diff --git a/AUTHORS.md b/AUTHORS.md index 3409243a3a..d76bfcb03a 100644 --- a/AUTHORS.md +++ b/AUTHORS.md @@ -29,8 +29,10 @@ * Raphael Portmann * Nicolas Colombi * Leonie Villiger +* Timo Schmid * Kam Lam Yeung * Sarah Hülsen * Timo Schmid * Luca Severino * Samuel Juhel +* Valentin Gebhart diff --git a/CHANGELOG.md b/CHANGELOG.md index 78543cf491..84de2f054d 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -12,24 +12,73 @@ Code freeze date: YYYY-MM-DD ### Added +### Changed + +### Fixed + +### Deprecated + +### Removed + +## 5.0.0 + +Release date: 2024-07-19 + +### Dependency Changes + +Added: + +- `bayesian-optimization` +- `seaborn` >=0.13 + +Updated: + +- `bottleneck` >=1.3 → >=1.4 +- `cartopy` >=0.22 → >=0.23 +- `contextily` >=1.5 → >=1.6 +- `dask` >=2024.1,<2024.3 → >=2024.2,<2024.3 +- `matplotlib-base` >=3.8 → >=3.9 +- `numba` >=0.59 → >=0.60 +- `numexpr` >=2.9 → >=2.10 +- `pint` >=0.23 → >=0.24 +- `pycountry` >=22.3 → >=24.6 +- `requests` >=2.31 → >=2.32 +- `salib` >=1.4 → >=1.5 +- `scikit-learn` >=1.4 → >=1.5 +- `scipy` >=1.12 → >=1.13 +- `xarray` >=2024.2 → >=2024.6 + +### Added + - GitHub actions workflow for CLIMADA Petals compatibility tests [#855](https://github.com/CLIMADA-project/climada_python/pull/855) +- `climada.util.calibrate` module for calibrating impact functions [#692](https://github.com/CLIMADA-project/climada_python/pull/692) ### Changed +- Use Geopandas GeoDataFrame.plot() for centroids plotting function [896](https://github.com/CLIMADA-project/climada_python/pull/896) - Update SALib sensitivity and sampling methods from newest version (SALib 1.4.7) [#828](https://github.com/CLIMADA-project/climada_python/issues/828) - Allow for computation of relative and absolute delta impacts in `CalcDeltaClimate` - Remove content tables and make minor improvements (fix typos and readability) in CLIMADA tutorials. [#872](https://github.com/CLIMADA-project/climada_python/pull/872) - Centroids complete overhaul. Most function should be backward compatible. Internal data is stored in a geodataframe attribute. Raster are now stored as points, and the meta attribute is removed. Several methds were deprecated or removed. [#787](https://github.com/CLIMADA-project/climada_python/pull/787) - Improved error messages produced by `ImpactCalc.impact()` in case impact function in the exposures is not found in impf_set [#863](https://github.com/CLIMADA-project/climada_python/pull/863) +- Update the Holland et al. 2010 TC windfield model and introduce `model_kwargs` parameter to adjust model parameters [#846](https://github.com/CLIMADA-project/climada_python/pull/846) - Changed module structure: `climada.hazard.Hazard` has been split into the modules `base`, `io` and `plot` [#871](https://github.com/CLIMADA-project/climada_python/pull/871) +- Ensure `csr_matrix` stored in `climada.hazard.Hazard` have consistent data format and store no explicit zeros when initializing `ImpactCalc` [#893](https://github.com/CLIMADA-project/climada_python/pull/893) +- `Impact.from_hdf5` now calls `str` on `event_name` data that is not strings, and issue a warning then [#894](https://github.com/CLIMADA-project/climada_python/pull/894) +- `Impact.write_hdf5` now throws an error if `event_name` is does not contain strings exclusively [#894](https://github.com/CLIMADA-project/climada_python/pull/894) +- Split `climada.hazard.trop_cyclone` module into smaller submodules without affecting module usage [#911](https://github.com/CLIMADA-project/climada_python/pull/911) ### Fixed - Avoid an issue where a Hazard subselection would have a fraction matrix with only zeros as entries by throwing an error [#866](https://github.com/CLIMADA-project/climada_python/pull/866) +- Allow downgrading the Python bugfix version to improve environment compatibility [#900](https://github.com/CLIMADA-project/climada_python/pull/900) +- Fix broken links in `CONTRIBUTING.md` [#900](https://github.com/CLIMADA-project/climada_python/pull/900) +- When writing `TCTracks` to NetCDF, only apply compression to `float` or `int` data types. This fixes a downstream issue, see [climada_petals#135](https://github.com/CLIMADA-project/climada_petals/issues/135) [#911](https://github.com/CLIMADA-project/climada_python/pull/911) ### Added +- Method `Hazard.check_matrices` for bringing the stored CSR matrices into "canonical format" [#893](https://github.com/CLIMADA-project/climada_python/pull/893) - Generic s-shaped impact function via `ImpactFunc.from_poly_s_shape` [#878](https://github.com/CLIMADA-project/climada_python/pull/878) - climada.hazard.centroids.centr.Centroids.get_area_pixel - climada.hazard.centroids.centr.Centroids.get_dist_coast @@ -50,6 +99,7 @@ CLIMADA tutorials. [#872](https://github.com/CLIMADA-project/climada_python/pull - climada.hazard.centroids.centr.Centroids.empty_geometry_points - climada.hazard.centroids.centr.Centroids.set_meta_to_lat_lon - climada.hazard.centroids.centr.Centroids.set_lat_lon_to_meta +- `scheduler` parameter in `climada.util.coordinates.set_df_geometry_points`, as dask is not used anymore, leaving all calculation to shapely [#912](https://github.com/CLIMADA-project/climada_python/pull/912) ### Removed diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index 9d465d7e5e..f21b73e951 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -22,7 +22,7 @@ Please contact the [lead developers](https://wcr.ethz.ch/research/climada.html) ## Minimal Steps to Contribute -Before you start, please have a look at our [Developer Guide][devguide]. +Before you start, please have a look at our Developer Guide section in the [CLIMADA Docs][docs]. To contribute follow these steps: @@ -65,21 +65,22 @@ To contribute follow these steps: ## Resources -The CLIMADA documentation provides a [Developer Guide][devguide]. +The [CLIMADA documentation][docs] provides several Developer Guides. Here's a selection of the commonly required information: * How to use Git and GitHub for CLIMADA development: [Development and Git and CLIMADA](https://climada-python.readthedocs.io/en/latest/guide/Guide_Git_Development.html) -* Coding instructions for CLIMADA: [Python Dos and Don'ts](https://climada-python.readthedocs.io/en/latest/guide/Guide_PythonDos-n-Donts.html), [Performance Tips](https://climada-python.readthedocs.io/en/latest/guide/Guide_Py_Performance.html), [CLIMADA Conventions](https://climada-python.readthedocs.io/en/latest/guide/Guide_Miscellaneous.html) -* How to execute tests in CLIMADA: [Testing and Continuous Integration][testing] +* Coding instructions for CLIMADA: [Python Dos and Don'ts](https://climada-python.readthedocs.io/en/latest/guide/Guide_PythonDos-n-Donts.html), [Performance Tips](https://climada-python.readthedocs.io/en/latest/guide/Guide_Py_Performance.html), [CLIMADA Conventions](https://climada-python.readthedocs.io/en/latest/guide/Guide_CLIMADA_conventions.html) +* How to execute tests in CLIMADA: [Testing][testing] and [Continuous Integration](https://climada-python.readthedocs.io/en/latest/guide/Guide_continuous_integration_GitHub_actions.html) ## Pull Requests After developing a new feature, fixing a bug, or updating the tutorials, you can create a [pull request](https://docs.github.com/en/pull-requests) to have your changes reviewed and then merged into the CLIMADA code base. To ensure that your pull request can be reviewed quickly and easily, please have a look at the _Resources_ above before opening a pull request. -In particular, please check out the [Pull Request instructions](https://climada-python.readthedocs.io/en/latest/guide/Guide_Git_Development.html#Pull-requests). +In particular, please check out the [Pull Request instructions](https://climada-python.readthedocs.io/en/latest/guide/Guide_Git_Development.html#pull-requests). We provide a description template for pull requests that helps you provide the essential information for reviewers. It also contains a checklist for both pull request authors and reviewers to guide the review process. +[docs]: https://climada-python.readthedocs.io/en/latest/ [devguide]: https://climada-python.readthedocs.io/en/latest/#developer-guide -[testing]: https://climada-python.readthedocs.io/en/latest/guide/Guide_Continuous_Integration_and_Testing.html +[testing]: https://climada-python.readthedocs.io/en/latest/guide/Guide_Testing.html diff --git a/climada/_version.py b/climada/_version.py index 3407d4bc6b..80952dacb9 100644 --- a/climada/_version.py +++ b/climada/_version.py @@ -1 +1 @@ -__version__ = '4.1.2-dev' +__version__ = '5.0.1-dev' diff --git a/climada/conf/climada.conf b/climada/conf/climada.conf index 71f4f72ba4..5fd35b1c42 100644 --- a/climada/conf/climada.conf +++ b/climada/conf/climada.conf @@ -67,7 +67,7 @@ "cache_db": "{local_data.system}/.downloads.db", "cache_enabled": true, "cache_dir": "{local_data.system}/.apicache", - "supported_hazard_types": ["river_flood", "tropical_cyclone", "storm_europe", "relative_cropyield", "wildfire", "earthquake", "flood", "hail"], + "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"] } } diff --git a/climada/engine/impact.py b/climada/engine/impact.py index e9afda5903..97bbd6d90f 100644 --- a/climada/engine/impact.py +++ b/climada/engine/impact.py @@ -937,11 +937,6 @@ def write_hdf5(self, file_path: Union[str, Path], dense_imp_mat: bool=False): The impact matrix can be stored in a sparse or dense format. - Notes - ----- - This writer does not support attributes with variable types. Please make sure - that ``event_name`` is a list of equally-typed values, e.g., all ``str``. - Parameters ---------- file_path : str or Path @@ -950,6 +945,11 @@ def write_hdf5(self, file_path: Union[str, Path], dense_imp_mat: bool=False): If ``True``, write the impact matrix as dense matrix that can be more easily interpreted by common H5 file readers but takes up (vastly) more space. Defaults to ``False``. + + Raises + ------ + TypeError + If :py:attr:`event_name` does not contain strings exclusively. """ # Define writers for all types (will be filled later) type_writers = dict() @@ -983,7 +983,7 @@ def write(group: h5py.Group, name: str, value: Any): def _str_type_helper(values: Collection): """Return string datatype if we assume 'values' contains strings""" - if isinstance(next(iter(values)), str): + if all((isinstance(val, str) for val in values)): return h5py.string_dtype() return None @@ -1037,6 +1037,8 @@ def write_csr(group, name, value): # Now write all attributes # NOTE: Remove leading underscore to write '_tot_value' as regular attribute for name, value in self.__dict__.items(): + if name == "event_name" and _str_type_helper(value) is None: + raise TypeError("'event_name' must be a list of strings") write(file, name.lstrip("_"), value) def write_sparse_csr(self, file_name): @@ -1240,10 +1242,18 @@ def from_hdf5(cls, file_path: Union[str, Path]): ).intersection(file.keys()) kwargs.update({attr: file[attr][:] for attr in array_attrs}) - # Special handling for 'event_name' because it's a list of strings + # Special handling for 'event_name' because it should be a list of strings if "event_name" in file: # pylint: disable=no-member - kwargs["event_name"] = list(file["event_name"].asstr()[:]) + try: + event_name = file["event_name"].asstr()[:] + except TypeError: + LOGGER.warning( + "'event_name' is not stored as strings. Trying to decode " + "values with 'str()' instead." + ) + event_name = map(str, file["event_name"][:]) + kwargs["event_name"] = list(event_name) # Create the impact object return cls(**kwargs) diff --git a/climada/engine/impact_calc.py b/climada/engine/impact_calc.py index 315a02c8a1..f050607c50 100644 --- a/climada/engine/impact_calc.py +++ b/climada/engine/impact_calc.py @@ -47,6 +47,8 @@ def __init__(self, The dimension of the imp_mat variable must be compatible with the exposures and hazard objects. + This will call :py:meth:`climada.hazard.base.Hazard.check_matrices`. + Parameters ---------- exposures : climada.entity.Exposures @@ -61,6 +63,8 @@ def __init__(self, self.exposures = exposures self.impfset = impfset self.hazard = hazard + self.hazard.check_matrices() + # exposures index to use for matrix reconstruction self._orig_exp_idx = np.arange(self.exposures.gdf.shape[0]) diff --git a/climada/engine/test/test_impact.py b/climada/engine/test/test_impact.py index a0b458ca55..0e70e993b0 100644 --- a/climada/engine/test/test_impact.py +++ b/climada/engine/test/test_impact.py @@ -779,7 +779,8 @@ def test_select_event_identity_pass(self): ent.exposures.assign_centroids(hazard) # Compute the impact over the whole exposures - imp = ImpactCalc(ent.exposures, ent.impact_funcs, hazard).impact(save_mat=True, assign_centroids=False) + imp = ImpactCalc(ent.exposures, ent.impact_funcs, hazard).impact( + save_mat=True, assign_centroids=False) sel_imp = imp.select(event_ids=imp.event_id, event_names=imp.event_name, @@ -1019,10 +1020,11 @@ def test_write_hdf5_without_imp_mat(self): def test_write_hdf5_type_fail(self): """Test that writing attributes with varying types results in an error""" - self.impact.event_name = [1, "a", 1.0, "b", "c", "d"] - with self.assertRaises(TypeError) as cm: + self.impact.event_name = ["a", 1, 1.0, "b", "c", "d"] + with self.assertRaisesRegex( + TypeError, "'event_name' must be a list of strings" + ): self.impact.write_hdf5(self.filepath) - self.assertIn("No conversion path for dtype", str(cm.exception)) def test_cycle_hdf5(self): """Test writing and reading the same object""" @@ -1120,6 +1122,15 @@ def test_read_hdf5_full(self): impact = Impact.from_hdf5(self.filepath) npt.assert_array_equal(impact.imp_mat.toarray(), [[0, 1, 2], [3, 0, 0]]) + # Check with non-string event_name + event_name = [1.2, 2] + with h5py.File(self.filepath, "r+") as file: + del file["event_name"] + file.create_dataset("event_name", data=event_name) + with self.assertLogs("climada.engine.impact", "WARNING") as cm: + impact = Impact.from_hdf5(self.filepath) + self.assertIn("'event_name' is not stored as strings", cm.output[0]) + self.assertListEqual(impact.event_name, ["1.2", "2.0"]) # Execute Tests if __name__ == "__main__": diff --git a/climada/engine/test/test_impact_calc.py b/climada/engine/test/test_impact_calc.py index 471706988b..c4ee5e26b4 100644 --- a/climada/engine/test/test_impact_calc.py +++ b/climada/engine/test/test_impact_calc.py @@ -70,6 +70,16 @@ def test_init(self): np.testing.assert_array_equal(HAZ.event_id, icalc.hazard.event_id) np.testing.assert_array_equal(HAZ.event_name, icalc.hazard.event_name) + # Test check matrices + hazard = deepcopy(HAZ) + hazard.intensity[0, hazard.intensity.indices[0]] = 0 + hazard.fraction = sparse.csr_matrix(np.ones((1, 1))) + with self.assertRaisesRegex( + ValueError, "Intensity and fraction matrices must have the same shape" + ): + ImpactCalc(ENT.exposures, ENT.impact_funcs, hazard) + self.assertEqual(hazard.intensity.nnz, HAZ.intensity.nnz - 1) # was pruned + def test_metrics(self): """Test methods to get impact metrics""" mat = sparse.csr_matrix(np.array( diff --git a/climada/hazard/base.py b/climada/hazard/base.py index daa4c3869d..f0d1f47441 100644 --- a/climada/hazard/base.py +++ b/climada/hazard/base.py @@ -27,6 +27,7 @@ from typing import Optional,List import warnings +import geopandas as gpd import numpy as np from pathos.pools import ProcessPool as Pool from scipy import sparse @@ -49,6 +50,21 @@ class Hazard(HazardIO, HazardPlot): Contains events of some hazard type defined at centroids. Loads from files with format defined in FILE_EXT. + Attention + --------- + This class uses instances of + `scipy.sparse.csr_matrix + `_ + to store :py:attr:`intensity` and :py:attr:`fraction`. This data types comes with + its particular pitfalls. Depending on how the objects are instantiated and modified, + a matrix might end up in a "non-canonical" state. In this state, its ``.data`` + attribute does not necessarily represent the values apparent in the final matrix. + In particular, a "non-canonical" matrix may store "duplicates", i.e. multiple values + that map to the same matrix position. This is supported, and the default behavior is + to sum up these values. To avoid any inconsistencies, call :py:meth:`check_matrices` + before accessing the ``data`` attribute of either matrix. This will explicitly sum + all values at the same matrix position and eliminate explicit zeros. + Attributes ---------- haz_type : str @@ -191,6 +207,33 @@ def __init__(self, if self.pool: LOGGER.info('Using %s CPUs.', self.pool.ncpus) + def check_matrices(self): + """Ensure that matrices are consistently shaped and stored + + It is good practice to call this method before accessing the ``data`` attribute + of either :py:attr:`intensity` or :py:attr:`fraction`. + + See Also + -------- + :py:func:`climada.util.checker.prune_csr_matrix` + + Todo + ----- + * Check consistency with centroids + + Raises + ------ + ValueError + If matrices are ill-formed or ill-shaped in relation to each other + """ + u_check.prune_csr_matrix(self.intensity) + u_check.prune_csr_matrix(self.fraction) + if self.fraction.nnz > 0: + if self.intensity.shape != self.fraction.shape: + raise ValueError( + "Intensity and fraction matrices must have the same shape" + ) + @classmethod def get_default(cls, attribute): """Get the Hazard type default for a given attribute. @@ -450,6 +493,75 @@ def sanitize_event_ids(self): LOGGER.debug('Resetting event_id.') self.event_id = np.arange(1, self.event_id.size + 1) + def local_return_period(self, threshold_intensities=(5., 10., 20.)): + """Compute local return periods for given hazard intensities. The used method + is fitting the ordered intensitites per centroid to the corresponding cummulated + frequency with a step function. + + Parameters + ---------- + threshold_intensities : np.array + User-specified hazard intensities for which the return period should be calculated + locally (at each centroid). Defaults to (5, 10, 20) + + Returns + ------- + gdf : gpd.GeoDataFrame + GeoDataFrame containing return periods for given threshold intensities. Each column + corresponds to a threshold_intensity value, each row corresponds to a centroid. Values + in the gdf correspond to the return period for the given centroid and + threshold_intensity value + label : str + GeoDataFrame label, for reporting and plotting + column_label : function + Column-label-generating function, for reporting and plotting + """ + #check frequency unit + if self.frequency_unit in ['1/year', 'annual', '1/y', '1/a']: + rp_unit = 'Years' + elif self.frequency_unit in ['1/month', 'monthly', '1/m']: + rp_unit = 'Months' + elif self.frequency_unit in ['1/week', 'weekly', '1/w']: + rp_unit = 'Weeks' + else: + LOGGER.warning("Hazard's frequency unit %s is not known, " + "years will be used as return period unit.", self.frequency_unit) + rp_unit = 'Years' + + # Ensure threshold_intensities is a numpy array + threshold_intensities = np.array(threshold_intensities) + + num_cen = self.intensity.shape[1] + return_periods = np.zeros((len(threshold_intensities), num_cen)) + + # batch_centroids = number of centroids that are handled in parallel: + # batch_centroids = maximal matrix size // number of events + batch_centroids = CONFIG.max_matrix_size.int() // self.intensity.shape[0] + if batch_centroids < 1: + raise ValueError('Increase max_matrix_size configuration parameter to >' + f'{self.intensity.shape[0]}') + + # Process the intensities in chunks of centroids + for start_col in range(0, num_cen, batch_centroids): + end_col = min(start_col + batch_centroids, num_cen) + return_periods[:, start_col:end_col] = self._loc_return_period( + threshold_intensities, + self.intensity[:, start_col:end_col].toarray() + ) + + # create the output GeoDataFrame + gdf = gpd.GeoDataFrame(geometry = self.centroids.gdf['geometry'], + crs = self.centroids.gdf.crs) + col_names = [f'{tresh_inten}' for tresh_inten in threshold_intensities] + gdf[col_names] = return_periods.T + + # create label and column_label + label = f'Return Periods ({rp_unit})' + column_label = lambda column_names: [f'Threshold Intensity: {col} {self.units}' + for col in column_names] + + return gdf, label, column_label + def get_event_id(self, event_name): """Get an event id from its name. Several events might have the same name. @@ -614,6 +726,52 @@ def _loc_return_inten(self, return_periods, inten, exc_inten): inten_sort[:, cen_idx], freq_sort[:, cen_idx], self.intensity_thres, return_periods) + def _loc_return_period(self, threshold_intensities, inten): + """Compute local return periods for user-specified threshold intensities + for a subset of hazard centroids + + Parameters + ---------- + threshold_intensities: np.array + User-specified hazard intensities for which the return period should be calculated + locally (at each centroid). + inten: np.array + subarray of full hazard intensities corresponding to a subset of the centroids + (rows corresponds to events, columns correspond to centroids) + + Returns + ------- + np.array + (rows corresponds to threshold_intensities, columns correspond to centroids) + """ + # Assuming inten is sorted and calculating cumulative frequency + sort_pos = np.argsort(inten, axis=0)[::-1, :] + inten_sort = inten[sort_pos, np.arange(inten.shape[1])] + freq_sort = self.frequency[sort_pos] + freq_sort = np.cumsum(freq_sort, axis=0) + return_periods = np.zeros((len(threshold_intensities), inten.shape[1])) + + for cen_idx in range(inten.shape[1]): + sorted_inten_cen = inten_sort[:, cen_idx] + cum_freq_cen = freq_sort[:, cen_idx] + + for i, intensity in enumerate(threshold_intensities): + # Find the first occurrence where the intensity is less than the sorted intensities + exceedance_index = np.searchsorted(sorted_inten_cen[::-1], intensity, side='left') + + # Calculate exceedance probability + if exceedance_index < len(cum_freq_cen): + exceedance_probability = cum_freq_cen[-exceedance_index - 1] + else: + exceedance_probability = 0 # Or set a default minimal probability + + # Calculate and store return period + if exceedance_probability > 0: + return_periods[i, cen_idx] = 1 / exceedance_probability + else: + return_periods[i, cen_idx] = np.nan + return return_periods + def _check_events(self): """Check that all attributes but centroids contain consistent data. Put default date, event_name and orig if not provided. Check not @@ -628,7 +786,7 @@ def _check_events(self): if np.unique(self.event_id).size != num_ev: raise ValueError("There are events with the same identifier.") - u_check.check_oligatories(self.__dict__, self.vars_oblig, 'Hazard.', + u_check.check_obligatories(self.__dict__, self.vars_oblig, 'Hazard.', num_ev, num_ev, num_cen) u_check.check_optionals(self.__dict__, self.vars_opt, 'Hazard.', num_ev) self.event_name = u_check.array_default(num_ev, self.event_name, @@ -816,8 +974,8 @@ def concat(cls, haz_list): """ if len(haz_list) == 0: return cls() - haz_concat = haz_list[0].__class__() - haz_concat.haz_type = haz_list[0].haz_type + haz_concat = haz_list[0].__class__(centroids=Centroids(lat=[], lon=[], + crs=haz_list[0].centroids.crs)) for attr_name, attr_val in vars(haz_list[0]).items(): # to save memory, only copy simple attributes like # "units" that are not explicitly handled by Hazard.append diff --git a/climada/hazard/centroids/centr.py b/climada/hazard/centroids/centr.py index 5548caef22..b02f7569d6 100644 --- a/climada/hazard/centroids/centr.py +++ b/climada/hazard/centroids/centr.py @@ -27,7 +27,9 @@ import warnings import h5py +import cartopy import cartopy.crs as ccrs +import cartopy.feature as cfeature import geopandas as gpd import matplotlib.pyplot as plt import numpy as np @@ -38,7 +40,6 @@ from climada.util.constants import DEF_CRS import climada.util.coordinates as u_coord -import climada.util.plot as u_plot __all__ = ['Centroids'] @@ -366,7 +367,8 @@ def append(self, centr): """ if not u_coord.equal_crs(self.crs, centr.crs): raise ValueError( - "The centroids have different Coordinate-Reference-Systems (CRS)" + f"The given centroids use different CRS: {self.crs}, {centr.crs}. " + "The centroids are incompatible and cannot be concatenated." ) self.gdf = pd.concat([self.gdf, centr.gdf]) @@ -477,44 +479,43 @@ def select_mask(self, sel_cen=None, reg_id=None, extent=None): (self.lat >= lat_min) & (self.lat <= lat_max) ) return sel_cen - - #TODO replace with nice GeoDataFrame util plot method. - def plot(self, axis=None, figsize=(9, 13), **kwargs): - """Plot centroids scatter points over earth + + def plot(self, *, axis=None, figsize=(9, 13), **kwargs): + """Plot centroids geodataframe using geopandas and cartopy plotting functions. Parameters ---------- - axis : matplotlib.axes._subplots.AxesSubplot, optional - axis to use + axis: optional + user-defined cartopy.mpl.geoaxes.GeoAxes instance figsize: (float, float), optional figure size for plt.subplots The default is (9, 13) + args : optional + positional arguments for geopandas.GeoDataFrame.plot kwargs : optional - arguments for scatter matplotlib function - + keyword arguments for geopandas.GeoDataFrame.plot + Returns ------- - axis : matplotlib.axes._subplots.AxesSubplot + ax : cartopy.mpl.geoaxes.GeoAxes instance """ - pad = np.abs(u_coord.get_resolution(self.lat, self.lon)).min() - - proj_data, _ = u_plot.get_transformation(self.crs) - proj_plot = proj_data - if isinstance(proj_data, ccrs.PlateCarree): - # use different projections for plot and data to shift the central lon in the plot - xmin, ymin, xmax, ymax = u_coord.latlon_bounds(self.lat, self.lon, buffer=pad) - proj_plot = ccrs.PlateCarree(central_longitude=0.5 * (xmin + xmax)) - else: - xmin, ymin, xmax, ymax = (self.lon.min() - pad, self.lat.min() - pad, - self.lon.max() + pad, self.lat.max() + pad) + if axis == None: + fig, axis = plt.subplots(figsize=figsize, subplot_kw={"projection": ccrs.PlateCarree()}) + if type(axis) != cartopy.mpl.geoaxes.GeoAxes: + raise AttributeError( + f"The axis provided is of type: {type(axis)} " + "The function requires a cartopy.mpl.geoaxes.GeoAxes." + ) - if not axis: - _fig, axis, _fontsize = u_plot.make_map(proj=proj_plot, figsize=figsize) + axis.add_feature(cfeature.BORDERS) + axis.add_feature(cfeature.COASTLINE) + axis.gridlines(draw_labels=True, dms=True, x_inline=False, y_inline=False) - axis.set_extent((xmin, xmax, ymin, ymax), crs=proj_data) - u_plot.add_shapes(axis) - axis.scatter(self.lon, self.lat, transform=proj_data, **kwargs) - plt.tight_layout() + if self.gdf.crs != DEF_CRS: + centroids_plot = self.to_default_crs(inplace=False) + centroids_plot.gdf.plot(ax=axis, transform=ccrs.PlateCarree(), **kwargs) + else: + self.gdf.plot(ax=axis, transform=ccrs.PlateCarree(), **kwargs) return axis def set_region_id(self, level='country', overwrite=False): diff --git a/climada/hazard/centroids/test/test_centr.py b/climada/hazard/centroids/test/test_centr.py index 1ee90ee1bf..745e544d5a 100644 --- a/climada/hazard/centroids/test/test_centr.py +++ b/climada/hazard/centroids/test/test_centr.py @@ -991,6 +991,25 @@ def test_equal_pass(self): self.assertTrue(centr1 == centr1) self.assertTrue(centr2 == centr2) + def test_plot(self): + "Test Centroids.plot()" + centr = Centroids( + lat=np.array([-5, -3, 0, 3, 5]), + lon=np.array([-180, -175, -170, 170, 175]), + region_id=np.zeros(5), + crs=DEF_CRS + ) + centr.plot() + + def test_plot_non_def_crs(self): + "Test Centroids.plot() with non-default CRS" + centr = Centroids( + lat = np.array([10.0, 20.0, 30.0]), + lon = np.array([-10.0, -20.0, -30.0]), + region_id=np.zeros(3), + crs='epsg:32632' + ) + centr.plot() # Execute Tests if __name__ == "__main__": diff --git a/climada/hazard/io.py b/climada/hazard/io.py index d50dcfc1d9..5248d45790 100644 --- a/climada/hazard/io.py +++ b/climada/hazard/io.py @@ -1008,7 +1008,7 @@ def write_hdf5(self, file_name, todense=False): if var_name == 'centroids': # Centroids have their own write_hdf5 method, # which is invoked at the end of this method (s.b.) - pass + continue elif isinstance(var_val, sparse.csr_matrix): if todense: hf_data.create_dataset(var_name, data=var_val.toarray()) diff --git a/climada/hazard/tc_tracks.py b/climada/hazard/tc_tracks.py index 89a44b3744..315e364f0a 100644 --- a/climada/hazard/tc_tracks.py +++ b/climada/hazard/tc_tracks.py @@ -331,11 +331,11 @@ def from_ibtracs_netcdf(cls, provider=None, rescale_windspeeds=True, storm_id=No When using data from IBTrACS, make sure to be familiar with the scope and limitations of IBTrACS, e.g. by reading the official documentation - (https://www.ncdc.noaa.gov/ibtracs/pdf/IBTrACS_version4_Technical_Details.pdf). Reading the - CLIMADA documentation can't replace a thorough understanding of the underlying data. This - function only provides a (hopefully useful) interface for the data input, but cannot - provide any guidance or make recommendations about if and how to use IBTrACS data for your - particular project. + (https://www.ncei.noaa.gov/sites/default/files/2021-07/IBTrACS_version4_Technical_Details.pdf). + Reading the CLIMADA documentation can't replace a thorough understanding of the underlying + data. This function only provides a (hopefully useful) interface for the data input, but + cannot provide any guidance or make recommendations about if and how to use IBTrACS data + for your particular project. Resulting tracks are required to have both pressure and wind speed information at all time steps. Therefore, all track positions where one of wind speed or pressure are missing are @@ -374,8 +374,8 @@ def from_ibtracs_netcdf(cls, provider=None, rescale_windspeeds=True, storm_id=No rescale_windspeeds : bool, optional If True, all wind speeds are linearly rescaled to 1-minute sustained winds. Note however that the IBTrACS documentation (Section 5.2, - https://www.ncdc.noaa.gov/ibtracs/pdf/IBTrACS_version4_Technical_Details.pdf) includes - a warning about this kind of conversion: "While a multiplicative factor can describe + https://www.ncei.noaa.gov/sites/default/files/2021-07/IBTrACS_version4_Technical_Details.pdf) + includes a warning about this kind of conversion: "While a multiplicative factor can the numerical differences, there are procedural and observational differences between agencies that can change through time, which confounds the simple multiplicative factor." Default: True @@ -1383,7 +1383,10 @@ def write_hdf5(self, file_name, complevel=5): df_attrs = pd.DataFrame([t.attrs for t in data], index=ds_combined["storm"].to_series()) ds_combined = xr.merge([ds_combined, df_attrs.to_xarray()]) - encoding = {v: dict(zlib=True, complevel=complevel) for v in ds_combined.data_vars} + encoding = { + v: dict(zlib=_zlib_from_dataarray(ds_combined[v]), complevel=complevel) + for v in ds_combined.data_vars + } LOGGER.info('Writing %d tracks to %s', self.size, file_name) ds_combined.to_netcdf(file_name, encoding=encoding) @@ -1506,7 +1509,6 @@ def to_geodataframe(self, as_points=False, split_lines_antimeridian=True): return gdf @staticmethod - @numba.jit(forceobj=True) def _one_interp_data(track, time_step_h, land_geom=None): """Interpolate values of one track. @@ -2435,3 +2437,18 @@ def set_category(max_sus_wind, wind_unit='kn', saffir_scale=None): return (np.argwhere(max_wind < saffir_scale) - 1)[0][0] except IndexError: return -1 + +def _zlib_from_dataarray(data_var: xr.DataArray) -> bool: + """Return true if data_var is of numerical type, return False otherwise + + Parameters + ---------- + data_var : xr.DataArray + + Returns + ------- + bool + """ + if np.issubdtype(data_var.dtype, float) or np.issubdtype(data_var.dtype, int): + return True + return False diff --git a/climada/hazard/test/test_base.py b/climada/hazard/test/test_base.py index 5df525fbfc..3dc7d8db98 100644 --- a/climada/hazard/test/test_base.py +++ b/climada/hazard/test/test_base.py @@ -124,18 +124,18 @@ def test_check_wrongFreq_fail(self): def test_check_wrongInten_fail(self): """Wrong hazard definition""" self.hazard.intensity = sparse.csr_matrix([[1, 2], [1, 2]]) - - with self.assertRaises(ValueError) as cm: + with self.assertRaisesRegex( + ValueError, "Invalid Hazard.intensity row size: 3 != 2." + ): self.hazard.check() - self.assertIn('Invalid Hazard.intensity row size: 3 != 2.', str(cm.exception)) def test_check_wrongFrac_fail(self): """Wrong hazard definition""" self.hazard.fraction = sparse.csr_matrix([[1], [1], [1]]) - - with self.assertRaises(ValueError) as cm: + with self.assertRaisesRegex( + ValueError, "Invalid Hazard.fraction column size: 2 != 1." + ): self.hazard.check() - self.assertIn('Invalid Hazard.fraction column size: 2 != 1.', str(cm.exception)) def test_check_wrongEvName_fail(self): """Wrong hazard definition""" @@ -212,6 +212,32 @@ def test_get_date_strings_pass(self): self.assertEqual(haz.get_event_date()[560], u_dt.date_to_str(haz.date[560])) + def test_check_matrices(self): + """Test the check_matrices method""" + hazard = Hazard("TC") + hazard.fraction = sparse.csr_matrix(np.zeros((2, 2))) + hazard.check_matrices() # No error, fraction.nnz = 0 + hazard.fraction = sparse.csr_matrix(np.ones((2, 2))) + with self.assertRaisesRegex( + ValueError, "Intensity and fraction matrices must have the same shape" + ): + hazard.check_matrices() + hazard.intensity = sparse.csr_matrix(np.ones((2, 3))) + with self.assertRaisesRegex( + ValueError, "Intensity and fraction matrices must have the same shape" + ): + hazard.check_matrices() + + # Check that matrices are pruned + hazard.intensity[:] = 0 + hazard.fraction = sparse.csr_matrix(([0], [0], [0, 1, 1]), shape=(2, 3)) + hazard.check_matrices() + for attr in ("intensity", "fraction"): + with self.subTest(matrix=attr): + matrix = getattr(hazard, attr) + self.assertEqual(matrix.nnz, 0) + self.assertTrue(matrix.has_canonical_format) + class TestRemoveDupl(unittest.TestCase): """Test remove_duplicates method.""" @@ -836,7 +862,8 @@ def test_concat_pass(self): haz_1 = Hazard("TC", centroids=Centroids( - lat=np.array([1, 3, 5]), lon=np.array([2, 4, 6])), + lat=np.array([1, 3, 5]), lon=np.array([2, 4, 6]), + crs="epsg:4326"), event_id=np.array([1]), event_name=['ev1'], date=np.array([1]), @@ -848,7 +875,9 @@ def test_concat_pass(self): units='m/s') haz_2 = Hazard("TC", - centroids=Centroids(lat=np.array([1, 3, 5]), lon=np.array([2, 4, 6])), + centroids=Centroids( + lat=np.array([1, 3, 5]), lon=np.array([2, 4, 6]), + crs="epsg:4326"), event_id=np.array([1]), event_name=['ev2'], date=np.array([2]), @@ -880,6 +909,7 @@ def test_concat_pass(self): self.assertEqual(haz.event_name, ['ev1', 'ev2']) self.assertTrue(np.array_equal(haz.centroids.coord, haz_1.centroids.coord)) self.assertTrue(np.array_equal(haz.centroids.coord, haz_2.centroids.coord)) + self.assertEqual(haz.centroids.crs, haz_1.centroids.crs) def test_append_new_var_pass(self): """New variable appears if hazard to append is empty.""" @@ -900,15 +930,23 @@ def test_append_raise_type_error(self): haz1.append(haz2) def test_concat_raise_value_error(self): - """Raise error if hazards with different units of type""" - haz1 = Hazard('TC', units='m/s') + """Raise error if hazards with different units, type or crs""" + haz1 = Hazard('TC', units='m/s', + centroids=Centroids(lat=[],lon=[], crs="epsg:4326")) haz3 = Hazard('EQ') - with self.assertRaises(ValueError): + with self.assertRaisesRegex(ValueError, + "different types"): Hazard.concat([haz1, haz3]) haz4 = Hazard('TC', units='cm') - with self.assertRaises(ValueError): + with self.assertRaisesRegex(ValueError, + "different units"): Hazard.concat([haz1, haz4]) + + haz5 = Hazard('TC', centroids=Centroids(lat=[],lon=[], crs="epsg:7777")) + with self.assertRaisesRegex(ValueError, + "different CRS"): + Hazard.concat([haz1, haz5]) def test_change_centroids(self): """Set new centroids for hazard""" @@ -1010,6 +1048,25 @@ def test_ref_all_pass(self): self.assertAlmostEqual(inten_stats[1][66], 70.608592953031405) self.assertAlmostEqual(inten_stats[3][33], 88.510983305123631) self.assertAlmostEqual(inten_stats[2][99], 79.717518054203623) + + def test_local_return_period(self): + """Compare local return periods against reference.""" + haz = dummy_hazard() + haz.intensity = sparse.csr_matrix([ + [1., 5., 1.], + [2., 2., 0.] + ]) + haz.frequency = np.full(4, 1.) + threshold_intensities = np.array([1., 2., 3.]) + return_stats, _, _ = haz.local_return_period(threshold_intensities) + np.testing.assert_allclose( + return_stats[return_stats.columns[1:]].values.T, + np.array([ + [0.5, 0.5, 1.], + [1., 0.5, np.nan], + [np.nan, 1., np.nan] + ]) + ) class TestYearset(unittest.TestCase): diff --git a/climada/hazard/test/test_trop_cyclone.py b/climada/hazard/test/test_trop_cyclone.py index 0452c6b78c..41ab3a81fb 100644 --- a/climada/hazard/test/test_trop_cyclone.py +++ b/climada/hazard/test/test_trop_cyclone.py @@ -26,25 +26,21 @@ import numpy as np from scipy import sparse -import xarray as xr +import climada.hazard.test as hazard_test from climada.util import ureg from climada.test import get_test_file from climada.hazard.tc_tracks import TCTracks -from climada.hazard.trop_cyclone import ( - TropCyclone, get_close_centroids, _vtrans, _B_holland_1980, _bs_holland_2008, - _v_max_s_holland_2008, _x_holland_2010, _stat_holland_1980, _stat_holland_2010, - _stat_er_2011, tctrack_to_si, MBAR_TO_PA, KM_TO_M, H_TO_S, -) +from climada.hazard.trop_cyclone.trop_cyclone import ( + TropCyclone, ) from climada.hazard.centroids.centr import Centroids -import climada.hazard.test as hazard_test - DATA_DIR = Path(hazard_test.__file__).parent.joinpath('data') TEST_TRACK = DATA_DIR.joinpath("trac_brb_test.csv") TEST_TRACK_SHORT = DATA_DIR.joinpath("trac_short_test.csv") + CENTR_TEST_BRB = Centroids.from_hdf5(get_test_file('centr_test_brb', file_format='hdf5')) @@ -150,32 +146,51 @@ def test_cross_antimeridian(self): def test_windfield_models(self): """Test _tc_from_track function with different wind field models.""" intensity_idx = [0, 1, 2, 3, 80, 100, 120, 200, 220, 250, 260, 295] - intensity_values = { - "H08": [ - 22.74903, 23.784691, 24.82255, 22.67403, 27.218706, 30.593959, - 18.980878, 24.540069, 27.826407, 26.846293, 0., 34.568898, - ], - "H10": [ - 24.745521, 25.596484, 26.475329, 24.690914, 28.650107, 31.584395, - 21.723546, 26.140293, 28.94964, 28.051915, 18.49378, 35.312152, - ], - # Holland 1980 and Emanuel & Rotunno 2011 use recorded wind speeds, while the above use - # pressure values only. That's why the results are so different: - "H1980": [21.376807, 21.957217, 22.569568, 21.284351, 24.254226, 26.971303, - 19.220149, 21.984516, 24.196388, 23.449116, 0, 31.550207], - "ER11": [23.565332, 24.931413, 26.360758, 23.490333, 29.601171, 34.522795, - 18.996389, 26.102109, 30.780737, 29.498453, 0, 38.368805], - } + intensity_values = [ + ("H08", None, [ + 22.74903, 23.784691, 24.82255, 22.67403, 27.218706, 30.593959, + 18.980878, 24.540069, 27.826407, 26.846293, 0., 34.568898, + ]), + ("H10", None, [ + 24.745521, 25.596484, 26.475329, 24.690914, 28.650107, 31.584395, + 21.723546, 26.140293, 28.94964, 28.051915, 18.49378, 35.312152, + ]), + # The following model configurations use recorded wind speeds, while the above use + # pressure values only. That's why some of the values are so different. + ("H10", dict(vmax_from_cen=False, rho_air_const=1.2), [ + 23.702232, 24.327615, 24.947161, 23.589233, 26.616085, 29.389295, + 21.338178, 24.257067, 26.472543, 25.662313, 18.535842, 31.886041, + ]), + ("H10", dict(vmax_from_cen=False, rho_air_const=None), [ + 24.244162, 24.835561, 25.432454, 24.139294, 27.127457, 29.719196, + 21.910658, 24.692637, 26.783575, 25.971516, 19.005555, 31.904048, + ]), + ("H10", dict(vmax_from_cen=False, rho_air_const=None, vmax_in_brackets=True), [ + 23.592924, 24.208169, 24.817104, 23.483053, 26.468975, 29.221715, + 21.260867, 24.150879, 26.34288 , 25.543635, 18.487385, 31.904048 + ]), + ("H1980", None, [ + 21.376807, 21.957217, 22.569568, 21.284351, 24.254226, 26.971303, + 19.220149, 21.984516, 24.196388, 23.449116, 0, 31.550207, + ]), + ("ER11", None, [ + 23.565332, 24.931413, 26.360758, 23.490333, 29.601171, 34.522795, + 18.996389, 26.102109, 30.780737, 29.498453, 0, 38.368805, + ]), + ] tc_track = TCTracks.from_processed_ibtracs_csv(TEST_TRACK) tc_track.equal_timestep() tc_track.data = tc_track.data[:1] - for model in ["H08", "H10", "H1980", "ER11"]: - tc_haz = TropCyclone.from_tracks(tc_track, centroids=CENTR_TEST_BRB, model=model) + for model, model_kwargs, inten_ref in intensity_values: + tc_haz = TropCyclone.from_tracks( + tc_track, centroids=CENTR_TEST_BRB, model=model, model_kwargs=model_kwargs, + ) np.testing.assert_array_almost_equal( - tc_haz.intensity[0, intensity_idx].toarray()[0], intensity_values[model]) - for idx, val in zip(intensity_idx, intensity_values[model]): + tc_haz.intensity[0, intensity_idx].toarray()[0], inten_ref, + ) + for idx, val in zip(intensity_idx, inten_ref): if val == 0: self.assertEqual(tc_haz.intensity[0, idx], 0) @@ -188,10 +203,14 @@ def test_windfield_models_different_windunits(self): intensity_values = { # Holland 1980 and Emanuel & Rotunno 2011 use recorded wind speeds, that is why checking them for different # windspeed units is so important: - "H1980": [21.376807, 21.957217, 22.569568, 21.284351, 24.254226, 26.971303, - 19.220149, 21.984516, 24.196388, 23.449116, 0, 31.550207], - "ER11": [23.565332, 24.931413, 26.360758, 23.490333, 29.601171, 34.522795, - 18.996389, 26.102109, 30.780737, 29.498453, 0, 38.368805], + "H1980": [ + 21.376807, 21.957217, 22.569568, 21.284351, 24.254226, 26.971303, + 19.220149, 21.984516, 24.196388, 23.449116, 0, 31.550207, + ], + "ER11": [ + 23.565332, 24.931413, 26.360758, 23.490333, 29.601171, 34.522795, + 18.996389, 26.102109, 30.780737, 29.498453, 0, 38.368805, + ], } tc_track = TCTracks.from_processed_ibtracs_csv(TEST_TRACK) @@ -271,227 +290,6 @@ def test_two_files_pass(self): self.assertEqual(tc_haz.fraction.nonzero()[0].size, 0) self.assertEqual(tc_haz.intensity.nonzero()[0].size, 0) -class TestWindfieldHelpers(unittest.TestCase): - """Test helper functions of TC wind field model""" - - def test_get_close_centroids_pass(self): - """Test get_close_centroids function.""" - si_track = xr.Dataset({ - "lat": ("time", np.array([0, -0.5, 0])), - "lon": ("time", np.array([0.9, 2, 3.2])), - }, attrs={"mid_lon": 0.0}) - centroids = np.array([ - [0, -0.2], [0, 0.9], [-1.1, 1.2], [1, 2.1], [0, 4.3], [0.6, 3.8], [0.9, 4.1], - ]) - centroids_close, mask_close, mask_close_alongtrack = ( - get_close_centroids(si_track, centroids, 112.0) - ) - self.assertEqual(centroids_close.shape[0], mask_close.sum()) - self.assertEqual(mask_close_alongtrack.shape[0], si_track.sizes["time"]) - self.assertEqual(mask_close_alongtrack.shape[1], centroids_close.shape[0]) - np.testing.assert_equal(mask_close_alongtrack.any(axis=0), True) - np.testing.assert_equal(mask_close, np.array( - [False, True, True, False, False, True, False] - )) - np.testing.assert_equal(mask_close_alongtrack, np.array([ - [True, False, False], - [False, True, False], - [False, False, True], - ])) - np.testing.assert_equal(centroids_close, centroids[mask_close]) - - # example where antimeridian is crossed - si_track = xr.Dataset({ - "lat": ("time", np.linspace(-10, 10, 11)), - "lon": ("time", np.linspace(170, 200, 11)), - }, attrs={"mid_lon": 180.0}) - centroids = np.array([[-11, 169], [-7, 176], [4, -170], [10, 170], [-10, -160]]) - centroids_close, mask_close, mask_close_alongtrack = ( - get_close_centroids(si_track, centroids, 600.0) - ) - self.assertEqual(centroids_close.shape[0], mask_close.sum()) - self.assertEqual(mask_close_alongtrack.shape[0], si_track.sizes["time"]) - self.assertEqual(mask_close_alongtrack.shape[1], centroids_close.shape[0]) - np.testing.assert_equal(mask_close_alongtrack.any(axis=0), True) - np.testing.assert_equal(mask_close, np.array([True, True, True, False, False])) - np.testing.assert_equal(centroids_close, np.array([ - # the longitudinal coordinate of the third centroid is normalized - [-11, 169], [-7, 176], [4, 190], - ])) - - def test_B_holland_1980_pass(self): - """Test _B_holland_1980 function.""" - si_track = xr.Dataset({ - "env": ("time", MBAR_TO_PA * np.array([1010, 1010])), - "cen": ("time", MBAR_TO_PA * np.array([995, 980])), - "vgrad": ("time", [35, 40]), - }) - _B_holland_1980(si_track) - np.testing.assert_array_almost_equal(si_track["hol_b"], [2.5, 1.667213]) - - def test_bs_holland_2008_pass(self): - """Test _bs_holland_2008 function. Compare to MATLAB reference.""" - si_track = xr.Dataset({ - "tstep": ("time", H_TO_S * np.array([1.0, 1.0, 1.0])), - "lat": ("time", [12.299999504631234, 12.299999504631343, 12.299999279463769]), - "env": ("time", MBAR_TO_PA * np.array([1010, 1010, 1010])), - "cen": ("time", MBAR_TO_PA * np.array([1005.2585, 1005.2633, 1005.2682])), - "vtrans_norm": ("time", [np.nan, 5.241999541820597, 5.123882725120426]), - }) - _bs_holland_2008(si_track) - np.testing.assert_array_almost_equal( - si_track["hol_b"], [np.nan, 1.27085617, 1.26555341]) - - def test_v_max_s_holland_2008_pass(self): - """Test _v_max_s_holland_2008 function.""" - # Numbers analogous to test_B_holland_1980_pass - si_track = xr.Dataset({ - "env": ("time", MBAR_TO_PA * np.array([1010, 1010])), - "cen": ("time", MBAR_TO_PA * np.array([995, 980])), - "hol_b": ("time", [2.5, 1.67]), - }) - _v_max_s_holland_2008(si_track) - np.testing.assert_array_almost_equal(si_track["vmax"], [34.635341, 40.033421]) - - def test_holland_2010_pass(self): - """Test Holland et al. 2010 wind field model.""" - # The parameter "x" is designed to be exactly 0.5 inside the radius of max wind (RMW) and - # to increase or decrease linearly outside of it in radial direction. - # - # An increase (decrease) of "x" outside of the RMW is for cases where the max wind is very - # high (low), but the RMW is still comparably large (small). This means, wind speeds need - # to decay very sharply (only moderately) outside of the RMW to reach the low prescribed - # peripheral wind speeds. - # - # The "hol_b" parameter tunes the meaning of a "comparably" large or small RMW. - si_track = xr.Dataset({ - # four test cases: - # - low vmax, moderate RMW: x decreases moderately - # - large hol_b: x decreases sharply - # - very low vmax: x decreases so much, it needs to be clipped at 0 - # - large vmax, large RMW: x increases - "rad": ("time", KM_TO_M * np.array([75, 75, 75, 90])), - "vmax": ("time", [35.0, 35.0, 16.0, 90.0]), - "hol_b": ("time", [1.75, 2.5, 1.9, 1.6]), - }) - d_centr = KM_TO_M * np.array([ - # first column is for locations within the storm eye - # second column is for locations at or close to the radius of max wind - # third column is for locations outside the storm eye - # fourth column is for locations exactly at the peripheral radius - # fifth column is for locations outside the peripheral radius - [0., 75, 220, 300, 490], - [30, 74, 170, 300, 501], - [21, 76, 230, 300, 431], - [32, 91, 270, 300, 452], - ], dtype=float) - close_centr = np.array([ - # note that we set one of these to "False" for testing - [True, True, True, True, True], - [True, True, True, True, False], - [True, True, True, True, True], - [True, True, True, True, True], - ], dtype=bool) - hol_x = _x_holland_2010(si_track, d_centr, close_centr) - np.testing.assert_array_almost_equal(hol_x, [ - [0.5, 0.500000, 0.485077, 0.476844, 0.457291], - [0.5, 0.500000, 0.410997, 0.289203, 0.000000], - [0.5, 0.497620, 0.131072, 0.000000, 0.000000], - [0.5, 0.505022, 1.403952, 1.554611, 2.317948], - ]) - - v_ang_norm = _stat_holland_2010(si_track, d_centr, close_centr, hol_x) - np.testing.assert_array_almost_equal(v_ang_norm, [ - # first column: converge to 0 when approaching storm eye - # second column: vmax at RMW - # fourth column: peripheral speed (17 m/s) at peripheral radius (unless x is clipped!) - [0.0000000, 35.000000, 21.181497, 17.00000, 12.103461], - [1.2964800, 34.990037, 21.593755, 17.00000, 0.0000000], - [0.3219518, 15.997500, 13.585498, 16.00000, 16.000000], - [24.823469, 89.992938, 24.381965, 17.00000, 1.9292020], - ]) - - def test_stat_holland_1980(self): - """Test _stat_holland_1980 function. Compare to MATLAB reference.""" - d_centr = KM_TO_M * np.array([ - [299.4501244109841, 291.0737897183741, 292.5441003235722, 40.665454622610511], - [293.6067129546862, 1000.0, 298.2652319413182, 70.0], - ]) - si_track = xr.Dataset({ - "rad": ("time", KM_TO_M * np.array([40.665454622610511, 75.547902916671745])), - "hol_b": ("time", [1.486076257880692, 1.265551666104679]), - "env": ("time", MBAR_TO_PA * np.array([1010.0, 1010.0])), - "cen": ("time", MBAR_TO_PA * np.array([970.8727666672957, 1005.268166666671])), - "lat": ("time", [-14.089110370469488, 12.299999279463769]), - "cp": ("time", [3.54921922e-05, 3.10598285e-05]), - }) - mask = np.array([[True, True, True, True], [True, False, True, True]], dtype=bool) - v_ang_norm = _stat_holland_1980(si_track, d_centr, mask) - np.testing.assert_array_almost_equal(v_ang_norm, - [[11.279764005440288, 11.682978583939310, 11.610940769149384, 42.412845], - [5.384115724400597, 0, 5.281356766052531, 12.763087]]) - - # without Coriolis force, values are higher, esp. far away from the center: - v_ang_norm = _stat_holland_1980(si_track, d_centr, mask, cyclostrophic=True) - np.testing.assert_array_almost_equal(v_ang_norm, - [[15.719924, 16.037052, 15.980323, 43.128461], - [8.836768, 0, 8.764678, 13.807452]]) - - d_centr = np.array([[], []]) - mask = np.ones_like(d_centr, dtype=bool) - v_ang_norm = _stat_holland_1980(si_track, d_centr, mask) - np.testing.assert_array_equal(v_ang_norm, np.array([[], []])) - - def test_er_2011_pass(self): - """Test Emanuel and Rotunno 2011 wind field model.""" - # test at centroids within and outside of radius of max wind - d_centr = KM_TO_M * np.array([[35, 70, 75, 220], [30, 150, 1000, 300]], dtype=float) - si_track = xr.Dataset({ - "rad": ("time", KM_TO_M * np.array([75.0, 40.0])), - "vmax": ("time", [35.0, 40.0]), - "lat": ("time", [20.0, 27.0]), - "cp": ("time", [4.98665369e-05, 6.61918149e-05]), - }) - mask = np.array([[True, True, True, True], [True, False, True, True]], dtype=bool) - v_ang_norm = _stat_er_2011(si_track, d_centr, mask) - np.testing.assert_array_almost_equal(v_ang_norm, - [[28.258025, 36.782418, 36.869995, 22.521237], - [39.670883, 0, 3.300626, 10.827206]]) - - def test_vtrans_pass(self): - """Test _vtrans function. Compare to MATLAB reference.""" - tc_track = TCTracks.from_processed_ibtracs_csv(TEST_TRACK) - tc_track.equal_timestep() - track_ds = tc_track.data[0] - - si_track = tctrack_to_si(track_ds) - _vtrans(si_track) - - to_kn = (1.0 * ureg.meter / ureg.second).to(ureg.knot).magnitude - - self.assertEqual(si_track["vtrans_norm"].size, track_ds["time"].size) - self.assertEqual(si_track["vtrans_norm"].values[0], 0) - self.assertAlmostEqual(si_track["vtrans_norm"].values[1] * to_kn, 10.191466246) - - def testtctrack_to_si(self): - """ Test tctrack_to_si should create the same vmax output independent of the input unit """ - tc_track = TCTracks.from_processed_ibtracs_csv(TEST_TRACK_SHORT).data[0] - - tc_track_kmph = tc_track.copy(deep=True) - tc_track_kmph['max_sustained_wind'] *= ( - (1.0 * ureg.knot).to(ureg.km / ureg.hour).magnitude - ) - tc_track_kmph.attrs['max_sustained_wind_unit'] = 'km/h' - - si_track = tctrack_to_si(tc_track) - si_track_from_kmph = tctrack_to_si(tc_track_kmph) - - np.testing.assert_array_almost_equal(si_track["vmax"], si_track_from_kmph["vmax"]) - - tc_track.attrs['max_sustained_wind_unit'] = 'elbows/fortnight' - with self.assertRaises(ValueError): - tctrack_to_si(tc_track) - class TestClimateSce(unittest.TestCase): def test_apply_criterion_track(self): @@ -650,7 +448,6 @@ def tearDown(self): if __name__ == "__main__": TESTS = unittest.TestLoader().loadTestsFromTestCase(TestReader) - TESTS.addTests(unittest.TestLoader().loadTestsFromTestCase(TestWindfieldHelpers)) TESTS.addTests(unittest.TestLoader().loadTestsFromTestCase(TestClimateSce)) TESTS.addTests(unittest.TestLoader().loadTestsFromTestCase(TestDumpReloadCycle)) unittest.TextTestRunner(verbosity=2).run(TESTS) diff --git a/climada/hazard/test/test_trop_cyclone_windfields.py b/climada/hazard/test/test_trop_cyclone_windfields.py new file mode 100644 index 0000000000..f91ac075ab --- /dev/null +++ b/climada/hazard/test/test_trop_cyclone_windfields.py @@ -0,0 +1,247 @@ +import unittest + +import numpy as np +import xarray as xr + +from climada.hazard import TCTracks +from climada.hazard.test.test_trop_cyclone import TEST_TRACK, TEST_TRACK_SHORT +from climada.hazard.trop_cyclone.trop_cyclone_windfields import get_close_centroids, MBAR_TO_PA, _B_holland_1980, H_TO_S, \ + _bs_holland_2008, _v_max_s_holland_2008, KM_TO_M, _x_holland_2010, _stat_holland_2010, _stat_holland_1980, \ + _stat_er_2011, tctrack_to_si, _vtrans +from climada.util import ureg + + +class TestWindfieldHelpers(unittest.TestCase): + """Test helper functions of TC wind field model""" + + def test_get_close_centroids_pass(self): + """Test get_close_centroids function.""" + si_track = xr.Dataset({ + "lat": ("time", np.array([0, -0.5, 0])), + "lon": ("time", np.array([0.9, 2, 3.2])), + }, attrs={"mid_lon": 0.0}) + centroids = np.array([ + [0, -0.2], [0, 0.9], [-1.1, 1.2], [1, 2.1], [0, 4.3], [0.6, 3.8], [0.9, 4.1], + ]) + centroids_close, mask_close, mask_close_alongtrack = ( + get_close_centroids(si_track, centroids, 112.0) + ) + self.assertEqual(centroids_close.shape[0], mask_close.sum()) + self.assertEqual(mask_close_alongtrack.shape[0], si_track.sizes["time"]) + self.assertEqual(mask_close_alongtrack.shape[1], centroids_close.shape[0]) + np.testing.assert_equal(mask_close_alongtrack.any(axis=0), True) + np.testing.assert_equal(mask_close, np.array( + [False, True, True, False, False, True, False] + )) + np.testing.assert_equal(mask_close_alongtrack, np.array([ + [True, False, False], + [False, True, False], + [False, False, True], + ])) + np.testing.assert_equal(centroids_close, centroids[mask_close]) + + # example where antimeridian is crossed + si_track = xr.Dataset({ + "lat": ("time", np.linspace(-10, 10, 11)), + "lon": ("time", np.linspace(170, 200, 11)), + }, attrs={"mid_lon": 180.0}) + centroids = np.array([[-11, 169], [-7, 176], [4, -170], [10, 170], [-10, -160]]) + centroids_close, mask_close, mask_close_alongtrack = ( + get_close_centroids(si_track, centroids, 600.0) + ) + self.assertEqual(centroids_close.shape[0], mask_close.sum()) + self.assertEqual(mask_close_alongtrack.shape[0], si_track.sizes["time"]) + self.assertEqual(mask_close_alongtrack.shape[1], centroids_close.shape[0]) + np.testing.assert_equal(mask_close_alongtrack.any(axis=0), True) + np.testing.assert_equal(mask_close, np.array([True, True, True, False, False])) + np.testing.assert_equal(centroids_close, np.array([ + # the longitudinal coordinate of the third centroid is normalized + [-11, 169], [-7, 176], [4, 190], + ])) + + def test_B_holland_1980_pass(self): + """Test _B_holland_1980 function.""" + si_track = xr.Dataset({ + "pdelta": ("time", MBAR_TO_PA * np.array([15, 30])), + "vgrad": ("time", [35, 40]), + "rho_air": ("time", [1.15, 1.15]) + }) + _B_holland_1980(si_track) + np.testing.assert_array_almost_equal(si_track["hol_b"], [2.5, 1.667213]) + + si_track = xr.Dataset({ + "pdelta": ("time", MBAR_TO_PA * np.array([4.74, 15, 30, 40])), + "vmax": ("time", [np.nan, 22.5, 25.4, 42.5]), + "rho_air": ("time", [1.2, 1.2, 1.2, 1.2]) + }) + _B_holland_1980(si_track, gradient_to_surface_winds=0.9) + np.testing.assert_allclose(si_track["hol_b"], [np.nan, 1.101, 0.810, 1.473], atol=1e-3) + + def test_bs_holland_2008_pass(self): + """Test _bs_holland_2008 function. Compare to MATLAB reference.""" + si_track = xr.Dataset({ + "tstep": ("time", H_TO_S * np.array([1.0, 1.0, 1.0])), + "lat": ("time", [12.299999504631234, 12.299999504631343, 12.299999279463769]), + "pdelta": ("time", MBAR_TO_PA * np.array([4.74, 4.73, 4.73])), + "cen": ("time", MBAR_TO_PA * np.array([1005.2585, 1005.2633, 1005.2682])), + "vtrans_norm": ("time", [np.nan, 5.241999541820597, 5.123882725120426]), + }) + _bs_holland_2008(si_track) + np.testing.assert_allclose( + si_track["hol_b"], [np.nan, 1.27, 1.27], atol=1e-2 + ) + + def test_v_max_s_holland_2008_pass(self): + """Test _v_max_s_holland_2008 function.""" + # Numbers analogous to test_B_holland_1980_pass + si_track = xr.Dataset({ + "pdelta": ("time", MBAR_TO_PA * np.array([15, 30])), + "hol_b": ("time", [2.5, 1.67]), + "rho_air": ("time", [1.15, 1.15]), + }) + _v_max_s_holland_2008(si_track) + np.testing.assert_array_almost_equal(si_track["vmax"], [34.635341, 40.033421]) + + def test_holland_2010_pass(self): + """Test Holland et al. 2010 wind field model.""" + # The parameter "x" is designed to be exactly 0.5 inside the radius of max wind (RMW) and + # to increase or decrease linearly outside of it in radial direction. + # + # An increase (decrease) of "x" outside of the RMW is for cases where the max wind is very + # high (low), but the RMW is still comparably large (small). This means, wind speeds need + # to decay very sharply (only moderately) outside of the RMW to reach the low prescribed + # peripheral wind speeds. + # + # The "hol_b" parameter tunes the meaning of a "comparably" large or small RMW. + si_track = xr.Dataset({ + # four test cases: + # - low vmax, moderate RMW: x decreases moderately + # - large hol_b: x decreases sharply + # - very low vmax: x decreases so much, it needs to be clipped at 0 + # - large vmax, large RMW: x increases + "rad": ("time", KM_TO_M * np.array([75, 75, 75, 90])), + "vmax": ("time", [35.0, 35.0, 16.0, 90.0]), + "hol_b": ("time", [1.75, 2.5, 1.9, 1.6]), + }) + d_centr = KM_TO_M * np.array([ + # first column is for locations within the storm eye + # second column is for locations at or close to the radius of max wind + # third column is for locations outside the storm eye + # fourth column is for locations exactly at the peripheral radius + # fifth column is for locations outside the peripheral radius + [0., 75, 220, 300, 490], + [30, 74, 170, 300, 501], + [21, 76, 230, 300, 431], + [32, 91, 270, 300, 452], + ], dtype=float) + close_centr = np.array([ + # note that we set one of these to "False" for testing + [True, True, True, True, True], + [True, True, True, True, False], + [True, True, True, True, True], + [True, True, True, True, True], + ], dtype=bool) + hol_x = _x_holland_2010(si_track, d_centr, close_centr) + np.testing.assert_array_almost_equal(hol_x, [ + [0.5, 0.500000, 0.485077, 0.476844, 0.457291], + [0.5, 0.500000, 0.410997, 0.400000, 0.000000], + [0.5, 0.497620, 0.400000, 0.400000, 0.400000], + [0.5, 0.505022, 1.403952, 1.554611, 2.317948], + ]) + + v_ang_norm = _stat_holland_2010(si_track, d_centr, close_centr, hol_x) + np.testing.assert_allclose(v_ang_norm, [ + # first column: converge to 0 when approaching storm eye + # second column: vmax at RMW + # fourth column: peripheral speed (17 m/s) at peripheral radius (unless x is clipped!) + [ 0.000000, 35.000000, 21.181497, 17.000000, 12.1034610], + [ 1.296480, 34.990037, 21.593755, 12.891313, 0.0000000], + [ 0.321952, 15.997500, 9.712006, 8.087240, 6.2289690], + [24.823469, 89.992938, 24.381965, 17.000000, 1.9292020], + ], atol=1e-6) + + def test_stat_holland_1980(self): + """Test _stat_holland_1980 function. Compare to MATLAB reference.""" + d_centr = KM_TO_M * np.array([ + [299.4501244109841, 291.0737897183741, 292.5441003235722, 40.665454622610511], + [293.6067129546862, 1000.0, 298.2652319413182, 70.0], + ]) + si_track = xr.Dataset({ + "rad": ("time", KM_TO_M * np.array([40.665454622610511, 75.547902916671745])), + "hol_b": ("time", [1.486076257880692, 1.265551666104679]), + "pdelta": ("time", MBAR_TO_PA * np.array([39.12, 4.73])), + "lat": ("time", [-14.089110370469488, 12.299999279463769]), + "cp": ("time", [3.54921922e-05, 3.10598285e-05]), + "rho_air": ("time", [1.15, 1.15]), + }) + mask = np.array([[True, True, True, True], [True, False, True, True]], dtype=bool) + v_ang_norm = _stat_holland_1980(si_track, d_centr, mask) + np.testing.assert_allclose( + v_ang_norm, [[11.28, 11.68, 11.61, 42.41], [5.38, 0, 5.28, 12.76]], atol=1e-2, + ) + + # without Coriolis force, values are higher, esp. far away from the center: + v_ang_norm = _stat_holland_1980(si_track, d_centr, mask, cyclostrophic=True) + np.testing.assert_allclose( + v_ang_norm, [[15.72, 16.04, 15.98, 43.13], [8.84, 0, 8.76, 13.81]], atol=1e-2, + ) + + d_centr = np.array([[], []]) + mask = np.ones_like(d_centr, dtype=bool) + v_ang_norm = _stat_holland_1980(si_track, d_centr, mask) + np.testing.assert_array_equal(v_ang_norm, np.array([[], []])) + + def test_er_2011_pass(self): + """Test Emanuel and Rotunno 2011 wind field model.""" + # test at centroids within and outside of radius of max wind + d_centr = KM_TO_M * np.array([[35, 70, 75, 220], [30, 150, 1000, 300]], dtype=float) + si_track = xr.Dataset({ + "rad": ("time", KM_TO_M * np.array([75.0, 40.0])), + "vmax": ("time", [35.0, 40.0]), + "lat": ("time", [20.0, 27.0]), + "cp": ("time", [4.98665369e-05, 6.61918149e-05]), + }) + mask = np.array([[True, True, True, True], [True, False, True, True]], dtype=bool) + v_ang_norm = _stat_er_2011(si_track, d_centr, mask) + np.testing.assert_array_almost_equal(v_ang_norm, + [[28.258025, 36.782418, 36.869995, 22.521237], + [39.670883, 0, 3.300626, 10.827206]]) + + def test_vtrans_pass(self): + """Test _vtrans function. Compare to MATLAB reference.""" + tc_track = TCTracks.from_processed_ibtracs_csv(TEST_TRACK) + tc_track.equal_timestep() + track_ds = tc_track.data[0] + + si_track = tctrack_to_si(track_ds) + _vtrans(si_track) + + to_kn = (1.0 * ureg.meter / ureg.second).to(ureg.knot).magnitude + + self.assertEqual(si_track["vtrans_norm"].size, track_ds["time"].size) + self.assertEqual(si_track["vtrans_norm"].values[0], 0) + self.assertAlmostEqual(si_track["vtrans_norm"].values[1] * to_kn, 10.191466246) + + def testtctrack_to_si(self): + """ Test tctrack_to_si should create the same vmax output independent of the input unit """ + tc_track = TCTracks.from_processed_ibtracs_csv(TEST_TRACK_SHORT).data[0] + + tc_track_kmph = tc_track.copy(deep=True) + tc_track_kmph['max_sustained_wind'] *= ( + (1.0 * ureg.knot).to(ureg.km / ureg.hour).magnitude + ) + tc_track_kmph.attrs['max_sustained_wind_unit'] = 'km/h' + + si_track = tctrack_to_si(tc_track) + si_track_from_kmph = tctrack_to_si(tc_track_kmph) + + np.testing.assert_array_almost_equal(si_track["vmax"], si_track_from_kmph["vmax"]) + + tc_track.attrs['max_sustained_wind_unit'] = 'elbows/fortnight' + with self.assertRaises(ValueError): + tctrack_to_si(tc_track) + + +if __name__ == "__main__": + TESTS = unittest.TestLoader().loadTestsFromTestCase(TestWindfieldHelpers) + unittest.TextTestRunner(verbosity=2).run(TESTS) diff --git a/climada/hazard/trop_cyclone/__init__.py b/climada/hazard/trop_cyclone/__init__.py new file mode 100644 index 0000000000..452bf43646 --- /dev/null +++ b/climada/hazard/trop_cyclone/__init__.py @@ -0,0 +1,5 @@ +from climada.hazard.trop_cyclone.trop_cyclone import * +from climada.hazard.trop_cyclone.trop_cyclone_windfields import compute_windfields_sparse, compute_angular_windspeeds, tctrack_to_si, \ + get_close_centroids, KN_TO_MS, KM_TO_M, KM_TO_M, H_TO_S, NM_TO_KM, KMH_TO_MS, MBAR_TO_PA, \ + DEF_MAX_DIST_EYE_KM, DEF_INTENSITY_THRES, DEF_MAX_MEMORY_GB, MODEL_VANG, DEF_RHO_AIR, DEF_GRADIENT_TO_SURFACE_WINDS, \ + T_ICE_K, V_ANG_EARTH diff --git a/climada/hazard/trop_cyclone/trop_cyclone.py b/climada/hazard/trop_cyclone/trop_cyclone.py new file mode 100644 index 0000000000..6fc8347e97 --- /dev/null +++ b/climada/hazard/trop_cyclone/trop_cyclone.py @@ -0,0 +1,696 @@ +""" +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 TC wind hazard (TropCyclone class). +""" + +__all__ = ['TropCyclone'] + +import copy +import datetime as dt +import itertools +import logging +import time +from typing import Optional, Tuple, List + +import numpy as np +from scipy import sparse +import matplotlib.animation as animation +from tqdm import tqdm +import pathos.pools +import xarray as xr + +from climada.hazard.base import Hazard +from climada.hazard.tc_tracks import TCTracks +from climada.hazard.tc_clim_change import get_knutson_criterion, calc_scale_knutson +from climada.hazard.centroids.centr import Centroids +import climada.util.constants as u_const +import climada.util.coordinates as u_coord +import climada.util.plot as u_plot + +from .trop_cyclone_windfields import DEF_MAX_DIST_EYE_KM, DEF_INTENSITY_THRES, \ + DEF_MAX_MEMORY_GB, compute_windfields_sparse + +LOGGER = logging.getLogger(__name__) + +HAZ_TYPE = 'TC' +"""Hazard type acronym for Tropical Cyclone""" + + +class TropCyclone(Hazard): + """ + Contains tropical cyclone events. + + Attributes + ---------- + category : np.ndarray of ints + for every event, the TC category using the Saffir-Simpson scale: + + * -1 tropical depression + * 0 tropical storm + * 1 Hurrican category 1 + * 2 Hurrican category 2 + * 3 Hurrican category 3 + * 4 Hurrican category 4 + * 5 Hurrican category 5 + basin : list of str + Basin where every event starts: + + * 'NA' North Atlantic + * 'EP' Eastern North Pacific + * 'WP' Western North Pacific + * 'NI' North Indian + * 'SI' South Indian + * 'SP' Southern Pacific + * 'SA' South Atlantic + windfields : list of csr_matrix + For each event, the full velocity vectors at each centroid and track position in a sparse + matrix of shape (npositions, ncentroids * 2) that can be reshaped to a full ndarray of + shape (npositions, ncentroids, 2). + """ + intensity_thres = DEF_INTENSITY_THRES + """intensity threshold for storage in m/s""" + + vars_opt = Hazard.vars_opt.union({'category'}) + """Name of the variables that are not needed to compute the impact.""" + + def __init__( + self, + category: Optional[np.ndarray] = None, + basin: Optional[List] = None, + windfields: Optional[List[sparse.csr_matrix]] = None, + **kwargs, + ): + """Initialize values. + + Parameters + ---------- + category : np.ndarray of int, optional + For every event, the TC category using the Saffir-Simpson scale: + -1 tropical depression + 0 tropical storm + 1 Hurrican category 1 + 2 Hurrican category 2 + 3 Hurrican category 3 + 4 Hurrican category 4 + 5 Hurrican category 5 + basin : list of str, optional + Basin where every event starts: + 'NA' North Atlantic + 'EP' Eastern North Pacific + 'WP' Western North Pacific + 'NI' North Indian + 'SI' South Indian + 'SP' Southern Pacific + 'SA' South Atlantic + windfields : list of csr_matrix, optional + For each event, the full velocity vectors at each centroid and track position in a + sparse matrix of shape (npositions, ncentroids * 2) that can be reshaped to a full + ndarray of shape (npositions, ncentroids, 2). + **kwargs : Hazard properties, optional + All other keyword arguments are passed to the Hazard constructor. + """ + kwargs.setdefault('haz_type', HAZ_TYPE) + Hazard.__init__(self, **kwargs) + self.category = category if category is not None else np.array([], int) + self.basin = basin if basin is not None else [] + self.windfields = windfields if windfields is not None else [] + + def set_from_tracks(self, *args, **kwargs): + """This function is deprecated, use TropCyclone.from_tracks instead.""" + LOGGER.warning("The use of TropCyclone.set_from_tracks is deprecated." + "Use TropCyclone.from_tracks instead.") + if "intensity_thres" not in kwargs: + # some users modify the threshold attribute before calling `set_from_tracks` + kwargs["intensity_thres"] = self.intensity_thres + if self.pool is not None and 'pool' not in kwargs: + kwargs['pool'] = self.pool + self.__dict__ = TropCyclone.from_tracks(*args, **kwargs).__dict__ + + @classmethod + def from_tracks( + cls, + tracks: TCTracks, + centroids: Centroids, + pool: Optional[pathos.pools.ProcessPool] = None, + model: str = 'H08', + model_kwargs: Optional[dict] = None, + ignore_distance_to_coast: bool = False, + store_windfields: bool = False, + metric: str = "equirect", + intensity_thres: float = DEF_INTENSITY_THRES, + max_latitude: float = 61, + max_dist_inland_km: float = 1000, + max_dist_eye_km: float = DEF_MAX_DIST_EYE_KM, + max_memory_gb: float = DEF_MAX_MEMORY_GB, + ): + """ + Create new TropCyclone instance that contains windfields from the specified tracks. + + This function sets the ``intensity`` attribute to contain, for each centroid, + the maximum wind speed (1-minute sustained winds at 10 meters above ground) + experienced over the whole period of each TC event in m/s. The wind speed is set + to 0 if it doesn't exceed the threshold ``intensity_thres``. + + The ``category`` attribute is set to the value of the ``category``-attribute + of each of the given track data sets. + + The ``basin`` attribute is set to the genesis basin for each event, which + is the first value of the ``basin``-variable in each of the given track data sets. + + Optionally, the time dependent, vectorial winds can be stored using the + ``store_windfields`` function parameter (see below). + + Parameters + ---------- + tracks : climada.hazard.TCTracks + Tracks of storm events. + centroids : Centroids, optional + Centroids where to model TC. Default: global centroids at 360 arc-seconds + resolution. + pool : pathos.pool, optional + Pool that will be used for parallel computation of wind fields. Default: + None + model : str, optional + Parametric wind field model to use. Default: "H08". + + * ``"H1980"`` (the prominent Holland 1980 model) from the paper: + Holland, G.J. (1980): An Analytic Model of the Wind and Pressure + Profiles in Hurricanes. Monthly Weather Review 108(8): 1212–1218. + ``https://doi.org/10.1175/1520-0493(1980)108<1212:AAMOTW>2.0.CO;2`` + * ``"H08"`` (Holland 1980 with b-value from Holland 2008) from the paper: + Holland, G. (2008). A revised hurricane pressure-wind model. Monthly + Weather Review, 136(9), 3432–3445. + https://doi.org/10.1175/2008MWR2395.1 + * ``"H10"`` (Holland et al. 2010) from the paper: + Holland et al. (2010): A Revised Model for Radial Profiles of + Hurricane Winds. Monthly Weather Review 138(12): 4393–4401. + https://doi.org/10.1175/2010MWR3317.1 + * ``"ER11"`` (Emanuel and Rotunno 2011) from the paper: + Emanuel, K., Rotunno, R. (2011): Self-Stratification of Tropical + Cyclone Outflow. Part I: Implications for Storm Structure. Journal + of the Atmospheric Sciences 68(10): 2236–2249. + https://dx.doi.org/10.1175/JAS-D-10-05024.1 + model_kwargs : dict, optional + If given, forward these kwargs to the selected wind model. None of the + parameters is currently supported by the ER11 model. Default: None. + The Holland models support the following parameters, in alphabetical order: + + gradient_to_surface_winds : float, optional + The gradient-to-surface wind reduction factor to use. In H1980, the wind + profile is computed on the gradient level, and wind speeds are converted + to the surface level using this factor. In H08 and H10, the wind profile + is computed on the surface level, but the clipping interval of the + B-value depends on this factor. Default: 0.9 + rho_air_const : float or None, optional + The constant value for air density (in kg/m³) to assume in the formulas + from Holland 1980. By default, the constant value suggested in Holland + 1980 is used. If set to None, the air density is computed from pressure + following equation (9) in Holland et al. 2010. Default: 1.15 + vmax_from_cen : boolean, optional + Only used in H10. If True, replace the recorded value of vmax along the + track by an estimate from pressure, following equation (8) in Holland et + al. 2010. Default: True + vmax_in_brackets : bool, optional + Only used in H10. Specifies which of the two formulas in equation (6) of + Holland et al. 2010 to use. If False, the formula with vmax outside of + the brackets is used. Note that, a side-effect of the formula with vmax + inside of the brackets is that the wind speed maximum is attained a bit + farther away from the center than according to the recorded radius of + maximum winds (RMW). Default: False + + ignore_distance_to_coast : boolean, optional + If True, centroids far from coast are not ignored. + If False, the centroids' distances to the coast are calculated with the + `Centroids.get_dist_coast()` method (unless there is "dist_coast" column in + the centroids' GeoDataFrame) and centroids far from coast are ignored. + Default: False. + store_windfields : boolean, optional + If True, the Hazard object gets a list ``windfields`` of sparse matrices. + For each track, the full velocity vectors at each centroid and track + position are stored in a sparse matrix of shape (npositions, + ncentroids * 2) that can be reshaped to a full ndarray of shape (npositions, + ncentroids, 2). Default: False. + metric : str, optional + Specify an approximation method to use for earth distances: + + * "equirect": Distance according to sinusoidal projection. Fast, but + inaccurate for large distances and high latitudes. + * "geosphere": Exact spherical distance. Much more accurate at all + distances, but slow. + + Default: "equirect". + intensity_thres : float, optional + Wind speeds (in m/s) below this threshold are stored as 0. Default: 17.5 + max_latitude : float, optional + No wind speed calculation is done for centroids with latitude larger than + this parameter. Default: 61 + max_dist_inland_km : float, optional + No wind speed calculation is done for centroids with a distance (in km) to + the coast larger than this parameter. Default: 1000 + max_dist_eye_km : float, optional + No wind speed calculation is done for centroids with a distance (in km) to + the TC center ("eye") larger than this parameter. Default: 300 + max_memory_gb : float, optional + To avoid memory issues, the computation is done for chunks of the track + sequentially. The chunk size is determined depending on the available memory + (in GB). Note that this limit applies to each thread separately if a + ``pool`` is used. Default: 8 + + Raises + ------ + ValueError + + Returns + ------- + TropCyclone + """ + num_tracks = tracks.size + + if ignore_distance_to_coast: + # Select centroids with lat <= max_latitude + [idx_centr_filter] = (np.abs(centroids.lat) <= max_latitude).nonzero() + else: + # Select centroids which are inside max_dist_inland_km and lat <= max_latitude + if 'dist_coast' not in centroids.gdf.columns: + dist_coast = centroids.get_dist_coast() + else: + dist_coast = centroids.gdf.dist_coast.values + [idx_centr_filter] = ( + (dist_coast <= max_dist_inland_km * 1000) + & (np.abs(centroids.lat) <= max_latitude) + ).nonzero() + + # Filter early with a larger threshold, but inaccurate (lat/lon) distances. + # Later, there will be another filtering step with more accurate distances in km. + max_dist_eye_deg = max_dist_eye_km / ( + u_const.ONE_LAT_KM * np.cos(np.radians(max_latitude)) + ) + + # Restrict to coastal centroids within reach of any of the tracks + t_lon_min, t_lat_min, t_lon_max, t_lat_max = tracks.get_bounds(deg_buffer=max_dist_eye_deg) + t_mid_lon = 0.5 * (t_lon_min + t_lon_max) + filtered_centroids = centroids.coord[idx_centr_filter] + u_coord.lon_normalize(filtered_centroids[:, 1], center=t_mid_lon) + idx_centr_filter = idx_centr_filter[ + (t_lon_min <= filtered_centroids[:, 1]) + & (filtered_centroids[:, 1] <= t_lon_max) + & (t_lat_min <= filtered_centroids[:, 0]) + & (filtered_centroids[:, 0] <= t_lat_max) + ] + + # prepare keyword arguments to pass to `from_single_track` + kwargs_from_single_track = dict( + centroids=centroids, + idx_centr_filter=idx_centr_filter, + model=model, + model_kwargs=model_kwargs, + store_windfields=store_windfields, + metric=metric, + intensity_thres=intensity_thres, + max_dist_eye_km=max_dist_eye_km, + max_memory_gb=max_memory_gb, + ) + + LOGGER.info( + 'Mapping %d tracks to %d coastal centroids.', num_tracks, idx_centr_filter.size, + ) + if pool: + chunksize = max(min(num_tracks // pool.ncpus, 1000), 1) + kwargs_repeated = [ + itertools.repeat(val, num_tracks) + for val in kwargs_from_single_track.values() + ] + tc_haz_list = pool.map( + cls.from_single_track, + tracks.data, + *kwargs_repeated, + chunksize=chunksize, + ) + else: + last_perc = 0 + tc_haz_list = [] + for track in tracks.data: + perc = 100 * len(tc_haz_list) / len(tracks.data) + if perc - last_perc >= 10: + LOGGER.info("Progress: %d%%", perc) + last_perc = perc + tc_haz_list.append( + cls.from_single_track(track, **kwargs_from_single_track) + ) + if last_perc < 100: + LOGGER.info("Progress: 100%") + + LOGGER.debug('Concatenate events.') + haz = cls.concat(tc_haz_list) + haz.pool = pool + haz.intensity_thres = intensity_thres + LOGGER.debug('Compute frequency.') + haz.frequency_from_tracks(tracks.data) + return haz + + def apply_climate_scenario_knu( + self, + ref_year: int = 2050, + rcp_scenario: int = 45 + ): + """ + From current TC hazard instance, return new hazard set with + future events for a given RCP scenario and year based on the + parametrized values derived from Table 3 in Knutson et al 2015. + https://doi.org/10.1175/JCLI-D-15-0129.1 . The scaling for different + years and RCP scenarios is obtained by linear interpolation. + + Note: The parametrized values are derived from the overall changes + in statistical ensemble of tracks. Hence, this method should only be + applied to sufficiently large tropical cyclone event sets that + approximate the reference years 1981 - 2008 used in Knutson et. al. + + The frequency and intensity changes are applied independently from + one another. The mean intensity factors can thus slightly deviate + from the Knutson value (deviation was found to be less than 1% + for default IBTrACS event sets 1980-2020 for each basin). + + Parameters + ---------- + ref_year : int + year between 2000 ad 2100. Default: 2050 + rcp_scenario : int + 26 for RCP 2.6, 45 for RCP 4.5, 60 for RCP 6.0 and 85 for RCP 8.5. + The default is 45. + + Returns + ------- + haz_cc : climada.hazard.TropCyclone + Tropical cyclone with frequencies and intensity scaled according + to the Knutson criterion for the given year and RCP. Returns + a new instance of climada.hazard.TropCyclone, self is not + modified. + """ + chg_int_freq = get_knutson_criterion() + scale_rcp_year = calc_scale_knutson(ref_year, rcp_scenario) + haz_cc = self._apply_knutson_criterion(chg_int_freq, scale_rcp_year) + return haz_cc + + def set_climate_scenario_knu(self, *args, **kwargs): + """This function is deprecated, use TropCyclone.apply_climate_scenario_knu instead.""" + LOGGER.warning("The use of TropCyclone.set_climate_scenario_knu is deprecated." + "Use TropCyclone.apply_climate_scenario_knu instead.") + return self.apply_climate_scenario_knu(*args, **kwargs) + + @classmethod + def video_intensity( + cls, + track_name: str, + tracks: TCTracks, + centroids: Centroids, + file_name: Optional[str] = None, + writer: animation = animation.PillowWriter(bitrate=500), + figsize: Tuple[float, float] = (9, 13), + adapt_fontsize: bool = True, + **kwargs + ): + """ + Generate video of TC wind fields node by node and returns its + corresponding TropCyclone instances and track pieces. + + Parameters + ---------- + track_name : str + name of the track contained in tracks to record + tracks : climada.hazard.TCTracks + tropical cyclone tracks + centroids : climada.hazard.Centroids + centroids where wind fields are mapped + file_name : str, optional + file name to save video (including full path and file extension) + writer : matplotlib.animation.*, optional + video writer. Default is pillow with bitrate=500 + figsize : tuple, optional + figure size for plt.subplots + adapt_fontsize : bool, optional + If set to true, the size of the fonts will be adapted to the size of the figure. + Otherwise the default matplotlib font size is used. Default is True. + kwargs : optional + arguments for pcolormesh matplotlib function used in event plots + + Returns + ------- + tc_list, tc_coord : list(TropCyclone), list(np.ndarray) + + Raises + ------ + ValueError + + """ + # initialization + track = tracks.get_track(track_name) + if not track: + raise ValueError(f'{track_name} not found in track data.') + idx_plt = np.argwhere( + (track.lon.values < centroids.total_bounds[2] + 1) + & (centroids.total_bounds[0] - 1 < track.lon.values) + & (track.lat.values < centroids.total_bounds[3] + 1) + & (centroids.total_bounds[1] - 1 < track.lat.values) + ).reshape(-1) + + tc_list = [] + tr_coord = {'lat': [], 'lon': []} + for node in range(idx_plt.size - 2): + tr_piece = track.sel( + time=slice(track.time.values[idx_plt[node]], + track.time.values[idx_plt[node + 2]])) + tr_piece.attrs['n_nodes'] = 2 # plot only one node + tr_sel = TCTracks() + tr_sel.append(tr_piece) + tr_coord['lat'].append(tr_sel.data[0].lat.values[:-1]) + tr_coord['lon'].append(tr_sel.data[0].lon.values[:-1]) + + tc_tmp = cls.from_tracks(tr_sel, centroids=centroids) + tc_tmp.event_name = [ + track.name + ' ' + time.strftime( + "%d %h %Y %H:%M", + time.gmtime(tr_sel.data[0].time[1].values.astype(int) + / 1000000000) + ) + ] + tc_list.append(tc_tmp) + + if 'cmap' not in kwargs: + kwargs['cmap'] = 'Greys' + if 'vmin' not in kwargs: + kwargs['vmin'] = np.array([tc_.intensity.min() for tc_ in tc_list]).min() + if 'vmax' not in kwargs: + kwargs['vmax'] = np.array([tc_.intensity.max() for tc_ in tc_list]).max() + + def run(node): + tc_list[node].plot_intensity(1, axis=axis, **kwargs) + axis.plot(tr_coord['lon'][node], tr_coord['lat'][node], 'k') + axis.set_title(tc_list[node].event_name[0]) + pbar.update() + + if file_name: + LOGGER.info('Generating video %s', file_name) + fig, axis, _fontsize = u_plot.make_map(figsize=figsize, adapt_fontsize=adapt_fontsize) + pbar = tqdm(total=idx_plt.size - 2) + ani = animation.FuncAnimation(fig, run, frames=idx_plt.size - 2, + interval=500, blit=False) + fig.tight_layout() + ani.save(file_name, writer=writer) + pbar.close() + return tc_list, tr_coord + + def frequency_from_tracks(self, tracks: List): + """ + Set hazard frequency from tracks data. + + Parameters + ---------- + tracks : list of xarray.Dataset + """ + if not tracks: + return + year_max = np.amax([t.time.dt.year.values.max() for t in tracks]) + year_min = np.amin([t.time.dt.year.values.min() for t in tracks]) + year_delta = year_max - year_min + 1 + num_orig = np.count_nonzero(self.orig) + ens_size = (self.event_id.size / num_orig) if num_orig > 0 else 1 + self.frequency = np.ones(self.event_id.size) / (year_delta * ens_size) + + @classmethod + def from_single_track( + cls, + track: xr.Dataset, + centroids: Centroids, + idx_centr_filter: np.ndarray, + model: str = 'H08', + model_kwargs: Optional[dict] = None, + store_windfields: bool = False, + metric: str = "equirect", + intensity_thres: float = DEF_INTENSITY_THRES, + max_dist_eye_km: float = DEF_MAX_DIST_EYE_KM, + max_memory_gb: float = DEF_MAX_MEMORY_GB, + ): + """ + Generate windfield hazard from a single track dataset + + Parameters + ---------- + track : xr.Dataset + Single tropical cyclone track. + centroids : Centroids + Centroids instance. + idx_centr_filter : np.ndarray + Indices of centroids to restrict to (e.g. sufficiently close to coast). + model : str, optional + Parametric wind field model, one of "H1980" (the prominent Holland 1980 model), + "H08" (Holland 1980 with b-value from Holland 2008), "H10" (Holland et al. 2010), or + "ER11" (Emanuel and Rotunno 2011). + Default: "H08". + model_kwargs: dict, optional + If given, forward these kwargs to the selected model. Default: None + store_windfields : boolean, optional + If True, store windfields. Default: False. + metric : str, optional + Specify an approximation method to use for earth distances: "equirect" (faster) or + "geosphere" (more accurate). See ``dist_approx`` function in + ``climada.util.coordinates``. + Default: "equirect". + intensity_thres : float, optional + Wind speeds (in m/s) below this threshold are stored as 0. Default: 17.5 + max_dist_eye_km : float, optional + No wind speed calculation is done for centroids with a distance (in km) to the TC + center ("eye") larger than this parameter. Default: 300 + max_memory_gb : float, optional + To avoid memory issues, the computation is done for chunks of the track sequentially. + The chunk size is determined depending on the available memory (in GB). Default: 8 + + Raises + ------ + ValueError, KeyError + + Returns + ------- + haz : TropCyclone + """ + intensity_sparse, windfields_sparse = compute_windfields_sparse( + track=track, + centroids=centroids, + idx_centr_filter=idx_centr_filter, + model=model, + model_kwargs=model_kwargs, + store_windfields=store_windfields, + metric=metric, + intensity_thres=intensity_thres, + max_dist_eye_km=max_dist_eye_km, + max_memory_gb=max_memory_gb, + ) + + new_haz = cls(haz_type=HAZ_TYPE) + new_haz.intensity_thres = intensity_thres + new_haz.intensity = intensity_sparse + if store_windfields: + new_haz.windfields = [windfields_sparse] + new_haz.units = 'm/s' + new_haz.centroids = centroids + new_haz.event_id = np.array([1]) + new_haz.frequency = np.array([1]) + new_haz.event_name = [track.sid] + new_haz.fraction = sparse.csr_matrix(new_haz.intensity.shape) + # store first day of track as date + new_haz.date = np.array([ + dt.datetime(track.time.dt.year.values[0], + track.time.dt.month.values[0], + track.time.dt.day.values[0]).toordinal() + ]) + new_haz.orig = np.array([track.orig_event_flag]) + new_haz.category = np.array([track.category]) + # users that pickle TCTracks objects might still have data with the legacy basin attribute, + # so we have to deal with it here + new_haz.basin = [track.basin if isinstance(track.basin, str) + else str(track.basin.values[0])] + return new_haz + + def _apply_knutson_criterion( + self, + chg_int_freq: List, + scaling_rcp_year: float + ): + """ + Apply changes to intensities and cumulative frequencies. + + Parameters + ---------- + chg_int_freq : list(dict)) + list of criteria from climada.hazard.tc_clim_change + scaling_rcp_year : float + scale parameter because of chosen year and RCP + + Returns + ------- + tc_cc : climada.hazard.TropCyclone + Tropical cyclone with frequency and intensity scaled inspired by + the Knutson criterion. Returns a new instance of TropCyclone. + """ + + tc_cc = copy.deepcopy(self) + + # Criterion per basin + for basin in np.unique(tc_cc.basin): + + bas_sel = np.array(tc_cc.basin) == basin + + # Apply intensity change + inten_chg = [chg + for chg in chg_int_freq + if (chg['variable'] == 'intensity' and + chg['basin'] == basin) + ] + for chg in inten_chg: + sel_cat_chg = np.isin(tc_cc.category, chg['category']) & bas_sel + inten_scaling = 1 + (chg['change'] - 1) * scaling_rcp_year + tc_cc.intensity = sparse.diags( + np.where(sel_cat_chg, inten_scaling, 1) + ).dot(tc_cc.intensity) + + # Apply frequency change + freq_chg = [chg + for chg in chg_int_freq + if (chg['variable'] == 'frequency' and + chg['basin'] == basin) + ] + freq_chg.sort(reverse=False, key=lambda x: len(x['category'])) + + # Scale frequencies by category + cat_larger_list = [] + for chg in freq_chg: + cat_chg_list = [cat + for cat in chg['category'] + if cat not in cat_larger_list + ] + sel_cat_chg = np.isin(tc_cc.category, cat_chg_list) & bas_sel + if sel_cat_chg.any(): + freq_scaling = 1 + (chg['change'] - 1) * scaling_rcp_year + tc_cc.frequency[sel_cat_chg] *= freq_scaling + cat_larger_list += cat_chg_list + + if (tc_cc.frequency < 0).any(): + raise ValueError("The application of the given climate scenario" + "resulted in at least one negative frequency.") + + return tc_cc diff --git a/climada/hazard/trop_cyclone.py b/climada/hazard/trop_cyclone/trop_cyclone_windfields.py similarity index 58% rename from climada/hazard/trop_cyclone.py rename to climada/hazard/trop_cyclone/trop_cyclone_windfields.py index 686a88b214..e82c0b11e5 100644 --- a/climada/hazard/trop_cyclone.py +++ b/climada/hazard/trop_cyclone/trop_cyclone_windfields.py @@ -16,38 +16,28 @@ --- -Define TC wind hazard (TropCyclone class). +Compute Tropical Cyclone windfields (see compute_windfields_sparse function). """ -__all__ = ['TropCyclone'] - -import copy -import datetime as dt -import itertools import logging -import time -from typing import Optional, Tuple, List, Union +from typing import Optional, Union, Tuple import numpy as np -from scipy import sparse -import matplotlib.animation as animation -from tqdm import tqdm -import pathos.pools import xarray as xr +from scipy import sparse -from climada.hazard.base import Hazard -from climada.hazard.tc_tracks import TCTracks, estimate_rmw -from climada.hazard.tc_clim_change import get_knutson_criterion, calc_scale_knutson -from climada.hazard.centroids.centr import Centroids -from climada.util import ureg -import climada.util.constants as u_const -import climada.util.coordinates as u_coord -import climada.util.plot as u_plot +from climada.hazard import Centroids +from climada.hazard.tc_tracks import estimate_rmw +from climada.util import ureg, coordinates as u_coord, constants as u_const LOGGER = logging.getLogger(__name__) -HAZ_TYPE = 'TC' -"""Hazard type acronym for Tropical Cyclone""" +NM_TO_KM = (1.0 * ureg.nautical_mile).to(ureg.kilometer).magnitude +KMH_TO_MS = (1.0 * ureg.km / ureg.hour).to(ureg.meter / ureg.second).magnitude +KM_TO_M = (1.0 * ureg.kilometer).to(ureg.meter).magnitude +H_TO_S = (1.0 * ureg.hours).to(ureg.seconds).magnitude +MBAR_TO_PA = (1.0 * ureg.millibar).to(ureg.pascal).magnitude +"""Unit conversion factors for JIT functions that can't use ureg""" DEF_MAX_DIST_EYE_KM = 300 """Default value for the maximum distance (in km) of a centroid to the TC center at which wind @@ -62,1343 +52,456 @@ MODEL_VANG = {'H08': 0, 'H1980': 1, 'H10': 2, 'ER11': 3} """Enumerate different symmetric wind field models.""" -RHO_AIR = 1.15 -"""Air density. Assumed constant, following Holland 1980.""" +DEF_RHO_AIR = 1.15 +"""Default value for air density (in kg/m³), following Holland 1980.""" -GRADIENT_LEVEL_TO_SURFACE_WINDS = 0.9 -"""Gradient-to-surface wind reduction factor according to the 90%-rule: +DEF_GRADIENT_TO_SURFACE_WINDS = 0.9 +"""Default gradient-to-surface wind reduction factor, following the 90%-rule mentioned in: Franklin, J.L., Black, M.L., Valde, K. (2003): GPS Dropwindsonde Wind Profiles in Hurricanes and Their Operational Implications. Weather and Forecasting 18(1): 32–44. https://doi.org/10.1175/1520-0434(2003)018<0032:GDWPIH>2.0.CO;2 + +According to Table 2, this is a reasonable factor for the 750 hPa level in the eyewall region. For +other regions and levels, values of 0.8 or even 0.75 might be justified. """ -KMH_TO_MS = (1.0 * ureg.km / ureg.hour).to(ureg.meter / ureg.second).magnitude -KN_TO_MS = (1.0 * ureg.knot).to(ureg.meter / ureg.second).magnitude -NM_TO_KM = (1.0 * ureg.nautical_mile).to(ureg.kilometer).magnitude -KM_TO_M = (1.0 * ureg.kilometer).to(ureg.meter).magnitude -H_TO_S = (1.0 * ureg.hours).to(ureg.seconds).magnitude -MBAR_TO_PA = (1.0 * ureg.millibar).to(ureg.pascal).magnitude -"""Unit conversion factors for JIT functions that can't use ureg""" +T_ICE_K = 273.16 +"""Freezing temperatur of water (in K), for conversion between K and °C""" V_ANG_EARTH = 7.29e-5 """Earth angular velocity (in radians per second)""" -class TropCyclone(Hazard): - """ - Contains tropical cyclone events. +def _vgrad(si_track, gradient_to_surface_winds): + """Gradient wind speeds (in m/s) without translational influence at each track node - Attributes + The track dataset is modified in place, with the "vgrad" data variable added. + + Parameters ---------- - category : np.ndarray of ints - for every event, the TC category using the Saffir-Simpson scale: - - * -1 tropical depression - * 0 tropical storm - * 1 Hurrican category 1 - * 2 Hurrican category 2 - * 3 Hurrican category 3 - * 4 Hurrican category 4 - * 5 Hurrican category 5 - basin : list of str - Basin where every event starts: - - * 'NA' North Atlantic - * 'EP' Eastern North Pacific - * 'WP' Western North Pacific - * 'NI' North Indian - * 'SI' South Indian - * 'SP' Southern Pacific - * 'SA' South Atlantic - windfields : list of csr_matrix - For each event, the full velocity vectors at each centroid and track position in a sparse - matrix of shape (npositions, ncentroids * 2) that can be reshaped to a full ndarray of - shape (npositions, ncentroids, 2). + si_track : xr.Dataset + Track information as returned by `tctrack_to_si`. The data variables used by this function + are "vmax" and "vtrans_norm". The result is stored in place as new data variable "vgrad". + gradient_to_surface_winds : float + The gradient-to-surface wind reduction factor to use. """ - intensity_thres = DEF_INTENSITY_THRES - """intensity threshold for storage in m/s""" - - vars_opt = Hazard.vars_opt.union({'category'}) - """Name of the variables that are not needed to compute the impact.""" - - def __init__( - self, - category: Optional[np.ndarray] = None, - basin: Optional[List] = None, - windfields: Optional[List[sparse.csr_matrix]] = None, - **kwargs, - ): - """Initialize values. - - Parameters - ---------- - category : np.ndarray of int, optional - For every event, the TC category using the Saffir-Simpson scale: - -1 tropical depression - 0 tropical storm - 1 Hurrican category 1 - 2 Hurrican category 2 - 3 Hurrican category 3 - 4 Hurrican category 4 - 5 Hurrican category 5 - basin : list of str, optional - Basin where every event starts: - 'NA' North Atlantic - 'EP' Eastern North Pacific - 'WP' Western North Pacific - 'NI' North Indian - 'SI' South Indian - 'SP' Southern Pacific - 'SA' South Atlantic - windfields : list of csr_matrix, optional - For each event, the full velocity vectors at each centroid and track position in a - sparse matrix of shape (npositions, ncentroids * 2) that can be reshaped to a full - ndarray of shape (npositions, ncentroids, 2). - **kwargs : Hazard properties, optional - All other keyword arguments are passed to the Hazard constructor. - """ - kwargs.setdefault('haz_type', HAZ_TYPE) - Hazard.__init__(self, **kwargs) - self.category = category if category is not None else np.array([], int) - self.basin = basin if basin is not None else [] - self.windfields = windfields if windfields is not None else [] - - def set_from_tracks(self, *args, **kwargs): - """This function is deprecated, use TropCyclone.from_tracks instead.""" - LOGGER.warning("The use of TropCyclone.set_from_tracks is deprecated." - "Use TropCyclone.from_tracks instead.") - if "intensity_thres" not in kwargs: - # some users modify the threshold attribute before calling `set_from_tracks` - kwargs["intensity_thres"] = self.intensity_thres - if self.pool is not None and 'pool' not in kwargs: - kwargs['pool'] = self.pool - self.__dict__ = TropCyclone.from_tracks(*args, **kwargs).__dict__ - - @classmethod - def from_tracks( - cls, - tracks: TCTracks, - centroids: Centroids, - pool: Optional[pathos.pools.ProcessPool] = None, - model: str = 'H08', - ignore_distance_to_coast: bool = False, - store_windfields: bool = False, - metric: str = "equirect", - intensity_thres: float = DEF_INTENSITY_THRES, - max_latitude: float = 61, - max_dist_inland_km: float = 1000, - max_dist_eye_km: float = DEF_MAX_DIST_EYE_KM, - max_memory_gb: float = DEF_MAX_MEMORY_GB, - ): - """ - Create new TropCyclone instance that contains windfields from the specified tracks. - - This function sets the ``intensity`` attribute to contain, for each centroid, - the maximum wind speed (1-minute sustained winds at 10 meters above ground) experienced - over the whole period of each TC event in m/s. The wind speed is set to 0 if it doesn't - exceed the threshold ``intensity_thres``. - - The ``category`` attribute is set to the value of the ``category``-attribute - of each of the given track data sets. - - The ``basin`` attribute is set to the genesis basin for each event, which - is the first value of the ``basin``-variable in each of the given track data sets. - - Optionally, the time dependent, vectorial winds can be stored using the - ``store_windfields`` function parameter (see below). - - Parameters - ---------- - tracks : climada.hazard.TCTracks - Tracks of storm events. - centroids : Centroids, optional - Centroids where to model TC. Default: global centroids at 360 arc-seconds resolution. - pool : pathos.pool, optional - Pool that will be used for parallel computation of wind fields. Default: None - description : str, optional - Description of the event set. Default: "". - model : str, optional - Parametric wind field model to use: one of "H1980" (the prominent Holland 1980 model), - "H08" (Holland 1980 with b-value from Holland 2008), "H10" (Holland et al. 2010), or - "ER11" (Emanuel and Rotunno 2011). - Default: "H08". - ignore_distance_to_coast : boolean, optional - If True, centroids far from coast are not ignored. - If False, the centroids' distances to the coast are calculated with the - `Centroids.get_dist_coast()` method (unless there is "dist_coast" column in the - centroids' GeoDataFrame) and centroids far from coast are ignored. - Default: False. - store_windfields : boolean, optional - If True, the Hazard object gets a list ``windfields`` of sparse matrices. For each - track, the full velocity vectors at each centroid and track position are stored in a - sparse matrix of shape (npositions, ncentroids * 2) that can be reshaped to a full - ndarray of shape (npositions, ncentroids, 2). Default: False. - metric : str, optional - Specify an approximation method to use for earth distances: - - * "equirect": Distance according to sinusoidal projection. Fast, but inaccurate for - large distances and high latitudes. - * "geosphere": Exact spherical distance. Much more accurate at all distances, but slow. - - Default: "equirect". - intensity_thres : float, optional - Wind speeds (in m/s) below this threshold are stored as 0. Default: 17.5 - max_latitude : float, optional - No wind speed calculation is done for centroids with latitude larger than this - parameter. Default: 61 - max_dist_inland_km : float, optional - No wind speed calculation is done for centroids with a distance (in km) to the coast - larger than this parameter. Default: 1000 - max_dist_eye_km : float, optional - No wind speed calculation is done for centroids with a distance (in km) to the TC - center ("eye") larger than this parameter. Default: 300 - max_memory_gb : float, optional - To avoid memory issues, the computation is done for chunks of the track sequentially. - The chunk size is determined depending on the available memory (in GB). Note that this - limit applies to each thread separately if a ``pool`` is used. Default: 8 - - Raises - ------ - ValueError - - Returns - ------- - TropCyclone - """ - num_tracks = tracks.size - - if ignore_distance_to_coast: - # Select centroids with lat <= max_latitude - [idx_centr_filter] = (np.abs(centroids.lat) <= max_latitude).nonzero() - else: - # Select centroids which are inside max_dist_inland_km and lat <= max_latitude - if 'dist_coast' not in centroids.gdf.columns: - dist_coast = centroids.get_dist_coast() - else: - dist_coast = centroids.gdf.dist_coast.values - [idx_centr_filter] = ( - (dist_coast <= max_dist_inland_km * 1000) - & (np.abs(centroids.lat) <= max_latitude) - ).nonzero() - - # Filter early with a larger threshold, but inaccurate (lat/lon) distances. - # Later, there will be another filtering step with more accurate distances in km. - max_dist_eye_deg = max_dist_eye_km / ( - u_const.ONE_LAT_KM * np.cos(np.radians(max_latitude)) - ) - - # Restrict to coastal centroids within reach of any of the tracks - t_lon_min, t_lat_min, t_lon_max, t_lat_max = tracks.get_bounds(deg_buffer=max_dist_eye_deg) - t_mid_lon = 0.5 * (t_lon_min + t_lon_max) - filtered_centroids = centroids.coord[idx_centr_filter] - u_coord.lon_normalize(filtered_centroids[:, 1], center=t_mid_lon) - idx_centr_filter = idx_centr_filter[ - (t_lon_min <= filtered_centroids[:, 1]) - & (filtered_centroids[:, 1] <= t_lon_max) - & (t_lat_min <= filtered_centroids[:, 0]) - & (filtered_centroids[:, 0] <= t_lat_max) - ] + si_track["vgrad"] = ( + np.fmax(0, si_track["vmax"] - si_track["vtrans_norm"]) / gradient_to_surface_winds + ) - LOGGER.info('Mapping %s tracks to %s coastal centroids.', str(tracks.size), - str(idx_centr_filter.size)) - if pool: - chunksize = max(min(num_tracks // pool.ncpus, 1000), 1) - tc_haz_list = pool.map( - cls.from_single_track, tracks.data, - itertools.repeat(centroids, num_tracks), - itertools.repeat(idx_centr_filter, num_tracks), - itertools.repeat(model, num_tracks), - itertools.repeat(store_windfields, num_tracks), - itertools.repeat(metric, num_tracks), - itertools.repeat(intensity_thres, num_tracks), - itertools.repeat(max_dist_eye_km, num_tracks), - itertools.repeat(max_memory_gb, num_tracks), - chunksize=chunksize) - else: - last_perc = 0 - tc_haz_list = [] - for track in tracks.data: - perc = 100 * len(tc_haz_list) / len(tracks.data) - if perc - last_perc >= 10: - LOGGER.info("Progress: %d%%", perc) - last_perc = perc - tc_haz_list.append( - cls.from_single_track(track, centroids, idx_centr_filter, - model=model, store_windfields=store_windfields, - metric=metric, intensity_thres=intensity_thres, - max_dist_eye_km=max_dist_eye_km, - max_memory_gb=max_memory_gb)) - if last_perc < 100: - LOGGER.info("Progress: 100%") - - LOGGER.debug('Concatenate events.') - haz = cls.concat(tc_haz_list) - haz.pool = pool - haz.intensity_thres = intensity_thres - LOGGER.debug('Compute frequency.') - haz.frequency_from_tracks(tracks.data) - return haz - - def apply_climate_scenario_knu( - self, - ref_year: int = 2050, - rcp_scenario: int = 45 - ): - """ - From current TC hazard instance, return new hazard set with - future events for a given RCP scenario and year based on the - parametrized values derived from Table 3 in Knutson et al 2015. - https://doi.org/10.1175/JCLI-D-15-0129.1 . The scaling for different - years and RCP scenarios is obtained by linear interpolation. - - Note: The parametrized values are derived from the overall changes - in statistical ensemble of tracks. Hence, this method should only be - applied to sufficiently large tropical cyclone event sets that - approximate the reference years 1981 - 2008 used in Knutson et. al. - - The frequency and intensity changes are applied independently from - one another. The mean intensity factors can thus slightly deviate - from the Knutson value (deviation was found to be less than 1% - for default IBTrACS event sets 1980-2020 for each basin). - - Parameters - ---------- - ref_year : int - year between 2000 ad 2100. Default: 2050 - rcp_scenario : int - 26 for RCP 2.6, 45 for RCP 4.5, 60 for RCP 6.0 and 85 for RCP 8.5. - The default is 45. - - Returns - ------- - haz_cc : climada.hazard.TropCyclone - Tropical cyclone with frequencies and intensity scaled according - to the Knutson criterion for the given year and RCP. Returns - a new instance of climada.hazard.TropCyclone, self is not - modified. - """ - chg_int_freq = get_knutson_criterion() - scale_rcp_year = calc_scale_knutson(ref_year, rcp_scenario) - haz_cc = self._apply_knutson_criterion(chg_int_freq, scale_rcp_year) - return haz_cc - - def set_climate_scenario_knu(self, *args, **kwargs): - """This function is deprecated, use TropCyclone.apply_climate_scenario_knu instead.""" - LOGGER.warning("The use of TropCyclone.set_climate_scenario_knu is deprecated." - "Use TropCyclone.apply_climate_scenario_knu instead.") - return self.apply_climate_scenario_knu(*args, **kwargs) - - @classmethod - def video_intensity( - cls, - track_name: str, - tracks: TCTracks, - centroids: Centroids, - file_name: Optional[str] = None, - writer: animation = animation.PillowWriter(bitrate=500), - figsize: Tuple[float, float] = (9, 13), - adapt_fontsize: bool = True, - **kwargs - ): - """ - Generate video of TC wind fields node by node and returns its - corresponding TropCyclone instances and track pieces. - - Parameters - ---------- - track_name : str - name of the track contained in tracks to record - tracks : climada.hazard.TCTracks - tropical cyclone tracks - centroids : climada.hazard.Centroids - centroids where wind fields are mapped - file_name : str, optional - file name to save video (including full path and file extension) - writer : matplotlib.animation.