diff --git a/.github/ISSUE_TEMPLATE/bug-report.md b/.github/ISSUE_TEMPLATE/bug-report.md new file mode 100644 index 00000000..c34a00d6 --- /dev/null +++ b/.github/ISSUE_TEMPLATE/bug-report.md @@ -0,0 +1,25 @@ +--- +name: "Bug report" +about: Report a bug or other issue +title: "[Bug]: " +labels: bug +assignees: '' + +--- + +**Describe the bug** +Describe what the bug or issue is about concisely. + +**How to reproduce the bug** +Steps to reproduce the behavior: + +**Expected behavior** +Describe what you expected to happen. + +**Environment details** + - OS: [e.g., Windows, MacOS] + - Python Version: [e.g., 3.9] + - Package Version: [e.g., 1.2.0] + +**Additional information** +Add any other context about the problem here. Screenshots or other additional information can be attached too. diff --git a/.github/ISSUE_TEMPLATE/tool-request.md b/.github/ISSUE_TEMPLATE/tool-request.md new file mode 100644 index 00000000..1c433691 --- /dev/null +++ b/.github/ISSUE_TEMPLATE/tool-request.md @@ -0,0 +1,26 @@ +--- +name: Tool request +about: Suggest a new tool to EIS toolkit +title: "[New tool] -" +labels: enhancement +assignees: '' + +--- + +**Tool description** +Please describe what the tool does. Ex. "This tool calculates distances to known deposits" + +**Justification and importance** +Please give reasoning why the tool is needed. Is the tool essential or a nice-to-have? + +**Tool category and type** +The current categories are the directories under `eis_toolkit` package in this repository. You may also suggest a new category if a suitable one doesn't exist. + +**Implementation details** +What does the tool take as input and what is its output? Ex. "The tool takes raster data as input and outputs decimal numbers as an array". Is there a ready made library that already contains the desired functionality or should it be implemented from scratch? Is the library already part of EIS toolkit? + +**Who will implement the tool?** +Are you ready to implement the tool yourself or will you hope someone else to do it? + +**Additional information** +Any additional information e.g. usage examples, screenshots, links to scientific studies and other documents. diff --git a/.github/workflows/conda.yml b/.github/workflows/conda.yml index 16e64ae1..1c67cca3 100644 --- a/.github/workflows/conda.yml +++ b/.github/workflows/conda.yml @@ -3,7 +3,8 @@ name: Conda on: pull_request: push: - branches: [master, main] + # branches: [master, main] + branches: [main] jobs: conda-test: name: Conda Install & Test @@ -11,7 +12,7 @@ jobs: fail-fast: false matrix: python-version: ["3.9"] - platform: ["ubuntu-latest", "windows-latest"] + platform: ["ubuntu-latest"] runs-on: ${{ matrix.platform }} defaults: @@ -27,6 +28,9 @@ jobs: auto-activate-base: false mamba-version: "*" channel-priority: true + - name: Set GDAL environment variable for Windows + if: runner.os == 'Windows' + run: echo "USE_PATH_FOR_GDAL_PYTHON=YES" >> $GITHUB_ENV - name: Print conda environment run: | # Print environment @@ -39,3 +43,4 @@ jobs: python -c 'import eis_toolkit' # Run unittests with pytest pytest -v + \ No newline at end of file diff --git a/.github/workflows/docker.yaml b/.github/workflows/docker.yaml index 368c4664..e6117eaa 100644 --- a/.github/workflows/docker.yaml +++ b/.github/workflows/docker.yaml @@ -1,7 +1,8 @@ on: pull_request: push: - branches: [master, main] + # branches: [master, main] + branches: [main] jobs: docker-test: name: Docker install and test diff --git a/.gitignore b/.gitignore index 67d161cd..daeccda9 100755 --- a/.gitignore +++ b/.gitignore @@ -164,3 +164,6 @@ cython_debug/ tests/data/local/** !tests/data/local/**/ !tests/data/local/**/.gitkeep + +#configuration files +.vscode/launch.json \ No newline at end of file diff --git a/README.md b/README.md index eccf7db8..736ccce7 100755 --- a/README.md +++ b/README.md @@ -23,8 +23,6 @@ · Usage · - Roadmap - · Contributing · License @@ -34,11 +32,11 @@ EIS Toolkit is a comprehensive Python package for mineral prospectivity mapping and analysis. EIS Toolkit is developed as part of [EIS Horizon EU project](https://eis-he.eu/), which aims to aid EU's efforts in the green transition by securing critical raw materials. EIS Toolkit serves both as a standalone library that brings together and implements relevant tools for mineral prospectivity mapping and as a computational backend for [EIS QGIS Plugin](https://github.com/GispoCoding/eis_qgis_plugin). > [!NOTE] -> This repository is still in development. Check the [wiki page of EIS Toolkit](https://github.com/GispoCoding/eis_toolkit/wiki) for list of tools and [roadmap](#roadmap) for more details about the project. +> This repository is still in development. Check the [wiki page of EIS Toolkit](https://github.com/GispoCoding/eis_toolkit/wiki) for list of tools. ## Installation -We recommend installing EIS Toolkit in an empty virtual environment to ensure compatibility between package versions. +EIS Toolkit requires Python 3.9 or 3.10. We recommend installing EIS Toolkit in an empty virtual environment to ensure compatibility between package versions. EIS Toolkit is available in conda-forge and PyPI and can be installed with one of the following commands. @@ -48,10 +46,6 @@ pip install eis_toolkit ```console conda install -c conda-forge eis_toolkit -# On Windows, tensorflow must be installed from the anaconda channel -# Consequently, channel priority must be flexible, which can be explicitly -# done using --no-channel-priority -conda install -c conda-forge eis_toolkit --no-channel-priority ``` A Python wheel can be downloaded also from the [releases page](https://github.com/GispoCoding/eis_toolkit/releases) of this GitHub repository. @@ -101,11 +95,6 @@ eis --help > [!NOTE] > Please note that the CLI has been primarily designed to communicate with external programs and may be clunky in direct use. -## Roadmap - -- Milestone 1: **Beta release 0.1** (November 2023). The toolkit should have the basic funtionalities required for a full MPM workflow. Official testing phase begins. The plugin will be still under active development. -- Milestone 2: **Release 1.0** (May 2024). Most features should be incorporated at this time and the toolkit useful for actual MPM work. Testing will continue, more advanced methods added and the user experience refined. - ## Contributing We welcome contributions to EIS Toolkit in various forms: diff --git a/cba_out.tif b/cba_out.tif deleted file mode 100644 index da922333..00000000 Binary files a/cba_out.tif and /dev/null differ diff --git a/docs/raster_processing/proximity_to_anomaly.md b/docs/raster_processing/proximity_to_anomaly.md new file mode 100644 index 00000000..e4f9a2e1 --- /dev/null +++ b/docs/raster_processing/proximity_to_anomaly.md @@ -0,0 +1,3 @@ +# Proximity to anomaly + +::: eis_toolkit.raster_processing.proximity_to_anomaly diff --git a/docs/training_data_tools/points_to_raster.md b/docs/training_data_tools/points_to_raster.md new file mode 100644 index 00000000..5fccb9a5 --- /dev/null +++ b/docs/training_data_tools/points_to_raster.md @@ -0,0 +1,3 @@ +# Points to raster + +::: eis_toolkit.training_data_tools.points_to_raster diff --git a/docs/training_data_tools/random_sampling.md b/docs/training_data_tools/random_sampling.md new file mode 100644 index 00000000..83652052 --- /dev/null +++ b/docs/training_data_tools/random_sampling.md @@ -0,0 +1,3 @@ +# Random sampling + +::: eis_toolkit.training_data_tools.random_sampling \ No newline at end of file diff --git a/docs/vector_processing/proximity_computation.md b/docs/vector_processing/proximity_computation.md new file mode 100644 index 00000000..9f97b764 --- /dev/null +++ b/docs/vector_processing/proximity_computation.md @@ -0,0 +1,3 @@ +# Proximity computation + +::: eis_toolkit.vector_processing.proximity_computation diff --git a/eis_toolkit/__init__.py b/eis_toolkit/__init__.py index 3dc1f76b..9b102be7 100755 --- a/eis_toolkit/__init__.py +++ b/eis_toolkit/__init__.py @@ -1 +1 @@ -__version__ = "0.1.0" +__version__ = "1.1.5" diff --git a/eis_toolkit/cli.py b/eis_toolkit/cli.py index 86dafe56..3065b896 100644 --- a/eis_toolkit/cli.py +++ b/eis_toolkit/cli.py @@ -6,7 +6,9 @@ import json import os +from contextlib import contextmanager from enum import Enum +from itertools import zip_longest from pathlib import Path import geopandas as gpd @@ -14,9 +16,11 @@ import pandas as pd import rasterio import typer -from beartype.typing import List, Optional, Tuple, Union +from beartype.typing import List, Optional, Sequence, Tuple, Union from typing_extensions import Annotated +from eis_toolkit.utilities.nodata import nan_to_nodata, nodata_to_nan + app = typer.Typer() @@ -121,10 +125,18 @@ class ResamplingMethods(str, Enum): nearest = "nearest" bilinear = "bilinear" cubic = "cubic" + cubic_spline = "cubic_spline" + lanczos = "lanczos" average = "average" - gauss = "gauss" + mode = "mode" + # gauss = "gauss" max = "max" min = "min" + median = "median" + q1 = "q1" + q3 = "q3" + sum = "sum" + rms = "rms" class ValidationMethods(str, Enum): @@ -332,6 +344,33 @@ class KerasRegressorMetrics(str, Enum): mae = "mae" +class WeightsType(str, Enum): + """Weights type for WofE.""" + + unique = "unique" + categorical = "categorical" + ascending = "ascending" + descending = "descending" + + +class ReplaceCondition(str, Enum): + """Replace conditions for replace with nodata.""" + + equal = "equal" + less_than = "less_than" + greater_than = "greater_than" + less_than_or_equal = "less_than_or_equal" + greater_than_or_equal = "greater_than_or_equal" + + +class BufferOption(str, Enum): + """Buffer options.""" + + avg = "avg" + min = "min" + max = "max" + + INPUT_FILE_OPTION = Annotated[ Path, typer.Option( @@ -344,6 +383,18 @@ class KerasRegressorMetrics(str, Enum): ), ] +INPUT_FILES_OPTION = Annotated[ + List[Path], + typer.Option( + exists=True, + file_okay=True, + dir_okay=False, + writable=False, + readable=True, + resolve_path=True, + ), +] + INPUT_FILES_ARGUMENT = Annotated[ List[Path], typer.Argument( @@ -381,6 +432,48 @@ class KerasRegressorMetrics(str, Enum): ] +class ProgressLog: # noqa: D101 + @contextmanager + @staticmethod + def reading_input_files(): # noqa: D102 + typer.echo("Opening input files....") + yield + typer.echo("[OK] Input files read\n") + + @contextmanager + @staticmethod + def running_algorithm(): # noqa: D102 + typer.echo("Running algorithm...") + yield + typer.echo("[OK] Algorithm run succesfully\n") + + @contextmanager + @staticmethod + def saving_output_files(savepath: Union[str, Sequence[str]]): # noqa: D102 + typer.echo("Saving output files...") + yield + if isinstance(savepath, Sequence): + for file in savepath: + typer.echo(f"[OK] Output file(s) saved to {file}") + typer.echo(" ") + else: + typer.echo(f"[OK] Output file(s) saved to {savepath}\n") + + @staticmethod + def finish(): # noqa: D102 + typer.echo("[OK] Algorithm execution finished succesfully\n") + + +class ResultSender: # noqa: D101 + @staticmethod + def send_dict_as_json(dictionary: dict): # noqa: D102 + typer.echo(f"Results: {json.dumps(dictionary)}") + + @staticmethod + def send_multiple_rasters_dict_as_json(rasters_dictionary: dict): # noqa: D102 + typer.echo(f"Output rasters: {json.dumps(rasters_dictionary)}") + + def get_enum_values(parameter: Union[Enum, List[Enum]]) -> Union[str, List[str]]: """Get values behind enum parameter definition (required for list enums).""" if isinstance(parameter, List): @@ -397,22 +490,17 @@ def normality_test_raster_cli(input_raster: INPUT_FILE_OPTION, bands: Optional[L """Compute Shapiro-Wilk test for normality on the input raster data.""" from eis_toolkit.exploratory_analyses.normality_test import normality_test_array - typer.echo("Progress: 10%") + with ProgressLog.reading_input_files(): + with rasterio.open(input_raster) as raster: + data = raster.read() + if len(bands) == 0: + bands = None - with rasterio.open(input_raster) as raster: - data = raster.read() - typer.echo("Progress: 25%") - if len(bands) == 0: - bands = None + with ProgressLog.running_algorithm(): results_dict = normality_test_array(data=data, bands=bands, nodata_value=raster.nodata) - typer.echo("Progress: 75%") - - json_str = json.dumps(results_dict) - typer.echo("Progress: 100%") - - typer.echo(f"Results: {json_str}") - typer.echo("Normality test (raster) completed") + ResultSender.send_dict_as_json(results_dict) + ProgressLog.finish() # NORMALITY TEST VECTOR @@ -421,20 +509,14 @@ def normality_test_vector_cli(input_vector: INPUT_FILE_OPTION, columns: Optional """Compute Shapiro-Wilk test for normality on the input vector data.""" from eis_toolkit.exploratory_analyses.normality_test import normality_test_dataframe - typer.echo("Progress: 10%") - - geodataframe = gpd.read_file(input_vector) - typer.echo("Progress: 25%") + with ProgressLog.reading_input_files(): + geodataframe = gpd.read_file(input_vector) - results_dict = normality_test_dataframe(data=geodataframe, columns=columns) + with ProgressLog.running_algorithm(): + results_dict = normality_test_dataframe(data=geodataframe, columns=columns) - typer.echo("Progress: 75%") - - json_str = json.dumps(results_dict) - typer.echo("Progress: 100%") - - typer.echo(f"Results: {json_str}") - typer.echo("Normality test (vector) completed") + ResultSender.send_dict_as_json(results_dict) + ProgressLog.finish() # CHI-SQUARE_TEST @@ -447,20 +529,14 @@ def chi_square_test_cli( """Perform a Chi-square test of independence between a target variable and one or more other variables.""" from eis_toolkit.exploratory_analyses.chi_square_test import chi_square_test - typer.echo("Progress: 10%") - - geodataframe = gpd.read_file(input_vector) # Should we drop geometry columns? - typer.echo("Progress: 25%") + with ProgressLog.reading_input_files(): + geodataframe = gpd.read_file(input_vector) - results_dict = chi_square_test(data=geodataframe, target_column=target_column, columns=columns) + with ProgressLog.running_algorithm(): + results_dict = chi_square_test(data=geodataframe, target_column=target_column, columns=columns) - typer.echo("Progress: 75%") - - json_str = json.dumps(results_dict) - typer.echo("Progress: 100%") - - typer.echo(f"Results: {json_str}") - typer.echo("Chi-square test completed") + ResultSender.send_dict_as_json(results_dict) + ProgressLog.finish() # CORRELATION MATRIX @@ -475,22 +551,22 @@ def correlation_matrix_cli( """Compute correlation matrix on the input data.""" from eis_toolkit.exploratory_analyses.correlation_matrix import correlation_matrix - typer.echo("Progress: 10%") - - geodataframe = gpd.read_file(input_vector) - dataframe = pd.DataFrame(geodataframe.drop(columns="geometry")) - typer.echo("Progress: 25%") + with ProgressLog.reading_input_files(): + geodataframe = gpd.read_file(input_vector) + dataframe = pd.DataFrame(geodataframe.drop(columns="geometry")) - output_df = correlation_matrix( - data=dataframe, columns=columns, correlation_method=get_enum_values(correlation_method), min_periods=min_periods - ) - - typer.echo("Progress: 75%") + with ProgressLog.running_algorithm(): + output_df = correlation_matrix( + data=dataframe, + columns=columns, + correlation_method=get_enum_values(correlation_method), + min_periods=min_periods, + ) - output_df.to_csv(output_file) - typer.echo("Progress: 100%") + with ProgressLog.saving_output_files(output_file): + output_df.to_csv(output_file) - typer.echo("Correlation matrix completed") + ProgressLog.finish() # COVARIANCE MATRIX @@ -505,22 +581,19 @@ def covariance_matrix_cli( """Compute covariance matrix on the input data.""" from eis_toolkit.exploratory_analyses.covariance_matrix import covariance_matrix - typer.echo("Progress: 10%") - - geodataframe = gpd.read_file(input_vector) - dataframe = pd.DataFrame(geodataframe.drop(columns="geometry")) - typer.echo("Progress: 25%") + with ProgressLog.reading_input_files(): + geodataframe = gpd.read_file(input_vector) + dataframe = pd.DataFrame(geodataframe.drop(columns="geometry")) - output_df = covariance_matrix( - data=dataframe, columns=columns, min_periods=min_periods, delta_degrees_of_freedom=delta_degrees_of_freedom - ) - - typer.echo("Progress: 75%") + with ProgressLog.running_algorithm(): + output_df = covariance_matrix( + data=dataframe, columns=columns, min_periods=min_periods, delta_degrees_of_freedom=delta_degrees_of_freedom + ) - output_df.to_csv(output_file) - typer.echo("Progress: 100%") + with ProgressLog.saving_output_files(output_file): + output_df.to_csv(output_file) - typer.echo("Covariance matrix completed") + ProgressLog.finish() # DBSCAN VECTOR @@ -536,24 +609,22 @@ def dbscan_vector_cli( """Perform DBSCAN clustering on the input vector data.""" from eis_toolkit.exploratory_analyses.dbscan import dbscan_vector - typer.echo("Progress: 10%") - - geodataframe = gpd.read_file(input_vector) - typer.echo("Progress: 25%") + with ProgressLog.reading_input_files(): + geodataframe = gpd.read_file(input_vector) - output_geodataframe = dbscan_vector( - data=geodataframe, - include_coordinates=include_coordinates, - columns=columns, - max_distance=max_distance, - min_samples=min_samples, - ) - typer.echo("Progress: 75%") + with ProgressLog.running_algorithm(): + output_geodataframe = dbscan_vector( + data=geodataframe, + include_coordinates=include_coordinates, + columns=columns, + max_distance=max_distance, + min_samples=min_samples, + ) - output_geodataframe.to_file(output_vector, driver="GPKG") - typer.echo("Progress: 100%") + with ProgressLog.saving_output_files(output_vector): + output_geodataframe.to_file(output_vector, driver="GPKG") - typer.echo(f"DBSCAN completed, output vector written to {output_vector}.") + ProgressLog.finish() # DBSCAN RASTER @@ -568,23 +639,22 @@ def dbscan_raster_cli( from eis_toolkit.exploratory_analyses.dbscan import dbscan_array from eis_toolkit.utilities.file_io import read_and_stack_rasters - typer.echo("Progress: 10%") - - stacked_array, profiles = read_and_stack_rasters(input_rasters, nodata_handling="convert_to_nan") - typer.echo("Progress: 25%") + with ProgressLog.reading_input_files(): + stacked_array, profiles = read_and_stack_rasters(input_rasters, nodata_handling="convert_to_nan") - output_array = dbscan_array(data=stacked_array, max_distance=max_distance, min_samples=min_samples) - typer.echo("Progress: 75%") + with ProgressLog.running_algorithm(): + output_array = dbscan_array(data=stacked_array, max_distance=max_distance, min_samples=min_samples) out_profile = profiles[0] out_profile["nodata"] = -9999 out_profile["count"] = 1 - with rasterio.open(output_raster, "w", **out_profile) as dst: - dst.write(output_array, 1) + with ProgressLog.saving_output_files(output_raster): + with rasterio.open(output_raster, "w", **out_profile) as dst: + dst.write(output_array, 1) + dst.update_stats() - typer.echo("Progress: 100%") - typer.echo(f"DBSCAN clustering completed, output raster written to {output_raster}.") + ProgressLog.finish() # K-MEANS CLUSTERING VECTOR @@ -600,24 +670,22 @@ def k_means_clustering_vector_cli( """Perform k-means clustering on the input vector data.""" from eis_toolkit.exploratory_analyses.k_means_cluster import k_means_clustering_vector - typer.echo("Progress: 10%") + with ProgressLog.reading_input_files(): + geodataframe = gpd.read_file(input_vector) - geodataframe = gpd.read_file(input_vector) - typer.echo("Progress: 25%") - - output_geodataframe = k_means_clustering_vector( - data=geodataframe, - include_coordinates=include_coordinates, - columns=columns, - number_of_clusters=number_of_clusters, - random_state=random_state, - ) - typer.echo("Progress: 75%") + with ProgressLog.running_algorithm(): + output_geodataframe = k_means_clustering_vector( + data=geodataframe, + include_coordinates=include_coordinates, + columns=columns, + number_of_clusters=number_of_clusters, + random_state=random_state, + ) - output_geodataframe.to_file(output_vector, driver="GPKG") - typer.echo("Progress: 100%") + with ProgressLog.saving_output_files(output_vector): + output_geodataframe.to_file(output_vector, driver="GPKG") - typer.echo(f"K-means clustering completed, output vector written to {output_vector}.") + ProgressLog.finish() # K-MEANS CLUSTERING RASTER @@ -632,25 +700,24 @@ def k_means_clustering_raster_cli( from eis_toolkit.exploratory_analyses.k_means_cluster import k_means_clustering_array from eis_toolkit.utilities.file_io import read_and_stack_rasters - typer.echo("Progress: 10%") + with ProgressLog.reading_input_files(): + stacked_array, profiles = read_and_stack_rasters(input_rasters, nodata_handling="convert_to_nan") - stacked_array, profiles = read_and_stack_rasters(input_rasters, nodata_handling="convert_to_nan") - typer.echo("Progress: 25%") - - output_array = k_means_clustering_array( - data=stacked_array, number_of_clusters=number_of_clusters, random_state=random_state - ) - typer.echo("Progress: 75%") + with ProgressLog.running_algorithm(): + output_array = k_means_clustering_array( + data=stacked_array, number_of_clusters=number_of_clusters, random_state=random_state + ) out_profile = profiles[0] out_profile["nodata"] = -9999 out_profile["count"] = 1 - with rasterio.open(output_raster, "w", **out_profile) as dst: - dst.write(output_array, 1) + with ProgressLog.saving_output_files(output_raster): + with rasterio.open(output_raster, "w", **out_profile) as dst: + dst.write(output_array, 1) + dst.update_stats() - typer.echo("Progress: 100%") - typer.echo(f"K-means clustering completed, output raster written to {output_raster}.") + ProgressLog.finish() # PARALLEL COORDINATES @@ -670,30 +737,28 @@ def parallel_coordinates_cli( from eis_toolkit.exploratory_analyses.parallel_coordinates import plot_parallel_coordinates - typer.echo("Progress: 10%") - geodataframe = gpd.read_file(input_vector) - dataframe = pd.DataFrame(geodataframe.drop(columns="geometry")) - typer.echo("Progress: 25%") - - _ = plot_parallel_coordinates( - dataframe, - color_column_name=color_column_name, - plot_title=plot_title, - palette_name=palette_name, - curved_lines=curved_lines, - ) - typer.echo("Progress: 75%") - if show_plot: - plt.show() + with ProgressLog.reading_input_files(): + geodataframe = gpd.read_file(input_vector) + dataframe = pd.DataFrame(geodataframe.drop(columns="geometry")) + + with ProgressLog.running_algorithm(): + _ = plot_parallel_coordinates( + dataframe, + color_column_name=color_column_name, + plot_title=plot_title, + palette_name=palette_name, + curved_lines=curved_lines, + ) - echo_str_end = "." if output_file is not None: - dpi = "figure" if save_dpi is None else save_dpi - plt.savefig(output_file, dpi=dpi) - echo_str_end = f", output figure saved to {output_file}." - typer.echo("Progress: 100%") + with ProgressLog.saving_output_files(output_file): + dpi = "figure" if save_dpi is None else save_dpi + plt.savefig(output_file, dpi=dpi) + + if show_plot: + plt.show() - typer.echo("Parallel coordinates plot completed" + echo_str_end) + ProgressLog.finish() # PCA FOR RASTER DATA @@ -701,7 +766,7 @@ def parallel_coordinates_cli( def compute_pca_raster_cli( input_rasters: INPUT_FILES_ARGUMENT, output_raster: OUTPUT_FILE_OPTION, - number_of_components: int = typer.Option(), + number_of_components: Optional[int] = None, # NOTE: Omitted scaler type selection here since the parameter might be deleted from PCA func nodata_handling: Annotated[NodataHandling, typer.Option(case_sensitive=False)] = NodataHandling.remove, # NOTE: Omitted nodata parameter. Should use raster nodata. @@ -710,37 +775,44 @@ def compute_pca_raster_cli( from eis_toolkit.exploratory_analyses.pca import compute_pca from eis_toolkit.utilities.file_io import read_and_stack_rasters - typer.echo("Progress: 10%") - - stacked_array, profiles = read_and_stack_rasters(input_rasters, nodata_handling="convert_to_nan") - typer.echo("Progress: 25%") + with ProgressLog.reading_input_files(): + stacked_array, profiles = read_and_stack_rasters(input_rasters, nodata_handling="convert_to_nan") - pca_array, variance_ratios = compute_pca( - data=stacked_array, number_of_components=number_of_components, nodata_handling=get_enum_values(nodata_handling) - ) + with ProgressLog.running_algorithm(): + transformed_data, principal_components, variances, variance_ratios = compute_pca( + data=stacked_array, + number_of_components=number_of_components, + nodata_handling=get_enum_values(nodata_handling), + ) # Fill np.nan with nodata before writing data to raster - pca_array[pca_array == np.nan] = -9999 + transformed_data[transformed_data == np.nan] = -9999 out_profile = profiles[0] out_profile["nodata"] = -9999 # Update nr of bands - out_profile["count"] = number_of_components + out_profile["count"] = len(variances) # Create dictionary from the variance ratios array - variances_ratios_dict = {} - for i, variance_ratio in enumerate(variance_ratios): - name = "PC " + str(i) + " explained variance" - variances_ratios_dict[name] = variance_ratio - json_str = json.dumps(variances_ratios_dict) + # variances_ratios_dict = {} + # for i, variance_ratio in enumerate(variance_ratios): + # name = "PC " + str(i) + " explained variance" + # variances_ratios_dict[name] = variance_ratio + # json_str = json.dumps(variances_ratios_dict) - with rasterio.open(output_raster, "w", **out_profile) as dst: - dst.write(pca_array) + out_dict = { + "principal_components": np.round(principal_components, 4).tolist(), + "explained_variances": np.round(variances, 4).tolist(), + "explained_variance_ratios": np.round(variance_ratios, 4).tolist(), + } - typer.echo("Progress: 100%") + with ProgressLog.saving_output_files(output_raster): + with rasterio.open(output_raster, "w", **out_profile) as dst: + dst.write(transformed_data) + dst.update_stats() - typer.echo(f"Results: {json_str}") - typer.echo(f"PCA computation (raster) completed, output raster saved to {output_raster}.") + ResultSender.send_dict_as_json(out_dict) + ProgressLog.finish() # PCA FOR VECTOR DATA @@ -748,7 +820,7 @@ def compute_pca_raster_cli( def compute_pca_vector_cli( input_vector: INPUT_FILE_OPTION, output_vector: OUTPUT_FILE_OPTION, - number_of_components: int = typer.Option(), + number_of_components: Optional[int] = None, columns: Annotated[List[str], typer.Option()] = None, # NOTE: Omitted scaler type selection here since the parameter might be deleted from PCA func nodata_handling: Annotated[NodataHandling, typer.Option(case_sensitive=False)] = NodataHandling.remove, @@ -757,50 +829,53 @@ def compute_pca_vector_cli( """Compute defined number of principal components for vector data.""" from eis_toolkit.exploratory_analyses.pca import compute_pca - typer.echo("Progress: 10%") - - gdf = gpd.read_file(input_vector) - typer.echo("Progress: 25%") + with ProgressLog.reading_input_files(): + gdf = gpd.read_file(input_vector) - pca_gdf, variance_ratios = compute_pca( - data=gdf, - number_of_components=number_of_components, - columns=columns, - nodata_handling=get_enum_values(nodata_handling), - nodata=nodata, - ) + with ProgressLog.running_algorithm(): + transformed_data, principal_components, variances, variance_ratios = compute_pca( + data=gdf, + number_of_components=number_of_components, + columns=columns, + nodata_handling=get_enum_values(nodata_handling), + nodata=nodata, + ) # Create dictionary from the variance ratios array - variances_ratios_dict = {} - for i, variance_ratio in enumerate(variance_ratios): - name = "PC " + str(i) + " explained variance" - variances_ratios_dict[name] = variance_ratio - json_str = json.dumps(variances_ratios_dict) + # variances_ratios_dict = {} + # for i, variance_ratio in enumerate(variance_ratios): + # name = "PC " + str(i) + " explained variance" + # variances_ratios_dict[name] = variance_ratio + # json_str = json.dumps(variances_ratios_dict) - pca_gdf.to_file(output_vector) - typer.echo("Progress: 100%") + out_dict = { + "principal_components": np.round(principal_components, 4).tolist(), + "explained_variances": np.round(variances, 4).tolist(), + "explained_variance_ratios": np.round(variance_ratios, 4).tolist(), + } - typer.echo(f"Results: {json_str}") - typer.echo(f"PCA computation (vector) completed, output vector saved to {output_vector}.") + with ProgressLog.saving_output_files(output_vector): + transformed_data.to_file(output_vector) + + ResultSender.send_dict_as_json(out_dict) + ProgressLog.finish() # DESCRIPTIVE STATISTICS (RASTER) @app.command() -def descriptive_statistics_raster_cli(input_file: INPUT_FILE_OPTION): +def descriptive_statistics_raster_cli(input_raster: INPUT_FILE_OPTION, band: int = 1): """Generate descriptive statistics from raster data.""" from eis_toolkit.exploratory_analyses.descriptive_statistics import descriptive_statistics_raster - typer.echo("Progress: 10%") - - with rasterio.open(input_file) as raster: - typer.echo("Progress: 25%") - results_dict = descriptive_statistics_raster(raster) - typer.echo("Progress: 75%") + with ProgressLog.reading_input_files(): + raster = rasterio.open(input_raster) - typer.echo("Progress: 100% \n") + with ProgressLog.running_algorithm(): + results_dict = descriptive_statistics_raster(raster, band) + raster.close() - typer.echo(f"Results: {str(results_dict)}") - typer.echo("\nDescriptive statistics (raster) completed") + ResultSender.send_dict_as_json(results_dict) + ProgressLog.finish() # DESCRIPTIVE STATISTICS (VECTOR) @@ -809,26 +884,23 @@ def descriptive_statistics_vector_cli(input_file: INPUT_FILE_OPTION, column: str """Generate descriptive statistics from vector or tabular data.""" from eis_toolkit.exploratory_analyses.descriptive_statistics import descriptive_statistics_dataframe - typer.echo("Progress: 10%") - # TODO modify input file detection try: - gdf = gpd.read_file(input_file) - typer.echo("Progress: 25%") - results_dict = descriptive_statistics_dataframe(gdf, column) + with ProgressLog.reading_input_files(): + gdf = gpd.read_file(input_file) + with ProgressLog.running_algorithm(): + results_dict = descriptive_statistics_dataframe(gdf, column) except: # noqa: E722 try: - df = pd.read_csv(input_file) - typer.echo("Progress: 25%") - results_dict = descriptive_statistics_dataframe(df, column) + with ProgressLog.reading_input_files(): + df = pd.read_csv(input_file) + with ProgressLog.running_algorithm(): + results_dict = descriptive_statistics_dataframe(df, column) except: # noqa: E722 - raise Exception("Could not read input file as raster or dataframe") - typer.echo("Progress: 75%") + raise Exception("Could not read input file as geodataframe") - typer.echo("Progress: 100%") - - typer.echo(f"Results: {str(results_dict)}") - typer.echo("\nDescriptive statistics (vector) completed") + ResultSender.send_dict_as_json(results_dict) + ProgressLog.finish() # LOCAL MORAN'S I @@ -844,17 +916,43 @@ def local_morans_i_cli( """Execute Local Moran's I calculation for the data.""" from eis_toolkit.exploratory_analyses.local_morans_i import local_morans_i - typer.echo("Progress: 10%") + with ProgressLog.reading_input_files(): + gdf = gpd.read_file(input_vector) + + with ProgressLog.running_algorithm(): + out_gdf = local_morans_i(gdf, column, get_enum_values(weight_type), k, permutations) + + with ProgressLog.saving_output_files(output_vector): + out_gdf.to_file(output_vector) - gdf = gpd.read_file(input_vector) - typer.echo("Progress: 25%") + ProgressLog.finish() - out_gdf = local_morans_i(gdf, column, get_enum_values(weight_type), k, permutations) - typer.echo("Progress: 75%") - out_gdf.to_file(output_vector) - typer.echo("Progress: 100%") - typer.echo(f"Local Moran's I completed, output vector saved to {output_vector}.") +# FEATURE IMPORTANCE +@app.command() +def feature_importance_cli( + model_file: INPUT_FILE_OPTION, + input_rasters: INPUT_FILES_ARGUMENT, + target_labels: INPUT_FILE_OPTION, + n_repeats: int = 10, + random_state: Optional[int] = None, +): + """Evaluate the feature importance of a sklearn classifier or regressor.""" + from eis_toolkit.exploratory_analyses.feature_importance import evaluate_feature_importance + from eis_toolkit.prediction.machine_learning_general import load_model, prepare_data_for_ml + + with ProgressLog.reading_input_files(): + model = load_model(model_file) + X, y, _, _ = prepare_data_for_ml(input_rasters, target_labels) + feature_names = [raster.name for raster in input_rasters] + + with ProgressLog.running_algorithm(): + feature_importance, _ = evaluate_feature_importance(model, X, y, feature_names, n_repeats, random_state) + + results = dict(zip(feature_importance["Feature"], feature_importance["Importance"])) + + ResultSender.send_dict_as_json(results) + ProgressLog.finish() # --- RASTER PROCESSING --- @@ -872,18 +970,19 @@ def focal_filter_cli( """Apply a basic focal filter to the input raster.""" from eis_toolkit.raster_processing.filters.focal import focal_filter - typer.echo("Progress: 10%") + with ProgressLog.reading_input_files(): + raster = rasterio.open(input_raster) - with rasterio.open(input_raster) as raster: - typer.echo("Progress: 25%") + with ProgressLog.running_algorithm(): out_image, out_meta = focal_filter(raster=raster, method=method, size=size, shape=get_enum_values(shape)) - typer.echo("Progress: 75%") + raster.close() - with rasterio.open(output_raster, "w", **out_meta) as dest: - dest.write(out_image, 1) - typer.echo("Progress: 100%") + with ProgressLog.saving_output_files(output_raster): + with rasterio.open(output_raster, "w", **out_meta) as dest: + dest.write(out_image, 1) + dest.update_stats() - typer.echo(f"Focal filter applied, output raster written to {output_raster}.") + ProgressLog.finish() # GAUSSIAN FILTER @@ -898,18 +997,19 @@ def gaussian_filter_cli( """Apply a gaussian filter to the input raster.""" from eis_toolkit.raster_processing.filters.focal import gaussian_filter - typer.echo("Progress: 10%") + with ProgressLog.reading_input_files(): + raster = rasterio.open(input_raster) - with rasterio.open(input_raster) as raster: - typer.echo("Progress: 25%") + with ProgressLog.running_algorithm(): out_image, out_meta = gaussian_filter(raster=raster, sigma=sigma, truncate=truncate, size=size) - typer.echo("Progress: 75%") + raster.close() - with rasterio.open(output_raster, "w", **out_meta) as dest: - dest.write(out_image, 1) - typer.echo("Progress: 100%") + with ProgressLog.saving_output_files(output_raster): + with rasterio.open(output_raster, "w", **out_meta) as dest: + dest.write(out_image, 1) + dest.update_stats() - typer.echo(f"Gaussial filter applied, output raster written to {output_raster}.") + ProgressLog.finish() # MEXICAN HAT FILTER @@ -927,20 +1027,21 @@ def mexican_hat_filter_cli( """Apply a mexican hat filter to the input raster.""" from eis_toolkit.raster_processing.filters.focal import mexican_hat_filter - typer.echo("Progress: 10%") + with ProgressLog.reading_input_files(): + raster = rasterio.open(input_raster) - with rasterio.open(input_raster) as raster: - typer.echo("Progress: 25%") + with ProgressLog.running_algorithm(): out_image, out_meta = mexican_hat_filter( raster=raster, sigma=sigma, truncate=truncate, size=size, direction=get_enum_values(direction) ) - typer.echo("Progress: 75%") + raster.close() - with rasterio.open(output_raster, "w", **out_meta) as dest: - dest.write(out_image, 1) - typer.echo("Progress: 100%") + with ProgressLog.saving_output_files(output_raster): + with rasterio.open(output_raster, "w", **out_meta) as dest: + dest.write(out_image, 1) + dest.update_stats() - typer.echo(f"Mexican hat filter applied, output raster written to {output_raster}.") + ProgressLog.finish() # LEE ADDITIVE NOISE FILTER @@ -954,18 +1055,19 @@ def lee_additive_noise_filter_cli( """Apply a Lee filter considering additive noise components in the input raster.""" from eis_toolkit.raster_processing.filters.speckle import lee_additive_noise_filter - typer.echo("Progress: 10%") + with ProgressLog.reading_input_files(): + raster = rasterio.open(input_raster) - with rasterio.open(input_raster) as raster: - typer.echo("Progress: 25%") + with ProgressLog.running_algorithm(): out_image, out_meta = lee_additive_noise_filter(raster=raster, size=size, add_noise_var=add_noise_var) - typer.echo("Progress: 75%") + raster.close() - with rasterio.open(output_raster, "w", **out_meta) as dest: - dest.write(out_image, 1) - typer.echo("Progress: 100%") + with ProgressLog.saving_output_files(output_raster): + with rasterio.open(output_raster, "w", **out_meta) as dest: + dest.write(out_image, 1) + dest.update_stats() - typer.echo(f"Additive Lee noise filter applied, output raster written to {output_raster}.") + ProgressLog.finish() # LEE MULTIPLICATIVE NOISE FILTER @@ -980,20 +1082,21 @@ def lee_multiplicative_noise_filter_cli( """Apply a Lee filter considering multiplicative noise components in the input raster.""" from eis_toolkit.raster_processing.filters.speckle import lee_multiplicative_noise_filter - typer.echo("Progress: 10%") + with ProgressLog.reading_input_files(): + raster = rasterio.open(input_raster) - with rasterio.open(input_raster) as raster: - typer.echo("Progress: 25%") + with ProgressLog.running_algorithm(): out_image, out_meta = lee_multiplicative_noise_filter( raster=raster, size=size, mult_noise_mean=multi_noise_mean, n_looks=n_looks ) - typer.echo("Progress: 75%") + raster.close() - with rasterio.open(output_raster, "w", **out_meta) as dest: - dest.write(out_image, 1) - typer.echo("Progress: 100%") + with ProgressLog.saving_output_files(output_raster): + with rasterio.open(output_raster, "w", **out_meta) as dest: + dest.write(out_image, 1) + dest.update_stats() - typer.echo(f"Multiplicative Lee noise filter applied, output raster written to {output_raster}.") + ProgressLog.finish() # LEE ADDITIVE MULTIPLICATIVE NOISE FILTER @@ -1009,10 +1112,10 @@ def lee_additive_multiplicative_noise_filter_cli( """Apply a Lee filter considering both additive and multiplicative noise components in the input raster.""" from eis_toolkit.raster_processing.filters.speckle import lee_additive_multiplicative_noise_filter - typer.echo("Progress: 10%") + with ProgressLog.reading_input_files(): + raster = rasterio.open(input_raster) - with rasterio.open(input_raster) as raster: - typer.echo("Progress: 25%") + with ProgressLog.running_algorithm(): out_image, out_meta = lee_additive_multiplicative_noise_filter( raster=raster, size=size, @@ -1020,13 +1123,14 @@ def lee_additive_multiplicative_noise_filter_cli( add_noise_mean=add_noise_mean, mult_noise_mean=multi_noise_mean, ) - typer.echo("Progress: 75%") + raster.close() - with rasterio.open(output_raster, "w", **out_meta) as dest: - dest.write(out_image, 1) - typer.echo("Progress: 100%") + with ProgressLog.saving_output_files(output_raster): + with rasterio.open(output_raster, "w", **out_meta) as dest: + dest.write(out_image, 1) + dest.update_stats() - typer.echo(f"Additive multiplicative Lee noise filter applied, output raster written to {output_raster}.") + ProgressLog.finish() # LEE ENHANCED FILTER @@ -1041,20 +1145,21 @@ def lee_enhanced_filter_cli( """Apply an enhanced Lee filter to the input raster.""" from eis_toolkit.raster_processing.filters.speckle import lee_enhanced_filter - typer.echo("Progress: 10%") + with ProgressLog.reading_input_files(): + raster = rasterio.open(input_raster) - with rasterio.open(input_raster) as raster: - typer.echo("Progress: 25%") + with ProgressLog.running_algorithm(): out_image, out_meta = lee_enhanced_filter( raster=raster, size=size, n_looks=n_looks, damping_factor=damping_factor ) - typer.echo("Progress: 75%") + raster.close() - with rasterio.open(output_raster, "w", **out_meta) as dest: - dest.write(out_image, 1) - typer.echo("Progress: 100%") + with ProgressLog.saving_output_files(output_raster): + with rasterio.open(output_raster, "w", **out_meta) as dest: + dest.write(out_image, 1) + dest.update_stats() - typer.echo(f"Enhanced Lee filter applied, output raster written to {output_raster}.") + ProgressLog.finish() # GAMMA FILTER @@ -1068,18 +1173,19 @@ def gamma_filter_cli( """Apply a Gamma filter to the input raster.""" from eis_toolkit.raster_processing.filters.speckle import gamma_filter - typer.echo("Progress: 10%") + with ProgressLog.reading_input_files(): + raster = rasterio.open(input_raster) - with rasterio.open(input_raster) as raster: - typer.echo("Progress: 25%") + with ProgressLog.running_algorithm(): out_image, out_meta = gamma_filter(raster=raster, size=size, n_looks=n_looks) - typer.echo("Progress: 75%") + raster.close() - with rasterio.open(output_raster, "w", **out_meta) as dest: - dest.write(out_image, 1) - typer.echo("Progress: 100%") + with ProgressLog.saving_output_files(output_raster): + with rasterio.open(output_raster, "w", **out_meta) as dest: + dest.write(out_image, 1) + dest.update_stats() - typer.echo(f"Gamma filter applied, output raster written to {output_raster}.") + ProgressLog.finish() # FROST FILTER @@ -1093,18 +1199,19 @@ def frost_filter_cli( """Apply a Frost filter to the input raster.""" from eis_toolkit.raster_processing.filters.speckle import frost_filter - typer.echo("Progress: 10%") + with ProgressLog.reading_input_files(): + raster = rasterio.open(input_raster) - with