From 81a85bcc9cf1538ebe9f046dce0d43f02d4f1c84 Mon Sep 17 00:00:00 2001 From: Gregory Petrochenkov Date: Sun, 10 Dec 2023 15:43:52 -0500 Subject: [PATCH 1/8] Stac Client Progress --- notebooks/Catalog Tutorial.ipynb | 77 ++++--- src/gval/utils/loading_datasets.py | 323 ++++++++++++++++++++++++++++- 2 files changed, 372 insertions(+), 28 deletions(-) diff --git a/notebooks/Catalog Tutorial.ipynb b/notebooks/Catalog Tutorial.ipynb index e192e984..b3821de2 100644 --- a/notebooks/Catalog Tutorial.ipynb +++ b/notebooks/Catalog Tutorial.ipynb @@ -47,7 +47,7 @@ }, { "cell_type": "code", - "execution_count": 89, + "execution_count": 2, "id": "b66eca6a-007f-42d2-a476-054b70c2395c", "metadata": {}, "outputs": [], @@ -70,7 +70,7 @@ }, { "cell_type": "code", - "execution_count": 73, + "execution_count": 3, "id": "972505a1", "metadata": {}, "outputs": [ @@ -123,7 +123,7 @@ "1 ./candidate_categorical_1.tif compare2 agreement_categorical_1.tif" ] }, - "execution_count": 73, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -153,7 +153,7 @@ }, { "cell_type": "code", - "execution_count": 74, + "execution_count": 4, "id": "f09a9c91-8bc4-41bc-b11a-733412ba091d", "metadata": {}, "outputs": [ @@ -203,7 +203,7 @@ "1 ./benchmark_categorical_1.tif compare2" ] }, - "execution_count": 74, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -240,7 +240,7 @@ }, { "cell_type": "code", - "execution_count": 112, + "execution_count": 5, "id": "d6034a81", "metadata": {}, "outputs": [ @@ -391,7 +391,7 @@ "positive_predictive_value 0.700815 " ] }, - "execution_count": 112, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -435,13 +435,13 @@ }, { "cell_type": "code", - "execution_count": 114, + "execution_count": 16, "id": "18b0df29", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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", + "image/png": 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", "text/plain": [ "
" ] @@ -462,7 +462,9 @@ ], "source": [ "for ag_map in agreement_categorical_catalog['agreement_maps'].unique():\n", - " rxr.open_rasterio(ag_map, mask_and_scale=True).gval.cat_plot(title=ag_map)" + " rxr.open_rasterio(ag_map, mask_and_scale=True).gval.cat_plot(\n", + " title=f'Agreement Map {int(ag_map.split(\"_\")[-1][0]) + 1}'\n", + " )" ] }, { @@ -483,7 +485,7 @@ }, { "cell_type": "code", - "execution_count": 90, + "execution_count": 7, "id": "a86f7eca", "metadata": {}, "outputs": [ @@ -536,7 +538,7 @@ "1 ./candidate_continuous_1.tif compare2 ./agreement_continuous_1.tif" ] }, - "execution_count": 90, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -547,7 +549,7 @@ }, { "cell_type": "code", - "execution_count": 91, + "execution_count": 8, "id": "1b94b2cc", "metadata": {}, "outputs": [ @@ -597,7 +599,7 @@ "1 ./benchmark_continuous_1.tif compare2" ] }, - "execution_count": 91, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -616,7 +618,7 @@ }, { "cell_type": "code", - "execution_count": 101, + "execution_count": 9, "id": "6dad7a6d", "metadata": {}, "outputs": [ @@ -643,6 +645,7 @@ " \n", " 0\n", " 1\n", + " 2\n", " \n", " \n", " \n", @@ -650,41 +653,49 @@ " map_id_candidate\n", " ./candidate_continuous_0.tif\n", " ./candidate_continuous_1.tif\n", + " ./candidate_continuous_1.tif\n", " \n", " \n", " compare_id\n", " compare1\n", " compare2\n", + " compare2\n", " \n", " \n", " agreement_maps\n", " ./agreement_continuous_0.tif\n", " ./agreement_continuous_1.tif\n", + " ./agreement_continuous_1.tif\n", " \n", " \n", " map_id_benchmark\n", " ./benchmark_continuous_0.tif\n", " ./benchmark_continuous_1.tif\n", + " ./benchmark_continuous_1.tif\n", " \n", " \n", " band\n", " 1\n", " 1\n", + " 2\n", " \n", " \n", " coefficient_of_determination\n", " -0.06616\n", - " -0.329965\n", + " -2.829421\n", + " 0.10903\n", " \n", " \n", " mean_absolute_error\n", " 0.317389\n", " 0.485031\n", + " 0.485031\n", " \n", " \n", " mean_absolute_percentage_error\n", " 0.159568\n", - " 0.177735\n", + " 0.202235\n", + " 0.153235\n", " \n", " \n", "\n", @@ -701,18 +712,28 @@ "mean_absolute_error 0.317389 \n", "mean_absolute_percentage_error 0.159568 \n", "\n", - " 1 \n", + " 1 \\\n", + "map_id_candidate ./candidate_continuous_1.tif \n", + "compare_id compare2 \n", + "agreement_maps ./agreement_continuous_1.tif \n", + "map_id_benchmark ./benchmark_continuous_1.tif \n", + "band 1 \n", + "coefficient_of_determination -2.829421 \n", + "mean_absolute_error 0.485031 \n", + "mean_absolute_percentage_error 0.202235 \n", + "\n", + " 2 \n", "map_id_candidate ./candidate_continuous_1.tif \n", "compare_id compare2 \n", "agreement_maps ./agreement_continuous_1.tif \n", "map_id_benchmark ./benchmark_continuous_1.tif \n", - "band 1 \n", - "coefficient_of_determination -0.329965 \n", + "band 2 \n", + "coefficient_of_determination 0.10903 \n", "mean_absolute_error 0.485031 \n", - "mean_absolute_percentage_error 0.177735 " + "mean_absolute_percentage_error 0.153235 " ] }, - "execution_count": 101, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -754,13 +775,13 @@ }, { "cell_type": "code", - "execution_count": 110, + "execution_count": 15, "id": "c97ea173", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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", + "image/png": 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", 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" ] @@ -781,7 +802,9 @@ ], "source": [ "for ag_map in agreement_continuous_catalog['agreement_maps'].unique():\n", - " rxr.open_rasterio(ag_map, mask_and_scale=True).gval.cont_plot(title=ag_map)" + " rxr.open_rasterio(ag_map, mask_and_scale=True).gval.cont_plot(\n", + " title=f'Agreement Map {int(ag_map.split(\"_\")[-1][0]) + 1}'\n", + " )" ] } ], diff --git a/src/gval/utils/loading_datasets.py b/src/gval/utils/loading_datasets.py index e0481d7e..11449e25 100644 --- a/src/gval/utils/loading_datasets.py +++ b/src/gval/utils/loading_datasets.py @@ -5,16 +5,24 @@ # __all__ = ['*'] __author__ = "Fernando Aristizabal" + +import warnings +from numbers import Number from typing import Union, Optional, Tuple, Dict, Any import os import ast +import numpy as np import rioxarray as rxr import xarray as xr import rasterio from tempfile import NamedTemporaryFile from rio_cogeo.cogeo import cog_translate from rio_cogeo.profiles import cog_profiles +import pystac_client + +# from odc.stac import stac_load +import stackstac def load_raster_as_xarray( @@ -310,7 +318,12 @@ def _convert_to_dataset(xr_object=Union[xr.DataArray, xr.Dataset]) -> xr.Dataset if isinstance(xr_object, xr.DataArray): nodata = xr_object.rio.nodata - xr_object = xr_object.to_dataset(dim="band") + + xr_object = ( + xr_object.to_dataset(dim="band") + if "band" in xr_object.dims + else xr_object.to_dataset(name="1") + ) xr_object = xr_object.rename_vars({x: f"band_{x}" for x in xr_object.data_vars}) # Account for nodata @@ -320,3 +333,311 @@ def _convert_to_dataset(xr_object=Union[xr.DataArray, xr.Dataset]) -> xr.Dataset return xr_object else: return xr_object + + +# def dataset_from_stac(url: str, +# collection: str, +# time: str, +# bands: list = None, +# query: str = None, +# time_aggregate: str = None, +# max_items: int = None, +# intersects: dict = None, +# bbox: list = None, +# resolution: int = 10, +# chunks: dict = {'band': 1, 'x': 2048, 'y': 2048}, +# crs: str = None, +# nodata_override: Union[Number, list] = None, +# nodata_fill: Number = None) -> xr.Dataset: +# """ +# +# Parameters +# ---------- +# url : str +# Address hosting the STAC API +# collection : str +# Name of collection to get (currently limited to one) +# time : str +# Single or range of values to query in the time dimension +# bands: list, default = None +# Bands to retrieve from service +# query : str, default = None +# String command to filter data +# time_aggregate : str, default = None +# Method to aggregate multiple time stamps +# max_items : int, default = None +# The maximum amount of records to retrieve +# intersects : dict, default = None +# Dictionary representing the type of geometry and its respective coordinates +# bbox : list, default = None +# Coordinates to filter the spatial range of request +# resolution : int, default = 10 +# Resolution to get data from +# chunks : dict, default = {'band': 1, 'x': 2048, 'y': 2048} +# Blocks to break up memory for dask processing +# crs : str, default = None +# CRS to get the data in +# nodata_override : Union[Number, list], default = None +# Value/s to override the original nodata (primarily for cases when served data has none defined) +# nodata_fill : Number, default = None +# Value to fill nodata where not present +# Returns +# ------- +# xr.Dataset +# Dataset with the time slice and desired bands +# """ +# +# # Call cataloging url, search, and convert to xarray +# catalog = pystac_client.Client.open(url) +# +# stac_items = catalog.search( +# datetime=time, +# collections=[collection], +# max_items=max_items, +# intersects=intersects, +# bbox=bbox, +# query=query +# ) +# +# stack = stac_load( +# stac_items.item_collection(), +# bands=bands, +# crs=crs, +# resolution=resolution, +# chunks=chunks +# ) +# +# names = [f'band_{x+1}' for x in range(len(stack.data_vars))] +# stack = stack.rename_vars({x: y for x, y in zip(stack.data_vars, names)}) +# +# # Only get unique time indices in case there are duplicates +# vals = np.unique(stack.coords['time']) +# stack = stack.sel({'time': vals}) +# +# # Aggregate if there is more than one time +# if stack.coords['time'].shape[0] > 1: +# if time_aggregate == 'mean': +# stack = stack.mean(dim='time') +# elif time_aggregate == 'min': +# stack = stack.min(dim='time') +# elif time_aggregate == 'max': +# stack = stack.max(dim='time') +# else: +# raise ValueError("A valid aggregate must be used for time ranges") +# +# # Resolve nodata issues +# for idx, var in enumerate(stack.data_vars): +# +# if nodata_override is not None: +# idx = idx if len(nodata_override) > 1 else 0 +# stack[var].rio.write_nodata(nodata_override[idx], inplace=True) +# else: +# if stack[var].rio.nodata is None and stack[var].rio.encoded_nodata is None and nodata_fill is None: # pragma: no cover +# raise ValueError("Please provide a nodata_override as there is none present") +# else: +# stack[var].rio.write_nodata(nodata_fill, inplace=True) +# +# return stack.squeeze() + + +def _get_raster_band_nodata(band_metadata, nodata_fill) -> Number: + """ + + Parameters + ---------- + band_metadata: list + Metadata fro raster:bands extension + nodata_fill: Number + Fill in value for missing data + + Returns + ------- + Number + Number representing nodata + """ + + if band_metadata: + prop_string = str(band_metadata.coords["raster:bands"].values) + idx1, idx2 = prop_string.find("{"), prop_string.rfind("}") + + return ast.literal_eval(prop_string[idx1 : idx2 + 1]).get("nodata") + else: + if nodata_fill is None: + raise ValueError( + "Must have nodata fill value if nodata is not present in metadata" + ) + + return nodata_fill + + +def _set_nodata( + stack: xr.DataArray, band_metadata: list = None, nodata_fill: Number = None +) -> Number: + """ + + Parameters + ---------- + stack + band_metadata + nodata_fill + + Returns + ------- + + """ + + if stack.rio.nodata is not None: + stack.rio.write_nodata(stack.rio.nodata, inplace=True) + elif stack.rio.encoded_nodata is not None: + stack.rio.write_nodata(stack.rio.encoded_nodata, inplace=True) + else: + stack.rio.write_nodata( + _get_raster_band_nodata(band_metadata, nodata_fill), inplace=True + ) + + +def get_stac_data( + url: str, + collection: str, + time: str, + bands: list = None, + time_aggregate: str = None, + max_items: int = None, + intersects: dict = None, + bbox: list = None, + resolution: int = None, + nodata_fill: Number = None, +) -> xr.Dataset: + """ + + Parameters + ---------- + url + collection + time + bands + time_aggregate + max_items + intersects + bbox + nodata_fill + + Returns + ------- + + """ + + with warnings.catch_warnings(): + warnings.simplefilter("ignore") + # Call cataloging url, search, and convert to xarray + catalog = pystac_client.Client.open(url) + + args = { + key: val + for key, val in zip( + ["datetime", "collections", "max_items", "intersects", "bbox"], + [time, [collection], max_items, intersects, bbox], + ) + if key is not None + } + + stac_items = catalog.search(**args).get_all_items() + + stack = stackstac.stack(stac_items, resolution=resolution) + + # Only get unique time indices in case there are duplicates + _, idxs = np.unique(stack.coords["time"], return_index=True) + stack = stack[idxs] + + # Aggregate if there is more than one time + if stack.coords["time"].shape[0] > 1: + if time_aggregate == "mean": + stack = stack.mean(dim="time") + elif time_aggregate == "mean": + stack = stack.median(dim="time") + else: + raise ValueError("A valid aggregate must be used for time ranges") + else: + stack = stack[0] + + # Select specific bands + if bands is not None: + stack = stack.sel({"band": bands}) + + band_metadata = ( + stack.coords["raster:bands"] if "raster:bands" in stack.coords else None + ) + if "band" in stack.dims: + stack = stack.to_dataset(dim="band") + names = [f"band_{x + 1}" for x in range(len(stack.data_vars))] + og_names = [name for name in stack.data_vars] + stack = stack.rename_vars({x: y for x, y in zip(og_names, names)}) + for metadata, var, og_var in zip(band_metadata, stack.data_vars, og_names): + _set_nodata(stack[var], metadata, nodata_fill) + stack[var] = stack[var].rio.write_crs(stack.rio.crs) + stack[var].attrs["original_name"] = og_var + + else: + stack = stack.to_dataset(name="band_1") + _set_nodata(stack["band_1"], band_metadata, nodata_fill) + stack["band_1"] = stack["band_1"].rio.write_crs(stack.rio.crs) + stack["band_1"].attrs["original_name"] = bands[0] + + return stack + + +if __name__ == "__main__": + # from gval.utils.loading_datasets import dataset_from_stac + # + # candidate = dataset_from_stac( + # url="https://earth-search.aws.element84.com/v1", + # collection="sentinel-2-l2a", + # time="2020-04-01", + # bands="aot", + # bbox=[-105.78, 35.79, -105.66, 35.90], + # nodata_fill=0, + # ) + # + # benchmark = dataset_from_stac( + # url="https://earth-search.aws.element84.com/v1", + # collection="sentinel-2-l2a", + # time="2020-04-03", + # bands="aot", + # bbox=[-105.78, 35.79, -105.66, 35.90], + # nodata_fill=0, + # ) + # + # from gval.comparison.compute_continuous_metrics import _compute_continuous_metrics + # + # c, b = candidate.gval.homogenize(benchmark) + # ag = c.gval.compute_agreement_map(b, continuous=True) + # ag.compute() + # metrics_df = _compute_continuous_metrics( + # agreement_map=ag, + # candidate_map=c, + # benchmark_map=b + # ) + # + # print(metrics_df) + + from gval.utils.loading_datasets import get_stackstac_data + + candidate2 = get_stackstac_data( + url="https://earth-search.aws.element84.com/v1", + collection="sentinel-2-l2a", + time="2020-04-01", + bands=None, + bbox=[-105.78, 35.79, -105.66, 35.90], + ) + + benchmark2 = get_stackstac_data( + url="https://earth-search.aws.element84.com/v1", + collection="sentinel-2-l2a", + time="2020-04-03", + bands="aot", + bbox=[-105.78, 35.79, -105.66, 35.90], + ) + + ag2, met2 = candidate2.gval.continuous_compare(benchmark2) + + print(met2) From 9a81866de789a694ac240fae2baa4f193f9b8e99 Mon Sep 17 00:00:00 2001 From: Gregory Petrochenkov Date: Sun, 10 Dec 2023 22:28:30 -0800 Subject: [PATCH 2/8] Progress on unit tests --- .../Continuous Comparison Tutorial.ipynb | 2 +- notebooks/Subsampling Tutorial.ipynb | 6 +- src/gval/utils/loading_datasets.py | 193 +++--------------- tests/cases_stac.py | 164 +++++++++++++++ tests/test_stac.py | 63 ++++++ 5 files changed, 254 insertions(+), 174 deletions(-) create mode 100644 tests/cases_stac.py create mode 100644 tests/test_stac.py diff --git a/notebooks/Continuous Comparison Tutorial.ipynb b/notebooks/Continuous Comparison Tutorial.ipynb index 51b5c92b..e29eccab 100644 --- a/notebooks/Continuous Comparison Tutorial.ipynb +++ b/notebooks/Continuous Comparison Tutorial.ipynb @@ -710,7 +710,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.10" + "version": "3.10.13" } }, "nbformat": 4, diff --git a/notebooks/Subsampling Tutorial.ipynb b/notebooks/Subsampling Tutorial.ipynb index 96cab560..5c110a0d 100644 --- a/notebooks/Subsampling Tutorial.ipynb +++ b/notebooks/Subsampling Tutorial.ipynb @@ -1790,9 +1790,7 @@ "cell_type": "code", "execution_count": 25, "id": "a9e533c6", - "metadata": { - "scrolled": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -2269,7 +2267,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.10" + "version": "3.10.13" } }, "nbformat": 4, diff --git a/src/gval/utils/loading_datasets.py b/src/gval/utils/loading_datasets.py index 11449e25..a8031d66 100644 --- a/src/gval/utils/loading_datasets.py +++ b/src/gval/utils/loading_datasets.py @@ -335,111 +335,6 @@ def _convert_to_dataset(xr_object=Union[xr.DataArray, xr.Dataset]) -> xr.Dataset