*, optional - video writer. Default is pillow with bitrate=500 - figsize : tuple, optional - figure size for plt.subplots - adapt_fontsize : bool, optional - If set to true, the size of the fonts will be adapted to the size of the figure. - Otherwise the default matplotlib font size is used. Default is True. - kwargs : optional - arguments for pcolormesh matplotlib function used in event plots - - Returns - ------- - tc_list, tc_coord : list(TropCyclone), list(np.ndarray) - - Raises - ------ - ValueError - - """ - # initialization - track = tracks.get_track(track_name) - if not track: - raise ValueError(f'{track_name} not found in track data.') - idx_plt = np.argwhere( - (track.lon.values < centroids.total_bounds[2] + 1) - & (centroids.total_bounds[0] - 1 < track.lon.values) - & (track.lat.values < centroids.total_bounds[3] + 1) - & (centroids.total_bounds[1] - 1 < track.lat.values) - ).reshape(-1) - - tc_list = [] - tr_coord = {'lat': [], 'lon': []} - for node in range(idx_plt.size - 2): - tr_piece = track.sel( - time=slice(track.time.values[idx_plt[node]], - track.time.values[idx_plt[node + 2]])) - tr_piece.attrs['n_nodes'] = 2 # plot only one node - tr_sel = TCTracks() - tr_sel.append(tr_piece) - tr_coord['lat'].append(tr_sel.data[0].lat.values[:-1]) - tr_coord['lon'].append(tr_sel.data[0].lon.values[:-1]) - - tc_tmp = cls.from_tracks(tr_sel, centroids=centroids) - tc_tmp.event_name = [ - track.name + ' ' + time.strftime( - "%d %h %Y %H:%M", - time.gmtime(tr_sel.data[0].time[1].values.astype(int) - / 1000000000) - ) - ] - tc_list.append(tc_tmp) - - if 'cmap' not in kwargs: - kwargs['cmap'] = 'Greys' - if 'vmin' not in kwargs: - kwargs['vmin'] = np.array([tc_.intensity.min() for tc_ in tc_list]).min() - if 'vmax' not in kwargs: - kwargs['vmax'] = np.array([tc_.intensity.max() for tc_ in tc_list]).max() - - def run(node): - tc_list[node].plot_intensity(1, axis=axis, **kwargs) - axis.plot(tr_coord['lon'][node], tr_coord['lat'][node], 'k') - axis.set_title(tc_list[node].event_name[0]) - pbar.update() - - if file_name: - LOGGER.info('Generating video %s', file_name) - fig, axis, _fontsize = u_plot.make_map(figsize=figsize, adapt_fontsize=adapt_fontsize) - pbar = tqdm(total=idx_plt.size - 2) - ani = animation.FuncAnimation(fig, run, frames=idx_plt.size - 2, - interval=500, blit=False) - fig.tight_layout() - ani.save(file_name, writer=writer) - pbar.close() - return tc_list, tr_coord - - def frequency_from_tracks(self, tracks: List): - """ - Set hazard frequency from tracks data. - - Parameters - ---------- - tracks : list of xarray.Dataset - """ - if not tracks: - return - year_max = np.amax([t.time.dt.year.values.max() for t in tracks]) - year_min = np.amin([t.time.dt.year.values.min() for t in tracks]) - year_delta = year_max - year_min + 1 - num_orig = np.count_nonzero(self.orig) - ens_size = (self.event_id.size / num_orig) if num_orig > 0 else 1 - self.frequency = np.ones(self.event_id.size) / (year_delta * ens_size) - - @classmethod - def from_single_track( - cls, - track: xr.Dataset, - centroids: Centroids, - idx_centr_filter: np.ndarray, - model: str = 'H08', - store_windfields: bool = False, - metric: str = "equirect", - intensity_thres: float = DEF_INTENSITY_THRES, - max_dist_eye_km: float = DEF_MAX_DIST_EYE_KM, - max_memory_gb: float = DEF_MAX_MEMORY_GB, - ): - """ - Generate windfield hazard from a single track dataset - - Parameters - ---------- - track : xr.Dataset - Single tropical cyclone track. - centroids : Centroids - Centroids instance. - idx_centr_filter : np.ndarray - Indices of centroids to restrict to (e.g. sufficiently close to coast). - model : str, optional - Parametric wind field model, one of "H1980" (the prominent Holland 1980 model), - "H08" (Holland 1980 with b-value from Holland 2008), "H10" (Holland et al. 2010), or - "ER11" (Emanuel and Rotunno 2011). - Default: "H08". - store_windfields : boolean, optional - If True, store windfields. Default: False. - metric : str, optional - Specify an approximation method to use for earth distances: "equirect" (faster) or - "geosphere" (more accurate). See ``dist_approx`` function in - ``climada.util.coordinates``. - Default: "equirect". - intensity_thres : float, optional - Wind speeds (in m/s) below this threshold are stored as 0. Default: 17.5 - max_dist_eye_km : float, optional - No wind speed calculation is done for centroids with a distance (in km) to the TC - center ("eye") larger than this parameter. Default: 300 - max_memory_gb : float, optional - To avoid memory issues, the computation is done for chunks of the track sequentially. - The chunk size is determined depending on the available memory (in GB). Default: 8 - - Raises - ------ - ValueError, KeyError - - Returns - ------- - haz : TropCyclone - """ - intensity_sparse, windfields_sparse = _compute_windfields_sparse( - track=track, - centroids=centroids, - idx_centr_filter=idx_centr_filter, - model=model, - store_windfields=store_windfields, - metric=metric, - intensity_thres=intensity_thres, - max_dist_eye_km=max_dist_eye_km, - max_memory_gb=max_memory_gb, - ) - new_haz = cls(haz_type=HAZ_TYPE) - new_haz.intensity_thres = intensity_thres - new_haz.intensity = intensity_sparse - if store_windfields: - new_haz.windfields = [windfields_sparse] - new_haz.units = 'm/s' - new_haz.centroids = centroids - new_haz.event_id = np.array([1]) - new_haz.frequency = np.array([1]) - new_haz.event_name = [track.sid] - new_haz.fraction = sparse.csr_matrix(new_haz.intensity.shape) - # store first day of track as date - new_haz.date = np.array([ - dt.datetime(track.time.dt.year.values[0], - track.time.dt.month.values[0], - track.time.dt.day.values[0]).toordinal() - ]) - new_haz.orig = np.array([track.orig_event_flag]) - new_haz.category = np.array([track.category]) - # users that pickle TCTracks objects might still have data with the legacy basin attribute, - # so we have to deal with it here - new_haz.basin = [track.basin if isinstance(track.basin, str) - else str(track.basin.values[0])] - return new_haz - - def _apply_knutson_criterion( - self, - chg_int_freq: List, - scaling_rcp_year: float - ): - """ - Apply changes to intensities and cumulative frequencies. - - Parameters - ---------- - chg_int_freq : list(dict)) - list of criteria from climada.hazard.tc_clim_change - scaling_rcp_year : float - scale parameter because of chosen year and RCP - - Returns - ------- - tc_cc : climada.hazard.TropCyclone - Tropical cyclone with frequency and intensity scaled inspired by - the Knutson criterion. Returns a new instance of TropCyclone. - """ - - tc_cc = copy.deepcopy(self) - - # Criterion per basin - for basin in np.unique(tc_cc.basin): - - bas_sel = np.array(tc_cc.basin) == basin - - # Apply intensity change - inten_chg = [chg - for chg in chg_int_freq - if (chg['variable'] == 'intensity' and - chg['basin'] == basin) - ] - for chg in inten_chg: - sel_cat_chg = np.isin(tc_cc.category, chg['category']) & bas_sel - inten_scaling = 1 + (chg['change'] - 1) * scaling_rcp_year - tc_cc.intensity = sparse.diags( - np.where(sel_cat_chg, inten_scaling, 1) - ).dot(tc_cc.intensity) - - # Apply frequency change - freq_chg = [chg - for chg in chg_int_freq - if (chg['variable'] == 'frequency' and - chg['basin'] == basin) - ] - freq_chg.sort(reverse=False, key=lambda x: len(x['category'])) - - # Scale frequencies by category - cat_larger_list = [] - for chg in freq_chg: - cat_chg_list = [cat - for cat in chg['category'] - if cat not in cat_larger_list - ] - sel_cat_chg = np.isin(tc_cc.category, cat_chg_list) & bas_sel - if sel_cat_chg.any(): - freq_scaling = 1 + (chg['change'] - 1) * scaling_rcp_year - tc_cc.frequency[sel_cat_chg] *= freq_scaling - cat_larger_list += cat_chg_list - - if (tc_cc.frequency < 0).any(): - raise ValueError("The application of the given climate scenario" - "resulted in at least one negative frequency.") - - return tc_cc - -def _compute_windfields_sparse( - track: xr.Dataset, - centroids: Centroids, - idx_centr_filter: np.ndarray, - model: str = 'H08', - store_windfields: bool = False, - metric: str = "equirect", - intensity_thres: float = DEF_INTENSITY_THRES, - max_dist_eye_km: float = DEF_MAX_DIST_EYE_KM, - max_memory_gb: float = DEF_MAX_MEMORY_GB, -) -> Tuple[sparse.csr_matrix, Optional[sparse.csr_matrix]]: - """Version of ``compute_windfields`` that returns sparse matrices and limits memory usage +def compute_angular_windspeeds( + si_track: xr.Dataset, + d_centr: np.ndarray, + mask_centr_close: np.ndarray, + model: int, + model_kwargs: Optional[dict] = None, + cyclostrophic: bool = False, +): + """Compute (absolute) angular wind speeds according to a parametric wind profile Parameters ---------- - track : xr.Dataset - Single tropical cyclone track. - centroids : Centroids - Centroids instance. - idx_centr_filter : np.ndarray - Indices of centroids to restrict to (e.g. sufficiently close to coast). - model : str, optional - Parametric wind field model, one of "H1980" (the prominent Holland 1980 model), - "H08" (Holland 1980 with b-value from Holland 2008), "H10" (Holland et al. 2010), or - "ER11" (Emanuel and Rotunno 2011). - Default: "H08". - store_windfields : boolean, optional - If True, store windfields. Default: False. - metric : str, optional - Specify an approximation method to use for earth distances: "equirect" (faster) or - "geosphere" (more accurate). See ``dist_approx`` function in ``climada.util.coordinates``. - Default: "equirect". - intensity_thres : float, optional - Wind speeds (in m/s) below this threshold are stored as 0. Default: 17.5 - max_dist_eye_km : float, optional - No wind speed calculation is done for centroids with a distance (in km) to the TC - center ("eye") larger than this parameter. Default: 300 - max_memory_gb : float, optional - To avoid memory issues, the computation is done for chunks of the track sequentially. - The chunk size is determined depending on the available memory (in GB). Default: 8 - - Raises - ------ - ValueError + si_track : xr.Dataset + Output of ``tctrack_to_si``. Which data variables are used in the computation of the wind + profile depends on the selected model. + d_centr : np.ndarray of shape (npositions, ncentroids) + Distance (in m) between centroids and track positions. + mask_centr_close : np.ndarray of shape (npositions, ncentroids) + For each track position one row indicating which centroids are within reach. + model : int + Wind profile model selection according to MODEL_VANG. + model_kwargs: dict, optional + If given, forward these kwargs to the selected model. Default: None + cyclostrophic : bool, optional + If True, do not apply the influence of the Coriolis force (set the Coriolis terms to 0). + Default: False Returns ------- - intensity : csr_matrix - Maximum wind speed in each centroid over the whole storm life time. - windfields : csr_matrix or None - If store_windfields is True, the full velocity vectors at each centroid and track position - are stored in a sparse matrix of shape (npositions, ncentroids * 2) that can be reshaped - to a full ndarray of shape (npositions, ncentroids, 2). - If store_windfields is False, None is returned. + ndarray of shape (npositions, ncentroids) + containing the magnitude of the angular windspeed per track position per centroid location """ - try: - mod_id = MODEL_VANG[model] - except KeyError as err: - raise ValueError(f'Model not implemented: {model}.') from err - - ncentroids = centroids.coord.shape[0] - npositions = track.sizes["time"] - windfields_shape = (npositions, ncentroids * 2) - intensity_shape = (1, ncentroids) - - # initialise arrays for the assumption that no centroids are within reach - windfields_sparse = ( - sparse.csr_matrix(([], ([], [])), shape=windfields_shape) - if store_windfields else None + model_kwargs = {} if model_kwargs is None else model_kwargs + compute_funs = { + MODEL_VANG['H1980']: _compute_angular_windspeeds_h1980, + MODEL_VANG['H08']: _compute_angular_windspeeds_h08, + MODEL_VANG['H10']: _compute_angular_windspeeds_h10, + MODEL_VANG['ER11']: _stat_er_2011, + } + if model not in compute_funs: + raise NotImplementedError(f"The specified wind model is not supported: {model}") + result = compute_funs[model]( + si_track, + d_centr, + mask_centr_close, + cyclostrophic=cyclostrophic, + **model_kwargs, ) - intensity_sparse = sparse.csr_matrix(([], ([], [])), shape=intensity_shape) + result[0, :] *= 0 + return result - # The wind field model requires at least two track positions because translational speed - # as well as the change in pressure (in case of H08) are required. - if npositions < 2: - return intensity_sparse, windfields_sparse - # convert track variables to SI units - si_track = tctrack_to_si(track, metric=metric) +def _compute_angular_windspeeds_h1980( + si_track: xr.Dataset, + d_centr: np.ndarray, + close_centr_msk: np.ndarray, + cyclostrophic: bool = False, + gradient_to_surface_winds: float = DEF_GRADIENT_TO_SURFACE_WINDS, + rho_air_const: float = DEF_RHO_AIR, +): + """Compute (absolute) angular wind speeds according to the Holland 1980 model - # When done properly, finding and storing the close centroids is not a memory bottle neck and - # can be done before chunking. Note that the longitudinal coordinates of `centroids_close` as - # returned by `get_close_centroids` are normalized to be consistent with the coordinates in - # `si_track`. - centroids_close, mask_centr, mask_centr_alongtrack = get_close_centroids( - si_track, centroids.coord[idx_centr_filter], max_dist_eye_km, metric=metric, - ) - idx_centr_filter = idx_centr_filter[mask_centr] - n_centr_close = centroids_close.shape[0] - if n_centr_close == 0: - return intensity_sparse, windfields_sparse + Parameters + ---------- + si_track : xr.Dataset + Output of `tctrack_to_si`. Which data variables are used in the computation of the wind + profile depends on the selected model. + d_centr : np.ndarray of shape (npositions, ncentroids) + Distance (in m) between centroids and track positions. + close_centr_msk : np.ndarray of shape (npositions, ncentroids) + For each track position one row indicating which centroids are within reach. + cyclostrophic : bool, optional + If True, don't apply the influence of the Coriolis force (set the Coriolis terms to 0). + Default: False + gradient_to_surface_winds : float, optional + The gradient-to-surface wind reduction factor to use. The wind profile is computed on the + gradient level, and wind speeds are converted to the surface level using this factor. + Default: 0.9 + rho_air_const : float or None, optional + The constant value for air density (in kg/m³) to assume in the formulas from Holland 1980. + By default, the constant value suggested in Holland 1980 is used. If set to None, the air + density is computed from pressure following equation (9) in Holland et al. 2010. + Default: 1.15 - # the total memory requirement in GB if we compute everything without chunking: - # 8 Bytes per entry (float64), 10 arrays - total_memory_gb = npositions * n_centr_close * 8 * 10 / 1e9 - if total_memory_gb > max_memory_gb and npositions > 2: - # If the number of positions is down to 2 already, we cannot split any further. In that - # case, we just take the risk and try to do the computation anyway. It might still work - # since we have only computed an upper bound for the number of affected centroids. + Returns + ------- + ndarray of shape (npositions, ncentroids) + containing the magnitude of the angular windspeed per track position per centroid location + """ + _vgrad(si_track, gradient_to_surface_winds) + _rho_air(si_track, rho_air_const) + _B_holland_1980(si_track) + result = _stat_holland_1980(si_track, d_centr, close_centr_msk, cyclostrophic=cyclostrophic) + result *= gradient_to_surface_winds + return result - # Split the track into chunks, compute the result for each chunk, and combine: - return _compute_windfields_sparse_chunked( - mask_centr_alongtrack, - track, - centroids, - idx_centr_filter, - model=model, - store_windfields=store_windfields, - metric=metric, - intensity_thres=intensity_thres, - max_dist_eye_km=max_dist_eye_km, - max_memory_gb=max_memory_gb, - ) - windfields, idx_centr_reachable = _compute_windfields( - si_track, centroids_close, mod_id, metric=metric, max_dist_eye_km=max_dist_eye_km, - ) - idx_centr_filter = idx_centr_filter[idx_centr_reachable] - npositions = windfields.shape[0] +def _compute_angular_windspeeds_h08( + si_track: xr.Dataset, + d_centr: np.ndarray, + close_centr_msk: np.ndarray, + cyclostrophic: bool = False, + gradient_to_surface_winds: float = DEF_GRADIENT_TO_SURFACE_WINDS, + rho_air_const: float = DEF_RHO_AIR, +): + """Compute (absolute) angular wind speeds according to the Holland 2008 model - intensity = np.linalg.norm(windfields, axis=-1).max(axis=0) - intensity[intensity < intensity_thres] = 0 - intensity_sparse = sparse.csr_matrix( - (intensity, idx_centr_filter, [0, intensity.size]), - shape=intensity_shape) - intensity_sparse.eliminate_zeros() + Parameters + ---------- + si_track : xr.Dataset + Output of `tctrack_to_si`. Which data variables are used in the computation of the wind + profile depends on the selected model. + d_centr : np.ndarray of shape (npositions, ncentroids) + Distance (in m) between centroids and track positions. + close_centr_msk : np.ndarray of shape (npositions, ncentroids) + For each track position one row indicating which centroids are within reach. + cyclostrophic : bool, optional + If True, don't apply the influence of the Coriolis force (set the Coriolis terms to 0). + Default: False + gradient_to_surface_winds : float, optional + The gradient-to-surface wind reduction factor to use. The wind profile is computed on the + surface level, but the clipping interval of the B-value depends on this factor. + Default: 0.9 + rho_air_const : float or None, optional + The constant value for air density (in kg/m³) to assume in the formula from Holland 1980. + By default, the constant value suggested in Holland 1980 is used. If set to None, the air + density is computed from pressure following equation (9) in Holland et al. 2010. + Default: 1.15 - windfields_sparse = None - if store_windfields: - n_centr_filter = idx_centr_filter.size - indices = np.zeros((npositions, n_centr_filter, 2), dtype=np.int64) - indices[:, :, 0] = 2 * idx_centr_filter[None] - indices[:, :, 1] = 2 * idx_centr_filter[None] + 1 - indices = indices.ravel() - indptr = np.arange(npositions + 1) * n_centr_filter * 2 - windfields_sparse = sparse.csr_matrix((windfields.ravel(), indices, indptr), - shape=windfields_shape) - windfields_sparse.eliminate_zeros() + Returns + ------- + ndarray of shape (npositions, ncentroids) + containing the magnitude of the angular windspeed per track position per centroid location + """ + _rho_air(si_track, rho_air_const) + _bs_holland_2008(si_track, gradient_to_surface_winds=gradient_to_surface_winds) + return _stat_holland_1980(si_track, d_centr, close_centr_msk, cyclostrophic=cyclostrophic) - return intensity_sparse, windfields_sparse -def _compute_windfields_sparse_chunked( - mask_centr_alongtrack: np.ndarray, - track: xr.Dataset, - *args, - max_memory_gb: float = DEF_MAX_MEMORY_GB, - **kwargs, -) -> Tuple[sparse.csr_matrix, Optional[sparse.csr_matrix]]: - """Call ``_compute_windfields_sparse`` for chunks of the track and re-assemble the results +def _compute_angular_windspeeds_h10( + si_track: xr.Dataset, + d_centr: np.ndarray, + close_centr_msk: np.ndarray, + cyclostrophic: bool = True, + gradient_to_surface_winds: float = DEF_GRADIENT_TO_SURFACE_WINDS, + rho_air_const: float = DEF_RHO_AIR, + vmax_from_cen: bool = True, + vmax_in_brackets: bool = False, +): + """Compute (absolute) angular wind speeds according to the Holland et al. 2010 model + + Note that this model is always cyclostrophic, the parameter setting is ignored. Parameters ---------- - mask_centr_alongtrack : np.ndarray of shape (npositions, ncentroids) - Each row is a mask that indicates the centroids within reach for one track position. - track : xr.Dataset - Single tropical cyclone track. - max_memory_gb : float, optional - Maximum memory requirements (in GB) for the computation of a single chunk of the track. - Default: 8 - args, kwargs : - The remaining arguments are passed on to ``_compute_windfields_sparse``. + si_track : xr.Dataset + Output of `tctrack_to_si`. Which data variables are used in the computation of the wind + profile depends on the selected model. + d_centr : np.ndarray of shape (npositions, ncentroids) + Distance (in m) between centroids and track positions. + close_centr_msk : np.ndarray of shape (npositions, ncentroids) + For each track position one row indicating which centroids are within reach. + cyclostrophic : bool, optional + This parameter is ignored because this model is always cyclostrophic. Default: True + gradient_to_surface_winds : float, optional + The gradient-to-surface wind reduction factor to use. the wind profile is computed on the + surface level, but the clipping interval of the B-value depends on this factor. + Default: 0.9 + rho_air_const : float or None, optional + The constant value for air density (in kg/m³) to assume in the formula for the B-value. By + default, the value suggested in Holland 1980 is used. If set to None, the air density is + computed from pressure following equation (9) in Holland et al. 2010. Default: 1.15 + vmax_from_cen : boolean, optional + If True, replace the recorded value of vmax along the track by an estimate from pressure, + following equation (8) in Holland et al. 2010. Default: True + vmax_in_brackets : bool, optional + Specifies which of the two formulas in equation (6) of Holland et al. 2010 to use. If + False, the formula with vmax outside of the brackets is used. Note that, a side-effect of + the formula with vmax inside of the brackets is that the wind speed maximum is attained a + bit farther away from the center than according to the recorded radius of maximum + winds (RMW). Default: False Returns ------- - intensity, windfields : - See ``_compute_windfields_sparse`` for a description of the return values. + ndarray of shape (npositions, ncentroids) + containing the magnitude of the angular windspeed per track position per centroid location """ - npositions = track.sizes["time"] - # The memory requirements for each track position are estimated for the case of 10 arrays - # containing `nreachable` float64 (8 Byte) values each. The chunking is only relevant in - # extreme cases with a very high temporal and/or spatial resolution. - max_nreachable = max_memory_gb * 1e9 / (8 * 10 * npositions) - split_pos = [0] - chunk_size = 2 - while split_pos[-1] + chunk_size < npositions: - chunk_size += 1 - # create overlap between consecutive chunks - chunk_start = max(0, split_pos[-1] - 1) - chunk_end = chunk_start + chunk_size - nreachable = mask_centr_alongtrack[chunk_start:chunk_end].any(axis=0).sum() - if nreachable > max_nreachable: - split_pos.append(chunk_end - 1) - chunk_size = 2 - split_pos.append(npositions) + if not cyclostrophic: + LOGGER.debug( + 'The function _compute_angular_windspeeds_h10 was called with parameter ' + '"cyclostrophic" equal to false. Please be aware that this setting is ignored as the' + ' Holland et al. 2010 model is always cyclostrophic.') + _rho_air(si_track, rho_air_const) + if vmax_from_cen: + _bs_holland_2008(si_track, gradient_to_surface_winds=gradient_to_surface_winds) + _v_max_s_holland_2008(si_track) + else: + _B_holland_1980(si_track, gradient_to_surface_winds=gradient_to_surface_winds) + hol_x = _x_holland_2010(si_track, d_centr, close_centr_msk, vmax_in_brackets=vmax_in_brackets) + return _stat_holland_2010( + si_track, d_centr, close_centr_msk, hol_x, vmax_in_brackets=vmax_in_brackets, + ) - intensity = [] - windfields = [] - for prev_chunk_end, chunk_end in zip(split_pos[:-1], split_pos[1:]): - chunk_start = max(0, prev_chunk_end - 1) - inten, win = _compute_windfields_sparse( - track.isel(time=slice(chunk_start, chunk_end)), *args, - max_memory_gb=max_memory_gb, **kwargs, - ) - intensity.append(inten) - windfields.append(win) - intensity = sparse.csr_matrix(sparse.vstack(intensity).max(axis=0)) - if windfields[0] is not None: - # eliminate the overlap between consecutive chunks - windfields = [windfields[0]] + [win[1:, :] for win in windfields[1:]] - windfields = sparse.vstack(windfields, format="csr") - return intensity, windfields - -def _compute_windfields( - si_track: xr.Dataset, - centroids: np.ndarray, - model: int, - metric: str = "equirect", - max_dist_eye_km: float = DEF_MAX_DIST_EYE_KM, -) -> Tuple[np.ndarray, np.ndarray]: - """Compute 1-minute sustained winds (in m/s) at 10 meters above ground +def _rho_air(si_track: xr.Dataset, const: Optional[float]): + """Eyewall density of air (in kg/m³) at each track node. - In a first step, centroids within reach of the track are determined so that wind fields will - only be computed and returned for those centroids. Still, since computing the distance of - the storm center to the centroids is computationally expensive, make sure to pre-filter the - centroids and call this function only for those centroids that are potentially affected. + The track dataset is modified in place, with the "rho_air" data variable added. Parameters ---------- si_track : xr.Dataset - Output of ``tctrack_to_si``. Which data variables are used in the computation of the wind - speeds depends on the selected model. - centroids : np.ndarray with two dimensions - Each row is a centroid [lat, lon]. Centroids that are not within reach of the track are - ignored. Longitudinal coordinates are assumed to be normalized consistently with the - longitudinal coordinates in ``si_track``. - model : int - Wind profile model selection according to MODEL_VANG. - metric : str, optional - Specify an approximation method to use for earth distances: "equirect" (faster) or - "geosphere" (more accurate). See ``dist_approx`` function in ``climada.util.coordinates``. - Default: "equirect". - max_dist_eye_km : float, optional - No wind speed calculation is done for centroids with a distance (in km) to the TC center - ("eye") larger than this parameter. Default: 300 - - Returns - ------- - windfields : np.ndarray of shape (npositions, nreachable, 2) - Directional wind fields for each track position on those centroids within reach - of the TC track. Note that the wind speeds at the first position are all zero because - the discrete time derivatives involved in the process are implemented using backward - differences. However, the first position is usually not relevant for impact calculations - since it is far off shore. - idx_centr_reachable : np.ndarray of shape (nreachable,) - List of indices of input centroids within reach of the TC track. + Track information as returned by `tctrack_to_si`. The data variables used by this function + are "lat", "cen", and "pdelta". The result is stored in place as new data + variable "rho_air". + const : float or None + A constant value for air density (in kg/m³) to assume. If None, the air density is + estimated from eyewall pressure following equation (9) in Holland et al. 2010. """ - # start with the assumption that no centroids are within reach - npositions = si_track.sizes["time"] - idx_centr_reachable = np.zeros((0,), dtype=np.int64) - windfields = np.zeros((npositions, 0, 2), dtype=np.float64) + if const is not None: + si_track["rho_air"] = xr.full_like(si_track["time"], const, dtype=float) + return - # compute distances (in m) and vectors to all centroids - [d_centr], [v_centr_normed] = u_coord.dist_approx( - si_track["lat"].values[None], si_track["lon"].values[None], - centroids[None, :, 0], centroids[None, :, 1], - log=True, normalize=False, method=metric, units="m") + # surface relative humidity (unitless), assumed constant following Holland et al. 2010 + surface_relative_humidity = 0.9 - # exclude centroids that are too far from or too close to the eye - mask_centr_close = (d_centr <= max_dist_eye_km * KM_TO_M) & (d_centr > 1) - if not np.any(mask_centr_close): - return windfields, idx_centr_reachable + # surface temperature (in °C), following equation (9) in Holland 2008 + temp_s = 28.0 - 3.0 * (si_track["lat"] - 10.0) / 20.0 - # restrict to the centroids that are within reach of any of the positions - mask_centr_close_any = mask_centr_close.any(axis=0) - mask_centr_close = mask_centr_close[:, mask_centr_close_any] - d_centr = d_centr[:, mask_centr_close_any] - v_centr_normed = v_centr_normed[:, mask_centr_close_any, :] + # eyewall surface pressure (in Pa), following equation (6) in Holland 2008 + pres_eyewall = si_track["cen"] + si_track["pdelta"] / np.exp(1) - # normalize the vectors pointing from the eye to the centroids - v_centr_normed[~mask_centr_close] = 0 - v_centr_normed[mask_centr_close] /= d_centr[mask_centr_close, None] + # mixing ratio (in kg/kg), estimated from temperature, using formula for saturation vapor + # pressure in Bolton 1980 (multiplied by the ratio of molar masses of water vapor and dry air) + # We multiply by 100, since the formula by Bolton is in hPa (mbar), and we use Pa. + r_mix = 100 * 3.802 / pres_eyewall * np.exp(17.67 * temp_s / (243.5 + temp_s)) - # derive (absolute) angular velocity from parametric wind profile - v_ang_norm = compute_angular_windspeeds( - si_track, d_centr, mask_centr_close, model, cyclostrophic=False, - ) + # virtual surface temperature (in K) + temp_vs = (T_ICE_K + temp_s) * (1 + 0.81 * surface_relative_humidity * r_mix) - # Influence of translational speed decreases with distance from eye. - # The "absorbing factor" is according to the following paper (see Fig. 7): - # - # Mouton, F. & Nordbeck, O. (2005). Cyclone Database Manager. A tool - # for converting point data from cyclone observations into tracks and - # wind speed profiles in a GIS. UNED/GRID-Geneva. - # https://unepgrid.ch/en/resource/19B7D302 - # - t_rad_bc = np.broadcast_to(si_track["rad"].values[:, None], d_centr.shape) - v_trans_corr = np.zeros_like(d_centr) - v_trans_corr[mask_centr_close] = np.fmin( - 1, t_rad_bc[mask_centr_close] / d_centr[mask_centr_close]) + # specific gas constant of dry air (in J/kgK) + r_dry_air = 286.9 - if model in [MODEL_VANG['H08'], MODEL_VANG['H10']]: - # In these models, v_ang_norm already contains vtrans_norm, so subtract it first, before - # converting to vectors and then adding (vectorial) vtrans again. Make sure to apply the - # "absorbing factor" in both steps: - vtrans_norm_bc = np.broadcast_to(si_track["vtrans_norm"].values[:, None], d_centr.shape) - v_ang_norm[mask_centr_close] -= ( - vtrans_norm_bc[mask_centr_close] * v_trans_corr[mask_centr_close] - ) + # density of air (in kg/m³); when checking the units, note that J/Pa = m³ + si_track["rho_air"] = pres_eyewall / (r_dry_air * temp_vs) - # vectorial angular velocity - windfields = ( - si_track.attrs["latsign"] * np.array([1.0, -1.0])[..., :] * v_centr_normed[:, :, ::-1] - ) - windfields[mask_centr_close] *= v_ang_norm[mask_centr_close, None] - # add angular and corrected translational velocity vectors - windfields[1:] += si_track["vtrans"].values[1:, None, :] * v_trans_corr[1:, :, None] - windfields[np.isnan(windfields)] = 0 - windfields[0, :, :] = 0 - [idx_centr_reachable] = mask_centr_close_any.nonzero() - return windfields, idx_centr_reachable +def _bs_holland_2008( + si_track: xr.Dataset, + gradient_to_surface_winds: float = DEF_GRADIENT_TO_SURFACE_WINDS, +): + """Holland's 2008 b-value estimate for sustained surface winds. + (This is also one option of how to estimate the b-value in Holland 2010, + for the other option consult '_bs_holland_2010_v2'.) -def tctrack_to_si( - track: xr.Dataset, - metric: str = "equirect", -) -> xr.Dataset: - """Convert track variables to SI units and prepare for wind field computation + The result is stored in place as a new data variable "hol_b". - In addition to unit conversion, the variable names are shortened, the longitudinal coordinates - are normalized and additional variables are defined: + Unlike the original 1980 formula (see ``_B_holland_1980``), this approach does not require any + wind speed measurements, but is based on the more reliable pressure information. - * cp (coriolis parameter) - * vtrans (translational velocity vectors) - * vtrans_norm (absolute value of translational speed) + The parameter applies to 1-minute sustained winds at 10 meters above ground. + It is taken from equation (11) in the following paper: - Furthermore, some scalar variables are stored as attributes: + Holland, G. (2008). A revised hurricane pressure-wind model. Monthly + Weather Review, 136(9), 3432–3445. https://doi.org/10.1175/2008MWR2395.1 - * latsign (1.0 if the track is located on the northern and -1.0 if on southern hemisphere) - * mid_lon (the central longitude that was used to normalize the longitudinal coordinates) + For reference, it reads - Finally, some corrections are applied to variables: + b_s = -4.4 * 1e-5 * (penv - pcen)^2 + 0.01 * (penv - pcen) + + 0.03 * (dp/dt) - 0.014 * |lat| + 0.15 * (v_trans)^hol_xx + 1.0 - * clip central pressure values so that environmental pressure values are never exceeded - * extrapolate radius of max wind from pressure if missing + where ``dp/dt`` is the time derivative of central pressure and ``hol_xx`` is Holland's x + parameter: hol_xx = 0.6 * (1 - (penv - pcen) / 215) + + The equation for b_s has been fitted statistically using hurricane best track records for + central pressure and maximum wind. It therefore performs best in the North Atlantic. + + Furthermore, b_s has been fitted under the assumption of a "cyclostrophic" wind field which + means that the influence from Coriolis forces is assumed to be small. This is reasonable close + to the radius of maximum wind where the Coriolis term (r*f/2) is small compared to the rest + (see ``_stat_holland_1980``). More precisely: At the radius of maximum wind speeds, the typical + order of the Coriolis term is 1 while wind speed is 50 (which changes away from the + radius of maximum winds and as the TC moves away from the equator). Parameters ---------- - track : xr.Dataset - Track information. - metric : str, optional - Specify an approximation method to use for earth distances: "equirect" (faster) or - "geosphere" (more accurate). See ``dist_approx`` function in ``climada.util.coordinates``. - Default: "equirect". - - Returns - ------- - xr.Dataset + si_track : xr.Dataset + Output of ``tctrack_to_si``. The data variables used by this function are "lat", "tstep", + "vtrans_norm", "cen", and "pdelta". The result is stored in place as a new data + variable "hol_b". + gradient_to_surface_winds : float, optional + The gradient-to-surface wind reduction factor to use when determining the clipping + interval. Default: 0.9 """ - si_track = track[["lat", "lon", "time"]].copy() - si_track["tstep"] = track["time_step"] * H_TO_S - si_track["env"] = track["environmental_pressure"] * MBAR_TO_PA - - # we support some non-standard unit names - unit_replace = {"mb": "mbar", "kn": "knots"} - configs = [ - ("central_pressure", "cen", "Pa"), - ("max_sustained_wind", "vmax", "m/s"), - ] - for long_name, var_name, si_unit in configs: - unit = track.attrs[f"{long_name}_unit"] - unit = unit_replace.get(unit, unit) - try: - conv_factor = ureg(unit).to(si_unit).magnitude - except Exception as ex: - raise ValueError( - f"The {long_name}_unit '{unit}' in the provided track is not supported." - ) from ex - si_track[var_name] = track[long_name] * conv_factor + # adjust pressure at previous track point + prev_cen = np.zeros_like(si_track["cen"].values) + prev_cen[1:] = si_track["cen"].values[:-1].copy() + msk = prev_cen < 850 * MBAR_TO_PA + prev_cen[msk] = si_track["cen"].values[msk] - # normalize longitudinal coordinates - si_track.attrs["mid_lon"] = 0.5 * sum(u_coord.lon_bounds(si_track["lon"].values)) - u_coord.lon_normalize(si_track["lon"].values, center=si_track.attrs["mid_lon"]) + # The formula assumes that pressure values are in millibar (hPa) instead of SI units (Pa), + # and time steps are in hours instead of seconds, but translational wind speed is still + # expected to be in m/s. + pdelta = si_track["pdelta"] / MBAR_TO_PA + hol_xx = 0.6 * (1. - pdelta / 215) + si_track["hol_b"] = ( + -4.4e-5 * pdelta**2 + 0.01 * pdelta + + 0.03 * (si_track["cen"] - prev_cen) / si_track["tstep"] * (H_TO_S / MBAR_TO_PA) + - 0.014 * abs(si_track["lat"]) + + 0.15 * si_track["vtrans_norm"]**hol_xx + 1.0 + ) + clip_interval = _b_holland_clip_interval(gradient_to_surface_winds) + si_track["hol_b"] = np.clip(si_track["hol_b"], *clip_interval) - # make sure that central pressure never exceeds environmental pressure - pres_exceed_msk = (si_track["cen"] > si_track["env"]).values - si_track["cen"].values[pres_exceed_msk] = si_track["env"].values[pres_exceed_msk] - # extrapolate radius of max wind from pressure if not given - si_track["rad"] = track["radius_max_wind"].copy() - si_track["rad"].values[:] = estimate_rmw( - si_track["rad"].values, si_track["cen"].values / MBAR_TO_PA, - ) - si_track["rad"] *= NM_TO_KM * KM_TO_M +def _v_max_s_holland_2008(si_track: xr.Dataset): + """Compute maximum surface winds from pressure according to Holland 2008. - hemisphere = 'N' - if np.count_nonzero(si_track["lat"] < 0) > np.count_nonzero(si_track["lat"] > 0): - hemisphere = 'S' - si_track.attrs["latsign"] = 1.0 if hemisphere == 'N' else -1.0 + The result is stored in place as a data variable "vmax". If a variable of that name already + exists, its values are overwritten. - # add translational speed of track at every node (in m/s) - _vtrans(si_track, metric=metric) + This function implements equation (11) in the following paper: - # convert surface winds to gradient winds without translational influence - si_track["vgrad"] = ( - np.fmax(0, si_track["vmax"] - si_track["vtrans_norm"]) / GRADIENT_LEVEL_TO_SURFACE_WINDS - ) + Holland, G. (2008). A revised hurricane pressure-wind model. Monthly + Weather Review, 136(9), 3432–3445. https://doi.org/10.1175/2008MWR2395.1 - si_track["cp"] = ("time", _coriolis_parameter(si_track["lat"].values)) + For reference, it reads - return si_track + v_ms = [b_s / (rho * e) * (penv - pcen)]^0.5 -def compute_angular_windspeeds(si_track, d_centr, mask_centr_close, model, cyclostrophic=False): - """Compute (absolute) angular wind speeds according to a parametric wind profile + where ``b_s`` is Holland b-value (see ``_bs_holland_2008``), e is Euler's number, rho is the + density of air, ``penv`` is environmental, and ``pcen`` is central pressure. Parameters ---------- si_track : xr.Dataset - Output of ``tctrack_to_si``. Which data variables are used in the computation of the wind - profile depends on the selected model. - d_centr : np.ndarray of shape (npositions, ncentroids) - Distance (in m) between centroids and track positions. - mask_centr_close : np.ndarray of shape (npositions, ncentroids) - For each track position one row indicating which centroids are within reach. - model : int - Wind profile model selection according to MODEL_VANG. - cyclostrophic : bool, optional - If True, do not apply the influence of the Coriolis force (set the Coriolis terms to 0). - Default: False - - Returns - ------- - ndarray of shape (npositions, ncentroids) + Output of ``tctrack_to_si`` with "hol_b" (see _bs_holland_2008) and "rho_air" (see + _rho_air) variables. The data variables used by this function are "pdelta", "hol_b", + and "rho_air". The results are stored in place as a new data variable "vmax". If a variable + of that name already exists, its values are overwritten. """ - if model == MODEL_VANG['H1980']: - _B_holland_1980(si_track) - elif model in [MODEL_VANG['H08'], MODEL_VANG['H10']]: - _bs_holland_2008(si_track) - - if model in [MODEL_VANG['H1980'], MODEL_VANG['H08']]: - result = _stat_holland_1980( - si_track, d_centr, mask_centr_close, cyclostrophic=cyclostrophic, - ) - if model == MODEL_VANG['H1980']: - result *= GRADIENT_LEVEL_TO_SURFACE_WINDS - elif model == MODEL_VANG['H10']: - # this model is always cyclostrophic - _v_max_s_holland_2008(si_track) - hol_x = _x_holland_2010(si_track, d_centr, mask_centr_close) - result = _stat_holland_2010(si_track, d_centr, mask_centr_close, hol_x) - elif model == MODEL_VANG['ER11']: - result = _stat_er_2011(si_track, d_centr, mask_centr_close, cyclostrophic=cyclostrophic) - else: - raise NotImplementedError - - result[0, :] *= 0 + si_track["vmax"] = np.sqrt( + si_track["hol_b"] / (si_track["rho_air"] * np.exp(1)) * si_track["pdelta"] + ) - return result -def get_close_centroids( +def _B_holland_1980( # pylint: disable=invalid-name si_track: xr.Dataset, - centroids: np.ndarray, - buffer_km: float, - metric: str = "equirect", -) -> np.ndarray: - """Check whether centroids lay within a buffer around track positions - - Note that, hypothetically, a problem occurs when a TC track is travelling so far in longitude - that adding a buffer exceeds 360 degrees (i.e. crosses the antimeridian), which is physically - impossible, but might happen with synthetical or test data. - - Parameters - ---------- - si_track : xr.Dataset with dimension "time" - Track information as returned by ``tctrack_to_si``. Hence, longitudinal coordinates are - normalized around the central longitude stored in the "mid_lon" attribute. This makes sure - that the buffered bounding box around the track does not cross the antimeridian. The data - variables used by this function are "lat", and "lon". - centroids : np.ndarray of shape (ncentroids, 2) - Coordinates of centroids, each row is a pair [lat, lon]. The longitudinal coordinates are - normalized within this function to be consistent with the track coordinates. - buffer_km : float - Size of the buffer (in km). The buffer is converted to a lat/lon buffer, rescaled in - longitudinal direction according to the t_lat coordinates. - metric : str, optional - Specify an approximation method to use for earth distances: "equirect" (faster) or - "geosphere" (more accurate). See ``dist_approx`` function in ``climada.util.coordinates``. - Default: "equirect". - - Returns - ------- - centroids_close_normalized : np.ndarray of shape (nclose, 2) - Coordinates of close centroids, each row is a pair [lat, lon]. The normalization of - longitudinal coordinates is consistent with the track coordinates. - mask_centr : np.ndarray of shape (ncentroids,) - Mask that is True for close centroids and False for other centroids. - mask_centr_alongtrack : np.ndarray of shape (npositions, nclose) - Each row is a mask that indicates the centroids within reach for one track position. Note - that these masks refer only to the "close centroids" to reduce memory requirements. The - number of positions ``npositions`` corresponds to the size of the "time" dimension of - ``si_track``. - """ - npositions = si_track.sizes["time"] - ncentroids = centroids.shape[0] - t_lat, t_lon = si_track["lat"].values, si_track["lon"].values - centr_lat, centr_lon = centroids[:, 0].copy(), centroids[:, 1].copy() - - # Normalize longitudinal coordinates of centroids. - u_coord.lon_normalize(centr_lon, center=si_track.attrs["mid_lon"]) - - # Restrict to the bounding box of the whole track first (this can already reduce the number of - # centroids that are considered by a factor larger than 30). - buffer_lat = buffer_km / u_const.ONE_LAT_KM - buffer_lon = buffer_km / ( - u_const.ONE_LAT_KM * np.cos(np.radians( - np.fmin(89.999, np.abs(centr_lat) + buffer_lat) - )) - ) - [idx_close] = ( - (t_lat.min() - centr_lat <= buffer_lat) - & (centr_lat - t_lat.max() <= buffer_lat) - & (t_lon.min() - centr_lon <= buffer_lon) - & (centr_lon - t_lon.max() <= buffer_lon) - ).nonzero() - centr_lat = centr_lat[idx_close] - centr_lon = centr_lon[idx_close] - - # Restrict to bounding boxes of each track position. - buffer_lat = buffer_km / u_const.ONE_LAT_KM - buffer_lon = buffer_km / (u_const.ONE_LAT_KM * np.cos(np.radians( - np.fmin(89.999, np.abs(t_lat[:, None]) + buffer_lat) - ))) - [idx_close_sub] = ( - (t_lat[:, None] - buffer_lat <= centr_lat[None]) - & (t_lat[:, None] + buffer_lat >= centr_lat[None]) - & (t_lon[:, None] - buffer_lon <= centr_lon[None]) - & (t_lon[:, None] + buffer_lon >= centr_lon[None]) - ).any(axis=0).nonzero() - idx_close = idx_close[idx_close_sub] - centr_lat = centr_lat[idx_close_sub] - centr_lon = centr_lon[idx_close_sub] - - # Restrict to metric distance radius around each track position. - # - # We do the distance computation for chunks of the track since computing the distance requires - # npositions*ncentroids*8*3 Bytes of memory. For example, Hurricane FAITH's life time was more - # than 500 hours. At 0.5-hourly resolution and 1,000,000 centroids, that's 24 GB of memory for - # FAITH. With a chunk size of 10, this figure is down to 240 MB. The final along-track mask - # will require 1.0 GB of memory. - chunk_size = 10 - chunks = np.split(np.arange(npositions), np.arange(chunk_size, npositions, chunk_size)) - mask_centr_alongtrack = np.concatenate([ - ( - u_coord.dist_approx( - t_lat[None, chunk], t_lon[None, chunk], - centr_lat[None], centr_lon[None], - normalize=False, method=metric, units="km", - )[0] <= buffer_km - ) for chunk in chunks - ], axis=0) - [idx_close_sub] = mask_centr_alongtrack.any(axis=0).nonzero() - idx_close = idx_close[idx_close_sub] - centr_lat = centr_lat[idx_close_sub] - centr_lon = centr_lon[idx_close_sub] - mask_centr_alongtrack = mask_centr_alongtrack[:, idx_close_sub] - - # Derive mask from index. - mask_centr = np.zeros((ncentroids,), dtype=bool) - mask_centr[idx_close] = True - - centroids_close_normalized = np.stack([centr_lat, centr_lon], axis=1) - return centroids_close_normalized, mask_centr, mask_centr_alongtrack - -def _vtrans(si_track: xr.Dataset, metric: str = "equirect"): - """Translational vector and velocity (in m/s) at each track node. - - The track dataset is modified in place, with the following variables added: - - * vtrans (directional vectors of velocity, in meters per second) - * vtrans_norm (absolute velocity in meters per second; the first velocity is always 0) - - The meridional component (v) of the vectors is listed first. - - Parameters - ---------- - si_track : xr.Dataset - Track information as returned by ``tctrack_to_si``. The data variables used by this function - are "lat", "lon", and "tstep". The results are stored in place as new data - variables "vtrans" and "vtrans_norm". - metric : str, optional - Specify an approximation method to use for earth distances: "equirect" (faster) or - "geosphere" (more accurate). See ``dist_approx`` function in ``climada.util.coordinates``. - Default: "equirect". - """ - npositions = si_track.sizes["time"] - si_track["vtrans_norm"] = (["time"], np.zeros((npositions,))) - si_track["vtrans"] = (["time", "component"], np.zeros((npositions, 2))) - si_track["component"] = ("component", ["v", "u"]) - - t_lat, t_lon = si_track["lat"].values, si_track["lon"].values - norm, vec = u_coord.dist_approx(t_lat[:-1, None], t_lon[:-1, None], - t_lat[1:, None], t_lon[1:, None], - log=True, normalize=False, method=metric, units="m") - si_track["vtrans"].values[1:, :] = vec[:, 0, 0] / si_track["tstep"].values[1:, None] - si_track["vtrans_norm"].values[1:] = norm[:, 0, 0] / si_track["tstep"].values[1:] - - # limit to 30 nautical miles per hour - msk = si_track["vtrans_norm"].values > 30 * KN_TO_MS - fact = 30 * KN_TO_MS / si_track["vtrans_norm"].values[msk] - si_track["vtrans"].values[msk, :] *= fact[:, None] - si_track["vtrans_norm"].values[msk] *= fact - -def _coriolis_parameter(lat: np.ndarray) -> np.ndarray: - """Compute the Coriolis parameter from latitude. - - Parameters - ---------- - lat : np.ndarray - Latitude (degrees). - - Returns - ------- - cp : np.ndarray of same shape as input - Coriolis parameter. - """ - return 2 * V_ANG_EARTH * np.sin(np.radians(np.abs(lat))) - -def _bs_holland_2008(si_track: xr.Dataset): - """Holland's 2008 b-value estimate for sustained surface winds. + gradient_to_surface_winds: Optional[float] = None, +): + """Holland's 1980 B-value computation for gradient-level winds. The result is stored in place as a new data variable "hol_b". - Unlike the original 1980 formula (see ``_B_holland_1980``), this approach does not require any - wind speed measurements, but is based on the more reliable pressure information. - - The parameter applies to 1-minute sustained winds at 10 meters above ground. - It is taken from equation (11) in the following paper: - - Holland, G. (2008). A revised hurricane pressure-wind model. Monthly - Weather Review, 136(9), 3432–3445. https://doi.org/10.1175/2008MWR2395.1 - - For reference, it reads + The parameter applies to gradient-level winds (about 1000 metres above the earth's surface). + The formula for B is derived from equations (5) and (6) in the following paper: - b_s = -4.4 * 1e-5 * (penv - pcen)^2 + 0.01 * (penv - pcen) - + 0.03 * (dp/dt) - 0.014 * |lat| + 0.15 * (v_trans)^hol_xx + 1.0 + Holland, G.J. (1980): An Analytic Model of the Wind and Pressure Profiles + in Hurricanes. Monthly Weather Review 108(8): 1212–1218. + https://doi.org/10.1175/1520-0493(1980)108<1212:AAMOTW>2.0.CO;2 - where ``dp/dt`` is the time derivative of central pressure and ``hol_xx`` is Holland's x - parameter: hol_xx = 0.6 * (1 - (penv - pcen) / 215) + For reference, inserting (6) into (5) and solving for B at r = RMW yields: - The equation for b_s has been fitted statistically using hurricane best track records for - central pressure and maximum wind. It therefore performs best in the North Atlantic. + B = v^2 * e * rho / (penv - pcen) - Furthermore, b_s has been fitted under the assumption of a "cyclostrophic" wind field which - means that the influence from Coriolis forces is assumed to be small. This is reasonable close - to the radius of maximum wind where the Coriolis term (r*f/2) is small compared to the rest - (see ``_stat_holland_1980``). More precisely: At the radius of maximum wind speeds, the typical - order of the Coriolis term is 1 while wind speed is 50 (which changes away from the - radius of maximum winds and as the TC moves away from the equator). + where v are maximum gradient-level winds ``gradient_winds``, e is Euler's number, rho is the + density of air, ``penv`` is environmental, and ``pcen`` is central pressure. Parameters ---------- si_track : xr.Dataset - Output of ``tctrack_to_si``. The data variables used by this function are "lat", "tstep", - "vtrans_norm", "cen", and "env". The result is stored in place as a new data + Output of ``tctrack_to_si`` with "rho_air" variable (see _rho_air). The data variables + used by this function are "vgrad" (or "vmax" if gradient_to_surface_winds is different from + 1.0), "pdelta", and "rho_air". The results are stored in place as a new data variable "hol_b". + gradient_to_surface_winds : float, optional + The gradient-to-surface wind reduction factor to use when determining the clipping + interval. By default, the gradient level values are assumed. Default: None """ - # adjust pressure at previous track point - prev_cen = np.zeros_like(si_track["cen"].values) - prev_cen[1:] = si_track["cen"].values[:-1].copy() - msk = prev_cen < 850 * MBAR_TO_PA - prev_cen[msk] = si_track["cen"].values[msk] + windvar = "vgrad" if gradient_to_surface_winds is None else "vmax" - # The formula assumes that pressure values are in millibar (hPa) instead of SI units (Pa), - # and time steps are in hours instead of seconds, but translational wind speed is still - # expected to be in m/s. - pdelta = (si_track["env"] - si_track["cen"]) / MBAR_TO_PA - hol_xx = 0.6 * (1. - pdelta / 215) si_track["hol_b"] = ( - -4.4e-5 * pdelta**2 + 0.01 * pdelta - + 0.03 * (si_track["cen"] - prev_cen) / si_track["tstep"] * (H_TO_S / MBAR_TO_PA) - - 0.014 * abs(si_track["lat"]) - + 0.15 * si_track["vtrans_norm"]**hol_xx + 1.0 + si_track[windvar]**2 * np.exp(1) * si_track["rho_air"] / si_track["pdelta"] ) - si_track["hol_b"] = np.clip(si_track["hol_b"], 1, 2.5) - -def _v_max_s_holland_2008(si_track: xr.Dataset): - """Compute maximum surface winds from pressure according to Holland 2008. - - The result is stored in place as a data variable "vmax". If a variable of that name already - exists, its values are overwritten. - This function implements equation (11) in the following paper: - - Holland, G. (2008). A revised hurricane pressure-wind model. Monthly - Weather Review, 136(9), 3432–3445. https://doi.org/10.1175/2008MWR2395.1 + clip_interval = _b_holland_clip_interval(gradient_to_surface_winds) + si_track["hol_b"] = np.clip(si_track["hol_b"], *clip_interval) - For reference, it reads - v_ms = [b_s / (rho * e) * (penv - pcen)]^0.5 +def _b_holland_clip_interval(gradient_to_surface_winds): + """The clip interval to use for the Holland B-value - where ``b_s`` is Holland b-value (see ``_bs_holland_2008``), e is Euler's number, rho is the - density of air, ``penv`` is environmental, and ``pcen`` is central pressure. + The default clip interval for gradient level B-values is taken to be (1.0, 2.5), following + Holland 1980. Parameters ---------- - si_track : xr.Dataset - Output of ``tctrack_to_si`` with "hol_b" variable (see _bs_holland_2008). The data variables - used by this function are "env", "cen", and "hol_b". The results are stored in place as - a new data variable "vmax". If a variable of that name already exists, its values are - overwritten. - """ - pdelta = si_track["env"] - si_track["cen"] - si_track["vmax"] = np.sqrt(si_track["hol_b"] / (RHO_AIR * np.exp(1)) * pdelta) - -def _B_holland_1980(si_track: xr.Dataset): # pylint: disable=invalid-name - """Holland's 1980 B-value computation for gradient-level winds. - - The result is stored in place as a new data variable "hol_b". - - The parameter applies to gradient-level winds (about 1000 metres above the earth's surface). - The formula for B is derived from equations (5) and (6) in the following paper: - - Holland, G.J. (1980): An Analytic Model of the Wind and Pressure Profiles - in Hurricanes. Monthly Weather Review 108(8): 1212–1218. - https://doi.org/10.1175/1520-0493(1980)108<1212:AAMOTW>2.0.CO;2 - - For reference, inserting (6) into (5) and solving for B at r = RMW yields: + gradient_to_surface_winds : float or None + The gradient-to-surface wind reduction factor to use when rescaling the gradient-level + clip interval (1.0, 2.5) proposed in Holland 1980. If None, no rescaling is applied. - B = v^2 * e * rho / (penv - pcen) - - where v are maximum gradient-level winds ``gradient_winds``, e is Euler's number, rho is the - density of air, ``penv`` is environmental, and ``pcen`` is central pressure. - - Parameters - ---------- - si_track : xr.Dataset - Output of ``tctrack_to_si`` with "vgrad" variable (see _vgrad). The data variables - used by this function are "vgrad", "env", and "cen". The results are stored in place as - a new data variable "hol_b". + Returns + ------- + b_min, b_max : float + Minimum and maximum value of the clip interval. """ - pdelta = si_track["env"] - si_track["cen"] - si_track["hol_b"] = si_track["vgrad"]**2 * np.exp(1) * RHO_AIR / np.fmax(np.spacing(1), pdelta) - si_track["hol_b"] = np.clip(si_track["hol_b"], 1, 2.5) + clip_interval = (1.0, 2.5) + if gradient_to_surface_winds is not None: + fact = gradient_to_surface_winds**2 + clip_interval = tuple(c * fact for c in clip_interval) + return clip_interval + def _x_holland_2010( si_track: xr.Dataset, @@ -1406,6 +509,7 @@ def _x_holland_2010( mask_centr_close: np.ndarray, v_n: Union[float, np.ndarray] = 17.0, r_n_km: Union[float, np.ndarray] = 300.0, + vmax_in_brackets: bool = False, ) -> np.ndarray: """Compute exponent for wind model according to Holland et al. 2010. @@ -1439,6 +543,11 @@ def _x_holland_2010( Radius (in km) where the peripheral wind speed ``v_n`` is measured (or assumed). In absence of a second wind speed measurement, this value defaults to 300 km following Holland et al. 2010. + vmax_in_brackets : bool, optional + If True, use the alternative formula in equation (6) to solve for the peripheral exponent + x_n from the second measurement. Note that, a side-effect of the formula with vmax inside + of the brackets is that the wind speed maximum is attained a bit farther away from the + center than according to the recorded radius of maximum winds (RMW). Default: False Returns ------- @@ -1462,26 +571,37 @@ def _x_holland_2010( r_n *= KM_TO_M # compute peripheral exponent from second measurement + # (equation (6) from Holland et al. 2010 solved for x) r_max_norm = (r_max / r_n)**hol_b - x_n = np.log(v_n / v_max_s) / np.log(r_max_norm * np.exp(1 - r_max_norm)) + if vmax_in_brackets: + x_n = np.log(v_n) / np.log(v_max_s**2 * r_max_norm * np.exp(1 - r_max_norm)) + + # With `vmax_in_brackets`, the maximum is shifted away from the recorded RMW. We truncate + # here to avoid an exaggerated shift. The value 1.0 has been found to be reasonable by + # manual testing of thresholds. Note that the truncation means that the peripheral wind + # speed v_n is not exactly attained in some cases. + x_n = np.fmin(x_n, 1.0) + else: + x_n = np.log(v_n / v_max_s) / np.log(r_max_norm * np.exp(1 - r_max_norm)) # linearly interpolate between max exponent and peripheral exponent x_max = 0.5 hol_x[mask_centr_close] = x_max + np.fmax(0, d_centr - r_max) * (x_n - x_max) / (r_n - r_max) - # Negative hol_x values appear when v_max_s is very close to or even lower than v_n (which - # should never happen in theory). In those cases, wind speeds might decrease outside of the eye - # wall and increase again towards the peripheral radius (which is actually unphysical). - # We clip hol_x to 0, otherwise wind speeds keep increasing indefinitely away from the eye: - hol_x[mask_centr_close] = np.fmax(hol_x[mask_centr_close], 0.0) + # Truncate to prevent wind speed from increasing again towards the peripheral radius (which is + # unphysical). A value of 0.4 has been found to be reasonable by manual testing of thresholds. + # Note that this means that the peripheral wind speed v_n is not exactly attained sometimes. + hol_x[mask_centr_close] = np.fmax(hol_x[mask_centr_close], 0.4) return hol_x + def _stat_holland_2010( si_track: xr.Dataset, d_centr: np.ndarray, mask_centr_close: np.ndarray, hol_x: Union[float, np.ndarray], + vmax_in_brackets: bool = False, ) -> np.ndarray: """Symmetric and static surface wind fields (in m/s) according to Holland et al. 2010 @@ -1496,6 +616,12 @@ def _stat_holland_2010( In terms of this function's arguments, b_s is ``hol_b`` and r is ``d_centr``. + If ``vmax_in_brackets`` is True, the alternative formula in (6) is used: + + .. math:: + + V(r) = [v_max_s^2 * (r_max / r)^b_s * e^(1 - (r_max / r)^b_s)]^x + Parameters ---------- si_track : xr.Dataset @@ -1507,6 +633,11 @@ def _stat_holland_2010( Mask indicating for each track node which centroids are within reach of the windfield. hol_x : np.ndarray of shape (nnodes, ncentroids) or float The exponent according to ``_x_holland_2010``. + vmax_in_brackets : bool, optional + If True, use the alternative formula in equation (6). Note that, a side-effect of the + formula with vmax inside of the brackets is that the wind speed maximum is attained a bit + farther away from the center than according to the recorded radius of maximum + winds (RMW). Default: False Returns ------- @@ -1526,9 +657,13 @@ def _stat_holland_2010( ] r_max_norm = (r_max / np.fmax(1, d_centr))**hol_b - v_ang[mask_centr_close] = v_max_s * (r_max_norm * np.exp(1 - r_max_norm))**hol_x + if vmax_in_brackets: + v_ang[mask_centr_close] = (v_max_s**2 * r_max_norm * np.exp(1 - r_max_norm))**hol_x + else: + v_ang[mask_centr_close] = v_max_s * (r_max_norm * np.exp(1 - r_max_norm))**hol_x return v_ang + def _stat_holland_1980( si_track: xr.Dataset, d_centr: np.ndarray, @@ -1547,9 +682,8 @@ def _stat_holland_1980( V(r) = [(B/rho) * (r_max/r)^B * (penv - pcen) * e^(-(r_max/r)^B) + (r*f/2)^2]^0.5 - (r*f/2) - In terms of this function's arguments, B is ``hol_b`` and r is ``d_centr``. - The air density rho is assumed to be constant while the Coriolis parameter f is computed - from the latitude ``lat`` using the constant rotation rate of the earth. + In terms of this function's arguments, B is ``hol_b`` and r is ``d_centr``. The air density + rho and the Coriolis parameter f are taken from ``si_track``. Even though the equation has been derived originally for gradient winds (when combined with the output of ``_B_holland_1980``), it can be used for surface winds by adjusting the parameter @@ -1558,8 +692,9 @@ def _stat_holland_1980( Parameters ---------- si_track : xr.Dataset - Output of ``tctrack_to_si`` with "hol_b" (see, e.g., _B_holland_1980) data variable. The - data variables used by this function are "lat", "cp", "rad", "cen", "env", and "hol_b". + Output of ``tctrack_to_si`` with "hol_b" (see, e.g., _B_holland_1980) and + "rho_air" (see _rho_air) data variable. The data variables used by this function + are "lat", "cp", "rad", "pdelta", "hol_b", and "rho_air". d_centr : np.ndarray of shape (nnodes, ncentroids) Distance (in m) between centroids and track nodes. mask_centr_close : np.ndarray of shape (nnodes, ncentroids) @@ -1574,14 +709,14 @@ def _stat_holland_1980( Absolute values of wind speeds (m/s) in angular direction. """ v_ang = np.zeros_like(d_centr) - r_max, hol_b, penv, pcen, coriolis_p, d_centr = [ + r_max, hol_b, pdelta, coriolis_p, rho_air, d_centr = [ np.broadcast_to(ar, d_centr.shape)[mask_centr_close] for ar in [ si_track["rad"].values[:, None], si_track["hol_b"].values[:, None], - si_track["env"].values[:, None], - si_track["cen"].values[:, None], + si_track["pdelta"].values[:, None], si_track["cp"].values[:, None], + si_track["rho_air"].values[:, None], d_centr, ] ] @@ -1591,10 +726,11 @@ def _stat_holland_1980( r_coriolis = 0.5 * d_centr * coriolis_p r_max_norm = (r_max / np.fmax(1, d_centr))**hol_b - sqrt_term = hol_b / RHO_AIR * r_max_norm * (penv - pcen) * np.exp(-r_max_norm) + r_coriolis**2 + sqrt_term = hol_b / rho_air * r_max_norm * pdelta * np.exp(-r_max_norm) + r_coriolis**2 v_ang[mask_centr_close] = np.sqrt(np.fmax(0, sqrt_term)) - r_coriolis return v_ang + def _stat_er_2011( si_track: xr.Dataset, d_centr: np.ndarray, @@ -1663,3 +799,595 @@ def _stat_er_2011( # extract the velocity from the rescaled momentum through division by r v_ang[mask_centr_close] = np.fmax(0, momentum / (d_centr + 1e-11)) return v_ang + + +KN_TO_MS = (1.0 * ureg.knot).to(ureg.meter / ureg.second).magnitude +V_ANG_EARTH = 7.29e-5 + + +def _vtrans(si_track: xr.Dataset, metric: str = "equirect"): + """Translational vector and velocity (in m/s) at each track node. + + The track dataset is modified in place, with the following variables added: + + * vtrans (directional vectors of velocity, in meters per second) + * vtrans_norm (absolute velocity in meters per second; the first velocity is always 0) + + The meridional component (v) of the vectors is listed first. + + Parameters + ---------- + si_track : xr.Dataset + Track information as returned by ``tctrack_to_si``. The data variables used by this function + are "lat", "lon", and "tstep". The results are stored in place as new data + variables "vtrans" and "vtrans_norm". + metric : str, optional + Specify an approximation method to use for earth distances: "equirect" (faster) or + "geosphere" (more accurate). See ``dist_approx`` function in ``climada.util.coordinates``. + Default: "equirect". + """ + npositions = si_track.sizes["time"] + si_track["vtrans_norm"] = (["time"], np.zeros((npositions,))) + si_track["vtrans"] = (["time", "component"], np.zeros((npositions, 2))) + si_track["component"] = ("component", ["v", "u"]) + + t_lat, t_lon = si_track["lat"].values, si_track["lon"].values + norm, vec = u_coord.dist_approx(t_lat[:-1, None], t_lon[:-1, None], + t_lat[1:, None], t_lon[1:, None], + log=True, normalize=False, method=metric, units="m") + si_track["vtrans"].values[1:, :] = vec[:, 0, 0] / si_track["tstep"].values[1:, None] + si_track["vtrans_norm"].values[1:] = norm[:, 0, 0] / si_track["tstep"].values[1:] + + # limit to 30 nautical miles per hour + msk = si_track["vtrans_norm"].values > 30 * KN_TO_MS + fact = 30 * KN_TO_MS / si_track["vtrans_norm"].values[msk] + si_track["vtrans"].values[msk, :] *= fact[:, None] + si_track["vtrans_norm"].values[msk] *= fact + + +def _coriolis_parameter(lat: np.ndarray) -> np.ndarray: + """Compute the Coriolis parameter from latitude. + + Parameters + ---------- + lat : np.ndarray + Latitude (degrees). + + Returns + ------- + cp : np.ndarray of same shape as input + Coriolis parameter. + """ + return 2 * V_ANG_EARTH * np.sin(np.radians(np.abs(lat))) + +def compute_windfields_sparse( + track: xr.Dataset, + centroids: Centroids, + idx_centr_filter: np.ndarray, + model: str = 'H08', + model_kwargs: Optional[dict] = None, + store_windfields: bool = False, + metric: str = "equirect", + intensity_thres: float = DEF_INTENSITY_THRES, + max_dist_eye_km: float = DEF_MAX_DIST_EYE_KM, + max_memory_gb: float = DEF_MAX_MEMORY_GB, +) -> Tuple[sparse.csr_matrix, Optional[sparse.csr_matrix]]: + """Version of ``compute_windfields`` that returns sparse matrices and limits memory usage + + Parameters + ---------- + track : xr.Dataset + Single tropical cyclone track. + centroids : Centroids + Centroids instance. + idx_centr_filter : np.ndarray + Indices of centroids to restrict to (e.g. sufficiently close to coast). + model : str, optional + Parametric wind field model, one of "H1980" (the prominent Holland 1980 model), + "H08" (Holland 1980 with b-value from Holland 2008), "H10" (Holland et al. 2010), or + "ER11" (Emanuel and Rotunno 2011). + Default: "H08". + model_kwargs: dict, optional + If given, forward these kwargs to the selected model. Default: None + store_windfields : boolean, optional + If True, store windfields. Default: False. + metric : str, optional + Specify an approximation method to use for earth distances: "equirect" (faster) or + "geosphere" (more accurate). See ``dist_approx`` function in ``climada.util.coordinates``. + Default: "equirect". + intensity_thres : float, optional + Wind speeds (in m/s) below this threshold are stored as 0. Default: 17.5 + max_dist_eye_km : float, optional + No wind speed calculation is done for centroids with a distance (in km) to the TC + center ("eye") larger than this parameter. Default: 300 + max_memory_gb : float, optional + To avoid memory issues, the computation is done for chunks of the track sequentially. + The chunk size is determined depending on the available memory (in GB). Default: 8 + + Raises + ------ + ValueError + + Returns + ------- + intensity : csr_matrix + Maximum wind speed in each centroid over the whole storm life time. + windfields : csr_matrix or None + If store_windfields is True, the full velocity vectors at each centroid and track position + are stored in a sparse matrix of shape (npositions, ncentroids * 2) that can be reshaped + to a full ndarray of shape (npositions, ncentroids, 2). + If store_windfields is False, None is returned. + """ + try: + mod_id = MODEL_VANG[model] + except KeyError as err: + raise ValueError(f'Model not implemented: {model}.') from err + + ncentroids = centroids.coord.shape[0] + npositions = track.sizes["time"] + windfields_shape = (npositions, ncentroids * 2) + intensity_shape = (1, ncentroids) + + # initialise arrays for the assumption that no centroids are within reach + windfields_sparse = ( + sparse.csr_matrix(([], ([], [])), shape=windfields_shape) + if store_windfields else None + ) + intensity_sparse = sparse.csr_matrix(([], ([], [])), shape=intensity_shape) + + # The wind field model requires at least two track positions because translational speed + # as well as the change in pressure (in case of H08) are required. + if npositions < 2: + return intensity_sparse, windfields_sparse + + # convert track variables to SI units + si_track = tctrack_to_si(track, metric=metric) + + # When done properly, finding and storing the close centroids is not a memory bottle neck and + # can be done before chunking. Note that the longitudinal coordinates of `centroids_close` as + # returned by `get_close_centroids` are normalized to be consistent with the coordinates in + # `si_track`. + centroids_close, mask_centr, mask_centr_alongtrack = get_close_centroids( + si_track, centroids.coord[idx_centr_filter], max_dist_eye_km, metric=metric, + ) + idx_centr_filter = idx_centr_filter[mask_centr] + n_centr_close = centroids_close.shape[0] + if n_centr_close == 0: + return intensity_sparse, windfields_sparse + + # the total memory requirement in GB if we compute everything without chunking: + # 8 Bytes per entry (float64), 10 arrays + total_memory_gb = npositions * n_centr_close * 8 * 10 / 1e9 + if total_memory_gb > max_memory_gb and npositions > 2: + # If the number of positions is down to 2 already, we cannot split any further. In that + # case, we just take the risk and try to do the computation anyway. It might still work + # since we have only computed an upper bound for the number of affected centroids. + + # Split the track into chunks, compute the result for each chunk, and combine: + return _compute_windfields_sparse_chunked( + mask_centr_alongtrack, + track, + centroids, + idx_centr_filter, + model=model, + model_kwargs=model_kwargs, + store_windfields=store_windfields, + metric=metric, + intensity_thres=intensity_thres, + max_dist_eye_km=max_dist_eye_km, + max_memory_gb=max_memory_gb, + ) + + windfields, idx_centr_reachable = _compute_windfields( + si_track, + centroids_close, + mod_id, + model_kwargs=model_kwargs, + metric=metric, + max_dist_eye_km=max_dist_eye_km, + ) + idx_centr_filter = idx_centr_filter[idx_centr_reachable] + npositions = windfields.shape[0] + + intensity = np.linalg.norm(windfields, axis=-1).max(axis=0) + intensity[intensity < intensity_thres] = 0 + intensity_sparse = sparse.csr_matrix( + (intensity, idx_centr_filter, [0, intensity.size]), + shape=intensity_shape) + intensity_sparse.eliminate_zeros() + + windfields_sparse = None + if store_windfields: + n_centr_filter = idx_centr_filter.size + indices = np.zeros((npositions, n_centr_filter, 2), dtype=np.int64) + indices[:, :, 0] = 2 * idx_centr_filter[None] + indices[:, :, 1] = 2 * idx_centr_filter[None] + 1 + indices = indices.ravel() + indptr = np.arange(npositions + 1) * n_centr_filter * 2 + windfields_sparse = sparse.csr_matrix((windfields.ravel(), indices, indptr), + shape=windfields_shape) + windfields_sparse.eliminate_zeros() + + return intensity_sparse, windfields_sparse + + +def _compute_windfields_sparse_chunked( + mask_centr_alongtrack: np.ndarray, + track: xr.Dataset, + *args, + max_memory_gb: float = DEF_MAX_MEMORY_GB, + **kwargs, +) -> Tuple[sparse.csr_matrix, Optional[sparse.csr_matrix]]: + """Call ``compute_windfields_sparse`` for chunks of the track and re-assemble the results + + Parameters + ---------- + mask_centr_alongtrack : np.ndarray of shape (npositions, ncentroids) + Each row is a mask that indicates the centroids within reach for one track position. + track : xr.Dataset + Single tropical cyclone track. + max_memory_gb : float, optional + Maximum memory requirements (in GB) for the computation of a single chunk of the track. + Default: 8 + args, kwargs : + The remaining arguments are passed on to ``compute_windfields_sparse``. + + Returns + ------- + intensity, windfields : + See ``compute_windfields_sparse`` for a description of the return values. + """ + npositions = track.sizes["time"] + # The memory requirements for each track position are estimated for the case of 10 arrays + # containing `nreachable` float64 (8 Byte) values each. The chunking is only relevant in + # extreme cases with a very high temporal and/or spatial resolution. + max_nreachable = max_memory_gb * 1e9 / (8 * 10 * npositions) + split_pos = [0] + chunk_size = 2 + while split_pos[-1] + chunk_size < npositions: + chunk_size += 1 + # create overlap between consecutive chunks + chunk_start = max(0, split_pos[-1] - 1) + chunk_end = chunk_start + chunk_size + nreachable = mask_centr_alongtrack[chunk_start:chunk_end].any(axis=0).sum() + if nreachable > max_nreachable: + split_pos.append(chunk_end - 1) + chunk_size = 2 + split_pos.append(npositions) + + intensity = [] + windfields = [] + for prev_chunk_end, chunk_end in zip(split_pos[:-1], split_pos[1:]): + chunk_start = max(0, prev_chunk_end - 1) + inten, win = compute_windfields_sparse( + track.isel(time=slice(chunk_start, chunk_end)), *args, + max_memory_gb=max_memory_gb, **kwargs, + ) + intensity.append(inten) + windfields.append(win) + + intensity = sparse.csr_matrix(sparse.vstack(intensity).max(axis=0)) + if windfields[0] is not None: + # eliminate the overlap between consecutive chunks + windfields = [windfields[0]] + [win[1:, :] for win in windfields[1:]] + windfields = sparse.vstack(windfields, format="csr") + return intensity, windfields + + +def _compute_windfields( + si_track: xr.Dataset, + centroids: np.ndarray, + model: int, + model_kwargs: Optional[dict] = None, + metric: str = "equirect", + max_dist_eye_km: float = DEF_MAX_DIST_EYE_KM, +) -> Tuple[np.ndarray, np.ndarray]: + """Compute 1-minute sustained winds (in m/s) at 10 meters above ground + + In a first step, centroids within reach of the track are determined so that wind fields will + only be computed and returned for those centroids. Still, since computing the distance of + the storm center to the centroids is computationally expensive, make sure to pre-filter the + centroids and call this function only for those centroids that are potentially affected. + + Parameters + ---------- + si_track : xr.Dataset + Output of ``tctrack_to_si``. Which data variables are used in the computation of the wind + speeds depends on the selected model. + centroids : np.ndarray with two dimensions + Each row is a centroid [lat, lon]. Centroids that are not within reach of the track are + ignored. Longitudinal coordinates are assumed to be normalized consistently with the + longitudinal coordinates in ``si_track``. + model : int + Wind profile model selection according to MODEL_VANG. + model_kwargs: dict, optional + If given, forward these kwargs to the selected model. Default: None + metric : str, optional + Specify an approximation method to use for earth distances: "equirect" (faster) or + "geosphere" (more accurate). See ``dist_approx`` function in ``climada.util.coordinates``. + Default: "equirect". + max_dist_eye_km : float, optional + No wind speed calculation is done for centroids with a distance (in km) to the TC center + ("eye") larger than this parameter. Default: 300 + + Returns + ------- + windfields : np.ndarray of shape (npositions, nreachable, 2) + Directional wind fields for each track position on those centroids within reach + of the TC track. Note that the wind speeds at the first position are all zero because + the discrete time derivatives involved in the process are implemented using backward + differences. However, the first position is usually not relevant for impact calculations + since it is far off shore. + idx_centr_reachable : np.ndarray of shape (nreachable,) + List of indices of input centroids within reach of the TC track. + """ + # start with the assumption that no centroids are within reach + npositions = si_track.sizes["time"] + idx_centr_reachable = np.zeros((0,), dtype=np.int64) + windfields = np.zeros((npositions, 0, 2), dtype=np.float64) + + # compute distances (in m) and vectors to all centroids + [d_centr], [v_centr_normed] = u_coord.dist_approx( + si_track["lat"].values[None], si_track["lon"].values[None], + centroids[None, :, 0], centroids[None, :, 1], + log=True, normalize=False, method=metric, units="m") + + # exclude centroids that are too far from or too close to the eye + mask_centr_close = (d_centr <= max_dist_eye_km * KM_TO_M) & (d_centr > 1) + if not np.any(mask_centr_close): + return windfields, idx_centr_reachable + + # restrict to the centroids that are within reach of any of the positions + mask_centr_close_any = mask_centr_close.any(axis=0) + mask_centr_close = mask_centr_close[:, mask_centr_close_any] + d_centr = d_centr[:, mask_centr_close_any] + v_centr_normed = v_centr_normed[:, mask_centr_close_any, :] + + # normalize the vectors pointing from the eye to the centroids + v_centr_normed[~mask_centr_close] = 0 + v_centr_normed[mask_centr_close] /= d_centr[mask_centr_close, None] + + # derive (absolute) angular velocity from parametric wind profile + v_ang_norm = compute_angular_windspeeds( + si_track, d_centr, mask_centr_close, model, model_kwargs=model_kwargs, cyclostrophic=False, + ) + + # Influence of translational speed decreases with distance from eye. + # The "absorbing factor" is according to the following paper (see Fig. 7): + # + # Mouton, F. & Nordbeck, O. (2005). Cyclone Database Manager. A tool + # for converting point data from cyclone observations into tracks and + # wind speed profiles in a GIS. UNED/GRID-Geneva. + # https://unepgrid.ch/en/resource/19B7D302 + # + t_rad_bc = np.broadcast_to(si_track["rad"].values[:, None], d_centr.shape) + v_trans_corr = np.zeros_like(d_centr) + v_trans_corr[mask_centr_close] = np.fmin( + 1, t_rad_bc[mask_centr_close] / d_centr[mask_centr_close]) + + if model in [MODEL_VANG['H08'], MODEL_VANG['H10']]: + # In these models, v_ang_norm already contains vtrans_norm, so subtract it first, before + # converting to vectors and then adding (vectorial) vtrans again. Make sure to apply the + # "absorbing factor" in both steps: + vtrans_norm_bc = np.broadcast_to(si_track["vtrans_norm"].values[:, None], d_centr.shape) + v_ang_norm[mask_centr_close] -= ( + vtrans_norm_bc[mask_centr_close] * v_trans_corr[mask_centr_close] + ) + + # vectorial angular velocity + windfields = ( + si_track.attrs["latsign"] * np.array([1.0, -1.0])[..., :] * v_centr_normed[:, :, ::-1] + ) + windfields[mask_centr_close] *= v_ang_norm[mask_centr_close, None] + + # add angular and corrected translational velocity vectors + windfields[1:] += si_track["vtrans"].values[1:, None, :] * v_trans_corr[1:, :, None] + windfields[np.isnan(windfields)] = 0 + windfields[0, :, :] = 0 + [idx_centr_reachable] = mask_centr_close_any.nonzero() + return windfields, idx_centr_reachable + + +def tctrack_to_si( + track: xr.Dataset, + metric: str = "equirect", +) -> xr.Dataset: + """Convert track variables to SI units and prepare for wind field computation + + In addition to unit conversion, the variable names are shortened, the longitudinal coordinates + are normalized and additional variables are defined: + + * cp (coriolis parameter) + * pdelta (difference between environmental and central pressure, always strictly positive) + * vtrans (translational velocity vectors) + * vtrans_norm (absolute value of translational speed) + + Furthermore, some scalar variables are stored as attributes: + + * latsign (1.0 if the track is located on the northern and -1.0 if on southern hemisphere) + * mid_lon (the central longitude that was used to normalize the longitudinal coordinates) + + Finally, some corrections are applied to variables: + + * clip central pressure values so that environmental pressure values are never exceeded + * extrapolate radius of max wind from pressure if missing + + Parameters + ---------- + track : xr.Dataset + Track information. + metric : str, optional + Specify an approximation method to use for earth distances: "equirect" (faster) or + "geosphere" (more accurate). See ``dist_approx`` function in ``climada.util.coordinates``. + Default: "equirect". + + Returns + ------- + xr.Dataset + """ + si_track = track[["lat", "lon", "time"]].copy() + si_track["tstep"] = track["time_step"] * H_TO_S + si_track["env"] = track["environmental_pressure"] * MBAR_TO_PA + + # we support some non-standard unit names + unit_replace = {"mb": "mbar", "kn": "knots"} + configs = [ + ("central_pressure", "cen", "Pa"), + ("max_sustained_wind", "vmax", "m/s"), + ] + for long_name, var_name, si_unit in configs: + unit = track.attrs[f"{long_name}_unit"] + unit = unit_replace.get(unit, unit) + try: + conv_factor = ureg(unit).to(si_unit).magnitude + except Exception as ex: + raise ValueError( + f"The {long_name}_unit '{unit}' in the provided track is not supported." + ) from ex + si_track[var_name] = track[long_name] * conv_factor + + # normalize longitudinal coordinates + si_track.attrs["mid_lon"] = 0.5 * sum(u_coord.lon_bounds(si_track["lon"].values)) + u_coord.lon_normalize(si_track["lon"].values, center=si_track.attrs["mid_lon"]) + + # make sure that central pressure never exceeds environmental pressure + pres_exceed_msk = (si_track["cen"] > si_track["env"]).values + si_track["cen"].values[pres_exceed_msk] = si_track["env"].values[pres_exceed_msk] + + # extrapolate radius of max wind from pressure if not given + si_track["rad"] = track["radius_max_wind"].copy() + si_track["rad"].values[:] = estimate_rmw( + si_track["rad"].values, si_track["cen"].values / MBAR_TO_PA, + ) + si_track["rad"] *= NM_TO_KM * KM_TO_M + + hemisphere = 'N' + if np.count_nonzero(si_track["lat"] < 0) > np.count_nonzero(si_track["lat"] > 0): + hemisphere = 'S' + si_track.attrs["latsign"] = 1.0 if hemisphere == 'N' else -1.0 + + # add translational speed of track at every node (in m/s) + _vtrans(si_track, metric=metric) + + # add Coriolis parameter + si_track["cp"] = ("time", _coriolis_parameter(si_track["lat"].values)) + + # add pressure drop + si_track["pdelta"] = np.fmax(np.spacing(1), si_track["env"] - si_track["cen"]) + + return si_track + + +def get_close_centroids( + si_track: xr.Dataset, + centroids: np.ndarray, + buffer_km: float, + metric: str = "equirect", +) -> np.ndarray: + """Check whether centroids lay within a buffer around track positions + + Note that, hypothetically, a problem occurs when a TC track is travelling so far in longitude + that adding a buffer exceeds 360 degrees (i.e. crosses the antimeridian), which is physically + impossible, but might happen with synthetical or test data. + + Parameters + ---------- + si_track : xr.Dataset with dimension "time" + Track information as returned by ``tctrack_to_si``. Hence, longitudinal coordinates are + normalized around the central longitude stored in the "mid_lon" attribute. This makes sure + that the buffered bounding box around the track does not cross the antimeridian. The data + variables used by this function are "lat", and "lon". + centroids : np.ndarray of shape (ncentroids, 2) + Coordinates of centroids, each row is a pair [lat, lon]. The longitudinal coordinates are + normalized within this function to be consistent with the track coordinates. + buffer_km : float + Size of the buffer (in km). The buffer is converted to a lat/lon buffer, rescaled in + longitudinal direction according to the t_lat coordinates. + metric : str, optional + Specify an approximation method to use for earth distances: "equirect" (faster) or + "geosphere" (more accurate). See ``dist_approx`` function in ``climada.util.coordinates``. + Default: "equirect". + + Returns + ------- + centroids_close_normalized : np.ndarray of shape (nclose, 2) + Coordinates of close centroids, each row is a pair [lat, lon]. The normalization of + longitudinal coordinates is consistent with the track coordinates. + mask_centr : np.ndarray of shape (ncentroids,) + Mask that is True for close centroids and False for other centroids. + mask_centr_alongtrack : np.ndarray of shape (npositions, nclose) + Each row is a mask that indicates the centroids within reach for one track position. Note + that these masks refer only to the "close centroids" to reduce memory requirements. The + number of positions ``npositions`` corresponds to the size of the "time" dimension of + ``si_track``. + """ + npositions = si_track.sizes["time"] + ncentroids = centroids.shape[0] + t_lat, t_lon = si_track["lat"].values, si_track["lon"].values + centr_lat, centr_lon = centroids[:, 0].copy(), centroids[:, 1].copy() + + # Normalize longitudinal coordinates of centroids. + u_coord.lon_normalize(centr_lon, center=si_track.attrs["mid_lon"]) + + # Restrict to the bounding box of the whole track first (this can already reduce the number of + # centroids that are considered by a factor larger than 30). + buffer_lat = buffer_km / u_const.ONE_LAT_KM + buffer_lon = buffer_km / ( + u_const.ONE_LAT_KM * np.cos(np.radians( + np.fmin(89.999, np.abs(centr_lat) + buffer_lat) + )) + ) + [idx_close] = ( + (t_lat.min() - centr_lat <= buffer_lat) + & (centr_lat - t_lat.max() <= buffer_lat) + & (t_lon.min() - centr_lon <= buffer_lon) + & (centr_lon - t_lon.max() <= buffer_lon) + ).nonzero() + centr_lat = centr_lat[idx_close] + centr_lon = centr_lon[idx_close] + + # Restrict to bounding boxes of each track position. + buffer_lat = buffer_km / u_const.ONE_LAT_KM + buffer_lon = buffer_km / (u_const.ONE_LAT_KM * np.cos(np.radians( + np.fmin(89.999, np.abs(t_lat[:, None]) + buffer_lat) + ))) + [idx_close_sub] = ( + (t_lat[:, None] - buffer_lat <= centr_lat[None]) + & (t_lat[:, None] + buffer_lat >= centr_lat[None]) + & (t_lon[:, None] - buffer_lon <= centr_lon[None]) + & (t_lon[:, None] + buffer_lon >= centr_lon[None]) + ).any(axis=0).nonzero() + idx_close = idx_close[idx_close_sub] + centr_lat = centr_lat[idx_close_sub] + centr_lon = centr_lon[idx_close_sub] + + # Restrict to metric distance radius around each track position. + # + # We do the distance computation for chunks of the track since computing the distance requires + # npositions*ncentroids*8*3 Bytes of memory. For example, Hurricane FAITH's life time was more + # than 500 hours. At 0.5-hourly resolution and 1,000,000 centroids, that's 24 GB of memory for + # FAITH. With a chunk size of 10, this figure is down to 240 MB. The final along-track mask + # will require 1.0 GB of memory. + chunk_size = 10 + chunks = np.split(np.arange(npositions), np.arange(chunk_size, npositions, chunk_size)) + mask_centr_alongtrack = np.concatenate([ + ( + u_coord.dist_approx( + t_lat[None, chunk], t_lon[None, chunk], + centr_lat[None], centr_lon[None], + normalize=False, method=metric, units="km", + )[0] <= buffer_km + ) for chunk in chunks + ], axis=0) + [idx_close_sub] = mask_centr_alongtrack.any(axis=0).nonzero() + idx_close = idx_close[idx_close_sub] + centr_lat = centr_lat[idx_close_sub] + centr_lon = centr_lon[idx_close_sub] + mask_centr_alongtrack = mask_centr_alongtrack[:, idx_close_sub] + + # Derive mask from index. + mask_centr = np.zeros((ncentroids,), dtype=bool) + mask_centr[idx_close] = True + + centroids_close_normalized = np.stack([centr_lat, centr_lon], axis=1) + return centroids_close_normalized, mask_centr, mask_centr_alongtrack diff --git a/climada/test/test_engine.py b/climada/test/test_engine.py index aac7098d73..215d94ba5b 100644 --- a/climada/test/test_engine.py +++ b/climada/test/test_engine.py @@ -153,7 +153,7 @@ def test_emdat_damage_yearlysum(self): self.assertEqual(36, df.size) self.assertAlmostEqual(df.impact.max(), 15150000000.0) - self.assertEqual(df.impact_scaled.min(), 10943000.0) + self.assertAlmostEqual(df.impact_scaled.min(), 10939000.0) self.assertEqual(df["year"][5], 2017) self.assertEqual(df["reference_year"].max(), 2000) self.assertIn("USA", list(df["ISO"])) @@ -173,7 +173,7 @@ def test_scale_impact2refyear(self): ) # scaled impact value might change if worldbank input data changes, # check magnitude and adjust if test fails in the following line: - self.assertListEqual(impact_scaled, [28, 137, 999, 163, 304]) + self.assertListEqual(impact_scaled, [28, 137, 1000, 163, 304]) class TestEmdatToImpact(unittest.TestCase): diff --git a/climada/test/test_multi_processing.py b/climada/test/test_multi_processing.py deleted file mode 100644 index 6ca15c7cbb..0000000000 --- a/climada/test/test_multi_processing.py +++ /dev/null @@ -1,56 +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 . - ---- - -Unit tests in multiprocessing mode are time consuming due to the overhead of setting up -processes or threads. In order to keep the load on automated tests small that run whenever a commit -is made to any branch, this test module is meant to catch failures in multi processing mode in -a test file that is run nightly only. -""" -import unittest - -import numpy as np -import geopandas as gpd - -import climada.util.coordinates as u_coord - - -class TestCoordinates(unittest.TestCase): - """Test data coordinates methods in multi processing mode.""" - - def test_set_df_geometry_points_scheduled_pass(self): - """Test set_df_geometry_points with a scheduler other than None - - The same test runs with scheduler None runs in - climada.util.test.test_coordinates.TestFunc.test_set_df_geometry_points_pass - """ - for scheduler in ['threads', 'synchronous', 'processes']: - df_val = gpd.GeoDataFrame() - df_val['latitude'] = np.ones(10) * 40.0 - df_val['longitude'] = np.ones(10) * 0.50 - - # set_df_geometry_points with a scheduler other than None uses dask.dataframe - # methods to execute calculations in parallel - u_coord.set_df_geometry_points(df_val, scheduler=scheduler, crs='epsg:2202') - np.testing.assert_allclose(df_val.geometry.x.values, np.ones(10) * 0.5) - np.testing.assert_allclose(df_val.geometry.y.values, np.ones(10) * 40.) - - -# Execute Tests -if __name__ == "__main__": - TESTS = unittest.TestLoader().loadTestsFromTestCase(TestCoordinates) - unittest.TextTestRunner(verbosity=2).run(TESTS) diff --git a/climada/test/test_util_calibrate.py b/climada/test/test_util_calibrate.py new file mode 100644 index 0000000000..5432f62cd7 --- /dev/null +++ b/climada/test/test_util_calibrate.py @@ -0,0 +1,261 @@ +""" +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 . + +--- +Integration tests for calibration module +""" + +import unittest + +import pandas as pd +import numpy as np +import numpy.testing as npt +from scipy.optimize import NonlinearConstraint +from sklearn.metrics import mean_squared_error +from matplotlib.axes import Axes + +from climada.entity import ImpactFuncSet, ImpactFunc + +from climada.util.calibrate import ( + Input, + ScipyMinimizeOptimizer, + BayesianOptimizer, + OutputEvaluator, + BayesianOptimizerOutputEvaluator, + BayesianOptimizerController, +) + +from climada.util.calibrate.test.test_base import hazard, exposure + + +class TestScipyMinimizeOptimizer(unittest.TestCase): + """Test the TestScipyMinimizeOptimizer""" + + def setUp(self) -> None: + """Prepare input for optimization""" + self.hazard = hazard() + self.hazard.frequency = np.ones_like(self.hazard.event_id) + self.hazard.date = self.hazard.frequency + self.hazard.event_name = ["event"] * len(self.hazard.event_id) + self.exposure = exposure() + self.events = [10, 1] + self.hazard = self.hazard.select(event_id=self.events) + self.data = pd.DataFrame( + data={"a": [3, 1], "b": [0.2, 0.01]}, index=self.events + ) + self.impact_to_dataframe = lambda impact: impact.impact_at_reg(["a", "b"]) + self.impact_func_creator = lambda slope: ImpactFuncSet( + [ + ImpactFunc( + intensity=np.array([0, 10]), + mdd=np.array([0, 10 * slope]), + paa=np.ones(2), + id=1, + haz_type="TEST", + ) + ] + ) + self.input = Input( + self.hazard, + self.exposure, + self.data, + self.impact_func_creator, + self.impact_to_dataframe, + mean_squared_error, + ) + + def test_single(self): + """Test with single parameter""" + optimizer = ScipyMinimizeOptimizer(self.input) + output = optimizer.run(params_init={"slope": 0.1}) + + # Result should be nearly exact + self.assertTrue(output.result.success) + self.assertAlmostEqual(output.params["slope"], 1.0) + self.assertAlmostEqual(output.target, 0.0) + + def test_bound(self): + """Test with single bound""" + self.input.bounds = {"slope": (-1.0, 0.91)} + optimizer = ScipyMinimizeOptimizer(self.input) + output = optimizer.run(params_init={"slope": 0.1}) + + # Result should be very close to the bound + self.assertTrue(output.result.success) + self.assertGreater(output.params["slope"], 0.89) + self.assertAlmostEqual(output.params["slope"], 0.91, places=2) + + def test_multiple_constrained(self): + """Test with multiple constrained parameters""" + # Set new generator + self.input.impact_func_creator = lambda intensity_1, intensity_2: ImpactFuncSet( + [ + ImpactFunc( + intensity=np.array([0, intensity_1, intensity_2]), + mdd=np.array([0, 1, 3]), + paa=np.ones(3), + id=1, + haz_type="TEST", + ) + ] + ) + + # Constraint: param[0] < param[1] (intensity_1 < intensity_2) + self.input.constraints = NonlinearConstraint( + lambda params: params[0] - params[1], -np.inf, 0.0 + ) + self.input.bounds = {"intensity_1": (0, 3.1), "intensity_2": (0, 3.1)} + + # Run optimizer + optimizer = ScipyMinimizeOptimizer(self.input) + output = optimizer.run( + params_init={"intensity_1": 2, "intensity_2": 2}, + options=dict(gtol=1e-5, xtol=1e-5), + ) + + # Check results (low accuracy) + self.assertTrue(output.result.success) + print(output.result.message) + print(output.result.status) + self.assertAlmostEqual(output.params["intensity_1"], 1.0, places=2) + self.assertGreater(output.params["intensity_2"], 2.8) # Should be 3.0 + self.assertAlmostEqual(output.target, 0.0, places=3) + + +class TestBayesianOptimizer(unittest.TestCase): + """Integration tests for the BayesianOptimizer""" + + def setUp(self) -> None: + """Prepare input for optimization""" + self.hazard = hazard() + self.hazard.frequency = np.ones_like(self.hazard.event_id) + self.hazard.date = self.hazard.frequency + self.hazard.event_name = ["event"] * len(self.hazard.event_id) + self.exposure = exposure() + self.events = [10, 1] + self.hazard = self.hazard.select(event_id=self.events) + self.data = pd.DataFrame( + data={"a": [3, 1], "b": [0.2, 0.01]}, index=self.events + ) + self.impact_to_dataframe = lambda impact: impact.impact_at_reg(["a", "b"]) + self.impact_func_creator = lambda slope: ImpactFuncSet( + [ + ImpactFunc( + intensity=np.array([0, 10]), + mdd=np.array([0, 10 * slope]), + paa=np.ones(2), + id=1, + haz_type="TEST", + ) + ] + ) + self.input = Input( + self.hazard, + self.exposure, + self.data, + self.impact_func_creator, + self.impact_to_dataframe, + mean_squared_error, + ) + + def test_single(self): + """Test with single parameter""" + self.input.bounds = {"slope": (-1, 3)} + controller = BayesianOptimizerController( + init_points=10, n_iter=20, max_iterations=1 + ) + optimizer = BayesianOptimizer(self.input, random_state=1) + output = optimizer.run(controller) + + # Check result (low accuracy) + self.assertAlmostEqual(output.params["slope"], 1.0, places=2) + self.assertAlmostEqual(output.target, 0.0, places=3) + self.assertEqual(output.p_space.dim, 1) + self.assertTupleEqual(output.p_space_to_dataframe().shape, (30, 2)) + self.assertEqual(controller.iterations, 1) + + def test_multiple_constrained(self): + """Test with multiple constrained parameters""" + # Set new generator + self.input.impact_func_creator = lambda intensity_1, intensity_2: ImpactFuncSet( + [ + ImpactFunc( + intensity=np.array([0, intensity_1, intensity_2]), + mdd=np.array([0, 1, 3]), + paa=np.ones(3), + id=1, + haz_type="TEST", + ) + ] + ) + + # Constraint: param[0] < param[1] (intensity_1 < intensity_2) + self.input.constraints = NonlinearConstraint( + lambda intensity_1, intensity_2: intensity_1 - intensity_2, -np.inf, 0.0 + ) + self.input.bounds = {"intensity_1": (-1, 4), "intensity_2": (-1, 4)} + # Run optimizer + optimizer = BayesianOptimizer(self.input, random_state=1) + controller = BayesianOptimizerController.from_input( + self.input, sampling_base=5, max_iterations=3 + ) + output = optimizer.run(controller) + + # Check results (low accuracy) + self.assertEqual(output.p_space.dim, 2) + self.assertAlmostEqual(output.params["intensity_1"], 1.0, places=2) + self.assertAlmostEqual(output.params["intensity_2"], 3.0, places=1) + self.assertAlmostEqual(output.target, 0.0, places=3) + self.assertGreater(controller.iterations, 1) + + # Check constraints in parameter space + p_space = output.p_space_to_dataframe() + self.assertSetEqual( + set(p_space.columns.to_list()), + { + ("Parameters", "intensity_1"), + ("Parameters", "intensity_2"), + ("Calibration", "Cost Function"), + ("Calibration", "Constraints Function"), + ("Calibration", "Allowed"), + }, + ) + self.assertGreater(p_space.shape[0], 50) # Two times random iterations + self.assertEqual(p_space.shape[1], 5) + p_allowed = p_space.loc[p_space["Calibration", "Allowed"], "Parameters"] + npt.assert_array_equal( + (p_allowed["intensity_1"] < p_allowed["intensity_2"]).to_numpy(), + np.full_like(p_allowed["intensity_1"].to_numpy(), True), + ) + + def test_plots(self): + """Check if executing the default plots works""" + self.input.bounds = {"slope": (-1, 3)} + optimizer = BayesianOptimizer(self.input, random_state=1) + controller = BayesianOptimizerController.from_input( + self.input, max_iterations=1 + ) + output = optimizer.run(controller) + + output_eval = OutputEvaluator(self.input, output) + output_eval.impf_set.plot() + output_eval.plot_at_event() + output_eval.plot_at_region() + output_eval.plot_event_region_heatmap() + + output_eval = BayesianOptimizerOutputEvaluator(self.input, output) + ax = output_eval.plot_impf_variability() + self.assertIsInstance(ax, Axes) diff --git a/climada/util/calibrate/__init__.py b/climada/util/calibrate/__init__.py new file mode 100644 index 0000000000..2e947ee049 --- /dev/null +++ b/climada/util/calibrate/__init__.py @@ -0,0 +1,29 @@ +""" +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 . + +--- +Impact function calibration module +""" + +from .base import Input, OutputEvaluator +from .bayesian_optimizer import ( + BayesianOptimizer, + BayesianOptimizerController, + BayesianOptimizerOutput, + BayesianOptimizerOutputEvaluator, + select_best +) +from .scipy_optimizer import ScipyMinimizeOptimizer diff --git a/climada/util/calibrate/base.py b/climada/util/calibrate/base.py new file mode 100644 index 0000000000..d61644dc75 --- /dev/null +++ b/climada/util/calibrate/base.py @@ -0,0 +1,494 @@ +""" +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 . + +--- +Calibration Base Classes and Interfaces +""" + +from abc import ABC, abstractmethod +from dataclasses import dataclass, field, InitVar +from typing import Callable, Mapping, Optional, Tuple, Union, Any, Dict +from numbers import Number +from pathlib import Path + +import pandas as pd +import numpy as np +from scipy.optimize import Bounds, LinearConstraint, NonlinearConstraint +import seaborn as sns +import h5py + +from climada.hazard import Hazard +from climada.entity import Exposures, ImpactFuncSet +from climada.engine import Impact, ImpactCalc + +ConstraintType = Union[LinearConstraint, NonlinearConstraint, Mapping] + + +@dataclass +class Input: + """Define the static input for a calibration task + + Attributes + ---------- + hazard : climada.Hazard + Hazard object to compute impacts from + exposure : climada.Exposures + Exposures object to compute impacts from + data : pandas.Dataframe + The data to compare computed impacts to. Index: Event IDs matching the IDs of + ``hazard``. Columns: Arbitrary columns. NaN values in the data frame have + special meaning: Corresponding impact values computed by the model are ignored + in the calibration. + impact_func_creator : Callable + Function that takes the parameters as keyword arguments and returns an impact + function set. This will be called each time the optimization algorithm updates + the parameters. + impact_to_dataframe : Callable + Function that takes an impact object as input and transforms its data into a + pandas.DataFrame that is compatible with the format of :py:attr:`data`. + The return value of this function will be passed to the :py:attr:`cost_func` + as first argument. + cost_func : Callable + Function that takes two ``pandas.Dataframe`` objects and returns the scalar + "cost" between them. The optimization algorithm will try to minimize this + number. The first argument is the true/correct values (:py:attr:`data`), and the + second argument is the estimated/predicted values. + bounds : Mapping (str, {Bounds, tuple(float, float)}), optional + The bounds for the parameters. Keys: parameter names. Values: + ``scipy.minimize.Bounds`` instance or tuple of minimum and maximum value. + Unbounded parameters need not be specified here. See the documentation for + the selected optimization algorithm on which data types are supported. + constraints : Constraint or list of Constraint, optional + One or multiple instances of ``scipy.minimize.LinearConstraint``, + ``scipy.minimize.NonlinearConstraint``, or a mapping. See the documentation for + the selected optimization algorithm on which data types are supported. + impact_calc_kwds : Mapping (str, Any), optional + Keyword arguments to :py:meth:`climada.engine.impact_calc.ImpactCalc.impact`. + Defaults to ``{"assign_centroids": False}`` (by default, centroids are assigned + here via the ``assign_centroids`` parameter, to avoid assigning them each time + the impact is calculated). + missing_data_value : float, optional + If the impact model returns impact data for which no values exist in + :py:attr:`data`, insert this value. Defaults to NaN, in which case the impact + from the model is ignored. Set this to zero to explicitly calibrate to zero + impacts in these cases. + assign_centroids : bool, optional + If ``True`` (default), assign the hazard centroids to the exposure when this + object is created. + """ + + hazard: Hazard + exposure: Exposures + data: pd.DataFrame + impact_func_creator: Callable[..., ImpactFuncSet] + impact_to_dataframe: Callable[[Impact], pd.DataFrame] + cost_func: Callable[[pd.DataFrame, pd.DataFrame], Number] + bounds: Optional[Mapping[str, Union[Bounds, Tuple[Number, Number]]]] = None + constraints: Optional[Union[ConstraintType, list[ConstraintType]]] = None + impact_calc_kwds: Mapping[str, Any] = field( + default_factory=lambda: {"assign_centroids": False} + ) + missing_data_value: float = np.nan + assign_centroids: InitVar[bool] = True + + def __post_init__(self, assign_centroids): + """Prepare input data""" + if not isinstance(self.data, pd.DataFrame): + if isinstance(self.data, pd.Series): + raise TypeError( + "You passed a pandas Series as 'data'. Please transform it into a " + "dataframe with Series.to_frame() and make sure that columns " + "correctly indicate locations and indexes events." + ) + raise TypeError("'data' must be a pandas.DataFrame") + + if assign_centroids: + self.exposure.assign_centroids(self.hazard) + + def impact_to_aligned_df( + self, impact: Impact, fillna: float = np.nan + ) -> Tuple[pd.DataFrame, pd.DataFrame]: + """Create a dataframe from an impact and align it with the data. + + When aligning, two general cases might occur, which are not mutually exclusive: + + 1. There are data points for which no impact was computed. This will always be + treated as an impact of zero. + 2. There are impacts for which no data points exist. For these points, the input + data will be filled with the value of :py:attr:`Input.missing_data_value`. + + This method performs the following steps: + + * Transform the impact into a dataframe using :py:attr:`impact_to_dataframe`. + * Align the :py:attr:`data` with the impact dataframe, using + :py:attr:`missing_data_value` as fill value. + * Align the impact dataframe with the data, using zeros as fill value. + * In the aligned impact, set all values to zero where the data is NaN. + * Fill remaining NaNs in data with ``fillna``. + + Parameters + ---------- + impact_df : pandas.DataFrame + The impact computed by the model, transformed into a dataframe by + :py:attr:`Input.impact_to_dataframe`. + + Returns + ------- + data_aligned : pd.DataFrame + The data aligned to the impact dataframe + impact_df_aligned : pd.DataFrame + The impact transformed to a dataframe and aligned with the data + """ + # Transform impact to to dataframe + impact_df = self.impact_to_dataframe(impact) + if impact_df.isna().any(axis=None): + raise ValueError("NaN values computed in impact!") + + # Align with different fill values + data_aligned, _ = self.data.align( + impact_df, axis=None, fill_value=self.missing_data_value, copy=True + ) + impact_df_aligned, _ = impact_df.align( + data_aligned, join="right", axis=None, fill_value=0.0, copy=False + ) + + # Set all impacts to zero for which data is NaN + impact_df_aligned.where(data_aligned.notna(), 0.0, inplace=True) + + # NOTE: impact_df_aligned should not contain any NaNs at this point + return data_aligned.fillna(fillna), impact_df_aligned.fillna(fillna) + + +@dataclass +class Output: + """Generic output of a calibration task + + Attributes + ---------- + params : Mapping (str, Number) + The optimal parameters + target : Number + The target function value for the optimal parameters + """ + + params: Mapping[str, Number] + target: Number + + def to_hdf5(self, filepath: Union[Path, str], mode:str = "x"): + """Write the output into an H5 file + + This stores the data as attributes because we only store single numbers, not + arrays + + Parameters + ---------- + filepath : Path or str + The filepath to store the data. + mode : str (optional) + The mode for opening the file. Defaults to ``x`` (Create file, fail if + exists). + """ + with h5py.File(filepath, mode=mode) as file: + # Store target + grp = file.create_group("base") + grp.attrs["target"] = self.target + + # Store params + grp_params = grp.create_group("params") + for p_name, p_val in self.params.items(): + grp_params.attrs[p_name] = p_val + + @classmethod + def from_hdf5(cls, filepath: Union[Path, str]): + """Create an output object from an H5 file""" + with h5py.File(filepath) as file: + target = file["base"].attrs["target"] + params = dict(file["base"]["params"].attrs.items()) + return cls(params=params, target=target) + +@dataclass +class OutputEvaluator: + """Evaluate the output of a calibration task + + Parameters + ---------- + input : Input + The input object for the optimization task. + output : Output + The output object returned by the optimization task. + + Attributes + ---------- + impf_set : climada.entity.ImpactFuncSet + The impact function set built from the optimized parameters + impact : climada.engine.Impact + An impact object calculated using the optimal :py:attr:`impf_set` + """ + + input: Input + output: Output + + def __post_init__(self): + """Compute the impact for the optimal parameters""" + self.impf_set = self.input.impact_func_creator(**self.output.params) + self.impact = ImpactCalc( + exposures=self.input.exposure, + impfset=self.impf_set, + hazard=self.input.hazard, + ).impact(assign_centroids=True, save_mat=True) + self._impact_label = f"Impact [{self.input.exposure.value_unit}]" + + def plot_at_event( + self, + data_transf: Callable[[pd.DataFrame], pd.DataFrame] = lambda x: x, + **plot_kwargs, + ): + """Create a bar plot comparing estimated model output and data per event. + + Every row of the :py:attr:`Input.data` is considered an event. + The data to be plotted can be transformed with a generic function + ``data_transf``. + + Parameters + ---------- + data_transf : Callable (pd.DataFrame -> pd.DataFrame), optional + A function that transforms the data to plot before plotting. + It receives a dataframe whose rows represent events and whose columns + represent the modelled impact and the calibration data, respectively. + By default, the data is not transformed. + plot_kwargs + Keyword arguments passed to the ``DataFrame.plot.bar`` method. + + Returns + ------- + ax : matplotlib.axes.Axes + The plot axis returned by ``DataFrame.plot.bar`` + + Note + ---- + This plot does *not* include the ignored impact, see :py:attr:`Input.data`. + """ + data, impact = self.input.impact_to_aligned_df(self.impact) + values = pd.concat( + [impact.sum(axis="columns"), data.sum(axis="columns")], + axis=1, + ).rename(columns={0: "Model", 1: "Data"}) + + # Transform data before plotting + values = data_transf(values) + + # Now plot + ylabel = plot_kwargs.pop("ylabel", self._impact_label) + return values.plot.bar(ylabel=ylabel, **plot_kwargs) + + def plot_at_region( + self, + data_transf: Callable[[pd.DataFrame], pd.DataFrame] = lambda x: x, + **plot_kwargs, + ): + """Create a bar plot comparing estimated model output and data per event + + Every column of the :py:attr:`Input.data` is considered a region. + The data to be plotted can be transformed with a generic function + ``data_transf``. + + Parameters + ---------- + data_transf : Callable (pd.DataFrame -> pd.DataFrame), optional + A function that transforms the data to plot before plotting. + It receives a dataframe whose rows represent regions and whose columns + represent the modelled impact and the calibration data, respectively. + By default, the data is not transformed. + plot_kwargs + Keyword arguments passed to the ``DataFrame.plot.bar`` method. + + Returns + ------- + ax : matplotlib.axes.Axes + The plot axis returned by ``DataFrame.plot.bar``. + + Note + ---- + This plot does *not* include the ignored impact, see :py:attr:`Input.data`. + """ + data, impact = self.input.impact_to_aligned_df(self.impact) + values = pd.concat( + [impact.sum(axis="index"), data.sum(axis="index")], + axis=1, + ).rename(columns={0: "Model", 1: "Data"}) + + # Transform data before plotting + values = data_transf(values) + + # Now plot + ylabel = plot_kwargs.pop("ylabel", self._impact_label) + return values.plot.bar(ylabel=ylabel, **plot_kwargs) + + def plot_event_region_heatmap( + self, + data_transf: Callable[[pd.DataFrame], pd.DataFrame] = lambda x: x, + **plot_kwargs, + ): + """Plot a heatmap comparing all events per all regions + + Every column of the :py:attr:`Input.data` is considered a region, and every + row is considered an event. + The data to be plotted can be transformed with a generic function + ``data_transf``. + + Parameters + ---------- + data_transf : Callable (pd.DataFrame -> pd.DataFrame), optional + A function that transforms the data to plot before plotting. + It receives a dataframe whose rows represent events and whose columns + represent the regions, respectively. + By default, the data is not transformed. + plot_kwargs + Keyword arguments passed to the ``DataFrame.plot.bar`` method. + + Returns + ------- + ax : matplotlib.axes.Axes + The plot axis returned by ``DataFrame.plot.bar``. + + """ + # Data preparation + data, impact = self.input.impact_to_aligned_df(self.impact) + values = (impact + 1) / (data + 1) # Avoid division by zero + values = values.transform(np.log10) + + # Transform data + values = data_transf(values) + + # Default plot settings + annot = plot_kwargs.pop("annot", True) + vmax = plot_kwargs.pop("vmax", 3) + vmin = plot_kwargs.pop("vmin", -vmax) + center = plot_kwargs.pop("center", 0) + fmt = plot_kwargs.pop("fmt", ".1f") + cmap = plot_kwargs.pop("cmap", "RdBu_r") + cbar_kws = plot_kwargs.pop( + "cbar_kws", {"label": r"Model Error $\log_{10}(\mathrm{Impact})$"} + ) + + return sns.heatmap( + values, + annot=annot, + vmin=vmin, + vmax=vmax, + center=center, + fmt=fmt, + cmap=cmap, + cbar_kws=cbar_kws, + **plot_kwargs, + ) + + +@dataclass +class Optimizer(ABC): + """Abstract base class (interface) for an optimization + + This defines the interface for optimizers in CLIMADA. New optimizers can be created + by deriving from this class and overriding at least the :py:meth:`run` method. + + Attributes + ---------- + input : Input + The input object for the optimization task. See :py:class:`Input`. + """ + + input: Input + + def _target_func(self, data: pd.DataFrame, predicted: pd.DataFrame) -> Number: + """Target function for the optimizer + + The default version of this function simply returns the value of the cost + function evaluated on the arguments. + + Parameters + ---------- + data : pandas.DataFrame + The reference data used for calibration. By default, this is + :py:attr:`Input.data`. + predicted : pandas.DataFrame + The impact predicted by the data calibration after it has been transformed + into a dataframe by :py:attr:`Input.impact_to_dataframe`. + + Returns + ------- + The value of the target function for the optimizer. + """ + return self.input.cost_func(data, predicted) + + def _kwargs_to_impact_func_creator(self, *_, **kwargs) -> Dict[str, Any]: + """Define how the parameters to :py:meth:`_opt_func` must be transformed + + Optimizers may implement different ways of representing the parameters (e.g., + key-value pairs, arrays, etc.). Depending on this representation, the parameters + must be transformed to match the syntax of the impact function generator used, + see :py:attr:`Input.impact_func_creator`. + + In this default version, the method simply returns its keyword arguments as + mapping. Override this method if the optimizer used *does not* represent + parameters as key-value pairs. + + Parameters + ---------- + kwargs + The parameters as key-value pairs. + + Returns + ------- + The parameters as key-value pairs. + """ + return kwargs + + def _opt_func(self, *args, **kwargs) -> Number: + """The optimization function iterated by the optimizer + + This function takes arbitrary arguments from the optimizer, generates a new set + of impact functions from it, computes the impact, and finally calculates the + target function value and returns it. + + Parameters + ---------- + args, kwargs + Arbitrary arguments from the optimizer, including parameters + + Returns + ------- + Target function value for the given arguments + """ + # Create the impact function set from a new parameter estimate + params = self._kwargs_to_impact_func_creator(*args, **kwargs) + impf_set = self.input.impact_func_creator(**params) + + # Compute the impact + impact = ImpactCalc( + exposures=self.input.exposure, + impfset=impf_set, + hazard=self.input.hazard, + ).impact(**self.input.impact_calc_kwds) + + # Transform to DataFrame, align, and compute target function + data_aligned, impact_df_aligned = self.input.impact_to_aligned_df( + impact, fillna=0 + ) + return self._target_func(data_aligned, impact_df_aligned) + + @abstractmethod + def run(self, **opt_kwargs) -> Output: + """Execute the optimization""" diff --git a/climada/util/calibrate/bayesian_optimizer.py b/climada/util/calibrate/bayesian_optimizer.py new file mode 100644 index 0000000000..98fe302c03 --- /dev/null +++ b/climada/util/calibrate/bayesian_optimizer.py @@ -0,0 +1,851 @@ +""" +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 . + +--- +Calibration with Bayesian Optimization +""" + +from dataclasses import dataclass, InitVar, field +from typing import Mapping, Optional, Any, Union, List, Tuple +from numbers import Number +from itertools import combinations, repeat +from collections import deque, namedtuple +import logging +from pathlib import Path + +import pandas as pd +import numpy as np +import matplotlib as mpl +import matplotlib.pyplot as plt +import matplotlib.axes as maxes +import matplotlib.patches as mpatches +import matplotlib.ticker as mticker +from bayes_opt import BayesianOptimization, Events, UtilityFunction, ScreenLogger +from bayes_opt.target_space import TargetSpace + +from .base import Input, Output, Optimizer, OutputEvaluator + + +LOGGER = logging.getLogger(__name__) + + +@dataclass +class _FakeConstraint: + """Fake the behavior of the constrait for cycling the BayesianOutputOptimizer""" + + results: np.ndarray + + @property + def lb(self): + """Return the lower bound""" + return np.array([0]) + + def allowed(self, values): + """Return if the values are allowed. This only mocks the true behavior""" + if self.results.shape != values.shape: + raise ValueError("Inserting wrong constraint values") + return self.results + + +def select_best( + p_space_df: pd.DataFrame, + cost_limit: float, + absolute: bool = True, + cost_col=("Calibration", "Cost Function"), +) -> pd.DataFrame: + """Select the best parameter space samples defined by a cost function limit + + The limit is a factor of the minimum value relative to itself (``absolute=True``) or + to the range of cost function values (``absolute=False``). A ``cost_limit`` of 0.1 + will select all rows where the cost function is within + + - 110% of the minimum value if ``absolute=True``. + - 10% of the range between minimum and maximum cost function value if + ``absolute=False``. + + Parameters + ---------- + p_space_df : pd.DataFrame + The parameter space to select from. + cost_limit : float + The limit factor used for selection. + absolute : bool, optional + Whether the limit factor is applied to the minimum value (``True``) or the range + of values (``False``). Defaults to ``True``. + cost_col : Column specifier, optional + The column indicating cost function values. Defaults to + ``("Calibration", "Cost Function")``. + + Returns + ------- + pd.DataFrame + A subselection of the input data frame. + """ + min_val = p_space_df[cost_col].min() + cost_range = min_val if absolute else p_space_df[cost_col].max() - min_val + max_val = min_val + cost_range * cost_limit + return p_space_df.loc[p_space_df[cost_col] <= max_val] + + +@dataclass +class BayesianOptimizerOutput(Output): + """Output of a calibration with :py:class:`BayesianOptimizer` + + Attributes + ---------- + p_space : bayes_opt.target_space.TargetSpace + The parameter space sampled by the optimizer. + """ + + p_space: TargetSpace + + def p_space_to_dataframe(self): + """Return the sampled parameter space as pandas.DataFrame + + Returns + ------- + pandas.DataFrame + Data frame whose columns are the parameter values and the associated cost + function value (``Cost Function``) and whose rows are the optimizer + iterations. + """ + # Build MultiIndex for columns + index = pd.MultiIndex.from_tuples( + [("Parameters", p) for p in self.p_space.keys] + + [("Calibration", "Cost Function")] + ) + + # Create DataFrame and fill + data = pd.DataFrame(data=None, columns=index) + for i in range(self.p_space.dim): + data["Parameters", self.p_space.keys[i]] = self.p_space.params[..., i] + data["Calibration", "Cost Function"] = -self.p_space.target + + # Constraints + if self.p_space.constraint is not None: + data["Calibration", "Constraints Function"] = self.p_space.constraint_values + data["Calibration", "Allowed"] = self.p_space.constraint.allowed( + self.p_space.constraint_values + ) + + # Rename index and return + data.index.rename("Iteration", inplace=True) + return data + + def to_hdf5(self, filepath: Union[Path, str], mode: str = "x"): + """Write this output to an H5 file""" + # Write base class information + super().to_hdf5(filepath=filepath, mode=mode) + + # Write parameter space + p_space_df = self.p_space_to_dataframe() + p_space_df.to_hdf(filepath, mode="a", key="p_space") + + @classmethod + def from_hdf5(cls, filepath: Union[Path, str]): + """Read BayesianOptimizerOutput from an H5 file + + Warning + ------- + This results in an object with broken :py:attr:`p_space` object. Do not further + modify this parameter space. This function is only intended to load the + parameter space again for analysis/plotting. + """ + output = Output.from_hdf5(filepath) + p_space_df = pd.read_hdf(filepath, mode="r", key="p_space") + p_space_df["Calibration", "Target"] = -p_space_df[ + "Calibration", "Cost Function" + ] + + # Reorganize data + bounds = {param: (np.nan, np.nan) for param in p_space_df["Parameters"].columns} + constraint = None + if "Constraints Function" in p_space_df["Calibration"].columns: + constraint = _FakeConstraint( + p_space_df["Calibration", "Allowed"].to_numpy() + ) + + p_space = TargetSpace( + target_func=lambda x: x, + pbounds=bounds, + constraint=constraint, + allow_duplicate_points=True, + ) + for _, row in p_space_df.iterrows(): + constraint_value = ( + None + if constraint is None + else row["Calibration", "Constraints Function"] + ) + p_space.register( + params=row["Parameters"].to_numpy(), + target=row["Calibration", "Target"], + constraint_value=constraint_value, + ) + + return cls(params=output.params, target=output.target, p_space=p_space) + + def plot_p_space( + self, + p_space_df: Optional[pd.DataFrame] = None, + x: Optional[str] = None, + y: Optional[str] = None, + min_def: Optional[Union[str, Tuple[str, str]]] = "Cost Function", + min_fmt: str = "x", + min_color: str = "r", + **plot_kwargs, + ) -> Union[maxes.Axes, List[maxes.Axes]]: + """Plot the parameter space as scatter plot(s) + + Produce a scatter plot where each point represents a parameter combination + sampled by the optimizer. The coloring represents the cost function value. + If there are more than two parameters in the input data frame, this method will + produce one plot for each combination of two parameters. + Explicit parameter names to plot can be given via the ``x`` and ``y`` arguments. + If no data frame is provided as argument, the output of + :py:meth:`p_space_to_dataframe` is used. + + Parameters + ---------- + p_space_df : pd.DataFrame, optional + The parameter space to plot. Defaults to the one returned by + :py:meth:`p_space_to_dataframe` + x : str, optional + The parameter to plot on the x-axis. If ``y`` is *not* given, this will plot + ``x`` against all other parameters. + y : str, optional + The parameter to plot on the y-axis. If ``x`` is *not* given, this will plot + ``y`` against all other parameters. + min_def : str, optional + The name of the column in ``p_space_df`` defining which parameter set + represents the minimum, which is plotted separately. Defaults to + ``"Cost Function"``. Set to ``None`` to avoid plotting the minimum. + min_fmt : str, optional + Plot format string for plotting the minimum. Defaults to ``"x"``. + min_color : str, optional + Color for plotting the minimum. Defaults to ``"r"`` (red). + """ + # pylint: disable=invalid-name + + if p_space_df is None: + p_space_df = self.p_space_to_dataframe() + + if min_def is not None and not isinstance(min_def, tuple): + min_def = ("Calibration", min_def) + + # Plot defaults + cmap = plot_kwargs.pop("cmap", "viridis_r") + s = plot_kwargs.pop("s", 40) + c = ("Calibration", plot_kwargs.pop("c", "Cost Function")) + + def plot_single(x, y): + """Plot a single combination of parameters""" + x = ("Parameters", x) + y = ("Parameters", y) + + # Plot scatter + ax = p_space_df.plot( + kind="scatter", + x=x, + y=y, + c=c, + s=s, + cmap=cmap, + **plot_kwargs, + ) + + # Plot the minimum + if min_def is not None: + best = p_space_df.loc[p_space_df.idxmin()[min_def]] + ax.plot(best[x], best[y], min_fmt, color=min_color) + + return ax + + # Option 0: Only one parameter + params = p_space_df["Parameters"].columns.to_list() + if len(params) < 2: + # Add zeros for scatter plot + p_space_df["Parameters", "none"] = np.zeros_like( + p_space_df["Parameters", params[0]] + ) + return plot_single(x=params[0], y="none") + + # Option 1: Only a single plot + if x is not None and y is not None: + return plot_single(x, y) + + # Option 2: Combination of all + iterable = combinations(params, 2) + # Option 3: Fix one and iterate over all others + if x is not None: + params.remove(x) + iterable = zip(repeat(x), params) + elif y is not None: + params.remove(y) + iterable = zip(params, repeat(y)) + + # Iterate over parameter combinations + return [plot_single(p_first, p_second) for p_first, p_second in iterable] + + +Improvement = namedtuple( + "Improvement", ["iteration", "sample", "random", "target", "improvement"] +) + + +class StopEarly(Exception): + """An exception for stopping an optimization iteration early""" + + pass + + +@dataclass(eq=False) +class BayesianOptimizerController(object): + """A class for controlling the iterations of a :py:class:`BayesianOptimizer`. + + Each iteration in the optimizer consists of a random sampling of the parameter space + with :py:attr:`init_points` steps, followed by a Gaussian process sampling with + :py:attr:`n_iter` steps. During the latter, the :py:attr:`kappa` parameter is + reduced to reach :py:attr:`kappa_min` at the end of the iteration. The iteration is + stopped prematurely if improvements of the buest guess are below + :py:attr:`min_improvement` for :py:attr:`min_improvement_count` consecutive times. + At the beginning of the next iteration, :py:attr:`kappa` is reset to its original + value. + + Optimization stops if :py:attr:`max_iterations` is reached or if an entire iteration + saw now improvement. + + Attributes + ---------- + init_points : int + Number of randomly sampled points during each iteration. + n_iter : int + Maximum number of points using Gaussian process sampling during each iteration. + min_improvement : float + Minimal relative improvement. If improvements are below this value + :py:attr:`min_improvement_count` times, the iteration is stopped. + min_improvement_count : int + Number of times the :py:attr:`min_improvement` must be undercut to stop the + iteration. + kappa : float + Parameter controlling exploration of the upper-confidence-bound acquisition + function of the sampling algorithm. Lower values mean less exploration of the + parameter space and more exploitation of local information. This value is + reduced throughout one iteration, reaching :py:attr:`kappa_min` at the + last iteration step. + kappa_min : float + Minimal value of :py:attr:`kappa` after :py:attr:`n_iter` steps. + max_iterations : int + Maximum number of iterations before optimization is stopped, irrespective of + convergence. + utility_func_kwargs + Further keyword arguments to the ``bayes_opt.UtilityFunction``. + """ + + # Init attributes + init_points: int + n_iter: int + min_improvement: float = 1e-3 + min_improvement_count: int = 2 + kappa: float = 2.576 + kappa_min: float = 0.1 + max_iterations: int = 10 + utility_func_kwargs: dict[str, Union[int, float, str]] = field(default_factory=dict) + + # Other attributes + kappa_decay: float = field(init=False, default=0.96) + steps: int = field(init=False, default=0) + iterations: int = field(init=False, default=0) + _improvements: deque[Improvement] = field(init=False, default_factory=deque) + _last_it_improved: int = 0 + _last_it_end: int = 0 + + def __post_init__(self): + """Set the decay factor for :py:attr:`kappa`.""" + if self.init_points < 0 or self.n_iter < 0: + raise ValueError("'init_points' and 'n_iter' must be 0 or positive") + self.kappa_decay = self._calc_kappa_decay() + + def _calc_kappa_decay(self): + """Compute the decay factor for :py:attr:`kappa`.""" + return np.exp((np.log(self.kappa_min) - np.log(self.kappa)) / self.n_iter) + + @classmethod + def from_input(cls, inp: Input, sampling_base: float = 4, **kwargs): + """Create a controller from a calibration input + + This uses the number of parameters to determine the appropriate values for + :py:attr:`init_points` and :py:attr:`n_iter`. Both values are set to + :math:`b^N`, where :math:`b` is the ``sampling_base`` parameter and :math:`N` + is the number of estimated parameters. + + Parameters + ---------- + inp : Input + Input to the calibration + sampling_base : float, optional + Base for determining the sample size. Increase this for denser sampling. + Defaults to 4. + kwargs + Keyword argument for the default constructor. + """ + num_params = len(inp.bounds) + init_points = round(sampling_base**num_params) + n_iter = round(sampling_base**num_params) + return cls(init_points=init_points, n_iter=n_iter, **kwargs) + + @property + def _previous_max(self): + """Return the maximum target value observed""" + if not self._improvements: + return -np.inf + return self._improvements[-1].target + + def optimizer_params(self) -> dict[str, Union[int, float, str, UtilityFunction]]: + """Return parameters for the optimizer + + In the current implementation, these do not change. + """ + return { + "init_points": self.init_points, + "n_iter": self.n_iter, + "acquisition_function": UtilityFunction( + kappa=self.kappa, + kappa_decay=self.kappa_decay, + **self.utility_func_kwargs, + ), + } + + def _is_random_step(self) -> bool: + """Return true if we sample randomly instead of Bayesian""" + return (self._last_it_end + self.steps) < self.init_points + + def _append_improvement(self, target): + """Append a new improvement to the deque""" + impr = np.inf + if self._improvements: + impr = (self._improvements[-1].target / target) - 1 + + self._improvements.append( + Improvement( + sample=self.steps, + iteration=self.iterations, + target=target, + improvement=impr, + random=self._is_random_step(), + ) + ) + + def _is_new_max(self, instance): + """Determine if a guessed value is the new maximum""" + instance_max = instance.max + if not instance_max or instance_max.get("target") is None: + # During constrained optimization, there might not be a maximum + # value since the optimizer might've not encountered any points + # that fulfill the constraints. + return False + + if instance_max["target"] > self._previous_max: + return True + + return False + + def _maybe_stop_early(self, instance): + """Throw if we want to stop this iteration early""" + # Create sequence of last improvements + last_improvements = [ + self._improvements[-idx] + for idx in np.arange( + min(self.min_improvement_count, len(self._improvements)) + ) + + 1 + ] + if ( + # Same iteration + np.unique([impr.iteration for impr in last_improvements]).size == 1 + # Not random + and not any(impr.random for impr in last_improvements) + # Less than min improvement + and all( + impr.improvement < self.min_improvement for impr in last_improvements + ) + ): + LOGGER.info("Minimal improvement. Stop iteration.") + instance.dispatch(Events.OPTIMIZATION_END) + raise StopEarly() + + def update(self, event: str, instance: BayesianOptimization): + """Update the step tracker of this instance. + + For step events, check if the latest guess is the new maximum. Also check if the + iteration will be stopped early. + + For end events, check if any improvement occured. If not, stop the optimization. + + Parameters + ---------- + event : bayes_opt.Events + The event descriptor + instance : bayes_opt.BayesianOptimization + Optimization instance triggering the event + + Raises + ------ + StopEarly + If the optimization only achieves minimal improvement, stop the iteration + early with this exception. + StopIteration + If an entire iteration did not achieve improvement, stop the optimization. + """ + if event == Events.OPTIMIZATION_STEP: + new_max = self._is_new_max(instance) + if new_max: + self._append_improvement(instance.max["target"]) + + self.steps += 1 + + # NOTE: Must call this after incrementing the step + if new_max: + self._maybe_stop_early(instance) + + if event == Events.OPTIMIZATION_END: + self.iterations += 1 + # Stop if we do not improve anymore + if ( + self._last_it_end > 0 + and self._last_it_improved == self._improvements[-1].iteration + ): + LOGGER.info("No improvement. Stop optimization.") + raise StopIteration() + + self._last_it_improved = self._improvements[-1].iteration + self._last_it_end = self.steps + + def improvements(self) -> pd.DataFrame: + """Return improvements as nicely formatted data + + Returns + ------- + improvements : pd.DataFrame + """ + return pd.DataFrame.from_records( + data=[impr._asdict() for impr in self._improvements] + ).set_index("sample") + + +@dataclass +class BayesianOptimizer(Optimizer): + """An optimization using ``bayes_opt.BayesianOptimization`` + + This optimizer reports the target function value for each parameter set and + *maximizes* that value. Therefore, a higher target function value is better. + The cost function, however, is still minimized: The target function is defined as + the inverse of the cost function. + + For details on the underlying optimizer, see + https://github.com/bayesian-optimization/BayesianOptimization. + + Parameters + ---------- + input : Input + The input data for this optimizer. See the Notes below for input requirements. + verbose : int, optional + Verbosity of the optimizer output. Defaults to 0. The output is *not* affected + by the CLIMADA logging settings. + random_state : int, optional + Seed for initializing the random number generator. Defaults to 1. + allow_duplicate_points : bool, optional + Allow the optimizer to sample the same points in parameter space multiple times. + This may happen if the parameter space is tightly bound or constrained. Defaults + to ``True``. + bayes_opt_kwds : dict + Additional keyword arguments passed to the ``BayesianOptimization`` constructor. + + Notes + ----- + The following requirements apply to the parameters of + :py:class:`~climada.util.calibrate.base.Input` when using this class: + + bounds + Setting :py:attr:`~climada.util.calibrate.base.Input.bounds` is required + because the optimizer first "explores" the bound parameter space and then + narrows its search to regions where the cost function is low. + constraints + Must be an instance of ``scipy.minimize.LinearConstraint`` or + ``scipy.minimize.NonlinearConstraint``. See + https://github.com/bayesian-optimization/BayesianOptimization/blob/master/examples/constraints.ipynb + for further information. Supplying contraints is optional. + + Attributes + ---------- + optimizer : bayes_opt.BayesianOptimization + The optimizer instance of this class. + """ + + verbose: int = 0 + random_state: InitVar[int] = 1 + allow_duplicate_points: InitVar[bool] = True + bayes_opt_kwds: InitVar[Optional[Mapping[str, Any]]] = None + + def __post_init__(self, random_state, allow_duplicate_points, bayes_opt_kwds): + """Create optimizer""" + if bayes_opt_kwds is None: + bayes_opt_kwds = {} + + if self.input.bounds is None: + raise ValueError("Input.bounds is required for this optimizer") + + self.optimizer = BayesianOptimization( + f=self._opt_func, + pbounds=self.input.bounds, + constraint=self.input.constraints, + random_state=random_state, + allow_duplicate_points=allow_duplicate_points, + **bayes_opt_kwds, + ) + + def _target_func(self, data: pd.DataFrame, predicted: pd.DataFrame) -> Number: + """Invert the cost function because BayesianOptimization maximizes the target""" + return -self.input.cost_func(data, predicted) + + def run(self, controller: BayesianOptimizerController) -> BayesianOptimizerOutput: + """Execute the optimization + + ``BayesianOptimization`` *maximizes* a target function. Therefore, this class + inverts the cost function and used that as target function. The cost function is + still minimized. + + Parameters + ---------- + controller : BayesianOptimizerController + The controller instance used to set the optimization iteration parameters. + opt_kwargs + Further keyword arguments passed to ``BayesianOptimization.maximize``. + + Returns + ------- + output : BayesianOptimizerOutput + Optimization output. :py:attr:`BayesianOptimizerOutput.p_space` stores data + on the sampled parameter space. + """ + # Register the controller + for event in (Events.OPTIMIZATION_STEP, Events.OPTIMIZATION_END): + self.optimizer.subscribe(event, controller) + + # Register the logger + if self.verbose > 0: + log = ScreenLogger( + verbose=self.verbose, is_constrained=self.optimizer.is_constrained + ) + for event in ( + Events.OPTIMIZATION_START, + Events.OPTIMIZATION_STEP, + Events.OPTIMIZATION_END, + ): + self.optimizer.subscribe(event, log) + + # Run the optimization + while controller.iterations < controller.max_iterations: + try: + LOGGER.info(f"Optimization iteration: {controller.iterations}") + self.optimizer.maximize(**controller.optimizer_params()) + except StopEarly: + # Start a new iteration + continue + except StopIteration: + # Exit the loop + break + + # Return output + opt = self.optimizer.max + return BayesianOptimizerOutput( + params=opt["params"], + target=opt["target"], + p_space=self.optimizer.space, + ) + + +@dataclass +class BayesianOptimizerOutputEvaluator(OutputEvaluator): + """Evaluate the output of :py:class:`BayesianOptimizer`. + + Parameters + ---------- + input : Input + The input object for the optimization task. + output : BayesianOptimizerOutput + The output object returned by the Bayesian optimization task. + + Raises + ------ + TypeError + If :py:attr:`output` is not of type :py:class:`BayesianOptimizerOutput` + """ + + output: BayesianOptimizerOutput + + def __post_init__(self): + """Check output type and call base class post_init""" + if not isinstance(self.output, BayesianOptimizerOutput): + raise TypeError("'output' must be type BayesianOptimizerOutput") + + super().