rasterio.open(input_raster) as raster: - typer.echo("Progress: 25%") + with ProgressLog.running_algorithm(): out_image, out_meta = frost_filter(raster=raster, size=size, damping_factor=damping_factor) - typer.echo("Progress: 75%") + raster.close() - with rasterio.open(output_raster, "w", **out_meta) as dest: - dest.write(out_image, 1) - typer.echo("Progress: 100%") + with ProgressLog.saving_output_files(output_raster): + with rasterio.open(output_raster, "w", **out_meta) as dest: + dest.write(out_image, 1) + dest.update_stats() - typer.echo(f"Frost filter applied, output raster written to {output_raster}.") + ProgressLog.finish() # KUAN FILTER @@ -1118,18 +1225,19 @@ def kuan_filter_cli( """Apply a Kuan filter to the input raster.""" from eis_toolkit.raster_processing.filters.speckle import kuan_filter - typer.echo("Progress: 10%") + with ProgressLog.reading_input_files(): + raster = rasterio.open(input_raster) - with rasterio.open(input_raster) as raster: - typer.echo("progress: 25%") + with ProgressLog.running_algorithm(): out_image, out_meta = kuan_filter(raster=raster, size=size, n_looks=n_looks) - typer.echo("Progress: 75%") + raster.close() - with rasterio.open(output_raster, "w", **out_meta) as dest: - dest.write(out_image, 1) - typer.echo("Progress: 100%") + with ProgressLog.saving_output_files(output_raster): + with rasterio.open(output_raster, "w", **out_meta) as dest: + dest.write(out_image, 1) + dest.update_stats() - typer.echo(f"Kuan filter applied, output raster written to {output_raster}.") + ProgressLog.finish() # CHECK RASTER GRIDS @@ -1138,23 +1246,19 @@ def check_raster_grids_cli(input_rasters: INPUT_FILES_ARGUMENT, same_extent: boo """Check all input rasters for matching gridding and optionally matching bounds.""" from eis_toolkit.utilities.checks.raster import check_raster_grids - typer.echo("Progress: 10%") + with ProgressLog.reading_input_files(): + raster_profiles = [] + for input_raster in input_rasters: + with rasterio.open(input_raster) as raster: + raster_profiles.append(raster.profile) - raster_profiles = [] - for input_raster in input_rasters: - with rasterio.open(input_raster) as raster: - raster_profiles.append(raster.profile) - typer.echo("Progress: 50%") + with ProgressLog.running_algorithm(): + result = check_raster_grids(raster_profiles=raster_profiles, same_extent=same_extent) - result = check_raster_grids(raster_profiles=raster_profiles, same_extent=same_extent) results_dict = {"result": result} - typer.echo("Progress: 75%") - - json_str = json.dumps(results_dict) - typer.echo("Progress: 100%") - typer.echo(f"Results: {json_str}") - typer.echo("Checking raster grids completed.") + ResultSender.send_dict_as_json(results_dict) + ProgressLog.finish() # CLIP RASTER @@ -1167,23 +1271,23 @@ def clip_raster_cli( """Clip the input raster with geometries in a geodataframe.""" from eis_toolkit.raster_processing.clipping import clip_raster - typer.echo("Progress: 10%") + with ProgressLog.reading_input_files(): + geodataframe = gpd.read_file(geometries) + raster = rasterio.open(input_raster) - geodataframe = gpd.read_file(geometries) - - with rasterio.open(input_raster) as raster: - typer.echo("Progress: 25%") + with ProgressLog.running_algorithm(): out_image, out_meta = clip_raster( raster=raster, geodataframe=geodataframe, ) - typer.echo("Progress: 75%") + raster.close() - with rasterio.open(output_raster, "w", **out_meta) as dest: - dest.write(out_image) - typer.echo("Progress: 100%") + with ProgressLog.saving_output_files(output_raster): + with rasterio.open(output_raster, "w", **out_meta) as dest: + dest.write(out_image) + dest.update_stats() - typer.echo(f"Clipping completed, output raster written to {output_raster}.") + ProgressLog.finish() # CREATE CONSTANT RASTER MANUALLY @@ -1204,30 +1308,28 @@ def create_constant_raster_manually_cli( """ from eis_toolkit.raster_processing.create_constant_raster import create_constant_raster - typer.echo("Progress: 10%") - - coord_west, coord_east, coord_south, coord_north = extent - raster_width = round(abs(coord_east - coord_west) / target_pixel_size) - raster_height = round(abs(coord_north - coord_south) / target_pixel_size) - out_image, out_meta = create_constant_raster( - constant_value=constant_value, - coord_west=coord_west, - coord_north=coord_north, - coord_east=coord_east, - coord_south=coord_south, - target_epsg=target_epsg, - raster_width=raster_width, - raster_height=raster_height, - nodata_value=nodata_value, - ) - - typer.echo("Progress: 75%") + with ProgressLog.running_algorithm(): + coord_west, coord_east, coord_south, coord_north = extent + raster_width = round(abs(coord_east - coord_west) / target_pixel_size) + raster_height = round(abs(coord_north - coord_south) / target_pixel_size) + out_image, out_meta = create_constant_raster( + constant_value=constant_value, + coord_west=coord_west, + coord_north=coord_north, + coord_east=coord_east, + coord_south=coord_south, + target_epsg=target_epsg, + raster_width=raster_width, + raster_height=raster_height, + nodata_value=nodata_value, + ) - with rasterio.open(output_raster, "w", **out_meta) as dest: - dest.write(out_image, 1) - typer.echo("Progress: 100%") + with ProgressLog.saving_output_files(output_raster): + with rasterio.open(output_raster, "w", **out_meta) as dest: + dest.write(out_image, 1) + dest.update_stats() - typer.echo(f"Creating constant raster completed, writing raster to {output_raster}.") + ProgressLog.finish() # CREATE CONSTANT RASTER FROM TEMPLATE @@ -1241,23 +1343,23 @@ def create_constant_raster_from_template_cli( """Create constant raster from a template raster.""" from eis_toolkit.raster_processing.create_constant_raster import create_constant_raster - typer.echo("Progress: 10%") + with ProgressLog.reading_input_files(): + raster = rasterio.open(template_raster) - with rasterio.open(template_raster) as raster: - typer.echo("Progress: 25%") + with ProgressLog.running_algorithm(): out_image, out_meta = create_constant_raster( constant_value=constant_value, template_raster=raster, nodata_value=nodata_value, ) + raster.close() - typer.echo("Progress: 75%") - - with rasterio.open(output_raster, "w", **out_meta) as dest: - dest.write(out_image, 1) - typer.echo("Progress: 100%") + with ProgressLog.saving_output_files(output_raster): + with rasterio.open(output_raster, "w", **out_meta) as dest: + dest.write(out_image, 1) + dest.update_stats() - typer.echo(f"Creating constant raster completed, writing raster to {output_raster}.") + ProgressLog.finish() # DISTANCE TO ANOMALY @@ -1277,30 +1379,90 @@ def distance_to_anomaly_cli( """ from eis_toolkit.raster_processing.distance_to_anomaly import distance_to_anomaly - typer.echo("Progress: 10%") + if second_threshold_criteria_value is not None: + threshold_criteria_value = (first_threshold_criteria_value, second_threshold_criteria_value) + else: + threshold_criteria_value = first_threshold_criteria_value + + with ProgressLog.reading_input_files(): + with rasterio.open(input_raster) as raster: + raster_array = raster.read(1) + profile = raster.profile.copy() + + # Create nodata mask + mask = (raster_array == profile["nodata"]) | np.isnan(raster_array) + + with ProgressLog.running_algorithm(): + out_image, out_profile = distance_to_anomaly( + anomaly_raster_profile=profile, + anomaly_raster_data=raster_array, + threshold_criteria_value=threshold_criteria_value, + threshold_criteria=get_enum_values(threshold_criteria), + max_distance=max_distance, + ) + + # Apply nodata mask after processing + out_image[mask] = out_profile["nodata"] + + with ProgressLog.saving_output_files(output_raster): + with rasterio.open(output_raster, "w", **out_profile) as dest: + dest.write(out_image, 1) + dest.update_stats() + + ProgressLog.finish() + + +# PROXIMITY TO ANOMALY +@app.command() +def proximity_to_anomaly_cli( + input_raster: INPUT_FILE_OPTION, + output_raster: OUTPUT_FILE_OPTION, + threshold_criteria: Annotated[ThresholdCriteria, typer.Option(case_sensitive=False)], + first_threshold_criteria_value: float = typer.Option(), + second_threshold_criteria_value: float = None, + max_distance: float = typer.Option(), + max_distance_value: float = 0.0, + anomaly_value: float = 1.0, +): + """ + Calculate proximity from each raster cell to nearest anomaly cell. + + Uses only the first band of the raster. + """ + from eis_toolkit.raster_processing.proximity_to_anomaly import proximity_to_anomaly if second_threshold_criteria_value is not None: threshold_criteria_value = (first_threshold_criteria_value, second_threshold_criteria_value) else: threshold_criteria_value = first_threshold_criteria_value - with rasterio.open(input_raster) as raster: - typer.echo("Progress: 25%") - out_image, out_meta = distance_to_anomaly( - anomaly_raster_profile=raster.profile, - anomaly_raster_data=raster.read(1), + with ProgressLog.reading_input_files(): + with rasterio.open(input_raster) as raster: + raster_array = raster.read(1) + profile = raster.profile.copy() + + # Create nodata mask + mask = (raster_array == profile["nodata"]) | np.isnan(raster_array) + + with ProgressLog.running_algorithm(): + out_image, out_profile = proximity_to_anomaly( + anomaly_raster_profile=profile, + anomaly_raster_data=raster_array, threshold_criteria_value=threshold_criteria_value, threshold_criteria=get_enum_values(threshold_criteria), max_distance=max_distance, + scaling_range=(anomaly_value, max_distance_value), ) - typer.echo("Progress: 75%") + # Apply nodata mask + out_image[mask] = out_profile["nodata"] - with rasterio.open(output_raster, "w", **out_meta) as dest: - dest.write(out_image, 1) - typer.echo("Progress: 100%") + with ProgressLog.saving_output_files(output_raster): + with rasterio.open(output_raster, "w", **out_profile) as dest: + dest.write(out_image, 1) + dest.update_stats() - typer.echo(f"Computing distance to anomaly completed, writing raster to {output_raster}.") + ProgressLog.finish() # EXTRACT VALUES FROM RASTER @@ -1313,19 +1475,18 @@ def extract_values_from_raster_cli( """Extract raster values using point data to a DataFrame.""" from eis_toolkit.raster_processing.extract_values_from_raster import extract_values_from_raster - typer.echo("Progress: 10%") + with ProgressLog.reading_input_files(): + geodataframe = gpd.read_file(geometries) + raster = rasterio.open(input_raster) - geodataframe = gpd.read_file(geometries) - - with rasterio.open(input_raster) as raster: - typer.echo("Progress: 25%") + with ProgressLog.running_algorithm(): df = extract_values_from_raster(raster_list=[raster], geodataframe=geodataframe) - typer.echo("Progress: 75%") + raster.close() - df.to_csv(output_vector) - typer.echo("Progress: 100%") + with ProgressLog.saving_output_files(output_vector): + df.to_csv(output_vector) - typer.echo(f"Extracting values from raster completed, writing vector to {output_vector}.") + ProgressLog.finish() # REPROJECT RASTER @@ -1339,20 +1500,21 @@ def reproject_raster_cli( """Reproject the input raster to given CRS.""" from eis_toolkit.raster_processing.reprojecting import reproject_raster - typer.echo("Progress: 10%") + with ProgressLog.reading_input_files(): + raster = rasterio.open(input_raster) - with rasterio.open(input_raster) as raster: - typer.echo("Progress: 25%") + with ProgressLog.running_algorithm(): out_image, out_meta = reproject_raster( raster=raster, target_crs=target_crs, resampling_method=get_enum_values(resampling_method) ) - typer.echo("Progress: 75%") + raster.close() - with rasterio.open(output_raster, "w", **out_meta) as dest: - dest.write(out_image) - typer.echo("Progress: 100%") + with ProgressLog.saving_output_files(output_raster): + with rasterio.open(output_raster, "w", **out_meta) as dest: + dest.write(out_image) + dest.update_stats() - typer.echo(f"Reprojecting completed, writing raster to {output_raster}.") + ProgressLog.finish() # RESAMPLE RASTER @@ -1366,20 +1528,21 @@ def resample_raster_cli( """Resamples raster according to given resolution.""" from eis_toolkit.raster_processing.resampling import resample - typer.echo("Progress: 10%") + with ProgressLog.reading_input_files(): + raster = rasterio.open(input_raster) - with rasterio.open(input_raster) as raster: - typer.echo("Progress: 25%") + with ProgressLog.running_algorithm(): out_image, out_meta = resample( raster=raster, resolution=resolution, resampling_method=get_enum_values(resampling_method) ) - typer.echo("Progress: 75%") + raster.close() - with rasterio.open(output_raster, "w", **out_meta) as dst: - dst.write(out_image) - typer.echo("Progress 100%") + with ProgressLog.saving_output_files(output_raster): + with rasterio.open(output_raster, "w", **out_meta) as dst: + dst.write(out_image) + dst.update_stats() - typer.echo(f"Resampling completed, writing raster to {output_raster}.") + ProgressLog.finish() # SNAP RASTER @@ -1392,18 +1555,21 @@ def snap_raster_cli( """Snaps/aligns input raster to the given snap raster.""" from eis_toolkit.raster_processing.snapping import snap_with_raster - typer.echo("Progress: 10%") + with ProgressLog.reading_input_files(): + src = rasterio.open(input_raster) + snap_src = rasterio.open(snap_raster) - with rasterio.open(input_raster) as src, rasterio.open(snap_raster) as snap_src: - typer.echo("Progress: 25%") + with ProgressLog.running_algorithm(): out_image, out_meta = snap_with_raster(src, snap_src) - typer.echo("Progress: 75%") + src.close() + snap_raster.close() - with rasterio.open(output_raster, "w", **out_meta) as dst: - dst.write(out_image) - typer.echo("Progress: 100%") + with ProgressLog.saving_output_files(output_raster): + with rasterio.open(output_raster, "w", **out_meta) as dst: + dst.write(out_image) + dst.update_stats() - typer.echo(f"Snapping completed, writing raster to {output_raster}.") + ProgressLog.finish() # UNIFY RASTERS @@ -1418,12 +1584,11 @@ def unify_rasters_cli( """Unify rasters to match the base raster.""" from eis_toolkit.raster_processing.unifying import unify_raster_grids - typer.echo("Progress: 10%") - - with rasterio.open(base_raster) as raster: + with ProgressLog.reading_input_files(): + raster = rasterio.open(base_raster) to_unify = [rasterio.open(rstr) for rstr in rasters_to_unify] # Open all rasters to be unified - typer.echo("Progress: 25%") + with ProgressLog.running_algorithm(): masking_param = get_enum_values(masking) unified = unify_raster_grids( base_raster=raster, @@ -1431,22 +1596,23 @@ def unify_rasters_cli( resampling_method=get_enum_values(resampling_method), masking=None if masking_param == "none" else masking_param, ) - [rstr.close() for rstr in to_unify] # Close all rasters - typer.echo("Progress: 75%") - - out_rasters_dict = {} - for i, (out_image, out_meta) in enumerate(unified[1:]): # Skip writing base raster - in_raster_name = os.path.splitext(os.path.split(rasters_to_unify[i])[1])[0] - output_raster_name = f"{in_raster_name}_unified" - output_raster_path = output_directory.joinpath(output_raster_name + ".tif") - with rasterio.open(output_raster_path, "w", **out_meta) as dst: - dst.write(out_image) - out_rasters_dict[output_raster_name] = str(output_raster_path) - typer.echo("Progress: 100%") + # Close all rasters + raster.close() + [rstr.close() for rstr in to_unify] + + with ProgressLog.saving_output_files(output_directory): + out_rasters_dict = {} + for i, (out_image, out_meta) in enumerate(unified[1:]): # Skip writing base raster + in_raster_name = os.path.splitext(os.path.split(rasters_to_unify[i])[1])[0] + output_raster_name = f"{in_raster_name}_unified" + output_raster_path = output_directory.joinpath(output_raster_name + ".tif") + with rasterio.open(output_raster_path, "w", **out_meta) as dst: + dst.write(out_image) + dst.update_stats() + out_rasters_dict[output_raster_name] = str(output_raster_path) - json_str = json.dumps(out_rasters_dict) - typer.echo(f"Output rasters: {json_str}") - typer.echo(f"Unifying completed, rasters saved to {output_directory}.") + ResultSender.send_multiple_rasters_dict_as_json(out_rasters_dict) + ProgressLog.finish() # GET UNIQUE COMBINATIONS @@ -1458,19 +1624,19 @@ def unique_combinations_cli( """Get combinations of raster values between rasters.""" from eis_toolkit.raster_processing.unique_combinations import unique_combinations - typer.echo("Progress: 10%") - rasters = [rasterio.open(rstr) for rstr in input_rasters] + with ProgressLog.reading_input_files(): + rasters = [rasterio.open(rstr) for rstr in input_rasters] - typer.echo("Progress: 25%") - out_image, out_meta = unique_combinations(rasters) - [rstr.close() for rstr in rasters] - typer.echo("Progress: 75%") + with ProgressLog.running_algorithm(): + out_image, out_meta = unique_combinations(rasters) + [rstr.close() for rstr in rasters] - with rasterio.open(output_raster, "w", **out_meta) as dst: - dst.write(out_image, 1) + with ProgressLog.saving_output_files(output_raster): + with rasterio.open(output_raster, "w", **out_meta) as dst: + dst.write(out_image, 1) + dst.update_stats() - typer.echo(f"Writing results to {output_raster}.") - typer.echo("Getting unique combinations completed.") + ProgressLog.finish() # EXTRACT WINDOW @@ -1485,18 +1651,19 @@ def extract_window_cli( """Extract window from raster.""" from eis_toolkit.raster_processing.windowing import extract_window - typer.echo("Progress: 10%") + with ProgressLog.reading_input_files(): + raster = rasterio.open(input_raster) - with rasterio.open(input_raster) as raster: - typer.echo("Progress: 25%") + with ProgressLog.running_algorithm(): out_image, out_meta = extract_window(raster, center_coords, height, width) - typer.echo("Progress: 75%") + raster.close() - with rasterio.open(output_raster, "w", **out_meta) as dst: - dst.write(out_image) - typer.echo("Progress: 100%") + with ProgressLog.saving_output_files(output_raster): + with rasterio.open(output_raster, "w", **out_meta) as dst: + dst.write(out_image) + dst.update_stats() - typer.echo(f"Windowing completed, writing raster to {output_raster}") + ProgressLog.finish() # SURFACE DERIVATIVES - CLASSIFY ASPECT @@ -1510,22 +1677,22 @@ def classify_aspect_cli( """Classify an aspect raster data set.""" from eis_toolkit.raster_processing.derivatives.classification import classify_aspect - typer.echo("Progress: 10%") + with ProgressLog.reading_input_files(): + raster = rasterio.open(input_raster) - with rasterio.open(input_raster) as raster: - typer.echo("Progress: 25%") + with ProgressLog.running_algorithm(): out_image, class_mapping, out_meta = classify_aspect( raster=raster, unit=get_enum_values(unit), num_classes=num_classes ) - typer.echo("Progress: 75%") + raster.close() - with rasterio.open(output_raster, "w", **out_meta) as dst: - dst.write(out_image, 1) - json_str = json.dumps(class_mapping) - typer.echo("Progress: 100%") - typer.echo(f"Results: {json_str}") + with ProgressLog.saving_output_files(output_raster): + with rasterio.open(output_raster, "w", **out_meta) as dst: + dst.write(out_image, 1) + dst.update_stats() - typer.echo(f"Classifying aspect completed, writing raster to {output_raster}") + ResultSender.send_dict_as_json(class_mapping) + ProgressLog.finish() # SURFACE DERIVATIVES @@ -1545,10 +1712,10 @@ def surface_derivatives_cli( """Calculate the first and/or second order surface attributes.""" from eis_toolkit.raster_processing.derivatives.parameters import first_order, second_order_basic_set - typer.echo("Progress: 10%") + with ProgressLog.reading_input_files(): + raster = rasterio.open(input_raster) - with rasterio.open(input_raster) as raster: - typer.echo("Progress: 25%") + with ProgressLog.running_algorithm(): if first_order_parameters: first_order_results = first_order( raster=raster, @@ -1569,23 +1736,22 @@ def surface_derivatives_cli( slope_tolerance=slope_tolerance, method=get_enum_values(second_order_method), ) - typer.echo("Progres: 75%") + raster.close() - if first_order_parameters: - for parameter, (out_image, out_meta) in first_order_results.items(): - out_raster_name = str(output_raster)[:-4] + "_" + parameter + str(output_raster)[-4:] - with rasterio.open(out_raster_name, "w", **out_meta) as dest: - dest.write(out_image, 1) - typer.echo("Progress: 90%") + with ProgressLog.saving_output_files(output_raster): + if first_order_parameters: + for parameter, (out_image, out_meta) in first_order_results.items(): + out_raster_name = str(output_raster)[:-4] + "_" + parameter + str(output_raster)[-4:] + with rasterio.open(out_raster_name, "w", **out_meta) as dest: + dest.write(out_image, 1) - if second_order_parameters: - for parameter, (out_image, out_meta) in second_order_results.items(): - out_raster_name = str(output_raster)[:-4] + "_" + parameter + str(output_raster)[-4:] - with rasterio.open(out_raster_name, "w", **out_meta) as dest: - dest.write(out_image, 1) - typer.echo("Progress: 100%") + if second_order_parameters: + for parameter, (out_image, out_meta) in second_order_results.items(): + out_raster_name = str(output_raster)[:-4] + "_" + parameter + str(output_raster)[-4:] + with rasterio.open(out_raster_name, "w", **out_meta) as dest: + dest.write(out_image, 1) - typer.echo(f"Calculating first and/or second order surface attributes completed, writing raster to {output_raster}") + ProgressLog.finish() @app.command() @@ -1597,19 +1763,21 @@ def mask_raster_cli( """Mask input raster using the nodata locations from base raster.""" from eis_toolkit.raster_processing.masking import mask_raster - typer.echo("Progress: 10%") + with ProgressLog.reading_input_files(): + raster = rasterio.open(input_raster) + base_rstr = rasterio.open(base_raster) - with rasterio.open(input_raster) as raster: - with rasterio.open(base_raster) as base_rstr: - typer.echo("Progress: 25%") - out_image, out_meta = mask_raster(raster=raster, base_raster=base_rstr) - typer.echo("Progress: 75%") + with ProgressLog.running_algorithm(): + out_image, out_meta = mask_raster(raster=raster, base_raster=base_rstr) + raster.close() + base_rstr.close() - with rasterio.open(output_raster, "w", **out_meta) as dest: - dest.write(out_image) - typer.echo("Progress: 100%") + with ProgressLog.saving_output_files(output_raster): + with rasterio.open(output_raster, "w", **out_meta) as dest: + dest.write(out_image) + dest.update_stats() - typer.echo(f"Raster masking completed, writing raster to {output_raster}") + ProgressLog.finish() @app.command() @@ -1622,18 +1790,19 @@ def reclassify_with_manual_breaks_cli( """Classify raster with manual breaks.""" from eis_toolkit.raster_processing.reclassify import reclassify_with_manual_breaks - typer.echo("Progress: 10%") + with ProgressLog.reading_input_files(): + raster = rasterio.open(input_raster) - with rasterio.open(input_raster) as raster: - typer.echo("Progress: 25%") + with ProgressLog.running_algorithm(): out_image, out_meta = reclassify_with_manual_breaks(raster=raster, breaks=breaks, bands=bands) - typer.echo("Progress: 75%") + raster.close() - with rasterio.open(output_raster, "w", **out_meta) as dest: - dest.write(out_image) - typer.echo("Progress: 100%") + with ProgressLog.saving_output_files(output_raster): + with rasterio.open(output_raster, "w", **out_meta) as dest: + dest.write(out_image) + dest.update_stats() - typer.echo(f"Reclassification with manual breaks completed, writing raster to {output_raster}") + ProgressLog.finish() @app.command() @@ -1646,18 +1815,19 @@ def reclassify_with_defined_intervals_cli( """Classify raster with defined intervals.""" from eis_toolkit.raster_processing.reclassify import reclassify_with_defined_intervals - typer.echo("Progress: 10%") + with ProgressLog.reading_input_files(): + raster = rasterio.open(input_raster) - with rasterio.open(input_raster) as raster: - typer.echo("Progress: 25%") + with ProgressLog.running_algorithm(): out_image, out_meta = reclassify_with_defined_intervals(raster=raster, interval_size=interval_size, bands=bands) - typer.echo("Progress: 75%") + raster.close() - with rasterio.open(output_raster, "w", **out_meta) as dest: - dest.write(out_image) - typer.echo("Progress: 100%") + with ProgressLog.saving_output_files(output_raster): + with rasterio.open(output_raster, "w", **out_meta) as dest: + dest.write(out_image) + dest.update_stats() - typer.echo(f"Reclassification with defined intervals completed, writing raster to {output_raster}") + ProgressLog.finish() @app.command() @@ -1670,20 +1840,21 @@ def reclassify_with_equal_intervals_cli( """Classify raster with equal intervals.""" from eis_toolkit.raster_processing.reclassify import reclassify_with_equal_intervals - typer.echo("Progress: 10%") + with ProgressLog.reading_input_files(): + raster = rasterio.open(input_raster) - with rasterio.open(input_raster) as raster: - typer.echo("Progress: 25%") + with ProgressLog.running_algorithm(): out_image, out_meta = reclassify_with_equal_intervals( raster=raster, number_of_intervals=number_of_intervals, bands=bands ) - typer.echo("Progress: 75%") + raster.close() - with rasterio.open(output_raster, "w", **out_meta) as dest: - dest.write(out_image) - typer.echo("Progress: 100%") + with ProgressLog.saving_output_files(output_raster): + with rasterio.open(output_raster, "w", **out_meta) as dest: + dest.write(out_image) + dest.update_stats() - typer.echo(f"Reclassification with equal intervals completed, writing raster to {output_raster}") + ProgressLog.finish() @app.command() @@ -1696,20 +1867,21 @@ def reclassify_with_quantiles_cli( """Classify raster with quantiles.""" from eis_toolkit.raster_processing.reclassify import reclassify_with_quantiles - typer.echo("Progress: 10%") + with ProgressLog.reading_input_files(): + raster = rasterio.open(input_raster) - with rasterio.open(input_raster) as raster: - typer.echo("Progress: 25%") + with ProgressLog.running_algorithm(): out_image, out_meta = reclassify_with_quantiles( raster=raster, number_of_quantiles=number_of_quantiles, bands=bands ) - typer.echo("Progress: 75%") + raster.close() - with rasterio.open(output_raster, "w", **out_meta) as dest: - dest.write(out_image) - typer.echo("Progress: 100%") + with ProgressLog.saving_output_files(output_raster): + with rasterio.open(output_raster, "w", **out_meta) as dest: + dest.write(out_image) + dest.update_stats() - typer.echo(f"Reclassification with quantiles completed, writing raster to {output_raster}") + ProgressLog.finish() @app.command() @@ -1722,20 +1894,21 @@ def reclassify_with_natural_breaks_cli( """Classify raster with natural breaks (Jenks Caspall).""" from eis_toolkit.raster_processing.reclassify import reclassify_with_natural_breaks - typer.echo("Progress: 10%") + with ProgressLog.reading_input_files(): + raster = rasterio.open(input_raster) - with rasterio.open(input_raster) as raster: - typer.echo("Progress: 25%") + with ProgressLog.running_algorithm(): out_image, out_meta = reclassify_with_natural_breaks( raster=raster, number_of_classes=number_of_classes, bands=bands ) - typer.echo("Progress: 75%") + raster.close() - with rasterio.open(output_raster, "w", **out_meta) as dest: - dest.write(out_image) - typer.echo("Progress: 100%") + with ProgressLog.saving_output_files(output_raster): + with rasterio.open(output_raster, "w", **out_meta) as dest: + dest.write(out_image) + dest.update_stats() - typer.echo(f"Reclassification with natural breaks completed, writing raster to {output_raster}") + ProgressLog.finish() @app.command() @@ -1748,20 +1921,21 @@ def reclassify_with_geometrical_intervals_cli( """Classify raster with geometrical intervals.""" from eis_toolkit.raster_processing.reclassify import reclassify_with_geometrical_intervals - typer.echo("Progress: 10%") + with ProgressLog.reading_input_files(): + raster = rasterio.open(input_raster) - with rasterio.open(input_raster) as raster: - typer.echo("Progress: 25%") + with ProgressLog.running_algorithm(): out_image, out_meta = reclassify_with_geometrical_intervals( raster=raster, number_of_classes=number_of_classes, bands=bands ) - typer.echo("Progress: 75%") + raster.close() - with rasterio.open(output_raster, "w", **out_meta) as dest: - dest.write(out_image) - typer.echo("Progress: 100%") + with ProgressLog.saving_output_files(output_raster): + with rasterio.open(output_raster, "w", **out_meta) as dest: + dest.write(out_image) + dest.update_stats() - typer.echo(f"Reclassification with geometric intervals completed, writing raster to {output_raster}") + ProgressLog.finish() @app.command() @@ -1774,20 +1948,21 @@ def reclassify_with_standard_deviation_cli( """Classify raster with standard deviation.""" from eis_toolkit.raster_processing.reclassify import reclassify_with_standard_deviation - typer.echo("Progress: 10%") + with ProgressLog.reading_input_files(): + raster = rasterio.open(input_raster) - with rasterio.open(input_raster) as raster: - typer.echo("Progress: 25%") + with ProgressLog.running_algorithm(): out_image, out_meta = reclassify_with_standard_deviation( raster=raster, number_of_intervals=number_of_intervals, bands=bands ) - typer.echo("Progress: 75%") + raster.close() - with rasterio.open(output_raster, "w", **out_meta) as dest: - dest.write(out_image) - typer.echo("Progress: 100%") + with ProgressLog.saving_output_files(output_raster): + with rasterio.open(output_raster, "w", **out_meta) as dest: + dest.write(out_image) + dest.update_stats() - typer.echo(f"Reclassification with standard deviation completed, writing raster to {output_raster}") + ProgressLog.finish() # --- VECTOR PROCESSING --- @@ -1799,17 +1974,16 @@ def calculate_geometry_cli(input_vector: INPUT_FILE_OPTION, output_vector: OUTPU """Calculate the length or area of the given geometries.""" from eis_toolkit.vector_processing.calculate_geometry import calculate_geometry - typer.echo("Progress: 10%") + with ProgressLog.reading_input_files(): + geodataframe = gpd.read_file(input_vector) - geodataframe = gpd.read_file(input_vector) - typer.echo("Progress: 25%") + with ProgressLog.running_algorithm(): + out_vector = calculate_geometry(geodataframe=geodataframe) - out_vector = calculate_geometry(geodataframe=geodataframe) - typer.echo("Progress: 75%") + with ProgressLog.saving_output_files(output_vector): + out_vector.to_file(output_vector) - out_vector.to_file(output_vector) - typer.echo("Progress 100%") - typer.echo(f"Calculate geometry completed, writing vector to {output_vector}") + ProgressLog.finish() # EXTRACT SHARED LINES @@ -1818,17 +1992,16 @@ def extract_shared_lines_cli(input_vector: INPUT_FILE_OPTION, output_vector: OUT """Extract shared lines/borders/edges between polygons.""" from eis_toolkit.vector_processing.extract_shared_lines import extract_shared_lines - typer.echo("Progress: 10%") + with ProgressLog.reading_input_files(): + polygons = gpd.read_file(input_vector) - polygons = gpd.read_file(input_vector) - typer.echo("Progress: 25%") + with ProgressLog.running_algorithm(): + out_vector = extract_shared_lines(polygons=polygons) - out_vector = extract_shared_lines(polygons=polygons) - typer.echo("Progress: 75%") + with ProgressLog.saving_output_files(output_vector): + out_vector.to_file(output_vector) - out_vector.to_file(output_vector) - typer.echo("Progress: 100%") - typer.echo(f"Extracting shared lines completed, writing vector to {out_vector}") + ProgressLog.finish() # IDW INTERPOLATION @@ -1841,40 +2014,36 @@ def idw_interpolation_cli( pixel_size: float = None, extent: Tuple[float, float, float, float] = (None, None, None, None), power: float = 2.0, + search_radius: Optional[float] = None, ): """Apply inverse distance weighting (IDW) interpolation to input vector file.""" - from eis_toolkit.exceptions import InvalidParameterValueException - from eis_toolkit.utilities.raster import profile_from_extent_and_pixel_size + from eis_toolkit.utilities.raster import base_profile from eis_toolkit.vector_processing.idw_interpolation import idw - typer.echo("Progress: 10%") + with ProgressLog.reading_input_files(): + geodataframe = gpd.read_file(input_vector) - geodataframe = gpd.read_file(input_vector) - typer.echo("Progress: 25%") + if base_raster is None or base_raster == "": + profile = base_profile(extent, pixel_size, geodataframe.crs) + else: + with rasterio.open(base_raster) as raster: + profile = raster.profile.copy() - if base_raster is None or base_raster == "": - if any(bound is None for bound in extent) or pixel_size is None or pixel_size <= 0: - raise InvalidParameterValueException( - "Expected positive pixel size and defined extent in absence of base raster. " - + f"Pixel size: {pixel_size}, extent: {extent}." - ) - profile = profile_from_extent_and_pixel_size(extent, (pixel_size, pixel_size)) - profile["crs"] = geodataframe.crs - profile["driver"] = "GTiff" - profile["dtype"] = "float32" - else: - with rasterio.open(base_raster) as raster: - profile = raster.profile.copy() - - out_image = idw(geodataframe=geodataframe, target_column=target_column, raster_profile=profile, power=power) - typer.echo("Progress: 75%") + with ProgressLog.running_algorithm(): + out_image = idw( + geodataframe=geodataframe, + target_column=target_column, + raster_profile=profile, + power=power, + search_radius=search_radius, + ) - profile["count"] = 1 - with rasterio.open(output_raster, "w", **profile) as dst: - dst.write(out_image, 1) - typer.echo("Progress: 100%") + with ProgressLog.saving_output_files(output_raster): + with rasterio.open(output_raster, "w", **profile) as dst: + dst.write(out_image, 1) + dst.update_stats() - typer.echo(f"IDW interpolation completed, writing raster to {output_raster}.") + ProgressLog.finish() # KRIGING INTERPOLATION @@ -1891,45 +2060,34 @@ def kriging_interpolation_cli( method: Annotated[KrigingMethod, typer.Option(case_sensitive=False)] = KrigingMethod.ordinary, ): """Apply kriging interpolation to input vector file.""" - from eis_toolkit.exceptions import InvalidParameterValueException - from eis_toolkit.utilities.raster import profile_from_extent_and_pixel_size + from eis_toolkit.utilities.raster import base_profile from eis_toolkit.vector_processing.kriging_interpolation import kriging - typer.echo("Progress: 10%") + with ProgressLog.reading_input_files(): + geodataframe = gpd.read_file(input_vector) - geodataframe = gpd.read_file(input_vector) - typer.echo("Progress: 25%") + if base_raster is None or base_raster == "": + profile = base_profile(extent, pixel_size, geodataframe.crs) + else: + with rasterio.open(base_raster) as raster: + profile = raster.profile.copy() - if base_raster is None or base_raster == "": - if any(bound is None for bound in extent) or pixel_size is None or pixel_size <= 0: - raise InvalidParameterValueException( - "Expected positive pixel size and defined extent in absence of base raster. " - + f"Pixel size: {pixel_size}, extent: {extent}." - ) - profile = profile_from_extent_and_pixel_size(extent, (pixel_size, pixel_size)) - profile["crs"] = geodataframe.crs - profile["driver"] = "GTiff" - profile["dtype"] = "float32" - else: - with rasterio.open(base_raster) as raster: - profile = raster.profile.copy() - - out_image = kriging( - geodataframe=geodataframe, - target_column=target_column, - raster_profile=profile, - variogram_model=get_enum_values(variogram_model), - coordinates_type=get_enum_values(coordinates_type), - method=get_enum_values(method), - ) - typer.echo("Progress: 75%") + with ProgressLog.running_algorithm(): + out_image = kriging( + geodataframe=geodataframe, + target_column=target_column, + raster_profile=profile, + variogram_model=get_enum_values(variogram_model), + coordinates_type=get_enum_values(coordinates_type), + method=get_enum_values(method), + ) - profile["count"] = 1 - with rasterio.open(output_raster, "w", **profile) as dst: - dst.write(out_image, 1) - typer.echo("Progress: 100%") + with ProgressLog.saving_output_files(output_raster): + with rasterio.open(output_raster, "w", **profile) as dst: + dst.write(out_image, 1) + dst.update_stats() - typer.echo(f"Kriging interpolation completed, writing raster to {output_raster}.") + ProgressLog.finish() # RASTERIZE @@ -1951,46 +2109,35 @@ def rasterize_cli( Either base raster or pixel size + extent must be provided. """ - from eis_toolkit.exceptions import InvalidParameterValueException - from eis_toolkit.utilities.raster import profile_from_extent_and_pixel_size + from eis_toolkit.utilities.raster import base_profile from eis_toolkit.vector_processing.rasterize_vector import rasterize_vector - typer.echo("Progress: 10%") - - geodataframe = gpd.read_file(input_vector) - typer.echo("Progress: 25%") - - if base_raster is None or base_raster == "": - if any(bound is None for bound in extent) or pixel_size is None or pixel_size <= 0: - raise InvalidParameterValueException( - "Expected positive pixel size and defined extent in absence of base raster. " - + f"Pixel size: {pixel_size}, extent: {extent}." - ) - profile = profile_from_extent_and_pixel_size(extent, (pixel_size, pixel_size)) - profile["crs"] = geodataframe.crs - profile["driver"] = "GTiff" - profile["dtype"] = "float32" - else: - with rasterio.open(base_raster) as raster: - profile = raster.profile.copy() - - out_image = rasterize_vector( - geodataframe, - profile, - value_column, - default_value, - fill_value, - buffer_value, - get_enum_values(merge_strategy), - ) - typer.echo("Progress: 75%") + with ProgressLog.reading_input_files(): + geodataframe = gpd.read_file(input_vector) + + if base_raster is None or base_raster == "": + profile = base_profile(extent, pixel_size, geodataframe.crs) + else: + with rasterio.open(base_raster) as raster: + profile = raster.profile.copy() + + with ProgressLog.running_algorithm(): + out_image = rasterize_vector( + geodataframe, + profile, + value_column, + default_value, + fill_value, + buffer_value, + get_enum_values(merge_strategy), + ) - profile["count"] = 1 - with rasterio.open(output_raster, "w", **profile) as dst: - dst.write(out_image, 1) - typer.echo("Progress: 100%") + with ProgressLog.saving_output_files(output_raster): + with rasterio.open(output_raster, "w", **profile) as dst: + dst.write(out_image, 1) + dst.update_stats() - typer.echo(f"Rasterizing completed, writing raster to {output_raster}.") + ProgressLog.finish() # REPROJECT VECTOR @@ -2003,18 +2150,16 @@ def reproject_vector_cli( """Reproject the input vector to given CRS.""" from eis_toolkit.vector_processing.reproject_vector import reproject_vector - typer.echo("Progress: 10%") - - geodataframe = gpd.read_file(input_vector) - typer.echo("Progress: 25%") + with ProgressLog.reading_input_files(): + geodataframe = gpd.read_file(input_vector) - reprojected_geodataframe = reproject_vector(geodataframe=geodataframe, target_crs=target_crs) - typer.echo("Progress: 75%") + with ProgressLog.running_algorithm(): + reprojected_geodataframe = reproject_vector(geodataframe=geodataframe, target_crs=target_crs) - reprojected_geodataframe.to_file(output_vector, driver="GeoJSON") - typer.echo("Progress: 100%") + with ProgressLog.saving_output_files(output_vector): + reprojected_geodataframe.to_file(output_vector, driver="GeoJSON") - typer.echo(f"Reprojecting completed, writing vector to {output_vector}.") + ProgressLog.finish() # VECTOR DENSITY @@ -2033,43 +2178,32 @@ def vector_density_cli( Either base raster or pixel size + extent must be provided. """ - from eis_toolkit.exceptions import InvalidParameterValueException - from eis_toolkit.utilities.raster import profile_from_extent_and_pixel_size + from eis_toolkit.utilities.raster import base_profile from eis_toolkit.vector_processing.vector_density import vector_density - typer.echo("Progress: 10%") - - geodataframe = gpd.read_file(input_vector) - typer.echo("Progress: 25%") + with ProgressLog.reading_input_files(): + geodataframe = gpd.read_file(input_vector) - if base_raster is None or base_raster == "": - if any(bound is None for bound in extent) or pixel_size is None or pixel_size <= 0: - raise InvalidParameterValueException( - "Expected positive pixel size and defined extent in absence of base raster. " - + f"Pixel size: {pixel_size}, extent: {extent}." - ) - profile = profile_from_extent_and_pixel_size(extent, (pixel_size, pixel_size)) - profile["crs"] = geodataframe.crs - profile["driver"] = "GTiff" - profile["dtype"] = "float32" - else: - with rasterio.open(base_raster) as raster: - profile = raster.profile.copy() + if base_raster is None or base_raster == "": + profile = base_profile(extent, pixel_size, geodataframe.crs) + else: + with rasterio.open(base_raster) as raster: + profile = raster.profile.copy() - out_image = vector_density( - geodataframe=geodataframe, - raster_profile=profile, - buffer_value=buffer_value, - statistic=get_enum_values(statistic), - ) - typer.echo("Progress: 75%") + with ProgressLog.running_algorithm(): + out_image = vector_density( + geodataframe=geodataframe, + raster_profile=profile, + buffer_value=buffer_value, + statistic=get_enum_values(statistic), + ) - profile["count"] = 1 - with rasterio.open(output_raster, "w", **profile) as dst: - dst.write(out_image, 1) - typer.echo("Progress: 100%") + with ProgressLog.saving_output_files(output_raster): + with rasterio.open(output_raster, "w", **profile) as dst: + dst.write(out_image, 1) + dst.update_stats() - typer.echo(f"Vector density computation completed, writing raster to {output_raster}.") + ProgressLog.finish() # DISTANCE COMPUTATION @@ -2083,38 +2217,36 @@ def distance_computation_cli( max_distance: float = None, ): """Calculate distance from raster cell to nearest geometry.""" - from eis_toolkit.exceptions import InvalidParameterValueException - from eis_toolkit.utilities.raster import profile_from_extent_and_pixel_size + from eis_toolkit.utilities.raster import base_profile from eis_toolkit.vector_processing.distance_computation import distance_computation - typer.echo("Progress: 10%") - - geodataframe = gpd.read_file(input_vector) - typer.echo("Progress: 25%") - - if base_raster is None or base_raster == "": - if any(bound is None for bound in extent) or pixel_size is None or pixel_size <= 0: - raise InvalidParameterValueException( - "Expected positive pixel size and defined extent in absence of base raster. " - + f"Pixel size: {pixel_size}, extent: {extent}." - ) - profile = profile_from_extent_and_pixel_size(extent, (pixel_size, pixel_size)) - profile["crs"] = geodataframe.crs - profile["driver"] = "GTiff" - profile["dtype"] = "float32" - else: - with rasterio.open(base_raster) as raster: - profile = raster.profile.copy() + with ProgressLog.reading_input_files(): + geodataframe = gpd.read_file(input_vector) + + if base_raster is None or base_raster == "": + profile = base_profile(extent, pixel_size, geodataframe.crs) + mask = None + else: + with rasterio.open(base_raster) as raster: + profile = raster.profile.copy() + raster_array = raster.read(1) + mask = (raster_array == profile["nodata"]) | np.isnan(raster_array) + + with ProgressLog.running_algorithm(): + out_image, out_profile = distance_computation( + geodataframe=geodataframe, raster_profile=profile, max_distance=max_distance + ) - out_image = distance_computation(geodataframe=geodataframe, raster_profile=profile, max_distance=max_distance) - profile["count"] = 1 - typer.echo("Progress: 75%") + # Apply nodata mask + if mask is not None: + out_image[mask] = out_profile["nodata"] - with rasterio.open(output_raster, "w", **profile) as dst: - dst.write(out_image, 1) - typer.echo("Progress: 100%") + with ProgressLog.saving_output_files(output_raster): + with rasterio.open(output_raster, "w", **out_profile) as dst: + dst.write(out_image, 1) + dst.update_stats() - typer.echo(f"Distance computation completed, writing raster to {output_raster}.") + ProgressLog.finish() # CBA @@ -2131,29 +2263,155 @@ def cell_based_association_cli( """Create a CBA matrix.""" from eis_toolkit.vector_processing.cell_based_association import cell_based_association - typer.echo("Progress: 10%") + with ProgressLog.reading_input_files(): + geodataframe = gpd.read_file(input_vector) + + if subset_target_attribute_values is not None: + subset_target_attribute_values = [value.strip() for value in subset_target_attribute_values] + + with ProgressLog.running_algorithm(): + cell_based_association( + cell_size=cell_size, + geodata=[geodataframe], + output_path=output_raster, + column=column if column is None else [column], + subset_target_attribute_values=subset_target_attribute_values + if subset_target_attribute_values is None + else [subset_target_attribute_values], + add_name=add_name if add_name is None else [add_name], + add_buffer=add_buffer if add_buffer is None else [add_buffer], + ) - geodataframe = gpd.read_file(input_vector) - typer.echo("Progress: 25%") + ProgressLog.saving_output_files(output_raster) + ProgressLog.finish() - if subset_target_attribute_values is not None: - subset_target_attribute_values = [value.strip() for value in subset_target_attribute_values] - cell_based_association( - cell_size=cell_size, - geodata=[geodataframe], - output_path=output_raster, - column=column if column is None else [column], - subset_target_attribute_values=subset_target_attribute_values - if subset_target_attribute_values is None - else [subset_target_attribute_values], - add_name=add_name if add_name is None else [add_name], - add_buffer=add_buffer if add_buffer is None else [add_buffer], - ) +# PROXIMITY COMPUTATION +@app.command() +def proximity_computation_cli( + input_vector: INPUT_FILE_OPTION, + output_raster: OUTPUT_FILE_OPTION, + max_distance: float = typer.Option(), + max_distance_value: float = 0.0, + geometries_value: float = 1.0, + base_raster: INPUT_FILE_OPTION = None, + pixel_size: float = None, + extent: Tuple[float, float, float, float] = (None, None, None, None), +): + """Calculate proximity from raster cell to nearest geometry.""" + from eis_toolkit.utilities.raster import base_profile + from eis_toolkit.vector_processing.proximity_computation import proximity_computation + + with ProgressLog.reading_input_files(): + geodataframe = gpd.read_file(input_vector) + + if base_raster is None or base_raster == "": + profile = base_profile(extent, pixel_size, geodataframe.crs) + mask = None + else: + with rasterio.open(base_raster) as raster: + profile = raster.profile.copy() + raster_array = raster.read(1) + mask = (raster_array == profile["nodata"]) | np.isnan(raster_array) + + with ProgressLog.running_algorithm(): + out_image, out_profile = proximity_computation( + geodataframe=geodataframe, + raster_profile=profile, + maximum_distance=max_distance, + scale_range=(geometries_value, max_distance_value), + ) - typer.echo("Progress: 100%") + # Apply nodata mask + if mask is not None: + out_image[mask] = out_profile["nodata"] - typer.echo(f"Cell based association completed, writing raster to {output_raster}.") + with ProgressLog.saving_output_files(output_raster): + with rasterio.open(output_raster, "w", **out_profile) as dst: + dst.write(out_image, 1) + dst.update_stats() + + ProgressLog.finish() + + +# --- TRAINING DATA TOOLS --- + +# POINTS TO RASTER +@app.command() +def points_to_raster_cli( + input_vector: INPUT_FILE_OPTION, + output_raster: OUTPUT_FILE_OPTION, + base_raster: INPUT_FILE_OPTION = None, + pixel_size: float = None, + extent: Tuple[float, float, float, float] = (None, None, None, None), + attribute: Optional[str] = None, + radius: Optional[int] = None, + buffer: Annotated[BufferOption, typer.Option(case_sensitive=False)] = None, +): + """Convert a point data set into a binary raster.""" + from eis_toolkit.training_data_tools.points_to_raster import points_to_raster + from eis_toolkit.utilities.raster import base_profile + + with ProgressLog.reading_input_files(): + geodataframe = gpd.read_file(input_vector) + + if base_raster is None or base_raster == "": + profile = base_profile(extent, pixel_size, geodataframe.crs) + mask = None + else: + with rasterio.open(base_raster) as raster: + profile = raster.profile.copy() + raster_array = raster.read(1) + mask = (raster_array == profile["nodata"]) | np.isnan(raster_array) + + with ProgressLog.running_algorithm(): + out_image, out_profile = points_to_raster( + geodataframe=geodataframe, raster_profile=profile, attribute=attribute, radius=radius, buffer=buffer + ) + + # Apply nodata mask + if mask is not None: + out_image[mask] = out_profile["nodata"] + + with ProgressLog.saving_output_files(output_raster): + with rasterio.open(output_raster, "w", **out_profile) as dst: + dst.write(out_image, 1) + dst.update_stats() + + ProgressLog.finish() + + +# GENERATE NEGATIVES +@app.command() +def generate_negatives_cli( + input_raster: INPUT_FILE_OPTION, + output_raster: OUTPUT_FILE_OPTION, + output_vector: OUTPUT_FILE_OPTION, + sample_number: Annotated[int, typer.Option()], + random_seed: int = 48, +): + """Generate probable negatives from raster array with marked positives.""" + from eis_toolkit.training_data_tools.random_sampling import generate_negatives + + with ProgressLog.reading_input_files(): + with rasterio.open(input_raster) as raster: + raster_array = raster.read(1) + profile = raster.profile.copy() + mask = (raster_array == profile["nodata"]) | np.isnan(raster_array) + + with ProgressLog.running_algorithm(): + out_points, out_image, out_profile = generate_negatives( + raster_array=raster_array, raster_profile=profile, sample_number=sample_number, random_seed=random_seed + ) + out_image[mask] = out_profile["nodata"] + + with ProgressLog.saving_output_files([output_raster, output_vector]): + with rasterio.open(output_raster, "w", **out_profile) as dst: + dst.write(out_image, 1) + dst.update_stats() + out_points.to_file(output_vector) + + ProgressLog.finish() # --- PREDICTION --- @@ -2183,36 +2441,30 @@ def logistic_regression_train_cli( from eis_toolkit.prediction.logistic_regression import logistic_regression_train from eis_toolkit.prediction.machine_learning_general import prepare_data_for_ml, save_model - X, y, _, _ = prepare_data_for_ml(input_rasters, target_labels) - - typer.echo("Progress: 30%") - - # Train (and score) the model - model, metrics_dict = logistic_regression_train( - X=X, - y=y, - validation_method=get_enum_values(validation_method), - metrics=get_enum_values(validation_metrics), - split_size=split_size, - cv_folds=cv_folds, - penalty=get_enum_values(penalty), - max_iter=max_iter, - solver=get_enum_values(solver), - verbose=verbose, - random_state=random_state, - ) - - typer.echo("Progress: 80%") - - save_model(model, output_file) # NOTE: Check if .joblib needs to be added to save path - - typer.echo("Progress: 90%") + with ProgressLog.reading_input_files(): + X, y, _, _ = prepare_data_for_ml(input_rasters, target_labels) + + with ProgressLog.running_algorithm(): + # Train (and score) the model + model, metrics_dict = logistic_regression_train( + X=X, + y=y, + validation_method=get_enum_values(validation_method), + metrics=get_enum_values(validation_metrics), + split_size=split_size, + cv_folds=cv_folds, + penalty=get_enum_values(penalty), + max_iter=max_iter, + solver=get_enum_values(solver), + verbose=verbose, + random_state=random_state, + ) - json_str = json.dumps(metrics_dict) - typer.echo("Progress: 100%") - typer.echo(f"Results: {json_str}") + with ProgressLog.saving_output_files(output_file): + save_model(model, output_file) # NOTE: Check if .joblib needs to be added to save path - typer.echo("Logistic regression training completed") + ResultSender.send_dict_as_json(metrics_dict) + ProgressLog.finish() # RANDOM FOREST CLASSIFIER @@ -2239,36 +2491,30 @@ def random_forest_classifier_train_cli( from eis_toolkit.prediction.machine_learning_general import prepare_data_for_ml, save_model from eis_toolkit.prediction.random_forests import random_forest_classifier_train - X, y, _, _ = prepare_data_for_ml(input_rasters, target_labels) - - typer.echo("Progress: 30%") - - # Train (and score) the model - model, metrics_dict = random_forest_classifier_train( - X=X, - y=y, - validation_method=get_enum_values(validation_method), - metrics=get_enum_values(validation_metrics), - split_size=split_size, - cv_folds=cv_folds, - n_estimators=n_estimators, - criterion=criterion, - max_depth=max_depth, - verbose=verbose, - random_state=random_state, - ) - - typer.echo("Progress: 80%") - - save_model(model, output_file) # NOTE: Check if .joblib needs to be added to save path - - typer.echo("Progress: 90%") + with ProgressLog.reading_input_files(): + X, y, _, _ = prepare_data_for_ml(input_rasters, target_labels) + + with ProgressLog.running_algorithm(): + # Train (and score) the model + model, metrics_dict = random_forest_classifier_train( + X=X, + y=y, + validation_method=get_enum_values(validation_method), + metrics=get_enum_values(validation_metrics), + split_size=split_size, + cv_folds=cv_folds, + n_estimators=n_estimators, + criterion=criterion, + max_depth=max_depth, + verbose=verbose, + random_state=random_state, + ) - json_str = json.dumps(metrics_dict) - typer.echo("Progress: 100%") - typer.echo(f"Results: {json_str}") + with ProgressLog.saving_output_files(output_file): + save_model(model, output_file) # NOTE: Check if .joblib needs to be added to save path - typer.echo("Random forest classifier training completed") + ResultSender.send_dict_as_json(metrics_dict) + ProgressLog.finish() # RANDOM FOREST REGRESSOR @@ -2293,36 +2539,30 @@ def random_forest_regressor_train_cli( from eis_toolkit.prediction.machine_learning_general import prepare_data_for_ml, save_model from eis_toolkit.prediction.random_forests import random_forest_regressor_train - X, y, _, _ = prepare_data_for_ml(input_rasters, target_labels) - - typer.echo("Progress: 30%") - - # Train (and score) the model - model, metrics_dict = random_forest_regressor_train( - X=X, - y=y, - validation_method=get_enum_values(validation_method), - metrics=get_enum_values(validation_metrics), - split_size=split_size, - cv_folds=cv_folds, - n_estimators=n_estimators, - criterion=criterion, - max_depth=max_depth, - verbose=verbose, - random_state=random_state, - ) - - typer.echo("Progress: 80%") - - save_model(model, output_file) # NOTE: Check if .joblib needs to be added to save path - - typer.echo("Progress: 90%") + with ProgressLog.reading_input_files(): + X, y, _, _ = prepare_data_for_ml(input_rasters, target_labels) + + with ProgressLog.running_algorithm(): + # Train (and score) the model + model, metrics_dict = random_forest_regressor_train( + X=X, + y=y, + validation_method=get_enum_values(validation_method), + metrics=get_enum_values(validation_metrics), + split_size=split_size, + cv_folds=cv_folds, + n_estimators=n_estimators, + criterion=criterion, + max_depth=max_depth, + verbose=verbose, + random_state=random_state, + ) - json_str = json.dumps(metrics_dict) - typer.echo("Progress: 100%") - typer.echo(f"Results: {json_str}") + with ProgressLog.saving_output_files(output_file): + save_model(model, output_file) # NOTE: Check if .joblib needs to be added to save path - typer.echo("Random forest regressor training completed") + ResultSender.send_dict_as_json(metrics_dict) + ProgressLog.finish() # GRADIENT BOOSTING CLASSIFIER @@ -2351,38 +2591,32 @@ def gradient_boosting_classifier_train_cli( from eis_toolkit.prediction.gradient_boosting import gradient_boosting_classifier_train from eis_toolkit.prediction.machine_learning_general import prepare_data_for_ml, save_model - X, y, _, _ = prepare_data_for_ml(input_rasters, target_labels) - - typer.echo("Progress: 30%") - - # Train (and score) the model - model, metrics_dict = gradient_boosting_classifier_train( - X=X, - y=y, - validation_method=get_enum_values(validation_method), - metrics=get_enum_values(validation_metrics), - split_size=split_size, - cv_folds=cv_folds, - loss=get_enum_values(loss), - learning_rate=learning_rate, - n_estimators=n_estimators, - max_depth=max_depth, - subsample=subsample, - verbose=verbose, - random_state=random_state, - ) - - typer.echo("Progress: 80%") - - save_model(model, output_file) # NOTE: Check if .joblib needs to be added to save path - - typer.echo("Progress: 90%") + with ProgressLog.reading_input_files(): + X, y, _, _ = prepare_data_for_ml(input_rasters, target_labels) + + with ProgressLog.running_algorithm(): + # Train (and score) the model + model, metrics_dict = gradient_boosting_classifier_train( + X=X, + y=y, + validation_method=get_enum_values(validation_method), + metrics=get_enum_values(validation_metrics), + split_size=split_size, + cv_folds=cv_folds, + loss=get_enum_values(loss), + learning_rate=learning_rate, + n_estimators=n_estimators, + max_depth=max_depth, + subsample=subsample, + verbose=verbose, + random_state=random_state, + ) - json_str = json.dumps(metrics_dict) - typer.echo("Progress: 100%") - typer.echo(f"Results: {json_str}") + with ProgressLog.saving_output_files(output_file): + save_model(model, output_file) # NOTE: Check if .joblib needs to be added to save path - typer.echo("Gradient boosting classifier training completed") + ResultSender.send_dict_as_json(metrics_dict) + ProgressLog.finish() # GRADIENT BOOSTING REGRESSOR @@ -2409,38 +2643,32 @@ def gradient_boosting_regressor_train_cli( from eis_toolkit.prediction.gradient_boosting import gradient_boosting_regressor_train from eis_toolkit.prediction.machine_learning_general import prepare_data_for_ml, save_model - X, y, _, _ = prepare_data_for_ml(input_rasters, target_labels) - - typer.echo("Progress: 30%") - - # Train (and score) the model - model, metrics_dict = gradient_boosting_regressor_train( - X=X, - y=y, - validation_method=get_enum_values(validation_method), - metrics=get_enum_values(validation_metrics), - split_size=split_size, - cv_folds=cv_folds, - loss=loss, - learning_rate=learning_rate, - n_estimators=n_estimators, - max_depth=max_depth, - subsample=subsample, - verbose=verbose, - random_state=random_state, - ) - - typer.echo("Progress: 80%") - - save_model(model, output_file) # NOTE: Check if .joblib needs to be added to save path - - typer.echo("Progress: 90%") + with ProgressLog.reading_input_files(): + X, y, _, _ = prepare_data_for_ml(input_rasters, target_labels) + + with ProgressLog.running_algorithm(): + # Train (and score) the model + model, metrics_dict = gradient_boosting_regressor_train( + X=X, + y=y, + validation_method=get_enum_values(validation_method), + metrics=get_enum_values(validation_metrics), + split_size=split_size, + cv_folds=cv_folds, + loss=loss, + learning_rate=learning_rate, + n_estimators=n_estimators, + max_depth=max_depth, + subsample=subsample, + verbose=verbose, + random_state=random_state, + ) - json_str = json.dumps(metrics_dict) - typer.echo("Progress: 100%") - typer.echo(f"Results: {json_str}") + with ProgressLog.saving_output_files(output_file): + save_model(model, output_file) # NOTE: Check if .joblib needs to be added to save path - typer.echo("Gradient boosting regressor training completed") + ResultSender.send_dict_as_json(metrics_dict) + ProgressLog.finish() # MLP CLASSIFIER @@ -2475,42 +2703,36 @@ def mlp_classifier_train_cli( from eis_toolkit.prediction.machine_learning_general import prepare_data_for_ml, save_model from eis_toolkit.prediction.mlp import train_MLP_classifier - X, y, _, _ = prepare_data_for_ml(input_rasters, target_labels) - - typer.echo("Progress: 30%") - - # Train (and score) the model - model, metrics_dict = train_MLP_classifier( - X=X, - y=y, - neurons=neurons, - activation=get_enum_values(activation), - output_neurons=output_neurons, - last_activation=get_enum_values(last_activation), - epochs=epochs, - batch_size=batch_size, - optimizer=get_enum_values(optimizer), - learning_rate=learning_rate, - loss_function=get_enum_values(loss_function), - dropout_rate=dropout_rate, - early_stopping=early_stopping, - es_patience=es_patience, - metrics=get_enum_values(validation_metrics), - validation_split=validation_split, - random_state=random_state, - ) - - typer.echo("Progress: 80%") - - save_model(model, output_file) - - typer.echo("Progress: 90%") + with ProgressLog.reading_input_files(): + X, y, _, _ = prepare_data_for_ml(input_rasters, target_labels) + + with ProgressLog.running_algorithm(): + # Train (and score) the model + model, metrics_dict = train_MLP_classifier( + X=X, + y=y, + neurons=neurons, + activation=get_enum_values(activation), + output_neurons=output_neurons, + last_activation=get_enum_values(last_activation), + epochs=epochs, + batch_size=batch_size, + optimizer=get_enum_values(optimizer), + learning_rate=learning_rate, + loss_function=get_enum_values(loss_function), + dropout_rate=dropout_rate, + early_stopping=early_stopping, + es_patience=es_patience, + metrics=get_enum_values(validation_metrics), + validation_split=validation_split, + random_state=random_state, + ) - json_str = json.dumps(metrics_dict) - typer.echo("Progress: 100%") - typer.echo(f"Results: {json_str}") + with ProgressLog.saving_output_files(output_file): + save_model(model, output_file) - typer.echo("MLP classifier training completed.") + ResultSender.send_dict_as_json(metrics_dict) + ProgressLog.finish() # MLP REGRESSOR @@ -2542,42 +2764,36 @@ def mlp_regressor_train_cli( from eis_toolkit.prediction.machine_learning_general import prepare_data_for_ml, save_model from eis_toolkit.prediction.mlp import train_MLP_regressor - X, y, _, _ = prepare_data_for_ml(input_rasters, target_labels) - - typer.echo("Progress: 30%") - - # Train (and score) the model - model, metrics_dict = train_MLP_regressor( - X=X, - y=y, - neurons=neurons, - activation=get_enum_values(activation), - output_neurons=output_neurons, - last_activation="linear", - epochs=epochs, - batch_size=batch_size, - optimizer=get_enum_values(optimizer), - learning_rate=learning_rate, - loss_function=get_enum_values(loss_function), - dropout_rate=dropout_rate, - early_stopping=early_stopping, - es_patience=es_patience, - metrics=get_enum_values(validation_metrics), - validation_split=validation_split, - random_state=random_state, - ) - - typer.echo("Progress: 80%") - - save_model(model, output_file) - - typer.echo("Progress: 90%") + with ProgressLog.reading_input_files(): + X, y, _, _ = prepare_data_for_ml(input_rasters, target_labels) + + with ProgressLog.running_algorithm(): + # Train (and score) the model + model, metrics_dict = train_MLP_regressor( + X=X, + y=y, + neurons=neurons, + activation=get_enum_values(activation), + output_neurons=output_neurons, + last_activation="linear", + epochs=epochs, + batch_size=batch_size, + optimizer=get_enum_values(optimizer), + learning_rate=learning_rate, + loss_function=get_enum_values(loss_function), + dropout_rate=dropout_rate, + early_stopping=early_stopping, + es_patience=es_patience, + metrics=get_enum_values(validation_metrics), + validation_split=validation_split, + random_state=random_state, + ) - json_str = json.dumps(metrics_dict) - typer.echo("Progress: 100%") - typer.echo(f"Results: {json_str}") + with ProgressLog.saving_output_files(output_file): + save_model(model, output_file) - typer.echo("MLP regressor training completed.") + ResultSender.send_dict_as_json(metrics_dict) + ProgressLog.finish() # TEST CLASSIFIER ML MODEL @@ -2596,39 +2812,34 @@ def classifier_test_cli( from eis_toolkit.prediction.machine_learning_general import load_model, prepare_data_for_ml, reshape_predictions from eis_toolkit.prediction.machine_learning_predict import predict_classifier - X, y, reference_profile, nodata_mask = prepare_data_for_ml(input_rasters, target_labels) - typer.echo("Progress: 30%") + with ProgressLog.reading_input_files(): + X, y, reference_profile, nodata_mask = prepare_data_for_ml(input_rasters, target_labels) + model = load_model(model_file) - model = load_model(model_file) - predictions, probabilities = predict_classifier(X, model, classification_threshold, True) - probabilities_reshaped = reshape_predictions( - probabilities, reference_profile["height"], reference_profile["width"], nodata_mask - ) - predictions_reshaped = reshape_predictions( - predictions, reference_profile["height"], reference_profile["width"], nodata_mask - ) + with ProgressLog.running_algorithm(): + predictions, probabilities = predict_classifier(X, model, classification_threshold, True) + probabilities_reshaped = reshape_predictions( + probabilities, reference_profile["height"], reference_profile["width"], nodata_mask + ) + predictions_reshaped = reshape_predictions( + predictions, reference_profile["height"], reference_profile["width"], nodata_mask + ) - metrics_dict = score_predictions(y, predictions, get_enum_values(test_metrics), decimals=3) - typer.echo("Progress: 80%") + metrics_dict = score_predictions(y, predictions, get_enum_values(test_metrics), decimals=3) out_profile = reference_profile.copy() out_profile.update({"count": 1, "dtype": np.float32}) - with rasterio.open(output_raster_probability, "w", **out_profile) as dst: - dst.write(probabilities_reshaped, 1) - with rasterio.open(output_raster_classified, "w", **out_profile) as dst: - dst.write(predictions_reshaped, 1) - - json_str = json.dumps(metrics_dict) - typer.echo("Progress: 100% \n") + with ProgressLog.saving_output_files([output_raster_probability, output_raster_classified]): + with rasterio.open(output_raster_probability, "w", **out_profile) as dst: + dst.write(probabilities_reshaped, 1) + dst.update_stats() + with rasterio.open(output_raster_classified, "w", **out_profile) as dst: + dst.write(predictions_reshaped, 1) + dst.update_stats() - typer.echo(f"Results: {json_str}") - typer.echo( - ( - "Testing classifier model completed, writing rasters to " - f"{output_raster_probability} and {output_raster_classified}." - ) - ) + ResultSender.send_dict_as_json(metrics_dict) + ProgressLog.finish() # TEST REGRESSOR ML MODEL @@ -2645,29 +2856,28 @@ def regressor_test_cli( from eis_toolkit.prediction.machine_learning_general import load_model, prepare_data_for_ml, reshape_predictions from eis_toolkit.prediction.machine_learning_predict import predict_regressor - X, y, reference_profile, nodata_mask = prepare_data_for_ml(input_rasters, target_labels) - typer.echo("Progress: 30%") + with ProgressLog.reading_input_files(): + X, y, reference_profile, nodata_mask = prepare_data_for_ml(input_rasters, target_labels) + model = load_model(model_file) - model = load_model(model_file) - predictions = predict_regressor(X, model) - predictions_reshaped = reshape_predictions( - predictions, reference_profile["height"], reference_profile["width"], nodata_mask - ) + with ProgressLog.running_algorithm(): + predictions = predict_regressor(X, model) + predictions_reshaped = reshape_predictions( + predictions, reference_profile["height"], reference_profile["width"], nodata_mask + ) - metrics_dict = score_predictions(y, predictions, get_enum_values(test_metrics), decimals=3) - typer.echo("Progress: 80%") + metrics_dict = score_predictions(y, predictions, get_enum_values(test_metrics), decimals=3) out_profile = reference_profile.copy() out_profile.update({"count": 1, "dtype": np.float32}) - with rasterio.open(output_raster, "w", **out_profile) as dst: - dst.write(predictions_reshaped, 1) - - json_str = json.dumps(metrics_dict) - typer.echo("Progress: 100% \n") + with ProgressLog.saving_output_files(output_raster): + with rasterio.open(output_raster, "w", **out_profile) as dst: + dst.write(predictions_reshaped, 1) + dst.update_stats() - typer.echo(f"Results: {json_str}") - typer.echo(f"Testing regressor model completed, writing raster to {output_raster}.") + ResultSender.send_dict_as_json(metrics_dict) + ProgressLog.finish() # PREDICT WITH TRAINED ML MODEL @@ -2683,35 +2893,31 @@ def classifier_predict_cli( from eis_toolkit.prediction.machine_learning_general import load_model, prepare_data_for_ml, reshape_predictions from eis_toolkit.prediction.machine_learning_predict import predict_classifier - X, _, reference_profile, nodata_mask = prepare_data_for_ml(input_rasters) + with ProgressLog.reading_input_files(): + X, _, reference_profile, nodata_mask = prepare_data_for_ml(input_rasters) + model = load_model(model_file) - typer.echo("Progress: 30%") - - model = load_model(model_file) - predictions, probabilities = predict_classifier(X, model, classification_threshold, True) - probabilities_reshaped = reshape_predictions( - probabilities, reference_profile["height"], reference_profile["width"], nodata_mask - ) - predictions_reshaped = reshape_predictions( - predictions, reference_profile["height"], reference_profile["width"], nodata_mask - ) - typer.echo("Progress: 80%") + with ProgressLog.running_algorithm(): + predictions, probabilities = predict_classifier(X, model, classification_threshold, True) + probabilities_reshaped = reshape_predictions( + probabilities, reference_profile["height"], reference_profile["width"], nodata_mask + ) + predictions_reshaped = reshape_predictions( + predictions, reference_profile["height"], reference_profile["width"], nodata_mask + ) out_profile = reference_profile.copy() out_profile.update({"count": 1, "dtype": np.float32}) - with rasterio.open(output_raster_probability, "w", **out_profile) as dst: - dst.write(probabilities_reshaped, 1) - with rasterio.open(output_raster_classified, "w", **out_profile) as dst: - dst.write(predictions_reshaped, 1) + with ProgressLog.saving_output_files([output_raster_probability, output_raster_classified]): + with rasterio.open(output_raster_probability, "w", **out_profile) as dst: + dst.write(probabilities_reshaped, 1) + dst.update_stats() + with rasterio.open(output_raster_classified, "w", **out_profile) as dst: + dst.write(predictions_reshaped, 1) + dst.update_stats() - typer.echo("Progress: 100%") - typer.echo( - ( - "Predicting with classifier model completed, writing rasters to " - f"{output_raster_probability} and {output_raster_classified}." - ) - ) + ProgressLog.finish() # PREDICT WITH TRAINED ML MODEL @@ -2725,26 +2931,25 @@ def regressor_predict_cli( from eis_toolkit.prediction.machine_learning_general import load_model, prepare_data_for_ml, reshape_predictions from eis_toolkit.prediction.machine_learning_predict import predict_regressor - X, _, reference_profile, nodata_mask = prepare_data_for_ml(input_rasters) - - typer.echo("Progress: 30%") - - model = load_model(model_file) - predictions = predict_regressor(X, model) - predictions_reshaped = reshape_predictions( - predictions, reference_profile["height"], reference_profile["width"], nodata_mask - ) + with ProgressLog.reading_input_files(): + X, _, reference_profile, nodata_mask = prepare_data_for_ml(input_rasters) + model = load_model(model_file) - typer.echo("Progress: 80%") + with ProgressLog.running_algorithm(): + predictions = predict_regressor(X, model) + predictions_reshaped = reshape_predictions( + predictions, reference_profile["height"], reference_profile["width"], nodata_mask + ) out_profile = reference_profile.copy() out_profile.update({"count": 1, "dtype": np.float32}) - with rasterio.open(output_raster, "w", **out_profile) as dst: - dst.write(predictions_reshaped, 1) + with ProgressLog.saving_output_files(output_raster): + with rasterio.open(output_raster, "w", **out_profile) as dst: + dst.write(predictions_reshaped, 1) + dst.update_stats() - typer.echo("Progress: 100%") - typer.echo(f"Predicting with regressor model completed, writing raster to {output_raster}.") + ProgressLog.finish() # FUZZY OVERLAYS @@ -2759,22 +2964,22 @@ def and_overlay_cli( from eis_toolkit.prediction.fuzzy_overlay import and_overlay from eis_toolkit.utilities.file_io import read_and_stack_rasters - typer.echo("Progress: 10%") - - data, profiles = read_and_stack_rasters(input_rasters) - typer.echo("Progress: 25%") + with ProgressLog.reading_input_files(): + data, profiles = read_and_stack_rasters(input_rasters) - out_image = and_overlay(data) - typer.echo("Progress: 75%") + with ProgressLog.running_algorithm(): + out_image = and_overlay(data) out_profile = profiles[0] out_profile["count"] = 1 out_profile["nodata"] = -9999 - with rasterio.open(output_raster, "w", **out_profile) as dst: - dst.write(out_image, 1) - typer.echo("Progress: 100%") - typer.echo(f"'And' overlay completed, writing raster to {output_raster}.") + with ProgressLog.saving_output_files(output_raster): + with rasterio.open(output_raster, "w", **out_profile) as dst: + dst.write(out_image, 1) + dst.update_stats() + + ProgressLog.finish() # OR OVERLAY @@ -2787,22 +2992,22 @@ def or_overlay_cli( from eis_toolkit.prediction.fuzzy_overlay import or_overlay from eis_toolkit.utilities.file_io import read_and_stack_rasters - typer.echo("Progress: 10%") - - data, profiles = read_and_stack_rasters(input_rasters) - typer.echo("Progress: 25%") + with ProgressLog.reading_input_files(): + data, profiles = read_and_stack_rasters(input_rasters) - out_image = or_overlay(data) - typer.echo("Progress: 75%") + with ProgressLog.running_algorithm(): + out_image = or_overlay(data) out_profile = profiles[0] out_profile["count"] = 1 out_profile["nodata"] = -9999 - with rasterio.open(output_raster, "w", **out_profile) as dst: - dst.write(out_image, 1) - typer.echo("Progress: 100%") - typer.echo(f"'Or' overlay completed, writing raster to {output_raster}.") + with ProgressLog.saving_output_files(output_raster): + with rasterio.open(output_raster, "w", **out_profile) as dst: + dst.write(out_image, 1) + dst.update_stats() + + ProgressLog.finish() # PRODUCT OVERLAY @@ -2815,22 +3020,22 @@ def product_overlay_cli( from eis_toolkit.prediction.fuzzy_overlay import product_overlay from eis_toolkit.utilities.file_io import read_and_stack_rasters - typer.echo("Progress: 10%") - - data, profiles = read_and_stack_rasters(input_rasters) - typer.echo("Progress: 25%") + with ProgressLog.reading_input_files(): + data, profiles = read_and_stack_rasters(input_rasters) - out_image = product_overlay(data) - typer.echo("Progress: 75%") + with ProgressLog.running_algorithm(): + out_image = product_overlay(data) out_profile = profiles[0] out_profile["count"] = 1 out_profile["nodata"] = -9999 - with rasterio.open(output_raster, "w", **out_profile) as dst: - dst.write(out_image, 1) - typer.echo("Progress: 100%") - typer.echo(f"'Product' overlay completed, writing raster to {output_raster}.") + with ProgressLog.saving_output_files(output_raster): + with rasterio.open(output_raster, "w", **out_profile) as dst: + dst.write(out_image, 1) + dst.update_stats() + + ProgressLog.finish() # SUM OVERLAY @@ -2843,22 +3048,22 @@ def sum_overlay_cli( from eis_toolkit.prediction.fuzzy_overlay import sum_overlay from eis_toolkit.utilities.file_io import read_and_stack_rasters - typer.echo("Progress: 10%") - - data, profiles = read_and_stack_rasters(input_rasters) - typer.echo("Progress: 25%") + with ProgressLog.reading_input_files(): + data, profiles = read_and_stack_rasters(input_rasters) - out_image = sum_overlay(data) - typer.echo("Progress: 75%") + with ProgressLog.running_algorithm(): + out_image = sum_overlay(data) out_profile = profiles[0] out_profile["count"] = 1 out_profile["nodata"] = -9999 - with rasterio.open(output_raster, "w", **out_profile) as dst: - dst.write(out_image, 1) - typer.echo("Progress: 100%") - typer.echo(f"'Sum' overlay completed, writing raster to {output_raster}.") + with ProgressLog.saving_output_files(output_raster): + with rasterio.open(output_raster, "w", **out_profile) as dst: + dst.write(out_image, 1) + dst.update_stats() + + ProgressLog.finish() # GAMMA OVERLAY @@ -2868,26 +3073,199 @@ def gamma_overlay_cli(input_rasters: INPUT_FILES_ARGUMENT, output_raster: OUTPUT from eis_toolkit.prediction.fuzzy_overlay import gamma_overlay from eis_toolkit.utilities.file_io import read_and_stack_rasters - typer.echo("Progress: 10%") - - data, profiles = read_and_stack_rasters(input_rasters) - typer.echo("Progress: 25%") + with ProgressLog.reading_input_files(): + data, profiles = read_and_stack_rasters(input_rasters) - out_image = gamma_overlay(data, gamma) - typer.echo("Progress: 75%") + with ProgressLog.running_algorithm(): + out_image = gamma_overlay(data, gamma) out_profile = profiles[0] out_profile["count"] = 1 out_profile["nodata"] = -9999 - with rasterio.open(output_raster, "w", **out_profile) as dst: - dst.write(out_image, 1) - typer.echo("Progress: 100%") - typer.echo(f"'Gamma' overlay completed, writing raster to {output_raster}.") + with ProgressLog.saving_output_files(output_raster): + with rasterio.open(output_raster, "w", **out_profile) as dst: + dst.write(out_image, 1) + dst.update_stats() + + ProgressLog.finish() # WOFE -# TODO +@app.command() +def weights_of_evidence_calculate_weights_cli( + evidential_raster: INPUT_FILE_OPTION, + deposits: INPUT_FILE_OPTION, + output_results_table: OUTPUT_FILE_OPTION, + output_raster_dir: OUTPUT_DIR_OPTION, + raster_nodata: Optional[float] = None, + weights_type: Annotated[WeightsType, typer.Option(case_sensitive=False)] = WeightsType.unique, + studentized_contrast_threshold: float = 1, + arrays_to_generate: Annotated[Optional[List[str]], typer.Option()] = None, +): + """ + Calculate weights of spatial associations. + + Save path for resulting CSV is set using --output-results-table parameter. Output rasters are saved to directory + set with --output-raster-dir parameter. + + Parameter --studentized-contrast-threshold is used with 'categorical', 'ascending' and 'descending' weight types. + + Parameter --arrays-to-generate controls which columns in the weights dataframe are returned as arrays. All column + names in the produced weights_df are valid choices. The available columns for "unique" weights_type are "Class", + "Pixel count", "Deposit count", "W+", "S_W+", "W-", "S_W-", "Contrast", "S_Contrast", and "Studentized contrast". + For other weights types, additional available column names are "Generalized class", "Generalized W+", and + "Generalized S_W+". Defaults to ["Class", "W+", "S_W+] for "unique" weights_type and ["Class", "W+", "S_W+", + "Generalized W+", "Generalized S_W+"] for the cumulative weight types. + """ + from eis_toolkit.prediction.weights_of_evidence import weights_of_evidence_calculate_weights + + with ProgressLog.reading_input_files(): + evidential_raster = rasterio.open(evidential_raster) + + if deposits.suffix in (".tif", ".tiff", ".asc", ".img", ".vrt", ".grd"): + deposits = rasterio.open(deposits) + else: + deposits = gpd.read_file(deposits) + + if arrays_to_generate == []: + arrays_to_generate = None + + with ProgressLog.running_algorithm(): + df, arrays, raster_meta, nr_of_deposits, nr_of_pixels = weights_of_evidence_calculate_weights( + evidential_raster=evidential_raster, + deposits=deposits, + raster_nodata=raster_nodata, + weights_type=weights_type, + studentized_contrast_threshold=studentized_contrast_threshold, + arrays_to_generate=arrays_to_generate, + ) + + with ProgressLog.saving_output_files([output_raster_dir, output_results_table]): + df.to_csv(output_results_table) + + out_rasters_dict = {} + file_name = evidential_raster.name.split("/")[-1].split(".")