return xr_object -# def dataset_from_stac(url: str, -# collection: str, -# time: str, -# bands: list = None, -# query: str = None, -# time_aggregate: str = None, -# max_items: int = None, -# intersects: dict = None, -# bbox: list = None, -# resolution: int = 10, -# chunks: dict = {'band': 1, 'x': 2048, 'y': 2048}, -# crs: str = None, -# nodata_override: Union[Number, list] = None, -# nodata_fill: Number = None) -> xr.Dataset: -# """ -# -# Parameters -# ---------- -# url : str -# Address hosting the STAC API -# collection : str -# Name of collection to get (currently limited to one) -# time : str -# Single or range of values to query in the time dimension -# bands: list, default = None -# Bands to retrieve from service -# query : str, default = None -# String command to filter data -# time_aggregate : str, default = None -# Method to aggregate multiple time stamps -# max_items : int, default = None -# The maximum amount of records to retrieve -# intersects : dict, default = None -# Dictionary representing the type of geometry and its respective coordinates -# bbox : list, default = None -# Coordinates to filter the spatial range of request -# resolution : int, default = 10 -# Resolution to get data from -# chunks : dict, default = {'band': 1, 'x': 2048, 'y': 2048} -# Blocks to break up memory for dask processing -# crs : str, default = None -# CRS to get the data in -# nodata_override : Union[Number, list], default = None -# Value/s to override the original nodata (primarily for cases when served data has none defined) -# nodata_fill : Number, default = None -# Value to fill nodata where not present -# Returns -# ------- -# xr.Dataset -# Dataset with the time slice and desired bands -# """ -# -# # Call cataloging url, search, and convert to xarray -# catalog = pystac_client.Client.open(url) -# -# stac_items = catalog.search( -# datetime=time, -# collections=[collection], -# max_items=max_items, -# intersects=intersects, -# bbox=bbox, -# query=query -# ) -# -# stack = stac_load( -# stac_items.item_collection(), -# bands=bands, -# crs=crs, -# resolution=resolution, -# chunks=chunks -# ) -# -# names = [f'band_{x+1}' for x in range(len(stack.data_vars))] -# stack = stack.rename_vars({x: y for x, y in zip(stack.data_vars, names)}) -# -# # Only get unique time indices in case there are duplicates -# vals = np.unique(stack.coords['time']) -# stack = stack.sel({'time': vals}) -# -# # Aggregate if there is more than one time -# if stack.coords['time'].shape[0] > 1: -# if time_aggregate == 'mean': -# stack = stack.mean(dim='time') -# elif time_aggregate == 'min': -# stack = stack.min(dim='time') -# elif time_aggregate == 'max': -# stack = stack.max(dim='time') -# else: -# raise ValueError("A valid aggregate must be used for time ranges") -# -# # Resolve nodata issues -# for idx, var in enumerate(stack.data_vars): -# -# if nodata_override is not None: -# idx = idx if len(nodata_override) > 1 else 0 -# stack[var].rio.write_nodata(nodata_override[idx], inplace=True) -# else: -# if stack[var].rio.nodata is None and stack[var].rio.encoded_nodata is None and nodata_fill is None: # pragma: no cover -# raise ValueError("Please provide a nodata_override as there is none present") -# else: -# stack[var].rio.write_nodata(nodata_fill, inplace=True) -# -# return stack.squeeze() - - def _get_raster_band_nodata(band_metadata, nodata_fill) -> Number: """ @@ -512,11 +407,16 @@ def get_stac_data( Parameters ---------- - url - collection - time - bands - time_aggregate + url : str + Endpoint with STAC API service + collection : str + Name of the collection to call data from + time : str + Time domain of interest (in case of multiple times a time aggregate must be supplied) + bands : list, default = None + Bands of interest in collection + time_aggregate : str + Way to reduce time to s single timestamp in the case of multiple time steps (currently valid options are) max_items intersects bbox @@ -524,6 +424,8 @@ def get_stac_data( Returns ------- + xr.Dataset + Xarray object with resepective STAC API data """ @@ -551,17 +453,27 @@ def get_stac_data( # Aggregate if there is more than one time if stack.coords["time"].shape[0] > 1: + crs = stack.rio.crs if time_aggregate == "mean": stack = stack.mean(dim="time") - elif time_aggregate == "mean": - stack = stack.median(dim="time") + stack.attrs["time_aggregate"] = "mean" + elif time_aggregate == "min": + stack = stack.min(dim="time") + stack.attrs["time_aggregate"] = "min" + elif time_aggregate == "max": + stack = stack.max(dim="time") + stack.attrs["time_aggregate"] = "max" else: raise ValueError("A valid aggregate must be used for time ranges") + + stack.rio.write_crs(crs, inplace=True) else: stack = stack[0] + stack.attrs["time_aggregate"] = "none" # Select specific bands if bands is not None: + bands = [bands] if isinstance(bands, str) else bands stack = stack.sel({"band": bands}) band_metadata = ( @@ -584,60 +496,3 @@ def get_stac_data( stack["band_1"].attrs["original_name"] = bands[0] return stack - - -if __name__ == "__main__": - # from gval.utils.loading_datasets import dataset_from_stac - # - # candidate = dataset_from_stac( - # url="https://earth-search.aws.element84.com/v1", - # collection="sentinel-2-l2a", - # time="2020-04-01", - # bands="aot", - # bbox=[-105.78, 35.79, -105.66, 35.90], - # nodata_fill=0, - # ) - # - # benchmark = dataset_from_stac( - # url="https://earth-search.aws.element84.com/v1", - # collection="sentinel-2-l2a", - # time="2020-04-03", - # bands="aot", - # bbox=[-105.78, 35.79, -105.66, 35.90], - # nodata_fill=0, - # ) - # - # from gval.comparison.compute_continuous_metrics import _compute_continuous_metrics - # - # c, b = candidate.gval.homogenize(benchmark) - # ag = c.gval.compute_agreement_map(b, continuous=True) - # ag.compute() - # metrics_df = _compute_continuous_metrics( - # agreement_map=ag, - # candidate_map=c, - # benchmark_map=b - # ) - # - # print(metrics_df) - - from gval.utils.loading_datasets import get_stackstac_data - - candidate2 = get_stackstac_data( - url="https://earth-search.aws.element84.com/v1", - collection="sentinel-2-l2a", - time="2020-04-01", - bands=None, - bbox=[-105.78, 35.79, -105.66, 35.90], - ) - - benchmark2 = get_stackstac_data( - url="https://earth-search.aws.element84.com/v1", - collection="sentinel-2-l2a", - time="2020-04-03", - bands="aot", - bbox=[-105.78, 35.79, -105.66, 35.90], - ) - - ag2, met2 = candidate2.gval.continuous_compare(benchmark2) - - print(met2) diff --git a/tests/cases_stac.py b/tests/cases_stac.py new file mode 100644 index 00000000..9f6b3cd4 --- /dev/null +++ b/tests/cases_stac.py @@ -0,0 +1,164 @@ +""" +Test functionality for gval/statistics modules +""" + +# __all__ = ['*'] + +from pytest_cases import parametrize + +url="https://earth-search.aws.element84.com/v1" +collection="sentinel-2-l2a" +bbox=[-105.78, 35.79, -105.72, 35.84] + +bands = [ + "aot", + ["aot"], + ["aot"], + ["aot"], + ["aot", "red"] +] + +time = [ + ["2020-04-01", "2020-04-03"], + ["2020-04-01/2020-04-03", "2020-04-06/2020-04-08"], + ["2020-04-01/2020-04-03", "2020-04-06/2020-04-08"], + ["2020-04-01/2020-04-03", "2020-04-06/2020-04-08"], + ["2020-04-01", "2020-04-03"] +] + +time_aggreagte = [ + None, + "mean", + "min", + "max", + None +] + +nodata_fill = [ + None, + None, + None, + None, + 0 +] + +expected_df = [ + { + 'band': ['1'], + 'coefficient_of_determination': [-1.4498682989135663], + 'mean_absolute_error': [0.025393391237534115], + 'mean_absolute_percentage_error': [0.20440844788169352], + 'mean_normalized_mean_absolute_error': [0.20592052600829783], + 'mean_normalized_root_mean_squared_error': [0.2110757427848459], + 'mean_percentage_error': [-0.20540028455073725], + 'mean_signed_error': [-0.025329236900295846], + 'mean_squared_error': [0.0006775147935436701], + 'range_normalized_mean_absolute_error': [0.31741739046917644], + 'range_normalized_root_mean_squared_error': [0.32536392930255564], + 'root_mean_squared_error': [0.026029114344204452], + 'symmetric_mean_absolute_percentage_error': [0.22778286629345662] + }, + { + 'band': ['1'], + 'coefficient_of_determination': [0.5954048574471591], + 'mean_absolute_error': [0.011375152039973328], + 'mean_absolute_percentage_error': [0.09299193429857326], + 'mean_normalized_mean_absolute_error': [0.09198102377977661], + 'mean_normalized_root_mean_squared_error': [0.0950603218421001], + 'mean_percentage_error': [-0.03739148233378963], + 'mean_signed_error': [-0.004624147232424582], + 'mean_squared_error': [0.0001382026920448174], + 'range_normalized_mean_absolute_error': [0.10036898858799995], + 'range_normalized_root_mean_squared_error': [0.10372909504665788], + 'root_mean_squared_error': [0.011755964105287894], + 'symmetric_mean_absolute_percentage_error': [0.09373343991103603] + }, + { + 'band': ['1'], + 'coefficient_of_determination': [0.62257119465569], + 'mean_absolute_error': [0.017518999671533943], + 'mean_absolute_percentage_error': [0.19568589409159515], + 'mean_normalized_mean_absolute_error': [0.15469671510079602], + 'mean_normalized_root_mean_squared_error': [0.1715846316993916], + 'mean_percentage_error': [0.013368590460465646], + 'mean_signed_error': [0.0015139580160649765], + 'mean_squared_error': [0.0003775836662784796], + 'range_normalized_mean_absolute_error': [0.11450326582701924], + 'range_normalized_root_mean_squared_error': [0.12700334769555513], + 'root_mean_squared_error': [0.019431512197419933], + 'symmetric_mean_absolute_percentage_error': [0.15366954251075923] + }, + { + 'band': ['1'], + 'coefficient_of_determination': [-0.41271964773267755], + 'mean_absolute_error': [0.011908415300546453], + 'mean_absolute_percentage_error': [0.0882205870282268], + 'mean_normalized_mean_absolute_error': [0.08814209475433238], + 'mean_normalized_root_mean_squared_error': [0.08996232968540896], + 'mean_percentage_error': [-0.08717281243113986], + 'mean_signed_error': [-0.011777460658723765], + 'mean_squared_error': [0.00014772792439308438], + 'range_normalized_mean_absolute_error': [0.16772415916262606], + 'range_normalized_root_mean_squared_error': [0.17118785462101266], + 'root_mean_squared_error': [0.0121543376780919], + 'symmetric_mean_absolute_percentage_error': [0.09215897319648417] + }, # One more for a multi band example +] + + +@parametrize( + "url, collection, bbox, time, bands, time_aggregate, nodata_fill, expected_df", + list(zip([url] * len(time), + [collection] * len(time), + [bbox] * len(time), + time, + bands, + time_aggreagte, + nodata_fill, + expected_df + )), +) +def case_stac_api_call(url, collection, bbox, time, bands, time_aggregate, nodata_fill, expected_df): + return url, collection, bbox, time, bands, time_aggregate, nodata_fill, expected_df + + +bands_fail = [ + ["aot"], + ["aot"], + ["red"] +] + +time_fail = [ + ["1945-04-01", "1945-04-03"], + ["2020-04-01/2020-04-03", "2020-04-06/2020-04-08"], + ["2020-04-01", "2020-04-03"] +] + +time_aggreagte_fail = [ + None, + None, + None +] + +nodata_fill_fail = [ + None, + None, + None, +] + +exceptions = [ValueError] * 3 + +@parametrize( + "url, collection, bbox, time, bands, time_aggregate, nodata_fill", + list(zip([url] * len(time), + [collection] * len(time), + [bbox] * len(time), + time_fail, + bands_fail, + time_aggreagte_fail, + nodata_fill_fail + )), +) +def case_stac_api_call_fail(url, collection, bbox, time, bands, time_aggregate, nodata_fill, exceptions): + return url, collection, bbox, time, bands, time_aggregate, nodata_fill, exceptions + diff --git a/tests/test_stac.py b/tests/test_stac.py new file mode 100644 index 00000000..b1b9e1ac --- /dev/null +++ b/tests/test_stac.py @@ -0,0 +1,63 @@ +from pytest_cases import parametrize_with_cases +from pytest import raises +import xarray as xr +import pandas as pd + +from gval.utils.loading_datasets import get_stac_data + +@parametrize_with_cases("url, collection, bbox, time, bands, time_aggregate, nodata_fill, expected_df", + glob="stac_api_call") +def test_stac_api_call(url, collection, bbox, time, bands, time_aggregate, nodata_fill, expected_df): + """ + Tests call for stac API, (IDK if this data can be mocked, API calls in unit tests are dubious) + """ + + candidate = get_stac_data( + url=url, + collection=collection, + time=time[0], + bands=bands, + bbox=bbox, + time_aggregate=time_aggregate, + nodata_fill=nodata_fill + ) + + benchmark = get_stac_data( + url=url, + collection=collection, + time=time[1], + bands=bands, + bbox=bbox, + time_aggregate=time_aggregate, + nodata_fill=nodata_fill + ) + + agreement, metrics = candidate.gval.continuous_compare(benchmark) + + bnds = [bands] if isinstance(bands, str) else bands + + assert isinstance(agreement, xr.Dataset) + assert bnds == [agreement[var].attrs['original_name'] for var in agreement.data_vars] + + pd.testing.assert_frame_equal( + metrics, expected_df, check_dtype=False + ), "Compute statistics did not return expected values" + + +@parametrize_with_cases("url, collection, bbox, time, bands, time_aggregate, nodata_fill, exception", + glob="stac_api_call_fail") +def test_stac_api_call(url, collection, bbox, time, bands, time_aggregate, nodata_fill, exception): + """ + Tests call for stac API fail + """ + + with raises(exception): + _ = get_stac_data( + url=url, + collection=collection, + time=time, + bands=bands, + bbox=bbox, + time_aggregate=time_aggregate, + nodata_fill=nodata_fill + ) \ No newline at end of file From 4c5e4f0eabea3bb33d53cc2d659f21f8106b68e5 Mon Sep 17 00:00:00 2001 From: Gregory Petrochenkov Date: Sun, 10 Dec 2023 22:36:56 -0800 Subject: [PATCH 3/8] Formatting --- src/gval/__init__.py | 2 +- src/gval/accessors/gval_dataframe.py | 4 +- src/gval/utils/loading_datasets.py | 2 +- tests/cases_stac.py | 204 ++++++++++++--------------- tests/test_stac.py | 33 +++-- 5 files changed, 120 insertions(+), 125 deletions(-) diff --git a/src/gval/__init__.py b/src/gval/__init__.py index 9104792c..0e1c8645 100644 --- a/src/gval/__init__.py +++ b/src/gval/__init__.py @@ -7,4 +7,4 @@ ContStats = ContinuousStatistics() from gval.accessors import gval_array, gval_dataset, gval_dataframe -from gval.catalogs.catalogs import catalog_compare \ No newline at end of file +from gval.catalogs.catalogs import catalog_compare diff --git a/src/gval/accessors/gval_dataframe.py b/src/gval/accessors/gval_dataframe.py index e59a6364..9f532d2e 100644 --- a/src/gval/accessors/gval_dataframe.py +++ b/src/gval/accessors/gval_dataframe.py @@ -184,10 +184,10 @@ def create_subsampling_df( if "geometry" not in geo_df.columns: geo_df.set_geometry( geometries if geometries is not None else self._obj["geometry"], - inplace=True + inplace=True, ) geo_df.crs = crs - + if subsampling_type: geo_df["subsample_type"] = subsampling_type diff --git a/src/gval/utils/loading_datasets.py b/src/gval/utils/loading_datasets.py index a8031d66..d1323a57 100644 --- a/src/gval/utils/loading_datasets.py +++ b/src/gval/utils/loading_datasets.py @@ -465,7 +465,7 @@ def get_stac_data( stack.attrs["time_aggregate"] = "max" else: raise ValueError("A valid aggregate must be used for time ranges") - + stack.rio.write_crs(crs, inplace=True) else: stack = stack[0] diff --git a/tests/cases_stac.py b/tests/cases_stac.py index 9f6b3cd4..2977e705 100644 --- a/tests/cases_stac.py +++ b/tests/cases_stac.py @@ -6,139 +6,118 @@ from pytest_cases import parametrize -url="https://earth-search.aws.element84.com/v1" -collection="sentinel-2-l2a" -bbox=[-105.78, 35.79, -105.72, 35.84] - -bands = [ - "aot", - ["aot"], - ["aot"], - ["aot"], - ["aot", "red"] -] +url = "https://earth-search.aws.element84.com/v1" +collection = "sentinel-2-l2a" +bbox = [-105.78, 35.79, -105.72, 35.84] + +bands = ["aot", ["aot"], ["aot"], ["aot"], ["aot", "red"]] time = [ ["2020-04-01", "2020-04-03"], ["2020-04-01/2020-04-03", "2020-04-06/2020-04-08"], ["2020-04-01/2020-04-03", "2020-04-06/2020-04-08"], ["2020-04-01/2020-04-03", "2020-04-06/2020-04-08"], - ["2020-04-01", "2020-04-03"] + ["2020-04-01", "2020-04-03"], ] -time_aggreagte = [ - None, - "mean", - "min", - "max", - None -] +time_aggreagte = [None, "mean", "min", "max", None] -nodata_fill = [ - None, - None, - None, - None, - 0 -] +nodata_fill = [None, None, None, None, 0] expected_df = [ { - 'band': ['1'], - 'coefficient_of_determination': [-1.4498682989135663], - 'mean_absolute_error': [0.025393391237534115], - 'mean_absolute_percentage_error': [0.20440844788169352], - 'mean_normalized_mean_absolute_error': [0.20592052600829783], - 'mean_normalized_root_mean_squared_error': [0.2110757427848459], - 'mean_percentage_error': [-0.20540028455073725], - 'mean_signed_error': [-0.025329236900295846], - 'mean_squared_error': [0.0006775147935436701], - 'range_normalized_mean_absolute_error': [0.31741739046917644], - 'range_normalized_root_mean_squared_error': [0.32536392930255564], - 'root_mean_squared_error': [0.026029114344204452], - 