__post_init__() + + def plot_impf_variability( + self, + p_space_df: Optional[pd.DataFrame] = None, + plot_haz: bool = True, + plot_opt_kws: Optional[dict] = None, + plot_impf_kws: Optional[dict] = None, + plot_hist_kws: Optional[dict] = None, + plot_axv_kws: Optional[dict] = None, + ): + """Plot impact function variability with parameter combinations of + almost equal cost function values + + Args: + p_space_df (pd.DataFrame, optional): Parameter space to plot functions from. + If ``None``, this uses the space returned by + :py:meth:`~BayesianOptimizerOutput.p_space_to_dataframe`. Use + :py:func:`select_best` for a convenient subselection of parameters close + to the optimum. + plot_haz (bool, optional): Whether or not to plot hazard intensity + distibution. Defaults to False. + plot_opt_kws (dict, optional): Keyword arguments for optimal impact + function plot. Defaults to None. + plot_impf_kws (dict, optional): Keyword arguments for all impact + function plots. Defaults to None. + plot_hist_kws (dict, optional): Keyword arguments for hazard + intensity histogram plot. Defaults to None. + plot_axv_kws (dict, optional): Keyword arguments for hazard intensity range + plot (axvspan). + """ + + # Initialize plot keyword arguments + if plot_opt_kws is None: + plot_opt_kws = {} + if plot_impf_kws is None: + plot_impf_kws = {} + if plot_hist_kws is None: + plot_hist_kws = {} + if plot_axv_kws is None: + plot_axv_kws = {} + + # Retrieve hazard type and parameter space + haz_type = self.input.hazard.haz_type + if p_space_df is None: + p_space_df = self.output.p_space_to_dataframe() + + # Set plot defaults + colors = mpl.colormaps["tab20"].colors + lw = plot_opt_kws.pop("lw", 2) + label_opt = plot_opt_kws.pop("label", "Optimal Function") + color_opt = plot_opt_kws.pop("color", colors[0]) + zorder_opt = plot_opt_kws.pop("zorder", 4) + + label_impf = plot_impf_kws.pop("label", "All Functions") + color_impf = plot_impf_kws.pop("color", colors[1]) + alpha_impf = plot_impf_kws.pop("alpha", 0.5) + zorder_impf = plot_impf_kws.pop("zorder", 3) + + # get number of impact functions and create a plot for each + n_impf = len(self.impf_set.get_func(haz_type=haz_type)) + axes = [] + + for impf_idx in range(n_impf): + _, ax = plt.subplots() + + # Plot best-fit impact function + best_impf = self.impf_set.get_func(haz_type=haz_type)[impf_idx] + ax.plot( + best_impf.intensity, + best_impf.mdd * best_impf.paa * 100, + color=color_opt, + lw=lw, + zorder=zorder_opt, + label=label_opt, + **plot_opt_kws, + ) + + # Plot all impact functions within 'cost_func_diff' % of best estimate + for idx, (_, row) in enumerate(p_space_df.iterrows()): + label_temp = label_impf if idx == 0 else None + + temp_impf_set = self.input.impact_func_creator(**row["Parameters"]) + temp_impf = temp_impf_set.get_func(haz_type=haz_type)[impf_idx] + + ax.plot( + temp_impf.intensity, + temp_impf.mdd * temp_impf.paa * 100, + color=color_impf, + alpha=alpha_impf, + zorder=zorder_impf, + label=label_temp, + ) + + handles, _ = ax.get_legend_handles_labels() + ax.set( + xlabel=f"Intensity ({self.input.hazard.units})", + ylabel="Mean Damage Ratio (MDR)", + xlim=(min(best_impf.intensity), max(best_impf.intensity)), + ) + ax.yaxis.set_major_formatter(mticker.PercentFormatter(xmax=100)) + + # Plot hazard intensity value distributions + if plot_haz: + haz_vals = self.input.hazard.intensity[ + :, self.input.exposure.gdf[f"centr_{haz_type}"] + ].data + + # Plot defaults + bins = plot_hist_kws.pop("bins", 40) + label_hist = plot_hist_kws.pop("label", "Hazard Intensity") + color_hist = plot_hist_kws.pop("color", colors[2]) + color_axv = plot_axv_kws.pop("color", colors[3]) + alpha_axv = plot_axv_kws.pop("alpha", 0.5) + + # Histogram plot + ax2 = ax.twinx() + ax.set_facecolor("none") + ax.set_zorder(2) + ax2.set_zorder(1) + ax2.axvspan( + haz_vals.min(), haz_vals.max(), color=color_axv, alpha=alpha_axv + ) + ax2.hist( + haz_vals, + bins=bins, + color=color_hist, + label=label_hist, + **plot_hist_kws, + ) + ax2.set_ylabel("Exposure Points", color=color_hist) + + handles = handles + [ + mpatches.Patch(color=color_hist, label=label_hist), + mpatches.Patch(color=color_axv, label=f"{label_hist} Range"), + ] + ax.yaxis.label.set_color(color_opt) + ax.tick_params(axis="y", colors=color_opt) + ax2.tick_params(axis="y", colors=color_hist) + + ax.legend(handles=handles) + axes.append(ax) + + if n_impf > 1: + return axes + + return ax diff --git a/climada/util/calibrate/scipy_optimizer.py b/climada/util/calibrate/scipy_optimizer.py new file mode 100644 index 0000000000..12d46b661d --- /dev/null +++ b/climada/util/calibrate/scipy_optimizer.py @@ -0,0 +1,127 @@ +""" +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 . + +--- +Calibration with scipy.minimize +""" + +from dataclasses import dataclass +from typing import Mapping, Any, Dict, List + +import numpy as np +from scipy.optimize import minimize, OptimizeResult + +from .base import Output, Optimizer + + +@dataclass +class ScipyMinimizeOptimizerOutput(Output): + """Output of a calibration with :py:class:`ScipyMinimizeOptimizer` + + Attributes + ---------- + result : scipy.minimize.OptimizeResult + The OptimizeResult instance returned by ``scipy.optimize.minimize``. + """ + + result: OptimizeResult + + +@dataclass +class ScipyMinimizeOptimizer(Optimizer): + """An optimization using scipy.optimize.minimize + + By default, this optimizer uses the ``"trust-constr"`` method. This + is advertised as the most general minimization method of the ``scipy`` package and + supports bounds and constraints on the parameters. Users are free to choose + any method of the catalogue, but must be aware that they might require different + input parameters. These can be supplied via additional keyword arguments to + :py:meth:`run`. + + See https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.minimize.html + for details. + + Parameters + ---------- + input : Input + The input data for this optimizer. Supported data types for + :py:attr:`constraint` might vary depending on the minimization method used. + """ + + def __post_init__(self): + """Create a private attribute for storing the parameter names""" + self._param_names: List[str] = list() + + def _kwargs_to_impact_func_creator(self, *args, **_) -> Dict[str, Any]: + """Transform the array of parameters into key-value pairs""" + return dict(zip(self._param_names, args[0].flat)) + + def _select_by_param_names(self, mapping: Mapping[str, Any]) -> List[Any]: + """Return a list of entries from a map with matching keys or ``None``""" + return [mapping.get(key) for key in self._param_names] + + def run(self, **opt_kwargs) -> ScipyMinimizeOptimizerOutput: + """Execute the optimization + + Parameters + ---------- + params_init : Mapping (str, Number) + The initial guess for all parameters as key-value pairs. + method : str, optional + The minimization method applied. Defaults to ``"trust-constr"``. + See https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.minimize.html + for details. + kwargs + Additional keyword arguments passed to ``scipy.optimize.minimize``. + + Returns + ------- + output : ScipyMinimizeOptimizerOutput + The output of the optimization. The + :py:attr:`ScipyMinimizeOptimizerOutput.result` attribute stores the + associated ``scipy.optimize.OptimizeResult`` instance. + """ + # Parse kwargs + try: + params_init = opt_kwargs.pop("params_init") + except KeyError as err: + raise RuntimeError( + "ScipyMinimizeOptimizer.run requires 'params_init' mapping as argument" + ) from err + method = opt_kwargs.pop("method", "trust-constr") + + # Store names to rebuild dict when the minimize iterator returns an array + self._param_names = list(params_init.keys()) + + # Transform bounds to match minimize input + bounds = ( + self._select_by_param_names(self.input.bounds) + if self.input.bounds is not None + else None + ) + + x0 = np.array(list(params_init.values())) # pylint: disable=invalid-name + res = minimize( + fun=self._opt_func, + x0=x0, + bounds=bounds, + constraints=self.input.constraints, + method=method, + **opt_kwargs, + ) + + params = dict(zip(self._param_names, res.x.flat)) + return ScipyMinimizeOptimizerOutput(params=params, target=res.fun, result=res) diff --git a/climada/util/calibrate/test/__init__.py b/climada/util/calibrate/test/__init__.py new file mode 100644 index 0000000000..d527ff4958 --- /dev/null +++ b/climada/util/calibrate/test/__init__.py @@ -0,0 +1,17 @@ +""" +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 . + +""" diff --git a/climada/util/calibrate/test/test_base.py b/climada/util/calibrate/test/test_base.py new file mode 100644 index 0000000000..f7b5fb69f4 --- /dev/null +++ b/climada/util/calibrate/test/test_base.py @@ -0,0 +1,265 @@ +""" +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 calibration module +""" + +import unittest +from unittest.mock import patch, create_autospec, MagicMock +from tempfile import TemporaryDirectory +from pathlib import Path + +import numpy as np +import numpy.testing as npt +import pandas as pd +from scipy.sparse import csr_matrix + +from climada.entity import Exposures, ImpactFunc, ImpactFuncSet +from climada.hazard import Hazard, Centroids +from climada.engine import ImpactCalc + +from climada.util.calibrate import Input, OutputEvaluator +from climada.util.calibrate.base import Optimizer, Output + + +class ConcreteOptimizer(Optimizer): + """An instance for testing. Implements 'run' without doing anything""" + + def run(self, **_): + pass + + +def hazard(): + """Create a dummy hazard instance""" + lat = [1, 2] + lon = [0, 1] + centroids = Centroids.from_lat_lon(lat=lat, lon=lon) + event_id = np.array([1, 3, 10]) + intensity = csr_matrix([[1, 0.1], [2, 0.2], [3, 2]]) + return Hazard( + event_id=event_id, centroids=centroids, intensity=intensity, haz_type="TEST" + ) + + +def exposure(): + """Create a dummy exposure instance""" + return Exposures( + data=dict( + longitude=[0, 1, 100], + latitude=[1, 2, 50], + value=[1, 0.1, 1e6], + impf_TEST=[1, 1, 1], + ) + ) + + +class TestInputPostInit(unittest.TestCase): + """Test the post_init dunder method of Input""" + + def setUp(self): + """Create default input instance""" + # Create the hazard instance + self.hazard = hazard() + + # Create the exposure instance + self.exposure = exposure() + + # Create some data + self.data_events = [10, 3] + self.data = pd.DataFrame(data={"a": [1, 2]}, index=self.data_events) + + # Create dummy funcs + self.impact_to_dataframe = lambda _: pd.DataFrame() + self.cost_func = lambda impact, data: 1.0 + self.impact_func_gen = lambda **kwargs: ImpactFuncSet() + + def test_post_init_calls(self): + """Test if post_init calls stuff correctly using mocks""" + # Create mocks + exposure_mock = create_autospec(Exposures()) + + # Default + Input( + hazard=self.hazard, + exposure=exposure_mock, + data=self.data, + cost_func=self.cost_func, + impact_func_creator=self.impact_func_gen, + impact_to_dataframe=self.impact_to_dataframe, + ) + exposure_mock.assign_centroids.assert_called_once_with(self.hazard) + exposure_mock.reset_mock() + + # Default + Input( + hazard=self.hazard, + exposure=exposure_mock, + data=self.data, + cost_func=self.cost_func, + impact_func_creator=self.impact_func_gen, + impact_to_dataframe=self.impact_to_dataframe, + assign_centroids=False, + ) + exposure_mock.assign_centroids.assert_not_called() + + def test_post_init(self): + """Test if post_init results in a sensible hazard and exposure""" + # Create input + input = Input( + hazard=self.hazard, + exposure=self.exposure, + data=self.data, + cost_func=self.cost_func, + impact_func_creator=self.impact_func_gen, + impact_to_dataframe=self.impact_to_dataframe, + ) + + # Check centroids assignment + npt.assert_array_equal(input.exposure.gdf["centr_TEST"], [0, 1, -1]) + + def test_align_impact(self): + """Check alignment of impact and data""" + input = Input( + hazard=hazard(), + exposure=exposure(), + data=pd.DataFrame( + data={"col1": [1, 2], "col2": [2, 3]}, index=[0, 1], dtype="float" + ), + cost_func=lambda x, y: (x + y).sum(axis=None), + impact_func_creator=lambda _: ImpactFuncSet([ImpactFunc()]), + # Mock the dataframe creation by ignoring the argument + impact_to_dataframe=lambda _: pd.DataFrame( + data={"col2": [1, 2], "col3": [2, 3]}, index=[1, 2], dtype="float" + ), + ) + + # missing_data_value = np.nan + data_aligned, impact_df_aligned = input.impact_to_aligned_df(None) + data_aligned_test = pd.DataFrame( + data={ + "col1": [1, 2, np.nan], + "col2": [2, 3, np.nan], + "col3": [np.nan, np.nan, np.nan], + }, + index=[0, 1, 2], + dtype="float", + ) + pd.testing.assert_frame_equal(data_aligned, data_aligned_test) + impact_df_aligned_test = pd.DataFrame( + data={"col1": [0, 0, 0], "col2": [0, 1, 0], "col3": [0, 0, 0]}, + index=[0, 1, 2], + dtype="float", + ) + pd.testing.assert_frame_equal(impact_df_aligned, impact_df_aligned_test) + + # Check fillna + data_aligned, impact_df_aligned = input.impact_to_aligned_df(None, fillna=0) + pd.testing.assert_frame_equal(data_aligned, data_aligned_test.fillna(0)) + pd.testing.assert_frame_equal(impact_df_aligned, impact_df_aligned_test) + + # Different missing data value + input.missing_data_value = 0.0 + data_aligned, impact_df_aligned = input.impact_to_aligned_df(None) + pd.testing.assert_frame_equal(data_aligned, data_aligned_test.fillna(0)) + pd.testing.assert_frame_equal( + impact_df_aligned, + pd.DataFrame( + data={"col1": [0, 0, 0], "col2": [0, 1, 2], "col3": [0, 2, 3]}, + index=[0, 1, 2], + dtype="float", + ), + ) + + # Check error + with self.assertRaisesRegex(ValueError, "NaN values computed in impact!"): + input.impact_to_dataframe = lambda _: pd.DataFrame( + data={"col1": [np.nan], "col2": [2, 3]}, index=[1, 2] + ) + data_aligned, impact_df_aligned = input.impact_to_aligned_df(None) + + +class TestOptimizer(unittest.TestCase): + """Base class for testing optimizers. Creates an input mock""" + + def setUp(self): + """Mock the input""" + self.input = Input( + hazard=hazard(), + exposure=exposure(), + data=pd.DataFrame(data={"col1": [1, 2], "col2": [2, 3]}, index=[0, 1]), + cost_func=lambda x, y: (x + y).sum(axis=None), + impact_func_creator=lambda _: ImpactFuncSet([ImpactFunc()]), + impact_to_dataframe=lambda x: x.impact_at_reg(), + ) + self.optimizer = ConcreteOptimizer(self.input) + + +class TestOuput(unittest.TestCase): + """Test the optimizer output""" + + def test_cycle(self): + """Test if cycling an output object works""" + output = Output(params={"p1": 1.0, "p_2": 10}, target=2.0) + with TemporaryDirectory() as tmpdir: + outfile = Path(tmpdir, "out.h5") + output.to_hdf5(outfile) + self.assertTrue(outfile.is_file()) + output_2 = Output.from_hdf5(outfile) + self.assertEqual(output.target, output_2.target) + self.assertDictEqual(output.params, output_2.params) + +class TestOutputEvaluator(unittest.TestCase): + """Test the output evaluator""" + + def setUp(self): + """Create Input and Output""" + self.input = Input( + hazard=hazard(), + exposure=exposure(), + data=pd.DataFrame(), + impact_func_creator=MagicMock(), + # Should not be called + impact_to_dataframe=lambda _: None, + cost_func=lambda _: None, + ) + self.output = Output(params={"p1": 1, "p2": 2.0}, target=0.0) + + @patch("climada.util.calibrate.base.ImpactCalc", autospec=True) + def test_init(self, mock): + """Test initialization""" + self.input.exposure.value_unit = "my_unit" + self.input.impact_func_creator.return_value = "impact_func" + impact_calc_mock = MagicMock(ImpactCalc) + mock.return_value = impact_calc_mock + impact_calc_mock.impact = MagicMock() + impact_calc_mock.impact.return_value = "impact" + + out_eval = OutputEvaluator(self.input, self.output) + self.assertEqual(out_eval.impf_set, "impact_func") + self.assertEqual(out_eval.impact, "impact") + self.assertEqual(out_eval._impact_label, "Impact [my_unit]") + + self.input.impact_func_creator.assert_called_with(p1=1, p2=2.0) + mock.assert_called_with(self.input.exposure, "impact_func", self.input.hazard) + + +# Execute Tests +if __name__ == "__main__": + TESTS = unittest.TestLoader().loadTestsFromTestCase(TestInputPostInit) + TESTS.addTests(unittest.TestLoader().loadTestsFromTestCase(TestOptimizer)) + TESTS.addTests(unittest.TestLoader().loadTestsFromTestCase(TestOutputEvaluator)) + unittest.TextTestRunner(verbosity=2).run(TESTS) diff --git a/climada/util/calibrate/test/test_bayesian_optimizer.py b/climada/util/calibrate/test/test_bayesian_optimizer.py new file mode 100644 index 0000000000..71af9b3545 --- /dev/null +++ b/climada/util/calibrate/test/test_bayesian_optimizer.py @@ -0,0 +1,363 @@ +""" +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 calibration module +""" + +import unittest +from unittest.mock import patch, MagicMock +from tempfile import TemporaryDirectory +from pathlib import Path + +import numpy as np +import numpy.testing as npt +import pandas as pd +from bayes_opt import BayesianOptimization, Events +from scipy.optimize import NonlinearConstraint +from matplotlib.axes import Axes + +from climada.util.calibrate import Input, BayesianOptimizer, BayesianOptimizerController +from climada.util.calibrate.bayesian_optimizer import ( + Improvement, + StopEarly, + BayesianOptimizerOutput, +) + +from .test_base import hazard, exposure + + +def input(): + """Return a mocked input""" + return Input( + hazard=hazard(), + exposure=exposure(), + data=pd.DataFrame(data={"col1": [1, 2], "col2": [2, 3]}, index=[0, 1]), + cost_func=MagicMock(), + impact_func_creator=MagicMock(), + impact_to_dataframe=MagicMock(), + ) + + +class TestBayesianOptimizerController(unittest.TestCase): + """Tests for the controller of the BayesianOptimizer""" + + def setUp(self): + """Create a optimization instance""" + self.bayes_opt = BayesianOptimization( + f=lambda x: -(x**2), + pbounds={"x": (-10, 10)}, + constraint=NonlinearConstraint(fun=lambda x: x, lb=-0.5, ub=np.inf), + verbose=0, + allow_duplicate_points=True, + ) + + def _make_step(self, x, controller): + self.bayes_opt.probe({"x": x}, lazy=False) + controller.update(Events.OPTIMIZATION_STEP, self.bayes_opt) + + def test_kappa_decay(self): + """Check correct values for kappa_decay""" + contr = BayesianOptimizerController(0, kappa=3, kappa_min=3, n_iter=10) + self.assertAlmostEqual(contr.kappa_decay, 1.0) + + contr = BayesianOptimizerController(0, kappa=3, kappa_min=1, n_iter=10) + self.assertAlmostEqual(contr.kappa * (contr.kappa_decay**10), 1.0) + + def test_from_input(self): + """Check if input data is used correctly to set up controller""" + inp = input() + inp.bounds = {"a": (0, 1), "b": (1, 2)} + + contr = BayesianOptimizerController.from_input(inp, sampling_base=3, kappa=3) + self.assertEqual(contr.kappa, 3) + self.assertEqual(contr.init_points, 3**2) + self.assertEqual(contr.n_iter, 3**2) + + def test_optimizer_params(self): + """Test BayesianOptimizerController.optimizer_params""" + contr = BayesianOptimizerController( + 1, 2, kappa=3, utility_func_kwargs={"xi": 1.11, "kind": "ei"} + ) + result = contr.optimizer_params() + + self.assertDictContainsSubset( + { + "init_points": 1, + "n_iter": 2, + }, + result, + ) + util_func = result["acquisition_function"] + self.assertEqual(util_func.kappa, 3) + self.assertEqual(util_func._kappa_decay, contr._calc_kappa_decay()) + self.assertEqual(util_func.xi, 1.11) + self.assertEqual(util_func.kind, "ei") + + def test_update_step(self): + """Test the update for STEP events""" + contr = BayesianOptimizerController(3, 2) + + # Regular step + self._make_step(3.0, contr) + self.assertEqual(contr.steps, 1) + best = Improvement( + iteration=0, sample=0, random=True, target=-9.0, improvement=np.inf + ) + self.assertEqual(len(contr._improvements), 1) + self.assertTupleEqual(contr._improvements[-1], best) + + # Step that has no effect due to constraints + self._make_step(-2.0, contr) + self.assertEqual(contr.steps, 2) + self.assertEqual(len(contr._improvements), 1) + self.assertTupleEqual(contr._improvements[-1], best) + + # Step that is not new max + self._make_step(4.0, contr) + self.assertEqual(contr.steps, 3) + self.assertEqual(len(contr._improvements), 1) + self.assertTupleEqual(contr._improvements[-1], best) + + # Two minimal increments, therefore we should see a StopEarly + self._make_step(2.999, contr) + self.assertEqual(contr.steps, 4) + self.assertEqual(len(contr._improvements), 2) + + with self.assertRaises(StopEarly): + self._make_step(2.998, contr) + self.assertEqual(contr.steps, 5) + self.assertEqual(len(contr._improvements), 3) + + def test_update_end(self): + """Test the update for END events""" + contr = BayesianOptimizerController(1, 1) + + # One step with improvement, then stop + self._make_step(3.0, contr) + contr.update(Events.OPTIMIZATION_END, self.bayes_opt) + self.assertEqual(contr._last_it_improved, 0) + self.assertEqual(contr._last_it_end, 1) + + # One step with no more improvement + self._make_step(4.0, contr) + with self.assertRaises(StopIteration): + contr.update(Events.OPTIMIZATION_END, self.bayes_opt) + + def test_improvements(self): + """Test ouput of BayesianOptimizerController.improvements""" + contr = BayesianOptimizerController(1, 1) + self._make_step(3.0, contr) + self._make_step(2.0, contr) + contr.update(Events.OPTIMIZATION_END, self.bayes_opt) + self._make_step(2.5, contr) # Not better + self._make_step(1.0, contr) + contr.update(Events.OPTIMIZATION_END, self.bayes_opt) + self._make_step(-0.9, contr) # Constrained + + df = contr.improvements() + pd.testing.assert_frame_equal( + df, + pd.DataFrame.from_dict( + data={ + "iteration": [0, 0, 1], + "sample": [0, 1, 3], + "random": [True, False, False], + "target": [-9.0, -4.0, -1.0], + "improvement": [np.inf, 9.0 / 4.0 - 1, 3.0], + } + ).set_index("sample"), + ) + + +class TestBayesianOptimizerOutput(unittest.TestCase): + """Tests for the output class of BayesianOptimizer""" + + def setUp(self): + """Create a default output""" + bayes_opt = BayesianOptimization( + f=lambda x: -(x**2), + pbounds={"x": (-10, 10)}, + constraint=NonlinearConstraint(fun=lambda x: x, lb=-0.5, ub=np.inf), + verbose=0, + allow_duplicate_points=True, + ) + bayes_opt.probe({"x": 2.0}, lazy=False) + bayes_opt.probe({"x": 1.0}, lazy=False) + bayes_opt.probe({"x": -0.9}, lazy=False) + + self.output = BayesianOptimizerOutput( + params=bayes_opt.max["params"], + target=bayes_opt.max["target"], + p_space=bayes_opt.space, + ) + + def test_p_space_to_dataframe(self): + """""" + self.assertDictEqual(self.output.params, {"x": 1.0}) + self.assertEqual(self.output.target, -1.0) + + idx = pd.MultiIndex.from_tuples( + [ + ("Parameters", "x"), + ("Calibration", "Cost Function"), + ("Calibration", "Constraints Function"), + ("Calibration", "Allowed"), + ] + ) + df = pd.DataFrame(data=None, columns=idx) + df["Parameters", "x"] = [2.0, 1.0, -0.9] + df["Calibration", "Cost Function"] = [4.0, 1.0, 0.9**2] + df["Calibration", "Constraints Function"] = df["Parameters", "x"] + df["Calibration", "Allowed"] = [True, True, False] + df.index.rename("Iteration", inplace=True) + pd.testing.assert_frame_equal(self.output.p_space_to_dataframe(), df) + + def test_cycle(self): + """Check if the output can be cycled to produce the same p_space_df""" + with TemporaryDirectory() as tmpdir: + outpath = Path(tmpdir, "file.h5") + self.output.to_hdf5(outpath) + self.assertTrue(outpath.is_file()) + + output = BayesianOptimizerOutput.from_hdf5(outpath) + pd.testing.assert_frame_equal( + self.output.p_space_to_dataframe(), output.p_space_to_dataframe() + ) + + def test_plot_p_space(self): + """Test plotting of different parameter combinations""" + # Mock data + mock_df = pd.DataFrame( + { + ("Parameters", "param1"): [1, 2, 3], + ("Parameters", "param2"): [4, 5, 6], + ("Parameters", "param3"): [7, 8, 9], + ("Calibration", "Cost Function"): [10, 15, 20], + } + ) + + # Create instance of BayesianOptimizerOutput + output = BayesianOptimizerOutput(params=None, target=None, p_space=None) + + # Mock the p_space_to_dataframe method + with patch.object( + BayesianOptimizerOutput, "p_space_to_dataframe", return_value=mock_df + ): + # Plot all + axes = output.plot_p_space() + self.assertEqual(len(axes), 3) + for ax in axes: + self.assertIsInstance(ax, Axes) + self.assertTrue(ax.has_data()) + + # # Keep x fixed + axes = output.plot_p_space(x="param2") + self.assertEqual(len(axes), 2) + for ax in axes: + self.assertEqual(ax.get_xlabel(), "(Parameters, param2)") + + # # Keep y fixed + axes = output.plot_p_space(y="param1") + self.assertEqual(len(axes), 2) + for ax in axes: + self.assertEqual(ax.get_ylabel(), "(Parameters, param1)") + + # # Plot single combination + ax = output.plot_p_space(x="param2", y="param1") + self.assertIsInstance(ax, Axes) + self.assertEqual(ax.get_xlabel(), "(Parameters, param2)") + self.assertEqual(ax.get_ylabel(), "(Parameters, param1)") + + # Plot single parameter + ax = output.plot_p_space( + pd.DataFrame( + { + ("Parameters", "param1"): [1, 2, 3], + ("Calibration", "Cost Function"): [10, 15, 20], + } + ) + ) + self.assertIsInstance(ax, Axes) + self.assertEqual(ax.get_xlabel(), "(Parameters, param1)") + self.assertEqual(ax.get_ylabel(), "(Parameters, none)") + + +class TestBayesianOptimizer(unittest.TestCase): + """Tests for the optimizer based on bayes_opt.BayesianOptimization""" + + def setUp(self): + """Mock the input""" + self.input = input() + + @patch("climada.util.calibrate.base.ImpactCalc", autospec=True) + def test_kwargs_to_impact_func_creator(self, _): + """Test transform of minimize func arguments to impact_func_gen arguments + + We test the method '_kwargs_to_impact_func_creator' through 'run' because it is + private. + """ + # Create stubs + self.input.bounds = {"x_2": (0, 1), "x 1": (1, 2)} + self.input.cost_func.return_value = 1.0 + self.optimizer = BayesianOptimizer(self.input) + self.controller = BayesianOptimizerController( + init_points=2, n_iter=1, max_iterations=1 + ) + + # Call 'run' + with patch.object(self.input, "impact_to_aligned_df") as align: + align.return_value = (None, None) + self.optimizer.run(self.controller) + + # Check call to '_kwargs_to_impact_func_gen' + call_args = self.input.impact_func_creator.call_args_list + self.assertEqual(len(call_args), 3) + for args in call_args: + self.assertSequenceEqual(args.kwargs.keys(), self.input.bounds.keys()) + + @patch("climada.util.calibrate.base.ImpactCalc", autospec=True) + def test_target_func(self, _): + """Test if cost function is transformed correctly + + We test the method '_target_func' through 'run' because it is private + """ + self.input.bounds = {"x_2": (0, 1), "x 1": (1, 2)} + self.input.cost_func.side_effect = [1.0, -1.0] + self.optimizer = BayesianOptimizer(self.input) + self.controller = BayesianOptimizerController( + init_points=1, n_iter=1, max_iterations=1 + ) + + # Call 'run' + with patch.object(self.input, "impact_to_aligned_df") as align: + align.return_value = (None, None) + output = self.optimizer.run(self.controller) + + # Check target space + npt.assert_array_equal(output.p_space.target, [-1.0, 1.0]) + + +# Execute Tests +if __name__ == "__main__": + TESTS = unittest.TestLoader().loadTestsFromTestCase(TestBayesianOptimizer) + TESTS.addTests( + unittest.TestLoader().loadTestsFromTestCase(TestBayesianOptimizerOutput) + ) + TESTS.addTests( + unittest.TestLoader().loadTestsFromTestCase(TestBayesianOptimizerController) + ) + unittest.TextTestRunner(verbosity=2).run(TESTS) diff --git a/climada/util/calibrate/test/test_scipy_optimizer.py b/climada/util/calibrate/test/test_scipy_optimizer.py new file mode 100644 index 0000000000..01b04ea5f7 --- /dev/null +++ b/climada/util/calibrate/test/test_scipy_optimizer.py @@ -0,0 +1,147 @@ +""" +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 calibration module +""" + +import unittest +from unittest.mock import patch, MagicMock, call +from typing import Optional, List + +import numpy as np +import numpy.testing as npt +import pandas as pd +from scipy.optimize import OptimizeResult + +from climada.util.calibrate import Input, ScipyMinimizeOptimizer + +from .test_base import hazard, exposure + + +class TestScipyMinimizeOptimizer(unittest.TestCase): + """Tests for the optimizer based on scipy.optimize.minimize""" + + def setUp(self): + """Mock the input""" + self.input = Input( + hazard=hazard(), + exposure=exposure(), + data=pd.DataFrame(data={"col1": [1, 2], "col2": [2, 3]}, index=[0, 1]), + cost_func=MagicMock(), + impact_func_creator=MagicMock(), + impact_to_dataframe=MagicMock(), + ) + + self.optimizer = ScipyMinimizeOptimizer(self.input) + + @patch("climada.util.calibrate.base.ImpactCalc", autospec=True) + def test_kwargs_to_impact_func_creator(self, _): + """Test transform of minimize func arguments to impact_func_gen arguments + + We test the method '_kwargs_to_impact_func_creator' through 'run' because it is + private. + """ + # Create stubs + self.input.constraints = None + self.input.bounds = None + self.input.cost_func.return_value = 1.0 + + # Call 'run', make sure that 'minimize' is only with these parameters + params_init = {"x_2": 1, "x 1": 2, "x_3": 3} # NOTE: Also works with whitespace + with patch.object(self.input, "impact_to_aligned_df") as align: + align.return_value = (None, None) + self.optimizer.run(params_init=params_init, options={"maxiter": 1}) + + # Check call to '_kwargs_to_impact_func_creator' + first_call = self.input.impact_func_creator.call_args_list[0] + self.assertEqual(first_call, call(**params_init)) + + # Check error on missing kwargs + with self.assertRaisesRegex( + RuntimeError, "ScipyMinimizeOptimizer.run requires 'params_init'" + ): + self.optimizer.run(options={"maxiter": 1}) + + def test_output(self): + """Check output reporting""" + params_init = {"x_2": 1, "x 1": 2, "x_3": 3} + target_func_value = 1.12 + self.input.constraints = None + self.input.bounds = None + + # Mock the optimization function and call 'run' + with patch.object(self.optimizer, "_opt_func") as opt_func_mock: + opt_func_mock.return_value = target_func_value + output = self.optimizer.run(params_init=params_init, options={"maxiter": 1}) + + # Assert output + self.assertListEqual(list(output.params.keys()), list(params_init.keys())) + npt.assert_allclose(list(output.params.values()), list(params_init.values())) + self.assertEqual(output.target, target_func_value) + self.assertIsInstance(output.result, OptimizeResult) + + # NOTE: For scipy.optimize, this means no error + self.assertTrue(output.result.success) + + @patch("climada.util.calibrate.scipy_optimizer.minimize", autospec=True) + def test_bounds_select(self, minimize_mock): + """Test the _select_by_param_names method + + We test the method '_select_by_param_names' through 'run' because it is private. + """ + + def assert_bounds_in_call(bounds: Optional[List]): + """Check if scipy.optimize.minimize was called with the expected kwargs""" + call_kwargs = minimize_mock.call_args.kwargs + print(minimize_mock.call_args) + + if bounds is None: + self.assertIsNone(call_kwargs["bounds"]) + else: + self.assertListEqual(call_kwargs["bounds"], bounds) + + # Initialize params and mock return value + params_init = {"x_2": 1, "x_1": 2, "x_3": 3} + minimize_mock.return_value = OptimizeResult( + x=np.array(list(params_init.values())), fun=0, success=True + ) + + # Set constraints and bounds to None (default) + self.input.bounds = None + + # Call the optimizer (constraints and bounds are None) + self.optimizer.run(params_init=params_init) + self.assertListEqual(self.optimizer._param_names, list(params_init.keys())) + minimize_mock.assert_called_once() + assert_bounds_in_call(None) + minimize_mock.reset_mock() + + # Set new bounds and constraints + self.input.bounds = {"x_1": "a", "x_4": "b", "x_3": (1, 2)} + self.input.constraints = {"x_5": [1], "x_2": 2} + + # Call the optimizer + self.optimizer.run(params_init=params_init) + self.assertListEqual(self.optimizer._param_names, list(params_init.keys())) + minimize_mock.assert_called_once() + assert_bounds_in_call(bounds=[None, "a", (1, 2)]) + + +# Execute Tests +if __name__ == "__main__": + TESTS = unittest.TestLoader().loadTestsFromTestCase(TestScipyMinimizeOptimizer) + unittest.TextTestRunner(verbosity=2).run(TESTS) diff --git a/climada/util/checker.py b/climada/util/checker.py index 2bcbbef057..17e9fa76df 100644 --- a/climada/util/checker.py +++ b/climada/util/checker.py @@ -23,7 +23,8 @@ 'size', 'shape', 'array_optional', - 'array_default' + 'array_default', + 'prune_csr_matrix', ] import logging @@ -32,16 +33,24 @@ LOGGER = logging.getLogger(__name__) -def check_oligatories(var_dict, var_obl, name_prefix, n_size, n_row, n_col): + +def check_obligatories(var_dict, var_obl, name_prefix, n_size, n_row, n_col): """Check size of obligatory variables. - Paraemters: - var_dict (dict): __dict__ class attribute - var_obl (set): name of the obligatory variables - name_prefix (str): name to add in the error log, e.g. the class name - n_size (int): size expected from arrays and lists - n_row (int): number of rows expected in 2D arrays - n_col (int): number of columns expected in 2D arrays + Parameters + ---------- + var_dict : dict + __dict__ class attribute + var_obl : set + name of the obligatory variables + name_prefix : str + name to add in the error log, e.g. the class name + n_size : int + size expected from arrays and lists + n_row : int + number of rows expected in 2D arrays + n_col : int + number of columns expected in 2D arrays Raises ------ @@ -57,14 +66,20 @@ def check_oligatories(var_dict, var_obl, name_prefix, n_size, n_row, n_col): elif isinstance(var_val, (np.ndarray, sparse.csr_matrix)) and var_val.ndim == 2: shape(n_row, n_col, var_val, name_prefix + var_name) + def check_optionals(var_dict, var_opt, name_prefix, n_size): """Check size of obligatory variables. - Paraemters: - var_dict (dict): __dict__ class attribute - var_opt (set): name of the ooptional variables - name_prefix (str): name to add in the error log, e.g. the class name - n_size (int): size expected from arrays and lists + Parameters + ---------- + var_dict : dict + __dict__ class attribute + var_opt : set + name of the ooptional variables + name_prefix : str + name to add in the error log, e.g. the class name + n_size : int + size expected from arrays and lists Raises ------ @@ -75,17 +90,19 @@ def check_optionals(var_dict, var_opt, name_prefix, n_size): if isinstance(var_val, (np.ndarray, list)): array_optional(n_size, var_val, name_prefix + var_name) + def empty_optional(var, var_name): """Check if a data structure is empty.""" if not var: LOGGER.debug("%s not set. ", var_name) + def size(exp_len, var, var_name): """Check if the length of a variable is the expected one. - Raises - ------ - ValueError + Raises + ------ + ValueError """ try: if isinstance(exp_len, int): @@ -96,12 +113,13 @@ def size(exp_len, var, var_name): except TypeError as err: raise ValueError(f"{var_name} has wrong size.") from err + def shape(exp_row, exp_col, var, var_name): """Check if the length of a variable is the expected one. - Raises - ------ - ValueError + Raises + ------ + ValueError """ try: if exp_row != var.shape[0]: @@ -115,45 +133,46 @@ def shape(exp_row, exp_col, var, var_name): def array_optional(exp_len, var, var_name): """Check if array has right size. Warn if array empty. Call check_size. - Parameters - ---------- - exp_len : str - expected array size - var : np.array - numpy array to check - var_name : str - name of the variable. Used in error/warning msg - - Raises - ------ - ValueError + Parameters + ---------- + exp_len : str + expected array size + var : np.array + numpy array to check + var_name : str + name of the variable. Used in error/warning msg + + Raises + ------ + ValueError """ if len(var) == 0 and exp_len > 0: LOGGER.debug("%s not set. ", var_name) else: size(exp_len, var, var_name) + def array_default(exp_len, var, var_name, def_val): """Check array has right size. Set default value if empty. Call check_size. - Parameters - ---------- - exp_len : str - expected array size - var : np.array - numpy array to check - var_name : str - name of the variable. Used in error/warning msg - def_val : np.array - nump array used as default value - - Raises - ------ - ValueError - - Returns - ------- - Filled array + Parameters + ---------- + exp_len : str + expected array size + var : np.array + numpy array to check + var_name : str + name of the variable. Used in error/warning msg + def_val : np.array + nump array used as default value + + Raises + ------ + ValueError + + Returns + ------- + Filled array """ res = var if len(var) == 0 and exp_len > 0: @@ -162,3 +181,36 @@ def array_default(exp_len, var, var_name, def_val): else: size(exp_len, var, var_name) return res + +def prune_csr_matrix(matrix: sparse.csr_matrix): + """Ensure that the matrix is in the "canonical format". + + Depending on how the matrix was instantiated or modified, it might be in a + "non-canonical" state. This only relates to its internal storage. In this state, + multiple values might be stored for a single "apparent" value in the matrix. + Also, the matrix might store zeros explicitly, which could be removed. + Calling this function makes sure that the matrix is in the "canonical state", and + brings it into this state, if possible. + + See Also + -------- + `csr_matrix.has_canonical_format + `_ + + Parameters + ---------- + matrix : csr_matrix + The matrix to check. It will be modified *inplace*. Its ``.data`` attribute + might change, but apparent matrix values will stay the same. + + Raises + ------ + ValueError + If + `csr_matrix.check_format + `_ + fails + """ + matrix.check_format() + matrix.eliminate_zeros() + matrix.sum_duplicates() diff --git a/climada/util/coordinates.py b/climada/util/coordinates.py index 3b49c74b8b..e32e649060 100644 --- a/climada/util/coordinates.py +++ b/climada/util/coordinates.py @@ -46,6 +46,7 @@ from shapely.geometry import Polygon, MultiPolygon, Point, box import shapely.ops import shapely.vectorized +import shapely.wkt from sklearn.neighbors import BallTree from climada.util.config import CONFIG @@ -2412,13 +2413,21 @@ def apply_box(df_exp): LOGGER.info('Raster from resolution %s to %s.', res, raster_res) df_poly = gpd.GeoDataFrame(points_df[val_names]) if not scheduler: - df_poly['geometry'] = apply_box(points_df) + df_poly['_-geometry-prov'] = apply_box(points_df) else: ddata = dd.from_pandas(points_df[['latitude', 'longitude']], npartitions=cpu_count()) - df_poly['geometry'] = ddata.map_partitions(apply_box, meta=Polygon) \ - .compute(scheduler=scheduler) - df_poly.set_crs(crs if crs else points_df.crs if points_df.crs else DEF_CRS, inplace=True) + df_poly['_-geometry-prov'] = ddata.map_partitions( + apply_box).compute(scheduler=scheduler) + # depending on the dask/pandas version setting `meta=Polygon` in map_partitions + # would just raise a warning and returns a string, so we have to convert explicitly + if isinstance(df_poly.loc[0, '_-geometry-prov'], str): # fails for empty `points_df` + df_poly['_-geometry-prov'] = shapely.wkt.loads(df_poly['_-geometry-prov']) + + df_poly.set_geometry('_-geometry-prov', + crs=crs if crs else points_df.crs if points_df.crs else DEF_CRS, + inplace=True, + drop=True) # renormalize longitude if necessary if equal_crs(df_poly.crs, DEF_CRS): @@ -2644,37 +2653,23 @@ def set_df_geometry_points(df_val, scheduler=None, crs=None): df_val : GeoDataFrame contains latitude and longitude columns scheduler : str, optional - used for dask map_partitions. “threads”, “synchronous” or “processes” + Scheduler type for dask map_partitions. + .. deprecated:: 5.0 + This function does not use dask features anymore. The parameter has no effect + and will be removed in a future version. crs : object (anything readable by pyproj4.CRS.from_user_input), optional Coordinate Reference System, if omitted or None: df_val.geometry.crs """ LOGGER.info('Setting geometry points.') + if scheduler is not None: + warnings.warn("This function does not use dask features anymore. The parameter has no" + " effect and will be removed in a future version.", DeprecationWarning) # keep the original crs if any - if crs is None: - try: - crs = df_val.geometry.crs - except AttributeError: - crs = None - - # work in parallel - if scheduler: - def apply_point(df_exp): - return df_exp.apply(lambda row: Point(row.longitude, row.latitude), axis=1) - - ddata = dd.from_pandas(df_val, npartitions=cpu_count()) - df_val['geometry'] = ddata.map_partitions( - apply_point, - meta=('geometry', gpd.array.GeometryDtype) - ).compute(scheduler=scheduler) - # single process - else: - df_val['geometry'] = gpd.GeoSeries( - gpd.points_from_xy(df_val.longitude, df_val.latitude), index=df_val.index, crs=crs) + crs = df_val.crs if crs is None else crs # crs might now still be None - # set crs - if crs: - df_val.set_crs(crs, inplace=True) + df_val.set_geometry(gpd.points_from_xy(df_val.longitude, df_val.latitude), + inplace=True, crs=crs) def fao_code_def(): diff --git a/climada/util/plot.py b/climada/util/plot.py index 8db72763a9..ab7c540279 100644 --- a/climada/util/plot.py +++ b/climada/util/plot.py @@ -44,6 +44,7 @@ from cartopy.mpl.gridliner import LONGITUDE_FORMATTER, LATITUDE_FORMATTER from rasterio.crs import CRS import requests +import geopandas as gpd from climada.util.constants import CMAP_EXPOSURES, CMAP_CAT, CMAP_RASTER from climada.util.files_handler import to_list @@ -874,3 +875,80 @@ def multibar_plot(ax, data, colors=None, total_width=0.8, single_width=1, # Draw legend if we need if legend: ax.legend(bars, data.keys()) + +def subplots_from_gdf( + gdf: gpd.GeoDataFrame, + colorbar_name: str = None, + title_subplots: callable = None, + smooth=True, + axis=None, + figsize=(9, 13), + adapt_fontsize=True, + **kwargs +): + """Plot several subplots from different columns of a GeoDataFrame, e.g., for + plotting local return periods or local exceedance intensities. + + Parameters + ---------- + gdf: gpd.GeoDataFrame + return periods per threshold intensity + colorbar_name: str + title of the subplots' colorbars + title_subplots: function + function that generates the titles of the different subplots using the columns' names + smooth: bool, optional + Smooth plot to plot.RESOLUTION x plot.RESOLUTION. Default is True + axis: matplotlib.axes._subplots.AxesSubplot, optional + Axis to use. Default is None + figsize: tuple, optional + Figure size for plt.subplots. Default is (9, 13) + adapt_fontsize: bool, optional + If set to true, the size of the fonts will be adapted to the size of the figure. + Otherwise the default matplotlib font size is used. Default is True. + kwargs: optional + Arguments for pcolormesh matplotlib function used in event plots. + + Returns + ------- + axis: matplotlib.axes._subplots.AxesSubplot + Matplotlib axis with the plot. + """ + # check if inputs are correct types + if not isinstance(gdf, gpd.GeoDataFrame): + raise ValueError("gdf is not a GeoDataFrame") + gdf = gdf[['geometry', *[col for col in gdf.columns if col != 'geometry']]] + + # read meta data for fig and axis labels + if not isinstance(colorbar_name, str): + print("Unknown colorbar name. Colorbar label will be missing.") + colorbar_name = '' + if not callable(title_subplots): + print("Unknown subplot-title-generation function. Subplot titles will be column names.") + title_subplots = lambda cols: [f"{col}" for col in cols] + + # change default plot kwargs if plotting return periods + if colorbar_name.strip().startswith('Return Period'): + if 'cmap' not in kwargs.keys(): + kwargs.update({'cmap': 'viridis_r'}) + if 'norm' not in kwargs.keys(): + kwargs.update( + {'norm': mpl.colors.LogNorm( + vmin=gdf.values[:,1:].min(), vmax=gdf.values[:,1:].max() + ), + 'vmin': None, 'vmax': None} + ) + + axis = geo_im_from_array( + gdf.values[:,1:].T, + gdf.geometry.get_coordinates().values[:,::-1], + colorbar_name, + title_subplots(gdf.columns[1:]), + smooth=smooth, + axes=axis, + figsize=figsize, + adapt_fontsize=adapt_fontsize, + **kwargs + ) + + return axis diff --git a/climada/util/test/test_checker.py b/climada/util/test/test_checker.py index c645b2a51f..391191a7ec 100644 --- a/climada/util/test/test_checker.py +++ b/climada/util/test/test_checker.py @@ -41,25 +41,25 @@ def __init__(self): class TestChecks(unittest.TestCase): """Test loading funcions from the Hazard class""" - def test_check_oligatories_pass(self): + def test_check_obligatories_pass(self): """Correct DummyClass definition""" dummy = DummyClass() - u_check.check_oligatories(dummy.__dict__, dummy.vars_oblig, "DummyClass.", + u_check.check_obligatories(dummy.__dict__, dummy.vars_oblig, "DummyClass.", dummy.id.size, dummy.id.size, 2) - def test_check_oligatories_fail(self): + def test_check_obligatories_fail(self): """Wrong DummyClass definition""" dummy = DummyClass() dummy.array = np.arange(3) with self.assertRaises(ValueError) as cm: - u_check.check_oligatories(dummy.__dict__, dummy.vars_oblig, "DummyClass.", + u_check.check_obligatories(dummy.__dict__, dummy.vars_oblig, "DummyClass.", dummy.id.size, dummy.id.size, 2) self.assertIn('Invalid DummyClass.array size: 25 != 3.', str(cm.exception)) dummy = DummyClass() dummy.sparse_arr = sparse.csr_matrix(np.zeros((25, 1))) with self.assertRaises(ValueError) as cm: - u_check.check_oligatories(dummy.__dict__, dummy.vars_oblig, "DummyClass.", + u_check.check_obligatories(dummy.__dict__, dummy.vars_oblig, "DummyClass.", dummy.id.size, dummy.id.size, 2) self.assertIn('Invalid DummyClass.sparse_arr column size: 2 != 1.', str(cm.exception)) @@ -87,6 +87,28 @@ def test_check_optionals_fail(self): u_check.check_optionals(dummy.__dict__, dummy.vars_opt, "DummyClass.", dummy.id.size) self.assertIn('Invalid DummyClass.list size: 25 != 3.', str(cm.exception)) + def test_prune_csr_matrix(self): + """Check that csr matrices are brought into canonical format""" + # Non-canonical: First three data points will be summed onto the first matrix + # entry, fourth will be an explicit zero entry + data = [0, 1, 2, 0] + indices = [0, 0, 0, 1] + indptr = [0, 4, 4, 4] + matrix = sparse.csr_matrix((data, indices, indptr), shape=(3, 2)) + + # These checks just make sure that we understand how csr_matrix works + np.testing.assert_array_equal(matrix.data, data) + np.testing.assert_array_equal(matrix[0, [0, 1]].toarray(), [[3, 0]]) + self.assertEqual(matrix.nnz, 4) + self.assertFalse(matrix.has_canonical_format) + + # Now test our function + u_check.prune_csr_matrix(matrix) + self.assertTrue(matrix.has_canonical_format) + self.assertEqual(matrix[0, 0], 3) + np.testing.assert_array_equal(matrix.data, [3]) + self.assertEqual(matrix.nnz, 1) + # Execute Tests if __name__ == "__main__": TESTS = unittest.TestLoader().loadTestsFromTestCase(TestChecks) diff --git a/climada/util/test/test_coordinates.py b/climada/util/test/test_coordinates.py index 30c37f7251..9426ad1dd7 100644 --- a/climada/util/test/test_coordinates.py +++ b/climada/util/test/test_coordinates.py @@ -1272,41 +1272,44 @@ def test_pts_to_raster_irreg_pass(self): def test_points_to_raster_pass(self): """Test points_to_raster""" - df_val = gpd.GeoDataFrame() - x, y = np.meshgrid(np.linspace(0, 2, 5), np.linspace(40, 50, 10)) - df_val['latitude'] = y.flatten() - df_val['longitude'] = x.flatten() - df_val['value'] = np.ones(len(df_val)) * 10 - crs = 'epsg:2202' - _raster, meta = u_coord.points_to_raster(df_val, val_names=['value'], crs=crs) - self.assertFalse(hasattr(df_val, "crs")) # points_to_raster must not modify df_val - self.assertTrue(u_coord.equal_crs(meta['crs'], crs)) - self.assertAlmostEqual(meta['transform'][0], 0.5) - self.assertAlmostEqual(meta['transform'][1], 0) - self.assertAlmostEqual(meta['transform'][2], -0.25) - self.assertAlmostEqual(meta['transform'][3], 0) - self.assertAlmostEqual(meta['transform'][4], -0.5) - self.assertAlmostEqual(meta['transform'][5], 50.25) - self.assertEqual(meta['height'], 21) - self.assertEqual(meta['width'], 5) - - # test for values crossing antimeridian - df_val = gpd.GeoDataFrame() - df_val['latitude'] = [1, 0, 1, 0] - df_val['longitude'] = [178, -179.0, 181, -180] - df_val['value'] = np.arange(4) - r_data, meta = u_coord.points_to_raster( - df_val, val_names=['value'], res=0.5, raster_res=1.0) - self.assertTrue(u_coord.equal_crs(meta['crs'], DEF_CRS)) - self.assertAlmostEqual(meta['transform'][0], 1.0) - self.assertAlmostEqual(meta['transform'][1], 0) - self.assertAlmostEqual(meta['transform'][2], 177.5) - self.assertAlmostEqual(meta['transform'][3], 0) - self.assertAlmostEqual(meta['transform'][4], -1.0) - self.assertAlmostEqual(meta['transform'][5], 1.5) - self.assertEqual(meta['height'], 2) - self.assertEqual(meta['width'], 4) - np.testing.assert_array_equal(r_data[0], [[0, 0, 0, 2], [0, 0, 3, 1]]) + for scheduler in [None, "threads", "synchronous", "processes"]: + + df_val = gpd.GeoDataFrame() + x, y = np.meshgrid(np.linspace(0, 2, 5), np.linspace(40, 50, 10)) + df_val['latitude'] = y.flatten() + df_val['longitude'] = x.flatten() + df_val['value'] = np.ones(len(df_val)) * 10 + crs = 'epsg:2202' + _raster, meta = u_coord.points_to_raster(df_val, val_names=['value'], crs=crs, + scheduler=scheduler) + self.assertFalse(hasattr(df_val, "crs")) # points_to_raster must not modify df_val + self.assertTrue(u_coord.equal_crs(meta['crs'], crs)) + self.assertAlmostEqual(meta['transform'][0], 0.5) + self.assertAlmostEqual(meta['transform'][1], 0) + self.assertAlmostEqual(meta['transform'][2], -0.25) + self.assertAlmostEqual(meta['transform'][3], 0) + self.assertAlmostEqual(meta['transform'][4], -0.5) + self.assertAlmostEqual(meta['transform'][5], 50.25) + self.assertEqual(meta['height'], 21) + self.assertEqual(meta['width'], 5) + + # test for values crossing antimeridian + df_val = gpd.GeoDataFrame() + df_val['latitude'] = [1, 0, 1, 0] + df_val['longitude'] = [178, -179.0, 181, -180] + df_val['value'] = np.arange(4) + r_data, meta = u_coord.points_to_raster( + df_val, val_names=['value'], res=0.5, raster_res=1.0, scheduler=scheduler) + self.assertTrue(u_coord.equal_crs(meta['crs'], DEF_CRS)) + self.assertAlmostEqual(meta['transform'][0], 1.0) + self.assertAlmostEqual(meta['transform'][1], 0) + self.assertAlmostEqual(meta['transform'][2], 177.5) + self.assertAlmostEqual(meta['transform'][3], 0) + self.assertAlmostEqual(meta['transform'][4], -1.0) + self.assertAlmostEqual(meta['transform'][5], 1.5) + self.assertEqual(meta['height'], 2) + self.assertEqual(meta['width'], 4) + np.testing.assert_array_equal(r_data[0], [[0, 0, 0, 2], [0, 0, 3, 1]]) class TestRasterIO(unittest.TestCase): def test_write_raster_pass(self): diff --git a/climada/util/test/test_finance.py b/climada/util/test/test_finance.py index abf75a5f24..50edf9a15f 100644 --- a/climada/util/test/test_finance.py +++ b/climada/util/test/test_finance.py @@ -121,9 +121,9 @@ def test_wb_esp_1950_pass(self): wb_year, wb_val = world_bank('ESP', ref_year, 'NY.GDP.MKTP.CD') ref_year = 1960 - ref_val = 12072126075.397 + ref_val = 12433394725.2159 self.assertEqual(wb_year, ref_year) - self.assertEqual(wb_val, ref_val) + self.assertAlmostEqual(wb_val, ref_val) class TestWealth2GDP(unittest.TestCase): """Test Wealth to GDP factor extraction""" diff --git a/climada/util/test/test_plot.py b/climada/util/test/test_plot.py index 45e1d092bd..2428bb4b56 100644 --- a/climada/util/test/test_plot.py +++ b/climada/util/test/test_plot.py @@ -25,6 +25,8 @@ import matplotlib.pyplot as plt from matplotlib import colormaps as cm import cartopy.crs as ccrs +import geopandas as gpd +from shapely import Point import climada.util.plot as u_plot @@ -150,6 +152,21 @@ def test_geo_im_from_array(self): self.assertEqual(cmap, ax.collections[0].cmap.name) plt.close() + def test_subplots_from_gdf(self): + return_periods = gpd.GeoDataFrame( + data = ((2., 5.), (3., 6.), (None, 2.), (1., 7.)), + columns = ('10.0', '20.0') + ) + return_periods['geometry'] = (Point(45., 26.), Point(46., 26.), Point(45., 27.), Point(46., 27.)) + colorbar_name = 'Return Periods (Years)' + title_subplots = lambda cols: [f'Threshold Intensity: {col} m/s' for col in cols] + (axis1, axis2) = u_plot.subplots_from_gdf( + return_periods, + colorbar_name=colorbar_name, + title_subplots=title_subplots) + self.assertEqual('Threshold Intensity: 10.0 m/s', axis1.get_title()) + self.assertEqual('Threshold Intensity: 20.0 m/s', axis2.get_title()) + plt.close() # Execute Tests if __name__ == "__main__": diff --git a/doc/climada/climada.entity.rst b/doc/climada/climada.entity.rst index d36a85eab1..f7eac11700 100644 --- a/doc/climada/climada.entity.rst +++ b/doc/climada/climada.entity.rst @@ -15,12 +15,3 @@ climada\.entity\.entity\_def module :members: :undoc-members: :show-inheritance: - -climada\.entity\.tag module ---------------------------- - -.. automodule:: climada.entity.tag - :members: - :undoc-members: - :show-inheritance: - diff --git a/doc/climada/climada.hazard.rst b/doc/climada/climada.hazard.rst index 520c9aae3c..8e4767ae62 100644 --- a/doc/climada/climada.hazard.rst +++ b/doc/climada/climada.hazard.rst @@ -4,6 +4,7 @@ climada\.hazard package .. toctree:: climada.hazard.centroids + climada.hazard.trop_cyclone climada\.hazard\.base module ---------------------------- @@ -13,6 +14,14 @@ climada\.hazard\.base module :undoc-members: :show-inheritance: +climada\.hazard\.io module +-------------------------- + +.. automodule:: climada.hazard.io + :members: + :undoc-members: + :show-inheritance: + climada\.hazard\.isimip\_data module ------------------------------------ @@ -21,18 +30,18 @@ climada\.hazard\.isimip\_data module :undoc-members: :show-inheritance: -climada\.hazard\.storm\_europe module -------------------------------------- +climada\.hazard\.plot module +---------------------------- -.. automodule:: climada.hazard.storm_europe +.. automodule:: climada.hazard.plot :members: :undoc-members: :show-inheritance: -climada\.hazard\.tag module ---------------------------- +climada\.hazard\.storm\_europe module +------------------------------------- -.. automodule:: climada.hazard.tag +.. automodule:: climada.hazard.storm_europe :members: :undoc-members: :show-inheritance: @@ -61,11 +70,3 @@ climada\.hazard\.tc\_tracks\_synth module :undoc-members: :show-inheritance: -climada\.hazard\.trop\_cyclone module -------------------------------------- - -.. automodule:: climada.hazard.trop_cyclone - :members: - :undoc-members: - :show-inheritance: - diff --git a/doc/climada/climada.hazard.trop_cyclone.rst b/doc/climada/climada.hazard.trop_cyclone.rst new file mode 100644 index 0000000000..c703126ec1 --- /dev/null +++ b/doc/climada/climada.hazard.trop_cyclone.rst @@ -0,0 +1,19 @@ +climada\.hazard\.trop\_cyclone package +====================================== + +climada\.hazard\.trop\_cyclone\.trop\_cyclone module +---------------------------------------------------- + +.. automodule:: climada.hazard.trop_cyclone.trop_cyclone + :members: + :undoc-members: + :show-inheritance: + +climada\.hazard\.trop\_cyclone\.trop\_cyclone\_windfields module +---------------------------------------------------------------- + +.. automodule:: climada.hazard.trop_cyclone.trop_cyclone_windfields + :members: + :undoc-members: + :show-inheritance: + diff --git a/doc/climada/climada.util.calibrate.rst b/doc/climada/climada.util.calibrate.rst new file mode 100644 index 0000000000..e56cdb6b8b --- /dev/null +++ b/doc/climada/climada.util.calibrate.rst @@ -0,0 +1,32 @@ +================================== +Impact Function Calibration Module +================================== + +Base Classes +------------ + +Generic classes for defining the data structures of this module. + +.. automodule:: climada.util.calibrate.base + :members: + :private-members: + +Bayesian Optimizer +------------------ + +Calibration based on Bayesian optimization. + +.. automodule:: climada.util.calibrate.bayesian_optimizer + :members: + :show-inheritance: + :inherited-members: abc.ABC + +Scipy Optimizer +--------------- + +Calibration based on the ``scipy.optimize`` module. + +.. automodule:: climada.util.calibrate.scipy_optimizer + :members: + :show-inheritance: + :inherited-members: abc.ABC diff --git a/doc/climada/climada.util.rst b/doc/climada/climada.util.rst index 298ab3b87c..820fd43f7f 100644 --- a/doc/climada/climada.util.rst +++ b/doc/climada/climada.util.rst @@ -9,6 +9,14 @@ climada\.util\.api\_client module :undoc-members: :show-inheritance: +climada\.util\.calibrate module +------------------------------- + +.. toctree:: + :maxdepth: 1 + + climada.util.calibrate + climada\.util\.checker module ----------------------------- diff --git a/doc/guide/install.rst b/doc/guide/install.rst index b6f8718538..be60bc1e87 100644 --- a/doc/guide/install.rst +++ b/doc/guide/install.rst @@ -161,7 +161,12 @@ For advanced Python users or developers of CLIMADA, we recommed cloning the CLIM .. code-block:: shell - mamba create -n climada_env python=3.9 + mamba create -n climada_env "python=3.9.*" + + .. hint:: + + Use the wildcard ``.*`` at the end to allow a downgrade of the bugfix version of Python. + This increases compatibility when installing the requirements in the next step. .. note:: diff --git a/doc/index.rst b/doc/index.rst index 6afee00eaf..732290eeee 100644 --- a/doc/index.rst +++ b/doc/index.rst @@ -85,6 +85,7 @@ Jump right in: Impact Uncertainty Quantification tutorial/climada_engine_Forecast + tutorial/climada_util_calibrate Google Earth Engine tutorial/climada_util_api_client diff --git a/doc/joss/calibration-module/impfs.png b/doc/joss/calibration-module/impfs.png new file mode 100644 index 0000000000..317a5bbea4 Binary files /dev/null and b/doc/joss/calibration-module/impfs.png differ diff --git a/doc/joss/calibration-module/paper.bib b/doc/joss/calibration-module/paper.bib new file mode 100644 index 0000000000..bbb47f1e70 --- /dev/null +++ b/doc/joss/calibration-module/paper.bib @@ -0,0 +1,315 @@ + +@article{aznar-siguan_climada_2019, + title = {{CLIMADA} v1: a global weather and climate risk assessment platform}, + volume = {12}, + issn = {1991-959X}, + shorttitle = {{CLIMADA} v1}, + url = {https://www.geosci-model-dev.net/12/3085/2019/}, + doi = {10.5194/gmd-12-3085-2019}, + abstract = {{\textless}p{\textgreater}{\textless}strong{\textgreater}Abstract.{\textless}/strong{\textgreater} The need for assessing the risk of extreme weather events is ever increasing. In addition to quantification of risk today, the role of aggravating factors such as high population growth and changing climate conditions matters, too. We present the open-source software CLIMADA (CLIMate ADAptation), which integrates hazard, exposure, and vulnerability to compute the necessary metrics to assess risk and to quantify socio-economic impact. The software design is modular and object oriented, offering a simple collaborative framework and a parallelization strategy which allows for scalable computations on clusters. CLIMADA supports multi-hazard calculations and provides an event-based probabilistic approach that is globally consistent for a wide range of resolutions, suitable for whole-country to detailed local studies. This paper uses the platform to estimate and contextualize the damage of hurricane Irma in the Caribbean in 2017. Most of the affected islands are non-sovereign countries and also rely on overseas support in case disaster strikes. The risk assessment performed for this region, based on remotely available data available shortly before or hours after landfall of Irma, proves to be close to reported damage and hence demonstrates a method to provide readily available impact estimates and associated uncertainties in real time.{\textless}/p{\textgreater}}, + language = {English}, + number = {7}, + urldate = {2020-05-11}, + journal = {Geoscientific Model Development}, + author = {Aznar-Siguan, Gabriela and Bresch, David N.}, + month = jul, + year = {2019}, + keywords = {Python, TC}, + pages = {3085--3097}, + file = {Aznar-Siguan and Bresch - 2019 - CLIMADA v1 a global weather and climate risk asse.pdf:/Users/ldr.riedel/Zotero/storage/DP4G5785/Aznar-Siguan and Bresch - 2019 - CLIMADA v1 a global weather and climate risk asse.pdf:application/pdf;Full Text PDF:/Users/ldr.riedel/Zotero/storage/CKM943SM/Aznar-Siguan and Bresch - 2019 - CLIMADA v1 a global weather and climate risk asse.pdf:application/pdf;Full Text PDF:/Users/ldr.riedel/Zotero/storage/F78V926K/Aznar-Siguan and Bresch - 2019 - CLIMADA v1 a global weather and climate risk asse.pdf:application/pdf;Snapshot:/Users/ldr.riedel/Zotero/storage/P2QX8GI5/2019.html:text/html;Snapshot:/Users/ldr.riedel/Zotero/storage/IXJQUUZX/2019.html:text/html}, +} + +@article{bresch_climada_2021, + title = {{CLIMADA} v1.4.1: towards a globally consistent adaptation options appraisal tool}, + volume = {14}, + issn = {1991-959X}, + shorttitle = {{CLIMADA} v1.4.1}, + url = {https://gmd.copernicus.org/articles/14/351/2021/}, + doi = {10.5194/gmd-14-351-2021}, + abstract = {{\textless}p{\textgreater}{\textless}strong class="journal-contentHeaderColor"{\textgreater}Abstract.{\textless}/strong{\textgreater} Climate change is a fact; therefore, adaptation to a changing environment is a necessity. Adaptation is ultimately local, yet similar challenges pose themselves to decision-makers all across the globe and on all levels. The Economics of Climate Adaptation (ECA) methodology has established an economic framework to fully integrate risk and reward perspectives of different stakeholders, underpinned by the CLIMADA (CLIMateADAptation) impact modeling platform. We present an extension of the latter to appraise adaption options in a consistent fashion in order to provide decision-makers from the local to the global level with the necessary facts to identify the most effective instruments to meet the adaptation challenge. We apply the open-source Python implementation to a tropical cyclone impact case study in the Caribbean, using openly available data. This allows us to prioritize a small basket of adaptation options, namely green and gray infrastructure options as well as behavioral measures and risk transfer, and permits inter-island comparisons. In Anguilla, for example, mangroves avert simulated damages more than 4 times the cost estimated for mangrove restoration, whereas the enforcement of building codes is shown to be effective in the Turks and Caicos Islands in a moderate-climate-change scenario. For all islands, cost-effective measures reduce the cost of risk transfer, which covers the damage of high-impact events that cannot be cost-effectively prevented by other measures. This extended version of the CLIMADA platform has been designed to enable risk assessment and options appraisal in a modular form and occasionally bespoke fashion yet with the high reusability of common functionalities to foster the usage of the platform in interdisciplinary studies and international collaboration.{\textless}/p{\textgreater}}, + language = {English}, + number = {1}, + urldate = {2021-12-24}, + journal = {Geoscientific Model Development}, + author = {Bresch, David N. and Aznar-Siguan, Gabriela}, + month = jan, + year = {2021}, + note = {Publisher: Copernicus GmbH}, + pages = {351--363}, + file = {Full Text PDF:/Users/ldr.riedel/Zotero/storage/4HDPKWKF/Bresch and Aznar-Siguan - 2021 - CLIMADA v1.4.1 towards a globally consistent adap.pdf:application/pdf;Snapshot:/Users/ldr.riedel/Zotero/storage/RUUA7987/gmd-14-351-2021.html:text/html}, +} + +@article{virtanen_scipy_2020, + title = {{SciPy} 1.0: {Fundamental} {Algorithms} for {Scientific} {Computing} in {Python}}, + volume = {17}, + doi = {10.1038/s41592-019-0686-2}, + journal = {Nature Methods}, + author = {Virtanen, Pauli and Gommers, Ralf and Oliphant, Travis E. and Haberland, Matt and Reddy, Tyler and Cournapeau, David and Burovski, Evgeni and Peterson, Pearu and Weckesser, Warren and Bright, Jonathan and van der Walt, St{\'e}fan J. and Brett, Matthew and Wilson, Joshua and Millman, K. Jarrod and Mayorov, Nikolay and Nelson, Andrew R. J. and Jones, Eric and Kern, Robert and Larson, Eric and Carey, C J and Polat, {\.I}lhan and Feng, Yu and Moore, Eric W. and VanderPlas, Jake and Laxalde, Denis and Perktold, Josef and Cimrman, Robert and Henriksen, Ian and Quintero, E. A. and Harris, Charles R. and Archibald, Anne M. and Ribeiro, Ant{\^o}nio H. and Pedregosa, Fabian and van Mulbregt, Paul and {SciPy 1.0 Contributors}}, + year = {2020}, + pages = {261--272}, +} + +@misc{nogueira_bayesian_2014, + title = {Bayesian {Optimization}: {Open} source constrained global optimization tool for {Python}}, + url = {https://github.com/fmfn/BayesianOptimization}, + author = {Nogueira, Fernando}, + year = {2014}, +} + +@techreport{cred_2022_2023, + address = {Brussels}, + type = {Emergency Events Database (EM-DAT) Annual Report}, + title = {2022 {Disasters} in {Numbers}}, + url = {https://cred.be/sites/default/files/2022_EMDAT_report.pdf}, + institution = {Centre for Research on the Epidemiology of Disasters (CRED)}, + author = {{CRED}}, + year = {2023}, + file = {CRED - 2023 - 2022. Disasters in Numbers.pdf:/Users/ldr.riedel/Zotero/storage/JQRMMVC2/CRED - 2023 - 2022. Disasters in Numbers.pdf:application/pdf}, +} + +@article{eberenz_regional_2021, + title = {Regional tropical cyclone impact functions for globally consistent risk assessments}, + volume = {21}, + issn = {1561-8633}, + url = {https://nhess.copernicus.org/articles/21/393/2021/}, + doi = {10.5194/nhess-21-393-2021}, + abstract = {Assessing the adverse impacts caused by tropical cyclones has become increasingly important as both climate change and human coastal development increase the damage potential. In order to assess tropical cyclone risk, direct economic damage is frequently modeled based on hazard intensity, asset exposure, and vulnerability, the latter represented by impact functions. In this study, we show that assessing tropical cyclone risk on a global level with one single impact function calibrated for the USA {\textendash} which is a typical approach in many recent studies {\textendash} is problematic, biasing the simulated damage by as much as a factor of 36 in the north West Pacific. Thus, tropical cyclone risk assessments should always consider regional differences in vulnerability, too. This study proposes a calibrated model to adequately assess tropical cyclone risk in different regions by fitting regional impact functions based on reported damage data. Applying regional calibrated impact functions within the risk modeling framework CLIMADA (CLIMate ADAptation) at a resolution of 10 km worldwide, we find global annual average direct damage caused by tropical cyclones to range from USD 51 up to USD 121 billion (value in 2014, 1980{\textendash}2017) with the largest uncertainties in the West Pacific basin where the calibration results are the least robust. To better understand the challenges in the West Pacific and to complement the global perspective of this study, we explore uncertainties and limitations entailed in the modeling setup for the case of the Philippines. While using wind as a proxy for tropical cyclone hazard proves to be a valid approach in general, the case of the Philippines reveals limitations of the model and calibration due to the lack of an explicit representation of sub-perils such as storm surges, torrential rainfall, and landslides. The globally consistent methodology and calibrated regional impact functions are available online as a Python package ready for application in practical contexts like physical risk disclosure and providing more credible information for climate adaptation studies.}, + language = {English}, + number = {1}, + urldate = {2021-01-29}, + journal = {Natural Hazards and Earth System Sciences}, + author = {Eberenz, Samuel and L{\"u}thi, Samuel and Bresch, David N.}, + month = jan, + year = {2021}, + note = {Publisher: Copernicus GmbH}, + keywords = {global, Hurricane, Impact Functions, TC, tropical cyclones, Typhoon, Vulnerability}, + pages = {393--415}, + file = {eberenz_2021_natural_hazards_and_earth_system_sciences_regional_tropical_cyclone_impact_functions_for_globally_consistent_risk.pdf:/Users/ldr.riedel/Zotero/storage/P3CD2TJT/eberenz_2021_natural_hazards_and_earth_system_sciences_regional_tropical_cyclone_impact_functions_for_globally_consistent_risk.pdf:application/pdf;Full Text PDF:/Users/ldr.riedel/Zotero/storage/URDD6URX/Eberenz et al. - 2020 - Regional tropical cyclone impact functions for glo.pdf:application/pdf;Snapshot:/Users/ldr.riedel/Zotero/storage/KWHT8L7M/2021.html:text/html;Snapshot:/Users/ldr.riedel/Zotero/storage/GR53CKH8/nhess-2020-229.html:text/html}, +} + +@article{fischer_combining_2015, + title = {Combining engineering and data-driven approaches: {Calibration} of a generic fire risk model with data}, + volume = {74}, + issn = {0379-7112}, + shorttitle = {Combining engineering and data-driven approaches}, + url = {https://www.sciencedirect.com/science/article/pii/S0379711215000600}, + doi = {10.1016/j.firesaf.2015.04.008}, + abstract = {Two general approaches may be followed for the development of a fire risk model: statistical models based on observed fire losses can support simple cost-benefit studies but are usually not detailed enough for engineering decision-making. Engineering models, on the other hand, require many assumptions that may result in a biased risk assessment. In two related papers we show how engineering and data-driven modelling can be combined by developing generic risk models that are calibrated to statistical data on observed fire events. The focus of the present paper is on the calibration procedure. A framework is developed that is able to deal with data collection in non-homogeneous portfolios of buildings. Also incomplete data sets containing only little information on each fire event can be used for model calibration. To illustrate the capabilities of the proposed framework, it is applied to the calibration of a generic fire risk model for single family houses to Swiss insurance data. The example demonstrates that the bias in the risk estimation can be strongly reduced by model calibration.}, + urldate = {2023-11-27}, + journal = {Fire Safety Journal}, + author = {Fischer, Katharina and De Sanctis, Gianluca and Kohler, Jochen and Faber, Michael H. and Fontana, Mario}, + month = may, + year = {2015}, + keywords = {Calibration, Fire risk model, Generic risk assessment, Model validation, Probabilistic approach}, + pages = {32--42}, + file = {Full Text:/Users/ldr.riedel/Zotero/storage/J8DRJBIX/Fischer et al. - 2015 - Combining engineering and data-driven approaches .pdf:application/pdf}, +} + +@article{luthi_globally_2021, + title = {Globally consistent assessment of economic impacts of wildfires in {CLIMADA} v2.2}, + url = {https://gmd.copernicus.org/preprints/gmd-2021-192/}, + doi = {10.5194/gmd-2021-192}, + abstract = {In light of the dramatic increase in economic impacts due to wildfires over recent years, the need for globally consistent impact modelling of wildfire damages is ever increasing. Insurance companies, individual households, humanitarian organisations and governmental authorities, as well as investors and portfolio owners, are increasingly required to account for climate-related physical risks. In response to these societal challenges, we present an extension to the open-source and open5 access risk modelling platform CLIMADA (CLImate ADAptation) for modelling economic impacts of wildfires in a globally consistent and spatially explicit approach. All input data is free, public and globally available, ensuring applicability in datascarce regions of the Global South. The model was calibrated at resolutions of 1, 4 and 10 kilometers using information on past wildfire damage reported by the disaster database EM-DAT. Despite the large remaining uncertainties, the model yields sound damage estimates with a model performance well in line with the results of other natural catastrophe impact models, such as for 10 tropical cyclones. To complement the global perspective of this study, we conducted two case studies on the recent mega fires in Chile (2017) and Australia (2020). The model is made available online as part of a Python package, ready for application in practical contexts such as disaster risk assessment, near real time impact estimates or physical climate risk disclosure.}, + language = {en}, + urldate = {2021-10-01}, + author = {L{\"u}thi, Samuel and Aznar-Siguan, Gabriela and Fairless, Christopher and Bresch, David N.}, + month = jul, + year = {2021}, + file = {L{\"u}thi et al. - 2021 - Globally consistent assessment of economic impacts.pdf:/Users/ldr.riedel/Zotero/storage/LFF4PIQF/L{\"u}thi et al. - 2021 - Globally consistent assessment of economic impacts.pdf:application/pdf}, +} + +@misc{gabriela_aznar_siguan_2023_8383171, + title = {{CLIMADA}-project/climada\_python: v4.0.1}, + publisher = {Zenodo}, + author = {Aznar-Siguan, Gabriela and Schmid, Emanuel and Vogt, Thomas and Eberenz, Samuel and Steinmann, Carmen B. and R{\"o}{\"o}sli, Thomas and Yu, Yue and M{\"u}hlhofer, Evelyn and L{\"u}thi, Samuel and Sauer, Inga J. and Hartman, Jan and Kropf, Chahan M. and Guillod, Benoit P. and Stalhandske, Z{\'e}lie and Ciullo, Alessio and Bresch, David N. and Riedel, Lukas and Fairless, Chris and Schmid, Timo and Kam, Pui Man (Mannie) and Colombi, Nicolas and {wjan262} and Meiler, Simona and Villger, Leonie and Portmann, Raphael and Bozzini, Veronica and Stocker, Dario}, + month = sep, + year = {2023}, + doi = {10.5281/zenodo.8383171}, +} + +@article{riedel_fluvial_2024, + title = {Fluvial flood inundation and socio-economic impact model based on open data}, + volume = {17}, + issn = {1991-959X}, + doi = {10.5194/gmd-17-5291-2024}, + abstract = {Fluvial floods are destructive hazards that affect millions of people worldwide each year. Forecasting flood events and their potential impacts therefore is crucial for disaster preparation and mitigation. Modeling flood inundation based on extreme value analysis of river discharges is an alternative to physical models of flood dynamics, which are computationally expensive. We present the implementation of a globally applicable, open-source fluvial flood model within a state-of-the-art risk modeling framework. It uses openly available data to rapidly compute flood inundation footprints of historic and forecasted events for the estimation of associated impacts. For the example of Pakistan, we use this flood model to compute flood depths and extents and employ it to estimate population displacement due to floods. Comparing flood extents to satellite data reveals that incorporating estimated flood protection standards does not necessarily improve the flood footprint computed by the model. We further show that, after calibrating the vulnerability of the impact model to a single event, the estimated displacement caused by past floods is in good agreement with disaster reports. Finally, we demonstrate that this calibrated model is suited for probabilistic impact-based forecasting.}, + language = {English}, + number = {13}, + urldate = {2024-07-10}, + journal = {Geoscientific Model Development}, + author = {Riedel, Lukas and R{\"o}{\"o}sli, Thomas and Vogt, Thomas and Bresch, David N.}, + month = jul, + year = {2024}, + pages = {5291--5308}, +} + +@misc{kam_impact-based_2023, + title = {Impact-{Based} {Forecasting} of {Tropical} {Cyclone}-{Related} {Human} {Displacement} to {Support} {Anticipatory} {Action}}, + url = {https://www.researchsquare.com/article/rs-3682198/v1}, + doi = {10.21203/rs.3.rs-3682198/v1}, + urldate = {2024-04-29}, + author = {Kam, Pui Man and Ciccone, Fabio and Kropf, Chahan and Riedel, Lukas and Fairless, Christopher and Bresch, David}, + month = dec, + year = {2023}, + file = {Full Text PDF:/Users/ldr.riedel/Zotero/storage/HB9QP4MF/Kam et al. - 2023 - Impact-Based Forecasting of Tropical Cyclone-Relat.pdf:application/pdf}, +} + +@article{roosli_towards_2021, + title = {Towards operational impact forecasting of building damage from winter windstorms in {Switzerland}}, + volume = {28}, + issn = {1469-8080}, + url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/met.2035}, + doi = {10.1002/met.2035}, + abstract = {National meteorological and hydrological services issue warnings for severe weather events, typically based on stakeholder-agreed fixed thresholds of meteorological parameters such as wind speeds or precipitation amounts. Yet societal decisions on preventive actions depend on the expected impacts of the weather event. In order to better inform such preventive actions, meteorological services are currently working towards including expected impacts into their warnings. We develop an open-source impact forecasting system for building damage due to winter windstorms in Switzerland. It combines a numerical ensemble weather prediction model with exposure and vulnerability data. This system forecasts expected building damage in Swiss Francs with a 2-day lead time on a 500-m grid or aggregated to administrative regions. We compare the forecasted building damage with insurance claims in the canton of Zurich. The uncertainty of the impact forecasts is large. For the majority of days with severe winter windstorm damage, the mean forecasted damage was in the right order of magnitude, with one missed event and one false alarm. For thunderstorms and foehn storms, the rate of missed events and false alarms is much higher, most likely related to the limited meteorological forecast skill. Such impact forecasts can inform decision makers on preventive actions, such as allocating emergency response and other assets. Additionally, impact forecasts could also help communicating the severity of the upcoming event to the general public as well as indirectly help meteorological forecasters with taking warning decisions.}, + language = {en}, + number = {6}, + urldate = {2022-01-12}, + journal = {Meteorological Applications}, + author = {R{\"o}{\"o}sli, Thomas and Appenzeller, Christof and Bresch, David N.}, + year = {2021}, + note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/met.2035}, + keywords = {building damage, impact forecast, impact modelling, impact-based warning, numerical weather prediction}, + pages = {e2035}, + file = {Full Text PDF:/Users/ldr.riedel/Zotero/storage/I8WT8G7K/R{\"o}{\"o}sli et al. - 2021 - Towards operational impact forecasting of building.pdf:application/pdf}, +} + +@article{welker_comparing_2021, + title = {Comparing an insurer's perspective on building damages with modelled damages from pan-{European} winter windstorm event sets: a case study from {Zurich}, {Switzerland}}, + volume = {21}, + issn = {1561-8633}, + url = {https://nhess.copernicus.org/articles/21/279/2021/}, + doi = {10.5194/nhess-21-279-2021}, + language = {English}, + number = {1}, + urldate = {2024-04-29}, + journal = {Natural Hazards and Earth System Sciences}, + author = {Welker, Christoph and R{\"o}{\"o}sli, Thomas and Bresch, David N.}, + month = jan, + year = {2021}, + note = {Publisher: Copernicus GmbH}, + pages = {279--299}, + file = {Full Text PDF:/Users/ldr.riedel/Zotero/storage/4V3IQZIA/Welker et al. - 2021 - Comparing an insurer's perspective on building dam.pdf:application/pdf}, +} + +@article{Schmid2023a, + title = {An open-source radar-based hail damage model for buildings and cars}, + url = {https://nhess.copernicus.org/preprints/nhess-2023-158/}, + doi = {10.5194/nhess-2023-158}, + abstract = {{\textless}p{\textgreater}{\textless}strong class="journal-contentHeaderColor"{\textgreater}Abstract.{\textless}/strong{\textgreater} Severe hailstorms cause substantial damages to buildings and vehicles, necessitating the quantification of associated risks. Here, we present a novel open-source hail damage model for buildings and cars based on single-polarization radar data and 250\’000 geolocated hail damage reports in Switzerland from 2002 to 2021. To this end, we conduct a detailed evaluation of different radar-based hail intensity measures at 1 km resolution and find that the maximum expected severe hail size (MESHS) outperforms the other measures, despite a considerable false alarm ratio. Asset-specific hail damage impact functions for buildings and cars are calibrated based on MESHS and incorporated into the open-source risk modelling platform CLIMADA. The model successfully estimates the correct order of magnitude for the number of building damages in 91 \%, their total cost in 77 \%, the number of vehicle damages in 74 \%, and their total cost in 60 \% of over 100 considered large hail events. We found considerable uncertainties in hail damage estimates, which are largely attributable to limitations of radar-based hail detection. Therefore, we explore the usage of crowdsourced hail reports and find substantially improved spatial representation of severe hail for individual events. By highlighting the potential and limitations of radar-based hail size estimates, particularly MESHS, and the utilization of an open-source risk modelling platform, this study represents a significant step towards addressing the gap in risk quantification associated with severe hail events in Switzerland.{\textless}/p{\textgreater}}, + language = {English}, + urldate = {2023-09-27}, + journal = {Natural Hazards and Earth System Sciences Discussions}, + author = {Schmid, Timo and Portmann, Raphael and Villiger, Leonie and Schr{\"o}er, Katharina and Bresch, David N.}, + month = sep, + year = {2023}, + note = {Publisher: Copernicus GmbH}, + pages = {1--38}, + file = {Full Text PDF:/Users/ldr.riedel/Zotero/storage/AT4U3Z3I/Schmid et al. - 2023 - An open-source radar-based hail damage model for b.pdf:application/pdf}, +} + +@book{rougier_risk_2013, + title = {Risk and {Uncertainty} {Assessment} for {Natural} {Hazards}}, + isbn = {978-1-107-00619-5}, + abstract = {Assessment of risk and uncertainty is crucial for natural hazard risk management, facilitating risk communication and informing strategies to successfully mitigate our society's vulnerability to natural disasters. Written by some of the world's leading experts, this book provides a state-of-the-art overview of risk and uncertainty assessment in natural hazards. It presents the core statistical concepts using clearly defined terminology applicable across all types of natural hazards and addresses the full range of sources of uncertainty, the role of expert judgement and the practice of uncertainty elicitation. The core of the book provides detailed coverage of all the main hazard types and concluding chapters address the wider societal context of risk management. This is an invaluable compendium for academic researchers and professionals working in the fields of natural hazards science, risk assessment and management and environmental science and will be of interest to anyone involved in natural hazards policy.}, + language = {en}, + publisher = {Cambridge University Press}, + author = {Rougier, Jonathan and Hill, Lisa J. and Sparks, Robert Stephen John}, + month = feb, + year = {2013}, + keywords = {Science / Environmental Science}, + url = {https://cambridge.org/9781107006195}, +} + +@incollection{smith_human_2014, + address = {Cambridge, United Kingdom and New York, NY, USA}, + title = {Human {Health}: {Impacts}, {Adaptation}, and {Co}-{Benefits}}, + url = {https://www.ipcc.ch/report/ar5/wg2/}, + doi = {10.1017/CBO9781107415379.016}, + booktitle = {Climate {Change} 2014: {Impacts}, {Adaptation}, and {Vulnerability}. {Part} {A}: {Global} and {Sectoral} {Aspects}. {Contribution} of {Working} {Group} {II} to the {Fifth} {Assessment} {Report} of the {Intergovernmental} {Panel} on {Climate} {Change}}, + publisher = {Cambridge University Press}, + author = {Smith, Kirk R. and Woodward, Alistair and Campbell-Lendrum, Diarmid and Chadee, Dave D. and Honda, Yasushi and Liu, Qiyong and Olwoch, Jane M. and Revich, Boris and Sauerborn, Rainer}, + year = {2014}, + pages = {709--754}, +} + +@misc{oasis, + author = {{Oasis LMF Ltd.}}, + title = {Oasis Loss Modelling Framework}, + year = {2024}, + url = {https://oasislmf.github.io/}, +} + +@misc{delforge_em-dat_2023, + title = {{EM}-{DAT}: the {Emergency} {Events} {Database}}, + shorttitle = {{EM}-{DAT}}, + url = {https://www.researchsquare.com/article/rs-3807553/v1}, + doi = {10.21203/rs.3.rs-3807553/v1}, + abstract = {The Emergency Events Database (EM-DAT) compiles global disaster data resulting from both technological and natural hazards. It details the human and economic impacts from 1900 to present, with systematic recording since 1988. Serving the humanitarian, disaster risk reduction, and academic sectors, EM-DAT\&\#039;s transition to open access and increasing climate change concerns have expanded its reach and visibility. The dataset, freely available for non-commercial use, is downloadable as an Excel file. It is categorized by hazard type and standardized by individual disaster events for each country. With over 26,000 unique entries, the database is populated through the monitoring of textual documents and their manual processing. The data collection and validation processes involve systematic daily checks from predefined sources, searches for additional sources, and periodic thematic updates. The evolution of EM-DAT\&\#039;s data content mirrors societal and technological advancements in disaster reporting. Besides these progresses, known inconsistencies and biases in the data quality have been reported. The article acknowledges these issues, highlighting their potential implications for research and decision-making.}, + urldate = {2024-04-29}, + author = {Delforge, Damien and Wathelet, Valentin and Below, Regina and Sofia, Cinzia Lanfredi and Tonnelier, Margo and Loenhout, Joris van and Speybroeck, Niko}, + month = dec, + year = {2023}, + note = {ISSN: 2693-5015}, + file = {Full Text PDF:/Users/ldr.riedel/Zotero/storage/IU9SY5AV/Delforge et al. - 2023 - EM-DAT the Emergency Events Database.pdf:application/pdf}, +} + + +@article{knapp_international_2010, + title = {The {International} {Best} {Track} {Archive} for {Climate} {Stewardship} ({IBTrACS}): {Unifying} {Tropical} {Cyclone} {Data}}, + volume = {91}, + issn = {0003-0007, 1520-0477}, + shorttitle = {The {International} {Best} {Track} {Archive} for {Climate} {Stewardship} ({IBTrACS})}, + url = {https://journals.ametsoc.org/view/journals/bams/91/3/2009bams2755_1.xml}, + doi = {10.1175/2009BAMS2755.1}, + abstract = {The goal of the International Best Track Archive for Climate Stewardship (IBTrACS) project is to collect the historical tropical cyclone best-track data from all available Regional Specialized Meteorological Centers (RSMCs) and other agencies, combine the disparate datasets into one product, and disseminate in formats used by the tropical cyclone community. Each RSMC forecasts and monitors storms for a specific region and annually archives best-track data, which consist of information on a storm's position, intensity, and other related parameters. IBTrACS is a new dataset based on the best-track data from numerous sources. Moreover, rather than preferentially selecting one track and intensity for each storm, the mean position, the original intensities from the agencies, and summary statistics are provided. This article discusses the dataset construction, explores the tropical cyclone climatology from IBTrACS, and concludes with an analysis of uncertainty in the tropical cyclone intensity record.}, + language = {en}, + number = {3}, + urldate = {2022-07-11}, + journal = {Bulletin of the American Meteorological Society}, + author = {Knapp, Kenneth R. and Kruk, Michael C. and Levinson, David H. and Diamond, Howard J. and Neumann, Charles J.}, + month = mar, + year = {2010}, + note = {tex.ids= Knapp2010 +publisher: American Meteorological Society +section: Bulletin of the American Meteorological Society}, + pages = {363--376}, + file = {Full Text PDF:/Users/ldr.riedel/Zotero/storage/VTI5QP8D/Knapp et al. - 2010 - The International Best Track Archive for Climate S.pdf:application/pdf;Full Text PDF:/Users/ldr.riedel/Zotero/storage/STJ7VDN8/Knapp et al. - 2010 - The International Best Track Archive for Climate S.pdf:application/pdf;Snapshot:/Users/ldr.riedel/Zotero/storage/H3CEIX4U/2009bams2755_1.html:text/html}, +} + +@article{holland_revised_2008, + title = {A {Revised} {Hurricane} {Pressure}{\textendash}{Wind} {Model}}, + volume = {136}, + issn = {0027-0644}, + url = {https://journals.ametsoc.org/doi/abs/10.1175/2008MWR2395.1}, + doi = {10.1175/2008MWR2395.1}, + abstract = {A new technique for relating central pressure and maximum winds in tropical cyclones is presented, together with a method of objectively determining a derivative of the Holland b parameter, bs, which relates directly to surface winds and varies with the pressure drop into the cyclone center, intensification rate, latitude, and translation speed. By allowing this bs parameter to vary, a realistic scatter in maximum winds for a given central pressure is obtained. This provides an improvement over traditional approaches that provide a unique wind for each central pressure. It is further recommended that application of the Dvorak satellite-interpretation technique be changed to enable a direct derivation of central pressure. The pressure{\textendash}wind model derived here can then provide the maximum wind estimates. The recent North Atlantic data archive is shown to be largely derived from the use of the Dvorak technique, even when hurricane reconnaissance data are available and Dvorak overestimates maximum winds in this region for the more intense hurricanes. Application to the full North Atlantic hurricane archive confirms the findings by Landsea (1993) of a substantial overestimation of maximum winds between 1950 and 1980; the Landsea corrections do not completely remove this bias.}, + number = {9}, + urldate = {2018-05-16}, + journal = {Monthly Weather Review}, + author = {Holland, Greg}, + month = sep, + year = {2008}, + note = {00158}, + keywords = {TC}, + pages = {3432--3445}, + file = {holland_2008_monthly_weather_review_a_revised_hurricane_pressure{\textendash}wind_model.pdf:/Users/ldr.riedel/Zotero/storage/I9VLDHTK/holland_2008_monthly_weather_review_a_revised_hurricane_pressure{\textendash}wind_model.pdf:application/pdf;Snapshot:/Users/ldr.riedel/Zotero/storage/62ZZVVY9/2008MWR2395.html:text/html}, +} + +@article{eberenz_asset_2020, + title = {Asset exposure data for global physical risk assessment}, + volume = {12}, + issn = {1866-3508}, + url = {https://www.earth-syst-sci-data.net/12/817/2020/}, + doi = {10.5194/essd-12-817-2020}, + abstract = {{\textless}p{\textgreater}{\textless}strong{\textgreater}Abstract.{\textless}/strong{\textgreater} One of the challenges in globally consistent assessments of physical climate risks is the fact that asset exposure data are either unavailable or restricted to single countries or regions. We introduce a global high-resolution asset exposure dataset responding to this challenge. The data are produced using {\textquotedblleft}lit population{\textquotedblright} (LitPop), a globally consistent methodology to disaggregate asset value data proportional to a combination of nightlight intensity and geographical population data. By combining nightlight and population data, unwanted artefacts such as blooming, saturation, and lack of detail are mitigated. Thus, the combination of both data types improves the spatial distribution of macroeconomic indicators. Due to the lack of reported subnational asset data, the disaggregation methodology cannot be validated for asset values. Therefore, we compare disaggregated gross domestic product (GDP) per subnational administrative region to reported gross regional product (GRP) values for evaluation. The comparison for 14 industrialized and newly industrialized countries shows that the disaggregation skill for GDP using nightlights or population data alone is not as high as using a combination of both data types. The advantages of LitPop are global consistency, scalability, openness, replicability, and low entry threshold. The open-source LitPop methodology and the publicly available asset exposure data offer value for manifold use cases, including globally consistent economic disaster risk assessments and climate change adaptation studies, especially for larger regions, yet at considerably high resolution. The code is published on GitHub as part of the open-source software CLIMADA (CLIMate ADAptation) and archived in the ETH Data Archive with the link {\textless}a href="https://doi.org/10.5905/ethz-1007-226"{\textgreater}https://doi.org/10.5905/ethz-1007-226{\textless}/a{\textgreater} (Bresch et al., 2019b). The resulting asset exposure dataset for 224 countries is archived in the ETH Research Repository with the link {\textless}a href="https://doi.org/10.3929/ethz-b-000331316"{\textgreater}https://doi.org/10.3929/ethz-b-000331316{\textless}/a{\textgreater} (Eberenz et al., 2019).{\textless}/p{\textgreater}}, + language = {English}, + number = {2}, + urldate = {2020-05-14}, + journal = {Earth System Science Data}, + author = {Eberenz, Samuel and Stocker, Dario and R{\"o}{\"o}sli, Thomas and Bresch, David N.}, + month = apr, + year = {2020}, + keywords = {Exposure}, + pages = {817--833}, + file = {Full Text PDF:/Users/ldr.riedel/Zotero/storage/IHS57ZPE/Eberenz et al. - 2020 - Asset exposure data for global physical risk asses.pdf:application/pdf;Snapshot:/Users/ldr.riedel/Zotero/storage/PUTYCUM6/2020.html:text/html}, +} diff --git a/doc/joss/calibration-module/paper.md b/doc/joss/calibration-module/paper.md new file mode 100644 index 0000000000..ef8960cd6d --- /dev/null +++ b/doc/joss/calibration-module/paper.md @@ -0,0 +1,144 @@ +--- +title: 'A Module for Calibrating Impact Functions in the Climate Risk Modeling Platform CLIMADA' +tags: + - Python + - climate risk + - impact function + - vulnerability + - optimization +authors: + - name: Lukas Riedel + orcid: 0000-0002-4667-3652 + affiliation: "1, 2" + corresponding: true + - name: Chahan M. Kropf + orcid: 0000-0002-3761-2292 + affiliation: "1, 2" + - name: Timo Schmid + orcid: 0000-0002-6788-2154 + affiliation: "1, 2" +affiliations: + - name: Institute for Environmental Decisions, ETH Zürich, Zürich, Switzerland + index: 1 + - name: Federal Office of Meteorology and Climatology MeteoSwiss, Zürich-Airport, Switzerland + index: 2 +date: 01 May 2024 +bibliography: paper.bib + +--- + +# Summary + +Impact functions model the vulnerability of people and assets exposed to weather and climate hazards. +Given probabilistic hazard event sets or weather forecasts, they enable the computation of associated risks or impacts, respectively. +Because impact functions are difficult to determine on larger spatial and temporal scales of interest, they are often calibrated using hazard, exposure, and impact data from past events. +We present a module for calibrating impact functions based on such data using established calibration techniques like Bayesian optimization. +It is implemented as Python submodule `climada.util.calibrate` of the climate risk modeling platform CLIMADA [@gabriela_aznar_siguan_2023_8383171], and fully integrates into its workflow. + +# Statement of Need + +Natural hazards like storms, floods, droughts, and extreme temperatures will be exacerbated by climate change. +In 2022 alone, weather- and climate-related disasters affected 185 million people and caused economic losses of more than US$ 200 billion [@cred_2022_2023]. +Forecasting the impacts of imminent events as well as determining climate risk according to future socio-economic pathways is crucial for decision-making in societal, humanitarian, political, socio-economic, and ecological issues [@smith_human_2014]. +One major source of uncertainty in such computations is the vulnerability [@rougier_risk_2013]. +Typically modeled as impact function[^1] that yields the percentage of affected exposure depending on hazard intensity, vulnerability is difficult to determine *a priori*. +Using hazard footprints, exposure, and recorded impacts from past events, researchers therefore employ calibration techniques to estimate unknown impact functions and use these functions for future risk projections or impact forecasts [@eberenz_regional_2021; @luthi_globally_2021; @welker_comparing_2021; @roosli_towards_2021; @kam_impact-based_2023; @Schmid2023a; @riedel_fluvial_2024]. + +CLIMADA is a widely used, open-source framework for calculating weather- and climate-related impacts and risks [@aznar-siguan_climada_2019], and for appraising the benefits of adaptation options [@bresch_climada_2021]. +The aforementioned studies calibrate impact functions with different optimization algorithms within the CLIMADA ecosystem, but lack a consistent implementation of these algorithms. +OASIS LMF, another well-adopted, open-source loss modeling framework, also does not feature tools for impact function calibration, and regards vulnerability solely as model input [@oasis]. +With the proposed calibration module, we aim at unifying the approaches to impact function calibration, while providing an extensible structure that can be easily adjusted to particular applications. + +# Module Structure + +Calibrating the parameters of impact functions can be a complex task involving highly non-linear objective functions, constrained and ambiguous parameters, and parameter spaces with multiple local optima. +To support different iterative optimization algorithms, we implemented an `Optimizer` base class from which concrete optimizers employing different algorithms are derived. +All optimizers receive data through a common `Input` object, which provides all necessary information for the calibration task. +This enables users to quickly switch between optimizers without disrupting their workflow. +Pre-defined optimizers are based on the `scipy.optimize` module [@virtanen_scipy_2020] and the `BayesianOptimization` package [@nogueira_bayesian_2014]. +The latter is especially useful for calibration tasks because it supports constraints and only requires bounds of the parameter space as prior information. +We provide a `BayesianOptimizerController` that iteratively explores and "exploits" the parameter space to find the global optimum and stop the optimization process then. +This only requires minimal user input for indicating the sampling density. + +# Example Code + +Given a hazard event set, and exposure, and associated impact data in the appropriate CLIMADA data structures, the calibration input can quickly be defined. +Users have to supply a cost function, the parameter space bounds, a function that creates an impact function from the estimated parameter set, and a function that transforms a CLIMADA `Impact` object into the same structure as the input impact data. + +```python +import pandas as pd +from sklearn.metrics import mean_squared_log_error + +from climada.hazard import Hazard +from climada.entity import Exposure, ImpactFuncSet, ImpfTropCyclone +from climada.engine import Impact + +from climada.util.calibrate import ( + Input, + BayesianOptimizer, + BayesianOptimizerController, + BayesianOptimizerOutputEvaluator, + select_best, +) + +def calibrate(hazard: Hazard, exposure: Exposure, impact_data: pd.DataFrame): + """Calibrate an impact function with BayesianOptimizer""" + + def impact_function_tropical_cyclone(v_half: float, scale: float) -> ImpactFuncSet: + """Return an impact function set for tropical cyclones given two parameters + + This assumes that the input data relates to tropical cyclone asset damages. + """ + return ImpactFuncSet( + [ImpfTropCyclone.from_emanuel_usa(v_half=v_half, scale=scale)] + ) + + def aggregate_impact(impact: Impact) -> pd.DataFrame: + """Aggregate modeled impacts per region ID and return as DataFrame""" + return impact.impact_at_reg() + + # Prepare the calibration input + calibration_input = Input( + hazard=hazard + exposure=exposure, + data=impact_data, + bounds={"v_half": (25.8, 150), "scale": (0.01, 1)}, + cost_func=mean_squared_log_error, + impact_func_creator=impact_function_tropical_cyclone, + impact_to_dataframe=aggregate_impact, + ) + + # Execute the calibration + controller = BayesianOptimizerController.from_input(calibration_input) + optimizer = BayesianOptimizer(calibration_input) + calibration_output = optimizer.run(controller) + + # Store calibration results + calibration_output.to_hdf5("calibration.h5") + + # Plot the parameter space + calibration_output.plot_p_space(x="v_half", y="scale") + + # Plot best 3% of calibrated impact functions in terms of cost function value + output_evaluator = BayesianOptimizerOutputEvaluator( + calibration_input, calibration_output + ) + output_evaluator.plot_impf_variability(select_best(p_space_df, 0.03)) +``` + +Within the CLIMADA documentation, we provide a tutorial Jupyter script[^2] demonstrating the setup of all calibration data for executing code like the one above. +In this tutorial, we use data on tropical cyclone (TC) damages in the North Atlantic basin between 2010 and 2017 from the Emergency Events Database EM-DAT [@delforge_em-dat_2023], which lists total damages per cyclone and country. +As hazard event set we use the associated TC tracks from IBTrACS [@knapp_international_2010] and the windfield model by @holland_revised_2008. +As exposure, we use asset values estimated from gross domestic product, population distribution, and nightlight intensity [@eberenz_asset_2020]. +For an easier setup and visualization, we chose to only calibrate two parameters of the impact function. +However, parameter spaces of any dimension may be sampled the same way. +Executing the calibration with these data and the above code yields plots for the sampled parameter space (see \autoref{fig:pspace}) and impact functions (see \autoref{fig:impfs}). + +![Parameter space sampling with Bayesian optimization. The 'x' marks the optimal parameter set found by the algorithm. Colors indicate the respective values of the cost function (here: mean squared log error between recorded and modeled impacts).