[0] + raster_meta.pop("dtype") # Remove dtype from metadata to set it individually + + for key, array in arrays.items(): + # Set correct dtype for the array + if key in ["Class", "Pixel count", "Deposit count"]: + dtype = np.uint8 + else: + dtype = np.float32 + + array = nan_to_nodata(array, raster_meta["nodata"]) + output_raster_name = file_name + "_weights_" + weights_type + "_" + key + output_raster_path = output_raster_dir.joinpath(output_raster_name + ".tif") + with rasterio.open(output_raster_path, "w", dtype=dtype, **raster_meta) as dst: + dst.write(array, 1) + dst.update_stats() + out_rasters_dict[output_raster_name] = str(output_raster_path) + + ResultSender.send_multiple_rasters_dict_as_json(out_rasters_dict) + ProgressLog.finish() + + +@app.command() +def weights_of_evidence_calculate_responses_cli( + input_rasters_weights: INPUT_FILES_OPTION, + input_rasters_standard_deviations: INPUT_FILES_OPTION, + input_weights_table: INPUT_FILE_OPTION, + output_probabilities: OUTPUT_FILE_OPTION, + output_probabilities_std: OUTPUT_FILE_OPTION, + output_confidence_array: OUTPUT_FILE_OPTION, +): + """ + Calculate the posterior probabilities for the given generalized weight arrays. + + Parameter --input-rasters are the output arrays (rasters) of weights-of-evidence-calculate-weights-cli. + For each set of rasters, generalized weight and generalized standard deviation arrays are used and summed + together pixel-wise to calculate the posterior probabilities. If generalized arrays are not found, + the W+ and S_W+ arrays are used (so if outputs from unique weight calculations are used for this function). + """ + from eis_toolkit.prediction.weights_of_evidence import weights_of_evidence_calculate_responses + + with ProgressLog.reading_input_files(): + typer.echo(input_rasters_weights) + + dict_array = [] + raster_profile = None + + for raster_weights, raster_std in zip_longest( + input_rasters_weights, input_rasters_standard_deviations, fillvalue=None + ): + + if raster_weights is not None: + with rasterio.open(raster_weights) as src: + array_W = src.read(1) + array_W = nodata_to_nan(array_W, src.nodata) + + if raster_profile is None: + raster_profile = src.profile + + if raster_std is not None: + with rasterio.open(raster_std) as src: + array_S_W = src.read(1) + array_S_W = nodata_to_nan(array_S_W, src.nodata) + + dict_array.append({"W+": array_W, "S_W+": array_S_W}) + + weights_df = pd.read_csv(input_weights_table) + + with ProgressLog.running_algorithm(): + posterior_probabilities, posterior_probabilies_std, confidence_array = weights_of_evidence_calculate_responses( + output_arrays=dict_array, weights_df=weights_df + ) + + with ProgressLog.saving_output_files([output_probabilities, output_probabilities_std, output_confidence_array]): + posterior_probabilities = nan_to_nodata(posterior_probabilities, raster_profile["nodata"]) + with rasterio.open(output_probabilities, "w", **raster_profile) as dst: + dst.write(posterior_probabilities, 1) + dst.update_stats() + + posterior_probabilies_std = nan_to_nodata(posterior_probabilies_std, raster_profile["nodata"]) + with rasterio.open(output_probabilities_std, "w", **raster_profile) as dst: + dst.write(posterior_probabilies_std, 1) + dst.update_stats() + + confidence_array = nan_to_nodata(confidence_array, raster_profile["nodata"]) + with rasterio.open(output_confidence_array, "w", **raster_profile) as dst: + dst.write(confidence_array, 1) + dst.update_stats() + + ProgressLog.finish() + + +@app.command() +def agterberg_cheng_CI_test_cli( + input_posterior_probabilities: INPUT_FILE_OPTION, + input_posterior_probabilities_std: INPUT_FILE_OPTION, + input_weights_table: INPUT_FILE_OPTION, +): + """Perform the conditional independence test presented by Agterberg-Cheng (2002).""" + from eis_toolkit.prediction.weights_of_evidence import agterberg_cheng_CI_test + + with ProgressLog.reading_input_files(): + with rasterio.open(input_posterior_probabilities) as src: + posterior_probabilities = src.read(1) + posterior_probabilities = nodata_to_nan(posterior_probabilities, src.nodata) + + with rasterio.open(input_posterior_probabilities_std) as src: + posterior_probabilities_std = src.read(1) + posterior_probabilities_std = nodata_to_nan(posterior_probabilities_std, src.nodata) + + weights_df = pd.read_csv(input_weights_table) + + with ProgressLog.running_algorithm(): + _, _, _, _, summary = agterberg_cheng_CI_test( + posterior_probabilities=posterior_probabilities, + posterior_probabilities_std=posterior_probabilities_std, + weights_df=weights_df, + ) + + typer.echo(summary) + ProgressLog.finish() # --- TRANSFORMATIONS --- @@ -2904,20 +3282,19 @@ def alr_transform_cli( """Perform an additive logratio transformation on the data.""" from eis_toolkit.transformations.coda.alr import alr_transform - typer.echo("Progress: 10%") + with ProgressLog.reading_input_files(): + gdf = gpd.read_file(input_vector) + geometries = gdf["geometry"] + df = pd.DataFrame(gdf.drop(columns="geometry")) - gdf = gpd.read_file(input_vector) - geometries = gdf["geometry"] - df = pd.DataFrame(gdf.drop(columns="geometry")) - typer.echo("Progress: 25%") + with ProgressLog.running_algorithm(): + out_df = alr_transform(df=df, column=column, keep_denominator_column=keep_denominator_column) - out_df = alr_transform(df=df, column=column, keep_denominator_column=keep_denominator_column) - typer.echo("Progess 75%") + with ProgressLog.saving_output_files(output_vector): + out_gdf = gpd.GeoDataFrame(out_df, geometry=geometries) + out_gdf.to_file(output_vector) - out_gdf = gpd.GeoDataFrame(out_df, geometry=geometries) - out_gdf.to_file(output_vector) - typer.echo("Progress: 100%") - typer.echo(f"ALR transform completed, output saved to {output_vector}") + ProgressLog.finish() # CODA - INVERSE ALR TRANSFORM @@ -2931,20 +3308,19 @@ def inverse_alr_transform_cli( """Perform the inverse transformation for a set of ALR transformed data.""" from eis_toolkit.transformations.coda.alr import inverse_alr - typer.echo("Progress: 10%") + with ProgressLog.reading_input_files(): + gdf = gpd.read_file(input_vector) + geometries = gdf["geometry"] + df = pd.DataFrame(gdf.drop(columns="geometry")) - gdf = gpd.read_file(input_vector) - geometries = gdf["geometry"] - df = pd.DataFrame(gdf.drop(columns="geometry")) - typer.echo("Progress: 25%") + with ProgressLog.running_algorithm(): + out_df = inverse_alr(df=df, denominator_column=denominator_column, scale=scale) - out_df = inverse_alr(df=df, denominator_column=denominator_column, scale=scale) - typer.echo("Progess 75%") + with ProgressLog.saving_output_files(output_vector): + out_gdf = gpd.GeoDataFrame(out_df, geometry=geometries) + out_gdf.to_file(output_vector) - out_gdf = gpd.GeoDataFrame(out_df, geometry=geometries) - out_gdf.to_file(output_vector) - typer.echo("Progress: 100%") - typer.echo(f"Inverse ALR transform completed, output saved to {output_vector}") + ProgressLog.finish() # CODA - CLR TRANSFORM @@ -2953,20 +3329,19 @@ def clr_transform_cli(input_vector: INPUT_FILE_OPTION, output_vector: OUTPUT_FIL """Perform a centered logratio transformation on the data.""" from eis_toolkit.transformations.coda.clr import clr_transform - typer.echo("Progress: 10%") + with ProgressLog.reading_input_files(): + gdf = gpd.read_file(input_vector) + geometries = gdf["geometry"] + df = pd.DataFrame(gdf.drop(columns="geometry")) - gdf = gpd.read_file(input_vector) - geometries = gdf["geometry"] - df = pd.DataFrame(gdf.drop(columns="geometry")) - typer.echo("Progress: 25%") + with ProgressLog.running_algorithm(): + out_df = clr_transform(df=df) - out_df = clr_transform(df=df) - typer.echo("Progess 75%") + with ProgressLog.saving_output_files(output_vector): + out_gdf = gpd.GeoDataFrame(out_df, geometry=geometries) + out_gdf.to_file(output_vector) - out_gdf = gpd.GeoDataFrame(out_df, geometry=geometries) - out_gdf.to_file(output_vector) - typer.echo("Progress: 100%") - typer.echo(f"CLR transform completed, output saved to {output_vector}") + ProgressLog.finish() # CODA - INVERSE CLR TRANSFORM @@ -2980,20 +3355,19 @@ def inverse_clr_transform_cli( """Perform the inverse transformation for a set of CLR transformed data.""" from eis_toolkit.transformations.coda.clr import inverse_clr - typer.echo("Progress: 10%") + with ProgressLog.reading_input_files(): + gdf = gpd.read_file(input_vector) + geometries = gdf["geometry"] + df = pd.DataFrame(gdf.drop(columns="geometry")) - gdf = gpd.read_file(input_vector) - geometries = gdf["geometry"] - df = pd.DataFrame(gdf.drop(columns="geometry")) - typer.echo("Progress: 25%") + with ProgressLog.running_algorithm(): + out_df = inverse_clr(df=df, colnames=colnames, scale=scale) - out_df = inverse_clr(df=df, colnames=colnames, scale=scale) - typer.echo("Progess 75%") + with ProgressLog.saving_output_files(output_vector): + out_gdf = gpd.GeoDataFrame(out_df, geometry=geometries) + out_gdf.to_file(output_vector) - out_gdf = gpd.GeoDataFrame(out_df, geometry=geometries) - out_gdf.to_file(output_vector) - typer.echo("Progress: 100%") - typer.echo(f"Inverse CLR transform completed, output saved to {output_vector}") + ProgressLog.finish() # CODA - SINGLE ILR TRANSFORM @@ -3007,22 +3381,21 @@ def single_ilr_transform_cli( """Perform a single isometric logratio transformation on the provided subcompositions.""" from eis_toolkit.transformations.coda.ilr import single_ilr_transform - typer.echo("Progress: 10%") + with ProgressLog.reading_input_files(): + gdf = gpd.read_file(input_vector) + geometries = gdf["geometry"] + df = pd.DataFrame(gdf.drop(columns="geometry")) - gdf = gpd.read_file(input_vector) - geometries = gdf["geometry"] - df = pd.DataFrame(gdf.drop(columns="geometry")) - typer.echo("Progress: 25%") + with ProgressLog.running_algorithm(): + out_series = single_ilr_transform(df=df, subcomposition_1=subcomposition_1, subcomposition_2=subcomposition_2) - out_series = single_ilr_transform(df=df, subcomposition_1=subcomposition_1, subcomposition_2=subcomposition_2) - typer.echo("Progess 75%") + with ProgressLog.saving_output_files(output_vector): + # NOTE: Output of pairwise_logratio might be changed to DF in the future, to automatically do the following + df["single_ilr"] = out_series + out_gdf = gpd.GeoDataFrame(df, geometry=geometries) + out_gdf.to_file(output_vector) - # NOTE: Output of pairwise_logratio might be changed to DF in the future, to automatically do the following - df["single_ilr"] = out_series - out_gdf = gpd.GeoDataFrame(df, geometry=geometries) - out_gdf.to_file(output_vector) - typer.echo("Progress: 100%") - typer.echo(f"Single ILR transform completed, output saved to {output_vector}") + ProgressLog.finish() # CODA - PAIRWISE LOGRATIO TRANSFORM @@ -3036,22 +3409,21 @@ def pairwise_logratio_cli( """Perform a pairwise logratio transformation on the given columns.""" from eis_toolkit.transformations.coda.pairwise import pairwise_logratio - typer.echo("Progress: 10%") + with ProgressLog.reading_input_files(): + gdf = gpd.read_file(input_vector) + geometries = gdf["geometry"] + df = pd.DataFrame(gdf.drop(columns="geometry")) - gdf = gpd.read_file(input_vector) - geometries = gdf["geometry"] - df = pd.DataFrame(gdf.drop(columns="geometry")) - typer.echo("Progress: 25%") + with ProgressLog.running_algorithm(): + out_series = pairwise_logratio(df=df, numerator_column=numerator_column, denominator_column=denominator_column) - out_series = pairwise_logratio(df=df, numerator_column=numerator_column, denominator_column=denominator_column) - typer.echo("Progess 75%") + with ProgressLog.saving_output_files(output_vector): + # NOTE: Output of pairwise_logratio might be changed to DF in the future, to automatically do the following + df["pairwise_logratio"] = out_series + out_gdf = gpd.GeoDataFrame(df, geometry=geometries) + out_gdf.to_file(output_vector) - # NOTE: Output of pairwise_logratio might be changed to DF in the future, to automatically do the following - df["pairwise_logratio"] = out_series - out_gdf = gpd.GeoDataFrame(df, geometry=geometries) - out_gdf.to_file(output_vector) - typer.echo("Progress: 100%") - typer.echo(f"Pairwise logratio transform completed, output saved to {output_vector}") + ProgressLog.finish() # CODA - SINGLE PLR TRANSFORM @@ -3064,22 +3436,21 @@ def single_plr_transform_cli( """Perform a pivot logratio transformation on the selected column.""" from eis_toolkit.transformations.coda.plr import single_plr_transform - typer.echo("Progress: 10%") + with ProgressLog.reading_input_files(): + gdf = gpd.read_file(input_vector) + geometries = gdf["geometry"] + df = pd.DataFrame(gdf.drop(columns="geometry")) - gdf = gpd.read_file(input_vector) - geometries = gdf["geometry"] - df = pd.DataFrame(gdf.drop(columns="geometry")) - typer.echo("Progress: 25%") + with ProgressLog.running_algorithm(): + out_series = single_plr_transform(df=df, column=column) - out_series = single_plr_transform(df=df, column=column) - typer.echo("Progess 75%") + with ProgressLog.saving_output_files(output_vector): + # NOTE: Output of single_plr_transform might be changed to DF in the future, to automatically do the following + df["single_plr"] = out_series + out_gdf = gpd.GeoDataFrame(df, geometry=geometries) + out_gdf.to_file(output_vector) - # NOTE: Output of single_plr_transform might be changed to DF in the future, to automatically do the following - df["single_plr"] = out_series - out_gdf = gpd.GeoDataFrame(df, geometry=geometries) - out_gdf.to_file(output_vector) - typer.echo("Progress: 100%") - typer.echo(f"Single PLR transform completed, output saved to {output_vector}") + ProgressLog.finish() # CODA - PLR TRANSFORM @@ -3088,20 +3459,19 @@ def plr_transform_cli(input_vector: INPUT_FILE_OPTION, output_vector: OUTPUT_FIL """Perform a pivot logratio transformation on the dataframe, returning the full set of transforms.""" from eis_toolkit.transformations.coda.plr import plr_transform - typer.echo("Progress: 10%") + with ProgressLog.reading_input_files(): + gdf = gpd.read_file(input_vector) + geometries = gdf["geometry"] + df = pd.DataFrame(gdf.drop(columns="geometry")) - gdf = gpd.read_file(input_vector) - geometries = gdf["geometry"] - df = pd.DataFrame(gdf.drop(columns="geometry")) - typer.echo("Progress: 25%") + with ProgressLog.running_algorithm(): + out_df = plr_transform(df=df) - out_df = plr_transform(df=df) - typer.echo("Progess 75%") + with ProgressLog.saving_output_files(output_vector): + out_gdf = gpd.GeoDataFrame(out_df, geometry=geometries) + out_gdf.to_file(output_vector) - out_gdf = gpd.GeoDataFrame(out_df, geometry=geometries) - out_gdf.to_file(output_vector) - typer.echo("Progress: 100%") - typer.echo(f"PLR transform completed, output saved to {output_vector}") + ProgressLog.finish() # BINARIZE @@ -3119,18 +3489,19 @@ def binarize_cli( """ from eis_toolkit.transformations.binarize import binarize - typer.echo("Progress: 10%") + with ProgressLog.reading_input_files(): + raster = rasterio.open(input_raster) - with rasterio.open(input_raster) as raster: - typer.echo("Progress: 25%") + with ProgressLog.running_algorithm(): out_image, out_meta, _ = binarize(raster=raster, thresholds=[threshold]) - typer.echo("Progress: 70%") + raster.close() - with rasterio.open(output_raster, "w", **out_meta) as dst: - dst.write(out_image) - typer.echo("Progress: 100%") + with ProgressLog.saving_output_files(output_raster): + with rasterio.open(output_raster, "w", **out_meta) as dst: + dst.write(out_image) + dst.update_stats() - typer.echo(f"Binarizing completed, writing raster to {output_raster}.") + ProgressLog.finish() # CLIP TRANSFORM @@ -3149,18 +3520,19 @@ def clip_transform_cli( """ from eis_toolkit.transformations.clip import clip_transform - typer.echo("Progress: 10%") + with ProgressLog.reading_input_files(): + raster = rasterio.open(input_raster) - with rasterio.open(input_raster) as raster: - typer.echo("Progress: 25%") + with ProgressLog.running_algorithm(): out_image, out_meta, _ = clip_transform(raster=raster, limits=[(limit_lower, limit_higher)]) - typer.echo("Progress: 70%") + raster.close() - with rasterio.open(output_raster, "w", **out_meta) as dst: - dst.write(out_image) - typer.echo("Progress: 100%") + with ProgressLog.saving_output_files(output_raster): + with rasterio.open(output_raster, "w", **out_meta) as dst: + dst.write(out_image) + dst.update_stats() - typer.echo(f"Clip transform completed, writing raster to {output_raster}.") + ProgressLog.finish() # Z-SCORE NORMALIZATION @@ -3176,18 +3548,19 @@ def z_score_normalization_cli( """ from eis_toolkit.transformations.linear import z_score_normalization - typer.echo("Progress: 10%") + with ProgressLog.reading_input_files(): + raster = rasterio.open(input_raster) - with rasterio.open(input_raster) as raster: - typer.echo("Progress: 25%") + with ProgressLog.running_algorithm(): out_image, out_meta, _ = z_score_normalization(raster=raster) - typer.echo("Progress: 70%") + raster.close() - with rasterio.open(output_raster, "w", **out_meta) as dst: - dst.write(out_image) - typer.echo("Progress: 100%") + with ProgressLog.saving_output_files(output_raster): + with rasterio.open(output_raster, "w", **out_meta) as dst: + dst.write(out_image) + dst.update_stats() - typer.echo(f"Z-score normalization completed, writing raster to {output_raster}.") + ProgressLog.finish() # MIX_MAX SCALING @@ -3205,18 +3578,19 @@ def min_max_scaling_cli( """ from eis_toolkit.transformations.linear import min_max_scaling - typer.echo("Progress: 10%") + with ProgressLog.reading_input_files(): + raster = rasterio.open(input_raster) - with rasterio.open(input_raster) as raster: - typer.echo("Progress: 25%") + with ProgressLog.running_algorithm(): out_image, out_meta, _ = min_max_scaling(raster=raster, new_range=[(min, max)]) - typer.echo("Progress: 70%") + raster.close() - with rasterio.open(output_raster, "w", **out_meta) as dst: - dst.write(out_image) - typer.echo("Progress: 100%") + with ProgressLog.saving_output_files(output_raster): + with rasterio.open(output_raster, "w", **out_meta) as dst: + dst.write(out_image) + dst.update_stats() - typer.echo(f"Min-max scaling completed, writing raster to {output_raster}.") + ProgressLog.finish() # LOGARITHMIC @@ -3234,18 +3608,19 @@ def log_transform_cli( """ from eis_toolkit.transformations.logarithmic import log_transform - typer.echo("Progress: 10%") + with ProgressLog.reading_input_files(): + raster = rasterio.open(input_raster) - with rasterio.open(input_raster) as raster: - typer.echo("Progress: 25%") + with ProgressLog.running_algorithm(): out_image, out_meta, _ = log_transform(raster=raster, log_transform=[get_enum_values(log_type)]) - typer.echo("Progress: 70%") + raster.close() - with rasterio.open(output_raster, "w", **out_meta) as dst: - dst.write(out_image) - typer.echo("Progress: 100%") + with ProgressLog.saving_output_files(output_raster): + with rasterio.open(output_raster, "w", **out_meta) as dst: + dst.write(out_image) + dst.update_stats() - typer.echo(f"Logarithm transform completed, writing raster to {output_raster}.") + ProgressLog.finish() # SIGMOID @@ -3265,20 +3640,21 @@ def sigmoid_transform_cli( """ from eis_toolkit.transformations.sigmoid import sigmoid_transform - typer.echo("Progress: 10%") + with ProgressLog.reading_input_files(): + raster = rasterio.open(input_raster) - with rasterio.open(input_raster) as raster: - typer.echo("Progress: 25%") + with ProgressLog.running_algorithm(): out_image, out_meta, _ = sigmoid_transform( raster=raster, bounds=[(limit_lower, limit_upper)], slope=[slope], center=center ) - typer.echo("Progress: 70%") + raster.close() - with rasterio.open(output_raster, "w", **out_meta) as dst: - dst.write(out_image) - typer.echo("Progress: 100%") + with ProgressLog.saving_output_files(output_raster): + with rasterio.open(output_raster, "w", **out_meta) as dst: + dst.write(out_image) + dst.update_stats() - typer.echo(f"Sigmoid transform completed, writing raster to {output_raster}.") + ProgressLog.finish() # WINSORIZE @@ -3308,20 +3684,21 @@ def winsorize_transform_cli( """ from eis_toolkit.transformations.winsorize import winsorize - typer.echo("Progress: 10%") + with ProgressLog.reading_input_files(): + raster = rasterio.open(input_raster) - with rasterio.open(input_raster) as raster: - typer.echo("Progress: 25%") + with ProgressLog.running_algorithm(): out_image, out_meta, _ = winsorize( raster=raster, percentiles=[(percentile_lower, percentile_higher)], inside=inside ) - typer.echo("Progress: 70%") + raster.close() - with rasterio.open(output_raster, "w", **out_meta) as dst: - dst.write(out_image) - typer.echo("Progress: 100%") + with ProgressLog.saving_output_files(output_raster): + with rasterio.open(output_raster, "w", **out_meta) as dst: + dst.write(out_image) + dst.update_stats() - typer.echo(f"Winsorize transform completed, writing raster to {output_raster}.") + ProgressLog.finish() # ---EVALUATION --- @@ -3337,19 +3714,14 @@ def summarize_probability_metrics_cli(true_labels: INPUT_FILE_OPTION, probabilit from eis_toolkit.evaluation.classification_probability_evaluation import summarize_probability_metrics from eis_toolkit.prediction.machine_learning_general import read_data_for_evaluation - typer.echo("Progress: 10%") + with ProgressLog.reading_input_files(): + (y_prob, y_true), _, _ = read_data_for_evaluation([probabilities, true_labels]) - (y_prob, y_true), _, _ = read_data_for_evaluation([probabilities, true_labels]) - typer.echo("Progress: 25%") + with ProgressLog.running_algorithm(): + results_dict = summarize_probability_metrics(y_true=y_true, y_prob=y_prob, decimals=3) - results_dict = summarize_probability_metrics(y_true=y_true, y_prob=y_prob, decimals=3) - - typer.echo("Progress: 75%") - - typer.echo("Progress: 100% \n") - - typer.echo(f"Results: {str(results_dict)}") - typer.echo("\nGenerating probability metrics summary completed.") + ResultSender.send_dict_as_json(results_dict) + ProgressLog.finish() @app.command() @@ -3363,18 +3735,14 @@ def summarize_label_metrics_binary_cli(true_labels: INPUT_FILE_OPTION, predictio from eis_toolkit.evaluation.classification_label_evaluation import summarize_label_metrics_binary from eis_toolkit.prediction.machine_learning_general import read_data_for_evaluation - typer.echo("Progress: 10%") + with ProgressLog.reading_input_files(): + (y_pred, y_true), _, _ = read_data_for_evaluation([predictions, true_labels]) - (y_pred, y_true), _, _ = read_data_for_evaluation([predictions, true_labels]) - typer.echo("Progress: 25%") + with ProgressLog.running_algorithm(): + results_dict = summarize_label_metrics_binary(y_true=y_true, y_pred=y_pred, decimals=3) - results_dict = summarize_label_metrics_binary(y_true=y_true, y_pred=y_pred, decimals=3) - typer.echo("Progress: 75%") - - typer.echo("Progress: 100% \n") - - typer.echo(f"Results: {str(results_dict)}") - typer.echo("\nGenerating prediction label metrics summary completed.") + ResultSender.send_dict_as_json(results_dict) + ProgressLog.finish() @app.command() @@ -3396,23 +3764,21 @@ def plot_roc_curve_cli( from eis_toolkit.evaluation.classification_probability_evaluation import plot_roc_curve from eis_toolkit.prediction.machine_learning_general import read_data_for_evaluation - typer.echo("Progress: 10%") + with ProgressLog.reading_input_files(): + (y_prob, y_true), _, _ = read_data_for_evaluation([probabilities, true_labels]) + + with ProgressLog.running_algorithm(): + _ = plot_roc_curve(y_true=y_true, y_prob=y_prob) - (y_prob, y_true), _, _ = read_data_for_evaluation([probabilities, true_labels]) - typer.echo("Progress: 25%") + if output_file is not None: + with ProgressLog.saving_output_files(output_file): + dpi = "figure" if save_dpi is None else save_dpi + plt.savefig(output_file, dpi=dpi) - _ = plot_roc_curve(y_true=y_true, y_prob=y_prob) - typer.echo("Progress: 75%") if show_plot: plt.show() - if output_file is not None: - dpi = "figure" if save_dpi is None else save_dpi - plt.savefig(output_file, dpi=dpi) - echo_str_end = f", output figure saved to {output_file}." - typer.echo("Progress: 100% \n") - - typer.echo("ROC curve plot completed" + echo_str_end) + ProgressLog.finish() @app.command() @@ -3436,23 +3802,21 @@ def plot_det_curve_cli( from eis_toolkit.evaluation.classification_probability_evaluation import plot_det_curve from eis_toolkit.prediction.machine_learning_general import read_data_for_evaluation - typer.echo("Progress: 10%") + with ProgressLog.reading_input_files(): + (y_prob, y_true), _, _ = read_data_for_evaluation([probabilities, true_labels]) - (y_prob, y_true), _, _ = read_data_for_evaluation([probabilities, true_labels]) - typer.echo("Progress: 25%") + with ProgressLog.running_algorithm(): + _ = plot_det_curve(y_true=y_true, y_prob=y_prob) + + if output_file is not None: + with ProgressLog.saving_output_files(output_file): + dpi = "figure" if save_dpi is None else save_dpi + plt.savefig(output_file, dpi=dpi) - _ = plot_det_curve(y_true=y_true, y_prob=y_prob) - typer.echo("Progress: 75%") if show_plot: plt.show() - if output_file is not None: - dpi = "figure" if save_dpi is None else save_dpi - plt.savefig(output_file, dpi=dpi) - echo_str_end = f", output figure saved to {output_file}." - typer.echo("Progress: 100% \n") - - typer.echo("DET curve plot completed" + echo_str_end) + ProgressLog.finish() @app.command() @@ -3475,23 +3839,21 @@ def plot_precision_recall_curve_cli( from eis_toolkit.evaluation.classification_probability_evaluation import plot_precision_recall_curve from eis_toolkit.prediction.machine_learning_general import read_data_for_evaluation - typer.echo("Progress: 10%") + with ProgressLog.reading_input_files(): + (y_prob, y_true), _, _ = read_data_for_evaluation([probabilities, true_labels]) + + with ProgressLog.running_algorithm(): + _ = plot_precision_recall_curve(y_true=y_true, y_prob=y_prob) - (y_prob, y_true), _, _ = read_data_for_evaluation([probabilities, true_labels]) - typer.echo("Progress: 25%") + if output_file is not None: + with ProgressLog.saving_output_files(output_file): + dpi = "figure" if save_dpi is None else save_dpi + plt.savefig(output_file, dpi=dpi) - _ = plot_precision_recall_curve(y_true=y_true, y_prob=y_prob) - typer.echo("Progress: 75%") if show_plot: plt.show() - if output_file is not None: - dpi = "figure" if save_dpi is None else save_dpi - plt.savefig(output_file, dpi=dpi) - echo_str_end = f", output figure saved to {output_file}." - typer.echo("Progress: 100% \n") - - typer.echo("Precision-Recall curve plot completed" + echo_str_end) + ProgressLog.finish() @app.command() @@ -3514,23 +3876,21 @@ def plot_calibration_curve_cli( from eis_toolkit.evaluation.classification_probability_evaluation import plot_calibration_curve from eis_toolkit.prediction.machine_learning_general import read_data_for_evaluation - typer.echo("Progress: 10%") + with ProgressLog.reading_input_files(): + (y_prob, y_true), _, _ = read_data_for_evaluation([probabilities, true_labels]) - (y_prob, y_true), _, _ = read_data_for_evaluation([probabilities, true_labels]) - typer.echo("Progress: 25%") + with ProgressLog.running_algorithm(): + _ = plot_calibration_curve(y_true=y_true, y_prob=y_prob, n_bins=n_bins) + + if output_file is not None: + with ProgressLog.saving_output_files(output_file): + dpi = "figure" if save_dpi is None else save_dpi + plt.savefig(output_file, dpi=dpi) - _ = plot_calibration_curve(y_true=y_true, y_prob=y_prob, n_bins=n_bins) - typer.echo("Progress: 75%") if show_plot: plt.show() - if output_file is not None: - dpi = "figure" if save_dpi is None else save_dpi - plt.savefig(output_file, dpi=dpi) - echo_str_end = f", output figure saved to {output_file}." - typer.echo("Progress: 100% \n") - - typer.echo("Calibration curve plot completed" + echo_str_end) + ProgressLog.finish() @app.command() @@ -3548,24 +3908,22 @@ def plot_confusion_matrix_cli( from eis_toolkit.evaluation.plot_confusion_matrix import plot_confusion_matrix from eis_toolkit.prediction.machine_learning_general import read_data_for_evaluation - typer.echo("Progress: 10%") + with ProgressLog.reading_input_files(): + (y_pred, y_true), _, _ = read_data_for_evaluation([predictions, true_labels]) - (y_pred, y_true), _, _ = read_data_for_evaluation([predictions, true_labels]) - typer.echo("Progress: 25%") + with ProgressLog.running_algorithm(): + matrix = confusion_matrix(y_true, y_pred) + _ = plot_confusion_matrix(confusion_matrix=matrix) + + if output_file is not None: + with ProgressLog.saving_output_files(output_file): + dpi = "figure" if save_dpi is None else save_dpi + plt.savefig(output_file, dpi=dpi) - matrix = confusion_matrix(y_true, y_pred) - _ = plot_confusion_matrix(confusion_matrix=matrix) - typer.echo("Progress: 75%") if show_plot: plt.show() - if output_file is not None: - dpi = "figure" if save_dpi is None else save_dpi - plt.savefig(output_file, dpi=dpi) - echo_str_end = f", output figure saved to {output_file}." - typer.echo("Progress: 100% \n") - - typer.echo("Confusion matrix plot completed" + echo_str_end) + ProgressLog.finish() @app.command() @@ -3579,16 +3937,14 @@ def score_predictions_cli( from eis_toolkit.evaluation.scoring import score_predictions from eis_toolkit.prediction.machine_learning_general import read_data_for_evaluation - typer.echo("Progress: 10%") - - (y_pred, y_true), _, _ = read_data_for_evaluation([predictions, true_labels]) - typer.echo("Progress: 25%") + with ProgressLog.reading_input_files(): + (y_pred, y_true), _, _ = read_data_for_evaluation([predictions, true_labels]) - outputs = score_predictions(y_true, y_pred, metrics, decimals) - typer.echo("Progress: 100% \n") + with ProgressLog.running_algorithm(): + outputs = score_predictions(y_true, y_pred, metrics, decimals) - typer.echo(f"Results: {str(outputs)}") - typer.echo("\nScoring predictions completed.") + ResultSender.send_dict_as_json(outputs) + ProgressLog.finish() # --- UTILITIES --- @@ -3598,21 +3954,22 @@ def split_raster_bands_cli(input_raster: INPUT_FILE_OPTION, output_dir: OUTPUT_D from eis_toolkit.utilities.file_io import get_output_paths_from_common_name from eis_toolkit.utilities.raster import split_raster_bands - typer.echo("Progress: 10%") + with ProgressLog.reading_input_files(): + raster = rasterio.open(input_raster) - with rasterio.open(input_raster) as raster: - typer.echo("Progress: 25%") + with ProgressLog.running_algorithm(): output_singleband_rasters = split_raster_bands(raster) - typer.echo("Progress: 70%") + raster.close() - name = os.path.splitext(os.path.basename(input_raster))[0] - output_paths = get_output_paths_from_common_name(output_singleband_rasters, output_dir, f"{name}_split", ".tif") - for output_path, (out_image, out_profile) in zip(output_paths, output_singleband_rasters): - with rasterio.open(output_path, "w", **out_profile) as dst: - dst.write(out_image, 1) - typer.echo("Progress: 100%") + with ProgressLog.saving_output_files(output_dir): + name = os.path.splitext(os.path.basename(input_raster))[0] + output_paths = get_output_paths_from_common_name(output_singleband_rasters, output_dir, f"{name}_split", ".tif") + for output_path, (out_image, out_profile) in zip(output_paths, output_singleband_rasters): + with rasterio.open(output_path, "w", **out_profile) as dst: + dst.write(out_image, 1) + dst.update_stats() - typer.echo(f"Splitting raster completed, writing rasters to directory {output_dir}.") + ProgressLog.finish() @app.command() @@ -3620,20 +3977,19 @@ def combine_raster_bands_cli(input_rasters: INPUT_FILES_ARGUMENT, output_raster: """Combine multiple rasters into one multiband raster.""" from eis_toolkit.utilities.raster import combine_raster_bands - typer.echo("Progress: 10%") - - rasters = [rasterio.open(raster) for raster in input_rasters] # Open all rasters to be combined - typer.echo("Progress: 25%") + with ProgressLog.reading_input_files(): + rasters = [rasterio.open(raster) for raster in input_rasters] # Open all rasters to be combined - out_image, out_meta = combine_raster_bands(rasters) + with ProgressLog.running_algorithm(): + out_image, out_meta = combine_raster_bands(rasters) [raster.close() for raster in rasters] - typer.echo("Progress: 70%") - with rasterio.open(output_raster, "w", **out_meta) as dst: - dst.write(out_image) - typer.echo("Progress: 100%") + with ProgressLog.saving_output_files(output_raster): + with rasterio.open(output_raster, "w", **out_meta) as dst: + dst.write(out_image) + dst.update_stats() - typer.echo(f"Combining rasters completed, writing raster to {output_raster}.") + ProgressLog.finish() @app.command() @@ -3644,22 +4000,21 @@ def unify_raster_nodata_cli( from eis_toolkit.utilities.file_io import get_output_paths_from_inputs from eis_toolkit.utilities.nodata import unify_raster_nodata - typer.echo("Progress: 10%") + with ProgressLog.reading_input_files(): + rasters = [rasterio.open(raster) for raster in input_rasters] # Open all rasters to be unified - rasters = [rasterio.open(raster) for raster in input_rasters] # Open all rasters to be unified - typer.echo("Progress: 25%") - - unified = unify_raster_nodata(rasters, new_nodata) + with ProgressLog.running_algorithm(): + unified = unify_raster_nodata(rasters, new_nodata) [raster.close() for raster in rasters] - typer.echo("Progress: 70%") - output_paths = get_output_paths_from_inputs(input_rasters, output_dir, "nodata_unified", ".tif") - for output_path, (out_image, out_profile) in zip(output_paths, unified): - with rasterio.open(output_path, "w", **out_profile) as dst: - dst.write(out_image) - typer.echo("Progress: 100%") + with ProgressLog.saving_output_files(output_dir): + output_paths = get_output_paths_from_inputs(input_rasters, output_dir, "nodata_unified", ".tif") + for output_path, (out_image, out_profile) in zip(output_paths, unified): + with rasterio.open(output_path, "w", **out_profile) as dst: + dst.write(out_image) + dst.update_stats() - typer.echo(f"Unifying nodata completed, writing rasters to directory {output_dir}.") + ProgressLog.finish() @app.command() @@ -3672,18 +4027,45 @@ def convert_raster_nodata_cli( """Convert existing nodata values with a new nodata value for a raster.""" from eis_toolkit.utilities.nodata import convert_raster_nodata - typer.echo("Progress: 10%") + with ProgressLog.reading_input_files(): + raster = rasterio.open(input_raster) - with rasterio.open(input_raster) as raster: - typer.echo("Progress: 25%") + with ProgressLog.running_algorithm(): out_image, out_meta = convert_raster_nodata(raster, old_nodata, new_nodata) - typer.echo("Progress: 70%") + raster.close() - with rasterio.open(output_raster, "w", **out_meta) as dst: - dst.write(out_image) - typer.echo("Progress: 100%") + with ProgressLog.saving_output_files(output_raster): + with rasterio.open(output_raster, "w", **out_meta) as dst: + dst.write(out_image) + dst.update_stats() - typer.echo(f"Converting nodata completed, writing raster to {output_raster}.") + ProgressLog.finish() + + +@app.command() +def replace_with_nodata_cli( + input_raster: INPUT_FILE_OPTION, + output_raster: OUTPUT_FILE_OPTION, + target_value: Annotated[float, typer.Option()], + nodata_value: float = None, + replace_condition: Annotated[ReplaceCondition, typer.Option(case_sensitive=False)] = ReplaceCondition.equal, +): + """Replace raster pixel values with nodata.""" + from eis_toolkit.utilities.nodata import replace_with_nodata + + with ProgressLog.reading_input_files(): + raster = rasterio.open(input_raster) + + with ProgressLog.running_algorithm(): + out_image, out_meta = replace_with_nodata(raster, target_value, nodata_value, replace_condition) + raster.close() + + with ProgressLog.saving_output_files(output_raster): + with rasterio.open(output_raster, "w", **out_meta) as dst: + dst.write(out_image) + dst.update_stats() + + ProgressLog.finish() @app.command() @@ -3693,19 +4075,20 @@ def set_raster_nodata_cli( """Set new nodata value for raster profile.""" from eis_toolkit.utilities.nodata import set_raster_nodata - typer.echo("Progress: 10%") - - with rasterio.open(input_raster) as raster: + with ProgressLog.reading_input_files(): + raster = rasterio.open(input_raster) out_image = raster.read() - typer.echo("Progress: 25%") + + with ProgressLog.running_algorithm(): out_meta = set_raster_nodata(raster.meta, new_nodata) - typer.echo("Progress: 70%") + raster.close() - with rasterio.open(output_raster, "w", **out_meta) as dst: - dst.write(out_image) - typer.echo("Progress: 100%") + with ProgressLog.saving_output_files(output_raster): + with rasterio.open(output_raster, "w", **out_meta) as dst: + dst.write(out_image) + dst.update_stats() - typer.echo(f"Setting nodata completed, writing raster to {output_raster}.") + ProgressLog.finish() # if __name__ == "__main__": diff --git a/eis_toolkit/exploratory_analyses/descriptive_statistics.py b/eis_toolkit/exploratory_analyses/descriptive_statistics.py index 3bc064aa..e6db1283 100644 --- a/eis_toolkit/exploratory_analyses/descriptive_statistics.py +++ b/eis_toolkit/exploratory_analyses/descriptive_statistics.py @@ -1,16 +1,20 @@ +from numbers import Number + import geopandas as gpd import numpy as np import pandas as pd import rasterio from beartype import beartype -from beartype.typing import Union +from beartype.typing import Dict, Union from statsmodels.stats import stattools from statsmodels.stats.weightstats import DescrStatsW -from eis_toolkit.exceptions import InvalidColumnException +from eis_toolkit.exceptions import InvalidColumnException, InvalidRasterBandException -def _descriptive_statistics(data: Union[rasterio.io.DatasetReader, pd.DataFrame, gpd.GeoDataFrame]) -> dict: +def _descriptive_statistics( + data: Union[rasterio.io.DatasetReader, pd.DataFrame, gpd.GeoDataFrame] +) -> Dict[str, Number]: statistics = DescrStatsW(data) min = np.min(data) max = np.max(data) @@ -38,14 +42,25 @@ def _descriptive_statistics(data: Union[rasterio.io.DatasetReader, pd.DataFrame, @beartype -def descriptive_statistics_dataframe(input_data: Union[pd.DataFrame, gpd.GeoDataFrame], column: str) -> dict: - """Generate descriptive statistics from vector data. +def descriptive_statistics_dataframe( + input_data: Union[pd.DataFrame, gpd.GeoDataFrame], column: str +) -> Dict[str, Number]: + """Compute descriptive statistics from vector data. - Generates min, max, mean, quantiles(25%, 50% and 75%), standard deviation, relative standard deviation and skewness. + Computes the following statistics: + - min + - max + - mean + - quantiles 25% + - quantile 50% (median) + - quantile 75% + - standard deviation + - relative standard deviation + - skewness Args: - input_data: Data to generate descriptive statistics from. - column: Specify the column to generate descriptive statistics from. + input_data: Input vector data. + column: Column in vector data to compute descriptive statistics from. Returns: The descriptive statistics in previously described order. @@ -58,19 +73,33 @@ def descriptive_statistics_dataframe(input_data: Union[pd.DataFrame, gpd.GeoData @beartype -def descriptive_statistics_raster(input_data: rasterio.io.DatasetReader) -> dict: - """Generate descriptive statistics from raster data. +def descriptive_statistics_raster(input_data: rasterio.io.DatasetReader, band: int = 1) -> Dict[str, Number]: + """Compute descriptive statistics from raster data. + + Computes the following statistics: + - min + - max + - mean + - quantiles 25% + - quantile 50% (median) + - quantile 75% + - standard deviation + - relative standard deviation + - skewness - Generates min, max, mean, quantiles(25%, 50% and 75%), standard deviation, relative standard deviation and skewness. Nodata values are removed from the data before the statistics are computed. Args: - input_data: Data to generate descriptive statistics from. + input_data: Input raster data. + band: Raster band to compute descriptive statistics from. Returns: The descriptive statistics in previously described order. """ - data = input_data.read().flatten() + if band not in range(1, input_data.count + 1): + raise InvalidRasterBandException(f"Input raster does not contain the selected band: {band}.") + + data = input_data.read(band) nodata_value = input_data.nodata data = data[data != nodata_value] statistics = _descriptive_statistics(data) diff --git a/eis_toolkit/exploratory_analyses/feature_importance.py b/eis_toolkit/exploratory_analyses/feature_importance.py index e0f7baae..18197ffd 100644 --- a/eis_toolkit/exploratory_analyses/feature_importance.py +++ b/eis_toolkit/exploratory_analyses/feature_importance.py @@ -5,28 +5,32 @@ from beartype.typing import Optional, Sequence from sklearn.inspection import permutation_importance -from eis_toolkit.exceptions import InvalidDatasetException, InvalidParameterValueException +from eis_toolkit.exceptions import ( + InvalidDatasetException, + InvalidParameterValueException, + NonMatchingParameterLengthsException, +) @beartype def evaluate_feature_importance( model: sklearn.base.BaseEstimator, - x_test: np.ndarray, - y_test: np.ndarray, + X: np.ndarray, + y: np.ndarray, feature_names: Sequence[str], - n_repeats: int = 50, + n_repeats: int = 10, random_state: Optional[int] = None, ) -> tuple[pd.DataFrame, dict]: """ - Evaluate the feature importance of a sklearn classifier or regressor. + Evaluate the feature importance of a Sklearn classifier or regressor. Args: model: A trained and fitted Sklearn model. - x_test: Testing feature data (X data need to be normalized / standardized). - y_test: Testing label data. - feature_names: Names of the feature columns. - n_repeats: Number of iteration used when calculate feature importance. Defaults to 50. - random_state: random state for repeatability of results. Optional parameter. + X: Feature data. + y: Target labels. + feature_names: Names of features in X. + n_repeats: Number of iteration used when calculating feature importance. Defaults to 10. + random_state: Seed for random number generation. Defaults to None. Returns: A dataframe containing features and their importance. @@ -37,18 +41,24 @@ def evaluate_feature_importance( InvalidParameterValueException: Value for 'n_repeats' is not at least one. """ - if x_test.size == 0: - raise InvalidDatasetException("Array 'x_test' is empty.") + if X.size == 0: + raise InvalidDatasetException("Feature matrix X is empty.") - if y_test.size == 0: - raise InvalidDatasetException("Array 'y_test' is empty.") + if y.size == 0: + raise InvalidDatasetException("Target labels y is empty.") if n_repeats < 1: raise InvalidParameterValueException("Value for 'n_repeats' is less than one.") - result = permutation_importance(model, x_test, y_test.ravel(), n_repeats=n_repeats, random_state=random_state) + if len(X) != len(y): + raise NonMatchingParameterLengthsException("Feature matrix X and target labels y must have the same length.") - feature_importance = pd.DataFrame({"Feature": feature_names, "Importance": result.importances_mean * 100}) + if len(feature_names) != X.shape[1]: + raise InvalidParameterValueException("Number of feature names must match the number of input features.") + + result = permutation_importance(model, X, y.ravel(), n_repeats=n_repeats, random_state=random_state) + + feature_importance = pd.DataFrame({"Feature": feature_names, "Importance": result.importances_mean}) feature_importance = feature_importance.sort_values(by="Importance", ascending=False) diff --git a/eis_toolkit/exploratory_analyses/local_morans_i.py b/eis_toolkit/exploratory_analyses/local_morans_i.py index 3d03c1ad..45612161 100644 --- a/eis_toolkit/exploratory_analyses/local_morans_i.py +++ b/eis_toolkit/exploratory_analyses/local_morans_i.py @@ -6,7 +6,6 @@ from esda.moran import Moran_Local from eis_toolkit import exceptions -from eis_toolkit.exceptions import InvalidParameterValueException @beartype @@ -19,12 +18,12 @@ def _local_morans_i( elif weight_type == "knn": w = libpysal.weights.KNN.from_dataframe(gdf, k=k) else: - raise InvalidParameterValueException("Invalid weight_type. Use 'queen' or 'knn'.") + raise exceptions.InvalidParameterValueException("Invalid weight_type. Use 'queen' or 'knn'.") w.transform = "R" if len(gdf[column]) != len(w.weights): - raise InvalidParameterValueException("Dimension mismatch between data and weights matrix.") + raise exceptions.InvalidParameterValueException("Dimension mismatch between data and weights matrix.") moran_loc = Moran_Local(gdf[column], w, permutations=permutations) @@ -59,12 +58,18 @@ def local_morans_i( Raises: EmptyDataFrameException: The input geodataframe is