'symmetric_mean_absolute_percentage_error': [0.22778286629345662] + "band": ["1"], + "coefficient_of_determination": [-1.4498682989135663], + "mean_absolute_error": [0.025393391237534115], + "mean_absolute_percentage_error": [0.20440844788169352], + "mean_normalized_mean_absolute_error": [0.20592052600829783], + "mean_normalized_root_mean_squared_error": [0.2110757427848459], + "mean_percentage_error": [-0.20540028455073725], + "mean_signed_error": [-0.025329236900295846], + "mean_squared_error": [0.0006775147935436701], + "range_normalized_mean_absolute_error": [0.31741739046917644], + "range_normalized_root_mean_squared_error": [0.32536392930255564], + "root_mean_squared_error": [0.026029114344204452], + "symmetric_mean_absolute_percentage_error": [0.22778286629345662], }, { - 'band': ['1'], - 'coefficient_of_determination': [0.5954048574471591], - 'mean_absolute_error': [0.011375152039973328], - 'mean_absolute_percentage_error': [0.09299193429857326], - 'mean_normalized_mean_absolute_error': [0.09198102377977661], - 'mean_normalized_root_mean_squared_error': [0.0950603218421001], - 'mean_percentage_error': [-0.03739148233378963], - 'mean_signed_error': [-0.004624147232424582], - 'mean_squared_error': [0.0001382026920448174], - 'range_normalized_mean_absolute_error': [0.10036898858799995], - 'range_normalized_root_mean_squared_error': [0.10372909504665788], - 'root_mean_squared_error': [0.011755964105287894], - 'symmetric_mean_absolute_percentage_error': [0.09373343991103603] + "band": ["1"], + "coefficient_of_determination": [0.5954048574471591], + "mean_absolute_error": [0.011375152039973328], + "mean_absolute_percentage_error": [0.09299193429857326], + "mean_normalized_mean_absolute_error": [0.09198102377977661], + "mean_normalized_root_mean_squared_error": [0.0950603218421001], + "mean_percentage_error": [-0.03739148233378963], + "mean_signed_error": [-0.004624147232424582], + "mean_squared_error": [0.0001382026920448174], + "range_normalized_mean_absolute_error": [0.10036898858799995], + "range_normalized_root_mean_squared_error": [0.10372909504665788], + "root_mean_squared_error": [0.011755964105287894], + "symmetric_mean_absolute_percentage_error": [0.09373343991103603], }, { - 'band': ['1'], - 'coefficient_of_determination': [0.62257119465569], - 'mean_absolute_error': [0.017518999671533943], - 'mean_absolute_percentage_error': [0.19568589409159515], - 'mean_normalized_mean_absolute_error': [0.15469671510079602], - 'mean_normalized_root_mean_squared_error': [0.1715846316993916], - 'mean_percentage_error': [0.013368590460465646], - 'mean_signed_error': [0.0015139580160649765], - 'mean_squared_error': [0.0003775836662784796], - 'range_normalized_mean_absolute_error': [0.11450326582701924], - 'range_normalized_root_mean_squared_error': [0.12700334769555513], - 'root_mean_squared_error': [0.019431512197419933], - 'symmetric_mean_absolute_percentage_error': [0.15366954251075923] + "band": ["1"], + "coefficient_of_determination": [0.62257119465569], + "mean_absolute_error": [0.017518999671533943], + "mean_absolute_percentage_error": [0.19568589409159515], + "mean_normalized_mean_absolute_error": [0.15469671510079602], + "mean_normalized_root_mean_squared_error": [0.1715846316993916], + "mean_percentage_error": [0.013368590460465646], + "mean_signed_error": [0.0015139580160649765], + "mean_squared_error": [0.0003775836662784796], + "range_normalized_mean_absolute_error": [0.11450326582701924], + "range_normalized_root_mean_squared_error": [0.12700334769555513], + "root_mean_squared_error": [0.019431512197419933], + "symmetric_mean_absolute_percentage_error": [0.15366954251075923], }, { - 'band': ['1'], - 'coefficient_of_determination': [-0.41271964773267755], - 'mean_absolute_error': [0.011908415300546453], - 'mean_absolute_percentage_error': [0.0882205870282268], - 'mean_normalized_mean_absolute_error': [0.08814209475433238], - 'mean_normalized_root_mean_squared_error': [0.08996232968540896], - 'mean_percentage_error': [-0.08717281243113986], - 'mean_signed_error': [-0.011777460658723765], - 'mean_squared_error': [0.00014772792439308438], - 'range_normalized_mean_absolute_error': [0.16772415916262606], - 'range_normalized_root_mean_squared_error': [0.17118785462101266], - 'root_mean_squared_error': [0.0121543376780919], - 'symmetric_mean_absolute_percentage_error': [0.09215897319648417] - }, # One more for a multi band example + "band": ["1"], + "coefficient_of_determination": [-0.41271964773267755], + "mean_absolute_error": [0.011908415300546453], + "mean_absolute_percentage_error": [0.0882205870282268], + "mean_normalized_mean_absolute_error": [0.08814209475433238], + "mean_normalized_root_mean_squared_error": [0.08996232968540896], + "mean_percentage_error": [-0.08717281243113986], + "mean_signed_error": [-0.011777460658723765], + "mean_squared_error": [0.00014772792439308438], + "range_normalized_mean_absolute_error": [0.16772415916262606], + "range_normalized_root_mean_squared_error": [0.17118785462101266], + "root_mean_squared_error": [0.0121543376780919], + "symmetric_mean_absolute_percentage_error": [0.09215897319648417], + }, # One more for a multi band example ] @parametrize( "url, collection, bbox, time, bands, time_aggregate, nodata_fill, expected_df", - list(zip([url] * len(time), - [collection] * len(time), - [bbox] * len(time), - time, - bands, - time_aggreagte, - nodata_fill, - expected_df - )), + list( + zip( + [url] * len(time), + [collection] * len(time), + [bbox] * len(time), + time, + bands, + time_aggreagte, + nodata_fill, + expected_df, + ) + ), ) -def case_stac_api_call(url, collection, bbox, time, bands, time_aggregate, nodata_fill, expected_df): +def case_stac_api_call( + url, collection, bbox, time, bands, time_aggregate, nodata_fill, expected_df +): return url, collection, bbox, time, bands, time_aggregate, nodata_fill, expected_df -bands_fail = [ - ["aot"], - ["aot"], - ["red"] -] +bands_fail = [["aot"], ["aot"], ["red"]] time_fail = [ ["1945-04-01", "1945-04-03"], ["2020-04-01/2020-04-03", "2020-04-06/2020-04-08"], - ["2020-04-01", "2020-04-03"] + ["2020-04-01", "2020-04-03"], ] -time_aggreagte_fail = [ - None, - None, - None -] +time_aggreagte_fail = [None, None, None] nodata_fill_fail = [ None, @@ -148,17 +127,22 @@ def case_stac_api_call(url, collection, bbox, time, bands, time_aggregate, nodat exceptions = [ValueError] * 3 + @parametrize( "url, collection, bbox, time, bands, time_aggregate, nodata_fill", - list(zip([url] * len(time), - [collection] * len(time), - [bbox] * len(time), - time_fail, - bands_fail, - time_aggreagte_fail, - nodata_fill_fail - )), + list( + zip( + [url] * len(time), + [collection] * len(time), + [bbox] * len(time), + time_fail, + bands_fail, + time_aggreagte_fail, + nodata_fill_fail, + ) + ), ) -def case_stac_api_call_fail(url, collection, bbox, time, bands, time_aggregate, nodata_fill, exceptions): +def case_stac_api_call_fail( + url, collection, bbox, time, bands, time_aggregate, nodata_fill, exceptions +): return url, collection, bbox, time, bands, time_aggregate, nodata_fill, exceptions - diff --git a/tests/test_stac.py b/tests/test_stac.py index b1b9e1ac..db7b828b 100644 --- a/tests/test_stac.py +++ b/tests/test_stac.py @@ -5,9 +5,14 @@ from gval.utils.loading_datasets import get_stac_data -@parametrize_with_cases("url, collection, bbox, time, bands, time_aggregate, nodata_fill, expected_df", - glob="stac_api_call") -def test_stac_api_call(url, collection, bbox, time, bands, time_aggregate, nodata_fill, expected_df): + +@parametrize_with_cases( + "url, collection, bbox, time, bands, time_aggregate, nodata_fill, expected_df", + glob="stac_api_call", +) +def test_stac_api_call( + url, collection, bbox, time, bands, time_aggregate, nodata_fill, expected_df +): """ Tests call for stac API, (IDK if this data can be mocked, API calls in unit tests are dubious) """ @@ -19,7 +24,7 @@ def test_stac_api_call(url, collection, bbox, time, bands, time_aggregate, nodat bands=bands, bbox=bbox, time_aggregate=time_aggregate, - nodata_fill=nodata_fill + nodata_fill=nodata_fill, ) benchmark = get_stac_data( @@ -29,7 +34,7 @@ def test_stac_api_call(url, collection, bbox, time, bands, time_aggregate, nodat bands=bands, bbox=bbox, time_aggregate=time_aggregate, - nodata_fill=nodata_fill + nodata_fill=nodata_fill, ) agreement, metrics = candidate.gval.continuous_compare(benchmark) @@ -37,16 +42,22 @@ def test_stac_api_call(url, collection, bbox, time, bands, time_aggregate, nodat bnds = [bands] if isinstance(bands, str) else bands assert isinstance(agreement, xr.Dataset) - assert bnds == [agreement[var].attrs['original_name'] for var in agreement.data_vars] + assert bnds == [ + agreement[var].attrs["original_name"] for var in agreement.data_vars + ] pd.testing.assert_frame_equal( metrics, expected_df, check_dtype=False ), "Compute statistics did not return expected values" -@parametrize_with_cases("url, collection, bbox, time, bands, time_aggregate, nodata_fill, exception", - glob="stac_api_call_fail") -def test_stac_api_call(url, collection, bbox, time, bands, time_aggregate, nodata_fill, exception): +@parametrize_with_cases( + "url, collection, bbox, time, bands, time_aggregate, nodata_fill, exception", + glob="stac_api_call_fail", +) +def test_stac_api_call_fail( + url, collection, bbox, time, bands, time_aggregate, nodata_fill, exception +): """ Tests call for stac API fail """ @@ -59,5 +70,5 @@ def test_stac_api_call(url, collection, bbox, time, bands, time_aggregate, nodat bands=bands, bbox=bbox, time_aggregate=time_aggregate, - nodata_fill=nodata_fill - ) \ No newline at end of file + nodata_fill=nodata_fill, + ) From 79f088607f42f2add4bd06e78fe3d046fcdce036 Mon Sep 17 00:00:00 2001 From: Gregory Petrochenkov Date: Wed, 27 Dec 2023 00:51:58 -0500 Subject: [PATCH 4/8] Stac client tests passing --- .../comparison/compute_continuous_metrics.py | 54 ++-- src/gval/utils/loading_datasets.py | 260 +++++------------- tests/cases_stac.py | 190 +++++++++++++ tests/test_stac.py | 74 +++++ 4 files changed, 349 insertions(+), 229 deletions(-) create mode 100644 tests/cases_stac.py create mode 100644 tests/test_stac.py diff --git a/src/gval/comparison/compute_continuous_metrics.py b/src/gval/comparison/compute_continuous_metrics.py index 01a6b669..8e5e7359 100644 --- a/src/gval/comparison/compute_continuous_metrics.py +++ b/src/gval/comparison/compute_continuous_metrics.py @@ -17,7 +17,7 @@ from gval import ContStats from gval.utils.schemas import Metrics_df, Subsample_identifiers, Sample_identifiers -from gval.utils.loading_datasets import _check_dask_array, _convert_to_dataset +from gval.utils.loading_datasets import _convert_to_dataset def _get_selected_datasets( @@ -49,51 +49,31 @@ def _get_selected_datasets( Datasets with selected coordinates """ - is_dsk = _check_dask_array(agreement) + # is_dsk = _check_dask_array(agreement) cmask, bmask = ( xr.where(candidate[var_name] == nodata, 0, 1), xr.where(benchmark[var_name] == nodata, 0, 1), ) tmask = cmask & bmask - # Create a coord meshgrid and select appropriate coords to select from xarray - if is_dsk: - with da.config.set({"array.slicing.split_large_chunks": True}): - grid_coords = da.array.asarray( - da.array.meshgrid(candidate.coords["x"], candidate.coords["y"]) - ).T.reshape(-1, 2) - picked_coords = grid_coords[da.array.ravel(tmask.data).astype(bool), :] - else: - grid_coords = np.array( - np.meshgrid(candidate.coords["x"], candidate.coords["y"]) - ).T.reshape(-1, 2) - picked_coords = grid_coords[np.ravel(tmask.data).astype(bool), :] + # # Create a coord meshgrid and select appropriate coords to select from xarray + # if is_dsk: + # with da.config.set({"array.slicing.split_large_chunks": True}): + # grid_coords = da.array.asarray( + # da.array.meshgrid(candidate.coords["x"], candidate.coords["y"]) + # ).T.reshape(-1, 2) + # picked_coords = grid_coords[da.array.ravel(tmask.data).astype(bool), :] + # else: + # grid_coords = np.array( + # np.meshgrid(candidate.coords["x"], candidate.coords["y"]) + # ).T.reshape(-1, 2) + # picked_coords = grid_coords[np.ravel(tmask.data).astype(bool), :] # Select coordinates from xarray with da.config.set({"array.slicing.split_large_chunks": True}): - agreement_sel = ( - agreement[var_name].sel( - {"x": picked_coords[:, 0], "y": picked_coords[:, 1]} - ) - if picked_coords is not None - else agreement[var_name] - ) - - candidate_sel = ( - candidate[var_name].sel( - {"x": picked_coords[:, 0], "y": picked_coords[:, 1]} - ) - if picked_coords is not None - else candidate[var_name] - ) - - benchmark_sel = ( - benchmark[var_name].sel( - {"x": picked_coords[:, 0], "y": picked_coords[:, 1]} - ) - if picked_coords is not None - else benchmark[var_name] - ) + agreement_sel = agreement[var_name].where(tmask.compute(), drop=True) + candidate_sel = candidate[var_name].where(tmask.compute(), drop=True) + benchmark_sel = benchmark[var_name].where(tmask.compute(), drop=True) return ( agreement_sel, diff --git a/src/gval/utils/loading_datasets.py b/src/gval/utils/loading_datasets.py index 11449e25..2277b5d6 100644 --- a/src/gval/utils/loading_datasets.py +++ b/src/gval/utils/loading_datasets.py @@ -21,7 +21,6 @@ from rio_cogeo.profiles import cog_profiles import pystac_client -# from odc.stac import stac_load import stackstac @@ -335,111 +334,6 @@ def _convert_to_dataset(xr_object=Union[xr.DataArray, xr.Dataset]) -> xr.Dataset return xr_object -# def dataset_from_stac(url: str, -# collection: str, -# time: str, -# bands: list = None, -# query: str = None, -# time_aggregate: str = None, -# max_items: int = None, -# intersects: dict = None, -# bbox: list = None, -# resolution: int = 10, -# chunks: dict = {'band': 1, 'x': 2048, 'y': 2048}, -# crs: str = None, -# nodata_override: Union[Number, list] = None, -# nodata_fill: Number = None) -> xr.Dataset: -# """ -# -# Parameters -# ---------- -# url : str -# Address hosting the STAC API -# collection : str -# Name of collection to get (currently limited to one) -# time : str -# Single or range of values to query in the time dimension -# bands: list, default = None -# Bands to retrieve from service -# query : str, default = None -# String command to filter data -# time_aggregate : str, default = None -# Method to aggregate multiple time stamps -# max_items : int, default = None -# The maximum amount of records to retrieve -# intersects : dict, default = None -# Dictionary representing the type of geometry and its respective coordinates -# bbox : list, default = None -# Coordinates to filter the spatial range of request -# resolution : int, default = 10 -# Resolution to get data from -# chunks : dict, default = {'band': 1, 'x': 2048, 'y': 2048} -# Blocks to break up memory for dask processing -# crs : str, default = None -# CRS to get the data in -# nodata_override : Union[Number, list], default = None -# Value/s to override the original nodata (primarily for cases when served data has none defined) -# nodata_fill : Number, default = None -# Value to fill nodata where not present -# Returns -# ------- -# xr.Dataset -# Dataset with the time slice and desired bands -# """ -# -# # Call cataloging url, search, and convert to xarray -# catalog = pystac_client.Client.open(url) -# -# stac_items = catalog.search( -# datetime=time, -# collections=[collection], -# max_items=max_items, -# intersects=intersects, -# bbox=bbox, -# query=query -# ) -# -# stack = stac_load( -# stac_items.item_collection(), -# bands=bands, -# crs=crs, -# resolution=resolution, -# chunks=chunks -# ) -# -# names = [f'band_{x+1}' for x in range(len(stack.data_vars))] -# stack = stack.rename_vars({x: y for x, y in zip(stack.data_vars, names)}) -# -# # Only get unique time indices in case there are duplicates -# vals = np.unique(stack.coords['time']) -# stack = stack.sel({'time': vals}) -# -# # Aggregate if there is more than one time -# if stack.coords['time'].shape[0] > 1: -# if time_aggregate == 'mean': -# stack = stack.mean(dim='time') -# elif time_aggregate == 'min': -# stack = stack.min(dim='time') -# elif time_aggregate == 'max': -# stack = stack.max(dim='time') -# else: -# raise ValueError("A valid aggregate must be used for time ranges") -# -# # Resolve nodata issues -# for idx, var in enumerate(stack.data_vars): -# -# if nodata_override is not None: -# idx = idx if len(nodata_override) > 1 else 0 -# stack[var].rio.write_nodata(nodata_override[idx], inplace=True) -# else: -# if stack[var].rio.nodata is None and stack[var].rio.encoded_nodata is None and nodata_fill is None: # pragma: no cover -# raise ValueError("Please provide a nodata_override as there is none present") -# else: -# stack[var].rio.write_nodata(nodata_fill, inplace=True) -# -# return stack.squeeze() - - def _get_raster_band_nodata(band_metadata, nodata_fill) -> Number: """ @@ -496,11 +390,34 @@ def _set_nodata( ) +def _set_crs(stack: xr.DataArray, band_metadata: list = None) -> Number: + """ + + Parameters + ---------- + stack : xr.DataArray + Original data with no