diff --git a/.github/workflows/sphinx_docs.yml b/.github/workflows/sphinx_docs.yml deleted file mode 100644 index 86c0afd..0000000 --- a/.github/workflows/sphinx_docs.yml +++ /dev/null @@ -1,18 +0,0 @@ -name: Deploy Sphinx documentation to Pages - -on: - push: - branches: [main] # branch to trigger deployment - -jobs: - pages: - runs-on: ubuntu-20.04 - steps: - - id: deployment - uses: sphinx-notes/pages@v3 - with: - publish: false - - uses: peaceiris/actions-gh-pages@v3 - with: - github_token: ${{ secrets.GITHUB_TOKEN }} - publish_dir: ${{ steps.deployment.outputs.artifact }}h diff --git a/.gitignore b/.gitignore deleted file mode 100644 index 5e6209a..0000000 --- a/.gitignore +++ /dev/null @@ -1,137 +0,0 @@ -# Byte-compiled / optimized / DLL files -__pycache__/ -*.py[cod] -*$py.class - -# Personal ignore list. -*.png -*.pkl -dask-worker-space/ -slurm* -*.c -reporting/ - -# C extensions -*.so - -# Distribution / packaging -.Python -build/ -develop-eggs/ -dist/ -downloads/ -eggs/ -.eggs/ -lib/ -lib64/ -parts/ -sdist/ -var/ -wheels/ -pip-wheel-metadata/ -share/python-wheels/ -*.egg-info/ -.installed.cfg -*.egg -MANIFEST - -# PyInstaller -# Usually these files are written by a python script from a template -# before PyInstaller builds the exe, so as to inject date/other infos into it. -*.manifest -*.spec - -# Installer logs -pip-log.txt -pip-delete-this-directory.txt - -# Unit test / coverage reports -htmlcov/ -.tox/ -.nox/ -.coverage -.coverage.* -.cache -nosetests.xml -coverage.xml -*.cover -*.py,cover -.hypothesis/ -.pytest_cache/ - -# Translations -*.mo -*.pot - -# Django stuff: -*.log -local_settings.py -db.sqlite3 -db.sqlite3-journal - -# Flask stuff: -instance/ -.webassets-cache - -# Scrapy stuff: -.scrapy - -# Sphinx documentation -docs/_build/ - -# PyBuilder -target/ - -# Jupyter Notebook -.ipynb_checkpoints - -# IPython -profile_default/ -ipython_config.py - -# pyenv -.python-version - -# pipenv -# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. -# However, in case of collaboration, if having platform-specific dependencies or dependencies -# having no cross-platform support, pipenv may install dependencies that don't work, or not -# install all needed dependencies. -#Pipfile.lock - -# PEP 582; used by e.g. github.com/David-OConnor/pyflow -__pypackages__/ - -# Celery stuff -celerybeat-schedule -celerybeat.pid - -# SageMath parsed files -*.sage.py - -# Environments -.env -.venv -env/ -venv/ -ENV/ -env.bak/ -venv.bak/ - -# Spyder project settings -.spyderproject -.spyproject - -# Rope project settings -.ropeproject - -# mkdocs documentation -/site - -# mypy -.mypy_cache/ -.dmypy.json -dmypy.json - -# Pyre type checker -.pyre/ diff --git a/.nojekyll b/.nojekyll new file mode 100644 index 0000000..e69de29 diff --git a/LICENSE b/LICENSE deleted file mode 100644 index 3f9c52e..0000000 --- a/LICENSE +++ /dev/null @@ -1,21 +0,0 @@ -MIT License - -Copyright (c) 2022 Cédric Travelletti - -Permission is hereby granted, free of charge, to any person obtaining a copy -of this software and associated documentation files (the "Software"), to deal -in the Software without restriction, including without limitation the rights -to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -copies of the Software, and to permit persons to whom the Software is -furnished to do so, subject to the following conditions: - -The above copyright notice and this permission notice shall be included in all -copies or substantial portions of the Software. - -THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -SOFTWARE. diff --git a/README.md b/README.md deleted file mode 100644 index ea33c9e..0000000 --- a/README.md +++ /dev/null @@ -1,2 +0,0 @@ -# DIESEL -DIstributed EStimation of EnsembLe covariance diff --git a/diesel/__init__.py b/diesel/__init__.py deleted file mode 100644 index 702a1e9..0000000 --- a/diesel/__init__.py +++ /dev/null @@ -1,8 +0,0 @@ -from .cluster import LocalCluster -from .non_stationary_models import BaCompositeGP -from . import covariance -from . import gridding -from . import sampling -# from . import estimation -# from . import validation -from . import plotting diff --git a/diesel/cluster.py b/diesel/cluster.py deleted file mode 100644 index 24fb6bc..0000000 --- a/diesel/cluster.py +++ /dev/null @@ -1,40 +0,0 @@ -""" Define the various types of computing clusters that can -be used to run the computation. - -""" -from dask.distributed import LocalCluster -from dask_jobqueue import SLURMCluster - - -def UbelixCluster(n_nodes, mem_per_node=16, cores_per_node=1, - partition="epyc2", qos="job_epyc2"): - """ Provision a Daks cluster on the Ubelix cluster of UniBern. - - Parameters - ---------- - n_nodes: int - mem_per_node: int, default=16 - Memory per node in GB. - cores_per_node: int, default=1 - partition: string - Under which queue to submit the job. - qos: string - QOS queue under which to submit the job. - - Returns - ------- - cluster - - """ - mem_per_node = "{} GB".format(mem_per_node) - cluster = SLURMCluster( - cores=cores_per_node, - memory=mem_per_node, - death_timeout=6000, - walltime="06:00:00", - job_extra=['--qos="{}"'.format(qos), '--partition="{}"'.format(partition)] - ) - - # Manually define the size of the cluster. - cluster.scale(n_nodes) - return(cluster) diff --git a/diesel/covariance/__init__.py b/diesel/covariance/__init__.py deleted file mode 100644 index f3b34ef..0000000 --- a/diesel/covariance/__init__.py +++ /dev/null @@ -1 +0,0 @@ -from .kernels import matern32, squared_exponential, pairwise_euclidean diff --git a/diesel/covariance/kernels.py b/diesel/covariance/kernels.py deleted file mode 100644 index 72ee908..0000000 --- a/diesel/covariance/kernels.py +++ /dev/null @@ -1,115 +0,0 @@ -""" Dask implementation of the covariance kernels. - -""" -import numpy as np -import dask -import dask.array as da -import dask_distance -import dask_distance._utils as utils -from haversine import haversine - - -# @utils._broadcast_uv_wrapper -def pairwise_euclidean(coords1, coords2): - return dask_distance.euclidean(coords1, coords2) - -def pairwise_haversine(coords1, coords2): - return dask_distance.cdist(coords1, coords2, lambda x, y: haversine(x[0], x[1], y[0], y[1])) - -class matern32: - """ Matern 3/2 covariance kernel. - - """ - def __init__(self, lengthscales): - """ Build Matern 3/2 kernel. - - Parameters - ---------- - lengthscales: array-like (n_dims) - Vector of lengthscales for each individual dimension. - - """ - self.lengthscales = lengthscales - - def covariance_matrix(self, coords1, coords2, lengthscales=None, metric='euclidean'): - """ Compute covariance matrix between two sets of points. - - Parameters - ---------- - coords1: (m, n_dims) dask.array or Future - Point coordinates. - coords2: (n, n_dims) dask.array or Future - Point coordinates. - lengthscales_2: array-like (n_dims), defaults to None. - Can be used to override using the lengthscales of the kernel and use - different ones. - Note that for haversine metric one should provide only one lengthscale. - metric: 'euclidean' or 'haversine'. - - Returns - ------- - covs: (m, n) delayed dask.array - Pairwise covariance matrix. - - """ - if lengthscales is None: - lengthscales = self.lengthscales - - if metric == 'euclidean': - dists = dask_distance.seuclidean(coords1, coords2, lengthscales**2) - elif metric == 'haversine': - dists = (1 / lengthscales) * pairwise_haversine(coords1, coords2) - else: - raise ValueError("Metric not implemented.") - - res = da.multiply( - 1 + np.sqrt(3, dtype=np.float32) * dists, - da.exp(-np.sqrt(3, dtype=np.float32) * dists), dtype='float32') - return res - -class squared_exponential: - """ Squared exponential covariance kernel. - - """ - def __init__(self, lengthscales): - """ Build squared_exponential kernel. - - Parameters - ---------- - lengthscales: array-like (n_dims) - Vector of lengthscales for each individual dimension. - - """ - self.lengthscales = lengthscales - - def covariance_matrix(self, coords1, coords2, lengthscales=None, metric='euclidean'): - """ Compute covariance matrix between two sets of points. - - Parameters - ---------- - coords1: (m, n_dims) dask.array or Future - Point coordinates. - coords2: (n, n_dims) dask.array or Future - Point coordinates. - lengthscales_2: array-like (n_dims), defaults to None. - Can be used to override using the lengthscales of the kernel and use - different ones. - - Returns - ------- - covs: (m, n) delayed dask.array - Pairwise covariance matrix. - - """ - if lengthscales is None: - lengthscales = self.lengthscales - - if metric == 'euclidean': - dists = dask_distance.seuclidean(coords1, coords2, lengthscales**2) - elif metric == 'haversine': - dists = (1 / lengthscales) * pairwise_haversine(coords1, coords2) - else: - raise ValueError("Metric not implemented.") - - res = da.exp(- (1 / 2) * dists**2) - return res diff --git a/diesel/estimation/__init__.py b/diesel/estimation/__init__.py deleted file mode 100644 index 92ce1d3..0000000 --- a/diesel/estimation/__init__.py +++ /dev/null @@ -1,2 +0,0 @@ -from .base_estimation import empirical_covariance, localize_covariance -from .bayesian import InverseWishartPrior diff --git a/diesel/estimation/base_estimation.py b/diesel/estimation/base_estimation.py deleted file mode 100644 index c3e04ea..0000000 --- a/diesel/estimation/base_estimation.py +++ /dev/null @@ -1,27 +0,0 @@ -""" Basic covariance estimation procedures. - -""" -from diesel.utils import cov - - -def empirical_covariance(ensemble): - """ Compute the emprirical covariance of an ensemble. - - Parameters - ---------- - ensemble: dask.array [n_members, dim] - Independent realizations of a dim-dimensional random field. - - Returns - ------- - covariance: dask.array (lazy) [dim, dim] - - """ - # Estimate using homemade (float32) implemenation of da.cov - return cov(ensemble, rowvar=False) - -def localize_covariance(base_cov, localization_matrix): - """ Performs covariance localization. - - """ - return base_cov * localization_matrix diff --git a/diesel/estimation/bayesian.py b/diesel/estimation/bayesian.py deleted file mode 100644 index 70b7ab4..0000000 --- a/diesel/estimation/bayesian.py +++ /dev/null @@ -1,41 +0,0 @@ -""" Module grouping the methods for Bayesian estimation of covariance matrices. - -""" -import dask.array as da - - -class InverseWishartPrior: - """ Inverse Wishart prior for covariance matrices. - - """ - def __init__(self, lazy_scale_matrix, dof): - self.lazy_scale_matrix = lazy_scale_matrix - self.dof = dof - self.dim = lazy_scale_matrix.shape[0] - - if not self.dof > self.dim - 1: - raise ValueError( - "The number of degrees of freedom should be strictly greater than p - 1.") - - def posterior_mean(self, samples): - """ Compute posterior means given some data. - The data likelihood is assumed normal, so that we have a conjugate - pior. - - Parameters - ---------- - samples: dask.array (n_samples, dims) - Observed data. - - Returns - ------- - lazy_posterior_mean: dask.array (dims, dims) - Posterior covariance matrix (lazy). - - """ - n = samples.shape[0] - # Note that we use the biased estimate (normalization by 1/N). - sample_cov = da.cov(samples, rowvar=False, bias=True) - - lazy_posterior_mean = 1 / (n + self.dof - self.dim - 1) * (n * sample_cov + self.lazy_scale_matrix) - return lazy_posterior_mean diff --git a/diesel/gridding/__init__.py b/diesel/gridding/__init__.py deleted file mode 100644 index d9f51e3..0000000 --- a/diesel/gridding/__init__.py +++ /dev/null @@ -1 +0,0 @@ -from .planar_grids import SquareGrid diff --git a/diesel/gridding/planar_grids.py b/diesel/gridding/planar_grids.py deleted file mode 100644 index e4f1dda..0000000 --- a/diesel/gridding/planar_grids.py +++ /dev/null @@ -1,98 +0,0 @@ -""" Module for building standard planar grids. - -""" -import numpy as np -import dask.array as da -import matplotlib.pyplot as plt -import matplotlib.font_manager -from mpl_toolkits.axes_grid1 import make_axes_locatable -import seaborn as sns - - -sns.set() -sns.set_style("white") -# plt.rcParams["font.family"] = "Helvetica" -sns.set() -sns.set_style("white") -plt.rcParams["font.family"] = "serif" -plot_params = { - 'font.size': 18, 'font.style': 'normal', - 'axes.labelsize': 'x-small', - 'axes.titlesize':'x-small', - 'legend.fontsize': 'x-small', - 'xtick.labelsize': 'x-small', - 'ytick.labelsize': 'x-small' - } -plt.rcParams.update(plot_params) -plt.rc('xtick', labelsize=12) -plt.rc('ytick', labelsize=12) - - -class SquareGrid: - """ Build a 2D regular square grid with n_pts_1d^2 points. - The points are returned as dask chunked arrays. - - Parameters - ---------- - n_pts_1d: int - Number of grid points along 1 dimension. - block_size: int - Maximal size of the chunks for the chunked array. - - Returns - ------- - grid_pts: array [n_pts, 2] - - """ - def __init__(self, n_pts_1d, block_size=1e4): - # Size of corresponding covariance matrix in GB. - cov_size = 4 * n_pts_1d**4 - print("Builing grid with {} GB covariance matrix.".format(cov_size/1e9)) - self.X, self.Y = np.meshgrid( - np.linspace(0, 1, n_pts_1d), np.linspace(0, 1, n_pts_1d), indexing='ij') - grid_pts = np.stack([self.X.ravel(), self.Y.ravel()], axis=1) - grid_pts = np.squeeze(grid_pts) - - grid_pts = da.from_array(grid_pts) - grid_pts = grid_pts.rechunk(block_size_limit=block_size) - self.grid_pts = grid_pts - - def mesh_to_list(self, mesh_vals): - """ Flatten 2D meshed values into 1D list. - - Parameters - ---------- - mesh_vals: array[dim_x, dim_y] - - Returns - ------- - list_vals: array[dim_x * dim_y] - - """ - return mesh_vals.ravel() - - def list_to_mesh(self, list_vals): - return list_vals.reshape(self.X.shape[0], self.Y.shape[0]) - - def plot_vals(self, vals_list, ax, points=None, points_color='black', - vmin=None, vmax=None, - fig=None, colorbar=False, - cmap='jet'): - dx = (self.X[1, 0]-self.X[0, 0])/2. - dy = (self.Y[0, 1]-self.Y[0, 0])/2 - extent = extent = [ - self.X[0, 0]-dx, self.X[-1, 0]+dx, - self.Y[0, -1]+dy, self.Y[0, 0]-dy] - - im = ax.imshow(self.list_to_mesh(vals_list).T, - cmap=cmap, extent=extent, - vmin=vmin, vmax=vmax) - - if points is not None: - ax.scatter(points[:, 0], points[:, 1], c=points_color, s=3, marker='*') - if colorbar is True: - # Add colorbar - divider = make_axes_locatable(ax) - cax = divider.append_axes('right', size='5%', pad=0.05) - fig.colorbar(im, cax=cax, orientation='vertical') - return ax diff --git a/diesel/haversine.pyx b/diesel/haversine.pyx deleted file mode 100644 index 6f982e9..0000000 --- a/diesel/haversine.pyx +++ /dev/null @@ -1,31 +0,0 @@ -from libc.math cimport sin, cos, asin, sqrt, atan2 -import numpy as np -cimport numpy as np - -cdef double NAN = np.nan - - -## Equivalent to 3.1415927 / 180 -cdef double PI_RATIO = 0.017453293 - -cdef double deg2rad(double deg): - cdef double rad = deg * PI_RATIO - return rad - -def haversine(double lat1, double lon1, double lat2, double lon2): - cdef double rlon1 = deg2rad(lon1) - cdef double rlon2 = deg2rad(lon2) - cdef double rlat1 = deg2rad(lat1) - cdef double rlat2 = deg2rad(lat2) - - cdef double dlon = rlon2 - rlon1 - cdef double dlat = rlat2 - rlat1 - - cdef double a = ( - cos(rlat2) * sin(dlon))**2 + (cos(rlat1) * sin(rlat2) - - sin(rlat1) * cos(rlat2) * cos(dlon))**2 - cdef double b = sin(rlat1) * sin(rlat2) + cos(rlat1) * cos(rlat2) * cos(dlon) - - cdef double c = atan2(sqrt(a), b) - cdef double km = 6371 * c - return km diff --git a/diesel/kalman_filtering.py b/diesel/kalman_filtering.py deleted file mode 100644 index e855b42..0000000 --- a/diesel/kalman_filtering.py +++ /dev/null @@ -1,475 +0,0 @@ -""" Module implementing (ensemble) Kalman filtering. - -In DIESEL, an ensemble is a dask array of shape (n_members, dim). - -""" -import numpy as np -import dask.array as da -from dask.array import matmul, eye, transpose -from dask.distributed import wait -import diesel as ds -from diesel.utils import cholesky_invert, svd_invert, cross_covariance - -import time - -from builtins import CLIENT as global_client - -# Use torch for the sequential updating (which is done entirely on the scheduler. -import torch -torch.set_num_threads(8) - -# Select gpu if available and fallback to cpu else. -DEVICE = torch.device('cuda' if torch.cuda.is_available() else 'cpu') - - -class EnsembleKalmanFilter: - def __init__(self): - pass - - def _update_mean(self, mean, G, y, cov_pushfwd, inv): - """ Helper function for updating the mean over a single period. - This function assumes that the compute intensive intermediate matrices - have already been computed. - - Parameters - ---------- - mean: dask.array (m) - Vector of mean elements. - G: dask.array (n, m) - Observation operator. - y: dask.array (n, 1) - Observed data. - cov_pushfwd: dask.array (m, n) - Covariance pushforward cov @ G.T - inv: dask.array (n, n) - Inverse intermediate matrix. - - Returns - ------- - update_mean: dask.array (m) (lazy) - - """ - y = y.reshape(-1, 1) - mean = mean.reshape(-1, 1) - - kalman_gain = matmul(cov_pushfwd, inv) - prior_misfit = y - matmul(G, mean) - mean_updated = mean + matmul(kalman_gain, prior_misfit) - return mean_updated.reshape(-1) - - def update_mean(self, mean, G, y, data_std, cov): - """ Update the mean over a single period (step). - - Parameters - ---------- - mean: dask.array (m) - Vector of mean elements. - G: dask.array (n, m) - Observation operator. - y: dask.array (n, 1) - Observed data. - data_std: float - Standard deviation of observational noise. - cov: dask.array (m, m) - Covariance matrix (estimated) between the grid points. - Can be lazy. - - Returns - ------- - update_mean: dask.array (m) (lazy) - - """ - cov_pushfwd = matmul(cov, transpose(G)) - data_cov = data_std**2 * eye(y.shape[0]) - to_invert = matmul(G, cov_pushfwd) + data_cov - - _, inv = cholesky_invert(to_invert) - return self._update_mean(mean, G, y, cov_pushfwd, inv) - - def _update_anomalies(self, mean, ensemble, G, data_std, cov_pushfwd, sqrt, - svd_rank=1000): - """ Helper function for updating the ensemble members over a single period (step). - This function assumes that the compute intensive intermediate matrices - have already been computed. - - Parameters - ---------- - mean: dask.array (m) - Vector of mean elements. - ensemble: dask.array (n_members, m) - Ensemble members (one vector per member). - G: dask.array (n, m) - Observation operator. - data_std: float - Standard deviation of observational noise. - cov_pushfwd: dask.array (m, n) - Covariance pushforward cov @ G.T - sqrt: dask.array (n, n) - Lower Cholesky factor (square root) of the data covariance. - - Returns - ------- - anomalies_updated: dask.array (n_members, m) (lazy) - Updated anomalies (deviations from mean). Have to add - the updated mean to obtain updated ensemble members. - - """ - # Work with anomalies. - anomalies = ensemble - mean.reshape(-1)[None, :] - - # First compute the inverse of the sqrt. - _, inv_sqrt = svd_invert(sqrt, svd_rank=svd_rank, client=global_client) - - # TODO: Just trying to see where it goes wrong. - # Inverese of the other matrix involved. - _, inv_2 = svd_invert(sqrt + data_std * eye(G.shape[0]), - svd_rank=svd_rank, client=global_client) - kalman_gain_tilde = matmul(cov_pushfwd, - matmul(inv_sqrt.T, inv_2)) - - # Compute predictions for each member using batched matrix multiplication. - base_pred = matmul(G, anomalies[:, :, None]) # Resulting shape (n_members, m, 1) - anomalies_updated = anomalies[:, :, None] - matmul(kalman_gain_tilde, base_pred) - - # We remove the last dimension before returning. - return anomalies_updated.squeeze(-1) - - def _update_anomalies_single_nondask(self, mean, ensemble, G, data_std, cov_pushfwd, sqrt): - """ Helper function for updating the ensemble members during non-dask sequential - updtating. only processes a single data point. - - Parameters - ---------- - mean: dask.array (m) - Vector of mean elements. - ensemble: dask.array (n_members, m) - Ensemble members (one vector per member). - G: dask.array (1, m) - Observation operator. - data_std: float - Standard deviation of observational noise. - cov_pushfwd: dask.array (1, n) - Covariance pushforward cov @ G.T - sqrt: float - Lower Cholesky factor (square root) of the data covariance. - - Returns - ------- - anomalies_updated: dask.array (n_members, m) (lazy) - Updated anomalies (deviations from mean). Have to add - the updated mean to obtain updated ensemble members. - - """ - # Work with anomalies. - anomalies = ensemble - mean.reshape(-1)[None, :] - - # First compute the inverse of the sqrt. - inv_sqrt = 1 / sqrt - - inv_2 = 1 / (sqrt + data_std) - kalman_gain_tilde = (inv_sqrt * inv_2) * cov_pushfwd - - # Compute predictions for each member using batched matrix multiplication. - base_pred = torch.matmul(G, anomalies[:, :, None]) # Resulting shape (n_members, m, 1) - anomalies_updated = anomalies[:, :, None] - torch.matmul(kalman_gain_tilde, base_pred) - - # We remove the last dimension before returning. - return anomalies_updated.squeeze(-1) - - def update_ensemble(self, mean, ensemble, G, y, data_std, cov, - svd_rank=1000): - """ Update an ensemble over a single period (step). - - Parameters - ---------- - mean: dask.array (m) - Vector of mean elements. - ensemble: dask.array (n_members, m) - Ensemble members (one vector per member). - G: dask.array (n, m) - Observation operator. - y: dask.array (n, 1) - Observed data. - data_std: float - Standard deviation of observational noise. - cov: dask.array (m, m) - Covariance matrix (estimated) between the grid points. - Can be lazy. - - Returns - ------- - update_mean: dask.array (m) (lazy) - update_members: dask.array (n_members, m) (lazy) - - """ - cov_pushfwd = matmul(cov, transpose(G)) - data_cov = data_std**2 * eye(y.shape[0]) - to_invert = matmul(G, cov_pushfwd) + data_cov - - sqrt, inv = svd_invert(to_invert, - svd_rank=svd_rank, client=global_client) - - anomalies_updated = self._update_anomalies( - mean, ensemble, G, data_std, cov_pushfwd, sqrt, - svd_rank=svd_rank) - mean_updated = self._update_mean(mean, G, y, cov_pushfwd, inv) - - # Add the mean to get ensemble from anomalies. - ensemble_updated = mean_updated.reshape(-1)[None, :] + anomalies_updated - - return mean_updated.astype('float32'), ensemble_updated.astype('float32') - - def update_mean_sequential(self, mean, G, y, data_std, cov): - """ Update the mean over a single period (step) by assimilating the - data sequentially (one data point at a time). - - Parameters - ---------- - mean: dask.array (m) - Vector of mean elements. - G: dask.array (n, m) - Observation operator. - y: dask.array (n, 1) - Observed data. - data_std: float - Standard deviation of observational noise. - cov: dask.array (m, m) - Covariance matrix (estimated) between the grid points. - Can be lazy. - - Returns - ------- - update_mean: dask.array (m, 1) (lazy) - - """ - mean_updated = mean - - # Loop over the data points and ingest sequentially. - for i in range(G.shape[0]): - # One data points. - G_seq = G[i, :].reshape(1, -1) - y_seq = y[i].reshape(1, -1) - - mean_updated = self.update_mean(mean, G, y, data_std, cov) - - # Have to execute once in a while, otherwise graph gets too big. - if i % 100 == 0: - mean_updated = global_client.persist(mean_updated) - return mean_updated - - def update_mean_sequential_nondask(self, mean, G, y, data_std, cov): - """ Update the mean over a single period (step) by assimilating the - data sequentially (one data point at a time). - - Parameters - ---------- - mean: dask.array (m) - Vector of mean elements. - G: dask.array (n, m) - Observation operator. - y: dask.array (n, 1) - Observed data. - data_std: float - Standard deviation of observational noise. - cov: dask.array (m, m) - Covariance matrix (estimated) between the grid points. - Can be lazy. - - Returns - ------- - update_mean: dask.array (m, 1) (lazy) - - """ - mean_updated = global_client.compute(mean).result().reshape(-1, 1) - # Compute pushforward once and for all. extract lines later. - cov_pushfwd_full = global_client.persist(cov @ transpose(G)) - wait(cov_pushfwd_full) - - # Repatriate y to the local process. - y = global_client.compute(y).result().reshape(-1, 1) - G = global_client.compute(G).result() - - # Send the important stuff to torch. - y = torch.from_numpy(y).to(DEVICE).float() - G = torch.from_numpy(G).to(DEVICE).float() - mean_updated = torch.from_numpy(mean_updated).to(DEVICE).float() - - # Loop over the data points and ingest sequentially. - for i in range(G.shape[0]): - # Every 500 observations, repatriate a chunk of the pushforward - # and send it to the GPU. - if i % 500 == 0: - i_pushfwd_start = i # The index at which the local pushfwd starts. - local_pushfwd = global_client.compute(cov_pushfwd_full[:,i:i+500]).result() - local_pushfwd = torch.from_numpy(local_pushfwd).to(DEVICE).float() - - # One data points. - G_seq = G[i, :].reshape(1, -1) - y_seq = y[i].reshape(1, 1) - - # Now are fully in numpy. - cov_pushfwd = local_pushfwd[:, i - i_pushfwd_start].reshape(-1, 1) - - data_cov = data_std**2 - to_invert = torch.matmul(G_seq, cov_pushfwd) + data_cov - inv = 1 / to_invert[0, 0] - - kalman_gain = inv * cov_pushfwd - prior_misfit = y_seq - torch.matmul(G_seq, mean_updated) - mean_updated = mean_updated + torch.matmul(kalman_gain, prior_misfit) - - return mean_updated.detach().cpu().numpy() - - def update_ensemble_sequential_nondask(self, mean, ensemble, G, y, data_std, localization_matrix): - """ Update the mean over a single period (step) by assimilating the - data sequentially (one data point at a time). - - Parameters - ---------- - mean: dask.array (m) - Vector of mean elements. - G: dask.array (n, m) - Observation operator. - y: dask.array (n, 1) - Observed data. - data_std: float - Standard deviation of observational noise. - localization_matrix: dask.array (m, m) - Matrix used to perform localization. Will get Hadamard-producted with - the empirical covariance at every assimilation stage. - - Returns - ------- - update_mean: dask.array (m, 1) (lazy) - - """ - mean_updated = global_client.compute(mean).result().reshape(-1, 1) - ensemble_updated = global_client.compute(ensemble).result() - - # Repatriate y to the local process. - y_loc = global_client.compute(y).result().reshape(-1, 1) - G_loc = global_client.compute(G).result() - - # Send the important stuff to torch. - y_loc = torch.from_numpy(y_loc).to(DEVICE).float() - G_loc = torch.from_numpy(G_loc).to(DEVICE).float() - mean_updated = torch.from_numpy(mean_updated).to(DEVICE).float() - ensemble_updated = torch.from_numpy(ensemble_updated).to(DEVICE).float() - - # Loop over the data points and ingest sequentially. - for i in range(G.shape[0]): - # One data points. - G_seq = G_loc[i, :].reshape(1, -1) - y_seq = y_loc[i].reshape(1, 1) - - # Find the indices at which G_seq is non zero and extract those - # parts of the covariance. - _, obs_ind = G_seq.nonzero(as_tuple=True) - obs_ind = obs_ind.cpu().numpy() - - # Extract the concerned line of the empirical covariance. - cov_pushfwd = cross_covariance( - ensemble_updated.cpu(), - ensemble_updated.cpu()[:, obs_ind], rowvar=False).reshape(-1, 1) - - cov_pushfwd = cov_pushfwd.to(DEVICE).float() - loc_obs_cov = torch.from_numpy( - global_client.compute(localization_matrix[:, obs_ind]).result()).to(DEVICE).float() - - cov_pushfwd = torch.mul( - cov_pushfwd, - loc_obs_cov - ) - - data_cov = data_std**2 - to_invert = torch.matmul(G_seq, cov_pushfwd) + data_cov - inv = 1 / to_invert[0] - sqrt = torch.sqrt(to_invert[0]) - - kalman_gain = inv * cov_pushfwd - prior_misfit = y_seq - torch.matmul(G_seq, mean_updated) - - anomalies_updated = self._update_anomalies_single_nondask( - mean_updated, ensemble_updated, G_seq, data_std, cov_pushfwd, sqrt) - # Warning, have to update mean after ensemble, since ensemble use the prior mean in the update. - mean_updated = mean_updated + torch.matmul(kalman_gain, prior_misfit) - # Add the mean to get ensemble from anomalies. - ensemble_updated = mean_updated.reshape(-1)[None, :] + anomalies_updated - return mean_updated.detach().cpu().numpy().reshape(-1), ensemble_updated.detach().cpu().numpy() - - def update_ensemble_sequential(self, mean, ensemble, G, y, data_std, cov, covariance_estimator=None): - """ Update an ensemble over a single period (step) by assimilating the - data sequentially (one data point at a time). - - Parameters - ---------- - mean: dask.array (m) - Vector of mean elements. - ensemble: dask.array (n_members, m) - Ensemble members (one vector per member). - G: dask.array (n, m) - Observation operator. - y: dask.array (n, 1) - Observed data. - data_std: float - Standard deviation of observational noise. - cov: dask.array (m, m) - Covariance matrix (estimated) between the grid points. - Can be lazy. - covariance_estimator: function, defaults to None - If provided, then at each step the covariance is estimated from - the updated ensemble members using the given function. - Signature should be ensemble -> covariance matrix. - - Returns - ------- - update_mean: dask.array (m, 1) (lazy) - update_members: dask.array (n_members, m) (lazy) - - """ - mean_updated, ensemble_updated = mean, ensemble - - # Loop over the data points and ingest sequentially. - last_time = time.time() - for i in range(G.shape[0]): - # One data points. - G_seq = G[i, :].reshape(1, -1) - y_seq = y[i].reshape(1, 1) - - # Re-estimate the covariance if estimator provided. - if covariance_estimator is not None: - cov_est = covariance_estimator(ensemble_updated) - else: cov_est = cov - - mean_updated, ensemble_updated = self.update_ensemble( - mean_updated, ensemble_updated, - G_seq, y_seq, - data_std, cov_est) - - # Have to execute once in a while, otherwise graph gets too big. - if i % 10 == 0: - print(i) - now = time.time() - elapsed_time = now - last_time - last_time = now - print("Time since last persisting: {}.".format(elapsed_time)) - mean_updated = global_client.persist(mean_updated) - ensemble_updated = global_client.persist(ensemble_updated) - wait(ensemble_updated) - - # Repatriate locally, so we can cancel running tasks - # to free the scheduler. - # TODO: this is not clean and should be solved. - mean_tmp = mean_updated.compute() - ensemble_tmp = ensemble_updated.compute() - - # Cancel cached stuff to clean memory. - global_client.cancel(mean_updated) - global_client.cancel(ensemble_updated) - global_client.cancel(cov_est) - - # After cancellation can re-send to the cluster. - mean_updated = global_client.persist(da.from_array(mean_tmp)) - ensemble_updated = global_client.persist(da.from_array(ensemble_tmp)) - - return mean_updated, ensemble_updated diff --git a/diesel/non_stationary_models.py b/diesel/non_stationary_models.py deleted file mode 100644 index 9a7de0c..0000000 --- a/diesel/non_stationary_models.py +++ /dev/null @@ -1,204 +0,0 @@ -""" Implementation of non-stationary Gaussian process models. - -""" -import dask.array as da - - -class BaCompositeGP: - """ Composite non-stationary GP model as defined in Ba and Joseph (2012). - - """ - def __init__(self, global_covariance, local_covariance): - self.global_covariance = global_covariance - self.local_covariance = local_covariance - self.n_iter_vs = 5 - - def _compute_helper_matrices(self, pred_pts, dat_pts, y, vs_data, lmbda): - """ Compute the matrices involced in the global and local prediction. - This function centralizes computations that are shared across the different - prediction scenarios. - - pred_pts: dask.array (m, n_dims) - Coordinates of the prediction points. - dat_pts: dask.array (n, n_dims) - Coordinates of the data points. - y: dask.array (n ,1) - Observed data. - vs_data: dask.array (n, 1) - Local variance scalings at the data points. - - Returns - ------- - G_cov_mat - L_cov_mat - G_cross_cov - L_cross_cov - Sigma_sqrt - inv - - """ - y = y.reshape(-1, 1) - - G_cov_mat = self.global_covariance.covariance_matrix(dat_pts, dat_pts) - L_cov_mat = self.local_covariance.covariance_matrix(dat_pts, dat_pts) - - G_cross_cov = self.global_covariance.covariance_matrix(pred_pts, dat_pts) - L_cross_cov = self.local_covariance.covariance_matrix(pred_pts, dat_pts) - - Sigma_sqrt = da.diag(da.sqrt(vs_data.reshape(-1))) - - inv = da.linalg.inv(G_cov_mat + lmbda * Sigma_sqrt @ L_cov_mat @ Sigma_sqrt) - return G_cov_mat, L_cov_mat, G_cross_cov, L_cross_cov, Sigma_sqrt, inv - - def predict(self, pred_pts, dat_pts, y, lmbda, b): - """ Compute prediction given some data. - The local variances are estimated iteratively in an inner loop. - - This function returns the global and local part of the prediction separately. - The complete prediction is the sum of both. - - Parameters - ---------- - pred_pts: dask.array (m, n_dims) - Coordinates of the prediction points. - dat_pts: dask.array (n, n_dims) - Coordinates of the data points. - y: dask.array (n ,1) - Observed data. - lmbda: float - Ratio of the local variance to the global variance. - b: float - Tuning parameter for the local variances. - - Returns - ------- - pred_global: dask.array (m, 1) - Prediction at the given prediction points (global part). - pred_local: dask.array (m, 1) - Prediction at the given prediction points (local part). - - """ - y = y.reshape(-1, 1) - - # Initial guess for the vs is ones. - vs_data = da.ones(y.shape) - - # Iteratively estimate the local variances vs. - for i in range(self.n_iter_vs): - # Estimate the global predictor at the data points. - pred_global_data = self.predict_global(dat_pts, dat_pts, y, vs_data, lmbda) - vs_pred, vs_data = self.estimate_vs(pred_pts, dat_pts, y, pred_global_data, b) - print(vs_data.compute()) - - # Compute final predictions and return - # Get matrices needed for prediction. - (G_cov_mat, L_cov_mat, G_cross_cov, - L_cross_cov, Sigma_sqrt, inv) = self._compute_helper_matrices( - pred_pts, dat_pts, y, vs_data, lmbda) - # Estimate global mean. - ones = da.ones(y.shape) - mu_hat = ( - da.linalg.inv(ones.T @ inv @ ones) - @ - ones.T @ inv @ y) - - # Estimate the predictors. - misfit = y - mu_hat * ones - pred_global = mu_hat + G_cross_cov @ inv @ misfit - pred_local = lmbda * da.sqrt(vs_pred) * L_cross_cov @ Sigma_sqrt @ inv @ misfit - - # Trigger computations. - pred_global = pred_global.compute() - pred_local = pred_local.compute() - - return pred_global, pred_local - - def predict_global(self, pred_pts, dat_pts, y, vs_data, lmbda): - """ Fit the global part of the composite GP, for a fixed vector - of local variance scalings vs. - - Parameters - ---------- - pred_pts: dask.array (m, n_dims) - Coordinates of the prediction points. - dat_pts: dask.array (n, n_dims) - Coordinates of the data points. - y: dask.array (n ,1) - Observed data. - vs_data: dask.array (m, 1) - Local variance scalings at the data points. - lmbda: float - Ratio of the local variance to the global variance. - - Returns - ------- - pred_global: dask.array (m, 1) - Prediction at the given prediction points (global part). - - """ - y = y.reshape(-1, 1) - # Get matrices needed for prediction. - (G_cov_mat, L_cov_mat, G_cross_cov, - L_cross_cov, Sigma_sqrt, inv) = self._compute_helper_matrices( - pred_pts, dat_pts, y, vs_data, lmbda) - - # Estimate global mean. - ones = da.ones(y.shape) - mu_hat = ( - da.linalg.inv(ones.T @ inv @ ones) - @ - ones.T @ inv @ y) - - # Estimate the global predictor. - misfit = y - mu_hat * ones - pred_global = mu_hat + G_cross_cov @ inv @ misfit - return pred_global - - def estimate_vs(self, pred_pts, dat_pts, y, pred_global_data, b): - """ Estimate the v(x) local variance scaling using eq (18) - from Ba and Joseph (2012). Returns the local variances - at the data points and at the prediction points. - - Parameters - ---------- - pred_pts: dask.array (m, n_dims) - Coordinates of the prediction points. - dat_pts: dask.array (n, n_dims) - Coordinates of the data points. - y: dask.array (n ,1) - Observed data. - pred_global_data: dask.array (n, 1) - Global prediction at the data points. - b: float - - Returns - ------- - vs_pred: dask.array (m, 1) - Estimated local variance scalings at the prediction points. - vs_data: dask.array (n, 1) - Estimated local variance scalings at the data points. - - """ - s_2 = (y - pred_global_data)**2 - - # One needs the original (global) covariance model, but - # with lengthscales scaled by b. - mod_lengthscales = b * self.global_covariance.lengthscales - - gb_pred = self.global_covariance.covariance_matrix( - pred_pts, dat_pts, - lengthscales=mod_lengthscales) - gb_data = self.global_covariance.covariance_matrix( - dat_pts, dat_pts, - lengthscales=mod_lengthscales) - - ones = da.ones((dat_pts.shape[0], 1)) - - vs_pred = gb_pred @ s_2 / gb_pred @ ones - vs_data = gb_data @ s_2 / gb_data.T @ ones - - # Normalize - vs_pred = vs_pred / vs_data.mean() - vs_data = vs_data / vs_data.mean() - - return vs_pred, vs_data diff --git a/diesel/plotting/__init__.py b/diesel/plotting/__init__.py deleted file mode 100644 index 35fcb65..0000000 --- a/diesel/plotting/__init__.py +++ /dev/null @@ -1,2 +0,0 @@ -from .covariance_plotting import compute_variogram -from .covariance_plotting import plot_variogram diff --git a/diesel/plotting/covariance_plotting.py b/diesel/plotting/covariance_plotting.py deleted file mode 100644 index 8122969..0000000 --- a/diesel/plotting/covariance_plotting.py +++ /dev/null @@ -1,44 +0,0 @@ -""" Module for graphic representation of covariance matrices. - -""" -import numpy as np -import dask.array as da -import matplotlib.pyplot as plt - - -def compute_variogram(dist_mat, cov_mat, n_bins): - """ compute binned variogram (covariance as function of distance). - - """ - min_dist = dist_mat.min().compute() - max_dist = dist_mat.max().compute() - bins = np.linspace(min_dist, max_dist, n_bins) - - bins_midpts = bins[:-1] + (bins[1] - bins[0])/2 - - bin_means = [] - bin_stds = [] - for i in range(bins.shape[0] - 1): - bin_low, bins_high = bins[i], bins[i + 1] - - # Find indices where condition is satisfied. - inds_i, inds_j = da.where((dist_mat >= bin_low) & (dist_mat < bins_high)) - - # Compute mean and std over bin. - cov_mat_bin = cov_mat.vindex[inds_i, inds_j] - bin_means.append(cov_mat_bin.mean().compute()) - bin_stds.append(cov_mat_bin.std().compute()) - - return bins_midpts, np.array(bin_means), np.array(bin_stds) - -def plot_variogram(dist_mat, cov_mat, n_bins, outfile=None): - bins_midpts, bins_means, bins_stds = compute_variogram( - dist_mat, cov_mat, n_bins) - plt.plot(bins_midpts, bins_means) - plt.fill_between(bins_midpts, bins_means - 3*bins_stds, bins_means + 3*bins_stds, alpha=.2) - - if outfile is not None: - plt.savefig(outfile, bbox_inches="tight", pad_inches=0.1, dpi=400) - plt.show() - - return bins_midpts, bins_means, bins_stds diff --git a/diesel/sampling/__init__.py b/diesel/sampling/__init__.py deleted file mode 100644 index 7bed275..0000000 --- a/diesel/sampling/__init__.py +++ /dev/null @@ -1,2 +0,0 @@ -from .samplers import SvdSampler -from .samplers import CholeskySampler diff --git a/diesel/sampling/samplers.py b/diesel/sampling/samplers.py deleted file mode 100644 index 18c0370..0000000 --- a/diesel/sampling/samplers.py +++ /dev/null @@ -1,56 +0,0 @@ -""" Module for sampling from multivariate gaussians. - -""" -import dask.array as da - - -class SvdSampler: - """ Sample multivariate gaussian from SVD decomposition of - its covariance matrix. - - Parameters - ---------- - svd_u: dask_array - Left singular vectors of the covariance matrix - (as obtained from da.linalg.svd_compressed). - svd_s: dask_array - Vector of singular values of the covariance matrix. - - """ - def __init__(self, svd_u, svd_s): - self.svd_u, self.svd_s = svd_u, svd_s - - # Equivalent of Cholesky matrix in traditional sampling. - smat = da.diag(da.sqrt(self.svd_s)) - self.cholesky_lazy = da.dot(self.svd_u, smat) - - self.dim = svd_u.shape[0] - - def sample(self, n_samples): - """ Generate samples. - - Parameters - ---------- - n_samples: int - Number of independent samples to generate. - - Returns - ------- - samples: array [n_samples, dim] - - """ - samples = da.random.normal(size=(n_samples, self.svd_u.shape[1], 1)) - samples = da.dot(self.cholesky_lazy, samples).squeeze().T - return samples - -class CholeskySampler: - """ Non lazy. - - """ - def __init__(self, covariance_matrix): - self.cholesky = da.linalg.cholesky(covariance_matrix, lower=True).compute() - - def sample(self, n_samples): - samples = da.random.normal(size=(n_samples, self.cholesky.shape[0], 1)) - samples = da.dot(self.cholesky, samples).squeeze().T - return samples diff --git a/diesel/scoring.py b/diesel/scoring.py deleted file mode 100644 index fbaf344..0000000 --- a/diesel/scoring.py +++ /dev/null @@ -1,203 +0,0 @@ -""" Scoring functions to evaluate quality of probabilistic forecasts. - -""" -import numpy as np -import dask.array as da - - -def compute_RE_score(mean_prior, mean_updated, reference, min_lat=None, max_lat=None): - """ Reduction of error skill score. - This score compares a base prediction (mean_prior) with an enhanced prediction (mean_updated). - If the enhanced prediction predicts the reference better than the base one, then the score - is > 0, the score being 1 if the reconstruction is perfect. - - Note that this score averages over times steps and produces a spatial map. - - See Valler et al., Impact of different estimations of the background-error covariance matrix on climate reconstructions based on data assimilation (2019). - - Parameters - ---------- - mean_prior: dask.array (m, t) - Vector of mean elements prior to the updating. - mean_updated: dask.array (m,t) - Vector of mean elements after updating. - reference: xarray.Dataset (m, t) - Ground truth to be reconstructed. - Should be provided in dataset format in order to include - spatial information. - min_lat: float, defaults to None. - If specified, ignore the refions of low latitude - in the computation of the mismatch. - max_lat: float, defaults to None. - If specified, ignore the refions of high latitude - in the computation of the mismatch. - - Returns - ------- - RE_score: dask.array (m) - Vector of RE scores at each location. - - """ - # Filter out high/low latitude if provided. - if min_lat is not None: - lat_filter_inds = (reference.latitude < max_lat).data & (reference.latitude > min_lat).data - mean_prior = mean_prior[lat_filter_inds] - mean_updated = mean_updated[lat_filter_inds] - reference = reference[lat_filter_inds] - - # If reference is a nested object (like an xarray or a dask.array), - # get the underlying data. - if hasattr(reference, 'to_numpy'): - reference = reference.to_numpy() - - # Get rid of Nans. - mean_prior = mean_prior[~np.isnan(reference)] - mean_updated = mean_updated[~np.isnan(reference)] - reference = reference[~np.isnan(reference)] - - # Make sure shapes agree. - - mean_prior, mean_updated, reference = mean_prior.reshape(-1), mean_updated.reshape(-1), reference.reshape(-1) - - RE_score = 1 - np.mean((mean_updated - reference)**2) / np.mean((mean_prior - reference)**2) - return RE_score - -def compute_CRPS(ensemble, reference, min_lat=None, max_lat=None): - """ Computes the continuous ranked probability score (CRPS). - This scores evaluates how well a probabilistic forecast (given by an ensemble) - predicts a given reference. The CRPS is relative in the sense that it is used to - compare different forecasts, lower score being better. - The CRPS is a sum of a misfit term and a spread term. Here both are returned separately. - - See Jordan et al., Evaluating Probabilistic Forecasts with scoringRule (2018). - - Parameters - ---------- - ensemble: dask.array (n_members, m) - Collection of prediction vectors. - reference: dask.array (m) - Ground truth to be reconstructed. - min_lat: float, defaults to None. - If specified, ignore the refions of low latitude - in the computation of the mismatch. - max_lat: float, defaults to None. - If specified, ignore the refions of high latitude - in the computation of the mismatch. - - Returns - ------- - CRPS: dask.array (m) - Vector of CRPS scores at each location. - misfit: dask.array (m) - Vector of misfits (in the CRPS) at each location. - spread: dask.array (m) - Vector of spreads (in the CRPS) at each location. - - """ - ensemble = ensemble[:, ~np.isnan(reference)] - reference = reference[~np.isnan(reference)] - - n_members = ensemble.shape[0] - misfit = (1 / n_members) * da.fabs(ensemble - reference.reshape(-1)[None, :]).sum(axis=0) - spread = (1 / (2 * n_members**2)) * da.fabs( - ensemble[None, :, :] - ensemble[:, None, :]).sum(axis=0).sum(axis=0) - CRPS = misfit - spread - return CRPS, misfit, spread - -def compute_energy_score(ensemble, reference, min_lat=None, max_lat=None): - """ Computes energy score (multivariate generalisation of the CRPS". - This scores evaluates how well a probabilistic forecast (given by an ensemble) - predicts a given reference. The energy score is relative in the sense that it is used to - compare different forecasts, lower score being better. - The energy score is a sum of a misfit term and a spread term. Here both are returned separately. - - See Jordan et al., Evaluating Probabilistic Forecasts with scoringRule (2018). - - Parameters - ---------- - ensemble: dask.array (n_members, m) - Collection of prediction vectors. - reference: dask.array (m) - Ground truth to be reconstructed. - min_lat: float, defaults to None. - If specified, ignore the refions of low latitude - in the computation of the mismatch. - max_lat: float, defaults to None. - If specified, ignore the refions of high latitude - in the computation of the mismatch. - - Returns - ------- - energy_score: dask.array (1) - Energy score (scalar). - misfit: dask.array (1) - Misfit term of the energy score (scalar). - spread: dask.array (1) - Spread term of the energy score (scalar). - - """ - # Filter out high/low latitude if provided. - if min_lat is not None: - lat_filter_inds = (reference.latitude < max_lat).data & (reference.latitude > min_lat).data - ensemble = ensemble[:, lat_filter_inds] - reference = reference[lat_filter_inds] - - # If reference is a nested object (like an xarray or a dask.array), - # get the underlying data. - if hasattr(reference, 'to_numpy'): - reference = reference.to_numpy() - - # Get rid of Nans. - ensemble = ensemble[:, ~np.isnan(reference)] - reference = reference[~np.isnan(reference)] - - n_members = ensemble.shape[0] - misfit = (1 / n_members) * da.linalg.norm( - ensemble - reference.reshape(-1)[None, :], axis=1).sum(axis=0) - spread = (1 / (2 * n_members**2)) * da.linalg.norm( - ensemble[None, :, :] - ensemble[:, None, :], axis=2).sum(axis=0).sum(axis=0) - energy_score = misfit - spread - return energy_score, misfit, spread - -def compute_RMSE(mean_updated, reference, min_lat=None, max_lat=None): - """ Root mean square error. - - Parameters - ---------- - mean_updated: dask.array (m) - Vector of mean elements after updating. - reference: dask.array (m) - Ground truth to be reconstructed. - min_lat: float, defaults to None. - If specified, ignore the refions of low latitude - in the computation of the mismatch. - max_lat: float, defaults to None. - If specified, ignore the refions of high latitude - in the computation of the mismatch. - - Returns - ------- - RMSE: float - - """ - # Filter out high/low latitude if provided. - if min_lat is not None: - lat_filter_inds = (reference.latitude < max_lat).data & (reference.latitude > min_lat).data - mean_updated = mean_updated[lat_filter_inds] - reference = reference[lat_filter_inds] - - # If reference is a nested object (like an xarray or a dask.array), - # get the underlying data. - if hasattr(reference, 'to_numpy'): - reference = reference.to_numpy() - - # Get rid of Nans. - mean_updated = mean_updated[~np.isnan(reference)] - reference = reference[~np.isnan(reference)] - - # Make sure shapes agree. - mean_updated, reference = mean_updated.reshape(-1), reference.reshape(-1) - rmse = np.sqrt(np.mean((mean_updated - reference)**2)) - if isinstance(rmse, np.ndarray): rmse = rmse[0] - - return rmse diff --git a/diesel/utils.py b/diesel/utils.py deleted file mode 100644 index b4f8303..0000000 --- a/diesel/utils.py +++ /dev/null @@ -1,288 +0,0 @@ -""" Helper functions for the DIESEL package. - -""" -import numpy as np -from sklearn.neighbors import BallTree - -import torch - -import dask.array as da -from dask.distributed import wait, progress -from dask.utils import apply, derived_from -from dask.array.core import (Array, asanyarray, asarray, blockwise, broadcast_arrays, - broadcast_shapes, broadcast_to, concatenate, elemwise, from_array, implements, - is_scalar_for_elemwise, map_blocks, stack, tensordot_lookup) -from dask.array.routines import array, dot - - -from climate.utils import match_vectors_indices - - -# Get the client stored in the global variable. -from builtins import CLIENT as global_client - - -CHUNK_REDUCTION_FACTOR = 4 - -def find_closest_multiple(x, base): - closest_lower = int(base * round(float(x)/base)) - if closest_lower == x: return x - else: return closest_lower + base - -def cholesky_invert(A, debug_string): - """ Computes the (lower) Cholesky factor and the inverse - of a symmetric positive definite matrix using Cholesky decomposition - and backward substitution. - - Parameters - ---------- - A: dask.array - - Returns - ------- - L, A_inv: dask.array (lazy) - Lower Cholesky factor and inverse of the input matrix. - - """ - # Note that the daks cholesky implementation requires square chunks. - # Hence, to keep chunks of a manageable size, one possible trick is to make - # R into a matrix wiht shape divisible by CHUNK_REDUCTION_FACTOR, to have CHUNK_REDUCTION_FACTOR chunks along each - # dimension. - # Appending with identity matrix (in block diag fashion) allows us - # to recover the original Cholesky decomposition from the one of the - # augmented matrix. - - # If small enough then use only one chunk. - if (A.shape[0] < CHUNK_REDUCTION_FACTOR - 1): - chunk_size = A.shape[0] - A_rechunked = A.rechunk(chunk_size) - shape_diff = 0 - - # If already square, then proceed. - elif len(set(A.chunks[0] + A.chunks[1])) == 1: - A_rechunked = A - shape_diff = 0 - chunk_size = A.chunks[0][0] - - # Else append identity matrix to get a shape that is - # divisible by CHUNK_REDUCTION_FACTOR. - else: - new_shape = find_closest_multiple(A.shape[0], CHUNK_REDUCTION_FACTOR) - if new_shape > 0: - shape_diff = new_shape - A.shape[0] - A_rechunked = da.vstack( - [ - da.hstack([A, da.zeros((A.shape[0], shape_diff))]), - da.hstack([da.zeros((shape_diff, A.shape[0])), da.eye(shape_diff)]) - ] - ) - chunk_size = int(new_shape / CHUNK_REDUCTION_FACTOR) - A_rechunked = A_rechunked.rechunk(chunk_size) - - # TEMP: try to compute to see if fails. - try: - R = da.linalg.cholesky(A_rechunked, lower=False) - except: - print("Error in Cholesky") - print(debug_string) - try: - R_inv = da.linalg.solve_triangular(R, da.linalg.eye(R.shape[0], chunks=chunk_size), lower=False) - except: - print("Error in solve triangular") - print(debug_string) - - # Extract the part of interest for us. - if shape_diff > 0: - R = R[:-shape_diff, :-shape_diff] - R_inv = R_inv[:-shape_diff, :-shape_diff] - return da.transpose(R), da.matmul(R_inv, da.transpose(R_inv)) - -def svd_invert(A, svd_rank=None, client=global_client): - if svd_rank is None: svd_rank = A.shape[0] - # Compute compressed SVD. - # WARNING: dask return the already transposed version of v, - # so that A = u @ diag(s) @ v. - # This is poorly documented in dask. - u, s, v = da.linalg.svd_compressed( - A, k=svd_rank, compute=True) - u, s, v = client.persist(u), client.persist(s), client.persist(v) - - # Compute (symmetric) square root. - smat = da.diag(da.sqrt(s)) - sqrt = da.matmul(da.matmul(u, smat), da.transpose(u)) - - imat = da.diag(da.true_divide(da.ones(s.shape), s)) - inv = da.matmul(da.matmul(da.transpose(v), imat), da.transpose(u)) - - return sqrt, inv - -def cross_covariance(X, Y, bias=False, ddof=None, dtype=None, rowvar=True): - if not rowvar: - X_in = X.T - Y_in = Y.T - else: - X_in = X - Y_in = Y - if ddof is not None and ddof != int(ddof): - raise ValueError( - "ddof must be integer") - - """ - if dtype is None: - dtype = np.result_type(X, np.float64) - - X = array(X, ndmin=2, dtype=dtype) - Y = array(Y, ndmin=2, dtype=dtype) - """ - - if ddof is None: - if bias == 0: - ddof = 1 - else: - ddof = 0 - - avg_X = torch.mean(X_in, axis=1) - avg_Y = torch.mean(Y_in, axis=1) - - fact = X_in.shape[1] - ddof - - # Subtract the mean. - X_centred = X_in - avg_X[:, None] - Y_centred = Y_in - avg_Y[:, None] - - Y_T = Y_centred.T - c = torch.matmul(X_centred, Y_T.conj()) - c *= np.true_divide(1, fact) - return c.squeeze() - -def build_forward_mean_per_cell(mean_ds, data_ds): - """ Build the forward operator corresponding to a given - model grid and data point cloud. - This function only assimilated the mean observed value in each cell. - - Parameters - ---------- - mean_ds: xr.DataArray - data_ds: xr.DataArray - - Returns - ------- - G_mean: (n_data_mean, n_cells) - Forward operator for assimilation of mean data in each cell. - mean_datas (n_data_mean) - Vector of mean observed data in each cell. - - - """ - # Get the model cell index corresponding to each observations. - matched_inds = match_vectors_indices(mean_ds, data_ds) - - # Get unique indices. For the ones that appear several time, - # we will assimilat the mean. I.e. we assimilat the mean observed data - # in each cell where we have observations. - unique_indices = np.unique(matched_inds) - mean_datas = [np.mean(data_ds.values[matched_inds == i]) for i in unique_indices] - median_datas = [np.median(data_ds.values[matched_inds == i]) for i in unique_indices] - std_datas = [np.std(data_ds.values[matched_inds == i]) for i in unique_indices] - n_datas = [len(data_ds.values[matched_inds == i]) for i in unique_indices] - - mean_datas, median_datas, std_datas, n_datas = np.array(mean_datas), np.array(median_datas), np.array(std_datas), np.array(n_datas) - - data_lats = mean_ds.latitude[unique_indices] - data_lons = mean_ds.longitude[unique_indices] - - G = np.zeros((unique_indices.shape[0], mean_ds.shape[0])) - for i in range(unique_indices.shape[0]): - G[i, unique_indices[i]] = 1.0 - return G, mean_datas, std_datas, median_datas, n_datas, data_lons, data_lats - -@derived_from(np) -def cov(m, y=None, rowvar=1, bias=0, ddof=None): - """ Re-implementation of the dask.cov function. - The goal is to restrict the computation to float32 - to save memory, apart from that, the implementation is the same. - - """ - if ddof is not None and ddof != int(ddof): - raise ValueError("ddof must be integer") - - # Handles complex arrays too - m = asarray(m) - if y is None: - dtype = np.result_type(m, np.float32) - else: - y = asarray(y) - dtype = np.result_type(m, y, np.float32) - X = array(m, ndmin=2, dtype=dtype) - - if X.shape[0] == 1: - rowvar = 1 - if rowvar: - N = X.shape[1] - axis = 0 - else: - N = X.shape[0] - axis = 1 - - # check ddof - if ddof is None: - if bias == 0: - ddof = 1 - else: - ddof = 0 - fact = float(N - ddof) - if fact <= 0: - warnings.warn("Degrees of freedom <= 0 for slice", RuntimeWarning) - fact = 0.0 - - if y is not None: - y = array(y, ndmin=2, dtype=dtype) - X = concatenate((X, y), axis) - - X = X - X.mean(axis=1 - axis, keepdims=True) - if not rowvar: - return (dot(X.T, X.conj()) / fact).squeeze() - else: - return (dot(X, X.T.conj()) / fact).squeeze() - -def match_vectors_indices(base_vector, vector_to_match): - """" Given two stacked datasets (vectors), for each element in the dataset_tomatch, - find the index of the element in the base dataset that is closest. - - Note that the base dataset should contain only one element at each spatial locaiton, - so that the matched index is unique. - - Parameters - ---------- - base_vector: xarray.DataArray - Stacked dataset. - vector_to_match: xarray.DataArray - Stacked dataset. - - Returns - ------- - Array[int] (vector_to_match.shape[0]) - Indices in the base dataset of closest element for each - element of the dataset_tomatch. - - """ - # Convert to radians. - lat_rad = np.deg2rad(base_vector.latitude.values.astype(np.float32)) - lon_rad = np.deg2rad(base_vector.longitude.values.astype(np.float32)) - - # Build a ball tree to make nearest neighbor queries faster. - ball = BallTree(np.vstack([lat_rad, lon_rad]).T, metric='haversine') - - # Define grid to be matched. - lon_tomatch = np.deg2rad(vector_to_match.longitude.values.astype(np.float32)) - lat_tomatch = np.deg2rad(vector_to_match.latitude.values.astype(np.float32)) - coarse_grid_list = np.vstack([lat_tomatch.T, lon_tomatch.T]).T - - distances, index_array_1d = ball.query(coarse_grid_list, k=1) - - # Convert back to kilometers. - distances_km = 6371 * distances - # Sanity check. - print("Maximal distance to matched point: {} km.".format(np.max(distances_km))) - - return index_array_1d.squeeze() diff --git a/examples/first_example.py b/examples/first_example.py deleted file mode 100644 index edbff35..0000000 --- a/examples/first_example.py +++ /dev/null @@ -1,44 +0,0 @@ -import dask.array as da -from dask.distributed import Client -import diesel as ds -import matplotlib.pyplot as plt - - -def main(): - # Instantiate a local cluster, to mimick distributed computations, but on a single machine. - cluster = ds.cluster.LocalCluster() - client = Client(cluster) - - # Build a square grid with 30^2 elements. - grid = ds.gridding.SquareGrid(n_pts_1d=30) - grid_pts = grid.grid_pts - - # Construct (lazy) covariance matrix. - lazy_covariance_matrix = ds.covariance.matern32(grid_pts, lambda0=0.1) - - # Compute compressed SVD. - svd_rank = 900 # Since our matrix is 900 * 900 this will be a full SVD. - u, s, v = da.linalg.svd_compressed( - lazy_covariance_matrix, k=svd_rank, compute=False) - - # Construct sampler from the svd of the covariance matrix. - sampler = ds.sampling.SvdSampler(u, s) - - # Sample 16 ensemble members. - ensembles = sampler.sample(16) # Note this is still lazy. - - # Plot one ensemble. - plt.imshow(grid.list_to_mesh(ensembles[0]), cmap='jet') - plt.show() - - # Estimate covariance using empirical covariance of the ensemble. - estimated_cov_lazy = ds.estimation.empirical_covariance(ensembles) - - # Compute distance in Frobenius norm between true covariance and estimated covariance. - dist = da.linalg.norm(lazy_covariance_matrix - estimated_cov_lazy, ord='fro') - dist = client.compute(dist).result() - print("Frobenius distance between true covariance matrix and sample covariance: {}.".format(dist)) - - -if __name__ == "__main__": - main() diff --git a/examples/plot_variogram.py b/examples/plot_variogram.py deleted file mode 100644 index da9d107..0000000 --- a/examples/plot_variogram.py +++ /dev/null @@ -1,28 +0,0 @@ -""" Plot variogram. - -""" -import numpy as np -import pandas as pd -import dask.array as da -from dask.distributed import Client - -import diesel as ds - - -def main(): - cluster = ds.LocalCluster() - client = Client(cluster) - - # Build a square grid with 30^2 elements. - grid = ds.gridding.SquareGrid(30) - grid_pts = grid.grid_pts - - # Construct (lazy) covariance matrix. - lazy_dist_matrix = ds.covariance.distance_matrix(grid_pts) - lazy_covariance_matrix = ds.covariance.matern32(grid_pts, lambda0=0.2) - - variog_bins, variog_means, variog_stds = ds.plotting.plot_variogram( - lazy_dist_matrix, lazy_covariance_matrix, 20) - -if __name__ == "__main__": - main() diff --git a/reporting/approximation_quality/matrix_norm_vs_ensemble_size.py b/reporting/approximation_quality/matrix_norm_vs_ensemble_size.py deleted file mode 100644 index 942a9e1..0000000 --- a/reporting/approximation_quality/matrix_norm_vs_ensemble_size.py +++ /dev/null @@ -1,94 +0,0 @@ -""" Study covariance matrix reconstruction quality as a function of the ensemble size. - -Script will plot Frobenius norm of error matrix as a function of the ensemble size. - -""" -def main(): - import numpy as np - import pandas as pd - import dask.array as da - from dask.distributed import Client - from diesel.gridding import SquareGrid - from diesel.cluster import