empty. + InvalidColumnException: The input column is not found in the input geodataframe. + InvalidParameterValueException: Input parameter values for `k` or `permutations` are invalid. + NonNumericDataException: The input column contains non-numeric data. """ if gdf.shape[0] == 0: raise exceptions.EmptyDataFrameException("Geodataframe is empty.") if column not in gdf.columns: - raise exceptions.InvalidParameterValueException(f"Column '{column}' not found in the GeoDataFrame.") + raise exceptions.InvalidColumnException(f"Column '{column}' not found in the GeoDataFrame.") + + if not np.issubdtype(gdf[column].dtype, np.number): + raise exceptions.NonNumericDataException(f"Column '{column}' must contain numeric data.") if k < 1: raise exceptions.InvalidParameterValueException("k must be > 0.") diff --git a/eis_toolkit/exploratory_analyses/pca.py b/eis_toolkit/exploratory_analyses/pca.py index a501da13..b3494416 100644 --- a/eis_toolkit/exploratory_analyses/pca.py +++ b/eis_toolkit/exploratory_analyses/pca.py @@ -59,28 +59,30 @@ def _handle_missing_values( @beartype def _compute_pca( feature_matrix: np.ndarray, number_of_components: int, scaler_type: str -) -> Tuple[np.ndarray, np.ndarray]: +) -> Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]: scaler = SCALERS[scaler_type]() scaled_data = scaler.fit_transform(feature_matrix) pca = PCA(n_components=number_of_components) - principal_components = pca.fit_transform(scaled_data) - explained_variances = pca.explained_variance_ratio_ + transformed_data = pca.fit_transform(scaled_data) + principal_components = pca.components_ + explained_variances = pca.explained_variance_ + explained_variance_ratios = pca.explained_variance_ratio_ - return principal_components, explained_variances + return transformed_data, principal_components, explained_variances, explained_variance_ratios @beartype def compute_pca( data: Union[np.ndarray, pd.DataFrame, gpd.GeoDataFrame], - number_of_components: int, + number_of_components: Optional[int] = None, columns: Optional[Sequence[str]] = None, scaler_type: Literal["standard", "min_max", "robust"] = "standard", nodata_handling: Literal["remove", "replace"] = "remove", nodata: Optional[Number] = None, -) -> Tuple[Union[np.ndarray, pd.DataFrame, gpd.GeoDataFrame], np.ndarray]: +) -> Tuple[Union[np.ndarray, pd.DataFrame, gpd.GeoDataFrame], np.ndarray, np.ndarray, np.ndarray]: """ - Compute defined number of principal components for numeric input data. + Compute defined number of principal components for numeric input data and transform the data. Before computation, data is scaled according to specified scaler and NaN values removed or replaced. Optionally, a nodata value can be given to handle similarly as NaN values. @@ -93,7 +95,8 @@ def compute_pca( Args: data: Input data for PCA. number_of_components: The number of principal components to compute. Should be >= 1 and at most - the number of numeric columns if input is (Geo)Dataframe. + the number of features found in input data. If not defined, will be the same as number of + features in data. Defaults to None. columns: Select columns used for the PCA. Other columns are excluded from PCA, but added back to the result Dataframe intact. Only relevant if input is (Geo)Dataframe. Defaults to None. scaler_type: Transform data according to a specified Sklearn scaler. @@ -103,8 +106,8 @@ def compute_pca( nodata: Define a nodata value to remove. Defaults to None. Returns: - The computed principal components in corresponding format as the input data and the - explained variance ratios for each component. + The transformed data in same format as input data, computed principal components, explained variances + and explained variance ratios for each component. Raises: EmptyDataException: The input is empty. @@ -116,7 +119,7 @@ def compute_pca( if scaler_type not in SCALERS: raise InvalidParameterValueException(f"Invalid scaler. Choose from: {list(SCALERS.keys())}") - if number_of_components < 1: + if number_of_components is not None and number_of_components < 1: raise InvalidParameterValueException("The number of principal components should be >= 1.") # Get feature matrix (Numpy array) from various input types @@ -158,40 +161,50 @@ def compute_pca( feature_matrix = feature_matrix.astype(float) feature_matrix, nan_mask = _handle_missing_values(feature_matrix, nodata_handling, nodata) + # Default number of components to number of features in data if not defined + if number_of_components is None: + number_of_components = feature_matrix.shape[1] + if number_of_components > feature_matrix.shape[1]: - raise InvalidParameterValueException("The number of principal components is too high for the given input data.") + raise InvalidParameterValueException( + "The number of principal components is too high for the given input data " + + f"({number_of_components} > {feature_matrix.shape[1]})." + ) + # Core PCA computation - principal_components, explained_variances = _compute_pca(feature_matrix, number_of_components, scaler_type) + transformed_data, principal_components, explained_variances, explained_variance_ratios = _compute_pca( + feature_matrix, number_of_components, scaler_type + ) if nodata_handling == "remove" and nan_mask is not None: - principal_components_with_nans = np.full((nan_mask.size, principal_components.shape[1]), np.nan) - principal_components_with_nans[~nan_mask, :] = principal_components - principal_components = principal_components_with_nans + transformed_data_with_nans = np.full((nan_mask.size, transformed_data.shape[1]), np.nan) + transformed_data_with_nans[~nan_mask, :] = transformed_data + transformed_data = transformed_data_with_nans # Convert PCA output to proper format if isinstance(data, np.ndarray): if data.ndim == 3: - result_data = principal_components.reshape(rows, cols, -1).transpose(2, 0, 1) + transformed_data_out = transformed_data.reshape(rows, cols, -1).transpose(2, 0, 1) else: - result_data = principal_components + transformed_data_out = transformed_data elif isinstance(data, pd.DataFrame): component_names = [f"principal_component_{i+1}" for i in range(number_of_components)] - result_data = pd.DataFrame(data=principal_components, columns=component_names) + transformed_data_out = pd.DataFrame(data=transformed_data, columns=component_names) if columns is not None: old_columns = [column for column in data.columns if column not in columns] for column in old_columns: - result_data[column] = data[column] + transformed_data_out[column] = data[column] if isinstance(data, gpd.GeoDataFrame): - result_data = gpd.GeoDataFrame(result_data, geometry=geometries, crs=crs) + transformed_data_out = gpd.GeoDataFrame(transformed_data_out, geometry=geometries, crs=crs) - return result_data, explained_variances + return transformed_data_out, principal_components, explained_variances, explained_variance_ratios @beartype def plot_pca( pca_df: pd.DataFrame, - explained_variances: Optional[np.ndarray] = None, + explained_variance_ratios: Optional[np.ndarray] = None, color_column_name: Optional[str] = None, save_path: Optional[str] = None, ) -> sns.PairGrid: @@ -203,7 +216,7 @@ def plot_pca( Args: pca_df: A DataFrame containing computed principal components. - explained_variances: The explained variance ratios for each principal component. Used for labeling + explained_variance_ratios: The explained variance ratios for each principal component. Used for labeling axes in the plot. Optional parameter. Defaults to None. color_column_name: Name of the column that will be used for color-coding data points. Typically a categorical variable in the original data. Optional parameter, no colors if not provided. @@ -226,8 +239,8 @@ def plot_pca( pair_grid = sns.pairplot(filtered_df, hue=color_column_name) # Add explained variances to axis labels if provided - if explained_variances is not None: - labels = [f"PC {i+1} ({var:.1f}%)" for i, var in enumerate(explained_variances * 100)] + if explained_variance_ratios is not None: + labels = [f"PC {i+1} ({var:.1f}%)" for i, var in enumerate(explained_variance_ratios * 100)] else: labels = [f"PC {i+1}" for i in range(len(pair_grid.axes))] diff --git a/eis_toolkit/prediction/weights_of_evidence.py b/eis_toolkit/prediction/weights_of_evidence.py index 563fbb41..5a21a395 100644 --- a/eis_toolkit/prediction/weights_of_evidence.py +++ b/eis_toolkit/prediction/weights_of_evidence.py @@ -1,3 +1,4 @@ +import warnings from numbers import Number import geopandas as gpd @@ -5,10 +6,17 @@ import pandas as pd import rasterio from beartype import beartype -from beartype.typing import Dict, List, Literal, Optional, Sequence, Tuple - -from eis_toolkit.exceptions import ClassificationFailedException, InvalidColumnException, InvalidParameterValueException +from beartype.typing import Dict, List, Literal, Optional, Sequence, Tuple, Union + +from eis_toolkit.exceptions import ( + ClassificationFailedException, + InvalidColumnException, + InvalidParameterValueException, + NonMatchingRasterMetadataException, +) +from eis_toolkit.utilities.checks.raster import check_raster_grids from eis_toolkit.vector_processing.rasterize_vector import rasterize_vector +from eis_toolkit.warnings import ClassificationFailedWarning, InvalidColumnWarning CLASS_COLUMN = "Class" PIXEL_COUNT_COLUMN = "Pixel count" @@ -49,8 +57,24 @@ GENERALIZED_S_WEIGHT_PLUS_COLUMN, ] +GENERALIZED_COLUMNS = [GENERALIZED_CLASS_COLUMN, GENERALIZED_WEIGHT_PLUS_COLUMN, GENERALIZED_S_WEIGHT_PLUS_COLUMN] +WEIGHTS_COLUMNS = [ + WEIGHT_PLUS_COLUMN, + WEIGHT_S_PLUS_COLUMN, + WEIGHT_MINUS_COLUMN, + WEIGHT_S_MINUS_COLUMN, +] + +REQUIRED_FOR_GENERALIZATION = { + "manual": [], + "max_contrast": [CONTRAST_COLUMN], + "max_contrast_if_feasible": [CONTRAST_COLUMN, STUDENTIZED_CONTRAST_COLUMN], + "max_feasible_contrast": [CONTRAST_COLUMN, STUDENTIZED_CONTRAST_COLUMN], + "max_studentized_contrast": [STUDENTIZED_CONTRAST_COLUMN], +} -def _read_and_preprocess_evidence( + +def _read_and_preprocess_raster_data( raster: rasterio.io.DatasetReader, nodata: Optional[Number] = None, band: int = 1 ) -> np.ndarray: """Read raster data and handle NoData values.""" @@ -178,20 +202,9 @@ def _generalized_weights_categorical(df: pd.DataFrame, deposits) -> pd.DataFrame return gen_df -def _generalized_classes_cumulative(df: pd.DataFrame, studentized_contrast_threshold: Number) -> pd.DataFrame: - """Create generalized classes based on contrast and studentized contrast threhsold value.""" +def _generalized_classes_cumulative(df: pd.DataFrame, index: int) -> pd.DataFrame: + """Create generalized classes based on given index for cutoff row.""" gen_df = df.copy() - index = gen_df.idxmax()[CONTRAST_COLUMN] - - if ( - gen_df.loc[index, STUDENTIZED_CONTRAST_COLUMN] < studentized_contrast_threshold - or index == len(gen_df.index) - 1 - ): - raise ClassificationFailedException( - "Failed to create generalized classes with given studentized contrast treshold ({} < {})".format( - gen_df.loc[index, STUDENTIZED_CONTRAST_COLUMN], studentized_contrast_threshold - ) - ) gen_df[GENERALIZED_CLASS_COLUMN] = 1 for i in range(0, index + 1): @@ -200,7 +213,7 @@ def _generalized_classes_cumulative(df: pd.DataFrame, studentized_contrast_thres return gen_df -def _generalized_weights_cumulative(df: pd.DataFrame, deposits: np.ndarray) -> pd.DataFrame: +def _generalized_weights_cumulative(df: pd.DataFrame, index: int) -> pd.DataFrame: """ Calculate generalized weights for cumulative methods. @@ -209,17 +222,15 @@ def _generalized_weights_cumulative(df: pd.DataFrame, deposits: np.ndarray) -> p gen_df = df.copy() # Class 2 - class_2_max_index = gen_df.idxmax()[CONTRAST_COLUMN] - - gen_df[GENERALIZED_WEIGHT_PLUS_COLUMN] = gen_df.loc[class_2_max_index, WEIGHT_PLUS_COLUMN] - gen_df[GENERALIZED_S_WEIGHT_PLUS_COLUMN] = gen_df.loc[class_2_max_index, WEIGHT_S_PLUS_COLUMN] + gen_df[GENERALIZED_WEIGHT_PLUS_COLUMN] = gen_df.loc[index, WEIGHT_PLUS_COLUMN] + gen_df[GENERALIZED_S_WEIGHT_PLUS_COLUMN] = gen_df.loc[index, WEIGHT_S_PLUS_COLUMN] # Class 1 gen_df.loc[gen_df[GENERALIZED_CLASS_COLUMN] == 1, GENERALIZED_WEIGHT_PLUS_COLUMN] = gen_df.loc[ - class_2_max_index, WEIGHT_MINUS_COLUMN + index, WEIGHT_MINUS_COLUMN ] gen_df.loc[gen_df[GENERALIZED_CLASS_COLUMN] == 1, GENERALIZED_S_WEIGHT_PLUS_COLUMN] = gen_df.loc[ - class_2_max_index, WEIGHT_S_MINUS_COLUMN + index, WEIGHT_S_MINUS_COLUMN ] return gen_df @@ -239,10 +250,128 @@ def _generate_arrays_from_metrics( return array_dict +def _calculate_nr_of_deposit_pixels(array: np.ndarray, df: pd.DataFrame) -> Tuple[int, int]: + masked_array = array[~np.isnan(array)] + nr_of_pixels = int(np.size(masked_array)) + + pixels_column = df["Pixel count"] + + match = pixels_column == nr_of_pixels + if match.any(): + nr_of_deposits = df.loc[match, "Deposit count"].iloc[0] + else: + nr_of_pixels = df["Pixel count"].sum() + nr_of_deposits = df["Deposit count"].sum() + + return nr_of_deposits, nr_of_pixels + + +@beartype +def generalize_weights_cumulative( + df: pd.DataFrame, + classification_method: Literal[ + "manual", "max_contrast", "max_contrast_if_feasible", "max_feasible_contrast", "max_studentized_contrast" + ] = "max_contrast_if_feasible", + manual_cutoff_index: Optional[Number] = None, + studentized_contrast_threshold: Optional[Number] = 1, +) -> pd.DataFrame: + """ + Calculate generalized weights for cumulative methods. + + Perform binary reclassification into the generalized classes 1 and 2 according to the selected + classification method. Calculate generalized weights for the two classes. + + Args: + df: A weights table returned by weights_of_evidence_calculate_weights. + classification_method: Accepted values are 'manual', 'max_contrast', + max_contrast_if_feasible, 'max_feasible_contrast' and 'max_studentized_contrast', detailed below: + 'manual': Requires a valid row index to use as cutoff value. + 'max_contrast': Uses the maximum contrast value regardless of studentized contrast. + 'max_contrast_if_feasible': Uses the maximum contrast value if the corresponding studentized + contrast is greater than the provided threshold value. + 'max_feasible_contrast': Uses the highest contrast value for which the studentized contrast + is greater than the provided threshold value. + 'max_studentized_contrast': Uses the highest studentized contrast value. + manual_cutoff_index: Index of the last row to be included in class 2. + studentized_contrast_threshold: Studentized contrast threshold value used to check that class with + max contrast has studentized contrast value at least the defined value. Defaults to 1. + Returns: + The weights table with the addition of a generalized class column. If generalization failed, returns + the original table. + Warns: + ClassificationFailedWarning + InvalidColumnWarning + """ + df = df.copy() + + required_columns = WEIGHTS_COLUMNS + REQUIRED_FOR_GENERALIZATION[classification_method] + missing_columns = [col for col in required_columns if col not in df.columns.values] + + if len(missing_columns) != 0: + warnings.warn( + f"Failed to create generalized classes. The following columns are required: {missing_columns}", + InvalidColumnWarning, + ) + return df + + columns_to_drop = [col for col in df.columns.values if col in GENERALIZED_COLUMNS] + df = df.drop(columns_to_drop, axis=1) + + index = len(df.index) - 1 + classification_failed_warning = "" + + if classification_method == "manual": + if manual_cutoff_index is None: + classification_failed_warning = f"Failed to create generalized classes with the row index \ + {manual_cutoff_index}" + else: + index = manual_cutoff_index + + elif classification_method == "max_contrast": + index = df.idxmax()[CONTRAST_COLUMN] + + elif classification_method == "max_contrast_if_feasible": + + index = df.idxmax()[CONTRAST_COLUMN] + + if df.loc[index, STUDENTIZED_CONTRAST_COLUMN] < studentized_contrast_threshold: + classification_failed_warning = f"Failed to create generalized classes with given studentized \ + contrast threshold {studentized_contrast_threshold}" + + elif classification_method == "max_feasible_contrast": + df_studentized_contrast = df[df[STUDENTIZED_CONTRAST_COLUMN] > studentized_contrast_threshold] + + if len(df_studentized_contrast) == 0: + classification_failed_warning = f"Failed to create generalized classes with given studentized \ + contrast threshold {studentized_contrast_threshold}" + else: + index = df_studentized_contrast.idxmax()[CONTRAST_COLUMN] + + else: + # max_studentized_contrast + index = df.idxmax()[STUDENTIZED_CONTRAST_COLUMN] + + if classification_failed_warning != "": + warnings.warn( + classification_failed_warning, + ClassificationFailedWarning, + ) + return df + + if index >= len(df.index) - 1: + warnings.warn("Failed to create generalized classes.", ClassificationFailedWarning) + + return df + else: + df = _generalized_classes_cumulative(df, index) + + return _generalized_weights_cumulative(df, index) + + @beartype def weights_of_evidence_calculate_weights( evidential_raster: rasterio.io.DatasetReader, - deposits: gpd.GeoDataFrame, + deposits: Union[gpd.GeoDataFrame, rasterio.io.DatasetReader], raster_nodata: Optional[Number] = None, weights_type: Literal["unique", "categorical", "ascending", "descending"] = "unique", studentized_contrast_threshold: Number = 1, @@ -253,7 +382,7 @@ def weights_of_evidence_calculate_weights( Args: evidential_raster: The evidential raster. - deposits: Vector data representing the mineral deposits or occurences point data. + deposits: Vector or raster data representing the mineral deposits or occurences point data. raster_nodata: If nodata value of raster is wanted to specify manually. Optional parameter, defaults to None (nodata from raster metadata is used). weights_type: Accepted values are 'unique', 'categorical', 'ascending' and 'descending'. @@ -267,9 +396,11 @@ def weights_of_evidence_calculate_weights( that class with max contrast has studentized contrast value at least the defined value (cumulative). Defaults to 1. arrays_to_generate: Arrays to generate from the computed weight metrics. All column names - in the produced weights_df are valid choices. Defaults to ["Class", "W+", "S_W+] - for "unique" weights_type and ["Class", "W+", "S_W+", "Generalized W+", "Generalized S_W+"] - for the cumulative weight types. + in the produced weights_df are valid choices. Available column names for "unique" weights type are "Class", + "Pixel count", "Deposit count", "W+", "S_W+", "W-", "S_W-", "Contrast", "S_Contrast", and + "Studentized contrast". For other weights types, additional available column names are "Generalized class", + "Generalzed W+", and "Generalized S_W+". Defaults to ["Class", "W+", "S_W+] for "unique" weights_type and + ["Class", "W+", "S_W+", "Generalized W+", "Generalized S_W+"] for the cumulative weight types. Returns: Dataframe with weights of spatial association between the input data. @@ -279,10 +410,13 @@ def weights_of_evidence_calculate_weights( Number of all evidence pixels. Raises: - ClassificationFailedException: Unable to create generalized classes with the given - studentized_contrast_threshold. + ClassificationFailedException: Unable to create generalized classes for the categorical weights type. InvalidColumnException: Arrays to generate contains invalid column name(s). InvalidParameterValueException: Input weights_type is not one of the accepted values. + + Warns: + ClassificationFailedWarning: Unable to create generalized classes for the cumulative weights types + with the given studentized_contrast_threshold. """ if arrays_to_generate is None: @@ -297,13 +431,22 @@ def weights_of_evidence_calculate_weights( metrics_to_arrays = arrays_to_generate.copy() # 1. Preprocess data - evidence_array = _read_and_preprocess_evidence(evidential_raster, raster_nodata) + evidence_array = _read_and_preprocess_raster_data(evidential_raster, raster_nodata) raster_meta = evidential_raster.meta + raster_profile = evidential_raster.profile - # Rasterize deposits - deposit_array = rasterize_vector( - geodataframe=deposits, raster_profile=raster_meta, default_value=1.0, fill_value=0.0 - ) + # Rasterize deposits if vector data + if isinstance(deposits, gpd.GeoDataFrame): + deposit_array = rasterize_vector( + geodataframe=deposits, raster_profile=raster_meta, default_value=1.0, fill_value=0.0 + ) + else: + deposit_profile = deposits.profile + + if check_raster_grids([raster_profile, deposit_profile], same_extent=True): + deposit_array = _read_and_preprocess_raster_data(deposits, raster_nodata) + else: + raise NonMatchingRasterMetadataException("Input rasters should have the same grid properties.") # Mask NaN out of the array nodata_mask = np.isnan(evidence_array) @@ -343,18 +486,25 @@ def weights_of_evidence_calculate_weights( ) weights_df = pd.DataFrame(df_entries) - # 4. If we use cumulative weights type, calculate generalized classes and weights + # 4. If we use cumulative or categorical weights type, calculate generalized classes and weights if weights_type == "categorical": weights_df = _generalized_classes_categorical(weights_df, studentized_contrast_threshold) weights_df = _generalized_weights_categorical(weights_df, masked_deposit_array) elif weights_type == "ascending" or weights_type == "descending": - weights_df = _generalized_classes_cumulative(weights_df, studentized_contrast_threshold) - weights_df = _generalized_weights_cumulative(weights_df, masked_deposit_array) + weights_df = generalize_weights_cumulative( + weights_df, + classification_method="max_contrast_if_feasible", + studentized_contrast_threshold=studentized_contrast_threshold, + ) + if GENERALIZED_CLASS_COLUMN not in weights_df.columns.values: + for key in GENERALIZED_COLUMNS: + if key in metrics_to_arrays: + metrics_to_arrays.remove(key) # 5. Generate arrays for desired metrics arrays_dict = _generate_arrays_from_metrics(evidence_array, weights_df, metrics_to_arrays) - # Return nr. of deposit pixels and nr. of all evidence pixels for to be used in calculate responses + # Return nr. of deposit pixels and nr. of all evidence pixels for to be used in calculate responses nr_of_deposits = int(np.sum(masked_deposit_array == 1)) nr_of_pixels = int(np.size(masked_evidence_array)) @@ -363,7 +513,7 @@ def weights_of_evidence_calculate_weights( @beartype def weights_of_evidence_calculate_responses( - output_arrays: Sequence[Dict[str, np.ndarray]], nr_of_deposits: int, nr_of_pixels: int + output_arrays: Sequence[Dict[str, np.ndarray]], weights_df: pd.DataFrame ) -> Tuple[np.ndarray, np.ndarray, np.ndarray]: """Calculate the posterior probabilities for the given generalized weight arrays. @@ -372,14 +522,17 @@ def weights_of_evidence_calculate_responses( For each dictionary, generalized weight and generalized standard deviation arrays are used and summed together pixel-wise to calculate the posterior probabilities. If generalized arrays are not found, the W+ and S_W+ arrays are used (so if outputs from unique weight calculations are used for this function). - nr_of_deposits: Number of deposit pixels in the input data for weights of evidence calculations. - nr_of_pixels: Number of evidence pixels in the input data for weights of evidence calculations. + weights_df: Output dataframe of WofE calculate weights algorithm. Used for determining number of deposits and + number of pixels. Returns: Array of posterior probabilites. Array of standard deviations in the posterior probability calculations. Array of confidence of the prospectivity values obtained in the posterior probability array. """ + array = list(output_arrays[0].values())[0] + nr_of_deposits, nr_of_pixels = _calculate_nr_of_deposit_pixels(array, weights_df) + gen_weights_sum = sum( [ item[GENERALIZED_WEIGHT_PLUS_COLUMN] @@ -408,3 +561,74 @@ def weights_of_evidence_calculate_responses( confidence_array = posterior_probabilities / posterior_probabilities_std return posterior_probabilities, posterior_probabilities_std, confidence_array + + +@beartype +def agterberg_cheng_CI_test( + posterior_probabilities: np.ndarray, posterior_probabilities_std: np.ndarray, weights_df: pd.DataFrame +) -> Tuple[bool, bool, bool, float, str]: + """Perform the conditional independence test presented by Agterberg-Cheng (2002). + + Agterberg, F. P. & Cheng, Q. (2002). Conditional Independence Test for Weights-of-Evidence Modeling. + Natural Resources Research. 11. 249-255. + + Args: + posterior_probabilities: Array of posterior probabilites. + posterior_probabilities_std: Array of standard deviations in the posterior probability calculations. + weights_df: Output dataframe of WofE calculate weights algorithm. Used for determining number of deposits. + + Returns: + Whether the conditional hypothesis can be accepted for the evidence layers that the input + posterior probabilities and standard deviations of posterior probabilities are calculated from. + Whether the probability satisfies the 99% confidence limit. + Whether the probability satisfies the 95% confidence limit. + Ratio T/n. Results > 1, may be because of lack of conditional independence of layers. + T should not exceed n by more than 15% (Bonham-Carter 1994, p. 316). + A summary of the the conditional independence calculations. + """ + nr_of_deposits, _ = _calculate_nr_of_deposit_pixels(posterior_probabilities, weights_df) + + # One-tailed significance test according to Agterberg-Cheng (2002): + # Conditional independence must satisfy: + # T - n < 1.645 * s(T) with a probability of 95% + # T - n < 2.33 * s(T) with a probability of 99%, + # where + # T = the sum of posterior probabilities in all unit cells in the study area + # n = total number of deposits + + T = np.nansum(posterior_probabilities) + + ratio = T / nr_of_deposits + ratio_msg = "T / n > 1 may suggest lack of conditional independence.\n" + ratio_msg_bonham_carter = "According to Bonham-Carter (1994), T / n should not exceed 1.15.\n" + + difference = T - nr_of_deposits + + T_std = np.sqrt(np.nansum(posterior_probabilities_std)) + + confidence_limit_99 = 2.33 * T_std + confidence_limit_95 = 1.645 * T_std + + confidence_99 = bool(difference < confidence_limit_99) + confidence_95 = bool(difference < confidence_limit_95) + sign_99 = "<" if confidence_99 else ">" + sign_95 = "<" if confidence_95 else ">" + + conditional_independence = confidence_99 and confidence_95 + + summary = f""" + Results of conditional independence test:\n\n + Observed number of deposits, n: {nr_of_deposits}\n + Expected number of deposits, T: {T}\n + Standard deviation of the expected number of deposits, s(T): {T_std}\n + T - n = {difference}\n + T / n = {ratio}\n{ratio_msg if ratio > 1 else ""}{ratio_msg_bonham_carter if ratio > 1.15 else ""} + Agterberg & Cheng CI test:\n + Data {"satisfies" if confidence_99 else "does not satisfy"} condition T - n < 2.33 * s(T):\n + {difference} {sign_99} {confidence_limit_99}\n + Data {"satisfies" if confidence_95 else "does not satisfy"} condition T - n < 1.645 * s(T):\n + {difference} {sign_95} {confidence_limit_95}\n + {"Conditional independence hypothesis should be rejected" if not conditional_independence else ""} + """ + + return conditional_independence, confidence_99, confidence_95, ratio, summary diff --git a/eis_toolkit/raster_processing/distance_to_anomaly.py b/eis_toolkit/raster_processing/distance_to_anomaly.py index ba0294b9..0236b340 100644 --- a/eis_toolkit/raster_processing/distance_to_anomaly.py +++ b/eis_toolkit/raster_processing/distance_to_anomaly.py @@ -1,5 +1,6 @@ from itertools import chain from numbers import Number +from os import path from pathlib import Path from tempfile import TemporaryDirectory @@ -10,28 +11,10 @@ from beartype.typing import Literal, Optional, Tuple, Union from rasterio import profiles -from eis_toolkit.exceptions import EmptyDataException, InvalidParameterValueException +from eis_toolkit.exceptions import EmptyDataException, InvalidParameterValueException, NumericValueSignException from eis_toolkit.utilities.checks.raster import check_raster_profile -from eis_toolkit.utilities.miscellaneous import row_points, toggle_gdal_exceptions -from eis_toolkit.vector_processing.distance_computation import distance_computation - - -def _check_threshold_criteria_and_value(threshold_criteria, threshold_criteria_value): - if threshold_criteria in {"lower", "higher"} and not isinstance(threshold_criteria_value, Number): - raise InvalidParameterValueException( - f"Expected threshold_criteria_value {threshold_criteria_value} " - "to be a single number rather than a tuple." - ) - if threshold_criteria in {"in_between", "outside"}: - if not isinstance(threshold_criteria_value, tuple): - raise InvalidParameterValueException( - f"Expected threshold_criteria_value ({threshold_criteria_value}) to be a tuple rather than a number." - ) - if threshold_criteria_value[0] >= threshold_criteria_value[1]: - raise InvalidParameterValueException( - f"Expected first value in threshold_criteria_value ({threshold_criteria_value})" - "tuple to be lower than the second." - ) +from eis_toolkit.utilities.miscellaneous import row_points +from eis_toolkit.vector_processing._distance_computation_numba import distance_computation_numba @beartype @@ -44,6 +27,8 @@ def distance_to_anomaly( ) -> Tuple[np.ndarray, Union[profiles.Profile, dict]]: """Calculate distance from raster cell to nearest anomaly. + If `osgeo_utils.gdal_proximity` is not available, will print a warning and fallback to a slower implementation. + The criteria for what is anomalous can be defined as a single number and criteria text of "higher" or "lower". Alternatively, the definition can be a range where values inside (criteria text of "within") or outside are @@ -65,71 +50,63 @@ def distance_to_anomaly( Returns: A 2D numpy array with the distances to anomalies computed and the original anomaly raster profile. - """ - check_raster_profile(raster_profile=anomaly_raster_profile) - _check_threshold_criteria_and_value( - threshold_criteria=threshold_criteria, threshold_criteria_value=threshold_criteria_value - ) - - out_image = _distance_to_anomaly( - anomaly_raster_profile=anomaly_raster_profile, - anomaly_raster_data=anomaly_raster_data, - threshold_criteria=threshold_criteria, - threshold_criteria_value=threshold_criteria_value, - max_distance=max_distance, - ) - return out_image, anomaly_raster_profile - + import typer -@beartype -def distance_to_anomaly_gdal( - anomaly_raster_profile: Union[profiles.Profile, dict], - anomaly_raster_data: np.ndarray, - threshold_criteria_value: Union[Tuple[Number, Number], Number], - threshold_criteria: Literal["lower", "higher", "in_between", "outside"], - output_path: Path, - verbose: bool = False, -) -> Path: - """Calculate distance from raster cell to nearest anomaly. - - Distance is calculated for each cell in the anomaly raster and saved to a - new raster at output_path. The criteria for what is anomalous can be - defined as a single number and criteria text of "higher" or "lower". - Alternatively, the definition can be a range where values inside - (criteria text of "within") or outside are marked as anomalous - (criteria text of "outside"). If anomaly_raster_profile does - contain "nodata" key, np.nan is assumed to correspond to nodata values. + try: + from osgeo_utils import gdal_proximity # noqa: F401 - Does not work on Windows. + func = _distance_to_anomaly_gdal + except ModuleNotFoundError: + func = _distance_to_anomaly_numba + typer.echo("WARNING: Falling back to a slower implementation as 'gdal_proximity' was not available!") - Args: - anomaly_raster_profile: The raster profile in which the distances - to the nearest anomalous value are determined. - anomaly_raster_data: The raster data in which the distances - to the nearest anomalous value are determined. - threshold_criteria_value: Value(s) used to define anomalous. - threshold_criteria: Method to define anomalous. - output_path: The path to the raster with the distances to anomalies - calculated. - verbose: Whether to print gdal_proximity output. - - Returns: - The path to the raster with the distances to anomalies calculated. - """ check_raster_profile(raster_profile=anomaly_raster_profile) _check_threshold_criteria_and_value( threshold_criteria=threshold_criteria, threshold_criteria_value=threshold_criteria_value ) - - return _distance_to_anomaly_gdal( - output_path=output_path, - anomaly_raster_profile=anomaly_raster_profile, - anomaly_raster_data=anomaly_raster_data, + if max_distance is not None and max_distance <= 0: + raise NumericValueSignException("Expected max distance to be a positive number.") + + distance_raster_data = anomaly_raster_data.copy() + distance_raster_data.astype(np.float64) + out_profile = anomaly_raster_profile.copy() + out_profile["dtype"] = np.float32 + out_profile["count"] = 1 + + # We need to convert nodata to a negative number in case it is non-negative in the input raster + if out_profile["nodata"] >= 0.0: + old_nodata = out_profile["nodata"] + new_nodata = -9999 + distance_raster_data = np.where(np.isin(distance_raster_data, old_nodata), new_nodata, distance_raster_data) + out_profile["nodata"] = new_nodata + + out_array, out_profile = func( + anomaly_raster_profile=out_profile, + anomaly_raster_data=distance_raster_data, threshold_criteria=threshold_criteria, threshold_criteria_value=threshold_criteria_value, - verbose=verbose, + max_distance=max_distance, ) + return out_array, out_profile + + +def _check_threshold_criteria_and_value(threshold_criteria, threshold_criteria_value): + if threshold_criteria in {"lower", "higher"} and not isinstance(threshold_criteria_value, Number): + raise InvalidParameterValueException( + f"Expected threshold_criteria_value {threshold_criteria_value} " + "to be a single number rather than a tuple." + ) + if threshold_criteria in {"in_between", "outside"}: + if not isinstance(threshold_criteria_value, tuple): + raise InvalidParameterValueException( + f"Expected threshold_criteria_value ({threshold_criteria_value}) to be a tuple rather than a number." + ) + if threshold_criteria_value[0] >= threshold_criteria_value[1]: + raise InvalidParameterValueException( + f"Expected first value in threshold_criteria_value ({threshold_criteria_value})" + "tuple to be lower than the second." + ) def _fits_criteria( @@ -156,6 +133,32 @@ def _fits_criteria( return np.where(mask, False, criteria_dict[threshold_criteria](anomaly_raster_data)) +def _validate_threshold_criteria( + anomaly_raster_profile: Union[profiles.Profile, dict], + anomaly_raster_data: np.ndarray, + threshold_criteria_value: Union[Tuple[Number, Number], Number], + threshold_criteria: Literal["lower", "higher", "in_between", "outside"], +) -> np.ndarray: + data_fits_criteria = _fits_criteria( + threshold_criteria=threshold_criteria, + threshold_criteria_value=threshold_criteria_value, + anomaly_raster_data=anomaly_raster_data, + nodata_value=anomaly_raster_profile.get("nodata"), + ) + if np.sum(data_fits_criteria) == 0: + raise EmptyDataException( + " ".join( + [ + "Expected the passed threshold criteria to match at least some data.", + f"Check that the values of threshold_criteria ({threshold_criteria})", + f"and threshold_criteria_value {threshold_criteria_value}", + "match at least part of the data.", + ] + ) + ) + return data_fits_criteria + + def _write_binary_anomaly_raster(tmp_dir: Path, anomaly_raster_profile, data_fits_criteria: np.ndarray): anomaly_raster_binary_path = tmp_dir / "anomaly_raster_binary.tif" @@ -171,16 +174,20 @@ def _distance_to_anomaly_gdal( anomaly_raster_data: np.ndarray, threshold_criteria_value: Union[Tuple[Number, Number], Number], threshold_criteria: Literal["lower", "higher", "in_between", "outside"], - output_path: Path, - verbose: bool, -): + max_distance: Optional[Number], + # output_path: Path, + verbose: bool = False, +) -> Tuple[np.ndarray, Union[profiles.Profile, dict]]: + from osgeo_utils import gdal_proximity - data_fits_criteria = _fits_criteria( - threshold_criteria=threshold_criteria, - threshold_criteria_value=threshold_criteria_value, - anomaly_raster_data=anomaly_raster_data, - nodata_value=anomaly_raster_profile.get("nodata"), + from eis_toolkit.utilities.gdal import toggle_gdal_exceptions + + data_fits_criteria = _validate_threshold_criteria( + anomaly_raster_profile, + anomaly_raster_data, + threshold_criteria_value, + threshold_criteria, ) with TemporaryDirectory() as tmp_dir_str: @@ -188,41 +195,36 @@ def _distance_to_anomaly_gdal( anomaly_raster_binary_path = _write_binary_anomaly_raster( tmp_dir=tmp_dir, anomaly_raster_profile=anomaly_raster_profile, data_fits_criteria=data_fits_criteria ) + tmp_out_raster = path.join(tmp_dir, "temp_output.tif") with toggle_gdal_exceptions(): gdal_proximity.gdal_proximity( src_filename=str(anomaly_raster_binary_path), - dst_filename=str(output_path), + dst_filename=tmp_out_raster, alg_options=("VALUES=1", "DISTUNITS=GEO"), quiet=not verbose, ) + with rasterio.open(tmp_out_raster) as out_raster: + out_array = out_raster.read(1) - return output_path + if max_distance is not None: + out_array[out_array > max_distance] = max_distance + return out_array, anomaly_raster_profile -def _distance_to_anomaly( + +def _distance_to_anomaly_numba( anomaly_raster_profile: Union[profiles.Profile, dict], anomaly_raster_data: np.ndarray, threshold_criteria_value: Union[Tuple[Number, Number], Number], threshold_criteria: Literal["lower", "higher", "in_between", "outside"], max_distance: Optional[Number], -) -> np.ndarray: - data_fits_criteria = _fits_criteria( - threshold_criteria=threshold_criteria, - threshold_criteria_value=threshold_criteria_value, - anomaly_raster_data=anomaly_raster_data, - nodata_value=anomaly_raster_profile.get("nodata"), +) -> Tuple[np.ndarray, profiles.Profile]: + data_fits_criteria = _validate_threshold_criteria( + anomaly_raster_profile, + anomaly_raster_data, + threshold_criteria_value, + threshold_criteria, ) - if np.sum(data_fits_criteria) == 0: - raise EmptyDataException( - " ".join( - [ - "Expected the passed threshold criteria to match at least some data.", - f"Check that the values of threshold_criteria ({threshold_criteria})", - f"and threshold_criteria_value {threshold_criteria_value}", - "match at least part of the data.", - ] - ) - ) cols = np.arange(anomaly_raster_data.shape[1]) rows = np.arange(anomaly_raster_data.shape[0]) @@ -234,8 +236,72 @@ def _distance_to_anomaly( all_points = list(chain(*all_points_by_rows)) all_points_gdf = gpd.GeoDataFrame(geometry=all_points, crs=anomaly_raster_profile["crs"]) - distance_array = distance_computation( + distance_array = distance_computation_numba( raster_profile=anomaly_raster_profile, geodataframe=all_points_gdf, max_distance=max_distance ) - - return distance_array + out_meta = anomaly_raster_profile.copy() + + # Update metadata + out_meta["dtype"] = distance_array.dtype.name + out_meta["count"] = 1 + + return distance_array, out_meta + + +# def _distance_to_anomaly_gdal_alt( +# anomaly_raster_profile: Union[profiles.Profile, dict], +# anomaly_raster_data: np.ndarray, +# threshold_criteria_value: Union[Tuple[Number, Number], Number], +# threshold_criteria: Literal["lower", "higher", "in_between", "outside"], +# max_distance: Optional[Number], +# ) -> Tuple[np.ndarray, profiles.Profile]: + +# from osgeo import gdal +# from eis_toolkit.utilities.gdal import toggle_gdal_exceptions + +# with toggle_gdal_exceptions(): +# data_fits_criteria = _validate_threshold_criteria( +# anomaly_raster_profile, +# anomaly_raster_data, +# threshold_criteria_value, +# threshold_criteria, +# ) +# # converting True False values to binary formant. +# converted_values = np.where(data_fits_criteria, 1, 0) +# driver = gdal.GetDriverByName("MEM") + +# width = anomaly_raster_profile["width"] +# height = anomaly_raster_profile["height"] +# temp_raster = driver.Create("", width, height, 1, gdal.GDT_Float32) +# transformation = anomaly_raster_profile["transform"] +# x_geo = (transformation.c, transformation.a, transformation.b) +# y_geo = (transformation.f, transformation.d, transformation.e) +# temp_raster.SetGeoTransform(x_geo + y_geo) +# crs = anomaly_raster_profile["crs"].to_wkt() +# band = temp_raster.GetRasterBand(1) +# band.WriteArray(converted_values) +# nodatavalue = anomaly_raster_profile["nodata"] +# band.SetNoDataValue(nodatavalue) + +# # Create empty proximity raster +# out_raster = driver.Create("", width, height, 1, gdal.GDT_Float32) +# out_raster.SetGeoTransform(x_geo + y_geo) +# out_raster.SetProjection(crs) +# out_band = out_raster.GetRasterBand(1) +# out_band.SetNoDataValue(nodatavalue) +# options = ["values=1", "distunits=GEO"] + +# # Compute proximity +# gdal.ComputeProximity(band, out_band, options) + +# # Create outputs +# out_array = out_band.ReadAsArray() +# if max_distance is not None: +# out_array[out_array > max_distance] = max_distance +# out_meta = anomaly_raster_profile.copy() + +# # Update metadata +# out_meta["dtype"] = out_array.dtype.name +# out_meta["count"] = 1 + +# return out_array, out_meta diff --git a/eis_toolkit/raster_processing/proximity_to_anomaly.py b/eis_toolkit/raster_processing/proximity_to_anomaly.py