information + band_metadata : dict + Information with band metadata + + Returns + ------- + Xarray DataArray with proper CRS + + """ + + if stack.rio.crs is not None: + return stack.rio.write_crs(stack.rio.crs) + else: + return stack.rio.write_crs(f"EPSG:{band_metadata['epsg'].values}") + + def get_stac_data( url: str, collection: str, time: str, bands: list = None, + query: str = None, time_aggregate: str = None, max_items: int = None, intersects: dict = None, @@ -512,19 +429,33 @@ def get_stac_data( Parameters ---------- - url - collection - time - bands - time_aggregate - max_items - intersects - bbox - nodata_fill + url : str + Address hosting the STAC API + collection : str + Name of collection to get (currently limited to one) + time : str + Single or range of values to query in the time dimension + bands: list, default = None + Bands to retrieve from service + query : str, default = None + String command to filter data + time_aggregate : str, default = None + Method to aggregate multiple time stamps + max_items : int, default = None + The maximum amount of records to retrieve + intersects : dict, default = None + Dictionary representing the type of geometry and its respective coordinates + bbox : list, default = None + Coordinates to filter the spatial range of request + resolution : int, default = 10 + Resolution to get data from + nodata_fill : Number, default = None + Value to fill nodata where not present Returns ------- - + xr.Dataset + Dataset with the time slice and desired bands """ with warnings.catch_warnings(): @@ -532,16 +463,14 @@ def get_stac_data( # Call cataloging url, search, and convert to xarray catalog = pystac_client.Client.open(url) - args = { - key: val - for key, val in zip( - ["datetime", "collections", "max_items", "intersects", "bbox"], - [time, [collection], max_items, intersects, bbox], - ) - if key is not None - } - - stac_items = catalog.search(**args).get_all_items() + stac_items = catalog.search( + datetime=time, + collections=[collection], + max_items=max_items, + intersects=intersects, + bbox=bbox, + query=query, + ).get_all_items() stack = stackstac.stack(stac_items, resolution=resolution) @@ -553,8 +482,10 @@ def get_stac_data( if stack.coords["time"].shape[0] > 1: if time_aggregate == "mean": stack = stack.mean(dim="time") - elif time_aggregate == "mean": - stack = stack.median(dim="time") + elif time_aggregate == "min": + stack = stack.min(dim="time") + elif time_aggregate == "max": + stack = stack.max(dim="time") else: raise ValueError("A valid aggregate must be used for time ranges") else: @@ -568,76 +499,21 @@ def get_stac_data( stack.coords["raster:bands"] if "raster:bands" in stack.coords else None ) if "band" in stack.dims: - stack = stack.to_dataset(dim="band") - names = [f"band_{x + 1}" for x in range(len(stack.data_vars))] - og_names = [name for name in stack.data_vars] - stack = stack.rename_vars({x: y for x, y in zip(og_names, names)}) + og_names = [name for name in stack.coords["band"]] + names = [f"band_{x + 1}" for x in range(len(stack.coords["band"]))] + stack = stack.assign_coords({"band": names}).to_dataset(dim="band") + for metadata, var, og_var in zip(band_metadata, stack.data_vars, og_names): _set_nodata(stack[var], metadata, nodata_fill) - stack[var] = stack[var].rio.write_crs(stack.rio.crs) + stack[var] = _set_crs(stack[var], band_metadata) stack[var].attrs["original_name"] = og_var else: stack = stack.to_dataset(name="band_1") _set_nodata(stack["band_1"], band_metadata, nodata_fill) - stack["band_1"] = stack["band_1"].rio.write_crs(stack.rio.crs) - stack["band_1"].attrs["original_name"] = bands[0] + stack["band_1"] = _set_crs(stack["band_1"]) + stack["band_1"].attrs["original_name"] = ( + bands[0] if isinstance(bands, list) else bands + ) return stack - - -if __name__ == "__main__": - # from gval.utils.loading_datasets import dataset_from_stac - # - # candidate = dataset_from_stac( - # url="https://earth-search.aws.element84.com/v1", - # collection="sentinel-2-l2a", - # time="2020-04-01", - # bands="aot", - # bbox=[-105.78, 35.79, -105.66, 35.90], - # nodata_fill=0, - # ) - # - # benchmark = dataset_from_stac( - # url="https://earth-search.aws.element84.com/v1", - # collection="sentinel-2-l2a", - # time="2020-04-03", - # bands="aot", - # bbox=[-105.78, 35.79, -105.66, 35.90], - # nodata_fill=0, - # ) - # - # from gval.comparison.compute_continuous_metrics import _compute_continuous_metrics - # - # c, b = candidate.gval.homogenize(benchmark) - # ag = c.gval.compute_agreement_map(b, continuous=True) - # ag.compute() - # metrics_df = _compute_continuous_metrics( - # agreement_map=ag, - # candidate_map=c, - # benchmark_map=b - # ) - # - # print(metrics_df) - - from gval.utils.loading_datasets import get_stackstac_data - - candidate2 = get_stackstac_data( - url="https://earth-search.aws.element84.com/v1", - collection="sentinel-2-l2a", - time="2020-04-01", - bands=None, - bbox=[-105.78, 35.79, -105.66, 35.90], - ) - - benchmark2 = get_stackstac_data( - url="https://earth-search.aws.element84.com/v1", - collection="sentinel-2-l2a", - time="2020-04-03", - bands="aot", - bbox=[-105.78, 35.79, -105.66, 35.90], - ) - - ag2, met2 = candidate2.gval.continuous_compare(benchmark2) - - print(met2) diff --git a/tests/cases_stac.py b/tests/cases_stac.py new file mode 100644 index 00000000..873e7810 --- /dev/null +++ b/tests/cases_stac.py @@ -0,0 +1,190 @@ +""" +Test functionality for gval/statistics modules +""" + +# __all__ = ['*'] + +import numpy as np +import pandas as pd +from pytest_cases import parametrize + +url = "https://earth-search.aws.element84.com/v1" +collection = "sentinel-2-l2a" +bbox = [-105.78, 35.79, -105.72, 35.84] + +bands = ["aot", ["aot"], ["aot"], ["aot"], ["aot", "rededge1"]] + +time = [ + ["2020-04-01", "2020-04-03"], + ["2020-04-01/2020-04-03", "2020-04-06/2020-04-08"], + ["2020-04-01/2020-04-03", "2020-04-06/2020-04-08"], + ["2020-04-01/2020-04-03", "2020-04-06/2020-04-08"], + ["2020-04-01", "2020-04-03"], +] + +time_aggreagte = [None, "mean", "min", "max", None] + +nodata_fill = [None, None, None, None, 0] + +expected_df = [ + { + "band": ["1"], + "coefficient_of_determination": [-1.4498682989135663], + "mean_absolute_error": [0.025393391237534115], + "mean_absolute_percentage_error": [0.20440844788169352], + "mean_normalized_mean_absolute_error": [0.20592052600829783], + "mean_normalized_root_mean_squared_error": [0.2110757427848459], + "mean_percentage_error": [-0.20540028455073725], + "mean_signed_error": [-0.025329236900295846], + "mean_squared_error": [0.0006775147935436701], + "range_normalized_mean_absolute_error": [0.31741739046917644], + "range_normalized_root_mean_squared_error": [0.32536392930255564], + "root_mean_squared_error": [0.026029114344204452], + "symmetric_mean_absolute_percentage_error": [0.22778286629345662], + }, + { + "band": ["1"], + "coefficient_of_determination": [0.5954048574471591], + "mean_absolute_error": [0.011375152039973328], + "mean_absolute_percentage_error": [0.09299193429857326], + "mean_normalized_mean_absolute_error": [0.09198102377977661], + "mean_normalized_root_mean_squared_error": [0.0950603218421001], + "mean_percentage_error": [-0.03739148233378963], + "mean_signed_error": [-0.004624147232424582], + "mean_squared_error": [0.0001382026920448174], + "range_normalized_mean_absolute_error": [0.10036898858799995], + "range_normalized_root_mean_squared_error": [0.10372909504665788], + "root_mean_squared_error": [0.011755964105287894], + "symmetric_mean_absolute_percentage_error": [0.09373343991103603], + }, + { + "band": ["1"], + "coefficient_of_determination": [0.62257119465569], + "mean_absolute_error": [0.017518999671533943], + "mean_absolute_percentage_error": [0.19568589409159515], + "mean_normalized_mean_absolute_error": [0.15469671510079602], + "mean_normalized_root_mean_squared_error": [0.1715846316993916], + "mean_percentage_error": [0.013368590460465646], + "mean_signed_error": [0.0015139580160649765], + "mean_squared_error": [0.0003775836662784796], + "range_normalized_mean_absolute_error": [0.11450326582701924], + "range_normalized_root_mean_squared_error": [0.12700334769555513], + "root_mean_squared_error": [0.019431512197419933], + "symmetric_mean_absolute_percentage_error": [0.15366954251075923], + }, + { + "band": ["1"], + "coefficient_of_determination": [-0.41271964773267755], + "mean_absolute_error": [0.011908415300546453], + "mean_absolute_percentage_error": [0.0882205870282268], + "mean_normalized_mean_absolute_error": [0.08814209475433238], + "mean_normalized_root_mean_squared_error": [0.08996232968540896], + "mean_percentage_error": [-0.08717281243113986], + "mean_signed_error": [-0.011777460658723765], + "mean_squared_error": [0.00014772792439308438], + "range_normalized_mean_absolute_error": [0.16772415916262606], + "range_normalized_root_mean_squared_error": [0.17118785462101266], + "root_mean_squared_error": [0.0121543376780919], + "symmetric_mean_absolute_percentage_error": [0.09215897319648417], + }, # One more for a multi band example + { + "band": ["1", "2"], + "coefficient_of_determination": [-1.4498682989135663, -0.7514062048150743], + "mean_absolute_error": [0.025393391237534115, 0.10266655092378396], + "mean_absolute_percentage_error": [0.20440844788169352, np.inf], + "mean_normalized_mean_absolute_error": [ + 0.20592052600829783, + 1.5904259451440834, + ], + "mean_normalized_root_mean_squared_error": [ + 0.2110757427848459, + 2.8406238608292003, + ], + "mean_percentage_error": [-0.20540028455073725, 0.8574267097258231], + "mean_signed_error": [-0.025329236900295846, 0.05534935042165941], + "mean_squared_error": [0.0006775147935436701, 0.03362470648587737], + "range_normalized_mean_absolute_error": [ + 0.31741739046917644, + 0.06341747539921179, + ], + "range_normalized_root_mean_squared_error": [ + 0.32536392930255564, + 0.11326852052594609, + ], + "root_mean_squared_error": [0.026029114344204452, 0.18337040787945413], + "symmetric_mean_absolute_percentage_error": [ + 0.22778286629345662, + 0.8343404150271029, + ], + }, +] + + +@parametrize( + "url, collection, bbox, time, bands, time_aggregate, nodata_fill, expected_df", + list( + zip( + [url] * len(time), + [collection] * len(time), + [bbox] * len(time), + time, + bands, + time_aggreagte, + nodata_fill, + expected_df, + ) + ), +) +def case_stac_api_call( + url, collection, bbox, time, bands, time_aggregate, nodata_fill, expected_df +): + return ( + url, + collection, + bbox, + time, + bands, + time_aggregate, + nodata_fill, + pd.DataFrame(expected_df), + ) + + +bands_fail = [["aot"], ["aot"], ["red"]] + +time_fail = [ + ["1945-04-01", "1945-04-03"], + ["2020-04-01/2020-04-03", "2020-04-06/2020-04-08"], + ["2020-04-01", "2020-04-03"], +] + +time_aggreagte_fail = [None, None, None] + +nodata_fill_fail = [ + None, + None, + None, +] + +exceptions = [ValueError] * 3 + + +@parametrize( + "url, collection, bbox, time, bands, time_aggregate, nodata_fill, exception", + list( + zip( + [url] * len(time), + [collection] * len(time), + [bbox] * len(time), + time_fail, + bands_fail, + time_aggreagte_fail, + nodata_fill_fail, + exceptions, + ) + ), +) +def case_stac_api_call_fail( + url, collection, bbox, time, bands, time_aggregate, nodata_fill, exception +): + return url, collection, bbox, time, bands, time_aggregate, nodata_fill, exception diff --git a/tests/test_stac.py b/tests/test_stac.py new file mode 100644 index 00000000..1f2f03a8 --- /dev/null +++ b/tests/test_stac.py @@ -0,0 +1,74 @@ +from pytest_cases import parametrize_with_cases +from pytest import raises +import xarray as xr +import pandas as pd + +from gval.utils.loading_datasets import get_stac_data + + +@parametrize_with_cases( + "url, collection, bbox, time, bands, time_aggregate, nodata_fill, expected_df", + glob="stac_api_call", +) +def test_stac_api_call( + url, collection, bbox, time, bands, time_aggregate, nodata_fill, expected_df +): + """ + Tests call for stac API, (IDK if this data can be mocked, API calls in unit tests are dubious) + """ + + candidate = get_stac_data( + url=url, + collection=collection, + time=time[0], + bands=bands, + bbox=bbox, + time_aggregate=time_aggregate, + nodata_fill=nodata_fill, + ) + + benchmark = get_stac_data( + url=url, + collection=collection, + time=time[1], + bands=bands, + bbox=bbox, + time_aggregate=time_aggregate, + nodata_fill=nodata_fill, + ) + + agreement, metrics = candidate.gval.continuous_compare(benchmark) + + bnds = [bands] if isinstance(bands, str) else bands + + assert isinstance(agreement, xr.Dataset) + assert bnds == [ + agreement[var].attrs["original_name"] for var in agreement.data_vars + ] + + pd.testing.assert_frame_equal( + metrics, expected_df, check_dtype=False + ), "Compute statistics did not return expected values" + + +@parametrize_with_cases( + "url, collection, bbox, time, bands, time_aggregate, nodata_fill, exception", + glob="stac_api_call_fail", +) +def test_stac_api_call_fail( + url, collection, bbox, time, bands, time_aggregate, nodata_fill, exception +): + """ + Tests call for stac API fail + """ + + with raises(exception): + _ = get_stac_data( + url=url, + collection=collection, + time=time[0], + bands=bands, + bbox=bbox, + time_aggregate=time_aggregate, + nodata_fill=nodata_fill, + ) From 7dfe7cc30322944049cfce47af2bc452fe140da0 Mon Sep 17 00:00:00 2001 From: Gregory Petrochenkov Date: Wed, 27 Dec 2023 12:45:02 -0500 Subject: [PATCH 5/8] Adjust documentation and README --- README.MD | 105 +++++++++++++++++- docs/compile_readme_and_arrange_docs.py | 56 ++++++++++ docs/images/benchmark_catalog.png | Bin 0 -> 22975 bytes docs/images/candidate_catalog.png | Bin 0 -> 39259 bytes docs/images/catalog_agreement_map.png | Bin 0 -> 34419 bytes docs/images/catalog_agreement_map2.png | Bin 0 -> 36568 bytes docs/images/catalog_metric_table.png | Bin 0 -> 119405 bytes docs/images/continuous_agreement_map.png | Bin 0 -> 92422 bytes docs/images/continuous_metric_table.png | Bin 0 -> 22189 bytes docs/markdown/04_EXAMPLE.MD | 99 ++++++++++++++++- docs/sphinx/PYPI_README.MD | 105 +++++++++++++++++- docs/sphinx/SPHINX_README.MD | 105 +++++++++++++++++- docs/sphinx/SphinxCatalogTutorial.ipynb | 77 ++++++++----- docs/sphinx/SphinxSubsamplingTutorial.ipynb | 4 +- .../Continuous Comparison Tutorial.ipynb | 2 +- notebooks/Subsampling Tutorial.ipynb | 2 +- notebooks/livneh_2011_precip.tif | Bin 0 -> 1639226 bytes notebooks/prism_2011_precip.tif | Bin 0 -> 3494416 bytes 18 files changed, 510 insertions(+), 45 deletions(-) create mode 100644 docs/images/benchmark_catalog.png create mode 100644 docs/images/candidate_catalog.png create mode 100644 docs/images/catalog_agreement_map.png create mode 100644 docs/images/catalog_agreement_map2.png create mode 100644 docs/images/catalog_metric_table.png create mode 100644 docs/images/continuous_agreement_map.png create mode 100644 docs/images/continuous_metric_table.png create mode 100644 notebooks/livneh_2011_precip.tif create mode 100644 notebooks/prism_2011_precip.tif diff --git a/README.MD b/README.MD index 2a96fcec..d130a037 100644 --- a/README.MD +++ b/README.MD @@ -38,6 +38,8 @@ Or for bleeding edge updates install from the repository: # Using GVAL +### Categorical Example + An example of running the entire process for two-class categorical rasters with one function using minimal arguments is demonstrated below: @@ -55,7 +57,7 @@ benchmark = rxr.open_rasterio('benchmark_map_two_class_categorical.tif', mask_an negative_categories=[0, 1]) ``` -### Outputs +#### Categorical Outputs `agreement_map` @@ -69,8 +71,105 @@ benchmark = rxr.open_rasterio('benchmark_map_two_class_categorical.tif', mask_an ![alt text](https://github.com/NOAA-OWP/gval/raw/main/docs/images/metric_table.png) -For more details on how to use this software, check out this [notebook -tutorial](https://github.com/NOAA-OWP/gval/blob/main/notebooks/Tutorial.ipynb). +### Continuous Example + +The same can be done for continuous rasters as shown below (in this case +only a subset of the default statistics will be run): + +``` python +import gval +import rioxarray as rxr + +candidate = rxr.open_rasterio('livneh_2011_precip.tif', mask_and_scale=True) # VIC +benchmark = rxr.open_rasterio('prism_2011_precip.tif', mask_and_scale=True) # PRISM + +agreement, metric_table = candidate.gval.continuous_compare( + benchmark, + metrics=[ + "coefficient_of_determination", + "mean_percentage_error", + "mean_absolute_percentage_error", + "mean_normalized_mean_absolute_error" + ] +) +``` + +#### Continuous Outputs + +`agreement_map` + +![alt text](https://github.com/NOAA-OWP/gval/raw/main/docs/images/continuous_agreement_map.png) + +`metric_table` + +![alt text](https://github.com/NOAA-OWP/gval/raw/main/docs/images/continuous_metric_table.png) + +### Catalog Example + +Entire catalogs can be compared in GVAL, which are represented by +dataframes of maps. The following is a candidate and