnrvo6OjtIcj51mO94uZgmqRlyrOWfw88mVRsaJ8inNCnJbZZB/GhJtZ7blRrGRxz4phMgZN4abBiCVGKCRZG4nMvRycer1uN2/etFarlRQshgA2NjaSsGfWxHA4TPdfXl5OfWRBSx5Ag8HAWYp0metApQKp7+hZeh+G86TYRSCrNhnLfURCRLPpwcPSQKyRpGv0zLW1NRcy0L1brZbz1kgpm5+fL4RP9T65TaV7rK+vuzDZeDxO7vJms5m1MqNgiKGKq6srB1qNhJ1mHhtE5XY0mpLK0nNGCoAciJXu9RjqqlQqybLTmPb7UzZ2glrZz8XFRacgxmdGvIaZD/2SqNFsMnac0263m/6nz1utlhM0uZBJpHpgLUb9L6eQ6XcaA2YToHYMBUacIYlYOf4xq1DzTnwNPYw5ugg1hvXjdfEAJVGxZAmVjYWFBbeuIv5QP1mBgWPGbGNiASO+qVKpZMuEmE1lWLPZTIpatVp11xCPozF/8uRJ+u43vvGNZMz9u3/379L33nvvPTevCmO+99576YB966230v8XFxedTJQ8lTLH92ddTb6rPP7qN40Vyjwp7rGsVSSrjc+jYaK2sLCQZOyNGzcctvHg4MB5bMx8AtTJyYnz4suT1u/3U5UKJXeYFT1NarVaLSnG9NJQ3kTZpvtoffN8YL1ZrWOtIe23+fl5l8TCvaSmoupmk/UaE32Y2BITULg/NYa7u7vp+eyv3sls4u3M7dler5fN4js5OUmQlpWVlfS+OcoGXcOQ5ue150KxGo/HLhvGzAout3q97jKGhD+axed0enpaAMWZTb06EWyoDfLqq686HI9+39zcdPFbNsXEmeHFcAUPRhZcVYxXCobcpPEQZoaLmQdur6yspDEijomg+pjmzYwoMyvgozhmPIC1mEulkgur5jxwZ2dn6fP5+XkXOqOFo+t7vV7B63B8fOzCwuQDkxfn6urKgfcj70l0M0esVy4ludvtOiHJcdF19DjwuwR1E+yq91dIR2NJ9z4P4Rz+Q/fX52q0rCJ2SOPHLMa1tTXrdDqOZ6parTpl8fLyMq13HUaffPJJ6vfW1pYbgxzjv5m50ITux4NueXnZhbBiVQPylZlNFJXoTaCyFwGpZp4TTNcwzZwHDBtly6wkCjb2Ix7C5FaKIW4aYbS+eZCyb1SGmY2r9HY9e25uzq13HpqaU7Mp4z6LEZt5TJTGqN/v2wcffJDuL1jEv/gX/yJ9b21tzf7Tf/pPZjY5YEktsre358I6ZhOAu+SySgiZFalW9Hwzn/FsNllHzCqO6fZqGtNr16650JZ+19jl8KfRW02FjGXQ9Bz1h5Qjeg9yz/E96CUkjcqrr75qo9GowJe1sLDgjNMYUp6bm3PXEydE/J/GmpjSmHGpa8jwTpnL96fRTMWKEJb47uJhW1paSutK17G+KWFB/D4dJ9XqtPyYziIa1/qc64TzqkYZt7i4WFiHs9oXKlalUul/MbP/ysz2x+PxOz/7bN3M/ncze8XMHpjZfzsej09KExPrfzaz3zSznpn99+Px+Ie5+7KRw4hgQLPpRiB9Qq/Xc54Ls6Lwo4ufhyczHujhkdLGsMtHH33kwOj379+37e1tt+F/+tOfpkF+6aWXCh4rhkPUIvGgFCpVJ9ehGAFy2mT9fj/196WXXkqHNQ83ZvYxDs7Qif6vDS7BQYWHmZKao5OTk2yNtGazmbxUOaVG12mz0CtJvBUVYVp1/X4/LWzi0KRwxayuaOnHcdE9Y4s4AwoOubs5NzGrVdacrtne3k5z93kxej5X6zBmqUlZIziTeBm+u5k5bFeOm0Xvtra25ur5ra6uJk+DBCyzP09OTlxY7stf/nJ6/48++sjMJla45rTX69nu7q6tr687rxYNDfZHh2uj0XBYyKurqwLXnZkH2up+VP5Zn69erzsvFQH+avTW8H+xlh77rHX1/7D3bjGSXte936rqqu7qrurqe09NDzmcIYcURdK0JNMOJV8gwQ+CYwgHvgROAANBECAvyauRk6fk8TwEyMsBYpyH4NiG4TgwIDiwLd+PfJEsm7ZkU0PxInI4nGtNX6qrq7q6q+vWeSj9d/3W+r4mDeM8jIPZwKB7qr/6vr33t/fa6/Jf/1UoFFwRWY2Hgp04Et2nXC5ngLxmFwtxYmRiBpPux/uUy+X0PjSnx8fHbo/xoJHR8swzzzisi+TMt771rUS5QQ/U+vp6Wu9f//rXkzeGc8jSOQ8ePEjfv379elqz9PxJuVYIin8z86Vajo6OnOKsz7leR6ORM4IjKDkSC8sAjs+8KDtM9CX0/Jp5j2TE8el7aqurq6mPVIZi4gONszxPCj/L62/EWkmeRtC7nnt2duaojLTuI60FE2RiIgIhHrHkU7VaTd5KKrgXeYnUz729PWeERTwfo0Nm3kA6OjpylDmaC46JeL5IohrbP8dj9R/N7N+b2a/js39rZn92fn7+7wqFwr/9wf//ZzP7GTN7/gf//gsz+z9/8PMTmyaQsVgzT4YWrXIe3hHhPxwOM4cw/04iOj6frtONjQ0XFjTzOAizKb6GWC1NuO43mUwyniRtHm3adruduDlKpZKrgadWqVTcWMlWmxe26na76QCjAqkxM+srKh8MRTEUQIWPC4uYD31OD4XuoQNRSgO9NGSr1qFHwKjmUnMjLyE9j7yHrm+3204I07LkeojucvKEUTGURcl3SlZ+Mirrs4ODA8fgbDZ7tzxsyF+jDUwFbnd3N41vMBi4WnISbuq35oRzEwlAKdyuXr2aSXyQ65s0HJwLKkjE9UiJZ6Ff3aPT6bjv0QtZqVTSWqV3QO/azIf9eOgxbBbXndn0vegddzodJzzz5ANBtnmHKnEeMVwmZY3eGnohS6WSWz80KPKwmReBcIk75T65iANsfX09HUIKaTDENZlMnNKq7x4dHaXvtVotF4IVPo7G4yuvvGK/9Eu/ZGZThfurX/2qmXlAO+eT5M/Hx8f2d3/3d7a6uprkxDPPPJN4sbRvIrFlv993Xk56hjievPA1ubO0dvMwkTHUWqvV0lqrVCruWTJ2uU+Jvdvf389NmKKXcHNzMxPyjBmlxDLJy0kD8/j42K2flZWVDPyC8JNKpZIUUO6fcrmc5oZedK47nqeRbV73J7FtzOj9uPNd9T/VxzyeNvZ5MBikZBwaZrqXrtXc9Hq9xLeo8+zKlSvOWfPgwYMk55jAltc+UbE6Pz//y0KhcC18/G/M7Is/+P3XzOzrNlWs/o2Z/fr5VBv6VqFQWC0UCpfPz8/zWdx+0AaDQQLKRowVLfxY1VtcSGbZLC2zmcud2UDcHFxwzHLTAuJBo7+zeKmZ1+qZbaSFt7Oz4zxJw+EwUzBYxTDpWo59Yih0fn4+kaWS9ZyNwHB6l/I4PNQfZh7pmogP0/MpGKnpS6A1m800fxJ4VJg0N5wvPSseVOx7JHEsl8tJuA0Gg1xPGtdGBAKrxVR8hlu0HobDYbIKo8InZUpCp16vp/GzUDgxG2y1Ws3hPKRwsF8ME29ubqa5o+VMwaJ7mXmLj0BsjZNkhOqz2ZQSQ/1qNptJWJXLZXcwvPHGG2Y2PXjVB3o3yB/HRp4hHUw7Oztp7vb39937YwiLGKq8osZMqMgjOdTzo2fVzDLrO8+DG7FZkXCXWY6aG94jjmNraysZGgzZREOAPwk6rtVq6aeZLxWm58a57vdnnE8RvE4PG9m3mSWq+yg70Gy6llQC6fXXX09Kw9e//nUzM/ubv/kbJ9tGo1Fae+1220ajkQPe7+7upn0jQLs8t1QqGMbUfqNhcnBwkPBIm5ubF4YVzcxlquq7MUmBBdJjoWzN+9nZmaPKIAVNHpnm3Nxc2vskceX6jmGwyOkYPa9UDuidozLFv+cVmCZujYYJqR7Y4j5X37TvWEEiYsc4Ppalk0ygHDSbzSm91Xn0KPqbxrGxsZGJINA4/+CDD5wxure3l55L735eyzeFPrldkrL0g59Kk7tiZndx3b0ffJZphULhfygUCn9fKBT+/pPilU/ak/akPWlP2pP2pD1p/xraf27wera+gtl5zmd2fn7+H8zsP5iZbWxsnEc6f/1kyEuaqTT6TqdzYbokmZiljRIcG7O3ZLnv7+87gtJIsfDo0aPkXVM/Y2yWXFyi41cj5krW6cHBgeP/kJYeeVEYumHoVFo1Q14xJh2zwJg6L4uZWCn9ZFYds26iFWTmWccXFhaS1yOGNEiZIMtSn+3s7DiiP3qgGArMy/Ij1xbHyoK8xWLRhYpicVczX6Kn3+87j8GjR48y2VYMF7LUBdOmtWb1Gflv9JPeJnqj1BiKI3B5NBrZ2tqavf/+++larVuNr9frpXVI4k9anHneyXq9nrwJDx8+TNesr687j5DCdfSgkrJAVmIsMcKsWa0XUlyQm0ctZlvVajXn/SVmiHNHT47WmBjDt7e3XdkYhi+IQWEoJyaVMEPNzNPCMLQY+dbIgZYHaiccQvfS9fEz9jtmJDP8qOezKgN5ynjParXqPK3RazyZTFxI9+bNm2ndvvDCC/b666+b2WxNPv300/Z7v/d76fpbt27levm0Nvf395O368033zQzSx4xsf0//fTTDmqg9RMTPzSO7e3tTLp/p9PJJJXofXS73RR+ondYfW21Ws6rRFC7IgGMEqytrdnZ2ZnLUjTznGaU+fp7ZKKn94aymiHovH3NMTLTdTKZZM5g3ZueIWbcaWyUvQ8fPnQEoOqPrqcHOXrO6JHV70wkY2Nf2He2SCStc3I0mrGzM2NYnrHBYOBk3GQySXjTmJwS279UsXqkEF+hULhsZnr6PTN7Gtc9ZWYPMt8ObTKZMnczky3ikJrNpuMmMfOhvTip5PFgpk18ifFnu91OG2FtbS19lxmBdLUy9KHnEweiLBg2bVYqE3mbO4Y3uMh0DwoHbiCFBTQ3EYtBsjPix2KjwsENl5cVV61WnVs6ZkgypdfMh+hYBkb3u3TpUiatVmPSYdPv913JEvaLNcoYfhWG4OHDh1av1+399993AFMq4Jxr1RbkQc7541ywtMx4PM5QYRBIazbdqOo7+xLDBXp/VNDFyfPw4cPEMaQ1rDl+6aWX0twx5EfeOHqOie0gK7aEUrFYtEajkYDJzELUIdtsNtN8fOYzn0nPIYcQvyuDRtljup7ZiiyGzoxGuvs15gheJ9O41qkOyej2J1M8FXqS0srYIlbSbLa/SeJKmoioEOmavb09p3TGwsN6NvchDTIzX6yXuBT1J+JYeDCTB8nM40V1//n5+QwkY2FhwSlbjx49st/+7d82M7PPfvazibVdNALr6+uJxdzM7C/+4i+SwqSw2dWrV1MqPBVbrcVWq2WVSiWtE5ZUqlQqSV7zYGTt13a7na6hDI30LOQS01zTsNYavAhYXS6XM3XtzGZhY+0FjWNjYyNhjpaXl9Ncy1gg5tFsqiDGRAmurwiyZhiQcos8WlGx1fXxnDObrclyuezkSqSMycO1xhYVOnJwRbJOtYgh4zgj/QkTPcymZ61kiX7W63VnaPCZut/h4eHHjsPsX65Y/b9m9t+a2b/7wc/fxef/U6FQ+L9tClo/+iR8ldmMfHN1dTUJE5LKmXkWbS3maIHR2uPLJ2dLtNq1wKmUyPomiSiFLPl+WNtQm/7k5MR5PNgvAsDJZE7cmPoYs/zU50aj4cCRZp5TxWwqLAjIJ7jXLB+Qq0YcGq+Jae5m3hPCOR8MBqm/ZOY1mynG9+/fd300mwpWfW9zc9MplxIoxOUUCoVkYdRqtVxvBQ+gSqXiAK9m2Qw9pgLnZZdubm5m5i4S1QoMqX7nZeMwIaFUKiWPTavVct4CNTJEE+unw2N7ezutC/2UsrK2tpZIHLUW1tfXk4IXGZo15rfffjsdcJVKJX1OjIzZ7NAsFovOeyHFkLxCtGwXFhYytcm63a4rihvruM3NzbnyIJ1OJ62BUqmU7sfnnJ6eOsA2OeE0z3wODSiCgbXXV1dXHTmn5ofj4DxGZm61SqXilNvIzWaWxRqSoyxy3EUAPr8r4HO3280FzBcKBWfs6KAsFArpcCJVQl4pJjOfgn/z5s20rqRcX7t2zRXwLhaLaf1IafmZn/mZ5IH95je/mbB9VJRYg67ZbCZw/NNPP22f+tSnzMyTOW9ubiZZxVqULFWmdRT5ivIqWkwmM465mKkqBVH8iGZTuamzQ3OifaS9/9xzzzmwu2S6qDTMPAaJhig9Pep35PbLwzFR+aLxFiE6pPVhEpT+xojK2tpa+j/PXMkbKltMyuL/ue8iropN63RhYcHNU17CCfvDUmN0Woj2YWNjwynF8pK2Wq0MIXZs/xy6hd+yKVB9s1Ao3DOz/9WmCtX/UygU/nszu2Nm/9UPLv8Dm1ItvG9TuoX/7pPu/4NnZDRsdZyhB11LsClTJyPhp37mZWXQwjObCcyFhQVHUCdFT4vk+Pg4Q7mvzavPyYZL4jozc54TpuWTL4sWLxuFoRbQRem+p6enGeuU81IqlVyhTt6f1prmjiDcWERTCkSz2UyCYHl5OR1wGocEMQkb1X+CY1nXj5tJz2Uokp6bmC2ia46OjtK6qlQqTiE3mwpFCpfd3d30LqVwRMA6lRAKFH2PwGKzmYFAQc81UCwWXSkShhLUGNI0y4aIFKY087xDZjNm7MlkkgT9cDhMRsRzzz3nhAgr1mt+WcYmZnpqLdFjouLQN27ccIkSNGIODw/Te2XmJDNtedjTatY6IXN3/K7ZVIEn+ShrpNHDSMOE3gutSTK9Ly0tuVChmWUylcbjsfMK52FJGdJldhoPuHjgaP12Op1M1hh5uR49epQBIGv8avPzsxqDp6enDmite9br9fSeCXYn/QsV7egl++53v2vFYtEpmaS1eeWVV5JiRYVAe+XVV1+1W7dumdkU+K727W9/2ylA+r3dbtuHH35oZpbCkGZThY6lajQflGf6u/qnuZQCtb+/n94XS4R/A6QAACAASURBVLi0220nZ6MX02yanEBCa7OZsimFq9FoOOjK6empjUaj5LWNhuPly5dzweYEhOd5XfQ3XUsuNxZR57Usjh0zYjc3N51nmeFF7SsauDRu4k89l/fgOCIMRXO9srKSyQgmabUMJ4ZJtRc0h3RIRD5DUjLFMlux/XOyAv+bC/700znXnpvZ//hJ94xNlARUQmSpR6zJ0dGRUzBiFpQahbc8P6QgiNkA1NL1t9PTU7t9+7bLlKjVahk2Y71oKQ6NRiNZeOPx2C1QbdyFhYXEyWI229zD4dDxefDvGitd0bKkzs7OMngkNfWP4Sgy2ceMLbKua8ORrC7iMHiQRW4qM++JYKPiRCwd+UboRWDtRAqLvCLBnIOFhYX0e7fbdRa/5ocCkIobLSb9/u6772bwFDHce/fuXUcuqPtxfkgi2O/3Mwp6HsGjruchr36dnp6mNaV3FQXx+vp66sPh4WFSoN59911ndetdkfuNYauomBAjouuZeccxR89QNG5WVlbcu6EioMPz/PzcCUVioO7cueNC58Ph0AaDWUHura2ttD/1zPPz88wBpLmXUqzwk8ad5yXiXESvQB6NCuEGZjOlh+uE/EG8hkXc8zBWZn7Pc061LsbjcZoD1eyjAqRx8CCloq1nxCxZNY11fn4+8ZuZTVnb1RqNRlrzxPGQB0lhPn7vm9/8pv3Zn/2ZmU2VLBpeGtMf//Efp+tv3LhhP/3T02OL3ivhZvb29lJtPvWHVTbMpu9A6/fs7Mx5sykXtf/Oz8/d/pQ8jYqJ1mO5XM5ELGgInJ2dubON3ilmMtMooCKicVGekZ9O18eseYbIuPZJtROhJFEmMiPRbKbMROwt908MR8ZWLBaTF4zzQmcKS8GZzfYWK2/obww7x4x7ee63t7cz6yK2x4J5XY0EiJrsyB3y7LPPpkFRWMX6WxT0agy5aXNEFy4VK4bN6AHj4mA4R40u/ejhooeNIDpq8kofjwtJh93h4WHqt0I9x8fHrpYVyeXYCNKPMXNdz/AnMT0SVgwBLC0tuQOEwHOz6cbT9/IUrGhF04tz+/btXN4ZhjE5/2LplkDSWOv1uitzEtPWI95gfX3decQ0n1pTnU4nw8jLoq96jn5vtVqOc0qNtao4bll+k8nEKSH0ykbuGbOp9ap+ad3H2laTySQJvOPjY2ftEbfG8hVqBwcHrkYbn8P9SsI+PVtzwTR3s+mhTE+v7qF52dzcdDxWVP6pILNYu9l0XUbwt9Y3ZQX3COeaIRTWySPWMo9C4vj42Cl5MgbIocT9c3p66sgo82gCYgiRhcvV9Du9AqR24T1JphnLppjNZCKTP7jWSA2gcVIRpKchrxanuK/UvvCFL5hZFphNhUFzypDcF7/4xfT/y5cvJ7Z3raMId7h9+7Z94xvfSOOW/CW1gjyvejYNPjNf6SKWP1PrdDrpmkaj4cL3DIWpH2YzBfjg4MDtA7PpQU4CUXqGiH0iHokynHNAI4eKksatfb+4uJih3mBkIJ55EQMVZbSeRyNYfeGe5ZlDOcGi1XnP1vWkg1E/9J4jBITeV/6kt59nPTFkF8Fo1B4LxapYnFarXl9fTx2OldclfBuNhrOC1EgC1ul00uF6UZaDhEYUKhRMFBwEPUYSSvVPm5CemLW1tdyiv+PxOGnSg8HAFVXW36Mbkoe9Fg4VRIaZIgcWn23mlTlq3/Pz86kvzMrjvMR+MWMoFtql1q+NpJ+DwSCNieBnATq73a7z2KgvBBbXarV0UGnhx0M2Yqg0Po6Ta4PKMzOAWHqE1li5XLZOp2Pb29uuv1yf0VDQeJhZJgHAe1Dxj/2MngWW5oh1INXfubk5lwGk+9PrSKU7ekLVR+4X9uXs7CwBk/VOW62Ws9DzvIobGxsJjzUajdxhlBcK5LMZttPBtbGxkT5rNpsumYJNfaTgVh8iSW6pVPpEziuG805OTpJSuLKykt4B+0HZwnFw/cW51jWbm5s2GAycskp5t7y87BQaXRMBv5oD9Y9eOX1Pn62trWWqYzB0rT5H/AyZ509OTuyf/umfLLbPfOYzKQRHA4BhVIZgSqVSwmrV6/W05r/1rW+l5/Dd9PuzcjwyCL7whS/YD/3QD6XxMpzI66TAkbsrEu6yiedoeXk5GbzMKotec5YVI+bLbPpetK4jvrPZbCbFlrUP8/jINE+aR3rbdHZo/PEeND6pWBEwT5lIhY9rgeXi1KKjgiHaPEB+3G+8f169v0jGHEOKqqahuaInmvtEOsXe3t6FyQpqj4ViVSqVbG1tzcbjsQMlm1kG7MfDhS+Ti4fuUSo5Z2dnbrOa+bRj/YxEf8yuiS5PKThMv65UZgzSTN0380JVG5TYrzxPjBo3t5kHSLMveq42Cb9HkjthbWgBEWOxsLCQm07LuX7w4IELl9Hbomt1MGvjx7RaetfImD8cDnOxNgQ3C4xrNlOuSQhr5vFhDPMySyQWiY4WCV3qJJYz82SE8tK8//77aa1dvXo1g3kym65HCUzWyxR+7uzsLOO5oMLE0I5+RsMkNoYaNjY20v2U3WfmExL4Pmq1WuqjhAyxCfoZD8H19fU0F8RDse/n5+du3xHnRwwMMSr0KLMGqJlXDjudjq2srDhj5PT01Or1elqvca91u93Mobq0tOSyD6Og1vNpPDGDWb/nedbN/AGTR2IbqUYYNuNBp/mKTOP0bDCTNnqFafhofLEGZb/fd1mDlD1ms3dPJZ7M89wHwv/t7e3Zj//4j6exiW2dxaZjSSGtq1qtluSZaDMePnzoEoBOTk4yhKbE4Dz//POZudB+JhUNveyRfNLMQ0GYxae9x8ST6I0sFAopCiES6Far5dZ93v5ZWlpy2W/ET9Hg5pnGUKCA2YKZvPfeexkHQiy3NJlMXFSAc8can/Q8EsuVl2XIVqvVkrFMaiVeP5lMXP3UPH2ApKxmWaLdk5MTl0CifcYyaxw3iUIvao+FYlUsFtPA4gaNWio3ZavVSgs/TrYaXbfKzlpZWcm4qtUKhUKyEPVsehbigUtFiFa7DiMqARwfY7fU9IlfoHXGkhjq19bWlnNLU8laWFhI19F7xRRU/c5QS7/fT+Onl2w8HtvZ2ZmVy2V3Peszzc/POwvdzGdz6PCN1AP9fj+51InjiaEB1aPrdrtJAWTWHosMm82UWLJrM2lBVmIMZzLbSO+JgqPRaGQyI/VT75d0F61Wy1qtlu3s7DgF7urVq+mwvXPnTlIQNf/Ly8sZUHDkHONByky1qLzyEJai8Mwzz6TnE//Cw4v4scFgkFiXY+qy5nw4HKZ5JZ8V2dAZctPBcnp6muaRCgwLB5vlW5lmM6VD6ygqPqxvqb7S+1ypVNyepEJHxUqNnqZoQUsxodKysrKSYAEMhV/Ez0PjMWYu8RDQTxqKDIFGLjxdE7GsHEcMLY1Go0wWKOUTFTUzX/NQ9+IcLS4uOpmovn/00UdO3uj+r7zyiplNQ2Lcp4PBIPXr5s2bSen7+Z//+TQv4r8yM/vbv/3bjMK3v79v3/zmN1MfJWO0L9QfVqXgHo7ZwPq7Puda15hZWSB6KJlspWoGfF+R2iKPGmA0GqV7yONLnJfZ9B0Q2hCpQoi70j2j8a/n8nu8nmtYLY9fLEYUuGaIje12u7axsZH7rNgfer1iZCaW+2JlgaWlJZegkwf2v3LlSi5Ehe2xUKzMZnie6FGKL2d/fz+FinjA07PBw4AA97zJkHXAqtUE7pqZW8gxDJSXpWQ223Dkf+HPpaUlp5BIAMrDFtM56erWc+r1urNAGKKbTCbpOpaSUbhlbm4ufU6ldGVlxSm5tDjNpkoCQ0P9ft8lE8T6hPV6PYNniRgCfVfP0aYjrUWv18sII7OpMkGryWxW9oSgSGatmU0FCz0+FJaqhq45M/v42lAUiszWlNBnhgrHRPc9DyZ53hj2NssqGbo3kx3ywl36rsZGxUkCmAfd2tqao0eIjaGhmLna6XRclXqzaTaWlJlYWFzXcD0ywyriNOjVkqAkJ5vux8NkfX09E9rQ95gBFfE9DDeb+dJBc3NzmWLqZpY5+NUkrAuFgjOA6HWngsQ1HnFXeYk3+t7S0lKSAzFzSf0lGL1YLKb7U/k2yxKScqz0YrHskxrXvNl0DrmmIoWG2Uz5VBNeRvd48cUXHcZqMpkkQPG9e/cSh5sA7v1+350Lly5dsj/8wz80s5nHdTQapXsw7PylL30pzQHB4Gz04keQNXnkoud8eXk5yUzNm+b44OAgKXqaQ/KhkSZC/dNPRkA0TzEpgBmNkROMf+fZwr6YeZA5z2fuA2VSU8nTZ3nzmPd/4vkYOeG7oPKVRw1kNjuDiY9Uf8x8qaednR0XFuReiNmIH9ceC8Xq7OzMPvroI7t8+bID1qodHR0lJarb7eZqu+TOibiUyBTc7/eToH/ttdfMbPYypdDcvXvXWSOa+OFw6ASjPl9ZWUnPPT8/d4qa2UzRUt97vV7y6qyurjr8zmg0rbzOQ6hYLGboEMiqOxwOnSVMwLhcxORTWl1dTdfzQNna2sp1NVM5zWM3N5seoMpKk/L7zjvvpMr1ajr8mLGmPjx48CC9C6Zk7+7upmc9fPjQpfJKEdE9WNhZY41JENVq1aWL850qZX8wGCTBxzUQqReooNHd/HHhHDOvKJEGQgfg4eGhww5yTdBrKUWGAoJ7ZGlpKb0zZt4yTZ6C4sqVK0l43rlzJ4ND3NnZyRy++lmtVu3FF190+3EwGKT09zh3OlRpsEwmk/Tu9PdI8bG5uZmU3ZOTkwxDNcMC169fd3OnvvGeDFeazagrmKGoOdNcsw6j2mQycQpEVNCr1WpuDUy1yGNVKBQyilQk6Iz1S/PAv8ViMb3H4XDoaltqHFH2UnHie9L9tNd5b7PpwStva95BSsXcLB+GcXp6mkmEocw08wqMvKl6vtnUMBGdgdl0zej+v/u7v5uupTf7r//6r83Me/m/+MUvJgN8aWkp7TezWdg+YkvpHYn1/hSKNvMs+hrrwsKCtVot5/FlFi7lvNZyLPQe5R2VFbPpeyQ1QcyOj43hdq2PTqeT5B2z481mSlepVHL7IQ84Ho1FesF0DevBxvAujYp+v+/kfbE4q9hCbjH+JDVOzA5XOzw8dKTjkXg1tsdCsTKbLpizs7O0aOl6bbfbGeESGzc2rVOyuevgIkWAXnp0cx4cHKSDhGDPlZUVZ52RRZqZgLFfkeOG49AiU2xc7Le8hla8Nhn7RfoEM5/FIi9Jv993RUOZwqu2t7fnspqYsaH54QYS/4zmstfr2cbGhrOI1STwCNCP80FrWtgLNfWX8fuzs7OkSOkzvVPigGJcnUWi6/W6C0Hr906n46x8zf/u7q4TpNygzEgk/kH34Dsdj8fOfa8NnpfhZeZZ5vU5eaFiCE+NCh4zGGlB0rJnMWCOk8IkYrn0LrXHRqNRKph7enqa8CKRTZyZSQS453nK1Gd9j32gYqrxsNQTw1PkC5Mg3djYcB5n7Vfibo6OjlK/xDHEudA8MMM1QhxoIOk50SNEBVn91P3YmPlMyERexiNpX8rlsvt9OBw6IHaeZ1iN2B3KO0YPaLCoD5PJxHlN8sKf+o7ZdF+TMsBsKgM5B4R5mJm99dZbVqnMaA8+97nPOePsxo0b9su//MvueV/96lfdvtEzWR5qMpkk9vhPf/rTSf5HPjxerz22ubnpQk5mPnwfFW/ek0DsPA9MbHofeQTG0avDTMvJZOIyGdWfGN7UWiZmiRGbSNAtxYgYK4LRGdZni9hR9j/KBiZhUQ4z0sI9y/tSzqpfrVYrvTsWWm61Wo4PK57nsf1LizA/aU/ak/akPWlP2pP2pD1poT0WHqtisWj1et06nU7yqESLlSmVEd0/mUycJTw3N5esyUqlkn5nVkoEJ0obpadMnhzem25DPUv3kbVBd6KujdYNtXRymMzPz9v6+rrNzc2l1FyzqcWg0Icsk4cPHzpsFp8ra2dxcTGNnxlZcnVXq1U31zHEof7LFcvCyWbTMIvCVfJyLC4uJuvmo48+yiQIRI8V8TXlcjnDa2TmY+nF4qyQcrvdtkajYbdv307YKlncZEvOS29mKJTvNI9ALnoRaE1pHJ1Ox+GqWKuQ4+D3ZP0xe0x9ZAhPY9WY8grBkj+I6+uiuoacE1pgDK3lsVIzLKA9pevlsdvc3HRcV9yP9PhyD8cQFFukDGHNQIbINb/r6+sOAkCyVd2fRct7vZ7D/SiTiuSjZKFXGP7evXuOroTp4szA1L17vZ67nmEYvSd6tMjVF8HrzPalR5bYHLY8UG7c+/wsD7MiecOQFDN/+T16XwmqFxVGrH7BcczPz2fkBNd6HJPmlDx277zzjlsDxWIxydBf/MVfNLPpexGJKL04HPs777yT7tlutxM9Q7E4Lcp+cnLiCnivrq6m729tbWUwoEya0LvWT61J4pcKhUIq0TMajVyUhCF50tpo3pmFp+v0f8kS7Yu1tTUnBznX9O6SY468X5FXTV6vPHqd0WhKMhpLadGzmQenoDfQbCpz82Qe90P0ksfoFJO02u12+pwYxU6n4+qEXoRlVXssFCtNZrPZvDAEwAOB6a76yYOBSka/38/lqnr77bfNbMZ9FAVNuVx2+ICY+aOmCWY2AUMKEZjHMJuaBEi9Xk8cTRGH0Gg0Uv+l/NG1urGx4RaCBHm3201zEckLzabClAKY/aJbXeOsVCpuoTIDUo0lEpaXlxMOQG55KdG6NxWfyWSSKW6rMbMmnjaR5o6g3Vh5vFQqZcqcMIuyXC67Z+mdbW9vJ0Xp/v37SXhfBNRlwoHKuWhMmruI/9O9yNSv9/HgwQO3BtbX1zMhN+IQlLUZ23A4TAdKsVjM4KrG47EL5ZjNhOr6+rrjttFzJNyYfcS504Fj5tdUqVRyIQsp5cRoUNFlcgb7RRA4D2D1Z2lpySUK7O3tZUIoHwfC13c7nU6aG+43HbQ0eDQ3kb+H/e50Oo7aYnV1NRfQT3kUZVOz2XSHN/9GZYYYRhLWMnRIOREzsKlQkn2fz9Pc5ZFQMiRzdnbmyG3NZnKSGY3qO/E7zDIlbCHyVKl/zEgmHqder6fDXbLo537u59I6/cu//MsLw2zaA3/4h3+YfhfGjFQpGj+zCfW+tdYJT4n4Iq3f8XjscIOkLpES12q1nGFC3FZMOorP47zp3svLy2n+aejo+ghtoCETHQ7MCiW+K0IyYsiRLVJ06GdeFiDvSc6tvJqIJycnVq1W01zTaGd2JQlB2RcmfFzUHgvFSum8H0e6xUMqMoWXy+ULMUysn6ZWKpXSYooFhAmcu6jeUryXmRcuxDDEBU6hFZUcHShKEVV5Bd1nMpnYrVu3kuCam5tzxI+0MGhdEPBM7h/1h/POzMHnn38+d9FzLkjWyfnSZ+JHMfN4tOXl5TT3zJBkpg8bvQU7OztJsEuYFAqFJMBiKR0pRJcuXUqHJDFWN27ccFYTcUKax/39/fR81QgjVYCZr6lG/IE+y+MVivQJo9GsJlen03GHY6PRsCtXrli5XE7KEcHok8kk7QmOh2UwLl++nJQ44Rl3d3czNRbV51u3bmUIESmAI0ZECmyr1XL9oqKmRvxDr9fLpS9Q0yGspBOz2bznEbcuLi5msprkVeZYdXhVq1U3Z8pIixQX6hvxW1S0z87OkiExGo0S6Fnz0mw23RqI5Uc0d+RH0nqINf64NnmoMruS88f9GcllFxcXUx81b9GjtLOz47xULPkTcS8aGw/0SGMTM74nk0m6JwsQM+uY4HXVbzSbKQeUw51OJxGC8jnXrl1Lc/HCCy/YV77yFTObZhbq+ouwvCcnJ/btb3/bzGayrVaruSSTlZWVtF4PDw+TYadn0ohg2Suz2bqan59P8/TGG2/kylPKE2atEZvJSI+ZZx3XXpGCyDETFM6/ce3wvlFB0nNYAi4mrsR767ls8fxkaTe1PGWRipLkHaNRPC8jJou8jBwnKZ8+qT0WihUF7EWNXEgRxN3v990L4YKjghOzadi4KM2y2nW0zM2yXBys5ZXn1uc4zGYLgaDG0WiUyA+5EGQlzc/PuxR5WdbR8qF1yqybyNhOkKjZ9OBTSE3f+853vmPvvfeemU2VEB6O3W43Q/zJxVwsFhNwWYeV2fQ9ahxU+MhcTe8Os1jW1tbszTffdONdWFhwgsvMW9xm07CkxrG4uJjA1bVazVnCTG3WO2bWZ6VSsa2trSTU6eWUhSzwKzdtvV53YVGuGQorKVOtVsuBfKU4suAplaxicVbfLrJC60AklYKUsOhuHwwG6fBQaH5zc9Nl5nB98V7sL9mr88Ki5O4ifQENDiktuj+TWBgyjc82y1fQmBlLBfLu3bu5AnMymVHA5IVuYwi+2+06D/nDhw/txo0b6e8kdjXz+1ZzVCqV0jg/+OCDJEv4TiuViqvcIFlRLpfTemT2WPQ0R84q8q7pb7EaRGS55u9cQww/aa5XVlYcUSXHrj3DUlHFYtEWFhZsdXXVFWWPofTIl2XmPdb0xGq+Tk5O0jspFouJiPRnf/Zn016it4Lr6uTkJMm7mzdvmtnUE//5z38+XbOyspLWCXmh9P729vacUWk2i6awdiX56SRD6IErlUppf9RqtVzQt1qsIkCvjq5nGI7r2Mycoq+9uLi4eKEnibABKnXRA8U5Uh9iZnP0hvF3epFJa0FHTCxLpEavlq4niSyjHLVazXEbUsHPa4+FYmXmJy9OpD4zmw6K3Ddm05fN9O7Nzc30YmO9MN0rKj55C4/cU9F60+dqLCfCpudEFyg9bxRky8vLidwwFq/V2OgulxBZWFhwuBamYmuDMA1XB3C1WnUWNzE+DKdKIXnvvffcgrtx40ZyTTPcpFAr+bXIQcNwKTdHvV5P1cM5nyqTYjbFlrHSuuYnFvXUBqFnRte//PLLSYjv7++7A5+eEM3X1atXE86hWCza4eFhem9UdCN30M7OTpovYgzMvLeP60Nrhtw/miezaUhBe+DKlSuZgsJkVNd4tP7FOM754b01HzFEXigU0l569OiRs6zNZoc+a8NxT3Etqz169Cj166mnnnIKbXxmZANfWlpK61p92N7eTtc3m00n/JiZRc+Y5uDWrVtmZpkQZ61WS9fQQ8z1wndOT6XGtru766xgKtcsbk6Pksa2tLTk8F5qDGOura0lr4NwmZ1Ox3m4er2eO5T1vjRHrBDBgvBsVGbNzIV6aWxpbxMjSuVA71KGFrOvdf/19XU7Pj627e3t1Ed6c82mh3+Ur6enp+n+lUrFhVn1nt58883U92vXrqV5/bEf+zH7sR/7MfuN3/gN+9M//dP0Pdanfe+995JHXXv3wYMH9p/+039K17/yyispW1qZZXfu3HEZ1FoPUuRkIDOjUm1rayu3QgD5wPr9fpKv6iujInktUjGISFct7zyjoVip+IoVPBu11kqlkgv1Rk9pVAJJsEuvU17lCrPsua5GxSrKWP2ue9HbJvx1nhdWa21hYeFfR0mbQqHgXlic7Mj9Iwsqr8SKmfdA5AHRVldXHYeHWkwHjWEzM4+vYd21i9rH/T1a1NVqNWGeYqFfNYZ1CEyXK1oblFq6rr97965zq5tN55aLs9VqpQ2qzbyxsZEExP7+vktD/exnP2tmUwVC7+3OnTuOfVoWjoS/mffu9Pv9dJCrX4zNm00PBh24BGeyCr0Eut59rBK/sbHhQKAq38A1QOxKtVpNB9Dq6qqrLfjee++ljSaBSVC9DuDt7W1H6RHXUqToePrpp9NB2mq1nOUsIUwCwDyMwsHBgVs7ZEMvlUppXshCHQWF+iyhtLq66gwZeUjE43Pnzh3HH0NFjVYr1/z5+XnqV6PRyBSCJXM4C0RrLvU3eVhjGn8sf6L76B5ixOd41TQ3sRitZA5Jdzmmk5OTtAfpMaX3LpaekrLFdah5ISg5Ku0srB2tdn7PbKpwMKVffVAfmQwQPR8kVsxL2uAhRd4terJOT0+TxzWC68kkHyk97ty5k/BQlBl6vt6HzgQZp2bZ8i9qpVIpeZQ7nY698MILZjbbsz/3cz/neKz6/X4yGhly03gIGTCbKm7qp+7daDRcX/K8xQcHB2k8xKR1u930fCpKly5dcgkiNKbVYhka0ptEzxA9VJG+hIW/8+h4dM5EJSbOfzxTY93KpaWl1H89n6XolpaWMtAV4sJ0T64jycrIs0UjRPPLKhPRS6Z3NhgM/nXwWJXLZWs0Gk6I0qNBzZksy+Ty4WS32+3MwClwyYrN55yfnztmZQm3yDWj9kleNrMsARyxNGY+Hl4qlez4+Nh6vZ4bp9lMuG1sbLgXrGsUKowcNjs7O87iJteHma+Zp/4KC8WQnNiMIyhYm5MYpKOjIxdqyAsDseYWXfzy7ly/ft1ZvO1224U7xN+k63u9Xuq3nsPMQbOp0qI+PnjwIOOuN5t6UZgVSe+cFDEB77U+WLcwMozzwGa4S3028yUrJpOJy+6hckCsgpQQssZzrdHbsLCwkJ4VFS79PWY8xuyrubm5pNhSOYncWVxLWmuFQiEpBRGfQUyanqlr2C+tDwpbfU5jSPcYj8e5BbEJ7CXTtu4hJZnygnNGHiezbMmqjY2NdLBT+ZJX8fDw0CnL9C5ovlgXjXxZeczxukbzpPW2trbmDpJ2u+0yuCK+k8pZ5PGhwcs1kOddOT09TeN46qmnnByI7zKGaObn5901Cp0T7E7lcmlpyQ4PD11WF/fXwsKC81pqvz969CitH4bbhV969tln7Sd/8ifT937zN3/TXn75ZTPzFRj0Pvb3912/+v1+8oBK+frc5z7nQsJ5bWFhIfW/Wq06/GKEtph5TOHm5mbqg57Z6/UcaaiZL4Ejo4yyhzIhMpybeUcBz46IP4qcV/ou+RD1zLwsYDOPldVzl5aW3P7R2UzuLoYTz87OnPdZbTKZuD5HZw4dPWaecPjk5OTCPqs9ForV+fm5O1CiF4UZKwQ5a/KiJXd2dpbRdsx9HQAAIABJREFU1MfjsUt5VuNC5WQyZHCRO5WZEvK66XeOTdfyJ3EDzPRiqC7vkGq32+k5CwsLmdpwxKzERi8dQyMxHT+mzl+/fj0JlE6n44CaPDzU93feeccVR41eMrOsVSEBIIU3KiGFQiEpdPr8+Pg4CXrVkTKbWYkxg+vWrVv2oz/6o2Y2FUT6O7FsxBBUq9UMhQY9aSxObOaxMZpDKpzR4mdcXwLt/v37rqYbDQYpcvPz8+mdbW5uJmWOAGZarcx00fsajUbpvURDhvgZhvOYbZWXOTMej13YRm1ubi4d1hFgqtDD4eFhJnOp0Wik/pJdnv2ixbq4uOgyPZmt+t3vftfMpu9KoZxms+k8cvoe+3Dp0iXnfY1A8