new file mode 100644 index 00000000..be8529b7 --- /dev/null +++ b/eis_toolkit/raster_processing/proximity_to_anomaly.py @@ -0,0 +1,56 @@ +from numbers import Number + +import numpy as np +from beartype import beartype +from beartype.typing import Literal, Tuple, Union +from rasterio import profiles + +from eis_toolkit.raster_processing.distance_to_anomaly import distance_to_anomaly +from eis_toolkit.transformations.linear import _min_max_scaling + + +@beartype +def proximity_to_anomaly( + anomaly_raster_profile: Union[profiles.Profile, dict], + anomaly_raster_data: np.ndarray, + threshold_criteria_value: Union[Tuple[Number, Number], Number], + threshold_criteria: Literal["lower", "higher", "in_between", "outside"], + max_distance: Number, + scaling_range: Tuple[Number, Number] = (1, 0), +) -> Tuple[np.ndarray, Union[profiles.Profile, dict]]: + """Calculate proximity from raster cell to nearest anomaly. + + The criteria for what is anomalous can be defined as a single number and + criteria text of "higher" or "lower". Alternatively, the definition can be + a range where values inside (criteria text of "within") or outside are + marked as anomalous (criteria text of "outside"). If anomaly_raster_profile does + contain "nodata" key, np.nan is assumed to correspond to nodata values. + + Scales proximity values linearly in the given range. The first number in scale_range + denotes the value at the anomaly cells, the second at given maximum_distance. + + Args: + anomaly_raster_profile: The raster profile in which the distances + to the nearest anomalous value are determined. + anomaly_raster_data: The raster data in which the distances + to the nearest anomalous value are determined. + threshold_criteria_value: Value(s) used to define anomalous. + If the threshold criteria requires a tuple of values, + the first value should be the minimum and the second + the maximum value. + threshold_criteria: Method to define anomalous. + max_distance: The maximum distance in the output array beyond which + proximity is considered 0. + scaling_range: Min and max values used for scaling the proximity values. + Defaults to (1, 0). + + Returns: + A 2D numpy array with the distances to anomalies computed + and the original anomaly raster profile. + """ + out_image, anomaly_raster_profile = distance_to_anomaly( + anomaly_raster_profile, anomaly_raster_data, threshold_criteria_value, threshold_criteria, max_distance + ) + out_image = _min_max_scaling(out_image, scaling_range) + + return out_image, anomaly_raster_profile diff --git a/eis_toolkit/raster_processing/reprojecting.py b/eis_toolkit/raster_processing/reprojecting.py index bcc173f1..48bb3b6d 100644 --- a/eis_toolkit/raster_processing/reprojecting.py +++ b/eis_toolkit/raster_processing/reprojecting.py @@ -5,7 +5,7 @@ from rasterio import warp from eis_toolkit.exceptions import MatchingCrsException -from eis_toolkit.raster_processing.resampling import RESAMPLE_METHOD_MAP +from eis_toolkit.raster_processing.resampling import RESAMPLING_METHODS # Core reprojecting functionality used internally by reproject_raster and reproject_and_write_raster @@ -57,7 +57,22 @@ def _reproject_raster( def reproject_raster( raster: rasterio.io.DatasetReader, target_crs: int, - resampling_method: Literal["nearest", "bilinear", "cubic", "average", "gauss", "max", "min"] = "nearest", + resampling_method: Literal[ + "nearest", + "bilinear", + "cubic", + "cubic_spline", + "lanczos", + "average", + "mode", + "max", + "min", + "median", + "q1", + "q3", + "sum", + "rms", + ] = "nearest", ) -> Tuple[np.ndarray, dict]: """Reprojects raster to match given coordinate reference system (EPSG). @@ -77,7 +92,7 @@ def reproject_raster( if target_crs == int(raster.crs.to_string()[5:]): raise MatchingCrsException("Raster is already in the target CRS.") - method = RESAMPLE_METHOD_MAP[resampling_method] + method = RESAMPLING_METHODS[resampling_method] out_image, out_meta = _reproject_raster(raster, target_crs, method) return out_image, out_meta diff --git a/eis_toolkit/raster_processing/resampling.py b/eis_toolkit/raster_processing/resampling.py index 4f29f5c9..aff2989a 100644 --- a/eis_toolkit/raster_processing/resampling.py +++ b/eis_toolkit/raster_processing/resampling.py @@ -9,14 +9,22 @@ from eis_toolkit.exceptions import NumericValueSignException -RESAMPLE_METHOD_MAP = { +RESAMPLING_METHODS = { "nearest": warp.Resampling.nearest, "bilinear": warp.Resampling.bilinear, "cubic": warp.Resampling.cubic, + "cubic_spline": warp.Resampling.cubic_spline, + "lanczos": warp.Resampling.lanczos, "average": warp.Resampling.average, - "gauss": warp.Resampling.gauss, + "mode": warp.Resampling.mode, + # "gauss": warp.Resampling.gauss, # Not available for rasterio.warp apparently "max": warp.Resampling.max, "min": warp.Resampling.min, + "median": warp.Resampling.med, + "q1": warp.Resampling.q1, + "q3": warp.Resampling.q3, + "sum": warp.Resampling.sum, + "rms": warp.Resampling.rms, } @@ -63,7 +71,22 @@ def _resample( def resample( raster: rasterio.io.DatasetReader, resolution: Number, - resampling_method: Literal["nearest", "bilinear", "cubic", "average", "gauss", "max", "min"] = "bilinear", + resampling_method: Literal[ + "nearest", + "bilinear", + "cubic", + "cubic_spline", + "lanczos", + "average", + "mode", + "max", + "min", + "median", + "q1", + "q3", + "sum", + "rms", + ] = "bilinear", ) -> Tuple[np.ndarray, dict]: """Resamples raster according to given resolution. @@ -84,6 +107,6 @@ def resample( if resolution <= 0: raise NumericValueSignException(f"Expected a positive value for resolution: {resolution})") - method = RESAMPLE_METHOD_MAP[resampling_method] + method = RESAMPLING_METHODS[resampling_method] out_image, out_meta = _resample(raster, resolution, method) return out_image, out_meta diff --git a/eis_toolkit/raster_processing/unifying.py b/eis_toolkit/raster_processing/unifying.py index 8cb959c3..f756064c 100644 --- a/eis_toolkit/raster_processing/unifying.py +++ b/eis_toolkit/raster_processing/unifying.py @@ -8,7 +8,7 @@ from rasterio.profiles import Profile from eis_toolkit.exceptions import InvalidParameterValueException -from eis_toolkit.raster_processing.resampling import RESAMPLE_METHOD_MAP +from eis_toolkit.raster_processing.resampling import RESAMPLING_METHODS def _calculate_snapped_grid( @@ -128,7 +128,22 @@ def _unify_raster_grids( def unify_raster_grids( base_raster: rasterio.io.DatasetReader, rasters_to_unify: Sequence[rasterio.io.DatasetReader], - resampling_method: Literal["nearest", "bilinear", "cubic", "average", "gauss", "max", "min"] = "nearest", + resampling_method: Literal[ + "nearest", + "bilinear", + "cubic", + "cubic_spline", + "lanczos", + "average", + "mode", + "max", + "min", + "median", + "q1", + "q3", + "sum", + "rms", + ] = "nearest", masking: Optional[Literal["extents", "full"]] = "extents", ) -> List[Tuple[np.ndarray, Profile]]: """Unifies given rasters with the base raster. @@ -159,6 +174,6 @@ def unify_raster_grids( if len(rasters_to_unify) == 0: raise InvalidParameterValueException("Rasters to unify is empty.") - method = RESAMPLE_METHOD_MAP[resampling_method] + method = RESAMPLING_METHODS[resampling_method] out_rasters = _unify_raster_grids(base_raster, rasters_to_unify, method, masking) return out_rasters diff --git a/eis_toolkit/training_data_tools/points_to_raster.py b/eis_toolkit/training_data_tools/points_to_raster.py new file mode 100644 index 00000000..d9a86dde --- /dev/null +++ b/eis_toolkit/training_data_tools/points_to_raster.py @@ -0,0 +1,201 @@ +import math +from numbers import Number + +import geopandas +import numpy as np +import pandas as pd +from beartype import beartype +from beartype.typing import Literal, Optional, Tuple, Union +from rasterio import profiles, transform +from scipy.ndimage import binary_dilation + +from eis_toolkit.exceptions import ( + EmptyDataFrameException, + InvalidColumnException, + InvalidDataShapeException, + NonMatchingCrsException, + NonNumericDataException, +) +from eis_toolkit.utilities.checks.raster import check_raster_profile + + +def _get_pixel_amount_radius(radius: int, x: Number, y: Number) -> int: + raster_radius = math.sqrt(x**2 + y**2) # RADIUS OF A SINGLE PIXEL + r = radius / raster_radius + return math.ceil(r) if r - math.floor(r) >= 0.5 else math.floor(r) + + +def _get_kernel_size(radius: int) -> tuple[int, int]: + size = 1 + (radius * 2) + return size, radius + + +def _create_grid(size: int, radius) -> tuple[np.ndarray, np.ndarray]: + y = np.arange(-radius, size - radius) + x = np.arange(-radius, size - radius) + y, x = np.meshgrid(y, x) + return x, y + + +def _basic_kernel(radius: int, value: Number) -> np.ndarray: + size, _ = _get_kernel_size(radius) + + x, y = _create_grid(size, radius) + mask = x**2 + y**2 <= radius**2 + kernel = np.zeros((size, size)) + kernel[mask] = value + + return kernel + + +def _create_local_buffer( + array: np.ndarray, + radius: int, + target_value: Number, +) -> np.ndarray: + kernel = _basic_kernel(radius, target_value) + array = np.squeeze(array) if array.ndim >= 3 else array + + return binary_dilation(array == target_value, structure=kernel) + + +def _create_buffer_around_labels( + array: np.ndarray, + radius: int = 1, + target_value: Number = 1, + buffer: Optional[str] = None, + overwrite_nodata: bool = False, +) -> np.ndarray: + out_array = np.copy(array) + out_array = _create_local_buffer( + array=out_array, + radius=radius, + target_value=target_value, + ) + + if buffer == "avg": + out_array = np.where(out_array, target_value, 0) + out_array = np.where((array != 0) & (out_array != 0), (array + out_array) * 0.5, (array + out_array)) + elif buffer == "max": + out_array = np.where(out_array, target_value, 0) + out_array = np.where(array != 0, np.maximum(array, out_array), out_array) + elif buffer == "min": + out_array = np.where(out_array, target_value, 0) + out_array = np.where((array != 0) & (out_array != 0), np.minimum(array, out_array), (array + out_array)) + else: + out_array = np.where(out_array, target_value, array) + + if overwrite_nodata is False: + out_array = np.where(np.isnan(array), np.nan, out_array) + + return out_array + + +def _point_to_raster(raster_array, raster_meta, geodataframe, attribute, radius, buffer): + + width = raster_meta.get("width") + height = raster_meta.get("height") + + raster_transform = raster_meta.get("transform") + + left = raster_transform[2] + top = raster_transform[5] + right = left + width * raster_transform[0] + bottom = top + height * raster_transform[4] + + geodataframe = geodataframe.cx[left:right, bottom:top] + + if attribute is not None: + values = geodataframe[attribute] + else: + values = [1] + + positives_rows, positives_cols = transform.rowcol( + raster_transform, geodataframe.geometry.x, geodataframe.geometry.y + ) + raster_array[positives_rows, positives_cols] = values + + unique_values = list(set(values)) + + if radius is not None: + x = raster_transform[0] + y = raster_transform[4] + radius = _get_pixel_amount_radius(radius, x, y) + for target_value in unique_values: + raster_array = _create_buffer_around_labels(raster_array, radius, target_value, buffer) + + return raster_array + + +@beartype +def points_to_raster( + geodataframe: geopandas.GeoDataFrame, + raster_profile: Union[profiles.Profile, dict], + attribute: Optional[str] = None, + radius: Optional[Number] = None, + buffer: Optional[Literal["min", "avg", "max"]] = None, +) -> Tuple[np.ndarray, Union[profiles.Profile, dict]]: + """Convert a GeoDataFrame of points into a binary raster using a provided base raster profile. + + Accepts a base raster profile and a geodataframe with points to be converted to binary raster. + By default, the points are assigned a value of 1, and all other areas are set to 0. If an + attribute is provided, the raster will take the corresponding values from the attribute column + in the GeoDataFrame instead of 1. The base raster profile defines the template for the raster's + extent, resolution, and projection. Optionally when the pixel size is square, a radius can be + applied around each point (with units consistent with the raster profile) to expand the point's + influence within the raster. In the case of overlapping radii with different attribute values, + a buffer can be used to resolve the conflict by selecting the minimum, maximum, or average value + from the overlapping pixels. + + Args: + geodataframe: The geodataframe points set to be converted into raster. + attribute: Values to be be assigned to the geodataframe. + radius: Radius to be applied around the geodataframe with units consistent with raster profile. + buffer: Buffers the matrix value when two or more radii with different attribute value overlap. + 'avg': performs averaging of the two attribute value + 'min': minimum of the two attribute values + 'max': maximum of the two attribute values + + Returns: + A tuple containing the output raster as a NumPy array and updated metadata. + + Raises: + EmptyDataFrameException: The input GeoDataFrame is empty. + NonMatchingCrsException: The raster and geodataframe are not in the same CRS. + InvalidColumnException: The attribute column was not found in geodataframe. + NonNumericDataException: Some numeric parameters have invalid values. + InvalidDataShapeException: The pixel size is not square. + """ + + if geodataframe.empty: + raise EmptyDataFrameException("Expected geodataframe to contain geometries.") + + if raster_profile.get("crs") != geodataframe.crs: + raise NonMatchingCrsException("Expected coordinate systems to match between raster and GeoDataFrame.") + + if attribute is not None: + + if attribute not in geodataframe.columns: + raise InvalidColumnException(f"Attribute '{attribute}' not found in the geodataframe") + + if not pd.to_numeric(geodataframe[attribute], errors="coerce").notna().all(): + raise NonNumericDataException(f"Values in the '{attribute}' column are non numeric type") + + if radius is not None: + transform = raster_profile.get("transform") + x = transform[0] + y = transform[4] + + if x**2 != y**2: + raise InvalidDataShapeException("The pixel size in the raster are not square.") + + check_raster_profile(raster_profile=raster_profile) + + raster_width = raster_profile.get("width") + raster_height = raster_profile.get("height") + + raster_array = np.zeros((raster_height, raster_width)) + + out_array = _point_to_raster(raster_array, raster_profile, geodataframe, attribute, radius, buffer) + + return out_array, raster_profile diff --git a/eis_toolkit/training_data_tools/random_sampling.py b/eis_toolkit/training_data_tools/random_sampling.py new file mode 100644 index 00000000..e785be2f --- /dev/null +++ b/eis_toolkit/training_data_tools/random_sampling.py @@ -0,0 +1,97 @@ +import geopandas as gpd +import numpy as np +import rasterio +import rasterio.transform +from beartype import beartype +from beartype.typing import Tuple, Union +from rasterio import profiles +from shapely.geometry import Point + +from eis_toolkit.exceptions import EmptyDataFrameException, NumericValueSignException + + +def _random_sampling( + indices: np.ndarray, + values: np.ndarray, + sample_number: int, + random_seed: int, +) -> np.ndarray: + + indices_negatives = indices[values == 0] + + total_negatives = min(indices_negatives.size, sample_number) + + np.random.seed(random_seed) + negative_indices = np.random.choice(indices_negatives.shape[0], total_negatives, replace=False) + Negative_sample = indices_negatives[negative_indices] + + return Negative_sample + + +@beartype +def generate_negatives( + raster_array: np.ndarray, + raster_profile: Union[profiles.Profile, dict], + sample_number: int, + random_seed: int = 48, +) -> Tuple[gpd.GeoDataFrame, np.ndarray, Union[profiles.Profile, dict]]: + """Generate probable negatives from binary raster array with marked positives. + + Generates a list of random negative points from a binary raster array, + ensuring that these negatives do not overlap with the already marked positive + points. The positives can include points with or without attribute and radius, + as in the points_to_raster tool. + + Args: + raster_array: Binary raster array with marked positives. + raster_profile: The raster profile determining the output raster grid properties. + sample_number: maximum number of negatives to be generated. + random_seed: Seed for generating random negatives. + + Returns: + A tuple containing the shapely points, output raster as a NumPy array and updated metadata. + + Raises: + EmptyDataException: The raster array is empty. + NumericValueSignException: The sample number is negative or zero. + """ + + if raster_array.size == 0: + raise EmptyDataFrameException("Expected non empty raster array.") + + if sample_number <= 0: + raise NumericValueSignException("The sample number should be always be greater than zero") + + out_array = np.copy(raster_array) + + total_rows = out_array.shape[0] + total_cols = out_array.shape[1] + + indices = np.arange(total_rows * total_cols) + + indices = indices.reshape(-1, 1) + + values = out_array.reshape(-1, 1) + + sampled_negatives = _random_sampling( + indices=indices, values=values, sample_number=sample_number, random_seed=random_seed + ) + + sampled_negatives = sampled_negatives.reshape(1, -1) + + row = sampled_negatives // total_cols + row = row[0] + + col = np.mod(sampled_negatives, total_cols) + col = col[0] + + out_array[row, col] = -1 + + x, y = rasterio.transform.xy(raster_profile["transform"], row, col) + + points = [Point(x[i], y[i]) for i in range(len(x))] + + sample_negative = gpd.GeoDataFrame(geometry=points) + sample_negative.set_crs(raster_profile["crs"], allow_override=True, inplace=True) + + return sample_negative, out_array, raster_profile diff --git a/eis_toolkit/transformations/binarize.py b/eis_toolkit/transformations/binarize.py index 4487983c..3a2a63a2 100644 --- a/eis_toolkit/transformations/binarize.py +++ b/eis_toolkit/transformations/binarize.py @@ -56,7 +56,8 @@ def binarize( # type: ignore[no-any-unimported] NonMatchingParameterLengthsException: The input does not match the number of selected bands. """ bands = list(range(1, raster.count + 1)) if bands is None else bands - nodata = cast_scalar_to_int(raster.nodata if nodata is None else nodata) + if nodata: + nodata = cast_scalar_to_int(raster.nodata if nodata is None else nodata) if check_raster_bands(raster, bands) is False: raise InvalidRasterBandException("Invalid band selection.") @@ -73,11 +74,13 @@ def binarize( # type: ignore[no-any-unimported] band_array = raster.read(bands[i]) inital_dtype = band_array.dtype - band_mask = np.isin(band_array, nodata) + if nodata: + band_mask = np.isin(band_array, nodata) band_array = _binarize(band_array, threshold=thresholds[i]) - band_array = np.where(band_mask, nodata, band_array) + if nodata: + band_array = np.where(band_mask, nodata, band_array) - if not check_dtype_for_int(nodata): + if not nodata or not check_dtype_for_int(nodata): band_array = band_array.astype(inital_dtype) else: band_array = band_array.astype(np.min_scalar_type(nodata)) diff --git a/eis_toolkit/transformations/clip.py b/eis_toolkit/transformations/clip.py index 22bd5aa6..d089e0b0 100644 --- a/eis_toolkit/transformations/clip.py +++ b/eis_toolkit/transformations/clip.py @@ -99,11 +99,13 @@ def clip_transform( # type: ignore[no-any-unimported] inital_dtype = band_array.dtype band_array = cast_array_to_float(band_array, cast_int=True) - band_array = nodata_to_nan(band_array, nodata_value=nodata) + if nodata: + band_array = nodata_to_nan(band_array, nodata_value=nodata) band_array = _clip_transform(band_array, limits=limits[i]) - band_array = nan_to_nodata(band_array, nodata_value=nodata) + if nodata: + band_array = nan_to_nodata(band_array, nodata_value=nodata) band_array = cast_array_to_int(band_array, scalar=nodata, initial_dtype=inital_dtype) band_array = np.expand_dims(band_array, axis=0) diff --git a/eis_toolkit/transformations/coda/alr.py b/eis_toolkit/transformations/coda/alr.py index d9b5af9b..ab880c8c 100644 --- a/eis_toolkit/transformations/coda/alr.py +++ b/eis_toolkit/transformations/coda/alr.py @@ -1,88 +1,88 @@ -from numbers import Number - -import numpy as np -import pandas as pd -from beartype import beartype -from beartype.typing import Optional, Sequence - -from eis_toolkit.exceptions import InvalidColumnException, NumericValueSignException -from eis_toolkit.utilities.aitchison_geometry import _closure -from eis_toolkit.utilities.checks.compositional import check_compositional_data -from eis_toolkit.utilities.miscellaneous import rename_columns_by_pattern - - -@beartype -def _alr_transform(df: pd.DataFrame, columns: Sequence[str], denominator_column: str) -> pd.DataFrame: - - ratios = df[columns].div(df[denominator_column], axis=0) - return np.log(ratios) - - -@beartype -def alr_transform( - df: pd.DataFrame, column: Optional[str] = None, keep_denominator_column: bool = False -) -> pd.DataFrame: - """ - Perform an additive logratio transformation on the data. - - Args: - df: A dataframe of compositional data. - column: The name of the column to be used as the denominator column. - keep_denominator_column: Whether to include the denominator column in the result. If True, the returned - dataframe retains its original shape. - - Returns: - A new dataframe containing the ALR transformed data. - - Raises: - InvalidColumnException: The input column isn't found in the dataframe. - InvalidCompositionException: Data is not normalized to the expected value. - NumericValueSignException: Data contains zeros or negative values. - """ - check_compositional_data(df) - - if column is not None and column not in df.columns: - raise InvalidColumnException(f"The column {column} was not found in the dataframe.") - - column = column if column is not None else df.columns[-1] - - columns = [col for col in df.columns] - - if not keep_denominator_column and column in columns: - columns.remove(column) - - return rename_columns_by_pattern(_alr_transform(df, columns, column)) - - -@beartype -def _inverse_alr(df: pd.DataFrame, denominator_column: str, scale: Number = 1.0) -> pd.DataFrame: - dfc = df.copy() - - if denominator_column not in dfc.columns.values: - # Add the denominator column - dfc[denominator_column] = 0.0 - - return _closure(np.exp(dfc), scale) - - -@beartype -def inverse_alr(df: pd.DataFrame, denominator_column: str, scale: Number = 1.0) -> pd.DataFrame: - """ - Perform the inverse transformation for a set of ALR transformed data. - - Args: - df: A dataframe of ALR transformed compositional data. - denominator_column: The name of the denominator column. - scale: The value to which each composition should be normalized. Eg., if the composition is expressed - as percentages, scale=100. - - Returns: - A dataframe containing the inverse transformed data. - - Raises: - NumericValueSignException: The input scale value is zero or less. - """ - if scale <= 0: - raise NumericValueSignException("The scale value should be positive.") - - return _inverse_alr(df, denominator_column, scale) +from numbers import Number + +import numpy as np +import pandas as pd +from beartype import beartype +from beartype.typing import Optional, Sequence + +from eis_toolkit.exceptions import InvalidColumnException, NumericValueSignException +from eis_toolkit.utilities.aitchison_geometry import _closure +from eis_toolkit.utilities.checks.compositional import check_in_simplex_sample_space +from eis_toolkit.utilities.miscellaneous import rename_columns_by_pattern + + +@beartype +def _alr_transform(df: pd.DataFrame, columns: Sequence[str], denominator_column: str) -> pd.DataFrame: + + ratios = df[columns].div(df[denominator_column], axis=0) + return np.log(ratios) + + +@beartype +def alr_transform( + df: pd.DataFrame, column: Optional[str] = None, keep_denominator_column: bool = False +) -> pd.DataFrame: + """ + Perform an additive logratio transformation on the data. + + Args: + df: A dataframe of compositional data. + column: The name of the column to be used as the denominator column. + keep_denominator_column: Whether to include the denominator column in the result. If True, the returned + dataframe retains its original shape. + + Returns: + A new dataframe containing the ALR transformed data. + + Raises: + InvalidColumnException: The input column isn't found in the dataframe. + InvalidCompositionException: Data is not normalized to the expected value. + NumericValueSignException: Data contains zeros or negative values. + """ + check_in_simplex_sample_space(df) + + if column is not None and column not in df.columns: + raise InvalidColumnException(f"The column {column} was not found in the dataframe.") + + column = column if column is not None else df.columns[-1] + + columns = [col for col in df.columns] + + if not keep_denominator_column and column in columns: + columns.remove(column) + + return rename_columns_by_pattern(_alr_transform(df, columns, column)) + + +@beartype +def _inverse_alr(df: pd.DataFrame, denominator_column: str, scale: Number = 1.0) -> pd.DataFrame: + dfc = df.copy() + + if denominator_column not in dfc.columns.values: + # Add the denominator column + dfc[denominator_column] = 0.0 + + return _closure(np.exp(dfc), scale) + + +@beartype +def inverse_alr(df: pd.DataFrame, denominator_column: str, scale: Number = 1.0) -> pd.DataFrame: + """ + Perform the inverse transformation for a set of ALR transformed data. + + Args: + df: A dataframe of ALR transformed compositional data. + denominator_column: The name of the denominator column. + scale: The value to which each composition should be normalized. Eg., if the composition is expressed + as percentages, scale=100. + + Returns: + A dataframe containing the inverse transformed data. + + Raises: + NumericValueSignException: The input scale value is zero or less. + """ + if scale <= 0: + raise NumericValueSignException("The scale value should be positive.") + + return _inverse_alr(df, denominator_column, scale) diff --git a/eis_toolkit/transformations/coda/clr.py b/eis_toolkit/transformations/coda/clr.py index d5a82215..a6022b4f 100644 --- a/eis_toolkit/transformations/coda/clr.py +++ b/eis_toolkit/transformations/coda/clr.py @@ -1,79 +1,79 @@ -from numbers import Number - -import numpy as np -import pandas as pd -from beartype import beartype -from beartype.typing import Optional, Sequence -from scipy.stats import gmean - -from eis_toolkit.exceptions import NumericValueSignException -from eis_toolkit.utilities.aitchison_geometry import _closure -from eis_toolkit.utilities.checks.compositional import check_compositional_data -from eis_toolkit.utilities.miscellaneous import rename_columns, rename_columns_by_pattern - - -@beartype -def _centered_ratio(row: pd.Series) -> pd.Series: - - return row / gmean(row) - - -@beartype -def _clr_transform(df: pd.DataFrame) -> pd.DataFrame: - - dfc = df.copy() - dfc = dfc.apply(_centered_ratio, axis=1) - - return np.log(dfc) - - -@beartype -def clr_transform(df: pd.DataFrame) -> pd.DataFrame: - """ - Perform a centered logratio transformation on the data. - - Args: - df: A dataframe of compositional data. - - Returns: - A new dataframe containing the CLR transformed data. - - Raises: - InvalidCompositionException: Data is not normalized to the expected value. - NumericValueSignException: Data contains zeros or negative values. - """ - check_compositional_data(df) - return rename_columns_by_pattern(_clr_transform(df)) - - -@beartype -def _inverse_clr(df: pd.DataFrame, colnames: Optional[Sequence[str]] = None, scale: Number = 1.0) -> pd.DataFrame: - inverse = _closure(np.exp(df), scale) - - if colnames is not None: - return rename_columns(inverse, colnames) - - return inverse - - -@beartype -def inverse_clr(df: pd.DataFrame, colnames: Optional[Sequence[str]] = None, scale: Number = 1.0) -> pd.DataFrame: - """ - Perform the inverse transformation for a set of CLR transformed data. - - Args: - df: A dataframe of CLR transformed compositional data. - colnames: List of column names to rename the columns to. - scale: The value to which each composition should be normalized. Eg., if the composition is expressed - as percentages, scale=100. - - Returns: - A dataframe containing the inverse transformed data. - - Raises: - NumericValueSignException: The input scale value is zero or less. - """ - if scale <= 0: - raise NumericValueSignException("The scale value should be positive.") - - return _inverse_clr(df, colnames, scale) +from numbers import Number + +import numpy as np +import pandas as pd +from beartype import beartype +from beartype.typing import Optional, Sequence +from scipy.stats import gmean + +from eis_toolkit.exceptions import NumericValueSignException +from eis_toolkit.utilities.aitchison_geometry import _closure +from eis_toolkit.utilities.checks.compositional import check_in_simplex_sample_space +from eis_toolkit.utilities.miscellaneous import rename_columns, rename_columns_by_pattern + + +@beartype +def _centered_ratio(row: pd.Series) -> pd.Series: + + return row / gmean(row) + + +@beartype +def _clr_transform(df: pd.DataFrame) -> pd.DataFrame: + + dfc = df.copy() + dfc = dfc.apply(_centered_ratio, axis=1) + + return np.log(dfc) + + +@beartype +def clr_transform(df: pd.DataFrame) -> pd.DataFrame: + """ + Perform a centered logratio transformation on the data. + + Args: + df: A dataframe of compositional data. + + Returns: + A new dataframe containing the CLR transformed data. + + Raises: + InvalidCompositionException: Data is not normalized to the expected value. + NumericValueSignException: Data contains zeros or negative values. + """ + check_in_simplex_sample_space(df) + return rename_columns_by_pattern(_clr_transform(df)) + + +@beartype +def _inverse_clr(df: pd.DataFrame, colnames: Optional[Sequence[str]] = None, scale: Number = 1.0) -> pd.DataFrame: + inverse = _closure(np.exp(df), scale) + + if colnames is not None: + return rename_columns(inverse, colnames) + + return inverse + + +@beartype +def inverse_clr(df: pd.DataFrame, colnames: Optional[Sequence[str]] = None, scale: Number = 1.0) -> pd.DataFrame: + """ + Perform the inverse transformation for a set of CLR transformed data. + + Args: + df: A dataframe of CLR transformed compositional data. + colnames: List of column names to rename the columns to. + scale: The value to which each composition should be normalized. Eg., if the composition is expressed + as percentages, scale=100. + + Returns: + A dataframe containing the inverse transformed data. + + Raises: + NumericValueSignException: The input scale value is zero or less. + """ + if scale <= 0: + raise NumericValueSignException("The scale value should be positive.") + + return _inverse_clr(df, colnames, scale) diff --git a/eis_toolkit/transformations/coda/ilr.py b/eis_toolkit/transformations/coda/ilr.py index 421ca99d..ed8831f1 100644 --- a/eis_toolkit/transformations/coda/ilr.py +++ b/eis_toolkit/transformations/coda/ilr.py @@ -1,100 +1,100 @@ -import numpy as np -import pandas as pd -from beartype import beartype -from beartype.typing import Sequence -from scipy.stats import gmean - -from eis_toolkit.exceptions import InvalidColumnException, InvalidCompositionException, InvalidParameterValueException -from eis_toolkit.utilities.checks.compositional import check_compositional_data -from eis_toolkit.utilities.checks.dataframe import check_columns_valid -from eis_toolkit.utilities.checks.parameter import check_lists_overlap, check_numeric_value_sign - - -@beartype -def _calculate_ilr_scaling_factor(c1: int, c2: int) -> np.float64: - """ - Calculate the scaling factor for the ILR transform. - - Args: - c1: The cardinality of the first subcomposition. - c2: The cardinality of the second subcomposition. - - Returns: - The scaling factor. - - Raises: - InvalidParameterValueException: One or both of the input values are zero or negative. - """ - if not (check_numeric_value_sign(c1) and check_numeric_value_sign(c2)): - raise InvalidParameterValueException("Input values must both be positive integers.") - - return np.sqrt((c1 * c2) / np.float64(c1 + c2)) - - -@beartype -def _geometric_mean_logratio( - row: pd.Series, subcomposition_1: Sequence[str], subcomposition_2: Sequence[str] -) -> np.float64: - - numerator = gmean(row[subcomposition_1]) - denominator = gmean(row[subcomposition_2]) - return np.log(numerator / denominator) - - -@beartype -def _single_ilr_transform( - df: pd.DataFrame, subcomposition_1: Sequence[str], subcomposition_2: Sequence[str] -) -> pd.Series: - - dfc = df.copy() - - c1 = len(subcomposition_1) - c2 = len(subcomposition_2) - - # A Series to hold the transformed rows - ilr_values = pd.Series([0.0] * df.shape[0]) - - for idx, row in dfc.iterrows(): - ilr_values[idx] = _geometric_mean_logratio(row, subcomposition_1, subcomposition_2) - - ilr_values = _calculate_ilr_scaling_factor(c1, c2) * ilr_values - - return ilr_values - - -@beartype -def single_ilr_transform( - df: pd.DataFrame, subcomposition_1: Sequence[str], subcomposition_2: Sequence[str] -) -> pd.Series: - """ - Perform a single isometric logratio transformation on the provided subcompositions. - - Returns ILR balances. Column order matters. - - Args: - df: A dataframe of shape [N, D] of compositional data. - subcomposition_1: Names of the columns in the numerator part of the ratio. - subcomposition_2: Names of the columns in the denominator part of the ratio. - - Returns: - A series of length N containing the transforms. - - Raises: - InvalidColumnException: One or more subcomposition columns are not found in the input dataframe. - InvalidCompositionException: Data is not normalized to the expected value or - one or more columns are found in both subcompositions. - InvalidParameterValueException: At least one subcomposition provided was empty. - NumericValueSignException: Data contains zeros or negative values. - """ - check_compositional_data(df) - - if not (subcomposition_1 and subcomposition_2): - raise InvalidParameterValueException("A subcomposition should contain at least one column.") - - if not (check_columns_valid(df, subcomposition_1) and check_columns_valid(df, subcomposition_2)): - raise InvalidColumnException("Not all of the input columns were found in the input dataframe.") - - if check_lists_overlap(subcomposition_1, subcomposition_2): - raise InvalidCompositionException("The subcompositions overlap.") - - return _single_ilr_transform(df, subcomposition_1, subcomposition_2) +import numpy as np +import pandas as pd +from beartype import beartype +from beartype.typing import Sequence +from scipy.stats import gmean + +from eis_toolkit.exceptions import InvalidColumnException, InvalidCompositionException, InvalidParameterValueException +from eis_toolkit.utilities.checks.compositional import check_in_simplex_sample_space +from eis_toolkit.utilities.checks.dataframe import check_columns_valid +from eis_toolkit.utilities.checks.parameter import check_lists_overlap, check_numeric_value_sign + + +@beartype +def _calculate_ilr_scaling_factor(c1: int, c2: int) -> np.float64: + """ + Calculate the scaling factor for the ILR transform. + + Args: + c1: The cardinality of the first subcomposition. + c2: The cardinality of the second subcomposition. + + Returns: + The scaling factor. + + Raises: + InvalidParameterValueException: One or both of the input values are zero or negative. + """ + if not (check_numeric_value_sign(c1) and check_numeric_value_sign(c2)): + raise InvalidParameterValueException("Input values must both be positive integers.") + + return np.sqrt((c1 * c2) / np.float64(c1 + c2)) + + +@beartype +def _geometric_mean_logratio( + row: pd.Series, subcomposition_1: Sequence[str], subcomposition_2: Sequence[str] +) -> np.float64: + + numerator = gmean(row[subcomposition_1]) + denominator = gmean(row[subcomposition_2]) + return np.log(numerator / denominator) + + +@beartype +def _single_ilr_transform( + df: pd.DataFrame, subcomposition_1: Sequence[str], subcomposition_2: Sequence[str] +) -> pd.Series: + + dfc = df.copy() + + c1 = len(subcomposition_1) + c2 = len(subcomposition_2) + + # A Series to hold the transformed rows + ilr_values = pd.Series([0.0] * df.shape[0]) + + for idx, row in dfc.iterrows(): + ilr_values[idx] = _geometric_mean_logratio(row, subcomposition_1, subcomposition_2) + + ilr_values = _calculate_ilr_scaling_factor(c1, c2) * ilr_values + + return ilr_values + + +@beartype +def single_ilr_transform( + df: pd.DataFrame, subcomposition_1: Sequence[str], subcomposition_2: Sequence[str] +) -> pd.Series: + """ + Perform a single isometric logratio transformation on the provided subcompositions. + + Returns ILR balances. Column order matters. + + Args: + df: A dataframe of shape [N, D] of compositional data. + subcomposition_1: Names of the columns in the numerator part of the ratio. + subcomposition_2: Names of the columns in the denominator part of the ratio. + + Returns: + A series of length N containing the transforms. + + Raises: + InvalidColumnException: One or more subcomposition columns are not found in the input dataframe. + InvalidCompositionException: Data is not normalized to the expected value or + one or more columns are found in both subcompositions. + InvalidParameterValueException: At least one subcomposition provided was empty. + NumericValueSignException: Data contains zeros or negative values. + """ + check_in_simplex_sample_space(df) + + if not (subcomposition_1 and subcomposition_2): + raise InvalidParameterValueException("A subcomposition should contain at least one column.") + + if not (check_columns_valid(df, subcomposition_1) and check_columns_valid(df, subcomposition_2)): + raise InvalidColumnException("Not all of the input columns were found in the input dataframe.") + + if check_lists_overlap(subcomposition_1, subcomposition_2): + raise InvalidCompositionException("The subcompositions overlap.") + + return _single_ilr_transform(df, subcomposition_1, subcomposition_2) diff --git a/eis_toolkit/transformations/coda/pairwise.py b/eis_toolkit/transformations/coda/pairwise.py index c64d195f..593517b8 100644 --- a/eis_toolkit/transformations/coda/pairwise.py +++ b/eis_toolkit/transformations/coda/pairwise.py @@ -1,73 +1,73 @@ -from numbers import Number - -import numpy as np -import pandas as pd -from beartype import beartype - -from eis_toolkit.exceptions import InvalidColumnException, InvalidParameterValueException -from eis_toolkit.utilities.checks.dataframe import check_dataframe_contains_zeros - - -@beartype -def _single_pairwise_logratio(numerator: Number, denominator: Number) -> np.float64: - - return np.log(numerator / float(denominator)) - - -@beartype -def single_pairwise_logratio(numerator: Number, denominator: Number) -> np.float64: - """ - Perform a pairwise logratio transformation on the given values. - - Args: - numerator: The numerator in the ratio. - denominator: The denominator in the ratio. - - Returns: - The transformed value. - - Raises: - InvalidParameterValueException: One or both input values are zero. - """ - if numerator == 0 or denominator == 0: - raise InvalidParameterValueException("Input values cannot be zero.") - - return _single_pairwise_logratio(numerator, denominator) - - -@beartype -def _pairwise_logratio(df: pd.DataFrame, numerator_column: str, denominator_column: str) -> pd.Series: - dfc = df.copy() - - result = pd.Series([0.0] * df.shape[0]) - - for idx, row in dfc.iterrows(): - result[idx] = single_pairwise_logratio(row[numerator_column], row[denominator_column]) - - return result - - -@beartype -def pairwise_logratio(df: pd.DataFrame, numerator_column: str, denominator_column: str) -> pd.Series: - """ - Perform a pairwise logratio transformation on the given columns. - - Args: - df: The dataframe containing the columns to use in the transformation. - numerator_column: The name of the column to use as the numerator column. - denominator_column: The name of the column to use as the denominator. - - Returns: - A series containing the transformed values. - - Raises: - InvalidColumnException: One or both of the input columns are not found in the dataframe. - InvalidParameterValueException: The input columns contain at least one zero value. - """ - if numerator_column not in df.columns or denominator_column not in df.columns: - raise InvalidColumnException("At least one input column is not found in the dataframe.") - - if check_dataframe_contains_zeros(df[[numerator_column, denominator_column]]): - raise InvalidParameterValueException("The input columns contain at least one zero value.") - - return _pairwise_logratio(df, numerator_column, denominator_column) +from numbers import Number + +import numpy as np +import pandas as pd +from beartype import beartype + +from eis_toolkit.exceptions import InvalidColumnException, InvalidParameterValueException +from eis_toolkit.utilities.checks.dataframe import check_dataframe_contains_zeros + + +@beartype +def _single_pairwise_logratio(numerator: Number, denominator: Number) -> np.float64: + + return np.log(numerator / float(denominator)) + + +@beartype +def single_pairwise_logratio(numerator: Number, denominator: Number) -> np.float64: + """ + Perform a pairwise logratio transformation on the given values. + + Args: + numerator: The numerator in the ratio. + denominator: The denominator in the ratio. + + Returns: + The transformed value. + + Raises: + InvalidParameterValueException: One or both input values are zero. + """ + if numerator == 0 or denominator == 0: + raise InvalidParameterValueException("Input values cannot be zero.") + + return _single_pairwise_logratio(numerator, denominator) + + +@beartype +def _pairwise_logratio(df: pd.DataFrame, numerator_column: str, denominator_column: str) -> pd.Series: + dfc = df.copy() + + result = pd.Series([0.0] * df.shape[0]) + + for idx, row in dfc.iterrows(): + result[idx] = single_pairwise_logratio(row[numerator_column], row[denominator_column]) + + return result + + +@beartype +def pairwise_logratio(df: pd.DataFrame, numerator_column: str, denominator_column: str) -> pd.Series: + """ + Perform a pairwise logratio transformation on the given columns. + + Args: + df: The dataframe containing the columns to use in the transformation. + numerator_column: The name of the column to use as the numerator column. + denominator_column: The name of the column to use as the denominator. + + Returns: + A series containing the transformed values. + + Raises: + InvalidColumnException: One or both of the input columns are not found in the dataframe. + InvalidParameterValueException: The input columns contain at least one zero value. + """ + if numerator_column not in df.columns or denominator_column not in df.columns: + raise InvalidColumnException("At least one input column is not found in the dataframe.") + + if check_dataframe_contains_zeros(df[[numerator_column, denominator_column]]): + raise InvalidParameterValueException("The input columns contain at least one zero value.") + + return _pairwise_logratio(df, numerator_column, denominator_column) diff --git a/eis_toolkit/transformations/coda/plr.py b/eis_toolkit/transformations/coda/plr.py index d0721db0..3b58cca0 100644 --- a/eis_toolkit/transformations/coda/plr.py +++ b/eis_toolkit/transformations/coda/plr.py @@ -1,126 +1,126 @@ -import numpy as np -import pandas as pd -from beartype import beartype -from scipy.stats import gmean - -from eis_toolkit.exceptions import InvalidColumnException, InvalidParameterValueException -from eis_toolkit.utilities.checks.compositional import check_compositional_data -from eis_toolkit.utilities.checks.parameter import check_numeric_value_sign -from eis_toolkit.utilities.miscellaneous import rename_columns_by_pattern - - -@beartype -def _calculate_plr_scaling_factor(c: int) -> np.float64: - """ - Calculate the scaling factor for the PLR transform. - - Args: - c: The cardinality of the remaining parts in the composition. - - Returns: - The scaling factor used performing a single PLR transform for a composition. - - Raises: - InvalidParameterValueException: The input value is zero or negative. - """ - if not (check_numeric_value_sign(c)): - raise InvalidParameterValueException("The input value must be a positive integer.") - - return np.sqrt(c / np.float64(1 + c)) - - -@beartype -def _single_plr_transform_by_index(df: pd.DataFrame, column_ind: int) -> pd.Series: - - dfc = df.copy() - # The denominator is a subcomposition of all the parts "to the right" of the column: - columns = [col for col in df.columns] - subcomposition = [columns[i] for i in range(len(columns)) if i > column_ind] - c = len(subcomposition) - scaling_factor = _calculate_plr_scaling_factor(c) - - # A series to hold the transformed rows - plr_values = pd.Series([0.0] * df.shape[0]) - - for idx, row in dfc.iterrows(): - plr_values[idx] = scaling_factor * np.log(row.iloc[column_ind] / gmean(row[subcomposition])) - - return plr_values - - -@beartype -def _single_plr_transform(df: pd.DataFrame, column: str) -> pd.Series: - - idx = df.columns.get_loc(column) - - return _single_plr_transform_by_index(df, idx) - - -@beartype -def single_plr_transform(df: pd.DataFrame, column: str) -> pd.Series: - """ - Perform a pivot logratio transformation on the selected column. - - Pivot logratio is a special case of ILR, where the numerator in the ratio is always a single - part and the denominator all of the parts to the right in the ordered list of parts. - - Column order matters. - - Args: - df: A dataframe of shape [N, D] of compositional data. - column: The name of the numerator column to use for the transformation. - - Returns: - A series of length N containing the transforms. - - Raises: - InvalidColumnException: The input column isn't found in the dataframe, or there are no columns - to the right of the given column. - InvalidCompositionException: Data is not normalized to the expected value. - NumericValueSignException: Data contains zeros or negative values. - """ - check_compositional_data(df) - - if column not in df.columns: - raise InvalidColumnException(f"The column {column} was not found in the dataframe.") - - idx = df.columns.get_loc(column) - - if idx == len(df.columns) - 1: - raise InvalidColumnException() - - return _single_plr_transform(df, column) - - -@beartype -def _plr_transform(df: pd.DataFrame) -> pd.DataFrame: - dfc = df.copy() - - # A dataframe to hold the transformed values - plr_values = pd.DataFrame(0.0, index=dfc.index, columns=dfc.columns[:-1]) - - for i in range(len(df.columns) - 1): - plr_values.iloc[:, i] = _single_plr_transform_by_index(dfc, i) - - return plr_values - - -@beartype -def plr_transform(df: pd.DataFrame) -> pd.DataFrame: - """ - Perform a pivot logratio transformation on the dataframe, returning the full set of transforms. - - Args: - df: A dataframe of shape [N, D] of compositional data. - - Returns: - A dataframe of shape [N, D-1] containing the set of PLR transformed data. - - Raises: - InvalidColumnException: The data contains one or more zeros. - InvalidCompositionException: Data is not normalized to the expected value. - NumericValueSignException: Data contains zeros or negative values. - """ - check_compositional_data(df) - - return rename_columns_by_pattern(_plr_transform(df)) +import numpy as np +import pandas as pd +from beartype import beartype +from scipy.stats import gmean + +from eis_toolkit.exceptions import InvalidColumnException, InvalidParameterValueException +from eis_toolkit.utilities.checks.compositional import check_in_simplex_sample_space +from eis_toolkit.utilities.checks.parameter import check_numeric_value_sign +from eis_toolkit.utilities.miscellaneous import rename_columns_by_pattern + + +@beartype +def _calculate_plr_scaling_factor(c: int) -> np.float64: + """ + Calculate the scaling factor for the PLR transform. + + Args: + c: The cardinality of the remaining parts in the composition. + + Returns: + The scaling factor used performing a single PLR transform for a composition. + + Raises: + InvalidParameterValueException: The input value is zero or negative. + """ + if not (check_numeric_value_sign(c)): + raise InvalidParameterValueException("The input value must be a positive integer.") + + return np.sqrt(c / np.float64(1 + c)) + + +@beartype +def _single_plr_transform_by_index(df: pd.DataFrame, column_ind: int) -> pd.Series: + + dfc = df.copy() + # The denominator is a subcomposition of all the parts "to the right" of the column: + columns = [col for col in df.columns] + subcomposition = [columns[i] for i in range(len(columns)) if i > column_ind] + c = len(subcomposition) + scaling_factor = _calculate_plr_scaling_factor(c) + + # A series to hold the transformed rows + plr_values = pd.Series([0.0] * df.shape[0]) + + for idx, row in dfc.iterrows(): + plr_values[idx] = scaling_factor * np.log(row.iloc[column_ind] / gmean(row[subcomposition])) + + return plr_values + + +@beartype +def _single_plr_transform(df: pd.DataFrame, column: str) -> pd.Series: + + idx = df.columns.get_loc(column) + + return _single_plr_transform_by_index(df, idx) + + +@beartype +def single_plr_transform(df: pd.DataFrame, column: str) -> pd.Series: + """ + Perform a pivot logratio transformation on the selected column. + + Pivot logratio is a special case of ILR, where the numerator in the ratio is always a single + part and the denominator all of the parts to the right in the ordered list of parts. + + Column order matters. + + Args: + df: A dataframe of shape [N, D] of compositional data. + column: The name of the numerator column to use for the transformation. + + Returns: + A series of length N containing the transforms. + + Raises: + InvalidColumnException: The input column isn't found in the dataframe, or there are no columns + to the right of the given column. + InvalidCompositionException: Data is not normalized to the expected value. + NumericValueSignException: Data contains zeros or negative values. + """ + check_in_simplex_sample_space(df) + + if column not in df.columns: + raise InvalidColumnException(f"The column {column} was not found in the dataframe.") + + idx = df.columns.get_loc(column) + + if idx == len(df.columns) - 1: + raise InvalidColumnException() + + return _single_plr_transform(df, column) + + +@beartype +def _plr_transform(df: pd.DataFrame) -> pd.DataFrame: + dfc = df.copy() + + # A dataframe to hold the transformed values + plr_values = pd.DataFrame(0.0, index=dfc.index, columns=dfc.columns[:-1]) + + for i in range(len(df.columns) - 1): + plr_values.iloc[:, i] = _single_plr_transform_by_index(dfc, i) + + return plr_values + + +@beartype +def plr_transform(df: pd.DataFrame) -> pd.DataFrame: + """ + Perform a pivot logratio transformation on the dataframe, returning the full set of transforms. + + Args: + df: A dataframe of shape [N, D] of compositional data. + + Returns: + A dataframe of shape [N, D-1] containing the set of PLR transformed data. + + Raises: + InvalidColumnException: The data contains one or more zeros. + InvalidCompositionException: Data is not normalized to the expected value. + NumericValueSignException: Data contains zeros or negative values. + """ + check_in_simplex_sample_space(df) + + return rename_columns_by_pattern(_plr_transform(df)) diff --git a/eis_toolkit/transformations/linear.py b/eis_toolkit/transformations/linear.py index 8d642b0f..c95299fe 100644 --- a/eis_toolkit/transformations/linear.py +++ b/eis_toolkit/transformations/linear.py @@ -5,12 +5,8 @@ from beartype import beartype from beartype.typing import Optional, Sequence, Tuple -from eis_toolkit.exceptions import ( - InvalidParameterValueException, - InvalidRasterBandException, - NonMatchingParameterLengthsException, -) -from eis_toolkit.utilities.checks.parameter import check_minmax_position, check_parameter_length +from eis_toolkit.exceptions import InvalidRasterBandException, NonMatchingParameterLengthsException +from eis_toolkit.utilities.checks.parameter import check_parameter_length from eis_toolkit.utilities.checks.raster import check_raster_bands from eis_toolkit.utilities.miscellaneous import ( cast_array_to_float, @@ -90,12 +86,14 @@ def z_score_normalization( # type: ignore[no-any-unimported] for i in range(0, len(bands)): band_array = raster.read(bands[i]) band_array = cast_array_to_float(band_array, cast_int=True) - band_array = replace_values(band_array, values_to_replace=[nodata, np.inf], replace_value=np.nan) + if nodata: + band_array = replace_values(band_array, values_to_replace=[nodata, np.inf], replace_value=np.nan) band_array, mean_array, sd_array = _z_score_normalization(band_array.astype(np.float64)) band_array = truncate_decimal_places(band_array, decimal_places=out_decimals) - band_array = nan_to_nodata(band_array, nodata_value=nodata) + if nodata: + band_array = nan_to_nodata(band_array, nodata_value=nodata) band_array = cast_array_to_float(band_array, scalar=nodata, cast_float=True) band_array = np.expand_dims(band_array, axis=0) @@ -164,10 +162,6 @@ def min_max_scaling( # type: ignore[no-any-unimported] if check_parameter_length(bands, new_range) is False: raise NonMatchingParameterLengthsException("Invalid new_range length") - for item in new_range: - if not check_minmax_position(item): - raise InvalidParameterValueException(f"Invalid min-max values provided: {item}") - expanded_args = expand_and_zip(bands, new_range) new_range = [element[1] for element in expanded_args] @@ -177,12 +171,14 @@ def min_max_scaling( # type: ignore[no-any-unimported] for i in range(0, len(bands)): band_array = raster.read(bands[i]) band_array = cast_array_to_float(band_array, cast_int=True) - band_array = replace_values(band_array, values_to_replace=[nodata, np.inf], replace_value=np.nan) + if nodata: + band_array = replace_values(band_array, values_to_replace=[nodata, np.inf], replace_value=np.nan) band_array = _min_max_scaling(band_array.astype(np.float64), new_range=new_range[i]) band_array = truncate_decimal_places(band_array, decimal_places=out_decimals) - band_array = nan_to_nodata(band_array, nodata_value=nodata) + if nodata: + band_array = nan_to_nodata(band_array, nodata_value=nodata) band_array = cast_array_to_float(band_array, scalar=nodata, cast_float=True) band_array = np.expand_dims(band_array, axis=0) diff --git a/eis_toolkit/transformations/logarithmic.py b/eis_toolkit/transformations/logarithmic.py index 241646f5..b59ec99b 100644 --- a/eis_toolkit/transformations/logarithmic.py +++ b/eis_toolkit/transformations/logarithmic.py @@ -101,7 +101,8 @@ def log_transform( # type: ignore[no-any-unimported] for i in range(0, len(bands)): band_array = raster.read(bands[i]) band_array = cast_array_to_float(band_array, cast_int=True) - band_array = replace_values(band_array, values_to_replace=[nodata, np.inf], replace_value=np.nan) + if nodata: + band_array = replace_values(band_array, values_to_replace=[nodata, np.inf], replace_value=np.nan) band_array[band_array <= 0] = np.nan if log_transform[i] == "ln": @@ -112,7 +113,8 @@ def log_transform( # type: ignore[no-any-unimported] band_array = _log_transform_log10(band_array.astype(np.float64)) band_array = truncate_decimal_places(band_array, decimal_places=out_decimals) - band_array = nan_to_nodata(band_array, nodata_value=nodata) + if nodata: + band_array = nan_to_nodata(band_array, nodata_value=nodata) band_array = cast_array_to_float(band_array, scalar=nodata, cast_float=True) band_array = np.expand_dims(band_array, axis=0) diff --git a/eis_toolkit/transformations/sigmoid.py b/eis_toolkit/transformations/sigmoid.py index 20147c12..401608fe 100644 --- a/eis_toolkit/transformations/sigmoid.py +++ b/eis_toolkit/transformations/sigmoid.py @@ -100,12 +100,14 @@ def sigmoid_transform( # type: ignore[no-any-unimported] for i in range(0, len(bands)): band_array = raster.read(bands[i]) band_array = cast_array_to_float(band_array, cast_int=True) - band_array = replace_values(band_array, values_to_replace=[nodata, np.inf], replace_value=np.nan) + if nodata: + band_array = replace_values(band_array, values_to_replace=[nodata, np.inf], replace_value=np.nan) band_array = _sigmoid_transform(band_array.astype(np.float64), bounds=bounds[i], slope=slope[i], center=center) band_array = truncate_decimal_places(band_array, decimal_places=out_decimals) - band_array = nan_to_nodata(band_array, nodata_value=nodata) + if nodata: + band_array = nan_to_nodata(band_array, nodata_value=nodata) band_array = cast_array_to_float(band_array, scalar=nodata, cast_float=True) band_array = np.expand_dims(band_array, axis=0) diff --git a/eis_toolkit/transformations/winsorize.py b/eis_toolkit/transformations/winsorize.py index b450febe..913d6e3b 100644 --- a/eis_toolkit/transformations/winsorize.py +++ b/eis_toolkit/transformations/winsorize.py @@ -129,13 +129,15 @@ def winsorize( # type: ignore[no-any-unimported] inital_dtype = band_array.dtype band_array = cast_array_to_float(band_array, cast_int=True) - band_array = nodata_to_nan(band_array, nodata_value=nodata) + if nodata: + band_array = nodata_to_nan(band_array, nodata_value=nodata) band_array, calculated_lower, calculated_upper = _winsorize( band_array, percentiles=percentiles[i], inside=inside ) - band_array = nan_to_nodata(band_array, nodata_value=nodata) + if nodata: + band_array = nan_to_nodata(band_array, nodata_value=nodata) band_array = cast_array_to_int(band_array, scalar=nodata, initial_dtype=inital_dtype) band_array = np.expand_dims(band_array, axis=0) @@ -148,12 +150,14 @@ def winsorize( # type: ignore[no-any-unimported] current_transform = f"transformation {i + 1}" current_settings = { "band_origin": bands[i], - "percentile_lower": cast_scalar_to_int(percentiles[i][0]), - "percentile_upper": cast_scalar_to_int(percentiles[i][1]), - "calculated_lower": cast_scalar_to_int(calculated_lower), - "calculated_upper": cast_scalar_to_int(calculated_upper), "nodata": cast_scalar_to_int(nodata), } + if calculated_lower: + current_settings["percentile_lower"] = cast_scalar_to_int(percentiles[i][0]) + current_settings["calculated_lower"] = cast_scalar_to_int(calculated_lower) + if calculated_upper: + current_settings["percentile_upper"] = cast_scalar_to_int(percentiles[i][1]) + current_settings["calculated_upper"] = cast_scalar_to_int(calculated_upper) out_settings[current_transform] = current_settings diff --git a/eis_toolkit/utilities/aitchison_geometry.py b/eis_toolkit/utilities/aitchison_geometry.py index ca2b03e8..80796637 100644 --- a/eis_toolkit/utilities/aitchison_geometry.py +++ b/eis_toolkit/utilities/aitchison_geometry.py @@ -1,47 +1,45 @@ -from numbers import Number - -import numpy as np -import pandas as pd -from beartype import beartype -from beartype.typing import Optional - - -@beartype -def _normalize(row: pd.Series, sum_value: Number = 1.0) -> pd.Series: - """ - Normalize the series to a given value. - - If no value is provided, normalize to 1. - - Args: - row: The series to normalize. - - Returns: - A series containing the normalized values. - """ - scale = np.float64(np.sum(row)) / sum_value - return np.divide(row, scale) - - -@beartype -def _closure(df: pd.DataFrame, scale: Optional[Number] = None) -> pd.DataFrame: - """ - Perform the closure operation on the dataframe. - - If a scale value representing the constant sum is not provided, assumes the standard simplex, - in which the sum of th components of each composition vector is 1. - - Args: - df: A dataframe of shape (N, D) compositional data. - scale: The sum to which each data row should result to. Default is 1. - - Returns: - A new dataframe of shape (N, D) where each row has been normalized to the given scale value. - """ - - dfc = df.copy() - - for idx, row in df.iterrows(): - dfc.iloc[idx] = _normalize(row, scale) if scale is not None else _normalize(row) - - return dfc +from numbers import Number + +import numpy as np +import pandas as pd +from beartype import beartype +from beartype.typing import Optional + + +@beartype +def _normalize(row: pd.Series, sum_value: Number = 1.0) -> pd.Series: + """ + Normalize the series to a given value. + + If no value is provided, normalize to 1. + + Args: + row: The series to normalize. + + Returns: + A series containing the normalized values. + """ + scale = np.float64(np.sum(row)) / sum_value + return np.divide(row, scale) + + +@beartype +def _closure(df: pd.DataFrame, scale: Optional[Number] = None) -> pd.DataFrame: + """ + Perform the closure operation on the dataframe. + + Assumes the standard simplex, in which the sum of the components of each composition vector is 1. + + Args: + df: A dataframe of shape (N, D) compositional data. + + Returns: + A new dataframe of shape (N, D) where each row has been normalized to 1. + """ + + dfc = df.copy() + + for idx, row in df.iterrows(): + dfc.iloc[idx] = _normalize(row, scale) if scale is not None else _normalize(row) + + return dfc diff --git a/eis_toolkit/utilities/checks/compositional.py b/eis_toolkit/utilities/checks/compositional.py index 7b68c51f..771f7940 100644 --- a/eis_toolkit/utilities/checks/compositional.py +++ b/eis_toolkit/utilities/checks/compositional.py @@ -1,51 +1,49 @@ -import numpy as np -import pandas as pd -from beartype import beartype -from beartype.typing import Optional - -from eis_toolkit.exceptions import InvalidCompositionException, NumericValueSignException -from eis_toolkit.utilities.checks.dataframe import check_dataframe_contains_only_positive_numbers - - -@beartype -def check_in_simplex_sample_space(df: pd.DataFrame, expected_sum: Optional[np.float64] = None) -> None: - """ - Check that the compositions represented by the data rows belong to a simplex sample space. - - Checks that each composition is normalized to the same value. - - Args: - df: The dataframe to check. - expected_sum: The expected sum of each row. If None, simply checks that the sum of each row is equal. - - Raises: - InvalidCompositionException: Data contains NaN values or is not normalized to the expected value. - NumericValueSignException: Data contains zeros or negative values. - """ - check_compositional_data(df) - - df_sum = np.sum(df, axis=1) - expected_sum = expected_sum if expected_sum is not None else df_sum.iloc[0] - if len(df_sum[df_sum.iloc[:] != expected_sum]) != 0: - raise InvalidCompositionException("Not each composition is normalized to the same value.") - - return None - - -@beartype -def check_compositional_data(df: pd.DataFrame) -> None: - """ - Check that each compositional data point belongs to the set of positive real numbers. - - Args: - df: The dataframe to check. - - Raises: - InvalidCompositionException: Data contains NaN values. - NumericValueSignException: Data contains zeros or negative values. - """ - if df.isnull().values.any(): - raise InvalidCompositionException("Data contains NaN values.") - - if not check_dataframe_contains_only_positive_numbers(df): - raise NumericValueSignException("Data contains zeros or negative values.") +from numbers import Number + +import pandas as pd +from beartype import beartype + +from eis_toolkit.exceptions import InvalidCompositionException, NumericValueSignException +from eis_toolkit.utilities.checks.dataframe import check_dataframe_contains_only_positive_numbers + + +@beartype +def check_in_simplex_sample_space(df: pd.DataFrame, tolerance: Number = 0.0001) -> None: + """ + Check that the compositions represented by the data rows belong to a simplex sample space. + + Checks that data has not NaN values. + Checks that each compositional data point belongs to the set of positive real numbers. + Checks that input dataframe is closed to either 1 or 100. + + Args: + df: The dataframe to check. + tolerance: Small tolerance value to allow floating-point imprecision. + + Returns: + None. + + Raises: + InvalidCompositionException: Data is not within the expected simplex sample space. + NumericValueSignException: Data contains zeros or negative values. + """ + if df.isnull().values.any(): + raise InvalidCompositionException("Data contains NaN values.") + + if not check_dataframe_contains_only_positive_numbers(df): + raise NumericValueSignException("Data contains zeros or negative values.") + + row_sums = df.sum(axis=1) + closed_to_one = (row_sums - 1).abs() < tolerance + closed_to_hundred = (row_sums - 100).abs() < tolerance + closed_to_million = (row_sums - 1e6).abs() < tolerance + closed_to_billion = (row_sums - 1e9).abs() < tolerance + + is_valid = closed_to_one.all() or closed_to_hundred.all() or closed_to_million.all() or closed_to_billion.all() + + if not is_valid: + raise InvalidCompositionException( + f"Input data is not closed to 1, 100 (%), 10^6 (ppm) or 10^9 (ppb) within tolerance of {tolerance}." + ) + + return None diff --git a/eis_toolkit/utilities/checks/raster.py b/eis_toolkit/utilities/checks/raster.py index a93a32fc..78b52a66 100644 --- a/eis_toolkit/utilities/checks/raster.py +++ b/eis_toolkit/utilities/checks/raster.py @@ -3,6 +3,7 @@ import rasterio.transform from beartype import beartype from beartype.typing import Iterable, Sequence, Union +from rasterio.crs import CRS from eis_toolkit.exceptions import InvalidParameterValueException @@ -37,21 +38,25 @@ def check_matching_crs(objects: Iterable) -> bool: Returns: True if everything matches, False if not. """ - epsg_list = [] + crs_list = [] for object in objects: if not isinstance(object, (rasterio.profiles.Profile, dict)): if not object.crs: return False epsg = object.crs.to_epsg() - epsg_list.append(epsg) + crs_list.append(epsg) else: if "crs" in object: - epsg_list.append(object["crs"]) + crs_object = object["crs"] + if type(crs_object) == CRS: + crs_list.append(crs_object.to_epsg()) + else: + crs_list.append(crs_object) else: return False - if len(set(epsg_list)) != 1: + if len(set(crs_list)) != 1: return False return True @@ -119,6 +124,7 @@ def check_raster_grids( Returns: True if gridding and optionally bounds matches, False if not. """ + if not check_matching_crs(raster_profiles): return False if not check_matching_pixel_alignment(raster_profiles): diff --git a/eis_toolkit/utilities/gdal.py b/eis_toolkit/utilities/gdal.py new file mode 100644 index 00000000..7ce98be3 --- /dev/null +++ b/eis_toolkit/utilities/gdal.py @@ -0,0 +1,20 @@ +from contextlib import contextmanager + +from osgeo import gdal + + +@contextmanager +def toggle_gdal_exceptions(): + """Toggle GDAL exceptions using a context manager. + + If the exceptions are already enabled, this function will do nothing. + """ + already_has_exceptions_enabled = False + try: + if gdal.GetUseExceptions() != 0: + already_has_exceptions_enabled = True + gdal.UseExceptions() + yield + finally: + if not already_has_exceptions_enabled: + gdal.DontUseExceptions() diff --git a/eis_toolkit/utilities/miscellaneous.py b/eis_toolkit/utilities/miscellaneous.py index 7e0fcb02..145bc8fd 100644 --- a/eis_toolkit/utilities/miscellaneous.py +++ b/eis_toolkit/utilities/miscellaneous.py @@ -1,11 +1,9 @@ -from contextlib import contextmanager from numbers import Number import numpy as np import pandas as pd from beartype import beartype from beartype.typing import Any, List, Optional, Sequence, Tuple, Union -from osgeo import gdal from rasterio import transform from shapely.geometry import Point @@ -358,20 +356,3 @@ def row_points( point_xs, point_ys = zip(*[raster_transform * (col + 0.5, row + 0.5) for col in cols]) # point_xs, point_ys = transform.xy(transform=raster_transform, cols=cols, rows=row) return [Point(x, y) for x, y in zip(point_xs, point_ys)] - - -@contextmanager -def toggle_gdal_exceptions(): - """Toggle GDAL exceptions using a context manager. - - If the exceptions are already enabled, this function will do nothing. - """ - already_has_exceptions_enabled = False - try: - if gdal.GetUseExceptions() != 0: - already_has_exceptions_enabled = True - gdal.UseExceptions() - yield - finally: - if not already_has_exceptions_enabled: - gdal.DontUseExceptions() diff --git a/eis_toolkit/utilities/nodata.py b/eis_toolkit/utilities/nodata.py index 3637e475..43456762 100644 --- a/eis_toolkit/utilities/nodata.py +++ b/eis_toolkit/utilities/nodata.py @@ -4,7 +4,7 @@ import numpy as np import rasterio from beartype import beartype -from beartype.typing import Any, Callable, Dict, Optional, Sequence, Tuple, Union +from beartype.typing import Any, Callable, Dict, Literal, Optional, Sequence, Tuple, Union from rasterio import profiles from eis_toolkit.exceptions import InvalidParameterValueException @@ -101,6 +101,60 @@ def convert_raster_nodata( return out_image, out_meta +@beartype +def replace_with_nodata( + input_raster: rasterio.io.DatasetReader, + target_value: Number, + nodata_value: Optional[Number] = None, + replace_condition: Literal[ + "equal", "less_than", "greater_than", "less_than_or_equal", "greater_than_or_equal" + ] = "equal", +) -> Tuple[np.ndarray, dict]: + """ + Replace pixels values with nodata for a raster. + + Can be used either for replacing all pixels with certain value with nodata, or for replacing all pixels with + values less than, greater than, less than or equal to, or greater than or equal to the target value with nodata. + + Args: + input_raster: Input raster dataset. + target_value: Value to be replaced with nodata. + nodata_value: Value that will be used as nodata. If not provided, nodata is determined from input raster. + replace_condition: Whether to replace pixels with certain value or values less than, greater than, less than or + equal, or greater than or equal to target_value with nodata. + + Returns: + The input raster data with specified pixels replaced with nodata, and updated metadata. + + Raises: + InvalidParameterValueException: Nodata is provided and not found in the input raster. + """ + + if nodata_value is None: + nodata_value = input_raster.nodata + if nodata_value is None: + raise InvalidParameterValueException("Nodata not provided and not found in the input raster.") + + raster_arr = input_raster.read() + + if replace_condition == "equal": + values_to_replace = target_value + elif replace_condition == "less_than": + values_to_replace = raster_arr[raster_arr < target_value].tolist() + elif replace_condition == "greater_than": + values_to_replace = raster_arr[raster_arr > target_value].tolist() + elif replace_condition == "less_than_or_equal": + values_to_replace = raster_arr[raster_arr <= target_value].tolist() + elif replace_condition == "greater_than_or_equal": + values_to_replace = raster_arr[raster_arr >= target_value].tolist() + + out_image = replace_values(raster_arr, values_to_replace, nodata_value) + out_meta = input_raster.meta.copy() + out_meta["nodata"] = nodata_value + + return out_image, out_meta + + @beartype def nodata_to_nan(data: np.ndarray, nodata_value: Number) -> np.ndarray: """Convert specified nodata_value to np.nan. diff --git a/eis_toolkit/utilities/raster.py b/eis_toolkit/utilities/raster.py index 2228002f..648e36da 100644 --- a/eis_toolkit/utilities/raster.py +++ b/eis_toolkit/utilities/raster.py @@ -5,6 +5,7 @@ import rasterio from beartype import beartype from beartype.typing import Literal, Sequence, Tuple, Union +from pyproj import CRS from rasterio import profiles, transform from eis_toolkit.exceptions import ( @@ -170,3 +171,53 @@ def profile_from_extent_and_pixel_size( } raster_profile = profiles.Profile(raster_meta) return raster_profile + + +@beartype +def base_profile( + extent: Tuple[Number, Number, Number, Number], + pixel_size: Number, + crs: CRS, + dtype=np.float32, + count: int = 1, + nodata: float = -9999.0, + driver: str = "GTiff", + compress: str = "lzw", + round_strategy: Literal["nearest", "up", "down"] = "up", +) -> profiles.Profile: + """ + Create a raster profile from given extent, pixel size, CRS and other optional parameters. + + If extent and pixel size do not match exactly, i.e. raster width and height + calcalated from bounds and pixel size are not integers, rounding for the width and + height is performed. + + Args: + extent: Raster extent in the form (coord_west, coord_east, coord_south, coord_north). + pixel_size: Desired pixel size. If two values are provided, first is used for x and second for y. + If one value is provided, the value is used for both directions. + crs: Coordinate reference system. + dtype: Data type. + count: Number of raster bands. + nodata: Nodata value. + driver: Driver, defaults to GeoTIFF file format. + compress: Compression mode. + round_strategy: The rounding strategy if extent and pixel size do not match exactly. + Defaults to "up". + + Returns: + Rasterio profile. + """ + if any(bound is None for bound in extent) or pixel_size is None or pixel_size <= 0: + raise InvalidParameterValueException( + "Expected positive pixel size and defined extent in absence of base raster. " + + f"Pixel size: {pixel_size}, extent: {extent}." + ) + profile = profile_from_extent_and_pixel_size(extent, (pixel_size, pixel_size), round_strategy) + profile["crs"] = crs + profile["dtype"] = dtype + profile["count"] = count + profile["nodata"] = nodata + profile["driver"] = driver + profile["compress"] = compress + return profile diff --git a/eis_toolkit/vector_processing/_distance_computation_numba.py b/eis_toolkit/vector_processing/_distance_computation_numba.py new file mode 100644 index 00000000..9a109ba5 --- /dev/null +++ b/eis_toolkit/vector_processing/_distance_computation_numba.py @@ -0,0 +1,218 @@ +from numbers import Number + +import geopandas as gpd +import numpy as np +from beartype import beartype +from beartype.typing import Optional, Union +from numba import njit, prange +from rasterio import profiles, transform + +from eis_toolkit import exceptions +from eis_toolkit.utilities.checks.raster import check_raster_profile + + +@beartype +def distance_computation_numba( + geodataframe: gpd.GeoDataFrame, raster_profile: Union[profiles.Profile, dict], max_distance: Optional[Number] = None +) -> np.ndarray: + """ + Calculate distance from each raster cell (centre) to the nearest input geometry. + + Pixels on top of input geometries are assigned distance of 0. + + Args: + geodataframe: The GeoDataFrame with geometries to determine distance to. + raster_profile: The raster profile of the raster in which the distances + to the nearest geometry are determined. + max_distance: The maximum distance in the output array. Pixels beyond this + distance will be assigned `max_distance` value. + + Returns: + A 2D numpy array with the distances computed. + + Raises: + NonMatchingCrsException: The input raster profile and geodataframe have mismatching CRS. + EmptyDataFrameException: The input geodataframe is empty. + NumericValueSignException: Max distance is defined and is not a positive number. + """ + if raster_profile.get("crs") != geodataframe.crs: + raise exceptions.NonMatchingCrsException( + "Expected coordinate systems to match between raster and GeoDataFrame." + ) + if geodataframe.empty: + raise exceptions.EmptyDataFrameException("Expected GeoDataFrame to not be empty.") + if max_distance is not None and max_distance <= 0: + raise exceptions.NumericValueSignException("Expected max distance to be a positive number.") + + check_raster_profile(raster_profile=raster_profile) + + raster_width = raster_profile.get("width") + raster_height = raster_profile.get("height") + raster_transform = raster_profile.get("transform") + + # Generate the grid of raster cell center points + raster_points = _generate_raster_points(raster_width, raster_height, raster_transform) + + # Initialize lists needed for Numba-compatible calculations + segment_coords = [] # These will also contain points coords, if present + segment_indices = [0] # Start index + polygon_coords = [] + polygon_indices = [0] # Start index + + for geometry in geodataframe.geometry: + if geometry.geom_type == "Polygon": + coords = list(geometry.exterior.coords) + for x, y in coords: + polygon_coords.extend([x, y]) + polygon_indices.append(len(polygon_coords) // 2) + segments = [ + (coords[i][0], coords[i][1], coords[i + 1][0], coords[i + 1][1]) for i in range(len(coords) - 1) + ] + + elif geometry.geom_type == "MultiPolygon": + # For MultiPolygon, iterate over each polygon + segments = [] + for poly in geometry.geoms: + coords = list(poly.exterior.coords) + for x, y in coords: + polygon_coords.extend([x, y]) + polygon_indices.append(len(polygon_coords) // 2) + + # Add polygon boundary as segments for distance calculations + segments.extend( + [(coords[i][0], coords[i][1], coords[i + 1][0], coords[i + 1][1]) for i in range(len(coords) - 1)] + ) + + elif geometry.geom_type == "LineString": + coords = list(geometry.coords) + segments = [ + (coords[i][0], coords[i][1], coords[i + 1][0], coords[i + 1][1]) for i in range(len(coords) - 1) + ] + + elif geometry.geom_type == "MultiLineString": + # For MultiLineString, iterate through each line string component + segments = [] + for line in geometry.geoms: + coords = list(line.coords) + segments.extend( + [(coords[i][0], coords[i][1], coords[i + 1][0], coords[i + 1][1]) for i in range(len(coords) - 1)] + ) + + elif geometry.geom_type == "Point": + segments = [(geometry.x, geometry.y)] + + elif geometry.geom_type == "MultiPoint": + # For MultiPoint, iterate over each point and add as individual (x, y) tuples + segments = [(point.x, point.y) for point in geometry.geoms] + + else: + raise exceptions.GeometryTypeException(f"Encountered unsupported geometry type: {geometry.geom_type}.") + + segment_coords.extend(segments) + segment_indices.append(len(segment_coords)) # End index for this geometry's segments + + # Convert all lists to numpy arrays + segment_coords = np.array(segment_coords, dtype=np.float64) + segment_indices = np.array(segment_indices, dtype=np.int64) + polygon_coords = np.array(polygon_coords, dtype=np.float64) + polygon_indices = np.array(polygon_indices, dtype=np.int64) + + distance_matrix = _compute_distances_core( + raster_points, + segment_coords, + segment_indices, + polygon_coords, + polygon_indices, + raster_width, + raster_height, + max_distance, + ) + + return distance_matrix + + +@njit(parallel=True) +def _compute_distances_core( + raster_points: np.ndarray, + segment_coords: np.ndarray, + segment_indices: np.ndarray, + polygon_coords: np.ndarray, + polygon_indices: np.ndarray, + width: int, + height: int, + max_distance: Optional[Number], +) -> np.ndarray: + distance_matrix = np.full((height, width), np.inf) + for i in prange(len(raster_points)): + px, py = raster_points[i] + min_dist = np.inf + + # Check if the point is inside any polygon, if polygons are present + if len(polygon_indices) > 1 and _point_in_polygon(px, py, polygon_coords, polygon_indices): + min_dist = 0 # Set distance to zero if point is inside a polygon + else: + # Only calculate distance to segments if point is outside all polygons + for j in range(len(segment_indices) - 1): + for k in range(segment_indices[j], segment_indices[j + 1]): + # Case 1: Point + if len(segment_coords[k]) == 2: + x1, y1 = segment_coords[k] + dist = np.sqrt((px - x1) ** 2 + (py - y1) ** 2) + # Case 2: Line segment + else: + x1, y1, x2, y2 = segment_coords[k] + dist = _point_to_segment_distance(px, py, x1, y1, x2, y2) + if dist < min_dist: + min_dist = dist + + # Apply max_distance threshold if specified + if max_distance is not None: + min_dist = min(min_dist, max_distance) + + # Update the distance matrix + distance_matrix[i // width, i % width] = min_dist + return distance_matrix + + +def _generate_raster_points(width: int, height: int, affine_transform: transform.Affine) -> np.ndarray: + """Generate a full grid of points from the raster dimensions and affine transform.""" + cols, rows = np.meshgrid(np.arange(width), np.arange(height)) + cols = cols.ravel() + rows = rows.ravel() + xs, ys = transform.xy(affine_transform, rows, cols, offset="center") + return np.column_stack([xs, ys]) + + +@njit +def _point_to_segment_distance(px: Number, py: Number, x1: Number, y1: Number, x2: Number, y2: Number) -> np.ndarray: + """Calculate the minimum distance from a point to a line segment.""" + dx, dy = x2 - x1, y2 - y1 + if dx == 0 and dy == 0: + # Segment is a point (Should not happen) + return np.sqrt((px - x1) ** 2 + (py - y1) ** 2) + t = max(0, min(1, ((px - x1) * dx + (py - y1) * dy) / (dx * dx + dy * dy))) + nearest_x, nearest_y = x1 + t * dx, y1 + t * dy + return np.sqrt((px - nearest_x) ** 2 + (py - nearest_y) ** 2) + + +@njit +def _point_in_polygon(px: Number, py: Number, polygon_coords: np.ndarray, polygon_indices: np.ndarray) -> bool: + """Determine if a point is inside any polygon using the ray-casting algorithm.""" + for p_start, p_end in zip(polygon_indices[:-1], polygon_indices[1:]): + inside = False + xints = 0.0 + n = p_end - p_start + p1x, p1y = polygon_coords[2 * p_start], polygon_coords[2 * p_start + 1] + for i in range(n + 1): + p2x, p2y = polygon_coords[2 * (p_start + i % n)], polygon_coords[2 * (p_start + i % n) + 1] + if py > min(p1y, p2y): + if py <= max(p1y, p2y): + if px <= max(p1x, p2x): + if p1y != p2y: + xints = (py - p1y) * (p2x - p1x) / (p2y - p1y) + p1x + if p1x == p2x or px <= xints: + inside = not inside + p1x, p1y = p2x, p2y + if inside: + return True + return False diff --git a/eis_toolkit/vector_processing/distance_computation.py b/eis_toolkit/vector_processing/distance_computation.py index c18722d4..e51b39e3 100644 --- a/eis_toolkit/vector_processing/distance_computation.py +++ b/eis_toolkit/vector_processing/distance_computation.py @@ -3,90 +3,142 @@ import geopandas as gpd import numpy as np from beartype import beartype -from beartype.typing import Optional, Union -from rasterio import profiles, transform -from shapely.geometry.base import BaseGeometry, BaseMultipartGeometry +from beartype.typing import Optional, Tuple, Union +from rasterio import profiles -from eis_toolkit.exceptions import EmptyDataFrameException, NonMatchingCrsException, NumericValueSignException +from eis_toolkit import exceptions +from eis_toolkit.raster_processing.distance_to_anomaly import distance_to_anomaly from eis_toolkit.utilities.checks.raster import check_raster_profile -from eis_toolkit.utilities.miscellaneous import row_points +from eis_toolkit.vector_processing.rasterize_vector