benchmark catalog +for continuous datasets: + +`candidate_catalog` + +![alt text](https://github.com/NOAA-OWP/gval/raw/main/docs/images/candidate_catalog.png) + +`benchmark_catalog` + +![alt text](https://github.com/NOAA-OWP/gval/raw/main/docs/images/benchmark_catalog.png) + +With the following code a comparison of each pair of maps can be run +with the following code (note the first pair of candidate and benchmark +maps are single band rasters while the second pair are multiband +rasters): + +``` python +import pandas as pd + +from gval.catalogs.catalogs import catalog_compare + +candidate_continuous_catalog = pd.read_csv('candidate_catalog_0.csv') +benchmark_continuous_catalog = pd.read_csv('benchmark_catalog_0.csv') + +arguments = { + "candidate_catalog": candidate_continuous_catalog, + "benchmark_catalog": benchmark_continuous_catalog, + "on": "compare_id", + "map_ids": "map_id", + "how": "inner", + "compare_type": "continuous", + "compare_kwargs": { + "metrics": ( + "coefficient_of_determination", + "mean_absolute_error", + "mean_absolute_percentage_error", + ), + "encode_nodata": True, + "nodata": -9999, + }, + "open_kwargs": { + "mask_and_scale": True, + "masked": True + } +} + +agreement_continuous_catalog = catalog_compare(**arguments) +``` + +#### Catalog Outputs + +`agreement_map` + +![alt text](https://github.com/NOAA-OWP/gval/raw/main/docs/images/catalog_agreement_map.png) ![alt +text](https://github.com/NOAA-OWP/gval/raw/main/docs/images/catalog_agreement_map2.png) + +`catalog_metrics` + +![alt text](https://github.com/NOAA-OWP/gval/raw/main/docs/images/catalog_metric_table.png) + +For more detailed examples of how to use this software, check out these +[notebook +tutorials](https://github.com/NOAA-OWP/gval/blob/main/notebooks). # Contributing diff --git a/docs/compile_readme_and_arrange_docs.py b/docs/compile_readme_and_arrange_docs.py index 3713d46b..fc9f9be2 100755 --- a/docs/compile_readme_and_arrange_docs.py +++ b/docs/compile_readme_and_arrange_docs.py @@ -84,6 +84,62 @@ def compile_readme(): 'width="700" height="180" />', ) + sphinx_contents = sphinx_contents.replace( + "![alt text](https://github.com/NOAA-OWP/gval/raw/main/docs/images/" + "continuous_agreement_map.png)", + '', + ) + + sphinx_contents = sphinx_contents.replace( + "![alt text](https://github.com/NOAA-OWP/gval/raw/main/docs/images/" + "continuous_metric_table.png)", + '', + ) + + sphinx_contents = sphinx_contents.replace( + "![alt text](https://github.com/NOAA-OWP/gval/raw/main/docs/images/" + "candidate_catalog.png)", + '', + ) + + sphinx_contents = sphinx_contents.replace( + "![alt text](https://github.com/NOAA-OWP/gval/raw/main/docs/images/" + "benchmark_catalog.png)", + '', + ) + + sphinx_contents = sphinx_contents.replace( + "![alt text](https://github.com/NOAA-OWP/gval/raw/main/docs/images/" + "catalog_agreement_map.png)", + '', + ) + + sphinx_contents = sphinx_contents.replace( + "![alt text](https://github.com/NOAA-OWP/gval/raw/main/docs/images/" + "catalog_agreement_map2.png)", + '', + ) + + sphinx_contents = sphinx_contents.replace( + "![alt text](https://github.com/NOAA-OWP/gval/raw/main/docs/images/" + "catalog_metric_table.png)", + '', + ) + sphinx_contents = sphinx_contents.replace( "\nSee the full documentation [here](noaa-owp.github.io/gval/).\n", "" ) diff --git a/docs/images/benchmark_catalog.png b/docs/images/benchmark_catalog.png new file mode 100644 index 0000000000000000000000000000000000000000..31e3a0bd3f4f3191be2287e148e8f4ac7e874e4b GIT binary patch literal 22975 zcmeIacR1F4|37|~l7y6|RH&q6Z&?Wqd&}M{d(TwJN=qfX2q7euY_gJ-kYr^gTS<1l zk9XI7-=FLLeLuhN@3@cS_x2ZFBvy?Gfdg zjDl9bvL0mK9pC+nH=9=Xk=mV?lVyG_qx)k^$-dGBKA|$ZNb+NPXnp10UYV^i^@WR^2?W6llF@03tF4BPSLKAoZOsLU2kt+ zi`t=6#)RG%)J@A(Oq#be23!WXBRS5lymhM{6m0T} z+L*AHN3G6lfHwDmgKl=!395Kn`nK~V?)m6%X|IQ$iJ0sEbJ)mrC%2EBdNA&43G;6ZIGS4ThjZrM?b4%)hD&3Z|nSZ 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z$Lx+3w;x^zEXuZj4hB{>aTO0)*T(I=^>{9DRt~s>AUGJ<4O|55Za&%dK0GF74RDZY z*IV_=zyV3%c%iSQrKOzr>zLJ>7sO@Rr{{rAY1z8f?KZH>z8bhvI0HBq@(P#=rvsBsoPXFeb+9l*c}Cqz>$Oc>Hq7P4$M9DvSih^ zZDD86p6%-B*s)SI5V(eO)9tsQVuYFBrr_>AzIJC|+AXq>k+LXAn4WqhDk>^vYgFme zQ=+y%9yIR)7AH0H($n|Mv}d@(_v-cQufXL3FM!K`Kks~(5*7QS}sz96Ex%l{|olG&(;`?zLxUlUjup(RuTqaueVqyEgt><|; zM1X$(cgEq5;lH)>`t9$AIobkGQw4+nrvErz?VJlbnH2;Y7?f79LzoN-4IHl+p==I? zrYb0tfvIDG9F#pk$`|B;>et43EHEei54{t}59K&82wq`^vIQJizQWY7T)5Acai#wa R=jRlVkf*Dk%Q~loCIF!~M=byV literal 0 HcmV?d00001 diff --git a/docs/markdown/04_EXAMPLE.MD b/docs/markdown/04_EXAMPLE.MD index 3db0e9d3..296b89a0 100644 --- a/docs/markdown/04_EXAMPLE.MD +++ b/docs/markdown/04_EXAMPLE.MD @@ -1,5 +1,6 @@ # Using GVAL +### Categorical Example An example of running the entire process for two-class categorical rasters with one function using minimal arguments is demonstrated below: ```python @@ -16,7 +17,7 @@ benchmark = rxr.open_rasterio('benchmark_map_two_class_categorical.tif', mask_an negative_categories=[0, 1]) ``` -### Outputs +#### Categorical Outputs `agreement_map` @@ -30,7 +31,97 @@ benchmark = rxr.open_rasterio('benchmark_map_two_class_categorical.tif', mask_an ![alt text](../images/metric_table.png) -The same can be done for continuous rasters as shown below: +### Continuous Example -For more details on how to use this software, check out this -[notebook tutorial](https://github.com/NOAA-OWP/gval/blob/main/notebooks/Tutorial.ipynb). +The same can be done for continuous rasters as shown below (in this case only a subset of the default statistics will be run): + +```python +import gval +import rioxarray as rxr + +candidate = rxr.open_rasterio('livneh_2011_precip.tif', mask_and_scale=True) # VIC +benchmark = rxr.open_rasterio('prism_2011_precip.tif', mask_and_scale=True) # PRISM + +agreement, metric_table = candidate.gval.continuous_compare( + benchmark, + metrics=[ + "coefficient_of_determination", + "mean_percentage_error", + "mean_absolute_percentage_error", + "mean_normalized_mean_absolute_error" + ] +) +``` + +#### Continuous Outputs + +`agreement_map` + +![alt text](../images/continuous_agreement_map.png) + +`metric_table` + +![alt text](../images/continuous_metric_table.png) + +### Catalog Example + +Entire catalogs can be compared in GVAL, which are represented by dataframes of maps. The following is a candidate +and benchmark catalog for continuous datasets: + +`candidate_catalog` + +![alt text](../images/candidate_catalog.png) + +`benchmark_catalog` + +![alt text](../images/benchmark_catalog.png) + +With the following code a comparison of each pair of maps can be run with the following code (note the first pair of +candidate and benchmark maps are single band rasters while the second pair are multiband rasters): + +```python +import pandas as pd + +from gval.catalogs.catalogs import catalog_compare + +candidate_continuous_catalog = pd.read_csv('candidate_catalog_0.csv') +benchmark_continuous_catalog = pd.read_csv('benchmark_catalog_0.csv') + +arguments = { + "candidate_catalog": candidate_continuous_catalog, + "benchmark_catalog": benchmark_continuous_catalog, + "on": "compare_id", + "map_ids": "map_id", + "how": "inner", + "compare_type": "continuous", + "compare_kwargs": { + "metrics": ( + "coefficient_of_determination", + "mean_absolute_error", + "mean_absolute_percentage_error", + ), + "encode_nodata": True, + "nodata": -9999, + }, + "open_kwargs": { + "mask_and_scale": True, + "masked": True + } +} + +agreement_continuous_catalog = catalog_compare(**arguments) +``` + +#### Catalog Outputs + +`agreement_map` + +![alt text](../images/catalog_agreement_map.png) +![alt text](../images/catalog_agreement_map2.png) + +`catalog_metrics` + +![alt text](../images/catalog_metric_table.png) + +For more detailed examples of how to use this software, check out these +[notebook tutorials](https://github.com/NOAA-OWP/gval/blob/main/notebooks). diff --git a/docs/sphinx/PYPI_README.MD b/docs/sphinx/PYPI_README.MD index f1109ae6..7d036596 100644 --- a/docs/sphinx/PYPI_README.MD +++ b/docs/sphinx/PYPI_README.MD @@ -38,6 +38,8 @@ Or for bleeding edge updates install from the repository: # Using GVAL +### Categorical Example + An example of running the entire process for two-class categorical rasters with one function using minimal arguments is demonstrated below: @@ -55,7 +57,7 @@ benchmark = rxr.open_rasterio('benchmark_map_two_class_categorical.tif', mask_an negative_categories=[0, 1]) ``` -### Outputs +#### Categorical Outputs `agreement_map` @@ -69,8 +71,105 @@ benchmark = rxr.open_rasterio('benchmark_map_two_class_categorical.tif', mask_an ![alt text](https://github.com/NOAA-OWP/gval/raw/main/docs/images/metric_table.png) -For more details on how to use this software, check out this [notebook -tutorial](https://github.com/NOAA-OWP/gval/blob/main/notebooks/Tutorial.ipynb). +### Continuous Example + +The same can be done for continuous rasters as shown below (in this case +only a subset of the default statistics will be run): + +``` python +import gval +import rioxarray as rxr + +candidate = rxr.open_rasterio('livneh_2011_precip.tif', mask_and_scale=True) # VIC +benchmark = rxr.open_rasterio('prism_2011_precip.tif', mask_and_scale=True) # PRISM + +agreement, metric_table = candidate.gval.continuous_compare( + benchmark, + metrics=[ + "coefficient_of_determination", + "mean_percentage_error", + "mean_absolute_percentage_error", + "mean_normalized_mean_absolute_error" + ] +) +``` + +#### Continuous Outputs + +`agreement_map` + +![alt text](https://github.com/NOAA-OWP/gval/raw/main/docs/images/continuous_agreement_map.png) + +`metric_table` + +![alt text](https://github.com/NOAA-OWP/gval/raw/main/docs/images/continuous_metric_table.png) + +### Catalog Example + +Entire catalogs can be compared in GVAL, which are represented by +dataframes of maps. The following is a candidate and benchmark catalog +for continuous datasets: + +`candidate_catalog` + +![alt text](https://github.com/NOAA-OWP/gval/raw/main/docs/images/candidate_catalog.png) + +`benchmark_catalog` + +![alt text](https://github.com/NOAA-OWP/gval/raw/main/docs/images/benchmark_catalog.png) + +With the following code a comparison of each pair of maps can be run +with the following code (note the first pair of candidate and benchmark +maps are single band rasters while the second pair are multiband +rasters): + +``` python +import pandas as pd + +from gval.catalogs.catalogs import catalog_compare + +candidate_continuous_catalog = pd.read_csv('candidate_catalog_0.csv') +benchmark_continuous_catalog = pd.read_csv('benchmark_catalog_0.csv') + +arguments = { + "candidate_catalog": candidate_continuous_catalog, + "benchmark_catalog": benchmark_continuous_catalog, + "on": "compare_id", + "map_ids": "map_id", + "how": "inner", + "compare_type": "continuous", + "compare_kwargs": { + "metrics": ( + "coefficient_of_determination", + "mean_absolute_error", + "mean_absolute_percentage_error", + ), + "encode_nodata": True, + "nodata": -9999, + }, + "open_kwargs": { + "mask_and_scale": True, + "masked": True + } +} + +agreement_continuous_catalog = catalog_compare(**arguments) +``` + +#### Catalog Outputs + +`agreement_map` + +![alt text](https://github.com/NOAA-OWP/gval/raw/main/docs/images/catalog_agreement_map.png) ![alt +text](https://github.com/NOAA-OWP/gval/raw/main/docs/images/catalog_agreement_map2.png) + +`catalog_metrics` + +![alt text](https://github.com/NOAA-OWP/gval/raw/main/docs/images/catalog_metric_table.png) + +For more detailed examples of how to use this software, check out these +[notebook +tutorials](https://github.com/NOAA-OWP/gval/blob/main/notebooks). # Contributing diff --git a/docs/sphinx/SPHINX_README.MD b/docs/sphinx/SPHINX_README.MD index 732fea9f..5b0c9112 100644 --- a/docs/sphinx/SPHINX_README.MD +++ b/docs/sphinx/SPHINX_README.MD @@ -38,6 +38,8 @@ Or for bleeding edge updates install from the repository: # Using GVAL +### Categorical Example + An example of running the entire process for two-class categorical rasters with one function using minimal arguments is demonstrated below: @@ -55,7 +57,7 @@ benchmark = rxr.open_rasterio('benchmark_map_two_class_categorical.tif', mask_an negative_categories=[0, 1]) ``` -### Outputs +#### Categorical Outputs `agreement_map` @@ -69,8 +71,105 @@ benchmark = rxr.open_rasterio('benchmark_map_two_class_categorical.tif', mask_an -For more details on how to use this software, check out this [notebook -tutorial](https://github.com/NOAA-OWP/gval/blob/main/notebooks/Tutorial.ipynb). +### Continuous Example + +The same can be done for continuous rasters as shown below (in this case +only a subset of the default statistics will be run): + +``` python +import gval +import rioxarray as rxr + +candidate = rxr.open_rasterio('livneh_2011_precip.tif', mask_and_scale=True) # VIC +benchmark = rxr.open_rasterio('prism_2011_precip.tif', mask_and_scale=True) # PRISM + +agreement, metric_table = candidate.gval.continuous_compare( + benchmark, + metrics=[ + "coefficient_of_determination", + "mean_percentage_error", + "mean_absolute_percentage_error", + "mean_normalized_mean_absolute_error" + ] +) +``` + +#### Continuous Outputs + +`agreement_map` + + + +`metric_table` + + + +### Catalog Example + +Entire catalogs can be compared in GVAL, which are represented by +dataframes of maps. The following is a candidate and benchmark catalog +for continuous datasets: + +`candidate_catalog` + + + +`benchmark_catalog` + + + +With the following code a comparison of each pair of maps can be run +with the following code (note the first pair of candidate and benchmark +maps are single band rasters while the second pair are multiband +rasters): + +``` python +import pandas as pd + +from gval.catalogs.catalogs import catalog_compare + +candidate_continuous_catalog = pd.read_csv('candidate_catalog_0.csv') +benchmark_continuous_catalog = pd.read_csv('benchmark_catalog_0.csv') + +arguments = { + "candidate_catalog": candidate_continuous_catalog, + "benchmark_catalog": benchmark_continuous_catalog, + "on": "compare_id", + "map_ids": "map_id", + "how": "inner", + "compare_type": "continuous", + "compare_kwargs": { + "metrics": ( + "coefficient_of_determination", + "mean_absolute_error", + "mean_absolute_percentage_error", + ), + "encode_nodata": True, + "nodata": -9999, + }, + "open_kwargs": { + "mask_and_scale": True, + "masked": True + } +} + +agreement_continuous_catalog = catalog_compare(**arguments) +``` + +#### Catalog Outputs + +`agreement_map` + + ![alt +text](https://github.com/NOAA-OWP/gval/raw/main/docs/images/catalog_agreement_map2.png) + +`catalog_metrics` + + + +For more detailed examples of how to use this software, check out these +[notebook +tutorials](https://github.com/NOAA-OWP/gval/blob/main/notebooks). # Contributing diff --git a/docs/sphinx/SphinxCatalogTutorial.ipynb b/docs/sphinx/SphinxCatalogTutorial.ipynb index e192e984..b3821de2 100644 --- a/docs/sphinx/SphinxCatalogTutorial.ipynb +++ b/docs/sphinx/SphinxCatalogTutorial.ipynb @@ -47,7 +47,7 @@ }, { "cell_type": "code", - "execution_count": 89, + "execution_count": 2, "id": "b66eca6a-007f-42d2-a476-054b70c2395c", "metadata": {}, "outputs": [], @@ -70,7 +70,7 @@ }, { "cell_type": "code", - "execution_count": 73, + "execution_count": 3, "id": "972505a1", "metadata": {}, "outputs": [ @@ -123,7 +123,7 @@ "1 ./candidate_categorical_1.tif compare2 agreement_categorical_1.tif" ] }, - "execution_count": 73, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -153,7 +153,7 @@ }, { "cell_type": "code", - "execution_count": 74, + "execution_count": 4, "id": "f09a9c91-8bc4-41bc-b11a-733412ba091d", "metadata": {}, "outputs": [ @@ -203,7 +203,7 @@ "1 ./benchmark_categorical_1.tif compare2" ] }, - "execution_count": 74, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -240,7 +240,7 @@ }, { "cell_type": "code", - "execution_count": 112, + "execution_count": 5, "id": "d6034a81", "metadata": {}, "outputs": [ @@ -391,7 +391,7 @@ "positive_predictive_value 0.700815 " ] }, - "execution_count": 112, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -435,13 +435,13 @@ }, { "cell_type": "code", - "execution_count": 114, + "execution_count": 