LocalCluster - from diesel.covariance import matern32 - from diesel.sampling import SvdSampler - import diesel.estimation - - - cluster = LocalCluster() - client = Client(cluster) - - # Build a square grid with 30^2 elements. - grid = SquareGrid(30) - grid_pts = grid.grid_pts - - # Construct (lazy) covariance matrix. - lazy_covariance_matrix = matern32(grid_pts, lambda0=0.2) - - # Compute compressed SVD. - svd_rank = 900 - u, s, v = da.linalg.svd_compressed( - lazy_covariance_matrix, k=svd_rank, compute=False) - - # Construct sampler from the svd of the covariance matrix. - sampler = SvdSampler(u, s) - - - results= pd.DataFrame(columns=['Ensemble size','Repetition', 'Error (Frobenius norm)']) - - n_reps = 100 - sizes = np.arange(10, 1000, 50) - sizes = np.concatenate([sizes, [1500, 2000, 3000, 4000]]) - for ens_size in sizes: - for rep in range(n_reps): - print("repetition: {}".format(rep)) - # Sample ensemble. - ensembles = sampler.sample(ens_size) - ensembles = client.compute(ensembles).result() - - # Estimate covariance using empirical covariance of the ensemble. - estimated_cov_lazy = diesel.estimation.empirical_covariance(ensembles) - - # Compute distance in Frobenius norm between true covariance and estimated covariance. - dist = da.linalg.norm(lazy_covariance_matrix - estimated_cov_lazy, ord='fro') - error = client.compute(dist).result() - - results = results.append({'Ensemble size': ens_size, - 'repetition': rep, - 'Error (Frobenius norm)': error - }, ignore_index=True) - - # Save at the end. - results.to_pickle("error_vs_ens_size_results.pkl") - - # Plot results. - import matplotlib.pyplot as plt - import seaborn as sns - sns.set() - sns.set_style("white") - plt.rcParams["font.family"] = "Times New Roman" - plot_params = { - 'font.size': 16, 'font.style': 'oblique', - 'axes.labelsize': 'small', - 'axes.titlesize':'small', - 'legend.fontsize': 'small' - } - plt.rcParams.update(plot_params) - - fig, ax = plt.subplots(figsize=(8,6)) - fig.set_size_inches(6, 6) - - my_palette = sns.color_palette("RdBu", 6) - my_palette = my_palette[0:2] + [my_palette[-1]] - - ax = sns.lineplot('Ensemble size', 'Error (Frobenius norm)', data=results, - palette=my_palette) - - # Logarithmic scale. - ax.set_yscale("log") - - plt.savefig("error_vs_ens_size", bbox_inches="tight", pad_inches=0.1, dpi=400) - plt.show() - -if __name__ == "__main__": - main() diff --git a/reporting/approximation_quality/matrix_norm_wishart_vs_ensemble.py b/reporting/approximation_quality/matrix_norm_wishart_vs_ensemble.py deleted file mode 100644 index cfcd403..0000000 --- a/reporting/approximation_quality/matrix_norm_wishart_vs_ensemble.py +++ /dev/null @@ -1,107 +0,0 @@ -""" Compare covariance matrix reconstruction between a bayesian approach -(inverse wishart prior) and a purely empirical estimate. - -Script will plot Frobenius norm of error for both approaches. - -""" -import numpy as np -import pandas as pd -import dask.array as da -from dask.distributed import Client -import diesel as ds - - -def main(): - cluster = ds.cluster.LocalCluster() - client = Client(cluster) - - # Build a square grid with 30^2 elements. - grid = ds.gridding.SquareGrid(30) - grid_pts = grid.grid_pts - - # Construct (lazy) covariance matrix. - dim = grid_pts.shape[0] - lazy_covariance_matrix = ds.covariance.matern32(grid_pts, lambda0=0.2) - - # Compute compressed SVD. - svd_rank = 900 - u, s, v = da.linalg.svd_compressed( - lazy_covariance_matrix, k=svd_rank, compute=False) - - # Construct sampler from the svd of the covariance matrix. - sampler = ds.sampling.SvdSampler(u, s) - - # Simple scale matrix for the prior. - scale_matrix = da.eye(lazy_covariance_matrix.shape[0]) - - results= pd.DataFrame(columns=['Degrees of Freedom','Repetition', - 'Error (empirical)', 'Error (bayesian)']) - - # Replicate analysis 50 times. - n_reps = 50 - dofs = np.linspace(900, 1500, 20) - for dof in dofs: - # Create inverse Wishart prior. - # TODO: This time use a well-specified prior. - scale_factor = dof - dim - 1 # Scale so that the mean is always equal to the scale matrix. - prior = ds.estimation.InverseWishartPrior(scale_factor * lazy_covariance_matrix, dof) - - for rep in range(n_reps): - print("repetition: {}".format(rep)) - # Sample ensemble. - ensembles = sampler.sample(20) - ensembles = client.compute(ensembles).result() - - # Estimate covariance using both approaches - lazy_empirical_cov = ds.estimation.empirical_covariance(ensembles) - lazy_bayesian_cov = prior.posterior_mean(ensembles) - - # Compute distance in Frobenius norm between true covariance and estimated covariance. - dist_empirical = da.linalg.norm(lazy_covariance_matrix - lazy_empirical_cov, ord='fro') - error_empirical = client.compute(dist_empirical).result() - dist_bayesian = da.linalg.norm(lazy_covariance_matrix - lazy_bayesian_cov, ord='fro') - error_bayesian = client.compute(dist_bayesian).result() - - results = results.append({'Degrees of Freedom': dof, - 'Repetition': rep, - 'Error (empirical)': error_empirical, - 'Error (bayesian)': error_bayesian, - }, ignore_index=True) - - # Save at the end. - results.to_pickle("error_empirical_vs_bayesian_results_well_spec.pkl") - - # Plot results. - import matplotlib.pyplot as plt - import seaborn as sns - sns.set() - sns.set_style("white") - plt.rcParams["font.family"] = "Times New Roman" - plot_params = { - 'font.size': 16, 'font.style': 'oblique', - 'axes.labelsize': 'small', - 'axes.titlesize':'small', - 'legend.fontsize': 'small' - } - plt.rcParams.update(plot_params) - - fig, ax = plt.subplots(figsize=(8,6)) - fig.set_size_inches(6, 6) - - my_palette = sns.color_palette("RdBu", 6) - my_palette = my_palette[0:2] + [my_palette[-1]] - - mean_empirical_error = results['Error (empirical)'].mean() - std_empirical_error = results['Error (empirical)'].std() - - ax = sns.lineplot('Degrees of Freedom', 'Error (bayesian)', data=results, - palette=my_palette) - ax.axhline(mean_empirical_error, color='r') - ax.fill_between(results['Degrees of Freedom'], mean_empirical_error - 2*std_empirical_error, mean_empirical_error + 2*std_empirical_error, - color='r', alpha=.2) - - plt.savefig("error_empirical_vs_bayesian_well_spec", bbox_inches="tight", pad_inches=0.1, dpi=400) - plt.show() - -if __name__ == "__main__": - main() diff --git a/reporting/approximation_quality/matrix_norm_wishart_vs_ensemble_illspec.py b/reporting/approximation_quality/matrix_norm_wishart_vs_ensemble_illspec.py deleted file mode 100644 index 5d62639..0000000 --- a/reporting/approximation_quality/matrix_norm_wishart_vs_ensemble_illspec.py +++ /dev/null @@ -1,108 +0,0 @@ -""" Compare covariance matrix reconstruction between a bayesian approach -(inverse wishart prior) and a purely empirical estimate. - -This one considers an ill-specified case for the prior, where we have the wrong lenght scale. - -Script will plot Frobenius norm of error for both approaches. - -""" -import numpy as np -import pandas as pd -import dask.array as da -from dask.distributed import Client -import diesel as ds - - -def main(): - cluster = ds.cluster.LocalCluster() - client = Client(cluster) - - # Build a square grid with 30^2 elements. - grid = ds.gridding.SquareGrid(30) - grid_pts = grid.grid_pts - - # Construct (lazy) covariance matrix. - dim = grid_pts.shape[0] - lazy_covariance_matrix = ds.covariance.matern32(grid_pts, lambda0=0.2) - - # Compute compressed SVD. - svd_rank = 900 - u, s, v = da.linalg.svd_compressed( - lazy_covariance_matrix, k=svd_rank, compute=False) - - # Construct sampler from the svd of the covariance matrix. - sampler = ds.sampling.SvdSampler(u, s) - - # Ill-specified prior. - scale_matrix = ds.covariance.matern32(grid_pts, lambda0=1.0) - - results= pd.DataFrame(columns=['Degrees of Freedom','Repetition', - 'Error (empirical)', 'Error (bayesian)']) - - # Replicate analysis 50 times. - n_reps = 50 - dofs = np.linspace(900, 1500, 20) - for dof in dofs: - # Create inverse Wishart prior. - scale_factor = dof - dim - 1 # Scale so that the mean is always equal to the scale matrix. - prior = ds.estimation.InverseWishartPrior(scale_factor * scale_matrix, dof) - - for rep in range(n_reps): - print("repetition: {}".format(rep)) - # Sample ensemble. - ensembles = sampler.sample(20) - ensembles = client.compute(ensembles).result() - - # Estimate covariance using both approaches - lazy_empirical_cov = ds.estimation.empirical_covariance(ensembles) - lazy_bayesian_cov = prior.posterior_mean(ensembles) - - # Compute distance in Frobenius norm between true covariance and estimated covariance. - dist_empirical = da.linalg.norm(lazy_covariance_matrix - lazy_empirical_cov, ord='fro') - error_empirical = client.compute(dist_empirical).result() - dist_bayesian = da.linalg.norm(lazy_covariance_matrix - lazy_bayesian_cov, ord='fro') - error_bayesian = client.compute(dist_bayesian).result() - - results = results.append({'Degrees of Freedom': dof, - 'Repetition': rep, - 'Error (empirical)': error_empirical, - 'Error (bayesian)': error_bayesian, - }, ignore_index=True) - - # Save at the end. - results.to_pickle("error_empirical_vs_bayesian_results_ill_spec.pkl") - - # Plot results. - import matplotlib.pyplot as plt - import seaborn as sns - sns.set() - sns.set_style("white") - plt.rcParams["font.family"] = "Times New Roman" - plot_params = { - 'font.size': 16, 'font.style': 'oblique', - 'axes.labelsize': 'small', - 'axes.titlesize':'small', - 'legend.fontsize': 'small' - } - plt.rcParams.update(plot_params) - - fig, ax = plt.subplots(figsize=(8,6)) - fig.set_size_inches(6, 6) - - my_palette = sns.color_palette("RdBu", 6) - my_palette = my_palette[0:2] + [my_palette[-1]] - - mean_empirical_error = results['Error (empirical)'].mean() - std_empirical_error = results['Error (empirical)'].std() - - ax = sns.lineplot('Degrees of Freedom', 'Error (bayesian)', data=results, - palette=my_palette) - ax.axhline(mean_empirical_error, color='r') - ax.fill_between(results['Degrees of Freedom'], mean_empirical_error - 2*std_empirical_error, mean_empirical_error + 2*std_empirical_error, - color='r', alpha=.2) - - plt.savefig("error_empirical_vs_bayesian_ill_spec", bbox_inches="tight", pad_inches=0.1, dpi=400) - plt.show() - -if __name__ == "__main__": - main() diff --git a/reporting/approximation_quality/variogram_comparison.py b/reporting/approximation_quality/variogram_comparison.py deleted file mode 100644 index 6c18a15..0000000 --- a/reporting/approximation_quality/variogram_comparison.py +++ /dev/null @@ -1,74 +0,0 @@ -""" Compare true variogram with ensemble estimated one. - -""" -import numpy as np -import pandas as pd -import dask.array as da -from dask.distributed import Client -import diesel as ds - - -def main(): - cluster = ds.cluster.LocalCluster() - client = Client(cluster) - - # Build a square grid with 30^2 elements. - grid = ds.gridding.SquareGrid(30) - grid_pts = grid.grid_pts - - # Construct (lazy) covariance matrix. - lazy_covariance_matrix = ds.covariance.matern32(grid_pts, lambda0=0.2) - lazy_dist_matrix = ds.covariance.distance_matrix(grid_pts) - - # Compute compressed SVD. - svd_rank = 900 - u, s, v = da.linalg.svd_compressed( - lazy_covariance_matrix, k=svd_rank, compute=False) - - # Construct sampler from the svd of the covariance matrix. - sampler = ds.sampling.SvdSampler(u, s) - - # Alternative sampler. - chol_sampler = ds.sampling.CholeskySampler(lazy_covariance_matrix) - - # - - # Sample 30 ensemble members. - ens_size = 30 - ensembles = sampler.sample(ens_size) - ensembles = client.compute(ensembles).result() - - chol_ensembles = chol_sampler.sample(ens_size) - chol_ensembles = client.compute(chol_ensembles).result() - - # Estimate covariance using empirical covariance of the ensemble. - lazy_ens_cov = ds.estimation.empirical_covariance(ensembles) - chol_lazy_ens_cov = ds.estimation.empirical_covariance(chol_ensembles) - - # Compute variograms. - true_variog_bins, true_variog_means, true_variog_stds = ds.plotting.compute_variogram( - lazy_dist_matrix, lazy_covariance_matrix, 20) - ens_variog_bins, ens_variog_means, ens_variog_stds = ds.plotting.compute_variogram( - lazy_dist_matrix, lazy_ens_cov, 20) - chol_variog_bins, chol_variog_means, chol_variog_stds = ds.plotting.compute_variogram( - lazy_dist_matrix, chol_lazy_ens_cov, 20) - - import matplotlib.pyplot as plt - plt.plot(true_variog_bins, true_variog_means, c='b') - plt.plot(ens_variog_bins, ens_variog_means, c='r') - plt.plot(chol_variog_bins, chol_variog_means, c='g') - - plt.fill_between( - true_variog_bins, true_variog_means - 3*true_variog_stds, - true_variog_means + 3*true_variog_stds, color='b', alpha=.2) - plt.fill_between(ens_variog_bins, ens_variog_means - 3*ens_variog_stds, - ens_variog_means + 3*ens_variog_stds, color='r', alpha=.2) - plt.fill_between(chol_variog_bins, chol_variog_means - 3*chol_variog_stds, - chol_variog_means + 3*chol_variog_stds, color='r', alpha=.2) - - plt.savefig('variogram_comparison.png', bbox_inches="tight", pad_inches=0.1, dpi=400) - plt.show() - - -if __name__ == "__main__": - main() diff --git a/reporting/paleoclimate/cornell_Nov_8_diagnose_stations.ipynb b/reporting/paleoclimate/cornell_Nov_8_diagnose_stations.ipynb deleted file mode 100644 index 3cdfaf0..0000000 --- a/reporting/paleoclimate/cornell_Nov_8_diagnose_stations.ipynb +++ /dev/null @@ -1,636 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "4aeb4e26-e963-41c5-a273-754ab850f5b9", - "metadata": {}, - "source": [ - "# Diagnose stations.\n", - "\n", - "The twentieth_century_station script, which is supposed to be our current state of the art, fails miserably. \n", - "The prior is within 0.5 degrees of the data (RMSE), while after assimilation we are way off.\n", - "\n", - "This notebook aims at diagnosing what goes wrong." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e97aef55-38e0-4dab-8cbc-ce1a73367af5", - "metadata": {}, - "outputs": [], - "source": [ - "# Directly copied from the script, to set things up.\n", - "import os\n", - "import numpy as np\n", - "import dask\n", - "import pandas as pd\n", - "import dask.array as da\n", - "import xarray as xr\n", - "from climate.utils import load_dataset, match_vectors_indices\n", - "from climate.data_wrapper import StationDataset\n", - "\n", - "\n", - "from dask.distributed import Client, wait, progress \n", - "import diesel as ds \n", - "from diesel.scoring import compute_RE_score, compute_CRPS, compute_energy_score, compute_RMSE\n", - "from diesel.estimation import localize_covariance \n", - "from diesel.utils import build_forward_mean_per_cell\n", - "\n", - "\n", - "base_folder = \"/storage/homefs/ct19x463/Dev/Climate/Data/\"\n", - "results_folder = \"/storage/homefs/ct19x463/Dev/DIESEL/reporting/paleoclimate/results/twentieth_century/stations/\"\n", - "\n", - "# Build Cluster\n", - "cluster = ds.cluster.UbelixCluster(n_nodes=12, mem_per_node=64, cores_per_node=3,\n", - " partition=\"gpu\", qos=\"job_gpu\") \n", - "cluster.scale(18) \n", - "client = Client(cluster) " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "bb1e6861-67e5-4915-962d-f6ce49c004f2", - "metadata": {}, - "outputs": [], - "source": [ - "cluster" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "84fee6e7-aa21-4eee-9d0c-1a4dbe0d6b28", - "metadata": {}, - "outputs": [], - "source": [ - "# Add to builtins so we have one global client.\n", - "# Note that this is necessary before importing the EnsembleKalmanFilter module, so that the module is aware of the cluster.\n", - "__builtins__.CLIENT = client \n", - "\n", - "\n", - "from diesel.kalman_filtering import EnsembleKalmanFilter \n", - "from dask.diagnostics import ProgressBar\n", - "ProgressBar().register()\n", - "\n", - "TOT_ENSEMBLES_NUMBER = 30\n", - "(dataset_mean, dataset_members,\n", - " dataset_instrumental, dataset_reference,\n", - " dataset_members_zarr)= load_dataset(\n", - " base_folder, TOT_ENSEMBLES_NUMBER, ignore_members=True)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "97650df1-63b6-4717-a6cc-9134f0515235", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "stationDataset = StationDataset(base_folder)\n", - "print(\"Loading done.\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "19c95897-7c40-45b4-812a-9dc138f2efdd", - "metadata": {}, - "outputs": [], - "source": [ - "from climate.kalman_filter import EnsembleKalmanFilterScatter\n", - "helper_filter = EnsembleKalmanFilterScatter(dataset_mean, dataset_members_zarr, dataset_instrumental, client)\n", - "\n", - "my_filter = EnsembleKalmanFilter() \n", - "data_std = 0.1\n", - "\n", - "# Construct localization matrix. \n", - "lambda0 = 1500 # Localization in kilometers.\n", - "lengthscales = da.from_array([lambda0]) \n", - "kernel = ds.covariance.squared_exponential(lengthscales)\n", - " \n", - "# Build localization matrix.\n", - "mean_dummy = helper_filter.dataset_mean.get_window_vector('1961-01-16', '1961-01-16', variable='temperature') # Dummy, just to get the grid.\n", - "\n", - "grid_pts = da.vstack([mean_dummy.latitude, mean_dummy.longitude]).T\n", - "grid_pts = client.persist(grid_pts.rechunk((1800, 2)))\n", - "localization_matrix = kernel.covariance_matrix(grid_pts, grid_pts, metric='haversine') \n", - "localization_matrix = client.persist(localization_matrix)\n", - "progress(localization_matrix)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b89b5500-78c2-435d-b23d-fdf4c789c808", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ea248242-c12c-4540-9e26-cf70b871d030", - "metadata": {}, - "outputs": [], - "source": [ - "year, month = '1982', '07'\n", - "assimilation_date = '{}-{}-16'.format(year, month)\n", - "\n", - "mean_ds = helper_filter.dataset_mean.get_window_vector(assimilation_date, assimilation_date, variable='temperature')\n", - "ensemble_ds = helper_filter.dataset_members.get_window_vector(assimilation_date, assimilation_date, variable='temperature')\n", - " \n", - "mean_ds, ensemble_ds = client.persist(mean_ds), client.persist(ensemble_ds)\n", - "\n", - "# Get anomaly.\n", - "anomaly = helper_filter.dataset_mean.get_window_vector(assimilation_date, assimilation_date, variable='anomaly')\n", - "climatology = mean_ds - anomaly\n", - "\n", - "ensemble_anomaly = ensemble_ds.data - climatology.data.reshape(-1)[None, :] " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a4af0fce-12ef-4131-880c-588ae5189969", - "metadata": {}, - "outputs": [], - "source": [ - "# Load data.\n", - "data = stationDataset.get_station_data(year, month, \"16\")\n", - "data_df = pd.DataFrame(data, columns = ['temperature', 'climatology','latitude','longitude'])\n", - "data_ds = xr.Dataset.from_dataframe(data_df)\n", - "\n", - "# Rename the date variable and make latitude/longitude into coordinates.\n", - "data_ds = data_ds.set_coords(['latitude', 'longitude'])\n", - " \n", - "# data_month_ds = data_month_ds.where((data_month_ds > -100.0) & (data_month_ds < 100.0) & (da.abs(data_month_ds) > 0.0001), drop=True)\n", - "data_ds['anomaly'] = (data_ds['temperature'] - data_ds['climatology'])\n", - " \n", - "# Build cell-averaged forward.\n", - "G_mean, d_mean, d_lons, d_lats = build_forward_mean_per_cell(mean_ds, data_ds['anomaly'])\n", - "G_mean = client.persist(da.from_array(G_mean))\n", - "d_mean = client.persist(da.from_array(d_mean))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d96b2193-c237-4628-a9f2-289860c4de47", - "metadata": {}, - "outputs": [], - "source": [ - "\n", - "pred_data = G_mean @ anomaly.values\n", - "print((pred_data - d_mean).compute())" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "0d0320bf-be7f-4b10-b880-2534934d7894", - "metadata": {}, - "outputs": [], - "source": [ - "# Load HadCRUT reference\n", - "ref_ds = xr.open_dataset(os.path.join(base_folder, \"Reference/HadCRUT.5.0.1.0.analysis.anomalies.ensemble_mean.nc\"))\n", - "if month == '02':\n", - " ref_date = '{}-{}-15'.format(year, month)\n", - "else: ref_date = assimilation_date\n", - "ref = ref_ds['tas_mean'].sel(time=ref_date)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "664a0578-16bb-4cc0-836e-9481e4835414", - "metadata": {}, - "outputs": [], - "source": [ - " # Regrid to common extent.\n", - "unstacked_prior = helper_filter.dataset_mean.unstack_window_vector(anomaly.values, time=assimilation_date, variable_name='temperature')\n", - "regridded_prior = unstacked_prior.interp(latitude=ref.latitude).interp(longitude=ref.longitude)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "56c8875a-5883-436b-b491-556be2a5d69a", - "metadata": {}, - "outputs": [], - "source": [ - "# Now restack.\n", - "stacked_ref = ref.stack(stacked_dim=('latitude', 'longitude')).isel(time=0).compute()\n", - "stacked_prior = regridded_prior.stack(stacked_dim=('latitude', 'longitude')).compute()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "fd157864-e59f-4228-a073-4d40af7f7be0", - "metadata": {}, - "outputs": [], - "source": [ - "print((stacked_ref - stacked_prior).compute())" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "8980ea03-43c1-4b56-886c-7de0af6820f0", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "# Compute the forward on the restacked grid.\n", - "# Build cell-averaged forward.\n", - "G_mean, d_mean = build_forward_mean_per_cell(stacked_prior, data_ds['anomaly'])\n", - "G_mean = client.persist(da.from_array(G_mean))\n", - "d_mean = client.persist(da.from_array(d_mean))\n", - "print((G_mean @ stacked_ref.values).compute())" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "1999a24d-58b3-4a11-847a-1a91b55da576", - "metadata": {}, - "outputs": [], - "source": [ - "nan_inds = stacked_ref.isnull().compute()\n", - "vals = stacked_ref.values\n", - "vals[nan_inds] = 0.0" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "4e0650c0-ce11-4646-8cb3-9e172d1ade71", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "# Conclusion: the regridding to a coarse resolution (that of the reference) does not work well with the data (too much averaging).\n", - "print(((G_mean @ vals).compute() - d_mean).compute())" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f84e1a87-7527-4071-b59a-4c93cd8f4872", - "metadata": {}, - "outputs": [], - "source": [ - "# Try other solution: regrid to finer grid instead.\n", - "# Regrid to common extent.\n", - "unstacked_anomaly = helper_filter.dataset_mean.unstack_window_vector(anomaly.values, time=assimilation_date, variable_name='anomaly')\n", - "\n", - "regridded_ref = ref.isel(time=0).interp(latitude=unstacked_anomaly.latitude).interp(longitude=unstacked_anomaly.longitude)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b0208c63-964f-4a03-a8bd-46b0482097bf", - "metadata": {}, - "outputs": [], - "source": [ - "# See if now predict correctly.\n", - "stacked_ref = regridded_ref.stack(stacked_dim=('latitude', 'longitude')).compute()\n", - "\n", - "nan_inds = stacked_ref.isnull().compute()\n", - "vals = stacked_ref.values\n", - "vals[nan_inds] = 0.0\n", - "diffs = ((G_mean @ vals).compute() - d_mean).compute()\n", - "import seaborn as sns\n", - "sns.histplot(diffs, kde=True)\n", - "\n", - "prior_diffs = ((G_mean @ anomaly.values).compute() - d_mean).compute()\n", - "sns.histplot(prior_diffs, kde=True)" - ] - }, - { - "cell_type": "markdown", - "id": "aedabd23-7bfc-4f15-aeea-77340ad124e9", - "metadata": {}, - "source": [ - "# Try to run one round of assimilation." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "432ef8c4-25c5-4b37-8563-b82fc92c5665", - "metadata": {}, - "outputs": [], - "source": [ - "ES_prior, ES_aao_loc, ES_seq_loc = [], [], [] \n", - "RE_aao_loc, RE_seq_loc = [], [] \n", - "RMSE_prior, RMSE_aao_loc, RMSE_seq_loc = [], [], []\n", - "\n", - "dates, months, years = [], [], []\n", - "\n", - "\n", - "# Loop over years.\n", - "for year in range(1990, 1991):\n", - "## Loop over months.\n", - " for month in ['01']:\n", - " # Prepare vectors.\n", - " assimilation_date = '{}-{}-16'.format(year, month)\n", - " mean_ds = helper_filter.dataset_mean.get_window_vector(assimilation_date, assimilation_date, variable='temperature')\n", - " ensemble_ds = helper_filter.dataset_members.get_window_vector(assimilation_date, assimilation_date, variable='temperature')\n", - " \n", - " mean_ds, ensemble_ds = client.persist(mean_ds), client.persist(ensemble_ds)\n", - "\n", - " # Get anomaly.\n", - " anomaly = helper_filter.dataset_mean.get_window_vector(assimilation_date, assimilation_date, variable='anomaly')\n", - " climatology = mean_ds - anomaly\n", - "\n", - " ensemble_anomaly = ensemble_ds.data - climatology.data.reshape(-1)[None, :]\n", - " \n", - " # Load data.\n", - " data = stationDataset.get_station_data(year, month, \"16\")\n", - " data_df = pd.DataFrame(data, columns = ['temperature', 'climatology','latitude','longitude'])\n", - " data_ds = xr.Dataset.from_dataframe(data_df)\n", - "\n", - " # Rename the date variable and make latitude/longitude into coordinates.\n", - " data_ds = data_ds.set_coords(['latitude', 'longitude'])\n", - " \n", - " # data_month_ds = data_month_ds.where((data_month_ds > -100.0) & (data_month_ds < 100.0) & (da.abs(data_month_ds) > 0.0001), drop=True)\n", - " data_ds['anomaly'] = (data_ds['temperature'] - data_ds['climatology'])\n", - " \n", - " # Build cell-averaged forward.\n", - " G_mean, d_mean, d_lons, d_lats = build_forward_mean_per_cell(mean_ds, data_ds['anomaly'])\n", - " G_mean = client.persist(da.from_array(G_mean))\n", - " d_mean = client.persist(da.from_array(d_mean))\n", - " \n", - " # Estimate covariance.\n", - " raw_estimated_cov_lazy = ds.estimation.empirical_covariance(ensemble_ds.chunk((1, 1800))) \n", - " # Persist the covariance on the cluster. \n", - " raw_estimated_cov = client.persist(raw_estimated_cov_lazy) \n", - " progress(raw_estimated_cov)\n", - " \n", - " # Localize covariance.\n", - " loc_estimated_cov = localize_covariance(raw_estimated_cov, localization_matrix)\n", - " loc_estimated_cov = client.persist(loc_estimated_cov)\n", - " progress(loc_estimated_cov)\n", - " \n", - " # Assimilate all-at-once.\n", - " # -----------------------\n", - " mean_updated_aao_loc, ensemble_updated_aao_loc = my_filter.update_ensemble(\n", - " anomaly.data, ensemble_anomaly, G_mean,\n", - " d_mean, data_std, loc_estimated_cov)\n", - "\n", - " # Trigger computations and block. Otherwise will clutter the scheduler. \n", - " mean_updated_aao_loc = client.persist(mean_updated_aao_loc) \n", - " ensemble_updated_aao_loc = client.persist(ensemble_updated_aao_loc)\n", - " progress(ensemble_updated_aao_loc) # Block till end of computations. \n", - " \n", - " # Save data.\n", - " np.save(os.path.join(results_folder, \"mean_updated_aao_loc_{}.npy\".format(assimilation_date)),\n", - " mean_updated_aao_loc.compute())\n", - " np.save(os.path.join(results_folder, \"ensemble_updated_aao_loc_{}.npy\".format(assimilation_date)),\n", - " ensemble_updated_aao_loc.compute())\n", - " \n", - " # Assimilate sequential.\n", - " # ----------------------\n", - " mean_updated_seq_loc, ensemble_updated_seq_loc = my_filter.update_ensemble_sequential_nondask(\n", - " anomaly.data, ensemble_anomaly, G_mean,\n", - " d_mean, data_std, localization_matrix)\n", - " \n", - " # Save data.\n", - " np.save(os.path.join(results_folder, \"mean_updated_seq_loc_{}.npy\".format(assimilation_date)),\n", - " mean_updated_seq_loc)\n", - " np.save(os.path.join(results_folder, \"ensemble_updated_seq_loc_{}.npy\".format(assimilation_date)),\n", - " ensemble_updated_seq_loc)\n", - " \n", - " # Compute scores. \n", - " # Before computing, have to put into unstacked form.\n", - " unstacked_updated_mean_aao_loc = helper_filter.dataset_mean.unstack_window_vector(mean_updated_aao_loc.compute(), time=assimilation_date, variable_name='temperature')\n", - " unstacked_updated_mean_seq_loc = helper_filter.dataset_mean.unstack_window_vector(mean_updated_seq_loc, time=assimilation_date, variable_name='temperature')\n", - " unstacked_updated_ensemble_aao_loc = helper_filter.dataset_members.unstack_window_vector(ensemble_updated_aao_loc.compute(), time=assimilation_date, variable_name='temperature')\n", - " unstacked_updated_ensemble_seq_loc = helper_filter.dataset_members.unstack_window_vector(ensemble_updated_seq_loc, time=assimilation_date, variable_name='temperature')\n", - " unstacked_prior = helper_filter.dataset_mean.unstack_window_vector(anomaly.values, time=assimilation_date, variable_name='temperature')\n", - " unstacked_prior_ens = helper_filter.dataset_members.unstack_window_vector(ensemble_anomaly.compute(), time=assimilation_date, variable_name='temperature')\n", - "\n", - " # Load HadCRUT reference\n", - " ref_ds = xr.open_dataset(os.path.join(base_folder, \"Reference/HadCRUT.5.0.1.0.analysis.anomalies.ensemble_mean.nc\"))\n", - " if month == '02':\n", - " ref_date = '{}-{}-15'.format(year, month)\n", - " else: ref_date = assimilation_date\n", - " ref = ref_ds['tas_mean'].sel(time=ref_date)\n", - "\n", - " # Regrid to common extent.\n", - " # Note that it was found out (see cornell_Nov_8_diagnose_stations.py) that regridding to a coarser grid (that of the reference), \n", - " # for comparison, lead to poor performances. The postulated reason for the discrepancy is that a coarse grid cell would contain \n", - " # too many highly different datapoints during assimilation.\n", - " #\n", - " # Hence, we instead regrid the reference to the finer (assimilation) grid.\n", - " regridded_ref = ref.isel(time=0).interp(\n", - " latitude=unstacked_updated_mean_aao_loc.latitude).interp(\n", - " longitude=unstacked_updated_mean_aao_loc.longitude)\n", - " stacked_ref = regridded_ref.stack(stacked_dim=('latitude', 'longitude')).compute()\n", - "\n", - " \"\"\"\n", - " regridded_prior = unstacked_prior.interp(latitude=ref.latitude).interp(longitude=ref.longitude)\n", - " regridded_prior_ens = unstacked_prior_ens.interp(latitude=ref.latitude).interp(longitude=ref.longitude)\n", - " regridded_mean_updated_aao_loc = unstacked_updated_mean_aao_loc.interp(latitude=ref.latitude).interp(longitude=ref.longitude)\n", - " regridded_mean_updated_seq_loc = unstacked_updated_mean_seq_loc.interp(latitude=ref.latitude).interp(longitude=ref.longitude)\n", - " regridded_ensemble_updated_aao_loc = unstacked_updated_ensemble_aao_loc.interp(latitude=ref.latitude).interp(longitude=ref.longitude)\n", - " regridded_ensemble_updated_seq_loc = unstacked_updated_ensemble_seq_loc.interp(latitude=ref.latitude).interp(longitude=ref.longitude)\n", - "\n", - " # Now restack.\n", - " stacked_ref = ref.stack(stacked_dim=('latitude', 'longitude')).isel(time=0).compute()\n", - " stacked_prior = regridded_prior.stack(stacked_dim=('latitude', 'longitude')).compute()\n", - " stacked_prior_ens = regridded_prior_ens.stack(stacked_dim=('latitude', 'longitude')).compute()\n", - " stacked_mean_updated_aao_loc = regridded_mean_updated_aao_loc.stack(stacked_dim=('latitude', 'longitude')).compute()\n", - " stacked_mean_updated_seq_loc = regridded_mean_updated_seq_loc.stack(stacked_dim=('latitude', 'longitude')).compute()\n", - " stacked_ensemble_updated_aao_loc = regridded_ensemble_updated_aao_loc.stack(stacked_dim=('latitude', 'longitude')).compute()\n", - " stacked_ensemble_updated_seq_loc = regridded_ensemble_updated_seq_loc.stack(stacked_dim=('latitude', 'longitude')).compute()\n", - " \"\"\"\n", - " stacked_prior = anomaly.values\n", - " stacked_prior_ens = ensemble_anomaly.compute()\n", - " stacked_mean_updated_aao_loc = mean_updated_aao_loc.compute()\n", - " stacked_mean_updated_seq_loc = mean_updated_seq_loc\n", - " stacked_ensemble_updated_aao_loc = ensemble_updated_aao_loc.compute()\n", - " stacked_ensemble_updated_seq_loc = ensemble_updated_seq_loc" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "be6894ae-355a-4f40-b1c7-0ebf56855b92", - "metadata": {}, - "outputs": [], - "source": [ - " ES, _, _ = compute_energy_score(stacked_prior_ens, stacked_ref, min_lat=-70, max_lat=70)\n", - " ES_prior.append(ES) \n", - " \n", - " ES, _, _ = compute_energy_score(stacked_ensemble_updated_aao_loc, stacked_ref, min_lat=-70, max_lat=70)\n", - " ES_aao_loc.append(ES) \n", - " \n", - " ES, _, _ = compute_energy_score(stacked_ensemble_updated_seq_loc, stacked_ref, min_lat=-70, max_lat=70)\n", - " ES_seq_loc.append(ES) \n", - " \n", - " RE_score_map = compute_RE_score(stacked_prior, stacked_mean_updated_aao_loc, stacked_ref, min_lat=-70, max_lat=70)\n", - " RE = np.median(RE_score_map)\n", - " RE_aao_loc.append(RE) \n", - " \n", - " RE = np.median(compute_RE_score(stacked_prior, stacked_mean_updated_seq_loc, stacked_ref, min_lat=-70, max_lat=70))\n", - " RE_seq_loc.append(RE) \n", - "\n", - " RMSE_prior.append(compute_RMSE(stacked_prior, stacked_ref, min_lat=-70, max_lat=70))\n", - " RMSE_aao_loc.append(compute_RMSE(stacked_mean_updated_aao_loc, stacked_ref, min_lat=-70, max_lat=70))\n", - " RMSE_seq_loc.append(compute_RMSE(stacked_mean_updated_seq_loc, stacked_ref, min_lat=-70, max_lat=70))\n", - " \n", - " dates.append(assimilation_date), months.append(month), years.append(year)\n", - " \n", - " df_results = pd.DataFrame({ \n", - " 'date': dates, 'year': years, 'month': months,\n", - " 'RMSE prior': RMSE_prior, 'RMSE aao loc': RMSE_aao_loc, 'RMSE seq loc': RMSE_seq_loc,\n", - " 'ES prior': ES_prior, 'ES aao loc': ES_aao_loc, 'ES seq loc': ES_seq_loc,\n", - " 'RE aao loc': RE_aao_loc, 'RE seq loc': RE_seq_loc})" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "bbaf2922-d020-417b-afd7-cec1195d3ac3", - "metadata": {}, - "outputs": [], - "source": [ - "import torch\n", - "G = torch.from_numpy(G_mean.compute())\n", - "_, obs_ind = (G[10, :]).reshape(1, -1).nonzero(as_tuple=True)\n", - "print(_)\n", - "print(obs_ind)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "50ec1517-df81-47ad-9203-5bd1afceb4dc", - "metadata": {}, - "outputs": [], - "source": [ - "df_results" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "16fb9f18-b29e-40ae-8d94-64bcfd426a4c", - "metadata": {}, - "outputs": [], - "source": [ - "1 - df_results.iloc[3]['RMSE aao loc'] / df_results.iloc[3]['RMSE prior']" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a85ffc39-79c5-4a19-aec0-29057783e3d4", - "metadata": {}, - "outputs": [], - "source": [ - "import matplotlib.pyplot as plt\n", - "\n", - "plt.figure(figsize=(20, 15))\n", - "\n", - "plt.subplot(221)\n", - "regridded_ref.plot(vmin=-6, vmax=6)\n", - "# plt.scatter(data_ds.longitude, data_ds.latitude, c=data_ds.anomaly, cmap='viridis', s=10, alpha=0.5)\n", - "plt.scatter(d_lons, d_lats, c=d_mean, cmap='viridis', s=10, alpha=0.5, vmin=-6, vmax=6)\n", - "plt.xlim(0, 100)\n", - "plt.ylim(0, 100)\n", - "plt.title(\"Reference\")\n", - "\n", - "plt.subplot(222)\n", - "unstacked_prior.plot(vmin=-6, vmax=6)\n", - "plt.scatter(d_lons, d_lats, c=d_mean, cmap='viridis', s=10, alpha=0.5, vmin=-6, vmax=6)\n", - "plt.xlim(0, 100)\n", - "plt.ylim(0, 100)\n", - "plt.title(\"Prior\")\n", - "\n", - "\n", - "plt.subplot(223)\n", - "unstacked_updated_mean_aao_loc.plot(vmin=-6, vmax=6)\n", - "plt.scatter(d_lons, d_lats, c=d_mean, cmap='viridis', s=10, alpha=0.5, vmin=-6, vmax=6)\n", - "plt.xlim(0, 100)\n", - "plt.ylim(0, 100)\n", - "plt.title(\"Updated mean (aao)\")\n", - "\n", - "plt.subplot(224)\n", - "unstacked_updated_mean_seq_loc.plot(vmin=-6, vmax=6)\n", - "plt.scatter(d_lons, d_lats, c=d_mean, cmap='viridis', s=10, alpha=0.5, vmin=-6, vmax=6)\n", - "plt.xlim(0, 100)\n", - "plt.ylim(0, 100)\n", - "plt.title(\"Updated mean (seq)\")\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a5ac33d5-6563-4a4b-b8d3-171ae2de8392", - "metadata": {}, - "outputs": [], - "source": [ - "d_mean" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "2592517d-1aba-479e-9b48-5eb6c66ce335", - "metadata": {}, - "outputs": [], - "source": [ - "ref_ds['tas_mean'].sel(time=assimilation_date).plot()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e60680f8-bf69-4279-b960-30ef604f4b68", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "64d9e5d1-6f87-4065-b0cc-c1559ceeb689", - "metadata": {}, - "outputs": [], - "source": [ - "(stacked_ref.latitude > -75).data" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.13" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/reporting/paleoclimate/first_test_climate.py b/reporting/paleoclimate/first_test_climate.py deleted file mode 100644 index 28d7d17..0000000 --- a/reporting/paleoclimate/first_test_climate.py +++ /dev/null @@ -1,66 +0,0 @@ -""" Try to run DIESEL on climate data (Valler, Franke et al). - -""" -import numpy as np -import dask.array as da -import diesel as ds -from dask.distributed import Client, wait, progress -from climate.kalman_filter import EnsembleKalmanFilterScatter -from climate.utils import load_dataset - - -data_folder = "/storage/homefs/ct19x463/Dev/Climate/Data/" -results_folder = "/storage/homefs/ct19x463/Dev/DIESEL/reporting/paleoclimate/results/" -# data_folder = "/home/cedric/PHD/Dev/Climate/Data/" -# results_folder = "/home/cedric/PHD/Dev/DIESEL/reporting/paleoclimate/results/" - - - - cluster = ds.cluster.UbelixCluster(n_nodes=8, mem_per_node=24, cores_per_node=4, - partition="gpu", qos="job_gpu") -# cluster = ds.cluster.LocalCluster() -client = Client(cluster) - -# The loading function returns 4 datasets: the ensemble members, the ensemble -# mean, the instrumental data and the reference dataset. -TOT_ENSEMBLES_NUMBER = 30 -(dataset_mean, dataset_members, - dataset_instrumental, dataset_reference, - dataset_members_zarr)= load_dataset( - data_folder, TOT_ENSEMBLES_NUMBER, ignore_members=True) -dataset_instrumental = dataset_instrumental.chunk() -print("Loading done.") - -# Extract one window vector. -# Use a helper Kalman filter for simplicity. -helper_filter = EnsembleKalmanFilterScatter(dataset_mean, dataset_members_zarr, - dataset_instrumental, client) -time_begin, time_end = '1961-01-16', '1961-06-16' -n_months = 6 -# First get the mean vector and data vector (stacked for the window). -mean = helper_filter.dataset_mean.get_window_vector(time_begin, time_end) -ensemble = helper_filter.dataset_members.get_window_vector(time_begin, time_end) -y = helper_filter.dataset_instrumental.get_window_vector(time_begin, time_end) - -# Get rid of the Nans. -print(y.shape) -y = y[np.logical_not(np.isnan(y))] -print("After removing NaNs") -print(y.shape) - -# Get forward. -G = helper_filter.get_forward_for_window(time_begin, time_end, n_months) -G = da.from_array(G) -G = client.persist(G) -print(G.shape) - - -# Estimate covariance using empirical covariance of the ensemble. -raw_estimated_cov_lazy = ds.estimation.empirical_covariance(ensembles) - -# Persist the covariance on the cluster. -raw_estimated_cov = client.persist(raw_estimated_cov_lazy) - -data_std = np.sqrt(0.9) -mean_updated_one_go_raw, ensemble_updated_one_go_raw = my_filter.update_ensemble( - mean, ensemble, G, y, data_std, raw_estimated_cov) diff --git a/reporting/paleoclimate/plot_scores_synthetic.ipynb b/reporting/paleoclimate/plot_scores_synthetic.ipynb deleted file mode 100644 index cebc854..0000000 --- a/reporting/paleoclimate/plot_scores_synthetic.ipynb +++ /dev/null @@ -1,558 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "a9a55753", - "metadata": {}, - "source": [ - "# Plot results of the synthetic test case." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "79367852", - "metadata": {}, - "outputs": [], - "source": [ - "%load_ext autoreload\n", - "%autoreload 2\n", - "\n", - "import os\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "import dask\n", - "import pandas as pd\n", - "import dask.array as da\n", - "import xarray as xr\n", - "from climate.utils import load_dataset\n", - "\n", - "from dask.distributed import Client, LocalCluster, wait, progress \n", - "import diesel as ds \n", - "from diesel.scoring import compute_RE_score, compute_CRPS, compute_energy_score, compute_RMSE \n", - "from diesel.estimation import localize_covariance " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e304eba3", - "metadata": {}, - "outputs": [], - "source": [ - "# base_folder = \"/storage/homefs/ct19x463/Dev/Climate/Data/\"\n", - "base_folder = \"/home/cedric/PHD/Dev/Climate/Data/\"\n", - "\n", - "# results_folder = \"/storage/homefs/ct19x463/Dev/DIESEL/reporting/toy_example/results_paper/synthetic/\"\n", - "results_folder = \"/home/cedric/PHD/Dev/DIESEL/reporting/paleoclimate/results/synthetic/\"\n", - "plots_folder = \"/home/cedric/PHD/Dev/DIESEL/reporting/paleoclimate/results/plots_synthetic/\"" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6c2aeb5c", - "metadata": {}, - "outputs": [], - "source": [ - "cluster = LocalCluster()\n", - "client = Client(cluster)" - ] - }, - { - "cell_type": "markdown", - "id": "5c94cc8c", - "metadata": {}, - "source": [ - "## Define colors manually." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "5b35d357", - "metadata": {}, - "outputs": [], - "source": [ - "import matplotlib.pyplot as plt\n", - "import seaborn as sns\n", - "\n", - "sns.set()\n", - "sns.set_style(\"white\")\n", - "plt.rcParams[\"font.family\"] = \"serif\"\n", - "plot_params = {\n", - " 'font.size': 32, 'font.style': 'normal',\n", - " 'axes.labelsize': 'x-small',\n", - " 'axes.titlesize':'x-small',\n", - " 'legend.fontsize': 'x-small',\n", - " 'xtick.labelsize': 'x-small',\n", - " 'ytick.labelsize': 'x-small'\n", - " }\n", - "plt.rcParams.update(plot_params)\n", - "\n", - "my_palette = sns.color_palette(\"twilight_shifted_r\", 10)\n", - "my_palette_r = reversed(sns.color_palette(\"twilight_shifted_r\", 3))\n", - "my_palette[6]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "fd6a91c3-4b48-485a-a1da-a0935b7d8528", - "metadata": {}, - "outputs": [], - "source": [ - "color_aao, color_seq, color_prior = (0.4981443546207415, 0.13569380302451714, 0.314135190862664), (0.7387914002459927, 0.4205367299231533, 0.34913260148542435), (0.8398783988412087, 0.7603990719977968, 0.7136714781112923)\n", - "color_true_cov = my_palette[6]" - ] - }, - { - "cell_type": "markdown", - "id": "139b71a2-1f79-4923-9957-f7144f34539e", - "metadata": {}, - "source": [ - "# Synthetic." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "16f5e135", - "metadata": {}, - "outputs": [], - "source": [ - "df_scores_synth = pd.read_pickle(os.path.join(results_folder, \"scores.pkl\"))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "862cac7b", - "metadata": {}, - "outputs": [], - "source": [ - "df_melted_synth = pd.melt(df_scores_synth, value_vars=df_scores_synth.columns, var_name=\"metric\", value_name=\"loss\")\n", - "df_melted_synth['experiment'] = df_melted_synth['metric']\n", - "\n", - "df_melted_synth.loc[df_melted_synth['experiment'].str.contains(\"prior\"), 'experiment'] = 'Prior'\n", - "df_melted_synth.loc[df_melted_synth['experiment'].str.contains(\"aao loc\"), 'experiment'] = 'All-at-once'\n", - "df_melted_synth.loc[df_melted_synth['experiment'].str.contains(\"seq loc\"), 'experiment'] = 'Sequential'\n", - "df_melted_synth.loc[df_melted_synth['experiment'].str.contains(\"truecov\"), 'experiment'] = 'True covariance'\n", - "\n", - "df_melted_synth.loc[df_melted_synth['metric'].str.contains(\"RMSE\"), 'metric'] = 'RMSE'\n", - "df_melted_synth.loc[df_melted_synth['metric'].str.contains(\"ES\"), 'metric'] = 'ES'\n", - "df_melted_synth.loc[df_melted_synth['metric'].str.contains(\"RE\"), 'metric'] = 'RE'" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6e1bd699", - "metadata": {}, - "outputs": [], - "source": [ - "plt.figure(figsize=(10,6))\n", - "ax = sns.boxplot(x=\"experiment\", y=\"loss\",\n", - " data=df_melted_synth.loc[\n", - " (df_melted_synth['metric'] == 'RMSE') & (df_melted_synth['experiment'] != 'Prior')\n", - " ], linewidth=2.5,\n", - " order=['Sequential', 'All-at-once', 'True covariance'],\n", - " palette=[color_seq, color_aao, color_true_cov])\n", - "ax.set_ylabel('RMSE')\n", - "ax.set_xlabel('')\n", - "# ax.set_ylim([0, 1.5])\n", - "plt.savefig(os.path.join(plots_folder, 'scores_RMSE'), bbox_inches='tight', dpi=200)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "0b2598d3", - "metadata": {}, - "outputs": [], - "source": [ - "plt.figure(figsize=(10,6))\n", - "ax = sns.boxplot(x=\"experiment\", y=\"loss\",\n", - " data=df_melted_synth.loc[\n", - " (df_melted_synth['metric'] == 'ES') & (df_melted_synth['experiment'] != 'Prior')\n", - " ], linewidth=2.5,\n", - " order=['Sequential', 'All-at-once', 'True covariance'],\n", - " palette=[color_seq, color_aao, color_true_cov])\n", - "ax.set_ylabel('Energy Score')\n", - "ax.set_xlabel('')\n", - "# ax.set_ylim([100, 200])\n", - "plt.savefig(os.path.join(plots_folder, 'scores_ES'), bbox_inches='tight', dpi=200)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f856a660", - "metadata": {}, - "outputs": [], - "source": [ - "plt.figure(figsize=(10,6))\n", - "# df_melted_mod_n400 = pd.concat([pd.DataFrame({'metric': ['RE'], 'loss': [np.nan], 'experiment': ['Prior']}), df_melted_n400], axis=0)\n", - "\n", - "ax = sns.boxplot(x=\"experiment\", y=\"loss\",\n", - " data=df_melted_synth.loc[\n", - " (df_melted_synth['metric'] == 'RE') & (df_melted_synth['experiment'] != 'Prior')\n", - " ], linewidth=2.5,\n", - " order=['Sequential', 'All-at-once', 'True covariance'],\n", - " palette=[color_seq, color_aao, color_true_cov])\n", - "ax.set_ylabel('RMSE Skill Score')\n", - "ax.set_xlabel('')\n", - "plt.savefig(os.path.join(plots_folder, 'scores_RE'), bbox_inches='tight', dpi=200)" - ] - }, - { - "cell_type": "markdown", - "id": "4a9d9927", - "metadata": {}, - "source": [ - "## Ordering" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "61661896", - "metadata": {}, - "outputs": [], - "source": [ - "df_scores_order = pd.read_pickle(os.path.join(results_folder, \"synthetic_ordering/scores.pkl\")) \n", - "\n", - "df_melted_order = pd.melt(df_scores_order, value_vars=df_scores_order.columns, var_name=\"metric\", value_name=\"loss\",)\n", - "df_melted_order['experiment'] = df_melted_order['metric']\n", - "\n", - "df_melted_order.loc[df_melted_order['experiment'].str.contains(\"prior\"), 'experiment'] = 'Prior'\n", - "df_melted_order.loc[df_melted_order['experiment'].str.contains(\"aao loc\"), 'experiment'] = 'All-at-once'\n", - "df_melted_order.loc[df_melted_order['experiment'].str.contains(\"seq loc\"), 'experiment'] = 'Sequential'\n", - "df_melted_order.loc[df_melted_order['experiment'].str.contains(\"truecov\"), 'experiment'] = 'True covariance'\n", - "\n", - "df_melted_order.loc[df_melted_order['metric'].str.contains(\"RMSE\"), 'metric'] = 'RMSE'\n", - "df_melted_order.loc[df_melted_order['metric'].str.contains(\"ES\"), 'metric'] = 'ES'\n", - "df_melted_order.loc[df_melted_order['metric'].str.contains(\"RE\"), 'metric'] = 'RE'" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3d19db2c", - "metadata": {}, - "outputs": [], - "source": [ - "plt.figure(figsize=(10,6))\n", - "ax = sns.boxplot(x=\"experiment\", y=\"loss\",\n", - " data=df_melted_order.loc[\n", - " (df_melted_order['metric'] == 'ES') & (df_melted_order['experiment'] != 'Prior')\n", - " ], linewidth=2.5,\n", - " palette=[color_seq, color_aao])\n", - "ax.set_ylabel('Energy Score')\n", - "ax.set_xlabel('')\n", - "# ax.set_ylim([100, 210])\n", - "plt.savefig(os.path.join(plots_folder, 'scores_ES_ordering'), bbox_inches='tight', dpi=200)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "2acd1394", - "metadata": {}, - "outputs": [], - "source": [ - "plt.figure(figsize=(10,6))\n", - "ax = sns.boxplot(x=\"experiment\", y=\"loss\",\n", - " data=df_melted_order.loc[\n", - " (df_melted_order['metric'] == 'RMSE') & (df_melted_order['experiment'] != 'Prior')\n", - " ], linewidth=2.5,\n", - " palette=[color_seq, color_aao])\n", - "ax.set_ylabel('RMSE')\n", - "ax.set_xlabel('')\n", - "# ax.set_ylim([1, 4])\n", - "plt.savefig(os.path.join(plots_folder, 'scores_RMSE_ordering'), bbox_inches='tight', dpi=200)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c6a90a64", - "metadata": {}, - "outputs": [], - "source": [ - "plt.figure(figsize=(10,6))\n", - "# df_melted_mod_n400 = pd.concat([pd.DataFrame({'metric': ['RE'], 'loss': [np.nan], 'experiment': ['Prior']}), df_melted_n400], axis=0)\n", - "\n", - "ax = sns.boxplot(x=\"experiment\", y=\"loss\",\n", - " data=df_melted_order[df_melted_order['metric'] == 'RE'], linewidth=2.5,\n", - " palette=[color_seq, color_aao])\n", - "ax.set_ylabel('RMSE Skill Score')\n", - "ax.set_xlabel('')\n", - "plt.savefig(os.path.join(plots_folder, 'scores_RE_ordering'), bbox_inches='tight', dpi=200)" - ] - }, - { - "cell_type": "markdown", - "id": "60afae38-ce92-46a9-8270-8ab0a6421cdd", - "metadata": {}, - "source": [ - "## Plot evolution." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "225a4973-fb33-4e65-a4b9-6b4ef5d4e18b", - "metadata": {}, - "outputs": [], - "source": [ - "results_evolution_folder = \"/home/cedric/PHD/Dev/DIESEL/reporting/toy_example/results_paper/synthetic_different_noise/\"\n", - "df_evolution = pd.read_pickle(os.path.join(results_evolution_folder, \"scores_merged.pkl\"))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "295ebae2-2fbe-4d30-ad5a-2a3d58aa9d03", - "metadata": {}, - "outputs": [], - "source": [ - "df_evolution['data std'] = 100 * df_evolution['data std']\n", - "df_evolution_melted = pd.melt(df_evolution, value_vars=df_evolution.columns, var_name=\"metric\", value_name=\"loss\", id_vars=['data std', 'repetition'])\n", - "df_evolution_melted['experiment'] = df_evolution_melted['metric']\n", - "\n", - "df_evolution_melted.loc[df_evolution_melted['experiment'].str.contains(\"prior\"), 'experiment'] = 'Prior'\n", - "df_evolution_melted.loc[df_evolution_melted['experiment'].str.contains(\"aao loc\"), 'experiment'] = 'All-at-once'\n", - "df_evolution_melted.loc[df_evolution_melted['experiment'].str.contains(\"seq loc\"), 'experiment'] = 'Sequential'\n", - "df_evolution_melted.loc[df_evolution_melted['experiment'].str.contains(\"truecov\"), 'experiment'] = 'True covariance'\n", - "\n", - "df_evolution_melted.loc[df_evolution_melted['metric'].str.contains(\"RMSE\"), 'metric'] = 'RMSE'\n", - "df_evolution_melted.loc[df_evolution_melted['metric'].str.contains(\"ES\"), 'metric'] = 'ES'\n", - "df_evolution_melted.loc[df_evolution_melted['metric'].str.contains(\"RE\"), 'metric'] = 'RE'" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c61b0511-d2b8-4206-b22b-a0daa821a0d4", - "metadata": {}, - "outputs": [], - "source": [ - "plt.figure(figsize=(10,6))\n", - "ax = sns.lineplot(data=df_evolution_melted.loc[(df_evolution_melted['metric'] == 'RMSE') & (df_evolution_melted['experiment'] != 'True covariance')\n", - " & (df_evolution_melted['experiment'] != 'Prior')], x=\"data std\", y=\"loss\", hue='experiment',\n", - " palette=[color_aao, color_seq])\n", - "ax.set_ylabel('RMSE')\n", - "ax.set_xlim([0, 50])\n", - "ax.set_xlabel('Noise std [% of model std]')\n", - "leg = plt.legend(fontsize='small', title_fontsize='10')\n", - "\n", - "# change the line width for the legend\n", - "for line in leg.get_lines():\n", - " line.set_linewidth(6.0)\n", - " \n", - "plt.savefig(os.path.join(plots_folder, 'scores_RMSE_evolution'), bbox_inches='tight', dpi=200)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "4d03d183-0fc3-4192-99c7-9843c9233bb1", - "metadata": {}, - "outputs": [], - "source": [ - "plt.figure(figsize=(10,6))\n", - "ax = sns.lineplot(data=df_evolution_melted.loc[(df_evolution_melted['metric'] == 'ES') & (df_evolution_melted['experiment'] != 'True covariance')\n", - " & (df_evolution_melted['experiment'] != 'Prior')], x=\"data std\", y=\"loss\", hue='experiment',\n", - " palette=[color_aao, color_seq])\n", - "ax.set_ylabel('Energy Score')\n", - "ax.set_xlim([0, 50])\n", - "ax.set_xlabel('Noise std [% of model std]')\n", - "leg = plt.legend(fontsize='small', title_fontsize='10')\n", - "\n", - "# change the line width for the legend\n", - "for line in leg.get_lines():\n", - " line.set_linewidth(6.0)\n", - " \n", - "plt.savefig(os.path.join(plots_folder, 'scores_ES_evolution'), bbox_inches='tight', dpi=200)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "09c7bd9a-0ef3-46b2-ba24-290def7803a8", - "metadata": {}, - "outputs": [], - "source": [ - "plt.figure(figsize=(10,6))\n", - "ax = sns.lineplot(data=df_evolution_melted.loc[(df_evolution_melted['metric'] == 'RE') & (df_evolution_melted['experiment'] != 'True covariance')], x=\"data std\", y=\"loss\",\n", - " hue='experiment',\n", - " palette=[color_aao, color_seq])\n", - "ax.set_ylabel('RE Skill Score')\n", - "ax.set_xlim([0, 50])\n", - "ax.set_xlabel('Noise std [% of model std]')\n", - "leg = plt.legend(fontsize='small', title_fontsize='10')\n", - "\n", - "# change the line width for the legend\n", - "for line in leg.get_lines():\n", - " line.set_linewidth(6.0)\n", - " \n", - "plt.savefig(os.path.join(plots_folder, 'scores_RE_evolution'), bbox_inches='tight', dpi=200)" - ] - }, - { - "cell_type": "markdown", - "id": "9365c327-48da-453d-bc31-1da7a1555368", - "metadata": {}, - "source": [ - "## Plot spatial situation." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "babf29ee-76c4-4e8f-9ea2-9c67956d96d4", - "metadata": {}, - "outputs": [], - "source": [ - "results_folder_spat = \"/home/cedric/PHD/Dev/DIESEL/reporting/toy_example/results_paper/synthetic/\"\n", - "\n", - "rep = 0\n", - "\n", - "ground_truth = np.load(os.path.join(results_folder_spat, \"ground_truth_{}.npy\".format(rep)))\n", - "data_inds = np.load(os.path.join(results_folder_spat, \"data_inds_{}.npy\".format(rep)))\n", - "\n", - "mean = np.load(os.path.join(results_folder_spat, \"mean_{}.npy\".format(rep)))\n", - "ensemble = np.load(os.path.join(results_folder_spat, \"ensemble_{}.npy\".format(rep)))\n", - "\n", - "mean_updated_aao_loc = np.load(os.path.join(results_folder_spat, \"mean_updated_aao_loc_{}.npy\".format(rep))).reshape(-1)\n", - "ensemble_updated_aao_loc = np.load(os.path.join(results_folder_spat, \"ensemble_updated_aao_loc_{}.npy\".format(rep)))\n", - "\n", - "mean_updated_aao_truecov = np.load(os.path.join(results_folder_spat, \"mean_updated_aao_truecov_{}.npy\".format(rep))).reshape(-1)\n", - "ensemble_updated_aao_truecov = np.load(os.path.join(results_folder_spat, \"ensemble_updated_aao_truecov_{}.npy\".format(rep)))\n", - "\n", - "mean_updated_seq_loc = np.load(os.path.join(results_folder_spat, \"mean_updated_seq_loc_{}.npy\".format(rep))).reshape(-1)\n", - "ensemble_updated_seq_loc = np.load(os.path.join(results_folder_spat, \"ensemble_updated_seq_loc_{}.npy\".format(rep)))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "244ab785-5289-4051-9e7c-b7ef3bb09877", - "metadata": {}, - "outputs": [], - "source": [ - "# Build a square grid with 80^2 elements.\n", - "grid = ds.gridding.SquareGrid(n_pts_1d=80)\n", - "grid_pts = grid.grid_pts" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "73834ccf-4c8b-4d2e-9dc0-60d6cc3653c9", - "metadata": {}, - "outputs": [], - "source": [ - "cm = 1/2.54 # centimeters in inches\n", - "\n", - "spat_cmap = 'RdBu_r'\n", - "# spat_cmap = 'rocket'\n", - "\n", - "# Prior\n", - "fig, ax = plt.subplots(1, 1, figsize=(10*cm, 10*cm))\n", - "grid.plot_vals(ground_truth, ax, vmin=-3, vmax=3, cmap=spat_cmap, colorbar=True, fig=fig)\n", - "plt.savefig(os.path.join(plots_folder, 'ground_truth_synthetic.png'), bbox_inches='tight', dpi=200)\n", - "\n", - "fig, ax = plt.subplots(1, 1, figsize=(10*cm, 10*cm))\n", - "grid.plot_vals(mean, ax, vmin=-3, vmax=3, cmap=spat_cmap, colorbar=False, fig=fig)\n", - "\n", - "# Add location of data point.\n", - "data_coords = grid.grid_pts[data_inds, :].compute()\n", - "ax.scatter(data_coords[:, 0], data_coords[:, 1], s=1, color='black')\n", - "\n", - "plt.savefig(os.path.join(plots_folder, 'mean_synthetic.png'), bbox_inches='tight', dpi=200)\n", - "print(compute_RMSE(mean, ground_truth))\n", - "\n", - "fig, ax = plt.subplots(1, 1, figsize=(10*cm, 10*cm))\n", - "grid.plot_vals(ensemble[0, :], ax, vmin=-3, vmax=3, cmap=spat_cmap, colorbar=False, fig=fig)\n", - "plt.savefig(os.path.join(plots_folder, 'ensemble_0_synthetic.png'), bbox_inches='tight', dpi=200)\n", - "print(compute_RMSE(ensemble[0, :], ground_truth))\n", - "\n", - "fig, ax = plt.subplots(1, 1, figsize=(10*cm, 10*cm))\n", - "grid.plot_vals(ensemble[1, :], ax, vmin=-3, vmax=3, cmap=spat_cmap, colorbar=False, fig=fig)\n", - "plt.savefig(os.path.join(plots_folder, 'ensemble_1_synthetic.png'), bbox_inches='tight', dpi=200)\n", - "print(compute_RMSE(ensemble[1, :], ground_truth))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "0dc11d0e-88d7-4fba-b0cd-8cb02ea6dc5c", - "metadata": {}, - "outputs": [], - "source": [ - "# All at once.\n", - "fig, ax = plt.subplots(1, 1, figsize=(10*cm, 10*cm))\n", - "grid.plot_vals(mean_updated_aao_loc, ax, vmin=-3, vmax=3, cmap='RdBu_r', colorbar=False, fig=fig)\n", - "plt.savefig(os.path.join(plots_folder, 'mean_updated_aao_loc_synthetic.png'), bbox_inches='tight', dpi=200)\n", - "print(compute_RMSE(mean_updated_aao_loc, ground_truth))\n", - "\n", - "fig, ax = plt.subplots(1, 1, figsize=(10*cm, 10*cm))\n", - "grid.plot_vals(ensemble_updated_aao_loc[0, :], ax, vmin=-3, vmax=3, cmap='RdBu_r', colorbar=False, fig=fig)\n", - "plt.savefig(os.path.join(plots_folder, 'ensemble_updated_aao_loc_0_synthetic.png'), bbox_inches='tight', dpi=200)\n", - "print(compute_RMSE(ensemble_updated_aao_loc[0, :], ground_truth))\n", - "\n", - "fig, ax = plt.subplots(1, 1, figsize=(10*cm, 10*cm))\n", - "grid.plot_vals(ensemble_updated_aao_loc[1, :], ax, vmin=-3, vmax=3, cmap='RdBu_r', colorbar=False, fig=fig)\n", - "plt.savefig(os.path.join(plots_folder, 'ensemble_updated_aao_loc_1_synthetic.png'), bbox_inches='tight', dpi=200)\n", - "print(compute_RMSE(ensemble_updated_aao_loc[1, :], ground_truth))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d3c5cce3-7389-43d3-9019-73acf5800493", - "metadata": {}, - "outputs": [], - "source": [ - "# Sequential.\n", - "fig, ax = plt.subplots(1, 1, figsize=(10*cm, 10*cm))\n", - "grid.plot_vals(mean_updated_seq_loc, ax, vmin=-3, vmax=3, cmap='RdBu_r', colorbar=False, fig=fig)\n", - "plt.savefig(os.path.join(plots_folder, 'mean_updated_seq_loc_synthetic.png'), bbox_inches='tight', dpi=200)\n", - "print(compute_RMSE(mean_updated_seq_loc, ground_truth))\n", - "\n", - "fig, ax = plt.subplots(1, 1, figsize=(10*cm, 10*cm))\n", - "grid.plot_vals(ensemble_updated_seq_loc[0, :], ax, vmin=-3, vmax=3, cmap='RdBu_r', colorbar=False, fig=fig)\n", - "plt.savefig(os.path.join(plots_folder, 'ensemble_updated_seq_loc_0_synthetic.png'), bbox_inches='tight', dpi=200)\n", - "print(compute_RMSE(ensemble_updated_seq_loc[0, :], ground_truth))\n", - "\n", - "fig, ax = plt.subplots(1, 1, figsize=(10*cm, 10*cm))\n", - "grid.plot_vals(ensemble_updated_seq_loc[1, :], ax, vmin=-3, vmax=3, cmap='RdBu_r', colorbar=False, fig=fig)\n", - "plt.savefig(os.path.join(plots_folder, 'ensemble_updated_seq_loc_1_synthetic.png'), bbox_inches='tight', dpi=200)\n", - "print(compute_RMSE(ensemble_updated_seq_loc[1, :], ground_truth))" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.11" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/reporting/paleoclimate/twentieth_century.ipynb b/reporting/paleoclimate/twentieth_century.ipynb deleted file mode 100644 index 416011b..0000000 --- a/reporting/paleoclimate/twentieth_century.ipynb +++ /dev/null @@ -1,976 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "f5d63573-85f8-425e-b325-feecb96de037", - "metadata": {}, - "source": [ - "# Assimilate GLSD data with DIESEL for 20th century.\n", - "\n", - "This notebook runs assimilation of GLSD data using the DIESEL version of the Ensemble Kalman filter. It compares sequential and all-at-once assimilation on the whole 20th century." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "aa5b5043-2a78-4111-859b-e59950f8947c", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/storage/homefs/ct19x463/.conda/envs/climate/lib/python3.8/site-packages/dask_jobqueue/core.py:20: FutureWarning: tmpfile is deprecated and will be removed in a future release. Please use dask.utils.tmpfile instead.\n", - " from distributed.utils import tmpfile\n" - ] - } - ], - "source": [ - "%load_ext autoreload\n", - "%autoreload 2\n", - "\n", - "import os\n", - "import numpy as np\n", - "import dask\n", - "import pandas as pd\n", - "import dask.array as da\n", - "import xarray as xr\n", - "from climate.utils import load_dataset, match_vectors_indices\n", - "\n", - "\n", - "from dask.distributed import Client, wait, progress \n", - "import diesel as ds \n", - "from diesel.scoring import compute_RE_score, compute_CRPS, compute_energy_score \n", - "from diesel.estimation import localize_covariance " - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "d6eb785c-8692-41cf-94a4-705d89e4e34b", - "metadata": {}, - "outputs": [], - "source": [ - "base_folder = \"/storage/homefs/ct19x463/Dev/Climate/Data/\"\n", - "results_folder = \"/storage/homefs/ct19x463/Dev/DIESEL/reporting/paleoclimate/results/twentieth_century/\"" - ] - }, - { - "cell_type": "markdown", - "id": "ad792b7b-ea36-4515-a2ff-e427f3de441b", - "metadata": {}, - "source": [ - "## Build Cluster" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "a4abf061-64dd-495c-ba16-78a39ae1d2ab", - "metadata": {}, - "outputs": [], - "source": [ - "cluster = ds.cluster.UbelixCluster(n_nodes=12, mem_per_node=64, cores_per_node=3,\n", - " partition=\"gpu\", qos=\"job_gpu\") \n", - "cluster.scale(18) \n", - "client = Client(cluster) \n", - " \n", - "# Add to builtins so we have one global client.\n", - "# Note that this is necessary before importing the EnsembleKalmanFilter module, so that the module is aware of the cluster.\n", - "__builtins__.CLIENT = client " - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "51f22768-5a73-4a98-84c7-b2e108260860", - "metadata": {}, - "outputs": [], - "source": [ - "from diesel.kalman_filtering import EnsembleKalmanFilter \n", - "from dask.diagnostics import ProgressBar\n", - "ProgressBar().register()" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "f10014e1-c7bd-4096-a316-847918c128a2", - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "295fc0ebca6a4a00944e4b55c7be7d2b", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Tab(children=(HTML(value='