\label{fig:pspace}](pspace.png){ width=80% } + +![Impact functions for tropical cyclone asset damages in the North Atlantic basin found with Bayesian optimization. The dark blue line shows the optimal function given by the parameter set noted with 'x' in \autoref{fig:pspace}. Light blue lines give the functions whose cost function value is not greater than 103% of the estimated optimum. The orange histogram denotes the hazard intensities observed.\label{fig:impfs}](impfs.png){ width=80% } + +# References + +[^1]: Other common names are "vulnerability function" or "damage function". +[^2]: See \url{https://climada-python--692.org.readthedocs.build/en/692/tutorial/climada_util_calibrate.html}. diff --git a/doc/joss/calibration-module/pspace.png b/doc/joss/calibration-module/pspace.png new file mode 100644 index 0000000000..271a12ed67 Binary files /dev/null and b/doc/joss/calibration-module/pspace.png differ diff --git a/doc/misc/citation.rst b/doc/misc/citation.rst index cfc7d66509..91570dbe49 100644 --- a/doc/misc/citation.rst +++ b/doc/misc/citation.rst @@ -26,9 +26,13 @@ If you use specific tools and modules of CLIMADA, please cite the appropriate pu * - :doc:`Uncertainty and sensitivity analysis ` - Kropf, C. M. et al. (2022): Uncertainty and sensitivity analysis for probabilistic weather and climate-risk modelling: an implementation in CLIMADA v.3.1.0. Geosci. Model Dev. 15, 7177–7201, https://doi.org/10.5194/gmd-15-7177-2022 * - :doc:`Lines and polygons exposures ` *or* `Open Street Map exposures `_ - - Mühlhofer, E., et al. (2023): OpenStreetMap for Multi-Faceted Climate Risk Assessments https://eartharxiv.org/repository/view/5615/ + - Mühlhofer, E., et al. (2024): OpenStreetMap for Multi-Faceted Climate Risk Assessments : Environ. Res. Commun. 6 015005, https://doi.org/10.1088/2515-7620/ad15ab * - :doc:`LitPop exposures ` - Eberenz, S., et al. (2020): Asset exposure data for global physical risk assessment. Earth System Science Data 12, 817–833, https://doi.org/10.3929/ethz-b-000409595 + * - :doc:`Impact function calibration ` + - Riedel, L., et al. (2024): A Module for Calibrating Impact Functions in the Climate Risk Modeling Platform CLIMADA. Journal of Open Source Software, 9(99), 6755, https://doi.org/10.21105/joss.06755 + * - `GloFAS River Flood Module `_ + - Riedel, L. et al. (2024): Fluvial flood inundation and socio-economic impact model based on open data, Geosci. Model Dev., 17, 5291–5308, https://doi.org/10.5194/gmd-17-5291-2024 Please find the code to reprocduce selected CLIMADA-related scientific publications in our `repository of scientific publications `_. diff --git a/doc/misc/climada_publications.bib b/doc/misc/climada_publications.bib index f00b9cc168..416d85da19 100644 --- a/doc/misc/climada_publications.bib +++ b/doc/misc/climada_publications.bib @@ -55,10 +55,12 @@ @article{Kropf2022c @article{Muhlhofer2023b, title = {{{OpenStreetMap}} for {{Multi-Faceted Climate Risk Assessments}}}, - author = {M{\"u}hlhofer, Evelyn and Kropf, Chahan M. and Bresch, David N. and Koks, Elco E.}, - year = {2023}, - publisher = {{EarthArXiv}}, - url = {https://eartharxiv.org/repository/view/5615/} + author = {M{\"u}hlhofer, Evelyn and Kropf, Chahan M. and Riedel, Lukas and Bresch, David N. and Koks, Elco E.}, + year = {2024}, + journal = {Environ. Res. Commun.}, + volume = {6}, + publisher = {{IOP}}, + doi = {https://doi.org/10.1088/2515-7620/ad15ab} } @article{Muhlhofer2023c, @@ -71,3 +73,32 @@ @article{Muhlhofer2023c issn = {0951-8320}, doi = {10.1016/j.ress.2023.109194} } + +@article{Riedel2024a, + title = {Fluvial flood inundation and socio-economic impact model based on open data}, + author = {Riedel, Lukas and R{\"o}{\"o}sli, Thomas and Vogt, Thomas and Bresch, David N.}, + journal = {Geoscientific Model Development}, + volume = {17}, + issn = {1991-959X}, + doi = {10.5194/gmd-17-5291-2024}, + number = {13}, + month = jul, + year = {2024}, + pages = {5291--5308}, +} + +@article{Riedel2024b, + title = {A {Module} for {Calibrating} {Impact} {Functions} in the {Climate} {Risk} {Modeling} {Platform} {CLIMADA}}, + author = {Riedel, Lukas and Kropf, Chahan M. and Schmid, Timo}, + journal = {Journal of Open Source Software}, + volume = {9}, + issn = {2475-9066}, + url = {https://joss.theoj.org/papers/10.21105/joss.06755}, + doi = {10.21105/joss.06755}, + language = {en}, + number = {99}, + urldate = {2024-07-29}, + month = jul, + year = {2024}, + pages = {6755}, +} diff --git a/doc/tutorial/climada_hazard_Hazard.ipynb b/doc/tutorial/climada_hazard_Hazard.ipynb index b4ad76b36a..7532e4ea30 100644 --- a/doc/tutorial/climada_hazard_Hazard.ipynb +++ b/doc/tutorial/climada_hazard_Hazard.ipynb @@ -57,12 +57,20 @@ "execution_count": 1, "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/vgebhart/miniforge3/envs/climada_env/lib/python3.9/site-packages/dask/dataframe/_pyarrow_compat.py:17: FutureWarning: Minimal version of pyarrow will soon be increased to 14.0.1. You are using 12.0.1. Please consider upgrading.\n", + " warnings.warn(\n" + ] + }, { "name": "stdout", "output_type": "stream", "text": [ - "2022-03-16 22:09:56,965 - climada.util.coordinates - INFO - Reading C:\\Users\\yyljy\\climada\\demo\\data\\SC22000_VE__M1.grd.gz\n", - "2022-03-16 22:10:01,099 - climada.util.coordinates - INFO - Reading C:\\Users\\yyljy\\climada\\demo\\data\\SC22000_VE__M1.grd.gz\n", + "2024-07-17 11:42:32,497 - climada.util.coordinates - INFO - Reading /Users/vgebhart/climada/demo/data/SC22000_VE__M1.grd.gz\n", + "2024-07-17 11:42:34,660 - climada.util.coordinates - INFO - Reading /Users/vgebhart/climada/demo/data/SC22000_VE__M1.grd.gz\n", "event_id: [1]\n", "event_name: ['1']\n", "date: [1.]\n", @@ -73,14 +81,12 @@ }, { "data": { - "image/png": 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", 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2MmmYe7FNUImoPN+ieTRg+4+XaBz0qb3is+2uAtmd61PVI06QQHM0xEpbeL3dEelNfKeG5SoGby90TfApNj8J8oQQogskgWbioRr5C1Ls/EiZ1IBU0285Ccy90KI9EQJgZyx6b8pRfbFNEkiQ183SQ4uDr2DmeFl13EyYeaJJ3Frdaxg1ErySzfjfz9EaDdj2ziKV51u0xkL6b5U3ec6F8fvm0LEmt8fb6KUAENUTJr5TI7fXY9dP9OD1SaQnzp4EeUIIsQGci/ZhX3kJ1vVXdC5rHAwY+UoFy1Hs/mgPg2/PY6W7q3xHrJKCwTvy7PhAieLlabxem76bs+z+B724JbuTGQrnYqYf3fjmD2JlqQGHbXcVaBwOGLt/DgC3dOYn4dXnW2R3egzeUehk/XZ+oEzcSpY0dRHrQ8cQzsZ4Pd3zZlr99YCRL82Chl0/0UPh4o0v3411sik+xPK656dbCCHOE87OHUSvvQ6Ybm/sPd6wwT8WEtZiUn0OpSsylK7IMPFwjeoL7dPeNC82TnanR+myjPl8h8kWJJGm8myLuJGQBAnj35wjDrTsw+tiXtlm6PYCc3NzHPv2HHFb489E9L0px+jXzqwbYv31gPEH5hh4a57sDpex++cYfFuBwkVp/BlpvHSuNEdDipd1VzfGYCZm5odNtr+z2Nn7KcSZkkyeEEKsIWfPrlMeEx05uuJ1pasz5PemiJrHz/wH31bg4l8dYM/P9FK+JtN1m/PFUq3RgNlnm2h9PDJvHwspXppm4LY8Q3cWsdKWBHhdLqjGzL1qul4WLkqDhpnHG+R2eZ19eWei9qrPof8zS1hPGH5PqTNuY/aEPZtifbWOBjgZC6eLRgRkd7oM/ViB2mttph+XnwVxdiSTJ4QQp8nKZEhaLezeHlS5BLU6JBo92EdzXxn7okHsB548o/tuHAzoe5MJEirPtihdkSFqJQQzEUN3Fhi4NU/fm3LMvdym8lyLsLI5htWeb3QMU99tUHm6Rf7CFOkBBztjdbpp6kSjY0nNdj1t9stamIAPoDESoLUm1WPTHgvP+K7jZsLRr1bZ9aEybtHmyN9WaB098/sTp2dhH6ydsohq3fFuy9CdBVqjIePfqnVF5YbprtkFCzmJbl/fRuqety+EEGKzSDTOQD/KdSEITYDn+6hGk/SROrFr4ezeeUZ3HVZjjn2rSuGiNLs+0kPx0jTlKzM4WYvZH7YAsFxF+aoMe+/uZfh9JbK7u6N5gFgqaiboWGN5itSgg3Kg9lqbkS9VuubEUqysdEWK8pUZYj8hPeTQf2uOHe8vA6bc72zp0DRcslMWShL051R6m4tONEGlO0pk3bKNk7OpPNOUDL9YE5LJE0KI0+AMbwfHpnnFdrAVmcNz6OkZ1MV7oVLDGpvE7s3QuGaY9LYe7ANj6Fab6LqLUI883bmPaHRsxceovx4w9vdmv48/HVO6Mk3fzTnaE6b1euHCFHbavEeX2+WR2+URVCMqz7apvdyWdvzrzPIUqX6HJNL4Eyc5QVQwdEeBwsUpWqMhsz9s0jgYLN9eX3SlzM75N1A0FC9Jk4SaqGEycGuVRW9PRMTthNSgS/OIZPLOBSdn0XN9ltprPro7Yjwy203Q2TrWJQuC+Tl03a37V7hxJMgTQohVUraNbreJL9lNeqyB2n8YtW0QVSygk4RkoIx1bJbU/mOoCwZRfozu74WRUewnX+78U7QQ4Cnbhjd0BovvvJHU4Rnq+w90Lpv6XoP6fp/hd5ewU4qReypkhlxKV6dJ95t9QV7JYfCtefpuzjL3cpvqc23CqgQTa8Ut2WSGXXJ7TFCtbJN2mXulTeNQQOGiFMFsTO1ls3/LchUDbzUDrscfqFHf72/k8sUZGv/7GtErDlMHZzv75tZD7GssV1J5Z8PrtbEcRVhPiJsrn/g7ecXwLUV0qJn6Xn3F4861zLCLPxmh5U06sUYkyBNCnPfsUom4epJOebdehzM6S3TwEIQR9nP7UdsGzU6Ato8OQ+LnXsYZ3k4yPYvKZXAn6qgkgVYbshl0fWmbfB3HOH092Lt24DRskoFenNcnSPqKS45tH4sYuafC8HtL7PxgmdFvVBn5YoX0kEP5qgz5fSmUrbA9i56rs5SvytA8bPb1SXbg7Hh9Nrs+3IOyTdZl6tEG/W/OoWxF8ZI0xUvS+FMRme0uPddnSCc5itqU343fP0f9QHDqBxHnL8tkluKWZCTOhHIVO95XIrPteCOcuG32MQezMVqDssFyFFZKMXRZiZmxWY5+tULc7p6AKt3v0DgifyvE2pEgTwhxXlKOi45C7MsvgZHRZY+xMhmIY9TzB0jaPsq2SVptM/ZgfAKUIpmZxcrnsEsliCISv40z1A8zFfRQH/rAYVQqhZXPkTSXdkuLZytE/ZeB58GhUSgWsJrBsgUoYTXmyN/MMvyeErt/oof2REjjUED9YEBtv0/xkjS5vR460rQnIpyczY73lwlmIyrPtZh7ud01pUndKD3okNnpMfvU8dfJ8hTb31UirMaMfKXSeZe9+kILnUBuj4eTtai+0EbZULw4zbZr08y83GDuVZ+4ISfu4uTS/Q6Wo2iNH38zpnhZmtKVaZRSNA4HVF9syR7OFbh5i8w2l4mHa7TGQtyijdfrkOqxO0Psk8g0OtKxZu6lNkceqBL73fX9VLbquixerDWx7q41vVG3r28jSZAnhDgvWZftQ1XqMFOB/j6sC3dj+SFaKTg2STwzS9IyjU6cUrGT6bNSLlZ5PtPmuijfRwchet8OiDVWrQ6NFsnuIaxmQBwE2J5Hskwmb4FdaUE6ZZq3OGWSw0exr7wE1fBN9vAEcVtz5CsVcns9ChelKV+Vwc4s7qGlPEV2p8eReyvoBMpXZxi4LU/fzTnmXjJdOeWEcanBtxdI9Ttkd7gEszFJoEkNONgpxeGvVhadgOn5StjGwWDRZXOv+HhBk8rB9rqW94mtY2GwejBt3oFxyzZDdxRoHPKJ25rSFWl6rs1QfanN9A8ana6QwlioeA9mY4IZ83Hi7+WJlKXo3+t0575lBRKviLUkQZ4Q4rwUP/fyoq/t3h6imVnz+dWX4bgu0bEJ1M1Xw3Qda3iQJOuiXjhIUqubLJ9loYf6CIbypJ49DOkUaqAPPVtFvXqY4MZL8Bo7SCanSfw2dm8P8fxjnEg1W6A1SRCQHBoxFz7/yopr14lpzlJ/3ZzIWCmFk7NQtkJZdPaLtcZDSGB8PMTJW5SuzFC6PE35mgyNg6aUs7UGHQK3krAek93hkd1x/LLR+6oSFIt1E9bMOwbZnR6NQwHZYRcda8bun0NHoBwoXZGh98YsuV0e4w/M0e6i5hwbSVnQd3MOYPOXuyq6YmyC2DokyBNCCFgUfMXPvgSAc+lF6BcPEtVqx6+b/3/SbGKl0lhxjPvKARJAZbOocgmVTkFvGacemODNby95jAWmJDQhev3gGa898TWBf/ImK1E9YfoHDWaebFC42GQAd36wjD8TUX2+Rf1AcNJmBecDfzrC67GJ6gmZbS5hPcbN253ZdkKsh/Z4RPNoQO9NWRqHArweh7AWd0qrdQSVH7WoH/DZdleRnR8qM/Nkk5knz+9h2XZase2dRdJDLmN/X930XWtVFwZ5Midvc5MgTwghVhC9/BoAzp5dJhv3hj11id8mmWgfvyAIoFJBOS6qOmcyc8vc70IpJkGAnpklnppex2exmI5g7sU2cy+2yQy7nVLOwbcps8fvcEDjUIA/ef5lCsJ6TGbY5ci9FYZ/vIRyFbM/alJ7TTpjivVVea7F8I+XGLw9T1iNcQtplMOiPbRRLeHIVyr03pCl96YsSaSpPNPauEVvECdnUb4uQ+myDDrWHP1qhfb45v97FfsaOy1vKIm1I0GeEEKcShgt2zRlJToKT/reYvz8KyYQtG106APZs17imWiNhrRGQ6yUIrfbI7fbo3x1hr6bckSNuBPwNY8E50XDFjdvE/sJUS3h8BeXZl2FWC+NAwGT36vTd0uOxsEAZStye1JLR29omHmyieUq+m7O0RwJCGY2dwZrNayUMjNB93rkL0iRhJrZZ5pUnm2R+FsjkxNWY9ySnJaLtSM/TUIIcQonG1x+pnQUoqPQdOrcYImvqb3qU3vVBwsy21wT9O3xKF2eIYk0rdGQ9rEQfzoimIkI57ZYaadlOmVWnj//MiOiO1R+1MLrscnu8GgdCylell5xvuL0Yw2yuzy2/Vhxy74h4fXYZi7lnhTpIQdlKdqTIVM/aFB9sd11nSjPhuUpwlpMbk9qo5eySIIm7vJySCnXXJkEeUIIcQrOZRfDbJXo2MRGL2X9JcczfFOPNnBLNrndHtnd3qJOnkmo8WdMwOdPx/P/jzbtu+rZYRc7bdGQmXZiA1Wfa1O6PEPUTMjudHHyFlF96RsqOoGp79fZ8f4yqT4Hf3rrpNrzF6XovSFLqtd0wWweCZh4uE7jcLClRpIoC0pXZShclCI9aEY96ERLAxaxZiTIE0KIU4heehUAO58nrtc3eDXnVliNqTzbovKsyXDZWYtUr43X55DqdUgNOBQuSWPNd/SMGjH+dGwCwGkT+AWVmGU3J3aRnhuy+DMR/tTWOVkWm48/HdEaC8ECpRSZbe6Ke0KboyGxn5C7wNsSQZ5yYOjOIoULUzQO+Ux9v0FrNOiMK9lK0tschu4o4BZs6gd9qs+3sNKW+TsqAZ5YIxLkCSHEKaibr0aFMeroJJxnQd4bxc2EZjOheeSE0QsWeKX5AcR95v+FC1O415m9hjrWBLMx/nzQ50+bADBud8fZTM91GbLDHke/WtnopYhu8oZK6vw+DydvM/dy22SsFViuWtO5dco2c/Mah3wyQ+7JZy0m0DwckNvjMfPE5u+02f+WPLndHqP3Vbd0Rr14WZrBt+VpT0aM3Tfb1V1Bpbvm5iZBnhBCnIJ+7Fk0YA0ObPRSulMyP4h4Nqa+//jFlqc6gV+qzyHV55Dfl8JyT8j6zcS0jgZUn2+f9oBiZUPh4jTZHS5OziKsJ6ZsdCrCn4lPORLCLdsM3JojtzvFzA+biwNXcd7b9RNl0gMudjWFc3FE/oIUOtb0XJ9l5okGPddmcYs21RdbTHxnbd786X1TDsszvx860finaKpSPxiw/Z3FFcs6u5lyFdkdLql+h/Sg2Qc88VBtywZ4Xo9N35ty5PelqD7fYuK79a6vcBCbmwR5QgixStHE5EYvYVNJAk17PKQ9fkLwpMAt2ibj1+fQd2OO3C6P/jfnmXq0TuW51qo6eeb3eQzclsfOWrTHI6J6jFu0ye/1sDyzb1DHmjjQJH5CEmhiX6NDjZ21cEs2TsYinIsZ/UaVxsGteWIpzlxzJCQ94JLblaKFDZi9cE7GYvBtBQAaIwGlyzNMPGxO2L1em3Du+Iw7ZUHpygy5vR7KVkS1mMahYEkJpnKg59osvddl8aciCpekqfyoRVg5eZDXHAnQsSa3x6P6fPukx3YNC0qXpem7OYedtoiaCf50xMRDNcK5mPI1GZychTK/xovzNNq8BlE9wZ8KzVD4c5nIUSbTmup3cHIWlmte02A2JqjEi7K6VkqR2e6S2eaS2emS7neJmgljf1+l/rr8vRHrT4I8IYQQ5442+/zCagyvB/TdmOtc1f/mPP1vzhM1YmafbtE4HJjjFihI9Tn03Zwjt9ujfsBn6tHG4mMAp2CR6ps/CfMsrJTCTinzuacIq3HnvhsH/S2550ecvenHGigLim8v4mRtklAT+wmWa3eOye3yAMjv8ShfmyWzzSVqJRz71hzNkZCeG7OLfsbZ5lK4OE1+n097IsQt2aSHXNyijeWYDF6q36H6UoupxxqnXGMSaFpjIfkLUpsiyMvt9Ri4NY9TsKi94jPzZINwLiE95DD49gKpPockSIgaifm9XKb5sHKU+d12FFEzYfrxBnMvLn3uXo9N8fI0+b2mY2Uwa/YHF8spGMpipSzsjMJKW9hpCzut0JEmaibELU3cSsxHO8HJWaT6TdZxoRIh9hN0ZN40Uup4dULc0tgZhZMzPydhLaY1HjLzRJPG4WBTZe9irYl1d5dDdvv6NpIEeUIIITbM2H1Vtv94adFlTs5m4LY8A7dB9fkW9YMBPddmSA+5WK5pNT769SqNQ8u/Gx7VEqKavFMuzt70Y00K+QYMa2aeaGClLfpvPh60+TMRbtHu/AzXXm2bDPXNOZojFUqXpc1xUxGxn5AZdmkdDXHLNpkdLspRKEDZxwOH6cebVJ9b/SiP2n6fwbflcUv2kjc8Toflre3+wjdK9Ttsf2eR5tGQ0a9XCWZjlA29N2XpvT5LezJi5J7K4sz/ye5v0KF8ZYah2wsULkpRPxBgOZAecvF6HbySTdRKqL/WJokh1WOT35eiMJAmOtbuBHLBjNkfnPgJylHYGQsnY+EWbdJDDnbGIm4m+FMR9YM+/qQpCV/4XikHvJKD12Pj9tjYaXN8WDXB3WYroxVbhwR5QgghNkz9QMDhL87Sf2uO7LC35PrSlRlKV2ZojYdMP9HAn4hoT4SSfRPnTP31gKlvzaITTfHy9KLr7IxFfb9P8VJzeeHi49dvf1exk82pPNti7uU2g2/PU7oiQ1CN0AnYtkInmvZkSHMkZPZHTZLTbEhUe6VN301Z+t+co30sJH9hiomH6viTq+u4Wbg4Rc/1ZmRBbX+b8ftrp/X4q5UedFC2IgkSvLJN4aIUxcvSWGmL2aebTD/ZPK0slz8RcWyiRu21Nn035eh/Sw4da/zJiMYBn+mJkPrBxZkzZSkae2HqYOXkTW1Og47oNJQSoptIkCeEEGJD+VMRR++t4uQtMsMuTna+fCpjEbcTGocCWqPSFEVsvLh1PGKY+WETJ2OR3uZQfbHVaR40/N4SXslkjRojAV7ZJr3NYe5lmHioTuNQQGaHe3zP6rHotJsOnUjHMPFwneF3l8hfYEoTt7+ryKEvzJ5yYHjx0hRDdxapH/BpHg3ouTrLzBPNden4WH2hTRJoBt6ap3BRmthPqL/mM/uj1lllIJsjIc2RytotVHQkdH91abevbyNJkCeEEKIrRPWE2ivLzwQTohuEtePBSP11f9ls2ciXZhm4LU/h4hS5XR5JkJDbfTxL3TgUrFhqfKYaBwNq+30KF6bwpyOzH+2Sk+/TSw04DN5eMJ0eH65jpRQ9V2dNxs1R5Pd6tKciWqOhGRmxBmqv+dRf91GOIom0nKELsY4kyNvErFQaHcfoSN7hFkIIIdZbMB0TtRKcjMXQ7QUOf3F2yTFJoDn27RpBNab3hiz1AwH5CzywWNegpvZam8KFKVJ9DvXXfXquy1Lb769Y/tlzXZZwLmbiETP+oed6M9fSztrsur1AEmp6UxZaa/ypiNbRkOZoSHssPLvMYwJ6Hff+CSEMCfI2scQ379DZhQI6CDtfCyGEEGJ9HPt2jf4352iOnTwblx50THfOS9NMP9lY96zViaWZjZGA7E6XbT9WZPTvqkuOtbMW+X1m7EJ+X4riJSlye1JMP9ag5/oM9f0+49+q4eQsMjtcssMe+YtS9FyXRSea1mhI9cU2jQM+WrJxW1aMJu7yYePdvr6NJEHeFhDX1meTtBBCCCEWax4OOHz41OWWEw/WKF+TxZ+cbwCy3usaDQlmI7weh+wOl9qrPrn5PXpvZGcUSinKV2UoX5XBn4kY/UbVjHJwlRnfoOdLqF/2qb1syqjdkk12h0vhohTb31kkaiXUXm5Tea4lXSSF6DIS5AkhhBBCrLG4rZlexay7NZPA1A8aDL+7ROEi0+Vz9unmsocGMzFTjzXwJ0L8mZhdHy7Tc22WzHbXBGy15QO2sBpTrcZUX2jjlm1KV6QpXpamdFWG2aebzD7d7AyCF0JsLAnyhBBCCCG2gIXZbZPfqxPMRDSPrLBnX8PsUyYAVBa4RRu3aHe+VhanLMMMKzFT32sw/XiT3usz9FyXpXR5mmMP1miOSK+ArSDW5qObdfv6NpIEeUIIIYQQW0D5ajODr/Kj1Q9TxzL/m/1Rk55rspSuyJC/MEXcTtAxZp5cDGE9ob6/Tf1AwInboHSomX6sSfXFNoNvKzD83hIzTzaZebKJbJcSYuNYG70AIYQQQghxdtySTf6CFJZn0XdzDuWqRdcrV+EWlznt06C1JpiNmXjI7PEPqzFeySHV65Dud0kPuRQuTLH9XSX2/IMenPzS+4lqCaN/VzWZvRuzDL+nhJVSS44TQpwbEuQJIYQQQmxycTNh+okGjYM+5Wsy7PxQCcs7HmTteE+RvT/bx8Db8otup2PwJyMy212qL7RpT4Yo28yxC+diWuMhYT1Ga5OW88oOe+7uxcktfwo5+1ST0a9VSQ847P7JHlIDUjS2WSWb5EMsT4K8LcrKZDZ6CUIIIYQ4R5JQM/NEk4nv1Bn58ixu3mbbO4oAOAWLzLBH82hA+coM2Z3uots2j4Zkd5qB7dOPNUj1OZ0M35F7Kxz8/83w2p9OMfI3Zi6g5Sh2friMW7KXXUvzSMjhL80SNxN2frhM8fL02j9hBdmdLtmdLukhB7X8UoQ4b0mQt0UlrdOoxxdCCCHElhHMxIw/MEdut0f5mgxu3kRAEw/XCWsx2d3eouObRwKcrIXXY9McCWmOBigHvJJD6Yr5AC2B9rGI2WdMwxa3YLP3Z3rZ8w966H9LjvTg4oxdVE848pUKcy+2Gbq9wNCdBdQaJvV6rs+y4/1ldry/zK6P9LD35/vovTG7KHspxPlMgjwhhBBCiC2mORIy+0yTvltyndJKZUF7IiLVvzjaao+HJJEmu+t4Nk8pRRIk9N2UWxQ4zT7TIm6ZIrmomWBnLXquzbLrJ3oYuO0NpaAJTD5SZ/yBOfL7Uuz68PL7+c5Ee9x08Kwf8Dn6tQr113x6rs+y92d76bkus6YB5fkqQRF3+UeCBPUrkSBvi7Pzx//g2oUCVmodSiaEEEII0XWmH2+QtBNKV5stHG7RxslaRI3FO5l0DMFMhNdrMn7t8YjGIR+tQTmK4mXHzx3iZsKReytUX2yhI42dOn4qWb46s+xevdqrPiN/M4vlKXZ9pNx5nLPRGg2ZeKhGbo/HjveVaR4NOPi/Z6jt9+l7U449P91Ldpd76jsSYouSIG+Li+t1rHQK5bjEtRqJ397oJQkhhBDiHNARTD/RJDNkgp3SFRlSfTbB9NKJ5cpRnTl7ALM/amGnLJJQk92xOFgKZmMmvlPn4P+e4fX/b5qjX60w/sAcR79aWRJAdm4zEzPy5VmiVsLwu0trUlZZfaHNwc/OENZiem/MomPN5MN1Dn1hlnAuZsf7ymy7q4CdkWyPOP9IkHceSNo+OpLBpEIIIcT5Zu6lNsGsCepyuz0sz6JxKFhynJ22FgV57TFTwmnZ4PWtXPsYNxOaR0Jqr/orD19fOLatGbtvDiulGLy9cIbPaLGokTDxcB23aLPn7l5KV6YJqzFHv1pl/IE5Mjs99vyDXoqXpsgMuwy9o7BiwxixWKI3x4dYnlQsiyXsfJ64Xt/oZQghhBDibGmY+kGD4XeXmHm6SXssJJiNFx2SGnBwslZnnxuY/XRJqFEWWO7aZcKiWsKxb9cYfneJ9niGyrOmUVzx0jR9N2cBULYiaib4UxH+ZETzaEAwE694n83DAYc+P0P/m/MMvq2A5Spmn25Re9WnMRIw8JY8Q3eaTqOposOef9BDYyQgqES0xyPqr/syuF1sORLknUeUbaPjlf9IWpkMOgjWNMBz9uwiOjSyZvcnhBBCiNPTOBjQGg/JDrtMP9pYcn3hkhRRI6Z59A2ZuESjtcJOr225Y+NgwOwzTQZuy+P12Ux9t4HXb+PkbGaeapAEGqdgk+53yO9LMeDkCaoxjQM+9YM+7WPRkqAsbmmOfbtG1IjpuyVHWE+ov+aTtM3ltVfbJntoYj1yuzxyuzy4GppHA0b/rope+RRJiE1HgrzziJXNoqNo5fEKSp00CDwTenp2Te9PCCGEEKdv6gcNdn2oTP7CFPX9/vErLChclKb2SntJ4KQTAI2yLJTNmgZBU99vEMzEDNyWI7vDY+bJJjrR6Ahmnz5+nqIsyOxwyV+QonBJmp7rskTNhMYhn/ZERDAd4U9F82uF6ceaOHmboTsKNA4F6NA8qeaRkJEvVkh9OAs9888v1jRGArI7PLa9o8jY389JRu8ECx0su1m3r28jSZB3PtEaZdtYWVMOkTSbi65+49drQco+hdi6nO3biMbGN3oZQohVaI+FNA759N2co37Ah/mgKLvDw8lY1F7xl9xGJ7pznOUq4nhtI6C5l9s0RwO2/ViRwbfnaR4N6b0pS2s8pDUazq/BjINojoTwUJ30kEPughT5PR7FS9MoS5HEGn8yon0spD0RkYQay1Gk+pxFJahJqJl5vInvNBi8s4CyzTETD9UYurPA4NvzTHxHzlvE1iBB3nlEAi4hxFpw9u0FPwBHmhcIsZlMPdZgz0/1ktvldZqvFC9J4c9E+Mt03NQxnSBPuQraa5/mimpmJMNF/7SfxqEAZcO2dxQ5/MVZ4ubSTp3tYxHtY5EpO7Ug1e+QHnTJDDnkL0jRc22WJNZMPVpfFOCdqPZagJ1t0P/mPG7BpnBJmonv1Bi6s0jc1kz/YGlJ6/lIMnmbmwR55xG7UCCu1TZ6GaviXHax+WSmQjQxubGLEUJgpdJYQ/3Q9qHtE42OYZfLODt3gO8TTU5t9BKFEKcQTMcE1ZjsTpfGoQA7o8jtTTHz5ApBTWJKGoE1GXmwIg2tsZDe6zPMPtMie6vHvn/URzgXc+zbNVpjK3TtTMCfiPAnIqrPmYsWxiXErZMHpLNPt0j1ORQuTpPb5dEaDZn8bp2B2/IoBVPL7F0UYjOREQrnkc0S4AFEL71K9NKr6MHejV6KEOcVu1DA8rzO186+vTi7d2KVCkSHj4BSJJUqAHGlQnTkKGTSK92dEKILLeyt639LniTSVF9cfoauTjTJQpC3hh02lzP+zRpJqBm4Nd+5zC3aDP1YgdNJ1sQtfcoAb8Gx79RoT5gAsnR5msqzLSYeqdFzXdY8rpwli01MfnxFV4ufe3mjlyDEeUUN9pMEx2doRa8fJDp8pJNRj45NLNq/q2xbBhUJsUk4BQuvZNOejMgMuxQvSTP9aIPEX/53WCfHA8L1DvLiZkLtVbMvsLbfZ/yBOQDcgo1XXp/ScB3B2H3zj1M0j1F9rs3Y/XMULkwxdMfazPLbrBKtNsWHWJ4EeWJdOHv34OwY3uhlbBr25ZegHHejlyEEtH2ci/bhbN+2qsN1HKPnM3tCiO6WHjC7dBI/Yfu7ijRHA+ZeXj6LZw7Ux8s11znIA8jvSwHQOhpQe9XvlGlmd3knu9lZiRoJR75Sofri8Y6e9f0+49+qUbg4Rf+bc+v22EKsJwnyziOW52FlMufksaKDh4iOjp6Tx9oKWntLcOPlG70MIczvba0OpzFORZo6CbE5JPO9VXa8v0xQiRn7xtxJj9fJ8QxXtMoSyDPlFCxSfSYIXZjXN/l987dl4FYzT2+9tMbCJV016/t9Jh+p03Ndlt4bs+v22EKsF2m8ch5JggC7cO5KD6S9+up5X38cDVg3XkVzR5b0vY9t9JLEeSw6NrHRSxBCrIPmYTMUnURz9OtznRlyK1GOItVrThWDZbpvrqWea0wgFbUSwqp5k8mfiKgf9MnvTTH87hIHPzuzrmt4o+rzbayURf/NObxem2PfrqHX99vQVaS75uYmmbzzzDltvhKs0A1LrCh58jnyPzqGs3vnRi9FCCHEFnTkKxWO3FtdRYBnyjuTSBPOxSTB+mXynIJF+WpTaTTzxOKulgujHtyCjVs692NbZp9qMnZfldzuFLs+3INblFNnsTnIT6pYN9H09EYvYVOKDh4yXQyFEEKItbbKWC27w0PZCstRK48wWCN9N5l9b7GfMPfS4j2C6QGnsy9wYTzCuVY/EDDy5VmUC7s+2kN29/rtERRirUiQJ7qS3duD3duz0cvYMM7gwEYvYdNSjnvO9p6erc2yTiHE+UVZULrS/H1KIs30E+s7M654qRnDMv14s9PNc0ESaZRtgju9dDb6ORPMxox8qUJrLGT4PcXzYp9ejLUpPsTy5DsjulI8M4vKZk179vNQ45YLNnoJm5aOQqye8pJ5b91EOS52bw9WuYTlpTZ6OUII0WGlFRf90wFy89mqyjNNotr6RVd2+nh2bu6EDpcLFo132MAgDyAJNGPfmGPm8Sa9N2UZfk9xfYfEC3EWpPHKecDKZbHzIdGc2Y9ned6iOVjd6nxuy+7UV9/ZUCyVTE2v+c+4XSoRV9fmZ1JHIfHMLADKUsDWf0dYCLE5bH9nsfP57DNNZn7YPMnRZy9ua47+XZVgNlqSxQOITwjydJfM5Jx5qkl7MmLbXQV2/2QPR79aIZzb4AhUiDeQIO98oPWiFuebIcADwLbRp9HGfStxv/vsRr9huamtx8+48mSOoRBi65t8pI6dsWiNnrvmac3DK//NToLj/xqGc91zTtAcCTj8pVl2fbhM+doskw9vvVEyehMMG9ddvr6NJOWaW4CVXZwFsDIZrFS6c3nSXFr+sBnoIMAZ6N/oZZxzdj6PNbx9o5exqa3FXjfluNiFgin7zGSIJqfWYGVCCNHdgtn4nAZ4p7JQrhm3k64bXxDVEmqv+uQvSOGWbIqXpihemsbJy+m12HiSydsKogjluOjI/FFOWi3scpm4UpkvBTt3LM9DZTLE1SpWymykTvz2KW61AqVI5rbeO2MnY2UyqL6e87pUdS1YxQJJ6/Tf3HD6+jpdYXUUgptH2Tbx5DkcPSKEEKIjiUyQN/fyGZ5LrLPa6z4912XZ+zO9ncuSSDP7dJPZp5tdF5iK84cEeV3ALuSh7p/x7ZMg6ARXCye2caVyWvdhOhKmz36Onm2DMoHlGQd385Jm0zyvEwLYrc4q5KFu9j8sBOri9FiZDCRnVuy6EOA5w9vRczVINKSlTFMIITZKezxk6rEGlWfWd2/gmfInImafbdJztameahwO8Kcieq7PUrw0zdSjDSwHcntTxK0EO21hZyxiP6E5EjD3UnvZvYjdQIahb24S5HUB7Qc4hQIqnTrjkrAkCJaUbZ6KlUp3AjFlqbUZlJ5o9Brvh7LSKZJWcn7sz1MKHUUmSLGk3OOMXHYByUsHzvpuVC5LdGwCJNAWQogNo2MzkLybTX2vgZu3yWx3aRz2qT7XZu6lFv1vyXca2YRzMbGfELc0YS3GzlgM3Jan94YsMz9smmBPsn5iDUmQ1wWSICAOa3CWQVbSbJ5W9kfHcSdLtlaNKs42e7dA2TZWJmMaxiwEsL6/5QO9pFYnaXb3P2bdzhqdIjqDUs0TRaNj5r5SaZTnrs0bIEIIIbYmDWP3zYGiM2w+nEsYu2+O9DYHnZiM3xu5JZveG7IM3Jqn/5Y8jUM+9f0+jUPBhs4EFFuDBHlbTFyprLrVu7LtNQvKznr/3RvvL58nrlaPl2pGEcrz0Gdx8m6Xy+hWe83WuB4kwFsD0dq9EZD4bejinxchhBBdZJkJD+3xldNzYTXm2LdrTD/RoHBhivyFKbb/eIlgNmL8WzX8yY1N7cXaItbdXVUUd8dUja7U3a+cODPDQ6s6TMdxZ1i05Xln1cky8dtgr92P00KQurAXT+VzcJZZvLhS6QR4dm8Pdrl8Vve3VtaiE6Q4bmFf3ZmyC4U1WokQQghxalEtYfbpFiNfqjB6XxWvx2H3R3s2ellik5Mgb4uxSyXiF19Z1bGdAMpxwXG6ukW8bvtrO/ssTiCKzLiC+UB3o5xJF0ixsrN5PZ2dO6Q0UwghxIY5sQmLU5DTdHHmpFxzi1lNmeYbKds+rTLB4/P33nCbeP0KyJVtr+n9xdWqydi4junLtFkGxItT0smZ125ER46u4UqEEEKI02PNn5kfe7BGVN/YjXkJiqTL80HJcjWyApAgb8uysll0EEJy8nruE7Njqx5VEEXgLP7RsctlsBS265hmKWtM9fWedWMaWPwcFzI2zvZtsA5rFhtDWUr+5AshhNiUFjJ5qQGHnZeX0ZGmNRpSfbFN3JRuLGL1ujs8F2cuirBypzdS4VSD05Vtm9JOZaHe0N5fB4HJtim15nvMlOOuyXBw5bi8sV2VcmQG2lajUimcfXs3ehlCCCHEaWseCWiMBBQvTRPVYxJf03Nthgt+rpeBt+axUjIXTqyOZPK6mLNvL9HrB8/otiqTIWk0cXp7sft74eDsKW9zqj1vOo6xS3lQCtVbXpT9SprNTvmmXS5jZ9IQRmuyv8nK58BSWJ53VvvylstS6igkGhs/m+WtOTufJ2m1tvy4iPWifZ/4NH9vrEyGpNVafTZbCCGEWAc6htGvLX5j2/IUpSvS9FyfJX9himPfrtE8vP7bTGQY+uYmmbwudqYBHpg9ZzoKzQnv1Oq6DS6MQTipJEH7PsnRpYGRclyUbRNXKijHAb02RXMqk0a32me112rJfc5n8E53gPyJt10vOklQqdS6PsZWZpVLpzzGGehflHG2CnkAlCvvewkhhOguSaCZfbrFoc/P0D4WsuO9JUpXruKcTZzXJMjb4nRw8qyElUpjl0omgxHHWNnsSYOYuFaDOF521pyOQnMfmQxJtQaei9PXd9bPIZ6YNJmttcywnFC2aV95yWk1dlnvTE/SbKJcKSM9U/H0zCmPiSanOl1N7XLZlBlns1hDg9j5/DqvUAghhDh9cUsz9o05Zn/UZOCteTLDcq4gViZvW28Sdj5/Rg1NkjAAcp370EkCcdIJ0hK/jZM31+soNE0rThHEJEGwYumklcmYIDAIsLTpebTa4ezLWa/yuUWlkIfHUJ6Hml93N1DZDJzh9+x8t9oyV7tQQMcxKmPeDbW2DUK9sS6Ng4QQQoi1MvX9Bqk+h213FTj8xVni1vq0G9scw9Cl1dpKuvuVE8fZ9lnPc4vrdZPZs61FZYrJCfvmThXkLJRkrlQ6qTwPHKdT+qkyGUjOvBuUmt+Lt260Rvs+JHrVAd65GKKeVOfW/TG2otVm4ezeHtRAH1ZPGWzbfL8TDWs8qkMIIYRYcxrGv2XO3XZ8oIydkX1pYikJ8jaJuFpdkyyTjsLFTVIKBVQmg3KdU54gL2TVrExmxexaXK2a+9YJKptFh+EpS0ZPJgmCNc2uWak0drmMlc1iFwpm/5ayli0/XYn2/TVbz4riZMOHtG82VibTycqdUhASvX6Q6OgoyfQMRBHJ2LGua8IjhBBCLCduJBy5t4qdthi4VbYZiKWkXPN8p/WiUkorm11xMPpCYKfDCGfnjpMOjk6CADufM6MW0iksOK1AasFalGtaqfSi8lSL+QYbroOu1eEUoyOWrCmThvn9XOtFuY6ZRdgl5aObglLo8ORzITuHFvKd7rDJOr+WQgghxHoIqzHVF1r03ZRD2YqJh2rE7bUrXzTD0Ls7S9jt69tIksk7z71x/5HyPJPdW6FszcpmzZ6n1qkDNqUss7csTsA+sx81HYXYpdLqOn+uIPHb2Pm8yeDNZyFVJg1BSFyvk7Rap9VsQ7fXL5PXKXMtl4grlXV7nC0pTlArzIZUjtsps1WOC5b86RNCCLH5VZ5pMfVoncwOl96bchu9HNFFJJMnFtHzWbzlmlcoxyVpNnEGB0gqVexC4aRz8KJpM7rBLpUgMhkWZdunPf9Nh+GSIeanw/I8dBCQzAe0zvB24ompRRnC02m2sVKmcy0sZBxPliUVi1mpNFYx39lv2rnc81ALTYV2bUe/9Lr5PAqJjo5uyFqFEEKItZSEZryC5SpKV2eY+n4dLWN2BRLkbWrOQD9k0uhClvj5V9bkPhf2v50YwFmZDChl9oll0kQTk6cM8BZYqTRxtYozOIBqtkx3zyA8vRLMKDqrfXlWoYAOAuz52XO63TZdROfXh05O6/7t3h7imVMPlxfrz/I8VC5DPGvmQp7YkdQqFEjqDRM4y+slhBBiC6u/HtB7Y47UgEN7fHVbF04lwSLu8qI/08ddLKe7XzlxUtHkFNHhI6h47X/A41oNu1AAzJ4l7fskfrsT2K0mwAOTmVK2DXGCThKsXPa0ZtKthaTeQBULYFmodArdaKHyOfP8ziBDGM/M4gwOrMNKF7NLpx7qfb5T+RzxzOySNw0szzMZ49PcbymEEEJsRv5sRBJpUv0yO08YksnbAqKXXl2X+z0x0NNRdHxra6JPrxtlHBNX50xJXRihXAdLmzl7q2qsoqyzasBiXbDL3I1jQxhh9ZbRzRbYtukAuor9hW+k/VNn/hbGVJxNeefZzBjc6qxMZsWMahIEWLYtTVWEEEKcHxLQsUat4fvoMidvc5MgTyxrYdi59n0z926+66YOIzO7LpM55Qn0wky9BbrRQuUyZgC155mOm6sok1SZNMlZNCFZCILtyy9Bew4qjs1erUoV3fZNE5bT7fypls8QWZ6H8jySto8+3bLUN5Dg7uSscmnJz6ByXKyLL4Bjk7DKTptCCCHEZqVs2PMPevGnIuyUReJL0COM7g7PxYZJgmDx0PMTGlokQYBeRXBmpVMkftuUzdkWWArdMCflq22+ohx3zbpM6qwHjoUKY/B98DxThlpvnP59tdqLBsovSIIAPNfs+TvL0Q9iZc6O4WU7ZCrXQc3VSS7eRVyroWzbHAsyd1AIIcSWk93p4RZt8vtM34HGiIxeEoZk8sSylG2bMklLoTyPaGKyc52VzUKcYPf2mNlkzday3SkXLtORyWjZ+TzYgG1DkpCEUWeGnXJMYJQEgTkZnx9QrqMQZ6CfaHLqrJ9T8uRzWNks0Xz5pDO8HZ3oMwvGLAXLlAjYhYI0ZTkHkplZrIE+rGzWzGK0beJqFeWYP2nq6Vewh7cDoOsNnL4+klXuIxVCCCE2C6/HJvYTgtmYuZfbxI0z70b+RgkWSZfng6Txysq6+5UTG0bHMcpS4DhLMmk6CLHyOZJG0+xns+1VzZmL63Uz4DtJ0EGIsm1TKgmmAYqy5jt5Wp09f8pxSapza/a8Ttwfl8xWUN6ZbVBWjtPJZi6UpCrbJjmD/X1i9axMxvy/XIIo7nytQxOoq6EBsC2sC3bRvmIH0egYcbVKND19Vh1axeak33od1o1XLbms8VNv3qAVCSHE2rA8RXaXS+HiNP5UxJF7Ksy9KOcg4jjJ5IkVJUEAK5wYR9PTJrCzLBPwJAl2Po/KpNFtf8Xum3GlcvxEvVQAdXx/n+nCefxYZdvmsrOYkbccy/PQie7s51rYf3g64loNu7dnUfmpKUGNz6pJzHKcPbsgimW2G0AcYxcKpnGOUqhsBuXYxMcmsEslksNHUKkUKpMm/eIosivv/KYeeZoEcC67uLM3Vz3yNDlMJj/aPYgzOk10+MiGrlMIIVZL2dBzfZae67NYtiKoREw/fvrbTsTWJ0GeOG1WLotKmf1N2g9Imk2sYsGULzoO8eRUpwxzOUmr1Qn0sG2swX6SQyMmy6c1yvMgCEzQpKw1C5gWhmPrVhsr5YCVJa5WTYnoKhrJvNFCWaay7UWB3VrvxUsmp03m6jynHNf8jIDJIkchnJBlXmhUo+IY/fJrG7FE0aXaO0s4Ly2+LBodg9Ex2L5tYxYlhBCr5PXa5C9IkepzSA852GmL2WeazL3sE1bXb/J5rBWx7u5RRN2+vo0kQZ5YNWeg33SVtG2SSrXTeETt3UWy/2DnuIWAydm9E1yXaP+BJUGfDiN0FEGrbcYzOC5EEWoh+Fs4bg0DpiQIYCboZBzjmUonOFspwFtNVk7HMXYpj/LcNdk7eOJjW5m0aTDinl+/qgv7PsHMWnS2byMaG1/Vz8Nqm/qI84cVrPwzEY2Nn8OVCCHE6Rm6q0Dx4jSxn+BPRtRe86m+0F42uHOLFukhF8tTNA4FRPW1rYQSm8v5deYozt7C6ID5jpkqlYVGE/I5krk6VjplRi4kmmRqBmv7EM6lF0HbN9k6jgdOut7A6imjHIdkcgrleehWa0nppOV5WKXimgZQSb2BlcuedEzBQhnnQlfGk5V0xtVqZ6bgWlGea5rUbB+EuaWNbbYyK5c1JbALexyjyDS1keYp4gx4E3W0/PwIITaZ8jUZihenOfZgjblX2rBCzGZnLfpvyVG4OGW6eyeanusSjtxTIVrDRixic5EgT6zKwv477QedRiM6ilBxgm4nqHwOu1g0e/gc2wSBe4ah6Zts3WwVZ99eotcPdrIxSRBAZQ6Vy5gALzF/iBa1ulcWVj4Hydp1T4rrddNZc+7kJ3yns0/P2bOLZGLtglAA5XmmLDaMlu3kuZW9MaDXQShNbcQZq1zXRzHj0tpTIHPPDzZ6OUIIsSIrpeh7U47cbjMaofJ8i7mXVv73LzXgMPwes6Vj8rt15l7xsTzFrg+X2f7uIiNfrnCmDShjLOIu79EYS3fNFUmQJ1YlrtfhjWMSZgLTBMP3IU5MQFI0zVRUPgeTsyRzNdOJ89K9WC2/E8CpTMa0vHcdc9tiAT01bco1lep09DRZv6XtM862uUlSqaLSqdUde4pgz8pmieazlGsprphyUruQh1WudatYKM8EcAYHFo3wEOJ05T//KAmQ+aH52rrhSpKnnt/QNQkhxBvZGcWO95dxcha1V33qB31aR825jpVWZIddsjs8rLRFVI+ZebLJtrsKRPWY0b+rErc1Tt7CTltMP9Zg211FSldmqD53ej0HxNYgQZ44KwvlT065ZEo5kwQ9UyGuVHCGt6NSKXTbx56pobOpxRm7bLZzGyyLJAiw8zkA7HKZuFJBRyHKc4lnq2vW3MTM5LPQbb+zV9DKZCCOlw3oTpzbdy4p20Z5LjrloGa3TpmZM9APjnPSvVC61UbZ9nzzHdlULdaWBHhCiG7j5C12fKCE5VrUXvNJgoTW0RDlwMBteYqXpVFKEcxGhPWE0pUZChelsLMWh78wSxJq+t6Upee6LMo+/u+mk5V/Q89XEuSJNXFipkXZNlYmg263iSsVk+2bnoX8MDiOGbDuOMdn1C0Eehw/uVeZ9PFsjmWhXGdJcxQ7n+8MXLey2UUz8Jaz0BBGRyFx3QSJdrmM7bkkzSb29m0kR45il8to3zejIeL4lPe7HqWUyjk+vy9+/hWci/at+WNslNXsrdTNZmeEhm6c4vsvhBBCbGJuyWbH+0u4BTPYvHyVaULXPBoyeHseJ2sz9b0G9df9zh67He8vkd3pmaCvGrProz24ZZuZHzZpHAqwXEVYi4lqZ74nL9EWie7ucs3kPNvOcjq6+5UTm9LCybnK50wny4E+VDYDR8bRQYBSFmhthpy7LngeumaCNR0E6CQhqTfmAy3XnOSf0DGxM37hhAzPKQMxzABzu1xedFlcqZjZfnFMMj1jxizYNlY+Z0pIg1NnDK2e8imPOV3KMs/NymWxrr1iy2WzTgxi38gulcwYDds2IxP2DJ/DlQkhhBDniILsbo+dHzQBHkDUSKi+YN7U3vbOIjqGw1+cpfJsa1ETFbdsjvdnYoqXp/F6bI78TYWZJ5r4kxGt0fCsArzz1W/+5m+ilOp8PPjggxu9pDO2KYK8drvNH/3RH/FjP/Zj9Pf3k0ql2LlzJx/84Af58pe/fMrba635L//lv3DZZZeRSqW46KKL+Pf//t8TnGSv1R133NF5gW3b5vnnT17ec/Dgwc7xn/70p0/3KW4pSRCgSkVINGpowHTfTBLimVmsUhFSHmQzWIMDJLMVdKXaKfvUcYzyPKxs1nTfzGVRqZQp5ZwP0JJWC8vziGs103lz/vhTiWs1M4w9mzXjCVJpUzroedj5PFa5ZB6r2QTPIzo6arqFnkI0OmYa06wRZ+8e/DuvRV17KeRyWBMz6LFja3b/G8nO53EG+lcst7V7eyCOzZDzvbuw+ntREzPneJVCCCHE+ipenuaCf9jHjveWcHImYGuMBMw82aRwaZrafh/LVcy9uHRcQmrAwc2b2wQzEaUrMtQPBPhTS3sYiNV75pln+M//+T9v9DLWTNeXa7788st88IMf5JVXXll0+dGjRzl69Ch/+7d/y4c//GE+//nPk0otf0L+K7/yK/zFX/xF5+v9+/fzyU9+ku9+97t89atfxZ7vFrmSJEn49Kc/zV//9V+f/RM6T0RHR3F27iAZO4aVz6GjyGTggtB033RssBQkmrhVxxkcIKnVsXp70H0lqNSg3gBMds/p6yOanu6MNVi0d85xOhm3zj6uFTh9fSTNpmn4gikPZX7fl643zDp3bAelsMtllOdihdE5249neR56eobUeA6dsokOHjKXp9Ln5PHXm8pliY5NLLl8ofQ2npk1gZ7jEL/4yvHGPkIIIcQWse2dBQoXppl7qY1y6Xw+81SD3T/ZQ3MkYObJBoULU8sGbj3XHJ8p7BZtUn0OU4+u/ail86m7ZpIk/JN/8k+IoojBwUEmJpaeq2w2Xf3KTU5O8s53vrMT4N1999187Wtf46mnnuIrX/kKH/jABwC45557+KVf+qVl7+PrX/86f/EXf0FPTw9/+Id/yKOPPspf/MVfMDw8zDe+8Q3+9E//dFVr+dKXvsQzzzyzNk/sPBEdOWrGLdi22YNn26ZTZpJAFKMLWZTrYJdKZhxDKgWFHKrRNtfHMUm9TlyrmQAva7J6CxZGOSTNphkaznwm8GRBu23N7wf0TEkgQBCiMmlUJn18VEMQmA6fcQK21QmyrDcMa1+w0EzmbFn9fajBfqx6C+vlwzhDg+Y5nuOmL+slqS3/j1Bcr2N5npk1mGgomAY8ca12WqMshBBCiG5WuChF4cI04w/MoWNN4cI00080OPZgjfw+c44z/kANyzHbNOL24vOL7T9epHBxmqhh3tAuXppm7tU2zZEzb0gn4A//8A95/PHHueyyy/jlX/7ljV7OmujqIO+3f/u3GRkxrel/53d+h8997nO8973v5frrr+eDH/wg9957L7/xG78BwOc+9znuv//+JffxhS98AYC/+qu/4l/8i3/BLbfcwi/90i/xta99bdH1KykWi3ieh9aaf/tv/+1aPr3zghroM3ur0inUYD8qnSKemUXP1dAHjqByWVQ6hXIc4kqF5PBRov0HSKZnOw1cFvZvWdsGTRkoZt+WVSqi49g0eYkiEySUyyfN5CX1BqqvF7V90OwTVMo0b5nfuGuVChCGJOOT5r50YvYXplOmJPPEvYGeZ9aRSqODcE2ybbq3CJU59MQUKpdF95ZNaekqylE3g5UCcGfnDpOhvXwvKuWRHD56jlcmhBBCrC/LU/TfmqdxyCd/YYri5WmOfbvGzBOmr0Cqz8GfitChxvJMkJeE828+K+i9IUv+AhMITjxcZ+6VNscerDHx4NbpwL0RRkZG+OQnPwnAH//xH+OdOK95E+vaIC+OYz772c8CsHfvXn7rt35r2eP+43/8j/T19XU+f6OjR83J4p133rno8uuuu47e3t7O9Svp6enhV37lVwC49957eeKJJ07viWxRJ2ucsURuPvsVx6C1GY9Qrx9vljKf7Tvxvq18DixlRjAs7N+yLJjvyKmyGVNqqaxO4KDyuU4Z5nLschkrl0XPzaFd2wRUu7d39vrpKDKNPoLQPHY+Z7KPQWhm+qVSizKJSRCQ1OvHs2z6zLN5lufh7BhGRQmkU7RvvYxktgKjx7B379gyw9B1EJpmPAuBezZrOofOB8/6sWeJjk0s6aQqhBBCbHalK9NYnsIp2GSHXUb/rsrcy+Ycwk4rMjs92vPlmfkLU8StBK/HYeBteS74h3303ZxDJxp/JmL43SWmH2sw91IbvfJ722clAWKtuvpjLeqofv3Xf516vc4v/MIvcMcdd6zBPXaHrg3yXn31VSrzA7Hf+c53YlnLLzWdTndekO985ztMTS1uzz44ONi57kTPPvssMzMzbNu27ZRr+cQnPkE6bbI053s2byGgWs2cOuW4RK8fRFsW4e5+dDaDHuiB/h6cyy4GIKnMmeDNtnF274QoMuMS6g2zT27+5L+TJXMdnB2m26I1NIjdUwLPNeWXmcyi9vzOzh04l16EMzSIXSqZQEkp4plZ4mdfgkOjcHjMBIxhZAaxF/Po3qLJnNkWuC4qncLZvROtk87eP+sN7/JYxfySskLluKsKhq1MBqtcIjo6CnN1wgsGce97HJVJE1erRK8f3DJBj9XXg8pmsC69ACuVxirkicu5TndVIYQQYkuyoHxVhrASk+p1GH+gRvPI8XOpvlvMNoXZZ1q4RYvS5RnsjMWO95UoX5nByR4/D1743Eptrc7bG+ELX/gCX/3qV+nt7eX3f//3N3o5a6prg7yZmeMd9RYCtZUMDQ0BJvv33e9+d9F1H/7whwH4R//oH/FHf/RHPPbYY/zVX/0V733vewH4qZ/6qVOuZXh4mF/91V8FzB6/73//+6t+HlvJiUGLlcmYMsZVBDFqpoKKNDrjgm2jPQdtKzPbzm+bBixAcmzK7N9LpUj8Nsp1zb44wNq9A5otaLTQ9YbZ15dyTSDY9lE9ZRMgzgehlueRDJbRlmVGMNiLf9TtQmH+E9t0cyyX0MU8RPNvhw30QpyQzFbMPD/L7CtcyNrpRJsyzYUMVGNpEHayrGKnK6jnmXl8foBz0T6a1+zEnTINZ+KZ2VN+bzcb3VeCdAoOj5k9lzsGiHMu7Nu10UsTQggh1k1+XwonZ6NsiJoJjcOL3xhO9Tk0DvrEjYSe6xZv0QhmIxqHfY59p8br/980dtqc04RzMiLhbFQqlc62r9/7vd9jYGBgg1e0trq2u2Yul+t8Xq1WT3rside/+OKLfOhDH+p8/dGPfpQPf/jD3HPPPfzzf/7PF93urrvu4td+7ddWtZ7/9//9f/nTP/1Tms0mn/zkJ/nmN7+5qtttJToK0VFoslhKmVLGZOUywoVsX1KZw36uiQ4CuHAvKgiJ+/LY24ew4oRkYrJTuukM9EMqheO54Dg4jkM0PU306n6TXYsiEygGAartm+6XmQzJxCQ6CDtBVxIE8NTzpjSwXDJBXqttGqlAZ2SDs2OYZLCMijXKDyEMUbNVUxYax1jbh47PqFt4znGM8kyGz7Its3bbMjP9TshwrpR9s8tlU2qqE1MS2ttD9NrrUK2SjWLiweLZvVBdTE1XzciJchl72yDxj17BvfSCjV6WEEIIsa56rjVbV7weh7H75zixKaSywc5aJGPmwsbhwIxFeN0Edol//GCv5/j2Fh2u71aOBIuke/NBAJ31zc3NLbo8lUqt2HV/wW/+5m8yPj7OrbfeumWarZyoa1+5iy66CMcxMejDDz+84nFJkvDII490vj58+PCi65VS/PVf/zX//t//e/bt24fruuzZs4d/82/+DV/72tc6j3EqQ0ND/LN/9s8AeOCBB3jooYdO9yltGUkQmKzZifvlTna83zZZmyAgefUAWBbaMgPRsRRWT7nTtXIhe6WDkKRSNVmfhftpNkmCAKtYmB+WbZE0miawi5d/Nyuu14nHJ4gOjUA6RTQ93bnO2bcX3WphVZuoICI5fBRdrUGpYD5s2+zPUwpcBzIZVCplsoTNJrreQC2UEUfRqr4XnWPnvx8qnTYB3sJVBw+hH3t2dfezCcUTppxW79tBMtSDjkLUVAX9+sgGr0wIIYRYH16fTXrAVD5Nfq9Ofb8ZDWSnFf235tj7c304GYvaaz5WStH/ljztqZDxb82R35di+L1F9v1CHxf8Qh+7PtqDPxPx2p9NbuRT6jq7du2iVCp1Pn73d3/3pMc/8sgj/Nmf/RmO4/Df//t/R6mtV/ra1Zm8u+66i/vuu49nnnmGL3zhC/z0T//0kuP+x//4Hxw6dKjzda22tMOQ4zh84hOf4BOf+MRZrek3f/M3+eM//mPq9Tqf/OQnl+zzO1+cWKJpZbPHG6iswC6XO7POdBSi0y7ORBVch2RkFOW68wPKHeJaDd1smsYntm3KJR0Xq5jvBIALc9Y6++yYL43UCZyQTbM8z2TiPI/4wOFOKaazeyfBvkF8W+FNpGGuATMNM2RdKfSRUaz+PhPchSFJXwmlNWq2aoI6z8POZFDpFMnc/M+b42DZ9qr2zi2MebCKeaLRsdP4zm9+nUD4pQOoC3cDLDs3TwghhNgqonrC9OMN6gd8gpmFXgOKHR8o4+Qt5l5qU32hTTgXM/yeEnZKcexbdXa8v0xm29JtMbVX/XVrtrJZjYyMUCwer4Q6WRYvCAL+6T/9p2it+fjHP87VV199LpZ4znVtkAfw6U9/mgceeIAoiviH//Afsn//fn7+53+ebdu2ceTIEf78z/+c//gf/yOu6xKG5uSxtY4NKvr7+/mX//Jf8pnPfIaHHnqIb37zm7zjHe9Yt8frNspxF3WQTFpmf5qVyZgAaX5Q+RvLFjv76uavj599adn7tvt6UK028cwszuAApFImqHJd9PTMktvEC2W6rVanMYuOTPdGPT8w3Wq2CHf349p7iV561ZR8plK4T79OctEuOHqM8Io9qEeeNs9pPmBNRsdMkHjB7vknm0C5iMrNl4w6Dsn4BFZfL6pWRweBKUc9CevE2XwpB91onXJ4+1az8HyTZhOW+TkQQgghtprE18w8efwNceXA8HtL2FmLkb+pEFbMecDAW/Nkd7lMPlJn211F3KIpzay+2KJxOEBZivxej/5bcuT2eIzdVyVurV/JZqwtYt21RX8AnfUVi8VFQd7JfOYzn+HFF19k9+7dfOpTn1rP5W2org7y3vzmN/Mnf/In/Oqv/ipBEPBbv/VbS0YpuK7Lf/pP/4mPf/zjABQWGmqsk3/1r/4Vf/RHf0S1WuWTn/zkmgR5ylLrliY2923+f9YS09YXnSyaFqD9CHv+HRNlKUiiFR9vucuVbZusWRRiObbZ3+Y64HmmKYtto/rKEARmMHkhTzQ5hV0qmXLNKESHJlPoFPImU+Y62K5DUp3D+u7TxPOPrdst9MhRrL4etALLc/EOHCNaZl12fy9U51CZfvM9bM6/gZBOQaOJvXsYPTkNYYCeb8iy0vO2XM88pu+bMQxxjEaDSh8PVldpTV/Tc00nm3Pd62hTv55iCXk9txZ5PbeWbng9vV6bgbfl8XpsRv92jmguIdXvUL42Q/HiNNUXWvTemCMJEuoHfbxem8mHG53bNw4ENEcCBu8osOsjPYx8sUISrS7QU1qxJjMHNrGXXnqpU8r5X//rf13UA2Sr6eogD+CXfumXuO666/gP/+E/cN9991Gvm1brtm3zzne+k9/93d/tXAZmrt166unp4eMf/zif/vSnefTRR/n617/Oe97znrO6z749ZVxrfQYvKktRHi6YLpQnaZJyVo9hW+j5PXFWupek7Xeus9JpiGOScOX9alZ2fo5eGMHOIiqdmm9MEpM0mtgDQxAAwxeA1iYDduU1AOjpCgDJMmW6J+fD9FFU0cEqZIhTPdg7t8/PyYvQk1OgAC8N2zKoIEJvz8KxGfCbqH196GwK3DYqP0h8bDW18QEq76IswHJQhR5IeehqCVXKAwqarVN21TwXr6k4d+T13Frk9dxa5PXcWjb89VSw/ceLuHmb2mtt9r4zS6rPxsnYRO2E9njI8A15U975dIP+NxdJfE3/3uNBqZ1V7HhvGYB2FNF/oVrUmOVkwiSAA+vxxDaP//yf/zNBELBv3z6azSaf//znlxzz3HPPdT7/1re+xfj4OAAf+MAHNlVQ2PVBHsD111/PF7/4RaIoYmxsDN/32bFjB5n5Zh1//Md/3Dn2iiuuWPf1fPzjH+cP/uAPmJ2d5d/+23971kHe9KEKjjqN4eKnQVkKtGbqUOWc/EGz0mmS9vxgz3wedEDcOP4OlOV6JOHiskbLMyUMVjGPDquolIdu+2ZAtlIwOULcaJixDa6L6u+FlyeIxsZxdu0APyCaOLNxA05vL9pxSEbmUKNNrF3DUKuTzFaxykWS6gRW04a2D23f7LnTGivOojMx8fNHl5SnnvT745pg3nwPxuHN12C/Pk3cOIydy6GKBaKxUwd55/I1FetLXs+tRV7PrUVez61lo1/P8tVpWolNaw4YhMZEyNRjIa2xCMuFwTsLTO6vUX2uzcBbc9T9OmNfn8OfH5C+IHVVgp22OPjZmdPamxfpVTaIm5egSOjuLPbprs+f7xHx+uuv8zM/8zOnPP53fud3Op8fOHBAgrz14jgOu3Ytnad1YnfNm2++ed3XUSwW+Vf/6l/xiU98gieeeIKvfOUrXHvttWd8fzrRaLV+f2y0nn+Mc/AHTcfaZKlsm2iuZgKzVBrleegwNOMPfL+zPw8gng8KVSGPVsoMHLcss3/NcUzzFTeF6u8DrQlfO/42VHjoyKrWdeLjnShutrBm50jCCKUVqlozA9sHeolfO4TV34d2HLBCSKWIp0x3zuTAiCn/TPQp9+It4rpm/16iTRD8o/1oz8Wen80SHl1dI5Zz+ZqK9Sev59Yir+fWIq/n1rJRr6eTt+i9yQQISZAwck+l04SleFmawbfnqR/wqT7fZsf7SrTGQo59u0bUWFpfOf2DBtt/vIRbspcEgCejtfwMn0+6ezflKjQaDf72b/8WgEsvvZRrrrnmnDzuv/yX/5L+/n4APvWpT8kvzrzEb2OlU2aGXCoNiSZptYirVdNsY35A+HIBlw5ClGsymiaTlxCNjpnmJpm02QvnnlnGc7nHA9NoJRozaXh72yDkc6h2iH7tkGnSkk6RZFzIZo538pzP3C00UbG81ZXaOtu3oUMz58/yPFQmjcplUa6L7i9DfvO8OySEEEKI1StfkyGJNLGfUH2p3QnwSlelGbqjQPXFNuPfrFG6Mk1QjTn6d9VlAzyA4uUZ/JnotAI8YfzlX/4lWuuTfpzYjOXb3/525/K9e/du3MLPwKYP8n7v936vMzbh13/918/Z4+bzef71v/7XADzzzDN86UtfOmeP3W0WRioox+10uVSlAiqd6gwrXxDPzC4Kik4cxxBXq0THJjrz57ROsHt7iMcnUOUSUW+OaP8BrOsXl+Q6F16AlUrjDA2e9ppPFB05ip6YQs9WUI6D1gnRa69jzbWg3uyMO7Dmg7F4fi/oSgHkkvsfGyfx22aPYhCAN1+WGsXEz79CtP88L5QXQgghtqjCxWmC6Qg7ZdEcMWWTdsbMxKs822Ty4TrKUeR2p6i90l6xQUr+whS53R6zT518fNVaWOiu2e0fYnld/51543DzE33hC1/gM5/5DADXXHMNH/vYx87VsgD4Z//sn7Ft2zbABJvnKx2FJsBLp8A2P1LR4SOgFHGthlXILz4+0Z0g68S9bMpxzUcmgw4jlLJQysIq5kmOTeBM1Uluv4GwN2NKHQGnz5RwWgN9psxyGScGmSeuGeZn6Z0QdOogIJ6ZJa7VOg1QkiNjJJOmTNMZ6DdNYc6Alc2a2ycau1yGQg6Vz3W+Z0IIIYTYelIDDk7Gws5YRI2Y5lHz5nBubwqlYPoJE7CVr0yjHKjt95e9n/yFKbbdVWDu5Ta115Y/RogFXb8n76qrruLWW2/lp37qp7jyyitxHIcDBw7w+c9/ni9/+csADAwM8LnPfQ73DEv5zlQmk+H/+X/+H/6v/+v/Ympq6pw+drfRUUhcX7yhd2E+XjQxueRYmA/qPBe0Npkz3zd794IAq7/XlGdiRheo+REN3sgM0WAJlUljXbibOGVDAipJTJOWec5AP8nwINgKnt+Ps3cP0cFDyy9eWZ09ewtZuYXZfwAqlzEZNyCpN8BS2Pl8p4voapuuKMsCy8LeOUxybAI1OWNKWGWfhxBCCLFl5XZ7xH6CnbGovWqydOltDr3XZ2keCUh8jddj0/umHJVnWkS1pWm88rUZ+m/JUXvV59iDp9tRXJyPuj7IS5KE++67j/vuu2/Z66+99lo++9nPnpOumsv51V/9VX7/93+fo0ePbsjjb2bKc80evDgG10FpjSoVodWCTBqi+ZZR2Qz4AVTnoKeE3QwIL9uFu3+caPcgioTEcbBrLZwLLyAaKOJnHGw/Rlug/DZWFGGXy53Ac0EnqEulFzVn0WGE3duDKpeIDx/tBHJJq4WdzxPX651AcLXdNVWxYEo1R8dMA5og6AyRPzGoFEIIIcTWkRl28Scjsjs97LTFtncWKVyYoj0ZMvFwHSwY+rECYTVm+vHGotvaGcXQjxXJ7fKY+WGT6ccasMr3ht2STVSPT6sD54liLOIuL/rr9vVtpK7/zvzZn/0Zv/iLv8gVV1xBb28vnuexc+dO3ve+9/FXf/VXPPHEE1x55ZUbtr50Os0nPvGJDXv8TS1Ojo9JgPkZdQHKcSAMzSB0x0Z7LuQyUCxArWEGiduKZKAHZ2QSe3wWu9ZCzcyRHBnDfv513GqbxLVQsSkNjY4cNQPWl2FlMqZk0nE6JaNg9g/iOtg9pePHet7xDJ9tm+YsepWTRf0AkgSVzaIcBzU0gLN7JyqbNQ1ZhBBCCLHlOHnTBXPulTbFS9OkhxzGH5hj5EsVolpC7w1ZUn0Ox75d6wRkyjFdN3f/VC+pPoejX60w/YNVBHhq/nY/2cPen+ml94bsuj+/rebTn/50p9nKHXfcsdHLOWNdn8m7++67ufvuu8/54z744IOrPvZjH/vYOd8PuGU4zvGulbaNjmNUNgueZwJAQB8cIW61cC6+kHi4H7vSwDtSIe7NEVy8DacWYB0eB8/D2j4EtoWansMz2+hIPJNpiyaPl9R29uEpywSZWkMcY/eUzJ472zaNYF5+rXMb5biLmqwsNF5ZDSuVNvv95psEOQP96KkZVDYDUbTqkk8hhBBCbC7BbER6m8uReyrMPNUknIs7jVVKV2XouynH9BMN/MkIt2RTuiJN8bI0lqeovx4w+XCNuH08uitensbrsY8/wAmBX2bYJT3g0jjk45ZsUgNnvpUp0YpEd/mcvC5f30bq+iBPbF3KdSAyIwWUskw2zY+g1TbBn21BFJO0Wji7d8JMBcsxg8mTvhL2TIMo10OU93DbvhmHqRTYnskSevNdP61lEtbz5ZIQQzTfbVNZZoxDsUAyWzm+zvmmMjoIVlsh0bmdch2SVst01fRPaNiSSpHMzKLfUD4qhBBCiK2lcShg6PYCyoawYt7AVjb0vyVP+aoMs8808aciht9XIrfLI24lVF9oU31h6f680pVpBt9WIJiNOHF610JRVNRIGPnyLFiwa08KHWuUA1oKhs47EuSJDaODwARWbR8rlwU/Bschmp7Gns962fk8zp5dYNvQW0ZpTbSzH/ulQyRBQCoM0bU6qrdMMjmNlUmbrFwQmGARwHGwC4VOFg3olFxantcZiGr1ltG+b+7PPv4O2XJNZVb1/KIQHYWL9tstdOeMjhxdsiYhhBBCbD12xiIJdSco83pstr2jSKrPIazF5Pel6Lk2S/tYyPi35qjv95fdR+f12sdHLny3sfSAN6i+0KJ4WZqBW/NMPLT66iOxNUiQJzaOssxAcEt1xhXYvT3YhQLa9814hHzWBG3zQZd2LMKii719EDUyip6eNSWPbR+VTpnAznVNJs+xzT47pcz+vhMCqhObrCjbNkPcgxDCCOV5RNPTZ/30LM8zQewJ++2iySnsqy/Devl1CfCEEEKI80DhohT1gz4kZij6wK3HR0vZGYvaa22qz7fxJ835gp1WuAM2btHGLZgPr9cmPegSzEZMPXrqAA9g4qE6SaTJ702d0bqTTdB4Jeny9W0kCfLExtGJCb4AZ2gQ7QeodBqyFmq+EUoydswMVQfIpFF+hHYtdNbDKhZIKlWzh6/ZRM1nBVUqZe5Xa9Aa3WqZLp4nUJkMdjZrsolxYjJ6vo+OYzPWYA0oz+vs21vYbwhAkqx6gLoQQgghNi+v1ybV6zD9aIP+23L0XG0aoUSNmNkftZh7qY2dtihcnGLwbXncko2dOh64RM2EsBYTVGIqz7ZoHAxOq1tmeyyk55osmWGX2lHZ/38+kSBPbJgkCLBatgmAghBs2wRtnmeGhMeBKeP0PAgjWpeVAbD8BNUOiEbHcIYGodk0M/XixASDvm/GLkQxRJEJ+oKlf9h0q43KzQ9KD8LTaqSyGnG9bhquxDE6CrGvuRxVqUEsc/GEEEKI80HP9VniVkLjSEAcapRSzL3YJmqYMs3h95TIbHOJ/YTGoYD66z5h1QR1YS0+6710jUMBjZGA4feU2P+FNsytzfMS3U+CPLGhFvaq2fk8xDFWIX+86UqcgOcRHR0FwB0bB8B//820d5ZIW5eRvHIQq7dsdhxHkZmvN1/0rnuLqFZAcnRs0Qw6K5NBB4HJECrzbpl2HLMPT1mLOl2udgbecpwLL4B6g6RWxx7oI3ntECqTWXH4uZVKmwYtQgghhNj0chd4FC9OM/7AHCQmqxZWY3pvzFK6LA0KmkcCxu6fo3Fw+X14Z0snMHZflT1399J7Yxa+vfrbJtoi0d1dDtnt69tIEuSJrrCQRXNSKaJjEyc9NvXVx8xt5r9OxsZx9u4x2bwwMlm8Vhs11zB77Obn2S2UYXYCvlbLzMiLY3Si58spF/+FXQjwTicAc/r6TEfNsWMozyO55mKswxMkzeaKpaAnrk8IIYQQm5udVgy+vUD9gE/tVR9lQ++NWcpXZ9GxZvrxBnMvtReNRlgvylHEzYTcrjPbmyc2JwnyRFc544YnrVYnOHR27jBNV+b35GHbptvmvEXZuUSbBjCuQllqxb1yp5Nh03GM1dtjgs4kwX7lMHHV1EcsF8xJgCeEEEJsLQNvK6CAiYdquCWbbe8s4PU4VJ5uMvtMiyRY3+BO2eDkLDI7PPpvyQFw5IHquj6m6C4S5IktQTeaODuGTWCltSndbLXRQUgynyVcGICuE20arcQxynU6e+c4YWzCWa0lCNATTVAW1t6dqEIeK4zAslCOQ9JsYmWzKMsy2cM4MTP15rt8CiGEEGLzyl+UonBhirG/r5Lqc9j+riJRM+HIlyv40+s/sC5/UYqh2wtYrmluN/dym9qrbba/qwh/vvr7iVHEdPew8W5f30aSIE9sqLPZ87bALpXMGAUwAZ7nQhSZAEopUBboBJVKmcBu4bjEHNsJ+Dj9IM9Kpc36lYWyFDrRnayc5XlEPTmcqQR2bkfVGuh6w+w/XHj+mTS67aP99ll/H4QQQgixseysxeBb89ReaxPMxuz8SJnWeMTY/XPocP1LMxdGNMy90mbupTZhLSY96LLtHUViZCL6+UR2K4oNdarAxspmV7xOOe7x66NoUQaPMDJjE3YMoqPQdNh0Hax8DmVZqFQKa6Hxim2bAC0+/R3POo5NFm6+1HNR05ZL9+GMThP35lBRDCkP4hhVyKNyWVDKjHywbex83uwPFEIIIcSm1XdTFp3A9BNNht9TIqol5yzAcwoW/bfkmP1Rk2PfqtEaC+l7U47t7yyik3V/eNFlJJMnNtRCR8s37oezslkT9NiW+b/nEs1311ygoxArk4YkMcHWfDkmrbbJ4GmNmpw1jVCaTRPoJQk4jumuaVkk8wPJleehgLi+NOi0C4UVB5frKMTKZYmrVexCgaTZ7AR+2nPQ/SXsQ8fAsggu3o5TzJDYNvZMzQR+cYz2fYBFHUCFEEIIsckoyF+QovpSm/6bcyhXMXpv5ZwEeAB9N+VIYo1btNlzdw9O3kYpGLt/DjutKN3kndb9SXfNzU2+M+KcUs7ioeQLM+TAlD4qx8XKZEwA1mqhowhyGXBXeD9iflyCchx0vTH/IKpzOXECKTM4Xfu+GbBeb5A0m8T1Onq+8YqO40WZvIX9e8pxSVon3ycXV6s4O4bRQWiarnieCf7Gps1aigVIpbAiTVQwna10PgPlYmedEuAJIYQQm1tmu4udsToz8Ka+Vyeqn5sUWm6PR/HSNCSQ3enROBQw9WidkS9XqO+fH88gZ/3nFcnkiXNqpfLMhSyeCapapgwziiDxINHE4xM4F19oDg6CznDzZHqGJAhw0imTnQsjcF2TsYsitE7QlSrKdc2A9STBHuhD+z7xzCz2YD+6Nj8EXWvschnlOkSTUydd7xtFR0exPA/L80iCwAx4n6uRjI6hFjKBBw9hARpQe/dArU5clU5XQgghxFaQv8AjrMV4PQ5BNab2qn9OHtfyFEN3FAiqMW7eYubJJjNPLe7aXT/gU7rJXeEexFYkQZ7oOspxj48+AKIjR3GGt8NczXTPBBPEpTxUNtsZj6AyaZJKFatYMMfYNgSh2ZsHZsC6Uua2cYKzeye0fRP8AbreIKk3UJbqBGunIwkCE5wGgSlBzWZwSkVo+0vGJOjZigR4QmxCwXveRPb1WaKXX9vopQghuozX49CeiLDTFuHcOkw2X0F+Xwo7Y5FEMeFczOzTS8cyJb5m4uH6ad2vmR7c3d0rz913efORxK04a85AP3Zvz2k3Dnlj6SaYzJlyHdOgxHHQrTZOX5/JtkUxNFskk9MktTokGlXM4+zbS9JoQhhhDQ1CJkN0bIJkegbdbKIcxzQ6iRNT0hnHJuCLzf48inlTDmrb2L1lkiA47QCv85xcF2egH+W5JiDNpMFzUfNZvgUS4AmxOWWfOECwvbjRyxBCdCE7axE3Y+y0ImmfmzJNp2DRe5NpQucWbCYeqq/YZKVx8MzObcTmJEGeOHuFPCqfw+opn9bNViqF1POZMB2EZm6cbaEyaROY5XJY5RLW7h0mYPMD8APTOCWbAcu842SXSlgD/fMdLyOTwQOU56JDk91LJqfM7Rf23GlNUm9gl8sn7eq5nIWAVeWyJvi0bRNAKmXKTqMIJd0zhdj0oskprEDa1AkhlnKyFlEzwU5ZxP76N1txyzY7P1DGyZjT+blX2rTGZByTMKRcU6yKXS6b4EtrEwAtZMI818yby6Qh0Tjbt5kySdsCZREdPHTaj/XGJig6CFEpD1IeeraCKhfNY28fRNvzTVbKeajUiQ8fNWMSBvrQroNdvgSOHCOZrWD194KaL/FMYqxCgWh6Glv3oHrKJvtmW539fiezUH65MOfPKuZRykI3TQMVlUlDrW6asYTz+wfrp1cmIYToPsntN2CFEuQJIRazXIWdtogaCVZaES+TyXNLNql+B7dg4RZslKdQFiilQEEwG9E4FNCeiPDKNulBl1S/bco/GwlRLUbHYKUUTtaidEWGqB4T1hO8ks304w2stKJ4cRo7a6FjjbIUbsEivc0ljEP43Oqfk3TX3NwkyBOromy7s0eOTBoyaZKjY1gLnS/bvgmO5pubYFnQWFoTfibiSgUAp68P5TidxipKW6Zb1EKgV8hhL3TWbLZQhRw0TUdN5bmm3NOxzYdyoNU2+/IaLZPNSzSEkRmxsNCpcwV6PhBUlsIeHCCamDRdQR3HjFCwbZNVVAplWZ2gdS2GvwshNpDW8L2nN3oVQoh5pSvTFC5O4/XYWK4irMVMfrdB8/C5LU3M7jIVPe1jkcnoNY4HeVZaMfDmPIVLUyiliP2EsBaT+NqUVs532i5dnqH3hpwJzmyF1pqwEhO1ElKDLm7eQtnm9omvmXu5zdSjdXa8vwwlmwt+rg+AJNZE9djcRwxxK6G238dvnZtGMKI7SJAnViWp1bDm58Ap3zd75LRGz1Q6mTvdamNl0iRjx0y2L5/FGRo0d1AsQBCga3WSuTp2Xw/RsYnTWkM0PQ2ArRMzxLyQN/v3IkxwZluQ8kjyGayxSRO4OY4p14xjVKIhVCaYsy3IZkzgV8hDYz6ocx10218xEFsI0hauV55HNDFpvkfzYxCcgX6Sufp8ZtA2Ad78aIUz3esnhOgOVpCgbroa/cSzG72UDmffXqLXD270MoQ45/relKX3xhy1/T6Ngz5JoMnvS7HjvSVq+30zwqCx+sy75SnstMJKW9hpCzut0JGmfSw65f3k9qbwpyKUbd4ADmYjANLbXLa9o4DlKCYfqeNPRaDATllYnsJKKZSlaI4EBJWY9KBDetDFn4loHwvR0RseSGHadJ9g7BtVUv0OVtoCDa2xkLi5dL2RljeZzycS5IlVSYIAK53Cmi/T1K2WuSyVNg1P8jkzc65WN0PJLQWtNvHubQAorYEM9BWxa220Z2MP9oLWxM+9fFpr0fUGcRBg1Rsk/nxjloVM2UAf1uwceqAHEsACtW0AlDKlnUeOmaHoC6WmWhMdPISzY5j42CRW2ly3UsZNuY4ZjzD/eCeWlS5YGL+gohArn180A+/E2wohNqFHn0Fde8Ubz7HOGev6K0h++AIAzuAAutk6HuC95Vr4/jMbtDIhzr30Npfafp/x++c6l1VfaFO4KEX/rXku+Id9BJWI9nhEezLEchSlPWmsXTmslIWVVvPBnIWdUih75U6S7YmQ+oGA+gGfsLL43/Hy1RkKF6WYfqKJkzNVT17ZoXhJmuLladrjEZVnm/TfmsfN24tum0QaNPS/JUd7POLI31ZoH3tjZHeCZf74xG1NayzEyds4eYvcLrfzuZO3cfMWds5i7nAD7l/FN3bhfrVF3OXlkN2+vo0kQZ5YFSs13/gkMQ1FlM7hqALkMsSjx9DDA8Tl3TDnow6NmpJKpbD8ENUOTaYt7aEdC+3ZqFYAUUQyUMa64UqSp55f/WJs2wwcXwiWHBtdq5lB5DuH0NkU6tAoqr8X7TowW4VsFipVVDoFrbaZxddThkzaZBtTnimxVIrEb88PZFdLMm9Jq4Wdz5t9eNksKp8z95XJLAnmlOOarp8nUhYQY6XSpqmMEGLTCXsz2Kc+bF1Yk1UW3p9fqCLokABPnGd0DKl+G6dgEdWOZ65qr/k0Dgdkd3tkhlzS2xwKl6TQMeTSHr7rELc0UT3Bn45IWtrsjwOCaow/ERG1EpJ2guUpsjs9chek6L0hS/8tOYJqRNLWoEA5ilSvw+zTTWZ/2MRyFUE1YuiOArGfMPndOu2xiB0fLBHMxBz7Vo2omZD4CUmgO0PKB27LU74yg1uwCatveDPYAid3PGBb+Nz8f/7zzOJgJ2omRPWYqJ7QGAmI25rMRXLafz6RV1usisqkodU2+83KJVQYguOQ9JdgsAfaAfaTL5ssn22hg8CMVFDK7M0r5tFKofwIKlXToe6GK9GOhT1dR5VKxNWqCSa1GW1w4ly5E50YTFmZDMlspTOAXE1XUakUeucQScpFtQOifcMorbF1QjI5jbIsrIE+iGKi114HwM7nzfMsFXGyGZLKnOmO+YYgz9m905SBtn2SVhtroRENdP6vLAudJCjLAp2YzKZtk7RaneygBHhCbF72t59Ev+161MM/POePHR05eka3UzddTf2CHLm/fhTefC08KgGh2PwmH6mx44Nl9tzdS+VHLWafapKEJtWVBJr6az6NQwFDt+cJZmKiuZgoZzP5QoOwGWO5CstRFC9LU7oyjZrv0B3WYuZebKMBy1EkoaZ5JGDioRqZbS7ZXR7KBjQoV5EEGjttMfzeEu2xkLFvzBH7mrid4OQsdn2kh3AuZvTrVZJgmVRcApUftShfmWH4PUVaoyGNwwFOziK3J0V2h7soyxj7CVHdBHH+RET9QEBUi4kaxwO75cYoTD93kgyh2HIkyBOromwbXNc0Pmm1TWMVwJprEfUXTLZuvjmLSqfMvjdLkXgOalsvKoxR01V0qYBa6MI5No3uLaLTbmfEgRmBYO7D6e8lOnzkpOtaCPhMFs4y67It1FwDZiqwdwfaUaggITo0AphOodGhESzPM1m5+W6eid825ahOBiuKSd7QfMUuFND1hgkiO6WcKYgirEIB0inTsEWp+YDY/IWVfXhCbD2JZ21YNu9M6CeeJfcEBO+7Gacewe034B2tdN7oEmIzCucSDn1+hp7rsvRcm6V4aZrpxxvUXm139rLlL/AoXJTu3KZYzONdsTgCioOEqUcbNI8EOFmLwiVpeq7PkoQaHWmUq7BTJmM3+d06jUPm33W3bDP840Wcgo2yTIOT8nUZeq7Pcuw7NZojATveXyYJNaNfe0OAZ4Flq05QWrzUrNErOyhLUboig4417WMhU482CGYjwvnAbsk+vVVKTnOsg0aRdPkwdN3l69tIEuSJVVloerKs10zvk45a7fjnk1OLy8dHxxbfdmx80ZedLF2rhY2Zd6cG+9GejX714JKAaWHvnLJtdKKJjo4uvv/nXl6yRzmuVEzWb362Xjxf8mR5HsnktNlfGEWodArbc4lrtePloUFIPDOLvbDXznPNn5eUtzhQtS2SZvO0B8QLITaH1EtjbMb3xL2vPdb5PALiO28EwGmG6B/8aINWJcSZ0xHMPNFk7sU2fbfkGLq9wODb8wSzMf5kRGs8pPpii8JFaZRjOk/G7YS5l9s0DgUkoSasxp0ALJiJaR4JOUZt0eP03JCl98Ys0483SAKNcmDH+0okoebwX892SiyVA4NvL7D9HUVi38zMO/TXM8TtxQFW741Z+m7MMfN00+wVvNwEefWDPmPfmMPJm5l7yMQWcYZkt6LoSs7wdnSjherrQTvm/fJlM2Lz9QjK87Ay6ZMOMV8YWA6mYUpcrxNNTGKXy+b6TAbihKRSRRXynWPtfB6rUDCP0ddjAr4gQKVSKMcxQ9i1NsPUG03TuMU1j3ViaakQYvNxtm/D2TGMdeNVOJdeRHL7DQBEuwc2eGVrw/72k3iPv4wKNmPIKsRxUSPh2LdqHPz8DBMP1mmNhqT6HIZuL9AaDzn8pVnaxyJ0rAkbMT3XZilfkyGqxcuXUL5BeyzEshX2/N63vptz2GmL0a9XF+2h0xEc+1aNye/VsVPm2OEfLy25P7dozm3KV2UoXJhi5skmQSUirJn7iuoS4ImzI5k80bWs3jJ6poL2faxyaUlzEzDBmuV56DCaH6cQLXscsPJ8uigyYw/mu3VamYwZCdH2UZm0GcQOqHwWPT17/HZxAj0l9OgxVKkIQYBdKprbNlvSSVOILSCarzawpmZg53a8g5PEN1+NBuyrLyN+9qWNXeAaiOt1+OELqFuukWye2PTCSmy6X8437h66o8C2O4ud623PonkwYObxJoNvL7DrJ3sY+8acGW1wEnFgIi4rpXByFqUrM1R+1IIE7IwymboTYsXKj1o0DgVse0dhSTdOLGiOBBQvTjPxcI36az46huLlafQqAs5zRbprbm4S5InuVMyjHQtVsVG2TVKdwxrehpqtEM/MLjp0IcNnadPgRIcRynGxinnimVmcnTvQ1TniWm25RyKu17GSBCubJWm1zMehEZzdO9HVmtnnlyQQRqYBjeeiFzp0Ts0S12o46ZTJ4mkNcSz78ITYYhK/TbL/gPlifn9v691vwum9gaDskn/8MNEby9E3Gf2DH5HccQPWg09t9FKEWDMTD9VojJh/k1tHQvb+mEPvjTlmnmoy8qVZtv94kV0fKePPRgTTMXE7wXIVylWd3V7VF9uduXfZHS5os5+u9/osvdebCiKtNUlbE7US4nZC3NLErYTGoQAdw+DtedyijVs0XTEXmrxYjhlY7vXaS4aoC3E2JMg7z1nZLMqySE4yAHxDRDFKa3RPAZXLoMYj9NSMGeOwgiQIsLLZ488jjLBLJRN4nYIOQqJmEzufJ67Xcfr6zKiIbMYEeLaNbrdRtr0oyNS+b5Y7OdUpB+2q76MQYt1433gc64YrcWo+zWt3knUd4pGTN4vqdhLgia1GJ1Dfb/6tVpZi7mWfaLRF/1vylK/O4E9FTD/RwC3aeL0Oac8hCbWZX5eAnbUYfk+Jkb+ZZfbpJv235PGnzNy71mhIEoGyMbP2MmbcQnaHt+r1DdyaNx07d3sEszH11/31+laI84wEeecxK5U2e8fi2JQWdlNwEsVm3t3kbKdcCkwjFiubxervhTBadB1A0myaLF46ZUo5M2niY5Odd8xWstC8RWXS2J5rgskogkzadAoNQ5TjoKPINF1p+6ajp23jDA4QTUwuO1dPCLG1Lcz49DCNTNRt15EMZ7Fz00TPvryhaztT0TtuwvnmExu9DCHWTeXZNs3RkOwOl94bc4S1mJknm9gZi8x2l8x2BydnEzUTxr85x7Z3FOl/c47Rv5vDn47ouTZL6aoM7YmI5gETlFlpxcCtebI7POJ2QnsiAq2xUhb2/NB1K6WI6gnBTIQ/HRHOJfRcm6FwcZqomXDk3sqq9geeK4lWJLq7u1d2+/o2kgR55wnL9Yj9xe8OKc9FBwHEMVah0FWz2/T0DMxWiKrVRZfH1Sp2oUA8egwrk8bu7UE3WovWrmybpO13umTqaH68w0ksdOlM5uqoTBrdapvSzCA0s/Icx2QEw8g0XfHms3atFtgWdqFg5uZ5ngR6QpzPvv8Mzp3XEpUz2L09S8rLNwPbl73EYuvzJyP8yYgk0Ay+vUDxEtPdMvYT2sciWqMh2d0e2+4qMvtMk213FilemmLuZZ/aaz67fqJMdqdH7VWf9KDD9neXUBYce7DG3MvtxW29T2ClFfk9HuntLr035DqXz73c7qoAT2x+EuSdJ5LweOCxENDoIARLoeb3ojnD2xftKbHnB5RvhJX2zwFmtMG2Qag3zNy6dAqnXDSBnFLg2ESHRogrleO3cdyTNkFZyGKqdAq0Jq7VsC0L4hg8F/zAzAh0HZPBi2OwbeJmE0cV0CCZPCEEAMmBw6iDs7R+/CZSMz7q2ddIms2NXtaqaUudN5OnnOHtJFPT6/a32//AzaSPtfB703jfeHxdHkOcneoLbRqHA9yiTeJr/JmoE6Cl9zvs+nAPcTOh+mKLoTuLZLa3qTzXwslaNI+GpAYddnygjD8VMXb/HHHz5Hvqht9dIrPNXXSZGb4u3bjF2pIg7zy0ENAkfhsrlTaZvN4eE7jMs1Jp4mq1U4rYTXQQEr+6H2fPLvN1GKLyObN3zrFXtQfv/8/efYfHVZ2JH/+ee+9U9WpLLnLHxoDBgI1FaKGEFhYCWbIhJIQQSCEFNiGVsul9yeaXZEMCgbAkBEJCJ6FXN0zHGNyLbMvqdTTl3nt+fxxpbLmqz0h+P88zj6WZW87MWDPzznnP++7LrkGt3xlDBRyU74DnobvXA6qcKMQT6O72Dbv2EOxpyq7dVDqYFkIcnIKPvYw+bh5q5hR4451MD6fPrOTYn8nb9tVquo7sIhhyCYcKaKnLA6U5fs5aXvnnoUx4Lk5wexvemnWDOk/tAofEuAizrlx+4I334Zp1q/jv6XMGNQ6xf26Hb1oW7CZe65Js9cibEWbHM+3Ea11KF+WQPzuMn9J0rE1QWp1DstVl68Mt6AP86QSL7HSA17klyY5n2vG6/H3O+mWah4WX5d3Wsn18mSRB3kFCOd2pmbtfH3BMmiGAZWHn5qJ90+fNshTadXFKSvbfDH2k2Zb59rW2His/16yVa20zjcldFwKBPXbxE/G9zkzaeXmovNz0DOauxVO0mwLtY+Xlol3X9L8Lh0wgGQ6hUimcKVW4Gzelj2dmC710ARchxEFu6RuovDycqkm43VU5s53d0sVYDvPsObPonJtk5iWvpa/r6XpYB1yx8p80X5BDfTKPjccN7lyFazQF1w88wAMkwMuw1ne6KF2QQ8eGBG3vxWlfGydSGSBR76ICimhlkNqn2g4Y4AEE8nYuHWl9u+uAs35CDIaEv2OYcgJYkQhWYOeMkl3QuyGn19GRvk3HE6i8XPxYDK+1FatqIqq4yKQrZhGlLHRbO6qnbYFSoBQ6nkCnXEgkTXXM3ewa4FlBU/nKa2/H3ba9V6N0FXCwQmGsSAS7sNAUVRlfbtI17e6Zwu5AD9fFKd/ZFNkuLMSKRNCe1+uYQoiDl9fejrtpC/Zhh2R6KH3irVqNe9ox2IfPzvRQhpwVifDu54qZ852GfW7zr7n5LD/SZsMCk2Jb872BP28Ff1oy4H1Fdmh9q4uOjUkqzyxg3PvzsIKK2JYUylFUnlVAqsOjc1Pf0n3VLlMrg0g6EqJPZCZvDNNuCt938VNhrHAY7XomeAuF0+vTeqpK6kQClIXf2IQVCpueUJu3YlWON33isoh2XVQ0YhqOWxa6M2aCKstCx2L4vt5vmuRem6q7qfTsmwoGUOGgKapiKZzKCuiKm8DO87r/9U3lzZ41e3X13dVKu1M7kylJ1RRC9OK9PXqqbTpPriB10vwx901w/OTDmPODjRAK7Xc7Z0oVWApv4yZmjK9ndDfGEIOhfah9oo3Y5jCli3KIVBbR/FqMkoU5+AnN1odb+1wwZdceeFYg+1e+SnXN0U2CvIOAchz8eBztmxchq6QIv7klHehoz0sHfXZBASoUxK+Lm8IsgF+/7288M8L3TepkJGyCqa4uVG5Oryp2PYHqXnffJcDbdTuvowPlBEwrhbzc9Myg8rVZf+f5qFDQVNr0PHQkSLIkitOexK7LRadcdFcclMIqK8Gv2Tq8j4MQQgwj67mx1TPPnjsL5yu1nDhuHb62WBBdx7/ajuCNo/ZMmetJw1eWIn5F00gPVWShtvfixGqSTLqoiPIT8mh7L079Sx39qogZ3+HSsrKL3ClB4jvki2AxvCTIOwho1wVMQIP2wXWxSor3GoToZHLnbJ+bwl27PuvSDlVuDrqjEwKOmW3biz63g9ituXpPPz0di6Vn5bBts+7PT6ETye51eQ6qsRU7J2iatqdcrPxc01MvHOruqxfoXvMoFbOEEDtJUaaRZ807FC8nyLp3iulMhvji9Kd5JzGBTxe/wNUcP6TnUguPQC97c0iPKbKD2+mz9eEWnIhFrGZgf8P1L3RQ/8IQD2yY+Fj4WT6fn+3jyyQJ8g4iPYGPpbVp0BmN4sdiWNEoKhAwM2KO06toiBUMpmcAs8XuDdABaOqdD98TYOF5+y2NvWtZcysYRIVCqGDQpIRGwqbCJuycOWzvwN1cY9b8hUM4WxpMYn1uDn5nDJ0wTdJVNIoVCZvj2PZ+2zcIIQ4u2k2ROHcBkS0d+KOk6mb8vAXkbGjDe+vdTA+lXzb/VzXzT19FQ7V5nGcuNtffOeNk3LXreXiIAzxAArwxLtnokRzTpYnEWCHh70FIxxPQHXzYeXmoYBCVEzXBUPesVJrjmBmqUUZZyqRP+horGMSKRnvfbtsmEHQCZoYT0sGgTiTQySS6p7VEd5EVd3stXkcHVihsqo2mUmYmMBSEogKs8jLsSRPRvjZpmwC+NufoLvQihBAA4X++hpUYPbN50ZpOUiXRA2+YRTZ9r5rv/MefaajeS0N6qXohhBjjZCbvIKB2T0ksLgTPx2uvS19nxWI4ZaXoijLU+i2QMNf7sRgqOXo+iDjlZfjtHahIGMtxzJq97uDVikTMRj1FZ3yNCjgm1TIYRAWDeO3tZpZO+yb1MpFAJ1P4nc3Yubmm4Ivr4kyqhK64aeMweQJ0xiAawd9Wa2bwPA8VDpnZwI5O9EHQd0oI0XfaTZEcn4c1whNjTmVFumVMf/ivroST5g/DiIaOf/J8UrkOm8+FJ87+bz43Gf7w7Sk4z1binrwNgP/a8Arj7S6umjzw8xz1Gjx8TzUTftg9LVh9JPVHRsnf7LL5AxYzv7B0CO6NEJnnaYWX5YVNsn18mSQzeQcB7fVeVO5u2gKJBHaumaGzi4uw8vJMamJTG6ogH7u4KL19zzq1USEQMOvnOrtM6wPbxopEUBEzW4dSO2fs3BR4nrl/tg1KYRcWopNJVCgElkpXYLOrJqIK8k3apuNAa5s5XMCBZBIiYXS4e+2i1ib9NZnCb203M6W2vcdQhRAHt9j4IO7px4zsSS0Lp2rSgHb1A5n/yGDn5u78wm43tV9KMP36dzhh3nt8bvLONMxVGypJPVHFue80c+PUo7lq8vsAOHtl776p4efGc8KbiQOO4a0Lp/DjT/0Ra96hADiNHThdmpKvbcQu7eN6cCGEGGaZf8UWQ64vqYFufQMqL9cUWIknTPqh55uCJtEI+Bpn5nSA/a5pyzbejnpTaMa2zHrCZBLtuqYHYCRsWh8AKAvlBPCTSfxk0gSGyRReSwt+V9wEf+EwWApVkAcBM+ntx2KmumbPGsZI2KS4RkOouGlHofLz0KmU6d9nW+C6+Im4BHpCiF5y716Kckc2bdCt2TrgpuzOkyvguHlDPKK+63kN3Vcxq1hHiIDyqYvncvXaNen3sJ++7x7mFW+lLpnfa/tH5/buGxs/qZYXjth/awUAd/1GZgUa2HJ2IVY0ivveWrywouOEOgqfGl0prUKIsUuCvDGgp/plz9qyvnK31+In4lilxTgV41E5EZhcAS2tMKHctCoYZVTAMTNtYAqodLdY8GMxvKZm/EQcp2I8WAorEsaKRruDwRRWQR7OuHLsshKzXiORNEFhIomua8BvaDLBYFu7WdOYmwO+xq9vNMUIkkmTCtvdR08FHNO7L5nco/iKFQqnny9ZryfEwct+5pWRn80DnOlTB7ZfS+zAGw2TbV9emC4K1kO/70gaHzbNymdc+ioL8tfjn7KV/zdjJu6adQDcMnMaj607lKdrZwFQuTSfrsen8vvNL6b37a+rq47nZ1fcyrbPHMmme47gR1/5PQDFf1g80LsnRNbp6ZOX7RexdxLkZbGedMoD6Umn7Kme2Z9qmFYkApYN0YipLNmVhJCZlfILotiFhf0edyZZuTlgWSjLAqVMY/PcHJPiEwyavnhNLWaWDUww1zPD5vmm8bvvQyAAlkq3aFB55rnoVSXT89Gui1VcZALHnlnCYBCrYpyptply0+maPYVedg5W7XlMIURGZaJlTGhb24ifsyc7ob90bf0QD6TvYhM1sQuPS//edskiZvz3u5Sca5rM/3jjMi7L385vNr+0x74fnb2Ck8avZc3tx/DCS3OJfjuXD33vOniohB0PzOGadav6PZ5vrzqfiedtxLI0dzcsHPgdE0KIYSCFV7LYrm0M9tsGYLfbtJva2frAd/exl1mA748rgvY4/tbtWNEourbOBElOIVYsiTunCqjC7kzivbnnm6BTUmJSPbOEu5/G7XZurimI4jgmmNs1uNI+fiyG32iCOmdKFWAayeP7kHKxKsbh1WwzM3jBoKmq2dpmqmwGAjt77mkNoQBWoUkF8hubQVloN24KvESjptceYIdCpjBMIiHBnhBZwJ4yCXft+hE9Z3J8HvbKET0l7rtrUAuPwFqzBa9pL9Un9yF51HTaq4IU3r5kGEe3J/uIOcz82itEnyrk3dnVTD5tE/mnLGHdXTu3+eZZl+KtWg3A7ze/yAtdVXzv7ospW7Sdl47YAMBMVgCggZLl3Tv+Dv6bOenjWKEw7R88ku3nJZn9kw7KfttFw0l7PkYl577H5uuqSRwep2Zh9/t1JCK9UYUQWUGCvCzX08uuv3oCPzsnukd1zZ6g0R9XhFXfCkHzzbXf1YVVXgaAbm5BhYKoggh+0EZ1xs2sn697NRrPpgDvQHTKNcGV55nASpk2C+mZ0F3emN2Nm0ygF4tB97o7UinscWX4zS2oVNAcx/NQWkMyaY4VCUNPCwqtwbKw8nNNURvbNo3VbdtsE3DMmr+Aefz1AJ5nIcTQSk4oxFo7suf0HYtMrNjVtgWV5dCPIC9eGqBjgqJk7iy8lauHcXS99XzJ2HFCHROpw//+ntuo7tfe1bcs4Ae1UZ5YcgQz/hXDvifQr65mfiJOzt+WMuNv4FmK7b+tpva+PK4/7GFumTmt17aVP9ktPTPL+soKMRhaW/g6u5P+dJaPL5MkyMtyug/tC5QTMJUi93oAjbLsXtuiLKxQGLVpO16b+fZRBbtnnjo78TtjWDlRUAprzRbUlEp0Y3O6OElPtUrd2YUKOHuskchWfiIOie7ZtEgEbAs/FmNfb8nuxk3mW9k6k57kTJyA7ug0QVrPekWl8Ns6wLbMc9XRCYAVDqF936SPOo7ZxwmYQDoSMY+1az6QaIAud8ABvRBi6FjPvYp9+GxUysN9d82InDO8rT0zrZUXv46aPBFn5vT0+rUDyXvwdSKPlhFfMZ7ACM4+uqcfw/p/V8z69Mu9rr9yzfp04NUzA/vYB27mS1XVzMC0MhjsY+ssXcX4u5u58WsfY86La5hfsJn7Nh1JU00BBSsdWmf5hOstJn1nca8vQYUQIpMkyMtyPamX+0vX3D3AsyKR7pkqCz8ex0+ZctNWMAjKAkvhd3VhBxzQvtk+5aLjcVQ0ihUJg2XjbtyEXVyE/+pKU4XT88zasmAQ3dFpxnTgatNZo6f4iZ9MmrYHu7AiEZOu2p022TOr53d1Yefl4bW349ZsxZlQmd7H21GfPq5VkG/SPx3HFF5RCqIR9JZtZs2f1lglRVhKmVm/RKI7WNTmMS0qBN+XIE+IDLALC2FyBdqx0AEbtbEWCvNxKsbjbq8d9vPrQOYq77qba3Aqxpv+oDkR3M01+92+5aKjmBV9h9cOm0TFv0ZmjMkzjyX4z5eZ9cSet+06s3b2ylYCyuXsR65hJsuGfBwTfriYth/Cs0Qo4T1Kuq8vH/IzCSHE4EmQl+WsYND0bOtjGwMrFE4HKFbQxh43HrssD7vJw4+Zkv86mcSKRHbOwCkF2sdrasbJzQHPT7/R645Ok6bpeWjXtAjwWltxSkqwLGXaDrS27ms4WaVnzduuM5+qO33S7+oyTdFzovgdHb0Caz8WSwd6fkMTVlkJuqXV9MjraajeFTczc0rtLOQS7Z7x7IqbpujNLeacwaDZTunupulRMzOoJc1HiJFmFxbiz5iEvbkW3dGJjsVwF83DbulC5URxJk7Ardk6rGOwOhNkspZxTyBrTTkMx3Fw12/c97ZhRWGgCzdn/8d0ykrBcQYdJNtzZxEb57C/GsQ/2Pgy35xyLL9cfirFS4KM65TXUiGGgofCI7urV2b7+DJJgrws1zPr1OfF3NpPByhWwc6eQCoUxG9pNccKhbHyc81MUjBgeuMpk9O8+7e4podcwAQ0mJRHKxgcVWvx9qB9c58shfY8tK/Tv/cErD0BnhUKm1TMhJmy9BNxqG8E7ZtZO0ygRvfsnxWJoCwLVZgPTS3mdJ5nZj57gm/AystFJ0x/PlzXFIKRIE+IEee1tMCKFnqVqFryRq8UP/uwQ8xsm1L4rw4uR9GZOAG/pAD/jXd2XtmVHSl+yaIwoQ17D2itUBg/EWfiJ9axeMcUptzbsN80SLe+gXW/WMT0awcX5L17XR5TKrfBHXve5hwyg+v/+VcOcXycQ2ZQ+nyAoj9KCwMhhABpoTBq7CvA27Xcd7pPXvdMktfcPcO2y+ySFQziJ+KmmppSZpYpFEpXe9wb7aZ6nX80NUffVU8/ul4l0pVl1s0BOA52bi7KCWDn5QHdQZ3rpu+zcgL4iTh+MmnW4NmmVQO+xi4owJpYCbaFt7UWnegOFPPNsaxIxDxHSqFjXWb2rqcoTncRFyFE9vHefg9raz1+cAi+F00m90jP7Jo9Hqdq0uCPPUhd5QFSR5j+eXZeHvYRc9j03WrW3HE0bQ9MYNO9R/CJipdo7wynq1jur9XPzG+9Tu011QMez/qfLWLOD1rJCez9Ped/nriDG6cezb+6yth6Zjml97414HMJIcRYIzN5o9yu6/F6FnwrJ4BdXGQqOEYjkEyaMv6YAM2KRkFrvPb27vV7KVROJP1N7b5YkYgJaDwfPxFHOQGsSPdavWAQHYthlZaYtMTuNEYwrSAGU1Rk91nMnnTLAx1TOQFUMIAfi6Urg9rFRXhNzelAzy7Ix+8yBWRwXbzuYM5rN4/rrmmbzrhy/LZ2rJyoWfPoeenz24WFJhjcuh0VDJqZ0twc8xzYNioUQsdiJqAOBtGua2YIY12owgJIJNJVNoUQ2cetq4e6etQxh6NXDDyY6DmOU1mBu207AIHHV+BWHwmbtgzRaAdGK/C/3cC6ixZyQfUK3p6/iqo3d96eC/z6xA9T9fxrHPeGyxPbZlMW7cA7yaT+r/nfBVhxiy+c/i8erj2MmBug4pyXYR/FwXYvGrbre1D9Q7Nxd7i4762Fk83vFdemaLjZYlZxPetaSjnjufmEbgxzy0wYx+LMFK85gJ614EKMRr4m65uNS0HbfZMgbwyyC/LxWtuwwiH8bbXoacUmPRBlZqFisXRjbr+ry/x7gBLaewuotJtKB0NOJIJWlmkZkJeL19TSK2AcTEERnXL3WkF0b8e0QuF0YRkAFQ6ZsWkf3dGJ19SMXVxk3ni7zGOBb1Iu9/YhZNdZS3dHnflhl4DTzs3FjydMIZVgEKWUqWgaNWsbcU2grevq0b7Gb2/f2WDe1/jxBKqpGRUOoeSVSois5+YFCR4xZ699Q/t1nCnjoDvIA0gUB4mEwli5Oel0eP2+IwnUNONu3DSoc/VVvFiRb/k8fd7PuWry+/a+kTIf+J7ZMZPk/eXkX9bAyv9ehJVQzPyMSZV8lAIsthCFfVYvBkz7Gidg0t+V1es9oyKvjY53itK/tzTmEvt+ktLbozQtj5M/LkLe8lcHe5eHnenNup8K2EIIMUwkyBuD3MZGU7AlPw/luWYWqTyKqixHvbMW7abQbsqk2agwXnt7el/V/Ya7+zeP+wvS7MLC9IcSqyuOTib79IbW1ze+3bfZ1zfCyjJBrBUKY+flmaIw3bN25sOEg/L1zusCjgkgbdusz+vnuGCXhvWRMCoY2Lm2rrsPH0pBPIFVVoq7dRvKCeC1tJhgVPsoS2Hl5Zp9HPlzFCLb2c+8godpzo3v4xZGUC++3u/jWF3JXsVWwnVdJkMiEiZ+3gKimzrg1dX4c6Zh583Ge+vdfh3fmVCZ7vHp50ZQSTedYrkv4//wGhtOmMlV799HgIdpMQEQOWMD2382nrpFLUznwI3R02vFg0F09xdavVP/zXtOz2yee/I2prItfesv3/cXvrz4YvIeeBVfWegMz3oeSPy8BYQfXH7ADBkhhBgu8qlyjPKTSfxt27GjEXQ8iGr18RuaewUzXkeHCXC6g5qeAK+v6SVWyAQ2XktLugqodl2TprhL4LgvPdU6hyqVJf0BwvPAs1CRsFl0aplgS1lWem2ism10umm5n/7Q0TOu/vLa23EiYZN+GQmbALOlZa/bKts29z0YNHkGbndTdZXdKRFCiJ16ZvKcwkLUITOgvTOdftmn/XOCtH5iEYV3mADJbuqEivG4VePQtiJekYNfdRh+wKJjgkV5PzNE3a3bev3uzJy+3+2VE2DLNUfxsdnP8AI712g706eaBvHPv9Zr+zW3HsvMTx04uOvhdxf92nVZwV6320tA9OONy/jVjlPJXxFOv85nM2dcObGrWsh5Otf0S+1r4TQhsow/CpqhZ/v4MkmCvLFOKQg4+M11oP09iqaYAMsEWT3BzYGCLrugAJQyQUz3G7JVXmb6xEUj/aoUpz2v1xugnZdnGpQfYAy7z7b1/Nxz/7SbglhsZ6GTZMqsjevuDdizzYHefO2Cgj63iHC7m6b3BLjKCZj2DLt8aNFuameBHMwHGhVw0MkkKifap/MIIbKH19ICLS2ml1s/grxESYjSJzbidb9OkEzSsaCKQIdL/TybYKtN+0yPqbO2EUyEaK8/DsuFnHuXDmicOhLEP2k+wXU7IDcKza3Ej5hMy/QggU5NyyzFzR+9lf+ePqfXfu66DVh76ZM+81Mv73nlAez+WnggygnQ+cgkPvXjaiL1PuV/M+mg2Vz8a81txxDMTZLYHqJMbTdr3u3M9UEUQhy8JMgbg3q+IU1XxUwk0a43oDfGdIrNLt/Aeq2te3wL6zc2mYbq3WmgzrhydKyrVyrovvTMqJk1gvFeAZ5yAthFBfhtJi1SRcL47ab4yf6CL7ugID3W9HncFJY2qULaNfdlfwGeFQyanoI9qZXds5V+V9f+01dzc3fOGIaC0G7WCFqhsAkygzsfOycaNbN4AFJ4RYhRK/hPE/Q406dilwVh4/7XOec8/Q6ML0dZirrL59M5AY499R0idoqrClfS6OXy0HkLcddsphCANel9L1pVx9/m9K8Ft/fmKizo1SrCeaKO0icg94VyCk6o47+vn7Ov3Qds10wRZamd7Wl2KWrVY9dUzp5lBdEPrCfK+iEf11BSToDO8+bTcVkrutbC25zDrK8sSReCkfV4QohMkDnOMUi7qfRsVa/r9qOndcAex+oOuHZPodFuKh1I9bByoqaoiFLp9gF9G7CfPreydqYspj8UtLaZGa/u1FDlBPYZ4Dnjys16vFTvJu09s2d+H9cL9myrbNustVOWKc7ieaaYyl7YBQXpwBClUI5j0le7WzOgfRP8+RqSKVN4JRYD1zXrGBtGce9BIQRgZr7c8YUH3M5rbydZWYC3cC6tszRV315M3aIWNi3opMxp4x9zSnHX7GUKDfjbnHJqvjXw1gS7S3oDm2mynpnQp+16UtR7+q7uLcCDna/P6eUDo8TqW+YRG2eTTDnMunI5077S9zRWIbKZjxoVF7F3EuSNUdrzTKsEwI8net1mBYM4ZaU4JSXYeXk4JSXgOFg50XQwZEUi2MVF5jjBYK8Uw7TKcThVk3CmTcHKzUGVl0JFOUydiMrPTTf33vXNWjmBdHVJ5QRwykrTlS1V9wxXTz+59IeC3Bys0M4CMftbxO7uqMNrb+8102ZFIgNe99fz2KmciFlD53mo7sfVqazofY54AhWJgOOgEwl0T6CttUnFdBwT+HXF0xXq8DwTEEaj6SIuQojRTb2+mo6LF5L6wLG4px8DkP53V/ZLb7F9UYTp1/QOCn447Yi9HnfXdXUTv7+Ydb9YNPAxOgE6P3wcNfcdRvLkvqWZXrCqodfvm5+sAiD2r2n7PId2U71ef3te1w9kNMx+XbCqgXmvWVy36J+kcmDihW9nekhCCJEm6ZpjlLJtU8J/L6yJldAVx+9uc6CcAPb4cnR7R7rcs7Ltnf3kumex0sd2AljhEMr1INZlKkoWFqBDDn44iEp5UJCLUgqrO4Vz1/10V3znz8kUdl6eCeBcUwl09xRPVVKMctrw6/pfocwUhwmigsG9zv45ZaW49Q172bP73N0zi8pxwNm57s4KBvFbdh4vvaYwEjaN5X0Nnm/2Ky1BN7eYIBG61wV2F3pxHPOYy5oNIcaU3L8u61XQKbS9fY8+btpN4Qfgl5sW80TnHB4+tIj92X1mLzyljZZdirf0Rc+Xf1u+eCReGKZ9tYO95ybsdOeWlzh52We4u6adzb8ZB1EXqznI9GvMGrn2x8YTeMIjcPrOVg9jvW3A5r8dznNN24h7Dm8c5VPB4kwPSQghepEgbwyyCwtRpcVgW6jmFqyAjRUIogoL0LEu/LwoVjCAKszH0Rpd15AOOnrelL2O7mbg3ZUid32ztkuKwPWgqQXyck27AEB1xCEcRAds/EAAckM4yoL2DvyOThNQFpTh1+4wPZHiCaycKCo3h54QR0UjOOFQ78CrKw6ej7VbD6m+8BNxVHeA2jNr6DU1m+psykKn3HSvu13vf5oyLRH89g7T6gBwJk/E27YDHYulZ/N0W7tZL9gZwyorgVgXflsHqjCEbmwyx7Is0wA95WKNK8XbtsMEuikXnUgMqmG8ECK7rflWhGn/sef1XZNcvlTV99TL6S+HWXes+cJrwodW8otNS7j2jgPP6DnjyokdVcX24wO407qYWL6NnE+5uJtrDrjvpZOOZ+L7bPxACTOfWbbH7eNuXgw3w4YfVfOpc5/gmSMLx1yAZxcV4s6azJorghSPa6My3Mq7fz2Eqz77IP+gNNPDE2JYeFrhZXkz9GwfXyZJuuYYpPJz0U3N6B0NUJBv1oEphY6Z2SY/J4gfDeIVRdEBG5WfD6kUOpVKN0m3Cwqwi823yrumR9p5ebg76kww4tjge5BKgW2Zn7v/R3lhBz9gmaCvO23RikbBNwVM7KICk6IZCpr0RTDj1Bq/rcOcv7AQZ/JE/PpGU+wkEUe7rgn2on2vRKksZQJTQCkrXX5bhU2ZcO15JtjaWzP0dLlvB7eu3qS2YmZKrVDY3HdAlZaYtMtAAFIuBIMm6GtuQeXmmNlExzFpsaXF0BXHCodMCmcwkJ75E0KMTdrb+weRYN2e37X+2zv7/iKrJ8Abv6SA9sdmcG1VH1M2HYft1QGSZS6Ty5s4q2IlVfc1cMKbiX3uUv/Q7PTP9svvElj6zn5PMfXri/nfxaf0bTyjRPyDx5I8aS7rrp3DjgU5qA6b0puCxG6fQPKkNhJ+gNV/ODbTwxRCiD3ITN4Y5JfkY0UjJmiwFUwox+q08FvNjJr97iaYOB7r9dVorSEYxO+M9Qpydk1ttHNz0z+rogIs1zXVIkuKTcuESPd6vUQSu6EdOmPYuTn4uRH8SADLcSAngsrLhUgYVZwPteZDjNfUYs5RNdEETI5D8ogqgs+/iYpG0U0tWJMqwbawGprRrturz1Kfvi22bXRnzJw/mcIqLUkXT9Fd8f326jMzfgoVDuEU5JsZT6XMbJ3nmdk5MOmYoaCpptm9jQoGUJFi/MYm074hFIJ499gDAVQ0Yspr5+aY2UpLpVtSCCHGhvrPVVP2m8X87X3/y4d+9SVmfmFnC4TUGcdQcHT9Hvs8cKj5Munb699gktPOA+2H8+jc3oWuahe1sujVDnLfcLlzSTUzP7N8n2NwKivwxxVRcHQ9fz38j7zUVcVND/07VlLhTe5iOq/tdb/mHXmUdf/c14bes65cDqOoaMr+2EfMYcexDk7QJlWQIJlvkbfeQq94i/wVkP9/8CgFzKL/7SSEEGK4SZA3Bqm4awK8ZBJCQZSnTXClwfI8Uxyko8s0RVfKBCu7sfPyTDsDN5WeCVQBB7riqFAIy9f4jU1YloLOLpO2GQri50XxJhQR2FCH1d4B4RAEAiit0bkRExT5vglowMxieT50dKLjCZg0nkB70rRtcBxUOIQOOeCDKshDJVPYvp9Oa9w90LNC4XTAlm62nnLN/UwkUMoyAZ7nm9nH7tnDfU32+8mkmZwMOCbtUykzU+d55n7sUkhFu27vtXWOYwJXZXWvkfRAW+Y+9zRAj4QhGEC57s5iLEKIUW/D9xfBtE6mXLyYrd+o5oVYM7N/15xel7fhh9XogOaFI27nExy/12N8b9q8/Z7j7fke4BC8N86W66uZ9N19rAuLRuiYmkdHvIM/tSwg5dvYXYrkhCRzbmje55o81dX/tcJ2cVF6PfdoT9nUazcBRWgLIpsdild65K2qO+AaRiHGCmmGPrpJkDcGKd83AUVHDNCQDJv1c+EwBBzwfXRTC8qyTLn/UGiPY6RTGFO2CYZSrtk3EEBFuvu9+WbGinAIrzwfK+5idXRhtXbijytCOxZWWxzV1GLWqrmF0BXDb2hCFRfhlJSYGcH8XDPTFgnj5oVRvsYpK4VAAL+5BbckBzvuYlmgwKR7RsJ4jU0miNv1vgcDWCXj0I1N6KSZoVOWMoFdysb3PKzCAvCSvSpc6v1Ue1OhkAlAAVVoGsGjFLR3oBMJsBS6o9OkXkYi5vFNJEwarOdhFReaA1kWujNmZu9CQUgkTKDo+aZHXkD+HIUYzVovXUTiwhZi9WVMveqFdOGVCT9czP2nzqP5pBLK3jYZAt+84D7uPf9E3rqgcNDnvf6IR7i7bAHJ7+55mxUM4pbmkShQlPxfDlN/VE9AeSy/3gSE+w1Yov2vSrzj4jmU/nbxqA/wAPxYjII1GjUXxr+cxH58hQR4QohRQz5VjkHue2vTP6tmhWfHcBuae1V662Vv/YpiMey8PBPAxBNYZSUmMAQTjASDeDvqUbGYCbpqNIRD6JZWs+7N99AFuaiWNnSsC6uoEJpbzZq6vFyzXU7UBFyOTWrOJAJvb8RZv9302MvNMevXKsdjL34ba+ZUkuPzsFIeTiSInxvGKitC1Tejkim8lhasYBA1rsxU/Aw4kEhgF+TjtbahLGXaGngefkNjdzsEU+VShUI7q4payvyeTKXTk7TbPXOXtKCj0wRlloJgML0mr+cxsyIRdGfMzOK1d5hCKx2d6GQKqyDPzOiFgqaSaH6e2bF7PSTt8vFBiGzmlJfh1tX3mqXadO8RVH34TQAK7lyCuktROmUHDcAN61+j0I5zbdUiNr82gVA+VC3P4bX6CfxldhJYw1XPf4LSywPcdePPuLqq94zeJ1dvpiZZzO/ePIFpH917SiXAnw6ZBOxsg+CfPJ/gqhr85hY6Pjif7e9TqBS4xT7/d0jfetttuHseh1y+ksGsFN5fKny2q/1yNX4Aqu6vpfCFFho2NrOPd1AhhMhKEuSJfdKJBH4yaZqLt7ShigrQHZ2oQAB/+w70grlYbV2mZUJTC7q0EBUMoGu2QyKBbmhCFRV2r4VLQk6OSUuEdMN0FXBAKYLr69Ap11S51Bo1rhS3JBc/aBHs6ATXw3J9rLhHoiIfy/VRrmmNgOt2B6QWdMYgmTJr3spKwbawMYGaTiZNumbP+ZNJrNwck27Z3cbAT8T3CHp1qnvMySSquNDs29hsZgC74mZG07axIhH8ri5TfGWXpu7YNlZFsQkIwxZ4Pn57B8pxTLpmIGBmX5WkHAiRbaxoFBUOsf6aOcw8YQMd8ek0LxnHpP8yM2HTv9EBh8zo9eXa1u8fwuZkkhVdLcwI1QIw7aumzcGmH0IxqwFofXQm4SVBim5bzK1fOJ4rVm/kj/92Bt4qc/udH3w/7ntrmda9Zu7sla17rM3b1Ya75+FvixKc3EGiayJFz8+g+YQ42rNYdMg6Wj6S0+eZqEmlzbjHzsZ67tU+P1b1n6um4tGt6XPEPngMkfv3rMY5GgQ6oXMCNC4aR1FTKWyUdXfi4OOj8LO8eqU0Q983CfLEHuzCQryWFvxkEqe8DGwbv6IE37KwcqIm4Jo8kUReACueQsUS+JPGYTV1gGObxt+BABTlQWsH7uYa7MJCdHEuVmcCf/NWk8ZYkA9K4Tc1Y+XngdZYkQjk5eIW5eA7CivuET98MuE1O7ASLnZTO15OACvpoeJJU61TWWbmz3UhPxcdCqAaWkwqpFLp6p4qGEynZfrdBVf87lk2tI/29p6yqSyFikRQoSB+QxNWbk73WkbfFFPR2szEwc6iK7aNjifS32KrWMwEtY4DVnfhls6u9JpA3bNWUgiRcT1re+2qidR8sILOSZrp/7mYJBA+7RhCX2xi+39WM/3f1vG9qrv2qHB5ytTVfG7Cw3zitEt59L21HPqKw+qPTuFH//o/vjLlOE5/u4O7f3UG4y59j4LGNQC8dhQ8e+l/UP/NBPnLq8k9t5bGl8qJVxZjddqcceJrvBcLE/tXJdEPrN/ruKd+5I09rhv3r4m4m2swTWma+3T/1/zxGC4qeZV7PzaOWc/17TGr/XI1429e3CuIHK0B3qbvVYMGy4Wc7Sm8oj2XNAghRLaTIE+kOWWlpp9dR2f6Or+1HZUTwdqyw1wRCaPzouhwkMj6JrOuzPVQbgRcF3fjJtNgvGYrjq5Et7WbmcBEAqul0wQ1mHVuRCOmkEskDJGICYza2iGZxLEtU3EyEsZpcdD5Oejlb6ELCgi2tkNOBOIJU9hFB9CxLlRRIdqxUG3d7R1c095BhU2rA7+1zRRB6V775nd1mcqhASfdzHyvbDu9dk51N3BXwaBJu9TarMvrjvGUbe9MUbJtcF2s/Dx0rKv7OgulTAGbnqI0+J4J/lxJ1xQiG/SkarvrNjD+5g1mlr77NufJFdjTq2k5JkXsxB1cy84A744tL/GNmrNY982JfOzZarRvZvfeOdoF1vKVKcdx2Ks2v3vrfUz73eI9ZtUK7lxCwZ3dv/wKouwM5tZVH8maS0IUV7XQ9wYy9KkP3u4i64IsffBY8qf2rfCKFYlQuGF0pmXuTbge4qUQbIHwhkYCfW/NKoQQWUPyw0SaTrmmn9suBUBUMGCCp+7eb7qpBR0O4uYG6ZpWTGLmOLoOn4hqaUe3tJqZuIJ8nNkzTdplWQl62iT03Bn4BVGzXg7T9FwX5EI0aoIwxwREKhI2VT2DAXRxvllb110d06msQHd1oV0Xb9sO0Bpv63az1m7GJFITi0wl0VgMLNvsm5dr0jgDAbNOzrbwOjrSfe68jg6TYnmgkt+Whd/RaYK4aAStfdxt203KZjyBFYmYWU7HgXAIFY3uTAFtbjGFbIoK0zN/hEImcOzsTBde8dvah/PpFUIM0O5fApX8bjHffN8jgJn1OvQVh4+/t4VPTDqexc/P5eP/8zR/37wce84s81oIzF4RZPN/VfP2fI9p//H6/s930nzW/Hohq29ZQOOnuxulL34d5SpKzn1vyO8fQOxf05i6PMqa247BdyBSG2f8L/ZRrXM37sI5pKJ9T5lyZk7HOvqwgQ512E3482qczu6UzUPL0oW3hDjYaJRJ2cziy77rowsJ8kSaTibTgYkViWBFIqiiAkgk07N7KhRCrd1C4I31KF8T2tpGoDWJX1tngqfuSm60d0IiCb7GamnH3t6Ita0BXZSH1R149QSGuC464JiCK4EA1qEzUHWNKNeHtg4zW6YURCNYEyvRqRR2ealJp5pYiSovxdrRTGC9Cfx0SZEJ9JSFbmo2s3iNzSYA7V6/p1M7++1ZwaCpwLmvxyXlmpk22wR6pghLCrugAL+13RRbSSTQXXEzE9rcYh7HcAjt+/jJpGn5oDV+U4tZw9dzPsdULPUbmvbaykIIkZ3+NqecdTcfxzULn+Ciopdp9026dekb8GbXZFr9FN6q1bjvruHqtWt4/JFjmPqLlQc8rhWJkCwMgK+YdeVyGo91Wf+TRay9cz5Vh27f6z7OlKr0z2tuO6Zf96P144uoe3A2swrreHz54Ux6wCY+IQVL9kz93BcvaJH3l6UH3rCbu2Yd/itvs/0r1f0a60hx6xt44HM/pfnkLqKb2vHj0r9UCDH6SJAndvJ8/Hgi3VdOhUKmgmROBLu76ArhEKq0GJ1IEF7XAJ6Ps70Jq6wUZ0Il7hHTcOq6G6kHTQESM2NneuSp7Q1QVIBfWWoCHEvh1tXjrVqNKinCH1+M6oxDMIDeWmsCJssyffaiIfy8KNaECtP3r6jQVPQMOvhNzWbGTimU6+HW1ePvqMNv68BraDRVLrUpbqLy80xBlsJCk66pLFMhdJem77uyS4pMhU7bNmsHC/OB7qDYtnAbG02vu0jYbFNYYAK+1rZeKZg61oUKOKZaJ5iqmx2d0D1LsM/qp0KIrLLlBhOcFM1s4oToatp1mLBKcW/NUpoOVSS1wyenHM9hr9qsuf1orv9/n2DyjYvxWlsPeGy/q4uuYgvyUnQ9PpVDv7eNadct4Z4TfkdBKM7H3tuK9cwEvr3+DcYvKeCadatYe+UECl8s5eq1awjWBNlyfTUb7t5/j70e0z/zLq1bC3jm9UOZefUyLFcTaOxfM/PA4yv6tX2PZP6AdhsRpz90LYumbzCZF0IcpHytRsVF7J2syRNpO1sGdC8wi8WgpQUAu6AA3dVlZps6HazJE3pVkwPguHkE1m43AZ3vm8qTxfkmaAOTopifCy1tWA3NJjjahbt+I86ESlPUJCcKHZ1Y0Sg64KCa2sz6uqICtGOj2uP49Y34iTh2Xh5WWQl+fSNWIoHf0YkVCpvqlcEgOp7Aj8VwIhGIhHG3bjOFFbqbvSvbRvtmvZ2dm2vaLOySnuU3t6DdFNoLoJtb8HfU9Rq3cgJ4TaaggVNSgrttO1YwiL9blU6v+7EEsJWC7tRPEqbKp9/VhXZTZmYxGDQbdjeLV0WFuBs39fcpFUIMg0nfMWmMH5+6jI2pEuaHalmfKOfDqy/i7LOW86/n3kfi4Rrenr+WWc6b/e4Z50YUVmOQ+HPj4Q+1vK9c8/V1H2LjjlJ2/HYqBX9/na9d+Bny/ryE/2YOU1hMC/D/mEkVO1Ms0+uDn55IwnNI3Tqe3Ctr6HID1GwoY84hNWxsi1D4ts2436/AB4KPLCc6s+8zbNZRh+K/9k6/7l+Pqhv6lg46XJJnHYvT5WE9u2cFUbvT4ssVT3CD/lgGRiaEEIMnM3miT3QqheruXYfr4m/cy2L+pW90Nx1PmQAmJ4qf012VzLLQkZBZf+b7UFa89xM53c3XW9vSa+pUu5nx0qkUdMTMrF5HJ2gfp7wMr70d4glUIGA+0ChlZhCTKVN9MxgwgVhLS7qfnp+Im6C2p22B1qZKZ3dg6JSV7rzv3TNsOpna62zbrh/gegLX3QO8XVmhMCo3B5WfC0rhNjbix2LpNFls26S+5uWiolGTKqoH061KCDGU/JPnm3+1Ra1bSKL7m+StLQVErSShJvjctGcBBtQUPFYJgaoOiv64hNyPtPD3h45Hf68ce12EvL8sxU/EyfvzkgMeR3se0efHse6NSeR8IkHu3UvZ9NJk6pZVMO1ujzW1ZeSeuY6WOV6v16zcrX1/vbHaB5bK6IwrH9B+Q6ltSgDr2Vdp/+gi2j62CLXwCJLnLDA3KphgJ1ExWY8nhBidZCZP9IlVOR5d34gqN73nsBWsXL3Hdn5nDJVMmfTIeALV0GTaGngepFy0ZaFCQXTNdpyZ03HXrOu9//YdpkF6MGiW0jo2aN/sk0rhNzaha+vQbsrMmtXVA91BaDgEOVFUZyzdPsGtqzdBlW2DVmaWLplMNzPWbgqcAMpmj5lF5QSwiwpw603x8b5+WLOiUZP6muj94Uc5AVQwYILkpmYThHZfr1NuevbQzs3FikZxt9fu3LlRyrsJkWlWMIg1vhz9yho84NG5Baz/y5G8UDWTJaunM2fKNl4+2ib2e4/ff/1CQgyshcCU+9vRK94CwGtqpup6M+NV9Uz/jxU7cQez8mK47aawU8+xAEoqF7HmfxdQUNlK6oxj0mmXOff2fX0drW39Gk/yrGMJNcZxl7/Vr/0GqvGqakp+t/cZw9LfmuvzNnbB4tfRQBDTRujPF/2KG7afgbthE0wpGpGxCpFtfG3h6+yeD8r28WWSPDKiT/wt28yMUmsbfmjv6zXsw2dj5URNQNbZhbIs0xy9Kw4Tx6d7yOmuuJkRbGrp9W2uM32q+QDVFUcX5UFejpkVjCfAcbpbICgTKNE7KNPxhFm/pxS6rAi/o9MEeMGgKZiyS5GV3Wk3lQ6wrGDQzKaBqTLq635/45wO3mwbZ1w5VjSaPq9Opszsoeeng8xdg8ee7fxYrF/nFEIMPz9pikz1tIIBmPWtFjpSIez6ALPzTauZYEGC8IPLB3yexnl5O6tqDoGe16Rd2bm5FN77CjM/s5yUa6Ptga1r6fkSrK9CTQn0CAV4LZ9YRMkbnQfcTltgHzEHAGfGNJrPmUOnDvJ2YwX2YYeYjY47wiwnEEKIUUKCPNEnVlmJqQipFFZ3+oo1fy5qweFw3Dys+XNNY/NJ46DArKb344mdH4Y21KDycvFnTUInU7iHTTEpm6EgzszpOJUVZtYunkBVjkNtb0BvrUU3NuM1NuNu3YZdWGhaEuwlAPJjMZNulEjCxq3pFgkoK729n0ymZ/FUwNkj4LOCQdOUPL2vMvtGIub3nu0iEZxx5dgFBQCmYTzdM3h0B42JONrzcHfUpcfWE8ylU0Uh3bpBuymz/tDXeB0dg36+hBDDw08maT1zNlv+djhg1hLfUPUQt134v1xW/BJHrLBItoRY8+sFAz5H50Romjd01XZ3zyoATPuY7hTNSd/V1B/Vv2IrA6WXvYl+35Ejcq78DXGzjOAA7FgK781VALhr11P8whYCeBxbtpmtpxaz+aY5xCrMem4hhBgtJMgTfdPVUzwliB/trmgZdNC2+S/kBx3c/BAq6aJDjgly3JRZB9fRYdbXWRb2jhZUMEBgS6M5Zvf6OTwPHQqgkyn8zVuhIK97DZyfLkbit7eb9Xf74dZsNcGlY8agggGTqtnNCoXTM3d+MmlSI3tmz5JJVDCIH0+YD0WuC56Hu3FTrw9JflcX7o46vNZWrFAYv6XVBKDWgf+cdk/57PW76w5o/Y4QYmTZcc34W0Js/L6Zbbu3+VgCymWc7XFB4cvklXcSLOlizf9byNZv9H9GLlkwshUd/dfeIW+TxqmaNCLnc1q6zBeEw8wP9a2Zu/9q79YW7uYaPv7QZzmv6FXUSc1E1wbYeq6X1b39hBgOma6aKdU1B0fW5Im+i5qiIFYihQ46KNfHD9soH5Tnm4aUylzsggJ0Mmkunmd6wwUC6LY20yA9GkLbNjpkgw8qJ4TV1oW2FFZ+Hn7NNtPaoDtA218hk92pYAAdi5liK7sHhdpPp2PieaZdBGYWTidTpm2Er1EBJ12ZzopG95k+mQ7++jG+Xe1ahbM/91EIkTmR+81auymPw5pbj2Xt2x6vXQpXrlnPIYE65pVuxXtfHX/a9CJPd03ijz+c3K/jl74ObVP7FqAMlcJ7X0lnYQy3xPg8go0xhjuUDW9owj3wZns140tL+fpbV9J+uE/ejBThdUH8VwbWKkIIITJBgjzRJ25j416Lf/R8f6K7f95XgpHX0pJux0B3u4FdaWCv9dz2kmZ0IPtbz9YTSNnFRZBy8drb003SVcDBa2kx6Zq5uaaheTBogtZwCK+peWdJ8iEigZ0Qo9vMT72c/vnrD1xCqFlx5F/eBeCClR/ntIr3aP+P4/rVLFwrSOWO7Gxe4uQjBtzvrr+cJ1egeta6DRPvlKNxNvRvveDuiv+wmBJLUTqliOjG5mEPSoUQYihJuqY4KHlNzSbACwbRPWv1bDu9Ls/vjJkedbZlCrB0G8oAry961vkJIbLftK8sYeIPd7Y2mFtcy4oLZ7Hoqy/vZ689RetcdGBkQ4rIaxtH9Hze2+8N6/GDK1b3KcMi9YFjh3UcQoxmPmpUXMTeyUyeOKj0pEdawaApyrLLTKH2fbNOr7DQpHJ2xU26aDQzfyZWKGzaQkilTSGyglNZgbttO2rB4ay9OJfxSzUFr9fjbdiMdegMvDdXsfa/FxKLBDl03greahpP7tp1/GPxQmb2o51C8LGXKaocuuqafVJciHdEFaF3tvZu3zJKee3tsI813OqYw2mem4vyoeDOA/cbFEKI0Uhm8sRBxU8msfPyTDPycAhl26baphNAJ0zVUN1dZEbZtlmj5/ng+Sb4G0E9vQXtYunRJESmORMqIT+Xjn9OZ+3Fudx47t/42U9+TfP/KH69/tl0dcYZ1yzj2uMe44nXDsP7czlbrq9m1p/6/0VN2bLGIW2jcCD+hs3YT70yJgK8A7HrmiXAE0KMeTKTJw46PcVYrGgUlLVHnzo/EU+vBbQ8zxST6WYXFOC1tg7r+KxgEKuk2DRN7+jEKi7EiUSkfLcQI8g6+jC6KqLUz3OYeOpm3nttEnOP2UBzfRnTj9rCg/VHck7pm/xmzl188dSPA+sAEwz+7p6ZzPzBc2hfUwADWsvlvf0e8XOrWXPbMcy8fPjXyh0s64OdCZW0LpwoAZ4QfTAaqldm+/gySYI8cVDpVc1yb/32dvugo1Puzkqbvo+OJ4Z9jH4yieqKo5NJlOOg2ztQSibdhRhJO25yadkGM69aDN+H6dQQByZhZrragL9QwV+oANbhPjkZ57TNuFu3Ea+YQXh/B++jyb9dyaYvzB2CI4kenUdNJOfevhfBEUKI0Uo+OYqDSn+/re7pqed1dJim5gOo9jkQXksLfiyG196O19RsqpsKIYaVf+JRAKy5/RhaGnKZedXyPu/7wOx7Abjkna1QlMQKBAc9Hq+1lYnfW0zNt0d4fd4Y5JSUkDrjGOz4Xus4CyHEmCNBnhBCCAHUHW16aFY+7DDzk3tPkTzsVZvr1r3NpnuPSF/nPjmZi9dewNZvVvOjP1+EH7epuXvGkI0rUaRZfcsCuh6fOmTHPNhoz8NKSRMEIfoj003OpRn64EiQJ4QQQgDxMtj2tWpy/mbS+Wav2Dkb58yYBsDb8z1iOsT3j7wfgD/XLCY/mGBDQwnxUs3Ue+sZ/5zCGsKuatP/cwmzrlxO3bKKITvmwcZracF+5hVSeSPbZF4IITJF1uQJIYQQQLLUZco3l3PCmwn++MYiWuvaab8vxO3zb+cjDx5H/tQgl01fxpZkMfdsPZpPvfcSxz71Babf6jPp+dcAcC1FMNHI+DuHvnl21fWLabyqmlQOtE/zmHl139syCCPyD3nMhBAHB5nJE0IIIYBZVy7HOWQGLxwR4kcL/477QBmTv6/58nsXo0oSJJIBfv3gWfzs5TP431l/5u7txzLzE69gdQd4I6Hkd4sZ/4vFFE9pIXnOghE771gR+9DCTA9BiFEj02mYkq45ODKTJ4QQQnRz31vLlWvWc8PtH2Pi7xbjAztWLiJSp6j88eL0dl+iGshcW5OmujxKHul7YRhhRP++DP/k+VjPvprpoQghxLCSIE8IIYQAtvztcErzO7jtxPFM3L64120lK90MjWrvrJZApocwatkvvjXkqbRCCJFtJMgTQgghgKJ7c4hssXG3b+h1/fT/XMKOL1UzLkPj2pvp1y5hw93zcOMOMy97JdPDGXWUE0C7qUwPQ4hho2wb7XnpfwdiNKRDZvv4Mqnfa/JOPvlklFL9ujz77LP9HthNN900ZMd/7733uPDCCyksLCQvL49zzjmHV17Z95vis88+2+v4H/nIRw443ssuuyy9vRBCiNEn3OSilq/c623Z+DnCS9koS7PxB9JHrz+0m5IAT4xpVjCYDuwGGuCJ0W/YC69YlsXMmTOH+zT79NZbb7Fw4UL+/ve/09raSkdHB48++ijHH388Tz/9dJ+Occ899/DWW28N80iFEEJkkvPEir1++LeCQRLFGRjQAcy49FVmfPxV7K5Mj2R0sgsKMj0EIYaMcgJYQdP2xU8mMzwakQ36na75xz/+kc7Ozv1u884773DxxRcDcOqppzJhwoSBja7bgQKsqVP33SD2iiuuoLW1lXPOOYcvfelLBINBbrvtNv70pz9x2WWXsW7dOgKB/a9t0Fpz44038ve//31A4xdCCDF6+ckkqQKfui9WU/4/iw+8wwgrWSnf1A+E19qa6SEIMWS0mxrytaYa8MnCNIZdyPrafet3kLe/gKrHnXfemf754x//eH9PsYfDDjtsQPtt2rSJ5cuXc+yxx/Lggw9iWWbi8qSTTqK1tZUHHniAJUuWcOKJJ+7zGKWlpTQ0NPCPf/yDV199lfnz5w9oLEIIIUavYIuFlaUZfg3zbCY2HDWirRyEEEJktyFP1/R9n7vuuguA3NxcPvShDw31Kfps69atAJx44onpAK/Hqaee2mubffnSl75EKBQC4MYbbxyGUQohhMhmq39/LJNvXEzpb7NvFg9g2h3b2HB+ONPDEEIIkUWGPMh76qmn0oHTRRddRDQaHepT9Fl5eTkAL774Ir7v97rtueeeA2D8+PH7PcbEiRO58sorAXj44YdZtmzZMIxUCCFEtlKJYV++PigbLq1E25K0JIQYWpluci7N0AdnyN+5/vSnP6V/HopUzcGYMWMGc+fOZdmyZVxwwQU8+eSTPP/881x++eXcd999VFZWUl194Kpk3/jGN4hEIgDccMMNwz1sIYQQWWTm1ebLvcYrq7HmHZrh0RjOxAnEz1vAml8dR8FaTfnLio6Lj8v0sIQQ2cqCgsNkxv9gMqRBXkdHB//4xz8AmDx5MieffPKQHPf000+npKSEYDBIeXk5J598Mj/60Y9obm4+4L633HILkUiEBx98kNNPP52TTjqJP/7xjwSDQW699dZ0Kub+VFRU8NnPfhaAxx9/nBdffHHQ90kIIcToUnLLYlLFkYyOYes3q+HpiWz5SBWJQpuZX1hK/l1LKF66g+ZZFtZR2RGECiGyQ87UIFUfKWLGp0spXZib6eGIETSkQd59992Xrrx56aWXDlnPuCeffJKmpiZSqRT19fU899xzfOMb32DatGk88MAD+923urqal156ibPPPpvc3Fyi0SinnXYazz33HGeeeWafx/D1r3+dnJwcQGbzhBDiYNb54eNo/uQiWj6xCHvurGE/n11YSPMnF9F66SLsOPD+Gib9fRsFf1qS3sZdu54pDzSz+ZxCABo+W037f8jMnhAHMysIFafnE8i3ie9IUftMe7/2z3QapqRrDk6/q2vuz1Cnah5++OGcf/75LFiwgMrKSlKpFO+99x533XUXjz/+OC0tLVx44YU89NBDnHXWWfs8zlFHHcUjjzwyqLGUlZVx9dVX8+Mf/5hnnnmGZ555hlNOOWVQxxRCCDG62M+8Qg6Q0/37js9WU7pyNU5lBe627UN6rrovVmPHoasMJn7fFH3p6ezmrt+4x/bem6uY+CbELjyO0t8uxj95Phu/X82Ub2VnwRghxPAad0o+KNj6SCtdNSlcnaUlgsWwGLKZvJqaGp599lkAjjvuOGbNGty3m1/+8pd58803+c53vsO5557L/PnzWbhwIR//+Mf517/+xf/+7/8C4HkeV1xxBV1dw98N9qtf/Sp5eXkAXH/99cN+PiGEENmtZbYp6jXUAR5ApEHTcohOB3h9Yc07lB0LzDfb1rOvZm3bByHE8ItUBvBiPl018kJwMBqymbz/+7//S1ew/MQnPjHo4xUWFu739quuuooVK1bwhz/8gW3btvH3v/+dSy65ZNDn3Z+SkhK+/OUv893vfpeXXnqJf/3rX3zgAx8Y9HGVpYYstXXvxzb/irFBntOxRZ7P0W3mNctgl+duKJ/P4sdXY7sz+3ysTTctwotopn1taXpMXq5O7+9Mnoi7uWbQ4xooZ0IlBAPgedARw2trR7vZ/eFT/j7HloPt+dSuRgVU+v4qrcA/wE67GA3pkNk+vkwasiCvpwF6KBTi4osvHqrD7tdVV13FH/7wB8C0RBjuIA/g2muv5Ve/+hUtLS3ccMMNQxLklVQVErCCQzC6PSlLUViZB0qhfSmxPRbIczq2yPM5etkTK/FqtvW6bmifT00+NtaUoj5tXXr7u+aHXbb3kiFKu3+3chV+H4/VX1ZuLn5HBwD2lEnQ1oHXtLM4Wur4Q9GOwun0cDpTJAuCBN/cCL6PH08My5iGgvx9ji0H2/PpdETInxnGO8EhtiVFyk/ChkyPSoyUIQnyVqxYwTvvvAPAueeeS1HR8LyJ7O7QQ3dWETtQU/OhUlhYyLXXXssNN9zA8uXLefjhhzn33HMHdczGTS04KjBEI+xNWQq0pmFTy0HxgnYwkOd0bJHnc3Sq+9wi2mb6HHJrOamSKMEN9biba4b++RzfgWMX4q7r/yez1kuOI74jSe2/z6ZklUv4oZcHP55d2IfPpmN6PsEWlx1fiBNyIjQ15jH5PgtyQ/iTStG2IrqtC+flWmhqwW1oHNIxDDf5+xxbDrbns2FzM9M+WYI9VdPwQnO/1+TJTN7oNiRB3q4FV4YiVbOvtM7MH+iXv/xlfvnLX9LY2MgNN9zAOeecM6jjaV+j1fDdF627z3EQvKAdLOQ5HVvk+Rx9rATMumEVhEOoN99l149OQ/l82u9sItXSMqB98+9cQv3PF4Gtqbcd9JGLmPRfQ1eExX1jFYHio2mcE2LS1dtZ/bNyihYHCT60GP/Eo0gUB4lsi+MFbFROGP/dhiE790iSv8+x5aB6Pn1ofj1GydE5FB4Zpv6VZKZHJEbQoIO8VCrF3XffDZgKlPurcjnUemYPASorK0fsvHl5eXz1q1/l61//Oq+99hr333//iJ1bCCFEZq357UJmfnYxHkDr8J7L60eAV/eFapQPZb/eGchNv68LN+pgJzwCq7fy7v8cR946CxRE6jXRuhSJQgfL1ejudTsdFRaBTo3ywYlrAjGfyLYYnZNziPxj2c7zXV1NKg8m/HAxLjDtP3amrlrPv0ZPR0GLfi0DEkIMoaaXYxQeFqHkmBxUvg/PZHpEYqQMurrmY489Rn19PQAf/ehHcZwh7cqwX7/73e/SP5900kkjdl6Aq6++mvLycgBuvPHGjM0qCiGEGFkVz2ZnepAbgT0ylxa/TnhTM3VHR/AnljPji0uZ8EgtpW8kKH56A8G6GL4DeS/XEL1vKfkvrCPUqgm1aQrvWELh6w2EHlqO1ZkgUhun7ovV6PcdiVpwOAUbXUpWujgTJ2Tk/goh+mb7E214CZ/cqaF+7ae1GhUXsXeDDvIG0hvv9ttvRylTUfKmm27a4/a33nqLtWvX7vcYt9xyC7feeisA48eP54ILLuj7oIdATk4OX/va1wAz3kcffXREzy+EECIz6o7Jzg8V45fHceJ7Xu+uWUf+Jo/Oyaa7n7t2PfYzr+Bur8V/4x1QCrfGrGt36+opuHMJ0fuWdu/sAeCv3wRL3qB8RYzWGRE6J+WQs2w94QeXm2qZQois1VWTYsMdTWy8a3StiRWDM6hpt+bmZh5++GEADjvsMObPnz8kg3rllVe44oorOOWUUzjrrLM4/PDDKSkpwXVd3n333XQzdADbtvnd735HTk7OAY469D772c/ys5/9jO3bt9PQMDrXGgghhOgbteBwak7LY9pXs7O5uPXsqxQ/C6kPHEvk7a34Tc343T1kY2U2pbcuY/ecE//Eo/AC0HLZIrSCoj8u6XV7T8EXP9m9lmfx6xR23323Z5vttcNzh4QQQ8rP3kK2YhgMKsj761//SiJh/sf0dRavrzzP48knn+TJJ5/c5zYlJSXceuutnHfeeUN67r6KRCJ885vf5Atf+EJGzi+EEGLk2M0xJvzgrUwP44As16furCmUPwb+VhPkWSmN3suMm/X8a5RumgSux+aPTiF1dTXl/y87g1ghxMjyUfhkZ+ZCj2wfXyYNKsjr6Y1n2/aQ9qg7++yzufXWW1myZAmvvfYaO3bsoLGxEa01xcXFzJs3jzPPPJPLLruM/Pz8ITvvQHz605/mJz/5CVu2bMnoOIQQQgyvjjklhNesy/QwDij07nby7Eq2fWgK5b8yxVDapyiCFx1Hzt+W7rG9u8m8f1X+1Gy7+aZq7CRMeLoTlr4xcgMXQggxZAYV5L300ksD2u+yyy7jsssu2+ft5eXlXH755Vx++eUDHNngnHzyyX0upBIKhdi8efMwj0gIIUSmDWOnmyHlbt3G9iunkLNL+9jJNy5m+1eq6cvChsk3LWbdzcex7iMRpu8ZEwohhBgFRq4UphBCCDGK5axqYO33q5nyLZPO6EyoxN267QB7jbx1Nx9HcGI7Jf+y09fZc2YRret7lDr9yya6W/eLRVhJxYQXXKKr6nDXbxzq4QohspQ0Qx/dBl1dUwghhDgY6IYm7DisvmUBAG5VeYZHtCdn+lQKV1kktubA4tfT13urVtM8u/8fhqZfu4Sqf8aJvl5D88LxQzhSIYQQw0lm8oQQQog+8FpamPRdM4u3/meLCDUoJmRZjZJ1l1VQdf1iSvZy24z/Xo2eM4sdJ5dS+tu+D9x69lVcoHP8FPKGbKRCCCGGk8zkCSGEEP007StLSBZmehS9bfpuNVXX7zt4c+sb2HJe/wK8XQXbBzoyIcRolOkm59IMfXAkyBNCCCEGYOYv12d6CL2kqvbSCX03+afsGPDxc3a4B95ICCFEVpAgTwghhBiAtkVV1Hy7GmdKFWAKsdgZbOuj2wMH3KbuzYGvIww9tJxtX63GO+Vomj+5CCsUHvCxhBBCDC8J8oQQQogByFvTSmQHuBs3kTh3QcYrbc78/LIDbuOHNGv+38IBn2Py/60ntHo71ocbqL/PBLfrfr6IDT9ehD1n1oCPK4TIPj3VNbP9IvZOgjwhhBCin9TCI/DeepdQm0/Tp6oJPbwce84s2k+dnZHxdD0+tU/bzfjSUrBg69er2XD3PDr+Ob1f53G31xI7YgIFZ6+h5Nz3AJj+n0uY+rUleKtW93vcQgghhodU1xRCCCH6wSkrxV32JgAt/97B+N+atEVv1Wqi7ymYUjTiYzqsaDvr+rjtzM/tnPFznq2kvyvtgo+93M89hBBCjDSZyRNCCCH6wa1vSP9sL8mn8bBgBkdj/HP5vAHt5568jQ0/rB7i0QghxoJMV82U6pqDI0GeEEIIMUAVP1uMtsA/aX7GxrDu5uP6tB5vX0pWamavGHygas2fS+uliwZ9HCGEEIMnQZ4QQggxQPYRc2if5lG7YGelSSscHpEqm+t+sYjVtyxg+peX4kyoHPBx8v9vCe8ek8R/ahLrfzLwIM1/dSVuWL5VF0KIbCBr8oQQI0Ydc7j5wQKV8vBfeyezAxJikN79bD6562w6J/s4VZNQ0TCN503A+8lz+CfNp31yiII7lwzZ+ayjDmXbSYUETmvE3+wz68rlAENS2dM6dQvT2DKoY5T8fmCN1oUQ2UePguqVkq65bxLkCSFGjJV0UZ1xYjNL6axwKKsdD76PTiTxWloyPTwh+sWeO4u81TYT791E+7ET0fk5tM4tZMo9b9IAWM+9SsEQnavhM9W0ndhFwTMRYhM00859j5IhOrYQQoixR4I8IcSIaT68kPy7lmBPKabsmW00nTqVotcaUa2dIEGeGGVUUxsTH/Vxa7YSqdmKBxS6M4flXLnbXEo/+hrAsAd3m/+rmikPtrH5zHym/HED7rbtw3zG4eVMnAA5EXTQQbV1guPgrtuQ6WEJIcSwkjV5QogRk3+XSVsLrdmBbmgkka9wi3MgN5rhkQnRf4lDJ+C+t7bXdbv/PlTqjh6572SVD5vPyseOM+oDPAC3Ziu0daAdC298EX5B1AR+xw2sIqkQBwsNaJ3ll0w/SFlMgjwhxIixDzsE6+jDIOXiHT6dwnUplKdJleZkemhC9It11KHEiwLp352y0p03Hj70s3mJcf3tZjdwk/5rMfFyn8SCzhE753CyIhFIprA278BeuxU3N0h8dgXJosy3vhBCiOEiQZ4QYsSo7Q2opAtKgVJE1jfh1LXh5gQOvLMQWcCeOwt7ziz8196hYOkWvFOPBkAnUzs3emvNkJ83d83Irq6Y8qCLei8H/b4jR/S8w0GnegfIwZWbCb+1meirmzI0IiGEGH6yJk8IMXKKCkBrvEllODta0+tiwrV1eBkemhB94a1cjVNehjOlCreyiJYZIcrrZqNqG/bY1j5iDt6bq4bkvHbqwNsMpca5QQrWaQK1bYzcHOLw0G4Kt7ERu6AA5XR/7PF982WTEGKffBSK7P478bN8fJkkM3lCiBGTqijADwex69vSH7CUE0AnkxkemRB959bV49fVs+kan2Cbj/fWu8SPnIJT0rskylAFeAB5527HKS8bsuMdSNscl/yNCdy160fsnMPNa23FbWw0l/oG3Lr6TA9JCCGGjczkCSFGTGBLE7qlFaIRdDwOmG/ZZeG0GC2cslLwNat+Mo3CcDuFb8Zhzix4YgUMoiH5gQQsb8SCEuUEiG5yCDS2jvoZdqesFLd+z1lWIYQY6yTIE0KMGHfjJqxIBBUK4rd1ZHo4QvSLM2Ma7tr1eKcezSGfews/Ee8VBL37tcnM/PLwVKPMDSSJPTUJ69TBNSvvC+2mKF7lseqLBRz6X5VD0mg9UyTAE2LgtFZZ32x8IONramri0Ucf5eWXX+aVV15h27ZtNDQ0kEgkKC4u5vDDD+ff/u3f+MQnPkFubu4wjHpkSJAnhBhZSqFjXWh3hBcZCTFI7YeXkdcZw/c01rhS/M016dv0CUcx44tLwRqeD0Txk2pxnpmANWFkgq7I/cso/fQsiEaG/VzDyT9pPtZzr2Z6GEKILPL0009z6aWX7vW22tpaamtreeKJJ/jxj3/Mfffdx7HHHjvCIxwaEuQJIUaUsm289vZMD0OIfov8YxkuYG2v3aMYiXrhtREZw0gEePWfq6Zthmb6OUvY8p/VKD2O8b9YzLqbj+OQX+3ALc+HJW8M+zj6wtlH0NtT9MaN2EijBCHE7iZMmMCJJ55IdXU1kyZNorKykng8zubNm7nrrrv45z//yZYtWzjjjDNYuXIllZXDl44/XCTIE0KMKAnwhBiYlj9MJp+tw36e9qma6DaFd8rRdFb5hBpNjbbpX16KC6SmH0PWND3x/T2ucqZNwVfKBICerPgVYqB8rVBZnq7pD2B8559/PhdddNE+b7/kkkv4n//5H770pS/R0tLCz372M37xi18MZpgZIdU1hRBCiFEgmaewj5gz7OeJblNE6zSpfIeS1xWT/mtxr9sDj68Y9jH0le7oxJk2pfd1uRF0wCYxuwKnXdLChRC9Oc6B57g+97nPpdfjPf/888M9pGEhQZ4QQggxCrRN00PalmFfJv51Aw1nxAk/sIz8DXu2N2m5bNGwj6GvvPZ23PUbe1/35ir0irewn3oFlmZHWqkQYnRxHIdwOAxAIpHI8GgGRtI1hRBCiCxXe001ubObRuRcNR+ZSvFTJs3RfuaVETmnECL7aG0u2Wy4xvfUU0/R0GCq886ePXt4TjLMZCZPCCGEyHYKrMeKR+RU43+xmIb5mi3XV+/19vZJCrXwiBEZixBCjJT29nZWrVrFd77zHT70oQ+lr//iF7+YwVENnMzkCSGEEFnKmj+X5sPyGf+LxQfeeAjN/MJSdnxp70FeqBW2nZBLxbIDH0ctOBy9/K0hHp0QYiSMpj55bW1tva4PhUKEQqED7v+zn/2Mr371q3u9zbZtfv7zn3PCCScMfqAZIDN5QgghRJZZ85uFrL5lAfGKHHK37rkubiS0T9uzciUAPlT8bGfQmTzz2F7r9Oo/tzM4lABPCDESJk2aREFBQfrywx/+cFDHO/nkk3nzzTf50pe+NEQjHHkykyeEEEJkEWfGNIh6zLrMVLGsvaaaCbWz8FauHtFx2PG9f4M/7uUOYhcsJHddm+lFF7UovH1J+vay34zsrKMQQmzZsoX8/Pz0732ZxQP45Cc/yZlnnglALBbj3Xff5U9/+hNPPfUUH/nIR/j973/PwoULh2XMw02CPCGEECKLrP/YeKbetXP2bvx/L8YbwfM3XlWNGwE/sPeKBvbGWqiYSu0JRXBCNRV/W7tHc3gAa96h+G+8M7yDFUIMm9GUrpmfn98ryOurkpISSkpK0r8vWLCAj3/84/zoRz/iG9/4BieffDIPPPAAZ5xxxpCNeaRIkCeEEEJkkck3ZXYmrOR35vzj9nG7u6OOyP11RHp+38d2EuAJIUarr3/96zzwwAMsXbqUT3/606xbt65P/fWyiazJE0IIIYQQQohdnHfeeQBs3ryZ5cuXZ3g0/SdBnhBCCCGEEKIXX6tRcRkupaWl6Z83bdo0bOcZLhLkCSGEEEIIIcQutm7dmv45Nzc3gyMZGAnyhBBCCCGEEKKb7/v8/e9/T/8+d+7cDI5mYCTIE0IIIYQQQvSi9ei49Ndtt91GKpXa5+2+73Pdddfx1lumz+fxxx/PtGnTBvowZszoKhMjhBBCCCGEEAN07bXX8s1vfpOLLrqI6upqpkyZQk5ODi0tLbz22mvccccdvP766wDk5eXx61//OrMDHiAJ8oQQQgghhBAHjR07dvDrX/96vwHcIYccwp133sm8efNGcGRDR4I8IYQQQgghRC8mHTLbm6H3f5+lS5fy5JNP8swzz7B69Wp27NhBc3Mz0WiUiooKjjrqKC644ALOP/98gsHg0A96hEiQJ4QQQgghRJYKlTnkzwmTavZoe68LP5npEY1us2fPZvbs2Vx99dWZHsqwkiBPCCGEEEKILDXp/EKUbWbUSqtzSDZ7bPl7M9rt54EswB/y4YksJUGeEEIIIYQQWcp3NQrY8Uw7+bPDRCcEmHppCU0rYqgARCcGCRbY+B50bU3SsLQDP7HnccafkQ//7Pt5tVajIF0zu8eXSdJCQQghhBBCiCxVv7gDZUF0QoBtD7dS92IHVkBRdnwupQtyiVQEwFI4EUX+7DDTPl5K4eGRXscoOjJCtDKQoXsgMkFm8oQQQgghhMhS7e8lGHdSHqES87G9bWWctlVxcqqC+Amfrm078zYjEwNUnJ5P2fG5FMwN0/xmjJzJIXKqgnS1xjN1F0QGSJAnhBBCCCFElprwbwUoS9G5ZZeKKz50btizAktXTYr1f2xk3Kl55M0IMe7EfLTWpNp8ttzX3K/z6u5LNsv28WWSBHlCCCGEEEJkkg2BXItUa+/KKJVn5ROtCNKxKUHTy7E+H27HU+3seK6d/JkhYptTuJ0+vkREBxUJ8oQQQgghhBgh+XNClB6Xi5fQJOpTJOpdSo7NQdkKrTXaB53SWAGFshWxbUm2P9bW/xO50LZqLxVYxEFBgjwhhBBCCCGGwbhT88idGkLZgAY/pbEcBQpQkDstRN70ML6raXkzRiDPxsm1sCMWqXavu1pm32fwhpJU1xzdJMgTQgghhBBiiJVW55A/M0yqwyNR72KFFMFCG4KKhiWdtLzZhR21yJsekibnYshJkCeEEEIIIcQQyj80ROHhEZJtHpv+3LTP7byYT8tbXSM4MnGwkCBPCCGEEEKIIRKZGKD8hDz8pGbzPfsO8LKelNcc1aQZuhBCCCGEEEPBhglnFaB92HxvM9o98C5CDAcJ8oQQQgghhBgC40/JAwtqn2zD7fAPvIMQw0TSNYUQQgghhBgkO2qROz1EqtXba6PyUWcUVNck28eXQTKTJ4QQQgghxCCVn5gLQO1T7RkeiRAS5AkhhBBCCDFoOZOCuO0+iXpZiCcyT9I1hRBCCCGEGISCw8IoW42pdgham0s2y/bxZZLM5AkhhBBCCDEI+YeE0b4eU0GeGN0kyBNCCCGEEGIQAgU2bqdU0xTZQ9I1hRBCCCGEGCgLrICia2sq0yMZUnoUVNfM9vFlkszkCSGEEEIIMUh+ShaIiewhM3lCCCGEEEIMlG8uOVVB7KiFFxsjaZtaZX8fumwfXwZJkCeEEEIIIcQgNCzrpHRRDlMvLaZxWSfNr0sBFmEkk0neeOMN1q9fT21tLZ2dnQQCAQoLC5k8eTJz585l4sSJQ35eCfKEEEIIIYQYhJY3u+janqLy7AJKFuaABc2vSqB3sNqwYQN/+ctfeOyxx1i+fDmuu//eiePHj+eMM87gggsu4Oyzz8ZxBh+iyZo8IYQQQgghBilR77Lh/xrxE5qSo3NG/afsnj552X7JJvfffz/vf//7mTFjBtdffz0vvfQSqVQKrfV+L9u3b+dPf/oTF1xwARUVFXzjG9+gpqZmUGORmTwhhBBCCCGGggf1SzoYf0o+hYdHaHlDZvMOBvfffz833HADK1euRHdHnuFwmCOPPJIFCxZw9NFHU15eTnFxMUVFRXR1ddHU1ERzczOrV6/m5ZdfZvny5WzZsoXGxkZ+8pOfcPPNN/OpT32KG264gfLy8n6PSYI8IYQQQgghhkj7ewnGnaTJPyQsQd5B4P3vfz/PPfccWmsikQjnnHMOH/3oRzn77LMJBoP9Ota6dev485//zN13382qVav47W9/y1133cWdd97Jueee269jjfKJZCGEEEIIIbJLosElWGQTHjeK51P0KLlk2LPPPktpaSk/+tGPqK2t5Z577uH888/vd4AHMH36dK6//npWrlzJkiVLOPPMM2ltbeXVV1/t97EkyBNCCCGEEGIIbX+iDTRUnJGf6aGIYfbzn/+cjRs3ct1115GXlzdkx124cCGPPPIIK1asYOHChf3efxR/vSCEEEIIIUT2cdt9OrckyZnc/9kcMbpcc801w3r8+fPnD2g/CfKEEEIIIYQYaqO8J7rWCp3lzcazfXyZJOmaQgghhBBCDCEn1yKnKojXNcojPTFqSZAnhBBCCCHEECpdlAsKtj7alumhiIOUBHlCCCGEEEIMoch4Bz+pSTa4mR7K4GS6cmaWV9YE+NCHPsQbb7wxLMeOx+PcfPPN3HLLLf3eV4I8IYQQQgghhpAVtHBjkqp5MLj//vuZP38+//Zv/8bzzz8/JMesq6vjpz/9KVOnTuU///M/qa2t7fcxJMgTQgghhBBiCCkH3A4v08MQI+Bb3/oWoVCIhx56iFNOOYXJkydz3XXXsXTpUpLJZJ+Ps3XrVv70pz9x5plnMnHiRL7+9a+zY8cO3ve+93HhhRf2e1xSXVMIIYQQQoghYkctlFIkW0Z3kCfVNfvmu9/9LldeeSXf/e53ueOOO6ipqeHnP/85P//5zwkEAhx++OEcddRRlJeXU1RURFFREV1dXTQ3N9Pc3MyaNWt4+eWXqaurA0Brk4c6d+5cfvjDH3LuuecOaFwS5AkhhBBCCDFEtGfSNC0n8wGIGBmTJk3illtu4aabbuLXv/41d9xxB9u2bSOZTPLKK6/w6quv7nf/nsAuEAhw3nnnceWVV3L66acPakwS5AkxRPLnhMifHcHr9GlbEydR7+J2SD6+EEIIcTDxE+C7mkhFINNDESOssrKS73//+3zve9/jxRdf5JlnnuGFF15g6dKldHZ27nWfmTNncsIJJ3DCCSdw9tlnU1ZWNiRjkSBPiCFQeW4BORODaF9DOeROCwHd38xo0D5oV5Nq84jVJGlbHSfVIgGgEEIIMRYlm11CJY6pfjFa3+6zqILlPmXp+JRS6cCtR3NzM/X19TQ1NREOhykrK6OsrIxgMDgsY5AgT4hBqjzHBHixrUm2PtSKFYa8GWECeRZOjo0dsbDDFnZYESp1CJcHKJ6fg/Y0qXaPrlqX9rVxumpSmb4rQgghhBgCzW90UXFaPlMvKWbro60kG0f3+jwxeD3r8UaKBHlC9FOwyKLirEKciIWyQNkqHeAB+HFofTu+z/1DZQ55M0NEJwQJFNjkH2JTMDuM1hov5hNvcOncmKB9TQI9ytvrCCGEEAejjrUJmss6KTwiyuQPFbH+jgb8vhdaFGLQJMgTop/GnZpPIM8i2eKhXU3HhiTNr8b6vH+i3iVR7wImN9uOWuTPChGdHCRU7JAzOUhuVYjyEzWpVo9tj7WSah2tuR5CCCHEwalhSYxYTYrKswuY9KEiNt3dnOkh9ZPqvmSzbB9f5kiQJ0Q/hYocks0em+8ZmhdrL+bT/HoXza93mStsyJsWMrN9k4JUfaSY1pVd1L+49wW7QgghhMhOsS0p4jtcwuPkI7cYWfI/Tog+CpbYlFXnomxF23v7TsccNA/a15h0zUChxYSzCyk8LIqTa7P9n23Dd14hhBBCDDntZWl1EDGmSZA3xjj5FsFCG+UourYm0QdzLQ8bcqqChEsdgsUOTq6Fn9R4nT6pdo9Uq0ey1SPZ5O4zTz4yMUDRvCiRigCWo9Ba01WbouWNrhG5C6kWn41/bmLi+QXkTgkx4bwCtj0sgZ4QQggxWji5Njo1CgM9qa45qkmQN0aExzuMPzUfJ9dCKZOfrLXGT2rsphANmxm9JXz7Kf+QEIVHRAkW270eC+2D6k4v77m+R8/t2tVoV2OFLJRtttNa43b4tG5I0PZePCMVsmrub6XynHxyJoWY8rEiul6THHQhhBBiNLDDCi8u0YgYWRLkjQE5VQEqPlAAQMf6BLGtSdAQnRAkOilI/iERpo8rwevysSMWXkKTqEvR9FqMeG32lG/Mm2XWoDk5FtrVJJtdWlf1rZ9cZEKA4qOjRMYFULZC+5p4nUvbe110bU3tUbgkUGARLHYIFtg4+TaBXCvd6kA5ilSbR6rFI96QovWdLvxhzM7sq22PtFF0dJSSY6JUnplPzrGAArfTZ8vfm6VqlxBCCJGFlK3w3YPkm3aRNSTIG+XyDgkx7uQ8tAdb7msi2bzzRaRtVQJlKbwTHJjgY4cUiQYXJ88iOjlITlUI39W4HR7aA+UoLMf8qyzwk5pkq0fbe3Ha30sM+ditqEXR4WFyp4YIFNjpWTM0oCBncoiieTn4KU2iyaVjbYKWVV3QHZcGCi1Kjs0hZ3IQK2ChtalG2fpOnJa3u/Y7c5lq9Um1JhltpUyaX4nRtSXF9HOj3c+TIlhoYQUt/KS8gQghhBAZZ0H5CbmESh3z+cqGVOso7JMn6ZqjmgR5o5QVtZj4wQKChSbPe9O9zbjte/+QH9uSouGFZrS/8y/BCkPJ0Tlm5izXBkD7JlXRT2q0p7HDFpGKANHKIONO1qRaPNrXJ2h5M4bfx5jPCkPpwlycXAsrYGEFFMpR2GGFFVQopfA9M+vWviZO66o4dL8OBossCg6LEp0UJFzuEBkXoLQ6Z49zeF0+ratiNK3oPChmsxINLqk2D1WqcLt8au5vxu2QAE8IIYTIOAumfqwYO2KZL5sV+AlN44rR9rWyGO0kyBuFnFyLqn8vQgUUHeuT1D7V1u/1dn4c6l/qhAPNZVlQcGiY/FlhQiUOJUfnUDw/ao6R1CRbPJpf7aRz054VXnJnhBj//rydLUw0Zt2br9EpTUdNkpa3u4hv33vKaLLZp/6FjvTvOdOC5E0LoSyF1uAnfZpej+EeZD3kgoUWuVNDNNbE2PSX0dZzRwghhBi7ShdGcaI2ja900vRy33voCjHURlWQt3XrVm699VYeeughNm7cSHt7O2VlZVRVVXHKKadw8cUXc9hhh+2xn9aaX/7yl/zv//4vGzZsYNKkSVx22WVcd911BIPBvZ7r5JNP5rnnngPAsizefPNN5s6du8+xbdy4kalTpwJw4403ctNNN/X5fk08v4Dah2PpGaz9cfItqj5cjHKg9ql2OtYOfRplLz60vh2n9W2zKC0yMUDejDCBPItAgU243KHyrELcmEfjik7a3jHjKT42SvH8KNrVbH24lfiOwa/961yfpHP9QTBVdwAFh0VQSrHtUamyKYQQQmST/NkRvKQ/NgI8rcwlm2Xx+D7ykY9w+eWXc/rpp+9R8G8kjJog7w9/+APXXnst7e3tva6vqamhpqaGl156ifb2dm6++eY99r3iiiu47bbb0r+vW7eO66+/npdeeomHH34Y27b3e27f97npppu49957h+S+7C5Y7FB1URGb/rr/WZlgkcWkC4tRNmx/vI3ODSMf8HTVpOiq2WXWzoHy43PJmxVm3In5lJ+gwTeLjN1Oj033NmVF0ZKxRDnmhWJf6blCCCGEGHmVZ+VjhywaX5HUTAH33HMP9957LxMmTOATn/gEl112GdOnTx+x81sjdqZBuPnmm/n0pz9Ne3s7kydP5gc/+AFPP/00K1as4IEHHuCnP/0pxx9/PJa159157LHHuO222ygqKuJ//ud/WLp0KbfddhuVlZX885//5Pe//32fxnDffffxxhtvDPVdA6DtvTjBIofKcwv2uU2wxGbyRcUoC7Y91pqRAG+vXKh7roN1v2+gYXkHsZokyWaXplc72XCnBHjDQhYZCyGEEBmXMy1I2Ym5TLqwkBmfLiWnKkS8LjU2ZvHEoBUVFaG1pqamhh/84AfMmjWLk046iT/96U/EYsP/fyTrZ/KWLVvGf/7nfwJw7rnncs899xCJRNK3H3300QB85StfIZXac13YPffcA8Add9zBBz/4QQAWLlzIUUcdxVFHHcU999zDZz7zmX2ePz8/n3g8TjKZ5IYbbuCBBx4YsvvWo+GlTnKKI+RMNC8W9c939Lq97PgcCuaa+7z1kVa6tmZnh/PmV7toZmSahB/MdHcZ5kiFQyxL/y8IIYQQY1XOtCDj35+P1Z1Zoz1Nqs2j+a1YetnKWKC1uWSzbB7f9u3befDBB/njH//I448/jud5vPjii7z44ot84Qtf4N///d/55Cc/SXV19bCcP+tn8j772c/i+z5VVVXcfffdvQK83QUCgT2u27p1KwCnnHJKr+uPPPJIiouL07fvS1FREVdccQUADz74ICtWrOjvXeiTrQ+3kmxxKZgTpux4U0Ey/9AQ0z9VQuHhUbwuny33t2RtgCdGTuOKGNrXjD89P9NDEUIIIQ4qVhAqTjXvvw1LO1j/p0bW/r6BTX9tHlMBnhi8YDDIRRddxCOPPMLmzZvTs3laa9rb27nttts44YQTmD17Nj/5yU/Yvn37kJ4/q4O8JUuW8NprrwHw1a9+lZycPcvnH0h5eTlAuohKj7feeoumpibGjx9/wGN861vfIhwOA3DDDTf0ewx9teneZlJtPoWHR5n+qVLGnZgPKOpebGfDnU0k6rKncbnIHD8Bre90YYctyk7MzfRwhBBCiING6aJclK2ofaKN5te78GJjeH28HiWXUaCiooKvf/3rrFq1ipdeeolPfepT5OXlobVm9erVfOMb36Cqqopzzz2Xf/zjH7ju4D/zZ3WQt2uhkw9/+MPpnxsbG1mzZg0tLS0HPMb5558PwMc//nF+/etfs3z5cu644w7OPvvsPY67L5WVlVx11VWAWeO3ZMmSftyLfvBg01+aaH3XLGRreaeLdbc2pCtbCtGj7d0EyTaPgjlhAoVZ/WcshBBCjBm500N4cZ/OTVlSG0GMOosWLeL3v/8927dv54477uDkk08GwHVdHnvsMS666CIqKyu59tpreeuttwZ8nqz+dLhs2TIApk2bRllZGb/73e845JBDKC0tZdasWRQVFXHooYdy8803k0zu/Y/twgsv5Pzzz6epqYmrr76ahQsXctlll1FTU8Opp5663/V4u/rGN75BNGr6w11//fVDcwf3oe7Zdtbd2rDH2jwhdrX90VYAJp5XiBXK8GCEEEKIMS7vkBB20KLtPak/IAYvEolw6aWX8vTTT/P000/3yi5saGjgl7/8JUceeSQnnHACjz76aL+Pn9VB3jvvvANAVVUVl1xyCZ/5zGdYvXp1r21WrVrFNddcw2mnnUZra+sex1BKce+99/K9732PadOmEQgEqKqq4tvf/jaPPPIIjtO32jPjxo3j85//PABPPfUUzz///CDvnRCDk2rzaX4thh2xmPrxUnKm7b3noxBCCCEGyYbyE/LwU5qGZQdJ9cyePnnZfhml4vE4f/7znzn99NM59dRT2bFjB1prtNZMmzaNYDCI1pqXXnqJD37wg1x44YUkEn1f95m1QZ7v++mgbfHixfzlL39h/Pjx3HnnnTQ1NRGLxXjuuec47rjjAHjhhRfSBVJ25zgO3/rWt1i3bh3JZJKNGzfy3e9+l1Cof9Mf1113Hbm5Zg3UcM/mCdEXjctjbP9nK2ioOC0f9t/yUQghhBADUHFGPsqGHc+1wxhehieG37Jly/jMZz5DRUUFl156KU899RS+75OTk8Pll1/OSy+9xNq1a6mtreXXv/51uljL/fffz49//OM+nydrg7xYLIburouaSCSIRqM8++yzfOxjH6OoqIhIJMKJJ57I008/zbx58wD429/+xvLly4dtTKWlpXzxi18E4Pnnn+fJJ58ctnMJ0Vedm1LUPt2GshTFR0UzPRwhhBBiTAmPd8iZHCTR4NKxVipoiv6rra3lpz/9KYceeijV1dX8/ve/p7W1Fa01CxcuTK/R+8Mf/sCiRYsAKCgo4LOf/SwrV67koosuQmvNXXfd1edzZm2fvJ5qlj2uuOIKDjnkkD22i0QifP/73+fcc88F4O6772bBggXDNq6vfOUr/PrXv6a1tZXrr7+e0047bdDHVJZCqeGZbjbHNv+KsWFvz2lsYwrtayITAqhX5bkeTeRvdGyR53NskedzbBno81nxgQLQsO3R1lH9f0Fp1a9ZSKXNJZtl8/hc1033yfvXv/6F53npCazS0lI+9rGPccUVV3DooYfu9zi2bfOVr3yFv/3tb2zcuLHP58/aIM9xHMLhMPG4qSz5gQ98YJ/bnnrqqTiOg+u6w9bHrkdRURHXXHMNN910E0uXLuWxxx7jrLPOGtQxS6oKCVjDs55KWYrCyjxQCu1n8V+C6LN9Pad5uflELY/kFMnZHE3kb3RskedzbJHnc2wZyPNZeGSE/HFhWt7poriiaJhHOLxSfhI2ZHoUB4+KigqampoA0FpjWRann346n/rUpzj//PP32t97X0pKSgD61Voha4M8gEmTJrFmzRoAJk6cuM/twuEwpaWl1NbWUldXN+zjuuaaa/jlL39Jc3MzN9xww6CDvMZNLTiq7090fyhLgdY0bGqRN6gxYm/PqRWCgk6Lrm0pGja2ZXiEoj/kb3RskedzbJHnc2zp7/NZ9r4cdJlF47YuNj3aPAIjHF6uTmV6CAeVxsZGACZPnswnP/lJPvnJTzJ58uQBHau4uJgbb7yxX/tkdZB36KGHpoM8z/P2u23P7X2tljkY+fn5fOUrX+Fb3/oWK1as4IEHHkivCxwI7Wv0MM43a919DnmDGjPSz6mlKTsuh/zZEQBa3u6S53kUkr/RsUWez7FFns+xpS/PZ96sEGXH52KHLJJtHlvua0KPgWIrPamCfd+B7G82nsXju+iii7jiiis4/fTTB70sq6ioqN9BXtYWXgE48cQT0z+vX79+n9u1tbXR0NAAwIQJE4Z9XABf/OIXKS0tBeDGG2/s/x+OEINgBaDi7DxmfKqUwsOiaFez/fE2OjdIc1YhhBBioCaeX8D49+ejbEXD8g42/bkJX2qtiAG45557OOOMM4at7saBZHWQd8EFF6QfmH/84x/73O4f//hHOsg64YQTRmRsubm5fO1rXwPgjTfe4L777huR8wrh5FtUnlNIdEKQZLPHtsdaWH97owR4QgghxCAUzosQGR8kti3JutsaaH5Vmp6Lgbv88sv51Kc+xfbt2/u8T319fXq/wcrqIG/q1Kl8+MMfBuAvf/kLTz311B7b1NbW8u1vfxuAYDDIJz/5yREb3+c///l0d/r+9K0QYqCsqMXkCwtRNtQ+0c7me5rp3CQ59kIIIcRgxTYn0FrjJ7T0woPMNzkf5c3Qb7/9dm6//Xaam/u+nrOtrS2932BldZAH8JOf/ISysjJ83+fcc8/lG9/4Bi+88AIrVqzgN7/5Dcceeyw1NTUAfPe73x2xdE0w7Ru+/vWvA6TTRYUYLsqBKf9ehHIUjcs66dwoM3dCCCHEUEk2+6TafXImD0/FcyFGUtYHeVVVVTzyyCNMmDCBeDzOj370I0488USOPfZYPv/5z1NTU4NSim9961tcd911Iz6+q666akQDS3GQsqDq4mKskKJxSSexGpm9E31nBeUDixBC9EXzK50oW1F+Um6mhyIOQj2t44JD8L6d9UEewLHHHsvbb7/Nd7/7XebPn09BQQGhUIipU6dy2WWX8fLLL/O9730vI2MLh8N861vfysi5xcFjwgcLcHItml+L0fJ2PNPDEaOMn5RZXyGE6Iu29xIkW1zyZ4cpOjKS6eFklh4llzHkpZdeAkgvBxuMrG6hsKvCwkK+/e1vp9ffDbdnn322z9t+9rOf5bOf/ezwDUYc1HKnh4iMD9C1PUXj8pjp8yNEPykngLJt/IR8SSCEEPuz6d5mpl5STMnCHNyYT/tqKa8pDuw73/nOXq//zW9+Q3l5+X73TSQSrFu3jgcffBClFNXV1YMez6gJ8oQ4GOXPCVF+Yp5pkfCv1kwPR4xi2k3JFwRCCNEXHmz6axNTLylh3Cl5uDGfLlkmIQ7gpptu2qNdgtaa3/72t30+htaacDg8JEvQRkW6phAHo8J5EcpPzMNPaTbd3Sx9esTgKXnJF0KIvvATsOlvzeDDhLMLCJbYmR7SyMt0GuYoTNfUWqcvSimUUr2u29clFAoxZcoULrnkEpYsWcK8efMGPRaZyRMiC5UsiFJ0VBQvrtl0d6MEeGJQlBMAkFRNIYToB7fNZ8sDLUw6v5BJFxSx6e4m3A7prSD2zvd7/9+wLAulFG+//TaHHnroiI9HvtYVIsuUHZ9D0VFR3E6fDXdJgCcGT9k22pVUIyGE6K9Encv2f7WhbJj84SIsKVYs+mjy5MlMnjx5SCplDoTM5AmRRXKmBik4LEKqzWfTX5ukGasYEjKDJ4QQA9e5KUnd8+2Un5hH1cXFbLjrIHl/zsJ0yD1k8fg2btyY0fPLTJ4Q2cKG8e/PR3uw+d6D5A1ECCGEGAXaViVoejWGHbWY/OGiTA9HiAOSIE+ILFF5Zj7Kgbrn2tFupkcjxoqe9XhCCCEGp+nlGG3vxgkVOUw4ryDTwxFivyRdU4gsYAUhOjFIotGlfY0swhNCCCGyUd1zHTg5FjmTQ4w/LY/aJ9szPaTho5W5ZLMsGN/73/9+AJRSPPXUU3tcPxC7H2sgJMgTIguUHJeLUoqGxR2ZHooQQggh9mPbo21MurCQ3OkhSjs9GpbEMj0kkUHPPvsswB498p599tl0C4W+6tl+92MNhAR5QmSBvBkhvLhP1zbJ0xRDQzkBtJuSqppCCDEMttzXwpRLiik8IorbqWl5syvTQxIZcuKJJ+41KNvX9SNFgjwhMszJs7CDFi3vyBuEGDoS3AkhxPDa+Ncmpl5SQumiHNxOn451Y2u5hdLmks2yYXw9M3l9vX6kSOEVITIsd0oIgNimsfXmMFYo2870EIQQQmQjFzb9tRHtasaflkekUuZORPaQIE+IDItUBNBa07lFZl6ykpKXSSGEEHvnx2Hz35rBhwnnFBIskvcMkR3kf6IQGRYostEe0hcvS2V72qO0SBBCiMxKtfrUPNQCCiZ9qBg7OkY+XutRchnl1q1bx7Jly9ixY8eQHneM/C8UYvRSCvDHwKuUyAjtpg6YUmqFwiM0GiGEODjFa122P9GGcqDq4iKUZG4e9Orr6/nNb37Db37zG1pbW/e4fe3atRx99NHMmjWL6upqJkyYwEUXXURLS8uQnF+CPCGEGO26U0p7ZvWUE+gV2PmJeEaGJYQQB5PODUnqXuzACiqqPlIsn7IPcvfddx9XX301v/rVrygoKOh1WyKR4KyzzuL1119Ha43WGt/3+cc//sH5558/JOeX/35CCLEfoyEdsieldNd/JbATQoiR17YyTvNrMZwci8kXFmV6OCKDHn/8cZRSXHjhhXvcdvvtt7Nu3ToAzjvvPH75y1/ywQ9+EK01L7zwAvfee++gzy9BnhAZ5iU0yslcHxWxf9m+Jk8IIUR2aVweo311glCJQ+W5BQfeQYxJ7733HgALFizY47a//OUvALz//e/n/vvv5wtf+AIPPPAAp512Glrr9O2DIUGeEBkWr0uhLIWTJ3+OQgghxFiw45l2OrckyZkYZNz78zI9HJEB9fX1AFRWVva6vquriyVLlqCU4sorr+x12+WXXw7AK6+8Mujzy6dKITIstjkJQO60UIZHIg5kNKRuCiGE2DflOCP2Wr7tkVYSDS55M0OULIiOyDmHkmJnQ/SsvWT6QdqPngIqltU73Fq2bBmpVAqlFKeddlqv26ZOnQpAXV3doM8vQZ4QGRbbmkJrTaRCAohsJ6mbQggh+mPz35pxO3yKjopScJhUOj6Y5ObmAlBbW9vr+meffRaAuXPnUlTUe91mIGA+CzrO4MuzSpAnRKb5oFOaYLHUWxYHdqB2CUIIIfZvpL+w2/TXJvyEpuz4XHKmBkf03IOi1ei4ZKnZs2cD8M9//rPX9ffddx9KKU466aQ99ukJCMeNGzfo80uQJ0QW8BIaO5S9L1QiOygngPa8TA9DCCFGLe26GTgnbLqnGe1Cxen5hMfLl7oHg3POOQetNbfccgu//e1vWblyJddddx0rV64E4IILLthjn1dffRWAiRMnDvr8EuQJkQUSDS5WUFF2Ym6mhyKymKSLCiHE6OTFfLb8vQk0TPxgIYEC+Qg+1l199dVUVFSQTCa5+uqrOeKII/j5z38OwKJFizjllFP22Oehhx5CKcUJJ5ww6PPL/zAhssD2f7WRavEoPDQiOftjmLJtKd4ihBAHqWSzz9ZHWsCCyRcVYWX7270eJZcsVVBQwJNPPsn8+fPTDc+11pxwwgncc889e2z/xhtv8PLLLwNw+umnD/r8Ml8sRJbY/ngrVReXECqRP8sxS1koS4FtS9qlEEIchLq2udQ+2c740/KouriEDXc1wshnkIoRMmfOHFasWMGGDRuora2loqKCKVOm7HP7P/7xjwBUV1cP+tzyaVKILGCFYeIFRWitaX4jlunhiCGgbBuUhXZTWMEgfjIJ2gcVMHWfGX1BXvp+CCGEGLCOdQkaohal1TlM+fdiNv65KdNDEsNs6tSp6fYI+zJv3jzmzZs3ZOeUdE0hssCEcwqxAoodz7aTavEzPRwxFNTOl1fta6xQOJ2qqSyFcgJ7VMrM1lROK2iqwUmAJ4QQQ6PlrS5a3ozh5FlMurAw08PZu0ynYY7ydM1MkyBPiCwQKnFINLi0v5fI9FDEfignkA54DritZaqlWsEgyrbxE3Ezk6d9tK97NhquoQ4pCe6EEGLoNSyJ0bE2QbgsQOXZ+ZkejhhjJF1TiAzLnRFCWYr2NRLgZTvtpg74paGy7T172VkKKxoFzwR55mA+2vO6Z/UclBPIymBKUjSFEGL41D7Vjp1jkTM5RPlJudQ915HpIYkhlkqlePjhh3nxxRfZsGED7e3teAdYl6+U4qmnnhrUeSXIEyJTLBh/aj65U4JorWlZ2ZXpEYkhoD0PZdtoX5uWB04A5SjQu8ze6VT3LJ5n0jaVAu2bPnhZ1CYhW9NHhRBiLNn6YCuTLy4if3YYt9OnaUV2rM1XunsJeRbL9vE9++yzXHbZZWzZsiV9ndb7HrRSCq21+VwwSBLkCZEJFky9pBg7auHFfBqWdY7GOhwHtX3NcPUERulgTfugNX5Xl0ndDAbxE3GUY1I1e17QURb42fWfoC8zl0IIIQZv873NTL2kmOKjo7idHm2rJLtntFu5ciVnn302iUQCrTXBYJCZM2dSXFyMZQ3/cg0J8oQYaTZU/bsJ8JpejdH0cnZ8Yyf6TjkBtOeZ9Xm2DZ6XDvi0m0J1r9vrSd30u8wsrfY1+DvTNZUTMAGe9k1A5WdPSJVts4pCCDGm+bDpr01MuaSE8hPz8GKazk2SKj+aff/73ycej2PbNt/73vf4/Oc/T25u7oidf3Ss+hdiDAiVOVSeU8CMy0sJ5Fu0vt0lAd4opj0P7ZsZut1n9PxkEisU3qOwinZT4LpYkUivVEjtZV9FVQnwhBBiZPlJM6OnPaj4QD6hsgzPxWS6auYor6757LPPopTi2muv5Wtf+9qIBnggQZ4QI6Lo6CiTPlRIdGKAVLvH9ifaqH+pM9PDEgPUEwDtGgjtvn7NT8TNv8kkVjCIFQqnUzz9rq5e6ZxWOIQVDmNFIials6fVgm3LujghhDiIuB0+W/7RDMCk8wtx8uWj+mjV1GT6H55//vkZOb/8zxFimDg5FhP/rYBJFxZSckwUL+az4c4mNt3dTOd6ScEYa/Y786UsszbPts0MH6Qra6IsU3XT1+iUi/a8namcnmfSP/sQ6A1FMCgBC/sFqAAAk95JREFUpRBCZF6y0WPro61gQdVFRVjhTI9IDMS4ceMACAQy894qQZ4QwyAyIcCUjxYTHh8gVOLgdvpsurcJL5Z9aXli+PQEfj2zevgaPxHfma6pLLSbwk+l8JMJE9zZdvr6dNCle/+/2bVXX8/Pg02vlDV4QgiRPbpqUux4uh0VUFRdXAz2gfcZcplOwxzl6Zrvf//7AXj99dczcn4J8oQYYoXzIkw4twCArQ+3svaWBjb+XxN+PMMDEyNq11kxKxg0gdsufXGUpcC2sAJB0yevu0iL9nU62EsHXbuv7fN1n5uy764nbXR3EuAJIUR2aV+ToHFZJ3bYourfizM9HNFPX/nKVwiHw/zsZz+js3Pkl+hIkCfEEMqZGqT0uBy8uGbjX5ro2iofnA9a3SmXYNbl9aReWpEIOuWaqpzdvXKUY+8RBPZU79zbDJt2U72KvezRfH03PUGdFQqbWUElL/1CCDEaNL/eRctbXQTyLSZ9qDDTwxH9MHfuXO644w42b97MaaedxqpVq0b0/NJCQYghNO6UPPBh412NaDfToxGZZII0G9UdWO2suGkanPpdXWb2LhAAy0IFHFQwmA70VHdq5wHP45see1bIBIO7BopWMGgqgHYHhPs6nhUK9+lcQgghRl7D4k6cHIu86WEqzsxn+z/bRuS80gx9cC6//HIA5syZw7JlyzjssMM44ogjOOSQQ4hGo/vdVynFrbfeOqjzS5AnxBAZf2oedtBixwvtEuAJo7vgSnrWTfugFcpSZimB50E4BL5GWRa+152m6XnptXs9PfZgL43Wd/l5b+mWB+q7l672KQGeEEJktdon2nFyLHKnhCg7IZf6FzoyPSRxALfffjuq+4tdpRRaa958803efPPN/e6ntZYgT4isYUHujBDJFpe2lfKBWZgUyr0GT55nqmxGo+C6Jojz8rpn4BQ6mQTHwQqFTVrnIOxrnV1PCuju/f2EEEJkr5r7W6n6SBEFh4ZxO32aX5Veu9ls8uTJ6SAvEyTIE2II5EwJopSi+U15wT1YKScA2k+nS+6aNtnDTyZRTgClNNi7vPAr1V1oxduZsmkpdGLPtXhDoady5z4DUSGEEFlp0z3NTL2kmJJjo7idHu3vJYbvZFqZSzbL4vFt3Lgxo+eX1fdCDIHcqSG01nSsG8YXW5HVtJvab0GTXVsd+Im46Y0H2Dk54JgUTWXbZibP173SNIdrvBLgCSHEKOPDpr824ac0407OIzpZ+puKvZMgT4ghEC5z0B74EuMd1JSlDtjaYNfed9rX+IkkuF56LR4yuyaEEGI//CRsvqcZ7UHlmQWEyiQxT+xJgjwhhoCf1FKVXgDsd53b/2fvvuMkK8u8/3/OqRy7OkxPT08e8jDkDJIzSEYFWZBVFFF3QVcRl5+A7q6rz7qPuj66uisIRiQPAioOYcgzQx4Z4uQ8nbsrh3P//qiZhmE6d3VXVff3/XrVa2q6Tri6T52qc537vq97x1g4y+15vyjK9sIshUQCtrfmjXQOPBERmRzycYf1D3QCMOO8GO7IGFyElHuS8yqfDL0vxhja2tpYv349hT6GdZSSLktFSiC+NotlWwSadTdtMtsx5q7f12yrd5Lz3scHPuR3zKdX6oIo/cUkIiLVK9uaZ9OfurBcMPtjtdj+ckckfSkUCvzyl7/kuOOOIxgM0tjYyNy5c3n77bd3Wu6hhx7i+uuv59/+7d9Ksl9dkYqUQM9baeoPDVIzP0BqU0+5w5FyMk6fP94xZ11fBVk+qJRj8XYkdzumbBARkYkluT7Hlsd6aDo5wpxL6ln9G83TW0m2bdvG+eefz5IlSzBm4G/iuXPncu6552JZFmeffTYHHnjgqPatljyREsgnHPJxh/A8H7av3NFIudg+/+AteePYqrajtbB3InYREZlw4u9laH0uge2zmHNpXcmu7ndMhl7pj0pVKBQ455xzeOGFF7Btm0984hP8v//3//pdft999+Woo44C4P777x/1/pXkiZTI1ie6wYIpR0fKHYqUy/ZWvAETPZerpLscatLoZNK9Y/005k9EZGLpXJ6i/eUkrqDN7I/XljscAe644w6WLVuGx+PhkUce4fe//z1f+MIXBlzn3HPPxRjDM888M+r9K8kTKZHUpjyFtCE4SxfQk1WxWmYay35/3h47ECj+6/UWn9tW789Kss9hzJ23Y6yfJkEXEZl42pcl6VqRxhtzM/PC2PsvuCG8m4+a/dSjYzz9/ve/x7IsrrnmGk499dQhrXPQQQcB7DJebyQ0Jk+khLJteQLTVeRisrI87mIXSef9/iMmVxwcsSOx6p0qQUREpMRano7j8ttEdvOx22cbsF3v33TMZrOwfBgbq4bqlRUc3+uvvw7AOeecM+R1pkyZAkBbW9uo96+WPJESyif7Lrohk4RjsNyenbpk7mhps1yu4kOVLkVEZAxt+Ws36W25nRI8gOQW9eIYT52dncD7idtQ5HLFawZXCYZ2qCVPZCy4AVW3mpRMPtdnhU3L5Sq25mlCRRERGWPr7+sEwDfFTXiul+hefgJTdZNxPNXV1bFt2zZaW1uHvM6bb74JDC8x7I+uNkRKKDjDg8kZJXiT1I5umH12x3S71YonIiLjKtOSp21pktW/bqf95cTwVq6AypmDVtas4O6a++67LwBPPfXUkNf5zW9+g2VZHHbYYaPev5I8kRLx1tq4AjbxNeoOMVn1VznTcrkw2VxvQZZSV9gUEREZTOer6XKHMKmcf/75GGP4yU9+wrZt2wZd/mc/+xmPPfYYABdddNGo968kT6REovsEsCyLjleGeadMJgwn088XqGX3zldn8jkVXhERkcpnquRRoT772c8ya9Ys2tvbOemkk1iyZEmfy7399tt85jOf4Ytf/CKWZbFgwQI+/vGPj3r/GpMnUiLuoI0xhmyHiq/IzoYzzYGIiIhUP5/Px8KFCznhhBNYsWIFRx99NNOnT+99/YorrmDbtm1s3LgRAGMM9fX13HvvvViW1d9mh0wteSIlUsgYLMvC1jQ0IiIiIpPeAQccwLJlyzjyyCMxxrBhw4be11555RU2bNiAMQZjDIcffjhLlixh9913L8m+1ZInUiKJtRli+waI7RekfVmy3OGIiIiIjFyFd4cEKj8+YPfdd+e5557j6aef5sEHH+Sll15i27ZtFAoF6uvrOeiggzj33HOHPGH6UCnJEymR5LochaxD3YFBut5IU9CceSIiIiICHHvssRx77LHjtj911xQpoS1/7QYbZn2sFlRAUURERETKQEmeSAkl1+dofS6By28x7eRoucMRERERGZFyz4E35LnyKpRt27jdblasWDHkdVauXNm73qj3P+otiMhOut9OAeAK6fQSERERmayMGVkWOtL1PkhXoSIlNuP8WgA6XtZ8eSIiIiIyPJpCQaQCeSIucl0FEms1N5qIiIhIpXn55Zf5zne+w5lnnsnMmTPx+XyEw2H23HNP/v7v/55nnnmmLHG1trYCEAqFRr0tVdcUKTFjDJTgDoyIiIiIlNbxxx/PU089tcvPs9ks7777Lu+++y633347l19+Ob/4xS/wer2j2t9QW+USiQQ//vGPAdhtt91GtU9QkidSeg5YqqwpIiIiUnE2btwIQHNzMx/72Mc49thjmTVrFoVCgeeff57//M//ZOPGjfz6178mn8/zu9/9bkjbnTdvXp8/P+200/B4PAOum8lk2LZtG47jYFkW55xzzvB+qT4oyRMpMeMYLHWEFhERkWo2QSdD33vvvfnOd77DRRddhMu18135I488kssvv5xjjjmGd955h9///vd8/vOf57jjjht0u2vWrNk1PGN6k8qhOvLII7n++uuHtU5flOSJlJjltiikKv1TUURERGTyeeihhwZ8vaGhgf/8z//sbU275557hpTkfepTn9rp/3fccQeWZXHuuecSi8X6Xc+yLPx+P9OmTePoo4/mpJNOKknhFSV5IiVk+8H2WKQ2qeiKiIiISDU64YQTep+vXLlySOv88pe/3On/d9xxBwD/9m//xvz580sW21ApyRMpobqDQ1iWRceryXKHIiIiIjJilT7ZOIxdfNlstvf5h7t0DtXNN98MQGNjY0liGi4leSIlFJ7nw8k5pLfkyx2KiIiIiIzA4sWLe5/vs88+I9rGjiSvXFQeQqSE3CGbdKsSPBEREZFq5DgO3/3ud3v//7GPfayM0YycWvJESsQTs7Esi8w2jccTERGRCaDCu2vu0N3dvdP/fT4fPp9vRNv6wQ9+wNKlSwG44IILOPTQQ0cdX6FQYMWKFaxevZqenh4KhcKg61xxxRWj2qeSPJESCc0qfpgk1ivJExERERkvM2fO3On/N998M7fccsuwt7N48WJuuOEGoDiW7r//+79HFVcymeTb3/42t956K+3t7UNez7IsJXkilcJTUxyYq5Y8ERERkfGzfv16otFo7/9H0or3xhtvcMEFF5DP5/H7/dx9991MnTp1xDElk0lOOOEEXnrpJYwZ/yZRJXkiJWK7i3OaOBqSJyIiItWuiiZDj0ajOyV5w7V69WpOO+00Ojo6cLlc3HnnnUOaG28g//Ef/8GLL74IwP77788Xv/hFDj74YOrq6rDtsS+LoiRPpERSW7JE9/JTd0iQ9mVjM4WC7fNjcpkx2baIiIjIZLNp0yZOOeUUNm3ahGVZ3HbbbZx33nmj3u7dd9+NZVkce+yx/PWvf8Xj8ZQg2qFTdU2REul+M0Mh41C7f3BMtm+5PTiZ9JhsW0RERGSyaW1t5dRTT2XVqlUA/PjHPx71WLgdVq9eDcBXv/rVcU/wQEmeSEnFV2exPRa2t7Tbtb1eTF5j/URERGRkbP/wlt8xGXqlP0aqq6uL008/nRUrVgDw3e9+ly9+8Ysj3+CHBAIBAGbMmFGybQ6HkjyREvI3uDDG4GRHvy3L7cFyubDcHoxT6Z3iRUREpFJZbph5fm25w6gYyWSSs88+m5dffhmAG2+8ka9//esl3ceCBQsA2LBhQ0m3O1RK8kRKyHJZJduWyecwhULx32G04lnukXcJ2LHuaLYhIiIilcMdtpl7eT2uoC77AbLZLBdccAHPPvssANdeey3/+q//WvL9fO5zn8MYw29+85uSb3soVHhFpJRsC5zRb8Zye0bcPXM03TrVJVRERGTi8E1xM+O8GJYLWhbHh7dyFVXXHI5LL72URx99FICTTjqJz3zmM/ztb3/rd/lQKMTcuXOHvZ9PfvKTPPjgg9x9990ceuihfPWrXx1+sKOgJE+khDwRm3TL6OdQGE2yZfv8oy7QomRPRESkujUcHSK2XwAMbPpTFz1rVZ0b4L777ut9/vjjj7P//vsPuPzxxx/Pk08+Oez9PPXUU3zuc59j/fr1fP3rX+eee+7h0ksvZa+99iIYHLxI32incFCSJ1JKFhSSo2vKG06S1leLn5NJ9/lz2+vFyZZgsKBIP3aMIzWFAqCbBSIi5eBrdNN8RhR30EUuXmDTw51kO0rQzUiG5YQTTsCy3h/Gs2zZMpYtWzakdS3LIp8fXaOBkjyRUrGLJ2UhM7q+DaWYJuHDF9eW26MET8bcjvGjts8PttXn+1CJn4jI2PA2uGn8SBj/VDcYaH85QdvSkc/bO9rqleNhJPEZM36/1Hju68OU5ImUiLfWBYCTKQy6rOUqLrujxeODhtPiNtQLZl1Yy3hyMmlc4TB2MFhs2csUuwhZgQAmlcI4ppgMeotzjegGhIjIyEX28FF/WAh3pFhYJdOSZ/Nfuskn1HpXTk888URZ968kT6REGo8NY4yha8XgLXGWy7XLhW1vcmep+pVUN9vrxeTy21vz8uByFf+fzYLbjZXPF8fKu91QxrucIiJVy4b6Q4PU7BvA5bMxjiGxOsu2Z+KjHjYipXH88ceXdf9K8kRKIDDdg3+qh/TWHLmuwT9c+2q52PGzUnTXFCmHD96oMIUCJpPbqYvmh1uUTXZ404OIiExWdtCm/tAgbr+N7bcITPVguSwKWYeOVxO0Lk2WpLr3TiZodc3JQkmeSAnUHxYCitWrRCYry+vF5fVSiL9fpnugJG7H+L0dNzZUHEhEZFeWG+ZeWovtsXvHeOXjDm3LEvS8o4qZ0jcleSIl4K11kU84OPqslUnK/lByN1RquRYRGdiUY8LYHputT3TT/fY4XmioJa+qKckTKQHbY5HtGLzgishEZZzRf9OqFU9EZFeRPfzkk4XxTfBkWL797W+XfJs33XTTqNZXkidSChY4WQ10lsnFDgZxksXy3BpbJyJSerUHBrDdFm1LUuUORQZwyy237DQnXimMNskraRm/66+/Hsuyeh8jmR1+hx1/rKE8BtvP22+/zUUXXUQsFiMSiXD22Wfz0ksv9bv8k08+udP2L7nkkkHjvfLKK3uXl8nJGeX8eCLVxA4EehO8UtsxxYiIyGQXOyCIkzd0Lh//JG/HPHmV/qgUxpiSPUqhZC15r732Gj/4wQ9KtbmSWb58OcceeyxdXe8XxHjkkUd47LHHeOSRRzjppJMG3cZdd93FjTfeyH777TeWoUqVsn07JkFXS55MHk4qNWaFUvqaP1JEZLIJzvTgDth0vqFWvEpX7jnx+lKSJM9xHD772c+Sz+dpbGxk27Ztpdhsr+XLlw/4+ty5c/t97aqrrqKrq4uzzz6ba6+9Fq/Xy2233cavfvUrrrzySlauXInH4xlw+8YYbr75Zu67774RxS8TW6CpOKFzrltJnkwuYz2G7oPTL4iITDY1+wQwxtD6/PCLWsn4KveceH0pSZL3X//1Xyxbtoy9996bCy64gH//938vxWZ7LViwYETrrV27lqVLl3LYYYfx4IMPYtvF3qnHH388XV1dLFy4kOeff57jjjuu3200NDTQ2trK/fffz8svv8zBBx88olhk4grv7sMYQ/c7utMmUgp2IICTSmHZFijRE5FJyjfFjZMzmHyZAlB1zao26jF569ev55vf/CYA//3f/43X6x11UKWyceNGAI477rjeBG+Hk08+eadl+nPttdfi8/kAuPnmm8cgSql23pgLHHBUCV6kJJxU8YaJk80WEz0RkUnIHbLJdan7uozMqJO8L3zhC8TjcT71qU9xwgknlCCk0mlsbATgmWeewXF27kq3ePFiAJqamgbcxowZM/jc5z4HwEMPPcSSJUvGIFKpZiZvQNehIiXjCod7n2taBRGZjALNbizbIrVJn4EyMqNK8u666y4eeugh6urq+I//+I9SxVQyu+++O/vuuy9LlizhggsuYNGiRTz11FN8+tOf5t5776W5uZmjjz560O184xvfIBAIAKMvZyoTj5NHSZ5IibhqanaaVN3e/tkrIjKZ1B8RxhhD+8tjU8V4SEyVPKRPI07yOjs7ufbaawH43ve+x5QpU0oW1Iedeuqp1NfX4/V6aWxs5IQTTuC73/0uHR0dg677P//zPwQCAR588EFOPfVUjj/+eH75y1/i9Xq59dZbe7tiDmTatGlcc801ADz66KM888wzo/6dZGKw/eBvdJevv7zIBFP4QCVkeL/rpojIZFGzwI+/0U1qcw5H85/LCI04ybv++uvZsmULRx99NJ/5zGdKGdMuFi1aRHt7O7lcjpaWFhYvXsw3vvEN5s2bx8KFCwdc9+ijj+bZZ5/lrLPOIhwOEwwGOeWUU1i8eDFnnHHGkGO44YYbCIVCgFrz5H2zP1GP7bVoezFR7lBEJhzL7cH2+csdhojIuPHW2kw5OoyTNWx8qGvwFUT6MaLqms888wy/+MUvcLvd/OxnPxuzCcD3228/zj//fA4//HCam5vJ5XK8/fbb/Pa3v+XRRx+ls7OTiy66iD/+8Y+ceeaZ/W7noIMO4uGHHx5VLFOmTOFLX/oS3/ve93jiiSd44oknOPHEE0e1TaludtDGHbDpfidN52tqbRApNTvgp9DTU+4wRETGRc0CPw1HFMckb3igA8o8M1OlTTbel0qPr5yGneRls1k+97nPYYzhy1/+8phNEH7ddddxyy237PLzI444giuuuIKf//znfP7zn6dQKHDVVVfx3nvv9Y6bGytf+9rX+OlPf0pPTw/f/OY31W1zsis4GGNw+TQgT6RUPjjBeqGnBzsYxEkmd3ltIJpfT0RKyfbCzAtr8URcYIGTNRRSDrmeAtmOPN1vpcl2DC8js/0QmObF1+DGG3MTnOHB5bNx8oati3uGvT2RDxt2d83vfOc7vPnmm8yaNWtMpxSIxWIDvn711Vdz1VVXAbBp06Zxmai8vr6e6667DoBnn32Wv/zlL2O+T6lcTgZyXQWCs7zMOK8Gb0NJpp0UkQ9wkklcNTXF59sTvB3/h50Ls1huz04Jnh0IYFfQtD4iUp2aTq3BU+Mi05En3ZLHyRvcYRfBmV5qDwgx+xP1zPv7emIH9N3Y4G1w03h8mDmfrGO3z9Sz+9UN7HblFJpPr6H+kBDheV6woO2lBCt/0UrP2xqIJ6M3rKvSt956q3ei8x//+Me9Y9TK5eqrr+YXv/gFUJwS4bLLLhvzfX7lK1/hxz/+MZ2dndx0002cfvrpo96mZVtj1uW1uG0019QY2bCwk1kfryMwzcvsi710vJKgbdnYdt3UMZ1YdDx3Zvu8u7TCWRa4a2O9RVmcnm4s28L2+nAy6ff/dk6xApLL58M4BSgUMPncLn9b27u94JYxOLn3WwZL0QKo4zmx6HhOLCM9noWUg2VZJFZl6Hh150lxvTGbmv0CRHb3MeWoMPWHBkltzlFIGbx1Lry1bmy3hTEGkzfkkw65Hod8V55MW570tgLZ9vfnwhvL95plrOF1Aa2G6pWVHl8ZDSvJ+8EPfkA2m2XevHkkk0nuvPPOXZb529/+1vv88ccfZ8uWLQCcc845JU8K58+f3/t8sEnNSyUWi/GVr3yFm266iaVLl/LQQw/x0Y9+dFTbrJ8dw2OPzd1my7aINUfAsjCOzoSxEH/WUHewj0Czh8hxEby5bnKdY9fNQsd0YtHxfF9vd8wpO38e2pEITk8P1Nb2sVZw4G36fVBwcHK53u3bHk/v6x/8eSnoeE4sOp4Ty0iPp7MGQvuFiZwUwZvtopDceV2zHrrXG6J7uwnv7qdm3+LNe+MY8okCqS154ivTFOKG4pxLLsCFFx/eKBAt4S85gJyThdXjsy8pv2EleZlMsfl41apVXHrppYMu/y//8i+9z1evXl3yJM+Y8nzgXnfddfzoRz+ira2Nm266ibPPPntU22tb24nb8gy+4AhYtgXG0Lq2U19QY6h1VQfuoM3sy2phWpbWV8euWISO6cSi41lke3042f6mxen4wDKl68Zke7zgduGkBp+OZ6h0PCcWHc+JZTTHs+f3Pcy4oAbffg7r/tDZ5zKta7Y/sbc/Kmx6pbzRWOXJpKoHEa1YsaL3eXNz87jtNxKJ8LWvfY0bbriBV155hQceeGBU2zOOwYxheSBjtu9DX1BjKhcv4GQMvnr3mP+tdUwnFh1PwOPBpNMDLlJIp7EDgZLMnWf7/OBxU4jHcYXDmFweJzPw/odKx3Ni0fGcWEZ6PNPbcqS35PA3eQZft0Jrpgy3cUTVNavbsAqv3H777cU+xQM8PliM5Yknnuj9+Zw5c0odOz//+c97nx9//PEl3/5AvvSlL9HY2AjAzTffXLZWRaksmdY8roBNoLmq75+IjL8hfoY6qVRJ5s5zMmkK8Ti2z08hHi9ZgiciE5ft237ZPOJZpkXGT1neprfffjuWVeyv3Nc0CcuXL+e9994bcBv/8z//w6233gpAU1MTF1xwwViE2q9QKMTXv/51oBjvI488Mq77l8rU/kqx1PuMc2tpOGrgsUIi8j7jDO3WtysSwfKWsHu7CmqIyADcYZuGo0PMu7IeX52bxLpsxbbUiXxQRTY3vPTSS1x11VWceOKJnHnmmey3337U19eTz+d56623eidDB3C5XPz85z8vS6XPa665hu9///ts3ryZ1tbWcd+/VJ7Uxhyr7mhl3uUNhOb6aX0+We6QRKrCjrnwBmJ7vSWfHL0UXT9FpLp4ojb1R4YITvdgubff6NleSdIUDMYBbLDdVm+1y0LWoeP1JK3PJcoW97hTdc2qVpFJHkChUGDRokUsWrSo32Xq6+u59dZbOffcc8cxsvcFAgH++Z//mX/4h38oy/6lMrlDLrAhs1UDnEWGShOYi8hYC0xz03xWlJpcsSNbPu6Q7cxhcmD7LGyvhe2xsNwWJm/IZgyZbTniqzOkNlVYFRWRQVRkknfWWWdx66238vzzz/PKK6+wdetW2traMMZQV1fHAQccwBlnnMGVV15JNDpOdWf78dnPfpb/83/+D+vXry9rHFI5AtM8WJZFz2pNZioyVIMleHZwe/fnEk11ICKTT9PpUVx+m8S7WVqej5PvVr9Lmbgso4ohZdPd3U1NTQ0ncN6YTqHQMKeW1jUdqgw2TtxhmzmX1ZHekmPDwq6Sb1/HdGKZbMfTVVMDgJNIYnncg3aXtL3eYiW8fA5XOEwhHh+PMEdssh3PiU7Hc+KoOzRI/aEhnHVuVv15y6Q8nnmT40kW0tXVNWAjyY7r032+8B1cJSh0NZYKmTRv/vSfB/2dJiPVBxIpsXzcIbW5WGY5NHtskneRalXo6qLQ1YXlcWMGaZXbMUm5yeew3DqXRGTkonv5cXKG7rfUy0YmByV5ImNg0yNdYKDukPEvCCRSDZxUClMoDLzMB5JAk89VfCueiFQu22NRSKt7pkweFTkmT6TamTyYAthelWcXsX1+sC1VshSRsrHcFk7SAfS9PFQWlf/XqvT4ykkteSJjILqPD9tjFefTEZnkdkw03ls8ZYh2jN8TERkty4ZCRi15MnkoyRMZAw1HhXHyZnLNpyMyACeVgmHW+XISSWyvd4wiEpFJxYJCavIVW5HJS901RUqsZoEfl9em9QWNHxLZwXJ7YJAxeB9m8jnNcysio2b7wLIsnGQBXfoOgyZDr2p6p4uUWKDJgzGGjtc1/khkB9vvG7Sa5nixfdBwZBh3yMb22bi8Fpm2PNue6sGpjBBFpITC83wAZDryePGVORqR8aEkT6TEPFEXOBQfIlLkcu1ULXM4LJcLaxTr7xRG0Gb2JbXYnu3D9R0wDnhiLsLzfMRXZdj6ZA8mP+pdiUiFqD8shHEM3W9naZilqtcyOSjJEykxV8DGyav/gMgHOYkkltuDyeeGva4pFDCFAnYwiJNMjiqOppMj2B6LrU/20PP2+/NlBaZ7aDw+Qng3H+F5PrY83kP8Pc2nJVJJ3GGbfMqBAXp+215w8vTeaJ1+bg3uoIv2lxO6+TpMlik+Klmlx1dOSvJESiyfdPCHdGqJfFApJjQfbYIHEJjmIddZ2CnBA0htzLH2d+0EZ3mYdmqUppMjbPNC9woleiLl4g7bNB4fJtDkwXJbWFaxBd4YAwZy3QUSazIk1ucIzfYS3dvf20rvZA2Wy8J2WyTWZ2hbmsSyVXBfJg9diYqUWKYtT2CqBztob5+TR0SAEbXilZLtB8u2SG7sv9tncl2O1b/vYM4namk8NoLLa9PxqsbXioy3KR8JUbNvAIB8wiGzPkcuXsD2WNheG0/UxlvnpvbAELUHFtcpZBx63stg2cUbOk7W0PJqku430uX7RUTKREmeSIkl12WJzQ9Qs6dPF4ciFcQbK37l5eMD33xxkg5rftvG7EvqqD8iRD7l7NLyJyJjp+mUCJHd/eR6Cmz6cxfZtv77Z/qnuvE3eci05Eht0mDaklJ1zaqmefJESiyxJotxTG81LxGpDNn24gWgp8Y16LJOFlb/th2TNzQeF8HWdH0i42LKR0JEdveT3pZjzW/bB0zwANJb83S+llKCJ/IhSvJExkCuu4C3Vg3lIpXEyYKTcwjvNsQbMAXY8lgPlg3NZ8XGNDYRgbpDg9TsGyDbmWf9fZ3lDkfg/da8Sn1Iv5TkiYyBbGcBSzmeSMVpfymJy2sz86IY9hByvcSaLMmNOfxT3fim6KSWyuWtd9FwdIjp59bQcFSw6t6vNQv81B0SpJB0WHtXR7nDEal61fUJIFIlMm15wnN8+Ka4ybSoC4lIpeh4NYW/yUNotpd5VzaQacmzdXHPgF3Ctj7ew9zL66g/NMimP3WPY7Qig7PcMPfyely+4n17YwzBZi+1BxTnhst2FmhbliCxevTzTA6JXRwn5w65iK/KDD5tgQ1TT4gQ2cOHkzGs/UO7pjoQKQEleSJjoPudNHUHB6k/PMSmh7vKHY6IfMDmP3cTaHbTcFQY3xQ3sy6uJbk+y6ZH+k7gCkmHfNwhML2EA/Ns8Na6yMcLOKrpIqMQ2z+Ay2fT816a9leSZNsKeOtdRHbzEZzhxdfgpvn0GgpZh5530rS+kMCU+N6jHbSZcVYN3loX2Ow01UGuq0Dbi8ld5p30NripOyhIeI4Xy2WR7cizfmEHzjjlojI4zZNX3ZTkiYyBfJdDtqNAcIaHKceGaXk6Xu6QROQDUpvyrL+3E3fYZtppUUKzfEz/aA0bH+r7pkz322nqDw0R3t03rEnSfVPcBKZ58E1x4425cIfsYovLhy6EMWAK4OQNJmcoZB2ctCGfcsi05ul+M6WLX9mF7YfoXgGMMWxZ1NP782xbgba2JG1Lk+CChsODRPcOEFtQHPOW3pan9fk46S2jz/Yie/iYekIE7OJ+s515Mq15ClmHyO5+Ak0epp0SxZxoKGSKTXQun43lsjDGkE84tC1LqIKtSIkpyRMZIxse6GDmhbXE9g3gDthsflTdvEQqTT7usP6+TqafU0NwhpfQPC+JVbtmU+2vJKk7KEjjceFdkjxPzCbQ7ME/xYO3zk399CjRtI3l+lAi5xTn8cq058l1Fch2FXD5LNxBF3bAwuWzsb0Wts/CFXRjbS8CGt3DT8ORIQppQ3pLju630yTWKOOblGyoPTBAdE8/7ogLa3tlhZ53B0iQCtD6fJLW55MEZ3qoPzyEv9HNjPNiFFIOXSvStL+cHFEXyWlnRgnN8mLyhk2PdJPauPNcmN0rMsWYDwoS2c2Hy188HzIdedKbc3S8lhp0ShMRGRkleSJjxMnC2js7mHFBjPA8n1r0RCrYxke62P0zDdQfFuozyaMArS8kmHJMmN0/20A+6eDyW1huqzeRAzCOwXJZ5HoK5DoLpFvzpLfmSG3OwcCV4PsVmuMlurcf/1QPoTlewnN9GMcQX5Vh21M9auGbLNwwb/vYO1MojrVLt+TpeWfo0wck1+dIru/E9kPDkREiu3mpPzRE3cFBkhtztD7XQ7Zj8KTLW2sz47xaXH6b1NYcG/7YCf2F4EDHS0k6XkoO/XeVylANFSwrPb4yUpInMsY23N/JnL+ro2Zvv5I8kUpVgHRLHn9j/1+LnctTOAWHuoNC2D6LfMIh110g05YnvS1PalMWk7NomOPQuqYT45Tm6iOxJtvbcmd7oWZ+gOj8AOHdfITn+dj4SBepDblBtiLVbuqxEWyvResLcTpeTY1qW04atj3Zw7YnIbKnj7qDgwRneJj18TrycYeO15J0/S29y3q2H+oPD1Ozjx8MJYlFRMaGkjyRcRBfmab2gJCqbYpUsO530gSmRoju5aO7n/FB3SsyxS5o/bDGeGIiJ1usENrxaorADA/NZ9Yw/awaNj7UqcmgJzhvravYKlbipKrnnQw972Rwh22mHB0mNNtL40ciNBwZJh8vkE84FFIO/iYP7pCNZVnkEwU2/LGTXKe6WopUKs2TJzIOOlekMcbQeGy43KGISD+63yyep9H5gXKHMiSpDTnW39MOZvtk7fpGn9Ast4UZw5wqH3fY/Gg37/1vKy3Px8knHdwhm8A0D+HdfLh8Nsn1WdY/0MHqX7crwZsEdlTXrPSH9E0teSLjIN/lkFibJTzHR2z/AJ2vq3uLSMVxINNa7LLpDttVURAi2+Gw7akepp4YpfHYMNsWq0v4ROXyWSXrAjyYztdSdL72ge8pG81dJ1JldN9PZJxs/nM3pmCome8vdygi0o+tjxer4DadEilzJEPX/XaGXE+B6F5+3bqdoGwvuII26W1lGnupBE+k6ujrQGQcOXmDy697KyKVKtvhkN6Wxz/VgydmV02XtJZn4zSfUUP9wcHi3GhSsUKzPQRn+/CEbFwBG9tvY3ssnIxDrqtYkTW1aXtF1u1vv7pDQ1iWRdff1AtExpGqa1Y1JXki48zlt3HX2OS7quPiUWSy2bKomzmfrKPp5Cjr7+0sdzhDkliTxckZonv5leRVqOh8H1OOjmC7d5470ckbTN7gDtl4alyEZvvgkOLrplAs5mPZFk7OkFirKqoiMjRK8kTG0YYHO5l1cS0Nh4XYsqin3OGISB/yPQ6pTTkCzZ6qqoib3JAlNMeL7QNngLmxZfw1HBmk9sAQTs6hdWmSrhVJnF1nKADA2+AmON2Df4obb60bUzDE12bpekPJu4gMnZI8kXGUbSvgZAyBZk+5QxGRAWx5rIe5l9cx9aQI6/7QUe5whqT7nTThuT4ie/j7nONMyieyhx8n67DytrZBl8225sm2VseNBZnYqqF6ZaXHV04aHCQyzgoZg7W9u46IVKZCslgR11frpmZBdRRLSqzNYowpdveTijOW0x+IiHyYkjyRcWbyBstWkidS6TY/2k0h7TDlmDD+piro+OJAIe3ga6iCWIfB9oK/yY3tLXckI5frcbC9+twXkfEzsb4JRKqAkzdY+q4XqXwOrLuvgzmfqGPGuTG2Pd1D95uVPdgt01ogOMMzIeY1azolQng3H5b1fqGSfNwhvjpD+0uJqhp36GQd0Oe+VBtV16xqSvJExpmTNWpDF6kS+W6Hdfd1MOO8GI3HRQg2e9nyWOUWTYqvyRCa6SU810d85fCzoECzm9gBQSwLjAGc4jZ73h7Ctlzgn1K8rEhvGd2Ystj+AcK7+cj1OCTXZ8knCgSmeQk0eajdP0hsQYCuN9O0PF0dk7+7w66qT7pFpLooyRMZZ4Vk8Zve9tNvdTURqRzZtgKrftXGrPNriezhxzfFzdp7OqACa2P0vJ2m8SNhYgv8w0rygjM9TPlIBE901ztQ4bk+Gj9iiK/O0LYsQb5n+2eYD6J7BQjN8eKrd2N7rZ1a3QpJh3RrnvaXkmS2Df2P5YnZNBwVwskY1t7Z3pscdVCcIy4ww8PUEyLE9g0Q3cPHtmfjQ0tCy8gbc5HtKJQ7DBGZRJTkiYyz+JoM0b381OwToOMVTWwrUhXysO6eDqYcF6ZmHz9zL61j9e/aocKu200esh0F/FOHVsHX9sOMc2vx1rrAQHxVhm2Le3CyOxaA2oOCxOb7iezhI7qnH+OY3q6HlmX1JnSJjTnS23LgsgjN8OCtcxOa5SU0y8u2p4be1TW2IIBlWWxY2N5n61dqQ441v2kntl+A+iNCNJ0Ype7gApsf7SLbVmEHBKg7NIhlW3S9qc97qTLqrlnVlOSJjLPE6ixO3lB3UFBJnkiVaXkqTiHpUHdIkDmX1LHmt+3lDmkXncuTTD0+SuyAAJ2v9f8ZE53vo/GYCNgQX5Vl6xPdmA83uDnQ8VKSjpeSeBvc1Oztw9fgxskXeyXEV2VIrMnusu2Ol4r/uiM2sy6upfG4CN4aF60vDD7Xm2+KB+MYsh0D92/sXJ6i840UU0+IENnDx6yLatn4cBepjZU1YXhsQYBC1tG0FiIyrjQySKQM2pYksL02U44NlzsUERmm9heTdC5P4Ym4mHpSpNzh7KL7zQxOztBwRAhvH5U2PbHtidexEZyCYeMfO9ny1z4SvA/JtuZpeSbBhge62PRQF1sf7+kzwfugfI/Dmt+2kY87xA4IMu306KDxe6IuCqkhDmBzYOvjPay7pwMMTD+7hsCMypmH1BW0sX0WyXUD/51EREpNSZ5IGXQuT5GLF6jZx49dHVNwicgHtD6XINOeJ7KHj+CsykkqdtjwYCcAM8+PEd3XT3g3H8GZHqafW8PsT9ThrXeRWJtl1e1tpDaN7eBCJwtrfttOemue8Fwfsy6u7ffqw1Nj4/JbpIcxhg+K4ybX3dOOMTD9rBoC0yvjmHhjNpZlkdHk5lKFdkyGXukP6ZuSPJEy2bKoGyyYdnpNuUMRkRHYsLADHJh2Wg1WhQ1+yLTk2fyXLiwXTD02wrRTo0w/O0ZgmodMS541v29n85+7x7Xi44YHOul+J4233sXcy+sJzd114ruGo4u9G1peGH7VzGyHw7q728FA85mVcUxSW/MYY/DWVUAwIjKp6FNHpEzSW/Kkt+QINHnwN7lHXXJcRMaXk4HNj3Uz7dQoM86Nsf6+znKHtJPE2hyrft2Ov8GF7S22kKU258panGTr4z1kOwvUHxqk+fQa8skCHa+l8NW7Cc7w4ArY5Hoc8l0jyz5znQ5bHu+h6ZQIM86Lsf7eztL+AsNVAFOA4MwqnsldRKqSWvJEymjTX7oAmHKMxuaJVKPEqiyJ1Vn8jR6azxx8vNl4c5IOyXU54u9l6PpbuiKqT3a8nOS9W1vpfCOF7bOZclSYyB4+LJdFYm2WjQs7R7X9+MpM8ZhM8VB7SLA0QY9C5+tJ3AGbxhMqb/ymyIBMlTykT2rJEykjJ13sVuWb4i6ejWrME6k6mx/tZsb5MUKzfcy5zE3qZavcIVW+ArQ8Hafl6TjRvXwkN+V6598rhc2PdjP3ijrqDw2SWJMpa3LbtjRJzfwAoQocuykiE5da8kTKrOvNFJZlEZql7jwi1WrDA520v5zAFbRpPqOG0Bydz0PV/XampAneDhsWdoKBGefESr7t4crFC9g+XXKJyPjRJ45Ime2oIuerV8O6SDVrW5pk00NdGANNp0Y0RUqZ5bocWpcksH0WzWeXt8BVemse22VV1PQOIoOxjKmKh/RNSZ5ImWU7CsXqazWucociIqOU2pxn0yOd5HocYvsGmPXx2oqo8jhZdb6WIrUlR2iml9mX1JZtyprW5+MYxzDlaCX+IjI+lOSJlJsDGHBHlOSJTAROFtbd2VGcLqDWxbxP1eOfqkyvXDYu7KLrzRSeGhfzLm8gsqdv3GMweUhuzOGtdeGu0aWXiIw9fdKIVIBCysFbqyRPZCLZ+ngPWx/vwXJZzDg/xvRzasrWkjTZbVscZ+NDXRjHMPXECM1nRcf9Cqj1uR4sy6J2QWB8dywyUuWumqnqmqOiJE+kAiTW57A9FkFVXxOZUHrezbDmd+2kt+UJNHuYd0UDU0+KYAf19TveUhtzrLy9jfTWPMGZXuZeVjeuV0HZDgdTMASm6XNeRMaevmVEKkDrCz2YAjSfXoNHXXlEJpR83GHD/Z1sWNhJPuEQ2cPHvMvrmH1pHbH91KozrgrbK6G+lMQVtJl5QWxcd59LOHg0/lqqhGWq4yF90yABkQrgpGHjw53MODfG7EvqyLblcfIGd9iFy2dh2RbZrgItz/SQ3lL+yYxFZPjSW/Ks+W07vilu6g8PEWz2MOWYMPWHBdm6OE58ZabcIU4a7S8m8da4iOzhp+mUCFsW9YzLfrPteTyzNb2GiIw9JXkiFSK9Oc+6ezqYekKkdzoFJ2fIxx2MA946F9PPiZGPO2T+psmWRapVpiXPpoe7AIgdEKD+sBBNp0TobHTR+nyyzNFNHlse68ETcxHezUdte56Ol1Njvk+TV7ODiIwPJXkiFSTbVmD9vZ19vmZ7ofG4COHdfMROjLDljfZiZU4RqVqdr6Xo/FuKOZfUEds/SGprnsSqbLnDmjTW39/J3L+ro/6wENmOAonVY/y3V44n1aQaCptUenxlpME/IlXCycKWRT20PZ/A9lg0nx0td0giUgoFWHdPOyYP006JalzueHJg7V3tmAJMOzWKt3Zs//aWS70wRGR86JtEpMp0/i1drNTX5FHRBpEJwsnAhgc7wYKZF9aqn804ctKwYWEnUPzb22M4jZ5vihuTU9ODiIw9JXkiVajl6TgmZ6g7JFjuUESkRDItebY93YPttZh1YW25w5lUMi15tjzeg+W2mPWxujHbjztkk+lQ8SypDuWumqnqmqOjJE+kSvWszODy2/ibdMtfZKLoXpGh550Mvjo3TadEyh3OpBJ/L0PHK0ncIZsZ59eUfPvh3X1YtkVyvcZcisjYU5InUqXaliQwxlB/eKjcoYhICW19ood0S47I7n6mn1v6ZEP617Y0SWJtlkCTl8bjwyXdduOxYUzB0P6KKqiKyNhTkidSpZwsZDsLBJo8OpNFJpj193aSWJ8h2Oxlzt/VYfvLHdHksfnP3WQ78kT39hNo9pRkmw1HBXH5bNpeTIB6a0q1MFXykD7p0lCkinW8msSyLWoPUAEWkYlm08PdtL+cwB2ymXtZg6pujqP1D3SAoSRdZr31LmL7BcknC3S8MvZz8YmIgJI8karW83YGJ2+oma8kT2QialuaZPOj3VhumHVxrVr0xomTgbalCWyfxdSThp/oBWd6iOzlIzTPy8zzYwBs/GNnaYMUERmAkjyRKpdcn8UdtnFHdTqLTESJ1Vm2PFas/DjjXFXdHC8dr6aIr87iq3Mz+9Ja3KEhfMbaMOvjtUw/O0bTiVGaT6vBcllsebyHbIcz9kGLlFC5q2aquuboqCyfSJVrXRInNKeOhiPCbPlrd7nDEZExEH8vQ3pfP/4dY3CVL4yLrYt6iATDuKfazLmsjp73Mmxd3NPnuDpvvYvpZ9fgCtj0vJcmviaLy28RX5WlkNQBE5HxpSRPpMrlOh0KSYfQbG+5QxGRMdT9bprANC/heT7i72XKHc6k0fl6mp5ED1NPjhDZw0dkDx+5rkLxEXfAgkCTB2+tC4C2F5N0vKQKmiJSXkryRCaAzhVpGg4LEd5dF38iE1Wuu9ga5PJbZY5k8sm05Fn7u3ZCc7zUHhTEV+/GU+PCsorHwhQM6a15tizqJh9Xq51MENVQvbLS4ysjJXkiE0DHK0nqDwlSd1BQSZ6IyBhJrMmSWPP+ZOa2F7CKhVpERCqJKjWITAQOpLfl8da5ihcdIjLhWNu/sY3uXFcMJ6sET0Qqk5I8kQmibVkCy7KoPyxU7lBEZAz4Goqdb7Lt+TJHIiKTRbkrZ6qy5sgpyROZIFIbcxQyDtG9/TqzRSag6J5+jGNIb1aSJyIiA9OloMgE0rokXpxL65yacociIiUU3ceHp8ZFckN28IVFRGTSU5InMoF0r8iQ3JAjMM3L3E/VEzsgUO6QRGSU3DU2jcdGcHKGTX/RXJgiMk6MqY6H9ElJnsgEs+nhLtpfTuDyWkw5Kszun2ug6ZRIucMSkRGaeV4MLNj4x64+J+EWERH5ME2hIDIBtS1N0rY0SXS+j9oDQ0R29+MK2MWLRBGpGnWHBXEHXbS9lCDTorF4IiIyNGrJE5nAuldkWPu7dhLrswSne2k+M1rukERkGGL7BihkHNqXJcsdiohMMuWunKkKm6OjJE9kEtj0cBfJzVlCs33UHhwsdzgiMkS21yLTphY8EREZHiV5IpPEpoeLXTU9EZ32IlXDAiejW9UiIjI8GpMnMkn4phRP91yXKjeIVAuTN3jr9FUtImVgtj8qWaXHV0a6pS8ySfgbPACkW9X1S6Ra5Hoc3CF9VYuIyPDom0Nkkqg9OIBxDKktuXKHIiJDlN6aw3KB7S93JCIiUk2U5IlMAg1HBnEHXHS8mgQ15IlUjcS6LJZlEd5NWZ6IjC/LqY6H9E1JnsgE5wraxPYPkk8UaFuqMuwi1SSxNosxhtAMb7lDERGRKqIkT2SCm3lhDCzY/Gh3uUMRkeFywMkZfA0qviIiIkOnbw2RCaz57CiesIv2lxOkt6qfpkg1ynUX8NXq61pExpmqa1Y1teSJTFC1BwcJzvCS3JRVN02RKpbemsdyWbjD+soWEZGh0TeGyAQUaHZTf1iQQsph44Nd5Q5HREYhsSYDQHg3X5kjEZHJxDLV8ZC+KckTmWDsoE3zWTFwYP29neUOR0RGKbk+hzEGv8bliYjIECnJE5lALDfMviiG5YJNf+kin1BtYZEJwRQr5YqIiAyFbguKVDnbCzX7BQlO8+Bv8mC5oP3lJMl1mvRcZKIwDrj8SvJEZBwZU3xUskqPr4yU5IlUsdqDg9QfFsSyLIwxFJIOLc8niL+XKXdoIlJKjsFyW+WOQkREqoSSPJEq1XB0kJp9AxRSDluf7FHLncgEZrktCqlCucMQEZEqoSRPpArVHxEkH7XJdTus/UM7aOidyMRmQT6uJE9Exk81VK+s9PjKSR38RapM80ejhGb6yLTlWft7JXgiE50nZmNZFrluJXkiIjI0askTqSIzL47hb/CQ2pJjw32a/05kMvDVewDIdinJExGRoVGSJ1INXDD7E3V4Ijbd76ZxVutiT2Sy8Na6AMi06bwXkXFktj8qWaXHV0bqrilS4WwvzL2smOB1Lk+x7Yl4uUMSkXHkibowxpBtz5c7FBERqRIVneS1t7fzm9/8hmuvvZaPfOQjzJs3j2g0is/nY9q0aZx22mn85Cc/IR4f+KLXGMMPf/hD9t57b3w+H7vvvjv/+q//Sjab7XedE044AcuysCwLl8vFG2+8MeA+1qxZ07v8LbfcMpJfV2QX7pDNnMvqcQVs2pYmaH0uUe6QRGScWTu+qTX+VkREhqiiu2s+/vjjXH755X2+tmXLFrZs2cJf//pXvve973Hvvfdy2GGH9bnsVVddxW233db7/5UrV/LNb36TZ599loceegiXyzVgHI7jcMstt3D33XeP/JcRGSZPzGbWRXVYbtj2VA/db2ruO5HJqJAxWJaF7QNHHwMiMk5UXbO6VXRLHsD06dO59NJL+fGPf8wDDzzA0qVLeeqpp/jNb37DmWeeiWVZrF+/ntNOO41Nmzbtsv6f/vQnbrvtNmpra/mv//ovXnjhBW677Taam5v585//zP/+7/8OKY57772X1157rdS/nkif/E1uZn+sDssFmx/tVoInMokV0sUmPHd44BuSIiIiO1R0S97555/PxRdf3O/rl112Gf/1X//FtddeS2dnJ9///vf5v//3/+60zF133QXAHXfcwTnnnAPAEUccwUEHHcRBBx3EXXfdxec///l+9xGNRkmn02SzWW666SYWLlxYgt9MpH+h2V6mnR4FAxv+2El6s8bhiExmqc05AGILAmxbrDG5IiIyuIpuyXO7B89Bv/CFLxAOhwF46qmndnl948aNAJx44ok7/fzAAw+krq6u9/X+1NbWctVVVwHw4IMP8uKLLw4pdpGRiOzlY9oZUYwD6+5pV4InIqQ25MjFC0T38oMa80RkvBhTHQ/pU0UneUPhdrvx+/0AZDK7dmlrbGwEYPHixTv9fPny5bS3t9PU1DToPm688cbefdx0002jDVmkT7UHBph6QgQnZ1h7ZzvZDlVZEJGilmfjWLbFtJOj5Q5FRESqQNUneY899hitra0A7L333ru8fv755wNwxRVX8JOf/ISlS5dyxx13cNZZZwHwsY99bNB9NDc3c/XVVwPFMX7PP/98iaIXKWo4Kkj9ESEKaYc1v20jH1eCJyLvS6zOkm7NEZrrJXZAoNzhiIhIhavKJK+np4c333yTb3/721x44YW9P//Hf/zHXZa96KKLOP/882lvb+dLX/oSRxxxBFdeeSUbNmzg5JNPHnA83gd94xvfIBgMAvDNb36zNL+ICDD1xAix/YPkexxW/6Zd1fNEpE/r7+ukkHZoODJEaLan3OGIyAS3o7pmpT+kb1WT5H3/+9/vnYcuGo0yf/58br75Zrq7u3G5XPzwhz/k2GOP3WU9y7K4++67+dd//VfmzZuHx+Nh9uzZ/H//3//Hww8/PKRxfwBTp07li1/8IlBsPexr/J/IcDWfFSW6l59se4E1v2uHQrkjEpGK5cC6uzsxBZh2Wg22t9wBiYhIpaqaJK8/J5xwAq+//jrXXnttv8u43W5uvPFGVq5cSTabZc2aNfzLv/wLPp9vWPu6/vrre4u8qDVPRmvmhTFCs3wkN2VZd3dHucMRkSpQSDps+lMX2NB0qsbniYhI36omyfv7v/97li9fzvLly1myZAl33HEHJ598Mk8++SSXXHIJS5YsGfMYGhoaeruEPvXUUyxatGjM9ykTkA2zL6nF3+ihZ2WGjQ92lTsiEakiqY05Mq15gjO8eGJV8zUuItXGVMlD+lQ13w719fUsWLCABQsWcPjhh3PFFVewaNEi/v3f/53ly5dzwgkn8Oijj455HF/96lepqakB1Jonw2d7Ye5ldXhqXHS+kWLLX7vLHZKIVKHN2z87pp1WU+ZIRESkElX0ZOhDccMNN7Bw4UJeeOEFPvvZz7Jy5cohj7MbidraWr785S9zyy238MILL/CnP/2JM888c1TbtOziWMOxUNx28V8pLztgM/tjMWyfRcfLSdpfSo3ouOiYTiw6nhPLeB3PQtyQWJclNMtLaLaX5PrcmO5vstL5ObFM9uNpGQtUvHvSqPokD+Dcc8/lhRdeYN26dSxdupSjjz56TPf35S9/mR/96Ed0dHRw0003jTrJq58dwzNGI+gt2yLWHAHLwjhq0y4XV9hi2ilRLJdFx2tJ7DY/DXP8I9qWjunEouM5sYzn8SyshOi+UcLnR9j4cJcu3saAzs+JZbIfz5yThdVDX74aqldWenzlNCGSvIaGht7na9euHfMkLxqN8tWvfpUbb7yRF198kYULF3LAAQeMeHttaztxW2NTDtuyLTCG1rWdk/IDrRL4GtxMP7mGnkQPWx/rIb4qO6rt6ZhOLDqeE8t4H8/cohT1R4TwLiiw6UF1/y41nZ8Ty2Q/nnmjFv/JZEIkeRs3bux9vqP65Vj7x3/8R37wgx/Q2trKzTffzP333z/ibRnHYMbwVoQx2/cxCT/Qyi0ww8P0s4pjZjY+1EVqY2k+YHVMJxYdz4llPI9nx6spAs0eQrN81B0aoG1pcsz3Odno/JxYJvPxNGby/c792bp1K0uWLOl9LFu2jO7u4o2ym2++mVtuuaW8AZZA1Sd5juNw33339f5/3333HZf9hsNhvv71r/O1r32N1157jXvvvXdc9ivVI7ybj6ZTIpgCrH+gk2xrvtwhicgEtOmRbuZeXkftQUHiq7NkWvRZIyIl4Jjio5KNML6mpqYSB1J5Krq65m233UYu13/Lh+M4XH/99SxfvhyAY445hnnz5o1XeHzxi1/sfZN873vfG7f9SuWrWeAvJnh5w9q72pXgiciYWndvJwBNJ0XKG4iISJWZN28exx13XLnDKLmKbsn7yle+wj//8z9z8cUXc/TRRzNnzhxCoRCdnZ288sor3HHHHbz66qsARCIRfvKTn4xrfIFAgBtuuIHrrruO1tbWcd23VK66w4LUHRzEyRjW3NmGky53RCIy0RWSDok1WUJzvASa3aQ26caSiEh/brrpJo444giOOOII6uvrefLJJznxxBPLHVZJVXSSB8U+sz/5yU8GTOD22msvfv3rX4+q+MlIXX311fzHf/zHTuMCZfKaclyYmn385BMOa+9sx+g6S0TGydYnupl3ZQONx0dZ+/v2cocjItWuGiYbH2F83/rWt0obRwWq6O6aL7zwAj/+8Y+58MILWbBgAVOmTMHtdhONRtlrr7245JJL+MMf/sDrr7/OYYcdVpYY/X4/N954Y1n2LZVl2mlRYvMD5LoKrPmdEjwRGV9OFuKrMniiNqHZY1OxWUREqkNFt+Ttvffe7L333nzpS18a930/+eSTQ172mmuu4Zprrhm7YKTiNRwdIjzPR2prjg33d5Y7HBGZpLY+2UN4no8px0ZIrFVrnojIZFXRLXki1cD2Q2xBgHyioARPRMrK5KH7nQyesIvQPG+5wxGRKmbx/oToFfso9x+pginJExmlppOjYMHmv2oiYhEpv21P92AKhsZjxmfeWBERqTwV3V1TpBq4Qy5MHtJbdh2EZ3uh8bgInhoXLc/G+1xGRKSkCtD1ZorYgiCRvXz0vJ0pd0QiImNqx0TmO/h8Pnw+X5miqQxK8kRGywIw4IbpZ9Xgn+LBchd/9MF+BDPOjbHpz10k1/U/96OISCm0PJcguneAhiPDSvJEZGSMKT4q2fb4Zs6cudOPb775Zm655ZYyBFQ5lOSJjNKOPG7WhbV4a13kehxyXXkst4XJGzpfT1HIGGaeH6P5zBrycYeW5+IkVmd7txGY4SE8z0d8VYbUBiWBIjJKDnT9LUntgSFqFvjp+psm7BSRiWv9+vVEo9He/0/2VjxQkicyehbYHhtfnU3Pe2m2LOrpc7GVt7XSeFyEyO4+mk+vwckZ8okC7rAL211MFWv28dP+cpL2Zcnx/A1EZAJqfSFJzYIg9YeFlOSJyLDtKG5SyXbEF41Gd0ryRIVXREbN9hVPo0LG6TfBg2LVu62P9/Dera10/i1ZTPBCLgoph7aXEqy9u51C0qHu4CDBmZrjSkRGr+O1JC6fTXh33dUWEZlM1JInMko976YJTPOw8Y+dQ1uhAC3PJIDELi+t/UM78z7VQNMpUVb9sq2kcYrI5NP+UpK6g4PU7Osn/p7G5omITBZK8kRGqfW5XZO1kXKy0PZykobDQkT38dH9pi7KRGQUHMh1O/inqHeAiAyT2f6oZJUeXxmpu6ZIhel4KYlxDLF9A+UORUQmgPiqNLbbItCs+7oiIpOFkjyRClRIObiCOj1FZPQ6XktijKH+8FC5QxERkXGi23oiFSifcvDGdHqKyOg5aUhvyeFv8hBodpPalC93SCJSBSxjsCp8nryRxvfMM8/w3nvv9f7/rbfe6n3+6quvcvvtt++0/JVXXjmi/ZSTriJFKlC+x8FXX+4oRGSi2PTnLuZd2UDdoSE2PthV7nBERMrqF7/4BXfccUefry1cuJCFCxfu9LNqTPLUH0ykAqW25LAsi9Acb7lDEZEJwMkADtgeq9yhiIjIOFCSJ1KBut9MYYxhyjFhYvsFiM73Ed3Lh79Jje8iMnyemI3lskhvyZU7FBGpFk6VPEbg9ttvxxgz5Ec10hWjSAVystD1RoqafQNMOSa882t5Q2Gtm9Y15YlNRKpPdE8/AN3vaFoWEZHJQEmeSIVqeSZBx6spPNHiHXjLZeGf6qZm3wB1Bwbp2uan47VUucMUkSrgCroAyLSp6IqIyGSgJE+kguXjDvn4+30REmuydLySYv8vRqk7JKgkT0SGxOWzil2ORti1SUQmn4lcXXMy0Jg8kSpj8pBYm8X22vin6j6NiAzO9lmgayERkUlDSZ5IFep+K40xhuazawjO8pQ7HBGpcLbHQje8RUQmDyV5IlXISRtan4tjuy2az6xh9iW15Q5JRCqYO+zCyaivpogMg6mSh/RJSZ5Ilep6I8PK21pJrM7ijblpOCZU7pBEpAJ56124/BbpFhVdERGZLJTkiVQxk4fNj3aTTxaILQhojJ6I7GLaKVEAWp6OlzkSEREZL0ryRCaA9Q90goEZ58ZoOjlS7nBEpEKE5njxxFwkVmd3qtQrIjIoY6rjIX1SkicyAeS7HTY+0kUh4xDZw0/TqUr0RAQaj4+AA5sf7y53KCIiMo6U5IlMEKkNOVb/qp1sZ57wPB+uoE5vkcms9sAA7oBN5/IkaDieiMikoqtAkQlmy+M9AMz+RC2BZo3RE5ms6g4JUsg6tL6QLHcoIlKFLFMdD+mbkjyRCSazLc+2Z+LYHovp58SYeVEMlOuJTCqBZje2x6brjVS5QxERkTJQkicyAXW/kWbV7a0k12fxNbiZfbHm0ROZTGr2CWCMoeM1teKJiExGSvJEJignC5se6Sa+KoOnxoW33lXukERkvGz/dnfS5Q1DRKpYuatmqrrmqCjJE5ngWl9IANBwuCZLF5ks8vEClmURnOUpdygiIlIGGqkjMsHlexyynQWCs7zMuayOlufiJFZnyx2WiIyhtmVJYvsFmXZaTbHbdr0bLCikHHI9DrmuPOnWPIk1WdD0eSIiE46SPJFJYMP9HUw9OUpohpdpp0VpfzlJ+zKN1RGZqEwetj7ZQ+OxYUJzvJicwRhwB934poBl+YrLGUM+4ZDamKNrRYr0Vs21ICJFllN8VLJKj6+clOSJTAJOFjb/qRvbC7M+XkfdwUFsj0Xrc4lyhyYiY6TnnQw972SKAzM+dCHkidkEpnuJ7ObD1+AmsqeP6F5+TMGQ7SgQX5uh8400TnLgKyh3jU2gyYOvrjjm18mDKRhMHpy8s/1fg8kbnJzByUG2Pa/WQxGRMaYkT2QScbKw5vftzLmkjtr9g9TsGyC9LUfn6yl14RSZqPpIqHKdDrnONN1vFCuzuMM2Nfv6Cc324a1zUd8Qou7gIE62mJxZ1vYVLcCysD0Wlgus3heGzhgDDhTSDrm4Q7Y9j5NxKOTA5AxOzsHJsv1fg5NxcHKQTzlQGPmfQURkMlGSJzLZFGDNb9upPShAdO8AgSYPwWlecj0FNv25i2ybrqJEJpt83KFtSZK2JcVu3IFmNzX7BPBP82C5LMCAYfvDkOt2yHUXyHXmSbcUSG/LgQOWG2yvXfzXbWG5tyeEbrBdNpbXwlfrwhtz4Qq58E9xE5g6tOIwxhjycYetT3ST2qRupSJjrhqqV1Z6fGWkJE9kkup4JUXHKylwQeNHwkT39jPr4lp63smw9YmecocnImWU2pQntWmknwPD64tpucHlt7F9FrbXwuW1sDw2thdsj43ltnB5wVvrJjjDS9PJUVb/un2EsYmITA5K8kQmuwJsWxyn/aUk08+uIbqXn1y8oMIsIjIuTL7Ykkh88GVnX1qHO6TZn0REBqNPShEBihdZa//QgZM3RHbzlTscEZFdeCJ2sXCLiIw9UyUP6ZOSPBHZickbbM/wiymIiIyl0Dwvlm3R816m3KGIiFQ8ddcUkZ2kW/IEZ3jw1rtUhEVEKkZgWrFAS7eSPJFxYRmDVeGFTSo9vnJSS56I7GTbU8ViC3UHBcsciYjI+1w+G2PMoHP3iYiIkjwR+ZAdkxoXy6aLiFQGJ2+K8/KpD5KIyKD0USkivdxRm6ZTa8CBrU90lzscEZFeJl/sluXy2hTyas0TGXOaJ6+qqSVPRADw1trM/ngdlgs2L+rGyZY7IhGR9zm54sWc7S1zICIiVUBJnoiAC2ZeWItlw6ZHukisVoYnIpXFbE/yXD5duoiIDEbdNUWE0GwvtsemdVmC5PpcucMREemfcjyR8WGASu8Zrd6a/dJHpYj03iHH0aeliFSmwDQPxhjSLZoMXURkMEryRITk+hymYKjZJ1DuUEREdmVDcKaXXI8DyvFERAal7poiAkBiXZbQHC/eWptsR6X3zxCRyaThyBCWbdG2JFHuUEQmDU2GXt3UkiciALQ8Gweg/vBwmSMREdlZzT5+CmmH+MpMuUMREakKSvJEBIB83MHJGvyNauAXkcoR3deP7bHpWJ4qdygiIlVDV3Mi0ivbnsff5Cl3GCIivWoXBDAFQ8dLyXKHIjK5GCp/svEKD6+c1JInIr26301jWRax/VWARUQqgztsk4trnLCIyHAoyRORXt0rMpiCIbZASZ6IVAbjgKWrFRGRYVF3TRHZSXJDluAsL+6wTV53z0WkjGw/2F6LXFeh3KGITD7GVEF3zQqPr4x0b0ykytk+iM73EWguzT2b1heKVTabz6wpyfZERIbLHbaZelKEuZc1AO9X/xURkaFRS55IFQvM8DD9rBos2wLAyRtSm7O0v5gkvXVkMwZnOxziq7JEdvOx22caSK7P0ro0Tq5TrXoiMnqRPX34mzzkEw6ZbTmcvCHQ7CXQ5MFb68IdsHtvQRdSDh0vpkb8eSYiMlkpyROpRjZE9vTSeFwE48DWxd14oi6ie/gJzvASmukjnyyw7Zk4iVXZYW9+y1+7SW32U3dwkNBcL6G5dXStSNPytO6mi8gIuGDKkSGiewewPVafixhjcLKGTHueTGuezjfSZFuV3ImUjQP0fbpWDt1/7peSPJEq4ZviZtppUdwhm2hNlJ4eF07esP7+DrJtxfEq7cuS2EGbhsOCRPf0M+3UKNueidP9RnrY++v6W5quv6VxR2xmnh+jZr6flufioKExIjJEthemnhglNNuLZVvkUw7tLyfoWpHCE3Xjn+rGdluktuRIb1FCJyJSKkryRKpA3WFB6g4OgoF0ax5Xd5bWt+J0vJ7a5S6Wk3TYtjhO65I4cy6tp/EjYYARJXoA+R6HtpcSTD2ueKE2kpZBEZl8/FPdTP9oDMsNuW6H1hfiJFa///mRacmTaVFiJyIyFpTkiVS4plMihHfzUUg6rL+vk0LK0DDHpnNNesBuCk4a1vyujTmfrKfxmDA9b6cxI7yeynUWm+88IdfINiAik0ps/wANR4XAwOa/duvmkEgVsozBqvDqlZUeXzkpyROpULWHBKk9IIDLa5NuybH+/k5w6C2yMhROBrYs6qb5rBqaTo6y+S/dI4ollyhmk65ApXfOF5Fym3Z6lNAcL07GsO6+DvLdGjQjIjLelOSJVJjADA/NZ9Rguy2crEPbiwnaX0yOeHvJ9TmyHQVCc0Y+912+Z0eSp1lXRKRvtg9mXVyHJ+IitTXHhoWdKoogIlImSvJEKoh/qpvpZ9WAKc4L1bk8VZLtbnmsm1kX1zLlmPCIW/OMMf1WxRORyc3ftH38nQs6Xk/S+lyi3CGJyGhpMvSqpiRPpEJ4a21mnBsDA+vue79iZilk2wrk4w6hWd4RrR/Zw4dlWZqrSkR2UXtggPojQuDA5ke7dyquIiIi5aG+VzJhNRwdouHIYLnDGBJ3xGbmRXVgwYY/dpY0wduh47Uklssitl9gWOvZXmg8NoIpGDrfLE3LoohMDM1nRak/IkQhbVjz+3YleCIiFUJJnkxI9UcEqd0/SO2BIWx/uaMZmB20mf3xWiwXbP5L15jNFdX1tzROwRDbf3hJ3vRzil2wNj/WDWrIExHA9sOcv6sjNMtHemuO1b9uG9F4XxGpYDu6a1b6Q/qk7poyIdXMfz+RcQdssunKvPiwvTDnE7VYbostj/WQWJsb0/0l1mQJz/PirbXJdgztb+IO2zhZoxLoIgJAcJaHaafVYLmg/ZUEbUtGXhhKRETGhpI8mXACMzy4fMVpB/xTPNQfFmbzou7Kq/LmgtmX1GF7LbY93UP8vcyY77L1+TjheXU0HB1h08NdQ1onvipLbN8AU44N0/J0fIwjFJHx4K21Cc7w4Y7aeMIuLBuMA8Yx4Lz/3DhgCsW75S6/C3+TB0/ULo6/+3PXmN+YEhGRkVGSJxNOw+EhjDFs/GMnMy+oJTzPx+6fbcDJGuKrMrQuieOkyx0lzDg3hitg07YsQfeKsU/wAPJxh2xngeAMz5CnU2h5Ok5wuoea+X7iK9OkNqnPpkg1cgVtGo4IEZrtxfZZWFaxWq7po7vTjtc+yJhiAphYnWXrk904atwXmdiqoTtkpcdXRkryZEKxveCb4ibTksfJwto/dBDe3Udkng//NA/Rvf1E9/Kz8ZEuUhvKdwe69pAggake4msydLw8vsVMtiwqTqcw7bQo6+/rHNI66x/oYN4VDUw/J0b7y0nal6l7lkg1ie7jo/HYCFjgZAzdb2foeS9Npr2Ak+znZo8NtnvHvzaFtIPRPR4RkaqgJE8mlPrDQliWRevS9+doir+X6e0KGWh203xWjOln17Dmt+1lKRTga3RTf2iQfLLA5j+PbM660ci2FUhtzBGY7iHQ7B5Sy5yThnV3tzP93Bh1BwdJbcyqRU+kSjSfFSU404uTM2x6ZBjFnRx6W+uciuvvLiIiA1F1TZlQInv6KWScflvpUpvyrL+/A4CmU6LjGVqRC6Z/tDjZ+foHOsd//9tt/msXGJh2es2QPwWyHQ5r72wHIHZAdUxNITLZzbmsjuBML+mteVbd0TZm1XtFZAJyquQhfVKSJxNGcGax4Er3OwMPuMu2Fci05PFPdY/7GTDj3Bi2p1hoJd9dvk8mJwOtzyewvVYx6RzGerluh9BML7ZvDAMUkVGLzvfhibjoejPNhgc6ofTTb4qISIVSkicTRv32gittSxKDLtvxWgrLsqgd5pxxo1F3aHEcXmJtlu43x6fQykA6l6dIbswRmOah9sCh/x22PtENFjSdXIaWUBEZsobDwzh5Q8tTqoorIsNnGVMVD+mbkjyZEGwv+BqKBVeGUhggvjKDKRiie4/PTOm+Rjd1h5RvHF5/Nj3URSFtqD8ihLfeNaR10lvyZNsLBGd6sTSqV6Qi+ZvcuPw2XSvGt7CTiIhUBiV5MiHUH75rwZXBpLbm8NS4xj5RccGMHePw7u8c450N34YHOsAUu5IO9ROhbWkcy7KoP1Rj80Qqkbe2eNMmtVHz2ImITEZK8mRCiOwxcMGVvnRu77JZd/DYJiozz4theSy2Le4h31N5I4RzXQ7bnunB9lrFRG8IEmtzODmHyJ7j0xIqIsPjDheTvFy3Cq2IyAjtmCev0h/SJyV5UvUajgzi8tl0vTG8bkmJtVmcnEPN/LEbl1d3WBB/o4fEmizdb5d/HF5/uldkSK7PEmjyUHvI0JLexJosroCNt1YfIyKVxh0snpfZMhZ4EhGR8tHVmVQ1T8wmdkCQfKJA29LhT9DdtSKFy28T2av0pSJ9jW7qDi7GtvkvlTMOrz+bHukmnyxQf2gQ35TB+7C2bi9wU39EeKxDE5FhcgVtjDGqqCkiMkkpyZOqVntgcSyeK2gz94o6QnO9w1q/dUkSUzA0HB4qbWDuD4zDK+N8eMO1/oFOMDD9nBoYpA5LPu6QjzsEZwzvby4iY8/ltzV/lIiMjmOq4yF9UpInVW3bUz10vZki05bH5bOZdmp0eO9qB7rfSeMOuYjtV6JumzbMvrgWy2OxtULH4fUn3+2wbXEPtsdi5nmxQZfvejOF7baI7KlJ80QqictnYQq6+BERmayU5El1c2Db4jjr7+mk6600lm1hD7NhadvTcQoZh4ajQriCozwl3DDn0jo8NS66VqTpqeBxeP3pfjtDYk0Wf6OHusMGHp/X8WoK4xhqD1CVTZFK4graFDJK8kREJisleTJhuAPFMShOepgrOrDpT11gwYxza0a8/0Czm3l/V487bNPxSpKWp6t3AuLNf+kmnyhQd3AQd3iAjwkH0tvyeOtcw06uRWRs2H6wPRbpLZo+QURGodxVM1Vdc1SU5MmEkU87WJbF7EvrmPWxWprPjGIPscJ/ekue7rfSeGNuZl4cG9aZ4Q7ZzLwwxvRzYthei9YXEiMqAlNpNj7SBcDUEyMDLtf2YqI4FcWhJR7XKCIjUrNPAMuy6H53uHe8RERkolCSJxNG95tp8skCroCFp8ZFcJaXmefXDnn9bYvj9LyXxlfvZt4V9bgjA58e0X39zPpELXP+rg7fFDepjTlW3dFK52vDm8qhUmXbCmTa8gSaPQN2Y01tyFHIOkT20Jx5IpUgNMeHMYbkOrXkiYhMVoPXSRepEpmWPKt/1d77/9mX1OIODVIi8kO2LOohtjVPw9Eh5nyyjkLKIbUpRyFjsN0WrqCFr96NK2BjWRbGMaS35tj2dJxs28SrVb71yTizLoox9cQImx7u6ne5xOoskT19eBvcZFs1+bJIOflqXeTj1VPwSUQqVTV0h6z0+MpHSZ5MWIWMwVMDDUeHaH0uMeT1OpenSG7OUX9YkECTh/BuPizLAiiO+csYUpty9KxM072i+gqrDEe2NU+2vUBwhgfbT7/jHduWJojs6aPh8CCbHqn8OQFFJjLLZVFITbybTiIiMnRK8irA3Cvq8HqHUrXCGtEi0WiUaPcY9sy1Bvzv+LJ2fm5ZFrX7B4ntG+C9/20d8mayrXk2/6mYrFjbzxIzSRuoti7uYeYFMaaeGO39m3xYPuGQ63EITlf1FZFyM47B9pT1k1hERMpMSV4FyCccrGwfXWuG2wLd1/IW5O0Cua7Ch17feeFdVi3FvgdafKSt60Naz+z01FvrpmflyFvcJmtyt0NmW55sZ4HQLG9xgvR+Ggi63kgx5agw0b18dFfh1BEiE4UpgOVWkicio1QN1SsrPb4yUpJXAdbf24nb8ozJti3bIjXHonVNJ8bRiSAj0/5SkmmnRKndL0DHq30XlulcnqLhiBCxA4NK8kTKyBSMkjwRkUlO1TVFZFDx9zIYxxDZc4AKmg6ktubwxjRnnkg5OXmwhldzSkREJhgleSIyJJnWPN5a14CfGu3LinPm1R+mOfNEysXlt3Cy6rkhIqPkmOp4SJ9GlORt3bqVBx98kBtvvJFTTjmFmpoaLMvCsixuueWWkgV3yy239G53sMeTTz454LbefvttLrroImKxGJFIhLPPPpuXXnqp3+WffPLJnbZ/ySWXDBrvlVde2bu8yETT9UYKy7Komd9/a15qUx4nbwioAItIWVhusL0W2Q5V1xQRmcxGNCavqamp1HGMqeXLl3PsscfS1fX+PF+PPPIIjz32GI888ggnnXTSoNu46667uPHGG9lvv/3GMlSRitX9dobG4w01e/vp+ls/cykATsbBFVAnAZFyqDskiGVZdK3oe+ysiIhMDqMuvDJv3jxmzJjBU089VYp4+rV8+fIBX587d26/r1111VV0dXVx9tlnc+211+L1erntttv41a9+xZVXXsnKlSvxeAYufGKM4eabb+a+++4bUfwiE0GmPY+vbuCPDacALpV0EimL2IIAhYxDYnW23KGISLUzTvFRySo9vjIa0aXYTTfdxBFHHMERRxxBfX09Tz75JCeeeGKpY9vJggULRrTe2rVrWbp0KYcddhgPPvggtl1sYTj++OPp6upi4cKFPP/88xx33HH9bqOhoYHW1lbuv/9+Xn75ZQ4++OARxSJS7brfStP4kQjRfXx0v6kKmiKVZMqxYWyPTctz8XKHIiIiZTaiPlXf+ta3OOuss6ivry91PCW3ceNGAI477rjeBG+Hk08+eadl+nPttdfi8/kAuPnmm8cgSpHq0LUijTGG6N6BcociIh/ga3RTM99PtqtA5+vqqikiMtlN+IEzjY2NADzzzDM4zs5NuosXLwYGH2M4Y8YMPve5zwHw0EMPsWTJkjGIVKQKOFBIOnhr+q/PbvIGy6XiQyLjxVtrM+O8GBjY+MfOcocjIhPFjsnQK/0hfZrwSd7uu+/Ovvvuy5IlS7jgggtYtGgRTz31FJ/+9Ke59957aW5u5uijjx50O9/4xjcIBIqtFzfddNNYhy1SsXJxB9vbfxKXactjucAdmvAfLyJlF53vY9bFdVgWbHyki3xc41NERKSKkrxTTz2V+vp6vF4vjY2NnHDCCXz3u9+lo6Nj0HX/53/+h0AgwIMPPsipp57K8ccfzy9/+Uu8Xi+33nprb1fMgUybNo1rrrkGgEcffZRnnnlm1L+TSDXKtufBot8JzzteTQLF8UEiMjb8TW7mXFbH1OOiGKeY4KU25ModloiIVIiqSfIWLVpEe3s7uVyOlpYWFi9ezDe+8Q3mzZvHwoULB1z36KOP5tlnn+Wss84iHA4TDAY55ZRTWLx4MWecccaQY7jhhhsIhYqTPKs1TyardEsOy7IINPed5WXbCuS6HUKzvGrNExkD086MMuO8GO6QTeeKFCtvbVWCJyKlV+5JzjUZ+qhU/BXYfvvtxze/+U3++Mc/8tJLL/HCCy9wxx13cNpppwHQ2dnJRRddxJ/+9KcBt3PQQQfx8MMP09PTQyKR4K9//StHHnnksGKZMmUKX/rSlwB44okneOKJJ0b2S4lUsfTm4sWkv7H/4rxbFnWDBdPOiI5XWCKTwozzagjP9pFpybPqV620PKVKmiIisquKTvKuu+46Xn/9db797W/z0Y9+lIMPPpgjjjiCK664gr/85S/87Gc/A6BQKHDVVVeRSo19RbGvfe1rRCIRAL75zW+O+f5EKo6r+LHhFPpfJNOSJ7kui6/BTWheP/06RWRYZlwQIzDNS3xNhvX3deKkyx2RiIhUqopO8mKx2ICvX3311Vx11VUAbNq0aVwmKq+vr+e6664D4Nlnn+Uvf/nLmO9TpJLsqKyZ7x4gywM2/bUbHJhytMbmiQD4p7lpPD7M9I/WENlr8LHgHzTzohiBqR7iqzJs/nP3GEUoIvIB5a6aqeqaozKiydArydVXX80vfvELoDglwmWXXTbm+/zKV77Cj3/8Yzo7O7nppps4/fTTR7U9y7awrLEpOV/cdvFfmRjKfUy9NcV7Q7luZ+AYHIivzhDezYe/0UOmNT9OEVaXch9PKa2+jqc7ZNN0RgRfnRvLsjDGEJzhpfbAPOvv7hx4gy6YeUEMX52bnpVptj4W13tlHOn8nFgm+/G0jAUqwDtpVH2SN3/+/N7ng01qXiqxWIyvfOUr3HTTTSxdupSHHnqIj370oyPeXv3sGJ7+ShWOkmVbxJojYFkYDU6dEMp9TGvnBohE/dTECjihgZe1Wy1qDq7BOspL53L1LetLuY+nlNaHj2donoe6A0NgQXprjrZlCZwcTDkmTHS2B472ktrUd9EUb4OLKceEsd0WibVZCisLNMypHeffaHLT+TmxTPbjmXOysLrcUch4qfokz5Spmfa6667jRz/6EW1tbdx0002cffbZI95W29pO3JanhNG9z7ItMIbWtZ2T8gNtIir3MXXvlcfpyrDt3cGnLwEIHmGRzGdpXdMzxpFVp3IfTymtDx7PpjPCuKfbdLZ3semh7p1as7O+BI3HRunc1k3Pmuwu22k4MkBgvyDxRA8tz/TQ/dauy8jY0/k5sUz245k3w6zCa6j87pAVHl45VX2St2LFit7nzc3N47bfSCTC1772NW644QZeeeUVHnjggRFvyzgGY43du9SY7fuYhB9oE1U5j6nLb4PDkPbtDttYlkW2s6D33wB0jk4swbke5hxfi+2xSG3JsfGPXbt0kao9KIRxDD2rMpgPvGb7YeZ5tXhr3eR6CmxY2KkJzstM5+fEMpmPZ7kaRqQ8KrrwylD8/Oc/731+/PHHj+u+v/SlL9HY2AjAzTffrJNHJgWXz8IUhvZeD84qtlCnNmsOL5n4Inv4mHN5HfUHh7Fsi9bnE2xcuGuCF5rnxRN20f1OBvOBoaqBZjfzLm/AE3PR9VaaNb9tV4InIiIjUrYk7/bbb8eyigVHbrnlll1eX758Oe+9996A2/if//kfbr31VgCampq44IILxiLUfoVCIb7+9a8DxXgfeeSRcd2/SDnYXgsnN7Qkzz9le5K3UV3NZOLyxGzm/F0dTSdHcXktut9Ns+q2Njpf73tan8ZjwpiCYdtT73dh9ta7mP7RGACb/tTFtifVvVlEyqzcVTNVXXNURtRd85lnntkpAXvrrbd6n7/66qvcfvvtOy1/5ZVXDnsfL730EldddRUnnngiZ555Jvvttx/19fXk83neeustfvvb3/Loo48C4HK5+PnPf04oNEgViDFwzTXX8P3vf5/NmzfT2to67vsXGW+2xxpy60K6JUfNPgFi+wdpX5Yc48hExl/sgAANRxa/e7reTNHybIKGWf0XR3HX2LiCNvGVmZ1a+GaeHwNg/cJOMttUiVZEREZnREneL37xC+64444+X1u4cCELFy7c6WcjSfKgOMn5okWLWLRoUb/L1NfXc+utt3LuueeOaB+jFQgE+Od//mf+4R/+oSz7FxlvlsuikBlakte9IkP9YQ51Bwex3dD6vBI9mThCs700HBnCyRg2LOwg2zHItCJAbL4fy7Jof+X9c6H+8CC2x2brU91K8ESkcjgOFT/nglPh8ZVRxY7JO+uss7j11lu56qqrOOSQQ5gxYwaBQAC/309zczNnnnkmP/rRj1i1ahXnnXdeWWP97Gc/y8yZM8sag8i4saCQHHr3iLV3tpGPO8T2D1J/RHAMAxMZP7Yfpp0WxRRgzZ1tZDuGdqHhrXVjjCHbVuj9WWy/AIWMQ/eKzFiFKyIik8yIWvJuv/32XbpkDteVV145YAtfY2Mjn/70p/n0pz89qv2M1AknnDDkQio+n49169aNcUQi5Wf7wbIsconC4Atv52RgzW/bmXt5HbUHBul4LYmjKfOkyk09Lgo2bHq4a1jvZ9tj7VTyu/bAALbHpnVpvPRBiojIpFWxLXkiUnm8tcX7QiOp+Lf5r90ATD0+WtKYRMadC0JzvOR6HFIbhlc5NrU5h2VbROf78NTY1B8WopB16Hi57yItIiJlU+6CKiq8MipVP0+eiIwfb9QFQK576C15O6S35CkkHQLTPaUOS2RcTTkytH2KhOG3vnX+LU1s/yBTj4v29hbZ/Eh3qUMUEZFJTkmeiAyZe0eS1zmy4hDJDTkie/qwgzZOUoOlpfrYPqiZHyCfckisHv7UIIWkw8rbWmk8NozLb9P6Qpxcl84FEREpLSV5IjJk7lAxycuO8KI025XHsvx4QjYZJXlSZWwvzLyoDmzY+vgoWt8c2LZYY/BEpMJVQ3fISo+vjJTkiciQuUNWsYvZCPMzT6SYJOozWapNdB8fjR+JgA1dK9Ik1w9vLJ6IiMh4UpInIkPm8tuYkTbA2RDd008+WSDbqrnApDrYPpj+0Ri+BjcmZ9j8l26S65TgiYhIZVOSJyJD5vLZmPzImuEajgphuSxaX0iUOCqRsVGzwM+Uo8JgQ2Jtls2Pdlf8vMAiIiXjGHaa86USORUeXxkpyRORIbO9Fk5uZB+ooRleTMHQ844mfJbKZvthxkdr8TW4KWQdNv+pe9hTJYiIiJSTkjwRGTLLbeEkRtaUkWnP44m5sNxg1FtTxlHsgAANR4TAAlOA7nfStDwbhz5mAontH6DhyOKy8VUZNi9S652IiFQfJXkiMmSWDYX0yK54e95JE9nNT8PRYVqeUmVBGR9Tjg1TM9+PkzEkN2QJNHuIzQ9Qs7ef+KoM255L4CQdPDU2006vwVfnppBx2PyXLlKbdDdCRCYvYxzMiAfij49Kj6+clOSJyNBZjLjwSmJtjnyiQM1eflqeiat1RMZc/eFBaub7yXUVWHtXR+97LjTXy5SjwoR38xHZ3Y8xBssqVo7teS/NlkU95Q1cRERklJTkicjQOeDyWSNevXVJgqaTokw5JkTL0yrAImNn2ulRQnO85BPOTgkeQGJ1lsTqdvxNbiJ7+vHWuMi05el+M0W2Q3cfRESk+inJE5EhcwoG2zuMJM8FNfv4iezux1vr6l23Zn6A9LY8PW+rCIuMnu2Dmn0DBGd4cQdtPBEXlssivS3H+oWd/bYap7fkSW9R12ERkT4ZU/nVKzXxbr+U5InIkJn84Emev8lNbN8AgWYPrqDd2w0un3BIb82R3pYjtn+QqSdEcIdcdLycHKfoZSJqOjVCeJ6v931m8oZsV4HuN9N0Lk+VOzwREZGyUJInIkOWTzj46nf+2LD9ENsvSHiOD2+s2IJijMHJGpIbcnS/nSb+3s4tdp3LU8z+eB31hwVxh20VYpHhs2HWx2rx1brJtOVpXRLXJOUiIiLbKckTkSHrejPF1OOizPpYLfmkg7/Rje21iq0ojiHbWSCxJkPn39IUkv2PbXIysPq37cz6WC2x+QE8YZtNj3SP428i1cwdspl5cS0uv0XXmym2LdZNAhGRkjNVMBm6umv2S0meiAxZ94oMtfvn8da68Na5KKQc4iuzdL2ZJrVxmK0oDqz7QwfTz6khNMvHrItjrLuvU1U3ZUA7umcCtL6QoPM1dckUERH5MCV5IjIsa+/sKOn2Nv6xi6knRYjs4WPOJ+tYd1c7TrZ027fcHkxe3fgmivA8H/mEw6Y/d5Nt1Tx2IiIifVGSJyJlt/XxHvLxArUHBZlzWT3r7uognyhNk57J57BcLrBsJXtVwFNjUzPfj3+qFyfrkIs7FFIOvgY3/gY3lmURX5lWgiciMtYcB6wK716jydD7pSRPRCpC29IkuXiBxmMjzL60jvUPdJbsQt4UCtheV6WPLJjUbC/Muawel88GwDgGLLCsYjXXHcV8UluytL+kiqwiIiIDUZInIhWje0WGQsJh2uk1zLowxqY/d6li4iQx9YQotrdYSKVrRZpMSzHBt73gjrjIdhQ0XlNERGSI7HIHICLyQYm1Odbf34lxoPnMGqL7+Ea9Tcvtef+5yzXq7Ulp2V4IzfGS6yqwbXG8N8EDcLKQbVOCJyIy7oypjof0SUmeiFScTEuetXe242QNjcdFqDssOKrtmXwOJ1us5mIKhVKEKCU09YQoWLB1cU+5QxEREZkQlOSJSEXKxx3W/LaNQtKh7uAgU0+MDHsbttc7qtdlfARnecn1OKQ3q5iKiIhIKWhMXiVwA2pcENmFk90+afpFtUT38uMO22z8Y9cw1t95LoYPT6dgCgVNsVABLBdk25TgiYhUEuM4mAqvrmlUXbNfasmrAPMur6fx+LCOhkhfHFh3dweJ9VmC073M+kTtiM+VDydzplDAsq0SBCkjZQdtLMsiF9edLhERkVJRWlEB8kmHmn0C7PbpBkJz1X1MpC+bHu6i680U3piLuZfVYY++Hguwa2ufjC9fXbEQTr5bd2NFRERKRUleBVj3hw42L+oGYNppUYKzPIOsITI5bVscp+3FJK6gzZzL6nFH9BFW7Xy1xVEDmQ511xQRqSjlrpqp6pqjoiukChF/L8Pq37VjCtB8eg2+KRouKdKXjpeSbH2yB9tjMfuSugHPFU2XUPnc0eLXUKZd3TVFRERKRUleBXGSDpv+1AUWzLwwRv3hoysbLzJR9bydYePDXVgWzLwgRmh2392cTaGA7fOXfP9KHkvHE3FhjMFJqrumiIhIqai5qMKkNuZY87t2Zpwfo/agIPE1WTLb1I1J5MNSG3Ksu7eDmRfUMu2MKNueidP9RnqnZexgsCRdOSyXa6f59Sp5rj132Kb2wADBGV5cARvLBuMUHzim+K8B229h2Rb5eIGW5+Ik15Wnwqiv3o3Jq7uNiEjFcQxYFf75rO6a/VJLXgXKxx02LOzEsiwiu5eouoTIBJRtK7D299snTf9IeNfW73weJ5Ua9X4qOanbwXLD9HNqmHNZHbEFQdxhF07WkI87FDIGUzBgge2xsH0WhZQh11XAE3XRfGYNjScMfx7CUnCHbXXVFBERKTG15FWofI+DMYbQHB+tSxOgxjyRPuUTDqt/3cbsT9RRe1AQd8TF1sd6gNJWzqzk+fRi+wVoODIENqS35ml5Nk6mZWgfGrYXZlxQS83efmw3bFnUM8bRvi8024tlWSRWZ8ZtnyIiMkTGABXelV4tef1SS14FS6zO4onY7HZlgwqxiAzA5GHNb9vJtOWJ7uFn+rk1Q17Xcg+tmm0lJnix/QLM/VQ9U44J4xQMm//czYYHOoec4EFxwvl1f+gg3ZIjvJuP2AGBMYz4A2yYelIE4xi63hx9a6uIiIi8T0leBdv8aDdtyxLYbotAs6ZVEBnM+ns6SazNEGz2MvuSoU2aXonJ24BsaDgyyG6fKSZ3tseic3mSVbe1kVg78pbL9fd3UkgbGo4M4Z829jeVZpxbg+21aHkujqOGPBERkZJS81CFs1wWAKlNVXYhKlImm/7UzZTjwtTs42fu39Wx9g/tY55E2D4/TiY9+IKj2YcXphwTIbK7D8tlUcg4tC6N0/FyiVrBHFh/bwdzPlnHtFOirP51+4jjnHpCFP80D5YNGMgnHXKdBbKdeWyfTXiuF3fQRWJdhq6/je3fTURERsY4BlPhhVeMumv2S0lehfPVuTHGUEhVeJ9okQrS8lScfLxA/WEh5l5Wz9p7Osh3D/0cslwuLJer3zF9H+ziafK5MU/wag8OUn9oEKziGMS2ZQl63i595pqPO3S9kSK2X5Dw7j7i7w1vH66gzZxP1mG5oJB2cDJgucATdeGtdRG2ioWknLyh840ULU/HS/47iIiIiJK8itf1ZorQHC+zL6mj49Uk7S8nK34MrEgl6Hg5Rb7HYepJEeZ8oo4ND3aS3jq0sWqmUMBye7C93j4TPZPP9SaCY30PccYFMQJTPeRTDlse6ya1YWxb9VueT1CzIEBsQWB4SZ4Nsy6OYblg81+7SazK7vK6t8Ymn3Jw1HgnIiIypjQmr8Il1+XY/Gg3APWHhtj9qgbqDtUk6SJD0fNuho1/7ARgxnkxQnN3nTTdcnuKCd2HJk13Mun+W/K2z5tXyuqdu7Bh9idqCUz1EF+TYfUdbWOe4AHgQK7HwVc3vAnfZ5wXwxWwaVuW3DXB277dbIcSPBGRqrFjktVKf4zSunXr+OpXv8o+++xDKBSirq6Oww8/nO9///skk8kS/CHLQ0leFUiszrLyF61sXtRNIWOoOyTY58WqiOwqtSnPunvaMQWYdlqU2H47V480+Vxv8RU7OMQbKNbYf3TOOC+GJ+aic0WKzX/uHvP9fVAh5fSOBx6KKceFCUz1kFiTpePl6v1CFBGRyeXhhx9m//335z//8z956623SCaTdHR0sGzZMr72ta9x8MEHs2rVqnKHOSJK8qpIfHWGnndSWJZFcLqqbYoMVbbDYfXv23EyhoajQzQc9X4y5wqHcUUi4LIx2aG1lI11Rc7epGl1lpanxn/cmu0deoIX3cdHzT5+sp15Nv9lfJNRERGRkXrttdf4+Mc/TldXF+FwmH/7t3/jueee47HHHuOzn/0sAG+//TZnn3028Xj1jSHXmLwqMveTdbhDLkzB0PWWao6LDIeTdFj1mzbmfLyO2P5B3CHX+xN/2zbk88VumGWeUiG61weSpkfLkDS5wBtzkd42+PhFX6ObxuMiODnDuns6xiE4EREZLxO9uuZ1111HMpnE7Xbz6KOPctRRR/W+dtJJJ7HHHntw/fXX89Zbb/F//+//5aabbipFyONGLXlVxBW0ybTmee9/W8m2Dn2yYxHZLg9rftdOpiVPZHc/M86vwUlnMCUYW7ejUMto+JvcNB5f3qSp9oAglmXR8drg3S6nn1UDBtbf14HRR5KIiFSJZcuW8eSTTwLwmc98ZqcEb4d/+qd/Yp999gHghz/8IblcdU1npiSvyuSThXKHIFL11t/XSXxNhkCTl1kXh3GyKZxUalRTIZh8blSFWALT3Mw4NwYGNtxfvqSpZr4fUzB9F0/5gNpDgrj8Nu2vJMl1quSviIhUjwceeKD3+d///d/3uYxt21xxxRUAdHR09CaF1UJJXhVxsgZvnXrYipTC5j930/lGCk+Ni7l/V4+7pkwfhzbUHRKg+aPFVrF193WQ7ShP0tR4QgRP2EXX24Mnu7EFfpycQ/syFVoREZmQyl01cwyraz799NMAhEIhDjnkkH6XO/7443ufP/PMMyPaV7koY6gi2c4C/ik6ZCKl0vJ0nHxPgfojQsz5RB1tLyboehOcVGpM9hfZw0d0vh+X18b2WFgeC5ffIlzjp3VDik0Pd5LrKk+CF9nDR3QvH5mO/KDFXrz1Llx+m553NDZYRESqz5tvvgnA7rvvjtvd/7X13nvvvcs61UIZQwXIk2MoMyp3r43jqg1h1RlybUPry2UZi5yTJW9yoxqcKpVDx7S0Wl7J0bk2QfMZNUQP9OKbmWfLIot8vHRz4LnDNjPOj+Hy2RhjMPkCecdg0obstgKJ7gIbnmvBOOU5nu6QTe0xPjLJDGvuaWewt1Ww0SaXy9H+Tg95U11jFMaazs+JRcdzYpnsxzPP8D6vh3p9Wk47fqfu7p0Llfl8Pnw+X5/rpNNpWltbAZgxY8aA26+trSUUCpFIJFi/fn0JIh4/SvLKyOv10tTUxDNbHhnaCi9tfwyHA6we5jpS2XRMS68d+N0Ybj8O/GYMtz9aCeCXw1h+xfaH7Ern58Si4zmx6HjS1NSEd5AiYcO+Pi2zcDjMzJkzd/rZzTffzC233NLn8j09PTutO5gdSV61TaOgJK+M/H4/q1evJluCyn4iIiIiIgPxer34/f4Bl6m261NjDJa18/yu/bXiQbElb4fBEt4Pbis1RkM5xoqSvDLz+/2DnmwiIiIiIuNlIl+ffvD3Gkoim8kUx58HAoExi2ksqLqmiIiIiIhMCpFIpPf5ULpgJhIJYGhdOyuJkjwREREREZkU/H4/DQ0NAGzYsGHAZTs6OnqTvA+P+6t0SvJERERERGTS2GeffQB47733yOf7r1j/1ltv7bJOtVCSJyIiIiIik8ZHPvIRoNgV86WX+i9dv3jx4t7nxxxzzJjHVUpK8kREREREZNI4//zze5//8pd9zyHkOA6/+tWvAIjFYpx44onjEVrJKMkTEREREZFJ4/DDD+fYY48F4NZbb+X555/fZZn//M//5M033wTg2muvxePxjGuMo6Ukb5xs3LiRb3/72xx22GFMmTIFv9/PzJkz+chHPsI3v/lN/va3v/W77sqVK7n22mvZd999iUQihEIh9t57b6699lreeeedIe1/06ZNXHnllUyZMoVgMMjxxx/PokWL+lx2/vz5WJbF1KlTB93uHnvsgWVZWJbF//k//2fAZRcuXNi77Pe///0hxT0ehnts1qxZ0/t7DPUxZ86cEcc3Z86cku3jN7/5DQceeGDv7/jVr36V7u7uXZa76667erf705/+dMBt/va3v+1dNhqNUigUBlz+oIMOwrIsampqBl12vIzm/NzhySef5NOf/jR77LEH4XCYaDTKHnvswYUXXshPf/rTASt46fzsXymOzQ7JZJJ58+aV5LzcQefn+BjJ++D111/nhz/8IZ/85Cc54IADaG5uxufzEQ6H2XPPPbniiit47LHHhrR/naP9G8mxcRyHN954g9tuu42rr76aAw44ALfb3fv7rVmzpmTx6RyV/vzoRz8iEAiQz+c57bTT+Pd//3deeOEFnnjiCa6++mquv/56APbcc0/+6Z/+qczRjoCRMfe///u/JhKJGKDfx7XXXtvnuj/72c+Mx+Ppd71AIGB++ctfDrj/jRs3mhkzZuyyrm3b5te//vUuy1999dW9y7z11lv9bnfLli07be+jH/3ogHH80z/9U++yL7zwwoDLjpeRHJvVq1cPuHxfj9NOO23EMc6ePXtI+5g9e/aA2/nWt77V53oHHXSQicfjOy27efPm3tcvueSSAbf7+c9/fqftvfjii/0u29XVZWzbNoA544wzhvw3GEujOT+NMaa7u9t8/OMfH/T4vPLKK32ur/Ozf6M9Nh/2wd9xKOfMUOj8HHsjfR+cffbZQzo2F154oUkmk/3uX+do/0Z6bG6//fYB11m9enXJYtQ5KgN58MEHTTQa7fd9seeee5p333233GGOiCZDH2M//OEP+fKXvwzArFmz+PznP8+RRx5JNBpl48aNvPPOOzzwwAPY9q6NqnfeeSef//zngWJf4K9+9auceOKJeDweXnzxRb73ve+xdu1arrrqKqZOncqZZ57ZZwxf/vKX2bBhA0cddRT//M//TG1tLffeey8//OEP+fznP8+ZZ55JfX197/LHHXccP//5zwF4+umn2Wuvvfrc7tNPPw2Ay+WiUCjw7LPPYozBsqwBlw+FQhxyyCFD+fONqZEem+nTp7N8+fJBt//v//7v/O53vwPgU5/61KjjPe+88/jXf/3Xfl/3er39vrZixQq+9a1v4ff7ufHGGznllFNYt24dN910E6+88gr/8i//wne/+93e5Zuamthjjz149913e49bfz78Pnj66af7Pb7PPvssjuMAxfdZuY3m/ITigO0zzzyTZ599FoAzzjiDSy+9lD333BPHcVi7di3Lli3j7rvv7jcGnZ99G+2x+bBXXnmFH/7wh/j9fjweDz09PSWNV+fn2BjN+8DtdnPwwQdz/PHHc+CBBzJt2jTq6+tpaWnh9ddf52c/+xmrVq3ivvvu41Of+hR33XVXnzHoHO3baI6NMab3uc/n48ADD6S1tZWVK1eOWbw6R6Uv55xzDq+//jo/+tGPePjhh9mwYQNer5fdd9+dj33sY3zpS18iGAyWO8yRKW+OObG98MILvXdcPvrRjw54pzCbze70/0QiYRobGw1gIpGIeeONN3ZZp6Wlxey2224GMHPnzjWZTGaXZdLptPH5fGbmzJm73Gm69tprDWDuuOOOnX6+bt263jsYl19+eb8x/+M//qMBzAUXXGB8Pp8BzOuvv97nsolEwrjdbgOYU045pd9tjpfRHJuhyOfzprm5uff4JRKJEce64y7kpz71qRFv45ZbbjGA+a//+q+dfr5hwwYTDAbN3Llzd1nn05/+dO/7YOXKlX1ut62tzViWZQDziU98oveueH++8Y1v9G7zmWeeGfHvUwqleA9cc801vXf0B2pRdxzH5HK5XX6u87NvpT4/8/m8OeSQQwxgvv3tb/eeU6VsydP5WXqjfR/0dc59UCqVMkcffXTv7/zaa6/tsozO0b6N9tgsWbLE/PjHPzZLlizpvXb51Kc+NaYteTpHZbLRmLwxdM011+A4DrNnz+bOO+8kEAj0u+yHB3P+6U9/Ytu2bQBcd911zJ8/f5d1Ghoaeu8crV69mj/84Q+7LNPW1kYmk+Hwww8nFArt9NrJJ58MFPvTf9DMmTN7+6YPdAdqx2snn3xy712n/pZ//vnne+ch2THQtZxGc2yGYtGiRWzatAmAiy++uOx3gXYc4w9Xhpo+fTp77733Lu8B2PkuYX/H9ZlnnsEYw5QpU/jMZz7T+7P+7NiO3+/nsMMOG94vUWKjfQ+8+uqr/OxnPwPgK1/5CldeeWW/61uWhdu9a8cJnZ99K/X5+aMf/YiXXnqJvfbai69//eulDLUkdH72bbTvg77OuQ/y+/384z/+Y+//n3rqqV2W0Tnat9Eem8MPP5wvfelLHH744QO2oFUKnaNSjZTkjZHnn3+eV155BYCvfe1ru3w5DGbZsmW9z88444x+l/vga/fee+8ur9fW1uJ2u3nxxRdJJpM7vfbkk08CxW4FH7bjS2TNmjVs2LBhl9e7u7t5/fXXe5fdMd9Ifx9kH/x5ubsYjPbYDMWOkrtQmq6ao9XY2AjsPN8LwJYtW3j77bcHfA/A4Mf1Ix/5CEcddRQul4tt27bx9ttv77JsJpPpfV8fccQRZf1iL8V74L//+78xxuDz+bjhhhtGFIfOz12V+vxcu3YtN910E1A8ZpV4Qanzc1fj8TkNEA6He59nMpldXtc5uqvxOjaVROeoVCMleWPkg2NwPvaxj/U+b2tr491336Wzs3PA9dvb23uf7/hw6Us4HO5tJerrLmQgEOD0009n7dq1nHbaaTz88MM899xzXH/99fzwhz8kGAxy1lln7bLeB79E+truc889R6FQoKamhgULFvROEDnYB5nX6+WII47o9/cZD6M9NoPp6enhgQceAGD27Nll/0KG9+eD+drXvtZbPeqee+7h5JNPJpFI7PR32GHevHlMnz4d6Ps9ADt/QYXDYfbff/+dfv5BS5cu7b2IKvffZLTvAWMM99xzDwAnnXRS73icfD7PunXrWLt2bZ8XjB+m83NXpT4/v/CFL5BIJLj88ssrdo4jnZ+7GuvP6R3uvPPO3ud77733Lq/rHN3VeB2bSqJzVKpSWTuLTmA7+vnPmzfPOI5jfvazn5k999xzp4o9++yzj/nBD37Q51i66667bkiVljKZTG9/bsBs3rx5l2XeffddU19fv0vFIMuyzC9+8Ys+t/vWW2/1Lvf5z39+l9dvvPFGA5gzzzzTGGNMa2trbxyrVq3aadlcLmeCwaABzDHHHDPg3208jPbYDOa2227r3c43v/nNUce7YzzB3LlzzX777WeCwaAJBAJmzpw55uMf/7i5//77jeM4g25nx/iRDz8WLFhgurq6+lznkksu6V1uy5YtO72WTCZ7K78uWbLEGGPMP/zDPxjAXHHFFbts69/+7d96t/XXv/51BH+J0hnte+Cdd97pXe7b3/62aWlpMZ/73Od2qjLn8/nM6aefbp566qkBY9H5ubNSnp+///3vDWBqa2vN1q1be38+FmPydH6W1lh9TjuOY7Zs2WIef/xxc+GFF/Zua6+99up3DJ/O0Z2N1bEZ6zF5OkdlslGSN0ZisZgBzIknnmguvfTSPj8YdjyOPfZY09nZudP6P/vZz3pf/8EPftDvfh577LGdtrXjg+LDVq5caT7+8Y+bWCxm/H6/Oeqoo8zDDz884O8wdepUA5h99913l9eOO+44A5jvfOc7vT/bZ599DOw6CP2FF17oje+GG24YcJ/jYbTHZjAnnHBC7/qlKLs7lPLPxxxzjNmwYcOg2/rpT39q5s+fbzwej2lqajL/8A//YDo6OgZcfsc+7r777p1ee/zxxw1gQqFQ78XRH/7wh94v0w8744wzDGDcbvcuBQzG22jfAw888EDv69/61rfMtGnT+l3fsizzH//xHwPGo/PzfaU6P9vb23v/Rj//+f/f3r3GRlG1cQD/b1vo/UJtE2JbW4WyC6VC8daWxEZEbTSpNyRiqoXQD0qqiaAJCdGgFZCUxBgl+ga1RCRqSChy8RalpYuYtGub2oZIEUO9oaW10Dst2+f90Mxxp91d9jKzkuX/SybZzp6ZObPnPGfmzHbP+Z/uPTM6eYxPYxndTufl5XncPjs7Wzo6Orxuzxj9l1nXULM7eYxRutawk2cCp9OpnshpI2bNnj1b9uzZI//8848MDw/LsWPHpLCwUAX/ihUrdPv49ddf1UhamZmZ0tvbO+04Y2NjsnTpUl1D9c033xh2Ho8++qi6Se3p6VHrL126JDExMQJA9y1FZWWlAJDKykrdfmpqalT+Pv/8c8PyFwgjysabrq4utf/i4mJD8pybmytlZWXy9ttvS0NDg7S2tkp9fb1s3bpVsrKyVD7nz5/vd4f0Strb29X+n3vuOd172pxBy5YtU+v++OMPld71gul0OtU8NLfffruhefSXEXXA9dtabR+lpaXS1NQko6Oj0t3dLe+8844kJyerdIcOHTL0PBif3uNz7dq1AkCKioqmPaU3spPH+DSeGe20u05eZGSkbN682eO3MMFijPp3DTWrk8cYpWsVO3kmGBgY0F1I4uLi3E6IOjw8LIsWLVLppn4LV1VVpd6z2Wxy8OBB6e/vl5GREbHb7VJSUiIAdJOlG3kj+eabb6r9HjhwQK0/fvy4AJCZM2fKyMiIWq9Nbmq1WnX7KSsrE2BymHmzLqa+MqpsPHH9d4p3333XkDx7e0rY398v9957rzrm888/b8gxNRMTE5KamioAZPHixbr3li9fLgDk5Zdf1q3PyckRAPLxxx+rdS0tLSqPL7zwgqF59JcRdeCtt97S7eOee+6Ry5cvT9uH3W5Xw4zn5eX59C9BvmJ8eo7PY8eOicVikaioKLfD4hvZyWN8Gs+Mdvr06dPS3t4ubW1tUl9fL1u2bJGMjAyZMWOGlJeXy8DAgOHnwRj17xpqViePMUrXKnbyTDA+Pq5rBKc+vXF1+PBhj43L6Oio3H///dOeProu8+fPl3Xr1qm/GxoaDDsP10Zlw4YNav22bdsEmP7bgJ9//lml137/MjExoX7LUFBQYFjeAmVU2Xhis9nUE05vFxYjXbhwQX3G8fHxAf2O0BvXGwztKef4+LgkJCQIMP23AeXl5QJA1q1bp9a53ux89tlnhubPX0bUgV27dun20dLS4nEfK1asUOncdTgCxfh0Xzajo6NitVqnfS6ujOzkXQnj039mt9Oavr4+ueOOOwSA3HzzzdLf3x9s1nUYo/6VjVmdvCthjFK44uiaJoiKikJMTIz6+7777vOY9u6771Zz+TgcDt170dHROHToEN577z0UFBTAYrGo96677jps2LABzc3NulH8Zs2aZdRpYNGiRUhOTgagH+nJdTQoV3PmzFHDCGvzvJw8eRK9vb0Aro7RoIwqG3eamprw008/AQDKysqQkpISXGZ9lJycjMcffxwAMDQ05FNe/aGV28TEBE6cOAEAaG1txeDgICIjI1FYWKhL726UOO21xWKZVm9CzYg6kJiYqF6np6ejoKDA4z5c929k2TA+3ZfNli1bcOrUKWRlZWHz5s2m5dVXjE//mdlOu0pJScHu3bsBAD/++CO2bdvmf2a9YIwGXjahxBilcMVOnkmysrLU68zMTI/pYmJikJaWBgBq8nNXERERWLt2LVpaWnDx4kWcPn0av//+O7q7u7Fjxw7Ex8ejvb0dwGTjm5uba9g5REREoLi4GADQ0tKCoaEhXSPlrqGZ2jhdTXP7aIwqm6lc58Z76qmngsih/xYsWKBeu5uUNRiuc/1ow0BrNyCLFy/WzTMF/FsvOjo60NfXB+DferBw4UKkpqYamr9ABFsHfN1+alpf6pGvGJ/uy2b79u0AgOXLl+Pw4cP45JNPpi1DQ0MAJm/otHVHjx4143QAMD4DYVY7PZXNZsO8efMAQE2LYhTGaHBlE0qMUQpH7OSZxLXBcDqdXtNq72tPvDxJTEzE3LlzkZGRgYiIyaIbHBxEW1sbACA/Px+xsbHBZHsa7aJy+fJlfP/99+jo6MCFCxdgsVjUxciV1jhpDZnr3DCuDd1/yYyyGR8fx6effgpgcl5DbxPYm0FETNv3kiVL1GS3U2883JVpXl4eZs2aBRHB8ePH0dnZib///hvA1XOTEmwdCGT7qfswAuNzetmMjY0BAGpra7Fq1Sq3S09PDwCgp6dHrXv11VeNPhWF8ek/M9ppT7SOSFdXV0Dbe8MYDa5sQoUxSuGInTyTuAbiL7/84jFdf3+/uuHQJs30x/79+9W/a65cudLv7a/EtQGy2+2qYdIaoam0i1ZbWxsGBgbU0yqbzYb09HTD8xcIM8rmyJEjKu0TTzwR8gvayZMn1evrr7/e0H1HRUWhqKgIANDc3IyRkRFVru6eRFssFpXetc4AV89NSrB1ICUlBfn5+QCAs2fPYmJiwuM+zpw5o14HEuPeMD4DbztDifHpv1DWA+2bm6nfqBiBMcoYDdcYpasfO3kmefjhh9Vv6Orq6jymq6urU0+Q/A3eS5cuobq6GgAQGxuL1atXB5ZZL2677Tb17WBjY6PH3xJoCgoKEB8fD6fTib179+K3334DcHU9fTKjbFz/VbOiosKAXPru4sWL6lvEuLg43HrrrYYfQyu/sbExfPjhhzh//jwAz/VAWz/1AnW11AMj6sAjjzwCYPJGxtu/+u3fv1+9NvoCzficXjYyOaCY1yU7OxsAkJ2drdY1NDSYcj6Mz8CE4hoKTP5WTPsGb+HChQHk1DvGaOBlEyqMUQpbIRzk5ZqzcuVKNaKSu/nrzp07J5mZmWoo5akTcZ4/f16Gh4fd7nt0dFQee+wxNdrS9u3bTTkHEVFTNcTGxsrs2bMFgOzdu9dj+mXLlgkAuemmm1T+PvroI9PyF4hgy8ZVb2+vzJw5UwBIfn6+X/nQPlt4GE3siy++8FgHRCaHs3Yd/vnZZ5/16/i+qq+vV8fQyjU3N9dj+sbGRgEmp/fQPse5c+eakrdABVsHuru7JTExUZW7u6HN9+zZoz63Bx54wJTzYHx6j093fB1dk/H53wqmHrS1tcmJEye87v/PP/+UBQsWqM9u165dhp+DCGPU1xgNZHRNxiiRZ+zkmejs2bOSnp4uACQmJkY2btwojY2N0tzcLDt37lSB66mTtm/fPklJSZGqqio5cOCA/PDDD2K32+WNN96QefPmqW0feughGR8fN+08XnrpJd2QyQCkq6vLsPT/hWDLxtXOnTtV2h07dviVjytdoEpKSiQ1NVUqKytl9+7dYrfbpbW1VRoaGmTr1q1yww03qO2tVqv09vb6dXxfjYyMqI6stqxZs8aw9P8FI+qAa9lbrVb54IMPxOFwyNGjR6WqqkoiIyMFgCQlJUlnZ6cp58H49P8Bl1GdPManuYKpB3V1dQJMThz92muvyZEjR8ThcIjD4ZCDBw/K+vXrJSUlRW1/1113idPpNOU8GKPuY7S2tla3LF26VG1TU1Oje89ut7vdB2OUyDN28kzW1NQkGRkZ0xpsbbFYLLJp0ya32+7bt8/jdtq2Tz/9tIyNjZl6Dl9//bXuuFlZWV7Tf/XVV7r0oZiLKhDBlI0rbZ6lyMhIOXfunF958OUC5a0OaMudd97p97cZ/iouLtYd8/333/eavqioSJe+trbW1PwFwog6UF1drTpz7pb09HT57rvvTDsHxqf/jOzkMT7NFWg90Dp5vizl5eUyODho2jkwRt3ztXwASEVFhdt9MEaJPGMnLwT6+vqkurpalixZIsnJyRIdHS033nijrF69WhwOh8ft/vrrL6mpqZHS0lLJycmRuLg4SUhIEKvVKs8884w0NzeHJP+Dg4MSFRWlGppVq1Z5Td/f36+76X3yySdDks9ABFo2ms7OTnWepaWlfh//Sheo5uZmef311+XBBx8Um80maWlpEhUVJUlJSWKz2aSiokK+/PJLmZiY8PvY/tq4caPugnPq1Cmv6V988UVd+jNnzpiex0AEWwdEJstpzZo1kpOTI9HR0ZKUlCS33HKLvPLKK9LX12dq/hmf/jOqk8f4DI1A6sHo6Kh8++23smnTJikpKZE5c+ZIQkKCzJgxQ9LS0qSwsFDWr18vra2tpuefMepeKDp5jFG6lllETBw3loiIiIiIiEKKo2sSERERERGFEXbyiIiIiIiIwgg7eURERERERGGEnTwiIiIiIqIwwk4eERERERFRGGEnj4iIiIiIKIywk0dERERERBRG2MkjIiIiIiIKI+zkERERERERhRF28oiIiIiIiMIIO3lERERERERhhJ08IiIiIiKiMMJOHhERERERURhhJ4+IiIiIiCiM/B8OeRil1KdGKgAAAABJRU5ErkJggg==", "text/plain": [ - "
" + "
" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -144,25 +150,59 @@ "name": "stdout", "output_type": "stream", "text": [ - "2022-03-16 22:11:00,548 - climada.util.coordinates - INFO - Reading C:\\Users\\yyljy\\climada\\demo\\data\\SC22000_VE__M1.grd.gz\n", - "2022-03-16 22:11:04,941 - climada.util.coordinates - INFO - Reading C:\\Users\\yyljy\\climada\\demo\\data\\SC22000_VE__M1.grd.gz\n", + "2024-07-17 11:42:43,761 - climada.util.coordinates - INFO - Reading /Users/vgebhart/climada/demo/data/SC22000_VE__M1.grd.gz\n", + "2024-07-17 11:42:45,776 - climada.util.coordinates - INFO - Reading /Users/vgebhart/climada/demo/data/SC22000_VE__M1.grd.gz\n", "\n", " Solution 1:\n", "centroids CRS: epsg:2201\n", - "raster info: {'driver': 'GSBG', 'dtype': 'float32', 'nodata': 1.701410009187828e+38, 'width': 978, 'height': 1091, 'count': 1, 'crs': 'epsg:2201', 'transform': Affine(1011.5372910988809, 0.0, 1120744.5486664253,\n", - " 0.0, -1011.5372910988809, 1189133.7652687666)}\n", - "2022-03-16 22:11:09,308 - climada.util.coordinates - INFO - Reading C:\\Users\\yyljy\\climada\\demo\\data\\SC22000_VE__M1.grd.gz\n", - "2022-03-16 22:11:13,503 - climada.util.coordinates - INFO - Reading C:\\Users\\yyljy\\climada\\demo\\data\\SC22000_VE__M1.grd.gz\n", + "raster info: {'crs': \n", + "Name: REGVEN / UTM zone 18N\n", + "Axis Info [cartesian]:\n", + "- E[east]: Easting (metre)\n", + "- N[north]: Northing (metre)\n", + "Area of Use:\n", + "- name: Venezuela - west of 72°W.\n", + "- bounds: (-73.38, 7.02, -71.99, 11.62)\n", + "Coordinate Operation:\n", + "- name: UTM zone 18N\n", + "- method: Transverse Mercator\n", + "Datum: Red Geodesica Venezolana\n", + "- Ellipsoid: GRS 1980\n", + "- Prime Meridian: Greenwich\n", + ", 'height': 1091, 'width': 978, 'transform': Affine(1011.5372910988517, 0.0, 1120744.5486664253,\n", + " 0.0, -1011.5372910988517, 1189133.7652687666)}\n", + "2024-07-17 11:42:47,608 - climada.util.coordinates - INFO - Reading /Users/vgebhart/climada/demo/data/SC22000_VE__M1.grd.gz\n", + "2024-07-17 11:42:49,532 - climada.util.coordinates - INFO - Reading /Users/vgebhart/climada/demo/data/SC22000_VE__M1.grd.gz\n", "\n", " Solution 2:\n", - "raster info: {'driver': 'GSBG', 'dtype': 'float32', 'nodata': 1.701410009187828e+38, 'width': 501, 'height': 500, 'count': 1, 'crs': CRS.from_epsg(4326), 'transform': Affine(0.009000000000000341, 0.0, -69.33714959699981,\n", + "raster info: {'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", + ", 'height': 500, 'width': 501, 'transform': Affine(0.009000000000000341, 0.0, -69.33714959699981,\n", " 0.0, -0.009000000000000341, 10.42822096697894)}\n", "intensity size: (1, 250500)\n", - "2022-03-16 22:11:17,739 - climada.util.coordinates - INFO - Reading C:\\Users\\yyljy\\climada\\demo\\data\\SC22000_VE__M1.grd.gz\n", - "2022-03-16 22:11:17,780 - climada.util.coordinates - INFO - Reading C:\\Users\\yyljy\\climada\\demo\\data\\SC22000_VE__M1.grd.gz\n", + "2024-07-17 11:42:51,380 - climada.util.coordinates - INFO - Reading /Users/vgebhart/climada/demo/data/SC22000_VE__M1.grd.gz\n", + "2024-07-17 11:42:51,397 - climada.util.coordinates - INFO - Reading /Users/vgebhart/climada/demo/data/SC22000_VE__M1.grd.gz\n", "\n", " Solution 3:\n", - "raster info: {'driver': 'GSBG', 'dtype': 'float32', 'nodata': 1.701410009187828e+38, 'width': 20, 'height': 30, 'count': 1, 'crs': CRS.from_epsg(4326), 'transform': Affine(0.009000000000000341, 0.0, -69.2471495969998,\n", + "raster info: {'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", + ", 'height': 30, 'width': 20, 'transform': Affine(0.009000000000000341, 0.0, -69.2471495969998,\n", " 0.0, -0.009000000000000341, 10.338220966978936)}\n", "intensity size: (1, 600)\n" ] @@ -220,7 +260,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "2022-03-16 22:11:26,805 - climada.hazard.base - INFO - Reading C:\\Users\\yyljy\\climada\\demo\\data\\tc_fl_1990_2004.h5\n" + "2024-07-17 11:42:51,462 - climada.hazard.io - INFO - Reading /Users/vgebhart/climada/demo/data/tc_fl_1990_2004.h5\n" ] } ], @@ -248,14 +288,12 @@ "outputs": [ { "data": { - "image/png": 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", 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sjH36fODAAXPzzTebhIQEk5CQYG6++eZz3tRNn39QMV0p0J+ioqKA76luWAqXPofi53JFL878M3PmTN8+H3/8scnNzTVJSUkmISHB9OzZ0yxatKhax7V7j42xR58j/fdfIKG67pg6dapJS0sz8fHxplevXmddDc+YH74X99xzj9/2N954w0jyW6gKOJ3DGGPqf7wKAAAAAMIb9ywBAAAAQACEJQAAAAAIgLAEAAAAAAEQlgAAAAAgAMISAAAAAARAWAIAAACAAAhLAAAAABBAA6sLCAelpaU6ceKE1WUAAAAA9aZRo0ZyOp1Wl2FrhKVzKC0tVXp6uvbv3291KQAAAEC9SUlJUVFREYHpLAhL53DixAnt379f//nPf5SYmGh1ObZXVlam999/X7m5uWrYsKHV5UQkehwa9Dn46HFo0Ofgo8ehUZc+f/HFF+ratateeOEF3XrrrUGqMLx4PB61adNGJ06cICydBWGpmhITEwlL1VBWVqb4+HglJibyCyNI6HFo0Ofgo8ehQZ+Djx6HRl363KVLFw0fPlx/+tOfdO+99yo2NjZIVSLSsMADAAAAIt6ECRP073//W++8847VpSCMEJYAAAAQ8bp3764rr7xSTz/9tIwxVpeDMEFYAgAAQFSYMGGCPv74Yy1btszqUhAmCEsAAACICrm5uercubOefvppq0tBmCAsAQAAICo4HA5NmDBB7733nj799FOry0EYICwBAAAgavzP//yP0tPTNW3aNKtLQRggLAEAACBqNGjQQA888ID+8Y9/aPfu3VaXA5sjLAEAACCq/PznP1ezZs307LPPWl0KbI6wBAAAgKgSFxenX/7yl3rttddUUlJidTmwMcISAAAAos69996rBg0a6IUXXrC6FNgYYQkAAABRp2nTprrrrrv05z//WYcPH7a6HNgUYQkAAABR6f7779fRo0f16quvWl0KbIqwBAAAgKiUlpamq666SsuWLbO6FNgUYQkAAABR64ILLtDevXutLgM2RVgCAABA1GrdurW+/vprq8uATRGWAAAAELXS0tJUUlKiEydOWF0KbIiwBAAAgKjVunVrGWNUXFxsdSmwIcISAAAAolZaWpokcd8SAiIsAQAAIGq1bt1akrhvCQERlgAAABC1XC6XzjvvPMISAiIsAQAAIGo5HA61bt2aaXgIiLAEAACAqJaWlsbIEgIiLAEAACCqMbKEqhCWAAAAEDbWFx3U/E17tXbXAZV7Tb0cs2XLlowsIaAGVhcAAAAAnMsHn38jSRr9+sc6Xu6QJKW6nJo0OFN5Wam1Pu6xY8f0j3/8Q126dKmXOhFZGFkCAACArRVsLdb9czZV2r7fXaoxszaqYGvtHyg7bdo07d+/X3/4wx/qUCEiFWEJAAAAtlXuNZqcX6hAE+4qtk3OL6zVlLw9e/bo6aef1q9//WtdeOGFdaoTkYmwBAAAANtaX3RQxe7SKl83kordpVpfdLDGx37ggQeUlJSkhx9+uA4VIpJxzxIAAABsq+Rw1UGpNvtVWLp0qf75z39q1qxZatKkSW1KQxRgZAkAAAC2lZzgrNf9JOnkyZP65S9/qd69e+umm26qbWmIAowsAQAAwLa6pycp1eXUoSPfB3zdISnF5VT39KRqH3PGjBnatm2bPvnkEzkcjnqqFJGIkSUAAADYVmyMQ5MGZwZ8rSLmTBqcqdiY6oWe//73v3rsscd0xx13sFw4zomwBAAAAFvLy0rVcyM6V9qe4nJq+sguNXrO0qOPPipjjH73u9/VY4WIVEzDAwAAgO39pGNLLSqSXru1m/577KSSE05NvavuiJIkffrpp/rLX/6i5557Ti1atAhitYgUhCUAAACEje7pSWrYsGGN3vPdd99p3rx5+sMf/qCOHTvq3nvvDVJ1iDRMwwMAAEDEOXbsmObMmaNhw4apZcuWuv3229WyZUvNmjWrxmEL0YuRJQAAAESEEydO6L333tObb76pBQsW6OjRo+revbuefvpp/exnP1Pr1q2tLhFhhrAEAACAsFJaWqqvv/5a//nPf/TVV1/pP//5j3bu3Kn8/Hx99913uuSSSzRx4kTdcMMNysjIsLpchDHCEgAAAGzp448/1sqVK/XVV1+puLhYN910ky688EJ99dVXfvu1aNFCbdu21b333qsbb7xRWVlZFlWMSENYAgAAgK2sXLlSTzzxhJYsWaL4+Hi1bdvWN0J05513Ki0tTW3atFHbtm2VlpamuLg4iytGpCIsAQAAwHLGGH344Yd64okntGzZMnXq1Elz5szRT3/6U8XGxqqsrEyLFi3ShAkTWKABIcNqeAAAALCMMUbvvfee+vbtq6uvvlput1tvv/22Nm3apOuvv16xsbFWl4goRlgCAABAyBlj9O6776pHjx7Ky8vTyZMn9e6772rDhg267rrrFBPDZSqsx99CAAAAhFR+fr66du2qwYMHq3Hjxnrvvfe0bt06XXPNNXI4HFaXB/hwzxIAAABCpqioSEOGDNEVV1yhDz/8UDk5OQQk2BZhCQAAACHj8XgkSc8++6y6detmcTXA2RGWAAAAUCvlXqP1RQdVcrhUyQlOdU9PUmwMo0SIHIQlAAAA1FjB1mJNzi9UsbvUty3V5dSkwZnKy0q1sDKg/rDAAwAAAGqkYGuxxsza6BeUJGm/u1RjZm1UwdZiiyoD6hdhCQAAANVW7jWanF8oE+C1im2T8wtV7g20BxBeCEsAAACotvVFByuNKJ3OSCp2l2p90cGArzdocOoukCeeeEKffPJJMEoE6g1hCQAAANVWcrjqoFSd/Tp27KiXX35ZhYWF6tatm6666ioVFBTIGEaiYD+EJQAAAFRbcoKzTvvFxMTorrvu0o4dO/TWW2/pyJEjGjhwoH784x/rjTfeUFlZWX2WC9QJYQkAAADV1j09Sakup6paINyhU6vidU9POutxYmNj9T//8z/66KOPtGzZMrVt21a33HKL2rVrp2effdb3PCbASoQlAAAAVFtsjEOTBmdKUqXAVPH1pMGZ1X7eksPhUE5Ojt59911t3bpVP/nJTzRx4kS1bdtWEydOVHExK+vBOoQlAAAQdcq9Rmt3HdD8TXu1dtcBVm6robysVE0f2UUpLv+pdikup6aP7FLr5yxdcsklmjlzpoqKinTXXXfpz3/+s370ox/pf/7nfzR79uz6KB2oER5KCwAAogoPU60feVmp6p+ZovVFB1VyuFTJCaem3lV3ROlsWrdurWnTpumRRx7Rq6++qn/84x8aM2aM3nzzTeXm5mrAgAEaPHiwMjMz5XDU/XxAVRhZAgAAUYOHqdav2BiHemU009DOrdUro1m9BKXTuVwu/frXv9ZHH32knTt3SpKaN2+uJ554QllZWbrwwgv1q1/9Sv/617904sSJej03IBGWAABAlOBhquGtZcuWkqTZs2frwIEDWrhwoXJzczV37lz95Cc/UYsWLTRixAjNmjVLBw4csLhaRArCEgAAiAp1fZgq7MPpdGrQoEGaPn26vvrqK23cuFG//vWvtXv3bo0aNUrJycnKzs7WH/7wB23fvp1nOKHWCEsAACAq1PVhqrAnh8Ohyy67TI8//rg+/vhj7d27VzNmzND555+vSZMmqWPHjmrfvr3Gjx+vDz/8kOc4oUYISwAAICrU9WGqCA+tWrXSnXfeqQULFujAgQPKz8/XVVddpTlz5uiqq65ScnKyXnzxRUabUC2EJQAAEBXq62GqCB9xcXG69tpr9fLLL+vrr7/WJ598ouuvv17jxo3TTTfdpCNHjlhdImyOsAQAAKJCfT9MFeHF4XCoa9euevnllzVnzhy9++676tatmwoLC60uDTZGWAIAAFEjWA9TRXi5/vrr9fHHHys2Nlbdu3fXm2++aXVJsCkeSgsAAKJKMB+mivDRoUMHffTRR7r77rt10003adWqVfrjH/+oxo0bW10abISwBAAAok7Fw1QR3c477zz97fX/q1YXd9ZzTzyiD1et1bvvvK126T+yujTYBNPwAAAAEJUKthbrimkf6h9HO6r5jU9r55d7dfEll+rJGbOtLg02QVgCAABA1CnYWqwxszb6HlTcOPUipd72f9Qwtb0eG3urXlu0xuIKYQeEJQAAAESVcq/R5PxCnfmkpdi4BDUfNlGx552vBydMVLmXZzFFO8ISAAAAosr6ooO+EaUzxTR06vzsUTq4dYX+OrcgxJXBbghLAAAAiColhwMHpQrnXXKlGia30x+ffEzGMLoUzQhLAAAAiCrJCc6zvu5wxKjpVbdrx2cbNHfu3BBVBTsiLAEAACCqdE9PUqrLqaqerOWQ1O7SHho06BpNmDBBJ06cCGV5sBHCEgAAAKJKbIxDkwZnSlKlwFTx9aTBmfrDH6bpyy+/1EsvvRTS+mAfhCUAAABEnbysVE0f2UUpLv8peSkup6aP7KK8rFRlZmbqzjvv1JQpU3To0CGLKoWVGlhdAAAAAGCFvKxU9c9M0fqigyo5XKrkBKe6pycpNuaH8abf/va3+vvf/67f/e53euaZZyysFlZgZAkAAABRKzbGoV4ZzTS0c2v1ymjmF5QkKSUlRRMmTNCf/vQn7d6926IqYRXCEgAAAHAW48ePV/PmzfXEE09YXQpCjLAEAAAAnEV8fLxuueUWLV68mOcuRZkahaWpU6eqW7duSkhIUHJysoYNG6YdO3ZU2u/zzz/XkCFD5HK5lJCQoJ49e+qrr77yvb5jxw716dNHaWlpmjJlit97+/XrJ4fD4ffnhhtuCFjP8ePH1blzZzkcDm3atMm3ffHixcrIyPDdiOdwOOR0OrVnzx6/9w8bNky33XZbTVoAAACAKJSdna1vvvlG//73v60uJWS49q9hWFq+fLnGjh2rdevWacmSJTp58qRyc3N19OhR3z67du1S37591aFDBy1btkybN2/WY489Jqfzh5VGxo4dq1GjRmn+/PnKz8/X6tWr/c5z5513qri42Pfn5ZdfDljPb37zG7Vq1arS9ueff15vv/22mjZt6tvmcDj0+OOP1+TjAgAAAJKkPn36KCYmRsuXL7e6lJDh2r+Gq+EVFBT4fT1z5kwlJydrw4YNys7OliQ98sgjGjRokKZNm+bbr127dn7v++6773TZZZfp0ksvVatWreR2u/1ej4+PV0pKyllrWbx4sd5//33NnTtXixcv9m33eDwaP368fvzjH/vtP27cOD377LN64IEH1KlTp+p/aAAAAES9xMREXXbZZVqxYoXuvPNOq8sJCa7963jPUsUHTUpKkiR5vV4tXLhQ7du314ABA5ScnKwePXronXfe8XvflClT1L9/f8XHxysmJkYDBgzwe/3vf/+7mjdvrksuuUQPPPCADh8+7Pf6N998ozvvvFNvvPGG4uPj/V5LTEysdDxJ6t27t6699lpNnDixLh8ZsI1yr9HaXQc0f9Nerd11QOVe5lADABBMOTk5Wr58edTetxSN1/61fs6SMUbjx49X3759lZWVJUkqKSnRkSNH9NRTT+nJJ5/U008/rYKCAg0fPlwffvihcnJyJEmDBg3St99+K4/HoxYtWvgd9+abb1Z6erpSUlK0detWTZw4UZs3b9aSJUt8573tttt0zz336PLLL9eXX35Z7ZqnTp2qSy+9VCtXrtQVV1xRo89bVlamsrKyGr0nGlX0iF4FT0VvB7+wXF99d9y3PSXRqYcGdtBPOra0qrSIwt/l4KPHoUGfg48eh4Yd+pydna3p06dr9+7datu2rWV11Iea9jHarv0rOEwto/HYsWO1cOFCrVq1SmlpaZKkffv2qXXr1rrxxhs1e/Zs375DhgzReeedpzfffLPG59mwYYMuv/xybdiwQV26dNELL7ygOXPmaMWKFYqNjdWXX36p9PR0ffrpp+rcuXPgD+lwaN68eRo2bJhGjx6t7du3a82aNRo2bJjOP/98/e1vf6vy/B6PRy6XS7Nnz66UZAEAAIBwdOzYMd10001yu91KTEw85/7Rcu1/plqNLI0bN04LFizQihUrfM2SpObNm6tBgwbKzMz0279jx45atWpVbU6lLl26qGHDhvr3v/+tLl26aOnSpVq3bp0aN27st9/ll1+um2++Wa+//vpZjzd58mS1b9++0vDgueTm5lbrL1K0Kysr05IlS9S/f381bNjQ6nIiTrnXaPALy3Vf+2N67JMYHff6PzjPIallolPv/Sq70kP1UDP8XQ4+ehwa9Dn46HFo2KXPvXv3VpcuXfTiiy9aVkN98Hg81d43Gq/9K9QoLBljNG7cOM2bN0/Lli1Tenq63+uNGjVSt27dKi0puHPnTl1wwQW1KnDbtm0qKytTamqqJOmFF17Qk08+6Xt93759GjBggObMmaMePXqc83ht2rTRfffdp4cfflgZGRnVrqNhw4b8AKwB+hUcn+w64Jt6d9zr0PHyyoFoz6Hj+vTrw+qV0SzU5UUk/i4HHz0ODfocfPQ4NKzuc8+ePbV48eKw/15Xp/5ovvavUKOwNHbsWM2ePVvz589XQkKC9u/fL0lyuVyKi4uTJD344IMaMWKEsrOzdeWVV6qgoED5+flatmzZOY+/a9cu/f3vf9egQYPUvHlzFRYW6te//rUuu+wy9enTR5IqzQ9t0qSJJCkjI8Mv6Z7NxIkT9corr6ioqEgjRoyo7scHLFdyuLRe9wMAADWTnZ2tF198UXv37lXr1q2tLieouPav4Wp406dPl9vtVr9+/ZSamur7M2fOHN8+1113nWbMmKFp06apU6dOevXVVzV37lz17dv3nMdv1KiR/vWvf2nAgAG6+OKL9Ytf/EK5ubn64IMPFBsbW6MPdjZJSUmaMGGCSku5oER4SU5wnnunGuwHAABqpmLJ7JUrV1pcSfBx7V+LaXjVMXr0aI0ePbrGxbRp06bGD/r60Y9+dM66Ar0+ceJElhFH2OmenqSURKekowFfd0hKcTnVPT0ppHUBABAtWrZsqYsvvljLly/XDTfcYHU5QcW1fx2fswQgtGJjHHpoYAdJp4LR6Sq+njQ4k8UdAAAIopycHK1YscLqMhAChCUgzFQ8R6llov9UuxSXU9NHdlFeVqoVZQEAEDWys7NVWFiob7/91upSEGS1figtAGu996tsffr1YZUcLlVywqmpd4woAQAQfKfftzR8+HCLq0EwEZaAMBUb42B5cAAALNCmTRulp6dr+fLlhKUIxzQ8AAAAoIa4byk6EJYAAACAGsrOztbmzZt16NAhq0tBEBGWAAAAgBrKycmRMUarV6+2uhQEEWEJAAAAqKH09HS1bt26xs8JQnghLAEAAAA15HA4uG8pChCWAAAAgFrIzs7Whg0bdPjwYatLQZAQlgAAAIBayM7OVnl5udauXWt1KQgSwhIAAABQCx06dFCLFi24bymCEZYAAACAWnA4HMrOzua+pQhGWAIAAABqKScnR+vXr9f3339vdSkIAsISAAAAUEvZ2dk6ceKEPvroI6tLQRAQlgAAAIBa6tSpk84//3zuW4pQhCUAAACglmJiYnTFFVdw31KEIiwBAAAAdZCTk6O1a9fqxIkTVpeCekZYAgAAAOogOztb33//vT755BOrS0E9IywBAAAAdXDZZZepSZMm3LcUgQhLAAAAQB00aNBAffr04b6lCERYAgAAAOooJydHq1at0smTJ60uBfWIsAQAAADUUXZ2to4cOaJNmzZZXQrqEWEJAAAAqKNu3brJ6XRy31KEISwBAAAAddSoUSP16tWL+5YiDGEJAAAAqAc5OTlauXKlvF6v1aWgnhCWAAAAgHqQnZ2tQ4cOaevWrVaXgnpCWAIAAADqQc+ePdWwYUPuW4oghCUAAACgHsTFxal79+7ctxRBCEsAAABAPcnJydGKFStkjLG6FNQDwhIAAABQT7Kzs1VSUqIdO3ZYXQrqAWEJAAAAqCe9e/dWbGws9y1FCMISAAAAUE8SEhLUpUsX7luKEIQlAAAAoB7l5ORo+fLl3LcUAQhLAAAAQD3Kzs7W3r17VVRUZHUpqCPCEgAAAFCP+vbtK4fDwX1LEYCwBAAAEETlXqO1uw5o/qa9WrvrgMq9TM2KdE2bNtWll17KfUsRoIHVBQAAAESqgq3FmpxfqGJ3qW9bqsupSYMzlZeVamFlCLacnBzl5+dbXQbqiJElAACAICjYWqwxszb6BSVJ2u8u1ZhZG1WwtdiiyhAK2dnZKioq0n/+8x+rS0EdEJYAAADqWbnXaHJ+oQJNuKvYNjm/kCl5ESw7O1uSmIoX5ghLAAAA9Wx90cFKI0qnM5KK3aVaX3QwdEUhpFq0aKGOHTsSlsIcYQkAAKCelRyuOijVZj+Ep5ycHMJSmCMsAQAA1LPkBGe97ofwlJ2dre3bt+ubb76xuhTUEmEJAACgnnVPT1KqyylHFa87dGpVvO7pSaEsCyFWcd/SypUrLa4EtUVYAgAAqGexMQ5NGpwpSZUCU8XXkwZnKjamqjiF+mD1M65at26tjIwMHk4bxnjOEgAAQBDkZaVq+sgulZ6zlMJzlkLCLs+44r6l8EZYAgAACJK8rFT1z0zR+qKDKjlcquSEU1PvGFEKropnXJ05jlTxjKvpI7uELDBlZ2dr5syZOnjwoJKSmHYZbpiGBwAAEESxMQ71ymimoZ1bq1dGM4JSkIXqGVfVneKXk5MjY4xWrVpVp/PBGowsAQAAIGLU5BlXvTKa1eocNZnid8EFF6hNmzZavny5hgwZUqvzwTqMLAEAACBiBPsZVxVT/M4MZBVT/Aq2Fvttdzgc3LcUxghLAAAAiBjBfMZVbaf4ZWdna+PGjfJ4PDU+J6xFWAIAAEDECOYzrmoyxe902dnZ8nq9WrNmTY3PCWsRlgAAABAxgvmMq9pO8Wvfvr1atmzJ85bCEGEJAAAAEaXiGVcpLv+pdikuZ52WDa/tFD+Hw6Hs7GzuWwpDrIYHAACAiBOMZ1xVTPHb7y4NeN+SQ6cCWaApfjk5Obr//vt17NgxxcfH17oGhBYjSwAAAIhI9f2Mq7pM8cvOzlZZWZnWrVtXpxoQWoQlAAAAoJpqO8XvkksuUVJSEvcthRmm4QEAAAA1UJspfjExMbriiiu4bynMEJYAAACAGqqY4lcTOTk5evjhh3X8+HE1btw4SJWhPjENDwAAAAiB7OxslZaW6uOPP7a6FFQTYQkAAAAIgc6dOyshIYH7lsIIYQkAAAAIgdjYWPXt25f7lsIIYQkAAAAIkZycHK1evVplZWVWl4JqICwBAAD8r3Kv0dpdBzR/016t3XVA5d5Ajx4Fai87O1tHjx7Vp59+anUpqAZWwwPqqNxr6vXp4AAAaxRsLdbk/EIVu0t921JdTk0anFnls3OAmuratavi4+O1fPlyde/e3epycA6EJaAO+MUKAJGhYGuxxszaqDPHkfa7SzVm1sazPmwUqIlGjRqpV69eWrFihR588EGry8E5MA0PqKWKX6ynByXph1+sBVuLLaoMAFAT5V6jyfmFlYKSJN+2yfmFTMlDvcnJydHKlStVXl5udSk4B8ISUAv8YgWAyLG+6GClf/g6nZFU7C7V+qKDoSsKES07O1tut1tbtmyxuhScA2EJqAV+sQJA5Cg5XPXP89rsB5xLjx491KhRI563FAYIS0At8IsVACJHcoKzXvcDzsXpdKpHjx48bykMEJaAWuAXKwBEju7pSUp1OVXVOqYOnVq8p3t6UijLQoTLycnRihUrZAxT9u2MsATUAr9YASByxMY4NGlwpiRV+rle8fWkwZk8FgL1Kjs7W//973/1+eefW10KzoKwBNQCv1gBILLkZaVq+sguSnH5zwhIcTlZNhxB0bt3bzVo0ID7lmyO5ywBtVTxi/XM5yyl8JwlAAhLeVmp6p+ZwoPGERLnnXeeunbtqhUrVmjMmDFWl4MqEJaAOuAXKwBEltgYh3plNLO6DESJnJwcvfHGGzLGyOHg2sGOmIYH1FHFL9ahnVurV0YzghIAAKiW7OxsFRcXa9euXVaXgioQlgAAAAAL9O3bVw6Hg/uWbIywBAAAAFjA5XKpc+fOPG/JxghLAAAAgEVycnIYWbIxwhIAAABgkezsbO3Zs0d79uyxuhQEQFgCAAAALHLFFVdIElPxbIqwBAAAAFikefPmuuSSSwhLNkVYAgAAACyUk5NDWLIpwhIAAABgoZ49e2rnzp06fPiw1aXgDIQlAAAAwEJxcXGSpPLycosrwZkaWF0AAAAAYIVyr9H6ooMqOVyq5ASnuqcnKTbGEfI6jDGSJIcj9OfG2RGWAAAAEHUKthZrcn6hit2lvm2pLqcmDc5UXlZqSGvxer2SCEt2xDQ8AAAARJWCrcUaM2ujX1CSpP3uUo2ZtVEFW4tDWk/FyFJMDJfmdsN3BAAAAFGj3Gs0Ob9QJsBrFdsm5xeq3Btoj+BgZMm+ahSWpk6dqm7duikhIUHJyckaNmyYduzYUWm/zz//XEOGDJHL5VJCQoJ69uypr776yvf6jh071KdPH6WlpWnKlCkBz2WM0cCBA+VwOPTOO+/4vbZz504NHTpUzZs3V2Jiovr06aMPP/zQ9/rixYuVkZGhQ4cOSTr1F8/pdFZ6MvKwYcN022231aQFAAAACGPriw5WGlE6nZFU7C7V+qKDIavJriNLXPvXMCwtX75cY8eO1bp167RkyRKdPHlSubm5Onr0qG+fXbt2qW/fvurQoYOWLVumzZs367HHHpPT6fTtM3bsWI0aNUrz589Xfn6+Vq9eXelczz//fJXp+pprrtHJkye1dOlSbdiwQZ07d9a1116r/fv3+9779ttvq2nTpr73OBwOPf744zX5uAAAAIgwJYerDkq12a8+2HWBB679a7jAQ0FBgd/XM2fOVHJysjZs2KDs7GxJ0iOPPKJBgwZp2rRpvv3atWvn977vvvtOl112mS699FK1atVKbrfb7/XNmzfrj3/8oz7++GOlpvrfYPff//5XX3zxhV577TVdeumlkqSnnnpKL730krZt26b4+HiNHz9eP/7xj/3eN27cOD377LN64IEH1KlTp5p8bAAAAESI5ATnuXeqwX71wa7T8Lj2r+M9SxUfNCkpSdKpb/TChQvVvn17DRgwQMnJyerRo0elobQpU6aof//+io+PV0xMjAYMGOB77dixY7rxxhv14osvKiUlpdI5mzVrpo4dO+r//t//q6NHj+rkyZN6+eWX1bJlS3Xt2lWJiYl+x6vQu3dvXXvttZo4cWJdPjIAAADCWPf0JKW6nKoqljh0alW87ulJIavJrtPwzhSN1/61/o4YYzR+/Hj17dtXWVlZkqSSkhIdOXJETz31lPLy8vT+++/ruuuu0/Dhw7V8+XLfewcNGqRvv/1W+/bt07x58xQbG+t77f7771fv3r01dOjQgOd1OBxasmSJPv30UyUkJMjpdOq5555TQUGBzj///LPWPHXqVBUUFGjlypW1/dgAAAAIY7ExDk0anClJlQJTxdeTBmeG9HlLdh1ZOl20XvvX+jlL9913nz777DOtWrXKt63iGz106FDdf//9kqTOnTtrzZo1mjFjhnJycnz7Nm7cWC1atPA75oIFC7R06VJ9+umnVZ7XGKN7771XycnJWrlypeLi4vTqq6/q2muvDTh0d7rMzEzdcsstmjBhgtasWVOjz1tWVqaysrIavScaVfSIXgUPPQ4N+hx89Dg06HPw0ePQqM8+X31xc71004/11OLt2u/54d6klESnHhrYQVdf3Dzk38+4uDiVl5f7RpmCraafL9qu/SvUKiyNGzdOCxYs0IoVK5SWlubb3rx5czVo0ECZmZl++3fs2NGvsVVZunSpdu3aVSkl/vSnP9UVV1yhZcuWaenSpXr33Xd16NAhJSYmSpJeeuklLVmyRK+//roeeuihs55j8uTJat++faXhwXN5//33FR8fX6P3RLMlS5ZYXULEo8ehQZ+Djx6HBn0OPnocGvXZ5/EdztxyVCeKNmhRUb2dolqSkpL05ptvVrpHKJiOHTtW7X2j8dq/Qo3CkjFG48aN07x587Rs2TKlp6f7vd6oUSN169at0pKCO3fu1AUXXHDO4z/00EO64447/LZ16tRJzz33nAYPHizph2/smXM6Y2JifOn2bNq0aaP77rtPDz/8sDIyMs65f4Xc3FzfNwhVKysr05IlS9S/f381bNjQ6nIiEj0ODfocfPQ4NOhz8NHj0IjkPr/++uv6xS9+UWnhg2DyeDzn3Cear/0r1CgsjR07VrNnz9b8+fOVkJDgW67P5XIpLi5OkvTggw9qxIgRys7O1pVXXqmCggLl5+dr2bJl5zx+SkpKwBu72rZt6/vm9OrVS02bNtWtt96qxx9/XHFxcXrllVdUVFSka665plqfY+LEib73jBgxolrvadiwYcT9jxlM9Cv46HFo0Ofgo8ehQZ+Djx6HRiT22ev1qrS0NKSfqzrniuZr/wo1WuBh+vTpcrvd6tevn1JTU31/5syZ49vnuuuu04wZMzRt2jR16tRJr776qubOnau+ffvWqLCqNG/eXAUFBTpy5IiuuuoqXX755Vq1apXmz59facnAqiQlJWnChAkqLQ3d+vkAAABAIF6v15aLO3DtX4tpeNUxevRojR49usbFVPecl19+ud577706HWPixIksIw4AAADLGWNsuWw41/51fM4SAAAAgLoxxthyZAl1WDocAAAAiDTlXqP1RQdVcrhUyQmnHk4b7Gcueb1eW44sgbAEAAAASJIKthZrcn6hit0/3NuS6nJq0uBM5WVV/TyfumJkyb6IsAAAAIh6BVuLNWbWRr+gJEn73aUaM2ujCrYWB+3chCX7IiwBAAAgqpV7jSbnFyrQcgYV2ybnF6rcW70FD2qKaXj2xXcFAAAAUW190cFKI0qnM5KK3aVaX3QwKOdnZMm+uGfJxqy4wRAAACDalByu3vN3qrtfTTGyZF+EJZuy6gZDAACAaJOc4KzX/WqKkSX7IsLakJU3GAIAAESb7ulJSnU5VVVccejUP1p3T08KyvkJS/ZFWLIZq28wBAAAiDaxMQ5NGpwpSZUCU8XXkwZnBu12CKbh2RffFZux+gZDAACAaJSXlarpI7soxeU/1S7F5dT0kV2CdhvEiRMntGTJEiUmJgbl+Kgb7lmyGatvMAQAAIhWeVmp6p+ZErIFtsrLy3XLLbdo5cqVWrRoUVDOgbohLNmM1TcYAgAARLPYGId6ZTQL+nmMMRo7dqzeeustvfXWW7r66quDfk7UHNPwbMbqGwwBAAAQfI8++qhefvllvfLKKxo+fLjV5aAKhCWbsfoGQwAAAATXM888o9///vd69tlnNXr0aKvLwVkQlmzIqhsMAQAAEFyvvfaaHnzwQT388MMaP3681eXgHLhnyaZCfYMhAAAAguvtt9/WnXfeqXvuuUdPPvmk1eWgGghLNhaqGwwBAAAQXB988IFuvPFGXX/99XrxxRd5CG2YYBoeAAAAEEQfffSRhg0bpquuukqvv/66YmNjrS4J1URYAgAAAIJk27ZtGjRokDp37qy5c+eqUaNGVpeEGiAsAQAAAEHw5ZdfKjc3V23atNG7776r+Ph4q0tCDRGWAAAAgHq2f/9+9e/fX3FxcSooKND5559vdUmoBRZ4AAAAAOrRd999pwEDBujYsWNatWqVUlJSrC4JtURYAgAAAOrJsWPHdO211+rrr7/WihUrlJ6ebnVJqAPCEgAAAFAPjDH62c9+pk2bNmnp0qW65JJLrC4JdURYAgAAAOrBN998o0WLFum1115T9+7drS4H9YAFHgAAAIB6YIyRJLVs2dLiSlBfCEsAAAAAEABhCQAAAAACICwBAAAAQACEJQAAAAAIgLAEAAAAAAEQlgAAAIB60LBhQ0nS7t27La4E9YWwBAAAANSD5s2b69Zbb9VDDz2kHTt2WF0O6gFhCQAAAGGv3Gu0dtcBzd+0V2t3HVC511hSx4svvqg2bdro+uuv1/fff29JDag/DawuAAAAAKiLgq3FmpxfqGJ3qW9bqsupSYMzlZeVGtJamjRpon/84x/q3r27xo8fr+nTp4f0/KhfjCwBAAAgbBVsLdaYWRv9gpIk7XeXasysjSrYWhzymjp16qQXXnhBM2bM0D/+8Y+Qnx/1h7AEAACAsFTuNZqcX6hAE+4qtk3OL7RkSt4dd9yhG264QXfccYd27doV8vOjfhCWAAAAEJbWFx2sNKJ0OiOp2F2q9UUHQ1fU/3I4HHr55ZfVsmVLjRgxQsePHw95Dag7whIAAADCUsnhqoNSbfarb4mJiZozZ462bNmiCRMmWFID6oawBAAAgLCUnOCs1/2CoUuXLnr22Wf1f/7P/9E777xjWR2oHcISAAAAwlL39CSlupxyVPG6Q6dWxeuenhTKsioZO3asrrvuOv385z/Xnj17LK0FNUNYAgAAQFiKjXFo0uBMSaoUmCq+njQ4U7ExVcWp0HA4HPrrX/+q888/XzfccIPKysosrQfVR1gCAABA2MrLStX0kV2U4vKfapficmr6yC4hf85SVZo2bar/9//+nz755BM98sgjVpeDauKhtAAAAAhreVmp6p+ZovVFB1VyuFTJCaem3lk9onSmHj166KmnntIDDzygnJwcXXPNNVaXhHMgLAEAACDsxcY41CujmdVlnNP999+vDz/8ULfeeqs2bdqktLQ0q0vCWTANDwAAAAiRmJgYvf7664qLi9NNN92kkydPWl0SzoKwBAAAAIRQs2bN9Oabb2rNmjX67W9/a3U5OAvCEgAAABBiffv21aOPPqqpU6fK7XZbXQ6qQFgCAAAALDBs2DB5vV4VFhZaXQqqQFgCAAAALNChQwfFxMRo69atVpeCKhCWAAAAAAs4nU5ddNFFhCUbIywBAAAAFsnKyiIs2RhhCQAAALAIYcneCEsAAACARbKyslRSUqKSkhKrS0EAhCUAAADAIllZWZKkbdu2WVwJAiEsAQAAABa58MIL1ahRI6bi2RRhCQAAALBIgwYN1LFjR8KSTRGWAAAAAAuxyIN9EZYAAAAAC1WEJWOM1aXgDIQlAAAAwEJZWVnyeDz6+uuvrS4FZyAsAQAAABaqWBGPqXj2Q1gCAAAALNS2bVs1adKEsGRDhCUAAADAQjExMbrkkksISzbUwOoCAAAAED7KvUbriw6q5HCpkhOc6p6epNgYh9Vlhb2srCx9+umnVpeBMxCWAAAAUC0FW4s1Ob9Qxe5S37ZUl1OTBmcqLyvVwsrCX1ZWlv7+97+rvLxcsbGxVpeD/8U0PAAAAJxTwdZijZm10S8oSdJ+d6nGzNqogq3FFlUWGTp16qTS0lLt3r3b6lJwGsISAAAAzqrcazQ5v1CBngJUsW1yfqHKvTwnqLZYEc+eCEsAAAA4q/VFByuNKJ3OSCp2l2p90cHQFRVhkpOT1bx5c8KSzXDPEgAAAM6q5HDVQak2+9WnSFlwwuFwKCsri7BkM4QlAAAAnFVygrNe96svkbbgRFZWlpYuXWp1GTgN0/AAAABwVt3Tk5Tqcqqq8RqHToWU7ulJIaspEhecyMrK0s6dO3X8+HGrS8H/IiwBAADgrGJjHJo0OFOSKgWmiq8nDc4M2fS3SF1wIisrSydPntTOnTutLgX/i7AEAACAc8rLStX0kV2U4vKfapficmr6yC4hnfYWqQtOXHLJJZJYEc9OuGcJAAAA1ZKXlar+mSmWL6hg5wUn6uL8889XWloaYclGCEsAAACottgYh3plNLO0BrsuOFEfWBHPXpiGBwAAgLBixwUn6gthyV4ISwAAAAgrwV5wotxrtHbXAc3ftFdrdx0I6UIRWVlZ2r17t44ePRqyc6JqTMMDAABA2KlYcOLM5yyl1PE5S1Y/uykrK0uSVFhYqG7dugX9fDg7whIAAADCUn0vOFHx7KYzx5Eqnt0UilX/OnbsKEnavn07YckGCEsAAABBVO41lq8eF8nqa8GJcz27yaFTz27qn5kS1O9ffHy8YmJi9P333wftHKg+whIAAECQWD2lC9VXk2c3BXs1wAYNGujkyZNBPQeqhwUeAAAAgqBiSteZF+AVU7oKthZbVBkCsdOzm2JjYwlLNkFYAgAAqGfnmtIlnZrSFcpV1nB2dnp2EyNL9kFYAgAAqGc1mdIFe7DTs5sIS/ZBWAIAAKhndprSheoJ9rObaqJBgwYqLy8P+nlwboQlRDwrHywHAIhOdprSheqreHZTisv/+5LicoZk2fAKjCzZB6vhIaKxChEAwAoVU7r2u0sD3rfk0KkL8FBM6ULN1Pezm2qDsGQfjCwhYrEKEQDAKnaa0oWaq3h209DOrdUro1nIv0+EJfuoUViaOnWqunXrpoSEBCUnJ2vYsGHasWNHpf0+//xzDRkyRC6XSwkJCerZs6e++uor3+s7duxQnz59lJaWpilTpgQ8lzFGAwcOlMPh0DvvvOPb/uWXX+r2229Xenq64uLilJGRoUmTJunEiRO+fRYvXqyMjAwdOnRIkuRwOOR0OrVnzx6/cwwbNky33XZbTVqAMMEqRAAAq9llShfCj12WDufav4Zhafny5Ro7dqzWrVunJUuW6OTJk8rNzdXRo0d9++zatUt9+/ZVhw4dtGzZMm3evFmPPfaYnM4fflCMHTtWo0aN0vz585Wfn6/Vq1dXOtfzzz8vh6Nyit++fbu8Xq9efvllbdu2Tc8995xmzJihhx9+2O+9b7/9tpo2berb5nA49Pjjj9fk4yKMsQoRAMAO8rJStWrCVXrzzp76Pzd01pt39tSqCVcRlHBWdhlZ4tq/hvcsFRQU+H09c+ZMJScna8OGDcrOzpYkPfLIIxo0aJCmTZvm269du3Z+7/vuu+902WWX6dJLL1WrVq3kdrv9Xt+8ebP++Mc/6uOPP1Zqqv8Pk7y8POXl5fkde8eOHZo+fbqeeeYZeTwejR8/Xj/+8Y/93jdu3Dg9++yzeuCBB9SpU6eafGyEIVYhAgDYRcWULqC67BKWuPav4z1LFR80KenUzYler1cLFy5U+/btNWDAACUnJ6tHjx5+Q2mSNGXKFPXv31/x8fGKiYnRgAEDfK8dO3ZMN954o1588UWlpKRUu46KGhITE/2OV6F379669tprNXHixNp8VIQZViECAADhyq5Lh0fjtX+tw5IxRuPHj1ffvn2VlZUlSSopKdGRI0f01FNPKS8vT++//76uu+46DR8+XMuXL/e9d9CgQfr222+1b98+zZs3T7Gxsb7X7r//fvXu3VtDhw6tVh27du3Sn/70J91zzz3n3Hfq1KkqKCjQypUra/hpEW7s9GA5AACAmrDLyNLpovXav9ZLh99333367LPPtGrVKt82r9crSRo6dKjuv/9+SVLnzp21Zs0azZgxQzk5Ob59GzdurBYtWvgdc8GCBVq6dKk+/fTTatWwb98+5eXl6Wc/+5nuuOOOc+6fmZmpW265RRMmTNCaNWuqdY4KZWVlKisrq9F7olFFj+zQq8evuVj3z9kkSX4LPThOe91bflJe+/3DzVnZqceRjD4HHz0ODfocfPQ4NKKpz/Hx8ZKC+1lreuxou/avUKuwNG7cOC1YsEArVqxQWlqab3vz5s3VoEEDZWZm+u3fsWNHv8ZWZenSpdq1a5fOP/98v+0//elPdcUVV2jZsmW+bfv27dOVV16pXr166S9/+Uu1a588ebLat29faXjwXN5//33fX1yc25IlS6wuQZL0dPeqXztRtEGLikJXS32zS48jHX0OPnocGvQ5+OhxaERDn8ePHy9JWrRoUdDOcezYsWrvG43X/hVqFJaMMRo3bpzmzZunZcuWKT093e/1Ro0aqVu3bpWWFNy5c6cuuOCCcx7/oYceqpQSO3XqpOeee06DBw/2bdu7d6+uvPJKde3aVTNnzlRMTPVnE7Zp00b33XefHn74YWVkZFT7fbm5uUpMTKz2/tGqrKxMS5YsUf/+/dWwYUOry5F0ahnxDXsO6b9Hjqt5k8bqekHTsH6uhR17HInoc/DR49Cgz8FHj0Mjmvp8zTXXqFWrVnrllVeCdg6Px3POfaL52r9CjcLS2LFjNXv2bM2fP18JCQnav3+/JMnlcikuLk6S9OCDD2rEiBHKzs7WlVdeqYKCAuXn5/slw6qkpKQEvLGrbdu2vm/Ovn371K9fP7Vt21bPPPOMvv32W7/3V8fEiRP1yiuvqKioSCNGjKjWexo2bBjx/2PWJzv1q6GkPu1bWl1GvbNTjyMZfQ4+ehwa9Dn46HFoREOfvV6vSktLg/o5q3PsaL72r1CjBR6mT58ut9utfv36KTU11fdnzpw5vn2uu+46zZgxQ9OmTVOnTp306quvau7cuerbt2+NCqvK+++/ry+++EJLly5VWlqaXx3VlZSUpAkTJqi0lGWjAQAAYC92WeCBa/9aTMOrjtGjR2v06NE1LqY657zttttq/OTdQHVPnDiRZcQBAABgO3ZZOpxr/zo+ZwkAAABA/bLLyBIISwAAAICtEJbsg7AEAAAA2AhhyT4ISwAAAICNxMbGEpZsgrAEAAAA2AgjS/ZBWAIAAABshLBkH4QlAAAAwEbssnQ4CEsAAACArTCyZB+EJQAAAMBGCEv2QVgCAAAAbISwZB+EJQAAAMBGWDrcPghLAAAAgI0wsmQfDawuAAAAAIg25V6j9UUHVXK4VMkJTnVPT1JsjEMSYclOCEsAAABACBVsLdbk/EIVu0t921JdTk0anKm8rFSWDrcRpuEBAAAAIVKwtVhjZm30C0qStN9dqjGzNqpgazEjSzZCWAIAAABCoNxrNDm/UCbAaxXbJucXKoYFHmyDaXgAAABACKwvOlhpROl0RlKxu1R7HccJSzbByBIAAAAQAiWHqw5Kp/u+3BCWbIKwBAAAAIRAcoKzWvudHx9HWLIJwhIAAAAQAt3Tk5TqcspRxesOnVoVL6NlImHJJghLAAAAQAjExjg0aXCmJFUKTBVfTxqcqUaNGsrr9cqYQEtBIJQISwAAAEANlXuN1u46oPmb9mrtrgMq91Yv2ORlpWr6yC5KcflPyUtxOTV9ZBffc5Yk8awlG2A1PAAAAKAGzvVQ2XPJy0pV/8wUrS86qJLDpUpOcKp7epJiY06NL1WEpZMnT/r+G9ag+wAAAEA1VTxU9sxxpIqHylaMDp1LbIxDvTKaBX4tNlaSuG/JBpiGBwAAAFRDdR8qW90peVU5fWQJ1mJkCQAAAHVW7jVVTiuLlHNX96Gy64sOVjlqVB2EJfsgLAEAAKBO6noPT7icu7oPla3uflUhLNkH0/AAAACiRG1XcDubint4zhxxqbiHp2BrcZ3PYZdzV/ehstXdryqEJftgZAkAACAKBGME5lz38Dh06h6e/pkp9T4tzopzVzxUdr+7NOB5HTq1BHj39KQ6nYelw+2DkSUAAIAIF6wRmJrcw1PfrDh3dR8qW9dwxsiSfRCWAAAAIlgwV3AL1T08djp3dR4qW1csHW4fTMMDAACIYMFcwS1U9/DY7dzneqhsXTGyZB+EJQAAgAgWzBGYUN3DY7dzS2d/qGxdEZbsg2l4AAAAESyYIzChuofHbucONsKSfRCWAAAAIljFCExVkcGhU6vi1XYEJhT38Njx3MFEWLIPpuEBAABEsIoRmDGzNsoh+U1Zq68RmGDfw2PXcwcLS4fbB2EJAAAgwlWMwJz5nKWUOj5n6XTBvIfHzucOBkaW7IOwBAAAEAUicQQmUrF0uH0QlgAAAKJEpI3ARCpGluyDBR4AAAAAGyEs2QdhCQAAALARwpJ9EJYAAAAAGyEs2QdhCQAAALARlg63D8ISAAAAYCOMLNkHYQkAAACwEZYOtw+WDgcQNOVew/M8AACoIUaW7IOwBCAoCrYWV3pSfGo9PikeAIBIFRNzavIXYcl6TMMDUO8KthZrzKyNfkFJkva7SzVm1kYVbC22qDIAAOzP4XCoQYMGhCUbICwBqFflXqPJ+YUyAV6r2DY5v1Dl3kB7AAAASYQlmyAsAahX64sOVhpROp2RVOwu1fqig6ErCgCAMNOgQQOWDrcBwhKAelVyuOqgVJv9AACIRows2QNhCUC9Sk5w1ut+AABEo9jYWMKSDbAaHoB61T09Sakup/a7SwPet+SQlOI6tYw4APti6X/AWows2QNhCUC9io1xaNLgTI2ZtVEOyS8wVVxmTRqcyUUXYGMs/Q9Yj7BkD0zDA1Dv8rJSNX1kF6W4/Kfapbicmj6yCxdbgI2x9D9gD4Qle2BkCUBQ5GWlqn9mCtN4gDByrqX/HTq19H//zBT+XwaCjLBkD4QlAEETG+NQr4xmVpcBoJpqsvQ//28DwcXS4fbANDwAACCJpf8BO2FkyR4ISwAAQBJL/wN2wtLh9kBYAgAAkn5Y+r+qu5EcOrUqHkv/A8HHyJI9EJYAAICkH5b+l1QpMLH0PxBahCV7ICwBAAAflv4H7IGwZA+shgcAAPyw9D9gPcKSPRCWAABAJSz9D1iLpcPtgWl4AAAAgM0wsmQPhCUAAADAZlg63B4ISwAAAIDNMLJkD4QlAAAAwGYIS/ZAWAIAAABshrBkD4QlAAAAwGYIS/bA0uEAAABAHZR7Tb0/l4ylw+2BsAQAAADUUsHWYk3OL1Sxu9S3LdXl1KTBmcrLSq31cVkNzx6YhgcAAADUQsHWYo2ZtdEvKEnSfnepxszaqIKtxbU+NtPw7IGwBAAAgLBW7jVau+uA5m/aq7W7Dqjca0Jyzsn5hQp0poptk/MLa10LYckemIYHAACAsBWsaXDnsr7oYKURpdMZScXuUq0vOqheGc1qfHzCkj0wsgQAAICwFMxpcOdScrjqoFSb/c5EWLIHwhIAAADCTrCnwZ1LcoKzXvc7E2HJHghLAAAACDs1mQYXDN3Tk5TqcqqqBcIdOjUdsHt6Uq2Oz9Lh9kBYAgAAQNgJ9jS4c4mNcWjS4ExJqhSYKr6eNDiz1s9bYulweyAsAQAAIOwEexpcdeRlpWr6yC5KcfmfI8Xl1PSRXeq0wATT8OyB1fAAAAAQdiqmwe13lwa8b8mhU6GlttPgqisvK1X9M1O0vuigSg6XKjnh1DlrO6JUgbBkD4QlAAAAhJ2KaXBjZm2UQ/ILTPUxDa6mtdRmefCzISzZA9PwAAAAEJaCOQ3OaoQle2BkCQAAAGErWNPgrEZYsgfCEgAAAMJaMKbBWY2lw+2BaXgAAACAzbB0uD0QlgAAAACbqZiGZ0ygtf4QKoQlAAAAwGYaNDh1t4zX67W4kuhGWAIAAABspiIsMRXPWoQlAAAAwGYIS/ZQo7A0depUdevWTQkJCUpOTtawYcO0Y8eOSvt9/vnnGjJkiFwulxISEtSzZ0999dVXvtd37NihPn36KC0tTVOmTPF77913362MjAzFxcWpRYsWGjp0qLZv317pHAsXLlSPHj0UFxen5s2ba/jw4b7XFi9erIyMDB06dEiS5HA45HQ6tWfPHr9jDBs2TLfddltNWgAAAAAEnR3CEtf+NQxLy5cv19ixY7Vu3TotWbJEJ0+eVG5uro4ePerbZ9euXerbt686dOigZcuWafPmzXrsscfkdP7wsLCxY8dq1KhRmj9/vvLz87V69Wrfa127dtXMmTP1+eef67333pMxRrm5uX5LJ86dO1ejRo3Sz3/+c23evFmrV6/WTTfd5Hv9+eef19tvv62mTZv6tjkcDj3++OM1ag4AAABghYqwZOXy4Vz7SzJ1UFJSYiSZ5cuX+7aNGDHCjBw58qzv69q1q1m3bp05ceKEGTJkiFm4cGGV+27evNlIMl988YUxxpiysjLTunVr8+qrrwbc3+12m4KCAr9tksyDDz5oYmJizGeffebbPnToUHPrrbeetVa3220kGbfbfdb9cMqJEyfMO++8Y06cOGF1KRGLHocGfQ4+ehwa9Dn46HFoRFuf58+fbySZb775JijHr801bjRc+5+pTvcsud1uSVJSUpKkU6t1LFy4UO3bt9eAAQOUnJysHj166J133vF735QpU9S/f3/Fx8crJiZGAwYMCHj8o0ePaubMmUpPT1ebNm0kSRs3btTevXsVExOjyy67TKmpqRo4cKC2bdsmSUpMTAx4vN69e+vaa6/VxIkT6/KRAQAA8L/KvUZrdx3Q/E17tXbXAZV7Wea6vthhGt6ZovHav0Ft32iM0fjx49W3b19lZWVJkkpKSnTkyBE99dRTevLJJ/X000+roKBAw4cP14cffqicnBxJ0qBBg/Ttt9/K4/GoRYsWlY790ksv6Te/+Y2OHj2qDh06aMmSJWrUqJEkaffu3ZKk3/72t/rjH/+oH/3oR3r22WeVk5OjnTt3+r55gUydOlWXXnqpVq5cqSuuuKJGn7esrExlZWU1ek80qugRvQoeehwa9Dn46HFo0Ofgi9Yef/D5N3pq8Xbt95T6tqUkOvXQwA76SceW9X6+aOtzbGys4uLidPz48aB85poeM9qu/Ss4/neoqsbGjh2rhQsXatWqVUpLS5Mk7du3T61bt9aNN96o2bNn+/YdMmSIzjvvPL355pvVOrbb7VZJSYmKi4v1zDPPaO/evVq9erWcTqdmz56tm2++WS+//LLuuusuSdLx48eVlpamJ598UnfffXflD+lwaN68eRo2bJhGjx6t7du3a82aNRo2bJjOP/98/e1vf6uyFo/HI5fLpdmzZys+Pr4GHQIAAADs6dixY7rpppvkdruVmJh4zv2j5dr/TLUaWRo3bpwWLFigFStW+JolSc2bN1eDBg2UmZnpt3/Hjh21atWqah/f5XLJ5XLpoosuUs+ePdW0aVPNmzdPN954o1JTUyXJ7xyNGzdWu3bt/FbdqMrkyZPVvn37SsOD55Kbm1utv0jRrqysTEuWLFH//v3VsGFDq8uJSPQ4NOhz8NHj0KDPwRdtPS73Gg14foXfiNLpHJJaJjr13q+yFRvjqLfzRluf16xZo4EDB2rDhg268MIL6/34Ho+n2vtG47V/hRqFJWOMxo0bp3nz5mnZsmVKT0/3e71Ro0bq1q1bpSUFd+7cqQsuuKBWBVac9/jx45JOrZjRuHFj7dixQ3379pV06n+eL7/8slrnaNOmje677z49/PDDysjIqHYNDRs2jIr/MesL/Qo+ehwa9Dn46HFo0Oe6KfcarS86qJLDpUpOcKp7elKlIBAtPf5k1wHtOXRcp2JRYHsOHdenXx9Wr4xm9X7+aOlzw4YN9f3338sYE5TPW51jRvO1f4UahaWxY8dq9uzZmj9/vhISErR//35Jp9JgXFycJOnBBx/UiBEjlJ2drSuvvFIFBQXKz8/XsmXLznn83bt3a86cOcrNzVWLFi20d+9ePf3004qLi9OgQYMknbqJ65577tGkSZPUpk0bXXDBBfrDH/4gSfrZz35Wrc8xceJEvfLKKyoqKtKIESNq0gIAABBlCrYWa3J+oYrdP4ykpLqcmjQ4U3lZqRZWZo2Sw4FHlGq7HwKzw9LhXPvX8DlL06dPl9vtVr9+/ZSamur7M2fOHN8+1113nWbMmKFp06apU6dOevXVVzV37lxfEjwbp9OplStXatCgQbrwwgt1/fXX67zzztOaNWuUnJzs2+8Pf/iDbrjhBo0aNUrdunXTnj17tHTpUr+11c8mKSlJEyZMUGkp/xMDAICqFWwt1phZG/2CkiTtd5dqzKyNKthabFFl1klOcJ57pxrsh8BiY2MlWbsaHtf+dVjgIVpULPBQ3Zvfol1ZWZkWLVqkQYMGRcUQuRXocWjQ5+Cjx6FBn2uv3GvU9+mllYJSBYekFJdTH46/Qu8VLI6aHlf0Zb+7VIEuIiv6smrCVfV+z1I0/V3evHmzOnfurPXr16tbt271fnyucaunTs9ZAgAAiFTriw5WGZQkyUgqdpdqw55DoSvKBmJjHJo0+NTN9mdGoYqvJw3OrNegFI3s+JylaERYAgAACKC699z898jxIFdiP3lZqZo+sotSXP5T7VJcTk0f2SUq7+Wqb4Qle6j1Q2kBAAAiWXXvuWnepLH+G+Ra7CgvK1X9M1POuUogaoewZA+EJQAAgAC6pycp1eU85705XS9oqvc+D3V19hAb4wjK8uAgLNkF0/AAAAAC4N4cWImwZA+EJQAAgCpwbw6sUrF0uJXPWQLT8AAAAM6Ke3NghYqwVFZWZnEl0Y2wBAAAcA7cm4NQ+89//iNJatWqlcWVRDem4QEAAAA289lnn0mSLrnkEosriW6EJQAAAMBmtmzZonbt2qlJkyZWlxLVCEsAAACAzWzZskWdOnWyuoyoR1gCAAAAbGbLli269NJLrS4j6rHAAwAAAKJOudfYdoXDb7/9Vvv372dkyQYISwAAAIgqBVuLNTm/UMXuUt+2VJdTkwZn2uLZWVu2bJEkwpINMA0PAAAAUaNga7HGzNroF5Qkab+7VGNmbVTB1mKLKvvBli1b1LhxY1144YVWlxL1CEsAAACICuVeo8n5hTIBXqvYNjm/UOXeQHuEzpYtW3TJJZeoQQMmgVmNsAQAAICosL7oYKURpdMZScXuUq0vOhi6ogL47LPPmIJnE4QlAAAARIWSw1UHpdrsFwxer1fbtm0jLNkEYQkAAABRITnBWa/7BcPu3bt17NgxwpJNEJYAAAAQFbqnJynV5VRVC4Q7dGpVvO7pSaEsy0/FSng8Y8keCEsAAACICrExDk0anClJlQJTxdeTBmda+rylzz77TM2bN1fLli0tqwE/ICwBAAAgauRlpWr6yC5KcflPtUtxOTV9ZBfLn7O0ZcsWderUSQ6HPR6QG+1YjxAAAABRJS8rVf0zU7S+6KBKDpcqOeHU1DsrR5QqbNmyRQMHDrS6DPwvwhIAAACiTmyMQ70ymlldhp9jx47piy++YHEHG2EaHgAAAGADhYWF8nq9hCUbISwBAAAANrBlyxY5HA5dcsklVpeC/0VYAgAAAGxgy5YtysjI0HnnnWd1KfhfhCUAAADABipWwoN9EJYAAAAAGyAs2Q9hCQAAALBYSUmJvvnmG8KSzRCWAAAAAItt2bJFknTppZdaXAlOR1gCAAAALLZlyxbFxcUpIyPD6lJwGsISAAAAYLEtW7YoMzNTsbGxVpeC0xCWAAAAAIt99tln3K9kQ4QlAAAAwELl5eXatm0b9yvZUAOrCwAAAACiwdGjR1VUVKSioiLt3r3b92fXrl36/vvvCUs2RFgCAAAA6klJSYk+//xzvzBUEY6++eYb335Op1Pp6elq166dfvKTn+i+++5TTk6OhZUjEMISAAAAUA8OHTqktLQ0lZWVSZJat26tdu3aqX379srLy/OFo3bt2qlly5aKieGOGLsjLAEAAAD1oKSkRGVlZXrzzTc1bNgwOZ1Oq0tCHRFnAQAAgHrgdrslSR07diQoRQjCEgAAAFAPPB6PJCkxMdHiSlBfCEsAAABAPagISy6Xy+JKUF+4ZwkAAACohXKv0fqigyo5XKrkBKe+++7UNLyEhASLK0N9ISwBAAAANVSwtViT8wtV7C79YePWT9TY6VTDhg2tKwz1iml4AAAAQA0UbC3WmFkb/YOSTi3wUBbrVMHWYosqQ30jLAEAAADVVO41mpxfKBPgNe+J7xXT+DxNzi9UuTfQHgg3hCUAAACgmtYXHaw0olTBe/yYYhrFq9hdqvVFB0NcGYKBsAQAAABUU8nhwEFJkrzHjyqmcdw590P4ICwBAAAA1ZScUPXDZr0njsnRKP6c+yF8EJYAAACAauqenqRUl1OOAK+Z46fuWUp1OdU9PSnktaH+EZYAAACAaoqNcWjS4ExJqhSYvCeOKaZxvCYNzlRsTKA4hXBDWAIAAABqIC8rVdNHdlGKy3+qnaPsmK7p0k55WakWVYb6xkNpAQAAgBrKy0pV/8wUrS86qJLDpUpOcGrQyyd02YWtrC4N9YiwBAAAANRCbIxDvTKaSZKMMfJ4PHK5XBZXhfrENDwAAACgjo4ePSqv16vExESrS0E9IiwBAAAAdeTxeCSJsBRhCEsAAABAHRGWIhNhCQAAAKijirDEPUuRhbAEAAAA1JHb7ZbEyFKkISwBAAAAdcQ0vMhEWAIAAADqqCIsJSQkWFwJ6hNhCQAAAKgjj8ej+Ph4NWzY0OpSUI8ISwAAAEAdud1upuBFIMISAAAAUEcej4ewFIEISwAAAEAdeTwelg2PQA2sLgAAAAAIhXKv0fqigyo5XKrkBKe6pycpNsZRL+9jZCkyEZYAAABsoLYX8qiegq3FmpxfqGJ3qW9bqsupSYMzlZeVWuf3cc9SZCIsAQAAWKy2F/KonoKtxRoza6PMGdv3u0s1ZtZGTR/ZJWCfa/I+j8ejli1bBucDwDLcswQAAGChigvy04OS9MMFecHWYosqiwzlXqPJ+YWVAo8k37bJ+YUq9/rvUdP3cc9SZCIsAQAAWKS2F/KRpNxrtHbXAc3ftFdrdx2o98+6vuhgpSB6OiOp2F2q9UUH6/Q+7lmKTEzDAwAAsEhNLsh7ZTQLXWEhEorphyWHq+7v2far6fu4ZykyMbIEAABgkdpeyEeCUE0/TE5w1mq/mrzPGMPIUoQiLAEAAFikthfy4S6U0w+7pycp1eVUVesKOnRqNKt7elKt33f06FEZY7hnKQIRlgAAACxS2wv5cFfb+4hqIzbGoUmDMyWpUp8rvp40OLPSMu01eZ/H45EkRpYiEGEJAADAIrW9kA93oZ5+mJeVqukjuyjF5T9Cl+JyVrlseE3e53a7JRGWIhELPAAAAFio4oL8zIUOUiL4OUtWTD/My0pV/8yUGj/4tzrvY2QpchGWAAAALFbbC/lwVTH9cL+7NOB9Sw6dCov1Pf0wNsZRq1UFz/W+irDEPUuRh7AEAABgA7W9kA9HFdMPx8zaKIfkF5jCcfohI0uRi3uWAAAAEHK1vY/IjiruWUpISLC4EtQ3RpYAAABgiUiZfujxeBQfH68GDbi0jjR8RwEAAGCZSJh+6PF4uF8pQjENDwAAAKgDj8fD/UoRirAEAAAA1IHb7SYsRSjCEgAAAFAHjCxFLsISAAAAUAfcsxS5CEsAAABAHTCyFLkISwAAAEAdcM9S5CIsAQAAAHXAyFLkIiwBAAAAdcA9S5GrRmFp6tSp6tatmxISEpScnKxhw4Zpx44dlfb7/PPPNWTIELlcLiUkJKhnz5766quvfK/v2LFDffr0UVpamqZMmeL33rvvvlsZGRmKi4tTixYtNHToUG3fvt1vn0OHDmnUqFFyuVxyuVwaNWqUvvvuO9/rixcvVkZGhg4dOiRJcjgccjqd2rNnj99xhg0bpttuu60mLQAAAAB8vF5vxI4sce1fw7C0fPlyjR07VuvWrdOSJUt08uRJ5ebm6ujRo759du3apb59+6pDhw5atmyZNm/erMcee0xOp9O3z9ixYzVq1CjNnz9f+fn5Wr16te+1rl27aubMmfr888/13nvvyRij3NxclZeX+/a56aabtGnTJhUUFKigoECbNm3SqFGjfK8///zzevvtt9W0aVPfNofDoccff7xGzQEAAADO5ujRozLGRGRY4tpfkqmDkpISI8ksX77ct23EiBFm5MiRZ31f165dzbp168yJEyfMkCFDzMKFC6vcd/PmzUaS+eKLL4wxxhQWFhpJZt26db591q5daySZ7du3G7fbbQoKCvyOIck8+OCDJiYmxnz22We+7UOHDjW33nrrWWt1u91GknG73WfdD6ecOHHCvPPOO+bEiRNWlxKx6HFo0Ofgo8ehQZ+Djx6Hhl37/PXXXxtJZ72etaPaXONGw7X/mep0z5Lb7ZYkJSUlSTo1DLlw4UK1b99eAwYMUHJysnr06KF33nnH731TpkxR//79FR8fr5iYGA0YMCDg8Y8ePaqZM2cqPT1dbdq0kSStXbtWLpdLPXr08O3Xs2dPuVwurVmzRomJiQGP17t3b1177bWaOHFiXT4yAAAA4OPxeCQpKu5ZisZr/wa1faMxRuPHj1ffvn2VlZUlSSopKdGRI0f01FNP6cknn9TTTz+tgoICDR8+XB9++KFycnIkSYMGDdK3334rj8ejFi1aVDr2Sy+9pN/85jc6evSoOnTooCVLlqhRo0aSpP379ys5ObnSe5KTk7V///6z1jx16lRdeumlWrlypa644ooafd6ysjKVlZXV6D3RqKJH9Cp46HFo0Ofgo8ehQZ+Djx6Hhl37fOjQIcXFxalJkya2q+1salprtF37V6h1WLrvvvv02WefadWqVb5tXq9XkjR06FDdf//9kqTOnTtrzZo1mjFjhq9hktS4ceOAzZKkm2++Wf3791dxcbGeeeYZXX/99Vq9erVv7qPD4aj0HmNMwO2ny8zM1C233KIJEyZozZo1Nfq877//vuLj42v0nmi2ZMkSq0uIePQ4NOhz8NHj0KDPwUePQ8OOfX7zzTf15Zdf6ssvv7S6lGo7duxYjfaPtmv/CrUKS+PGjdOCBQu0YsUKpaWl+bY3b95cDRo0UGZmpt/+HTt29GvsuVSsdHHRRRepZ8+eatq0qebNm6cbb7xRKSkp+uabbyq959tvv1XLli3PeezJkyerffv2lYYHzyU3Nzcib9yrb2VlZVqyZIn69++vhg0bWl1ORKLHoUGfg48ehwZ9Dj56HBp27fP8+fN1yy236KuvvgqrqXgV0werIxqv/SvUKCwZYzRu3DjNmzdPy5YtU3p6ut/rjRo1Urdu3SotKbhz505dcMEFtSqw4rzHjx+XJPXq1Utut1vr169X9+7dJUkfffSR3G63evfufc5jtWnTRvfdd58efvhhZWRkVLuGhg0b2up/TLujX8FHj0ODPgcfPQ4N+hx89Dg07NZnj8ej77//Xk2bNlVsbKzV5VRbdXoYzdf+FWoUlsaOHavZs2dr/vz5SkhI8M0TdLlciouLkyQ9+OCDGjFihLKzs3XllVeqoKBA+fn5WrZs2TmPv3v3bs2ZM0e5ublq0aKF9u7dq6efflpxcXEaNGiQpFNJNS8vT3feeadefvllSdJdd92la6+9VhdffHG1PsfEiRP1yiuvqKioSCNGjKhJCwAAAAAft9ut8847L6yCUnVx7V/D5yxNnz5dbrdb/fr1U2pqqu/PnDlzfPtcd911mjFjhqZNm6ZOnTrp1Vdf1dy5c9W3b99zHt/pdGrlypUaNGiQLrzwQl1//fU677zztGbNGr8bu/7+97+rU6dOys3NVW5uri699FK98cYb1f4cSUlJmjBhgkpLS2vy8QEAAAA/kfpAWolrf6kW0/CqY/To0Ro9enSNi2nVqpUWLVp0zv2SkpI0a9asah83UN0TJ05kGXEAAADUicfjCat7lWqCa/8ajiwBAAAA+EEkjyyBsAQAAADUmtvtJixFMMISAAAAUEuMLEU2whIAAABQS5F8zxIISwAAAECtMbIU2QhLAAAAQC1xz1JkIywBAAAAtcTIUmQjLAEAAAC14PV6dfjwYe5ZimCEJQAAAKAWjh49KmMMI0sRjLAEAAAA1ILb7ZYkwlIEIywBAAAAteDxeCQRliIZYQkAAACohYqwxD1LkYuwBAAAANQCI0uRj7AEAAAA1AL3LEU+whIAAABQCxUjSwkJCRZXgmAhLAEAAAC14PF41KRJE8XGxlpdCoKEsAQAAADUgsfjYQpehCMsAQAAALXgdrsJSxGOsAQAAADUAiNLkY+wBAAAANSCx+PhGUsRjrAEAAAA1AIjS5GPsAQAAADUAvcsRT7CEgAAAFALjCxFPsISAAAAUAvcsxT5CEsAAABALTANL/IRlgAAAIAa8nq9Onz4MGEpwjWwugAAAADAzsq9RuuLDqrkcKmSE5zqnp6ko0eOSBLT8CIcYQkAAACoQsHWYk3OL1Sxu9S3LdXl1L3dzpckRpYiHGEJAAAACKBga7HGzNooc8b2/e5SPfTmR5IIS5GOe5YAAACAM5R7jSbnF1YKSpJkJHlPHJMkndckIaR1IbQYWQIAAEDUCHT/UWyMo9J+64sO+k29O5P3+KmwVOQ2+nHQqoXVCEsAAACIClXdfzRpcKbyslL99i05XHVQkiTvie8lScfUsP4LhW0wDQ8AAAARr+L+ozNHi/a7SzVm1kYVbC32256c4Dzr8bzHj0qS2rZsVr+FwlYISwAAAIho57r/SJIm5xeq3PvDHt3Tk5TqcqryBL3/fd/xY4ppFKdeF7ao73JhI4QlAAAAVFLuNVq764Dmb9qrtbsO+AWJcHOu+4+MpGJ3qdYXHfRti41xaNLgTEmqFJgcOrXAQ9Pzzw94vxMiB/csAQAAwE9N7u0JB+e6/6iq/fKyUjV9ZJdKvUhxOXVZuwRtP3h+fZYJGyIsAQAAwOdszxYaM2ujpo/sEvTAFGjFuro41/1HZ9svLytV/TNTKtVz152zecZSFCAsAQAAQNK57+1x6NS9Pf0zU4I2/ayqUa3Hr7m41sesuP9ov7s04Gdz6NRoUVWhLDbGoV4Z/gs5eDwewlIU4J4lAAAASKrdvT316Wwr1t0/Z1Otj3uu+48kadLgzBoFQI/HI5fLVeuaEB4ISwAAAJBU+3t76kN1Vqyr2K82Ku4/SnH5T7VLcTlrNbWQkaXowDQ8AACAEAh0H47dVlKry709dVWdUS1J2rDnkPq0b1mrc1R1/1Ftvg9ut5uwFAUISwAAAEEWLqvL1fXenrqo7mjVf48cr9N5At1/VBuMLEUHpuEBAAAE0dnuwxkza6MKthZbVFllwbi3p7qqO1rVvEnjej93bXDPUnQgLAEAAARJde7DmZxfaKsHvtb3vT3VVTGqVVUMq9je9YKmQTl/TXi9Xh0+fJiRpSjANDwAAIAgqcnqcvUxNay+1Oe9PdVVMao1ZtZGOeS/qIPjjP2sdvjwYUkiLEUBRpYAAACCxMrV5eqq4t6eoZ1bq1dGs5CElLONaj03onPQz19dHo9HEmEpGjCyBAAAECRWri4Xrqoa1fKWn9SiIqurO6UiLHHPUuQjLAEAAASJlavLhbNAK9Z5yy0qJgBGlqIH0/AAAACCxMrV5RA8brdbEmEpGhCWAAAAgsiq1eUQPIwsRQ+m4QEAAASZFavLIXgqwlJCQoLFlSDYCEsAAAAhEOg+HIQnj8ejhIQExcQwSSvS8R0GAAAAasDtdjMFL0oQlgAAAIAa8Hg8hKUoQVgCAAAAasDj8fCMpShBWAIAAABqgJGl6EFYAgAAAGqAe5aiB2EJAAAAqAGm4UUPwhIAAABQA0zDix6EJQAAAKAGmIYXPQhLAAAAQA0wshQ9CEsAAABANZWXl+vIkSPcsxQlCEsAAABANR05ckSSGFmKEoQlAAAAoJrcbrckwlK0ICwBAAAA1eTxeCQRlqIFYQkAAACopoqwxD1L0YGwBAAAAFQTI0vRhbAEAAAAVBP3LEUXwhIAAABQTR6PRw6HQ02aNLG6FIQAYQkAAACoJo/Ho4SEBMXEcBkdDfguAwAAANXk8XiYghdFCEsAAABANbndbsJSFCEsAQAAANXEyFJ0ISwBAAAA1XTgwAGesRRFCEsAAABANZSXl2vVqlXq1q2b1aUgRAhLAAAAQDWsX79eBw8e1KBBg6wuBSFCWAIAAACqYdGiRUpKSlL37t2tLgUhQlgCAAAAqmHRokUaMGCAYmNjrS4FIUJYAgAAAM5h//792rhxI1PwogxhCQAAADiHgoICORwODRgwwOpSEEKEJQAAAOAcFi1apO7du6tFixZWl4IQamB1AQAAAKh/5V6j9UUHVXK4VMkJTnVPT1JsjMPqsupdKD7nyZMn9f777+v++++v1+PC/ghLAAAAEaZga7Em5xeq2F3q25bqcmrS4EzlZaVaWFn9CtXnXLt2rdxuN/crRSGm4QEAAESQgq3FGjNro1+AkKT97lKNmbVRBVuLLaqsfoXycy5atEgtWrRQ165d6+2YCA+EJQAAgAhR7jWanF8oE+C1im2T8wtV7g20R/gI9edctGiR8vLyFBPDpXO04TsOAAAQIdYXHaw00nI6I6nYXar1RQdDV1QQhPJzfv311/rss8+YghelCEsAAAARouRw1QGiNvvZVSg/Z0FBgWJiYpSbm1vnYyH8EJYAAAAiRHKCs173s6tQfs5FixapZ8+eSkpKqvOxEH4ISwAAABGie3qSUl1OVbVwtkOnVovrnh7eF/6h+pwnTpzQkiVLmIIXxWoUlqZOnapu3bopISFBycnJGjZsmHbs2FFpv88//1xDhgyRy+VSQkKCevbsqa+++sr3+o4dO9SnTx+lpaVpypQpvu0HDx7UuHHjdPHFFys+Pl5t27bVL37xC7nd7oD1HD9+XJ07d5bD4dCmTZt82xcvXqyMjAwdOnRIkuRwOOR0OrVnzx6/9w8bNky33XZbTVoAAABgW7ExDk0anClJlYJExdeTBmeG/fOWQvU5V69erSNHjkRtWOLav4Zhafny5Ro7dqzWrVunJUuW6OTJk8rNzdXRo0d9++zatUt9+/ZVhw4dtGzZMm3evFmPPfaYnM4fhkHHjh2rUaNGaf78+crPz9fq1aslSfv27dO+ffv0zDPPaMuWLfrb3/6mgoIC3X777QHr+c1vfqNWrVpV2v7888/r7bffVtOmTX3bHA6HHn/88Zp8XAAAgLCTl5Wq6SO7KMXlPwUtxeXU9JFdIuY5S6H4nIsWLVJqaqo6d+5c52OFI679a/hQ2oKCAr+vZ86cqeTkZG3YsEHZ2dmSpEceeUSDBg3StGnTfPu1a9fO733fffedLrvsMl166aVq1aqVLz1mZWVp7ty5vv0yMjL0u9/9TiNHjtTJkyfVoMEP5S5evFjvv/++5s6dq8WLF/u2ezwejR8/Xj/+8Y/9zjlu3Dg9++yzeuCBB9SpU6eafGwAAICwkpeVqv6ZKVpfdFAlh0uVnHBqSlq4jyidKdifs2LJcIcjsvpWXVz71/GepYoPWnHDm9fr1cKFC9W+fXsNGDBAycnJ6tGjh9555x2/902ZMkX9+/dXfHy8YmJiNGDAgLOeIzEx0a9Z33zzje6880698cYbio+P99s/MTEx4PF69+6ta6+9VhMnTqztxwUAABGk3Gu0dtcBzd+0V2t3HQj7Zw+dKTbGoV4ZzTS0c2v1ymgWcUGpQrA+5549e1RYWBi1U/ACicZr/1qHJWOMxo8fr759+yorK0uSVFJSoiNHjuipp55SXl6e3n//fV133XUaPny4li9f7nvvoEGD9O2332rfvn2aN2+eYmNjA57jwIEDeuKJJ3T33Xf7nfe2227TPffco8svv7xGNU+dOlUFBQVauXJlLT4xAACIFAVbi9X36aW68ZV1+uX/26QbX1mnvk8vVcHWYqtLg00sXrxYsbGx6t+/v9Wl2EK0XvvXaBre6e677z599tlnWrVqlW+b1+uVJA0dOlT333+/JKlz585as2aNZsyYoZycHN++jRs3VosWLao8vsfj0TXXXKPMzExNmjTJt/1Pf/qTPB5PrVJiZmambrnlFk2YMEFr1qyp0XvLyspUVlZW43NGm4oe0avgocehQZ+Djx6HBn2u7IPPv9H9czbJSGp82jXboSPf61dvbtBzIzrrJx1bVvt49Dg0Qt3nJUuW6Morr1R8fHxEfm9r+pmi7dq/Qq3C0rhx47RgwQKtWLFCaWlpvu3NmzdXgwYNlJmZ6bd/x44d/Rp7LocPH1ZeXp6aNGmiefPmqWHDhr7Xli5dqnXr1qlx48Z+77n88st188036/XXXz/rsSdPnqz27dtXGh48l/fff7/SsB+qtmTJEqtLiHj0ODToc/DR49Cgz/6e7l71ayeKNmhRUc2PSY9DI1R9vuWWWySdum8pEh07dqza+0bjtX+FGoUlY4zGjRunefPmadmyZUpPT/d7vVGjRurWrVulJQV37typCy64oFrn8Hg8GjBggBo3bqwFCxb4raQhSS+88IKefPJJ39f79u3TgAEDNGfOHPXo0eOcx2/Tpo3uu+8+Pfzww8rIyKhWTZKUm5urxMTEau8frcrKyrRkyRL179/f7y866g89Dg36HHz0ODTos7/1RQc1+vWPz7nfa7d2q/YzeuhxaISyz0uXLtV1112ntWvXVgoCkcLj8Zxzn2i+9q9Qo7A0duxYzZ49W/Pnz1dCQoL2798vSXK5XIqLi5MkPfjggxoxYoSys7N15ZVXqqCgQPn5+Vq2bNk5j3/48GHl5ubq2LFjmjVrljwej+8b2aJFC8XGxqpt27Z+72nSpImkU6tnnJ50z2bixIl65ZVXVFRUpBEjRlTrPQ0bNuQHYA3Qr+Cjx6FBn4OPHocGfT7lv8dO6nj5uRcA+O+xkzXuFz0OjVD0uaCgQM2aNdOll14asSvhVaeH0XztX6FGCzxMnz5dbrdb/fr1U2pqqu/PnDlzfPtcd911mjFjhqZNm6ZOnTrp1Vdf1dy5c9W3b99zHn/Dhg366KOPtGXLFl144YV+5/jPf/5Tow92NklJSZowYYJKS0vr7ZgAAMD+khOc596pBvshMi1atEgDBw6M2KBUXVz712IaXnWMHj1ao0ePrnEx/fr1q/Y5KvzoRz8653sCvT5x4kSWEQcAIMp0T09Sqsup/e5SBbp6cOjUQ02rOwUPkWfXrl3auXOnnn76aatLsRzX/nV8zhIAAEA4iY1xaNLgU/egnDlmUPH1pMGZEftMIpzb4sWL1bBhQ1199dVWlwIbICwBAICokpeVqukjuyjF5T/VLsXl1PSRXZSXlWpRZbCDRYsW6YorrlBCQoLVpcAGav2cJQAAgHCVl5Wq/pkpWl90UCWHS5WccGrqHSNK0e3YsWP68MMP/VZfQ3QjLAEAgKgUG+NQr4xmVpcBG1m2bJlKS0s1aNAgq0uBTTANDwAAAJC0du1atWzZUh06dLC6FNgEYQkAAACQtHv3brVv3z7qlwzHDwhLAAAAgE6FpXbt2lldBmyEsAQAAACIsITKCEsAAACIeocPH1ZJSYkyMjKsLgU2wmp4AAAAYaLca1juPEiKiookiZEl+CEsAQAAhIGCrcWanF+oYnepb1uqy6lJgzN5kG492L17tyTCEvwxDQ8AAMDmCrYWa8ysjX5BSZL2u0s1ZtZGFWwttqiy4Cr3Gq3ddUDzN+3V+qKDQT3X7t27FR8fr+Tk5KCeB+GFkSUAAAAbK/caTc4vlAnwmpHkkDQ5v1D9M1MiakremSNpjWONpnWXPvj8Gw28NK3ez1exuAPLhuN0jCwBAADY2Pqig5VGlE5nJBW7S4M+8hJKVY2kSdL9czYFZSRt9+7dLO6ASghLAAAANlZyuOqgVJv97O5sI2kVJucXqtx7tj1qbteuXdyvhEoISwAAADaWnOCs1/3szoqRtPLycn355ZeEJVRCWAIAALCx7ulJSnU5VdWdNA6dWhWve3pSKMsKGitG0vbt26cTJ04QllAJYQkAAMDGYmMcmjQ4U5IqBaaKrycNzoyYxR2sGElj2XBUhbAEAABgc3lZqZo+sotSXP4BIcXl1PSRXSLqOUtWjKTt3r1bDodDP/rRj+rtmIgMLB0OAAAQBvKyUtU/M0Xriw6q5HCpkhNOBYZIGVGqUDGSNmbWRjmkgAs91PdIWklJic4//3w5nZFx3xfqD2EJAAAgTMTGONQro5nVZQRdxUja6c9ZqvDciM71PpLWsGFDlZWV1esxERkISwAAALCdM0fSmsc30H8/X6efdGxZ7+dq3LixTpw4Ue/HRfgjLAEAAMCWTh9JKysr06LPg3OeRo0a6cSJEzLGyOGIrGmNqBsWeAAAAEBUa9y4sSQxFQ+VEJYAAAAQ1Ro1aiRJOn78uMWVwG4ISwAAAIhqFSNL3LeEMxGWAAAAENUYWUJVCEsAAACIaowsoSqEJQAAAEQ1RpZQFcISAAAAohojS6gKYQkAAABRjZElVIWwBAAAgKjGyBKqQlgCAABAVGNkCVUhLAEAACCqMbKEqhCWAAAAENUYWUJVCEsAAACIaowsoSqEJQAAAEQ1RpZQFcISAAAAohojS6gKYQkAAABRLTY2VjExMYwsoRLCEgAAAKJe48aNGVlCJYQlAAAARL1GjRoxsoRKGlhdAAAAACJfuddofdFBlRwuVXKCU93TkxQb47C6LB9GlhAIYQkAAIQFu19s24Ud+1SwtViT8wtV7C71bUt1OTVpcKbyslItrOwHjCwhEMISAACwvXC42LYDO/apYGuxxszaKHPG9v3uUo2ZtVHTR3YJam3VDY+MLCEQwhIAALA1qy+2w4Ud+1TuNZqcX1ipJkkykhySJucXqn9mSlBGv2oSHhlZQiAs8AAAAGzrXBfb0qmL7XJvoD2ih137tL7ooF9QOZORVOwu1fqig/V+7orweOb5K8JjwdZiv+2MLCEQwhIAALAtKy+2w4ld+1RyuOqaarNfddUmPDKyhEAISwAAwLasutgON3btU3KCs173q67ahEdGlhAIYQkAANiWVRfb4caufeqenqRUl1NV3Y3k0Kl7iLqnJ9XreWsTHhlZQiCEJQAAYFtWXWyHG7v2KTbGoUmDM301nFmTJE0anFnvizvUJjwysoRACEsAAMC2rLrYDjd27lNeVqqmj+yiFJd/gElxOYO2Ql9twmOjRo0IS6iEpcMBAICtVVxsn7kEdArPWfJj5z7lZaWqf2ZKyB6WWxEex8zaKIfkt9BDVeGxcePGOnz4cFDqQfgiLAEAANsL9cV2uLJzn2JjHOqV0Sxk56tpeGRkCYEQlgAAQFgI9cV2uKJPP6hJeGSBBwRCWAIAAEDEqm54PHTokM4///zgF4SwwgIPAAAAiHr79u1Tq1atrC4DNkNYAgAAQNQjLCEQwhIAAACimtfrVXFxMWEJlRCWAAAAENUOHDigsrIywhIqISwBAAAgqu3bt0+SCEuohLAEAACAqEZYQlUISwAAAIhq+/btk8PhUEpKitWlwGYISwAAAIhq+/btU3Jysho2bGh1KbAZwhIAAACiGsuGoyqEJQAAAEQ1whKqQlgCAABAVCMsoSqEJQAAAEQ1whKqQlgCAABA1CovL9f+/fsJSwiIsAQAAICoVVJSIq/XS1hCQIQlAAAARC0eSIuzISwBAAAgau3atUuS1Lp1a4srgR0RlgAAABC1/vnPf6pz585q2bKl1aXAhghLAAAAiEput1sLFizQyJEjrS4FNkVYAgAAQFT65z//qRMnTujGG2+0uhTYFGEJAAAAUWnWrFm6+uqrWdwBVSIsAQAAIOp89dVXWrZsGVPwcFaEJQAAAESdN998U3FxcbruuuusLgU2RlgCAABAVDHG6I033tDQoUOVmJhodTmwsQZWFxAuPB6P1SWEhbKyMh07dkwej0cNGza0upyIRI9Dgz4HHz0ODfocfPQ4NOqzz1u2bNG2bds0adKkqL3Gi9bPXVMOY4yxugg7Ky0tVXp6uvbv3291KQAAAEC9SUlJUVFRkZxOp9Wl2BZhqRpKS0t14sQJq8sAAAAA6k2jRo0ISudAWAIAAACAAFjgAQAAAAACICwBAAAAQACEJQAAAAAIgLAEAAAAAAEQlgAAAAAgAMJShDt58qQeffRRpaenKy4uTu3atdOUKVPk9Xp9+xhj9Nvf/latWrVSXFyc+vXrp23btvkdZ8eOHerTp4/S0tI0ZcqUgOcyxmjgwIFyOBx65513fNu//PJL3X777b4aMjIyNGnSJL/l2BcvXqyMjAwdOnRIPXv21JgxY/yOPX36dDkcDv31r3/123777berd+/etW1PvQhFj++++25lZGQoLi5OLVq00NChQ7V9+/ZKtSxcuFA9evRQXFycmjdvruHDh/teC+ceS/bp86FDhzRq1Ci5XC65XC6NGjVK3333ne91+nxKVX0+ePCgxo0bp4svvljx8fFq27atfvGLX8jtdges5/jx4+rcubMcDoc2bdrk2x7OfQ7F3+W//OUv6tevnxITE+VwOPz+jlbYuXOnhg4dqubNmysxMVF9+vTRhx9+6Hs9nHss2aPPkf77L5BQXXesXbtWV111lc477zydf/756tevn77//ntJUmFhoZo3b66ioiLdcMMNGjhwoN97Fy9eLIfDoccee8xv+xNPPKFWrVrVVysQKQwi2pNPPmmaNWtm3n33XVNUVGTeeust06RJE/P888/79nnqqadMQkKCmTt3rtmyZYsZMWKESU1NNR6Px7fP1VdfbaZPn24++eQTc/nll5tVq1ZVOtcf//hHM3DgQCPJzJs3z7d98eLF5rbbbjPvvfee2bVrl5k/f75JTk42v/71r3375Obmmk2bNhljjHnooYfMxRdf7Hfs66+/3rRp08bcfPPNftvbtWtnHn744Tr1qK5C0eOXX37ZLF++3BQVFZkNGzaYwYMHmzZt2piTJ0/69vnnP/9pmjZtaqZPn2527Nhhtm/fbt566y3f6+HcY2Ps0+e8vDyTlZVl1qxZY9asWWOysrLMtdde63udPp9SVZ+3bNlihg8fbhYsWGC++OIL869//ctcdNFF5qc//WnAen7xi1/4fq58+umnvu3h3OdQ/F1+7rnnzNSpU83UqVONJHPo0KFKdVx44YVm0KBBZvPmzWbnzp3m3nvvNfHx8aa4uNgYE949NsYefY7033+BhKLva9asMYmJiWbq1Klm69atZufOneatt94ypaWlxhhjbr31VrN48WJjjDEzZswwTZo0MWVlZb73/+Y3vzFt2rQxffr08av9qquuMjfddFNQ+oLwRViKcNdcc40ZPXq037bhw4ebkSNHGmOM8Xq9JiUlxTz11FO+10tLS43L5TIzZszwbevatatZt26dOXHihBkyZIhZuHCh3zE3bdpk0tLSTHFxcaWwFMi0adNMenq6McYYt9ttCgoKfK+99957RpLZt2+fb1vLli3NSy+9ZFq3bu3b9tVXXxlJZsmSJdXsRnCEqsen27x5s5FkvvjiC2OMMWVlZaZ169bm1VdfDbh/uPfYGHv0ubCw0Egy69at8+2zdu1aI8ls376dPteyz//4xz9Mo0aN/C5mjDFm0aJFpkOHDmbbtm1+YSnc+xzKHn/44YcBL+K//fZbI8msWLHCt83j8RhJ5oMPPgj7Hhtjjz4HEkm//wIJRd979OhhHn300YDnP3nypJk7d67v6x07dhhJZu3atb5t3bt3N3/+859No0aNzNGjR40xxhw/ftzExcWZV155pQ6fHpGIsBThpk6dai644AKzY8cOY8ypUJOcnGxmz55tjDFm165dRpLZuHGj3/uGDBlibrnlFt/XCxcuNAkJCaZBgwZm2LBhfv/SfvToUdOxY0fzzjvvGGNMtcLSI488Yrp27RrwtSNHjpiGDRv6aty2bZtJTEw0paWlJiEhwezcudMYY8zrr7/u94POKqHo8emOHDlifvWrX5n09HRz/PhxY4wxH330kZFkXnvtNdO5c2eTkpJi8vLyzNatW6s8Rjj12Bh79Pmvf/2rcblclfZ1uVzmtddeC3gM+nz2PhtjzCuvvGKaN2/ut23//v2mdevW5uOPPzZFRUWVRpZOF259DmWPq7qI93q9pmPHjuaOO+4wR44cMWVlZeYPf/iDadmyZcAL/nDrsTH26HMgkfT7L5Bg9/2bb74xkswLL7xgevXqZZKTk012drZZuXJllTW1atXK/P73vzfGnPpHgQYNGpiSkhLTsWNH8/777xtjjFm+fLnfP44BFQhLEc7r9ZqHHnrIOBwO06BBA+NwOHw/MIwxZvXq1UaS2bt3r9/77rzzTpObm+u3rbS01JSUlFQ6x1133WVuv/1239fnCktffPGFSUxMPOu/3vTu3dvcddddxhhj/vznP5tBgwYZY05NgfrLX/5ijDHm5z//ubniiiuqPEaohKLHxpzqw3nnnWckmQ4dOvj9QH/zzTeNJNO2bVvzz3/+03zyySfmxhtvNM2aNTMHDhwIeLxw6rEx9ujz7373O3PRRRdVes9FF13kV8vp6HPgPlf473//a9q2bWseeeQRvxry8vLME088YYwx5wxLxoRXn0PZ47NdxH/99dema9euxuFwmNjYWNOqVauI6bEx9unz6SLt918gwe57xWh+UlKSee2118zGjRvNr371K9OoUSNfmDzTTTfd5Dv2woULTWZmpjHGmHvuucc3lXHy5MmmTZs2dfvwiEgs8BDh5syZo1mzZmn27NnauHGjXn/9dT3zzDN6/fXX/fZzOBx+XxtjKm1r3LixWrRo4bdtwYIFWrp0qZ5//vlq1bNv3z7l5eXpZz/7me64444q97vyyiu1bNkySdKyZcvUr18/SVJOTo7f9quuuqpa5w2mYPe4ws0336xPP/1Uy5cv10UXXaTrr79epaWlkuS7cfaRRx7RT3/6U3Xt2lUzZ86Uw+HQW2+9FfB44dRjyR59DnT8qs5RgT4H7rMkeTweXXPNNcrMzNSkSZN82//0pz/J4/Fo4sSJ1a47nPocyh5XxRije++9V8nJyVq5cqXWr1+voUOH6tprr1VxcXHA94RTjyV79Pl0kfj7L5Bg973i993dd9+tn//857rsssv03HPP6eKLL9Zrr70WsKYrr7xSq1evVllZWVj2FBazLKYhJNLS0syLL77ot+2JJ57w3UBa3eHwqvzyl7/0/atkxR9JJiYmxuTk5Pjtu3fvXtO+fXszatQoU15eftbjfvDBB0aS+frrr01ycrL56KOPjDGnbups1aqV2bNnj5Fkli1bds4a/387dw/Sxh/GAfz5R9IcwiloCdb3t8FFB5VqHGyrIjoJDoIN2EEHNwdFBAUnXxYRHARxiJBJBAcHobQIHVR8AaMuim8tGKWIQjL41uHbQTx7+ot/lUaT9PuBW+4ud5dvwl2e3P2eYAt2xioXFxeIjo42HmuYmZmBiNx5DOHt27cBBwCHU8ZAaOT82MfwAOYciN/vh8PhQHl5Oc7OzkzLampqYLFY7pxXoqKiAu4jnHJ+zu9yoDseX79+hcVigc/nM83Pzs5GX1+fclvhlDEQGjlfi9Trn0qwc9/d3YWIwO12m+bX1dUFbM6wvb0NEcHs7CwKCwsxPj4OADg4OIDVasXx8TE0TcPY2NiD3yf9O3hnKcKdnp6KxWL+mKOioox/ZjIyMiQhIUG+fPliLL+8vJRv3749qCVpR0eHrK2ticfjMSYRkcHBQXG5XMZ6Xq9X3r9/L/n5+eJyue4c020lJSVis9lkeHhYzs7OpKCgQERECgsLxefzycjIiGiaJsXFxQ/KIZiCnXEgAOTi4kJERAoKCsRms8nm5qax/NevX/L9+3dJS0tTvj6cMhYJjZwdDof4fD5ZXFw0li8sLIjP5wu4D+Z8l9/vl8rKSnn16pVMTU2Jpmmm5UNDQ7K6umqcU6anp0Xk6h/rnp4e5TbDKeeX+i7fPgYRuXMcFovF1OL5T+GUsUho5CwS2dc/lWDnnp6eLomJiabrnchVK/xA17usrCxJSUmRqakp8Xg88u7dOxERefPmjaSnp8vAwICcn5/Lhw8fHvVe6R/x0tUaBdenT5+QlJRktPCcnJzE69ev0d7ebqzT39+P2NhYTE5OYn19HfX19XdaeD6G3Bqz5PV6kZ2djbKyMuzv7+Pw8NCY7lNaWgpd11FVVWWaX1FRAV3XUVZW9qTj+9uCnfHOzg56e3uxvLyMHz9+YG5uDjU1NYiLi8PPnz+N9VpaWpCUlITPnz9jY2MDjY2NsNvtODk5CbjtcMkYCJ2cq6qqkJeXh/n5eczPzyM3N9fUOlyFOd/w+/0oKipCbm4utre3TeeDQE0gHjJmCQifnJ/jvHx4eIiVlRWMjo4aXe9WVlaMMYxHR0eIj49HbW0tPB4PNjc30dbWBqvVarSxVgmXjIHQyDnSr38qz5H74OAgYmJiMDExga2tLXR1dUHTtHubMzQ0NEDXdeTk5JjmNzU1Qdd1ZGZmPu0NU8RjsRTh/H4/WlpakJqaCk3TkJmZic7OTqO7F3A1GLO7uxsJCQmw2WwoLS3F+vr6k/d5u1hyuVwQEeV0n+7uboiIqb0ocHU7X0SMwd8vLdgZe71eVFdXw263w2q1Ijk5GR8/fsTGxoZpvcvLS7S2tsJut0PXdVRUVATshnctXDIGQifn4+NjOJ1O6LoOXdfhdDr/d1A3c75x/biSatrb21O+5qHFUrjk/Bzn5essbk8ul8tYZ2lpCZWVlYiLi4Ou6yguLsb09PSDthvqGQOhkXOkX/9Unut3R19fH5KTkxEdHQ2Hw3FvNzzg5rNobm42zXe73RARU6Mqoj/9BwB//34VERERERFReOOYJSIiIiIiIgUWS0RERERERAosloiIiIiIiBRYLBERERERESmwWCIiIiIiIlJgsURERERERKTAYomIiIiIiEiBxRIREREREZECiyUiIiIiIiIFFktEREREREQKLJaIiIiIiIgUWCwREREREREp/AYw7RcSUHuZTwAAAABJRU5ErkJggg==", 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" + "
" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -318,7 +356,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -333,7 +371,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -342,7 +380,7 @@ "True" ] }, - "execution_count": 8, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -381,7 +419,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -393,14 +431,12 @@ }, { "data": { - "image/png": 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", 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", 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" + "
" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -453,6 +489,7 @@ "- `select()` returns a new hazard with the selected region, date and/or synthetic or historical filter.\n", "- `remove_duplicates()` removes events with same name and date.\n", "- `local_exceedance_inten()` returns a matrix with the exceedence frequency at every frequency and provided return periods. This is the one used in `plot_rp_intensity()`.\n", + "- `local_return_period()` returns a gdf.GeoDataFrame with the return periods at every centroid for user-specified threshold intensities. \n", "- `reproject_vector()` is a method to change the centroids' CRS. \n", "\n", "`Centroids` methods:\n", @@ -471,7 +508,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -481,7 +518,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -490,7 +527,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -502,10 +539,8 @@ "Number of historical events between 1995 and 2001: 109\n", "Number of events in 1999: 16\n", "Year with most hurricanes between 1995 and 2001: 1995\n", - "2022-03-16 22:12:55,499 - climada.hazard.centroids.centr - INFO - Convert centroids to GeoSeries of Point shapes.\n", - "2022-03-16 22:12:57,355 - climada.util.coordinates - INFO - dist_to_coast: UTM 32617 (1/2)\n", - "2022-03-16 22:12:59,926 - climada.util.coordinates - INFO - dist_to_coast: UTM 32618 (2/2)\n", - "Number of centroids close to coast: 41\n" + "2024-07-17 11:42:52,690 - climada.util.coordinates - INFO - Sampling from /Users/vgebhart/climada/data/GMT_intermediate_coast_distance_01d.tif\n", + "Number of centroids close to coast: 67\n" ] } ], @@ -542,19 +577,19 @@ " \n", "## Part 5: Visualize Hazards\n", "\n", - "There are three different plot functions: `plot_intensity()`, `plot_fraction()`and `plot_rp_intensity()`. Depending on the inputs, different properties can be visualized. Check the documentation of the functions:" + "There are three different plot functions: `plot_intensity()`, `plot_fraction()`, and `plot_rp_intensity()`. Depending on the inputs, different properties can be visualized. Check the documentation of the functions. Using the function `local_return_period()` and the util function `subplots_from_gdf()`, one can plot local return periods for specific hazard intensities." ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Help on method plot_intensity in module climada.hazard.base:\n", + "Help on method plot_intensity in module climada.hazard.plot:\n", "\n", "plot_intensity(event=None, centr=None, smooth=True, axis=None, adapt_fontsize=True, **kwargs) method of climada.hazard.base.Hazard instance\n", " Plot intensity values for a selected event or centroid.\n", @@ -590,7 +625,7 @@ " ------\n", " ValueError\n", "\n", - "Help on method plot_rp_intensity in module climada.hazard.base:\n", + "Help on method plot_rp_intensity in module climada.hazard.plot:\n", "\n", "plot_rp_intensity(return_periods=(25, 50, 100, 250), smooth=True, axis=None, figsize=(9, 13), adapt_fontsize=True, **kwargs) method of climada.hazard.base.Hazard instance\n", " Compute and plot hazard exceedance intensity maps for different\n", @@ -624,7 +659,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 13, "metadata": { "scrolled": false }, @@ -633,80 +668,78 @@ "name": "stdout", "output_type": "stream", "text": [ - "2022-03-16 22:13:50,822 - climada.hazard.base - INFO - Computing exceedance intenstiy map for return periods: [ 10 50 75 100]\n", - "2022-03-16 22:13:51,449 - climada.hazard.base - WARNING - Exceedance intenstiy values below 0 are set to 0. Reason: no negative intensity values were found in hazard.\n" + "2024-07-17 11:42:55,987 - climada.hazard.base - INFO - Computing exceedance intenstiy map for return periods: [ 10 50 75 100]\n", + "2024-07-17 11:42:56,078 - climada.hazard.base - WARNING - Exceedance intenstiy values below 0 are set to 0. Reason: no negative intensity values were found in hazard.\n" ] }, { "data": { - "image/png": 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", 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", 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", 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ZZ56B67rBeS1Kw+FrX/saDj74YADAZz/7WXzgAx/AK6+8AqD6UZY0oRwrWJaFU089Nfa8/mCupy6S8Jvf/AYAcNJJJ8FxnNh4nZ2dga/GpUuXxsY77bTTYs/pMgPRcus6ffbZZ0f1QTtanH322bBtG67r4ic/+Unk/NKlS/HCCy8EcccCQ0NDWLFiBZ577rmgrdJnkdRWxwJa2Of1r389FixYkBhXTxaS2oDGxo0b8dJLLwX39MwzzwTPmbun3/3udwCA3XffHccff3xD90Bx4IEHxrqYaWtra2gcSUOSSNVZZ50VLJz8+c9/Ns796U9/AgDst99+waINh4997GORNCPBBz7wgdhzul92d3dj27ZtI76GQCCYXBD3CgLBTgS6up2mfLg9kGT5oKvZSfG0FYROWilKpRKuueYa3HzzzXjyySdRLpdj89qyZUvsuWw2i5/+9Kc49NBDMTAwEHwIn3322TjppJNi040lpk2bhqlTp8aenzJlCoD4uqgHK1asCKxFl156KS699NK60mnrG4fFixfHntNlBqLlPu2003D55ZejWCziyCOPxDve8Q68613vwhve8Abst99+O8SCCgDz5s3D2972Ntx999244YYbcMEFFxjntSWrpaUFp5xyyoiv09fXhyuvvBK33XYbnn/++cC6wyGprY4FHnvsMQBVi0699RzXBu677z5897vfxV/+8pfESUPtPZXL5UAN8w1veENdZYhDUhsExqbvAEA+nw8WPzhMmzYNixYtwquvvmoofRaLRbz88ssAEFhI43DwwQcjm82iXC7HKrHWg0b6ZZK1WSAQ7DoQi55AsBNh2rRpwfHGjRt3+PWbmppiz1EqXj3xqGVOY8uWLTjiiCPw6U9/Go8++mjiJA+oWlGSsO++++Izn/lM8P/s2bNx1VVXJaYZSzQ3Nyee13WRNEFIw0jbgabXcUgqN33Otc9wn332wc9+9jN0dXWhUqngN7/5DT7+8Y9j//33x4wZM3DGGWfULVc/Wnz0ox8FALz00kt46KGHgvChoSHcdtttAICTTz4Zra2tI8r/1VdfxQEHHIAvf/nLePbZZ1OfYVpbHS1G0g64NvDlL38Zxx13HH71q1+lWoZq72nr1q2BxP9oLeb19h1uHGkEU6ZMSbSCA8CMGTMAVO9Po7u7OzhOo9Fns9lgwYfm0ShG2i8FAsGuC7HoCQQ7EebMmYPp06dj06ZNePLJJ+F53nbb6zQeOO+884J9d+973/tw9tln44ADDgh8cFmWBaVUcM/6ozIOPT09BnVv48aNeOqpp9j9ODsr6EfdxRdfXLe1cntRf9/3vvfhLW95C2677Tbcc889eOCBB7Bp0yZs3rwZN998M26++WacddZZ+PGPf7xd2+573vMezJgxAxs3bsQNN9yAo446CgDwy1/+Er29vQBGR9s888wzsWLFCliWhY985CM49dRTsc8++2DatGnI5/MAqrTC3XffHUB6Wx0tdDt497vfjcsvv3xEefzpT3/C1772NQDAnnvuic997nN4wxvegPnz56OlpSWYEJ111ln4n//5n8R72lHW24mAeu51ez9/gUAg4CATPYFgJ8PRRx+NX/3qV+jv78d9992HN77xjeNdpDFBb28vbr/9dgDABz/4QXZvFWCupKfhk5/8JFauXAmgSnvt6+vDGWecgSeffDJWrGJnA6WGlkql2H1NOxLt7e342Mc+FuxNev7553HXXXfh6quvxtq1a3HTTTfh4IMPxnnnnbfdypDNZnHmmWfim9/8Jm677TZcddVVaG5uDmibe+yxB4455pgR5f3CCy8Eohdf/OIXg8lRLRppq6PF1KlTsXbtWnR3d4+4DVx//fUAgK6uLjz00EOYPn06Gy/uvqZMmQLbtuF5HtauXTuiMuxobN26Fa7rJlr1tLWU0iO7urqC4yQaNABUKpXAkkfzEAgEgu2NyWMKEAh2EVArxI6kIW5vvPzyywFVM0n0pV6Rj1tvvRW33HILAODjH/84/ud//gdA1cqyPScYOxq77757sD/yj3/84ziXhse+++6LCy64AA8//HBgSdST+noxEgvRRz7yEQDVPUu//OUvsWLFCvz1r38FMDpr3rPPPhscj6atjqXVSwsPPfLII+jp6RlRHvq+3vjGN8ZO8pRSePzxx9lz2Ww2mGSOhRrmjkCxWEzcN7d58+ZA8IVOoPP5fCAI8/e//z3xGk888UQwtiXtB9ye2JUsrAKBIIRM9ASCnQzvete7AoXLu+66C7feemvdaeOsZBMBdD9e0n6m6667LjWv1atX4xOf+ASAqjDMt771Lbz3ve8NZM5vuOEG3HHHHaMs8faHdkVRLBZj4ziOg3e+850Aqh/599133w4p20gwf/78QKinUXcDui6A5Pqg2GeffQKa7g033IAbb7wxoP4mKS2moZ62qpTCD3/4w8R8RnJPcdAKl+VyGVdeeeWI8tD3ldT/fve738W6rACqlFmguodRq8FOdOhFoLhzmnb5pje9yTj3lre8BQDw3HPP4eGHH47NQ1tKaZodjXrGEoFAMPkgEz2BYCeDZVm4+eabA8GTM888E9ddd13iHpANGzbgox/9KM4888wdVcyGseeeewarznET0uuvvx6//OUvE/NRSuGss87Ctm3bAp9iWsTgqquuCvZMnXPOOamUq/GGFrTQbiHi8IUvfCGgnp1xxhl48cUXY+N6nodbbrkFq1evHruC+rjzzjsTBTxWrVoVuDVYtGhRQ3lTcY+0+qDQVr17770X3//+9wFUP7bnzZvX0PUpqK+5uEnCl770pVSL3kjvicNZZ50V+KW75JJLcNdddyXGf+ihh3D//fcbYfq+HnjgASxfvjySZvny5cECShz+/d//PbDannPOOXj++edj426PNjgSfP/732cnai+99BIuueQSAMCsWbMCX3Uan/jEJ4J9pueccw5rSf3DH/6AH/3oRwCAI444AoceeugYl74+1DuWCASCyQXZoycQ7IQ44IADcPvtt+PUU0/FwMAA/u3f/g3f+973cMopp+Cwww7DtGnT0N/fjxUrVuB///d/ceedd46LO4ZGMG3aNLz97W/H3Xffjd/+9rd417vehXPOOQfz58/H2rVr8dOf/hS33norjjzyyET/X9/+9rfxl7/8BQBw0UUX4XWve11wrrW1FT/5yU9wzDHHYPPmzfjwhz+M3//+99v93kaKo446CsuWLQucvi9ZsiRYmW9vbw/UAA844AB885vfxPnnn49Vq1bhkEMOwUc+8hG87W1vw5w5c1AsFrFixQosXboUv/jFL7BmzRo8/fTTo5rscLjyyitx+umn413vehf+5V/+Bfvuuy86OjrQ3d2NRx99FFdffXVgLUqbMNTi4IMPRqFQwPDwML70pS8hm81i4cKFwYf23LlzWbXX97///TjvvPPQ19cX7LXSlt2R4pBDDsF+++2H5557Dt/73vfQ09OD0047DbNmzcKrr76KH/3oR7jnnntw1FFHGYqftdACMQBw/vnn44tf/CJmz54dLHjstttuyGTqe03n83ncfvvtOO6441AsFnHiiSfixBNPxMknn4w99tgDlmVh/fr1eOyxx3DHHXfgySefxNVXX23sUzzjjDPw29/+Fv39/TjmmGPwhS98AQcffDAqlQruvfdefOc738HAwAAOOeSQWPrmrFmzcM011+DMM8/E+vXrcdhhh+FjH/sY3v72t2PWrFno7+/HM888g1//+td48cUXx33iMX36dDQ1NeFNb3oTPvvZz+Ltb387HMfBAw88gMsvvzzYj3jVVVcFTto19t9/f3z2s5/Ff/3Xf+Hpp5/GIYccgv/8z//EIYccErh0ufrqq+G6LvL5fF1shO2Fo446Cn/961/xj3/8A1dccQXe8Y53BBPypqYmzJ07d9zKJhAItiOUQCCYMACgAKhjjz22rvhPPPGEOvLII4N0Sb+ZM2eq6667ruEy/fWvfw3y+Otf/xob76KLLgriJeHYY4+Nvcfly5erefPmxd7Dfvvtp1avXh38f9FFFxnpn3zySZXP5xUAddRRR6lKpcKW4Ytf/GKQx/e+9720KoggrU7OOussBUAtXLgwMZ+0OnviiSeC+6n9nXXWWZH41113nWpubk5tC7lcTr388ssN3ZPGsmXLgng33HCDcU4/26Sf4zjqsssuayhfjf/4j/+IzTepzB/72MeCeFOmTFHDw8OxcevFo48+qjo7O2PL84Y3vEE99dRTqff0/ve/PzaPZcuWNVyupUuXqvnz59c1Jtx0002R9GeeeWZs/EKhoH72s5/V1b5vvPFG1dTUlHh9Lv3ChQtj2zdF0jhSD+g9PPzww2rKlClsGS3LUpdffnlsPq7rqk9+8pOJ99nZ2an+9Kc/senT2n294+oNN9yQ2G5Wr14de48jrUOBQDDxIdRNgWAnxkEHHYSHHnoIf/zjH/Hv//7veO1rX4sZM2Ygk8mgo6MD++67L8444wzcfvvtWLlyZaCCOFGxcOFCPP744zj//POxxx57IJfLoaurC4ceeiiuuOIK/OMf/4hdeS4Wi/jgBz+IYrEYWO7ilPQuuuiigEL1uc99LqATTjQcdNBBWLp0KU499VQsWLAgYlGoxcc+9jEsW7YMF198MZYsWYJp06Yhk8mgpaUFe++9N973vvfh2muvxZo1a7DnnnuOeXlvv/12/PSnP8WHPvQhHHTQQZg1axYymQxaW1ux//7749xzz8UTTzyBL3zhCyPK/4orrsAPf/hDHH300XX5P9M444wzguMPfOADgfuD0eDQQw/FE088gY9+9KOYP38+stkspk2bhiVLluB73/se/vrXv9al7HrzzTfjG9/4Bg4//HB0dHSMWjTjiCOOwMsvv4xrr70W73rXuzBnzhzkcjkUCgXMnz8fb33rW3HppZfihRdeYKncN910U+COorW1FYVCAbvvvjs++tGP4h//+AdOPfXUuspx1lln4ZVXXsEXv/hFHHrooejs7EQul8OCBQvwhje8AZdeemkgjDPeeP3rX4/HH38cn/zkJ7H77rsjn89j2rRpOOGEE3DffffhggsuiE1r2za+973v4f7778fpp5+OBQsWIJ/Po729HQcddBC++MUv4uWXX47s79vRmDt3Lh555BF85CMfwR577GHsDxUIBJMXllLi3EUgEAgEkxfXXHMNPvnJTwIAHn/88UChUrDr4kMf+hBuuukmLFy4kN2PKBAIBJMBYtETCAQCwaTGjTfeCKBqIZVJnkAgEAh2FchETyAQCASTFvfffz8eeeQRAI0LwAgEAoFAsDNDVDcFAoFAMKmwYsUKFItFPPvss/jMZz4DAJgzZ86Edi8iEAgEAsFYQyZ6AoFAIJhUOPbYY7FixYrgf8uy8N///d8iQCEQCASCXQpC3RQIBALBpER7ezuOOuoo/O53v8OJJ5443sURCAQCgWCHQlQ3BQKBQCAQCAQCgWCSQaibkwDDw8MolUrjXQyBQCAQCAQCwXaE9ouZhp3h27DeexGMHDLR28kxPDyMjo4ZKJX6xrsoAoFAIBAIBILtiFmzZmHZsmWJE6Th4WEsWtiK9RvdHViyxlHPvQhGB5no7eQolUoolfpwxNFfgJPLAwCUZYURnOqxsgFAoSU3jI7mAbQXBlFRDhzLxXAli+V9s+Ep249XhXLIsc6S5K3j0jSwomFeNkzj5sy/AOAxYYrcQt3QaZh7UDZhKOtyk2tY+jQlMivmvBcmslzzLwBYXjTMrlSLNjufxfqBMlTtedf8CwBWRacNC2FXdN7RMBrPKLenzPKTMsKLpjGqXUXTGnm7+jwJ1NcjeQfXU8z1jDDmHkiBlO0/OLsaOHf3TShXMtiwZorxLJVjk2Pa/nU+lnGOnle2BcsCpnfmsLG3BKXC+NU0iKQxYMU3XJrW80de2je8TPS80Q+YrNl+MpK+49e30QaT2qU7Osa/vm83S8KCOqHxoud1PVoAZrVmsa5YhpsNy6Oc6rHxfHT/58IQjg9WhrRbp9pwbYf0t0y1MjIZLwjLZsIKyjvV43y2HIQ1Zcr+30p4L/6DK3thgUqu44eFDcUl5wfL1QFyuBg2lLJ/7JVI4xquHttlMlaVqsd2KQyzwyIGx1Z4W2GfYNp8Wrs0xgkAs5uyWD9YDoMT2ptViYbZpFxgmh43RtGw8P3FFDWmv+jHoEi/1HXhMWHmGOMfxIwXQRs1zjPtVrc90gah26gTVopNj/22aZM26vh5Z0g8fZzxH7oFYLaVxyZrEMoCKn57HK6EN1sqV8MqZdJG/WNVDgtularHXBsEALtYPbaYOYjFDS1cGB3zmfZohHFtmXsGSWMHEDwHRZ6HZUfjKde/uPG9oMMQDQNg+XGN9l/xw0hfxeAwXv3OV1EqlRInR6VSCes3uljx2G5ob5uYchy9fR4WHro89V4Eo4NM9CYJ7OYm2PlqRzE+TINBqBo2iBb0q6lYM6TgZWw028PYq2Ut9mnejI3lTmxTzSiprJmW5GkOgIjGYyZ6FJbf4mw6qcszYXWOS9wkiyK8/2hY2kSP/Xig10ZCGE2brYZn8jk4FQcKcZO6MEw/QptkpMMs8kLRt0XDaD3o5BY3qavQNPGTOnMiR/LWLzs6qdNl5GbMNn0zK+NU5Dr62FhYMCdopWInpkzvw5ZNeSgrOrmjaTwSBmbyp88rpzrRyzTl4ZSc6kTP+BgjeTMNgPto5D76LH+iY5ERmB7b+uOxzolc6qJFCoJ2UrNAAZAPGfBtsP6LkEM9wSUTOX3/FgmzmUmf/si2AGQKOdiWA0UnepkRTPT0fZGPY/1RR8P0x7NFP6yzFXK+4oc5JKx67GTCr7WsX+EOmcjZ/szCdsO0dNKXK1croJIJB0ov41dKhjQuv/EYH9YZy/gLmBM9p1j9S59/MJZzk5V6x1P4z6kpBxtOuH7DLIhZlWiYHietmIle0gKdzdxLWl+l521mcqDbns0tynATPWOMIeeZSQaYSYZiJnq6fXOLEtXw6ETPtvWihRdJY/sVZSkg6xSQsVwoC7D9ClJkoqdK1WOrEhbc8hcZPDL5Q9H2z5H2nYm2R2NSkzCkWMyCAPctAqQ8a2NBsPrXI/UYPA9a38Zz1c+DXjs6+bOZiR6CtkwXDskz1JM6OvkLvg3IgiAaQ3ubjfY2Jz2iYNJCJnq7LPyJn1fACwPzsKBpE+bnNmEBgA1uB9aUp8IVUVbBBEZfTwHTZvaiuWUYA4PN410cgUAgEAgmFDwoeA1PD3cMvEZWIwUjhkz0JgkqeQsq70/M2FUrsiJUE1ZBHs9Z82ArDzPtbZif2YrOzCD+ac1Ar1X9gGZpPBxVkqP2EOhVO5MuwYQlzDGN1T2GkmjQjxLKmAqWuhmG2cwKdEhTNNNa+m/MeeMaNedrYcRLs/jo+qYr1QljvmH50/XJUSoBcNTOgFZK0uj0hr5vaPqrzc4vhx9m0CYtI6xUyaFYzKCtfQj9Qy1hPMYKmLqSH8SvxlVWdaU+Qt3kaMpJVjxSBpa6yVDAjDwbsEDUxovrJywS2mDdtLg0bI93uqr+LKaJGmEJlh+AVKPBAY7GYwzRUCSN5+dEKZeapukxFUUt9ppC59H2RtI4flybWu91ZzbMrow1PIVpkURtNKxpOh+uHmnV2eb52kcfnKfPgAljn5tROCYfbfGpt12mgbtXymzQx9y4m8IQMe+VqVzuvlPacviwo3nTNphmlNdt0yENQNM9PdK+A2YHoXOzlkjKtNDhKf2NtfxyzB1yrNsR+5rkLL+g/U2/qygDJJrGaP+ESh4ggaapiAURnPWWQltV6QXFOCdoEDLREwTwYGOtNQVbVSv2wEbsr9ZglerCKmsq+BmFQDC+GBwooKV1aLyLIRAIBALBhIOrPIxyS/V2g6smpqVxskG4eYIIhq0cnsVcrLCmYj66MQO9410kgYBFJuPCsgCLW+YVCAQCgUAg2IUhFr1JAjdvAfmoyARH3UxSDgs3jFtYYU9BrljGosomDGcdbMm0xihakrBAHSSlwJRWwom6BNSOMExTHyyONkOO2fMcrYgrYwrVxsg7RSiAS8uVkaXicPkxbJfgkKo9EpqHjsvRNa24e9VhXpSnYtaJT930uLAo/4pX9CMbz+km9eAZ0Zu1ImH5QhnZrIs5c7ZgzbrpZlqMjr6lLAvK4vtTXN5JokWsqmZMWRPbaJ1iLDRvQ92uTjoYL1AUTcy1HbY8DLWtVrQjEWk0vnrAUdxIQRRTHkXbpU9Zo1Qql5zXlE1FOhxH3dS0OC4sw6QFAMcPd8h5TeOk4jheIDxDKZ6679CxgRlI6h23U9pJRPHQrrbHIEqUXcrSOVPHxIBenlLuMSKkBGMY7bcMnZOjlPL0yjSaZpSGmdYBAnox1+EYUBa9BQXL32ug09NnpNubTdqgQ4SsNALxK0PUhB7r89Fy2wxNk3vvxiFgEtvRMDDjEoggDFvfBm0y+mAV17iYbxWWzkrHaGiaJsnHp3l69BqZtMYuEJiQiZ4gEf/MTUdGeXjN0Dq8VJiB9fmO8S6SQBBg46ZOzJjejZbmIVRfvkIxFggEAoEA0GIsE3NyOFHLNdkgE71JAi9rAb7qtrGyVuN7ygizmbCIlc/GM02zsPfARiwe3ohK3sL65vbqeUboJLAI0p3erOkjPAwFWqIrXZyghGFB4qx3XBhnOWjEolebX205mPQ0H8svF2cF5PKxGKET1pKj64kToEGM8IqXMLimWP4M614gvELjMlZAP54hJa6YFU+6uulFV4k5YZX+wWZ4mywsmLsJ2byLUjkL1pednTIBTBNo4Y6teOudYvoE64+MEXcxjkcjxsJYzQHSp7g+kWZBSQpLLlbdYEVU0sCNJ8Z5rlKY9CnWpKB5e0y7jClvIIRBaiVRoIVc2HSv4o8JJMxiLHqhXy9SBi0Bz1nxQKonrZtw1ru0uiNCMIG1xQvDImkasOgF5eH6zvZEnWIscfeSVGdsE02xBlLrncVUGleNqc+aeQEGVmfipkELs1jGOOi3N84nIOi4Tt8TUQETcBY9rr6Ngvs5p4xvQZunSSvRtEYfVVyD8++VhAXtmwqsMAwoi9aJDjOevxWNJ2IsggYhEz1BOiwLL7bNQM5zsVv/Vqxvakt/SwgEOwjDw3koBTQ3DVcnegKBQCAQCOBNWOcKmMAlm1wQMRZBfbAsrGruRItbxr49G5DxUojyAsEOgufZGBwqoK15cLyLIhAIBAKBQDBhIBa9SQI3Z0EVfDoQpYgl0TQ5Oifj306f39zUjOec6direwuat5Xw97nzAMsyqQQcZQMcFyUKgykSbGaO0iE4PzvVQkbDEtNwNEv6TwwNM+l8EnXTdqs/FVduJu9U4RVNG0mja7JlTRMCiKdh0jSWRy7I+t7zj+mz9CMqstZkkdoPfOYxlCyDzunXxVAph7aWwSi9cSwMz4ZgBC2jDgvPh9RlmkaXNRoW63vMYsKY8gRpU8RYjOfL0H2T2mBqGFeOEfStehFQuGv+ryeN6cuRlocpeJCGtFvGJyQ9z/kr85gH5jF0Tg6UuhmKsZD+GDkA8aPHZBhHcUxpU4lIaCfBcc0zYoVXuDAGaWIcOxyjoZxyYz6i8Vh/fCylkORTZx8LKMGo0jVtS1X9iPr52ykdTAuz2JTOyYixgPGjF9A1wW9r0MIsaX700sCNowlDlXE9mjTor+yeCkKv1ImM7yn/no06Yd7BrN++6HtHIKgXMtETNITV7R0oOg4O3rgezZUyBrO58S6SQIByxUHWqQAiyCIQCAQCAQDAVQpu3ZuddywmarkmG2SitysjRfyBFyux0J1vBgB0DA1jMJOrEQJhdkJzl+Y2l9OVvIr5FwhX2bgwGp5mqajHAlAtV8r5pPkEs+lb1UqM12RjVll0lZBR9mbLSK0EobBDsiCO/od9LnFWEG1NtIlVjpGpDyx5hpXL/8ewkCULrwRWPprGPyx7Gdg2YGcUKlR4Jaj7aBqat3Y/4mWrdkWVqVqxFcjqNMAKs3iGRdsP4wRaOOtkjNBLktx9miiLrno7pv3ybhNGHsbmndbcuMA0y0iclaTWYsQIr3DP3ACrvOT/acgHRDxKbthQOPcKGjQs54QDnOMPbA4Z4FhrilbFoA3AYvoTY03mLMMUnOGTHb8Yix4rDsRZnRlZfGNMj7mH4HSSNdyImBKWdJ7WU5LLhbS2zLlA4Cx1hA2hXwSKiGpZdHzTQlZkEFKsJdrPTgtjqarlyFNVORfWOu0fG1ZubbwzLHrVa7uGiAi5OOOGJonlwD3LWJEsri0zVsAkASJlmvHCfKxoWNgmop1HMWlN0TFy7LtNSLYVwnBTIRDUA5noCRpGxXEw7GTQWiqNd1EEAgBA0RdhacoX0VeWYU0gEAgEAnGvIBAxFsGIsLXQhFkDA6wDZYFgR6NUzqJUcdDWNDTeRREIBAKBQCCYEJCl710EHG1sZBlV/yxv78IR61Zh382b8dy0aQEvgRA6EtNzdDBjw3XFioTZZX2OxHOjx5wYS5rfOo/Z4JxG8WQ3RSfULaVuGrQSTSFhN5lHqT00mh3wyxCJV43rb6hnRCYsSknUfw3KZXARHsFzI/Qc1LYDhP6DKLVFU3c4uiY9Nig5DHVT03QcC71DLehs6ceagWlBCULqTwp1M+vTlDLVYC9TpXMqIJYCxvqRZChpQdviqJsx/ZKjMfGqAXzZItEY2hjXZHjhFUIRY/pTmj9Krr4t5npJYfS4VqyiLip2rYJL3HFK2Eh8j7lefeupmhaXtcNBLUMq2vPbMBVj0f7MKoYfPX+coBSvQCSLpyFzVOJEf4sEKnJgIvAjyrQto/0nCWbECAtxPtUSfQI28O4LLpPSvtly1UndZNjzfD50e4R+z5EbNMcRTa8k47Kmcxo+H6vHLqVuwoLyqZshTZNcW3cj0s4zjPqX67dLSnuHceynZ8TfjHeCZ56j4PyS0uNU33tM/+bcbbLPjVJpdWSujdJ4jG9gk6ZZ/U9TOOl54zXgxHQ0gSAGMtETjAj9uTyenzodr9myEWXbwstTpqZ/BQkE2xHbBlsxra0Xbfkh9BWbx7s4AoFAIBCMKzwouBOUIinUzR0DmehNJjTYZ+qWz47BmtYOOJ6Hfbo3o7lcwQtTp6OYrS6zpWZtrLYx1jstrMJY7zj5ZYC3DNpMGs5Hp95HbqwMMot2Rp1xm/A5AxsFt/E7weLDJ6YWu0gRahLrTfhWbZCx4qtdGxgWPc5iyaUh8XQ9s8IMjPAKa8Uj5znXBsYzyoT5DHl5DJZymNa6DT2VZjMNt/pLFia0e5HAoueE1teGZOgZgRb2+TL5se4XmLiJIhkkS9Y1B8BaDhoNi1vxTrLejeZ6sedrrwEk60AZz5KsnLNWoPiM0ta0XFr5vghLmmUv4wuvUDl77tghFhTbt9RZjEXPopcLLM0qGobQ2k7TBNYbOl5ydc+cSlGf562wjFWRKysr6sK10Zi+1Sg4vRS2/NTqVu+mGK7cNB/NkODYAHHvNG2JI9akWuGVaphl/IWq+iR1Ldu4Z5fcTCC8Qtoga8X2xVi0KEs1jMRzohZmMO5qRiLGksTi4Zg0rAhWHHTejAUVnoqGUZcLwXcJPzCH1jvSlxmBFmTixyWBgINM9ASjwsr2LhQzGey3eSOOWbUMG1pasbKjA9uam8a7aIJdDhY29Xdh4ZQNaM4MY7BSGO8CCQQCgUAwbhAxFoGIsQhGjQ0tbXhw3m54qWsa2kpFvH7tGuyxZUvK0rpAMPboLTaj6GYwpalvvIsiEAgEAoFAMK4Qi94kQd2+4RoBJ4QQszG9bDtY2d6Fle2dWNTTjb26t2DGwADWtbahN5dHXz6PsuOENE1Kq+BEVLTPPE5shYaNII3ti4eYVDn/H0oBSaDPURgsJc5nGkmrrGp8thrrXnaJcolMgZZo4TgxA8Vxkph4rG89gPcVxdG0gg31UZomS9ck5TWpPVHqphcJs9BdbMW0Qi9WD0yHykTz4cRRvNp4I9xvygm0IIl+FHcZhgIZpEnp69wGfvYSTP+uWxwlphABndcMRG2gFnixGEpxHHWztjyWPl/P2BfQ03jqInuzdYJS1zyfnlkm9+XaUcqlhsNQM6m4BT3WtDuDuumnoRTPYNwy/JbpMS/ax6rn/bDYgcTPmxFoCc7F0Wf9Z1Q3vZYm1cWlVGialhHeSBM6qht6KGBFsqJQHFUwrj655qYFUyh1jxm/A8ohvV4KxVn71KN0fde/nkPEWDxVFfBSACu8ktW+HClVWLdBcsGK49OVqW89YwzWHFCSj6tp9pTuGKXeB+fi/NFpeqVLI+uyRtNzdRfnE9JiwoK4TBs0XiP6+dK+Shom/zr1x0lC1/REjEXQIMSiJxhbWBaWdU7BI7PnYiiTxV5bt+B169fijSuW4aD162B7db41BYIRorvYhoztoT03MN5FEQgEAoFg3OAqNaF/jeK4446DZVkN/e69997Y/O655x6cdNJJmDdvHvL5PObNm4eTTjoJ99xzzyhqfWJBLHqC7YLupiZ0NzXBUgpNlTKmDA1hny2bsee2LXhpyvTxLp5gEqPo5tBfLmBm0zZsG2jByJbzBQKBQCAQ7MywbRt77bVXJFwphY9//OO47rrrjPA1a9bgjjvuwB133IFzzjkH1157reHKZGeETPR2FYyRAlkqAuWwkHs2bOWxtjmPjuEipg0O4p/tqFHdNP8CMTTNhDDjPEvnJBQpouQZFDvwM0XD6HGU2skhuAqlmqQpNXLx6n5GjLoXQ7U0fO/4YXQjdOCPj5abo7FQ6OfG0VgYOiensMnRNY24DqX7cPkgmo8DrC92Yc/WdWjNDaHXbeGV3AwlTk2rQfg3lfsYf4pT0GT9lqVRN41Mo+dYpiETj1ObY9MYlFufNpTSV03lOD8NQ0lKU/5kXd3VS/fjaMiNgKPK1tkHqZqmpkPbhF7p+AX3iAqi9n/nOOEA5viVSn3nUUqmpnFSipxOYxnUTf2QSLl0/48d3/wkTLs1qpOjSoKJV/NcI+200UfElQuEKs/5MOPopY1cs8Eycm01Tu05UHll65GUUlMkjeE7uv1BGcdaBZLS7JV5DuGYp9ulpRBsLVAI20KO7IXQ7dFiKMcUWkG2bIeDLKcMS6mbwTu4ZiwHQBQrQb4xwiBuOwenFmu0nSSaJkdXB3k0xjipD6IDM6VKW8xIaNB0Gdp7mILEa1B10wMrND4hMJJy3XDDDRgYSGbrPPfcczjllFMAAG9605swd+7cSJwLL7wwmOQdfPDB+I//+A/sscceeOWVV/CNb3wDTzzxBK677jpMnz4dl1xyyQhKOnEgEz3BDsNgJotZbv94F0OwC6Cv0oT+SgGzc93oHWoZ7+IIBAKBQCAYJRYtWpQa5yc/+UlwfOaZZ0bO//Of/8Q3vvENAMBhhx2G+++/H01NVaX4173udTj++ONx7LHH4tFHH8XXv/51nH322dhjjz3G6A52PGSiN0mgHNNFUjRCeBisnJfJaWbzuGHxYPzQJIJZYM9VPJRtG1YlffN0EMb40ePEVgDie48RXqFWPM6nzkh8eHH+wVTtudrr1In6hVmC0gRHpqVOh1kkzD9fia74GvenhU7i6sRhwpKsfLQebCbMWBH106ZaPqMCLdWVagtbyu1YUNgIy/bg2dS6QePVXIf+ZcrA+lnkrECMNa0Rf03cfbOGOGaF2U5qqzQ8pS0nWUbMQkTLmLZS3bBvvZjrBeaHtDRjDBVTt6HmCxFj8ftEhgpPMCIqrGXEcBpa/UPFWLQoRoaIXlQY33pBfzN8mZFy11i0AYDRjiEJokFxvhW5ZzQqUgnTt1grNTO+pzWNEZUrKVGcVb3O8rBZcr5KDcthdODSoiaGIIp/XlukLQAebLiq+obQljxD6Idrt4iG6edhtGlm/Fecbz1qdWT87QXMnZEY7rn3EmedI0j7Tgjy5FgOBtvBqkkAw+qqM7WYNMat7oDxbWeG53n46U9/CgBobW3FSSedFInzne98B5VK9aPw6quvDiZ5Gs3Nzbj66qtx5JFHolKp4Morr8TVV1+9/Qu/nSBiLIIdA6UwpTiI3oz4NhPsGPS7BVgW0O4MjndRBAKBQCDY4XChJvRvrPHnP/8Za9asAQD867/+K5qbm43zSincddddAIB99tkHRxxxBJvPEUccgcWLFwMA7rzzTkOxdmeDTPQEOwQLBrahvVzEuqa28S6KYBdB0cth0M1hakZ86gkEAoFAMNnxP//zP8ExR9tctmxZMBE89thjE/PS51evXo3ly5ePXSF3MIS6OUngOYDlU3HYTeEplAW75AdROg9znEpDjBF6KJSrZvJeuxl2uX7qJqVD2JxPPI7aWVHRNMbGbJ8iYfIqzL+RY821sSJBFCwlp86FINbvEycykJa3UcZogQJaGd0IH3DOaDyVWIbwXknBdZuhzxLRjen62KBFGtQeTXMhYXUqX9F81rud2D23EQWviCGVr54PfPiRNDVUUmVX+5SqjUf7RAJNM43OyZW1bjonR8NOEzdifG5xYgWszzyjECqalp7WYh1G1vH9rW6/ffSaNWywWrGPJEr19obuRwZNlalIO6FEJp2TCLP4eeeIgMuwFmghNE2boW5qoQvK7zd96unz5BkxFDmLoWYHeaRQN0dDZzdojynnWf9/TNthr1NvgZh+mya2lCbqwebNhIVCLuT5McIkhg9SPd6SNJ5bLbCnBVYAuK6NCmwoKxT6cch9BX4bKTW7Tg6lIcaiqcZEyAi+yIgiwmlamIXdRkK/Dbi2x76rSBhH0+R863GUTPos2T6R8IAtc3QMj6IPPnx30vI01nlcVf1NRIx1ufr7+3HHHXcAABYsWIDjjjsuEuf5558PjvfZZ5/E/Oj5559/vq79gRMRYtET7BCsbuoEAEwpiW8zwY7DFrcNw14Wc52t410UgUAgEAgE2wm//OUvA0XOM844g3WLsGrVquB43rx5ifnNnz+fTbezQSx6kwTKNle9NJIkhNnVdEagpfY4CKtzYclSQEVloAA0lSuw7QYsECkWPdO6p4x41fP1mtNq/sYd121VI8f1WmqYZRfjudW7LJNivWXDGCn10PLH5x3WCVmp1YfUCqZXIDkxEkaABQjrJNbilxBmwsLaShd2z21EkyphEHnW3QFn0VPaokfu33Pi0xjpYwRcEtPS8wn3xVniGhE14SxeSeIoadYyrmyGjHmQhLaTqAW53j7IuWHg8yECDlpeni82jySrVYxln7Nu6CbOn6PWu+pxhqigcGIt1JqS9Qc7KtCiXTvYRKBFixEZbZE5Zl0uMNL2bH3HWK+g/FMpbSvRQg4+zOLGTsZ6E8Tn+k4jqNOKz4ppNdLWE/Jmi8W9q71oIbUoCxAKs1AxFte24MKGUoDr34TrhQXT7Yxrt1yY4WYkEy0PzTsQZqFME/8eFHEpoO/BsPLR7wCmbQXtLRrEW/7SxqAU4ZXwPcB0Cq79AsG3jmHZs6LxvJG02wmO3t5e4/98Po98Pt9wPmm0TQDo6wu3crS2tibm19ISKnb39++8ivFi0RPsEDR5ZVgA5pa2IeeVU+MLBGOFLW4bhlUWc+0t410UgUAgEAh2GLwJ/gOqlrOOjo7gd/nllzd8n6tXr8a9994LoCqksvfee7PxhoeHg+NcLpeYJ51sDg0NNVymiQKx6Al2CAadHJ5pno29hjbi8P4V6HUK2Jhpw4Zsx3gXTTDpYWGNNwV7OBvQ7BUxgMZXCgUCgUAgEIw9Vq1ahfb29uD/kVjzbr75Znhedep41llnxcYrFELl91KplJhnsVgMjmtdMOxMkIneJAEVY2GRRi/kaFqUxqmpEQ1uoqd5d6MVj+WaMMftwTS3H4uLG2G7wEbbn+xxNE3DP5A+RyhgnB+9FG5DuDHb4FzVeTME9dI4uWgpdL+AskHT6LpIoQWZwjs6c0J94ehnmi6TIuSifetVy+NTdqgoQNCQCG3O59IYfus4QRQnSlmp27deHPy4m1Ub5qqtmGNvxUv2bP965No1FDBlA16GoW7SEdOuSRtXxgbDIuH6NEe5TKA9cwIsND0nOGBxNNx6KXf0PKWSMQItYT7ken47Si23LqsOU3y8uqnXaUihhVLZ7bDZctS2MBvOz1hAd2N8lFXDvch5fZ0sociVfcpmhYpfBH2Q3Iwh1sL0R0ZkJLiHFDpvhPqmwLZbAykiJByS/I3ykhdMGdLaxAj6MudbzahHHc4U0qi7pDqJE0HR/Yi8G4OxlwrwaAokHZc9C54vAVLxxVoc8rA0zdNxog+QtkudJpsJC0F9S+ovT7olxNPUTirG4h/Td4PSaWl7Y2ic9b4mjLGMji1BBHLMfQclbL2gQV7A4Sb3x7wn2X5EabgNirF4sOCOznPldoP279ve3m5M9EYC7SQ9n8/jlFNOiY3X1hYqv6fRMfV+PyCd5jmRIdRNwQ6FazlYlZmC/8vOxzq7A3tWNmK625ueUCAYFSys87owxepHAcmreAKBQCAQCHYOPProo3juuecAAO9+97vR1dUVG5cKsKxevToxXyrAQoVZdjaIRW+SQDlgxVg4cDLmrAowZ90bhUXPuLZnYbmaDgce9nI3QLkWulW40mIFFj264m+eo2G1cbl7SDyXtjk+OE9W4/zKMFYE4y8HZfk/gN24z8lzgwkyhHGYpRrjdJIFgrP81etyAQjk8g2RDY9ZtdTxOIuVIQFOztsJaTgBlxjLGMUm1Ya52II56MarmMkKTyinmlxliEWPjJL1WvQoOLGdVOEJ7vkzcvehJY5eMD7MOE6xyrBW/sAKEm0HFIZRghNo4UQNUvogJ5QQuFZIiccWLO28lqSneY/IJBgFJ6yS0RY7GkaVpTwzLT02LIh+p7cZi55lWPRogbTFzyhkJCzJOsvK0CN8Puw4mTLmseeYsZOF8dwiQeG5OIGWpD7KhLFujWLKo5iwJEu7N5IvNYMNY5l/gUDoxNMWQACeCi162npXJgpUegyi4j8Zn0pDLX8Z3+LnkRpQVDFFF4sItGh3D1R4RVv3qFsIzT6hwjKsK6gY9zJBmI7PhdG24zIRuHZLn68OY9xeRDuHTs40JOZ9Wqc3i10KVIQlibYJAPvtt19w/MILLyTGpef33XffEZZu/CEWPcH4wbLwCmZiC1qxF9ZjNrYinUcjEIwMCjY2eJ2Yjj5kUUlPIBAIBALBTgxPTezfaFEul3HrrbcCAKZPn453vOMdifEXLVqEOXPmAADuu+++xLj3338/AGDu3LnYbbfdRl/YcYJM9ATjC8vCPzEL69CFBdiCA7ESr8Vy7OesEoqdYMyxQXXAg4XZ2DbeRREIBAKBQDAK3H333di0aRMA4AMf+AAymWTzt2VZOOGEEwBULXYPP/wwG+/hhx8OLHonnHAC65NvZ4FQNycJLBWzwT0lTXDM+K1jRRE4nkMjYOkpFlZhGnrQjJlWD0rIoNMawP7OSrzizkKP12KUJ46uWTelQcdrpPxM3qGoy3YAI4RQc6p6nhNooRQS1j9enWHR/fsmvTCgcREKDVe52ocZpWk6HC2M5OOHe4xACyeiQtN6ht87y0hTgYMNVjtmqh6stLrg+gpG4fV8ClOMHz0wNDYK1u/VSPqJblyGL6yav8aFmWOGzkiPk4ROjHh02ZVJq5gCGXTOpPKmlKsuerWqDdOU6mi5DUEII83Ie7EpshKlUupjmxmgON96owXnwyygbMaJsfg3YfR13b+5tpwWlgKWhstQfNMG1yTKcerYyeUXZRc2JKKUmDm9VyaMG4OD+BwtMAZs3TLCK3psobRIz7PgqSp52PMz8iht0q9TTesEgIrlUy5TCkbP63bvZIivR9eLXE9vJaAiQqpmTK/eAznmqJu6TtKeJeM7kvUJSvNm3tX1iu1w7c3scCpaRreBjgbAncBiLGNRrnp859Xi05/+NH74wx+iUqngU5/6FO6//35DVXNoaAif+tSnAACZTAaf/vSnR13O8YRY9AQTBn2qGf/0ZmOlNx3PluejVzVjz9w6dNgD6YkFgjqx1u6CDQ+zRARIIBAIBIKdEt3d3fjtb38LANh///1xyCGH1JVu7733xuc+9zkAVSGXJUuW4LbbbsOjjz6K2267DUuWLMGjjz4KAPj85z+Pvfbaa/vcwA6CWPQmCzxi8ap3kThNkj1uk/pooK1ETN50BV55Nl4pzcIemfXYI7ceLwzPw7CX99OS1fJ6ZbrrBWdBoHk2ajWEb21F+GskmzThgeB0jBBCsCpvrBzrJUha3348Kr+tV1PjrCD6ORgrnlGBlmA5kljntNWNs+LRcHOTPWMFZFZT6QZ/LaRCxQxcJ4NNqh1z3G6szXRAWXbUKmGF98WJUdBr1yu/bmCM+hPrciEtbzaNiqTl5OeDvhd3PUbNIKgLuqKfYFWMFWOpGd8sfV7Vk0/UyjcKI94Og0canFfnwKZFMRxiBdHCLB4Jo8eh5YT0HeZyiSJCcYIp/sCX9j4JxipObCpuXGbeW0nuDoywtOocYyNImlWOZYjoiPSdp0+RAlKRncRmzcj0m6ImFjxVFWPx/GvT8d/1H5JLyuPatl9EEs+zjb+10G4XqEXL1QIudpgmcPtDRcJ0uWkYI8xiuG7QUVPGaotz60TAiREFWadZZ/XrkloLOd80BpiG0iBzazLjtttuC3zd1WvN07j00kuxceNG/PjHP8YTTzyBU089NRLnIx/5CC655JIxKet4Qix6ggkLBQvLijMx7OWwZ34dMpYIaAjGBmucLuTgYobbN95FEQgEAoFgu0BTNyfqbzTQvvMcx8Hpp5/eUFrbtvGjH/0Iv/vd73DCCSdgzpw5yOVymDNnDk444QT8/ve/x/XXXw/b3vmnSWLRE0xoeLDxz+Js7FtYhUWFDXh5aM54F0kwCTBk5bDVbsFctxsbnHaM+fK9QCAQCASC7Ya//e1vo87jne98J975zneOQWkmLmSiN0lAxVjqpVxyNM24tJyPurTyBNnUSWPjaGOWB1SQwcrhGdizeR067QH0ui2ReEZ2KT5uWASOj0b3wV+3QEsKS4PzzZRIC2Q2lNPrKIZCYvjW0360OFGDGLpTQPOibcOORgx0RSgNk/XRxdBvOLqnkY8uY5SuCYSUTS7NquwUHFRchalePzZl22ryqx4rgKepkXsdkVhDkGHCuRik+szj8uaolql0R5/OSf1W6uvRZ240Cv8PbTNcGbxoxJA+SsJS/OxxIlSBqEfa/VEwPiP1P9tDMGWskXXCh6TpYJwYi2X41iMZ6GMn2pdp37KY9s2FGa7CXABujPAEA07UwrgeJxKWQiVN9P/XAMZMbCnpvcxQANNpgSk8VYaarWJ86ynPp276loyK4aOxWvkuaRMcPdNluKmargmE9GI6dFR8P3palKV67EcgvvWU9q1H3g2cMIvixg7mubF+eeOowsxzY9jq7HcASwc1wvx3ovEh5P8x2n9jDc5TlkGrnUiYqOWabNj5bZKCXQI9bjN6y02Y37QJY+W0WLBro88uYKvdjIWVLTvHhi2BQCAQCASCBiATPcFOAgubSu3I2S6ytuzVE4wNVmSnolmVMaMie/UEAoFAIBBMLgh1c5LAoqqbnHImQxswqZIqkpZTvEstB6emxlnnU2iK4b2EgUWVAwAUUEbZzUbKGKSlFCFGlXEk4BXRwASa8YEaqo0+5uilDDhGjuHLjU1E0mj6FUf3o76HmHgWp25m0OqiSn1hnahIGKewGUu/8eMadE//2OPSUDG5TDRNnFpmv1PAZqcFC0tbsTHbFnJkLJ++WZOW86PHU2lJnSU8X5OGOUY0Fv18mTDjPEfdZuhHbL9kKJ4A6cuGb8VovFAFkyljHOWUCQsUN7lyU0oaZ7FNpXPrtKTNTzA2Qc6nbFIKlGNHVTd1mGuTDsX41DMo0EEfJRfUY2sKfbyW7msp8O+gaJABzpenMW7VSaUM2+X2w4i6bwrlOqSuptAwG3kH1V4njrrph9O2VXGrjYG2LTdhW0eOUIodQiUO2qhL8gmom0RptuL76KOUYq0QS8vNKHAa7ZahbgZtNGUc5Not+z3BjbHMt0hsW/Xj2iTQ0370qMJqg0PQZPejJ0iHWPQEOw2KXhZDbhbzWjajySmOd3EEkwQrslPRpMqYWRa/egKBQCAQCCYPxKI3SRArxsJa+aIrgqMRY2lkhSlcgbWYMKY8NRaPV/tnY/eW9VjcuRq9pSb0FpsxUCpg2M2TxFFrAme9GgmMVdLIATnm6sRC8pIyt9IXNYKYdWJHovFlpM+fs3Jqn0nkOQfnmTJUr60zImmCMlBrSvR6rJ88YvHjhFe0JU8xVj4jb06shRS71so7YOexMduKhaWtWNfSVvWrZ4MVW2F96nHWuxSBlsBCHGMZa1j/g7Foxfm6C/tW9Hq8+EnUNMBaVUCbLck7qIBoGXhLHF/uWkukpcMU6rZuxFUr9zy4rqqte2Nl2VMjMAPliCMubW3JEGuJUtXzZWK9swMxFlK51F+ZPuQs1mybj5bLsF7UPFfKONFhQKz7T1JGpgwp1kT2fTKSx8VYgdhoKYyVuq5BjlmfgEw/idtWnMw+YdgXJCa16GnLGPVr5/mZu8Sa5vnjtk0qWVvyqBUvY0UtejRNybcWlithu3Uzvm894idPC7PQdxX1ncr6VvSzpLs+WAYU9wzoezLBume0MTcalmp95vqUFY6oYRkaGzNc2HAnqE2nTn0mwSgxMZ++QBCDkpfFi9vmYUXfDNiWwpzWLVg8dQ0Wtm9AOhdLIOCxPD8VeVXBnGGx6gkEAoFAIJgcEIueYCeEhe5iG7qLbbA9D52Ffixo34S+Uh+2DrePd+EEOyGGnBw2ZNuwcGgr1hekDQkEAoFAINj5IRO9yQIqSMDRoShNixNWSaN7pWwarxf1UrvYtBxtwrLRXWxH+/AgZrduRU+xBW5Ksw42uDuJ0VKh61RNhA3FlNrEiR7Q89yz1nXCxLNiNr0HNBbDF5rOmj5ffRAVUYmjRdbrMzBwf0bSUgEXToyHo595GWBZyxTM2NaHOaUeuO2FwI8eRwWtHqtIPmy5NbWHo7bF9TFGrISlOyb4fWKFTmKuzdE0LYZ+FBwT2pRFRXZ0mJG1bhRR2lhDYiw1aSh1M83XFYsximcyWy3jLz1O8xnl+bXmkdqjaTT1zSPUzYzfIDOk0+s0nPiF7dDnRmna/rEh0GJFwrQoiGLobLF3l/DuYCnAKTTk0WgWsf4d0+hzTNtKc1sX0xEi10nLO0k4y8BoyCyRCtU0fr89kvFf+8zToixAKMySz1RIWLXAHF0TCKnGlLpZzlTzrGQJVdS/dplQN4Py0O0k9NCriQfA4fwxIoo62bo8GNo3fRcHrz/m3QAg6Eesz0jqW7JBvqOawH70RkJdFzQOoW4KJgU82MjYHpqyItIiGBmGnRzWNbVjwUA37IQ9qQKBQCAQCAQ7A8Sit6sgzToXnOTTsEIgSUtiMXkmLeCkrpJqywizCpx3SvCUVd0EXlYxhfPTcMsb1tisLCVuhN8OYOuTrv5x5dEr8Jw1jVoiAvcJJD9mZdkI0/EMeX0VLWuwurl9V/Q87YWDsxYyAi6vtk/BrKE+zCz1YkWuybfoxakexOdtWkFqTwJW2f9LN9Zz1itGHIV1i8AJmHBuVmjeCdaytDIYDYqx7rHDBGupIWkDK2Y0LO7als4rxQo05iAVZTFjJl2p1qvp1Fimwyqk8VR8awldffcQvX9qJdHHnECLYUFxohYUatGzdBtnLNaGtD8jjsK5cKkdJ5TNW684EZ00twBxRrLa84oL5MY8zupCM+DKzY2DTFnYMsRB58lZdGi59Zhhb4cGrsJyaKaKIta0wO0NGbcqvjsEh9BGdNuzGlDByfoCLtlM2Ja1MIubJWJDfj+hYiz0GaqMLjcJ48SGrGgYy2xh2gw75o8ETPXwYmrk3eE2dkFxryAQi55gUmDt4FQMVXKY37YJUwsiqCEYGYpOFmtaOjBrsA9ZVzTBBAKBQCAQ7LwQi55gUmCg0oSXe+Zh785VaMsNorvYCttSaM8NoiU7jIxdgWUBebuMopfF2qEpGPTyqNqePEzL9qGn0oIisuN9K4JxxvLWLuwztAW79Xbjpa5p410cgUAgEAgEghFBJnqTHWnCDElJGSplKuq1xHN+9Jhrc1TRWBEVBWwptmN+y2YckF9eDVLAYCWPspeBUhaGKjm05QaxuH0Nhtwsim4OTU4ReaeCmV43Npc70J4ZQEU5+GdxTmIZg2IxVItYf3uj2u3tZ5NGG2E2plNwVKskSpIywgiFJIHaaT42/TBJPoxoCXtcJ+03DR4d6RL8g3kZoJjNYL3Tjnnr1mBZVweKVsowadCL42mqilLldD0So2G99Mu66ZxxdO2ENCZNTZl/a/NpFCraKdKomXWJyKgY+jBHARwJ6DiYVgH6WdMiMnROLiygc3qUzhkOdhk7amHWYZTimfEYIQz/2CZ0TkrdDCrLCNMUQXIvSb714hjOlfhzRrxoc+MpnjSNDksbG5itAMErKEbIisuyYWp+nNALM/7VXsM4zzyq2DbNvjzjr2O8I4gfvcDfKvWZ59M0XdJO9H5mlzQKLdqSJW2W0oZtpmy2f5PaBx8AVLIVIz8AUD7TwthGTa4dbDmgPlgzUfp0MPam+C1ME+up61w94GiczFjOvdOT4CrbeDYTCe72pNcLAshETzBpkLPLKDglvNo7C47lwYOF/lITXOWYE4ZhhbbMELpy/cjYLgbdPFYOz8C8wmbMyW9FRdmoNDqaCiYV1rW0YZFlYffurXh++ozxLo5AIBAIBAJBw5CJ3mSEsfoXXYIcjVhB2gpkapok8Q1uI3yK5Y9iTmELunIDmF7oxct9c9DvNrECLoCFvkoz+tzmMG/bwksDc5HLlNHuDGJmbptfiAaX6barAETNX8TUCSdgE2dh1EmYTejsxnRaj75VyiJLq0kCLWO1qNiIZY+zDAZGEsYlgxZeqWQsLO/swp5bt2BVRwf6c/lIPvW6e9CVb7ip0PVErXx0kz1nlfPMc0ZYisXOTMOYJfxxgkp3s/3aTxtnYQnOp7hc4BJzgjDseZqftujVY/lLKvcYgzW0x4ms6DDGDUPccRjmu1egAi2+1S5LLCNaAt8h7c1xwjSe7QvBUOEhR1tGou2WEzVh3fZQ0Gepg1KeG2e9Ml4TrPJKNF5tfkaxmPEyrTxp7W1EY10SO4Pt31QkR9We5pGQD+1P9Lxhifb7t0fGKteniFRsIsbih5VtapFObiDaWk6tfdq6VyECLdrCaPQnOnbqdpsh5XbNv0A45tNiBcbQMWKSGOAeTAojJ3gH03gNbh33YMGboHIc3nYdhQUaE/PpCwSjRMEpNZzGhYNBr4B+r4CM5WFuditsiGVvV8WKjk4MZrPYd/OmUXL/BAKBQCAQCHY8xKInmDRYMTQDW8uDGKw0oTIKb+j9bjOGvCxm57qRtSpYXpo5hqUU7CxQloUXpk7HYevXYvZAP9a1to13kQQCgUAgqBviXkEgE71JhCRaCSu80FDm3C5lP2gEfZX1qYZoWCN5u8rBNq+tWtQabpBBSWQ25puXVshbZZQ8B+vKXbHl1v6l0u7FUiEtht3MnwTOn08KrYR7/sZjS/CjZ4qxxFMzaT6oU6DFpD1GBVrq3vSehrS0/nkvG3NfOo4FbGlpxvqWFizeshm9+RwGNIUTKW2TPgQtYEGeeiCIQymOpAxaw2BEwiucb70R+NFLFHKiAi2Gz0Qfab71OHppAlUu6XycHz3unpXRgUne9fbIgNpF7m8ECjWKoWkGYaQ+jWMV9bPHQfvUKxHanPZrRumaBm3YVpGwULQo2k9MUSamECnPKNF/GKVActRFhuLP0jm58qQ9KqZ5pFJFk7jJMWM1N5YnjVupY3pK3QYX9JiKpM9cWQGVM6Ax0neQYmiTfnztTw8AbL/tOURto0waSuD3sc5xnvrWc11NMyYCLBzlmLbbgJpP+5v/l77zdLG2B4Ej7V3N0fAZqvSIvt8EuzSEuikQ1EDBwoZyF7KWi65MP7bvbh7BRMcL06aj5Dg4YvVqtJQapwQLBAKBQCAQjAfEoicQMFhbmgJAYW52KzqcQbxanomSEh97uyKKmQxWdHTgNZs2wU0SExIIBAKBYAJhYrtXkEX0HQGZ6E0SWB5CWhGlTTHUrpEIS6TRHZMT15colc5ZJwyFtkAGkUaIRozel4U1lWno8VqwT2EN5qsteKU8a+SFihSyzmhMnaTVE3ueUj8CKiEBo7DJhXF+9gy9Go7uqWkznDplHLVJ+7UjVBvW/51j/q3+Q8pbL2UrBRmvSqKb29eLV6dMqdJ2NUWIlltTiGg9agqRN4JGzVAy0xQ2uXiWYsYEljZJ4iVQIE31yiiN02xbmhYYR5uM5snFq722oRJYL3Utbuyrt32Moh1xCpqcEmes6qY+X+fgSJU4tT8zh0gMUnVDK6ndWil9kBkHalULFaXTAw3XY5xPxIBJmZJ3WtuKZBh3nqMzc2Vg8mDpl2njNwfdfzkaPQgNn7uewSVlxqXavgTwfYtTgCW+7jzfl6NL8q6QRlPyonvoOTpz0OZJPprG6RKqKH1PaJ95qJB3kB+m6HtJU1TJV7CmcSpmzAMIS5dhwHLvXaOWuGfOtGW2Dca8gwWCeiATPYEgAf1eE3rdJiFv7uJY0dEJx1PYo3srpg4N4ZG5c8e7SAKBQCAQJKLqXmFiMlEmarkmG2SiN0lgeUTfghNrGKUYS5CGLMTVbd0zlmCTdpyTJHrluIFxgLXecBv8mQumWixrV6S3B3bQbJITAgisfB4XRgVDopahNIGWMG30mIbRVVnOUsf6v7Oj8VixhrRV6VpLD13ZtgBYFl6dMgXdrXkcvmodZg71Y22htXqeCgHYWgiAFMdllnyTLM215Ui4l6Q2z1rs6DG13jEiBIFV1mAIKPMcYDxrbd1jBVoI+HL7aRXtl6ZQRG0aC0Tso/aCdVoIq5dmLH5jZA0OrXbEmuAfUotHmh+9Sp1b6rXQhUeeSyDGQm46Q4RZXP/YIxa/oD+S9q3FLGh/CwQsOCsfUO2vI2WO1WuJ46x8zPk4H41BEB0T9d8UsRlOEIYDa8lhLEMW0+T5DPnMdZ9RzPhmFIGOb0lIYZAE1jZyTguz2FQQiBFmoUJGZX+Ap6Iu+piKsej7KlfCvL0MEWbRY1OGtFv/2h7xrQfm24h771hlcswJi+kg5vuFgvPRZ5xn8mEbs6w6CxrExCTuCgQTBDY8NNtFDMv+PAGArS3N2NpUwOzevvEuikAgEAgEAkEixKInECQgZ1WQsTy0WcOYavdis2rD9jftCSYyNrU2Y8/N3WguljGYlwUAgUAgEExMeLDhTlCbjifmyR0CmehNElSpmyo4puHVv1GaVkP5c/2REdkYCRKFOYyw5AuFG6WjFCkO3Ab/WtrMEHJYWZqGBbnNaHeGsK3UggpG7oydRaNjXQqVhj1PaVX6FL1Xm4nnJYSRcE6ghWVXsdRNQvEjo5HHUGhYURcuXgr1haMNGpTFmv+NcmUUlk9vx7yeXhy4biMe3mO2cV5TNwO6JrlOqo86BnXTNBu5P4bameTXjqWA0rT0QtpnHtcuGR9eqSIZaffv+T/u/lLqiR0bWD970YiUFmcy05Pj1obRPSqsGEWdg2vglwxAzq5Ezmd8SmbWCeMVCa0u8KNHaJqcQItiqMnBM6e3XuP/UwGp62Ocj7qgOuPobozwSGIShirZEPT7lMmT9WtnUKGTs2Z969WLtGuraDS+wuu7vvEe8B+2Ijfo+Txel9I17bBROP4Ab1A33WpYmQi15Pz2SmnGAXWT0DmpWItX8fsRpWm6TL/0ommD7QhONB4ABF2Le5YcNbeRd7VOw/jOpZ8+IsYiaBQy0RMIUrDB7UTBLaHNGhr7SZ5gp4Nr23hq7nQcuWwddtvci2Wz2se7SAKBQCAQRCDuFQQy0ZsksDwVI8aiLQwkLFjxq7+ThQtvzLLUSARaOOEVO3qeWvGC83HXCO6rvkKYbhgUExYeF1UGU+0KHMsLJ3vbk8EZY2GMjR6zcmhZ0fNBGCOiwgkqmBY7upk9uuLPFy56PU7UwaMy1364Z0fDUt0rcJZhpoysGAcJVJlq0T2yqV9v8O/uKGDZjDYsXr8VG6flMVioUjhVILxCLlRnN6tXUKR+Kx+xzqQKNEXNEpYbH2a4zzAaHCLnA4sPvQmdp3EvUStfklXOIr80NxTBSj1iUOcz0u3ESklALXGB1YKEuX7DzhBzASfGQiXpM4wZQVs8ck5oxctoMRbyXHKMewVqJdHhrhO1yihq5dODhyGi5AcZls1IUXlwlgomrYr5x4o2UVa/Is1ynlQ2TqClLktzDVhjIr1XTtwqoR45K6bxj9F3GKWXFHDPUDHCK/pCHkmgXakY7hWIyErZqjYa6uKj4veJHLE6awt0llisdbsuueGgT/N2/TGaulLQFj1DWIxjgzDiXtSip48NozlXpUnfKnFWPubVEVQpbYMjYGQJdm1MzGm+QDDBsMVtgwcbi501sFnehmBXw4tzuzCcdXDgq1saWjQRCAQCgUAg2BGQiZ5AUAdKyOLF8mw0WWXs5axLXdEXTH64jo2ndpuGKf1F7LZBVDgFAoFAMLHgwZ7QP8H2h1A3JwnS/OiZNCZmkpIybwkZFim72VkaZpR+ydJTGOGV9HjRInC3ksZY0ZSOOLEGSykMooCXvNlYbK/FHs56/FPNhII9agonRz+qFyydk6HFcnu+jST6uTD0K8MnHEPj5Os7GprqJ49SNzkKTZ3UTY7GmibQQstgoUpHU7bvFY76YyLHlqPQ3ZnH8hltWLx6GzZ2NGMgm2MuyFyPoxkzdLBUOmeD8arHjDKDzidFjCV4rjG00CCpQ+9LZ0SoXSMqNxNWk59xOY5exwjQxKUZEdihlaEF+4eUpqlpoXQ/jSHM4udDqW1aeCVDHkLWP/ZIWCDGQihwWULjLPuUzQonxmL40fMpcOT5BhTfRgZChknIvmJ09DgxFubK3JhXe926wImjMO9YTqCFE4kB095MUaP4MigmLA2x1M7aCGn5GQJczJjPUG71+9QjewE88t3h+hxIl7TvUHiFtlHXOAeEfaLkhC+MCvGj5/o0To8IYiktzEIp5wxVNthSwNA1q/egD8IwTeNkn3XKlgnufcuJpBn0YSEUCRqETPQEggbQq5rxT28m9rQ34DDrVRSRxRbVijWYglHP+AQ7JV6c14XpPUM4cNlmLN1rdgMblAQCgUAg2H5wlWVMqCcSJmq5JhtkojdJYFr0qHgCE8atCNVpTrI8Y91ShzIRSazt2ZfrzJu7O8NSxVl5jBXY0OLXrdrwlJtHpz2AZquI+fZWuMrGenSN6F7HpH4YK15qVGblEF40zLDiUdGThCbDSsoz1jtuIzwNZ10ycJvnmXgAWIseJ+qjaq0XDqBU1aJniFFkosvkFbtK4TzyxfVYtLEXy2d0xFuldD4jsF5x1vmkeKxl3zhP7ivFSlZ7vTSRIMOiG/hPIUFJ5eYsceTYMER4froRWD6NNsqMf4GVL8UebiaNVoameSvmHE3LuVKgYbavdkKtd1rMwjbCqsd5ohih02SIFY8Ks+i8aZjrt3uXtH/dPxRRXgks+3F90K7+lE1qL8kqwY2HMW0i0bXBaN9BnO4Ik1+SQItKs9jRPLmIjEgWd89GnfjvaJVm0gv6JT+QB2WnlnidEXWLo/sJbRMMaYC2ZZcRXtFtL8NYommYFn2hbZW6aQhchVALeuAqhGm39L2kddbcaBgA6EsaFj99/+UwbKysrqxA0WhZB4JdDjLREwhGgCJyWI8coAAbCtPQh/XoGu9iCcYJW9t8Fc613djY3oxBTeEUCAQCgUAgGCfIRE8gGCVc2VAsQFWFc8a2IRy4chMe3n2OUDgFAoFAMK5wYU/YbxRXzJM7BDLRmySoUjd9ygL1maf96HE+syjS+hsnahFQpJJpWunno2UIKCJUrIArFqVQ1OkqiKM2BlTBFIoQV+4mlDCMrC54bbHDY6t6XM/QFlB/6ogbTRw9ZsVIOD96DMWRUleoz0SOahcmZoLShFW4d1HKXImjc3qcTz1K49F1QuPVbHpXJMxwEGY0UrMtu7aNpxZMw5H/XI/dNvdi+bQOI9Mkn3Cx5zlxlFSaZnKfD+jccX00CfVSpeuc5PJ9LIYXyvVB/39TMIaJF4TF0DV1uCE8wXRCJh+zONF6DFzPUXrZKNYAvJSHoMVYKJNM+9ajFDhKbdMUOi8T5s2JWgTnK5QWF6Vu0iJWhY2q/S1IVW9zi3vmOxCcD9JUeqmuZjq+NUDjTMi6fpEZbsw3Gp5PwzWoolZA/Qx2MzBd0FCdDoS8KJ3TYsLCJLptVUgb1FRM2r4D35JcWAwfN9hmQSn3fuVTQTCL0PSDtMF7icSrpSGj5r3FvU+S3rs0iHmw7FBM26DMjQQNQiZ6AsEo0YQyetA83sUQTAB0tzZh2bT2qiP1tmYM5rPjXSSBQCAQ7KLwlA0vbeP+OMET/7M7BDLRmywgq9qpwguBRHByJzMk0oNAkg+n/ayP6co4awVMDuNWfjm9FIqkla40a1Gw8hYnfVyzAX6a6sM8tQUb0YEsXBSRbUwCu94l3VGOg4GVk94XVxzO7UVNHkBNsfXG9BRLVXAqRUQlVTwhQVjFdM0QbTQRcYiaeKrGeqcyKugfVGSAExwwzaAWXpo1BTN6B3Hg6k14eNFsWIoreM3fmmPeLUrNORBhkrSwOs+HUvljh3qFMFiXC8Z5/3noFXugWj/6F1wwmk+a+A3r4oQRqwgtFcw4SLK0rGgbpGHausdZ9uIsFUlwjLyrD9Mm5QpFW8J4eScUa9H3VfHCjuL4Lhdcxr2CaQXRfYaEESsgPETFWBhBJA3Oij1aBM83xcJiJqo3c/9vmkWese6lXoJ7BXPnuPctFU5jXClYNfEsnY9mBOl+Rse8RIUaxkJOrXikben+Q/uE47+EqMgKZ73zWGtheBykp1+3Og0V0/LHOs56Z7yrqHsFP9ym9e3qMpAw5tMoOJU25jPv6rg0AkE9mJjTfIFgAqOgSthTrYcFYCE2V8MMopRgV4br2Hhq7nRMGRzGoi09410cgUAgEAgEuyjEoicQNAKlMF9tgQ2gF01Yj04swibMw1aswtTxLp1gB6J1sIR9l2/DxvZmrJzeCs8O1826W5rw6tQO7L1hKzY3N6O/kB/HkgoEAoFgV4SIsQhkojdZoFSMz7yoMIOmLLC+sGI2/bJ0kchJBPwF098evQ7DoWFoVcFZFaWNcEzQWKRQESMXZGiB9EJNVhnTK/0AgJnoxUv2TGzxWpFHuUr3SNpIbYGtP45WNmbUdYZCwgkKBHUS49cuSMtRTRiRAY726lFKCufLjqMpMecNipj2x0coOawfvQSaJgDiH8z/P6PCm6D+FkmaprKL6T3DmN4zjHlb+vHY3tMxbPtuFSo2XpoxBdP6h/DaNRuxdLe5UIQPlES5pOFpIiNJ1Gz2WQHhfVH6IUttjG+EDVHguDRshOjYkCaIZCn/R4vD1B1HpTOEnGLyjhSccRBmMraidLdAJMuKhhm+7EagsmD7V88QBS6dTxZuJF6B0DU5Ohyl15WdanutOMS3nr+YQX1LIsEfGQDYLuoSYxlzkQmGSjciMGNQqiBKQl8FENLH0wRaksod1wc532v6fWyIUkXDLM8K3t2KcUio2y2/rYFSKauBWnQFAGz6Lte+7ghV3LarjcYhYVo8iGurnI9JAMg4tKJ1oJ+WCgvprSkGNVm3ZRJGv2V0MONnz+boszSIo/NybT6lH4gYi6BRTMxpvkAwQVGp6TKLvM3YaLfhKXv+OJVIMF7oa6kKrbw8pwO5ioslz67D9J7BYC+ZZ9t4ct4MtJZK2HvT1vEsqkAgEAh2QXgAXGVNyB8zJRdsB8hETyBoAC5srLfa0W0142V7BgCFfb116MLAeBdNsINRzNooZWwoC/jba2ajv5DF617dgDc9vRIHrNmItuEi+gp5vDh9KhZt7cGUwcHxLrJAIBAIBIJdCELdnCSo+tHz/2FoFZQiwfnR05SjNFaASefUmUepVsrgNpHzHK2Eoy7WxqeZG5SVqCRkqj82RgQxDTpPFxba3SE0qzI61BCeyczFayprULBqqJtxlIwkaid74Zjj0YCrJ02BZKlrpAgcFSmFchiw3aifoTrLaNCmdBkpRcynbJqKng3SNMl5y/EJwo4HeF4QFhSrhhba15JF21AZpbyDv+83E+3bKpi1bQBzt/ajc2gYD+w9H8undGB6/yAOXL8Bf1uwoEqJ03Qg+kxTqIZsWJ1Ku+wzMq4dpXOHKpjjyBVKaY9JqpvsOBjTn3jVzWhYME6qKJ0NACyfv0Wpm3ZA06Q0teqxTQqh80mjcNI0nJpm1qdxlgkPu8mpikVRf2QV0gF0uEvCSm41fbkS5uM62h8Z6Qi6P9E+X6PAGUfd3GEKmwmIi5fk69BghXKqmwlKnABCX7eMumPdr6U0+jRHOeXohRbZEkH6UkDh5PZwpLyXAtVVco5SLbVfuwqpAN0/ymSvs+PzIjklzqxD+JMEFYYqrfurTSmZmu5Pvo1URb9PCMWT1Jm+pDLo/P4Bt9WBo4rH0Hl1nnX71hMI6oRY9ASCBuDAQ7OqfjTZUMihEkpOC3Y59LVk0TZYqv5jWehpyePFuVOwcko7mkuVIPzpOTNgewqv2bhR3toCgUAg2CHwYE/on2D7Qyx6kwSWG64is8IM3MqSscc4ap2jfvYsveHeuKj/hzMcxIi6hBvFmU3h3Ao7XTmrvQYAi/wXTLjqXFllRV1S5myu7eDx3ALsW16HMhxstVvgwYKTxDafCPNAZj+5cf92zUl6zFlVwK8Ss/XIPEvWYsdZXRkBl7rFVoDQkudEz1NLHetnSvcnO74C+lqyWLi2H7brVa0clgKUwqyeAWxubQqiDmczeG76DBy0YT12696GVS1TItmx4ikp1lLWyrUjEGfuSPCPxsF85tFEnAWOPqpAjKUey19NmPEotc8w2pYZsSFOjMUgFeg+wQivUHB+9ALrXMzDrNe/nv544n3rRa2BFFk7tJJkfCuKQ8Qt7Ixv0auEYSrjl4sIXZhW9+r/HrHo2dxwGZyMhm3PIZQVJTMijCBT5hGyhtoUgZbQJx5TnJSx00gTsFiY93sNKcaqbeJGP2LesfrYi0YzQRPZfpIwUdly/LKG8XQbphY9x2/LXL+KQzZTbddUHMbzv2k86i84sE4T6yO1+DHCQ4qz8vHGxuq5mDGIZRAwvvVEqFLQKGSiJxA0iEE7j8fyuwUD7qCVQ4c3hFWNUBMFkwI9bVlYAKb3DGPDlGYUShW86enVAIAXZk8x4m5obcWrxU4s3rIFA04BWwvN41BigUAgEOwqcJVtULInEiZquSYbRlTLjz/+OC677DK84x3vwPz585HP59Ha2oq9994bZ599Nh588MG681qxYgUuuOACHHrooejs7EQ2m8WUKVNw1FFH4Wtf+xo2bdo0kiIa2G233WBZVupvt912S83r5ptvxkEHHYRCoYD58+fjc5/7HHp7e2Pjf+hDHzKucc8996ReQ8f90Ic+1MBdCsYDTV4JbWoYA1ZuvIsiGAf0t+SwtS2PReuqY8CijdW/y6e2Y0trdCL38pSp2NLUjAM2r0OhUt6hZRUIBAKBQLBroWGL3rHHHov7778/El4qlfDyyy/j5Zdfxo033ogzzjgD119/PXK5+A/gW265BR/72McwWKNG193djaVLl2Lp0qW46qqrcPvtt+Nf/uVfGi3qmOOrX/0qLrroouD/1atX41vf+hb+8pe/4IEHHkBLS0tqHl/+8pfx9re/fczLZrsqEE0g7BtYFYbO6ceje8uCIy9KU6DhFg0LKB4kb52TscmY4cYY9ASGTKgpUOReAqogLYJB44vmE4SkUXKSNtTXHpPMu7wB2FBYYU0DvBghAFqsRmlAI6ANcbQZdoM3rW6XiZdGEUmhwwXRGBEVbiEv1TfbaGiK3ANxGI4UoTUrZSVeSgX+poCOgRKggM7+ItZ0teC52dMiogbV/y08NWMmjli9Gq/dtA6PTZkHT+/mT6Ea1lIX08KM/mv4hfL/0joJnk1cBzBh0JmSVCsAwmesK2sT1Nefp+mVKsjCUj69qU6BGpaGBoTjHm2XrGiNn2YE1vtGqGZhsYgohF9pJeKkLuMfZ73wVe4wrbbony+ReFpsBQj962VIpRXdTCRexae+eRkijuHV9B0AivjjUxlAOcoQYwkeK6W4WUxY0IcitzRmaESUKyl9nE/MMGJyGPP6Yn3iKSYsrQsmCZUZrHUFckPcYB49pbgxyC+QsR2Z1o8dzVvTKitEjKXk+9ajNGNN4yzTdkn9P/riQTahewZCR2TMt3xBIaMsfpaKjm+E2qnfYYbwUJCG3J8+HsH7lBNoSXvHCgRJaHiit2bNGgDAnDlzcPLJJ+Poo4/GggUL4Louli5dim9961tYs2YNfvKTn6BSqeCWW25h81m6dCnOPPNMuK4L27Zx1lln4YQTTsCcOXOwcuVK3HTTTfjNb36DLVu24Pjjj8czzzxTl8UtCSeccAIuueSS2PNJk9LnnnsOF198MQqFAr74xS/izW9+M1auXIkvf/nLeOKJJ/C1r30NV1xxRWoZ/vGPf+DXv/41jj/++BHdg2BiYRB5WABaUUQvmlLjCyYfgs8ApZCvuOhuKSTGLzsOnpw2G4dvXIXFvZvwfMfM7V5GgUAgEOx68GAZarsTCRO1XJMNDU/09tlnH1x22WV43/veB8cxlzWPOOIInHHGGViyZAleeukl/OxnP8PHP/5xHHPMMZF8LrvsMrhuddnj6quvxic/+cng3Ote9zq8733vw2c/+1l8+9vfxsDAAL797W/ju9/9bqPFNdDZ2Yn9999/RGl//vOfw/M8fOMb38CnPvUpANX7XbJkCfbee2/cfvvtqRO9adOmYfPmzfjyl7+M97znPbBSl+Hqh+WRD07OlQKzskZXsrR1zygRlWfXK3SMCoFpK9Gr7Yz6Bz1mN5zHKBzUxmNVVEBW46n10g+xE/KLy7sOC1PWX8azXMC2yGIojUathYzgQN0ryiNpLoz1jrUCJeSdJklvuF/Q+dD784cJz4mGpQoK1LsKGlNGLrnlWyPsTNRM4FW0UgsC6w0rjoDQovf8vClY8vw6zN8wANeyka14oRUPIBamMGwgk8eLHdOx37aN2JBvQ3e+mbUIGFXCWlCjdxjGoxFJubWgALXyB8+NrF7r9FzfIeAsemltWnH9TQfF1HeSewWLu9UUy2eaxS+weBrNZPsvp9OPH4tUpLZaUKubttBl3DAs45sTPOoqIbDoRa14AJDxaSAVi7hkyFSpxaVK+JlQ9t/5LrXo+ZaYQK6+evHgUCnfqpcJ3x5U7CK8WX2SBDHtZLQWuFEhod1y76VYSyRnLEsY84x89GNNs3Iy4ymXT8SlSO31uZcHY+U2XyfM2FGnKapCLHX6+RsWPf96ZdKWqciKtt5lnGgFUVGXiv7+od9Bul2T/GhbVVqshQoP6XrkXGU0MFxERHDIP9vDDYlg10HDe/R++9vf4v3vf39kkqcxbdo0fOtb3wr+/8UvfsHG+9vf/gYAmDp1qjHJo/jyl78cHD/00EONFnVMoS2Zb3zjG43wuXPnYp999gnOJ+E//uM/AABPPvkkfvWrX419IQU7HFNVP/pQQJ8l1rxdFT2teaye2oK913RjW3Mec7b1o3NwmI3bVC7h8HWrsPe2jaj4lM3X9K6HrdK+2gQCgUAgaAxajGWi/gTbH9ullo877rjg+JVXXmHjlEpV31OLFi2KzaejowPTpk0DABSLxbEr4AgwY8YMAMB9991nhK9fvx4vvvgiZs2alZrHueeei5kzqzStiy66CJ4nH3c7Oxx4KI1kw45gUuHFeV1wPAXXttFbyOHI5Wtw+PI1WLx+M+b19KBraAg5t4Ku4WF0FoexYKAHB3avBwDkPBf79WwY5zsQCAQCgUAw2bBd3CvoSRyAWMvfXnvthf/7v//DsmXLYvPp7e3F5s2bAQB777332BayQbz3ve/FpZdeis9//vPo7e3FG9/4RqxevRoXXXQRBgYG8PGPfzw1j+bmZlxwwQU4//zz8eyzz+K2227DaaedNibls7xQjIUKrwSm/zQ6p95QzAm0gFA2CY1D50k3K1u18WvShH6oaD7+ASOsojjmh+G4ChEYG8UZOqdOZOTN5VMHXciGQgVEZWAUtKJUMRIGnPgL69cwTfyEKb/N0NnYY4MW6P9lhFfqDavNcyygsnQTvr8xn6Fu6kvbthfSz0hFGYY3TVlyLQw7WWxsb0bHYBGPLJqNOVsHMKt3ALP6BtBU7om9nd5MHu2VImYU+7EyO4z+jL+/L46mWE9YGhhhBsXwxjT9mgoTBDdCKZ4sjY05n0IB5caGJPplKB6RHM9Iw9E1ybHhP0uPQcmlHjNo4RXLqNDwIdl+/VTIg7P9Nlqywld5xmOom76wSsEJlV4pBTTv0zi1AEs1bvW4mA3TaPqo4Y/Mp40afv5qfI8pC4YYi9aEsRl/Y43saODa3vakdrKCYIoJYyj8abR3Tngl6KspfZ6lO6dQN4O2XvvCqO0/9HRQRkovVkwZOR4uGoYW/7EJpdj1xxFK8aSUTE3ZzJIw3TYdQtPU/iEDP5AgwkKGnzzSlv1vHY6maQi0aN963KdK3Ps04VtGMWECQb3YLhY9avXad9992Tgf+9jHAABbtmzBtddey8b52te+Fhx/4hOfGHW57r//fhx44IFoaWlBc3MzFi1ahFNOOQV33nlnuB8lBocddhjOO+88DA0N4f/9v/+HI488EieffDKee+457L///gbNNAkf//jHMXfuXADAxRdfHOxTFOycsOHJhmIBAKCvKYuWYgmebWNNZzseWzAb9+25EH/afXc8PG8eerP5IO6zHTPxUvs0tFeK2JSrqvXuO7iB3XMnEAgEAsFI4MKe0D/B9seYW/Q8zzNESU4++WQ23jnnnIMHHngAt956K84991w89thjOP744zF79mysXLkSN998M+644w4ACFQuR4ta6+Hy5cuxfPly3H777ViyZAluu+22YBLG4corr8TixYvx3//933j55ZcxdepUnHzyyfjqV7+K9vb