16, "id": "18b0df29", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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1s2w/evQoAKBXr17ZHteiRQsAwOXLl/N1jFL9+vWzdJuytraGnZ0doqOj4e3tneWYChUqAMjoKlYQeXB1dUXVqlWzbK9SpUqW6xSU9PR0Vfe3ESNGaHxcdHQ09u3bh1u3biE2Nla1Tl1aWhqio6MRExMDOzs7jc6Vl3t2/vx5AMi2u6mNjQ28vb2xa9cute1nz54FAPTt2zfbboaDBw/Grl27VOkyy+nvNCd//fUXYmNjUbt2bTRq1Ejj4zT9vyMgKioKADQa2/e+ZTw8PT2z3d66dess2yQSCb766qssS3L4+/tjyJAh2LlzJ06dOoUrV67g3r17CAsLw/Tp07F3714EBQVp3OVy48aNWdZ+9PLyUusO3LNnT/Ts2TPLsQkJCfDz80OVKlU0Gl/5rkGDBmHQoEFITU1FWFgYfv31V8ydOxf79+/HoUOHPni/Fy5ciG3btsHOzg6bN2/Osu6nctx4eno6fvzxR/Tt2xdAxvu4adMmhISE4MqVK/jf//6XZXgAANXninLsKRHpB1YgiYoxIQQmTZqEZcuWZbtwNPB2HBCQUZFITU2Fvb09TExMsk1fvnz5HCuQ5cuXz3a7csIdHx+fbNdPU3r58mWWY5o1a5Zj+nePUSpbtmy2aS0sLBAdHZ3tfuUX0swTnOQl30rlypXLNq2lpWWW6xSU6OhovH79GnZ2dhpPsvH7779jxIgRSExMzDFNQkKCxhXIvNwzZWXaxcUl27TZ/V0pJ/Nwc3PL9hjl9mfPnml0vvdRTrJSsWJFjdLn9v+OgLi4OABv/z/eZ8mSJTm+7zlRrgMpkUhgamqKSpUqoVu3bqhUqVK26e3s7DB8+HDVeOFHjx5hzZo1WLp0KS5evIilS5dixowZAIBSpUoByP5zAMioXCmNGjUKP/74o8b5/uqrr/D06VMcP35cbaxmbhkbG6NKlSqYP38+7Ozs8OWXX2LmzJlYtWpVjsf89ttvmD59OszNzfHnn3+qHrJlpvzctLCwyPYB0NChQ3HlyhWcPn0622tYWVkBAGJjY/NQKiLSFlYgiYqxbdu2YenSpXBxccGyZcvQpEkT2Nvbw8jICKmpqZDJZDl+wc2LnCqdyqfUHTp0gKOjY47Hly5dOssxn3zyCczNzXM8JruJZqTS908w/aH97+YhN/nO7TW06dGjR6pZNZcvX47OnTujbNmyqpaVpk2bIjg4OFd/I/m5ZwXp3ZaSzHL6Oy0oRf1/VxxYW1sDKLyK9bRp0/I1OY+rqysWLVqE9PR0LFu2DH/++aeqAlm7dm2cP38e165de+9Dk7zYv38/TExMMG/ePMybNy/LfuVEYUDG/3CdOnU+eM7Bgwfjyy+/xN69e3OsQB44cABDhw6FkZGRanbk7Li6ugLIeCiT3f+csqKvbGF+l/LBgY2NzQfzTUS6gxVIomJs9+7dAIC1a9eic+fOavuUs+plVqpUKRgZGeHly5d48+ZNtl+0M095rylla9znn3+O3r17a3xMSEgIpk2bludp7/MrL/nWptKlS8PU1BQxMTGIjY394JeygwcPIjU1FZMmTcL48eOz7M/ub+RD8nLPypQpg5CQEDx58iTbLojZ/c0pZ4Z99OhRtudUtoTm1BqdG8qW0dDQUI3S5/b/jgAHBwcAyLF3g65o06YNli1bptba2LFjR/zvf//DH3/8gYULFxb47Lpv3rzJsQUPgGqfpq14dnZ2kEqlOXYbPX36NPr06QMhBLZs2ZJtl38l5VIor169yna/8v3Mqcux8rjcLJtCRNqn+4/IiSjPlME5u+6U27dvz7LNyMgIDRs2hBAiy5gzAAgKCkJ0dHSu8/Hxxx8DePvFurCOKWhFmQcjIyO1rm55YWBgoGqN+Omnnz6Y/n1/H2fOnEFkZGSW7cq143LKa17umbKb8s6dO7Psi4uLU42rzEw5lvKPP/7IMr4MyOh+lzldfnz00UewsbHBjRs3sh3v+q7c/t/pog+9zwXNwcEBTk5OePLkCZKTk4vkmtn5UMvw/fv3Aag/mOjUqRM8PDzw+PFjLFiwoEDzExYWBpEx4WGWF5DxP6/8XdMW1rNnz0KhUGTbJfvvv/9Gt27dkJKSgvXr13/wIVDTpk1RqlQpREREICQkJMt+ZeVWWdF81+3btwFAo5ZTItIdrEASFWPKCVt++ukntS9GZ8+exeLFi7M9ZtSoUQCAmTNnqo0fi4mJweTJk/OUj969e8PT0xObN2/GvHnzsoz/E0Lg/PnzqslUAGDkyJFwcHDAokWL8NNPP6m6Riqlp6fjyJEjuHXrVp7yVFj5zitnZ2dERkbmeyzQ1KlTIZFI8O2336oWdFdKT0/HwYMHVb8r/z5+++03JCUlqbY/e/ZM9XfwrtKlS8PIyAihoaHZVtzycs+GDh0KY2Nj/Prrrzhz5oxqu1wux5dffpltt0YvLy/UrFkTYWFhmDlzptrf9+7du7Fr1y5YWFjgs88+y7YcuSGTyeDv7w8gY33Hd1s94+Li1FqI8vJ/py27d+9GtWrV8Omnn6pt/9D7XBhatGgBuVyOa9euFcn1stOtWzesXLky25bQS5cuqbqRZp5kTCqVYtOmTZDJZPjmm28wZcoUVdfMzKKjo7OtZAE5vw95tXjx4mxbBa9cuaIa1zl06FC1fSEhIejQoQPi4+OxYsUKVff29zE0NMTEiRMhhMCYMWMQHx+v2nf8+HEEBgZCIpFg5MiR2R5/+fJlGBsb59hFloh0VBHN9kpEWhASEqKajt7T01P0799ftGjRQkgkEjFp0qRsl45QKBSqJS0sLCxE9+7dRa9evYStra2oX7++aq2vZ8+eqY7RZMr/u3fvCnd3dwFAODg4iHbt2omBAwcKb29v4eDgkO0U/MHBwaJ06dICgHBxcREdO3YUAwcOFG3atFEtq7B7925V+g8thZDTsgNCvF0nMvNSEXnJ94fuRU7XGTt2rGoZAx8fHzFs2DCxaNGibM/xIYsXL1YtWVK/fn0xYMAA8fHHHwsHBwdhbW2tSpeSkiKqV68uAAgnJyfRu3dv0blzZ2FmZiaaNm0qmjZtmu1yDF27dhUARPXq1cXgwYPFsGHDxC+//JLneyaEEMuXL1ctf9K6dWvRv39/UaFCBWFjY6NaDmDz5s1qx/zzzz+iVKlSAoDw8PAQAwYMUC1DY2hoKLZt26aWXpO/05zWgUxLSxM9evQQAISxsbFo27atGDBggGjevLkwMzMT3bt3V6XNy/9dQSzjcfXqVdGoUSPVy8TERAAQderUUW37+eefsy1vdvfkQ+9zQa8DGRgYmGXplcyU1+vdu3eWZTGUr9GjR2ebl+zWgcxO7dq1VX8/9evXF3369BG9e/dWWwO3a9euIi0tLcuxZ8+eFU5OTgKAkMlkomXLlqJ///6iR48eon79+sLIyEgAENWqVVNbB1WID68DmR28ZxkP5d9po0aNRL9+/UTPnj1VZQMg+vbtm6UMyjLa29vneH8XLFiQ5VqpqamiXbt2AoBwdHQU3bt3F82aNRMGBgYCgPj222+zzeP9+/cFANGhQweNy0xEuoEVSKJi7vbt26Jr167CwcFBmJmZibp164qffvpJCJHz2oOpqaliwYIFonLlysLY2FiULVtWjB07VsTHx4tKlSoJiUSitti3Jl/MhchYi3H+/PmiXr16wsLCQpiYmAg3NzfRvn17sWbNGvHixYssx4SHh4spU6aI6tWrCzMzM2FmZiYqVqwounfvLgIDA0VCQoIqbWFUIHOb77xWIBMTE4Wfn59wcXERhoaGuf4y+a4zZ86Inj17CgcHB2FkZCTKlCkj2rZtm2UNzJiYGDF69Gjh5uYmZDKZqFChgpg6dapISkrKsSIQGRkpBg8eLJycnFRfEt+953l5r3fs2CEaNmwoTE1Nha2trejVq5cICQkRn3/+eY7r7D169EgMHz5cuLi4CCMjI1G6dGnRo0cPcenSpSxp81OBFEIIuVwuAgMDRcuWLYW1tbWQyWTCzc1N9O3bN0sFJbf/dwVRgVSe432vd8v1vorLh97ngq5AJicnC2tra+Hp6Zntfk3Wgcz8gCRzXjStQN67d0+sXLlSdO3aVVSpUkVYWFio/n86d+4stmzZkmUtxMySkpLEihUrRNu2bYWjo6MwMjISFhYWomrVqsLHx0fs3r0728pnQVcgV61aJXr16iUqVKggzM3NVZ/j3bt3V3volpkm9zen/KWmpoqFCxeK6tWrCxMTE2FlZSXatGkj9u/fn2P+586dKwCInTt3alxmItINEiE4FRwRaebp06dwd3dHpUqVVGNXiAqTXC5HrVq1cPv2bTx//hxOTk7azhIVIn9/fyxfvhx//fWX1ibPosInhICHhwcSExMRFhYGQ0PO6UikTzgGkoiy+Oeff5CWlqa2LTIyEr6+vkhPT8egQYO0lDMqrkJDQ7OM/0xJScGUKVPw33//oW3btqw8lgDTp0+HhYVFgU9GQ7plz549CAkJwdy5c1l5JNJDbIEkoiw6dOiAy5cvo06dOnB0dER4eDiuXr2KxMRENGjQAGfOnCn0tfSoZPn+++8xa9YsfPTRR3BxcUF8fDxu3LiB8PBwlC5dGufOnUPVqlW1nU0qAnPnzsXs2bNx48YN1KxZU9vZoQImhEC9evWQnp6OGzdu6MWauUSkjhVIIspi69atCAgIwM2bNxETEwMDAwNUqVIFn3zyCfz9/WFmZqbtLJYYmsyECGTMmrlkyZLCzUwhunLlCpYuXYqLFy/ixYsXSE9PR9myZdG+fXtMnz5dtRZjSbF+/XqcO3dOo7TTpk1DtWrVCjlHREREGViBJCLSYRKJRKN0rq6uCAsLK9zMUJHx9fXFxo0bNUp78uRJjdcAJCIiyi9WIImIiIiIiEgj7HhOREREREREGmEFkoiIiIiIiDTCCiQRERERERFphBVIIiIiIiIi0ggrkERERERERKQRViCJiIiIiIhII6xAEhERERERkUZYgSQiIiIiIiKNsAJJREREREREGmEFkoiIiIiIiDTCCiQRERERERFphBVIIiIiIiIi0ggrkERERERERKQRViCJiIiIiIhII6xAEhERERERkUZYgSQiIiIiIiKNsAJJREREREREGmEFkoiIiIiIiDTCCiQRERERERFphBVIIiIiIiIi0ggrkERERERERKQRViCJiIiIiIhII6xAEhERERERkUZYgSQiIiIiIiKNsAJJREREREREGmEFkoiIiIiIiDTCCiQRERERERFphBVIIiIiIiIi0ggrkERERERERKQRViCJitjs2bMhkUi0nQ0iIiK9wxhKpH2sQFKxFRgYCIlEovZycHBA69atcejQIW1n74NCQkLg7++Ppk2bwsTEBBKJBGFhYdrOFhERlQD6HkN37dqFfv36oUKFCjAzM0PVqlXx5ZdfIjY2VttZI9J7htrOAFFhmzt3Ltzd3SGEQGRkJAIDA9GpUyfs378fXbp00Xb2chQcHIyVK1fC09MTHh4euH79urazREREJYy+xtARI0bA2dkZgwYNQvny5XHz5k2sXr0aBw8exN9//w1TU1NtZ5FIb7ECScVex44dUb9+fdXvw4YNg6OjI37//XedDn7dunVDbGwsLC0tsWTJElYgiYioyOlrDN2xYwe8vLzUtn300UcYMmQINm/ejM8//1w7GSMqBtiFlUocGxsbmJqawtBQ/fnJkiVL0LRpU5QqVQqmpqb46KOPsGPHjizHSyQS+Pn5Yc+ePahRowZkMhmqV6+Ow4cPZ0l77tw5NGjQACYmJqhYsSJ+/PFHjfNpZ2cHS0vL3BeQiIiokOhLDH238ggAPXv2BADcvn1b4/MQUVZsgaRiLy4uDi9fvoQQAlFRUVi1ahUSExMxaNAgtXQrVqxAt27d4OPjg9TUVGzduhV9+vTBgQMH0LlzZ7W0586dw65du/DFF1/A0tISK1euRO/evfH48WOUKlUKAHDz5k14e3vD3t4es2fPRnp6OmbNmgVHR8ciKzsREVF+FKcYGhERAQAoXbp0ns9BRAAEUTEVEBAgAGR5yWQyERgYmCV9cnKy2u+pqamiRo0aok2bNmrbAQhjY2Nx//591bYbN24IAGLVqlWqbT169BAmJibi0aNHqm3//fefMDAwELn911u8eLEAIB4+fJir44iIiPKiOMVQpWHDhgkDAwNx9+7dPB1PRBnYAknF3po1a1ClShUAQGRkJH777Td8/vnnsLS0RK9evVTpMg+of/XqFeRyOVq0aIHff/89yznbtWuHihUrqn6vVasWrKys8ODBAwCAXC7HkSNH0KNHD5QvX16VzsPDA+3bt8fBgwcLvJxEREQFrbjE0C1btmDDhg2YMmUKKleunOvjiegtViCp2GvYsKHaBAADBgxA3bp14efnhy5dusDY2BgAcODAAcyfPx/Xr19HSkqKKn12601lDmhKtra2ePXqFQDgxYsXeP36dbZBqmrVqqxAEhGRXigOMfTs2bMYNmwY2rdvj2+//TZXxxJRVpxEh0ocqVSK1q1bIzw8HPfu3QOQEVy6desGExMT/O9//8PBgwdx7NgxDBw4EEKILOcwMDDI9tzZpSUiIiou9C2G3rhxA926dUONGjWwY8eOLJP/EFHu8b+ISqT09HQAQGJiIgBg586dMDExwZEjRyCTyVTpAgIC8nR+e3t7mJqaqoJrZiEhIXk6JxERkS7QlxgaGhqKDh06wMHBAQcPHoSFhUWe8kNE6tgCSSVOWloajh49CmNjY3h4eADIeBoqkUggl8tV6cLCwrBnz548XcPAwADt27fHnj178PjxY9X227dv48iRI/nKPxERkbboSwyNiIiAt7c3pFIpjhw5Ant7+zzlhYiyYgskFXuHDh3CnTt3AABRUVHYsmUL7t27h2nTpsHKygoA0LlzZyxduhQdOnTAwIEDERUVhTVr1qBSpUr4559/8nTdOXPm4PDhw2jRogW++OILpKenY9WqVahevbpG54yLi8OqVasAAOfPnwcArF69GjY2NrCxsYGfn1+e8kVERKQpfY2hHTp0wIMHDzBlyhScO3cO586dU+1zdHTExx9/nKd8ERG4jAcVX9lNQW5iYiLq1Kkj1q5dKxQKhVr6DRs2iMqVKwuZTCaqVasmAgICxKxZs7JMFw5AjBkzJsv1XF1dxZAhQ9S2nT59Wnz00UfC2NhYVKhQQaxbty7bc2bn4cOH2U6hDkC4urrm+n4QERFpSt9jaE7xE4Bo1apVru8HEb0lEYKzfhAREREREdGHcQwkERERERERaYQVSCIiIiIiItIIK5BERERERESkEVYgiYiIiIiISCOsQBIREREREZFGWIEkIiIiIiIijRhqOwPFmUKhwPPnz2FpaQmJRKLt7BBRARJCICEhAc7OzpBK+SyOqKAxhhIVX4yh+o0VyEL0/PlzuLi4aDsbRFSInjx5gnLlymk7G0TFDmMoUfHHGKqfWIEsRJaWlgCA27euqX4mouIhISEBHjXq8n+bqJAwhhIVX4yh+o0VyEKk7HJjaWkJX7dpWs4NERWEXTFr1H5n1zqiwsEYSlT8MIYWD+x0TERERERERBphBZKIiIiIiIg0wgokERERERERaYRjIImIdIhcLkdaWpq2s6F3jIyMYGBgoO1sEBGRFjGG5k1uYygrkEREOkAIgYiICMTGxmo7K3rLxsYGTk5OnJSBiKiEYQzNv9zEUFYgiYh0gDLwOTg4wMzMjJWgXBBCIDk5GVFRUQCAMmXKaDlHRERUlBhD8y4vMZQVSCIiLZPL5arAV6pUKW1nRy+ZmpoCAKKiouDg4MDurEREJQRjaP7lNoZyEh0iIi1TjtcwMzPTck70m/L+cfwLEVHJwRhaMHITQ1mBJCLSEexykz+8f0REJRdjQP7k5v6xAklEREREREQaYQWSiIhyLSwsDBKJBNevXwcAnDp1ChKJhDPgERERfYC+x1BWIImIdJyvry8kEkmWV4cOHbSdNZWmTZsiPDwc1tbW2s4KERGRCmNoweMsrEREeqBDhw4ICAhQ2yaTybSUm6yMjY3h5OSk7WwQERFlwRhasNgCSUSkB2QyGZycnNRetra2ADIGvq9fvx49e/aEmZkZKleujH379qkd/++//6JLly6wsrKCpaUlWrRogdDQUACAQqHA3LlzUa5cOchkMtSpUweHDx9WO/7y5cuoW7cuTExMUL9+fVy7dk1t/7vdbwIDA2FjY4MjR47Aw8MDFhYW6NChA8LDw1XHpKenY9y4cbCxsUGpUqUwdepUDBkyBD169FCl2bFjB2rWrAlTU1OUKlUK7dq1Q1JSUkHdViIiKgEYQws2hrICSURUDMyZMwd9+/bFP//8g06dOsHHxwcxMTEAgGfPnqFly5aQyWQICgrC1atX8dlnnyE9PR0AsGLFCvzwww9YsmQJ/vnnH7Rv3x7dunXDvXv3AACJiYno0qULPD09cfXqVcyePRuTJk36YJ6Sk5OxZMkSbNq0CWfOnMHjx4/Vjlu4cCE2b96MgIAAnD9/HvHx8dizZ49qf3h4OAYMGIDPPvsMt2/fxqlTp9CrVy8IIQrwzhERUUnHGJo77MJKRKQHDhw4AAsLC7VtX331Fb766isAGWM8BgwYAAD47rvvsHLlSly+fBkdOnTAmjVrYG1tja1bt8LIyAgAUKVKFdV5lixZgqlTp6J///4AMoLSyZMnsXz5cqxZswZbtmyBQqHAhg0bYGJigurVq+Pp06cYPXr0e/OclpaGdevWoWLFigAAPz8/zJ07V7V/1apVmD59Onr27AkAWL16NQ4ePKjaHx4ejvT0dPTq1Quurq4AgJo1a+b+5hERUYnGGFqwMZQVSCIiPdC6dWusXbtWbZudnZ3q51q1aql+Njc3h5WVFaKiogAA169fR4sWLVSBL7P4+Hg8f/4czZo1U9verFkz3LhxAwBw+/Zt1KpVCyYmJqr9TZo0+WCezczMVIEPAMqUKaPKU1xcHCIjI9GwYUPVfgMDA3z00UdQKBQAgNq1a6Nt27aoWbMm2rdvD29vb3zyySeqbkdERESaYAwt2BjKLqxERHrA3NwclSpVUntlDn7vBjaJRKIKIqampkWaV6Xs8pSbrjMGBgY4duwYDh06BE9PT6xatQpVq1bFw4cPCzqrRERUjDGGFmwMZQWSiKiYq1WrFs6ePYu0tLQs+6ysrODs7Izz58+rbT9//jw8PT0BAB4eHvjnn3/w5s0b1f6LFy/mK0/W1tZwdHTElStXVNvkcjn+/vtvtXQSiQTNmjXDnDlzcO3aNRgbG2P37t35ujYREZGmGEOzYgWSiEgPpKSkICIiQu318uVLjY718/NDfHw8+vfvj7/++gv37t3Dpk2bEBISAgCYPHkyFi5ciG3btiEkJATTpk3D9evXMX78eADAwIEDIZFIMHz4cPz33384ePAglixZku8yjR07FgsWLMDevXsREhKC8ePH49WrV5BIJACAS5cu4bvvvsNff/2Fx48fY9euXXjx4gU8PDzyfW0iIio5GEMLNoZyDCQRkR44fPgwypQpo7atatWquHPnzgePLVWqFIKCgjB58mS0atUKBgYGqFOnjmrMxrhx4xAXF4cvv/wSUVFR8PT0xL59+1C5cmUAgIWFBfbv349Ro0ahbt268PT0xMKFC9G7d+98lWnq1KmIiIjAp59+CgMDA4wYMQLt27eHgYEBgIwnu2fOnMHy5csRHx8PV1dX/PDDD+jYsWO+rktERCULY2jBxlCJ4HzohSY+Ph7W1tZ4+ug+fN2maTs7RFQAdsWsAQDExyegnGslxMXFwcrKKl/nfPPmDR4+fAh3d3e1QfYljUKhgIeHB/r27Yt58+bl+njex+KFMZSo+GEMLTxFGUPZAklERFrx6NEjHD16FK1atUJKSgpWr16Nhw8fYuDAgdrOGhERkU7TZgzlGEgiItIKqVSKwMBANGjQAM2aNcPNmzdx/PhxjnEkIiL6AG3GULZAEhGRVri4uGSZuY6IiIg+TJsxlC2QREREREREpBGtViAXLFiABg0awNLSEg4ODujRo4dqSlyl0NBQ9OzZE/b29rCyskLfvn0RGRn53vPOnj0bEolE7VWtWrUs6YKDg9GmTRuYm5vDysoKLVu2xOvXr1X73dzcspzn+++/L5jCExER5QNjKBERaYNWK5CnT5/GmDFjcPHiRRw7dgxpaWnw9vZGUlISACApKQne3t6QSCQICgrC+fPnkZqaiq5du0KhULz33NWrV0d4eLjqde7cObX9wcHB6NChA7y9vXH58mVcuXIFfn5+kErVb8ncuXPVzjN27NiCvQlERER5wBhKRETaoNUxkIcPH1b7PTAwEA4ODrh69SpatmyJ8+fPIywsDNeuXVNN8btx40bY2toiKCgI7dq1y/HchoaGcHJyynG/v78/xo0bh2nT3k4NXrVq1SzpLC0t33seIiIibWAMJSIibdCpMZBxcXEAADs7OwBASkoKJBIJZDKZKo2JiQmkUmmWp6HvunfvHpydnVGhQgX4+Pjg8ePHqn1RUVG4dOkSHBwc0LRpUzg6OqJVq1bZnvP7779HqVKlULduXSxevBjp6ekFUVQiIqICxRhKRERFQWcqkAqFAhMmTECzZs1Qo0YNAEDjxo1hbm6OqVOnIjk5GUlJSZg0aRLkcjnCw8NzPFejRo0QGBiIw4cPY+3atXj48CFatGiBhIQEAMCDBw8AZIzzGD58OA4fPox69eqhbdu2uHfvnuo848aNw9atW3Hy5EmMHDkS3333HaZMmZLjdVNSUhAfH6/2IiIiKmyMoUREVFR0ZhmPMWPG4NatW2pPMO3t7fHHH39g9OjRWLlyJaRSKQYMGIB69eplGWeRWceOHVU/16pVC40aNYKrqyu2b9+OYcOGqcZ+jBw5EkOHDgUA1K1bFydOnMAvv/yCBQsWAAAmTpyodh5jY2OMHDkSCxYsUHuiq7RgwQLMmTMnfzeCiCiTqMcvEPcyociuZ13aEg7l7YvselQwGEOJiLJiDC0cOlGB9PPzw4EDB3DmzBmUK1dObZ+3tzdCQ0Px8uVLGBoawsbGBk5OTqhQoYLG57exsUGVKlVw//59AECZMmUAAJ6enmrpPDw81LrpvKtRo0ZIT09HWFhYtmM9pk+frhYw4+Pj4eLionE+iYgyi3r8Ar7VxiPtTVqRXdPIxAiBd1ZoHADlcjlmz56N3377DREREXB2doavry++/vprSCSSHI87deoUJk6ciH///RcuLi74+uuv4evrW0ClKFkYQ4mIsmIMLTxa7cIqhICfnx92796NoKAguLu755i2dOnSsLGxQVBQEKKiotCtWzeNr5OYmIjQ0FBV0HNzc4Ozs3OW6c7v3r0LV1fXHM9z/fp1SKVSODg4ZLtfJpPByspK7UVElFdxLxOKNPABQNqbtFw9rV24cCHWrl2L1atX4/bt21i4cCEWLVqEVatW5XjMw4cP0blzZ7Ru3RrXr1/HhAkT8Pnnn+PIkSMFUYQSgzGUiChnjKGFR6stkGPGjMGWLVuwd+9eWFpaIiIiAgBgbW0NU1NTAEBAQAA8PDxgb2+P4OBgjB8/Hv7+/mpPL9u2bYuePXvCz88PADBp0iR07doVrq6ueP78OWbNmgUDAwMMGDAAACCRSDB58mTMmjULtWvXRp06dbBx40bcuXMHO3bsAJAxRfmlS5fQunVrWFpaIjg4GP7+/hg0aBBsbW2L8jYREemsCxcuoHv37ujcuTOAjMrF77//jsuXL+d4zLp16+Du7o4ffvgBQEbL1blz57Bs2TK0b9++SPJdHDCGEhHpN32NoVqtQK5duxYA4OXlpbY9ICBA1QwbEhKC6dOnIyYmBm5ubpgxYwb8/f3V0iu75yg9ffoUAwYMQHR0NOzt7dG8eXNcvHgR9vZvm5MnTJiAN2/ewN/fHzExMahduzaOHTuGihUrAsh4Erp161bMnj0bKSkpcHd3h7+/v1r3GiKikq5p06b46aefcPfuXVSpUgU3btzAuXPnsHTp0hyPCQ4OzrKERPv27TFhwoRCzm3xwhhKRKTf9DWGarUCKYT4YJrvv/8e33///XvThIWFqf2+detWja4/bdo0tTWsMqtXrx4uXryo0XmIiEqqadOmIT4+HtWqVYOBgQHkcjm+/fZb+Pj45HhMREQEHB0d1bY5OjoiPj4er1+/VrWe0fsxhhIR6Td9jaE6MYkOERHpp+3bt2Pz5s3YsmULqlevrhqP4ezsjCFDhmg7e0RERDpLX2MoK5BERJRnkydPxrRp09C/f38AQM2aNfHo0SMsWLAgx+Dn5OSEyMhItW2RkZGwsrJi6yMREZUY+hpDtToLKxER6bfk5OQsawoaGBio1grMTpMmTXDixAm1bceOHUOTJk0KJY9ERES6SF9jKCuQRAVo9L37GH3vvrazQVRkunbtim+//RZ//vknwsLCsHv3bixduhQ9e/ZUpZk+fTo+/fRT1e+jRo3CgwcPMGXKFNy5cwf/+9//sH379iyTuxBRyfJ0Zw083VlD29kgKjL6GkPZhbUIaDLRARHRu6xLW8LIxKjIF0G2Lm2pcfpVq1bhm2++wRdffIGoqCg4Oztj5MiRmDlzpipNeHi42gLz7u7u+PPPP+Hv748VK1agXLlyWL9+PZfwoGwxhhJRXjCGFh5WIIvAm5QUbWeBiPSQQ3l7BN5ZkatFifPLurQlHMrbfzjh/7O0tMTy5cuxfPnyHNMEBgZm2ebl5YVr167lIYdU0jCGElFeMIYWHlYgi4ChIW+zrivobqcFcb61lSsVQE5I3zmUt89VMCIqbhhDdV9BdzstiPOV632rAHJC+o4xtHBwDGQRMDQw0HYWiIiI9BJjKBGRbuFjvSIgkUi0nQVCwbcyFrYP5ZctlERUEjCG6gZ9m9zmQ/llCyVR3rEFkoiIiIiIiDTCFkgqNvSthTG/3ldetk4SEVFu6FsLY369r7xsnSR6P7ZAEhERERERkUbYAkl6r6S1PGoi8z1hayQREeWkpLU8aiLzPWFrJFFWbIEkIiIiIiIijbAFkvQKWxtzL7t7xlZJIqKSh62NuZfdPWOrJJV0rEASEemw58lxeJWaXGTXszU2g7OZdZFdj4iIqLAwhhYOViBJZ7G1sfCwVVI/PE+OQ8djq5GqSC+yaxpLDXHoY79cBcAzZ85g8eLFuHr1KsLDw7F792706NFDtT8xMRHTpk3Dnj17EB0dDXd3d4wbNw6jRo1673n/+OMPfPPNNwgLC0PlypWxcOFCdOrUKa9FIypR2NpYeNgqqR8YQwsvhnIMJBGRjnqVmlykgQ8AUhXpuX5am5SUhNq1a2PNmjXZ7p84cSIOHz6M3377Dbdv38aECRPg5+eHffv25XjOCxcuYMCAARg2bBiuXbuGHj16oEePHrh1i1/SiIjowxhDCy+GsgJJRET50rFjR8yfPx89e/bMdv+FCxcwZMgQeHl5wc3NDSNGjEDt2rVx+fLlHM+5YsUKdOjQAZMnT4aHhwfmzZuHevXqYfXq1YVVDCIioiKnjzGUFUjSOaPv3Wf3VS3gfafC0rRpU+zbtw/Pnj2DEAInT57E3bt34e3tneMxwcHBaNeundq29u3bIzg4uLCzS6TXnu6swe6rWsD7ToVFF2Mox0ASEVGhWrVqFUaMGIFy5crB0NAQUqkUP//8M1q2bJnjMREREXB0dFTb5ujoiIiIiMLOLhERkc7QxRjKCiTpBLZ86Y7M7wUn1qGCsGrVKly8eBH79u2Dq6srzpw5gzFjxsDZ2TnLE1Iiyj22fOmOzO8FJ9ahgqCLMZQVSCIiKjSvX7/GV199hd27d6Nz584AgFq1auH69etYsmRJjsHPyckJkZGRatsiIyPh5ORU6HkmIiLSBboaQzkGkrSK4+50G98fyq+0tDSkpaVBKlUPNwYGBlAoFDke16RJE5w4cUJt27Fjx9CkSZNCySeRPuK4O93G94fyS1djKFsgiYgoXxITE3H//tsHDQ8fPsT169dhZ2eH8uXLo1WrVpg8eTJMTU3h6uqK06dP49dff8XSpUtVx3z66acoW7YsFixYAAAYP348WrVqhR9++AGdO3fG1q1b8ddff+Gnn34q8vIREREVFn2MoaxAUpFji1be9TBPUv28J8m8yK7LcZHaYWtsBmOpYZEvgmxrbJarY/766y+0bt1a9fvEiRMBAEOGDEFgYCC2bt2K6dOnw8fHBzExMXB1dcW3336rtgjy48eP1Z6wNm3aFFu2bMHXX3+Nr776CpUrV8aePXtQowaf5lPJxhatvPu3yWbVz9WDfYrsuhwXqR2MoYUXQ1mBJCLSUc5m1jj0sV+uFyXOD1tjMzibWefqGC8vLwghctzv5OSEgICA957j1KlTWbb16dMHffr0yVVeiIiIAMbQwoyhrEBSkSnpLY+ZWw915Xx5acVUvo9siSwazmbWuQ5GRFT8lPSWx8yth7pyvry0YirfR7ZEFg3G0MLBSXSIiIiIiIhII6xAEhERERERkUbYhZUKVUnrtlrQ3VQL24fy+74urpxYh4iocJW0bqsF3U21sH0ov+/r4sqJdUifsQWSiIiIiIiINMIWSKJc0LcWxvx6X3mLchkRIiLSf/rWwphf7ytvUS4jQlTQ2AJJREREREREGmELJBWK4jb2saS1PGpC7Z5waQ8iogJT3MY+lrSWR01kvifVd2a0RnIsJOkLViCJiHRYclo4UhSxRXY9mdQGZkZliux6REREhYUxtHCwAkn0DrY25p7yns3grHIFKjktHEef9IBCpBbZNaUSY3i77NE4AC5YsAC7du3CnTt3YGpqiqZNm2LhwoWoWrUqACAmJgazZs3C0aNH8fjxY9jb26NHjx6YN28erK1zXtxZCIFZs2bh559/RmxsLJo1a4a1a9eicuXKBVJOIiocbG3MPeU923Pv7dwC7NGTf4yhhRdDOQaSiEhHpShiizTwAYBCpObqae3p06cxZswYXLx4EceOHUNaWhq8vb2RlJTxUOH58+d4/vw5lixZglu3biEwMBCHDx/GsGHD3nveRYsWYeXKlVi3bh0uXboEc3NztG/fHm/evMlP8YiIqIRgDC28GMoWSCow+jjuka2NBSu7MR2ZsVWy+Dl8+LDa74GBgXBwcMDVq1fRsmVL1KhRAzt37lTtr1ixIr799lsMGjQI6enpMDTMGoaEEFi+fDm+/vprdO/eHQDw66+/wtHREXv27EH//v0Lt1BEWqCP4x7Z2liwsptbIDO2ShY/+hpD2QJJREQFJi4uDgBgZ2f33jRWVlbZBj4AePjwISIiItCuXTvVNmtrazRq1AjBwcEFm2EiIiIdoS8xlC2QlG/61vLIVseioXwynXmtK+UTdrZEFk8KhQITJkxAs2bNUKNG9q0pL1++xLx58zBixIgczxMREQEAcHR0VNvu6Oio2kdUXOhbyyNbHYuG8rtK5jWXR3PG82JNn2IoK5BERFQgxowZg1u3buHcuXPZ7o+Pj0fnzp3h6emJ2bNnF23miIiIdJg+xVB2YSUionzz8/PDgQMHcPLkSZQrVy7L/oSEBHTo0AGWlpbYvXs3jIyMcjyXk5MTACAyMlJte2RkpGofERFRcaFvMZQtkFRisOuq7njK5T6KDSEExo4di927d+PUqVNwd3fPkiY+Ph7t27eHTCbDvn37YGJi8t5zuru7w8nJCSdOnECdOnVU57h06RJGjx5dGMUgog9g11XdkXnoELuz6jd9jaF5boEMDQ3F119/jQEDBiAqKgoAcOjQIfz7778FkjEiItJ9Y8aMwW+//YYtW7bA0tISERERiIiIwOvXrwFkBC3llOQbNmxAfHy8Ko1cLledp1q1ati9ezcAQCKRYMKECZg/fz727duHmzdv4tNPP4WzszN69OihjWIWOMZQIiLS1xiapxbI06dPo2PHjmjWrBnOnDmDb7/9Fg4ODrhx4wY2bNiAHTt2FEjmiPKLrY66jxPr6Le1a9cCALy8vNS2BwQEwNfXF3///TcuXboEAKhUSf1J+cOHD+Hm5gYACAkJUc0+BwBTpkxBUlISRowYgdjYWDRv3hyHDx/+4JNXfcAYSvqCrY66jxPr6Dd9jaF5qkBOmzYN8+fPx8SJE2Fpaana3qZNG6xevbpAMkZEVNLJpDaQSoyLdCFkqcQYMqmNxumFEO/d7+Xl9cE02Z1HIpFg7ty5mDt3rsZ50ReMoUREhY8xtPBiaJ4qkDdv3sSWLVuybHdwcMDLly/znSnSfbq+dAdbHnVH5ifYmZf0eBdbIrMyMyoDb5c9SFHEFtk1ZVIbmBmVKbLrlUSMoaTrS3ew5VF3ZP4+k3lJj3exJTIrxtDCk6cKpI2NDcLDw7MM9Lx27RrKli1bIBkjIqKMAGiG4h+MShLGUCKiosEYWjjyNIlO//79MXXqVEREREAikUChUOD8+fOYNGkSPv30U43Ps2DBAjRo0ACWlpZwcHBAjx49EBISopYmNDQUPXv2hL29PaysrNC3b98s09K+a/bs2ZBIJGqvatWqZUkXHByMNm3awNzcHFZWVmjZsqVq0CoAxMTEwMfHB1ZWVrCxscGwYcOQmJiocfmoaPUwT1K9SH893VlD9SIqjhhDSRf922Sz6kX6a/S9+6oXUWHJUwXyu+++Q7Vq1eDi4oLExER4enqiZcuWaNq0Kb7++muNz3P69GmMGTMGFy9exLFjx5CWlqaaaQgAkpKS4O3tDYlEgqCgIJw/fx6pqano2rUrFArFe89dvXp1hIeHq17vLsoZHByMDh06wNvbG5cvX8aVK1fg5+cHqfTtLfHx8cG///6LY8eO4cCBAzhz5gxGjBiRiztFRESkjjGUiIj0WZ66sBobG+Pnn3/GN998g1u3biExMRF169ZF5cqVc3Wew4cPq/0eGBgIBwcHXL16FS1btsT58+cRFhaGa9euwcrKCgCwceNG2NraIigoCO3atcvx3IaGhu9dLNPf3x/jxo3DtGnTVNuqVq2q+vn27ds4fPgwrly5gvr16wMAVq1ahU6dOmHJkiVwdnbOVVmp8LDFsfjiuEgqjhhDGUN1CVsciy+Oi6TCkud1IAGgfPny6NSpE/r27ZvrwJcd5fSzdnZ2AICUlBRIJBLIZDJVGhMTE0il0ixPQ9917949ODs7o0KFCvDx8cHjx49V+6KionDp0iU4ODigadOmcHR0RKtWrdTOGRwcDBsbG1XgA4B27dpBKpWqptN9V0pKCuLj49VeRERE2WEMVccYSkSkHzRugZw4caLGJ126dGmuM6JQKDBhwgQ0a9YMNWpktDo0btwY5ubmmDp1Kr777jsIITBt2jTI5XKEh4fneK5GjRohMDAQVatWRXh4OObMmYMWLVrg1q1bsLS0xIMHDwBkjPNYsmQJ6tSpg19//RVt27bFrVu3ULlyZURERMDBwUHtvIaGhrCzs0NERES2112wYAHmzJmT67LrE/app/xQPul+32ysRMURYyhjKKD7s6+SblP2uHrfbKxERUHjCuS1a9fUfv/777+Rnp6u6rJy9+5dGBgY4KOPPspTRsaMGYNbt26pPcG0t7fHH3/8gdGjR2PlypWQSqUYMGAA6tWrpzbO4l0dO3ZU/VyrVi00atQIrq6u2L59