-100.0) & (data_month_ds < 100.0) & (da.abs(data_month_ds) > 0.0001), drop=True)\n", - " data_vector = client.persist(da.from_array(data_month_ds.data))\n", - "\n", - " \n", - " # Get the model cell index corresponding to each observations.\n", - " matched_inds = match_vectors_indices(mean_ds, data_month_ds)\n", - "\n", - " # WARNING: Never try to execute bare loops in DASK, it will exceed the maximal graph depth.\n", - " G = np.zeros((data_month_ds.shape[0], mean_ds.shape[0]))\n", - " for obs_nr, model_cell_ind in enumerate(matched_inds):\n", - " G[obs_nr, model_cell_ind] = 1.0\n", - "\n", - " G = da.from_array(G)\n", - " G = client.persist(G)\n", - " \n", - " # Estimate covariance.\n", - " raw_estimated_cov_lazy = ds.estimation.empirical_covariance(ensemble_ds.chunk((1, 1800))) \n", - " \n", - " # Persist the covariance on the cluster. \n", - " raw_estimated_cov = client.persist(raw_estimated_cov_lazy) \n", - " progress(raw_estimated_cov)\n", - " \n", - " # Construct (lazy) covariance matrix. \n", - " lambda0 = 1500 # Localization in kilometers.\n", - " lengthscales = da.from_array([lambda0]) \n", - " kernel = ds.covariance.squared_exponential(lengthscales)\n", - " \n", - " # Build localization matrix.\n", - " grid_pts = da.vstack([mean_ds.latitude, mean_ds.longitude]).T\n", - " grid_pts = client.persist(grid_pts.rechunk((1800, 2)))\n", - " localization_matrix = kernel.covariance_matrix(grid_pts, grid_pts, metric='haversine') \n", - " localization_matrix = client.persist(localization_matrix)\n", - " progress(localization_matrix)\n", - " \n", - " # Localize covariance.\n", - " loc_estimated_cov = localize_covariance(raw_estimated_cov, localization_matrix)\n", - " loc_estimated_cov = client.persist(loc_estimated_cov)\n", - " progress(loc_estimated_cov)\n", - " \n", - " # Assimilate all data.\n", - " mean_updated_aao, ensemble_updated_aao = my_filter.update_ensemble(\n", - " mean_ds.data, ensemble_ds.data, G,\n", - " data_vector, data_std, loc_estimated_cov)\n", - "\n", - " # Trigger computations and block. Otherwise will clutter the scheduler. \n", - " mean_updated_aao = client.persist(mean_updated_aao) \n", - " ensemble_updated_aao = client.persist(ensemble_updated_aao)\n", - " progress(ensemble_updated_aao) # Block till end of computations. \n", - " \n", - " # Save data.\n", - " np.save(os.path.join(results_folder, \"mean_updated_aao_{}.npy\".format(date)),\n", - " mean_updated_aao.compute())\n", - " np.save(os.path.join(results_folder, \"ensemble_updated_aao_{}.npy\".format(date)),\n", - " ensemble_updated_aao.compute())" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "9dd91b52-1b06-41cf-b7fc-2ddde6994c19", - "metadata": {}, - "outputs": [], - "source": [ - "# Construct localization matrix. \n", - "lambda0 = 1500 # Localization in kilometers.\n", - "lengthscales = da.from_array([lambda0]) \n", - "kernel = ds.covariance.squared_exponential(lengthscales)\n", - " \n", - "# Build localization matrix.\n", - "mean_dummy = helper_filter.dataset_mean.get_window_vector('1961-01-16', '1961-01-16', variable='temperature') # Dummy, just to get the grid.\n", - "\n", - "grid_pts = da.vstack([mean_dummy.latitude, mean_dummy.longitude]).T\n", - "grid_pts = client.persist(grid_pts.rechunk((1800, 2)))\n", - "localization_matrix = kernel.covariance_matrix(grid_pts, grid_pts, metric='haversine') \n", - "localization_matrix = client.persist(localization_matrix)\n", - "progress(localization_matrix)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "e64c0039-ceff-4a9a-960c-10faa2b7db95", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/storage/homefs/ct19x463/.conda/envs/climate/lib/python3.8/site-packages/dask/array/blockwise.py:288: UserWarning: The da.atop function has moved to da.blockwise\n", - " warnings.warn(\"The da.atop function has moved to da.blockwise\")\n", - "/storage/homefs/ct19x463/.conda/envs/climate/lib/python3.8/site-packages/dask/array/blockwise.py:289: PerformanceWarning: Increasing number of chunks by factor of 11\n", - " return blockwise(*args, **kwargs)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Maximal distance to matched point: 113.08002097917435 km.\n", - "0\n", - "1\n", - "2\n", - "3\n", - "4\n", - "5\n", - "6\n", - "7\n", - "8\n", - "9\n", - "10\n", - "11\n", - "12\n", - "13\n", - "14\n", - "15\n", - "16\n", - "17\n", - "18\n", - "19\n", - "20\n", - "21\n", - "22\n", - "23\n", - "24\n", - "25\n", - "Maximal distance to matched point: 113.08002097917435 km.\n", - "0\n", - "1\n", - "2\n", - "3\n", - "4\n", - "5\n", - "6\n", - "7\n", - "8\n", - "9\n", - "10\n", - "11\n", - "12\n", - "13\n", - "14\n", - "15\n", - "16\n", - "17\n", - "18\n", - "19\n", - "20\n", - "21\n", - "22\n", - "23\n", - "24\n", - "25\n", - "26\n", - "Maximal distance to matched point: 113.08002097917435 km.\n", - "0\n", - "1\n", - "2\n", - "3\n", - "4\n", - "5\n", - "6\n", - "7\n", - "8\n", - "9\n", - "10\n", - "11\n", - "12\n", - "13\n", - "14\n", - "15\n", - "16\n", - "17\n", - "18\n", - "19\n", - "20\n", - "21\n", - "22\n", - "23\n", - "24\n", - "25\n", - "26\n", - "27\n", - "Maximal distance to matched point: 113.08002097917435 km.\n", - "0\n", - "1\n", - "2\n", - "3\n", - "4\n", - "5\n", - "6\n", - "7\n", - "8\n", - "9\n", - "10\n", - "11\n", - "12\n", - "13\n", - "14\n", - "15\n", - "16\n", - "17\n", - "18\n", - "19\n", - "20\n", - "21\n", - "22\n", - "23\n", - "24\n", - "25\n", - "26\n", - "27\n", - "28\n", - "Maximal distance to matched point: 113.08002097917435 km.\n", - "0\n", - "1\n", - "2\n", - "3\n", - "4\n", - "5\n", - "6\n", - "7\n", - "8\n", - "9\n", - "10\n", - "11\n", - "12\n", - "13\n", - "14\n", - "15\n", - "16\n", - "17\n", - "18\n", - "19\n", - "20\n", - "21\n", - "22\n", - "23\n", - "24\n", - "25\n", - "26\n", - "27\n", - "28\n", - "Maximal distance to matched point: 113.08002097917435 km.\n", - "0\n", - "1\n", - "2\n", - "3\n", - "4\n", - "5\n", - "6\n", - "7\n", - "8\n", - "9\n", - "10\n", - "11\n", - "12\n", - "13\n", - "14\n", - "15\n", - "16\n", - "17\n", - "18\n", - "19\n", - "20\n", - "21\n", - "22\n", - "23\n", - "24\n", - "25\n", - "26\n", - "27\n", - "28\n", - "Maximal distance to matched point: 113.08002097917435 km.\n", - "0\n", - "1\n", - "2\n", - "3\n", - "4\n", - "5\n", - "6\n", - "7\n", - "8\n", - "9\n", - "10\n", - "11\n", - "12\n", - "13\n", - "14\n", - "15\n", - "16\n", - "17\n", - "18\n", - "19\n", - "20\n", - "21\n", - "22\n", - "23\n", - "24\n", - "25\n", - "Maximal distance to matched point: 113.08002097917435 km.\n", - "0\n", - "1\n", - "2\n", - "3\n", - "4\n", - "5\n", - "6\n", - "7\n", - "8\n", - "9\n", - "10\n", - "11\n", - "12\n", - "13\n", - "14\n", - "15\n", - "16\n", - "17\n", - "18\n", - "19\n", - "20\n", - "21\n", - "22\n", - "23\n", - "24\n", - "Maximal distance to matched point: 113.08002097917435 km.\n", - "0\n", - "1\n", - "2\n", - "3\n", - "4\n", - "5\n", - "6\n", - "7\n", - "8\n", - "9\n", - "10\n", - "11\n", - "12\n", - "13\n", - "14\n", - "15\n", - "16\n", - "17\n", - "18\n", - "19\n", - "20\n", - "21\n", - "22\n", - "23\n", - "24\n", - "25\n", - "Maximal distance to matched point: 113.08002097917435 km.\n", - "0\n", - "1\n", - "2\n", - "3\n", - "4\n", - "5\n", - "6\n", - "7\n", - "8\n", - "9\n", - "10\n", - "11\n", - "12\n", - "13\n", - "14\n", - "15\n", - "16\n", - "17\n", - "18\n", - "19\n", - "20\n", - "21\n", - "22\n", - "23\n", - "24\n", - "25\n", - "26\n", - "Maximal distance to matched point: 113.08002097917435 km.\n", - "0\n", - "1\n", - "2\n", - "3\n", - "4\n", - "5\n", - "6\n", - "7\n", - "8\n", - "9\n", - "10\n", - "11\n", - "12\n", - "13\n", - "14\n", - "15\n", - "16\n", - "17\n", - "18\n", - "19\n", - "20\n", - "21\n", - "22\n", - "23\n", - "24\n", - "25\n", - "26\n", - "Maximal distance to matched point: 113.08002097917435 km.\n", - "0\n", - "1\n", - "2\n", - "3\n", - "4\n", - "5\n", - "6\n", - "7\n", - "8\n", - "9\n", - "10\n", - "11\n", - "12\n", - "13\n", - "14\n", - "15\n", - "16\n", - "17\n", - "18\n", - "19\n", - "20\n", - "21\n", - "22\n", - "23\n", - "24\n", - "25\n" - ] - } - ], - "source": [ - "# Now sequential.\n", - "for month in ['01', '02', '03', '04', '05', '06', '07', '08', '09', '10', '11', '12']:\n", - " # Prepare vectors.\n", - " assimilation_date = '{}-{}-16'.format(year, month)\n", - " mean_ds = helper_filter.dataset_mean.get_window_vector(assimilation_date, assimilation_date, variable='temperature')\n", - " ensemble_ds = helper_filter.dataset_members.get_window_vector(assimilation_date, assimilation_date, variable='temperature')\n", - " \n", - " mean_ds, ensemble_ds = client.persist(mean_ds), client.persist(ensemble_ds)\n", - " \n", - " # Load data.\n", - " data_df = pd.read_csv(os.path.join(base_folder, \"Instrumental/GLSD/yearly_csv/temperature_{}.csv\".format(year)), index_col=0)\n", - " data_ds = xr.Dataset.from_dataframe(data_df)\n", - "\n", - " # Rename the date variable and make latitude/longitude into coordinates.\n", - " data_ds = data_ds.rename({'date': 'time'})\n", - " data_ds = data_ds.set_coords(['time', 'latitude', 'longitude'])\n", - " data_ds = data_ds['temperature']\n", - " \n", - " # Prepare forward.\n", - " date= '{}-{}-01'.format(year, month)\n", - " data_month_ds = data_ds.where(data_ds.time==date, drop=True)\n", - "\n", - " # Need to clean data since dataset contains erroneous measurements, i.e. \n", - " # either extreme values (10^30) or values that are exactly zero for a given station across time.\n", - " data_month_ds = data_month_ds.where((data_month_ds > -100.0) & (data_month_ds < 100.0) & (da.abs(data_month_ds) > 0.0001), drop=True)\n", - " data_vector = client.persist(da.from_array(data_month_ds.data))\n", - "\n", - " \n", - " # Get the model cell index corresponding to each observations.\n", - " matched_inds = match_vectors_indices(mean_ds, data_month_ds)\n", - "\n", - " # WARNING: Never try to execute bare loops in DASK, it will exceed the maximal graph depth.\n", - " G = np.zeros((data_month_ds.shape[0], mean_ds.shape[0]))\n", - " for obs_nr, model_cell_ind in enumerate(matched_inds):\n", - " G[obs_nr, model_cell_ind] = 1.0\n", - "\n", - " G = da.from_array(G)\n", - " G = client.persist(G)\n", - " \n", - " # Assimilate all data.\n", - " mean_updated_seq, ensemble_updated_seq = my_filter.update_ensemble_sequential_nondask(\n", - " mean_ds.data, ensemble_ds.data, G,\n", - " data_vector, data_std, localization_matrix)\n", - " \n", - " # Save data.\n", - " np.save(os.path.join(results_folder, \"mean_updated_seq_{}.npy\".format(date)),\n", - " mean_updated_seq)\n", - " np.save(os.path.join(results_folder, \"ensemble_updated_seq_{}.npy\".format(date)),\n", - " ensemble_updated_seq)" - ] - }, - { - "cell_type": "raw", - "id": "2dff9cdf-9a40-41d8-b39d-efee6ed141b8", - "metadata": {}, - "source": [ - "year" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "3e9690de-16bf-46b6-841a-cf19ff5c020e", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "1816" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "year" - ] - }, - { - "cell_type": "markdown", - "id": "2cecc54b-439a-461c-9a7b-13cb2c419bc8", - "metadata": {}, - "source": [ - "# Run Assimilation: All-at-once (aao) vs sequential (seq)." - ] - }, - { - "cell_type": "markdown", - "id": "c296b30c-54b4-467b-b818-a9c1c9b2f8a7", - "metadata": {}, - "source": [ - "## Compare the different updates." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6e85c269-d963-417e-9908-fbc8c96017bd", - "metadata": {}, - "outputs": [], - "source": [ - "# Basic plotting functions.\n", - "%matplotlib inline \n", - "import matplotlib.pyplot as plt\n", - "plt.rcParams.update({'font.size': 22})\n", - "plt.rcParams['figure.dpi'] = 100\n", - "import cartopy.crs as ccrs\n", - "from shapely import geometry\n", - "\n", - "def plot(unstacked_data, ax, outfile=None, vmin=None, vmax=None):\n", - " # ax = plt.axes(projection=ccrs.Mollweide())\n", - " # ax.set_global()\n", - " unstacked_data.plot.contourf(levels=30, ax=ax, transform=ccrs.PlateCarree(),\n", - " vmin=vmin, vmax=vmax, cmap='RdBu_r',\n", - " add_colorbar=False, add_labels=False,\n", - " #cbar_kwargs={'ticks': [-30, -20, -10, 0, 10, 20, 30],\n", - " # 'label': 'temperature'}\n", - " extend='both',\n", - " )\n", - " # Center on Europe\n", - " ax.set_extent([-25, 30, 30, 75], crs=ccrs.PlateCarree())\n", - " ax.coastlines() \n", - " ax.set_title('')\n", - " ax.set_ylabel('')\n", - " if outfile is not None: plt.savefig(outfile, bbox_inches='tight', dpi=120)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "2a2aeab2-4a85-4ad2-a772-e384df5a2168", - "metadata": {}, - "outputs": [], - "source": [ - "cm = 1/2.54 # centimeters in inches\n", - "fig, axs = plt.subplots(6, 3, figsize=(60*cm, 50*cm),\n", - " subplot_kw={'projection': ccrs.PlateCarree()})\n", - "\n", - "for i, month in enumerate(['01', '02', '03', '04', '05', '06']):\n", - " mean_updated_aao = np.load(os.path.join(results_folder, 'mean_updated_aao_1816-{}-01.npy'.format(month)))\n", - " mean_updated_seq = np.load(os.path.join(results_folder, 'mean_updated_seq_1816-{}-01.npy'.format(month)))\n", - " \n", - " unstacked_updated_mean_aao = helper_filter.dataset_mean.unstack_window_vector(mean_updated_aao, time='1816-{}-16'.format(month), variable_name='temperature')\n", - " unstacked_updated_mean_seq = helper_filter.dataset_mean.unstack_window_vector(mean_updated_seq, time='1816-{}-16'.format(month), variable_name='temperature')\n", - " ref = dataset_reference.temperature.sel(time='1816-{}-16'.format(month))\n", - " \n", - " plot(unstacked_updated_mean_aao, axs[i, 0], vmin=-20, vmax=30)\n", - " plot(unstacked_updated_mean_seq, axs[i, 1], vmin=-20, vmax=30) \n", - " plot(ref, axs[i, 2], vmin=-20, vmax=30) " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d7b83a7c-1fb0-428a-8f0d-259f4fec3512", - "metadata": {}, - "outputs": [], - "source": [ - "unstacked_updated_mean_aao = helper_filter.dataset_mean.unstack_window_vector(mean_updated_aao.compute(), time='1816-01-16', variable_name='temperature')\n", - "plot(unstacked_updated_mean_aao, vmin=-40, vmax=40)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "398995a3-5eca-485c-bcb1-f09fb066b992", - "metadata": {}, - "outputs": [], - "source": [ - "unstacked_updated_ensemble_0_aao = helper_filter.dataset_mean.unstack_window_vector(ensemble_updated_aao[0, :].compute(), time='1961-01-16', variable_name='temperature')\n", - "plot(unstacked_updated_ensemble_0_aao, vmin=-40, vmax=40)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "74ed872d-6cca-4ef9-83d7-ff5ae8045d66", - "metadata": {}, - "outputs": [], - "source": [ - "unstacked_updated_mean_seq = helper_filter.dataset_mean.unstack_window_vector(mean_updated_seq, time='1961-01-16', variable_name='temperature')\n", - "plot(unstacked_updated_mean_seq, vmin=-40, vmax=40)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "34a9b724-3242-4b01-9cc1-a66dbc20c536", - "metadata": {}, - "outputs": [], - "source": [ - "# Plot difference.\n", - "plot(unstacked_updated_mean_aao - unstacked_updated_mean_seq, vmin=-7, vmax=7)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e8f05a39-1b0f-41c6-98a4-5394708c4abd", - "metadata": {}, - "outputs": [], - "source": [ - "# Plot original data (before updating.\n", - "unstacked_mean = helper_filter.dataset_mean.unstack_window_vector(mean_ds.values.reshape(-1), time='1961-01-16', variable_name='temperature')\n", - "plot(unstacked_mean.temperature, vmin=-40, vmax=40)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "1d6e7e6a-7c51-4367-8f1f-af2512da887b", - "metadata": {}, - "outputs": [], - "source": [ - "# Plot station data.\n", - "df = data_month_ds.to_dataframe()\n", - "# Could reset coordinates if you really wanted\n", - "# df = df.reset_index()\n", - "cm = 1/2.54 # centimeters in inches\n", - "fig = plt.figure(figsize=(40*cm, 25*cm))\n", - "ax = plt.axes(projection=ccrs.Mollweide())\n", - "ax.set_global()\n", - " \n", - "ax.coastlines() \n", - "\n", - "df.plot.scatter('longitude', 'latitude', c=data_month_ds.name, cmap='jet', ax=ax, transform=ccrs.PlateCarree())" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "107ed9b2-3b83-4c61-a331-2e1cd46f6703", - "metadata": {}, - "outputs": [], - "source": [ - "# Plot error wrt reference.\n", - "plot(unstacked_updated_mean_aao - dataset_reference.temperature.sel(time='1961-01-16'), vmin=-7, vmax=7)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "cf6d003d-aa35-4f0f-9f07-56c734ba4004", - "metadata": {}, - "outputs": [], - "source": [ - "plot(unstacked_updated_mean_seq - dataset_reference.temperature.sel(time='1961-01-16'), vmin=-7, vmax=7)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "5bf2ad55-f006-4d05-96cd-8fff375ceeee", - "metadata": {}, - "outputs": [], - "source": [ - "# Plot original error.\n", - "plot(unstacked_mean.temperature - dataset_reference.temperature.sel(time='1961-01-16'), vmin=-7, vmax=7)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "978c96e1-9007-47ce-a7af-18556b4f6893", - "metadata": {}, - "outputs": [], - "source": [ - "helper_filter.dataset_members.dataset_members.time.values" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "cd282313-2a5e-41b4-bfa3-f3c5dc318937", - "metadata": {}, - "outputs": [], - "source": [ - "(dataset_reference.temperature.sel(time='1816-12-16') - dataset_reference.temperature.sel(time='1900-06-16')).plot()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6bdfd694-65d4-4558-8e8b-db2d6bd15a24", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "1a4f31a7-8666-4206-a7ba-fb185c4c7b2d", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.13" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/reporting/paleoclimate/twentieth_century_n1200.py b/reporting/paleoclimate/twentieth_century_n1200.py deleted file mode 100644 index dba9090..0000000 --- a/reporting/paleoclimate/twentieth_century_n1200.py +++ /dev/null @@ -1,262 +0,0 @@ -#!/usr/bin/env python -# coding: utf-8 - -# # Assimilate GLSD data with DIESEL for 20th century. -# -# This notebook runs assimilation of GLSD data using the DIESEL version of the Ensemble Kalman filter. It compares sequential and all-at-once assimilation on the whole 20th century. - -# In[1]: -import os -import numpy as np -import dask -import pandas as pd -import dask.array as da -import xarray as xr -from climate.utils import load_dataset, match_vectors_indices - - -from dask.distributed import Client, wait, progress -import diesel as ds -from diesel.scoring import compute_RE_score, compute_CRPS, compute_energy_score, compute_RMSE -from diesel.estimation import localize_covariance - - -# In[2]: - - -n_data = 1200 - -base_folder = "/storage/homefs/ct19x463/Dev/Climate/Data/" -results_folder = "/storage/homefs/ct19x463/Dev/DIESEL/reporting/paleoclimate/results/twentieth_century/n{}/".format(n_data) - - -# ## Build Cluster - -# In[3]: - - -cluster = ds.cluster.UbelixCluster(n_nodes=12, mem_per_node=64, cores_per_node=3, - partition="gpu", qos="job_gpu") -cluster.scale(18) -client = Client(cluster) - -# Add to builtins so we have one global client. -# Note that this is necessary before importing the EnsembleKalmanFilter module, so that the module is aware of the cluster. -__builtins__.CLIENT = client - - -# In[4]: - - -from diesel.kalman_filtering import EnsembleKalmanFilter -from dask.diagnostics import ProgressBar -ProgressBar().register() - - -# In[5]: - - -cluster - - -# In[6]: - - -TOT_ENSEMBLES_NUMBER = 30 -(dataset_mean, dataset_members, - dataset_instrumental, dataset_reference, - dataset_members_zarr)= load_dataset( - base_folder, TOT_ENSEMBLES_NUMBER, ignore_members=True) -print("Loading done.") - - -# In[7]: - - -from climate.kalman_filter import EnsembleKalmanFilterScatter -helper_filter = EnsembleKalmanFilterScatter(dataset_mean, dataset_members_zarr, dataset_instrumental, client) - - -# In[8]: - - -my_filter = EnsembleKalmanFilter() -data_std = 0.1 - - -# ## Run Assimilation. - -# In[9]: - - -# Construct localization matrix. -lambda0 = 1500 # Localization in kilometers. -lengthscales = da.from_array([lambda0]) -kernel = ds.covariance.squared_exponential(lengthscales) - -# Build localization matrix. -mean_dummy = helper_filter.dataset_mean.get_window_vector('1961-01-16', '1961-01-16', variable='temperature') # Dummy, just to get the grid. - -grid_pts = da.vstack([mean_dummy.latitude, mean_dummy.longitude]).T -grid_pts = client.persist(grid_pts.rechunk((1800, 2))) -localization_matrix = kernel.covariance_matrix(grid_pts, grid_pts, metric='haversine') -localization_matrix = client.persist(localization_matrix) -progress(localization_matrix) - - -# In[ ]: - - -ES_prior, ES_aao_loc, ES_seq_loc = [], [], [] -RE_aao_loc, RE_seq_loc = [], [] -RMSE_prior, RMSE_aao_loc, RMSE_seq_loc = [], [], [] - -dates, months, years = [], [], [] - - -# Loop over years. -for year in range(1902, 2000): -## Loop over months. - for month in ['01', '02', '03', '04', '05', '06', '07', '08', '09', '10', '11', '12']: - # Prepare vectors. - assimilation_date = '{}-{}-16'.format(year, month) - mean_ds = helper_filter.dataset_mean.get_window_vector(assimilation_date, assimilation_date, variable='temperature') - ensemble_ds = helper_filter.dataset_members.get_window_vector(assimilation_date, assimilation_date, variable='temperature') - - mean_ds, ensemble_ds = client.persist(mean_ds), client.persist(ensemble_ds) - - """ - # Load data. - data_df = pd.read_csv(os.path.join(base_folder, "Instrumental/GLSD/yearly_csv/temperature_{}.csv".format(year)), index_col=0) - data_ds = xr.Dataset.from_dataframe(data_df) - - # Rename the date variable and make latitude/longitude into coordinates. - data_ds = data_ds.rename({'date': 'time'}) - data_ds = data_ds.set_coords(['time', 'latitude', 'longitude']) - data_ds = data_ds['temperature'] - - # Prepare forward. - date= '{}-{}-01'.format(year, month) - data_month_ds = data_ds.where(data_ds.time==date, drop=True) - - # Need to clean data since dataset contains erroneous measurements, i.e. - # either extreme values (10^30) or values that are exactly zero for a given station across time. - data_month_ds = data_month_ds.where((data_month_ds > -100.0) & (data_month_ds < 100.0) & (da.abs(data_month_ds) > 0.0001), drop=True) - data_vector = client.persist(da.from_array(data_month_ds.data)) - """ - # TODO: Here there is a change. We instead try to assimilate a randomly chosen subset of the reference as data. - date = assimilation_date - ref = dataset_reference.temperature.sel(time=assimilation_date) - stacked_ref = ref.stack( stacked_dim=('latitude', 'longitude')) - data_ref = stacked_ref.values - data_ref_lat = stacked_ref.latitude.values - data_ref_lon = stacked_ref.longitude.values - # Get rid of NaN's. - data_ref_lat = data_ref_lat[~np.isnan(data_ref)] - data_ref_lon = data_ref_lon[~np.isnan(data_ref)] - data_ref = data_ref[~np.isnan(data_ref)] - # Select a random subset. - data_inds = np.random.choice(data_ref.shape[0], n_data, replace=False) - np.save(os.path.join(results_folder, "data_inds_{}_n{}.npy".format(date, n_data)), data_inds) - data = data_ref[data_inds] - data_lat = data_ref_lat[data_inds] - data_lon = data_ref_lon[data_inds] - # Put into a dataframe. - data_df = pd.DataFrame({'temperature': data, 'latitude': data_lat, 'longitude': data_lon}) - data_ds = xr.Dataset.from_dataframe(data_df) - data_ds = xr.Dataset.from_dataframe(data_df) - data_month_ds = data_ds.set_coords(['latitude', 'longitude'])['temperature'] - data_vector = client.persist(da.from_array(data_month_ds.data)) - - - # TODO: here back to traditional. - # Get the model cell index corresponding to each observations. - matched_inds = match_vectors_indices(mean_ds, data_month_ds) - - # WARNING: Never try to execute bare loops in DASK, it will exceed the maximal graph depth. - G = np.zeros((data_month_ds.shape[0], mean_ds.shape[0])) - for obs_nr, model_cell_ind in enumerate(matched_inds): - G[obs_nr, model_cell_ind] = 1.0 - - G = da.from_array(G) - G = client.persist(G) - - # Estimate covariance. - raw_estimated_cov_lazy = ds.estimation.empirical_covariance(ensemble_ds.chunk((1, 1800))) - # Persist the covariance on the cluster. - raw_estimated_cov = client.persist(raw_estimated_cov_lazy) - progress(raw_estimated_cov) - - # Localize covariance. - loc_estimated_cov = localize_covariance(raw_estimated_cov, localization_matrix) - loc_estimated_cov = client.persist(loc_estimated_cov) - progress(loc_estimated_cov) - - # Assimilate all-at-once. - # ----------------------- - mean_updated_aao_loc, ensemble_updated_aao_loc = my_filter.update_ensemble( - mean_ds.data, ensemble_ds.data, G, - data_vector, data_std, loc_estimated_cov) - - # Trigger computations and block. Otherwise will clutter the scheduler. - mean_updated_aao_loc = client.persist(mean_updated_aao_loc) - ensemble_updated_aao_loc = client.persist(ensemble_updated_aao_loc) - progress(ensemble_updated_aao_loc) # Block till end of computations. - - # Save data. - np.save(os.path.join(results_folder, "mean_updated_aao_loc_{}_n{}.npy".format(date, n_data)), - mean_updated_aao_loc.compute()) - np.save(os.path.join(results_folder, "ensemble_updated_aao_loc_{}_n{}.npy".format(date, n_data)), - ensemble_updated_aao_loc.compute()) - - # Assimilate sequential. - # ---------------------- - mean_updated_seq_loc, ensemble_updated_seq_loc = my_filter.update_ensemble_sequential_nondask( - mean_ds.data, ensemble_ds.data, G, - data_vector, data_std, localization_matrix) - - # Save data. - np.save(os.path.join(results_folder, "mean_updated_seq_loc_{}_n{}.npy".format(date, n_data)), - mean_updated_seq_loc) - np.save(os.path.join(results_folder, "ensemble_updated_seq_loc_{}_n{}.npy".format(date, n_data)), - ensemble_updated_seq_loc) - - # Compute scores. - # Before computing, have to put into unstacked form. - unstacked_updated_mean_aao_loc = helper_filter.dataset_mean.unstack_window_vector(mean_updated_aao_loc.compute(), time=assimilation_date, variable_name='temperature') - unstacked_updated_mean_seq_loc = helper_filter.dataset_mean.unstack_window_vector(mean_updated_seq_loc, time=assimilation_date, variable_name='temperature') - # Clip to common extent, since reference does not contain the sea. - ref = dataset_reference.temperature.sel(time=assimilation_date) - unstacked_updated_mean_seq_loc = unstacked_updated_mean_aao_loc.where( - xr.ufuncs.logical_not(xr.ufuncs.isnan(ref))) - unstacked_updated_mean_seq_loc = unstacked_updated_mean_aao_loc.where( - xr.ufuncs.logical_not(xr.ufuncs.isnan(ref))) - - stacked_ref = ref.stack( stacked_dim=('latitude', 'longitude')) - ES, _, _ = compute_energy_score(ensemble_ds.compute().values, stacked_ref.data) - ES_prior.append(ES) - - ES, _, _ = compute_energy_score(ensemble_updated_aao_loc.compute(), stacked_ref.data) - ES_aao_loc.append(ES) - - ES, _, _ = compute_energy_score(ensemble_updated_seq_loc, stacked_ref.data) - ES_seq_loc.append(ES) - - RE = np.median(compute_RE_score(mean_ds.data, mean_updated_aao_loc.compute(), stacked_ref.data).compute()) - RE_aao_loc.append(RE) - - RE = np.median(compute_RE_score(mean_ds.data, mean_updated_seq_loc, stacked_ref.data).compute()) - RE_seq_loc.append(RE) - - RMSE_prior.append(compute_RMSE(mean_ds.values, stacked_ref.values)) - RMSE_aao_loc.append(compute_RMSE(mean_updated_aao_loc.compute(), stacked_ref.values)) - RMSE_seq_loc.append(compute_RMSE(mean_updated_seq_loc, stacked_ref.values)) - - dates.append(date), months.append(month), years.append(year) - - df_results = pd.DataFrame({ - 'date': dates, 'year': years, 'month': months, - 'RMSE prior': RMSE_prior, 'RMSE aao loc': RMSE_aao_loc, 'RMSE seq loc': RMSE_seq_loc, - 'ES prior': ES_prior, 'ES aao loc': ES_aao_loc, 'ES seq loc': ES_seq_loc, - 'RE aao loc': RE_aao_loc, 'RE seq loc': RE_seq_loc}) - df_results.to_pickle(os.path.join(results_folder, 'scores_n{}.pkl'.format(n_data))) diff --git a/reporting/paleoclimate/twentieth_century_station.py b/reporting/paleoclimate/twentieth_century_station.py deleted file mode 100644 index c617718..0000000 --- a/reporting/paleoclimate/twentieth_century_station.py +++ /dev/null @@ -1,237 +0,0 @@ -""" Run 20th century assimilation, but with station data from CRUTEM dataset this time. - -""" -import os -import numpy as np -import dask -import pandas as pd -import dask.array as da -import xarray as xr -from climate.utils import load_dataset, match_vectors_indices -from climate.data_wrapper import StationDataset - - -from dask.distributed import Client, wait, progress -import diesel as ds -from diesel.scoring import compute_RE_score, compute_CRPS, compute_energy_score, compute_RMSE -from diesel.estimation import localize_covariance -from diesel.utils import build_forward_mean_per_cell - - - - -base_folder = "/storage/homefs/ct19x463/Dev/Climate/Data/" -results_folder = "/storage/homefs/ct19x463/Dev/DIESEL/reporting/paleoclimate/results/twentieth_century/stations/" - - -# Build Cluster -cluster = ds.cluster.UbelixCluster(n_nodes=12, mem_per_node=64, cores_per_node=3, - partition="gpu", qos="job_gpu") -cluster.scale(18) -client = Client(cluster) - -# Add to builtins so we have one global client. -# Note that this is necessary before importing the EnsembleKalmanFilter module, so that the module is aware of the cluster. -__builtins__.CLIENT = client - - -from diesel.kalman_filtering import EnsembleKalmanFilter -from dask.diagnostics import ProgressBar -ProgressBar().register() - -TOT_ENSEMBLES_NUMBER = 30 -(dataset_mean, dataset_members, - dataset_instrumental, dataset_reference, - dataset_members_zarr)= load_dataset( - base_folder, TOT_ENSEMBLES_NUMBER, ignore_members=True) - -stationDataset = StationDataset(base_folder) -print("Loading done.") - -from climate.kalman_filter import EnsembleKalmanFilterScatter -helper_filter = EnsembleKalmanFilterScatter(dataset_mean, dataset_members_zarr, dataset_instrumental, client) - -my_filter = EnsembleKalmanFilter() -data_std = 0.1 - - -# ## Run Assimilation. - -# Construct localization matrix. -lambda0 = 1500 # Localization in kilometers. -lengthscales = da.from_array([lambda0]) -kernel = ds.covariance.squared_exponential(lengthscales) - -# Build localization matrix. -mean_dummy = helper_filter.dataset_mean.get_window_vector('1961-01-16', '1961-01-16', variable='temperature') # Dummy, just to get the grid. - -grid_pts = da.vstack([mean_dummy.latitude, mean_dummy.longitude]).T -grid_pts = client.persist(grid_pts.rechunk((1800, 2))) -localization_matrix = kernel.covariance_matrix(grid_pts, grid_pts, metric='haversine') -localization_matrix = client.persist(localization_matrix) -progress(localization_matrix) - - -# In[ ]: - - -ES_prior, ES_aao_loc, ES_seq_loc = [], [], [] -RE_aao_loc, RE_seq_loc = [], [] -RMSE_prior, RMSE_aao_loc, RMSE_seq_loc = [], [], [] - -dates, months, years = [], [], [] - - -# Loop over years. -for year in range(1993, 2000): -## Loop over months. - for month in ['01', '02', '03', '04', '05', '06', '07', '08', '09', '10', '11', '12']: - # Prepare vectors. - assimilation_date = '{}-{}-16'.format(year, month) - mean_ds = helper_filter.dataset_mean.get_window_vector(assimilation_date, assimilation_date, variable='temperature') - ensemble_ds = helper_filter.dataset_members.get_window_vector(assimilation_date, assimilation_date, variable='temperature') - - mean_ds, ensemble_ds = client.persist(mean_ds), client.persist(ensemble_ds) - - # Get anomaly. - anomaly = helper_filter.dataset_mean.get_window_vector(assimilation_date, assimilation_date, variable='anomaly') - climatology = mean_ds - anomaly - - ensemble_anomaly = ensemble_ds.data - climatology.data.reshape(-1)[None, :] - - # Load data. - data = stationDataset.get_station_data(year, month, "16") - data_df = pd.DataFrame(data, columns = ['temperature', 'climatology','latitude','longitude']) - data_ds = xr.Dataset.from_dataframe(data_df) - - # Rename the date variable and make latitude/longitude into coordinates. - data_ds = data_ds.set_coords(['latitude', 'longitude']) - - # data_month_ds = data_month_ds.where((data_month_ds > -100.0) & (data_month_ds < 100.0) & (da.abs(data_month_ds) > 0.0001), drop=True) - data_ds['anomaly'] = (data_ds['temperature'] - data_ds['climatology']) - - # Build cell-averaged forward. - G_mean, d_mean, d_lons, d_lats = build_forward_mean_per_cell(mean_ds, data_ds['anomaly']) - G_mean = client.persist(da.from_array(G_mean)) - d_mean = client.persist(da.from_array(d_mean)) - - # Estimate covariance. - raw_estimated_cov_lazy = ds.estimation.empirical_covariance(ensemble_ds.chunk((1, 1800))) - # Persist the covariance on the cluster. - raw_estimated_cov = client.persist(raw_estimated_cov_lazy) - progress(raw_estimated_cov) - - # Localize covariance. - loc_estimated_cov = localize_covariance(raw_estimated_cov, localization_matrix) - loc_estimated_cov = client.persist(loc_estimated_cov) - progress(loc_estimated_cov) - - # Assimilate all-at-once. - # ----------------------- - mean_updated_aao_loc, ensemble_updated_aao_loc = my_filter.update_ensemble( - anomaly.data, ensemble_anomaly, G_mean, - d_mean, data_std, loc_estimated_cov) - - # Trigger computations and block. Otherwise will clutter the scheduler. - mean_updated_aao_loc = client.persist(mean_updated_aao_loc) - ensemble_updated_aao_loc = client.persist(ensemble_updated_aao_loc) - progress(ensemble_updated_aao_loc) # Block till end of computations. - - # Save data. - np.save(os.path.join(results_folder, "mean_updated_aao_loc_{}.npy".format(assimilation_date)), - mean_updated_aao_loc.compute()) - np.save(os.path.join(results_folder, "ensemble_updated_aao_loc_{}.npy".format(assimilation_date)), - ensemble_updated_aao_loc.compute()) - - # Assimilate sequential. - # ---------------------- - mean_updated_seq_loc, ensemble_updated_seq_loc = my_filter.update_ensemble_sequential_nondask( - anomaly.data, ensemble_anomaly, G_mean, - d_mean, data_std, localization_matrix) - - # Save data. - np.save(os.path.join(results_folder, "mean_updated_seq_loc_{}.npy".format(assimilation_date)), - mean_updated_seq_loc) - np.save(os.path.join(results_folder, "ensemble_updated_seq_loc_{}.npy".format(assimilation_date)), - ensemble_updated_seq_loc) - - # Compute scores. - # Before computing, have to put into unstacked form. - unstacked_updated_mean_aao_loc = helper_filter.dataset_mean.unstack_window_vector(mean_updated_aao_loc.compute(), time=assimilation_date, variable_name='temperature') - unstacked_updated_mean_seq_loc = helper_filter.dataset_mean.unstack_window_vector(mean_updated_seq_loc, time=assimilation_date, variable_name='temperature') - unstacked_updated_ensemble_aao_loc = helper_filter.dataset_members.unstack_window_vector(ensemble_updated_aao_loc.compute(), time=assimilation_date, variable_name='temperature') - unstacked_updated_ensemble_seq_loc = helper_filter.dataset_members.unstack_window_vector(ensemble_updated_seq_loc, time=assimilation_date, variable_name='temperature') - unstacked_prior = helper_filter.dataset_mean.unstack_window_vector(anomaly.values, time=assimilation_date, variable_name='temperature') - unstacked_prior_ens = helper_filter.dataset_members.unstack_window_vector(ensemble_anomaly.compute(), time=assimilation_date, variable_name='temperature') - - # Load HadCRUT reference - ref_ds = xr.open_dataset(os.path.join(base_folder, "Reference/HadCRUT.5.0.1.0.analysis.anomalies.ensemble_mean.nc")) - if month == '02': - ref_date = '{}-{}-15'.format(year, month) - else: ref_date = assimilation_date - ref = ref_ds['tas_mean'].sel(time=ref_date) - - # Regrid to common extent. - # Note that it was found out (see cornell_Nov_8_diagnose_stations.py) that regridding to a coarser grid (that of the reference), - # for comparison, lead to poor performances. The postulated reason for the discrepancy is that a coarse grid cell would contain - # too many highly different datapoints during assimilation. - # - # Hence, we instead regrid the reference to the finer (assimilation) grid. - regridded_ref = ref.isel(time=0).interp( - latitude=unstacked_updated_mean_aao_loc.latitude).interp( - longitude=unstacked_updated_mean_aao_loc.longitude) - stacked_ref = regridded_ref.stack(stacked_dim=('latitude', 'longitude')).compute() - - """ - regridded_prior = unstacked_prior.interp(latitude=ref.latitude).interp(longitude=ref.longitude) - regridded_prior_ens = unstacked_prior_ens.interp(latitude=ref.latitude).interp(longitude=ref.longitude) - regridded_mean_updated_aao_loc = unstacked_updated_mean_aao_loc.interp(latitude=ref.latitude).interp(longitude=ref.longitude) - regridded_mean_updated_seq_loc = unstacked_updated_mean_seq_loc.interp(latitude=ref.latitude).interp(longitude=ref.longitude) - regridded_ensemble_updated_aao_loc = unstacked_updated_ensemble_aao_loc.interp(latitude=ref.latitude).interp(longitude=ref.longitude) - regridded_ensemble_updated_seq_loc = unstacked_updated_ensemble_seq_loc.interp(latitude=ref.latitude).interp(longitude=ref.longitude) - - # Now restack. - stacked_ref = ref.stack(stacked_dim=('latitude', 'longitude')).isel(time=0).compute() - stacked_prior = regridded_prior.stack(stacked_dim=('latitude', 'longitude')).compute() - stacked_prior_ens = regridded_prior_ens.stack(stacked_dim=('latitude', 'longitude')).compute() - stacked_mean_updated_aao_loc = regridded_mean_updated_aao_loc.stack(stacked_dim=('latitude', 'longitude')).compute() - stacked_mean_updated_seq_loc = regridded_mean_updated_seq_loc.stack(stacked_dim=('latitude', 'longitude')).compute() - stacked_ensemble_updated_aao_loc = regridded_ensemble_updated_aao_loc.stack(stacked_dim=('latitude', 'longitude')).compute() - stacked_ensemble_updated_seq_loc = regridded_ensemble_updated_seq_loc.stack(stacked_dim=('latitude', 'longitude')).compute() - """ - stacked_prior = anomaly.values - stacked_prior_ens = ensemble_anomaly.compute() - stacked_mean_updated_aao_loc = mean_updated_aao_loc.compute() - stacked_mean_updated_seq_loc = mean_updated_seq_loc - stacked_ensemble_updated_aao_loc = ensemble_updated_aao_loc.compute() - stacked_ensemble_updated_seq_loc = ensemble_updated_seq_loc - - ES, _, _ = compute_energy_score(stacked_prior_ens, stacked_ref, min_lat=-70, max_lat=70) - ES_prior.append(ES) - - ES, _, _ = compute_energy_score(stacked_ensemble_updated_aao_loc, stacked_ref, min_lat=-70, max_lat=70) - ES_aao_loc.append(ES) - - ES, _, _ = compute_energy_score(stacked_ensemble_updated_seq_loc, stacked_ref, min_lat=-70, max_lat=70) - ES_seq_loc.append(ES) - - RE_score_map = compute_RE_score(stacked_prior, stacked_mean_updated_aao_loc, stacked_ref, min_lat=-70, max_lat=70) - RE = np.median(RE_score_map) - RE_aao_loc.append(RE) - - RE = np.median(compute_RE_score(stacked_prior, stacked_mean_updated_seq_loc, stacked_ref, min_lat=-70, max_lat=70)) - RE_seq_loc.append(RE) - - RMSE_prior.append(compute_RMSE(stacked_prior, stacked_ref, min_lat=-70, max_lat=70)) - RMSE_aao_loc.append(compute_RMSE(stacked_mean_updated_aao_loc, stacked_ref, min_lat=-70, max_lat=70)) - RMSE_seq_loc.append(compute_RMSE(stacked_mean_updated_seq_loc, stacked_ref, min_lat=-70, max_lat=70)) - - dates.append(assimilation_date), months.append(month), years.append(year) - - df_results = pd.DataFrame({ - 'date': dates, 'year': years, 'month': months, - 'RMSE prior': RMSE_prior, 'RMSE aao loc': RMSE_aao_loc, 'RMSE seq loc': RMSE_seq_loc, - 'ES prior': ES_prior, 'ES aao loc': ES_aao_loc, 'ES seq loc': ES_seq_loc, - 'RE aao loc': RE_aao_loc, 'RE seq loc': RE_seq_loc}) - df_results.to_pickle(os.path.join(results_folder, 'scores.pkl')) - diff --git a/reporting/paleoclimate/twentieth_century_wellspec.py b/reporting/paleoclimate/twentieth_century_wellspec.py deleted file mode 100644 index d0d7874..0000000 --- a/reporting/paleoclimate/twentieth_century_wellspec.py +++ /dev/null @@ -1,213 +0,0 @@ -""" Run a WELL_SPECIFIED version of the 20th century assimilation. - -Here, well-specified means that instead of using the "reference dataset" as ground truth, -we generate the ground truth by sampling from the estimated covariance model. - -""" -import os -import numpy as np -import dask -import pandas as pd -import dask.array as da -import xarray as xr -from climate.utils import load_dataset, match_vectors_indices - - -from dask.distributed import Client, wait, progress -import diesel as ds -from diesel.scoring import compute_RE_score, compute_CRPS, compute_energy_score, compute_RMSE -from diesel.estimation import localize_covariance - - -# In[2]: - - -n_data = 1200 - -base_folder = "/storage/homefs/ct19x463/Dev/Climate/Data/" -results_folder = "/storage/homefs/ct19x463/Dev/DIESEL/reporting/paleoclimate/results/twentieth_century/n{}/".format(n_data) - - -# ## Build Cluster -cluster = ds.cluster.UbelixCluster(n_nodes=12, mem_per_node=64, cores_per_node=3, - partition="gpu", qos="job_gpu") -cluster.scale(18) -client = Client(cluster) - -# Add to builtins so we have one global client. -# Note that this is necessary before importing the EnsembleKalmanFilter module, so that the module is aware of the cluster. -__builtins__.CLIENT = client - -from diesel.kalman_filtering import EnsembleKalmanFilter -from dask.diagnostics import ProgressBar -ProgressBar().register() - - -TOT_ENSEMBLES_NUMBER = 30 -(dataset_mean, dataset_members, - dataset_instrumental, dataset_reference, - dataset_members_zarr)= load_dataset( - base_folder, TOT_ENSEMBLES_NUMBER, ignore_members=True) -print("Loading done.") - -from climate.kalman_filter import EnsembleKalmanFilterScatter -helper_filter = EnsembleKalmanFilterScatter(dataset_mean, dataset_members_zarr, dataset_instrumental, client) - -my_filter = EnsembleKalmanFilter() -data_std = 0.1 - - -# ## Run Assimilation. - -# Construct localization matrix. -lambda0 = 1500 # Localization in kilometers. -lengthscales = da.from_array([lambda0]) -kernel = ds.covariance.squared_exponential(lengthscales) - -# Build localization matrix. -mean_dummy = helper_filter.dataset_mean.get_window_vector('1961-01-16', '1961-01-16', variable='temperature') # Dummy, just to get the grid. - -grid_pts = da.vstack([mean_dummy.latitude, mean_dummy.longitude]).T -grid_pts = client.persist(grid_pts.rechunk((1800, 2))) - -localization_matrix = kernel.covariance_matrix(grid_pts, grid_pts, metric='haversine') -localization_matrix = client.persist(localization_matrix) -progress(localization_matrix) - -# Build a sampling localization matrix (different from the real localization. -sampling_kernel = ds.covariance.matern32(da.from_array([2000])) -sampling_localization_matrix = sampling_kernel.covariance_matrix(grid_pts, grid_pts, metric='haversine') -sampling_localization_matrix = client.persist(sampling_localization_matrix) -progress(sampling_localization_matrix) - - -ES_prior, ES_aao_loc, ES_seq_loc = [], [], [] -RE_aao_loc, RE_seq_loc = [], [] -RMSE_prior, RMSE_aao_loc, RMSE_seq_loc = [], [], [] - -dates, months, years = [], [], [] - - -# Loop over years. -for year in range(1950, 2000): -## Loop over months. - for month in ['01', '02', '03', '04', '05', '06', '07', '08', '09', '10', '11', '12']: - # Prepare vectors. - assimilation_date = '{}-{}-16'.format(year, month) - mean_ds = helper_filter.dataset_mean.get_window_vector(assimilation_date, assimilation_date, variable='temperature') - ensemble_ds = helper_filter.dataset_members.get_window_vector(assimilation_date, assimilation_date, variable='temperature') - - mean_ds, ensemble_ds = client.persist(mean_ds), client.persist(ensemble_ds) - - # Estimate covariance. - raw_estimated_cov_lazy = ds.estimation.empirical_covariance(ensemble_ds.chunk((1, 1800))) - # Persist the covariance on the cluster. - raw_estimated_cov = client.persist(raw_estimated_cov_lazy) - progress(raw_estimated_cov) - - # Localize covariance. - loc_estimated_cov = localize_covariance(raw_estimated_cov, localization_matrix) - loc_estimated_cov = client.persist(loc_estimated_cov) - progress(loc_estimated_cov) - - # Localize for sampling. - sampling_localization_matrix = localize_covariance(raw_estimated_cov, - sampling_localization_matrix) - sampling_localization_matrix = client.persist(sampling_localization_matrix) - progress(sampling_localization_matrix) - - # Create ground truth by sampling. - svd_rank = 2000 - u, s, v = da.linalg.svd_compressed( - sampling_covariance_matrix, k=svd_rank, compute=False) - # Construct sampler from the svd of the covariance matrix. - sampler = ds.sampling.SvdSampler(u, s) - ground_truth = mean_ds.data + sampler.sample(1)[0] # Note this is still lazy. - np.save(os.path.join(results_folder, - "ground_truth_{}_n{}.npy".format(assimilation_date, n_data)), ground_truth.compute()) - - # Build forward and data. - G = np.zeros((data_month_ds.shape[0], mean_ds.shape[0])) - # Select a random subset. - data_inds = np.random.choice(ground_truth.shape[0], n_data, replace=False) - np.save(os.path.join(results_folder, - "data_inds_{}_n{}.npy".format(assimilation_date, n_data)), data_inds) - - # WARNING: Never try to execute bare loops in DASK, it will exceed the maximal graph depth. - G = np.zeros((data_inds.shape[0], mean_ds.shape[0])) - for i, model_cell_ind in enumerate(data_inds): - G[obs_nr, model_cell_ind] = 1.0 - G = da.from_array(G) - G = client.persist(G) - - noise = da.random.normal(loc=0.0, scale=data_std, size=data_inds.shape[0]) - data_vector = client.persist(G @ ground_truth.reshape(-1, 1) + noise.reshape(-1, 1)) - - # Assimilate all-at-once. - # ----------------------- - mean_updated_aao_loc, ensemble_updated_aao_loc = my_filter.update_ensemble( - mean_ds.data, ensemble_ds.data, G, - data_vector, data_std, loc_estimated_cov) - - # Trigger computations and block. Otherwise will clutter the scheduler. - mean_updated_aao_loc = client.persist(mean_updated_aao_loc) - ensemble_updated_aao_loc = client.persist(ensemble_updated_aao_loc) - progress(ensemble_updated_aao_loc) # Block till end of computations. - - # Save data. - np.save(os.path.join(results_folder, "mean_updated_aao_loc_{}_n{}.npy".format(date, n_data)), - mean_updated_aao_loc.compute()) - np.save(os.path.join(results_folder, "ensemble_updated_aao_loc_{}_n{}.npy".format(date, n_data)), - ensemble_updated_aao_loc.compute()) - - # Assimilate sequential. - # ---------------------- - mean_updated_seq_loc, ensemble_updated_seq_loc = my_filter.update_ensemble_sequential_nondask( - mean_ds.data, ensemble_ds.data, G, - data_vector, data_std, localization_matrix) - - # Save data. - np.save(os.path.join(results_folder, "mean_updated_seq_loc_{}_n{}.npy".format(date, n_data)), - mean_updated_seq_loc) - np.save(os.path.join(results_folder, "ensemble_updated_seq_loc_{}_n{}.npy".format(date, n_data)), - ensemble_updated_seq_loc) - - # Compute scores. - # Before computing, have to put into unstacked form. - unstacked_updated_mean_aao_loc = helper_filter.dataset_mean.unstack_window_vector(mean_updated_aao_loc.compute(), time=assimilation_date, variable_name='temperature') - unstacked_updated_mean_seq_loc = helper_filter.dataset_mean.unstack_window_vector(mean_updated_seq_loc, time=assimilation_date, variable_name='temperature') - # Clip to common extent, since reference does not contain the sea. - ref = dataset_reference.temperature.sel(time=assimilation_date) - unstacked_updated_mean_seq_loc = unstacked_updated_mean_aao_loc.where( - xr.ufuncs.logical_not(xr.ufuncs.isnan(ref))) - unstacked_updated_mean_seq_loc = unstacked_updated_mean_aao_loc.where( - xr.ufuncs.logical_not(xr.ufuncs.isnan(ref))) - - stacked_ref = ref.stack( stacked_dim=('latitude', 'longitude')) - ES, _, _ = compute_energy_score(ensemble_ds.compute().values, stacked_ref.data) - ES_prior.append(ES) - - ES, _, _ = compute_energy_score(ensemble_updated_aao_loc.compute(), stacked_ref.data) - ES_aao_loc.append(ES) - - ES, _, _ = compute_energy_score(ensemble_updated_seq_loc, stacked_ref.data) - ES_seq_loc.append(ES) - - RE = np.median(compute_RE_score(mean_ds.data, mean_updated_aao_loc.compute(), stacked_ref.data).compute()) - RE_aao_loc.append(RE) - - RE = np.median(compute_RE_score(mean_ds.data, mean_updated_seq_loc, stacked_ref.data).compute()) - RE_seq_loc.append(RE) - - RMSE_prior.append(compute_RMSE(mean_ds.values, stacked_ref.values)) - RMSE_aao_loc.append(compute_RMSE(mean_updated_aao_loc.compute(), stacked_ref.values)) - RMSE_seq_loc.append(compute_RMSE(mean_updated_seq_loc, stacked_ref.values)) - - dates.append(assimilation_date), months.append(month), years.append(year) - - df_results = pd.DataFrame({ - 'date': dates, 'year': years, 'month': months, - 'RMSE prior': RMSE_prior, 'RMSE aao loc': RMSE_aao_loc, 'RMSE seq loc': RMSE_seq_loc, - 'ES prior': ES_prior, 'ES aao loc': ES_aao_loc, 'ES seq loc': ES_seq_loc, - 'RE aao loc': RE_aao_loc, 'RE seq loc': RE_seq_loc}) - df_results.to_pickle(os.path.join(results_folder, 'scores_n{}.pkl'.format(n_data))) diff --git a/reporting/toy_example/base_vs_localized.py b/reporting/toy_example/base_vs_localized.py deleted file mode 100644 index 934796b..0000000 --- a/reporting/toy_example/base_vs_localized.py +++ /dev/null @@ -1,129 +0,0 @@ -""" Compare the performance of raw covariance estimation with localization -on a simple 2D example. Comparison done with the RE score. - -""" -import os -import numpy as np -import matplotlib.pyplot as plt -import dask.array as da -from dask.distributed import Client -import diesel as ds -from diesel.kalman_filtering import EnsembleKalmanFilter -from diesel.utils import compute_RE_score -from diesel.estimation import localize_covariance - - -results_folder ="/home/cedric/PHD/Dev/DIESEL/reporting/toy_example/results/" -# results_folder ="/storage/homefs/ct19x463/Dev/DIESEL/reporting/toy_example/results/" - - -def main(): - # Instantiate a local cluster, to mimick distributed computations, but on a single machine. - cluster = ds.cluster.LocalCluster() - client = Client(cluster) - - # Build a square grid with 30^2 elements. - grid = ds.gridding.SquareGrid(n_pts_1d=30) - grid_pts = grid.grid_pts - - # Construct (lazy) covariance matrix. - lambda0=0.1 - lengthscales = da.from_array([lambda0]) - kernel = ds.covariance.matern32(lengthscales) - lazy_covariance_matrix = kernel.covariance_matrix(grid_pts, grid_pts) - - # Compute compressed SVD. - svd_rank = 900 # Since our matrix is 900 * 900 this will be a full SVD. - u, s, v = da.linalg.svd_compressed( - lazy_covariance_matrix, k=svd_rank, compute=False) - - # Construct sampler from the svd of the covariance matrix. - sampler = ds.sampling.SvdSampler(u, s) - - n_rep = 30 - for rep in range(n_rep): - print("Repetition {} / {}.".format(rep, n_rep)) - # Sample 30 ensemble members. - n_ensembles = 240 - ensembles = sampler.sample(n_ensembles + 1) # Note this is still lazy. - - # Use the first sample as ground truth. - ground_truth = ensembles[0] - ensembles = ensembles[1:] - - # Trigger computations. - ground_truth = client.persist(ground_truth) - np.save(os.path.join(results_folder, "ground_truth_{}.npy".format(rep)), ground_truth.compute()) - ensembles = [client.compute(ensemble).result() for ensemble in ensembles] - - # Estimate covariance using empirical covariance of the ensemble. - raw_estimated_cov_lazy = ds.estimation.empirical_covariance(ensembles) - - # Persist the covariance on the cluster. - raw_estimated_cov = client.persist(raw_estimated_cov_lazy) - - # Perform covariance localization (use scaled version of base covariance to localize). - # Maybe should persist here. - scaled_covariance_matrix = kernel.covariance_matrix(grid_pts, grid_pts, - lengthscales=da.from_array([0.5 * lambda0])) - loc_estimated_cov = localize_covariance(raw_estimated_cov, lazy_covariance_matrix) - - # Prepare some data by randomly selecting some points. - n_data = 500 - data_inds = np.random.choice(ground_truth.shape[0], n_data, replace=False) - - # Built observation operator. - G = np.zeros((data_inds.shape[0], ground_truth.shape[0])) - G[range(data_inds.shape[0]), data_inds] = 1 - G = da.from_array(G) - - data_std = 0.01 - y = G @ ground_truth - - # Compute ensemble mean. - mean = da.mean(da.stack(ensembles, axis=1), axis=1) - - # Run data assimilation using an ensemble Kalman filter. - my_filter = EnsembleKalmanFilter() - mean_updated_raw = my_filter.update_mean(mean, G, y, data_std, raw_estimated_cov) - mean_updated_loc = my_filter.update_mean(mean, G, y, data_std, loc_estimated_cov) - - - - RE_score_raw = compute_RE_score(mean, mean_updated_raw, ground_truth) - RE_score_loc = compute_RE_score(mean, mean_updated_loc, ground_truth) - - print("RE score raw: {}.".format(da.median(RE_score_raw, axis=0).compute())) - print("RE score localization: {}.".format(da.median(RE_score_loc, axis=0).compute())) - - fig, ax = plt.subplots() - grid.plot_vals(ground_truth, ax) - plt.savefig("ground_truth", bbox_inches="tight", pad_inches=0.1, dpi=400) - - fig, ax = plt.subplots() - grid.plot_vals(mean_updated_raw.compute(), ax) - plt.savefig("mean_updated_raw", bbox_inches="tight", pad_inches=0.1, dpi=400) - - fig, ax = plt.subplots() - grid.plot_vals(mean_updated_loc.compute(), ax) - plt.savefig("mean_updated_loc", bbox_inches="tight", pad_inches=0.1, dpi=400) - - fig, ax = plt.subplots() - grid.plot_vals(mean, ax) - plt.savefig("mean", bbox_inches="tight", pad_inches=0.1, dpi=400) - - fig, ax = plt.subplots() - grid.plot_vals(RE_score_raw.compute(), ax, points=grid_pts[data_inds], - vmin=-10, vmax=1, - fig=fig, colorbar=True) - plt.savefig("re_score_raw", bbox_inches="tight", pad_inches=0.1, dpi=400) - - fig, ax = plt.subplots() - grid.plot_vals(RE_score_loc.compute(), ax, points=grid_pts[data_inds], - vmin=-10, vmax=1, - fig=fig, colorbar=True) - plt.savefig("re_score_loc", bbox_inches="tight", pad_inches=0.1, dpi=400) - - -if __name__ == "__main__": - main() diff --git a/reporting/toy_example/plot_scores_synthetic.ipynb b/reporting/toy_example/plot_scores_synthetic.ipynb deleted file mode 100644 index 068a0b8..0000000 --- a/reporting/toy_example/plot_scores_synthetic.ipynb +++ /dev/null @@ -1,1624 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "a9a55753", - "metadata": {}, - "source": [ - "# Plot results of the synthetic test case." - ] - }, - { - "cell_type": "code", - "execution_count": 89, - "id": "79367852", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The autoreload extension is already loaded. To reload it, use:\n", - " %reload_ext autoreload\n" - ] - } - ], - "source": [ - "%load_ext autoreload\n", - "%autoreload 2\n", - "\n", - "import os\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "import dask\n", - "import pandas as pd\n", - "import dask.array as da\n", - "import xarray as xr\n", - "from climate.utils import load_dataset\n", - "\n", - "from dask.distributed import Client, LocalCluster, wait, progress \n", - "import diesel as ds \n", - "from diesel.scoring import compute_RE_score, compute_CRPS, compute_energy_score, compute_RMSE \n", - "from diesel.estimation import localize_covariance " - ] - }, - { - "cell_type": "code", - "execution_count": 90, - "id": "e304eba3", - "metadata": {}, - "outputs": [], - "source": [ - "# base_folder = \"/storage/homefs/ct19x463/Dev/Climate/Data/\"\n", - "base_folder = \"/home/cedric/PHD/Dev/Climate/Data/\"\n", - "\n", - "# results_folder = \"/storage/homefs/ct19x463/Dev/DIESEL/reporting/toy_example/results_paper/synthetic/\"\n", - "results_folder = \"/home/cedric/PHD/Dev/DIESEL/reporting/toy_example/results_paper/synthetic/\"\n", - "plots_folder = \"/home/cedric/PHD/Dev/DIESEL/reporting/toy_example/results_paper/plots_synthetic/\"" - ] - }, - { - "cell_type": "code", - "execution_count": 91, - "id": "6c2aeb5c", - "metadata": {}, - "outputs": [], - "source": [ - "cluster = LocalCluster()\n", - "client = Client(cluster)" - ] - }, - { - "cell_type": "markdown", - "id": "5c94cc8c", - "metadata": {}, - "source": [ - "## Load Data" - ] - }, - { - "cell_type": "code", - "execution_count": 92, - "id": "04f6b294", - "metadata": {}, - "outputs": [], - "source": [ - "rep = 0\n", - "\n", - "ground_truth = np.load(os.path.join(results_folder, \"ground_truth_{}.npy\".format(rep)))\n", - "data_inds = np.load(os.path.join(results_folder, \"data_inds_{}.npy\".format(rep)))\n", - "\n", - "mean = np.load(os.path.join(results_folder, \"mean_{}.npy\".format(rep)))\n", - "ensemble = np.load(os.path.join(results_folder, \"ensemble_{}.npy\".format(rep)))\n", - "\n", - "mean_updated_aao_loc = np.load(os.path.join(results_folder, \"mean_updated_aao_loc_{}.npy\".format(rep))).reshape(-1)\n", - "ensemble_updated_aao_loc = np.load(os.path.join(results_folder, \"ensemble_updated_aao_loc_{}.npy\".format(rep)))\n", - "\n", - "mean_updated_aao_truecov = np.load(os.path.join(results_folder, \"mean_updated_aao_truecov_{}.npy\".format(rep))).reshape(-1)\n", - "ensemble_updated_aao_truecov = np.load(os.path.join(results_folder, \"ensemble_updated_aao_truecov_{}.npy\".format(rep)))\n", - "\n", - "mean_updated_seq_loc = np.load(os.path.join(results_folder, \"mean_updated_seq_loc_{}.npy\".format(rep))).reshape(-1)\n", - "ensemble_updated_seq_loc = np.load(os.path.join(results_folder, \"ensemble_updated_seq_loc_{}.npy\".format(rep)))" - ] - }, - { - "cell_type": "code", - "execution_count": 93, - "id": "7fb060c4", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Builing grid with 0.16384 GB covariance matrix.\n" - ] - } - ], - "source": [ - "# Build a square grid with 30^2 elements.\n", - "grid = ds.gridding.SquareGrid(n_pts_1d=80)\n", - "grid_pts = grid.grid_pts" - ] - }, - { - "cell_type": "code", - "execution_count": 94, - "id": "16ee95dd", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "cm = 1/2.54 # centimeters in inches\n", - "fig, axs = plt.subplots(4, 4, figsize=(55*cm, 40*cm))\n", - "\n", - "# Prior\n", - "grid.plot_vals(ground_truth, axs[0, 0], vmin=-3, vmax=3)\n", - "axs[0, 0].title.set_text('ground truth')\n", - "axs[0, 0].set_xticks([])\n", - "\n", - "grid.plot_vals(mean, axs[0, 1], vmin=-3, vmax=3)\n", - "axs[0, 1].title.set_text('mean')\n", - "axs[0, 1].set_xticks([])\n", - "axs[0, 1].set_yticks([])\n", - "\n", - "grid.plot_vals(ensemble[0, :], axs[0, 2], vmin=-3, vmax=3)\n", - "axs[0, 2].title.set_text('ensemble 1')\n", - "axs[0, 2].set_xticks([])\n", - "axs[0, 2].set_yticks([])\n", - "\n", - "grid.plot_vals(ensemble[1, :], axs[0, 3], vmin=-3, vmax=3)\n", - "axs[0, 3].title.set_text('ensemble 2')\n", - "axs[0, 3].set_xticks([])\n", - "axs[0, 3].set_yticks([])\n", - "\n", - "# All at once update.\n", - "grid.plot_vals(ground_truth, axs[1, 0], vmin=-3, vmax=3)\n", - "axs[1, 0].title.set_text('ground truth')\n", - "axs[1, 0].set_xticks([])\n", - "\n", - "grid.plot_vals(mean_updated_aao_loc, axs[1, 1], vmin=-3, vmax=3)\n", - "axs[1, 1].title.set_text('updated mean (aao)')\n", - "axs[1, 1].set_xticks([])\n", - "axs[1, 1].set_yticks([])\n", - "\n", - "grid.plot_vals(ensemble_updated_aao_loc[0, :], axs[1, 2], vmin=-3, vmax=3)\n", - "axs[1, 2].title.set_text('updated ensemble 1 (aao)')\n", - "axs[1, 2].set_xticks([])\n", - "axs[1, 2].set_yticks([])\n", - "\n", - "grid.plot_vals(ensemble_updated_aao_loc[1, :], axs[1, 3], vmin=-3, vmax=3)\n", - "axs[1, 3].title.set_text('updated ensemble 2 (aao)')\n", - "axs[1, 3].set_xticks([])\n", - "axs[1, 3].set_yticks([])\n", - "\n", - "# Sequential update.\n", - "grid.plot_vals(ground_truth, axs[2, 0], vmin=-3, vmax=3)\n", - "axs[2, 0].title.set_text('ground truth')\n", - "axs[2, 0].set_xticks([])\n", - "\n", - "grid.plot_vals(mean_updated_seq_loc, axs[2, 1], vmin=-3, vmax=3)\n", - "axs[2, 1].title.set_text('updated mean (seq)')\n", - "axs[2, 1].set_xticks([])\n", - "axs[2, 1].set_yticks([])\n", - "\n", - "grid.plot_vals(ensemble_updated_seq_loc[0, :], axs[2, 2], vmin=-3, vmax=3)\n", - "axs[2, 2].title.set_text('updated ensemble 1 (seq)')\n", - "axs[2, 2].set_xticks([])\n", - "axs[2, 2].set_yticks([])\n", - "\n", - "grid.plot_vals(ensemble_updated_seq_loc[1, :], axs[2, 3], vmin=-3, vmax=3)\n", - "axs[2, 3].title.set_text('updated ensemble 2 (seq)')\n", - "axs[2, 3].set_xticks([])\n", - "axs[2, 3].set_yticks([])\n", - "\n", - "# Truecov update.\n", - "grid.plot_vals(ground_truth, axs[3, 0], vmin=-3, vmax=3)\n", - "axs[3, 0].title.set_text('ground truth')\n", - "axs[3, 0].set_xticks([])\n", - "\n", - "grid.plot_vals(mean_updated_aao_truecov, axs[3, 1], vmin=-3, vmax=3)\n", - "axs[3, 1].title.set_text('updated mean (truecov)')\n", - "axs[3, 1].set_xticks([])\n", - "axs[3, 1].set_yticks([])\n", - "\n", - "grid.plot_vals(ensemble_updated_aao_truecov[0, :], axs[3, 2], vmin=-3, vmax=3)\n", - "axs[3, 2].title.set_text('updated ensemble 1 (truecov)')\n", - "axs[3, 2].set_xticks([])\n", - "axs[3, 2].set_yticks([])\n", - "\n", - "grid.plot_vals(ensemble_updated_aao_truecov[1, :], axs[3, 3], vmin=-3, vmax=3)\n", - "axs[3, 3].title.set_text('updated ensemble 2 (truecov)')\n", - "axs[3, 3].set_xticks([])\n", - "axs[3, 3].set_yticks([])\n", - "\n", - "plt.savefig('test.png', bbox_inches='tight', dpi=200)" - ] - }, - { - "cell_type": "code", - "execution_count": 95, - "id": "675f4e65", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_15557/753562997.py:12: PerformanceWarning: Slicing with an out-of-order index is generating 33 times more chunks\n", - " data_coords = grid.grid_pts[data_inds, :].compute()\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.8084630065417957\n", - "1.2835222641711297\n", - "1.3152859504746937\n" - ] - }, - { - "data": { - "image/png": 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\n", 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\n", 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ADyv8Zl5yeeNwMg2Z99B4CmfCCPcqqNaVN4EqwLIAy4y6zPZbgUmIAMscvjCpJyHFwMeeyYCkAFgq3nB2tEwI9+PzRLADqGcwFPcqzuETMe9P5tbE34zO8fB0sS/kMTMWCwuzphguprGfNzxkiIMNI9HH1EKBLSs1nj/VMzmKji2jVgE7vwQdrN6eq8+lcyDr+vphddxuiF/1eDAGgh+/Bn78BjAozVeGK9TxGjXvMNGI4yK4XwTHotjD3ez4g4UWpYcWk1FxfYcBOs4Qd5TEgsTUJhuwmZCbiXlptL81spTtOsafGJI+dvDLU500TwY2zgo4AutQo8zda5AFVCvkeN8MQ72/RZWCeriFODA5HSCTGghKARrnBMAMh4RMBicQrz2HFoK4wbAUKXFqZKp2jLUCdpyrQdy8ikSMgnUmw9OefVLbgmSAa9wFGUyEo3kJy5BwGHMz9s/aDCL28KJD7LuJ1r4UE9STq6LenPT71AhmntOoRSntVYAask+ckY10BqkYmLBYqKHhGIOlQqie9SCeVdcUw7vGrXkF48EYCNo/Qb16zXK4CXX3GHW8Qs173E+C46J03KfHpWEOh6U09z4Ck24cjsH1bGjwykCUkwnEzf1cs+VO55iafhGf5MFIYPveDkxmQ7MzGVPRsxEGOK52KTcas6YuqxmAOk/AMkMOt/r7eG/hRoFMM8qsk5dFmaiAZzPs4DzE4ATKahBIeB1eMCvICTUUzo1YDQt1AIvFoyfh1N88ttjcCVObq2jXyz6SNN2pgFxttQ4eYpRasZ+VqKR407ACo5+Vzm6nFvAGH54W56RktBo4Db7Dk3t17sVtGKrReKp9YaAavwQdvHQCHKDnV2pFquppERM2kbFdp/PnIlBwN7KyHGR9FePBGIiaBjUOedDdhxMqZyXjWBhRrNjKHzswuA0tzu0s/jPmHlZs/76dUB6fnhtuFFIyt1Ccem3IuXkNnluPOfTEpJPMfxxjALCiR3uBlRT1GqQ041AX+10C2cmPzUhd5OmvFEKJSyMu/vDa6EVQSt27MGNWz4QW7TEnkCyoxJpiFi0WI9LYuXsR/Rq7V6FMS4KgogpWntyQCACHFCYjsYSU4Onu6xyI7dAMFbXvrpXgAVS153znrkZDb4ZwaxC3GIRfio0xiv/zecTG/yjQc5BSz/I+Gnck7EfOp/Hn7GhPzvX9jAdjIGRn2YpkBmL3GPdFi6zuZsEkvXjnuAjuDZiUWnE/lVbE41mMY3jsv4GObLuHscvrm3k1VCRYKswnJPukPB3n4msA2AfK7uMxYZ81f77PjCwTaD6AprtGbmreghsFy0KIGwfLTNTDbcMdWrjhhsNGNwxsxiGFhX5qKPxv/trV8x5eGEV7tSiWCUTL+QsQsQ0R5PEaybgMYulMAI196JM7Wcao2o9vhddjAi+ASMKhCAbWtPb1KGYU5MSDOGf0t/+Xql7gLBXJuC9ZdK/37EWVioUJ2VKZVGZN9wYCm3u+F0cIyzzlLQCybYCjESISNOQl8IqqrWnWwPGwDcfTsgp4u+e5gKb7y8fyEuPBGAh4FWEeUTljEqPSWsXfYoxJTWcq7uB1Fp0tuU5xrgyEgz/Sr3qjWi+CMetnl1objTrSpbfFO4DtPnQeUPL6C8ce2g9BsxZlXoUYJKUZBU2vm2cgRfGGUtagpHsSZkAAoJa+g0fjEPGGS+OicbDHzTjE9xgwtwXsAAXlwGY8SD0m4myhVt+x464XcQn1IPQSODFoYMaQRGN4qGfmGYubcD+dQbsFn0+NQ20cF5GKwsrCHSoBQqhsuIbhBEqFZatlwcpjqI7PbMcWm/FrB7V9bFt/qWrMqGqYBV7vPIwInIZUrBuHhlVZZqye466+/HgwBqIOOwUlLaxQjKG2jMXdtAYmD+ZFrIp15JRe7boB1cOJzfdOiwQcItvrK0oKsWwwAjHk0Dy5PQ4GhFnz2qrxQNgnxphMM8GMARUDKK0Cs85Ti/nPUqBtECdUzB1DABQM5ARKm0yEexKbxQ6oQaBzBsPByO1nOEofMy4UVC82BUNErKlYmMu7TKhJkNMIYY3ziXplqtvYZM8RNBwRWyyJCEjd8GIRK4WmFmZs+YMr5mRIefZqzA4+N90Q0e8vUH5CMUBRw1pS/CCPqGVZGYSasnoVcWwxCrs2kR/ioUEirMIvD2v8G8bEWr/DhNEBWILOp5gKBzRsXY6n9/Z9jAdjII60w50kzIugVuA+GIjvG5X65rjgveOsWgHHpVGp76flxDh4uDEXwTIVSJEV23GZC6Y5gcKuU6TiyT4DIyC1LxKmHmLESeUb82DxLWHN2R+TuolXWUOLRADf3wOL1vPTclB69DKdcBdWw9KU3RMwwlM0AHE8I4zANtRwD+PkMy7gFr5T2eP2nK305mk013sBklWlloyaFozjNSqo4UvABqEnGEahhqOIGsFZgFoTjoO0zMaxSEt9TkvHH9x7cOOw86KtISnlfeP5uSwAIJhKB5apKHGLlwpkXbBj3oNS8JYAw8O2F8uqgGmNO/jrge416QblRCpqokD6Gj2f0bCskQRUjmoUPIMUsauygD9sIYZ7Ci6M4o+nomzJufRiLH2uC4dcBClrRRVVLxLpj32QAV9TwC+W8N6yAdDOGQePCQlo+g4ORDa9Bv97pEnHEUHGbc1v+/LUjYTRptWbWI+zXsH2Ne9TROQk1x8Ng4VHNIz9NVVAlVFl43nIArK0YNspN1FaY1XaFktS7VrXnkJmrXEp4ZrFMCJ6F2v9B6dynz/Prh2ink4RghjjUcMBZUv6Iu8G4hSQWnkD1A3DVumRKYKZnXKuf+vl6ASYhzB1fkwMLzw9LhewoZccD8ZA3MyCq2NpCkMKSKpx+F93M46L4Om04E9vpuY13JsH4VyHCFLeTwXLVFCKYD4uKIsaiGJxekoMKaqERSGn/vr1jCIVV0PCcRFIA4TUwg/J8AjRUEIxCzR+vRuH7O/jEGta3KqqPwnEGRh4tbucCyuAoYF94AQy7MF/XxyhnDvqQtRSjCpdQAv6Z3imwio/PTSpADBPPUxJaQ2iAivD1ozENquxIYQx88rV9uE5fTfIWvbMxnJlPNllzEVwXBKWopjFk31uc8CHaz8kJlyNSRWjmHA9JDMuIdPEfJKxqhVWIKZaDEAFi7JtfecH0CT15GTRu9pUXfEULg0XLkqhpD8xmQYFgRetsaH5vitJOZnOmbhVQMsBdLx7zre92HgwBuJ2FozH0sq1n04aUhyL4Pv3M+6ngqeHBe/dTRvpsTVq3YDJuWCZC0QqlknaYw81UmaUoiEGGVINAO/dqYG4tgkllq48FDG8gVVWgSoGe+yDEAqCqNN0o/tcidQwJEFNo1r+LCBR2nOV8+lIwtAJTsPYQE3aGgg3CCE1CjGDcM5IcGl4BjADeWgTWUvD1VD4YwCovjmdM1C8MXhV0FIW7nHUTszyGLyfp47Ii0iVgNwVwBYhZGY8HjMOi4AJeDTmVdaiiOBqzLg2HYjr4byB6OlTO/yASVT/LcAEUUoz1GgwuZcBoztXBIzYPquzbt2bjN/hm0o871hGngzURhXQ8Ra0mMzcfFgbhI3xrYc7yP1TvIrxYAyEgpLSMgmqCFUwmzfw9LDgflLcIYYEccSMRVkEZRF1Ec0oVHsegHoTi4CZIEVQirTQZfTiL6Njz0UnoRiabgooSlAR9T6ItcZfnORDPY+uNAnSIh7OQK1ay8C5cfUxGrfgnCgAsKZCS0EVAxNd6CUyJqUvfndc1mGMfpYbCQCaCTEadjVvoTu853e/lmnZYBUrHGLzUzmpB0Xde5AtTTiWtwNgYisHJyQxfEGsmMuyGENSL8HnQRFquMPo2o7k4jAdoDyHLZ3LSgkMCyFNg7dyf/j9PQ0xYpoW0rUrqxeGV7QTjx6GcxvatYihmRvZSFBrWYzSsl2NkfsBx4MxEH96N+H+5mhCpoL3Dgveu59xNxV8/+aIu6ngfiq6wwcDEEc1zEGkYpkLllkgi2A6LpDFUOqlexDESnQipoZRfH9IzfBkA7hmybrrEOFaUgO4fKLNYtoCWVUOSq2aKkNnzxUDnfbjtd7kMkEsl36WSbk6Mf0/hWItDlyJKgXknkOrySghKxIFYUrzKvT1lhmZ53UxFwDksWMewwhgPg9comdKeHelNTSmPlWzUudr3gEpa5aKlE/gKkq+WwKWvvNz9t2RGLs0BoNCWIQgVafvcRFIhaqJjRl3s6qZD6wZDybg9f1geg/qQcT7B6yxi615kNqNAwCjYaMZBkAXvXsT7XMISHYrE9e28B18dU0MdUy7F+Ov8ZS4g5G0eFgx9aK9xklRsBsikPtbyN2raVz1YAzE02OBWEgxl4o/vZ1aOPGnNxPup4L7uWA6LM0TkK1PB9hChxmIoiQXMxQiFeLEIhlBzKiZscy6WJgJ9/b4akx4elhWcnQDu1Zhl6BjhpI0MoDF0XNlxs2OlJcKMfr2BGX15WzNTDxd5hWSDjbF4eQpYE3z5VNjoReh9IW/CQHOysxZaFEX3b1W+2Cs1XgWuGlZj0qkwjKc1TikUb0GNxTEcAl8ASylCEBqYwSmYBBRli6iS4yRMxaTfJuFcJV1sV8PBUDu9Ppau5wcmy6oeQ9qNOgEqIwg4nZ4yGEXDHbIzSAoDuFhR7XPIy/FMC+MLOxQI1Cpy9SxeZ363qA05qphsqno3VSTrkLKZUYtoWDvA4wHYyDul4JqRqBIxXt3E35wN2NaBO/dzS1dOR/dQHTA0Uc1LwBQA1EWsWsrKIvunGIqS7JM4DyiLEBZFKxcJv2OewA3hwVP9kvTHdA6DjQEXCowE1osO0vFwNI4ECURRgCMipwIKbjRJNV6WWi6rEm6Vd35T7yI1U5hgrOtitDozkQgS83WBYoXMLcFLuXQQcugP+khAi3QsGOebEfTcCOmP2tx4HJDxeak4j4pQfJea2g4ow5XqMMeIEahTptf2kJCT+exZi14y9a086eyAMwq2GqvHSz1R6R4BFNpJKpoIJg6s3VI1NLP6tZ3C+Eg89Yr8NEzEP53HVvDEEONqDLlCt6Kd6sh8POXWpG2vsuG1l05qRXh3AzIiqgGoCmJLR+yEON7T49IuMf9tDSvwQ3D4XaCFMEyabjQPIilniDHjRC1CKqpKsmiJCQJbjdxApuRmDO39x3vZ5QieI86wcap27vMmPfZeA+luadO2BmYcVyk9Va4HtjYf9Q8j3Pip5kHpGFsbmXzEtAptF7zQHlGDamtKtLo2nBD4Yt4mfW9VsXpoUU1MdtaCmoRrfp0JuVK5DZpliMDJJ0r0VKpeVCC1jBq9S0x6u4RZPcESBlL2mOSilIqplIa2h8XkFQF8IqlIGuChn/9htoCKOpqiOBqvEZVKXAkVo8kkTa/OSwFj2flv0QAUnteqLEYE63c/ThiGlKgUm8V6/4V5wxBe3/cs7g2ZJXJStNRmzcBVhanZ2wci6m1msgtjDafgWS4VXWlrx6e+tyI9/eZvJqXGA/GQBxmwbCUVldxPxVM5gW4cYhhQymKJwBYEaBqQPHjj4THPgSaHqoWljigSQtZ1yZpdRuOS4wWTmgPB/ucWsFClv6CKTHbbqToGip34kutaK6nu7UD9500rXYIQNVFpHkLK5YiSQc87TWUqoKNwCmA6JPIjEON6UkAwKCYBtCBUSkANirJVt9Bg4YQrQo3jS2ccKp8rWjNcrYGQi8goRhqJxHQRXehIynLeRStVwe05UCFisyIoHkSQC+3d+xoiwXEkcizErQGFNA9hZWBu0BbaX/jnsk4B/ZKRVMJqGdeoCC2fUkItU7KYu0LW6j5rAN7ifFgDMTT+wmVJ9wclBV5cz/jeD9DiuBwq7u6hhilYQkeLvhYLf7wuAaX2heE74JqPHRXcEMhxcBNMwrXYzK2pk7abYFXlM+/m9WDuB4SHo9i8XFq7q23d9OmKNTTWRw6VFHfVQAgUQKRypznvO8exDLpZEhj9yBmzxgQaNxr2GC1Gk6sisZBtCOLloYXAdvCr4AqRUk5rQo1zwE7CyXSCNk9AjhD9k8w0YgltCUo0jtobYE8oOsHexepijOLyT0oj62JsRuvQcaTIACjwEI9wlozGo2XEq8xwnfEjIouQs1QeWbKX7Fts+jjHHbrdG6fIwRYNeY6rImjMTLNnciG27gk+gqLKqoR2pitEozDhRqQlx0PxkD88H7BXCfcHhdUqTjcTjgeFsgSDcSCMt2jSkFZJu37sBnnGsWce03d1hxAPRHFNVjTnmYg7qayEii5b7n2Pgm9lNyJOU/2Gcf9ACbg8S4bcg5cD2KknC6pFvtotHJw4CSH7orKiRISZ+TdqF5DmVCXQXdb4mYkaFQ+PuUBNAyos5+/GocyWYjCer5a4DXB6zFqHs04DD28yKOK4GY1DEij1tHsnqCmjCMG3EwFRdAMRAVUu8Pm7FY1aZdVnq7WaiGDLg6ivigaDuOdwSw2H81bSWNqLQqn0g2R90mN1zByEtqcQN/FS8MDVMFMPY44T07PIT7eGgVgzXnQ15xMS/VEQQ2ohFSQq1YHPIIAbW5ErGI9TvkuFl7MU9sUPuh4MAZiNkUo5ycU28WLcRQUU1DDAJg3cIYzEDEGoBsMNxTn3nNuVKdbb+jbwLpq8NzvMWuq9GB4xFIqZm231fj+g/ErAM2IaPGSci28mbAXnqrJ0kIeI3FqeMQEtgwIseEUnAFeQDWr+x/4E5cKtNxI1CKKSRhj82SsSr+5hxWcW7FSKR1n6D1Fa6u+BXrNglBnJEoD6+zcNAA/d2NWj6loqDWksek6AIHAZN/VCEgWZmxDCxUa7t/tpp/DfdiOSLvfegsAmseg3989h57m1HGGdnE6ImDp/w/XIaauX+V4MAbi6WEBoGFFrRXT/YLj/YxlFkx3d5Blal5DLad4wnbEv3m6b7s43J0+N8T4FBMUDwH0xt9Np+pF0XBcjRpO3E+5AZtaVpwxsECGhEEITB1EA8qKyddk8h0EtfiZSRv9Osi5t47gAyfk4Vo/wxeuV4tyAnsTnWUGjvfgcUAtpaWJFcwNj01bQqXVzLgM2h+Vd1eQ4UrDiV1vSbDwiCKq0XAooh7E3BvxajOj9TV2irNU1UOoQO+CxtAiL2JF6uPiWHEkZtDCqGkCEyOnbJwNNUT+lb4GIyHJvQYAWICWdoX9FlKsiKphE+xZCWltEGJ7vjUzsnt/QDQG6/DC+RDRo9DjqhY+6f81Ncq9L2d7sZOo7KexZz9kIcYyFyApuakUzVbMx4KyFCzTPYoJpkjwIM6NrQexHbRB4onTSu+hSm2EmCqaq3ewMo5YYu7Dw5B77jqXU1aq78AMSW4QlG3ATKf15+g0Y+8urv0j2drPWaxOFYDWEahPYpmRQfkVBOjiBTQFmQd9nFUImABw4pYqdi0JKQJiAaXSwUqgYQ+VU+M2tH4lw16xhloxBRff2bDaZ1NOOAYKMqp/pKlPWsXg1VKY4eZ1w6A3SH8RaxxOrMaCDwAxssXurZv56WTQ8m3S/HWpWuad7LbEnb4BmhXgC4bB2/Q1rwHdIJwDRD1Xc8mDqNVwGzM3aetRRZq1FJMlfHXGAXhABkKKQObSQgtZKsrSU5TbEQ3Aub8/y7s4RyA6fX/nVGxVqfw5DxH89Skz0kInmY8p0LYHrpjNAPEm3efDbVGxUyyVUJL+7q3cCEmqVQA6Gw9aAp6yZjk4gWoGcgbtrvTch0GNBADiWVOc6AZCz0XWNGxOij2EbAVStjaICqJJFSNA9evjvTu9NUBsFcBMqokiaEItidNKG1IqQCkbB0LPCcRd5s6zNp7taanh7DcaK80KPz+vhfBmN8ShjSBZ6UjXoyimLuUt8xAWdsSJtgbBX6O35jSsiXtODHscKyHq2RbNkFoGxzEZr9504NI4L8p/Oc94fdnxYAzEdFhQioYVVSqmowKSHlbIvM5YbD2EmKkAcMJ5ADSkSOOVvaOn7bRgC6tqvlqVru2FXNOsv72mA0Cjevv7lnndw7PrEFjj2HSaJgPQJPu3w9maThEeEmOW2kg/ACOx0rorlNarlX/aDq+WGVgOOqn21+pFLDNYBHJk8DJrJgPGc4AZihQmlwGWlAdg3KHmPep4rYZivEYBYynVPAi0BrOKO0jzHLzGxs+XhSBM7VodF1HOFiUsSXfOzBX7NBquwoAMxv8obbE0o2BEs7Z4+s3t99RIWOTKUGkE6k5fk9HTy+xCNmoMXDuokIUeIWzQbmm9zP/c4LD4z4GTwDZr1Q1Do55HMp1JFLq6tofc7bPy2OnyH3A8GAMhpaJQJz91SvR5MHI7olcgF94nAPgFgBxveiNSrfGsgIRWtR5RW6IaipUyKzdj0+3JPRBAF7t7HhHsnKWuvl8ni72ubT2C2Yg/WDT0gCitW9hRd3WVs7nOlTOIc/ca8qAZnKxhAjH3MslLw+sxTG6teRFGm/Y4uXoWQNay7V1kOGA2rHwHiEq9laoelWcRqGq4JhGIxQIIUEkASutQIxqOGIbYiIKztaaG1URjoovUQzYPF2pTDqtULasCy7j0zlsx+3T2EgbsY6uo3b6/eR091U3h/FY/MZUZ57R7DucA3vcxHoyBKEUgJK2gaiW5HlKVvO0rCUCCXJsbB5mnhlc0DyKPEAMmZR4hWcumyyJIibW6M4kt9AphgZTuNrrwjI8OKKvX4EaFpK5CjPtpaYzMxL0Ckc2wRIS/nScFQ7EU7JG0yMxnUQbmwka40nAjkVK+E5lXlAZ1v9MAGkZwSqi7K9B0UHf0MIDHAkoMJ+ZyYvCgGRC2ruo0jL2R0bBvocVSu9BPMSJUdIQ8O+E8kyKh0bFlc5gILBXHpQA5tc8DtKx7svMarL4D1u5PF4hRzp2KbTcltsZrHkYQl7XSWfVAJCsQaerb2scDzZMg8xrcCABooYjrfRDQjEUcXm8CnAcpt/fb/5JiOOGtFgNbluaDFm/JAixLwx0af2VQb+9VjIdjIBZBgZKhHFF3shP7rrcJK9xrcE9BlqkZizLdN/wiGghA032cR8iibliSK5QiSOBVSThCVNPEQTahADHpQs8AIKhG2y614xYaZiyYlt44GFh7ENvSdS9TBqoyiZbSdCn8eFwK7yonVYsmIIvWiABATtYaPuviBi264PePgOmgRCoAWGYkpwSP2tmM8qivtdfXvDdR4b0aCWJMS1cB8zYESoaqxhXo4VT0kroxZAwJmgYuFUDBMamWZ61AIm90a3qQREiUkXLf+b194WpYnL7S+bT54o2LXbQHSUCiuEtjrBIjAWBmJA/fCJDkG4V+3kpV2vRF22c3ctN5UZyTEbwDKr1RUjMKoT1j1zM10N45D67FMe5BHzYDcW4wp2YkttmHZ41Gr96QRWJqdM2uLKiSIGzAJBsPYiOCujUObSSCgfEagpC1BDSPwftHjhmt2hBYG4hz2ZBiNF0nznjoU1h7OTSRVXTEu4LahGxutV64Nmm72nVvnsOjGVpP/VrWAoY/VM4KTBJpRabhDM41EHO74yVysV/BhesG5x+swxGpsGRi71PRzm+7A0e9iXMpoTCqFNXbCHqSmxesuAaeOVAAkDq7mcL5Qd19Mhk4hGvu3JBnGgYfrinpqtQrYxC6eYXKznM9UXqB3vuTFdyOB20giBPSM060ii5h7TrdqzVlmVBCsYp7JP6chy3eMUov5mPQkjQbkRgLK0Cp7z+d4MSElLnNyQRu98qxipj5cGNxP5VVw5ftcBl3p3MnS4tqJaDupjD9g4E1u8GLgntChFEURCsVbQeDl19jk+a8eoQ6DKhzV5KiPGqmY9yDn7yhXsb+kbIlDaCcjQi1iPdLNf1PxyBCuMTUQdzYezKSik5dc433ixBmqg3FdxKVaiew6lU4s9C9Q1vgrcJ1WVYxeiN5nU6mnjLc3mv0EAFAX5TWNhFAl4ADDKthkF93A0ebUWo9Q85jKLQctMy9hg7usgCTMmNrwOea9kdZe0ouFPxBxwt9yje/+U38+q//OqZpwmc/+1n86q/+Kh4/frx6zbvvvot/+k//KYgIV1dX+Lt/9+/i85///EsfEDNBcBpOAOdTm9t9ww3EpVGmg9JX84iSRws1JpTp3j7/CsX0Cc8ZCH8uWaOT9jg5dqSGoIqK63ofjtj1yWsvxpURWP8+Bu5FYWMicjXVN/3uWew3EyQDQO27rtOuYy8HYk155s6PaDvOoOGWhxbIA3j/qGcuLMSonLEsmy5nlt6UipWvEME47xYVn79ENzYahBkeAFZVSYRV385MmtLVlK3J+AENfNTCtQS4hiZ6inPlQdCZBWu/zxaLuYdxRjwW9v3k6ll5NDyjy+JXoeap9JTl0rATmo8tjPC2i60nCnBiEE4YlGfC8fc7nmsgvv/97+MXf/EX8Tu/8zt466238PWvfx2/9mu/hl/+5V9ur/mv//W/4utf/zr+5b/8l/jkJz+J3//938ff/tt/G9/85jdf+EAIBNeHZOCkS9GlE3YM4lLF5qURadsyzygrbwKQC2lUxzFqTuCqwiMli3oU4MafKNCqUOHzBsJLyc81mR1zl05r77XiIWEH/wwUJawAzlrRQpKLo9VaqBgujb3Mu4GSltZsgi+WtfAUpi/SbeEVkxK8ZoiKtYiCkUciDEnD/DXn47K6dDwnH6tz24RP1d1524nVthg+4ffT05tmXNoOv/pCxzdqxzc8BADWQrHFWaenwOiq+1YKJfuRNh2NTRCHaRjDdEAr4fZU/ysmQz1rPNdA/MEf/AE+//nP46233gIA/MzP/Ay+/OUv45d+6ZcaIjuOI37lV34Fn/zkJwEAn/vc5/C//tf/wjRNGMfTrMO5kUYGDQleUcmZIctlK1iCGnMLLyxzUZ9z8bRQSfsGiC149yCWfHtStxENDg9j80CG/WMzKGY0xLQpjSbrnsccVkByJSrruuWGwn+e7HMr/IpGYyXIaiKusyXkS1am4pB4tfj0BDduM3H3IHb7Btq1BjnjXmXjOKEO1+o1DPsGdDr5ycOJ9rGO8gsBCci2IAfunblnqU1SEECglVNjs25TgL1uKZRNV2UXageqDEMZ1QiIytp5NqOeOX8PR1o3t9UXyjr+9yyCZw0CWUmLoxQjqEDfyXPwxtr1teOi4NGtLN/SvZ7liDpPkOlgXdtL66imh7hhC5/pnPYim+SLjOcaiO9+97t488032//ffPNN3Nzc4Pb2toUZn/70p/HpT39aD6xWfO1rX8Of//N//oWNA2Bup8X1IlXJ8Zuyal9w5zCBDja+mGWNmEXZpEOx9NfE125rOmSZNGVah+Y1ZKlNx3WrmQkAhVUol4qgDu4pdI/BDUNsKBwBTN+9nWvQ2sbVulVsOD9cZ4GTxvBezOUGYq/dzbQAawiyccarOMlE6PA7pSxEZRwO1u1KKhnpy7Cg2jEJfg7J6IWGewNl0ZRlOxYGeM2J8CIzuEJTxIE2mIAbhlXmoFbtph7aIup7z7j8Di5yUuVy8yZW4HHMxpjx8WrM1mZRSm/YHIYX4n2QS/e88VwDISJnc7d8BmC7u7vDL/zCL+C73/0u/sk/+ScvdSBpYPCguX5OUA6C1JUxKIhGoi/u+PtFh6tNAUA53l+s+tymyXgYwcFQECfwYVS2HGueOxVfhNSqFX20+DtTU9X2od2hpHWFGrOlP4OhLI2FuLl+z4jrV4O4xeW9GU8Ccu7ut4cVw153Puu8XhwktPNIrISmajgB4JkHmOhLxSyqgVGqelLHM0bz3HAPRW+3KUkrSqspyKqpz2zeQtudAQeDAOx6qBHOvzqA51oLsAXKlo1o3arcW1BDUWcrFnSvYZ5W7v5JJXEemgdBeVh7FoHb495Em3PtO9zQdINzMi4UIv7IirU+9alP4Vvf+lb7//e+9z28/vrruL6+Xr3uT/7kT/C3/tbfwk/+5E/it37rt7Df71/uQHICdrYz14oq3CXqqxsKaXqGURNime5RjvcvZSD8M/ynhFZlW4GZJphiVG3ihGTfxXm0qtDXwEwt5KSwUs+CnJmRh6QNfBy3qAlXi9Kyk1RMS2ml4yXiFrRmKnb33IA8d9W3DVypN+3RN/rvrCAkUWue3AxDUt2HJYCDbBS/bhwQ6gz6eXsB11IqcDXgsKhaGE+leT2ejQG6kXPPyYHKWoHidGWpSnkmTWuQZ3nChuVXpuEk8XEgL9UCSBWTg2MkYqTEIDr6jWsCsXK8Rz3cqddwPGAl3wd0LkL4/tjSkDxlzEkzRqGx8rZDWpMEDDyO9jtmK1Iw8HG8QsGY5/Ixv/CFL+Bb3/oWvvOd7wAAvvGNb+CLX/zi6jU3Nzf4a3/tr+Ev/sW/iH/0j/7RSxsHIOJNPQ7ddjqKI9Kw40J/mbGVpVvpV1qdgr6u61ue+16x15dFi83UU6ztpwpQisvkVStE04bCpXR1budOrEvJX+xGr2Tbz122LR8ipj8dqDNZ+lVBVtyZ/aPstxskLT/vP4PRwZ0H4erR2hNT/8bcFZ4u3h/zRoDumVT4gjdVbHQD4D8l/Hj5+CJ11d5Ru8b3+pGWMXE6+TYVGtKJTXvBBYBdgmCe9KfY62bDDWK44BmJ46F3aPcf6Z/dDMGPEJA8N57rQXz84x/H1772NXzlK1/BPM/4zGc+g3feeQff/va38dWvfhXvvvsufvu3fxt/8id/gt/93d/F7/7u77b3/rN/9s/wsY997IUOhBMDmY3ODACMAtE0ZnGvASZW617DQQu6jvdYDjdnFaaeNaIBeNHXR4vuHkuZDl2qTUYswaLTJhRzo8eVwVy05DoodDurMmIPEaD0/pLeq9Fl3Xss3xdnOAhb7KwVR55uc0uS1INQ99twB9eVwJoIxQBgmSYAq7oB+5P/BYPVWHj598DuGVTr4F6A0j0gr9eYRUzmjSGkHoPA2IxAK2yKBsRPJfIwvB1eRRexaYQuYHUO+6zZpJoIewdx06lxXM8HXbyxPwmAi95EUwAPKeXqYUfEQ0RWRujEaMTxitKZl8YL8SDefvttvP3226vn3njjDbz77rsAgJ/92Z/Fz/7sz36gA+HEQGLQotLxKtRhd92Nae1alLLMrdpTadWvRmLreWNlIKQARu9evOpxmcBnWJ8xfeqPS9JU5bjLcAamMi55Q7KK09l2bI7GwBvSGjGJ1u3+PAUIoxHXAJbpB449nDCPQlYYAFphGhFhCExC0ouxTgMaSrvL+9YwqFRSOnhVEpiIg6uyMmaqHVFNxUlMbUu/x0utFwBkxDHvN+IrMRqCztGojdxVzatwqrpqOih3ZPTq2WFUPU4OZeNxbAlKG9JSnCdt97c+pMQJdXEDMauxcFbrubl2Bn9o+EXs7v6Kyru348ExKYkJ9bSP+qpICsAHCi1e1XAU+aTU3P7edqjn0MSdQr39c/QcgCi82kMwJlWfcgUjwmmI0UhSwJpF6M95OGFegxOefLfdDt3BA/Lu9QMx7i0C8ILEWSse4eFGbTwJZmqdp9q1kNok3ti2/l4uTS1z085W6ooo596CQLEPrwdZxGQNa5e/c21QbUGQukEhvR6u4ERVjG5uSpUBw6HU+4xWl+oLGS8tUz85SXu+K3ZFaUB9zTOASeC8cfg/4E08OAOxHVG4pUo9yVwoOPmj8R7ODZECloJyPBiFW0DW8FaAE2+CrMTawcqtBsUWgwBw0r5ewwsOIUaXoWOi3k0citjr7nuGOchuILrXELtexcEw74HRagZWTWS3BVNufPKIMY3YZ7ZYnzAlQuJk7FBelYIDMI9RyWDOlfDHQ+rAaKK1AXWDVszzmaU2sdyn06JaFdIZoL1fJwEYcTUwAMZUKkZXy0qDZn5Gq49YZpBLy3v1ZB7WYYVVm7beqScgom8om6vsrzuz6VEwSqt6C1cYD6PGe/wBx4MxEOyE+zAi+h8Nhf6/nAhl/ChGZFvGVOgl1SuR0upJ3Dj0v5/PdGyVq+Lw8IKDm+8t4h003I6acnP/6xastOd8UfkCW50HYR2yeOpPegFRIxGFzyQxbgIxiFTVm+G1F6HzdUgFbw2Fd1L3LIXUXs8B0IlnWUIocVgK7mZNl3sjaJGKo+E9CzNyIpRKJjDMDbgsFWArUIPR1r3LOQ2jdiBj0dSlSO+SDvMmAPMUOkt1NTbeZ3vO37e9hzgTskZKdZxXm/9/kPFgDIQYEnYuJegU7LigXhXX/GXH2ZzzSwwO5+S/lUMR2JZnHufgQfjnqJit6yKuJc2kwnYaAyT9D6H8OL42rrOtkfGpFsHIlkrbipi4/fHvNMPE0FAncVXBKtGdexAysZg1XySOiEuUWlq7AJeC256LhxJ3TTRXcDPp71m8tBwYkjQv7Hoo5oElTEU/Nw971MkAWzeKYcH30KKsrktbzG3xDx2jaK97RghxadM740U0byLyKubpla2PB2MgqqNiZwYZHVeNRGpu+//dI8rIn7qEvCJTxdDCu4qnzLqwjWp8SUxkZTCIWhPhRKoqZfjuSdpQ14Ht6PacyHlcAcAqrIgIv36vPRGrHqPnYNWHDoBqPdkCEIFkQUq5Cd4MhiqO1gaArWy9HUc4vlJMCAfAjIokaAt8WwUKoKlni1Q8nRbcTAVSK947LK3v69EA4Osx4WpMrTO71Apm4H5gJXqNCbvxkRKnnHLNS2dHOvCIYd3op4GLQdYwqIXXZV55Fme94ABuAm6I1vqaxEm1RiMeAehn/yirOR/CiPyI9fMPw1hsR/UY9Dljy/WI6cxznkR7n2Urzi0SgTINYxFSTO9dPJbN/7cCrB90KI6xfszmTTxrlFoD8ttTFuc8jq6gXc1bUK9hKtJaFniGqF3jjAZozsUzIBaqJMv+pAyU3L0Dw5m8UlTDD2NSAide2km4cck4nAM0gecCkJ5CbUV3z+rE/hLjwRiIWlWZOcq6MZ1Sihu9OY9Nfk536x8dmSRSapnXVaDnXus1G/r/Hk6kpB5FygROjBSKtrYVn/7YoRrnPLSCJ6c6e3qS0OIGwdo58xRfXKyx2cszDULFWt/R06cIoFvsBGXDwyAmbTlIpLot+5xaXYljBH6M0QA0PcsYsp+pyYkpzpujehBTEfzgbrb2iWsSmnsSd2NpIrH3SzYFK2DcjUhj1nteBbQwMO5Vvi+AivC2hHpgLfQIly0YjtC74pz3cMkYePiy9U7cOJiaNQEfTg/iXBEWW3ghjcJsCzIlpKydoziPK4GY/7PHGCTsgmE4l8qMOplkncQBf52HGtyMQwrGYBdEZaI3kThUPzJa3QbQF7YY5dA9CCcLxeHt5RyAXKUut8Z2s9jt5Fvar2EQjBVIeW50A6REKl045gkxMKMT41oHLl/QYfFvpfTjcP3Lm6ng5rjguAjeu5taAySn61+PvUju8S43cZubY8H1mDBwxbFUZGbshh1cdt8zNg1/sEbJFA0GoNYseAyOVUTjIFPPfngLAgUZn+MBbEKQCFhWfjHv9UXGgzIQPlQ0RqfORZk3G8/avX9U40W//xSnCPiDE594/RPHuXAiXh33Hmo0FDjj7qLzJTz7sRJK3fD4m8ZikGM7O56RWmPS41BQNQDR0JaCzGvvoJ3TGeOwmOWYRU6yPNFoeFjhXsNyZi65Afb3eo1ISzfXqirW5jWtvafQ6DiPJ9WWxKlrOLQQwEINvGBq/hnZCDVOsvncZ7/nZceDMRBEBEoMtkwGs5JdLs1H12QA0HpdOMPy/+SIdOt+LGmltr2lV+v71tkPJzl5eLEfkjX/TbgerY9GTs2beNbwRWHkw1BUtTYoFMITImDkILg6HQx0LJ3TYAuBjIJtB25fKv01QAs1zh+gNAl3srQsvCALGg8NzBAR1fP0oq3aC7q8v8Ys2vPU+336YvYRQ4inhwU3hxnT0g0FoFWz+RmGeDYgc5+50cT344iaF70Wpj2JMoF2sOY9FbVYDZLjC8vcr5cBk45TNKIUHK/i1rzoLFNycz3746Lg5fY1+UMYYviIbEri012TNq69YxIAXthARAPj40XEZs4dS/zMZ42zHgTRSWgRcYetHN3qeEUXRGblBzCMSejf1wTa1VjU6pyG7jmQZSKolTh3dWa06s9sb2RsUU4tVW4n9GwPA/17ld3pfUfsbwxrwHsubEBjQToT8n4uJ2rgXpxVpOLmMON+Kk00eMtM9esbwzQ3Rok6VZugACYFwLJWAblRDBqYAED1lBhVn8N2jFXDcawyE6vXl9bsqGMZw/o9r2A8GAORMoOsWKsPgUBLejW8pZY+BPqFiLt3GiMF2yxysMRsLeT8NwW9QlkmzHfvPdNIkNdcxM8aRnC+fHN49frOe4ge+fOyFt5spkht3cJnEbAASVKTiBfqhKlae0frdizGtEys6UdaJlVLcqFU9yBaWEEd8ApqSICDZegpz21oUgVUufWuSMRatg3lQnDx4/MiLm11x0yAaFozDmkehWYlPGXp10d/es/U+6msQgy/rq4HOubure3sJ6fOTr0E1lZiMxa1g7RRlMYxmXHX05O7K13Iy9TbDXBSo74t4Y7syO28cvaklY9fGkQfMgziekzALuPAa9yBqEKSqjUxm5I0BqQ8orACk8wJGMbW76K913gKMdPgi9r/DqAxMst0vxKxPQd8pvEKnEek3VX7rDTukVxV+Qwvon/3oKBkNnDSzof4PPYQqzm9wvOYjXhEaJ2zRYDS9C1rCyEG9ty8LkrPXGhNB0DHu2Yc+PC0U6aXRQ0nJwsvRrT6Afco9CT10yNRygdzIFERqEyonLWvBbs2pwKRXvSlgLRyHcBapyFEzTAU6cbBwwbPTMRmys5ziM2LosG9MqPwZJ/xeJcxJML1oF3Z914dy46PbEK1UOWKDK2OBdZposgTIQ5y9kXFgEVQU8cnthkPymMDHVeSgD5vrW/JiiyFTRj7YQMpk+XvFKBEI0cJzHOoa7ZhHM5ai6HHOaOgBuR0p9fQwhDt0D18G3KsvIeQTXleqrO/p/cA9fBCj/U0Do5jCbtfEUAYjRHIpOXRQ9Vdl0AAa4bCnA7j5m8+1BWVXSzVCU/LYmpGOjkr6SSHWBOelHsIEj7rfEGSexbcWJXMnVVJgQnp2MT8AhFeNJyxteESHkcDcU413MHJ1jmdvXyeQgp588VbEDYAt5FzQpFR4opVeWjtBqr1HIkZjdVXhdLwlWF40cX/YQwxPnY9QK4HjJO2qLtPjGkuTVhF70NCSpaq8vAgWN6YdkzjFdJuD+KEYf+4A5o5YgbqrXgX8aYxaR5EU7AOIc1wpUK1ef8Y46PX4CpTecztM7eDicBZDV4eEjgz8sDIoypKeZnxNsxYLQSO4q/cBGuHWXe/uWj7v+uBcZUThKrpJ5hAC4UmszCNxeWgIcZyRL2/0Ty7C5pwarL4vHPUM1s8Ha5hU3RZA5v+XPW0YFFvBJy1H4gTlSpQGVbVqRWWwkr0EtO0BNDYlH49jotgWkozAndTaYbhPgCSpVYkIVyNKQgEd0B4l1Q8eJcZ+6yhxWBhhodnK6w3cDw0tckAeGUgVVLXOecG9A7a5hF5VAM5W0OccY+VAA2wDivOGohOr25Nej0s8efTxpi9z/FgDMSTqwF0Pax6WN4zYcqiJKqFIEsFZwIu9M0A+m6drx5hvH4dnAeMu4w88mrX9lFrxTIVlDJAFpWTU42JK6TxqhkHd9/SeAUeBuTxCnn/GMS60PNgi3yTcXBPiLPWTeSRGzCZh3QSXvjQCS4A+ERVapcZk/f3JO3HOYtXJ2arUNQCrBx26JbONK/BjQPP9yiHu6BwNOlEmyfQMKou6bViFirlfiHE0BvQ06KSQLRoybQU7SMqC1IaLYSonfTFms2oDEgi63+hhVSO3SUmZT1vPIfjIrifurGY5rIKUyUzrox/4vjD9Ziwz4x9TivcYWdq4y3bc36SdSPYKmP9y7wojkDoRDKkAbRX7UlhVur1MunCDsIwJ8VXm3nepOr8NRyKtiIH5wJt/2XHgzEQ+yGBx9RvvBkKAJhCLQYzofJlbsS69iHZ4uwu/XYBV6mouYK4YgFWGAWAVcUmcWpeCecRKdsCD3UVseHOmjFpqU0jRcUajHOpNh9uHKLCdfMkii6wmQUlMzhQhAdsdj5sooxYZAW0+oFVUZGojBrlQdN4WL9n21073IT+/Ll7FI4l8jGqZ1iIzhKgtpwH5zasOqnX3oHdaexuLM5lhJqBcnAUjoecPfRnjxiO1np6/sTqXbiaeHyflEYEeSGadDMKmw0ppfOSee9zPBgD8eOv7bD72DV+cDejSMXV3YQf3M09npxV6HQ+GsaQx4YJnBvMCSmrOx93a9/p9TVaLpyHBO8qngft7F3KNZbJmrGGEKMbBW5eybDLzQjkIa0whl5kRk2HcQtIOmi2baDjI6pLeUw9WaikKdGMYRGInU9TuAaa0pSXhScCyLIV+mOCrMcD6jL1/gthBxP7TXkADWHiN0PRFZmRrXmNcyei8TC0v7bjIgxQqKIkAvnLKppehIYWTr/WTMbRPQXzHDysmEvX+QQcV6XmpTmN3b0IFYyhUNdynowWRyU+NbRAK1bTL17WfwOaOhVVAdLYHpP0951Iym2Uqi5JGzTvIefeWezDlsV4NGRcWRzvN79IxZ31siy1ImVWV11qBwy3kkSIXgQ1+bVW95DWYi0itUvsW9wmQ4UUaU1ugF6/4FhC8wTM6LhhcIyBWSXMzoUP24IsT7PFv/mIhiG+JzYGHjPjeqgbrQS7FiFDAGDtOXjWouke2kQstptxArEy9CJLsHXIPqOHQYCmT2CG49zNNnSfET0IBVfVOKiuQ1ysTWey9nRmzFa45+CYld7gdkQnRtk1NbqOZ1DnIjSFrgp06b0tzyNI7DWP4VLKt6WIeb27y6JYRpW+GtvnLq23aBUGLaeZipiRi1ofl6juLzsejIG4HhlXQ8IsgkS6q05Lac1kilRMAfXXnSHB1ZuAdYpRZfEtHrTceWwxEncWQNOplSuqpCa7z8H4eLes9j5LV7pRcMPgjMhtodW54c/vMm8m7xp3iDn8aFx8N3S1aI+luwR+L+ICeph8NhyIw8uZpbSOUS1mBnqse04kxydqVIfexuv+WiLThtCUJ1VCITca1WqPTmXpfORwLRIRCjxMUD5NHpIWwCVuRrjdk2d4CkpX9/Kq5wzZGIxYDh/POdDVV/iAz1m/JtHQcAaRNgWmZcGqQjhe++hFnzNCH2A8GAPxY/sR+ys9nFmkEWEAdOAyc9vV9eZ37kHaZBzKMoGnA6qMKLuElKQRlKIHAKD1ptDPlbNVgj5i+OAexH5Iq9x6b6WXToBHJ/JsR1/43H5PSznzd/c6UjMsQ1IG5sCkeXxP1TnPiXtR1kmvDD0pxHJlAIY/AF4z0LpQ7Z6Rh+TNDraV0ffnq2cm1IdgRtOSBNQYc728iC8VsXlXNl8a8b68cT3gakx4vB9wNSTT0TC+SSBguS6F1rWoV6aitrVnMOII6eKmjeE/G+Pg16Dpf0ZOiVPWgZZyJlmA6d48rkPrO9q0LoFuYKIx/jAaiGz56Jx0B9i656nF8mjc3MhHiOO034VX/3X6dgQTAZ1g6poyyEDQbcYjajd4GOHejk/Ex/vcDINXCwKdyzAtUc7ew4b1zfQQ4lljdW2im8xoJeGM5zfGPTdW4QKwqkTsYihhnMOBLngNq7eRLghBZ1eqCnYndj1vxHArCSFSECLu4AY7c+8Beunz3XcQoBW/SXi+XsputA+4YEQVqT5t/+d/a9eMNOsDaBghUG8ilZWK1TPHhy6LYS7gPjEOkHbzfPHEBeNt7jh4EFsGo8wzSra+FWWPVARVevdtpLDgN6vIEfAtoKjHo79j+PBkn81AJLxhqVoXQ2WiVjtQas/RK/rOq/PqeEM5OWf/3qsxtzTdo1FZgI/HhOuBscsJV7YI3INoCL2fpsfSPkk5K6ruxJ1lXmdxvGJwmU13seflG5LuIQl08dRzu5gDmmVp7wVnZGJUZtSqpLgiaClPDZ8IYkQmSYS9nXtiwrSkFoLq/9esycf7jCd7ZW5+/PFO71FSvoPjD+2eV5eiExwWNKr1TNWOq1p3s9z4HDDiWBUDXx3ADF29azQE7jlYa0PtXhYb+tr7rZsXlqnjRH4NOQHzdN5ArfCRZ28wLzoejIEYje56My0A1E0+h+prmhOmn5BQJYGHITAfe11FmZI9/xgl0QpTiOXVwGkKzHcdfxxBxOjmRwMxMOPJLrXJ5UamayGKSr5LJ/QAvcAIQMNdTpWte8bjeky4GlL7rse7jNd3A4ZEGO0nETVKdeLA/4ixsE1SyllZk4CFGmmFoNd5Cjn3AJCF9zRjccG9jWlSqhVVCERLc7lH7xzOVVmh5lEMSWs0VBIO2KXaSE9FlCSkBWvUvDQPvR7vhxbyPRpzMwyuaZlDQw61DdIaCwtrJiWR9dJIyl4FEziZslTNungZqDV1I2G1LNFT8BaGIDa1bDXOBWzhVYV3ER04g1JWvgigknfAGuPYiojXqmX+25TzBxwPxkAArtbs25yO7UIBel4bwGq3Owk1ilbRae9LjSljMdg2ro+PtymxLYjoRiMx4fEuNwzAmXnrHcoZgYzBwhhg3T3r0th21XKj6RRh/15n/8VGOmzpO/ceSC/e+guaN8FWf3EhJAjcCGBYhRoV6KIpMf7eTFYKp6oYYNUGSWUB0qi9M5hAlt50IRlXzcpJw4hEPfMz5dRwnSR1Y8C5GRO9Pr2H6bb1n9jih1VuQgRFGGL4jSplK31diV/SDKziBdLwhHaaIYSInoQbBxCv2gwU0bCq3fPo6QVaN4hXRYZN0cr/9gqNxIMxEHFaOmC0GKjnaU9Z5ARLiPURMS6LjXzLdMDCV+BsaU3p6tlbibfrQMm9GnN7rmUmLEvgIVEiwtXgbis1um4chyI4LqXl9Q9LwSzUjV9I63raDlhnLzyGvhoTnux0N3xtn7FPjNf3GY9HlZW/Ghg7WwhjCkpRQAe+fPIw61ZJrIInAFprOFFXtulfhNQn7HX+e8XuW5aGvOtJLN2jYF6j/u5eJy08SpwxcsaSCCzqVZbKTY6eZ1We1qItNA+wSMXVKC3ccMP92j7jekhg6iDuFvj09OmsJAsw9QIyHdk8MQ0/MhOQM4bRWglkLWSrw9J7hEQj6d5a3rWWhgXKcvX77QCtptI1zUvGjN2N10Cx61lUjwJWXk6h4Eu9PCs/AE7rZd7neDAGwmXP4lgx5NrCPvNe559bwZX3oogNecsyQpZk9+6UWecT6yqItXj86qi379jJJlzLHARjMRqHH+hhIC864WZWSjQAYCkYE2PaCCysNROjt6OTfp+VInw1JFwPCfvkpco6+XfJjZjRrL2QKuTs42hdt9zQWgepNppATNitxFNtIUW3zIBhFJySTmAAjZZNHIQqLD43PgSqmDqTII26IAm6QBZWKvk+ZJquh9Sa3wAZpVaMSzcQ+5yQGHg8uoFAM+A+vD9ol7fr7Evt+G3eF5UW7jLBDJOgVvUkc953nCBe62ggANRh38KJ+Yxh8MfeUtA96cxs1PRJdSjsWsa10rgRDmwSffgwiFp792WvVIyEqbloTYaDjCK1pyw5Kb/dPIm0CTUujS1RKXoNjiskUmxk69LvMuN6SM1YqPu6Bb6AagVIs+X1z41OFxacC6nisWr/TWopzV1mjEn/76BeK/MuUy81XrEZayNI0fMmkpV5N+9stnh4RdYxgUDzJJqBGdHi8ZNBrMbDSqcbai8LEmWAFax0rsOQCLuqhLlByRMNuGQhpIGsW1YHIb1C09W7BjMyxUhv7j1IRdO49BJY7/rtBl3nph76VNQopqrV3kSMxOM6jNsYiMU+r4bfbqRUtLv2HqRVxXSIqHUcz3lEXY76+YuGHdVAyzbjXmEVp48HYyBm0e5Hd3PB/axiozeHGXcmPLpMxYqqpBGgfHAetTQ/eA3bJruyTJA6oBRBymvPwfsjXI25ZSH2ZgCYulbAwIQnYwYzcJUTdlnj+5x6POtrezUREjBYfn1gwmx3NGofuq5BlEbbsi+zYRzX5j083iWMifBoYDweTA+iLqCjyce5StS2dkIvzEpF6plj1VZOCVMOZlJKWsacrTfE8aAZETMWrdqw3awOaNK40/g6jRpuWAw/7h5bZoNBECyisf9oHA+pFXO4Nkj9uu/affPMWGrVrABUkKZqqDkjiOBW5d/ojdHXezjq2a5r0QzRnAlpMWPdNCPQGKtK1VHDILWHi24M/Pt0ntSWTnUYJJlxqBVgUuJgyntgWHqPDr+HVUA1nen8/SHDIMQs6lJORT9ElPrcPIjaMQRvphN/fGxzxf6eLREqZiScoz8wY2eiLk1IxAhJmXu2gEHI3Bezb8ilAU8q9R5rCnxsQ6hL2Yv42CnCCkgGDgQTMkErMG3hUwlYwFav4YRyfTqhVrLtG25EFQHlwA2AZUCWqefpPQXqgxO8n0RTXh4U9a9lsd13RJUFZE1/E5N5Yfodfm8AwSAK+ipL1q4Rda/BdR78+Xbdqy36cGgn2paoSKShzAD3JhRoTqLciIhVuLEAKtLGKXMj5EbAPZdWuuHGwgFSF/ipOn/A2tM0G8hJKVuoZ95ZLWvQ8hWOB2MgpiI4TAVPJ5UpvznMJjqq3kNZBMskK4DRB4ddSgYVf3GVqLWilN7NSLOOGQsvAY679EptKCfskqYOx6Rxv4cVycg0bZKJUn8rrY/V3dZj0UIjP9e7WK5sBVn+szMmafMsvLCIYaEFkCGtfLvJxwUPYlsb4J4D1arFWtNBY9jpoJyHVvo99/oMYB1KLJOCm8ygYiSeYehciiiL5rJ/w6iwhBQt/rLQohODJlDRIq+c9hhsV52d4ZhgzXa57a5x5ORFcXYbWhjhWENvrOPhRgS+41ANTDUKR+OmJBLM4g2Tu0K3l+0QnOYeLnXt4Wb0MP16wuaN1/tk7jBCIjU4iSrSeK21G20XsswFOzU7hhgfMibl02NBHRf84F4Xy5/eTHjvbsb9XDAfFyyzoBSBlE6F9sY6KTOYxxZieEhRxdvdWen3hj0X+QxP9hlXQ8LjMWOw1OUT4zY8GpPF92ihRmZ1YQE0ybQKYCZSDwcVJH3H9czMXLTI6LAInh4WTEvB08OC96yK9X4qWphGPb16ZWXwfs5RBSmzGiuabrQ683ijRkIWRb8Xc0fPVQMuM0SKGoLjQUOz6QA53isyPx0g04wqgmKVrS5EwonBQw5qzQfzIIYefgxDk4ZfCZ/oB6jxAYBRe040klCZUUVJSWPKKALMrN5aqWKcFG22q6Xv4Z46MWxjOFwB2+N+7/K9BGXsLfTjxsHDBBflOTghy2pzfA74dw5p/d3AWQetzQug4xGJjKTFzteQkFFhZM4YxysF9Z0f4dkT79dR5cPHpJxFq/DiLjotijdEcHI7mAgl9llgEwINBVz6s7aoTauBOwfC8+3OJ3B1Id2prTzZgELdPTYpROKWJjt3e7yDdqxIjNJoDYyVCuFO2PLXLtInOICmncCRIBN/WiXgaRenVfXmPJnMnKDOWu7du6erUXbpPVdfFgDEhm0ADcgkST20sMfqyV3e0VZVnw3g68aEyKtyu0RdNAD8jLXg12prHLz+Yv3a7qFtny/VXP5S0fAFqqbCDZPq9yzIKU38UmPieIwi3hpRMIAxF0FmrVfxruVU0Riw6mI4yIvGk3hVlZzAAzIQ7x0KSp7x//3hAfdTwfdvJtzdTSiLYDoWyGIexBkj4SPqQ2RTg1LJuStwZgz7hDxqafaQuAF/o6UNNX2oAOXruwGPd8l4DoTRDMlVdjIS1JW30enFnSxTa3cvVa5dlajnop7CzWHB3VTw3t2Mp7cTRCoWU0MiJixDwhTCH2D9OSdzzjIUjj3U6RAW/gSXNXOJsxY+zPMqxCiHI6QIymGCzIsaCKukSmPW6+wyeXCpvY14SWRXMnch1mEM8mjqVayqGzf0bC/kciDQmxf7zuol+z5cGp+5N/vtYQXONt7pQGXPbriR8IV9XASLPXfcguQRJyLfbNa3JhqdGAJtu4cBCmgzSfNQPWsDsNLQB8Z+vAbJAq6iefQygRxrdqm/VzBeyEB885vfxK//+q9jmiZ89rOfxa/+6q/i8ePHZ1/7e7/3e/j5n/95/Kf/9J9e6kBu5wXH+9l6KBbcHxfMxwVlqc04PKvLFlkKYcus5Dw2DciUupJTjPG9gGdwrv+gdOa9VfztExt9GRjJYneP8YGg/px71eRK5ESxB+f7HxaVY1dV5oLb44LpuKBKbaFUSlo3IoVxMyQrAlOatk7ozfl7mfBWfNbwBDUA0g1GKd0o+GuKYDGjEB8D6BhEEfCYwaIhRjX6Om2SFQCakWhS7Z7ZYM9wjKvy8HDj+kP/KHKGaJfI91266XpohnLV7HcOC9wNgRsLYO3i+++W8fC0JxS0fNGWTKvSAK8pCqxNZ9pGwBJAy5iUUMnqtTw0JhCpOHFmrVjNnEFp6D0/LZ1N2+v5AcZzP+X73/8+fvEXfxG/8Ru/gX/37/4dfuInfgK/9mu/dva13/nOd/DOO++8rwNZSl0BdWXpgOQ5YHI7tn/vataMlIKOg5WMbytFO7+g90RolZLcFZka4HeByqqeQ9+Vqse3xu2YZc2YnEzgRIoahlKkGURP6c6lqye5ByGyTtG1cl+j5Z7IljWqtDSD4T9yOGC+PWA5TCiHCYv9lMMRZZr1xz0J6d7ExeHENe4GYZX2tLSoS+vHysa60ZHwu+pLjmI6MfwA6107duXyn1bS7YZCQocuQftpMvub90dj/8yfEn+ke47t7702x1Opi3XwivfVX18sBKtVwcxqG0QFrKQ+Nbr8qzIMPp7rQfzBH/wBPv/5z+Ott94CAPzMz/wMvvzlL+OXfumXVuXQ9/f3+Pmf/3n8wi/8An7u537upQ/kT+8m3KQj3rubcTBgcj6WtnguhRZSXUUoFmIZYcok51RUNmHYJZWHs7Lszn9Iq8yFew878yB2iTEy1DjMhzWlFgDChPbdvfjNFd29nOPh4OTNYcbNYWkg7GTnupiSdyoMWbhJ5r03KFD53v2Mx2PC3VwwFS1oWqRiZ1RprRocAUydPj1PLayQ4z2qCdTOtwcNIeYFy/1kYOTcjIBMC7bNh6pImzRpb39LayYmDZq9oHGvxmAYm0K2Vo66QQh6EXnQx2m0GLtfz3ZfCZZW1nm3y4xdcYYlr4hPQPAOpBtmX8BuBM4P1jQnh+yHxEzIszerOKInEElu2zqciINJFSskE8xMECEMqYApQYgwldo6pGWrYfEmPoQFkFdnJJ5rIL773e/izTffbP9/8803cXNzg9vb21WY8ff+3t/DX/krfwWf/exn39eBzIvgWFUopu2gBlBGD8LBxeoUVXv+tK2dirlEyTmXm3OtCc0SuNS5se9SV2NKFivGxrYrEDAOX0C2SzUMAjY5bTeKNRfuWbhMWnHPaakARA2wPefNYo5L34kqvICoaqrRqgaxhAKsyGz0su1ZQ4dymNQzmBYsh6MCkpOGFw2cLKIGoAg49f6RZ70Ir/YMmQv1HLh3gso5eAxmHIhaMZMfdws7bA0xKR+ikZJCennFUMOp2y4VDWuQ2o3DJcZqAxSlb4CxUfA5Itulort1lTA3QH37cjEBYvfpWRT4nC1uEkFTuhK770UqkPxaUdMCfZXjuQZCRE6EUwCAAyD127/928g54y//5b+MP/7jP35fB3KYC6baXa71d9EJLywKx0TvIWr2ucCMKz8Ra4YiCohcOffB2JNj6k1UWlihH7z+8ngjwk5YltpouqVWLAIcFk9tltYN6m4qzRjG82znBjM2gmYko8y7/lQkqphKRR2slNiZdpxBe+uSJQU07XWBHu/V/Q8LvYoErEFQ7HF7zjIXpQh4yCjTjDQOkGlBYVZjvL2hEXvIozXhIb1WXvZsGgktNDLlqRrCC4cBYhbAjQOzZpogSrv2SRI9hzhi1+9Vz5HNTr4lp50jsMW/t88Pf1+k08Tb52Q3Dr0uxPkT+n+ClIpdphN8wsMMwIHvigqlYicORtcv0CsazzUQn/rUp/Ctb32r/f973/seXn/9dVxfX7fn/tW/+lc4HA748pe/jHme2+Pf/M3fxI//+I+/0IEc5oIJumA0ranPk+0cESkGTi32lvXHPDaJOcUeFH+IpCivt2ihxaB8hxxSmW2XMlEQvQnSVzExah5ROTfDsFhMeVxqCy9uJg0vWmhh5C+RdQk682kJuIgCtVMS3E8Lbo4LdplNO0OLm/ZLReaMnXU6r2UCVwGlSTcl4zoosSmBUgcdayn2W42Dg5TRS/CshcxLe1zmBTzmdXjnzVxML4Ly2AvAzJDWNLTr1ia2PxdKoT3mX10fQpPI3+eE4yJgTii1gAMZyo1DxGl03nSj4Gzd7XxKfLrA1POzTE6TBbxsTABgQi/XT9wbH0VtEdcc0YZB1UIMfb/pg6+Mox+mG84KzaCRCfA8lzb/kuO5BuILX/gC3nnnHXznO9/BW2+9hW984xv44he/uHrNv/gX/6I9/uM//mN86UtfwrvvvvuBD87Rae+ABfQQ41ljqwvh1rqFDZsfpk50YXdhn/0F/XeIlXUy95vYm82GPLwZkNOPXJ8j87o1n48WmtQAeloRE1WYJ5NA1cCrqoIwbTcfhlU6khLjheF5QNW+0b22auS155YJEa+Vpjg8PvOcew9eq7D6KOoMRq96TKQuedkspi1e4PfgkgexHVEesL/Oj6gv9PjaZz2+9LdEa1HlOByQ9WhqfT46d1dy/D9KHsTHP/5xfO1rX8NXvvIVzPOMz3zmM3jnnXfw7W9/G1/96ldfiSEAujXNYwIthGHnxJx6PsU5ApgKKhEK86ppL9DDi9Zwx+oq3IO4GnMTenHvYZ+j4EoXWvHRujoDq8ne6vsdf3CgMsS7sSO1/7ghyENqsvucuZ2rYyaumO07VGNiGrtxnxNuZ3Ozh4T9/jXNkXNWT4Iz+A0A07FxIWS8Rz5M3TMoPcwAoJow83o3ImZwaAXgXkYtsiZiRQzCQMoWNnCQXnMxW85NK6ESN0JZdWJT7YVOPvze7DNjLhUDV5QqiF3Bo1F2/slkTNYptO4DzhuK7cL332PzAKThWPH1UWx4i1eoYnldeR+JCW5hfe5FEWJmCgprCNmbjcGIhvYVVXa+EA/i7bffxttvv7167o033jhrHD796U+/NAcCcJRXtRcmogbcedu9VmgV4suUWRH/zK3AzYeHFk3o1p7z2oYx4A5ehOXgpSPEK+zF6/rbAfeYz1luXrEn8JRVd3fnclqMpfJ5arzyoDc0lU78cQVvNpWqPgEt7TkLEhU8PS4YeMDC1cqFjZK7ewRaBqXYVAGlUZW+pwOYGcMjD0fUOHDipmSmHsLp9IiMSMcvajMOgQzRSFLpRKdyZSxcl9FCNE/leYVjLGzyFCWTXle1h0oi4kIYKuEYQouWwrTfDvSqLmh/DHTy0/OMRGJq+IIv8iKlgZA+tkalv9+NAzeWbLtkTtXmzuh1Wr3OTfS0O/fXt/tB1DgQH7rWe2NmUNCBnDKj1mSGobQ0Pokai5S190HKjFLqSUu9bTu8bR+MmFZ6EfXkDzo6KLVWo4Z5DPH4ibp34dyNqJEJePlwz5UvoiBWlgq2NF7KGWlgZdYNk/Ij9o9A+0f6PeMe2fAHPkyozOAAXjqVGughBSVlUUY2ZX9N70CmJ73u17B67BPZQErP6zcjW7uICtD/H0M4MoqQd8WagbNVmjFrcTQswQHfbeu+Z42tkdhiFTF16Y/PvSZK9nu9TUx/tmbCyfVHuBXm9eyNP35OOPwBx4MxEK9dDViuhnbDxsy4OSyaCZhKA/Nat++ZWtxOTCuNiNj3IiV1i30XjlqTbqnjBY+2otZ6GRGOfQ+2f9rMMzbXMAKkEdCaEmNq7eK6d+E5dOdqRFCriOpnuEwZoBOoSMYx6Q6nPAnC1fgadnkElgkJQMoZ9XAHAKDbp+CrW1BiyLwgH0Ys9yZjZoxLQEMQAC3EeG4HaWdRrtKaqes+OEhpYrWTdA/M1ZYaphNqbVZhhuNFrLtsSYDUtXvvnIebw2JNfpf2ONbAbA1EDAO2YOT5hd8XfZH1a9ev65/n4sNeMXw1JAxMeH0/NOOwz2ocxqThRmJg5K5w5RKCFFv/vcLxYAzEkBjDmHE9GpvS5N+KVNwDq5ZqHnoAjEqnLEtvauMxvHsQ0XvoHgSa4pCPeO+l4pkm+mWtt7ae7/Jo2xg1/t+fi7qZPhab/ImqpTwLhBn3qaAimWqSahcwCcZhD3CGzPfgMoH2AO0fgUUgUpD3IyS2GnR8wcu0N+2tYqhxUohl/IfmSZwLL4xc5mBkNA5RZem5pE3LbjLDzrVfIw8zpqKeghuDqDVyyUDE4fdqm6W4NM5lNuL/nd6fOFbmds2R5jkkakWCahS6cJBzcyhK4r9iRWvgIRmIrACd75S90YzdQCIrv7UqOqkABCJARtd6AAxrSIyUNROQh4RsWpOxzR2AQG9VSxBdWGADAgVdhQrABTsSsZFYtFbAyTw+ab3AqPBaTt8nauLe08HH1kBEMV2fJGJ04WMh3M0Cpl6W7CXH6oUwElkadPdEpcrmEen1I8SKp6oU8DKDh1ssQ264xJY0FYeXfCuTMpR16x/tx1KbwXvo1OCM2fGbYBy0avU0g+FmiJz8ZnI1BONDZMUmXCSGAy4Vq2dLMBarn+0OLKeL3e/HuXt0rr/quRDDhY+HpAplORGeWLpd2xikJiG4N63R0Zi9TACVSZXAqyir1+eiG4tlUpXtVzAejIG4yoxhn1c9IrzXgVvhaRHcc+k5bu7Nd/OmrMSBP2bC9dXQul55g5uVy2gxqhuHs56aGwdDQ13otZKKilYmQLQKUEhxBEekXZYe6FWZ3kDHd7QtNhJ3mWhUXDMzsaX0CnCH0gzNcZEWwx4X68OxU48iM+G18Qq7xyNoukMCwPtH4OsnaGpQt0/Bh1st9z4eWjVn5EY4ccrxCE4BOTf+g4cXNY2oaUAd9o3v4PyHxYxBqUr28kpL1370e9LYkybGYoqBICKMbCFJJiRJEAHuDLj02H4Vzl1SED9HruLzoUZe/Z9XwHc0ABFIjPUirga2y4zXdxnMhNdNqTwxsE/cvAUvGMwQ0HRr4cSsBYPRYwi6o1Rmreh9BePBGAjHAZx9NmY+6ZQUFzUzoVYys8AnYYaKyFBLD572tjgNDi46jufUmAA4X0oVnDcGKpyXhzBeiejn4hM3M6FsztHL0Ptr++PI0Cu1goW0A1QF2OTZAZ1ogGAujGI6aLNUjEMGhj1qHgAZQeMC2lmtxDKroZAEiNh5LKjlFG9oYGUs6w46HCvGaUvBdUq1SM9M1BZWKD0dcKq63Rjy5/tCA3q2STkCWubtnIjngc/nCGkvMi7No86rWddd+N9W/T2oK6N7FzYPJzKj9Vf1cIKWqdUAUZkQdSkB9GpeOC7xauTnHoyByNbYZMwMLNIsM9CNhadBi1S4EoPYgtA4VF/v3IFxUE/BS6WVOZkDMhwbp6yPZ+tFRHXo1iUKUJidM5itlJxqq+UYEqPU0rgVEnYgt+9btDvuTt2o9d1pl9fZjGTMu+PiBkCstkSPb2Cn4w5NGxMQjGnEbv+6pj45I32sos6Tsh9dmev2hwAzUlImlRSBoCtL8ZA1zBiHRqmGVWuq3JyFE+YxeNOYQhm1wrCB7sEVqSchHsGMADqhzfUfE+vfK8Nk6QAgYS7GFSnSPDedQ7QyzKvHWgm2GrH1ontx3hpBn0vNI3y8z01gyHujMHXWpIsQee8UF0OOXoNLGO4SqzJ5FVA5AmXpUoIiXTEsehAuEARole7Ne2dW2cuPB2MgtNHsKcsRWFNSSww5wsKKDXeZFaT0G9na1dnv0W7KdocpBn6aHV4fYDAOcI+hKlEL1gOCmyuM9gmNdEWnqc5zI60MRMcddtET2h537a7yVAiJpTXvGZhNAMUFXzVXXqpg2D0GpwwxJSLKU1sj9ajalAJo2DDN9rcMwdJIU2ShhdZbWBl3Ho3SneEdvhsGkUYtbQawSK9ZaSClhRUn88MyTIl1Vyb4rtwNihu/vV2ju7m0HTy3cEDOzq9mJC7ci2icFWjmVVjhRmFI1LIRg1UIA2gApGMPiktpoyOCKXBHjGFSTKEJEM8H0HxvYe7U1cJc9MekAwGgzhPK0/99dn697HgwBuJ54xJyvGrDR1Ej8FzM2MMMXyweDwI6IYioSZk3otQl7YcwofwopOJiqNJLh883x8l8aiDj8ft5dUGTy6h7Iv0ONtHcUpXGtHLnq2IpFKorqYGL3EKGihmU+KT0u3EhWtVmf++JEIxlMDwAcrp4K5wMx0+Ehj0A3Tgw3JvotPhktOsC9SySdM/NM1dSz9dXAJef979d2rBWXh71hR+zET7HPJzIrPNqbAai91EdEyFV61Xi4YQsTR2Myty9hmVZif102UDjzC8z6ny8eF4vMx6UgZDa0WWnwZ5bTI44t7RnrPRj5UfEtu+PLbS4HhNeM1cwqlZ7LJiNoeagGHAhjbkRNQHnVlIc12w3Gr3k2KsxI5IOoOEK0XuIvUG3HcMBYLYwM5YuR4Mzi2EQopoJM4kqK5u8egW6658HoFDXbgBAhzvVqDRxF8cjfDFT4laQhTyAxj1otwftr1tRlms8ODA5izcK6h5ErWdCC3JjiLOLqhOGgArFd9xgToVAOeF6ENwYDuPXsMhpI+Zz1w5Yd3CP3oN7oVehX0rrnZIYT8bUwthd1h6jA/f+Kfvcmxxl0YwETUe0Rkeeoai1SQzQcgCmo6p/HW67yPDhViUDl64aBikof/ohCzHiiOw24PkFNXE472Hbc9P/726gYx7t/6w3kl22HKeFMYDRWd0KhAKjaBg8RQeEFF4wEp5yuzS2noMfN9ALd0pVQZOYedwSfdQwURNpHUCrLI1UXC6a4u4Z1JAuJOamDdFUq7zmwkKNXv7Oq3oLvw5OTVddAw031POqdo5ovyNz0PEHJwklQquwFcdaRF9TgeYhugex9SLjtb7Mejz9aXMpdTxhSNzCjGvTPM1GaqLoLbBm7DIBKBP4eKuewnw43+jIvYbpaBqjpkBueqNy+xSuJTrf3rcs03xzf3F+vcx4MAZiFkHdyM5td9rV402pdJUKaj0R+s3fmdXfnbH43jnrKndZ+853tziX0HLOJ5oQHNxmQ+Cj2x8XbKT7xp+oG9AnX1p3pjYjBqDRcVkUlOMESD1F1KMxHJIL4gTWKGwhnlPD3hivJmNv/3ehWpeQ24YljRQVOA8VXWkrZi685qJeCMzYjtO1OTx2J0AXlgm0DmkEV8LCiiVUgm0AhOJaowYoToteVwe+L3kQ0VDH++LXdpe4dXO/HrjxGHprBGuuZB7QymvwHiZHa1dQJsUWgLYB1RI6qE8HyP0tIAXiHsR0gNz+UBXBTC7QGa/TzQGvYjwYAzEVwcHa7U2L4Ad3qpsABLJU7epSZZF2MbYjAnuxtZ5Xbu6yuoGv71Xwc5c3saDz4uFVip2QooVH1BZAqyMI8bRPfMDf3ku0vWO5GwdPc0bj4C6sN6GNBsIXuaZMzbNyADcYp2YcuPP6eyrN02hQhFxK/12lVXyuRsMdTEsi6kta5sPJUl6p2URg8thDi6qhhbIk16GF/27hneFAbrBbeOGuuMfqrmTOGWNYlHvbGABt+FukA5IxjAUue6luJK7G3OaSZx+uhoQno24yT8aMR6Per+uB21xq84phYcICPj4FzZqVqLc/0Ora6dA6dZ/oiQJrr+HpDyD3t1gOE44/uGmKYOVgFPkiONx9yDAIEZzsrluWmxsHp1qfKE+lTkgBTgujXClqsF2ZCQ3oYlBLR7YCmDP0Var1VKY9eA/xiC5pGJ6bjOcAMDcOTN0wAL6A3A+H8R/WeX/HKrIZAmavfFxnWZxs07Izfg4bI6Fgpd2obTFWo2MHarX/9hCs4UU4Oy5FkNtwox2z1x5U0Z4QVVCrIHFevTbyULSvZyeRjTm1+xHvwTm6u3ujbqi6oe7FVR7+tA2G0DIumnlQg0az4g0o06qTWQMZty0EAMUYlsnwhrlpiZbDhDLNzYto13N5NcIxD8ZAHE2D0kOLxnjbGIZiXkOszdjWW7h77roPV83qp+ZBXI/JemsiWHo0YkoLLWRjJIhPG708Rxz8UvpsG1ok1gKex7us4KmXeQdyzRYXEYvrr0vdeBC0ah14Zd5ITt1gaherpQNhsqi61DJf7vMYJ24gRgE4fU+ovdAS+Ms4EpMbOnsrqFcsethH5tVtUP7KGTQzwAvy7nG7rtmuAbPS9HXhFjARZvE5ls5iXvE+JdZu76MZgjaHhoTXG+itgOTA2jvFQ1aO3AVviTjdAMd7yOEW8t6fwtsRVOuc7qK/cUQPYn56g8VUyOfbe5X+M2MBwArvPmREqcNcsLhWoxkH7zLllZpR3dofs4GLAFbZi12IGV1z8moIGhDc3T9HlVfxbZlOuA+r8ZJFMYobUMjH9+xFo+jmHgoNYXLHhrTbtnLJJvu2T0YirLqTr8VG0Ip9Ou6gRkL7ZpSeWw9jq9T1QsMLsixr8iwj0d4S2JIRoBwYaD1HDX+gZQZIcQiqKpAzsOpSjEkNLi/AXFI3tFxQhHHM0vQiLhVsuYF4ZMrn+6wGfG8ch8ejNVfKjKtBj/U6k2YkjoozNB6DEZzq7Q9RD7eQ+1vI0x+oQbZ+JQC6wG8wwI5B1CKYn95hvj2gTHNXJjcjAWiZwVbs5/2OB2Mg4jiXm45g5LmxlaLzXdmb4vhEc9COCeuMBQLJKbLTnlUhZwi6v79Sl0IDfCHX5uJ6LK3pNmnVnDGtGbt1u3FgQjMO2rfRORH2PZzOCv16j0c3Dn6OgNGTX6agx9vpra55Otnp2nUJjwnm8lsoRNayTsOAas1uNl+3mQLOSTlXf0BgeDcpKgtSGiDmebBds31mM06e8m1JaLCQ8kb4FKzs4al6czn12honPKnniV4q4I2V3Mvxoio3xN4MubVFtI5n86RZI+nycX4kbqzrhXtGicGy4ae8gvFgDMSyiQN9gcTLEbMWcUG4fmNahRaBPTkkXBnK7BmLXaZGbR2d2gr0XPTWe7jkQZzRhXBST/KJZN5AIsJr+6GBXU8NhF0BqiHT0uTG+LRfKNtk3EQvLcZ3LkfMwXtPCY2hoa5vrTqBQwbjJFTgBPD2Od78ne1yFFAVVFnaQqnESJQtEqsAXJiVwA5Syjq1HHkOyeoryEKi9tuKkprsu92L3XitYKRlSkqqyDzgelAa9t2sDZJdUBhAa2QD9LDNjXNiZUc60BvDiivjzzwalAmJMoGne51Hsmg4VKv+v0zao2SZe+d074NqrQioCKgUIKX1dbXCuTItK3A+Gd1dvD0B1EA8iwD2MuPBGAgfmQNPnqiJhUTPwY1DNTTch++8MbxoKH4jQ+mCibL2LZ/uCyUaBx9uCDbFMZX4LF/ChVUdxBpYWroN6Gg6EPQeqBuUYWMctoy8xgc4cw0dFfFdt6kRwRaaLbzVuW2NoIilN4ulFNNpItI7Zm3HxgMjWZBSBqCWs6KCqqqVi3SGpP8GOqrDMSUb8aC2Gy9aWSvZOk1pn8qcxhZGFiIItL5ntDWnWhrUhJFnqZjtRs6+WQVQOAq47HNCtuvv3IbM1DAGLIeVEfNrQOYx1NDIyJsou/gvivQ0cint+taiHdajB8GpewpOg/fn+QV5Q88bD8ZAiNReARm8iFKpVW5uoe6VKG14TyO0tNRe5xKonl9Amjmk+0I590khjP+O9OHNIMMHKqrtPLoY94khBhiKqFbDXDr5KVb+RR7/2oPoIZLrEroE2bYrhWyWcuc9WOiyzdKcGyciMBvNZU4KpgFrmXsPOTa8Cs5oWIQveA3NSPU9gNanxr95lSzy37U2z2c1qoAqd68FwJhGlEoopJ86WxhBlFAzcJ8KhkU9jeNSMLMS0IbgQcCO1VmSrS0j+Vzqno6nw32TuaTw5LT0asSyukzgcVgt/ho8Ah9pzKogPuT2GgCrloj+Pr5AAXjZ8WAMRLEsReKuAZGYMIJRWEBi1n4LlBO1hryxqKkr8lAHKXNqtfa7rAIcqAKaDnCxjZOQ4sIiakKs4Thg7EaIluzukupm8l7DnblIc3NnUWVqH76buVHoeAMsp84gAvapZzQ83++LH/BU65oxSQZKuhHKBKy6hEkwfM6eBJTjAAsbmAFZC8I0w+BciLxG38njbXucOQMWNtYKSPQmWDGKZw26ZMzifSqLFjWVjDwCj4exNzIyHoZrT1xlwqFUqyxNXeBWTlPoPp/2WbNfA6vKk3d91xBiXl/XM3NmRWWX0loSVgA8lJUojz+O8n6JGXi0R3Ihn/2IKOjj7xlekfT9gzEQcawVg09njXsL8SZGkNI9kL5j6s5L1Hd5DYc7et+s/TnP4QVH3Pncg6iVkCogVDEmNim4iiF0p27H3UIM/aTGnjTjwBYedM5GONf23WS7dEVtKUMHB9fHe1HDkBMQcAgv/45y6pEYterivR22s1d/DG7eQjduZw7hUgj9PNA4/JB1GMukaCUDYDOcJo+BoRG1GICAnbS1+X6/Lw5I+zxyLYrnDjfArtMQmxqH8I0Sr0R5gJ4+buGEMCT83wWGa+rkwRMZwPc5HoyBKPXZGQoPM6IUfR64lXa7IO2KcEQ9ezAwt7DC48dWa++WH3i2UYj1CvFp2IQmQoZOQHdDxZScvIx5lwlFdOeMXmBFcKPd1bbfjby1SskquJo2ry1twq+byDQDEo67yaRbSTYBVngVqgINnCTvO+Ll3a0Rj3oOuhMmYBh7n804LDy7tPAdg4jn3wBKpvV98YXmjE1/zv8si4r4+HOsbQByUpwiESAgpFLBVrx2XHR3LgIMXFckN59Hjv0AaDL8i6ghTq1jmHmlxCrtJwmNnWqdr2j/CFiMszAd1GtzYpqcehEU614ApJTALiZsB1QmJU7xoO9Jw4+wL8aPYsxLwQ5oFGQfvuCJCSRqDBr/IWQvtsahvZ9605GBNR3lNGqaj+aWTl38Mw7vhXFBsp2qtJ0xtcnorj6hcoWYzqUvVu8SrZwAfUsveV5Xg/rfPc5lAnapYycZp+BitiKpCiBVOgEWVwuU1K+vVvJdAe3C5aFFCDHaW7xyM4Xu3XkEBjcgeW1EA/hbPS1M1Nrkeau5jkXY/TbvyHfsFgoBen5V2jHHe0W1ohbVq6gOFpJ5N6QNjnfWpEc3DVGcCypuK6wFX1JpdS9iVshHLMJLzMjDPmw0I1Y6kWKkOzFDbNeLjvfAMqh48DJrirMUeL8RYjYjoQa53cdxjyoFaVCjwkNuTEo1FK9maT8YAwFc1nzY9qs8MQ5pLScHPLvOX/GCC0yzMAnPpTAvfibWXgD7LgWLs931Z8UIgLWB8LRfCc+5dxAxhE4dRwdWw3Gr86ALgYkvUphbQRXZQnNvgrwjuFhOvrRz8+sStR/cTa7RmF7wtF50+GLU62he4za0iF7EuSECom7ASaDGkNEKvIhHnSdSm0GCVFOvUjR1Wx/SPt5TqKLHW6SqinoaNbtVFrSO23b92vHY/fGmxhBTDz/DNYmhRj/1HuZVp76X0loS1MInvKD3Ox6MgZit0/WW9urDG8g4/sC5d5tySblz8vDPHTG1af8HgKalHjMX20kvYpnPLmR71rHbxoObSe3eREVXdga619GbpGjRD2Kx0pbl6TumhQ4pys3DPxdgYpWCq7bjOx4DC5mGERInqxcSmQo2ZQPbrK1eDZ5Dq+AM1yyKx5zvtwmkJivXhVU8HU1TKCYj1nSpk6TifYuPwzk1D0MYKLMawxEYeUQhQoVgKkAxIlesNN2OYukYBTT1mBch5OKh3x4pqVGvoW9FtVSnfsis99C8tBZqWM1L5DowFIc4UQ0HQNlA5GHsIRoz0m7divL9jodjIKy24lnaDy4GAwD7ITVj4GXRXdK+G4ltodQzhUwDWFkh3UhsXxY/QwQE6cYkPO+feem7+nmppmUlRrZGMmo0SLuH+SIBNNceyDcnn1WDi2+LqaasOxsQPlvj5vZ+sf6ZaQFkBGhRl9ZiY3eJW0jBrGEF9z6biEI6sezbn8NJproN27wb69INRcu4nMsOEKMLBPbr3wrqzIvYXiM3VrQkpDEjMWMRu8bsbf/U+1NPQQ+6ZV+qeoWoaE289HoqXlGqNMOWOesU8s7by6SGGQDEpPmAk4UfhxgISR5hbIq5mvcacIoPnQexCEAXjMOYGMVyYL7wr8ZuIHbBe3AdxxZq2GKu0BtcalUEm7m51a0zcphwzx3b8EPO72AnZKtzOx2bq2yudGoScIwW9Ta5saBu7M9tjqstAIwWQhhgBvUcWps7xzjSqPwPIm1OXByPGDtwtuDE/W34gl1fvSabdnvR67LK1zicxyG1rohr3k6v1VwYbdlTmavru806tYK68FofzI1xiTJrRSVnZM6m2UEY0IV41JvQtDHXCiHzgFoaOeBlISysVbEjARpITpwV4OAMsGMjYaF7yBAvceRDbK9/O6dk4r1uLAEehvOvfcnxYAyEyrc7kNeNQSzB9ToGAKZQ3dWFo7FwkZWI7M9FMLBqAhQCChg5jwpoVdEbV81r2Cz+BkY+bzRArnb31kMYn7yhTLkbiI4B1DyiaUSmoX1ue48Dqq5RcY7EZTt2Q/LdK7LP3e6KAzPy7jFQVR2ciHVRVlHQcjqugMo2UZdFZxDn9TU75z1swiqGG291G5yG5fwOApCqKS1J6SzFGprFhGvYGhqF7yIDYdtrAd3yt6AzLxh3jwEob2Ui42g0bwGmggXT9rDOZiKt6a9zVrzuw4lsO6PFa8UwI+c9aD6a/mfUAL2cdahFU5ir68/hWvupmCAOpIDG+eLnvcx4MAZiS0y5JP/lBqLjDVp3kTd/jyS0Rs1GUDOCEp16N2THz18QmLzwmmgcWmbknMZgmNxtIftnJmlpxxYquPFxCm/4rDZalsAWhuERlbORofz7PIVnhyfWLNiyGbDsQOWs3+dCMJeuxcoz2kz0sEuee//WEfZQigmrgqcT4+vXMB4DsAZUt8e4fVwHvU9QrCBRRoGHc5q+JMtmCKml8LsuVWnic/FrKZr9YE2h+zEo49IbEpPaJweEgecah9UpFKO944x34IbCQcv3CRBvx4MxEM5jAHpHLR/ndBNchLYpMQWL4OIpEYc4LIIxMSbx9FpFMv1EAD1vzX3RnexK24seb4qPSNaJO553QiqTpeK6nBgPY99p615dX5eJbwCinH7WNmQBVqCgup0JoKm5oUijZVRqELhxJJ8wZk3PkeMKnIG8xlaasImUDlhuAckz1YS+8Nv9bM/2v2t2RkFYmo9a1+CP/bwDuW1bWNYyLOFanBuV2ErFzdNaBqRBNxxYalpgDF/DRyqREajcKKgXoddC591szXtaK0CugJB5JBUnbLV229yT0PqLNGRUkS4QvFUO9/dshz+XP2QhxpipLXhgq7DUvQaXELsa00qSzUEuH9EBma0Y534p2GXPb+uunYgxjteoZzID9cziiy6zZwaI0RZw9B5aoY7RcGtRcVExkkxMqTrwxzvttUEu/OoGAji/OLZxqbPzbMcnca9k7DgB+o5eBAB7Vy4gpYw07IFlUqXruFNbSXKdDupReDHRuAen1K9NGi6CswSo6Es7FqzPL0jfNUDWQwxZVPLdtBPqOc2KbJRvABQN75njqMTAQtqYeHGdTcaY96iWck7NmKoHGsvSpdZGz/bhlPmBK4CEzIpbZAEkUUtznr02zqrMA6gU61xmfIiNwW3G4YyRUI7K7ux3vOx4MAbilAm5YUVyFw+NhVgAWkluwnkuhRi3XmXtAEZFcW4FA6WSMuGqUVarAJXcI3+5ERZT9CZa/b8Lg7gmAGwiLxOaOOwwakoM6JNpA3yq8tNp/8UVH4M7D0Df52DlerKJgWtex3ExrQugiclAjU+152opQA5ZkfgdIgBfAnG7dwTgBIxc6ShYsxgvkfbrCKAtlHZNmJUunvPaWMXz8Wu5Mu72fAs57eMCJyU6AcyEYlajXccojdXeT+3+nPX82gvNO0ipz49nZDjiiCrjr2q8kIH45je/iV//9V/HNE347Gc/i1/91V/F48ePV6/5L//lv+BXfuVX8PTpUzAz/sE/+Af43Oc+98IH8trVgB97vFthDIAzCDvFNdYp+ONshiIKgUj1jIjgUKi1d3PBmFKVQdeFaoHECcNw3Vzd6irDrmod42l3wQFbzGHSG+ruTU9wvA+S5ffdRfdheezKGj9qcVQCDbXJ7EeBkeoGZisuawxKsj4XDLR41LkamiUBNvSnFcnLz7OGRdLKk13HALOBZbYwx72eM2egKviLtFiWSNSYEK8Uwpsx8OvWFqn93ZvQVuk9IZZJu36FpjHw8wTUe9jZNRkFJEPzqrwL2tZIaAyxgIpxOLxru99fIpD0xsxePDeXioGVResAJnOvuHWVMq/6bJwOKQY0L83gtjYDQEt9NoFgPz9XmIoFc1uj4OS1H1WI8f3vfx+/+Iu/iN/5nd/BW2+9ha9//ev4tV/7NfzyL/9ye839/T3+xt/4G/iH//Af4u2338bv/d7v4ed+7ufwb//tv33hA3m0y3i8z63PQBTriB2SG8OOw2vs74UrsEgLNYopJy+lV+c1bYYhQSobgOTKQBUlUUuxZR6txqK7+auU3TnX1Se4axXUqorFplrshmI1ZDMJ8rhKd0kwBOc8kNX3J+2EBSnWjyIpWi5aAk1lAVu4Efc43/SkpYA3ReTSvaDW4g0AmVHDMivnyhc/zFvxx8V1G0II4U1oI/gIrAFZDyvcwM4T6uG2hRhoQq+8ZiJGGX4pAMZuJMK9qsAqpCSBHpPT1a2fR7J6GjaOBhHaZqNCM3oBPUXLlkxiS+M2fMW9IzOCqxApiAA7JlGFzQPrz61eG88d6AYk/Yio1n/wB3+Az3/+83jrrbcAAD/zMz+DL3/5y/ilX/qlVjj1H/7Df8BP/MRP4O233wYAfPGLX8SnP/3plzqQ/WCiLombUfC+irGdHtC9t/Z/ByOLWnDIOlYsRmOepeK4FIgVbmkRlb5mYJhugANp+libnuQTtN3JRjpZfCJitbBaKGAiIS6hj3OTYjvOuYlbt3qVejQDYs8RoIrJVlCFYmh9NQk4oMu/ufvs19Z28xMgtImbFGyFYqoU7Qju4YAbpcbJAAALF7ay9dt0Zfu+jjk0b2GZV8ahueF2DapdU2IJqkyph0UpaZVrwJBOhocapMeSyAv99M+u5FUrY58qBqZWIs5Mq03OC+sGJpOeU6Fd9yKqn0sDO12oJ4ZPwTgEnUrFmrh5DVsv4lWM5xqI7373u3jzzTfb/998803c3Nzg9va2hRn/7b/9N3ziE5/A3/k7fwf/+T//Z7z22mv4+Z//+Zc6kNd3Az62H1bt0FdS72eYYe4VFEPkkRhSBWAlrfg+PRfBDCCJGxrt/HwoAhd3dZ1KV7rOSWv9Pbzx41jFn1ADMjb8IlxOwx3qMulEtrZodTqE8l2/iS/gDgaj0HbyNrnKiVdCnED768aAZM6oMoLyUSnYadRz4n5eDCATQItKtGOZ2o7vsuwuattqNFjdY1pmXYDT0cIMc+Vt92ve1haENC/rHODa+k5aaNbUnx2kDFLxvh9QHqwKlS3MM2O2f6S0ZOAky9G8ioCDVDIDSepNjGlEStwyMEUqxlQtpdm5ES6Ln1hVqB4NWnG7owI+3KpxnO5Bi3bSEp8fsjl/dgB841VAMatmGDyUiEYhD0C6e/6ceoHxXAMhIqsSax8cdrhlWfD7v//7+K3f+i381E/9FH7v934Pf/Nv/k38+3//7zGOL8YJ9wxFbGUWFZxbGBEQYyHL5ZvxdRcQICwhvnaNQQA4LtI+q7mDZEpClg0hi8+1ia8i2J4MiLuti9RWhLz26uL1Hbeh7nEhpLTGEID1TrAZNXoOwTg0ryIqEnEBFkPzl7nv7F5VSAuIRnjG3o2du7+qVal6ld0glXYcqx3KvQrHT8yDaByDEPdrk56pZ2Smo3IutmEX1uGUGwf3wtwzi8fUhqd0F+0rWu17q/DJznoxy+FMTMAqsrgxXF3TswIoSecgoYcU2pyITMjWPI55CiQ3/XGv8mw2CljNg3V4Eb2GTWhhuMRZKcD3MZ5rID71qU/hW9/6Vvv/9773Pbz++uu4vr5uz33yk5/ET/7kT+KnfuqnAAB/4S/8BXz1q1/F//gf/wM/+ZM/+UIHcpUZT8bcPIiWS7YRxWO8jZ2Ilg3P0PAiUfcaHL+wN8Aj7ljnPxfNWR8XgVQygFNTp84zJPcgav9c2Ke5avLsXkSSHm6gA3urHcJ333izgTXghM2EMDBQd2xZP9++R1ZApqZNDcwbRtB0AOUMmndAUq7DuMsoxKtzoknbzGvPjN7cpbprP7uLL+24AQDWbbof+wIaREMNoKeEQ6rWgccTwNXPa+7pzHYNHYfwx/5+x2P8/wCoFANqGTWlZmg0JSud61GL0tpjSLXBYNSrIGTWNHAlxsQVmWsr5/fKTlf62iXGWCfQNIGm296H83Cj13Q6oN7f6saxzHZtL7Qb4NT6ZZDXwwCt9H51L5hBw48ozfmFL3wB77zzDr7zne/grbfewje+8Q188YtfXL3mz/25P4d33nkHf/RHf4TPfe5z+I//8T+CiF4Kh3h9N+B1a06yrb1v69zcOTFu/FzEVJErhJzPb7s/ax8KQMFKH0Vq+7/HjHORJgwrVYVm95khmZVQJepJAFjpZiabEAQBE5sWgxXl6Bf0XL3Fy+udjluMvC2hXhuM0SaOAm40jNamre+63rZt1WNBCjCMEO7aDZxGSMotA5O93gJQt3++b41keb7XXgyH29ZufuVJLMAqm2FGRNygjfvOSQhGrRk0c6+3x7/ykPzvK5l4A2mnA2SaUaULuhIz0ngEMYNbhsB22XEPDDPEeScGmmpdxnKqDBXSrwB6vc6wB4ixTwN247WFKal5Esl0Omi6AR+fAmUBT7eodz8ERFBuf2jGrXfkXnXWCvMjGgcX5aFx38KMlZCPHycn0PAjat778Y9/HF/72tfwla98BfM84zOf+QzeeecdfPvb38ZXv/pVvPvuu/jEJz6Bf/yP/zH+/t//+7i/v8c4jviN3/gN7HYvbsWGBCvfhtXj44R0xgCkaq3+uTJc70Pho3fBdnj+dHjOWqT2kAW1CYHUCoBrqxVoy1sqKAFsoiyO/rfxElTXlTdxSboNfSdxsM0fA/MawQfs8aCuv+1QxKnzC+w1dZNFaAQvc4HrMvXdeUNO6ilQwyAMj/AS5lbkZZmEJl3XmJgGrIYQbIWnmLeg3xGOYZna+5oeo3Q9RleHZvMwKPv79DqRG88VOKqZltW981SsPV79ze/1fNDnkhZ8KbYy2e9jY4DCPIaGRzW5++hBnQszgnfgKcy0TnmeAJIvQd9+3nihXMjbb7/dMhQ+3njjDbz77rvt/3/2z/5Z/PN//s/f94FcDwnXWQlRbhgYtFJZ6iHG2hDEMSQCVwIgzUCI1IYh+GBCS5U6+w3oBkNl0cVUqmnlhQC9Uo9RkZmRLV3KtEkRxkUA9EWy8hZsUcUJgBAfEwHIvTJUipYMc9LFz8ZNbJ/Lp16I7bhsnkEtWp+hYrTSjAQt2gGqzhPk5gfa8i1wD9xLALBayOKLb55BZoxo3IN2KtAKE7SNoYSGEGt8wb9rFUL4udl1dEOwHLSXRC2CYr8pMdKQG/NwADTdCwP3ltn0HBNo/wh8pde0Wjp4NSJr1cIa4tSp8ZxRs3oT1e+jSO/8tRxQb95DXWaU2x+i3j1tbNoa2bRbw9A4HX4GaNwGGkbQ/pFe28hejaMKkH+EBuJHMa4GbY0X+1hGlSXdyS0sgANCXXdR05v+TstoSNUQhNb6jMCaUxFHMRK+kBfi1KYs3f4O7RwNeP2/hS5EyERrBiTQJ0CI0SkF47DZJRqqvtFbrFWAcQeONQj+mWd2oG4crGvTMEKO99ZVS+ngrSDLw6LjPcrhVhfS/W3f7QK92WP+rp04g5J1hVqstX0eLZVneIs3g/E7tMnENCzheIAYz0EOh9ZOLg7/3nKYmmFQQ1GMgbgGfjlxy7Q4LuF05obvbBS5AbQQSAL2sTK6psUZDbsbAEhBub+F3PwAkAK5faqhWimtAc76XnFThFp1xrKNA8yg3ZX+HvfAuG8Fde0zVozRD5nkXLacsWIPRsKxSrotBx5AkGnTseq0ZaFGMm/CDUUcMX3ahV9pZaDiONehOzOdRi5bAtXWAFzaLbYuYSRkxedkQUXXKGy4xLjvn739rNbr0b7LEfpARiJf9NNh4y2cJ2T52PZyYE7NE6BhWGEAq9MzOTsAAduQ3m3KdRmLnHyPf1fsBVFLgRTRxW8LzP8mAJJ5OJomtRCIk4Udft025+nhANA8KSB4JHlA515w8w4b0/Nw10HIwy3m2/tgzNb1FSSMWow9PFoBYTp/3Vtbgs1ci1Oxnrnm72c8GAPxeCQ8HvrJuiBokQop5mVXNEXoRXqhTDQeQ7jovvCdBrsdkVuRNnYhKk9t5qa94PQpL6NuNy4CR86t3+INQAAoWWsHPD8fPYgW+xr9twooDaAq4N0VZOw8AQSwq5bSAa2YK18WALqL1Ul37WYgbAdsrv6KnNVb8xVrQb+6LPOC4ZGeZ82mmCQFhNMdenXt5g7a+UJyD+HktW40ikCsI7a/TsMHac9RYpAwyuEItmshAIgP6uks02UPYnstngEituEZCQuV5tsDqog22z1MzZAB6tmoJ8mmXK1eRJURxIwMII2z1bvYPeC+cTjd3+n4q5E+ZNWcXAtSXTY7p7J3WDb8B/MenCAlUledkLoikXkJpgS0+r4LnkIc/pnbar1zo73iXE49AIur3TjmsiPYtC2U8hgXCqbpF5pylJF5eGdue/BYNN05nExiP4YWNpjXUGXtQaxqHbDmGvSduy9WPVSGTDN4RAPfSNay7R6rw7yNNvnNjY+4gkynBkKPpXsNVdbH0M6vbIBLLC3t2WTf7D7AU8PxPJe5Xws3FuHzKaW+U4fSfzmoUVgOkxqIIphv71EOvbiudcAas2VeBq0FSawZmDGfeE0nI1QXew8UB1PrC8zvFxkPxkDQfDRrrpYxWb2Ar03f7Ct63nkOTEof0Ti0xjM4xSD0tf0irgyQnH9dxCJaIQ5Z/wbXnwjdsmLWoS2K514IwxuivkIgT7WjlI6wN8owJ+U6GEbhmYQYN/vEoVotTQnEKtN67AvhXE4eALYknBoXqUhbvKuxNYK2KNXF57PGp/1/A/S2SxC+V49LF9fq2Ozv6ulkAAtSmjc8jp5a1ffYdT0eFDeQArm7a7LyHtpc+j6xlGuZFpTDEWLe0BIMBIDejdu7Zo3D6jMKM9Iya8i9TIojwcBd6s2KtXUB9U2FueNXH3A8HAPh6DmnrhNJ2dR3alsZLgE2l9iNWf+4apRjEl/t8wmr1Gg0sP58BVoJuJwxKIB1uQJMQqx/l3PuV7ULPoI8GOF0op9ejGAYSGXoTm64FxdVUTpfEKBtOhbhOOLZtO7bQAccIxXcDcS5c7gwmttsTWhVx6C0WD9qXqzSneFzlWkqzeDItLQQYmsMzl+2ELLBQhBLebb/ByCQzTASJ8Ul+NCOw6+FA6XLrYYLACDzvAoTttfBwy6Zlh76nPGGKDHSftSwYtNNi0fNxLQ07/GAmu9CqrY0oR6Xr6sue+9NfF7BeDAGwhcW1e4uceMlXB6XFnJsNdegho3X5USsanRqii92Ul/4fLbMh3M0vJSX7G0EnOpNPvPgzwN4/QADjrENXRpgCZB1jIJVTqKFHueAEqtgJJdYX+MVq2NrJ96B1HY1nmfkXvHYhhFxbMVUVu+znT4alzItvY2dh38LUI0v4Z5TnXXX93DBDUMEGWU+/b5m4MLjesbAOZCqmIh5JVuPpAgoeZm/hmsNQHZey7n59hI8nGeNB2MgWsntKs25fs05rpN3Zir1tOQpVn16MdK54d2vgB5qMPlzAcjkHlqMSdvgJVJhUhVYnVQibZlAxYqbWizfc//NnXbjMA+gPGizmqgExAZUcm7exPoE7f21qlw9gOqybPoftGKpdrI2mSSDxkWBS8+CNLd/WsXUJ2QcZtAwIo32nUFQ1YE2db+3BJ7Az4BlG4axk7g4NbDOP4skncUU4thmBMo02+XR92MGhPtnepagDBlsbn8yLMBDA0DTqPPtATLPmG8PaizCgt+GGL7gV8cWQ6DneGHnzks9qRmcD2pIjPnqHBP3JDDuGyZBUZ7gA44HYyDOWUDFIGpTF47jHFi4wiKYGiOzpU8JrbWbDydiudJzb36rngXQPY3EaGHFmEhL0wlIMgX15dDlOaQKAYQCIy9AUqYheTotD8pxiKw9rxfwEILPlClXAcRk3uoQMg7LiYYs2NrglaX1ylBKtIF2w6jFXX7Mm7f7oq9Aa++2avIyZDT9xAvD03Stg1dIw3on60uGIP6+NHzxFhHAXPy4OMuhZw/YjIWHCrFL9nI4YrEsxPTDOywHD3dqJ2WN3N5/0gFrKxMXmu2evy6nBsS9HTpaCGj1NbRMoOmgm964N+/XQo1XZByAh2Qg4ggL4Fmn+iIZBq/raBmOraduVGnvf+CxhmIWtPocb/5L0Pr+gTWej0bBVZBIivZadBrxvGbOVWGl/AI9Vn/ecOOwMRD6XJdvUw5JRZOj34wuaOsNe5fQj3PuKVjf2dv3h2IyAEhJSWvcFy4HD+BkeOZi9XmnVZwx7UfGDYClLC/xIvz7V59z4TikeTgMnmdQSogBU/H6jvupsTXLvKBM+iopXpcjKBPAiSCQVWerc8fQMj3RQwoe17OGhxoAGg4BoJG3VFvDaph9s3gF4+EYiNVuqQuhlHpCiALWUEI3CgGoZDMK3Pss9PZ1PSzxd2lDXX0s7IrGneId3+tsz5EEfP9DNQrHG+XdywIcblGXGTIdIFaU44zErfcg5r4zW7qsFNT91HLYbgwqEaorBHnPDKB18lbVK/1zzlBsItQEnB1ifApTekqv94yC/n2T+4/6BL7IveZimcGRI7DlB8QCK+E1fuElykaqqnlQd9939MQo04JUCoQFgmWVunRA8Jx3sVLlil5O2O23CzPiB2USLAdLuU6CMhVQouZtpJHBQ0JNhDQmNRKbxb9iRQ79+32sjCF3Y9H6bNo5LGUCzQs4MXhW74FGpbHT4Q40T0hGAxcAtBzxKsaDMRCxZVslbe4i6MZhi0XqJkeNAMWhnsJp1IwOHmb29Cfaa3zKELSOArAsBgEVnSXpRiUzMLqu4PEONN9p4dNy1PqGeVKasOXPG8lmCZV6ljVo6D7QCneaAvUlwMkMRuuMJdW6hcULowK8rRnQpiKxXW+Gln2TtZ4fd4pH7K60T6QkLSTasgt9MSNkC7bGIJ5b/E4x1an4uvC5rRiJWQvzzINIY9aO1YAaGP+8EA50ULC2Hb4bEvu/9/+wSXBu11b+hayMgn52RS3VDAStDIwAoKLMXSnSVNj975FCHccWb7k0VgCreyHOGjU2J/LQNiFKQ/NOP+h4MAaipfYuaT2GjIQAjYPgT/SajPgeajUbntXwlvIVPTxnoFGxY+GnGyU3Lpk7GEnLETSpbgIdblCslLfe3zZG4Ip8NE8r95gBjSUvXYvtY78+6MZhltowAvckajVeBmsXp1WjHnTOBIVsRh0EKHsQTYpHjHsFwoJq9qr+YCOISuO+K2UF7sQKhwi06l6o1I0EcQKSie2OWoWaARRmyLwg7Udzs1OnUBdpEUqZCspUdCFLtb/rotav2ngQW+qsDTUwamgUsJT2XjcQGBMYAilk99Rp3ZYVS+tQQs9vHUZsH2+Nh9PGJWRc/HEVMfznoOC2FLAZCEgB767Qams+4HhQBqIh9bYQfGhFZdWQQXR5q2YlN8EYyCldGlBQUkMNU/lxrkIYKXEPMaxhbhyN41Am8OEpIAv4eAu6+wHqdEB5+r8hd081nDjer8VNQnFO3AkygDQ8o6Xfhknpo1Tt81GkYiq1lZqXWu1cq4VEFftEIMpN2MYNY784Cmix6SEgjUrdtswLAzrpoqhNqNBsgGWUhIs8ishxsGKxuvQQcSUS4xWtpag8nBTQMIMHNaxsjWQiXVnmBYt5D/P9ApmLhQUzpFQ1GHNkUnp44R5EnzA1uGFVulGJz1MisKhHoKlJ81ZKxyY4kb4O5sEM/t5NdmZjEGLY0YrRPDszr59PY1Ya+pAxAuBxsOPR+yN5BJUPWYix3TWf5SER6ARs9CGyKhqEbPWbz+SMKagq2ROrv7duT8UKmzyNaWW7zVvYCIB4fFymXrPQdoMiJwmGs3X8Z65LNWPW8BNnl6ICQhAydS1yUPb8bpkCIcu5FMS5A5a2aPWre8l0FClpWIMXdUlRLUs/H6CneEtQ0grPr043JWtaDICtjgSTLopp6RmS4BEoiaq2kMB3fZnlBFisqTbDwCFciR5GNAoNkEyk3gX0s1MjY9WL3si5EbGG838/ZaluH7fMBksjYqXRNyXV8DipzXif4wEZCDrxHpr8wZmTjfJzYo1USyWUBMxiRCloJ++BKwgWmiTueeLAFyAAIFYPw55rf5tUYQllUs+hTMD9U8jT/61su6c/UO2EUhrqDaAx5zw+ZoupKXHfWRrLcIBrCUrgPajugIK3pXrT2O5BNBzCN2IzDkSWnSldSHV7uQdjdqZhj1pmgBfUMoP2qibNQAMum4EY98C4W3k4JIu6usuMmoI2RfQOpGdYViNgFi4qq7tvaZ+p1+bQeAqeXl14ves621JxAw0VOh5h8wWdD1NekOzlXoE+Pl3c1bwVTqTZqSSoplHIQ25EqHM4w7l0sNPVYdehX6rgBVmoxWPW3/t5VYUr04fMg6iwhdC0D2LvSOcprN/juMMs0vQlpRbDHIr1MgDGpAZiAFDYexQEQHDTjwFAawMHl2GzVCZNN8B0hNz8oAmqlPf+FNPTO8i0NDKNfpx9lk1kAZCvYpEQ996Ww6hAUwizOuU6W3WrnvNiHaYnr2YVtF6mjrEk1rStS+MV7mAugEY9JwL2Sam5ikfs1EtKpt0YNBAqkbbwM/5EKyATbZijYrUKMDYJNZeM86rJpYc5Kw3OPACSuhdlmR4axrZImBNyCzPW3ofjDY5BeBYiegOAYQyn06+9LnoDnDqg2YFGakClfq8YVbqa8TEDZUZF5gUka6PiC9znxpb3sS1E22Zo6phB7k0cJjVChwNob7TsebJq3Q8+HoyB8OExtYTHSpiy52uXufdeBHPRx2zalMwapx+Wgn1OWEo1gpSKjPaqT1uEm9oF5xM0eXZv9tL6NJhw6jy3+gEv0DlHqXWOfYxB+QzTkKJxCMbSrwvQr8E59EJM7CaZToX4GxigSkZdV2PJMP1Ny9aoElYXcq3e1dvUk2owVs2z8awT0N7n7QM1IyEqQ9cO8BnaEP4YQOWi3sISn0urjMrzeAOA8xPOP39y7S783Q1GNwzcshgx5fmiYcYKl+DzWQztxakck/Iy2Qh/7SvKYAAPyUBQT282lL4ohdp3ykUqjouGEzfHgru5YJaqv4usunAdF8FxYQxc8LGrAbvsPQxgDU2smQkREnED8ZyeTGXunaW9j4EUlLsfWpXfnVb6LTPmu07DXQ7HZhB88vAwdFLMkNuPew/IQ/upFl74byRt4lvsWhTpXoMbCzWkIeQqFUNiLFJBJtvXm/ygyfmTgb+lElIeVbTEjCJkUG9ho8VY7Rjjc9oZvarnMS6aJt1UaMZKyd5QOIjYxDRpVk4E5bEL8B7vWwp0O7ZeAg8GRJ6pV9ouZH+vA5PrKdlDizQmpCGBjPNwKVXqaVQyryJyNjhQwt04OO379Jx6OnP1/2cZxlAUCLxY+PS88XAMBNAmXDMS5i24cVhEF/4s2vTmbi6Q2p9D6S40AOt1IRgSQWrCLjMGrpbtcPWqDmIm4lUdA8miPIcyAfMEcW1Go0a7JyEGQnrGotcG5BVxptUoJKf6duk5yqPuvsTaNs1+V87mNaHVnNSqIVg940X4eaNo4RaRApcweXYiQiUV4a3uqdXaWZqclB/BgtZDI4qUeMfxqFUBALw0ui/yoKFEDBd8SLFQIk7mkBEJvStaeXNrbHwax2+9NU6kVY1brHcTOuihnE+Drj9PPYU0JKSxGwjiF/QYQpHZtmZEz3ud/jz3Gj3+Z3tML+JRvZ/xcAxEIAF5z4pSta/mVHQHvJsKnk4LpKrX4Abi4AYCa7q11IqFGXe5T1BtoFq1mtFo2MxK1FrlO0IDGX0c2YCXrfNJGGG1Bc7e48GMRhCKUUk4ey5iDwG0jXgMGQiZzOthqlrFCTWsQ3idV59emj690tVDm6yuvAvS8NqDaK+J1+ncb6DhCKdf2s9/pQ/BVlwVtSH8c55Jy5YAJDLSuP47sDUQPZPUPYiYFemZC39/vsrNMEQPIo74PkC9DjYB3TTmVsLNwwAeM9KQm2BMXOAcdDabdJ7IuggtpZU3GjecVzkejIGoSXfPxQzD1MKJivcOC46Leg0/PCwoteJmWnA7FRSpuLPfQG+Ws8uMqzFhYEapFdeD4HooKHXAPieUKqiVW7k2YC4lMagaOGm4Q6urWKazxx7z2gmAsCCNQ58cYSLoYxM7HTpASXkEhhE1Db2eP42d92Ceg5KhFGdJ7NgMoRoI6fRroC9+ar970Vr7ARobM3HWkmcfl8g2htucpNJqj4G34UUjTwWSFVlYddoHxND7oEvhVGwvsmrXPbFyE4aEtMlYpDHE+IFBGYdsQgwnWAFooQIA5H1WanVSDYcTYtO8nACncfH7Qk7jgHw1tv8Pj1Sdum0WQNfkLNLKzaO2hI80ZKT9iOThqgHdKjH4YWNSwmPq3o/Ce1PMlsb0UELFYiqmRTTdZ7+B0E3LH2dtsHO0xaFK1QWZkylYE2rqoOhqWFZj1RoturTcPYReqVfOhxXMvcZgs2vqa4Py1IY1WStWNSmNVVp1hbdCM7uI59TGKGQvthWt7XThYKNP/vPFXvpBDNTSrtNZHQyXk9uOYAyicVjJpOUMLNyucXwvgNU116eDAQj4QBrivTg9b9rSsplAqVdrumFJY5fTb17gZsSydz23dTHWyoscss6HoBe6mhdSQCLIUO/mnKk+Ka3fluW/gvFwDERM5VmufxFrvFukKUgdFn18P5fmOUzLqQcBKAOySMU+a8fwLITjoskoB/m092bXg6BNyrP932PpPCjINu7B+0eoy4zxSdcrkHlpqS/eTCgy/QMyurK3UKNxHzyHETXvUPOIWToGE0MMB1RTm/D9nBfpBnab6VBwsr9fww+jB0OvR3KcAUCrEAX6Ql9pTXTiWOs7uSy9BuVZ4RintttVohPgE1W6RiTQdBAQS7Qd7AVAacE2Dk8DN8OQxp5W1NdvuQw9++Shh2cpvCw8m+fAoZisnc5w6kFEzcnh0RXyfgSPGeOTa9VyyCP40ZMeagUNDm8vwEbXT64VCnTSHTN4v0eTwvfP4AS8IEbyvPFgDEQ13UjP8S+WppxLbeHFsagXUaqGFeo5yEUPAtCmwHOpOFgTnFkEWIBlqMYdUCOSCNaQBxd3TeIEDGPDI7zvAwDsTJA28iCa1qAZCkqpu9W7fWuhpgZibGFFzco1WJaesXCgvm2STK0TuYcOgHpb7m0ssq5aBdwwUAsvAFjHMjQFrxTl6/x6tHJywDtyd1ZpKHE/3Foefl5pYegX+e7fd7tGCHPwM17vwQhsJpBSTdjGjUIah5YF4ERIA7fwwK+/hwVurGO874t/W/UZF3oPMca22PN+FzxGAyH3HSPwkcahScrl671+xn4PfvyGGYgB/Oi1jr84Q9UNqxf9eT2PEaHYwt2V4LFtPOfSyB9kPBgD8aLDm+XkZ1jIxITEfGIs2mdwd7fPjsg/IFbCUHO7e/mzMv00lq4WS6cip66mE2MiMBd+yPCXyC+I4daWSdqk79CJUe1UzWvSkKJXqW71OKP3sB3RmJz+cd2mr3larm15EZhchxTtuQ0pbH3heEW9dvdZiUnrdGFnOtLKSKw/bl1KHUutW50DM2rqzFd/nYOK25By9d5xnVflIbfNwY1DU4KyojjaPzoNMfwahu5pbUY15un6mrSy+eiJvILxYAzEdlImK8YCBLtsqLNUzLliqLoI1NtgAAumRcww6CeMmbHL3H7v7ffA2qQ3M7VycK/2BNDb5jlaX0VrFfIYcAhTijaWX80D6OqRCqzsQ7GWaz+EHaKBkz5RsgGTaVBx2jwq70F6uOU4RAcbu86Fd8v2yksmPcfKGXXIgTthh77V1ghWwL2IXuYe3P1IHrNwgpbZalSmXqQWVLFjeLFqMei8j8FYmbFQr00IgacjeAdUKxDj3RXSXmX50n5EuhpBiVH2Y2NSaqXl5jxDSLDSYAgAc//qUywh73fgMa+8ESB4ECFEaR7OfsTw6EqVwq5fAz96omHF6x8HXz2ye65SceLEsw2ew0U1Pfh431PrVjG8wnf8usbM0CsYD8ZAxEHBjZbalaMHYQxJwAKMSRe8YhAM55t4LX5iwmgGQtWf7HcQj6H2u++qVrHUJi0J1FBkAdVNrt7pwEZJrssMZlYdBWtg017bXEHDHXL/XTkr94FzoFXXVpQVy885yOdRFeVouKJVEKolzpDxCpkYKWXMgpWkXrzO2+Hp3lXat2lZLo0f0gyFFIgVqXnvzpUnEWJj7whGrtRtjV9WfSr0IgMuv1dFDUopjVDGQ1EUv2EQrDjDVKD9iS57mNE4NK8g1nWcMxCWeXBMqb3W1bRS5zk45pT2O9C1GYUnb4AfvaYbw5Mfg+S9EuKsO7h9kW5IzStbUMtOH6cRNO5Ay6L6ml53YdeaWvbiQx5itNJuUh0GQA0BFmCXgbkwZqrY5YpSE6ZFcDUmTIvlns2LuB4TroakZeFmTNpv5qZIfdKj01F8W7SaWVABllpFJzkAcNLFL2m9kMJEryJr2ffIfYi/z+hfnBvNg4CGF50abniAcxf89cvUSFfJwxc5bSIUR1Oo2o6onlzUKLj30uX01h5DDY+b5P24B++uTCVrVMGTiEFEEJRU7Qpk8bk3BTbtCB4zeBg0HBjyiUBMNBIRF7rESrxENorYxbYSM5oSf03ej+jNgR+pgbh6BL5+Aow7yHClnmIaUYer05CgeOg2ArxWBaPk7QV764CTOZYHUNoY3Pc5HoyBIDgFWll+UhlYBImAMiTt2j1T66IFe/2cBWPmFmIAaiT2mbFLahRe32VcD8qkvB4SBgP4Ru59NNotYjMKSQAJ/Sjc/QuTmKp3ftaqQy+NxsQ969F1xhpIueI+ZK9t6DG4GwD1EtBEchpVmpQi7osVsqhortVZeFikf89AWpB8pwoxRJzc7l1s70nEGGAeCy1HfWxdwGM/TT1XO4/YeDYsFBmu9DqPV+vu2KHKljxr4wbC5dXmqT3O+yOKFb8VK1SK5fQ+vE+HnvPSFviz9BriYMtGuGcQPQgejP247+lq2j/Sc796hPT6x9Xjee3HUHevoeYBsn8dddgDnDGJa3n0kUyElgkN56FlAs33Gm7kPWh3bR5kuO5+31IC8i1exXgwBgLoeIDUapqSOpkVkFSa9JAIKMqInElLlgujnYk3zxlYjYOHFf7Tu2GR1W6ghRttRC/CKhRh9QPV5dyaQhN08Tvwz4eWkqtuJNy1jp7DB7hG/ptEVgsq7vKK/puIqRsLwOjXKoqjCM/zB0XvQaL720HJFVekXcaIsKsHofTrTb2J08qd6l4WVELnZEgAK90Ly4Pu2AYg8piR3IOYtFR9S59uvSyKnGWWXtRo4O45bBW7taN4eM7T15adonGvNTZ5jzrszGtSnGkWYCrrZDRBsSfHmRLlPl0cDM4dHLYDX3kZzbC+gvGgDASARuTxlnZgK12GLnB2UVoHJEWNRmI2w6Ar6HpI6i0kwuu7wUKM3stiTKZQTT3MaHE39boEAlp5MyxkaDFii48zML1kccw5I2ELL+WMyoQkqlAkBjSqxsUZ7CCmZe24qocCACBq6GJLQ5BVcZrn0FiXfniEznVwl9fwBt/VvFHwaVPb1F1dS7/x1SM1CsM1ZPdIjcRwpcBsKNRjUje6nZPjQctOMaPpoOBuSqj7PdJ+MpByp4v30Ht7RkGVVU0Ea7Ma7wLGACiwI89Jw6Wxcy5WBLcoqLPylvScaf8IGEZI3qEO16icUHjEVBRk9zKCduls3qunWBuYnnlE2mmVrVKqx+ZZtA0iMF9rujmdX+9jPBgD0UhAAISsSU0FqJJ5ENUsqismmbFIKrNWpDaMIRHhevBwgvF418OKq8wNe2j9Ms4cC3EGknSJeABggYufkBTUqPe4KUc+O2KKE+jpTWC1qMELMmcMpmdBqJ3fbxPG+QHb4UaLZAHEeiQQA0tvaZisAIyDtYmGwUMLKlPnexia3shRtVoXa+9rWRqRiRyQtCwNjXsNK9IIGa9Qd08AZkjetwXi+h8e8hFljOO1YihzhiyTKn/tH6HuH6FOB9D+GsOjbhCWwwRvY1fmRbUSZBtydEJVyzxsDELMUnDEHwbrI5J72Ki/NfZ3qjNfP1HDeP0Edf9YjeHuMWTUa3A/ixkI9SDiXWypa9soM9s1YTcWA8b9aODw0tSjarsvev/rsH/2XHzB8UJ+yDe/+U186Utfwk//9E/jK1/5Cm5uTq3T7/7u7+JLX/oSvvzlL+Ov//W/jv/+3//7Sx9M0zB4xnD8wX+3vpxMzYCwhR4aZlAzBrFVXiKcGIdnks9inp6DHsIZV+5ZxVxnXrz+8efQsyy8WcRxxz/5uG2qEB0r8edafOufBw/v1rjDyfEFhewW97bw4sKdc5fcsxXe1mDDnC1WqSq1tgrTagYtiug4NbktSstArKpkAwW5HcaF8CGOmN1w5aiVcbDvtQ9cH8eWMv3/a+9rQy47r7Kv+7733uc8z0xmYpLKRNp0/NOKNBRFsGogYtqatiSBgiExBKoReRFa/CNNVUwL0TS2RREtYiOV1phSEDItFn/0R3yNP0XSglRrMfhRM0lN22TmmfOcve97vT/Wx732PufMnMxM0oe+Z8HM85zPZ3+ue61rXetaWqmZbL/R58FMV95/909L2+ReV7JcITtmENatTV3TipAI+tjU8Su0S0YQL774Ij70oQ/hiSeewOnTp/Gxj30MH//4x/HhD3/Y3rNYLPAbv/EbOHPmDN74xjfiL/7iL/Dwww/jz/7sz7bekKHU0p4ShAC4PgQnFJO5y1OH9yo4GQOw33KqcXzGKUYKAcfayPoPbhrW1DmMKhH6XIiCm8k4MzgMIkQA2W4e06Zc+nZwAc4A1j4QhlwQZIzz18SrfCkIkGlcmbGDFDsmhpUKII4u8xgBYqyEmramFK6aYWAfFS7Z5sGiopFgjqNSG+dBS6d5WSsXJpojowW9krVoONTwm7EHisl4HtTMUJo5+sJt+kttt5bzrJFjFFyo0ynvDYfUlFqE2R5//2yOsJwjxoQknY8xReRlv5ankFwlAvDViVT7JdrGqhCMLUm0oIArMIoArXzrtD3isRP8uW4OamaMrwiVvi9Th1Cp8QAHdEXIgEHTbHCanUpADIS+BBNhbtt9W/C8gjl1x1dvssuwSzqIp59+GjfffDNOnz4NALj33ntx11134aGHHrKLNucMIsLLL78MADh//jxms9mmr1xr1j9AlRhE09cKTD0qi6JUCgEJsHKmRg3zJhnOMGvq8JwuYnxDALwybQB1SF8LZO+dOhMNt60dfFiOV9WYRLBV/oYnUaUkN2SW1uYBCNxmnIKmNrXy4KMH2zbhT7BzoNXRAZOIIAgeYbwJdQpAldnTnFZCWVPUkrB2PD7QScsVt59RwcWGb3AB6JZOCMg7CKBGkHzRc3jdJP4sO+JDkafLdkOGmNDMlxjAjqCZz1Ak1VA1bKA6CwCjFIJPqwMiNRpoKn3ZnIE3dSDqILSbspsDjazwqf7Mkkpl/6+gTpXX0xUVg2DZF6PAy3lvIzvSkAlDUonBCmgGAOUqpRiXdBDPPfccTp06ZY9PnTqFc+fO4fz58zh+nL3UsWPH8JGPfAT33HMPrr32WpRS8MQTT7yiDSnWlFQfb5JWU1Pth+hTiyRy+IYCu6qF87Jml0B7g3/v1Fy4rdOgWURGWsMl1fAiKLzBW4R/Gs6v2b5CcgNpSZYKgv4eCwLV0uyoQ1K3uRQECJ4ySR20nGkYhC+lKvZQijlBcv0WVS2qUo5Nrk62x9PIr9i0nwO8mifhQoQUEdEidgNS33B/RWQKvHbVAqgpiU9RXNqgFRirPk1shTYeqyZDpc5LJCM/p1eTMOJZP1VbzuU/Lm1zM2EETCAoE4Ai6Wfh76BCUvnhKHO4CocX2MJBlFJW6uMAEJ03/Zd/+Rf8yZ/8Cb70pS/hpptuwmc+8xm8//3vx5kzZ9Z+dp3paqKAoEYNHEmQKEzVvVaHoIrNUbkPTcS8SZingL0mGCeiQamUZL3xpIzmG4U85Tv4m8eXED0QSDILQsfrTcfsAY51uXqRUc7MuZAUgAqTg+zGV+q3mB4DIvB07NjxBR+iRB9xdaXz3I2CGs8qVOL3TasWgKUYTKs+ZMWs5YKl9nI2yT0AtcRZEnQG51hjszHchgrWun2eRxKkq3TjpVJLnZr758zdtTFxe/SJfVMUVyvLqvSl/5r5rLZd+9ZpZbpKz8Sox8FL53mGqDZLifCw4QA6eFmAYZ9KRMA4Lt5f5sJK5PU5nX/C13wpBIk3Mbi+Iu3WBYDzy20K2Je2SyI3N954I55//nl7fPbsWZw8eRL7+/v23NNPP40f//Efx0033QQAuO+++/D1r38d3/72t7feEBU60Z8K0qju4kV3Qp2E8B8s5IocNTQBTPAxBiCNo4hL2EXfr/iDUoynfRibmpfgwEyfAujjdb/rU7K6KGhFIfLsTr0YN4CnK4DoCIAcLFrgqkVvUQOU8yAzF6xbc7SPk7RtdADj+J/Ypc7rpcxWeYcXxK4dcyNk2ngcKTo1llIYDmG4hOerpJpmaKekqH8FrwIGVEexpgFNo7jpQKYQeAI9sIp/WTThMDiS+8HATAV4pQO6l7LpUAj91YjQsEUEccstt+DRRx/Fs88+i9OnT+Nzn/scbrvtttF7fvRHfxSPP/44vvWtb+GGG27Al7/8Zbz+9a/Hddddt/WG9JkBK4/Oa/lLrU0BsyYiFn5fI7jCTPotrukazBquWHSp4g7QfgXNqzUkDxF+UMLKIV0XPYhOo1ebWpGiG4nKVD6AkWg8o9KVOj2HIWgfgr+hUJu3QLX6AAS0gmivrT5swiRclcOwh9zXLk0BJKHtxsUN9BWsZSsF5ekxDAFBooSsxC8ACME4MKOWdO9AQzTgkPol8wx0CphI9LeixjQ6laI67tu9ldtgknBtU29yYBwhOIFdrdwYOUpTCznPRZ109E4iyoK3euMqxrCNMSMorDrXyff2237hJeySDuL666/HI488gg984APo+x433XQTHn30UXz1q1/Fb//2b+PMmTP4qZ/6KTzwwAO4//770bYtTp48iU9+8pOvaEMWmXCYi+EKGipZSAYIvyFJJYOl5MxBpIC9JjHPIQTMUuDRc2VAXF4whWrDDYBRXukBUQBIkxst+BVWex88g9CvpmpTfrxj2HHTUbMZUHTOAiFaGKrSc7ytXDBrU2Bl6hCZO9Av7GZfiUC8Y/DOLi+5HKr0adUj0IagflkdhTqI3uk96E01+lPZKYVL6lQGpNAhRgHetFoFjhiDpI0VQ4KlPOrstCkplsxShVmFfHIdFVgKYnfeejT8fAnfW1GVx5txVUI1K7QBKlYciUp1UmYSYWgpd6pMbimzu8YAxZ9ZZVyrcfkSoZUXK+59sOk+tnytHAQA3Hrrrbj11ltHz1177bU4c+aMPb7vvvtw3333XfaGqFCMTtHVOQ7jgxnMgbQJiMQXkAKTnuuQIrdCa4hszkFXvCTVh4vlutPVeBuLCUBfS30OvBrlzr42vib8vphNRWBMog9Ua/UF3I1KcbwfYlN8xfAUBzzaT+8EgfHvK4dsMpB4ki4FKuYclHsByGNs5nfIwTReQQBksnU2jIe1OTqERra7aZHa3vQdtPNyqgnBz7nzAqwAk37EYNBGKffaCJx026rnl1CBd0BTB1qJBLxzyOXiuiejw+yqfkAdpHSldmSYlC8tBuwvemNCtmk8LzOII2jieMhKCKpUzS3g+62UNiMhHJ7jfHpYIAz9CqK/gvDD8SO2CJ0phNq+PN8HhpZD3W4+KovF/RM88LZpka75gdqPoBTuMCFebXAUhWik7eBl5VQvIgLoGm72gbEfJ1WKMjAYSo4lKTJxZXEedEEafRwgx4/zmFatvI4Jwk8lIwxgnGJ5yEBrv7CW9iZ1PDAZ1bnpwqBRg4ri2PFJ4xWbqCA0M4Q5p46mRzH0rD41LCvIKFWXkYS/ffWE29C0LJC7qdKk0YSr1ISYgG4mEYMKDitxiX9mkVDMRCsT0bTM2U+uuRSCJBRueydoRZTy+wh7LrSCd1yuHR0HsRxwbJllhF7APBE7Cwk3rVcg1AarCCXTqIMQKnUAwuF5Hpmnwialzm0g7YQEVm7G6eo3Um4uk8chgiDaibPiLrLe6ugAEI5dgzg/BsRUOxkl9PTfBWB17oRuigvF1TEoeAVwC3cMPLBXj1OrDkhDfMU3/PfLazaI+MJ50OLg4idLRsONn8u2wiqng+d6an7eA0SgxAIziA1SaJCCRHHSg57iGvwBgHXZyoocqIAG7kkgKnyTUkFYHqLEiFAKOzrbpjXbrNsN1HM3iSQoa0TUjqNC+YziEOocTCbAMRwpxBHvYTw+kSUUS1mt0iESolQ0Aqpz0Oufz79esVICLWTzU66GHRkHsRxYezIFTh3ayF2dyVDeVceQImpjl6taWFPRtEx5Kc6D/rIunQgRiBjxDiw87+Zyg8j3j8g1qQ68jY2QhZwGwvRvWN3c8Qcm5p0DEStacw4voTqB+zdI5egig7Ikbc9El4xWVtSgvEmlaCV60Pb2USrCYGaQzlLuAh1EdLexcl4Jq1c0AUYGUw6Dfrb2mCxHaVJoSRYEUfqSc0NDXwlctr0FOgp+6jw843Xl8aiXZhL5TY9nmMgH+nMHjJzDCoBZggHoRoV31z+hKpiTnNYUN0vuXY4dGQfx8rLH3qJHpkZSBuneLITWNVhpB2Yba19FK86hCbC0AsMCoT8cr55r8kO1KVqudGqCIyEBYCISr8Sc4xMQG27tlZAdwOjCoWaOImGndvSt5Ss4I+EOIKgCNxnA5annJE9qBAHUdINH7ynLLiKGiKaVvzvIUJgix8GVKkfzPyTftq5FVIA3AFUsRre77+1z5cJ51m+Y7SHEhNh2POla9i3NI7rYSQdt7VjVfciFZ60CQIwSjaXKkQjDoo667w/499xbNJG6OcpC0iWZggYAI8UrVWRady4cUMvszWJVKIppBDD7maUeUyKoI8BoENRSFNpVhFlX/RS4jyhGAIqloUoMAjCcTdXYC7h1v4cCmPHiPJJXYEfGQRwKhXoQ76eeVVeWCOHpSwjKvzPDrBO6KURZ2er3a8DFdbgDv3ARzGHiTChE/v7ENN6ArjLmtPrgbnBqqvYgtbPVNGLT31bClN/MUeTgqi7symyeCKgyTQnE0Q8BKfF2hVjsgp5WHkamqYOaix74sQrj+M9I5ObwIlLBl2aGMCxBDRDywGzQEJGxem6K7jDGZUDdr06OKTM9u+q85ByElqrDBiqYmYXxWZKoQ8ug4DX7YaCrwwdGzv9i0YM/rsT8hQJ23NoysMgFpbhrPgQABS2iefwQ6j/u8JQ0DMKepACKGoXw81fLjoyDODjk6VnzhokJPtzSxp2IKvLSCDkqAJXZmJ1T2LY+739Of9eLgAqsLVorCC7FIK0C2FcUE0BBiJxWJM5hqZlzqRJ+E6PtJ1BTHQOdXPTAISpJOiEAlcn1V8AKQI0qil5MwlIEoLM4DY+I3K5uUvTwuXbFU2xCVpThvDmPnrdqguMMoAjrsmkRY0KIDetYaoqVGjSxQxDHp87AU481cuIeBQVmA+skpA6qhRDkOFAZgFRY8FbTB9UIle8cTcMeVWkEnG0wSjPMtqk2bXD62m/ECyCZQlotOhBa57J9y4Be79qhrGlKbU+QhQNAs0X36jZ2ZBzESxd6nDwcpOOS0GeefDULUcKuOpE7iYNIvJSZboE2E6298aemZCSwYyEBDH3vBTkQUaMCco/NSqlTsAFLHxRj0NbmQsAw0ChdmJpWURSoAxR48m3APpIA4HLRQjB+vnat6rDiFGWSt+IRbjuNfOTydjhHEURVGq7kSUOPkKSOMunJIICxAHUupcjUblFzahqZxyF4zpx1KgZiWUHlfCgxTOd92CEOQC+EuBQD9mbHmf+hTic7RmMqiJIWsJMQ4R8nsDvidNhpjAa4MsAtjnQaPbhrCoGb7zSS8UYWDZFNi9PGQ40iYgzQNivrJYocJc9EMElB+ULBGsAUo8gloKCg+X5LMXJhkDIXqLobAEwiiapGbYpHDogcVRi25S2ITZuyRgChXgwbXy9jjAMw9BqAKUrrqu87V6dGso+ECtBq5cKbJ97YmwDQpLknE2wC14pDWrMS1hGA6zkBo/TCNqY+9j0oNpUbAA2tYRY08NRutIN1ltrPCft/irvoTRZCQA4sEwCQTPSWqC5EhCRiORoxhGBR0iab7ldwqdK2Zu31617T0oMz7xy8xSjKaQJKRin96sBmxiVku0NAEmBaQcyrZUfGQSz6jAvLjMXAJ2khU7RSCFU0gyCIPd8Eo9PmooYwSRnC6DWJGjQ1gKQQBRZ2A5AoQCIA1Jtr7cl33trUkQqQ5UbJziGMLvg13+a1KkrUVaF+dmpT1h3XzSWiEEC3yBpovAI9Jj4V05tHIwhgTCWW5/Tm5vb1BAKDeDpuryx7k3nzgisQUd+oKUrbIYpgL1rReQCQmrmpitk+Ue070Z4ErmSwY8gEtJE43Wij7Feu6YZWOFIyrMF6S5YL/t3pdwCoqZZiLv2SRxQACLLdvlw8wiKo2N8Oco1p+3awlEFv+PEdrfNgdFRDCtxSoO0DAUCigaPeEKumR4oWUXCUeHVCiCPjIC70BReWA5YDh7o6bi/GUOvHgUufow7RTfjButemK6aVPzU+hzTas3OwXJhqvj+mOU++Dnwz6+ubCE2VTTfGDCJQtQAkU87uu6cLjT7UbVGQCqhEIwWw1Czy8uVfiDMoaWU+hTFBHbVYQ24CTPeiLBagwrJvvpNSOydb6aTU7yQhlwVxwqGZS+TSIIQG0fAVPnY6p7RGTbwVBQUtBSwioQPQxQZN04E8HpUHK/OaOQq5jbbrVRmbHZ92aXIbu5DCSgIGcTahrAKUemxTtL8f5UavHZxVTd2bdijHGIQUGNCIpkkrVTpQsXGHEFo3YkTTzLlhkYDlmu++XDsyDkJNu9OKB3EKN/UUWTtGofXFACPx5hvNOw0tO07KU6NyIlZXck+dBSYpBPzn9H1jp+A/WwJY5h5cvZmy6HRRiABksLcI9653WGsPie73NNICRkClPQZGKYYP0WupMINKQZaWaip1dB2Vgogq2mKVgRIvCiQrzmL/UJ2DRk0xMGfGOhoDLyJJxXPyWExXw391arULl7U8bCiuePnYLEF9W6sYQy9OLAKQRjvUsjgBq5UpUok/vmkpwloCtAsZpZ5bVWJX7CHCCRXT2Knb3yn693Rx2HhYX7EdGQdR5CTz9G3goM+YJWag7bcJ1CXRCSCkQACk2SYyCKUh3YopsGjAUhg7BUPxo5UmVSdQ+ex+IO70L0ydQX0vsDkbXW+BAhCpEl5oXNkgwDCYBJJaOAAhx6xbNSysjTW0DcOyzrkgAoTTgRAYzLMuU/3jji9Sct0rISDRcoH+/AKlH5CXA/LikN8rYixRdRhiRJyzJJ+VR/0Fb6XierwV0NNJ75nIOhXnFFGEkh+C6jWyilTTNBw5DDXsp5yrHOBygXLhPMr5l1D6AcNiibxYwovHNKrnoWCr9X0UG29gA3NbcJRSBtk3AEVSAbAqdZc4QuiY9os21zkvtcco4JpZgyYCnaQV+nvIhxh1EodYo0MqCKHh9BI1KL5SOzIOAoCM0ZO8vfDNFqVWHAcgtolvWk07JJxOlgeG0WCbFbtUeSpEK0GqyrKuWB47UNO0ITsQ0prOMMYr1p2v6UmMgVmRGkloxcKvCBGBgUgoS5LW+sXR925ahcqamzOg8hfWofQj5mSxfL70A4YLS5S+YhDBDcAt7YDYNfb+TRoZfDPLn0M95lm4A+owACCxtBJaWUiGopgNXyOtgMv+MGuXqpKgdBp7XiwxXFiORGtDjEhS9SCvB3HIXZ4aTfioDCEazuUjNQUcTeEsAq1MjWPpxBplaBm/SbXxkMvVayIIf54dBvp95yD22mizNBVg0TSjzwWHgRlmvfQH9xloIuehKbBgirEfFUCKFdUGsBotGMBTefTKldfmGsUSpjgCbx//rNqChMOBbOVTG0njubudaOw4NEhMQVOR9Wc5SDjhW4X9pO5C5FS+NYKQvLT0VV9yunp7wM0/pxe62wbK2To/Sz9wt+QkZYheZVr1FqQZqoqwOKGbNO5b4MhBf6+MQ7VMJKpYBW1MiCBkAbU5yBw7N23YomFpQ4azzNHozy8wLA7ZKXQt93PEiNyJuEw3r/MwfWt7TKAovSCNRHqBqe1cds8gKmiaOboY0IPQpQiAj+e8iWhHEYRLMdRhTK4E7dL1KuGIDftr6AK39tJ5xXZkHMT+rMHxeWsNWgCLXsRAOOgzdGZCmwKy1vkzpxup4Ro6UuG8U2daAADS+CZQboOb6qSNNjrpSOvvQ6mpg68W6G2gJ6GX1a0Qb+tUrCPGilq3ji6rN7U5DQIoELJw8Au4n6LA0Ysl79ZURD42cjwJqqdQJfmUPxIOl/BDd/W4mFPQ48IbLjssI97USeioPVGXystBJN3yaJqVphipY6Xo2DY28p5/7vPEqUamm6dOzgFZ16MqJB0OxY5rdb5RmLYRQyI5DvzZQHALhVY2CujwAorIAi5fOo/D75xDXvYYzi8wLJZutmY0h8dKVC+x/oR07EK5HYBzGHMEFbhQ4lw44OtxeYBZt48mBhBYYbsphBiSLTSaDqrYsnJ97NxKG7+lzU4uMRNHr9rz8X3nINom1YarOF4NAZWiU8aZNCgRh8QaDtsFAfXiAoTZxT9ZIeVzijuQixYyjZ3DWlBRcAZtxumzUmjHK2mlzRJKCaKHMMpV1paliBisTBchz2o0oRjF+DU3vRwVoJyWgcc08lAdg4XoA9dcp6GtiLBMI4fRNjhJN1bS6qDaCda7oKtgiCDtRiwKTtYoLm+46pVpCkxA45XmLCeNV4pgJhwB5X5A6QdENCj9gJAjaM7gazHwVaoYvpdDuz1dF2tVnhoAapnjISS+FBuL7EpgTM2nAxo1cPq4ZmejP1cOVHeLBVEFw6/UjoyDuOF4h2uPd1z7jZxOqDaEWiYGqtoUpX+ew/BcCEEakri3II6dwohHX8uYaLiHYhBwUUeh+V59YD3g6IlKiyFL0w2Dq9MLOSegpTDm2cMaCS9pikXU3yesQnEPHpzy08ttjKFGDa4EOELfJyW7kSN14/Y0PKdhOXIOISWkpOzLiGavQ5p3aOYd0vFreLL3sWsQr7mWNRvafVC3Lx2uc3ceUNMMASYTl2xGxyW5fZ2em1GzkrTpl+JmiUrVovQc/ejvdV8iT+daDkhdg9wPiKbmPbf0QpvZAMY3Qkx8jGNCaCWFi8waJSJQTJh1x/mcFACIoxK68iV0P7ICMTEgNVXK3rpBC6GArL+DxwkUw2mu1I6Mgzg5b3DdHtfaPVgTFY9wvHXkgpyYio0CdEqa0ulCpY6N5y9EjSTU44okGGKDfhLK+mlGmw5z7bsnHPQFvZyURS7WcMb7Iqtaw+FwFBQ8Bq5ZX6xeXaiWPKMAspvGAOjXMMlq4hxigBegtQqG6nNi7AxWIixlqpZBxu0tbS5ndmrRqvkYO56C3ezPObXY3zenEK/5AYTjJ4HUocyO2XxOnXI9CDjttRNMBMyDte646SyIi5nNKSlj3KQsJXpYDshuvmoo0RwHADSZOROh7YRQ1XGruY9SMs85sTb4YYk426spGxUgdWhSg9TMJWosTI+WKMlT7fkaIHMUusu+5K4Rry5whYBloddO9v61sllKmKk+oAJqgMy6GK8co4vjkldGqRHENERWvoOCkLJSF/ndy3ZpquNPnL94R7wNea821FzMMo0BxsuxGCrib8/Za+67fdXiIraSgqgzEQdhbeHTsfPOOaicfOxam7Clczoh4+EUmGSnXvkv68LjOvXdYS3bHjdTvhY+R3RDclJELFra5O8bTd/amKbIGAOjlfcIDSqJLIrjNbLVAGSZjyrzV5WvocQ4jVI1rYoINjhHHwO13G79OajXr/JwLqVrua0dGQfxuuMdbjg+G60SuhDXiUJRUpA63BSoOTY7AzcFyomdGkdew3rJr7N4X59iFEllFkPZeFF6RP1wyPJe/i59Xj9rfPvIdW/rjSiSAUkk9ErpsSEERCIpiyoQ6l4LevFhlFJg+s+/DkBVte3Y5SUgMzBIuAO2CssKq8NoNKUIMSKdkKhh7xjiD7yOgclj16LsnQSlDmV+DXLsOCwuDEIOgsT76C1GPderDEHlEIzItYIJmUAPMZU7qgL2/BhCTGgWS3QnjiEvK6FKZfGjVF607Amgit5Kx6qZNHXZdDHVzmha41DE/YzQDaDcswRfyaB2hnkz5/J+IaaFwJfTaRIZegfCz3uujkYQB33GQb+55+SV2JFxENd0CSdndXNG9GG5hlvp3POXSEBdLT3xx1rA3YUO370Ro0UKZCnF2DkoaUsv1Rh5sriuZpkgEYP33jT23pegXjhRo1dkBk6uaQAyfpM9Dtx4pDZ1DKhRA6k0n3coeWlq1vZzWI4qFrFt7Geaz6D9G3HvGMJ8H3H/BNB2KO0eYw9StVhmchyHcckYGJeIN9m610j/hcAEptgg6ugB6UzVygoA5LZK4EcHrAIwLgcVTaWydIPqBiTualWW6VTYVvo6YhJZvGEmWJjgCjEguGtGr0fbEWC0IPpyuwLog2I3pc6uvRp2ZBzELEXst/Vu8sCgXofW8IJgilJW+UANgzV6sDKemiOzoBSmSchL0QF9AIwKu67TzjuHqcUQkFElzA1P0fZ09x0+Gll3kdcIoK6QybvHqMlosN+j+6ztNlGVbtNKz5Rwo3oKIdYSnYzaK1IaHA0lHnpG9t20KkBuppiq+Gsjw3ZbBiKpnZtoTl9gq152TnrdMd0YXU0iLyW4BRImqqiBUTMDUUHcPwHafxklJqTjC3QnFlaitV4RcRDNfGYlT592oJSaSpRY0w2TohNnYQrm2lLOGEbloXRWXYrq8QEbGOWvg++VHRkHcXKWcHKWRkBMZTLyxePvIaWhphjQoCD0C5YbWx5IWNybiItWLgLVkXbebMUNlXQUIwEZIyehaYK3abtuUl4zXPNN0C7NYKCrfhZgB6BpRiOsuYjqHBRw1G1V4FMdZ217ZisCaFluChhBbFqp4IgrVzzBazosF5U7cPAyMPRMTT44sLKgHUOhKKf5jFOJpkWYH0M8dgJx/xpQd5wdQ3cMfeKw+sJQ9RCGTJZa+N4XxZ/0OOr+6TUyjSiBut/LQkjdPkdAs+MoqUGIDdL1GWFxHqFpMQfjB2neIS/G8nlK7hp1peoxKpll9KeKVfAphjS+CQOTTL6PB1tTiEDeQ0qdnS89r/XaYOeZJHq1bUOAI72/anZkHEQTRTpOzjaDL7wKBDAXwHvVKlIbgNIb/sCA2pjjX30zqm6D5NvsvcflpRQD+sI3dSasjSQ8GLnOfBVGSVGX02FnHIZRLirbGrVxbew812Y1a2jHap76PBobOPS1FVpYhDQwlVrzcQ27gRo92PAZN57OBsk0nbVuaylTnZjuy/Sw2jkSIM8cBWp25f22XjMq5hu0upU6IA0I3YzLjhfO2+zN9lh1DkaQanl0H4DqHLxNIlS7xnQGq+NGjLpBXfpmw5EmZ0ZLnuvMY2/1GPmr/OrZkXEQ+03AfqK6ujfRqgxecEUPnDEDqSAeXmB2YF4iDIe1fFey0VE5HBQkGeATl5dIsRH5Ov6zXQoYCmHe8EkuBUx8ukicF2OQ9uQAJKBBLdPWyV8aVdTPGZgpKcI0zdALgWeOBvu8NnKZCE2Zgqewv20g5TqjMpqiBUCmky/NGRhjUvQeqF+uTKoC3A0kClKhdVPE2o5Ti2YGpA7LvoxmSWrO7bklgMdQ+AZQajlQ0099j+6zXiNZjsUyg1mL3R7CIOSskhFShyhzNDD0Mqy3CtzWkzBV7ca4euN+Nwl8aDSxRq4u1ujNn4eIJOeQm/CIKtAMuHPpnQLx9aaAJl+B0ip+lVRrj4yDiIuXEA/mNf91oXBURWgNkwHWnxSZudAf8OzN3CMMC5snWbQe3fH3sWMQheq45AsmFbRpDuJmRmRy3PcmcU48wMpZU+xBb3ITAXGhsVfnbmMcRRzTngJNMwpVrEIvCBPolVSFe04ARHagOQYjxuhgV6BGG8HltwBqdDVUXoNGC+oQUIppJCg4iZJXCUUOyAsynk4Zk2haxNkeSuqA1AHCd6iEnoq8ryvNNbHeFL4iwxHC+hVTnU1CMGp2IiCkDmnWAalBKQNC4SgiASyC080RFufhRwp6dSx+LJwQN/tzRBSzrlVpdhMw009Xs1Z6dRKlAKEwwzISULhZz+uC2HXg0kzdV2i1R51KBOZNQnOpxsQt7cg4CI0ArDSp1Fu5sdcNfPHlzFAyRw46bFYFQQCEYQAaN8eiyPdJ+JcaGPVVc/5MVTI+RmDEtcZm2q+exBQq0Gk9F1LyrN8huxo2VzM0DbLHmkppmhRYVaiEIGBkvXH07zLL25U47XucvmTvGpmcRsJI82HSa2Hb5MC9mmLIjSIRnFKqVVTVUgtyaPwasFZTC3MOLiIidwynptohmm5kGeUIbaSSVHOkxdm0xm0wENK+cLVsqM5BtS9Gx0SdgfIuZAIbVC4fGBHS9DzztbcaMUzTTN7HGjWBMIoWLzJR4RXZkXEQ6cJ3EQ/ammKE6iBMPj6E2mzldChDf8A3zfKQ5yBMQ8Sml+G5nXV8RirSu98ghohZO0cTIzKVCv5J2KsYgpKhqpq0A43krGkq0KaIeeL5Bv7z8NGpkqv0nshAlxSQrb0mgJOiG3hSubcQG3RNB+1S1TZ4NQNxS+afwwIoA4/ZO2R9huJWT9/1qHhEEa7AdGp23YZKRlolRbXc3BQb9L0AkwLEaZObd5xatVBwNqizDeAmLJ+ExFUPkQuNoiYmG/Et3+n2hMiamHNmmEYA1HYyIVwmc5XMIxuzivCuxixrHWbTWjk1zo/JaEYu906HJ+nEtxQbdCmCZGEiDTTcMZliEtrWXpmVMIxm25mel7Ij4yCwOA8s9qx2bGEZALQyNCWMp1HZirhcjEbHjZSKrDTFK2VAzRX59YHDXwCpnTMGEaoX3wT8rBtv5qOGeYqjzlSAS5SZslNXlwtYcsj6/9iqfFzhaEmVktSZKnYTItB0aCYTu0wgxpPIlodWtsTQj0BI7xhIwulpQ9bUUXg5OegN0naSXjSm7E2oKH1WfAfulEVCm5QWpe3qlfTFrfA1MgjOGWrjHJ82ErUpTR25wzNTYEm6Ycm9OJnT2rgnDXlKeioFGJbGeUBJlR257jhYFJXqYiRplmExqbORfKOpahIVtqmT9v0gaac7vg6PsEwsgpvbUFPRXAKimxR+pXZkHEQ5PKhTkACrIauSj42yc/MnAhFfwMry6wVM86aOJrkhLjEizvT1BhjqZ9p2HwBEuQoAwkrYezHzfAc9Sfppf2F5IlhVhSGbLuUzmIpNuEvG8xjKYPGnjrgbWXb8EEnDSJ2CU1gyByHajN4xlEnO7VH9Gj24XFtl5l0UyOK7MMWodc5QOxmN8+LAXsu9AYRA8PwPTlfG08HZWdCYOzI1i1RDTTeiXA96DdrvnGqFlJj/Ia3sAFc8QssKU3G2Z+K/cY8jCLRCL1fJASu9VxFlULFzPL3m/CMtc0+zCH/NfN9RrcvL30HZq3MYyCdRbhZibaWtiLKtgiXbSlhnY0Z+rWnr98YEWhxIiUvSjeYQ1M7QigAoUbTGoTYFtCWglMApyIbEV2XJVffB5428sfxDFbpHbeHiJLgXIUhfyHiFrN+jWIoAtammZTZ/073XwNs88Li6Besh6CRvWi6Qz70M05XcYuhQjRjkJpmQo9AwU5KaFpBhQTq0tqCugko319OtU9MCQh2OFGA/Ab0RAnIkmQPBPIrx61oyDSt9KnUnojEtERugE+xAIygAo56LkkANmK7dTb6q40nioe0QT1zH4wbn+wj7J0CxQWlm1j2sKbOdHxKgVP5m0jR62jSn2yxPFaxO0SKi9ft6mXZkHARlXsn4Jp5MXJYLnoTXDsBOoJdc9/kze3u3qg2o7Da4ISkxIs5YQ5FC4NU2LJFCO0LQUwhGcfUlxYvZNoGHTy/WsxRQacMYrySjJjR1GuIk7HlgDOSStD1bxYL/DYullS6t58BHCWt+N/1GvcNdtBa8GE8Io4Yi5TusW+UqA9VVLtxP/sOyWxkmoMM/aYolWxSBTVFEkPmruqLLtnNlYAMHfooAxmiphE8prILjVcs8Uc0R9gLJNLAQuUIXx6+PhvRs4LNcTcegdnQcxOIC6HBeoXwvjKG/TE+YRhGHC6vlkwMoSZF0GeDivyO0LcLigFc7AHF+jCOJdo5QMmZ7VWS0LwFdiohBOjgDBPkaA5Ur+zR5iQCbydjnYhqLnL/qc/x3QgCGzOWuXIIBUjE1QJFRc8lRx/1PCuNmK4c9UL+0tKIcXpBIgqXXvDL1lD4dsqtUuJ/aEakCriYlpyCc/ONSJm9TCODmsghT2FJT7GH6nC/z2emXmicJkp8L81Eu1qZkp0S3LbG4rYX/YUCYH6tAsC916oyM6XUYE/ecdHMGJk9cD3RzlGYO6vb45k5d/ZxFD+482QYqQl7q+yYOqUh6NhI4clwhyGtXw46Og7hwDuXAlYaAlWhh7XNK6JFVcXB0WS09RWPDeXAy1Yt56EHz80wLTh2oYYcyb46hFwehYNecotTXC5DH0cRY3GYsMWNS/kSmHaE0Y6CgBC6n9qWAA6Yokuj8vqbwTdA0c6mdB7v5yYWrWsblYyN4TR5kPOFQWZHqJBYL9AeqSt2PtCVT2yCkxPqMvjU61uMaYrTcm3/yvzjbQ06iOZlqedMOv0QEKv5zKRuV+dyXxCCgJwBIyrH18FqdlWE3YgKKtGA3DUJMfMNpHjRMKdT1c/HYCW5Km+2hzE8yrTx1TA4T/o6eJ2unL1JJ030kBard9a56HDqvRUz7V4xkhkokvEq+AcCWDoKI8OCDD+JNb3oTHnjggZXXn3rqKXziE5/AcrnEm9/8Zvze7/0ejh8//sq2RDUGAGg3HOT38ftWx7xN2X0qXJLzpE7tCD6xbWp9f7ngnHnoEcvAF0nJzFiEltg4TNU+jRZcEuVNXy85nwujyXoDKPawsuuaZhRVzWKtzSzsTFPwLgRKXOr1Ar1rbdLGvfqyHLuJbJxnSJZY7GYMKfLzm4bCOv7DxYrw2meig4ULjZutLoONbrJ64CBrpMC1zqo0Ya43nUQSCCQ3dAPEwdKNIJU0lMI4BLTSFiv+0nRSoahiRNR0o9QAYA4OqPBDqs594+T50c7GkaTcVINSo4urZZd0EN/4xjfwkY98BF/5ylfwpje9aeX1F198ER/60IfwxBNP4PTp0/jYxz6Gj3/84/jwhz/8ijYkHy5RFoweh7Q69n6d6YWtwBrlYhHEplxaLWkjTtugKwVRQu8w3+d/TYum20OTOuQmgEiZkJHl44grpKUAOVYNCBOUKUAJhOKEVtkH1qFAxoNw7eSxz2hjFUcxBrNQcFMkdLEz9DyUxspkvOMOj7CIopY4i5UxnaPNLP6iykq+hFn6GoH5Y6dt0SwIo0rVnUUROrZOD0ZoqvRdk3yVRo7P5I42dqU5SX4yhjAiS9X0gx8xdsD3ue+GBSoGghDrYObAYT6JQ2BCXmC8JkSeEyK9Kj59RZExhRrxzvY5Ykgtyt5JBiRTh4zVaznFSv7DNKpwNtKclDSNqGo/sCxi7YJVViqnqitfd1l2SQfx+OOP4xd+4RfwQz/0Q2tff/rpp3HzzTfj9OnTAIB7770Xd911Fx566CGEV7AcZBleElIE+ku/H6gOooiDKDLfQBl/034Bb6x41JoGQCs3TFwcAABCu4+4vABKA7pmH5mK9dtH4lV9LlTsvhTup8AYuDRClWyCSrcrol/KBHEWbAKIwFAQQ0abAmJISLkgURCpfxHPSY00AmGEilcdh2HkMEbzKJQA5aIsdarZRVqjY5aiVZe0zFfL0W41TWklstGbOEXW58yFowgJjBDcfA+dKUZUqxCVY0YCGOv3Vmcq0yiYeGTpyFRMhjhaKDzR3XgNKlOox8qnHoL1hG6GkUmkQSGadB5SB+r2jffhp5LrdhTldSAipa7+vel3AyPH4LUfjEsizkE1SbwuydWwSzqI3/md3wEA/MM//MPa15977jmcOnXKHp86dQrnzp3D+fPnt0ozsjS7PP/yBQDrV/tNRpnTktLL6Lc+gwZeAYuoFm+y2DZIywGhaTBrEtplRlhGNOkFhL1jwPmCcmwAxRbLZo7FIJO/rH+Adf/Mc2ta6SKJKVwyEIvQDOJUlsNq33/XcP9GlyIOmoQmBew1kUe/R/6dezuAJh8COSOQu/G9UI42reUBYbgADBnl3LdRDs5xC/d3z6G/sEA+OMRCJmOVPqMMMnxXlqHYDxxtxYjQDmj6ASFGdADirEdolwhLIKQWcUFIiwKKLcp8wTdO6jC0c2vQ0l6MofheDG020moF76N2w2qZMwXUlnkLq+sMk4Kxk1YHodL/MQBdBECiy2lt7qKHQQVhOHTl4UPUpoc1N3FI4iDmzNBMHcrsZZSQoBPaxp9xrFhoJETrvxsASVRE4O9iMJZsoPOyaDQKKyEfDgUvvvA8gHp/Xa5dMUhZSlkbKcQtyeAvvPACAOD/fP7/Xumm7GxnO5vYCy+8gDe+8Y2X/fkrdhA33ngjnnnmGXt89uxZnDx5Evv7+1t9/i1veQsef/xxvO51rzPJ9J3tbGdXZjlnvPDCC3jLW95yRd9zxQ7illtuwaOPPopnn30Wp0+fxuc+9zncdtttW39+Pp/jJ37iJ650M3a2s51N7EoiB7XtE35nX/3qV3HXXXcBAK6//no88sgj+MAHPoB3vetd+Nd//Vd88IMfvOIN29nOdva9t0BXi3K1s53t7PvOLiuC2NnOdvb/h+0cxM52trONtnMQO9vZzjbazkHsbGc722g7B7Gzne1so70m7d7bdHtuek/OGR/96Efx93//98g545d/+Zdx7733vhabvdG22Z8zZ87gz//8zxFCwN7eHn7rt34LN998MwDgJ3/yJ0f09AceeAB33nnna7oPU9tmnz760Y/ib//2b3Hy5EkAwA//8A/jD//wD4/kOQIuvU9PPvkkPv3pT9vjl19+GWfPnsXf/d3f4YYbbjiS5wm4/O7qyzpP9Crb//7v/9Lb3vY2+vd//3ciIvr93/99euihh7Z+z1/+5V/Sr/zKr1Df9/Sd73yHfv7nf56eeeaZV3uzN9o2+/ONb3yDfuZnfobOnj1LRERPPfUU3XrrrfbaO9/5ztdwiy9t2+wTEdHdd99N//iP/7jy/FE7R0Tb75Pacrmku+++m5544gkiOprniYjo3/7t3+j++++nt771rfTYY4+tvH6176VXPcVY1+35xS9+caR4c7H3fPnLX8Z73/teNE2DkydP4j3veQ++8IUvvNqbvdG22Z+u6/Dwww/jB3/wBwEwnfxb3/oWlssl/umf/gkxRvziL/4i7rjjDvzxH//xFTfUXKlts0/L5RL//M//jMceewx33HEH3v/+9+Ob3/wmABy5cwRst0/ePvWpT+G6667DPffcAwBH8jwBtbv69ttvX/v61b6XXnUHcbFuz23e8z//8z+48cYbR68999xzr/Zmb7Rt9uf1r389fvZnfxYAh4OPPPIIfu7nfg5d1yHnjJ/+6Z/GY489hscffxxPP/00PvvZz77WuzGybfbp7NmzeNvb3oZf//Vfxxe+8AW89a1vxa/92q+BiI7cOQK22ye1F198EZ/+9Kfxm7/5m/bcUTxPAHdX33HHHRtfv9r30quOQWzT7Xmx9xDR6DUi2rpT9NWwV9K9enBwgAcffBDPPfccHnvsMQDA3XffPXrPL/3SL+Gzn/0s3ve+970q27uNbbNPb3jDG/CpT33KHj/wwAP45Cc/if/6r/86cucIeGXn6fOf/zxuu+02vOENb7DnjuJ52sau9r30qp/FG2+8Ec8//7w9XtftebH3TF97/vnnRx7ytbZt9gcAvvnNb+Kee+5BSgmf+cxncOLECQAMjH3ta1+z9xERmuZ7Kw26zT597Wtfw5NPPjn6HBGhbdsjd46A7c8TAHzpS1/Ce9/73tFzR/E8bWNX+1561R3ELbfcgmeeeQbPPvssAKzt9rzYe2677Tb89V//NYZhwEsvvYS/+Zu/wdvf/vZXe7M32jb7c+7cOdx///145zvfiT/4gz/AfD63177+9a/jj/7oj5BzxmKxwOOPP453v/vdr+UurNg2+xRjxO/+7u/iP//zPwEAf/VXf4U3v/nNOHXq1JE7R8B2+wQA3/3ud/Ef//Ef+LEf+7HR80fxPG1jV/1eukJQdSt76qmn6I477qDbb7+dfvVXf5W+/e1v01e+8hW68847L/oeIqK+7+nhhx+md7/73fSOd7xjLXL7Wtul9udP//RP6Ud+5EfozjvvHP178cUX6eDggB588EF617veRe94xzvoE5/4BJVSvsd7tN05evLJJ+k973kP3X777fS+972P/vu//5uIjuY5Itpun5555hl6+9vfvvLZo3qe1D74wQ/acX4176VdN+fOdrazjbZjUu5sZzvbaDsHsbOd7Wyj7RzEzna2s422cxA729nONtrOQexsZzvbaDsHsbOd7Wyj7RzEzna2s422cxA729nONtr/A8ej1zkW81cvAAAAAElFTkSuQmCC\n", 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\n", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "cm = 1/2.54 # centimeters in inches\n", - "\n", - "# Prior\n", - "fig, ax = plt.subplots(1, 1, figsize=(10*cm, 10*cm))\n", - "grid.plot_vals(ground_truth, ax, vmin=-3, vmax=3, cmap='RdBu_r', colorbar=True, fig=fig)\n", - "plt.savefig(os.path.join(plots_folder, 'ground_truth_synthetic.png'), bbox_inches='tight', dpi=200)\n", - "\n", - "fig, ax = plt.subplots(1, 1, figsize=(10*cm, 10*cm))\n", - "grid.plot_vals(mean, ax, vmin=-3, vmax=3, cmap='RdBu_r', colorbar=False, fig=fig)\n", - "\n", - "# Add location of data point.\n", - "data_coords = grid.grid_pts[data_inds, :].compute()\n", - "ax.scatter(data_coords[:, 0], data_coords[:, 1], s=1, color='black')\n", - "\n", - "plt.savefig(os.path.join(plots_folder, 'mean_synthetic.png'), bbox_inches='tight', dpi=200)\n", - "print(compute_RMSE(mean, ground_truth))\n", - "\n", - "fig, ax = plt.subplots(1, 1, figsize=(10*cm, 10*cm))\n", - "grid.plot_vals(ensemble[0, :], ax, vmin=-3, vmax=3, cmap='RdBu_r', colorbar=False, fig=fig)\n", - "plt.savefig(os.path.join(plots_folder, 'ensemble_0_synthetic.png'), bbox_inches='tight', dpi=200)\n", - "print(compute_RMSE(ensemble[0, :], ground_truth))\n", - "\n", - "fig, ax = plt.subplots(1, 1, figsize=(10*cm, 10*cm))\n", - "grid.plot_vals(ensemble[1, :], ax, vmin=-3, vmax=3, cmap='RdBu_r', colorbar=False, fig=fig)\n", - "plt.savefig(os.path.join(plots_folder, 'ensemble_1_synthetic.png'), bbox_inches='tight', dpi=200)\n", - "print(compute_RMSE(ensemble[1, :], ground_truth))" - ] - }, - { - "cell_type": "code", - "execution_count": 96, - "id": "927dcf6e", - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.32205177575516\n", - "0.36184092691906883\n", - "0.3461131143670416\n" - ] - }, - { - "data": { - "image/png": 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HQkbKrgFF29DCLr15DQ0U2qQ0AyHKcYeyOsV6S5KiWvdrJUr2cKOWjLos7f2SUdcGIIpxCOnIMBnMSJC6U93UdL6DGQYNM5ROXSLeoO5th/JH47B15fX8jQznNFRUv75+XTwV3UBJ55oYgOeTCXLu8iYrpOfdfxs2wYOhyOZObFboaBg231Mcf6gOVEbswYxDNHjRSEhmzjAtduNgeAQF8DyRehC2sNgCEij4FM65gcqekkXwQPIKfr8ZCLtYiVgno8SmBQBKRSWAKil0K89r1f/PFUTiVSABVTMaHHArVgDTsxjBEJgX4O7ejnGI4YSNOCU6L/IkQ9FzH7rH7gsTgGXzGl2YcGJMSgZCarLLSATM4XlHjccbuQ57BuGemhvzKsQwVHeXgRZa7O6DTUB7DXGx240B9EjjPWPrdWyvy8bI1fBYfIGSd6PnYEB4DCkic7KR6wjbsBRoi41fK2fS7ocaIAayPtLqhs+PS2tOXsR4NAaC1gW0HEHMIGLBE8xKJlY+v6XPqnP9AcKa7OJBq+gsbyz5EIsdbcIDzbXbeg0tJdVQYcCSqm3YreRxNtBCC7tY5inkFZRl4tNeaMEJ4D69uDUWlJKEFgY4xnGXEWAxuFW/P07OjjGpAGWHPxgAGanUFm4AwZPQ9yHXpnlbIb4mO2eEbEZiZ24XfT9DwpOlyLVYYcaEkZIyIcMKe3JOdf99BPJZDO/MeNeN4bPXsZbEMmMlWAe5XSxskPNhHoUVZBmb10hRltq06+GP0SuyzFeePdNF22yWhhn+GoZXFZRv/uHpyX0X4/EYiGrVjdysJLeLlvzmZEAppllPMlN1d7ClPIEVFQXkF1m2bTerpy65eQmWHYrPbdy9TqL3DkKoceIK73gPNvE9JNDJKru9nGy/OywjsTeMNo1oGLh/bfuSEk7m7jkPgptx2EPzGYRCVQysnnfblghdDn87xOAAlZonmbjHPGqSW5jyuhsmdIewVylrhkN/bxtidHVBMWKpvZFgA2EB2LQ1SrUXWzF391RcfOI+dmClGbNlRpmPuiN5f5GIxXm3N7vn4HnHozEQKFlW2XgDZnF1Kb6nN0TiwXPNiQgV4tbNZiCK3pwQLKPW2pWkG+HJYuWYmWir3umdG/V17hPD8e32XFr7PiKfkLLSF3+01R/D6KFDXSLI2E/0Dog7N7Zhh/EeQrn3dt9rCUZtE1rYSuseHsQ4m1EuSgW2XyzBWDuRyv/fshzV/z8RQEVSd0RAKlU9PgYTN6/QYvFtWLd128+MLoRi7o3QXhqTxPCNVlbNPVHKC7OMa+NMSgXESe6/u3eqYVllnVHnoxiHZW7p6m29jWJW5ebFSD4+GgNB6y1oPZ6CXegvHq3NWFicPCiSO/CAYRhQIaQWCTfEcNTNxYi4gpFwomHgnWtXNHW3NTbdtndhDGeGGQloZqIWPnEd5bl6COvST+RzXoPv0qasO2g7+Od3jEPzdlJ/XNSHIeDBDYLVIlRYmCdGNbrqbi8BbOsWCktYQQBmqooBWZ1Cyw4YTtRqMUj/f/A6BtlXA/mK+JMVp7hQPJYN6cv2rsMcmMBVjAPzqfWItG+j8Bu9WryIxgzdHRFEXWYxDsdrlGffEgMxH1Fvj7rphlhn+/vNd858+fONR2MgBN0v6CN9yOpm6auNi2uehaxCMqmSGohCJMAmJO5wwMuIJcFrAJq7F42DXT+7BSx8+bYMwyA8/RgAyw6nQJ+5wA6Qefb7cS/AqF+4/3Yw5oYNVUSsqOFFe2Mv5VlCRZfxBUiZtaQeISDXq+j1sKK+Sv6G3AvbkMiASA2JOiPh2AP5NvZb9sgMcJXQs5B5Ov0sjxiXpdOd7h+AzHuHGTEjyi2zpLtLRr09ngoERXwCQJ1vH/Y794xHYyDIgLw7LHtM8wA4Ac+QB03tMaY0ISX20MPAry36DEQArfcGtrbAJgHQHolIeRttP22fK/H+SrUzhPpbPdVY1xk0jJutxg5LaAdyj2pQ5z1sjIqxJ/W3Ih5BtaDWAqoMlFVSavrapz2x11nkkAasFV7EVNFS1hUIbFh4KtqUkbakIwCefTIK/GFIu8Vc5lFMyp2QqshBFw4AdRBQU891NQ/JgFcNYSsP7g2ZoTOPpZAV/PXGLUq+bdWdjClqvBvTe7B7ze8QYkh6QkctqMqBqfMR5eYZUDLK9TsoxyNqKcjzilqKyg4qnpIYy9P3GQZBeZYQw8aOdd8FyUhXV8MnbJVIK5KKfaQ0hCyIXJU9LOGccb/La6hVVq4K9Cm4zlDspPLuwCNkTCd6jU6EuiOL0XkPZzyJmL2gpLTqc0bGuRsFhHVXYKXWxhUwUlGBFjiVZhQAuHRgqU1C0Gjyln0yqrxtbyDfOSEfZuBySF6nkYvE+bkCo4Kgk4YelQdQnpVlGgxEauSvRcMiw1JsiIECLirvkr0stDBjBqAr+Gthkho2NMPiGFS8V8yDWBcJK+Yj6jpjeecp1mdiINabWTAiwD0ISgm31+8zDwKbfG//f8VdRNqs0C03rPGiEXkiSq1ZkO6SUhPbuMvra4CauZtt8Ga7ROJ2E0oDV58Di9iOVkOhaVBLWXY7GEIO6E32oBCDT9OaexjE1iD0O9jCCUBpyL23ZjjDVlrO5NrMOJjK1BLCDlvBmRRjKIQS2K2lVlwMQCrqZWiOZKEW1jABVCVNCiYwMaAKU7K5bqns0BzCoqog63aB6HQrbHFhtFCCGzPU7i2j75uOiAHiJRQYdvwOO7/b1Oa6oCyrew9lWVBycSMBAJQL6vI+o1pTWYBl7sGWvaIkmHssbial1NxDHiRXzAkYJjmhpKi9hiOD0YQh6TOg9xAiKBXj57hgWJVohmIXmn7LVUAyj28tzNhOspiTt7dsJbGwx0BLK9gKuIMbgVD+HeNP//+ttxEp1fr6hH25TXlqmCH7HI5Dz7dxT+Ys3sKqaku1qtyciqwY7T0ag2MuWNWLkOInMQ43i+yzAZ6x6GlKotMBtIK7ITXptsPAeDINQnO2AihqGguN+JY0tFSZQ/UaSvAcPETS6yRsUPEiGhFKTkcnPwicgNgxa2HAql/7agA4g4IOZjfUiyjzguXZEeuzG+RlxfrsKAYiCEOkacB8fGBq/J7xaAxEXbTeYJUDO1FntmElr6ZToHUDlARpj3G/eRQEoNYkhS72PTZxiVssWNtjt4rsxMUOdkLiUqrBEznHOJQDk7cNy9C6CtJQxTez58MAqmoo0AwYlTb5Y9bDjGtHqDqnFLUZ5zQo20EXSWgoDmTegWmEmmqUxe+nIUR1xXJTK7fn10t2pfLrORoIOV/ToFqiA7sy+e3KLhG45oohyXuAxPx1SuLZsaZMS1vVSWnf20yCXW/ziOw+IPXd3DNILZNiIcTWEPh5DV4EAA8t4m8379fAeDpZWGrOyMuKMq9Yj3P3CMC9iLIMyDvq7u9mPB4DUUNx0l0aCHYTx7x9Kc1QxMlpJB4HODUc4WAoagHpqs/UNDBln8LzsK+xes5cRLmRqHfT7wkxthL0biQ2n9tiEyi57c/WCHi2o9VhyK7EugtjTXLzxu4zDpEfoM9zbbyHqItgIKQbjBLCCgcoVVehFOTS2hiInmPvQWyHaSzYMBm3UcWCxiWDVRHmakpYC1ATYPqlTbLudPICLQOzJUNZ+tQqfvd0QuT/29iDryQsq2BIpk0qQ0nTxcXv4UrcXUP/vIGSpaDMK7KGE1VvXmJGWd9nIQbWVUAYCzPWZeNN9BOhQ+EVZKOLA2idQZxAF5cthh4LalLjUStqUeuspCsDGLu0HdpKYjdMHLm2m0tqRQgJUjiWFPSi9XY3ho/9LPz3twIuGzezOlGJQFXDjGUW76Ekr/Z0FqSGFxQNlhkGe35CtEre82LLCfB94kGUnnnAUlrfC3PH1wIPK8w7KFXUnwG4AnQuFddLwXEVodd3jqsaiYybOXcNYwYm73cyDbKNqZWbLOBSkiuB3a4FiQjXS8bVnEQgNmh+OJ2eG4ZgtPs4eiAb0igJolNhqmKRk2Ef95II9azifdR9v+tMmvdFQiNPE0RQd5BrMk5+jSRbkVEW8R7W44I85y49XHJFru8zPQgfJ4pK6ln4arso7qBcdDYl5uJeRR3Q0WhRCohCmg4s+INhEzq2WKWd8r0wY694y/EK+673CFJ2IxoLlJYShawoEdAkqHdiGMTWO+iAyXs8B99uL3vRdBGFFRkEXQ3UrU2s1liUBj5GoVdpflT8tWk7bj2I5lUU5EK+nQkWMxFuqYicW5F2CA56Vsk0FDUU1WjgcrGEMwOcUOrjayFktQIsr+NBMzBVk8BUW8ixl76188MwQST9UwPtWbG9zynmUHJFydKkp+YCSixG5MzvPe94XAYiqCeZB1FzdoqpDV8hTRrt4iD18esMCloJdEBbCaGAG9BwCYamJPfR+pOMwQPHHUmRB3z4DvwijIrSKNoDpPwb6EOKkp2d6WOPMfnAUY0v4FJsagxQffJHQ9qHGGc4DhzFVVg0JDSEsDJx2y7+xbGWihTCkVIreJXPr0MCk+iE7AkTN35FCBMsVAjpb5vo0WsYWFou+jZ6/nMVObtKQcvSMzjhUrjnUV2YqBDhQgH2yjN4mIBhaXwVjXM4McoODllzQWHaxc3ezXhcBsKGpXNub8RgWOgBdGQQy2DQfASGETRMYMUjbAqIGIqGEgZU2iTUNIYj9Xel9La7qJb/LF32vQ7PtPT7FD2TWtYQUmgadD1j1KKntMUrbOwZFPu98Fed+xD+ajMC0WuwYbhD3M5GnPTWEtGMRsxitG3bscTGSfZ6Vu2LpSTcaquEQ24ZjyVX5VQUz4SIwKz4fuYRRMNgQrCsGRGpAoaXVTshjrTaNB53bdR/oIUaYmR6/GNkYEoTMBQRiRknEYDRMJr1vt+OmouHGZRqC0nf43icBiIODTPMMNRtZiO3iQGriIRmB9alCzeEAahjW2Og7xEkq1Grltyj3SRb5lw0DvGmAjYhzgNH3cbA9xksaozGkxBjw4948D6oB1bROCR7x/G8lPNkJ6g0l944A0ADDicVKj4n23ZqLE69CRuWSWECRpawBGAsJArpoxrIrWcTw4Vt7xNZ8ZvAiyuU6yA+NdCeDdEwrJ0/UrU0LThUwyngJKO1Amx8FTpzTSmxZ7pe5Hg8BiKchIjEA0CZF+RldeTWYi07WYO9Llm4L5zAzKhJgbphAk0K9KkCk6RD68mktHoLU/uRHRDBmoSeXGWf7OTXtXbflIHOlRdvh+8HhcYt23y4pnFt4lLREucBLRVasgOX9+pRhkrAqqsuDQ00rVpu7L8Z9r3RhGsX7zZRlCr1CgwUalKBrm5dKwBGUg8kXZHjC5J56KfZNtSII+IWhmcAYiwMxAQGjElkDI2VuZSKQ/ieqH5umQnDGEx1LDFhUONJZQXWrTBLm1KlNvB2KcaraGGQtHawc1lQIALNM1dcDAcgr6jDQQzRdABNB3DJ4FEUrokZnAh7V7lsXbh3OR6RgdAUoWkr6pA+ES2tk4/q0mUlTSUWMZFpAOOoJKlROOzLLKvpOoOG1jRFXPIzbM2wO37fc5O8i/X85kW0Zi7o9BLNKNBmhTpJb1qYsMUf4uttCBRfqyfRlY2fO89bCXzVoNhyKmR/6DTseoBXFNmFhtwmNRrMJL1PADBVr2tIzI74RwNgE6rzHPT7LbWamHAz5+4zxp+IIUqpDAyMpVRsq1zsFw0LsJaKRmpyWXq7xi4NF84nsbv2kV1aFauxpk7GKM21upq3wZuAGBJRwtbOXmkCjRNoGFGHUcNrDniEGFd+UR17w3g8BgLqJltmwsRTdcWnxIrWy6M/zwUYBdW1JrS+eprAq0p3eVwWHgnyfcKmKaoz0LwIAKKcTc0NBFpIAahtM5cz9Ns0ifvoPZCmvDojmDMwhEuxB1LuGAoHJP31jpG4QxuiulEILMwVqIPsH6sX4bUYVE7qFxsOU0NtQXW9BKc8MwFFvAnJHDRPYoTgAiPLxDFZwNg1yry5mD0ygpZ0XauSY/Xte8OWSwXSfjaq0wPpjgldvUQXPm4Hncr/O3CLtthEunkpco5QFLAEee2KALTSsQyKQdA4gdYFaRyQDhNqLkhTwyOy7wq9MGPxaAwEpanl4QEJC/goAiqJwYVR84ZZFgwGANQkN3JdZ2dmutewzMB0IVafB3h1onxRy3CU1SXvzFCAyCfjNsTwsEJXFVpv4a311kWe5xmmcC3chY0HwcqkjET8nRtO9lWrPoPbX3lwA1StNiUloa4bNrFX0LWewuCVi/InlMJusmZJiseoZP0N7jCYSDJzWkUl9yKKUk1HANZa8SKcz4gDlE14EdsWxO1u152+FJrubI2c+6zKiNb6IDbsierSZiwsbdrYl5CwsTRj3y6LXKuaWobHvAdLYW5rUVCKp15h2BdJDw4CMAwDeJAgKL30CuhwBQYwvXKF9TiDE8vjuILSDK04Ee/i9v0IUprugbngWqREKQFnik8s/NgdljJVoJNrKPGVD7fHUjSx0VbqzpuwXexWlnbTuNJyVCPutA+1EW+crO863Ujt5gzeBOl+x9LxszjEpgLQhh2neTluA2qRu3dn9fRzopOq1DaRk54oVsFaKI/AjIAtdGPkCmwwhhaytM/lWiUFgJbW3MMnbOyxMptnQLr/zVPYfsvJt56TJQCcWNe4IGj7DHjqV46zYRKlkrJzGycC2gu1EoGGCXVYwOOANA3qQQyopSCNA/Is+8TpfelBKK2UWdq9DSNolEixDiOSoknRIBhYyaPqEm41Fo1KvB2GPBswZ4bDOBHyJfKAk/vV3/fuR6ZlYd6CvqasUveqCgQArh3IqRmKc+nFOEwqzlKcJAKuPSWbvYJ0C77a71ifDQD70mXGSjXSGRfwOLkuhBlYdul2ABVeq3BupI2bDu67efOJUdgcvm4bU6i3a8aoYKNgDY1XsQaDEPkTMRvRenBQZ+Rs7tokF6KT7hcPqEllAc1oAjKRrZenpnMtbwKcEr72Rq1AJaOrAwNXjGmU3xqvQC+9LOf9pVdwsazg8Yj1eIt0mJCPs2MSgLA9X8R4NAbC3VklidAwSkUmAJoOqIAbCateq6k4PsEG3MQbfPsbOYMGDSViWBEutL8Xxm41pqHYpUhYkWcJK5bbZigibXwrNGrgoIUAnl6MP2y+engMRqLbxvbL3tuucCGs8D6gul8n4YdL0IkHV5cRNFyEc1XuJIPFMCCC6dG1956p3JdJAz1guB3mrudScZsYI2dtmtRk5hszszpVu/XYsM5q8A5rpL+1XQi8DwuqqutLqDnEil0zsEo/lz4axfkhxjC9a5icfgWUaQpkkixMna6A9Yg6XYJf+TOo0wHpeC3n8/AMNResxxn5eAseB188x/sWnAeOR2MgAPQTJNZacFvNSElQxT+jxoEDgcSyIXfQiB0sfN6xCUu6FGYJhqOWZhS2xuH/1nAC2JlUahQ4NU9m08ovhhikXIoaQw2gKU09x65FlSUzDsZi3OonGKa/57kZyYqYsDAU0JRuaVyaR7DtcmXGQ/bhFHM4N2oAGbMFckHJ2+2zvvbQ4IHDGjqVIkaPmBS3IA81vMMZD5qyl5QnSkY6NFJWCTQAfr/pQQCqvDSMkmobp+CK6+N89MxBCtWe5g7TMMqJU0/EvYlhgjWmtXTirgsO9OrGm2wBEFY3xRu8z8I6S+Yiz6CsFPGtbiDg3oAZPXvuRVLdzmxSmyf7ugEstyP8tj8uc9uv+XiKRViV5zB5mhjD5IbQDaPZYtpf6UvpMwaONXArsBIC02m5dBd6GFAMm6wVa9ZokBJGlg5qSykuO2fpw1KlVZ5115a+FPq7qXkPlr3w81oR6mpkltZq+yaTN/Ggr2U76wAeadXGoowUdOurkcOPaGJUGj1VwsBJ32XcsGQzLg4vo5QVNN4ilSyp/OMzXLAU6pXrayzXR5eeu/zTb6Oq9Ze+9CX87M/+LOZ5xvd8z/fgZ37mZ/DkyZNum7fffhv/+l//axARLi8v8U//6T/Fxz/+8YfviSH2hpoPo9ygAzzU8GFkoC3WoKkgMLdY2l6zTMB7G7mG53uZi27zWt1joJI1tFg8UxH7aZ4dYd/OGoNNUZnvawQk7yoM23gMFl7UZceARfm5Vc9hye4lxbCMKYE26+VdsbZpSpoMm3W2bkSkjZBw+Gx0v62dgY3ECVdjO/alMBJV1buU82bZCmuDF9Ob5rFEQ2e2zQvNtKjLfC1b+avurOmemj6GGYa7/NSunLxKqrcQsCaDnAuOq+z7OB6A6SVQmsB5RgJQLg6etaPpAD5cAyWjzAum56W6nhn3wujf+MY38OM//uP4uZ/7Ofz6r/86vuu7vgtf+MIXum3+5//8n/j85z+PX/zFX8Tbb7+Nf/SP/hF+5Ed+5F3tUJRB63o1BA0Dq7ugYdS/yd/z/wvGwVbovZqC7j2jt2qM2S5yu0F3iUOR6xCP5aSceuM97ICo99KrX8Q4p2EZX0cAdTs2x7kVSNnV+Qmzz9OIASgcWOodRrbnfXEWoddfYLRu36Tf77gGtR6Y5jW05jVGeBL8wzkspFJw22OBFKM1z0DoFqaSZX+xh2dLczZD4duriE78rLxv7xWsBrLWVj4/54o6HlCHC9ThAji8BL56BXT1MujwEljToHR4CenJy0iXB7yIca8H8eUvfxkf//jH8cYbbwAAfuAHfgCf/vSn8RM/8ROeFpqmCT/90z+N7/zO7wQAfO/3fi/+6I/+CPM8Y5qmc1/dDe9FQEr2GcYG5K0m/y4U6r78G244LD4DM+jiUk4YJ2C6kFQRD6hplN8ZtO6eGDWNDYEGNz3FDamm9e8MpKXN3zaVeVIYFb0e84wG+W39QBfeeJixN+vuKye3ugztzGX75riINgeOGImBxI5BQJv27KSGEyXlCcjqWoHdNKNNSMD6RDT16SnJhD0MrevUrudAEpujVNX+BFJVViYLueqggjFXJWHJBUzVOxpa89yLgXExmDxdkspMmAJ28xaBFi4AUEEg6RCWE4GzeTut16gZgjWb5mYNilkFt2vuBHrjWAhIawGzZExMPq9UuADvS+OIYZxw8YRR5yvQckQaJ2AR1et6fOYhZKrfJgPxta99Da+//rq/fv311/H06VM8e/bMw4yPfvSj+OhHP+on8nOf+xz++l//6w82DgD6lR2lAZMAuv6SsWjFXO9OLYmdltpW6qG1rDdPwQ0SNc+BeuOwnXonkGeYnA+St99Jvwr2sMOc3P7Ouf+z8xW9m63h4ASUY/+5cx7CNnw7sz+iubmPP5wbzOhi/hRW9IHgaeOYjfE+rQAyyFf8LSMydR4EtONVQal9h6vWLZzafgSjFjEPoC0SDagEkGvnBcn/t+ZAkSjlKt6lL31fNtwdZpEu5FqxMKEUXXyThLqm+1lqxTQe5FwRo+YFxAM4JcFlpgPquoAurh9+Ye4Y9xqIUsqJ0o4c0OkNe319jR/7sR/D1772NfziL/7ie987wyNGBcwcixh70M8yHcMoqlKcxJOYLuQkWtjACdb3AOZNMKOmyT0H6+lgKkBAWNFYVi0n0oSJaFqYlBJQLH3Zn6OtN9EBkxbyvNvhmERrNNSyQks7l54RMrZk6Y3EjpgMcdo1UNa128BFUjKU2e7d3aQWKhgeIfUNcyOb2bBrJTvjb0eFpu1KbEYAWvdxoZ8z7coLVaEaWTwKU4ey7moy5Eu38oOts7d+PwGkMnZOpa7wEGEpBdeLpGCvl4yjGoU11w5/sA9G2ToDW2NHekBqQkZmXBxeAdajAJflArRIWI1aQOsKfvJtMhAf+chH8Nu//dv++utf/zpeffVVXF1dddv9/u//Pv7hP/yH+O7v/m788i//Mg6H53dxOgVozcHbcxon7W69uVEDqSdmMTBOkhrSEMJvthhWaO56DfHjOQMhCkRNopxop7OieiQ0qiGLWRY/RgrPOXwurP57hiJyHLbnoEC8CGzCEyi3gsNnYgo4nuOY6ow8krs8Cci5kUQjtJNZG1aXwR3fAd4+TwRXAOQZPN80D2LDDnXvD71x8NVdL1Trl0naAasia2s80324GBiXGlqY4AtTa+FXAUBXb1Mtd33NYhyMZsTM+4hVmkdVsVqyGAYX6FWpvVJxaiDQvJFFjcVRhX6ZCC/nAeWibXMYCBNf4OLqO0QoabgFpVEMRF5Aly8mi3HvkvWJT3wCv/3bv42vfvWrAIAvfvGL+OQnP9lt8/TpU/zQD/0Q/ubf/Jv4l//yX74r47CdFJSCRHsHUrY/GqfQ+KXd9HuAZA9O9rlscwtL7Y1DBCkfrNATjkMUgGSCSQaFdre7K5V51zk6a0x0uIQ+h3PpH31+Ik3n4ZTiq53gEA+LNQgtU8FAC1e2f3u/H2sfLPX5ANaBhR8eYig4mdj2vS0ChBY2nWSd7dCt2KoquKgTeSlNur9T3LLQI9DEy+bP3jfRXyN/eZOh0hoLRcAUVtBlj8m85RfDYLj3Wz70oQ/hc5/7HD7zmc9gWRZ87GMfw1tvvYWvfOUr+OxnP4u3334bv/Irv4Lf//3fx2/8xm/gN37jN/yzv/RLv4Tv+I7veP69IoYllCR0mFpsvG0Rx6ljT9Y0qjTaBpA0jGEnrIiyYO5BQF6b+2xF0EmLjpKGLAR4xV21NCDH49h5DtwNLlqIUGsXgsjxNqNgE5aStJSrBP391jioojTDCQDj5LwIMV6TpMm2GRcET2JjTIxk5pNKwwwrjxZxmOorsWUqJL2ooivQ1bcsngU6wVosPCRr79f4BnpkXvcQVbTNc7G6j0Q9SBlxiG3zX0A8xXh1GG2Cm2ewHS38qF0vkDipgdOQCFD+xMbQyXbKitRQtXUTkxSocSbG4SB1Q0DDJrbUgHc5HmRm3nzzTbz55pvde6+99hrefvttAMAP//AP44d/+Iff+94EJqCs8nbTBmNhI0i2Yxj8ZrIbyvAGccnVqhK1FmvE3hQlklvWYv0Yq0u6JyJUkhveG6uw9dWo3UrulZXYrLgxK6Gvd4FNmyR7RiLI+MfvFHxS3yunv2leVi0sGaFh1LLuUWtKkgvGyO/03ka3/3E/gcZ+1FShGQ35HjlnzDJBKWzv7Mk7PAb73Urs9Q3CN7BKSXSVktthobuBlBHzGFIzEqTeDJEok1stxj71uhflBXqsohTjX6BJ7AVuhH/Plgkfvm/JQGGheUv3raIpWvFUptQqaedcUCvhIk2oaXW3p6at4sW7G4+ISRmGTjhKSXGH094A1ixHDMnQJs4mXXgi267jIQFDdGOLrkRV35ebSNKyMDzCszCbY7HHuEI+JOtxbmyPJxgVIU7121UqLeW54WTI8wRgU/odGJaRKFVttUfyMEEwCDGiNvmbEER1j4HC9ifHE/82xrxVR/bn/wVxgR48vGAsHEAUEcpVQFLWuh5mIBU56FEVknNRTGarTRG+1GquIqM0jr23zaO7L+x83vF4DIQyCd1tJhZXnVofCCCAfDxIKGFGwbCFNPSGIeIOm8EUmHJdfNvcV9s1trhSclFSx59UpcoyJLXvHdo92vNYfWlvB5LVyT2/udi7Bk+Nkt0gldH0N61mQmXpyDQg1kVo7fq5GlMPWl0q4jtSi8GBG1FVM4NqQWJGgVQektYPZAXWaiX/fu8fESoqKwyAVKNj2ZKk1zZNktqDaCSY2+7d2i3b8JyWopV1B2EY3adt9GDvRcM3JnGO9JN+2kZAMw9yXVJuxuO4FgyFuv6jBljK7/Q/7IBufAz1JLsmgBneOPoFGYnHYyBs7K725eT/Iq+h9ebc0XN8DjAwrkrRuFcNp0kBTKpK2rGMgj0WwJfvuP+RRWgl2htj0Y2tp0HB5b/D8LjYafRfo5FEoGw/BKQsWVK2W/89gIkW+1qWJ2IMWesYDBQk2ln97DjS4LyWiDsYfdmzCLVxEoCHeYI2zq3ID51KDTfZfG8KWYngCYypfXtOAFBEpxMFi7HA3bid7pvVq7ByOE73+w5g+H1pIOKkUqANQBer+wqqbmhccTqDEI2JbRd/yh5JGqYkZcgkFi8hBjXmGp9cI2pt/WSf4GpLbX/PXMT7Qoy9cu7nGLG7eEwfG+grOp0KWHIL4zqcJzYHXmZNNc+gop7HOiONjMrSzwLF4nolEqlHYQCm0ZntlJSA5YgnWGAl0xXiNZjHEBvpmv3bcxwSnQRLPqykulVL6qN6iOfqU03FuihrkpgQJ3QFXOMyejMmhVeK1FMcV/nNIwGjeloleBFNZEe+e0iGnTCuxoSrUVK1UxL+RqSmm6rZfZmg5x2PykDIRLYbVEk6gY/QTfQU2JHW9+LcZHSjcmokzHWseienKpvl2pwB3z7GnuF7rZmMFG4V1O0SY5+v9d4LtwdwnpSmnzEaJ6DnjvdE4yShwzA1OjsgxmBHNr2us6zq8xEucLtOMpmHBWDGMBzkJm25jY5ZGDNBQdNEKxqNHCTnMmdrAGxt/SRjYR3DLYTZZp2Z+90XcM/EZZoGpIQn2tgGcEGYZiTsfT1tujiYMpYNO674GoBmGML50yu3loQr7XZ+MYTUaIhpjBhl3oIZh0TA1ZhwOUoG5pDUSLDQ1akW0HJ0rMiVzF7AeDQG4iSd5+/bk9K59MaGPOchdOMBYYb9jHmQcpv3406X7o6xVbXeXrzYkXxvnDs353/wTPii50p6aKh3xtbGUBO5YZaJQeD2vKS2/1F+rhYFKVuXKK7a1g7NODS+Qb9b0ROQzJGlB9vzBlLWdxVevJfRMjTtF7eCTecEnGo1YLM6/TtXNaKlIsYrQu5qYKilZqXgLKRn2f6oKZtteCQn99y7HI/GQHioEDGE+KjPq6c+VeJLn5+M6GZtDcQm08AAnITPolydaiTG6HYU4sK7DqUDMHbi97gveynRsJ8PGvE7y7572RkhZVDSODVwMwKUZZNWLgVW1BVBympuLTEST0Ax9W/50orTPiKu/whxx20BMJDYKiVLbcBkrY2fArTrEtmLW5hkS2zLVTwEwFKjogG59SLixwhoSuZcPTsh398bOsfOQ+l4/K5B6dcjJ0yJNZ1uaVD9bMhemJDOwACBvLCNCbgc1IMggG6fBqkBlTgspbFS3+N4PAaCAxUa6I2BAZCbzIR5ZxF5totGxF4cZCPeMnHVIiK5eZgkdV9rd3MDCIIhhn7TSazX5fTPxYFbA3GfEegyMZvtn7dYzL4P5jlwZyQsawHzFGJIpkQ1l+0r1hdCSFopTQATCiQet1WflGrtE4jaNanYeghW4ly9psEMQ8VptmKP3brNQmxXduMj2ATOxSZ33e2eVlEBggDTO9+7Pa6ogh13NxVpniPGDhiKErJ27t3IzYi1IpOK3FwOpMJEvYq69GSRL6RtEd67HI/GQPR8hgY+AugzFjBWW0tH2jUl0pUCMun38OG7HC+GgNC+CujWNXxJvAEaiLrBFu7yGh46NunZF6kT4a0FDJi0lCY256erkn13GofbLuh7sbsZBwEQ7b1+8uylMg1neMg+7IUAscvHuW+xc7KTvOiEbbby+PE3i5HdOGTKWH6781iCgTDcZtQwYkxNV9P7r7iRVuOw0Tx9EePRGAikSdhfwySTIU2d+EbOQleyRimyorSPF10BjDrbTWREjKG5gDblSD0CplbSXYJV6FrL6RcOBLlIOVykUhpQtCMgE0cN4cUuRhI9qMD+9L89g3SPIbJsRtTcoBLqWyArz4kU/th0NjAMp/gP82lVJfXpyDi2RgFo9GljtFoYsPf5WCC1rcC0LEY0TH0peHu/6r1FZgGCmbD9t4xCrafeSDQIRtX2pr7xvIOwhOOaUlPcjouYa2Zyb2T8OxVr4NubJpI83yg43gOTlM/lcp5vPB4DoR6EVWCuIe60OngzFkDT/4uDUZFqc88MMe9PvrpgflOIUehXg80F7ngMuq3137RQItYTxAKkk+NUF1/Tj/vnIhSUAacpXPueu7yW7W/u/H9kqJL3ENrRgghdznbxHH3vIXwlpwvUWJXZrqU1muk4KWe+qzMUmyzG3ogRU9sPuZ61ORNt+/iaTr2fuBDF9osnkxXSsXutLWw1Y7M1EO3ebSpacpBz8xry7O0VfHE6ud/ed1mMhKp8hlz7HPgccuBLkZXFSm/jIHgvFSSuQnOFpdnUw6jN4kNXIKNSR/69GYkIVLrhqEGHcvPcrblud9fo177t/wUgdWMgTnQot17F3vPNthVFytJDw18ArW1hMBLW9k3oz5NInqXBy+iduxAmN9B7DzFEiOdUiE/9GbBJUtEyShRmptlr+/7EskLbar8lRDWhmCCtvxdy6PftcJL0e+3/23c0piO6DmsnIGEt0kqPGClxh3HVWptamf2O3Ut5ddDR77c8913bamvMBEjI+P7DIIYRNU04ar77Zi2CYpeKY25afibAYbRboF04V0wmLRDipp4sJ19dNXXZRCREwJ8U6i3MCDhNGRD6bNSeXGfAUON19lSTI8mAA0b3kaVi342TMt3ItYiva/EeP50xYMB6ZxgfoyNuASDlbNQqCJ1R2WvOp9oV+nvFdDTGC9ThIN7eeAihoFwrwxGABiKeMwjRgDTeAbVeqKC2qO+cwkqKU1S51os2/xwTuagsIBPaUoV7RVi2r5aK3RqK6IGa8TLylE1mWmdRNq/V3f9uX9dbOe8UPDFu3hd5Fqk3Bt6cKbb82zRlQskoHf6Qznebe87xaAwEiDt3UxqPiMewZBHcWEqv69cMhPLoUyObjIlQlDbRDIWkqhKMEEMB1aZTSbk4YuigzYDtdXzuhxPu/q4ic5vFiO9pKrLa/1lT4bj9ewEr4+8Fj8INyUbhqgshrIAqVMYaaJz1ulnIsAcc2rW1cR+22ADj80Pk5BotLVFTu47GwbAHswu0+d4SjIllq/aGnxoK3qa59rm1QDDQsPssYjgxSQFdUWON8D1Q/GDrjdr3R7yhFpyoktvvvN88iEIJcxE1nlyBYy44riKe8c3jimMuuF0LrpeMqNpjw26EKC1mOoSHJOrGpiZEBFi1PMOYfBXgRlYxmjKA4BGUduEjznDyXFfILdi37XZU8+l2w9B+2/ga0avQVac1r9HJa/tJDKJ+v6UCs/XzNC/F54FtBzSDEIyChBNBiWs8CE5EgzMfoxLXVqoNgDMrgVOs4mRVt1BhZ9vu9FUpmiMAlYVDYfUPjZUIV81OyikwDyAaAjFu8mOWxSq6b3EXGC3UJPMYi6zqDhyq+9+NvChATAAdhYAWz/X2HgsLzwnorR5GJ5C8FQN6l1mn7Xg0BmItgjvMRVSBj2vFN48LllzxjePixuF6yZjXgnkVgwE0sOpiYFxOyQ3EYZD+B1djcrGQMoVuTNA4kiF6iqWGOLY3El3nLF0pOnBSR4wFfag1rzG2Lz1j0VWwi/alGKGrBSQUsG1Dupd4aKGG1a4Y+BnCI2tF1snS1bKvOmSVqSqq44ZiK7SDRmQywHgPU4hGYasSDsArK7dEIxtbvkH83qwui6W3ByasBWBWvIlI2YtNyIaoeQDbYXuXa1UmLbmRsNNooQUBMpEtvZhnoTvXClqPnUfQDrbV7dh1pL2sVFl7lS3fQX2+rjjXqc2MQ+zT+V7GozEQwm0IkuHKWzfPQVq9V9zMGblUXM8Z89pOUmL2foyJCbnKRDRwCgODFbtgv5Eh8W6VfHQlKLdCEWTg9OLZ2EtNQgFE1bGQA9s3FgDQ9aBwI6LCKspYhArQnEwtjWFRIGnL6DQH78MEbNWfFSNhAGfY1j9qwr5qGAz/qKGMfiu0U96DceAdw7A1BrugISnUopiFlWVbd3H5HNmmjma4B0CnxiiOByRkdMO2gPQLh2IHEYeKoeQd5LcT47BdgAB0ndtjv5UXPB6NgbheCy6WgmsNK/7oesY3jytuNcR4ervies745vUcDIScuNjF+WpKSEx4chjw5DBiYMJ3XE24GhmHIanUup5QEm8jOWVXUWQGAAJrfQJ4kMmqqkhNWm4AkYJ9dQgXN/dGIozO8luX7dBxG+siDEcrjKoFNY9iMJy4odkMHgCGajZsQhzzJuy91GJdq8MQnvOGrZqkX0gl8lDChH1LBXJuqlsm9BvTlNuQYK+3CNBP0sgnkP87zTScq99Q0wcLkkqFew2k38HqJVr6cK/se5fHocClZFJqY9xGY2ZGIa8NnJyPjg3U2M3dv7xNaFaB4y2QfVcthYeqKXXCyJ1C+vsvxJDw4nrOnq24XrIYiJsF13PGzbzinePqIcbNLCcnGoibOSMx4Va3mQbGNDDsUI8rAwOwZEYdRLOg0W3JUXjj6vuNu/Ukoqu/WQU8DRnGiUsYm+au0q4PLBfcVbqjSEstTQRmA1Sa6IrFrxUFoCShh4UTtqIBDaGzw4nxcAgnPENRGsnH+jzINWvYwjkvYW9Er8EnMPV0bN9X/8xmAsHYiRUokhQ2HQpb/y1zZYdsXbnOJpUCQBmBy3Ojw51K7ps2W/9TM/zbInRrubgujWMSJvXemdxiWJVI5BZt+0Cyuxveffh4NAbidq24XSvemVcspSreUHBcM945rriZV9zMGU+PK3KRUOO4mFteUYt0Rk4KUprRuBgYTw5ZW7ippLhyLFYPR1pxlngR1anacqO0E29uuzxq3J9KwwJsdb+LqBJCiv59WWmqcRNydo/F3FYDJ21/4qiAeArmTcR6dd+vMPGCR9JpeapoizFZa0hhGv+k1B549J85N/tw6jW0upZ9jkH32c13kfJXtE4bqYb8pJgPASRD5mG7L3sAaKTtO3BZAU56T3FIoUYMwb+gSN9T7VpWl3n3XEg7gtIIauhDhBNAG8BuVe/O8/p+9CDeuc24fXaLP7lZsZSCb9ws+OOnM27mVR8zbuaMb90sqKViXTLWOcvNuzYDIZ4y4eYw4ulxFdCSCfM6YT4MuEiMdUhgjkKmyRV+sqJSXBs5ShZMyaFTmjQGXJsKEqA8AkWfSyDKxFgR6NJRXWftmAcHRPJtPmqvUWHOVZbCKMcGYPvIDoDVsro34XTuGvgEwZvwmyumLocJdTigAo4HRaxhy2YFTj2Hc1WOZgiMm2Lsw0gS2o7eoe/fZwALKpKSjojE0JvmQzQq0Utp9RLhUoRDKBCjYKXaRABydd0Q1i8c9JzFVoxUK8o6u1foHsS2OVEUBubsLQ9dnf0uYxDYq13qO277fhOtvS0FZRWPYVEP4WZePZSwsKKsQpRa54yci6Sgs7wvBkJdysSYlVZ5PWdcThm3K4sHUYrzKMTbkBu3qBdh6TqgKSIRegKNqSCJNycYxUlTn3PjATlqE4uthRv3goL3gLa/ThcG/GbVvQ83T+1AspPfM15DMDwFDYBsVGhsHtvMiqt0hx+EUCIWMXkzXmDXQNwXsBQ1LCYcW1WDYo/TEveHwuP2NyLIavU9DcBueIdQ9c9fbzf622bI4dgIeDhfIRiCk6bTOMUwzpLznnM8GgPxzeOC6bDim7cSSvzx01v88dMZ81rwzesFR/UY5uMiBmIp7kGscz6BB8pasC4Zt2PC15gwrwWvXWUJOS7ksBMRlkE4EjaxTJSWKmENKTQgeORESMRIaXJNBFIugqceIyYRiSyhUa69li/X7Tedt4X6vDY8QkGxygOY2eXZbB+Tub1AS3fmVQDOSqB6mpVxRSztUVo8tGgGE7g7rDgpVfZVun+d1HMTeLQARVZYb7vnXg3vGrL43siDVnQSgKJygYAxEOJkN2CyUemFJGdGooZj66pI9TsKgIymr1nVIxlUvq9yateeE0z098SDjMObPQlAbB3ZXGM1HG+X7TBvD/CK586rAJpswnscj8ZA/MnNAh5n/MG3bjGvGX/8dMY3rxfczBnX1zOyTvj5ZhVl4NsV61Ik3JhncevCWG6fYLodMIwSTsxZPJFpYFwfxP1iIlxkVgqunPChmG1vQFccBoLZjZ6IpbGP8g5EsUkzGim1GDRyIc41zgVOV6WSlVsxCwOvrEA2CfvB97SESdwmo1KqoROQVOTFhE+iy/rANKbvlsfqrTTZqmkHK4KzcAKb4qM8O1091rC4gQgcgTu7kSWpbxApfyFHZWoZiBo8wHbtgtFCXy5uxmFP0s4AzKLfU9iObxAcQcWOqJYQQoR2AlE1fKe/C3HqqO0nKdDoLZhRCdjRCR41XOBFjEdjIFZRCsG85i5LseTixmFdioYVUv6dV0nZ5fkGJbaw54R1nsEaYhhecZPYMx8vHwY8meRCWauzxEknBlS5uhcgsZUQaDeTrIS2gV2o04wFsDEO27EhUO2BVLvkG/svvaOJ6DTkcM5EOZ1kQFutqZVtb39lO2G2IN9WUs68ha6YCRDjsM6O/HsNSzAQdWMkGmdDjaxyCsQgsxoeBkAASwbKrl+UeTkHgJ5zxrcgJikoWrmFGx6WxX01j0CzURXY5yxsmjm3H9p4A9tQYsvCdIPSjuRF6Yc8GgPxh09vUetRPYiCbzybcXuzYJ0zjtcL1iUjr8U9iPX4DMvxKWopWG+eas65Tcz16lUsx5cwTJcAgNubFcfLAblUPDkMboSMebmUiquxgC5HDCzVoCuH1BvazcXUqkYrQdSUahGl6CRuJopxKGQVceOw4zV0xiA0zbVu5ifxpGESm+8x4hegoru2khsVmxqp58RlBTy0sBXU6iqqf38/Y4ysF7tjm9KyaxhY9sWMQiw0iiInGxLRVjSoP2FtvykvqERI40GMRBqwcuvPcVfNkrQqNNEWw1isUrj11AQQQig79goiUZlOaQKGg2QuAPDFpRgPzUh1Kl2A9JIFXGeDrDm1iy9vvIZ4PqwtQMw62TbhXNXxfeZBfOtmQaYFT43ncFwljJiLP65LxnK8QVlnLMdnyLf6/OaphximvFxKRlln5IsjeJhQVvE4hjFhXiTtOQ2My2nAyxcDhiRNTYxQ1cnYyTcL9sABkyAAsLQXSwy6l/oC3DjskadO+l96XnxnFVCk3MBKy7LYPCho7nDeehL2FTF7ATiOUTrjcL52AmghBQAXUGVqMuxMcFk0MRC3nuGhdWlp221pvLrNfg5LPiUR6XaeXo4TKg0Y0gQwI1cKeMSpF3ROjSrKCSzZzrOUitcqrRFqBlbWimImsIodARIC0DChFi2pt+seshTejV49iRPh5T3jEDVY3VsZmtHojP77LYuxZByPq4cVy+2K+TYL2KjGYZ1nrLMYhbLMHlqUde4MBCAuHOuky/MNZpaMw3wrHsg3x4QnB/EorpeMg2U8FrkhRuYQu1oaTlxMc6+LrjwVBK+FMBdzGz+fGSehRCDNuBsaL368YZ53bOPU8BiJTtFr2JtCHSwQwgoBKKmVQLsRCCQi00qwdPBO2GThhheVbXeCZIZW2zZiKQHHkAbLurMaD8ZfYrRGvbRJaRBtMZeKkkWZOiuMY418cqnCzvXwYgCGIv1D9L4koHmH6kHEbvRmtLfU664va/x/6inwFnKcCxHf7Xg0BuKPns44zrc4PptRcsHx2YLbmwU5V8zXR+T5But8g/XmGWrJWI5P3YPI802jD+uoajhYrfpw+RLy/Ko8nxJKLsi14snFgGlgzK8c8MphQCLC7cq4GBKuCncKw4SmIWEXIpG0nUuJgTQ5kg3W9nQphX6YjRoLBOOg3oI0tWmrSteU2MRZYpyL09W9L6ludSVMfeWBZT7MMNgKG5WetivsSSEVN6xhUDByZLhnQIsIqsKeV61+zHMraovgLaA9V8VrIK1F2St68kc/H7OL11DJct7GC/EmABBL+jZWiGaI3JxVkwrLsmdjOoVcdyHXDAzSlzTlgjlLWHUxHoBhlt8dFvmOCykIPAkvLcQYJmA0Xs2Z5k9xwenIbJp1Ul5OrkKDtzM1v5hq78djII5LwYqMdckoqxChcq4oq4CQeZ1RlgVlnWXyL7M/3xoHACjrAgoeBHHCwgnL4RK1AOtB0qQ3JPTsa81wLLngSOJSLkxYUL36U1iXkuWw7kyeH6+huCuuaPQwW953LtdVhTcupX3/c449byDG18Cp+33faJ5DawPgkytWuUYA0gHJZhxOtAzWGYA2ZoZ+5uSAFEsJK6c5AAZcwkhlug0H3Uwz8AxZoWOZd6XabROHNfoxRqXxRLL1II0eXshodM5J9BCD5yAHdP7annAfeMNZCYC6yem/iPFoDMTtcUHOC+abFTkXzLcZy1EMwjrfuHHIbhSyewnnhv1fXmeQGol1nlHKiPl29XDjT68XXE4JAxOuL/pTYoU/pRSMiTAlRjb2nqLlWW8SZrHuVIdutTfPoJbsMSkAxx1oGJsXMUwiDLtdMayhrYJSdnMApzfyabcoBVjRGwkPKcLnYsp0b8R6CVZMZje0ODdq6Y3DuoT/0o5f64y6wju4A+iwG3/POqtTa15sqzHKepId2J6vuJeEZvQSw0lXI7OEF5FApSmMWkn7bSjek4ZGd9dzSaMYRzajqPtfiTowcq8vSsxYbP/MCObaiuas5B7AneDs84zHYyBuFqzzguOzRQzE9bVmKTLy7dFxhrLMbhjEgzh/JmopYlRub/y9+fqAYbrEfDNgvlhRshCxrjTl+erl6BbZshsmOJNrCzdyATIB4IpcCFmLhi4si6F9PgiCVlf1aKyVXTcUsBIdiKm5k1pViWHaaDOI4TAmpR/vJmZmEDIEOxHgUp3nYBhqWHUeOjhMJJtYJth6No0bRyxS2zAN63yUcwG5fjWeK2vua0xxNSBS7t48CCOSnbQsRI+19MckeERVwaCKikKa0gQh13wiimvcEJPbG9Ik+zEcZINMLTwKBmvrNezxHSInYhd30JBkyX0RnR3Z+n7zIGoBClVk5T00LyFUPb6r79Xv2rSUE/KPxplauOVdpMMfCpC1q9KuLoF9p96dXTNfv7jkPARpe7fpXMX9DbO9Sc7Gpv7btZvoNlFKVVFesV2eAi3+ufaZ3eN5L+MuLyKOLWksnp+7tmfNcACtz4fpZjjg1ybZNqTaYi42LI0tIsbVwctEFBt3n+6W/u72+guguuGfbEa333dsu5UDLOH6xcViS2p7L+PRGIh1zVhLxjqvHkqU5VTb39xLY58BuNOL2I5obGqR7sq5VOdFzGsRAVQuKDVhD8e3+6Qv9tFJavEvLRJuyM56MU4t3HpcGmlGwwr3HEzVKfQI8dBil/F4WkAVjrgjeG3HOZBzK0vvWo+bacvUiqK6/9m4wxQnzEkBW+4ASw8NUo/L2DlrhjUUNhmYy6ml/AbhFpQqcgJ5YxA6g6G065aNcX9Et2Ow4hOxfqQ7d1AcwijXemxWIOf4zM5jTI3LbTT0FcGbEMN7xlh4UULXc9QTzsq7HQ9CvL70pS/hU5/6FL7/+78fn/nMZ/D06dOz2/7mb/4m/uJf/IvPvSNlrfo3h/Ah+6MN0psiGoh3M6reHbU0F9H+llK6AiUgEmTkydY4AG0V8VSXT4ih5b4j915fU0rdto45mHiLodbBtYzZBy+oKg2gin9rUVXwzZ+l6eJfdzw7K5EBstvb7y7vCuhX826YcVgXWA/QOh/l7/YoFZHhz8OSbU2Lje48tpL1pViJP8Lxtz//OCR0GrnRxkeGP3eOB6FL99p5iVmGiBl13t+JjkT2bE9HO987NstoQD1H/e2KZhjqznV7t+NeA/GNb3wDP/7jP46f+7mfw6//+q/ju77ru/CFL3xhd9uvfvWreOutt97VjnR9D7j3EuyPhwm8897d39s3gmnffT9HYas8ZK+3lYpG4e1O5s4FjpyHuE9bDv7uX+hN6lWc2Kwg5k0U+bNJkYuIu0hj3PjXjIiFXFlvOivWuuumi6nQs8DmdnJszkN/yvLGw8tuNHYZqNx7D50R1Unan5fwPPxV9AaP9C8ZCMtN9NZA2UQmfqufiV5avPbRU4iGoVbRlnTDt7YmvFtlsO13fRvHvQbiy1/+Mj7+8Y/jjTfeAAD8wA/8AH7t137tBOi5ubnBj/7oj+LHfuzH3tWOiNEn8DD5xKckklw8yF+aLjEcnmC6ehXj5RMMB/nj4X7WWPQ6eJikYEf1I2zE1SRWJ0obdgEqo46BVS56K3atEdgD6rruXLzjOgPK6Scv/BF3ufcm1tqa2q66Ipoa16ztAY65qJZnxc1S8WwpuFn7v9tVVMOPa3Wx4Fm/dymt74gZm1xa2bevljoc07ALCahBi4wqduRe0Pkz3p9lNvRPMhoivmIZD08TjuphaTOfMl2iDgd5nK4wF+CYq4sR3aoYsmmczrn6nzEnK+BA9MiiiH6R5PFyYO+sPSXCkEh5MGosgG5iU6g3obKK8vV8RD1eo9w8Qz0+Q9G/enyGcnsDzLdonbNUMXvTqevbOe7FIL72ta/h9ddf99evv/46nj59imfPnuHJkyf+/j/7Z/8Mf+fv/B18z/d8zwvbOWLRWuxWXtV65GECKx+Ch+ksH2I7+MyNud8cFmALKTjk/I1QE1YNRkDx7yiq6n8g7MtmlfXnAaQ0TMAAthPB2GpeBbyWwI+vtmMiCH/DmxNXnCg976H9VTYMr+GFod12xC3VGTwg0fZrXkQ1QpjLt4vH0IWU9p1KoTeVLfkdapyHjecgGE1D+C2MjJkbgoi/5ApQAqiGylR1Iyyb5epVAIjke41EZ/dDd903HkNM76IUZ1g6BX8AaAUqF/A4Nd3TWvp87Ld53GsgSim7MmIckPdf+ZVfwTAM+Nt/+2/j937v997djgwJNSWMhxFllZU+zze725aS3SOoU8sICKvy6NtRjPM3huEOUPnEWJi3IOBUFD9p7qZULkJasvuq0aoVsa6yQu3c/FSTg1p161JujINlLFpo0DyKlnKTlVBW/f5YxsRuGKxPhBkL64wOnGY6ALUNyjAiiHoTVSEOVS2QSmF/pW1A3g2ZKkrDYmLVo54XMwgAArlIt3W26eAYTR0O0rNjmLCChYGrfwXNGwIatmTGkgnaOqABk/Ha28bbfvGt5B+YGFKQFsVr89rUxZZZFMKCXiWAxo3Q4yd7z8LZWsUIO9lMww+wEtOkNgSlZVkqgJ0p+67GvQbiIx/5CH77t3/bX3/961/Hq6++iqurK3/v3/27f4fj8YhPf/rTWJbFn//CL/wC/tyf+3MP25EpgacRJReUkdX9P13tTbUpz0es08HJUOs4oSwzYhpRtm/GYftowzIZq/4BvQvN1DozxRjUYtSBCQNBjII1UNHWfOZaenwdVI6rNTzJTfU4FmJth2EDZgTMKFi4USq8RWEpwFJKV3QGACOXTWvCBswBQBCnQ0wAEkj9bwg9WfcFpbpeY9OGaN6QSfKL+hZD2p0JkckLlQaIWG8JHIfdMwCV4FMy2XCQVoBpQp0uvWeHNV+6XSvmXBykbCQiMZxmLB1wLHJeLGUt50U9yNo0NH1XuHXypvm6FaQtx9YrY72VBjfHZ2IgAMEc9hSmlHZNJcui4aAlnXgmbNRx1mpUNW5FDcqecve7GfcaiE984hN466238NWvfhVvvPEGvvjFL+KTn/xkt82//bf/1p//3u/9Hj71qU/h7bfffq4dSQOBBgInBrFkNOqwZyDkwGuZkNQQ8CDPu1XHt7/79YP3j1vn5Yhie7prx6Wk4Fqe8PFdbShc+OjW3BGibMFBzyzo+6U04ox1mPLPUntSSGKSxK2RLIfKqN5IWjjSmh13+4NW5+BnmBnCNNJj3oZPhiUArsDkBj5yQzzzs6ObwC3ta6lfAyQLLLVpIZkcw+KFFSVUacILt7bmSRfopkql73vD3kAjj5kI2rBGW6+UdxczuDdhr8O+kUZ/JoLzoPTkA8a9BuJDH/oQPve5z+Ezn/kMlmXBxz72Mbz11lv4yle+gs9+9rPPbQjOjVeeXOB2ugAnRslFaiWWjGLpyFq7UGddEtZlRFkLmBPW+YB0e+k3mE1K4oTh4rLLeIhXofHjJpuxTXsBcDn1RAZMkQNViQgDZNWQoqRrcS3XW1k9VLjUVo8u1886IdYZnHTFKCuQyXPoFYAssaeratWQw7n4pTU5llVTH1GDhDsLx0hEFcEs8v6j9vzo2uZtfguQz4kWZFXpNTEYJsNWCQCzh6CEkNfXIjakSc7VODWuACBFTCoDLz/WSFE0TnCNjHESnGEYFaQckXnCqmDr7SrG4VYB2ArguGY3nCa+ax5BIgKmhIHF0DEqhmod2No94AxSXRAo37YitPXYF6RpiFnmo+ANBrxiE2aa55hCwV7Izvh2hkdULWIjlnCOyRw7cSIg90Lc9/cyHkSUevPNN/Hmm29277322mu7xuGjH/0o/vt//+/PvSOvvjRhPVzgT1iUg0VSTk5QKdUXVEsjp4ExjAUlFwCvgo8TVjUAxp3I6wy2jEjgT2yHGQkzDJYOi3bCcIdEMe7Um2YVVxKmdaAodLm9USR+Qb3d4CmKwldjV2o/jKrIt+tJFux6Ex1giepuc/QccoUb2PYNBVzJJ7nJxvOZPKWxMW2Iyy2Tbiny8QJjGlaJ1avs32A6nYCCc6IE7iSiWlqLQdvGgMuNhkKrVRmbx6BVrjUNXSYnZmFmrdq91bCjlJbW9ca+TOBVQq6SgNF6bVATBuo6ec9HNQSLtNnLa3tu1y+rcTDcYZkbMBnuAb0B5R6z49wMp4xX7Y1SiiCaxC4GRHreATHmib+NBuLbMS5HRlV9hlkVqjmxgHLKeIxDsC5CWQnDWACIclRZZwcsT/kU7I/MmuakBjidO6mSwWhGQlKbrTgJG7l7S29VJfXsxZyys/nkhnDarbmuxA5MMfGJbHutgg8IUPV857w0t0A9itPhlPN2NmTV0hXY6hdylQK2XCtSBYqBn4Fu3gR9TUR3aKnBsUo2w8+LrqYmshI1FDb8EPAg0Zt5UtW0NNEZyyUX9x5Kqe5JcZXKXaBg4CR4SrIQS8MKxZgsjPCOWquUrrvXUKsYA8/KZHRtDbYjSgHYPWBVnpEybiOGL3buYJJ78O1fkAPxeAzE//c7rpBeeQlPjwvmtXhrPTMY3epeqqtclywqUeuSMd8OSNMBZV28RDyOYbrEME3ggTGMCWmQRjtTYifD7A0JMci7RE+JcEgEmq81rLhxYIrWI7DMvnp4b4R1QxvvRENkZawAkIqvFCirTpikoN6ARAzrmJc17rRYGyx9Pkol8RJqDuyd0yHhhCF32/87zYAAwEKtazaQBLTVzYgqJusloY6vqX8L3bgVVakbiKpt7SvPoEEUmFxqz/gPsfPUqLwHTprBkJTmnHteQ6nS7uB6ycGDMCBXmLKWxk5a8j0yoxTxfICKUc8dEam+hYKP69GVsWi5lmO5vUGdjygBa+r6YoTCtE4tzJi0zJ6d2WVi2jkDWqc1YvEkAIDFm7AxnjH4zzsejYF4+WLAdDVqpWTFpIYiF724gRZrDXpvmJBXKe4y8LKsFXng3d8QwhUjpeY9PIRRCcS0psR7W7WkmNosSu5xz2GHFrzVKewA1k2q04g3NXgRJnRSq7SdM29C9lVWciafjifZjFy12jMaiTDKJkTx88BwZEwyPhKiZAU7cwW4QKX5tFhMTj4IK2pN3pHM03qlgGoIPYYAFQaZNtNZaOXvEqe7RH/AZEyIuOExGlpoSthDp1IdX8AALEXPXaUuxLTQwfQ1LUshXsSq3bQWNwzO54jcjo3HuGX4noQY53LxmuasCF5m7jVKKZ+XQXie8agMxOVhFL2FWnE1JVyrLI4Yit6buNZOW9bAd10y0qBYQi4YRsZy29OweWBMFwmcxIMYpoSU2Pt3TgPjQh+jGrPJpJOBWkxAWSSlaSuJ3iyGOyDeMOZBdKXLQU0KO9mVGGJY/puk90PRdEoqkIyPTnRi4DAkANkRd5vgvOMN+OR3IZz2XvQgonEZBYUU17wAai6RKfwWyVxoKDthiopJenxkWYiq6d5SgCJZCRFbUV2HSBZLkzNNvS6lRNp46wy/5IrjKt5C8yBab1GoYS1MuKitAKq7Z/R4qGiGolsMzDjEBSH33bTuqUYW8FW8hygU1GlMGs1+c39IPVnLEnXp8a3H+i7HozEQ33E54JWXJlwvGaUCx4vslt7jyBMDsboy9bwWPD2uGMZb7ZuRsaggDCArS1LDQEy4uBwwXgwYE+NySriakhgGxSLGZJLt5L0dRL1ZOA8CTCpAtR5bxuJ43YqKFKDyHo3mTj50xG5axtEnxsSDqBh5nIzuOQ1CuuEVLeVZmgHwyk99XNZ2Y/VA3rZgjVCYtCOZbDuyKW6ljpxjBrZ6BoXAFm6YUSir4yv2Xi0raFWw1uL8GI8Po+gtEKMOU8cLyeodHJVKfb1k3CwNpLT0phkIJgvJxKiMLMLFzRNDE8JZjf7ciFCUF89S1Ntjf71jOns7LGU7jDCRIOd2cDR+5EJB2BhXH+G5s1drAS/7JMPnHY/GQExDwpjEktuNntRrYFKEfgfnSZzx8mHA9ZzFgFwMqidRUXWVLgp0psQYJmGgcRKg0tStzTDE9vAWnxLg1Gofuqq7+9/RhTfU4dxulgoFoUqjDN/JzdjStuMNoXlvuZFrx9EwDQNwRc7wCR1HxBhcrqw0Y2IuuW+vuMII+aFSqmQxNNwYuLUxJBIDQ5VcsMb0MSPb0vuIGu4iF17PqeAuAPYL2oCuHN1o1BZOxEK0vr9oMxD9OXhA3L7ht3S8FmNHPkC/ZJe01xHM+jTnXfuzfU7be+Y9jEdjIF6eEr7jcvR01O2a3XNYNItRKrDopD9eWMFNweU0qAexuDdxM2fc3AqAk3WFZCbwIIbhUlWtExNeuxpxOQ24nBJemgYxVIm9ua+1W7M/R7EDSabGlWRV7sOyBGMRwDY0oCoK127R6zgk1aWTqaxIPHiNQGJx/WVyqhISVYxgTV8WLIpXLgDgLrl8t/UpBaxhrxnkluo1qGYsAt6xGoixkKQFASwMXKqgq333iMaVEFpwM8CJhhZ1WSiVU3vOrUgpSvU7iMeDNFCqVrEqaU27d26W4qGF3StxJKoYiT3VOapyWOKeBEd6/rdsxk42b9t82cYOUY+GqXE7jDY+DH5MhrFEItjJPbE1AJZFA9SrfZ9hEBeJcTUm1wA8JHaAKeauzT085oJVSUGHIWEpBU+PQ2cg3jnKCTOcInoJEW94chhxNSVcTgkH7dV5MbBXcVr9BQEtfx9AK4tDW/Xh0kIMYN/NBEJ+f9PRGdi9IQiWAxejlEjc0KQpR8MjANHILFQVhGUUc68DIGnndVEgGMCJK25MTONCjKXiMDBYKZm5EnJSb6tYhSs7ZwRQL6M28DSRAITZAF8ijNZLklc3EFSkKXI8H24cUtBjLC3McOwhSyNowSAqbpbsmTBPZ5ttJtF/GNko9S283ELYpF4G1SoZi3XpmbKAk/X6qt2QznTiV8jOhNDCq3e3oUW4F9yI2r0R6zSsOdELGI/GQFiNg2kAQjltXKtyeWTFsnADYBwhqP4yVKQC4DDgdi2Yht5VvA0GYggGQkILdvxh0hVkZHZClHEgTPfhQUmPjlZ7B0Dl3kPMYUeK8R0u5kPczzNjm8IspXkMORhhW3GzrvwAkFh0EJnEkygk12WRNEYrB9ff4UrIqO7Os/IjjDfhwrpeSanchgKp49g7zs15MTWvCpx4nC28aNmvOKzQzp8/9BrfM3blBH3/z4SUZyp6TxsHcW8k7LELN95nac6LxDgMhKXIsQ3MUB3ZUEBlqSqhEV/pCncxsK+CV2NCqRXH9cL7cFohlhkHAA13IPEWDoN4C08mwUKuxoSrMbmKkLTjM3ezXQxSV9OwhurKSA2L6NJZWo3oqLXlvjWFZyvGVn/Q3wtdrysaMLkde7U62xg7Mg5tpT2q9xXTy804yLmydoVC6S240EovC1NiGXRVrob9dKLGm5Dmt2hYDLWu6eAixVs5yN4rmBmFc3I0DBoqmRd0XAue3q6d5mhicoFiAylF60MXh9Q0PvwcholXd7wKuUwJlfcAyU1aM1SvnsjfK++hEyzeASfFTwzGpAMqq+pH7LQLeBfj0RiIgVpXaAG3NEeOVoNRtf9ALpJia96E3JyjTvhcKy5S8W5ZBk5FynDLUkhqcEjiMVhocaGhhlVsurt5DvyJIFVusvzdCKvJliyzXTXssXVcotNt3sMiEUFJU5NasmSJzDBYetlGCvs/6fmBajUupYJrlV4iWWo8hpIAltYAUO0FaDaTlT+BohMS4knE46zEIiEf4/ogA1994YBnXBalVltNinmPscYml6oLhH5lSGebxqaBv9yoJO/JazvbSjGMuCh0xmGn8bLgUdW3P/m995sHwUpdrqSCoVojUCu5oSgAUM2QMICCUiQGTtQKkkoFFgWd5HU7WWYkzDiwxp6jgpJXo3gQhyE1yTF7ZJKcc2kehKsYc1gVUhKegwrVnnTOsvSWvY7gZLg5TpqlRD4ADMFvRrLj9Vj6E8HzqtUNAtB7FC2Or4HB2uJ2AEgct6+YlN/NuZ3vUmVicjXwsl3PqlyJpCCqZTgYTXn73dXa6nU3nHATVuwV4DXlMOoeTRlsD3N6rszANkuhqU3vwxpedz0ygodor6VUPh6oPFRoCFZLuw9tf1/QeDQGYtTqSPUZZLUp5kGgGQqlkBIRBhXZGDNp6JFwNTaAzcA2Wy3NgMQiHQAtW0GktGt4xSYRRF5MDYk1o+1GqBWoRpltR4ZIp6XpIGHFxaVrG0Ql5ohgn3RwDmK4bhzQjAAAj//tuRx/n8oz4NczQ7lhELdrccNgoUYcl1NFLgXTkJCYcDklZ1E6fZnEWNyuBSXJqlw0lEgMrdGwc6dApqZljZz5vMMOcSlNlFfAas1k6XFMQwqeRPt80nDD9lHCH3LmpHE2TnQibeyUo9PGEGwbJdEw3q1HaoQwBGyqFjEWtguRUh1JUy+oc86jMRBeest6k2s5cUELYJmAUs3iN49iTJLOsxJmGcXl5WIu34xCLPUdUyNEmcpS0pvYPQ1s7tsY96WmhiReRJF4VIuNnGsf6bRWOLbxHqJ73b0X2HTP4zzaZLivMU6Tze9X3XVn5T2hvVMNfBW4pyL9RPSDwYi4/6cchj3ZOh/vUjvB9jXuszy///tsLWbglGewNQ6qZ7FXwt0ZjW1IeU+4sRtyKiBZ7S0DcrGT6XhB41EZiETiZlZNi8Vww0KM5PZCMQESL2NgKVJqwCbvutLbZrwAQoWkKgSpoRq56UB49aYWF8kHQ0igXgNNB8BDiaWBlMaYuzjI/00HYLrQkmXRNOhWjeg5xPeoV7Xem/cMqb0isklJnkWwsuwCMZYlV09JWrbC6mHiMICvGYYivIO1+MRfchEMgglZJ5L99pgEgB6ZURUAVB4XSLGluvEeOsKPPWbpL1F5ANHguhSG326rGPdqeG7XgouBHXuxH22hpHi0AxNonlu9zZ73EIuubDZFjYfYqNcWhTEAkFaUpc2ZXdQXm0ViM9xIVN7I98A5Gi9iPBoDwQggJQydl3y+YxHqVtvZcMwhyqTpfT2wGpXNBDJD4IxDtFw8IDcHo0nJsYKniYCtWpAP0sY4nIQbkZLIyA2jcPiDm0mHlyTE8KrEvnO35b9jB2fPj+t5eUj2wh/RPCU9QyqUAyQt807cjKaN+Hy7EsdGQ7btMmjDodIAS7k+DAkhjEsh6WtrXygMT/l/svItJyLFbJFMzsoQVx8rUhqaAafGXYhpyq2BiPsdFw7ecCBMudy5LtnqMHYUpjUzUYHTju1mGLxNIIUFIJCgjFJt75nhicBkxBmApvu52Q4Aan2fGQjAVgLym6sq0cYaqYpXdeqPWuQXjQcRkEurixdko908RC0YiYrOHEhRvHkEcLqK6AX2G4RFY5E4ndZfKDhpikHxBumfJzUW4T0/Sw2cPBc1kOYVJUTrBzMh5wrrpclMTnqycvbEjFwyEguFes+j2I4cskrWAV0a21bNLMneFzNKzwE0xHO+7bdpFauGfbi3pxfXDN19+9++r3k3HTi5E164+pNVnAJd+CA1FupBmIr3mWsdMacOoMa7SFbVcn4Fec7xeAyEFiMJk9JwBjjwdd/KKaP3JEqKPSu3XkPbNnIGYgct73tBUJUoBSi3q4hfaBIPwfgPUfMBkJXkcAWn1JpbaR20lAtRI80WVkBlx6WYAhquYCzFRkSSCVrVsFWGgIUO1grZybI/AHA1yup/OSWsmga08wC0CRbZqDZMzapU5Zzk6hgStPJzVKEZrhUjxOCMTHo8O8YislXDa6/wrIyUCgadjAMTLgbJQl2OCUyE46HgnePqlcBxRK+oBFDSrnciAsriXoMIyZr32Dp1S2g5dliJG4VRQ0cARYvUYt8TuSfOLAzmZaDNAQA4OVv3GLH3Oh6NgXB9BRVFAUtIYStPVqNR692AmxfgEFDRavqNsCPb6G+e+Xy7SWQ1plCg4xcg3BCShtIKxQGgmlypOhKlaJy6aj3vvWn8e5IKRaiMmAmuyv7Xk+yEnJnNeTSvB81LSoyTuowxCU16gYQaTiBTDkguFbOKBvepzlMDAShrski2wjyIotkSMwwogjGZa19Pb3c9qHL6OvxRbVoIiRiZBC/KVUlviaVOhIUtO69A4p70tT0uhD3xXhdlZ/LFfePgB0VClGIMsWy7prG73tuUdgdEb4wD0BuJ+4ZUwb7vQgwdehFMHcfz1GoYKuFe9zTpzVmrAp+1qQHHT0biFNDASvs/Dz1O4uHNDWzIsj8vXcgBoGsy64y5aBxCDOrswHJqFLacBzvOjusBpTdDJiQ08jLA0pStk1qQwlKctlDFxVCRa/Iyegsv5rXdcEnDkOfRPfQK2YAVSI1L+w7HDzye6kFKK1STa8XAOiONjIml21UBIQ+MJxcDmAm3ueCZyhgmJtzMK6ahlfULtZ4xpDPtE+8Y1sBpKxUHoAsl6nDRGX43FtEgbMBIE8ABmoEwL1rOJW/OS+0eX+R4PAai1A61rro6gBgpGXJPDw6trOu1fvVuHLd3e0devvc8yEEo5IR2qxdWS5ZB3BSIOXWpKQMhrXM3hoOsNONBQgyIEKzVM0Th3D2nKRqFXU/C8Rwt3AKUPFY03GDPbADwwqxEAjrK6luUXak378abOCfT151TM7q80dgwHAR0sjpGbQN/XbUUzYyE/v+QBlyNB1UZL8h1xO3aJu6SJXS6meV2t/DpYmAv0rJwcq9J1HZUIqE0xwK7SGIzoHkYw/MLMRJqOGLIXP0611Njb+C5Xc+we9QZh+bhePjzAsbjMRBAH8cxmiinaTFif1LH4SsrCElTo9YrIP6/gI/738YIwKR6Dx11dWskau3RZoTPRpQ5/Hk6K4CQRhe2m8S8A2A/rHqIrbTuS3EFsuMuPnFVJl1LwpkkHblohsMm1JYTYSOWcJ8bxpT1/QJOMy4nRsKsY8hqcLsHXAEcwDjIt9qExyDVwU+TUMGPKyMPAUch8rJ109i809btpRrt+un/74KP9l7AFVqvjriI1c4r9N/Q0kUiOs8y/b+APdh4PAZCIPXmyheA0FaRSgwilpMUXaw44sUiibGpQt1tnRROutoHKS3lefZe0RuBqN203hqNkoieqAcRvyMW4/jNooBk5UGL1FqHrId4EHujnDEbksGpegjkFbNOZBoSmNo5td8dk6YjOZ/wCWLhm5HNxtQmntW2tFYB5DUuRj6L/SZkP9G5z746Gv/EvDgNMSSDNIBKBqcR44V0fJuDOs6Shfdws/Qr66UW5IlRYWPWqHEWDIGS4gZZ+3lU0V2o0fgDzThYvw7zFocLgBllOMg1LibL366xnW9fDPQaWrodgPTpMP4IS1HbNu15kn5/AePRGAhHdR0EVLVjIjEW23zvOQNh+eM0IfkJbESrc9mLbcrTsYedlCYQvITU708tBagkQJoZsrjvqWUwLC5diqzOracFXCVpG4veN2wVjOCmvS+ZHDkPFm6MABIVpNxUnJlJyrcB5MpYsmhhSru/0mc0NGQYmd3zGJM8t8K3RPCqycE/07IG5s255xYwHzKKs+lAhmvil48YdblF5QRab/Hq1XdgKQ1TWgtwGFgVrhuzVnQ/xEAY54UJXh1ak1wnIgYGURi3NGuciB2wyKkPK8aDqm5XHHNrlehhZGlKWKecneqhxZQUU2M1qsx9KXwEVPcKBd/leDQGAoB7CbuT3yballm3HQViJGpx4ZKt1Jp/ZchsiPG4a9+oDzMCN4G2+2ZApaCUzagQnaLYMFezelwajcN2ot83etJY/xk7FxZuGB4oxKkKFCu4ykCStCeygJisxXNQNkonIpPahLdJFslGZkC8fSEZRmIhzY6/Fq+vZbj00QG5cG0E+xlQtWnRmCbpfsbiF1wMktodASzqKY3MHf7g57C2CctJMkrgQfp5QELek/6p5h1asVW4xhI6No/BNTRrk1HckvrMGIjvp53AKoR1upca3hCr3m2Lye14NAaiUvL0nq0gu01sN8y6XT5CZS3s4s5IMFr7B4/p40Whpp8IJgxmobXHpPEK/HcNnAyeAlEzYF2FHtRLGg7ajTp0oS6tl4PpFgA9lXrPg7CotQ+IqnsRADotBtvWWuUVPX4JFdhrWRI1YdeFC4bSqNoiDNsLq1glLDPhoGnSGE6IV0FuOGy1bnqfIaUMNO8hz9KprIgwMOZblZcP7QS0IxlNQmFPl08EJE4TXn3pQ2A9FqYBl0PSTujFFAA79iWp4bTzL7SUAyjptbb+Hta92/aVe7JbTaOGGhNmDR1nVbnKRcKftey3BLRh2Z6LQbyuWoGaoKpdcq5GzYCR3vu+SG3l89/DeDQGoqBNaFt45c3NSoJgHMqpmAhps1hZqe2zIlS7XVG3cy6qHFFF1xmK3AsIxTFbz0GPYWssulJuNQ5Ik+opaql1Ea8hl9BwNuzhroHw92q3AjYgVvzlkwyBFSOFcANMmj4WX8rwikRAUmNRjFSkYHJLWaJhDYnuDiuoRWXmPbTUMgLu0K4xlVWMw3wUA3HzDKYFWVdlq2qVLK8LKE2CHaQRlxevYOCKtQBEYuyGQh1YLWFO8LygFG0CFhI17mE8OEZGQJPrt3kYMSblO7hmpuIOq15vEegR3cwox7+tOh5ZFLpMNVzlN7AWQmJR62KyDurt/uPnUU6/ZzwaA2FdmRMPLZVFG96BriyV9L8iHrFFmaPLpYhxQfMcYq4Z+j6TuNFU9V44kUGzlOUmq2G/s9kX32LDuTck2yXbQ4hh78k+6Vdufmov2LBtSI9N3Hf7nlMXPrq0NYQbfuqKAo0aUmTt5yn7JscnuLJVxKpRCG67Gwe2GhcNQ2BktKi3EYrhQv2D9Rup1qns9ohyfCbewyIGwlv0DRMKJwwv3QhIvNxgmK5APGBKQqRiNbt3YTqqnoesHh2RnJRhOOhCpKBlCHV6jkssyW9ho13b45pF9Uq9ithmwM6r0NNLkCRQsmBpCu/CY1HvloewiJEI7byA8WgMxK02OTGgLPHQMR87195QbftwdPmBXq4NcjOYu97FgmZ3HDUWtNj4/USy0lgTWlARTgQAl/3aGqe9dFisyhwOyBWYixxvBbp2ceZ63lWmfZc8e0dIYnPh64kXsTcMuEQukEWIpWQ7hhhVwg77LUtfjtrv1EMMVpk+je9HbkahPW9A50AQb2E5ems7Xm6A4zPU+Yj8zT9uBuL6W8C6oMwL8rKCEyMdLoBhBN88k2O5OHjGi9OEJ4dXQaSp2qWceGmsoViGlKjnLIZi1TSPhUUEYOQLpOmghWLhOvv1kcfFQ0dR2zatiqe3GcdcsOSCm6U48GtsSSuesz4d1qnbzulQtOCOKqakHdKzAqolA2kCjeP9F/wB49EYiKKuddMPqJ5RaJY15CCYUTWRH/X5tgKfZ38vAIH2GmqlKyTUEK+j1TTsjnOgqv2fPSpTMgcjJYYALhZb/L1Y9LQ9T/ccWFB9Mg0GqIHcG3u2xus61JMQmTjlOpTiMUIU4LGbmBnNMEVPgShgDW0iOJZhNQ9VNRWVnOYt7dbFm9PU2yPKsiLPK8qyouj+8JhRh6mpiecZtBxRdV9HjeWdGwL1tjZXt3kXtcnjaWLYFo9cVXpgx/I6x6G00KHo+60Du8jhHdfs/BdbDMZqIZ6EGMia+ShScBh5MsapMS/X0q8PnQf3jUdjIGYFcuyGFeGW6rURgLmoyocYWgl0Dflfj/d5wFrlfl7DBDSegYGBNnGqhRwJqJWwoCqIVpGq5J13xx18DHc7Q6s4Q6+tLNpdzyLGwdrGAfuewrk6FM8EOPvJLJ+dz1Nuxzk/JCkeYSI8XIFESeLhSgL6hYmRFIMAVN+TZdKNZizIrmfTAiUCJgsrygqar735Mc03wjW4eYry7FvSFPfZt1COz1Bvj7j906eouSAvK8q8ghKj5gJKjAkAX74kBuX6KWgUXgSvR0zpAACYsyC0GaaXGRYhyPsA3JhkNQhECEZFuSDFPJB2Poz0JL065DquRYyCdf0SYd2KWwUu44KwaFuAQ1VQNxHGNeOaCYeBMec2J+YETGlCTcLTIGIJt/h95kEs6mZbU1kqFlo0sVkiIKl7JSvR4OizDXfzsoF9AlCZJTcDYRctxqJ63zhKbC3mKlPzBpibVtkWQN2kNN2AadGOGyb97QhazUFsda8npo1tM13ZJes30bCUnHXx5x6H2N7I9l6sq7BnQqOmk5Anb9oKGAZBQNPRADCkluo0jCEpgYoApLqClhneIb2soPkGPD9DXWaUd/4U5emfisfw9E+Rj7dYnpmByJ2BKMsKFjol6OJPBLQ8XGG4fAm1rKjzEwwH6TkxaPpQFJn6e6BU+E2k4m39uQ7chMQ1hHR1k0uCZiwKCsRQmHE45oJbNRZHpbKb3J8prycmLEXupdGzRhmlihaoG4gsoPI0HkB5kQVylCzOixiPxkDYyiripnay5V9P51UA3IRgDEiKa3vx72tgVOwzaV6DWO3mYtttUDUurVY9qt9xNsa4L7zglguX7zJZNnTei8n5RzR7zxgAzbOIDXp5x9WV/2t4LbDvgZTa9Bnit7gtqa1GZS9WMcVvywYwZAI1ZejGh2D9jUToalwsrKCySo9Ta3hs7QNy9pCi5oySC6r+ARAlcRZPQk6kpspzhrXyE26M7m/AZSyMNc2Q7eizScrlqBVZuSGsnN8tSS3Xdq9ZJsPaGXr4UU7/zBuz0BNZPMtShcuxloqhSOOhqr9TmbuUZ71P0u6B40EG4ktf+hJ+9md/FvM843u+53vwMz/zM3jy5Em3ze/+7u/ip3/6p/HOO++AmfHP//k/x/d+7/c+eEdWDTGaHgNgVKS+4q+qqyexoPy//J/jCRtsoekttloHc+fjGLXxC0C6HZ3OB8vRRx7GJoPhkmGm60Dsv20uZyTLzBpWxEbF92lIxtGKrVr1ahw5w0OmvcEMcIn9L+R9E9iJdmrg06wIU9s2kXgO0ui49xy8AK6soLyCllsHJOn2maYzjyjX74gHcXyGciMg5fLsiHyckecV63FGzUXwh1zAhZEXAY/LsqpQD3vXbRpEQ4LyipQmJyDVCqSgbbo1Dts0s4QcVS6zUtflVDe8zLYFrBWgfMvTecX1krFkaSp8vQj+8M5x7VTEAXiZ/aXqJ07KTBX6elG8Rxo4CwtU8IrL6Qq1rKLhNV6euVueb9xrIL7xjW/gx3/8x/Grv/qreOONN/D5z38eX/jCF/CTP/mTvs3NzQ3+/t//+/gX/+Jf4M0338Rv/uZv4h//43+M//Sf/tODd8TisknvTmP8Af2q1hbKvZXQ/uf0wtonzCjYRLQhk0c6dQHCxbdKx1p31pZI0goMSh+B+9A6ULcUlRkHM2DWDcraDQLnw4mwE2cLzuTY652vW4lzCC+oTRRPTYafKDqx4rDYXD7TU6m3YQXVIirRtYhxWGdpFbcepYXh8ZnzHE5AyWVFPt6izCtqKShqJIABRdWrzbOg4EVQra3CtqxIlKS6VUMF6cexn/qMqeBqj2YoEFLhxnxFu98sdFxy7bCHW20LeDNnVQ4vXjW7Ve8amDBz0TAwY2XG9SCG5GpMmLXMXajy0nSojgVI3yYM4stf/jI+/vGP44033gAA/MAP/AA+/elP4yd+4id8Zf+v//W/4ru+67vw5ptvAgA++clP4qMf/ehz7UjxFVbdZ443YiMC5XDB2v+2sSctFsVrLQsQV+kTXQhsymqN4ffQqjnjTTi77XRfI2lL7mPjQzTDcGrA7g4ntoKt8h137ajiCpqpyKWCLRuhk35b/pzQDHCjobXtYvGVE6HQXrcaC1FoshJ6qhXWbCh2SZcwoXQy7mQuTi4gZunUPg1I4wBOLP/P7W8P0TdK+H1j60H5mVNjIDJ7tGHpNp5JbOATO37dzMKFuJlXDy2syU/DITJuV1Y8omDJwklZcsXC+qfuz8q6yBBh4kGYyS9g3Gsgvva1r+H111/316+//jqePn2KZ8+eeZjxv/7X/8KHP/xh/JN/8k/wP/7H/8Arr7yCH/3RH32uHXHyCAXgh/u0XpzU26a+9p6N7WQ5t9IKwCbPx2AYKKyAlobzODbqQkRwMoYXodbC48saMBHAi3Vim3qJNdvx2j56Ckxz4yayeu544/mInkiHX+jBsv52ayIDX11d29GOqwIAnVSNcvA6WkqzByih3gNloU9jnSUVmWeU2xtPZcYmyHWZpQAO0EyFCfAwEgZg1OfjgHS4QDpM4MuXBKScDioaOzTau15b69wmbRb0nOr/2+mKtPS8a+ThIYZ9r3kauVRcLxlP5xW5At+8XfHN44qlFPzBt25xM6/eYHrb3MdAypOeskTaTZ39nrkYGDkJppKoInGVrNn0bQoxSim7IhocQJB1XfFf/st/wS//8i/j+77v+/Cbv/mb+Af/4B/gP//n/4xpehiaagj+mAK6Xqlzi7dGwesFSh8uAOjibWOmyX7bSte2HTex5yk1WR83RUL3ApQ4XX22no9Jr0dw0pmUMfcGY9edYgnnPIo44vnxc1ru/iwr1hNsiYD/FcoTads6Nw2N52BGpvO+wl/rI6mAooKS3in7TEViTGvaax5HUGKQ9aLY6Xu5R2LzY0XzALZYwnZsvVTDbjy0sIxUsRaA1jdUPIdZw4vbkMEwxe0o6ZeYMK8i3HO7Fs9YLEUo40ux5lCMUT0IgJDZVCTe+7jXQHzkIx/Bb//2b/vrr3/963j11VdxdXXl733nd34nvvu7vxvf933fBwD4G3/jb+Czn/0s/s//+T/47u/+7gftiOeBwz0hHkSb/BHEix2cz4F6Xm3IoYuWAWxaYGTbefMcW/U2jD9B3M9gDvEG3NyEJ8amYav+2/YX6bZ2bHrgnsrcekL3CbVsRwnnMpGsnOXMVzj/xEMGecMqGSJWaU+98Al9dml33BWuhaYzxBI2pElu1+EwoWjIwWocpleuwOOA6bVXwC+/BpoO4JdekZJrFYz1sM/sroY/gkdQO6578OF+0TI3X/+vwHEkASMzllLxdM740+sFN3PGN68XXM8Rg2jZDWbr7tYMhPQSTS7Xv+QqBoGNMyNUeOmmbnU9L2bcayA+8YlP4K233sJXv/pVvPHGG/jiF7+IT37yk902f+2v/TW89dZb+J3f+R187/d+L/7bf/tvIKLnwiHWYjFUuDql9xpawUvr5ByNRhxN2gyhmpAAU2smQUGN5deKiuCNfL39Wp6de3+iaA3cuzrZOOeZnJyLQJQqVcCnjqgPuBEzqvMev2E7Yihm/Sii3NzeXhGZK97CL0uJ7q2w0VvrtDXK1nvYy5eenkMLKYzjYCNBPYfE4HHAxWtPhF798ncgvfohCS9eegVlvJTiqTQKH0VBaD82Um/IlMBrM5gMASGt/SMsfFAsa1tkFe/ToiHGN29XLLnij5/N+MNvHTGvBX/8dMbT4yqY25xRSnVMikh0NZPep9Mgwrs38+pZjaUULFp0JmEIO/ANSBOp5QVZiHsNxIc+9CF87nOfw2c+8xksy4KPfexjeOutt/CVr3wFn/3sZ/H222/jwx/+MH7+538eP/VTP4WbmxtM04Sf+7mfw8XFxbvese1JL5vwQrgEsa5+c7d6QCnpKJbzuAvyeV2Xodqg3j0GWmpzOx5gGGyi5NpuzIeMvjPYpiFMBR6CU1uY8jxp071hXsH2ve3wSOQ9eLjkvSbEiyCIMaDC4HDLEgs4yeMAujh4ybf/8YDYlWzvWhkIaQbAeoo8dGzD3lzhOFILMUrQ9tSwolbktSAbj2MTThpXYtuVPN7nEVuqMOD7xYQWNh7Eg3jzzTc9Q2Hjtddew9tvv+2v/8pf+Sv4N//m37ynnTGJdHtuhsFOeK6tPDY25zU3LI5p4ODjtlCidfNuHb2NHmxhxcDa0FfpwWRyZ3vpzDAiWm5pNXHNWTEPQiLhVwgbrzr4ZMKyNjqjF46j1H1A0s/hBsS177pvSCjbRGq2RxhDiI65umczN/sn+xzCMSt+4+T0YGlZyI3kxAwuwkLEumCEkqFycdCSxwE0SnMaVq+BX3oF/MqfAdKEMr2EOj2REvvxgKwr7XafmQSoNFal5Arisbe4MJfmPRgrcus5GOZwmwu+dVxwuxZ883rGO0cBJp/druI55IK8huyMMlBrqaBzmSru72WrphXs58UaB+ARMSltxJu64wgEoxClz+5r776902OLNjMU5hYP3JN7COiyFycj3vRxVA0CaQWIkZhRyMRjhaLryk6wbByBd8qyS0U3Yy3O5DtWioi47xmHuwxMhaUxd4BpMsPWgw+xZ4dvG/a/+72I1aQBqPprg5T5Y1hQh1HAxlJkwsS2hoDLqVmWgsYR/OQ1yV4c1CikEfXiCeoo/U8zGh8lCsRGSMj4HHenhmMtTfEFa/V7s9VXPJtXJUJJtuKpkqIWNRAA3INgvRnuigyicQB6/MmAYWev3n0IDx6PzkDYcPDxHJS8GdseDY4EG/DoTD71IhJ74ZAh7p6335tAW4GY+4q3SOXv0TgAtYYUG7WV6SQrQfCUqJ0LMxJFc93WPbtxfJvnAJyCu3cNC1diSrnUlrKLtqIAJ+rKRjaK2wBiOKxkPlLPayGQNyMmrUQc5G7UvqUo2bMaZDGgdSzT4IovLrVJ8uhNkWHdrJKoLVmRXK09i1b2b49u93zDqdMBC4sL2LzmLlMRDfZdMoK8yWZ4qjPc00wGwMes0Xs8oM14NAbCXKaz9Qfb1FKg7+01cJlSAyat/8HFwC6kejUyLocEwS0ttGgCJv0qKao9olpdzxqHTidR28PZ1B3SBGLCqJuMAOagAzkqSMLUp/YieCiduKvqRwJjSFduQzMAHYC2HbaqS/2L6DQyiVgtgUEkNzppB66KlvmwwZ3hkBexNSDDvAtChoRaUP0CCROkDaErg5UVdMmOQfAwCi/CCFObtKcYhkt5fPIqkCbU4YA6aXvD6QorDV1FL9Bq7bxkGjjRAN0bdg4shDB2ZKmNKZlr9cyEZSoMf5g1HKk7P0JEEmIwgQfGlAScvJqk0c/lNOBySt757HJkHAb5GxOHULXxUF7EeEQGoj3f8gDuWgFj78iu6U2KSsumeNSUj3oyT/Aktj9grMgqRsHUpHq8IeyfKy5rezg0PCIRN44AdAJtfjBOQHOHLQVqo6g3IZM/GInwGXl+9rTJRzZpU0s1m9fjtGEF8h7CPNwbVc+XYRECGkpGiAyPUPXoCoCmIuQtc2mMExEawlBK6j1og9yk3dKH0TUhoZJvBS1krbXdT9GTkON/+DFFoHzJVauRm6cgYKQYilU5DgB2jUMcZiS23sOkRmP0DJvd3y2kiNfn3V6r7XhEBkLZdhaYowbuAsM6RwP2HN6VGpCeAUArNLpI7EbBvIaRRfGIWTwMkzwz90x+VR+r1l/oDe2/FMV0d/Qy4yMZmzKvDs5JPwMpLU8KoRsGUqg24KkGerXewSbkshQLCWzWBuOxSbudnGczqHVT2JWhHpyAwmNiZ3oS6arLUifgHj/OhGO2L/ab6Ccfm6wgIGxK60BWS7gQBXRFoHVtocVGLYc0HDGpeQ8pTDwFzciV2rxQi9jEeWpehWEp0WAYWmHnooUUPZAcKzNNa+QcPub7TxSUEQlpEO1UC4nFc0iYhoSrKbVOYIldENg8B8u4xVYCL2I8GgPRZNQjetf+f4DSYam/ICffwxGAFA/iakxuIEwleLAsxY73UDR2dhFRu+kAAR4Bb+YCAKibDtSbbAdhArL0VUg8SX9RbmESF5MVYyTKJxkNG7ZamdrQMRekQh6HAn2IsT2/29E8DJ0kXk0rOD4TOyAialJybVgxFNbw4dwItsdJSFaeT0QYjcBkmI7KpSENqKWAygEYTfvxFCh2XgOxg5EYDt5vJNdW9m9U92gUzHBEw2C3lNeb6GfWDVBeSk/Uy8FARMxh3TEQxORZCi+MG6TFJKtRuJzEKFxOA66mhJemwZv8XI3SNewwNM3PkVs7Q8tqvIjxaAwEqSWUOBduJHKVhi5F5oSsnHa+N1CAMSEBeDrT0kCmsjxw7M1AnfewHfY7sjvstfa7GY3oOUQpfPu/QA6KrD35/mAcu+M5HyZs2XwxxNju/0PczRbKyMTn2iZMUoA1hkP3pdy3Nspek9n+KvJ+3txI+08Q4J3L/CesEnN73gPQWTsjzn78NTzaPnQewj3GIY5z6eJd3VD1ds55D1suTsQgtqFFCtiY3NfsC2rMWHjo+n70IC5Y3CYLG0qpDpdv6wgsx7/sudC0wSA0rDDeQ+vJYOXMsfqQPNbOIbVYa8M6our2Xnt47wQFOAMryvmnQW5WS3cakcs2N+8n16byVCrt3pxrrh6WOMHmjhB3W4fSaj/ieRbXzRmcAAApBjoxi9wbOhttAu7sTPAqij4SMabhACTp3k2c4H03rVFOCVT3dkAhO9I6W8kuc2dvS21AZOw7sm1c08FJ+phL66Vh91+kxZ8b0Th4rw1WANzJeXIxhkm8XAsrrqaEJwfxHqxF4OXIHl50IQbDeTxJ722U5e6de+B4NAZiTE2vEICk8HRs6cBbevVew5FYW3Ex7MuvOyV4sy+dkYAQoLzajy3WGwBeQ05v00MSkNhZ03jWR7GWFYkH51lYaioxoVRqq0QlrKyUndIv15b2ZKUHo1TY7bCXMrXvlP/fHOvmLi+kxDQSGm+x0IdbJBXPWRSu7c9h+97q7/W/XUv1FU9SwIzEk9Rc1CL4REl+Lnf5KMFAROn57X4CAYsIBtG9htobhO1xWI3FkltocW6c5eToMKyHNdQgJhzG5LTqlw+DhhkDXpoGHAZufUQT66OCldwWRCsVEEGe91nrvSExRpbuTqZLacNu+hG2ksqqax6Exd1xIlgBlgE+xjuw+Jf8xu73w7gKEbUvuj/WUGfPZff29LV2NzGBm3t8h56EfSUzecEak068LcqH/dTudsRN7iJHAc34Clmr+nusE0cLCRvTchN2WIiyZxgQdj+u0JadNcyHLTtkup5paJWghslsQ7ytQPADpdbi6ax+jBtjWdt5MK2O/vU+3hPHXohhjEkzDhZWmIGQ56lrdBxbGRrW0LJwLbRggkv5vYjxaAzExKLYWzcu61a8xZDlXBpwtCesEvtCdBoFZkB2EHi7yR1c63alyiqqF8Y6LBN00odiLgoXJ3ZgrgiGhNjTly2GtB4T8t2lime15IKyc9/b/hvOYu/tlbbvFXNtG7YAzXtgRgjhMmhIDeRTAzFoqtXCM9oBVoH91CIA7V6mxrpYo2ajGTOSpUCBdn5rke5pQC8E88CCuWrpTjRtUDvuvcluUY1xHzy9WUKZfmmGwASPzmUvmEXGkBREN4NwOSW8fBgwDYxXrya8fBjw5EKAyYuBHZgUT0EVwbl5EVNiDFXD2+VWFLpewHg0BkImBlzSzFd4NDDRQCWAsAAq0NFUl23y221jYKQZBw7vn0sF7a2Avl0hkIqU9rbD+A6qb2AGwtiUPMidxsUfWW/mbpXnFhYBUCyiBq3M/dTl3rAqT/mNELoFjkQ0Dn01Z5XIRkOXRCRCqbo/VJunVat5Y73XF0ekNstr26/mndXgSRgNvRIL49K6WcnMwm7PVkttninI2hsRj+h5Jm2beN63+MO5UCJvsBIXgrEFwfEs6gyEeRBGjrIwYuTW39RS9y0dGrwIkyTIs9PS3+t4PAYi6YqvYbVdVJnY5Go9LU9dVVAGqvpzGnsDLY20HVtUPo7zc/CMEEdUmYrl4PZYVuFElFVBt6HRYquFPOLnjkmwCBPLkbAqGIZ0Hk3fYg17nP3tce6L08iOWahn5Cvpb0nCGSDhOkF7RFaS79r+0hZ/6Had+qedopX999YYnMUgQgaDeNcgAbbYiEwcUe2AYEDDKk8Zt0nt31Vql0r2nhablGY0Hh3pCQ18bjwHVlBy9PdemhIuUmP+GqZGaAufLXL22toWCgbxPgMpL5hxkehk1T6tHtQWcGzK0+gEMvbmznZ+WIpuG4cCpzly2Qf7DiEwkSbgGu7QQgsqWVSVu99XkHIFaMyAqSuTuMnWX6GSZg4Guznl8+f6Y+wJ5WyNghmMyEp10Z0debsRcCMhIYaADykrxRsMJJ34JWA2gHsQ/fXqz2t3XuppmMdo8XT7knKaweg+xH2DZDRvs7NHZB6pPNr/1aqK5qUtNKVWgOvuzxYNmaxga8t92BoHAO4p2HvmPbx2NSIx4clh9BDj5amFFi9PA8ZEHl5E5m8nCGxGoawaYtyeP1/PMR6NgTB8IN7vEUg06MxuROtdYWpACejEPupm8tt3Wfxc1AOJYyuX78U0ikv4DbXd+bCq1ZxPawY0TefciABWxmMsVVzGUgngilFbr+1hJSkRkE9XOODUONw1tpkgE2Dtfq9YYRg1oBLKxIRwGuxaME5Vr+N57U/M5iW1tB+A+72He8ZdWxNBu2o1MZhI23dDsVNMd26cwx3MINiYVAzmwkFJ9uejPjfRItMedc5D8B6iIPCppN/7LMQwBafnZXhI/FpPXFgLPWxQlYKhxO1mJTcmOjmA/Ru51BYGgJr7WzS0KMWxh7rOrTdkGHRQQ7GqiA4xDuOVtt+TdGYuBv71naG7xVQfLTevu+HP90Zf/t1i6OLnqjqGk2svrlMUw4lSdRUE6PekmN0BpJ3djpHezhvLgtjB2c1uHBUHfM17KMZgDRiRW300jj3s2vfD9DgqAEry20YA29Krdf4iFxE09irMVX7Msku2kufwa9EQJCZvgmNy9vae4Q6vXU2YBsZL04CXLxJGZjwxWnVivKz8CAMmiSgUF6KlNlcR//WO6Mv7LMRgUsXmzfv9TRYmPAwkO/2umOkI36RouVUWti+PxiSSZXzhYELa00iIKtcaXpRZVJkB8SbikMKig78exgNSMtYfoTIw51BtiN77ieekKFfCjMjRCiLLKUbRgMlTenCkrJvsvfEssuILrFiIZYysyU6paEVnCLTrzntrxhdoQHFBS885aKxus+E41GUu6gm2I4VeO5hE9EJhgKe4CZVEYq4tKM1T8OyVsVIdVyHHhgCZuKUW9+xi6BDBcqB5CxMakH45JSQWvsOrlyPGRHgyDXiixuBlr7tgXAwE63NqDYmc/8CEBOXe5CU8zqjL+zDEiLoCvVVXXAAtZRZ59RYO2OoIaPHN9keqrHBMlp5rbfzst+pme/n9tto8ZLhhsFCjFDEanMSQrFoAlmdIAZfEmbnI6mWGzwBbIHIy5L1axeU378lYj9Zs135W+CEWEiiI+cBMiA0733tcCjl/rR0d0MKErXGoLUo5GV01Yqi9cDQ+ckgirV2rZk17Q/6P/TulDqSB3XY8diwWZVINmSm3YlXDPsGGFqquvTAkQiqt9UAMI5IR3DajYQ/JMYiLoRUUHkKIcRhSl4HrGxI1XgRq7s+VGdP3XRZDY6vYGCd2Se6eq3dgPHprwltrz41Ygm/tqUNvDGOoBjpXHbBJ1VraEzG4kqf3KuknQy8MghoGK00uxUONui5SeTgf5d6bLsBl1azdgIuLlzCOB1gj39hXNHoz0WBWEKZwLkZuXoKtgLHACBCl5VRsIrbUpzXN2RulKN7hr9v21mTH8CC7Jbfq0PGrGWokQnzBPgEgYJt23jLP7GyxlhZ5OQicBtRawBqiEZGmzqEYlZ1LCt/Rzqkci4QWgFyLpEYCKEg84LhmLDkJy7RULINlNAYkzsGDSLuYRExjHgbGqxcDxiSPTzTEEEMBF4lxohTLGZtcsgCg22Prir4egbKi3DxDOV7vXs/nHY/GQNglIyJfeWySxFr+KEUnXkJ1b2HRNmdbtWHAelNCC4SacQCagYjbF62Y5MAerPq9aRtqxGHGQftC+nvrIgpJwygXFACp8pHd5IPm/bOeg3PhlsXYWZ2BFQZywhvCmKqDJB5FFt2O2TAFM8jO1kQM4+7Xv7T4/cQp2diabfNb+04D2uTXGtgWU8axb8bWQBAgHBP9HEoBqLSwAhrKxA9gnxdhac5aq57XZsDkvtO0M1uIUTEWxpiK0FsSIVc+qb8w4tQQwpBLzUhYpuIwCAnqahSv4TA0w9AZCGpGjwDhO1gTotAIuayqwv4CxqMxEDZsYhhDLSpXm2GonYHoDYMJh55LAY6JsfhEgXMMtnF7EcACXAkja8PgqiQeZc4NSdvN2yRP2rCl+yK5qasaBVFIKiBO4HSDWiaAGKyeSBoPWufBzZuKXyeuC2qtWJSXwQSXOV9LOy4CnGhl3pR0ZSoYITe9Vceea0ModOv+vVOhGXjBWb+fp2PP4PhbEXcw45DNg6gnBqICUjCXBqBsq0HZMY7taByZ8F0ewjVP0SDiXCtSUcOWBCuDMkuXLKFHEScCuTQsIqsHAvTpTlc487qK9l4iYEobA4GGz7B5WXZu8uwpTkux19ujLEYvYDw6A2HYw1pax6w5N29hLQ3Bt47YR5X9ipLjBsjFVJ+o8MhNFt/fMxBjYhFm0dJaW2LnXLW6E5hGmdxV1Yz0i520U5fQKQoAsbqDw4i6zOCSBbhUUKlyQl1uRBOBRHpNP9i+0/L9LApDcqzNcLYQhfy8LQW4mlLXzTyzGlESmbk9KrbVvRg5CNwDljWkNKNBOJfqk6EEt40XkQi+CvpNvy4txIi1BWYo9Dx5vJ0muXmUFyFGgvvPhLL7SHIw3UspGBMDLT0xW8hmhvhqTCEslfM4MmEprPcjdQucfNbEiQgvTwOGRHh5Snj1MGBkxksT46XR8IjWl8VDq9oaErvXUFbwfCPGdLmWTuiaRau3NyfX4N2MR2MgojU3jnu1sKKECZCj7H2TGd9Kj3sKL9sFAkzPnAnIIZ7YW+0WWQqAUpAr6/401z8X8SaG2HNhS/ONbeSg69Gq/sssnatRirSnJwbVwbfz16aHWdsNLAfEQJraBFDA1ZZHyX601KW43a1aNJsIDKorWD1EHh9oGE1E7KOy0u6wSZbuDl2sE7eFFG4YdjAIKquEGFsOQCnbZEbLfGzDFX9s59fOf+JBcBUmUK5OsBpYMhquiq6M3qLfNer/M1UsZrvUOIxMznEYmZ1C3cSV5fMDQT0qC1NbqteMA4VHqlVCi3WRcPb9BlLacAWgCtH4q61QZsnV+xCYB2H9CcxrsFWyA8b0Zkx6Uey9LcswjjGxr5pLqUgK9A2clDyj1aRMGJPqINYizWJHxRs4+epW1xnESYDJZRbjwQl1GMWTOKzSR3K4QF1v4doGJP0mPdaOxohngBhDGlpYAiltXxVciw2DAcLCAlQisayOG5qmpz1ZO3wlgKvgNKwCL6VI815m+437u6Yb4Js4dcbBWaplbeGFUobNmwAAqPRcHDRWNwqVU5vcJQFVPIiONw201djTVvq+hkiVgpYHMQYN9UZl+Qr1vBXbAQpmUquduV3FYCylYGTNMqWmdHY1JhwS48lFwtUo1+ulkXE5qHp1mYXXYOeg2+/+/NAqXmm5eYZ686yB4+83DwII4FuxVnxyw13PGcdcpBnJ2hqjrsFA2I1psbathk3KDmqdq6PmUdNyG1cr/AauFbf6dq7SN7FC9s8AqHGYROasFlFi5tTCA6CBlNCQA2jpO06o8xE8HwFO2uuhaS3CNDEjlZhIMQ+VXONBOAFEGIYJKU0YB3YDISEbgRieroOCtoXE3V83yKKwCSssNTFqTG4ZEUAwmQX77efiEFDUJkjFwHD+AaDBm6/+4ebPMzBLPt8k8LeDxknEZbTykwYAZQCodp5XLKjbTZna95Gg0rVoJXCawMwK8ho3oqlHJ5YQbdSFpJSKRBm5VgyFvI3ikFrbBeM5XI0Jl4OQoC4HwgVladJ0+0yOfasvYsdg/1dW1OM16rqIgTheo66zPJ/fZzwIG43dZ+mzSPBp5cmxUYmFHPb/HZKM2sRhXL1JYfdtRHBiJE7Djy1gKGnPUMfIqa1a9pl4Y9tKti6oywJKRYzEMnefrSWDLy5lRasFUA+iGQvNU7DQt6uuaoZRUJqEul5qVxgG9MxIP0/3hBcRg2CdIPE7tu3n5Hfa5w23iNkBhGcx1o5/sdP3idtsStdDuI3tszHci8YhvD4JW6LQTK2opQCpkcEIPamLSUK3WG27QL2ljO7+MuNgSmfy1xo1DUzqWSoOs867xszqflCLeFUWToRwtq4zyvuNSRmnnsT7TfPhdpXW6ce14GYp7jVYl61jaE5iFXWRzWbUVmH/sQNGY+1LrvdEWLZvnc36+cQU76GyZjWKcDCdPBVIVLVkECfQMoPmI8Ds3aLAjDpqR6lhdONBsa199DDSpHoKWjCWVkyjdGAfGZglDdMdoysz14Yf7AGMsUlPIXkd191t9sh0JGLZOtBPJPn/NjlwO4PWW9DS8vn1eI2q+fxYAGcNfcGN30ppVe+q1bv0rQf2jUPXRLiWVi5u31NWDTVkqnCFFAqSeKTGT2FKKANOvAkDM6P84SuHhIEJl4MAkwMThuUadPuO4Anzjes5eDexeN+oN1XXBfX2Rh6Pz1BungHrgnJ9jfXmfepB1Gr5a01hlh6UNK/BDEYu0qjktqumK8pmO+XA23uGWIrHoM111avYehF3jaKoedMjIJ3M2/5TQPVGrQWUi8iCJWkSQ+siBmBZQOPYGQZ/VKNAwyjmdBglXTpOqFpSXjQOJwioPzJh1bCq1jsM3AOGpTw5GBR5H9plGl2FaFPmZoyGD40GBvb1F16mXDIoL5Kum49iGALQK+cvy/ldFzGYUY5uGzpYs6PuQpTT11uAORiQCr3EJJT7MRG4oBP29fOj3zPqPTtqmBe9h4klVJ0S4SKRZG2Wmy4jgfkWtWTxYoLn5F6C4VjHa9nu9ijGtGSsxxn59n3nQciQKjt9jlMtg1xMrKNVz8U+BPOqPQ9LVuMATEPyz7YVUjAG5yXjfI8HV3pKTbqOqDEAz1YZanfqOGopKLm491n1eXcL2wQAAMUt3Ogwoy6zeifyfi0ZdFBcQieLi70qwfihNm+vnmA7to15+m5eMVzRuL22XqTyruX2oRogulJroVFdZlkl7Q9wN9q6fcdzVQs3klWtTVCG+LSp0dZdP3ftzgjP2DHbfZq0iE9+hpzpuBYBsr2Mnpsu6mCqUGYcvNBK0ruYb8U4RsMYjcQyeyhR56NUEKsxLcuKfJyRl1O85t2Mx2MgqlXJAZXaDRTVkCx9uRQJL27mjFU9COtiNIduyduuW7kkV0aaBhbdBdMkSAAi6w7W91Ck8Kx4xvLTsS7f1a3tYlp4oYPUFQaAkgtqbm3fAYCWFZxuQczgdZHQgVnCDvu+QXgWQsbi3rOYDmBjaYa8P+VZdCdYzIS4xFpPoDhCXGdMUzJ2KIvZnkQUmvVE3AEOxll2Sb6veWsltVJ8JmMEEgYU0HwNmq/Byw1we43y9E/bTb+pjCUuqIOaHjMOJbXVPqz6Zyf/NrTof6D7c96J/bcxGj2lrGQ1F9ZRjkwQMwKs6lKuwUujeBEXicDP3gHlWYDJ41OZ9NfvCKEOkv068Z6CF1GOkrnI19dYnh1RlhXLsyOWZ+8zqjXQu7+0oUNvR6xAzJtO32YE4raR3QYAqdQupLBVcbT6AJ8U0Mcgfkt9w989GvBdo5bTbXMuIK3spJQlnBDpY+1VKc9d5Vk9DGIFOddFJ42CXCUJ8YgHMJKDlHc4Bs89YpWoPfYNagUMHjeLsZ03S2/Guou6zr5CYidzIRkgTWdmIZ3Jfzzg/G89hi0z8wGalr7/pkliixcJBZ+oAkX+T/6rGVerwrTGTchz7zkpj0E8KPUSzCDYKEVrfrJ7WTVn5HkV72FekecF5f3qQSQH0+R5ZXg7OtY8s4UXt9YUVf/sve0wz2IaWiFOZyz0om5hAxPrMP49E1Qyv3kQ5toiPvoXJFDSHL16FFwYNVumonSPAJCxggsLRmGGAuId2HfKnzFw1KNY5P9pXQWwK41ARDS0ykAt0irB8JnmQ65NmZqD9+C9SzdGs/jkkDRpIkLhVkrt6WQNz3r0nkTfYFZwcj0ChjsogcwxCDOG7pWNZ++hBlBiX7sSOO89+AYtlXxODNdDDIZW05LrY0jlb2+JLVsh7R8JlGfwfCOgbF5AeXGpgO4cLHPL3tj9YjU+64Ll2Q1qLlivj1ieHZHnRUKM217V7N2OR2MgSC8qEfQkS5ltBVy4E4Cnyua14GZeHaQ0HMJCjMSEbDe0Ps6rgJfmURhP3npwCLYhVYqxU3InPR5cRXkP+y5rABNpGFEVUKRckKZBQo3oOgYA0wwIJQabV8GLeBg2SYZRbiBmsGEdJaMerkDj1PgEeUUaJ3jvTzIQtiJWtHa7HgyChRl9Faxux9B2iPCCLabWCMhEeK0blIiuNsGThOKZC8xHlOtvoVo+P+fgXks1bC1Zju2+UYpY8j1DcBfuICe6l9DfGAdWr6BK6sKFZ2IRXzSQdrasNd5AAN0+FaOwHuX4s3IXbtVAHK9Rbm8a6WmTyah67+TZ8IYV67MbLM+OqLlguT5ivX4xWYwH+VRf+tKX8KlPfQrf//3fj8985jN4+vTpyTa/8Ru/gU996lP49Kc/jb/39/4e/vf//t/veYck93y3T9yHGv3zPQHRbUu0c2XOAPzmbq97sO8h3jp5KKC4QhJmJOuj1wAERaRoOErujUhLd4Wb/C69xjNjr33EOWm1rpVA99c8DOaNEWXqtm/SaeZZoOXza2mZiuBC6wm4c8dp6/aFyX9itJ8jDNz8yO7bhpMBcm4IBqzDF5cmTa/06RJZorFatXEZTDZADKS8LvOCsmgYsay+wORlRc3FsS1ZYAru6yL+0HGvB/GNb3wDP/7jP45f/dVfxRtvvIHPf/7z+MIXvoCf/Mmf9G2OxyN+9Ed/FG+//Tb+/J//8/ilX/ol/PRP/zR+4Rd+4eF7Uqq6wxLdWY7ceP8ukAE4D8JCi+s5d6KhAFyQY+stxLGH1nvfDII3/LXHBk7ezYegYQKGBTRMsupdqIqUFW1p6orHwS+qGQLzJKLB6L4+tWxGf/6y4A53GIs9/IFZyU/qAcT+Gt5mPhEuhgRTyrZwxFJ4VhRndTFD7UHOg/Z1aCk+DS+Wo/Iebr3AyFbMDsEHPM0rnpnwROji4KneLhyoBS5Dt2VKbo2EYTvdNrVXPN+kQZng6lmKbdsnO6+BqRHAjARl2g1YZw8tLGvjmILJFpbsIYSHo+E+qaWIB6FhxXItHsR6nLEeQ3Hbexj3ehBf/vKX8fGPfxxvvPEGAOAHfuAH8Gu/9mtN0BVAzhm1VrzzzjsAgGfPnuHi4uL598YyGVCLvFmx44hKwhGDiH/nlIb3viuOTjU4AJLkK2BbDU+YeLHsexiFNj1MoHGSx+nQ/sYJPI3gcUAaB/Eq9G9vnHt/7zzGET0fSy2eG7bC955B35vBNAwuBsYh9Y/eop7jdgmHQZu+aIgxMDnngcoqBkJZgSfGwXeOBZRUKjoZpd0IY3tchucd5/Ak9IuCe0L6N7L8DQSkuiKVGbweQbdPwbfPlOfwDLRcCxEqL11Kty6zGwcDKvPx1tOWRcOJsqzCc1hWrDczVt1m1W3W44z1ZkW+/TaBlF/72tfw+uuv++vXX38dT58+xbNnz/DkyRMAwEsvvYSf+qmfwt/9u38Xr732Gkop+NVf/dXn35sHosj3ja1nsFUVju/bY5v0DZyTEMNuhKboY5NNVlwFHI1ubTGspjqN0ISSPZzocvcYhfCTxJXksa0Sflo8u9G8B9o8ulexEzcbJRxA1ywmEbk2hknRxZSmdUi3vgxW1zKwCNFwqS5xZ708gOLnxPukGlBp7jb12pMnq3xIC9sx0jiq55AEh2B2QxHb9W2P3TyGh2Yo+hNXAO69h+1dZLeVg9U1NBq2cMKqMqPGpokce9pyf0JHbGrrZXo4EcKLFz3uNRCllF6KXAcHt+x3f/d38fM///P4j//xP+JjH/sYfvmXfxk/8iM/grfffnv3s7vjBeTf9noSnBqKRru2PwfQdNUTpFmKaQxtj0KhWywCPAA8oA5AzZJupANgqtdVGZC1ZNAgqySFVdIAOQLgknUAYu2Bx9rBMNhEiR6JTCLdH06oaVBlrv2GsyKCIqzSQg1HYNZeDCwhxuUgqVIzlLUChZum58isdRgiTpPUuCYS1uDlIA2ALxJhouLkIFFgnnvcIRS7Ge+DDi+JN3ZxCb56WT20QbQ4tGCtAxg342zmYg+MqUXbArK3K6A0+S3agZD2vaEa1ftTxJqKKJ0X+6dYaOEpzaI4RI8rlB1DkOcFZV7l/5cVec4ouaLk+sKMxb1m9SMf+Qj+4A/+wF9//etfx6uvvoqrqyt/78tf/jL+0l/6S/jYxz4GAPjBH/xB/D//z/+DP/mTP3nwjrwrC78Z51znIRgKMwrDzraeoaDQLdkotuo9WIx5srd75Bpf2Vu4EcMOuLs8gi8uVfV6bO8Pk/9FJiWF5/a+T6SUUG2i6CjBe7hrROMQgbaRuaV2PcULz+nb+Zq0AfPB28aJqI13VlfQziXScg/Q+alMG+Og50UMoDxiUCN4LhX5wCa+3SU8R9fePLfwovMaguRbVMOKryk3HML6p9S4GASAcjvMOLTXQsOOnsX/jXGvB/GJT3wCb731Fr761a/ijTfewBe/+EV88pOf7Lb5C3/hL+BXfuVX8Ed/9Ef4s3/2z+I3f/M38dGPfhR/5s/8mYfviU6soviSCcYA7Vpb/J+IvEXZvBaVEW8GAMCmUzLrn4iFGpPyckxgIr+hvdUZW++CPrSwZsCCjwQDw6LdIH04EwiTpDcPV1JxZ65/KZ6OBBSsRLs5zINoee+dm8VYlGZ0OIEuDqDDS7IfaZTVNA3NkyhNFdKMRRPllZJ2CwusqCgRXA4t9mRIDCNvu1R8IlEAA+D9O+03mOBemHeBWhSss2pFoBm6AZ4a7o7x8iXHcGoaYUVqXQl8HAY+nvMcdsDJblRpElxNfEYBdCAkhmPBl5G9QjghGMscwgo1BlZ4plmb51GgrprlMY8iehY2OBFoeH4DuTfuNRAf+tCH8LnPfQ6f+cxnsCwLPvaxj+Gtt97CV77yFXz2s5/F22+/jb/6V/8q/v7f//v4oR/6IYzjiFdffRX/6l/9q+fakUqpk04TtxjBSGjEzoSSCFdTwryKkjAgHIe11K5rkXkL1vMwsXwusax2UXLcDIO51Ych+aQwN9uMhICoLSqyJrOCNwgHwWIQSqIYRcPU2G9bduBeObi5oPE9ORHuMdAk2RGaDsB0IRNlOKCOFyqDJxPIWgNY+ztZxU0qvzh7VM5v6wN5oeKpEiY0hL61p2vXSXqaVC1eCkxTSN3BYRCNyKGu0hbOag9qRTGDadWZk3oQ7i2M4KtXmtcwHO4MJ07O5V3/H4yEe7HRgygrUEN2BOjp7Eqxj2EFLbcNZ9Bj7DyGWF/hXkO+11h4aGGpzVkfl7ULKSgx0nhaLPhuxoOIUm+++SbefPPN7r3XXnsNb7/9tr/+wR/8QfzgD/7ge9oZrwI84wt3+XhuPQZO6NNovRDjXzQaTgby1GWfuehSVffteATIrPy6CLhVARAPoFSd5FTLBkjDuOE1KIgZFamU/2BEIQ9XFIuw1dRKwH1fNru6jay2vTytgzQhnBeGpzhJDaOw1LV2g0T+IKngPWkLPtvWit3EMIVVNx5XUrp44DWYcaBhaqGTYit+vs0zs7H1GF4Q8O31HfYbe9+7DU+seMyo0d1CsKNv8YARyVLyM//3wgvgETEp19q4/LYq2TAk3FR4XG+ASZmUySnW0UAMbkTY8QUr+TZ0PhHhchQq9SG1oiwDJMkmBuDlyV1fBaBJlBVIjbXpCtjNq/oM8sOXd1N9bZUKNxggN4ZMEmqAnMbghZOEFICsrjaJeHBFKSah/5KyKYkJaWrG1TAZwwsY6oVRowhvvfhY6txfMzPkLTSbEmnrwdAirpammTGoH+NgLLe0cEoo46Uft7UK8PPFvKu8JEMn4WZC73kfZNfNDICFGQiT3r7LCV4lGAMLGZpepFOlt6I39torM5fW11XLvM0ItDBCQosS6i4cpMyl6a8m8p4l73U8GgORNf1mN7Rda5ugYyKMRSYwhzRdrhVTECKJRClLy5lgaKNNy3OXAVPjYMCaudXmIt87FD+RWDUBlZo+YoK6qVo/UPb6XO8MjVnjqlWjh8Cipr2tF6hqLKoah1KbRybHozUuKtVOAUAE0GjQLOXIdh5GxV/ivscQcM/kmTH1hjjWgXqb2nTyU5+p4YtLycTwAKTJq1xdQVzp+SegYtmQhEJ4YOfJxWQKGlZBfGok/GALQM1w3OWZRONe1qBsvinddsakGQUr0FJDIIfUpzlbxqKxKutGV5QSgb+dIca3ZVScnTimIiyaDIxEKkGXyOPnvbCkCyMcw5ALa8bByC62/R27h1Jraw9IhFD53MIJB7JEJxG1NOVlAOA+5XYue2Olyz40XnZgzryEiOBzzKAMamjVONj3UpPdIwCVqodWXoIdDKp5ENsFyY49Mgojec5IZXJe0QA5OxY/flIhnHYMlqUxL8izMjscD1vFo/ew9dCaY9N7B6DUndv+Arz30KQLLcxjyD0A7fUmAbA8Fz6YVxHreKqmNfvffTE0a+ARGQjrP2At9gCbfFK0dUHsRKZSKy4G3jTJkc9EBB3d9/QjpvM66fGwca1AIdVYLOpiFy3xLa2BbQo3U01Di6/PlIFX4PwNaJqUO/9l+f5KrR9G3TGMBJIbR4+hwoxfddJS3Lpv62bhhHZwqgWoIqbq+0CsFGPe7G9/on2i5tZOL67ulYem4XC4UoozNfwkTXq8BAxTr+wdXXp3960beO9BkH1mw7b08CGGK/G6nCvceojxqNZdbWnqT9bMxjyIUK7dKjiz1FqEGgsBI7MDk2WR0CIH7gOAFlaYVMALGI/GQNTaGwcArmFg/RsGzSaUQsAAkaJPrXUe0BR/zoVg2/+3gqw9sVqVixBXHAK82Rwo0FUTAYfYpMHkpb63ufFOxh03X0xRWijmYr6BtANYlmH/2CXcqkDpNzA+A2soYc+9g1MwdCYlv/V8dn/yTBrw5Lh1wluvTZ/IjqUEz2l7vsJvnfAYdoaHSdGLqCHUeJ7hONNmfwycBHrjEIhQAJr2g4cXUpTVsSM3OET7ieLeQy0VxISS6wvDHmw8GgNhxmHL9rN0WQIBXHE5SCpsyYQ8WD+GdNo670S1Wd/vBE1Ot7cuU6S5fbMblVQMREVBEjWyFDGBA9XabtDOtcUWyGuji+vLqVdgnkCp0lls28jYMgYAnMBk5853geCalIP/v4YHgefgXkNenfwDoIGlKiV/YijOTVigBxCBblJZ+GXnLio4dWFFMCT2/TFTcPL65BwHoxFCDfda4rjPmHdp0I1R0uc1Zxe8iUVYAKTjGtDqLlRTcj3OnrYsWrEpnkQDLWOYYcZBDAlASYoTKfELCzMejYFYamtxZoMhGgajufIVKCw3+qiAVsXWMLTndp9Y/4rYrs/a9AFwlWZm9Uq0RYpCDUoGqtpYWIsE1avhEG7EPY/7Uku7PQ1E3bt8HegXvCkzBLVKKGbZAzN0ki1opKSR1aiG1CSgQje61lt2gfVxgIYSs7L9Aj1YD0ImlK7sQEatqe/01R3M6aTpgUmWz6uyeIe3mGufhgZIRqzFO0wV308rmzaW4mmTnSkoXJfuNwmMul14t+DvjqE4kepXQ2gNdKuJv5iBsIyGSdWvixRkqSHIRzEgWfkNovmwCDA5r43/sKzIs0oXzhk1V1AiV0wnPm1r8G7HozEQQFsN4zAjwRBAjHUZ9JV9c2Xjq+qqytaQnlGMbBKrUWt1zML7G5SCxAnwiSiKxgUQPIJFi9CAy03v2q47t63+8v3tvd1zEDwp26Z1N2+NjIFggCpEOBXQFnoWaqg6lOpRxvPT80DgaccuFDCyjx2HP7GJH872Oa8+rvA2wgpeof8f3ws4hG+/873dY3x+hl/gZdz3ZCFO2Jlb47cXwpxkUoqHFAZOxloLy1ZkS1POa2NHLor3RM9hp2grgpPmMVCSXijn1LSedzwaA1GKrJQ2qU+8Ps823B1jtRW4PV8AlRYVFibrhDXB1lJFxyAVYDHCUGlFR0YeYpIOU6JWTD5hUoHE9vF4ao+r+Gv1BLbHZ4bRkqCx6tIYpqWiaxBk0AuzNhnWVGRR+TfxDtCpHRlpKYYTtM6tqMiUlQFsW8gTIKt6fE07LrpfjHNGQT8rVj9M2kCA2vuerdGKq7d6PT4R435zch5JZxx2QclNSBONg3kcMXTStK2ncMuKcnvj2pqeunSNh4JyPKIW0W0wFah8vHUPIRZluTFYGuchzwXrce08CD8ExSDyuyBh7Y1HYyBqrbrq6o2MNols8vjrAMjJ63aD9hOSdMLZ91Zvt9fowtV7fJrBSETeb9FqNXgtXtWYWFZq26cMDX824U3kZjQcoW1jx5Ho1GCY9maFPM56o1gnc9lGP18k9GIGaEhyZrhKpqGQYw4EdGlLqabUG9sMg1UkbklHAUexXTWwzzt97RiKPTzgxEgAvQdypsLSHy21WXILLQJj8T2Nrfew/e+AO7gyVC3eiVzEZxev0rQO7wgZizXoOqzPbiQbcTM3AxFIUvZofIeSK9bj6oahbio33Xa937IYRf+8KQuoMxI2erS+NxxAo/8yxR4GAiREqk+M0WI2wP6PFWzIVRr3jglYsvT2tAm4FLRV2u7fzoPpDUP13za6ctt/Dvvz4BJ5PyfPtz3ZXwT2gjZDFxJE3gAgy/3e/50D/E5+fMtFCN+9Nzb/d5LajGPHOJxI0m33ZcshAfpUa5cW3cFUYpbmTAbFajAsVRnDhhxTlopBbL0HAI45lGygZO34EDHVWS3GfAHj0RiI7CGGvE626lfAmtucAGzUG4qiMWaCNMwBV1AlBD5iN8x7sEbAQM+jMPr2rUvPiWdhzw/akIdc4ryNWhs4aiFBHMLYtFctJWJvMeRcFKdHW91D+6HmgTSG6Ln5aedpYBGLRdaiom0vyKhbAARcgP17oIbgJGUIA/zaTlSihjFshhPIiEG0gyUgGDGdCLHjtZdP19qrP9tnIzNz0NCIh1OyGTfOhb1/KkPXsAXHaMqG35FnYF1PFaJKAdbFsxTLsyPyUdSgbv/kafMmbtYuMwG0iW8GAYBL2hf1IOLIuSJN70MPQtbXerKqWubAsgnRMtpNbxRi21aAQziqFr2JOLapz9ibckFTmgIKciXv8FxYOmdHAt5JBzDFCGLfSyAYs1IArRGRJsXnJ3ccUoBJp+/d9zk7TwZIuhbD6i3vdmN8HWSGokivDYKWQlNoUnMfgegOmnJFmIQRIwCaSx/SmZHO7OzE3QMPHsQG9NxLo9p7J+nbvfRqyJyQGqqytlaBsWzfiU7mMcyLhBUuH7diPYqBiLhCL2DcjEV7r+hhcr/d+y2L4cQkMpBO+jUYrTkah9p9rmqmowcF3a2vjVgkBqh6K7jIwjTjUPovV++DBWMw40EVYyENN9ApX3fHtEN6MIr81qB4nYQWRVVxH1SajYAk71X09SbQ02JfZ0VVAlJSa29nRhRxVV6d83ASagCnoYR+3hFyDTe693CHFwGchhSRN0K8LyoLNONwJqXZ1TpsV3+tBMXO3x51veumZSQuYmeT9kaiGVYvvApFV4ZFtJZ4K9bjrXoQM9bjgjxnrMcVy43gDHlpBqLJzJnB6O8p0nsg5+wAZUovpg4DeEwGAm1Cy70lyHuG/Id12iJdxWtt4YgbiQBQmq5EVhygVHiKUGjvzThYc2CgZ2X6vgVPRMDLUGdwxjjEbWLTGYBdSdpG61XZiqNAzYgkVhq6bl+DlwX/Vv0tNwZB6IaaCpS4wXMTMzFhkxI8ip1VWSZZEe/BZNiIUfPax/J+TGGVDo/nwg0bnScRPYc8N68hK9HIengCDX9gVfb2dgIhM0KhXJwYdZhaEdjQSuY7ENV3rPcgHNA1g+XK1HMDJJfFww1ph3dEWRbc/ulTNxA3f3JEzRXzMwkxAHhfzVhnEUMMOUw7PqncpETgICNQXpCNeDQGImpJmrfQFUOF/xNukhiL7f93YGNIGUZCVWx5v+VDyL6070gctyFY4x4HBncUsQEId8OtCIAC7wx2rkK0K25CMwjiIZD3DZW2brT7WTMOtMFn/P+jq156t/3EZY+TDmiNcbs0YfLv9TNxB3DZeRMPHVv3HthNZ/o+u5E4M0ssfdmFFtwZBwCnRmL7NbX2Hs82tND3YtrylOzUQopaFHg8Yxii95BLFgYv2NpQo6C44XjfMSmruviWy797W/Vuq5KCqE8pihuuoKeFFEVAQ+MRRBHXLqOxcSAM7LTt8gnlrh+d8EoNK6UaiWjRmoRdW/lj6bXX3lTAvorCv+dGpFAboGueRJcB2PsDTrMBWnbuPIIQVlABQBWVuYGW28zDVsFzL4SJHINtKnEDmu411ukEfgFY13Ps8R98w1g+34rgavgeofrHTEZvUA3UravpPrTS7TofUeZFQ4kZy7Mb8Ryuj5ifSu+K+dmMGlKXEXgsoQHO1oOQQ2T5/wVIY0KaWEINJhTa3MjvcjwaA5EVBzBVIqmSPN3O2JZy3uTkGYvRPmehhYUZTRqtBt6DhRut4Ww0FN3z3CadZURiZmJPgyJX8rPLxNrHsn2nqVYRtT8OlZW2jQzqvJY9MDMuytttPbw4Sc/VOwG/7aQzI1G3ExwQd5sYlIb7WXw7BqLLmmisH+P7zsOJoiu2nwF/qKbvCbhxA6dT7INYiF/qQZh+hpzPRp//f9u7thA5iu7/q6ruyeonBryRiMH4YnwwiCAoGlDMPWETCBgSIRCNn/wRFN8SFYxCNPESFBERsiFgjBFBiIrigw8R46NIFCR4weAlZhONt1x2Z6ar/g9Vp+pUdffuJLubb9D+wbIz3T09Vd1Tp8/5nZvXJLgQK7rJmKxQC30urLlBMQ/FyKjvvt0+2Ub7VMcSk2e6MNpqEUVbl7QFEgzEZ0WKQVH4loh+WhRJ2VvVkXHRNwKCgpKAILmrMF4BFx6HkG4jcrIX8I7faSCWP1diXhTaQE0gm44vfJFso8hIuy02G6IYCySp7qi/luSq5O/HhC5s2Ty2UAwQP5lpMafboi+uEQycc2DnqosvqB6jDkKCTAwSNOPEXHDhwFG6KtH4WYBWotXYQ8rFZUlLsFWpTVINSkIXGkIKLyQktSWoIL2pq5kpDLTU1uQoNPBP4yBsxeTAKQiE0mcp8eY/4/7TZdMGPpSZ8hZIlnYK7R8ypDnoxGshrW/VmxlVAUhccAAxd1JVdj+kk/Pal4Fol867wKNFiTvwaddg2lTkZaALEWxq0qKi3UBsQkShxDY+wHsGlEps6LDgAACdtq0VaQrfLMjorqugRdYwaoVCyYTgKrs7LhIaVf99fEPh52dorNH/AqYLQNrK3cadu2QGOdMCKAsHfwg3KRx3I5i5Y1z5OK49mNER3+2Kd8Uic6Joc9JReM6JvBC06I02kGSj6qD92opqcDdcQyhhuQklfEPqiaJvBAQQFrwUYeEIIUrEWx2MYRpEzTFBVau/gHUd7sr8RPU5iEeghChCnccjBY9ZEEAIbKKFxQuisJwBqq7tu32BEb0mOZ4+Y0Kqtf1QFfHHJp7mNdBnjY5jI5JISZ6/EAkGn06uy4IgfZ1ASFcYhRX35eCmhimKoI4ZJsj48XW/CU7o0nsvMKorVduwah3lV3BiMtSR1J5zSGs52FFKaPo1F47oToOj3I2WFOzXLqDzc9dkOfpGQGTcfy9CzgDfRoQbB8U+AOHpm95n4hjoj47n67uuszWHVIEnkVqUrh4VxfUJXr7MHRXIRXVxGljNJ7SPN56joHJtvmtTqehK4ao8Z6DME7DKS7JOqCZJSil4mbR4OyMjXUhy5MEon6isNTg3JRV2ta/j6t1+aBU+faFUOM4VBo7a9UW5CdRvpIAwymkRjjOgMQltU/0lXX/436CvUJW4gUlARNdpnOhF29Vd+HgFqWyX9zpzQMEudgXptQ1TGOTQvhYvhy6YF4OqHU0QfSMgqPdCVIresfBExHC3HYE8C8aRljbAyqaHFwhCQOtAUnIyqiAyiuw9dn4ujLhpUWgDLYGceTRo3FQpO1OhDyUvlBtiEsKPEXDEqhs/rWgiFkW3DdE54wSE87/zEmgys+o2cz9SuzgPnnNglPM+0DmYzVyE6kdVTXQFACM1IAtIIiwrBIxItQAqGkPt9ox2Yclt7xqMyEdXm5JCoUXmyv2zrluePJQKpuvGlppHIPOjADpt5yPvetPIB4sJicwLVvp8CKWmZru2I1jbR5+aunRuB6msG5KaMlPcQigqm/mFHQRH+F0RcalaGp0zwgdSoe3CrRMtVhfW1ND4hwkIb4s7Rp/UdG6TUzQgEEwICdcfEkDdYywtnlFF9tTxB36/CG3vpXJeBc1UeHeOXMZNb+k8vFN2ijHDAlI3JLHnhQ7cQ0k953EKYwQlCQkhJaBrjhlDgyiNb6zxs3iLuOJzqKhU+i5dwGRM+3HxDd68IdSYF5VzkcqXggtjYTUiqniTir9SUdwa86YK1MFduqpPQll+QSjhm92QoKD9SikYV05OAxCF8GSmlMITlVOBvhEQ1NuRaw0kILiJwUE2tnDcgxSWlBRGoLDMXMTgW3InPgd/mhN4lCSPiOTgFZ1CQx8EU0IEAcHrX1JT26rzScBFgzJb2SRpzczEMBJAoUOJffqMdu3iootVXsQRp0G5BKx4amVlJsAuXLdYkSU/Ib7Q0gQrUtfbo9DaFmqlFGgvKOh7pLIVkvKWJyMhi8SUKMdAVIKe8lrauAgXCYpu20WGOiFLGoRh43bajnDRp9zcoHqTpevqIjNlqwOZZ8gAdEdayP8zgKLdRattYxWyC7KoIpSijmLkXtUhiErlyrowC4OuEiiUdpcgxEqE7/8H9sXIpK1bQC3uouKpVeSVJ9uArhFE5Fq70QkGnqRVFTHJF37U9l6EDEl7XFmI8PN4ASGDWUGdrWuPjVynjiCr+A5vv7PkKtIMyEww0dOPPV3rnuyVT0XjC5yUCq0CzHPg+oKOtSi5YABAdRJgtA9J9kTe6AiiMmw0RMC2FKTISAAia8X1KLhJopPISv7auzuJK7BeFxQS6Iw4HsMmofFrRt25AVgTg+eAkMeCmWHWJHLjdf/VQAtCSeT/uQBGa8jcFn1RrTZ0oT23kHITQlrCt+PCrzun2l5AWGFCBKcsJXEJ16RoMtA3AkIhBBoJEadz16qxtFAgA6nkPR42o9OGY9vzdhAWqTaxWuYjIIlYZLwBlccnEB/Be1qmjXmAYLYU2pRMi5jfOLtrVRl/MBb4tePh1Ym7rkS40X8po4V6Vt9Xdd94fQSKPNRlbcV+f7K4kWhGPFgqOS4eUuF4Bxk/cIwGNCDQjfNAgMCbJMK0MnuUaz/ULUySKSFdv0y73GSewRQaQmoAVgCQUBA+x0LCFBrKeTlkrqBc/ITqBE2jQFE2u5WANKKSyDxb9I2AsCaG8B2cFFwpNCA8PTmYq065JBshJXLn1chNqAmhpH2i5675DqRAbne577aXOPI2CFtJivZH2kbgNP17IMRupDBOw7HZpPG++oCwdIP0vENty3sg5EsUOjbJ6CnHqkdRiTQUbVdctQ0zesaXRvOLlrQGoLz4knFEeRa8tgQlgelUELnXVEMhBc+pJyFVaaPFgiHiKnTBzmHJUbvPJoDR9fTXi0wMprFFPTa7cVXq8LFYaxFSQTqhkA20YAoNmdsOa9q9N177CMIBYALC1ZDIBkLmp2rJMTM/Adj+8n+eKl+ns0TfCAhSzzNi7okxJrs7hTAAChgtbMt4IaFUy9etJDPDGAo0cunjFPIshQ9OCmaBwICSvsZjroK5QQRpVbUnvsjrTBFbdo7qVaDnUFgjxo4drYx+5DEHpW3tciYieRO6HSsY6EfPf/wYOwYl+g72Pg3jLnkZqF4jL/hCQsGVh4frtEWvhVKV5GT6ZE8JTa9FdNtWM5EKyJI4EDYPMrvskOJYB25alDQKabkOGquE1RqygRZs9KSGybOoIpRw2gZBKIWi3fGCgrwgquXcwm47eTbssMP1V91JUB/QRwJCpeZEXaQdwUt9F5wjYX3cQkLBOK+HqzjtKjJJaZAr6XpfwOcscK2BOoBLEddWsFGd9UVdghYR+AUfn2GoxoM1N+xr4bUJIaoFTzpX+2dqtQe46+dTrRF+6IHw7MYp3joIh9oMSQBR6TaWSl3XOnBcM6MOPBoyMjFsVKTInImRLv4Ktd/vJw6jwkQKwU+FzzT1HbSrgqAQawoh85WPu4IQdgvc8hCZDYln8RxCKetRcsfa8Upo562w32UFRVZY00RL4c0OO7zAYajOP42kFM6sKKxf3Ku/da4ltmis0uHIzWwAhRHIpIGyFWeghHDZkBK5NFGrPACso3doXMtJ0sxxEPWu1gDqSclDdyk0ttCuxaBLIqNITPoMRV+WvDVCwJdD67btfF2Qkj1BEt7MVWT3njpk0XUVuguQWdEe8cVOwInJFFIGc0OqWHshlyr/TibYSwupCqWFKG3sAicpmclT672IhJmq3BcyL9l7uKcwFwRjjreIj6UmODXFc4WSkG7JcXOiin8A4KMvAaBzagSylUG3ibNou/gJEUhKbbwHY1pXAT/XD71X9I2A8NpDRTpyVbKOMxQsDAvzRXiKS2EDj6QjLIkE5fD5EZQvIcM20iByGUrF10YmOviYCPdfG5IWtpeGlQkuY1WEz9CwouFVcQ0uPJoHPkWoeHJ7kwIIwkHHZkWpjkE0KeWFAx9HLVIS0MQLf0wwb4TQLBgKuTcRAGby9Ngur654bRW5WaU1RHNPoz5riFG+TSoJ3XGaRCGhSlWvpOcrvICQ2psSMs+g3PcRyQl0oVoKwhWypR+daqlSG4ZzRd8ICKDMFkc/tFSS1yVMgAVbCdIg4NOtp2UyCpySzvNAZgRFc+bSkqYkIIgbicZVpV7zcQrb5FaqzPXUtJsLrz0EaUC5F9LzJOEclFQF570QfB9QreZzrYsVWiVy0sce8I5P6Y88y93XqBDJmLeALLcLjhdbSRBVzI7IySQoigQPuQZRI4BZVKRwXhX/GogWcGRasG7h54Sqz42R++HrQXBPTRFXqE7JSMAuesUEBIWTUws+PjfSIuxn2xCWjXdDs4Ima09OOmf/CAgKeSXeAYh+4KVFmaq0BMdDWELRVrjObMyydf3Yk/jDgwlhBcSAkhDCCooWuVpd/whPmPIn+BjwZkDWQiYzZEpCuGjMQsDzIGEsoUYE4BaDkDAqs+aWy9Yw6bzTsSSClQJ8hLEmhh49YzWG1LQAvApvT+t+ZFluhYJSEFkOkbU8/xDVb+Q8B73vWvdhVRhyMFm0TSNHy3MNfj9NqeA1HpgAqCovVyUY2Lm4NjFukFUdKJCLg8+RCF9GRPrvZxwDRVXKPLPCl8YN+ysVeQdKF57ApF6dAGDyzMdLhLlJqFaGvDU5JGVP+pkxBhs3bsTOnTsr9+/fvx+Dg4NYvHgxHnroIZw8efLcRjNOskslxlio3LtAWaEUCEV/VA9S+vgJV5vCbRPMTRdpEBRRl25PnpzWIxOOoZBy5bgO+79csr5EWqZ8wxiuzsrLxH34LH+AL9rSYpEy+sHabUmV6LHIUp9am2gNKVIzocJsqEra4sfWCgc3BztchdTUqJ0bfS7962VMZ4FQWzIdNx+/8sLE/lcst8Ntk07I+PfniaT87rvv8OSTT+KLL77AtddeW9p/4sQJPPLII9i7dy9mz56N5557Ds8//zyeeOKJCQ/OVzmmbtCAv0lRgVFfNow9JYR1dSpJSVVWg5DUbwPBbcm5BuIbMilcQo7rGcFarPUybjsI13WKvAhCIssHkClbvUgCvrFOGDcjQQ2ZSxJQmV3McO8TQjJ8eYVmFQk25suvCFAS7IlLZgWyHGLagDM1cttjwpVoq9VegKTidBz/4Nl7Cp3Ocmc6qEBMVnEeLA6CFpN3e9ICY6ZRL6YFjww1WkJUVXxlXEQa7zD++aWNS0m8FF5zUHbMojUQj5viN7SGBJBpjUJKKNa7kzgKft5soIX8TEVMyTlgXAGxZ88e3HXXXbjyyisr9x84cABz587F7NmzAQBr167FypUrsXnz5rPqEFUXGhoJCX+wrPxLbWGKeKQ4CJsKHWfX8sQwJeE9Fz5Qy8Qqem3QFn8LgGdbUoSekQqiMwKjMhvcJUOBF6pFkFaLovMbIQEVCrRAqPrir3VuRcOyJpOnepXq759eecsKCyltl2zeYCb1oNBr8l5wtTsRDvElDDkWIm+V9kdmDxdiTjiILA9CwplAAHpvB+hs/lhYJNcEsCabn2bvGq9UEoVzU9J7EY2/FWeskrBkBYSlMynIs6HbXWQA0t4YamAasvGjVnrCuALi8ccfBwB8+umnlfuPHj2KGTNm+PczZszAyZMncerUKVx00UXjDqBwF2B4+JhdKbprOX63KAGUA6W41iBCbwOjlP3RQqDjSEFfgxKI+mUCwdbPnXuRSEorIIztPEVBW1QHQFcICDem0nsSECp37wWMzP046afG+5zQmMhbYv9MuBYFuy5gHyRwE4eIyaIL0R0FTNdFSo4CRRemPRqiAqlGA9PGRGbHLfIWRD4NkAKyNQAjczsX1WKECZkTXQjdcfxDAVO0AW2sx8SYWEAY5j0pukyIVFxflxQmpLLaFOD4kNxpEZkTEAJCtYInoFcBAfgncRSRmRbQ0YZ5MbqWwzHaXsvOqOd2UNgOW3p01E3VoOh0g4BwZoCcNs2OKW8DrdEwR6c5eCE1egZ65DSoM1d3tA3dKWA6XVDHeumEmOxo/DZqNYiiQhifDSZMUmqtq6MLe3Q/HT9+HACw/r//N9GhNGjQIMHx48dx9dVXn/PnJywgZs6ciYMHD/r3w8PDmD59Oi688MKePn/99ddjz549uPzyyye1I1CDBv9mFEWB48eP4/rrr5/QeSYsIObNm4dnnnkGhw8fxuzZs/Hmm29i/vz5PX9+YGAAN91000SH0aBBgwQT0RwIvdkBCb788kusXLkSAHDppZdi69ateOihh7B06VJ8/fXX2Lhx44QH1qBBg/89hKkt5dugQYN/O85Jg2jQoMG/A42AaNCgQS0aAdGgQYNaNAKiQYMGtWgERIMGDWpxXtK99+/fj+3bt6PdbmPOnDl4+umnS2HYdccURYFt27bhk08+QVEUuPfee7F27drzMexa9DKfd955Bzt37oQQAhdccAEee+wxzJ07FwBw8803R+HpGzZswIoVK87rHFL0Mqdt27bhww8/xPTp0wEA11xzDV588cW+vEfA+HPat28fdu3a5d///fffGB4exscff4zLLrusL+8TYPN2Nm3ahGuvvRYbNmwo7Z/UtWSmGL/99pu55ZZbzPfff2+MMebZZ581mzdv7vmY119/3dx3332m0+mYP/74wyxevNgcPHhwqoddi17m891335nbbrvNDA8PG2OM2b9/v7n99tv9vkWLFp3HEY+PXuZkjDGrV682n332WWl7v90jY3qfE6HdbpvVq1ebvXv3GmP68z4ZY8y3335r1q1bZ2644QYzNDRU2j/Za2nKTYyqbM/33nsv6qQ81jEfffQRVq1ahSzLMH36dCxfvhzvvvvuVA+7Fr3Mp9VqYcuWLbjiiisA2HDyX3/9Fe12G59//jmklLj77rsxODiIl19+ecIJNRNFL3Nqt9v46quvMDQ0hMHBQTz44IM4cuQIAPTdPQJ6mxPHjh07cMkll2DNmjUA0Jf3CQjZ1UuWLKncP9lracoFxFjZnr0c88svv2DmzJnRvqNHj071sGvRy3yuuuoq3HHHHQCsOrh161bceeedaLVaKIoCt956K4aGhrBnzx4cOHAAu3fvPt/TiNDLnIaHh3HLLbfg4YcfxrvvvosbbrgBDzzwAIwxfXePgN7mRDhx4gR27dqFRx991G/rx/sE2OzqwcHB2v2TvZamnIPoJdtzrGOMMdE+Y0zPmaJTgbPJXj19+jQ2bdqEo0ePYmhoCACwevXq6Jh77rkHu3fvxvr166dkvL2glznNmjULO3bs8O83bNiAV155BT/99FPf3SPg7O7TW2+9hfnz52PWrFl+Wz/ep14w2Wtpyu/izJkzcezYMf++KttzrGPSfceOHYsk5PlGL/MBgCNHjmDNmjVQSuG1117DxRdfDMASY4cOHfLHGWOQpQ1wzzN6mdOhQ4ewb9++6HPGGOR53nf3COj9PgHABx98gFWrVkXb+vE+9YLJXktTLiDmzZuHgwcP4vDhwwBQme051jHz58/H22+/jW63i7/++gvvv/8+FixYMNXDrkUv8zl58iTWrVuHRYsW4YUXXsDAwIDf98033+Cll15CURQYGRnBnj17sGzZsvM5hRJ6mZOUEk899RR+/PFHAMAbb7yBOXPmYMaMGX13j4De5gQAf/75J3744QfceOON0fZ+vE+9YNLX0gRJ1Z6wf/9+Mzg4aJYsWWLuv/9+8/vvv5svvvjCrFixYsxjjDGm0+mYLVu2mGXLlpmFCxdWMrfnG+PN59VXXzXXXXedWbFiRfR34sQJc/r0abNp0yazdOlSs3DhQrN9+3ajtf4fz6i3e7Rv3z6zfPlys2TJErN+/Xrz888/G2P68x4Z09ucDh48aBYsWFD6bL/eJ8LGjRv9dZ7KtdRkczZo0KAWTSRlgwYNatEIiAYNGtSiERANGjSoRSMgGjRoUItGQDRo0KAWjYBo0KBBLRoB0aBBg1o0AqJBgwa1+H8dt68WNMrWXQAAAABJRU5ErkJggg==\n", 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\n", 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# All at once.\n", - "fig, ax = plt.subplots(1, 1, figsize=(10*cm, 10*cm))\n", - "grid.plot_vals(mean_updated_aao_loc, ax, vmin=-3, vmax=3, cmap='RdBu_r', colorbar=False, fig=fig)\n", - "plt.savefig(os.path.join(plots_folder, 'mean_updated_aao_loc_synthetic.png'), bbox_inches='tight', dpi=200)\n", - "print(compute_RMSE(mean_updated_aao_loc, ground_truth))\n", - "\n", - "fig, ax = plt.subplots(1, 1, figsize=(10*cm, 10*cm))\n", - "grid.plot_vals(ensemble_updated_aao_loc[0, :], ax, vmin=-3, vmax=3, cmap='RdBu_r', colorbar=False, fig=fig)\n", - "plt.savefig(os.path.join(plots_folder, 'ensemble_updated_aao_loc_0_synthetic.png'), bbox_inches='tight', dpi=200)\n", - "print(compute_RMSE(ensemble_updated_aao_loc[0, :], ground_truth))\n", - "\n", - "fig, ax = plt.subplots(1, 1, figsize=(10*cm, 10*cm))\n", - "grid.plot_vals(ensemble_updated_aao_loc[1, :], ax, vmin=-3, vmax=3, cmap='RdBu_r', colorbar=False, fig=fig)\n", - "plt.savefig(os.path.join(plots_folder, 'ensemble_updated_aao_loc_1_synthetic.png'), bbox_inches='tight', dpi=200)\n", - "print(compute_RMSE(ensemble_updated_aao_loc[1, :], ground_truth))" - ] - }, - { - "cell_type": "code", - "execution_count": 97, - "id": "7b76182b", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.361786137238143\n", - "0.3713881983704567\n", - "0.36430857864241306\n" - ] - }, - { - "data": { - "image/png": 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\n", 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\n", 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\n", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Sequential.\n", - "fig, ax = plt.subplots(1, 1, figsize=(10*cm, 10*cm))\n", - "grid.plot_vals(mean_updated_seq_loc, ax, vmin=-3, vmax=3, cmap='RdBu_r', colorbar=False, fig=fig)\n", - "plt.savefig(os.path.join(plots_folder, 'mean_updated_seq_loc_synthetic.png'), bbox_inches='tight', dpi=200)\n", - "print(compute_RMSE(mean_updated_seq_loc, ground_truth))\n", - "\n", - "fig, ax = plt.subplots(1, 1, figsize=(10*cm, 10*cm))\n", - "grid.plot_vals(ensemble_updated_seq_loc[0, :], ax, vmin=-3, vmax=3, cmap='RdBu_r', colorbar=False, fig=fig)\n", - "plt.savefig(os.path.join(plots_folder, 'ensemble_updated_seq_loc_0_synthetic.png'), bbox_inches='tight', dpi=200)\n", - "print(compute_RMSE(ensemble_updated_seq_loc[0, :], ground_truth))\n", - "\n", - "fig, ax = plt.subplots(1, 1, figsize=(10*cm, 10*cm))\n", - "grid.plot_vals(ensemble_updated_seq_loc[1, :], ax, vmin=-3, vmax=3, cmap='RdBu_r', colorbar=False, fig=fig)\n", - "plt.savefig(os.path.join(plots_folder, 'ensemble_updated_seq_loc_1_synthetic.png'), bbox_inches='tight', dpi=200)\n", - "print(compute_RMSE(ensemble_updated_seq_loc[1, :], ground_truth))" - ] - }, - { - "cell_type": "markdown", - "id": "80a36c8a", - "metadata": {}, - "source": [ - "## Compute Performance Metrics" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "337b7f50", - "metadata": {}, - "outputs": [], - "source": [ - "# RMSE\n", - "print(np.sqrt(np.mean((mean - ground_truth)**2)))\n", - "print(np.sqrt(np.mean((mean_updated_aao_loc - ground_truth)**2)))\n", - "print(np.sqrt(np.mean((mean_updated_seq_loc - ground_truth)**2)))\n", - "print(np.sqrt(np.mean((mean_updated_aao_truecov - ground_truth)**2)))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d35f1e32", - "metadata": {}, - "outputs": [], - "source": [ - "# Energy score.\n", - "from diesel.scoring import compute_energy_score\n", - "\n", - "es_prior, _, _ = compute_energy_score(ensemble, ground_truth)\n", - "es_aao_loc, _, _ = compute_energy_score(ensemble_updated_aao_loc, ground_truth)\n", - "es_seq_loc, _, _ = compute_energy_score(ensemble_updated_seq_loc, ground_truth)\n", - "es_aao_truecov, _, _ = compute_energy_score(ensemble_updated_aao_truecov, ground_truth)\n", - "\n", - "print(es_prior)\n", - "print(es_aao_loc)\n", - "print(es_seq_loc)\n", - "print(es_aao_truecov)" - ] - }, - { - "cell_type": "markdown", - "id": "c7bf9585", - "metadata": {}, - "source": [ - "## Scoring part." - ] - }, - { - "cell_type": "code", - "execution_count": 99, - "id": "5a95104f", - "metadata": {}, - "outputs": [], - "source": [ - "df_scores = pd.read_pickle(os.path.join(results_folder, \"scores.pkl\"))" - ] - }, - { - "cell_type": "code", - "execution_count": 100, - "id": "bd4390ba", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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RMSE priorRMSE aao locRMSE seq locRMSE aao truecovES priorES aao locES seq locES aao truecovRE aao locRE seq locRE aao truecov
00.8084630.3220520.3617860.32205247.29287120.62981125.46559220.6298110.9167920.8828830.916792
11.0094780.2945630.3693110.29456357.74726918.51653825.83151618.5165380.9598590.9448290.959859
20.9485510.2990430.3738890.29904354.42884018.91096426.40024418.9109640.9449080.9209180.944908
30.9745580.3460450.4673210.34604555.79520122.20186833.33396122.2018680.9314460.8818720.931446
41.1186030.3166810.3980250.31668164.25060820.18771428.24631020.1877140.9519760.9277030.951976
50.9124930.3208640.4061920.32086452.47444620.62378629.03439720.6237860.9330060.8990560.933006
61.1109270.2754580.3849070.27545863.93140317.08015427.05793117.0801540.9608960.9359500.960896
71.0032000.3536350.4412600.35363557.51004822.51091930.99465022.5109190.9325160.8835060.932516
80.8895980.2900980.3688640.29009851.12363918.39970226.11404118.3997020.9380190.8942560.938019
91.0084930.3476140.4196660.34761457.63757322.25916429.64086522.2591640.9391200.8971720.939120
101.0675120.3479470.4318050.34794761.23329522.64100431.04284522.6410040.9488990.9161510.948899
110.8969190.3475830.4346750.34758351.79212122.59303731.31414422.5930370.9261880.8878270.926188
121.1079100.2968700.3621640.29687063.90464518.82663225.50791018.8266320.9538090.9288140.953809
130.8898370.2936530.3801450.29365351.28149118.63738626.79590218.6373860.9431190.9155210.943119
141.1427110.3524810.4198430.35248165.99644522.62449929.66650622.6244990.9511140.9292450.951114
151.0021790.3033820.3388260.30338257.53246719.43520623.83363219.4352060.9616710.9428600.961671
161.0467540.3262440.4375660.32624459.83000020.88971331.25354520.8897130.9528210.9246190.952821
171.0649180.3326010.4166280.33260161.21782621.21841529.50937521.2184150.9301300.8936850.930130
180.9554510.2896150.3362220.28961554.81078718.29897923.51277318.2989790.9417500.9188240.941750
190.8299080.3064510.3792540.30645148.07771119.34143826.69741019.3414380.9223520.8793240.922352
\n", - "
" - ], - "text/plain": [ - " RMSE prior RMSE aao loc RMSE seq loc RMSE aao truecov ES prior \\\n", - "0 0.808463 0.322052 0.361786 0.322052 47.292871 \n", - "1 1.009478 0.294563 0.369311 0.294563 57.747269 \n", - "2 0.948551 0.299043 0.373889 0.299043 54.428840 \n", - "3 0.974558 0.346045 0.467321 0.346045 55.795201 \n", - "4 1.118603 0.316681 0.398025 0.316681 64.250608 \n", - "5 0.912493 0.320864 0.406192 0.320864 52.474446 \n", - "6 1.110927 0.275458 0.384907 0.275458 63.931403 \n", - "7 1.003200 0.353635 0.441260 0.353635 57.510048 \n", - "8 0.889598 0.290098 0.368864 0.290098 51.123639 \n", - "9 1.008493 0.347614 0.419666 0.347614 57.637573 \n", - "10 1.067512 0.347947 0.431805 0.347947 61.233295 \n", - "11 0.896919 0.347583 0.434675 0.347583 51.792121 \n", - "12 1.107910 0.296870 0.362164 0.296870 63.904645 \n", - "13 0.889837 0.293653 0.380145 0.293653 51.281491 \n", - "14 1.142711 0.352481 0.419843 0.352481 65.996445 \n", - "15 1.002179 0.303382 0.338826 0.303382 57.532467 \n", - "16 1.046754 0.326244 0.437566 0.326244 59.830000 \n", - "17 1.064918 0.332601 0.416628 0.332601 61.217826 \n", - "18 0.955451 0.289615 0.336222 0.289615 54.810787 \n", - "19 0.829908 0.306451 0.379254 0.306451 48.077711 \n", - "\n", - " ES aao loc ES seq loc ES aao truecov RE aao loc RE seq loc \\\n", - "0 20.629811 25.465592 20.629811 0.916792 0.882883 \n", - "1 18.516538 25.831516 18.516538 0.959859 0.944829 \n", - "2 18.910964 26.400244 18.910964 0.944908 0.920918 \n", - "3 22.201868 33.333961 22.201868 0.931446 0.881872 \n", - "4 20.187714 28.246310 20.187714 0.951976 0.927703 \n", - "5 20.623786 29.034397 20.623786 0.933006 0.899056 \n", - "6 17.080154 27.057931 17.080154 0.960896 0.935950 \n", - "7 22.510919 30.994650 22.510919 0.932516 0.883506 \n", - "8 18.399702 26.114041 18.399702 0.938019 0.894256 \n", - "9 22.259164 29.640865 22.259164 0.939120 0.897172 \n", - "10 22.641004 31.042845 22.641004 0.948899 0.916151 \n", - "11 22.593037 31.314144 22.593037 0.926188 0.887827 \n", - "12 18.826632 25.507910 18.826632 0.953809 0.928814 \n", - "13 18.637386 26.795902 18.637386 0.943119 0.915521 \n", - "14 22.624499 29.666506 22.624499 0.951114 0.929245 \n", - "15 19.435206 23.833632 19.435206 0.961671 0.942860 \n", - "16 20.889713 31.253545 20.889713 0.952821 0.924619 \n", - "17 21.218415 29.509375 21.218415 0.930130 0.893685 \n", - "18 18.298979 23.512773 18.298979 0.941750 0.918824 \n", - "19 19.341438 26.697410 19.341438 0.922352 0.879324 \n", - "\n", - " RE aao truecov \n", - "0 0.916792 \n", - "1 0.959859 \n", - "2 0.944908 \n", - "3 0.931446 \n", - "4 0.951976 \n", - "5 0.933006 \n", - "6 0.960896 \n", - "7 0.932516 \n", - "8 0.938019 \n", - "9 0.939120 \n", - "10 0.948899 \n", - "11 0.926188 \n", - "12 0.953809 \n", - "13 0.943119 \n", - "14 0.951114 \n", - "15 0.961671 \n", - "16 0.952821 \n", - "17 0.930130 \n", - "18 0.941750 \n", - "19 0.922352 " - ] - }, - "execution_count": 100, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_scores" - ] - }, - { - "cell_type": "code", - "execution_count": 101, - "id": "a18fafbe", - "metadata": {}, - "outputs": [], - "source": [ - "import matplotlib.pyplot as plt\n", - "import seaborn as sns" - ] - }, - { - "cell_type": "code", - "execution_count": 102, - "id": "5757d9bf", - "metadata": {}, - "outputs": [], - "source": [ - "df_melted = pd.melt(df_scores, value_vars=df_scores.columns, var_name=\"metric\", value_name=\"loss\")\n", - "df_melted['experiment'] = df_melted['metric']\n", - "\n", - "df_melted.loc[df_melted['experiment'].str.contains(\"prior\"), 'experiment'] = 'Prior'\n", - "df_melted.loc[df_melted['experiment'].str.contains(\"aao loc\"), 'experiment'] = 'All-at-once'\n", - "df_melted.loc[df_melted['experiment'].str.contains(\"seq loc\"), 'experiment'] = 'Sequential'\n", - "df_melted.loc[df_melted['experiment'].str.contains(\"truecov\"), 'experiment'] = 'True covariance'\n", - "\n", - "df_melted.loc[df_melted['metric'].str.contains(\"RMSE\"), 'metric'] = 'RMSE'\n", - "df_melted.loc[df_melted['metric'].str.contains(\"ES\"), 'metric'] = 'ES'\n", - "df_melted.loc[df_melted['metric'].str.contains(\"RE\"), 'metric'] = 'RE'" - ] - }, - { - "cell_type": "code", - "execution_count": 173, - "id": "2b685865", - "metadata": {}, - "outputs": [], - "source": [ - "# Set plot parameters.\n", - "sns.set() \n", - "sns.set_style(\"white\") \n", - "# plt.rcParams[\"font.family\"] = \"Helvetica\" \n", - "plt.rcParams[\"font.family\"] = [\"Arial\"] \n", - "plot_params = { \n", - " 'font.size': 25, 'font.style': 'normal', \n", - " 'axes.labelsize': 'medium', \n", - " 'axes.titlesize':'medium', \n", - " 'legend.fontsize': 'medium', \n", - " 'xtick.labelsize': 'medium', \n", - " 'ytick.labelsize': 'small', \n", - " } \n", - "plt.rcParams.update(plot_params) \n", - "plt.rc('xtick', labelsize=22) \n", - "plt.rc('ytick', labelsize=22) " - ] - }, - { - "cell_type": "code", - "execution_count": 136, - "id": "9407ef22", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure(figsize=(10,6))\n", - "ax = sns.boxplot(x=\"experiment\", y=\"loss\",\n", - " data=df_melted[df_melted['metric'] == 'RMSE'], linewidth=2.5)\n", - "ax.set_ylabel('RMSE')\n", - "ax.set_xlabel('')\n", - "plt.savefig(os.path.join(plots_folder, 'scores_RMSE'), bbox_inches='tight', dpi=200)" - ] - }, - { - "cell_type": "code", - "execution_count": 137, - "id": "e335bfc2", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure(figsize=(10,6))\n", - "ax = sns.boxplot(x=\"experiment\", y=\"loss\",\n", - " data=df_melted[df_melted['metric'] == 'ES'], linewidth=2.5)\n", - "ax.set_ylabel('Energy Score')\n", - "ax.set_xlabel('')\n", - "plt.savefig(os.path.join(plots_folder, 'scores_ES'), bbox_inches='tight', dpi=200)" - ] - }, - { - "cell_type": "code", - "execution_count": 138, - "id": "c4b37cfa", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure(figsize=(10,6))\n", - "df_melted_mod = pd.concat([pd.DataFrame({'metric': ['RE'], 'loss': [np.nan], 'experiment': ['prior']}), df_melted], axis=0)\n", - "\n", - "ax = sns.boxplot(x=\"experiment\", y=\"loss\",\n", - " data=df_melted_mod[df_melted_mod['metric'] == 'RE'], linewidth=2.5)\n", - "ax.set_ylabel('RE Skill Score')\n", - "ax.set_xlabel('')\n", - "plt.savefig(os.path.join(plots_folder, 'scores_RE'), bbox_inches='tight', dpi=200)" - ] - }, - { - "cell_type": "markdown", - "id": "5046286e", - "metadata": {}, - "source": [ - "## Now plot evolution of scores for different noise levels." - ] - }, - { - "cell_type": "code", - "execution_count": 181, - "id": "8cd2d4fb", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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repetitiondata stdRMSE priorRMSE aao locRMSE seq locRMSE aao truecovES priorES aao locES seq locES aao truecovRE aao locRE seq locRE aao truecov
000.0021.0181890.2976940.3706970.29769458.44237818.65349625.97110818.6534960.9631190.9363190.963119
100.0051.0181890.2976020.3643770.29760258.44237818.62310525.36018918.6231050.9631440.9413900.963144
200.0071.0181890.2975000.3601320.29750058.44237818.59940924.95335718.5994090.9630460.9436970.963046
300.0101.0181890.2972930.3543680.29729358.44237818.55938024.39572218.5593800.9633560.9454060.963356
400.0301.0181890.2951470.3334580.29514758.44237818.22772122.18443118.2277210.9639520.9482450.963952
..........................................
7750.0070.9153960.2959310.3835110.29593152.60002618.88306327.48608018.8830630.9379380.9179800.937938
7850.0100.9153960.2959080.3813400.29590852.60002618.85205627.19598218.8520560.9375640.9178610.937564
7950.0300.9153960.2957180.3675740.29571852.60002618.62910125.41810118.6291010.9381660.9158950.938166
8050.0500.9153960.2958010.3584390.29580152.60002618.42230424.15084618.4223040.9371930.9108950.937193
8150.0700.9153960.2962330.3532670.29623352.60002618.25209523.29112518.2520950.9357090.9090710.935709
\n", - "

82 rows × 13 columns

\n", - "
" - ], - "text/plain": [ - " repetition data std RMSE prior RMSE aao loc RMSE seq loc \\\n", - "0 0 0.002 1.018189 0.297694 0.370697 \n", - "1 0 0.005 1.018189 0.297602 0.364377 \n", - "2 0 0.007 1.018189 0.297500 0.360132 \n", - "3 0 0.010 1.018189 0.297293 0.354368 \n", - "4 0 0.030 1.018189 0.295147 0.333458 \n", - ".. ... ... ... ... ... \n", - "77 5 0.007 0.915396 0.295931 0.383511 \n", - "78 5 0.010 0.915396 0.295908 0.381340 \n", - "79 5 0.030 0.915396 0.295718 0.367574 \n", - "80 5 0.050 0.915396 0.295801 0.358439 \n", - "81 5 0.070 0.915396 0.296233 0.353267 \n", - "\n", - " RMSE aao truecov ES prior ES aao loc ES seq loc ES aao truecov \\\n", - "0 0.297694 58.442378 18.653496 25.971108 18.653496 \n", - "1 0.297602 58.442378 18.623105 25.360189 18.623105 \n", - "2 0.297500 58.442378 18.599409 24.953357 18.599409 \n", - "3 0.297293 58.442378 18.559380 24.395722 18.559380 \n", - "4 0.295147 58.442378 18.227721 22.184431 18.227721 \n", - ".. ... ... ... ... ... \n", - "77 0.295931 52.600026 18.883063 27.486080 18.883063 \n", - "78 0.295908 52.600026 18.852056 27.195982 18.852056 \n", - "79 0.295718 52.600026 18.629101 25.418101 18.629101 \n", - "80 0.295801 52.600026 18.422304 24.150846 18.422304 \n", - "81 0.296233 52.600026 18.252095 23.291125 18.252095 \n", - "\n", - " RE aao loc RE seq loc RE aao truecov \n", - "0 0.963119 0.936319 0.963119 \n", - "1 0.963144 0.941390 0.963144 \n", - "2 0.963046 0.943697 0.963046 \n", - "3 0.963356 0.945406 0.963356 \n", - "4 0.963952 0.948245 0.963952 \n", - ".. ... ... ... \n", - "77 0.937938 0.917980 0.937938 \n", - "78 0.937564 0.917861 0.937564 \n", - "79 0.938166 0.915895 0.938166 \n", - "80 0.937193 0.910895 0.937193 \n", - "81 0.935709 0.909071 0.935709 \n", - "\n", - "[82 rows x 13 columns]" - ] - }, - "execution_count": 181, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "results_evolution_folder = \"/home/cedric/PHD/Dev/DIESEL/reporting/toy_example/results_paper/synthetic_different_noise/\"\n", - "df_evolution = pd.read_pickle(os.path.join(results_evolution_folder, \"scores.pkl\"))\n", - "df_evolution" - ] - }, - { - "cell_type": "code", - "execution_count": 182, - "id": "c73408b0", - "metadata": {}, - "outputs": [], - "source": [ - "df_evolution['data std'] = 100 * df_evolution['data std']\n", - "df_evolution_melted = pd.melt(df_evolution, value_vars=df_scores.columns, var_name=\"metric\", value_name=\"loss\", id_vars=['data std', 'repetition'])\n", - "df_evolution_melted['experiment'] = df_evolution_melted['metric']\n", - "\n", - "df_evolution_melted.loc[df_evolution_melted['experiment'].str.contains(\"prior\"), 'experiment'] = 'Prior'\n", - "df_evolution_melted.loc[df_evolution_melted['experiment'].str.contains(\"aao loc\"), 'experiment'] = 'All-at-once'\n", - "df_evolution_melted.loc[df_evolution_melted['experiment'].str.contains(\"seq loc\"), 'experiment'] = 'Sequential'\n", - "df_evolution_melted.loc[df_evolution_melted['experiment'].str.contains(\"truecov\"), 'experiment'] = 'True covariance'\n", - "\n", - "df_evolution_melted.loc[df_evolution_melted['metric'].str.contains(\"RMSE\"), 'metric'] = 'RMSE'\n", - "df_evolution_melted.loc[df_evolution_melted['metric'].str.contains(\"ES\"), 'metric'] = 'ES'\n", - "df_evolution_melted.loc[df_evolution_melted['metric'].str.contains(\"RE\"), 'metric'] = 'RE'" - ] - }, - { - "cell_type": "code", - "execution_count": 189, - "id": "0657d965", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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oXdfx6quvAgDOPvtsyLKMSy+9FIcOHcLzzz8fFZiPHDmCRx55BK+99lqTH7+1xd0IpuWcc87BOeec0+B5x3olctpppzVp3YfL5cIf/vAH/OEPf2j0xxBCCCGdzYIFC6CqKi655JJjlvAbO3Ys0tPTUVBQgJUrVx73415xxRWYPXs2rrjiCpx//vmQJAmbNm3Chg0bkJaWhiNHjkQVQ7f2VHz66afQdR2XXnrpMTfsZmdn49FHH8UjjzyCq666CmPHjkVmZiY2btyITZs2IScnB//4xz+ade0+nw//+Mc/cN999+Hqq6/GuHHjkJGRge+++w7bt2/HxIkTMW7cOADA/fffj40bN+KNN97Ad999h+HDhyMUCuE///kPSktLccstt8T16CUQpyOYhBBCCIlfn376KQBjbeSxiKJonxOLzj5333037r//fiQkJOCjjz7C559/Dl3X8fDDD9v3v3z5cvv8U089FTfddBPC4TDmzp3bqC44V111Fd59912cfvrpWLNmDd5//31UVFTgjjvuwIIFC+qts90YF1xwAebOnYvTTjsNq1atwrvvvotwOIz7778/aumez+fDv//9b0ybNg3hcBgffPABFi9ejBNPPBHPP/88HnzwwWZfQ2vhGLWnaRH5+fkYO3Ysli5dGlUbixBCSPu0ffv2qO5whLQHjf2+jXVuoRFMQgghhBASUxQwCSGEEEJITFHAJIQQQgghMUUBkxBCCCGExBQFTEIIIYQQElMUMAkhhBBCSExRwCSEEEIIITFFAZMQQgghhMQUBUxCCCGEEBJTFDAJIYQQQkhMUcAkhBBCCCExRQGTEEIIIYTEFAVMQgghhBASUxQwCSGEENJsW7duxQMPPIBzzjkHJ598MoYPH45JkyZh1qxZKC0tbevLazNLlizBL7/8Yr+/du1a9O3bFw8++GCz77Nv374499xzY3F5LY4CJiGEEEKa5f3338eVV16JJUuWYOjQobjxxhtx0UUXgeM4vPrqqzj//POxY8eOtr7MVvd///d/+P3vf4+SkhL7tuzsbEybNg3jxo1rwytrPWJbXwAhhBBC2p+CggI8/vjj6NWrF9577z0kJydHHX/vvfcwY8YM3Hffffjiiy/AcVwbXWnrKywsrHVbTk4O7r777ja4mrZBI5iEEEIIabIVK1ZAVVVcffXVtcIlAEyZMgUDBgzAL7/8EjVVTDoHCpiEEEIIaTJFUQAAO3furPecRx55BLNnz0ZWVpZ9W1lZGWbOnIlx48Zh4MCBOP300/GnP/0Je/bsqfM+/v3vf+PSSy/F4MGDcc455+CVV17Bli1b0LdvX7z00kv2eQ8++CD69u2LtWvX1rqPG264AX379kV+fn7U7Xv27MGf//xnnHHGGRg4cCDGjh2LmTNnory8POq8l156CX379sWWLVvw2muvYcKECRg4cCDOPvtsPP7446iqqrLP7du3Lz799FMAwI033oi+ffsCqH8N5r59+/DII4/gvPPOw6BBgzB48GBceOGFeP755xEMBuv92sY7miInhBBCSJOdccYZ4DgO8+fPR2VlJS6//HKMHDkSXq/XPueUU06J+pjCwkJMnjwZeXl5GDVqFMaPH4/CwkIsXrwYy5cvxxtvvIGhQ4fa5z/88MP48MMP0aVLF1xxxRUIBoP4f//v/2HVqlXHff3ff/897rzzTiiKgnHjxiEnJwfbt2/Hm2++iWXLluGDDz5ASkpK1Mc89thj2LNnD84//3yce+65WLx4Md555x0cOXIEzz//PABg2rRpWLJkCXbs2IHLL78c2dnZ9V7Dtm3bMGXKFOi6jnHjxqFr164oKSnBkiVLMHv2bOzbtw8vvvjicX+ubYECJiGEEEKarE+fPpg+fTqeeuopfPPNN/jmm28gCAL69euH4cOHY8yYMRg5ciR4vnqy9LHHHkNeXh4ef/xxXHnllfbtt956K6699lrcd999+M9//gNRFLF27Vp8+OGHGDBgAN566y0kJiYCMKbep0yZclzXHg6Hcd999wEAPv74Y/Tr188+9sEHH+Bvf/sbnnjiCTz77LNRH5efn48vvvgCOTk5AIA77rgD48ePx+LFi1FUVIT09HTcfffdKCgosAPmyJEj672OWbNmIRAI4N1338WIESPs2++9915MmDABS5YsQTAYhMvlOq7Pty1QwCSEEEKOU+XmFajctKytL6NRfIPPhW/QmJjc10033YShQ4fi7bffxrfffouKigr8/PPP+Pnnn/Gvf/0L/fr1wxNPPIEBAwbgyJEjWLp0KQYNGhQVLgHgpJNOwqWXXooPP/wQq1evxujRo/H5558DAO655x47XALAwIEDcd111+Gtt95q9nUvXboURUVFmDp1alS4BIDrrrsO//rXv/D111/jb3/7W9SI7CWXXGKHSwBITEzE0KFDsWzZMuTn5yM9Pb1J13HjjTfiwgsvjAqXAJCeno7c3Fxs3LgR5eXlFDAJIYQQ0rmcfPLJePbZZ6FpGn7++WesX78ea9aswffff48dO3bg5ptvxvz587F3714wxhAOh6PWTloOHToEwJg2Hj16tF3e6OhpdgA47bTTjitgbt26FQCwd+/eOq9FEASoqoqdO3di2LBh9u0nnHBCrXMTEhIAVK9JbYozzzwTgLEudceOHcjLy8P+/fuxbds2+xo1TWvy/cYDCpiEEELIcfINGhOzUcH2ShAEDBo0CIMGDcJtt92GgoIC3Hvvvdi8eTPmzp2LAQMGADA2BR1rY5C1waayshIA4PF4ap1T1671pqioqAAALFu2DMuW1T/yfPRmH4fDUescq/wSY6zJ11FYWIgnn3wSixcvtoNk165dMWTIEGRmZiI/P79Z9xsPKGASQgghpEk0TcPEiRMBAAsXLqzznOzsbDz00EO49tprsXfvXnsa+Prrr8fDDz/c4GNY0+KFhYW1NsrULGBusYKeruu1jgUCgaj3rdD64osvYsKECQ1eS0tgjOH222/H9u3bcd1112HixIno27evPSV/9dVX19r13p5QmSJCCCGENIkgCGCMYc+ePdi4cWO951mjb1lZWTjppJMAAJs3b67z3K+++gqzZs2yp8YHDRoEAPjhhx9qnVvXfUiSBADw+/1Rt+u6jry8vKjb+vfvf8xr+ec//4lXX30VZWVldR5vSGOKyu/cuRPbt2/HqFGj8Le//Q3Dhg2zw6WiKNi/fz+A5o2MxgMKmIQQQghpsltvvRUA8Oc//xnbt2+vdbyiogLPPfccOI7DVVddhS5duuDMM8+0p8xr2rt3Lx577DG89tprdsi66qqrwPM8XnrpJRw+fNg+d//+/XjnnXdqPV6vXr0AGBt4avrXv/5Va6r7vPPOQ1JSEubOnVsrZC5cuBAvvPACFixYYK+vbCpRNCaII5FIvedY0+2lpaVQVdW+XdM0PPHEE/Y11zzWntAUOSGEEEKa7KqrrsKePXvw1ltvYdKkSRg2bBhOOukkOJ1O5OfnY+XKlfD7/XjwwQft0cgZM2ZgypQp+Pvf/46FCxdi8ODBKCsrw6JFixAIBPDXv/7V3qXdr18/3HfffXj66adx2WWXYdy4cdA0DYsXL7YDXE2XXnopXnrpJcyfPx9FRUXo168ffv75Z/zwww8YMmRI1Eirx+PB008/jWnTpuG6667Dueeei+7du2PPnj349ttv4Xa78dRTT0WVWGqKLl26ADDKEK1btw6///3va53Ts2dPDBs2DBs2bMCVV16J0047DZFIBKtWrcKvv/6K1NRUFBcXN3sUta3RCCYhhBBCmuXBBx/EBx98gMsvvxxFRUX4+OOP8eabb+Knn37C2LFj8eGHH+Lmm2+2z8/Ozsb8+fNx8803o7CwEO+++y5WrlyJoUOH4s0338SNN94Ydf+33XYbXnnlFXTr1g2fffYZVq5cieuuuw733ntvrWtJTk7Ge++9hzFjxuDHH3/Ee++9BwCYO3cuhgwZUuv80aNH46OPPsKECRPw448/4u2338auXbswceJEfPzxx3V+TGNNnjwZZ599Nnbv3o0PPvigzrWUHMfh5ZdfxnXXXYeKigrMnTsXy5YtQ/fu3fHaa6/h/vvvB2C05GyPONZeJ/fjXH5+PsaOHYulS5dG1cwihBDSPm3fvt1eu0fa1vz58zF9+nRMmzYNd999d1tfTlxr7PdtrHMLjWASQgghhJCYooBJCCGEEEJiigImIYQQQgiJKdpFTgghhJB2ZdKkSZg0aVJbXwY5BhrBJIQQQgghMUUBkxBCCCGExBQFTEIIIYQQElMUMAkhhJBGotLRpD1py+9XCpiEEEJII/A8D13X2/oyCGk0Xdeb3e7yeFHAJIQQQhrB6XQiEAi09WUQ0miBQAAul6tNHpsCJiGEENIIXq8XZWVlNE1O2gXGGMrKyuDxeNrk8SlgEkIIIY2QnJwMVVVx8OBBhMNhCpokLjHGEA6HcfDgQaiqiuTk5Da5Diq0TgghhDQCz/Po1q0bSkpK8Ntvv0FV1ba+JELqJIoiEhMTkZGR0WZrMClgEkIIIY0kiiIyMjKQkZHR1pdCSFyjKXJCCCGEEBJTFDAJIYQQQkhMUcAkhBBCCCExRQGTEEIIIYTEFAVMQgghhBASUxQwCSGEEEJITFHAJIQQQgghMRW3AXPNmjW48cYbMXLkSAwdOhQ33HADVq1adVz3+cgjj6Bv376YP39+ncdvuukm9O3bt95/v/7663E9PiGEEEJIZxCXhdbnz5+P6dOnQ5ZljBo1CrquY+3atZg6dSpmzJiBa665psn3uXLlSsybN++Y5+zYsQNutxtjx46t83hb9fMkhBBCCGlP4i5gFhYW4tFHH4XP58P777+P3NxcAMDmzZtxyy234PHHH8eYMWOQmZnZ6PssKyvDQw89dMxzCgoKUFZWhtNPPx3PPvvscX0OhBBCCCGdWdxNkc+dOxeRSAQ333yzHS4BYNCgQZg6dSrC4XCDI5FHe+yxx1BWVobBgwfXe8727dsBAAMGDGjehRNCCCGEEABxGDCtdZbjxo2rdey8884DYEx3N9aXX36JhQsXYtq0aejTp0+9523btg0ABUxCCCGEkOMVVwGTMYbdu3eD53n06tWr1vGePXuC53ns3r0bjLEG7+/w4cOYMWMGBg8ejP/5n/855rlWwCwvL8dtt92GUaNGYciQITHZXEQIIYQQ0pnEVcAsLy9HJBJBUlISZFmudVwURSQnJyMYDMLv9zd4f3/9618RDofx1FNPQRCEY55rTZE/+uijKCwsxPDhw5GTk4N169Zh6tSpePvtt5v3SRFCCCGEdDJxtcknGAwCAFwuV73nOJ1OAIDf74fX6633vPfffx+rVq3C9OnT6xwNramkpASHDh2CKIqYOXMmLr74YvvYwoULcf/992PmzJkYMWIE+vfv35RPiRBCCCGk04mrEUyeb/hyGjM1/ttvv+GZZ57B8OHDcdNNNzV4fkpKCr777jt89dVXUeESAC688EJMmTIFmqbhgw8+aPC+CCGEEEI6u7gKmG63GwAQDofrPcc6Vt8op6ZpeOCBBwAATz75JDiOa9Rjp6SkoGfPnnUeO+eccwAAP//8c6PuixBCCCGkM4urKXKv1wu3243S0lKoqgpRjL48VVVRWloKh8OBhISEOu/jm2++wcaNG9GtWze88MILUcc2bdoEAPjwww+xZs0ajB8/HuPHj2/wutLT0wEAoVCoOZ8WIYQQQkinElcBk+M49O7dG5s3b8b+/fvRu3fvqOP79u2DrutR9TGPFggEAAB5eXnIy8ur85yNGzdi48aN6NGjB8aPH481a9bg008/xYABA3DzzTfXOj8/Px8AkJWV1czPjBBCCCGk84irKXIAOOusswAAS5YsqXXMum306NH1fvykSZOwc+fOOv9deeWVAIyp8507d+Luu+8GYIxMfv7553jnnXegqmqt+1ywYAEA4Mwzzzyuz40QQgghpDOIu4A5adIkOBwOvP7669i6dat9+5YtWzBnzhw4nU5MnjzZvv3AgQPYs2cPSkpKmv2YZ555JrKzs1FQUIBnnnkGmqbZxz755BN8/fXXSE9PtwMqIYQQQgipX1xNkQNATk4O/vKXv2DGjBm49tprMWrUKDDGsHbtWqiqipkzZyI1NdU+/y9/+QvWrVuHadOm2SOSTSXLMp599lncdttt+Ne//oVly5ahX79+yMvLw/bt2+F2u/HSSy/B5/PF6tMkhBBCCOmw4m4EEwCmTJmC2bNnY/DgwdiwYQO2bt2KoUOH4q233sKll17aIo85dOhQfPrpp7jssssQDAaxfPlyFBcXY9KkSfj8888xZMiQFnlcQgghhJCOhmONKSxJmiw/Px9jx47F0qVLkZOT09aXQwghhBBSr1jnlrgcwSSEEEIIIe0XBUxCCCGEEBJTFDBbGK1AIIQQQkhnQwGzhakVRdBD/ra+DEIIIYSQVkMBs4UxVYUWCbT1ZRBCCCGEtBoKmK1AD9IIJiGEEEI6DwqYrUAPVrb1JRBCCCGEtBoKmK1AV0JgmtLWl0EIIYQQ0iooYLYCpqrQlUhbXwYhhBBCSKuggNkaGKBHQm19FYQQQgghrYICZivgRBF6qKqtL4MQQgghpFVQwGwFnChDD1LAJIQQQkjnQAGzFXC8AKZGwDS1rS+FEEIIIaTFUcBsJQwAU2mjDyGEEEI6PgqYrYQDg66E2/oyCCGEEEJaHAXMVsIJEjTa6EMIIYSQToACZivhBIk2+hBCCCGkU6CA2VoEESwSAtO1tr4SQgghhJAWRQGzlXAcZ2z0oY4+hBBCCOngKGC2Jgba6EMIIYSQDo8CZiviBIE6+hBCCCGkw6OA2Yp4yQHNX97Wl0EIIYQQ0qIoYLYiThChqxGaJieEEEJIh0YBs7UxBhYJtfVVEEIIIYS0GAqYrYwXJag0TU4IIYSQDkxs6wvobDjJAd1fDsYYOI5r68shhBBCSCfEdA1MiUBXQtCCVQgd2BPT+6eA2co4XoCuq2BKGJzsbOvLIYQQQkgHxxgDU8JgShhaqApaoBIsEgQDwMHYI8LxsZ3UpoDZBhgDtHAAPAVMQgghhMQQYwxMU8AiYejhALRgBfRgAGCaESgFEbwog3P5omZSOb4yptdBAbMN8KIE3V8O+FLa+lIIIYQQ0o4xTYEeCUNXgtADldCCVWC6Bo4B4Dlwogze5QbHte62mwYD5pNPPolu3brh+uuvb9YDTJ48GT/99BO2bdvWrI/viDjJAS1QbnwD8EJbXw4hhBBC2gFj3WQYWiQEPVgJPVgFXQ0bYZLjwYkSeNkZF9miwYD59ttvY9iwYfUGzLFjx2LQoEF4/vnn670Pxljzr7AD4nge0HXowSoInsS2vhxCCCGExBnGdDAlbIxOhvzQghVgkTAYWPW6SVGGGKfL7Y57irygoABZWVmxuJZOhZMcUMoLKWASQgghnRxjDEyNGKOT4QD0QAX0UACADgYOHC+Y6ya97aYCDa3BbCO87IQaKIeuhMFLjra+HEIIIYS0EqYq0JUQ9HAQetBYNwmmAeDMqW4ZfDsKk3WhgNmGOHDQ/GXgkzLb+lIIIYQQ0gKYph61brISuqaCAwPAg5Mk8A53zMsEtTUKmG2Id7ihlhVCTMxo169SCCGEEHJU8fJQFfRApVF/0pjoBidI4CQHRIe7rS+1xVHAbEOcIBrfgOEABKenrS+HEEIIIY1kr5uMGGFSC1aBhQPmxmYOvGhswuHdCW19qW2CAmYb4wQJamUxBUxCCCEkjulqxCheHglAC1RCD/oB6EbrZ14AJ8ngjype3plRwGxhjOnHLNPEO1zQKkrAUrqCE+jpIIQQQtoa0xToSgR6JAA9UGXUm9RVcIwBPN9mxcvbE0o0LazkP28iYcAQJAwdX+dxjuPBGINaWQyJNvsQQgghrSq6eHkV9GAlmBoxj3LgRBGc7IDId/x1k7FEAbOFcaKEUN72egMmAAguD5TigxC9KeBEqRWvjhBCCOk8jOLlZr3JYBW0YCVYJFijeLlkjE7GafHy9qRRAfPHH39E//796zzGcdwxj3d2ckZ3KL+thx4J1fsNa7V0UsoPQ07Nac3LI4QQQjqkmsXL9XAQWqDcLF7OYHRWtIqX07rJltCogHm8rR478xMnpfcEfl2HyOH9cHbrV+95vMsDpfQwRF8avXIihBBCmsgoXh6GHglCD1ZAC5jFyxkAXjDXTXpo3WQraTBgPvnkk61xHR2WlJ4D8ALCh/YeM2ByHA9OkKCUHIQj64RWvEJCCCGkfWG6ZpQHioSgh6qM1oqaAs6Y6AYndszi5e1JgwHz8ssvb43r6LB4QYKUmo3wob0Nn+twQ6sqgR7KAE9liwghhBBz3WQYeiQMLVRZXbycMXBc5ype3p7QJp9W4Mjoiapt/4WuhMBL9U9/cxwHTnIgUlwAR9c+nXppASGEkM4nuni5H1qoCizkB8DAmFm8XJA6bfHy9iSmAbO0tBS//fYbUlJS0K1bt1jedbsmZ/YEfl6JyOFf4czpe8xzedkF1V8OPVgJgX6ACCGEdGC6vQknAC1QBT1UBTAqXt4RNDpgKoqCzz//HJs2bcIDDzwAr9drH6uqqsKjjz6KRYsWQdd1AMCJJ56IBx54AGeffXbsr7qdkdK7ARxvrMNsIGACRsiMFOXD2b0fLUYmhBDSITBNrbEJx+jTresKOADgeFo32cE0KmAWFBTgtttuw6+//goAuOWWW+yAqWkabrnlFmzdujVqt/nu3btx55134sknn8Qll1zSApfefvCiDDm9GyKNWIcJALwkQ/OXQ60qg+RLaeGrI4QQQmLLKl6uK2GzrWIVmBo2600am3CoeHnH1uDLBE3T8Lvf/Q779++HLMs4/fTT4XRWryN89913sWXLFgDAiBEjsGjRImzYsAGPPfYYeJ7HY489hsLCwpb7DNoJOasXIkV50JVwo87nnR4oRwrAdK2Fr4wQQghpPsZ06JEQNH85IsUFCObtQHDfZgTzdyJyeB/0QDk4QYDgToDoToTgTgAvu+wa0KRjanAE84svvsDu3btxwgkn4J///CdOOCG6hM4777wDAPB6vXjllVfg8/kAANdccw38fj+efvppfPTRR/j973/fApfffji6nIiqzcsRKfwVzuzcBs/nBBEsFIBacYRaSBJCCIkL1cXLI+a6yXLooSAAc92kIFLxcgKgESOYS5cuBcdxmDlzZq1wuXPnThw4cAAcx2HixIl2uLRcc801EEURK1asiOlFt0dyRg9jHebBxk2TA9UtJJmqtOCVEUIIIXVjmgItWAWlvAjhQ3uNkclff0b4wG4opYfANBW8y22MTnoSITg9xvQ3hctOr8ERzJ9//hnp6ekYNGhQrWPff/+9/XZdm3k8Hg969OiB/Pz847zM9o+XHJDSchA5tKfRH0MtJAkhhLQWu3i5EoYerDT+qZGjipfT1DZpnAYDZmlpKXJz657S3bBhAwCA53mceuqpdZ7j8Xjw22+/HccldhyOLieiasu3x+xLfjRqIUkIISTWahYv10NV0IKVYJGQ0aMbMIqXixJE2dXWl0paga6EESnKi+l9NhgwOY6DptW90WT9+vXgOA79+vWLKltUU2lpKRISml7Pcc2aNZg9ezZ27twJRVEwYMAA3H777TjrrLOafF+WRx55BPPmzcOTTz6JSZMm1ToeCoXw9ttv4/PPP0d+fj58Ph/GjBmDe+65BxkZGc1+XIszOxdVm5cjfGgPXN0HNOpjqIUkIYSQ41G9bjJsFC8PVoKFAgB0Y083L4AXZSpe3knoShhK8QEoxQWIHMmHUpwPtawIpVWN24TcWA0GzPT0dBQUFNS6fevWrSgtLQXHcTjttNPq/NgjR44gPz+/3hHQ+syfPx/Tp0+HLMsYNWoUdF3H2rVrMXXqVMyYMQPXXHNNk+4PAFauXIl58+bVe1xRFNx1111YvXo1unTpgtGjR2Pv3r346KOPsGLFCnz44Yfo2rVrkx+3JjmjBzhRRrjgl0YHTIBaSBJCCGk8pirQlRD0cAB6sApasApgGgDOrDcpg3d5aZ1kJ6ArESjFBbXCJGCUleTdCZBTs+E6YTBCuguY/0TMHrvBgDl8+HB88sknWLt2LUaOHGnf/tlnn9lvjxs3rs6PnTdvHhhj9U6f16WwsBCPPvoofD4f3n//fTucbt68Gbfccgsef/xxjBkzBpmZjd9ZXVZWhoceeuiY58ydOxerV6/GmDFj8NJLL0GWZQDA888/j9mzZ2PGjBmYPXt2ox+zLpwgQu7SC6GCXU37OGohSQghpA7VxctDxprJQCV0XQUHBoAHJ1Hx8s5CVyJQSg5AOZJvhskCqOWFgFmjnHf5IKflwHXCYEip2ZDTcqI6BlYePBzT62kwYF522WX4+OOPcd999+Gpp57CoEGDsHz5cvz73/8Gx3HIzc3FKaecUuvjvv/+e7z++uvgOA4TJkxo9AXNnTsXkUgEv/vd76JGPgcNGoSpU6di1qxZmDdvHu65555G3+djjz2GsrIyDB48GJs2bap1nDGGt956CxzH4eGHH7bDJQD84Q9/wOLFi7F8+XLk5eUddwtMZ9dclOftgFpZArEJRdSphSQhhHRuRvHyCHQlBC1YBd1aN8mBipd3MroaMaa5j+QjUlwA5Uh+3WGy58mQ0nIgp2VDcCe26jU2GDBPPfVUXHHFFfjkk08wdepU+3bGGERRxGOPPRZ1/meffYbly5fjm2++ga7rGD16NEaMGNHoC1q1ahWAukdFzzvvPMyaNQsrV65sdMD88ssvsXDhQvzxj39EXl5enQFz586dOHz4MPr374+cnOjd2jzP49xzz8Ubb7yBlStXYsqUKY3+XOriMGtghgt2Qew3qkkfSy0kCSGkc2CMgSlhc91kFbRAJVgkaExsMoAXRWOq29O6oYG0PjtMmkEyUkeYlNKy2zRM1qVRrSL/8Y9/IDMzE2+//Tb8fj8AoGvXrnjsscdqjV6+9NJLKCgoAGMMp5xyCp599tlGXwxjDLt37wbP8+jVq1et4z179gTP89i9e7dR0LWBqeLDhw9jxowZGDx4MP7nf/4HjzzySJ3n7d69GwDQp0+fOo9b17JrV9OmtusiJqZD8CQhdOAXeJoaMKmFJCGEdEi6GgGLhI3i5cFK6EE/wDRjV7cgGiWCqHh5h6erESglB6EcybdHJ9WywzXCpBeSPTKZDTk1B7w7IS6/LxoVMDmOwz333IPbb78d+/btgyRJ6NWrF/g61nT07t0bXbp0wSWXXILLLrsMkiQ1+mLKy8sRiUSQkpISNU1tX6woIjk5GcXFxfD7/fXuXLf89a9/RTgcxlNPPQVBqL9uV1FREQBjQ1NdrNuLi4sb+6nUi+M4OLL7ILh/K5iuNbmemNVCUvQkUi0yQghph5imGOWBlKCxZjLoN9ZNMgA8Z27CcdNMVQfHVAVKyQF7vWTkSD7UskKA6QAA3mmGyR4DjJHJOA6TdWlUwLQ4nU7079//mOccz0aYYDAIAHC56q+7ZfVBbyhgvv/++1i1ahWmT59e52hoTYFA4JiPaz2mdd7xcmb3RWDXekQKf4Uj69jXdjRqIUkIIe2HsW4yDM3ahBOsgq6Go4qX07rJjq86TBZAKc6vJ0xmG2Ey1Zjm5t2J7SZM1qVJAbOl1TUiejRmDhMfy2+//YZnnnkGw4cPx0033dTox63vibQeszGP3RiOnFxAEBHcv7XJAROobiEpelPAiY0fISaEENJyrOLlTIlAC1ZBC1aARcJgYODAzOLlMkRqmtGhGWHyICLF5jT3EWua2wqTHmNksvsAY5o7Lafdh8m6NBgwdV2PyQM1Jjy63cYruHC4/mKf1rH6Rhs1TcMDDzwAAHjyyScb9YRZjxsKhZr1mE3FS044s3MR+nUr2MiJTf6mohaShBDStqKKl4cD0AMV0OsoXs5RvckOLTpMWtPcR4XJ1By4uveHlJYDKTUHgqfjhcm6NBgwBwxofEHw+nAch23btjV4ntfrhdvtRmlpKVRVhShGX56qqigtLYXD4ai3O9A333yDjRs3olu3bnjhhReijlk7yD/88EOsWbMG48ePx/jx4+0uPUeOHKnzPhtao9kczh4DEfptG5TifMhpTS99RC0kCSGk9djFyyMh6IGKeoqXe2jdZAfGVAVK6UFjmtssWq6U1hMmU3MgpWVD8CR1ijBZlwYDZqymhRuD4zj07t0bmzdvxv79+9G7d++o4/v27YOu68fsDGStk8zLy0NeXt19NTdu3IiNGzeiR48eGD9+vH1/1m7yo+3ZswcAmtyR6Fhc3U9CGccjuH9rswImtZAkhJCWwTT1qHWTldBVBUZOoOLlnYERJg/Z3W+UIwVQSg8dFSaz4c3pDzmNwmRdGr2LHAD69++Piy66CCeffHKLXdBZZ52FzZs3Y8mSJbUC5pIlSwAAo0ePrvfjJ02aVGefccDYVf7xxx/X6kV+4oknIjs7G9u2bcPBgwfRpUsX+5iu61i2bBk4jjuuPuhH4x1uOLqciOD+LUgYdn6zvimphSQhhByf6uLlYWghoxMOU8x1k8zchCM5IDpoE05HxTTVKA1UnF89OlkzTDrckNJy4M0ZAzktG1JaDoXJRmgwYL7wwgtYuHAhvv32W2zbtg3bt29Ht27dcNFFF+HCCy+st3Zkc02aNAlz5szB66+/jjPPPBMDBw4EAGzZsgVz5syB0+nE5MmT7fMPHDiAYDCI5ORkpKQ0vzbktddei+eeew5//etf8fLLL9vrMl944QXs378f48ePR/fu3Y/vkzuKs8dAlH/3KdSyw5CSs5r88dRCkhBCGs9eNxkJQQv5oYWqwEJ+c6aOM4qXCxJ46pbWYTFNhVJ60F4vaU9z6xoAgHO4IaflwJsz2hyZpDDZXA0GzAkTJmDChAkIBoNYunQpFi5ciP/+97949dVXMXv2bPTu3dsOm7EIYDk5OfjLX/6CGTNm4Nprr8WoUaPAGMPatWuhqipmzpyJ1NRU+/y//OUvWLduHaZNm4a777672Y978803Y8WKFVi9ejXGjx+PoUOHYt++fdi1axe6du2Khx9++Lg/t6O5egxA+XcLENy/pVkBE6AWkoQQUh+7eHkkAC1QCT3kB5huNOrgBXCSTMXLOzAjTB6K6s2tlB6KDpOp2fAOPNsIk6nZELzJ9P0QI40uU+RyuXDxxRfj4osvRlVVFZYsWYKvvvoK3333HWbNmoUXXngBAwcOxEUXXYQLLrgAmZnNr9E4ZcoUdO3aFXPmzMGGDRsgyzKGDh2KO++8E6eddlqz7/dYZFnGG2+8gddeew1ffvklli9fjvT0dFxzzTWYNm2avREolgR3AuSMHgj9uhUJQ85r9v1QC0lCSGfHNBW6YoRJPVBl1JvUFbN4ubkJx0nFyzuqqDBZXGOa2wqTsssYmRx4NuRUc5qbwmSL4thx7uIpLy/Hf/7zHyxcuBDr1q2DpmngeR5DhgzBRRddhPPPP/+4pq7bq/z8fIwdOxZff/guuiS6wTvqLnFUuXUlKtZ9icwr/wIxIbXOcxpD85dDyuxJLSQJIR2eVbxcV8LGyGSwEkyN2Mc50ag3Sd3OOiY7TJrdb+oLk1JqdvU0N4XJBhUcPIwLrrsFS5cuRU7O8ZdAPO5C64mJibjqqqtw1VVXoaSkBIsWLcLXX3+NDRs24Mcff8QTTzyBkSNH4o033jjui+2IXD0GomLdlwj8sh4Jw85v9v1QC0lCSEdkFC83600Gq6AFK8EiQbN4Oezi5VSurWNqVJhMzYZ3wFnV09y+FAqTcSCmnXxSUlIwefJkXHbZZfjkk08wa9Ys+P1+rFmzJpYP0w7VP0gs+lLgOmEQKreuhKv3MEiJzau1SS0kCSHtXc3i5Xo4CC1QbhYvZ2BAjeLltG6yI2KaCqXscPSayZKDdYZJa3SSwmTsMMSmsY4lZgEzEAhgxYoVWLRoEVatWoVQKATGGHiex6mnnhqrh2l3RE8CmOoHjlHiInHkJQgV7ELZmvlIO//2Zv+wUAtJQkh7wHTNCJKqYqybDAeghwNgkRAAZrwm5wUqXt6BMV2DUnoYypE8c3SyAErJgRph0gk5NQfeAWeavbkpTMYSYzqYqhpLS8xyTCwUjOljHFfADAQCWL58uR0qw+GwHSqHDRuGCy64ABMmTEBaWlqsrrfd4RwecJoCpqnghLq/3II7AYmnXoCyNZ8iuPtHuPsMa95jUQtJQkicYIwBmvEHTFcV6JEgmBUkNRVGiuQAcOAE0fhHI5Mdkh0ma7RTVEoPApoKwAiTUmq2GSatkclU+l6IEcYYmKYYS010DRwAcAJ4pwuCNwOC0wNOcsAhF8b0cZscMP1+vx0q//vf/9qhkuM4DBkyBBdccAHOP//8mLZVbM84joOYkoVIYT5ET/1lhNx9R8L/ywaUr/sCjm79IDSzcDq1kCSEtCZjNFIxRiNVczQyEgILB8GYDmNEkgPH8+BEEZzkAE9FyzsspmtQSw/bO7lrhUnJCSktG97+p0NKqzkySaPUsWCHSVWpDpPgwDndEBPT7TDJSXKtr3msA32jAqbf78eyZcvsUBmJROxQOXjwYDtUHk9poo5M9CRB4QrAdL3e1mIcxyP5jCtQ+NkLqFj/FZLPurpZj0UtJAkhLcH+o6UqRj/usB96OFi9e5sB4DhwgmBsvKGSQB0e0zWoZYX25ptIsTnNXTNMpnatDpOp2RASUun7IkasWQJdtUYmjVkBzuGGmJAKwek1gqTkaJOveYMB86677sLq1avtUAkAgwYNwgUXXIALLrgAWVnNKxDemXCCBDEpDWp5CQRX/SOTUkoXeE8ejarNy+HuPQyOLic26/GohSQhpDmMdVnWaGQELBysDpJMA6zxEI63p7VppqRziAqT1o7uqDDpMKa5+58OyawzKVKYjJmoMKlpAGcuMJFdEH2p4J0e8LKjzcJkXRoMmMuWLTNOFEWMGjUKF1xwAbp27QoA2LdvH/bt29eoB2qpAunthehLhVLa8PoG3yljEdy7CWWrP0HGZX9s1mYdaiFJCDkWpqlmkIxAV0LQw0FjbaQSts6AtTYSvAje4a539oV0PHaYrDHNrZYcBNMUADXCZL/TIJl1JilMxhZTFWPWQFPNklwcONkJ0ZcC3ukFb01zx3FZwkZNkXMcB03TsHr1aqxevbrJD8JxHLZt29bkj+tIeNkFwZ0APRIEL9dddB0AeFFG0umXo/g/b6By83IkDB3f7MdTq8qg+csgepObe9mEkHYqai2WGjF3ageNtZG6ahRPY8xYGykYPbhpk03n02CYFGVIadnw9BtlhslsiAlpFCZjiJmb4ZimmFHSCPG8NwmCHSYdcR0m69KogHmczX6O++M7CikpA+GDu4FjBEwAcOb0havXKajcvByuXqdASmpem0rB5UX44F7oKV0gpWTRLwRCOqAGS/6AM/7Pmzu1ZQd4njbZdEZM16CWF1UXLC8ugFJ8oFaYdPcbBTnNmuamMBlL1WHSHJlkxi563p0I3uWFIDvbZZisS4MBc8eOHa1xHZ0C73Ado+R6tMSRExHK32nUxrzgd80aVeAEEYInAUrpIeihKsgZPcBLjibfDyGkbVHJH9JUTNehllevmTQC5VFhMjUb7n4jzQ44ORATKUzGktHS1BqZNKe5RRm8OwG8ywfBGpmsp4Rhe9cxP6s4xQkSeNF5zJqYFsHlQ+LwC1G2+hMEfvkBntzhzXtMjofoSYQeDiD023bImT0hepOadV+EkJZVq+RPyBqNDINBM0+ikj8kmhUmlSMFiBSbo5MlB8DUOsJkqjXNnU7ramOo5kwCYPSe4gXJCJNOL3jZaUx1d9AwWZcW/0wVRcErr7yCe++9t6Ufql3gPT5olaUQGvFN5s4djsAvG1C+/is4u/WH4PI2/3EdbjBVQfjQHuhJGZBSunaIIXhC2qMml/xxUckfYjDCZBGUYrPG5JECKCUFNcKkZITJ3JE1prkpTMZSzReCADMazAgSeLcPvMsDXnaDl2RwQufuqNekgPnrr7/il19+AQCcfPLJDda9/OGHH/Dwww9j//79FDBNoisBavmRRp3LcTySzpiEws9eQPm6L5Ey+trjemxOlCAIiVDKjkAL+uHI7EklRghpIdUlfyLQlQj0SI1NNlTyhzRCdJgsMDrhFB+wX4hwogQpJRvu3BE1prkpTMZSdJjUwcCB50UjSCZngJdcxsgktWeupVEB8/Dhw5g+fTq+++47+zae53HFFVfgf//3fyHLctT5fr8fzzzzDD788EPouk7rgGrgJAe4Jmx6kpKz4Dt5NCo3LUOozzA4u/Y5vsfnOIieBOjhIEJ52yFl9IDkSzmu+ySkM2tSyR+BSv6QujFdh1pRFD3NXStMdoU7d3j1NHdiBn0vxRDTdTDNnOZmRpjkOB6C2ws+KcOc5nZSmGykBgNmZWUlrrrqKhQVFUXtBtc0DR999BH8fj+ee+45+/bvv/8eDz74IA4fPgzGGGRZxh133NEyV98OcZID4IVjdvU5mm/wWAT2bULZmvnIvOxPMfnm5h0uME1C5PA+sGAVpLRsmjInpB6MMXt9lTGtTSV/SPMxpkMtPxI9zV1cUB0mBQlSKoXJlmSESeOFIXQGYzu3AMHlBZ+QBl52g5Mdxs8y/Rw3S4MB84033kBhYSFEUcTtt9+Oc845BzzP46uvvsLbb7+NhQsX4sYbb8TgwYPx5ptv4rnnnoOu62CMYfjw4ZgxYwZOOIFaFlo4joPg9kIPh8A1cjqMEyUknTYJxYtfR+WmZUgYNiE21yKIENyJUCuLoYWq4Mg8Abzj2CWUCOnIGlfyBwBvro2kkj+kAdFh0pzmPlJ3mJRSsyGnWdPc9II/Vmp2qALTARhhknd6jC44DrdRtFyUKUzGUIMBc9WqVeA4Dk8++SQmTpxo3z5gwABkZWXhiSeewFdffYWtW7fi6aefBgD4fD488MADuOqqq1ruytsx3p0AzV8BNGG9lTO7D1wnDkXllhVGbczk2PR9NwJvAvRICMG87ZAzukP0pdIPGemwqOQPaSmM6VArjhjT3Efyq9dMWsslBBFyajbcfU41enNTmIw5I0yatSZ1zfy55cG73GaYdBlL1ShMtrgGA2Z+fj4SEhKiwqXl2muvxbPPPouVK1eisNBog3jGGWfgiSeeaHADUGcmyG5EGl0Rs1riiIsRyt+BsjWfIO3CO2K6q5SXjXUlkcP7oQcqIafndPodcKR9q7/kT8isSWfiBWO3NpX8IU1ghMliY63kkXyjE05xQVSYlFK6wt17mBEmU7MhJmVQmIwhu1uVUjNMcuCdbgjeDAgONzjZSWGyjTQYMP1+P/r371/nMVmW0aNHD/zyyy/gOA7Tpk3DtGnTYn6RHQ0nO8HxEpiqNGk9peDyInH4RSj770cI7FoPT9+Rsb0uXoDgSYIWKEcozw9H1gngnZ6YPgYhscI01fina4CmQtciYJEImBKCHgkdVfIHNdZGeqjkD2mSqDBZbI1OHgBTQsYJNcOkNc1NYTKmolqf6ho4MAA8OKcbYmI6BKfHGJmUZPr5jhMNBkxVVWvtEq/J4/GA4zhce+21FC4bieMFyOndED64u8l9wt19TkVg9w8oX78Qzu4nQXD5YnttHAfB5YOuhBHM3wE5rZsxhUOv/kgrYYwBumYHSOgadHOHNpQIdCUMpkSAGrMA5j5tcIIAmBttqOQPaQ7GdGgVxVG9uesMkycOqZ7mpjAZU9YyFt1sqWj8+eHAyS6ICakQnF4jSEoOCpNx7LgLrfPmrrbbbrvtuC+mMxE8iRC8KdBCVRCaMErIcRySTp+EwgWzzNqY17XI9VkdB5SiPGjBKjjSu1FpBnLcjPCogmnVAZJpCnQlZHSrUcNgigIGHZw5ic2Y8X1vhEcBHC+Ac3npRQ85brXCZHGBsQGnZphM7lIdJlOzISZnUpiMoegwqRmzDYARJn2p4J0e8LK5ZpK+7u1KzDr55OTkxOquOgWO4yCnZSP02zZjuL8JPzhSUiZ8g85B5U9LEOo9DM7s3Ja5Rl6A4E2CFqxCMG8HHFknHFc3IdKxVYdH658GpobtEUemhI2F9zXWPzLGgeM54/ufF8DxIjiXg8IjiTnGGLTKYrMsUI01k5G6wmQ25NQcCpMtgJkb7Iz+3AAHHpzsgOBLhuD0GYMbEoXJjqDzNMWMQ7zkgJSajciRPIiepCZ9rG/QOQju/cmojXn5n1t0dFFweaCrEYTyd5rTQek0LdHJ2DuvawZINQI9EgZTQnahceMPhjV1zRntDnkB4M3d2BKFR9LyjDBZUr2T2yxebodJXjCmuXudAsnsgCNRmIy56jCpVtdmkBzgPUlGvUnJYfxOoK97h0QBs42JiWlQK4qhR0JNWjPGiRKSzpiEI1+/hoqfliLx1PNb8CoBXpTBuUVEivOhBSshp3cDLzla9DFJ62BMB6wpaytA2msdQ+bIowJmBkM7QHKCUfiZF8GJEoVH0iaiw2T1VDeLBI0TeAFSShcjTKYavbmlpEyjsxKJGbujlaaYsxQcONEB3p0I3uWFIDspTHYyjfoJKy4uxoIFC+o9BqDe45bLLrusKdfVaXAcD0dGdwTzdjR595ujS2+4ew9D1ZYVcJ94CqTkrJa7UAAcz0P0JEELBRD8dSt42QXe6QVvvhLlJZlKG8UZxnRjo0zNNY/mhhmmhGuHR3uYga+etqbyPSROWGHS3sldT5h0nTDI6M1NYbJFMF0zlr1oin0bJ8rg3T7wzhphkr7unVqjnv1ff/0V06dPP+Y5xzrOcRwFzGPgnR5IKZlQy4oguBOa9LEJIy5GKG87ylZ/grSL7myVqWvB6QbgBtNU6IFyaJXGiwwGBl6QwLt85sJs45cML9ZfhYA0X3V4rN40YwVHXY0AkXB1G0OY4REwdlnzAkC1H0kcY4xBqyqtsZPbCJVRYTI5qzpMpmZDSs6iUBNj1d2tVKM/N4fq3/Mun9mfmwYXSG2N+kms2YO8OY734zsDKSkLakVJ02tjOj1IGHExylZ9iMDOdfD0G9WCVxnN6nJSE9M16KEqaFVlAHRjooTjjfU2Ti91UWgkZpbpqS7Xo1WHRyUMKBEw3eg6w+z91jBGHq3d1tTGkLQTTFOhlhdBKT0EpeSgESaLC8DCAeOEmmHSmuamMBlzNZsTGL9bAJ4XjZFJlwe85DJ2dFOYJI3Q4E/njh07WuM6Oj1OECGnd0f44B6I3qQmfay79zAEfvkB5T8shLP7AAju2NbGbAqOF8DJLqDGoCXTdehKCFqw0qg5AwDgwTuNKXajQK7cacpQ2OHRLBKuq4q9UcYuEK5rsH7BczX7X/M8OF4EZKddIoyQ9oLpGtTKYqilh6GUHjL+X3YIavkRs0c0qsNkj4HV09wUJmOO6bo5MqnAGAwAeF4ygmRShrEESnJQeTrSbPQTG0eM2phJ0EL+ptfGPGMSCj99HuVrP0fKOVNa8CqbzghFTkCq3sRk9YtVy4uglB02N45w4GQneNEBCCI40ey8IojG1D9vbSoRzPf5uAukUeFRU6Frqr1RRjdL9Vh/SBkYYNZ4tOs7mgGdo/BI2jHGdGhVZdEhsvQwlPJCQFPNszgICSmQkrLg6nEyxORMSMlZEBPSKEzGGNN1MM0Mk7oOxtWYWUpMt2eWaDkTiSX6KY4jRm3MnObVxkzMgG/wuajc+A1CfU6FM6dvC17p8eM4HpwkA1L1LzS74K4SBMK6WVdRBxgzJoI52NPB1l4UWN1bhBphVJCqwykvmBtWjEBaHU6FJk/R1w6Pir1Rxu4uY43CmJ9PrfDocFN4JB0GYwx6sAJK6WGopYegWCOTZYerW3UCEDxJEJMz4c3uAykpC2JyJsSkDAo0LcB48a6YMyHMWHzNCUaYTEgDL7vBmdPctEyJAICuM4QVDRX+SMMnNwEFzDhj1MbsisiRAoiexCZ9rFEbcxPK1nyKjEl/ane/vDmOA0QJHBo/JcMYM0Kdbv5SVUJGINV1Y0E6M4MoB6BmfUbGjOAnioAggefFqFFTcDD7Woehq2Fz5PHo1oQw7kMQwHECOKeb6oOSDksL+Y0QWXbYnuJWSg9Vb7oBwLu8kJKy4M4dboxGJmdBMqdbSexVh0nFfHFrhEne6TG64Djc1cuPKEwSAJrOEI6oCCsa/EEFlf4IgmFjVqHwcEVMH4sCZhwSE8zamEq4SbUmOUE0amMunI3KjUuQOPzCFrzK+MBxHMCZm1qa+LHMCqa6Bl1TokdNAbOntdldxumh8Eg6BT0SglpmBcjD9tt6sNI+h5NdkJIz4e41GGKSObWdlEmdvlqQtayIqRFjhovjYK1lN8IkbaAk0TRNR1jREI5oqAxEUBVUEAyp1vQfRJ6HJPHwuY3vmaA7toNSFDDjEMcLkDN6IJy/A6yJvywcWb3g7jMcVVtXGi3PUrq04JW2bxzHAwLf5GBKSEfAVAVKeWGtqW2tqtQ+hxMliElZcOb0NUcjjTDJuxMoxLQgxphRsFxVwDTV/Fpz4J1uCN4MCA43ONlJYZLYVE1HOKIhFFFRFVRQGVAQDqt2b3dB4CGLAhI8rfc9QwEzTglOD8TEDKiVxRBcTdsVnjD8QoTytqF09SdIv/guGnkjpBNjumaWADoMtaw6TGqVxdXLPngBYmIG5IwekPqOtMOk4Eum3x8tzF57bo1MGvu5wTndEBMSIDi9xshkExtxkI5LUY2RyVBYNUYmAwrCimYvB5PMMOn0tm23PQqYcUxKyYJaWWq8gm3CrkrB6UHiiIkoXflv+LetgXfAmS14lYSQeMB0HVpVSfXUtrVesrzILHsFgOMgJqRXt04010mKCalxV5GhI4oKk5pRiozjOHCyC2JCqhkmZbPtKoVJAiiqZo9MVvoVVAYjUBRzGRcHyIIASeLhcsRfnIu/KyI2TpAgp3dD+PBeiJ6kJn2s68QhCOz5EeVrP0fkSB4SR0yk9VGEdACMMWj+cjNAVodJtawwqnWf4E2BlJwJZ7f+kJIzjd3bielU17AVWb25dbV6qpKTzTWTTo9RZ1LqHPV/ScMiioawoiEYUlEZjKDKr0DRjM1bHMdBFgU4RAFuR/v4GaaAGecEbxKEikTo4UCTWvpxHIfUsTehcvNyVG5ejlDeDiQOvxDu3OH0ypiQdoAxBj1UFbVj29hwc9iolmDi3QmQkrPg6TfKmNpOzoSYlNmkDYLk+DHN3ICjKXY5NU52gvcmQXJ6wUtOCpMEgPGzrag6QhENgbCKKn8YVUEFqmYsWeE4wCEKcMoCPO24axIFzDjHcRzk9BwEf9sGTtKbVEOREyUkDB0PV6/BKFszH2WrP0Fg949IOn0SpOTMFrxqQkhT6OFArTWSatlh6CG/fQ7vcENMzoK791AzRBphknrJt77qMKna9W45yQHek2TUm5QcxjQ3hclOjzGGiKojFFYRrLFmUtetMMlBFnm4HBIEvmNt2KKA2Q7wshNyajYiJQcguhOa/PFSUibSLvgdAr9sQPm6L1H42Sz4Th4D3+BzabqMkFakKxG77I+1RlIpPQw9UG6fw0kOSMlZcHYfYK6RNHduO720Y7gNGGHS3NHNGWOTnCiDdyeCd3khSA5jRzeFyU5P1xki5prJQMjYye0PGmGSARA4DpLEw+OUwHewMFkXCpjthJho1caMgJeaXquK43h4cofD2a0/ytd9icpNSxHY9xOSTp8EZ9c+LXDFhHReTDPboFpT2+bGG62qpPokQYSUlAlH195m+Z9MiMlZEDxJFCTbCNM1MCVir2VlYOBFGbzbB97phSA7jZFJamXZ6ek6s9dM+kNGwfJASIXGdIABAm/s5Pa4JPCd9OeZfkraCaM2ZjeE8neCE5vf4ktweZEy+lqEeg9D2XfzUbzodbhOHIrEERfTJiBCmojpGtSK4hqjkUbPbbXiSHXbUI6HmJgOOb0bxNzh5oabTAi+VGob2oaYrhnT3KpqdP3iAF6QwLt8xj/ZCV6Sjc5epFPTrDAZMbvfBCMIBBW7N5wxMtm5w2RdKGC2I4LLBzExHVpV2XGHQWd2H2Re9idUblqGyi0rEMrfgcThF8Hd51QaPSHkKIzp0KrKoqa2jf8XVpcAAgchIQVSUhZcPU+2p7bFhDQa8WpjRpg0WyrCmK7kedEYmXR5wEsu8GZ/btK5HauVIoM1Mlnd/YbUj37rtTNySlcEq8qaXBuzLpwoIWHYBLh6nYKyNZ+g7L8fIbD7BySdfgWkpIwYXTEh7QdjDHqwonqjTY12iUyN2OcJniSIyZnwZuca5X+SMyEmZYAXY9tqjTQd03UwLQKmKAB0M0xKRpA0+6LzkoPWn5M6WymGwqo9Mnl0K0XSNBQw2xlOlCCl50A5tB+CNykm9yklZyLtwjsQ2LUe5esXonDB8/ANOge+QefQL2HSYWkhf40AecguUM4iQfsc3uWFlJQFd+4Ie42kZIYU0vbsMKkqgK6DcRw4jjd2ciekgXe4jd3dFPw7vWO1UgQDRNFqpUjlvWKFAmY7JHpToLmLoYeD4B2x+UPHcTw8fUfC2f0kYxPQT0sQ3PsTks6YBEeX3jF5DELagh4JVU9p2x1uDkEPVtnncLILUnIm3L0G2+V/xKRMWpccRxjTq6e5dc2sWi6Ad3ohJqSBl93gzGluGm3q3OpqpRhRNWOOO45aKXZ0FDDbIY7jIKd1QzB/OzjdEdONAoLLh5TR1xmbgNZ8iiNfvwZ372FIGHExBKcnZo9DSKwwpkMPBaAHK6EFKqAFKqCWF9phUvOX2edyogQxKQvOnH5mUXKzlqQrgUJJHDHCpFFr0tgsxcww6TG64Dhcxm5ukaYuOzurlWIwrKIqUKOVovltEc+tFDs6+oq3U7zDBSkpC0rpYYieptfGbIgzOxeZl/8JFT8tRdWWFQjlbUfCiIvh7j2MfqGTVmEHx0AFNDM86maAjHo/WFljo42JFyAlZUDO7Gn325aSsyB4k6iTVZxhmmp0vzF3c4PjAPDgnS4IvkwI5jQ3hcnOzep+c3QrRVXX7ZHJ9tZKsaOjgNmOScmZ0CqLoauRFlljxIkSEk89H+4TT0HZ6k9QtupDBH75AUlnTIKUSJuASPNEBce6wmOgwh6NtEv91MDJLgjuBAjuBIhdTjTedvkguBPAW297k6jwdZwx1kuaBcvN55UDjKLlTi94hwu87DKCpCTTC4FOzOp+E67RSrEyqEDXmNnT3eh+43SIHa77TUdCAbMdM2pj9kSoYCc4T8utO5KSs5B20Z0I7FyH8h8WovDT5+E7eTRcvYZATMqgUQUCoDo4Vo8sVkALVB4VHI3b6gyODrcdFMWkDDtECi6fERzNt2njWXxjTLe730A3d+Qy4/cV7/RA8CYbo5JWkKQXAp0aY8zeyR0MKagMKkYrRcYABvBW9xtH5+h+0xY0neFwiR9bdhXF9H4pYLZzgtsH0ZcKLVAJwdVyayQ5joen3yg4uw9A+drPjfqZm5aBdyfA2bUPHNl94OjaB4LL12LXQNqGERz9ZlgsrzHiWNn44GiGQykpozosuhPAm4GSgmP7wxgDrPqSmgrAeu4FcE4XRF8CeKcbvCgbYZKe307v6O43VX4F/pAZJmGESVkUOk0rxbagqjoOFvuRX1iF/MJK5BdW4eARPxRNhxIoafgOmoACZgcgpWVD+3UbmK61+GiA4PYh5ZwpUE+9AOEDvyBc8AuCedsR2L0BACAmd4HTDJty1glUHiSOVQfH6LWN1SOOlfZ0dV3BkXe47bBYKzhaU9UUHDsEu1C5pgCM2bdzksNso+gxa0vKtFaSADDCZJi637SpsKLhYFEV8ouqzEBZhYPFfui68Sy4HCJyMrw485SuyEn3wYEqPLAsdo8ftwFzzZo1mD17Nnbu3AlFUTBgwADcfvvtOOussxp9Hxs3bsTs2bOxceNGhEIhdOvWDRdeeCFuu+02OJ3OWuffdNNN+P777+u9v//85z/o0aNHsz6flsSLslEbs/A3CJ7EVnlM0ZcCse9IePqOBNN1KCUFCBf8gtCBX1C1bTWqtq4EeAGOzBPgMEc4pdSutK6qFTDdDI7B6HWNNdc2aoHKxgVHc4e14K6xxtEacaTuNB1O1DpJXTfXuwGcIBoh0plWvU5SlGh6mwCg7jfxIBhWUWAHSWNksrA0YL8e9Lok5GR40b9nN+RkeJGT4UVKgjPq+Th8SI3pNcXlX4j58+dj+vTpkGUZo0aNgq7rWLt2LaZOnYoZM2bgmmuuafA+/vvf/+L222+HpmkYPHgw0tLSsGXLFrz44ov45ptvMHfuXHi90TXuduzYAbfbjbFjx9Z5nx5P/JbpEX0p0CqKoUeCrV4EmuN5yGndIKd1g2/wudCVCCKH9yFcsAuhA7+gYsPXwIavwTvccHTtbQbOXIje5Fa9zvbODo6B8qiSPDV3Uxshsqru4Oj02FPSUnKWGRyPCo8UHDsFxhhg7d7WqoMAx/HgnW4IniTjhYZkjkjS9wQx1dX9JhhS7bJA1P2m5fmDih0i8wuNEcojZdUNIhK9MnIyfDilT7oRJjN9SPS0/vMRd781CgsL8eijj8Ln8+H9999Hbm4uAGDz5s245ZZb8Pjjj2PMmDHIzMys9z7C4TD+/Oc/Q9d1vPzyyzjvvPMAAKFQCPfeey+WL1+OOXPm4N5777U/pqCgAGVlZTj99NPx7LPPtujn2BI4joec3g3BvO1GSY82HCnkJRnOnL5w5vRFIgAtUInwAWN0M1ywC8F9mwEAQkJa9frNLid22u4oRnCsqmOU8aip62MGRyMoSslZ5trG6vBIwbFzszbc2NPbHADw4GQneG8SeIcxvc2LMiCIFAqI7VjdbzgAgmB1v6Ew2VIq/JHoMFlYidLKsH08JcGJnAwvRpyUiZwMH3LSvfB54mNpWtz9xZk7dy4ikQh+97vf2eESAAYNGoSpU6di1qxZmDdvHu6555567+Pbb79FWVkZxo4da4dLAHA6nbjrrruwfPlyrFq1Kipgbt++HQAwYMCA2H9SrYR3uCEld4FaVgjBHT+bbQS3D+7eQ+HuPRSMMahlhWbg3IXA7g3w7/gOMAOyo2sfCL4Uo1+wVbZEdoF3OMGJjnb1S6xWcKyvJE+oKmpdm6U6OCZASulSY5TRWufoo+BIbEzX7HqSTNeMcpLgqtdJOtzgJaexc1uUaLkKiULdb9oWYwxllWFzRLI6UFb4IwCMQJ+e7MIJXRNxljnFnZ3uhdsZv2vc4+4v06pVqwAA48aNq3XsvPPOw6xZs7By5cpjBszx48djxYoVdR7z+/0AAFGM/tS3bdsGoH0HTACQkjKgVRaDqUpcbq7gOA5Sciak5Ex4B5wJpqmIFP1mr9+s3LS0zrBlfDAPXnaagdMIn0YAdRp/PGWn0eFDrnHMOs/hitl6MaZrZnCsv35jQ8HRGmWUUrpWh0UzPBoh0kvBkdTJ7nKjKWCa2TIRDDwvGbu2fV7j58Sa3qZ1kuQo1P2mbemMobg8FDUyWVBYCX/IWK7Cc0Bmqgd9uyeb6yV96JrugVNuX89HXF0tYwy7d+8Gz/Po1atXreM9e/YEz/PYvXs3GGPHHM3q0qVLrduKiors6e9LLrkk6pgVMMvLy3Hbbbfh559/RjgcxsCBA5u8uagtcYIIKa0bwof2QBST2vpyGsQJIhxZveDI6oWEYROgK2HowSrokSD0SBAsEoIeDkCPhKCHg2Dm7XokCD0chOYvhx4JQA8Ha3dzOfqxRAmcXB1Eq0dHrUDqBGcGVXBcVBkeY8NMZQPB0WtPSVcHxxprHF0UHEnjHXOdpMMFwZNgTm/TOklSP6ssEHW/aRu6zlBYGojafFNQVIVQxPh7JfAcuqR5cHLvNOSk+5CT6UWXNA9ksf2/MIyr30jl5eWIRCJISUmBLNdeQyCKIpKTk1FcXAy/319rk0593nrrLSxbtgwbN24EANxxxx2YMmVK1DnWFPmjjz6K3NxcDB8+HPv378e6deuwbt06PPTQQ7jpppuO8zNsHaI3Cao3GVqwqt31D+clB3ipeVMwTFXs4GmE02CN90NgVlA1b9f85dBLD9lBtm5c9Yij2wcptWutNY5WPUcaKSLNFd0u0VonyRmj8+7E6jJAkgxOaLmmCqT9qqv7TVVQgXZ09xtZgCBQmGwJmqbjUEl0mDxQVIWIaqydl0QeXdM8GNYv097JnZXqgSh0zOUqcRUwg0FjF5TLVf9mD6u8UFMC5qJFi/DTTz8BAGRZxqFDh1BSUoKUlBQAQElJCQ4dOgRRFDFz5kxcfPHF9scuXLgQ999/P2bOnIkRI0agf//+zfnUWp2cmo3Qb61TGzNecKIEQZQguJvem53pOpgStsMndN0YjaTgSGIoap1kXe0SnR5aJ0kaVG/3G726YLkxxS1RK8UWoqg6Dh6pitrJfeBIlRHoAThkATnpXow6uYux+SbDi4xkd6d6PuIqYPJ8w79MWX3r847hxRdfRFJSEvbu3YvnnnsOCxYswLZt2/Dpp59CFEWkpKTgu+++Q0VFBXr27Bn1sRdeeCF++uknvP322/jggw8wY8aMJj9+W+AlB6S0HEQKfzUX99PC7GPheN5Yr+nonDvZSWzV2S4RAM9LRmcjnwdCzb7b9CKG1KNm95tASEGl1f1GZ2aNSep+09LCEQ0FR6rXSuYXVuFQsR9mnofbISIn04vRp+TYI5OpSa5OX0A+rgKm2+0GYJQZqo917FijnEezShr1798fr776Kq644grs3LkTixYtskcrU1JS7BHNo51zzjl4++238fPPPzf6MeOBlJgOXnYiUpQH1V9m7DimP2SExEztdZLMmOHm6miXaE5vE1Kfmt1vAiEFFQGj+40Ooy+3wPOQqftNiwqG1Khd3PmFVSgqDdgvEr1uCd0yfBjQK9UIk+k+JCe0rwonrSWuAqbX64Xb7UZpaSlUVa2101tVVZSWlsLhcCAhoenToAAgSRIuuOAC7Ny5E9u2bYuaDq9Peno6AKOOZnsjuHxwdusHtbIYSlEBAIB3eemHgZAmqrddYtQ6SZnaJZJGie5+Y5QGCoYU41uLM1opypIAr1umMNlCqgKRqDaK+YWVKC6v/juf5HMgJ92LoX3T7WluqvnZeHEVMDmOQ+/evbF582bs378fvXv3jjq+b98+6LoeVR+zLosXL8ayZctw2WWX4bTTTqt13NpApKrGzsw1a9bg008/xYABA3DzzTfXOj8/Px8AkJWV1ZxPq81xHA8pIR2iOwlK6SEoZYXgZUenLWxOyLFEtUusuU6yrnaJkkzrJEmDana/qQoaNSYDNbrfUCvFlsUYMwuWV2++yS+qQlmNguVpiU7kZPgwamAXc2TSC687PgqWt1dxFTAB4KyzzsLmzZuxZMmSWgFzyZIlAIDRo0cf8z727duHBQsWIBQK1RkwrVqbVs3LUCiEzz//HBs2bMD1119fa+R0wYIFAIAzzzyzWZ9TvOBECXJ6N4i+FESO5EPzl4F3eOKyXiYhLe3Y7RI9ELzJEBxuWidJmkTTdIQixprJqkDE6H4TUaGbpfWsVoo0EtYyGGMorQgfNc1dicqAAsDI9BkpbpyYnWhPcWene+Fyxl0cavfi7is6adIkzJkzB6+//jrOPPNMDBw4EACwZcsWzJkzB06nE5MnT7bPP3DgAILBIJKTk+01lJdeeileffVVLFq0CF988QUmTpwIANA0Da+88gq+++47ZGdn4/zzzwdgBMfs7GwUFBTgmWeewQMPPABBMP6YfPLJJ/j666+Rnp6OK6+8sjW/FC2Gd3rgyM6FVlWGyJF8IBIC7/SAa8QmK0Lao+p2iSoAq92mAM7hhOBNNl5oSbKxVpJecJFGslopBsNGK8WqQAThiFn8ngGiaLVSpE2WLUFnDEfKgtE1JgurEAhXFyzPSvWgf88UZJtT3NlpXjhkerFYF2sHfKxwrDnbslvYe++9hxkzZkCSJIwaNQqMMaxduxaqqmLmzJm49NJL7XNvuOEGrFu3DtOmTcPdd99t375gwQI89NBD0DQNAwYMQFZWFnbs2IGCggKkpKTgjTfewEknnWSf/+OPP+K2225DIBBA9+7d0a9fP+Tl5WH79u1wu9148803MWTIkEZ/Dvn5+Rg7diyWLl2KnJyc2HxhWgDTVChlhVBKD4EXJfAOd1tfEiHNwpgOaFp1KSBdj26X6PQYG24kJ62TJE12dCvFykAEilpdsNxqpSiK9EK9JWj1FCwPWwXLBQ5d04ypbWsnd5c0LyR6Puqk6wwRVUNE0cHMLUylRw7j97ddFbPcEncjmAAwZcoUdO3aFXPmzMGGDRsgyzKGDh2KO++8s84p77pcdtll6NGjB1577TX8+OOP2LVrFzIyMnD99dfj9ttvt3eWW4YOHYpPP/0Ur776KlavXo3ly5cjOTkZkyZNwl133YVu3bq1xKfa5jhBhJza1Zg2Ly6A6i8zyhqJtPaExBfGGKBrxmikrgGa2bmJY+bfeB6c7DA6MznSqF0iabaarRStvtyKGt1KUZao+01LUTUdh4r9UVPcB474jecAgCzy6JruxfD+mfbmm6wUN4QOWrD8eFlF+BVFg8aMigQ8z8HnlpCW7IbHKcIpCzjsVWL6uHE5gtkRtJcRzKNpgQpEivKgK2GjrSH9YSatxA6Qumr02NY1gAHM7LVtB0jJYbT4lJzgBBGcKBptEnmRRiRJkzDG7JHJY7VSlEWewksLiagaDhaZYdJcN3nwiB+aWWTSKQvINkclu2UaYTI9yU01P49BUXVEFA2KpsP6MrldEhI8MjxOCQ5ZgEMSav2+jHVuicsRTNJ2BHeCUdao4giU4gMAJ4B3uukPNzluxw6QRis7TpKNHdqSA7zspgBJYqauVoqVQQX60a0UHWKn6rbSmkIRFQU1ygIVFFbhcEl1wXKPU0R2hg+jh1oFy31ITXRSmaZjUDUdiqIjYs3oMMDlFJGa5ITHJcEpi3BIQpsEcgqYpBaOFyAlZULwJEEpOQi1ohi8wwlecrb1pZE4VmeABOwCxUcHSE5yGptqzBBJAZLESr2tFK3pQbOVosdB3W9aSiCkoKCoCnmHzTWTRVU4Uhq0fx8keGTkZHhxcu9U5KQbI5NJPipYfiyazqAoGsKqBsYYOBjfxz6PjASPBy6HESbjZbSdAiapFy854MjsCTExHZHC36D5y43d5gJ923RGDQdIGCOPkhOcx0kBkrSKmq0U/SEFVVYrRVbdl1sWqftNS6oMHFVjsrAKJRXVBcuTfQ7kZPhwar9MYyd3hheJtLP+mHTGjJFJVbN7zAsCB59HRobLDbdThEMW43oTEyUF0iDB6YGzW1+oVaVQivIBMCNoUoHpDqU6QBobaY4ZIN1Oo4ONKIETJONFh0ABkrQszQqTEQ3+oILKoNFK0foeFTgOErVSbDGMMZRXRaKKlecXVqK8KmKfk5bkQvdMH04/uQtyMn3ISffC46LNUMfCGIOi6YgoOjTd2MjEcxw8TgmpiQ64XTIcsrEWuD39jqWASRqF43hIvlSI7gQoZYehlhXBih6MceB4ztgQxAvV/29HPwidQV0BkhmNswGYAVKUjd3X7kQKkKRNRbdSVFDpjyAYri6IT91vWhZjDMUVIXOtZPXIZFXQLFjOARnJbvTOSbJ3cmene+FyUKxoiKrqCKsaVKsyAQPcThHpSS5z3aRRpaC9L9+g7wTSJJwgQU7NgZSUZXZAMcvGqBHoahgsEgZTw2DhIBh0WNGFMVAIbQVR4VFTowMkY+baRwcEd6KxI9uawhYkCpCkTaiaDkXVzZ2vKgIho2h5sEYrRav7DYXJlqEzhiOlQeTVCJIFRVV2oOd5Dl1SPRjQK9Vuo9gl3QuHRFVGGmKNukdUza5M4JAEJPkcSHAbI5MOuWNuLKOASZrFCCX1f/tE1Sw0A4+uRqArIUCJQFeMEFo9AWv97HHgBIECTz2iA6RmFBe3ionXCJC87IoKkDCfL1rWQFobYywqRIYjGoIRFcGw8U/XmTWIAwAQBav7DYXJlqDpDIdLjqoxWeRHWDGWxIhmwfIhuen2yGSXVA8VkG8Eq3i5oujQwcCYUYA/wSPD53HD6TBGJ8U42YTT0ihgkhbBcZwdaixHv9atXThbNUJoOAg9HAALVoGBmYMYzNgkYgWlDlqfk5nBEeZGGitAWiPBnCCBk51GeJSd4AWztSEFSNKGdL06REZUDeGwimDYCJKhiLkUw8SBgyhwEAXaxd3SVFXHQbtgeXWNSUUzC5ZLPLLTvRgxIMvogJPpRWYyFSxvDKuGakTRoOo6OI4Dz3Hw1ihe7pCMqe7OigImaTMNhVDGmDENryrGFHwkBBYOQA8HoGsq7PkGcNUjqnE+6lkrQIIBdog2Pw/ZBU72VgdIQQREiQIkaVOazqCY68YUVa8xCqkhHFGjzuU5I0CKAg8vbbhpFRFFw4Eia+ON8e9gsd/egeyUBeRk+HDG4K52jcn0JBcF/EaKCpPmbW6XhIwE9zGLl3dmFDBJ3OI4DpwoA6IMwBN1jOkamBoBUxXoStgIneFgm4961hUg7WuxAqTkAOf2gq9RxocCJIkHmqYbLeXMP6ahGlPZilK9IQEABN4MkSIPB62NbFWhsFojSBojk4WlAVgDxR6XhG4ZXvTvmYNsc5o7NcFJz1EjaeaO7oimGfsHADgdIpITHPB55DYtXt6eUMAk7RLHC+BkFyC7jj3qaQXPcBC6fvyjnszchQ1zWt8odgvUCpAuD3jZFb0GUpQoQJI2Z6yF1OwQGQyrCIU1+MNK9XpI88dE5I1RSIdIfbfbij+o2OWACsyRyaKyoH080SMjJ9OHU/qk2yOTiV4K/I2l25twdHsph1W83Oc2ipc75fgpXt6eUMAkHUqtUU9vsn2s7lHPAFiw0o6d9qgnx5lhkqHm72mOF8HJDnAOjzGFLTmiN9F00LWhpP2w1oZZ/0KK0c0mFDbWQ+o6s3dnA8amGonWQ8aFCn8kqlh5fmElSivD9vGUBCdyMrwYflImctK9yM7wIcEjt+EVty96jXWTmvm7nec5JLhlZLhls3i5AEmk3+OxQAGTdBpNGfVkmgZeNoqK2+GRAiSJE7puFGa2RiOtTTX+sGqsh6xRHZ+HMZUtiBwVII8TjDGUVYbNYuXVgbLCX12wPD3ZhZ5dEnDmYJ/d/cbjpFHkxqqreDkHwOuSkZrggMspwekQ213x8vaEAibp9I416klIW9GsAGn+PxhSEQwrCIY1KKpWo8CX2Vvb3FRD6yHjC2MMR8pDUSOTBYWV8IeMjVEcB2SleJDbPdmc4vYiO80LJxUsbxJVM0YmFVU3175zcDtFpCU54XXJHaZ4eXtC38GEENJGoouMawiEFITCxrpIVdPtqWwGaz0kB0nkqVtKnNJ1hqKyQNQUd35hFUIRo8akwHPokubByb3TkJNu1phM83TqUjbNEVW83CSLAhJ9DnhdElwOscMWL29P6LcUIYS0kMYWGdfNhktWaR+ng/44xjtN03GoJBAVJA8UVSGiGtOxksCja7oHw/pl2lPcXVKoYHlT6YxBUYwXYDozCrtJAg+fR0KW221MdXei4uXtCQVMQgg5DjWLjCuqjpA5jU1FxjsORdVx8Eh1fcn8oiocOFIFTTOeW4ckIDvDi1EDu9g7uTNS3PQioYlqFi/XzJ8bq3h5arILbnNHN434tg8UMAkhpAENFhmvuamGioy3a+GIhoIj1Wsl8wurcKjYD7NeOdwOEdkZXpx9SrbdSjEtyUXPczOoqo6wqhnLQWCsCHE5JaQnu+F1U/Hy9o4CJiGEIHpTTTjSyCLjtKmmXQuGVBQURe/kLiwJ2K8XvC4JORlenHRCKrplepGT7kNygoOe72aoWbzc+gI7HCKSfUbxcock0LrJDoYCJiGk06hZZFxRNQRCqr2pRtGqW8BRkfGOpyoQiWqjWFBYiSPlIft4kteBnAwvTsmtUbDcQy8emkPXGSKqhoiiQzenuuWjipc7aN1kh0cBkxDSYdRaD9mIIuPWphoPjZx0CIwxs2B59eab/KIqlNUoWJ6aaBQsHzmgi70Bx+emguXNUbN4uRUmeZ6Dz2VMdbudxrpJKl7e+VDAJITEPcYYNJ1BVXWoOjOnszWEIzrCirEOMqLo9louAFRkvBNgjKG0Ihw1xZ1fWInKgALAeC2RnuxGr66J1TUm071wU8HyZrGKlyuKDlXXAbP6gdclIyXBATcVLyc1UMAkhLQpa9TR+GeEx7Cqm6FRQyisQdH0qN3YFoHnIfIceIGj0j4dnM4YisuCyC+sQp45MllQWIVA2ChYznNAZqoH/Xqk2FPc2eleOGQaOWuumsXLwRmB3ekQkWoWL7c24VA1BFIXCpiEkBZhjzrWCI7HHHWssYmG5zkIPAeB5yGJPJwy7STtTDSdobC0usZkgTnNHbYKlgscuqR6MKhPuj0y2SXNA5mmYZtN0xkURUPYKl7OAFkSkOB1wOc2i5dLAgRaN0kaiQImIaTJGhp1DEc0RFQadSTHFlE0HCkLoqgsiKJS4/+HS/w4cMRvjJoBkEQe2eleDO9vFCzPSfchM9VNG0SOw9HFywFjPbLXIyHT7bY74UhUFJ4cBwqYhBDbsUYdI4pRuqcxo46iwFP9OgLAKE9TXBEyA2TADpJFZcGojTcAkOCRkZHswuknd7FrTKYnU8Hy42EXL1d1aLrRp1vgeKN4eaID7hp9ugmJJQqYhHQSNUcdrQ0zNOpIYkFnDGWVYXMkMjpElpQH7SLlgFGoPD3Zhd45SUhPciE9yYW0ZOP/Tpn+JB2vOouXO0SkJ7mM4uWSAActOSGtgH6aCWnnjmvUkeMgCDTqSBrGGENVQEFhzVFIc1SyuCwEpcYOflnkkZ7sRna6F0Ny0+0gmZ7shsdFO7hjRdOMkcmIahYv5wCHbBYvd5ubcKh4OWkjFDAJiWMNjjqa66ho1JHESjCkorAsgCNlQRSWmiOS5miktckGMLoZpSa6kJHsQv+eKUhLciEjyY20ZBcVKG8BNYuXM/MVoiTw8Lll+DweOM0+3bQ2lcQLCpiEtIG6Rx2NFoVNG3Xk4HBJ9MecNIm1uaawLIgj5ihkYWkQR8qCqAoq9nkcgOQEJzKSXejZJQHpyW5zJNKFZJ+TXrS0EMYYIqoORdGgMQYws3i5W0JashseKl5O2gEKmITEGI06knigajpKykNRo5FHzGntsqrozTWJHhlpSS4MPDHNDpAZyS6kJrgg0k7iFmd1wlE0HdaPvNslITnBDY9TsutN0gtJ0p5QwCSkCY4uzVPXqKOm62DmeigadSQtyd5cY01j22sjAyipCEVvrnEaGz16d0uyQ2R6kgtptLmmValmJ5yIVl1v0uU0ipd7XBKcskjFy0mHQL9VCDHVLOdhFBzWEYmoRlHwBkYdBd4IkDTqSGKNMYbKgHJUiDTePlIWhKpVf0/KEo/0JDdyMnwY0jfD3liTnuSizTVtIKp4ufk0SRIPn0dGgsdDxctJh0YBk3Q6um7007VK8wRCCvxBBcGIBqYzMDBw4GjUkbSqQEiJDpA1Sv1Eba4ROKQlGqOP/XumID3JbY9GJtDmmjZjFy9XNejm0LEgcPB5ZGS43HA7qXg56VwoYLYSYwpVg8BzNJLQSmruuowoKvwh1QiSZu9iiygY7Qi9Lgk8/XEmLSgc1bkmOkT6a26u4YCUBCfSk1w4oWtijTI/xuYamj5tW4xZL1KNJTGAsQzG46ouXu6QBcgiT4GfdFoUMFtYSUUIxbuK7N3ADEC/HsnwumUARgjyhxQwBrMLijFqxpvTrqRh9jSUOSLpDynwh1REIqoxK2Wuh5QEHpIoIMFNozyk5dTcXHN0vcjyqkjUuYkeGenJLgw6Mc0ehUxPdiM1wUmba+KIataaVFTdXlttrWk11k0anXAo+BNSjQJmCwuEFLh8QKLXAQCIqBp2/laG3jmJCIYUHCoJQNV08Bxn5CAOYFZlGp4zQxEHUeQhiwIk0QhJosCb07ec3aKvo4dSu6iwoiEY0RAMKqgKKogomvFLHwAH62vGw0FBkrQQXTc21xxdL/JIWRDFFSHUXKrrcYpIT3ajT7dkZCS7jCltc3ONQ6YyM/FG0xkiihZdvFwSkOhzIIGKlxPSaBQwWwGP6l9EsiiA6cCu30rB8xy8TqneBd6MMeg6g8YYQmENgaBqv2+cYJ5o3b1ZK00UeEiSEbJEwXh0nufAgQPHAzwAjufBc+btHGfcBWcENCuTGR8DwDxu3V7z/Kj3a5xjHeS46suzDh597tEhUDXXR0YUHaGwMRrpDylQFN3+YB5G6JZEHi4HfRuT2GOMoSIQMetEBlFYWh0mi8ujN9c4JAFpSS50y/RhaL8MpCW5kWG2QPQ4aUlMvNKZESYVRYdu/kIVeR4JHhk+jxtOh0TFywlpJvrL3AaMV8ANj1xw1iaTJty3boZSXWMIKiqMGr1G3wdm/seaNmYwjlmv0mtU1THULLVjBUWOq74P6zqtj6sj8Ea9jaM+6KhzrPWPOmNgjIHjjI02ksjDIQpwO+gPNYm9QEhBUalZdPyoae2wUntzTUayCyedkIIMc3d2WrKLll20A3aVCEWDquv27xdvjeLlDsmY6iaEHD8KmB0MzxlFupuUSuMAqxFaaaMNiTV7c83RO7RLA/CHqjd9cRyQmuBEmrm5JiPZZbdATPI5aI1dOxIVJs3b3C4JGVS8nJBWQQGTxIWa0+yENAVjDBFFR2UgYv5TUFweXS+y1uYar4z0JDcG9UmvUXTcjdREJ02HtkOauaM7omlgzJgUcTpEJCc44PPIVLyckDZAAZMQEncYYwiEVVQFIqj0K6gMGsGxygyRVQHFDpOVgYixu/coHpeE9CQXcrslGwEyucbmGpoGbbd0exOObjc+sIqX+9xG8XKnTMXLCWlrFDAJIa1C0xn8QcUMiNXh8OjAWBWIoDKo2MWqa+I5wOOS4XNL8LqN/tk+twyfW4bXLZlvS0hJcMJNm2vaPb3GuklNZ+DMjYkJbhkZbtksXi5AEukFAyHxhgImIaTZVFW3Rxft4GiOOFYdFSD9QaXWXi/A7HZihsREr4ycDC+8Lsm+zeeR4DVDpZuK4XdYOmNmvcnq4uUcAK9LRmqCAy6nBKdDpOLlhLQTFDAJIba61jNW1Xi7MhBBlTkKWemPIFSjhWFNDkmwRxmtbjTW6GL0aKMMp0wbLTobTdONkUlrmpszNve5nCLSvC54qXg5Ie0eBUxCOrhYrGcEjM4lPrcMr0tCdpoXvh7mVLWrOjha4ZFKvRCgujSQoupQNd1uJiGJvLFG1iXD5RAhSzzt6Cakg6GASUg7FIv1jBwHeyq65nrGmqOL1iik1yXR7mpyTNaopKLq0M1RSQ6AyykhNckJj9N44eGgwuWEdAoUMAmJEy2xnjE73VtrdNEKjrSekTQHYwyKpkNRjFFJiyTy8LglpLlkuM1RSVmkKW5COisKmIS0kNZYz+itsa7R55LhdNA0I4kdTWdQFM0YlTRf0nAAXA4RKYkOeF0yZLP7jSTSqCQhpBoFTEKaIGbrGR2iHQ6z07zwdo+elq452kjrGUlLs0clVR1qje9ZUeDh9Rijki6zxS2NShJCGoMCZgv7YtVeHKjIgyTyEHgOAm/01pZEwV7YLksCXA4RLocIt1OE2yHB5RThdohwO423qTRHy1E1HVVBBf5A3dPRMVnP6JLg9cj2Jhlag0baiqYzKKoGRak9Kpnsc8Drlu3fSzQqSQhpLgqYLSw9xQ1dlMBxxi92VWN2oIlUaIgoGsIRDaGIijpyi03gObidIlwOyQihTiuQSnA7xFqB1H7bIUIUuE4VTnXGEAgZo4xVNUJjVdAIida0tPG+gmBYrfN+rPWMXpeEBE/1esaoaWlrPaNTolEdEleiRiU1HdaiXVHg4XGJSEt2G6OSEpUDIoTEXtwGzDVr1mD27NnYuXMnFEXBgAEDcPvtt+Oss85q9H1s3LgRs2fPxsaNGxEKhdCtWzdceOGFuO222+B0OmudHwqF8Pbbb+Pzzz9Hfn4+fD4fxowZg3vuuQcZGRnN+jxGDciCx5cOl/PYX2rGGMIRDYGQikDYCD3+kIqg+X7AfltFIKSgwh/BoeIAAiGl3rV7Fp7n4DT/kDhlo/OFUxbgkEXz/8b7Vr9e65y6zpOE1h9JZYwhrGhGSDRHEY3AWD2yWBWMDpCsjrDOwWgf6HFJ1VPT1nS0q3q3tNct0XpG0q7Yo5LWDm5Ej0p6XBIc5s83jUoSQlpDXAbM+fPnY/r06ZBlGaNGjYKu61i7di2mTp2KGTNm4JprrmnwPv773//i9ttvh6ZpGDx4MNLS0rBlyxa8+OKL+OabbzB37lx4vV77fEVRcNddd2H16tXo0qULRo8ejb179+Kjjz7CihUr8OGHH6Jr164t9jlzHAenQ4TTISIFtcPvseg6QzCimiFUMUOoEUiDYRVhRUUooiEUqR4tDYRUlFSE7WORiFbnruSj8TwHpyRAlgUIPAee48Dz5j/rbbOdm8AbI6dCrePVH3P0ORyAYFi1Rxn9ZphUtLrXMjplwQ6GaYlO9MxKgNct2YHRqtPodUvw0CgjaecYM2ZBIqoGRdPBAWAMkKxRyUQnXE6JRiUJIW0u7gJmYWEhHn30Ufh8Prz//vvIzc0FAGzevBm33HILHn/8cYwZMwaZmZn13kc4HMaf//xn6LqOl19+Geeddx4AY4Ty3nvvxfLlyzFnzhzce++99sfMnTsXq1evxpgxY/DSSy9BlmUAwPPPP4/Zs2djxowZmD17dst94seB5zl4nEaAAlzNug+dsRrT9RrCERVhRUMorCGkGO9bt4cimt0bWNcZdAbz/8b7ms7AzLdVTUe4xrHq/5sfc9T5OmNw2RtgZHRJ9dQeZbQCpEum0RjSYem6GSStUUlmrPV11hqV5KkXNyEk7sRdwJw7dy4ikQh+97vf2eESAAYNGoSpU6di1qxZmDdvHu6555567+Pbb79FWVkZxo4da4dLAHA6nbjrrruwfPlyrFq1yg6YjDG89dZb4DgODz/8sB0uAeAPf/gDFi9ejOXLlyMvLw/dunWL/ScdB3iOg1MW4ZRFJLb1xRDSiVijkoqqIWKOSgLGC0drZN5lFimXJWPmgBBC4l3cDf+sWrUKADBu3Lhax6ywuHLlymPex/jx47FixQo8/PDDtY75/X4AgChWZ+udO3fi8OHD6NevH3JycqLO53ke5557bqMelxBCjkXXjbXWlYEIyv1hVPjDqPBHwMCQ6HOgZ1YC+vZIwaDeaRiSm4E+3ZKRleZFotcBl0OkcEkIaTfiagSTMYbdu3eD53n06tWr1vGePXuC53ns3r0bjLFjbsDo0qVLrduKiorw7LPPAgAuueQS+/bdu3cDAPr06VPnfVnXsmvXrsZ/MoSQTqvmqKSi6fbvK2tUMiXRATeNShJCOrC4Cpjl5eWIRCJISUmJmqa2iKKI5ORkFBcXw+/3R23SOZa33noLy5Ytw8aNGwEAd9xxB6ZMmWIfLyoqAgCkp6fX+fHW7cXFxU36fAghHZ+uM7MHtwaNMXvjjcMhItHngMcpVVdhoLWShJBOIq4CZjAYBAC4XPVvVLHKCzUlYC5atAg//fQTAECWZRw6dAglJSVISUkBAAQCgWM+rvWY1nmEkM5JVXV7BzeMfTcQBGNUMjnRAbdDMrrd0KgkIaSTi6uAyfMNLwlldRU4bMCLL76IpKQk7N27F8899xwWLFiAbdu24dNPP4Uoivbj1jflbj1mcx6bENL+HD0qCQAwRyV9Zkcmp1zd7YbqpRJCSLS4CphutxuAUWaoPtaxY41yHs0qadS/f3+8+uqruOKKK7Bz504sWrQIF198sf24oVAoZo9JCGkfVNXodhPRNLvbDc9z8NQYlZQlHg6ZNtkQQkhjxVXA9Hq9cLvdKC0thaqqUTu9AUBVVZSWlsLhcCAhIaFZjyFJEi644ALs3LkT27Ztw8UXX2x36Tly5EidH9PQGk1CSPzTmTkqqdQelfR6JPjcbjhlkUYlCSEkBuIqYHIch969e2Pz5s3Yv38/evfuHXV837590HU9qj5mXRYvXoxly5bhsssuw2mnnVbruLWBSFWNHtTW/Vm7yY+2Z8+eqPOaQhIFhFUNEX91O0enZLRiBIxpd2YWUKY/aITERs1RScaMtolWQ4Jkn7WDm4dDEiAIcVetjRBC2r24CpgAcNZZZ2Hz5s1YsmRJrYC5ZMkSAMDo0aOPeR/79u3DggULEAqF6gyYVq3NAQMGAABOPPFEZGdnY9u2bTh48GBUiSNd17Fs2TJwHNekPuiWjGQXunRNh6YzaJqOUFhFYWkQZVVho6AyB4g8b3e4AQdw4MCOatzIc0e1WKzxNiEdgfViS2dGRyfGAGZ1f2LGcaujjbFV2/y/fQfV78uiAK9HgtflhtNR3YObXsQRQkjriLuAOWnSJMyZMwevv/46zjzzTAwcOBAAsGXLFsyZMwdOpxOTJ0+2zz9w4ACCwSCSk5PtXeGXXnopXn31VSxatAhffPEFJk6cCADQNA2vvPIKvvvuO2RnZ+P888+37+faa6/Fc889h7/+9a94+eWX7XWZL7zwAvbv34/x48eje/fuTf58OI6DKPAQBQCSALdTQkqiC6qm2z24LZrdOlG3WyhqmvFHNaLqUFUNiqJD0XSjBWNYg6pX9+i2/uYCMNvK1QijQvX/efojS2JIt1p9muGQ6bWDog6jfI/9DWp9C9Z8HWW+2BIE4+dCkngIPA9R4CDwPASRh8hzEITafe05DkY/e46zv+8JIYS0HY7F4dbo9957DzNmzIAkSRg1ahQYY1i7di1UVcXMmTNx6aWX2ufecMMNWLduHaZNm4a7777bvn3BggV46KGHoGkaBgwYgKysLOzYsQMFBQVISUnBG2+8gZNOOsk+PxKJ4Oabb8aGDRuQnp6OoUOHYt++fdi1axe6du2KefPm2Ws1GyM/Px9jx47F0qVLa3UHiiWrh7dW458VUjVNR0TVEY5oRiBVjPVnum485Rxn1Ouz/tjzZuDleeMPuvF/+kPdEdUZBHUjCDKzV3zUKLr5JseZb9Y4xAscxKggaL6oMr9/RJG3v584OxgiKiDyFAoJIaRNxTq3xN0IJgBMmTIFXbt2xZw5c7BhwwbIsoyhQ4fizjvvrHPKuy6XXXYZevTogddeew0//vgjdu3ahYyMDFx//fW4/fbb7Z3lFlmW8cYbb+C1117Dl19+ieXLlyM9PR3XXHMNpk2b1qRw2Zo4zhjREZpQv9kIoTpUjdlT95rOEImoiKg6IooGRdURskZIj56SNEdHOc7MpjXe5ow5fvP96LetJQEcV/02jaY2njVKyHRmBsG6p5PtEAiYz1X0+9bXXRCMICgIPGQeEAQekshDMF9giCJfIwDiqBHD6ttp2pkQQsjR4nIEsyNorRHMlma1vDNGRKsDqaLq0AEwXYeRQY2R01ojYUeNiuk6s8+1Rs+MBzIf8Oi1dTUCEgPAwww1Vtgxwy3PceZt8RV4jmddIQfOqL1qvW+OMAuCMVoomkFQEKpHCUWhekRQqBkE7a8X7IBICCGEWDrFCCaJHxzHQRI5SGi5nbZWCGP229VT/VYotcKrZq4/VTUGVTOWAai6GYIjGjTdCLN1BdWaG0K4uoIqX72Zyroue10hqz1iaK0rtFoD1tpwYjxQnesKBXNa2V5XyEcHQc4MgrSukBBCSHtEAZO0OWu63XwPACAdx/1ZYbRmMD06qKqqDlXXoaq6PSprB1VVgw5mryuUBGNdocBXry20RgyjRwgRtdOf1hUSQgjprChgkg6H5znw4IAmrEslhBBCSOxQhWFCCCGEEBJTFDAJIYQQQkhMUcAkhBBCCCExRQGTEEIIIYTEFAVMQgghhBASUxQwCSGEEEJITFHAJIQQQgghMUUBkxBCCCGExBQFTEIIIYQQElMUMAkhhBBCSExRwCSEEEIIITFFAZMQQgghhMSU2NYX0FFpmgYAOHToUBtfCSGEEELIsVl5xcovx4sCZgspKioCAEyZMqWNr4QQQgghpHGKiorQo0eP474fjjHGYnA95CihUAhbt25Feno6BEFo68shhBBCCKmXpmkoKirCwIED4XQ6j/v+KGASQgghhJCYok0+hBBCCCEkpihgEkIIIYSQmKKASQghhBBCYooCJiGEEEIIiSkKmIQQQgghJKYoYBJCCCGEkJiigEkIIYQQQmKKAmaMrVmzBjfeeCNGjhyJoUOH4oYbbsCqVava+rJIPebPn4++ffvihx9+qPP4vn378Kc//QmjR4/G4MGDMXHiRMydOxe6rrfylRLAKAQ8d+5cXHHFFRgyZAgGDRqEiy66CK+88grC4XCt8+n5iy+apuGdd97BJZdcgpNPPhkjRozArbfeihUrVtR5Pj1/8ausrAxnnnkm+vbtW+dxeu7iy4IFC9C3b996/z3//PNR58fi+aNC6zE0f/58TJ8+HbIsY9SoUdB1HWvXroWiKJgxYwauueaatr5EUsPGjRtx6623IhAI4L333sOpp54adXzHjh2YMmUKqqqqMHToUKSmpmLt2rWoqKjAxIkT8eyzz7bRlXdOmqbhrrvuwooVK+B2uzF48GCIoohNmzahoqICgwcPxttvvw2XywWAnr949MADD+Czzz6D1+vFsGHDoCgK1q9fD0VRcM899+D3v/+9fS49f/Htj3/8IxYuXAgA2LlzZ9Qxeu7iz5NPPol//etfOOOMM5CSklLr+Lhx43D++ecDiOHzx0hMHD58mA0cOJANGzaM7dy5075906ZNbOjQoezkk09mhw4dasMrJDUtXryYDRkyhOXm5rLc3Fy2fv36qOO6rrOJEyey3NxctmDBAvv24uJi+/ZFixa19mV3ah988AHLzc1lEydOjPpZKi4uZtdccw3Lzc1lzz77LGOMnr949NVXX7Hc3Fw2YcIEVlRUZN++a9cuNmzYMNavXz+2b98+xhg9f/Huiy++sH935ubmRh2j5y4+XX/99Sw3N7fBHBLL54+myGNk7ty5iEQiuPnmm5Gbm2vfPmjQIEydOhXhcBjz5s1rwyskAHDo0CE88MADuPvuu6HrOtLS0uo8b/Xq1di5cydGjBiBSy+91L49JSUFjz76KADg3XffbZVrJoZPP/0UAPDQQw8hMzPTvj0lJQV/+9vfAABfffUVAHr+4tHnn38OALjvvvuifu769OmDiRMnQtd1rF69GgA9f/Hs8OHD+Pvf/44hQ4ZAEIRax+m5i087duxAWlpa1O/OusTy+aOAGSPWOstx48bVOnbeeecBAFauXNmq10RqmzVrFj777DMMHDgQ8+bNQ69eveo871jP57Bhw5CamooNGzagqqqqRa+XVEtOTkavXr0waNCgWsd69uwJACgsLARAz188evHFF/HFF1/g7LPPrnXM7/cDgB1Y6PmLX3/9618RDocxc+bMOo/Tcxd/8vLyUFFRgQEDBjR4biyfPwqYMcAYw+7du8HzfJ2BpWfPnuB5Hrt37wajJa9tqlevXpg5cyY++uijehenA8Du3bsBIGo0uqYTTjgBuq5jz549LXKdpLbZs2fj66+/htvtrnVsy5YtAICsrCwA9PzFI1mWkZubC1mWo25fvnw5Fi1aBLfbbf9Ro+cvPr3//vtYtWoV7rvvPvTo0aPOc+i5iz/bt28HAKSmpuLvf/87zjvvPJx88smYMGFCrQ2SsXz+xBhce6dXXl6OSCSClJSUWr88AUAURSQnJ6O4uBh+vx9er7cNrpIAwO23396o86yRsPT09DqPW7cfOXIkNhdGmo0xhhdffBEAMH78eAD0/MW7UCiEBx54ALt378aePXvQtWtXPP300/bUOT1/8ee3337DM888g1GjRmHKlCn1nkfPXfzZtm0bAGMjcmJiIoYNG4bMzExs3boVL774IlatWoV//etfcDqdMX3+aAQzBoLBIADYu1fr4nQ6AVRPBZH4Zj2n1vN2NOv2QCDQatdE6vZ///d/WLduHdLS0jB16lQA9PzFuwMHDmDx4sVRoyA1dyLT8xdfNE3DAw88AI7j8OSTT4LjuHrPpecu/lgjmBdccAFWrFiBV199FXPnzsWXX36Jfv36YePGjZg1axaA2D5/FDBjgOcb/jLS1Hj7Yj2n9f0itZ5Pel7b1gsvvIDXXnsNsixj1qxZdvkNev7iW1ZWFr7//nusW7cOs2bNgqIo+Pvf/47XXnsNAD1/8WbOnDnYuHEjpk+fjq5dux7zXHru4s+LL76Ir776Ck8//XTUEqOcnBw89dRT4DgO8+bNg6IoMX3+KGDGgPWE1VXo2WIdO9YoJ4kf1nMaCoXqPG49n3WtByQtT1VVPPLII/jnP/8Jh8OBl19+GcOHD7eP0/MX39xuN5KTk5GYmIgLLrgAL7/8MjiOw//7f/8P4XCYnr84smPHDrz00ksYPXo0rrrqqgbPp+cu/jgcDvTu3bvOJXz9+/dHVlYWAoEA9u/fH9PnjwJmDHi9XrjdbpSWlkJV1VrHVVVFaWkpHA4HEhIS2uAKSVNlZGQAqH+dSVFREYD616mQluP3+3HHHXdg3rx5SEhIwBtvvIHRo0dHnUPPX/tyyimnoHv37qiqqkJeXh49f3Hk+eefh6IoUFUV9913X9Q/q6uL9X5JSQk9d+2QtfY5GAzG9PmjgBkDHMehd+/e0DQN+/fvr3V837590HW93l1ZJP706dMHQPWOupoYY9i7dy8EQcCJJ57Y2pfWqZWXl9vtV7t06YL33nsvauTSQs9ffGGM4emnn8Yf//jHOl+EA7BHV1RVpecvjlhr7VavXo0vvvgi6p81TWq9HwgE6LmLM1VVVXj44Ydxzz331Puzl5+fDwDIzMyM6fNHATNGzjrrLADAkiVLah2zbjt6lIXEL+v5XLp0aa1jP/74I0pKSjBs2DCqCNCKIpEIbr/9dvz888/o3bs3/v3vf9f7oo2ev/jCcRyWLl2KhQsX2sXUa8rLy8O+ffvgdrtxwgkn0PMXR959913s3Lmzzn9W3VLr/ZycHHru4ozH48E333yDxYsXY/369bWOr1y5EqWlpcjNzUVmZmZMnz8KmDEyadIkOBwOvP7669i6dat9+5YtWzBnzhw4nU5Mnjy5Da+QNMWIESPQp08frF69Gh9++KF9e0lJCR577DEAwC233NJWl9cpvfjii/jpp5/QpUsXvPvuu3bNy7rQ8xd/rr76agD/v717D6sp3/8A/t6lKCkcNKTbyOrCcJTEiHYxDArPRCS5RTLOYcLEOBM9ck7H3VTMGDEM6Zg6paJ5aoiYYUqkTtSktNNN5VLZFbV36/dHz1qnpd2NPdX5zef1PJ7HrO9a38v+rj3747u+3+8Cdu/ejSdPnvDHy8rKsGnTJshkMixZsgS9e/em/vsfRn3Xs4hEIv675+/vj7KyMj7t8ePHfJ+sW7cOgHL7T8TSUi6lCQ0Nxa5du6CmpoaJEyeCZVkkJydDJpNhz549gtcukZ7B3d0dKSkpCA0Nxfjx4wVpGRkZWL58OWprazF27FgMGTIEKSkpqKqqgouLC/z9/bup1n88lZWVsLOzw6tXrzBq1KhW38AEAPv37wdA/dfTNDQ0YP369UhKSoKmpiYsLS0hl8uRnp6O2tpa2NnZITg4mH9UTv3X81lYWEAulwu2mAKo73qaV69eYdWqVbhz5w40NTVhZWUFAEhOTkZ9fT1WrlyJbdu28ecrq/8owFSyq1evIiQkBA8ePIC6ujpMTU2xbt06TJo0qburRhRoK8AEmuahBAYG8l9EQ0NDLF68GAsXLlT4Hl7y+7h+/TrWrFnToXOb/9hR//Uscrkc586dQ2RkJPLy8qCiogKGYfDJJ5/AxcWlxZZv1H89W2sBJkB919PU19fj1KlTiI2NhUQigbq6OiwsLODu7s6/oKI5ZfQfBZiEEEIIIUSpaA4mIYQQQghRKgowCSGEEEKIUlGASQghhBBClIoCTEIIIYQQolQUYBJCCCGEEKWiAJMQQgghhCgVBZiEEEIIIUSpKMAkpIsVFRXB1NQUpqam2Lp1a7vnJycn8+crQ1BQEExNTeHq6qqU/HqKhw8fdvoad3d3mJqa4tChQx2+xsHBge8PRf0SFxcHJycnjB49GmKxGIcPH0ZDQ0Or+bm5uWHcuHF49uxZp+v/rh49eoR169bBxsYGH3zwAcRiscJNs/9XcX0VHh7+znk1/x7KZDIl1K51LMsiLy+v09e92d7mdeb+bNmyRdnVJUQhCjAJ6UYXLlzA1atXu7sa/9Py8/Ph4eGBHTt2dGm5hoaGsLS0hKWlJX/sl19+waZNm5CbmwtjY2NIpVJ8/fXXgtewNXflyhWkpqbCw8MDf/rTn7qq6gCAmpoaLF++HImJiXj16hVGjhwJDQ0NDB8+vEvrQYQyMjLg4uKCr7/++p3z6tevH3+PdvX9RUiv7q4AIX90vr6+uHTpEnR0dLqkPDc3N8yePRsaGhpdUt7v7eLFi/j5558FgV5XWLNmDRYuXCg4FhQUBJZlERISAltbW/7dvRcvXsTatWvBMAx/rlwux4EDBzBo0CCsXLmyS+sOAKmpqSgvL4dIJEJkZCRGjBjR5XUgLZ07dw4ZGRkwNDR857wsLCwQFhYGANi2bRuioqLeOU9COopGMAnpRiKRCBUVFdi9e3eXlTlw4ECMGDECw4YN67Iy/whkMhn+85//wNjYGLa2tgAAHR0dzJs3D0BTQNdcREQE8vLysH79evTt27fL6/vixQsAwKBBgyi4JIQoHQWYhHQjNzc3AEBMTAyuXLnSzbUh7+LZs2eQyWQYMGCA4PjAgQMBAFVVVfyxuro6BAUFwcjICC4uLl1aT05jYyMAQF1dvVvKJ4T8/0aPyAnpRu7u7sjOzkZqaip27twJKysr9O/fv1N5lJWV4dSpU0hKSkJxcTFUVFRgYGCAjz76CMuWLYO2trbg/KCgIAQHB8PS0pJ/fMa5dOkSIiMj8ejRI1RUVKBfv36wsLDAvHnz4OjoCBWVlv8mffr0KU6ePIlr167x5b///vuYM2cO3Nzc0Lt37061Jy8vDyEhIcjIyEBxcTFUVVWhr68POzs7LFu2jJ9LVlRUhGnTpvHX3b17F6amptDT00NiYiJ/vLq6Gt9//z1+/PFHFBcXQ0tLC2KxGBs3buxUvdqjra0NkUiE58+fC44/ffoUAARTIE6ePImKigr4+vqiVy/l/G/41q1bOHfuHNLS0lBZWQktLS2MHj0aLi4umDFjBn9ecnIyli1bxv93cXExv1ApICAAn3zySZvlODg4oLi4GAkJCSgsLERISAgyMzPR2NgIhmHg5eUFsVgMmUyGU6dO4cKFC3j8+DE0NDRgbW0Nb29vhSOmcrkckZGRiImJQXZ2Nurq6jBo0CBYW1tjxYoVGDVqlML6lJeX47vvvkNiYiJKS0sxcOBAzJo1C+vXr2+zHfX19QgLC0NcXBxyc3PR0NCAoUOHQiwWw8PDA0OGDGnz+o5KT0/HqVOn8ODBA5SWlqJ3794wNjbG9OnTsWTJEmhpaQFo2S+xsbGIjY3FhAkTcObMmXduLyFdjQJMQrqRSCRCQEAA5s6di4qKCvj7++PAgQMdvv7WrVv461//ipcvX0JNTQ0mJiaQyWTIyclBdnY2IiIicOzYsQ6tQA8ICMCpU6cAAHp6ejA1NUV5eTl+/vln/s/evXsF19y5cweffvopKisroaamBiMjI7Asi/v37yMzMxPR0dEICQnB4MGDO9SetLQ0rFq1CrW1tdDW1oaxsTFev36NnJwcZGVlISoqCufPn8fQoUPRu3dvWFpaorS0FKWlpdDS0gLDMIKySkpK4OHhgUePHkFVVRUjR47E69evER4ejqSkJGhqanb4s26PhoYGLCwscP/+fVy/fh1Tp05FdXU1YmNjAQDW1tYAmkY6T5w4gbFjx2LmzJlKKdvf3x9nz54FAPTv3x9mZmYoKyvDjRs3cOPGDcyaNQv79u2Dmpoav/Dj+fPnkEgkUFdXx+jRowGgUwtBTp8+jdDQUGhra0NfXx8FBQVIS0uDl5cXgoKCcObMGSQnJ0NXVxfGxsbIycnBTz/9hNu3byMmJga6urp8XlKpFKtXr0ZaWhqApvvPwMAAEokEMTExuHjxInx8fFrMVc3Ozsbq1atRUVEBNTU1MAyDqqoqnDx5Ejdu3EBdXZ3CupeXl8PT0xNZWVkQiUQYNmwY+vfvj9zcXD4oPnr0KKysrDrVD29KSEiAt7c3P7JtYmKCmpoaZGRkID09HTExMfjXv/4FLS0tvl8KCgrw7NkzDBw4EEZGRoJ5u2/bXkK6BUsI6VKFhYUswzAswzCsRCJhWZZlT58+zR/76aefBOf/+uuvfFpzRUVF7J///GeWYRjWy8uLraio4NMeP37MLlq0iGUYhhWLxWx1dTWfFhgYyDIMwy5evJg/lpubyzIMw37wwQfsr7/+KignKiqKNTMzYxmGYdPS0vjjT548YSdMmMAyDMN++eWXbFVVFZ9WUFDALly4kGUYhl2yZEmHPxvuGn9/f/b169eC9syYMYNlGIb19fUVXKOoPZyVK1eyDMOwTk5O7OPHj/nj6enp7JQpU/jP9eDBgx2uo729PcswDPvDDz+0SLty5QpramrKmpubs46Ojuz48eNZhmHYLVu28Of4+fmxDMOwKSkpHS6zLSdOnGAZhmEtLCzYs2fPsnK5nE+Li4vj7xF/f3/Bdf/+979ZhmFYe3v7TpXHtZ/Ls66ujmVZlq2qqmKdnJxYhmFYMzMzdsKECez169f567Kysthx48axDMOwQUFBgjzXrl3LMgzDTp48WXD/vX79mj18+DBfXnx8PJ/W0NDAzp49m2UYhl22bBn79OlTPu3atWuspaUlf13zvmpsbOS/G66urmxeXh6fVl1dzX7xxRcswzCsjY0NW15ezqc1/x42NDS0+znJ5XJ28uTJLMMw7PHjx1mZTManZWZmshMnTmQZhmGPHTsmuG7r1q0swzDs5s2bBcfftr3t5UvI74XmYBLSA7i7u/MjXDt37uQXYLTl2LFjqK2tBcMw+OqrrzBo0CA+TV9fH8eOHcPgwYNRUlIieMSmCLf3obGxMWxsbARp8+fPh6urKxwdHVFfX88fP3HiBCorK+Hg4AB/f3/Bo3gDAwMcPXoUWlpaSE1NRVJSUvsfAppGaADA2dlZMDdQX18fW7duhb29PfT09DqUV3p6On755ReoqqoiODgY+vr6fNqYMWM6NVLcUQ4ODjhy5AhGjhyJ/Px8aGpq4tNPP0VAQAAAQCKRIDw8HPb29nx/A00jeE+ePOHnRXbU69ev+e1sNmzYADc3N8E0hlmzZvELyM6dO4eioqJ3bSLPxMQE27dvR58+fQA0TRHg5hQ3NjbCx8cHU6ZM4c83MzPjR2zv37/PH7937x6/VVdgYKDg/lNXV8fGjRuxaNEiAMD+/fv5tISEBOTm5kJHRweBgYGC0Vc7Ozv4+voqrPeVK1eQlpaGIUOGICQkBO+//z6f1q9fP/z973/H2LFj8eLFC35E/208f/4cFRUVAAAXFxeoqqryaaNGjYK3tzemT5/e4Skxb9teQroLBZiE9AAikQj/+Mc/oKGhgadPn8Lf37/da65duwYAcHV1VbhQQ0dHB87OzgCAy5cvt5kXtyVKdnY29uzZA4lEIkjfsWMHDhw4gAkTJvDHuDznzp2rMM9BgwZh8uTJANDhvT65euzcuRO3bt0SbFDu4OCAb775BmvXru1QXtznY2VlBQMDgxbp1tbWMDEx6VBenTFt2jRER0cjMzMTSUlJ2LhxIz/P8uDBg2hsbMTmzZsBNAUhnp6eGD9+POzs7GBra9upTcFTU1NRXV2NXr168cHdm2bPng1dXV3I5XL+M1GGqVOntpiT2zz4t7Oza3ENN69RKpXyx7h7Y8yYMa1uNbVq1SoAQEFBAXJycgD8t3+nTZumcIuvOXPmoF+/fi2Oc/ft9OnTFU6REIlE/D39LnvUDhgwgK/Xli1bkJaWJvgHhIuLC44cOdLhRV5v215CugvNwSSkhzAwMMDmzZuxe/duXLp0CR9//LFgcUZzUqkUZWVlAMDPn1OEWxiRn5/fZtmjRo2Ck5MTYmNjcfLkSZw8eRJ6enqYNGkSbG1tMWXKFH4xAtC0SXdxcTEA4OjRo/j+++8V5sud8+jRozbL53z++edYt24d0tPTsWLFCmhqasLa2hoffvghxGIxjIyMOpQP8N82N5/D9iYzMzPk5uZ2OM93ce/ePcTHx8PZ2RkjR44EAPj4+ODGjRtwdXXFqFGjEBYWhi+//BIaGhpwdHRsN0/uczU0NBT0T3MikQgWFhYoKytr9z7ojPfee6/FMTU1Nf7v3Or55hQtaOLa0NoiHgAwMjKClpYWpFIp8vPzwTAM3xbus1RUFxMTE35eJ4cLUK9evcqPmL+puroaQNOIM8uyEIlErdatNaqqqtiyZQt8fX2RlJSEpKQk6OjowMbGBpMnT4ZYLFb4GbbmbdtLSHehAJOQHmTp0qWIj4/H7du34efnh/Hjxys8r6amhv97a4FF87Ta2tp2fyj37duHiRMnIjw8HOnp6SguLkZERAQiIiLQu3dvuLi4wMfHB+rq6oIRKO4Huy0vX75s9xygaVQsIiICx48fx7Vr11BTU8P/OAcEBMDKygq7du3q0MgjFyS0tZCnqza3B4C9e/eiT58+2LBhAwAgKyuLX4Dj5+cHABCLxfxIbUcCTK4f2hu54u6D5vfNu2pvgZSiHQcU6Wgb+vbtC6lUyrfhbfuXK49bHNYWuVyOmpqaNr9jbXFxcYGhoSG+++473Lx5E1VVVUhISEBCQgJEIhHEYjH8/Pw6FGj2tPuZkPZQgElID8I9Kp87dy6ePXuGXbt2KXxnePONuZsHe2/i9l7U1NRsdxRGJBJhwYIFWLBgAZ4/f47k5GSkpKTw2x9x8zi5ETZObGxsm6OEnWVubo6DBw+ioaEB6enpSE5Oxs2bN3H37l3cuXMHK1asQEJCQrsBDje3ra3P59WrV0qrd1suX76MO3fuwNPTkw8mMjMzAUCwUnnw4MEwMjJCTk4OpFJpu4ENdx+0F8BzwUl3bOjeno62gUvnzn/b/uXuXV9fXyxdurTT9e0sGxsb2NjY4NWrV0hNTcXt27dx48YN3L9/H1evXkVpaSkuXLjQ7vezJ93PhHQEzcEkpIfhHpUDwI8//oj4+PgW52hpafHz2bhARREurb1Hy1KpFJmZmfzjSm5vvZ07d+Ly5ct8kBsdHQ2gaUEHt6iorUfMv/32G7KysgSbjLdGLpejoKAAt2/fBtD0yG/8+PFYv349QkNDERoayr/56ObNm+3mZ2xsDKBppLA1XfF4nHslZP/+/eHp6ckff/bsGQC02Ce0M6ON3AKVgoKCVgOPxsZGPHjwAACU8vpBZePa0Hzhz5vy8vJQW1sL4L9taK9/WZZFXl5ei+PcdQ8fPmy1vNLSUty7dw/l5eUdaIFi9fX1yMvLQ3p6OgCgT58+sLW1hbe3NyIjI3Hw4EEATfOeuUV2bXnb9hLSXSjAJKQHWrp0Kb+g5ty5cwrPsbe3BwCEhYUJVndzqqqqcOHCBQBNj57bEhgYCGdnZ+zZs6dFmoqKCiZNmgSgKVjiiMViAMDZs2cVrn5++fIlli9fjvnz5+P06dNtlg80/eDPmDEDy5cv51ffNjdu3Dh+9Kp5edzID8uygvO5+av37t3jA6zmsrOzkZGR0W693lV4eDgePXqEdevWCR4DcyNST548EZxfXl4OFRWVFhvkK2JlZQUdHR3IZDKEhoYqPOfSpUuoqKiASCQSrOruKbj7OCMjA3fv3lV4Drea+7333uP3dOX6NzExkZ+P3NzVq1cV3kdceXFxcXyQ/6bt27dj0aJF2LRpU+ca08z169cxe/ZseHp6Kvx+fvjhh/zfm3+v2rufO9teQroLBZiE9EDco3JNTc0WPzScNWvWoG/fvsjJycHGjRsFP5aFhYVYu3Ytnj59Cl1dXSxfvrzN8ubOnQuRSIRr167h+PHjgtXbJSUl+OabbwAIVwZ7enpCU1MTd+7cweeffy54g01xcTE8PT3x4sUL9OvXr9UVzs2ZmZmBYRjI5XJs2rRJEHjV19fj0KFDkEql0NTUFMxN5YLO8vJyyGQy/ripqSkcHR3Bsiz+8pe/CEZ+Hj58iA0bNrT62SpLbW0tgoODoaenhyVLlgjSuG2KoqOj+W2pEhMTUVRUhLFjxwqmIbRGQ0ODHxUNDAxEaGioIPiOj4/Hjh07ADTNB+RGwXqScePG8ffVhg0bkJyczKfV19cjMDAQP/zwA4CmRVFcACYWi2FpaYna2lp4eXmhsLCQvy41NRV/+9vfFJY3e/ZsMAyD6upqeHh4CEYypVIp/Pz8cPPmTYhEIsGIc2dNnToVAwYMQGVlJbZu3YrKyko+raamhv/H3NChQwULd7j7uaSkRJDf27aXkO5CczAJ6aH09fWxefPmVrcs0tfXR2BgIDZu3IjExETY2dnBxMQEcrkcubm5aGxsxLBhwxAcHKxwRW9zo0ePxmeffYZDhw5h//79+PbbbzF8+HDU1dWhsLAQMpkMBgYG2LZtG3+NoaEhDh8+DG9vb1y8eBHx8fEwMTFBQ0MDJBIJZDIZNDU18e2333b4DTGHDh3C4sWLkZKSgunTp2P48OHQ0NBAUVERqquroaqqil27dgnaY25uDqApqJ0xYwaGDBmCsLAwiEQi7Ny5EyUlJbh79y7mz5+PkSNHQiQS4eHDh9DW1saECROQkpLSobq9De6VkPv27WuxldSIESPg6OiIixcv4qOPPsLQoUORm5uLXr16wdvbu8NleHh4oKioCGFhYdi1axeCgoKgr6+PJ0+e8I94Z86c2aMDkL1798LLywtpaWlYtmwZ9PT0MHDgQOTn50MqlUJVVRWfffYZ5syZw1+joqKCAwcOYPXq1Xjw4AFmzpwJhmFQV1cHiUSC4cOHQ1dXt8UjZTU1NRw9ehSrV69GVlYWHB0dYWxsDA0NDUgkEv5R/BdffNHuyH9b1NXV8dVXX8HDwwNxcXG4cuUKDAwMoKKigsLCQtTW1kJDQwP//Oc/BfcGdz/fvXsXH3/8MUxMTBAcHPzW7SWku9AIJiE9mJubm2DvyTfZ2tri0qVLWLFiBYYPH478/HyUlpbC3NwcmzdvRnR0dJvbvzTn5eWFI0eOwM7ODurq6sjJyUFFRQXMzc2xadMmREdHC17vBzSNaHLlGxgYID8/HwUFBfyIXUxMTKt7GypiYmKCqKgouLq6Qk9PDyUlJcjNzYW2tjacnZ0RHR0NJycnwTUTJ06Ej48P9PT0UF5ejqKiIv7939ra2jh9+jS2b98Oc3NzFBcXo7y8HDNnzkR4eLjC/TGVhXslpLm5eYs6cwICArBmzRr06dMHEokEY8aMwYkTJ1psdt8WkUgEPz8/nDhxAtOnT4eqqiofZNjb2+PIkSMIDAzs9Dvhu1L//v1x5swZ+Pv7w9raGi9fvsRvv/2GAQMGYMGCBYiIiFA4mjhs2DCcP38eGzZsgLGxMR+QOjs74/z5861uYq6vr4+oqCj4+Phg7NixqKioQE5ODvr27YuZM2fi7Nmz7Y76d4SNjQ3Cw8Mxb948DB48GBKJBI8fP4auri7c3d0RFxeHiRMnCq6ZP38+Vq9ejcGDB6OoqAhZWVn8qPTbtpeQ7iBif+9nRIQQ8v+Ig4MDiouLsXv3bixcuLC7q0NIh2zbtg1RUVFwcnISvBGJkN8LjWASQgghhBClogCTEEIIIYQoFS3yIYSQt3D8+HFERkYCaNoqipCe5sGDB/wiwYKCgm6uDfmjoQCTEELeQkFBAf1okx7t5cuXre4tSsjvjRb5EEIIIYQQpaI5mIQQQgghRKkowCSEEEIIIUpFASYhhBBCCFEqCjAJIYQQQohSUYBJCCGEEEKUigJMQgghhBCiVP8HIyghu3751N8AAAAASUVORK5CYII=\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure(figsize=(10,6))\n", - "ax = sns.lineplot(data=df_evolution_melted.loc[(df_evolution_melted['metric'] == 'RMSE') & (df_evolution_melted['experiment'] != 'True covariance')\n", - " & (df_evolution_melted['experiment'] != 'Prior')], x=\"data std\", y=\"loss\", hue='experiment')\n", - "ax.set_ylabel('RMSE')\n", - "ax.set_xlim([0, 50])\n", - "ax.set_xlabel('Noise std [% of model std]')\n", - "plt.legend(fontsize='small', title_fontsize='10')\n", - "plt.savefig(os.path.join(plots_folder, 'scores_RMSE_evolution'), bbox_inches='tight', dpi=200)" - ] - }, - { - "cell_type": "code", - "execution_count": 191, - "id": "95c58dc1", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure(figsize=(10,6))\n", - "ax = sns.lineplot(data=df_evolution_melted.loc[(df_evolution_melted['metric'] == 'ES') & (df_evolution_melted['experiment'] != 'True covariance')\n", - " & (df_evolution_melted['experiment'] != 'Prior')], x=\"data std\", y=\"loss\", hue='experiment')\n", - "ax.set_ylabel('Energy Score')\n", - "ax.set_xlim([0, 50])\n", - "ax.set_xlabel('Noise std [% of model std]')\n", - "plt.legend(fontsize='small', title_fontsize='10')\n", - "plt.savefig(os.path.join(plots_folder, 'scores_ES_evolution'), bbox_inches='tight', dpi=200)" - ] - }, - { - "cell_type": "code", - "execution_count": 190, - "id": "cda3b435", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ], - "text/plain": [ - " data std repetition metric loss experiment\n", - "0 0.002 0 RMSE 1.018189 Prior\n", - "1 0.005 0 RMSE 1.018189 Prior\n", - "2 0.007 0 RMSE 1.018189 Prior\n", - "3 0.010 0 RMSE 1.018189 Prior\n", - "4 0.030 0 RMSE 1.018189 Prior\n", - ".. ... ... ... ... ...\n", - "77 0.007 5 RMSE 0.915396 Prior\n", - "78 0.010 5 RMSE 0.915396 Prior\n", - "79 0.030 5 RMSE 0.915396 Prior\n", - "80 0.050 5 RMSE 0.915396 Prior\n", - "81 0.070 5 RMSE 0.915396 Prior\n", - "\n", - "[82 rows x 5 columns]" - ] - }, - "execution_count": 158, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_evolution_melted.loc[(df_evolution_melted['metric'] == 'RMSE') & (df_evolution_melted['experiment'] == 'Prior')]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "16f5e135", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.11" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/reporting/toy_example/plot_seq_aao_comparison.py b/reporting/toy_example/plot_seq_aao_comparison.py deleted file mode 100644 index 2e061c0..0000000 --- a/reporting/toy_example/plot_seq_aao_comparison.py +++ /dev/null @@ -1,123 +0,0 @@ -""" Compare the performance of the sequential Ensemble Kalman Filter with -the version that assimilates all data points in one go. - -""" -import os -import numpy as np -import matplotlib.pyplot as plt -import dask.array as da -from dask.distributed import Client, wait, progress -import diesel as ds -from diesel.scoring import compute_RE_score, compute_CRPS, compute_energy_score -from diesel.estimation import localize_covariance - - -import time - - -results_folder ="/home/cedric/PHD/Dev/DIESEL/reporting/toy_example/ubelix_results/" - - -def main(): - # Instantiate a local cluster, to mimick distributed computations, but on a single machine. - cluster = ds.cluster.LocalCluster() - client = Client(cluster) - - # Build a square grid with 30^2 elements. - grid = ds.gridding.SquareGrid(n_pts_1d=120) - grid_pts = grid.grid_pts - - ground_truth = da.from_array( - np.load(os.path.join(results_folder, "ground_truth_0.npy"))) - ensemble_updated_one_go_raw = da.from_array( - np.load(os.path.join(results_folder, "ensemble_updated_one_go_raw_0.npy"))) - ensemble_updated_one_go_loc = da.from_array( - np.load(os.path.join(results_folder, "ensemble_updated_one_go_loc_0.npy"))) - ensemble_updated_seq_raw = da.from_array( - np.load(os.path.join(results_folder, "ensemble_updated_seq_raw_49.npy"))) - ensemble_updated_seq_loc = da.from_array( - np.load(os.path.join(results_folder, "ensemble_updated_seq_loc_49.npy"))) - - # Now compare scores. - # RE_score_one_go_raw = compute_RE_score(mean, mean_updated_one_go_raw, ground_truth) - # RE_score_one_go_loc = compute_RE_score(mean, mean_updated_one_go_loc, ground_truth) - - CRPS_one_go_raw, misfit_one_go_raw, spread_one_go_raw = compute_CRPS( - ensemble_updated_one_go_raw, ground_truth) - CRPS_one_go_loc, misfit_one_go_loc, spread_one_go_loc = compute_CRPS( - ensemble_updated_one_go_loc, ground_truth) - - CRPS_seq_raw, misfit_seq_raw, spread_seq_raw = compute_CRPS( - ensemble_updated_seq_raw, ground_truth) - CRPS_seq_loc, misfit_seq_loc, spread_seq_loc = compute_CRPS( - ensemble_updated_seq_loc, ground_truth) - - ES_one_go_raw, ES_misfit_one_go_raw, ES_spread_one_go_raw = compute_energy_score( - ensemble_updated_one_go_raw, ground_truth) - ES_one_go_loc, ES_misfit_one_go_loc, ES_spread_one_go_loc = compute_energy_score( - ensemble_updated_one_go_loc, ground_truth) - - ES_seq_raw, ES_misfit_seq_raw, ES_spread_seq_raw = compute_energy_score( - ensemble_updated_seq_raw, ground_truth) - ES_seq_loc, ES_misfit_seq_loc, ES_spread_seq_loc = compute_energy_score( - ensemble_updated_seq_loc, ground_truth) - - - fig, axs = plt.subplots(3, 3) - grid.plot_vals(ground_truth, axs[0, 0], vmin=-3, vmax=3) - axs[0, 0].title.set_text('ground truth') - axs[0, 0].set_xticks([]) - - grid.plot_vals(ensemble_updated_one_go_raw.mean(axis=0).compute(), axs[0, 1], - vmin=-3, vmax=3) - axs[0, 1].title.set_text('all-at-once (no localization)') - axs[0, 1].set_xticks([]) - axs[0, 1].set_yticks([]) - - grid.plot_vals(ensemble_updated_one_go_loc.mean(axis=0).compute(), axs[0, 2], - vmin=-3, vmax=3) - axs[0, 2].title.set_text('all-at-once (localization)') - axs[0, 2].set_xticks([]) - axs[0, 2].set_yticks([]) - - grid.plot_vals(CRPS_one_go_loc.compute(), axs[1, 0], vmin=0, vmax=3) - axs[1, 0].title.set_text('CRPS all-at-once (ES: {})'.format(ES_one_go_loc.compute())) - axs[1, 0].set_xticks([]) - - grid.plot_vals(misfit_one_go_loc.compute(), axs[1, 1], - vmin=0, vmax=2.5, - ) - axs[1, 1].title.set_text('misfit all-at-once') - axs[1, 1].set_xticks([]) - axs[1, 1].set_xticks([]) - - grid.plot_vals(spread_one_go_loc.compute(), axs[1, 2], - ) - axs[1, 2].title.set_text('spread all-at-once') - axs[1, 2].set_xticks([]) - axs[1, 2].set_yticks([]) - - grid.plot_vals(CRPS_seq_loc.compute(), axs[2, 0], vmin=0, vmax=3) - axs[2, 0].title.set_text('CRPS sequential (ES: {})'.format(ES_seq_loc.compute())) - axs[2, 0].set_xticks([]) - - grid.plot_vals(misfit_seq_loc.compute(), axs[2, 1], - vmin=0, vmax=2.5, - ) - axs[2, 1].title.set_text('misfit sequential') - axs[2, 1].set_xticks([]) - axs[2, 1].set_yticks([]) - - grid.plot_vals(spread_seq_loc.compute(), axs[2, 2], - ) - axs[2, 2].title.set_text('spread sequential') - axs[2, 2].set_xticks([]) - axs[2, 2].set_yticks([]) - - plt.savefig("scores_sequential_vs_one_go_bigdata_2", - bbox_inches="tight", pad_inches=0.1, dpi=400) - # plt.show() - - -if __name__ == "__main__": - main() diff --git a/reporting/toy_example/report_mem_usage_paper.py b/reporting/toy_example/report_mem_usage_paper.py deleted file mode 100644 index 9fc2cb6..0000000 --- a/reporting/toy_example/report_mem_usage_paper.py +++ /dev/null @@ -1,232 +0,0 @@ -""" 22.09.2023 - -Report memory usage for Kalman all-at-once paper. - -""" -import os -import numpy as np -import pandas as pd -import matplotlib.pyplot as plt -import dask.array as da -from dask.distributed import Client, wait, progress -import diesel.covariance, diesel.cluster, diesel.gridding, diesel.sampling -from diesel.scoring import compute_RE_score, compute_energy_score -from dask.distributed.diagnostics import MemorySampler - -from dask.distributed.client import futures_of - -import time - - -# results_folder ="/home/cedric/PHD/Dev/DIESEL/reporting/toy_example/results/" -results_folder ="/storage/homefs/ct19x463/Dev/DIESEL/reporting/toy_example/results_paper/report_mem_usage/" - - -CHUNK_SIZE = 3000 -n_pts_1d = 300 -n_data = 10000 - - -def main(): - # Instantiate a local cluster, to mimick distributed computations, but on a single machine. - # cluster = ds.cluster.LocalCluster() - cluster = diesel.cluster.UbelixCluster(n_nodes=15, mem_per_node=64, cores_per_node=4, - partition="gpu", qos="job_gpu") - cluster.scale(20) - client = Client(cluster) - - # Add to builtins so we have one global client. - __builtins__.CLIENT = client - - # This has to be imported later, otherwise we do not know - # which client to use. - from diesel.kalman_filtering import EnsembleKalmanFilter - from diesel.estimation import localize_covariance, empirical_covariance - - # ---------------- - # Start profiling. - # ---------------- - ms = MemorySampler() - with ms.sample("collection 1"): - - # Build a square grid with 80^2 elements. - grid = diesel.gridding.SquareGrid(n_pts_1d=n_pts_1d) - grid_pts = grid.grid_pts.astype('float32') - - # Chunk it so that localization matrices built out of the coordinates - # are chunked too. - grid_pts = grid_pts.rechunk((CHUNK_SIZE, -1)) - wait(grid_pts) - print(grid_pts) - - # Construct (lazy) covariance matrix. - lambda0 = 0.05 - lengthscales = da.from_array([lambda0]) - kernel = diesel.covariance.matern32(lengthscales) - true_covariance_matrix = kernel.covariance_matrix(grid_pts, grid_pts) - - print(true_covariance_matrix) - - # Compute compressed SVD. - svd_rank = 1000 # Since our matrix is 900 * 900 this will be a full SVD. - u, s, v = da.linalg.svd_compressed( - true_covariance_matrix, k=svd_rank, compute=False) - u = client.persist(u) - s = client.persist(s) - wait(u) - wait(s) - # Aggressively clean memory. - client.cancel(true_covariance_matrix) - print("Finished waiting.") - - # Save for later. - u_res = client.compute(u) - s_res = client.compute(s) - np.save(os.path.join( - results_folder, "svd_u.npy"), u_res) - np.save(os.path.join( - results_folder, "svd_s.npy"), s_res) - print("Saving SVD done.") - - # Construct sampler from the svd of the covariance matrix. - sampler = diesel.sampling.SvdSampler(u, s) - - # Repeat the whole experiment several time for statistical analysis. - ES_prior, ES_aao_loc = [], [] - RE_aao_loc = [] - RMSE_prior, RMSE_aao_loc = [], [] - n_rep = 1 - for rep in range(n_rep): - print("Repetition {} / {}.".format(rep, n_rep)) - # Sample 30 ensemble members. - n_ensembles = 30 - ensembles = sampler.sample(n_ensembles + 1) # Note this is still lazy. - print("Sampling done.") - - # Use the first sample as ground truth. - ground_truth = ensembles[0] - ensembles = ensembles[1:] - - # Compute ensemble mean. - mean = da.mean(da.stack(ensembles, axis=1), axis=1) - - # Trigger computations. - ground_truth = client.persist(ground_truth) - mean = client.persist(mean) - - # Save ensembles locally so they survive cleaning. - ensembles_local = [client.compute(ensemble.astype('float32')).result() - for ensemble in ensembles] - # Aggressively clear memory. - client.cancel(s) - client.cancel(u) - # Persist on cluster and stack. - ensembles_cluster = [client.persist( - da.from_array(ensemble).astype('float32')) - for ensemble in ensembles_local] - ensembles_cluster = da.stack(ensembles_cluster) - - # Chunk so later computations fit in memory. - ensembles_cluster = client.persist(ensembles_cluster.rechunk((-1, CHUNK_SIZE))) - print(ensembles_cluster.chunks) - - wait(ensembles_cluster) - wait(mean) - print("Finished waiting on ensembles") - - # Stack ensembles so are in the format required later. - - # Save for later. - np.save(os.path.join( - results_folder, "ground_truth_n1d_{}.npy".format(n_pts_1d)), ground_truth.compute()) - np.save(os.path.join( - results_folder, "ensemble_n1d_{}.npy".format(n_pts_1d)), ensembles_cluster.compute()) - np.save(os.path.join( - results_folder, "mean_n1d_{}.npy".format(n_pts_1d)), mean.compute()) - print("Saving starting conditions done.") - - # Estimate covariance using empirical covariance of the ensemble. - raw_estimated_cov_lazy = empirical_covariance(ensembles_cluster) - # raw_estimated_cov = client.persist(raw_estimated_cov_lazy) - - # Perform covariance localization (use scaled version of base covariance to localize). - # Maybe should persist here. - # grid_pts = client.persist(grid_pts_local) - localization_matrix = kernel.covariance_matrix(grid_pts, grid_pts, - lengthscales=da.from_array([2 * lambda0])) - # TODO: delete once bug found. - # TODO: Result of this comand is found to increase memory footprint - # but drastically increase speed. - localization_matrix_pers = client.persist(localization_matrix) - - # localization_matrix = client.persist(localization_matrix) - loc_estimated_cov = localize_covariance(raw_estimated_cov_lazy, localization_matrix_pers) - loc_estimated_cov = client.persist(loc_estimated_cov) - - # Wait for the localized estimated covariance - # to be loaded in distributed memory. - wait(loc_estimated_cov) - del localization_matrix_pers - - # client.cancel(raw_estimated_cov_lazy) - # client.cancel(localization_matrix) - print("Finished waiting on localized estimated covariance.") - - # Prepare some data by randomly selecting some points. - data_inds = np.random.choice(ground_truth.shape[0], n_data, replace=False) - np.save(os.path.join( - results_folder, "data_inds_n1d_{}.npy".format(n_pts_1d)), data_inds) - - # Built observation operator. - G = np.zeros((data_inds.shape[0], ground_truth.shape[0])) - G[range(data_inds.shape[0]), data_inds] = 1 - G = da.from_array(G) - - data_std = 0.01 - y = G @ ground_truth - - # Run data assimilation using an ensemble Kalman filter. - my_filter = EnsembleKalmanFilter() - - # ------------------------- - # All-at-once assimilation. - # ------------------------- - # Localized version. - # We have to re-persist the ensembles, since cleaning - # of the covariances wipes them from the cluster. - ensembles_cluster = [client.persist( - da.from_array(ensemble).astype('float32')) - for ensemble in ensembles_local] - ensembles_cluster = da.stack(ensembles_cluster) - - print("Starting assimilation.") - mean_updated_aao_loc, ensemble_updated_aao_loc = my_filter.update_ensemble( - mean, ensembles_cluster, G, y, data_std, loc_estimated_cov) - mean_updated_aao_loc, ensemble_updated_aao_loc = ( - client.persist(mean_updated_aao_loc), - client.persist(ensemble_updated_aao_loc)) - progress(ensemble_updated_aao_loc) - progress(mean_updated_aao_loc) - wait(ensemble_updated_aao_loc) - wait(mean_updated_aao_loc) - print("Finished assimilation.") - print(mean_updated_aao_loc) - - np.save(os.path.join( - results_folder, "mean_updated_aao_loc_n1d_{}.npy".format(n_pts_1d)), - mean_updated_aao_loc.compute()) - np.save(os.path.join( - results_folder, "ensemble_updated_aao_loc_n1d_{}.npy".format(n_pts_1d)), - ensemble_updated_aao_loc.compute()) - - # ---------------- - # End profiling. - # ---------------- - # Save memory usage dataframe. - df_memory = ms.to_pandas(align=True) - df_memory_path = os.path.join(results_folder, "mem_use_df_n1d_{}.pkl".format(n_pts_1d)) - df_memory.to_pickle(df_memory_path) - - -if __name__ == "__main__": - main() diff --git a/reporting/toy_example/sequential_vs_one_go.py b/reporting/toy_example/sequential_vs_one_go.py deleted file mode 100644 index 37c8645..0000000 --- a/reporting/toy_example/sequential_vs_one_go.py +++ /dev/null @@ -1,416 +0,0 @@ -""" Compare the performance of the sequential Ensemble Kalman Filter with -the version that assimilates all data points in one go. - -""" -import os -import numpy as np -import matplotlib.pyplot as plt -import dask.array as da -from dask.distributed import Client, wait, progress -import diesel as ds -from diesel.scoring import compute_RE_score, compute_CRPS, compute_energy_score -from diesel.estimation import localize_covariance - - -import time - - -# results_folder ="/home/cedric/PHD/Dev/DIESEL/reporting/toy_example/results/" -results_folder ="/storage/homefs/ct19x463/Dev/DIESEL/reporting/toy_example/results/" - - -def main(): - # Instantiate a local cluster, to mimick distributed computations, but on a single machine. - # cluster = ds.cluster.LocalCluster() - cluster = ds.cluster.UbelixCluster(n_nodes=12, mem_per_node=32, cores_per_node=2, - partition="gpu", qos="job_gpu") - cluster.scale(12) - client = Client(cluster) - - # Add to builtins so we have one global client. - __builtins__.CLIENT = client - from diesel.kalman_filtering import EnsembleKalmanFilter - - # Build a square grid with 30^2 elements. - grid = ds.gridding.SquareGrid(n_pts_1d=120) - grid_pts = grid.grid_pts - - # Construct (lazy) covariance matrix. - lambda0=0.1 - lengthscales = da.from_array([lambda0]) - kernel = ds.covariance.matern32(lengthscales) - lazy_covariance_matrix = kernel.covariance_matrix(grid_pts, grid_pts) - - # Compute compressed SVD. - svd_rank = 900 # Since our matrix is 900 * 900 this will be a full SVD. - u, s, v = da.linalg.svd_compressed( - lazy_covariance_matrix, k=svd_rank, compute=False) - - # Construct sampler from the svd of the covariance matrix. - sampler = ds.sampling.SvdSampler(u, s) - - n_rep = 1 - for rep in range(n_rep): - print("Repetition {} / {}.".format(rep, n_rep)) - # Sample 30 ensemble members. - n_ensembles = 30 - ensembles = sampler.sample(n_ensembles + 1) # Note this is still lazy. - - # Use the first sample as ground truth. - ground_truth = ensembles[0] - ensembles = ensembles[1:] - - # Compute ensemble mean. - mean = da.mean(da.stack(ensembles, axis=1), axis=1) - - # Trigger computations. - ground_truth = client.persist(ground_truth) - ensembles = [client.persist(ensemble) for ensemble in ensembles] - - # Stack ensembles so are in the format required later. - ensembles = da.stack(ensembles) - - # Save for later. - np.save(os.path.join( - results_folder, "ground_truth_{}.npy".format(rep)), ground_truth.compute()) - np.save(os.path.join( - results_folder, "ensemble_{}.npy".format(rep)), ensembles.compute()) - np.save(os.path.join( - results_folder, "mean_{}.npy".format(rep)), mean.compute()) - - # Estimate covariance using empirical covariance of the ensemble. - raw_estimated_cov_lazy = ds.estimation.empirical_covariance(ensembles) - - # Persist the covariance on the cluster. - raw_estimated_cov = client.persist(raw_estimated_cov_lazy) - - # Perform covariance localization (use scaled version of base covariance to localize). - # Maybe should persist here. - scaled_covariance_matrix = kernel.covariance_matrix(grid_pts, grid_pts, - lengthscales=da.from_array([10 * lambda0])) - loc_estimated_cov = localize_covariance(raw_estimated_cov, scaled_covariance_matrix) - loc_estimated_cov = client.persist(loc_estimated_cov) - - # Prepare some data by randomly selecting some points. - n_data = 500 - data_inds = np.random.choice(ground_truth.shape[0], n_data, replace=False) - - # Built observation operator. - G = np.zeros((data_inds.shape[0], ground_truth.shape[0])) - G[range(data_inds.shape[0]), data_inds] = 1 - G = da.from_array(G) - - data_std = 0.01 - y = G @ ground_truth - - # Run data assimilation using an ensemble Kalman filter. - my_filter = EnsembleKalmanFilter() - - mean_updated_one_go_raw, ensemble_updated_one_go_raw = my_filter.update_ensemble(mean, ensembles, G, y, data_std, raw_estimated_cov) - - # Trigger computations and block. Otherwise will clutter the scheduler. - mean_updated_one_go_raw, ensemble_updated_one_go_raw = ( - client.persist(mean_updated_one_go_raw), - client.persist(ensemble_updated_one_go_raw)) - progress(ensemble_updated_one_go_raw) - - np.save(os.path.join( - results_folder, "mean_updated_one_go_raw_{}.npy".format(rep)), - mean_updated_one_go_raw.compute()) - np.save(os.path.join( - results_folder, "ensemble_updated_one_go_raw_{}.npy".format(rep)), - ensemble_updated_one_go_raw.compute()) - - mean_updated_one_go_loc, ensemble_updated_one_go_loc = my_filter.update_ensemble( - mean, ensembles, G, y, data_std, loc_estimated_cov) - - # Trigger computations and block. Otherwise will clutter the scheduler. - mean_updated_one_go_loc, ensemble_updated_one_go_loc = ( - client.persist(mean_updated_one_go_loc), - client.persist(ensemble_updated_one_go_loc)) - progress(ensemble_updated_one_go_loc) - - np.save(os.path.join( - results_folder, "mean_updated_one_go_loc_{}.npy".format(rep)), - mean_updated_one_go_loc.compute()) - np.save(os.path.join( - results_folder, "ensemble_updated_one_go_loc_{}.npy".format(rep)), - ensemble_updated_one_go_loc.compute()) - - - localizer_loc = lambda x: localize_covariance(ds.estimation.empirical_covariance(x), scaled_covariance_matrix) - localizer_raw = lambda x: ds.estimation.empirical_covariance(x) - - # Stupid, but have to transfer to local node - # in order to preserve from cancelling. - mean_store = mean.compute() - ensemble_store = ensembles.compute() - - chunk_size = 1 - mean_updated_seq_raw = client.persist(da.from_array(mean_store)) - ensemble_updated_seq_raw = client.persist(da.from_array(ensemble_store)) - running_estimated_cov = raw_estimated_cov - # for i in range(0, G.shape[0], chunk_size): - for i in range(0, 50, chunk_size): - print(i) - running_estimated_cov_lazy = ds.estimation.empirical_covariance(ensemble_updated_seq_raw) - running_estimated_cov = client.persist(running_estimated_cov_lazy) - print("Estimating cov.") - progress(running_estimated_cov) - - G_chunk = G[i:i+chunk_size].reshape(chunk_size, -1) - y_chunk = y[i:i+chunk_size].reshape(chunk_size, -1) - - mean_updated_seq_raw, ensemble_updated_seq_raw = my_filter.update_ensemble( - mean_updated_seq_raw, ensemble_updated_seq_raw, - G_chunk, y_chunk, data_std, running_estimated_cov - ) - mean_updated_seq_raw = client.persist(mean_updated_seq_raw) - ensemble_updated_seq_raw = client.persist(ensemble_updated_seq_raw) - print("Start progress.") - progress(ensemble_updated_seq_raw) - print("End progress.") - ensemble_tmp = ensemble_updated_seq_raw.compute() - mean_tmp = mean_updated_seq_raw.compute() - client.cancel(ensemble_updated_seq_raw) - client.cancel(mean_updated_seq_raw) - client.cancel(running_estimated_cov_lazy) - client.cancel(running_estimated_cov) - mean_updated_seq_raw = client.persist(da.from_array(mean_tmp)) - ensemble_updated_seq_raw = client.persist(da.from_array(ensemble_tmp)) - - np.save(os.path.join( - results_folder, "mean_updated_seq_raw_{}.npy".format(i)), - mean_updated_seq_raw.compute()) - np.save(os.path.join( - results_folder, "ensemble_updated_seq_raw_{}.npy".format(i)), - ensemble_updated_seq_raw.compute()) - - print(ensembles.compute()) - mean_updated_seq_loc = client.persist(da.from_array(mean_store)) - ensemble_updated_seq_loc = client.persist(da.from_array(ensemble_store)) - running_estimated_cov = raw_estimated_cov - # for i in range(0, G.shape[0], chunk_size): - for i in range(0, 50, chunk_size): - print(i) - running_estimated_cov_lazy = localizer_loc(ensemble_updated_seq_loc) - running_estimated_cov = client.persist(running_estimated_cov_lazy) - print("Estimating cov.") - progress(running_estimated_cov) - - G_chunk = G[i:i+chunk_size].reshape(chunk_size, -1) - y_chunk = y[i:i+chunk_size].reshape(chunk_size, -1) - - mean_updated_seq_loc, ensemble_updated_seq_loc = my_filter.update_ensemble( - mean_updated_seq_loc, ensemble_updated_seq_loc, - G_chunk, y_chunk, data_std, running_estimated_cov - ) - mean_updated_seq_loc = client.persist(mean_updated_seq_loc) - ensemble_updated_seq_loc = client.persist(ensemble_updated_seq_loc) - print("Start progress.") - progress(ensemble_updated_seq_loc) - print("End progress.") - ensemble_tmp = ensemble_updated_seq_loc.compute() - mean_tmp = mean_updated_seq_loc.compute() - client.cancel(ensemble_updated_seq_loc) - client.cancel(mean_updated_seq_loc) - client.cancel(running_estimated_cov_lazy) - client.cancel(running_estimated_cov) - mean_updated_seq_loc = client.persist(da.from_array(mean_tmp)) - ensemble_updated_seq_loc = client.persist(da.from_array(ensemble_tmp)) - - np.save(os.path.join( - results_folder, "mean_updated_seq_loc_{}.npy".format(i)), - mean_updated_seq_loc.compute()) - np.save(os.path.join( - results_folder, "ensemble_updated_seq_loc_{}.npy".format(i)), - ensemble_updated_seq_loc.compute()) - - # Compare sequential and one-go. - fig, axs = plt.subplots(2, 3) - grid.plot_vals(ground_truth, axs[0, 0], - vmin=-3, vmax=3) - axs[0, 0].title.set_text('ground truth') - axs[0, 0].set_xticks([]) - - grid.plot_vals(client.compute(mean_updated_one_go_raw).result(), axs[0, 1], - vmin=-3, vmax=3) - axs[0, 1].title.set_text('all-at-once (no localization)') - axs[0, 1].set_xticks([]) - axs[0, 1].set_yticks([]) - - grid.plot_vals(client.compute(mean_updated_one_go_loc).result(), axs[0, 2], - vmin=-3, vmax=3) - axs[0, 2].title.set_text('all-at-once (localization)') - axs[0, 2].set_xticks([]) - axs[0, 2].set_yticks([]) - - grid.plot_vals(client.compute(mean).result(), axs[1, 0], vmin=-3, vmax=3) - axs[1, 0].title.set_text('prior mean') - axs[1, 0].set_xticks([]) - - grid.plot_vals(client.compute(mean_updated_seq_raw).result(), axs[1, 1], - vmin=-3, vmax=3) - axs[1, 1].title.set_text('sequential (no localization)') - axs[1, 1].set_xticks([]) - axs[1, 1].set_yticks([]) - - grid.plot_vals(mean_updated_seq_loc.compute(), axs[1, 2], - vmin=-3, vmax=3) - axs[1, 2].title.set_text('sequential (localization)') - axs[1, 2].set_xticks([]) - axs[1, 2].set_yticks([]) - - plt.savefig("sequential_vs_one_go_bigdata", bbox_inches="tight", pad_inches=0.1, dpi=400) - # plt.show() - - # Now compare scores. - RE_score_one_go_raw = compute_RE_score(mean, mean_updated_one_go_raw, ground_truth) - RE_score_one_go_loc = compute_RE_score(mean, mean_updated_one_go_loc, ground_truth) - CRPS_one_go_raw, misfit_one_go_raw, spread_one_go_raw = compute_CRPS( - ensemble_updated_one_go_raw, ground_truth) - CRPS_one_go_loc, misfit_one_go_loc, spread_one_go_loc = compute_CRPS( - ensemble_updated_one_go_loc, ground_truth) - ES_one_go_raw, ES_misfit_one_go_raw, ES_spread_one_go_raw = compute_energy_score( - ensemble_updated_one_go_raw, ground_truth) - ES_one_go_loc, ES_misfit_one_go_loc, ES_spread_one_go_loc = compute_energy_score( - ensemble_updated_one_go_loc, ground_truth) - - - fig, axs = plt.subplots(3, 3) - grid.plot_vals(ground_truth, axs[0, 0], vmin=-3, vmax=3) - axs[0, 0].title.set_text('ground truth') - axs[0, 0].set_xticks([]) - - grid.plot_vals(mean_updated_one_go_raw.compute(), axs[0, 1], - vmin=-3, vmax=3) - axs[0, 1].title.set_text('all-at-once (no localization)') - axs[0, 1].set_xticks([]) - axs[0, 1].set_yticks([]) - - grid.plot_vals(mean_updated_one_go_loc.compute(), axs[0, 2], - vmin=-3, vmax=3) - axs[0, 2].title.set_text('all-at-once (localization)') - axs[0, 2].set_xticks([]) - axs[0, 2].set_yticks([]) - - grid.plot_vals(CRPS_one_go_raw.compute(), axs[1, 0], vmin=0, vmax=3) - axs[1, 0].title.set_text('CRPS raw (ES: {})'.format(ES_one_go_raw.compute())) - axs[1, 0].set_xticks([]) - - grid.plot_vals(misfit_one_go_raw.compute(), axs[1, 1], - points=grid_pts[data_inds], - vmin=0, vmax=2.5, - points_color='magenta') - axs[1, 1].title.set_text('misfit raw') - axs[1, 1].set_xticks([]) - axs[1, 1].set_xticks([]) - - grid.plot_vals(spread_one_go_raw.compute(), axs[1, 2], - points=grid_pts[data_inds], - points_color='magenta') - axs[1, 2].title.set_text('spread raw') - axs[1, 2].set_xticks([]) - axs[1, 2].set_yticks([]) - - grid.plot_vals(CRPS_one_go_loc.compute(), axs[2, 0], vmin=0, vmax=3) - axs[2, 0].title.set_text('CRPS loc (ES: {})'.format(ES_one_go_loc.compute())) - axs[2, 0].set_xticks([]) - - grid.plot_vals(misfit_one_go_loc.compute(), axs[2, 1], - points=grid_pts[data_inds], - vmin=0, vmax=2.5, - points_color="magenta") - axs[2, 1].title.set_text('misfit loc') - axs[2, 1].set_xticks([]) - axs[2, 1].set_yticks([]) - - grid.plot_vals(spread_one_go_loc.compute(), axs[2, 2], - points=grid_pts[data_inds], - points_color='magenta') - axs[2, 2].title.set_text('spread loc') - axs[2, 2].set_xticks([]) - axs[2, 2].set_yticks([]) - - plt.savefig("scores_sequential_vs_one_go_bigdata", - bbox_inches="tight", pad_inches=0.1, dpi=400) - # plt.show() - - # Plot members. - fig, axs = plt.subplots(3, 4) - - grid.plot_vals(ground_truth, axs[0, 0], vmin=-3, vmax=3) - axs[0, 0].title.set_text('ground truth') - axs[0, 0].set_xticks([]) - - grid.plot_vals(ensembles[0, :].compute(), axs[0, 1], - vmin=-3, vmax=3) - axs[0, 1].title.set_text('ensemble 0') - axs[0, 1].set_xticks([]) - axs[0, 1].set_yticks([]) - - grid.plot_vals(ensembles[1, :].compute(), axs[0, 2], - vmin=-3, vmax=3) - axs[0, 2].title.set_text('ensemble 1') - axs[0, 2].set_xticks([]) - axs[0, 2].set_yticks([]) - - grid.plot_vals(ensembles[2, :].compute(), axs[0, 3], - vmin=-3, vmax=3) - axs[0, 3].title.set_text('ensemble 2') - axs[0, 3].set_xticks([]) - axs[0, 3].set_yticks([]) - - grid.plot_vals(mean_updated_one_go_raw, axs[1, 0], - points=grid_pts[data_inds], vmin=-3, vmax=3) - axs[1, 0].title.set_text('mean updated raw') - axs[1, 0].set_xticks([]) - - grid.plot_vals(ensemble_updated_one_go_raw[0, :].compute(), axs[1, 1], - vmin=-3, vmax=3) - axs[1, 1].title.set_text('ensemble 0 raw') - axs[1, 1].set_xticks([]) - axs[1, 1].set_yticks([]) - - grid.plot_vals(ensemble_updated_one_go_raw[1, :].compute(), axs[1, 2], - vmin=-3, vmax=3) - axs[1, 2].title.set_text('ensemble 1 raw') - axs[1, 2].set_xticks([]) - axs[1, 2].set_yticks([]) - - grid.plot_vals(ensemble_updated_one_go_raw[2, :].compute(), axs[1, 3], - vmin=-3, vmax=3) - axs[1, 3].title.set_text('ensemble 2 raw') - axs[1, 3].set_xticks([]) - axs[1, 3].set_yticks([]) - - grid.plot_vals(mean_updated_one_go_loc, axs[2, 0], - points=grid_pts[data_inds], vmin=-3, vmax=3) - axs[2, 0].title.set_text('mean updated loc') - axs[2, 0].set_xticks([]) - - grid.plot_vals(ensemble_updated_one_go_loc[0, :].compute(), axs[2, 1], - vmin=-3, vmax=3) - axs[2, 1].title.set_text('ensemble 0 loc') - axs[2, 1].set_xticks([]) - axs[2, 1].set_yticks([]) - - grid.plot_vals(ensemble_updated_one_go_loc[1, :].compute(), axs[2, 2], - vmin=-3, vmax=3) - axs[2, 2].title.set_text('ensemble 1 loc') - axs[2, 2].set_xticks([]) - axs[2, 2].set_yticks([]) - - grid.plot_vals(ensemble_updated_one_go_loc[2, :].compute(), axs[2, 3], - vmin=-3, vmax=3) - axs[2, 3].title.set_text('ensemble 2 loc') - axs[2, 3].set_xticks([]) - axs[2, 3].set_yticks([]) - - plt.savefig("ensembles_sequential_vs_one_go_bigdata", - bbox_inches="tight", pad_inches=0.1, dpi=400) - # plt.show() - - -if __name__ == "__main__": - main() diff --git a/reporting/toy_example/sequential_vs_one_go_different_noise.py b/reporting/toy_example/sequential_vs_one_go_different_noise.py deleted file mode 100644 index 2ab0527..0000000 --- a/reporting/toy_example/sequential_vs_one_go_different_noise.py +++ /dev/null @@ -1,208 +0,0 @@ -""" Compare the performance of the sequential Ensemble Kalman Filter with -the version that assimilates all data points in one go. - -In this one, vary the observation noise level to see if it has an effect on the accuracy. - -""" -import os -import numpy as np -import pandas as pd -import matplotlib.pyplot as plt -import dask.array as da -from dask.distributed import Client, wait, progress -import diesel as ds -from diesel.scoring import compute_RE_score, compute_energy_score -from diesel.estimation import localize_covariance - - -import time - - -# results_folder ="/home/cedric/PHD/Dev/DIESEL/reporting/toy_example/results/" -results_folder ="/storage/homefs/ct19x463/Dev/DIESEL/reporting/toy_example/results_paper/synthetic_different_noise/" - - -def main(): - # Instantiate a local cluster, to mimick distributed computations, but on a single machine. - # cluster = ds.cluster.LocalCluster() - cluster = ds.cluster.UbelixCluster(n_nodes=12, mem_per_node=64, cores_per_node=3, - partition="gpu", qos="job_gpu") - cluster.scale(10) - client = Client(cluster) - - # Add to builtins so we have one global client. - __builtins__.CLIENT = client - from diesel.kalman_filtering import EnsembleKalmanFilter - - # Build a square grid with 80^2 elements. - grid = ds.gridding.SquareGrid(n_pts_1d=80) - grid_pts = grid.grid_pts - - # Construct (lazy) covariance matrix. - lambda0=0.1 - lengthscales = da.from_array([lambda0]) - kernel = ds.covariance.matern32(lengthscales) - true_covariance_matrix = kernel.covariance_matrix(grid_pts, grid_pts) - - # Compute compressed SVD. - svd_rank = 900 # Since our matrix is 900 * 900 this will be a full SVD. - u, s, v = da.linalg.svd_compressed( - true_covariance_matrix, k=svd_rank, compute=False) - - # Construct sampler from the svd of the covariance matrix. - sampler = ds.sampling.SvdSampler(u, s) - - # Repeat the whole experiment several time for statistical analysis. - ES_prior, ES_aao_loc, ES_seq_loc, ES_aao_truecov = [], [], [], [] - RE_aao_loc, RE_seq_loc, RE_aao_truecov = [], [], [] - RMSE_prior, RMSE_aao_loc, RMSE_seq_loc, RMSE_aao_truecov = [], [], [], [] - data_stds = [] - repetitions = [] - n_rep = 20 - for rep in range(n_rep): - print("Repetition {} / {}.".format(rep, n_rep)) - for data_std in [0.002, 0.005. 0.007, 0.01, 0.03, 0.05, 0.07, 0.1, 0.2, 0.3, 0.5, 0.7, 0.8, 0.9, 1.0] - # Sample 30 ensemble members. - n_ensembles = 30 - ensembles = sampler.sample(n_ensembles + 1) # Note this is still lazy. - - # Use the first sample as ground truth. - ground_truth = ensembles[0] - ensembles = ensembles[1:] - - # Compute ensemble mean. - mean = da.mean(da.stack(ensembles, axis=1), axis=1) - - # Trigger computations. - ground_truth = client.persist(ground_truth) - ensembles = [client.persist(ensemble) for ensemble in ensembles] - - # Stack ensembles so are in the format required later. - ensembles = da.stack(ensembles) - - # Save for later. - np.save(os.path.join( - results_folder, "ground_truth_{}_std_{}.npy".format(rep, data_std)), ground_truth.compute()) - np.save(os.path.join( - results_folder, "ensemble_{}_std_{}.npy".format(rep, data_std)), ensembles.compute()) - np.save(os.path.join( - results_folder, "mean_{}_std_{}.npy".format(rep, data_std)), mean.compute()) - - # Estimate covariance using empirical covariance of the ensemble. - raw_estimated_cov_lazy = ds.estimation.empirical_covariance(ensembles) - raw_estimated_cov = client.persist(raw_estimated_cov_lazy) - - # Perform covariance localization (use scaled version of base covariance to localize). - # Maybe should persist here. - localization_matrix = kernel.covariance_matrix(grid_pts, grid_pts, - lengthscales=da.from_array([2 * lambda0])) - localization_matrix = client.persist(localization_matrix) - loc_estimated_cov = localize_covariance(raw_estimated_cov, localization_matrix) - loc_estimated_cov = client.persist(loc_estimated_cov) - - # Prepare some data by randomly selecting some points. - n_data = 300 - data_inds = np.random.choice(ground_truth.shape[0], n_data, replace=False) - np.save(os.path.join( - results_folder, "data_inds_{}_std_{}.npy".format(rep, data_std)), data_inds) - - # Built observation operator. - G = np.zeros((data_inds.shape[0], ground_truth.shape[0])) - G[range(data_inds.shape[0]), data_inds] = 1 - G = da.from_array(G) - - y = G @ ground_truth - - # Run data assimilation using an ensemble Kalman filter. - my_filter = EnsembleKalmanFilter() - - # ------------------------- - # All-at-once assimilation. - # ------------------------- - # Localized version. - mean_updated_aao_loc, ensemble_updated_aao_loc = my_filter.update_ensemble( - mean, ensembles, G, y, data_std, loc_estimated_cov) - mean_updated_aao_loc, ensemble_updated_aao_loc = ( - client.persist(mean_updated_aao_loc), - client.persist(ensemble_updated_aao_loc)) - progress(ensemble_updated_aao_loc) - - np.save(os.path.join( - results_folder, "mean_updated_aao_loc_{}_std_{}.npy".format(rep, data_std)), - mean_updated_aao_loc.compute()) - np.save(os.path.join( - results_folder, "ensemble_updated_aao_loc_{}_std_{}.npy".format(rep, data_std)), - ensemble_updated_aao_loc.compute()) - - # Version with the true covariance. - mean_updated_aao_truecov, ensemble_updated_aao_truecov = my_filter.update_ensemble( - mean, ensembles, G, y, data_std, loc_estimated_cov) - mean_updated_aao_truecov, ensemble_updated_aao_truecov = ( - client.persist(mean_updated_aao_truecov), - client.persist(ensemble_updated_aao_truecov)) - progress(ensemble_updated_aao_truecov) - - np.save(os.path.join( - results_folder, "mean_updated_aao_truecov_{}_std_{}.npy".format(rep, data_std)), - mean_updated_aao_truecov.compute()) - np.save(os.path.join( - results_folder, "ensemble_updated_aao_truecov_{}_std_{}.npy".format(rep, data_std)), - ensemble_updated_aao_truecov.compute()) - # ----------------------------- - # End all-at-once assimilation. - # ----------------------------- - - # ------------------------ - # Sequential assimilation. - # ------------------------ - mean_updated_seq_loc, ensemble_updated_seq_loc = my_filter.update_ensemble_sequential_nondask( - mean, ensembles, G, y, data_std, localization_matrix) - - np.save(os.path.join( - results_folder, "mean_updated_seq_loc_{}_std_{}.npy".format(rep, data_std)), - mean_updated_seq_loc) - np.save(os.path.join( - results_folder, "ensemble_updated_seq_loc_{}_std_{}.npy".format(rep, data_std)), - ensemble_updated_seq_loc) - - # Compute scores and save. - ES, _, _ = compute_energy_score(ensembles.compute(), ground_truth.compute()) - ES_prior.append(ES) - - ES, _, _ = compute_energy_score(ensemble_updated_aao_loc.compute(), ground_truth.compute()) - ES_aao_loc.append(ES) - - ES, _, _ = compute_energy_score(ensemble_updated_seq_loc, ground_truth.compute()) - ES_seq_loc.append(ES) - - ES, _, _ = compute_energy_score(ensemble_updated_aao_truecov.compute(), ground_truth.compute()) - ES_aao_truecov.append(ES) - - RE = np.median(compute_RE_score(mean.compute(), mean_updated_aao_loc.compute(), ground_truth.compute())) - RE_aao_loc.append(RE) - - RE = np.median(compute_RE_score(mean.compute(), mean_updated_seq_loc, ground_truth.compute())) - RE_seq_loc.append(RE) - - RE = np.median(compute_RE_score(mean.compute(), mean_updated_aao_truecov.compute(), ground_truth.compute())) - RE_aao_truecov.append(RE) - - RMSE_prior.append(np.sqrt(np.mean((mean.compute().reshape(-1, 1) - ground_truth.compute().reshape(-1, 1))**2))) - RMSE_aao_loc.append(np.sqrt(np.mean((mean_updated_aao_loc.compute().reshape(-1, 1) - ground_truth.compute().reshape(-1, 1))**2))) - RMSE_seq_loc.append(np.sqrt(np.mean((mean_updated_seq_loc.reshape(-1, 1) - ground_truth.compute().reshape(-1, 1))**2))) - RMSE_aao_truecov.append(np.sqrt(np.mean((mean_updated_aao_truecov.compute().reshape(-1, 1) - ground_truth.compute().reshape(-1, 1))**2))) - - data_stds.append(data_std) - repetitions.append(rep) - - df_results = pd.DataFrame({ - 'repetition': repetitions, - 'data std': data_stds, - 'RMSE prior': RMSE_prior, 'RMSE aao loc': RMSE_aao_loc, 'RMSE seq loc': RMSE_seq_loc, 'RMSE aao truecov': RMSE_aao_truecov, - 'ES prior': ES_prior, 'ES aao loc': ES_aao_loc, 'ES seq loc': ES_seq_loc, 'ES aao truecov': ES_aao_truecov, - 'RE aao loc': RE_aao_loc, 'RE seq loc': RE_seq_loc, 'RE aao truecov': RE_aao_truecov}) - df_results.to_pickle(os.path.join(results_folder, 'scores.pkl')) - - -if __name__ == "__main__": - main() diff --git a/reporting/toy_example/sequential_vs_one_go_order.py b/reporting/toy_example/sequential_vs_one_go_order.py deleted file mode 100644 index 0cd2a84..0000000 --- a/reporting/toy_example/sequential_vs_one_go_order.py +++ /dev/null @@ -1,151 +0,0 @@ -""" Check the effect of observation ordering on the sequential assimilation. - -""" -import os -import numpy as np -import pandas as pd -import matplotlib.pyplot as plt -import dask.array as da -from dask.distributed import Client, wait, progress -import diesel as ds -from diesel.scoring import compute_RE_score, compute_energy_score -from diesel.estimation import localize_covariance - - -import time - - -# results_folder ="/home/cedric/PHD/Dev/DIESEL/reporting/toy_example/results/" -results_folder ="/storage/homefs/ct19x463/Dev/DIESEL/reporting/toy_example/results_paper/synthetic_ordering/" - - -def main(): - # Instantiate a local cluster, to mimick distributed computations, but on a single machine. - # cluster = ds.cluster.LocalCluster() - cluster = ds.cluster.UbelixCluster(n_nodes=12, mem_per_node=64, cores_per_node=3, - partition="gpu", qos="job_gpu") - cluster.scale(10) - client = Client(cluster) - - # Add to builtins so we have one global client. - __builtins__.CLIENT = client - from diesel.kalman_filtering import EnsembleKalmanFilter - - # Build a square grid with 80^2 elements. - grid = ds.gridding.SquareGrid(n_pts_1d=80) - grid_pts = grid.grid_pts - - # Construct (lazy) covariance matrix. - lambda0=0.1 - lengthscales = da.from_array([lambda0]) - kernel = ds.covariance.matern32(lengthscales) - true_covariance_matrix = kernel.covariance_matrix(grid_pts, grid_pts) - - # Compute compressed SVD. - svd_rank = 900 # Since our matrix is 900 * 900 this will be a full SVD. - u, s, v = da.linalg.svd_compressed( - true_covariance_matrix, k=svd_rank, compute=False) - - # Construct sampler from the svd of the covariance matrix. - sampler = ds.sampling.SvdSampler(u, s) - - # Repeat the whole experiment several time for statistical analysis. - ES_prior, ES_aao_loc, ES_seq_loc, ES_aao_truecov = [], [], [], [] - RE_aao_loc, RE_seq_loc, RE_aao_truecov = [], [], [] - RMSE_prior, RMSE_aao_loc, RMSE_seq_loc, RMSE_aao_truecov = [], [], [], [] - - rep = 0 - # Sample 30 ensemble members. - n_ensembles = 30 - ensembles = sampler.sample(n_ensembles + 1) # Note this is still lazy. - - # Use the first sample as ground truth. - ground_truth = ensembles[0] - ensembles = ensembles[1:] - - # Compute ensemble mean. - mean = da.mean(da.stack(ensembles, axis=1), axis=1) - - # Trigger computations. - ground_truth = client.persist(ground_truth) - ensembles = [client.persist(ensemble) for ensemble in ensembles] - - # Stack ensembles so are in the format required later. - ensembles = da.stack(ensembles) - - # Save for later. - np.save(os.path.join( - results_folder, "ground_truth_{}.npy".format(rep)), ground_truth.compute()) - np.save(os.path.join( - results_folder, "ensemble_{}.npy".format(rep)), ensembles.compute()) - np.save(os.path.join( - results_folder, "mean_{}.npy".format(rep)), mean.compute()) - - # Estimate covariance using empirical covariance of the ensemble. - raw_estimated_cov_lazy = ds.estimation.empirical_covariance(ensembles) - raw_estimated_cov = client.persist(raw_estimated_cov_lazy) - - # Perform covariance localization (use scaled version of base covariance to localize). - # Maybe should persist here. - localization_matrix = kernel.covariance_matrix(grid_pts, grid_pts, - lengthscales=da.from_array([2 * lambda0])) - localization_matrix = client.persist(localization_matrix) - loc_estimated_cov = localize_covariance(raw_estimated_cov, localization_matrix) - loc_estimated_cov = client.persist(loc_estimated_cov) - - # Prepare some data by randomly selecting some points. - n_data = 500 - data_inds = np.random.choice(ground_truth.shape[0], n_data, replace=False) - np.save(os.path.join( - results_folder, "data_inds_{}.npy".format(rep)), data_inds) - - n_rep = 20 - for rep in range(n_rep): - # Shuffle data ordering. - np.random.shuffle(data_inds) - - print("Repetition {} / {}.".format(rep, n_rep)) - # Built observation operator. - G = np.zeros((data_inds.shape[0], ground_truth.shape[0])) - G[range(data_inds.shape[0]), data_inds] = 1 - G = da.from_array(G) - - data_std = 0.01 - y = G @ ground_truth - - # Run data assimilation using an ensemble Kalman filter. - my_filter = EnsembleKalmanFilter() - - # ------------------------ - # Sequential assimilation. - # ------------------------ - mean_updated_seq_loc, ensemble_updated_seq_loc = my_filter.update_ensemble_sequential_nondask( - mean, ensembles, G, y, data_std, localization_matrix) - - np.save(os.path.join( - results_folder, "mean_updated_seq_loc_{}.npy".format(rep)), - mean_updated_seq_loc) - np.save(os.path.join( - results_folder, "ensemble_updated_seq_loc_{}.npy".format(rep)), - ensemble_updated_seq_loc) - - # Compute scores and save. - ES, _, _ = compute_energy_score(ensembles.compute(), ground_truth.compute()) - ES_prior.append(ES) - ES, _, _ = compute_energy_score(ensemble_updated_seq_loc, ground_truth.compute()) - - RE = np.median(compute_RE_score(mean.compute(), mean_updated_seq_loc, ground_truth.compute())) - RE_seq_loc.append(RE) - - RMSE_prior.append(np.sqrt(np.mean((mean.compute().reshape(-1, 1) - ground_truth.compute().reshape(-1, 1))**2))) - RMSE_seq_loc.append(np.sqrt(np.mean((mean_updated_seq_loc.reshape(-1, 1) - ground_truth.compute().reshape(-1, 1))**2))) - - df_results = pd.DataFrame({ - 'RMSE prior': RMSE_prior, 'RMSE seq loc': RMSE_seq_loc, - 'ES prior': ES_prior, 'ES seq loc': ES_seq_loc, - 'RE seq loc': RE_seq_loc, ) - df_results.to_pickle(os.path.join(results_folder, 'scores.pkl')) - - -if __name__ == "__main__": - main() diff --git a/reporting/toy_example/sequential_vs_one_go_paper.py b/reporting/toy_example/sequential_vs_one_go_paper.py deleted file mode 100644 index 22a7065..0000000 --- a/reporting/toy_example/sequential_vs_one_go_paper.py +++ /dev/null @@ -1,226 +0,0 @@ -""" Compare the performance of the sequential Ensemble Kalman Filter with -the version that assimilates all data points in one go. - -This is a synthetic toy example, so the ensemble is produced by sampling from a Matern 3/2 -model with lambda = 0.1 on the unit square. - -This version is the one used for the article. - -We compare 4 different assimilations: - - 1) all-at-once (localized) - 2) all-at-once with true covariance matrix. - 3) sequential with localization at the beginning only. - 4) sequential with localization at every step. - -Note that, according to Nerger (2014), the difference between aao and seq -is bigger when the observation noise is smaller that the model standard -deviation. -Here, the observation noise std is set at 1% of the one of the model. - -""" -import os -import numpy as np -import pandas as pd -import matplotlib.pyplot as plt -import dask.array as da -from dask.distributed import Client, wait, progress -import diesel as ds -from diesel.scoring import compute_RE_score, compute_energy_score -from diesel.estimation import localize_covariance - - -import time - - -# results_folder ="/home/cedric/PHD/Dev/DIESEL/reporting/toy_example/results/" -results_folder ="/storage/homefs/ct19x463/Dev/DIESEL/reporting/toy_example/results_paper/synthetic/" - - -def main(): - # Instantiate a local cluster, to mimick distributed computations, but on a single machine. - # cluster = ds.cluster.LocalCluster() - cluster = ds.cluster.UbelixCluster(n_nodes=12, mem_per_node=64, cores_per_node=3, - partition="gpu", qos="job_gpu") - cluster.scale(10) - client = Client(cluster) - - # Add to builtins so we have one global client. - __builtins__.CLIENT = client - from diesel.kalman_filtering import EnsembleKalmanFilter - - # Build a square grid with 80^2 elements. - grid = ds.gridding.SquareGrid(n_pts_1d=80) - grid_pts = grid.grid_pts - - # Construct (lazy) covariance matrix. - lambda0=0.1 - lengthscales = da.from_array([lambda0]) - kernel = ds.covariance.matern32(lengthscales) - true_covariance_matrix = kernel.covariance_matrix(grid_pts, grid_pts) - - # Compute compressed SVD. - svd_rank = 900 # Since our matrix is 900 * 900 this will be a full SVD. - u, s, v = da.linalg.svd_compressed( - true_covariance_matrix, k=svd_rank, compute=False) - - # Construct sampler from the svd of the covariance matrix. - sampler = ds.sampling.SvdSampler(u, s) - - # Repeat the whole experiment several time for statistical analysis. - ES_prior, ES_aao_loc, ES_seq_loc, ES_aao_truecov = [], [], [], [] - RE_aao_loc, RE_seq_loc, RE_aao_truecov = [], [], [] - RMSE_prior, RMSE_aao_loc, RMSE_seq_loc, RMSE_aao_truecov = [], [], [], [] - n_rep = 20 - for rep in range(n_rep): - print("Repetition {} / {}.".format(rep, n_rep)) - # Sample 30 ensemble members. - n_ensembles = 30 - ensembles = sampler.sample(n_ensembles + 1) # Note this is still lazy. - - # Use the first sample as ground truth. - ground_truth = ensembles[0] - ensembles = ensembles[1:] - - # Compute ensemble mean. - mean = da.mean(da.stack(ensembles, axis=1), axis=1) - - # Trigger computations. - ground_truth = client.persist(ground_truth) - ensembles = [client.persist(ensemble) for ensemble in ensembles] - - # Stack ensembles so are in the format required later. - ensembles = da.stack(ensembles) - - # Save for later. - """ - np.save(os.path.join( - results_folder, "ground_truth_{}.npy".format(rep)), ground_truth.compute()) - np.save(os.path.join( - results_folder, "ensemble_{}.npy".format(rep)), ensembles.compute()) - np.save(os.path.join( - results_folder, "mean_{}.npy".format(rep)), mean.compute()) - """ - - # Estimate covariance using empirical covariance of the ensemble. - raw_estimated_cov_lazy = ds.estimation.empirical_covariance(ensembles) - raw_estimated_cov = client.persist(raw_estimated_cov_lazy) - - # Perform covariance localization (use scaled version of base covariance to localize). - # Maybe should persist here. - localization_matrix = kernel.covariance_matrix(grid_pts, grid_pts, - lengthscales=da.from_array([2 * lambda0])) - localization_matrix = client.persist(localization_matrix) - loc_estimated_cov = localize_covariance(raw_estimated_cov, localization_matrix) - loc_estimated_cov = client.persist(loc_estimated_cov) - - # Prepare some data by randomly selecting some points. - n_data = 1000 - data_inds = np.random.choice(ground_truth.shape[0], n_data, replace=False) - """ - np.save(os.path.join( - results_folder, "data_inds_{}.npy".format(rep)), data_inds) - """ - - # Built observation operator. - G = np.zeros((data_inds.shape[0], ground_truth.shape[0])) - G[range(data_inds.shape[0]), data_inds] = 1 - G = da.from_array(G) - - data_std = 0.01 - y = G @ ground_truth - - # Run data assimilation using an ensemble Kalman filter. - my_filter = EnsembleKalmanFilter() - - # ------------------------- - # All-at-once assimilation. - # ------------------------- - # Localized version. - mean_updated_aao_loc, ensemble_updated_aao_loc = my_filter.update_ensemble( - mean, ensembles, G, y, data_std, loc_estimated_cov) - mean_updated_aao_loc, ensemble_updated_aao_loc = ( - client.persist(mean_updated_aao_loc), - client.persist(ensemble_updated_aao_loc)) - progress(ensemble_updated_aao_loc) - - """ - np.save(os.path.join( - results_folder, "mean_updated_aao_loc_{}.npy".format(rep)), - mean_updated_aao_loc.compute()) - np.save(os.path.join( - results_folder, "ensemble_updated_aao_loc_{}.npy".format(rep)), - ensemble_updated_aao_loc.compute()) - """ - - # Version with the true covariance. - mean_updated_aao_truecov, ensemble_updated_aao_truecov = my_filter.update_ensemble( - mean, ensembles, G, y, data_std, loc_estimated_cov) - mean_updated_aao_truecov, ensemble_updated_aao_truecov = ( - client.persist(mean_updated_aao_truecov), - client.persist(ensemble_updated_aao_truecov)) - progress(ensemble_updated_aao_truecov) - - """ - np.save(os.path.join( - results_folder, "mean_updated_aao_truecov_{}.npy".format(rep)), - mean_updated_aao_truecov.compute()) - np.save(os.path.join( - results_folder, "ensemble_updated_aao_truecov_{}.npy".format(rep)), - ensemble_updated_aao_truecov.compute()) - """ - # ----------------------------- - # End all-at-once assimilation. - # ----------------------------- - - # ------------------------ - # Sequential assimilation. - # ------------------------ - mean_updated_seq_loc, ensemble_updated_seq_loc = my_filter.update_ensemble_sequential_nondask( - mean, ensembles, G, y, data_std, localization_matrix) - - """ - np.save(os.path.join( - results_folder, "mean_updated_seq_loc_{}.npy".format(rep)), - mean_updated_seq_loc) - np.save(os.path.join( - results_folder, "ensemble_updated_seq_loc_{}.npy".format(rep)), - ensemble_updated_seq_loc) - """ - - # Compute scores and save. - ES, _, _ = compute_energy_score(ensembles.compute(), ground_truth.compute()) - ES_prior.append(ES) - - ES, _, _ = compute_energy_score(ensemble_updated_aao_loc.compute(), ground_truth.compute()) - ES_aao_loc.append(ES) - - ES, _, _ = compute_energy_score(ensemble_updated_seq_loc, ground_truth.compute()) - ES_seq_loc.append(ES) - - ES, _, _ = compute_energy_score(ensemble_updated_aao_truecov.compute(), ground_truth.compute()) - ES_aao_truecov.append(ES) - - RE = np.median(compute_RE_score(mean.compute(), mean_updated_aao_loc.compute(), ground_truth.compute())) - RE_aao_loc.append(RE) - - RE = np.median(compute_RE_score(mean.compute(), mean_updated_seq_loc, ground_truth.compute())) - RE_seq_loc.append(RE) - - RE = np.median(compute_RE_score(mean.compute(), mean_updated_aao_truecov.compute(), ground_truth.compute())) - RE_aao_truecov.append(RE) - - RMSE_prior.append(np.sqrt(np.mean((mean.compute().reshape(-1, 1) - ground_truth.compute().reshape(-1, 1))**2))) - RMSE_aao_loc.append(np.sqrt(np.mean((mean_updated_aao_loc.compute().reshape(-1, 1) - ground_truth.compute().reshape(-1, 1))**2))) - RMSE_seq_loc.append(np.sqrt(np.mean((mean_updated_seq_loc.reshape(-1, 1) - ground_truth.compute().reshape(-1, 1))**2))) - RMSE_aao_truecov.append(np.sqrt(np.mean((mean_updated_aao_truecov.compute().reshape(-1, 1) - ground_truth.compute().reshape(-1, 1))**2))) - - df_results = pd.DataFrame({ - 'RMSE prior': RMSE_prior, 'RMSE aao loc': RMSE_aao_loc, 'RMSE seq loc': RMSE_seq_loc, 'RMSE aao truecov': RMSE_aao_truecov, - 'ES prior': ES_prior, 'ES aao loc': ES_aao_loc, 'ES seq loc': ES_seq_loc, 'ES aao truecov': ES_aao_truecov, - 'RE aao loc': RE_aao_loc, 'RE seq loc': RE_seq_loc, 'RE aao truecov': RE_aao_truecov}) - df_results.to_pickle(os.path.join(results_folder, 'scores.pkl')) - - -if __name__ == "__main__": - main() diff --git a/requirements.txt b/requirements.txt deleted file mode 100755 index 4386eba..0000000 --- a/requirements.txt +++ /dev/null @@ -1,10 +0,0 @@ -numpy -cython -scipy -scikit-learn -pandas -xarray -dask -dask-distance -netCDF4 -cartopy diff --git a/scripts/download_mpi_ge_temperature_data.sh b/scripts/download_mpi_ge_temperature_data.sh deleted file mode 100644 index 6867791..0000000 --- a/scripts/download_mpi_ge_temperature_data.sh +++ /dev/null @@ -1,1140 +0,0 @@ -#!/bin/bash -############################################################################## -# ESG Federation download script -# -# Template version: 1.2 -# Generated by esgf-data.dkrz.de - 2022/04/20 10:23:00 -# Search URL: 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- -#These are the embedded files to be downloaded -download_files="$(cat < 10#${ver2[i]})) - then - return 1 - fi - if ((10#${ver1[i]} < 10#${ver2[i]})) - then - return 2 - fi - done - return 0 -} - -check_commands() { - #check wget - local MIN_WGET_VERSION=1.10 - vercomp $(wget -V | sed -n 's/^.* \([1-9]\.[0-9.]*\) .*$/\1/p') $MIN_WGET_VERSION - case $? in - 2) #lower - wget -V - echo - echo "** ERROR: wget version is too old. Use version $MIN_WGET_VERSION or greater. **" >&2 - exit 1 - esac -} - -usage() { - echo "Usage: $(basename $0) [flags] [openid] [username]" - echo "Flags is one of:" - sed -n '/^while getopts/,/^done/ s/^\([^)]*\)[^#]*#\(.*$\)/\1 \2/p' $0 - echo - echo "This command stores the states of the downloads in .$0.status" - echo "For more information check the website: http://esgf.org/wiki/ESGF_wget" -} - -#defaults -debug=0 -clean_work=1 - -#parse flags -while getopts ':c:pfF:o:w:isuUndvqhHI:T' OPT; do - case $OPT in - H) skip_security=1 && use_http_sec=1;; # : Authenticate with OpenID (username,) and password, without the need for a certificate. - T) force_TLSv1=1;; # : Forces wget to use TLSv1. - c) ESG_CREDENTIALS="$OPTARG";; # : use this certificate for authentication. - f) force=1;; # : force certificate retrieval (defaults to only once per day); for certificate-less authentication (see -H option), this flag will force login and refresh cookies. - F) input_file="$OPTARG";; # : read input from file instead of the embedded one (use - to read from stdin) - o) openId="$OPTARG";; #: Provide OpenID instead of interactively asking for it. - I) username_supplied="$OPTARG";; # : Explicitly set user ID. By default, the user ID is extracted from the last component of the OpenID URL. Use this flag to override this behaviour. - w) output="$OPTARG";; # : Write embedded files into a file and exit - i) insecure=1;; # : set insecure mode, i.e. don't check server certificate - s) skip_security=1 && use_cookies_for_http_basic_auth_start=1;; # : completely skip security. It will only work if the accessed data is not secured at all. -- works only if the accessed data is unsecured or a certificate exists or cookies are saved (latter applies to -H option only). - u) update=1;; # : Issue the search again and see if something has changed. - U) update_files=1;; # : Update files from server overwriting local ones (detect with -u) - n) dry_run=1;; # : Don't download any files, just report. - p) clean_work=0;; # : preserve data that failed checksum - d) verbose=1;debug=1;; # : display debug information - v) verbose=1;; # : be more verbose - q) quiet=1;; # : be less verbose - h) usage && exit 0;; # : displays this help - \?) echo "Unknown option '$OPTARG'" >&2 && usage && exit 1;; - \:) echo "Missing parameter for flag '$OPTARG'" >&2 && usage && exit 1;; - esac -done -shift $(($OPTIND - 1)) - -#setup input as desired by the user -if [[ "$input_file" ]]; then - if [[ "$input_file" == '-' ]]; then - download_files="$(cat)" #read from STDIN - exec 0$output - exit -fi - - -#assure we have everything we need -check_commands - -if ((update)); then - echo "Checking the server for changes..." - new_wget="$(wget "$search_url" -qO -)" - compare_cmd="grep -vE '^(# Generated by|# Search URL|search_url=)'" - if diff -q <(eval $compare_cmd<<<"$new_wget") <(eval $compare_cmd $0) >/dev/null; then - echo "No changes detected." - else - echo "Wget was changed. Dowloading. (old renamed to $0.old.#N)" - counter=0 - while [[ -f $0.old.$counter ]]; do ((counter++)); done - mv $0 $0.old.$counter - echo "$new_wget" > $0 - fi - exit 0 -fi - - -############################################################################## -check_java() { - if ! type java >& /dev/null; then - echo "Java could not be found." >&2 - return 1 - fi - if java -version 2>&1|grep openjdk >/dev/null; then - openjdk=1; - else - openjdk=0; - fi - jversion=($(jversion=$(java -version 2>&1 | awk '/version/ {gsub("\"","");print $3}'); echo ${jversion//./ })) - mVer=${jversion[1]} - if [ $openjdk -eq 1 ]; then - mVer=${jversion[0]} - if ((mVer<5)); then - echo "Openjdk detected. Version 9+ is required for retrieving the certificate." >&2 - echo "Current version seems older: $(java -version | head -n1) " >&2 - return 1 - fi - else - - if ((mVer<5)); then - echo "Java version 1.5+ is required for retrieving the certificate." >&2 - echo "Current version seems older: $(java -version | head -n1) " >&2 - return 1 - fi - fi -} - -check_myproxy_logon() { - if ! type myproxy-logon >& /dev/null; then - echo "myproxy-logon could not be found." >&2 - return 1 - fi - echo "myproxy-logon found" >&2 -} - -proxy_to_java() { - local proxy_user proxy_pass proxy_server proxy_port - eval $(sed 's#^\(https\?://\)\?\(\([^:@]*\)\(:\([^@]*\)\)\?@\)\?\([^:/]*\)\(:\([0-9]*\)\)\?.*#proxy_user=\3;proxy_pass=\5;proxy_server=\6;proxy_port=\8#'<<<$http_proxy) - local JAVA_PROXY= - [[ "$proxy_server" ]] && JAVA_PROXY=$JAVA_PROXY" -Dhttp.proxyHost=$proxy_server" - [[ "$proxy_port" ]] && JAVA_PROXY=$JAVA_PROXY" -Dhttp.proxyPort=$proxy_port" - eval $(sed 's#^\(https\?://\)\?\(\([^:@]*\)\(:\([^@]*\)\)\?@\)\?\([^:/]*\)\(:\([0-9]*\)\)\?.*#proxy_user=\3;proxy_pass=\5;proxy_server=\6;proxy_port=\8#'<<<$https_proxy) - [[ "$proxy_server" ]] && JAVA_PROXY=$JAVA_PROXY" -Dhttps.proxyHost=$proxy_server" - [[ "$proxy_port" ]] && JAVA_PROXY=$JAVA_PROXY" -Dhttps.proxyPort=$proxy_port" - - echo "$JAVA_PROXY" -} - -# get certificates from github -get_certificates() { - # don't if this was already done today - [[ -z $force && "$(find $ESG_CERT_DIR -type d -mtime -1 2>/dev/null)" ]] && return 0 - echo -n "Retrieving Federation Certificates..." >&2 - - if ! wget -O $ESG_HOME/esg-truststore.ts --no-check-certificate https://github.com/ESGF/esgf-dist/raw/master/installer/certs/esg-truststore.ts; then - echo "Could not fetch esg-truststore"; - return 1 - fi - - if ! wget --no-check-certificate https://raw.githubusercontent.com/ESGF/esgf-dist/master/installer/certs/esg_trusted_certificates.tar -O - -q | tar x -C $ESG_HOME; then - #certificates tarred into esg_trusted_certificates. (if it breaks, let the user know why - wget --no-check-certificate https://raw.githubusercontent.com/ESGF/esgf-dist/master/installer/certs/esg_trusted_certificates.tar - echo "Could't update certs!" >&2 - return 1 - else - #if here everythng went fine. Replace old cert with this ones - [[ -d $ESG_CERT_DIR ]] && rm -r $ESG_CERT_DIR || mkdir -p $(dirname $ESG_CERT_DIR) - mv $ESG_HOME/esg_trusted_certificates $ESG_CERT_DIR - touch $ESG_CERT_DIR - echo "done!" >&2 - fi - -} - -# Retrieve ESG credentials -unset pass -get_credentials() { - if check_java - then - use_java=1 - else - use_java=0 - echo "No suitable java for obtaining certificate - checking for myproxy-logon instead" >&2 - check_myproxy_logon || exit 1 - fi - #get all certificates - get_certificates - - if [[ -z "$(find $MYPROXY_GETCERT -type f -mtime -1 2>/dev/null)" ]]; then - echo -n "(Downloading $MYPROXY_GETCERT... " - mkdir -p $(dirname $MYPROXY_GETCERT) - if wget -q --no-check-certificate https://raw.githubusercontent.com/ESGF/esgf-dist/master/installer/certs/getcert.jar -O $MYPROXY_GETCERT;then - echo 'done)' - touch $MYPROXY_GETCERT - else - echo 'failed)' - fi - fi - - #if the user already defined one, use it - if [[ -z $openId ]]; then - #try to parse the last valid value if any - [[ -f "$MYPROXY_STATUS" ]] && openId=$(awk -F= '/^OpenID/ {gsub("\\\\", ""); print $2}' $MYPROXY_STATUS) - if [[ -z $openId ]]; then - #no OpenID, we need to ask the user - echo -n "Please give your OpenID (Example: https://myserver/example/username) ? " - else - #Allow the user to change it if desired - echo -n "Please give your OpenID (hit ENTER to accept default: $openId)? " - fi - read -e - [[ "$REPLY" ]] && openId="$REPLY" - else - ((verbose)) && echo "Using user defined OpenID $openId (to change use -o )" - fi - - if grep -q ceda.ac.uk <<<$openId; then - username=${openId##*/} - echo -n "Please give your username if different [$username]: " - read -e - [[ "$REPLY" ]] && username="$REPLY" - fi - - - - if [ $use_java -eq 1 ] - then - local args= - #get password - [[ ! "$pass" ]] && read -sp "MyProxy Password? " pass - [[ "$openId" ]] && args=$args" --oid $openId" - [[ "$pass" ]] && args=$args" -P $pass" - [[ "$username" ]] && args=$args" -l $username" - - echo -n $'\nRetrieving Credentials...' >&2 - if ! java $(proxy_to_java) -jar $MYPROXY_GETCERT $args --ca-directory $ESG_CERT_DIR --output $ESG_CREDENTIALS ; then - echo "Certificate could not be retrieved" - exit 1 - fi - echo "done!" >&2 - else - args=`openid_to_myproxy_args $openId $username` || exit 1 - if ! myproxy-logon $args -b -o $ESG_CREDENTIALS - then - echo "Certificate could not be retrieved" - exit 1 - fi - cp $HOME/.globus/certificates/* $ESG_CERT_DIR/ - fi -} - -openid_to_myproxy_args() { - python - </dev/null; then - #check openssl and certificate - if ! openssl x509 -checkend $CERT_EXPIRATION_WARNING -noout -in $ESG_CERT 2>/dev/null; then - echo "The certificate expires in less than $((CERT_EXPIRATION_WARNING / 60 / 60)) hour(s). Renewing..." - get_credentials - else - #ok, certificate is fine - return 0 - fi - fi -} - -# -# Detect ESG credentials -# -find_credentials() { - - #is X509_USER_PROXY or $HOME/.esg/credential.pem - if [[ -f "$ESG_CREDENTIALS" ]]; then - # file found, proceed. - ESG_CERT="$ESG_CREDENTIALS" - ESG_KEY="$ESG_CREDENTIALS" - elif [[ -f "$X509_USER_CERT" && -f "$X509_USER_KEY" ]]; then - # second try, use these certificates. - ESG_CERT="$X509_USER_CERT" - ESG_KEY="$X509_USER_KEY" - else - # If credentials are not present, just point to where they should go - echo "No ESG Credentials found in $ESG_CREDENTIALS" >&2 - ESG_CERT="$ESG_CREDENTIALS" - ESG_KEY="$ESG_CREDENTIALS" - #they will be retrieved later one - fi - - - #chek openssl and certificate - if (which openssl &>/dev/null); then - if ( openssl version | grep 'OpenSSL 1\.0' ); then - echo '** WARNING: ESGF Host certificate checking might not be compatible with OpenSSL 1.0+' - fi - check_cert || { (($?==1)); exit 1; } - fi - - if [[ $CHECK_SERVER_CERT == "Yes" ]]; then - [[ -d "$ESG_CERT_DIR" ]] || { echo "CA certs not found. Aborting."; exit 1; } - PKI_WGET_OPTS="--ca-directory=$ESG_CERT_DIR" - fi - - #some wget version complain if there's no file present - [[ -f $COOKIE_JAR ]] || touch $COOKIE_JAR - - PKI_WGET_OPTS="$PKI_WGET_OPTS --certificate=$ESG_CERT --private-key=$ESG_KEY --save-cookies=$COOKIE_JAR --load-cookies=$COOKIE_JAR --ca-certificate=$ESG_CERT" - -} - -check_chksum() { - local file="$1" - local chk_type=$2 - local chk_value=$3 - local local_chksum=Unknown - - case $chk_type in - md5) local_chksum=$(md5sum_ $file | cut -f1 -d" ");; - sha256) local_chksum=$(sha256sum_ $file|awk '{print $1}'|cut -d ' ' -f1);; - *) echo "Can't verify checksum." && return 0;; - esac - - #verify - ((debug)) && echo "local:$local_chksum vs remote:$chk_value" >&2 - echo $local_chksum -} - -#Our own md5sum function call that takes into account machines that don't have md5sum but do have md5 (i.e. mac os x) -md5sum_() { - hash -r - if type md5sum >& /dev/null; then - echo $(md5sum $@) - else - echo $(md5 $@ | sed -n 's/MD5[ ]*\(.*\)[^=]*=[ ]*\(.*$\)/\2 \1/p') - fi -} - -#Our own sha256sum function call that takes into account machines that don't have sha256sum but do have sha2 (i.e. mac os x) -sha256sum_() { - hash -r - if type sha256sum >& /dev/null; then - echo $(sha256sum $@) - elif type shasum >& /dev/null; then - echo $(shasum -a 256 $@) - else - echo $(sha2 -q -256 $@) - fi -} - -get_mod_time_() { - if ((MACOSX)); then - #on a mac modtime is stat -f %m - echo "$(stat -f %m $@)" - else - #on linux (cygwin) modtime is stat -c %Y - echo "$(stat -c %Y $@)" - fi - return 0; -} - -remove_from_cache() { - local entry="$1" - local tmp_file="$(grep -ve "^$entry" "$CACHE_FILE")" - echo "$tmp_file" > "$CACHE_FILE" - unset cached -} - -#Download data from node using cookies and not certificates. -download_http_sec() -{ - #The data to be downloaded. - data=" $url" - filename="$file" - - #Wget args. - if ((insecure)) - then - wget_args=" --no-check-certificate --cookies=on --keep-session-cookies --save-cookies $COOKIES_FOLDER/wcookies.txt " - else - wget_args=" --ca-directory=$WGET_TRUSTED_CERTIFICATES --cookies=on --keep-session-cookies --save-cookies $COOKIES_FOLDER/wcookies.txt " - fi - - if ((use_cookies_for_http_basic_auth_start)) || ((use_cookies_for_http_basic_auth)) - then - wget_args=" $wget_args"" --load-cookies $COOKIES_FOLDER/wcookies.txt" - fi - - if((force_TLSv1)) - then - wget_args=" $wget_args"" --secure-protocol=TLSv1 " - fi - - - if [[ ! -z "$ESGF_WGET_OPTS" ]] - then - wget_args="$wget_args $ESGF_WGET_OPTS" - fi - - - #use cookies for the next downloads - use_cookies_for_http_basic_auth=1; - - #Debug message. - if ((debug)) - then - echo -e "\nExecuting:\n" - echo -e "wget $wget_args $data\n" - fi - - - #Try to download the data. - command="wget $wget_args -O $filename $data" - http_resp=$(eval $command 2>&1) - cmd_exit_status="$?" - - if ((debug)) - then - echo -e "\nHTTP response:\n $http_resp\n" - fi - - #Extract orp service from url ? - #Evaluate response. - #redirects=$(echo "$http_resp" | egrep -c ' 302 ') - #(( "$redirects" == 1 )) && - if echo "$http_resp" | grep -q "/esg-orp/" - then - urls=$(echo "$http_resp" | egrep -o 'https://[^ ]+' | cut -d'/' -f 3) - orp_service=$(echo "$urls" | tr '\n' ' ' | cut -d' ' -f 2) - - - #Use cookies for transaction with orp. - wget_args=" $wget_args"" --load-cookies $COOKIES_FOLDER/wcookies.txt" - - #Download data using either http basic auth or http login form. - if [[ "$openid_c" == */openid/ || "$openid_c" == */openid ]] - then - download_http_sec_open_id - else - download_http_sec_decide_service - fi - else - if echo "$http_resp" | grep -q "401 Unauthorized" \ - || echo "$http_resp" | grep -q "403: Forbidden" \ - || echo "$http_resp" | grep -q "Connection timed out." \ - || echo "$http_resp" | grep -q "no-check-certificate" \ - || (( $cmd_exit_status != 0 )) - then - echo "ERROR : http request to OpenID Relying Party service failed." - failed=1 - fi - fi -} - - -#Function that decides which implementaion of idp to use. -download_http_sec_decide_service() -{ - #find claimed id - - pos=$(echo "$openid_c" | egrep -o '/' | wc -l) - username_c=$(echo "$openid_c" | cut -d'/' -f "$(($pos + 1))") - esgf_uri=$(echo "$openid_c" | egrep -o '/esgf-idp/openid/') - - host=$(echo "$openid_c" | cut -d'/' -f 3) - #test ceda first. - - if [[ -z "$esgf_uri" ]] - then - openid_c_tmp="https://""$host""/openid/" - else - openid_c_tmp="https://""$host""/esgf-idp/openid/" - fi - - command="wget "$openid_c_tmp" --no-check-certificate ${force_TLSv1:+--secure-protocol=TLSv1} -O-" - - if [[ ! -z "$ESGF_WGET_OPTS" ]] - then - command="$command $ESGF_WGET_OPTS" - fi - - #Debug message. - if ((debug)) - then - echo -e "\nExecuting:\n" - echo -e "$command\n" - fi - - - #Execution of command. - http_resp=$(eval $command 2>&1) - cmd_exit_status="$?" - - - if ((debug)) - then - echo -e "\nHTTP response:\n $http_resp\n" - fi - - - if echo "$http_resp" | grep -q "[application/xrds+xml]" \ - && echo "$http_resp" | grep -q "200 OK" \ - && (( cmd_exit_status == 0 )) - then - openid_c=$openid_c_tmp - download_http_sec_open_id - else - if [[ -z "$esgf_uri" ]] - then - echo "ERROR : HTTP request to OpenID Relying Party service failed." - failed=1 - else - download_http_sec_cl_id - fi - fi -} - - -download_http_sec_retry() -{ - echo -e "\nRetrying....\n" - #Retry in case that last redirect did not work, this happens with older version of wget. - command="wget $wget_args $data" - - #Debug message. - if ((debug)) - then - echo -e "Executing:\n" - echo -e "$command\n" - fi - - http_resp=$(eval $command 2>&1) - cmd_exit_status="$?" - - if ((debug)) - then - echo -e "\nHTTP response:\n $http_resp\n" - fi - - if echo "$http_resp" | grep -q "401 Unauthorized" \ - || echo "$http_resp" | grep -q "403: Forbidden" \ - || echo "$http_resp" | grep -q "Connection timed out." \ - || echo "$http_resp" | grep -q "no-check-certificate" \ - || (( $cmd_exit_status != 0 )) - then - echo -e "\nERROR : Retry failed.\n" - #rm "$filename" - failed=1 - fi #if retry failed. -} - -#Function for downloading data using the claimed id. -download_http_sec_cl_id() -{ - #Http request for sending openid to the orp service. - command="wget --post-data \"openid_identifier=$openid_c&rememberOpenid=on\" $wget_args -O- https://$orp_service/esg-orp/j_spring_openid_security_check.htm " - - #Debug message. - if ((debug)) - then - echo -e "Executing:\n" - echo -e "wget $command\n" - fi - - - #Execution of command. - http_resp=$(eval $command 2>&1) - cmd_exit_status="$?" - - - if ((debug)) - then - echo -e "\nHTTP response:\n $http_resp\n" - fi - - - #Extract orp service from openid ? - #Evaluate response.If redirected to idp service send the credentials. - #redirects=$(echo "$http_resp" | egrep -c ' 302 ') - #(( redirects == 2 )) && - if echo "$http_resp" | grep -q "login.htm" && (( cmd_exit_status == 0 )) - then - - urls=$(echo "$http_resp" | egrep -o 'https://[^ ]+' | cut -d'/' -f 3) - idp_service=$(echo "$urls" | tr '\n' ' ' | cut -d' ' -f 2) - - command="wget --post-data password=\"$password_c\" $wget_args ${quiet:+-q} ${quiet:--v} -O $filename https://$idp_service/esgf-idp/idp/login.htm" - - - #Debug message. - if ((debug)) - then - echo -e "Executing:\n" - echo -e "wget $command\n" - fi - - #Execution of command. - http_resp=$(eval $command 2>&1) - cmd_exit_status="$?" - - if ((debug)) - then - echo -e "\nHTTP response:\n $http_resp\n" - fi - - #Evaluate response. - #redirects=$(echo "$http_resp" | egrep -c ' 302 ') - #(( "$redirects" != 5 )) \ - if echo "$http_resp" | grep -q "text/html" \ - || echo "$http_resp" | grep -q "403: Forbidden" \ - || (( cmd_exit_status != 0 )) - then - rm "$filename" - download_http_sec_retry - fi - - else - echo "ERROR : HTTP request to OpenID Provider service failed." - failed=1 - fi #if redirected to idp. -} - - - -download_http_sec_open_id() -{ - #Http request for sending openid to the orp web service. - command="wget --post-data \"openid_identifier=$openid_c&rememberOpenid=on\" --header=\"esgf-idea-agent-type:basic_auth\" --http-user=\"$username_c\" --http-password=\"$password_c\" $wget_args ${quiet:+-q} ${quiet:--v} -O $filename https://$orp_service/esg-orp/j_spring_openid_security_check.htm " - - - #Debug message. - if ((debug)) - then - echo -e "Executing:\n" - echo -e "$command\n" - fi - - #Execution of command. - http_resp=$(eval $command 2>&1) - cmd_exit_status="$?" - - - if ((debug)) - then - echo -e "\nHTTP response:\n $http_resp\n" - fi - - #Evaluate response. - #redirects=$(echo "$http_resp" | egrep -c ' 302 ') - #(( "$redirects" != 7 )) || - if echo "$http_resp" | grep -q "text/html" || (( $cmd_exit_status != 0 )) - then - rm "$filename" - download_http_sec_retry - fi #if error during http basic authentication. - -} - - -download() { - wget="wget ${insecure:+--no-check-certificate} ${quiet:+-q} ${quiet:--v} -c ${force_TLSv1:+--secure-protocol=TLSv1} $PKI_WGET_OPTS" - - while read line - do - # read csv here document into proper variables - eval $(awk -F "' '" '{$0=substr($0,2,length($0)-2); $3=tolower($3); print "file=\""$1"\";url=\""$2"\";chksum_type=\""$3"\";chksum=\""$4"\""}' <(echo $line) ) - - #Process the file - echo -n "$file ..." - - #get the cached entry if any. - cached="$(grep -e "^$file" "$CACHE_FILE")" - - #if we have the cache entry but no file, clean it. - if [[ ! -f $file && "$cached" ]]; then - #the file was removed, clean the cache - remove_from_cache "$file" - unset cached - fi - - #check it wasn't modified - if [[ -n "$cached" && "$(get_mod_time_ $file)" == $(echo "$cached" | cut -d ' ' -f2) ]]; then - if [[ "$chksum" == "$(echo "$cached" | cut -d ' ' -f3)" ]]; then - echo "Already downloaded and verified" - continue - elif ((update_files)); then - #user want's to overwrite newer files - rm $file - remove_from_cache "$file" - unset cached - else - #file on server is different from what we have. - echo "WARNING: The remote file was changed (probably a new version is available). Use -U to Update/overwrite" - continue - fi - fi - unset chksum_err_value chksum_err_count - - while : ; do - # (if we had the file size, we could check before trying to complete) - echo "Downloading" - [[ ! -d "$(dirname "$file")" ]] && mkdir -p "$(dirname "$file")" - if ((dry_run)); then - #all important info was already displayed, if in dry_run mode just abort - #No status will be stored - break - else - if ((use_http_sec)) - then - download_http_sec - if ((failed)) - then - break - fi - else - $wget -O "$file" $url || { failed=1; break; } - fi - fi - - #check if file is there - if [[ -f $file ]]; then - ((debug)) && echo file found - if [[ ! "$chksum" ]]; then - echo "Checksum not provided, can't verify file integrity" - break - fi - result_chksum=$(check_chksum "$file" $chksum_type $chksum) - if [[ "$result_chksum" != "$chksum" ]]; then - echo " $chksum_type failed!" - if ((clean_work)); then - if !((chksum_err_count)); then - chksum_err_value=$result_chksum - chksum_err_count=2 - elif ((checksum_err_count--)); then - if [[ "$result_chksum" != "$chksum_err_value" ]]; then - #this is a real transmission problem - chksum_err_value=$result_chksum - chksum_err_count=2 - fi - else - #ok if here we keep getting the same "different" checksum - echo "The file returns always a different checksum!" - echo "Contact the data owner to verify what is happening." - echo - sleep 1 - break - fi - - rm $file - #try again - echo -n " re-trying..." - continue - else - echo " don't use -p or remove manually." - fi - else - echo " $chksum_type ok. done!" - echo "$file" $(get_mod_time_ "$file") $chksum >> $CACHE_FILE - fi - fi - #done! - break - done - - if ((failed)); then - echo "download failed" - # most common failure is certificate expiration, so check this - #if we have the pasword we can retrigger download - ((!skip_security)) && [[ "$pass" ]] && check_cert - unset failed - fi - -done <<<"$download_files" - -} - -dedup_cache_() { - local file=${1:-${CACHE_FILE}} - ((debug)) && echo "dedup'ing cache ${file} ..." - local tmp=$(LC_ALL='C' sort -r -k1,2 $file | awk '!($1 in a) {a[$1];print $0}' | sort -k2,2) - ((DEBUG)) && echo "$tmp" - echo "$tmp" > $file - ((debug)) && echo "(cache dedup'ed)" -} - -http_basic_auth_func_info_message() -{ - echo "********************************************************************************" - echo "* *" - echo "* Note that new functionality to allow authentication without the need for *" - echo "* certificates is available with this version of the wget script. To enable, *" - echo "* use the \"-H\" option and enter your OpenID and password when prompted: *" - echo "* *" - echo "* $ "$(basename "$0")" -H [options...] *" - echo "* *" - echo "* For a full description of the available options use the help option: *" - echo "* *" - echo "* $ "$(basename "$0")" -h *" - echo "* *" - echo "********************************************************************************" -} - -# -# MAIN -# - -if ((!use_http_sec)) -then - http_basic_auth_func_info_message -fi - -echo "Running $(basename $0) version: $version" -((verbose)) && echo "we use other tools in here, don't try to user their proposed 'options' directly" -echo "Use $(basename $0) -h for help."$'\n' - -((debug)) && cat< 1)) || (("$#" == 1)) ) - then - openid_c=$1 - else - read -p "Enter your openid : " openid_c - fi - - - #Read username. - if [[ ! -z "$username_supplied" ]] - then - username_c="$username_supplied" - elif (("$#" == 2)) - then - username_c=$2 - elif [[ "$openid_c" == */openid/ || "$openid_c" == */openid ]] - then - read -p "Enter username : " username_c - fi - - #Read password. - read -s -p "Enter password : " password_c - echo -e "\n" - - fi #use cookies - -fi #use_http_sec - - -#do we have old results? Create the file if not -[ ! -f $CACHE_FILE ] && echo "#filename mtime checksum" > $CACHE_FILE && chmod 666 $CACHE_FILE - -#clean the force parameter if here (at htis point we already have the certificate) -unset force - -download - -dedup_cache_ - - -echo "done" diff --git a/setup.py b/setup.py deleted file mode 100755 index c43f6d5..0000000 --- a/setup.py +++ /dev/null @@ -1,85 +0,0 @@ -from setuptools import setup -from setuptools import find_packages -from distutils.cmd import Command -from setuptools.extension import Extension -import os -import sys -import io -import subprocess -import platform - - -# Make sure numpy and Cython get installed first. -from setuptools import dist -dist.Distribution().fetch_build_eggs(['Cython>=0.29.15', 'numpy>=1.18.0']) - -import numpy as np -from Cython.Build import cythonize -# import Cython.Compiler.Options -# Cython.Compiler.Options.annotate = True - -if "--line_trace" in sys.argv: - line_trace = True - print("Build with line trace enabled ...") - sys.argv.remove("--line_trace") -else: - line_trace = False - -PACKAGE = "diesel" -NAME = "DIESEL" -VERSION = "0.0.1" -DESCRIPTION = "DIstributed EStimation of EnsembLe Covariance " + VERSION -AUTHOR = "Cedric Travelletti" -AUTHOR_EMAIL = "cedrictravelletti@gmail.com" -URL = 'https://github.com/CedricTravelletti/DIESEL' - - -requirements = "requirements.txt" - -ext_modules = [ - "diesel/haversine.pyx", -] - - -def generate_extensions(ext_modules, line_trace=False): - - extensions = [] - - if line_trace: - print("define cython trace to True ...") - define_macros = [('CYTHON_TRACE', 1), ('CYTHON_TRACE_NOGIL', 1)] - else: - define_macros = [] - - for pyxfile in ext_modules: - ext = Extension(name='.'.join(pyxfile.split('/'))[:-4], - sources=[pyxfile], - define_macros=define_macros) - extensions.append(ext) - return extensions - - -n_cpu = 4 -ext_modules_settings = cythonize( - generate_extensions(ext_modules, line_trace), - compiler_directives={'embedsignature': True, 'linetrace': line_trace}, nthreads=n_cpu) - - -setup( - name=NAME, - version=VERSION, - description=DESCRIPTION, - author=AUTHOR, - author_email=AUTHOR_EMAIL, - url=URL, - # packages=find_packages(), - packages=['diesel' 'diesel.haversine'], - include_package_data=False, - install_requires=[# io.open(requirements, encoding='utf8').read(), - # 'mvnorm @ git+https://github.com/CedricTravelletti/torch-mvnorm.git#egg=mvnorm' - ], - classifiers=[], - # ext_modules=ext_modules_settings, - ext_modules=cythonize([Extension("diesel.haversine", sources=["diesel/haversine.pyx"])]), - include_dirs=[np.get_include(), '.', './diesel/'], -) diff --git a/tests/test_InverseWishart.py b/tests/test_InverseWishart.py deleted file mode 100644 index 2bb0946..0000000 --- a/tests/test_InverseWishart.py +++ /dev/null @@ -1,38 +0,0 @@ -""" Tests for diesel.estimation.bayesian.InverseWishartPrior - -""" -import dask.array as da -from dask.distributed import Client -import diesel as ds - - -cluster = ds.cluster.LocalCluster() -client = Client(cluster) - -# Build a square grid with 30^2 elements. -grid = ds.gridding.SquareGrid(30) -grid_pts = grid.grid_pts - -# Construct (lazy) covariance matrix. -lazy_covariance_matrix = ds.covariance.matern32(grid_pts, lambda0=0.2) - -# Compute compressed SVD. -svd_rank = 900 -u, s, v = da.linalg.svd_compressed( - lazy_covariance_matrix, k=svd_rank, compute=False) - -# Construct sampler from the svd of the covariance matrix. -sampler = ds.sampling.SvdSampler(u, s) - -# Sample 16 ensemble members. -ensembles = sampler.sample(16) -ensembles = client.compute(ensembles).result() - -# Build a simple inverse wishart prior. -dof = 10 -scale_matrix = da.eye(ensembles.shape[1]) -prior = ds.estimation.InverseWishartPrior(scale_matrix, dof) - -# Compute posterior mean given the data. -lazy_post_cov = prior.posterior_mean(ensembles) -post_cov = client.compute(lazy_post_cov).result() diff --git a/tests/test_SvdSampler.py b/tests/test_SvdSampler.py deleted file mode 100644 index 5f88ad3..0000000 --- a/tests/test_SvdSampler.py +++ /dev/null @@ -1,42 +0,0 @@ -""" Tests for diesel.sampling.SvdSampler - -""" -import dask.array as da -from dask.distributed import Client -from diesel.gridding import SquareGrid -from diesel.cluster import LocalCluster -from diesel.covariance import matern32 -from diesel.sampling import SvdSampler - - -cluster = LocalCluster() -client = Client(cluster) - -# Build a square grid with 30^2 elements. -grid = SquareGrid(30) -grid_pts = grid.grid_pts - -# Construct (lazy) covariance matrix. -lazy_covariance_matrix = matern32(grid_pts, lambda0=0.2) - -# Compute compressed SVD. -svd_rank = 900 -u, s, v = da.linalg.svd_compressed( - lazy_covariance_matrix, k=svd_rank, compute=False) - -# Construct sampler from the svd of the covariance matrix. -sampler = SvdSampler(u, s) - -# Sample 16 ensemble members. -ensembles = sampler.sample(16) - -ensembles = client.compute(ensembles).result() - -# Plot results -import matplotlib.pyplot as plt -fig, axs = plt.subplots(4, 4) - -for i, sample in enumerate(ensembles): - axs.flatten()[i].imshow(grid.list_to_mesh(sample)) - -plt.show() diff --git a/tests/test_kalman_filter.py b/tests/test_kalman_filter.py deleted file mode 100644 index fe5509e..0000000 --- a/tests/test_kalman_filter.py +++ /dev/null @@ -1,106 +0,0 @@ -import numpy as np -import matplotlib.pyplot as plt -import dask.array as da -from dask.distributed import Client -import diesel as ds -from diesel.kalman_filtering import EnsembleKalmanFilter -from diesel.estimation import localize_covariance -from diesel.scoring import compute_RE_score - - -def main(): - # Instantiate a local cluster, to mimick distributed computations, but on a single machine. - cluster = ds.cluster.LocalCluster() - client = Client(cluster) - - # Build a square grid with 30^2 elements. - grid = ds.gridding.SquareGrid(n_pts_1d=60) - grid_pts = grid.grid_pts - - # TODO. - grid_pts = 90 * grid_pts - - # Construct (lazy) covariance matrix. - kernel = ds.covariance.matern32(lengthscales=da.from_array([0.1])) - lazy_covariance_matrix = kernel.covariance_matrix(grid_pts, grid_pts, metric='haversine') - - # Compute compressed SVD. - svd_rank = 900 # Since our matrix is 900 * 900 this will be a full SVD. - u, s, v = da.linalg.svd_compressed( - lazy_covariance_matrix, k=svd_rank, compute=False) - - # Construct sampler from the svd of the covariance matrix. - sampler = ds.sampling.SvdSampler(u, s) - - # Sample 30 ensemble members. - n_ensembles = 30 - ensembles = sampler.sample(n_ensembles + 1) # Note this is still lazy. - - # Use the first sample as ground truth. - ground_truth = ensembles[0] - ensembles = ensembles[1:] - - # Trigger computations. - ground_truth = ground_truth.compute() - ensembles = [ensemble.compute() for ensemble in ensembles] - - # Estimate covariance using empirical covariance of the ensemble. - raw_estimated_cov_lazy = ds.estimation.empirical_covariance(ensembles) - - # Persist the covariance on the cluster. - raw_estimated_cov = client.persist(raw_estimated_cov_lazy) - - # Prepare some data by randomly selecting some points. - n_data = 60 - data_inds = np.random.choice(ground_truth.shape[0], n_data, replace=False) - - # Built observation operator. - G = np.zeros((data_inds.shape[0], ground_truth.shape[0])) - G[range(data_inds.shape[0]), data_inds] = 1 - G = da.from_array(G) - - data_std = 0.01 - y = G @ ground_truth - - # Plot data location. - fig, ax = plt.subplots() - grid.plot_vals(ground_truth, ax, points=grid_pts[data_inds]) - - # Compute ensemble mean. - mean = da.mean(da.stack(ensembles, axis=1), axis=1) - - # Run data assimilation using an ensemble Kalman filter. - my_filter = EnsembleKalmanFilter() - mean_updated = my_filter.update_mean(mean, G, y, data_std, raw_estimated_cov) - - fig, axs = plt.subplots(1, 2) - grid.plot_vals(ground_truth, axs[0], points=grid_pts[data_inds]) - grid.plot_vals(mean_updated.compute(), axs[1], points=grid_pts[data_inds]) - plt.savefig("compare_reconstruction_raw", bbox_inches="tight", pad_inches=0.1, dpi=400) - - fig, ax = plt.subplots() - RE_score = compute_RE_score(mean, mean_updated, ground_truth) - ax = grid.plot_vals(RE_score.compute(), ax, points=grid_pts[data_inds], - vmin=-10, vmax=1) - - # Compare with localized version. - # Perform covariance localization (use base covariance to localize). - loc_estimated_cov = localize_covariance(raw_estimated_cov, lazy_covariance_matrix) - mean_updated_loc = my_filter.update_mean(mean, G, y, data_std, loc_estimated_cov) - - fig, axs = plt.subplots(1, 2) - grid.plot_vals(ground_truth, axs[0], points=grid_pts[data_inds]) - grid.plot_vals(mean_updated_loc.compute(), axs[1], points=grid_pts[data_inds]) - plt.savefig("compare_reconstruction_loc", bbox_inches="tight", pad_inches=0.1, dpi=400) - - # Also run with the true covariance. - mean_updated_exact = my_filter.update_mean(mean, G, y, data_std, lazy_covariance_matrix) - - fig, axs = plt.subplots(1, 2) - grid.plot_vals(ground_truth, axs[0], points=grid_pts[data_inds]) - grid.plot_vals(mean_updated_exact.compute(), axs[1], points=grid_pts[data_inds]) - plt.savefig("compare_reconstruction_exact", bbox_inches="tight", pad_inches=0.1, dpi=400) - - -if __name__ == "__main__": - main() diff --git a/tests/test_non_stationary.py b/tests/test_non_stationary.py deleted file mode 100644 index ed381d8..0000000 --- a/tests/test_non_stationary.py +++ /dev/null @@ -1,58 +0,0 @@ -import numpy as np -import matplotlib.pyplot as plt -import dask.array as da -from dask.distributed import Client -import diesel as ds - - -def main(): - # Instantiate a local cluster, to mimick distributed computations, but on a single machine. - cluster = ds.cluster.LocalCluster() - client = Client(cluster) - - # Build a square grid with 30^2 elements. - grid = ds.gridding.SquareGrid(n_pts_1d=60) - grid_pts = grid.grid_pts - - lambda0 = 0.1 - lengthscales = da.from_array([0.1, 0.4]) - kernel = ds.covariance.matern32(lengthscales) - - cov_mat = kernel.covariance_matrix(grid_pts, grid_pts) - - # Plot covariance to verify everything works. - fig, ax = plt.subplots() - grid.plot_vals(cov_mat[1000, :], ax, points=grid_pts[1000].reshape(1, -1)) - plt.show() - - global_lengthscales = da.from_array([1 / np.sqrt(2.44)]) - local_lengthscales = da.from_array([1 / np.sqrt(578.09)]) - - dat_pts = np.array([0.93, 0.95, - 1.05, - 1.34, 1.355, - 1.265, - 1.45, - 2.2, 2.3, 2.4, 2.5]).reshape(-1, 1) - true_fun = lambda x: np.sin(10 * np.pi * x) / (2 * x) + (x - 1)**4 - y = true_fun(dat_pts) - - myGP = ds.BaCompositeGP( - global_covariance=ds.covariance.matern32(global_lengthscales), - local_covariance=ds.covariance.matern32(local_lengthscales)) - - lmbda = 0.019 - b = 1 - - pred_pts = np.linspace(0.5, 2.5, 200).reshape(-1, 1) - true_fun = lambda x: np.sin(10 * np.pi * x) / (2 * x) + (x - 1)**4 - preds_global, preds_local = myGP.predict(pred_pts, dat_pts, y, lmbda, b) - - plt.plot(pred_pts, true_fun(pred_pts)) - plt.scatter(dat_pts, true_fun(dat_pts)) - plt.plot(pred_pts, preds_global) - plt.plot(pred_pts, preds_global + preds_local, color="red") - plt.show() - -if __name__ == "__main__": - main()