2uspQKBTw//7f/8O5556LF198ET/96U9x5plnjuq+AMCqKOhFL3MlWxl/AZClt6h1zrDOMJY6zv2Cxax4xa1khku6zI57bnWLs/LEiEzolfBU2neSyESMFS/OPYMDFcrjN4KRfM9zq8D1zjHT0jD1HaxUU9loJh/DMuQwYXZCPLoymlGRNAaS6ow1TwLw87RI3o5v3csQi17tbdmWBzvjQSEUmwAQyuuTY71KXsxkkHM9WK4pvGJ5Cvtv2IDmcgUrWjvxSscUKM8BlMK8gR60Vqq09BavhHnFbVid7+ItUAn3b4rRKCYsmkZxwiuMogQVVlKRg5o8E9oWe47rypyVA7yFwVLhLwhj3CsE+XBhpHCUDaF2ACPbHJYt4y9QU1w/3HC54B9TK58WXqHxtCUvQ0xoeTsUY8n6lZUhnX3YzVb/EtGWkm/lK2fCeNq6Z4hWECuJclXVvQIRY9E3Ru/V4tY8ubaTNH5tB3BWmZGE2UZ7q/lLj2mb16fIEMQJvKSeZyx6uilozwQWzL4UPBujkfphHtNx48bgMYB2mQAAth21ztnkOONXNLXy6dQlx4mksYjrGS3MoijbgxFmUdRdjRv97mDdMDBjOgeOkRP7PSUQ1IExn+h95zvfwSOPPAIAOPHEE3HYYYfxF85kcMstt+CEE07AFVdcgeuvvx7XX3+9EeeNb3wjvvCFL+Atb3nLqMqUy+Vw/PHH461vfSv2339/dHR0YNu2bVi6dCmuueYarFq1Cn/729/wlre8BUuXLkVHR0dsXp/4xCdGbV386Ec/iq9//etYuXIlvvrVr+IDH/gAMpntwqIVbGfYUHDFoicAUMw4sADkKi7Klg0ohbm9fejqH0JzuYyHZy3AgG/Vsz0AlgVbKWzOtWDecA/Klo2Fw1uxNteB7US2EAgEAsEuBE9ZJq16AmGilmuyYUy/Ju677z5ccMEFAKriJddcc01i/BdffBE/+9nP8Mwzz7Dnly5diptuugnr1q0bVbkeeeQR3HXXXTj33HNx7LHH4qCDDsJxxx2HL3zhC3j22Wfx1re+FQDw/PPP4+KLLx7VtepBLpfDhRdeCKAqVnPjjTdu92sKxh42PGRRqe7RE+zyGMxXLSBtw9U9ylMHh3DA+o2Y09+Hda1twSRPo1Apo+BVsC3bhJJlY3OmFRl4mFoe2OFlFwgEAoFAMPkwZhO9Z599FieeeCIqlQoKhQJ+/vOfBwqTHB544AEceeSR+PWvf4158+bhJz/5CdavX49SqYRVq1bhe9/7HpqamvDTn/4Uhx9+OJ5//vkRl62zszP2XFtbG26//XZMnToVAHDdddcZYjLbC2effTZ23313AMAll1wy6mtayhdkqf251R+88BfQM5QiP6Dqj0qxP+qvqvZHBRG4MHody0PkF5xj0ltu9Gdcz8hL33f0GvyPqS/jx5U1/HVhAI6lsBWt5nOopVaQDe6xz24c6BjKqu8H8mPj2OTHpAnSOswvo8KfcU5Ff5mEn01+TFrL9oKf7VR/Dv3Z1Z/tKNiOgmVXKT2Wo2DZ4Y82Ut2s9U0P5LIoOg6mDgwBCpjeP4DhjIM/7bYHnp0+M9KWFvZ3o2zZ6M60wAIwaOfQ4xQwq9RbpW95fN0b9co8S1gWYFnxz9B/VmaY5f/o84yGwbEApzYecz5Dfo5l0MApOOolNy7RcSSSpmYcMNPUnI8Zw4K0bEcJf2HRLPID8wvPc9Cr7NqZsQcrCHM9O/iZ16n+vJRfxXNQ8RzkbDf45Z0K8k4FTU45+OXtSvBrckr+LzzfnCn5v3LwK/i/bMYNfhn/Z2e84Acn/KmMAjIKnoPgF/RzZuxIG0+Mdm1Xf2xbHwm4/sadj0mT9FNMnmz7Ty5W4rs4+u4F857027r/s1wr+FW56v6Puw7XxzwL8CwjXjg28r/EfmLE88+T4KBOLBX89Pjt2B5sS8Em5yxLIWu70Z/fful7IBjzyQ+Z8Be+Y2LeZ0k/brxMHcuj7U3EWASNYkwmesuWLcNb3/pWdHd3w3Ec3HrrrayTdI1isYjTTjsN27Ztw6xZs/Dwww/jgx/8IGbOnIlsNot58+bhk5/8JB544AEUCgWsXr16TPaxxaGjowOnnnoqAGBgYACPPvrodruWRiaTCQRcVqxYgR/96Eejyo9838kPO+Y3BQPoV3mUkN1h15TfBP5ZFrpbCpg6OAwLQEu5jP5cHpZlReIW3DLmDPVidaELnmUHioobMu3ocgeRV+UxKM8k/Y33cx7pL+HjfHv84j6yw5+V8kPihCL4jXe9ym/X/I1VXxnv+xjBrxF4E0BwJe7nyRaFHYJRbwxbu3Yt3vzmN2Pt2rWwLAs//vGPccIJJySmueeeewIH45/61KdifdC95jWvwQc/+EFcf/31ePTRR/Hkk0/ita997WiLzGK//fYLjutxfj4W+OAHP4jLLrsML730Ei699FKcffbZKBQKI8prelcOOVtTw8iSj79p2nC5oGGFQ0awoEbCjGO7Jl5MmmBDMhcGBKOUMq4Tzdu4du31YhBem7keJw6CaLzY6/kbwGnYTDuHYbRiNnKRNEY92dXspzZlq6uUNeXRdesxYawIAb0X5v7ovSgujBlbtRCCIWrBhBngxBP0JnSj7vzoYTUFwiuGGAstoxN30RhkaMFpeapmGupKQW/Sz1uhyITeZ1lBdZ/ddOThWdWVZM8mm/FpIZ1o5TtTp2Lelm1Ym8mitb0dTeUK5uaq/ZI26YWlAbS2t8NrnYkF8NDV3obuXBt67QJah4fxGqeC9ZkWs+61yAipd62nQQWROLcYnDALm7fhckBFw7SiACfnXntcC2M53g/ixoEY1w21Aj4WgM7mDOApeFQiXcfLhmm11wBF3ngeOa/z9LJhIXV6j7YtLfpAKsUiN6Yn7DZRYdAuEGySxvHPO4qmrYaZw254PuN3uAyp/KyfJqvCRpHzVTbyKmzfTU6VMZIhvlupe4Ws3xfoh1deVTtsC8J3UptdPe5zQhrysL+/vEQqt0I6trJtzLAzgBvWlOX3HZs8Ayvnh4XFDo9JGNve6JimL0LHp6R3A6JhAHnEjBiVYf1lwoK+xYif0PO17wn6N1JeHc+KnuPy4d4TdJeBdnGh41sApmWyodsYfwzm3p3m+0RFrhdWFDPwIOwzVCQuqD+SRrs4oXpnuj9R4SCbfN9kdT8hD0T3vS4nzGjIHwBKxKVIJeu7DzGUmGij8N09kPHG9pNbZRKm352k3epjhwkD+OfKwR1w8XRyFIHAwKgmeps3b8Zb3vIWvPrqqwCAq6++ui7LG6VhHnLIIYlxDz300ECk5YUXXthuE7009wrbA47j4KKLLsLpp5+ONWvW4Ac/+AHOO++8EeW1eUsRWX+AMBQ2RzHRox9c4cSjzkmdzYSR6xh5NzrRixkI2ZeQnw870Uv5QDVfntF8ptl96EcT1hPqJpe3nujBAtb3l/zJA7mQnug50TB2kpyiaMl+ANQ70fOSw7g0FJzqZjDRI/noD2r64a2Icpri2msiSFqjafkTPScsrP5gLiD0D6Zlnsv+RA8A1qhi9VmRh6Xosa+2ZlXCsF7Lw/TeXgz0tmJdsYh523qwvrkdru3A9tnZObeCxZvW4ZmWKVg9VMb0Uh96B3rxz+apKNsV5Io25pRXoy9vYcAKP7IDNUn6Iezfgl0hEwZ9q6kTvehHlqGwyk30mLC4D85aGGpySeNA3NihJ2NE2dfNABt6SnCzYRr9AeuRhQVfQNKc/BkLD34YVd3068cj9aR1Iy2i6GcZH5TV8w6ZHejJmkO++nUbdMhDcqzoRM+hkzH/wWbJjCHnt+scCSv4X5J54jOvnKkqu2aN/MLjvFWNS+XOeyvVydw21RyEbfOq8baRRjgIX50TYeWWScdWvr+zNZVKONHzP4pt+nFc0mEIw/xjK2WiZ4w3un0zEz1uzI97DwTjn9EnzL9x17OYMJvJJ1WdmBmr08Z0dqKn86YTvawZZvnHa4vVd1Qw0aOLcXoBjpvoGe8v/aRjJnq6rZMx3+Li+cd0wUMfZ0kfo30r51d6llS+Xtxzyfg94C/Qmu3W8eORhQqiummV/YleOQzT47puv0C4QEHbst/F4DDtG6h/oqcGy8kRauAp2/CpOZEwUcs12TDiWu7p6cHb3vY2PPfccwCAK664Aueee25daanCZKVSSYgJlMvEwet2VKbU9wEAc+bM2W7XqcWpp56K17zmNQCqdTg4ODiifPh9IvJT2H4/C4AHa7teQ37b8WeR3xjl2ZfPoeTY6BocwsqOdjhKYU5frxFn+nA/AIVVTR1QANorQxi0syjZGSgAK3NTAADTK33b9/7VTvob73Yz1m1vO/1Gz3erpqn7muNdp/LbtX5j1VfG+z5G8BMIGsGIZk6Dg4N417vehccffxxA1c/df/7nf9adftGiRcHxAw88gHe/+92xcanzdZpuLNHT04PbbrsNANDc3BzrEmJ7wLZtfOUrX8HJJ5+M9evX4/vf//6I8rFcFayOUesdpXTVQhmrab7Fiq5o0pVs7TOMpNGRFaJhrJ88kMUqmqT2HM2TplWRpAa4VdKwLphlspTVXSOFrgsSwYaqUvnMgsdeLu7SQRKmToybTckz8UJcWm4lm4Zxq+BM5XMr3hSKOxesxJM2RumadQqZKt8nHvWTZzHUH2OlXtOBSDy94luxbP/7doSvU8tCd3MBUwaH8M+pUzGQy6KlXA6EQACgsziEvkwBruXAVkBneQg9maZqPSuFhaUtULCwOtdplkK3a2oF9SN4xg1GO4pByQ0oUkxjpxErOpBphHRFP62tB8Xhx4R681FMcahIRO31TEukn5QJo+HU/2EQN4W6arAc/It7XrRuad6h703K7auGUesEpQprawS18iX51uNArXiU+qathDYZ87VFpCUTCoVpH33DZNFVl8u0fIdlqLgWLFtB+X4pq4ls//7CsnlJzyBmHAzGenLeZsagesflVATjVjRvjgody6SIvk5Yiw5Lm9dlQEw8nTcTZvSdGkaG5Z+3astuvCf8dz7zHcD51tOsh2pSMtYzN6GzMdzS6ncQ7W/+e8Il7YSw6+H6GTm0cv22SS16us27mTBM91uPUDyN+9JbDhjfwYbl0y8vZenodklY1qyvTsU8f+NZxYlGCQQxaNiiVyqVcOKJJ+Jvf/sbAOC8887DJZdc0lAeb3rTm9DcXKWDXHPNNXj6aZ5xfPfddwdO0+fOnYuDDjooEue4446rih1YFpYvXx45f88992BoaCi2LP39/Xj/+9+PLVu2AAA+8pGPIJ/Px8bfHnjf+94XUFK//vWv79BrC0aGdmsQBauMQbI/TyAAgPVtrZgyPIzWYhHDTgYF12QtdJSH0ZOtUjIznosWr4Qep4CcV8E+w+sxt9yDV3PTUKQbmAQCgUAgaBAurAn9E2x/NGzRO+200/CHP/wBAPAv//Iv+MhHPhLrBw8AWlpaIpa4zs5OXHDBBfjyl7+Mvr4+HHXUUfjUpz6Ft7zlLejq6sKGDRtw11134Yc//CE8f5nviiuugG03zjS94oorcPrpp+Okk07CG97wBuyxxx5obW1FT08PHnroIVx77bVYuXIlAGDx4sX4yle+0vA1RgvLsnDxxRfjve99LzZv3rzDry9oDDY8LLI3olc1YbNqG/0qsWBSYV1HKxZv2IL5Pb0YzmTQOVQ0zuddF0NOdRLX7lYVOnsyTdh3cD063SG8nJ+OdZnOHV9wgUAgEAgEkwoNT/R+9atfBcd/+ctfcOCBBybGP/bYY3HvvfdGwi+88EJs3boVV111Ffr7+3H55Zfj8ssvj8TLZrO47LLL8MEPfrDRogbYunUrrr/++kDUhcMxxxyDW265BVOmTBnxdUaDE044AYcddtiIXTtYrgdLq7YZG8XjKWgGRUZTMYzolHLpUxY4ShJNwmTDUxLTaKM6frT8VszMiqMIBopihrgHU3B9zCmV1aDTGkDBKuNFd3Y14WjpldsTo6AssfukU+41UcDFiznW4Db9M7CyRPxCq2qSMJO6GQ0LBDMI/UgLxdhWlVRkQxlp6oaq0pI2tLZiZn8/Vrd3IO8OBJsrLKVgQ8FTNiwP6HCH4MHC4qGN6HCH8FTzHHRnW/j60WGUVZSJBMGzorQog8YYULyj5y1CtQoEENxon6d0Jq6fcH2e9tsk4YE0CmeE91yzccVi6H7B/cXVSQLd06R4MhxvFT1viPb4GXiE4lnxqYv0BRwIVDB09eqxn3edNM00OicVq9DHVPRC0zwzXhiv2VfvNKibSisVRu8PqKqW2pYy6NXwqW+0XFq90GPGCYMpSKn5zHPjqJQBLZAJSwX3qLl3FUeV5NRAQZjL3Dsoga7Z0HmOZk/vP4bOqZtFQEk2GqH/h15PUxKNMMuPTsYJSuPkipjwPBSlfTL9gDumNE3NmzT6hH9BxyHvE7+h2eT94xo0Tf+YtGVVo1JKw4wb1PUaI7bDvqqZ96ni3g0JEDEWwfZTN0mBZVn4zne+E7hPePDBB7FixQoMDg6itbUVe+65J4499lj827/9G/bee+8RX+eb3/wm/vznP2Pp0qV48cUXsXnzZmzbtg3Nzc2YM2cOXv/61+O0007DW9/6VuMFOx746le/ine+853jWgZBGhSmWX3wlIUihFon4LGhpQULe3qQdyvIe66/CGMHsvoVfyNKR2UINhQ6K0N4Wk/yBAKBQCAQCMYADU/0xtoNwaGHHopDDz10xOk5ayHFYYcdtkPFVWpx44034sYbb6wr7jve8Y4R169dUbA4K0hidsTyoVegjNXLZCuYFiZQRiLfWkBX78mKmOVEV7qC6zBLrNzUm7Py0WsapU4SaDE2yjMrcMxxFwbQZQ/g5cosKFSFO9IsGlDb0dCXsHodC+68vn26OZyr5jqbJytJblhT/BVfKvhD0zvMjflh1CdeJuvL1DOrskBoveMEWqiohd7Nb1kqaEW2XW1pnsWXkQvUUbvzTSjaDtqKVdpma6mMQSuPrG+ucB0LsDy0V4oYcHJockvYkg8neUmL+5x/MI+M5MFCvBeNB4SrzabLTFUbLbBAGdZQf7uhilkU4yzxnM8wTvyIFWipc8HXaEd+ppyVB4zFjh5bjDXJ6FucqIsxZvpiDvQZBfVIrAmByxEqVhGlFdhEk1/fl5cymqSd5xBY9EgFaEsedcmQ8cvTnAnVsCv+Qyq74eBRzoTl9lwbNjxYpI9qv4eUaRHUrWHZ19abmIJzYi2cRStpLIuzluk2ylgQjWySrsdZ8UieaUJX3EBgRQ5q6ocxxHECLbW+/iw/TD9u3fQ4oSej/zuaIUDe+ZyYmiFgFLmFsNnTQupyMxZyWgZqvdPjv5GNirajUEQozEe/Rzyj/ZJS6vZKrXf6eg7p3/69Eq8+gTCLxYi2VP/xz6f4bYxzdyQQxGHcLHoCwc6IWfY29HkFbJW9eYIkWBY2tLZgdl8/AGDmUB+WNecNi17W82BDYdjOoMltzDeSQCAQCARpcIEJK3rCuOIVbAcIQVYgqBNNVhHt9hDWe53jXRTBToBlnV3BnqeFfd1oLQ/D0RY9yzb3r4xLCQUCgUAgEExmiEVvssBTidN2i+G+GJTLgCJCKFeIUjFMGpN/nlARUn1meVFKB0c14XxmJcYjZTfL7afhBFpoUqRjptODknLQrVpj47A0NYp66ZDszmwmfspCXZJQgLER3DHjp5aPXpsTFKCPt95lO3pxLRhCaJqOpmmSsJxPsckSqg2lZOpmZjPUTRo2XAnpjJYuPnd/nOMng36jKalA0cliRXsnduvphrIsLBjsxrpCOwA90YvkWB8SqLcAob4xggk0mKNNEk9nZDihAgaapkWS0r4VpCB90GbGjqBuaTzzbwTah1dam2domBwFSjE0TZPaacXGM2hqBgU2mrcWoXBJjeo2Sn3PaYaYRTqMSyrD8de/OZEVTmylQtJW/EZRJtfLU+ob06icBIddtO8UnCqft+iEbkRKmfClUKk4cODBzoRvBeW/BxQVydLUzQyp2yAeTwEcC8S9Tzh6IdtXuY7MUerTqKS1+aUhJg/2HdTIe6eW0smNI8zYUa9vvWo4s83Cij5r7rvEC3z60jZhRc671CGfXmAjfUJnSWmfOq1B/2eEWUyfr1GqaNCWHdqgdBgJojRN0wuPkWY0K4EixiKQWhYI6oADF1PtPmz0OswJskCQgGWdXajYDhylMH14AHl/31PF8Ao8YvfsAoFAIBAIBLEQi94kgeV5oTiKIc9d5yektobRlWoqzNGoIiljsQMA2Mwyqb5e2iUYKxdvBYxaJY2VQ2bTv8WtnJHjmXYPHEshAzdazw1Y2Li8k0w77Iowc/+xq8XcZn7mXhPFAWLq28vWxEO4gZ/OY/Tmc4+IlHo5ZfwFAGQJldE3b9jkvLbkZYiog7bk5ciud8eOulqgFghNpyy6RCLeX9XV9ahQXVlWNfcHallwtaAEtVib8Vw4WNHShb36NsOBwrzBbVWBF2XDNhU7eCEQTjCEPkudBfd8CTghFNZ6m2bRYPqvKeATtd7pejKaqG4fzPWM4qQNX4H5lQR50bHM4tplWt0GLidImH+vinM5QY/pfWkLBLUCJFjsVYwpxgsEJRiLHqJhFS98MAOVXPRCKeh38/7fMO1gpRqWISIxzX5Flcj1yuTYzdjIwIXjucSi55fVaPN+HyRWIC3aQo2LvoeHKupkFSSFcW59DNTbtxhXCmlWvHpFtFhWRL3vy7SwmvJE3g+0jLpPUetdguWPZQ0hXNriXDKAESuxmIpyK6TC6ZcsYxlzNWOBWtC9aFjwvqDvEONY3z/JXI8JXjSMimTpbDw6XtLz5N0TBvphtE7EPCNoEDLREwjqgH4BdNqDWCk7iAUNYGOhFXv1bQYAtFeK6M3koayQ3NhWKQYfIgKBQCAQjBVcZRuU1YmEiVquyQapZYGgDvS4zVAK2Oi2j3dRBDsZhjNZbMk3R0/4y/15VYEzxm5rBAKBQCAQCMSiN0lguSoUsHBSIuuPytFaEZhlAsXQYUA2TbPnA8dfKdcb629hg1bD7WCvIgMXe+Y2YEAVsIEqbibQr0ZcjqS8uQ3+TB4N+dSrTU8pOYxqhyFWoOOSdqBdaXG0OUrT9HK+L6Qs4bswoiaUIqePqPCKpmzmMiFfh4qx2Ax1U1M26Sb84PGHKiFQqlotnkviGaIQUeou649NAa+2TMXU4iCAqlXPcb2AyrPNaULBK7O+Bw1xEFVzjh4zz78Rf4qsXyt9juvnsUIQVuR8QJc0Aq1oGXUZjLojdF6OfqeDGB9dnM+8OBEZ9ryme1KKdyDQQujhpI2y3dbiBj0zPsDXKSeyYqRnxFgCOicZ3zWNsxEK54BP3Rx2Q861pmy2GPzJKkqEp1YiPvUqGQc55SKbodRNy/gLEH9mXnSAo7Q46vdM+3VM9aOnHwEVoEqiMJP09VKhOcpdml/WtD6qx4iGxvSEd0difH1cG5d7tzCCOMaWEYaHaFAb/cIZvgW1z1PG/6ch0ML46DNonMz3jxZXcclYrvsJpYU6NkP1J+9ET/vMI2FKi4MZjUuXmwRlomOjcZ6j+zpMWIPmGQVrRL41dwRE72DHQCx6AkEK5uU2w4KHV0ozZWASjAi9uQI2Z0Or3sxSX7BPxYKqEWcRCAQCgUAgGD3EojdZ4CpYWqSCWmCc+ImJZayWj2IC04h1zqqJhxgrX8plGgVrBUuDr8TR4Qxgk9uBErJpKUYFi1vxZValU11OMPkY66EJK95G29GrltQ1AbdKTjPP6mtQi0f1Lye8YgiZcCvndMXfvxAVW9GWvKZM6HCcE14ZThBeqR6HlhELVXEL5fmpyQ1S6w0YMZYky9HypqmYVq5a9RYPbsRjLfMBAGU4KKgyL8aSYi1MsuixYhT0mLVUkIh69ZomTek8nFUqFHfghRmCINZKkHw9SzHpGMtgksUOCK12RpieexvWab/dGiwF0tZ1cAqrQrc3Tm81ToxF1y1n5Ut1ueAfNiLQos/T/qQteS2ZYiR+Mc6il3WQ8yrIq0qouh8IYZB6ZKx8nMsFj44tuh5pG9BRGesdBSvAxZ0n4KzKrDWcsSCmiboE52rYANFCMHkkD6P8dWru39LHtfXCWSUNFwhRS1xoxSbPymP6DOdKgWn+/DuN9A2Sj8vYL3Sb4Vw3GO8LVoyF3IOj7zVqdqTvvERXIZQBQL7Ag/5Nsg4Mo2Q88dIYWwJBDWSiJxAkoMUuImt56PWaxrsogp0c/ZkCNmdbMK08AADI+U6USlameqzU6OnUAoFAIBD4EDEWgdSyQMDAhoc52S1YlN+AYS+LPpnoCcYAy5umBscHDK4FAJRsBxaATIKDaoFAIBAIBIJGIRa9SQLL8wCfVkQFNSxNK0igcFKkLbAog3IZFVQIfQpFqZn0mNvMzlozGDGGONoLK0KRIDzD0X101lOzvZiT60ZF2XhpeE40Ai33WCGBpgOQ+0Ny2EiEObh4QVgMBSoIp+cDuicJ0xvKs5TawikYMKBUUu2bjoioaOGVghOKsVAqjqZsstQ2UsiAzqnpY7DguT45iKFrAoQOSN1taJ9pHL3SA/rtPDZlWzG93B9m6Vdgk1tCn11dULBJmiAfXbUMtYsNi6Nu6mj0WXOUNGbMSBMH5fx2Bl2QEZRgKXdMGzSu4YXRLC+BusnQxiymPgGwfgt1PRKXcaHYkCGoQOmufphBL9Rh0XuhNM3wuG5yOSuywFI3g8KEh2k0Tt2PqPCKpmy2Ogx1M0Oom9Snnuug4JrUzcCHGdO/PWaQoXRNm/Zlpp8EfadOunKaGIvxTkx6f3Htm8aj4yRXDsYnZm1ZjMtxlEoagQFL8a/JO8mPHri6DcbMaCEtElEZvveilcZpFgX1Z7QT/4D6xGRonJTCGfjWpF3VbyhUbEWHOSSMbhUIxkRSHlf7DqV+MjO6LGEY5xOSjjea+Wwzz8jYZtCgecZTVqqo03hhopZrskEsegIBgya7hGEvi2cGF2DQK4x3cQSTCMsLU4z/HeXBg4U2L/rxLBAIBAKBQDBSiEVvssDzwg3CdPXLXz6yyJw+sMqlCQakWATqhpFPdOd6khiLZURUkXhp4hBscTgrWM05x/JQ9jJwVQZWTNyxArvYHDWMhZaRtLC0CyZYWHiBFhLGrG5zD8F4lhnOlFFfGdlTxopn9UI5YnapkCVPvWLIWU7MMD++V21xyqquDCvAkMA2LXXRFVqbWZWvFQIZtPLot3No9UpwYaHNLaLfyqHNG44KJHCr9pyoQ4oVwABnGdd9MNU1S3IYp0rLWbQsj8mIE2Ogku1x1uuarALLJyOsEudeQT8314meV8wzV0bexJqABDAr9WZfjlq0DCMIou02OEetXEiw6FEw1r0csYxzwivNdjWsmVj0HP/GDDEWYt0rZzNosipotkrEoudb6qiLE79uy160X8IQbQnLrQLLSTSNFeoz8f2p5lwESdXHvBvT3kumyyE/3vZgaydYLVlRE6YtGvlwaRh3JpyFPLZOmPMBG4L5NrC4dwwn5IKwP3KWdiqywrne0ZY8er2ME6bR7dWj+fhjhuEVRLdr+u7TzyDOVYgOSxnzZVuboFHIRE8gYOAqGwU76itKIBgLPF+YjcMGV6BiOej0BrHZaUWbNzzexRIIBALBJIILm1UinQiYqOWabJBaFggYDLp5NNklWCKQIdgOGLJz2JhpQ15V4EAhrypoUmU41FQkEAgEAoFAMAqIRW+ygBFJqQ0PYCfES5N3Z6l7UQoFK9BScxy9NgkLKAtRygZLZ2OKRaGYiMZG8ZrrDVQKsCygwxnEtkorT5ELdkpz+TBlJDSzNL9+vLAME5G7f4ZWU+9mfo5KRgvL0XlZii/lnOW9aBgH5rSTDyc+hXyVi9VWCGljXYUhACblzHNDf4c2Q/nhaEAjQgK9MvUSCliRnYIZlT5YAFq8EiwAbV4RPXZzSPOhdatFbeoUaOHo0QY4ilTKukY99xUbZuTN8L3qXFOp1epQdg3jmPVB6FOyGB9t9Nige/rHlpMcRvPR83TqZ08LUxk+Id2AqBXG00IvNJ4VDrhaB8gmd6spkDSsom+cjNW2itLU6HHGjq/8NAqo7Vd4R2YoCCsrU4ylGSW02lHqpuF6jLlO2edmelQQidA9g1IbfdCKhAV6Mo1QN32wQ3ACPdI45sIQQ79LKgdHszZEsJLz5vz/pW7H4NLU5peGtLqllFtORSr6KM2+p9MydH6Led94Rt62f7kwnuX3SyrQUnGZrQBetJLtDKF4BjTr6PgW5xNS36zhelFvKaAXatCPnoixCMSiJxAwGPZy6HcLmJ3byjo1FghGi2E7hw1OGwAgi+oMoVUJfVMgEAgEAsHYQCZ6AkEMhtwcmuwSslYlPbJAMAKscqYa/0/z+lkrtkAgEAgEAkGjEOrmZIIVcHsi4GmadeZH0pt+9My/1WszeTN00FQ6J1ecJIVBI3OaqCatEY1QNrxoubuy/Vhf6kJJZRMVyFhVspowyw9jFQgjB0Q5jnI2AsotSctR/Cg4qihH51SRoPR65i6n20SeqJLlqtYqSqVJYwhr5AuhIE6rT9nsyocUsYxfQRlSUfTYHoU1VlN6zDqhD4kJqz1nxCN5+8dFO4v1Tjtmub0AgFZVxBQ1gK1ojWapL0NGbbadMNTNummcXD40KI2KxfUJTYuk9DK2MMmZs9Vs+VS1NMopQ8206Tip+xalxbrmX4AMnYRKSKlygS8wQq8KKJuMSqCyKA0x6iDMoJX5mbuUzhn4B6NjWbQeA4VBUseZUew/9qiyLTQtNMyP0jgrbgYtqoRWpxg8w4pWL2ReQgMq9OnnZrV/S6LOafD0/D+EDqd9lxlUWe2jjFHipEil1AcZkni6aClbClhKpcHTYy6o+zdDzWR9mo4AtUOashJ6ItMHw3daSl8mzyPoJ/SdoBVtjTavc6MUT/8cpWZy3xhM86b9Tfntlfan4HqkjVFlWINCrNM40T4Y1EWWREzzCckO3H48mk2DX+0ebHgT1KYzUcs12SC1LBAwsKCQsTwMe9n0yALBKLDKMf3qzfAnfQKBQCAQCASjgVj0Jgssy7TakXAAxpSei8etEhoLSzYTVq9l0ChPTVqmDACxUBhOdRhhjcb1PSJFMbL2/zr+Mr6n7Oo1Ei16UXMZJ3SCuHzqKWxtPglpOXGQdOEVJixtAz/jgElb8rQVDwCy+Sr11bDo+X/tGBEIvSLaSix67fnq/jUqHJHxHaBlY/zo6bicAIvFrBxbll82q1oGBRidItUPVwNWXo2incU6pwOz3R4AwFQ1AEe5cC2HF0Kg/2iBlqixyIzHPEPWbyWzKM9277i2qsO5x0qtZYwFLWADxGQ+Fvv2qbWLE2YxrHcOE2bHhwEIhVdcch19X0Sghasm2x9kFbGMcWIstN26/gOzSLxAZIX6CfTDDNGVlPrkrG1JcujUykcth+2ZITR7BbRaxeCa2m+fmb56skzOVXzHhl6FlJuxkijyXD1t3eOs06RpOZz3HIZpkWjZIxHY/p1iVWcZGzRJ9FGyYiwjAje+EyT1N5YhYiRm4nH5UDEW/X5gLN90n7y27hn1xL2XaH8L/PaRdhL0HSsSRu+JEwxxMnQA0GWIWvQM33q63dIw4yHqb4fkfBr9aneVBXeCip5M1HJNNohFTyBg0Ow7CR5y85FzHfYA2u3BHV0kwSTGqkyX8f9s1TNOJREIBAKBQDBZIBM9gYBBa2YYJc9BiSHEz89txt6FtZjpbIMFJaqcglGjZGWx1u4I/l/obYHNbTIRCAQCgUAgqBNC3ZyMiPOplxCP86On6s2HgBNoSbXOj8Z6n6IOYSVkzlFkNEem1RlCf6XJj6TIjQEritOxuGktFmQ3Y0F2M1xl4anSQpSRYSlCloqKsXCb+Q3qT7ApnCkjQ4cxKDIpVMIkH32N0IE44RXLp2xmiP+7fK5K3aQ0Td2cHBLGCae05UN3AzmfnknFVsKw8HoZ4mhJU9VyTni+5MYPeyzFNYZyaTHnOWpnvSJCa5wpmOX1BvWwh7cRL9uzEtMkCbTECrAkULaM9uYxYUkUZpJniixDIn04NXW96ypp8Si10W8eivOZRynlNhdGyuvTz2hYQIt3GDEKw+dlNS319UXz0TQ3g86pr0fd8flt3qb1OArfkR4zKLiUpsk4X6S0T9tSsC2gPVMMylHxK5rS4ko+ZbNUCRtz2afIedkwHmHFBiIsyuDI+X9o3TKUep3EphROrv8mn+bp2ilbIRJpkeQ4Ks8Tk7bOd2hd7+KxoEgzz8Bi6sKgnOtjzg8sKZReWDUEWoxB2DLiAWB9WerknG89O4Zz6vi+8uitcNsPdF+n1wtEgjJxL2u/PIzgl220+UiSRIgfPYFY9ASCGtjw0Jwpot8tsOf7vCaUlR04BXYshdfkVmGK3Yf6v0IFAhMlK4P1xKo3Q/WhyxsYxxIJBAKBQCDYmSEWvckCyyKCKSkqC8xmblZ4gwFrqbOjecfp5ycu4NC8OclqbS2ImUvxQhkq9rrU2hdsgFYWmrPDsCxgoFQgojBheVqdIWQtDy8Nz0FRZaBsYEFmM/bMbsA8tQXPl+dVXTKYRTDuh9vMz8ajdcJZ5xKuAcRYAbkVaHaVmCksLU/WP58LVzQd/zibDX0PFrJVTfOMQ6x3ft5xFj29sZ1a4kLrXJi3FmHJ22FYhZhldJ50hTa0eNBN7/qv3uhPlrbj5u4Jc3q2LXIr2jXPYFWmCzNLPXBhIwcX+7lr8Q9rEUpWJtkSF93THy+sxJ1nyq1X4zlrMevOAWBNEBYTrV4xIsokCFwkcFb8ei2WccwEfV+cKwVyL3ZgnSNhJI2rBVzoSn7gpoEbJ0kRGBcvVM49cK9A8rb9Yyoo4fpy8LZD+lOdK+d0hZ0TStDnqcVOW/yolY8eVy0Kfh/0G1Nbpmqpp8Irw76lvZgNfSCU/Ap1XSKLT+TuXa/aKAwrh3+es97R5+Ixbdmi7heCC5Lz0aDoOwL8+5TrjyyDImqc4t0wNGIMGSvDCXdtlrHgt0uWNhPWheE2Qj8b2m61ywXmZWVxFUXyNlNErYCcixMthKRIGWybHlfPO4arIGX8BagFOWqdo31HGWbnqBqPbsO0X6qojlEilLJZq/xEgBqjcq1ZswY/+tGP8Jvf/AbLly9HX18fpk+fjoULF+KNb3wjTjnlFOy///6x6e+55x5cd911eOSRR7Bp0yZMnz4dhx9+OM455xy8/e1vH5MyjidkoicQ1KAlM4yKZ2PYy7HnZ2R7/L/bULDK2OK2YW1lCjqdQRSsCjrtAWxEB8bu7SrYVVC2MljndGCeuy0I29tdj2ecuZD2JBAIBAJBiOuvvx6f+cxn0NfXZ4SvXr0aq1evxt/+9jf09fXhyiuvjKRVSuHjH/84rrvuOiN8zZo1uOOOO3DHHXfgnHPOwbXXXmsspu1skImeQECQtcvoyvejr9yEuA/rnkoLujID6HSqypstahguibsoswnz1Rb0qSYs96ajCPHFJ6gfq50uzHZ74EDBg4V2NYR53laswtTxLppAIBAIBBMCV155Jc4//3wAwIIFC/Dxj38cRxxxBNrb27FmzRq89NJLuPPOO2HbvOXwwgsvDCZ5Bx98MP7jP/4De+yxB1555RV84xvfwBNPPIHrrrsO06dPxyWXXLLD7musIRO9SQJlWQF9waAn2YiGMcIrLNWS0ip0PgxNk6NzxjGFkoRADCYGQxtLFb9okEqnbJMiMr9jA7qaqnuiVvdPNyg91bgKu+U3YWq2unI06OXQU2nGGrf6AT7gFQBLwbE8NNllzLB7cICzEk95C1BG1hBjSb1/FQli7yWob8OXFy139HxwXCfdLRY+rUwRepmmmrmEXqXpWa35YhCmKZlUWIXSK/VxgdA0tcuLJifkVzX7x/2V0Po65IYTay3wMEwEHrTYg6aF0fLqe1G2FTou4yh3COliVAhC048oIydgdlHKDUeB9I9dZLBGdWJBuTugly7wtqLbakZPpsl8vgyNp17aWJoYC1fGoF0yfZWGG2n0MePCjaOAxtFCgzFB0xVRFelwc1aNsIpP0yL15GkhFK4fkLwpDTOIS8J0syZMYcPtl13xqY10bNF90E3uXCq4YHgzBq1MizlQv1+63ZLrVdzoR42mcVZIvzT6m5+3jbBBeX5llKlfSr/BDXthHxv2F7IovaxMGqbn2bC8nE+prl6n2VdAqWSIzzz/OtQPpsvQME1UOyF9bDqqR2ihtt9XPZKRrWmo5CuIil5Y+hkzYzU3fFOwFE+ahuuj3LcoR6/WzaOR7eDMu5Ol8DeYH4BkpQeGrjkimI7tdObkOuTQf4iGEIo+Z1ClddZhGTnfeka5vWgf1XFpfwoozoYYS5RTamVJ/TC8d1aMp8F1YxeWsRA9kTCacv3973/HZz/7WQDAu9/9btx+++1oamoKzh966KEAgM997nMol6Oc7H/+85/4xje+AQA47LDDcP/99wfpX/e61+H444/Hsccei0cffRRf//rXcfbZZ2OPPfYYcXnHExOTuCsQ7GC05gaDSV6xksVAOSrEMjvfjanZPnSXW/Bk/254bmgB1pSn+Q4WLPR7TehTzdjmtWKtNwXPVBbAhocuiwpqKExHDw5Sy3G4+icWq7Uioy+IYHW2CxVYqPhDtAVg3+H1yCg3OaFAIBAIBJMcn/jEJ+B5HhYuXIhbb73VmOTVIpuNzo6/853voFKpruZcffXVkfTNzc24+uqrAQCVSoWlfu4sEIveZIETWvSM6XtgvQuDAisPK9BAVqAM6x2Tj5YQNlbJmXg0m2AVMbqRml3J55ZyaRhn3fOYNDazchRcT2H3KeuD4M2D7bC8mnJ7wGAljyE3i67sAFqdIWwqd2JDqRMuqzyh4MLGgCqgDUPYpDoBBcxR3Zhvb8EWtGIj2jEX3VikNuEVaya/2sosWhpGF8Y6xwrmpAgBjGhPtN6ETiwV2mrhEauCFlKgAg2t2SEAQI6YRgwLg3+X1HrX4lv0mokeer/v0H7IDS16w8SipwUeqEuFsr8qWyEWPV1ez62K0nvKhnKrW/iNVWAn2va8DF1tRhRaoIOzgsVYxkqug9VeF+YPd2NVvhPzi9uQVxXsXdyAp7vmRK/BdRNq7eOsd5y1IKW7ceI+aVY51qKXYL2LdWdRU2cWqtYYLwtDREWvyhvt268LzopnXIdej3OvkOBygR5bFSt6PqrLYCCoZ9KeFKl8LexALWf6mFrxPGass62o6bfC9DfaB0MLW5i26GnLYDQ/ujpPz2csFxVlI2+Vg4erxZOanLAvt/nXo31VWyApQ4BHmCYQkckSy6iftyHnzlnGyHHw2Ij1lhuEuXG59hwQw+KgYVEtjkQYwkFcv2XeuyneiKoXr+mHbD4jEHIzwMXlwrgxQfdv7mEBgYndEEfh/L7o92CKywU6/ocGPZuc15Z2UuyA2UH7sl8uh6E2AEGlKub7xnitZOttIX5aNXHdGHCfavVg6dKleOKJJwAAn//859HS0tJQeqUU7rrrLgDAPvvsgyOOOIKNd8QRR2Dx4sV48cUXceedd+K73/3uTrlXTyx6gl0ezbmi8X/3cBsbr7fcguf75+P5vnnoLrdhVq4b+7csx6xsN7LG10AIGx5c/0vfgsIcayvWqk68hNlYiynYiHZ0YnBsb0gwKbA63wkFCxYUSpYDD8D08gAcTyzAAoFAINg18fOf/zw4Pvnkk4PjLVu24OWXX8a2bdsS0y9btgxr1qwBABx77LGJcfX51atXY/ny5SMr8DhDJnqCXR75LLEQlQopUsQWhrw8Vg9PwzMDC7Gt0oo52S04sGk59i6swYLMJizOrsHhuX9ijr0VeauCor+ZK4cKHEuhW7UGufWhCXlUkFOcrrdgV0bFdrCq0InZxV6syXeEFgbx1SgQCASCXRR///vfAQC77747pk+fjh/84AdYvHgxpk2bhr333htdXV3Yb7/9cOWVV6JUKkXSP//888HxPvvsk3gtep6m25kg1M3JAovQLo2NxNEwToxFx9N0zGoYosdpPvMCWiBv3g7yIfQEVqCETWzGrz2Gm/ABzJyzHL1huvoJva5nCrqLrYR+FqWXUqpFxctg1fAMrClNRWdmAFOzvZiZ6SGXtJGxPBRVFlAI9uJVlB2Uux8FwAI61aDvkiH+XlmhmwRqJoCQDsRQbVjfa1w8I++USYamlxEqmaaVlQlVUguhdOVCayalcToBdTMcpFudquW11w259JqyadA1ifBK0T+m1y5Xor653Ip/bAiv+JQcMkqywiOkSrRHDsVSF61IGEtxBIKReWWmE/OK25C1XPRl8mirFAFHwaulg6bwxiiNM0mshRNe4fw6Ge2S5K3Tc2ItRt0xYktJokRGep3Wv7bK1NDLuLas64tT7SDHrM/AFN96xjFzneCYlocTZgnGRkbAAaGwg0XGVt2GaX07DD8voJySG6Tjm6ZsZkjD1fTLIqkoLaxkW1GrMl0gyxJVm2angoJbQdYpEepmdWGLirZomnZHbigsg6aPplA3DTqrFlQyaHP6JROmCcQ4aH3Tvuz/NYbJRgVa2MBkuqfRTrjzCTRjCqMtJ+TDuUtlr4cUin/au6FOpL5idDxDbIWpXJuOt1F6ZTBOpPrWoxTnKmhzZF0hMh8zTpYb9EJwNEbF5Z5plLo5cf3ojbRczz33HABg4cKFOP300/Gzn/0sEuf555/H+eefj1/96lf4zW9+g46O8Ptq1apVwfG8efMSrzV//nw23c6Eifn0BYIdiOFS9eu8VMmg4jW+9uHCwZZKO3oqVZ74hnIHnijuhm6vxT/vOzBmnLaWkcFWtGAOttWxgUKwq8G1Haxs7sLsoV6sbOkEABy+aRUcT0RZBAKBQLDzo7e31/gVi8XYuJ7noaenuqD+0EMP4Wc/+xlmzZqFn/zkJ9i6dSsGBwdx3333BfvuHnjgAXz0ox818qA+91pbW5EEuv+vv7+/4XubCBCL3iSBsqyqIIs+1kiw1HCWH9MyxK10RfNpxL1CmB2nDkHOc9Y7L7oqSy11jS4oKj9tyc1iuJxFR2EAvUWyqddiVnoNK4BpitQr4GtLU1BxHDi+6HcWFVhKoaQyUArowBCGVSgessbqwgFYjSlqAFtRHXS4Fd8kS0ycsAr7PDhxiMDyx6yMpkBFm0mwqg7wghHa7UGRCC9MyYbqpFqsodkJB/z+SiGSRrtSKBHxh2FyXselFgFdDmrRCywspG4sJ9reDDEPfX8g0BYdzjKWJlBC6oymX9HZiflD3Zg93AsAKHgV7DbQjVc6piVLpMe1CSe6uh30b86aZnTwmnM1ZdXXMcKYOkGCRY8VJSLpqfVCOahaN7mVempp1BatOGshI1aR5EqCulcw2kTFLEM1zIrGYwQcWOssMwDQvhUKeJC2zCwYaeEVm1i2DeEVLbJCLBkl3zxJ3R1woi0aGWLFa7HDfpu3yshalaoVT7tNsXyLHunf2rpXsUMae6svwBQnJuH6Vp2yE17by+rxhozfekjwaD35QcbzjV6HNltWoIUDM4Abxj3udhL6MD3Fud4xyqirwkjElIfJxwLT7rlXdRyDhDtfL7jxhkHQtzi6C2g/I+e1dY+x6Ka5XKAWdL092tB+0WMeCdN152SoG4Y006mfH/0neEak3TpcQ0nKz4I3Qd0r6HJRqxkAXHTRRfjKV77CphkcHAzGuGKxiObmZtx7771YvHhxEOeYY47BX/7yFxx55JF48skn8Ytf/AKPPPIIDj/8cADA8PBwEDeXyyEJ+Xw+OB4aGkqIOXEhFj2BAED/cBOa80VMa+nB4hmrkHOivO409PmUwlanOoi4sNHrNmG+swVT7T4UrDJc2GizzMGiH03oRQEzVU8kT4HAtW2saOtCZ3EIPbnqS2fKsAj4CAQCgWDnx6pVq9DT0xP8vvCFL8TGLRRM11cf/ehHjUmeRlNTEy699NLg/1tvvZXNg9vDR0Gti0kuHCYyZKInEADoHWpGLlPB7I6tyGUq2GP6OrTlGvuY1qtTdugiG8sr06FgYffMBuzmbEbG8tCrooNFPwooQARZBDxWtXaiYjsB7bejXBSqr0AgEAh2erS3txs/akWrRSaTMSZqb3vb22LjvulNb0ImUzXnP/roo0F4W1uorJ5GxxwYCJlGaTTPiQqhbk4WWISyaYgw6LAot0lxYiwMLZDmk+YzL8lvH0DEGmI2qYdhUV4JS+fkqJQpCNIQSsbQUAE9A83oaKlO7jK2h0Vd67F5oB3re6eEm7MZOmu1jAoLCpvhKQv9blOwhFJUOTxZWggHCrMqOWwsT0UJjrHEYimFVhThWqFIS5LwirF/mfGJyImscDQ+81mqaDwubQplJ3BXRB6MFmYpe1ExlhLZE0mFGWZkqjTFQTcc8Ad9pZMhj/rJy/p/qZ+8MB9N06S0Ue2TiwrGBOW2q9N1y1YAQ920GDoUpdJYrP8kfWGSlKEuGgIuNfQrDxaWdXRhr+7NQZxWt4i+XCFSxiA/o52EETifchy1KxReiWbO0RmrJ6xIWNDn6fWYvpzqRy8QFAmL7DlVX3oGnVOzcFOEZdjjOsvDPisAtu5bhDaqqXSGr3s7Op7WUsGr1yG0sYB+GqVp2tTfnh19Xro/GmIslMbp31iJ+NvTlE3OvyUNy/m0yWZK1yTc1rxdQc52q3RNTd0MxFgIzdoPqzhEoMUrG2UBTBq27uu0zwf9m/jR04twlNaqKZu24ZeQ3ms8jZMVaKFhDC0yVcAlSaGFXJArA9tvGT977Bsy7bXJjflGGEMF59KmXJvR90lOSsVWaJvX4w1T4QZNM2F/hOFbj0TQNE7ajsK6DdNoyqb5acT0QZbCGwa6/nUsQuhUDfrRc5UV+pacYBhpuebPn4+XX34ZQLKYSqFQwLRp07B+/Xps3LgxCKdpVq9enXgtKsBSSzHdWSAWPYHAx9otUzFQJBOLUh5Tmvuwx7S1cDhZM4Ip2T50Zvrx6vBMVFTt+okFDzZcOCgjg9o3/iJsRBuGsBJTx+xeBJMPq9o6ULbDD9p9t25MiC0QCAQCweTDfvvtFxy7bvK3mT6vLXu16V944YXE9PT8vvvu21A5JwpkoicQ+PCUg+WbZ2PbYFWQpTlXxNbBNmRsF3t0rYOdMNmbku1Dv1tAj9uIaV9hkbURM9CLVzATPVZLehLBLgvPtrGssyv4v6NURGspXp1MIBAIBLs2tHuFifobCY455pjg+NVXX42N19vbi82bqyyYuXPnBuGLFi3CnDlzAAD33Xdf4rXuv//+IP1uu+02ovKON4S6OVlgWYRWwdE0aVz/L0fd4qiJKecphYKjBZrULj8shX0Q0DSpJFpAgeNl+dg8E65D8w5orJ6FNVumo1zJYHp7D6a19GJzfzu6mvsxv3UzVvTPhL5xfY85q4T2zBCWD86IpXtZflktQkmda2/FDKsXr6iZ2Ix2o2ya0Ui9PaiEMFrfhNnIqmkGtDIjDSMnFrQTnhZm+RQSJxNOgLWvoGw25DNpP4XUt1YhUz1fcMJ9iZTulfUn1VQtTCvvUbqHPm/40WLOu4banqbfhLdqU/olqtQ02/KgUEPjoc9V1yPHdzZ8xoVtixTMPAeY1E79jA3aqMLKXBt26+1GoVKNvHfPJvxj7hxWxZZT2gxusKbYHJUUTD2FTqz46wT+qoywqOJdeE/RMrDXAwJ/dcFfVNu/ygIepUr6zcjl0lKfiJRy6R/TfHRf8Ix6jN6D0Y/0eSsaZjE+Gqm7UY+hvRvKv5GD8Ji2eb1H2KDmB88yGgaE1Eg6Juac+lx4aCplbzlGqEBZKFcKGKq0RqibfW6416bXV9UdIHRtrZrblgkXNCpelF5K+7fH0KcrQT3RcvkUT8N5Hkf3o3UbjaaHRNaXHc0t2gVZNU3EDDdBPI85mUZD1k2Lf3WaaWvDUynOPl2bBCbSUIEY53MxcWNgKPuSSgtonNz56KdRTTX541eMiq1+fxv0aT12Eq54QOFmPqesGLqiCtotDWSopNkGOa6TECeeeCI+97nPQSmFO+64A+973/vYeHfccUfw/I4++ugg3LIsnHDCCbjmmmvwwgsv4OGHHw7cMVA8/PDDgUXvhBNOMMaCnQli0RMIIrCwsWcK1myrUimntfair9SEzqYBzGvdjPbcADJkY0abU1XR7K3Up8iURwn72Gswz96KNd6UyCRPIIiDZ9t4dUpo1Zs6NIQ5xCeQQCAQCAQaHix4aoL+Ruj2YdGiRTj55JMBAD/72c/w5z//ORJn/fr1uPDCCwFUXSicffbZxvlPf/rTAZ3zU5/6VMR1wtDQED71qU8BqNI+P/3pT4+orBMBYtGbJFCWFVrvWEENy4xL/tJ4nABL7HkmLA2hKAIjGMCs7hthroqGseIJKUuDzG52bnWvu78dnU39aMkX0Zobwoa+Tkxt6cXUpl6U3Aye614IAOgtNwMA2p0hbNHmtJhV0IxysV9uDVxYeKEyBz2qJbAMUWtCYIkjYUHWNEwLa2RoPLKyGlj8GIsOsfKonL95PButXLrRnYowZHKa+x4uZeeyviWOipH46XM0nm9CaSJuLArEf5a26OXtqBIpR/egFg3OakGbhGIsTPq8nfHFWCwvtOjZTKOnMJaWo6uyirEwBPGolccoj79KzpR75fQ2LOruRlO5WkcHbtiIjZ1NKGVqhnP6zA1LrR9O/YzpRuFF43FWPtZHF8jYQ89z/i+ZaEH/jbGgBgZhagXLAm7OFMQIktOm7FeNIVBBx0ndT4zxz/wbew+MFdDw4ceMZR4zVgdjECd4BWoFou3btwxSsYY6l29pn9EWMdq/Pb8Rcz7zOFCRlP5yaKlzoGC7+f+fvfeOsuSor8dvdXhp8u7O5qS8igghCYEkBMYiCAWwMMbYJli2wAEbf3+2cQJ0CDImHGNzbDAGDNjGAQzCkgwmC4QRQmgFymG12jSbZnfySx3q90d3dX9q+tOv38yGmZ2te85qWtVV1dXd1dWv6966H4igmlyEVixLoJNj00FktkQNmgbc6McXZfs9aswSb7eDrBkLZfmS/GQ7uWLaM0aYwfiGCJHt7PTR4lhelt3j4k1ms/GPFvPO45QydD9baSE7R8xFOjHwLDNI+2q2cFEsXy62aMfvAOb3QpSs7ls2sz58C/JfvUrtGtJ2q2tDxij1fmRNkrj3bs45cUx0Em/UIe9qv7vncanjAx/4AL7zne/g4MGDuPbaa/G2t70N11xzDarVKu6991785V/+ZWK08p73vEeTbgLAmWeeiT/8wz/E+9//ftx33324/PLL8fa3vx2nnXYatm3bhr/6q7/C1q1bAQB/9Ed/hDPOOOO4n+PRgvnQMzDogMPT/egpH4RjhZBS4JGDm7C69zCWVyeTPOqjxGd1XTrW2odhIcSD/ubYmMXAYG4ILYGnVg7h/D2pA+c5ew/hgQ2rFrBVBgYGBgYGxwebNm3CnXfeiVe96lXYs2cP3v/+9+P973+/lkcIgT/7sz/DH//xH7N1vO9978OBAwfw6U9/Glu3bsVrX/vaTJ6bbroJ733ve4/JORwvGOmmgUEHTDWjGedQCizriT7u/NCBJSQGS1NYVp7E5p79aAYupv1Kbj0WQgxZ01hpT2AkWGY+8gyOCLsH+1B30z60dnIaK6ZMEHUDAwMDgxQSkURyMf7TQ2DMHZdccgkeeughvOc978FFF12EgYEBlMtlnHLKKXjjG9+IH//4xx0/0izLwqc+9SnceeeduOGGG7B27VqUSiWsXbsWN9xwA/7nf/4Hn/zkJ2FZJ/ankvm1uVQgiDSiKGZe1zHx6HZno4A5NzdvIXmSxsi9GNmnZtbSYY2yJudk5CeCO79AQsJCo1WCbYcoOT763QYm6j1YVpnE5v7I3n7aq2BXfRhSWqy0BVJiS2U3VjsV7Ax7sN8fSI08wEvEJCPJVGnUjEVtSyrt0PbH6WS/0h1p8d/iBd42WejtlPzMJbHIgnMl06y6qbwykU0RyZlKK9mpcKoSb7tEc+MSvVNJ+Jk018reYCU/yzVjYU0o4r9UIqNkNSo/BCxLRreRdFY9vlJcVGTlORSqjCZnjWWT0uks3eRMDxBGwqin1g3ggmcOAQDatoXN4+M4uIJMNlDjHHqYICsLTaRNVH7FKS4ZOWehdDuRc3Y3XmjmB+SWJ6o6O5VcSSt6LjTTgw7GE4JKKqm8jpFzswQ9J11jYlTSsYgJa5cehqq0VIwuTV5K5bVxGq2vg4hAV89G9QRa/6VSM66NsfyMyrWt7uScbaJDn2xXYftlBF4lOd+SFenQZ/xypoySawJArx2ZsNSIxFszY3HsOI3EyVQGLQX9zWOMLmTIPct0f/yXxm3s8Bsw16iZe08wryD2KnPvL24JA02S2Xyz+5NQibPzcs86c2w9Ni7zEPJdL1uk8AJ0hrrmmiyaM6tinKVY2So9heQ3FtndQRZL30XKrCXvZxMn8RcqHh8dyx1jxkIxODiIv/iLv0jW480H11xzDa655pqj2KrFBfOhZ2BQgPGZXqweOgwA6CvPYKpVw5Oj6wEnBCCSHxt56LfqqFlt7PDWY5tnzefdZWCQwZ7lvThzzzgqXoCZiovhyQYqbR/NkhnWDQwMDAyQGJ8sRizWdi01mF8ESwTSFsSMhRphxNtdhlLgDFjo9rF4LjmDlmSWMWDS6IQW2WZNWJi6SYl0K2CmQeP/GZ/sxbK+SZRdH64VJCxiGBsAaDN0Qi36FgAkVroTWFcexXRQxnRYARDNTLOGKgybwBm0aOEVYvMUyuhpFvFxuiRMnVrYTZkoOzZWcd10Crpcipi6Mg2fQFi1ihPtD0jD1Uy/TY0e4jRq1+7EzhoOCblA2buy8DJpTrxNwzRwjB7L3rEGLVnzgGRWVYjo36xzhp3tR/Q6qm1u1jYICIOijHW0h4xssiYyVvw3TXtyYz/O3zYGzxUIhcDaqSlsX9OvTioFY3CikwnZGfjkOPQ6hZkkHcw4kcybh8wzWDCeaJb0iskjZgTSiQxZqBnL7OPS/9GYDybUAsfOFKkPOLMWizPK4E6fmq3E56WZxAS0j6pqSH9TY0dO/0/Tor+hZlBCaUcVXiFN8lUaMTpRz3eJPJdF7J4vLQQQmGxXk36h2H3Kzikmr9dJ2bsBJ0qrWWmaRybWlDGLzxi0cCFVKKsexMxIQE2JHKZD0mcwUV9kWb4iaEZAqgz/+Kf5sqQT35/mw3xx7BVFh35bmE+NAxzbDejmYEkRpaYgaVwoiU5t4MqS3dq4HGTHWHac0MZg9XuKPIPKgIkxSRLab5r4eeIoflK3Rd/VcV5q6hIWBAg3MJiNE1t4amBwHCClhR0HVuHA+CBGpwe6LreqNI4NlVEc9AfwRHP9MWyhwcmKA0PRGtKV401M1VwMTpkA6gYGBgYGBgYRDKNnYNAFPN/FwYlBfsaXQVm0sbZ0GPvag9jtrTjCJccGBjzapZTdGJxpQyiq0MDAwMDgpEcoLTYk0WLAYm3XUoP50FsqEIKNTZPEdWLknNozxpqxZKWdxwusZIOVQzCryxkJqIZkZTqzL1eGIzL7E5keLRPLQVZUJhFKgb2NoeQpk7ZAaEdyQNZ4pes0mUnT5JpE+qEknTQOjxVLMS2yqFtJNsslYpjiRNtVN5VN9ZJtJZGiRgi+UEYIWTmnQy6U2tbMVjQzliB3v0PlnB0MWgAigaQZGKOTJG6dlfYNKTVvAgC6jNNW8ZMKpJuJtI18g4WcNJNpt2YsE2bTZCgwsrqKtfsiidtA3YPtBoAQCLU4eSkYPwUir6QZVZ8n/U3FnuIMGmbvmHXAwnd60RijuiaVu1kS0pGzzFiybUzkqoyJBECeM3a8YLZpGme8knOc2UW0IdZS0lSSkZHPa/LhDvI6auoQxC8CanRjaf0t+xxZsWSTSjPVs06f5a7j7EmyPjmIBsVet5ns73EiNrrfTs1YBuzISZbG2GwStykvvnE0tI2Kw0fHJc6MJnkGmbRoR3zN6HPEjcuckq6gL7Nx9pIKSd1MPewjVtBHkxiNRXJPMuB1NCZh+reWTRl+cUtGkJOWrM1gfncUPE+slJyWUZJzulzDYjIm4zJJC5ky2rjNtMvS92lluMEY6fvY0mShsXSTVG7ZxozFYG4wH3oGBkcZFkIsL01hzOuFNOpog2OM8cFS8qEHAD0NHzM1w+wZGBgYnOwwZiwG5kNvKYELpZCkgaRli3JmK9y2bs8csxd0EbJqCmdGAKRTXBxTp5msKPYuTUr259TNmj1wdatdxfHN07zc9UlY0DTRsX2srx2ELQLs84YgLaHNAksnanLYbdgEi0nTDFoKpjeZlfacGQln/jF732wsK0ez7ZrdOcfyxdsqPwAMl6cBACtLaeD5Ve5Esj0TRrbr9bCUpDXi7UaQpjX96KK0g/TicSwZZ9DCOSEkZiwQCGMGIp/YVnbZaaqVMHpk8XxcyspZhJ/U3fVUtY7xweh6BBZgh8CG/dN48ox+2KQ/aUYw6hwpUxEyHTxk+kRyfWjD81m8Iwa9l7FsWqjwEIgI9EBInWn3mWYVMW0My8vZy/PhU7LN5pUEBfuL0OmaamYdWeMRK3t6GmvBMsgd0nxtYCpotozGAF9YyWV2YlZCGzvibY8McM1YimyRtnpkoGzF2x6pR7VRUx+osC+U5UvMjdLz82ioBeZey/i8Q6bvkAgQLItdaOrDKXLUNvPOy+tDHcktru48cMdhr0mX+TocoxAFv186n3TO+z8pStVO6vcC/Q3Fl5p9aM0kSaVSAxqmDZoxC8MmcqErcs1zDAxyYOgGA4OjBomz+ndhsFTH9uZqtM16KYPjgHrVRtu14DvRcL5p1wyswPwaMDAwMDAwONlhGD0Dg6MAR/gYqkwnQb177QbG/d4FbpXBSQEhMDZQQt90uo5p9f4GRtbWFrBRBgYGBgYLjRAiUZUsNizWdi01mA+9JQIpkGoIimLmKRCDlk6x9TLHySRmJQvaemNGslSUlshGQipjkNrf2fs7yTSPGJyhBFG7nTG0J4kNBQAr3Ens8ocBAKEdXZXQikxTJMDH0eOMVzSJZ3yuXCw3GjOIk6TR62TN2geitKHx78LszeY09ctjGSZA5VfUCCHaHib5VrqRZHOVk8o1m2HKgE4E0UfKhJ9+rNSDaH/dJ9LNWLKpJJwA4PlZGVehoYZKUnIupHH0QiJ7pPJKtf6SXpGQkW4mZck9cuysG0OeRLYj4iITQy5WHG6iUbVRbQTYsGcGBzaUkufZIbEQlXyNnpeS+Uku3p4mZ8u2kZWcalLC/LJcPXmxBdMHjtYdQNpBYoIEII1xRuP/dbjn0X5GIxXOKkuRI8NLj5M9B3Z84+qeSzdgzCqUCY9FXgQy7nyakZMmyYz+akoy5r6pvuNTaTL3biGwIRFIC5aQHZXISobZ0qSZblLH7HwANWNJz7XXyYYYSaSbJF/AGbRQAxvm/CWz9CCJUUjyKe+YXNMiTktVID/suI+RSOrxXbNtYI1ewJiraNJtpq0Fzxbb3jnmKzR1YcpbdIhVZlvM8g7tt4r6/UINUWjekHngkqRsP9GfbzXGFjScdI4wvuD09IPQCPEM5gbzoWdg0AX6azPoq9WxZ3JFxhK4Yre0j7xW4GBbe/XxbqLBSYyJQRd2CLRdC9VGgL4pH30TPqYGjXzYwMDA4GSFMWMxMB96SwVCEOOVvIXEKm92H2c2Mrv+DHjnjvgQOauHOaYuMVlhmDpuNpFh8bR6uHYVzDpzoCEpemsNDPbMoBmUcXBmEIDEuv6D6C01UCYfeYe8PuxoDSNwrAwLKK34H/QZSsXaaeydKsOEUtCYivgCSW6GFTzDkBqUZGf3OdalSF5BZ9uXlyOmjhoqKOtzxeIBwBp3DADQIusYx4OUvZsOKlGal6ZNeVFakxivNP2oPGXx6Laa/eSMZ9gZZtptwzgUBmUvqMGFyscYYWhVKpaPZourdHKssjuxXxoxEEuF6wM2AktnhTfsruOJ5X1aGwDARTTV7QeE+eRCN3S4TrR9LKPH5C0yEZDMIMSWUWMIACFCWGGgsZOK3ZPMMCEYxlI/NqXG1M7umcE0H0PlcOYQOazM7KJ65dkyUpvljxK1nqVCTtAwO7SEzLKp6rmnfUIxZ3T85sIr0DRXhLAgNUaPK6OOxzF2NKQCHTOUgqDPScM0cHUnJlGU0etg0AKk1yLg+qOWT5UlB4zLWDn3lzPZmL0vN62IGWO6cqrYYfLPrlvqeYtMVljjFfWcFRhQ6e1QByRJLPPZ+UER3HOrwitYNC2f3eMMWmjTOIMqQS6ACuOg3QJr1oVFHrtHS8XqC/o0M0obA4NOMBywgUEXmG5WAQCr+w9DQGJt/yiWV6fQDnTGZLk7hfWlQwvRRIOTGNISGFteQrkRQAqgWbWwYm8LTtvEXDIwMDAwMDhZYT70DAy6wGSjJ9leOzAKNSvXV2rAC208NbUGY+1etEJHCwlgYHC8sGtTDdVmCCGBViVilFftahYXNDAwMDBYklDSzcX6z+DYw0g3lwoEOn626wvBsxKJwjLsMTmNCJONlRKSDCpmXpjNxxmvsLH1SD36wTs2LZOPSjt0aavAgckBrOyfwLLaFEIp0A4cOFYAS4SYlD2YbPYkse5Cl5pDxBuxtE4iNWCh+4ti5rGSHbXInMhLdEMNSf9EeTsYlGgxs5iLR9NU3oDJt4rINJX8isbJU/IrKtekxiuTfkX7CwCTsXRz2ks/pNuxTJPKNX0/K0mkMeNSqVGBHDmRxZFzpvHolEy5MChaLOOi9whZSWmejDOpJT4OrYfG7ZtZYWNywEH/hI+eqQCH1pSwZlcT+88oQ5C+ZcdlSsQQJkjig2VlapyMNE+6ycVj4+rhjFcSiWfOcWabuggANgLYMoBw0msnE+kmU1aTPXF9giSFzHPCyaa0BjMyVTZ2qDKHyFaX250yLhnpubLGG9rh4vtCMgqns3RbbftB5/lghzkJx0nl7JaQEGK2dDNM9iX5lOSUDITUoEnBI7LJfrcZl03b4Iqs0ZGSeOpx+zjpJiNtpWY06jqRd01qZEOvXdy/SX2aMUsHSTKbxr1DKebxLmflo0S6meyfa1vzynYpXaVyz26/A/TfAdk0No6epZ7BfAknwMs4tTh7bGy9+P5zMYa132LkmB1knJI8E9JINw3mCMPoGRh0icMz/QilwGSrioMzA7BFAEtIjLb7F7ppBgaAENhzaiQxdnyJmQEblUaIwQNeQUEDAwMDg6WIhWbsDKO38DAfegYGXcIPHeyeXIH+cgMrapMJu9UIyyheIW9gcOxxaFUJjVo0rNemQkwP2Fj1TNZu3sDAwMDAwGDpw0g3lwhkXvw7la7JEJkKmHx6/UwRTosgGc0GI2PinbyyZURA05i6ObnmPJA6lpJEm0o2ou2xVh/qh8rYsnw3bEQyos3VA1jmTuGZ1ko0bTfOT+pWDpoi/gfoko3EYZOkKbdNKndJdtJ2z97Q5a6smyAjpevkjFg066bJPZm8ymGzHpaTtKkwkmFyck26Pe2nZZRkc6adpjW96HpTuWbgZ2UuunQz/1x1aTGTT3PWU3+4hyObRKU5VizTpI5u9H7YdlbapuS5VCqpHj2ab9/pZZzyswZW7mph57kVbHi4iZ6Gh3ZPdI3s2KmzW7dMCu7+Sub+6+aVWVkgl69IIjq7jADgyBAuAl0q6jL1KWlyXkxArj2MdDOVTTGyT7rNSFc56SYnC82TuyVyT6pMFNquCFamhankmL4m6Hbc3zSHTVWGysezqsjkeK7F7Yz6piUkLCJyU/3VIpq6VM6Zpqn2eERyOeTWyaGjemzRWbqp5OM0vmfixEmkmzSmXuLYS2JQqmdYe+0ksmdmYM65l+qx55Yr8K7JmVPK7YLM7o4ZaY8WkpFdsv2blFeOlrMloJjlcsuNdVT2nVwUptm0r3LXTtvOv/adJJzR/u5knNLi7nU2jdaXOHHS8ZuerPodwEk46b020k2DOcIwegYGc0SLOG0KADtbK1CxPJxb24nV9pi2XsTA4Hjj0HqyhrFmIXAFhp9pL2CLDAwMDAwWAgstzTTSzYWHYfSWCqx0ZkozFEnYoizjx0xo67Ny84g9lxTNWTyebNOYeZLLl882cLNy0Q71N3v+er74OlHGzsmmaYYp2nUUeGZqFTb37YdjhRhwZvBQawPWu4ew3j6E1fY4HhXrUEc5rluVjbYlUrMVlY6cNNA4eh1uh3ae1GTBjdkbkqZm751SOlPtupF5QqWUmihU3WhtV4+bfiTo7F1380SleIa9xdw4W5vRT9vYji/GJGHv6jGj1/RITK12tO23KO2QIokvRg6dGFgwM6NptxPpBbe4/eCnyRKGldtHDBziPqhFPyP3OojZlFBj7xSjR5oQp9F8oSWw88wqNj7RwGk/rmNyuYNlOzzsOLMGaacvV4upm0MRs9cte8fFN6PgDFooknONjxEfCWI2p6pm00X22bFoRob5powf6zXF0OqcGU0he8c5phQxepk2ECEGvbZHafq22z6hmC+HYeKAiDELQgu+tFJGLx5cLXKCyiTIJZRGGJ9MQPK5hFa0GRawE2xyTlx/rJbS9aycGRVvWqM2aD7F8tHSVHWRLZPEraPHS+5vNo1Ve9CytGrF0hW90iUpyLyXWdMi5OfLZ6eZY3di8ugzpj3/s/KB9Fs29i/Jx7F7Kt5kzlIMhohk420mXZg8i6kCitTH3UONqWWYT8PoGcwRhtEzMJgHxtu9OBibsAzYDfTbdezwVuKnwWZ4cLAFI+DfZgYGxx57N0fSVwGgMhPC9SSWjxhWz8DAwOBkgkQ0abEY/5lfSMcH5kPPwGCe2NVagXY8M73ZPYiS8NCGi13hcpThowLjdmiwMAhcCyOnRh975WaIep+NVduNKYuBgYGBgcHJBCPdXCKQliDSTbKDiZmXyiGyck6tzgKFACtjSHbSbSJzCvP3awuhmXyF4cpUESrJ5M6VyRfG20rCmbefyitD28K29hqcXdkNR4TYUt6Dh7ABU6hAAui1GqhbJYROdHlDOyovkco5aZ1Uuqckm1Q+Cm6xupi1D9Ckm0qmabmpTsVxI+mTQ0wGquXoo1TJNYFUskklTt1q6l3hZ7apSUJFRMdpiXS9I41xVfcjmWYrSC9UvR3lbbZJmXZURnppWU0OoxRydLG+MmZhJDBCxpdUCMgwnnGk9VEZJ6fT7HR56HOZGBiQa0vilXWSH2lmLFxavL1rYw1rn47jjHkStWaI8miI+rLomnLSTYuZY+0kYQO6j5kXFPQdThbXbX8rMpbhFLVa1fH9sDSZbpybmHUI9YzmyF45uWcq52SukxbzkmsYA07uZ/Htmd0Gbchn8nFyRi5foElco22fXDw6ltuWRCAF/NAiEtq4v5GXFSfnTI5HNLc20WGrmJLdSjcpVN/qJdJ0n7Qn7CTTJPDVvSRpRCDKllUSYTokxEOifnuTB5w/h6Ruel/neilmDV+zZaWs/LJIhln4myA+Hn1O2OUjzDtPxZajMSHp3uSdmG2C5tOj4u3RDIkhCq07O75TsXjyzNNnUG1S8yfVHirn5My9yDs/OTZdjmKkmwZzhGH0DAyOADNhBY96awEAZeHjbOwBANRRQr9sLmTTDE5ytCs29q2LWL1KM4QEsGan6ZMGBgYGJwsW2mzFmLEsPAyjt0QgLUFmshhGiytTxPJ1ffAsY0dndLVZqzhdM15R+zWLZG46MQstlEAy3UaNCWKLbCt7XlqaYuwYFg9IGbjQofujvxN2Dx4L12CLtRc10cZmcQCHrF6sC8bwjL0cTceBiOsI3Vgzr4VSUO0haQxTJ12ZSRNMPsXiAYAVs3sOMVkpxUxeyc0ar/SVjkzepyzWKXuntrm0Zpiyc9NB6hbZjJm8tp8OUV4cQkGxeAAQqu0WYRNol1Gu8jSRY/RmuxVYImX+NHaG1K36usagJzkxG9ossTIW0Vg8krkjs5KdTdbOT51eKPHMxl6s3tNMklfsbeGpM/vglyyW0ePYKe0cGMYuzzyl2/2zkWf0wrVLiOi8CoaJBJZFjD4IU5c8P9r9jVkw8jwJhmEoYlgTppJUnaR1NATiwV3Pbu9bHjjWLmUn0woDhlpSrIMfpM8l7Vt+KOHL2WYsikFmyhDZhGKTS+S4mqFKfGI2oextJrxC0n5yLr1OSztG1NasqRMf4iPbRwOmjCQXXg/JoP5mr6dFFf8JW5Qty5BcGooUMLP3C1WPqr9T2ARuTOQYO/ru1x7cLCvHsnfdDh30t4y6hcyYqI2d6oaFHbPpbWR+v6h7IzRalWlD8juHYfkItMutLol2/83HkcHcYBg9A4OjgHH04rEwYvb6ZBO7nUGEsLDJP7TALTM4mVHvcXBwRepcaofA6j2NBWyRgYGBgcHxwkIzdobRW3iYDz0Dg6OEcfTgXutUPGyth4TAiD2AVcEkSqFfXNjA4Bhhx+Ye7f/X7mp0T4EZGBgYGBgYnLAw0s0lAgkk8kPJSB/0NKHt6wqMmwEf/y4/jZbXpJ3KjEVm9SB62e7knCw0WWR8nbh4gzQfUfGESZw9uj+We5KnyHdtADZOC/ZjTTAJAChZPtqOE8XQU2YsWj3x+dCYeUyakpdpMfFiKRo1Y7AcYrwSb1OZZkXFzOOMV8gN5kw4OLhEn8PJNEuxGUtFpKYHB2UfAKARUrlmKuNsxpLNNpGDKWlY6JGbFMfPEx6R5DAL/HWtndpJzyJ9JgQAYYlUi0WNRajULr7mkuvr7BQaubaxZJOL9Ubbo+3nAm2p3IzsU5kDjPVVMD7gYnAiNtxphBg44OMwYfq6lW6yZ3UMvxk5UxdtmADv+zAbrPEIkXFy8krt2ZtVT951spj9HIIw20G6vY6cdJO7TkVluVxFxisclBkLHTs88tzalkQYWroZS5xXM3+J5dqhlY4d3Iy/rVmdRPfQZsYgChWPr99J16iqOKAeGfR9MjCrYxexDtz98BPpJm0EMWYJVd00Q1YqrmScXAu0kl32HTYm3uxKZ0s6mTLsmMdKU0kSLaNuF/u7hI55TD4mjRqZqTGavSRF8lJOjq8ZpqgOzGlls9k4mb32KmJieWrXSS3roNcx6HJgNjCIYRg9A4NjAFemPzYMd2Kw0HhmU6/2/+t31ReoJQYGBgYGxwsLLc000s2Fh2H0lgpsYsbCzSxRe94jeLZ4hq0gTTNm4fYz+dRsW8BMCR7hlxPL3iWhKfiF0mpbM2tRYRGYUAmHrRpWNGbwaG0VJiuVyDAC0bXPMHpMe5RHgTZTGW8LK2u2YpNQCTRsQikOpVBxCKPnRNPEPe6RBdB24pl3x+KMV/xMWlOm7F09ZvLqlMUj2+14dl8ZsABAoEIoEAMT+DGb0Cpg9OgzYWXT6OJ5EdehzFj0Sfds5UXGBKlhDqlHdeVc2ixmrzgmkjZHsVx6YpRGZp33D9QwXZtCbz26LysOtVCaDtGoOlo9eSYjHZpw1NApLECHUpCzAu8WsWlpPq4Mw/zNwbSmU3gCLmwARbcMOndNuB9MHEOYZ6LDlQ+C7uaBk/AZhAahLIkvrNzwCtTBJIzHXnodQiZWAL2OKqwCve5qv2ulY1C/Ha1LpeO3FxuvBOAZvaQNyF4nzjBIIh2/ZBi1i5q/SPJ+CxMzGlqPYqzJ8dQ5EYOWbtk7DXnsHcjQR/5F7WDKckxcJ0aPGp1wBiez6fnM8eJ8lF1XRSmrxjjGsMfjYpPQFM6ghRZR72rK1Kn7Tw3R8g+Ra/SSjmLZviVotzThFQzmCMPoGRgcA4yUBjBhV3BK4xBcs0bP4DjC8UOsHq2jf4p8yAuB7ev7tHzr98wc55YZGBgYGBxPLDRjZxi9hYf50DMwOBYQAo/0rIaAxHkT+9DvNWbxDgYGxwab9k7h2Y8fwvMfOIAt28ZhB9H09N6VNTRL6dTw2r11WIHpkwYGBgYGBksVRrq5RCCJ5oIzXuEMWigS+QGo/IBZuE9j5kn9b24aI4Ogi5AFF1tP/QBlpB0iz7WAlV92SHNoWvyXMVvJ269kmiFj4CJtoGm7eLR3FS6cGsFzxnajLIewp3c5pBCaJFPJUrQ0NqaQkm6Sa8eYsTgk7peKa1ci0s1yvF2yU91UxY60QSET2Cdv1m3ai8w8euyUOZqyowDdvXY1TQsjA4Q6MV6Z9qN8M35qCFL3GTMWIt0MY5mmkmsCgFKNiiArpYn+Z9ZfpP1NO61EPhsnhtR4hdNh5sg0GclSckm1mFKZ6nQUSH6SupkKdgz3YePeaVS8EJtHprF2fx0jK2s4PFDGyHANp+6ZAgCUfImV+5oYWd2T9i3m4nFt7FYemQclT+OOl19G75sCkVwwyBNFzmGyOJFkMvtCVrqZ7qeywZCVdqox+Oh8VB+pTJOrh4uP2G0b1F0JtDiR6bYf2AikjYDE0WuroUeTpDEHSp6d9KcKlYo78YDrkjh6nhqESX1KfumROHlKsknlmg4xcumxozh7ntPZoCU10eDSSBsYCWSozbVn+4mNbD3qVDVVq1Wwn3kvp21Njy6t+B9yljjY+t+ovfoxtONwck6AlYCm8eiyZbT+rdqj9Z00QyKrpL87EuMsesBEP4/ZsKgIh7lmXBw9rR7BZOSycTJOOnaE2T4hjBmLwRxhGD0Dg2OIsVIPDpYje/tyGGDNzOQCt8hgqcNzbfzfeavRiNk7J4yknBc9dij5yFM4a/vEQjTRwMDAwOA4QEqxqP8ZHHsYRm+JQFrobLyizRzFSUUzVTnsHskQ/2UWVHMmKmQ/b7LCpdG2MbNjnHkKTVMhJ0gamLQwSaN1Z7c1UoFlAbNpP12+Bi/auw0AMOg1sdseYM1YOHMQytRx7BQHyiYodo/ajzvxtkPSFKNXtdNV/xxzQBm/qZiNG2vX0rqt/PAKB/10jZhi96gBS8tPhyNlwuJ76YWSfnxsGkpBmbFQR3WGgNMmci2GqSL9TSCeDVcz47Qwdz+0mfx4P+lbnUIu5L7numT0WEZQAE3XxT1nr8JZu8ex5lAdZY+jS4CSF6I66aPeG137YvOXnPbOE3MKzTArrwAQWBb80NZ3dWhjPoOab6LCmbYIznhBK0MZvegvZ9RCoZ63onydylLow2mWdaJIDFUY84xu20PrpuEjAiERyCjkQpIlfqzbjBkLC/LstAm7Z8dt00MqRGOLJ7JhGjwmfELZyhpH0fboIRmyjJ46V47lox3T59g9csqK3aMGLSHDOqlhmw1xEFWk5aP72eeNMHqhA4QuEuMwgH+/FcZ74Bg9RtGgn5dSWtAHLq4gZNLoWKz1nbge/eJGRa3ZufQtNdZL5nBRGzPN5o1XkvdO9kJxoXC08vT3m8WcPz+UGxjkwnzoGRgcawiB0UoPhgD0tlsL3RqDkwSNiosHTh/GU2vbGJ5oYrLHhW9buPyhfQCAp9f24dSRKWzYP43HewcXtrEGBgYGBkcdIUTXjr7HG4u1XUsNi1q6ef/99+PWW2/Fy1/+cmzYsAHlchm9vb0488wz8aY3vQl33313x/LPPPMMhBBz+rd58+bc+kZGRvDGN74Rw8PDqNVquOqqq/DNb36z6+Nfdtllhed8yy23JPmfeeaZwvwGJwaeHBgGALTtrH23gcGxxHSthO1r+3FooIqJ3jL+77xVAIAV402EAhjvKxfUYGBgYGBgYHAiYtEyeldddRW+973vZdLb7TaefPJJPPnkk/jMZz6DX/u1X8MnP/lJlEolppa546yzzmLTR0ZG8NznPhe7d+9O0r73ve/hpS99KT772c/iV3/1Vwvr/tGPfoQ777wTr3jFK45KWzUIocsTk/ToD2vQokkW4g1GSUDzFhqvBPn56H5u4bYu51T7uMXM/GrmxGSFM1EhUxpqPT6/yJyRgtK6OTlngXQzMmZxsMvux7I9u1EJ25ix00evo7yWkY3NBUp25ZDYe8qEpWRTyVK0v0akm1UrMlnRZEpkbkjJNKe8SpKmZJxUAqXiWR3yUunmuBeZtegGLCSOnhddn9AnF9yLtjXjlUD/mwtSTRr3KE1TfUrEgh8RkjppPio1SiS3tN9mzRPSAxbIeLQd3aWxSjymP9HjjPdUcPd5q3HFQ/uwe0UPJmrl9Dqzck0icjpaE7Cc+UfX3Vsk/w0tC+Fs6WanA2ariTZZHXt2H3fPOZmmJu3sEI9QMmm0Kxc985wUk1XnFdy4pMtQk5V4zKCz7mo8KZShEqlZYFkIYSHg4ujRqWZGksbN+PskqJgXj1sNpO//UPrxcdOyfhIzL01TYxQ9FypxT8fO9I70uZGM0yeDh6+km0z8M1bOSba5q6gZtKgyVIaZSMpJNs14RG+DljfPHAVRH5AF0s2Okk1u7Mg7Hicl5X6DMGlJGSpnpDJORl4qmScuWY5CjbOY9y43lHPXXpdkZpqQXk+NXqEHyv9tpD0bJo6ewRyxaD/09uzZAwBYu3YtfvEXfxFXXnklNm7ciCAI8MMf/hAf/vCHsWfPHvzzP/8zfN/H5z//+Uwd69atw4MPPlh4rL/8y79Myr/hDW9g8/zBH/wBdu/ejec973n4sz/7MwwNDeG//uu/8JGPfARvectb8PKXvxzLly8vPNY73/nOY/OhZ7DosaevD6fYDs44fAgP9Kxa6OYYnMSY7Cnj4U1DOH3PBNaNzuBnpy3DnuHehW6WgYGBgcFRxGKOV7dY27XUsGg/9LZs2YJbb70VN954I+xZcrfLLrsMv/Zrv4bLL78cTzzxBP7t3/4Nb3nLW/CCF7xAy+e6Ls4777yOxwmCAN/97ncBAH19fXjlK1+ZydNqtfCVr3wFGzZswDe+8Q309EQuipdffjnCMMTf/M3f4M4778TrX//63OOsWLECo6OjuP/++/HlL38Zr3rVq7q4CgZLCdIS2D3Qj1PGxiCkZBdqGxgcL+xY049dq3px0ROjOHVkynzoGRgYGBgYLDEs2g+9O+64o+P+FStW4MMf/jCuu+46AMAXv/jFzIdeN/jmN7+JkZERAMCrX/1q1Gq1TJ5Dhw6h1Wrh0ksvTT7yFF784hfjb/7mbxIGMg9vfOMb8bnPfQ4HDhzAu971Lrzyla/UYg0dKXLj6CkwTpx6nLxYcqlVSoozaUpqwMXr0eLtkRg3gnHY5OWc2XzSijUPjKsmTS+OmRc7pxVIPItcNzkpBh97SCZpB3t7cMbhwxiqN3C4p6ZOVv8LpFKUeXQRrltR6Sbnuqmc52gcqVocR6pmpXHyqOtmxfK0sgAwFcfHO9xOnxM3PraSawLAeCzxnGqn68OaxHXTj6WEkko3Y8mm0BzIsu52jJJS66Os6yyJhSTiepPjUH0Rld0kElAi52QfFEbOyTVWS8+XYRdl46Sb2phAHOhC2Ni9vBcXbTuISjNAs5x9JfATEnnSvS47LCsl61yWi1sIW0AGs2y62Wua1VKx8izGBU8yabnSTEb6xccoZPKx9XF63WyStpuTD3eoLtoRl9X6iZVtTzIEd24ELROEAgEEAiGSpluhlS3ExZtMdqX1+VZaVrn2UmZASSgdKq+M9YclMlapc7DJAelxVLrmxBlfwD4nNdVSYyInM+XknHSbNQsm45uSBUr6WCaum7y+MHmd0DGvkyySVk2km0lZZszrNvxl7iPNyh2Vrpe885OXLDlgmM3H2mRa2ROkTpzJbwxtfUScj0r9ud83tNmcvJSTzyftB5MI9jom8Ubz2tMFFnMYg8XarqWGRW3GUoQXvvCFyfa2bdvmVcfnPve5ZDtPtjk0NATHcXDfffehXq9r+xQbuHr16o7H6enpwdvf/nYAwIMPPogvfOEL82qvwYmNqXIJDcfBxjETT89gcWC0vwLPFjh1xPRJAwMDAwODpYRFy+h1g3Y7ZRpmyzu7wdTUFG677TYAwKZNm3IZwWq1ipe+9KW488478ZKXvAR/+qd/iqGhIdx22234yEc+glqthmuuuabweL/1W7+FD33oQ9i7dy9uueUWvPrVr4ZlHaVvbUFnkbLs3ey8wGzOQS0Ol7OzaZm5mDLQGLtZ+wB9BivZn53eFMyUp6TXhzVbybJ7RTHzWBMV1mwlWwYMe8el5cbJswSeWrEM5+87gGcaTYzXKoXmGUcbaiabMnHKcECxdEA6k91jpbPXZUH3+1o+uj0pUoMWxeTReHvjrWi/xui1UjOWMGH0yAysYvTINLjFmLGwTB03c6qtlE/zC0AzY5FcTDwgiXGkp6l83FRuB6YpJ7lwwpM1EenMXiXxH+OinnDw1OpBnLVnDM8M92Om4uY2La1n7h20UwyvaDu+vzkGDmJ2PgCQFoQ/awyV2fPPVjL7QCqNPvPZGX/F5EnKujADpR7/Mm5vB8MX7TDzMF3q1iWHspOayY66ppQsV+wdKR+qsVwblrtrLzVjSeqjTJzapq9yjr1Ih63EWIpl9IhpixrrqG2PisFXZMZCWT5u7FQGLdy50HiCnBmLdlpxn5IuOem4vPZqVOMgF7+W5BXMs9XJ6EQgMioLnVm/DRj2jjVRKQBr0Ma0m2O89JinTBtoGTVea3H2ZCafZMfJfJYvKjOr/bPOIVOEU5ewsfyQMJWCqVC/H4YFM5gbTmhG76677kq2zz777DmX/+IXv5gwdK9//es7Sik/8pGPYPny5fjBD36Aa6+9Fpdffjk++MEPQkqJv/3bv8WqVcXmGtVqFX/6p38KAHj00UdZAxmDpY89/X2YqJRw9v7RnF+/BgbHF8+s7EOzZOOsPWML3RQDAwMDg6MEZcayWP8ZHHucsB96YRji/e9/f/L/v/iLvzjnOqhss5ORCgCcfvrpuPfee/Ga17wGg4ODqFQqeN7znoc77rgDN910U9fHvPnmm7F+/XoAwLvf/W4EQZEvvMGSgxB4bNUKDDZaWDM5vdCtMTBAaFl4avUgVo/XUWn7xQUMDAwMDAwMFj1OWOnmX//1X+Pee+8FALzqVa/CxRdfPKfyO3fuTBjB5z//+Tj99NMLy5x66qn4j//4j7k3lqBcLuPP//zP8Vu/9Vt48skn8bnPfQ5vetObjqhOIJKXdYyjx3zSa4u1k+xEXsLG2WMYKE3G0K2Oo7O0rZPxCme2QrcLzVG4fFxsvaL9HcqwaSKSg0kAh/oq2N9Xw5kHDmPfshpCy0qlYqASEpKmpIIkn6XSwIOTi1nMam4lueRkmDSNyji92CHAI04BXiyh9oh2dbQVyTOp2Uo7iMv6ab4gSC9aGKdT45VUKkzOL26aRedLuJhSjEROW3BPJElKumn5UY/Unh0q41H9iNatpH2cVLTD85mbzhgKsBm5hfx5MkUlEdL2C+zt78M51mGsPziDbasHi+vs1KrOKsX0UecMJYpOWdMxicSkZ+4NYsYgmqQkYlocxWw+rcpEFputWzI6VE0+ybSLe34LDQyYNqb1UMkpPY5KY8Z8+pyo8yPXPGTMZuiltSEhIRAKQeLoxc8OHfMZuSNn0KJJRdVzT+SeVhwzzyLPjpJzBsyN0+KEMi9KVpJJ6qnGcvfAzcpH8xgLla7LOYO4DSBpKn/aLi5OHkXSb7VnSyVm8yX/D0C6EmEY35WOxiPc4MC3J+94wOz3aTx22kyaNgYTrakC3d9hDBYO87wRqPtmab9FOr+DOo5bTBs4Q7c5YY4iIGPGYnBCMnp33XUX/uRP/gQAsHLlSnzsYx+bcx3/8i//AhkPfkVs3tHGTTfdhM2bNwMA3vOe98DzvM4FDJYkHlu9DBXPx+bRLkwwpET/dNtIPQ2OGQLbwr6BHqw/PGX6mYGBgYGBwRLACcfoPfzww3jVq14F3/dRqVTwhS98oav1cbPxz//8zwAihu2XfumXjnYzO8J1XbzjHe/ATTfdhO3bt+PTn/403vzmNx9RnVp4hRwGIs3cXZ3c4un5+ATolcZN4GbjqG1+p9mvnJk82cGsRTdtQTbNyk/T9zNp1DwgMf9gXCTIPYIFzFRL2Lm8H6ftH8OuFb3wNOMZnb3rrXt4/gMHAAAPnDeEg8NVrW46m24x21bBTU/yEbZPMXkWsmlAalxAy6jj+GF6UdqhE/9N07wg2vYJixcwoRRAzVgUE0UZHGYBPzVmUTP+mpW+lU2j/U0AsFwg9prRF89zzKDWH7MsH388hqnp8pngTUaKKDTm2WLas3uwD+vHprFssoXDvdW8okcObha8y9ldOqEvQmTtxtkxL3tfdKeE+K829Rn3N2ocxazj1u61ul9syIUsXShzTB/SZnXJ6LIgrEQnQxik5yCYvhyQj35LGWcxISDy6g4B+LAR2FZqFKJYQDJWp+8YmqYYljTNJ4yfFTM+lDnz4wfOIoO1I7MMmxfX49hESUCOo1QJVJ2gmDU6DipjFsryJSEXChk9ZidlGFUS3a/Gv5zwCmmdTP9m2kOfp9ABQpnf6zgTFa4NXbNO9BQSRo+aqHQoS8MwcIwfA/quVodmmT3KdpPnP+m/GqPXJbuZjN/0uSS753HNDAy6wQnF6G3fvh0veclLMDY2Btu28e///u/zip1377334rHHHgMAXH/99RgcHDzKLS3G61//+kQu+r73vQ+tVqugRGcIYf7N+R8W/t9Tq4YAIXDm3rGO+Waq6ZzMhQ+NYcWhZn5+WfzPPgDYX6nA+no51ieKrv5J8q/bMh3bsgjugfmX/hvrqWCyUsKZB8YgpFzw9ph/S/RfF2PU0fwH9p84yv+yx5hTOxf6nph/J8y/uUAuAsOVvH9Gunl8cMIweiMjI/j5n/95jIyMQAiBT3/607jhhhvmVddcTFiOFRzHwTvf+U68/vWvx65du/CP//iP+N3f/d151zc8WIJbitZCdcvocYE39fAJMpuuhUWI95FFBaqMVo/mNyMz9SRl6OKEuN3sejy61olsqwDoIQmOrsIr0ODoiliis3sx6aSxgaHD7CdpgUorcfmkliYADDuRbb0EmbV0gMnNa3HuwUm0fQuNcmxtz6zH23HxRpz/xDgA4LI9AX42WEZoCdh+CGHLpO1lpGEKqnGDeoKokcKXGNhTRuVBK5ot9wDfa6AHUd+pBGlQczcOmG4JMglhN9PNOG8pTEMpVINou89LA6Z7fhRWwQrTUAoVGW33kba2BQmvoMKluGT2Pu4UFnk5JEs5iwLKki7BMsNqO65nRcVNfq8hh9FL+ig9DrMmEOzxstO3bN1F9czeR+tjsuXXnU7rT25ci3P2Hca0B4z3lDPFjyo0FqC7lz5lIFba6TOVZuhwuIJ7wPYJjp0vPB5TJud6HxfEx8lj3bj9XLiHhIljq+DrtgCsQBkWwiSHCkbuyKwawCI0lx3Hc7BJGzSFgQpqLonSIH6fuCQEQimMtktkMa/aLgckHyljx2OnS5QIQfzSYJUPYTp+ufF4q/4CQIXsr8UvjSb5CebF2z4JCxGogY28l1gpQREb1CWjxz5PNG+n4x0po5coZAqeNwUtfEJeQzodW+h/AYj494SgzB/3O+k4MXqSeZ/4MsAzTPUGBnk4IT70RkdHcfXVV+Ppp58GAHz0ox+d9wea53mJocrKlSvxspe97Ki1c674lV/5Fdx666147LHHcOutt+I3fuM35l3Xgak2nFIcc4cZd9j3u/ZRp/+NtskLPv4I0/ar3cyHnkU/2rQykjk286GnmlgkwyQfdepjTtJF8U724099jGkffy6y+dL3MtmfpgXxb2D67RvGvyPCWXIPEZfd47eiUycXcmTARnVXHQPb92J8dR/Ge8tQvw/oj6d9vSGm11g49/FxAMDwgw0EjsDwwRb2rynjsfMHAABV4WHwQBu+a6ExGKA6HqC2p43yIQlnCvAA1FdKuAcEgstakK4Py20AAMpu6gIa2lHoESf+CwC23Ui2rSD6mJNBGh9vxusHAIyG6fmNxi/kMfKjbjLuBzMkX9tPt2UrutCiRT702tG9sZvkQy/+BiXfn3wfpfMTTGw9miYQGbHsa7Tje8XkA3nOCuTF3PEy+WfV3clQgP1oBZMvL00wdROMWBb6202URw5gZMPq3PZySIwgusw3V2MBrQ4AcIE9Xmt+1bAf/3k/HvPL8vuZSasuPzKPOIZmpw/Y3LqzH56J+RMrQ80eL9eMxY4+8PaFzeQ+OVAfcNkPJj3WXZzPCjNpQPqx5tC0OLYe/ahTH31lO/2QqzJpNE6okmy2yKDPxdETcbv9MP2o8xKzKRIn1EsnxKZFlF4XaZl6POnVIpNfyuhKM6hJPvTSJNq5OsarLPjQk1aIPVb8PHX7AcM+fEKrOw+SfU6YCWauLD1RbnKvSwhmIKAmYPpvlQ7XhObrML5xxnhaewrKhH47m6EDJHL6xCLAIm3WksOi/9CbmJjAS1/6UjzyyCMAgPe///34nd/5nXnXd+edd2J0dBQA8LrXvQ6Os3CXwLIs3HLLLXjta1+LvXv3zstURkFKcMZaJENBGjcwdbufScsNqNptPSpJZre5tKL9bFqXzZpLs4vSJLKnGVoCj64fwnOePoiV49GH1DcvWQvPtbV8EsCeNTVU2j5O2z6NZWPpgO94MnrxCYHKVIAz753Rynr9QGuFAISE3ZZwDgOyJ4Q83Y9esNwiTCZNFOznmArJ/NCV3G+VnO3ZaV12nVx081sl715128aitCOpby7751q2t9XGYL0JJwgRup3P/1i1ay7Iu0/zqedY42T9USORvU8dx4Qu0+ZSpjOryv1CVy3P2V+Ylj1e1+3OtuCIx7e5lD0az9NcjtdNWrdlTwTMp91F7wkDg05Y1B969Xodr3jFK3D//fcDAP78z/8cb3/724+oTirbfMMb3nBEdR0NvOY1r8H73vc+PPjgg3j/+98/b6ZSWvwEXLK/QCKQZqRfRAVlGJnmfMAxI5l9AG+2wsnduJAL1LKZDcPA5KPbgknjZuaKflAwH0LCkti/vIrDB0tYNhV9vF36yEH86IJhffF4XGb7Kb0ILYEztk0BAMYHXCwfbePshybRP+6j2gjguQLbLu5BLfTQ6rOwbKyFwYdDWG3AWysRrJBwt7QAIjudDc5sxQWRSMXTqNRwoBlTn3QWvB0br3hEAqVMWKgBi6Sz1rHhADVeEb5KI9eEY6Lpfua8lMlKnpxTMXqWF//o0e4VUyYnlMbsMkU/POlzlJh6cDLs+UgJtXONr+Os9qybmMT5+yLDn4lyGU+uWB5df/qhz1TNgebrWmEwRwj1b1ZdXS/7YMY3LURNMGvnnBrHMCeC+Z8i+WgB5sro5hsCCe0PQCbEmHAPWv9OPlBIPiK/s4SARBT+R6UGSYiPuEwoIWR0S4SNZNlbaEdtokY21JhFjT3UPEOZtWimVPGD6ZCGe0xaQB5wxei5hE1UjJ9rUTOWrGt2oNplZ9s6e1uh83qldDxVwoi8/ElfLmTkmP1WCNghk1eVj9+ThTNUzFcr0w4txI3qM9xPEK6rHinzrarmpKLaAEY204ciU482fnOPNZPG/S7Tw2KwTTYwmBMW7Ydeu93Gq171KvzgBz8AAPz+7/8+3vve9x5RnYcPH8add94JADj//PNx4YUXHmkzjxhCCNxyyy248cYbceDAAXzmM59Z6CYZLASEwPY1/Vg2FbHN/TMe1h6oY8+GGpt3x+Ze7Njci4u2HoLjhdh2Zi9Oe2IabVdg20U1TK5w4JctlMd8bPxhE6UZiZnNAuPnW+gZir50XMe8RQxS+JY+c9HT9lAvdZgJMDCYL6TE6pEmlh9uoTYVoDoTdAwZ2aoJNIZsNIYstIYEmkMWQpf5se1L9OwI4Q0KYCXm9NFsYLAUEULo8tRFhHCRtmupYdF+6P3yL/8yvv71rwMAfu7nfg433XQTHnroodz8PT09OOWUUzrW+e///u9otyPGZDGweQqvetWr8OxnPxtbt25NZKUGJx8ODlZQL9uotaLZ4jUHcz70CHZu7MGFD4xhz0aBnzx3CO2yBWcgKr9qWxMbH2lietjCoSut6MePgUEO9vf14lunnYLhmRmsnZzCRXv24oeb1mOyeowNWQxOKgyOtXHGE5Pom0rXufmOgG8DTlsmH3wzAzbG1zsIXIHqdLTWeOWjPuIleGj1CrQHBYIKICtAWAL6ngpRmgQACW8ZUD8LaG5CR+WCgYGBwVLGov3Q+9KXvpRsf/vb38YFF1zQMf9VV12F7373ux3zKNmmbdv4lV/5lSNu49GCEALvfve7cd111x1JJZ21DERLoJQB3S5HyNufLiAg0q4jIYm49msOmyofX0ZJOgvXQjDx7zrFyaN5uZh57PqRvDSmPYn0yRH4v2etwnnbxjA02cLQZBuVdoBW2Y7zSe0vABxeXsLeNRWc+egU7n3BcrTLNpxYc7Z8j4fGoIVnrqigr5yu5ws7rAYPmBk2G7wRQhBfgKZMzQOUZNMnx1Dx81TsPADw/Gg7JHH0JI2Zl0g3QdLiv0SmqTwRtHxcHy6QGiX3II6jJ4Ko7tnSTdYljpNpMv2Ii+WnSTM5eRIr96FSumwbWIknmP1Mmi9s7O3tx76ePlyxYwfWj03iUXclOTZfZSd0LDKfuYfZsqpZjcpzf5wNVlLKmjrMoZGdhuAimWW3Mky6e47Szby+nCQxcQR1l0BG4hlfbyrXpOYYoRDRcX3g5767L3NM3xbRh15JoN4rELoCTluid9xH9bEA+84oY+855Wh8lxK1mQDVsQDVsRCVyRDulITTkrDbQGtAYORlFuy6xMBTIQZ+CPTeD9QvsdDerDtauvHNpjE/qbunMoehxisqrULkmkq6ScdVTpqp7Wcufl7Mvdx8mlSQk4Jmy0r2ZRVBIHI0tewgNmPJLy8Kjp2WzTkn9XuiSNZ9HEQnGtulYvlpJ8jJNLP6Uq2p7D1CNiM10elQt1Z1NqkjknBIixCLtV1LDYv2Q+9o48knn8SPfvQjAMDVV1+N1atXF5Q4vrj22mtx6aWX4t57713ophgsIDzXxtYtK1CSPl70w71YeaCBXRt68wsIgSfP7MfKAy2s2dXAjtPTvAc3lbDpZw2c8r0mpi4UaA2bQdWgO0ghMNrTgxUzM8WZDQy6gBVKTPS7GJj0sG91BQdWlTExXErWsTkxVefEa8ScVoj1Tzew5vEWVuxoY3qFHa/Xi0LbtPosjJ1hw+u1IqfN+AeyY0l4gwLhuhD2NND3U6DvbgutnRLe8yL2z8DAwOBkwaL90JNH2Q/2jDPOOOp1FmHz5s1zOqb6EJ0PpJVjDpJk4H7kEyZOrb+mDBptu5UpksyEaVUXxTBLbJfp7K+a/WJmyShjJxjGjjWryKbpBi1M3RwTU8DesCYyyhY/r12Wno/up0xd4AocWlbG6oMN7Nlc06qxiDmAEBHLeGBdCWt3NzByRiX5oTR2qgtrWYCVWz2s+naIQ1cD3rBIYkVxsaBcQo2puFZ0lnuGxMKrx3bi00H6y6kRROxe3U+1Uq3Yarzlk/hQMZMXeumFEoTRU14HLKPHpdHLqcU4yu7vlJYwetSMJScuJdcn0v7G5KNssGpDmM0HgH3eEiaeZe+YNuQw/DwLlDXZGK3UsHFiAtWWh4br5pftdogrmmeY4zyEYl5FMHt461CRKMg1L4axYAa+AzvL02rZsrnHY6vMsm6zd2X2q21m/Nb6EWsmFefTCqWbISyEwkLbsvGji4YBAJatPsqCJG8Ym6go/6XAtbDz7BoOrC9jzfYmKlNhfGgJ25MobfdhPRBJOGfWWphZbaE1YMEOAvRtC9H3VDSOBOVI1lneKeAecDH9wgDBylR9QMdBixhClS2W3s2FxdPB7P4eO1JYcAYtsiBNC7UQQ3eVFpl8MjG/IexsXIjWLYQkIZDUsaHtjxLpwTPNIWn0ePMYAFTScV5OrjPb9Bxmp5D/0c4vZrlZBVTObywuiStj5mtPaOzfvx/79u3DzMwMXNfF4OAgNmzYgErl2M1ALdoPPQODkx37hqs4/7Fx9Ez7mOnt/Kju31jBmp0t9B/24a1N0xvDFnb8fAmn/08LlT2AN3yMG22wZHC4WkUIYHmjjt3uwEI3x+AkRbPPxs4L07XKivmzfIn+gx769gbo3xlg2RPpDFDoAlNnCXi9Au6kRO9OCbQBqynQ9w0bM1eEwCnH/VQMDI47QikgFqlEslu58omM//u//8NXv/pV3HXXXdi6dSvq9Tqb75RTTsFzn/tcvOQlL8G1116L5cuXH7U2mA89A4NFigMrKmhULFxy7yFsP7UHo8MV1Gs2m3emz0ZgA70TPsbWznqsLYGgBlgNtqiBAYvAsjBeqWBFvY7d/eZDz2BxIXQEptc6mF7r4IAIUJqSKE1J2E6I5koBQYbB5oU++u4Hak9E63977rLQrtvwzw3yD2BgYGAwD+zfvx//8A//gH/6p3/Czp07k/ROCr+nn34a27dvx7//+7/DcRy87GUvw2//9m/jpS996RG3x3zoLRGETvQPQKHMIZETEEmOUk+KgMg5qemDWoQdZuWempwz1PNHlWelTYKRL+hpKl9W4qjJS6kkkzEcSORwBXK3QjMWZj9ncNDZjEUmFz8/nlWKwLHw4+ctxynbZnDaU9M4/alptEsC+zdUsOuMKir1EO0+gZ6JACt3N9Hot9E/6aFOvgUrdizTDCQsV6JihyjHaZrJgJWVbrpxwDkVGw/QjVfGg2iWnUo3Z/xI2ln3SRkv2m576XDje1Ejpc9LN9k4eqpvFcXR61K6SU4/I9205iLd5PoEIwGW1KBCXYo8MxbJ7OckctlmkVX9ORnIuWbqmVX5oUoNmyfGYYUSUqQ2J/r1PIJZ2fm4N6mxCIBwov7QSbVOinRox9z2FUpFOaVVYRsYXVi3+tKiNjKSy241YEXx/5LxmMrRiTFLKCVCy0IYWuTUooeBxq0T8YMrwvSAar8gD48mXUyMjiSafUCzL42f55AybcvGoYskBAJUnxAI+oDyjx1gWqB1aZjUQ2PvscsQLHV6RO4Zb3skdqgyqqJGL7Tuqt2KD5Geq8rru1nTloDcTHVeeWyIksX7RCofhh3uNTUEiY078mS4xbH55pZv1rqO/HxHKxhnt2NVTt1cmALJbCXvAWacLDJb0SvPVqTF/esCUrIK0UWBxdqu+WJkZAS33norPvWpT6HdbicfdrZt49xzz8VznvMcrFy5EsuWLcPQ0BAajQYOHz6MsbExPPHEE7jvvvswOjoKz/Nw++2344477sA555yDd73rXXj1q18973aZDz0Dg0WMwLHw1Jm9EFKit+5jps/Guu0NrNrVRKktUe+zUZ4Jkhi3jV4JwNUrkRL2DNDuWWKjqsExx6FaDWeMH0Z/q4mJSnWhm2NAcNroGHzLwo6h/pzFkwYahMD0JYBoSpT2Ac0LApQftBDOlNC+qm1+DRkYGMwb7373u/HBD34Q9XodUkqsXr0ar3nNa/ALv/ALuOSSS1Ctdvf+3L59O7797W/j85//PO666y48/PDD+KVf+iU897nPxSc+8Qmcd955c25b0USogYHBAqPSCLB+VwODhzyMri7joUv7cXhVCXtOqaA8E6DZY6PRm0/3OFOA8ASCZce33QYnPhpONGlQCozEbTFBSInTDx7GOftHcenOEVjh3IxDTloIYPq5EZttHxaovyiANWKh/LUy0FzoxhkYGJyouOWWWzAzM4OXvvSl+PrXv47du3fjIx/5CF7wghd0/ZEHzJfS0gABAABJREFURGv1brrpJnzrW9/C7t27ceutt2L58uW45557tLBzc4GZw1oiCB2B0FE0f5ouOFcntY9K4BJHPyKVI1KcRPkTZKUWmmyOkV/wMk3aIJFNi+WeVMaT8Px5rnTpSaT7O7hlduvYmbefrSeR81HHUpnsk1Z0aei1TeqbJaFRaFZtjA+6GBz3sOHpOp6+rAe7V0YDx+gWF4EjsPmndVSnQ4yd6qDspJrEmuOhPB413Br2UbIARzlsEle5JD6USONDtWKZ5lSYDlIesY4c83oAAJNeKt2sB5HbZoNIN5XbpiYlit02BRM7D8iRaTKumyiSbjJpsSIVls98FQsl3ZSwPBlLN3nHM04W3EkCrKmQ4/aEdAQmZRIVH0fCMnJPzvmTlXPStml184xQ1fPT/VKkMeoYmTVtd6EkqYOeMU9dNbtuAQCB0CW/fJXs2RXFlMseOCf/PGSh/Dlmb1ye0rK31YYF4IkVy3DqoTFcuHs/tq5bDaiYdZlmc51i7uCk6Yn6kvZ52ifixz6EpcncBqbaaPbb8J3YbVNVQ92HE7dIuhQgPZDK6xGJpCOy0kaVrx3agAuMXyqx/HsSjY0C9Zf6qH7LQfmOCtovATCQjtdxwzP1sNJNMjb68bZjpYNVVZPFR9sqtAQAeE4cW1STc8bXxknPmXPd9Ehaqx0NKlSuKTtJNykE0jGFiQXXCd3LNdWBZu3vINnkxkFORjkXJD8n5lINMxYk7uM0jfu9kLic0kYUHZz5vTRn6aaJo3c8cN111+Gd73wnnvOc5xy1OlevXo0/+ZM/we///u/jYx/7GHp6euZVj/nQMzBY7BACh5eXMDjuYeigB8uXyUd9UIpe8PvOLWFsk4uZFRZ64GnF3UNA2B8C5UzNBga5cMIAF+0fwZRbwmEj21xU6G9Ga7x2DvVjqlLCc3bvw7qJKewZ7F/glhWj5AW47IH96G34eHpTL7adevzb3Fwv0Nwcovc+oH6DRP0aD9VvuXDvqMD/+RbkKsOQGhgYdI+vfOUrx6zuarWK//f//t+8y5sPvSUCScxYtLBOHWblLG7xMGUVyCxgwuhpbEIHExXKxDCzdvNLY5i/QqaOmcnuxM7RwxWZsTAx8dhYhslxUjMWdiaTOVd1/zxHmRIAvVMeZpZHN1sZq9ilEH4vUEaAmpN+6JVsH+4hG3I4TAxXlAlLmbB3ZSvarpM4eWqGms5Ua4yeH5mxTHjpR8C0FzF6yoAFSE1YAhIzD7FhADQWL8vusUxdttvmzgyrMpS9s3z9L4VMGD3AVmYsgr+/PKMncvNJ8kxIxjhJY/c6GhNk6+bYu1zGivHl4A5jBxJuGOBArQeBsNXFyORjn9v5/E4uIp1mNVIASRy9buvWkvKUAR0ysrEM2TIMY5/H1HVMzO63ZIjNY+MYq1Tgw8HBai/29PXhrIOHMNLbnxpUafc/bk+3zI52XKLsiP9qsfXi60/jRNJ3gkQYl4zSzts2BtcPMVNxUGmECOOxQMVo84P04XHjNMpiWYQl84LooDZRJ6i20bR2kHUrHr84xPAdQOkeF1MvlGi/VKL/u4Dz1TKaL/DRPlUdMC3jJsYrWTUEHRutmAas2n4mHwC4zOCjylMmUrGdNE1dC8piNojRVRJ7j4ytCaNXcPulEJChbsYyL9YlqSCnbCdSSlO25OcvFA1obFrn/WniPNaxK4M1jeZmmL/04SHt6u542v0wjJ7BHGHW6BkYnACgTmxzGhuDaC2KHDYz1AZzQ8tx8MTgCqyfnkTNay90cwwAWGGI54yMoOZ5eHR4RZL+zNAgSkGIjRMTOXrfhYaEFUps2j+F1WN1PLZpECUvQL26cHPNsiwwdRFQ2iVgHwJkGWi8xIe/MUTluw6sh8w8uIGBwYkPM5IZGJwAaFTS2dt2rfv5GXtMQIQCcoX50DOYO/b09eOs8VEMtJqou6WFbs5Jjwv270d/s4X71q3FZCVdGztVLmGkrxdnHxzFxvEJjPT3YW9/L+ql43jPpMSymSY2jE1hoNlC2fNhSUBIicH+fkxOTgIAdg734nBfBW4gMdG3sH2qsQnoe1Ci9qDA1AslYAPNFwQo9QLle0sIpgVwmW+mxA0MDI4Z9u/fjzvuuAOjo6M45ZRTcN11183JwKUI5kNviSC0kQSI7VYWGWrahjifZqxCJDucbC6JrcccjyplaD2MRrJT3WDaoJ9LVlbHxjDTZFoik3ZEZixMPl3iyelOWHuIdGuWwnNiIJVC+pW0ciVPqlntWB0lUCLSJveQhLQk3BXt5MeKMmFxSb56EEk2OZmmMhaQEmjvLCG4uwZIgYkbA0Ckck0AmG5H9TRpzLzYhCUkMfNkIs2kck1ktjnpZmEaqUdJNqlSKknTlzLGhSOJjeVF+aTMkQKDlworEx7O6IcaL7CSJCrJ7BQTkylDZXOJBJLWx6iKumGGfctGw3bQO4vR48YTIO9ZLj5OHrh2J/8fp4mQPwZ7fnnn3CkvI7lkzW9kZN4TGaIwEtdumficC1bz2lg1MwMA2DQ2gaF6E7v7+uHbNiAEHly5Grv6G1g/OYlTDo/hjEOHMV4uY29fH/b19gIAqr6PqVIJodX5y2VZvY6NExMIhcDu/n4crlYBIVBrt7F2agqlIEDDddBwXAS2wLJ6A8MzdfR4HqZLLg72VdF0negdYwPD1Rr2DbkY6yljulrCUCOyuPRgIVRmOiKrQ1ZxXanskUoW1fgnAzomRmU0M5a4Q/rk2rYR1dNa46O8G/ClgBU/uP5FEuj1UPqRg3C6hvCKFlABvLgdlpUdJ3VpZmxuRQYZLkYp/QWWjLchNXVR8fjS82vGhZrE8Cok+5UUVhaZsTBJ0iJx9I6IGOYqZ7KxyxWY/QXvS+7Qx5TYZp9lbk0BV1Zym90feo7SzVAKfXnNIkJeLMilhEcffRTvete7IITAP/zDP2BwcFDb/9///d943eteh0ajkaRt2LAB//3f/40LLrjgqLTBfOgZGJwIEAKPXtSLs++fxqk/rOPpy6rJL8favgAbvh8itIHGBoH2RUAYmzNZhy2EgxL2PJ90GQLBThfBbhfhAQfhARKjr6vFUQYnOjzbhmPs+ztieWMGFx0cQQjg8aFh7OofPOrHaDguHl4+jP52Cz1eG6ePzeCU8TE8smIl9vdFH3LjlSrGq1U8MjyM4ZkZrJ2exlmjo9gyOpr89gwBTJXLGK9UMF0qYaZUwozroh1/MK6bnMR5Bw5gqlSCkBKXjIygZdto2zb62m14loW662LV9DRKcb9oOA5Ge6p4aGAYh2uVdKICgHAkAqeM3dJOJ656SpgpO9g8MoWx/oV1ibKnookV9wAg+oDqTy1YM0D7hR5kj4/y9x3Y/1WDPMtDuDGE6AkhKwKishglsgYGBosJt912G774xS/iBS94QeYj78CBA/jVX/1V1Ot1LX3nzp247rrr8Mgjj8zbaZPCfOgtEYQuINRv8AKzCnbWnWHL9NAGs8rStC6ZP70MVw9Nyxq9JN8VOXUnjB5lTmz9r7ZdwMRxIRk0sCwgdzJzB+chMzUU3eCB/T5KdYl2j4AlJIJ4HAjLQGW/RPV/BWau9hEOALYngIo+m6xMWKgZD2e80h530f5mH8JRfZiQQiL8+RaaQSQdq5OZ5XbM3nk0lIKabaf3kmHiwDB1HHtV2HcKmVqG8lLVWVFe9TezX8zKC+SY9mRpINoH1aQ9a+6TUzfPFuv7csFdnyJTG5JkSZlrsMKPCUxa0bdiEenQyYyFe9yK+gGXl+7vwBLOZudm7IjVtgCcPXYQQ80GnhxYgZbjZsoWIpf6ExjpGcSe6JsOVc/DlSPP4MzDh3Cg1qe3EzYO1PpwoNYHJwiwsj6DUAjUXRf9rRYGW02sqNexYWIi6UKBEAiFgB2G2N3bj0dWDANAkrcc+Ng2OISDtZ6EEbTDEG4YoOk4kA5ReASzb5aAoKyTAzy1egDP2nEIvVM+pnpKCH2VNS0bxCyfxtJTcyTVt6g3TJzoEDMWP26vCNOyqp6J8wWWf19i2dcBwInC3wCQP3XQuiSAdWMD9k9KsH7mIvhZdJ99AKUXTcE5s50wjGUiG1BmKxbp9AmLh5Tpa8q0fyQmWaSelhWNvdRgS7F89DpZ5FyT0D0c69zhvZTpdXNlW1jGjq+Du2+8yiHuUwV1S+65LXgH80NGl+esjaeMRKLTofOkFt1ijmWkPMbs5hFgsbbraOJb3/oWhBC49tprM/v+/u//HtPT03AcBx/4wAfw4he/GP/7v/+LP/mTP8Hu3bvxyU9+Er//+79/xG0wH3oGBicIvHL6Q8lphWj3xO5r/RaaawF7Bhh9ocDwdyVqdzmYvs4HShLikAUp5yAdA+Dvd9C8cwCiImGd00T4SAViUxth00Z4gQe5IQCMP8dJAQnBxuM0SNGyXfxkxTqcd3gfymGA1Y1pDDdnsKN3CDv6hhAUSCXnDClxzuH9CAGM9PR1zOrbNkb60hAGk+UKdmMAQDRJV/M89HhtVHwflpSRXLOvPxkwxitVjMfhNWb/xgwsC4HNn5uQEnYYwglC1EIL/V4L9ZKTxIvbs7wX5+84hKGpFqZ6Fm6tnrdCYN8NgDsGlJoS/gBQ3gn0PWDDXxMCmwA4MvMRYK1mbHsNDAwMCHbu3AkAeNaznpXZ96UvfQlCCLz+9a/H2972NgDA+eefjyeffBL/+I//iK985SvmQ8/A4GTD3o1lrNnZwuCIj/qy9PGdPF9g+H8lKruBxmUBev/XgbNHQG72YT3uQo44EOu6+2Hi73FR/9oArOUBnGc10P5OL8TGNpyXzKARGEOOkw2hONLQxCcHxso1/GjlRpw3tg/LWg3YUmLz1BjWzkxg28CK6INsTtGZ87GyPo3lzQbuH16L0dr8pT1SiEi6eYSmLW4QoL/ZxECzhYH4bzlIabj+2IxFAti1rA8PbxqC60ehwRfFMh1LwFuerrGtnwvUngnhbrMgNwHhqhD2IwDObgNrfVTXN41008DAoBAHDhwAAAwPD2vpo6OjePjhhwEAr3vd67R9119/Pf7xH/8x2X+kMB96SwRBBYB6V3NGCAUGFpzkSlM+cKYXiSSLkSzk1cPIPTvGzKPHYwwqqBRHyeVoPLLQjRd4U+kma9CiKiRtoNsdjF44g5bcepI07qJk91OpjW1JHF5XwpqdLax6qo3955SSRfozQw6qp/ro+5lE8xXRRbNagL25DSx3IbeWUd4QGSBwRgF2HB8q3Gdj8n8G4a5pwT2jifo3BiBW+bBfFJlAeOQCqGPTWDiBzKZJVobJmFWwi/Az2XRw+aiMqxTff9YwJdsGFUcvdIHAjUwJaFnNUCWJo4hMmta3GPlwIt2kfZWOxlwfZSTHSRrzXOYikTZld7GmJoieVyHB3w/uWadp8e99q2BJZ9EPftaMJYiMduTsHfQv2c47huDKMO3qJHEFAE842Dq0DpunD+PU6cOYKFXgWRbOHduPDdPjeKJ/GBOl6hF93KxqTuG8w/txsNKDw6WeyGiIHYNypG1HAEtK9Hht9Hkt9Lbb6Gu30Ou1UIk/6jzLwkS5gl0D/Wg4LnzLQuAKrKjWsGdFP4bqDZy5/zCGpxqwpITnWBgZ7IX0BdSDFBZ0YG1Yjp9h+v3s2FFbbDoOxGMVlX2G8cDUDqmTUQwpYU0K+Mtjw4hNIezhEGK3A3lhG2EZyUVV8tKQtCyUKi0rQwVS2bxN9tvxy06Te8YPTa/bTOuOj0NjA9I4g358L9S7LzqdDh1AvWuBSJMav8+TOHzdfs8Wjenajvyq+fdA58GKP06RFjxbdVLNnJZeFAwuHRrRdbxR2sZgbg9zJN1cDLMpWZwMQhFlstJsNrX0H/zgB5BSolwu44orrtD2rVmzBgAwPj5+VNpgPvQMDE4gTC1zEFqAFQJ9BwI016f7Dl9go2eHj8p34wDla8LoBXFRC+E3agj2O7BX5bN6MgQmvzUEZ5mH8qYmpr8zAPf0JuQLmrqLqsFJBQmhrec0KIAQeKZvORpOCeeM78Ohcg/uX7YOp0+N4uJDu7G/0osn+1egSdbvdQsnDHDq5GGMlyv42bI1R7XZVhhioN1C2U8ngASAchCg12uhLzaBUZ8UDdvBVKmMPX39mHZLmKhU0HAcQAjdBdaRKLtlTAbARLWM0Z4aTj90GC3Hxo7VffBzpJ8LCiHgr5KwDwmE8aK99lVtlP+nBPHVKsKfD2AtM2ZUBgYGnbFs2TIcOHAAO3fuxGWXXZakf+tb3wIAXHLJJSjNUlT4fvQ77WgYsQDmQ2/JICgBiM3LiowQkm2OBchhBjqV6daMQUunxhzsImyu3ZKpO8uwhA5J69DDi8IwzIup6wRByhQslBcMoyeEBASw7bk1nPHDOpZt97BjbRxLywfgABMbJYa2A9KWaJUcDEBCbvQgKyHkThfu6hbKMZNXJoyeJUPMPFNDMO6i75JxTP1gEOXzZ1B9/hSmgmoy80atv5U1sk8upEoLNUZPaH8ByipzF5n0rQK2iAXdHzeNKk4VkyeZsBcylimGrkBQihg9yrRpzHAnlldjAZmycZ0ci6e3J3sulIBIrmOXPgC54AqFapeIrn0XrCE3TihviSKT1kJVI8fohXG9TDcS3LOa9/wyfgpctkJTF9LGA6U++AMWzp/YCycM8cDgOqxozeC06VE878AOPDKwCgcr6fq6pN0519YOQzx7bA/KgY/H+lcCIZHUcudlZZPo+ZUCH4OtBgZbTQy2GujzWmy4OF8ITLtlTJQq2N07gGm3jKlyCb4KMZBcExltSgDavRYQVsToSgB1t4QH1q2OdjkhBidaWDbTRG+7jWrbR2ALPHD68mjtH3OdLfLchvFDZRMzEvV8B+TBVP2S5qMhC5K6yQ2cOt/Csq8D/mMuvLNCoAdwXtaE/c0KvP/qhzitDbHKh7XMgrXMh1dNH0zVRmqCZSNL31gMpUPNWBQDScdTxRa2XcLoMUqLgIScYIn4uE6vGQ1CEaMnIINo3BMcc9QtM1QwCM1Zwcz9njhSGTTz3k33FdTNXtAuy+T8NuoIqirw53beUopFzOgtznYdTTzrWc/CN77xDXz+85/Ha17zGgARu/eFL3wBQgi86EUvypTZsWMHAGD16tVHpQ3mQ8/A4ATD1EoX+04vYWo4+/jOrLIxtD2ACATKT0ng/OidJQZ8hDOdZ86nHumHO9yGDASsaoDq86eO1pIigxMYUkSSPYO543C5Bw8MrsMFEyO4cGwPfjq0Fgcqm3H2xH6cM7EfP7FLmHY7hxewwwDrGxNYXx+HE4b46dBaTJTmGExXSvT4bQy2GxiIP+xqQfRB0bAdjJeq2NvTj7FyFXXH1X5Yhiq4JDB3lVoBBuotXP7kSCbdDiWCBVYReCuB+ulA9Uc2rMMCwWoJDAgEL2+itAMIHytDbiuhEV+Mei1A6bQmyufXgUFj1GJgYAC89rWvxde//nXcfvvteO1rX4srr7wS//mf/4n9+/fDsiz88i//cqbMj370IwDAqaeeelTaYD70DAxOQIycG/3Qc/Spc5SmQkhHorURqN0vIDcJiH4J2bAgVuX/WPdnbNSfqWHgyjEAQNiwo1l5M0IYYO7ktUGKiVIV9w+tx4Xje3DR4d346dA6PDqwCs85vBvnj49gZ88QaoEHS4bYX+nDuBvFyBRSYmVzCqdPj8IJo33be5d1Jfm0wxD97Wb6YdduwpFhFEPPLeNgtRfj5QomylW0bId1iD8eaMUOnCODPVg7Hq0D/ulpyzS2aiExdQlgDwRwH7ZRekJADYihK4G+AGK9D7sSwFrpQUxbaD9WReuhGvwLptF36WSisjEwMDg58frXvx6f/vSncffdd+MLX/gCvvCFLyT73vSmN2HLli2ZMsqN8+d+7ueOShvMz7glgrAMVrrJSi6ZmHlg0lhJJhP3jJWF0npoQztJOxmZnl53dgU33Z9KN5EF9wOGi0fGGGvQ8rq0U2bzMcejsk/O9CGRjpA0Jf3R4kMxUqNE0hOf88yAxErfR3NIwDkg4X+vB+HlLTiTDkprJ2EJmciJqKzo0GM9EJZE7dQZHPjSapTXNuFZNoScZcDCbNN2hYxMM5FnFJmtFO1nYzQy+RhJHr2vSsbJmbFARMVDB/ArUXE2BiNypJtWh3w5EtAkjZHfcSYymty1SErYAWw4J2YuoOZ7GC9Vi+XYSOWZVKbJpc0HrBlLGP3TDFOYa9JRzjm70o6N0Ovj2hVl0P+3Lsq4f2A9LpyIP/b61+HBvjV4zsQunDF1EA3bhZDAusYkmpaDluWgEngoywCjbg+e6B9Gy3YBmUphtXYIiZWtaQw3p9Hrt1ANPFgAPGFholTBjp4hjJcqmChVkhh4SfvpvSq4Fqo/co+YJr3Wro+EZeuSMyWRbDounlg5hDMPjMG3BDzbwshQb2IIEvrZMU9SWbAdxodO606MV4jEUxHS3BiaByeM1jjXzwVwbgDRBip1H2JKoDwjgSkLctKC/3QZ2FaGvdqDu6WBcMZG/aFetLZXsfLn96O6LjJhoEYwyniFyjmVlJ7KR5XxSqDJ42PpJtFw020vpkKVERUAhPF5UwlosxlNGEgi8UQoUslmJzOWghmBwomDTmYnXJkunrE0vUPbtMGKOWC342i38suCJSqs+VVBG+Ys3UTnJi4kFmu7jiYsy8JXv/pVvOtd78IXvvAF7Nu3D2vWrMEb3vAGvOMd78jkv/322/HMM89ACIGrr776qLTBfOgZGJygEIFE74SP6WXpS35qtYP6RqD3cYnx5wisuMsGnooecztHTiQlMPHIAHpOmcH495YhbNhYdsN+TAkzHX0yww4DXHh4L3r8Nnb1DC50c054NO0S7h/YgGdN7sGzJ3bh0b7V+OHQKQDiDxUp0e83sbI1DUeGmHQq2Fvux4zT+Tl0wgBnT+/HcGsGk04Zh8o9mHFLmHArmHFKOBbum0cTTw0Potb2sG58Gg9tGOInYBYBZAmQVQm5XAJ2ur656vnwnypD7nHgPV6F9AUGrx/FzD392PuldejdMomhS8eAIRN41MDgZERPTw8+9KEP4UMf+lBh3iuuuALbt28HAGzatOmoHN986BkYnKAY2u/hjJ/M4ODGEvZenLqN1E8VqO2UCKuAFBKyJCFKIeo/6UPvi8fRGi1h8qcDCCZsLH/WGCaf7oM3XoI3XoKwQyx/6UE4Az7gmQ+9kxk138NQu4GdPYPYX+ld6OYsCbRtB1sH1uO8qb141uQIDrs1PNWzIvqYEwKTbhWTTnfr7yqBh5WtKaxtTsCRIX46uBaHyj28wmAxQwj8bN0wHtm4PHbg7NZzfnFAVCTc85pwzg8QTluY/pcVCKdsDL5yFPLREsbuHcL0432Y2jKB1VccgFMzbp0Gxw/GjOX44eGHH8a55557RHUMDQ1haGjoKLUogvnQWyIIKyFQUtIuugNxGpEQcBK4TrI4ks7JNDXpDxszjamzSH7HuS52iP8H5EgoOTDyq46x9Ziy2vEK3f3IybAXIP5jZdNCct+Ui5qSITVKEZM3vLON/WeW0e6J9k8N21jm+FHA9F4gaNioXT6N1nf6MD46jMNjdnKA+p7Uvteu+Bi45jCc1R680IEX6wt9SZ02C+LohXobo/+J9ut9UKWRU+YcXTk5L6m6a0kP48QZMjGiw9joLygBQcBIN6n8spNMs0DOmch+86SEHSSCktOp5kmljwBuGN2QXbVBBLHDIhf3iZVpEvJYxc/rOmZUAbSzl7F8s+jadZBzUhSaD+meJJ0bl5MWwMZPa+uwwpvBqc1RXDK+EzvLQ9heWY5obV7nJjjwsb41jg3NcYQAxko1PNUzjKbtRtc/cSXMb39X6Yz8LrmHBWOefgoCkGKWdDd+/ol8zrcE4AOSxJsT8QssJNprJdcEiFyfNFINo9rYqeqcwzeWkkM6ZC2AE1+AkOhng/jYjgCCfZEc0l3Rhi0kaudPovfsKUw91I/x+wYxs6sHZ/zS07D6o3qofH7AjuJteVZ2vA3IxVXjcou0q022E9dNIlNtx4dpt9L1nXK2RBOIJLPx/4su4+h17K+5Dwovm583utYAZmWa3HKM/OOIbLZOKlRt6QEzVnNlC+TlmnTfYFHhggsuwObNm/GKV7wC1113HV74whfCdeceRudo40Sb+zMwMIjR6El/EAzsT6VEsAUaawR6d4aQvVHgX/fMFipXT8Ja7mPwwokka2UoWj/Sf9YkNv/KDpRWG3mRQQQ3jH4d+pZ5TRx1CIHRUi9+3LcJ2yvLsbE1hrPr+1AK890ahQyxuXkIz5t4Buub49hVGcQPlp2Kh/rWomkv/I+JkxlSAu3HK2je3QdnQwvOEAmR4EgMXDiBM395G2QI7Lnr6FimGxgYLC5IKbF9+3b83d/9HV72spdhxYoVePWrX43PfvazOHjw4IK1yzB6SwRhOQTK8Uwna2CSTh2pmbpCsxW6kJhj0zoZuBQwNRo4kqsD65jH6BXOjs0+LGVdHP3v7G01YcoyMOTAkkljobEN+XkpWxYms4nRX8+1MdNvo2cywPIdHvZvjuRfoRQYO8vGum/6EBMWgmUSISxYp/oonerDfjS6gLXhBrwZB6e+fAfC00IEsNDy42Dr4zZaU2VgTYBWkF6IJtlWi/7bxAddsY6aiYKaGdZi5qmTIteB6VusiVBRf+JMdJg0zZeBxKgTiO536MaMXl4cPa5uJtadohi4NlCwfauA5T5aq9m5582RISQAH1bmGdXuAWX04t+3FsPy5z233TdS/yuAqH/MZvRUNo69K2BQ2eMxmKsZS24aBHa5y9CAiy3N/VjpTWPSquCw04OWcOAJG64M0BO2MBTUUQvb2FVaht3lQfjChgzjZlrZ43AKh1zGkmU3sufXaYjN80MSyg2CMvpxbkHX46lNjQ2fzfzpxitSKQhI5wpj9o4SVckYSyctGHaPGqYkcULJQKEmPajKwQ9DhNtcyO/UYG9uoXTFNKSIY3CSm2D3hVh52ShGvr0ajckyyv1eYZy9ME6jn/F9djQ51yAf9zWy3bSjAavlpB+b7fnEquiWqVIIsvdS626FbPlRGsy6NWNhme/of+iz0+3zzcb37fI3jV5RzrZKmqvy17ixHDfs2LEDt99+O26//XZ897vfxdTUFL70pS/hy1/+MoQQuPTSS3Httdfiuuuuw/nnn3/c2mWmag0MTmAcXhnpD2uTAVZuT9m41nILY+dGL/ewTx9Nw2aUvuo5B7HmuQfQv3E6U+/MtwaBO3sAQ/CdtHDCEL6wutAzGhwpRt0+3NN7Ch6vrIInbGxoj2FLaz/Ob45gS2s/lvszaAsH99c24pnKcvhicYQfOCnRBsQOG+IweS6mLKAcovrSSVg9+RrlnnV1AAKNQ5Vj304DA4Pjig0bNuC3f/u38dWvfhWHDh3Cl770Jfz6r/86Vq5ciTAMcc899+Ad73gHLrzwQmzevBm/+7u/i//93/9Fu31sf2gZRs/A4ATGgfVlrH+qAQFg40MNVKYCNM+V8PoExs6z4ZzbhD0q4G8vwd7chhCA3RPN9G7/2kYAwO67V6Pvgin0XziRTB3byzwEB11gaxm4aIFOzmBB4cjQyDaPI3xhY7/bjwNOPwDAkiFcGU3ftyx3UTpmnmxwdwjU7rVgNaKfTv4VLWCLB7SKb07QsrD9vzbBckNUhlrHuqkGBhEWsRnLUh7UarUaXvnKV+KVr3wlAODee+/FHXfcgdtvvx0//elPsXPnTnzsYx/Dxz72MdRqNVx99dW47rrr8IpXvAIrV648qm0xH3pLBdUAqMScPplQVAvSBZFVSD8rK+AXGRO5p5LdMJItzuiFleHNrn825iHdLJKfdhpHqAwvjD9wNLkmt+yFkYOxi7k1V5f8NhTBstKTUbIiGh/K7xPYe3oFa59qIrAjYxaxA/BrQGuZQKlqwdluoe33wRry4T67jr6zx+H0BugpN1EebOPg1uU4+NNlmPxZP3CKB+HKdGR4sIz2esBfHv0vlW42/egCeT6RMfmMdFP1N03OJ7S/0TYy20VyzkSmxhii0PRuY91p8jGVL8/Ag5O2sX1QnSsn8c1UF4Ht/1nJNSuVPhI5DJVuBgF8YbFmS7nSzS5ja85ZFgZkYriJOG8mf5dmJIXSzk4okm4y7WHjFnJVJ6Y7FtpxJxQh9BPPOQYAQPVr+pxwvhq0ng5GR5Lpo6yEM1e7GRXQn5PskgJWwh+PHZq5kybdjHaEJE3JQenY4icXhXRWi1zbGDYZb0PmJpW2CVgNAX9TAGeHjXDCRntHGdYjJdjnthKDlGSsJifT2leGX3ew9jW7gX6JZugiIKIqZbISMprbvtioBQDq8drZMjGEcYhWumTH+20i3YzlnJ5NZPZObCzjzxpQ44vNdmvVJ6j5TZtZCqKQM3byz0z2es/nO6DT8yhFtjNrh0icfEgZTgKtVarqpvuzg1ASy5MzaKF1c78naJIxbT0hcemll+LSSy/Fu9/9buzevTv56PvOd76DmZkZ3HbbbfjKV74CIQQuvvjiROL5rGc964iPbaZrDQxOcOzaUsHI2WUIGa0Naw4D9XUCdhuwDgsEG0OUnjeNcMxB69v9GP/JEGqb6+hdV4fb42PtFfux4tf2o3reDOQhG+H2EsKHK5AliXAgRN99WHJaeoNiuGFoJIIGBgT154WQroS9K/rpVHrQgf2NCuTKENZzGh3Lqo9Vu2p+qRsYnMxYv3493vKWt+DOO+/EoUOHcNttt+E3fuM3sHr1aoRhiHvvvRfvete7cNFFF2Hjxo34nd/5HTz44IPzPp5h9JYI3KoHqxLdTmorrSyUNYbFYSzwVX6GQdDSCTOoZlsFLcSwDizbUMB4dTJwYM1fyHG6NavQGL34SWBZPIDMpstsGlu53kYBROecnPfcv5wUu0cNAyxIQAAHt5QwvtHBqsfaWLbTgzMjMXmeQO/6FqzdNtr39sBa5sPe3EL57DpaoasZAQRlgfJzZzD5bAvwALHNwdSgC6sO9H8X8A86aC8TaPhk0X9s3NImjF7Sz4KsGQtllQVnuc+xt5Qt4pgRxdjRe8GxdyRNGaVQ9paaUYi4XiniJuWYqHAztKz9fKjakDWj0IgKbka8gC0vDIvSLRg2Ra3Ro8YqhWYsgZ6vCHMJuaDyavdaMnVwjB43pdnJWSS3EQXZOhg3cB4LLMOWc+24EBHJIRgzFoawY9NywTHWXBMYRmN2NYkhy+w2cmO1n1aUsLikcnoc9XhY1IxFjTeUGlE+ZTR0Q5edjzJ7XtnG9IVA79a0zsZL2whXS8BykjQ1RlODFSs2dWlLB/UwilFKwyZwTF5vbLxCx+qy5TFpKXtXiYO5K1MWAHBjls910jK+Ms6Kmb3oHSUh7SiAS9Iy7v3cYsYlzYwlHt9IkjWPcSl5JObA7HUd7oF5nqQK+0Eby7B7HMuthXpKxokszZ2rSOLayDzs2nXuAjIlaRcdFmu7jieq1Squv/56XH/99QCA++67L2H7tm7dit27d+PjH/84Vq1aNW8DF/OhZ2CwRODVLOy+qIL2eQEGfiax7F4J3FuFhIRzdhPl509DOIDtFPzAcQG5xUfguQiGoo/g6v4Q7WWG3TmZUA49TDnGNMLAgKJ5FuCfHaD0tEDP3TbCYdmV5Fc48cdP2wipDAwMeFx88cW4+OKLccstt2BkZAS333477rjjDtRqtXnXaT70DAyWGPx+gUNXCIzXJYabdchlIfr6Z+ZXmQV4vYBTP7ptNFjcKAce+oI2dtaWLXRTDAwWJRJWp0tW2l3eBiyJ6Z/1oe+qOizH0BkGxx5yEZuxLNZ2LRasXbsWb37zm/HmN7/5iOoxH3pLBH21FmQ5lmKQuD/KKMMn0k0lr2MfMm7BPE0nUjJVjyYliBfHa2mc9EMyOgdO2sMaT+QYeCRtZNIYaHK+onjDSopSEAtNyV2lpqVLz0/K6NTEEQxwNO6eHUtMqImAo7Z7AX9ZJMLxiE5VbfNp6Qkq45WqBQSeQMNz0fTSC5X0LY+RbhL5VSLxZdPIeTESQO7+crHsKLRua+t/gVSyGTLSTVpOOtG90urjYvRx8jPGCIM1P6LZOkiYaZ2c3K1QDlSEWVK64eYMQgCH7VpxLEO63eXh2HNl5KN6Bv2vQNRPZpsTcH5IrOawUEvJ5Cv6bT7H82INYYoOwZmeMPJhLRYY0wTttLpUlHeSnHaMtzdbYqueHZqmJJdM/9aWFNCLFsuhtdh6sTxTEvMj9UoMyLtDGcpqryImbimVbrZj3XcIkdw7z7chHcAS6YCSSDepzN6V6LlyAjPfG8DTT52C6ql1DL34MGZDyTUBoBQPkBXhJWlKsknlmlU73d8IopA7FWLG0oy321Y6EKpt347f2dHJpu8XZnwTrfidz8nsGQOqQrlmXoecBbav0v3dfjMzz7wkjUz7N3lXMTJOaadpidyTHodZolL0+6XjGETTzBJPgznCfOgZGBh0hLSyP6YNljZ6gjbqVgmBsOeznNTAYMlDTRhZk0DQpcK5ck4D7ro2Jm9bhvbe8rFrnIGBguYNsMiwWNt1jPDAAw/g+9//PrZv346pqSkEQecfVkIIfOpTnzri45oPvSWCgXIDsho9NG0/va1tJ2ZqiGGG2tYmTjlWjdkfEpONMIimoyRjvBFS4w12hpZOZUttH92mhjAce8GxDawxRZhtD52V48COQczsPjuTzZ6fSNtBpkmlMrApag/TIDX7auXcOD+egVZ/AcCLtz0ry+jRfH5ooeeZED2jIaZX2PBCGy2mH2lGPwHD6MV9gUxAJ9v0XlqMTT89rYSoLrgvXHgFyt4ljB5dcjhrcX1oR2UyV5VjYLIeBCzDwqHQOIjpyyzLd5QZPSElJET0gV8we88yjNx5dWK7isoCGpOn/lph1G+4Rf0aW8awXLnsXqaiDvtmgzuHWfty6y4ab9Q2PQfG/Ed2ut45zeEOLbmdMjeJfw7i7dlmLJyBh1J5aPfIyo6NNJSCVLSc9vxHA0lA3kv2PB6KQJmnEOfZECnL114h0dsXovwzB+M/p5tkKSbPJidtxTcraDoI6w56X3pIU1WwxitCGa8Qs5U4rWansfjqYSnZrtpR8OVmkKovKk7M6JHwOO34R6YfRG0QAEQoE5OahNVqU9eyOE0z1mJMRubBfKeFOxc9ok8D9vkn/QlyVkoOu0eN6uI0zaBFXZ85qCG6xbzGd4MFxUMPPYTf+I3fwI9//OOuy0gpzYeegYHBsUdlb/QSC9yTa+btZEcIMcsxzsDgxELPjId61dHcbo8mrFb0T8UY7RayEX1tOIN+QU4DA4MTHU8++SSuvPJKTE5OQsaTWf39/RgYGIBlWQWljw7Mh56BgUEupk+z0LsjwMQaM1ScTLCgz2QbGJxIcL0AV9x3ACGAe56zEvXBo+wYHEos/78Q0gamLp5bUXtNG1afj8lvDWHoFw/qAbINDI4yTHiFhcU73vEOTExMwLIsvP3tb8db3vIWbNy48bi2wfx6WyLocT3YpUj6QePntGKpRtvOyjnpIvNEXZijmVbpdDG7krbock6VRupm5Je61mb2PiQ6iFCTOYhsfZqMLX8xPxfrb16EBb08jGQjkR2xq9Alr2nhFpfH+TTfAWW8opmxhFp+IDVRAYBSLGNqEe1iS0aSHjdMJUJqf5vka4c22iuA0loLax5uYXRlOZF9Aul9oPdDJvIUKu2JN5j7pq39YyQpkhmhCiW19DBOtp4kFlKOdFPtk/HytNz4YMyt7CRj61rOR9KLJJBs3R11eN2hL2iiYTEORYxcla26SOLc6Vp0KXstBK1HbXDmBwAfZ4+pp2sjCE4+XlB319e2qB5VDWd+QvslZ0whMkmFbSjMpyS39DqqeHMkG7t8gMreVXW03b7anSaGwgK8MKn/ufcfwGNbBrB3TU17vyXLEbRYdum2rwYKZhnN4MMByqPA3hfZCNwoD5XPq+0kzQNEA5B1C2JFgPJzZ9D45gDklAW7Xz+AzQyEdMzvsSLJJpVr1qx2st2K3/UNYtCi3gku0ceX4th6XvxXALDsEJYVQgIIgjl+HM9FutkJx+DHPxfzVGY2yLKOPPl40kfTxOR3CTV1YcfvrMS1axytMXGJQHQ5O3LVVVfhu9/9bsc8X/va1/CJT3wC9957Lw4ePIjh4WFceumluPnmm/Gyl73siNv6ne98B0II/OEf/iFuvfXWI65vPjAfegYGBh2x85wqzv/OJNY93sDkGcZAYKnDlgH6ghb2VAYWuikGBvNCy7Ux2eMisAXsQOLcRycwNliC13t0pFLOjAQEYLcLTBBnBKyfuhBPOPDVROTGNsKGAEohLPckoDQMFhaz1scuKixgu6SUeMtb3oJPfOITWvqePXvw5S9/GV/+8pdx88034+Mf/3jXH5YcpqenAQCvetWrjqi9RwLzobdEYAmJmhPN4Gm2yvFMHmX5Gn52pj6Z3cyhL9RC84CEblDsXpvM/KmF3XQhvMYcqtkvbXY/O53MGQpwDJI2BR2qc0AmTZtZYxZKF4KZbU8YPSubprFXCk5qXS3s9OBWHMCcBjJ34v2uk52BdW2yWD++19w9B4C6iO61RaYBLWZKcMaPPuDqpG+ofjJTcrHr9Bo2PV7H4xskvFJs5qNmLcnFTRktkhYPkuxYmcOqJJPVRQYWyTGYfCDsHcO6ssYcs9gbmXO8OTSNJ7GZGeZCW23GeCNhYgpCTnRtjiCBQFrwhQXXCqL7wLFTeTPMHVhH3iSJpM2uI69ukj/XNGd2WVVFDvPLGet0i6J+0DF0QVFf5u41V55hUDkUZeP2d0sQd7x0s+9dJ+ZEu//xmE8StdA9apOuw7MFAIFnVvfigm1jAICxgRLqJQc2OaB6l1nkhREwD4pFjGD8uMy+57hY2/Qw9NMQu9apMZYwekKi/ARg/9gFXMB7VgCnx4d1dwVyZwnoC1C5dhJe2UpcppRhDDVwUWkzpI2K8aMsXpMxZlGmLNH+qI1NJ31PKLWPescIGZ2rjYjRk8pYjb7LAvUOTpOUMMSiHVeFx1msHxh5SFg3Tg5A3nkcyy+ZAYXr3zk/XzpeKyPvZfFbv/Vb+O3f/u3c/T09Pbn7/uIv/iL5yHv2s5+NP/7jP8Zpp52Gbdu24QMf+AC2bt2KT3ziExgeHsZ73/veebdx06ZNePzxx+dd/mjAfOgZGBgUot5nJy6HBksfQkrN3dDA4ETD0FT0oTPR4+L+85fFM01H6cvDFphZbWHFzxg+L5TouU+g8oSAd1aA9nN8oAQ40kNoS6Am0bOuAXGUlw0aGJxsWLlyJc4777w5l3vqqafwgQ98AABw8cUX43vf+x6q1SoA4JJLLsH111+Pq666Cvfddx/+6q/+Cm9605tw2mmnzauN119/PT74wQ/i7rvvxnOf+9x51XGkOD6WLwYGBic0alMBfEegVTZDxlJHNfRgQ2LGLhVnNjBYpKhXHHi2wL3nrkTgHINxyxJZFiaUGPqhRPlJYPqyEO3nRR95AKJp9dN9YG1gPvIMjhukFIv630Lgr//6r+H7Ebv90Y9+NPnIU6jVavjoRz8KAPB9Hx/5yEfmfaw/+IM/wKpVq/CBD3wAO3funHc9RwLD6C1B1JxUslGJF2TXg/RHmzLoCBk9AJVZalKUeCaUllGSTSrnVGYdbRJvLZBZuSe7KJ6Rboak7kTZQ6QkoWYEYmllaV7dwEXof+cC+mZXIZw4HUeo/wIQ6j+xtNNyiBQnlmk6RJKptunieSXZrDjpIntOrkvvWzu+H3U/vf8Oo2lT+6l0s+VFw4Mf2KhMhaj32AiJGYsy3tEG6yJ55qw0Kp8MmO+KbqU/he8LRrqpy+KknmZJIovLa0T2oF1L2pLzL3D6YKSNWv9mzuVIpZs1Pxo/piplPdYgyZcnOe0Yy5Ipw0kzBVffrDYC0SlLBwjdLqSbyvyHu3aqshyw8ip6QE5+ybWnoG42Y4G8tNt7zRpPcLJQkd0vmW+kLpWiSd7ZcfQ4qTQn503zkSPSZ0bFR6TmX3Fcz21rB7Ft7WBUJI4ZZ5GyiYRbW0ZAt6M66fstmRoPAWfCh18RiZzTFxaGfhKguhOYuFKgtVGgQi6eT/TVnpKNklimzTArs3dVGiPnpKAyTrXdoss1bPWeIKZc8TvDt6O2CAnYdgAHASSA0InfyyROahJ7lkrFHfVOz95gcZwiSHQtgaToWmcvmf3ZMuwQwj2LzDM2Oz1Tt1HSHDVIKfGVr3wFALBlyxZcdtllbL7LLrsMZ511Fh5//HHcdttt+Nu//dt5rdVbtWoV7rzzTlx//fV47nOfi/e973248cYbMTAwcETnMReYDz0DA4NChLYwL5uTBD1+G56w0LYM7WBgwGFwu4+hbQFGz0l/Qlktib5tEuPnW2hvNIOlwSLCibZW8hhi+/bt2LNnD4DIlbMTrrrqKjz++OPYvXs3nnnmGZxyyinzOuazn/1s3HXXXbjsssvwm7/5m7j55psxPDycYRJnQwiBbdu2zeuYFOZDz8DAoBCtsoUVzY7+cgZLBL1+C9NuKcc9x8Dg5EZpKsTq+z2Mn2pj9GwnIflquyQggelTBYzo2cDg2OMLX/gC/u3f/g07d+6E4zhYvXo1nv/85+ONb3wjXvSiF7FlHn300WR7y5YtHeun+x999NF5f+j9x3/8B26++WZMT09DSgkpJfbv319Y7kjcPinMh94SAuem2ONEblxlIu1rxA5cnMMmJ9ek6T6VosS6Lpqm5JxtovnyNadOK1OGk24GjAxTyUaplCZk4vrRMp1i/eXGyukEKrtQMiDSbhU/kAs2TWMUWcR10+7gsFkiaUqyWWOkmyWLl246cSBCzYmT0UgqyWaTSDeVDDcILDRKDkqeBDyBMHa4S+RSXJxEVpKW1Y2FTKi2vCKZY2gZO9fD5dVkk7OkZFJE++WsNugq1Q4NKVBkqkq1fRYjEdKkPSKTltRTtASpoH+nkkSBHr+N8XIlkdLycSlz6u6wn00rkG6ysbmIVCpwEMUxY86Pk27mmQl1dN1k7uV8Xr/sLSiQbrIOnIxMrajuoucoOS9G4kyvY9LP8h+dzLGVvTsXw0zLpuS1BecstfsaSwTpHJRqI5FzhiJKDMgzpmScIXXd1GKhxQcK9YfLnQggJDC6qQRP2olrpzUp4PUKNEtO4jpJZfIe3Y7Zcou8hNR2iwyKyoHT4i5eDmqxAyeNnare+cp9E0gdONU4LyTgBCFcBJCCvDuJG3TyviVO20rOycXLpe9YcQznCfPG6I7oILmUOf+TusUWPIRcB1cu3TmjR0d34tzGndx45JFHtP9/6qmn8NRTT+Fzn/scXvnKV+Izn/lMRiK5a9euZHv9+vUd69+wYQNbbi743ve+h1/5lV9BGP8W27x5My644AIMDAzAso6P54H50DMwMChEqxINSOVWgEbVDBtLFUJK9Hgedvcdv/UDBgYnEqaHHTT6LZz6vQaefkEVrWXR2OjUJfzaAjfOwGAWFtL0pAiqXZOTk1p6uVxGuZwfs7dWq+H666/Hi1/8YmzZsgW9vb04ePAg7rrrLnz84x/HoUOHcNttt+GGG27AN77xDbhuOsExNTWVbPf29nZsHw3PoOLhzRXve9/7EIYhhoaG8G//9m94yUteMq96jgTmF9sSQTTjJ+JthtkjcXaqVsQCBWRmKZ05pPHW6ALwaNujxivx4nKfsHdePOXbJrOJdL9i8ijLp5g8yvKFDMvnJ4wdSWOYQS/Isok+YfTUdhh2P5vCzkCrtpK61XS0nEW5C8SMnowYPZsweorJc0iaYvJKhImtsYxezNgSRs8JialLfB80g5bk+qTMnmL06LULkusk0HCjekqNADOlKG9ifkNmzpMYV4zxghZmqdRpBTsBx9Qc4Ywmy97MThNg213QNMLydflizTPeYJgT6TDXjGMniw7JGQTEJ9nTasOCxFTNRRjfo6JZZZ2VE0wac9xOJgO5dev1CADSjWMuMvXQsopNCBmWrwiCeegLzR+KTFYYFBmzyMxGup+P69X5eNph1HnRoYyL29ihXR0NWqSeyDVRgElU0NpFjhSzaZo6Qw1/PjVwmaVCQLTuGEgNwgDApqoSqUxG6OFswAIev6IX53xrCv07A4wMROOhMwM0Byx4gQ0npo41BQyjhtFj5ql3LGUBYzOtMB2rbeYCBcxNou+EamzGokxZgNSYpW2njJ5rBSiJiNHz4/SAMnpKDUPSwClkVH7SLs376Diwe53MTYAcJnrWvtkZuPi+HY/HsPMsY02q5MaTEy4e4RxBmTMAeNe73oVbbrklN/+ePXswODiYSb/66qvx1re+FS9/+cuxdetW3HXXXfjYxz6G3/u930vyNJvNZLtU6iyyph+bjUaj4Cx4bN26FUIIvOc971mQjzzAhFcwMDDoAs2KjVAAffXjZKNmsCDobUWOfdMFL0ADg5MZoSvQ7LNQmU4/eJxGCK9qflIZLDLIRf4PkSxyYmIi+fenf/qnHU+J+8hTWLVqFb74xS8mH3EqTIJCpVJJttvtNjqh1SIESYFxSlEdz3ve8+ZV/mjAjEoGBgaFCG2ByT4XyyaaqLR8OL5xlVuK6G230bYttB0j9jAwyIWUCFyBgf0+eg75EIGE2wT82uKUyBkYLGb09/dr/zrJNrvBqaeeiquvvhpAtG5vZGQk2dfX15dsF8kxZ2Zmku0imWceTj/9dADAxMTEvMofDZi3+RJBKAXCWBoSahLIbJoyZrGQlWnaTBqQxu7xBJVpRtueRaWSKi1lfrT2xFoFj5VzkhhtHeScmuyTkay0AxqvKG4PlXPGUpOgwMGCM6uhUNJGn1ynRHJqUUmpkm5K2Iikm9R4xU1kmiTNUjHz0uuo9leI/EZJcso5ZiwclMkONWgpKgMAY0MlrN9dx4vu24vRgTLuPXt1YRkglRXKIxu7O2I+0pZO8jvpADKMzFgKtZsymyS42EucyYamZ5KZ/VqzOpnI5MiBMm2gmzKbYaDVwGSljLAUpjJMRnKpNYvbz5mosMYqWe2S4PIx+wUiM5+wpMvGkv20HvX4h9l82jYnySJNYO/5PFS6bHftUu5ZGHuPQddLdDhZ8BGkaf/P9f+j1W4ONMZqkB2XgyCWJpPnKdS0vbEkkXRCAYnKdIAzfjKN2mQIryQQ2FZyrMAWCCH4d5WVtkctcbDIO8hiYuupd6xFHGjCLqWbLnknqPcDfU+oeLrqvRLF0ZNwRAgp0vdSQJYmyPj6BPTGxPJZLQYv00ZWxnksJIkd+ky3z47k+irQOW4pTVPxBqlOM8nHjN8AH9dXZe0y5qdBinPOOQd33nkngEjquXbtWgC6Acvu3bs71kENWGbLS7vFL//yL2Pr1q347//+b7zwhS+cVx1HCsPoGRgYdIWxoTLc+MfRiolWQW6DEw12EGL5TAMH+oyjhIEBh/6DHs77/iREADx6eS8eeEk/GgN28gFg+Ut8MZXBCQixyP8dG0hu0SiiD0CFxx57rGMddP/ZZ589r3b83u/9Hi6++GL8/d//Pb761a/Oq44jhWH0lggavgM7Zq00AxM7ZvTIA9VKDDqy8juNxSPTbSqvmmEECHvHLDLXwjQwx6Ft5Jgzjqlj85E0ZWVNzUra8QykzVhbc05UeSwex3ipvDTcA2f+IqWIFrrDQSm2ruZCKVRIuxWTR+251Ta9nuoeuWR1u4/0HrlxXr9g6rTXzWrV1TmL+G9zWL82VdFCu2QjtMm5OvEsODFCkNxMZZcQdud2s2N5t8fL8eQWiCzFQzs7d86ygJLJwLFcHONB2ITcUAsK6ryoBXzRLPHsNmhp5NlpB3j2joMQEtg3XIUsh0kZ+pzwRi5MnZwFPmeYIpl7UMDoUVv00AWCUG+DFczKT4/NpeXtZ69ZNh/FXJnlObFzR4nd61iWwmLycewdB/qcCHKfOuTrdC6UxZbkV4syKEqMigCAGzMYBlmxfNJO0+hYbsUdSY1//aMezrp3GpMrHDx9cQ9CR8TNk6hMRR2o3SfYsESzkZix0Pdk3IlpyIVmHGqB1mnHZmqacRqpO2SUKkqpQ8s48fkl4zwitU/F8qLwMowZTXIZybMTMs91GI//GqFFmpXcw2P5XVw0Dnbqyxw7R8swady5yIIxRn83Sj0fkIyFgpgISbNMvivQ0AuKzQOAU045BWvXrsXIyAjuuuuujnV873vfAwCsW7cOmzdvnlc79u3bh09+8pO4+eabcd111+F1r3sdfvEXfxFnnnlmV+v+Nm7cOK/jUpgPPQMDg64QOgJjgyUMjUcfhUMTbewfnt8CZYPFhQ2HpjE82cSPT1+JRtm8FgwMKHoP+Tjz3mlMLXfw1MU9gDNr0msi+mhqDRiRlMEiAzE9WXQ4Ru16+umn8Y1vfANAtF5v3bp1yT4hBG644QZ87GMfw2OPPYZ77rkHl112WaaOe+65J2H0brjhhnkHL9+8eXNSVkqJf/3Xf8W//uu/dlVWCAHfP/IvezMqGRgYdI2fXLQM3/y51ahXbAwZ+eaSwWh/BaEAlk03izMbGJwEqE752PToDM780TTO/uEUpgejjzzKACZ5JwK0egRC1yygMjA4lrj99ts7fvzs378fr371q+F5EfP9O7/zO5k8b3vb2+DEhmNvfetbM6ETGo0G3vrWtwIAHMfB2972tiNqs5QykZKq7W7/HQ2YqdslgpbvwPKj2xmSF5GSQLaJ8YZjZYPYcBITToJCpY0qVh41TFFlaH30eNwi7aStjJyzSNapyVTjeYsKWXCuJIltssC9HWYlrtxxdPlpd+1Wpi/0mgShBSGBqnTQK1oZqYgyx6HGKxU7K91UshubkcXmSWW5/Vy7FaiEc7Z0k25PrXCwarSBZ87qgWdnY+9J8oOHxq5KK8omcaBGCZbNaITUMXJWz3NxjwqPicgQQbh+pg10Uk/VTeU3yXbR8eJs2vnR68xMwSkZkCalpEHlmDbObquemG5Olxw8va4fp+6exJOn9EOKOQTZZaWb9JrEG0w/0O9bXBETlw/ISmUj6aZEKEL9HnDSzcQQhtRXIKvKlNUaXrB/HujWoIQtQ3Ek3xr0dnSQbnLGQbnXQcT3Scr0snWSoRYcL6QyTSXdLJB4J88Tueeqz4SkT4SJzFji7B9PwQok6gM2dp1dxYFNZfYXkxASlXqIdl9aDzfeUrMxK24vNTXpGFuPLJmw4weqzEj4ozKxiQq5kMrcy9XeDVEZtdRBSKAS+KjZnnbPtfekip1aIu88JfHU+o6SmVMjF3JNupTXd3z25vGcsNJz9hmiUmCSrozFOAk/f+R0Kz5nyUgz8w7NGVSF4VEacE5QvPWtb4XnebjxxhvxvOc9D5s3b0a1WsXo6Ci++93v4h/+4R8wOjoKALjiiivYD70zzzwTf/iHf4j3v//9uO+++3D55Zfj7W9/O0477TRs27YNf/VXf4WtW7cCAP7oj/4IZ5xxxrzb+0//9E/zLnu0YD70DAwM5ozdp1YxvKeFzU9P48lTBxa6OQZHAa2SDYjFq/IxMDheWLu7gUojxNbLB9FeRj7gcp6OwAEcI3AwWIxYgtLNkZERfPSjH83EyKO48cYb8clPfjI3VMP73vc+HDhwAJ/+9KexdetWvPa1r83kuemmm/De9753fo2M8YY3vOGIyh8NmA+9JYKm78JizFiUhb5j0YXbWdYt2ZczPcUxesp4hGO+aFoR46dQZMDCsXxFBi3qeL1u+hZOmMgwS5vksXgdmciCEA++jBi9vsBB227kGhkoFi/ajmZZS1Y25IIjaFrM8pGpQZfca4+hcrj2cuAMWhIsA/acWcGmx+uoeAG2n1ND4FrwVR8MO98jDlwui5yLHc+m2h2YPUBnr9QMdMixfB3aUYKNmmhBYlY/IO1RbBQ1cFCMZtE5c2yplcPuJXUnDASZJSdtTtrPlKXMmWTSAKAkfPi2gFsNtP1cfXl1q/tOWdwwYKihTkwrbStlYFR76Oy2JRFCpiweAEFn4FUad7z5MHqM2U4ho8ft78Ro5eXrcKCjxuxxxDith2E0uqkydKOwAJkzKLoODGMtXcJ823L27mxZmkEjkBXDEvXPtftmMNBoo2/Kw9B4G3vXVTDRU4IbZI2zZj+fgStgT8vMs0LfMRZhHRPTMsrE2Yq9y4ZccESW0aMhF2xBw+vE+0k70vduWqbHbuttkEDV8dDrtLV7QMMQJaS7Np5G25qYTlGoJGRQ10oLZqwqesbmIz5QYD25RLaPRf/DtIcxrWGh6tFUBcxYzRSlioXQW6xfbccHn/3sZ3HXXXfhhz/8IZ5++mmMjo5icnISvb292LBhA57//OfjDW94Q2GAcsuy8KlPfQo33ngjPvGJT+DHP/4xRkdHsWLFClxyySV485vfjJe//OXH6ayOLcyHnoGBwbwwckYF7aqF0x6oo1oP8ODzDLN3IsMJQgSOWbZtcHJiy7ZxuIHE2GAJPztvEIfXuF2XDVwByyvOZ2Bw3CHF3GZmjifm0a6rrroKV1111VFrwjXXXINrrrnmqNW3GGHe6gYGBvODEDi0rgQAmBo0c0YnOhxfwncW6Q8CA4NjjHufNQzfFrCDEH1THlYcaKHSCLqgamJGr31yMy0GBgaLE+bX2RKBF9poetEMZFukt9WOpWZUUqIMPookWZyMUZOiMDJNNo2IEZSEtMjohJVpYu7STVWGHqMWS1aoaQ0nKdXi1XW4VgHXBsKMhDJa+DTgu5BOvatJrGos3dRkmnF7dHMbxmyFShc7mNpo7e2wnzNoASIDIACoD1pYPtrCZGihWbbhtCV8x0YQG7Jw16dbuJp0M9T+RvuzUiraRxNZkcym5RnwCAn0SwuePQMp9P7LPTMeMVlQMk5NzskcR/Unei5aDEsrK09l6w7VNU7TVBvzzFRCRmokpUBJBghdoFLOpya486dSUhU/krZRxoG2wiA7r6jH4EvbkhxvVhujiqgTQgjYoW74k8gwOakY/5yIDiY6nWL55aLb3/zzkGvO63HqVtpWIJvkJJBd1etISMFJN7OyOPb8qCFSgfHK7PriWjNpyghJKSCnaiU8cP4ybNo1jTV7G6jsiGPHugITy1w8dU4fgp6oD9sgY7CQCF1EjJ6UgBDpsgaLH5dUurasQck5SRkVE0/Fvo2OraSZdLxIt5VJC5XzqziyvXa6hEGNN8n7RAKloI2q04oMdNQ4yZifcXJOilAF1yuQYbOx57gO0IXcnv+fLsAofHVzqzRDp3azPxHYttAXNLebK0Ta4GfN9AwWHr/wC7+Ad73rXXjWs5511OtuNpv4+Mc/jlqthptvvnnO5Q2jZ2BgcETYdUkFlg9s+VodF/73FM7/2jSe/dUJVKbMC+lEguNLBIbRMziJMTZUxgMXLMf3L1+Nu68cxk8vHMTIpir6x31c+MMx2Hnro0QcPN2QegaLDFIu7n9LBbfddhsuuugi3HDDDUmg9SPFgQMH8MEPfhCnnHIK/r//7//Dvn375lWPYfSWCFqeA+lFt1NozIDMpnVgp/LMWATD1HFpapYxjy1MZxGzTAadoVSziHQWVG3T2UTKyiimsk0WrltBtu7U6CRdPm4zbaDMmd3lGzzIm06UQC+aCN2GNltaBGqH7SQW2Wm7ylY2ngxlkNSMMDcrq80mq/tVcJrKihuIjGKcSYmebRKtVcD0gAXPsTCwLUBlXMLt92C7fnK/OEObIlaZ9hNlTMOn8R+V6vx9ps9w7LRjhRASGPIFHGc6YvRyQoWk9ejGO0DWjCdzXkzYi6JnQtWjPRNMmgrxkWe6kzJ6s8xYwgB+WaCvkm8fyLGFHjlX1fd8wt4pxo+mcbP2ssM+AAgD3TxDABAyhLCDlJEDUsZPY+IYwwSOLSRIHglaptvwGRSd8s6FLeOelW5NXboFw7Bp4To6sXydztMOAId5Rpm2CpZiIUndxrPgWBmG0curr122cahsY3K1iwNryrjk+2MYPNDGoXW6i59thbDDEKEFSGHNImxy+nIH4zG6T71PLPq8xaEWXMIqBtr4Hj0fLhmreu0oPqYWeid+Nya/B6RAyfYR2F6u+0ne2AHorGJyLjTsSYESpxO7V2ig1WU/KQoZwx2HC91TpEToGoxxFN+wdNMqmwnUxYg///M/x4c//GHcfvvtuOOOO7Bu3Tq89rWvxS/8wi/goosuQqlU6qqePXv24Fvf+hY+//nP49vf/jaCIICUEldeeSVuvPHGebXNfOgZGBjMG32PS9SekZA2EI5KTJwqUJ6UGN3iIHQF3KkQw097OLClBHQ3zhksEGxPot1jRB4GBrPR7HEwOeBg1Y4WDq3NDmS2H8k3DQwWHSQWL9O8WNs1D7znPe/BzTffjPe85z347Gc/i927d+PDH/4wPvzhD8N1XZx//vl49rOfjZUrV2JoaAhDQ0NoNBoYGxvD2NgYnnzySfz4xz/GgQMHACAJln7uuefiL//yL3HttdfOu23mQ8/AwGDeaA8CPRIYebGDlf/nY8XPAng14OA5DixPYvN3WnDa0fqV0XMcBCUBaR8J5WBwrGD5UTwwAwODLHadVsO5909iYNRHY7U+IWL5QGhkzwYGJzU2bNiAT3ziE7jlllvwd3/3d/jsZz+LkZERtNtt/OQnP8H999/fsbz6uHNdF9dffz1uvvlmXH311UfcLvNaXyLwfAtgpJtqYTcXCyxk4sgVSuns7AJvKpvg4oNRGRsXmyuVe4Lki/5yMk0qB3GJHCiRX/pp3S0nkjY2SYy6khVNvZZsfUE9bQtNA1LJHic/LIpLZ0ECErD9MqZQyciREsMY5tg+kd84SqZHpKlKxkPluDN+Ki2aCaLZ57qfzkLX/di0h9yX+RjrtEMbwYoQy6QHe7/A+EYHKx/y4daBNT/yUZkIIQLg4NkOVjzmY9kzPrwa0B4U8PsExs61IApmwYtkmkoCSe+BMrKJ9kd5Q9KPVJwqmqbFKJRAv2WjUpoChNTlTowEmkpllYyTmid4jHRTleXMdug50vvqxc+ruuf0eI0gvZD1+J5zcSKj8+bNaBw/RKkCDJYbbDmA7xOqPwGpjJPKVVVsxYDGgkJWApZnjpPU7ev1CABu6KIET4/bqGL5aaY8aiP31DLtYSVZSfl8iXZ+5WSblWl2J80sGqM5GRufr0gPx9Sn7j81Rykw/xGI4sA5oZc5Q1rP7OPmtbHo9DpJ9mgcTHVeeXEy1fuhFL9j/I0S9R0WTn14GiNrHYSlqAJHhKgEAaQrUXW9JA3gx68oPdDyAekYRJcUKJk+leirbVcbO+h2tJ8+T8qYpULi7YXW7JifAq7lo2z72rhHj12K36P0XKas6H3juVlTqrz4pdw7k+s/ecZRQH7/7bb/d5KDanFHmTGKa8+8pJtdgjY1gInjsdixdu1avO9978N73/te3H333fjOd76D73//+7jnnnswMzPDljnjjDNw5ZVX4sorr8Q111yD4eHho9Ye86FnYGAwb3j9FsZOsbFma/ry8SoCvQd8TK12sPOSCvxlwPgmB2d8rQm3Drj1SEsy8HiIibMFJrdYkDZg1wG/38yKH28IX6K2S8JpwsjPDAzyIARGLnZxyrdbWPVDH3uvdABLoLYzRO/TEtObzNhlsAixxOLonUgQQiQfbwpjY2M4ePAgDh8+jEqlguHhYQwPD3e9hm8+MB96BgYGR4R9z3ZhtyX690QzwG4zmn8cW++iMWjDRQCvx8LICxyUJyRqe0JUR6M8/Y9J9OwMgBCwm8Dhiy1Mn2bWiR03hBIrfhiitie6H+ZDz8AgH16fhT2XlbDh7jY2/K+PxkqBgadCTG8SGL24W2cNAwODkxVqfd7xhPnQWyIIPRu+H7vRFTiLidjqSTOOCjmNUDaJxvdSMs4iOYySXAFAO97WjseUSZRWjCTLdujx0jIqlBaVVTTjWG9UplmK5ZxUNsM5iHKym04x/+j+2fmEBMqBixmUcyexOIkkJ1OkaW6o3BvTtmoyvliy2SSLr1T8Oxr/rVsHVbpfyXN8WBjdBPTvaWLvWSWMrXdxzrdmUJ0MML0mPUZrrUBrrYAVyORDr3GOhHMQcEcB6QDLfxzC7gvgrUXmXNU9oG5y6hy0e0B6dr8Tuc1RaZOSPoYkuoyKR1iz24AUqAoXfnkCEFIrS68zByUHpfJKtU1lyKpOl5FpAUBFePHxyHMklSw0vb/NeHsyqCZp9fj+UzdQT5NAp7Kq8n023BELYQ9gzQj0VFsol/W+S9vgMhJmJQ8G0v7GuY5yEm4KJeeyc6SyrbgPt+P+KyRQDUvoQRs+lVd1kI1pMfoKXPnYeItdOuwVufepY2tjNecM2aXDJNeart0pc8pwY3kyTljZMSHPSVhIoAobPbKVGfvo+XOxIzlZPwduXObin2lyRiamKy1TjmWKFSL7rzg+sAmY7AlRfVBg4CmB5vkBgmeHGGScL7nxG5glFZ91DpyrMpVPViwvTktVFGq8oAiY6Fl0/FLlk3FJCtjCR1lErpuWZWeOrbZLmpwzaq961wLdx7yl6BQTl1vCkbekYK4oWnpBY4JyrqOcnPNoQ3Nile0OObMQMtdEdcGxWNu11GA+9AwMDI4Y06tsHN7oYNUTbSzb6cErCxw6haeHxs+1MPQQ+cj2AREKqN8gg98WaG6S8FcAckjAXyW7t6E26BrukxZKD9toXuLDGhcoPWlDGkbPwKAQ/jAw9XMSoi1hV1Rw8IVtk4GBgQEH86G3RBD4FuBFv4a1WVk1GcXMWmqz10x8KG4GOrDT2a0gYfQ6z/jS+Fl+3MagTX65q6x0Yi2h9EgbnKz5C2ULVRwfbia/7aX5XCfapsYyakaQxgKiM8xqBrZoljzP1ERIoBY4qEt3joxe2m7FMDph1rSDzmjWCcOimDw626pYsLZPGb1sGzrFTgTSmU7VT3ada0GKJkr1EAfPdoFy1K7MNYsPJgVQHgHssXjWdk0I67CAaAlUdghgBwDYkCWJ4CIPzrktQOhMlZqhpnGmONatN2b2gHTGnLJuKq0iPEAKuEEFrjseM3rZeItRy/LZPTqbzjN68cw4ZfG0GfrsrG1bRvewHqZmO1NhBQDQF6TnNxVUMuenGbjE2/UHhmCd3kL/hTPw/qcPEjYGeupJnCbeMEbtS899Oj4eAEzHjB41BGrHxjRcLEOKIkMk1Zfb8f0XEuhtuxiwGmiT85vdL+nx8vqyAlcm0MaTLFNRNJPfif0rinlK0S1r0a0ZRV7M1Nlts5n4jhzrmsvYhAJ9oY261cyMfVw9RYZQHPL6TNKEDgyyHjs1bU/FzrJXaSzTOJ+bpunsVTZ+qcMwdZy5k4Uwk48zXqFpHHunnStnCBWfdzLuSAFphdG7TkhYTMfmGEa13XDS9w4XE7AIqUkUE/OUaT8Fd8/nY5w2+7izt5XBFReXttvYuPOB1oYgP86pgQEH86FnYGBwVBCULOy+KPrRz8mwKA6+EoANDN4jYY8JhDUJa1RAeOQFOhDCmrAg2gLOPSXgGQv4uQZQya3WoEvIpgDqFqxNHoQA5MH4Q6lkaAkDAwODJQOJxcs2L9Z2LTEY1wMDA4PjjrAXCKtA48oAjYsDTN3oo3m1vs6k+fMe/CtakE78NtjnALf1AIfMsHWkCA/FrNhQzAq04/W9rnnzGhgYGBgYLBUYRm+pwLcgYzMWXUEQy4FovCKh7Yo2E+lm1nhA2yS/scOYtRE2kc0wcs6ASDcTyaZPKlIsTpg9tnSz7Q5InDxBpI1hKGc3O5VNkUQV14uLn6RJN21OuokMODnYbEZLSKAndDETliBF8UJyVQ+VEiXSTSJ3KtnZjx4a10xJNtvEjEVJNqk5Bifd7BQnEUjjmdlcLCwG7HmWJfxzI+FReU0L4ct9iKcdiMdLqP1XGeKsFsQFTYT3x2YjVQn7m2XIV80AZcBWi/5zDGMSqRk5dp+VNWhRUsqKFUk3RVBFyZmEEBI9ViqVcUHjNkbXJyiQFYXMfFpyPCILrWhmLFlGdCaWJ06FqURKSTfHrZ40nx3HtcqRbjali32ProIz4GHtysMQFvAMlgEAVvdOwCrpphBUuspJN6eIEcyUHbVn2kmlm9NBtN0Os68bXXIXu7Zqcj5qMlSK/8b9WwI1VCCdOlrk/NqcQU+BLFRBk2zFfYvGI/QZWahWnpGGdZJ0FRlCFUoWu3Qz6Dofc30cRrrpMPeIk9wB0bUa8B2EzkzyelH7HUa6SQ1MVHvy2q+ObRdI99R9CTgJJ2k37W8qHqc2TiRjcFZKSc9ZSSn1/pQeR5Wn8m9OFsvJNKkJC3cO3SJQ0tXkeJEZS2hFZiyd6qQycyXdpLFDuTGvsD1Kpkmk+QGyaRw4uX63fZ43gSF9WTNCybaHM5452tDiwAZzM2Mx4RUMzNS4gYHB4sHaAPKiNrA8ANb6kI+XEd5fhVjjAVc2gKvrgCcgvldBhyVyBh3gTTqob+vFwHPG0zW8MQyjZ3C8Yc0A1UcBZ3ShW2JgYGCw9GAYvaUCT0B68a82OpOVUDXdMnokG2XY4m1J2DvYcVqYpsk4xkFI8oWUvVPbZC2WULIxn8wCq/KU5LMUe0Nm2wjrpqYtwlCfTY42suYJehiKmImk7CRhDj07msGzrewMPMd4aSYLsRlLABu+tBmLcWY2WpnfyPR4oYxnji1mITi5UNR4pZ1Y0hPGI2byvAIzFj++zpyVenRec/vSYtkOxpIcVaB640TUhm0lyIMOrEsb8FVYkNN8WI+6CJ8MEZyRbQO3kD5gjk2Zulq83WO1IKVAaNVRs6chhMxl3ew5LjAgtw2V+DpWSF8ug5rDZIOnToStuExqvKLaRmf8+2LrTE+m/aBJQjJYLWAXgFVrx1CLTWqeifetLk2QNmQZPWoek7Rb0Nn9aLtoxj9lWrOMBj0X2seUyVBdMZpSwJVVCHcaLXquMePHseY6WxbC2m9BTAvgNB8QOuOjZu1pH/UZtpAzZqDoNNOvhXNRxkKz2sjlnSu6NXIpCqWh2DabYWIp60CftzAU6LdsCGcmEwfCsQLgGzVgvwMIwHpJHe6m9LnkDEq0MYiZ7eGYsUBmGba0rTwTmYZA6WyEorYDZizj0mh7OPaO3nM76bdHf1ZL1R1ohjFh1A4hAeZZ58a8mjVHhomAuz5UfRAwaZyxktZG9RwV9PlOxixcOAfaDspYeuHR5UuK2Nm2l2V0DQw6wXzoGRgYLFpYp3rAqcoVDsCPy9FH3poAcoMPI0qYO9SkDpVz922cguXM/0PihIMPlL5Zgr0vnvToDyFXGop4QbAsBEYlUJIIv1+FXNMCM89hYGAwHxgzlpMe5kPPwMDgxMCIDfy0DFmWCF/WjNgB86KYM1pT0a9oi7DhZ/zCMwvUmoVB5asubGLqIw5a5kNvoXBJExi1gdj5NbivBuf59QVulIGBgcHSgPnQWyrwiTU91YgpeQPRFyZKBCp9iBM1hQiVucTpArSerHRRQsk5SSIxY0nc/ah0089KKBLZKFUpWNnzC0h8vFAxFPS0ZLY9SRxB7YCxLJDIUEOLyiZjCSCR2glGpqmuhWMT+ZkTy52sAI4VHLEZS4lIiVS7qFyz4aXT4ZxMU8k4wyB7LlTNkpwfaZfrpMfmpJ1K2qrFm4vTvDArH605JL4b2VYyP00iNyhQPS1EaZuFmV1V+GvS+Eo01lU7rGbqobLBlhtJ+2g8OiXdrFktQApYfj9K/hAgZBRbL8ZyezrZVtLIGdkd/UBlZpXEjMXLpNH2UEzF5zBOzm+KbCsomdsMOb96u4TJHX1oT7vY88NV6Ns0Bb9XYP+PV0MGAssuHQOgS9JKiSyUGLnEdVPp0hSJo6fMYab9NK0VS0mpxDORYtFFgrFM2UPW6ABgDC6kQCit5J/CgNuIroMm8SPy8l0OwkPptQGA0kQIl8RapMfRjBASCWBnswa9nizrzEnxuLiFnDxtrpLhPHQrB3TpGNTBRIQzIwGAMLTQE7YBt5G8czTprgOE1zYwfdsyBGMugocqGDx7Au6wx8rDu5U20usUIHsvZ+/LK1MUw67EyKcDJo5eoElbY+kmuY52YkZEzWiyz1ux+VO2D3Yl3ZUCltWLql3XfhsciQRyPmgTGbbqU1SGzt3LovvfCZysP0+G7Kn3Er2vVneqEq4v50lEO6HuZCW1HWEYvQXFa1/7Wvz6r/86rr76aohug5weZRjdk4GBwYkBF2hcHsAfkqg+aty6ukXgWXj8C6fhqds3Y+d31qG6oomNL92D8UcGcOAHK3HwnuGFbuJxRfj1GgDAelYTGAyAnhD2uSYI8ULCKkv0XTWe/P/kXUOQhmA1MDA4wfGf//mfePnLX45NmzbhHe94B7Zt23bc22AYvSUCEYiUGaMMG2PGkkwqcLMLMmc7YcRIPbHxijYpw5m6EDMWxeRxLB4Ym36NdEzMZkiatq0yaxRjBsk1YXYGlPkjzKFi+kLNjEX9JbOf8TlQRi/NL2EJCSmAsu1n9nO26tR+3GGCkDdjs5UWDZ9AwiY0WhGbooW4UIwevQfMuSiyhUr86BVLu1H2/KlpjUqjpgYqxEVAFrK3CeOnmL5QM8eI8jbPlFj1owDl79pweySkC/in2wjTCAMJxmNjDmXQAaTsXt1JmbheOzY6icMrlP0+wIsYvWWExSsR1k3NPB/0+5O0epjP7tH7q5g8alpCzWH6rIiVorPS42H0gTIZZCPGD9qp1C0N+xBdu4NPDmJmfxWbbtyB2poGfOFgfFcf9n5rTdSucpC0m7IJXszkcYYXlLGZpoxevE0ZVGXGQu+/qlKxfQCISRQ9HgltELOACRsoAQcCIQT6CRvX42Q/2tQ5iFDisByIij9WQu28afRcMtUxREiozejH/TbHwCMt090cqsVcW7eA0aOYqyFScXuyIQloGss6FZixQApUwgpK7kwyoKt+rdWzMYS40MHYA0PwDpRQHQ1QXpu9l5pZD5SCgGH+mHOh4JixoutpJe3OGhTRNC7cQ8AwnjqjlzWeSdtPGb3OhjLdIvNcSwHYdcCe1l+8DPT+b2XS5nxs8KycYvICzRCFYdXIvezUF4rYx4A5F82MRcTtyWGBjwQcU8mxjY5jzFhOJAwNDWFsbAy7d+/GrbfeiltvvRVXXHEFbrrpJrz61a9GrVY75m0wjJ6BgcEJhelNFg6fY8GZkajukeh/SKLy1EK3avGiNVmGXQ3Qs66RfLw7/emPBVE6CfQzMaQvkh+xsmWh/pN+NB859i9ag+4wfOVBDD/vIAa2TKA0MH83RwMDgxhykf9b4ti7d2/C6lmWBSkl7r77brzpTW/CmjVr8Ju/+Zv4v//7v2PaBvOhZ2BgcGJBCIyd72DP1S72XWvDGwDsmYVu1OJF/WAVTo/OICfsOADLOXk0clZZYuAXDqHnqnHUnjOJ6rnTKJ3SLC5ocFwgBDB8ySGse8leOD1zXItkYGBgsMhQKpXw6le/GnfeeSd27tyJW2+9FWeeeSaklJiamsKnP/1pXHnlldiyZQs+8IEPYO/evUe9DfOSbt5///342te+hu9///t46KGHcODAAbiui7Vr1+Lyyy/HTTfdhCuuuKJjHZ/5zGfwpje9qavj/dM//RPe+MY3zqepAIDNmzdjx44dhfk2bdqEZ555pmOef/mXf8GHPvQhPPbYYxgeHsYv/dIv4Z3vfCf6+/vZ/G984xvx2c9+Nvn/r371q3jZy17W8RhqweYb3vAGfOYznylsNwBYvkiklJyhhmaOkuxMNzsZtACpSYvUKlf7OsshWOMVWiT1Vuhcj2oDqU8SeaWSWmrKL+60Rf4+7ZpQqU0s3aRyzsSshMbei+VpkjkZKQVCKTKTWJVYxlkk3VSo+6ncTck0fSLX9KlMM5Y7+m1icKGMWQJ6L+N+Qpsdy9kk7RJUksvIPYNQVU0+JGIZZ2gLkibj/FlZHD0vCiWroRJAS0jMrJcYfDjE9OmAN6xfdyX3pHLHdiwrbASpzHI6lvuVLR+QAr1eL1y3BgipScUqITUUiepRBiQAMOr1RXUzEk4qyVJyxppN5ZrpB8eAnXUd5IxXlGTT1gwqUkOcxlgZh54axOqr9if762EJYZ+APeQhGHPhrG+jHkTmJHkx7GaDSqWm/3/23jvMkqM6G3+ru2+ePLOzO7N5tatVWEkIJKFgBYQQP4ISychYMkIgg4EP8IMJxgYhQAYbDBjb8GGCcCBJBIP4jDEYSUgo57BarTbP7sxOTjd3d/3+uF19T0+fe/tO2J3RbL3PM8/tqa5wurs6VJ233uNUhU0UZTNBxHE4AQs1xUipu7Yfo6p6DWOgAhfhe6rZKkDECkiR86jOKRfrDQDQW/mruX8GKM1MUbqCcb0WZr6UsyHGxDKrZdvRBGdXLeqeKSUsM41ma6oqxuKde9rHOOERDvQ+irOx5+pTjUP2zeEccvcJ7au+XaRu7riMAHVTUa7D+QLUTe/+qCXKUo+eyAmrqHYr8UMLMMwcG9uVtsmJJEWJxHDgnltsuzRuHUPn5KikUWJDvDCPOhYiAtagEE4U7bdRER1OtIfCMmYrxiKiP64WC0vVriOEnp4efOQjH8FHPvIR3HvvvfjWt76FW2+9FZOTk3juuefw0Y9+FH/1V3+FSy+9FNdffz0uu+wyWNb8V9jN+s688MIL8ZKXvAQf+9jH8Mtf/hJ9fX0olUrIZrPYuXMnbrnlFpx//vm49tprUSotL+rFTTfdhGuuuQaPP/44isUi+vr68IUvfAEXXXQRstnGXAof//jHj7CVGhrHFsZP8gaAI4tsyBLEgQdWIZ4uo+2kiUC6EZPoeNMgOq8ZQNMfTNQoraGhoaGhobHQOOecc/Av//Iv6O/vx3e+8x1cdNFFAADbtvFf//VfeMMb3oDe3l78+Z//OZ588sl5tTXroeLBgwcBAL29vXjjG9+I888/H+vWrYPjOLj33nvxhS98AQcPHsS//du/wbZtfPe7342s87//+7/R29tbc/+aNWtmayaLK664Ap/+9Kdr7o/HawspPPPMM/jkJz+JZDKJj33sY7jkkkuwf/9+fPzjH8ejjz6KT33qU/jsZz8bacODDz6In/3sZ7j88svndAy1IGwBpesQ9MrU9qAxzrmaQif+BB+dtIrw5FHbwomkHU4IoV7V1NNIKGjKAxUoyngLWdGWKG+i576itqptkwivcPOgMoKLrrx3SSLQws1GTpcrnpNCQHilsl0mnhEaSsHxhHBcIogjVYgLel2U9454J5WqPp355MJUUIV81VHcgECLN1NJbFRiLVSamnr0yqYX7oKcN8sThaGz0rbXTmGljcwOYPI4ARDPoe3L4VfTJkuV80i9hjm7cu+3xvOABAwnAeGkKh49gwovVLfVzCsVI1HerdFSVRlGCcHQa5o0KzcrFRGZNomoiVXZ5hb6U2+f772jcvdemcOPd2LwmU4cd+EBWLFwf8ojDqQBuKY/a11s0LtBZ7mpEJASGYqSpFdwRHiu0SbesjI5fHUN/XMmBTKIwTDzSJPzSD2jCmq2nRMWoeCEMDhEzfLPRSQlqm1O4MPfNwevFOct4LyXc2kv4GGTErDTAZGP+QiP0POgBFCoPVFe0FB981woVLWB2uUwafTcipptUwEOTujH9+hFCAJFCYbMFPqpePSKiBn5IEuDaYfW7Xv5mHs5CpxHk6Le+eFCLgTsiRJeqeOJnE0fnA84r6TLePRoPosRctN44SKVSuGaa67BNddcgzvvvBNXX301Dh+uMHCGh4fx5S9/GV/+8pdx7rnn4qMf/She/epXz7qNWQ/0TjjhBNx88814/etfD9MM0qvOPvtsXHPNNTjvvPPw3HPP4Xvf+x7e+c534oILLqhb5/HHH48NGzbM1pRZo62tDdu2bZtT2VtvvRWu6+Jv//Zv8d73vhdA5XjPO+88HH/88fjhD38YOdDr6urC8PAwPv7xj+Oyyy5btJgaGhrLDeMvMbDqv1y0PCMxeYq+r5753UbsvG8DVp8+gNWnH8ao27TYJmloaGhoHGUIGSmiumhYqnYdTRQKBfz4xz/Gt7/9bfz2t7+FlBLSm+HetGkTDh48iGKxiHvuuQeXXXYZrrzySnz3u99FIpGIqLmKWU/B3H777XjTm94UGuQpdHV14Qtf+IL//2233TbbJpYklCfzZS97WSB99erVOOGEE/z99fChD30IAPD444/jxz/+8cIbqaFxjMJuEZjeLND0nAy6AY9BZCeS2HHfBqw/5yA2v+wAG0VFQ0NDQ0NDY3Fw//33453vfCd6enpwzTXX4De/+Q1c10Umk8Hb3vY23HPPPXj++ecxMDCAf/qnf/IFXH7605/ic5/73KzaOiJx9BTXFMCiBAc8Euju7gYA3HnnnQGv4MDAAHbs2IFVq1ZF1vHud78bX/jCF3D48GF84hOfwFVXXQXDmD3dgYOwq1Q8EaApIpxWjypJM9ZnXFbTWL4ibY9rJ1woci031xBHJWVoqoJJi6JrBuieoQ1AqPNYQ3gllAYB6YmxUKoNR7tRtBpFKQSqceYCFEePfhigaxKKpFSCK5RyqfoJI2ojyCBJzSpRsRkJZpsR/6FpisZJ63G9fu+QGH0GFZExvXV3VpWGpZg9NrlnhCeSYxsGpnuB5ucciAkD5daKAUqMhTvfE1SgxYtNZAgJIQHLjgN2DBBAQlTzJQl1UwlyTNhVkZSsXZllo9dtvJQM2ZD22stZ1Vm55hihcXoU0CYiMtJq5dEIJocqtNGebUN181F60rRdm7bOgdK+qPCKpWiaRDBAUV8ppU5RraJodpTGqSibvoCNFGiSFuJWDhmzPnVTUQm5uGWBfPMUN5lPXLtG6ZecoMZCIYruVg+1aLGGBBwjD9PM+s8HTniFixkWRYtVAiiBNOZa+2Ujzl1U3MJ6CNI0JZNG83JUS0+gipqgHt+BfOGyrgzvj6JuzkyTECgJB0nDDlA3y5Re6L1IXSZtvlD20GtQ73o5qGpAUHtKCMdgrSdgQymn3LnlbJwParXRqNCNVSfmJ4ulHMZgqdp1hDAwMIB/+7d/w7e//W3s2LEDQPU766UvfSne/va3481vfjMymerSj9bWVrzrXe/CDTfcgKuvvhq33XYb/uM//mNWeh9HJLwCFWGp5fl7oeHKK68EAPzFX/wF/uZv/gb33XcfbrvtNrz85S9HNpvFG9/4xsg60uk0PvKRjwAAnn76afzgBz84kiZraBxTKHYKSAEkh4+xt8cMKIVVK6nl6TU0NDQ0NBYLtm3jxz/+MS677DKsW7cOH/nIR/Dss89CSonOzk68//3vx1NPPYV7770X119/fWCQR2GaJj74wQ8CQGR0gJk4Ih69O++8098+8cQTI/O/9a1vxfbt2zE2NoaWlhZs3rwZl1xyCd71rndh9erVC2bXXXfdhVNPPRW7du2ClBIrV67EWWedhauvvhpXXHFF3TVzZ5xxBt73vvfhy1/+Mv7yL/8ysG/btm0Nj67f+c534vOf/zwOHjyIT37yk3VpsBoaGo1DxgRK7QJNu124FhCfkJjqAQqdx1a4UNfzYorZzvweQzi8oxNd68YRT2thA40XPiYnUvjBdy7E+k2HkUyXkG4qYt1xh9HWqQOMamgsJnp6ejA6Ogqg4r0zDAOveMUrcP311+PKK69ELBaLqKGKzs5OAJXB42yw4AM913UDoiSNeLrowHBkZAQjIyO4//778YUvfAFf+tKX8Kd/+qcLYtuePXsC/+/duxd79+7FD3/4Q5x33nn4wQ9+UHdg+aUvfQlbt27FP/7jP2Lnzp3o7OzEG9/4Rtx000014+jNRDKZxF/+5V/i3e9+N3bs2IH/+I//wLXXXjuv4wIA4VT+Kv+QHQaTxsUu4SiVnOomB64+juJIs5J4dMrGAOuJbZChQ0axRhjVzaj4eWySqofeMV6lNG6f69EPHaOaWHZNCFmhiDjCgBQz44cZgV8AsDxKkk3zedtlGjPPSwvQNQlNU9kjScw8dZ4Dl9rrOwHKqTvjd8axKj5s8PqG6bOK4ikIv0ipe7rEVtMMU60ClEtLxSgkdVvBCze8Beh5sIzueyt5W551sf3ydEDdU50rk1wjdR5L3rWypQHHtQABFGX1QVwgHaDsxYxTseMAIO8pbFJlVP8akeubm8P4QtEheUXLaoVFt2JDDnH/WudIXL+cFz8w58RImbm/CvKkHkXjTBhcGrXRU4uVtC97NGRynqhSobLXj1EoBQwnhbydQZFcl4KhVE45upoL6QBP3n48AODF73/Sa6e29zOKzplzq/TbKCXL2SJtVKm7SaNcJ2cVZbkwr/XZ2l0rlp0JADIJx8n4Dx1lY7NZpSNHqR/67ZCHkFLBNchDQdE4ad/yy0ZwxShtjqMA18NsVDenvD6cZq4pRzmkUH1sZLAZzz2+FpbloFS2kMsmsf3J9Whuz2JqLIN7frMNV/7pnWhqyzesupl347CcJISQdRUv6bVS1yiK9sghEMuSo9qqGIUMJbrWefLj7EUocXL36lxiASoUyHui0XuHi//nMIqf9D4ou8srbNlyx8hIJe7TunXrcN111+G6667DunXr5lRXR0cHPvGJT8y63IIP9L74xS/igQceAABcddVVOOOMM2rm3bRpE173utfhnHPOwdq1awEAu3fvxo9+9CPcdtttKBQKeOc73wkhBG644YY52xSPx3H55Zfj0ksvxbZt29Da2orx8XHce++9+OpXv4oDBw7gnnvuwSte8Qrce++9aG1trVnXu971LrzrXe+asy0A8Pa3vx2f+9znsH//ftx00034oz/6owUJiqihcaxjcq2FqZUWkmMuVj1RglFuYO3nMsPUYAaJpqIWYakBYQIrXjKMoYe7sO/Xq7H+kmghLQ2NxYaUwFB/GwqFOLpXj+Fn3zo/lOfKd96BTEsBP/uX8yGERLp5doNVDQ2NhcUb3vAGvP3tb8crXvGKeSvtt7e3L/5A78477/TXoHV3d+OrX/1qzbxXXXUV/uRP/iR04GeeeSb+8A//ELfffjte97rXoVwu4wMf+AAuv/zyhgRPODzwwANoa2sLpV900UV4z3vegze84Q341a9+he3bt+OTn/wk/v7v/35O7TSKeDyOv/qrv8INN9yAXbt24ZZbbsHb3/72edUpXOKhoRNxXMg4zss1cycal76NFmOhsddUGm1Shu1hPENs27RJTmTFF2MJr3CP9AbSupXXkSZ68dro8SkPlUs8bK7rQkjAEZWF61IEvXfUa+eXUTN5xEglwkK9d47XnkOETFyyrQ5S0JiHsnYHENTTpLRmqOePC65I+kz1GtBCXiL1/LkqBh/tKGEhIBkQh/F+yVNLzdDS85ScdrD+7jyKGQM7z8ygYJuBYzBVzEBGoKXkVDx6ZcdE2YlBiqDnK2VUvTfKY5B3qt4yFTOPE9ihUNdyNp69mBGOBeeLjHi/2aEk+p5ciVWnD2LKpfH9qtvKu1dyqdfREzCYw6w8PVZ1/HEixsJ59JTXjnoS1Yw29Qy5dNuzzbdbAnEnjSmUkLOq1yhtVo6vXsy8+Nk5tDgTGHmsA8nTs0i3FUNtc+Bm6qlHb8oJC+/UA+d1TBMBHjq7Tz3LfnnvGKk3QXl0OTGOqOOjqHqlGisTEGAh596VBiw7gzGxwn8uKNva3GpMSOVB4z0t1WOJE+9rQcRCNiqPTonx6HECPOVA3MbqtopX2WI0JoJEj1nZSL1UOVl9Tky6FQGnpKh69FoYD6IDganRFJ763WYc3NHtp6/eehhnXvEUpkYycGwDux9eAzPmoNxkoG+oC1NjGZz9hieQFQnA5T2Z6nqpviOlQN5pgbRzgJD+OaWeZJUW9Dopb9n8ZtTUuTIDMRiVR696zVVfDnrxiBhZHU8dl89lZgJrxZDkjl89Twtu9R7kYodGxvVjPHrqOUKfE84sVaUFlm4Yg2NhLvKHP/zhYpuwcGIsTz/9NK666irYto1kMolbb70VK1eurJm/tbW17uj2ta99rT9yzeVy+OY3vzln27hBnkJzczN++MMf+tzXr3/96wExmSOF6667Dps2bQIAfPrTnz4qbWpoHAsopQzYMYF8q4F8y7HjznNtgZ0/34hURwFrzu5fbHOWPNrPHUX3awZgteh1ehpLE6WChf/+xrmBQR4gseaEQfQeP4yt5+zDSefvQcfqCbT1TEEIoDBdGXhMHG5CbrLxWFsaGhoLj7e97W24/vrr0d/f+Dt5aGjIL7cQWJCvoD179uDSSy/F2NgYTNPE97///cgg6Y3gHe94hz8YpOv4Fhqtra1485vfDADIZrN46KGHjlhbCpZl+QIu+/btm9dAFvBmbfTf0vyTR+FvsY9xCZ1PaQkcOiWJ9oM21j5TgHDlrM8nZmxX/wTzV92/0Nc1uu3q38iODhQnEtj8yn0wzdr55ZSJ0i+aYT4cm1H/kbKbs79Wnrn+1T833J8QAukNBQjMvqz+O3b+5BH4a7Ru03LR3jOJ9lWTOO8Nj+Plf/IgLv/zu9CzZcTP47oC4wPNaFs1XSnjrX/e/rtNuPPfXoL8ZHLWds3l+JfieV5oG5fMn8YLBrfccgtuueUWjI2NNVxmcnLSL7cQmDd189ChQ7jkkktw6NAhCCHwrW99C1dcccVC2Ibu7m50dXVhaGiooYDk88FJJ53kbx/pthT++I//GDfffDOee+45fOYzn8F1112HZDIZXZBBTyIGK+5RQ8hzwH8mMM8GGVHnkXic+G3Syr1tyaTNyiCWAtqgXVy+MANyxnqvsFqJEgowXUI/cSskk1YZhy1NSAEknKoXIW1UvLkZuzr76scec6ppSY8aQikiigJYInzGAPVDxQgySew5y1Nl5I6Z6xQGv1uyqXXqCaioyFBGSnMyvPNHbVT0I0HyWV49lqxSZCQEsAqYflEJW3YX0JKIY3Bj9Tyqa0PpTAmPs5p0bAgJtLppFMvtkABSMl0t6xB6ldcpUiSOnvTi0aWIYE7Goxo6XCcjrDiL9Jmk1z/iTtXTb9gVSp80q2m2R/MrmAUU+ruwYqMFI92NQh6wiV2C9qNnMkhOxeGYLkqlSjuWJx7DxX/kUIuGxF12S1E2CX/I8NqJk5iQpkf3pDpNlMYVokNKQDrNkHYSjlm9n2wzzI4QnmUWpS4SWpU0atMTA1RZhgJZJtRdqWhcJM2JetB6SHrHQO0vkviAjgj3W0UBp9RNRZ+V5BqZfllyj0VcahUT0SLnqZ6YCaWXBei8bhyJcgsmigW/D6h7wbGqNNWiJzyTMSh1NWykQ2xQ19UmDykVe9FmqJsUVRGsaj7ar4veczlHKJUZQqudCXpuVT+jtN4sFUTy0iktclJU2qMCPADwkqtGPVpk5Xk0mQ+ek9xYAql4J5q6ppDNboBImmhpaUHb2glMDbRh96MvxrozBkI2lmT4fWIXulG2mwEh/WudIHx+ZS+txxdjYZ4dMuIFLJj+ROmO1T4YFvpxAte8vpCPvxSCKUMFWKLsVf2Rvm/V+aNplmcjfU6oY6hF664uQyCibN7xSxrXMD/LdZdLeXC4VO1aZpjXQG94eBiveMUrsHv3bgDAV77ylQVRkKSQs+QjL/V2KEzTxCc+8Qm85S1vwcGDB/F//+//xfve97451dVfKMP0AoU2PNCLuMeOBK+btafOQG9Wg7ZGB3qNngs6wFHrugLB372HOA367Q04lGqm2hYAYnAxgFxl8EDWZjR5L/YS+eiJex/Hk2Sd1ZT3cZAVJIi6dwsXEV5vBQCuGnDYZKBX9gZ6doOjfxownRn8skqt3DWga/S8OunaScMlH5RqPZpgPrLpBxXCazjUYGVgNTBSsLH+mUlk25ox1V55IVsz1rUB1Q+YtFmueAUBFMxJSAE4seoaHUGCmqsPm3ESuDeLyjUqkOuR8z6oohTdaJDxtFmxh/YJV9lB0iwviHrCzGFyqtIvyolKoPSCWY3HkyWDvum9ACYLKG2yUfaUTIuisbWFvi01bkaufNI7Fnq+1bmgfdXyB3rVax450IPAuDWBpFW9n7iBnqqTUywFguc+VJZZ60c/4HI2XQvp3aMGHeg1dk5L3nkKXt/qGjZOydFfJ8qoqtKPWotZbxcVHNxfZ0TWVtYLGB1YW0n6et5JAJCYNvOhgZ5pVe+tuKn6cn0lTsmsBbSZdXZR6qPVfGTdFv3I9gbZjlm9BtKst14vPIngutX7zibrZIvec900qn01rgZ4zJpASa6lnHFO+p9fg1xpBJn1eyBNCSsJnPjGYaRaCtj3QC+e//1qFOw4VmwZRefqyaq13ppBm6jYlt04Solh0DV6JhmMup69ghnocfd+1Lo9TmlTUgVcNcgMpHllAi+j8KCeO2fUHscrE1QQjRjoeXlLdGLFO39FZo0evSdUGrcmEKgOIul+rh7Hrj3ZoLE8UChUnj3xeDwiZ2OY80BvYmICr3zlK/HMM88AAD772c/i3e9+94IYpTA4OOhLk/b29i5o3TOhjuNotEXx5je/GTfffDOefvppfPazn8U73vGOOdUjHMAfW3Af2RFprJALxXwGfbQdbgA3Y1+tsuyxEChp/8DuOu0F7WIGiQH3lQgl+iELSOgC15smd8xqmnANCACuWfkUUEyzmVVHpfkvAmJ42RNmoWEK6LZkKw8fX91vPjogJO/T6jmLmjHwmmPeb4HjE0YoXZIPAWUiP1PL23BgQwor9xXQdaCEyQ5vMOPtox4NVd4RonKthPCFc6hgSNGlH5TeYAWM3dy1ZC2sgopHKAclFatR24ZBB8feQNewIZpd5PemMF2Ow7AkcuTDo+jVLacMYMLzArcJ/yO3yHwosR9uEYMWbr86ZxYTzoKmqY9/KpVvkQHYzA8kISse7ZJroVim16iyTQcyaiBHB2207pgbHmQqmIHJhvAERJ6c50lvoJcj3vmcXXsQnbSqgyi/XweeedV21GREmfRBJQSRZ0Jl0POlPhSpTL0h6g/6qgM96pWoLdBCPSzUnqydQGu5BYcN078X1AA/G6t+yEzHKueshXjBmk3Ge0FM5QZrfpob7tNcPpu5pwGgxarYNkXsaZWVbS70Ag1xMuVUBnhUBGnKCU8IUIEidaxpI0PSwoO+gAfKAQae7UTb8ZOYRhz5w0lIVyDRVoIjk+h4yShy+QQOP9eBvkdWoetFI+g57zAMS/piTTnl7ZcCwm5C1igHBnrURiUUlCYDVM7DptDoxBFQ7W9lMkka8+qMkzR1D9L+zYUkCNgBRoxllgItQPV+yznxUBp9T3D3jsFMSkaFYzGEFSpTdmf5Mabo7UsRS9WuRcY999wDAHMWoJyJOQ30crkcXvOa1+CRRx4BAHzsYx/Dhz/84QUxiOLrX/+672m78MILF7x+hYmJCfzgBz8AAKTT6bohIRYahmHgxhtvxBvf+EYMDAzgn//5n49a2xoayx5CYHB1Ar1789i7LQ3XbPzD44WGzJZpTD3ZgsM/X4Weq/iF3/KgR9EUEuVe/ZbVWFqQWYHieAKJddprEQXXFtj3/9agPB1DYTSBx792EtxSddCSaC+iqSeL9Io81l14CNMDGQw+1omp/Rmse+VBoGsRjdfQWKa46aab2PR//ud/Rnd3N7tPoVgsYteuXfjZz34GIQTOPffcBbFp1gO9UqmEq666yh9xvu9978OnP/3pWdWxd+9ejI2N4fTTT6+Z5/bbb8enPvUpAJUg49dddx2b76KLLvKFWvbs2YMNGzYE9v/yl7/EhRdeiFQqxZQGpqen8aY3vcn3HF5//fVIJI6uUtXrX/96nHbaaXj88cfxuc997qi2raGx3DGyMoF1z+eRGbcx1RmWqF8uSPQUYbWU4Zb52WgpAeeRynPQPdUGrOU76NV4gcABxGEDkIDtxFH6dTPyAFb9Wd9iW7bk0febXkzuaQEA5AZTWHHqCFo3TsGIuSiMJpDrT2O6P43RHW2QJOROcTSJnd87Dh3njqD19PFFsl5DY3nixhtvDEUUkFLWDTc3E1JKJJNJfOhDH1oQm2Y90Lv66qvxq1/9CgBw8cUX4/rrr8dTTz1VM38mk8HGjRsDaXv37sXLXvYynHPOObjsssvwohe9CN3d3ZBSYvfu3bjttttw2223+d68z3/+81i9evVsTQVQoZS+5S1vwete9zr8wR/8AY477jg0NTVhYmICv//97/G1r30N+/fvBwBs3boVN95445zamQ+EEPjkJz+JK6+8EsPDw3OrwyE09iiqpAhl813oAQZPre1ZGxfejlpHp/aLWZTlPlsld6wMBdSPhRegbpKD9tkUhBap3p2E2ig90ROXKDC4prfEWwpIb/l5o6IXUVD10Nh5tG7JXVcwafWuL6V4UsaWospGrXlU24RJpc4dzScIl1LRYt3AOguPukhj6zEx8QxKNTMlck0mHANIjzuY6Iir8IfsNXClqKzRk5VtiRkCDe7sSRCKFkfXAUetBVG0s+lylSKk6gkKE1TXnpX7Y7AnY2i+cBw5JxGkqbkm8Ms0kPXEO05xq2tzUKVS8uts6lM4G6Z4kmU26lho3VzMKG6/gpAVql3JNQP9qOAJy1gByqHrpTmhNJpuMmtCKQzBUDcJTXG67NHh7Gqa2s6Ta2mZlfZsWT+sDl0Tp9Z/UqqoarvkEHEIR1E3qyfF4ta8km3u/KjjD1BcmXy+fYSuWiDnJF+OwXbSGJCGf51cKWAWJTp/Xr2/1ZmQm8o4VGwFALRYlTqbiAgKvQc56qaiYgbzhSl5NrNO1A6sLaxcr2myZlJRLlvJ2sImj3LJ0TTpOZmmdD8vXa1fBYBxszIJ02RV+0TKo3FSCqtPw11fRGKsgNS6HDpeNA4z6UKiQvGPdZbQfnwB7RiFlEA+m0DhUBJT+5tQeC4F2AZGf9+J8T0tcC8oAM2VhcnxUhoTsgyI6r0QD1A3y55dJZJWOT/c+k1OTAfgY2ZysedUnMEyjaPnlam15tmn4TP7g9RdK2RjlfZL1liT56S632jfUv2kRPoR96yOoo+bzP2mrgE9frtRdScFTd086qDvejXoa0QHJJlMoqenB+eeey4++MEP4rTTTlsQe2b91fLjH//Y3/7f//1fnHrqqXXzX3jhhbjjjjvYfffeey/uvffemmXT6TS++MUv4oYbbpitmQGMjo7iG9/4Br7xjW/UzHPBBRfgu9/9Ljo6OubV1lxxxRVX4IwzzjgqoR00NI4lSEMg22qhedzGco4u54x5gaL7EoivKgWD5xQF4NE2c1eUKk9+HT5OYzEhgWIHkBj1/o1JFLe5iJ8+S1XBYxRNx0+j6fhpAPVFcoQAzLSLzOYcxCYb6RdNY+IXnXAmLLj9MeDHFnBuATiuXLMODQ2NxuC6wfXLhmFACIGnnnoqoO5/NDHv8ApzwUte8hL8+7//O+6991489NBD6O/vx/DwMGzbRnt7O04++WS8/OUvx9vf/vZITmsUPv/5z+M3v/kN7r33XuzYsQPDw8MYHx9HOp1Gb28vXvrSl+Lqq6/GpZdeWjeA+9HATTfdhFe/+tVzK+wSbwsjBMF5xngJfJKNbNdT74zEHMqotjkTOU8kwHuJ6qlucoIZrGIpqqJekihQIuYGfwEIT4HTtMJiBbZjoOy9kktEqTPvzfjHqfCE53ainoGSXbldyzZZPO4oD2JA1aRqj+dtpAqbwgmncSe6UdVV1lvMFJbELjW5FbCbnDLXoxUKM9whXUaghOi4wLTIefQ8VSMdCazfnUX7vhJG1lZm06moSdWEikev4FrI2bGQiA/1kqjZeF5+P+w5camaZINTmZxXymA8Ua4UiJ1QQHJ6GtnHmlGYSqB8IVEDNRMQbypBWkDOiAP2jL7lNCbG4nuQI25qN+BVDuc1veOyyUvRnwWnAgZEeMAX3vF+hQSSroWcjLGeA96jZ/L73fC5VfbUCylQC/T8NByyYsbxAUHvnUKReO+UN4p6E9R2wOvq2RP0JhDlRBFu2+9bVOhJScST41PiFzbjLVPbjjTguFWPni0NlOPA3pdXvWAt8SIAA00kFAgnEkO9/OpclAPthcVY1HXlPNG1vNPc/uoxV9tTnryAeqMKHxHwSIf324E0TxzEraYlvOtCvUpcfzRF+H1D+7JShJx2kkAzEL9iEvlftgCDFqQExJ0p2PtiMM6wUDYtSEHuGSoe4tlNPWOqbmoX93zgPGwBVV3vIe6Sh7najjEhHmqBa0eJnvCCMeG0NPEg0/ZyRliMRXm5OQ+yGfCaO4H8wMxr5JWn70HlvRS0H81uhs6PbboEsVTtWkisW7cOQogFU9CcC2Y90FuIMATNzc14y1vegre85S3zrquWt1DhjDPOOKriKjMxm6CHr3rVqxYlzIOGxnLH/o1ppHIOTnxqCoOjJew6KQOZWF5r1IQAkqdPo3QoAXeMkRtXy5RrRxLQ0NA4BiBSEnh1DrgrBey2IOMS1l4L6SkT2Y0GSltdYPkuZ9bQOGrYu3fvYpuwOB49DQ0NjaMJaQg8e0oLRrviOP6ZKTSPlXFocwrZZhO5ZmtZPAmdSRPZX7XBHbcQv3gKjiuA52KADYj1ZKCnoaGhYQF4WR52ZwrWk15IkrJA6iETqYdMlLc6cLpdYKUAWuTc2DwaGhqLjmXweaMBVGibfhxRRoyFpXNGUDcbfrDP4gVQlwLK0Qe53bXivym6J+3V9YRCOIonrZDGY7U8QY2YDKVReqHhUTZp/DPTCBNZKP2yZHhrp4zqFKoqQUUWFL2OllUiLFRVTdLg6IqmSWP51olBG8VS4+i8LC2YE/CgMfhUe+RaSUJZglpwzgRrlwx1k14rSgdV18MSwIsfG0X7eIXOaOUljnsy6x/HUG8cfcelUWi1ICBQkhbyRoW6SalbBXI9kmbleqWJeIJP92Oom7WEMOqBEwyh1B9l28RABu5/ZYA4YL+mALvFhPjPNIxRQECg6X7g8FWAmxY+ZZPSNbmYY5KhrnFpHKKICUJU2jMJfVBt0zhRHB2MUjcLbgw5EQ8889R+k6Fu0rS4Wb0B4oy4CHfdFgr1zmOJUMBshlZGqWaKpmmTe4cT1okSulHn2SWCSGq/JcIUT0oVdWW4vZn2OFLA9YSogOrxOySf6pcBkRjmugTaYWianMiKxQhhzAWKNjrN0EuDVNna16Cyn6GNMgGzi15g7rITFvqg4OIa0nyKFphzGCGfkwAcbyO+y0B6vwS8mOqxHSZiO0xQ114x4QJJCae3jNgZOYi0RCHcNHvMnG10v4qjSuOpOiouLaUKu4rOSYVM6sd1ZOm3zDsq6b0cKVWUZkt4NM4EoXOmzcrzn9I51fmmFNd6sfUqNhqBsgARiSIPVIeJeVoXWozlmIce6GloaCx7nPjshD/Io3jqtBYkCy7W7cmi++A4XAMoNBnARguT6yVkbOlPY8tRozLIa3FhvDIHxEwYd8UhJoHiqS6ST5iwWwA3GV2XhobGMQgLKB/vYnqDxITjwhwD4qMSVr+A1U8mG4oGkHRg743D3hNH8rWTQJfmgmtoXHzxxQAqKpu/+c1vQulzwcy65go90FsmoB69qHAGvgdmFrMpbJEGv4EjxVG8SlnxF8Z7FxUCggpzVL1AUXZ5HjsyWeYy3jtJhVc8b5FBhFdMM+w5sEwXQlZmHS24IYEP5VmJEW+RmvEuEe+d8uQ5xHvneoIqkhFbAeqLsRjcmm7OW1qjn0SGVfAzVn4MOunqzdRSryIVq5KeAAT13vkTtFwa9VSQ+HDSEcjky+gZyGNfT0WqfH1/1t/fcbgMw5U4uCaNqZYYmooltIzb2LCnBOwpYMfZTSilyAwr8QIVPE8s9YwlLds7vMY8elHeIk5QhGJqOIXEfyXgZiQKlziAlQC2W8jsMjF+pkDzEwL51cDBl8YrYjhO1TtART3ULDnnieNEJmazlJgTI1FCOCYNi+H1a9qvAh6oGW0LCZSkiYKwWPEHKvRgq/bI+aTeJNtUcu+NeV2jPJpRZbg5edWPqDeMFSNhvETUw1ZmxFhU33GZ8wlU+1lAeEUJhgjm4cnYw3mnVLorRUCMRZ17KtokvOPPiaoHybeb6YO0HS5EAj2PlpLzR/j61hIW4tKpR0xBCSs5CJ+7gN0R+5XQCfXoqDqp94oD6+UjacpuGgpEeY6ph1xKAScuYK8CnF4X2CaRNIsQ0wLmXhPWbgNixAS2lIBhE4X/boF95SREUrL3ai0b1Tljvcqcl5MRaKFp1ANX/74N92XqnVPnOS54wRNlY8aoirVk3USoHiVQQz3NyrtH7aPX1WWEYlRfcMmzTDLsi7rQHr2jAqUXMlPU8Y477oAQYlb6Gyr/QglE6oGehobGssZxBydRjJt4dlMbpCHQvyqNsx8dAgD0DuQx3hrDqsECnjytFf0bUxiQQCmbQNf9Uzjh99N4/twMyqn6H7uLgmmBxH8nIJMS5VMdxJ40YR42YA4ZKG5xAWnCKAHjZwpIHRxdQ0NjjpBNEvY2G2KbC/G0BfPBOLDCAXICzm8zMF85zc9eaGgcI7jgggvYgVmt9KMJPdDT0NBYthCuxMqxHPasaYb0vIgTLXFsP64VJ+6aAABMtsTRNlFGOVYdzBUyJnac24St905jyz1Z7Dw3g3J6aQ32jAfjkKLyAZa8Iw43JeF2u5i+0EF5rUTmFwaKqyrr8nTMPA0NjXlDAHKbDWQc4LdpYGMZcq8F9+EkzLPy0eU1NJYpakUAiIoMcDSgB3rLBELCj7/CimNwaYzwyhwYSQHUo+4FM5LdDEWQNUNRttxwWqA5ynJhjsunZxIKhUrj6JpAVYRFkDRF2TRITDxFSbPMIJ1TyArtw2SomwqUpqni6FFaoO1R2xwitqJEWChdE1zMPHJOFGUzcB5V3+GosBGMgwAtmMugrgFDqaV9VVDamGdvYN25Xw/l9nmUWlqRUzW4rVBAzJEYbErDLVYqE5aLfSuacdy+KcRtF+sOZHGwJ42R5hTMUoUsVIaF0VgKj5wRx2kPj2HL77J48qw2FJqrj0wl5lEOxDCr0HeSJqESeSfSMsLUS45KRhGIdeadgCk7AZEHWvtM5E4EyvEY4gByxwlkTzNRcgy03S8RG5c4cFEc+bIRtFHFY6TURSc8iOUEMxT7pNHYcLWgKHtKlAWo0ioDdD56385oW8gKHaoEK0CbU/lMKojk3Y8xIu7hGOG4b0dCeKUezZOKbCiqYRR9lKNu0uvhMGIs6v6g59ZiRH242HqNHosd6CdBe8quGRBjcVxlD6FXCjdUjxJoqUcLnbnfF6NBOC0Qo7FB+nTWToTSoq5LPVtr2a2olDaNdahoqCLsLjMYKnctKAqgHRDRCdJehaxQRW3pPZW86kuUkqmotBsdmIfKMJ63gI023MdSyHcC2BCcUQrQNQl1zfb3V49BiZQEYjS6jECLEm0h7wF6/8e8+DFUoIWLn5cU3rOaoW5SkRQzon8kvHoyREyt4FE3C5II2Sg6JyPQApDYi8xxBQSaZknd1HH0NJbWFLWGhobGAqJzqoCiZWAyHQxWKg2B+05bgclM5eXbOVLApj2TSOaDHyqFtIknXtoGKYDNT08dcXtFWcIoShgFCWtKQhSYTCWg+X9MSAsobAbKPcD0NqDpKSA2BDQ9J9G8U+Lwiy3ku/QjXkNDY+HhvLQM2SqBYRNytQ1xRxLYr/mbGhpLDdqjp6GhsWyRsB0UYharHJNLx/D707vROlXC2sEs1h/IYtPeaYx3xFHe0IWBTgkIgVLSxKH1KWx6NgvDlgGxlwWzc0ii80EHsclgupsARl4LJHcDqYMmIIDiZglzXGDiMhtuuvJhlT++MtBL7QKSuyUmTxCY2KQf7xoaGkcIFmC/rIjYz5JAuwTW2BC/TkGeVwS2lqPLaxwdSDF/qtaRwlK1axGwa9cuDA8PY8OGDVi5cuWC1q2/BJYJhMu7wX2FOoa6eUTvMVo3R6+spZypUIdKWDOOnkqjTgxVD1V0VEqcZPLRV9UkcdtAaJpKYVMQSqaKn2fQOHoe/cQ0KSXFgZCAJR3EhNPQeedi5inKpkupm4yqJg09pZghQdVNlUbyceeWi63Hne8G6bocdTNAzSR1K3FLStNkFVSZNEkooFZZVqg+RUJZUrQx7/qOpSyMb0zimXUuVg9ncfKucbTY0+grxzDUkYQlHLQPlDHVZCFrxwCPDls2K3UG6bXedbOqHzoJT4mTU800hAQcia77XbhxYPQcAWkCsWmJlscAowis+FElb6nTQHwEMEYB1wImM3HYXv9of9IB4CK5B8iuMHDw5DjKRFXTJjb66o5OmLpJKXdsrLcFpm7SfqLUHQWXj7SpUioUWxMlmKyNDqVuetdcUaKBIL16tiqas1Ed5aAom5QWKxgqoWAecDKCuskdi/SpsuR8khtXpTucPDEDNi5ZDeqmdIVHsTVDzz6aT9FnBYmjWGIoizajwBlFi1Tn1JJh6ia9L+m5z9nxUJqfj3kQ1qJpcmmcWioXt9BXEK1hY6PwaZrk3CkKoGpXoKqQKgHY6uFKnyNe2349TYB7ro3knTGUzinBSDmw7k7CzhpwX2QHbKVcCUXZNOg5cxS9lsbHY5Q4vX4bON+MKielRar2qDKmom5Sxc64inVXQ3WTg6KIO7KqxFkwKn1HUTgraWHqZpHsT3hrKjiaKT3+fIP3qMbSwNDQEG699VYAwFve8ha0trYG9j///PP4wz/8Qzz22GMAKoqbV155Jb7xjW+gra1tQWzQPUZDQ2PZYjoRR0uhCNOJXs/imAbi5Wq+RNHB+r5pnPXQMNomSti9qbnGqHZ+SPYD1jQw9lKB/EaBwjrBTmDER4ByC2CUK+ssY+OV9LanHLTtrNjtJIBDL40fETs1jmFIidSIg9SIA7OoF9ZoVOFsdFHe6iD2QAz28TbKLy7DejQO8544oEPsaRzj+NGPfoT3vOc9+MpXvhIa5BWLRbzqVa/CY489BiklpJRwXRc/+clPcOWVVy6YDdqjt1wg68fRC2St4+WrVVZNKEV5Bhv17rBevAiBlkbFQQLeO0bARZUPzMorL08glhvdFoF8QCVGWyUpPLPoutTzVzkFAt75aeAbXMXKc+ksudomJ1wwdtFtwXg02XiEjXpQmXo4cA4SGv6O884FPKyc944RdVEdjqaRdesYSDXjeHcUvcNZHGxrqexXnizisVWensNNGXRnCmgBcPJz43AEMNaSxBPbOjGRSUDkyQy15+W1iVqn8sCWYtWDidueOIDy9pYkWgfKyAw76NjnwDWBQrPAUDoNeJPCkxtddE+X0bkrOLMcmwTG15poO+Bg1S9dTPQYaO13YccA0wZ2n5HGtBEDylXBC4D3OgbiMaq4hXTm3A3f4JK7L+cD7tlRw6OlhI58MRZUZvUdaczw6FW2qUiSv4vxWAJVb6JBOul8vXaNgIsZSOP/sbEqiV3KAxflkYyq248pV6Oe9oMlHPdwdcFoOSGQbTMxtD6BiVWxkF3uDE9NGWZF5GOO8w+0/3JeQM6jycaWo2IsXic2GYEWiqg0zrvn2xIhxkLrsbx3RiB2prddom3P4+bjBGrUr5BAyjVREkHPK/Uw+vEWA/eOROElQNsQELsjgenX2IinbCR/b8IdSqF0vg23I2iz8rCZzHmkwirKo0dFW1xPRIl6kKPEWmJ1RpxcvETOq1axJ/xMKTOXQ3ntqPeOS0uTeHz1EPBOx0p1cjKQWLrx6paqXQuIX/3qVxBC4PWvf31o3y233IJdu3ZBCIHLL78cL3/5y/HrX/8aP//5z/G73/0Ot956K974xjfO2wY90NPQ0Fi2KMRiGEqnsXF0HIdam4PqnAwmMwn8/qRerBdxDNjNKFkmOJGzTK6MrfvHMdYax8GNqVl50Dbcl0fTCKEVOcDQCTPEYkyBwVNisNPA1BoT5bTAujtLyAy7KLQayGUl0qMuWvttHN4aR+eeMkbWW8h2WsfEy1Pj6KJ3RwET3Rb6TkwhmXWQmnTQMmRjywNZ9G9O4OBJKT9vcspBx8Eysm0mxrrDAcY1liFMIHehjabbLTT93ELhAhu519hI3WMieXsM5dMclE9xNIdM45jDjh07AABnnXVWaN/3vvc9AMDFF1+Mn/70pwCA9773vbj00kvx61//Gt/73vf0QE9DQ0MjCrs6OnBOXx96J6Z8r14UbEOgGLe8MRNZ6+W4OH7fBNYNTKMcM7BypIA1h3OYbLFwcE0auY4aj1QpkRm2kZxw4VpAKS3w7P+XQTLrIDHpYqonPJqUpsDo1uqH8uhmC5nhEhJTErsvSmLbj3Ow4wLjayys3FHCeI/+qNY4MjBtiWLaQL7VRL7VxFgvcOgEYNXOAtZsLyDbZmKyK4ZVuwro2VmEa1U8zNkWE3tOzgDti30EGkcabgsw/Vobqd+ZSP+XhfJJLgoXl2HtNBF7zIR5wEDxD2zINj0TdTShwyssLoaGhgAAvb29gfR8Po97770XQgjccMMNgX1ve9vb8Otf/xoPP/zwgtigB3rLBIJQN1kqZQSdk/UCMLQ5tmwEBVQ22PZchFcarTuyPeVgIZ4ZSWhcQtFAAou+w+26Rlj0wBASApXTaQhZk75E45op2lwgFlYdcZSa4jY+/Y6mzfil5SPEWFjqJq1bBH6CIPnUmvgAXZP2mXrCK4JJI/lozEDpCGSRQn+mCScdHobjWDjcmgm3rWiBSlgnZsB35Xn7TMfFS3YPoy1Xws6eNuxZ3YKuiTxWTuSwtj+L3v4CRtrimG6KoSlXxlOntMGOGSgIB5ufncaaA3lI79xMN5uYzCcxaQBoA5Dn48dRaldmrBKQuAATsT2VA37+5AyytgUgBzFuYLo1wQtBEBqnH5uKivoo6ialAHPCK745s+Dg1XuZB6oJC7TQc+LfW0Y1mwsDrjQCtEFftIV0Cj8GHxFOoufJv5UjhFDmAp9qytTH0Sdt8oyJEmNhBXNm1AcArhJbCQiwNGQ+pASGehJYcbCI508wIA3hl92/MY3UqIPND+UqeQWw//gU+jam0TRh47inp7Ht3km4Gyz0bzRgx6NdOkFaYHgCJPBMVMI7MnzOeIqjSfJ5KTWom9y5N5i6OREd3lZC3fR6nBmgJFbSqGCK4dY7lvnBnkH7FbJCkS1IC1Lw/VGJ2XB0TlsaQAbIXQo0P+Uiud1AbHscpeMkpi92kX7QQOo/YyieKFF8kQPE+DiiZXL8MSXaEoj56K1HJtTM4PIJ755gxFo40RZa1mGEXDiYjLAWRb04epSuSWP4sSIs3jGkjSpd04nrwPQvJIyPjwMADCN4fe+//36Uy2UYhoFLLrkksG/jxo0AgMHBwQWxQQ/0NDQ0lj2e7lwJyMM4bagfO2QX9rW2YuZgxXAlYq6NZNnGKthonxiDZVfCPJuuRHOhBMtxcf+WlRhrTkIYEoPtaQytSKJ9soimvI3O8RI6xysv5T/43SCyGQsx20Uy72LnSU0Y7Y6ja6CI0ZVxxsraMMsS63ZUXvA9+4ro2VdEIWWgmDJwyr0TyKVNDM+yTg2NRjHUm8DqPQW0D5WDfVcI7HhxM1pHykjkXIytiKGUMiGlwFR7DI+d14YV/SWcfsDGSY/n8fgZbVooaLnDAPKnAYUTJZI7JZJPGbAGBLLnO4gdEkg+aSB20EL2/7OB5GIbq6FxZNHU1ISJiQkMDAwE0u+44w4AwMknn4z29iDlIRarTApY1sIM0fRAb5lAuOC9O/UEU6K8c9ykVYT3rm4+ag/1wNQReKDHIrn2orxA/s5wnUFPhZdIPVYOmY1lz6MSAqGJah/NJ71kWZn9nXG+lLR9YHG5t009LL69Aa9L2GMnmO2otHpiLDUFWJj99SaeAzaoMiTcEivMwl3fqP5EnmpKvVpaBp5pWYWiGMEJw8NozpUwlM6gqVxCa7GAllIBCbc6i9vU0oL+QhEFy4I0KrOth5qbcKitGZNWAiIPX8zFtQTuOnE1mvMl5NMWusdzmGiOo3ssj3TBhjSBw5tTGO+IAxIYX5mq9IMZE7Mz+ww9vkTBQdkSiNmV9GePb4EUwMn3TyGbsfDoKR0oCxMo8iIiwb7uzXiTkBtVoR9aKJi/ki+c1ijYe5nLUPP+DnZcAcA1DbiuMeOYVacgXgAYM5NYigAn/jIb52XDjhf/OSFnJkV6FWsJyswE9UmoeubqpRxPGxhvi2HNzjwOdyRDg7WhNqvimQaCOvoABrpSyA3baO63gaIIiBdxnlg7wsao4693rDR/NV/Vy0e9ewpB752Xxlyj2YQ9UF60gIiIVCEH6vfLhYbyhgoJlF0TJc+jp9oMhHtwVQgE4tFjvHy2MAATKJ4oMb1Gov13QPN/Wxh9BZBdC3T8SiJ+v4Xs+dSjp7x31OsW9uip/hEPeOzIdh2xlsD59t7bVPxFedVqibH4IJdDCbTQMkpwJRg+ofKyo168hCAvQKZJ5f2jHsaSpcVYXkg44YQTcP/99+OXv/wlXv3qV/vpP/rRjyCEwIUXXhgqowaFCxVPTw/0NDQ0jg0IgV0tXcjHYtgyMYTVuUmUhYGJRBJ9LS3IWzEUYyZKloXWpiYccMuVdyRVYqzx/pdCYDKdgLAkDnU1AYbEnlTlJW+oGIzzeKsVkybuPq8bL7vzMAAv9MP+LEY7EnhiWxscg1GM0dBYKAiBvcdl8KKHx9E5VMJId6Lhoh3DRbRMSdx1altgkKdxbMBpFhi+VGLFfwGZ7cDE+UD+eCC1A8gqLruGxjLFa17zGtx33334+te/jhNPPBEXXHABvvOd7+Dpp5+GEAJXXXVVqMwjjzwCAFizZs2C2KAHehoaGscUDmZacSjdghhsFE0LEMJfuyW9UBgtS4leJiUyWRstk9XZ3437sjjUk8L2ra0Vj/IxMDOqsbgY74hhvD2GdXtzsxroJQsuJATG27VY0DELSyC/WaLpMUAUATcJUGeWxhGEXMKiJ0vVrgXEe97zHvzzP/8z+vv78Z73vCew75xzzsHLXvayUJmf//znEELg/PPPXxAb9EBvmUC44OPoSSaNoSH6uyIolxR+jDow+aLqYeilwcoZG5m6uThrDQvPcHRHTsgEqPKgKL3Qo34EYioxNJ6qGIv0xVjKJC6UHwsrQNMM/gJEMIOhQHI0zMDxcLRJLrYeU5alXNL9UWmiwXz0WNV5pueboWn625T2SKhjhrcdEF4xKwVsMwbTF4VRNNxKVYYrYJYrPji1r5KPePcsESgb2E/yOZZHjaKBBKPozkSYZOu+cRx3aDKQ9fnVLXhubStQ5G7Meo0Q0DiRiqZMqW2qHpdJi3pBR+xnLVMPn1r3rxLMoecRAnAMlhcqSeeRLscLZ5JI3a6QoXyBazijvVmBoRdycwuB9qIouTPLchz+mplrt0GLHujN4JSnx2FNSxRSdT4fSD2DHUnI/iKO3z6F7ce3+TErZ0JR7aLOZtRRNUpTlS6QKLqIlx24hqiIoQgD6ayN9LSDVMGGKwQmumIYWxEDhCB1V+sx6rRXS0RFiWwEaaFmoD7umI4EfLEgWaFmlmfE0XMITVNRWwOxDBk6p8HQPifXu2h61EVsl4AsAk4KKEnDv6CKsknPiWs4gX0z9/v5KHWzjlhLIB9UGifQMj/Pc0FW7g0qopLxaJiUrklpnOp5nCbUTEULjZGXWtmcJXVTY1HR2tqKX//617jmmmt8Tx0AnH/++X54BYrHH38cDz74IIQQeMUrXrEgNuiBnoaGhsYShulWP5aKMQM71rahb2XTIlqkcaxiuCsBxwBWDhawb31jfbCQsrB3QwyrnjyEsmXguS2tR9jK2jBtF01TNjqGS1gxUEQ6z6srOiZQSJkwHYk1+/KYbjGxf3MaUx1WQ8qhGmG4SYFSBxAfBWAAbiqyiIbGssCJJ56Ihx56CHv27MHAwAB6enqwYcOGmvm//e1vAwDOPffcBWlfD/SWC2p59MCkMRPnnNBJQHiEqUdNjgUmlTkPImMHnRFlRTaY9jjvTZQYC1tGJTFCAEEXGsmrQleQ7wKhxOcYEY2ZggoCgOsYcIQBKQCHHKzje+rIbKMvxkIPRu0U4TTOE0PsZkVUOE8d4+Ws5XXz627UyxcoK+vXzXh0uWupvKr0mtPlai7n0TPC+dR+Rd00DcAseuvYA2VFuAx5ivqewUDdjCeqwXu0d2wSGwamAQDPrWrD86vaKztmKmxz92AtcAIWdTzaNUN3zCw7i/bqFa6Z3VfC8PoOULmYtuGnqSQAM8SkmIceU7Uk9lSfieHrNm8Pi9egYOrm7FoqcGBirCWB1rEynNUzBjw1LpwAMNIcR6cEppIxOLbhH5frMIOmiHPLeUFZ7yUxJ52zsXHPNLoH8zBdoGwJDK5IYufmJIoJA8IFTFcCEsilTRSSJpRGS9t4CZt3TuGkR6b8+p48vRUjKxIBG+biiYsKuaEwG6GX2YKGVyjDRAlmILwCfVeZKjQL4+WjIS5U+AEamsEQEq5w4UgBKw+Uk5VwDnQ//QV4zyd3LoLeOyWOwnj0iMS9H2aGCrR422VB7aofSoFD0vPAJUVYeCVTI7xCi1F5qNNjjXsiLAZ5mJXNWXJeJZYuRXKp2nWEsHHjRj90Qi2cdtppOO200xa0XT3Q09DQ0FiC6B2dwmkHhgFU3od9Hc2La5CGBoCJ5jjWHM5WJsUaHIm2T1Q+bgc7j74bZ01fFlt2TqIUN7F7YzNGOhPIZizISNsrX6ETbXE8fEYHVvflsXXHJNmjMVtYeYl8p4HEhItil/aMamgcDeiBnoaGhsYSQ6pYxqkHhvw56R097SjE9eNaY/Ex2RxH4sAUEiUXxURjaq9tkyVMNsdRih9dddjO4QJO2DGJA6vT2LmlGa7JMCTqwLRd9PTnseZADpmcg+kmC3s3ZTC6onExGg0PjoSVA8pNAtZeIKdj6B0daI/eMQ/95bBMICShF0ZRgBjKpc+GiBIyYaidgtsfUU9AUERtc5RLhPNFah9EUABZcDG8aJUctZWhuyn6pQxQMw0ICTgu4AhRoW4SSqaiadoOTfPqCVAbPYoNFVFRNM5a7JIGqV/+teSYq1w/AX9O6vWjwLH4VJsaYhOqLzNiOwH6pLKb1G3YhNrFUim9Jijl0qnuEwCMGGCWOOpmuB5J49GpumkZK2w3f59IZIolbB0aQdd0zr8dnuhZgYNtLRBFpkyjaPCFGqRphvtWpBjLLKmdnFellqNFnXvhnUfhbYuyCBbiKJcz99VKn3E9aLuBNIbCPhdIjrq5xOiaMzEZqwxykpMuci0keHoNwwWAZMHBQCoOt2SGd9ZF/Ws4M95kIJuQ2LAni1zSxFPrO4CyCMTtrFQvsXXvBEoxAwe7M/5ANJMrY/3ANFYPZmE4EoNdKTy1uQnjrfHKBS8SKiEn0DPTvjrH0EiZyNdXg9TOuuI9AIqwkEcMElVKpmnQY1Vp1ZtCtR1j6Jw0LmFiunL7ONKAWQRyLSZK5D2oxFwM8nGgaKUBoRfVJ9wan68MddunbpKXkKJ7UoEWRdmkceuioGiqgTJelTS2nsvE6CuQOHuqvElfZlK1UbWxwAVK1VjyKJfLuP3223H33Xdjz549mJqaguPU72dCCPzmN7+Zd9t6oKehoaGxRHDywBAypbL/KXDPhtWYTOmpb42lg5KnIhsvN752yRVA11geJ+8Zwd5VLcimjk6ohb6VGZyycwxnPzmIsmd3IWHiwMomTGZi2LpvApsOVdbeHb9/ApOZGIQEWrNllCwD+3qacKA3g0Ki8qm01NZMvpCQGfIGf6XKQCXfqambRwNCNrCOepGwVO1aaNxxxx1461vfigMHDvhpss6AXQgBKSXEAj1w9EBvmUC40he4YGe3GU8M24dm4dFjZ6AZDxobsoF6xlQS893AevZcfr+fTr0u3L3ke4ZIPsbLyQo8MPZIIo6iZiBdxqPnwoAjK/ORDvHeKTEWWqbq0SPeIkd59KppvCBK+MJxYSo4sR32fFJEeXmVIA6Xje0n5FhoH2WFV+rYUGNizCxXKg2ErmDOmfLASdfz6NmA0ahHj0kTtIwd9ir6+8ixJFwb7fkCHCHgAni4pxfTVgpGuXaZuaDuy5X13oX7Wy2PHlt3vfY4b3+ER4+KsRgmYJREULTH32Y6eETb9ACkL5hCbajv+W8Y9eyJEGhhcSQ/mAL3rcTmg5OQALJmAijWoGLOsGdXdzvSxSK6x/JYOziNx9Z3Y6CtaXZeV1V14FrXvx77W1tR3mRi1VgOQla8RV2jRawbyKIQM5EsO3hmTTv6upqweiSL5nwJQkrsWdGK/o40XMOAgARmetNJew4a6xORXrcaXsnG8tWvWqGeM0gAsIWJsmtBArA9ESnTCodNMALeO0+0xaymxc3KA1kxV7r2FNH7RCUsQOeOMrLtBnKxGCwn/JIJhCbyvXfMfirARR/CXl43IASjxGGqdVteGZs8rC0vnANNC9jGUGd8Tx4xoew97GPSDqVNOdV1qtR753v0QNNsL1/V7oKrAxC+kPD000/j1a9+NYrFIqSUiMfj2LJlCzo6OmAYR2eyQw/0NDQ0NBYRputiRT6LVblJCACWlHi6awVGU+nFNk1DI4D1w1PYMDSJZ1d3YCoVjy7gIZuI4bkN3RCuxCkHhnDa/kFMphLIJY/8J0h/RxP6O6qhIISUWDWWRedUEYc6MhhtrnjM965sOeK2HIvofaaAVTuLKKUF4jmJidUxDJzceN/R0Hgh4zOf+QwKhQJM08SnP/1pvPvd70ZT09ENj6QHehoaGhqLBSlxTv9+pO3qLO3e1jb0tbQuolEaGgykxKbD4zjY0YQ93XPrn64h8OTaLnRNFbBxaAJPr+1cYCOjIYUIDf40jgyahm2s2llE38lJWI6Lrt1l9J2ePKIB4DU0lhLuuOMOCCHw53/+5/jwhz+8KDbogd4ygQjE0Yug/vjBh8K7IkUGoqib9fKR+gN1c97rBmmcLGUp4h1SLz5WrTiCii6mYqIBgOAW4SumCaUKGpV0xxVwoMRYgtROIBhTSm0H4uipMo4IpQmSVis23czjimT7KIonrYOhdgauCyfGgnA+n3Jag2bLiuhwlCWOsinD22pdCEAFU8g5mxFHzyhX/iSCIjKS6bdUeEWlUZqmv38GVdh0XawsTvmxogBgV3MH9qQ7YJZEuD2OXjwPsKwwru9wwklg8jWwP9xgnX2ocfxEjMUwBcyiCISWrFdn5Llj6NzBNObmmQu9knkGsZTTJYTWfAGpsoNDTc0QhcYpRwKAcA2IsvdMg4H9bS3YODKOnZ0dKJsNKHHWYOGyz/JGEdUZOFoo985j3gNc1YFc9extlJo5B6GXeveiAOAaJlynQt2E965zbULd9GicJnl2Ki0qk9AwXauSuPZADoW0gYMbk+juK8EslSBzAk7S6ws0/p1neMyoPtQVTZMKpljsmoLqp6yKo0fj3xnei4bGqFNiLBydMxYRO88ymBcPKaJEXcokyKp6z1NqJqWpqnQq6mKKmGd3tUyxlgiNxpLE6OgoAODKK69cNBv0algNDQ2NowxDurhgcBdOnBjEnqYOPNrRi/tWrMOe5k6t+KCxJJGLxWALgd6J6XnXtb+9BQLA2rHJ+RumsWTRNO5gsssChMDESgvSAFbsLS22WccW5BL/W+ZYuXIlACAWOzoCVBz0QE9DQ0PjKEJIibOH9vkP36FkE0YTGUzHdGwujaWLsmXCkhJrJqaQKNvRBeqgZFk41NKE9WMTEFoufllCOBKpKQfZ1ooHyk4YGNoQx8rdRQhHX3ONYwMXX3wxAOCxxx5bNBu0D3iZQLjwZ0cCjI56lI96tM4a+1lq5yyom5ziZZVySvIxsXB8G2ooQ7LUTobu5itDcmmEmkm3FY0lQNNR6n9m+KUViKMnK6qbkAKyouEW2K+UNYPKkGGapvBVN8nhMQqSgXA+aj/3XqXHH06qplHqWiAW3oyMMyuYCSYfG0+Rps+FrhkBpWLpq9QCcBUN1RG+6qYfR49R1QRJp/b4/Yj20RnqnKdMH0LKrXwo392+EaJcaZOPGcgcwJF0+NHrwShscnH0Iuma86BusoquhD1pWBV1VMGUmRXFlaFN1lN+XSj6bPB5K8N1R7VzFL+XLdeBIwRGkynYrgWjpE50/XKGrDw+zaIIZD3Q1Ia1E1PoHclioKm5/jmNPCfkutWpZzYMz7o0XUadlbcmKpG2V2dn5HKMiHYaPW7LADyKrU/dtMi5VUsKrOpDT/jvTkLDlALNk2UYEhhLJ1DKmbBiLvp7U1i5u4TEqIupzlhQaZpRrFY0TkpxZOPkkTK2ZxAXe4/WY3s3M6WCKsqlzVEzAVjqYc+9BwmqCpvV0DgmQyk1yYVR6XwapW5q1c0XEj74wQ/ihz/8IT7/+c/j6quvRiaTOeo2aI+ehoaGxhFG3LX90euElcKYlcLv2jahbOi5No0XBjaOjQEAnl7RzQ5uZovpeAIjyRTWT4yjru6/xgsSiUJlUJTMOzj/N8NYuzOPcsJbpzY/h7DGLKDi6C3Vv+WOk08+Gd/5znewf/9+XHLJJdi+fftRt0F/ZSwTBMRYAjsqPzI8cVzf21dzf9jjx81EsrHzAN5TV8/LR6ciGhVooTPwjKACuDQuRhv15CjvDfHoqW0ujcKVAkICjhRwYEDKxmPmSc57Ry+RE9xXc5uLjxYhGCC4NMYL2LAwBfXYMZPlrHePOdZadYbKkm0uzSACLcKbjZaG9MRYJMySDHn0qIALGO9d1TOs2pJY7w5jtTOO52Mr0G+14aDoQF+iAygHP3i4elhveMSMvn9uZ/MS5dgA3DVoNI5exHXxm5iNR28Gg0B59MzCDO98lMejDiK9qvU8rTUrbdQerw/OxqPXaHuNokZ7huti/cQE9jW3w3ZiMGfhVRcAhBAwCyKU5UCqHS8aO4SOqSLGEym+8BzsXQjM7G/BNO6FWqeOWmi4rzIey1p1M17HRlzQAoDhChglw4sf6pW3ST1WZdshXj7hCbTQZ6NhCaBc+X/rU5V1mPFpFzJbScubJsq2CYe8L2PepmRs5QRYqEBLIPaet20TITPl3Qvm80TQAqItXrw9coMH2q4jHGcSZk/OiYfaU6gpxqI8fmDSqNfRaUC8SGPJ4G1vexsA4MQTT8T999+Pbdu24dRTT8XWrVuRTtcPoSSEwDe/+c1526AHehoaGhpHAElZwkvK+/z/Jw3mQ1ZDYwmjuVTA6twk2op5GJAYSDUvaP0jiTSyZgzrsmP8QE/jBYuJ5spgRwqBUgwoxwzES5UBSzmuyWRHFceA52yp4pZbboHwJiWEEJBS4oknnsATTzxRt5yUUg/0NDQ0NJYKTOlgJSYwJJtR8iSxu53KTLYDgSfia5E1tNiKxgsHpuvgzKEDcIXAULIJu1o6kYstcKBrIXAg046tk4NI2WXkrcVTptNYWORTFu4/swtt4yVs3TmJsY44knkHEvApnBoayx3r1q3zB3qLBT3QW0ZgqVHcvjpr6APdkaPaBfh16pehc9aykaFkytDGTEMaBEdtq0NPDYqxyMBvqCwjYONTTKiwCrOgXLgV6qZ0BVwlxhIQXlEx88IcIUHT1DabRmyl55ERa0E9OmcEfTKy09SbOeROLdcealA7jXA+ThCmcVrd7DsZdx5bzSyOF/0whcQadxSPYgNK0kK/bMekSGHcSAOuCIbbYiiAnDgQS8nkDmG+75E61z8ytl69+oCGKaQNC5xQ6ma8IsYyF7pmJAW2jhjLvCmVdZ5V8xV6WaiQclIakBDYm+7E/nQ7gMq5nm27AoDpieaEsgjgcKwZm8QItowP4enmnsD7ZLZ9olbaXM4p923G0yYV7Tvcdq1m69IvmRMZWHoR2phZPpyBp70HKxAADAgYJRGgrMuAGItnmyORLJWRKdkoJQSyiRjcRDWf4y09SE442LpzEn3daRxqT+PFT45iojlWoTY6gEU4wIr6aVA6p/d+s0ma6dEwBUPXBKrUR7o/boZFXRQl0yZqY2o/pWu6RJhFvdcdGv/Py+uQ860EVYKx/CppZuBFV91UsfK42HoBEZn6If40lhj27t272CbogZ6GhobGXCDgYqM5iBXGFKZlAk0owoREGkWUYMEWJiaQOaLriDQ0jhQyjhrVHVnelysMPNe0AtumBjBWmMShVOsRbU9j/jBcFxfs7INFlIvzcRPTyRiyyRiyaQuFuImTdo3j0Io0ntrSjt7BLLpGi3j8pPZFtPwYhMTSpW4uVbuWGfRAb5kgMDkXMdteT4ulpieubj2SpHmeqAgPS8BLoGYOa7RdF5z3rt5M7KzqpiIr4TRlMBVM4UIlVOZJK7OByqMXDKXg/Tp05jTs0WNDKXBiI9z+qJAMdbx3tby8ddMiOhfbHufdoOW9NegRytYzKmqsV/nOWSKfLw3vHUkX/ZP4T5tih9FpVIJHp1D5KB50mzEp0wHvnd8/OA8DFSCK8iYhnLZgg8h6nrqI68Z6/Ahm62Gqda+qegLhFcpeGAzu3Eadm4jnBFvPAnnd6rY9m+t7hD6UmpwCTsr1I2/E0G+0wSzOsoIZ/UjU8uh5GEEzDsXy2JwdwiSSyJmzpDlHXcsZIU4CRWsYxV2PegysWmFYWDB1++UZ5TRWV6XWs5PzxHvvEYNTuyTPeWGQ6+R5rVwaPsb7YhRmJVSQbQAPr12FpO0gUy6hqVhC50QB64YqoRXycRPPrOlALCtxwnOTONSZxsHWZhilsICLY4W9V45ZOSkGCZVgKhE0xotH91NvoWLa0DKcQIvy5LnUY0dOrvLeRaVVwzkwHj0mfIIqNdMe5f0zyEdG2dUuPY3ZQQ/0NDQ0NOaAaZlEzHWQlQmUYWFKJjElktAuPI0XMrrLk9iaH0TWiOPpdA9ccXTWU+1KdqHFyeOk/AAeyaw9au1qzB6uYeDBdb04c/8hnDgwgmd6unCwM1MdCZsOkiUbpaQB1zCw9cAYBCSe3qi9eUcbSzmMwVK160hDSonR0VHkcjn09vbCNI+skqp+kmpoaGjUQEYU0GOMYovZjw4xFdh32GnDs+XV2O+uQL/bjmmZgh7kabyQ0Vmexgn5wxiMNeHRzBoUjaMnjuIKA9tTq5B0y9iaPwwdW29pYzKZwAPreuEaAmfvPYQLdvRh0+FxJMo2pBDIJ2JwjconZtt0EUNtKZRjOjSAxrEJx3Hw7W9/GxdccAHS6TS6u7uxceNG7NixI5Dv9ttvx4c+9CF85jOfWbC2tUdvuYOjUtXeVXvxeJ28AQaJlMGdQEA8ZFbxp2qBo7uAp9o0mhZVtx8fL0Dj8QRcmJh4VIwFbqWYdCt0l4oYS7Vy1/G2KU1T0TgJbcaf/YqiYTa4n6PcBVhjXMy0uQjmcB0lovOZ0oUFB0UR/tCktCifFUnK0i6maJdGBMfNp256dZumg+OsAXR41EwAKLsmJtymkLmSfJCqTSqso2wLMLLqUbeoPbQPhjbAXoP5xNGLpGnWS5tFPQsxHhYATI+66Uack4aFWZjrwYo7LdB4Pko4qtFLuFDTC2tK45gUKTxvrIRRrl3rbC6lkIAZ9yi2oZ3Bf4tI4LnYKmwt9+O07EEcMDswbqTqcyZrNkzsNYO/QPS7iLtv/X1Mn3etcFqtNrj7n7WLuZlnUphpewCh+1OhDy7e6gyBFoEKtdMoK+qmt4NZmqCYhFmRxH2r16Azl8fq6UlsOTyG4wdGsWtFG57vboeMVfKbtkQ5YUCWvNh1qh5C3VTvQcOsNuh470mTUDfVe9ek8Wsp/dIrT9/BDkPTdLyDMAPCK977m1Iz6ftd7Q+kVU4kF3uPUjN9GmZN6qZKC9/1lO5ZdmbzYNdYChgcHMSVV16J+++/P/C9wGHjxo24/PLLIYTAa17zGrzoRS+ad/vao6ehobHksN4expnFvTituB+W5BaWHClIdBvjOCHW7w/ycm4c+8pd6LM7j6IdGhpHF6Z00OLmMWQ2wYR7lO+7KkbMJmyP9SImHZxSPoiTyv0wpV6XtGQhBEYyaTy+eiX+d8t67Opqx+ahcawbnfSzmFIG1r1pHEXIJf63zOE4Di677DLcd999MAwDf/iHf4h//Md/rJn/5JNPxjnnnAMA+MlPfrIgNmiP3nKBCIoU+OBuJMZ7Uy87QCYUGXGIqNANVKxFGRk1S8rawXg5Gp2Bj5yVV9u07kCoBW8BODeLGJhZVTOeZDZRiIpHT1ZmAiUAlwivKHcEFWNRHj1B8zECLVyohMDJ40IpKI8eMystmLKsx6YW6r3LuX5SI7zAAasDvc4EmmURLy7ux47YKkyY6VAbnOgBJ9ZCZ2grs+kSaVFEm5FDk1FA0ihhxGnGGmvUy9UCVwoMOG0YKLXD9WoKzIIznkp1bmVAHSd8fNW0sOcPQN3wCgHU8egFsjX4Qo0SVmH7SeD4Zc39rKDEPCAACFvCKEu+2zV4bmheyYWKOQIevflcy0bBhtupU1+LLMIAEHNcnGPvhgTwoNgEWzRGt6sXXsEXzWmgHimACWTwONLoEFlsdg/j1GIfnhG9sMUsPlmY54RLnzdWtb165dndER5rX2CLmBv01IXTGOdOwwItATYI50Gvx+zwxvMCqIjm1PXoeb+Bd5WXzQFcWNjd0ol0ycZxg+Poa2mBaxgwHQlXmhCeRw+eV8oloRuEWUkLePm8NCraosIv2JQBQjx+fogjUsaUYWaH5e2nA1AuPFJAmMVLpyEZLO8i0ZAMtgrTQEIzVAVa6nv0TOZGonbb7uJMwGjMDd/5znfw4IMPIhaL4fbbb8crXvEKAMB73vOemmUuv/xy3Hvvvbj77rsXxAY90NPQ0FhyKAkLT8RW49TyQcTh4OTyITwu1iBrJOdVbxMKWGFMos3IIiGCL0w1yCtLE/vsLuwqroCE0KvuNJY9YtJGEwoAgIysSGwKAG3IYRjNi2OUEBhFE55CDCfJgzhF9uFprEaJoXNrLC3s6mjH6skprJzKor+1GYaUcBY5aPSxCi3Gsrj43ve+ByEE3vWud/mDvCicfvrpABBavzdXaOqmhobGosGQLprdfEA+WmHCTOOA2Q6g4hU+sdyPmLSxsTyEDeXh6nrQBrFODGGbeQArzYnAIG/EacJuuxv9ThseLW3AY+WNmJJaPVPj2ECTzOMlci/WyxEAQBemUfY+DZplfjFNAwDkRAJPijUwILFFapGWFwJy8TgmE3GsmM75aWKReHqGLbFqVwG9z+WRHtfeMI2jiyeeeAIAcNlllzVcZsWKFQCAkZGRBbFBe/SWOzh6mQwlhfYFyhJE7K7SOWuUmS0CDDCOzsbROBulaZKDlYqSSamZzHaQuhmuhxNo8RfrOwZcWSFhSIa6GUXTVBQZwQi01Iyjxwmv1BFj4WiItequSyurQ0M70T2IZhTwuLkWa9xRrHSnYMPAXnRiwGyDKR3YMAAhsM/qRLubQ0qWEJMOTi31ISXLvrF7Y5UHYq24VVSsZQRNSKKMrJvASjGBuHCw3+lEv9vhDxqFJ5wjXHK+A+cz3JsDhyoYanIdwQWO4hkoH0jjOJlM3fOgbgafE+EHRaNiLHyMxggjGp3xV9dKAKYNmGXJV83UF03d5K9HqHwUpbZRsG0c3QmGLdZh5BHHgNOGjeYgDCER8+6clXISk6U0xmVTRC21YUgJIylhFmXdd0G947YRx37RhS3WAFIlGyU04NVjqJuUharmltwAXZ8zrMb2zGY46iZJc2nbyh76PDXCaTP3Vf4R4Xzc/cgJdDFiLP5zHt795FFsfXsYu6nwjK+/Rt5VrgMMJzJYMz0BswiUDAuJogvDo25Kj5IJKixihWmaLJ1TvYtNUpbaSOiXhiNx0sPjaBkvwzUF1jxXwK5tGQyuS4byA4BsUIyF0i/VfkrxVPujBFoMQgFV9Ew6FOWEWWxXf7a/kDA+Pg6gOnhrBOVy5RtnocIuaI+ehobGUUNM2mhHDhZcnOD0o8PNYVykMGI0YbM9hJPKB3FOaTe22IMAKh9/z8ZWAQDKMJGSZUyKykt6jTPu19vi5tHi1vc+TCOFHejFQdmJnW4PCtLCoNt6ZA5UQ2MJw4SDFIoAJAoyDhMOhmULJmQ6kG9cpnG82Y/2GaFFFgMlb17aBDMS0lhy6G9qQcx1sXZyAkXLRNI+yt40KXHCk5NonijjyZe24v6Xd+Dw2gQ2P5nF+u1ZWIVjpB9xAihL6W+Zo6OjAwAwPDzccJnt27cDmN3gsB701MBygYyYbWemvIPeO2b2m2lGMBkC68S5slHuvaN9s/vnhMwc+jOwdFo2vEh7LvQT3zEihRdioVKTv9/3nDEL6gOzsuGZ3IbDK3COCsbrwoZcqDVzjDrgdhpBmegMSgCAZllA0vPSdbgVqs8qZxKTRgouBIbNZuy1unCcPQQAKIgY+s1WxKUD4QKd7hROsAdgQ+C+xObA9JXvYaaXzQCmkMJj7kY/r+8lZe6hwGFFXH7pX92wRxdM3woIsND7qI4CRJS3cF6YjfdOpQX6hwylsWE6ohpvAEIQkQ+2aDWxYfEThiEQPN+Mx3Y+YD16tTofkzbHZ+fGxCA6rWlMOwmkRAkTThpmSaKUsJBDHFk3AQmBfeUV2Bo/iB4xjony3Lx6ApX7y7DJrcAIZ9WlYovqAM85gvPT7L0V0f99jxa9vUMbPFjvHfM+DWZgbGE96NU0QwmuUDaIl+aHXvDsCXn/GI8m1SnzvZczWCN5xHEo04LjRscwnExjZW4asYILxzB8ERZ6/CrkAvXUSc/LFwjRZKpnDBFgoeIw3v5kwcGKw0Vs39qCkaYU4ADPbm1BNpHDxuez6N1dwNiKGPafmEau2ZtE8ERdYkw4B7qfCq+UfOEV6qmTgV9aJpBGylhMZ+DEWhypP9tfSDj55JMxODiIu+66CxdffHFDZf793/8dQgiceeaZC2KD9uhpaGgcFcRRxouxN5RuQvqz9UNG9WPy+PJhnFAeQMIto99sRdHL0+lOY9RowkGrsn6v3c0CAHZaK4/wEWhovNAh0WFOod3MouDGAAgM2y3YW6zcO0lRQkla2FteiX3lbgACLowAlW2xEPNGJjZ00O0XCna1dMIVAr25KZhSomf66HmGiwkDxbiBtX05dA4XKyNyIXBgYwa/v6gLz5/chGTOxbbfTyI57URX+ELFYnvsjnGP3pVXXgkpJf7pn/4Jg4ODkfm/9rWv4Te/+Q0A4PWvf/2C2KAHehoaGkcF5ToEgiGjGTvNbjxvrsRj1jrsiK3Es7FV2Gd1omjEACF8SeuZQdR3mStxT3wzRsxFUgfU0FjisISNLmsSJyb7sCl5GONOGk/n1+LZwhocKK2ACwMxYaPZyGPSSaPbHEeTkQcgkXUTaDHy6DbHF/UYMqKIorT8UCcaSx9Fy8LdPevxROcqDKdSR3X9qWMZeORFHbBNgdMeG8NZ942gY6iiKGvHDBxel8Tj57RCCmDFweJRs0vjyOJDH/oQhBD+3x133BFZ5pe//CVe97rXYc2aNUgkElizZg1e97rX4Ze//OW87XnHO96BdevWYXR0FBdffDHuv/9+Nt+OHTtw/fXX493vfjeEENi2bRve9KY3zbt9QFM3lw0q4hEepaFRemSAUhmmhbHUTobZyDIzo57nc6FwRtDGqjGHmDJcGhdwza7Bm/EWWgdEVBSFhKNf0fPE8byijp+j59Q7Fi6NbjOL8A0nnC+Kutmo0iX3QpdS4EmsxSnmAdgwYBGj8jKOCSMNSCCLBLIiEbJ3SDRjXDjYLzrhukbl8ghAQgTj8ZE2/W7LULIELeMLHMhqnrkIbzDH7dcZoIAx91vERxAbJ/OoUDfD1zxABeb6UaAPMs+leczk+jRjARiOhGHXEGOJQBSdsyq8QmhjXOzMeUByY5YFvqaWsHFK6z4AwEQ5jZ35XkzbKe+R510bw8XG9GE40sRYqQmnNe0FAAyU2nCw1AkLLtbHh1EuWBh3ZkfhNCRgMBTFECLEnTqMaYy5TUHhpTplAnpYZvAXqIqwSEZsBAAr/sW+gzg6p3ru1BB68dM5ijPNR4RSZtrA0T5pOo0e4z/zuTRC3YRTLce88qvmcu8qRvCrcl8aGIo3YzBVmYgzytUylO5pMGl+g1T8xXsHSyJ+EhBm8b6DpCkwETdx3ykJtE8Xcdy+KZz66AR2bWjC7g3NsD2hi1zagpWVKJYsP0ZfmdIwCU9VUTdprDtFxTQZuielYwqG4hknL2GVHoyzF35AOM4y9j7OE48//ji++MUvNpxfSol3vvOd+PrXvx5IP3jwIH7yk5/gJz/5CW644QZ87Wtfi3w/10IikcB//ud/4qKLLsIzzzyDc889F6tXr/b3X3vttRgcHMTBgwd9mzo7O/GjH/1ozm3OhJ4a09BYRjCki7RTnHXogflDIokS1BvchAMTDgRcrDOG8BJrF04z9uIU8wAA+IM8FwKPinWYFOlaFfs4aHZgr7kCLvPy09DQ4GFLEyXXRNGxsDu7CtN2KrA/bpRxUtN+ZMwC9ha6kTaK/sA5YxbwkswutJkVenSLmZtZ/VFBk8gjLhwMyZZFaV/jBQwhMN6WwMOnduL5jc3YtHcapz01BtOuvIMKSQPJwvIdPKk4ekv1b6Hgui7e8Y53wLZtdHd3N1Tmr/7qr/xB3umnn47vfe97eOCBB/C9733Pj2X39a9/HX/91389L9tOO+00PPjggzj77LMhpURfX5+/79FHH0VfXx+klJBS4qyzzsL999+PzZs3z6tNCu3R09BYJrBcB38wsdv//9H0GkyJVJ0SC4cN5hC6zUlk3TgEgLRRQkHGYMJFzJs6VoM7WxoYFs0YQgumRIJ3iWpoaCwQBPblurE504/VqREczHeC+ogyZgFxw8HhYhvWJoeRNMrIO3HEDBsFN4Zms4CE5wrKuskabRxZxFB5hpS0EIXGXCEE9mxoxlRTDNu2j+Hce4fwxCntKKRMtEyUo8trLGn8wz/8Ax588EGccMIJuOqqq/A3f/M3dfM///zz+Nu//VsAwBlnnIG77roLqVTle+nMM8/E5ZdfjgsvvBAPPfQQPve5z+G6667DcccdN2f7Nm/ejN///vf43e9+h5/97Gd4+OGHMTg4CMdx0NnZidNPPx2XX355w0HVZwP91FwmEK7kY6Y1CD9mToD3RjMwVDMvQ2BSZg7f7GxcP4Yh5e+nx8dRTQO0SW8fR9MMcFLCvDhJJvmkonQGvEmVSgPKicoWls8zo00/neHIzKwQhLJWg7rpwMC+RDvWF8cAAKfn+vC79JZQGb+fRFDuWMohOY8ZUcD6+BAG7Da0ihxcKVCUMVhwYUsDSVF9eU44aUzIFCZlCjmZgGtWzqMRoE/y26FjpeebU++jZTgK1cz6yLZfn1GpU86okKNScBRAg8SH8qlQ9PhUd+I4WTVwNGh+7LWOoquRi8WpvLLUTa49dQ1qHNPM8kIAwvaom079c+fD4Cvn4uNV91EjG7v+DSOi7EKpe+ZKKRx0u7CmZRjCkTg03Qnp3XxTThrllImViXFMllPYO70SBSeGF7XvQa6cRNnMYrDchsFSG1wYDXW5mSq3ITD3sr+LOccTSMOVQIcxhQHR7qf7sekijFKhxwIUb4bOyVE32fsuYHCdfcyzCoB/TgJFuecy965i2qXP5XoKmzTNYOLoGUQdlTtu3xy6ZELRNMm582mYxEYaglSxF6VFaNFeBpek+c8EorqplkwE7mVy/ytVTpeWsb00U2KgJYOJ0xI4fccwjt8xib6eDNYWcrDzJkS8ks8mL6YAldII0zRVGs1neie1RM1Wip5m9SLQGH2WDCt6cnH0HHeWYkS1vjuWAhbIrgMHDvhet69+9asNrcv74he/CNsL+fGVr3zFH+QppNNpfOUrX8E555wD27bxpS99CV/5ylfmbev555+P888/f971zAaaA6WhsUwghcCeVBfubtp0ROo34GKVNYYucxKrrDGclOxDxijiuPhhJAwbhpDIiCJazDxMuJhyk3imuAYPFY7Dc+VeDLjtyMkkFn50oqGhEYWRQgv6pjrRlZrEhtbDvnS7I03sGF+DHeOrsWu6B3knAQkDZddEV3wSMcNF0Y0tqgiKAxOjsgk9xjiEjqOnMU/kkxZ2r21G63QZ+aQJA0D3SP04rBpLF3/2Z3+G6elp/Mmf/AkuuuiiyPxSSvznf/4nAOCEE07A2WefzeY7++yzsXXrVgDAT3/602D4rRcQtEdvmUDYsx+1B7wgapKpxjc45/GTatqOE6DgvE6ImPyM8OhwHjvOe8cLSpB8aqbPYAwLePlIPUqEJSD6UfnHdcgMnJolpoZHiLE0Gq+snhgNzf8H0xX65oFYOztLLGbM5Fa2a3vvVN2rY6PoiY0zhlUx7mQwWUpj2knCITLoYkbdhvcPK1QTATdCMCHQdzjhldBGdds1K35qaVQEXiR4T1wtKE8eFUIwOG8TU09kjD5WPGSBB83UO1enD9b06NXry274fmPj8jX4MhUCMGwXRsmdhRgLn5EXXvGSgsaFC9fwEjZkTaS4z5yrZjFmt6BcsrCufRBbWg5i79gqlF0LrjBRhOkdSuUY90+twNqmYUzbSUwWMpV7lhM3UqYG3g0RAlTq3EbdW6S9PtmJ08Q+dItJDKAtuJ8+E7im6wivBARaqLOE8eixnt86fa/W8fnv04CR4fb8+K40TQJN5SJOGz+IgUQLdjV18SIrdrgewaQF4ug54fdDgGnB2OOfW87LVyO+q+9h5d7LtB71hUqvSx1mB0Df1WEvH8h7cDxR8eC4toHBtiROem4cj4kuDLcnYRBxF8F47ziPXzDenuflo3HyTI8BRTqCa1a3HVd5Aav1KIEWKuBiu9o/Q/HDH/4Qt99+Ozo6OvB3f/d3DZXZs2ePL35y4YUX1s174YUXYseOHejr68PevXuxcePGWdlnGAYMw8ATTzyBk046qaEyu3btwpYtW2AYhu91nA/0QE9DY5lhe2IVYtLBoVhrkAY0T6i1dlICRRnDULkVg+VWP8qWA3PhAklraGgcEUwX09g13IsNHYexufMg9o93I2uH1/JOlTN4ZiztDYQW/8YuII5h2YzVYhSH0Qq5BGxaLHQXppBwHazPj2Eg2Yw8EtGFNAIwvYFV23QJz61tw/EHxvGSZ4bw+PGdGFx1dNa2HxUsY+rm+Pg43ve+9wEAPve5z2HFihUNldu+fbu/fcIJJ9TNS/dv37591gM9AHP2BC6UB1EP9DQ0lhmGYkcmntzeUjcOFFaEKFyuDmCsofGCQtGOY+foamxoG8Bxnf2YLKZweLoDOWfmgGFpDaYGZQtWYApJlI7pwQ1dx5ixS8hbx+65mCtSxcrE5fEHJnD8gQmMNcVRSJg4becI7s90Y7I5vsgWakThQx/6EAYGBnDuuefi+uuvb7jcgQMH/O01a9bUzbt27Vq23NHAQoVX0AO9ZYKKGEtjo3/J0GbqxckDqnQOyXzTczTN2oIK4Qx1reaolDVoMxzNhaNkVqlkJBsnRkMWc/tUFEqB9KsmVAzv12DOk5Si8gfM4PGoX5oWvh51aXG1FuYzAj0+NYcKhkgmX0iMQ0C6gJh5xcjDqN5jKbAwn0tE+Jxw4glMtmBfZeJQBQRVuKZnUgkNj76J4KwaF2cwILzi056YMrQezB5smQUXYwnfjVHx74L3DEMB9i9SFMWTqZxZjqXy+WIsZZe9lsFCTBoDNqCzEbE/cBCNtVOtLyLDAr3o/We+SZ9pBvYM9qAllcXKlnFs6TyIiXwGh6fbUbTjvjhGgJLnUTKFAEzhIGWW/HAN3LuhsgONzdwzlDwfXloBlY/vJMrIC35w459Tct3cOsIrbOw80iZLU2Ty8cbUSK/TjQLPGHXd6HtHAofirViZn0JMOsgiEaSKe9tGFF2fEWMRKi4laZsKofjnjDsnlALLLYWg1H31mKB0TvU8DVBAPbqjRWiYfhw9ciyEKswuKfEFXKpJQ+kMfr1tHWLSRqZQxpb+CaS9wd+aviyePC6lKq/abbozk3xqJ4235/iiLcQur2PKAF2zum0zQi+Od4Jst5rmOrOj8i10GIOFhLJrcnIykJ5IJJBI1J+8uPvuu/GNb3wDlmXNOs7d1NSUv93UVD8uaCaT8benp6cbbmM+GB4eDrU9H2iyr4aGhoaGxjEJgcl8E3YOrkbfWBdSsSK2dPVhTesg4mZtyfmMlccpHXuxufUQjiYvzPVGH9YxLshSMi080Lwe97RsQt7Unqe5ohQzMZ2K43B7Bnef2IOh1kr4kLVDWVj2sd3HjibWrl2L1tZW/y8qNEKpVMINN9wAKSU+8IEP4JRTTplVe4VCwd+Ox+vfP3TAmc/PXbCn0YFoNpv11T3nE86BQnv0lgkMJzjLpFB30XzAMyZD+QU37cjMNs5lXVZUGVZEZT6T2xHfImyIB6a8YL2AjOePzO4dlTBxnOeTpAdmchkhDDY0h1+2hkeLbZyB30+I54/zzpJ2VN6AmEG9Jubyrcks5vevn4AvxiKZaw6AX//InFs/vMBCfTcw9+2sEOWVU+AYAtzxBfbL8H6/TNilG0hjPH/1bKx49FwI2218rBFxM1JvtX9bk1l3f3/A+d7gDc48nwOXMsJbGCnzXw+qbnovU2+DEBjPNWM834T29BS6m8fRlprGaL4Zh3PtKHvqGUICmVgeG1v6q2WVyETAq0Lb9u4lg1xiTkTJKx94zs8QP+nCFCSAMZH279FQe962S75uGhVj4c4x570KCH3UC4FQA0L1qShPNPf8VmIrjgilVdLDaYJL88VYvHsWFW+a4cwwix6r/35jvHz0+abS6O3NiLUEQi6odzC9biqNCpj4+4ldpGk/7BN9BysvYMAg7x1DSj6+vguXPFEJZr35wASeXdcR9Ax7F1vQur1QQQHhGc8rR713vkevhhiL6z0UacgFte2Qjzs52/AKLwAcOHAALS0t/v9R3rybb74Z27dvx7p16/CJT3xi1u0lk9V4oKVSqW7eYrHob88MwcBh0yZe9fzSSy9FLBaLbGtwcBCu60IIgcsuuyyyvUagB3oaGhoaGhoakBAYzbVgLN+EjswUupvG0Z6axlQphaITQ9Isozmew7SdRNmxkLTqfyQtBEw4SKKMDkyjF2MYQRPKQn+6aCw8ijELk6kYWvJlbDg8ib4VTZjO1P84X/JolDq9GPDsamlpCQz06uHZZ5/1PX5f+cpX5kRvbG6u6hhE0TGz2ay/HUXzBIC9e/eG0qSUvspnozj77LPxoQ99aFZlakE/LTU0NDQ0NDR8SGlgONeG0XwLOjKTaInn0BrPoujEcSjbiaFiKzY0H4btHplPCAEX68UwOsQ04p4bypXAAXSijwRM19BYaEykEzCkhJDASftG8cCJ3UeJlqPRCL74xS+iVCph06ZNyOVy+P73vx/K89RTT/nb//u//4uBgQEAwGWXXYZMJhMQYOnr66vbHhVgocIstfAnf/Ingf+/853vQAiByy+/HG1tbTXLCSGQTCbR09ODc889FxdffLEWY9EIQjgSwgxP2/jCE+wCd0rdCeavJDI0JlZEpD6/MqgxUqfjckyx+fbzGdSfWnWzsZei2l6qs2QUdehAjdofoHjOJVyD6luBihSNhbGLtBkwkRXwQEP1sIJBlIbJ9RNfjIHSb8JUQ56yxNwnxLB50TiDPKVQWsM01qi4dhwNk7OBmqPOj8ukRdXNCcG4tfcL4T33bDnj+BUNnVHgCWSMuMH9/sH0W9IngnTPOnXSY+FonJzdDNgYdRxotnq0SFLnzJhxDgwMFtoxWKgMrnyaownEDBsFJ+7Tz2pSII0K3c61qmeKp276W9iKfrQgj8NoxTSSKCCGvIjDFQbbDj0uX3glirrJxckj+7kYpf49H7i/G7vhBKEA+zHuGPGrudAwOeEVLrZegIbPiLEYduUvSN0Mb3N0TkpD9GmY9BFDxVpUPtpO+NFRfZ7SW7lGbMGZ9bC0f+66BQ5W4HBrGmtHprF3RTM2DE1h/cAU9q3yvE112NWBXuDFunOjbnpajf9ekqE0+kiQzuyomy8EMZbZQFEpd+/ejauvvjoy/6c+9Sl/e8+ePchkMoF4ds8++2zd8nT/iSeeGNnet7/97cD/3/nOdwAAn/nMZxqOo7fQ0AM9DQ0NDQ0NjVlBQsBYsIWnEq3IIYky2pBDG3LYjl5MiIVRndPQaBSHW9MYaEuhdzSL/vY0TjwwhvGmBCaadAiL5YKNGzeit7cXhw4dwp133lk371133QUAWL16NTZs2DDrttQawu7u7lmXXSjogd4ygbBldYEwM+MbXPSvEulKaG9Wtlb9fmGSyAhqLPTEUWBWjpkFDmauVxGph/Vuhvc1KgQSqMYrtNSYHpEzZ8x+NfsbXMA/hyushFU42XAuFAbITDDnqWLqqSVqwIn6sF7eGddfCpJGZ7RZzyDx7nCiRmLmBjFsFp44VlhJnQvGO1cLsw53UCfEQdjIenUzHk2uHs4GJm/Vo+fyZej5NhBOi/DEVUWrqMgIN30f9u41LNAyX5i12+G8dwGHBiOeEvAWqjLUC0a83NNuEh3WFMyyDRtW4D5xreC5l6aAtBDy6Kn61rojWIsxuACKiGGHsQpjRoa/J2Z4HUM2WsoGeqzBX1qPGxBooX2UeW41iGqfZ7x4CHvTAmkR3jl1PxpMWZo3WE/FIIMRY1H7KuJGqIRXkNVzFRQgYzy/vhgN9USFGRucb4t7ndBLpJyggWvg200ZSczzj3mZR3n5Ks8OgSc2dOGipw4iH7cwkY7j9OeHcM+2HpTjZui4fDaEoMevbK0ejcv6Masw/HNGwit4zxGTePncZSjGMhvccsstuOWWW+rmufHGG/HJT34SAPDb3/4WF110UWC/EAJXXHEFvvrVr+LZZ5/Ffffdh7PPPjtUz3333ed79K644oo5USnnIhaz0NDhFTQ0NDQ0NDRmhbFyEyzh4tT0XmxK9CMlitGFaiCOygjkAfM4PGJtwIjRHFFCQ+PIoWyZGGxJoXMyj0c3r4DlSJy6e6Rhmu6Sglzif4uE97///bCsymzQe9/73lDohHw+j/e+970AAMuy8P73v/9om7hg0B49DQ0NDQ0NjVkh5ybxxPQGdMSn0B2bwDbrAAbsNhywu2Zd16RIYaWcRJvMYkTUGeRJiaSsxPcrIIb6NA4NjbljoD2NNaNZtORKePy4Tpz53BA29k9iT2/rYpumsQA4/vjj8cEPfhCf/exn8dBDD+G8887Dhz/8YRx33HHYtWsXPve5z+HRRx8FAPzFX/wFtmzZMu82HcfBM888gz179mBqagqOEy16cO211867XT3QWyYwXOlTMChHgqV7qQ1K+1EzVYH4R4QaMTNfrcqjdzGZZxo2C9ShYUaWOVrfCD6dU/r0jqUwL8jRogQnDkDSKPWn8Wusjp+h0riUfkN3e+eJ0mrccD6fxhNuLghG9IFl3xGKl4r7FSD7MYv5A7QxtZ9QwKplwvxRMQvKpV+S5lO0K65srXh0XAy7Wca1m5OICkev5GzkhFyYvEIAwnUgHAdg3peC5YDR/sYIqjDPPz62KLm+AV6MCNY3o52qPQi3x6HBe4yN/xagbnKUO466ydQpmHxekg0Th2UbDpda0RsbRa85hkOyHY5R/bSQVoUe6TLUTUM42GAPo0dOYlSkMWYG1+SpdlKyiF5nHF1yGpZbOXk5Eccz1irkjESApqm2WeEVNq3GtfSVnEiaW/uCBO+JSj6Orkm3GxVZYfPVeC5X6Z4yooz37FBUT1F5HglHArJaJkBNF8wzT+0P3KqVf2hMuMDtqG43et08OwL3m7KbFlZp9FI4tJB6d4SSIsW96L7DrWkMtqSwbe8o7trWi12rWnDC/nEYLrBrdXPVgDr1BJIiBFr8c0ZFewyVRvqoM0si3iJ7zupike36zGc+g8HBQXzrW9/Co48+ije/+c2hPNdffz0+/elPz6udXC6Hm266Cd/85jcxOjracDkhhB7oaWhoaGhoaCw2BPrddvQY4+g0ptCP9lAOQ7poQQ6mrHx5pmQJK91JGHAxbDQhJ+LY6AzBlBJ7zC6UhQVTOljtjmG1O+Z/EjsQmDBSSMkyTir046H0hqN5oBrHCoTAE+s7ceEzB3HS/lE8sbEL0gCO7xtH78g0nl/div7O9GJbqTEPGIaBb37zm3j961+Pr3/963jwwQcxPDyMrq4unHnmmfjTP/1TvOpVr5pXG7lcDhdddBEefvjhoGr3UYQe6GloaGhoaGjMCw5MTMoUWkQ+NNBrlXmsdvf5a/HgAGWYGDUyEJDodqdQhoGY5+04gHa0uzlscgYrsv9ePdMiAVsYaHdzEABKOLaFKTSOLIpxC8+s68Rpe4Yx0J7Bc2vbcbg9jS0Hx3H68yPYfHAST2zpwGRTfLFN1SC48cYbceONNzac/9WvfjVe/epXHxFb/u7v/g4PPfQQAODUU0/Fu9/9brz4xS9GR0cHDOPoyKTogd4ygbCl7+bnFP/YOHEI56M8FRrPxacszZ5ptnA4Cg0GVLnYmDm8at+8GlInP8D9mR2vdDbxaDjFxCpNk6H7OCSN7vd+G6VwBuh8HtWGUnLoti+cyNCQORW8mofPxNny83J2E2VXRd2s2ZCvksZQv7h+xKggBmOdkd2c0qW6DgwlqWEFTaBhmmbDapkcBZijXLKx9Ti+U9hWrowQAGxPddNl2guoblZOboCuydHUSUdR9LQgdddLc/nnQJX6t3hKnJKJmcepalL6HRszkrl3WCVaQvvMIoEuMeWnWdLBRjmIDTCw10hij9mForAgBeBCAEJgS3kADgSmjCTa3Rz6zHbkzAROKfbBhUBWJNAm89hjduGg1Q5pAim3hE5nGiOJDKSJYMy8eqqbNNYsG0eP9j3vPAbo7IpyTtL8+ySssMlRLqP2zy2OXvge5eup5vNtcKv3k+FIX3VTgbvWAco9o0TL0dVdck/4qpvVIhCxoK1AVbQyqPwZPr7ATaoop4FblHnH+pKt3HOwWrivI4NVo1ls2zeC37X0YCKTwEMnrETrdBHb9ozi7CcP4+ETVmCkI1k1h+GK+pusEidYZWDXs80gdrv2LOPoYbZfE0cPS9WuhcStt94KIQTOP/98/M///A9isdhRt0GrbmpoaGhoaGjMG1mZQELYSMoSICVOQh9aZQ77RAe2Wz3IG5Wg564w/A/a/VYnho0mWNLF81Y39pmdAIBxI40YXGRkCTuslThoVb2EeSOOPqsdhpQwZbSggYbGnCEEnlzfCdOVOG3XCCy7MjibaErg3pNXYbw5gRP3jvGTVhrHPPbs2QMA+OAHP7gogzxAe/SWDYTjQthqZi4sBBDwlojwbBu4NNa7FzEHs1CCKgtV9yzri/ROBTxxTD2CyTcXiAbPt4egYERU3vC15r1AM/bN2N9we56XgI9hVN+FGvRENXguuNh8zPnhDmvmJLCckcaKunCz39QzZHkZbM4LxLkayT/0eihvi7N0PyjqeuqiUM/zV7NOWUnnvIB04lt5AQMlF+iB0qinjn0G03rUPr64720jIlqsp65eGhMTj+YNipEglEbj6HF1jyEDGwa6xQT6RAcybgl7zS64VgbSLPndWXnbhKyoZ+6IrSKNVH62m6uwzy2jJEzYRtVwNwY02wUclx9Gm5PHWCyFR9Kr/evgxsJ2zzymwPHXunz+AzD88AgKhnjCK1FeNy6OHpMWiI/n7efrJt65OvHxaFrABleip2kE0gWGptsgYUA4lbqCtx35nlCCWMwz1iAnxfUSDXDPvKp3L1ANEyeXE2Nhb7fAWN973wS+g7w9zEM/GBNPvatIWQkUYxYe3diF0/cM4fynDmGgI42JTAIDHWk8u64N5z11GL2DORzqznhlVIPUKkmbrYB49xzlvTPoQ9/bR718Db4HA8e5VF8ZS9WuBUQqlUKhUMCaNWsWzQbt0dPQ0NDQ0NCYN1wYOIxW9MgJrJBTKMNERhaqGaRE0i1hRXkKhmT4yRRCIG/E4YiArC3ay1mcPt0HSzrYle5EezmPFcXskTmgZY4VmQl0N09gfcdhHBNf3fPAUGsad5/ci9GmJLrH8njRrmGc/cwASpaJgfYUthyYCKooa2gA2LZtGwCgr69v0WzQAz0NDQ0NDQ2NBcEBdGJINGOTHIIFByNGEyzpoNuexOnFAzizuA8nFQZwSv5gw3Wa0sHq8hhenN+P07KHMGkm8EjzWuzLdGAslsKmqZHogaNGCEPZFgBAU6KAttT0Iluz9JFPxPD4cStw54tW4+5tq5AoOzhl9wieW9uKZMnBi54bQaysqcQaVdxwww2QUuLf//3fF80GTd1cJhCu9GeTODEWnpFJM7LktVBFkfNV85nQYmg1kVRKhorBLdwOHIqin9CyRthwyVAowJ1bSoGcL2VT1aiYH8w5YdeY16qIoxohnMa1N3Nfrf1sXi72FkP34mhRwQrJbpfroxHgjoeZ3mIuJREeIWWpuV49bqDjKsoSTfL2W6Qih7lwNFaSolwzFCpBzqNPU6R9nqHcBmJK+ZTTaj0+VckNH0uAhurdE4KWJRQxnw7JCBwEqFT+ZvhYBLnvpBuQawjkq6gMGJ7QSlhkg42ZZ/Kd2d/PxdEzwmUkpU/Sa+gLoZBEX9Sk/j1RbSOcRNvkYuFJ1u5qkh9bjrsH6TZHTWbs5ujqihYqIfC8uRL7ZCd6nXFssofQZecwWZ7EiJlG0jFgwUVcOnDismKvlGgv57CuMIYmuwQAiEsH+5LtGIo1YVu2H3HXxnCsCc9nujAaTwNCwLWA59q6cOZQH/7g8B48srIX41YSM6HEQ6KEnATXH91wZkHithnlcD6fhkkplRx1M0J4xWAonn78O6a9wH6OzknFtFxgcKoDzbE8kvEyVrcOYyzfVqGGc89BgNw/lPbvUWZJmmGH02gcVcNLd2najNsbIK/d8O2EqGE9F7dVMt88wViudbj+IM9gCUymE+jrasLaoWlMpxN4bHMXtu0ZxVlPD+H+k7pRjpnBeJpcjM3AsXrnjOqzcOJes4yjJyTmvZLkSGGp2rWQ+KM/+iP87Gc/w6233oozzjgDH/zgB4+6DXqgp6GhoaGhobGgKMNEjzsOExJ7zQ7szHTAhcAZ2X0YjDXhmUwPIICMXcTx2UG02YVQHYaUODF7GLYw8GjrBhTNmL8GT2EqnsTO9k5sHRtGW7GA8XR4oKfBw5UG9o2swtaeAxACWNs9iFLBwshkM2Y1oXaMIp8wkSw7MFyJgc40plMxvHT7Ybz0mUHcf1I37LgmzR3ruOuuu3DDDTfgwIED+PCHP4zbbrsNV199NbZu3Yp0OjoO4wUXXDBvG/RAb5lA2LI6E84tqI/wyvgz1DI4x7TgYDxjjcrzR6t+zPgFmY1kZqoDnj81q0e9LtysNQ05obYX6jRF1VNPPICxdc77PUR6C2fkq2T2ZiXpshrldQikhctyMu4cgusg6lvHXWsfdIH/zL5DZ0FreVjUbnoMyutC7iN1LqgwgfRnk4kNxEvgq3PL8Mx5wDun6qGeIeY+aNTjFwxxobyKVOgk/JwIPFu4tmeWpeWZYwk+gqqdRsw0XAAwK+dccl5O1stFErn9nPAG4wUMevnIYZl16gk8g8IexihxEM57x4VSmOlhC+Rj7kG6HfD4cfawxxKur6IxIbAT3dhSHsR6ZxRt2Un/lHeXp7HddLEhP4p1uTEUzMroLW9akBBwhMBwIoPpWAJri+N4pGM1colKnkDYBMvFltERbJwYx1Q8DlO4WJmbwuGmphmCN2HvDbeeKtD1GI+eukeDHja1j+YLpwWFUGqX4URbAuETOG8hu5/xqlOPnrdp2yZ29/dgU08/TANY1TGKZKyIQ0NdXsbwfSsCgkBh4RXlyaPPpUDYBGUDeQgpD5zB9DfG0TqDNcHsp2W4EEb+vcw83wKeSNKOZ4j0Gm2dLiMXNz0yhsB0Oo77T1yJlz5zGGc9M4gHPM9eZa96vvHPDsUqol5ll3lvaTGWFxYuuuiigHf5wQcfxIMPPthQWSEEbNuOzhgBPdDT0NDQ0NDQWHAMmS2YiKVxfMzFcCqBgmHBgYGV9iTOGtuPuGtjb6YD01YCp0704/nmLrSUC7BcFy12EeuzYxiNpzAWT4XqTjg2tg31o61QwJ62NvRMTWHj2BhMKbG3rYBnV6xYhCN+YSJfSqBveAVObC4CANqasxgaa0XZXhw5+KWOlWNZnL5nGKYrsXtVc2A0OJ2O4/6TunH2M4M4ae8YHt/StYiWaiwFSLm4I1o90NPQ0NDQ0NA4IigZFkbMGFrtEaxyyzCkRMYtYTSWxmPtvcibMZw2fghZK47e3AQ6S3lMxBIoGyZ2Nq/AwXRriIbSmc/i5NHDkAJ4sGc1MnYJCcfBnRs2oDs7jZOGhjGUzmAkE02N0qhgMpfBgcMtyGVjcFyBsq0/DzmsHM/i1H0jGG1KoL89g8Md4UmI6XQcO9e04qS9Y3h2vYNifHZBzjWWD377298utgl6oLdcIBwXwlEqIww1wAzTL/iKCCWD29/gxAQbMy2iPEvdC7NvgnVQJgazn2uuKsZSn0rG0So4GtN8BVjqCaE0TOeslVavfB26ZmB3FM2WxvhRggyU2qPSuNhbNWxl44ypbJTO2KBACxf3iYupJGSlJiErZSTZF7KDtbvy65Lj94UHaFlFvwlQ4IJCCYF8qEG5ZKipLO2zQWqnZO4nTsgleDDcfcSkMfeyYCiltCwr4ELOnTRMSNMMCNlw8c/8/sgJsFDbmDIcNZmja9I6WXEUWk89YZWa90QwH93Pxb9z2Xh71Wzc/mCsv9o2sKItlBZKty0gZtjoLk9j2ozDNgzsTndgT1MnUm4Jp0wcQkcph6c6VuHk0QFsb1+BAy1toBBSorlURFO5hJ7xSXTm8xhOpfBEz0qULAvdQ1nkYxYKCQv7463omZrGltERjDSlAMHLX1QFWvgbvEqvJP2RiWvni7FEUDcjxVpYuqdk8nlpDBWU7p8pvALMeOa5Qfq4EIBTNjE52QQp6fONWc5A61F9hnxN+jRMYiMnzEKPS7UTEGiZ2S41P5zkH8fMMlxadWe40sA96ArEbAed03msH51E53QBA60pPLGhC2XLrDwTJWZy6nGoM4OT9o5hxVgBfd1N1d2B25f0TP+5Vn9pgnS4g4jAMUCRXKq48MILF9uEpR1e4ZFHHsHNN9+MV73qVVi7di0SiQSamppw/PHH47rrrsPdd98957pzuRw2bdoEIQSEENiwYUNkmUOHDuGtb30rVqxYgXQ6jQsvvBC//vWva+bfu3evX78QAmeffXZkGzfeeKOff+/evbM4Ig0NDQ0NjaUHFfC8L9WGR9rWYjSexiljh3DO4D40l0t4vKMHcdeBAWAkFfbCbRsewNkDB7Bt5DAs18Wjq1bh4d5elKzK6CLmOCiaKgq7wK7OdrQViliRzR2tQ9RYhojbDk45MISLt+/Di/cPwpASD2/sxsPHdaNskfXDrkSqaKMlW0RzroSY7aAcMzHeFMeK8fwiHoGGxhL26F144YW46667QumlUgk7d+7Ezp07ccstt+Caa67BN77xDcTj8VnV//GPfxx79uxpOP+hQ4fw0pe+NBD08K677sIrX/lKfOc738Ef//EfR9Zx//334xe/+AVe85rXzMrWRiBcCWF701GBmdrKWJ56QarCK4EaZt8m4wRbcDCVc56YmnbU8QKyXpAabdf1us0FgVl7dqov0O7M7VBaDbu4MBX16gnsivQmVjIEhFeUp4KkuYz3gvNEcPuDRipbyQw7J+rBTfUy3iTJ9Al6vqTwkull4fpbxMwxF86Bi0IgGHdqwMvHedjc8LlVM/m0Pt5Tx3j8uHuLnk+ONDAHARdVAe/FpPUhVKYaPgEVxQVThGbgA/lQfQ4G77s5eO+YMAy8gAnnLeM8bPW93I167zgbAveWdz+6jKc9aA9TN+dhZPq3O8OLR7cdy8BoPI3N2SGsLoyj2S4ha8Wwu6UDluti8+QImuwSDja1IJuKQXU+VfdYJomeXCXO2+7ONhxuyXi2VfJZrgvbFH5/GmpOYSiTwkmHh/D7dBxlbxDIyutTjx31gnnpymNXSav8BrxpdnBfIF+NEAh8eAXJpKn2qF3qN3zvBOqxww+rgHNOHStxgAvHq5fz2KlMMytiXryc499gzj0NTeNXzfS3QGgKdQtScRfuecSde0agJ/A+MWWgwpUTOawdm8Kezhbs6WxDIVXpR8J1sWo8hxVTOTTnS2jOl2DOOA/jmTgMKdE1kYdwZfUepd9igRuJSfPNpsyG2X2E6PAKGkvWo3fwYCWYam9vL973vvfhtttuwwMPPIB7770Xf//3f4/Vq1cDAP7t3/4Nb33rW2dV96OPPoovfelLSCaTaG5ubqjMBz7wAfT19eGcc87Bz3/+c9x99934wAc+ACkl3vnOd2JkZKShej7+8Y/PylYNDQ0NDY0XOp5t6cZIIoOpWBKPd/TgqfZVWD81ht7cJCbiCTzS3YunO7vZsk2lSmw92xCYTCaCO6VES7GILJ3sFQJP9ayAgMSL+w6j7nIFDY0aONTahIJloiNbQMyTwEyWbJz7XD9evHcQrdkSplJxPLumAw8cvxJ3n9SD35+4Co9v7EQ+biFdsBFzJJLF+SsnamjMFUvWo3fCCSfg5ptvxutf/3qYZnAh69lnn41rrrkG5513Hp577jl873vfwzvf+c6G4k04joN3vOMdcBwHn/jEJ/DNb34TU1NTdcsUi0X853/+J9auXYv/+Z//QSZTmU0877zz4LouvvzlL+MXv/gFrr322pp1dHV1YXh4GI888gh+8pOf4KqrrmrgLGhoaGhoaLzwUbBieKqtB0DFm3jcxDAsKfHb3o2wDZN4GMODsrFUCusmJlGwLHTm8mgam8DzXe0oWybWj00gXbZxuDkTbC8Ww+O93Xjp/n505vJamGUeEJ7LUOLYEhVxDQMPr1uJ0/qGcN7zfcglYkiVbJQsA/du6cFYUxLSc+VRZ+F4cwIHVzTBkBVKZz6xZD+1NRYQN91004LXuRDOoSXb+26//fa6+7u6uvCFL3wBl112GQDgtttua2ig9+UvfxkPP/wwtm7dig9/+MP45je/GVlmZGQExWIRZ511lj/IU3j5y1+OL3/5y74Hshbe+ta34l//9V8xODiIT3ziE7jyyisDsTXmDUf6PBFBHbVumM7pY4GEV+bE3ZzPBGsN6iYbR49Jk6ENsNQWju7GmkPFIxaIyMpSRRkqlUA4LUpQpW5sxQDlTDL5Iuhn9YRXIqmbYfoZd94FMztPKVeSER7h6JyzZMAwhsz4JdssBZDQFP3jo3ZxVGGGfhkUYwkKKtD2avVfRdmMrFvlM7h8JI1SyDh6KcfjYsQhqkImtD56LWccq6j0H2mIAN3Jj8dnhDs4J5ISyBtF0+TonBx1MyI+HhsTj6NhcnHvRETd7L1VO422U49mXLM9VTf5mqCx7mB618msdHEhXRw/MYzufBYAUI4JQEjWBvURfai9CdmkhRMPD2PbwBAAoGiZyMcsnHh4BHs6WjGaCasfjqZTFY9MgwM9YVcPzGAomX6azaQxVEGOhhncL0P7WTonpS5ySw8Y4RX+PcjROb02UBtreoaRyRRwoH8FprM1zuNsXn0slVT9kuekEsyh4S+9c1FLD0rdPuz+gGPNo+YSwxWVlL7HJ5Mp3LNpLdaNTSBh2yjGTBzsbEbJMgGXvINpPD61rEEIZBPxyvGq45rDdx/9xpDuLIl4EvP73jqSWKp2zRFKY2MhsRADvSVL3WwEF110kb+9a9euyPz79u3zT9pXv/rVhtf1tbe3w7IsPPTQQ8jlgou777jjDgDAqlWr6taRyWTw4Q9/GADw5JNP4tZbb22obQ0NDQ0NjeWC4yZHsW56AknHRjYWa/jDdyKVxH3rV+N/N69H0TTRWiji1EODONjajGe7O/lCQiAbjyFd0tS5+cCKVc7f2p4htDRPL7I1Rx+uIbC3sw3P9nRiT1dbZZCnocFASrlgfwuFJevRawQlj7cPIETv5PBnf/ZnyGazuOaaa/Cyl72s4XZSqRRe+cpX4he/+AUuvfRSfPSjH0V7ezt++tOf4ktf+hLS6TRe/epXR9bzrne9C5///OfR39+PG2+8EW94wxtgGAsz1hauS6awqnNZwqs/2GeYxb5ehpoLfbkZf84Obka/0TJMeyw4cYBZoG57AQ8C2a9EKOjMKXPpqmIUEUZwIhOB42Jm/1Qa9VRQYQrU3o4UVJilly9U5wxEecvY/eScGJzkOdMHubLBqeywd4v18onqPuHta1TUJcoz7O+ii/5niE2EzOZkvtn2OA+T8tiRtEDdtT1+AYEW2WA+2h/dGb+kPM2nZu0D3nCVkSorBERdVMbqrzREILwBAN+7FxnigPPoRYY74NJIGZO7HuHOznrD1T3IiBsFyyBUJtrrxh0fsx1Vj28XSfO+IlwrnKbyukZlvwQwlYjDyQqYUsJyXWyYHMWh5mYUPAVN9tyqhoVAe7aIhOOgazqHQszC093dgTAkM98JtmHCctyKty7gdfM8OoynjW6zIit2/Xycd65R4ZWAl8/3FnLUhnASwL9OOJGgmYJQQnheV1NU0sg7YWyyGSs7x2A7Jro6JjCeb6pm8Ptt+H6iaQEv7yzf29yz2KgxdldZuS+rgNfZTyReXISfS7DCVIvA+8S7F7ioCFHvXRHwAjIGc5jl978WYzl6WAox8zi8oAd6d955p7994okn1s37/e9/H//v//0/tLe34/Of//ys2/rSl76E++67D/fccw9e+9rX+ulCCPzDP/wDVq5cGVlHKpXCRz/6Ufyf//N/sH37dnz3u99tSK1TQ0NDQ0NjOaC/qQWHWpvQkc+jd3oKm0bHsHlkFAfaW7FjRQccs/bkp+FKbB0aRtkwEHNdPN3ZWTfmJgCUTBMt5eJCH8YxhdHJZrS1TCNmOohZDlrSOUzmMtEFNTSOISyFmHkcXrDUTdd18dnPftb//41vfGPNvGNjY3j/+98PAPjsZz+L7m5e2aseNm/ejAceeABvetOb0NbWhmQyiXPOOQe33347rr/++obrueGGG7BmzRoAlYWbjuNElNDQ0NDQ0FhGEAKj6TSeXLUSv920Ebs72rF+bAI9k/VpgR25PNK2DSElJhIJ9Dc3RTY1kUyguViE6XIueo3GIDAy3gLTdFEqW+hqG8eyW2ClobFM8YL16H3xi1/EAw88AAC46qqrcMYZZ9TM+xd/8Rc4fPgwzjnnHLzjHe+Yc5ubNm3CD37wgzmXB4BEIoGPfexjeNe73oWdO3fiX//1X3HdddfNq04AgOtW6JsAJB2/C4YOpXZLhmtAUxhKWpAhp8QRePEADtW4Z6QM0x6btrBrXCMRaNtfKM7Q1Myw4Ry9WkpR+QNmcCQVJZNmVvQzQpVkqGR1qVsg5zFcdZBK6FNFmXyB6aAw1Sw63l7EfpWN0mI5muLMfTX285kjjJjBaKpFdwncMRxNM9y9a4QEZPhVlJ7l36NhGzkbOGpmkOJZnyLk07fovczRtesIuQBVOjMrMkPrUTQ1hl4aoERzigputa9KQ1T+mNup4Zh4qE+5ZOlXjNgQzcvRNSPFWBQ1k6Fr0u1AvDpWRCbcXqNxKwP7ObtVPD6GphlIo3VblTxSyGpXsFQ+CRsCk6nKevmuXA6H2ppCXj0BoDVfwJkH+itVSomdKzqCz+MamLYSMACkC2VkrWS1To5ySSmZbni/qCO8wout0LoJJbMeTZOJjxegbnIeTCY+HAuOZuxU9ykqNBPyEhP5DFY6YxBCIhWz0ZQpYLqQZmnIbj1xn1mAf76F83E0zmAMPyaNqVtRNl3mmQ6AfDGT9zL7YA78BIxg7zHwlzXyJdQI5BzKHC0sVbuWGV6QHr0777wTH/nIRwAA3d3d+OpXv1oz71133YVvfetbsCwLX/va1xZcEWcuuP7667FhwwYAwKc+9SmUy+X6BTQ0NDQ0NJYpDjdn8NyKdnRP5fDSfYdY79uasWoYpOc72zGcbixcQtKujALKDazj16gNKQ30j3YgZlVGhitaxqG/1DU0lj5ecAO9p59+GldddRVs20YymcStt95ac31csVjEDTfcACkl3ve+9+HUU089ytbyiMVi+Ou//msAwJ49e/Ctb31rkS3S0NDQ0NBYJAiB57s78MCGHrTli+jI5kNZnu7pwl3HrcU969fi+a7OGi6QMNrzeeSsGIrWC5bAtGQwmWvChBdeIZ0oIp0oLLJFGlFQ7JSl+qdx5PGCevLt2bMHl156KcbGxmCaJr7//e/XjZ33mc98Bjt27MDatWtx4403Hj1DG8C1116Lv/mbv8Hzzz+Pz3zmM3jrW9+KRCIx5/oqN43rbyv4NM4I9SeFQEwZRqGLxpcRDB1K7TcI36kmDWJmGmWuMVRRjs02HxiMXS7H9wCq1FeqJugdmODits3gbobogIEAQsx5ZKgffhqTL8DWZba5MlyMvkCD6rio6hilyKgJcqYe9hOMYxzViCPHUTfrqW3WUpishpQK9+taFFCBSt/gWJ9cmcBhieA+msapsgU7OKUFc5U3WI9iEgXOHU+1nFmnJDTkerTQqBh9lCpeV6mT2qKURpm0QDtEfVOYBmAZQWqbOnUcTbNGHD21KRlFW8HQNGupd7IquPXymcx+aiMT645T3Q1Q5n1qdv20wPlh7PaZsgxNk1PdREAtlLRtysqfmJEGBB7CQsCnkJcto0onh9d/hUAuEYeIVd8z6h1E49/NRMKueKDMMgL9yadPMgqadDtAyXTDZdh4e3VUNQP7A1RRLy1wb4XT+Bs4vM2pQoNReQWl/5oCiAn2eaK2B6c70JLOQQigq2UC2fHKwC94zcN9mX1O0iRVhN7KSp2XLhlg6OEBc9X1IGk8Db3yy72CaFk38Oz07LHozeOlMcqvgdh6TIw+WfPlotKYE9UAXVlDg+IFM9A7dOgQLrnkEhw6dAhCCHzrW9/CFVdcUbfM5z73OQDAJZdcUjMAezab9X+///3vA6jQQS+++OIFtD4My7Lw8Y9/HNdeey0OHDiAf/mXf8F73vOeOdfXsSqFWNxbf0Cf8JZaC1J9CitufmAtiFoTQeWQA/tVGYT2ByTC/bUXJB+3LiQioDYbZLuevHitMlwQXq4ebzDj1vhY8fPS9XimG05TLysjSD0SALpQWYciSb7KTlUo/JVFP9D99zHJZ3jn24iRotxHCJMWWNfgvVDZjxpmPQrAfxxyH5R+IOTAh7eXvc5gK5TW6ECP6XuSW9dUg88gAHRmYpAzvndqgT2GOoOphsDJqderZzbnrlHbuME2sz6G+wgN9JkZ+wCwAdP9/YH2yMf+TBsE0NYS99a8hgd67JdljWdHtd+Gn0tg02jZ8Mczu56Yy8cN0Gqso3O5ZydXz4x9gbq5yR2g+mxl9nPPxOCzs77dMCVWxGKQknQp/xkc7Ljdho2Wlhac4Bros+LV5yAzcQBUX3XC5E54BcVVPegePoyz7Dz60m3VQ2bW4IF5/tFHud/3okIpqEEbzRcRUN0vE5jcYEYjHAKjmmB9wXzMQI/sam+JAwLsGr1qOwnI+Cq0pnJoaQGcmEDJic94z6tvgwh76dyW6kfsBET9e5D9DmD6LduXo/p3YLJFhsr49w5JYgd6/sCazmjTQhEDeA+2KXEgnKyhURMviIHe8PAwXvGKV2D37t0AgK985Su49tprI8upOHvf/va38e1vfzuyjauvvhpARSL1SA/0AOAtb3kLbr75Zjz77LO4+eab8fa3v33OdY0eysKKeW8DqzNbp7gAAQAASURBVPr0kGrbCKe5MZLmDfToTBU30HNjTBodHHoDDm5hdiU9vL/ew5V9CDODiMgyzMx4cNDqpRHvVTAWlAz8Aqh6uuhb0Rv8iRkPbQHAhYGDshAWY1HV0I8Zte1UDTe8WWujRAZ6XihJgyzz5LbpoM5Po/nqxXUKfOiEX1yS+3jgXrzc5GWN8Ux9j164UFC0J9z3+AkKWkH1V20enixV1rHX+jhmBpl1PdY17GXh1z2b0WFjdjU6YOZjAjLnnvkQDnpqGU+FG9xH9wfKcp469UEsAAhgaLwIEAELLpCW3ydqfBz68fECzwmmDCeiwtXDDvRo3TPsInVyz8tA2+zzlCnDfjCTNMY2bgDHCq9QG9Tziz47Z9QtJdDnFEm4Um9rhuj0UAxYNTmJ1qkp3N+0gRhGnnkBcRRvIMilBTxDFtYNHMRQVxKO9y7kPHGcdy9ygObH1qs2yA3+aGxQ1Y4RIbxSHVg2NgioVKpG6EwZzqOnurn3OzReDA70mDJDUwlsbBtFOlbC+HQKk6VM0BvO9CfOdq7/R07KcoPIiAlfl/FEV9/5TL4awkLVbwcZKhNkC0jGbhnOZ4avEf/sIO3ZswwVIlHzPbvoWKp2LTMs+YHexMQEXvnKV+KZZ54BUAmP8O53v3uRrVoYGIaBG2+8EW9+85vR399fV1QmEpJ8MzH0BC5NNpg2lzK1vn257/uFSFuMumcLSf5q7Y8q34hd8z3WuvYwibVizNdrb16IMJazZ7bXUiL6eh2TiOooR/kmlPCeOQ3kq2e2n1bj+TczjXMgRtXN1tNgX43aP5d7+WhcSvq/rJGXohizMNSUgmOIyiCAuwY1ytaElDBdBxICroi2Ya7tRV4DJgOXNu8XT70yDfRbKWvfB2rTcU3sGu1F0ioh5yRD+biyrKlzeFZHnZKj/R0Q9R5pJF+tMnPZr6ExE0tajCWXy+E1r3kNHnnkEQDAxz72MXz4wx9uuLyUMvJv/fr1AID169f7aXfccceROBwWb3rTm3DKKacAqAxiFZVUQ0NDQ0PjWINtGDDrUJcbQdyxsXZqHMdNDONlB3dhbW4Ch9ItcNmFaxpzgYSBvJ2MzqixuJg5a7nU/jSOOJasR69UKuGqq67CPffcAwB43/veh09/+tOLbNXCQwiBG2+8Ea9//esxODiIW265ZW4VudKneVHRA47G5LdNFzMrWgFd6Mus+6glejFzfy2xBn8mE+E0Lo4emHwcfa4m6s1qMjayM6w1yqhzJSnVxheZoGmVJdcC0hcYCIYwFPSnst+n08hQvighl0jhHY5eyayE57zBnI2NIrj2hKk7cL5lOM3fx9fJZWAFg3yOoGBKkASVjW+ah8q8QC8wyS3Gp83VEWaQTFpgB82g6E4R94TgLnqANhW+bsIR4Xo4QQUZ3Ddzf9WdUG3XNUSAOj6zvF/U7/PMWh/Ap7Txa345SlqNtU71KGsRFFAu9hhLcY+ibqp8tO06a/AC6RzdjVLbWLqbDKUFKO5GpZw0iCRSHddINhHD2rEpj3pYMSia6lw9mNZCHmcM9YVeeatzExgymzEZS1XqZNYqNyzGEsgnA/uCZcmziNLnuTLMGj34+ZiHMH1f0utah7JZd92mqOSTRiWOHk/Dr1NPRJ+nOOoxcckzw09T25Req1ivEfYFYtB61yh4v4W/A6oLaun3WXg9A9e0ZG9WDY3GsGQHeldffTV+9atfAQAuvvhiXH/99Xjqqadq5s9kMti4cePRMm9BcdVVV+H000/Ho48+iuHh4cU2R0NDQ0NDY1Ewnkpg89A4kmUbhVh81uU7inl/kDceS2J3cyeyIo7Txw9iTWEcz3gDPQ0NDY1jAUt2oPfjH//Y3/7f//3fyBh4F1544VGlXC4khBC46aabcNlll829kkgyPOOKC7qVwnZx3rs6XrxGzJg1Ijw6Rx2B2TQZTvO9qmTGz8tX8eyFjZec19VPox4Ir+4GFUupaYGJc3am1ktiJiBrOZXUTLicIZEeaAMIKOZVK2c8dpynLqK/Vd1AvKemroeZmyUXldMi3Aba5lCvTERZ1js3h74e6QVlum11JynCeSeZY6EeJuXxC5xvEb7W/mVzyH3iXyvGI0vLk/4rTRGUOp/RdrWw9xvliYvwuvn3HnPv1KqHFXpp1NPOeOoC4iicQBWnPsx68Zl6IsStql4+4vn3VTdJGg3TYUnAcgEi/qSebwYTFkF53DqmShho8UIOBTzEZFsJVJG0uOPAFgK7Ml04mGwFhIBhV/pPGSbEDIEq6kELePdY752Xj3rqZgjKNATV1xnBoEBYCdX/o9qoI7ISTKuTT1TSpIGARy9KZKWm4MqM9ig4G+t5tOeivh20w9vHpPHniTWbLePvivgOYllDnI2BZwLnnZ3dS2Epx6tbqnYtN2jC+hLBa1/7Wpx11lmLbYaGhoaGhsaioWRZyMUstBWig3ELKXHiyCBai9UA67uaOnFP90YcTLX5Aw0hJVJuCTlj9h5CDQ0NjRcylqxHTy64a4jH3r17j1jdGzZsmNVx3H///UfMFg0NDQ0NjRcCxpNJdGVzFfeHEIg5DpqLRZiuhHArA7eY62DtxCRaS0WUDAsTiYon0DUMuJix/k2rPmgcqyDrzZcclqpdywxLdqCnMQcwlExfoKUmF0Hlm1EHgvTDemVYplQkjYG0U4c2Krm0WmY1+tBodC0zR/eLoqkyC66lT2cTlb+ZNnA0EC4wM5e/HgUMqPrsI6gv1XNLqHIcr4JjkhC6E0f3lAzVhKNmzkWMpUrJIX2eUEWr4iCUI8TU6Qu0eFVKGQjM7dcXQdlhbfTzNU7NrEtpabCf14zv12D/Z2lMETRUn11J7xOPfsWJ8QRov34csTBVDIBPtZVUvMjw+jGlb9Y7P7WuZR16ZVTMT46extIia8Xem2EbR5Wj5dlYYAzFMzKOHifgEhXL1BdekeF8JC0UH0wgeF2YyPKCUDv3N7fhpVN9OHP/IUgh0JnP1e22Q/GML3aSLNpYWZrClJHEhJkEhIC0BcrCQsYu+SFl2UDnDdI0DUaMJXi89ZdC+OCeZQZ3w3EPHqY+Uj6KmhyyUVTKzhRj4ei8dSmgDaB6b5E0vw/S5zeTj6Mmc9y0iCDzTBfkKdwRz/96aFhgLgD3/2fv3aMkye76zs+NiHxnZb2rq1/T3dPzfumBJIQkkPBDYOxFyCAZm+V1wCw2sHj3rLGNfSTAGCQOAh+zWmwZY7xe2wvY2PjssmAQtoSRBkloNKMZzbN7pt/V9cyqyndGxN0/MiPyRuUvM7Kqu6dreu73nO6Muu+IuDci7v197/fXGy5Kxe0xb1WKPpeFxRDsRM/CwsLCwsLi0GA7n+fJpWXu2qmChmfnFtnMF3F1yJF6jSONGiW/y04mx4vTC9S9HGjN8eY29+6s4/SXD18sLHIlP8OCXyOnfVa9yu0+NQuL0dCar3lhhUqzw6cePk4rZz/RLW4cthfdMRi1xC7sAJbEP/SYlUNGWLSG8g5W47XgmmFcM/eDRAvF9txA2SOsIFqw1EluIUSY1064zCoSWTFXIONVUnPlXLA6CLLpolVCWJXU0iqhdPtHnZ9k3Yukps2yQ+HaRXEjrrdkYR5urLzeLQuvDBeTLHPwq+hZl2JJ83Gr4HuLmdBqN7kVcGx1E+e9IUVuaUU7pV2iJV5wm5CwtIp93ogOVDI6skC4KtnfxrQt1b2AIOojWxNGWN0mtVSMux9pY9lIqgV3B+PEWETLHwPLYMK9imDRi+JDMyyy8u214kVVB6onI59wJRBVbIT1szvdXmPXs1Osz0+B1pzZ3eTN21cpBl1CYDNX5Hx5nrVMGZRiptnkVGOLhW6d65kyvnI53tnm3uYabhCy3N1hyylQ1/khS95o9wq9BjkHEmM5wEtIeJgN+kzKc1CySqUItAyX10unVf9VNak4yn4ZAmaYVHaKxVp0FWI2R3opjDt/8Tkgx+/HajkSgnhb7xjmd1vM1dsATDW6tPP9ga2kl9pkUFrL76VDgMParjsNdqJnYWFhYWFhcSiRDQPO7m4C0HJc/mTxFL7TmwHkOj5n6+scbe2iga5yWOjWE9/jd3c2qKks573FV7/xFhb7QCM7+CSvlnK3sSUWdxLsRM/CwsLCwsLiUKLjevy35bu5q1bl7tomc+0Ga/kyJxpV7t7dxOtTIRS93U2X8zNczU7jBPBQ8xqVsMWLmSM0nBxOmmXfwuI2opnL8ORdCzRzHl3PvTkiQiaT6LDhsLbrDoOd6N0p0HrgF8x8mQmblAcCLVI5w+mi4vuh7A006VcinVOMN+sZChqKG9neVwvjqHYJimfvV4kKFuODVApNM45P0MsivtMw5awXLoQJ9JRYRCWljSIE+oVJd9KCH7VJaZxJUY8oyuhb/RZLVEHA8D8lqR6YDR5EqX4ZEb0scQ8mvZc3Ss0cc+0npgrfgk37WkkdJSVP/zdB0xHYvHHRku884w/TdWLoKkJXJfvyODpQil+vBE1xDAVuFHUtlGiaooDL6CaOhCSsJLQnzAxnjX3wmcIaBtUyPm8zTDrX6HljpotEWMx7GZg3JPo3fNKJ+xtRvE2/dv3xG2qXlVyFI80aj1ZX4vjr2SnmunU0ivO5BVYz5V6DQwgD+HLmBG/pvMJRf5tzbj5Bs4wpnL4ZNkzJTFA3++/YUXnG4UD0aaEP7o3bGz/OT2paXdrp9QWtZZryWFrwAc5vYuEVs9+OvxSiIJSINJrmuMKl7xdpG4LAKR/5TdNPe3lhahAUPevShGcsLMbATvQsLCwsLCwsDi0cHfI1G68MfYcHKJSGL5ZP0Or7yEvuTVUEONZyYGFh8bqFnei9jpFYYY9EVJzhMACiFbXQWPGMrE5moSkv1HHWuwO9jKU9ysLKmrzaJoWN2Ci9tw4wBFrMcsad4H4QWeqMgpzhe6QEWXglrOQmwoSVellQ4OYgXvFOs2iF5sWNEhhpoz/MFd9+woQF1SxnnLqMuUpuuFIYiLEIWccUNwo3ZN2bsB+lecIQV7yltJOuyh9g9T40rndsvTPvlbTCbtyD2K2C2U1c0B4JIaOEe4Y9GGnZkKwSkssFSfwoIT8/LCgR5QkFa+F+MLHFI6rPFGjpW+ASFkvTKidcl9jiJ1j0ZJcp5ngyjgMFjkq614gsfoJAS0J3IgAvDDjW2EYBW16Bqltkxm8wHbQ41tkB4LH6VS55s1x3K/GzQAVQ0i1KusNlNYcK9iesMnD3YcT7Uh6RnjEcNJxqYiuY5M5jpLBQZEHebx9TxO5Keha9Mf1fEFE5UJ8W3lVpwivR+Y1y4RQlTRUjGjeeRp3LGDaMLP4y3DdGly2MKdHCuL+PC6X3neVVw2Ft150GO9GzsLCwsLCwOHTwwoCv3Twf/z3rN5n1m4RAw8kS4DAdtijoLvd1V1kMdrngLVBTOSphkzPBGg0ybFK+fSdhYWFhcRthJ3oWFhYWFhYWhw6+cnihtIgbhLQdjxAHrRU53WXOb7Dg16k6BWbCJue9BY4GVd7YuUSAwkXTwuM57yh6jJXXwuKORrRH9jDisLbrDoOd6N0pUA64fd6BQPNIcvcEPqMgrCIJqqRRIGO9kBG0ClFkY1LKmkS5TNE8kTDeJ+CI8gRqZ0Tp0SbdR6TV9G+Iy+T0FuG2jdsoLvnJM48leopENRH98Y1qgyQUIPiek/rEUHqQN7gLaRM0zbgNRl8NpU6RwrmMqTZqiLo50h9dzBEaLnliwZS0PBP6GZLyJq6CecmEPqqEfiKLFQzf34khCQYpoe8YSPpjjAKTbdRKJamE48QhUvwgpvV/2W/lAQQlJJpaCuI8xls7El4JhbBYJMWo27xOCb930SkYY0f5w5S8mAou+RFM0CIH8U6ocByjPAa+66Qwp5ss52pmJiFG4QS9Oq9npnkgXGExrFFVBa6pGa55M8wEDcq02QmL1MhBoER/mjElU6L/GxDHiTCeRuWRMPa+p5U3bqwiUy4nhXZ6+TUpfVkcywerL0IsZJQyTiRhocQ9HPGcgBFjJyFQlEw/VLcz/PyL85hjS7pH44SMMD7VEts19FCYG1jpWIv9wU70LCwsLCwsLF5bUIrnM8ushE1q5OMv5apTokoJYRuhhYWFxesOdqJnYWFhYWFh8ZqDVoqqW7T+8e4geDrA0wHL4TahUlz25vBfSz4FtOb+1Q0a2QwX5yqpLIJbDSvGYmEnencKlBr4uHIGD8U4TKKASf72EnROk5/QryaVhhnRYQwKkJAnoZIV+fWT+C6jqJSThkVtDIVogRY2iu4iUQnj8zJoSrE/uhEURx32aDGOl/JlElOEJF7sMG1GCeqcveM9v4n2DIIkdc40WmikxJpQ7IyVEU3ab/9gBE0zThcKeSSKnKD8mlBndIQ+bLZH2q8T04907xR0/xw0yYuS8s7e94trBDVzvwqcokKoicT9F+iX8XNCyCOqyaXUZyDuZ5IynllO3G/H9xMzLHT71CvTV6eUR3q0jKFCJ+Il6uYIv5XjqGYJmpqXjANS/WNJFNCIdmb6zgsz/eepSd2MypbomslajIL6Y9mgV0YUSolKmOiDxjNRBb18TnfghTKmbpp+7fqUzYSPOiHdXlXOvXVLeWIIFD8lKH8mIPgtNZ8hekI686g+MwnS/Nal+rqbAAoInV6fSlI3hXbvR6lyVGV78mgXymGLxzqXcAAfB4WmrNt8OXMsvg9aonia42gM5T5B3YzGoEhXF8YOo85fJ+KcUHP35jYA+dDnhaNz/Tb2+47x7lfGeFRCP4re6447yKODWyiRbXFHwk70LCwsLCwsLCwsbisWwhoBLs9ml9hxCpRp8WjrKve1VjmXXyBQbnohtxlefyFyJ5/l7GqV1UqRail/+xqkGbtIeFtxWNt1h8FO9O4UuA64kjmpD8F6pwQxFnMzfkLgIra6CWUmjE79VeAUC4tKWbQfJ5iSsHYIuhtp4i+SqIsel848HFV3hOj6GYtu8aqlN4gPg8GSoGjdG2NtSNwXadU1VTyi/4sQZq5Uxn7EzBts1BP7ozOiA+EmjBM9kCx/GNY9JdxgoQ0JcYhEHxVMg5J5NjbF9opXQb8P670NYwg3RD8ZkVe0LIyz6AlxCcEIYRE46W9RuJeRAFOaQIuARB7hFozPM+JeMnzftOrnk8btpFY8I60oeiFYdMVVfuNYtLol/Nr1f6WwEZaY2CohWDIS1jtv2KIXW/JSrS7jrdeq2w8UvrfVKDGWAJSrcLrGrYl82JnWu24yzjxO+uAzjsPhciT/eBOPnRHPo0FgP1nCEj267MRzWRAZGdmOKJ34HlBDcWlW5Ukt8Nrt/2PQ9kmFhW6GH71tN8+JYIsCHTbcEpteiefUEvc11zjS3aHlZMjqgKcqx6hmC8NtwBhTwrta8i2ZJkomWe+SVv5+WP+anL2+CUCl1QHg0ctr/PcHj0K2l8nxBh3YfPcPdPOGO4JjCr2ksYEsLPbgNUR8trCwsLCwsLCwuBOx6ZRY8aa4u7PBXFAH4Hp2msenT/NScZFi2MXTh3eic2JzlzPrO4mwqVaXe69t36YWWVjYiZ6FhYWFhYWFhcXthlJcyM4DsNwdTJg6jsfV3AwvFRYAmO02mNT1zKuJrD9Mnbg6W+TulW0q9fZtaBGxGMth/Wdx62Gpm3cItKsGtn+TfiKljeiF+xll0QZnk8Yg0B3jqiWaXjJ4KHBSOmfa8z1xWpHIihkf1ZHY9JxMD0najVipHr6OsaiLWXbYrzNQ6EAN6XvoIKJ0DF80yYePEmiaagSNRwyL0pph4Z44M8+IsiNBAklEJU20J/bbaFKyEnTPfphJGw2HqaRRG3SizxsNju+rQD9NDI4BLVSp/ksoYKgj3nQBtVHUTSl8vz719tHW+DomREQEiqsjXTsjjyC8Eot1GO3X/UCBmCvTPmFAWTPYUzHVbMTzJi4mhXKqpRs7js45irrZf6OGkhiLO5xOoniOpPhFog8SdVPwzaWF50kaEvcjFnoyAvv9xPR1F+fdI8ASNzvs0Tcd36BuRj7zBMplIq8QJh2bNM1YjMU0/AinL/qeTNPIEp6Tot87ieI4QjxlTBNHNCJZx6jjUXT+ccVqRxG6fdGc/fqEnKTte7Gn7Dc1LjIV9iZEC0Gds61VNjMltrwCoXK4VJpFuZqztQ1wNOfL86CUOI5Ev3Ym5Vh6JkTvXWHM99o4THEfjEvNuaNTnDs6haNgeavOm86v88zZWUpf6fKG8xt89k1LONlBJ3Ndk7rZfyear684zDgXK8ZisU/YiZ6FhYWFhYWFhcVtRUb3JjEBioabZb7b4ER7m6CmWMuVeam0wIXSHCGKe2vrKA3npuY50AbBW4F4XzuUWr0VhwcuVKkVMxxbb3D2wg4v31O6BSuGY6A54Cz8VcBhbdcdBjvRu1OglLHULViijLBYWAUhXWIz+nC86QJBsrBJFh0pPtU4IVgGx6aDgRUoLYtk0BxhiYvzSJaKKJ15rlHZpqhNMIiMw82yo5dDwuoUxxphgjVFWN2VrHtSmCjQIpQzyr1CbKERrHeSRVcJ7hUSbhgk8QSz7Gg1Nkw0ovdrrl4b5cTWPWHVNqnPEgkc6L4Yi8YJNFrvXd29yS/o/bhXEPOPjhpZhmjmH2MGTFiIh58nacIr8Zg/wKVbdLY5wSZPcopQMh0o1XNbksYaEOpOc68wXqDFKMeUbHeEMG84LLIshGnCKmnWu+hYCjMxqRiHed/GnL/5bhBdIOxxWaB8RDGWRJ7IyicIrySsfAkrYK9ER4iXnjcSUl2TmBhjLZYFSuRxsl/XB2J9ifKEepzhPKllJ8RYhuuJygyFsIOM771l/8nU6d51VIPncTHosuDXOFmv8rbORZ6dWeLi1CyhC/dvr6OU5vm5hUEed9jyHY2P1DZKfd4cT3tcKZhhCVcJbkjR75m9l6pNct2QWsnj7KVdplttvvKGabSr8NxBx429Y5kMof6xY3S8wLEWPYv9we7Rs7CwsLA4lHDQ5JTP29Q5HA6vCIOFhcVNgFKJyTFK0fCyXCjN8Sfzd7GdzfOGrWvcs7PGpfIMz80scqpW5R3XLrLYqE2wgvzq4dzxKb748Byffvsym9NZfE/x5GMzzG50eOjJ7Ve1rbd7H57dn3d7YSd6FhYWFhaHEteZYU1PAfA25xzKTvYsLF6X6LoeX54+yvOVRU7Vq9xfXeNKaZovLhyn47i8ae0ab71+maX64ZjwNQoZ1hYKhI7i4vEyM9td2nmXr7xxmvm1DqfO1W93Ey1eJ7DUzTsE2nVlWtk4qpkgfpGY+if4UH3xBElQQWLImJQ8k8Yo+V+6lXROiUo4RnhkJOU0PgeTxhGFGHTWmFZzk140ApXKbGRE+xR9ohkRkhhLgvoTROVJ6QQenllpImy43YaLOqOYfp5RggkClTCmdppl931HJmhaAo1TSXmM6mJxmH5aFepefRqUSb8SFRyGg1IxIQU42UejGyJRfA+AxHkJ9bl74sw2CGPHTCv2N8kfnSR6sOeczrGM0poFVeOks8EFFhNt3CvGMtZv3wga8th4IUyiVIIhqJJG3XSScb1y9HA6k5IZXVOTShbT1ARatCGOkqA7j4PZtXyV+IUBLTJNRMWkQzpBL051k2FD9cXtTit7kEkUawnHjFGJeplCqZzU8iDRJ9N83R0IwnN5Uj9646iLil7/DT1Gi7FI/f8glE1BeAip7KE2KC7mZwg8xYObqyy2auSCgKtTUzw5c4TT1Spvur7CM0uLXJyvDJWT9q4ahMnP2IEgmpEn9sdn9Ms+/ToSW9lYytI673D8aoOXHy1x8Z4ip15qsDGfY3cmgwlJjMVEp337J7EWry1Yi56FhYWFxaHGSxzlJY6wRQkPu0fFwuL1jCvlaZ5eWKKRyfDS7ByL9Tr3bG7y1JEjXJ0qc3ZzEyc8PNZ/7Si2ZzKUar0Vkkv3FGhMuZx5qfYqVK4P9z+LWw5r0btT4Cp0f96uUgaPJCU+ENYwrXzmiqEeCovTClYZc7N+crVdj4xPWAtjC+Jw2WZQIl4yeAgWPckFhGhhMc9LDR2gic7FKEawWE4shGAK3Ywz+SQsI8NWLslSk7BoCWFjLXEjrCCiKICwciyJOkTXNtFXE0JA/SBBpz9xbSJLpGtcO0HUxlz9ji2DgsR51F4V9tuowewco67FQSFa8SAWkZHqkKTZ06wOYjnSanpKfWkuN7RwvSe13qVdzzVV4V59jQVqbFDiBXWU0Olb0VLGcqpFY8I2SmIrCYue4DYhFl4R8kjCK6ZFPmGpE8RYlOACYyD4ZDQ8iCzfjEWiH0VuX0zrXWTlk0RUzPm3adHzB/9iY2uY/DXPYa+QS69s45wFy6E55qV7PM7lyCjLmPS+GYdU1sQI8ZSJyk4bOwex6ElNcHt9WxtpxXNIK0dA2jgZqsM4To6n3h25MlfhylzPand1rsxXXbrGOy5d4uJchdxuwBuuXefp4wt0Mt7A1YJosR9+XyTfjXpvdMJ1gxLEWJy+Jc90n9Aqucxsdel0eyd/8USJB5/dgabCz5gqaMZh5O7BGMt+xy50WewP1qJnYWFhYfGawFVmAZinToXmbW6NhYXFYUAjm+GPz5yg5XlUmm2+ePIIs/UWX/fCZY5v7qYufr8aaBVdcu0Qr9ub/O1M9yibpbo/LtsN43YLrlhBltsPO9GzsLCwsHhNoK7yfEbdw5PqJDsUbndzLCwsDgkUUOx28cKQrB/y4vIcWinecHmNP/v0RR66vM5MvXXb6ILV+QyhgtMv98RiGkWPUMFUrZue2cLiBmCpm3cItKviHcLa5KaPo+okKH4C71HwqSfRJpOiJf0wZzjdqDxi08bQZkTxl1EYR9OUKJ778qnUvyaT+q1SvSx6KHqcUsDIans5x1Alx7VjKG4MHWgk3UcsRw2HRX3BvLYR9cWkhYkU3+H6EucalW2wWRI0zuggQefs37dRnVD10itfD98sRyhbwEFonWaWmLIq+LIkTdREak8KjUukkonphhubpG4O339J1GTSdotwFHXyqH7d2pGfSyZuyGeecX6SsIroC8+8Jl6UV6BpmmGx/zOTrjlM41QJ4RWBpin46owpjoZAi0jjNIeJRN2MxE+MMNGPnunXLuj/M6mbUTnCczlB3QyGw5wRoi+TQOrz0tg4CGS/pCNooRMusY/rtxJl2iw7Eb8nb6IcY8hGVGht5k/zxzfmmkl0TTNcFHUxqctqOF18fnvGho/iibuWuGe1yiPX1hLNygYhp9d3Ob2+Sz3ncXW+BArynYCO59DKuWxW8uwWvN7zdcQ5RVTKBL2y317HoGlGx8ro4DUvy/mzZe55qUa+HvKlR2apFTMUdwL8rivq5unoe8oY30HbUjct9gc70bOwsLCwsLCwsHhNY2W6zMpcCbTGJSDrh0y1OtxzbZuZRgeAUtvn3qvbcZ52xsHzQ1zdO16bKXD+2BS1Yvamt+/C6TL5esiJaw2Uht1Shulap7eIN04h/UagmXyj6auNw9quOwx2oneHQDuOIX0+WP5S0ZJnMOGIGvWwiS0sxiraGMGUpJuC4RXoia18+3kQjNlwL7UxUXa0cjZsLBmNaFUyUVFapjFIWCql5b3RVpdRySRxlEnDBu4cbk7ZCYtV7BZihAVZWKmPpa1NQZy+pS4hd20ueMYWKPPEhldlTcTuFUI93P+kdqXdlnF9wlzxP0C/FVe8BWipnhSr28RhKWVPLASRMtZHWhOif1E64TmQ6kpBChsromKESRY/QTwimUcn4oD4ecIIMRZJeEVLoiai9U4NpVMJ8ZTR8VJYUqClHzbKouf30jjdCS16wnNglOuGQaD0IBQsQ5ILhJvs9sCsZ7QYy2TlxOmlsTPKqh4dSJa4tLHsMSTGMqnwimjRFNyMAISZqP8LZSfq0yPLltyM9MrsHQeuQxOHZtljdTHP0laTu6/uMLvbQQPP3j3DvRd38D2Hy8slWlmXQjvg6FqDE2t1ri8UeP7MNM2C0fB+e8z3VkxiMMZt5BbB9wcnGAS9hq9NFThxrcG7P3OdUCny3YBjV5tcXiozfLL9H1MYr5PysLew2AM70bOwsLCwsLCwsLgzoRSrc0XW5gs9612oaedcquUcZ67tcOpKDS/UdDyHWtHDC0KOrDeZ2u3wx285QujePDmLlcUi/73ocf8r28zstrm0WOKRlzfpZBxWZ4s3rZ4IsYL0IcRhbdedBjvRs7CwsLCwsLCwuOPhe068NXxnKstTlXmcIGR+u81UvUul1sV3AjIEFNsB972yw3NnZ25qG3ZLWTanc8xtt1idLZDxQ9744gafemOWdiaTXoCFxT5gJ3p3ChyF7q86mbSC2LeeyZGJxFoOwglPUC77tAqBAplMNywAYPqKkuicE9M4BbEONSJ6KFDwk2eu2yUWm6JCb4RTbsgJS77+En70xjh+EiWJR9FrxtFv0uhAadSdOD6FFjmGFmr6dFQJJ3XDS30R/czsO7G/uXCYStOLENot0jmThyo6lb3XWuiYSUrqULQ4zkTqltknxpWXgpg2NuK+jRPZSaNXjhVbMcPT+pGECc81VXhGolRLdQjlJGiakYiKOxyWEJmQaJqJPP0w855HVDOBpmnSkJPCK/0fg8YV0zOD4TAlPZcTNEwzT/9Xom7uoWHuDYvpnGOom06QpG7GfvGE52BaWCrFOQ6cjKZ5I/7tTKQKoqRRN9Mom3vSHWR8p9Owe313JHVTwEBEZRAWeslfAJ0x+rowJsb5tUs+v4dpz+Y4kfzaxWECLVQpDR5sLOXYVLl+dSHlus/ySpOt2QxuJimAkqBuMoyIshn4gxPU/fGm+xTOCwsVpre7fNUL67yyNEWoFPdd2OHLJxcHBQmUaqdlxfIt9gc70bOwsLCwsLCwsLAAUIpaOcNL92QmXoDaL3zP4U/PLnJ2ZZv7r1RRwMmNGq/MV9gt5m5eRVaM5XUPuzRgYWFhYWFhYWFh8WpCKc4dnUnMJR+7tD6C0mRhcTBYi94dgtBTI3xh9cMM33qqP783/Y1ppz/nH+X3Jw40KaB9WoGg+DWShimocko+8wa0UDWc7iBKnCYi9pnQLolSmShHoEMpUxErohBKHNBQDXxcibSaYZqWxOdL0h2jsOHikmUKCQQaj8CEvXmYlAJkJhXoV0n/j/2gxL0aVtubmM7ZT9vbwD6sumle+2j8pKpJxnUIVLJEXolqJoSZtEBJ3W8cTctMm4gfQ8mUKGWjlgjH0Momvk5p2HPftDuCZr0P+ug4lUAt+MlLKnHqofi9Pr6G6pbaE1PgjE5n0Csj6pdJuSSmVxrPhOi5nPB/F+UdhCWUMyV1yzHUzQQFNGrfiGeQVv044X6kDcuxCpLI7w4J42iao/zo7Zc2Lfqq3I+ipzTOol+jD8bnLI3pEXVPTN10e31bG3VOOm4T5yr4iQwzwphIqG4Oj5OxNF1BnTLZIIH2bJYT0f6FLDocnIxE90y0J1IGNfKEMU3THKvOUJjye8e7uQyhUky3Okw3OyxttFibKhn+fQd5Qn9/HVNpsdmHAoe1XXcarEXPwsLCwsLCwsLC4jbgj+4/yR+fPcnzS3MAvOHqasLNiIXFjcBa9O4QaNcZWOXMjcL9Te86YbLoh5lLVdHqpmuukhnlxCIqZjFR2cLK2ohnlLgCOyaPZOQaZS2UVjonhtQGScxAWG5OCBiY1rs4sJ9ND8pRwqqlMi2s4wRX0jarCxavG1qpliw/gB7j4yjdj95wWAITCgWpyI+ecQ/0cFeXrQmi6aAfHure2NF7xon0xExYxke3VbLeiZY4DGthmvVOslSI93zY3HIgsYYUy9jedqXVN66bjyzbSBdZi1LrEwpP5OkfSz7zZD96w1a83vGwUIR4QtHYMYWFonYLwiqJ44T1TkrX/zVW/seJqMBASCXNohfXZ5pQx4yxg0ASNRkVL9YjdCrRejdOtMRIO/G5SOMyLU/aOBL6oCRQI177Ca38UR2KnrBQbNGLLdrDDAm5Txtlx+yDYQEWALzhcTLw2yoInRg3QXo3poqbjbsTRrrYEhfKN0YJJqg4rdlRIuEw89ugL8ySHGPJ8X1papr7VzfJhCF3re9yqTLdizMFmNo3MLgsXpewFj0LCwsLCwsLCwuL2wjfddnO9YRY7tncwAlvgqM5rQ/3P4tbDjvRs7CwsLCwsLCwsLjNqPf96GXDkHK3c5tbY3EnwFI37xBoU4zFoAbENDVzYagfnxRj6R2nbY5N1QsRaCVJquUwvzLegy1RMkdRKcdhBKVlEoi+p8xyhPgEVTCifpiCClH8KDGWOF7II9BiTfEbkVKaoHb226OGgsSwND9jNx3muQQjUyUhUIrNa5e4h0q4cQLdcajPREIsmoRggEQbE8NEYaTx6UwhkIlpmjG9LIU+KtEmR9EYx6QT6W5pkGia46hk+yg69vs1Kd0vxa9Zwj9eRF0TaJqJMLOcKDzt+kjnH9EiDd9bCDSvBI1LpFwqIaz3mxBg8YV4iaY5KXXTxIj+FrM8hXsdj1Qx7zD1eCjtOExMcRbqHiHWshdp40l6t0rt14LfxlAQYxn1jpXaMEp4ZSidB2Gm/+iLaMiS8JDU7sQY7KdLCLQYz2iv14GU+Z4cR8NNOdc0RDROk8IZvUbCwHQAqJK/eyqMtqkkqJ2CrzspLBZJSoixmPGD391MlkwYcqK6y/MzhUQ5wT7nflaMxcJO9CwsLCzGYG5mh7nZHTSKWqPA6tYMQeCmZ7SwsLCwsJgQThgy32pytVSh6Hco+N3b3SSLOwB2oneHQDuyewXJECVaeeKVM8FakggX6tDDq2SMWPFVenilK17BlDZUD9V2myBZGPuQLJFJC2pPyLkn2d9PJwmGiMu75rFK1NHL3P8ZsSotWuoQwsZZ9EZZ+caVLcHsn/H1GbY+muGJ6xTlEWSzlRAG8kr2wCIibKw326h6aeZmd/C8kGYrS6XUYLpcp9nOcnF9iTDcM+ETRFak8SaupguiLrJFz0gnWaomtFSYkF0uCGFSGRPe/4ldLuzDWqhdwaIntXvM9TTTJoRX+tY5M4y+VSJx2z3hwSUh0Z5euqSrBJX8JWlhQ7LUhUKYZJ3rWw4cwYqXiA+H4yWxKTNvmhhL5K5EEgcyLSxh1K9T+mAC4/qR2cRxVt4ReUVBlTEW7VT3CSkWvYFrDiMssioLLoxGWrlElsdwOXvdhyh6fV6HOulewbC6xRZrU2QlfnYY3w7jwgDV72jKlS6KcRiJmqQye9Lenf0fU9QlcocguTBJsHkOYL3TyTgzfrQYC5zZqeKFIVfy09xd2yAT+jjdPePNsjkt9gm7R8/CwsJiJBTnLh6j2cpSyHdw3RDH0ZQKbcr5Firpwc3CwsLCwmLfcHTIyfo2V4oVWl5vn95YtdBJoQ/5P4tbDmvRs7CwuOPh9FeQw3D/a1th6PLK5WXKxSa5fIdivo1SmpOLawC0uhm2GyV2W0WaYY5xJilXBQTaGZvGwsLCwuL1hel2i2wYcKU4fbubYnGHwU707hBoV8XUl8S+9YgiExhLJ5FwxUGkbRMUuck+VtMEXKIylUmrECh5Me1iBGVFYpdKJL2D+NtT0iUTaKoxPWWEX7ehvMax5AsI6fykdCPOxRQMKXZ7nP9tp4BWTro/qoMgpkMJN0GiShrpEhvlI5eQwXDnSdI0ox31cnPy5Tbzx2oUpjooBdvrRVYvTwMq2Xfc4QugldkiRa1RZLddgm1wXZ+zx6/iuSH5TJf8dJUj01UA2n6Gpp+lE3p0A5cuGQLtsFTYYirTouFnebZ5snfGRt/IeW2OuNtklc+OLrASzgx8Y6IHlM00SmIa5XZvOiM+SfEeLnusj7o0HIC6Oa4eRY9eFvn9GipbomkK1848NkVWZDGWiLpm1mcOyOF2Sucg+sTzIwpYks6191j0mSdRLk0/eZEfPYO6KVI7BeEVBOqmI4zLsdTNQCfpZxFVdogqvQcp1Mxx1M4D+YmU6pb6/4QCLYn2CH+I4j8Jv4zDYXG15jtGy+FxfNRuQVglqlf14yPqZkxPNumV0bExJmL6ZYKmqRO/e48dR3gXRNfEXIxTwvNd/GyRyjNpmsMdKaZpTuir0mxO0meekE7yb2mMp0qnxZndTS6XptnMlQAodbqEQCvM4nTA1y7ZoIXTuTHqphVjsbATPQuL1xK0Zm63zcn1XaYbHXYKWaqlPFuFPLuFbG/ipDWlTpfZZouZepuc71PqdCn1N3Y3XY/nppfYcku3+WRuHTI5n7njNabmWsnwrM/NsKYFgce5a8dYnt2kkOuQ9QZf0DmvS84bvYm+6A2/qT0CHsleQinYCQrc5W0wF9YIcPBUSEm1uRAucI3ZG267hYWFhcXtQS7o8saNK2R1yEynyXPTS6zly2h6b6Zs6NN2M9TdLCfCKo4O0XaXlcUNwE707hCErmGVEFZOE6uWkSCKaS2LVqAkYRXjDxUKSzCCRWuksVAIv9nCKwlLjWjSE8KkyoU8onVSguk+IuyJMisjv9bSMnKy8FzHZ26nTanlU251KfX/eaGmnvPYKBeoNDscrW7gaPAdRSvjkesGZMIQDexmszQzGdYLRV4oFGm5HvdtrPOmzatcy03xcmmelpuRmj3WfcAk8WMuiTzVSqzA9zfhe4mO2/sNhbD4PahZOLbD7FI9UXS37bK9WWJrrTwoM+GSwKhPAZ5DmFE9n64JFwiD4+Pzm0wXGnQDl5afYa0+TYCLUprpfJ3pTJ0Qhbtn2fJKey4eq9Gq+ZTTiJtTcZsAlJ02HVycfr8oOG3CPazPfYuo7AnfGyZKxE9qidsPxpSzH2uhVr12psrUKyHM/HZyktYNILZeJNJJYqvB+AaroYNBnoQVILLojRBrcCRXCqLAw3BYbNETwoCBhUmwDCvhHoVK6FxjLHraVT1r0d74Ca1zJia11EnPG3EcjHq+jev/Ka45RAgvjDTrXejtfb4NykmKpQ3fD+k9aLo4iIVejLDQ1YRez6JH7EpkUFFsvRMscuL7NBE2aGMYuSEy2xgJr4RC3zJvQshwujRE10Sw3olWN8FiZ8anu1IQ0vXbcGZnC1drPjt3ijdsX+XR6gpNJ8MzpWV85fDw7gpPFY/T0FkUUG53qJMflGOOWwuLCWAnehYWhwlas7jT5K71XZa2mjhA23Oo5zNsF7NcnSuxVcyzVcqB6tEPnTBkut5hptEm3/XpKI+dfI5qIUfQ/3J1uoMX1xPzxznW2OG+nXWOtndpOL3Vw47y6GqXrvJoK5e6ztNVHnndodDniyjzf6XRKBzH+Nbrfxw4GXBVyHSujqtDap0ChJqu71FrF27FhWNqtsnyqWoc0qxlqe/maNaytJrj985NWsd0oU4x18J1QqYLDQAybkCGgKzrs9Ls7a/YDKbwVECoFYHrcDy3zmYwRSvMEOKCAw4hZdVCKU1LZ7gUzOMS0sUl4/jMUSevepbBKgUuqXmjKRoHjdIaH2fP17iFhYWFxWFE2W/jojnd2CQb9lZeCmGXh+orfLl8jMd2r/BI4yrPFpYJUcwHdepuPqXUMdB6zMr7bcZhbdcdBjvRs7C4ndCa45u7LO42KHa6lNo+mSBkp5DhK6fmuDZboptxo6Q97FnJDB2HrXKerXLvZaA6g+Vfab8GSnG1NM26V2am22Su06Dod5kOmmR1QEYH8ZSoi0vGNBGbq9YTuJLrBB5hqKjk61HVNNo5Ll9fxA9u5PGjyWQDMtku+WKX+eXdOKZZy3Ll/Fxvr0e8an9jE6FCts3R+Q0KuWHaZbObxXMClkpVrjdnY5qNHy/NK660FxNWgIJq83DhUvx3qOHZ4AQ13ZsEawUXWaCk2ig0uxSAkPvCa1Roko3uSf/+PumdoOYkJ9BZ7ePpgIbK2omghYWFxSHAU9PHWG7tcLq+hacHL+i249F0Mny5eJzHGld4oHmda16FE90tVpxpOsp+rlscDLbn3CHQrhpQN81vupjmYCbuJ0vQPYYFUZKiH/0/pE34qY0bDkoTaJHET0SBkrT6onMV2H6JT1+BPjoxTVOqexSNxeCSFtsd3nhllZlmm81ijt1ClpWZIhuVAttTGVCqR0+RyoyCJErpHtrUEPphvuuy7pZZz5dx+lvKnC6gNTntM91tUQw71HWOWp86EqVTaBy/V2lMKSMSadA4XQi0g6/dPkWsl76YbXFibo2zJ66wsT3NxkYlbpApzBJTlYwO4GVDZud2KZXaZHPdoXPrtF2unF+kG/QsZjjyBZBETUKvV6F2FUpB6Cp0xkERcHRug9liTbiQEGqF42gybsBqc5og0ysnNH3iCT7zun2RlUAr1nSFZWebrOsP9H3cXgN3nHx8Hd4avDKY4O1B2/UIDarZve0VloPe5HfTKfJ04fggsURzjMImFHUZ5TNsYtplCm0uTpYy8GI/elKck0w3FGb6BxOEJ2IxCk94EJgVCr6y0hCdV0TXNI8lGqZZdoJ+KdA0o2e+IwivmGIsIrUvhRYphgm0z733NdwjmjOpYJCEVBEhSaBo6MDIO6IN4wR8krRfoT5pESyFuhnRNBN547INUZNYtER4qY2CNN76ZZpiQ7gD608UnvB1J1I2hWds/CAb0URpC0N0PsZ5iQJkMVVSpleKkKibgg9KkRYr+cKbkKZptjsag2HoctWbZXWqwunWJic6VQBm/SZfvX2BFzNLPJM5xsPdq7gqRKN4tH2Zl9wj7KrCvqmbVozFwk70LCxebWjNia0dHlpZp+15fOaeY1RL+eReiNv5BFSKtsqw7mbAHUzuILlVJNo7Zr77nf4eTifx4tVEXxiNToGXrh9nqVxlcaaK33HZ3i2PaUpINueTz3eZX9xBKU19N8/WZolu1yOf67C4vEMYKLY3yjhuCMEEpsYJMJWvc3JhNf57p1mg2p6i2c3SDT1cL2A2v0vGDbhYW6TuFyaycgJ0yPBid5l7Myssq22uhxU21OjrYKKFxwVvnjVnKnZCv/dbazrs7fNbc8ssBjXOtlc5l10E4OHWNXK6y1OFE/jq5lwrCwsLC4vJ4Tsu5/KLXMtUuKe1xmzQxCPkwe4KK26FFXea40GVLVXE1SGPBZfZUCVWdCa9cAsLA3aid4cgdFW8Aq1MyfqBOvsgLBwOi60qpthKwprWX/1yhuNlCXyxGFl45WbNaQRRgImFXoS8B8EoqqQCiu0u962tM19vUOl0uDw9xVeWF/Bzao+1b0SD07Tmx4UnJo6C5Vda3U6rR1yVF2UPEulCXK5vzTFV6Pml033dlGKuRS7XJZvxyWa65HJdMpnBcmu9nmPl2hy+P3hstVo5HA8KhRbzy9ssOrBybZbtanLSZAqqRLPVXL5Dpdwgn2uTz3UJQoeWn6XbzTA7U8QJepO87VaRa7V5OkGmZ6lTgAtdx+VaZ75nSVdAZrCqH7o6vixhLLwyaIPnBBxhu389FBed+aQVcMhKoPiidxeeDmk6mfg6jxJj+UrhKG9oXGE2aNBRLsf9bULHATTzQe+Cn+2s8mxxOWn1TLHoiQIVBsT+M6ZPJazPkxkLhsvekya2TqW4TxDFWMx5ryc8PASxhuTqvkqm29vW6DC2sA0CRbGVFJGVce4VHEl4RXgMDB3vCZMESpRkWJb6CfTFWPa4wZAscWMeHaPqiZ8pohXMCBtX5iiLnmS9HuPORBZWGVGxTqYDo29KVm7T+ixZncyixXsp0FiiY7ON3sD8E1nylDNckU6zpkULUOINZnA/TOtdbBkbtrpJY1CJoi17wvfEi0JHwreRZLFLlCO5M0lJJ+YNoUWOpzPHWVA17vVX8QhZDnbiNK4OeYYTLLHDEb3NvXqLTw+foYXFSNiJnoXFq4BCt8sjW1ts1WpU83m+cnSBrWK0p+r1yV9QSsfUnYWZbZbmqmgN3a5Hp+2xs1Ok087Q6Xh0OhnR2XkYOqyvTYOuoJRm8UiVI8tbBIFDbbc4sm7XCTh1/DphqGi2cmxsV3DdkGy2y0x5l5mS5uWrFdbqFTpkx5yFJqMCCm6bgtMhH/06HRzV+9BpkaER5qirHCGKPF0W2MXrv/EvM4c/wf6LrvLoChMbCTU3z+fLp7irvcnxbm9CebKzBcCl7Ay7bp4HmyuUdi+ykSnRVS5r2TJtx64WW1hYWLyqUIoNd4pdp8Dp7jpLerDn3MflBJvUyfECRwmCmf2VrTm8nxiHtV13GOxEz8LiVkFrlpo1jtd3mG038Wdm+O+n7qLrusl9Ea9DOCok4/l0uhlAMz+zzWa1zOraLKCSVuIJobVidWUG19UcO77B5UsOjbqsVjY9XQM05y8eJwjdeH+r9hSuE/LW6S1aQZZukInpmI4KyXtdCm6HvNcm73UouB28/op3oBXNMEc9zLMWVHquFggpuh2Kqs206ql0+jhcZ5q6znGvWmGWOhu6THPshHL/6DgeLxWW6Doup9ubvfMDTnaqrGSmeLGwyHy3zql2bwJYDto8N7UMgNI6uWfSwsLCwuKWoqM8XnSXuaznKOsW5bBNkTbLVMn0TZkNx+cLt7mdFq8t2IneHQLtDehpCdqMQBeI6TeJzdH9Pwz6WIJ+KVAfYiqCWZ9A45CFV4Y5RFJ9qRhFRRoDiSGUSvGUmIiSMIPubZ6ebze4Z3uDSrfNZq7AuZl5WJonCAKUTqeuSpvQx+1c1sbJK4GmeRC/Z0PCCXvy6glomqPqm5veRSmot/IU8x1cR7O9W0Y7e6x2Zn8a5zPWoOtcXZnnhBty/MQ6F68s0WrlEtRN1/OZm91lu1buWdJcYv94oQtaObS6GeZKO4QKMp5POdOi5LVQkZUuzNAMc+x0izTI0QyzdLQX0y/1WBrm4LiJx33BdR7lEn/qnqLbt+yJ4ihROSn02b2Uy1eK86zlyrT7ZR/t7HC8XWW5uxtf3l03x/nSPKELx5tV7qutsZkt8uXpo33K5/A5SJjUF5ooCCPSlVPyKEb70UuIY+iRYYlwV3hYSdTMBK3dqDSN+hWn65VjCqbElEshzDwWw/RwmCMIr6T5xBxH4RyZLip7jBjLXtGcsfXtg7opiuwIYWmCYGZb99YjjVtxXJr9SfCJpwUhE4k+nHjOqeF+O3hXyRdqUupm/Pg2L4SjiTqx4w133DAQHkJShf0+qEdxr+PvEoGmmaBFjhlvI0SQ4vEhbIVw0oRXhK0nZnvGiR+JY95sN0KYJKwUQocsm2TZjMvWZAgoqjbZYJv9wIqxWNiJnoXFAaG0Jhv65H2fvN9loV1nsVWL39PVbJ4vLB5ns1hEAcdcFwJZNfH1hKzb5chMz4o0P71DpVSn67u0OjfLoqW4cnWBkyfWOHlslbWNGaq1Mlo7OCrkrqOrhKFidWt2ZAkbjQoZdjlZWacbujT8HJfri9R1jlaQQeMYk7qDt7TmFHhKneTN/iuc9td5MbN88MLGoO7m4uNLhVku52eoBE1Kfoe6l6XqFYhmsffW1lDATKfJ6fom56cWbkmbLCwsLCwmgaKLx7b2BpNtC4sJYSd6Fq87lMNWX5Ww5/C76WRoOlkyOqAUtFj0a2RDn1A5BEoR4BA4DiGK0FHkA59c2CUX+onF110vy0uVBXzl0Mp4bOaKWP9lw+gEHters0yXapTyLbZrJTZ3Bm4Wbga0drh8ZZEjRzY5srjFwtw2tUaBXLZLxvN5+cpRgnD0DK3VzfHKzgnQIaE7SHcjk7pR8JXLBXeBe4JVVsJpdp1b4VA+Ca0U1UyRaia5j1ExMCj0nPpu8XJ53tI4LSwsLCwsXoOwE707BNpRhJmI5jCwhw9UpIZpDEnq5nCYRKVSAoUkoZzV31s10iQfFzpMu5A+oif1Zad0yNFwm7zusuGU2VYFFvQuvnKpOqU4XV53eLRzuddsHBxCXKPkENhyi2xnpnpxWuMS4qB7jsQDTdv12M7kaboeLSdDK+PRdjIEjpOk1/U3QSvVP9sBKyZxP7RBz4jDUtQNx16UBP1o+HpLPrNif3wptECxCSksnuE4xVpthrXaTGIvXoJyGjVk0omV8CQLcLmyscTqdpf5mW3yuQ6hVlxaW6IdZtEZ45pEapme4UfPVWjtEhplm5TMUKBnSVY+2Ydb/7df3DWnwhG9zVl/lSfydw32DAp0uP1SN4fy7M2bKFvx2YVTHG9sc7S5Q0aH5HSXhpdNr29E2WPFYtPomikY60dPomY6QhgkKZsRJn12pvjZIvb7NQiSlDEHSpyDMInGmab4F5dpPjul58kB6LVD5ZlhSo5XfXqtpLopFz6+LeI4coX4tOfWuPqM/On16aEwJKqwSd2M0pr9UQ2HRY9BJfmvG3F+agyVPvmMTYYpwHFCHB0mdyMEw/0/0bckJVrxG2N8OeI4keicY8YYGP3fzCPUN87/nUgFNfOYYeO2tUjfWFK7jPYmx7IezmO4O5oIoe79O4w4rO26w2AnehaHFhnVpeB0ANgNC4SJr35NkTYF3aGk28xSJ6e7tMlwNNimjUeu71n0RXeJ6940AGe7a3Rx+WL2FIHjgNZkCcjTxVcOdS+DjmazaS94YR+VxeFD189wbcOgHx5G45RSvJRZ4o3tS5zqbvCKM3/D1uBc0OVks8pMt0lGB1zPTfFyaY5Qje+wDS/Li5VFzlXmqXTbNDI3VyTGwsLCwsLC4tWBnejdIQgzEPSV0ZWwciauIpmWP1HNwMgjrSZLZUcTImk1mZ7FBPZYRvqWlagJeafN8dwG024j/tYNtOKVYIl1PcVRXWVZVckpP5avr5HnWfcoTbIs6l1mdCOWKL43WGXDKZPTPrO6wXPe8uBjVyk6eHScPSIYo3AA8Zebjld7Eewm1Wfe80GhwirvQSo1JkWixcsQYxmIoxh9MBJj8XpFhZ4i9Hpr46FgnYPx1jspT6JdgkDLjpPnAnOcbm8yF9apuTkabpa6m2XXzdOJXDCM8HU31W1xvFWl7Xrkgy5L7RqBcljNl1ls1znV3KLrOVwoz7EXop6C47DlFWSBilGWIcFEF1uLJfGLtEakjTdHo90RshSCxW6kFS9eqR+uRAk+w0aJrUT5k9aGflyK9VJJ6VJYF5LlZNwClOT/zUwrxku3ReoTo8RYImueYdGbWLRHgGhhm9DX3X4syOOswKKlPU3cRxL6EcQ6tJBsf6IVwwIm0QXQgkUvvjZA6DiEoYM2ExhjQgvjRPQdGfndHdWXo7xmmDB24uMUq/kk/ur2tkG2DA63Syonzaou+iqWxq/gt1iyzifE9ParSK0Z2cdvOw5ru+4w2ImexaGBS8DR3CZLmW1aOsOFzhJVXcRRIfdmrnHWu86RsEpJtVmjwlo4xa6bjydt0QtrTVVY11PMBvVYkng53Ga5b+nbcMqjmmBhcVtxITdP1S1yvFOlEHZZ6Nbx+n14283zXPEITUe2sD20u0IxHPB6Xpha4EphGgUcb/Yc8F7PT93yc7CwsLCwsLA4HLATPYvbjozymc3sciRbxVEhV9rzXA9m0Ci0C0r3nFLXwhxlp801PcMFvQgkFvoS0ErxvHOUu/QGFd3idLBBVRU45y1ZYQmLQ41tr8B2pifIotHktM+03+R0a5M31S7zxcpJWu6wY/Nnp47w0O4KhbBHWT67u8HR5g4NL8tWtsBsp8lyc4dXpubjPEvNXcrdNrVsjtV8eWhsuGGAS29f6l5kgoATtW3mWg0UsFYosVEsEKI4vVNlutWimfF4dn6RVsY6YrewsLCwsHi1YSd6dwhCT6Eyw5QNFfFABLqPtHl6lH8o1aefHYSykKBuRjRNV6HQnMyvMZ/ZQQNb/hRX2vN0tQeqRxzRQAYfT4VcCeaYZ5ejTpWGzrLGtEgNiU5r2ynyZVXE0wGuCmnjpe57mlT8ZV+4WfPKSRt0sxp+C2kVA9qkUYlnXCifySDS+YZpnEm6V0TTNNL1n4Shq3r9zh2IR5hiLKHpH0/ylSUJr4wRdZD2gSaPFS0ytDIZNnNF3rx9mTfvXOJiYZaGm0Ub46TjuHx27jQ5fEp+h0LYoeR3KPldSn5vr6uLHpyPgrvXNij4Pk5d03Q9LlRmuVKqEDoOWsFDW2scq+9yYWqGF2fnCfrXrNxp87arV8iEg8E/12rCFrRdFy8MuTo1xVK9zqNr1/n8iRPp3UlSf0qhbmqH0X70BIEOLdA1waRcGs/EcaIOkviDmSeNIhYlk/rvqGewEC+VM1YZdoSoiUTJFf3RjaNxjujLqP54MkVzUuie45BKOZVougKbUVbwMctJ1tErc7TQiSS2oiWRH0ZQMSMxErM5UdnCOBj1KtNCH5Xe+XvLAyDjoP2ePJnory5+5wvbQ1KEVUTqZppo0ZixI4mkJI7TqJsCVTKmbo5sY+/HSRFoGVwTPZRu5PgW6aW9P0xRJr1PMRbFiP52CHCQT6PNzU1+53d+h89//vP86Z/+KVevXmV9fZ12u83c3ByPPvoo73vf+/ju7/5uyuV09tbv/u7v8olPfILPfe5zrK2tsbi4yNve9jZ+4Ad+gG/8xm88QAsPH+xEz+K2QKE5U7hGxWtyuT3PereyR2xlgA4eXe2y5GwT9t+6bSa3EPjKxVfjvn4sLA4/uo7HF6dPcF99jTONjYRabISql+fpmaNs5kqE7kBtFge8MMA3NtFWOm0C5dB2XZ5cPMqpnS3u31rj7u0NVotltrN55psNAE7UtskFPk8u9/z8KTSZMCRkeCtYru8rcqbVIhcEtDz7mrGwsLCwuHH84R/+Id/5nd8pxq2srLCyssLv//7v89GPfpT/8B/+A29961vFtFprfvAHf5BPfOITifArV67wH//jf+Q//sf/yA/8wA/wT//pP0W9xllg9g18hyDMgIrmPokVI2ljdhQnhQkrUGa8uZId6ZkYG4rjTdrSyigDK8qx3DoVr8G51lF2/NJQe+KFRw2guNBZ5Ez2OgAvhMvskPT/deggPRfUiHAJt3AFbuzq3qu88mcKopiVK3f0hUoVzDEXm2OhBFl4JU7nGmIs/d8g0xNjkSx2ILtXkMQaxlnvxglYSOi4Hk9PH+3RmUNjCdmBst/mwZ3rvH3jApdK06wUp6h7WVCK0IWu41Dqdlhs1Dha26Xc7dJ2XS5Uptku5niquMyLfpeT29ssNeqcqO3QdD3+9OhRckHAI6urnPPb7OZy7BRytFyXfBDwxWPL+K7DieoOLy3M8fD1NeYbTaY6PSvil44ujzyfVExi0XNHWPRMEY3IsiKIOvSO+9YEyd1BIOQZIZE+yGOWPVyfhOQzL+V4bx5JDThlnCSKi8eJES/1b6Fs0aJnxEcWci1Z9CRLZFq7E2Nm2Ho3sMSNFgYaBekdJNUnuqlwhf4mCKKkNsjsb9E5CGJTox7VkoBJnFiw8iWboFDd5M1JdXcQlz2cbpRFT8wjWdgkplGqIJJQjmCpky2RQnmS1S3NBcI4690IMZaxbhqMdI6/XzEWLbMlDgMO2K7jx4/zdV/3dbzjHe/g5MmTHDt2jFarxcWLF/k3/+bf8Lu/+7tcunSJ9773vTzzzDMcO3ZsqIx/8A/+QTzJe9Ob3sSP/diPcfbsWc6dO8fP/dzP8cQTT/CJT3yCxcVFfvqnf/qGTvN2w070LG4psk4Xxw1pBlmix2zBabGcqXKlM89OUBpfQB9bYZlqtze58z1rnbN4fUMrRcfcN+fApuvxuflTnGpscrK+zZnaFr5S+MoldBQ538dFEyjF9WKZ5+YX2SgUEh+RzUyGFxYWeIEFnDAkdBxQvQWg01tbPLi6xudOHAdH8Sd39eiYrWxvhWmz2Buf18sl5hvNuMzQUSith/b/Hd3ZpeO6bJQO+aKNhYWFhcWhwLd8y7fwbd/2bSPjv+M7voN/8k/+CT/6oz9KtVrl53/+5/mFX/iFRJqXXnqJn/u5nwPgLW95C5/+9KcpFHr74t/61rfyzd/8zbz73e/mC1/4Ah/96Ef53u/9Xs6ePXvrTuoWw3oAs7hlOFZa56G5izxQucw95asU3DagOZKt0tEe17sz+yovxImpmxYWFsPoOi4vVhb5oyNn+OL8cc5PzXO1VGGlWObF2Xk+d+QE/+2uMzy9dISNYnH0Rh96UusRtFI8u7jIXKvFsd2e25JmJiOKrFycmebLy0s0PY/dbJa3XbrCN7x4jrs3NuM0hW6XN6xc561XruKGKaYuCwsLCwsLwJtgK8Df/Jt/M96f9+lPf3oo/hd/8Rfx/d7Gx1/6pV+KJ3kRisUiv/RLvwSA7/v843/8j2+w1bcX1qJ3hyD0QEl3c8wGaJm6aXz4ibQKg57S/w4s6C7TbgPQKA2NIIdCs1TY5mprjkaY42R+jQcql+O8F1qLE3DwRiCF2iSSAW8hxVo8DYnadDswYeWHYbN2wq9d5MlJovOmwZi8DOiVRlhfEChMUIoH6VT/N/L7lfCJZwqzTEjJvJnUzV7kmLiobcpho1hko09xTtDvvNFiFSL6N2GjUmBlp8SDa+sEnmJlqjQ0UXTDkEevrXJ0tz5UTDYMYjrdW65cjcOP7+6wMlWinRl+gKXd/x51U/ajlxReGUNDYwTlMhJoSaFhynRPIUyaz0rPif1Qk6P+bcZHfesGaYpiX5Z8Qgo0zL3Qqn+v9uaRyjYplyPueYzo2DiZAV17tHDKyHaafwhljztXZdQXs3Bv9Lka9VGTCitxLlO2WcjbNVQiTAGOUjidvh89gUo5oCQO05lTfUemCo8IecaFjaJuChTIiamUIg3VjB9N00zUF5c9vBVmJJ01HH7nDdpt9O99+tFT+nC83yXcqnZ5nkc+n6dWq9FutxNxWmt++7d/G4AHHniAt7/97WIZb3/727n//vt5/vnn+U//6T/xT/7JP3nN7tWzEz2LG4JDyAOFy7iEBDh4/SdTK8hQ83OstGfQjkOrkeGR8kUAzjWXqfrWl52FxWsNzx2Z5+GVNd505TrVfI6VSo96vTJVJnAUX3VphZlWeyjfhZkKzy8O3DpEHy6BUjx0fZ2z61t85swJWsJkz8LCwsLCYlJ88pOfZH19HehN5ky8/PLLXLlyBYB3v/vdY8t597vfzfPPP8/ly5d55ZVXOHPmzK1p8C2GfaveIUiz6ElWrkk3PYO5SmZuFId5VcMl5MnOKTpkeNC7zJTbQinNy+0jhJ6D0tAmy1P103S1u6c1r9JqkyDqIMuApAUOl5PIMulq/M066T2rsvvBYV3lg4F17yBNTAqvRL+GmIGbjIOB2wTtYoixMCzGIloghPgJrXejLHdxuNRJJUOFaZ0R8iaESSRXAxNYZQAabobPnz7GfL3BvaubnF3fwg01D6xu0urvnV2ZKrFQb/CH952m0mwTOA47hVyi3V86vsybL18jE4ZUczlmWm3edf4Szxxd4Nr0wKl7mn5F7F5BuBiiRUMSVgFZeEVyHzPGYmcep8qvx5FGE1OYAdFxmNKPxvad8UHpFr1xFusxGCnG4gr9UrDYjbLyDYRgjPgxlrhEm8TrPT5PtKCvpHQpwis39LxNWIOjjjCiL8fPiWGLn8jiMYRKehY9NdqiN0ZEZT+CKWMtdWPaaIY5QnlmWkcQNUm36I0RVjGPJUuc9JxInMsYi50ZL11bw4qn9y3GgjzYDwNuYrt2d3e5fPkyv/mbv8nHPvaxOPx//p//50S6Z599Nj7eOwncCzP+2WeftRM9izsfjg6ZpkGeLg4a1wlZVDts6yKdvruD5zoncAghVOg9b8qutt3NwuJOwEa5wEb5OAALO00eXllnrVzg/PwMb7twjZWpEoHjsFUqiPl3Cjn++913cXZjixNbOwBkwpCHVta5VimT5u/SwsLCwuL1jZ//+Z/nb//tvy3Gua7Lxz72Mb72a782EX7p0qX4+MSJE2PLP3nypJjvtQb75W2RCkeH3KU3OEoV5UCgFQEOIYouHpf9uUT6EOdWbomzsLA4RFifKvKpqbvivxVQ7Pqi0qYJ33V4/sg8q+Uib768gu84PHdk3k7yLCwsLF5H2NnZSfydy+XI5XIHLu8973kPH//4x3nooYeG4nb7YmJAqkP1UmmgCl+r1Q7cntsNO9G7QxBkQWX7f4icnOGgkZQMrckSoF3wlcfd3TWWg95AfNo5zrbqSbJH1AhlCu9FtAmDXiDTSoabJtHPkhSh/gfgpCISKUhlDaRRqcaESWIFo+NvgL8Q+ZEyKpn0kmiBAqWkdo+glykhXvTHFVGMTGWCCYUiJhVgMYVcEoIpkc88wZejFsVYBtTNSIzF7INmOaLoxYR0N5EqKfghS9LYxlwUiZqX8Os1HJ6gbkp0v0mhk4P5qRMLfPX5a7zl4lWuzZRpeR4d16GZzdARXKNsTuX5gwdOxR1K6ZD5WpPFWoOpZo89sFoucXG2QuA4CfEIFe6hPxknkaBuSoIJghhL8rz2/BrHCfpVCh1s8IAzgqSxxXCY6BMyjQIs9bcJIfm1E+nKkocbgR5vtilJ3dTDZUfHZr9Ne3Y6RmfYU3byZPbEmYdm3lC44KIYi/kgHK5vQF2UbvCeeoabJufZm25E2bLohxLCkuUpekPQ6fSiJhYrkeiVY6iZo9s4aMfedNJ4kujRiboT9Q3TJsf7Ex5RtkSvjCnew31LzDuqbIECKuUJ9y3GohP+kQ8TonaZljOAD3/4w/zET/xEav7v/d7v5Ru/8RsBaDQaPPfcc/yf/+f/ySc/+Um+/du/nX/+z/85X/3VX53I02q14uNsNss4mJPNZrM5JuXhhp3oWfSgNTNhg0W/xnxQI9N/WtZUDtd4QtdVzq64W1hYjMRWqcDnzyzzwLVNHr2ynojbLOZ4bnmeajGfzGQ8U86uVblvdYu265ALes+euUaLpd06n7/raMLtg4WFhYXFaxuXLl2iUqnEf09qzZufn2d+fiDy9ba3vY3v+q7v4iMf+Qh/7+/9Pd7znvfw27/927z3ve+N0+Tzg3dPp9MZW76p2LnXBcNrCXaid4cgzPasensxzlikwt6KypHODne1tyiGXRpOhqvZaXbdPG6oWfB3yRByxZ3hqjedcL8QreqaK1CO32+PklfLxZW1qDzJoucO13eQlepJsS9XCJNa/IyytRIWacdYxuSC9tEuYdl6UutdWrvGS7Kb9UXLm5Nf3EnXHwcWhkHZo9wmDIcNlxP2LXra7Vv19qZLscqNFWORLJ9p1mnTKBU9rU3hCcmCJORNWvf6x4KVL5lfGqRqOEpItlEp8seVIirUZP2ArB9QaXU4tb7D15y/yssL07ywPJuctKlBcSHg9B8IHdchG4TMNVt81aUV/vToMbTTc4OhHFD+ngsX9W9REGXUc2n4HOK4SS125rF5i8ZZ4qQ+kWLlFS3o+3DdMQ6iRU/qJ1J5Y/qEdrToBiNhGez361RRGglmfGB0pKF0KQUlziHq69Kzc5z50rDkjbD8DjrF+DaM629Ja5lgTRSsaeOsVwpwHHC6SYueaHWbUMhNZPOY4WlhY6yK0nfFqLontd6JVjdB1EWuT7AWJ8rRw2GJ8xfig6g9g0Cnu0+/oyF7xHwOEfrtqlQqiYnejeLv/t2/y2//9m/z+OOP89f/+l/n3Llzsf+9qamB2FcaHbNeH7gJSqN5HmbYid7rFK4OOdba5mSzSk77rHslni8cYcfJxy9DFcJapj8oxn1QWlhYWAjQjqKd8WhnPHYLOa7OlDmzts2917c4stPgqZOLbJXyFDpdZhptFJqV6RKZIMR3Ha5VpmhkPB65tsbJ6i4LjSZvXFnhS0eXLbPAwsLCwkLEN3/zN/P4449z8eJFPve5z/GOd7wDSAqwXL58eVR2ICnAspde+lqCnei9DlD0Oxxp75DRAZkwJBv6lIMOjg65np3iUmGOJn1zoJ3IWVhY3CJopTi/NMP1SpHHLq/x9nNXeWW+wsnNXbz+qvXF2SmePr4IDCwjL8/PcKK6iwaO1Os8unKdp5eP3K7TsLCwsLA4xFhYWIiPL1y4EE/0TIGW5557bmwZZvyDDz54k1v46sFO9O4QhB5JURStKfsdCkGXR6vX8B2XluvRVQ6tjMd6ocRKfoq228sk+XoSaQ5p/qEEWqAy2VmBsCk8rs+gl0V+1EbR5gSMFfBIY+zshxo0CUT65E2C2JbxVKKJaaFmiRNeE1OYQAv0K1G44QaQFIfoi60IPvHMuhPCK1GYkScqM6JuhgZ1U6JhJo7TRDai4zQ6pwRJCCJFrGJcXrMd2hsMQiVRNyVEY2wkdVENBUo0pnopw2fvO8pjF9Y5s7HD6lSBp04ucna1ytFqvVeBGvCc69ksXzq2zIPX18gHAcdqNYqXfDZP3IUTjlifMuhssXDDKP9Y43zcHcRwOCF1N7WfSLTgNLrnpHROCQLdNzF+43aPpvUCQ1SxIX+H8blO2JcTbRTqlkR2UujMaYuakj+6ARNeaOyEQh+9NgoVCnTOcT7qRtJCpToE6ubechTguOD2xVjG0TTF85LolSn+6FIpoGKYHi5P+G5JEzUZe34SDTMlPlFOMFyfSM00HqQStVOmku6Ph/laEGO5FYicokOSdnnmzBmOHTvG1atX+dSnPjW2jE9/+tMAHD9+nNOnT9+Sdr4asBO9OxDZwOeB7VWWWnVCoJot8KX5Y4Qq+ZYfty/FwsLC4pZCKZ46tcBzx+foeL2ZwPpUkTPrOxQ7Po1cJpH8+lSZzWKBr3nlMtnAp+B3eePaCkeaTdYLRVZKZbbzr90N8xYWFhYWN44wDPmt3/qt+O+HH344PlZK8b73vY9f/uVf5rnnnuPxxx/n7W9/+1AZjz/+eGzRe9/73od6DW8VsPJldxK05khzl7evXWCm0+J8eQ7fcTg3NT80ybOYDIWgw2Jnl6X2DsWgs8eUYWFhcUNQik7GjS0kW6U8GphryFLWXdflwvQ0ntb4jsOFqWm28gWW6nW++toV7tlcP7Sr1xYWFhavOvQh/7dP/Oqv/irdbndkfBiG/NiP/Rhf/vKXAXjnO9/J3XffnUjzt/7W34rFWX7kR35kyHVCs9nkR37kRwDwPI+/9bf+1v4beohgLXp3CDJOh4e2t1hu1Fgplnl2fpGu6/GSnhummfR5AAkFOomGKdA8lEHjif3HCAsd5rRSmxQLJ1meeaxN7s4N+IJKxQSULEeH3N+4zmK3lkjWUS5b2QLVTIFqrkDdzSYKcggJlZOgIWUDn9lWi7JX7jmRdoYrdsIQQn1zpOMFOliq2JxAAVMMh6X50RunxHmjkOi8EQ0z8o23N16kbjpCuj1KnHqEH700JUORunmTVBBFml4mTMSNzjtM40zQNR3hjTvmJTxSdVOgdo6bd+31M+Y7Djv5LHP1Npdn9pxUv5yLMzNsFfI8dv06J3e3eSHUXJia4YHqOndvV5lrtnh6fpmWkzHq6f+aFK801U3peu9py95jiaY5KeVSVGxNoVxP3N8iSnXauBSom1K8FvpL4hqadH1NT+FVKO9Gnw1KUNiUKJdx4Ci/dkLYeNqkULbgt28kdVMoW8yzt14zzyjq5lhKolCmSd30Bn70zPCxbRxTtkQfNfObFMjJlTGl+gQqpaSWmUqljNKnUDcDod0indNIJ6hzivWYYaEQ5psNev3hf/1f/1d+/Md/nG/7tm/jHe94B6dPn6ZUKlGtVnniiSf4V//qX/GlL30J6ClsfvzjHx8q47777uN/+9/+Nz7ykY/whS98gXe+8538nb/zdzh79iznzp3jox/9KE888QQAf/tv/23uvffeV/MUbzrsRO8OwTuvXELlCzy5uMz10kA+1irTHQx3dbZY6NZ5obDEarYMCipBi+mgyUynyb3tNZxab+JXzRUIlGK606IYdGk5Lju5PLvZHAW/y3J9FweotCqc3N1lJ5djJ58lGwQUuj4F3yfnB4TATjHLVilPK+fihJp8NyDf8cl3AnJ+/82lB+7RNVArZFmfyrNeKbBdHO8AdC+U1mTCXtnZ0CfXDXB1iKs1hFB1ijR11vYji1cNm8UCR3brY9Ps5vI8fuIkX9XpMn19heX6LjUvQ9nvMtNp8a5rr7CVzbNSrHCtMIW+ieQVpTWuDglw7LiwsLCweJVx/fp1Pv7xj4uTuAj3338///pf/2ve8IY3iPH/6B/9I1ZXV/nVX/1VnnjiCb792799KM33fd/38dM//dM3rd23Cwea6H3xi1/kd3/3d/mjP/ojnn76aVZXV8lkMhw7dox3vvOdfN/3fR/vete7xpbRarX4vd/7Pf7gD/6Az33uc7z44ovs7u5SqVS47777+IZv+AZ+8Ad/kOXl5QOdmInTp09z4cKF1HSnTp3ilVdeGZvm//q//i9+/ud/nueee47FxUX+yl/5K3zoQx8a6QPke77ne/hX/+pfxX//f//f/8c3fuM3jq0j4gJ/93d/N7/2a7+W2m6AZ5YW2VicJ3AcJraHmytQkXXO9McjCa+Yq2j9pOYnVBxtLngKq3o6bfUvbRVdwgFEWEalX+7ucDU7zbXcdC/AgS2nxEa+BKWexa8StJjtNJntNFBoNnJFXsn1JndT3TYnd7fpOg4vzC2wUi5zKu/RzmWZbrWYazZpux61XJbVSpFm1sNRIXO1Fos7TfJdn8BRtDIuraxHtZyjnXF7l2DPYvJ0vc09K9s8cLVK11VslApcmZ1itVIEFEprSp0upU6HUqdDud2l1O1Q7HbJ6GFTRogiVL18Lus0VYZ1p8y6O9VTZ+3feMmaoIYOEC2YkyIpvNJvnym24kVhw+nMY7OcUPQPNkin+r/ancyil2p1GydgkQbJKieJrGSM+xhZzRHSwcAHmBRmQAviENEfWrBeJJotWTJMf2SCoER0vFnKc2Zzm3zXp+0Kr6jYOONwrVThyvF8b5VbKRaaNd64ca0Xj+KB6ipH6zs8WTlG4LgDURZk655kvTPncve0Vjne2Y7/9nHwVe9foBy6jst6psRKrjIYJ/v0o7gfP3qyb70x1rtE2HD/EK3AUnuk/iJZnYAwVIR7xVj2iYQBLRi+SQnLsOQzDqEvp/Zb1S97OE+aTzxxqE9q0ZN8z01o5RoVH5dplr0nTgHKH/ajly6OIoRJvuXGWBPTy5YsX0a6FF+9omVsXH2CpXFU2ZKVL6aPixZE87tLsmgKFr+Er2JJOWoMtObQbjk5QLsef/xx/uAP/oD/+l//Ky+88ALXr19na2uLYrHI0aNHedOb3sT73/9+vuVbvoVsdvTCt+M4/It/8S/41m/9Vj7xiU/w+c9/nvX1dRYWFnjrW9/K//Q//U/8hb/wF27k7A4N9j3Re/e73x0r0ZjodDq8+OKLvPjii/zar/0a3/md38mv/MqviBf6qaee4l3vehe7u7tDcZubmzz++OM8/vjj/OIv/iL//J//cz74wQ/ut5m3BD/1Uz/Fhz/84fjvy5cv87GPfYw//MM/5I/+6I8olUqpZXzoQx9KnegdBCvTUzg3g/ZngatDctpnx8uNTBMqh61cka1cEdR8HG463h6E9Sxw9WyWK7MzvcmDqZzo9Y89zaWFSpxnFEQjQqCZabSZ32lxZKvJV124Tstz8R2HYqcbf591HYd6JstuJsdarkzH8eg4Ll3t0nVc/NCN93O6Hc1M0GCxW2M52OZksEWTDBuqzLrqT/oOJEloYTEam8WeoMobL1/n/NwMa+ViuuWsH79eKPPlmWUerq4AiidnjvHw9gpv3L7KF2dOcKP9dcMrJSZ6HiGeHsh+NnSGB7p1XEKu5GdvqC4LCwsLiyQeeOABHnjgAX74h3/4ppT3Td/0TXzTN33TTSnrsGLfE71IsvTYsWN84AMf4Gu/9mu56667CIKAz372s3zsYx/jypUr/Ot//a/xfZ9/+2//7VAZOzs78STvne98J3/pL/0l3vKWtzA/P8/a2hq/9Vu/xa/8yq+ws7PDX/trf42pqambMrN+3/veN9YMO272/5WvfIWf/MmfJJ/P8/f//t/nz/25P8fFixf50Ic+xBNPPME//If/kI985COpbfj85z/Pf/7P/5lv/uZvPtA5WNx6OP2l6eAmCNhkgoD7N1a5Wq7APmmV+4F2FFvlPFvlPOcW5phqtjmxtYsKFLW5DA03Rz2ToeO6KD+ayA3yK7/3ay7ya6XY8kpsU+Kct8R02GDRr3FEb3NCb9EgwxoVVphF2wmfxU1C13P58tEF7tra5S2XV7heLvLk8SOETOanY7UwRddxeWTrGg9vr9B0M0z7Le6ub/ByYUHMUw5avLl5KRH2J6VTtJzkmN3KlPh05R4qQZOMDvB02PdPGnCku0tW91bb7XiwsLA4DFD6YGSSVwOHtV13GvY90XvggQf4mZ/5Gb71W78V102+eN/+9rfznd/5nbzzne/khRde4N/9u3/HD/7gD/J1X/d1iXSO4/DBD36QD3/4wwnnhRHe+9738hf+wl/g/e9/P0EQ8CM/8iO8+OKLNyxvOjMzwyOPPHKgvL/5m79JGIb83M/9XKzG8/a3v513vvOd3HffffzGb/xG6kRvYWGB9fV1PvShD/E//A//w02Vaw0zIWQn9JcQVSuJsYzYPO30JwLKN/JEh+bG++jYl8sRKaASZcXMv7fdtxgzQU+BqWl85I3z8TRKrAQgH/gcq9U4VqvxzNzMeAGAFFpRHGXGjaCr7hZzPFvMobp9+pF53yKamnTfJGEV+pM+t8S2LqH0EtO6wVK4y0k2mHdqvKSXaZEdCKaY1JcbENZJCq/0y074zBtOJ9E90+icMQwqnFbCbUihzcniGHqo3RPDZKRF5+AZrYqODQuw4w4LtCQeNZEYk/GWjaiY2nwmRBRAUQXEPBZonCZNU6JI+ZHjQiPM8LF5ZXqaK5UKSzsNHlu5zpsvrvDF5WNopQbp+ufl+MlyALZyRR5fPM2p2iaL7d5+v1PNLa5np6hHlnqj2c6eO113sj0xF8XQ/dVKUfWKg7D+qey08xxrbXOpMMN6vjRMh5T6iUDNTBXtMe+bSN3cE2fUI9E1zbQSJTFRTnQzJTp2guKYfLeokD00tv09zBOp0yiAgh+9cVTKkR+bIgVUqm/CskWKoBB2A3ROMK7VhPWZ1E3XB7dP3UwVfRlq4zBNc6QfPSGPeC5jKJ6SkIsZnirqIvi6G+dvrxfejxcolxItNJlOonMaFyXub1IeI+x1LsZisX/s+9Pr//l//h8++MEPDk3yIiwsLPCxj30s/vvf//t/P5TmHe94B7/+678uTvIivO997+Mv/+W/DMC5c+diBZzbhciS+fVf//WJ8OPHj/PAAw8knDOOwo/92I8B8OSTTyZ8fFjcfjg6ZN6v8UBzhQeaK1TdAg33xi1wTWOPUaXVuuHyDgO0UlSdEi+pZZ7mJC4hj6qLLLHNwXfhWFjsgVKslst88dhR5ptNzlS39pW967i8VFnks4uneb68CMCp5qaYdtsr8FJugR0nx9P5o3yheFc6XXQPVnNTPDFzgvVced95LSwsLCwsbgVuierme97znvj43LlzBy7n67/+6/kP/+E/xOW8+c1vvtGmHRhLS0sAfOpTn0pYBVdWVnj++ecnEo35oR/6IT72sY9x/fp1PvzhD/P+97//pu2r07kQnRteWhz3vZHYPB8Or+ibFr9oBVcZK8LxpnjD+qYE682ksssJoYRIUGKEu4f9YpxFbiao80BnhQwhNSfLK7l5Lmdn5MSTfr/105mWglK3C27PmqASi3aTCQXI9UTWGSPMuEfRfTCtx/GqfiIs+QuGlS8h0240MYSayvPl4C5OscbdzioVmrwcLokCLJJ1Lg0HcpWQkkeUu48jJ2pWOowbHOZ0st5Jskfjw2xjJLhiWu77ljzXEGNxvUhZySjPaE8UnOhu/fEfGBdK0OmRYfblWO7esOhIFgh/WOgittQFybBqpsSFygxnqlUuFWcI+21U9B5Ljr/nXIT7e7k8w1auQKidkffhcmGWywVjX90YS9woC/JYa1rCyhvFCda5tH4iiVZNKNBiIiFwItzryDKWsPxGz4wUPYiEqJcG5YLTNeSBJrUWSQiH+1aaK4GxGNUGqY2SlW/cuYw6p3GWOkEwZVIhl1Hxk1ynnhiLxulqSX8pteybJ8YybBnbl0UvsqIdqOz+HyNcN8gWv35YKIUJ1rlE2eFwvAnBypfIMwnuMDEWi/3jlqh3dDqDzT+jLH+ToN1u35Rybga+5Vu+Bej51PjZn/1ZHn/8cf79v//3/Nk/+2ep1+t84AMfSC2jWCzyd//u3wXgmWee4dd//ddvZZMt0qA1J7ubPNK5yq6b53PFU/xp+RSXcnPom+Rg3jUeZNlgn2pZrxGEOLzMEV7Uy8yqGg+5l6moBllGOzW1sNgPLlRmcXTI8frOgcuoZ3K03Ex6QgsLCwsLizsEt2Si96lPfSo+fvDBB297ORE+/elP89hjj1EqlSgWi5w5c4a/8lf+Cv/pP/0ndMrKwlve8hZ+9Ed/lGazyY//+I/zNV/zNXzgAx/gK1/5Co888ggf+tCHJmrDD/7gD3L8+HEAfvInf5LgDv34P+xwdcCDnWuc9je46M3xdP7YkPDCzYBjmEV28qNVPO8EbDDFM8FJXAIeyF7ljbkLnPLWbnezLO4AtD2Pa8UpzuxskQmkDbwWaVBak+92cfZrEbCwsHjNItoje1j/Wdx63HTqZhiGCVGSSSxdEp588kn+3//3/wXg4YcfvikTvZdffjnx9yuvvMIrr7zCb/zGb/DOd76TX//1X48nYRL+8T/+x9x///387//7/86LL77I/Pw8H/jAB/ipn/qpkX709iKfz/PjP/7j/NAP/RDPP/88/+bf/Bu+67u+64bOC8DJBji50ZNG0U+WQYHREQfOpGsaAh6hiqib40U9nIiSZdKCpM3VptBLv5zAMNpGZZvMVpVGF0pjAWhNloBC2OGsv0pWBzyTPcqmV5ZpfGnswjFCCFFjgv71arkuW/kCdPvW7oRYQZ/aZ1I6xlFuHSFd4uTV0KEpwqD2+PcCRD9iMU1NooXBgJJmtLvh5HhSnyLn+9znXaOo2gYt1OhvAqVSQipNU/IPlqCIjqln3DUe1a4Rl3lvWETXBNCZ/v31hjto4rYlxuNwHVF+xxtccKdP2fQyg8HhOr0wsw85JpU0Etkw6g77F1C74XDYPtaiYsqe+RwZR+0ynjGx4JMgEgVwvrzAYusib9q4yhMzxwkdFzL9chOCIVFms2H9qP0QQ8aKmhjVCccJOmckxpNC54zCZhtN2p5LI5sFpTm7vsl0q81WvoCrw557lFyWejYrC+XEFNCBkMPdG1VOb1bJBr0LWstmWC8VuTA3TSM7sHImRFJiQQnjfgjnPMhr/GHcayfsPccjYShIoQBKSKEpHoi6GaUbVe8Ymmaqbz2hrTdDMCW1PrPMCemOJnXTCQwqtFSPUKdU9sAvr5l3MkrmxOkCIZ2RXxZjMds4TPEcR80E43y0cB3NsFjoRaBujvKjJ9Qni7HY2ZHF/nDTJ3q/+Iu/yOc+9zkA3v/+9/OWt7xl32W0222+//u/P7Z2/czP/MwNtSmbzfLN3/zNvPe97+WRRx5henqaarXKZz/7WX75l3+ZS5cu8cd//Mf8+T//5/nsZz/L9PT0yLL+xt/4G/yNv/E3bqg93//9389HP/pRLl68yE/91E/x1/7aX8Pzbsl2SQsgrzsc87dYCGtk+m/RXZXjiVtkxTPRymT4/LFjNLJZ5l8n+gwahzYZMgRUdbpvSQuLSdBxPb40d4w3bVzhDdtXebayjNKZ3kfQa1z8xAsC3nhlBUdr5po90abfu+8sj167zrGdGqGCuXoT33HI99+LvlJcmJnh/NwswYi93kprHry+zl3VHV6ZnWa9XCAbBMw0WhzdqXF6a5sXF2Z5aXHuVTtXCwsLC4tXDzd1dvGpT30q3oO2tLTEL//yLx+onB/+4R/mC1/4AgDf/d3ffcM+5z73uc8xMzMzFP6e97yHH/7hH+bbvu3b+C//5b/w7LPP8pM/+ZP8wi/8wg3Vl4ZsNss/+Af/gB/4gR/g3Llz/Nqv/Rrf//3ff0NlejkfNydQmqQF30g23Vi9DfzeUndorLBr4+Mh3rNmVBGt6prfGJHVJWH5kzZhm3uQXSFdZGEyrAkDFw/D59RrYz++/3ch7HDS32Qx3KWLyzV3ml0nT0d51FROlggf9b044XekZAXbKBV72ZVRjrQcm1hFFCqMRVSkpVo5q/T9K1kdYtEWSXhFslgax9Lqfl53yaiAbQoDlwuS5WOUqIUQFrsXkNwnjLDoiWVKltg4cvCjnb6xKMVSbF7vyJIXZoxMfZEk5QoFmeMgYcmKrpmx+tu35DmGZTCy5GU9w6LXt8o5IxouWvQkmfOoXcZF1JPK4ifEEYbFhiJLnuMPhyXEWEzLvw918jxVOcZjO1f5mo1XqHQr3LezQ9PxeKGyxEbOWFiQrK/mOEmz8gr9O1WMJbLeSf0xIcaSvN7FboeFRjP+eyeXxcPn2E4NgMvTU0y1Ozx5/Ahdx6HS6rBQb3B6s8pd29u0PZdAOWTCACfUdF2HUClyfkA2CPjy0UUuz1bia3JlusJzSyHvfeFl7l3fYqNYpFooiFZX0cIkWXn3uFSIL0kIylUJ0ZyJXRJISBP32qdFz8TE1jszz5hzGRU2VsAkYeUf065R203GWOLGuTVS9MaY4+ve2onQxr1tHVVf7OLgIBY9yeooWd0S52I+zISwsdbC8RY7UVDFhFRfRI+W2ihZ7MzjlDzK3+d2HyvG8rrHTduj98wzz/D+978f3/fJ5/P85m/+JkeOHNl3OT/7sz/Lr/zKrwDw1re+lY9//OM33DZpkhdhamqK3/iN32B+fh6AT3ziEwkxmVuF7/3e7+Xuu+8G4Kd/+qdflTpfLyiEHR7oXOPNnQtMh03Oe4t8IXeai5kFttwyNSf/mrcAHHaE/U+hvf7JLCxuFDuZAn8ye5ovV45yvjjH81NLNL0MD22vkH2N7t9rG4yOjuvw5IkjdA0BspPVXcrtLm+7cJVQOWyWCrywNM9/P3OSl+dmWC8V2cnnWJkqc2m2wkapyHYhz5WZKT57+kRvkrcHoeOwUu5NjMtt+/6xsLCwuBNxUyx6L7/8Mu9973vZ2trCdV3+7//7/x5ykj4J/tk/+2f8+I//ONBzzP47v/M7lEq3nvo1PT3Nt3/7t/Pxj3+cer3OF77wBd7xjnfc0jo9z+NDH/oQ3/M938OFCxf4F//iX9wQJdQ0Fk2SdlTYpNvSRoWN27a2n7onDUuUo0Pu8rc4HmzSUR7nvCVW3UosCf5qTO3GGQT3c49utL698Qe5tpPeSymsRO/DsUn2QNsfJbxaU/O992o/9R4kz7hy0sqWDMSCkTdZzrg9PCn1HQQ3Y8zvPfYdl81smVwuy7Uww2q+zNs2LvDwznWemD2274Wc273s0854fPb0cVyt2Srm42dWPZvBdxwuzlV45OoagaNw0PHevFY2w8sLs3vM+H2rohkk1KmALx9bInNlhQdX1+l4HmvG+/ZGxu3e+7b32TeunJs13m7WM2bSd97NqucgbdhvHVJ9ifukUu6REKmE+FHDcGzbUsqW2pCIV0LYpG2Q6p6wIKkNjGrjhA0T8+y3w2lkC/NhwGFt1x2GG57oXb16lT/35/4cV69eRSnFr/7qr/K+971v3+X8u3/37/ibf/NvAnDq1Cl+//d/n4WFhRtt3sQwnbdP4vz8ZuB//B//R37mZ36GF154gX/0j/4R3/u930s+nz9QWacyGbxMb1N94sEk8GCieD8YGHSjPR6mf7PQiA9dQaxFEF5Qkn8sieYhiDWItBKT2hVRJBJ+tnq/HgH3tDbI53xW3ROseBWUUhwZR6ERKKcI9EIzPE14IQ4z6HW6/9JczGZA9Z+7wsNaooAlHuoR9c+8p1I6QeHB9KOn+oYCxxS/8fr3TaLK5obDAJnaFYLSmlP4VKiwRI5u0OuXkkgKIyhwY+9H2j3AgPDCHedTMUq6kM2AI7yHTKZNv+1hdhAYUzYNX3eqL5hi+qCMfCcmqJDm0zgKN/JEIiyuIcbi9a0+Oc83wvrxxolKLqe00SeCPmfXNy5u0D8OGX4mhKPIIAPHjXFQ1PcS43tPnJnFEUSZgJ7wipkZWMhlQPeq3cyc4v7aOr7rJymco5AYM8Nh8SUT+6AeDsO45BLdM+FvTxi3hd5+4aNG0LlHzsbJGq7H8nad913bYLuYZzefpZHNUM9k6Jh7vMd8PCW6fv9+rJw6Q2V9na/brfNiNs9moZAoRxQMSqPzOcnDxUwm6e9QWmyQ2j+O4mecg7TQkeaXNPV7WaBkjvX/JiBVyESkfUpUQiPPmPISdaa1cQ/tVSmYK/Tvkx60I1VkJq5DesgY8ZNSMoV6pHuefBgL5Yh0z5Q2RIGjaKFaOLGhdmE8cCekZibKEfIY6To+8NJwNguLUbihid76+jp//s//ec6fPw/AL/3SLx1IQfI//+f/zHd913cRhiFHjx7lk5/8JCdOnLiRpu0bae4VbgVc1+XDH/4w3/Ed38GVK1f4Z//sn/GjP/qjByprhRYu0YPSeFEID6ToQdo1vrKjD7zA+BoJQ/O4lzaxjyh2mC5M9EYo58UvFyFedKxuTCwidc7E5C+AbOjzSOsKDR3wufxxGk4O2n0fbpNO9Mbs/wLG7ikTVR4NNS3t9K+5A1c6nf7+r+GGiRM9sz1RHzX3bUVh5gdqYuLdn+gJ98gx3Nw5fUW8xPXuIqQbHEsTbzcIuU+voKnzLLNcIkQFPete4jr1nzza/D69gf144sc2HHiiB3Ct1ZloohcYgWG/g5vqlY7bu0AJx9PRHjxzoicdG88lpz8APMNbdaZ/nFeDG5Pt3xBTkdGcrGkhLBr3kz4TzGdDUqk32sub8kzo9zPHF8LMhRyJiblnBf1qs4N24CoumXbAzO5Frk0fYT1fFjIPlwPIEz1p0haP+RETPXExQg+lG2t2HWEuujxboFRwObLTYHF3h6nVNnP9j8JrlTLPLi/Q8dyRE4BCp8vRnRprpSK1fC7R365Upni02WD55ZfZWFriaqUi7w+LnKiPmwRBog3R4+pa0xhP4yYjaWHS+0SKv8GJnjjJGjfREyc/KW0Q97qlTPTG1DcqXg1d+OH3rlK949Vq+2ATPVN1U2qDqTA5bqInXLOJ9+CNLEePacM+JmPSPrs97QIGTs0PUnbKRK8btIbrtrAYgwNP9La3t/mGb/gGvvKVrwDwkY98hB/6oR/adzmf/OQn+eAHP4jv+8zPz/P7v//7nD179qDNOjCi8wA4duzYq1bvt3/7t/MzP/MzPPPMM3zkIx/hr//1v36gctqBQ8mV3r5CkBJWt/oY+V6KyzY+GAWn4vHHkfEhJEp2i5O/QVA08TBrEIwFKKV5oHYNj5AvlU7QdLNjV7TNuqWXbNo+6ZRFS2nPdyJMaxLS1WBcsxHvraE2SnFCG9ICw8TEsv+jhuOV9PELFHSbubBOLgwo6jYuISXd+5h7Th2jqkqJstPcHiQmfVG8EsLSPqyFD2V58idfSUXvQz7wxl/r6GM/ND/6I4ueYdFVkVsE03VBZC03J39KDR2brhTcbF94JTuY/eQzvQleITuY6HX7fkoC46ST3+r9SaYRH6UNjLEa9o+TCz7DeZM3abhCSTY+nmQIzwFjHiu79oguHb1+FLqDuKdnjvDQ9nUeq17jfHmO81NzTETjHDepEyZtiQmhaPmXJ4JxddF1lNgX5s3aMxmtZbPU5nOcm58FrSl0AhbqDe5f3eBd5y6yMlVmO58jVIoQB6U1Bd9nttlksd5AAac3qvy3M2eS36pa8eX5IwTa4ZHVVcLQ4Xpxqt/WZBuGwhDC9rAzVAaMtYiJJ3qy0MdwPQey6KVMYMYLhqTUF8eNf6inTVBFgRZhkpF6LuI7TyfjVG8ipLr9l9SEEz0xPhDyCkIoiW8QaQIn1Dd4GY8oO2aamPUJL3DRVcJwukRrxEnmmDyiOwcjXcqkTpz8jRHOkqC0Hi3cc5txWNt1p+FAE71Go8Ff/It/kS9+8YsA/P2///f5O3/n7+y7nM985jO8733vo91uU6lU+L3f+z0efvjhgzTphrC9vc2v//qvA1AsFg/kEuKgcByHn/iJn+ADH/gAKysr/B//x//xqtV9J2CpW2M6aPGl0vHeJM9icmhNqdsh7we0yOA7Llk/IB/45IMuTZVl18vR4zBqMjqgFHQphW2Wgl0qYYsARYhi2ynSxmNNVdhUJTo6k1q9hcXNRuC4PDV7lDO1Le7e3WCq2+KZ2WV8kw96p0ApWhmPyzMVVstF7t6oslRrcFd1J5HMdxS1bJZGJkOp2yUbhmSDgLZyyfs+0+0WM6020+0WXt8ScWJ3O57ooTXlbod80GUrW8TRGq3VSJcOFhYWFhaHB/ue6HU6Hd7//vfzx3/8xwD86I/+KD/90z+974q/9KUv8Rf/4l+kXq9TKpX4nd/5Hb7qq75q3+W85z3v4VOf+hTQE4U5ffp0Iv53f/d3efe7300h2newB7VajQ9+8INsbGwA8H3f933kcjkx7a3Ct37rt/KGN7yBJ598ko9+9KOvat2vZTg65O7mOmuZEtVMMV1O+06H1ixvNTi+Wme21kYrCJVDoFRvhV8pAqe/0o+m0mqTDcdfNA10lYujNV5ESwSqTpGvZJbZdMq9666SFppUiXQLi1sFpXh5ao6dTI5HtlZ429olnpw7Sj3z6j7XX010PI/njizw3BFwwt4KfkSB9R0HN9R8/fmXe2kdh4fW1phutcj1ffI1PY/tXJ4wCKAL050Wp7c3cbTmSKNG2e+LK7keub6y6W4mx8XSLKv58mRWUwsLi1cf1r3C6x77nuj91b/6V/kv/+W/APBn/syf4fu+7/t4+umnR6YvlUqcOXMmEXbu3Dm+4Ru+gWq1CvTcC0xPT48t58SJE2PdJIzCRz7yEb7jO76Dv/yX/zLvete7OHv2LOVyme3tbT7zmc/wT//pP+XixYsA3H///fzET/zEvuu4USil+Mmf/Em+5Vu+hfX19QOV0WlkcfJ9YYbs8MaWxP6g/jtZomlpk89nvLsj2plJLzN2IQ3KlqhNRj3RZCyxNykQ9n3EPtyG9/BEQSdrVbLa51z5eI/WN27OMkocZkxY2v6ZmJJllq32HgCqv1tKM+B0mNck2sNj1jOWF2qGRdzEkDe9tM7RrQZbpRwvL1UAheNrXK1xAo0bahzd+1UoLs5Ms1ko0MxkKDV9vDDE1w4dx6PleZSbHab8NrlugFbQdLK0wgwtJ0OonME1M09177Uxmyjt0TOpm9L+x8S+JqFsKZ0z3NcTkPLsqSb0esIqQ68hae+VSd3sHzuOQbns+7gzxVii8RgE8niLaJqm8Eou0xsARYOmWcj0PsAD4WR8YyybYz0WgjGpm/143x/chKDb34/nG2X322vuAzWhdDSWBc6lNAYFClyiH4zYrxbHO730WhiX6+USf5I7yRvWr/G29Uu8OD3P1fL0WEuUSNMU+nJiISPu9P0BrlRyTIyhACotDB6zPWI5aVT43s7s6JnuBDDTbOAZVDzXD7lSrLCTy7OdzdN1PCrtFl+1fhkAV2vu3d6gqxw2ckVemloADfftrvFyaZ6uclls13i0usJmpshXysv4CWWlwaETDlM3J92PJ1I3Ezzk4fOfWIxEaKsULvprG0UlHVffhLRQUTBkxP7H8fVJVMnh+AR109exH70YkpKTVHean7zAjB9Nv0zzmSdODhL3Y58+7NLSSUgpZyxG+eWbuO7X+4q2xX6x74neb/3Wb8XHf/iHf8hjjz02Nv273/1u/tt/+2+JsD/6oz9idXU1/vt/+V/+l9R6/+W//Jd8z/d8z77aGmFzc5Nf+ZVfif3zSfi6r/s6/u2//bfMzc0dqI4bxfve9z7e8pa3xI7iLcYjG/icrm9yJT9jKZvA/E6Lo1sNnji7wNXpqThcxSIr5mR7+MO7Te8ammIrtUyeWiaPYzwlzHgLi8OOZibL546c5P6tNe6rrnN2e5Oml6Hgd1ktlnl2blHca2xivl6n3O2yncuxVRwwQ7K+z3KtxlK9znSrhad7CwNtz6XlebQzvd+9xy3PIzQnm3sFlbS+6Rayaq7AE4tHaXoZ6l62V/6eScajm9dw93xk7mRyPF9Ziqmvj2f7+241XC1MM9+u8eDuKm/avsRTU8dpO5aybWFhYXGYcFP86B1m/PzP/zyf/OQn+exnP8vzzz/P+vo61WqVYrHIsWPH+Oqv/mr+6l/9q7z3ve9NSH3fDvzUT/0U3/RN33Rb2/Bawd21DTSKV4q3Z2J+2JD1e7O2ev6OH9IWFvtC6Dg8O3+E89NznKhtkw0CNvJFTu1uUem0eGlmgeX6LpVOi3omy04ux042Ty2XodJu88bVlbisjUKht9et02G21UID66Ui5+bn6LguSmnyXZ9c4JPzA2YbLfK+TzZIrsL7jqLrOHihxg1DUPD4qRPsZrO859wFckHAdj7HSwuzrE7duC9ZrRRrxfJYy9azs0c4Wt+h4Hc5V5knFwTcv73Kw9UVnhzhm3AjV+aLTpY37lzh4do1npg6mWB9WFi8Wpg/uoPrhaxdqNzuphwuaCa3Nr7asMzNVwVK3w6/AhY3DTs7O0xPT3PyF/4hznxvNTVTGJhdMn26l/TuTfjR69OzTDqXk0I1iyldUg8aQd2UaFxacgEQqW52h8Ommm3evnqRF6YXuFycHcSPkdU2LVFuJzoBob2m6qSp3yApR46hHybz9qibxwpZrrQj9wpC2ZJSn+mGQZB2j+IdrXnXM1cptrusTxVpZV1aGY9zczOgVNJvYXSqRtjAlcIwVXY/7hWG2g/xNQ0lVc0RfvTG0TQTfat/LaS8YLDchDyj9NUVcDyT40q3PewKw8wT1W0qbEbUzezgomRzvQtp0jljNwXGeHON+GjsZbxBOaVsr+PmvcFNCAUaZrff+ZrdgXWl3R1cfEk5MyrH7wxuSNjtu1QRXCUg+MHsHQ/TsGN3HgnXHf10Qt8xIfYJNfgzHlMJ6uZkr7SpdpuH11aZ7rQJgcuVaYrdDtPtNhlj7+pmIc/nTx5nqVbnrq1tvDCkmfFYLxa5Xi7TzQyumVS30uCEIblOSN73e/+6faq04xAqxT2bm6wVizy9dIR3X3iFXBAQ9s/x6cjdQQSBpii6s5mUUoj87FQhnG5scKaxyVPlYyg0W26xR9veQ58sBy3e3LjE+ewCV7KzQ1TKo+Us13YH7hXi1kp0xn3QMMc981Nl+oU2mBjn2kEuWyhkxOeVTG2UypbojGPKG1GnSN2MKIRRnIKluRxrG/tzr6DGUiDldol0T1EFc095Zvye+u5+y3WUgstfmaVdyyTLmVAtc980TLPMST+lzUWfUTLfEeJ7NMjjBy3+4NIvs729TaUyelIbfRt+/Zv/Hp57MP/Mtxp+0OK/fvFnU8/F4sZgl/8tXlPwwoCHtq7TcDNcLs3YFaE+tKP47IPL3LVWY367xXK1Ts4P2c1kWa3cuEXAwuJOw24ux+PHTzDV6eBozXY+31tM6bskKPhdtILNYgGU4vpUmevlvm++fT53QsehmXVpZnuT7737ujJhyOmtLQA+feoUb752jal2m81ikUdXV5lqt7laqbCbzTJyheImww1DzjQ2AXisdhWAtnL5SukoO25S3Kzm5lnxKpzsbnE1M/OqtdHCIsLW1RJzx+ssntrl8jOW6RPBulewsBO9OwRu00HXeyvLXeMjIiz0XriZzGDpPLLOJURS+oemry9TwMURHPxGVgclLfnt8f8UIbIIJgQeokNXgdbcf7nKYrWJVlDN59koFWh7HoW2z9nNLXKBzxePHCfIqoSVQBJKiAVcDvA8MT/G4uxKiBcsSEnfcwMrh5hnzArtuDgzXgNd1+Xc8jTnF2ZAa95y/jqPXF3nhSCgkcmyVcyjlRq0IWFB7Ft5TG2QcVa1qFJI+L8ThU7GWfTMfiKJrCTi9cg8pmUw6Zsu2YZe2sjMy0hoT6OdcMiil9jOFVlTzTHjDoe5/WPTYhcZYHLuYFx6Zp7+OWS9QQcv9YVXQkFhJhDCxPHLwOKnJZ95CeGVfr/tDsLiPZ8JcQRJRcRoR2xhGrYCivdSEjwCw+pu3F9PowM9Ok8qFDumGmf/njYyHo3E63HIFmUUIVhQzHMVLN97hVWu58vcozdZ2G2wXixzrTjFfLPJS5V5am6WU7tVTm9v0/AyXC+UWS2W2cnkDPEbs+49TTaOR1mGJGtatq/I2VIeee3zdP4opzqb3F+/zp/mThkPiN7PqqpwVO8w1WlTV/lE2SroWXS1ETaqDaLwiNSfkC1HE4uxpFrGpLBhK5dolU4rOwqThFdu1KIn+FmTrGV7r5NS4AQaxw97yUSL8GhLXaogjCgEk2bR2lOvmW6PxW77ap6543VyJR/lh7K1bFIr3yhfdY4w/kOh7EkxzooHA0teot12cmSxP9iJnsWhQa7jU252ObuyzXYhy04hy8Jug1NbA79QjYzH55dPUM/euVLpNwVK8eW7Fnjz+VUeu9JTct3NZXjixDL1rBWvsbA4TKhnc9S9DG9eu8aLM/Mcre+yVijSyGQ5PzPPy9NzzLUaHGnUOF7f5szuFk3XY7VQ5nphil03x80WcGkrjx03RyVoU3XzZLVPKWzTUhkUGr1n0ttUvedKTncTEz0Li1cDoe9w4U/n4oU1CwuLHuxEz+LQ4GueW6HY7lkwnj82x/pUAdVxKHS6uGFImwy+4+D4+1qyf92infH47D3HccKQSqPLo1dXeef5y1yaqXBptmInfBYWhwhXSxXu3d7g3mrPp+vT80fiOK0UG4USG4USz80sMdtustSosdzY5VStyka2yFdmjtBxb+IrXSmeKh1nptOk6hU42t3BoedmZSpsU3eyhKjeXk+l8HHwcSjpDpv0LCVngjVcrVHhAivakSejWjNLg7zu0iDLLsWbdw4Wryv4HVd0NfO6hrHf8tDhkDbrToOd6N0hcOsKne094EyfWRGlS+cHYZlcxGc08vdpZQm6l0FZi+IdgzYQ0cEk6qYZFhr+vLp9P12hseoW+C7FRpdi22dlrsBLJyvsZLOgetS5RtYFXIgoZCPEMVRMNTPCxlB2EuzSFPbpwP+dERaxKsw2CL71UAwTvtIomcPRIo1pULl8TXB6+4Oq5Ryfuec4Z9a2Ob2xzemtbar5HFemp7g2Xcbv+1CQaJpJuuJwvEibk/zoiQI0Ql4Y68MsQbmMaMijqHtCOQj+79ReWqAbxLQZsy+bvvAcgaYZHZs0TK9PzzTD8n1OccagbmacwXE03vLugLoZCrTBiMaZaGP/2BnxFh0IKxnCKxGX1BRZiYRXBDEWNeyqsxcujrdheqEorBOHCdRbGKJ2KnqUXe2BHpXnJkNJDw8z3h8+V4m6KtELL5RnuVqcZqmxS9PLUHPzCWr6wKeaouoWqU4VebG4yEKnzgM7qzy8tcJTlWMQPW9T6IypdMcQNC5VyuDDNTVLx8twj3+dNwT1OF2IokGW62qadcocD7bYCYrUyHOUbQAqIeT8Ds9zjAA38Sybpc797rW4vGvhDJeCeUBNTjkVqJ2TiqOMLFsSqInrG02BHFm20I5UqugewZRRbUitMy57NHVRKSDQKF8nJgepfu32xI2s7yDiKOPSjaw7ol8OgkTBFIlyKaUzFyfGGQtvdDIlCK8c2gmaxWsKdqJncVvhBCF3v1zj9OVdGjmXF05NUy9kEh+XFjcHgePw0pFZzs/PslSrc3xrlwevr/Pg6jqrpRJXKhU2M6U7Qx5da2aabUIXdvKvnoCFhcWBoRRd1+VKeWZfedZzZb4ypXhs5xpv2r7CV4rLtN1b489u0ynz+UyJgu5Q1B0UGi8MqegmZ/UqdXpWvoe4TJUSOxSo0OQys5S5wBLbXCMplLFFiQvhAifUBq7SHHWr+NrlWjg7ohUWFhYTQ+vDO2E8rO26w2AnencIVDBYTdbGKnDYF08IvYFJI3J+63qDlaNouI36HI6k2E1BCdmSx1A6x7BaRBLzXd/F9UMe+1KV8q7Py6fLvHxiitBVuAREOaTHQJg06QzKjhbEBCuBaPGRNtFL0vxmtLmiF8lhq+GwvWUq6K3o91f1lWCBS+x5caOV00GYFu7SwMJgrjoKK5DmoqWhIJj1fY5t1zhR3eWrrl6j7bhcK09xtVyhoYb3QUoWuHES+MBY650W3Eck8k9qvUuUE/KmC2scrfasDtuFLF86u9hbQDDSSpY61XeF4TghLgEaUILbA/PYFax3WUFkJTAs25IF3Tz2+vEdw09HZL0zy4msfGZYdNwxLHaSK5XQsN4lXKDEgf12pVkqUsRYREhWXuH+Ju6/9JDS/X83aR6vxlyHoeM4j3BsWu/C4XTjJfmN8iSLjynq0i9zW5X4UukEDzWu8badC1zIznPFm46dwada9KQwod29Y0U7zNEmF+dZZYbruslpZ5VMP8McA6ufF/R6qhvoHitkz3lfZ4ZtXeTB3BUyKuC4u0m1W6YdZoavgwnR4jfGWiZaoozzm7SctHRC2SYG4jfG+J9AMKUXJhQ6TjjFzD/GxYFS4PghTjdMuFdIIM1St6c9I9NNanUbY+UzMbGgSmrZE95ESZTlIDDf2zEbyFREi5pgnPTNqtvidQM70bO4bbj7xTqlWsCfftU8O5Vs4sPT4tVBx/N4ZX6GC5UZptodTlR3OVbb5fROlWo2z1NzR/FfY4+JrB9ytFrn2WNzbJcyPHJxgzedW+UzDx0jtC9JizsUu16eL0zdxZnmBmc66xzvbrHhltlyizRVlqbKcCst27sUeDq8iyOqyhG1TUF1CXXvu3TZ3eaKzrMSzozM39ZZQq2ohkXKTot5b4ernflb1l6LOx+LjzRwPc3Kl6yLIYvXL15bX3AWhwbl7S73PF3j8tkCG0cPoICpNbMbHVaP5tipWFGQ2w6l2M3neH4+zwtzCyw06jy4ucZb1i7zhbmTsRX4tYDAUYT0jCoblQJfvHuJdz13lXuvbPH8SetfyeLORaBczuWWuJKZ4XinylxQ55jf2ycX0lPGbCuPrA644M2x6ZZvav0axYqe5Xo4i4dPni4Pe5cBuBAsAuAQ7mFlDNDWGQqqQy3IM+02uIqd6FkcHOUjhn+lW7l59zAj5PDuXLACqa8K7ETvToTEITF9ZnWH+YyRiIRJpQsNSkNCeEVrzjxTp7wTcPczDdZn8wSZXpmxaItJSTDak3UDMs2QqZWAYiPgwkNFMl6Pi+Qbqh8RVTThHywzTLVI7FuOfaqpoXjTz1rsys505XcAGqfkH0vKG/+pB/ScBE0zuraJDH0arunDrX8PdZKHOVxfgmqV9HU1CbRSrJXKNJws77h+gYVOjWvF6eF046ibE9I0RT9pINP4JFpo7BNvUHaQUaxPF1jeaXDhZJnatMcLd03zwIVttqczrC72nD2b9z+iZzp96qZLiOf4aJI+6DzBP54popLt92UzLNf3hWdSKWvt3uKIHvHxIYW3g97j2qRpRkPBVJqLaJqd7uDx7htKtUEn8qM37DMvIYQi0GJV3C8TjR3Ei/RKoY/GceYfw20YRyVUgPLoCcOYltoDfNRIfuT2+rpLYATdb0DdHI5P9Q83LswoU/Lhlig7hDZZXnaXeNnRZAgohR0Kuvcvr7uUdZtT3U2aOkdbZWThkZHUzb1t1EIYhLiU3d047JHMpV7zNKx0Z9n0p7grt8Zqd5pamCdPlwutJR4qXmTKbeIqTS7s0tWZ0eIYk9I0x9GPR4maSKIukh896WNVpA2aecbRQodpimk0zES7RfahHm7rnvumFKhAo4KQhFLjpFTJFEppap5J6Zxx2Sm0UCDogpsBz/PxW3sWF8aJn5hle8Zn8s1ghCTomsKChyOdi/mysp/tFvuD1aG12DeOXWoyve3z3CNTZLohxy82J8p36skGb/6dKm/8gx3OPl2nOu+xtXRrRAMsbhxNL8tmrsjp2hZuKM1mR0NpzYndKsdq23jB/vLeDKzMFpnbbZHt9up++dgUAHM77Ve9LRYWtw1K0VUe206RFXeGl70lns0cx8ehTJuH/CsjJww3Co+AZa/Kll/mpfYRrnTmOd8+wqo/w7HsFo8UL1Jxm5zJXeeNxVd4oHgFjeJSewG3P1u5u7iCY5f9LQ6Iq5/rueo4+XVNlHtr+vlhh9L6UP+zuPWwSwN3CtQoq1N/Bb7rDIUFoSHG0u8J5sPQFJ6IrDEnL9W474Ual04WqZZzKL3LqXN1rh/J0yx6sSUvNBqjlEKFmuk1n0ijoptVnHt7iawbxsvSjrQUaZQTRAtwrrkiZljv+gaTxAJdmDjlXnuC5C8AvhBmYsxCXmKVNEq3pxzVL1u2/g1b6pRwFN0DFaRYL0ZZBKLoCRcltQPPzS7ytuuXeOPWVZ6YP05orEBG1/sNG1fJ+12uF6doZjw0MNducmJ3Bw3cV+2pel4vl1gvFntlKM3prSontneoFvI8vzQn+gCTxDoSy1P9/ppwleBqNmbyKGCm2WK9UMBTPWGV+pSLl+3dhISrhMglQd+i5xGQUz5a7XGVYAizZGLhlYH6USTCkjUsepGLhI47sOhFFrvdzoD2bI4Z7Q53lMgiGLkogYF1z3xfRmF+10jXMU3akWiTKRgSHQt9S3B7oZTcB+N2CH1w0n430s1I7KfDSBuo3ngwx6Az3OnHuX0wyxQtNaLVRWy6aE0aJ8YyqauAUWWL7Q6Gw/ame56jlGhzmnWW/F02dKUXIbZbsNhKlr84TLOY2eZIpoqjNVda88zmMqy2ZtFAlTJHvGps2DCFqaZpsNmcYtapM51tUHLb3F+8zPmdZbp9YRbJ6jy50I1wLol4huJVwnLUDwuGw0yMdcNglilar4bbm+riYIxwSrLsYWthnEaBCkKUv8eiN6mlblK3B0Z+8UP/IC4QRAtqSHF28GyeOdVm6wUvJY9Utvks7j9HD2LZizq8acVzU2wt4vV57WyjsDgcsBO91ynyHZ+vefYaXc/hvz92NBHn+SHFpk+p7VNs+GzO5ujmFPe9uMPiepsLdxV56d4pnFBz9WiepdU2b//jDbanMzQqLu28Q6foEHiKTCck2w2pbHTJtEKef0dPvr825yYpDBaHEo1MlicWjvPm9St81fplrhSnaboZWq7HA5urzLV71lxfOZza2SLTn1n7SvHC3DxXylPctbPNkXqd47u7+ErxyuwMlXabpXqDlakSi7U6S7U61yplAuMluJ3PsVIpoQ/wUg2cSN20h1KjiwJq5deABVlr5s93aFUcmtMuR59p47U1z7+5bMeMxU3Bjiqyo4tUaHKadTpk2KVw08o/nt0g0A7PN4/T0XvHnOKp3dMczW2ylNuJv5mvNufY7JYBxcXGIve5V8i5PgWvw9nKNV7cPk5gP3It9gE3P5goVc4EVF9yZYXhOxnWvcLrHnaidwfCCUPObFaZabbJBj6+6+C7qv/rELqwVG1S6AQUOgEPvrJJ3g8otHyKLZ+sv2fwvbxLqKCTdXjq0RnWlnKgFKGrePbhGV58MGRhtcXiapupbZ+FlYBMd1BGN6Pwc4qX3lJid+E18KFtkcBONs8XF45x3/Y6D26vDsW/MLPAhakZUArVX6oNXOJJyUvz87w0P0/B7/Dmq9e4Z3OL3WyWJ48e4epMz8XDvWtbzNebg1V0NHdvVGmvOFyerfDC4tzBFjL75UX9sZM5/Gz1wk7IiSeHKaans00uPnjzPsYtLM5xhAe4ysPqMlVd5DxLdOLPgoN+ECuutBe4K7/Gidw6l1pLQFJwK9Aul1uL7PhFThbWySofR4UU3A4tP4uvPV7cPc49U1fJu13yXpelQpVrDSvOYjE5apddpk8HKAeUA04GAsvet3idwU707jC4YcAbL60w02qxUSywnc/jhiGeDsl3Arygi6s1tVyWL55d5L4rVY5sNmnmPHYLWa7PFWnkPZp5l3bR5U3PrOOEcPlYkWvLBQLXiWmJEbUzcByuLxe5vlyMBSqy2scNNN2MwmTkuTryHzYIy0diFWrYZ5j2BrSJ2P2CybVPHPd+TOpm5GcsQaWK6D7GxCGiADmGD8JJRFZ6mcfnibUo9IiN+0LhEY0zUd04UQuT2yOtWAp0NgkJwRQG9W0XCny+cBI3CMkFPnnfJ6t92q7HZimafOjYfUGCctk/rmczfP7UUSqtDmvlYs8xu4JOxuOZY4t7hFc05VaHk1u73L1epZbPcGW+t89uL03T/EVrlKtRfR+RjhvieCGdUq/wmUaHzZneR6cr+MdznRClIaczFN1un7o5uKkJ4ZV+eN6kbjpRWDcOy/Xju+H4mepOO9/LW+3lfektRZwAGhWX8qbPXc80mVnp8pVHKmzP9c7B79M4TfGWsC/GEnSG6doAui/MkqAAS4ILQweD46QYi5An0Y303tjxMDuC5KszXgzojScn2MN2kyzAEp1PotylUQBFYQohvxQmCr0IYWY6qW0SJXNCquje8w9x+QonmFe7nHQ3eLP7SrzIvkORdb9C1S/F4lGTCpRsdCv4vsvJ4hoPFi9SDc7idHVcdpS23inxXKPIcnGTxeI2y/kqoVZcqS2w2apwbvMYlVwdBey0izihTnaPaDhKlu4Uf3RDcXvDI8GUhNDNZLRJkfaZuEfDFEGR7in41kv1ZTdOPGWM/zulgCCEPnVz35TMSUVbTEiCKmk+8WKFtRE3zri23W1Y+ROX0rGQ5qpDUBcambh20klI71NvfLwUF9E0jbDENhS5gH67BiHas1Zti/3BTvTuIGQDnzevXqXgd/n8iWNUC4X4o9j8OMbrh3maL9x7BADlRR/MpqNzzZ+8qR/vjJ2hDCH0FKEXPcSsef5OQagcml6WppdlMG+Z/P62MhlamcmsurV8lmePzTPdbLO8XY8nekPQmuNrNY6v15nbaXF5qcQLp6YTTWuWPLZmMpx+ucbm0dlDTYGsLmbwM4qjL7Y5/6YirSmXZsVFdxVnnmswtePHEz0LixuHYjOcohqWmFc1QOOimXN2OZtbIcgqmmEWX7sE2sXXDpudCs1wvFud7W6J2k6eByqXWCpUWVElNHCstMFMtkbDz7NSn+v9Nua53pil4LW5d/YqlWyDzVYFX7tstiqj90RaWKSgte7QWo9Wpl6HHclSN1/3OPw8JovJoDVvuX6FbODzJyePUy1YipfFnYGV6RILtQZeIC82nLm+wxvObYCGC0emuOt6nYVqC0haRM6fnWKq5jO/2nkVWn1wdPMOL3xNCSfQPPzpXRZf6XGNZta7NEoOa8s5XN8qEVrcXIQ4rAcV1oNprgczPNc+ydPNu7janaMVZtEosqrLfGaXe0tXmOSjOdAuV2qLFLw2D8xc5NTUdebzu2y3y2Qcn2Pl9TitxiHv9azZ1xuzt+o0LSwsLF5XsBa9OwTFsEu52+FLC0dpqDxO/1s2UtNMClRFNJxBoI6OjR4RGPGRv2zT95ju80pMtci0BZqISukKHwmRvzGzjUEwqNDpWyWDEdTNsE+DUMbyr6SwqSKhDjNMUBhUaTTOKE4KM77DnYDxqpvjSpUoOQKVLkH7FONTEPm1SzTB4MhFZZsqpxHNU/KZZ96j6FT2UDPHti++H5qV2SIPXdtgqVbjyny5R9PUGi8I8V2H6UabzUqWzz+6CFozW29xz+WdXnZH47gazw2pLXhszWc4c67GzjGXrDesoBlRN/NhhpLXRqsBRRNkNc2swfct9CmbuURYbzC2w8Hgivq3qbQZGhdjp5Ljqa/NcOqZBqe+3KRR8Fg9kue+r+zyNZ/eAOCJN8yyk1e08h6h4SdPR2PGoGZq41hFaptm2KTzRuO+DBpuUo77yYYFLQ9klRGpllI5EnUxrb402qRAi5R82aVSKcdRMielfZrlTJhHiRRAobwx7e6QZb2dSaSreHXOTq1QCDu0g8gH356BbNRd6xa5UK3gtK8xV+z51ev6LpudCsuVDYq06AQeoFgublJtlGg3Mwm3Cgkl1sRzpE8VT7nZaQqcYrr4vo2nUkrXO+4nibzDlMRxfu0SZaYpVt4EKqVSQBiigj4XWihnrErmOBVLob6hsEn92u1H3TMuO4Wyud+85os8UlNOKBFH20yM53IkNOYK6dKQMMnsk41iLXqve9iJ3h2C2U6DEMVGrni7m2JhcVPRynpsFXOcub5NI+tRnc7y2PkNTmzUWZ3JU2z5tLODl+3F42Uee26r9/ee98gr95R5059sceRSm60zh1sYSLuK84+UyDVD7nmyxufeNU91NsOZ5+osrbd505NbdDzFf33nsdvdVIvXEepBHj90eHCm5wDdDx2e2TgzNk8QulzbXWSrVebs3DWOTFW5sLVE289y7+IVtO65BXEcTaOTZ7FcxQ8ddlolgpS9rRYWFhYWo2EnencIjrR22c3lIXT2WI2GV38cYeFssFpummcMa1m04mv61tPDPryizcWmOETGEFSJrG2m6EcU5hrLqZEVJfAG7Qn7loPQsESExuqYyvQtjJKfOfO0ojmBadGLrBymRU+w7jmS2MpwUDLQsAKocVY5wR+fNlb84kPTj9QY92eJ+BSBi4F/NDOvIP5iWoGj7y/Bz1rSCjhs+UM4F2mhMrIMPndylkcvrPOOF1ao5T3KLZ+16TwztQ6hgudOTeP0LXTNqcGHoeuEeG4Q98HmgsPaXVnOPF2n0slQW3JpzTh0+zfbVT27mqs0nhP2xFiMZXfTj15slTMaHh0HiTCnn94ZSpew6AnHHd/luQcqvO2PNzh2rsX5u6f40oMFKrsdHn1+i2LTR7dVPBaB2D+eKbYiHaeJsUy6cJzu600Nh00MwepodDEVueEcYQUbatcIS9w4i16qlU+yjE1atmiJM8pLzaOH6xtnvRth0RuIgwjt2WN10ji8uHGccrbFyek1PCfkzNQ16p08fn9SVm2UiO6dA6hA4/iarmHVPjW7SquTYadRxA9d5so9a9+xmQ38wMF1Qo5VNlmtzrBRn0Gh8VyfTugx6BfScys6ATMwxXoXpxPiE/d3+HpLOGyOoPclrKJ1Ois31YfdmPrM8DSRlXF+9Ea1J84rWAZT86SUE/UpqY2Spc7sgw5DYdpwJzRpn9GuXfiw2B/sRO8OwZTfwZ9Q5MLC4nbgyHad0+vbbBXzvLI0TWcf6mGbU3k+/dgxFnZaHNuoc22+yLnjFVSmRxxWxpMs2xnMxpvF4TouPlrAzymOvNRm8fke1bI57bB6X4bGXYp9U2NuMdoFl+vH8ixfb3J9KU/LyVBoBZSavdWH42t1Li1UbnMrLV5P6IYZtloZtlpl5gs7TOfqHJnawunPgDKOz1ptZihfELiEGq5X5whDh6Nz63huQMfPoDW0ulmubi7Q6mZxnZDFqS2W57bwvIBKsU7WC2h0slyrztPs9FRqc16HjOdTaxc4bGPXwuK2I+TwDgu71fxVgZ3o3UEoBt30RBYWtwln1qrMNdpUmm1ObO3ymXuP08rtY3VSKdanC6zP5uMgRzBf3P98b39e11PUpoYfcdpVXHmwwMYjLrmaJl8Nmbngc+rzbTrPKrZPu2SXQtyixi8wWrIqBKcFbluhGuA0FG4rAw0FrQxkgJymk/dQOU3oKVROQ07jOIAHmabuWaVCKLf9+DjXBieE3azHzkyG6myWY5dbfM3n1hNNaGZdri5YurbF7YJiozHNRmMahxBXhSyWtlksV9msT4kOzsPQoZxvcnH9CPV2nrnyLp7rs90osVWbQvcHXBC6rGzNkfECFio71JoFVrfLzE9tc/fiNbbqZTp+hiPTWygF240Sl7cWY1cQFhYWFhZ2onfH4KnKMtMZB3SSahMxuhJ0zijeG34h6hF/6LBP0xTYCaHhkkEJgilBOPhSjnyS+UY5kT8z3+DDuH26XzYzELXwI/9ghoiGSdOMPhASH+aROIhJ04wYIsY3SEzTNAVYDOGRKFpyUSfBMebcKuwvqOkB5SfB0hhHBxN8/SXobP2Eo0gf8bmaNNT+SSR85kWMFDNvP14nNpkbCaJra9J5Y3qKHkpXLeeZa7RxtCbjBxzd2eX80elB0YJYS4I+2+8nyuhbTr/vOUYb1pZzlGo+1dksmT6dNyGo0hf9yXkBzEJrFi6fypLfCpl9PmD+WZ+Zi12mdno3MciBn1cE+Z7l0GmC2wS3pfrN9PrXCcKCS1CAIK9RdVBbEHYyqA6oroqv71T8aw5MWamnmXMTE+LdQoZqOce54xXqXhYCUF2DAhQVYwzWhMhGTN1EjN83pHpSqITjMJIWt2ecKEC5PVq15Ecu1WdayhgcJ6KyL+rmOCqlSAs1H7xCnhS6pygYIuU1738cn5JHoIoO4hUBLms708wVdzg+vc6VjaXecy/QKL/nR+/K+gJ3La1yemGFy6uLrG4MFDbz2TZ+4OIHA6ftl68v4rhhvFdvu1ZkbmqXxekqrhOyVZui0clzbG6NI4HLyvb80EXRB5n7STQ+AfEzz6gkemaqYAQ1XaLwC30wvh3mGIu2PyQovsMnmNxmIbx49oq6KHqqa65LQozFvI5xvx1uT7JB0cdBCnVTUtkJEx1OaPeEtFCDFhnTL808Eg1T8sFpYpx4yg3SdeM+I5SjJQrohFBaHzoqcYTD2q47DXaid4cgVA5Fv8ND1RWemV++3c2xsBjCC0dnaGdciu0uS9sNGrlb8/g5f+/A317WnLmnoDXrcPFtGRxfc6KWY7edxW1pcp0Qt6XxWuAGmu6MonUUgiIEBUUn7/Qnd5Dv7wXMGnv5IufpHd9FdUF1oFbPo3xNLcih3d7HYs3Poh0IHUXLzxA6itKmz9HVJkdWm1xbKNDKuZy5UmOq2eX/b+/Mwy2pynP/rqra83T2PvPQE01DMyoyCRhQnDAhEEgkmhjnJxojotE4XBNDolGj3uC9XsXHKJAQLnJBRZM4QEBkFrBbZGoaez7ztM+ehxrW/WPvqr1q728PZ+o+5/T6PU9zilWrVq2ad63vrffzlQ08uUNe65K1hWmpGJ3vxUj3DDb3TuLwdL9rfrYQxKHJAYz0TWO4dxaHp/oAMPi9JZwwPAEAKJS8GJ/tQansBcDqDFkY5jNRzGciUJXqC6DC4NV09ERSmEwlsHa1ahKJRHJ0kS96G4SXp8bh9VaSKD/Haz/+bPMQ7hqBq5a5zFaI6J64DDUyR3z0btkJ2jWhbSF8Z3oqw1GapzacbKdQUESji+popFhmR/fEvop9dGpajSOrrg+l7W0RR0HtwURhRI/pjaON7QbTnJQM1Ig3r027RsTtXUFEyyi4a151FFBMcUH1i4ryivuJMlFx/A6EUX40LuPqD9GOPW1qDPsHKt+SMTVR+as0CSXZ3jhEtFiM3jkRPWGHiuY/diTPI0T0fNW0CHZ6BAAwqqO/XGdgGmBEGZLwgTPAIzjwiO04y9oRaxMoGN6Gen618u1s2f6x6gXSvCI/zZVqic9Lwu3YsDTAAgoBH2a3hPDMZl5Jh8AY9sfj2DadxubZNAIZC0WvVjMTQi1i1yyCZkdyFHGZxUb0mkTGqGhcxxG9DiNwYkRP0QC1XDdQ3y6VALU+KhJHRd3apFegDTzq+gVhJLvT6FyTPlLmILWILtUv4VomjV7aRPTsaZOoJ5DVgzis92Hr4BS6Alkw0wfF5E7zhbwPkzPdGBmYQdSfAwCMDNZkyR7NQH9XEkcm+6orERqvuVLBslQwVJ5l5bIGReFQLatifKQ03t+X/f7npHOoFdXuk0I9Zz+KFRu74w5oNCotmN24K6JFRaKEVnjziJ8rgmJHBu3eMABqRdrOeZNlnGUFVQ0R5XOquSJ2jdFEEvF54qiBrMb5i7ln2dE9yqCljWmPKzJIYZvICMolV/qZRcKpqKHr94scxJAsDpkwfYNQVCo/En8b7jnGPZFIJCsOY84PkpJHw4HeKMqaivMPjMNf7jxqKZEcLfLFAHJFH7oiWXJ+JhdAvuDDyOAsRgZnYVYHKArFyuBHSV/cOHRXOItCyetyt5VIjnscJ9U1+k+y6sg74gbBY5l4MdKLw8GuY90ViUSyypS8Gn554gAYR+VlT5cve5K1x2yyC0F/CeFggZjLcGisHwdH+3HgyAD2HtiE8alupLMhaKqF+YXOnWT93hLCgSJmU7GV67xEIpFsAKR0c4OwOzKCoj+KejMW54NrItovSiCp3HqiWYlTzjl6cgVwABm/F2VNczduTwqGGZZHlFpWJXKCiYpaNcywBEmCaUs3BTcOW7KnCXI+S2vUb7jy6CmEZsdqUa8sJoUT+l0debJYoxRHEQ1cmkjAmP2XkJs4Ulriw/x2tDJoadYMadDSqCQTjFVca2ycT5i6kGWENFU0DBBNVpw0RIK0U7Glm6JpT/VcUIV6Lulmddqv1dxx/JR0U9gZjAM+04DHMsEZoAvfB1mE9ks0G7Kn83pNklmomr8YZq0dW7IpRi2MsrAevTLNdeG4GpW2WbWsCB+eHBnGeUfGcP7BcTw5OISS5nHnx7Oh1FeUGUuH52BbU5M20kUSalmROmkjA6B4KsZHhCJtadLNdjnz2ko3CUkm1Z8W7Ygy+bb7kbrfUDnxiLZpc5jG+e2korTktlJYyPqQzgQx0J/GOAuirHsa6pWylWtBUSwYZRWeQOV66elKYW4+WjFmcT1jhHtQtbg3lkJJ15DJBWvnsLgfq/f35cYPWgULXW2TOUiFupTxBnFOcOLcqUsh2EgraSeVj0689plSkSryWkdcLqZVyS4T5YyUsQqxOpeBC+VGQ5qsELJIu4woarpy+3h0atDSDovYt64DbDuVUQduEdiSTVfuvUW2Y/EWN9VjTIeGR5LlIV/0Nggl5egcyhNnk9gxm3SVJQN+PLFlEFY7LfsGRLUsaBaHrsgkppKjT8HjwRObKi97505UXvbKzNt+QYnkKDE9G8dAfw4nbJvAwkIIU9Nx8Lo3JqZY2Lp1El5vbfQh3pVFvCuLXN4Hy1Lg95UxPdeFdC7csI6Qv4CFbBjL/whPIpFINhbyRW+D0FvKYjZQyaclRuUc23zi+Sc+au1lRHMTLoy22OXjoShi+RJ683nnkRovFKHoDFxhtYEnwejBEiMMZjV65xHnV6Mynsbh9HbGG5rWaI5hRyJ9JRMnHMzCY1goeisW9QWfiqJXQ8GroihEIzkjRuDEwdbqDrQ/LlcsC+dMTKCrVIluzvpDGA12IYVApU1qxNsCHREgIl72qH2n35tTBi2VKXtUthGXQYtt6iJG4ojIX1vjlRamLq62wRvKROMV+7iL8+2onSqWERE9VyoFInoXrEb3bDdMwB2pYxzwwYBPMSopE4RzWTcbX+hdEb3qxoplJaOyjBjR06sROyqKB8CJLDMhomebp7iMV3SGErz4VWIEZ8+N4tzxcexKDKOketqmKRAho21kpKZNvcVG9Nqto0XbDAAzKhF1TkTi2pqkUOtsk+6grdFLq+2nImOU0YvVWK/ZNqyYiUqLSB0ddaLvkxSWqWB8tBvlMtDTl4LXq2PscA8s4RoJhwuulzygEnCZnelCIFiCppkolrwYHpiDcUSFbmgYGpgD58DETDdUlaM7lsH8QhS6nZpBjILY2+e6VzUaq7hoETmhlnHtO6vxvssFQ41a1LVRDcNd9yI76kYYhnCiTGiHiiC70/q4n2lglT46Ziz2M0FMEaES7VCmLaSpifB7ovrrg1FRR/H8twePqTQMpnC+tIvuUbQyaGkGNd9eXCHqWY3XTtMPphwFjRi9a0wZxa3jb0Bdsjzki94GIa962ldaAYoeD3YPDQFo8YBcI5z/qxn4y4LMk7kVhRYDSh4VBZ+GnM+D+bAPC74Acj5PWwkHZww+Q4euqJjyR5Ao5/GK+TEsaAE8GxmAKS8tyVGkqHnwq+7Ky94r5sewKzGMsnJ07gkSSTssrmAhGUah4MHI5lmMbJnB6KFe52VPrQ7YWRbD1FQXyiUPisVKaoVkMlKVOnJs2TyF/r4kymUPgoESAODELePOejTFdF70Ar4iAr4SCiUf8mX/Ud1eiWTNsJZNT9ZqvzYY8tfoBiGjBSDFg25ePDGGlz1fk5keHArjwEgUvpIJf8mEv2DCXzYRKBuI5UoYmc2CASipChZCfiT9fswHA0j5faiXBHHG8EKiH2fMTWBTfgFZrSKX6zIK2FxI4oC39yhuqUTS+LK3O16J7Ekka4ViwYcjB3sxsmUWm7fO4MihHpim6orSp1Mh0BJMhsmpBLZsnoLfV4vGp9IhTM4mam6bjKM/kUR3VwacV8bsZlNRTCXjTdqVSCSSjYt80dsgMAs1cwXxWUZ8E+5IREQ1hC3JEFUMYo464ttpZ7Ldh76iHLT6MBZz+NmTokKEOTnaaoVWNd+ey6CDkPFZnCGY07FltGLrPd/lxVzch7HBIEwvkPN5kIPHJSnlhgLNsBBLlZHIlhDPFXHiTBIan4euKJgPBjAXCmDaH0ZJq1w28/4gHhrahoFMFjG9iDlfECXuwaQvUsuNh+qxAaBYlbyGHHXR0BZmJUIKN1qRQiy7qpFWhZDauJwgqn0QTVaUuvqAY7wieO24ZbpEHkXbhEesZ+e4E38oetTGPHqUnNMnyDlz1fx3FmdgnM4rCbglmeWqFNNlIlSdL5bZbdnnL1CTbHJd2AGCERCzpZuCTFPR7b+iQUu1rPpXhwdPh0bwsuwozp4bxXPhQWQ0/9JkmBQdSi1J+SWhLluudFMxqvuFMChZjBlLy7x27Qxa2sgm69sT51PGK0uSbi7BRIURsjJSkkn2ofWJ4tpnDFAMC4pRyaOnGx6MHujByLZZ9PWnMDmWcO7disIRChaQywZqC6MmQS8XPZiZjaG/bwGWxXB4tA/Fks+Ve6wvsYDurgwm5+KYT0WQiGXQn0gi4C3j8EwvLNtcyTYWIeScQJP7aIt7q7hHGCX7FD+FIMxhyH3r6N7F9VBGLq2lnfb9mFFyT6u2Dq4wcFWpzLKbJ9xhXHJO27RlMXLOVsuI6zNb6DBVYWjbcp3YjetrZfpBGbSIiGWtfAhcpk3V9blyItrb3KQvjoGPcIwIOefixyrWcERv2fZIkk6QL3qSDUcoq+Pcp+ZQ8il46qwEFuI+1w/vZhiagtloELPRyreOSgmIFUroyRbQkylg59QsdmIWY9Eo9nT3AFBhMQWTwSgmUbECV8qVtpTGTwclkqNCUfVgd2QTTs9O4KzMKPYFejDlicCQhkGSNUK55MH8dAS9gykk58JQFAuFvBeWxTAwlMSRgxrKZToanUxGUCj4oOsaTO4+p/2+EnriaQDAQrpizjKfiqJY9mJT/zR6YylMJROrvXkSiUSyZpAvehsFYQSuQ4f09nbnAs7AFBk5aj3a5Bqhs0cyXRGfapno9GF7oyiNo4DiaKkhpojQLIBzvGzvPEo+BU+c2wNTbT4CJ/aLs8YROK4qWAj7sRD2Y193ApppYtN8Fjvm5qBwjucSAw1tUikJnDLxGPHG+a7jRkVQxXZsCMMUl/041R8ygsjd88RlqSieuB7XMo2ROjvVBhPNVqplqmCm447oNRqv2PNF4xWVKhNObK06rQllSnUHpsoBpyxveJy/jAM+y4u0xcFZLXIHAGUhHUK5arJiGrWNddKHEJFxMUIOO7onRvHESF11WikTET3Rg6BaxsQUHxZgQMPTvmGcWJ7BjsIMTizMIKUEMK+GMKNGUG7m0ruUiB4xv21Er0WErZ2JjBNpQ0XFwAzUmagQ0bkWkbZm86kUCHYUTExN0X67GqNpHUf0lpLugOwXEYlp1w5hGANi37aDgVfWa3HXNqRmgoglcugfSgIcMAwFU2NxjGybxeZt08hn/eAAspkAMulgrT2FoVTw2o1XyyoNhwO1fH2DPXMYn+4BwFDI+5HOhBAJ5DE9F68u1BhVc+1vddGhk86x2xajfKx+AvR+psxYXNkOWkT8iGihc89mlWce1+yIXrWu61y3dzgRqROvQft50iSi55yH7aJ8TG3cFpOI2FFmLZ1G+VyGMJQpm9gOEXVzUjeIx41yMmtcn7hdfPGhOomkI6R9j2TD4CuZOPeZWSSSZfx2exSmtrKnt6GqONTVhX2JBAayWffDRyJZY3Cm4CVfPx4PbMNL3j6YTMEWfQ6vKB5Cl5k/1t2THPcwTI92wevT4QvoSC+EYJoqjhzoQ3ohBEW14PGYGBqZRyhMJVxvZH4hir37RzA22YNYJI/h/lko1R/5uYIfPq+BUKCztiSSDYFtxrJW/0lWHRnRk2wIehYKePlLc7AUYNcrEkjGfau3Ms5hMSbV5ZJ1QVnRMKnEMOmJQeMmdhYncXppDFNqFGnVj6ziR4550XGyYIlkhSjmfTi4p6KMMCz7m1cFM5NdACrBmU1bppHoztS+22sB5wyWpSCdDcGaYBjqn8XWkUkcmexFNhdEruDHlsFpzKUimE425vOTSCSSjYZ80dsgMA5aIkWZdVDLm8TMNp/0UEqThpkAmOvj4qpMRPzo3ZaaUBJBUUrpyEHEPEOAYnG84sVZJMM+PH1CL4wgByrO29C8xMdyjsyjsePuD/PFddsyF6A/l0My4K/uH+7ME5fnhLKjojWrThP5sygzlrYyTQrXynnjMsw9T1yEzJPXRBba0nhFFcpsowWhzJZsUnJNoCbJdJdV5ZxCH2q59QS5JjEt5l5M6xWrdVuuCQClqiV7Ua9IN8Pcg7xpgTNAN2oXgiFOV/PeWYJ0EyYh07RNBlz5JNFQJpqs2JJNpWYuWDNeEcpsExZWZ/7TDAsqXlCHMMKS6LUy6DfTYADKUDGnhDHDIsgwv/tCaDOisaQ8clTbxPxWed0q0k0OxeD09US100Tt1Vpe2dg2IySVzfvdom1CSkkt23wbCNkcJa9sIc0EhG0kTCtIQ41FyP7BKu0yzp0+1WRqHGbVkIhR9xMOZFIB9A2moICDc+bK78pqzbjXByCXDeBQaQDDQzPYMjiFg6MDODzeh3gsg77EAsKBAsZnelAQUi+4mrH7uoTxD1LhKEpBnXuw+1nW0A7VeBvlYktpp0sWXLesbcaisDozlhYGLkKZ61MIe9ISt0983lSnXXkd7X43bljHck6xcjs5p/OMJZZtZt7itC3uW0rOSUhA7TZdeQLFbahOays84GaJB3ON0cokR7JiyBc9ybonli9BszheHImj7FGhwGi/0DLwmiYyPu+qrkMiWTUYw6iawKiagMIthHkJCSuLHiuLQaRggiHHfEiyIMaVOCyp8JccQ0pFDxgDvD4dpeLi7rtl3YMjo33YsmkKmwZncHC8H7m8H4eLfejrSWLr0CTmUlHMJOPyGymJRLIhkS96knVPIluErjCkg0fn5etgvAunzMyipGjY15UAFRmUSNYDFlOQZgGklQAO8h5ErQIivIQwL2LESiLCi3hBGZKyTskxo1TwwjQZevpTmJ7oQllfXG5Iw9RwZKIPW0YmsW1kAl6PAV3XUNIrL5A9XWmEg9Xonr6Kkn+JRCI5BsgXvQ0Cs2gJICnTJGY6UpN2kpxOcwuJygYxF5ydH07M8WOr4USDKkfGI8jZCOkSAITyOgoeD1CsuiDy2mltyzxVT4f5DlyOlYLsRJAxHuqOQeUWdszOI1Yq4dn4APRqTp+aUyWxLdVpjjqVB6VYqVvW1WYbqRgphiCcMUVaOmxSLp5C+XIcNim5plgultmSTVGmacuFXE6bYp7F6vy08AOuaFRlmoJ003bWLOsaGAdK0FA0LXC4XTVNvSbd5NVyLuS6Y1UZmmI0XoRMlDHZ0k2jsQwQc+YRZXUOm5V2GstE2qW6rNZClgeRRcXhMI4sdvIJxI0C0gg2X6xT+SUlOexQrkm1wwCoBqDqddeO7YzZRipK5rVrI3d0lqEklc3asRq3teawSUk3RbmXuEyHksy6eU377Vp3CwlokzZblomSRIVV2jJr0k3nfirUo+SOlfkME4cTGBhJYtuOKZSKGgp5HwLBEkolD6Ymu8ChwOsx4PEa8HoNcADFkg/5vA8AQ7ngweHD/ejrSyJf8CMULMDr0WEYCvTqtb9taAJzqRjmUlGYlupIRF2fGTj9atxkCk48B1zbzRuPfzupqNOfZtddC2mnS9XvqAtZrUsqA9eq0s0WTp3kA4eSeIrPBjEfHZEzz3HoXIac07UMJecU+0NJXO1rotkPoVbnuispLCHndK5VYpuFcneeveqnAMsZaOOWW566llir/dpgyBc9ybonGQxgOJWFxzCha0cnV9j+njjSHj/OnJrEK6cO42AkjqzHi5QSkNEPyYYgiRCK8CCBbOsXPYlklcnn/DiwdwChSBGhSBHBUAnlsoZQqIQTT5pw3XLN6nevqspRLHkwPRVHvuAHA0dZ9yDgLyGbCyLRlQEAaJqFfaODiIby6I6lkYimkS0EkCkGkC0EYPDFRRAlEolkLSFf9DYKXBi1E4qpb9VrI3Ct67kMHqjRRub60zifwI7kuUY6q+9mYhmZM44cdWeY08IAZjAwX8B4NApT6IQzmC5EU1TSoKV1vx3jEaHeXCiIRzdvxpkTU9i5MAMAeDHai9FQFz1yqlT/1bdN5OuyB7rEQULyG3MqytkmwuLM6tB4pVmOvprxihBhq0bvOjVeoaJ4YrkYqSONV+w8eWIUT9jodLkSybPNVgCgVM2FR+XHM02lak6iQdcrET1LF81WhOmqeYoYlbMjea5InV0mXk92RM9qLKu0U/1LmbEQy4hRvnZGGS2je655DAXVAz90KAYHg1Xx1aj/Zo9X/hNjeWStQJP5zSN1Df2ionhAQ/SKsco+UQxel+ur/bJN+0DktSP708SMpbZMu3ZaRNCI6GR9m7X5xDwqMtguYkdFTsjIcIuIHhGdczrELbcZi30/Fo1XnNrMvSzsaDhDbiGAbLrmvql6LESieRimCl1XUS55YFkKAI5AqIye3hQ2b55GPu9FMFiGrqvweEz4fcKFBWD7yASyeT/2jQ0hGsojEsxjKDEHxoBc0YeJ+W6UdG8t52sbFUstV2mTfUJINsjLklgP/RwQ78stIn6tPEtYRXFjqZWIXn3ED0DNPIh46LtMdCz330o1IWcoFZVbgSifq5iK8lHmKGQ7xINXpJ2JSKt8e9R1J04T1yBredNuw1pOY7BW+7XBkC96knVPWdWQ9AewdWEBE5HIUV13SdPwq/4RgHOcMj+NEzJzGA9GIVNUSjYCOe7HoJJElOVxkjYOBiDL/UhaIRS4FyYUeJiJPjWFmFLAmJ7AuJk41t2WHCeYhoqF+Qjx4lVJkn7kkA/RWB6DQ/MAgMNjfeCcIdGVAVM4YpGcMwAVDhYRLeYwl+rCXCoGRTURChTQG0vhhIEJHJnpRbYcOrobKJFIJMtE/hqVbAhejPcgXC5j88LCsekAYzgYjUPlFoby6WPTB4lkhZmyYrDAsNMzDoVVBqV9TMeIOo+TPRM41TOGHZ5JeKohNQ9bXcdbiWRxMKRTIby0dxhHjvRA1z0wDA3Ts3Gks0GXysAwFYQCRef/TUtFOhfG/olBZIt+bO6bRnckhZYyCYlEIlljyIje8Uwr+RTglqJYDUW1/6EkoGJF0ShCkDHWty2an1BaUkoCZU/nmB+HI104cX4eE9EISp7KqW0pSkN/LMKMxJGfuPL+NK7H1e+6bcl7PdAVFQFTB/c11oMCZ2iF/KlAmEO4IOSjyzL8FPeDcyw7LANqxiui/NKRbgpyTjt3oij7dJQtjWViuapwKDpHfFxHqYuhEFddOfFEGadNRjReqTr0iTJNOy+eIcgwnZx4ekW6aSgqLF2tGOgI0k0m5LqrSTIF6abungc0yXVnNi9zLdNGpumUidKmTr9vb6c+4gCHipfMIZzkG3dkslN6F6b0LniZAQUWLK5C5xpOCowiygro5hkkjYirncZ1N5dSNu1/ndSMsYpsU9E5ee0sRrrZSpK5uHbaSCRtKMMYu22rsczdnxbySWHdi5FmktK2To1XbBThOhHLLVZZl2XVtoFIOOr4fLjuS41SSfIZ5ZKXV5cRDoxlKshnA656WtUlbO/+EZiWgngsg/6eJDRmwDRVx1CDQ8GR6T70x5MY6JpH0FvAZLobZdvMibwvExJPSobZ6VA79d2361xtNH8Sj5WjZlSoerX+cZVV/nHUzg9OHA9XLtvqeSvcv2zzEErO6doeV07EFZBzig20y+tHuaA5621zHZBJD6l6wv6mDFpcOfWo1VT3yXIe9DKP3nGPfNGTbBj2dSUwkM/glJkZ/Hpo8Kivf3NmAT7LxETg6MpHNyycI36kjJHnivCUOEwVeO73wlKHcJTJW348V9yEfi0FzisvegBDuWpSYf/mMbiKiFbEkG/eedGLaVls9U9DhQWDq8iYAWSMINK6H7o0uZAcQ+w0DX5fGblCAKlMCH3dC+iKZDC30FVXm2EqmUC+5MdQ9yxOGhjFXDaCyVQCXN6QJBLJGka+6G0QuCpEmNqMIlIj5u3GVajxpFbLuEd0hWUo1xdy5LyzeuK2GoqKF+O9OHN2Ej35HGZDte8pXC7HZmNYjTsmGqLxBrFBwgilYyKjAf3ZLHbOz2IqEEI66HeZaDiD5SpgaZXVuo6BHdF0jbBXZwlXqLM+wVjUPuZivXaROmcUWTxIjt252HZjmXtguXG+k3JBjNRh8ag6x/ZdOcSnazuy0KXCZMxltmJW83XkRbMVozZtR+/KRm2n2ekSLOFYW9X53GBgALiqOOk6FDGKJ0TvbJWiK3pnp0AQI3UtInquelbjMkqbKKBiNEaB2qUVaEWz6JsJDePl7so66xq0l4mqeQBA0fDCY1Y2YGtoGnndh1Q5BI9iIOIpIO6fBgtU0luUTA84Z+CoRBEsMJQNL5KFMExe56BLRfRMXtkH7SJ6NqtpoiLWJdshohudmqgI62wb0Wtp9NKkbTKq0VhErbtm/S6uULhOwACLV4w8hHthQ9t2e5YYBeGueUBddIOKsBJRPvueJ14n5byGYtGD7nga+bwf3FSwkA6jJ55GseRDruCvNCasLpsL4qX8CLqiWfR3JRH2FTE614ui7nOvzzaRERamTsdO742U+YtLAUOeg9RC4jR3l7FKm05Ez94eysCFiCaKKQBaRfmqFSp/XekXViDKJ1RmJnF+uzxWmke5m5qfdGrM0iLy7doPRCS6ad2lIs1Yjnvki55kQzEZDGM4GMApU7N4ZGugnQHhipH2VeSC08HwUVrjxmbbb3KIzuvIRxUE0xZmtngx+jIfLWGSrAn2ZQfR7U0j7s3htNghFE0vVGbhcLYPRjW35WQBUJmJsFZAxFOARzGgKhYYOBjjYOBI+LOI+bLYlxzC0oYJJJJOYJifi2JoeA5er45y2YPpuS74fWVsHppGqezB2FQPirrXtRTnCuYzUWQLfoz0zOKE/nFMp+KYycUgz1eJRLLWkC96ko0FY3i+rxcXHTyMbfMLeGmo66isNqBXQjkm2zgyHtW0MJjMoT+VR8Gr4fnN3au3Lt0C9wJgDN6Cie7xyv4MpC0cOdWPqe0+VyoGydojZwaQywcwXjDQ40sjpBUxWYw7L3k2JleRKoeRKofJb9QingJOiE8g4s0jI10OJatINusH50AoWEC57AHnCg6N9SPoL6G/dx4jA9PYNzoETnxMVza8ODA5iN74AvpiSXSFskjmw9BNDQFvCfmyD6mSlPFLJJJji3zR2yBwRZD2tZNrUt8MU222WSelwmxZUeyHW4tT+dNGctYql584nfN5cSARxwnzSYz1hJD3eQBBXsfNxq1lRP4z8UNy6jMMRyqrcdjKwVPnp3DQKmNBCyLtqUSgLF7pmn2MeF2/KVmRo74RZZqEdNOWbHKV7ist563UDZXLCJZ1zESD1Q5ycKUiuzn3pSn0ZmoOdCVNwQtb6Bc9UuZCGKtQ9ThniCR1nPHkAkyNYaHXg76xMoBKyrr954SQGrC/5aosbwkbU6h+Z0PJNQHAqE6bokzTNlcRc+LZx1+viq08CpQSq8hsiZx4gGCYIpijUGWkiQp3z6uftiWbrrbNRjlULV8VIQtcAuSyHXoRVOpyWFAxU4pjplqkUA2Qcu3K/+QMH7JBPzZFZzCXLSNbCqCge52TmHGOrlAWuWIAzPBA0blL9kz2cTkmKu3aATGfMlSh1tepiUr9dCfLLCaPXst1iPcW4t5pL2oRN3WnQlU+Vnfeu+5LjmmTeHMkzFgos5amuffcZe73NQbOFWQyQfT3LiAezUI3NBRLXswvRDA22YvtW8YRC+aRyoSry9e3zTA9X3Hw7OlKoS+y4Dh5pgtBpIo1hYdbXmr3uw2U+pJ6WLv2mf23+XUnNu5KgyiYsdjSRuaSZDb2gcrr53yOQMg5AdQ+i6Bcdig5pyh7biHnFNfDqYSz7eTMdtuEPLhaoaEdu79trydKjdJpmavN1rPJ+mtVIrlGu7XRkC96knVBSC+hrGgwFOLjDoJ93XEMpTM4bWwWT24bWOXeAQvBAO4/cQt2TCVx0vwcgDns7h7CXGBtRiQY5zj7wBTCJR2PnzgAU1Fw0d5xPD8SR6iou17yZqJ+PH1Cz8p2gHOAMaiGhZ1Pp5GNamCA85I3N+DB/peFwXzySXD8wXB4vg+DsTn0RBbQH0vCtBRkCgFMpLoR9JQwnJiFbqhIlbcf684uG1UzEeoqoasvB8Y4ckk/FiaCMI3O7nWS5TE5EUcu64fPX4bHa6IrlkG8K4OyXvl5ZJu2tKJY9mF0rh8Ah6aaOHnoCHIl/yr3XCKRSNojX/Q2CFxt8oG7M3LWrgF7AWFZ4vtuinbRQNI8xWW1XP3TJBLhM3VcMHMYAPDLns3IeH10RWGFlsrwfH8Pzh6bxNB8DuNxYWSVCEU6JhuuEWZxmtgDdrSsOppY8mh4brAPWb8Xp8zMIuf3wNJqi9rHiMM9suyMklImOW0ienYkjzcxY+G1gWcwztGTzaMnn0c8X0S4VJFHRoo6proqP0pOHU06y+oKw7NbuzGRCNV2WtNQRnU2ZcbilFX+f/BwHifuycLwMHDG4CtZyEU0JGbKyEZV7Ds9hEKiskGqaBFe3ZhsqXb8y7qdKkEwW9Fr01Y1AseFiJ49TaZKqJqxKJyRET0ytYFOlBH1yIgdkT6hMs1d9cQ2XdE7op2ljN4u1sCFipa1bY9o2x0FEI41FIzP9GKccwR8JYT9RSSiaezwjTqj7apqIhHMYNbwurvgtN0mWkb0x22YQmxXCxOVpu1zDoDD6zWg+UxoHguaZsIbNOAL6fD4LHAO5OZ9MHQFkZ4Cor15zB8OIT1RNQSpX6er7bp5raJ8zezMW7XdzA7exo5o0C1Xojcmd6VXsNtsF71rFeUTJ9umZCD0J47awVKQToWAVAhggKJY6O1dQFc8BwDoimRg6AoMQ4VXNcC5At1Ua1vM3C0bpopUPoT+WBKFshf5csBVr1KX1/WL3LzOoXZPOzMWp8w+zpVFuGKfso3PRCfKR6VpEMxP7EgeFeVzzRcNU6h+Ow9HoR1GXA+uKGB1RUSUj0y5gMbzu/l9gog6UikQnOWJa6fJ1x0tjVdc5k6LvL9LM5bjHvmiJ1nzlJXaaXru7GHsj3TjUDje1pFqJhLCRDSEM8em0ZvNI+fzYDQeQdG7uqd91lv5eH84k8Zv491o8RPoqNGfzuK0iVn4TBNlVYHXrDx1DIUhGfKh4PPgheE4ThmrvOilAl7sPrEXef/KW+APjhXBOKBYgGZU+tE1X8bBnUGMb/UDCoMqNR0SAABDoeRHoeTHQiaM3q4FKIqFmWQXYuEcTticQ6KrgPmFSN13VByaaoApHLqu4ehcgxy+YBnlgqdy9goWw72b0oj1Fir/xytSYr2oIjfvQzHrQTHrgVmu9D95OIj4pjx6tuWgeSzMH16bqoCNimUp8HgNGIaCQsGHcKiAcKiAsq4hGKgoDgxTQSYbxHwqipLReI8cm+vF9oExDHbNYz4XgcI4kvkIrI4T50kkEsnKIF/0JGsezhj2hxPYmp3HeDCK7Zk59BRzeC7ej4LH23LZp4f7cY45geFUFgCQyBXxxAmrm2NvLhTE3ngPTkrOYiCXwZO9m6Crx+ZSixRL2LSQxuZkGmVVQUHT4DMMcACzET9eGO5GNuAFwLF/IIbJeBCaZSHj9wLq6vw4LvsU57OdbETD1IgfU0M+V5J5iaQew9QwMdfjjALPLsTQndDRG59AT3wBpVLlXqCqJjya6Qz8G4aCsfEeFAorI6VTVAteXxmax3YLBVSPiXC0CH+oFt7VSypMQwHnQCCsY+ZIBNk5P0xDIVMuAByKZkHTLMwdDMMoKejZmkMh40Eh2fo+J1lZLEsBUzgszjAx1Y1IJI9YJIdc3oe5hRiCgSK6IjnEY1lk837Mp6LIlAKoRfk4PKoBReEY8syBA+iNLODwfD/yZSnplEgkRw/5ordBEPPoiRIwG1FC0UpW1WwWqQahKhMKP0pWRRm0UNJTm/FgFCdk5zHnC2EiGMVpySmcN3MEjw5sqb1E2RJBoybtGM5mECuUnHYUi9dkeoSk1G3AIsp8CBlXVb7ilmFW6h2IxzCcTSGk6zA0BktF5R+RR89RlbQwfAFo4xVOmbFUZZqbkmnsnJqFPctnWsj4Vezv78ZEVwglv904d45HIUBE8JqdMKzuLwSFJxrLlGo7z58Vhco5LM39IsmEE8o2XBElW8VSpW9UTjxRrskFkxWu25LMWpmdF8+VE8/Of2dLN1lNuknlxKvUJcrIXHeNZbU8eqJUsM18Su5ZleItRbrZqbyy7TJkHqkO103lm2vSTr1hCmPAwlwECymGUDAPv69yEA3DB8NQoesauMXQ3ZPGppFpzM1GkZoLw7JszR6x71vktfP4dcQHc4jECw3XqmkwFHMeJMdD4BwwSgq8AROKakFROeaSQaTHKyMZDJZbsuasj6NvRxqhhI5yXnFkx9HeAgqzauu8di7pJlEmrIOkUwlVSzmneO90G3gw3j6PnlNfXJ0tv2viuEsawVC59+xzhjBtqTdoASrf7cUTWUQjeUTCBczMdOFIKgTGOCyTYTYXw+xsDNFIHvGuDDYPTqNU1jA23YuSrmFz94xjyrJ3dARgwEjvDDYnprB/Zghl01PtQaPkVGQpMk4qaOg8Y8nvLGo3bUtlsKpmLI5q0nWd1OlUKxWqMzuTcwLCte5SO7aQcxKferh2nUsWWpXmL0WmuQQzlo5NjUjTstYH2DGWsZZwIthYFmjN9RrAWqP92mDIFz3JuqCkepBXPegu5bAn3o8nezfhoqkDOH1+Es8lBlCui5gploXTpqcxlM1iJhhEb76SzHlvz+qlCHDgHKdPzyCo69g9MABDVY+6ePO8g+NIFGqGKrNhPw72xDAdCQLOj6xjJI9UGJbz3JJIRHRdw0JSsLGvO60LR3zo6VlAT28aPb1pFAte5HM+5DM+FArelr+mFcVCIFRCrCePUKwEo6xgbiKCXMoHo6wAnIGbgP0DsG1i8jb4QpVRAW/QgmUCxbSK1JgMdR9tLFPF3GwM83NR9PUn0deXdP0mL5U8ODzWh3QmhFQuhIC/hP6eJDYPTqFU9iAUqAwujs8lYFRH6A7N9uOEvgls7ZnEvpkhmJY025FIJKuPfNHbIHAFtR84VAQNbcqoSBwRvXMVESNr9e3Vz2dEH8l2iN9ek4EItuSS2B/tRlnV8HRiCGckJ3HRxEHMBELIeH2wGAMYMJxNI2DoeLq3HzPBEM6ankBB05DyBmoRGJUYOWz2m48qZ407xWNYeNn4FBL5An7T34/pcLj2Q0/l4FplDJdbxOgmMbgl/hbgWnV0jzJjqTOOOdgbBWaBhZAP8yE/prsCNZMCe8RbXKZVeKfJPqk3WXGX1dpTiLad0dsm67V3WaFUizDaqRIMl9lKdSOEiJ2YSoMR0TsnoieYsYhRNwZAUQG1XI2+ElE8COWKRZRRpi1tonOu+ZTxijMCLbRNRfSaGW7YdPjeQUfYiMj2kiKD7ugcOa9+2bp1MwYw04JiWHWqgcZ9MjcWxcJUCKFIEcFICV3xHLp7M7BMhkLOC6OsVGSWJoOiWVA1C16/AV/QAGNAOa9ien8UmTlfw42xrekL9fJHbZMFzOwJoWtTEd6wCdXL4Y+aGHpZFsUFFQuHfSjMedzHt9MXSzLq2mY03b5BLOWzMjICU4votUqvIN5QnIg+YcAiLtM2JYNtZCVeq2RoyN0MBzA1kcDsdAyaZsK0FHi9BoZHZhAN55BMRsEAFPM+HB7rx5ahKeclr1j2IJmpDUBwQ8HhqT5sG5zAlsQUDswOuG7cTnSPSmcg9NHud7PxCSd6R6TZaVW//hlImro4YhkxEt8Y5aKifK6oW/V8dEX5rMZlyCgfde+gfm+4zgnunofas4eM3rWL2InRQtIsidgnrdoTaZeSQZqxSBaJfNGTrBuOhLswkk/hZXPjeDYxgKQ/iEcHtmA4m0JfIYvuYt6R/5VUDU8MbKp8awbgqaHhVeuXYlmI54tI5IrYPJ8GAPxqaAjzweCqrbMdk7EwJmPh2o+0tr/GJZKNjWmoSM+HkJ4PAeDw+3UEoyX4Q2X4AgZUzYKiWjCNyktfuaghNRNEMe2BUaq6LK7yD5PCggfFZOWxrGgWNL8Jb8hCeKCMgTPzSI95MbfHh+YjUpLVwDRVmKYKMLgdPgUsS8HBsX70di+guyuDmWQM9cdJNz04PN2Pbf2TGEnM4Mh8X0MdiUQiWUnki55k3aArKnYnhnBmcgKvnDqM/dEEDkXiOBRN4FA0UbPLdo1ert4PM49hYstsGltn0vCaFgyFYSwawf7uOMps5d0qJRLJSsFQKnhQKtRdp4tJybDKWIaCcoahnAGykx5EBnV0n1yAolqYeU4w/pAcNWKxLPoHkjAMFalUoxsq5wpKpUq6j1w+QB6iYtmHI/N92Nw9hYHYPCZTR+FzAolEctwiX/Q2CE0/M+lUzklAfrdNzKc/8G7TELU+SrHF3POzHj8e692CEzLzODE9h4hewrPxgYrEwVZ+iBIRpVFWUctbV9c44Mq94+q30KavbOCE6RQ2z2bAOHCkK4Ij8RiyXg9Y1eiBEbIStxlL4w9KTuQmsuWaAIQ8ekKZ/VLryiMlrtuWyDT2x71MowyVlFWKngdapeOqWttYe9qj1ZxH7HZ0wUSlnXSz5phYW8aWbFo6kRNPkGtCmO+YoxA588T8d049oyrd9FTmN0g3CWMWUqZp8IZlXGYs1ePvXpaQaVqN563r3DJ503pLopXMUqSd6o+UKVLrI/rd5MWqPs+mLd1khtWBBLSNJKuVrHIxxgst8tqRfWsnsxTmZ0Y1mGU/+s4oopwtIXXAK/SxjTSz3Xrse49L7mbnI1Ma6y3m5deqrsuqSTep3HvtjFdsOl3GVc++BkXZOyGFFmXxjJDkhUIFGLqKA/sHwLlSUZDYs6vLBv1FlMoecFNxRf/E+3s2F8AES2Coex66rmE+G3M6LJ5jzn1baXxWkZ9RALVj2Fpx2DhPVLJyYQWEx4poalOTnApt2fLSJrJY29GZNEQTFyHknM71REkzxYbaJQK2iBXayxJ58lx0en13nG9PXJj4weRqZ5EDPFK6edwjX/Qk6w5LUfDbWA/SXh/OSE4ir81jf3T1R0VDRR0nTC1gZD4LU1FwsCeGgz0x6FxeRhKJZPXJT3uwcMBEfHsZxaSKUlLmZTua5LJ+hCNFqKoFw6D2PUcwUEI23z6FQjIbhddjYqBrHrqpIV2U+RIlEsnKI3+hStYt04EI9pk6TkzPIePxYToUXpX1RPMlbJ9NYmAhj5Km4sWhOA73RGFWh3+Z3qYBiUQiWSEW9nsR7DaR2FHCxBN+SAnn0SOdCqKnL42+gQUsJMPweAxwzpDJBqBpHD3dC/B6DWRmOvs+eyoVh0czMJKYwYEZDQVdOqxKVhjLDtGuQdoZh0lWBPmit1FgIPPoOXIvQkHhusR43UyAliSITl4r9fuihUKQE4XithwMxREpl3B6chLjpahTnvN4MRkMo+zRGpap5VQS23av0acbGMhmMZjKIl4oIefV8OxID8biYVhKdSeYjfITZx3UYC+rrVQMAnLqWyBO1KvKgOpz5jVsjEsu1FjG1MaTwi4TpUZkih9hGbUq3dTU2gmnVaWbitCO7bppCWXl6mg4F2RhoozTXrUp5MSzJZtcGEln1TKXNLNJfjynP9WyZjnxGAAmSjeJnHj1y9TKqpJMl0zT7pewTyhXTXG+vbzVeE7Qcs4OpUSLoY3Usr4PzdshlqGkn5QzXot+M1bZbmZaAJk7tHNJZUtJ5iKkWy3baSLJbN1HevuTL3kwcE4J/piO4rzaXprZar1AbRvIAKGwgRZRoV1Q0eKVfFmC6ya1PucOTOTEa0anyzj1xOvSvp8qwv2EyOHmupdbCmanYhgYTiISKTjFg7A3UcHcXASFvL8i63S5KxOSTIthfLobWwYMbOmZxJHpPndCdUfiSCzrsjsWt5Y3zCaxTzdXHtjq9SwcKvenAK5Fq0WND2ZS4kkdS1dDzeWc4inoKDNdx4WYprStvPH4gpBSuj7/IKWW4vlWLRGP0SKdOOuXr3W78SBSZRJJK+SLnmR9wxie7+rHjvQM4qVC5bnBgOFcCictzGAuEERJ0cCVys2XMyDr9WI0EkX9Dd5vGOjN5TCYziJeKIIzYCYcxK5NfZiKh+QNViKRrAkKsxWjlvCIWXnRkxw10gsh5DJ+KAqHP1jC4EgSADA/F8V8MlIZvFrEIeG8knZhU98MtgxMYjbVhdl0FLxVTgSJRCLpEPmit0HgCmqjo/SnAw2Qry1tRufdH0o3ro8cBaTad43aNy5DjdpRbTMOWEzBi7F+4cN1QLNMDOXS6C7lEdbLYOBgHFDAsSWdwkA2C0NV4DMM+AwTPtOAgspg21wogGcHejEZC8FQ1ep284Z9Q0Yl7FFLKtyp1Oa7c0E17hMnF5TLeKXxo3cyokdE9xhhvMKEtu35imCsolDrE7AjeRqxjCokl1OqG2YKhgJK9cCawoi3RUQLuBCds0fHmS5G9KplRMROLHdF4nSqXvWvnUfPqEX0qNx5lTaJ6B2VM88gyogoHzXNqGgRWdYY5VsSi42+NW2n9ezWZiREPWLdjAHMsM1YOmynmQFLi360NWBxLdNiPe0idVReuxbLFOcZ/HEOmBa9TLtjRJlZiE2RxitUss9OonzcHdGjmjvaBi32/cR1oggRKEJBYjdgcgUmAEP344jei2LBA0u4kVPBItc+Ye7GuaXg0EQfeuMp9HYtIBFJI5mJIJkNQzc0V79Y9bhwUQFBPDvbUmfawoCacQevReNce7GFQYvbjIXoC9Ex1651or3is4owR7Hvt1TkD7XfDFworN1biXPe9dyt/qmLcgr/514Wtftj28ifXeRqu10U0K649EFmzi3wdjkzjxFrtV8bDfmiJ9mQGIqKw5E4DsXiANwPgv58GiOZNFSLI+v1Yi6ooahpKGkqkmE/dPvlTg6oSiSSNUo5zRDZZIEp3C1dlBw1OFdQyFe/q1v2IWCYSXZhIRtGIppBIppGb1cKhqmgUPahWPYinQ+iaLQ3epFIJBIb+aInOe6YCkcwFY64XuScabXNKLhEIpGsAYpzCphiItDPkZ+QL3obBd3wYGo+gZmFGIL+Evy+EgLeMuLhDHqiKYzN9yKVXx3jMckGhPO1a3oi0yscFeSL3kZBAZkwnDJoWY4Bk0uJQbVH/N6g0tlQeeaoZsTIviPJaNIhJz9eO3mpI9khPrznlPwC7vx6dZBSGUI/x6t1G+bY6yY+qG9rvELkvxM/enckm0QZI8oUokyUYYrYufJEVYldVxG2UrXbEfanvR5LKDN18SBVGrUE4xVbxukyG6Jy4rly5oGYT5QJue5s6aZaBmHGImyDI/dsXWafg245JyHNJKWbjRJA16lFmAeQkqQlsCjJok0LJU7HOeiatFe/PGMATN5curncPHqdmpmQ6xHLrMZ6na6jxf7W0xz5aSCx00BhQnHLnIGKO4iN0kaewAjZGClNpxZuI+dk1SqUdJNqpp1Bi9iIIy8XrnnWeEOtyfp563piHj3e2HYrOSf1CQNr0m1qGUrOmc0FkM0FAMagqQZO2jyKke4ZaMzAXKbuO3O7GUfa2TDL/Zxwdi5RT1yEeH7Tnw8Q+7sdlAGZeCxbyTmbKYrtad56GVsqyYjnvFtSKfbXabBWZD9P20g8nTmLMXqhjo0c05EsEilOk0gkEolk3cEw9xsFqh+I7ZAj4xsdy1KQzFQieQOJJE4aHsVAfA5BXwEAh6qYWPbojkQi2XDIiN4GgSu1UTQqouf6uNgxGVjCeogyd4SheT1AGKFtMU+c7/54vlrWxHjEiV5SVszEijgVDhSLxIXsQXkqWkptTLMNJKN3dX0Q+0GOnBKRSFckTljG7iORXkEsU6uGKq6yanROFcoUIbpnl4tpEdTqtOIyCmispxA7TZxv2pE80Z68WuZKn+BE7BrLKuXuv64yKlWCy4yFt06vYEfdXGYs1e0nypgQDbQjdUo7MxbR7r3T9AMmdfItno4jekuI8i06nQFVzgAYJqCb5PVGRuyapTjodLvamahQ61ns+qwmYVFiGSMN5A4DoUGOheeIHBM2VJvtonxkO0RZJ1E+Xo262pvQatWuFAfVCItC35gXa9bSNg2DsAudyJJ473RmivWq88RuO88LITIkbENdNh9344q7zOsxoGlm5d4r1E/ngoiG8uiOZmBaDKrCUSx5MLUQR67odz+sVMJYxVYD2JGt6jbY6RXs+S6jE8qgxdk+otBVgSgTH7KW3R9iEfH4U4oFsR27H+1UTE47jS4y4m8D1ibKzYjukKFPe38uwujFmeOKWC4ypMftA7oGkdLNo4J80ZNIJBKJZJ1SnAci2wDHNliyIfB6dAwNzsLv0xvmTcwkkMxEMDUXh99XRjhYBDgQCeWxpX8anANlQ0Ou6MdsKgade4/BFkgkkrWAfNGTSCQSiWSdYuQqfz1hQE8f275IVo7hoRlwznB4vA+6ocGyGLZtmoCmWkhlbDMWhmLJh2I1yfpsKgavV0fQX4TPoyMazCMSKODgzCDKhufYbYzk2GFZoHPkrAFkeoWjgnzR2yBwlTsfkrsUQnYZoS4SpQ0dyzibyCYbqlEfcDdZlBMVHSUGKV0UlqXMWIiP+V0yDkpiQmhpXPINtVGfYctyOLEjqF3DGWpmLNQOr5PsoL4eJfskJKcuSZItm3SZEFSrtTFecUxUVLGsMT+e6mq7Ubqpm5WTsKzXbjdlo1JmCmYrpl5zQuBmtbxdzjyDMGhxGW7YDdbKWqpYxPr2rm2mJCRlwbYRQuP+dp/LhGECdVzVxg/zGXUti4fcltQuV8LZKocdJSldRnsAFi3xZKwyn1kWLVdtl8tOpJUks1MJZ7P1tJJkLiVHXx2lWcAyGQK9HHpSmKEQdyGXVI4wa6Fy61Flrn4RnaJMRrggPbTNWlrei8V1COttZ9bSQsbp/hSgteyTEdI+MVdrQx+IpxprZiJD3MvFR5CiWPB5DUxOxVHI+hCLZdGdSENTLZRKGmDW7Sbxflv0IFX0gDNgVolh2/AEBmJzODzd71qG2blaFeF6snjl2uZCh1yHo1a3YZ8sJdebmI+OupfxxnoN663rol2TNd46XdRkqIQ017WeJitq1TiVg5PYP0uRe1rSjUWySOSLnkQikUgk6xRuAsVpIDjAkN4rv3nZCFiWgmzWj+7uNLq709BU03lPyOYCxBIcg71zsCwF0/Nd4JwhFCiiK5KF12MCaJR/SiSS4wP5ordB4KoQvTPd5QA9cO4aTHUsiZew7iaDljZtZtMRE9KshJhHRcF443zX6F9LtQARIoQwqidGaiz3iKgIFeWrfenuXk3tI/w20TuiX3Y9RkTxXOXiZimNxit2BE4lDFpcUT7ReMVOpUCUlY3arcWsboQpjG7bkTwyigeAE9E724SFMl5xG6LUplt+s0RE+RivmRKQEMfDZUJABUGcYyREBoSolIOrneq5RdmKi8fS3lZi5Nh9zi9BItMqJYFFhRXR8TdijIp8dZrOwGkEFTMWo86MpVUEjpQ2EPXa9aGZsUqrqF2H6+PN5EwtcmGVkgqi2xUAppMihokpDtpFzux+d2rQ0i7y5zpvq3Vc6RWq62vXR7KvjdG9xUTqxG4BcKk9eLPrsW4ZKoLofs41Poxc9yUiMlh/z5+ZjGHT1hnksgHMzUYQT2QRCheQnItUom5iFzQLXdGKhjcazsG0FPi8hnNovB4Dp245iGLZi0wuiPl0FGb1fuuYs7FKHxWzeqgcE60m+8fuNqFiWErQyXkuL8Ggg4zuKcQ5I162zu2UiNi6Gieet6527AMnLGM/1hazKS2MXlxQkfpWSDOW4x75oieRSCQSyTomP8oROxnYcpWGwiRH6iWO0uyx7pVkOZTLHux7adj5/+mpODAVJ1+ifL6yM61pFgpZHyanuxGN5BCPZTEzH4NpKggESujpSiERSyOdCyERzWA2FYXfW0bQX0Q0EkXCn63m2GbgnIGj8tfiDCXDg0wpiHQxiCW9zUkkkqOOzKMnkUgkEsk6pjTPMfoTAwvPWdDCDAO/U8mvJzk+KJc9yGZrBzzgL8HnKyNfqJTlC34k01GMzfTht6PDyBf9SEQzAICeWEUaOrMQx3w2itlMF+YyMSzkI0gXQ8iWAijoPpRND4LeErYkprGtexJ+TwlrNlIkceCWtab/LYVdu3bh85//PN70pjdh06ZN8Pl8CIfDOOmkk/Cud70LDz/88KLa++lPf4qrr74aIyMj8Pl8GBkZwdVXX42f/vSnS+rfWkNG9DYKKq+ZgwgGDoT6ynm7F8tsWYnSJvdMx9LLJsswqrDVstTH6s20oJQchhNlrQYim5pj1HdCkGyKnXTkQk00rKT8tPGDa1Li2diF1lJQoJbDzpULrzItSi6dMpcZS2XaQ5SJ04qwrSWzcktxyTSr8ixLkGnx6jQl1wQAVjVhcUk3q5+ZUMYrzaSb9rTLoMX+S370j84hk2FVS1zHoFHa5Kh9XMdN+B/V3VdxNaJ5gCNdI2SodF/RPE9bPfa+pWSaYs6/dvnqKFrJGTs1I2EATBMwDHqbKVlopzn6mpW1M1Rpsf2kJLOdVLTTY6UoCPQq6DpVQWqvhfhpKphq1lSVrcxPgNr5QRm0UIYS7SRX9VLL+vrO/wvra9dHCuI7BFJeaZ8gCrEtQl/bSkAdyXWjntF1zTuSeVEW2Ch3dV231POL0ILzxm7DKikYO9ILpljw+3VEYzkM9CZRKFbSKmwZnkKprGFiphuFoh9jkz3QhqcQ9JcAAOlcCPOpGBSPD3MLmvtwuZ5VDEFfAcOJGezoHUdR92Am04VUIVz3DCKMR5p8zdBqn5BSyg5xHw/7/G59r25373QM39pIPGv1FrEB9nEXt5/4AWAd5/GZSy65BA8++GBDeblcxksvvYSXXnoJt9xyC/7sz/4M3/72t+H1Nk8twjnH+9//fnzrW99ylY+NjeEHP/gBfvCDH+DP//zP8c1vftN9za8zju8zRiKRSCSSDUDkBAWKhyF+morsIQt69lj3SHK04ZaCQsGHqckEDh/pgyUMpPm8Bnq6UvB4dPi8OtK5IACgpGvojqUQC2fhUQ2wNlb8+VIAeyc34eBMP8qGB5sSMxjqmgWT0T3JUWBsbAwAMDQ0hOuuuw533XUXnnjiCTz22GP453/+ZwwPV+TOt956K975zne2bOtv/uZvnJe8s846C7fffjueeOIJ3H777TjrrLMAAN/61rfwt3/7t6u3QUcBxrn8GnI9k06nEYvFsPmfPgfVU3HjUozafDsi4iqzo3dEGSPKAGGUlOpEu4GOFRqVo9MLtJlPmLE4kTOlsYzXvEFgCdNc5Q3znbaFEV+7Xv02MwDDXh/GjFJDeoVafxojPmS4lNqfYrRIrU0r1WlFSJFgT4sRPY9WOdiaUE+zI3pq7UTwCCFfO1pYNmvCADuVgiFE74zqj41iuZbHyShV6lllYYeWatN2KgWlzBrK1NrnKM756iqjIn4WUSac64pg6sIADMS8mEpWjpW4rEK23Wi4IKYfsOspRJkrMmY1zhejZU6bVuPIsSvdQXW+GIkj0yEQKQno6JwYdbH7J5YRyzQzK6GWcep1mH7A3j4GdI+EMTeadZvWtFq2U+OUDvtQmb1CkTrKQp6ox0TDlFquFGy+0oPsYQupPRbMYt1C1YgGY0pDmasd14qIMsqspc2yleMUwdxYtraJ9rrJZYk+NhtNJ6JyDcsK810RFsU9r+V0FWd5Kn1Eq/r1yxDrqBl+tanXth13GWMWvD4DI5tmoGnu82khFcLUbALDgzMIB4uIRqNIpyvJGI9M9SCTCzVpu9ZGVziDwcQcdMODmXQMmUIIJuGS0zaiR7CciF47lUPLdbdTJIgstu1lYhhFPPLff4dUKoVoNNq0nv3b8NLAH0NjzaNaxxKDl3F/4Y622yJy+eWX4+1vfzv+8A//EKqqNsyfnZ3FRRddhL179wIAfvGLX+Diiy9uqPfb3/4Wp5xyCgzDwDnnnIMHH3wQgUDN0Tafz+OSSy7BU089BU3TsGfPHmzfvn2JW3pskRE9iUQikUjWOaU5jkAfa3zJkxzXcK6gVPIim6n9iC0UvchkA5iejcPiCkYnenF4vA+5gs+p49UMqrkGFrIR7J8Ygm6qGOmexclDhzGSmEbYn4f8hk+y0vznf/4nrrnmGvIlDwB6enrwP//n/3T+/6677iLr3XDDDTCMyjn+ta99zfWSBwDBYBBf+9rXAACGYeCrX/3qCvT+2CBf9CQSiUQiWedkDpjwJRT4Euv3WxLJ6jE1Fa8kWwegqSaCgSKGBmahMAvbNk1g89A0gv4SFtJhvHRwGHOpWMdtl3QvDs0MYu/4CGbSXfB7ytjaM4WdA4cxGJtFwFOEfOk7Rlh8bf9bBV796lc70/v27WuYzznHD3/4QwDAzp078cpXvpJs55WvfCVOPvlkAMDdd9+N9SqAlGYsGwSucXCtchJagl7CNspwl6FaVoNSpLgkJK0G99qd+x1eG9Q30W7jEaKiuIytFhHlIlQ7VLfs+aLEj/q43JVbr6EIoHLr2QNPCq8ZrxBmLK5ceC2HYFrLORnhMiKWOd/8u6SilWlV0CnaZiuUAQsgGq/UOkvlzLOlm6I5gj3NTUHiJcoiq5JjsUwhjFWcsmZmLLxxGUdeSX5432Ta7jcxnzwdXRKw6jFXGs9wVw4n95oqf0RDCUIh5hiziGo+4qR3GRNwYhlHFtpovELLPpvko6PMSGxp43Ly1lHlrNoP06TvMc1y/bUqa2ei0ml/26ybdJtr15/q+SMu68g4LQv5UaC8oCLxMgUT9xvuE8Wyz0FhWSqHXdv8eES+PapeMwMXu5hM5oq6mViaQQvRzJLy7RHb5XzC0KHRi1sKTcg0xTx7VD+onJjt7ltKY5m9/KEDA/B4DJTLGsKRIoZHZnHi1lEAwMREN9LpGKYmK/n3FOJZ1cpEinMO3dQwW45VUjZ4yoiFcoiFcugOZ1DSPZhIJpArBUkJ6ErRaV7elj9LBEO7jmWYopx7FcdaVrPtjUK5XPuWg4r8HThwwPnW75JLLmnZ1iWXXIIXX3wRo6OjOHjwILZt27aynT0KyIieRCKRSCTrHQ7M/9pAoF+Bv1f+GpQ0wjlDuewBwJDN+jE1GUcmE8TBQwNIZ0IwjJUa+2co6j5MLSSwd2wEByb7oZsqtvZNYTgxDUZlB5dIVohf/OIXzvQpp5zSMP+FF15wpnfu3NmyLXG+uNx6Qkb0JBKJRCLZAOTHLBgFjuCwguKs/DEtaQXDwkLYiS6unns8Q74UwKGZALqCWQx3zyJdDCJdCK/WCiUinANr9cV6FaSQlmXhi1/8ovP/b37zmxvqHDlyxJkeGRlp2d6mTZvI5dYT8kVvg8C9liPdFGUKlu22RslKREOw6rRFyDTE6U5z3FDuhO4ON19WXB91H3D1gZCsUTIX0r1TdNUkXDc55bpJ5ONzO3o2yuLEXHecVTddISQ7xDKsnW7EPi5UewBU22GTKNMEN02v2ui6abttik6bJcFhs6BXXDQNwcLbsF03hTLb4tsQXDVtt01WrtVT9Nq0fc64XFDtronnkz0t3Mlc53W16+JedNIVEee3U18FLLViGC7mCaSknZbLYZC7+g8AzGSueZU+NEq3XB23dzklJSVkqG0fly75nb2sIDWqFjI0SvK4S0rHGrpFum5SNwqxnmVb/zbKC5tKADtlKUl4bWnjch00W8k0221LOydSQsZoty06cRYmLAT6WBMpqLAsJeOk3CQ7zSdIuYHayzfLo+eSV1Lrq1ZwyUzbnB9Uvxebb6+ubsMqqELhmefcW1w5XUWpfHNduDv3GiHrpxxEiRxunco5XaahnFfcfzlqu4Jsh3jIUnJOscwEFjJh9MWSCGolZMyQ+1jW9cu1LR3gVXUoioWi7kWTI7QkGWZbVv5d5bjDdnq18fl88Pl8TWq35oYbbsATTzwBALjqqqtwzjnnNNTJZDLOdDjcesAhFAo509ns+sxZI1/0JBKJRLLhCPRz+BKAFgCKM0D28LHu0dHByHEE+uVXGZK1Sa7oR8i/ktawHL2RBfRFF8AYkC97MTbfC49mON+Uc85QNjSUdQ84mZ9i48ItDr6a+R6WgW1uIkbNAODv/u7vcP311y+6vV/84hf45Cc/CQDo6+vDjTfeSNYrFmvnX6uE6gBcL5yFQmHRfVoLyBe9DQLzWoCnelMTRsm4nUdPGIGzBzCZOJqm22VCm2JkxI54tQswmY1l4iLO4F+bdgg/Caew2YifU+5aYd1f1KJE4v3eKSOieK5yMWeePXBMRPlc9ZxRUN7SeIWJI+zkh/nVP66R00o9pUlEz86Vp4r58aqROq8Q0bMjeWLOPNuYRYzi2RG7ynSl4yUhP55ZLbNcEb3q6K4u5MmrRvIUvTHS1DDtbEw16iJ8KO/4fDQJ4tjFroAWuR9r9RkqAQSuVk4lV8ROjFQ7UVexrHq9ideBfZIKxjOWPVIvtm1RJ654vjVG4u3lXYYpxEXmjt412jHZkTzXD4LqMq6xcdtYRghVuKLOpBlLvQMH4DgUWaLbDhH5oyIfnUTsFI6BCyuTeg6IbAGi24HpX3IYObsdYZ+0iuRR0bsmN0IyetdpzkBnVrObY2VfMdcNsHrchPVZZRXMo1ZMapx6dshauFkR0T3SoEWEipbZZeJxqc+3Z0f16m/sZNSNilQRkU2RTn+7tzFoEWlp1kJtv4BjwkIasAAgTJQc86MW0T5xkoryiU2Tz8kWUT7GKvcgO6JXH/ET215SlM9uwlYFWICq6IiHs1BVE9lCANlCgN5+hSEWzCARrkRiiroXc6koGOMY7p6F31vGbDqGQsmHoe5Z7BgYa2wDlWfW6GwvcsUAOb+t0ugYvS9tdAOWI0eOuPLoLSWa99xzz+Gqq66CYRjw+/2488470d/fT9b1+/3OtGjcQlEqlZzp+hQM6wX5oieRSCSSjYXFMPU4R+/ZgFIdh/DFgcFXA5MPAnqm5dLrGqvMocgnu2SNUjY0xMI59MXnkYilwTmDYarojmaQygUxOtsLj2oi6C/Cqr4UhoNFJMIZZAoBGJaKiD/vvPSVdA37JwdRLFdeDvZPDiLgLaNY9kKvmssoigWvpqM3lsKWvikcnOpHvrQ+f7RvRKLRaMcJ0ykOHDiAN7zhDUgmk1BVFd/97nfJJOk2kUjEmW4nx8zlcs50O5nnWkU+DiQSiUSy4chPMIzdzzF4Sa1M8zMMXMwrL3upY9e31cTSOZjCwFSAEwoLieRYspAOIx7JoqcrjflMGFPzCVhcQTSYw6a+GcRCh1Ase+D36s4ypsUwnuxGMlt5GfB5yjixGrWbnE84L3kAoBse6IbHtU7TUlEoqzg848OOoTFEg/nqix4VotxgcAtr14xl+f0aHx/H6173OoyPj4MxhptuuglXXnlly2VEA5bR0dGWdUUDlnqJ6XpBvuhtEDSvAe6pPNW566P4qpSOMlYhJIcuKZ2A1eI+yAjjFcrowgVvMu3ujmueI4GkFG5NcHwgCOMVl6mBY8ZCyDXFcsp4hZCxuOScjtkKp3OY2bIpUX6pNu4UUipYXUYRjT5cee8apZu2uQplvCLm0dMt21hFFcoajVcsMWde1WRFzI/nnDyC8QqzzzNhWZeBCXXC2ZIkSnLb7HlhN2MSZVQ9W1Gm1qSbTXPr2dvoyltY+eO63ggzFls2rYj5Kan1iO3Y15ar44v/oVIThYonqW16IRyPaplLpklI6dzeGY3b6uTcU4j1QXTbaWHQ4toAe7+LzdF56YwckDkAxAWHbc3PMHgJx+Eficu3kOcRZiu8mQFLC5lmc0kmXb/5bOF42Ne6mMuyWL3m/Rx6xpaK1/LtORAyzqZ59pxl7JmEvJLKtwcAqqD7r98FVDudGrQAtYeYK99gY7dJnGbEZcXzvzqXzJknFhHbXz8PdZJMe9pl4EJ8e+A884h2CDmnCG3gIhTV3Toc6aZZPU4dGLi4uttWzln5Y5kqDowOwuvRUShXjFMYODLZANBXqeP36ijrGg6MDwIADK5UG6isrGR6cGCyH1pV8tk5DNmiH7FQDn6PDr+3BNNSMTmfQDYfqNsIe/s6u247NapbFKtnhbohmJ2dxetf/3rs378fAPC1r30Nb3/729sud+qppzrTe/bsaVlXnE+lalgPyBc9iUQikWxYFl4AzAIQ3gJoIQ5uAKm9x7pXq0c5WXmD8cYV6BkZ0pOsPSxLQbHkq/8AGEemeuDRTAT9JSxkwjDtEVrifSff7Du7NqRyIQR9JZiWgrlUDEF/CZv7pzE5F8d8OrakNtcy68GMZSmkUim88Y1vxPPPPw8A+OIXv4i//Mu/7GjZbdu2YWhoCOPj466cexQPPvggAGB4eBhbt25dcn+PJfJFb4Pg8Zrg3kp4wGCCjb1jGCLcKe2R3JoywjGcEG8IiiGMblLfR1PGK9SIXzuoa90uU4iypl/PNy5TM1ERyuxB4DbpE1zRPScKSIy2EhEdlxGGwivFjF7GHnkVo3i2uQqj6tXW5kTvVNGAhYroCeGyVsYruiWarajVMjGKJ0QOqtOmUMYd4xUh6mBHtMpi9K72QX6tTNiwRj8R8jyh0mK4aBG9E0ddxTQMrNqupVX/n/C0EKeZ2XiduFMpUAtX/ljCRrnbaTRmoMrgRAQXEeYmavFqZETstx3JY0T0jkrDUJlRvY7EfWZHdEwqmkQYtIgRLSoISG1AGzIHODIH0N6ApY3xSluzFSp6R6yn4+gegWjaZEf3xDIzDxgFC5HtGorTJswCJ9MwkNG9JukXnHVTaRjITtZF9+r3Qac/8EhTpjYVVtqshTJe6TQSJ9LOwKWNCUtDkagAWYaBi1ONVftYjbwyKsXLSkT5ROruidlsxco+6ZRx8c+KUMj7sT8/JJRw9CcW0J9IIl/wo1j2Lm59lOhgGYrQjW68shLk83n83u/9Hnbt2gUA+PSnP41PfOITHS/PGMOVV16JG2+8EXv27MHjjz+OV77ylQ31Hn/8cSeid+WVV9LX2Trg+PKZlUgkEolkg5PcXUZwRMWmPwgitEWO50okzWGYnu9CsezBcN8sGJn4V7JWKJfLuOqqq/DII48AAK677jp87nOfW3Q7H/7wh6FplXvjtdde25A6oVAo4NprrwUAaJqGD3/4w8vr+DFEPgEkEolEctyjeABFA5iHwSpzmLn2y6xV0i/qyB0x0XOeFwOv9iP9Wx2zvyy3tY+XSI5HOBjGpntwwvAk+uIpTM/Fj3WXVo4NZsby1re+Fffccw8A4NJLL8V73vMePPvss03rh0IhbNu2raH8pJNOwsc+9jF88YtfxFNPPYWLLroIn/jEJ7B9+3bs27cP//RP/4Tdu3cDAP76r/8aO3bsWHRf1wryRW+D4DWz0O1ceIKUjhlVwwyXvM6WUokSsKo5hPDhuSVKyWz5nWAe4XgniDKt6nwu1HMNkFWnXWpHooySLzgyvSbmGFb1bHapqmyZHuGdIMqn7GVd0i5hG7hWM1SpXzcnylhdHj0GwNA5LF6sfOcuyq9UW7oplJHSzWoXCOMVMY+eKNO067rlnFXTHqGdgmO8UiszqjtNlGu6jFn0ynyzLEiF7Vx5gvGKMy0eX/t8Es8d8Xyj0pW1+JHqSr1GnFsuVZW9TlE2atbqMwC6bsLQ9Yp0s4m81EkV1aSd2jLcNU+c726PN843iTJhY2t9IMpEWag47aynsczdTvN67oMpbhexw+3tFy9CKkcdiIPuWqZuPgPKpgc61+vWZ7n/Cuvj1I0AQHCYoe+V9k2gUp49ZGHuVzqsErEMmSdQuKcI66HKloWgL3dyoRJlRo5h7OdFhA9p6D7Pj+6LFUw+UKzdg0UZp31TpcpE7LyNrnx7zetVpoGy4YVuFAV5PSEmomRRjJhHmbW0a8eRnLar32F7Ha6Xt5N6tZMzdlhGrkch6rokl+4yxgCjBJjlgvsSJCTnrnUTxiucOm7Uupv1t1W9lVbPVbe1UObI5UzAysLQ6e//2DK+KWu3LaRk05bXCkWmubhk8wb0NTvAY4jfD3XI97//fWf6/vvvx5lnntmy/iWXXIIHHniAnPeP//iPmJ6exk033YTdu3fjLW95S0Od97znPUuKGK4lGF/O15CSY06xWMS2bdswOTl5rLsikUgkEolEIllFBgYGcODAAVfi73rWy2/DTrZFZLHfybV60bP58Y9/jG9961t48sknMTs7i56eHpx77rl43/vehze96U2LWt9aRL7obQCKxSLK5fKx7oZEIpFIJBKJZBXxer0dvRith9+GnW6LZOnIFz2JRCKRSCQSiUQi2WBI102JRCKRSCQSiUQi2WDIFz2JRCKRSCQSiUQi2WDIFz2JRCKRSCQSiUQi2WDIFz2JRCKRSCQSiUQi2WDIFz2JRCKRSCQSiUQi2WDIFz3JuqNcLuM73/kOLrvsMgwODsLn8yEcDuPkk0/Gu9/9bjz++ONNly0Wi/jhD3+Ia6+9Fueffz4SiQQ8Hg+6u7txwQUX4Prrr1+xvDNbt24FY6ztv61bt7Zt69///d/x8pe/HH6/H5s2bcLHPvYxpNPphnr/7//9P6fdb3zjGy3bvO2225y60WgUpmm2rH/WWWeBMYZYLNa27lJZzrG1LAvPPfccbrrpJrzvfe/Dy172Mmia5mzjwYMHO+7H+Pg43vnOd6K3txfBYBCXXHIJ/vu//5use+qpp4Ixhv7+/rbt7tixw+nPl770pZZ1f/jDHzp1v/KVr3Tc90549atf3dG5Kf5rloto3759uO6663DaaachEokgFAph586duO6667B3794V6a+8lhbHSh7fQ4cO4ZOf/CTOPvtsdHV1wePxIJFI4MILL8RnP/tZzMzMtO3PRr+egJXd5zYPPPAA3v3ud2PHjh0Ih8OIRqPYsWMHrr76anzjG99ANptdcn/lNSWRHCdwiWQdcfjwYX7GGWdwAC3/feQjH+GWZbmWffrpp3kkEmm7bDQa5Xfcccey+7ply5a26wLAt2zZ0rKdv//7vyeXO+uss3g2m3XVnZiYcOa/5S1vadnu+9//fld7Tz31VNO6qVSKK4rCAfDLLrus432wGJZzbDnn/JZbbmm53IEDBzrqx9jYGB8ZGWlYXlEUfuuttzbUf9/73ufU2bNnT9N2JycnXe1dfvnlLfvx0Y9+1Kn7+OOPd9T3Trnkkks6OjfFbR8dHW1o55vf/Cb3eDxNlwuu0h+aAAAW5UlEQVQEAvzmm29edn/ltbQ4Vur43nbbbTwYDLZctru7m993331N+3I8XE+cr9w+55zzdDrNr7nmmrZt7N69e8n9ldeURHJ8IF/0JOsGXdddLwJnnnkmv+WWW/hjjz3G77nnHv6Zz3yGh0IhZ/6XvvQl1/IPPfSQM++iiy7iX/jCF/i9997Ld+3axX/2s5/x973vfVxVVQ6Aq6rKf/zjHy+rv/aD9Morr+TPPPNM038vvvhi0zaee+45rigK9/v9/LOf/Sx/7LHH+B133MFPPvlkDoB/4hOfaFhmx44dHAAfHh5u2b/TTjvN2VYA/IYbbmha98c//rGz7z7/+c93vA86ZbnHlnPOb775Zme+z+fj559/Pt++fbtT1umLnv0D64ILLuD/8R//wR9++GH+kY98hDPGeCgU4rOzs676t912m7OOf/mXf2na7p133una3/F4nHxhtTnvvPM4AB4Khbiu6x31vVP279/f8px85pln+B133OFs1+tf//qGNm6//XZnfldXF//c5z7HH3nkEf7EE0/wb3zjG875L68lN6t9LXG+Msf30UcfdbZHURT+rne9i9999938iSee4HfddRf//d//fWf5UCjU9Po6Hq4nzldmn3POeTab5RdddJFT77LLLuP/+q//yh977DH+yCOP8P/7f/8v/8hHPsJHRkZW5EVPXlMSycZGvuhJ1g133XWXczO/4IILuGEYDXWeeuopJ8IQj8ddD/RHHnmEX3PNNfy5555ruo67776bM8Y4AL59+/aWPxzaYT9I3/GOdyy5jeuvv54D4P/7f/9vV/no6CgPBoN827ZtDcu8+93vdvbTvn37yHbn5uac7fzjP/5jDoBfffXVTfvxqU99ymnz4YcfXvL2NGO5x5Zzzn/5y1/yr33ta/yXv/wlL5VKnHPO3/GOdyzqRa9YLHKfz8c3bdrUMBp93XXXcQD8X//1X13lhw8fdtbxZ3/2Z03b/tCHPsQB8Kuuuor7fD4OgP/mN78h6+ZyOa5pGgfAX/e617Xt92rw8Y9/3Nmu+shLLpfjfX19HACPRCLkNTUzM+O8aG/bts05JktBXksrT6vjyznnl19+uTP/61//OtnGX/3VXzl1rr322ob58npy026fc875X/zFXzgv162i4ZZlLeuFVV5TEsnxgfxGT7JueOSRR5zpT33qU1BVtaHO2WefjcsvvxwAkEwmsWfPHmfehRdeiDvuuAOnnnpq03VceeWVuPrqqwFUvj3avXv3SnV/SYyNjQEAXvOa17jKh4eHsXPnTme+yMUXX+xMP/TQQ2S7Dz/8MDjn6O3txXve8x6nrBl2O36/H+eee+7iNqIDlntsAeC8887DBz/4QZx33nnwer1L6sfc3BxKpRLOO+88hEIh17zXvva1ANCwzzdt2uR8x9Jsf4vzXvva1+Lss89uWf+xxx6DYRgAgN/5nd9Z/IYsE8uycNtttwEAwuGwc03Y/OQnP8H09DQA4MMf/jB5TfX09OCLX/wiAODAgQO44447VrnXrTlerqVOaHd8gdo12d3djQ984ANkO5/5zGec6UcffbRhvryeanSyz3/961/jm9/8JgDgr/7qr/DOd76zaXuMMWiatip97RR5TUkkax/5oidZN5TLZWf6hBNOaFpv+/btznSpVFr0esSH1r59+xa9/ErS19cHAPjFL37hKp+cnMSLL76IgYGBhmXEHzLNHqR2+ate9SpccMEFUFUV09PTePHFFxvqlkolPPnkkwCA888/f8kvUa04Wse2HfF4HJqm4amnnkI+n3fNs40TWu3zgwcPYnR0tGF+Op3Gb37zG6fuq171KgDtjw/g/mF0tLjvvvucH2l/9Ed/hGAw6Jpvnw8AcNlllzVtR5z3ve99b4V7uTiOl2upE9odX6B2TW7btq1pO7FYDD09PQDo61FeTzU62ec33ngjOOfw+Xz45Cc/ebS7uGjkNSWRrH3ki55k3bBjxw5nev/+/U3r2S9njDHXMp0i/mChIktHkz/4gz8AAPz1X/81vvCFL+Dxxx/HXXfdhde+9rXI5XJ485vf3LDMCSecgOHhYQDAgw8+SLYrPkjD4TDOPPNMV7nIE0884eyT1fqRdLSObTsCgQDe+MY34tChQ3jDG96A//qv/8Kjjz6Kj3/84/jqV7+KYDCI3/3d321YTtwv1D5/9NFHYZomYrEYTj/9dFx00UUA2v/Q8Xq9OP/881di0xbFv/3bvznTb3/72xvmz8/PO9P2jz2KcDjs/KBtdi4eLY6Xa6kT2h1foHZNHjhwoGk76XQas7OzAICTTjqpYb68nmq02+ecc9x1110AgEsvvRTd3d0AAMMwcPjwYRw6dGhVBreWg7ymJJJ1wDEVjkoki2BqaoqHw2HHTIX6jmvXrl3c6/VyAPxtb3vbktZzxRVXOHr/559/fsn9tb+B2LZtGz/jjDN4MBjkgUCAb926lV9zzTX8Bz/4QUffANrfstT/O/3003kqlSKXectb3uLUm5ycdM3L5/POt26//OUvOeecX3vttRwAf/vb397Q1j/+4z86bd17771L2BPtWa1ju9hv9Djn/KWXXuLd3d0N+5sxxr/97W+Ty+zZs8ep9/73v79h/qc//WkOgL/pTW/inHM+OzvrfIOyf/9+V11d1x2nw4suuqijPq8kmUzGMb7ZvHkzeY5++MMfdra3lRteqVRythMAn5iYWFKf5LW0cnRyfDnn/Otf/7rT1xtvvJGs87GPfazt9hzv1xPnne3zvXv3Otv8D//wD3xmZob/+Z//ucsp2ufz8Te+8Y38wQcfXHaf5DUlkRwfyBc9ybrizjvv5H6/nwMV+2bbjezee+/l119/vfNQfMUrXsGnpqYW3f6vf/1rx+XrtNNOW1ZfO7Gvvuiii5pabIt84xvf4Keeeir3eDx8YGCAX3vttTyZTLasb6/jzjvvdM27//77OeB2n7Pd4KiP5y+77DIOgGua1mCosJKsxrFdyose55zv27ePX3PNNbyrq4v7/X5+wQUX8P/6r/9quUx/f3/T8+biiy/mgNsR7pRTTiHNKB5//HGnz5/85Cc77vNKIaap+PSnP03W+eY3v+nUaeWGd99997nOd/uH22KR19LK0cnx5bzygmT/IFcUhb/3ve/lP/rRj/iTTz7Jv/e97/Grrrqqo3Y4P76vJ8472+d33323U+fv//7v+eDgYNNznTHGv/zlLy+rT/KakkiOD+SLnmTd8eyzz/J3vetd5IOpv7+f33DDDTyXyy263WKxyM855xynrR/+8IfL6ueOHTv4FVdcwf/P//k//IEHHuC7d+/mP//5z/nnP/95vmnTJmc9p5xyCl9YWFjWuup55plnnPY/9KEPuebZeY8uvfRSp2xsbMypLz7YTdPk0WiUA+DnnXfeivaRYqWP7VJf9JbCH/7hHzo/wkTL+FKp5LzAiiPx733vezkA/t73vtfVzpe//GWnz8tNS7AULr30Umf9zazVDx8+7LgYjoyM8Lm5uYY65XLZZRMPgP/3f//3kvokr6WVo5Pja2NZFr/99tv5y172MvKafM1rXsPvueeeVennRrmeOO9sn990001OHdtF9LLLLuNPPPEELxaLfHp6mt944408Fos59f7jP/5jyX2S15REcnwgX/Qk64pSqcT/x//4H7y3t7fpKOS5557L//M//3PRbds/FIDlWU7btBrRTKfT/A1veIOzvo985CPLXp+IZVk8kUhwAPzlL3+5a97rXvc6DoB/5jOfcZVv3bqVA+C33367U7Zr1y6njx/72MdWtI/1rMaxPZovev/rf/0vZ1133323U/7www9zANzr9fJCoeCU26P8J598sqsdWzqsKEpT6dNqceTIESfx8Ctf+cqWdT/4wQ8627tz507+ox/9iKfTaV4oFPhDDz3kJJAWE6ov9YepvJZWhsUcX845f+GFF/gVV1zhqBzq//n9fv6nf/qnfHx8fMX7uhGuJ8473+df+9rXXPv29a9/PSlhf+ihh5z2TjvttCWnAJLXlERyfCBf9CTrhmw260h2VFXlH//4x/kLL7zAS6UST6VS/J577uGvetWrnFHgr371qx23/fnPf971MnE0JCALCwvOtyuhUGhZecYoxB849oisruvOt3D13zO87W1v4wD4Bz7wAadM/LG13AhnK1br2B7NFz3xR8dHP/pRp/wLX/gCBxq/D/rtb3/r1LelqJZlOefEWWedtar9pbD7CjT/LsumWCzy3/3d3236Um5HAz7wgQ84///AAw+sSr/ltdQZizm+Dz74IO/q6uIA+JYtW/itt97KJycneblc5keOHOFf//rXeTwe50Alqruc75kpNsL1xHnn+/xf/uVfXNfOrl27mtb9oz/6I6fe008/vRrdlteURLJBkC96knXDRz/6Ueemfsstt5B1dF3nr3nNa5wHSCcPQfF7o507d/KZmZmV7npT/vIv/9JZ9yOPPLKibX/lK19x2rYlS0888YTzMpXJZFz1b7zxRg6An3HGGU6Z/YOCMUbK81aK1Tq2R/NFzzRNR1Ylyojsl6FPfOITDcsMDAxwAPx73/se57wiXbX7e911161qfylOPfVUDlSkY/Pz823rm6bJv/3tb/OzzjrLZbrS3d3NP/rRj/JsNsvf8573rPqPUs7ltdQJnR7fYrHIh4eHOQA+MDDQ1ETn2WefdWSU55xzzor2dSNcT5x3vs+/+93vOn3t7e1t2ab4Uvid73xnpbvsIK8piWT9I9MrSNYFnHPcfPPNACo23u94xzvIepqm4bOf/SyASoJae5lm3H777U4y4C1btuDee+918kIdDcRE01Ry2eUg5iuybaztpLMvf/nLEQ6HXfXtXFTPPvsskskkgJqd9emnn45EIrGi/bNZrWN7tFEUBRdeeCEAYNeuXcjlcrAsy0kkbe9fkXpb+GOZ7+upp57C888/DwC4/PLLEY/H2y6jKAre8573YNeuXUilUnjppZcwOjqK6elpfOUrX0EoFMIzzzwDoHL8ViMlho28llqzmOP705/+1NmH1157LZkPDQBOO+00vO1tb3Paf/rpp1esv+v9egIWt883bdrkTI+MjLRsV6w7PT29zF42R15TEsn6R77oSdYFU1NTTu6us846q2Xds88+25nes2dP03o/+tGP8Pa3vx2WZWFwcBD33Xdf2wfsSsM5X7W2X/GKVyAUCgFo/OEjPmRtTjvtNMTjcXDO8fDDD2Pv3r2YmpoCsLo/klbj2B4r7P1kGAYee+wxPPvss1hYWABjzPkRKmL/eLF/6Ih5pahjtJqIeb6avWy3IhKJ4MQTT8Tw8DAUpfJoyWazzo//M844A4FAYGU6SyCvpdYs5vi+8MILzvQrXvGKlnVX85pcz9cTsLh9Lr5UmabZsq44X9O0JfauPfKakkjWP/JFT7IuEB9mhmG0rKvrOrmcyH333YdrrrkGhmGgu7sb9957L7Zv374ynV0E9mgvAAwNDa1o25qm4YILLgAAPPnkkygUCs6IKTUazhhz6j/00EOu0fDV/JG00sf2WCLuJ3Ef2j9S6rF/rD799NPIZDLO8dm5cyd6e3uPQo8r6LqO7373uwCA3t5evOlNb1qRdr///e87yYyvueaaFWmzGfJaas5ij+9auSbX6/UELH6fd3V14YwzzgAAHDx4EJZlNa27b98+Z9pOPr4ayGtKIln/yBc9ybogkUggGo0CAB577LGWPz5+8YtfONPbtm1rmP/oo4/iyiuvRKlUQjQaxc9+9jOcdtppK9/pNqRSKdxxxx0AgGAwiHPOOWfF12GPdJbLZfzbv/0bZmZmANAPUrG8/kG6miOmK3lsjzXnnnuuE7V68MEHnX3YbH+fddZZCIVCME0Tt912G44cOQLg6I9Q/+QnP3HOjT/5kz9ZkR/spVLJkdoGAgG8853vXHabzZDXUmsWe3zFa0vsO8VqXpPr9XoClnZNXX311QCAdDqN+++/v2m973//+870ar3kyGtKItkgHLvPAyWSxfHWt77V+Xj7+uuvJ+vMz887H78D4D/72c9c83fv3u04yYVCIf7www8vqS+2dTyamHz85Cc/4fl8vunymUzGZV997bXXLqkf7fj5z3/urOOEE07gAPiOHTua1n/wwQc5ULHEHxkZ4QD4iSeeuCp9E1mJY0txNM1YbOxzIxAIOOYQt912W9P6do4t+/gA4P/+7/9+VPpqY+csA8B/9atfdbTMzMxM03O8WCzyN7/5zU6b//RP/9S0HXktrT6LPb7JZJIHg0EOgEciEf6b3/yGrPfjH//YsfofHh7mpmmudNfX5fXE+dKuqenpaR6JRBzTESodxK233uq0+3u/93tkO/KakkgkNvJFT7JueOGFF5wfHwD47//+7/O77rqL79q1iz/66KP8n//5n/nmzZud+a997Wtdy//2t7/lfX19zvwbbriBP/PMMy3/Ncs11O5Beskll/BEIsHf+9738ltuuYU/9NBDfPfu3fyBBx7gn//85139PPnkk1fNMaxQKHCv1+usCwB/17vetWL1V4rlHlubm2++2fVPTNj95S9/2TXvoYceWpVt+du//VvX/gPADx06tGL1V5r5+XknQfPpp5/e8XJ33nkn7+rq4h/84Af53XffzX/1q1/xhx56iN9www38pJNOcrblD/7gD7iu603bkdfS6rLU4/sP//APTr/D4TD/1Kc+xe+//36+e/du/tOf/pT/xV/8Bdc0zalz6623rkr/19v1xPnS9znnnH/96193nc833XQTf+qpp/j999/PP/jBDzo5DaPRKN+7dy/ZhrymJBKJjXzRk6wr7r33Xt7T09PwIK//d+mllzZYWd98881tl6v/d/PNN5P96ORB2kn7F198MR8dHV2FPVXjwgsvdK2znR33BRdc0NE+WGmWc2xtFnNs3/GOd6zKdtxzzz2u9WzatKll/Z/97Geu+lu2bFmVfjXDti0HwL/0pS91vNydd97Zcv8yxvj73/9+Xi6XW7Yjr6XVZanH17Is/uEPf9iVNoP65/F4+Je//OVV6/96u544X/o+t/nsZz/bNEk9UEm/0CrdgbymJBKJzdpzM5BIWvC6170Oe/bswXe+8x385Cc/wXPPPYeFhQVomoaBgQGce+65+JM/+RNcccUVYIwds35+5StfwX333YfHHnsML774ImZnZ7GwsIBgMIihoSGcf/75eOtb34o3vOENq97Piy++2LEkB5p//yDOf+yxx1zLHw3Wy7Ftx4UXXghN05xvDdvt7wsuuACqqjpOekf7e5Nbb70VAKCqKv70T/+04+V+53d+B1/+8pdx3333Yc+ePZienoaiKBgeHsall16Kd7/73SvyXY+8lpbHUo8vYww33HAD3va2t+Hb3/42Hn74YRw6dAj5fB7hcBgnnngiLrnkErzvfe/DSSedtFrdX3fXE7D0fW7zN3/zN7jsssvwjW98Az//+c8xMTEBn8+HHTt24IorrsCHPvQhdHV1Lbl/8pqSSI4fGOer6J8rkUgkEolEIpFIJJKjjnTdlEgkEolEIpFIJJINhnzRk0gkEolEIpFIJJINhnzRk0gkEolEIpFIJJINhnzRk0gkEolEIpFIJJINhnzRk0gkEolEIpFIJJINhnzRk0gkEolEIpFIJJINhnzRk0g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", 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", 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", 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", 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", 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", 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", + "image/png": 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", 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", + "text/plain": [ + "