+PjY2cw5OGnSGJL1nMzc3uJ8ozvI09Jp/czJqCYTT2lVGJ7vZ67Rh55Ko3MXNR3zGZlv0g/wv1LxXJ+fj6t00Kh4BjDIwaOxLkaE+dSY2doKE8m8neSuHa7XSfrKW90PYvVqx8vvfSSfe5zn0vX/vmf/3laf5/73OdSuRfJm2az6RJzlLVpNpMbN2/eTPMiL6/GzLnT+Gq1mmNsj4qR5oMUJfqbFOJOp5NkWyxUr71BzHKj0Uj9brVaTimV3Go0GsnYbLfbriyO2Wx/a65XV1czTg4qMlGhYdSIeGDWNbyIzZ9edmIH81qxWMyl9SEkg9+l1z1COvLaY6FYqWmTxwy/QqGQDlMKVCoK3GTLy8sZ7hceJB9H6UCBFstLxI3N+DE9VvpZLpczpSCi4Dg4OEhjo+Vp5hXAaH2aeQxOdN/r/3t7e2ncCjEdHBy4lOhYOoe8TPH5pVIpU5PQzM9ptVp1xYhPT0+t3++7EMhgMHCKtOaaGVWcq62trQyegsVOK5WKo3Mwm1WHV7tx40aagw8//DAJA+IKXn311RQuPD4+Tl4qPVtlE6icCuBNziM2riPOE13eel8MP25vb9v29nZ6D0zxJxZI86F1RDoEzaXuwbWp91woFNz+OTw8zKRpc1wsy8I1Oh6PnaUoIa71srq6mmGs1xwcHR25IuZ8tvqrOdQcmE2VKY2j3W5nSoWokQZC74uszVoXOnDorYlh9vn5eaegEh5A73b0splN1xH3AQ0DjpVUMroXwd/0+J6cnLgaimbT0CxpHdrtduoX9x15lIiFMctSaRB8TKOGoHru/fPzc+cVVnZopFHQ/igUChmPBkO+lUrFeYwIkietg36PGLE8aAffBfcz1+mP/MiPJMyVynNtbW0lBWlraytTzFprTHJzd3fX3nrrLTPzUYe88mhmU+VM35U3amtryxmBg8HAlXximSLNV/QqxrI/vV7Ple26iAZC/3/22WdTUWzSQEg2ac3p/9vb20mGxvWlz2gAtNttFzXSHJLbio3vKQ/GIzA6k590Db21kWIkJnUxweoihgC2x0qxIocHJ5CKFXEuzDCIEz43N+dCTNT6+SwKYNIEDIdDBwzUs+lFucgdqM/n5uacR8tsJkwkQNbW1tJiJL/MeDx2ZG5s5E1S6ELjkZWhefqLv/iL9CwdDI1Gw3liKIAuXbqUsFDaxB999JGzKrm5qXxp7i5dupTZmIVCIUNGKOFycHCQLEJm0HEBHxwcJEuJtQ9J7CpFillBzWbT1YxTePG9995L3hZiv6rVavo+w49MhtA4NPekcCCY3mw617r+6OjIHaAEcko5aDab6cDS+9J4de/hcOj6E8vKxP1D4cMMQmKm+L6oNOjZkWRPLbrLtc6jt1GNSki1Wk3rlJgSHeTdbtdlLHEO6BHl3KnlAW8Hg4EdHR05PBKxavwelTX1S8rq7u5ueo/Ly8tuvS0sLGTSvyPnEz0BzDqNWaz8O2WJ5jFmFfOZH374oTu0GWbinOqZNEbV2u22ra6upvVwdnbmSEQ1jkhGSywXa5Oqac2ofwxFSRFQX4hxm5ubc/Ka4W3y8nGO2Ijzo6cnekGLxaJT4K5fv576IDm1tbXlQvCER7z00kvpXgS66/dvfetb9vrrr5uZJ6llbdDhcJgJqx0dHWW8bpwDzQ29w9HQViNND5MVmBDBedN4pCSZTSMgUvaltEhGS7beu3cv9YcZnCxfFc+3GG2hIc+QnNnMACqVSumZkSrCLFu8Wt8xm64jeqPUrzwPJ8fxce2xUKwURtMEUsGI/9cEsBYbw3Bmvn4ZNXAysGszc2FzcY5Gs5pbmuzz8/OMe1CCjOEgauZ6jvpKBmozTyjITCcunkql4l64GjMotKD1PR32+/v7aUxSBH7kR34kua7jotZC/cmf/En7u7/7OzOb0i1IMEa8mcZRr9ed1RY9BxsbG5kwGIXbiy++6CwmEriZTdPOY9KCmc8WleBikdSNjY0Uyuh2uym7sdVqJQ8XuWH29vYcIzu9lWaeOZtklWbTw0xuYgmHk5OT9N12u+0UDnobafnpXUaFSZmRx8fHjoFcjfg5AlFbrZYLZ2k8EhDj8TiXYT2uDa11ks7S4leIxMyTlbIgebTQOR+qW0d8RswiInWIxqq+tFqtDODabLq+9F6KxaJjLKdHwWxmcev7pAWhAkDsCgHV6guTIKIngII5gvx1/yhXIrM1FVF6H7XWOp2O6xdJcPM8gfQuRY++5mt1dTUpr2Ke1/yyMTysA5rA6liflQakig/nGcF7e3vu0GXlBioQmjvtESk9Wnu9Xi/tD8pH7a8HDx4kj5PZFFqgzGdGQvIyRM2mc7qzs2MPHjxIfbl3714a7/e+972031kken193a1vyXDJl8hVyIoGCwsLzvNuZo7VPFb+IBidBMPqYyz+TijKP/7jP6bvSlZpDvRT77jRaKQ+M/SmNZJHCq6QO890yWTOu9ksWhCpQCKWuFQqued0u13n8ZUSTiMpOl3yZMtF7UmtwCftSXvSnrQn7Ul70p60/0ztsfBYmXnAbMwUyGN6LZVKyRIh/b/ZzEJhPD9aNXkeoHK57FK9I55jdXU1Y9lHK69UKiVrgGDXWN6DLl+GI2RlxSxGhlPoSRA3k6xTPVtem3a7nakXdXBwkJ4TLQNxy3z2s5+1L3zhC+leYiQmVQXnmnNhZg7obDa1EIh5IjsxrTSGPmi1fvrTn05hPIXnGFJhqJf92NraSmP9/ve/nzwS165dS/NOy15ZRmqykNWvq1evJs8fmbLZOJ9Mg+Z8mM0s6nq97vh7ZPWR1Z+t3+8ny1lzQOLPUqnkLG5manG+8hi9zbzni6nxzIiLFqHmiFl2TDIwm1reXLuVSiVZrnkgao01zpuZL6uhRoA0w0Cq2be0tGTLy8vJW0Hvt94hMy/1XI2RoQZRPDD0ZTbzWB0fH6d9cvXq1TRfxDeqz+yLWh6/D8NcDPfOzc2lEDbDxWzLy8su1BXnSMz0mi9+Hmsymk2xMzFMq7nR/+fn55MHQNeynqWa/q+wEnm8SFURw9HE4Ggvj8fjJPtIz8LnzM3Nuey3YrFoa2tr6QyJYOt6vZ6yor/2ta+Z2VQOas2srq6690UaFr2Pb37zmy4sqP3Jtf7DP/zDybses/XMpu+C5xyzR8/OzlL0Q9ivUmlWYUT3kveYY9T9q9Vq+jthGNE7yRC3xqSfkkEMRyqZKC9xJ+KWJStilr3W7v379528JQ2L1vPq6mp6PpO7eHbTI1ipTMl2mYC1tLTkzgHyO/6rCQWqETOVd5iYedQ+0zHzUPosJtvv9zOAwSgU43OYuadG4UIFkCnDCi8yPVnP1qLQxiIr9dWrV129ObXV1VVX5oRCXf2LL139rNfrmRTy999/36XrU2nTfH3jG99Iz+n3+26DNpvNlDqsed/f38/lC+GhSmwWm4QvwyeM65uZPffcc0lgiIOGPGILCwspjBnfqcKet2/fTkqL+n98fJwpmk0cD0k5zbwgJKjXzHMPKazNNPa5ubkLWf9ZOFprRu8w4hYIdpdiywyoK1euOEWKgF4eLhJK+/v77npmH3LP8CCJQHyzbKFqrauLcI4sT8FQP4U69xUbn0OahshHYzZjT6ciy0ZlIGZPEgir0LHZTCbEEIvex3A4TH0n1QDlkcar8TMUyBCd+hyzmSPerNvtOuJPyipmnOnn6empCzkqbMfn3L17N63HpaUlx4OknzH5RfPLMC37SmOX96JRy/sRd5mXCci1wTA178HrKpUZcShlPMH97AMzdTXXH374YZJH9+7dc+THJATW/ul0OumdMxuV5bt2dnaSXKpUKi70bzaFd3D8nHdSXOj6hw8fZoDWcY57vZ4rg5WHPe50Oo6CR8ZcxCfq72YzLFaj0ciQVtNYIam12Swkf3x87ADlVAhJw8M1QF63PPLTCG3QXtF8saAzC6jrPpIJhI5c1B4bxYqx8VqtlrHORAvArCYS/eVRKHCCzbJCsN/vu8NeNeDMpoIx0tbHop+M90ooMU5OZmc9e2FhwZFmsjDycDjMeCvMLCMoIlD9+PjYlZAxm5EmfuELX0gLn4VUyQXGZ+n3ZrPp8BQUFuyTxl2v1x3AWPMuq+Du3btuoZbL5SSAtWm5QekVUX/MpoKTTM2s30dGen5Pitj29nYSXOpLu93OHAzkBNKYmF2jdylFJ6+mmX7vdrupX1SaSS/CjJVisZjWpASI5loW/ac//el0PdOd6W3hO6XXTmPd29tz75D7jbxJWt/1ej2tLXoGI68NvVpa4zzYaG3SS8KfGge5rQjMZ/ZspVJJ75LZknyn4ueZm5uzbrfrgMbqx0WJIpPJJN2TWVdaj6SJMJsdvO12O42f2XnR00dvhNbM8vJyhkbl+PjYeSuIZ8xj0F9cXHQ4GVrcMUNUf49gcjNfnosKie7V7/edTKTCR2wZS6XoM8kxZkXz3mYe20IPGytQHB0dJeXv8PAw44klpk59JI+fmS98zTnS9ZKnSnT5+7//+4SPWl5ednPAJAfN8XPPPZchA47A7d3d3bTGPvOZz2R4qc7OzpzXhQW8WcpKa6Pb7WYMF/1OZVbrSrRHZl4WVyoV5w2X3NAeWFpayhjVWr+NRiPXQ0njhn/X76xWQCJSYR41N1Ju6W1iRRJdRz40jUnrSeuOFAsRCxn57z6pPRaKlcrUaHNEBSimuptNDzpmCBJAKCXg9PQ0CZdyuZzxLrXbbSeser1eeuHn5+fWbrddSnatVnOW8NraWjrg8sCpLPVABW4ymaQ+zs3NufIG2sCcA/aB6cxSrBYXFzO8QzqEqRBEd7DZdPNxTPR4SRAwRKn50f91+G9ubqZFTmWNBX+Z3aEK43qW+p5nkfJ5TK2eTCbJMnr22WdT35kdtru7m66/evVq6iNDuBSinU7HhSQEQtUmHI1GmUxBCTdtUM7X6elp+i6FCQ8vJmKQA0fCgQLMzIdMBVilZ6Xf72c8PCLRZVaX5oWs6mbmvJkUzMye1FxFi5iCWcJWCv2zzz7rlIC33nor/Z+eA1qbWiP0INAL2O12099oHUdyw7m5OVtdXbVGo+GyITWfCsFoPev+zz77rPX7fXvhhReSfOKeLRQKuR7fSqWSFG7WGFS/qLBqzrUPlpaWMmzSLFKtPmisupYwhEajkalTqjFLttTrdUd6GCkCzKZKOUO6+lteYXf9n95tvUuWIZNMjJ5D9lfrmlmf9BZHT4j6WCgUksy5iDuQYSa+OzVmM6rPt2/ftpdeesleffVVM5sm03Ctc10T1M5kBkUOtMbefPNNp1Ts7u4mI1NZg/qu+q1SYWaeTV/9bbVazoiKFUjMpu9IlDL7+/vJ+D06OkrnJeXB5cuXnVGh37VeWCFC71YytF6vJ4OSiu1F3JA0lPNkNOUwW6VSSX3v9XqZusCs4qCx6gymQ0BnFOlM1K/JZFp38JNY180eE8VK7l4toEgnTzZaTRQpGGgZ8XoKCFrXJGCMLb6wcrmcFguZu3X/aHFHr0se5qNWqyWl6Pz83JV6+OCDD6zRaDjlZ2FhIXmGxM5u5g9VbaZYDJfZZiRDZDZkXp0vVWVXfyUgYsFlZmRJYEuAkGvsueeec5b7/Px8BjNRKBRcdkZebTOWTaHlsL+/n8ZMz9Di4mI64ElNoGvJ2WTmPSYkClVjpmfE+eg5DIGwqCeFVVybLBwtJuEYYtRB9OGHH2bWaQyFU7kkB4zGdnp66jyf5EjSuyXpIcNl5NeJYV8+99atW06RiYozQ1c6VDudTlpja2trGYyb5kFrjXUTa7Va2n88hMfjse3t7dn169etUqmkdShBfOnSpUxml55L9n4aR8Qj5ZHx7uzsJCOJ80jFlq1SmbHWaz0wFE0D0Cy/ziTpCqiQ6G9m0/klpQEP4Ygv0zjz8HQ0ushjRSVSRlClUkmKKz1a6kf0aETWe/WHISDWDdzb20uyhO8j8llRkY77JXrpuOep8Go9rK+vJ/m0trbmxsDsMc07MXfymjMqYDaVYcq4U1Hnp556ypUUun79err+7bffzl0XmpeTk5MMJ6SZz0Sn7FteXk5yi2snMpOTSsbMc1FpTnTNnTt37I033rCVlZUUpn/qqafSO6YXycw7KNS3/f39dB3hQmaz85pVKubn513RaLUY3tT5ybHpXOTeM/MZ3Mx6vag9FoqVmR90oVDIcKOYTScpKi5q5Kmg1aiFValU0ufUvqnw0AWvz/v9viuSGl2YEbsSObXyKmtvbm46d7CUGeIjKFxYH6nVaqUFwe/FQq7aOLr2zp07adyf/vSnc4Gq1WrVLSAJMhZHjUSEJIGURU+eJ+GeRqORW5CRUkPjkECbm5tzXCr0TubF9BnC44ZYW1tLCs9oNEpKl8Zx7949926IAZHyWy6XExbi5OQkk36vedU7u3PnThor3x0Pewp3XqP5PDo6SvMTC7D2+/3MoUpuGApIzaXZ9H1IkI9GozQv9+7ds3v37jkeOd2HB4Puz/0Ta7vxEDLzhVejEhs9MGbT90wXfUzJ1zywjwz/dbtdK5fL6SBXP2u1mu3v7ztCT6aGa14iZxgTPvgZcWNs9ESpj/HQ5meENqjpb1QYWCNU/Yshfip4xJeaec4oUm/oWZKtsfj5YDBwqfkRu8J1oX5F7NPq6qojFo3YsIgLPTw8dGWlCElQOzg4SB7dWq2WoT4hZioagew/lVK+i4il1fdlgFy9etXefPNNM/PVDzRG9Utrk3JLWL3r169nEhnkgVeyUKPRcJ4eGsGbm5tJzuTtSWJvuddOT0/tgw8+SM/UWDc2NmxjY8PVlOX8qQ8xqUvlh3it5uCNN96wK1euWLvdTmV+FAnQ/aLH08wbuOvr65mKB9EIIAGu2YwHjAS4McGAdCWaT71fRkbiuP7VKFbioWIsnZNEpUWHzGAwSEqDLEPy5ega0uPrZbIUCTdrp9PJcG7QLU9eEPWH/Yu/6xn8qcaYMUue6B607KhMxSKuGhs3MO+vz1955ZUUyplMJknQEm9CfNhgMEiWNkORzBLTs82m7mIpd1IOms1mGlPkWNnd3bXd3V27ceOGIxZV63Q6znWbR9wqRl32p9/vO3I/eiJKpVlRWClY8fq5uTnnhTPz771SmRW3pZeHh07E9sVSHnouC8jygDPLlmQx895UCRhtcioM/X7f9Y2hF3ohI26OWDQzr/z2+/1MHcTd3V3Xp+iFU/8uIn1VMefl5WUHxtc7IoaO++qjjz5yeJwIfl5fX3f7WsJQWKoYgmJtUc2V3oPWQr1edwc/2fTJp0SBn+cxivuH8xELIw+Hw/TZwsKCU5wEyOe9WFewWq26Q1h7Oe4rNXJu8aAjYJyYIALBOddra2sZRYZjpDzlXLVaLaekk3yTniY14vP6/Vk9Uma1xQLeXFeaO0YxiJGkvNL7GI/HCbj83HPPJcWq3+/bw4cPkyzVOI6Pj5PxcnJykuSJrvvUpz5l7777bnpOp9NxJXDMpiFBGimsXUolR0p0s9l0ayYqwpoH4pM5PjOP+dT/acjl4bDoxVZfNQeTycTd4/bt20l+Xb161RlP+pxwkvn5+aSM6lrJb80X9wYNUb1HGqNmHhqkcR8eHjpZRTmh7zabzQxUKa89FopVqVSyp556yoHOGQaicOXhpQXHrAYzLyQIhOU1Zlnwba1WS4edXiS9UlGI5Fk70cUcFa3YmGkT0/Q//PBDM5vik/Typb2TybxarWYwGbqXLPCVlRXnJcvDX0wmE3vnnXfMzGMIVldXXaFh3p811ujGNZsC16VsPf30007ZyAP0Hh8fu3pPsnA0bjMvpLe3tzNA1fF47IRCt9t1xKKj0Sj9M5vOP8Oi1Wo1CWKtQVr+6+vrzoI0myr2vV4vzenOzk6ar16v56x7NYJ+qRQxa08CQhgIKl2FQsHu3r2b5nEymSSFKCrxEuTMFGMB4ggyVyuVSkmQE1+j+9++fTtj1ekds5oACw1zf+jerHgwHo8TdqNSmRXZZripUCikvrNYLOWH5kxzPRwOrdvtJtqIYnHGPL+xsZGRExH0SxLV0Wjkyh6pEdshUlK1+E50f/3kIaU+MAmCwHG1ZrNpGxsbmVJR+skwk+ZLIH4zj8mix4PhE74vYlwZ/qbxsrCwkPqZp3xyHDERSH1ot9uJmmVubi5BC2K4jeOOHg2zmYKj0DLHIowhPeQcG2WV1u/y8nJamzdu3EiGqgxJZfGpkey3VqtlPG+EKai/zBw0mxomUuaonJhN535zc9PeeeedJKNKpZKDWGjMERfEMy0qZxsbGxmqAc0r76M1W6/XM9mE8WexOCPmXVpaSu9ECqTZVG6yLqTKBzEKFMllufdJYGw2lXvcXxG3zIxBs6mHmPue65N4srzQf2yPhWIljxU5LGgxMHuJQOeYsRUFLBuVIB5QbOSuYF0geivYYjqmrqdGT6tCjbgFelHo1rx//35SLPb391Mf8jARrPml+2oR6frz83NXAkOWbeS7oeJKgLCZL9LJ9Fs9V3/TBlpcXEzhxCtXrrgsSx32nN/T01MH/IyKpsbFtRGzUXZ2dpxF3m63U1hIh3O73XY4KW4UpkXnlUwS/snMXLiiUqmkuXz66aeT0JVrP2aYfu9733PWf1RyWO4l0oTQOo3KmuaEB4Ou73a7DkQdXdpSzJhdoz5qzLdv305CP+4HhgS0B+jVYhjIbBbe7Xa7CXcSsUFUNNVWVlZcZiizOPVsJiS0Wi23n818tQYWP481yQie1/tdW1tzmEeOX43JAeoTeb4ISNYcaP7p8YlJIGoaH7NaT05OnPVP+aS+r6yspHsOBoPMIUEvsMZEgLe4/LRPo6w9Ojpyng/dQ+McDAa5WYnEyRK3ReM44uykiPV6PWc06t7RyOSz9JM4XbVKpZKiIGbe46x33Gq1ErfV7//+72euM5u+I+2xer2eWYPr6+vOCLxz507qs/B80dPCOaACTmWZfGUE7PMeDMfGd7i2tpapwad1K46wUqmU5qjRaGR4IfU+GI6lEhkrKph5FvZr1645zDUpEcyy3GSbm5vprJE3ixxozIQ3m54/yvglbRONfFa1oLEQ5V5eeywUKzW6dhUWYqX3Wq3mXlgMlVy0kfgZAZ5518ZJo+szLkB6xuixygsRxu+xPySOM5u6JG/duuUsrQjGo2JJNzoX7+LiottwOty73W6GOE5Nc7O1tZUBRp6enmYUOIKFCbI0m24OLezvf//7LtSnTdnpdNI4RqNR8hIJ76X+SKlYWlpyXrKoBPX7feftXF1dTetEyuSlS5ec54bg4tPT03Tg65qIC1Gf9C50GOnznZ2ddMgrzFiv191Bt7i4mCEwNDN3oOQlYZjNNvnOzo7LBo1ucrNpVhupH8gZxowwsxmhIkMZmkt6bkh9wMZ0bb2j+fl5l5zBg0TjG4/HSZBfunQpA2hlxqn6JY8G+W7o2SbVwO7ubnofxDxqnPQUkLrBzFyGmZ5TrVZzvS6k0KDBoDkhn5jeMwHZcV5pNcfU/Dzviuqjmk3nn1mYtNzVn0iarH7zvcZ3rHtTJsbDXv9nQWWGpNQnytpisehIWmNJr26369bFaDRymMC8iEEsShxDXsQ9ERu2srKSKdirZxKkHbFib7zxhplNyZXVFyq6r732mr3//vuuFie50YSrMpvJhHv37iXPDZVMM79WuT/zKE94VrbbbSfjYsIHgeNmHujNyI/OB85FvJf6uL29na65c+dOetccPzkFWeNVBKyUcRGj2O/PavtJX2i32844jthBzZf6Su6qbrfr5oDjjmdmXnssFCsuWDOvkBDke3Z25mpixdBfHklonrWSh98ymy6C9957z3Z2dlx4LgpPtbzn5bX4zJhuGkHfyoxiaEe/M7tHB+ji4mJyORNbs7S0lBYwQam1Wi0toFi4lhuR3kF9L27USO5GoGqlUkmeiO985zsZkKvGTAA2vWq8TgdRoVBwdaNICmc2tfQoRDY3NzMZjUdHR+l71WrVuYglcEg+OplMkhAnQ3PEHZCDJYZFR6ORC/dQ6Zybm8vUM2u1WmkNKkwYvZAbGxtprPfv30/9oXK9ubnpPGu0ciN9gtaf5r7VaqU1pvVVrVZzKUzMvDBlAgnXLkHlrA1KPF/kiRmNRm6dMiSyubmZDBNds7y8nCERfPHFF81s5r0bDodpTT3zzDPJEqcXb3NzM9WIYyYmlQcKWeIi87BTKysr6T1rjHoeaxKS10n7K+I6ovdG42YtO7OZZ1h96PV6udmNaoQ+qEVwOe9fLBZdAtB4PHbcQWY+zEbvesw0o6eZB/VkMnE13HQfyU0ybefVD1TTnBF7FSEO+j1WylDTunv06FFaG2IW/4M/+AM3rueeey7NhfYRz5NKpeIwrqQm0HPfffdd+4mf+In03bzxUIEi3mwwGFiv17PV1VUXHmQort/vJ1mo/TAajVy91U6n4zLu9JN9j6FzclKaTeUHveWRoV33YMRIBpn6Sqb5iPUjnxwJcxnN4nrQtTFpJFYniFn/F2UBx/ZYKFZy2fEg+6SmgsUqSZDXois+7+8UgNq8Dx48SJp05MPhy6ElQ2s59ic+O2YTvvzyy/ZgjZasAAAgAElEQVTd737XLZbt7W17/vnnzcxr23rO1tZW+jutAQqI6BkjCF8YkchvwoUY6QuYVqzFHjN7SE7JjKFyuexCeDpgq9VqylC5c+eOK37LOaaFxVJGZt7jc35+7gCenGu9h/Pz83T/ra0t5/LNy6jZ3NxM/aIwpvdubm4urR9SVRB/EnELOuxJBMp7R3Z7HYgEXaoPV69eddk9bFyPOqCPj49TOEt9JIO52RRHIiVKfSA/Eg9Rzu/8/HxaR+fn52ncW1tbbn3q/VWr1aTwkHtOAuz4+NiNYXt7O62B8/Pz1AfND0NDGkccP0kNi8UZ1Qv7d/36dYdRyfOKMEy2tLSUvk8qCY2z2+06TI/ZTAmeTCb2wQcfOHA3geTdbtd5j+R9ZjkVZlHGg4GccZovpt3rusuXLztlsdfrOUXAzBtmg8HARQCOj49te3vbms2mC/8xkywejuqv5ityluXxpRHacHh46JQ7zXnMCoxQEXrFBoNBWgP0LB8dHV34vqUUUw6bzZJArly5ksnSZqQhKm3Kbrx161b62927dxO+59q1a5kMZo2HHqt4FkwmE9fvS5cuOSoQ3ZNrjdEKFkNXixjCPCzX/Px8biLRiy++mO5H2d1sNnPPVxlMjx49cmS1mjONUXKPhh+TmvKwZ3HdxH0TPbYxY/Si9vHI6iftSXvSnrQn7Ul70p60J+2f3R4Lj9V4PHZWPrXearXqsCCRXM7s4pAcQ3B53qsYkpN2enp66tino3at7+iaYrHosDR5z4y/R6uJwPy5uTkrl8spG4ZhJoblWP4mWnR6DjX7aEEWi8XkuYrzcXZ2lsFn7O3tZVLVCV7XPcgKrr8/9dRTLswiNy9B6iTflNUR6SNoMaivsdAx54JrSf1qNBopDNbr9XL5bUgGWKvV3JpRmIGsx0wCIOEm+xiTHSImrVQqJQtqc3PTZcXwXsoWnZubS16aZ599NoV9Yw0xzeXy8nLyFHa7XWs2m3Z+fu7KBXHOWBBaLVp+DAUyPEZPiPrd6/XcvtXav3LlSuoDS1kwpEHvQqlUSt5BraPvfe97aW1ubm66MAU9LHrX5Csbj8e5DNzE3rHocbE4K8nB8BXrKRKjyBRyjUnf0zrhGiShJdn2uX7UXxa2jtw7ZrO9wLVKWg6FZRjuixiSyOLPPX50dOS8GfSYkY+MIZWYiKP5ILmp+q36bTEEI+oMzhnxW+TEI3kof3Kuzs7OUkiZnFQsXt3v952HQ5mn2se/8Au/YGZmv/Zrv2Zm0zDeyy+/nL6rudTapcfZzHv/SYoquRk9VkxC0PWk6CC5LuERq6urLgNTc6Q5HwwGbo/Te6W9HOtQ5lHKEFvHjO/RaJS8pgzxs77mycmJyzo081nHLGdkNoXTHBwcWLlcdjVcNe5KpeLenTx2xWLRRSwixjry8uk7n9QeC8VKwj0vFMg0ycFgkBY9hZzaReByXkfl4aKQY6fTsW984xuZ+1wUdycQmIdwnlszPlcLfGdnx42J7l0C8DgmhgyYUchnEvwaw4kkOtT9GS6MLLfHx8dpocX3oPscHR2lxU8Q6NLSUgqvtlote+utt8xsqnxp8ZPTRE1zTlyEDnuSuWlz1Gq15DrXXCvEQBCr5unDDz90c6ZNPDc3lw6v+/fvu/JBmhNmaK6srCSBqTRoM8vgu/Iagd4ab6PRSOPTHCukxfci93en07H33nvPzDxOSCVZzKaHvZR1vcd+v5/Wrv4mZW08HjsclK6nQpKXIUMySwJhW62WtVqt9D6YeaVWrVYzWWBM59Z8aYwKu3AeyXpuNhXYMYs1EhDrXXLu+N7n5+fTehgOh3b37t0MdoVUBQcHB+lZCk0ro05zoflhf9rtdrqepKSaV85T7HvcO2b5iQ+6Pq8CRSzzdXp6mt691szBwYGjUshr5EyjzFUSQAyxLCwsuEObiUyFQsGq1WpGqRD5MA9jHdQLCwsuczTOQWzkdiKea3l5Oc0Tk4FUu1b0HWazRJUvf/nLZmb2ta99LX1GbGGeMqU54/jMpvua1CF8n9wnOiOLxRmFxEX1YNvtdsJQqe9ra2sO98RnEpxO6gmOgUoe+6f3USqVnAzRPovYOUIhIrUH5ygSkkoud7tdl1GptTEcDt1eZaUL3Z9juiiTVGP5pPZYKFZarDEjx8wDRSNBHn9+XCPHBTcVOV14/36/7zTpmE0SwWuTycS9qLyswCj0YixXHEt8Xl72gRY2Lb/RaFbsOc9DY+atDgLH+fni4qLLDNGG1IF1fHycUbaI1zCbCjR5g+i943taXV1Ni/j+/ftJ+JD6IOKnaDlojFQAOGbOOy0WzacoEDRfXG96Pg+subm5NGcRLK85GA6Hri6jqDJIchc3ZMxoVC07M2+1aw7m5+dtfn4+FX+9fft2EoBvvvlmeib79v7777vyERGoytpfuq8s91arlcvrROJHHvxSQug5YH1NrZXoAW63245Bmoeq2fSd09okPYXu+alPfcodXrRCyR9E/jIJ9dFolK5nRmW1Wk0K8vHxsRO8ZlMPVeSfosIRPY23b99OFraEPLFlZlNlJ2KaNBc8kEkzkqe8E9NkNtufk8nEEUjqHvTKRMM2en4mk4mjD7mIJFbPpOeNcu+ihB5+LqWGRp/Z9N3EQ5XGaKvVch4SjocyT3NAIlQmCvCeEZOle0tJUf/kaf7oo4/s29/+thv3xsaG4wJk9moel9lgMEgG05e+9KVceg967POK0RO3p7mhF1vPliF3fHzsvIlRSVbfWZg5UknoPvoeDTxlWUfSaMptRgFYnDxyCOr+pFrRmDY2NtJZdHZ25gxoZRUPBoO0fuippCHAvkWd4aL2WChWZuZIBJmtRIqF09PTj6VUUItK1HA4dNmEUblSy/OY8XcJlhh2Y+hDFhmJ16Lyl3d/pnFGJYxZXkwP1vX7+/sZMLmuJRGnFrLu/0d/9EduLK+99lpalLp3o9FIluH6+no6ACNIVoK20Wg4rhcqDWqj0cgVZVWasbwvTz/9dPqeLPs8JVMCfzKZpHT8CL7Vu2b9KBLBsRxQbOx7BDmenp66FP2joyPH+mw2fUeaF3oy1eJBwpAXuVtoyTJjtVQqpbBgPAzVojLHUJjZdE1LqdB7YGiyVCplanVxDTLkFpVsM8+QHcOKTGzQ/DL5QWOKNQhPT0+TAqS5IUkueal4n7Ozs6TAPHr0KL0bZiJy7127ds29Y/Wf2Zw8hJmRJX4tAulJxxBbLG5uNt0PVMzVoidc73JjY8MpPxobv9Pv99Pe089Wq+XClVQgWXuUHoTIas450E8ChLmm9PcIw9BeZfYX+f4YzqJnk5memgtm9kV5rfdI2oxyuZzkLL01vL7f76f9Rq4+rQHJWvFbfeUrX7Hf+q3fMjNLNQCvXbuWzre4NzXWer3uKirIgJQsZDkYM1/doFqtpvWpfrHkj+5J49rMr692u53JIjTzCQaM0jA0HKMxNNSjwbC4uOg8VjRm6WXUc/JK26hRYdf9CSOq1WrOu8+ahloD9BDrrFD/SO8QQ+N57bFQrOi+LJfLTot/+PCh01jVNMHD4fBCT0+eFUbvVcRV5JWgYNP1UZhMJhNXEDl+n9ar/pbn1WKGA1ueNTCZTNJGVmkFFrfU9cziiwff888/76xgHkjqX7PZdBtejWEa9p3Ek2S2Z4il1+ulMVIQ6IBfWFjIlALiRlLfpBDNzc2l62L1cabIM8zHOmN87ww7090cPWn0Em5vb9vx8XGaV45Vv6+trTnFolKZ1XqjgGJaswQosxzr9bpTuH7oh37IzKZr/KWXXrKbN29mqEt0f9Y0oyCiMsf5ZXhZ76ZarTpesDzPLon2zs/P0zxpvOqDvjscDtMY9V6Oj48dzUQMT0VqiWKxmN7dRfX7xuNxek6v10ueBob0eABxbI1GI/WbuDnu30qlkjG+5ubmnCDOU87YYikjlm/icxhClILINRplCL1UxCuqT1rX5+fnTr6RtJmHJ/nQ8gyZ5eXl3Oypi7xbqvOoa0ajkfN2EiNq5olGdV4wNF2v1zN71sxzV8Vs2UhVoc81B4VCIZ0Bkj2kt1F2rzzHr732mv3Kr/yK3bt3z371V3/VzKbKF98dn0XYhNpkMknjkzKgZ5IzSnNJ/i21SsVXDOH5o2eVSqV0/93dXfdOSftCL1VexITvlFgus5k8oaJGzsHJZJLWQB6zP6NX0WCI563ZdO9ISaazplQqJV2ChuLJyYkjdDWbOQx0/dnZWYbfMa89FopVrLJOnhVaIWQbjzQIZt71HO+vTV6tVjOhBt3jIg8ThQ4bWW31TKac8/CicOX9uTDo9eKGi2zQul7/V8mDp556ys1Ls9l0Al5N3p2NjQ0nsNvtdqYGYLVaTQLj7bffTtcy7HJR/Sr2mYtxf3/fMdxGVnKGoKKyTCuIY/2nf/onM5uuAdEYmPk0YG1QgbbNZjgfHdDf/e53zWzq1cpz/Wscc3NzTqgSx6D10uv1XIg20k1E9mqO7erVqxlWec2NDuVarWavvvqqm7tSqeTKRAwGs/pi6+vrGc/BxsaG84CZZXFzZl5ZkZDhZxSQVL4o1BnSNJutQ9ZRI6kkx8m+kNQvrxxUp9NxRoD6W61Wrd/vW6PRcEoTy2QwXLC6uurY0COPWGTtFx6I92y1Wq7cRyTmjWG2er2e5oBcW5SJNIDoCeX7YP1A/lxcXHSp+WbT90K2d86BlMnBYOAM0ejBVaOBTHkZD0JSPai/UlboNdAzWTJKc6M1Q8JRGisM66spbK97SPaQzoXXN5vNVM9vfX09yRYqFXG/iD5Ga/OFF16wn/qpnzIzsz/+4z92RbM5R5HQ1MzjQWkk8noqnWRzl1eXZ4tZPl7o+Pg4yTaWZjLzJa94vlEpy5PJxF7lEflGLz49b9ozVIgI0o9joXcyL6mJThk+k4Xmy+Vy+l37VPcnvCUSk+e1x0KxMptt8sFg4BQj1lrTi2eGDicpWtxqLMB8+fLl9HcedM1m02VWUFnLs3zMPN5GFlzUytWff1YmATZrHkg9auWRFJSAczPPPLu9vW3b29v20UcfOUHERdLv95MAZ//JMB5ZzDVmHsrMdmOGhlqhUHDhkchZlZf9IUVuPB6n+nMSUN///vedIOChSuX2+9//vplNPWOaF+GJYlHPhYUFZ9lK8JLlmhgY1pAiR5bmbm9vz3li1ffl5eX0vRs3biRlql6vp2fxoCPjdL1eT+OQcv3gwYMMwaaEHsu4yC0ei+yy8bDRc4htiXuPB3ZMxDCbrSM9R+v22rVr6btSeBYXF11/eNgMh8O0LxmuZbif61fj5/7d2NjIJDWUSr74bL/fd+Ep9ZFep4uEtPrOgtA8gKiksFhuZL3WnPFgINC70+k4ZVQtz5LXWKMHnCFKhsfMZsoav8d3PZlMrFarpf1D744aPb5q9BrH+/OQZrFtrgcCk/XONjY2HM8RPdpq9LwtLCw4PI7ZVMYRg/nWW28lDxQ9aCRc5RjMvKfTbGq0vf7662Y2DTNKxor/SvfRvK+srLjzQuOW90XGVB5ejdnsXAPcDzwn1Bfi85aWltx6yzt/4u/Knr8o+50KD39GJ4PeAyEy0cjW55wbQlPyQnURz8WSVDT6NzY2nEEc70+uuo9rj4Vixdh1qVTKbGzWu4statH6zGz2IsjCyxdMq4sZO6PRyG16ZYDo+XkhxNiHizICY/+Ir4m4pdgicDEvO4GLan19PVOAdG1tLc01Q7BmU+EdvQqrq6suPJP3Di4iTjWbbVwe9vRiUKAy9h6ztGLaK5XVV155xZWDoGCkV0ih01KplN6hQlwxLBdDH2qycJnZonmKdaziGiKWS3/r9XoOHKvMyX6/n7IbKRQbjUZu2JXYKSoHxBdqTEyvJz4iWt4Ex/IdRfyO2VRBI9Cd/ZGQ0gEX322pVMp4F/r9vgvzUfhRPmjO6fHo931ZI+J0xuNxSv+P1m5ULLmvi8Vipv/CUKmRZJKCW3NRqVTSuqNwZiibc0o5E7MYSTqqcdATErGofGdHR0e2srLispxZLYHzoLWn2mpq2jex7h+fq9+73W4GMF8oFJzMqNfrad1qblj7cH9/33msFA6t1WpJCYgGbJ4crVar6R11Op0MiPvg4CB5qMymYT0erpIhMtQvXbqUxiFZKu8GFXCt5c9//vP2V3/1V2Y2w11pPJItlMkMf8mb/vnPf97dn1QcBLHTQxShKNp7ZMmXUrO8vJxLo8L5JWkr1zK/d3Jy4hJW8qhbuN63t7fT/emNzDtb6ECg17BY9ATdMdJgNl2DPBdiVQNGyfRsrZOLFLfYHgvFih6iUqnk6oIRH5WXTkzhQ08XrZThcJjcs/RExD7QM5UHzqTgMvMWQ15W3kVWIzXwmHWl71EwMYsk4qziTy5CatdSGhluOjk5cYtqeXk5ecGYccfMi7wMpJWVFQd+1Vju3r1rlUrF7t27l8m04WZl6ExNQlTeJ5ZI0ff0nLOzM6dMxI1i5hXq8XicuUZzoufRjUylmZiyeAhqXeje7XbbrWsq8swy1Do6ODhI1rHeXaVScWOTZWg29U5JiWT6Pj12zWYz9UeCc2Vlxe2piw5h/dzd3U175vz8PFPw1my6dmh46OBVvzhfkU6BCio9JHrO4uKi24csXyKB9+jRo+QBYzaq2Wztq4/yvEZLPIYa6DXTHBLD1+12MzXtiP008+EQhmQog3q9XnrHhUIhlw6Asq1cLqd3VS6XM8W6874fs5CZ7Ual8fj42O0NFjrWuHd3dzPZhlKYqPzQg6j/S36IQkFtaWkpAwlgWLJYLDrDhEkLLPocefbMsoe9+kyll6B7ebHNsmfOzZs37dVXX3WgbHon+VPr7h/+4R9S+Ztnn33WvvSlL5mZ2e/8zu+k+7799tuOW49rM9JASPZKWavVammfUcEg5QnPJSVZsB6f2Uw+0KjU9/WTezcPZ8t91+/P6vcRGsOzksoWE9Vo1GrNMnv1Ihwyva+xCkne72Yzo5T7hrguMy+jzHwGYV57LBSrYrGYLHXGYs3yJ4TWPwu/ssVConqpjO1z4gnCjGGreD/222x2yC8vL7v0TFr+8WWaXYxHilw7eeVy8sgA9Sy1SmVWUZxKgjZ+DBspi4KbcG9vL1lzdCFT0DODzWwWVpPlR7I+jt3MYzG0eOUhNJstcHqKzDyfVa1Wy32nKysrzj2vazqdTgajEwHbo9GsTBGLFGucjUbDKbFMLSa+SAphu912ihUxhcxkktCUoHzhhRfc+m63206Z0vWsN0mll+SAzEbiOqX3kN+jUNRcc/9QKK6srOQaCXlZWnk4iUjguby8nITX6elpBuhsNl07eUSdLJXD/x8eHtr8/LT2HMu/kM6F62dhYSGtTb0vZrqWy2XHl1WpVNJ7IOZSfW+325l1p0ZQdFToY6OSWSwWMwWhaWx+nFFHAkaChqkcaV4fPXqU5rNQKGT2W0yNj0q3mccUUcaYTd9BzFrmXF+5csW9GxpY9DJzDUaAtj7XPcfjsauxZ+ZpB8ym+5pcg2YeX8pSR9GboucPh0N75513zGw6/zJeX3vttXSfvb29FEkgmbCM7MFgkFkXNPgi/Q3ry9L7rfurMcuO8ouyQPNHRYmGP5OVuK4ZjpQCOhrN6gIzs1Dfjwqi9queT3iCGvvK8H2e103zxnNMa4CJQzGkTyOTY76oPRaKFQ8caqVqUSmJru48BYOHM13nZjMNW6y4ZlOch1yz2gT6rlp8jl6c8C3PPfdcUlq4gS8SkPF+cYNGMHHMLjS7WMHS9eSgMptuPBZQZsiEQGAt3OFwmBZXvV7PzdI7OztzXEl54T8qVvQGFYvFlOVFS1nzrntoXqVQHx0dJSwDmdSp8HGj7u3tuawjNVrt/BtDXuoriQsfPXrkNjdB7RT6+v3KlSsZ8K3Gp+cUCgVXPNhsKtQofIj12dzczITlYuJGvV7PBaPnAVi57w4PD5OSQAJChtyih0FC/eTkJL0/1p7U/aOXg8JQ6yumzkc8jplXDliJgAq0vq9xbG5u2mg0cgcN+09hTAudHlkWiuV8b29vW71et4ODg7Q2m82m86hEQlCNlSEyJsVo7iKug4dHXNeUoVGWqJ2dnaW/Mc19OBymvpECodfrpfe9srKS4ZdTo+GjOYvrRP2KHoW8CheSG5ubm7lrgLg4FguWMkIWc31Or12EmYzH41T02GxKnZAHMSFsROdXBOjrezs7Oyli8tFHH6XrWEfvy1/+sv3Jn/yJmU1DhHFfF4vFDA5UjTJCBz7ndnd3N7e6RK/XS3uEZJ7PPPOMjUajjFHO/VEsFtPn9JbH/UMIjgxw9TVigll1gffhPohnpBozQ7XeFCaP4URCFVZXV5OirGcfHR1l9k9MBrnoTFd7LBSrUqnk8BS0uHnwXxRa+6RGRUHC9/XXX7dXXnnFXSfFil4Usi1HT5IWrw71nZ2djJfjov7kfXbRuLi58gTlRQcmrWVyYWlD3Lp1y2X0abE988wzaRy7u7tJ+DCWTYuUGAqOg0B3xriJcWLfqcTq8+imlUt9cXExWbjNZjPzTLPpJtABSv4eCeDt7e3Uj5iltby8nLGeVldXnYVLQCTxFMwokdBlXN9sdiDRm8myErJeG42Ge0eVSsU9q1ar2Z07d9J71uGudnR0lOZQStvly5ed4M3DTFHZYRjm7OwsN3sp4qoiKehoNHJ7SWPR9WbTvUYgPSkJ+CySaqov9CASf6R5MpuGOogHjHs07j+GSfkOWCyY8olcWFpPzOwj9xL3b2R1zyv8elHijNkMuM/DIB5AMcxLT2beujTLhn50eNFTqc+oQOvzPM4uejD4XBWZZsbj+vq68wjSS8BsLV1PyhM1csPpe8xyIy2I+k3gNgtxS5miQkJaEHqN5ufnnaKV58HjPF69ejUZ+ru7uwkSwD2ptSZ6HZ1f9ECr3bt3z2HJuLZ1H9LeFAqF9O6joZVHpSDuPrZKpeIMboab5QGnYTIajTLe9aiUEi4zHo9zIz8MS165ciXdX9U98lgC9Jzl5eU0T5oXGiYMg5pdzDwQ2yemqhUKhf+rUCjsFgqFm/jsfysUCvcLhcI//uDff4m//S+FQuH9QqHwbqFQ+PIn3f9Je9KetCftSXvSnrQn7f8v7Z/jsfqPZvbvzezXw+f/x/n5+f/ODwqFwktm9l+b2ctmtmNmf1ooFF44Pz/PApTQzs/PHW6CmvFwOLRCoZBhSM/LrKNVR1BwtVpNddDEinvt2jX3HOJuCLS7CBtlltXoL8pIycve0+cRtM77xnt9Em1D9HrRIstLlf2pn/opV8KDYHBmlDAmrrAB73d4eOi8EJpHWYrNZjNDjhm9AyT343vUnJB7y8xTKty8eTM3/Gjm3dTECugzWarybMk9X6lUXK0tMx9SXlhYcF5AtWq1msJmh/8fe+8SI+l15fmdeD8zMyryWQ9WFSmSIinq0ZKapiBBLfVuvGl4Y8AbY+BFe9FeGJiFZ+eFMYA3NjCrAdrwwgO07TbaG8M7ww1BQEPdbLZEUVSLFKkqVlVWMisfkZkRGZERkZERXoT+N373fDeq6PF4UAPUBQqZFfnF993vPs49j//5n5OTqGzIYDDI4GA2NjaCxXtycpKxAvf396OQAQG3zOwhGS49MYPBIIMTPDk5CYzpxEFw3fEeJCDUM3K5XBRO63a74TskZyRvkeZOlrE8SkwKoTeI4ZlUkdeLi4so3OdxS/JMMMQsygMmcDDky/VTLpdDmEnv0+v1Iu8d9y+tb80p5460LGwE9tMr7Mdcjbx8lINcW770kpeRlCNk0Oc4NxqNKB1f40jMqp4jS54kxz70u7KyEt0/5dHK5xd0LcwSI/s/G5ORUmW76MkzizkFJUPOz8/D3F1dXWUKA7OMmFl8JjARxj9Ha+n8/DzgBRuNRtgnfE6z2Qy1QHu9XpDLXFNaA/qeZFur1bJbt25FGYzkAvQlbfT7kydPIuodyVh6mcyyWGLdX/uTsoSlpASSN1usPyaWnJ+fZwrTK8pCLCC9pqlIx8XFRUQMywx49cOHrCVniO+U7GXI0eOuvwzVgtmXUKxms9lPc7nc3S91N7M/MbP/dTabjczsfi6X+8zM3jGznz3tS7lcLkzSxcVFtOlT6f0EffOA5uARpP0Hf/AHoQ4aQ45eCfFFMFMCjM8keZ2uYb+YzcDvMhShw5E1nlJZi7r3shizHxc/NnqmPyS5UCSYdnd3wzgRUK3QV7lczmRdcgFqo5EbhqSd5XI53JPEh2qFQiFsch44ZPItlxdFUxmm8fcjcFYC/mkhR25oHUIsjUSFgWNHoDcxNWp+vrSuiJHr9/sZkkRPWlmtVoNA1c9utxvmxocF8vl8mAd+RoVbBxZpKobDYZSNqGv0TkwMMJsL7xTBoPZGpVJJZmuaWZJKgez7zNIldrJWq0XhPq0DheY9n1Cj0Qjp+dobKysr4R6lUikDdFbTM33mkse/mc3HVPM7Go3C79PpnPPJl9xhmJTPZaUHT0HjDZPj4+OIZNSHuHwo0csSZlrynsRSEd/I8WSfuc6lXMroOjg4CO+0trYWGctSOJnZNx6Pwxr3WFLNwRdffBFkRblcDmuMGdXeOE5lg+qzdrudUWJ9RvJgMIg+84kXZvO1QCVDY0f2cp43VCC++tWvhszE9957z8ziw14hfY8/Jej64uIizJM3yFkBgfvZ13P0PyeTSYYZfX19Pdzv+Pg4GrvLy8vQRxoYPCv82WiWBZozvJuiJanVahFVh8+mb7fbUUbpkydPonWlMWIWsi/t5kHsy5wlav9fMFb/RS6X+0/N7H0z+2ez2ezEzG6a2d/imt3ff5ZpuVzuT83sT83mws0LGrVnZfwRM8Frb9y4YX/0R39kZvOimF5ZSWXqebA4cSZsy+pjeeI8vzD9O/kyFQLjeqUxpUQSd7WM1Z3fp2Il75E4jdS0aXq9XtD2Z7NZOFSIsfBMtlQ+DiT2W4EAACAASURBVA8PrVpd1Bzb3NyMLK/V1dWgfHEzeTI2syw4VmPR6XRCfyuVSlAeeHgfHBxE7ycPhnAbk8kk8miZZYnnlrXJZJIhsPTktvn8glhU70k8kd6VwGH1QwKEFAxmc8NAyvjjx4/NLOZpM4tTgbWGPc5Ea6bb7QaFigdBu90OynW3283Mw/n5eURwyL1E7JHP7jTL8kZpzrw1uMyDq/G6efNmpDwtE3bEV2pN0lInuJp7lYqi7nHt2rWwD7yCxLVDIllfzcAXc6bXUN9hX7zFzQK1OgwGg0FUb9B7przMWib7OBb0OBCIrLV7cnKSYdOmTNO9NK9nZ2fBAPBld9TPlZWV8Pvp6WkmM9XTIxwcHIR+kVWe4+O9NV7Ol8vliGGeCSSp8iV+n+vQplGXy+WCsnj9+vUwN1L+yd+ncWOS0Ouvv25mi0SqTqeTycKTx1hYK1/OhkpBaqxJBWKW5iJjI2UMx5z0CT4r2+M3iffyiWq6ptfrZRJczOZjqu9y/NfW1qJ39zpDpVLJeGh9UgoNjV6vFxHmmi32ri/Ptaz9mypW/8rM/hszm/3+539nZv+ZmeUS12ZdTmY2m83+3Mz+3Mxse3s7XEOh9GUaU2op6L/1rW+FBcc03Kc1D2YVmaBvfrHoYKBw03N5vRoXEz1DvvnQIq0WtVT2ROr5VKzotWNftMiurq6i4pN6hgTltWvXMiSNPiNmMlmUJKlUKtEGJqkhrSSGbrWgvbeFbmEPUuaGMJsfpCwarc3HMhZ6NwpqPodcYAxJM5lBTdlwPJw8a3CKH03XCKTcarXCRm42m6Gmndn8MPP7gyn6TJtm6/V6UbFarQFmY3oOGqYfaz40Lt1uN2Q6aZw0NrVazW7evGnD4TBKfFAfNW/aK1onvV4vUrL8Ianvks5CSkqv1wsKqBRnZh36sWEGGusEpoDcDO+enp6G57PGodlCfhwcHISxLBQK4Vkacx44vpEdn14gXt/v9yPQvO6t+1MhWaas0ovhPd1q5G1L/Z1rhJleZvO9N51Oo8ObdUHN0tm5DJVOp9OoXufjx48za0j91Bj4cLBXWvP5RU1J3csnEvgxG4/HEaWLCmz7d2B2+61btyJuPH9Pn83uSZPv3LljZgtlyXvDzCzDds9GA50y3jPvy0igke0rEqhNJpMwv1SkGcnwRoMaw39cg1zXNBL4PY7TYDCIeNzM5nuf8+ANsV6vF83NeDyO6pDqGiaseSiQjAmvlC9r/0aK1Ww2C+W/c7nc/2Bm/+fv/7trZi/h0ltmls1rds1b3KmDYRl+iuE+1k3b2NiISjB4xWo6nUYTeO/evcCqSzyCnkNXPQeVlgmf4xcN3y3FiEsPlJ884ciIJ/syoUDeO+X212fatDqQptNplOHF4sW6Lw+70WgU7sVMFF3D+oxmcVouLRAyr2teKSin00WttcFgEO4vz83x8XH0/oy3M4tFfczlckFxI9bMzILFaLao/TUcDpMZRbq/Qgr0tHhXsg5rYkA0BvpboVCIPB4cOwpYfY/eQI/78Zl++h4FhhQHKhW5XC6Z2boMh0UvihpxaizsyoOQ700FvVAohLGmsry2thaFAzSOxG1pPej7+knCT/Id0VL3WU18P10vz161Wo0Etvq1tbUVriH+Q88cjUaRYs39JOu60WiENPDz8/NQzsRsvie1zmlBExOj9+RB12g0grLZarUiXjFPKeCfw1AVlX7CIDgG9ADr+qurqxC2Y3UFs8X+45oiNujRo0f24MGDTGULUm4w5KP+nJ2dRWdHrVZ7aiSgWq1Gxpy8QtxHVMALhULgYBTtjvquxv22LLqQz+cD75SyfSeTScAGf/bZZ5mzQuucMoYkojLiSLhNXOjjx48j2aG+aU17rB9lm+ap2+1GGXYpKIqHyKiRxsUsVqpTjgaG1Pm9q6urYEBvbW1lQppPnjzJ6BQe6+jxfsucImr/v3iscrnc9dlsJn/pf2Rmyhj8P8zsf87lcv+9zcHrr5nZe8+632w2y1AZqPGF6UJ8+eWXzcxCDaa7d+8uZUGmwNFn+/v79tvf/jZc/+tf/zrjCSCh3Ww2y5SyUdMC7na7IZbMZ/nFxvdY5pLn+3qFyitVy5S5FNgyBYD340Qum2azmVl0/vCezWZBSKZKGKSsteFwGI3RbDYLQpm10Hz4k8I9FZ7yhHMMwcljo014586dTPq7PxhYcubw8DDQPXBjq3KASEVZ3NZ7FhQC5UHt644xlFMoFJJGAX9yPuk653tcXV1FIF8fcqxUKlEIsd/vR7QGfi2zTp/ZfN419yS7VCNOQs9WfUXtMdYhHA6HSc6YTqcTWbx6hgR9qVTKeOBkMLBkk/pDrN5gMIgOYT2/WCxGWD2FH8fjsXW73XBYSRFinUeufX12cXGRKe+h6+TtvHXrVhjfw8PDaAyazWYGgE1vQT6fD2EpKlY3b96MPLSkhLi4uMgYQLVaLdr7CpFxTz0Ll8N9SqLXVqsVhdl4iBNOIcXmwYMHGc+dxotjeXh4aC+99FJ0Dypw/L/WDhUGb3DrAO71emEsm81m5JVmeE/N4xxTB7V/l9R+ltJWr9cz55NwWJIx7XY7KEVnZ2fBoPAKnNYvjcmnJWxRsSJ5q1lsbPrzh2c6gfTkPGSyDt+fv3ualouLi0iezGaz4DUTt1+1Wo2ev4zQM6Xo8v0ZgWKS0LPaMxWrXC73v5jZj8xsI5fL7ZrZf21mP8rlct+yeZjvczP7z3//gr/O5XL/m5n9o5lNzOzPnpURaBYDBK+urpKgUTJtv/nmmyG7T4uaWCuzNJfLZDIJ4YKPPvooAuv6mL9vy3ijLi8vg+Ag2WOKe8osmzniS+FQSDwLa6bPU4uZffYkeZ5AUU2LlQepLHi6sz1DdT6/qOWmTVutVjOKj1qKA4Txe7M4Js/Gg1eHB4HbXrHSQalxrVQqUe02ecYkNPWT3iMJUYbQ+P4PHz606XRqKysrViqVIlyRahjKmuV9zebrVwoNQaksF5Gy3oh5IdaHwOzUuJnFXkh6U7yyqEbvAolCPRmrV1LpmWFYx4fL9T16J6mU+YxAMo0rZCLeL3pfdF+tSSqLfBbfg54krRMqVqznqEbwrn7yIPUYKxWRVqOXROvRZybRCJQX486dO5F3W2tzMBiEvjBz8/T0NCrYzMQC9k+NHgWGF9UIGfDcfprjSqUSrmH1AWLj+v1+5EmhN5ElbdrtdsYzvrOzEynGZnMPhZRc1vA0m8s2z2nIunpHR0dLS2JpnHyB+gcPHtjm5ma0rjl/qdIqPrpADzgdAVp3Ozs74bzykQYWidY8UEnmObK/vx/kJZMs6vV6BuPr5c5wOAzKmObUe219eJPv48eO+4r9JDEtvbm+UgKbzil6nFNnntl8nac8TpRt2p8+dO1Dzcval8kK/E8SH/+PT7n+X5jZv/hST4+/Z2aLzCufkbW1tWXf//73zWwO1tME+ZRQNSocGtQPP/zQPvvsMzObH8a0xnwJHLO4AHTKWtLnxJGkCAC9lZIKdVIRS3lr2GgZMD3bj0OKOJQprvqp6yQ4mPmkBZbP56NwCxctlSgpIdevXw+C6/DwMNnHlNvYLFuCgu9NS1RWCENMfszUB5I0MmPNe6o8xYKuM5t7m5idxmdcXl7a8fFxFDIpFov2rW99y66uroICoD7rAN3e3g6CjlgrFgnlGMiQGI1G0cGvVigUovFjCrH3cpgtFNfhMC5czJAxx4LeIu4fhi9Jqqc53tnZSSqI5+fnYc4otKiU+fR2NXql6TmRhSvhL8ue703GfWJKeKhKKalWq1HJLe13faYwHclQNb8sDUQmeSoxZCknto+HsDIdzeIacWq8/vHjx8l1Si/VYDAI/aFB5Q8cKShvvvlmGEcmTcgD6xUryRJSYjD726fZ05DzWbBm8/2yurqa8ZAxk1bt4OAgzOPOzk70Tvr84uIirI96vR6e+eDBg0xWmllMalwoFEL/9T7dbjdaXx6TpfnV/VgRQe/nw7vqv9lcxniv197enhWLxYDBbLfbYb8fHR0lFSuzRaiP3iYmTKSy2XW93lfzQkJmeYrUWHmEa5+s9dzDjBIQy+pxqikYjK4RnOeVV16J5pH9ZEk8hsPJwp8aO9bxfBZm+7lgXjdbntGjhXLnzp2Qsk8q/GXKBw+Kjz76KPwkQM1bEl5TZxiINQm9h0j39EV39fdlLOx+IZrNXaw+HOWtG/6fygQzHekJGA6HEefRslAS+Wb8wmKqLRlydX81CYJSqRThilLZlewDvQbEKPkSCRLS9+/fDwcax9pnFGoDscA2QyD+Hj4sR+Xh888/zxyqap1OJ9S51GHDcBuLYPNvu7u7Ge6tarUanitvGP9mFtfvu7q6ilLzxTPF/jMNmQeZnn16ehqFutfX1zPheZ+AwCaPAq9nUdazs7OkYCSOkd8lOJZ7hIYBU911WLAkjPYljQp9T0pCu90Oa9bz8KgPb731VnSQa8ykYPDw1U8q16urq5HXhN5Gs/ne0t6jh4jhQa0pPodZyN1uN6xlHqr0gJNp28wynjSCss3mxtHt27fDWOzs7EQ1N5kF58sJaX0NBoMwXgy7cs4pq2mY0HsipYVhJN1Hc6/5Ho1GQWn64osvohA3GdZTz/R7keeMvksFQ82HEAeDQaQs+zViZhklRP/XO+7t7YWswLfeeit4LT24+/333zezOUu81tH5+XkwGLivyRdI5S5FQZNa15ubm0nuKf3dQzf0nuQQ9Nnf+qkzgkkvnmZE7+PhKSy9ZBYXjveM7ZorrkeGg5nwwPliWPtplVXMniPFynP/eCWHoT4pQTwgzOKX3d3dDQoVgZ/LCjNq8dFNeHFxEWX9SOhSKJDbiBXgPUeJGieNCzLlUfLZE3x/jpM/OMxizMvp6WnkgfIWnoSQFmWj0cgA3BlfLpfLkStfz9/a2gqbmVZKuVyOYukca92/UChE/DV6H3pfOp1OVHMrVaqAnoBSqZQpcaG6hOqjrpelYzZXmohNEpC91+uFjUhhXSgU7OHDh1apVOzWrVvByve0AmaL9ezLrfBvLH3SaDQioKXGcXt7OwqLev4rX6yUodZbt26F+ZOwZkhb761rRFx4586dsJfoMdT/NR/kmSINRsoDZZbObOUBwb1EQDVxfJoPepz0Pc2DDlJmJvni2MTTKXy7ubkZQiD7+/sZoLU3Cpl5K4Wo1WqFsd7b24t4fVSShvdiaKjZbEbeRHk4t7e3w74+Pj4O1yiUkioqTS+cD/1NJpPoIOv1euGeAlazQDEPQFJTmMXhKhbF1vulEj/0/lo/9IDKIPEldHg4ks5Cc+PL3Mhz0Wq1Ql8fPXoU1mm1Wo32ZIqYlhxvLKrNff7o0aNwH61DwgR8osRwOAxnjcZuPB5HITc5FqRImc3Xieb0/v37AU/I51O5Pj09jQwChZUJXNc7+3NKrVgsRmSx9Jx6Cho/howkEb/G//vkDt2HtX+XZeVpPZ6cnIToCY18s5jYm+FwyiG9O/cDk0KWOQrC95/6139HLZfLZVKj9WL6vN/vZwC01JbPz88jvp/PPvssShc3iz0B3nqji5obmiEuFQLlxhsOh5GCJmVGAshTJFCxomVAgaMDy1try8KFeiaVg9PT0/AeFCzEmaRCM7pGC4cCT9dTqWDb3NxMYryEP0o1bToCzTVvw+Ew8ow8fPgwHGry2vl38P/3hz37VyqVgkDlHO7v74f+0toixsM/s1Kp2EsvvWTl8qIorg7AnZ2dDOGpJzJtNBrhOZ6jhYKRygm9A+qXlIQUG7U+29/fD3gu3Xt9fT0SFp9++mnohw6zR48eJXln9HulUrHV1dWkorSzsxOup6FDNvqVlZUMBIAZpGbzOdD+29vbC8of5YUOT++t1Ofcv/1+P4RSqGicnZ2Ffl1eXgYBvLe3lyEL9ApWs9kMn5E+gjQRNAKHw2HY4wxdcz9wXZOUU4ck96ze1yuwXGNsJEglH9BwOAyHNvsnpfHtt98Of1c/8vm8nZ+fR8qv3oN8aj7k6D0DbPV6PQNg1qHJrGy9B/nKeBhqnMwsrFWzucwmEWqj0YjIVtVoNDMT0Sw2JNVPjYHk887OTliTJycnmQxmz9HFs6hcLgfvoRwGZnGoeXd3N1AOSaFPcaDRi+3PWRou3nPlk5zMYjzSeDyO5DzD7WySfR6Kwoxgtaurq2D0pRLZljXCPGq1WuRM0DqhgVWtViMIgTfIzeI5fVatwOdCsWLxWTXPH1GpVDIpq+PxOCghn332WQTUlAue9yJehanwZovFzIU3Go3CItCArqysZDAfdBfrdy5Cjy/yIZHpdJGOy37r3QhC189lmAS+jxaHMvDYPD5G76I++2cWCoUou4dN/WWWo8949CFLfeZLHvCZ/X4/Cs0wPKLmy4GwMRWYmDw9kxlkHA+C8WezmT148CC6b7EYlyKazWb20ksvWbvdtsFgEA4ZevikwKk/zApUSwFbNzY2kl5ZbuxqtRqUHx+iI/+U+iWOqGKxGGHLOK9f+cpXwphJkZ5Op+H+3GtmC4Vua2srmg8ShWoufTaY5oChBO4pvj89m7R0pWDduHEjKnBtFpNems3n/fDw0HZ2duzk5CTDuq+mcOHe3l5YdxTGPqRO7y6Vcb0n54J7VhUYVlZWImuaVBIpT/vu7m6YM3LvqNVqtQgUXiwWg1JLzx75uohFISCeSiRxQgz18n1oIOv+pVIpyDR6a5rNZriGZ4HmjdQQWq/05ktuMZzKAtMcFxL26jmrq6uhX/pM701DnEqOxzd6LNfKykrG89bpdKIkCLbJZFGQWGM6Go2CF2t3dzdZvHlvby+83+7ubjDmdA058NQIb9G4UhFVW6ZQXV5eBkVlOp1Ga1Dz2m63o1Cfxonlg7xXPYXDpcJNw8H3U3+jp4uOlRTA3huGfNZsNrPRaBSdC1RQU6Wp2J7OcvWivWgv2ov2or1oL9qL9qJ96fZceKxyudxS7gyfLm+2cK0+fPgwhP+Gw2Eye4kx3cFgEMVxU+UKSqVS5K2QtcG0cQJRaakVCoXwN1lATIHV9312h6dM8Bk2p6enGUZggkM9yFjfpVdDmreswHw+H8JBekemYntXPcMhzCbRvTQWes7TeFHUCKonXo4hT1oJzFjReqCrv1wuR3PDkhx6t9PT0+CBubi4CFYNn1OtVsM6aTabEUBY15JEkMBTZlupPiW9WHq25oEFfUkDIQvSexZIBqix3tzcDOtH32NWja7nuOh7snBPT08jNz4L5jJtnHNJq1vzQVZkJlNorXU6nWhdMvy3v78fWZZ6Jvt+eHgYcCTCh3zxxRdhHgn2Z0aZ2YLFX5mhrVYrSkrxZIlaOyxoyzC+fw73oa5LVZEgpoj3XFtbC/i8s7Oz4OFrNBpRmJaVHlIeBoY3iH9hiE5ZcZ7Og3JoY2MjkkG6N/eefmfVi6urq7CuvMWvEBXfh2GXXC4X1pXWAsdL76O5UliO2dmsUddut6MxoPdF78qMSj+XjFgwvElqAPWLZxQLWHMN6PobN25E+0deHcoS9oP4N4Zr9/f3wzUPHz4MXlYmtlC2Eb9JzCi9O15u63rvYVLzGCiz+Z5hmTMSHqstK1fGNchEFMI5fCgwVSGF/fdUTPqpa5ioxubPnxSFRqo9F4qV2XwTkUDOp+RPp9MQVhCfx4MHD8LA1Gq1JO/TdDqN0omlCLGYK5vKkugaZpGYzTeJDwUyRParX/0qcpG//vrrGe6LVNgztai4gTxQn+zVqSKgzMhaW1vLZDlMp9OA+5ESQZf65eWlzWaziLuKruDUwvK0EnRpmy0WKRU4XZMiart27VqG70fYNc3R8fFxeP98Ph9dr5Bdu90OG7RcLgcBzGspFDnHR0dHAcTsM5nUGHpYWVkJB6LWKfmDRGyreSAAXMrWcDgM78Q1sLKyEoXBeZBoPBTq8QcFm96bYQJmt5pZhCVkmIAkkcyMZEKCGjEcnCPiHZjJSZc+S6PwED48PAzXS1FS+RSz+VhrXHQPj+/L5/N2dXUVDBaG69ROT09D35k9RaU/lYRiNl9jGgfN12g0CgpRv9+PMrU071tbW+GA63a7mX2te7K/qdIqPPQ9Qa3eg+t3WdmayWQSZd/pe7o/KQB4cN26dSuEsA4PD0OYVnPPeVY/U5mvvIZ1EPmTeFjt4UKhEOGVqMgzLEnCYC8fSZ2hd5Wip7JSuVwuPJ97XPfROtf8rq2thXPEK8VqlPOUoeSx+va3vx2u/+1vfxvBTz744AMzM/vRj35kZnOFg3uSMIYUDo1Gte6rdUtGdq75FDUBDXFm/qYogFJj4LOaqThTydU4eejJdDqNsIaps5OlqihD1TwJNWsVpgwmtudCsRIImXgln/7+4MGDsMkFYC4UCpHQpPdIBx1p/qk06fveI0WcR4qF2W+IFE0EAYUeeCoiNr23f6YfF7VyuRxxodAqrVarmbI90+miLM3h4WE4TIm58IBn1go0my82Zl1RiKSsXJ9R4t/TZ4nw8NKc0ovYbrej55DRmoLV8zip6Zqjo6PwzFdeeSUC62pefUYc+6i+kSqBG6vVagUhWiwWbX193Q4ODpLYN80R+8B7m8XM9h77pbliaQ4KMY9NpIGheeBap2XuD1uxvgtP9fDhw6TC5xv3FK1jsyxZrc+K9NQO3gBivTnN787OTgT898DSR48eWavVivhzRqNRSGZJkQWWy+VMmSN91ywW7h7IXKvVgvyRQsD1wIQIXjMYDCJeMXp/U41136iMq3nvOr1zWm88yGu1WrSX6D1JJQyRXsaXoqFsI77GbL4fOUd8PyasqI/Xrl3LlILS+JIYUntjc3MzrDHO4WQyCfLMg+UJhjdbKBNUjDVP3mtmNle2vEyU4ZpS3D3JZWqfco1Np1O7ffu2DQaDSFbcvXs3kBCTYVzK7Pr6ejTWpBliqSHJBHryNA8sZaZxVON64HnJWq5ftnllyt9TjWOU+psa6TF4PZNZUmSwy7xRrBDzrPZcKFa5XC4AND34Wy/S6/UyxSZZPoTp3mYLLww3PxldPU8GFzCJwnyjNq1GMKfZXHlhzTzPPKv+aINsbGwkXfpcJB4Ez/6YzRc9BRR/39/fD0KdQEO9H2vnqY9m8UHHzXx1dbU0LJg6ePSZnq0wwfn5eXimPqOFPR6PM4qwz5xhqSHPV0IBxdRnhZL0Dj5Dx/PtCLwuQXn9+vVIsWD1dM11q9WKDj2t3d/85jfRmMiy98zNLA2kdnFxEdVCo2JFQKrZ4qBjVpreW14/HsblcjlKFKCCRhC++ttoNKJ9wOfwHvTS6Hs81GhBq8j11dVVeP9yuRwpIWSNV7+Oj4/DGiQFAZW2TqcTDort7W2r1Wqhrwz7qpH36+joKKOQUFhr/2hcafHzPbSW+Q5mC1nEWolsXkZxvFi2xBO0NpvNDJO85kzvsb+/H8bx+vXr0X4TX6CHWKS8D37sdA1liBR0rjOz2MDROPB9a7VaJnIgRUjPPT8/jzwk3mjU5z593vedHgyzuJC3L9nE67jHzeYy3QP1fekctpTHzmcNmlkgGlZrNptRlEd9lHeL60zNV5vo9XpBEVtdXU3W/GS7uroK48okAJ5z/P1Zyggpd3R/NXKtsUbosuxknuNMVGBEQmun1+uF8fKF3s2y53+KP2tZe24UK/J7mFkG36HF45UaHQx+0zDTR4PtM/jMshkCT8sU8H01m0+4DlyWceA9aM3Jgvriiy+isgvk61LzyhYzpngg6tns63Q6jVKkJXS9YDJbCGotXOEger1eWHDT6TTCdqgVi8WwAI+Pj8O7c5w05l4wkf5CG7XdbkfKGb0ol5eX4SChNaJn+kOJm0X32d/fD2tLYbNSqRR58rrdbhD+xeKCcVnjy2K2al5QUtlhfSyNATmozGJ2eirFfq0R5yGFgIq9zz6kN0WhO/KE6e/r6+vR+qEizpqPHv+nps+n02mYI2ZSac2wRJCax6uYLRROEgqaxbgetbOzs7A2V1ZWIoJfNeGJzBZWu5jG5Z3iwTiZTKJ16j2IZMrmdeqzWTb7T59vb28ni1l7LrFlKd3EefAdhcXSXHhZubGxEdaflEwq194Tqz1JTw8zkvV/s6znnhQUWvviQ7tx40YUhmaBbuEXqbj2+/2AV/IEoVISrl27FsaLxjQVK2Y2j8fjMAckHvaHvFcY+WzieonF1H2UGafrPYFoCm9EDyLxTsJmkhxb709FXv3XOeNJlmkIM2tPz7m8vAxjTMLOarUaZZmrqc6kWQyt8LLYK1feM2cWh/2oVOtvxKOmOCipCDF0KPZ/VS8gp6PGggzvnBe+K4mul9UNVnsuFCtZW9REPaePFi1p5ckrQYvNLCYYJR9N6tnLmrwC3o3IzbaxsREOM96LYNeUK38wGNinn34a3lUklLSUfd9EMDgcDsPfZCX7aw8ODoJyMJ1ObWtry8bjcVgQTAf2deBIKqlFeO/evSgtXm06nUaCfNmiNMuyahNfwzI7tOpSoF2CcpvNZhQ+5sGUAo0OBgP75JNPzGyxgTY2NqJrSWpYLBaj8iT66a0qlvDQZ8QwaAPTuzccDsN9Op1OmEMC459G5upd8N1uNxyAEiJau8QJeSXbbOGNU5PnsFqtRiS5vnlyxkKhEJUN8f2/vLyMcGWlUin0mQcPuXb4OfmlmH6uZ9IDQ6VmNpuFvVIqlaJ6mBofKtenp6eBF67ZbEaKnlnM60ZeLfVZ/aOSp7knEJmNa3c6XZDXap49NQgNEyoMDNnznoQ+cC9TCefakzJDpaHZbGaSEzzmZXd3137xi1+Y2XwtC6PIUBpL9IzH47DeCHGgB8PTE2j/s/g2/8YkDzUqnQyrq29MPPG8dynIh1cWUjXsqFjRk0YGdN9/ynI9l1U02O7evRt4rQ4ODkKfpCgeHByEJA+zufGrc4EcXFSE/Zoym+9lXUMjn8aW75v3tLKlPtN46KfweGZxfU1P5slzEIcXzAAAIABJREFUPeXx9WvBh0L1O9eGWXwOqGl9eLnm23OhWM1ms7DovCXsPRpkzr64uAjCldYDswB89g6tSTPLhP24qGmREjhKAbys6C0VPl9XzywGqpKPKxWmMZsLELl3mbVGZYjcGlRyyOmi38krxbAe/0/8CTEfHgxK17WuPz09Da5pKXPyhNGS0KGkdz04OAjWmceiUNDS2mQ4gJtb70qOpnw+HylUerayafw1l5eX1ul0IgW52+1GJKlkkGeGF1tKQDMhgHgyGgy8ntirVGbM5eVlFLbjcz0Gxmw+HwpN0pOnvpnND0YPFjdb7BMJGFrxmt9cLheUJgm8brebyVD0LncmGIzH42S/zGIMCDM3CfhVm81mUVbg/v6+dbvdqGA2nzOZTMJePDg4CAJWoRhmhvlDT9dubGyENdjr9SJMC/vFMU3VLNVPjbUOxM3NzchTZzbfD/IGUfkzm+9Jv98Y6ms0GtHBJEWRnmNiTPWzXq9HeKP33nvPfvazn5nZXGFRmP+b3/xmGGserJubmxlIAMHIlUolKGIaQ3lkqDwTmE4mcY6n1gzHJhUe9N5Hs4WxoTmt1Wrhs3q9Hh22PF88/ESNc89DP5UVSKgB+ySjmWNitthvn3/+eab8FvGOGgtmB0pOewNajZ5zz2qu5rPwPD6LZ6pX5Dhn9ILxTPV71SxbZkb3k5yXLKLc93PCEkwpD7InnF7WngvFSuR2hUIhYxl42oXBYBAGsNVqhQPM45FS3goWV9TC9yzF3PDa7I1GIypN4QGe/hD0hx7vqd/X19cj6gNZx8TWSMjoGsbzvSI4Ho+jw540DyktnoKL3pTJZBKoAYrFYlSjTM+i14ULbzQaZQgIldaufvM+T548yYBEKVgYJzdbeGsY3mXIRt/3qe4M6VJpYoYoLaB6vZ4RFgR9+gPQbCE0NTa04p+W7EBQrt+sdLObxeELvZf6pvHRWEvZZNhPfSf7NcNYPlHAbB6C0OcpAk89R/0kxQXHnyV6GMpjGCgVBq/X6xkKBo0NsVTeM8N76P08XoZKRaFQyCiqUsR0n93d3YBFuX37doY9P5V2zvnyIRjffF00Gopm2ZDjaDQKCpW8QhsbG2H8f/e730VjMJnMi0N//vnn4TCu1+vROC5jtyZeSEqOFLzhcGgffvhhuPaXv/xlkGNkNdd+++M//uOM8iCvipQAjvXGxkY4DBmyun//fjR+lI9MbEg1YkxZicIX8Ob6oPGgvngyWjUSARM3TGWCcmxZljWVCd2P4V96U0U9RBLXTqcTKVztdjsj26rVasRCr3XoKygQnsN5UCiVso3vZpYFoS+LLHAsOL5eAfLZx7oPHSUeM6yfWod0wLDSg87l4+PjyKtPTPcyCiG150KxMluUtdHAaBHrxXXAXl5ehk1WLBaDQpICmpvFGQ/04ixzX/pCtGbzzZkS7mZp8PjTgIlqk8kkuGSpvUtY+xguS5kUi8UMWJgHoFlMt7CM0sCDb83iLEbRLpjNx139peeiVCpFiQCeE4thBC1GWXmPHj0K86w5bbfbyRIu6o/Z/ADQNa1WK5NZp7kijkQCm5gd9cevnclkEgQNhSgVElrohUIhgwm8uLiIQjaptUEMBYUlAfzMeuWcjUajiMJA7y/uLO0bhY+Jw5AngorByspKJKwYtmUqusci+nT8SqWSwXfRk0jKCDOLsvX08/LyMhwWLGei/+sahj28gk5+LPVX9+/3+wGPojE8OjqKQvzEDhJjRxyGvFcaNykCtLwJjPe4Tt2b+4eYN/VVSo3PiKOHmH3Xc8iAzs+3trbszTffDONFr6WnhtFYEzDvEzU++eQT+/u///twvc+m1Lj/wz/8Q3jPd999N/y9UqkE2SJD7tVXXw17nPhRrd39/X0bjUaRokJvgvaez8zV+hGztn43m4+r548j/u/atWvRPqUB5rG0lHka98lkEuEu1Vc2Yn2WcS/RW7O2tmZvv/22mc2rj5gtgOtmcaapmdnNmzft5s2bNhwOw7veuXMn9PHBgwdhzJaFu1i9YhmIfBnEhnLQKyc+45uyxFNyaG+XSqUIf83wIzMdzSxyGOinl8v0UtVqtUixoi7xNAiR2XOiWEmx4Ab2vCKaSJWhMJtrlJ73Rvfz2rOaBscfECydow2tLCoKVB8bThGR+jTZFBlgr9eLlDVt2GVavrBHIgoVbQMtHl5PGgJ6Gej+JVeT2hdffBHhF6jgasGlymHoOdqMIgxkpop+0tshjBTLdKi/LItgtggTUIBVKpVM+r3mT8K60+lEmCyv6HW73cgionLJ+m56t1qtFvVLypqUw8lkXsdPY81sRe+9IkiTXGn6O+d0bW0t8uB5V3qj0QhjRz4kjgXDOtVqNRzgxLywzAvd/vTE6jMpDho/9jmFc6GnSdd7b8Tp6WkUfkyFasziBBevZJKDTX0gcNsbHeIl4tiq31IUrl27FnGNafylBPpQOTFWjx49SuJ0BIPQ93WNxlUhSzPLeLCGw2GG2286ndrf/u3fhnsQtqCxabfbEXeV9gsLwHNs8vkFPxxllhTze/fuRUXZlx06uvfPf/7zaP+888474d10+G1ubobaeMViMUMjIH4mjbkfx1RNREJN6HXmWeCzAtU8lYDZfP0Rd5TK9FR/zGK8r77n+zkejzMF1QloN4vnQPQuZguZu7u7G8kbKVxmFhTq9fX1YBisrq5GGDfNNQ2vZrMZGRg+0UYYaTZ/hi/DkalJaWE9R7WUR1n3YViSyQe6n6/7Rw+8b/V6PYJksA+j0SgYQs/KdHwuFCtlCg0Gg7CAtQDM4rp3jx49SoYMOHjUdHkNiSG9QsVitAqpUTlKhfOWNSpTHthJi1qbi8BAWRE3btyI0pKlSW9vb0d4IXprPHbFh6LoIcnn8xkFQ+PALDvNx8rKiq2srNj+/n7ENUXv0fb2dgaASMoE4svMFkKl0+kEwdntdoOXxdNQMHtJ45TL5TJ8K/5AHwwGQSArE7PRaESeE++B8rw5rVYrPMcTpGo+Li4uIg9eihqBwuHi4iKsE3If+WwztX6/H95bihC5a6bTafDg0oul75rNDyaNS6PRWFpQW9fcv38/HM5ra2uZd0sVufaWZ7PZDPvTE/np80ajEYVdpNjVarXIg0kAKw8YKWukDvFZQqTo8CEWkj2q755kdXt7O8Jr8lo9mwDxjz/+OITLaCRQeRmPx+E91tbWwjOlzFWrVbtx40YYC72LxkIHIhUbGmk0gjRH3W43HMbtdjv83u12bXV1NWC0SA+h96W3WnOxsrISlCCzOd+Zp0zgHhgMBlHo0Mzshz/8YbiXWYxhowKjOd3Z2bHd3d0kuWy1Oq86cfPmzYxyzTmgHDVLZ4Ez+1hjQrmZokbg/xWJ0fVeNuk6ZlBznPRuvM7TlUiOvfHGG2Y2L6CusCCfpb+ZmX33u98Nn9GwLRaLGWJgs9j7enFxkaQkWgbjUd+9gZTixeJ1VGy8syLVWJ2ECiKNPj6vWCxGZ4rek/2lDKYS/6z2XChWZouB08b1ngpfJNMsToXnyzK7g03XEGslQSyrk8J6GSg9pVgRf0L3r29M9WTGo95bVsKnn34aHfwKZa2uroY+iznaLHbPmi2Ul/F4HIT00dFREPS8lr+//vrrYSN+/vnnETeMWcwnoud4cky+J1l3NSeaMx7wzLaQQkmB4DMrtchv3rwZBIq+x1RjfdfH0mntetLX2WyWwcswOaJYLGY8Vnr++vq6nZ6eWrPZjKgq1GgB0ctJ3Jw8V8QJqM8SdhQSKS8kFXs2hoeY3UhleTKZhIOa3jaGeZdZ3Hy2F2i6N/ew3o9eLIZH+v1+NGbL2J9liEkJOT8/j4pnn5+fR/tT9+D4eGJCD4DmQUrFzisvUnqLxWIYR/KaseXz+ZCRuba2liEsJAO7mt5Dc9bv98N7sEQNCzmbLeaPxKJcy3w3s4XixPdmk/Fx9+7dCMu0tbUVDnbuRe4D7u0PP/zQPvzwQ7t7924AuMsAMptjMWV0kEet1WoFDxdLnpDp3I85Q3fecGGWrg+DMYRHxSolB1g2huXamCxBDJZZTCuk6gZScHK5XBgPnxTDe2lsXnnllWBg+T5qPT558iQKk8rbVq1W7datW7a7uxv9PZ9f8DdOJpOgZJEOgWcns25TyonumWpUfklD4SmZdC2dKpJHorNI8T9qnjgfKcD+03BhzyI/fbrr5UV70V60F+1Fe9FetBftRfvS7bnwWKn0BV2ltDZ9/JZu1hSY7PLyMiIk9MUeybosTTVF+JjK6iJglM+ll4qeCGZ16V3USDjpwc/dbjdyr8vNmc/nMzQMZnMN2oNvzeaWCLNSdM10Oo1wR2qtVitZv45eC28xaOxoCTMjUZaP+qE5Iw8RvUvyyHncE8dQ2Yc+hMQxYfkVWVOsHyeL32cF8p7EbTCLk+uR156engbvFjFW+knrkWzczPaR19JzSBHInMqiNFusH+HRdC8/B7rW497M4pAFSSiJZZDnU+tM9xEwnI3Za9PpNMO8rbEhm30K6G1mUQIBC+R61vPJZBJZsimcI0M/xOj479DbSW4prR/2sd/vRyFQ9Ushu2azGWFxiIE6OzvL4LpIyaF943nner1eJL80Bjs7O6E2JT+v1+sRjlL7TWOrZ1O2af+JYoCUCZ6T6Y033gjv/f7775vZHHTuw19q+vzevXtRqEyJGI1GI3yHmEfPE6b30F6/vLzMZHCzZJDZXD4SE+hxqRrrVGKHT0Th++ieLJvy05/+NPRRHlYWSzabe73lVdL9W61W5D3ytS8Hg0Hklb9x40a4L6sGmC3mlsk14/E4Uy+SlCd6J8I8PLzGy1B6qVIEzoxk+MaIBiEZHrupvqfIQlOZzD6jmR4xeQlrtVokq7zuYGYRRntZey4UKwmpo6OjIAwp5Hw2gBYQeY1SnD5qGhwtnnq9Hg1eqi1LlydOySymEPCbkc/2QpuLhpwyup8PHei5zWYzWUKAQob9Yjjg9u3bEcOwNh/HgJXkWZuPmYU+ZMLSAX7szBbKpcensDaU5oOCiLiner0ehAuZle/duxeuk9D1cXOmoFPosnGsxbDLDJxcLhcp6xTYLLVwfn4eCEd9CMfji3y5EB2cUvZv3boV/f2LL74IB5wOdR6MZot5J/h8f38/UsDJji8FlQc8cST5fD6iOjGLWcdJs+DHkmFqKRZXV1dJol4S45JXR+td4TJSOWidbm5uhvnQ+BweHkbhTa5JFoymUr6sJIf2aaVSiQqWpw6GbrcbMtem00WhaoUoT09Po3XR6XQi/isfcms0Ghnh7hUUJjXkcrkg51ZWVqJ5ZVo614nWpBRVnylNig71xVPBUL5Vq1X76le/Gr33xx9/bO+99154JmUOKXDEJ8e/v/322+H5HPNqtRr2CsdJ65tgZrP5nvcZ0twPpFTQePrswp2dnSBvSP3jjVPdn3jMnZ2dUNdP/VBBZWZA+kxG4jZ7vV4mU1h90Zq9du1amHd/L83t0dFRwFcym5uch8sy5lNUCV6WURHi91LnOpWw2WwW5CBlBbM4U4kE/L5Z7Ajw52UKHyaDNZ/PRzU/edZR1nlAvG/PhWJVLpftzp07dv369cAi64WiGnFV5Kdapv0SpK3Fy9pu/nvLLOUUJxTj15zsSqUSBIFXTIhn0L2Is2Fcmp6jzc1Nazabtre3FxF6pjKjNE5qrFXFrBixfXvQIcnifB/z+XxkHR8fH2cAzMSJVCqV6Jlm2cr0jUYjWLgEGTMrhYWfd3Z2wj2Oj4+jg8JsYdGpnxsbG0EAp8qntFqt6LCnp0Jjw2LMnuCQIM2NjY0wphLAmqP19fVMQgKFkcpySInY2NiIUtc9q7bZfMxpZaZqLi4TkIVCIYz7MhyE3kvvbRZTB3iLnh6d1B4bDocZnJl+UpH22Y06BOiR0+80NkgcyXU4HC4IblPM0j6Nnftda+Xs7CwIY49TUhuNRhEGkjXYzOZrgenw3vJl6r9ZTJLrDQY9h8kv1Wo1yhr84IMPzGyu4HAe5AmdTqdhn0khJGhc76p5poLMZAt/wGls5OV98803w1y+9957yQiE+mMWZ7YJtylljd/TXmFavIyn1dXV6DmlUimTSeuTnJgdrjE0i2WW1gMxit5A1zgRF/nOO+/YO++8Yx9++GHGi0Kj0HuPzBbrgjx9ZnFRd/W5Xq8HXrPPP/88eS7cu3cvXLO2tpZJ4qrX68majk9rlKGz2SxjQBEXSW+xlzdcd3QQqC1zhjCCJcWHnF4pzJfH2nkcFd+BxsuzxuK5UKyUcs00dypWZmlGdAp3D4ojc7TZfPFQ0Ot37/pdtuE1sd6dyfIvXukyyypkOiD6/X5URsC37e3taGGfn5/b+fm57e7uBmF1fn4eDqOtra3IOlUrFotJj8bTFEt60FIAbC7y0Wj0TBZafZcHnQ9h8WBIgXyvX78exprKHEHm3iMmAcvMQbqKdX2lUokULXpcWNrGkyf6bBeBfDudTvR+JKqjonRxcZFZ5/1+P7K2fDjLKzTj8dh+97vfRe9tlial1e/eqjOLrberq6soy0/WOC05NZ8VRd4zZqFpfjudTqTA6EAhVxHZj/W+zAo2mystDENqH8il3+v1npm9w797i5vhQg/u1vWpciorKytBafn0008zhwszC83mnhy1QqFgZ2dntra2llQ+PRO7L3ljtlCOzGJyzMePH2e42czmCqqUP4bJ+b6lUimMAUsdkYuJe1YhN3qyms1m8F41m81I8ffGm/oipZ/RBS+fqcDpb1qvrVYrUta9IWMWh3Xy+XwmktFsNiNP4nA4jOSY9gT79cknn0RREnk6lNTwxhtvRBmVerbGVfdMeXyr1WrkHdMctNvtqD6uCFcFJ+E6M5vvGSUYvPrqq1GChppnrfdnRy6Xi/pI2Eur1Qr3ouHtk890H35PY01DcVnozVPSeKWLSr9PVkiF+NfW1qLxZRYsZS7XVbJfT/3rv6Mm3iluJm/Zq3mhb5bVpOv1elAmfAq7WkqRYuOEpLiDzOKaRZPJJCwC1tyiN4HP9Zgds2zWHg8gHShra2thQVSr1aUuSYZV+O4kVzWLix6bzVOlU+9NjBM3HIUumw6G1dXVDA2C+k6hRFZj790zmx9IDHEw01H3I36MP6mISZDRmvVrQNd460XWsWfv1wErj6MwCFoDem+lOquNRqNkYWuWieHc1ev1qKyO7s0wmDhrqEzv7+9HWB+y6ZulyzMQo6f34KGzzLPL8SK9iRorArAxa48YN92fZK++j2r9fj8K07D5sJDaMoOB75fKBk2VqTKLqSIuLi6i8ky6H4X1V77ylaggsZlllBQdqsr00t91aI7H4+B1Oj4+DvuEBw7Dq4eHh5FR540sesvM0lnO9JTW6/VkvTbKRMptYnTYKP9v375t3/nOd8xsEU70mYX+kPbkm54c8+rqKuwLn4FrFhsDHguptX91dRXuWS6Xo3JafA45mVjWyCzm+9PzFJJnFILYTcmJZrMZYRFJHUCYB+EEZhZCrPRmSqG+ceNGkOkaDx+2I6s5Q63qK7FMZnMlUvfXfiBkh0TflLP+vFK/iL1b5rGioUQicC/nPb8WHS3lcnkpeSuvSWXJsj0XipXKnkwmE3v33Xftgw8+yLhBqZD4AaL1ZDY/DDQhyyZBLRXqMptvCAKXlzGvp9hmh8OhPX782NbX1zO8TvSGpIS0nt9sNiOhoHdlbUIeMv1+Pwhes0Ul+bfffjsJtk8B7c3mni9tQlpAPFx87TZep74TmO/f3WxuKdNLlcKHsQ2Hw4gvy7vqn9VYpNbMgkVtluU8Ih+O92iaLeesEYVDqVSyRqMRvivrcTqdhvRws3jtSPiSg827qSlkyIQt4c4xZBiZysGtW7fC/EpR3N/fD4eXmu65vr4eDhVant5rzPCI2fwQ0UHCg5kM2Zubm0HJ8MB+zZf3NDL0QsydnqMDyHurUqHOyWRRYoQyho2yhbguNu/FppcmRWfB9bO5uRn6o3npdDrhs16vF0KHOrylfKQ8jiRcpceKxMrkISqVSoH00MsqKq76jPU3tX8bjUaSEoOFwpna7vmi1M+vfe1rYa+wSLzem3VizWKckN5ZxMlm83VBOAEPSo0d16OnulAjcJuJK8Tb+uoAHEv97Wn0L16pJUEt60wWi8Uo+UFKNz3LDH9J+VZImNAUGae9Xi/jzUxhRL0HnpCXFO3Kzs6OjcfjiHSV7+mjRnoPyhZPe8Nwom+sCqC+rqysZOA6Xnbl84uC3NVqNZPIZjY3Dinr/r1QrCT4dRikWFw1GOT20UJaXV2N6tdx8JdZ1KmsPR5ktMJSyhzvYTafELl6FY4g+7YnS2PIjXXmWLNpGWZK1k21uqjxJFesf+9yuRxtRJ8x53k+VldXwyErJYDcQ8zGUp99I9cVMVNeOSFWzGMfPLvtcDjMlDkyW4RWaVH5RAFivFgzT+NSLBYjy5V8PwTN8rtmC2HC2lKqKfj1r3/dzOZrlEkC/mDw3leGQpclZFxcXEThTik+FNhsXhjrWVqn/juFQiFYxfR8+e/739nni4uLqF6kmge4Utlm9pB32/uQKT0k6sN4PI5qi6VCBN5go2WeUp7z+XxyTNk/7tONjY0oEcMz0/d6vWjP1Gq14F3QM//xH/8xeLGowGguxuOxbWxshMOg0WiE+Xrw4EHwUqSgAWZxdjDDHnonP6fsgxrxL/TKmKWxOMQrttvtqOi4sv9eeumlMAe9Xi8TaifTvMaB69osDhceHh5Ghlcul7Nf//rXZrbw+N66dSsodvn8IuPaczjRAKfhNRwOM31otVqRsZOqd6fx8lAR9ffk5CTyFEspJoSBjTyGZNDX2tI7UhEjCfD9+/ft3XffDePi34lhXa+8PK3RqPMeR+1pKjLLZIv6Q+Jes9gTSkJPJh3RqNM9+H8qhZzr1D4wm6/vZeV+1J4Lxery8jIq3XB8fJwhESR4z2wuEFKkXmZZpnR+bjbXPnl4qtVqtaR7kIthmeeC2rUULCpt3lNkNt+Ag8EgLHoKAdZXY3vy5En4nNki3mUqrAVxa7S4U+nnZvPxlZUjS/nevXtBqfBhAXloKpVKJGhZ89F7G8zimky0ttQXhjzN5ptQFj1DsK+//noYVynmEkJSlpgRl/JyDoeLulmnp6dBqPIwuXbtWibzzIdMCVyW5c3smqOjo+BtMDN7+eWXgyWpsWC9smazGWGy+F4kl/XJAOVyOfJAtdvtKPSt2mLKUNra2orW8vr6ehJA+7Q9ReVGfUkBhFP4qlRTVpHmOZWy7e9Pb8V0Oo32D4kheUB4T5kaPZWkK1EjvslnAfNQbbVaUYkgszhsxDHReLzxxhvBqDk5OQlKk8dy0bNET4AvNmsWF0OmkaDvbW1tZcKopDLQQax10Ww2IyPNZ9XyffQc7RPJF8k9lQ6bTqcRW7ZX0NbW1qI5bbVaUVacWey19P0aj8d29+5d++u//usgKzqdTpjfGzduhLGm96rX60VZgjp3RNrJMBzf1ywGu2udsI+kqtEY6Hu63sM/UsWpu91uhAtT0xwq+1AynbKN3m19V7QxaqoqYLYwZhle9mcw95S+R1JsJauINsOPm7+H3i0FPXmWXFqGAdb3RqNRZHx7BVLvwCz+lMxgey4Uq4uLC/voo4+C4PKCZzabBcVBB1e73U6GaZa10WgUDUYKe5XKTvGfs/nrPZ4qBZL3yhotN8aDz8/PI5wZS5+oMS1eTf/Xxjk4OIienQLV+3f1YGXP0suFylRxuq5Z7yrlak+Bxfn7tWvXokO4Xq+HOTs8PAzX6T3Pz8+jOmIcC4aV6ImQMOSB6TFeukYlfdh3/SS2TgePwKJvv/12uMfp6WnIxDGbH2ap1GoKS44ZFT4pThRMtMR8+juB/ax5aDb3XNEz1W63k8Bo/s4wOf9PxcaX3uAz9Vnq/lyvWgMpQebDMPQs+72hz5kiL6VK9SpT+J3BYBApTl7WEPhqNl8LGl+tV+99W2Zxk9leh/fl5WVUm05tbW0togKQIUVPu+eBo9dFwGXdc2dnJ6wB7SPyZJnFuE9SJkyn06hv9KBqL/IgZ0q/vq/+0ivgM/LoZfbjyFqRBFlTyWD1Dl2zu7sbvDh37961t956y8xib4yvQyhPkp5J7il9Ti+NlzOEGEhhU1heSqf4wnSPlBFC44HKHaEbGq+vfOUr4R3NLGTfm8UUDJI9DH/qvf18MeRIA4StXq+HPpycnEQAcH2HMrBSqSQjE2ryEnIMfKOXjPPlufzoeeMZvGys1VKFyn17LhSry8tLe/LkSRLAbBYLSU3Mo0ePwsHvCdPMLOKb0T289Wu2PAswpRR5JYRg1n6/H8VpzeIYPAv0mi1c0XQ/EzB/cXERLSBiGxhvX6awacN+9tlnYWEz84rv6ck31fQ9bXr9neNEgcmsIpYKSFWap0t3MBhELn6z+eJnHbutra1I4RmPx1av14Ow7Pf7EQcYm9ZCtVqNcB56PsHVBLAWi8VwqBwfH2dc4Dp4uCbkJfAZTmZz7xqF1W9/+1t76623ogPjb/7mb+z+/fvhPVNksIVCIfQ5JQi8p5Njfe3atdAH3YOGi1l8OKkRg0Qjgkq2iH51DyoUWnfe7Z9KIvFgav8cM0sqTVQmPYkfvVrc1/JWpvhvdE+vPPrf2c7OzsI7aR16ALz3uuleLLasNbO5uRkUd84JlQNa3JPJxD799FO7detWxgNOcLfGQ17gn/zkJ/a1r30tGlvtV4ad1ce9vb1IznH/0uOqMDn3mMZFfVA/CfMQnUW32w371+PmngUiLpfL0RrUgfj6669HOCyt2WXrejJZkBzv7OwEg1fK7Pvvv5/hp+M91VJKgE9EIO0MPSo0xNWoEFCeFYvFTBTEe/iZpKRrSMFQKpUyWX7j8TjKru/1etE+SfE3co6U+c+xYNat2XydMpLhm8dAcR+ScJphcyp2agcHB6EPTE7gvT0JzPpyAAAgAElEQVT2i2ezP698ey4Uq6urKzs7OwsT6VPbK5VKBsza6/Ui5L8XvKVSKalEqHnQrVrKyubi5T24mLvdbpgUpmfrem1M72UjLoYcVefn50srtKfiu8vwOORfohJGriYPbpcAUg2+brcb3Y/WmcadIQi6vzVfGxsbkcLDcC8xPbSGeDDs7+9nMvvK5XKEFfPZTD6k12g0wjXdbjdSmNUY0qSCyENG76n+UBHgd/U+BNZSidVzX3nllYj7SAVqmTVoZhEmjGBxCQzOL5Ut8l8JuyMvjVlcicAsnRDA+poEH3M/MMTuSf+kuNEIopLhvVdq2vf6nleAGGIqlUqRF8sT2aoREMuEA7P0nOr+5C3iIe+B8SyeaxbzbPlMTyl39HpRUXrppZeCcvLJJ5+Ea46OjqLwkR87YhI1XvTsyXOhfu3u7maK+WpNsli8rm80GmEMqEisrq5GGEz1q1KpBKMjlXmnsdB7M2NY9xDInvcmKaZvXunycs1sXqeQxsuHH35o3/72tyOvBMHKrBIhJZOHtPq8LMqhfuvv3tCmXNY1TDZYFp3J5/ORDOl0OnZ+fp7B0onx/f333w/ngv7GNVAulyPFihEOjRerLLDYtJlFa4MGN/tYLBYjmWEWe8oYzuO+pGwrFoth7nVvnruemV2NMvHatWvRfk9li49Goyg68qy2fPZftBftRXvRXrQX7UV70V60/1ftufBYFQqFSPut1WqRRcj6cdJmmQZOS8e3VIjBeyhSjdlAzOyjdk1vk8JltLBodXj8CIGfcv+S0ZueipOTkyiUwXf15WRkUcva6PV6GevUv5sPgXrwf7vdDu9dq9WS+DNPrujLBxweHkbhAGYwEltBVzzHYDAYZDIzPfmiLCN9r1Qq2enpacR8rsa0eDbvJZN1RO4nhqz57ilMwNPuL0/WxsZG+O7Ozk4g/JRnUF4/WfGrq6th7M7Pz0NIgrXgfG0zel3NLABHzWJ2fLUUa3KKsZzt6uoqSQGQy+Xs9PTUbt68uRSXmGq0DL3lrXBMPr+oBDAajaLsUp8soaaxFrdVv9/PYMr0HgyRLgO2pqg62P/hcBj6nfJC6x4pAO3l5WVU+0+NdCXNZjMqq6Rnc/y4Z9lfAbCr1Wq4Xt4TUmHomlR5EnoGKSNPTk7CuE4mk4ys9WVjqtVqRAmha0jAy0aPNXGt2i9ra2uZjC+9jzBHBwcHAdDNrHQfYpK3mN59yj4fyfBZlGwpXK/uqzFhqSPyMnKf+r7wjDCLow/6TCHHt99+237yk59E/ZpOp6Hs0B/90R9F92dWq/rlo0Sp5A4mTBEPKm+ah9dcXFxElBN+vMzicChxfzzfmQTga1+azWUSy5ulcF30dDFk7L3hqfZcKFZXV1e2uroaDsR+vx8tILr/mKbq8U5qKW6pFGZKnz+raSGxlId/DhUiMhcvA68LR8PUZ/7dE+4xfMEMJ12vxcUMNbO5EFU8WC5wAnU5Lvqb3lkLcm9vL7xrvV4PWTxmC0yRVyJ9lhhLgJjN55EYJ49X8tlW+Xw+UnDN5oKbn6XqP1JZZeZOPp8PwpKuZW6gRqMRDjOSDvpQmebPY6L0HKYwM4zKMieao729vdAfCVMpEZoPEhNWq9VMini/389kaTGrRj8VYqrX65m1rPWTwpyw+VJAuk8ul4tCT2ZpsDrXjM8wJF+WJ8Il5pLp1Dw8KRgZLqSrX5hFn4JN8CspMZSMsYzVnTUEiQlkph1D4uoLaU+4H4fDYXgW912j0QgK+NnZWQZnd35+Hsm5ZrMZ0ZFI2dW4bGxsZOZL64ns4MQOeUJe9Vdrz9PmLJtDKk7qM2kB1IrFYqZOXbVajUhUPQaVWbjaw3t7e1H6PutPqh8Ed+/t7QVewNu3b4fvarxu3boVrTVm2T2raew+//xze+utt6IkENLUkL5GjbKbn1OZ9M4Bjcdbb70VEmxkmOXz+cCF+OTJkyTrOBuzrJedo8R3VqsLUuhUAXMzy4QG1RjuS5WeMovP+hS2jfelfMjlcpm16UmMx+NxtD+WYSzVngvFKpfLRVl+ZgtuILM5fYEXVv4gWKZM0ZJJMT57Xire30+6zxwiXkL3Zqp6Kh1VB6usJMbG19bWwmR7Nlo1LlQdVMyY84RqIis1Wwgr0TyYpYvCms35cBj3l4XHtGwzi3BV9IrI8qTniouRpKvFYtEajYadnp6G8RwMBpnDyzNK00PCWmjE8fR6vTB+FHicG1+ihenvGkcpkDzoZIHr/iqL0uv1onXEQyIFqCYLcqfTCda6n1P1l+twOByG60gqyXetVqvhHWlJElRK9m9Zb7VaLSm46VHxykuKA4ZFn31GXCr7UN+lh4OcZ2wEv5Lws1QqRYVw9Tm5s4SJq9frGcGawmctOzw8xkrrg3gvYge5n5dZ5V7JJE2EPidFiCe33NnZiTxcR0dHYY3pPhzfVBIEAchm83WhcSHVgfckqXE+tKYnk0mQdyxRZRYnxujgJUCbSiu/T2+bT1Kiga59zVI3KysrgX6EHnTO0WAwCBUNNIatVivssdPT0+hsGQwGmfPqyzQap/780Vwv83jyWTyTfCacvn/9+nX7sz/7M/vLv/zLoEyNx+PwTg8fPowMAO7J1N7317GQMhN6fNmkq6urCMvFAvdS7Hzm/LMaFU7KKl+8mWelN6jy+Xx0LvpEiZSiyfZcKFZm8YbY3t62l19+OfyNXgx+tmzxUotmCI7FHfUZv0sv2NHRUWRN6jl+YfvFXK/Xg7u1WCxGadBmc4vn448/Dn2kkkPL04Njaf2mrtHB4IF34/E4LGwJh42NjbCZDg4OIvJFsuQyw4ss2vRwEOyoMRgOhxmmei98J5NJVC3cLFaUyGGk5kMCuVwuHB7b29tR6QQ1hQP1PXqXzOKNZxYL8nw+H4S+yDSvXbuWUag0vpqP4XAYvtfv98PcbG1tRd5JrZOHDx+G7KxerxfWjPooAafD4/DwMOLQOjs7i0LjFxcXmTIyPnxH8ObR0VHECK/xYKo0vUH6v8aPn+k5LF/BeaRilFJefD1GCTApL/QSmM0NACq2ZI5mS2UpSQCnuLFShJAMgy1LftF1zKLM5xfkij7jWRZ8KrHGLK6nxqymJ0+ehLGp1+uZkFixWIwylxju5Bx5/jgzy/AKad3lcrlg8Far1Yx85LjpGr9O6OWQrKECqiZoBQ83JifwngT7E7bQ6XSs0+kEeXnnzp3QLxqwkkP0lPqUeo2BZMnu7m50/jyrAgQVJe+FU6NzYThclKoqFothnSw7f1JhdUJkvPyVTPnGN74Reec0Lg8fPoxA/lxXnK9lbOZq3F8XFxfhPpQrDFlvbm4GKpmUYudbSjfgM09PT8PZRdlSq9Uihcvv48FgkKltmzKUlrXnQrGazWbW6/UijZCDTXcms+qWvZwW3+rqakSEJzcosSh+wjwzeafTiSyzFL2A7m9mybCe+qvW6XTCe5DLiJvTc4h4LBNdmXT7UwHTu+qQ+8UvfhGuIdnlMqHAw873kYeZ3vVpGLbJZBId9qTQSLltUxa60rF1H4ZVGHbwYSv9v1arBfJWKUMHBwdLD/terxdhPfR3n8KcUnS5fsmOr8Pu29/+drB+9/b2Qh1BhmBJxshGz1Cqhlkul4sOR3KJkeSPjPD0XtCbycwZlt1hppzaZDKJqAk8UaR3oV9dXYX7LHOtUwlg/6lkMFSvZ3FOZ7NZeE63243WsNax7q8x0f8PDw8zjM0+fM7fl1my7D/XhvaV2MjN0jgezzpOAtZWqxXWtTyP+/v7EfM6FesUEz65j/h8eqfX19cjjKiXNf73fD6foaAhDxGxN2q6nn1UfzqdTvQeIossFotBEaNMJg7RbFGr8+WXXw5GzXS6qIChdy0UClGW4e3bt4Mnhwc5f/cYRWb36SdlpuaVeCGP4aXM//Wvf23f+c53orlh6JQtFbHxJLeSKdevXw+KzL1798Izj46OIq+3IhakFaLh4x0MbDT2j4+PI3Jb4kvNshhi3ftp9099zmcSg6lGJZ1nDeFI3kBPedWXtedCsZKGyUOaKbsCDpOfolqtRq5YfWYW44S4sKVY+bgzW6pqNQUAtehisZg59I6OjkKYb2trKwg3MmgfHh6Gz3lQP83NS6ZtT8bIhaiF4ctTEPxNz43nEVm2gL1F4pVaT9zmsTHeM1SpVMIhSCVAc1oulyOLgaVBJFxbrVa45zKwKEk2t7e3Mxi2YrEYYQmoRNLCYtka9VfeMq1PjePJyUlQmlZXVwNAmH18+PBh+P/h4WGwqkiix7kzWwgLgsA1hqenp5ER4TEX3pNzdXUV7RuuaxZ61vwdHh6GOaCXg4rEcDgM6246nUYhGTXviaVnyCwWeHwHPduv+Xq9Ht7jyZMnydCaQs36nMo7sYN8N1q5Xvl6GsaK70YLV2PhFUj9v9PphD5sbGyE70qZ2t/fzxDsagwIUJZc2d/fD6WvzOKanewD8S86MKm0TqfTCEysPpCuhPx03W43Utg9ZQ2LbXsZ0Wg07PLyMpKDpKqgl8xsvt9Go1G0DqlweuJc9b3T6YS9++qrr2YUq6urq2g//OAHPwjrQedSSvb75uEnDMFqnyyj7fDvbZYl4fQ4Y9/4NyY6mC1k1dbWVlCa9vb2gnzodrv2V3/1V2Zm9uMf/zh4+0qlUkRN8jT6h2V9IJFyu93O4KT8PZ9FX+EbMY1M/vBgfHqgPZk0CWrVL43Z0xIT1J4LxapSqdidO3cipYaLlgBiDQA9FF5BSrGKMyxHi9VzUtD61bNZbdyXSFDj5iRvlPpLDhqya/f7/WTpD244Am7l8r28vIzAz+oLQw5mcfkVDzLV/ZY1usb9gab+6L39IhRHEQUeN0eKPDKfXyQp0IJW/81iIX7jxo1w/+Pj43AwcOwUJjObK0e0SMzmhxixKOTLogLB8BGzysyy5SgYIhkMBplq9WZzxUrCen9/P7yfry1otjj0qXB570a/349CpFxLhULBGo1GxP3miTo9I7r6y3VNnJme7ZVyzrE3ABge1P19SI1rzbv01Yj9YnmJ6XQaZSKyXzzUNGY8sDwPEis9pCpBUK7wnRgmThFPqnnv6tXVVXjOxsZGxttChUT39EWK2U/P6t/tdsPf6T2ioqLradlXKpWw7k5OTqL31jPp2aAnq91uh3syq9bXxyPnkVmcgUrPvM86FT6nVCoFhYehfI/n0dycnJyEck53796127dvR2zpvV4vWteVSiUy7s2ytUxTQG9y/okvzWy+31OK+XQ6jcJgJIw1m8+pT+pS01nnMcOMXPAns+DeffddMzP7xS9+kTF0zOY1Y9955x0zmxs4PK/0PI9RTEVriF3V2TWdTqM5Td3jaUoVFTjKX70fDVx5dfX/FA72wYMH4Roq1ysrK5ks8Ke150KxKhaLduvWrbAJmfVkFtcv4qGlF/WHjDb6/fv3w6a6e/duuL8W7HA4jPBF9OpIi3/11VczhHBq9IxREdOEffrppxkFRNekCnPycOHBUCqVoo2oSvS07HR/YqHM0h4mH2JLZVhRgUil+PowgCcaVR9YXsZ7UQgi1j2kWJ2dnUVCgoobMyC9V8u7xldWVsKhmc/nM0R8jUYjmldiUZjNx9Ctwi0kczWLw8cipmONNpbjODk5CTgsVq/XOkqV+dGcDofDjAeKgHliw3hNsVgMwkLeAV2fCmERI1MqlcLaYgYnv8fwjXBF7XY7zC9ZzNW8h+ny8jIcgKlQg9k8NKP/n56eRmzd/rD2ffN0KfqbDmZPs1CpVAIO0hN/ms2F7zIWePWBtR251vmzWl2UOSG+kwXnudaJ25LByLFbXV2NDgZmu6q/uVwuooTwNe30HroPcZRa0ymFj7AEPVPrkVm6ywxjKjU0oD1sQaD1VqsV5LwPRXKM+TcdoDortra2Qtbl4eFhpJSORqOMh6lYXBRuZ6jdbHGY/+QnP4kyDVlhIxXCm0wWdTy/+93vZkKzT/NQeYXKP8eTWxIzJbl09+5de//99zP3Ozg4CF7MRqMR1kmtVsvASAic1/WE5uhnqr/8Pn9fFnb3jWeSFCWeu/zueDwOBjWjLQy/cl+fnJyE+X5WAWaz50SxKpVKtrW1FSbAL7qUckBrWhtGgOyf/exnZjbPvBNFvzxEN27cCM9hmQfdxytRH374YaSkeLChPtcG5aJ5/PhxCCHxQNnY2AiCfDweh4nyrNJ8VzWmTKewUV6ILFOo1FJZNmbZxePDrH5OKHS1qQiyZqhB/ZxO52nEDBPwXZktokOlXC6HueThtcxdzgOLCok2ebVazWBXNE7EPzBsp0NCz97f37fbt29HCvt4PA5C7Je//KWZxbQOv/nNbyI8iV/7jUYjKFk+BNfpdILw1ntTkZhOp5HBwO/rmeVyOaxND6jmfegaZxaWmWUy0di8ophqwseYxXQlmr+1tbVwH84FOZyUicl78n56D3IxaQwIXvVufm+d0lvgvbMMufHglZej2WxmFGGvODHblzQI9IB5vigfvtjb2wvPZ1KM2dwYEfO/nuND6ctCHYJQyOtptlBIrq6uIkoDhr/oOWAihlcqv/nNb0Zj89lnn0VKo9amL+kjDqNarRYpyX7fSCnQOBFbq3seHx+H+XrttddCsop/rn4fDAZRXUPKbMkHegl9ppoiCF5poCySvGCWuW8p5ZTP8fvLe7B439u3b4eqD5QH5+fnwcP3h3/4h5ESIq+z1rH2KferV6wUzUi9UyqEuAy/y+vo+To9Pc1UOqFRbTafPylKzL5mmTzCkVjQPDUPvj0XitVsNrO9vb2wCH2hTWbccfOJx0UWmxQr/d9jR9RIoOYFML1QZnEIzVv1yqTqdruhz91uN2R4CajK8I5Z1jNDj5HemZNXLBYzYRWCLs2WY6LUUnWczOKQCUOdDHHQNe8VK24EvymobK2srESbhdkhxH4RV0CPnLxErVYraTFImKbKDUiwjkajsHGoVFBwXl5eRnUczWKOH9YM9JQGeqderxcOytFoFA4jrQuNkdbEyspKVOhWP5dl9KR4athf/zvDSgyP6L1Ho1Em/GKWLubMxhIveqbGieEY761hv8g5ZDYXilyfKQD/+fl5mKNyuRw8khcXF1atVkO5DDWm6TMblULXGyn+MH/06FHkvZOHhKFjjYnZXHBLuL/xxhsZjwoxIGZx2R9xvvV6vUxWnJqnSjCbe8Lp3SGuU94YhpjG43GUneVLI+ka3bPX62WSWGgYmsXrdjwehyw99ZEhN+3FX/3qV9E9zRbrZ2dnJ8lpxRA7x4Wece1xYqfM5muKtCMaL33v5ZdfDrhIMwuKM6lbPL6I80Sjxofr/Gds9B4xA1PX7+7uRrAFzw1oFgPjCaqnYuUpP3SPmzdvhjmhbCMNQ7/fD/3S+5+dnYXxlNHL+6QcJj7ZSs2HmtV3nhE0nHkfzeX9+/fDHGjflUqlaI5Y8kkyq9FoBJlMo0DXq/1747EajUb2d3/3d0Ex+t73vhf9TYLz6OgoHLAffPBBoA/wYS8Nzu3bt+2HP/xh9BndyZPJJKO5e48PcUKVSiW5OEqlUrAwU4uMQET1QVbe1tZWZAWZzQ9vDzBNgV5TwEdtpKfx8lAJ4j3q9XpkQcsjMZvN7P79+7azs5MBUut5VJCYyuwPXo6BGlOJl4UYNH8e4O/HhZa/+sdnXV5eRkWlebhrnCgwzObzpyyiW7duBXJXhgtoaaoPtVrNGo1GlL3J99EG5TVcyx43p7EWcFbP0PcIDk1lqhUKhUjoe0vyaa53en3offGWpcdwsc7n5eVlptabDiutV4W5fT+47y4vL6N31f3pLfKCmSFFH+JOhSI8sJrKWS6XC+PoSQdT+LCDg4OAwfGhaIYxvSeQ95tMJplD1e9FZcmZxUo8W7PZjPBRuv/a2pqtra1lwuvD4TDIZXoOOIY+E9p7RdrtdngOvQCvvfaamS32EaEgNGZT68ErDT41Xu9qtljfuv+DBw+S4HHN6ePHjyM8jn6fTqfBG+XlGg9teZPpkV0GwKaxsrOzE4hIU4c3s+LVvLLGsRuPx8HJQFlFygTK9FdffTVgvKhYTafToKSnQq1s9NCZxRQSy7xUKYyVdy4QBpG613Q6Dev08PAwSS/D55Aqgoanx/CpERbxZdpzoVjJotGiYmMJlcFgEARku922H/zgB2YWlzMwW7j23njjjUwmIDPifNhI14mrSX0jcNunu+oabbjpdMF1pUPv4uIiWqhHR0dBYN+5cycoYAxjECNDoKT6siwrSe+5vr5u5+fnEU2AvkOgn7eoqFj6A317e3tpmNJndarfqQONjUKJWZwSRN5da7YQzqurq8F60nX9fj8SSrlcLqPo6rpU43qQZ6Pb7QbhyrIpzGbs9/vJzE0dpPV6PeAn1DSWx8fHYc0QD+a9gx4vUS6XA58TlftlRIokwiO/iw+Paqy911IKCr26y7xkWvv8rFKpRHNDPCBDulwzehbnpdvthv8ThKo14IXy+vp6uOcyahKvEGqMKXyfFVYnuJp4p8vLy4xHzLOcp8KZfF4ul4s8+VQWSROg8V5dXc1QbpjNvS/qe7lczgCkb968Ge2Nw8PDcFienZ1l+luv16Ox5oFFb6TefzabBdnGagZm8QFLRUBKKdcASZGJf2s2m3ZxcRFRQ0hGqD8nJycZxYqg+6Ojo2h+VRJsbW0tvMfJyUm0thlm8tQMailFRPc2M/v+978f3klUJPSOMpHKLD6PUnAV8pt5jKr6Qi/2cDi0t956y8ws4raicbq/vx8lIpjNzzBGKfQdvbMiEctCaKlxYWiPWDFS1/C7nU4nKJHMmOU56Z/PNanv6TNfVNrrGM9qXz6H8UV70V60F+1Fe9FetBftRXtqey48Vo1Gw773ve8lXYJ0P9MqkKVOEKe05hRJJ7mrmKnnM1rM0tkn/lm6l1ns5qX7V/1ltqHZ3OvBNFyzeWhI/d7a2oqsxlTpALOs1cv3NJt7DRjaS1nC7BepHxirVwjixo0bS+kpUpgeenF8OnsKVJh6Z92LHhDdk95Mfca0Z7PYs6Awcq1WC9aIr7N28+bNYLWLUHA8HofvVqvVpLeQoQBhpiqVSsTXQg/CdDqNPCx+DTNc6zOxUgDUl19+ObzT/fv3M0BfNYYMPF2E92ARr0JPIqk6vFdF1i9xjGqemkDjTq+HgP8cJ7N4fMfjcYR588kG1Wo1QyDq1wVlxGQyCWPrMZnk/SKVgx8Xs5gji9hFlufS331Ba/LZpfanp7Ywm3vXptMFz5T+r3vwXciTpnvu7OyEdSe86NXVVTRWzOKjh2tZirzZYu6JxdE9arVaJhFGUQLtf2L7vGdIjdxVFxcXYX1oPg4PD4NnRXtQfSbGinQwDLsyYqD3uXv3bgRJkOdcAH7JiGWZez5KUC6XIw/U2tpamEvNFzmffGZ1KrvQhxw9T5vuSY8Vw7o//vGP7Ve/+lW0dg4ODsI1Dx8+tK9//evRM4fDYSbq4t+V4cfUmPj++rBzCr+rZ5vNZSM9sQwhc7zUuJd4jjKr2IcONUZfpj0XilWhUIiyCPwAMPThi1FyAnwsfnV1NVKK9JOTn1JaptNp2OQUVgor0iWvRgZwEYRKWKcOYi0ICbR+vx+Uj3q9HsWIidHSWLDCNrEtbJVKJQIks95aKvuCiymfj2sYmqXZ8H38nKG9UqkU7sFx13X++SlGbwoV9Z3kphpjvSdDAGZzASvhScwHD/LUZhkOh2EOyIdGl7bPeFPoULxi165di8rc8LCt1+tRCRpfMoPYFR920vOvrq4CroDZVtxLnU4nUg6IF9S8+iK0pHBQY6kXCpllfUuBU8lbxb+RfZkZPVLcvEAeDofhPUqlUnhfrTUy0+sahljVpIhq3lZWVsLBJswcweW8n38Hs5hnr9vthnHyGB/2Q88bj8cZkL8/dLj/SMshzOB0Og0GGw0ps7gQNvF8uo/W4uPHjyNZsrKyEoWI19fXrdfrBcVDmWFqNBIIxtcYU0H04G8SmGr+NjY2kticlZWVpJyX/GC9Pk+LwmQoGsdUMLh+SGchXNjNmzdD/30pLX/oL2uTySScFWZmP/3pTwMRJ6kAPO2CGhV5wgV4NnosrO6TSozhmqRs29/fD9d1Op1gtEgBHwwGUaKE2QJ2QeXoWQon33EZxMA3zenBwUHGEGX5JF+GhqE+juGyZCC12Wz2bycrMJfLvWRm/9rMdsxsamZ/PpvN/mUul2ub2V+a2V0z+9zM/uPZbHaSm0uTf2lm/6GZDczsn85ms58/7RmXl5fW6XSijDQ1Kj/5fD46VM3ihecXIEGsKd6McrmcrDTPLKF+vx+EjlfcGL+WRZDPL9KNl9EnUHBJELB8iD+w6ZlJsZunALy6NlWDbVkhSyqprC8nS+zo6CiAuH1le+JVCIjVuPDg8M/UwTgcDiMAq+7Bd51OY26jRqMReYY8wJNM3sr02djYCILAZ5mQEJOeACrveifG4KnknZ+fR9XY1S+vWOq9x+OxbW9v25MnTyLPjLAaKaCt3ucb3/iGmZl9/PHHQcjQA0cvQ7fbjfaN5lDv4es7MqOR2APtAxodaqI/IACbioj3kOoz7jd/INHDoOs1H+vr62GMdb/j4+MMZkkKKOeMHsCVlRXb2NgIh8bq6qo9fPgwaRT5guJqPOj8s8xizjTiYMzmc6N76m+1Wi28GxVOs8Uhvra2Fu0N4mXokddcV6vVyFOpexLHI+VMz5HiRpA8SWs5XzzAtHaYnEClhYr75eVlOKhZGD7luTVbkN7qd3mn5AV+/PhxVCOU36c3NaVMeWWeSQiSPVtbWxGbP++r9iw8znQ6jaIwH330URhLzQepLLwRyCxqf1+zWBb7RBB6yDXfkkdbW1uRccYoT6fTCftM80VKG61XGtS+X964SjlJljX9nZ4qs5hJnSWoyJPnlSbJY11PHLJvTNz5t0UQOjGzfzabzX6ey+VWzOwfcrnc/3UcCgkAACAASURBVGVm/9TM/u/ZbPbf5nK5f25m/9zM/isz+ydm9trv//0HZvavfv9zaZPylPIekfMin89HB6jaMo3eZ/+YzQ9OHVi0rnR/s/lkicfk5s2bIVMiFa7R97hZdb3e55NPPlkaEiGPjITPaDTKMGObxeU1eLgsW4zT6YKJmqG9ZaSOl5eXkftc40RLWb9r46Qyb3xWnt5ZY9TpdIIFWygUopCA7uNDMrqGXoEUNUS3281YwnpfprprfplmbRaDldVfliSRwKMSqHszxOGLB0+n02h9ttvtSOn013S73ZD1ymw3/uz3+wFkmuKE0njpAGg0GsHTIG4lT4PB+zcajbA+dAh6Bn8qGPTO0dPDJA+uDb6rlDo+k/W7PGklvRte2Pp08idPnmSUx0ajEb4ngkl6YsyWh8zZN+4feiXp2dM1BP1y/av5CgytVivw8Mm4UWMpIyrvHqystaXDsFQqRYq3D+3dvXs3em+yhMtYIPnowcFBZr+niII5l55qQv2igaXrl8Egjo6Oor/5zHCzhdfRVzLQ2NRqtSgRhXQ3fCd6txX+m81mQRZIwfjOd75jZovD/oMPPniq18of9jwD6Y2TnPDj6sNiHAezGOpCpc+H3n09QbMYVP/zny/8IsPh0P7iL/7CfvjDH9q3vvWt8L7yUNHB4Bu9/illKgUn0fXeA84yVrompVixMWJQKBSsVqslE8PUF34/pWg/rT1TsZrNZl+Y2Re//72Xy+V+Y2Y3zexPzOxHv7/sfzKzn9hcsfoTM/vXs/lu/NtcLtfK5XLXf3+fZ7ZyuZzhjyAPhvdO0fXpFSlOmAQIQyP0MpgtFtajR4+CECSh6DLLh16S8XhsrVbLxuNxWGyHh4cRiSAbDyZOJDVnusI1+SyRsEz4kHqAwoIC3CuotKBl0XLhsVaW2cLlS/I31l1LhYQGg0F4bqvVihaq7uOZghkC9gKFHkay6JotPCa0qliIt1wuR1QJpJiQQC4UChmMji9L0Wq1Iq+O5pX9JoasXq8HBdzzZul3hQOkBHqPxsHBQfC8NZvNsGa9ZcZxUp9TLndvMNCa1CHS6/Wi0CIb61NSGVRGJesT8tmrq6uh7/QcLFPI6vV6eO/j4+PgofJhR7XJZBIZLWZzxUZK1urqqvV6PTs/P49IafP5fCSMqUx5slR9Tk+dp0+YzWbhHt5rQyONNC7M2KR8kGft7Ows3GtlZSVzsJHGwiz2hhMTp/ccDAYRFnAymWRKgd2+fTvcP5fLRUYQSTjV+v1+JEOIwVTza9ZndU2n02guvJdMTR51vrPPPtSzVlZWguxkXVCuNVLyeMoc8oeZLRQ4UfxMp9NAuLmsvpw3NKRcSokVCarGhfvV19jTNZS5Gxsbdnp6Gq1TzhdlJ0k1CXep1+uZqiOff/55MLx5b2/c8WxKVenwysyy5rODtf8pc71BxvvlcrmkN+ppoT9P2WC2vMh65r5f6qpF5+6a2R+Y2d+Z2baUpdls9kUul5PP8qaZ0bza/f1nkWKVy+X+1Mz+1Gyu8S/DWPEQ5uEYXqBYzChS/v88SIkvoheHPED1ej1Z/XwZMJdeNS5wKR2vvfZaJAioOFJwaGHMZrPoGiqTJLnzvFG+OOxoNIoOAwlVCSJWGdd9GM5S4wLTs4QN00EkxaPZbEZgUW4mtZ2dnchKIou2t7TJfK1+6ZqUdTSdTpOHN0MW/X4/3JdAZPXZLCbfY2hLAubi4iI6pM7OzsJ7cC2rFYvFyFPAdah3G4/HEXu7xtYLZaYJM3ysMfZ12TimZKj2jUzkZnEYlUaE5n4Zr4vHoUmB1LgoLMc0e/Wx3W5nqh/48G6j0ciEWnO5XFA2iJ0xm89fipiT+4eEk2rEftIIIw0J55Tp2tzPkgkscusB3GrT6YIvjDXrfNhI+5MewWq1GvahPG+eCNM/b2dnJyqh0+l0ov1AOUPvgOaUWCeNmdmCdNZ/Pp1Oo76rcY74PSm/1Wo18uIPBoMwHhsbG+G7Io0mTstjBxn6oUFMgL8a57FQKGTKBOkas8W+E07qRz/6UfibvM+eK4mt2WwGWUAv5LL2tPOH7+uxQ8sA41TiyfV348aNjGLV7XaDwff1r389GKeSicRF65nL3mVZ33iWyqBWP7QWibX07/U0RY3wilQSmFms9Pqkk2e1L61Y5XK5ppn972b2X85msy6tBH9p4rOMqjibzf7czP7czOy1116bcRB4YDFbY1nJG2b8sWmh0IuSz+czdPdmsQu70+lEQEfvlVF7GsCO96tWq9HC/OSTTzIuYh9S8ASf6q8ETalUig5hs+wGoiCqVqtRCRWzmMFazY/NMqCeBJmEkQR6vV6PNmpKseJhz9Dd01zbLI3hBcH/w97bxDiWZXd+hwySwWAwGIzPjMqMyszKrM6q6q7qbrWgVjd6IUHuTUsLywsB9mJs2AOPFzMwDHhlrQwYA3hhy/DKgIxZyMBYg4E8gAVDLcstAQJa7VKjWt1d36qsysrKrIyMjGBEMhgMBoNkkF6w/pe/e959kdn2zCDbyLsJBvne/b7nno//Occzh3yf3ojUkmnc+/v7SQ9QAmJTWhqCxdXPlNqf2kb2azAY2J07d8wsZjiYjyo195Q+V1dXkxpJFmIU8zBuvvjn6/V6+I5eZWaxtoBec5qns7OzIF3S9GYWaxL0HSXolIeQCuMo+Tra7XYmTVFqbKQJciYg7eFeajQakaBmlgX5yrRqFmvGmYTbx8sijTKLMYT9fj+iC9QQ8rP6IE9psxk2keml1AePgWI/PD148cUXQ70ax/3798Pn1dXVSJtLzZTWgNqgvb29QNu8Fljf+zRX6h/HXKlUAhMyHo8DZpA4OyZDNptplkgHfeBk/q56iPdMJQv3ntKam5s3b2aY6/fffz/jDalSr9ft7bfftu985ztR3SlNj1lM97iPtJeo9fdCA3FY3hKj/IsqV69eDSB7Cj2aa2rJyBxTIPJYaTKF/Oz7x89M+O09o4mZosmRWnRvClQ9VK7wDqI1jHQjha/05akYq0KhULYpU/XPJ5PJv/ri60cy8RUKhRfMTG4Bn5vZi3h928x2ntiRnEltNBpJDQUnw3PrPuhdtVqNVIe0L6twsYvFYqSl8pge3x61Wj4wmr67detWaEv9e/DgQYQPYX28BHn4iHnx4LvU5qHpgUA/sylx5ibx6nZfUkBSaW44N8S6+EOrseoZZkvnM17rowPFzc6kxyQUKXAh7fqVSiVIPJIkPfiS89Tv9zMpEqrVaqRJ8klb9UxqP6ifDLRpNiXiDPLp11fzQuJKLaoi+udhgxhAkkVt8qJlKZfL0VzklRQj2O/3w1yrnZdeesnM4uCy1OqkvMBS9ZrN1ohCx3g8jgQTXXzUpM3Pzwdt0/Lyso1GI9vf38/g0+gooT5K0CqVStF5KZVKUWBRryW7SGvAetQHnk8yAd5VnMyd1lACheZS+5racGomUyFq9JvS4UiLwbRd+/v7ER5HzEulMsvp+fDhw0i7I5wXNeHU0HoBRH3iuBlhvFQqWbvdtpWVlQgD6fezD4TrnQ28xcNsyvypXxRqUh6oxG6ZTRldMaXKh9jtdsPc+dAIdCbRMw8ePMiEBvH99QyiF3p9xoW5ubmMoxjn19OIl19+OSRnpiAnZuu73/1uMkE5z4wfK5/x/c57ptPpRIG2+Zs/TxpHHhOkcTCINZ+V4KNzybAo/1oYqy+8/P6ZmX0wmUz+AD/9qZn9R2b2337x93/H9/+kUCj8C5uC1o+ehK/y5hj/2YdMGAwGEZMlpkkSkv7SnCRisr29nbuB6I5P7v3u3bu2urqaSYaZAh0TIEmtD7Vdim57fn4ePDLItBDQzX4VCoVIpS8ipo3hParIXZOI5W2MvO9TjA8LU2nQNZfzkacGTqlWV1dXA0Hya8VDRk0I3c/J8HBNVXZ3dzPRkbXH6Ekk4smwFbwsfU4wtSVJrlqthmdoVlO/PBCTXnjMS0bNI02I1Ir6qNz8TAbGzyMZYY9N0DPU/lwE3OT+SZmPVY/Go4u33W6Hy4v90tiEkWORdoxj1XfeJM4LlX3R/C4vL9t4PLY7d+4EE5dZfN4WFxej6NNmcXgGs+nFyFha+k7l7Ows7KNU/Df9z0siFZmd9KxQKIR5PD09Df3x+L0UTojJgpmk3jMbSvGi+dra2orM6gwZoL3vcXN0PjGbrjMTHVcqlTBWajqIhfSxzDiHCwsLtr+/H+abYHjO8dnZWaS903yNRqOkRtfHM/J4JM6t5k2m7mq1Gs4ls4pobHfu3InWlTSKZstUXDeOy/fbA/8LhUKkiazVahmvvfPz88hzlO0QqkOLxmeffWZmFpmSfUkxPLzrPCOWwnyKzu/s7OTeUTwPoi90tOF9zHHrOf2lxcIs6+npBZm88jQaq++Y2T8ws3cKhcLPvvju923KUP3LQqHwD83snpn93he//ZlNQy18bNNwC//xkxogY+Ule0qZNBGktEWMpXF0dBSBeT2A0SyeNKoqzbKh+X3iV72j+j2zRQ2UN0+JIWKbOoxmM1div4k8nkIbiMHuOL48l3C9NxqNMoA+f2HlrY1Pl+FNCGxPY1WhJMO10eHd2trKqNn1V3n6yNTMz88nwwiYzXAP9Xo9zM3Ozk5YD16kmhPVI8lzfX09tEc8GLFMqZhBk8kkiodGpjd1sGme4nxxPAywOD8/n8FM+YuZhWp3/T09PX2ipMjgmJ740fzH77hn6JpvNtNcaQ4ajUZgrCgRUwrmfqvX6wEQr/E/fvw49JfzYjZj4Jh4moytTLKlUikKNVAsFiO8YCr/ps9RlyLMOhsp84YPpcGzYTY728wTSPB+t9sNDJI0by+++GI4S3l7wSzNCBeLxWgOVDcZuGvXroX5/eijj4L52my2xr1eL0olpXmRQNPv9yNmg0B9hpZIpZbR+2LUNzY27Bvf+IY9ePAgfNfv9yPtmcrHH38cznKtVotC4uickWmieddsxjgSQ+S18ho3GQ7V/eqrr0bMb4qxoonQp6dKMR5ms/Pvc1qyPxcVarNTmmKZbXVWxuNZypnbt2+HoKGextEKRKFJ97pvi/eF9opoPs12HiRPzC7NfxQaU9qxdrsd7t7BYBD2JKEtLPV6/V8PYzWZTH5oadyUmdm/k3h+Ymb/+Ikto1xkfmq1WpnJZ8wngitpxvFuv3r+8PAwHDivYuWi+Tb39vYCwfFYEUoa9EyktiXlvnv58uXIrChi6V25CbQWU5RS3fvo5iQQ2lTD4TDXdKg2fGESX3rUqHiToi9UPZvFjHG1Wg1YCTkMdDqd4E0joq31k7Q5HA5DX/wF5y9hsykhlNmPc6D51BzTbVjPEMzKkBs8qPRK0RzOz89HkiG1Gyrj8SwrOwGY3C98r1gshv6QcPH3lCeOL57J0btsm/swZdL1JaUlqtfrwZTkzeUMN5Eyl7EtMtJqZ3l5OWJ0yTSSIFKbp71x8+bNUL/Oyb1796I2maS40+lknBM8RobgdUazl1CQWi+V1LmbTCZhrggmJm2hQ49wVc1mMzJ/sj+cD12GDx48iIRWzpfqOT4+DszvK6+8EvZ1o9GI9hBNoTp7DG0hYaVQKGQ8PaXB0kVHzYov1Wo1MFqvvPJKuESpfdCeUfw6M7PXXnsthK4glnVtbS3SHKvQtN5qtTKC4snJSaSh1nj5W6fTsZs3b5rZlMZp/1y7di3yFqdHqcpkMomYE7ZNZxOevTyzmArH56EgKmSQarWa/e7v/q79zd/8TRQnTfN1cnIS9olXVvCzh5Gk+qo90el0MsnuiVH0ddGCxbngXU3Nv7SsP/7xjyMaorZ0F+lu0vedTudCGqjyC3kF/pss3DQ8cA8ePAiTz+CNPDQpgDRB1L1eLxnzhMQmBS7ms2rXbHaA/P/1ej25afyY2KYWkL9L6vKmmbW1tbA5OF80DREQ6qV+P35PzH3yYhVqsahqVyGjwn6rTS8xeVyV+i/G9d69e/bee++ZWRZMSy8Q1cuQEKPRKBDoQqEQGDEf2E2fZXLU+2Lk9vf3M3GFqIWcm5vLpKjx0hPf9ZcwCZH2AJNE02ONe7fVaoVxM1I9sUCc75S0e3p6GhFOr8XS+uUFuFXxeyQlPKysrISL2pu/uLY+4fdoNAoM2f3795Pn6ujoKKzByspKWPfd3d3oHIgpefToUWQ21wUnzePx8XF00TAOED0ECW7mWlNTl9LgMngm9wU11zxHZNa9OYpel9pLNPf5kBWcO7Wls/ziiy+GuTs5OUmanijM9nq9KNp9s9kMQiGZE4VpSOFr/AVIGplirvv9fmSRKBaLgbESJpbrtb29HTQe6odZfC+Yzeb75OTEyuWyPXz4MGIsNzY2ItMhGWazOIG3j5+k9+7cuRPapDD96NGjaJ/kQS6eBtOjwj1Dhoz7tFgsZjQ+ngniGniMs4r2ycHBQfIZf+emoDd5DkA7OzsZrSUjqfuSBz8RTZWGW8/Si5HhbnRfSjOnSPukkYRG5JUns17Py/PyvDwvz8vz8rw8L8/LU5VnTmNlFnO6zPWmZ3y8oxReplQqBUmu0WhkpPJKpRK1STU97ff0RvMRfIkXMDO7ceNGMnCp1zqkYo4QOOdzt0lCXltby2jS2O/RKI4kv7GxEVTN1FDQI8IDqlWnCjUxDFbnAZOsx8dDKhaLkUq/1+tFYPNWq2WNRiNIC17DyP7rmS9/+ctRdO9U39vtdtDebGxsROByJmFm/dSy5bkom8Xu1hqr2iJeRcVLbFRdS6piXjSa3rxpT30ej2cJeBnY0ku4alfarZOTk6QJwGuq+DtxG/o9L/2D2cxMLo0gJUofHbxYLIZxaK8vLCyEcb/wwgvR+9Rq6Xt5Raq/Ke9FBii9cuVKVI9ZfL7M4pQY0lbSK+j69esRpnFvby8TAoTaDyY5ZxmPZ9643jPJZ3vQ/KmdbrcbNL3UIGocWgfuJ4U7oRlFWr2Tk5MIM6V9dXZ2FsEpfKxAemSaTTV2XA8fc25hYSHCjS0tLQXNobRh9+/fD3R4PB5nsmTQklEqlaLwFF/60peCac3nsVP9vV4vzB1NoYRZnJychHYajUYGnjEYDEIdoinSgKhd3jXUntF8bTZbq+FwGOaOWTgu0iT7fcVgtNVqNVcLlsJf+dAtOjMvvPBCGHe/P4vYvre3l4zLNhgMgiaUOEnuO2/q1VweHx+HsDip3Jz+/tEcMdNEHlh9NBoFK0GxWIwiy6to32nsxL7yfOSVZ46x8heJXElbrVakgqPHBw+zCgNVPo37LsHpnU4ntKWN0e/37Stf+YqZzRZJRId4B10kVHPSM4tMFkMgPHr0KBwOBow0m2EnGPqBJj7W5y9vESsyWClAod5X4XowCatZ9uL2ZkFissyyJlKGZ+DaMR6ZiK4InoiU6rxy5Upk5lPf2BcC2QuFQlDfp/AInrlgBHCafDWOyWQSYdr0/d7eXpSMdWFhwVqtli0uLmZwMmYxY8xEotw7eXHb2G8VH1JhPJ6FHsgDcqpchNOgScxjPvzzpVIpEFHtncPDw5CeRftbWJp79+6FPU6hIzVHLMTRnJycBEGnXq/bcDiMTLVmceJphXxQPWZTxop4Oqb70b7T+3qeZsHRaJS5qNlvMuoem0WHB73DXIH+YuDce6/O1dXVTHwsj/2q1WoRU6gz6+dM52k8Hoe5JoOq9dVzTCqtMXa73dC31DlVf4i7NIudUnwplUq2s7NjzWbTVlZWQqYIFQKXeS9sb2+Hc5sy6VAgZt/M4lRNnHOtkWfmZY5mSqbDw8MwB9zjDCNQrVZDkFHic72CQYVesAx2SUbMxzf0QiOF8mq1GuFz6RzF86zS6/Xsj/7oj+z3fu/3ov1OT87xeOY5r/qIrVQfxLSkzNE01TGgsNkMR0clCgV0L5gx3ysdTZiZQXWwMHfwReWZYayIKeFgtIm2t7cDMfQHSM/wvVKpFCUK9jgtMjVmFiVcrVSmSY339vZCW3fv3g1EVNgPr5ngoczz5OAG73a7gWGgxwMxNBwvcWCcAwYK9cyRjxbP9BK+byq0ZTcajQzo2ke5pUaD3zNqMokb++kZG7OpFklt+gjEes8npdXzJBhkMnq9XoT96vV6tri4GA6edxigNyq1Bvp9fX09CsgoCfPhw4fhcpZn4enpqW1ubkbaCx/UVPPiibTHOwwGg3CwCaDlXvRr6rVgfr3yMBxkNlNJvn1J7XMyiB5jJU2dLhXOz2Awy324t7cXMTzUnhF3qQtT8+O1JBsbGxEOLpUjjfuQ6ZE4HrVz//796ELs9/uZILyenuVhRJiuSOs0Pz9vrVYro7GjJ2epVApCiC7y1dXVKMabxqsxaZxcL6boSQGdh8NhFB5DRfuVgTzVHoVJMfMMUcN66EHn0w/5OeI4+v2+dTode/z4cRRYdDQaZeinWez0MxwOM7TNO5nQ8qG90e12w3fr6+thXnzYH+3DxcXFMC+7u7uBOfc0T+N/4403ghDi8VF8L+VlTOtF3r2TKhSyyQSZzc7n/v5+UjhQ3T4cwng8DvOt3Hxms7U7OjqK6ODu7m5GIDk7O4u0U2pfcyvFQQqfyvs4zyObcQH17OHhYZR02Wy2b5aXlyNalFeeCcaKwNdut5uMQ7S2thYmjxw1J4mXd7vdzjAx/OuZEHoXShNydHQUDsHLL7+cYZpEVETYUovkC7n4vb29ZIwgqbC1EVNmEB/byywbMoIbTONg4Dx/STLVjca2sLCQ8QjzRJm/MV4TA/Hl5cqipkdSwtHRUdjYzO5Oqfbk5CRs9vX19UzqDrMp4aQHmj5T8iNx11xpjMzxprmQuXlzczOjRTCLPVOZBFYgSfWZF2MKZEowvo89xKzyeja17ponb1Zlm3lMlWe+80CuLFTTM66ZxuszJ+h/7hPNL5P7FovFCIyudfEpXzx4WGddjNDm5mYkWHnGypsdqJFMOR74hN/VatUmk0kmaj/NyxoH9123201qKKnBpdcrwetnZ2ehfkrZfs296Y6XHrXF9IgmDGM4HIb5SO17xkBS/SreMmAWe5rqe4Ym0dgYmJGFdeqiOzs7C0LvYDCw1157zT744IOMNlV9XFtbywjHeUKJ2Yy2sF/9fj/jFei1cbRYeHOmSrVaDczC1tZWMhi271tqrlOfvXCWupvIiA0Gg4gBVbDSl156yd58800zi2mIztRkMskoRcQcl8vlTBoaBtE1i507GACYtFU8ADXHvLs5X7VaLfISVaHVqFKpRM4J6qtog+ijztb5+XnuXcbyTDBWLN6DToeZ6u3V1dWw2Jw4L9WKeFE6zePKzWYxW+SK2ev1IqmHRMYsG24hhdtim6pTJpCdnZ3IBCCpQQyOV/12u92w4fWXKlzGVNFv/GsW55ETNkGFXDk1Y4ySbpYNdpkyVTEoqscSMJcbtTU8QGJmeCDoVlwoFCKTqdaaz9dqtUDkUnGjlpeXAwHz7um+LRX1W5KyiuZofn4+XJ5zc3MhwrcnQqno8LVaLWMKHAwGkVaEc0nCqv632+1IwiOWRoVrQebae4nyMvPxzTxTRc/QF198MWAYaIrS3HoPMppaGaCV88K0ITQnaU+WSqUIc6c22WfVpe+EVdK5HwwGEcGu1WoZM5vZjLHzNIS4QZrDRSeazWYQljyhZ6J3mvR8m2bxOolWnZ6eBgGvWq1G2CTOJ7WWPIOpOH9MWzI3NxfFGtOFqXHoLGoOzs7OQl0UEvTZe2COx+NQP0OgaC0XFxej80Y8JoVsCY0pwVP91Zi2trbCZ60X598szk2qsTKVEc2/WudUfkbRwqWlpfA7x7O0tBSFy1C/FZompYn2GM7UX/8c/+cZ9rAQfiY94Vqqjyklh5klFSSj0Sg6y6QxDB1C+qd3Nzc3A+NM+lmpVCKFCX9TH8Q8aWxkfn0mgsuXL0fMvfrxi5RngrHyUh+5ZW0+LsaHH34YuF8djKWlpUjCPDo6ioiKN+ukLhtvDmIhZkB13L17115//fWM66o+U3Wscu/evUAM+/1+RmKam5uLbNpms8UlDoyXKl2vU2YNf4D0rsapi8qDnM1ilT4lYm/W8m0RV9Xr9TKSmo9LdXBwEPqxvr4efuclwkj1i4uL4YKuVqsRrkmFEpDMf6lnWDwRVf3qowg9wfZmMQCal6qYKp9PjNiZFFBdf1utVhSwcTgcZqQ6Yo2oxVGdaielnaL20WswPXFnfZ6xmkwm4XJeW1uLcClmUybFfyeCnAr8S5NUo9GIsB3UUqlQs6k9pe+oGfPnnngwL6kTj0hcjM4g41ZxvqjRaDabkelW73Lv0CxWKBTCuxIe6UKvufFakrm5uQjD5+fIO38QmM/QE7zsh8Nh2Ff1ej3sH4LndZa8YKJ3zGKtNPcm50B0v16vBzre7/dDUmWOxSw23TN8hM+/2el0MkKM1vLx48fh/Ov5fr8fXcLz8/OBGWZCbjIKjPivORmN4kjupKGsX4XaeGowOa8pvJ3mxZvlCH8hTsu/n3pGgrEEOu1fall5p2p9GfZG33uzKIWLdrudidlFpwzNqQTVxcXFKO8m2yGdY1wq5vxlIeZP+CzOnZ5nCCL155cGY9Xr9QIynxNjNvPaOj8/D5M5mUzC4iiWSalUysSOIWPV7XYjBoDpLVSYsd1sSsTJ5GhRGQHYJ2Zm26lLqNvtRh5cjMtkNiUUXvJmQluPmaDGygMSmdqHDEZehm5K+PToU/2a8/n5+cwBSkmGalMeOypcK6b0IQaKEqEKTRyMcr2wsGALCwtJDxcvebBOroM0eSJ6vPTVL0b07vV60RxoH9AMJlzJ+fl5Mg+aL2TAtHbens8o1mqH0bep2TCbMc8p863ZdO189G+zOJGynjObzSODxep3xYppNpshTyQZJc2pLijNGc2nHJMXhsym86c5oRl9eXk51OM9tzTn7XY7g7uiROydLFLmjmKxGBiYK1euRGebOBrtI5od1K92u51JMUJNDqOw6z2uzWg0sPS3mgAAIABJREFUCkwjg5XKTMK5U5/0V3P3+eefR5AGeleqMCUW6dzjx4/DvvEYFubJ1BlmX3XeFXfKz129Xo8YPmpguA+o2dDzjUYjAombxThTtaN9Qg2c1qjX62U0vgyGqu+Yk5DJv1XOzs6iRNg0Zft9aRY7B3z88ccBv0mNixgA/ZZiBCgM5VkVeBdw/3rFgTdBttvtjEDgf6dFpFqdpQwSHV5eXg7zUi6Xo3MwHo8zjBM9B+v1etIKxLlkkFgmtKeTCe8OWk9Ic/1dovY8nCGvPBOMFSUjEhgzi0wKDx48MLNsNnE9z8kolUrRxam6SazzIq+LWF26dCnSIORpu56kWvVpC5ijK+Uar4uOHh6q//j42Obn56OkpiSKqcNETAY3kr+0yayxTTIzes6bMrzGqlqtRq7eXi28tbVlH3zwQUTEiNHR+nMNab6gFoOBPJ9Gne2dH8yyWCONlVIVQag+zQzbJPhVHl+afz1LAuHN1Ht7e0Eje35+HpmEms1mRhN6cHAQ5mk4HEZSFr26PBNvltbO+u/JIPnvVbivqJanlkrPkwhy3F7Lm0rvw4jPu7u7YSxXrlwJa6m10ZykzEIpTMZ4HIdpoNmMnlnUMvNs0xRIzycxE3RC4UW7vb0duXDTc1nFX4AellAqlaKwAqmE2v1+P5zDcrkc9vVwOMyYDs2y2h5dgpqL1dXV0FfRKuJUPNM1Ho/D/vcCBsH2ZN7lHeeD2oq5uHr1atgPhG5obB4HZ2b2zjvvROMoFAoRk0VaQNOw5peaz0qlEuaFNGE8HkcCA02aes4zfNIQffLJJ2FdNZ7xeJaZg9hSsxibmTpLKTOi/97jsFi8eVjjJk7Jj0djEo1moE7vVa8yGAwir3LVmQph5BlFOmdon9ASw9AeZNBoPaHW0odi0VqmMFup8kwwVtVqNaRXUNqRFEBMh5L2e/1dXFyMiMD5+XnGq6RQKCSZDbN4c5JRSXnReOLDsA7+GX+or169Gp754IMPIrOCxqbD5Dc6VfEp1SdDBLBQU6dxqb+aC7OYoPPy0Lv83WO5aHvXMykbO0ur1YqwOxrf8fFx5MygMhgMwuZfXV2NzDOpUiqVMl5/Hkug76Xp8YeMZrb19fVg46dmiDg0XrbyKCIzpEIQtdZDlxuxd0ypYTZl9LSv6Q2qveJNmyk8VZ75z7+b0liRWJPhu3TpUuSFy/GreLAttVNm8flhLj/P7FDa9BdcsVgM6yXm5SLpkhdOHsDVLNZu0GzvtWlqTylMzGZaWf31LtvSAJnNCDjjQHlTFjVS+nt+fh5p5mge45g8c6R3CUBW4fhoSmfx9EZnkpcT8ZpaUx/yg1hHXoyqQ0IgI3OrfY2RmvGUm72+90zL6elpYGwJxfBjJDZQjAJDT9AzbXNzM9LeiWnS/fbGG29kLmc6L+l5jZ93mebKO8KMRrN0RBQyfQgJj7vjd/ysvUXayLyXXpDd3NzMaKBUh+b6008/jTR7PJuFQsEODg5sdXU18o7W881mMyl0sJ5Go5GJaejvP95fXAMK7SqeCZZ5XsqRvPJMMFa1Ws1u3bplZrON9P7775vZbCCUgMrlcph4fXdychJxwLR9M/gesQEk2NRGpZgvEgUWplFoNpsZKU3vqhSLxaAaH41G9uGHH4b++z6qTylTDfuiQ8iD3Wq1wv/NZjOSmmk+MJsRDF6eKUxNitnhuNhn4oUqlUpE4JlPjAkzxUz4tWYR/oJmOV7Ufq61J/LwGXnhA9RHXi7EVpAgHBwchH7u7e0Fk9j6+nrGnKW1VR6s4XCYNCUwsCYxGTQT00yidrwGKqXBZG5Havs4F0/yFqzVaoHJNJsSVf0m4joYDCKGkxKh6jg8PIwYK2o5PW5ERVoE5qBTGY/HISyL5pwmce916c80sUmUnPOYs5RZv1gsRgELNW7NNXMQmsXYFPaLAWc99ktzRndzzQudOTwWReP3AXsVr88Lp9S8aXy8bLSWol/UEshxgphDxh+kZlcXValUCp8Z/0rFn3l6f1er1cAgafyXLl3KCIEeXN7tdiONITUvk8nE2u22raysBK0HtZD0ouRlq4TYqkN1ql8HBweZ4Jj6n3eA6mbcvJTji77P26cpxskzW14Z4DGQnU4no2Wmtsgsvus2NzfDHOgMXmTe7XQ6trW1ZZcuXQr1Hx0dRbHqtGcIx6E2ypt+/dj9/yk60Gg0MpAMH6PsSWmGngnGipeXZ7CEvaKaezKZZFTQDIimkjKliQB49Sg1QPzMCU/Zd2k+YHwTXcxUO6po821tbdnW1pbdu3fPbt++bWYxYJYSjy/0ZvGAP7MpMdb/+/v7oU5qzHweQUp+BACqPV2YPqGlWayp0jjo2ZFiSi9duhQ8JDkvZrPAsLy8i8ViRFyIJ/Dva1xewuFccs/oXY+16vf7UXgDEc88E5pfKxFez5TqfWISeEmz/VSCXjIexKednp5Gfchz7/YXuerh7xdpv0ajUYRxonZQf1dXVyMzic8oYDbdg6lAr8SS5an9R6NZbjER342NjRADyJtMUyBfFmozT09Po31FDa53OPF7my7i3W43PCfGZ21tLZLsFxcXoxASrFt9IZO3tLSUCVj64osvRiYWjZlrSM/NlZWVDEj+8PAwE0xZa8PApZwXrZcuYb+vzs7OQpvn5+eZYMMq6m+5XI6EEB81Xb8xRIfeFbNzdnYW6m80GtFck9mnJ6DqazQakZlWc7SzsxPWhpoTYjBZqEFkiAcxlh7ATs82zgHN22Im9NdrlvKK9xDMO/OenmreNJeMGs/6dK59Lj9aUOiwJRpOhshsZiEiQz2ZTEL9slgNh8MgCOg9MqkpxtFrLi+if/V6PUM7/B3j968vzwRjVSgUMoEvxWCpvPXWW0EzQw2OB3iz8ED7kheWnhHTKY2Y5ZucVFKZr7vdboajpxbMLE7M+cknn2TMVz4qcrFYjAL36cB74kuPSmmvUtFoxSww3o/Gocuz2+1GgFAlp9RvZlkNhYhDt9vN4HHMpoyVnn/06FFm7mq1WhQhm+ZFjyHQfHKNqBlg3cQdsW6z6cW2trYWAaFJgL07t9n0kGmtFhYWMqbWYrFo9+/fj+ZK7zO9j/AkZGx9uAT1+eDgIMLRMM0F5zoFNn8SIVY7/iIdjUZhD9IDzczss88+i+LwaIzUMGj/8LJkVOpicZb6yGMrVLwJX3OtvU5p1WOuOI6UKYAaztFoFDElKYyZiteCeYFMhcIHzQ3U6rCkPF31PDE7ZrGUTWybx6dJIJqbmwtnT7Sw0+lEe4eR54mzo7lN/dZe9AKXhzaIoZifn09iIIlB8tkwOB5iatRnjz1SHz1j4deL8cUYBZ/tkfYQg0rB1gcw5V4U48k14vMeRiJnK57ZPE2V76v/nMJX+cI9q7n2d2S73c7sa5rGub/MZpYUQneoCS6VStHeFtPHvcmzTsGTZund3d2kAwrn09NQ/wxpQqfTySgdvBXpSYzVk2f8eXlenpfn5Xl5Xp6X5+V5earyTGisvHeN2YwzfPXVV81syrn+6Ec/MrOZhiVlIvH1+kIpgRI/sSPeRmuWVpnq80XB2Cipm03VyKmxSr07Go1CLBZqSth3Bkvd399Pqpdpv97a2gpaGgZz0zj020VJq4n58B4keR5Tend7ezsy+UkSWlpaCi7EPnWB6mPamNFoFKUAUnmSpsAsaxbWe6lApBxrs9mMTAep5KWSLlVUF4OWSnpjuAYzi4KH0lxJbzpKdWpne3s71Lm7uxvFG9JcmVkU4JH4lzwvIRVKcClMhHdgODs7S3omEWOhc0ssytLSUghzwn2lECnFYjHSQPiz5+M1EdunOVT9TD2ifURQMKOB0+uJscNSc+el31qtljGr0MTvTeMHBwcZrBLjeNHkaWaRNyz3jNq8efNmGB/nq9lsRgBsaQDo1MB9vLy8nIE2qF3NC3Nr8jnN3dnZWRIb6oPPMt0JvV2ZoUDzqP6bxeB8xdo6Pz+PchbS0YQmTcJJtCe9CZwxqqhBVR2TySSZ05B0vl6vZ4JolsvlaE43NzfDXKovo9EocjbwlgZCLfi/xpGihV6rpf1DXB7NdmaxdtpDPhiUmlHo+T4D5pbL5eh532ezqTZTe6Ber0dx7lQf98/x8XGULNk7obHuvPkgrdrd3Y1MzfpL2vJLEW7BLL+j2pwvv/xyGPhbb71lZnG4AG/uU7JYr9pV4QEziz2TaM5K9Svleed/ozsnn/fxa1S0GW7cuBEWj8zI+fl5pG6nKjwF9O73+9Ely3AL+l0MjogaVbr+UNJDZ3FxMQJa5x1g4sxIsKnO19wsLy8HBpTR6HmhrK6uhmdIyImj8p5cGlNKvX9+fp67lprfk5OTQFzJjPq4WFx3n+vs8PAwg88jgTo9Pc2Yhki4UuZoMhy1Wi0EUtScpyIGk7ilMsB7gL+/gOj4wPrMpushIL43P5lNz6rHbJhN1917E7JfniByL6tdvsvQHj5AJQkuLzgJJPQoWlxcDOdiPJ7FGCKm0psDPGPJcZBhODo6isbFoJj6y4CRd+/ezQTt1bi4Rqpza2sr4yyhzxorcUVyprl79260vq+99pr95Cc/MbNskmCNU+3rPR/np1wuh8t2Z2cnwmxyjpgSR+eNCXJpPjKb0VGC4M/PzwOzp7uhUChEcyAPSAYtpmBPUyHHenZ2FnmLk6FTO97Rhl6vmieeczJWDOej9svlcsBgDofDTM7CFO16ElzF90/rSlOr+qG96LGV7CMxyTThqc9msWNStVoNc8C7y2wGdZFjj+pPOZh48zjjtdEUrpLHWHnGyWy6vhonzaEMUfJLgbE6PT21t956y15//fXMhtWkVqtVe/nll81stgnef//9ZLwWszRQl+BNz4iJMDB6udcW+HrVv5SnES9aXgSpaLAeg6XYXdxQOzs7geA8evQoYJxqtVqYD2rG6BrL6M8knMQMmM2ICBkFXd7EcHgcD+eCUrbWRhK8NjsDvjGFidfMlcvljKec8DvypiSuilKd+kOPULP4kDOYZx4OLhVZ2xfmvGL9vFQ9Xs6DcJeWlqL9k0c82U/V32w2kzno+Dy1cEwCS0bNa/PoGak+UoNJPAUD+/GMcG/+zu/8jpmZff/734/6qLlIBYBMrSklYY9XZOBJrwlkjk6V9fX1TOJrtcsiQkrtlS+cG+9ZxbK0tBQR7JWVlQyDyFhJPsI7LzdmZeClraCffs9K++BDPmhszF7BmEDEPaUsBdrXpNdm07hF0owNBoOM8GQ2PeeiM+VyOawPmTn1lXGdzGKts/rAPeAFE/YzBf6mw4tZ7FFKT3QPYj45OYnWiHSeQUQp3Ph7IRUPiloqT6/Vd42DHup0ROCdSuFzZ2cnMNp69tq1a5l2vIDF76rVarAqLC0tRc/4uJIam8cpaZ+rL5wXKkYk6Pg7n7iqPEExdW+zFIszb8LRaJQJkaK6tU9/KTRWp6en9vbbbyfDLpCj1Ab5xje+YWbTRfrZz35mZtNDmNJK8DMXWvVLIpApjlw8NTdknvIWKcUVe+JWLBajA2g2JYK8MHUI6VI6Go0idbUOLV1a6WXR7/fDfDQajSjlh/rsD14q1hT7TtMTtRV0C2eh5s8sZurMppca8wlKiqVkyNJqtcKh1xyRmHgTC9eKJgh6nFB7pcKcjCRcjKDNy4XMBMGZ/F3v0qzFuaEpJyUUqJBhT2lGUml79L8nPtyLXi3PtdRcX758OdRxdHQUSW08p2RyRARHo5H98R//sZnFl+ri4mK0B7zU7k3tPgiuL2S8xNSpfnr9kOHxwpyZ2Z07d8IceAYkr/2UpxhjjakdheNQodlTDBe1GYeHh9HZ1vwx1hXDDnhzDcep+aXmXJ8nk0lkpr19+3YmbAPPFMHuTFKrPmtOGDZGe8abB6mN0bu61FZXV8OYLwoqq/bK5XIQThuNRkRvGSaBcfxIq8igkiHQ90zplfLONos9SbvdrrVarUxuQj5PiwAZPsZr1O+87MkgmsXrKoaHv7darchELppMsyWFVs2J2XRNPT3lvJyfn0f3DBkzrpunQ54ZZwy0ZrMZ6ClDcrCkgP15McL4ji+qgw4PXCMfN++i8kwwVsPh0Pb29uzv/u7vwuDkMp3CM4lA3bp1KyzK22+/HUmBeeYp1SfJTe2QuKu0Wq2MXTs1oalFS2kfzGJixwuAeaD0DF1I6anHOhUdeTQaRRfd1atXw8FeX1+PNElmsS06deFqLuiVIqzK6upqNGYSJUpPPrGrTJs0Wehg37p1y27dumW7u7vhOdr9VY+YTb23u7sbJFyv3VC5SD2uS5PElIe/VqsFswKZGh+2Qc9TmkpF6Bax84EMaRalCTov1IBn+Pwe1P+MiM2o2PpLhojzpHAXZhaFmyAjxsKLl4wMsTOKM8R9Xa/XMxhEjtub0kejUcZNmxoKjsNjn1Tn5uZmaKdSqYR3eSlVq9XIlC7GIaWt8YJJu93OCAlmFmmD/H5TPerDw4cPI3wT5ywlbHI/MuwK6Q1pC2NQqX2lImI9fg7L5XJk4tE55NiYv29nZyfCSfpQFWZTM5uYhY2NjSDAaV+kMHapi5FMnIoPWil6QsxUrVYLmj/1zwcfLpfLAQ96fHwcRSA3mzKKpL+Li4thzaRlKxQKYV729vYydwljuGk8jDumdf3oo4+id7hPta+2t7czcbLMsiEO/L0wGo0izY1ZjGtbXV2N4q5VKpXIo5LFY8DMpvOuM6S5pYlc86T1oJDCOIdeG+ZDRPCO9PSfmkF6z5JuaF9zDukN+iQPzWeCsVL5/PPP7Yc//KGZzS7jr371q2YWExNNYqPRCIxRt9sNUW3NLCJszPWmwyGJxksJ1Wo10qzocHOzpwB3fIYgX35vlsaOUOtC0CwJZLvdjg4iN4QuE/aLYQoIdk+B7/wGTGk0GO2d88P16Ha7kWbGM1Yintr8H374YZgnEc/r16+H7z766CP7+7//+2jM2ui8gGk2S2kTeNkScEui4NeL+01hEAjM9epoHnoV4kgYj0v1as5UUuYjjodmDWoOaHZNRR3WX485IgOpelSUm5PAYhLItbW1aA8xarEnchzbxsZGlCbk6OgoPMfLmUws+9Xr9TL5MkulWcTtzz//PPTLA1B1YVPt7+tnUVqhYrEY5kB1jkazYLFiHGh2lXStvfbiiy8GwcTnNKWJSevFi9/vae3DWq0W9lvKmcQLdcTPqHAuVlZWovhADJVAIUxtUhtPpoLx3thes9kMmiqa+ZiuplqtZiK8M4SJT6/V6XTC2pTL5SjXotrhHBC/xPp1kXszssY3mUxCrMFSqRT2gdpeWVnJpHtSW6+88koYJwMWU1NKekktugS/5eXlELLFMxVkclRnvV6P+qBy0Zk3i5k5rYnA7ISwkOHTfBNLqjHpGe35+/fv5wplzJerc0NmTeeETKtZHOaImk8ySimFBsfuYQiaR7ZDbZ+3APnyTDFWZrMgZF5rI+JFaX48HofF+/rXvx4tGLl6SiGakJT6X3UymNvq6qq1Wq3Iw4slL+ps3qKSiSHTQk+mlNaFF4Bn7HTQSUBqtVomdoofp/rrQZEpVfQnn3wS1kZMqZnZr/3arwUis7q6Gmm7vOZAa6n53dnZCVKKLtvt7e0IMMoN/8ILLwSCrLpbrVYkeacY2n6/Hw7/2dlZhLfqdDp25cqV6D1vNqOanONh0ZoxdhgZer3vNT7M8cdMASks4GQyiTQmeoZSsIrvc8rTVXOmNll87DSNmwII9+lnn30W1WcWmwLFwDILgFnMZNA0m4cx6/V64R2aDcVsffLJJ2Fe+O7q6mrYy8fHx2Fe19fXM8BrsynYnvtXdEaCHJ/VZ81/p9PJpDnZ2tqK6iPB1l64f/9+5KDitSbaK7xgUkIN6YTHZuVp88ymTCOFAZ+QWHVq/skA+f2lvVwulyONpJhL4WXNpnuHDIzGoXFyD3pBeDwe2+PHj4M21ZuSaSJVf8ymZ8fvPRZPs+g52O/3M3if+fn5CHO4srKSMZudnJyEc7ayshLdQaK1ZJr8HvDCklk2kK0XrDwU5SLPaT3voQ9i6HgmSV+IJaN2UjCber0exrG/v5+Zaz+u5eXljNaMCg2P46SmMmUdyoPutNvtUH+z2YzqV/s023c6nV+uXIFm2UtDktOPf/xjM4ujlacYldXVVfvt3/5tM4ttwbdv347ywelizrtwyAh5bYpZvBhm6TQZ+l79TnHL7XY7ktKozk15F/kN4wHnXmNVqVRCv0lwqU3QgdB7InoMF6C+M4KyNDhm03nnQedYRVA156qXWcRT3hfqz/z8vP36r/96+F74u1arFbB17777bqTFSmkTj4+PM3gvsxnzsLGxkZFeUqYGmiH8YaVZwSxLoEqlad487wmUInJkpigF83t5QKktsynx98FJ9c5gMMiEnGBAV37meBi8dm9vL0pxwgtT7T58+DCaO5+vbzQaRZe3L48ePcqsh9/7qbQm+nz58uWk5u/8/DxcfIPBLA/geDyOLiaVpaWlKJSAv7C2trYyHmpMBvzCCy/YwcFBIMz0lqxWqxFjJUaTybwPDw/D82IsvCOGWdYLj58phKp4RpVjM4s1q4uLi4Fx0BlmBgZeLmSW9/f3wx5jmq9qtWqHh4f2yiuvRF6Oy8vLYYztdjvj2EHYAtMhmcVCYKFQsGq1Gpm4m81mRFt8UnfWaTZjvHxYFE/nT09PbWFhIRcsTbM5oRBM/ZOyQHBez87Owp4i2D0vXRohAVQ++HskpbEmA+OtG1rbFI5ycXExuj9oVuY5JAOqtZdG2FsNmK2Ee0x95L3kf/OWHP3m72yzOEMCn6dzFZnfZrMZYCFe6+zLM8NYSYMioqEBy41ck/6tb30rYnRS2oPV1VX7+te/HuqRCnc0msVB0saQ9oUT6DenlyyoGfBmI98vb54SgTo9PY1U6h4kfxE2i5uAmi5unk6nk8F+ePOj/peUIcZF7d67dy+YV9fX1+3LX/6ymc2kdrMY+Fmtxsku6/W67e3tBTDkgwcPzCzOd+e9ayqVSiDuN27ciLRj9N7S4Tw/P4+8gnxcIs2FDjbTgaicnp5mgPUqKcyO/5xi5sigF4tFa7fbUU42s+k8pzRJJCY+JQnNzWpXz/BC8Qzb0dFRMg2R6mN0ZLOZqpumusFgEOpQ2hBJ8bzgZW6vVqvhrIgg0YXebApI9Tk92f8UAfXSIk1oNKX72EvcG5KmKf16sysveP3G9FHqozS5OtvCsbz++uthHpnuqlqtRqYw4sM4JoZ4MJvNsc7P6elpxKx5bU2KJnjtNZkv0iGz6X4SjEC05OjoKLrY/bzoO5l1meaEGhAyvaSRV65cCePUxTsejzNastT+qFartrS0FJmL2Q7pEzGE9P4mgFlzaDaNp6gxSYNDPOHBwUEmUbLq1DpyPy4tLWWYWLWrM0WNITVNPnUZGYGUqTdPY6PPfIZWCtEqYV65lzRuxriidpPMtcySZjHD6TWxvANEN5jRgvhA0txerxcUEqrz/v374Vw2m80odZj6yZAyvV4vrJ/m/8qVKxGtunbtWqgzld6H5ZlgrGgC8ERWC/Lee++F57/1rW+ZWQzK9kyIiNtXv/rVcEg+++yzsIjaLFoggaLz0P7khElEpMFi+wyxwLAHZrMD6y9BBufUGDyhJTHxphJeKPreS3f0auj3+0FDRewHL5HxeBxtIDEf1FSQWKSYkG63G6Q0f5B4eenCLhaLgZmi+cTMAv7u5z//eWC4h8NhZB7kgZN5cXNzMyLInrHycbtSn6mlosSm3zgH/plisWg7OzuRA4Lq1ppRpc/vfbDCFJ5M46FgwvAPqkdaRzJn9FKiAMEccLycvOaAnqdmU9W5mInBYBD2ntphCh+z6RppbUTweHF47SA1TCrFYjHDbJll09tobpaWlqLQIinTFs3Kg8EgXPI0e2gttBc1TwqFwn330UcfRfhDjlEX78LCQhTiwptkvFbUm3WU/ihPIJOTC+ukWccLRnp2OByGNufn5zPeXOqLCk1YZjN6oTqOjo4ycy0hk+EsiO9SPkMVnQu1Q80YGW3uAaYqYQiaVquVCYGj9yTEX7lyJdKYmMVhMAaDQWTievToUXhOe7/T6YTzMz8/H2koRROPj48jJk8hCMrlcmAGuf8fP34cOaOkwgN5TJUXJEhvut1u6IvHFxPLpfNeKBTCOLnmpdIseG+r1YqwgNormn+1R+ylipjYl19+ObrHSEPoFKX76i/+4i+Cpq3ZbIZA42ZxPmIqN3wOSWpP/biFO8srzwRjZTaLBeQvC/5uFjNY3/zmNwMR91y5/mfIAi30w4cPw2b3DNb29vaFm5OMh9ls0zEfngfEXqR58t9TUvJaEW5wEVDmVOLl0ul0MqZAEr96vR4FauN70jDduXMncO+0pdOM4TEXdC83m25GSQs6eDoUSkKt+TObqp3FKN27dy9gd8xmEizDPVSr1XAgFxcXowSsnNOUeY0ASQ9w9cSH7tnFYjaDOufDLNYolUol29zctO3t7Ujb6iVxb7/v9/vRBcSAqY1GIxAmXczn5+eBUPj8ZQTvE3smKW15eTkTrkPPM2CriK3WUHOvPURzZ6VSCfOk5+jOr2fI6JnFWgZ6XJrF65H6bJZ1/ef8mU0ZGDJW6rfHhzHhNs2IZjFOS3tRZj+N/86dO5HZW+vlmSqNl+P2mDuzbFLclPmIc+EFBmJwuJe5Zz0jpjqZuYGx5/Q8L1VG16aGkVp50hwKlqI9xeIsf+LKykrQqotea99qXfwlqLnxQiBDcGg9+L0uY2/C7vf7YRzSvPNi9vRAGpf9/f1oH2q+er1eYBo0J2ZZ7I7Onsa5srKSEXR8nCcWj7FS2/xbqcyCYh8eHmbCXfjzyRALxLWR2alUZtHW/V0ufCYDL/N9BoJWHY8ePQrtc73M4gTsqoPPtNvtyMokK8z29nZ0B3qtJZlcjUl98CFYfHlmGCuzmNHwFyGz71KdAAAgAElEQVQXRxJisVi0b37zm2aWj9IvFotRuhgzsx/96EdBEpD9nRyoLj+a5VIXLJ8dj2eRximpb25u2uXLlyNiq2dSBy6Fc0n1QePRdwySpt+0ySidem8XzovZVLIW8J8JpDc2NoLG6iLmwAdQq1QqkeOB6uX4Go1GkDTu3LkT1ubk5CSaAxFvBtOsVqth7b13icZKT6Hz8/PMPKaSdOcx6qnfOBckQmxne3s7EzGYkvVoNMpkUvfmPK9RUF26jB4/fhza9KBj4ja0xxjZWeY7FXrEiZE+ODgIfdQcyzzCZLwa09WrV63ZbEaXhZ+7er0e+kAnCj7H94kfolYipcnjex7r6Oe2WCxmGB49L4aWggwvND82mrzoNabizXMUElTnRSBc7eWTk5PMZeohBHkCni4Uar04p6nxmE3XVM/TtZ0Xjbzz9K53jPBay36/n3RSIJhYGgftRd0BqawI7XY7Y/EwixnLw8PD0Ifr16/b9evX7cGDB6HfDPypcdBj0yw2W6pO9t9sSltSDk4+cCmfSWX+0DiIF9XcqF97e3tRIFf9TnPWaDSKsIBqT30/ODgIc6s7sdFoRGOrVCphfggv8I5DmqfhcBjdBxqbT/NDE6GEa4ZFoacpTZDae4IT6T1pOYvFYiRk6A7e3d2N7kPNkyAvZvHeZ5aIFBPLkr4hnpfn5Xl5Xp6X5+V5eV6el1+4PFMaK7Os2tAXcZ9mU0ld3K5wV2ZTLVHKXVuak69//ev213/912aWDXRollWVErvjXZhTHhqVSiWYVbz3IWM65Wkm9JzH8XjMBXMi0U6uPvh4HR6TIamAOKp79+5FmiRJpfv7+7a/v5+JIVKpVEI9V69ezXgMMb6YB+insAdmM7OR9w6TRHHt2rVIylG9Mg94LBclKZ9QV31NeQHmFZqsWQ8/0yQjkDg1K+on95qfH69l4dhYaMLz2kBiuMxmSWPNzL7yla8EbR/38cOHDyPXZ4I9VbR2+ktwqErKqcFjFCkppgIa+r6peLA2AdIyYaTSyZjF6UHYL79P/Fj93Es7S9yV+mYW7wtqNvx4pHWZm5sLz/FdmvvNYgld/Ux5T3mtiEoKVqDneQ71zIMHD0IfVlZWQlwmaZbH43FkamGC55S3ogcfp+adXpzUaGmPCPituacJmqFnqFFh9HLSR52fV199NZj5qM1RPWpba7S1tRX2GEPyaF7VDjVpKpVKJQqtwD3Du8vTfK/Z6/f7QXuk4J20Iqyvr0dwAmr29H232w3j5rxwTT2MROeduRgJ3qfTgtdOahz+vDBulnBQpI/Eaf70pz81X0qlUpjjvb29XAcYeium9r7quHr1akY7KKtNHhY79OXCX/8tFh/bwhcCJmnueOedd8L3Kt/61rfChcHLSwzAaDQKATQ9SJEeNSrET3mANAMGpjx0aKozm3kh0kORhYSeh4gMHQktc7SRuKrPnBcyBKVSKah38w4Q7eccD00m7OPbb78dGGOGcvB4Hx+9dmNjIzqUZEZTsXK4NjSheVAt14Nm3Xa7ncGJseSBcgkIT5nkvAnLe5mlzDRkpjzR9P+z0OuJjKVfL8bJMpvuc13MxWIx4DzY1mg0CqarSqUS5mptbS3MtbBv3jWepkauS14MGs5lyiPWMwcEcqeYEJld/dkik0pzIYkv21lZWQlzQtwacU96XrRD46ZDAANbqi2Gm6D5ghctgbz+gqBQ4T09z87OIiYzby+rMLyLL6KXvV4vmF5oFhIOqVKJk3N/9NFHEf7Mm+b9HHDcml86GJycnASvUs3B6uqq7e3tRfSBtNubK9UXren8/Hwm+Xez2QxMloRNMgKEephNGQC9S1rJMTHWmOrR8/3+LIcgQ3twn9Ccpnnh3uj1ehkow3A4jMKDeA86H3Pv8PAwrOXx8XGgGWQqGAKB3odeIFEhPRsMBpHXo9l0HX0gWNGQer1uvV7Pbt++Hcx5rVYrfJaTRmpOUyZ0fx+khNPBYJDBB/tne71elEruovLMMFYqKddrs3T+K16kjNBdKpVCSACmRaG0IjuqD05G4k67MPEEqX75DaziuWXGB9Ii6RIol8uBUHo8DWPvqJAgMkCg7xft2pzH1EarVqsRMdLFQEwGx9Tr9aJYKz4L/Wg0CkQm5QqsooPNOCrEvU0mkyAl+VQ73mvKu/NyHGLgvAcjiR//5wXvsS5PW7hPvTfURdgtz4jRnVhrJEnTLPbwUv81Z5Lw8xg7tr2xsZFJLaNCYKfZjAEgmFWXpvYdHR/8mImZSmlpPPiW+4qOHk+jceTe90xritiqPTLyEgpWVlYCM6E1rVar1m63wxlmdHp6x1IbVqvVAl7DM+z6zsdsSnlA0uknDw/K9ympU0PCupkUlxG9VcRgFYvFCHPy0ksvhYtHdRCzRmbLbMrQ6DmdT2oy6/V62Gce68OE6qr/0aNHgRHMiw12cHAQ6pSgwQjeOje8nGUhUJv05NX8qdADVX1QYmyNj+8w0bl3lND41KbP0al3SeOERxoMBpmUQNQcqjBBuTS+nl7r/7W1tQhsXywWbWNjI8JyUfj2oHaz6RppfOq/zlO73bZ33nnH/vzP/zzSquVhJC9ipvxn/u/rE66PoHcfhicVyiVVninGKu/CYobrVPHmhTt37kSaIm2sFNDbmx/oPqvNQUKUB2qm10gegeTfSqUSwhwwm3iepDEejzMSOkMsdDqd5GbyFw6ZxRRjRRd8AlI948F2VOfCwoItLCxE2rZSqZRJ9OkD5+Vp5tj+eDwOl3m32w11UnpRPZJsaI5QH32eKrMs05oyk/CC9+ubYlZTxXtd5f3G+jxTQTOPb380GgUtgy6ur33ta+Fds9jl/ODgIDD1lMAuXbqUdG6Ql99gMAgaLe9Zev369SSDSM+w1BxxbKm9z+d8PSkhwWtrU+ZEz1z7fqWYNe3Jzc3NKAGyCp1GeJGyv2yHGiea9vS3XC5Hpj+zWOpX/brIy+XyU2mpqGmhhstsxgxpzl544YUouG+KtvAM1+v1cD7lKXl0dBSYovE4BrsvLi4GZkEMweHhYWjz6OgomT2CMZw+/fTTSEv22Wef2draWiZ+kbwKa7Vaxtnl6OgoCPaqV+Ng+3pGJn59JiOivt+7dy9iLNRHMZDeG3AwGETCH8dqFudhVPFhHc7Pz6OsG2Tk5aRy+fLlQCcZ1Hdvby8DKjeLNatbW1th3Brz1tZWBrqiuVtdXc0Eh1Zon9QcvPPOO/bmm2/a7u5uco9dRGcvYqj8HZzHkImxfP/994PZW3OnOyUPZhDGf+Gv/xaL11A9yTRoFjMP9Lq5fv16wFMxSCZTkmgixZ1Snci69Tw1WWyfC5+Syj13zL/aePJaZGJOxpzxRX32kndKOvUqU5r0Njc37d69e9HBe/z4cViLlJbGmyg986HviEOguZJ1McI7maxUxHviBgaDQfDmVB1Uj4sIMcCi5oahFvT57OwsF6OSkpL8pc3C+UrtgTypK6WVuahQC8kAoV4D+95779kbb7wREUK9+/Dhw4wLv/qV0u6QsfQavZQGRH+JH/M4P5U8jzSVVFBQ1u/3ltesekaM/fO/s43T09PowhbR3d3dTQaVVdgY1UkNYsrkxrmmxyrjOulsUEurd7UWYtBS3nUqpGEp71UfVJMZDThnKdOthxD4PbO2thadUa7NwcFBxqTc7/ejy0tt+lRCogMMVKy7YDAYBBOi2ZQp1m+PHz/OQCvK5XJGO+gZOprSKGx54Z73CKEbTErO8ag9BmAVXaaZcWNjI2LGOe8aa7vdjrRXjIovhpYZM5hNJI9hGI9nuQ1ffvnlMO9MVeXNZkw9RW2WWWxOFLP36aefmpnZm2++aXt7e7m00ix9hp9UUs+mvmPoD0IKXn311ZCKKeVJzvLMMFZm8WHzjFZqIrUw165di6KBb25uZrAm1IT4lBZmWRd3mm263W7YGNwgvCQI+kup6n2wTn6nUq/XI2B6CltCOzIJd7FYjCQjFX8Jah51gB4/fpwJz6/Dwnkhs8E16Ha7mQjRi4uL0XylLpRicRa12PeRQfxUGo1GEjvCEAVqh664mjMfcJOmiPPz8+gAMf4XL+oUs8px8zdqaXghck2pkeI8e/Mfn/eMLpkRRZ7273nQbmocFGRoEspbO6894Z70GhDWk6e18yEZ8jTE7DvNFz4bfalUyqQvoVbHl/F4nPleeSWZW1LrxEjQPtq/NAc7OztRtgCN3xeayPx4mZA2j+EmE1ksFiOQNfd1ypRYLBZDaA19L+2l6IDXCKaELS8keLA119RftHp2Z2cnrNna2lrYy7u7u4HpIzM7Go0CM+GZDxXPKEiIrVarwYwumsBI6HrPCx7cIz6kCQVG9WEymUSMscebqs9MV+TP52QyiZK8c67b7XboExNJq2/NZjOy9nzwwQdmNg1XobEx2TYZR+5FYi3X19fDeWbOR/brxo0bIWyPnJeuX78eMMa3bt0Ka6c1FSBd2DlqsVn8d9yHeRaBJ5U8msQMCe+++669++679tprr4XMLnnlmWKstDA+bhU9CObm5oI2Sou6vb2dASNroqgJSZmzqDUxi6U2TqoYN15kvV4v8lrwddHrwF+E9Xo9aftlLkPflgqlO0o8nAMeTtqFySyaTbU6xC0sLS1lLnsWr3EYDAbhEFP165Nocg78/wyEyVhPJL4MzDoYDJKSufdC0fMPHz4McyAGwnsC8hIWwaEURmaOv5ul87WZZZkSroXGncL65GktOU/qy/LyctAErK6uZiRDPZtnJs676C8iVjRXiAn1MdfIROZdzKrLj/tJJlU9o3c//fTTcOZ0TpkAWv1LxYhifZRCqTVI4T6JJSOtunXrVhhHq9WKtMtm0z3h8Rn6TWZ09pGa1bxLg+u1vr4eGCNqCs1mpiIyB9TWirHwnk9klFhSNEb/+71PhttrbvPMvqrjypUrYQ74bKFQyADQVRYXFzM55ehpPT8/bzdu3LBWqxVF/PfetKpX9IEe54yzR8/c8/PzwBTycu73+6Eev3eYozDFQDMyvMdGaZ+LUSkUChGulQK8mOj5+fmoz/q+1+tFATpVKpVK0PYR/6tzNxgMIia+WJw5OWl+KWx2Op2MlUjvPwm/RAUCPdd9kN9fpOTRBBa1c/v27WSUeJZnhrG6CENVrVaD9HTjxo0gdTB4GDcPmRyW1MVMbc2HH36Y8QYxi9V+3HBUoZ6enmY2BImyivp88+bN8C5zM1HjwAOWijDvQbtkBL1JSJ/1DPEhVHczkXFKcvHRwEulUrhcyVjlaXfYFxWvhUwxZaPRKNJGqF6tF9fFM83CcOzv7yf3BdOHmFlkXkil+/B7h0xznqZF73Ff0UWb60XC4T09mb5Hz5NZT2GwKCmzrZWVFfv2t79tn376acSU+77zQlSbSttCcy3nJk96zNNAUUNL5t2bnVKavaWlJRuNRlHwSu5LPe+j0JvF2CTOGbUReSZgPU8YQqPRiPatLlCagDwWJ8XoUsjM81qlAKI9Q8D6YDAIjObOzk40p8zJqv57LCTNXClGif1OmWp94bkU1slsxhAwd6iZRZ6pKjRrzc3NBeagVCqFsyGmam5uLmKGKYgSME3Njdrymm0612heqBnnXisUCtGdoTpIZ9W+Z7CWlpZCf9TXhYWFKFUV55rJsXXGGSLFOwrI7Far1SKaIIbPM5QqxWLRbt68GT6rME8msyqQkeZ+lEPS2tpaaEPrn6cFZx9Yn9l03tWOdwzKK6m7Kc+0mKqn3W4HbVxeeSYYq0KhkDmI5Ahv3boVtFM6/LwsGA7BLB3G39trtXjeY4F4Ci6w7L++3pRpiRoHry7WQn3yySdRImGz6UHVBUcMli8pHI8OnBgw2sxpRtRFLjf7g4ODAKI3y+bcUkmZdzRGDz4kuJyXRd4akZlQm7ws9D0ZPT2n+RqPxxFgk+2pnpOTkzDXk8kk/O73H/NzpcIypC5536+LzHmcO79ejILMS0TPK7mrX3ezOLE2i9/7zApgFgN5NU5vBmAZjUbRu2rbj5tm1JRXr98PKaB3HpaLpl+ZF2iq297ejoQmn1NN/SITwb6z3ZSQxf3DC/Pzzz+PhC1+Vn1e2Ek5cfAMeg2yxq1zu7q6GoWSyYt5pHYo7JVKpUzIBjJU7EvedynHgFRRfwaDWe5F1unjaNHT22x6GROm8N577yXjZbXb7RAaw8+110ARWtFut6P0UGazvUKhVcwcY0RRsCNzvb+/HyVe1zi9GZmJ6VWn+rK1tZWJaq6ieph2aTQahXPZ7XYjxxQ5Ezx8+DDUQ6GCe4ztNBoNu3btmpnF51N9vH37djRGatu1NltbW6GPrVYrs59fffVVu3v3bhirV1R4RYTZlEamPIi5T58Gj5WHv0rtfTKIeeWZYazkcWQ25doZVp7qdR8jxyyfszVLx7+q1+tJrcj29nZID+IlDk98zOKcV8yp5jUm+t0sjt+i76WlKpVmmcU9GJAbhcRHzMR4PLatra1MfA0SbobwVx2rq6tRfQTf6tCTafRqfF3sJBb0UNzc3EyaFon7YeA1MkxejU+vIkqqZtM1zcOxEN8mQlAoFCI1PMHbZMi07h5Pp36wz960JqmNz/ECoHmTfWTcNa+h1HylxjcYDJKmwJS5UeNTfSmmQn32hIlj8sF0U559eYB1EmgyzikclmfoyVAyzYzWxrtnM82KLufXX389Yo4YKNcTaF90NpgY2yzWEEwmk4zgRaEuNS7NRUq4odBhZpFnLLVR0gaRRvIzNSBcA48DZR9SDFVKYDLL99wkXfEaVRXN1+npaeay9xCRra2tgJPq9/thPys+lNeALS4uZkK/HB4eRiZoHwxX66lxXL58OcRAHI/HAUvFnKZ3794NGqy5ubmgrWQOU5m9NCeMh+jH/fjx44he84wzxprmjuet3+9HAobmj1ablGk+Raso9DcaDev3+0EBsr6+HmkHiTUUE1ur1aJUQQznwfGQsUpBErzGjnsspXG6iD94UkkJiv77VHkmGKtKpWLXrl2L3C852b1eL3Np+kn0oGCVVGT0PMJVKpUCk8NDmQccZMyl8XickXaYiTwFAtezKeyHNBNsS/1lvA/Vd/nyZXv77beDZk9/m81mFDZCudt0uczNzUWaMW5EunyTeSAgVER8eXk5SEOl0iwWSKVSyYS70GfOhWca6vV6dNGRySgWZ9H3eXl7U4b3+KxUKmF9OW7fL8YVoyYt77LlpealNP+sx5P4C4vmRO8okPLC8gyMj+nlv8/rv2fiOdfeJJyngfLtpjSrvl2OlQy415JJY0DmXZoA9Z2523Z3dzPC0fLysh0eHoZ9ce/evSioZArrQxf88/Pz8JleeyxeC6ZCxobvnJ+fR5AG/15qj+h35nwjg5vy7Jqbm4twPH4P0Eyt+aHGOK8P+i5Ff8nw8bx7kL4uW0ZM39vbC3SRmhWPm9O7gijUajVrNpu2uLho1Wo1EgrL5XKGGR8MZjkAa7VaEN64d8ggMx7Z4uJiMgwEcU0KT6LPGo/XdpKZ8g4PCwsLYc8cHh5GGhsya/pMhwvRa62PNIXD4TAwhYzTqOLNv5cvX84oI0qlWXyzra2taC7oSKO+0Aq1tLSUxFItLy9H5yeFAfUlpZG6iHl6GjzVk8qTGKv/dxD65+V5eV6el+fleXlenpfnJVOeCY1VqVSKQNNm+XiclBTsJeKUijql7vSfyXF7actsKiF6MLpMAimQKT3mPDif4GOCv8l9M++b+kmw3t7eXpDkW62WlUrZNDWXL18O9evZwWAQtDXf/OY3I8n0448/DoHjNFY6B4xGowDu9s9wbAwGKsnE43Y4v5LSpLW8d+9epLHq9XpRP4U/8tI+P/u/CwsLwbTHkBz0Zvn444+DVEwgvcau8fk2Vbzkk6elUz0epE0tjsc6sR8pjZiXGFW8B2rKXTlldvbPpsJd5HlE+rFSI6figds+vAg1c34PtdvtsA8lqZ+enobn6YFlNjMxMZWJB5ITgKy5PDo6Cu/y7KeyGZjFmKxyuRzOvb47PT3NmEx8PCVqdJaXlzPa9cPDQ2s2m5H5Ve8eHx9H4SFUvFaJ/fWfPdwi712aDFO/c/+ORqPIzJZyDlDkes0Lo6arPz5WlKerhUIhaJLq9XomBI3PZcq5ztN4EOZQr9ejOHlM+6Pi94PmSfv1+Pg42vd8p1QqWbfbtUKhEGjVxsZG8Nj89NNPI3wa51d1Mfq+Cve82TRPqGjtZ599ltG8+TW9fPlyRktDc2KhUIjO0rVr1zKZRZiDlBoyalibzWboK3FtF+GkLjIFPm3Jey/P/PckDdczwVhNJhNbXV0Nh6bZbObGi8rzHMoDzaYIB7/jQd3Z2YlSK3jGKgVm8/F/SqVSIJzFYjEcDn0nRoCeRMJFETDo3fIJpNdG3dnZCQRqZWUlisSuTbm5uZlhPhqNRgC+Xr9+PTpAH330UQakbRYfBMaJ0YEfjUYRZoqmona7nbnESHQ98NRsGj2fa+O9WDQvaqfZbGaC7anQfVxtNZvNMCZ6KLXb7XAxtdvtMNdkslLBYvU/CZ3GT0KbMsnR2+wip4tUnSQmeW7rrJ8q/tR5UD3+N98vmpdV/JllX1IYHV5YZMb9PJvNounreZpqSNwZU8ozamZTE4T2A4UX1mM2uyiJrSRDwDZTcbHMppf2cDi08/PzaO/6571n2Pz8fDSOVKEJi4D18/PziOljm09r7kjRPY8FY5v+bPMcsi7RPJ9TUf8z0vbi4mIEwDab3hPsx8nJScbppdPpWLfbtW63G5nAzKbzqrkhHouOF3qeTHaxWAwC69HRUXhXtOHw8DBDm5gSJ5VsWHX4mHu6T7j+zWYzSpZOJi4VlLRarUbBXc0sw+Cvrq5Gwpl3PiEEQHV65ou/+/BAtVrNarVaJisB44OpLc718vJygLHs7e1d6N2v/l7EdF1Unua9X2qM1WAwiPI0pcC5nrDzYHuNFe3WvIBIcFQfuWJmcddzZlmpgsVrRWq1WrAlV6vV8K5PIKy+e+2Z92RTu9qQtNkfHBxEuDRiUzQOMTXqj/6SKNP7TEwGA88tLi5GuCteoJojSpMcN8NE5OF8ut1uuDjpWZiHleNlr887Ozu5mceJ+RChIRiel/T8/Hyo8/T0NEiZCt5aq9Uymiu2k6dh0l8CMPU+54VrmAf6TmlGWbyGiudHe8mnckoxcByjF1BSwXCLxVn09KfRkPB/gmN5/n2Cbc69mB7Nlx8T94EuQV72pCEew6m/7AvTiUhQSmmqfdwr7+TivUR9jDWzmKHz9NAsq2mXdq5cLodze5H2NFX8ReOf997W9HRM7RmvcfX1eY3U2dlZcL3vdrvBQUUMmHcUoIDI0CeKR8Uo5uq/2ZSpEK3odDqBth4fHwftJz2lue6np6cBMM/gpD6gtbTeN27ciMI5mMWaubwMI8TEmc3uqQcPHkTzSKaEwq8wtF5bJgar1WqFO6bT6dh3v/td+8EPfpCxDKlQ80Yaxjs4hQkm9vXBgwfhPFOr5pkXxamkZ2+eRp0lj4alfr+oPM1zvxQaq0KhEIGTS6VSdHnzovBJOM2yXkQp6dxrDiTF60I3m162qXhaImwnJyfRZmcCVTEQ9LJjRnAmdmQfzWabkG6yPtmpuPh+vx8YodFoZIeHh7axsZG57LWB7969GwgUA1qqnY8++sjef//90BaZLBEZXkZkSMyyTIna1hykGEL1hZGzdfAJNKdWz2sLVahC52HWX5pH+/1+mJcUA612UwwPL2OVixgHEigyBaqTXo+j0SgZyDaPUfN73ZsmaTo2m64HQ1uoHRK2PEYsJVRQS0YplmNNaZnNsi7UOjde66H6PHCe/zcajSjoo9lMO8k5NZtdKPfv3w8X3JUrV8IzuiR1TnVR3rhxIzr/NC+q+IvBewLOz88HOtHtdqPLlEEuydTlwSL4HR1nvNdynrOA/51MlDcv+z1P0xvPvddopQTe1FqK+ZBpiw5De3t7tre3F3k5l0pxQuxGoxEsDFoXOesUi8XMejH4s+KwjcfjiClKAf8JtO50OlH4ANXLvUbweCryPaPpq0+MWaU51P7Z3d0N9ZA+k4b4cAzaUzqfXkDp9XpJaANpD+eaKWb0DB0+RqNRRAtSZuJCoRDNNTV4LNL2bW9v2507d0J/88qTLEv+udR7ecKEiqdtpOWp8swwVt1uN1L9+hhG/vDWarUo4qrPIWUWa04oVXEBGNRsMIiTLWuTy7vh0qVLmaivTMtiFktptVotbDZqxngZkECy797LRoH+3nrrrTDulGdYylvLE4tWqxUO9IMHD5LBNRlqwGy2+UulUoSPYDRnhpvwjDDV32aztAVms8SrhUIh6itj1phd7M2RkpbUruak0WhEmkJdwnKfNpsSLsbD8sHqpLXSXPA3XoiMr0UGgwSHfSd+iUwWzRMp4SGFw+r3+9F5oGRJ8w33Ouc25TXmsVGaU5pRz87OkgSV2i3uASZDT5kxi8ViLkHlnDPYJYk+50D7Y3FxMUj53A9yl/fepp7oX2SqNZvuYTFO6iNhBd4MqPQjtVotcpenuTYvYCLngp7IqX7xO89Isx4+p7lM5UWjAFosFiOawBycqmNxcTGMSUysYgFJqKKwpeJNqNRKHhwcBMaKHmtP0r6SOaHZ7Pj4OOMdp/GLCRyNRoFufOc73zEzs7/8y7/M9FNz5jVFZrHnqN9vLHrn8PAw7BuOv1KphHlXyBu1qXPpI9ZTs+etG35PcC06nU7SW5RjIg3mOeQeoKe52VSDyzlnLEazGfOaFzcqj4liyTP55TFfeWeHypPxeGw/+9nPctt8ImNVKBReNLP/xcy2zGxsZn84mUz+x0Kh8F+b2X9qZvtfPPr7k8nkz754578ys39oZudm9p9PJpP/86I2dMloYdbX15M59Xhx+UuPlykDVl5EQLrdbuQC2m63g81bxLderwfmyYfMr9frSbMTN6o2uDdlEeZY6aAAACAASURBVLCqOpkYk+3cuHEjwhD4CNJkGmmKMZseDo+HYXC4fr8fEUUxUwz8ybWgq7HZTMIiaJYMrdphUk7NtS4gzS8jJXu1PyVeal/IdKS0KGRi19fXA4NLhoTq9FJplmOuUqmE/pAxESOo9rRnaP70REt9JCPhmRk9k3epp9ab+Cn10TNG0u5ozvQdx+zb8cwWCQ7XQheb2VRrQBdugp/NsthJ/pbSprCdvCCUpVIp0gDRtZ37ljhGai31jPaA9jPjCumCmZubs6WlpUhS7/V6ER6GTKdKpVKJ4ic9yaxRqVTCOcyTpslA5Dk5+DXm/knVmdIwmsXBK/OwZYzTxctewtF4PA57RYyN1oGQA6+ZZF/n5+cj4DYTZFOjpDN7fHwc9VFMifBXZrGAof/ZJxXeE7JyaI90Op2MedeHGPBJiT2ERCWFmdLcmMW02AcQ1vhoDlYfRfcISdGcMY1PHlNaKpXs+9//vv3Gb/xGMqiu166nLAIvvPBC2A+ai9PT0+g9CRlmZr/6q79qr776qpmZ/emf/qmZTRksTytSDNNFGChPc6iYyBM6RqNRmMsbN25YuVz+/8ZYmdnIzP7LyWTyd4VCYcnMflIoFP6vL377HyaTyX/HhwuFwpfN7N83s6+Y2WUz+0GhULg1mUxy00HLhi3txtbWVjTZKYApJXkPWiZXrk1AtanqG4/HIWqz2ZS4Mi2L6la9rVYro9r1izQYDKL8SSk1viesGgclARK3arUaDvPBwUEm2z03tb9kU2H+S6U4GSi1ETqcy8vLUQoZ75XlY+5wM1N6oRcItVEnJyfhGdV9cnISLslr165lUrt485S/UFLAfwLpfcyaVB2lUikCtorJIkHXRarLT5cxpc8UwN2bW/LUzyktnB8XCfP29nYE9Oz3+0lMGsftJbMUo0UNkO9PXhwrCkGaRxGkWq2WeT4FwqdQkBeMNWWmHY9jMLr2Gz2NOBeMj6dUHzQXqT3PANCRwQN0U4R5PB5HuKeUdrXf70emwxQDnhfTJ094zNtXohd5l4/a8VHB2fc8wD41/Wtra6HNBw8eBIHEz6cKcbaq4+rVqxGMwjNB1WrVHj16FBgPQgLOz8+jORPzXC6Xg4BI8xcZDW+W5hnXeuti9Ux8sTiLKSXGijgsCtYSRLzgQO1Zo9EI/c3z1JOjhNn0Pt3f37cvfelLwdqiwNd6/6WXXorS9JhN7zwpArw5Whqj733vexmNph8T6ySulUGv9e7x8XFGG651uHz5cpjr3/zN3zQzsx/+8IcRfOdJUf8vMvOlGGoqRvx503wsLS1dmILP7CkYq8lk8tDMHn7x+bhQKHxgZlcueOXfNbN/MZlMzszs00Kh8LGZfdPM/u+8F4bDYYRvunfvXnLCGFmZkbu9qYR/qZnxxeMwer1edAmYxRezv+iYZ4+MBy/yFEaFGjZyy+pnqVSKtDvdbjdsbF4oKRyGZy40bm4wYmjq9XoUdVzjp5cHAdXerpwyU6Qu41qtFmmG5ubmAoZKc93pdAIB8TnoiNVKafWoxfGXJ82SHoBJ/JG+FzGkZKuLeXl5OVw4ao8gXtXhMUl+bryEd5G9P4V58cz4aDSKojnnaaQYpZx98nOQKjxXel5eoWZThkD7p9lsZrxRfb3jcdZD0Y/XaxW135heg6Z3z3ybTfeAsBqDwSCYojwuhoUAdK2vLte5ubnwLpMea6z+jPizkdLO+ajiqXcvksL9Mz6sA82ueSZ1f2F6TFSxWIwYROajU1lYWIiYWDFTSjHD+j397na7oX7NdafTCZquk5OTKJixNDZ7e3vh+42NjdzLVrRleXk5Y65VH9U/MkrMXjEYDALdYsBV9uvs7CzjDT4ejwOdZU5P0Zrl5eUIP0ulwHA4TCb8ZjiJ8XgWoFrrwajromPa5wcHB/bKK6+Y2cyc752SeF4ZpoIR//P2FO9pzp3GxLYIx+n3+8Esv7y8bHt7e9ZsNsN5L5VK9u6774bn33///SQuTuVpzgxLrVYL46OQ1ul0wv6YTCZPND/+QhirQqFw3cx+xcz+1sy+Y2b/pFAo/Idm9pZNtVqPbcp0vYnXPreLGbGw2JQKUiB1Rug2y0atpUrTLJ5UMkGUiJnH6aWXXopcf82mXDyZFF6Y2mz7+/vhkF2+fDki7p44qV/aWHwmj4E7PDwMY/HYBv1VHR7zlQKSlkqzkBDz8/OR3Z4XGN/1AFuvbfKu7brcU16ZZlNpVheWDvbW1lZ45v79+9H66WDxYhKI0BeviSFDqf5qntbX16OLsNVqBVxEoVCwlZWViAlk/SkXZbOplJ23lqlCrVmeFpT/ax5YP6VB4vlUv++rxzTleWym+lgqlezq1avW6/WCB4/GL6KUyjmZMhekpEi1z7mjKS81Nxf1vd1uh7xz/X4/ytGp8+SxKCyaa11uNFd47KDSZdVqtSgUgOjK3t5eJDRRG8Q5yGOqUyUlQHqsUbfbzeCvyuVy0tnACwrMsch+qS1pRcziOHv0LDabnrnd3d2McMO4X2I0GFGd2iUyiwwdICwOLR+rq6sRfdC45ufnA52rVquB/nBv8f5ZXFyM4AFm0/2t+WR+P/bLzCLmU2MqFAqhfe7xhYWFcAfSMuFDTKjs7e2F/+fn58O8i2m8e/duYLa8d93e3l64LzWO9fX1QBc9s6L37t27F848BXQpPTxeazQaJSPVaw9QG2c23aca99nZma2urtrx8XGo96WXXgraZTOzr371q/bmm1N2Q1q5p6EJ/ncmvk6lBTs8PAxz8ujRo3Af5ZWnZqwKhULdzP43M/svJpNJp1Ao/E9m9t+Y2eSLv/+9mf0nZlZIvJ7xKS0UCv/IzP6R2fRgDofDsAnOzs4ijYU239bWVsQte+Bfnus0fyPDMB6PA9duNiWKWngdto8//jjSkKSkoUuXLkUbjITTXy56JpUPiSYgTxTJPPqgnDQ9eaKlMh6Pgzlrd3c3MAqNRiMXb6BxkKHUZZGKhUQPPbar/nhJnSYDMwsxfzRmqoiZZFZ9ZEBV9V/99e37zyryrOT/mhtpNhgfiVgyf7mrL7dv344OqhhXn1Q7T5Plv/PPcy7ZNgHzPuaM9lRKe5Tn4UIztWeMzbL7xTMTqfq4Bk9iOikweGzYRcyaLx4s7DVD/P8iswGxQ1qb4XCY9BQk00/TIXNnmqXjgeWZE/35Sc0xtUf8/fz8PPSTwGIxRayfCYjp9k/m+uTkJDAKpNWj0cjee++98L8Xdm/evBmEWS+U8VIjjVboA+YDZbv8fm9vL+wrOfzoXtHfUmmWloumf5V2u52rgSEzSFgBGT5q6ijUyexHK4j3VBdNpGdvuVwONJJMyMHBQaAzCwsLYZ20n9vtdqBt3rLy1ltv2VtvvWVf/vKXA45pdXU1ME0eT6z5un37dsjjm9Kue4GAeE/Sbq4v6cCVK1ciZtGnifOCzCuvvJLBj+7s7IT6GWMsdSeq0IKhO9+HktHcffrppxHmLlWeirEqFAplmzJV/3wymfwrM7PJZPIIv//PZvZ/fPHv52b2Il7fNrMdc2Uymfyhmf2hmdmlS5cm1KIMh8Nw+DY2NqKJ1+DInNBkZRZjRChlm+VrYvS8v8z877zsyVSo/larFZkFU8meyXylNFbEdZjF0oMkA2o2isViBLZlIZZIfb9y5UoS17W5uRlpY9THg4ODUK+YDkmIxCN50DVNiBwn+0km4OzsLBAfZo43m3nXkLBWKpVAdBkfjHPHJJ1k3HR4yRCyHvZxNBoFU2y73bY33ngjmgsyUapbGhKzGUP1K7/yK+G79fX1iMj4Q8596kvKzMi5bjabydhHHneTIiz+eTL0v/Vbv2VmZn/1V39lZlkztncmeFLhuXlSoWDEdlh43kkzOBeFQiHJnD1JI2QWezLleStyn+pSazQaUeTrFJaSeK+UMEDGzmx60dBrVxonXq4efuBpw3A4jBKtp7JEUBNCzebCwkJGUDCbOsbIdHd8fBwYN63BwcFBBhtLPFun07Gtra1woVIz5AOtqv2vfe1roV8///nPM2tJzZba1biZb1X9Ys7CvELTbWo/Mo6g2XQeGaRTe8MDxBm8lljOVqtlW1tbUXDmyWQS9lu73Y7yoKoOgvr1ndnsHt3f37dbt26Z2VSjdP36dTObhuFJWQPu3r1r9+/fN7OpQsHfr1Qw6K/GSiUCadvNmzfD50ajETRPjx8/tsFgmlieDKenYZcuXbLRaGTf+973zCz2Lv3bv/1b+8EPfmBmlhE2Wbz21AtoZKwYqiKvPI1XYMHM/pmZfTCZTP4A37/wBf7KzOzfMzMZPv/UzP7XQqHwBzYFr3/JzH78hDYyEl7oIFzU6QnYaDQyB9Sbq1KaE7q2j8fjCIz++eefZ6I+U+NAnALrJ9NweHiYkUaI1dH3xFipLW3+TqeTAV36+Fd5l5i+8wf38uXLgRnixqAZUITNbHpQpbEhTkIbTu9JauSlxk2ap1lQqVarwatuPB5H/UmB6nd2doLkfXh4GOqUqYixw1QHE7cyqrr6RVMGpVkmklbcm7m5uUCIWO7duxfMx+VyOTIVaXz+Qs2TWs2y6myWFFNBU06KUdPf1KVNLKCvk4y5GKrUfsvrlzdtepNXSivnNdBmsXDBmGjUHFDD5bEzKUaIz3imImWOJXCblxc1VvSMpbmc2DMW7QkmS09F8jaLg4am9gwFLGJu2C+z9Dox9hKf5dnodDqhv/R8pcaXgYJHo1GyTjoBpP5WKrNk6YuLi6G+g4ODSPD59re/bWZmr7/+eqRdF0ZHe0GaNQ9fUF+8hcN7MHsaaTZlahgQ2a+XGFJqOWkW9Q4C+o4YSO0rWSgePXoUMVbs19zcXBT8WPVpTmm+M5vtgcXFxWhtNEerq6tRSjE9v7OzE+IeUkD3DBbfY2w5/dW5qtVqUTBWr9USDEZ7n0KKnjGL6UO/34+YbuErGa+RNDKPbvnvaaV5kkD4NOLid8zsH5jZO4VCQf6Fv29m/0GhUPi6Tc18d83sPzMzm0wm7xUKhX9pZu/b1KPwH1/kEfi8PC/Py/PyvDwvz8vz8v+X8jRegT+0NG7qzy5455+a2T/9RTpCjt97u1GVSy8Icas+YCYlEs9Z+mCJ5HSZDJmmspS7Ptth0EgvkZrFGCn1lyp+vSNMAIOfmk2lhBR3TU1dSs3pAZV6ntHTqbFjqgWC9VTkuWIWu7JrDjyA3tupUyY3gq7NZlIBQYzqv1nWe4tpJcyyyUJ7vV6QdobDoVWrVRsOhxEYnqEUzCyDjyiXy/a1r30tmic/nlKpFPo2Pz8fJO7l5eWgKbwofMJFYGWPY/AhIjhWPZ8y+dH7zzsWpEDzxPrxXWp8fTt5Lsp+TByP/6xCMCxxOx6XmEqS7MuTTDvaI3nPea9JrgXHTHMBTUteu+6T0Qr0rsKE6eqX1xQS52UWg/H9WM7PzzO00OOnmHdUpVKpBBpRLpcjZwWzOJ+mWQySZ8oaamKIlzGbrb3oBsNN7O3tReN44YUXwmdhg4rFYgQs1nkWDkbj0f7wYUvkyak2V1ZWAl0xm66l14QeHh6G+Tw+Ps7QArNsUF3R3Gq1GvrkMXHEAtLiofdIkxcXF8Pe73Q6QUtDczidpNj/FCC+Wq2GOra2tgLdevfddyOsmPDHrVYrhCsijWEfqOnVXUFnLO9g0Gq1guafoU9UX7PZjOio6vHBnzVPlUrFXn/9dTPLxgBTO0+DySXNrVar9tZbb9lF5RfyCvw3VaRWTZm2GO+oUqlkGBeqDj3zwZgYVEPywiZOgGp5gstZ8rAVqUzzJKbevKn/tZGIKfr8888zTI0H8TImEPEZItQar4hYq9WKLkYdOK/CTmGZNOfD4TDUIQwczQ0ePL65uRmlVvBAZ7N4rcmcCTOkeRCho6loMplEmebNzD755JOozuXl5dAHEdxmsxmIxs7OTibyr9okkfjpT39qZlOiz9hnnDcCTKXif+WVVzKhI1TIHKQYEm8O9EyKx/ulvAZ9e2SUUoB5/k+zudlsj9Ozx5ukUqaBlGnP/8ZxqFwUTZ999sDi4XCYYbJSoP2nwYGp0FRmZk+MY0MzGF21NXcMa2IWhwKp1+vBtC0Gy4/ZM01mMYZwPB7n9leffWLmlLlyMJhFy2eOPV2ud+/ejcK1sM6lpaVoDtRHnVnNid6X6YkBNweDQYQD++STT8xsevF//PHHYa5/8pOfmNnUm5hjPTo6CswgmWdewuqLmJxCoRB5hpGhIUMgzOT6+rr9yZ/8SXieHppkMFKOOTQPVqvVKI2Yf2ZlZSVixA4ODkL/5+bm7P79+9HdlYpkr7XU+h0eHoY6tra2glmOTl137tyJPOo1748ePQrMrfc6TkERqBThHenDGqQSRuuZR48eRaZnrgex1aRtb/w/7b1rbKTpded33qrivXjtIpvN6R71XHUbjSRfxjIcwGs7iJ14EQXwxt7AQRbBAvslHzZAgmCTL0ECLIx8yQZBAhtBssgmzsWLTTYW8sGIvJKQCLaUaEfSaDTujHp6unu6OWw2L0WyWCySxXrzofh/6vec93nZMjKaZq/eAxBVrHrrfZ/rec7lf8753OesVqvZZz/72fC79fX1gOV6//33C0JWv9+P9kGz2QxrX2OkcUjRpRGsPDBcxIHe3NwsRKfxN555cXL5GQ+OFLOikEcgo8fElEWZeSAcs5/rfzEOaQu12qiC+quvvhod3u+++27hUKIWxYXE8WFxVkZ1LSwsFBiL+qM+7e/vB9+02jg9PR18/GIS+r3m4+zsLAhBTI9BBq3v9MwU8N9H8uigpIUrNf4nJycR83nppZfCdWKOrOdYq9UizZqHkfp9dnYW+nHjxo0wxiznsrm5GVk5pZGurKwEi2aZAOMttPqsrJ8poaIM/Mz/PR6KQhavI2Dd4+Z4nU8/0m63C0ERzC2VsmjpObwnFaky7Bf774UcP84sM+P7p3b5yE4P8mU0KGu9zczMRJownyni/tR61toS856dnQ3Pnp6eDgEqLBbso5y99ZYYOgq57BeVIw8E9uOitlDI8LXnJiYmCsKB7iMh4fDwMOJLXkiREEmANa1YmgMqm3fu3AlztrW1FfZis9mM9u3Vq1fD/k7lfFJbeZByPegafmc25CHCWn7pS1+yb3/72+F6gbs5/lNTU5EAfHx8bHmeR9dsbGyEVALkldrXrJihsfaWqKOjo0JyUo6dt37v7++HvnKuKSy/+uqrwUJDHOPXvva1MC4CoHvlmmcK13cZD+n1enbt2rUA1tc9JXS/8847kcCj9/TIMN9lKt2R2XDNqY+vvPJKEKz0nE6nE2FpiQPzBbdTdGkEq/Hx8bAQWCaDkS5mowXCopIe8HuR9Dw3Nxcmg0zALLYg0GTvIxk82NFr5T4yguZQ9cEfqkdHR8HMPT4+HmkatOSkwtkZMsxxGR8fD0zm8PAwAr4r6Z2/Dzc6F7ZZbMlTe2QFImiXFdXlVpuamooYNtM2qM17e3tRgjwS3RO6DwUi5hoiU2BAgg4p5q45ODgo1B9TcVdGgVHgVN9prWs0Rtnsr127VnCZpHI2pTRbEQVhv445RxTeeZ03Y+ueIq5T/2xaE3lQyzVH4cNrpmXCo+6REqx6vV7EaJkBW2PNOWLdOT6TCSc9s06V4eBh78dW65C5cXQNBXfuzZmZmYhXaU9wDLwbjFFj/lCiYtJoNCKeQHeKiMlJvbuYkY3MUp6yLHhLodpDq7rWPkP6NTZeQc7zPCq3orH1hx8DBxhZLYsvldONjY2oViz7Ld6iaDG1VX3d3t4Owg8T3NIDwHms1+vBhSlFtdvt2ltvvWVmQ6tdqiwYgenz8/NR+S+zmCfrekbccW9rvOhJYNJlzjWjub0CnIpGFJ83G2bI95/dvHkz7AOuwc3NTfuDP/gD+6Vf+qUQ3CMij/DnsxesUoE6dI03Go2o5iyJwRGagytXrkTKmYhtZ2qLqamp4G1QVKSiEUU+GOZJdCkEKzOLIsPYcDIuutQYRlqG7k+Z+ulKkmYo8prwkz5LYWRoeaNpkn2ihp5agDs7O5H7SAkHmfgx5UbhM3UoMjuwGJMwEX5Rez+y8pVQoPSHmLBK6h+FSN6/3++HTWsW4wb0ni453V8WMvrnJXgLx1SrjTJCX79+PZp34j90PdeMtzgwaZ6ec3JyErlY5S4l871y5Ur4/Pr160msnQ9/v8jyxvn1VhTeg5ieMuuSF27886lJpn6vMdD/F2VS16FGt3PKKuL7o/WztbUVpa9QexmdNj4+HuUw0n2o5XuBwePbuN+8IMbf/+AHPyjcj5YiHqh6Fl/1Xvf3ZVzU7729vSiDuyfmA1JfNSf+8NRzKOB4BUqvvKcsq96lnMLlEZqgdW8WKypUZKg8aZ40p8R+msUZ2CcnJwv1G3WNfsfINs3rwsKC9fv9yA3L52jepPiNjY2F5zBqm0Sr5+TkZPjtBx98EEXrqV93796N9o2PkBwbG4vWJdfv1tZWwfPi4QFcw7TWMMJVxgoJJioTQ0gJU+aIaAW+cuVKELTo/tJ8ffDBB0FRlaeFSoK3jPf7o3JXfqxTaVIGg0EYXyp7ZhZFXUph8FY9fc/xZa1E8c7BYBDGazAYRIKx2ejMemYEK7rk9vb2CmAyWmG0aelD9RvZ5+7wgg0PFI/d0H1+HEwGBSzPyKmF++fzPsQIvf/++2Y2DBHlItCm4EYqOzz1+t5779mVK1eihSXtmBY3D7CkCd6H714kxKYsf3SLUlA4OjoKzHh7ezssYApi3i2hV+K2OEd6z2ADs6FFTf/T1SDm58HzamO9Xg/PbLVawUSvudU1ImaN9iVONLYkf6Dp3kw5Qi2cr7RkpYQzvxZTmENiOHyJIIKKPeiaodIpXBeFarOhZUHj4V1PXBuam9u3b4d1qvGdnZ2NFI3BYBCsHhT0WEKHz6EikxoXuhxTqVr0OdeGiIISLaWswclneZgALaG+DbRQeLfo5ORkOCRZJJlEAfAily5pMBhECg7X+/j4qK6e+rq4uBgdQKxkoTnb3t4ObSPGSuPY6XRsbW0tuFgePnwYxm5+fj5YibiHOp1OOBempqYiJUtjuL+/H+0jrivxELWB1nimUiAxCGlmZiZ4A/r9fkHA1nhRaWS6DrPY7W1WxCxev37dDg8Pw729Ml2r1aI1plfWMNWzqDzSk0HrL4Uy796VK+zBgwcFXnb37t1gvZPwSyC9txDzzF5YWEgm1ObZwXQlajfTa+h6jcXx8XGBd3r3Ls8MwmK0ln0euG63GwHin0SXQrDK8zwCQ3OR5nkeWVqYH8gsNv16Fx0jzHgI6zrm6aCZkD71lKYn4sGQ2oiU0L1g5/vB67mQzCzKP8XrJXCxxp4HyfuDz6wIhE9Rr9ez9fX1ggWuzHqSYtzsg+8/gaJibiofY2YFgCsXtZ+HycnJJA7EbGgJFeYrldfHH8K6tt/vh3FfXFyM8BkvvPBCIUnc6elpwXVnli4WrHHyQirxZiRvNeKhnNKsvfBCgYnPLHMFkog1NBsyTN2Dh5vao7Wtdj18+DAA/6empiIBScx6ZWUlEvo197rH2NhYFGTCcWf/UjhKs7iobgrX5oUsPsMX1zVLl5XSb6gQePAxweUpDIh/po+ULmPo6v/h4WG4X57nER9ljT/+LuWe9pZVCuYab1p/uK9omZEVw/PQVAHnXq8XuQLVdkEW9CzRtWvXwvgeHh4WlJ2ZmRmr1WrBtaP2q43ezUbcnLcO0kVKQYyldg4PD6MC52ZxdDd5J/Nq0RK7u7tbiBrlvBNgzz6ajSzzCwsLATZxeHhYyLYv0vnWbrfD3nv55ZcLgpHZUFARtIG1+ciXFDwgLweVS7nrKWARlpI6AynEc39q7HyQ1uTkZPjMQ1zUVi/AMWmt7sX96zGNKV5bRpdCsDo7Owubs9frFTQtLf579+7ZvXv37IUXXgjf+5BwMysw2vHx8ShVv7Jik7lOTEyEg5R4Bi7slIanzz2+i4vBu9yYFI3RFsJY+ZBSmVdXVlbC4rhz505wrSkh3tjYWOiDfMZcwB7g3u12I8tYymVEq17qAPaCAN0qFIK8m0Zuze3t7aSlR/fwmLpms1mYDwL5fXtUqHQwGJWZ2NjYSFqdzEaHWZZloV0cu5RArzZQCEy5dcowVt4lx89SvydD82Hg/j48sL2Fywvwnvnyt2LcOzs7gekuLS0lXZ604H76058O+3V/fz8CIKtKfa1WC2uceAqt65mZmYJllf1IYYNIsl7wGroQdU+zeP+WCTbeisNrRNPT01FCT33vXYIUMjTGmpNOpxOlLkm5VP16pxCXEgC9MOUtnt5164VYfabPHz16FB1YKysrQThgHUbizTTGLM9CHBzxPTMzM1FkJK+nBcgnoZSVZXFxMTyP8zE/P29bW1vh+lu3bgUhbHV1tfAssyFP0HqnRdKnNKCFX9+NjY2FMda4TE9PR2cdBVbxDwqTXggZGxsLlkA9Z25uLvSzXq+HsfPrhAWbqdi1221bWFiIcM6CopgNrWgeIzcYjGor/tmf/ZmZjZQmngUp179fb7qG11O5YUS42Uh495HxOi90pqrmIPvk28KgNXpnRHNzc3Z6evrsuAJZqqBer0cmbVqDWK9Ih9pnP/vZwBQ14VoUilS4efNmdAiL0fvDiNq0FmSz2QyL1rsHeSB6dw2ZaL/fjzQALTpmW+50OsEM6RcbI6y0sff398NiZvSHfqffcGP6dtPKYlZk3ro2dYiJvNBFTV195zP1nhsuFVXlsTrMAXMRPo2bg8+t1WrBTz82NhZFDjFzNg8SuSuZPoHFevmcs7OzwAzLyuJ4K8lF1iZqeHylJleW+oPE/nhhQpTCbum3Xhjn96k0Ir4frOfYbreT5Vzm5ubCNSsrKwUBr9lsRgIcI+RoaSKezwsHKQwVD4aUgOpdPBQOtJ8ZVFGv1yOsoe4pF1e9Xg+Hnp4tXM/JyUngOVp3fm+y8QLtxQAAIABJREFUbXTf8GAXj6SA6Pt1EflAG1oX9Jz9/f1wSFHz1xjp2Rqv+fn5yOpC/iPqdrsRbk7jenR0ZKurq/b2229Hc/rcc89FB6Lml/xxcXHRNjc3w7kgRVTtff7558Nzut1uOHcODg6ivckoMEY6qh9UwNQe/Z/iBeI9BwcHUZ9YKokBHMT28HrxogcPHoR+z8zMRAJkKufeYDAIa4x7WPfrdrsFHKEE9qtXr4bzi5hf9VlGC0URKoeUj3Im3CCVVsVjdUX6TIYDRozyDGIkpZkl63kyqlTt0mfHx8cRz9Ec53leUJhSdCkEK7qGFCEoYlgxmalMqOPjo1pESiLIAsO6npYbutZE7XY7KlRJQWxzc9NarVbSn637e9yP2s7rRMyXpaSgnU4nShZISknItBbQ6qVntVqtCCRIa4HupzQXoh+H+aYwXiT/eeo6Hmqe+egZ1DzNRgzzIiyRXlOYKQqIDMfl2uM9iW85OzsrFL5meLxZbIUkMJPmaR/GnhqXi/7XPcsEqotcev5+qWtTFjW/hinY+aS0tNB6XIXeM+1E2Zzq81/4hV9Itm1hYaFgpVbb1A6vBVPoMosxh2WRnjs7O8HKOT09HQnOajMPoI2NjciFQ4FK1zJ6lmMmq4BXXtSejY2NSDlLrR8meNT9y4o8l1FKefIRVrVaLRw0TAhqNjrAeDC12+1IOPF9F4lvU2mh24zRh+12O7Rnbm4uGea/ublpu7u7QSjwVpTDw8NI0JXy5EHLuqbRGJV42t3dLfAor0QyQlL36HQ6UUJXjgFxXuIbh4eHkWBFHiJrDdMQLS4uRkB6rR9aFVlXcHJyVPJLa52Kju4hnnbjxo0Al9A9ic3VHMn9LwHoxo0bEX9I8TDvfUoFh3mlmtZwCay0XGmP7+3tRdbhbrcb1pb6xnQxjUYjWtdUZtWni+hSCFZmcdgkNyEtP5RmtYGZlZzul/n5+ciNRKHIR4mYFTU/1swzGzI2FlXl7/1CFNG8TmGJIFsxQp+QzmvY+lzPYc4RLVIyGLM4gk7ZjGliPTs7K2joKbeUKIX/UT+JwyCjSUVQ7u/vR5a11CHp+09gr7fk+Pv7tvm2E9A6Pj4eab+ad1/bzD9ze3s70mgo6BLTpFxgu7u7UYK6FHan7D37SWwXhcUnuW/8ffi71Pjrf1nWUvfZ3NwsuLgvaovIW36Z181slFFbz7l169YTBXkK4z56iphJHVIPHjwI2nWZS3VycjKE41NLlQXEX88UFmWkdSX+JV6nsWOUFl2enieQJ3JvpJQ+/d4shiWk1pp3p05OThbcvY1GI7gtj4+PI4u53u/t7QVlxLtiU8+anZ2NIpj9GvniF79YCDTSQfr48eNC4tXp6ekoezrHQGPKeqgpK7uu1VyxZuvx8XGhjR4D+fjx4zD+XH9q98TERDQGy8vL4V6yKHmLMAUSBqLobLp7924UUa1n+VqbXD8a95QypM9pnWq1WpHw9uabbxb4o/iejB0zMzMRoN2Pmd57a6nIz53nNUtLSwUe/vDhwzBGHmJE0r2pQC8sLEQpc/TbRqORTBDt6VIIVnT9nZ2dRb53argEN2oQWZiSk80J2traCszqxRdfDAKYxyyQMXsMw+bmZli8tBCZDTez7skFy4kWqM/Motwj7777rpkNBUQKXCx4mmKik5OT4ZkpUKbaKR9zKuEcy26IvIDi3Wqeces9D/sUAJHWDVku9FsvHKQ2Hl0+cvukBAgKG6l2auy8hsWizCICflPuR2/d8Jr41tZWMEWnclWpfwRMp1xrqcjVFGl+vABVhq/RM8vcRU+y/BHbxmuYFNRb9fgqohuPa4lWpjIrS0rIPDk5iQJTmEyY7iO1g5ge8pButxvxIoWaE3NDLbhWq0VKYQqkrfHw1qyUBk+IgXfp0uJKK7I/4FKueC8Q0LU5GAyivcpcf7yHxtTnYiIYm5UORIRb8Dl5noe1T1A220FeNDc3F9qlHErdbjdKkeILa4t0b0bEtVqtMCfEF/V6o8LznU4nKltF65VZnJdO/3uhRb9Vn7zS5DFEeZ5HgPlUckreY3d3NwgzTLxKo8Mrr7wShEVG+WlcPJyC36nPs7Oz9nM/93NmNhTmGLmuvpiZff/73zczs8997nNRlQFvBTWL1wMNAPRw8Hu2h9g99WdxcTHcg7yadHp6GlUFoMJPyxQjgoX9vIgu9h1UVFFFFVVUUUUVVfRj06WwWJmNtDuP+2k0RsVtGblDrVFapo86oJWBdbgYOi/yLjFqMmZDyVgStcyiauf+/r7t7+9H2AuaZ8vSAczNzUWhqykAPNvmx4VATbOhJkCTbbPZDO3nvWgN8olLU/m7qNGkLFZlFgVaQmjZIK6KWjafnTJH85WgxxSGRkRrUMp/L4sEc8X4vmZZVpgLRmCaDbVJzSXz5uj90tJStAZ0/3feeSd8TovV+Ph4AdPniW3k+Pjrfdt5z5Q70d/TJ5rk97SWMMEj95t3/XjXIzNnN5tNu3//vrVarQjD4bFUF4HwiTkxG+1XumZmZ2fD/rlx40aEr+T91NZ2ux0Fl6RoMBhE1ne5xVNWhrJag3w+IQbe5Zhyt/ucSGwrrSh+PRBu4YMyiM3h82WN2tjYiPaB+n98fBySPhOvKJ64tLQUWQdPT0+DhYDpOrgf2T+uQya7pMXKW/s0D4wM1Z7lGqfVbXl5OVh3Op1OlAvLA+Z9VODKykoBGO6ts5xvuZj5fHohpqenI7eriLyCFlat+/Hx8chSyDyCzCPFPGx0HZ6enhaiFXlevf766/a1r32t0C6z0Xx/4xvfCMmkl5aWktCN6enpZN42rdmLsIFcG8QiiiYnJyMsrayMPo0C8/UxJxvH5qJEvqJLIVj5KEAO4NnZWRC6tMDJ8Or1eviclc89QI5gaYXBlh1AeobuI/KRap4ZU1AhzuPVV1+NGJTcfycno4rnjUYjLP5U6LeIvmafioCge/1OAEf9jpFhXrDyY6V28Zn8LYmHcypii+TbWXZPfsc+sW1m6cMi1S4KlHqWT+znhVrd36wYHcQNzJqSeiUY9eDgIOR64TWvvPJKJBAoBcHu7m44sMhM6QYiSJuvF41Z6v8yehJeyH/PCEK1odPpFHA+dDl4ovucpTyehLGiu2BlZSXa+3fv3g33ZhoPzaf2IA80syG/kTC1vb2ddCmVtetJwlTZnAwGo4zPTLfAPdTr9cKhSXchr/NQBo9RMYshBOQ13n3b7/cjJeHs7CzqSyr58tnZWeTa0UEmsPHq6mrknqnX6xe6flJr2iuc/X6/EOiUIvIB8fQsyyI3X4p48GvcmQdKcy5ev7q6Wih2v729HaXyYTsloC0vL0fRbuRtqSoR5Ms++ljPpqBkFgP0GVxhZlFiabMhVkl7RALZ7Oxs4E+vvvpqwNy9/fbb4XdUeN955x376le/amZmv/mbv1kINtB4UBHwqUYIAfJzX6vF0almcdSiT9ki4ejevXtBSWCfjo6OksWeFxcXL1xboS9PvOJjoCzLSsHHeZ7bycmJZVkWWR+obXq8jFnMIFh3qAwcZzZiNMQGiQio1L18Fvj9/f0o54pCTZmkbnNzMyzCdrsdakO98MILhU2oDUrNjIkFm82mra+vRyHWPOwZaSjGtry8XNBmRQwVTyWo4zVPAkVzXIiH0FiSqXugrb8P/0/hScosaZ4owPGQ4O9SKQNITEDogx/8cwg4brfbQaA2G23slZWVyCoiLenu3bt269Yte/755yPQu7ckloGOU9eXCV++nylAfJm1S+TxbikLmvaG3+sprFytVisEifAab/Xinm232xHGSlaMTqcTKT4irYGU8EQBnBjPFDESUMT/WdtS5Ne5z4VFa6aIBwytOxJkiHHzghafx4hB7sfUHqDlhIlOZ2ZmCgBksxgvxPsREMzPqcDxO2LEvOWUFjc9U+Rr/ZEkVJydnUVzKSGL1onT09OguK+srESpEsyGvI3joraZDc8jb0knZmthYSGyYjP4ReNLYZ81S81iRYVRgYw6NIvPJbPh+vC19xg80G63ozEbHx8PZ4zuvby8HM6WVqtlb7zxRuiziIFlvV7PvvWtb5nZ0AAifJYPyOC8MyhLHiFey7FWO83i0kiaO3+m6zlTU1Nh3J977jn75Cc/GcbMbFRQm9GpPigiRZdGsBIDyvM8GixKtLqmVqtFOYZkUqYGdHBwEKpTv/jii0ESLUvo56OKpE2kDs8yYKgXfGR9IJMng3r8+HFY8NRSUtql/vf1inhY8DBSG7RoFErLSCMf7ZVyD9GtI+GSmr8fS2rQul/KncA+sb2+LSK/kcoOby84sy0XAXlTESO6jkDjlLDOdqVccfot+ycmdv/+/cC0vvrVrwZ3gK69fv16YQ2mBJ6yNBhlVpWLhCd/YNEClcqLps/pviHw3lsofdkMnx5BxPx0fC7TWlAZ0DUbGxtR/TqRX7cp10KqNEkZ1Wq1pGXKLE6OKQsILZj+QEkx/W63W6gXp+81XnQxM32EV8oooDExq1mxlEpqjOi+7/V6Qcs/OjpKWjYZHOCjvcyK+0f8SUTlTsRDm3VeU94FCga8z/z8fBC0T09PC9GjZrEAfHR0FOaYkWXMPyiBTOeQ1hDz30kgGwwGkdDHcVGbe71eWDOLi4sFuIJ+rzk+Pj4OXp+bN2+GZ4qXrKyslAYh0Rsky+7q6moUcKC6i+w3BeeTk5NQ8utnf/Znw++63W5URkzz9/Wvfz14ZyhoU3im8qC8dUxZoznnOld0sfpNSE+v10tW3jg9PQ1tl6vSzApzKt7i00CU0aUQrMxGEiGjOcwsCtllbhNJonfu3LE7d+7Y6upqtFGXlpaidAhlwoqIpkQynBSlInVS/yt6gKZllgR55ZVXkhFLngmlDm2a+Jm7hgeQFvLx8XFYVPV6PVzvk3Nyo9C8LqL/mc+htsmDv0yzZz9S0YSci5SmfdG9yyxOPHi1llqtVrAm6juavP0he3Z2Fq2p1IGSsqaYDRlrykry3nvvRYekGJ0Eg1arVZqaIfV8CsVmRVO77pFyB3urFK8hPqos63hq7FOZ3b3gXbYeRB988EHQHPnd2tpaFIHJ9c6IHtXgLLOs8rOykGwmhi0Tps7OzqIySOoP90Mq0pN4IO4DWna534gT3dnZCb/VIdXv94PCpkOGFgsJFqm58W6+VNu5/7VO9RvtW+YfzPM8/FaHfqvVitbmxMREwcrmlT6fX8q3ncJ0yqJlFs/72dlZVKdUQgMP4IWFhcC/WRg51S4fhchs8Azp19iNjY1FY6C5rtfr4UCv1+thLUtw9iWfNjY2oghyjbswbvRcqM0+U/329nY4U/0a6Pf7Ya1IIGL0PnOKUThpt9shnxXH7fbt2/Z7v/d79uqrr9pv/dZvhc9ffvnlSIHjWpLFyc+pF6r39/eDR+jw8DDIFd5qqTFaXV0NZ0Cz2YzK53F86FpnH8voUghWZFS1Wi0ZpjwYDCKrFrUIs6EfWJnWzYrFeFMaOt9764ZZ2iXIe/n7DAajekS3bt0Kk0kGRizVzZs3I7zW22+/bc8//3wpRolCC9uakvLN4hqDWgyNRiM6GL05XvdJ5fUpcxcQ91M2TmUCTwqXw99yLLwLi3Oawlh5hurDipnJXq+aq52dnbDJxEBOTk7CgdlsNpM4LGpJZqMD0ZuPKehqnbz44ouR1UXtKnPzcdNTMHmS8KXx0FikME+8D60DDHv3+4sHou6fSgvg9w9zK/EaCQDEmZiNFJadnZ3IiqIxnZmZsX6/H36fsphSmCtTtsys4P7jtXNzcxEAe2VlJVhedEiNjY1F2EwmjBWV4a2khfN+IuI6NWZyL7darfBMvUqw6na79vnPfz5KiHx8fBy5uDmveq63bHr3l9a39o8vqaODXJ9R0DOLM4BzrLmvKSynEp7Ozs4GaxNLj/B8odA0Pz8fhCZds729He0DppPwGcnVVvXJW5To/hNOcnx8PLKY8X7iM+QJVGq8WziVob/dbge+r7FYXFwM1iiNgcfeLSwshDbW6/Wkp6bdbkfCDq9Jlc751Kc+FfrxzW9+s2DpuXXrlv3RH/1R+P93fud3QsJs8h8pGD54gc/z54U+03z3+/1I8JbQ+fLLL0d4L/WDXibSzMxMVG2hjC6FYCUclVlxUlMuhH6/X6jkbhZHRPikg2JEZbgYs7h+kacU85WQontvbW1FC5jCjIhVzokVE8Pd3NwMTMhnc6cLgNYXtXtubi4y4ep3zz//fBInxYzTZnF+LrW53W6HxUqTuscUecGVBxfdcPwNXzlOZRpmrVaLxq4MsJ5qV6PRCOtHDKff7xdcLQRAayxZZoLWvhQ2iMIqteBGYxjFqfGmFZNuEDFalkzxQmlqLFNuUU8pHJnPjcZ7eiuJ7lGmpKRwZqm2EFNmZpGgr77R1fDCCy8k701sEHOTaS+lwPEaX4KlywSsslp7OriuXbsWKU3cT3q9du1atI4YnSeiMO5d6ZOTkwWhfDAYJa0kAJuYTx0QmlPhPUU7OzshL5dPeMo5017xkZi0wLA/TASaKq2SwjSZxfkKU/vfz00KJ+r3ZMoCxXV6cnIS4a30va9hyLNGFhAqI95apzGQsNZut8NnjC737WcWeJ5T4lviE/pfr3T17e3t2XvvvWdXr14NY3H16tUIN7a9vR36KAGD58bR0VEkWIgXEbzPvHwE/hMk32q1Qv7G/f19e/PNN80sVpYVXGU2hEL8+q//upkNrdG6LpXQli5qjuP4+HiwQG1tbQW3t1zXZsM5lBGm2WyG9b+zsxOELx8lrue12+1np1ZgrVYrAP9EDFPVNSkfZ71ejxYPF6wW8urqasRMfXJDbynyB5jaxo2xs7Nj77zzjpkNF49+S1Ay+8RyHI1GnJFd99b9tdi1sBkyS+sRmVYqAo9CCGv56VDw2gbdZsSlENxfZoFKWUKo4fI+F1GKoXpcFYWm1FjzGlot6H4VeUsCo7FY8Z0HYAqfwecTiyKGk4qk07qenJwMIFAyDn/QpAT/HycK6i9DHofmD73U81L4t9QhSZyaWVx+RYdHlmUhwzHrpZmN3LVcYxSyZDGgdq3/uR5S+5sWBL7Xta+99lqU9JHC58LCgi0sLERjx8Oo0+mUjttFgqjX1k9PTyO3uQQ99ffBgweBJwmPJhIP8QXPmeCRbWAqA/I/8SYJA969SAD97OysnZ6e2sTERPje86oUP/EW4TK3NtubUqZFU1NTEZ9nrVStLy+cEf+WshxPT08HAcorhlp3FMwePXoUxoxzQAvfwsJCVEybVlmz0bpkyhomlzYb8nR97xPd0lLG1BCiw8PDSECi61H3Pzg4SCY6pbeJBdufe+654F6TMOXPhXfeeSeM3a/92q+Z2dCKTwyxPxc8ZKVWG6VWKkslcePGjcigoXZ98MEHBSWLipjomRGs2Ojj42M7Pj4OE8RQUGJevNuOG0bkN0JZFJyupSZjFoM69/f3gxRPEOX7778fHbB0HaRKfExPT0cb/iJgt9fo2a5UHzy+xmzkYuPC06LyuUl0f0auNBqNQqiuB/XTEsPnEAPAbMYM501ZImgGLnPn6Rn8LuVSYZ+0pjT+9+/fL8y5tFsGEjDsl66tMoFS5MPiSXrOxMRE0LBeeuml8N5H7fh+kbzLxJPamvqu7HNvir8IA2VWBKD7/cnvfI4i3ZMZjT/xiU9E36cOz0ePHkXr1Nfh81rnjRs3gkZ6cHBgY2NjBdeOx2JoncsNxdD1fr8fKTu6dmlpKXIDXVQIXZ/xUKawqOek9onZUGhh1nSzId/40Y9+FJ5PEr8hRnBtbS0S/riWuSeJL1SfZNHSd7KAzM7OhjWu+RgbGwsCDPOb+T7xmUy5QUpZt72Q5QVrkeac7ZFwRJyN2uLzFnFMibHSntVYMIeVovsI9aBix3uOjY1F2dyJVTMrYrna7XYYa5VgWlpaCpaYmZmZAp5N/SXQnu1l22Qd7fV6kTvW99fjLOfm5sLYzM/PF0pVMWrQbMhzVbxZc/C7v/u7ATvIABmtY40lUxUxul79YyomRs5z7HypH7MY4822P4kuhWA1OTkZNqQ0fB/W7zvkD7JGo5hAzAOfidXwAGwShQYtnrfeeisAaLl5Z2ZmogSdDKUVMFklF8yGQgzBwqys7e+tzxiBYjZcsJ6ZpsZI5LVD1pgyK5o7aSWhRUz9ZEJHs3Q+LNa0u337dqS1KZcNn00qi9wqi0Qs6zdxWwyL1uFKTJrG3VeqbzabUWQLN20qT1qj0QjzxMK5YoL+sGk2m/a5z30utGFraysShH8cIsjZj0cZwD7FKPy4p8DuZS5HMtWUkEuLkjRE/U8zvUz0KysrkQuAArAOus3NzSiHEKOqzEbasw6mBw8ehHVIgcq7NEUrKyshYkiWNJb7aDabyZxHVKr8XKYEAj6XwgHdyJ50MK6trdlbb71lX/rSl8J63NzcDGPAVCz8f3NzMxKUtE7plqb1gf2m1VtKmgdMK3qY96S7djCIS+eMj4/qOTJ9BHMM0V2VWps8AwaDQZS0V884OjqKrP26XhYdXxiZbabATg+L+q7+aiwldE5OTkZpGnxBd5FPF2Q25BvkP2ajeaEVRs+WMLW2tha+//DDD6NIR7qmiQHlnJKfs26hPic+jcIp14y3tEq4kRD0zW9+M4LOsI+qNfjHf/zH9uUvfzl8R4un+sD/2catra2C4M/2msXuewqx4iWaqzKXdBldCsFqfHzcfvmXf9nMRvW4ZOHQ69HRUdQpdVwTOTs7W4hy8Qh/Woe8GZJErIbyCp2cjIpVcpIeP34cFvNgMAql5eR5jUkujrW1tbDxb926ZWZpl6QXLmme9q4hrwn797VaLYyDXn0IuL+/WRFj4Q9ObSBmy9b18/PzyXwtZNbetZZyJfn0EP7a1GHO3GP+nilLgr5LaWE7OzuFelRmQ6bsQ7TNhoxVODutNTEV4hZ85Bfdw96ilBKaLpoXkjefp+4vYiRTSmnx1oGUO4v5b3hoiMSwBQC/efNmVKi1LBs6LS0EnmqMWeTWbKTFHx4eFnAqvG58fDw6rCQksK3Xr1+P1krKheWxav57L2zRbZYa66mpqWisT09Pw3pjGhkKTQwnNxtGn5qNDuPXXnstKtyr9U4w/qNHj4LFYWpqKuJpWqMSPH2qgYODg9APb3H399CYbGxsWKvVisDatDr59Av6XcrS7b0RZsOxnZqaiiLQ/X5OnQkeNE7cFYVMCfzkdWaxK5CWc1KtVgu8wcM7zOLUBRpj5jLz/d7Y2Iiwiz7Kj/fR2GgcWWBafTSLhbylpaWCxdkrg2dnZxFEQkYErd033ngjWtf3798veD7efPPNMF7vv/9+sHr9/M//vJnFxa3VLhoOtD/4nP39/QLUgePAYAcfvFGrladYIV0KwSrLssBQBXYjkzAbMjbifsyGE62BzfM8Yoq1Wq0QETMYjCKXKNmajaIAxsbGoogt5mIRk3n48GH4HYGR1F5178XFxWjilpaWgmBVq43yTjFc1h+A3l3xJAHDk8cMqK8SGmVR08b21+ve9F2TKTYajULyR1otnnvuuUgYpSUihZUgrstbReiS8mPgNzaBvXyONhuj0VJZ9M2KuCr1k1Fa/X4/rFcfLehdjSKN9dzcnH3ve98zs1iTZCSoF5xTeCce2mXCFV1MKQsUie4/Hl4pN5E+93NG6934+HghB5sOZeGAfEmoVFJfZrqenJwMv+12u2E9e41eVkhqpyywzfHhHtbePj4+DvdYWFiIBFrOKw8Y5pby88WAAT2XwqhIB9/ExERpBCYPDvHKe/fuFSyx6pf46+rqaviu3W6H33piElG/licnJ0OEVFmqAt1f40IXNxMnc20SQsG9l1IUafniuk7hsfQ7BkiIt6bySfn3eubs7GwBR2c2SiUhqxSfq7VIt5zP9K+xm52dDddo3ubm5gqYWBF5FL0K3o0tolBIV7oEqEePHkVnju5zdnYWrn/06FEhkpp5vvgcszi5qQwRV69etc9//vPhmsFgEOGvdM8///M/D58xaEv0i7/4i4X+mw15cVnQBHFzPOPZFt5Pfen1eqXnLOkvj2qtqKKKKqqooooqqihJl8ZiJYmSQGGzkbRMTAutCgL5MTLBbCihyjKTwtns7OxEYcb0ezMhmzSs7e3tKN2DiFYUavYpU7RZsazG6uqqbW1tRaBlaY/SgDx4nNgBWkhS+CxvwfAFrDnGshoyJQRxagxp5T0fPHhQiCykVs36hBw/b40ixkvXeddXSrOnVc2D230AAfvRarXC/Poxl7laKTXMhutNGiyTxPX76Wy8e3t7BVOyx7exxqDcNYPBILivGF2q7/inz55kpfJ7Q//TeliGNSojj/3xVi2Crj1GR33jfWhxaDQahTUoouWabbh27Zo1Go3IGqh2+DamIjr5TN3fLC570ev1opQfqf5vbW1FLvFUwl8/vpynVLoIum3GxsYKfLLX6wV+Rywpc+4QtCsMktmQn3z7298O9xatrq4Gi8nBwUHgOcRM6XvyBrPh/tB1zLuk948fP45cZpqz7e3tKLXHRbg9f43nJ2Yxtk8QB0ZFz83N2f3796Px9YmhvSuz0WgU+IzGUcRCx0x+2mg0gqWK1p3T09NgeeO4ssyOrKaaI10zPz9fOHdYTivP8yjIQvfZ2dkJGKdGo1GKOU5FdGdZFtaDrHFLS0uRm4w5wWq1UdCUeOhgMIjc7a+//nqEV9bvONY6Z5S6QW3/lV/5FTMbriOd68THeS8C06KkzhQRsZIaC59TLkWXQrDyRZhJWlhMQyBhanZ2NhxwPrtss9ksMG6C9Y6OjqKNoAWQ53mIqCGjF3kfq8c+qC8MNfYRPQS46x48BLR41B9hRrQh+/1+knH7Ap2DwbBQMzEc3W43bEy5UVK+cvqU1W4/DiKVdWA4uY8SI6XwJD4KTc/wAktqo+genjEQr0FXQyoiUd9LoNJ8dLvdMB8nAjwuAAAgAElEQVRra2thzHgodDqd8Oz9/f3AcLQB9/f3w/1ZislsiE3xZud+vx+YX7/fj9yuqXBqmvG9i7Qs3YEXbv1claWw4HiVCQdcM2oX86GJ7t+/HyXJ9b+l25f9T4Fv9Zzr16+HuSkT/MxGjJvRUKzoQFIEodnwUGIZDa4hClyM4E3hfrwQy343m03b2tqKANJse7PZLKRY2NnZidxsPpLNbLhmqNTRBaIxpmBF1+zMzEx4T2FNiogPztjc3CxAGpgmol6vJ92PS0tLoY0+yXPK/a8xU59ExGbpoFXkt1duGD3mharUGiuLyk61yV/jlVLR8fFxWNeTk5OB1/sALLMYV2w2HCcf9LW2thaCrR49ehStUwl0zLC+t7cXPpdSp/bovF1fX0+mlSHPJ0Zvfn4+rEmm6qFrkkk719bWgmtQGdvpSudzaWz4xje+EcDuX/jCF8I1L730UnLOnoRJpdIqIoTIp1RJ0aUQrGixKmswDzJtlNnZ2cDkCJY0G5V3WFlZCcBwpkbwTF4HHRnr+Ph4Ie1Dv99PVrk3i7EQmlAmajOLQba6hr7ulZWVAs5Hi0IbaTAYZZQ9OjoKhzCp0+kEALDem8UWGe9bJsDYh8RTIPTWQQm8tDDxsPCWJ1JKIOBragOrPSIKhqkSPXwOrRKMZvTgRgkztHCxKC7vzXvOzc0VNuvExESUq8q3XwcVmYYYxcTERBQqrN8/fvw4vKdFxSydwsK3yc+vP7zZJwk5nCMfVctUFBT0CchncVRR2WFZq9WShxaVkEajEaw0HH+/rpmjTNdpvJrNZiTsEiAtIZvWmsFgEKJa19fXrd/vF7AmxILQqvbjRHpSk+Z+98KlLBAau42NjXC4ZlmWrEnISF3mDPrhD39YWA8cN30uJZaWbfEMZsk3G/EZYo2mp6fDvrp582a0Dmhl8IJMilLYwJQw43Fa3W43tE398yVkyiJsUxYdjhctUD6Vgu4jPpDneXQNsX39fj/wd8+XSOL7nU7HZmZmbHd3N6pRKXrllVeiPklQunbtWjjf8jwP4yQLlOZSrwcHB+E7JiVlWSCenzxjOB/67PT0NBLk2+12lJbCbLjWiSlLeTv6/VH9y62trSiB7+3bt+21114rlCJK5VC7KLWNV+ifCcHKzCKGTaKJWe+1sX3oLBc5JWUxnI2NjdJNw0SkdA2kQp39PbzwdXh4WNgoIuY2Yu09ZizWBGuBmMUpDmq1URmbbrcbWfA4bgLR0l3IvEqpyD/9lhl22QazOBEqiVYGCkX+4ORzRDzYy6xeqWv43hfelfZvNlozzEO0sbER6sj98Ic/DL87OzuzL3zhC2Y2ZF5MIqjEixRCaZmhdVDPbrVa4TM9W4xgYmKikO336tWrUe1CmvHVFgrTg8EgMp2bjRg7hZCU5sfIIT+ntML5z3hv3ccLLVwPZJB+PaQOR2/FEXmgvhg5gxSYdsDMIqFIrqNUjc52ux31jzl4KIi9/vrrZlZUfDR+9+/fD+2amJgolE3ylljm59HzHz9+HMEjeBC0Wq2wnlO54iYmJsL17A8L3Hc6naBMiue12+0o/L3T6URFcpnZXWPn3a08ZDUmGmsqHdeuXYtSOzBCOeWe9rwq5QanApeySmxubhay2Ot7H0UpGhsbs7GxMTs8PIxy3FEpTh2yy8vLYQy63W5oA11LPgBIlHJHM5KNc2g28vhQGO/1evbyyy+HZ4rfqD1mw+AlnV1TU1Ph/bvvvhv1Rf0+PDwMz9ra2ooS+JrFrl4zi3IXqt/MXO4LcatcDLO6M2p6f38/cn2TaDDRWIq3t1qtyIJLyIhPVaPvRU+CWJTRpRCsaLHylMINaeBOT09Lf6dN2+12kwtf99BiksZCjMPk5GRUn1DkJ9XXE/Pk8UDePNpoNMJndCPwfjTVNpvNoE2XpdhXioNOpxMJiBRMtAm8yzQV+ZDCpKT6SaHCC1kpQSs1NxeNozfjMyrIJ2wknozuI1mDvvOd7wSBiu1YWFiI3K4SCB49ehRZFETczMzlonF67rnnwgHlTdk6gB89ehSe2Wq1Qm6rdrtdKP1hFls+qeHqM4/B8WvQC87eKsJ8UcSp6bNerxdp3EwTQle22iH3ulmMT6PZvWxd+DWi59C9qP4xatBrqczTo/Xg03nQxcCyHoqmW1lZKVgOaN1U+1L5rWit5HNYxFb3YC6hs7OzKOJ5aWnJ3n333ShlwsbGRlhLTP5JKwotac1mM7hkPv3pT5vZcG98//vfD9f/6Ec/CvzvjTfeKOBNarVR5LXaKj6q8d3e3o7ylImuXLkSuYQ0HlevXk1GFvs1kFoz4jFeEKfL8fbt2wWeX6/XA3/3uZi4J1i6hml1iJcUMXltrVYLfWVJHK6RMsFKz5+ZmSlEZeqM03psNpuhDWNjY2Fsdnd3oz4RQ+xzvplZ5A1RW9UGztn6+rotLS1F0YdU7lnYWethZWUlclnT0k5MGNOJaGwePHgQ+DsVAFZRGAxGuSH/5E/+xMyGOSi1f82GNQzFg2hBo7GCa5BjXpZawtOlEKzMioBAETXe1dXVyFpCbYxAWbN4Aese9Xo9TJgWiBiDFoS3gvmN7TWas7OzQvbe09PT6P4+X4unzc3NwHwGg0GUC4b9o5ZN87oWBpmvWZxYNAUspg/ebBT+fpEfXffwuZDUllT/vEWMeKcUXcRMvYDI1Azz8/OFXDsUBMyGFiCG6//Mz/yMmcVrqdvthjFhIkWWXvHWHW+VJFFr97lopEEzNLjT6SRr2vHetNzQbecTQZIR6j4MoKC2z3XKpJHqa6fTCUzRKywprFGtVgvXX7lyJTBRCr9P0vw83oFWCVoONP7MNaZ7e6slw9OpYPhEt6wvqvGkpVd4O86bruEh2G637fj4OEo9odQQZkOh6JOf/KSZxcXA5R47PDyM1htzfemaR48eFWAIZkV+RbekV1Q/85nPRMLBzMxMsJozyCJlVdQe8WWKdnZ2QhsePHhQcB+ZDfcthZaUYOF5wkXrxvMtHsKcb1lUFhYWgpCT53k0ZsSQEYuna5hry59HEtzGxsYKNQa9N6TMSkL3uu+zeAQFffHUqampSJBX/wgd6fV6gf/s7u6G9mjOdX8KFWqD+nrnzp2wHl999dVo7MibGQhCV6DPOK+xEdXr9XCukm8wEOOdd94J+4Bt1L3v378fuRPv3LkTrHlSYNfW1pLeMfaFY/FMWKxEKfM/tVOz4YRqQ/CA6/f70aHKpGZaBAsLC+Gg0QDpvj4Pk9rjaTAYFHJ1eKFrfn4+AAB5YJrFvmbm/9DiIZaJxI1yenpaiPrxrh4yEB4GoqOjo4JmL6auMeI9/OFGolUk5Wr15vwyQLW+4zNTwGaz0Tj6QsHeRbi+vm43b94MWuvt27fDNcvLy2FD0oJQq9VCPpXJycnAXDVHtdqPlyRORM3QW0ol6LdardCuhw8fRtZMMhRa0rTeyLh1X9YGU5t1z9/+7d82M7Pf//3fD3Ptc5PR4qix29jYiA5jzjUFfQpWnN9UXirv3k3h7Eh09dVqtahun8hb6SQAsZ4aE0KyfyLmAWo2m+EaH6BhFgc6pNpsFgvIxDzqemn2XO8ElHNuiImUVW1jYyNKYKxneTwolT8dtjw4eWApGaPa7J+fCnBQOzVe3W43RH71er0og73o9PTUXnzxxXB9yrWXgh6IyqxXukdZFBdxteKjMzMz0X3KclrxDFDBcPaJbi7CSy4ir2jSEp6Kmvb5qbrdboAVzMzMhHyLnDvyrXa7HRXZ9jmbdH+NU6/Xs89+9rNmNtord+/eDcrTrVu3CrzK44nNRuPY7XYjayJLrXl3p9nI4vriiy9Gc8S+rq+vF8qB0athNhTE5O781re+ZWbDmoSKzH/hhReCm1Pk3f7PjMXKH6L+QCdugoegB8eJeOjot8vLy8EEKNeEviO4WJumTDuiW5Calw63F154ISp+6jecXolh0T3HxsYKwp/ZcOFrExwdHUXPFXmrEjWWlMDqk6d59wWjhTxYO0WMovRJEPlsvqbcPKlniry1SxGJZjHonL9/8OBBdGiq3x6UnCJih7QuarVatAbM7ImClja2GJ/uxcM+ZYlpNpuRe0/zPjExEcZib28v7AOGYJvFdcs0rn/4h38YriU2zCsAHryqdmoMUlaq/f39yF3L6KyUy8SXOXkSKJTWUllrO51O5ErzblBv5WQE8f3795NrYHV1NYyd7xPv4++rsRFRe2ZNMrpMBoNBOGCYtkTtX1xcjPa28DJMc8JM2OITxFSZxWV/vItQ7aeiQlcr3cHcg0wRoLEyGx289Xo9CC3Ly8vhubTGdzqd8P/U1FSULNm3UXSRoMXrCdj3xOod3NsUoBgNRuuor0M5Pj4e7R/yAwpFXOs8FwgBkSCYZVlSoNZaEe+Q4EoFbm9vr2A8MIv33vz8fBQE4QMQiK0yGwYcaB3Q4koICZ/FOWOSUfVvbGwsGjOtvYODgwhLpf7Ozc1FiiWJllUpxVqDvvg594SEsHa7bW+//baZDcdV57eELf2vwJVU1C3p0ghW2rA+R4z3ddKt5TUzMZSJiYkIQyJN7uDgoFDvT8KQTKJcGNx49MFz04yNjUVZus1isDL7Yha7JbiAKUx43IieI2J5CS5eZoRnNJnHOpkVs/6axRl0de/UIeG1r9SiTeFl/GbwYPcUeayNxpV1xfgZN6qY/L179wJz393dDRt4a2vLarVapM34fmRZFvpLhkvBiuuB60REoUnjrlc95/DwMLJmqk/UStfW1gKToUWJGp7I7xtaAnhw0VRPRr+/v1+oUTY+Pp4MGjCLI0OpELBPEhif5BonpYQtL8gwdUDKGupLOHU6ncB0e71eWPe01hCnRExUt9sNB454h6xG+vzKlSthrFnAVmuj2+1GSkytVrOlpaXIRXt8fBwBpH0uPLM4B48s5GYjJdFb18lDyAvIc7kGuCa4BihYqX969YWwuWemp6cjTI2oXq8H3sWi0qkIMG/BKlMa9Z4CBq8ZDAbhOWtra+FZZ2dn0d7m/tD4cW3o3jMzM4WQflrhL1IgzYbzrbESr+LaHwwGhdQWtGaZDfc1s92nBMqdnZ3gopyfn49KcV2/ft2Ojo5CPz2W8MMPPwxrnPnp9BzPf6VEUjnlGrx69WpkICB0halGGBWf57kdHBzYa6+9Fn730ksvhfVD6IbOv+3t7cgy1m63k9ZBBpDoegL519bWCrCQMro0gpVZ0Wqlz8ziCum65uDgICwyMuvFxcWwAFiIke4v/c7XXeJiTCUE9QNKVwZNv9pUtBSYjZg3D0YyWZ8IUMRDjYtN/dzf34+YKHPNkDFp8ZydnQWrxkV4oZRg5Q+vFOPzIFJS6nNaKFOuAH2nw0Tzvba2FlkTGK2nw7NWqwXM1J07d6Ikd2ZFdwn72Gg0CrW9fIkhUkpAPDk5Cdqg2i2BSf3b3t4Oh+PVq1dDexnpSPcxo2W81YevqdqGjIhhagIKmD5IwL83S5vDPVbErxkqDqn7l+HreE9/oDASk/3zr9zjun5tbS2q28jnqA0LCwtRWgHl1pFgpcOMEXS6XofY1atXo8hNCkpah51OJ7xXNJpZMcyfCTRT0AUmYOTardfrpSBp/l7E/E6p9UB8KQWyvb298L+v4ar7UIhtNpvBRXhwcFBINjw5OVnIL1WGSfL/85AmNRqN0K5WqxU906cR0fMkPFKIkoJEF7hZbO0nLoduV7ZrMBhEqQz0O17vU2KcnJzY3NxcdC4wVQIhMKJmsxklKPXCGqNYveBdr9fDdVS6yhQAnS95nkeWTa1xBmSZjfi1D2ZjAJksfTyHd3Z2IgV9enraer1esORNTk5G+OO9vb3IVW1WDNgiZjP1nGdGsCqzUKUOKnbKh/fqvYSVa9euRYA7bQ76jc1iC4OvcP0kYLIWEzOZU7LlwmbIciqJoAQlhkzrOTw0U5YePoeHjX5HYL5ZsV4arQ5mxVwfqXw8/vD0LlyPoeF3tOSkFnWZa4htYaZdb3nSAXR8fBz53Z8EPKSmT9eAX5scg5RVRc85Pj4O65SYweXl5ejwYIJAfU4XJn9PoTNlkaSiIfKRLWT+FNTKiAJJahzYFrNivUHdwwsKPju+v/eTnul/m8pVx307NzcXpU9IMUm2c2trq+AWpZVMr4zsZQSixoJzQjCt1hp/x8gzv770noExvrCsHxP+jmPD/jPvmNqgtsmKc3p6Gn0mnuf5rzwAdIFSsPPYSblyTk9PgyuHySbVdj2HhbY9do/C0d7eXmSBUqqM/f39sK/b7XbohwRBv+/Ozs6ifaa5oaLsc+h5yypT5hwfH0f7jYmkmXqHe0PvuXZoiWaQzCc+8YlgxeE+WFxcDIIbXdxeMDGLUxCZxfxCJGXXbLj+U/mhmFCzXq8HwU7P1lwSG8lAAa23Xq9n9XrdTk9P7U//9E/Dc65cuRJcdQxWkuDa6XSitbS8vBzA69rPxGYxmrxMaH8Sr7wUgpU0n4sAfgQrM8Igddg/fvw4SKvNZjMMghjRBx98UGDuWkSaPLWL3+k5HGw+VxuYjLDZbAZgplmcrDAFUtczd3Z2IkZIjFXqcGEeEl1Dc7hZnBmegqS3ILB/qUgJv7loYfLCXEqISR2UKdxVrVaLDj1aBqVtsgSQt1zwwCC+pSxbMvtDpiJhjBqjt1qlBF3doyxVx/b2tr300ktmNhQQOQY+67HZUDDm4eEFVYKJU4esT+DJ5JU06fP3FNYuwrnw+hTglm0tW288RDgWdElsbW1Frgc/FmVlOYghW11dTeZS49xsbW2FtBzb29uB+WtOb9y4EZi4xwQy076o0+mE62u1WpTKQc+dnp6O3Hg+hYasnGynnsNcV9yPHA9aF7iWGclHSxJz6tEylVKaaBE4Pj6OgjW8C5JjJvIRaGax9d4ne5Xgo/1B3kMB9uDgIAJU09qn8T05OQnRYRSEzUaH/tjYWITbVb8ZaVqmAFAA1/XdbrcQMOPnzwck+VJQZnGEL5/FnGHkIYPBCMjdbDYjXmA2tLqq7VQizEZGjJ2dnSgVC92oXsDWWGrtMrpSwo4EGq4BJjoV/6R3iutnY2MjzJOew3XnreR8r3V7/fr1YHGmK9DvH1q4LqKLVfeKKqqooooqqqiiin5suhQWq35/mISRkXTUxAg8m56eDpLt2NhYEhtlZpGFgs8RXRRV8qRQyjIXCIGAooWFhWREHfFkvV6vkDXcW9XW1tbC/RnyyfvRsnZ4eBhZ4fRKF4UH2JrFuC7v+kmBSfk/rRuilEuHFhCfF8RjulK/V11Cs9hV5i04tJbQzea1Df8ctkEaLrX3lBnYP7PRaARNq8x9NTc3F8J6mdiS13NNeQvQRdgSb12k25nZ2InF4rpPWXS8a9ZbF33bOc5sty+5k5ov/XZjYyMEnJgNLRYeE8Xr6cogtkcVCPwzT05Owj5jdup79+5FrmWP5VxZWSlY4/g8f++Tk5PSUO1UCaIULpMRaup/yrrBCgl+rfp5olvduytpAeb+5Jqim0tE983BwUGYm3q9br1er1A/lekvuIZlIWLqEA+OV3u3t7dDG+iS8lY4uRkJUp+ZmQlWLb369d9ojJI4b25uFqxjKXetxxf6cle0PLGOo8c2mcWBBDzTlpaWktb+RqMR6i6yXQcHB8FFuLi4WIgMX1tbi7wtejb7Ua/Xw5wQhF+r1aKKEORBek6e5wUYjuaU/aLXSO3xdXj5HB/BSHeixzmnIB0rKyvR/tR7enZYhaPdbttXvvIVK6NLIVidnJyEJF6p5JS93qgMzOHhYeTnZJoCEXPG0N1BXMW9e/fC92ZxlXjv5mGxSk9c1DRl0uR9//79IDQybQQ3gT9c6vV6VPJhbW2tED5+cnJyYQoA/z9dFFNTU+FZHDsvBNJ9lBKsvEsqhTXyzNl/dxGYmcwlVS+SB7Xuz/qRZkPGSYHSY7EuwkeJ+THkvdfrRULv2dlZwURcJpxzfG/cuBGYF4uN0hTPshAEp5JxpAIMPFPn/6m8Yyz6q+90PYUnj19KBZtcFCVK15NZDPplElclddze3o5cJr4Eje5BbJkvIaNr2EbdQy659fX1KKqWe54uaQUY0KWsg1vjlCrgvb29HSKMvCKlw4hrirnqvJDMeaeQwxQk+syvQz9fJycnkXLCMlpcJ0xnoffcj7wvI7NOTk4i7JnZEBdF1yFrNF65ciVy0Wm8/B73aUVarVaYy8ePH0cZ4JnawWwUAEUh1lfDSNX09PyXbrAn1RHUOHI/epyZWSxg8flbW1shoIV4oeeffz6sv8nJybAnGAlLwfTk5CR81+v1wm+lwG1ubhYKfHsMW61WiwRqtavb7RbSTJhZQWHzqWn8OLFMEIPN9BmDJvR7utnNhm5L1lvk/mf5KxpwtCYPDw/DucrAsqmpqfC5T8bt6VIIVmbDBfThhx8WMAAiAvq85aZWq0Wb/OHDh8koFkY7aPFKGyaI2CwWsMqEKt1LxEPVa/JaXOpHu91OaujUBBjZlwLY8nD1ua18xu56vW43b96M0iykDkceIl6T9sxARMubpxR4PaU104IgSuVWMos1cUbE+X4xMIEHb6relL8Ho2T0Owobvtabbxfno9frRZqUaHJyMhy8qWzuOzs7hZBn7o9Uwk1PXsDyEYQ8mFP5e8pSLPg5LAsyENGKxDmlcKCxePfdd8Nh73MxDQbDkhWMJKNVotVqRVYE/o6CBxMcmg33o0+hwTxSvL/IC7b+leDyhYWFsE78ASQhi3MwOTkZRffpe7MYk+Wt8DzgL7Jo8jPdb3NzM1pLsqamsHL+ftwPLK2ilCZmo700Pj4eKa+0mNO6xIATFi/mmGgcyf/JN5aXl4Mg0m637eDgwHZ3d+369eshHH9vb8+++93vmlnRIqbX2dnZQp4/ZqNPKVI+io/krfvaH6urq9E60XuC3Sko+iS9ug8josnz+dtGo1GoQEIMI6MmzeK0HGyv+sEM96enp1E5G9aU1Jh50Lrm6fj4OPA9rYcsy8I6PTg4KCTp5p7Tczi+vrariH1Tv+/duxeVhxKNj48ni1yn6FIIVpI4W61WEnXf7XaDhEhmRZOpBzlL+3z8+HGU/NMsZu4+b5asN09C/fNZ6oPPAm4WC4Rm6ZIkR0dH4bfavF/84hejPvnDSJ+p3Uw9ofaoffo8y7JweKVCmNVOCqzeQkELlK5JCUgU2ryLzrfRm5NFCwsL0Txo41KDprDnGQ9N2hyzT33qU/bgwYPIJOxdjr4Ehb+G7VRbOJ6MeNza2ipkQjeLc7JxnFIAeP/+4OCgkFPlIlegiIBmaq8kD4JP9ZXRibpObSh7tn7P9ZMScmily7IsYqIUmqXNNpvNEOVDRuhTTMgN9PDhwwIwnH0zG1pOUsKaKKVEeAGXlk1aXZiWod/vh37Q6mSWVkD4OZUgEflQyuXt7+kPIK4HzYvPmUYgOWEGIpY7qtVqBX49NzcX8cpOpxOES/E/1p1j7TYfqUarJKsZaL/Rvatwea05Xc9IRAKu+byJiYkoxYuIVp0y/kDlJcUreL2fU91zfn4+rFl+z7PPu/Z1P7aFwvPc3FxI88J7aN5ZV9Isrn6i+VhYWAh94bgQnM80DLVaLdzXu3OZi07zR3epgklarVZksdrY2CjUo+Q6np6ejixMkgdOTka1QTudTlgPEqqojIn8uVBGl0KwyrIsuAhSzJimRx/5ZRaHT+saLTBmahfD5mBJYvfm4r8sUfvnwmM4p1mcykBtnJqaCknOyHw800ylpNBYzM/PJyf78ePHlmWZ7e7u2tTUVGDqc3NzhTw/ZsUNz1Bbs4sXVArDRg2l7Hoe3h6roOevr6+HzeYTYvo2+ZIPqWg55n9h1Xaz4Xq8SGjxfeI4eiuJWRyqzbHe2dmJMh97i4fP7kvNfWZmxnZ2diLtPFXjyo89D29ffobXUDAvwxT6PvrxoMWXTJn3297eDkpQKofbYDAouLTN4ooH9+/fDwzv9ddfD20nNvPBgweB+XrLrtnQIkE3WKvVisbGr3+vYJilMX5lkZYizh+v39zcDBY5tVFCYwpTQyWTa82Hv5dF9Ip4ILFWop5DnsRUHTzo6Naem5uLXE98FXH/aZ3wIJydnS20U3OoMaK7P2XtU392dnbs+vXrtr6+HqpvEKsrYnSm/tczJQjMz8+HdZHC16Us6RSSU8IXMW/CSZkNLTef+tSnbH19PXLdUhCj9VG88uHDh4Us+zoDZCHiuqMCoecQLmA2tPApA3mtVgs8jNZGuu8J5cnzPKxJCdHem3NwcBDWg65pNBqRe5cwGe5NrUOfu4zryXs0dL3GSVbQmZmZQo5JRoleRFmKaX3clGXZYzM7NLOtJ11b0aWhllXz9SxRNV/PHlVz9mxRNV/PFv3/na9P5Hm+nPriUghWZmZZln0nz/Ofe9rtqOjHo2q+ni2q5uvZo2rOni2q5uvZop/kfFV5rCqqqKKKKqqoooo+IqoEq4oqqqiiiiqqqKKPiC6TYPVfPe0GVPSXomq+ni2q5uvZo2rOni2q5uvZop/YfF0ajFVFFVVUUUUVVVTRs06XyWJVUUUVVVRRRRVV9EzTUxessiz7jSzL/t8sy25nWfZ3nnZ7KhpSlmV/P8uyzSzL3sZnS1mWfTXLsh+dvy6ef55lWfafn8/hW1mW/czTa/lPJ2VZdiPLsq9nWfYXWZb9MMuyv33+eTVnl5CyLJvMsuz/zrLs++fz9R+df/5ClmXfPp+vP8qybPz884nz/2+ff3/zabb/p5WyLKtnWfbdLMv+9/P/q/m6xJRl2d0sy36QZdn3siz7zvlnP3Ge+FQFqyzL6mb2X5rZv2hmnzGzfy3Lss88zTZVFOi/NbPfcJ/9HTP7J3mev2Jm/+T8f7Ph/L1y/ve3zOz3P6Y2VjSivpn9O3mef9rMvmRm/9b5Xqrm7HLSsZn9ap7nnzezL5jZb2RZ9iUz+0/M7O+dz9eumf3N8+v/ppambVMAAAPZSURBVJnt5nn+spn9vfPrKvr46W+b2V/g/2q+Lj/9Sp7nX0BqhZ84T3zaFqs3zOx2nud38jw/MbP/2cy+/JTbVJGZ5Xn+f5rZjvv4y2b2D87f/wMz+1fw+X+XD+lbZraQZdm1j6elFZmZ5Xn+YZ7nb56/P7Ah83/Oqjm7lHQ+7kpXPnb+l5vZr5rZPzr/3M+X5vEfmdmvZb6abUU/Ucqy7LqZ/aaZ/dfn/2dWzdezSD9xnvi0BavnzOwD/P/g/LOKLiddzfP8Q7PhQW5mqrlSzeMlonO3wxfN7NtWzdmlpXO30vfMbNPMvmpm75lZO89z1Z3hnIT5Ov9+z8yufLwt/qmn/8zM/j0zU52cK1bN12Wn3Mz+jyzL/mmWZX/r/LOfOE982rUCUxJ8Fab47FE1j5eEsixrmtn/Ymb/dp7n+xcoydWcPWXK8/zMzL6QZdmCmf1jM/t06rLz12q+niJlWfZXzWwzz/N/mmXZX9HHiUur+bpc9Et5nq9nWbZiZl/NsuzWBdd+ZHP2tC1WD8zsBv6/bmbrT6ktFT2ZHsk0ev6qKrfVPF4CyrJszIZC1f+Q5/n/ev5xNWeXnPI8b5vZN2yIjVvIskwKL+ckzNf59/NWdNVX9JOjXzKzfznLsrs2hKz8qg0tWNV8XWLK83z9/HXThsrLG/Yx8MSnLVj9P2b2ynlkxbiZ/XUz+8pTblNF5fQVM/sb5+//hpn9MT7/N86jKr5kZnsytVb08dA5fuO/MbO/yPP8P8VX1ZxdQsqybPncUmVZlk2Z2T9vQ1zc183sr51f5udL8/jXzOxreZWE8GOjPM///TzPr+d5ftOG59TX8jz/Xavm69JSlmUzWZbN6r2Z/Qtm9rZ9DDzxqScIzbLsX7Kh5F83s7+f5/nffaoNqsjMzLIs+5/M7K/YsAL4IzP7D83sfzOzf2hmz5vZfTP7V/M83zk/1P8LG0YRds3s38zz/DtPo90/rZRl2T9nZv+Xmf3ARhiQ/8CGOKtqzi4ZZVn2ug2Bs3UbKrj/MM/z/zjLshdtaBFZMrPvmtm/nuf5cZZlk2b239sQO7djZn89z/M7T6f1P9107gr8d/M8/6vVfF1eOp+bf3z+b8PM/sc8z/9ulmVX7CfME5+6YFVRRRVVVFFFFVX0zwo9bVdgRRVVVFFFFVVU0T8zVAlWFVVUUUUVVVRRRR8RVYJVRRVVVFFFFVVU0UdElWBVUUUVVVRRRRVV9BFRJVhVVFFFFVVUUUUVfURUCVYVVVRRRRVVVFFFHxFVglVFFVVUUUUVVVTRR0SVYFVRRRVVVFFFFVX0EdH/B7i3gB4zU6OVAAAAAElFTkSuQmCC\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# Read a tif in python and Visualize the image\n", - "image_dresden = imread(dresden)\n", - "plt.figure(figsize=(10, 10))\n", - "plt.imshow(image_dresden, cmap=\"gray\", interpolation=\"nearest\")\n", - "plt.axis()\n", - "plt.show()\n", - "\n", - "# Crop the image\n", - "image_dresden_crop = image_dresden[300:700, 600:1400]\n", - "plt.figure(figsize=(10, 10))\n", - "plt.imshow(image_dresden_crop, cmap=\"gray\", interpolation=\"nearest\")\n", - "plt.axis()\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "image_pop = imread(swiss_pop)\n", - "plt.figure(figsize=(12, 12))\n", - "plt.imshow(image_pop, cmap=\"Reds\", interpolation=\"nearest\")\n", - "plt.colorbar()\n", - "plt.axis()\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "64832\n" - ] - } - ], - "source": [ - "# Thresholding: Selection of pixels with regards with their value\n", - "\n", - "global_thresh = threshold_otsu(image_dresden_crop)\n", - "binary_global = image_dresden_crop > global_thresh\n", - "\n", - "block_size = 35\n", - "adaptive_thresh = threshold_local(image_dresden_crop, block_size, offset=10)\n", - "binary_adaptive = image_dresden_crop > adaptive_thresh\n", - "\n", - "fig, axes = plt.subplots(nrows=3, figsize=(7, 8))\n", - "ax = axes.ravel()\n", - "plt.gray()\n", - "\n", - "ax[0].imshow(image_dresden_crop)\n", - "ax[0].set_title(\"Original\")\n", - "\n", - "ax[1].imshow(binary_global)\n", - "ax[1].set_title(\"Global thresholding\")\n", - "\n", - "ax[2].imshow(binary_adaptive)\n", - "ax[2].set_title(\"Adaptive thresholding\")\n", - "\n", - "for a in ax:\n", - " a.axis(\"off\")\n", - "plt.show()\n", - "\n", - "print(np.sum(binary_global))" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.6" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/doc/user-guide/impact.rst b/doc/user-guide/impact.rst index 9046118297..df7e459407 100644 --- a/doc/user-guide/impact.rst +++ b/doc/user-guide/impact.rst @@ -17,6 +17,7 @@ Additionally you can find a guide on how to populate impact data from EM-DAT dat climada_entity_ImpactFuncSet climada_entity_MeasureSet Discount Rates + Risk trajectories Using EM-DAT data Cost Benefit Calculation Probabilistic Yearly Impacts diff --git a/doc/user-guide/index.rst b/doc/user-guide/index.rst index 7f5d73820c..014fc43f50 100644 --- a/doc/user-guide/index.rst +++ b/doc/user-guide/index.rst @@ -3,12 +3,12 @@ User guide ==================== This user guide contains all the detailed tutorials about the different parts of CLIMADA. -If you are a new user, we advise you to have a look at the `10 minutes CLIMADA <0_10min_climada>`_ -which introduces the basics briefly, or the full `Overview <1_main_climada>`_ which goes more in depth. +If you are a new user, we advise you to have a look at the `10 minutes CLIMADA <0_10min_climada.html>`_ +which introduces the basics briefly, or the full `Overview <1_main_climada.html>`_ which goes more in depth. -You can then go on to more specific tutorial about `Hazard `_, -`Exposures `_ or `Impact `_ or advanced usage such as -`Uncertainty Quantification `_ +You can then go on to more specific tutorial about `Hazard `_, +`Exposures `_ or `Impact `_ or advanced usage such as +`Uncertainty Quantification `_ .. toctree:: :maxdepth: 2 @@ -19,10 +19,10 @@ You can then go on to more specific tutorial about `Hazard `_, Hazard Exposures Impact + Adaptation appraisal Local exceedance intensities Uncertainty Quantification climada_engine_Forecast climada_util_calibrate - Google Earth Engine climada_util_api_client How to cite CLIMADA <../misc/citation> diff --git a/pyproject.toml b/pyproject.toml index 49ce786bdf..3663d15f7c 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta" [project] name = "climada" -version = "6.0.2-dev" +version = "6.1.1-dev" description = "Framework for climate risk assessment and adaptation option appraisal" readme = "README.md" requires-python = ">=3.10,<3.13" @@ -65,7 +65,7 @@ doc = [ "ipython", "myst-nb", "readthedocs-sphinx-ext>=2.2", - "sphinx", + "sphinx>=8.1,<9.0", "sphinx-book-theme", "sphinx-markdown-tables", "sphinx-design", diff --git a/requirements/env_climada.yml b/requirements/env_climada.yml index aff3cf5bda..a7bd23305b 100644 --- a/requirements/env_climada.yml +++ b/requirements/env_climada.yml @@ -22,12 +22,12 @@ dependencies: - numexpr>=2.13 - openpyxl>=3.1 - osm-flex>=1.1 # this is only required for running the tutorials, not for the CLIMADA package itself - - pandas>=2.3 + - pandas>=2.3,<3.0 # petals does not run with 3.0 (... and issue #1215) - pathos>=0.3 - peewee>=3.18 - pint>=0.24 - pip - - pyarrow>=21.0 + - pyarrow>=20.0 # petals cannot be installed on win-64 with pyarrow 21.0 - pycountry>=24.6 - pyproj>=3.7 - pytables>=3.10 # this is the name of the pypi 'tables' package on conda-forge @@ -35,7 +35,7 @@ dependencies: - rasterio>=1.4 - requests>=2.32 - rtree>=1.4 - - salib>=1.5 + - salib>=1.5.2 # see https://github.com/CLIMADA-project/climada_python/issues/1081 - scikit-learn>=1.7 - scipy>=1.15 - seaborn>=0.13 diff --git a/script/jenkins/install_env.sh b/script/jenkins/install_env.sh index 3a5a9d44aa..50053a5155 100644 --- a/script/jenkins/install_env.sh +++ b/script/jenkins/install_env.sh @@ -2,7 +2,7 @@ mamba env remove -n climada_env -y mamba create -n climada_env python=3.11 -y -mamba env update -n climada_env -f requirements/env_climada.yml +mamba env update -n climada_env -f requirements/env_climada.yml -y source activate climada_env python -m pip install -e "./[dev]" diff --git a/script/jenkins/test_notebooks.py b/script/jenkins/test_notebooks.py index c7b969b0ff..94db8db5de 100644 --- a/script/jenkins/test_notebooks.py +++ b/script/jenkins/test_notebooks.py @@ -18,7 +18,7 @@ # collect test cases, one for each notebook in the docs (unless they're excluded) -NOTEBOOK_DIR = Path(__file__).parent.parent.parent.joinpath("doc", "tutorial") +NOTEBOOK_DIR = Path(__file__).parent.parent.parent.joinpath("doc", "user-guide") NOTEBOOKS = [ (f.absolute(), f.name) for f in sorted(NOTEBOOK_DIR.iterdir()) @@ -92,13 +92,14 @@ def test_notebook(nb, name): re.sub(r"pool=\w+", "pool=None", ln) for ln in c["source"].split("\n") if not ln.startswith("%") + and not ln.startswith("?") + and not ln.strip().endswith("?") and not ln.startswith("help(") and not ln.startswith("ask_ok(") and not ln.startswith("ask_ok(") and not ln.startswith( "pool" ) # by convention Pool objects are called pool - and not ln.strip().endswith("?") and not re.search( r"(\W|^)Pool\(", ln ) # prevent Pool object creation