O4YNG6Ya+zFy5EgMHToUAFC3bl2cOHECv/zyCxYsWJCnMkyfPl3tS0J8fDxcXFzydC4iIio+GEM/jDGUiEg/aFyBPHnypOrnpUuXwtLSUjWWAgBevXqFoUOHokWLFrnOhJ+fn2qAfbly5dT2eXt7IzQ0FC9fvoShoSFsbGzg5OSEChUqaHx+GxsbVKlSBffvZ7SelSmTMZ2vp6enWjoPDw9VNx0nJydERUWp7U9PT0dMTEyO40JkMplaVyEiIiKAMRRgDCUiKi7yNInODz/8gKNHj6oCHwDY2tpi/vz58Pb2xpdffqnReYQQGDt2LHbv3o1Tp05lWRMrs9KlSwMAgoKCEBUVhW7dummc38TERISGhmLw4MEAADc3Nzg7O2eZ7vzu3buqJ69NmjRBbGwsrl69qnoiHBQUBIVCgUaNGml8bSocnDinZMnc7aukTagj5M8Bxauiu6DUFhIDTnBSmBhDGUO1jRPnlCyZhx+VtAl1GEMLR54qkPHx8Xjx4kWW7S9evEBCQoLG5xkzZgy2bNmCvXv3wtLSUjUuwtraGqampgCAgIAAeHh4wN7eHsHBwRg/fjz8/f3VZntr27YtevbsCT8/PwDApEmT0LVrV7i6uuL58+eYNWsWDAwMMGDAAACARCLB5MmTMWvWLNSuXRt16tTBxo0bcefOHezYsQNAxpPUDh06YPjw4Vi3bh3S0tLg5+eH/v37c/Y4IioSQv4c4oU3gNQivKoxYH9U4wC4YMEC7Nq1C3fu3IGpqSmaNm2KhQsXqn1GKwkh0KlTJxw+fBi7d+9Gjx49cjyvEAKzZs3Czz//jNjYWDRr1gxr164tkMlmtI0xlDGUiAofY2jhxdA8zcLas2dPDB06FLt27cLTp0/x9OlT7Ny5E8OGDUOvXr00Ps/atWsRFxcHLy8vlClTRvXatm2bKk1ISAh69OgBDw8PzJ07FzNmzMCSJUvUzqPsnqP09OlTDBgwAFWrVkXfvn1RqlQpXLx4Efb29qo0EyZMwPTp0+Hv74/atWvjxIkTOHbsGCpWrKhKs3nzZlSrVg1t27ZFp06d0Lx5c/z00095uWVERLmneIWiDXzIuF4untZ+aC3CzJYvXw6JRKLReRctWoSVK1di3bp1uHTpEszNzdG+fXu8efNG47zpKsZQIqIiwBhaaDFUIoQQuT0oOTkZkyZNwi+//IK0tDQAGbOrDRs2DIsXL4a5OWeHAjKeMltbW+Ppo/vwdZv24QP0gK7MwsourPotP7OwarsL666YNQCA+PgElHOthLi4ONUae3n15s0bPHz4EO7u7jAxMVFtF2n/QkT3zNe580JSajckRtXzdOyLFy/g4OCA06dPo2XLlqrt169fR5cuXfDXX3+hTJky7316KoSAs7MzvvzyS0yaNAlAxhIVjo6OCAwMRP/+/bM9Lqf7qGsYQzVTHGOorszCyi6s+i0/s7BquwsrY+j76UsMzVMXVjMzM/zvf//D4sWLERoaCgCoWLEigx4VOlYcSfkFTNsVScreu2sRAhkVpoEDB2LNmjXvXZxe6eHDh4iIiFBb6N7a2hqNGjVCcHBwjsFPXzCGkraw4kjKhgBtVyQpe/oSQ/NUgVQyNzdHrVq18p0JIiLSf9mtRQgA/v7+aNq0Kbp3767ReZRj+RwdHdW2Ozo65riGoD5iDCUiIiV9iqF5qkC2bt36vX1wg4KC8pwhonex1ZGyU5JnZtVV2a1FuG/fPgQFBWVZB7EkYwylosRWR8pOSZ6ZVVfpUwzN0yQ6derUQe3atVUvT09PpKam4u+//0bNmjULOo9ERKTjlGsRnjx5Um0twqCgIISGhsLGxgaGhoYwNMx4btm7d294eXlley5lF53IyEi17ZGRkRp139F1jKFERJSZvsXQPLVALlu2LNvts2fPRmJiYr4yRERE+uNDaxFOmzYNn3/+udq2mjVrYtmyZejatWu253R3d4eTkxNOnDiBOnXqAMiYUOXSpUsYPXp0oZSjKDGGEhERoL8xNF9jIN81aNAgNGzYMMsU4UREVDx9aC1CJyenbJ94li9fXi1QVqtWDQsWLEDPnj0hkUgwYcIEzJ8/H5UrV4a7uzu++eYbODs7v3fdK33HGEpEVLLoawwt0ApkcHCwTk+dTkREBWvt2rUAkKUrTUBAAHx9fTU+T0hIiGr2OQCYMmUKkpKSMGLECMTGxqJ58+Y4fPhwsY4xjKFERCWLvsbQPFUg313oWAiB8PBw/PXXX/jmm28KJGNERCWe1BaAMYp2IWTj/7+uZvKwlHC2x7y7TSKRYO7cuZg7d26uz6/rGEOJiIoAY2ihxdA8VSCtrKzUZpCTSqWoWrUq5s6dC29v7wLLHBFRSSYxcAbsjwKKV0V3UaltxnWp0DCGEhEVPsbQwpOnCmRgYGABZ4N0WeapnosSl+8ovjJPK1892Cff51Mu6VEcl/OQGDgDJSAYlSSMoSVL5iWHihKX7yi+Mn8/2pNknu/zKb/nFcflPBhDC0eelvGoUKECoqOjs2yPjY1FhQoV8p0pIiKi4ooxlIiI9FmeWiDDwsIgl8uzbE9JScGzZ8/ynSnSLZmfSGmrNZKKl4JodSTSV4yhJUvmnhHaao2k4qUgWh2J8iNXFch9+/apfj5y5Aisra1Vv8vlcpw4cQJubm4FljkiIqLigjGUiIiKg1xVIJVrh0gkEgwZMkRtn5GREdzc3PDDDz8UWOaIiEqSvMzGRm/p+v1jDCUiKjy6HgN0XW7uX64qkAqFAgDg7u6OK1euoHTp0rnLGRERZWFkZAQASE5OhqmpqZZzo7+Sk5MBvL2fuoYxlIio4DGGFozcxNA8jYF8+PBhXg4rseQKPhHJDc6+SnmVeXyRPs3IamBgABsbG0RFRQEAzMzM1JZ5oPcTQiA5ORlRUVGwsbGBgYGBtrP0XoyhucMYmjucfZXyKvM8F/o0IytjaP7kJYZqXIFcuXIlRowYARMTE6xcufK9aceNG6fpaUsEmamFtrNARDrOyckJAFQBkHLPxsZGdR91DWNo3jGGEtGHMIbmX25iqMYVyGXLlsHHxwcmJiZYtmxZjukkEgmD3ztSXrNFjYjeTyKRoEyZMnBwcEBaWpq2s6N3jIyMdLrlkTE07xhDiehDGEPzJ7cxVOMKZOYuN+x+kzsGeVptk4hKIgMDA52uCFHeMIbmHWMoEWmKMbRo5Oljee7cuaqBlpm9fv0ac+fOzXemiIiIiivGUCIi0md5qkDOmTMHiYmJWbYnJydjzpw5+c4UERFRccUYSkRE+ixPFUghRLazG924cQN2dnb5zhQREVFxxRhKRET6LFfLeNja2kIikUAikaBKlSpqAVAulyMxMRGjRo0q8EwSERHpO8ZQIiIqDnJVgVy+fDmEEPjss88wZ84cWFtbq/YZGxvDzc0NTZo0KfBMEhER6TvGUCIiKg5yVYEcMmQIAMDd3R1NmzaFkZFRoWSKiIiouGEMJSKi4iBXFUilVq1aqX5+8+YNUlNT1fZbWVnlL1dERETFFGMoERHpszxVIJOTkzFlyhRs374d0dHRWfbL5fJ8Z4x009rKlQAAo+/d13JOSB9VD/bRdhaItI4xtOQq1/sWAODpzhpazgnpoz1J5trOAhGAPM7COnnyZAQFBWHt2rWQyWRYv3495syZA2dnZ/z6668FnUciIqJigzGUiIj0WZ5aIPfv349ff/0VXl5eGDp0KFq0aIFKlSrB1dUVmzdvho8PWxmIiIiywxhKRET6LE8tkDExMahQoQKAjLEaMTExAIDmzZvjzJkzBZc7IiKiYoYxlIiI9FmeKpAVKlTAw4cPAQDVqlXD9u3bAWQ8Vc08LTkRERGpYwwlIiJ9lqcurEOHDsWNGzfQqlUrTJs2DV27dsXq1auRlpaGpUuXFnQeqQToYZ6k7SxQMaOcpEI5aQWRrmAMpYL2b5PN2s4CFTPKyRKVkycSZZanCqS/v7/q53bt2uHOnTu4evUqSpcujd9++63AMkdERFTcMIYSEZE+kwghREGd7MaNG6hXrx6nIP9/8fHxsLa2xtNH9+HrNk3b2Sk0Bb2kB1sjixdtLd1RWC2Pu2LWAADi4xNQzrUS4uLiuG4fFQjGUHUlJYYW9JIebI0sXrS1dEdhtTwyhhYPeRoDSURERERERCUPK5BERERERESkEVYgiYiIiIiISCO5mkSnV69e790fGxubn7wQEREVW4yhRERUHOSqAvmh9amsra3x6aef5itDRERExRFjKBERFQe5qkAGBAQUVj5Ijyln6iro2VhJv2lr9lUiXcUYStlRzhhd0LOxkn7T1uyrRJrgGEgiIiIiIiLSSK5aIIneJ/OaQWyNLJnY6khElDeZ165la2TJxFZH0hdsgSQiIiIiIiKNsAJJREREREREGmEFkoiIiIiIiDTCCiQRERERERFphJPoUKHg0h4lCyfPISIqOFzao2Th5Dmkb9gCSURERERERBrRagVywYIFaNCgASwtLeHg4IAePXogJCRELU1oaCh69uwJe3t7WFlZoW/fvoiMjHzveWfPng2JRKL2qlatmloaLy+vLGlGjRqllubd/RKJBFu3bi2YwlOO9iSZ82kc5Um53rdUL6LijjGUslM92Ie9QihP1laupHoRvY9WK5CnT5/GmDFjcPHiRRw7dgxpaWnw9vZGUlISACApKQne3t6QSCQICgrC+fPnkZqaiq5du0KhULz33NWrV0d4eLjqde7cuSxphg8frpZm0aJFWdIEBASopenRo0eBlJ2IiCg/GEOJiEgbtDoG8vDhw2q/BwYGwsHBAVevXkXLli1x/vx5hIWF4dq1a7CysgIAbNy4Eba2tggKCkK7du1yPLehoSGcnJzee30zM7MPprGxsflgGspZ5qdYHA9ZvPAJN5F2MYYWf5l7U3A8ZPHCnlakz3RqDGRcXBwAwM7ODgCQkpICiUQCmUymSmNiYgKpVJrt09DM7t27B2dnZ1SoUAE+Pj54/PhxljSbN29G6dKlUaNGDUyfPh3JyclZ0owZMwalS5dGw4YN8csvv0AIkeM1U1JSEB8fr/YiIiIqCoyhRERUFHRmFlaFQoEJEyagWbNmqFEj4ylb48aNYW5ujqlTp+K7776DEALTpk2DXC5HeHh4judq1KgRAgMDUbVqVYSHh2POnDlo0aIFbt26BUtLSwDAwIED4erqCmdnZ/zzzz+YOnUqQkJCsGvXLtV55s6dizZt2sDMzAxHjx7FF198gcTERIwbNy7b6y5YsABz5swpwLtSvHBm1uKBLY9EuocxtPjjzKzFA1seqTiQiPc9DixCo0ePxqFDh3Du3DmUK1dOtf3o0aMYPXo0Hj58CKlUigEDBuC///5Dw4YNsXbtWo3OHRsbC1dXVyxduhTDhg3LNk1QUBDatm2L+/fvo2LFitmmmTlzJgICAvDkyZNs96ekpCAlJUX1e3x8PFxcXPD00X34uk3TKK8lgaYVyB7mSYWcE8oLXa5AFsXkObti1gAA4uMTUM61EuLi4lTdA4m0hTG05NC0Avlvk82FnBPKC12uQBbF5DmMocWDTrRA+vn54cCBAzhz5oxa4AMAb29vhIaG4uXLlzA0NFSNp6hQoYLG57exsUGVKlVw/37OFZdGjRoBwHuDX6NGjTBv3jykpKSodQlSkslk2W4ndZqOi1R+yLIiqX26XGkEiqbiSKSrGENLFk3HRSo/t1mR1D5drjQCRVNxpOJFq2MghRDw8/PD7t27ERQUBHd39xzTli5dGjY2NggKCkJUVBS6deum8XUSExMRGhqKMmXK5Jjm+vXrAPDBNLa2tgxwRESkdYyhRESkDVptgRwzZgy2bNmCvXv3wtLSEhEREQAAa2trmJqaAsiYAtzDwwP29vYIDg7G+PHj4e/vj6pVq6rO07ZtW/Ts2RN+fn4AgEmTJqFr165wdXXF8+fPMWvWLBgYGGDAgAEAMtbF2rJlCzp16oRSpUrhn3/+gb+/P1q2bIlatWoBAPbv34/IyEg0btwYJiYmOHbsGL777jtMmjSpKG8RERFRthhDiYhIG7RagVSOv/Dy8lLbHhAQAF9fXwBASEgIpk+fjpiYGLi5uWHGjBnw9/dXS6/snqP09OlTDBgwANHR0bC3t0fz5s1x8eJF2NvbAwCMjY1x/PhxLF++HElJSXBxcUHv3r3x9ddfq85hZGSENWvWwN/fH0IIVKpUCUuXLsXw4cML4U6UXJxYR7fpetdVopKMMZQ4sY5u0/Wuq0R5pTOT6BRH8fHxsLa25gQAGnhfBZJjILVHXyqQRTkGkhMAEBUNxlDNva8CyTGQ2qMvFciiHAPJGFo86MQkOkSaTqxDhU9fKo1ERJRB04l1qPDpS6WRKD+0OokOERERERER6Q+2QJLOeXdcZOaneezOWnj0reWRS3cQEWX17rjIzJ/t7M5aePSt5ZFLd1B+sAWSiIiIiIiINMIWSNJZ2T4d+/9WSbZE5o++tTZmxpZHIqIPy+6zsvrOjM9+tkTmj761NmbGlkcqCGyBJCIiIiIiIo2wBZL0ivLJ2YxMs8zxSer76XNrIxERFRxlq+See5xbQFP63NpIVFjYAlkEuNQmERFR3jCGEhHpFlYgiYiIiIiISCPswkp6KfPkAJwUIKvi1m2VE+cQERUctYlUODldFsWt2yonzqGCxhZIIiIiIiIi0ghbIKnY+FCrW3FroSxurYxERKQ9H2p1K24tlMWtlZGoKLEFkoiIiIiIiDTCFkjSe8rxcU8zLe2Rnfe12Oli6yRbGDn2kYiosCnHx43+/7GQOXlfi50utk6yhZFjH6nwsAWyCEgkEm1ngYiISC8xhhIR6Ra2QFKxkbm16kOtke8qqNY+ZUsmWw/zjq2ORERFL3Nr1YdaI99VUK19ypZMth7mHVsdqSiwBZKIiIiIiIg0whZIKpY0HRdZ0NjymHdseSQi0g2ajossaGx5zDu2PFJRYgskERERERERaYQtkFSs5WdcJBUNtjwSEemm/IyLpKLBlkfSBrZAEhERERERkUZYgSQiIiIiIiKNsAsrlRjamliHsmK3VSIi/aKtiXUoK3ZbJW1jCyQRERERERFphC2QVOJwYh3tYcsjEZF+48Q62sOWR9IVbIEkIiIiIiIijbAFkkq07FrE2CpZMNjaSERUvGXXIsZWyYLB1kbSZWyBJCIiIiIiIo2wBZLoHWyVzD22NhIREcBWybxgayPpG7ZAEhERERERkUbYAkmkAa4hmRVbHYmISBNcQzIrtjqSPmMLJBFRHgghtJ0FIiIivcQYqt/YAkmUCx9qdStuLZRsZcyeEAIpySnazgYRkV75UKtbcWuhZCtj9hhD9R9bIImIckEIAaEQeJOcqu2sEBER6RXG0OKBFUgiolwQAkiOfw1TM2NtZ4WIiEivMIYWD+zCSlSA3tflUxe7t7KLau4lxCRCKASMzWTazgoRUbHyvi6futi9lV1Uc48xtHhgCyQRUS68SXwDC1tzbWeDiIhI7zCGFg9sgSQqIgXR2pe5FZOth9ph7WAFA0MpJBKJtrNCRFRiFERrX+ZWTLYeagdjaPHAFkgiolyQmRoz8BEREeUBY2jxwBZIIj3CVkftY+AjItJPbHXUPsbQ4oEtkERERERERKQRViCJiIiIiIhII6xAEhERERERkUZYgSQiIiIiIiKNsAJJREREREREGmEFkoiIiIiIiDTCCiQRERERERFphBXIImJiJkP99nVQvVk1SKVvb7uBoQFKOdvBqpSlaptbjfJo3OUj2DhYf/C8FjbmKOVsB6lB0b2V5tZmKF3WDoZGXEa0oFnaWaB5z4aoWNsN5tZmaNajIarUr6jtbBERaRVjKGmCMZSoaGj102vBggXYtWsX7ty5A1NTUzRt2hQLFy5E1apVVWlCQ0MxadIknDt3DikpKejQoQNWrVoFR0fHHM87e/ZszJkzR21b1apVcefOHdXvXl5eOH36tFqakSNHYt26darfHz9+jNGjR+PkyZOwsLDAkCFDsGDBAhga5v62/XBqjupDbNPcP3Ds19PoN7UHOgxtDQNDAwDAy2cxiHr8Ep5NqqiO623/GeKjE7Kcr1XfpvhkYldUa5ixKG56Wjr2/e8Ifv9uF2JfxOc6f5ro+HlbDPr6EziUL63advPsbRz8+ThqtfKEibkJDv58HNdPFsxi94ZGhug/rQcq1nHH7Ysh+GPJfvh80xt9J3WHkcwQYbeeYHrHb5EUm4TZu6egXruaUCgETm+7gEW+qwskDwWp5SeN0apvM0Q/j8GW73YhNipObb/M1BiBISvVvggp/fD5Whz+JUij63z0cS10+KwtkuKTsW3hHoQ/iCyQ/BORbmEMZQx9H8bQtxhDiQqWVlsgT58+jTFjxuDixYs4duwY0tLS4O3tjaSkJABAUlISvL29IZFIEBQUhPPnzyM1NRVdu3aFQqF477mrV6+O8PBw1evcuXNZ0gwfPlwtzaJFi1T75HI5OnfujNTUVFy4cAEbN25EYGAgZs6cmetymppZoPJHFdCpUyc8efIE9drVwo/Xl6BWh6qYN38eOnbsiE8++QTb925FhbrlkZ6eDi8vL8TGxmLQzE+ynM93Xn/M+H0CwmLuw8fHB506dcKixYvQ0qcRVl1aADsnG1VaqVQKSzuLbPMlMzWGubVZtvukBlK1D+Ge4zrB/8eROHzqIPr164eOHTtizJgxSLd4g6m/jkX1jyvDzsMC034bl+VcZlZmMLN6ex1LOwsYGWf9AiGRSGBd2kr1JLhJt/oYNPMTJJhEY/jCwajtVR0DpvfC9p3bMHbcWNi5WqPX+E5o1a8p6revjUmTJ2HZ8qX4+NNWcK7olHEtWwuYWphkW0aJRAJLOwtIJJJs92dmaWuR7b0yszRVfXl5l8zUGGaWpqrjv9n+JYzKCrT0aYRPJnZRSyuVSlHbqzqsSlmiRYsWuHUr4wtEx44dERwcjAbt6+RYDrXzGEgxY6s/rKuYok4nDwyZ009tv5HMCCZmshyPtbF/e//NLE1hXdpKo/tDREWPMZQxNDPGUMZQoqIiEUIIbWdC6cWLF3BwcMDp06fRsmVLHD16FB07dsSrV69gZWUFAIiLi4OtrS2OHj2Kdu3aZXue2bNnY8+ePbh+/XqO1/Ly8kKdOnWwfPnybPcfOnQIXbp0wfPnz1VPatetW4epU6fixYsXMDY2/mB54uPjYW1tjZSUNzh8+Ai2bduGzZs3AwD++usvdOzYEaNGjUK3bt3w+vVrbN26FQ0bNoSvry+WLVuGV69eYe7cuehhOwRJcckAgCr1K2LN5e8xZcoUHDp0CN9++y1cXFzw66+/YseOHbh06RJsreyQ8CoRDi5vn3KmvE7Flu924vfvdqNSXXcMnT8ADTrUAQC8iopD4DdbIU+XY/jCQbAubaU6Lik+Gb/N24EB03ri1983Yv369ViyZAkcHBzw999/w9bWFt27d8eGDRtw/vx5/PLLLznejxdPoyE1kKJUGVsAwLWgW1jttx4RYS/g83VvfPJlVxjLjAAAp7dfQKu+TZGcnIxSpUrh9evXqvP4+vpi7969+Pbbb+HTdxAe/fsUL9LD8cknn6BKlSq4dOkSLh+6htpe1SEzzXifoh6/xC8ztuDFk2h8uWG0KjgqbVu8FwEzfoc8Xa7aJpFI0N2vA3r7d4GTmwMAICYiFuun/QYTcxkGfdNH9UXjryPXsXrsBgz6pg/aDW6pdu6HNx8j7mU86rSugVatWmHOnDnw8vLK8T4dPXoUDRs2hI2NDZo3b44FCxagRYsWAIDQG2FY5bcBH3/aCq37N8OLJ9H4buByePVvhq6jvGFhYw4AGDhwIDp37gwfHx8kxSdj/9qjqO1VHR6NKgMAnoQ8x89TN8HG3gqfLxwMq3e+IMnT5WpB/eD641g99hekpaTlmO+SYlfMGgBAfHwCyrlWQlxcnOrziUibGEMZQxlDGUN1HWNo8aBTHfDj4jK6I9jZ2QEAUlJSIJFIIJO9fdpjYmICqVSKc+fO5Rj8AODevXtwdnaGiYkJmjRpggULFqB8+fJqaTZv3ozffvsNTk5O6Nq1K7755huYmWU8IQsODkbNmjXVuvm0b98eo0ePxr///ou6detqVKZq1arB2FiGH3/8EWPGjEFywmuYWZriq6++wvjx4/H111/jxOazkJnK8L///Q+pqakAMj686tati1mzZqFJt/o4vukMAODjT1vh8ePHWL58Oe7evQuZwhT3rz3EsmXLEBERgcWLF2PZsmW49s/fOHbmCJ4/f47g4GA0a9YME2ZPQLUGldGkW31cv34do0aNQkxMDDp16oQJ60ZAIpHg4MGDSE9Px82bN3H16lW0bt0aYxaOgVQqxdGjRzFixAjVfa9VqxYAICYmBr///jueP3+O8ePHo3bt2hg4cCBmzpyJzp07Y926dejatSv69euHTZs2Yf/+/ShdujTGjx+Pn/75Aff+fojyNZyxes0qnDlzBu7u7pg6dSoAYM2aNUhNTcX48eMBAEuXLgUA9O/fH4GBgfjiiy9Qq5UnBg9egIEDB+Kvv/4CANRvXxtLlizB5cuXYWxsjK5du2Lqr2MhkUjwzz//4M+f9yM9PR3Hjh1D3bp1MXXqVFjZWWDp8Lfdr3zn9Ue/qd2xefNm7Ju0D1KpFP3798eUQD8IIRAQEICjR4+iVKlSGD9+PALvrgIALFy4EC1btkRAQAASEhIwatQotGrdCkeOHMH9+/exatUq7N69G59//jlevXqF58+f49mzZzhz5gx+/PFHXL9+HdWrV4eNjQ2AjG5gI0aMQHJyMkaMGIHlZ+cBAJYtW4YePXqg1/jOaD+0NdavX4+GDRvi9evXuHr1KqKjo3H58mX06dMH/af2wMWLFzFs2PdISUlBv379MHdPxj3+888/kZ6ejuvXr+Pq1av4448/8Oeff2L79u148+YNatasiXnz5uH8niu4fPBvjf7uiajoMYYyhjKGMoYSFQWdmURHoVBgwoQJaNasGWrUqAEAaNy4MczNzTF16lQkJycjKSkJkyZNglwuR3h4eI7natSoEQIDA3H48GGsXbsWDx8+RIsWLZCQ8HYcxMCBA/Hbb7/h5MmTmD59OjZt2oRBgwap9kdERGQZI6L8PSIiItvrpqSkID4+Xu3VvHlzpKWl4dSpU2jWrBnk6XLExcXhxIkTGDhwIPavO4rvB6/EnE+WYFyTr/DbnB34fvBKODo6wtLSEjdv3kTNFh6qa9RtUxP79+9H48aN4ebmhu8GLsfcPj/g5tnb6N+/P/bu3QsA+PfffzFq1CiYmppi5syZOH78uCqQ3rlzB926dUPHjh0xffp0bNmyBatXZ4x3uHTpEkaOHAlXV1dMnjwZ69atw549ewAA9erVw4oVK7Bx40Y8ePBAlSdzc3M0btwYFStWxKBBg9CyZUukpaVh6dKlWLduHcaNG4dGjRph5syZ2LNnD2bMmIFOnTqhQ4cOeBn9EtUaVoKPjw/CwsIwd+5ceHp6ok2bNkhNTUW7du1gaGiIQYMGYdCgQaquIFWqVIGRkRH+/fdfxMXF4dy5c+jQoYMqT0IIWFhYYPbs2fDz88OyZctU9+bBgwfw9/dHdHQ0Zs+ejf/++w9jx45Fx2Ft4VLVGQDg5OaAgV/1wsyZM/HLL7/gyy+/xLRp01Rjd77//nvV9gYNGqB58+Z48eIFgIwvVVOnToWPjw969eqFnj17IjY2FjVr1oSDgwM+/vhjDBo0COXKlcM///yDUaNGwcjICF999RUsLCywZcsWvHz5UlWWhQsXYvDgwRg4cCD69u2L//77DwCwfft2REREwPz/n5geOHAADx48QIUKFeDm5oZmzZph0KBBqFKlCm7cuIHu3bujR48eGDFiBCZOnIidO3cCyPiiN2LECLi4uGD69Om4ffs2pk6dCn9/fyxcuBAeHhl/f8YynXreRESZMIYyhjKGMoYSFRWdqUCOGTMGt27dwtatW1Xb7O3t8ccff2D//v2wsLCAtbU1YmNjUa9ePbVZ2N7VsWNH9OnTB7Vq1UL79u1x8OBBxMbGYvv27ao0I0aMQPv27VGzZk34+Pjg119/xe7duxEaGprnMixYsADW1taql4uLC8qVK4eIiAgYGhrC2toa8dGJiI6OhkKhgKOjI57efa46/vale/h9wW7cOpcxUUH58uURGhqK0mVLqdJY2JojMjJSFYif3s34EvDsfjicnJwQGfl2sHft2rXh5+cHz2qeWLFiBdavXw8g44nksGHD0KJFC5QvXx5fffUVNm3apDqua9euGDRoEJo0aYIhQ4bgwoULAIAZM2Zg3Lhx+OWXX1C9enXUqlULf//9N2QyGdzd3WFvb48GDRqgUqWMSQnkcjlWrlyJJk2awM3NDcuXL8eSJUvg6uqK5s2b4+OPP8a+ffvw4MEDBAcHY+bMmShbtix69eoFOzs7XLlyBVWrVoVUKkWDBg0gSzZXe999fX2xceNGbN++Hb169YKRkZFqn4GBAT777DPExMQgPDwcDRs2xKFDh1T7y5UrhxkzZqBaVQ+sWbMGgYGBSE9PR82Wnhn3zssTcrkcy5Ytw6ZNm1DWxgUvb8ajW7duAIAff/wRK1asgGeV6vD19UXr1q3V/nanT5+OVq1aoU+fPqhWrRr+/fdfODs7w8rKCtWqVYOzTTnY2mZ0Q2rSpAnGjRuHiuUqw8Ii61ibsWPHokWLFujUqROGDRuGjRs3vvfv0N7eHqVKlULFihVR0aUyHBwcsGHDBowaNQpdu3ZFw/qNMHv2bLXJLjp16oTPPvsMTZo0wevXryGRSGBsbIzKlStj4MCBAIC/TxTMxA5EVPAYQxlDGUMZQ4mKik5UIP38/HDgwAGcPHkS5cqVU9vn7e2N0NBQREVF4eXLl9i0aROePXuGChUqaHx+GxsbVKlSBffv388xTaNGjQBAlebdQAJA9buTk3rff6Xp06cjLi5O9Xry5AkkEgkyDzONfxkPe3t7GBoa4vnz53Cv+bZLUP32dTBi8WA4utmrtgkh1AZgx79MQNmyZfH8eUbQdKvhAgBw9XTBs2fPULZsWVVaNzc3AMDGWdvg5uaGyMhIpKen48GDB9izZw8GDx6MTz/9FIsXL1Y9IQOAMmXK4MXTaNy9+gAWFhZITs4YO3Lz9G34DhqK06dPIyYmBl5eXhg+fHiW+7Duy4wPZzMzM9jb2+NJyHNERkYiJSUF48ePx6effopPP/0048mfuTkePHiAN2/eYMiQIap91tbWeHd47qENJ9R+79u3L3bs2IENGzbA19dXbd+zZ89Qq1Yt/P7777h79y5evXqF6Oho1X5XV1dIJBJsnLkVdnZ2MDExwcuXL1XjMUqVtUN0dDSEEHBxccG2RXuxeOgaLB66BkIIPHv2DO7u7ti6cDfioxNQoUIFPHv2TO0eBu/L6AqU+R4q3b54D/evPVS9T+lp6ZjVcxGy4+bmhjN/BCPkyn24ubmpXQcApNKMv4/shjMrZ6l7+vQp3N3d8V/wXZzZHgx3d3fV35DyGlFPXmLOJ0vQuHFjjBs3DiNGjEDZsmUxY8YMCCHQqHO9bPNHRNrFGJqBMZQxNDuMoUQFT6vt6UIIjB07Frt378apU6fg7u6eY9rSpTMGswcFBSEqKkr1FEsTiYmJCA0NxeDBg3NMo5wsoEyZMgAynmh9++23iIqKgoNDxuDvY8eOwcrKCp6entmeQyaTqY01ATI+hJ2cnJCWlob4+Hg8uxcBj8ZV0L59e/zyyy9YsGAB7JxsYWImQ61WnkhMTIT3EC8Abz+wom7EqM539dgNdBvQDWPHjsWdO3cwc8ckPPr3CTwaVca07pPQs2dPVVrlk+BhC3xw584dlClTBoaGhqhQoQIaNGiA2bNnZ1uOdwO2Ur12tRAfk4jHd56hfLWy6NOnD7Zt2wYg42mlXJ4xeH7UD0OQkJCgetIZE/4K1dwqwcLCAuvWrYOLi4vaeUNDQ2FkZIRdu3ZluX8pKSmQy+UQQmDCjyPV9llbW6Nly5a4d+8eatasqRYU9u/fj9atW2Pt2rUAgHnz5uHGjRtq1xRCYPjCwYiKikJqairs7e0RHf4KAPDiSTRKly4NQ0NDPHz4EIO++QSNu3yEFr0bAwBcXFxw9+5dDPqmD2Smxrh37x5at279wXuovE/tBntlbDifMXucQiGAHOazCg0NxahRowAAv+z4WXX/LCwsEB8fj/bd2wMAnjx5kuU6FWq5Ash4En/v3j34+vqiSv0K2Lxls9r7IJVKoZArYFXKEhKJBH5+fvDz80NkZCQaN26MLl26oL53bZz8PetMjESkHYyhjKHKfDKGMoYSFSWtViDHjBmDLVu2YO/evbC0tFSNi7C2toapacbUzQEBAfDw8IC9vT2Cg4Mxfvx4+Pv7q61z1bZtW/Ts2RN+fn4AgEmTJqFr165wdXXF8+fPMWvWLBgYGGDAgAEAMj5MtmzZgk6dOqFUqVL4559/4O/vj5YtW6oGtXt7e8PT0xODBw/GokWLEBERga+//hpjxozJ8gH9PufPn4exsTFatmyJ4OBgtO3fGvL0NCxcuBBt27ZFfHw8unfvjuRXyfiu/1x07NgRQ4YMQXR0NGJiYlCnTh2keiShea+MWaukUkOYW9lh1qxZ6NSpE7755hu4uLhgyJDFuH37NjZs2KC69n///aeafeybb77BF198AQD44osv0Lp1azg4OKBBgwZ4+vQpXr58qfYktHRZW5QqY43jl96WZfz48XB2dkaDBg3w950r+P7779G7d28AgIeHB+bOnYstW7agQoUKqF69uuq4qo3KQyaTYdy4cejbty9mzZoFGxsbXL58GS1btkSdOnXQqlUrDBgwAOPHj4dUKsWpU6cwevRolC5dGuXKlcPSpUvh5uamFtwBIDAwMNv77u7ujmXLluHMmTN49uwZAgMD1SZtePHiBaZPn46uXbti0aJFGDFiBAwMDDB88Sfw/a4npAYGkEqlmDp1KgYMGIB58+bBuLQxtm3bhn79+mHs2LEYO3YsFi1ahFu3buHChQv4+eef1fLQoFOtLPny8PBAQEAAYmJi0KxZM9V2I2NDLAqamm1ZVq9ejfLlyyMhIQGBgYGq6fRbtGiBJUuWwMTEBMePH0dYWJjadbZt2wYTExN89NFHGDlyJFq1aoUaNWrA2toas2fPVo3ZUXIoXwpjVw/F2bNn8d9//6F+/fqIiIhAamoqnJ2dUaaePZp0z/6Ln74QQiApLhmKdAUs7Mzf242PSNcxhjKGMoZmYAwtGoyhpKTVCqTyyda70zEHBASoulOEhIRg+vTpiImJgZubG2bMmAF/f3+19KGhoWoDpp8+fYoBAwYgOjoa9vb2aN68OS5evAh7+4xuLcbGxjh+/DiWL1+OpKQkuLi4oHfv3vj6669V5zAwMMCBAwcwevRoNGnSBObm5hgyZAjmzp2bqzL++++/SEtNxfDhw7Flyxa0b98erxNj4eHhgf/++w+bN2/Gr7/+CjMzM/Tq1UsVTLZu3QpfX18YGBggPS1FdT6FIh0prxMxY8YMtGvXDps2bcKJEyfQvHlzrFmzBqamJlDI0wEAvXv3hq2tLX7++Wf0798fI0aMQFrqG1SrVg3nzp1DQEAAzp49i7JlM56EAkCrVq1UT/4EBOrXr696yubr64t9+/Zhw4YNsLS0xBdffIEBAwZAoZCjUaNGWL16Na5evQqpVKqalQ0AFHI5Ut4kYc6cOWjQoAH++OMPpKamolatWqpZ/bZs2YLff/8d69evh5GRERo0aKCa1vnIkSPYs2cPbty4gR49eqBbt25wcnKCXJ6GlNdJkEgkMDQ2QcWKFfHZZ58ByPjyMn36dKxfvx7u7u7YvHmzWnBo1aoVqlevjh9//BFeXl4YO3YsUlOSIRRyGEgF0uVyJCclZIzxqFYNv/32G4yMjPDJJxlrik2YMAHOzs4ICAhAmTJlcPHiRdV4jJEjR8LR0RHi/9dZ8/HxUbUMLFiwAFu2bMGtW7dQs2ZNNGjQIKOblHj79HTEiBGq8Tm+vr6oWbMmtm3bhuTkZOzfvx+VK1dGeloKpk2bBplMhg0bNqBLly5YvXq16kvhxIkTUaZMGfz3339wc3NDw4YNcfjwYWzYsAEKhQIbNmxAmzZtAGT8/xkaGma850KB8uXL4+jRozh9+jTs7Oywe/duuLq6Iin+bfclfSSEQHpqOpLjX8PWyYaBj/QeYyhjKGMoY2hRYQylzHRqHcjiJi4uDjY2Nnj+9BEcy5RD69at8dtvv8HWxgoJca9gYWkNMwtLGBhk1ONTU1MghAIGBkZo1qwZ/vzzT5jKjBD76mWWc8tMTGFhaQMT04wp0+Xp6UhKjEdiQixKOzhj46+bcOnSJdWgf3l6OhISYpGUEAdDQyNYWtvCzPztIsdvXicjPT0NMpOMp9YJcTFQKBSwtikNiUSClJTXkEqlkJmYQirNWNsoLS0VSQnxSEqMg4mpOSwsrWFkZAyJVAqJRAJ5ejpSU98g5mXGuBdTMwtYWFrDWJaxmK9CLkdyciIS4mJgZm4JcwtrGP7/IP709DQkJyXgdVIiLK1tYSwzgVSa8URTLpdDoZAj/lU03rzJGBdhZGQMGzt7GBgYIjk5EelpqbCytoOB4dtnJHK5HAYGBtizZw8CAgJUM8op5HIkJsQhIT6j640QAgohkJ6WDktrG9jY2MHIOOOJeVpqKuLjYiCVSmFhZQMjo4w1sl4nJyLu1UtYWNnC1NQc6elpeBUTBTNzS5iZW0IhlyPu1UuYmJnDxMQcBoaGqg/f9LQ0JMS/QnJSAqxtS6uOVyjkMDE1g0Ty9kM65c1rxL16ibT/L5+5pbXah3hqagriYl5AZmIKEzMLGBoaQSFPR3JSIoxlJqq/l9SUN/9fDgNYWdtCAIiPjUFaWgqsbUrBxNRcNW4o5c1rxMdGIzX17ZcwfZMR