" + ] }, + "metadata": {}, "output_type": "display_data" } ], @@ -724,28 +757,31 @@ "# 4. tropical cyclone intensities maps for the return periods [10, 50, 75, 100]\n", "_, res = haz_tc_fl.plot_rp_intensity([10, 50, 75, 100])\n", "\n", - "# 5. intensities of all the events in centroid with id 50\n", + "# 5. tropical cyclone return period maps for the threshold intensities [30, 40]\n", + "return_periods, label, column_label = haz_tc_fl.local_return_period([30, 40])\n", + "from climada.util.plot import subplots_from_gdf\n", + "subplots_from_gdf(return_periods, colorbar_name=label, title_subplots=column_label)\n", + "\n", + "# 6. intensities of all the events in centroid with id 50\n", "haz_tc_fl.plot_intensity(centr=50)\n", "\n", - "# 6. intensities of all the events in centroid closest to lat, lon = (26.5, -81)\n", + "# 7. intensities of all the events in centroid closest to lat, lon = (26.5, -81)\n", "haz_tc_fl.plot_intensity(centr=(26.5, -81));" ] }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 14, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -779,19 +815,21 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "2022-03-16 22:17:30,344 - climada.hazard.base - INFO - Writing results/haz_tc_fl.h5\n", - "2022-03-16 22:17:30,572 - climada.hazard.base - INFO - Reading results/haz_tc_fl.h5\n" + "2024-07-17 11:43:52,930 - climada.hazard.io - INFO - Writing results/haz_tc_fl.h5\n", + "2024-07-17 11:43:52,996 - climada.hazard.centroids.centr - INFO - Writing results/haz_tc_fl.h5\n", + "2024-07-17 11:43:53,014 - climada.hazard.io - INFO - Reading results/haz_tc_fl.h5\n" ] } ], "source": [ + "# If you see an error message, try to create a depository named results in the repository tutorial.\n", "haz_tc_fl.write_hdf5('results/haz_tc_fl.h5')\n", "\n", "haz = Hazard.from_hdf5('results/haz_tc_fl.h5')\n", @@ -807,14 +845,14 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "2022-03-16 22:17:41,283 - climada.util.coordinates - INFO - Writting results/haz_ven.tif\n" + "2024-07-17 11:43:53,325 - climada.util.coordinates - INFO - Writting results/haz_ven.tif\n" ] } ], @@ -831,14 +869,14 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "2022-03-16 22:17:55,836 - climada.util.save - INFO - Written file C:\\Users\\yyljy\\Documents\\climada_main\\doc\\tutorial\\results\\tutorial_haz_tc_fl.p\n" + "2024-07-17 11:43:53,337 - climada.util.save - INFO - Written file /Users/vgebhart/Documents/climada/outputs/temp/tutorial_haz_tc_fl.p\n" ] } ], @@ -865,7 +903,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.7" + "version": "3.9.18" } }, "nbformat": 4, diff --git a/doc/tutorial/climada_util_calibrate.ipynb b/doc/tutorial/climada_util_calibrate.ipynb new file mode 100644 index 0000000000..202296c734 --- /dev/null +++ b/doc/tutorial/climada_util_calibrate.ipynb @@ -0,0 +1,4072 @@ +{ + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Impact Function Calibration\n", + "\n", + "CLIMADA provides the [`climada.util.calibrate`](../climada/climada.util.calibrate) module for calibrating impact functions based on impact data.\n", + "This tutorial will guide through the usage of this module by calibrating an impact function for tropical cyclones (TCs).\n", + "\n", + "For further information on the classes available from the module, see its [documentation](../climada/climada.util.calibrate).\n", + "\n", + "## Overview\n", + "\n", + "The basic idea of the calibration is to find a set of parameters for an impact function that minimizes the deviation between the calculated impact and some impact data.\n", + "For setting up a calibration task, users have to supply the following information:\n", + "\n", + "* Hazard and Exposure (as usual, see [the tutorial](../tutorial/1_main_climada.ipynb#tutorial-an-example-risk-assessment))\n", + "* The impact data to calibrate the model to\n", + "* An impact function definition depending on the calibrated parameters\n", + "* Bounds and constraints of the calibrated parameters (depending on the calibration algorithm)\n", + "* A \"cost function\" defining the single-valued deviation between impact data and calculated impact\n", + "* A function for transforming the calculated impact into the same data structure as the impact data\n", + "\n", + "This information defines the calibration task and is inserted into the {py:class}`~climada.util.calibrate.base.Input` object.\n", + "Afterwards, the user may insert this object into one of the optimizer classes.\n", + "Currently, the following classes are available:\n", + "\n", + "* {py:class}`~climada.util.calibrate.bayesian_optimizer.BayesianOptimizer`: Uses Bayesian optimization to sample the parameter space.\n", + "* {py:class}`~climada.util.calibrate.scipy_optimizer.ScipyMinimizeOptimizer`: Uses the [`scipy.optimize.minimize`](https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.minimize.html) function for determining the best parameter set.\n", + "\n", + "The following tutorial walks through the input data preparation and the setup of a {py:class}`~climada.util.calibrate.bayesian_optimizer.BayesianOptimizer` instance for calibration.\n", + "For a brief example, refer to [Quickstart](#quickstart).\n", + "If you want to go through a somewhat realistic calibration task step-by-step, continue with [Calibration Data](#calibration-data)." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/ldr.riedel/miniforge3/envs/climada_env_3.9/lib/python3.9/site-packages/dask/dataframe/_pyarrow_compat.py:17: FutureWarning: Minimal version of pyarrow will soon be increased to 14.0.1. You are using 12.0.1. Please consider upgrading.\n", + " warnings.warn(\n" + ] + } + ], + "source": [ + "import logging\n", + "import climada\n", + "\n", + "logging.getLogger(\"climada\").setLevel(\"WARNING\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Quickstart\n", + "\n", + "This section gives a very quick overview of assembling a calibration task.\n", + "Here, we calibrate a single impact function for damage reports in Mexico (`MEX`) from a TC with IbtracsID `2010176N16278`." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2024-07-08 16:36:14,126 - climada.entity.exposures.base - INFO - Reading /Users/ldr.riedel/climada/data/exposures/litpop/LitPop_150arcsec_MEX/v3/LitPop_150arcsec_MEX.hdf5\n", + "2024-07-08 16:36:20,330 - climada.hazard.base - INFO - Reading /Users/ldr.riedel/climada/data/hazard/tropical_cyclone/tropical_cyclone_0synth_tracks_150arcsec_global_1980_2020/v2/tropical_cyclone_0synth_tracks_150arcsec_global_1980_2020.hdf5\n", + "2024-07-08 16:36:28,741 - climada.entity.exposures.base - INFO - Matching 100369 exposures with 6125253 centroids.\n", + "2024-07-08 16:36:30,939 - climada.util.coordinates - INFO - No exact centroid match found. Reprojecting coordinates to nearest neighbor closer than the threshold = 100\n", + "2024-07-08 16:36:34,188 - climada.util.calibrate.bayesian_optimizer - INFO - Optimization iteration: 0\n", + "2024-07-08 16:36:34,646 - climada.util.calibrate.bayesian_optimizer - INFO - Optimization iteration: 1\n", + "2024-07-08 16:36:34,996 - climada.util.calibrate.bayesian_optimizer - INFO - Optimization iteration: 2\n", + "2024-07-08 16:36:35,313 - climada.util.calibrate.bayesian_optimizer - INFO - Optimization iteration: 3\n", + "2024-07-08 16:36:35,690 - climada.util.calibrate.bayesian_optimizer - INFO - No improvement. Stop optimization.\n", + "2024-07-08 16:36:35,733 - climada.entity.exposures.base - INFO - Exposures matching centroids already found for TC\n", + "2024-07-08 16:36:35,736 - climada.entity.exposures.base - INFO - Existing centroids will be overwritten for TC\n", + "2024-07-08 16:36:35,736 - climada.entity.exposures.base - INFO - Matching 100369 exposures with 6125253 centroids.\n", + "2024-07-08 16:36:37,907 - climada.util.coordinates - INFO - No exact centroid match found. Reprojecting coordinates to nearest neighbor closer than the threshold = 100\n", + "2024-07-08 16:36:41,611 - climada.engine.impact_calc - INFO - Calculating impact for 292230 assets (>0) and 1 events.\n" + ] + }, + { + "data": { + "text/plain": [ + "{'v_half': 48.30330549244917}" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import pandas as pd\n", + "from sklearn.metrics import mean_squared_log_error\n", + "\n", + "from climada.util import log_level\n", + "from climada.util.api_client import Client\n", + "from climada.entity import ImpactFuncSet, ImpfTropCyclone\n", + "from climada.util.calibrate import (\n", + " Input,\n", + " BayesianOptimizer,\n", + " BayesianOptimizerController,\n", + " OutputEvaluator,\n", + ")\n", + "\n", + "# Load hazard and exposure from Data API\n", + "client = Client()\n", + "exposure = client.get_litpop(\"MEX\")\n", + "exposure.gdf[\"impf_TC\"] = 1\n", + "all_tcs = client.get_hazard(\n", + " \"tropical_cyclone\",\n", + " properties={\"event_type\": \"observed\", \"spatial_coverage\": \"global\"},\n", + ")\n", + "hazard = all_tcs.select(event_names=[\"2010176N16278\"])\n", + "\n", + "# Impact data (columns: region ID, index: hazard event ID)\n", + "data = pd.DataFrame(data=[[2.485465e09]], columns=[484], index=list(hazard.event_id))\n", + "\n", + "# Create input\n", + "inp = Input(\n", + " hazard=hazard,\n", + " exposure=exposure,\n", + " data=data,\n", + " # Generate impact function from estimated parameters\n", + " impact_func_creator=lambda v_half: ImpactFuncSet(\n", + " [ImpfTropCyclone.from_emanuel_usa(v_half=v_half, impf_id=1)]\n", + " ),\n", + " # Estimated parameter bounds\n", + " bounds={\"v_half\": (26, 100)},\n", + " # Cost function\n", + " cost_func=mean_squared_log_error,\n", + " # Transform impact to pandas Dataframe with same structure as data\n", + " impact_to_dataframe=lambda impact: impact.impact_at_reg(exposure.gdf[\"region_id\"]),\n", + ")\n", + "\n", + "# Set up optimizer (with controller)\n", + "controller = BayesianOptimizerController.from_input(inp)\n", + "opt = BayesianOptimizer(inp)\n", + "\n", + "# Run optimization\n", + "with log_level(\"WARNING\", \"climada.engine.impact_calc\"):\n", + " output = opt.run(controller)\n", + "\n", + "# Analyse results\n", + "output.plot_p_space()\n", + "out_eval = OutputEvaluator(inp, output)\n", + "out_eval.impf_set.plot()\n", + "\n", + "# Optimal value\n", + "output.params" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Follow the next sections of the tutorial for a more in-depth explanation." + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Calibration Data\n", + "\n", + "CLIMADA ships data from the International Disaster Database EM-DAT, which we will use to calibrate impact functions on.\n", + "In the first step, we will select TC events that caused damages in the `NA1` basin since 2010.\n", + "\n", + "We use EMDAT data from TCs occurring in the `NA1` basin since 2010.\n", + "We calculate the centroids for which we want to compute the windfields by extracting the countries hit by the cyclones and retrieving a `LitPop` exposure instance from them.\n", + "We then use the exposure coordinates as centroids coordinates." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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countryregion_idcal_region2yearEM_IDibtracsIDemdat_impactreference_yearemdat_impact_scaledclimada_impact...scalelog_ratiounique_IDAssociated_disasterSurgeRainFloodSlideOtherOtherThanSurge
326MEX484NA120102010-02602010176N162782.000000e+0920142.485465e+092.478270e+09...1.0-0.0028992010-0260MEXTrueFalseFalseTrueFalseFalseTrue
331ATG28NA120102010-04682010236N123411.260000e+0720141.394594e+071.402875e+07...1.00.0059202010-0468ATGTrueFalseFalseTrueFalseFalseTrue
334MEX484NA120102010-04942010257N162823.900000e+0920144.846656e+094.857140e+09...1.00.0021612010-0494MEXTrueFalseFalseTrueFalseFalseTrue
339LCA662NA120102010-05712010302N093065.000000e+0520145.486675e+055.492871e+05...1.00.0011292010-0571LCATrueFalseFalseTrueTrueFalseTrue
340VCT670NA120102010-05712010302N093062.500000e+0720142.670606e+072.676927e+07...1.00.0023642010-0571VCTFalseFalseFalseFalseFalseFalseFalse
344BHS44NA120112011-03282011233N153014.000000e+0720144.352258e+074.339898e+07...1.0-0.0028442011-0328BHSFalseFalseFalseFalseFalseFalseFalse
345DOM214NA120112011-03282011233N153013.000000e+0720143.428317e+073.404744e+07...1.0-0.0069002011-0328DOMTrueFalseFalseTrueFalseFalseTrue
346PRI630NA120112011-03282011233N153015.000000e+0820145.104338e+085.139659e+08...1.00.0068962011-0328PRITrueFalseFalseTrueFalseFalseTrue
352MEX484NA120112011-03852011279N102572.770000e+0720143.084603e+073.077374e+07...1.0-0.0023462011-0385MEXTrueFalseFalseTrueTrueFalseTrue
359MEX484NA120122012-02762012215N123133.000000e+0820143.283428e+083.284805e+08...1.00.0004192012-0276MEXFalseFalseFalseFalseFalseFalseFalse
365MEX484NA120122012-04012012166N092695.550000e+0820146.074341e+086.103254e+08...1.00.0047492012-0401MEXFalseFalseFalseFalseFalseFalseFalse
369JAM388NA120122012-04102012296N142831.654200e+0720141.548398e+071.548091e+07...1.0-0.0001982012-0410JAMFalseFalseFalseFalseFalseFalseFalse
406MEX484NA120142014-03332014253N132602.500000e+0920142.500000e+092.501602e+09...1.00.0006402014-0333MEXTrueFalseFalseTrueFalseFalseTrue
427MEX484NA120152015-04702015293N132668.230000e+0820149.242430e+089.239007e+08...1.0-0.0003702015-0470MEXTrueFalseFalseTrueFalseFalseTrue
428CPV132NA120152015-04732015242N123431.100000e+0620141.281242e+061.282842e+06...1.00.0012482015-0473CPVFalseFalseFalseFalseFalseFalseFalse
429BHS44NA120152015-04792015270N272919.000000e+0720148.362720e+071.129343e+06...1.0-4.3047332015-0479BHSTrueTrueFalseTrueFalseFalseTrue
437MEX484NA120162016-03192016248N152555.000000e+0720146.098482e+076.088808e+07...1.0-0.0015882016-0319MEXTrueFalseFalseTrueFalseFalseTrue
451MEX484NA120172017-03342017219N162792.000000e+0620142.284435e+062.285643e+06...1.00.0005292017-0334MEXTrueFalseFalseTrueTrueFalseTrue
456ATG28NA120172017-03812017242N163332.500000e+0820142.111764e+082.110753e+08...1.0-0.0004792017-0381ATGFalseFalseFalseFalseFalseFalseFalse
457BHS44NA120172017-03812017242N163332.000000e+0620141.801876e+066.118379e+05...1.0-1.0801162017-0381BHSFalseFalseFalseFalseFalseFalseFalse
458CUB192NA120172017-03812017242N163331.320000e+1020141.099275e+101.090424e+10...1.0-0.0080852017-0381CUBTrueFalseFalseTrueFalseFalseTrue
459KNA659NA120172017-03812017242N163332.000000e+0720141.848489e+071.848665e+07...1.00.0000952017-0381KNAFalseFalseFalseFalseFalseFalseFalse
460TCA796NA120172017-03812017242N163335.000000e+0820145.000000e+085.003790e+08...1.00.0007582017-0381TCATrueFalseFalseTrueFalseFalseTrue
462VGB92NA120172017-03812017242N163333.000000e+0920143.000000e+096.236200e+08...1.0-1.5708262017-0381VGBFalseFalseFalseFalseFalseFalseFalse
463DMA212NA120172017-03832017260N123101.456000e+0920141.534596e+098.951186e+08...1.0-0.5390662017-0383DMATrueFalseFalseTrueTrueFalseTrue
464DOM214NA120172017-03832017260N123106.300000e+0720145.481371e+075.493466e+07...1.00.0022042017-0383DOMTrueFalseFalseTrueTrueFalseTrue
465PRI630NA120172017-03832017260N123106.800000e+1020146.700905e+106.702718e+10...1.00.0002712017-0383PRITrueFalseFalseTrueFalseTrueTrue
\n", + "

27 rows × 22 columns

\n", + "
" + ], + "text/plain": [ + " country region_id cal_region2 year EM_ID ibtracsID \\\n", + "326 MEX 484 NA1 2010 2010-0260 2010176N16278 \n", + "331 ATG 28 NA1 2010 2010-0468 2010236N12341 \n", + "334 MEX 484 NA1 2010 2010-0494 2010257N16282 \n", + "339 LCA 662 NA1 2010 2010-0571 2010302N09306 \n", + "340 VCT 670 NA1 2010 2010-0571 2010302N09306 \n", + "344 BHS 44 NA1 2011 2011-0328 2011233N15301 \n", + "345 DOM 214 NA1 2011 2011-0328 2011233N15301 \n", + "346 PRI 630 NA1 2011 2011-0328 2011233N15301 \n", + "352 MEX 484 NA1 2011 2011-0385 2011279N10257 \n", + "359 MEX 484 NA1 2012 2012-0276 2012215N12313 \n", + "365 MEX 484 NA1 2012 2012-0401 2012166N09269 \n", + "369 JAM 388 NA1 2012 2012-0410 2012296N14283 \n", + "406 MEX 484 NA1 2014 2014-0333 2014253N13260 \n", + "427 MEX 484 NA1 2015 2015-0470 2015293N13266 \n", + "428 CPV 132 NA1 2015 2015-0473 2015242N12343 \n", + "429 BHS 44 NA1 2015 2015-0479 2015270N27291 \n", + "437 MEX 484 NA1 2016 2016-0319 2016248N15255 \n", + "451 MEX 484 NA1 2017 2017-0334 2017219N16279 \n", + "456 ATG 28 NA1 2017 2017-0381 2017242N16333 \n", + "457 BHS 44 NA1 2017 2017-0381 2017242N16333 \n", + "458 CUB 192 NA1 2017 2017-0381 2017242N16333 \n", + "459 KNA 659 NA1 2017 2017-0381 2017242N16333 \n", + "460 TCA 796 NA1 2017 2017-0381 2017242N16333 \n", + "462 VGB 92 NA1 2017 2017-0381 2017242N16333 \n", + "463 DMA 212 NA1 2017 2017-0383 2017260N12310 \n", + "464 DOM 214 NA1 2017 2017-0383 2017260N12310 \n", + "465 PRI 630 NA1 2017 2017-0383 2017260N12310 \n", + "\n", + " emdat_impact reference_year emdat_impact_scaled climada_impact ... \\\n", + "326 2.000000e+09 2014 2.485465e+09 2.478270e+09 ... \n", + "331 1.260000e+07 2014 1.394594e+07 1.402875e+07 ... \n", + "334 3.900000e+09 2014 4.846656e+09 4.857140e+09 ... \n", + "339 5.000000e+05 2014 5.486675e+05 5.492871e+05 ... \n", + "340 2.500000e+07 2014 2.670606e+07 2.676927e+07 ... \n", + "344 4.000000e+07 2014 4.352258e+07 4.339898e+07 ... \n", + "345 3.000000e+07 2014 3.428317e+07 3.404744e+07 ... \n", + "346 5.000000e+08 2014 5.104338e+08 5.139659e+08 ... \n", + "352 2.770000e+07 2014 3.084603e+07 3.077374e+07 ... \n", + "359 3.000000e+08 2014 3.283428e+08 3.284805e+08 ... \n", + "365 5.550000e+08 2014 6.074341e+08 6.103254e+08 ... \n", + "369 1.654200e+07 2014 1.548398e+07 1.548091e+07 ... \n", + "406 2.500000e+09 2014 2.500000e+09 2.501602e+09 ... \n", + "427 8.230000e+08 2014 9.242430e+08 9.239007e+08 ... \n", + "428 1.100000e+06 2014 1.281242e+06 1.282842e+06 ... \n", + "429 9.000000e+07 2014 8.362720e+07 1.129343e+06 ... \n", + "437 5.000000e+07 2014 6.098482e+07 6.088808e+07 ... \n", + "451 2.000000e+06 2014 2.284435e+06 2.285643e+06 ... \n", + "456 2.500000e+08 2014 2.111764e+08 2.110753e+08 ... \n", + "457 2.000000e+06 2014 1.801876e+06 6.118379e+05 ... \n", + "458 1.320000e+10 2014 1.099275e+10 1.090424e+10 ... \n", + "459 2.000000e+07 2014 1.848489e+07 1.848665e+07 ... \n", + "460 5.000000e+08 2014 5.000000e+08 5.003790e+08 ... \n", + "462 3.000000e+09 2014 3.000000e+09 6.236200e+08 ... \n", + "463 1.456000e+09 2014 1.534596e+09 8.951186e+08 ... \n", + "464 6.300000e+07 2014 5.481371e+07 5.493466e+07 ... \n", + "465 6.800000e+10 2014 6.700905e+10 6.702718e+10 ... \n", + "\n", + " scale log_ratio unique_ID Associated_disaster Surge Rain Flood \\\n", + "326 1.0 -0.002899 2010-0260MEX True False False True \n", + "331 1.0 0.005920 2010-0468ATG True False False True \n", + "334 1.0 0.002161 2010-0494MEX True False False True \n", + "339 1.0 0.001129 2010-0571LCA True False False True \n", + "340 1.0 0.002364 2010-0571VCT False False False False \n", + "344 1.0 -0.002844 2011-0328BHS False False False False \n", + "345 1.0 -0.006900 2011-0328DOM True False False True \n", + "346 1.0 0.006896 2011-0328PRI True False False True \n", + "352 1.0 -0.002346 2011-0385MEX True False False True \n", + "359 1.0 0.000419 2012-0276MEX False False False False \n", + "365 1.0 0.004749 2012-0401MEX False False False False \n", + "369 1.0 -0.000198 2012-0410JAM False False False False \n", + "406 1.0 0.000640 2014-0333MEX True False False True \n", + "427 1.0 -0.000370 2015-0470MEX True False False True \n", + "428 1.0 0.001248 2015-0473CPV False False False False \n", + "429 1.0 -4.304733 2015-0479BHS True True False True \n", + "437 1.0 -0.001588 2016-0319MEX True False False True \n", + "451 1.0 0.000529 2017-0334MEX True False False True \n", + "456 1.0 -0.000479 2017-0381ATG False False False False \n", + "457 1.0 -1.080116 2017-0381BHS False False False False \n", + "458 1.0 -0.008085 2017-0381CUB True False False True \n", + "459 1.0 0.000095 2017-0381KNA False False False False \n", + "460 1.0 0.000758 2017-0381TCA True False False True \n", + "462 1.0 -1.570826 2017-0381VGB False False False False \n", + "463 1.0 -0.539066 2017-0383DMA True False False True \n", + "464 1.0 0.002204 2017-0383DOM True False False True \n", + "465 1.0 0.000271 2017-0383PRI True False False True \n", + "\n", + " Slide Other OtherThanSurge \n", + "326 False False True \n", + "331 False False True \n", + "334 False False True \n", + "339 True False True \n", + "340 False False False \n", + "344 False False False \n", + "345 False False True \n", + "346 False False True \n", + "352 True False True \n", + "359 False False False \n", + "365 False False False \n", + "369 False False False \n", + "406 False False True \n", + "427 False False True \n", + "428 False False False \n", + "429 False False True \n", + "437 False False True \n", + "451 True False True \n", + "456 False False False \n", + "457 False False False \n", + "458 False False True \n", + "459 False False False \n", + "460 False False True \n", + "462 False False False \n", + "463 True False True \n", + "464 True False True \n", + "465 False True True \n", + "\n", + "[27 rows x 22 columns]" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import pandas as pd\n", + "from climada.util.constants import SYSTEM_DIR\n", + "\n", + "emdat = pd.read_csv(SYSTEM_DIR / \"tc_impf_cal_v01_EDR.csv\")\n", + "emdat_subset = emdat[(emdat.cal_region2 == \"NA1\") & (emdat.year >= 2010)]\n", + "emdat_subset" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Each entry in the database refers to an economic impact for a specific country and TC event.\n", + "The TC events are identified by the ID assigned from the International Best Track Archive for Climate Stewardship (IBTrACS).\n", + "We now want to reshape this data so that impacts are grouped by event and country.\n", + "\n", + "To achieve this, we iterate over the unique track IDs, select all reported damages associated with this ID, and concatenate the results.\n", + "For missing entries, `pandas` will set the value to NaN.\n", + "We assume that missing entries means that no damages are reported (this is a strong assumption), and set all NaN values to zero.\n", + "Then, we transpose the dataframe so that each row represents an event and each column states the damage for a specific country.\n", + "Finally, we set the track ID to be the index of the data frame." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Please save the pickle again; shapely 2.1 will not have this compatibility.\n", + " setstate(state)\n", + "/Users/ldr.riedel/miniforge3/envs/climada_env_3.9/lib/python3.9/pickle.py:1717: UserWarning: Unpickling a shapely <2.0 geometry object. Please save the pickle again; shapely 2.1 will not have this compatibility.\n", + " setstate(state)\n", + "/Users/ldr.riedel/miniforge3/envs/climada_env_3.9/lib/python3.9/pickle.py:1717: UserWarning: Unpickling a shapely <2.0 geometry object. Please save the pickle again; shapely 2.1 will not have this compatibility.\n", + " setstate(state)\n", + "/Users/ldr.riedel/miniforge3/envs/climada_env_3.9/lib/python3.9/pickle.py:1717: UserWarning: Unpickling a shapely <2.0 geometry object. Please save the pickle again; shapely 2.1 will not have this compatibility.\n", + " setstate(state)\n", + "/Users/ldr.riedel/miniforge3/envs/climada_env_3.9/lib/python3.9/pickle.py:1717: UserWarning: Unpickling a shapely <2.0 geometry object. Please save the pickle again; shapely 2.1 will not have this compatibility.\n", + " setstate(state)\n", + "/Users/ldr.riedel/miniforge3/envs/climada_env_3.9/lib/python3.9/pickle.py:1717: UserWarning: Unpickling a shapely <2.0 geometry object. Please save the pickle again; shapely 2.1 will not have this compatibility.\n", + " setstate(state)\n", + "/Users/ldr.riedel/miniforge3/envs/climada_env_3.9/lib/python3.9/pickle.py:1717: UserWarning: Unpickling a shapely <2.0 geometry object. Please save the pickle again; shapely 2.1 will not have this compatibility.\n", + " setstate(state)\n", + "/Users/ldr.riedel/miniforge3/envs/climada_env_3.9/lib/python3.9/pickle.py:1717: UserWarning: Unpickling a shapely <2.0 geometry object. Please save the pickle again; shapely 2.1 will not have this compatibility.\n", + " setstate(state)\n", + "/Users/ldr.riedel/miniforge3/envs/climada_env_3.9/lib/python3.9/pickle.py:1717: UserWarning: Unpickling a shapely <2.0 geometry object. Please save the pickle again; shapely 2.1 will not have this compatibility.\n", + " setstate(state)\n", + "/Users/ldr.riedel/miniforge3/envs/climada_env_3.9/lib/python3.9/pickle.py:1717: UserWarning: Unpickling a shapely <2.0 geometry object. Please save the pickle again; shapely 2.1 will not have this compatibility.\n", + " setstate(state)\n", + "/Users/ldr.riedel/miniforge3/envs/climada_env_3.9/lib/python3.9/pickle.py:1717: UserWarning: Unpickling a shapely <2.0 geometry object. Please save the pickle again; shapely 2.1 will not have this compatibility.\n", + " setstate(state)\n", + "/Users/ldr.riedel/miniforge3/envs/climada_env_3.9/lib/python3.9/pickle.py:1717: UserWarning: Unpickling a shapely <2.0 geometry object. Please save the pickle again; shapely 2.1 will not have this compatibility.\n", + " setstate(state)\n", + "/Users/ldr.riedel/miniforge3/envs/climada_env_3.9/lib/python3.9/pickle.py:1717: UserWarning: Unpickling a shapely <2.0 geometry object. Please save the pickle again; shapely 2.1 will not have this compatibility.\n", + " setstate(state)\n", + "/Users/ldr.riedel/miniforge3/envs/climada_env_3.9/lib/python3.9/pickle.py:1717: UserWarning: Unpickling a shapely <2.0 geometry object. Please save the pickle again; shapely 2.1 will not have this compatibility.\n", + " setstate(state)\n" + ] + } + ], + "source": [ + "from climada.util.api_client import Client\n", + "from climada.entity.exposures.litpop import LitPop\n", + "from climada.util.coordinates import country_to_iso\n", + "\n", + "client = Client()\n", + "exposure = LitPop.concat(\n", + " [\n", + " client.get_litpop(country_to_iso(country_id, representation=\"alpha3\"))\n", + " for country_id in data.columns\n", + " ]\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'res_arcsec': ['150'],\n", + " 'event_type': ['observed'],\n", + " 'spatial_coverage': ['global'],\n", + " 'climate_scenario': ['None']}" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from climada.util.api_client import Client\n", + "\n", + "client = Client()\n", + "tc_dataset_infos = client.list_dataset_infos(data_type=\"tropical_cyclone\")\n", + "client.get_property_values(\n", + " client.list_dataset_infos(data_type=\"tropical_cyclone\"),\n", + " known_property_values={\"event_type\": \"observed\", \"spatial_coverage\": \"global\"},\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will use the CLIMADA Data API to download readily computed wind fields from TC tracks.\n", + "The API provides a large dataset containing all historical TC tracks.\n", + "We will download them and then select the subset of TCs for which we have impact data by using {py:meth}`~climada.hazard.base.Hazard.select`." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "from climada.util.api_client import Client\n", + "\n", + "client = Client()\n", + "all_tcs = client.get_hazard(\n", + " \"tropical_cyclone\",\n", + " properties={\"event_type\": \"observed\", \"spatial_coverage\": \"global\"},\n", + ")\n", + "hazard = all_tcs.select(event_names=track_ids.tolist())" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**NOTE:** Discouraged! This will usually take a longer time than using the Data API\n", + "\n", + "Alternatively, CLIMADA provides the {py:class}`~climada.hazard.tc_tracks.TCTracks` class, which lets us download the tracks of TCs using their IBTrACS IDs.\n", + "We then have to equalize the time steps of the different TC tracks.\n", + "\n", + "The track and intensity of a cyclone are insufficient to compute impacts in CLIMADA.\n", + "We first have to re-compute a windfield from each track at the locations of interest.\n", + "For consistency, we simply choose the coordinates of the exposure." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "# NOTE: Uncomment this to compute wind fields yourself\n", + "\n", + "# from climada.hazard import Centroids, TCTracks, TropCyclone\n", + "\n", + "# # Get the tracks for associated TCs\n", + "# tracks = TCTracks.from_ibtracs_netcdf(storm_id=track_ids.tolist())\n", + "# tracks.equal_timestep(time_step_h=1.0, land_params=False)\n", + "# tracks.plot()\n", + "\n", + "# # Calculate windfield for the tracks\n", + "# centroids = Centroids.from_lat_lon(exposure.gdf.latitude, exposure.gdf.longitude)\n", + "# hazard = TropCyclone.from_tracks(tracks, centroids)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Calibration Setup\n", + "\n", + "We are now set up to define the specifics of the calibration task.\n", + "First, let us define the impact function we actually want to calibrate.\n", + "We select the formula by Emanuel (2011), for which a shortcut exists in the CLIMADA code base: {py:meth}`~climada.entity.impact_funcs.trop_cyclone.ImpfTropCyclone.from_emanuel_usa`.\n", + "The sigmoid-like impact function takes three parameters, the wind threshold for any impact `v_thresh`, the wind speed where half of the impact occurs `v_half`, and the maximum impact factor `scale`.\n", + "According to the model by Emanuel (2011), `v_thresh` is considered a constant, so we choose to only calibrate the latter two.\n", + "\n", + "Any CLIMADA {py:class}`~climada.util.calibrate.base.Optimizer` will roughly perform the following algorithm:\n", + "1. Create a set of parameters and built an impact function (set) from it.\n", + "2. Compute an impact with that impact function set.\n", + "3. Compare the impact and the calibration data via the cost/target function.\n", + "4. Repeat N times or until the target function goal is reached.\n", + "\n", + "The selection of parameters is based on the target function varies strongly between different optimization algorithms.\n", + "\n", + "For the first step, we have to supply a function that takes the parameters we try to estimate and returns the impact function set that can later be used in an impact calculation.\n", + "We only calibrate a single function for the entire basin, so this is straightforward.\n", + "\n", + "To ensure the impact function is applied correctly, we also have to set the `impf_` column of the exposure `GeoDataFrame`.\n", + "Note that the default impact function ID is 1, and that the hazard type is `\"TC\"`." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "from climada.entity import ImpactFuncSet, ImpfTropCyclone\n", + "\n", + "# Match impact function and exposure\n", + "exposure.gdf[\"impf_TC\"] = 1\n", + "\n", + "\n", + "def impact_func_tc(v_half, scale):\n", + " return ImpactFuncSet([ImpfTropCyclone.from_emanuel_usa(v_half=v_half, scale=scale)])" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will be using the {py:class}`~climada.util.calibrate.bayesian_optimizer.BayesianOptimizer`, which requires very little information on the parameter space beforehand.\n", + "One crucial information are the bounds of the parameters, though.\n", + "Initial values are not needed because the optimizer first samples the bound parameter space uniformly and then iteratively \"narrows down\" the search.\n", + "We choose a `v_half` between `v_thresh` and 150, and a scale between 0.01 (it must never be zero) and 1.0.\n", + "Specifying the bounds as dictionary (a must in case of `BayesianOptimizer`) also serves the purpose of naming the parameters we want to calibrate.\n", + "Notice that these names have to match the arguments of the impact function generator." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "bounds = {\"v_half\": (25.8, 150), \"scale\": (0.01, 1)}" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Defining the cost function is crucial for the result of the calibration.\n", + "You can choose what is best suited for your application.\n", + "Often, it is not clear which function works best, and it's a good idea to try out a few.\n", + "Because the impacts of different events may vary over several orders of magnitude, we select the mean squared logartihmic error (MSLE).\n", + "This one and other error measures are readily supplied by the `sklearn` package.\n", + "\n", + "The cost function must be defined as a function that takes the impact object calculated by the optimization algorithm and the input calibration data as arguments, and that returns a single number.\n", + "This number represents a \"cost\" of the parameter set used for calculating the impact.\n", + "A higher cost therefore is worse, a lower cost is better.\n", + "Any optimizer will try to minimize the cost.\n", + "\n", + "Note that the impact object is an instance of `Impact`, whereas the input calibration data is a `pd.DataFrame`.\n", + "To compute the MSLE, we first have to transform the impact into the same data structure, meaning that we have to aggregate the point-wise impacts by event and country.\n", + "The function performing this transformation task is provided to the {py:class}`~climada.util.calibrate.base.Input` via its {py:attr}`~climada.util.calibrate.base.Input.impact_to_dataframe` attribute.\n", + "Here we choose {py:meth}`climada.engine.impact.Impact.impact_at_reg`, which aggregates over countries by default.\n", + "To improve performance, we can supply this function with our known region IDs instead of re-computing them in every step.\n", + "\n", + "Computations on data frames align columns and indexes.\n", + "The indexes of the calibration data are the IBTrACS IDs, but the indexes of the result of `impact_at_reg` are the hazard event IDs, which at this point are only integer numbers.\n", + "To resolve that, we adjust our calibration dataframe to carry the respective `Hazard.event_id` as index." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + "region_id 28 44 92 132 \\\n", + "event_id \n", + "1333 NaN NaN NaN NaN \n", + "1339 1.394594e+07 NaN NaN NaN \n", + "1344 NaN NaN NaN NaN \n", + "1351 NaN NaN NaN NaN \n", + "1361 NaN 4.352258e+07 NaN NaN \n", + "3686 NaN NaN NaN NaN \n", + "3691 NaN NaN NaN NaN \n", + "1377 NaN NaN NaN NaN \n", + "1390 NaN NaN NaN NaN \n", + "3743 NaN NaN NaN NaN \n", + "1421 NaN NaN NaN 1281242.483 \n", + "1426 NaN 8.362720e+07 NaN NaN \n", + "3777 NaN NaN NaN NaN \n", + "3795 NaN NaN NaN NaN \n", + "1450 NaN NaN NaN NaN \n", + "1454 2.111764e+08 1.801876e+06 3.000000e+09 NaN \n", + "1458 NaN NaN NaN NaN \n", + "\n", + "region_id 192 212 214 388 484 \\\n", + "event_id \n", + "1333 NaN NaN NaN NaN 2.485465e+09 \n", + "1339 NaN NaN NaN NaN NaN \n", + "1344 NaN NaN NaN NaN 4.846656e+09 \n", + "1351 NaN NaN NaN NaN NaN \n", + "1361 NaN NaN 34283168.75 NaN NaN \n", + "3686 NaN NaN NaN NaN 3.084603e+07 \n", + "3691 NaN NaN NaN NaN 6.074341e+08 \n", + "1377 NaN NaN NaN NaN 3.283428e+08 \n", + "1390 NaN NaN NaN 15483975.86 NaN \n", + "3743 NaN NaN NaN NaN 2.500000e+09 \n", + "1421 NaN NaN NaN NaN NaN \n", + "1426 NaN NaN NaN NaN NaN \n", + "3777 NaN NaN NaN NaN 9.242430e+08 \n", + "3795 NaN NaN NaN NaN 6.098482e+07 \n", + "1450 NaN NaN NaN NaN 2.284435e+06 \n", + "1454 1.099275e+10 NaN NaN NaN NaN \n", + "1458 NaN 1.534596e+09 54813712.03 NaN NaN \n", + "\n", + "region_id 630 659 662 670 796 \n", + "event_id \n", + "1333 NaN NaN NaN NaN NaN \n", + "1339 NaN NaN NaN NaN NaN \n", + "1344 NaN NaN NaN NaN NaN \n", + "1351 NaN NaN 548667.5019 26706058.15 NaN \n", + "1361 5.104338e+08 NaN NaN NaN NaN \n", + "3686 NaN NaN NaN NaN NaN \n", + "3691 NaN NaN NaN NaN NaN \n", + "1377 NaN NaN NaN NaN NaN \n", + "1390 NaN NaN NaN NaN NaN \n", + "3743 NaN NaN NaN NaN NaN \n", + "1421 NaN NaN NaN NaN NaN \n", + "1426 NaN NaN NaN NaN NaN \n", + "3777 NaN NaN NaN NaN NaN \n", + "3795 NaN NaN NaN NaN NaN \n", + "1450 NaN NaN NaN NaN NaN \n", + "1454 NaN 18484889.46 NaN NaN 500000000.0 \n", + "1458 6.700905e+10 NaN NaN NaN NaN " + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data = data.rename(\n", + " index={\n", + " hazard.event_name[idx]: hazard.event_id[idx]\n", + " for idx in range(len(hazard.event_id))\n", + " }\n", + ")\n", + "data.index.rename(\"event_id\", inplace=True)\n", + "data" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Execute the Calibration\n", + "\n", + "We created a class {py:class}`~climada.util.calibrate.bayesian_optimizer.BayesianOptimizerController` to control and guide the calibration process.\n", + "It is intended to walk through several optimization iterations and stop the process if the best guess cannot be improved.\n", + "The optimization works as follows:\n", + "\n", + "1. The optimizer randomly samples the parameter space {py:attr}`~climada.util.calibrate.bayesian_optimizer.BayesianOptimizerController.init_points` times.\n", + "2. The optimizer uses a Gaussian regression process to \"smartly\" sample the parameter space at most {py:attr}`~climada.util.calibrate.bayesian_optimizer.BayesianOptimizerController.n_iter` times.\n", + " * The process uses an \"Upper Confidence Bound\" sampling method whose parameter {py:attr}`~climada.util.calibrate.bayesian_optimizer.BayesianOptimizerController.kappa` indicates how close the sampled points are to the buest guess.\n", + " Higher `kappa` means more exploration of the parameter space, lower `kappa` means more exploitation.\n", + " * After each sample, the parameter `kappa` is reduced by the factor `kappa_decay`.\n", + " By default, this parameter is set such that `kappa` equals {py:attr}`~climada.util.calibrate.bayesian_optimizer.BayesianOptimizerController.kappa_min` at the last step.\n", + " This way, the sampling becomes more exploitative the more steps are taken.\n", + "3. The controller tracks the improvements of the buest guess for parameters.\n", + " If {py:attr}`~climada.util.calibrate.bayesian_optimizer.BayesianOptimizerController.min_improvement_count` consecutive improvements are lower than {py:attr}`~climada.util.calibrate.bayesian_optimizer.BayesianOptimizerController.min_improvement`, the smart sampling is stopped.\n", + " In this case, the `iterations` count is increased and the process repeated from step 1.\n", + "4. If an entire iteration did not show any improvement, the optimization is stopped.\n", + " It is also stopped when the {py:attr}`~climada.util.calibrate.bayesian_optimizer.BayesianOptimizerController.max_iterations` count is reached.\n", + "\n", + "Users can control the \"density\", and thus the accuracy of the sampling by adjusting the controller parameters.\n", + "Increasing `init_points`, `n_iter`, `min_improvement_count`, and `max_iterations`, and decreasing `min_improvement` generally increases density and accuracy, but leads to longer runtimes.\n", + "\n", + "We suggest using the `from_input` classmethod for a convenient choice of sampling density based on the parameter space.\n", + "The two parameters `init_points` and `n_iter` are set to $b^N$, where $N$ is the number of estimated parameters and $b$ is the `sampling_base` parameter, which defaults to 4.\n", + "\n", + "Now we can finally execute our calibration task!\n", + "We will plug all input parameters in an instance of `Input`, and then create the optimizer instance with it.\n", + "The `Optimizer.run` method returns an {py:class}`~climada.util.calibrate.base.Output` object, whose {py:attr}`~climada.util.calibrate.base.Output.params` attribute holds the optimal parameters determined by the calibration.\n", + "\n", + "Notice that the `BayesianOptimization` *maximizes* a target function.\n", + "Therefore, higher target values are *better* than lower ones in this case." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2024-04-29 14:09:45,569 - climada.util.calibrate.bayesian_optimizer - INFO - Optimization iteration: 0\n", + "2024-04-29 14:09:46,990 - climada.util.calibrate.bayesian_optimizer - INFO - Optimization iteration: 1\n", + "2024-04-29 14:09:49,255 - climada.util.calibrate.bayesian_optimizer - INFO - Optimization iteration: 2\n", + "2024-04-29 14:09:50,873 - climada.util.calibrate.bayesian_optimizer - INFO - Minimal improvement. Stop iteration.\n", + "2024-04-29 14:09:50,874 - climada.util.calibrate.bayesian_optimizer - INFO - Optimization iteration: 3\n", + "2024-04-29 14:09:52,698 - climada.util.calibrate.bayesian_optimizer - INFO - Minimal improvement. Stop iteration.\n", + "2024-04-29 14:09:52,699 - climada.util.calibrate.bayesian_optimizer - INFO - Optimization iteration: 4\n", + "2024-04-29 14:09:55,673 - climada.util.calibrate.bayesian_optimizer - INFO - Optimization iteration: 5\n", + "2024-04-29 14:09:58,819 - climada.util.calibrate.bayesian_optimizer - INFO - Optimization iteration: 6\n", + "2024-04-29 14:10:02,072 - climada.util.calibrate.bayesian_optimizer - INFO - Optimization iteration: 7\n", + "2024-04-29 14:10:06,238 - climada.util.calibrate.bayesian_optimizer - INFO - No improvement. Stop optimization.\n" + ] + } + ], + "source": [ + "from climada.util.calibrate import Input, BayesianOptimizer, BayesianOptimizerController\n", + "from sklearn.metrics import mean_squared_log_error\n", + "\n", + "from climada.util import log_level\n", + "\n", + "# Define calibration input\n", + "with log_level(\"INFO\", name_prefix=\"climada.util.calibrate\"):\n", + " input = Input(\n", + " hazard=hazard,\n", + " exposure=exposure,\n", + " data=data,\n", + " impact_func_creator=impact_func_tc,\n", + " cost_func=mean_squared_log_error,\n", + " impact_to_dataframe=lambda imp: imp.impact_at_reg(exposure.gdf[\"region_id\"]),\n", + " bounds=bounds,\n", + " )\n", + "\n", + " # Create and run the optimizer\n", + " opt = BayesianOptimizer(input)\n", + " controller = BayesianOptimizerController.from_input(input)\n", + " bayes_output = opt.run(controller)\n", + " bayes_output.params # The optimal parameters" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Evaluate Output" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The Bayesian Optimizer returns the entire paramter space it sampled via {py:class}`~climada.util.calibrate.bayesian_optimizer.BayesianOptimizerOutput`\n", + "We can find out a lot about the relation of the fitted parameters by investigating how the cost function value depends on them.\n", + "We can retrieve the parameter space as `pandas.DataFrame` via {py:meth}`~climada.util.calibrate.bayesian_optimizer.BayesianOptimizerOutput.p_space_to_dataframe`.\n", + "This dataframe has MultiIndex columns.\n", + "One group are the `Parameters`, the other holds information on the `Calibration` for each parameter set.\n", + "Notice that the optimal parameter set is not necessarily the last entry in the parameter space!" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " Parameters Calibration\n", + " scale v_half Cost Function\n", + "Iteration \n", + "0 0.422852 115.264302 2.726950\n", + "1 0.010113 63.349706 4.133135\n", + "2 0.155288 37.268453 0.800611\n", + "3 0.194398 68.718642 1.683610\n", + "4 0.402800 92.721038 2.046407\n", + "... ... ... ...\n", + "246 0.790590 50.164555 0.765166\n", + "247 0.788425 48.043636 0.768636\n", + "248 0.826704 49.932348 0.764991\n", + "249 0.880736 49.290532 0.767437\n", + "250 0.744523 51.596642 0.770761\n", + "\n", + "[251 rows x 3 columns]" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "p_space_df = bayes_output.p_space_to_dataframe()\n", + "p_space_df" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In contrast, the controller only tracks the consecutive improvements of the best guess." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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iterationrandomtargetimprovement
sample
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20True-0.8006112.406088
230False-0.7994840.001409
250False-0.7941190.006756
290False-0.7917230.003026
401False-0.7811150.013581
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1424False-0.7652100.001556
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1735False-0.7640000.001240
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" + ], + "text/plain": [ + " iteration random target improvement\n", + "sample \n", + "0 0 True -2.726950 inf\n", + "2 0 True -0.800611 2.406088\n", + "23 0 False -0.799484 0.001409\n", + "25 0 False -0.794119 0.006756\n", + "29 0 False -0.791723 0.003026\n", + "40 1 False -0.781115 0.013581\n", + "44 1 False -0.777910 0.004119\n", + "55 1 False -0.772557 0.006929\n", + "88 2 False -0.768626 0.005115\n", + "92 2 False -0.768494 0.000172\n", + "93 2 False -0.768144 0.000456\n", + "113 3 False -0.767889 0.000333\n", + "115 3 False -0.767105 0.001021\n", + "116 3 False -0.766561 0.000709\n", + "122 3 False -0.766400 0.000211\n", + "142 4 False -0.765210 0.001556\n", + "146 4 False -0.764947 0.000343\n", + "173 5 False -0.764000 0.001240\n", + "216 6 False -0.763936 0.000084" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "controller.improvements()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To get one group of the columns, simply access the item." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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scalev_half
Iteration
00.422852115.264302
10.01011363.349706
20.15528837.268453
30.19439868.718642
40.40280092.721038
.........
2460.79059050.164555
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251 rows × 2 columns

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" + ], + "text/plain": [ + " scale v_half\n", + "Iteration \n", + "0 0.422852 115.264302\n", + "1 0.010113 63.349706\n", + "2 0.155288 37.268453\n", + "3 0.194398 68.718642\n", + "4 0.402800 92.721038\n", + "... ... ...\n", + "246 0.790590 50.164555\n", + "247 0.788425 48.043636\n", + "248 0.826704 49.932348\n", + "249 0.880736 49.290532\n", + "250 0.744523 51.596642\n", + "\n", + "[251 rows x 2 columns]" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "p_space_df[\"Parameters\"]" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Notice that the optimal parameter set is not necessarily the last entry in the parameter space!\n", + "Therefore, let's order the parameter space by the ascending cost function values." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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ParametersCalibration
scalev_halfCost Function
Iteration
2160.99374452.2556060.763936
1730.98572152.0265160.764000
1770.88114351.5586650.764718
1460.96728451.0347020.764947
2480.82670449.9323480.764991
............
350.028105118.9679246.298332
950.012842102.4493986.635728
1990.031310143.5379007.262185
990.025663141.2361047.504095
2320.010398147.1134869.453765
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251 rows × 3 columns

\n", + "
" + ], + "text/plain": [ + " Parameters Calibration\n", + " scale v_half Cost Function\n", + "Iteration \n", + "216 0.993744 52.255606 0.763936\n", + "173 0.985721 52.026516 0.764000\n", + "177 0.881143 51.558665 0.764718\n", + "146 0.967284 51.034702 0.764947\n", + "248 0.826704 49.932348 0.764991\n", + "... ... ... ...\n", + "35 0.028105 118.967924 6.298332\n", + "95 0.012842 102.449398 6.635728\n", + "199 0.031310 143.537900 7.262185\n", + "99 0.025663 141.236104 7.504095\n", + "232 0.010398 147.113486 9.453765\n", + "\n", + "[251 rows x 3 columns]" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "p_space_df = p_space_df.sort_values((\"Calibration\", \"Cost Function\"), ascending=True)\n", + "p_space_df" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The {py:class}`~climada.util.calibrate.bayesian_optimizer.BayesianOptimizerOutput` supplies the {py:meth}`~climada.util.calibrate.bayesian_optimizer.BayesianOptimizerOutput.plot_p_space` method for convenience.\n", + "If there were more than two parameters we calibrated, it would produce a plot for each parameter combination." + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "bayes_output.plot_p_space(x=\"v_half\", y=\"scale\")" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'scale': 0.9937435057717706, 'v_half': 52.25560598796059}" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "p_space_df[\"Parameters\"].iloc[0, :].to_dict()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Analyze the Calibration\n", + "\n", + "Now that we obtained a calibration result, we should investigate it further.\n", + "The tasks of evaluating results and plotting them is simplified by the {py:class}`~climada.util.calibrate.base.OutputEvaluator`.\n", + "It takes the input and output of a calibration task as parameters.\n", + "Let's start by plotting the optimized impact function:" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from climada.util.calibrate import OutputEvaluator\n", + "\n", + "output_eval = OutputEvaluator(input, bayes_output)\n", + "output_eval.impf_set.plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here we show how the variability in parameter combinations with similar cost function values (as seen in the plot of the parameter space) translate to varying impact functions. In addition, the hazard value distribution is shown. Together this provides an intuitive overview regarding the robustness of the optimization, given the chosen cost function. It does NOT provide a view of the sampling uncertainty (as e.g. bootstrapping or cross-validation) NOR of the suitability of the cost function which is chosen by the user.\n", + "\n", + "This functionality is only available from the {py:class}`~climada.util.calibrate.bayesian_optimizer.BayesianOptimizerOutputEvaluator` tailored to Bayesian optimizer outputs.\n", + "It includes all function from {py:class}`~climada.util.calibrate.base.OutputEvaluator`." + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from climada.util.calibrate import BayesianOptimizerOutputEvaluator, select_best\n", + "\n", + "output_eval = BayesianOptimizerOutputEvaluator(input, bayes_output)\n", + "\n", + "# Plot the impact function variability\n", + "output_eval.plot_impf_variability(select_best(p_space_df, 0.03), plot_haz=False)\n", + "output_eval.plot_impf_variability(select_best(p_space_df, 0.03))" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The target function has limited meaning outside the calibration task.\n", + "To investigate the quality of the calibration, it is helpful to compute the impact with the impact function defined by the optimal parameters.\n", + "The {py:class}`~climada.util.calibrate.base.OutputEvaluator` readily computed this impact when it was created.\n", + "You can access the impact via the {py:attr}`~climada.util.calibrate.base.OutputEvaluator.impact` attribute." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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0.000000e+00 \n", + "2010302N09306 4.578844e+06 6.995127e+05 \n", + "2011233N15301 0.000000e+00 1.383507e+08 \n", + "2011279N10257 0.000000e+00 0.000000e+00 \n", + "2012215N12313 0.000000e+00 0.000000e+00 \n", + "2012166N09269 0.000000e+00 0.000000e+00 \n", + "2012296N14283 0.000000e+00 0.000000e+00 \n", + "2014253N13260 0.000000e+00 0.000000e+00 \n", + "2015293N13266 0.000000e+00 0.000000e+00 \n", + "2015242N12343 0.000000e+00 0.000000e+00 \n", + "2015270N27291 0.000000e+00 0.000000e+00 \n", + "2016248N15255 0.000000e+00 0.000000e+00 \n", + "2017219N16279 0.000000e+00 0.000000e+00 \n", + "2017242N16333 0.000000e+00 8.060133e+08 \n", + "2017260N12310 0.000000e+00 9.235309e+07 " + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import numpy as np\n", + "\n", + "impact_data = output_eval.impact.impact_at_reg(exposure.gdf[\"region_id\"])\n", + "impact_data.set_index(np.asarray(hazard.event_name), inplace=True)\n", + "impact_data" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can now compare the modelled and reported impact data on a country- or event-basis.\n", + "The `OutputEvaluator` also has methods for that.\n", + "In both of these, you can supply a transformation function with the `data_transf` argument.\n", + "This transforms the data to be plotted right before plotting.\n", + "Recall that we set the event IDs as index for the data frames.\n", + "To better interpret the results, it is useful to transform them into event names again, which are the IBTrACS IDs.\n", + "Likewise, we use the region IDs for region identification.\n", + "It might be nicer to transform these into country names before plotting." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "import climada.util.coordinates as u_coord\n", + "\n", + "\n", + "def country_code_to_name(code):\n", + " return u_coord.country_to_iso(code, representation=\"name\")\n", + "\n", + "\n", + "event_id_to_name = {\n", + " hazard.event_id[idx]: hazard.event_name[idx] for idx in range(len(hazard.event_id))\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", 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s7e3Vttvb2yMuLk51v1mzZrh06RKSkpJQokQJ7Ny5E9WrV8e8efPg5eWFtLQ0/PzzzyhYsGCOxzIxMYGJiYnWvhYiIiLSHTpdAKXLPHZHCKG2LafZXa1atUKrVq2+6FgBAQEICAiAUqn88qBERET0VdDpS2CFChWCoaGh2tkeAIiPj89yVkhT/Pz8EBERgQsXLmjl+YmIiEh+Ol0AGRsbo2rVqggODlbbHhwcjDp16siUioiIiL52sl8Ce/PmDe7cuaO6Hx0djbCwMNja2qJkyZIYOXIkunXrhmrVqqF27dr47bffEBMTgwEDBmglDy+BERER5X2yF0AXL16El5eX6v7IkSMBAD169EBgYCA6deqE58+fY8qUKXj8+DEqVKiAAwcOwNHRUSt5/Pz84Ofnh8TERFhZsdkcERFRXiR7AdSwYcNsmxZmNGjQIAwaNEiiRERERJTX6fQYICIiIiJtYAGUSUBAANzd3VG9enW5oxAREZGWsADKhNPgiYiI8j4WQERERKR3WAARERGR3mEBlAnHABEREeV9LIAy4RggIiKivI8FEBEREekdFkBERESkd1gAZcIxQERERHkfC6BMOAaIiIgo72MBRERERHqHBRARERHpHRZAREREpHdYABEREZHeYQGUCWeBERER5X0sgDLhLDAiIqK8jwUQERER6R0WQERERKR3WAARERGR3skndwAifePkv/+zj7lnKkEQIiI9xjNAREREpHd4BoiIKAefO1t3b6avREmISNNYAGUSEBCAgIAAKJVKuaPkfZOscvGYBO3nICIivcNLYJmwDxAREVHexwKIiIiI9A4LICIiItI7LICIiIhI77AAIiIiIr3DAoiIiIj0DgsgIiIi0jssgIiIiEjvsAAiIiIivcMCKJOAgAC4u7ujevXqckchIiIiLWEBlAk7QRMREeV9LICIiIhI77AAIiIiIr3DAoiIiIj0DgsgIiIi0jssgIiIiEjv5JM7ABF9BSZZfWZ/gjQ5iIg0hGeAiIiISO+wACIiIiK9wwKIiIiI9A4LICIiItI7LICIiIhI7+TpWWBz587F2rVroVAo4O/vjx9++EHuSFrl5L//s4+5N9NXgiRERES6Lc8WQFevXsUff/yB0NBQAEDjxo3x7bffwtraWt5gREREJLs8ewksMjISderUgampKUxNTVG5cmUcPHhQ7lhERESkA3S2ADp58iRatmyJYsWKQaFQYNeuXVkes2zZMjg7O8PU1BRVq1bFqVOnVPsqVKiAY8eO4dWrV3j16hWOHj2Khw8fSvgVEBERka7S2QIoKSkJlSpVwtKlS7Pdv2XLFgwfPhzjxo3D5cuXUa9ePfj4+CAmJgYA4O7ujqFDh6JRo0Zo27Ytqlevjnz5cr7i9/79eyQmJqrdiIiIKG/S2QLIx8cH06ZNQ7t27bLdP3/+fPTp0wd9+/ZFuXLlsHDhQjg4OGD58uWqx/Tv3x+XLl3CsWPHYGxsDFdX1xyPN2PGDFhZWaluDg4OGv+aiIiISDfobAH0KR8+fEBoaCi8vb3Vtnt7eyMkJER1Pz4+HgBw8+ZNnD9/Hs2aNcvxOceOHYuEhATVLTY2VjvhiYiISHZf5SywZ8+eQalUwt7eXm27vb094uLiVPfbtGmDV69eoUCBAli7du0nL4GZmJjAxMREa5mJiIhId3yVBVA6hUKhdl8IobYt49mg3AoICEBAQACUSuV/zkdERES66au8BFaoUCEYGhqqne0BPl7yynxW6Ev5+fkhIiICFy5c+E/PQ0RERLrrqyyAjI2NUbVqVQQHB6ttDw4ORp06dWRKRURERF+LXF0C+zdTwi0tLb/4/2T05s0b3LlzR3U/OjoaYWFhsLW1RcmSJTFy5Eh069YN1apVQ+3atfHbb78hJiYGAwYM+E/H5SUwIiIizdO15ZpyVQBZW1tnGW/zKQqFArdu3YKLi8u/Dnbx4kV4eXmp7o8cORIA0KNHDwQGBqJTp054/vw5pkyZgsePH6NChQo4cOAAHB0d//UxgY+XwPz8/JCYmAgrK6v/9FxERESkm3I9CHr79u2wtbX97OOEEGjRosV/CgUADRs2hBDik48ZNGgQBg0a9J+PRURERPolVwWQo6Mj6tevj4IFC+bqSV1cXGBkZPSfghERERFpS64KoOjo6C960mvXrv2rMLqAY4CIiIjyvq+6D5A2cAwQ6ZtcDUw0lSAIEZGEvqgASktLQ2BgIHbs2IF79+5BoVDA2dkZHTp0QLdu3b5ooDQRERGRXHLdB0gIgVatWqFv3754+PAhPDw8UL58edy/fx89e/ZE27ZttZmTiIiISGNyfQYoMDAQJ0+exJEjR9SmpwPA0aNH0aZNG6xbtw7du3fXeEgpcQwQERFR3pfrM0CbNm3C//73vyzFDwA0atQI/v7+2Lhxo0bDyYFLYRAREeV9uS6AwsPD0bx58xz3+/j44MqVKxoJRURERKRNuS6AXrx48cmFRu3t7fHy5UuNhCIiIiLSplyPAVIqlciXL+eHGxoaIjU1VSOh5MQxQERERDKZlIv2M5MSNHKoXBdAQgj07NkTJiYm2e5///69RgLJjX2AiIiI8r5cF0A9evT47GO+9hlgpDlsrkdERLos1wXQ2rVrtZmDiIiISDK5HgSdk/v37yMiIgJpaWmayENERESkdbkugIKCgrBw4UK1bT/++CNcXFzg4eGBChUqIDY2VtP5iIiIiDQu15fAVqxYgR9//FF1/+DBg1i7di3WrVuHcuXKYfDgwZg8eTJWr16tlaBS+dJZYJ8b63Jvpq8mYhERUQ5yNeaQv4spk1yfAbp16xaqVaumur979260atUKXbt2haenJ6ZPn44jR45oJaSU2AmaiIgo78t1AfT27VtYWlqq7oeEhKB+/fqq+y4uLoiLi9NsOiIiIiItyHUB5OjoiNDQUADAs2fPcP36dXzzzTeq/XFxceybQ0RERF+FXI8B6t69O/z8/HD9+nUcPXoUZcuWRdWqVVX7Q0JCUKFCBa2EJCIiItKkXBdAY8aMQXJyMnbs2IEiRYpg27ZtavvPnDmDzp07azwgERERkablugAyMDDA1KlTMXXq1Gz3Zy6IiIiIiHTVf26ESERERPS1yXUBZGBgAENDwyw3Gxsb1KpVCzt27NBmTskEBATA3d0d1atXlzsKERERaUmuL4Ht3Lkz2+2vXr3C+fPn8cMPPyAoKAjfffedxsLJgavBExER5X25LoBat26d474ePXrA3d0dc+fO/eoLICIiIsr7NDYGyNvbG7du3dLU0xERERFpjcYKoLdv38LU1FRTT0dERESkNRorgFatWoUqVapo6umIiIiItCbXY4BGjhyZ7faEhARcvHgRUVFROHXqlMaCEREREWlLrgugy5cvZ7vd0tISzZs3x6BBg+Do6KixYHnGpFzMJJuUoP0cREREpJLrAujYsWPazEFEREQkGXaCJiIiIr2TqwKoXbt2SExMzPWTdu3aFfHx8f86lJzYCZqIiCjvy1UBtHv3bjx9+hSJiYmfvSUkJGDv3r148+aNtrNrhZ+fHyIiInDhwgW5oxAREZGW5GoMkBACZcqU0XYWIiIiIknkqgD6NwOgixcv/sX/h4iIiEgKuSqAGjRooO0cRERERJLhLDAiIiLSOyyAiIiISO+wACIiIiK9wwKIiIiI9M4XF0CNGjXCq1evsmxPTExEo0aNNJGJiIiISKu+uAA6fvw4Pnz4kGX7u3fvuBo8ERERfRVyvRhqeHi46t8RERGIi4tT3VcqlTh48CB7/xAREdFXIdcFUOXKlaFQKKBQKLK91GVmZoYlS5ZoNBxpwSSrXDwmQfs5iIiIZJTrAig6OhpCCLi4uOD8+fOws7NT7TM2NkbhwoVhaGiolZD/1oIFC7B69WoIIdCkSRMsWrQICoVC7lhEREQks1wXQI6OjgCAtLQ0rYXRpKdPn2Lp0qW4fv06jIyMUL9+fZw7dw61a9eWOxoR5cDJf/9nH3Nvpq8ESYgor/viQdAzZszAmjVrsmxfs2YNZs2apZFQmpKamop3794hJSUFKSkpKFy4sNyRiIiISAd8cQG0cuVKlC1bNsv28uXLY8WKFRoJBQAnT55Ey5YtUaxYMSgUCuzatSvLY5YtWwZnZ2eYmpqiatWqarPQ7OzsMGrUKJQsWRLFihVDkyZNUKpUKY3lIyIioq/XFxdAcXFxKFq0aJbtdnZ2ePz4sUZCAUBSUhIqVaqEpUuXZrt/y5YtGD58OMaNG4fLly+jXr168PHxQUxMDADg5cuX2LdvH+7du4eHDx8iJCQEJ0+ezPF479+/R2JiotqNiIiI8qYvLoAcHBxw5syZLNvPnDmDYsWKaSQUAPj4+GDatGlo165dtvvnz5+PPn36oG/fvihXrhwWLlwIBwcHLF++HADw999/w9XVFba2tjAzM4Ovry/OnTuX4/FmzJgBKysr1c3BwUFjXwsRERHpli8ugPr27Yvhw4dj7dq1uH//Pu7fv481a9ZgxIgR6NevnzYyZvHhwweEhobC29tbbbu3tzdCQkIAfCzUQkJC8O7dOyiVShw/fhxubm45PufYsWORkJCgusXGxmr1ayAiIiL55HoWWLqff/4ZL168wKBBg1QdoU1NTTFmzBiMHTtW4wGz8+zZMyiVStjb26ttt7e3VzVorFWrFlq0aIEqVarAwMAAjRs3RqtWrXJ8ThMTE5iYmGg1NxEREemGLy6AFAoFZs2ahfHjxyMyMhJmZmYoXbq0LMVD5p4+Qgi1bb/++it+/fXXL3rOgIAABAQEQKlUaiQjEWkYm3kSkQZ8cQGUztzcHNWrV9dkllwrVKgQDA0N1ZbjAID4+PgsZ4W+lJ+fH/z8/JCYmAgrq1z8oiUiIqKvzr8qgC5cuIBt27YhJiYmy8KoO3bs0EiwTzE2NkbVqlURHByMtm3bqrYHBwejdevWWj8+ERERfd2+eBD05s2bUbduXURERGDnzp1ISUlBREQEjh49qtEzJm/evEFYWBjCwsIAfFyKIywsTDXNfeTIkVi9ejXWrFmDyMhIjBgxAjExMRgwYMB/Om5AQADc3d1lO7tFRERE2vfFZ4CmT5+OBQsWwM/PDxYWFli0aBGcnZ3Rv3//bPsD/VsXL16El5eX6v7IkSMBAD169EBgYCA6deqE58+fY8qUKXj8+DEqVKiAAwcOqJbs+Ld4CYyIiCjv++ICKCoqCr6+H9fiMTExQVJSEhQKBUaMGIFGjRph8uTJGgnWsGFDCCE++ZhBgwZh0KBBGjkeERER6Y8vvgRma2uL169fAwCKFy+Oa9euAQBevXqF5ORkzaYjIiIi0oIvLoDq1auH4OBgAEDHjh0xbNgw9OvXD507d0bjxo01HlBqHANERESU933xJbClS5fi3bt3AD52TzYyMsLp06fRrl07jB8/XuMBpcYxQERERHnfFxdAtra2qn8bGBjg559/xs8//6zRUERERETa9K/6ACmVSuzcuRORkZFQKBQoV64cWrdujXz5/nVfRSIiIiLJfHHFcu3aNbRu3RpxcXGqxUVv3boFOzs77NmzBx4eHhoPKSUuhUFERJT3/avV4MuXL48HDx7g0qVLuHTpEmJjY1GxYkX8+OOP2sgoKT8/P0RERODChQtyRyEiIiIt+eIzQFeuXMHFixdhY2Oj2mZjY4Nff/2VM6eIiIjoq/DFZ4Dc3Nzw5MmTLNvj4+Ph6uqqkVBERERE2vTFBdD06dMxdOhQbN++HQ8ePMCDBw+wfft2DB8+HLNmzUJiYqLq9jViHyAiIqK874svgX377bcAPjZBVCgUAKBasqJly5aq+wqF4qscSMw+QERERHnfFxdAx44d00YOIiIiIsl8cQHUoEEDbeQgIiIiksy/6lz47t07hIeHIz4+HmlpaWr7WrVqpZFgRERERNryxQXQwYMH0b17dzx79izLvq913A8RERHply+eBTZ48GB89913ePz4MdLS0tRueaH44SwwIiKivO+LC6D4+HiMHDkS9vb22sgjO3aCJiIiyvu+uADq0KEDjh8/roUoRERERNL44jFAS5cuxXfffYdTp07Bw8MDRkZGavuHDh2qsXBERERE2vDFBdAff/yBQ4cOwczMDMePH1c1QwQ+DoJmAURERES67osLoF9++QVTpkyBv78/DAy++AoaERERkey+uIL58OEDOnXqxOKHiIiIvlpfXMX06NEDW7Zs0UYWIiIiIkl88SUwpVKJ2bNn49ChQ6hYsWKWQdDz58/XWDg5BAQEICAgIE/0NCIiIqLsfXEBdPXqVVSpUgUAcO3aNbV9GQdEf624GjwREVHex9XgiYiISO/8q8VQiYiIKI+Y9JmrHZMSpMkhsVwXQO3atcvV43bs2PGvwxARERFJIdcFEMfDEBERUV6R6wJo7dq12sxBREREJBl2MyQiIiK9wwKIiIiI9A4LICIiItI7LIAyCQgIgLu7O6pXry53FCIiItISFkCZ+Pn5ISIiAhcuXJA7ChEREWkJGyESEVHe97lmf0CebfhH2eMZICIiItI7LICIiIhI77AAIiIiIr3DAoiIiIj0DgdBExH9WxxYS/TV4hkgIiIi0jssgIiIiEjvsAAiIiIivcMCiIiIiPQOCyAiIiLSO3m2ALp58yYqV66supmZmWHXrl1yxyIiIiIdkGenwbu5uSEsLAwA8ObNGzg5OaFp06byhiIiIiKdkGfPAGW0Z88eNG7cGAUKFJA7ChEREekAnS2ATp48iZYtW6JYsWJQKBTZXr5atmwZnJ2dYWpqiqpVq+LUqVPZPtfWrVvRqVMnLScmIiKir4XOFkBJSUmoVKkSli5dmu3+LVu2YPjw4Rg3bhwuX76MevXqwcfHBzExMWqPS0xMxJkzZ9CiRYtPHu/9+/dITExUuxEREVHepLMFkI+PD6ZNm4Z27dplu3/+/Pno06cP+vbti3LlymHhwoVwcHDA8uXL1R63e/duNGvWDKampp883owZM2BlZaW6OTg4aOxrISIiIt2iswXQp3z48AGhoaHw9vZW2+7t7Y2QkBC1bbm9/DV27FgkJCSobrGxsRrNTERERLrjq5wF9uzZMyiVStjb26ttt7e3R1xcnOp+QkICzp8/jz///POzz2liYgITExONZyUiIiLd81WeAUqnUCjU7gsh1LZZWVnhyZMnMDY2zvVzBgQEwN3dHdWrV9dYTiIiItItX2UBVKhQIRgaGqqd7QGA+Pj4LGeFvpSfnx8iIiJw4cKF//Q8REREpLu+ygLI2NgYVatWRXBwsNr24OBg1KlTR6ZURERE9LXQ2TFAb968wZ07d1T3o6OjERYWBltbW5QsWRIjR45Et27dUK1aNdSuXRu//fYbYmJiMGDAgP903ICAAAQEBECpVP7XL4GIiIh0lM4WQBcvXoSXl5fq/siRIwEAPXr0QGBgIDp16oTnz59jypQpePz4MSpUqIADBw7A0dHxPx3Xz88Pfn5+SExMhJWV1X96LiIiItJNOlsANWzYEEKITz5m0KBBGDRokESJiIiIKK/4KscAEREREf0XLIAy4TR4IiKivE9nL4HJhWOAiIjo33Ly3//Zx9yb6StBEvocngEiIiIivcMCiIiIiPQOC6BMOAaIiIgo72MBlAmXwiAiIsr7WAARERGR3mEBRERERHqHBVAmHANERESU97EAyoRjgIiIiPI+FkBERESkd9gJmoiIKI/KVWdqUwmC6CCeASIiIiK9wwKIiIiI9A4LoEw4C4yIiCjvYwGUCWeBERER5X0sgIiIiEjvsAAiIiIivcMCiIiIiPQOCyAiIiLSOyyAiIiISO+wACIiIiK9wwIoE/YBIiIiyvtYAGXCPkBERER5HwsgIiIi0jssgIiIiEjvsAAiIiIivcMCiIiIiPQOCyAiIiLSOyyAiIiISO+wACIiIiK9wwKIiIiI9A4LoEzYCZqIiCjvYwGUCTtBExER5X0sgIiIiEjvsAAiIiIivcMCiIiIiPQOCyAiIiLSOyyAiIiISO+wACIiIiK9wwKIiIiI9A4LICIiItI7LICIiIhI77AAIiIiIr2TT+4ARDpjklUuHpOg/RxERKR1efoMUHR0NLy8vODu7g4PDw8kJSXJHYmIiIh0QJ4+A9SzZ09MmzYN9erVw4sXL2BiYiJ3JCIiItIBebYAun79OoyMjFCvXj0AgK2trcyJiIiISFfo7CWwkydPomXLlihWrBgUCgV27dqV5THLli2Ds7MzTE1NUbVqVZw6dUq17/bt2zA3N0erVq3g6emJ6dOnS5ieiIiIdJnOFkBJSUmoVKkSli5dmu3+LVu2YPjw4Rg3bhwuX76MevXqwcfHBzExMQCAlJQUnDp1CgEBATh79iyCg4MRHByc4/Hev3+PxMREtRsRERHlTTpbAPn4+GDatGlo165dtvvnz5+PPn36oG/fvihXrhwWLlwIBwcHLF++HABQokQJVK9eHQ4ODjAxMUGLFi0QFhaW4/FmzJgBKysr1c3BwUEbXxYRERHpAJ0tgD7lw4cPCA0Nhbe3t9p2b29vhISEAACqV6+OJ0+e4OXLl0hLS8PJkydRrly5HJ9z7NixSEhIUN1iY2O1+jUQERGRfL7KQdDPnj2DUqmEvb292nZ7e3vExcUBAPLly4fp06ejfv36EELA29sb3377bY7PaWJiwlliREREeuKrLIDSKRQKtftCCLVtPj4+8PHx+aLnDAgIQEBAAJRKpUYyEhERke75Ki+BFSpUCIaGhqqzPeni4+OznBX6Un5+foiIiMCFCxf+0/MQERGR7voqCyBjY2NUrVo1y6yu4OBg1KlTR6ZURERE9LXQ2Utgb968wZ07d1T3o6OjERYWBltbW5QsWRIjR45Et27dUK1aNdSuXRu//fYbYmJiMGDAgP90XF4CIyIiyvt0tgC6ePEivLy8VPdHjhwJAOjRowcCAwPRqVMnPH/+HFOmTMHjx49RoUIFHDhwAI6Ojv/puH5+fvDz80NiYiKsrHKxOCYRERF9dXS2AGrYsCGEEJ98zKBBgzBo0CCJEhEREVFe8VWOASIiIiL6L1gAZRIQEAB3d3dUr15d7ihERESkJSyAMuE0eCIioryPBRARERHpHRZAREREpHdYAGXCMUBERER5HwugTDgGiIiIKO/T2T5AREREedKkXDTZnZSg/Rx6jmeAiIiISO+wAMqEY4CIiIjyPhZAmXAMEBERUd7HAoiIiIj0DgsgIiIi0jssgIiIiEjvsAAiIiIivcMCKBPOAiMiIsr7WABlwllgREREeR8LICIiItI7LICIiIhI77AAIiIiIr3DAoiIiIj0DgsgIiIi0jssgIiIiEjvsADKhH2AiIiI8j4WQJmwDxAREVHexwKIiIiI9A4LICIiItI7LICIiIhI77AAIiIiIr3DAoiIiIj0Tj65AxBJwcl//2cfc89UgiBERKQTeAaIiIiI9A4LICIiItI7LIAyYSdoIiKivI8FUCbsBE1ERJT3sQAiIiIivcMCiIiIiPQOCyAiIiLSOyyAiIiISO+wACIiIiK9wwKIiIiI9A4LICIiItI7LICIiIhI77AAIiIiIr3DAoiIiIj0DgsgIiIi0jv55A6gq4QQAIDExMRPPi7tffIn9ycqxOcP9plj5NbnsgDS5dGlLIBu5dGlLICG8uhSFkCyPLqUBeBr+JOH4vcm50Plsfd3+t/t9L/jOVGIzz1CTz148AAODg5yxyAiIqJ/ITY2FiVKlMhxPwugHKSlpeHRo0ewsLCAQqH4V8+RmJgIBwcHxMbGwtLSUsMJv+48upRF1/LoUhZdy8MsX0ceXcqia3l0KYuu5dFUFiEEXr9+jWLFisHAIOeRPrwElgMDA4NPVo5fwtLSUvYXVka6lEeXsgC6lUeXsgC6lYdZcqZLeXQpC6BbeXQpC6BbeTSRxcrK6rOP4SBoIiIi0jssgIiIiEjvsADSIhMTE0ycOBEmJiZyRwGgW3l0KQugW3l0KQugW3mYJWe6lEeXsgC6lUeXsgC6lUfqLBwETURERHqHZ4CIiIhI77AAIiIiIr3DAoiIiIj0DgsgIiIi0jssgEivffjwATdv3kRqaqrcUSgb0dHRuH37dpbtt2/fxr1796QPRF+Ft2/fIjn5/9adun//PhYuXIjDhw/LmIp0DTtBa9iFCxewbds2xMTE4MOHD2r7duzYIVMq+T158gSjRo3CkSNHEB8fn2WROqVSKWme5ORkDBkyBEFBQQCAW7duwcXFBUOHDkWxYsXg7+8vaR7KXs+ePdG7d2+ULl1abfs///yD1atX4/jx45LmefDgAfbs2ZPt+3v+/PmSZtE1SqUSu3btQmRkJBQKBcqVK4fWrVvD0NBQ8iytW7dGu3btMGDAALx69Qo1a9aEkZERnj17hvnz52PgwIGSZ9IVBw8ehLm5Ob755hsAQEBAAFatWgV3d3cEBATAxsZGf7II0phNmzYJIyMj4evrK4yNjcW3334r3NzchJWVlejZs6fc8YQQQqSmporLly+LFy9eSHrc5s2bC3d3d7Fs2TKxc+dOsWvXLrWb1IYOHSqqVq0qTp06JQoUKCCioqKEEELs3r1bVK5cWfI8Qghx/vx5MXr0aNGpUyfRtm1btZu+srCwELdv386y/fbt28LKykrSLH///bfInz+/KF++vMiXL5+oXLmysLa2FlZWVsLLy0vSLLrm9u3bokyZMiJ//vyiSpUqonLlyiJ//vzCzc1N3LlzR/I8BQsWFNeuXRNCCLFq1SpRsWJFoVQqxdatW0XZsmUlzZKamirmzJkjqlevLuzt7YWNjY3aTWoVKlQQ+/fvF0IIER4eLkxMTMTYsWNFzZo1Jf87JXcWFkAa5OHhIZYuXSqEEMLc3FxERUWJtLQ00a9fPzFhwgRZMg0bNkysXr1aCPHxjVi3bl2hUChEgQIFxLFjxyTLYW5uLi5fvizZ8T6nZMmS4uzZs0KI//tZCfHxF7mFhYXkeXS1eE5KShKRkZHiypUrajepWFpaikuXLmXZfvHiRWFubi5ZDiGEqF69uhg/frwQ4v9eM69fvxatWrUSy5YtkzRLRi9fvhRz584Vffr0EX379hXz5s0Tr169kjSDj4+PaN68uXj+/Llq27Nnz0Tz5s1FixYtJM0ihBBmZmbi/v37QgghvvvuOzFp0iQhhBAxMTHCzMxM0izjx48XRYsWFXPmzBGmpqZi6tSpok+fPqJgwYJi0aJFkmYRQogCBQqI6OhoIYQQEydOFO3btxdCCBEaGirs7e31KgsLIA3Knz+/6odZsGBBER4eLoQQIiIiQhQpUkSWTMWLFxcXLlwQQgixc+dOUaxYMXHz5k0xbtw4UadOHclylCtXLts/ZHIxMzNTFT0ZC6CwsDBhaWkpeR5dK57j4+OFr6+vMDAwyPYmFV9fX/Hdd9+J1NRU1bbU1FTRvn170bx5c8lyCPHx55J+NsPa2lp1hiEsLEw4OjpKmiXdhQsXhK2trShevLho27ataNOmjShRooQoWLCgCA0NlSxH/vz5Vb/vMgoLCxMFChSQLEc6Dw8PsWjRIhETEyMsLS1FSEiIEOJj4Sz1H3kXFxexb98+IYT6a2jRokWic+fOkmYRQggbGxtx/fp1IYQQdevWFStXrhRCCBEdHS15cSh3FhZAGlSiRAnVL4GKFSuKP/74QwghREhIiCx/VIUQwsTERMTGxgohhOjXr58YNmyYEEKIu3fvSnqm49ChQ8Lb21tVIMqtfv36YvHixUKIj7+U7t69K4QQws/PTzRr1kzyPLpWPHfp0kXUqVNHnD9/XhQoUEAcPnxYrF+/Xri5ual+mUvh+vXromDBgqJUqVKiZ8+eomfPnqJUqVLCzs5OXL16VbIcQghhb2+v+mXt7u4udu/eLYSQ74+8EEJ88803omfPniIlJUW1LSUlRfTo0UPUq1dPshw2NjbizJkzWbafPn1alss827ZtE0ZGRsLAwEA0bdpUtX369OmSF8758+dXnY0qUqSIqjCNioqS5e9Cy5YtRbNmzcSUKVOEkZGRePDggRDi4+/o0qVL61UWFkAa1LlzZzFv3jwhhBDTpk0TdnZ2om/fvsLR0VG2cRwlS5YUhw4dEqmpqcLBwUHs3btXCCHEtWvXhLW1tVaPbW1trXat29jYWBgYGAhzc3PZr4OfOXNGWFhYiAEDBghTU1MxbNgw0aRJE1GgQAFx8eJFyfPoWvFcpEgR8c8//wghPo7DuXnzphDi4xipunXrSprl4cOHYuzYsaJFixaiffv2YvLkyWqXWqTSunVr8dtvvwkhhBg9erRwdXUV06ZNE56enqJx48aS5xFCCFNTUxEZGZll+/Xr1yX9NN+tWzdRvnx5ce7cOZGWlibS0tLE2bNnRYUKFUSPHj0ky5HR48ePxaVLl4RSqVRt++eff7L9fmlTmTJlxLlz54QQHwvWGTNmCCGE2Lx5s7Czs5M0ixBC3L9/X/j6+oqKFSuqhkcIIcTw4cPFkCFD9CoL1wLToBcvXuDdu3coVqwY0tLSMHfuXJw+fRqurq4YP368pKPr002aNAkLFy5E0aJFkZycjFu3bsHExARr1qzBqlWrcPbsWa0dO32GVW706NFDazlycvXqVcydOxehoaFIS0uDp6cnxowZAw8PD8mzdOnSBdWqVcPIkSPx66+/YtGiRWjdujWCg4Ph6ekp+QxCS0tLhIeHw8nJCU5OTti4cSPq1q2L6OholC9fXm2Ksb64e/cu3rx5g4oVKyI5ORmjRo1Svb8XLFgAR0dHyTPZ29tj/fr18Pb2Vtt+6NAhdO/eHU+ePJEkx6tXr9CjRw/s3bsXRkZGAIDU1FS0atUKgYGBsLKykiSHLvL394elpSX+97//Yfv27ejcuTOcnJwQExODESNGYObMmXJH1FssgPTA9u3bERsbi++++w4lSpQA8LE4sba2RuvWrWVOR4DuFc/Vq1fHtGnT0KxZM7Rp0waWlpaYMWMGFi9ejO3btyMqKkprxw4PD0eFChVgYGCA8PDwTz62YsWKWsvxNRg6dCh27tyJuXPnok6dOlAoFDh9+jRGjx6N9u3bY+HChZLmuXPnDiIjIyGEgLu7O1xdXSU7drt27RAYGAhLS0u0a9fuk4+VsyXJuXPnEBISAldXV7Rq1UqWDFFRUVi7di2ioqKwaNEiFC5cGAcPHoSDgwPKly+v1WMnJibm+rGWlpZaTMIC6D/TpR+mLrt06RKMjIxUZ1d2796NtWvXwt3dHZMmTYKxsbHWM/BnlXsbN25ESkoKevbsicuXL6NZs2Z4/vw5jI2NERgYiE6dOmnt2AYGBoiLi0PhwoVhYGAAhUKRpW8UACgUCkn7R124cAFpaWmoWbOm2vZ//vkHhoaGqFatmmRZ0n348AGjR4/GihUrVM08jYyMMHDgQMycORMmJiaSZ5JLr169sHjxYlhYWKBXr16ffOzatWslSqV7Tpw4AR8fH9StWxcnT55EZGQkXFxcMHv2bJw/fx7bt2/X6vHT39O5oe33Nwug/0iXfpg5SUpKwokTJ7Jt3jZ06FBJMlSvXh3+/v5o37497t69C3d3d7Rr1w4XLlyAr6+vJJ9Udf1npVQqsXPnziyN5PLlk79faXJyMm7cuIGSJUuiUKFCWj3W/fv3UbJkSSgUCty/f/+Tj5XyslONGjXw888/o0OHDmrbd+zYgVmzZuGff/6RLEtmycnJiIqKghACrq6uyJ8/v6TH79ChA6pVq5algeicOXNw/vx5bNu2TdI8ctuzZw98fHxgZGSEPXv2fPKxUp8Fql27Nr777juMHDkSFhYWuHLlClxcXHDhwgW0adMGDx8+1OrxT5w4ofr3vXv34O/vj549e6J27doAgLNnzyIoKAgzZszQ/tAIrY8yyuOOHz+uugUGBooiRYoIf39/sXv3brF7927h7+8vihYtKgIDA2XJd+nSJVGkSBFhaWkpDA0NhZ2dnaoPkLOzs2Q5LC0tVdM/Z86cKby9vYUQH2eJlChRQpIMuvyzunr1qnBxcVE1kqtSpYooUKCAcHJyynZ6MUkvY8PMjO7evSt5T6J0r169ynZA+PPnz0VCQoJkOQoVKpTt6zQ8PFwULlxYshzpJk2aJEsDxnQKhUI8efJE9e+cblK2lEhXoEAB1azXjC1AoqOjhYmJiaRZGjVqpJrwkdHGjRtFgwYNtH58FkAaJPcPMzsNGjQQ/fr1E6mpqaoXe0xMjKhfv774888/JcthYWEhbt26JYQQokmTJmLhwoVCiI+zAExNTSXLkU7XflY1a9YULVu2VOvQ/eLFC9GqVStRq1YtyfO0b99eNVslo9mzZ4sOHTpo9djpBWlublKytbVV9ZPJ6MyZM1qfUZmT5s2bi4CAgCzbly9fLnx8fCTLYWpqKm7cuJFle2RkpCzvbw8PD2FgYCBq1qwplixZIuLj4yXPoKuKFy+ualmQsQDasWOHcHFxkTSLmZmZ6u9CRjdv3mQfoK+N3D/M7FhZWal+MVlZWYmIiAghhBDnzp0Tbm5ukuXw8vIS3bt3F+vWrRNGRkaq5Q2OHz8uSxM5XftZmZqaqhrrZXT16lVZ/oDI+Yn+U5+Y5fz03KlTJ9GgQQO1LssvX74UDRo0EN99952kWdLZ2Nio3tMZRUZGCltbW8lyVKtWTUyePDnL9okTJwpPT0/JcmR07do1MXbsWOHs7CyMjIyEj4+P2Lhxo0hKSpIlj64YPXq0+Oabb8Tjx49VS82cPn1auLi4qDpmS6VMmTJi5MiRWbaPHDlSlClTRuvHZwGkQXL/MLNTqFAhVQ+XMmXKiIMHDwohPv6ClPIP/ZUrV0SFChWEpaWl2pts8ODBsnRD1bWfVaVKlcSRI0eybD9y5IioUKGC5Hl07RO9Lnjw4IFwcXERVlZWomHDhqJhw4bC2tpauLm5iZiYGFky5dSBOTw8XNL39+7du0W+fPlE9+7dRWBgoAgMDBTdunUT+fLlEzt37pQsR05Onz4tBg0aJOzs7CRf6mbIkCHZLnmxZMkSVWNaKX348EF06dJFGBgYCIVCoWoY+cMPP6h1XJfC/v37hampqShfvrzo06eP6NOnjyhfvrwwNTVVrRGmTSyANEjuH2Z2mjZtKjZu3CiEEKJ///6iRo0aYsOGDaJZs2aiRo0asmTK6O3bt+LDhw+SH1cXflYJCQmq2/79+0X58uXFtm3bRGxsrIiNjRXbtm0THh4esrx2dPETvS548+aNWLlypRg0aJD46aefRFBQkCyv33QNGjQQgwcPzrJ90KBB4ptvvpE0y759+0SdOnVE/vz5RcGCBYWXl5c4fvy4pBlycvnyZfHTTz+J4sWLS17AFytWLNvmqqGhoaJ48eKSZsnozp07Ytu2bWLLli3Zng2XSmxsrBg7dqxqKZf//e9/kn2g4CwwDYuNjcXy5ctx48YNVS+MAQMGwMHBQZY8Fy9exOvXr+Hl5YWnT5+iR48eqv4ya9euRaVKlWTJpQsePHiA5cuXq/UtkfJnlXlWWvpbMX1bxvtSz0rbs2cP2rdvjy5duqBRo0YAgCNHjmDTpk3Ytm0b2rRpI1mWI0eOYMGCBarZcWXLlsXw4cPRpEkTyTLoqjNnzqBJkyaoXr06GjduDODj9+vChQs4fPgw6tWrJ3NC+URHR+OPP/7Axo0bcevWLdSvXx9dunTBd999J2ljRlNTU1y7di1LT6Q7d+6gQoUKePfunWRZSB0LINIaW1tb3Lp1C4UKFYKNjc0np6C/ePFCslwpKSnw9vbGypUrUaZMGcmOm1nG6aCf06BBAy0myd7+/fsxffp0hIWFwczMDBUrVsTEiRMlzbJ06VKMGDECHTp0UE2TPXfuHLZv34758+dj8ODBWj2+Lk9nThcWFoY5c+ao/ZzGjh2L0qVLy5JHF9SuXRvnz5+Hh4cHunbtii5duqB48eKyZKlQoQIGDBiQ5bW6ZMkSLF++HBEREZLmUSqVCAwMxJEjRxAfH4+0tDS1/UePHpU0z6tXr3D+/Plss3Tv3l2rx2YBpEEnT5785P769etLlEQ3BAUF4fvvv4eJiQkCAwM/WQBJvRSGnZ0dQkJC9PqPxNegePHiGDt2bJY/HgEBAfj111/x6NEjrR4/c1PGnMhxlk5uuvoBBwD+97//oWvXrlrvapwba9asweDBgzF69Gi1s6nz5s3DwoUL0a9fP0nzDB48GIGBgfD19UXRokWz/NwWLFggWZa9e/eia9euSEpKgoWFhVoWhUKh9dcNCyANyu4XZMYfqBy/IJ8/f44JEybg2LFj2VbYUv9i0hU//fQTjIyMdGYdHhbP2bOwsMDly5ezXD64ffs2qlSpgjdv3siUTD6JiYmqTuWf626uzY7mGT/gfG7dPznW+gM+dsqOjo5GqVKlZG0ounz5crWC3cnJCZMmTdL6GY7sFCpUCOvWrUOLFi0kP3ZmZcqUQYsWLTB9+nTJm3cCLIA0KiEhQe1+SkoKLl++jPHjx+PXX39VXaOXko+PD6KiotCnTx/Y29tnqfal+sVkaGiIx48fo3Dhwmrbnz9/jsKFC0teHA4ZMgTr1q2Dq6srqlWrhgIFCqjtnz9/vqR5dKF41sVP9F27dkXlypUxevRote3pi9hu2rRJkhy6JON7Kafu5kIIvTwrle7t27cYPHiwqjC7desWXFxcMHToUBQrVixLx2qpPH36FGZmZjA3N5fl+ABQrFgxHD9+XNbL/+kKFCiAq1evwsXFRZbjy99jPw/JbmBd06ZNYWJighEjRiA0NFTyTKdPn8bp06dlH+ycU539/v17SdYBy+zatWvw9PQE8PGXY0a5XS5Dk16+fKl2P3PxLIUFCxbAwsICACRfRDOjxYsXq/5drlw5/Prrrzh+/LjaGKAzZ87gp59+kjybLiwrc/ToUdja2qr+LcfrNbcuXbqECRMmYN++fZIe19/fH1euXMHx48fRvHlz1fYmTZpg4sSJshVAdnZ2shw3o59++gmLFi3C0qVLZX/tNGvWDBcvXpStAOIZIAlERkaievXqspyur169OpYsWYJatWpJfmzg//6YjRgxAlOnTlX75KNUKnHy5Encu3cPly9fliWfrjt58qRsxbNcnJ2dc/U4hUKBu3fvajnN/7l8+TJatGiB5ORkJCUlwdbWFs+ePUP+/PlRuHBhSbPokuDgYBw+fBhGRkbo27cvXFxccOPGDfj7+2Pv3r1o2rQpDh48KGkmR0dHbNmyBbVq1VJb7+rOnTvw9PT8ooWR/ytnZ+dPFhpSv27atm2LY8eOwdbWFuXLl4eRkZHa/h07dkiW5ffff8eUKVPQq1cveHh4ZMmi7YkFPAOkQeHh4Wr3hRB4/PgxZs6cKdsZmGXLlsHf3x8TJkxAhQoVsrzAtL3qefqAOiEEVqxYAUNDQ9U+Y2NjODk5YcWKFVrN8Cl37txBVFQU6tevDzMzM9WlA11hZ2eHmzdvynLstLQ03LlzJ9uxY9ockxQdHa215/4vRowYgZYtW2L58uWwtrbGuXPnYGRkhB9++AHDhg2TJdP48eMxadIktfcV8PFy/IABA7R+iTAoKAi9evWCra0tXrx4gdWrV2P+/PkYNGgQ2rdvjytXrqBChQpazZCdp0+fZrncDnw8gyf1+3v48OFq99PP7h48eDDLpV0pWFtbo23btpIfNzvpA8CnTJmSZZ8kl3Al6TakJ9Lb82du2V+7dm0RGRkpS6Zbt26JqlWrCgMDA7Wb1EsJNGzYUG2dK7k9e/ZMNGrUSPV9SF8Pp3fv3tl2iNa2K1euqN3CwsLEX3/9JRo0aCDq1KkjeZ6zZ88KZ2fnbF/PcizgqAt0ZVmZjEqWLClq1qyptvDnsWPHhIODgyRryFWqVEm1ZtyWLVuEQqEQnp6esi5EKoQQ9evXF4sXLxZCfFzvKn3xTz8/P9GsWTM5o6ksXbpU9OzZU+4Yeo1ngDQo8ydXAwMD2NnZwdTUVKZEHweRGhsb448//sh2ELRUjh07JstxczJixAgYGRkhJiYG5cqVU23v1KkTRowYgXnz5kmap3LlylAoFFnGStWqVQtr1qyRNAsADBgwANWqVcP+/fuznSorld69e39yv5TfGyMjI9X3wd7eXvXasbKyQkxMjGQ5MgoPD0f//v1RuXJlzJ8/H7du3cKiRYvg7++PiRMnav34UVFR6NSpEwCgQ4cOMDQ0xPz581GqVCmtH/tTZsyYgebNmyMiIgKpqalYtGgRrl+/jrNnz35R/y1t8vHxwdixY7F27Vq5o+gtFkAa5OjoKHeELK5du4bLly/Dzc1N8mOPHDkSU6dORYECBTBy5MhPPlbqWVeHDx/GoUOHUKJECbXtpUuXxv379yXNAuhe8Xz79m1s3749y/RzqWU3OPzatWt49eqVqqeKVKpUqYKLFy+iTJky8PLywoQJE/Ds2TOsX78eHh4ekmZJZ2Vlhc2bN2PcuHHo378/8uXLh7/++kuyGadJSUmqGZQGBgYwNTWVret9RnXq1MGZM2cwd+5clCpVCocPH4anpyfOnj0r288qs+3bt6sGs2ubp6cnjhw5AhsbG1SpUuWTH2guXbokSaZ0ck4sYAGkYbrWtr9atWqIjY2VpQC6fPkyUlJSAHx8U+X0ppPj7EJSUlK2fSeePXsGExMTyXIcPXoUgwcPxrlz57KMx0pISEDVqlWxYsUKyZc0qFmzJu7cuSN7AbRz584s29LS0jBo0CDJZ45Mnz4dr1+/BgBMnToVPXr0wMCBA1XLyshlyZIlWLBgATp37ozQ0FAMHToUf/zxh2TjDg8dOqSaAZuWloYjR47g2rVrao+Ro0u2h4fHZ/sTSSFzwSGEQFxcHJ4+fYply5ZJkqF169aq32tSLmPzOZ+bWKDtAoizwDRI7rb92dm2bRsmTZqE0aNHZzvKvmLFipJn0gW+vr7w9PTE1KlTYWFhgfDwcDg6OuL7779HWloatm/fLkmOVq1awcvLCyNGjMh2/+LFi3Hs2LFsCwFt2rlzJ3755Redfd3cvHkTDRs2xOPHj2XNITcfHx9cuHABK1asQIcOHfD27VuMHDkSgYGBmDx5Mn7++WetHv9T3bHT6XM/IgCYPHmy2v30s7sNGzZE2bJlZUqlGxo2bIgyZcqoJhZcuXJFbWJBu3bttBtA3iFIeUuxYsXEkiVLsmxfunSpKFq0qAyJRJYBrBkHaks1mDUlJUUYGhqKq1evSnK83Lh+/bqws7MTzZs3F8bGxqJDhw6iXLlywt7eXtIBnCVLllQNps1OZGSkcHBwkCxPOl143XzK/v37RaFCheSOIbsmTZqIhw8fZtm+b98+UaRIERkSySv99fmpm6GhodwxhRBCxMXFicmTJ8sdQ1ZyTyzgJTANSkxMVGu6lc7b2xtjxoyRIZFuTCnOly8fHB0ddeJT4K5du9CyZUu4u7sjPDwcy5Ytg6GhIZKSktCuXTv4+fmhaNGikuV58uRJlrMrGeXLlw9Pnz6VLE86XXjdAMgydkz8/9YS+/fvl3x5BV1cViY4ODjb7b6+vrh69arEaeT3qTOlISEhWLJkSY5NWaUWFxeHyZMnY8KECVo/1uc6u2ck5etY7okFLIA0qFWrVti5c2eW3g67d+9Gy5YtZcmkKwOzf/nlF4wdOxYbNmyQbOBfdjp06IBChQqhR48e6N27d7b9J6RUvHhxXL16NcexNuHh4ZIWZOl05XWTuUFm+uWDefPmfXaGmKb98MMPn1xWRi6nTp3CypUrERUVhe3bt6N48eJYv349nJ2d8c0338gdT1KtW7fOsu3GjRsYO3asauHNqVOnypBMXhk7uz9//hzTpk1Ds2bNVEM1zp49i0OHDmH8+PGS5pJ7YgHHAP1HGdv2JyYmYu7cuahbt262bft/+eUXuWIiIiIi21H2Ug1OrFKlCu7cuYOUlBQ4OjpmWXtLqpkHjx49wtq1axEUFISoqCjUrl0bffr0QceOHbNkksKQIUNw/PhxXLhwIcuMr7dv36JGjRrw8vJSe51py549e+Dj4wMjIyPs2bPnk4+VY1Cr3CwsLHRiWZmM/vzzT3Tr1g1du3bF+vXrERERARcXFyxbtgz79u3DgQMH5I4om0ePHmHixIkICgpCs2bNMGPGDFmaMubkypUr8PT0lPzMePv27eHl5ZVlTOrSpUvx999/Y9euXZJluXjxIl6/fg0vLy88ffoUPXr0wOnTp1UTC7T9XmMB9B/patv+dHfv3kXbtm1x9epVtT4z6Z9epXrzZR4ImJkUPUsyO3HiBNasWYMdO3ZAoVCgY8eO6NOnj6p4lcKTJ0/g6ekJQ0NDDB48GG5ublAoFIiMjERAQACUSiUuXboEe3t7rWcxMDBAXFycapHNnMgxqDU+Ph43b96EQqFAmTJlsu3yq21yLyuTnSpVqmDEiBHo3r272pIPYWFhaN68OeLi4uSOKLmEhARMnz4dS5YsQeXKlTFr1izJZ1HmhlwFkLm5OcLCwrKcdb59+zaqVKkiy5JNcmEBlMe1bNkShoaGWLVqFVxcXHD+/Hk8f/4cP/30E+bOnauTvxik9ubNG2zevBlr167FuXPnULZsWVy/fl2y49+/fx8DBw7EoUOH1ArUZs2aYdmyZXBycpIsi65JSEjA4MGDsWnTJtWYG0NDQ3Tq1AkBAQHZLkCsLRcuXJB1WZns5M+fHxEREXByclIrgO7evQt3d3e8e/dO8kxymj17NmbNmoUiRYpg+vTp2V4Sk8rnep89ffoUf/zxh+QFkKOjIwYPHpxlqMacOXOwdOlSWfqgyYVjgDQkJSUFbm5u2LdvH9zd3eWOo3L27FkcPXoUdnZ2MDAwgIGBAb755hvMmDEDQ4cOlW0R0rt37+Lt27coV65crqbSapO5uTm8vLxw79493LhxI8vq8Nrm6OiIAwcO4OXLl7hz5w6EEChdujRsbGwkzaGL+vXrh7CwMOzfvx+1a9eGQqFASEgIhg0bhn79+mHr1q2SZbG2tkZCQkKWBozi/68fJ8cg/6JFi+LOnTtZiuTTp0/LssJ2aGioqgdauXLl4OnpKenx/f39YWZmBldXVwQFBeXYB0iKBT9z87tVm2vq5WTy5Mno06cPjh8/rjZU4+DBg1i9erXWj/+5RowZaXtoBAsgDTEyMsL79+91ZmBkOqVSqVqBvVChQnj06BHc3Nzg6OgoySKbKSkpmDZtGi5duoRatWrB398fP/zwg+oPl5ubGw4cOCDLWY7k5GRs27YNa9asUf3BGDlyJHr27Cl5FuDjTI3q1avLcuzsnD9/HsePH892xpNUnbv379+PQ4cOqQ3mbdasGVatWpXtjEtt0pVlZTLq378/hg0bhjVr1kChUODRo0c4e/YsRo0aJcnsonTx8fH4/vvvcfz4cVhbW0MIgYSEBHh5eWHz5s2ws7OTJEf37t114ucC6N7yP+l69uyJcuXKYfHixdixYweEEHB3d8eZM2dQs2ZNrR9flxoxsg+QBs2YMUP06NFDpKSkyB1F5ZtvvhE7d+4UQgjRuXNn0bx5c3H69GnRvXt3Ub58ea0ff+TIkcLOzk706dNHuLi4iFatWgk3NzexefNmsXXrVuHh4SG6dOmi9RwZnT59WvTu3VtYWFgIMzMz0bVrV3H06FFJM+i6X3/9VSgUClG2bFnRoEED0bBhQ9XNy8tLshwODg4iPDw8y/YrV66I4sWLS5ZDCCHMzMxUPUt0yf/+9z9hZmam6tdkamoqfvnlF0kzdOzYUVStWlWtp9X169dFtWrVxPfffy9pFqLcYgGkQW3atBEWFhaiaNGiwtvbW7Rt21btJoeDBw+KP//8UwghRFRUlChXrpxQKBSiUKFC4siRI1o/fsmSJcX+/fuFEELcvHlTKBQKceDAAdX+48ePS/qHrHTp0sLAwEBUrVpVLFu2TLx69UqyY39NChcuLNauXSt3DLFy5UrRpEkT8ejRI9W2x48fC29vb7FixQpJs9SrV08EBwdLeszcSkpKEhcuXBD//POPeP36teTHt7S0FOfPn8+y/Z9//hFWVlaS56HcSU5OFgkJCWo3KcXExIjY2FjV/X/++UcMGzZMrFy5UpLj8xKYBllbW6N9+/Zyx1DTrFkz1b9dXFwQERGBFy9efFFjrP/i0aNHqqmMZcqUgYmJidrsgzJlykg6U6V58+bo06ePTk1l1kUGBgaoW7eu3DGwfPly3LlzB46OjihZsiQAICYmBiYmJnj69ClWrlypeqy2xwsMGTIEw4YN04nlQXLbA2nNmjVaTvJRWlpatg09jYyMslw+JXklJyfj559/xtatW/H8+fMs+6Ucy9alSxf8+OOP6NatG+Li4tCkSRNUqFABGzZsQFxcnNYv43IWGGlVxqnVANRmqgAfp4EXK1ZMJ7pE0/+ZPXs2Hj16pNZATQ6fa5+QkbZbKWQ3WD+9tYTUg6ANDAzg6OiIKlWqfLKzsVRryLVu3RqvXr3Cpk2bUKxYMQDAw4cP0bVrV9jY2Ei+lh3lzM/PD8eOHcOUKVPQvXt3BAQE4OHDh1i5ciVmzpyJrl27SpbFxsYG586dg5ubGxYvXowtW7bgzJkzOHz4MAYMGKD11jE8A5THJSUlYebMmThy5Ei2g1ml6E30qdWiX716pfXj05cbNWoUfH19UapUKbi7u2f5dC/FLBpAnv5QOdGV5UEAYMCAAdi8eTPu3r2L3r1744cffpC1w/rSpUvRunVrODk5wcHBAQqFAjExMfDw8MCGDRtky6ULYmJiVN+TjIQQiI2NVZ3ZlMrevXuxbt06NGzYEL1790a9evXg6uoKR0dHbNy4UdICKCUlRbVK/d9//61qsFq2bFlJFjrmGSAN2759O7Zu3Zpt12Wpuh1n1LlzZ5w4cQLdunVD0aJFs7wJhw0bptXjc7Xor5Ofnx9+//13eHl5ZTvjae3atZJlefXqFbZv346oqCiMHj0atra2quaQxYsXlyyHrnn//j127NiBNWvWICQkBL6+vujTpw+8vb1lmwkVHByMGzduqGYWNWnSRJYcusTQ0BCPHz/O0rzz+fPnKFy4sCyNEK9fvw5HR0eUKFECO3bsQI0aNRAdHQ0PDw9JGyHWrFkTXl5e8PX1hbe3N86dO4dKlSrh3Llz6NChAx48eKDV4/MMkAYtXrwY48aNQ48ePbB792706tULUVFRuHDhAvz8/GTJ9Ndff2H//v2yjefg9f+v07p16/Dnn3/C19dX1hzh4eFo0qQJrKyscO/ePfTr1w+2trbYuXMn7t+/j3Xr1kmeSe5lZdKZmJigc+fO6Ny5M+7fv4/AwEAMGjQIKSkpiIiIULW/kMK6devQqVMnNG3aFE2bNlVt//DhAzZv3ozu3btrPcPnlm/JSMqfVfol0szevHmTZfkbKbi4uODevXtwdHSEu7s7tm7diho1amDv3r2wtraWNMusWbPQtm1bzJkzBz169FCNzdyzZw9q1Kih9ePzDJAGlS1bFhMnTkTnzp3VxrpMmDABL168wNKlSyXP5OzsjAMHDqBcuXKSH5u+Xo6Ojjh06BDKli0ra44mTZrA09MTs2fPVntPhYSEoEuXLrh3755kWXRlWZnsxMTEIDAwEIGBgfjw4QNu3LghaQGkC2c5Mp9tzvgzSr+fToo86Z2gFy1ahH79+iF//vxqx//nn39gaGiIM2fOaD1LRgsWLIChoSGGDh2KY8eOwdfXF0qlEqmpqZg/f77WrwpkplQqkZiYqNb49d69e8ifP7/2l7yRZK6ZnjAzMxP37t0TQghhZ2cnwsLChBBC3Lp1S9ja2sqSaf369aJDhw4iKSlJluPrsuPHj4tvv/1WlCpVSri6uoqWLVuKkydPyh1LJ6xZs0Z07NhR9teNpaWluHPnjhBCCHNzcxEVFSWEEOLevXvCxMRE0izffvutaN26tYiPjxfm5uYiIiJCnDp1StSoUUOW1827d+/EH3/8IZo0aSJMTU1Fhw4dxP79+4VSqZQ8i0KhEPHx8Vm2h4WFCRsbG8nzBAcHC09PT3Hw4EGRkJAgEhMTxcGDB0W1atXE4cOHJcmQ3jdLoVCIOnXqqPXS8vb2Fj/++KO4deuWJFk+5f79++LPP/9U/b3SJ7wEpkFFihTB8+fP4ejoCEdHR9X1zOjo6E/O1NC0zK3G79y5A3t7ezg5OWUZzCrHuCRdsGHDBvTq1Qvt2rXD0KFDIYRASEgIGjdujMDAQHTp0kXuiLJavHgxoqKiZH/dmJqaIjExMcv2mzdvStZdOJ0uLSszaNAgbN68GSVLlkSvXr2wefNmFCxYULLjp0v/XaNQKNC4cWPky/d/f1KUSiWio6Ml79gNAMOHD8eKFSuydBDPnz8/fvzxR0RGRmo9Q3on6F69emHRokWyrBWXGyVLlpR8IHZul8PgUhhfkUaNGmHv3r3w9PREnz59MGLECGzfvh0XL15Eu3btJMuhU63GddSvv/6K2bNnY8SIEaptw4YNw/z58zF16lS9L4B05TXUunVrTJkyRbV0SvrsIn9/f8l7bsm9rExGK1asQMmSJeHs7IwTJ07gxIkT2T5O27P10l8nYWFhaNasmdplN2NjYzg5OcnSGy0qKirbhXLTx5JJScoJA7l15MiRHGcGS9E7Sld+v3AMkAalpaUhLS1N9Slo69atOH36NFxdXTFgwAAYGxvLnJDSmZiY4Pr162pNGYGPZ8sqVKigd6to66rExES0aNEC169fx+vXr1GsWDHExcWhVq1a+Ouvv1CgQAHJstSrVw8//fQT2rRpgy5duuDly5f45Zdf8NtvvyE0NFTV2kEKPXv2zNUnaCn++CqVSqxfvx7NmjVD0aJFtX683Khfvz6MjIywYcMGVaa4uDh069YNHz58yLFg1AZdaEWS0eTJkzFlyhRUq1Yt25nB+tSziQUQ6SVXV1eMHj0a/fv3V9u+cuVKzJ07F7dv35YpmW7JuLq3u7s7qlSpIkuOo0eP4tKlS0hLS4Onp6cs06sPHTqEpKQktGvXDlFRUWjZsiVu3LiBggULYvPmzWjcuLHkmXSFqakpIiMj4ezsLHcUAB8/yLRt2xY3b95U6yBepkwZ7Nq1K8sHH22SuxVJZkWLFsXs2bPRrVs3SY+ri1gAaUlSUhK2bNmCt2/fwtvbG6VLl5Ylh1KpxIIFC3LsTfTixQtZcmXk7OyMRo0aYcqUKZL1dVm+fDmGDx+O3r17o06dOlAoFDh9+jQCAwOxaNGiLIWRvtGV1b1zcunSJUyYMAH79u2TNYeUy8rosurVq2PmzJk6VQQKIbLtSyT1z8ra2lrWViSZFSxYEOfPn0epUqXkjiI7FkAaEBMTg27duuHSpUuoVasWfv/9dzRt2lR1FsHMzAx//fUX6tevL3m2CRMmYPXq1Rg5ciTGjx+PcePG4d69e9i1axcmTJiAoUOHSp4ps0mTJuH+/fs4efIkoqKiJDvuzp07MW/ePNWAyHLlymH06NFo3bq1ZBl0VadOnRAVFYX169erWihERESgR48ecHV1xaZNm7SeITg4GIcPH4aRkRH69u0LFxcX3LhxA/7+/ti7dy+aNm2KgwcPaj1Hbsbv5cuXD0WKFEHTpk3RsmVLrWfSNYcPH8aYMWMwdepUVK1aNculSV0dACwFXWtFMmbMGJibm2P8+PFyR5EdCyAN6NixI2JjY+Hn54dt27bh1q1bKFWqFH7//XcYGBhg0KBBeP78OY4ePSp5tlKlSmHx4sXw9fWFhYUFwsLCVNvOnTuHP/74Q/JMpPusrKzw999/o3r16mrbz58/D29vb60vYRIUFIRevXrB1tYWL168QKFChTB//nwMGjQI7du3x08//YQKFSpoNUO6Xr16ffYxaWlpiI+Px4kTJzBq1ChMmTJFgmS6I2MPnoxnWIQM66Slk3ugb7oNGzZg9+7dCAoKUusFJJdhw4Zh3bp1qFixIipWrJhlhuf8+fNlSiY9FkAaUKRIEVXnyvRf1mfOnEHt2rUBAFeuXEHjxo3x7NkzybMVKFAAkZGRKFmyJIoWLYr9+/fD09MTd+/eRZUqVZCQkCB5JuDjpbmrV6/C0dFRrQGW1C5evKga41KuXDlUrVpVtiy6xMLCAqdOnULlypXVtl++fBkNGjTIdmq6JlWuXBnff/89/P39sXXrVnz//feoUqUKtm7dqtOn7vfv34+BAwciJiZG7iiS+tyg4gYNGkiU5CNdGuhbpUoVREVFQQihE61IvLy8ctynUChk+aCe0atXryTrSM1p8Brw9OlTODo6AgBsbW2RP39+2Nvbq/YXKVIEL1++lCVbiRIl8PjxY5QsWRKurq44fPgwPD09ceHCBdUidFIYPnw4PDw80KdPHyiVSjRo0AAhISHInz8/9u3bh4YNG0qWBQAePHiAzp0748yZM6o326tXr1CnTh1s2rQJDg4OkubRNY0aNcKwYcOyrO49YsQIScZ5REVFoVOnTgCADh06wNDQEPPnz9fp4gcA6tati2rVqskdQ3JSFzifs2LFCgQGBurEQF9dmfKdLr0/kS6YNWsWnJycVO/1jh074s8//0SRIkVw4MAB1dIYWiNx48U8SaFQiCdPnqjuZ+xYK4QQcXFxwsDAQI5oYsyYMeLXX38VQgixbds2kS9fPuHq6iqMjY3FmDFjJMtRvHhxceHCBSGEEDt37hTFihUTN2/eFOPGjRN16tSRLEe6pk2bipo1a4obN26ott24cUPUqVNHNG3aVPI8uiYmJkZUqVJFGBkZCRcXF1GqVClhZGQkPD09RWxsrNaP/7n3FOmekydPiq5du4ratWuLBw8eCCGEWLdunTh16pTkWWxtbVUdxCl7t2/fFgcPHhTJyclCCCHS0tIkz+Ds7CzOnDkjhBDi8OHDwtraWhw6dEj06dNHkt/DvASmAQYGBvjxxx9V13cDAgLwww8/qBpxJScnY9WqVTqx4vk///yDM2fOwNXVVdIFAU1NTXHnzh2UKFFC9b1auHAhoqOjUalSJa1fUsnMzMwMISEhWaZ1X7p0CXXr1sXbt28lzaOr/v77b0RGRkq+ureBgQGCgoJU76HOnTtj4cKFamdWAekXIKXs/fnnn+jWrRu6du2K9evXIyIiAi4uLli2bBn27duHAwcOSJqHA31z9vz5c3Ts2BHHjh2DQqHA7du34eLigj59+sDa2hrz5s2TLIuZmRlu3boFBwcHDBs2DO/evcPKlStx69Yt1KxZU+tXTngJTAPq16+v1gm2Tp06WZpbyTEDDPj4Yk9vkR8bG4v9+/fj7du3kp+mt7e3R0REBIoWLYqDBw9i2bJlAD4Wh4aGhpJmAT62f09JScmyPTU1VbKp+LoqLS0NgYGB2LFjB+7duweFQgFnZ2fVdHipphH36NFD7X7m1gRyDa6lrKZNm4YVK1age/fu2Lx5s2p7nTp1ZBkQ/u7dO/z222/4+++/ZRnoa2tri1u3bqFQoUKfbZMgdSuSESNGwMjICDExMWoz0zp16oQRI0ZIWgDZ2NggNjYWDg4OOHjwIKZNmwbg4+B5Kd7bLIA04Pjx43JHyOLq1ato2bIlYmNjUbp0aWzevBnNmzdHUlISDAwMsGDBAmzfvl2y69O9evVCx44dVQMSmzZtCuDjGSk5VhyfPXs2hgwZgoCAAFStWhUKhQIXL17EsGHDMHfuXMnz6AohBFq1aqW6/u7h4QEhBCIjI9GzZ0/s2LEDu3bt0nqOzLN2SLfdvHkz2w95lpaWWp8xmJ3w8HDVAP7MHbqlKOAXLFgACwsLAMDChQu1frwvcfjwYRw6dAglSpRQ2166dGncv39f0izt2rVDly5dULp0aTx//hw+Pj4APi6tIkmzSq1fZCNZNG/eXHz77bfi1KlTon///qJ48eKiV69eQqlUCqVSKQYNGiRq1qwpaaZt27aJ+fPnq40hCQwMFLt27ZLk+NbW1sLGxkZ1MzY2FgYGBsLY2Fjt33KsXq0r1qxZIywsLMTRo0ez7Dty5IiwsLAQQUFBMiQjXebi4iKCg4OFEOrjtYKCgkS5cuXkjEaZmJubq1ahz/izOn/+vLC1tZU0y4cPH8ScOXPE0KFDxaVLl1TbFyxYIFatWqX143MMUB5VqFAhHD16FBUrVsSbN29gaWmJ8+fPqy593bhxA7Vq1ZLl05lcgoKCcv3YzJdf9IW3tzcaNWoEf3//bPdPnz4dJ06cwKFDhyRORrps9uzZCAoKwpo1a9C0aVMcOHAA9+/fx4gRIzBhwgQMHjxY7og64e3bt1kuvUvdJNLX1xeenp6YOnUqLCwsEB4eDkdHR3z//fdIS0vD9u3bJc0jJxZAeZSBgQHi4uJQuHBhAB/7uly5cgUuLi4AgCdPnqBYsWJavc66ePHiXD9WFzpS08eWDQcPHszS/yfd5cuX4ePjg7i4OGmDkc4bN24cFixYoFpI2MTEBKNGjcLUqVNlyXPhwgVs27Yt2yWAduzYIVmOpKQkjBkzBlu3bsXz58+z7Jd6HFtERAQaNmyIqlWr4ujRo2jVqhWuX7+OFy9e4MyZM5K3moiKisLChQvV+rENHz5c9bdKm1gA5VEGBgZ48uSJas2m9Eo/fbFCKQqgzAsjPn36FMnJyWp9d/Lnz4/ChQtLviJyRrrwqUxXGBsb4/79+zmu6v3o0SM4Ozvj/fv3Eiejr0FycjIiIiKQlpYGd3d3mJuby5Jj8+bN6N69O7y9vREcHAxvb2/cvn0bcXFxaNu2LdauXStZFj8/Pxw7dgxTpkxB9+7dERAQgIcPH2LlypWYOXMmunbtKlmWdHFxcVi+fDlCQ0NVCwz7+fnl+L7XlkOHDqFVq1aoXLky6tatCyEEQkJCcOXKFdVyN1ql9YtsJAuFQiFatGgh2rZtK9q2bSvy5csnvL29VfdbtGghaW+ijRs3irp162bpu1OvXj2xYcMGyXKke/PmjfDz8xN2dnbCwMAgy01fGRgYiPj4+Bz3y9nTiii3PDw8xNKlS4UQ/zfOJS0tTfTr109MmDBB0iwODg7i2LFjQgghLCwsxO3bt4UQH3sk+fj4SJrlw4cPomHDhuLmzZuSHjcnlStXzrYf3ZgxY0SVKlW0fnyeAfqPwsPDc/3YihUrajGJutysXwRAsk9CpUqVwvbt27P03QkNDUWHDh0QHR0tSY50uvipTBcYGBjAx8cnxy7h79+/x8GDByU/bf/hw4ds13QqWbKkpDkoe15eXp+cXSX18goFChTA9evX4eTkhEKFCuHYsWPw8PBAZGQkGjVqhMePH0uWxdzcHNevX4ejoyNKlCiBHTt2oEaNGoiOjoaHhwfevHkjWRYAsLOzQ0hICEqXLi3pcbNjamqKq1evZsly69YtVKxYUXU5VVs4Df4/qly5MhQKRa76o0j5R0PKU7y58fjx42z77iiVSjx58kTyPHv37sW6devQsGFD9O7dG/Xq1YOrqyscHR2xceNGvS2AcjP4u3v37hIk+ej27dvo3bs3QkJC1LYLGRfZpKwyjxlLSUlBWFgYrl27JsuEAltbW7x+/RoAULx4cVy7dg0eHh549eoVkpOTJc3i4uKCe/fuwdHREe7u7ti6dStq1KiBvXv3SrbmVUbdu3fH77//jpkzZ0p+7Mzs7OwQFhaWpQAKCwtTjV/VJhZA/1HGMxeXL1/GqFGjMHr0aNVCqGfPnsW8efMwe/ZsuSLqhMaNG6Nfv374/fff1fru9O/fX7Luwhm9ePFCNUbJ0tJS1Yzsm2++wcCBAyXPoyt0rXDu2bMn8uXLh3379mW7qCXphgULFmS7fdKkSZKf4QCAevXqITg4GB4eHujYsSOGDRuGo0ePIjg4WJK17DLq1asXrly5ggYNGmDs2LHw9fXFkiVLkJqaKsvK6x8+fMDq1asRHByMatWqoUCBAmr7pczUr18//Pjjj7h79y7q1KkDhUKB06dPY9asWfjpp5+0fnxeAtOgGjVqYNKkSWjRooXa9gMHDmD8+PEIDQ2VKZn8nj59ih49euDgwYOqrqypqalo1qwZAgMDJan2M6pYsSKWLFmCBg0awNvbGxUrVsTcuXOxePFizJ49Gw8ePJA0D2WvQIECCA0NlaVZJv13d+7cQY0aNSTvdvzixQu8e/cOxYoVQ1paGubOnYvTp0/D1dUV48ePh42NjaR5Mrp//z5CQ0NRqlQp7S/2mQ1dWg1eCIGFCxdi3rx5ePToEQCgWLFiGD16NIYOHar1DzwsgDTIzMwMly5dUmsvDgCRkZHw9PTk+lL4eG33xo0bEEKgXLlyKFOmjCw5FixYAENDQwwdOhTHjh2Dr68vlEql6lPZsGHDZMlF6qpXr44FCxbgm2++kTsK/Qvr16/HmDFjVH/ciHKSfskyvYO2FFgAaZCnpyfKlSuH33//HaampgA+Dhrt3bs3IiMjcenSJZkTUk5iYmJw8eJF2T6VUfaOHj2KX375BdOnT4eHh0eWNZ30tV2BrmnXrp3afSEEHj9+jIsXL2L8+PGYOHGiTMnk888//+DFixeq5R0AYN26dZg4cSKSkpLQpk0bLFmyJMcJB/ogOjoaqampWcYA3b59G0ZGRnByctLq8VkAadD58+fRsmVLpKWlqf6IXrlyBQqFAvv27UONGjVkTigfpVKJwMBAHDlyJNvZPFLPEqGvg4GBAYCs6zdxELRu6dmzp9rPyMDAAHZ2dmjUqBG8vb1lTCYfHx8fNGzYEGPGjAHwcX1GT09P9OzZE+XKlcOcOXPQv39/TJo0SZI8jx8/xtKlS/Hrr78C+DjeMeOAcENDQ+zatUvSxaAbNGiA3r17Zxkov2HDBqxevVrr62yyANKw5ORkbNiwQXWZx93dHV26dMky0EzfDB48GIGBgfD19c12MGtOgyg17ejRoxg8eDDOnTuX5exBQkIC6tSpgxUrVqBevXqS5KFPO3HixCf3N2jQQKIkRF+maNGi2Lt3r2r5oXHjxuHEiRM4ffo0AGDbtm2YOHEiIiIiJMkzfvx4vHjxAgEBAQA+Xmrq3bs3bG1tAQB//fUXvvnmG0kXg7a0tMSlS5eyLHx6584dVKtWTftLNWm90xCREKJgwYJi//79cscQLVu2FPPnz89x/6JFi0SbNm0kTET09UpKShKDBg0SxYoVE3Z2dqJz587i6dOncsfSCSYmJiImJkZ1v27dumLq1Kmq+9HR0cLc3FyyPJUqVRKHDx9W3c+4EKoQQhw8eFC4u7tLlkcIISwtLdUWQU138eJFSb43nAavYbdu3cLx48ezvcwzYcIEmVLJz9jYOEuVL4crV65g1qxZOe739vaW9BMQ5U5ycnK2azpJ2VyUspo4cSICAwPRtWtXmJqaYtOmTRg4cCC2bdsmdzQ1iYmJOHr0KNzc3LJMUtEWe3t7REdHw8HBAR8+fMClS5cwefJk1f7Xr19nGdOmTffu3VNb56tp06ZqVybc3Nwkb0hbr149zJgxA5s2bYKhoSGAj8MlZsyYIcnEBxZAGrRq1SoMHDgQhQoVQpEiRdQu8ygUCr0ugH766ScsWrQIS5culbWXy5MnTz75Sydfvnx4+vSphInoU54+fYpevXrhr7/+ynY/xwDJa8eOHfj999/x/fffAwB++OEH1K1bF0qlUvUHTQ4dO3ZE/fr1MXjwYLx9+xbVqlXDvXv3IITA5s2b0b59e61naN68Ofz9/TFr1izs2rUL+fPnV7u0Hh4eLunCo6mpqUhISFDdz7wg7MuXL1Vj7qQye/Zs1K9fH25ubqrvzalTp1QFq7ZJ+9XmcdOmTcOvv/6KuLg4hIWF4fLly6qbvs8AO336NDZu3IhSpUqhZcuWaNeundpNKsWLF8fVq1dz3B8eHi75goCUs+HDh+Ply5c4d+4czMzMcPDgQQQFBaF06dLYs2eP3PH0XmxsrNof9Ro1aiBfvnyyT3s/efKkKtfOnTshhMCrV6+wePFiTJs2TZIM06ZNg6GhIRo0aIBVq1Zh1apVMDY2Vu1fs2aNpAPE3dzcsnRUz+jUqVOStyVxd3dHeHg4OnbsiPj4eLx+/Rrdu3fHjRs3UKFCBa0fn2eANOjly5f47rvv5I6hk6ytrdG2bVu5Y6BFixaYMGECfHx8VK0K0r19+xYTJ07Et99+K1M6yuzo0aPYvXs3qlevDgMDAzg6OqJp06awtLTEjBkz4OvrK3dEvaZUKtX+qAMfz6KmpqbKlOijhIQE1eDegwcPon379sifPz98fX0xevRoSTLY2dnh1KlTSEhIgLm5eZYzYtu2bYO5ubkkWQDg+++/x4QJE1CvXr0sl46vXLmCyZMnw9/fX7I86YoVK4bp06dLflyAs8A0qk+fPqhevToGDBggdxTKwZMnT+Dp6QlDQ0MMHjwYbm5uUCgUiIyMREBAAJRKJS5dugR7e3u5oxI+zhIJDw+Hk5MTnJycsHHjRtStWxfR0dEoX7685Os6kbrsFs/du3cvGjVqpDa+JPPlFm0rU6YMpk2bBl9fXzg7O2Pz5s1o1KgRrly5gsaNG+PZs2eS5tEFKSkpaNKkCUJCQtC0aVPV774bN24gODgYtWvXxpEjR7Q+Lik8PBwVKlSAgYHBZxcT1/YYP54B0qD0Nuvnzp3Ltmnb0KFDZUpG6ezt7RESEoKBAwdi7NixSK//FQoFmjVrhmXLlrH40SFubm64efMmnJycULlyZaxcuRJOTk5YsWIFL1XqgOwWOv3hhx9kSKJu+PDh6Nq1K8zNzeHo6IiGDRsC+HhpzMPDQ95wMjEyMkJwcDDmz5+PzZs3q3rslC5dGlOnTsWIESMkGZRduXJlxMXFoXDhwmqLiWcmRZ8vngHSoPTFNbOjUChw9+5dCdPonu3bt2Pr1q3ZzuaRY4zUy5cvcefOHQghULp0aVnXB6Lsbdy4ESkpKejZsycuX76MZs2a4fnz5zA2NkZgYCA6deokd0TSUaGhoYiJiUHTpk1Vl5r2798PGxsb1KlTR+Z0+uv+/fsoWbIkFAoF7t+//8nHOjo6ajULCyCSxOLFizFu3Dj06NEDq1atQq9evRAVFYULFy7Az89P1Z2U6FOSk5Nx48YNlCxZEoUKFZI7DumoKVOmYNSoUcifP7/a9rdv32LOnDl6PSOX/g8LIJJE2bJlMXHiRHTu3BkWFha4cuUKXFxcMGHCBLx48QJLly6VOyIR5RGGhoZ4/PgxChcurLb9+fPnKFy4MNsn6JiIiIhsrwy0atVKq8flGCANe/DgAfbs2ZPtD3P+/PkypZJfTEyM6rSzmZmZauXfbt26oVatWiyAKFsdOnRAtWrVssxOmTNnDs6fP69zDfdIN4j/v1ZcZleuXFHNDiP53b17F23btsXVq1fVxgKl/+y0XaiyANKgI0eOoFWrVnB2dsbNmzdRoUIFVfMtT09PuePJqkiRInj+/DkcHR3h6OiIc+fOoVKlSoiOjs52ABwR8HEtsOxWEm/evDk7dlMWNjY2UCgUUCgUKFOmjFoRpFQq8ebNG87S1SHDhg2Ds7Mz/v77b7i4uOD8+fN4/vw5fvrpJ0ne3yyANGjs2LH46aefMGXKFFhYWODPP/9E4cKF0bVrVzRv3lzueLJq1KgR9u7dC09PT/Tp0wcjRozA9u3bcfHiRUkbIdLX5c2bN1n6zAAfZ7QkJibKkIh02cKFCyGEQO/evTF58mRYWVmp9hkbG8PJyQm1a9eWMaHu+PDhA6Kjo1GqVCnkyydPKXD27FkcPXoUdnZ2MDAwgIGBAb755hvMmDEDQ4cOxeXLl7V6fBZAGhQZGYlNmzYB+NgM7O3btzA3N8eUKVPQunVrDBw4UOaE8vntt99Ua6MNGDAAtra2OH36NFq2bMlPZJSjChUqYMuWLVkGrW7evBnu7u4ypSJdlT4t39nZGXXr1pXtD7suS05OxpAhQxAUFATg4/qVLi4uGDp0KIoVKyZpM0SlUqmaoVeoUCE8evQIbm5ucHR0xM2bN7V+fL46NKhAgQJ4//49gI/dLaOiolC+fHkA0MvGWxmlV/fpOnbsiI4dO8qYiL4G48ePR/v27REVFYVGjRoB+HipedOmTRz/Qzlq1KgRB0HnYOzYsbhy5QqOHz+udmWiSZMmmDhxoqQFUIUKFRAeHg4XFxfUrFkTs2fPhrGxMX777Te4uLho/fgsgDSoVq1aOHPmDNzd3eHr64uffvoJV69exY4dO1CrVi2540nuc10+M+Kq3pSdVq1aYdeuXZg+fTq2b98OMzMzVKxYEX///TcaNGggdzzSUTmNK3z//n22l1T1ya5du7BlyxbUqlVLbYyUu7s7oqKiJM3yyy+/ICkpCcDHtdO+/fZb1KtXDwULFsSWLVu0fnwWQBo0f/58vHnzBgAwadIkvHnzBlu2bIGrqysWLFggczrpfarLZ0ZSdPykr5evry/X/KJcWbx4MYCPv1NWr16tttaWUqnEyZMnUbZsWbni6YSnT59mOTMGAElJSdnOnNOmZs2aqf7t4uKCiIgIvHjxQjWYXdvYB4i05nNdPjPSdsdPIsr70rvx379/HyVKlFBbgDR9EPSUKVNQs2ZNuSLKrkGDBujQoQOGDBkCCwsLhIeHw9nZGYMHD8adO3dw8OBBrWdQKpW4fv06SpcuDTMzM7V9ycnJuHPnjmq9MG3iGSDSGhY19G/Y2tri1q1bKFSo0Gc/Cb548ULCZKTroqOjAQBeXl7YsWMHl7fJxowZM9C8eXNEREQgNTUVixYtwvXr13H27FmcOHFCkgzr16/H0qVL8c8//2TZZ2Jigt69e2P48OFaX1eOZ4BIEkFBQShUqJDqUsbPP/+M3377De7u7ti0aROLJVIJCgrC999/DxMTE9VMlZxktxgnEX3a1atXMXfuXISGhiItLQ2enp4YM2aMZAvF1qtXD35+fvj++++z3b9161YsXboUJ0+e1GoOFkAkCTc3NyxfvhyNGjXC2bNn0bhxYyxcuBD79u1Dvnz5sGPHDrkjEtFXbOTIkZg6dSoKFCiAkSNHfvKx+tyVXxcULlwY58+fh5OTU7b7o6OjUaNGDTx9+lSrOXgJjCQRGxsLV1dXAB9nIXTo0AE//vgj6tati4YNG8objnRaWloa7ty5g/j4eFUvqXT169eXKRXpmsuXLyMlJUX1b8qZ3O+ppKSkTzYyff36NZKTk7WegwUQScLc3BzPnz9HyZIlcfjwYYwYMQIAYGpqirdv38qcjnTVuXPn0KVLF9y/fz/LbELOHqSMjh07lu2/M0svkvSVLrynSpcujZCQkBzbn5w+fRqlS5fWeg4WQBrGxVCz17RpU/Tt2xdVqlTBrVu3VGOBrl+/nuNpUKIBAwagWrVq2L9/P4oWLSr5NF36umzevDnHcSXAx+KnQ4cO2L17t4SpdIsuvKe6dOmCX375BXXq1MlSBF25cgUTJkzAzz//rPUcHAOkQZ9bDPXo0aNyR5TNq1ev8MsvvyA2NhYDBw5UdSCdOHEijI2NMW7cOJkTki4qUKAArly5orp8SvQppqam2L17t1p/mXSpqano0KEDLly4gIcPH8qQTjfownsqJSUF3t7eOH36NJo0aYKyZctCoVAgMjISf//9N+rWrYvg4GAYGRlpN4ggjalevboYP368EEIIc3NzERUVJV6/fi1atWolli1bJnM6oq+Pl5eX+Ouvv+SOQV+JhQsXigIFCoiQkBC17ampqaJNmzbC3t5eREZGypRON+jKe+rDhw9i1qxZolKlSiJ//vzCzMxMVKpUScyaNUu8f/9ekgw8A6RBFhYWCAsLQ6lSpWBjY4PTp0+jfPnyuHLlClq3bo179+7JHVFyuV0Og0thUHZ27tyJX375BaNHj4aHh0eWT4R83VBmEydOxJIlS3Dy5ElUqFABSqUSHTt2xOnTp3Hs2DG9XEQ34+/hqKgovqf+PxZAGlSkSBEcPXoU7u7uKF++PGbMmIFWrVrhypUrqFu3rmqZDH1iYGCQ43IY6ds5mJVykl0nWL5u6HOGDBmCP//8E8ePH8e4ceNw/PhxHD16VLI+N7rmU7+HAf19T3EQtAZxMdSs0juzEv0bfP3Qv7FkyRK8evUKlSpVgrm5OY4cOaK3xQ/A91FOeAZIg+7evYs3b96gYsWKSE5OxqhRo3D69GnVYqjsdkxEpD0ZGyCmpKRg1apVqFevXpbiR59n5J48eRJ16tRBvnzq5z9SU1MREhKiV721WAARkU7Zs2cPfHx8YGRkhD179nzysa1atZIoFX0NvLy8PvsYhUKh1zNyDQ0N8fjx4ywrwj9//hyFCxfWq0tgLICISKcYGBggLi4OhQsX/uRq0Po2XoFIEwwMDPDkyRPY2dmpbb916xaqVav2yQ7NeQ3HAGlQ+kCznPCXNdHnZWzNn7lNPxH9O+3atQPw8YNDz549YWJiotqnVCoRHh6OOnXqaD3H59Zpy0jblypZAGnQzp071e6npKTg8uXLCAoKwuTJk2VKRURE+s7KygoAIISAhYUFzMzMVPuMjY1Rq1Yt9OvXT+s5Mq/TFhoaCqVSCTc3NwAfz0QZGhqiatWqWs/CS2AS+OOPP7Blyxa9br9O9G+dP38ex48fz3bhRn0ezEr0b0yePBmjRo1CgQIF5I6C+fPn4/jx4wgKCoKNjQ0A4OXLl+jVqxfq1auHn376SavHZwEkgaioKFSsWBFJSUlyR5HNkydPMGrUKBw5cgTx8fFZ+lHw8iBlZ/r06fjll1/g5uYGe3t7tUvM+j6YlehrV7x4cRw+fBjly5dX237t2jV4e3vj0aNHWj0+L4Fp2du3b7FkyRKUKFFC7iiy6tmzJ2JiYjB+/Hguakm5tmjRIqxZswY9e/aUOwp9RWJiYuDg4JDl94wQArGxsShZsqRMySijxMREPHnyJEsBFB8fj9evX2v9+CyANMjGxkbtDSeEwOvXr5E/f35s2LBBxmTyO336NE6dOoXKlSvLHYW+IgYGBqhbt67cMegr4+zsnO1U7xcvXsDZ2ZlnnHVE27Zt0atXL8ybN0/VLPjcuXMYPXq0atC2NrEA0qAFCxaoFUAGBgaws7NDzZo1Vdc39ZWDg0OObdiJcjJixAgEBARg4cKFckehr0j6sg6ZvXnzBqampjIkouysWLECo0aNwg8//ICUlBQAQL58+dCnTx/MmTNH68fnGCCSxOHDhzFv3jysXLkSTk5Ocsehr0RaWhp8fX1x69YtuLu7Z1m4cceOHTIlI12UPsV60aJF6NevH/Lnz6/ap1Qq8c8//8DQ0BBnzpyRKyJlIykpCVFRURBCwNXVVbIB2jwDpEE5rXyuUChgamqKkiVLqvVe0CedOnVCcnIySpUqhfz582f5Q/bixQuZkpEuGzJkCI4dOwYvLy8ULFiQY8fok9KnWAshcPXqVRgbG6v2GRsbo1KlShg1apRc8WSzePHiXD926NChWkySvQIFCsiyCj3PAGlQxkaI2Z2CNTIyQqdOnbBy5Uq9Ow0bFBT0yf09evSQKAl9TSwsLLB582b4+vrKHYW+Ir169cKiRYtgaWkpdxSd4OzsrHb/6dOnSE5OhrW1NQDg1atXyJ8/PwoXLoy7d+9KlispKQkzZ85UzQ7O3OZC21l4BkiDdu7ciTFjxmD06NGoUaMGhBC4cOEC5s2bh4kTJyI1NRX+/v745ZdfMHfuXLnjSooFDv0btra2KFWqlNwx6Cuzdu1auSPolIyrwf/xxx9YtmwZfv/9d1XzwZs3b6Jfv37o37+/pLn69u2LEydOoFu3brLMDuYZIA2qUaMGpk6dimbNmqltP3ToEMaPH4/z589j165d+OmnnxAVFSVTSukkJiaqPoF9bn0ZflKj7KxduxYHDx7E2rVr1cZzEH2K3GcWdFmpUqWwfft2VKlSRW17aGgoOnTooFYsaZu1tTX2798v20xPngHSoKtXr8LR0THLdkdHR1y9ehUAULlyZTx+/FjqaLKwsbFRTUW1trbOtrpPv1TIaamUncWLFyMqKgr29vZwcnLKMnbs0qVLMiUjXSb3mQVd9vjxY9WMq4yUSiWePHkiaRYbGxvY2tpKesyMWABpUNmyZTFz5kz89ttvqsF3KSkpmDlzJsqWLQsAePjwIezt7eWMKZmjR4+qXtzHjh2TOQ19jdq0aSN3BPoK/fXXX7KeWdBljRs3Rr9+/fD777+jatWqUCgUuHjxIvr3748mTZpImmXq1KmYMGECgoKCZDnDy0tgGhQSEoJWrVrBwMAAFStWhEKhQHh4OJRKJfbt24datWph/fr1iIuLw+jRo+WOS0SUJzk7O+PAgQMoV66c3FF0ztOnT9GjRw8cPHhQdUY1NTUVzZo1Q2BgYJbmkdpUpUoV1fR3Oc7wsgDSsDdv3mDDhg24desWhBAoW7YsunTpAgsLC7mjyYotAui/CA0NRWRkJBQKBdzd3bOMXyDKaMOGDdi9e7dsZxa+Brdu3cKNGzcghEC5cuVQpkwZyTNMnjz5k/snTpyo1eOzACJJZGwRkB19bhFAOYuPj8f333+P48ePw9raGkIIJCQkwMvLC5s3b4adnZ3cEUkHyX1mgb4OHAP0H+3Zswc+Pj4wMjLCnj17PvnYVq1aSZRK97BFAP0bQ4YMQWJiIq5fv666nBEREYEePXpg6NCh2LRpk8wJSRdx7FjOlEolAgMDc5whd/ToUZmSSY9ngP4jAwMDxMXFoXDhwjAwMMjxcfo+04ktAujfsLKywt9//43q1aurbT9//jy8vb3x6tUreYIRfaUGDx6MwMBA+Pr6ZjtDbsGCBZJlUSqVWLBgAbZu3YqYmBh8+PBBbb+2VwjgGaD/KGP1nLmSpv/DFgH0b6SlpWW5fAF8vGTK9xvRl9u8eTO2bt2KFi1ayB0FkydPxurVqzFy5EiMHz8e48aNw71797Br1y5MmDBB68fP+ZQFfbF169bh/fv3WbZ/+PAB69atkyGR7khvEZCxwtfnFgGUO40aNcKwYcPw6NEj1baHDx9ixIgRaNy4sYzJSNfY2tri2bNnAP6vv0xON31mbGwMV1dXuWMAADZu3IhVq1Zh1KhRyJcvHzp37ozVq1djwoQJOHfunNaPz0tgGmRoaKhq/JfR8+fPUbhwYb2+BMYWAfRvxMbGonXr1rh27RocHBygUCgQExMDDw8P7N69GyVKlJA7IumIoKAgfP/99zAxMeHag58wb9483L17F0uXLpW9QWSBAgUQGRmJkiVLomjRoti/fz88PT1x9+5dVKlSBQkJCVo9PgsgDTIwMMCTJ0+yzEy5cuUKvLy89H7Fc7YIoH8rODhYNWXX3d1d8oZtRHlF27ZtcezYMdja2qJ8+fJZLjHv2LFDsixubm5Yt24datasiXr16sHX1xf+/v7YsmULhgwZgvj4eK0en2OANKBKlSpQKBRQKBRo3Lgx8uX7v2+rUqlEdHQ0mjdvLmNCeaWkpMDNzQ379u3DgAED5I5DX6GmTZuiadOmcsegr9Dbt2+zLP2gz2sPWltbo23btnLHAPCxGDty5Ahq1qyJYcOGoXPnzvj9998RExODESNGaP34LIA0IH3KZVhYGJo1awZzc3PVPmNjYzg5OaF9+/YypZOfkZER3r9/L/vpVvq6pKWlITAwEDt27MC9e/egUCjg7OyMDh06oFu3bnw9UY6SkpIwZswYbN26Fc+fP8+yX5+HI6xdu1buCCozZ85U/btDhw5wcHDAmTNn4OrqKknbGF4C06CgoCB06tSJjfyyMXPmTNy4cQOrV69WO0NGlB0hBFq2bIkDBw6gUqVKKFu2LIQQiIyMxNWrV9GqVSvs2rVL7piko/z8/HDs2DFMmTIF3bt3R0BAAB4+fIiVK1di5syZ6Nq1q9wRSQewANKCDx8+ZNtgqmTJkjIlkl/6qU5zc3N4eHigQIECavulvO5Mum/t2rUYNmwYdu/eDS8vL7V9R48eRZs2bbB06VJ0795dpoSky0qWLIl169ahYcOGsLS0xKVLl+Dq6or169dj06ZNOHDggNwRZbV9+/Yce+/oU5dsToPXoNu3b6NevXowMzODo6MjnJ2d4ezsDCcnJzg7O8sdT1bW1tZo3749mjVrhmLFisHKykrtRpTRpk2b8L///S9L8QN8nBrv7++PjRs3ypCMvgYvXrxQ/c61tLRUTUD55ptvcPLkSTmjyW7x4sXo1asXChcujMuXL6NGjRooWLAg7t69Cx8fH7njSYrXIjSoZ8+eyJcvH/bt25dth019pkvXnUn3hYeHY/bs2Tnu9/HxweLFiyVMRF8TFxcX3Lt3D46OjnB3d8fWrVtRo0YN7N27F9bW1nLHk9WyZcvw22+/oXPnzggKCsLPP/8MFxcXTJgwQe9mKvMSmAYVKFAAoaGhqsZ+RPTvGBsb4/79+yhatGi2+x89egRnZ+dsG48SLViwAIaGhhg6dCiOHTsGX19fKJVKpKamYv78+Rg2bJjcEWWTP39+REZGwtHREYULF0ZwcDAqVaqE27dvo1atWtkOGs+reAZIg9zd3VWdSAnw9PTEkSNHYGNjo2oVkBN9uu5Mn6dUKj85WN7Q0BCpqakSJqKvScYp1F5eXoiMjERoaChKlSqFSpUqyZhMfkWKFMHz58/h6OgIR0dHnDt3DpUqVUJ0dDSkPh8SGxsLhUKhamh6/vx5/PHHH3B3d8ePP/6o9eOzANKgWbNm4eeff8b06dPh4eGRpcGUvvWeaN26NUxMTFT/5iVByi0hBHr27Kl6/WTGMz/0JdL/2NPHMXR79+6Fp6cn+vTpgxEjRmD79u24ePEi2rVrJ2mWLl264Mcff0S3bt0QFxeHpk2bonz58tiwYQPi4uK0vh4YL4FpUPpq8Jn/0Ash9HY1+LCwMFSuXFnuGPSV6dWrV64ex7FllNE///yDFy9eqA3mXbduHSZOnIikpCS0adMGS5YsybGw1gdpaWlIS0tTnWHdunUrTp8+DVdXVwwYMADGxsaSZbGxscG5c+fg5uaGxYsXY8uWLThz5gwOHz6MAQMG4O7du1o9PgsgDTpx4sQn9zdo0ECiJLrDwMAAVapUQd++fdGlSxfO+CIirfHx8UHDhg0xZswYAMDVq1fh6emJnj17oly5cpgzZw769++PSZMmyRuUAADm5ua4du0anJyc0KpVK9StWxdjxoxBTEwM3Nzc8PbtW60enwWQRPT1TMjZs2exZs0abN26FSkpKWjXrh369OmT7fRmIqL/omjRoti7dy+qVasGABg3bhxOnDiB06dPAwC2bduGiRMnIiIiQs6Y9P/VrFkTXl5e8PX1hbe3t2o80rlz59ChQwc8ePBAq8dnHyAtSkhIwLJly+Dp6YmqVavKHUcWtWvXxqpVqxAXF4fly5fjwYMHaNKkCUqVKoVff/1V6y9wItIfL1++hL29ver+iRMn1NZhrF69OmJjY+WIRtmYNWsWVq5ciYYNG6Jz586qAep79uxBjRo1tH58ngHSgqNHj2LNmjXYsWMHHB0d0b59e7Rv3x5VqlSRO5pOiIqKwtq1a7Fu3To8fvwYTZs21fvOrET03zk6OmL9+vWoX78+Pnz4AGtra+zduxeNGzcG8PGSWIMGDfSu340uUyqVSExMhI2NjWrbvXv3kD9/fhQuXFirx+YZIA158OABpk2bBhcXF3Tu3Bk2NjZISUnBn3/+iWnTprH4yaBUqVLw9/fHuHHjYGlpiUOHDskdiYjygObNm8Pf3x+nTp3C2LFjkT9/ftSrV0+1Pzw8HKVKlZIxIWW0YcMGGBoaqhU/AODk5IQ5c+Zo/fgsgDSgRYsWcHd3R0REBJYsWYJHjx5hyZIlcsfSSSdOnECPHj1QpEgR/Pzzz2jXrh3OnDkjdywiygOmTZsGQ0NDNGjQAKtWrcKqVavUZjWtWbMG3t7eMiaUX6NGjfDq1ass2xMTE9GoUSNJswwePBj79u3Lsn3EiBHYsGGD1o/PS2AakC9fPgwdOhQDBw5E6dKlVduNjIxw5coVuLu7y5hOfrGxsQgMDERgYCCio6NRp04d9OnTBx07dsyyKCoR0X+VkJAAc3NzGBoaqm1/8eIFzM3NJZ3qrWsMDAwQFxeX5fJSfHw8ihcvjpSUFMmyHDx4EN9//z327NmD+vXrAwCGDBmCHTt24MiRI1pfVYGNEDXg1KlTWLNmDapVq4ayZcuiW7du6NSpk9yxdELTpk1x7Ngx2NnZoXv37ujduzfc3NzkjkVEeVhO7TZsbW0lTqI7wsPDVf+OiIhAXFyc6r5SqcTBgwdRvHhxSTM1b94cK1asQJs2bXD48GGsWbMGu3fvxrFjx1CmTBmtH59ngDQoOTkZmzdvxpo1a3D+/HkolUrMnz8fvXv3hoWFhdzxZNGqVSv06dMH3377bZZPY0REJA0DAwNVk97s/uybmZlhyZIl6N27t9TRsHz5cowYMQJ2dnY4duwYXF1dJTkuCyAtuXnzJn7//XesX78er169QtOmTbFnzx65YxERkR66f/8+hBBwcXHB+fPnYWdnp9pnbGyMwoULS/IhdeTIkdlu3759O6pUqaI2SH3+/PlazcICSMuUSiX27t2LNWvWsAAiIiK9ltsmuAqFAkePHtVqFhZAREREeuTWrVs4fvw44uPjkZaWprZP2wuQ6hIWQERERHpi1apVGDhwIAoVKoQiRYqoLd6tUChw6dIlSXKkpqbC1NQUYWFhqFChgiTHzIyzwIiIiPTEtGnT8Ouvv6oWjJVLvnz54OjoCKVSKVsGNkIkIiLSEy9fvsR3330ndwwAwC+//IKxY8fKtjQJL4ERERHpiT59+qB69eoYMGCA3FFQpUoV3LlzBykpKXB0dMzSGFfbl+N4CYyIiEhPuLq6Yvz48Th37hw8PDxgZGSktn/o0KGSZWnTpo1kx8oOzwARERHpCWdn5xz3KRQK3L17V8I08mIBRERERHqHg6CJiIj0zIcPH3Dz5k2kpqbKlsHAwACGhoY53rSNY4CIiIj0RHJyMoYMGYKgoCAAH5siuri4YOjQoShWrBj8/f0ly7Jz5061+ykpKbh8+TKCgoIwefJkrR+fl8CIiIj0xLBhw3DmzBksXLgQzZs3R3h4OFxcXLBnzx5MnDgRly9fljsi/vjjD2zZsgW7d+/W6nF4CYyIiEhP7Nq1C0uXLsU333yj1gXa3d0dUVFRMib7PzVr1sTff/+t9eOwACIiItITT58+ReHChbNsT0pKUiuI5PL27VssWbIEJUqU0PqxWAARERHpierVq2P//v2q++lFz6pVq1C7dm1JMvTu3RuJiYmwsbGBra2t6mZjYwMLCwusWbMGc+bM0XoOjgEiIiLSEyEhIWjevDm6du2KwMBA9O/fH9evX8fZs2dx4sQJVK1aVesZDA0N8fjxYxw4cEDtrJOBgQHs7OxQs2ZN2NjYaD0HCyAiIiI9cvXqVcydOxehoaFIS0uDp6cnxowZAw8PD0mOb2BggLi4uGwvxUmJBRARERFJxsDAAE+ePMH/a+9+QqLs+jCOXz7zOqWZo5IjkxSarQq0wBb90RLCjMjC2rQQEsVVVFTERFhZQdJCa+MiQhuDAiVbiErUGFoboYwUCkLLfxAYWVaWWTrPIl5heHued9V9hjnfD7iY+wRdy4tzfve5U1NTjeagAAEAYImOjg65XC7t2LEj7Pm9e/c0Pz+vnTt3/vEMf/31lzwez/8duv7TX4nnIkQAACzh9/tVU1PzP89DoZD8fr8jBUiSqqur5fF4HPm//gk7QAAAWCIuLk4vX75URkZG2PPh4WGtXbtW09PTfzxDpMwA8Ro8AACW8Hg8v/3i++DgoJYsWeJIhki4b0iiAAEAYI3i4mIdPXo07NbnwcFBHT9+XMXFxY5kiJSDJ47AAACwxNTUlIqKivTkyZOF25bHx8eVl5en1tZWJSUlmQ3oIAoQAAAWCYVCun//vp4/f664uDhlZ2crPz/fdCzHUYAAAIB1eA0eAACLBINBBYNBTUxMaH5+PmytoaHBUCrnUYAAALBEdXW1zp8/r9zcXPl8voh5I8sEjsAAALCEz+fT5cuXVVpaajqKcbwGDwCAJWZnZ7Vp0ybTMSICBQgAAEtUVFTo1q1bpmNEBGaAAACwxMzMjK5du6YHDx4oOztbsbGxYeu1tbWGkjmPGSAAACxRUFDwj2sxMTHq6upyMI1ZFCAAAGAdZoAAAIB1mAECACCKlZSU6MaNG0pMTFRJScm//tvW1laHUplHAQIAIIp5PJ6FCw89Ho/hNJGDGSAAAKLcu3fvlJqaajpGRGEGCACAKJeenq79+/ers7NT7Hv8QgECACDKBQIBffr0Sbt379aKFStUVVWloaEh07GM4ggMAABLjI2NqaGhQYFAQCMjI8rPz1dFRYX27dunxYsXm47nKAoQAAAWCgaDamxs1N27d+V2u3XgwAHV19ebjuUYChAAABa7c+eOKisr9fHjR83NzZmO4xhegwcAwDLDw8NqbGxUIBDQ+Pi4CgoKVF5ebjqWoyhAAABYYGZmRi0tLWpsbFRPT4/S09N18OBBlZWVKSMjw3Q8x1GAAACIcpWVlWpubtbMzIz27Nmj9vZ2FRYWLlyQaCNmgAAAiHLZ2dkqLy9XaWmpUlJSTMeJCBQgAABgHS5CBAAA1qEAAQAA61CAAACAdShAAABYYnR09LcfQw2FQhodHTWQyByGoAEAsITL5dLbt2/l9XrDnr9//15er9eqm6DZAQIAwBKhUOi3d/98+fLFuo+hchEiAABR7tixY5KkmJgYVVVVKT4+fmFtbm5Ovb29WrdunaF0ZlCAAACIcs+ePZP0awdoYGBAbrd7Yc3tdisnJ0cnTpwwFc8IZoAAALBEWVmZrl69qsTERNNRjKMAAQAA63AEBgCAJaanp1VTU6NgMKiJiQnNz8+Hrb9+/dpQMudRgAAAsERFRYW6u7tVWloqn8/H1+BNhwAAAH9eUlKS2tvbtXnzZtNRjOMeIAAALJGcnKyUlBTTMSICBQgAAEtcuHBBZ86c0devX01HMY4jMAAALLF+/XoNDQ0pFAopIyNDsbGxYet9fX2GkjmPIWgAACyxd+9e0xEiBjtAAADAOswAAQAA63AEBgBAFEtJSdGrV6+0bNkyJScn/+vdP5OTkw4mM4sCBABAFKurq9PSpUslSVeuXDEbJoIwAwQAAKzDDhAAABb69u2bfvz4EfbMpq/EMwQNAIAlpqendejQIXm9XiUkJCg5OTnszyYUIAAALHHy5El1dXWpvr5eixYt0vXr11VdXa3ly5erqanJdDxHMQMEAIAlVq5cqaamJm3btk2JiYnq6+vT6tWrdfPmTd2+fVsdHR2mIzqGHSAAACwxOTmpzMxMSb/mff772vuWLVvU09NjMprjKEAAAFhi1apVGh4eliStWbNGzc3NkqS2tjYlJSWZC2YAR2AAAFiirq5OLpdLhw8f1sOHD7Vr1y7Nzc3p58+fqq2t1ZEjR0xHdAwFCAAAS42MjOjp06fKyspSTk6O6TiOogABAADrMAMEAECU6+3tVWdnZ9izpqYmZWZmyuv1qrKyUt+/fzeUzgwKEAAAUe7cuXPq7+9f+D0wMKDy8nJt375dfr9fbW1tunTpksGEzuMIDACAKOfz+dTW1qbc3FxJ0unTp9Xd3a3Hjx9LklpaWnT27Fm9ePHCZExHsQMEAECU+/Dhg9LS0hZ+d3d3q6ioaOH3hg0bNDY2ZiKaMRQgAACiXFpamt68eSNJmp2dVV9fnzZu3Liw/vnzZ8XGxpqKZwQFCACAKFdUVCS/369Hjx7p1KlTio+PV15e3sJ6f3+/srKyDCZ03n9MBwAAAH/WxYsXVVJSoq1btyohIUGBQEBut3thvaGhQYWFhQYTOo8haAAALDE1NaWEhAS5XK6w55OTk0pISAgrRdGOAgQAAKzDDBAAALAOBQgAAFiHAgQAAKxDAQIAANahAAEAAOtQgAAAgHUoQAAAwDp/A9x3hZ3HX2JyAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "output_eval.plot_at_event(\n", + " data_transf=lambda x: x.rename(index=event_id_to_name), logy=True\n", + ")\n", + "output_eval.plot_at_region(\n", + " data_transf=lambda x: x.rename(index=country_code_to_name), logy=True\n", + ")" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, we can do an event- *and* country-based comparison using a heatmap using {py:meth}`~climada.util.calibrate.base.OutputEvaluator.plot_event_region_heatmap`\n", + "Since the magnitude of the impact values may differ strongly, this method compare them on a logarithmic scale.\n", + "It divides each modelled impact by the observed impact and takes the the decadic logarithm.\n", + "The result will tell us how many orders of magnitude our model was off.\n", + "Again, the considerations for \"nicer\" index and columns apply." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "output_eval.plot_event_region_heatmap(\n", + " data_transf=lambda x: x.rename(index=event_id_to_name, columns=country_code_to_name)\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Handling Missing Data\n", + "\n", + "NaN-valued input data has a special meaning in calibration: It implies that the impact calculated by the model for that data point should be ignored.\n", + "Opposed to that, a value of zero indicates that the model should be calibrated towards an impact of exactly zero for this data point.\n", + "\n", + "There might be instances where data is provided for a certain region or event, and the model produces no impact for them.\n", + "This is always treated as an impact of zero during the calibration.\n", + "Likewise, there might be instances where the model computes an impact for a region for which no impact data is available.\n", + "In these cases, {py:attr}`~climada.util.calibrate.base.Input.missing_data_value` of {py:class}`~climada.util.calibrate.base.Input` is used as fill value for the data.\n", + "According to the data value logic mentioned above, `missing_data_value=np.nan` (the default setting) will cause the modeled impact to be ignored in the calibration.\n", + "Setting `missing_data_value=0`, on the other hand, will calibrate the model towards zero impact for all regions or events where no data is supplied.\n", + "\n", + "Let's exemplify this with a subset of the data used for the last calibration.\n", + "Irma is the cyclone with ID `2017242N16333`, and it hit most locations we were looking at before.\n", + "It has the event ID `1454` in our setup.\n", + "Let's just use this hazard and the related data for now.\n", + "\n", + "NOTE: We must pass a dataframe to the input, but selecting a single row or column will return a Series.\n", + "We expand it into a dataframe again and make sure the new frame is oriented the correct way." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + "region_id 28 44 92 132 192 212 \\\n", + "1454 211176356.8 1801876.321 3.000000e+09 NaN 1.099275e+10 NaN \n", + "\n", + "region_id 214 388 484 630 659 662 670 796 \n", + "1454 NaN NaN NaN NaN 18484889.46 NaN NaN 500000000.0 " + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "hazard_irma = all_tcs.select(event_names=[\"2017242N16333\"])\n", + "data_irma = data.loc[1454, :].to_frame().T\n", + "data_irma" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's first calibrate the impact function only on this event, including all data we have." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "from climada.util.calibrate import (\n", + " Input,\n", + " BayesianOptimizer,\n", + " OutputEvaluator,\n", + " BayesianOptimizerOutputEvaluator,\n", + ")\n", + "from sklearn.metrics import mean_squared_log_error\n", + "import matplotlib.pyplot as plt\n", + "\n", + "\n", + "def calibrate(hazard, data, **input_kwargs):\n", + " \"\"\"Calibrate using custom hazard and data\"\"\"\n", + " # Define calibration input\n", + " input = Input(\n", + " hazard=hazard,\n", + " exposure=exposure,\n", + " data=data,\n", + " impact_func_creator=impact_func_tc,\n", + " cost_func=mean_squared_log_error,\n", + " impact_to_dataframe=lambda imp: imp.impact_at_reg(exposure.gdf[\"region_id\"]),\n", + " bounds=bounds,\n", + " **input_kwargs,\n", + " )\n", + "\n", + " # Create and run the optimizer\n", + " with log_level(\"INFO\", name_prefix=\"climada.util.calibrate\"):\n", + " opt = BayesianOptimizer(input)\n", + " controller = BayesianOptimizerController.from_input(input)\n", + " bayes_output = opt.run(controller)\n", + "\n", + " # Evaluate output\n", + " output_eval = OutputEvaluator(input, bayes_output)\n", + " output_eval.impf_set.plot()\n", + "\n", + " plt.figure() # New figure because seaborn.heatmap draws into active axes\n", + " output_eval.plot_event_region_heatmap(\n", + " data_transf=lambda x: x.rename(\n", + " index=event_id_to_name, columns=country_code_to_name\n", + " )\n", + " )\n", + "\n", + " return bayes_output.params # The optimal parameters" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2024-02-20 13:27:01,235 - climada.util.calibrate.bayesian_optimizer - INFO - Optimization iteration: 0\n", + "2024-02-20 13:27:02,398 - climada.util.calibrate.bayesian_optimizer - INFO - Optimization iteration: 1\n", + "2024-02-20 13:27:03,922 - climada.util.calibrate.bayesian_optimizer - INFO - Optimization iteration: 2\n", + "2024-02-20 13:27:05,573 - climada.util.calibrate.bayesian_optimizer - INFO - No improvement. Stop optimization.\n" + ] + }, + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "param_irma = calibrate(hazard_irma, data_irma)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If we now remove some of the damage reports and repeat the calibration, the respective impact computed by the model will be ignored.\n", + "For Saint Kitts and Nevis, and for Turks and the Caicos Islands, the impact is overestimated by the model.\n", + "Removing these regions from the estimation should shift the estimated parameters accordingly, because by default, impacts for missing data points are ignored with `missing_data_value=np.nan`." + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2024-02-20 13:27:12,398 - climada.util.calibrate.bayesian_optimizer - INFO - Optimization iteration: 0\n", + "2024-02-20 13:27:13,605 - climada.util.calibrate.bayesian_optimizer - INFO - Optimization iteration: 1\n", + "2024-02-20 13:27:14,991 - climada.util.calibrate.bayesian_optimizer - INFO - Optimization iteration: 2\n", + "2024-02-20 13:27:16,771 - climada.util.calibrate.bayesian_optimizer - INFO - Optimization iteration: 3\n", + "2024-02-20 13:27:18,916 - climada.util.calibrate.bayesian_optimizer - INFO - No improvement. Stop optimization.\n" + ] + }, + { + "data": { + "text/plain": [ + "{'scale': 0.9893201575415296, 'v_half': 46.03113797418196}" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "calibrate(hazard_irma, data_irma.drop(columns=[659, 796]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "However, the calibration should change into the other direction once we require the modeled impact at missing data points to be zero:" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2024-02-20 13:27:25,785 - climada.util.calibrate.bayesian_optimizer - INFO - Optimization iteration: 0\n", + "2024-02-20 13:27:26,845 - climada.util.calibrate.bayesian_optimizer - INFO - Minimal improvement. Stop iteration.\n", + "2024-02-20 13:27:26,845 - climada.util.calibrate.bayesian_optimizer - INFO - Optimization iteration: 1\n", + "2024-02-20 13:27:28,241 - climada.util.calibrate.bayesian_optimizer - INFO - No improvement. Stop optimization.\n" + ] + }, + { + "data": { + "text/plain": [ + "{'scale': 0.07120177027571553, 'v_half': 134.9655530108171}" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "calibrate(hazard_irma, data_irma.drop(columns=[659, 796]), missing_data_value=0)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Actively requiring that the model calibrates towards zero impact in the two dropped regions means that it will typically strongly overestimate the impact there (because impact actually took place).\n", + "This will \"flatten\" the vulnerability curve, causing strong underestimation in the other regions." + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## How to Continue\n", + "\n", + "While the found impact function looks reasonable, we find that the model overestimates the impact severely for several events.\n", + "This might be due to missing data, but it is also strongly related to the choice of impact function (shape) and the particular goal of the calibration task.\n", + "\n", + "The most crucial information in the calibration task is the cost function.\n", + "The RMSE measure is sensitive to the largest errors (and hence the largest impact).\n", + "Therefore, using it in the calibration minimizes the overall error, but will incorrectly capture events with impact of lower orders of magnitude.\n", + "Using a cost function based on the ratio between modelled and observed impact might increase the overall error but decrease the log-error for many events.\n", + "\n", + "So we present some ideas on how to continue and/or improve the calibration:\n", + "\n", + "1. Run the calibration again, but change the number of initial steps and/or iteration steps.\n", + "2. Use a different cost function, e.g., an error measure based on a ratio rather than a difference.\n", + "3. Also calibrate the `v_thresh` parameter. This requires adding constraints, because `v_thresh` < `v_half`.\n", + "4. Calibrate different impact functions for houses in Mexico and Puerto Rico within the same optimization task.\n", + "5. Employ the {py:class}`~climada.util.calibrate.scipy_optimizer.ScipyMinimizeOptimizer` instead of the `BayesianOptimizer`." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "climada_env_3.9", + "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": "3.9.18" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/requirements/env_climada.yml b/requirements/env_climada.yml index 646e7cd77c..9ebc16c2b1 100644 --- a/requirements/env_climada.yml +++ b/requirements/env_climada.yml @@ -3,41 +3,42 @@ channels: - conda-forge - nodefaults dependencies: - - bottleneck>=1.3 - - cartopy>=0.22 + - bottleneck>=1.4 + - cartopy>=0.23 - cfgrib>=0.9.9,<0.9.10 # 0.9.10 cannot read the icon_grib files from https://opendata.dwd.de - - contextily>=1.5 - - dask>=2024.1,<2024.3 # 2024.3 apparently instroduced a sophisticated locking mechanism which leads to read-only exceptions in several places + - contextily>=1.6 + - dask>=2024.5 - eccodes>=2.27,<2.28 # 2.28 changed some labels, in particular: gust -> i20fg - gdal>=3.6 - geopandas>=0.14 - h5py>=3.8 - haversine>=2.8 - - matplotlib-base>=3.8 + - matplotlib-base>=3.9 - netcdf4>=1.6 - - numba>=0.59 - - numexpr>=2.9 + - numba>=0.60 + - numexpr>=2.10 - openpyxl>=3.1 - pandas>=2.1,<2.2 # 2.2 is not compatible with the default pytables=3.7 and yields a very high deprecation warning number through geopandas - pandas-datareader>=0.10 - pathos>=0.3 - - pint>=0.23 + - pint>=0.24 - pip - - pycountry>=22.3 + - pycountry>=24.6 - pyepsg>=0.4 - pyproj>=3.5 - pytables>=3.7 - pyxlsb>=1.0 - rasterio>=1.3 - - requests>=2.31 - - salib>=1.4 - - scikit-learn>=1.4 - - scipy>=1.12 + - requests>=2.32 + - salib>=1.5 + - seaborn>=0.13 + - scikit-learn>=1.5 + - scipy>=1.13 - sparse>=0.15 - statsmodels>=0.14 - tabulate>=0.9 - tqdm>=4.66 - unittest-xml-reporting>=3.2 - - xarray>=2024.2 + - xarray>=2024.6 - xlrd>=2.0 - xlsxwriter>=3.1 diff --git a/setup.py b/setup.py index 2033378bdb..9278c04a01 100644 --- a/setup.py +++ b/setup.py @@ -38,7 +38,7 @@ setup( name='climada', - version='4.1.2-dev', + version='5.0.1-dev', description='CLIMADA in Python', @@ -65,6 +65,7 @@ python_requires=">=3.9,<3.12", install_requires=[ + 'bayesian-optimization', 'bottleneck', 'cartopy', 'cfgrib', @@ -90,6 +91,7 @@ 'rasterio', 'salib', 'scikit-learn', + 'seaborn', 'statsmodels', 'sparse', 'tables',