2AWSYpNhYi6DobFhvhd3TkhIgEeNuoiNjYW1tXUB5ZSIlBhDGUMZQ3UDYyi9i3MKFyLllOflyrvj1q1bOH36NICMJ2hfffWVKp2JiQnS0tIgl8vRvXt37NmzB5cuZfR7yW4igsykUimMjY3x5s0b1bYLFy7Azs5ONZmCTCZTrY2VmUQigampKV6/fp3tWIOcmJqaIiUlBYr/fzqYW0ZGRpBKpUhJyfphKpPJoFAokJZWMIvtymQytevs3bsXVlZWcHXNGNeQeTKFD50nu3wp721hP4eRSqWQSqVIT0/PNm/Z3cvsKKdQz+487zIzM8v130ZJlJCQwOBHVAgYQ7PHGJp7jKG6izFUP7EFshApFAo8f/4clpYZg6o1/eAxMDDIePr4/wPfc8vY2BgmJhlPKNPS0vD69essaeLj4+Hi4oInT56ourkUZ8ryRkdHqxbZTk9PzzKzW3FQkt5bbZZVCIGEhAQ4OzuzKw9RIWAM1R2MocUTYyjlFVsgC5FUKs0ypXpRMzIyUlvb6V1WVlbF/gMyM8NM3XEMDQ2LddlL0nurrbLyqSlR4WEM1T2MocUTYyjlFqv8REREREREpBFWIImIiIiIiEgjrECWUDKZDLNmzcrVelz6rCSVl2UlIipcJe2zpySVl2Ul+jBOokNEREREREQaYQskERERERERaYQVSCIiIiIiItIIK5BERERERESkEVYgiYiIiIiISCOsQBZznCOJiIgobxhDiYiyYgWymDp27BiSk5MhkUi0nZVCl56eru0sFJnDhw8jLCxM29koEsnJydrOAhGVUIyhxRNjKFHBYAWymHnw4AHatm2L9u3b48KFC9rOTqEKCwtD//79MXLkSMyZMwcxMTHazlKhUb6vnTp1wq1bt7SdnUIVFhaGnj17wsfHB2PHjsV///2n7SwRUQnBGFo8MYYSFSxWIIsRPz8/VKtWDQ4ODoiKikK7du20naVCExQUhI8++ghyuRylSpXC0qVLMXjwYAQHBwMAFAqFlnNYcN59X7t06aLtLBWaK1euoHHjxpDJZGjevDkOHjyITz/9FIcOHQJQvN5XItItjKGMofqOMZSKjCC9J5fLxc8//ywkEonYtm2bavvDhw9FSkqKFnNWeEaOHCkGDhyo+v3ixYuiS5cuomnTplrMVcE7dOiQkEgk4qefflJtu337toiJidFirgrPN998I9q3by/S0tKEEELcu3dPDB48WJQtW1bI5XIt546IiiPGUMbQ4oIxlIoKWyCLAalUiqZNm6J169bYv38/7t69izZt2qBnz56oXr065s6di0ePHgHQ/6dP4v8nNHj06BFMTExU2xs1aoTRo0fj6dOnWLhwIQD9LysANGnSBN27d8f27dtx48YNtGzZEn379kWNGjUwZswYXL16FUDxKCuQ0c1IIpHA0NAQAFCpUiVMnToVEokEkydPBlB8ykpEuoExlDG0OJQVYAylosMKpB56+fIlDh06hBs3biAlJQUAULVqVQwfPhzbtm1Ds2bN0LRpU8yYMQM+Pj4IDAzE3LlzkZSUBKlUv97yzGVNTU2FRCJBamoqzM3NoVAo1MZsNGvWDAMGDMCGDRsQGxurd2WNjY1VDe5XTmpgbW2NqVOn4vTp02jTpg1atWqFH374AVOnTsXff/+NyZMn631Z5XI5gIygVqZMGcjlctWXNQCoVq0apkyZgrVr1+Lly5d6V1Yi0i2MoYyhjKFE+aTtJlDKnW+++UbY2NiIpk2bCiMjIzFu3DgRGhoqhBDi0aNHYv78+WLTpk1qxyxbtkzUqVNH7Nu3TxtZzrPsynr//n0hREaZypUrJ4KDg9WOOXjwoKhTp47YunWrNrKcZ4sXLxaWlpaiX79+qm3K7ibJycli7dq1Yt26dWpdUHbs2CFq164t1q5dW+T5zY/sypqeni6EEOL3338Xnp6eYsuWLWrH3Lx5U9SuXVssWbKkSPNKRMULYyhjqBJjKFHe8TGEnoiOjsZnn32GgwcPYseOHfjzzz+xcuVKXL58GXv27AEAlC9fHr6+vujbty+At90UhgwZgnv37mkr67mmSVknTJgAAPj555/x4sUL1bHNmzfH8+fPIZPJtJDz3EtJScGkSZOwbds2tGjRAs+ePcOOHTvU0piamqJ///7w9fWFVCpVdUHq2LEjoqOj9WadsveVVVmG/v37o1SpUti5cydu376tOrZixYpQKBQwNjbWSt6JSL8xhjKGMoYyhlLBYQVSxyUmJgIAnj59ivT0dMybNw9t27aFjY0NRo0aBQMDA8TGxqrSly1bVvUBoeym8Pfff8PS0hKlSpUq8vznhiZljYuLU6VfsWIF9u7di61bt6q2xcTEwNzcXOeDX2JiIoQQkMlkqFChAoYNG4aFCxeidOnS2LJli6pLjfILjI2NjapMynXJQkJCIJFI4Obmpq1iaESTshoaGiI1NRUAMHPmTNy+fRs//vij6hwpKSlITU2Fra2ttopBRHqIMZQxFGAMZQylAqetpk96v/j4eDFo0CDRpUsXIYQQMTEx4uLFi6r9yu4KvXr1EtOnT89yvFwuFwqFQty6dUu0bdtW9O/fX2dnk8tPWf38/ETt2rVFly5dxNatW4WXl5do3LixePnyZdEVIBeUZe3atatqW2pqqurnDRs2iEaNGolly5Zle7xcLhdv3rwRt27dEu3atRNdu3YVcXFxhZ3tPMltWRUKhWrf4sWLRfXq1UWjRo3EqlWrRJs2bUTNmjXFo0ePiiz/RKS/GEMZQ7PDGMoYSgWDFUgdNGvWLCGTyYStra2wt7cXERERavuVffjfvHkjnJycxK5du4QQbz88Xr9+Lb7//nsxcOBAYWpqKoYOHSqSk5OLthAaymtZlR+iCQkJ4s8//xTt27cXjRs3Fp9++qlITEws2kJo6N2yRkZGCiHUP/Tj4uLE0KFDRZs2bcTt27eFEOr3YPXq1aJ///7C3Nxc+Pr6iqSkpKIviAbyWlbl+5qamiquXbsmfHx8RLt27cTQoUN19n0lIt3CGPoWYyhjKGMoFQZWIHXItm3bhKOjo6hSpYo4efKk2Lt3r6hcubK4efNmtumvXLkiXFxcxLNnz7Ls2759u5g4caK4detWYWc7TwqyrEIIkZaWJuLj4wszy3mmaVmVgeHgwYOiefPmYvLkyap9yjWdjh49KubOnav37+v7yqp8Wq6kq1/ciEi3MIYyhgrBGMoYSkWBFUgdERERIbp06SIWLFig2vb06VMhkUjEmTNnhBAiyyKwgYGBonXr1qrfT58+LVatWlU0Gc6HgirrmjVriibD+ZCXsgohxOTJk0Xz5s3FsWPHxI4dO8Rnn31WZHnOq4Iq6+eff15keSai4oExlDE0M8ZQosLFSXR0hIODA3bv3o1p06YBeDujVp06dXD69GkAyLJ2z969e9GqVSu8ePECXbt2RevWrVVrWgkdnlWsoMr6+vXros14HuS2rMoB/wMHDkRqaiq6deuGAQMGwMLCQu14XVRQZTUzMyvinBORvmMMZQwFGEMZQ6mosAKpIyQSCQwNDdV+t7e3h0KhQHJyMoC3HxYAEBkZicuXL2P//v1wcXGBEAIRERH48ssvVcfrqoIuqy7LbVmlUimeP3+On376CVeuXEG/fv0QHh6OFStWqI7XVQVdViIiTTGGMoYCjKGMoVRUDD+chArSuXPnUK5cObi5uUEIkeOHmXK9nnr16uHMmTMA1J88RUVFISkpCUZGRjh58iSaNGlSJPnPDZY1qw+VFQAOHDiAoKAgnD9/nmUlIsqEcSUrllW/4kpJKisVX2yBLCKPHj1Cjx490LJlSyxatAjA+5+EKRe7dXV1RUpKCp49ewbgbZcGd3d37N+/H8HBwTr3ocGy5r6swNvyjhgxAnfu3GFZiYj+H+MKywrod1wpSWWl4o8VyCIwbtw4VKhQAQYGBmjTpg3kcrlqnEVOlE+lypUrh2fPnsHS0hLA2w8bCwsLNG/evNDznlssa97KCrwtry6O0ShJZSUi3cK4wrIq6WtcKUllpZKBFchCdPnyZZiYmODChQs4f/48du7cCQ8PD1y9ehUymey9//jKD4fmzZsjMjISV65cKaps5wnLWnBl1aUxGiWprESkWxhXWNZ36VtcKUllpZKFFcgClvnDwNDQEHv27MFff/2Fxo0bAwAaNGiA58+f4+nTpxr940dHR6Nv375wd3cvtDznFcvKsup7WYlIt5Skzx+WlWXV97JSycVJdApQYmIiUlNTYWdnByBj6mXlgGdlVwQjIyPIZDLExsaiXLlyascnJyfj8OHD6NWrlyp906ZN0bRp0yIvy4ewrCyrvpeViHRLSfr8YVlZVn0vK5VsbIEsIF9//TVq166NHj16oH///rh7967ah4aSl5cXnj17hsjISADqUzKfP38en3zyCY4fP67T3RJYVpZV38tKRLqlJH3+sKwsq76XlYgVyHxKTU3F4MGDcfDgQfz444/4/PPPkZCQgA4dOuDq1asAMvqoZx7wXLt2bZw7dw6A+pTMDRs2xLx58+Dp6Vn0BdEAy8qyAvpdViLSLSXp84dlZVkB/S4rkYqgfLl586aoXr26OHnypGrb69evhZWVlejXr5+4d++eEEIIuVwuhBAiPT1d1KxZU0yfPl1tuz5gWVlWIfS7rESkW0rS5w/LyrIKod9lJVJiC2Q+xcXFISQkBLVr11Zti4yMhJ2dHc6dO4fjx49DCAGpVIr09HQYGBigWbNmCAoKApB1UVhdxrKyrPpeViLSLSXp84dlZVn1vaxESvyrzScrKyvUrFkTX3/9tWrbjz/+iA4dOqB8+fLYs2cPhBAQQsDQMGPOIuWHiLL/u75gWVlWfS8rEemWkvT5w7KyrPpeViKVomvsLJ5ev34tAgIChJGRkWjUqJEoVaqUcHZ2FmFhYSIoKEgYGhqKhIQEIcTbbgpnzpwRISEh2sx2nrCsLKu+l5WIdEtJ+vxhWVlWfS8rkRKX8cij9PR0GBoawsTEBL6+vqhevTquXbsGW1tb9OnTBwBw5coVVKpUCa9evYKFhYWqm0KLFi20mfVcY1lZVn0vKxHplpL0+cOysqz6Xlaid7ECmUvKDwxDQ0OkpaVh2rRpGD16NBo0aIAGDRqopT179iyqVKkCFxcXLeU2f1hWllXfy0pEuqUkff6wrCyrvpeVKCccA6khuVyu1n99+fLlKF26NPbu3au2Vs+DBw/w4MEDTJkyBTt27MDQoUMBqK8BpOtYVpZV38tKRLqlJH3+sKwsq76XleiDir7XrP5JT09X/Xz8+HFRsWJF4ejoKNavXy/S0tLU0m7evFnUqFFD1KxZU5w7d66os5pvLCvLqu9lJSLdUpI+f1hWllXfy0qkCVYgNfT48WPRoUMHYWRkJCZNmiSio6PV9isUCiGEEElJSeLChQvayGKBYVnfYlmJiPKvJH3+sKxvsaxExRMrkBr4448/hKGhoejcubO4ffu2trNTqFjW4qkklZWIdEtJ+vxhWYunklRWIk1IhGCn7A958OABIiMj0aRJE21npdCxrMVTSSorEemWkvT5w7IWTyWprESaYAWSiIiIiIiINMJZWImIiIiIiEgjrEASERERERGRRliBJCIiIiIiIo2wAklEREREREQaYQWSiIiIiIiINMIKJBEREREREWmEFUgiIiIiIiLSCCuQREREREREpBFWIImIiIiIiEgjrEASERERERGRRliBJCoAYWFhkEgkuH79eqGcXyKRYM+ePYVybiIiIm1iDCXSL6xAUrHg6+uLHj16aO36Li4uCA8PR40aNQAAp06dgkQiQWxsrNbyREREpAnGUCLKDUNtZ4CoODAwMICTk5O2s0FERKR3GEOJ9AtbIKnYO336NBo2bAiZTIYyZcpg2rRpSE9PV+338vLCuHHjMGXKFNjZ2cHJyQmzZ89WO8edO3fQvHlzmJiYwNPTE8ePH1frEpO5+01YWBhat24NALC1tYVEIoGvry8AwM3NDcuXL1c7d506ddSud+/ePbRs2VJ1rWPHjmUp05MnT9C3b1/Y2NjAzs4O3bt3R1hYWH5vFRERkRrGUCJ6FyuQVKw9e/YMnTp1QoMGDXDjxg2sXbsWGzZswPz589XSbdy4Eebm5rh06RIWLVqEuXPnqoKOXC5Hjx49YGZmhkuXLuGnn37CjBkzcrymi4sLdu7cCQAICQlBeHg4VqxYoVF+FQoFevXqBWNjY1y6dAnr1q3D1KlT1dKkpaWhffv2sLS0xNmzZ3H+/HlYWFigQ4cOSE1Nzc3tISIiyhFjKBFlh11YqVj73//+BxcXF6xevRoSiQTVqlXD8+fPMXXqVMycORNSacYzlFq1amHWrFkAgMqVK2P16tU4ceIEPv74Yxw7dgyhoaE4deqUqovNt99+i48//jjbaxoYGMDOzg4A4ODgABsbG43ze/z4cdy5cwdHjhyBs7MzAOC7775Dx44dVWm2bdsGhUKB9evXQyKRAAACAgJgY2ODU6dOwdvbO3c3iYiIKBuMoUSUHVYgqVi7ffs2mjRpogoSANCsWTMkJibi6dOnKF++PICM4JdZmTJlEBUVBSDjCaiLi4va+IyGDRsWWn5dXFxUgQ8AmjRpopbmxo0buH//PiwtLdW2v3nzBqGhoYWSLyIiKnkYQ4koO6xAEgEwMjJS+10ikUChUBT4daRSKYQQatvS0tJydY7ExER89NFH2Lx5c5Z99vb2+cofERFRbjGGEpUsrEBSsebh4YGdO3dCCKF6gnr+/HlYWlqiXLlyGp2jatWqePLkCSIjI+Ho6AgAuHLlynuPMTY2BpAx9iMze3t7hIeHq36Pj4/Hw4cP1fL75MkThIeHo0yZMgCAixcvqp2jXr162LZtGxwcHGBlZaVRGYiIiHKLMZSIssNJdKjYiIuLw/Xr19VeI0aMwJMnTzB27FjcuXMHe/fuxaxZszBx4kTV2I0P+fjjj1GxYkUMGTIE//zzD86fP4+vv/4aANS69WTm6uoKiUSCAwcO4MWLF0hMTAQAtGnTBps2bcLZs2dx8+ZNDBkyBAYGBqrj2rVrhypVqmDIkCG4ceMGzp49m2WyAR8fH5QuXRrdu3fH2bNn8fDhQ5w6dQrjxo3D06dP83LriIiohGMMZQwl0hQrkFRsnDp1CnXr1lV7zZs3DwcPHsTly5dRu3ZtjBo1CsOGDVMFL00YGBhgz549SExMRIMGDfD555+rAtL/tXPHKGoHcRSAX6xEbBYEC8FGLLyAlWBjp4V4BS0FS7W0sPQs/zuI3sLKK1hol3SBDSEZ0uxm+b7+N0z3eDPD1Ov13850Op0cDofsdru02+2s1+skyX6/z3g8zmw2y3Q6zXw+T6/X+zlXq9VSVVWez2eGw2FWq1WOx+O7tRuNRs7nc7rdbhaLRQaDQZbLZV6vl9NUAP6JDJWhUOrb918fkwN/db1eMxqNcrvd3oUXAPBnMhT+bwokFKiqKs1mM/1+P7fbLZvNJm9vb7lcLh+9NQD41GQofC0+0YECj8cj2+029/s9rVYrk8kkp9Ppo7cFAJ+eDIWvxQ0kAAAARXyiAwAAQBEFEgAAgCIKJAAAAEUUSAAAAIookAAAABRRIAEAACiiQAIAAFBEgQQAAKCIAgkAAECRHzjlXs96SOwcAAAAAElFTkSuQmCC", + "image/png": 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", "text/plain": [ "
" ] @@ -462,7 +462,9 @@ ], "source": [ "for ag_map in agreement_categorical_catalog['agreement_maps'].unique():\n", - " rxr.open_rasterio(ag_map, mask_and_scale=True).gval.cat_plot(title=ag_map)" + " rxr.open_rasterio(ag_map, mask_and_scale=True).gval.cat_plot(\n", + " title=f'Agreement Map {int(ag_map.split(\"_\")[-1][0]) + 1}'\n", + " )" ] }, { @@ -483,7 +485,7 @@ }, { "cell_type": "code", - "execution_count": 90, + "execution_count": 7, "id": "a86f7eca", "metadata": {}, "outputs": [ @@ -536,7 +538,7 @@ "1 ./candidate_continuous_1.tif compare2 ./agreement_continuous_1.tif" ] }, - "execution_count": 90, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -547,7 +549,7 @@ }, { "cell_type": "code", - "execution_count": 91, + "execution_count": 8, "id": "1b94b2cc", "metadata": {}, "outputs": [ @@ -597,7 +599,7 @@ "1 ./benchmark_continuous_1.tif compare2" ] }, - "execution_count": 91, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -616,7 +618,7 @@ }, { "cell_type": "code", - "execution_count": 101, + "execution_count": 9, "id": "6dad7a6d", "metadata": {}, "outputs": [ @@ -643,6 +645,7 @@ " \n", " 0\n", " 1\n", + " 2\n", " \n", " \n", " \n", @@ -650,41 +653,49 @@ " map_id_candidate\n", " ./candidate_continuous_0.tif\n", " ./candidate_continuous_1.tif\n", + " ./candidate_continuous_1.tif\n", " \n", " \n", " compare_id\n", " compare1\n", " compare2\n", + " compare2\n", " \n", " \n", " agreement_maps\n", " ./agreement_continuous_0.tif\n", " ./agreement_continuous_1.tif\n", + " ./agreement_continuous_1.tif\n", " \n", " \n", " map_id_benchmark\n", " ./benchmark_continuous_0.tif\n", " ./benchmark_continuous_1.tif\n", + " ./benchmark_continuous_1.tif\n", " \n", " \n", " band\n", " 1\n", " 1\n", + " 2\n", " \n", " \n", " coefficient_of_determination\n", " -0.06616\n", - " -0.329965\n", + " -2.829421\n", + " 0.10903\n", " \n", " \n", " mean_absolute_error\n", " 0.317389\n", " 0.485031\n", + " 0.485031\n", " \n", " \n", " mean_absolute_percentage_error\n", " 0.159568\n", - " 0.177735\n", + " 0.202235\n", + " 0.153235\n", " \n", " \n", "\n", @@ -701,18 +712,28 @@ "mean_absolute_error 0.317389 \n", "mean_absolute_percentage_error 0.159568 \n", "\n", - " 1 \n", + " 1 \\\n", + "map_id_candidate ./candidate_continuous_1.tif \n", + "compare_id compare2 \n", + "agreement_maps ./agreement_continuous_1.tif \n", + "map_id_benchmark ./benchmark_continuous_1.tif \n", + "band 1 \n", + "coefficient_of_determination -2.829421 \n", + "mean_absolute_error 0.485031 \n", + "mean_absolute_percentage_error 0.202235 \n", + "\n", + " 2 \n", "map_id_candidate ./candidate_continuous_1.tif \n", "compare_id compare2 \n", "agreement_maps ./agreement_continuous_1.tif \n", "map_id_benchmark ./benchmark_continuous_1.tif \n", - "band 1 \n", - "coefficient_of_determination -0.329965 \n", + "band 2 \n", + "coefficient_of_determination 0.10903 \n", "mean_absolute_error 0.485031 \n", - "mean_absolute_percentage_error 0.177735 " + "mean_absolute_percentage_error 0.153235 " ] }, - "execution_count": 101, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -754,13 +775,13 @@ }, { "cell_type": "code", - "execution_count": 110, + "execution_count": 15, "id": "c97ea173", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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", + "image/png": 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", "text/plain": [ "
" ] @@ -781,7 +802,9 @@ ], "source": [ "for ag_map in agreement_continuous_catalog['agreement_maps'].unique():\n", - " rxr.open_rasterio(ag_map, mask_and_scale=True).gval.cont_plot(title=ag_map)" + " rxr.open_rasterio(ag_map, mask_and_scale=True).gval.cont_plot(\n", + " title=f'Agreement Map {int(ag_map.split(\"_\")[-1][0]) + 1}'\n", + " )" ] } ], diff --git a/docs/sphinx/SphinxSubsamplingTutorial.ipynb b/docs/sphinx/SphinxSubsamplingTutorial.ipynb index 96cab560..b66a18d1 100644 --- a/docs/sphinx/SphinxSubsamplingTutorial.ipynb +++ b/docs/sphinx/SphinxSubsamplingTutorial.ipynb @@ -1790,9 +1790,7 @@ "cell_type": "code", "execution_count": 25, "id": "a9e533c6", - "metadata": { - "scrolled": false - }, + "metadata": {}, "outputs": [ { "data": { diff --git a/notebooks/Continuous Comparison Tutorial.ipynb b/notebooks/Continuous Comparison Tutorial.ipynb index e29eccab..51b5c92b 100644 --- a/notebooks/Continuous Comparison Tutorial.ipynb +++ b/notebooks/Continuous Comparison Tutorial.ipynb 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zj=245`*-u(3Z>^$>Chb7d_9XtEob0cWg;(+kNmsOeIGuP-h-|!8IS=PkO3Kx0U3}1 z8IS=PkO3Kx0U3}18IXZLvw>S*IAipc!~dA!t{5`!e|-GUeE#&=_5C3OG9UvoAOkWW z12P~3G9UvoAOkWW12P~3G9UvoAOkWW12P~3G9UvoAOkWW12P~3G9UvoAOkWW12P~3 zG9UvoAOkWW12P~3G9UvoAOkWW12P~3G9UvoAOkWW12P~3G9UvoAOkWW12P~3G9Uvo zAOkWW12P~3G9UvoAOkWW12P~3G9UvoAOkWW12P~3G9UvoAOkWW12P~3G9UvoAOkWW z12P~3G9UvoAOkWW12P~3G9UvoAOkWW12P~3G9UvoAOkWW12P~3G9UvoAOkWW12P~3 zG9UvoAOkWW12P~3G9UvoAOkWW12P~3G9UvoAOkWW12P~3G9UvoAOkWW12P~3G9Uvo zAOkWW12P~3G9UvoAOkWW12P~3G9UvoAOkWW12P~3G9UvoAOkWW12P~3G9UvoAOkWW z12P~3G9UvoAOkWW12P~3G9UvoAOkWW12P~3G9UvoAOkWW12P~3G9UvoAOkWW12P~3 zG9UvoAOkWW12P~3G9UvoAOkWW12P~3G9UvoAOkWW12P~3G9UvoAOkWW12P~3G9Uvo zAOkWW12P~3G9UvoAOkWW12P~3G9UvoAOkWW12P~3G9UvoAOkWW12P~3G9UvoAOkWW z12P~3G9UvoAOkWW12P~3G9UvoAOkWW12P~3G9UvoAOkWW12P~3G9UvoAOkWW12P~3 zG9UvoAOkWW12P~3G9UvoAOkWW12P~3G9UvoAOkWW12P~3G9UvoAOkWW12P~3G9Uvo zAOkWW12P~3G9UvoAOkWW12P~3G9UvoAOkWW12P~3G9UvoAOkWW12P~3G9UvoAOkWW z12P~3G9UvoAOkWW12P~3G9UvoAOkWW12P~3G9UvoAOkWW12P~3G9UvoAOkWW12P~3 sG9UvoAOkWW12P~3G9UvoAOkWW12P~3G9UvoAOkWW12P~3e@p}a2bb%ks{jB1 literal 0 HcmV?d00001 From 7accfcd952f2fba9437c8cf2341603b20ef77347 Mon Sep 17 00:00:00 2001 From: Gregory Petrochenkov Date: Wed, 27 Dec 2023 12:54:59 -0500 Subject: [PATCH 6/8] Remove commented code --- src/gval/comparison/compute_continuous_metrics.py | 14 -------------- 1 file changed, 14 deletions(-) diff --git a/src/gval/comparison/compute_continuous_metrics.py b/src/gval/comparison/compute_continuous_metrics.py index 7ff8e219..66a0781d 100644 --- a/src/gval/comparison/compute_continuous_metrics.py +++ b/src/gval/comparison/compute_continuous_metrics.py @@ -49,26 +49,12 @@ def _get_selected_datasets( Datasets with selected coordinates """ - # is_dsk = _check_dask_array(agreement) cmask, bmask = ( xr.where(candidate[var_name] == nodata, 0, 1), xr.where(benchmark[var_name] == nodata, 0, 1), ) tmask = cmask & bmask - # # Create a coord meshgrid and select appropriate coords to select from xarray - # if is_dsk: - # with da.config.set({"array.slicing.split_large_chunks": True}): - # grid_coords = da.array.asarray( - # da.array.meshgrid(candidate.coords["x"], candidate.coords["y"]) - # ).T.reshape(-1, 2) - # picked_coords = grid_coords[da.array.ravel(tmask.data).astype(bool), :] - # else: - # grid_coords = np.array( - # np.meshgrid(candidate.coords["x"], candidate.coords["y"]) - # ).T.reshape(-1, 2) - # picked_coords = grid_coords[np.ravel(tmask.data).astype(bool), :] - # Select coordinates from xarray with da.config.set({"array.slicing.split_large_chunks": True}): agreement_sel = agreement[var_name].where(tmask.compute(), drop=True) From 68975622242200b1098670b665712ed9ecfa8ed0 Mon Sep 17 00:00:00 2001 From: Gregory Petrochenkov Date: Wed, 27 Dec 2023 12:59:37 -0500 Subject: [PATCH 7/8] Update pyproject version and deps --- pyproject.toml | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index f388ea24..f7430c48 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -12,7 +12,7 @@ authors = [ requires-python = ">=3.8" keywords = ["geospatial", "evaluations"] license = {text = "MIT"} -version = "0.2.4" +version = "0.2.5" dynamic = ["readme", "dependencies"] @@ -37,7 +37,9 @@ dev = ["pytest==7.2.2", "colorama==0.4.6", "build==0.10.0", "twine==4.0.2", - "deepdiff==6.7.1" + "deepdiff==6.7.1", + "pystac-client==0.7.5", + "stackstac==0.5.0" ] [project.urls] From bfc1a6ec5ee6328862f95d1de3315f552bf2d566 Mon Sep 17 00:00:00 2001 From: Gregory Petrochenkov Date: Wed, 27 Dec 2023 18:14:23 -0500 Subject: [PATCH 8/8] Swap Fiona to pyogrio --- pyproject.toml | 4 +--- requirements/base.txt | 3 +++ tests/conftest.py | 2 +- 3 files changed, 5 insertions(+), 4 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index f7430c48..298de963 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -37,9 +37,7 @@ dev = ["pytest==7.2.2", "colorama==0.4.6", "build==0.10.0", "twine==4.0.2", - "deepdiff==6.7.1", - "pystac-client==0.7.5", - "stackstac==0.5.0" + "deepdiff==6.7.1" ] [project.urls] diff --git a/requirements/base.txt b/requirements/base.txt index c78c552d..21ea05fa 100644 --- a/requirements/base.txt +++ b/requirements/base.txt @@ -12,6 +12,9 @@ matplotlib==3.7.1 contextily==1.3.0 flox==0.7.2 xskillscore==0.0.24 +pyogrio==0.7.2 +pystac-client==0.7.5 +stackstac==0.5.0 ################################### diff --git a/tests/conftest.py b/tests/conftest.py index 9bec53a9..626fa19c 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -64,7 +64,7 @@ def _load_gpkg( geopandas GeoDataFrame. """ file_path = _build_map_file_path(file_name) - return gpd.read_file(file_path, *args, **kwargs) + return gpd.read_file(file_path, engine="pyogrio", *args, **kwargs) def _load_xarray(

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