import rasterize_vector @beartype def distance_computation( - geodataframe: gpd.GeoDataFrame, raster_profile: Union[profiles.Profile, dict], max_distance: Optional[Number] = None -) -> np.ndarray: - """Calculate distance from raster cell to nearest geometry. + geodataframe: gpd.GeoDataFrame, + raster_profile: Union[profiles.Profile, dict], + max_distance: Optional[Number] = None, +) -> Tuple[np.ndarray, Union[profiles.Profile, dict]]: + """ + Calculate distance from each raster cell (centre) to the nearest input geometry. + + Pixels on top of input geometries are assigned distance of 0. + + Rasterizes geometries and uses `Distance to anomaly` tool for computation. Args: geodataframe: The GeoDataFrame with geometries to determine distance to. raster_profile: The raster profile of the raster in which the distances to the nearest geometry are determined. - max_distance: The maximum distance in the output array. + max_distance: The maximum distance in the output array. Pixels beyond this + distance will be assigned `max_distance` value. Returns: - A 2D numpy array with the distances computed. + A 2D numpy array with the distances computed and raster profile. Raises: NonMatchingCrsException: The input raster profile and geodataframe have mismatching CRS. EmptyDataFrameException: The input geodataframe is empty. + NumericValueSignException: Max distance is defined and is not a positive number. """ if raster_profile.get("crs") != geodataframe.crs: - raise NonMatchingCrsException("Expected coordinate systems to match between raster and GeoDataFrame.") - if geodataframe.shape[0] == 0: - raise EmptyDataFrameException("Expected GeoDataFrame to not be empty.") + raise exceptions.NonMatchingCrsException( + "Expected coordinate systems to match between raster and GeoDataFrame." + ) + if geodataframe.empty: + raise exceptions.EmptyDataFrameException("Expected GeoDataFrame to not be empty.") if max_distance is not None and max_distance <= 0: - raise NumericValueSignException("Expected max distance to be a positive number.") + raise exceptions.NumericValueSignException("Expected max distance to be a positive number.") check_raster_profile(raster_profile=raster_profile) - raster_width = raster_profile.get("width") - raster_height = raster_profile.get("height") - raster_transform = raster_profile.get("transform") - - distance_matrix = _distance_computation( - raster_width=raster_width, - raster_height=raster_height, - raster_transform=raster_transform, - geodataframe=geodataframe, - ) - if max_distance is not None: - distance_matrix[distance_matrix > max_distance] = max_distance - - return distance_matrix - - -def _calculate_row_distances( - row: int, - cols: np.ndarray, - raster_transform: transform.Affine, - geometries_unary_union: Union[BaseGeometry, BaseMultipartGeometry], -) -> np.ndarray: - row_distances = np.array( - [ - point.distance(geometries_unary_union) - for point in row_points(row=row, cols=cols, raster_transform=raster_transform) - ] - ) - return row_distances - - -def _distance_computation( - raster_width: int, raster_height: int, raster_transform: transform.Affine, geodataframe: gpd.GeoDataFrame -) -> np.ndarray: - - cols = np.arange(raster_width) - rows = np.arange(raster_height) - - geometries_unary_union = geodataframe.geometry.unary_union - - distance_matrix = np.array( - [ - _calculate_row_distances( - row=row, cols=cols, raster_transform=raster_transform, geometries_unary_union=geometries_unary_union - ) - for row in rows - ] + # NOTE: Default value is set to 2 and fill value to 1 for cases where given raster_profile has 0 as nodata + rasterized_data = rasterize_vector( + geodataframe, raster_profile, value_column=None, default_value=2.0, fill_value=1.0 ) - - return distance_matrix + out_array, out_profile = distance_to_anomaly(raster_profile, rasterized_data, 1.5, "higher", max_distance) + return out_array, out_profile + + +# @beartype +# def distance_computation( +# geodataframe: gpd.GeoDataFrame, +# raster_profile: Union[profiles.Profile, dict], +# max_distance: Optional[Number] = None +# ) -> np.ndarray: +# """Calculate distance from raster cell to nearest geometry. + +# Args: +# geodataframe: The GeoDataFrame with geometries to determine distance to. +# raster_profile: The raster profile of the raster in which the distances +# to the nearest geometry are determined. +# max_distance: The maximum distance in the output array. + +# Returns: +# A 2D numpy array with the distances computed. + +# Raises: +# NonMatchingCrsException: The input raster profile and geodataframe have mismatching CRS. +# EmptyDataFrameException: The input geodataframe is empty. +# NumericValueSignException: Max distance is defined and is not a positive number. +# """ +# if raster_profile.get("crs") != geodataframe.crs: +# raise exceptions.NonMatchingCrsException( +# "Expected coordinate systems to match between raster and GeoDataFrame." +# ) +# if geodataframe.shape[0] == 0: +# raise exceptions.EmptyDataFrameException("Expected GeoDataFrame to not be empty.") +# if max_distance is not None and max_distance <= 0: +# raise exceptions.NumericValueSignException("Expected max distance to be a positive number.") + +# check_raster_profile(raster_profile=raster_profile) + +# raster_width = raster_profile.get("width") +# raster_height = raster_profile.get("height") +# raster_transform = raster_profile.get("transform") + +# distance_matrix = _distance_computation( +# raster_width=raster_width, +# raster_height=raster_height, +# raster_transform=raster_transform, +# geodataframe=geodataframe, +# ) +# if max_distance is not None: +# distance_matrix[distance_matrix > max_distance] = max_distance + +# return distance_matrix + + +# def _calculate_row_distances( +# row: int, +# cols: np.ndarray, +# raster_transform: transform.Affine, +# geometries_unary_union: Union[BaseGeometry, BaseMultipartGeometry], +# ) -> np.ndarray: +# row_distances = np.array( +# [ +# point.distance(geometries_unary_union) +# for point in row_points(row=row, cols=cols, raster_transform=raster_transform) +# ] +# ) +# return row_distances + + +# def _distance_computation( +# raster_width: int, raster_height: int, raster_transform: transform.Affine, geodataframe: gpd.GeoDataFrame +# ) -> np.ndarray: + +# cols = np.arange(raster_width) +# rows = np.arange(raster_height) + +# geometries_unary_union = geodataframe.geometry.unary_union + +# distance_matrix = np.array( +# [ +# _calculate_row_distances( +# row=row, cols=cols, raster_transform=raster_transform, geometries_unary_union=geometries_unary_union +# ) +# for row in rows +# ] +# ) + +# return distance_matrix diff --git a/eis_toolkit/vector_processing/idw_interpolation.py b/eis_toolkit/vector_processing/idw_interpolation.py index 2f0fb836..6dc7d92f 100644 --- a/eis_toolkit/vector_processing/idw_interpolation.py +++ b/eis_toolkit/vector_processing/idw_interpolation.py @@ -3,7 +3,7 @@ import geopandas as gpd import numpy as np from beartype import beartype -from beartype.typing import Union +from beartype.typing import Optional, Union from rasterio import profiles, transform from eis_toolkit.exceptions import EmptyDataFrameException, InvalidParameterValueException, NonMatchingCrsException @@ -18,6 +18,7 @@ def _idw_interpolation( raster_height: int, raster_transform: transform.Affine, power: Number, + search_radius: Optional[Number], ) -> np.ndarray: points = np.array(geodataframe.geometry.apply(lambda geom: (geom.x, geom.y)).tolist()) @@ -34,26 +35,47 @@ def _idw_interpolation( y = np.linspace(grid_y_min, grid_y_max, raster_height) y = y[::-1].reshape(-1, 1) - interpolated_values = _idw_core(points[:, 0], points[:, 1], values, x, y, power) + interpolated_values = _idw_core(points[:, 0], points[:, 1], values, x, y, power, search_radius) interpolated_values = interpolated_values.reshape(raster_height, raster_width) return interpolated_values # Distance calculations -def _idw_core(x, y, z, xi, yi: np.ndarray, power: Number) -> np.ndarray: +def _idw_core( + x: np.ndarray, + y: np.ndarray, + z: np.ndarray, + xi: np.ndarray, + yi: np.ndarray, + power: Number, + search_radius: Optional[Number], +) -> np.ndarray: over = np.zeros((len(yi), len(xi))) under = np.zeros((len(yi), len(xi))) for n in range(len(x)): dist = np.hypot(xi - x[n], yi - y[n]) - # Add a small epsilon to avoid division by zero - dist = np.where(dist == 0, 1e-12, dist) - dist = dist**power - over += z[n] / dist - under += 1.0 / dist + # Exclude points outside search radius + if search_radius is not None: + mask = dist <= search_radius + if not np.any(mask): + continue + + # Add a small epsilon to avoid division by zero + dist = np.where(dist[mask] == 0, 1e-12, dist[mask]) ** power + + over[mask] += z[n] / dist + under[mask] += 1.0 / dist + + else: + # Add a small epsilon to avoid division by zero + dist = np.where(dist == 0, 1e-12, dist) ** power + + over += z[n] / dist + under += 1.0 / dist - interpolated_values = over / under + interpolated_values = np.divide(over, under, out=np.full_like(over, np.nan), where=under != 0) return interpolated_values @@ -63,6 +85,7 @@ def idw( target_column: str, raster_profile: Union[profiles.Profile, dict], power: Number = 2, + search_radius: Optional[Number] = None, ) -> np.ndarray: """Calculate inverse distance weighted (IDW) interpolation. @@ -73,6 +96,8 @@ def idw( crs, transform, width and height. power: The value for determining the rate at which the weights decrease. As power increases, the weights for distant points decrease rapidly. Defaults to 2. + search_radius: The search radius within which to consider points for interpolation. + If None, all points are used. Returns: Numpy array containing the interpolated values. @@ -97,7 +122,7 @@ def idw( raster_transform = raster_profile.get("transform") interpolated_values = _idw_interpolation( - geodataframe, target_column, raster_width, raster_height, raster_transform, power + geodataframe, target_column, raster_width, raster_height, raster_transform, power, search_radius ) return interpolated_values diff --git a/eis_toolkit/vector_processing/proximity_computation.py b/eis_toolkit/vector_processing/proximity_computation.py new file mode 100644 index 00000000..8bd6400c --- /dev/null +++ b/eis_toolkit/vector_processing/proximity_computation.py @@ -0,0 +1,55 @@ +from numbers import Number + +import geopandas as gpd +import numpy as np +from beartype import beartype +from beartype.typing import Tuple, Union +from rasterio import profiles + +from eis_toolkit.transformations.linear import _min_max_scaling +from eis_toolkit.vector_processing.distance_computation import distance_computation + + +@beartype +def proximity_computation( + geodataframe: gpd.GeoDataFrame, + raster_profile: Union[profiles.Profile, dict], + maximum_distance: Number, + scale_range: Tuple[Number, Number] = (1, 0), +) -> Tuple[np.ndarray, Union[profiles.Profile, dict]]: + """Compute proximity to the nearest geometries. + + Scales proximity values linearly in the given range. The first number in scale_range + denotes the value at geometries, the second at given maximum_distance. + + Args: + geodataframe: The GeoDataFrame with geometries to determine proximity to. + raster_profile: The raster profile of the raster in which the distances to the + nearest geometry are determined. + max_distance: The maximum distance in the output array beyond which proximity is considered 0. + scaling_range: Min and max values used for scaling the proximity values. Defaults to (1,0). + + Returns: + A 2D numpy array with the linearly scaled proximity values and raster profile.. + + Raises: + NonMatchingCrsException: The input raster profile and geodataframe have mismatching CRS. + EmptyDataFrameException: The input geodataframe is empty. + """ + out_image, out_profile = _linear_proximity_computation(geodataframe, raster_profile, maximum_distance, scale_range) + + return out_image, out_profile + + +@beartype +def _linear_proximity_computation( + geodataframe: gpd.GeoDataFrame, + raster_profile: Union[profiles.Profile, dict], + maximum_distance: Number, + scaling_range: Tuple[Number, Number], +) -> Tuple[np.ndarray, Union[profiles.Profile, dict]]: + out_image, out_profile = distance_computation(geodataframe, raster_profile, maximum_distance) + + out_image = _min_max_scaling(out_image, scaling_range) + + return out_image, out_profile diff --git a/eis_toolkit/warnings.py b/eis_toolkit/warnings.py new file mode 100644 index 00000000..2f5da5f6 --- /dev/null +++ b/eis_toolkit/warnings.py @@ -0,0 +1,6 @@ +class InvalidColumnWarning(Warning): + """Warning class for invalid column.""" + + +class ClassificationFailedWarning(Warning): + """Warning class for classification failures.""" diff --git a/environment.yml b/environment.yml index 29c598e0..a49c6ca2 100755 --- a/environment.yml +++ b/environment.yml @@ -1,13 +1,12 @@ name: eis_toolkit channels: - conda-forge - - defaults dependencies: - python >=3.9.15,<3.11 - rasterio >=1.2.10,<2.0.0 - pandas >=1.5.3 - geopandas >=0.12.2,<1.0.0 - - fiona >=1.8.22,<2.0.0 + - fiona >=1.8.22,<1.10.0 - gdal >=3.0.2,<4.0.0 - geos >=3.8.0,<4.0.0 - scikit-learn >=1.2.1,<2.0.0 @@ -23,5 +22,6 @@ dependencies: - imbalanced-learn >= 0.11.0 - mapclassify >= 2.6.1 - esda >= 2.5.1 + - numba >= 0.60.0 # Dependencies for testing - pytest >=7.2.1 diff --git a/notebooks/distance_to_anomaly.ipynb b/notebooks/distance_to_anomaly.ipynb index 130ee5b8..777ed830 100644 --- a/notebooks/distance_to_anomaly.ipynb +++ b/notebooks/distance_to_anomaly.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 3, + "execution_count": 1, "id": "b3d379c7-d0d1-44e4-9cdc-169f047b35f6", "metadata": { "tags": [] @@ -16,25 +16,15 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 2, "id": "c3b27bf4-0a01-415f-a17f-a0f2b7fe68f7", "metadata": { "tags": [] }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/nialov/.cache/pypoetry/virtualenvs/eis-toolkit-tACG8vKh-py3.10/lib/python3.10/site-packages/geopandas/_compat.py:112: UserWarning: The Shapely GEOS version (3.10.3-CAPI-1.16.1) is incompatible with the GEOS version PyGEOS was compiled with (3.10.4-CAPI-1.16.2). Conversions between both will be slow.\n", - " warnings.warn(\n" - ] - } - ], + "outputs": [], "source": [ + "import time\n", "from functools import partial\n", - "from tempfile import TemporaryDirectory\n", - "from pathlib import Path\n", "from textwrap import fill\n", "\n", "import rasterio\n", @@ -46,14 +36,14 @@ "from tests.raster_processing.clip_test import raster_path as SMALL_RASTER_PATH\n", "from eis_toolkit.raster_processing.distance_to_anomaly import (\n", " distance_to_anomaly,\n", + " distance_to_anomaly_gdal_compute_proximity,\n", " _fits_criteria,\n", - " distance_to_anomaly_gdal,\n", ")" ] }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 3, "id": "02e9f227-8a87-487f-828e-c2d0d901e661", "metadata": { "tags": [] @@ -62,6 +52,7 @@ "source": [ "def _plot_image(ax, data, title, transform):\n", " plot.show(data, transform=transform, ax=ax)\n", + " plt.subplots_adjust(hspace=0.4)\n", " ax.set_title(fill(title, width=25))\n", " norm = plt.Normalize(vmax=np.nanmax(data), vmin=np.nanmin(data))\n", " cmap = matplotlib.cm.viridis\n", @@ -69,7 +60,7 @@ "\n", "\n", "def _plot_distance_example(threshold_criteria_value, threshold_criteria):\n", - " fig, axes = plt.subplots(2, 2, figsize=(8, 9))\n", + " fig, axes = plt.subplots(2, 2, figsize=(14, 10))\n", " axes = axes.flatten()\n", "\n", " with rasterio.open(SMALL_RASTER_PATH) as raster:\n", @@ -91,43 +82,43 @@ " _plot_image_with_transform(\n", " ax=axes[1], data=raster_data_criteria, title=\"Data fitting criteria (anomaly)\"\n", " )\n", - "\n", - " out_image, _ = distance_to_anomaly(\n", + " start_time = time.time()\n", + " out_image,_ = distance_to_anomaly(\n", " anomaly_raster_data=raster_data,\n", " anomaly_raster_profile=raster_profile,\n", " threshold_criteria_value=threshold_criteria_value,\n", " threshold_criteria=threshold_criteria,\n", " )\n", + "\n", + " end_time = time.time()\n", " _plot_image_with_transform(\n", " ax=axes[2],\n", " data=out_image,\n", " title=\"Distance to nearest anomaly (distance_computation)\",\n", " )\n", + " print(f\"The time taken by distance_to_anomaly function to calculate the distance to anomaly is = {end_time-start_time} seconds\")\n", "\n", - " with TemporaryDirectory() as tmp_dir_str:\n", - " distance_path = Path(tmp_dir_str) / \"distance_to_anomaly_gdal.tif\"\n", - " try:\n", - " distance_path = distance_to_anomaly_gdal(\n", - " anomaly_raster_data=raster_data,\n", - " anomaly_raster_profile=raster_profile,\n", - " threshold_criteria_value=threshold_criteria_value,\n", - " threshold_criteria=threshold_criteria,\n", - " output_path=distance_path,\n", - " )\n", - " with rasterio.open(distance_path) as distance_raster:\n", - " _plot_image_with_transform(\n", - " ax=axes[3],\n", - " data=distance_raster.read(1),\n", - " title=\"Distance to nearest anomaly (gdal_proximity.py)\",\n", - " )\n", - " except ModuleNotFoundError as exc:\n", - " print(\"distance_to_anomaly_gdal does not work on windows.\")\n", - " print(exc)" + " start_time = time.time()\n", + " out_image_ComputeProximity, out_meta = distance_to_anomaly_gdal_compute_proximity(\n", + " anomaly_raster_data=raster_data,\n", + " anomaly_raster_profile=raster_profile,\n", + " threshold_criteria_value=threshold_criteria_value,\n", + " threshold_criteria=threshold_criteria,\n", + " )\n", + "\n", + " end_time = time.time()\n", + " _plot_image_with_transform(\n", + " ax=axes[3],\n", + " data=out_image_ComputeProximity,\n", + " title=\"Distance to nearest anomaly (distance_computation_with_gdal.compute_proximity)\",\n", + " )\n", + " print(f\"The time taken by gdal.compute_proximity function to calculate the distance to anomaly is = {end_time-start_time} seconds\")\n", + " " ] }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 4, "id": "4af1be28-b9b0-4b60-ac04-6dfc8307ffda", "metadata": { "tags": [] @@ -137,14 +128,15 @@ "name": "stdout", "output_type": "stream", "text": [ - "Several drivers matching tif extension. Using GTiff\n" + "The time taken by distance_to_anomaly function to calculate the distance to anomaly is = 0.27871108055114746 seconds\n", + "The time taken by gdal.compute_proximity function to calculate the distance to anomaly is = 0.004987001419067383 seconds\n" ] }, { "data": { - "image/png": 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", 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", 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" ] }, "metadata": {}, @@ -157,7 +149,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 5, "id": "2b633218-be36-482b-a10c-915f71522ece", "metadata": { "tags": [] @@ -167,14 +159,15 @@ "name": "stdout", "output_type": "stream", "text": [ - "Several drivers matching tif extension. Using GTiff\n" + "The time taken by distance_to_anomaly function to calculate the distance to anomaly is = 0.689572811126709 seconds\n", + "The time taken by gdal.compute_proximity function to calculate the distance to anomaly is = 0.0010004043579101562 seconds\n" ] }, { "data": { - "image/png": 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", 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", 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" ] }, "metadata": {}, @@ -187,7 +180,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 6, "id": "2bba94fe-8e57-48a7-9d1c-ceb0cb589355", "metadata": { "tags": [] @@ -197,14 +190,15 @@ "name": "stdout", "output_type": "stream", "text": [ - "Several drivers matching tif extension. Using GTiff\n" + "The time taken by distance_to_anomaly function to calculate the distance to anomaly is = 0.36331868171691895 seconds\n", + "The time taken by gdal.compute_proximity function to calculate the distance to anomaly is = 0.0013973712921142578 seconds\n" ] }, { "data": { - "image/png": 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atWqhY8eOmD9/PgCgXbt2KCgowO7du1Wv0bhx47Bw4UJkZGTA39/fFu/WrRuuXr0qLc+YUwRjjDHGJK+++qoAIHJyclTxvLw81fOioiLRpEkT0blzZ1UcgDAYDOLYsWO22K+//ioAiA8//NAWe+CBB4TBYBDHjx+3xc6dOyeCgoLEfffdZ4vNmzdPABA9evQQVqvVFk9ISBCKoogxY8bYYmazWdSoUUN06NDBpbbHxsaKpKQk2/NPP/1UABC///67at2qVauqlisxatQoYTKZpDhjzuDb/Ywxxhjh8uXL0Ol0CAwMVMVNJpPt/69evYqsrCy0b98ev/zyi7SNrl27om7durbnzZo1Q3BwME6cOAEAsFgs2LBhAx544AHUqVPHtlz16tXxj3/8Az/99BOys7NV23zsscegKIrteevWrSGEwGOPPWaLabVatGrVyrYfV9p+o0GDBsHPzw+LFi2yxdavX49Lly7hkUcekZYPCwtDfn4+8vLybrpdxm6GT1IZY4wxJ6xevRr33nsv/Pz8EB4ejoiICKSmpiIrK0tatmbNmlIsLCwMV69eBQD89ddfyMvLQ1xcnLRco0aNYLVacebMmZtuMyQkBMD1W/n28ZL9uNL2G4WGhqJPnz5YvHixLbZo0SLccccd6Ny5s7S8+P+RhDeeTDPmLD5JZYwxxghVqlSB2WxGTk6OLfbjjz/asv0/+eQTrFmzBmlpafjHP/5BTrmk1WrJbVPLOqq0bVLxG/fjbNvtDR8+HCdOnMDWrVuRk5ODVatWYciQIeT8sVevXoW/v7/qyi1jztJ5ugGMMcaYN2rYsCGA61n+zZo1AwCsWLECfn5+WL9+PYxGo23ZefPmubSPiIgI+Pv74/Dhw9K//fHHH9BoNNIVUle52/aePXsiIiICixYtQuvWrZGXl4dhw4aRy6anp6NRo0Zl0m5WefGVVMYYY4yQkJAAAKoMda1WC0VRYLFYbLGTJ0/im2++cWkfWq0W3bt3x3//+1+cPHnSFr9w4QIWL16Mdu3aITg42KVtU/typ+06nQ5DhgzB0qVLMX/+fDRt2tR28m7vl19+QZs2bcqi2awS45NUxhhjjFCnTh00adIEGzdutMV69eqFvLw89OzZE7Nnz8aUKVPQunVr1KtXz+X9/Otf/4JOp0O7du0wffp0vPXWW2jTpg0KCwvx1ltvlcWhlFnbhw8fjkuXLuH7778nE6YAYM+ePbhy5Qr69etXVk1nlRSfpDLGGGOlePTRR/G///0P+fn5AIDOnTtj7ty5yMjIwMSJE/Hll1/izTffxIMPPujyPho3bowff/wRTZo0wYwZMzB58mTExsbi+++/t82RWhbKou0tW7ZE48aNodFoMHToUHKZZcuWoWbNmmRCFWPO4Mn8GWOMsVJkZWWhTp06eOutt1RTPFVmLVq0QHh4OL777jvp3woLC1GrVi28/PLLePrppz3QOlaR8JVU5hRFUZCSkmJ7XlKm78axVM4YMWIEatWqVSZtY4yxshYSEoIXX3wRb7/9NqxWq6eb43G7d+/Gvn37VFWsbjRv3jzo9XqMGTOmnFvGKiLO7mc2n3zyCcaNG4d77rkHO3bs8HRzbmn69OmIj4/HAw884OmmMCcVFBSgqKjIpXUNBgP8/PzKuEWMle6ll17CSy+95OlmeNSBAwewZ88evPvuu6hevToefvhhcrkxY8bwCWo5cacfBXyjL+WTVGazaNEi1KpVCzt37sSxY8fcSgQoD9OnT8dDDz3EJ6k+pqCgALVjA5Fx0XLrhQlRUVFIT0/3+s6VsYpk+fLlmDJlCuLi4vDll1/y58/D3O1HAd/oS/kklQG4Pqfd1q1b8fXXX2P06NFYtGgRJk2a5OlmsQqoqKgIGRctSN8Ti+Ag50YcZedYUbvlKRQVFXl1x8pYRZOSkqIa6sU8y51+FPCdvpTHpDIA16+ihoWFoVevXnjooYdU9ZnLyjfffIMmTZrAz88PTZo0wcqVK8nl3nnnHbRp0wZVqlSByWRCy5YtsXz5ctUyiqIgNzcXCxYsgKIoUBQFI0aMAACcOnUKTz75JOLi4mAymVClShUMHDjQ5XGz7PYICHTtwRhj7DpX+1Ff6Uv5SioDcP0ktX///jAYDBgyZAhSU1Oxa9cu3H333WWy/Q0bNmDAgAGIj4/HjBkzcPnyZYwcORI1atSQlp01axb69u2LoUOHoqioCEuWLMHAgQOxevVq9OrVCwCwcOFCPP7447jnnnswatQoAEDdunUBALt27cLWrVsxePBg1KhRAydPnkRqaio6duyIQ4cOwd/fv0yOibnHCgErnJtcxNnlGWOsInOlHy1ZzxfwSSrDnj178Mcff+DDDz8EALRr1w41atTAokWLyuwk9aWXXkK1atXw008/ISQkBADQoUMHdO/eHbGxsapljxw5oqr3PH78eNx111147733bCepjzzyCMaMGYM6depIE0qXXA2+UZ8+fZCQkIAVK1aUWsaPlS8rrHA2V9r5NRhjrOJypR8tWc8X8O1+hkWLFqFatWro1KkTgOu30h9++GEsWbJEVT7PVefPn8e+ffuQlJRkO0EFgG7duiE+Pl5a/sYT1KtXryIrKwvt27fHL7/84tD+bly/uLgYly9fRr169RAaGurwNhhjjDHmWZXuJPXbb79F69atYTKZEBYW5lBm+O+//46+ffsiJCQEAQEBuPvuu3H69GkA1+sel4yJtH8sW7bMto0JEyagZcuWMBqNaN68ucvtv1lbXGGxWLBkyRJ06tQJ6enpOHbsGI4dO4bWrVvjwoUL5GTNzjp16hQAoH79+tK/xcXFSbHVq1fj3nvvhZ+fH8LDwxEREYHU1FRkZWU5tL/8/Hy8/vrriImJgdFoRNWqVREREYHMzEyHt8FuP4sQLj2Y53E/yph3cLUf9ZW+tMKdpHbs2BHz588n/63kVu/IkSPx66+/4ueff8Y//vGPm27v+PHjaNeuHRo2bIjNmzfjt99+w2uvvWbLhouJicH58+dVj8mTJyMwMBCJiYmqbT366KOlzi3niFu1xRWbNm3C+fPnsWTJEtSvX9/2GDRoEADclgSqm/nxxx/Rt29f+Pn54ZNPPsGaNWuQlpaGf/zjH3C0ONpTTz2FadOmYdCgQVi6dCk2bNiAtLQ0VKlShSfj9iIlY6mcfbDbj/tRxnyDq/2or/SllWZMqtlsxtNPP423335bVdqOut18o1deeQX3338/3nrrLVusJEEHALRaLaKiolTrrFy5EoMGDUJg4N/pcx988AEA4K+//sJvv/1G7uunn35CcnIydu/ejapVq+LBBx/EjBkzEBAQ4FBbXLFo0SJERkbi448/lv7t66+/xsqVKzF79mzVLXRnlYw5PXr0qPRvhw8fVj1fsWIF/Pz8sH79ehiNRlt83rx50rqKopD7W758OZKSkvDuu+/aYgUFBcjMzHSl+ew2sULAwolTPoX7Uca8iyv9aMl6vqDCXUktzS+//IKzZ89Co9GgRYsWqF69OhITE3HgwIFS17Farfj222/RoEED9OjRA5GRkWjdujW++eabUtfZs2cP9u3b53SN5+PHj6Nnz54YMGAAfvvtN3z11Vf46aefMH78eJfbciv5+fn4+uuv0bt3bzz00EPSY/z48cjJycGqVatc3gcAVK9eHc2bN8eCBQtUt9vT0tJw6NAh1bJarRaKoqjGwp48eZI8zoCAAPLEU6vVSlddP/zwwzIZX8vKTkX+9V9RcT/KmHep6FdSK81J6okTJwBcn5D41VdfxerVqxEWFoaOHTviypUr5DoXL17EtWvX8MYbb6Bnz57YsGEDHnzwQfTv3x9btmwh15k7dy4aNWqENm3aONW+GTNmYOjQoZg4cSLq16+PNm3a4IMPPsDnn3+OgoICl9pyK6tWrUJOTg769u1L/vu9996LiIiIMrnlP2PGDFy4cAHt2rXDzJkz8dprr2HgwIFo3LixarlevXohLy8PPXv2xOzZszFlyhS0bt2arH7VsmVLbNy4Ee+99x6WLFliK+Xau3dvLFy4EBMnTsScOXMwcuRIfPDBB6hSpYrbx8FYZcb9KGOsXAkfN23aNBEQEGB7aDQaYTQaVbFTp06JRYsWCQDi008/ta1bUFAgqlatKmbPnk1u++zZswKAGDJkiCrep08fMXjwYGn5vLw8ERISIt55551S2ztp0iRx5513SvFWrVoJg8Ggare/v78AIA4dOuR0WxzRp08f4efnJ3Jzc0tdZsSIEUKv14tLly4JIYQAICZNmmT793nz5gkAIj09/Zb7W7FihWjUqJEwGo0iPj5efP311yIpKUnExsaqlps7d66oX7++MBqNomHDhmLevHli0qRJwv7t+scff4j77rtPmEwmAUAkJSUJIYS4evWqGDlypKhataoIDAwUPXr0EH/88YeIjY21LcM8JysrSwAQR36vJs7/Wd2px5HfqwkAIisry9OHUaFwP+p6P8qYJ7jTj/pSX+rzY1LHjBljS/IBgKFDh2LAgAHo37+/LRYdHY3q1asDUI+dMhqNqFOnTqlZnVWrVoVOp5PGWzVq1Ag//fSTtPzy5cuRl5eH4cOHO30c165dw+jRozFhwgTp32rWrAkATrXFEY7cxp83b55qPKiwu40+YsQIW6WnW+nfv7/q7wIADz74oLTco48+ikcffVSK25fki4uLI69+hIaG4rPPPpPiXHHKu1j//+HsOqzscT/qej/KmCe50o+WrOcLfP4kNTw8HOHh4bbnJpMJkZGR0u3hkmlLDh8+jHbt2gG4PofmyZMnpcnkSxgMBtx9991Scs+RI0fIdebOnYu+ffsiIiLC6eO46667cOjQIfK2dgln2sKYt7O4MODflQQBdmvcj3I/ynyTK/1oyXq+wOdPUh0VHByMMWPGYNKkSYiJiUFsbCzefvttAMDAgQNtyzVs2BAzZsywXeF74YUX8PDDD+O+++5Dp06dsG7dOvzvf//D5s2bVds/duwYfvjhB6xZs4bc/7Fjx3Dt2jVkZGQgPz8f+/btA3D9ioTBYMBLL72Ee++9F+PHj8fjjz+OgIAAHDp0CGlpafjoo4+cagtjvsAirj+cXYd5DvejjHkXV/rRkvV8gqfHG5S1Dh06iHnz5pH/VlRUJJ577jkRGRkpgoKCRNeuXcWBAwdUywCQ1p87d66oV6+e8PPzE3feeaf45ptvpG0nJyeLmJgYYbFYSm0XAOlx41jOnTt3im7duonAwEAREBAgmjVrJqZNm+Z0WxjzZiVjqfYdihTHz0Q59dh3KNInxlH5Ou5HGfNu7vSjvtSXKkL4SNkBxliFkJ2djZCQEOw7FImgIOcmGMnJsaJ5/EVkZWUhODj4NrWQMca8mzv9KOA7fWmlud3PGPMuViiwgC7IcLN1GGOMXedKP1qyni/gk1TGmEdYxfWHs+swxhi7zpV+tGQ9X+CTJ6lWqxXnzp1DUFBQqaUxGWO3lxACOTk5iI6Ohkbj/O0miwtXAFy5YsBo3I8y5nme6EdL1vMFPnmSeu7cOcTExHi6GYwxAGfOnEGNGjWcXo9PUj2L+1HGvEd59qMl6/kCnzxJDQoKAgDcdf8r0Or9bHH/jAJpWW1OkRRTiorljVpvPbWt4sAyACCoX0NUTKeV19UTMXJ7DjWFRrw3hVa9QatO3oHFT26b2Z+ImeR1rXp5pxY90Q7iHSkc/CwJrbyg1b55xLYU4s+qWOSYhpizQzE7tj1B/b2ImNReABqiLdpCYh+OvE5u9Ev227cUFeDA0qm2zyPzLSV/tzNnznh14gQrX/3CRkoxbXiY6rlCfXcVyd+1lquZUuy/mQtcb1wFlJ2djZiYGO5HS+GTJ6klt6a0ej/objhJ1RFHoyVOXBQtcXZAnVnYL+JgjQb7Ez4A9EmqlvigU7EyP0mVXxOhu/VJKtUxgTiphp44wTUQZ0flcJKq2G/P0ZNU4uSTPEl18K3k6Emq4uhJKjGeqLxPUm2bdPFWsVUosDr6x71hHVY2Sv5uwcHBfJLKbHSK3DFrNQbVc0VDfU/J21KIbfF7jVae/WjJer7AJ09SGWO+j2/3M8aYe/h2vxcLSr8GnfbvW/dKvny7ARb5MpRCpbVRt/Ltp5ClfulQVzmp5Yirq+StfeIKJsmdwrs6uX1mf73dc+o2PrGeH3HV1Cjv0kpdNSWufJJXHCnUlUQHrlaSPx6p4Q/Ucho5qCGmGVaItlFXQ0FdrSUWo7ZXlldNHf5Bbb+cm32cBRpYnLwlQL2MjFVmPfyHSTHFj+iEqds+xPejNjxUXtXfTx0gvvcU4i6gtorcSXQ3DpViGwoXyW1jDnGlH72+nm9w56YxY4y5TPz/bSpnHsKFW1Q5OTmYOHEiYmNjYTKZ0KZNG+zates2HBFjjJUvV/pRV/tST3DqJDU1NRXNmjWzjWFKSEjA2rVrbf+ekZGBYcOGISoqCgEBAbjrrruwYsUK1TauXLmCoUOHIjg4GKGhoXjsscdw7dq1sjkaxhiz8/jjjyMtLQ0LFy7E/v370b17d3Tt2hVnz571SHu4H2WMMcc4dZJao0YNvPHGG9izZw92796Nzp07o1+/fjh48CAAYPjw4Th8+DBWrVqF/fv3o3///hg0aBD27t1r28bQoUNx8OBBpKWlYfXq1fjhhx8watSosj0qxpjXKxlL5ezDGfn5+VixYgXeeust3HfffahXrx5SUlJQr149pKam3qYjuznuRxljZcXVftRXxqQ6dZLap08f3H///ahfvz4aNGiAadOmITAwENu3bwcAbN26FU899RTuuece1KlTB6+++ipCQ0OxZ88eAMDvv/+OdevW4T//+Q9at26Ndu3a4cMPP8SSJUtw7ty5sj86xpjXsgiNSw/g+rQtNz4KC4k5uQCYzWZYLBb4+anH1JlMJvz000+3/Rgp3I8yxsqKq/2oxeEEEM9yOXHKYrFg2bJlyM3NRUJCAgCgTZs2+Oqrr9CrVy+EhoZi6dKlKCgoQMeOHQEA27ZtQ2hoKFq1amXbTteuXaHRaLBjxw48+OCD5L4KCwtVX0LZ2dkAAE3WNWg0xJynqoYSGUZUkpQjyU7U1E1EMg01qJyaWorcpzsJUdQ8oURylsUk/9mLQtSxokD5GMwmeZcWIzWdlbwc9Xlw6zPiaEk3RxKnqNWotxU1J2o5lJZzObHJy1mhwOrksHjr///h7SehnzRpElJSUqTlg4KCkJCQgKlTp6JRo0aoVq0avvzyS2zbtg316tVzue1lxRv6UeZ5PYJGSDGN3dykACBMckKUUqemvByV2EQkeToyPzggf48IA9HJE1P0abNy5eWychzaJ3OMK/3o9fV8oy6q0yep+/fvR0JCAgoKChAYGIiVK1ciPj4eALB06VI8/PDDqFKlCnQ6Hfz9/bFy5Urbl0FGRgYiIyPVDdDpEB4ejoyMjFL3OWPGDEyePNnZpjLGvJg7U1DZT0BvNBLZzP9v4cKFePTRR3HHHXdAq9XirrvuwpAhQ2xXJj2B+1HGWFmo6FNQOX36HRcXh3379mHHjh0YO3YskpKScOjQIQDAa6+9hszMTGzcuBG7d+/Gs88+i0GDBmH//v1uNTI5ORlZWVm2x5kzZ9zaHmPM89y5RVWSdFTyuNlJat26dbFlyxZcu3YNZ86cwc6dO1FcXIw6deqU16FKuB9ljJUFvt1vx2Aw2H7Rt2zZErt27cKsWbPw4osv4qOPPsKBAwfQuHFjAMCdd96JH3/8ER9//DFmz56NqKgoXLx4UbU9s9mMK1euICoqqtR9Go3Gm34JMcbYrQQEBCAgIABXr17F+vXr8dZbb3msLdyPMsbYrbl9Km21WlFYWIi8vLzrG7QbC6PVamH9/3EvCQkJyMzMVN1m27RpE6xWK1q3bu1uUxhjPuT6WCrnH85av3491q1bh/T0dKSlpaFTp05o2LAhRo6Ua5R7CvejjDFXuNqPutKXeoJTV1KTk5ORmJiImjVrIicnB4sXL8bmzZuxfv16NGzYEPXq1cPo0aPxzjvvoEqVKvjmm29sU6QAQKNGjdCzZ0888cQTmD17NoqLizF+/HgMHjwY0dHRzrfebFGX8iGqZ5AcrRLlyDJUQhRV455CDWQnq1oRqxIVrKxG+c9ZHEQkSQXJ7SsKVO/XHCC3w+InhWB1MEmqzD8P1EvnQA6AIP40ZIw4Bg2VOEW85agYefyOvibU+HYXX09HK245sq67c0FbXaiU4spg/6ysLCQnJ+PPP/9EeHg4BgwYgGnTpkGvJ8qglQOv60fZbdOzijwtmIitLsWK74mTYhYDkYBLJuo62BiqEp5ZDmqKb92RkhUJieqDpksGKaa/cvWW22eOc6Ufvb5eBUycunjxIoYPH47z588jJCQEzZo1w/r169GtWzcAwJo1a/Dyyy+jT58+uHbtGurVq4cFCxbg/vvvt21j0aJFGD9+PLp06QKNRoMBAwbggw8+KNujYox5PVfGRVmoH3a3MGjQIAwaNMjp9W4X7kcZY2XF1fGlrvSlnuDUSercuXNv+u/169eXKqPYCw8Px+LFi53ZLWOsArJC4/IUVL6M+1HGWFlxpR+9vp5v9KW+kd7FGGOMMcYqFZcn82eMMXdYhAKLkwNbnV2eMcYqMlf60ZL1fIFPn6SKwmJ6IPkNFCLByGF2SVFUshKJquLhYMUpoScSogxE1Sh/IkkqkEqIkrdXFCzvtzjQbvtUkpTW9dsDVHKSw9fxqQJhVMwiH5e0HFXkizouopKWhahCRaXeUG0jj99RLiY2ObotT/VVFhcG/Ft85BYVq/i6mx6Rg80aqJ7mdmogLVIUQCXuOrZPi5yHBIvBsZW1xURFKKKasLaQWK5IHaOGQJpNcjvyI4jEqciqN2klc5Yr/ej19XyjL/Xpk1TGmO+yCg2sTg74t/rIYH/GGCsPrvSj19fzjb6UT1IZYx7BV1IZY8w9Ff1KKidOMcYYY4wxr8NXUhljHmGF84P3HajXwBhjlYYr/WjJer7At09SrRYAN5T30TiYnUJWjnKwuofUBupPTVygJga3W0xy2g2VEEVV9yATooKIhCgqFiiFYPZXX/q36uVbAVRikrZI3hbFapC3Z3Ww4A9d1Ului8ZCtNmuSpTGTKxHxMgEKyJpgUoW0Fsdu41S5glLZbk9F5OwnOHaPKl884eVv54NX5Ziub2aS7Hsmuq+uihY3haVrGT6i+gziFB+BNGfhzi2rraA6L8L5OX0uUSfdk29QcM1+XtPnyvvlBoqWRQdIsW6G4dKsQ2Fi+SVmcT1eVJ9oy/17ZNUxpjPcq3ilG90rIwxVh5crzjlG30pn6QyxjzCCgVWJy/HOrs8Y4xVZK70oyXr+QI+SWWMeQRfSWWMMffwlVRvpijq8aXUGFJqEn0HJ+VX7MYWCi2xfQ2xLZ28Tysx/rQ4mIgFuTH+lBprGiiPEzKbiLFD9mNGiTFN5GT5xHhRaqwl9XkQRseGblPTuVFtEcXEeFNp/LG8MQ2xHjWq3Ep8WqgxZ9RgTV2ed0z34dY4WPt1feOHOGOl6hE0QoqZW9SXYlfvi5RiuTXkD0BRsLrjoAp7aC/JnaGuwLHxnFY90e+HEp0wUaCkmBh7r82Td2K+RhSUsd8v0ThjtnywVGGAohC5I9XcEy/FeJwqA3z9JJUx5rNcmyfVN379M8ZYeXB9nlTf6Ev5JJUx5hFWocDq7BRUPlJvmjHGyoMr/WjJer6AT1IZYx5hdeEKgK9Mm8IYY+XBlX60ZD1fwCepjDGPcKXmtCs1qhljrKJypR8tWc8X+PZJqlarmsBfoZKYqJiriCIAwii/hJZAoxQrCpFngi8KIZKkiIQoh5Ok/IkJ8/2IAfnERP2wG3uvzZdfNw0xcT+ZTEXlrxETWAsd8Xr6UZkGxDFQiVNU4pzdB1EhbnEIYvJ9MjGLeCuZiSIFVEYRlWBGvSZlmYxE3s1xZ/v2h+oduWCMOSSxznNSLK9TYymWVUfu0/OiiATUACJRyG7CfNMF+QPnf5GYCD9PjlmJ/pGa9J8q9mKpLncuhpBiKVYcJB9rkVH+rlKEeh+aIqqwC5EwKkUAC1HYJquuSYqFiEbE2qyy8e2TVMaYz7JAgcXJs2Znl2eMsYrMlX60ZD1fwCepjDGP4Nv9jDHmnop+u983WskYq3As+PsqgOMPxhhjJVzrR53rS2vVqgVFUaTHuHHjSl1n2bJlaNiwIfz8/NC0aVOsWbPGpePjk1TGmEeUXAFw9sEYY+w6V/tRZ/rSXbt24fz587ZHWloaAGDgwIHk8lu3bsWQIUPw2GOPYe/evXjggQfwwAMP4MCBA04fn0/f7ld0WiiaWxwCVa6IGIshiCpR0Ku3bfWTB5RbgomEKKKSVEEokSQV4mBClDymHBYiIcpqJBKMqCJZ+URlJrtB79pCx5J/qGEt1D6p7UGRPyTFRCKSYnAwmYr4zWWfJGalilxZideDqnJFxKi3l1V+S8DiR+zDTCVsEe1zcOiQ9Lo7OuSIWs6Rj41vDGliFVwP/2FSzNpMrhqV0zpaiuVWl/uMgiqOZQSaMuR1/S6r1/X/S/5A63PlmJWqZkg0w/8vuQMTRFXFzFD5e9E/LFde1ygnU2UT+y0uVCcDawvkY6cSp6iKWxqLvANqfGRBpJyAnHjHU1Js7dkP5Z2wMhUREaF6/sYbb6Bu3bro0KEDufysWbPQs2dPvPDCCwCAqVOnIi0tDR999BFmz57t1L75sgRjzCNKak47+2CMMXadq/1oSV+anZ2tehQWUtPO/K2oqAhffPEFHn30USjUVD4Atm3bhq5du6piPXr0wLZt25w+Pu7xGWMeIaDA6uRD8OVbxhizcaUfvbEvjYmJQUhIiO0xY8aMm+7vm2++QWZmJkaMGFHqMhkZGahWrZoqVq1aNWRkZDh9fD59u58x5rtcuTLKV1IZY+xvrt5hKlnnzJkzCA4OtsWNRnmYxY3mzp2LxMREREfLw2duBz5JZYx5hCs1p32l3jRjjJUHV/rRkvUAIDg4WHWSejOnTp3Cxo0b8fXXX990uaioKFy4cEEVu3DhAqKiopxup2+fpFoFgBtGZhMDyKGlkmnkwxaBcnaSOUj9i8IcQFTnCJK3Xxgsx6gkqaIQKQSLiUgIIpKEqGQfpZioAmKWV7WvigLICTvUgHeHKxg5mIijEG3TEJWurMTxKwYiw0hHvHZ21aTISlVEOwSVOUW85hTqR63FT45RFae0jr7ut1s57NPiQs1pV2pUs4ojse7zUiy7b3M5Fiu/TyzEBSItUUXPkCO/+fVENlHgebnj0BYKu+dyPyWocXx6KrGUCBH9gz5HbpvxgvxdlR3kL8VCguVkKqNJTqbKt6tMVUx8h2iJhFxtgRSCQvR7CpFMZTbKf0NzTIQUq+xc6UdL1nPWvHnzEBkZiV69et10uYSEBHz33XeYOHGiLZaWloaEhASn98k9PmOMMcYYK5XVasW8efOQlJQEnU79o2X48OFITk62PX/66aexbt06vPvuu/jjjz+QkpKC3bt3Y/z48U7v17evpDLGfBbf7meMMfe4e7vfURs3bsTp06fx6KOPSv92+vRpaDR/X/Ns06YNFi9ejFdffRX//Oc/Ub9+fXzzzTdo0qSJ0+3kk1TGmEdYoYHVyZs5zi7PGGMVmSv9aMl6zujevTsEOe88sHnzZik2cODAUif7dwafpDLGPMIiFFic/DXv7PKMMVaRudKPlqznC3z7JFWjADdcYhYmeWS88JdjFn+5JBCZFBWijhUFEhWSAh2rGlUcJP8CoWLCTx5or5jl/eozb1016voG5RBVOUljN1bevnJKadsqiJD3SSUoUBQiiUmbT+xWIyfEkb/nqAQzuwQoQSVXEZ8CQSWmEb8iNVQiFpF0RVWcMlMVWogEArIKFaUMLzI60n+528fx7X5WorthiBxs3lAKXblPnvYmM05etThKzohScuQPevh++f1kuixnJ2mKqc8llbCUp3q+YU+K3DhCt3unSLH8aKLUIEGfTyR1/Skvl22Uszezasrr+vnJiVMakzpJzGyS+2SzP/FdkCeFoJU3D20RUUGRSCbLqyEnfyXWeU6KrT3xrryTCqq8bvd7Ct87Y4xVWBaLBa+99hpq164Nk8mEunXrYurUqaXetmKMMeY9fPtKKmPMZwmhgdXJSaiFk8u/+eabSE1NxYIFC9C4cWPs3r0bI0eOREhICCZMmODUthhjzNu40o+WrOcL+CSVMeYRFiiwODkhq7PLb926Ff369bPN61erVi18+eWX2Llzp1PbYYwxb+RKP1qyni/wjVNpxliFYxV/j6dy/HF93ezsbNWjsJCYIRzXp0L57rvvcOTIEQDAr7/+ip9++gmJiYnldZiMMXbbuNaP/t2XejvfvpKq1wGavw/BGiIPqi4KlbN4ioOIJCkyKcrueQCR/CLvkqwaZfEjEnb0jpUXUsgEG3lVOptIZtXLC+ry1PuockCuRKK9Ko+Cz2xeVYpl1SGqRhHJVFT1FA1VNYtIpjIbiF+BVJUoO1RSk9BSyU/UunKMOgZHM4qscv4erMRx2Vexub6PW2/f0XHxVCIdFSvrcfZWF25TlSwfExOjik+aNAkpKSnS8i+//DKys7PRsGFDaLVaWCwWTJs2DUOHDnW53cw9PRv/U4rl97xLiuXUkJNz8ohy4eZg4kNI9COGLCJ2jUiGJJKkqItOhWFy+/yPEZ2VA9K2vy7FunSYLsXyouROQ2OW22vIlvdhOi9/1rJD5O0VU2Xv7AjiO4SqqldMJFNRlfZ0RPIXWfWQquYXIZdu7G56RIptyP9CXrkCcKUfLVnPF/j2SSpjrFI6c+aMqt600UhPKbF06VIsWrQIixcvRuPGjbFv3z5MnDgR0dHRSEpKKq/mMsYYc4FTp9Kpqalo1qwZgoODERwcjISEBKxdu1a1zLZt29C5c2cEBAQgODgY9913H/Lz//51eeXKFQwdOhTBwcEIDQ3FY489hmvXrpXN0TDGfIYViksPALY+qORR2knqCy+8gJdffhmDBw9G06ZNMWzYMDzzzDOYMWNGeR6qCvejjLGy4mo/aq2IY1Jr1KiBN954A3v27MHu3bvRuXNn9OvXDwcPHgRwvWPt2bMnunfvjp07d2LXrl0YP368qlzW0KFDcfDgQaSlpWH16tX44YcfMGrUqLI9KsaY1yuZhNrZhzPy8vJU/Q8AaLVaWK23vqV5u3A/yhgrK672oxVyMv8+ffqonk+bNg2pqanYvn07GjdujGeeeQYTJkzAyy+/bFsmLu7vmZZ///13rFu3Drt27UKrVq0AAB9++CHuv/9+vPPOO4iOJgYcMcYqJHfGpDqqT58+mDZtGmrWrInGjRtj7969eO+998j60+WF+1HGWFnhMamlsFgsWLZsGXJzc5GQkICLFy9ix44dGDp0KNq0aYPjx4+jYcOGmDZtGtq1awfg+hWC0NBQW8cKAF27doVGo8GOHTvw4IMPkvsqLCxUZe9mZ18fFS4MBgjt3wO/LX5EQlSoHMuvIv9xikKIpCi7gh9UwpGVeAWtBiI5h3g/aArlIFU1SiMXTyGrNZGI/dof1/WYus1ZdeWMsCrb5NH4QelyglVBuFxyK5+oTOXW3BJU4pCZqrh169eJTJKiKlMR2xJEiqRCVKuCYzly5PvJvhoY4GDinBs/lOm2qYNW6jidYIULFaecPKgPP/wQr732Gp588klcvHgR0dHRGD16NF5/XU5U8QRv6EfLUo+7Jkmx3Frq/qDwbjnZsjBU/rsWhsnbp/pW4yU5gcmQKceoKnq6AseuqFuMcvtyasodx7UaUQ5tzxHfbZETzNr3fVuKUcmWVOIjiViuOF8vL1akPlbqU2g1EslUJuJ7tUBel/o+0xEJo1QSqcVI/P3DiTdPBeVKP1qyni9w+jRh//79CAwMhNFoxJgxY7By5UrEx8fjxIkTAICUlBQ88cQTWLduHe666y506dIFR48eBQBkZGQgMjJStT2dTofw8HBkZGSUus8ZM2YgJCTE9rDP7GWM+R7hwhgq4WTHGhQUhPfffx+nTp1Cfn4+jh8/jn/9618wGIipFcoR96OMsbLgSj/qSl/qKU6fpMbFxWHfvn3YsWMHxo4di6SkJBw6dMg2xmv06NEYOXIkWrRogZkzZyIuLg6fffaZW41MTk5GVlaW7XHmzBm3tscYY57E/ShjjN2a07f7DQYD6tWrBwBo2bIldu3ahVmzZtnGT8XHx6uWb9SoEU6fPg0AiIqKwsWLF1X/bjabceXKFURFlX6bxGg0lpq9yxjzTSWTSju7TkXA/ShjrCy40o+WrOcL3B45a7VaUVhYiFq1aiE6OhqHDx9W/fuRI0cQGxsLAEhISEBmZib27Nlj+/dNmzbBarWidevW7jaFMeZDSgb8O/uoiLgfZYy5wtV+1Ff6UqeupCYnJyMxMRE1a9ZETk4OFi9ejM2bN2P9+vVQFAUvvPACJk2ahDvvvBPNmzfHggUL8Mcff2D58uUArl8N6NmzJ5544gnMnj0bxcXFGD9+PAYPHuxaRqpOC2j/HjRtJQZQF5vkPwQ5SL8KkRRlN0ifSmqiEmKoQeBU8gsZo6pLUZWZzNR+5ZjFwaF3liD1yplx8mupK6gmx/Llxjl6rFTFJUeHyShUkpQjiTzUIlQVKo3rVaioZASFyuki1rXKOQuwkO8nImHLLkRWwyKQVVyIRBH76moWuhKpwyrrlVSv60dd1P3uFCl2rkuoFCsMV78xDZlEFSIimYaKGbLldY1X5M+CX6bcGVJ9leGq/CbW5MmZqoVRcjKoYpX7yGt11R1d60felZbZ8cVzUsxRhky5bQURjl0dp5KpdDnyMVgL5A7Bvv8SWuL7Ut4U+f1jJpKpqORgbRFVHZDYh58cFOFyFaoeQSOk2Pqc+fIGy1DP8MelmLV+TdVzs4V4ozuhol9Jdeok9eLFixg+fDjOnz+PkJAQNGvWDOvXr0e3bt0AABMnTkRBQQGeeeYZXLlyBXfeeSfS0tJQt25d2zYWLVqE8ePHo0uXLtBoNBgwYAA++OCDsj0qxpjXc2VCaV/JSL0Z7kcZY2XF1Yn5faUvdeokde7cubdc5uWXX1bN72cvPDwcixcvdma3jDFWYXA/yhhjjnF5nlTGGHNHZb3dzxhjZYVv9zPG2G3AJ6mMMeYePkn1ZkJcf5Q8pZJTiIHWZHKKnzyoXtglTilmeUC5tpBIdKIqRBFJPVSiExWjEgMCLsrtNRPJLnmRcpvJCiV2o96pykdXiWQqbQHxAlOozwOVYEQcv5Z4PaGRj8tCVDyB/QB/qhILFXMw0Yl6fylEFSoqc8rR9yaFSuyyT6ajkquo4zL7y8GiYHk5i11VMmuBo2VtaHyS6tsKq8ql6/Ij5PeEOUz9xtTlym9y/wwi+Yno9/T5RBUiomqUPlfOLP3ue7mCk6N6VntSigVF1JVimiL1sRX7y+3t3GWGFDOmX3KoHaK2XK2L+pxTMdNf8vasOrlTK4iQl7PvW6nkYCoxi6rcR33/UEm0FgcraZkD5I60KDJAihnEHVKsZ/VxclsuX1E931D0pbxTQmLNiVLsWsc4KZYTrf5ytRTpgT3SYg7jk1TGGLsN+CSVMcbcU9FPUn1joizGGGOMMVap8JVUxphHCDg/DYp7AwwYY6xicaUfLVnPF/BJKmPMI/h2P2OMuaei3+737ZPUYrOqzIW2mKp+RAy0J6o6afOJRBxhty5ZrUgOKQ5WjaJQlaSC/pSziQIPZEix4ugwORboL8WKiIpb9hWhBPHOKAohBujLuRPQFBMvlKOJU9TPO6rikoPVSKz2fx9HqlKVhmicoBKi3KhCRSWsOfyT1257Vp1jCQrFcjEdKUnq+rp2iVNu/hbnk1Tf5peRK8X8M+Q+KN+uM6H+hFTCoN9Vx5KkKLqr+Q4t56h1Fz6RYu36vy3FDFnqg6OqK11s4SfFTDXkpB6/q3K/T/XL1MeQ+t7TFcoxI/EamwOofsO16osUujIV8Z1MvCesDlZaLA6QXyglXP4upPJUtXZJuT0jRssLVZMT2DLbxkixnBj5y6Ao1PMJqCXr+QLfPklljPksPklljDH38EkqY4zdBnySyhhj7qnoJ6mc3c8YY4wxxrwOX0lljHmEEAqEk7/mnV2eMcYqMlf60ZL1fIFPn6Qq+QVQNH8POtZmy1k8xmx5aHRxIDEwnEgyKbYbkE0m5hCvoNBQFUDk5aiqHdT2CkPlC94BRjkDRlMojyqnknioykz2iQtCTyxDVOXSEAln+hx5n1S2hF7OuyAH1Qsi2YdKRKMG89vvlqyKUsb3E+jEKcfKWlHHRW2PfN/ZvXbUemYiIYpMkiIyCsikNjdYoTg9dYorU62w22P93ilSrEun6VLsan11ohBVzawomOgL9fIb2JAtL6cjEk+Kq8hJMmUtN5KodGSXlFoUJLetKELO9CkOlj/QmiPy8VNJQiF/ZEkxq0n+AOdVl78fqdfOjyh+JRR1W6h2UEmZjnK0j6P7Jcf6BIuf/PdSQuXXRBNgVD03B8g7zYkxSrFrMcQ5BPH3V+y/pt3sV13pR0vW8wU+fZLKGPNdPCaVMcbcU9HHpPJJKmPMI/h2P2OMuaei3+7nxCnGGGOMMeZ1fPpKqriWC6H8PdhTa5DHjhiI8SR+fvJ4EqEhztftfmmYA6gJzuXVqLE01FhDR8cfFgbLwdz64fJ+9cSYGGL8LYgxs/YxoSOWMcgNpuZvtubLY3/8LsvLVd1fJMXyIuQXL7uWfPxCQxUkoAacqpejxhVD69igIMWtQgBUzMEZ/qm3JjWGy25V6m9Ijemi2kYVlVDsizQUuPdLnG/3Vzzfff9PKda17b9Uz7Pqy+NF86rJf9dCIzFmm3hfaonPvcUof9C7aQdJsTTLUnmDhMR6L0gxa89oeb92ny/qO8M/Qh6Mn6eXx0YWn5U/rJGbL0ixdYfflGKUjt3fkGJWPfH9SEzwb98fFBMT/psdnMxfK3f7jo/F1xLvCaL/pvovq5GYWF8vf4Fb7QoLFAXK6xWGOVbERZ9DjKG2+/NbCsu/Hy1Zzxf49EkqY8x38e1+xhhzT0W/3c8nqYwxjxAuXAHwlY6VMcbKgyv9aMl6voBPUhljHiFQysxct1iHMcbYda70oyXr+QJOnGKMMcYYY17Hp6+kiqIidV5MzjVpGf0VOUnKzyAPqhdaeQC5lJxDJLWYieQXMjnHwZ8t1LpFIXIssy6VnSWHzH7EYsREzLCfzJ9KnCKKD2gKHJtw2u8SMWn03lNSTBt3hxS7VkNOtKAS1qjiCBq7F576xSmohChH74RQuU/U39rRvz+1Xwd/Sgq7BALqvUTmahFFEBxpL5Xs4AwrFCg8mX+Fp2z/TfU8yNhCWsail/tpix/xeSYSYqjPDLWuSGgmxXpGjJZiikHuXPIby/2SLl/+kNgn1FiD5M6wIF/eviFD/v6htu9okhTFcLVAihWGy/vVFsr7tW8xmcBWKMeEg4mgioWY9J74HiH7VmIXVHEeKvGVap/ZpI5ZiAQ+TZFjRRAc+S5QiG05w5V+tGQ9X+DTJ6mMMd/FiVOMMeYeTpxijLHbwCoUh0sa3rgOY4yx61zpR0vW8wV8ksoY8wghXEic8pXR/owxVg5c6UdL1vMFfJLKGPMIvt3PGGPu4dv9XkyYLRA3juDOlweGa7LkZCqDnkic0gVIMatOZ/ecqHxEVMAwm4hkKrJqEDVYXN4elSRURFQOIgeaE8ktWocqBREJUfYVhwDoiG3p5JccARlymRFFK+/DSlSKsVDJX1QCEJXIYzcwXhArUq8G+fml8qvIklvUcsRAfkcTrBxMFpAabXXjWIn3q8Wg3p7VZyYxYZ5kX9WJSlYK9K8rxa5Fy19PFjm/ClqiX9IQfWF+Nbkj0YTXk2K6PHllbaH8oQ7IoMqy2bVZkTtqfY4cCzor7zP4NyITxw15MfJ3HHU5TVskH6uw66u1xdIioHoXq96x70INsT0dkcBFLUdVHCMTsai+mqhWZf8dTO2T6voEcTZlIapAFlaxW8bNilMVHU9BxRjziJIrAM4+nFGrVi0oiiI9xo0bd5uOijHGyo+r/ShfSWWMsZsoj8SpXbt2wWL5+yrVgQMH0K1bNwwcONCp7TDGmDfixCnGGLsNyiNxKiIiQvX8jTfeQN26ddGhQwfnNsQYY16IE6cYY+w2uN65Ops4df2/2dnZqrjRaITRSAxYvEFRURG++OILPPvss1CoScYZY8zHuNKPlqznCyrUSaookkc4C6IKlUYnJ+dQyVRWvUn1nBoEbTXIMSrRx2KkBpDL62qp6hPEG5BMHCIGeOuJJCZdnryyfVWN4iDiWKl3C/HZ0Mo5UrD4ycOfc1vUlGJZdamMMGK/xCB4akC+I9siK4RRq1KJaWSMqnZCbNCNJCnqWKX3BPVWorZPHL85QG6wLiJfvb88OVGxvMTExKieT5o0CSkpKTdd55tvvkFmZiZGjBhx+xrGbmndX59KsR7NXpNiudXCpFh+BNXfyjHTJfn9S31Wqc+RIYd47+87JsW+y/pMinXTDlI9r9I8XlqmOFz+grDqiYZcdD1xqkdgkryP+5tKMer7hqrWJOVkEsvYV2oCQGa9aIlKWnriO0lXQCR1EQlsGjPRqRFJo4JIfLYQ38Eau6QrQVU5oxKmiXOBnFpyO9q3P6B6XnStCMdcLyRW4VWok1TGmO9wZwqqM2fOIDg42Ba/1VVUAJg7dy4SExMRHR3tXEMZY8xL8RRUjDF2GwjQF45vtQ4ABAcHq05Sb+XUqVPYuHEjvv76ayf3yBhj3suVfrRkPV/AU1AxxjyiPKdNmTdvHiIjI9GrV68yPgrGGPOc8pqC6uzZs3jkkUdQpUoVmEwmNG3aFLt37y51+c2bN5PT/2VkZDi1X76SyhjzDHcupTrBarVi3rx5SEpKgk7HXR5jrAIph0upV69eRdu2bdGpUyesXbsWEREROHr0KMLC5HHj9g4fPqy66xUZGelUM327x9ZqAeWGrA+LPDLeWlgoxTTZcjaRVkskUxnVL4/ZJF94NvvL61EDyC1E1ShBVOOwElkyClVRw+zYryB9rrxu2B95Ukx3OVf1vChavpWaXVMe95cXJbejOFBux9U4onoMkSNlJRLMyJJIOgeXc4SD1auo11xDFZ0hEjTcurfiYPUrR7I1qepl5lD5IIIi5c9IdLA6o96cW4j0W+/S4zZu3IjTp0/j0Ucf9XRTWCmsh45KMf/aLaVYQYTcjxQRoz4Uq9xXG7KJRByibwVRrWg9kSRFsa+u5ajud6dIsaIWchWuHs3lBLOiqnIlqfy+zeSdUIdKJPuQfZ99jNiWfcIRACjy1y8MxHeSIVvuNKnKX5piKmOW6sCpJGcqeZmoPmmfTEX0mflV5e3nNpUTSV++Z50UGxO3RfU8OzsbS7BI3kk5cWSmlDfffBMxMTGYN2+eLVa7dm2Hth8ZGYnQ0FCX28e3+xljnuHK7SkXfoh0794dQgg0aNDgNhwEY4x5kKu3+v+/L42JiUFISIjtMWPGDGkXq1atQqtWrTBw4EBERkaiRYsW+Pe//+1Q85o3b47q1aujW7du+Pnnn50+PKdOUlNTU9GsWTNb0kJCQgLWrl0rLSeEQGJiIhRFwTfffKP6t9OnT6NXr17w9/dHZGQkXnjhBZjNxOUoxliFVjIJtbMPX8f9KGOsrLjaj5b0pWfOnEFWVpbtkZycLO3jxIkTSE1NRf369bF+/XqMHTsWEyZMwIIFC0ptV/Xq1TF79mysWLECK1asQExMDDp27IhffvnFqeNz6nZ/jRo18MYbb6B+/foQQmDBggXo168f9u7di8aNG9uWe//998nJsi0WC3r16oWoqChs3boV58+fx/Dhw6HX6zF9+nSnGs4Y823uTEHly7gfZYyVFXenoHJkphSr1YpWrVrZ+pcWLVrgwIEDmD17NpKS5Dl5ASAuLg5xcXG2523atMHx48cxc+ZMLFy40OF2OnUltU+fPrj//vtRv359NGjQANOmTUNgYCC2b99uW2bfvn1499138dln8hieDRs24NChQ/jiiy/QvHlzJCYmYurUqfj4449RVETMAM8Yq7hKbjk5+/Bx3I8yxsqMq/2oE31p9erVER+vLkzRqFEjnD592qmm3nPPPTh2TC6McTMuJ05ZLBYsW7YMubm5SEhIAADk5eXhH//4Bz7++GNERUVJ62zbtg1NmzZFtWrVbLEePXpg7NixOHjwIFq0aEHuq7CwEIU3JECVDPRVtBooNyROCaLKBJVMJYhkKuTkSiGtv7oyiCFAfrmK/YkqFkQFFEG80tRyVqL6D/lWIkpOKVbiqgsxx7lCvE7WE+o3m/6M3OAQc5wUKww3ybFQeZ9mEzFonar0RP5soiqKyCGFGLivsav+pBQTryZ1l5Qai08sp3GwChXJwUpSZF/iQGUqs/yngaguD+6vFp4jxQxa+SAKzOpMN7OZKqPlOFdu31eE2/038oZ+1BukmZdIsZ4NXpRiRUHVpFhuFJXQKu+DqvAnNPK62nwio/M2017IlGI5dWOk2LU75CweMlGXqI5IVwckKj0RFaHsqz8ZiSQ0QVQ3pCoj6rPljtSQJf+40uQRP7iIDoD6GwqT/DcURJUsKknMvrLitRry9qt2OyvFFtaTk+ZidXI/eu1crPo5UeHMGa4Og3JmnbZt2+Lw4cOq2JEjRxAbG1vKGrR9+/ahevXqTq3j9Enq/v37kZCQgIKCAgQGBmLlypW2M+xnnnkGbdq0Qb9+/ch1MzIyVB0rANvzm82dNWPGDEyePNnZpjLGmFfifpQx5itK+qTp06dj0KBB2LlzJ+bMmYM5c+bYlklOTsbZs2fx+eefA7g+XKl27dpo3LgxCgoK8J///AebNm3Chg0bnNq30yepcXFx2LdvH7KysrB8+XIkJSVhy5YtOHbsGDZt2oS9e/c6u8lbSk5OxrPPPmt7np2dLdXuZoz5mHKaJ9UbcT/KGCsT5TBP6t13342VK1ciOTkZU6ZMQe3atfH+++9j6NChtmXOnz+vuv1fVFSE5557DmfPnoW/vz+aNWuGjRs3olOnTk410+mTVIPBgHr16gEAWrZsiV27dmHWrFkwmUw4fvy4NB/WgAED0L59e2zevBlRUVHYuXOn6t8vXLgAAORtrRLUvF2MMd9WWROnAO5HGWNlw93EKUf17t0bvXv3LvXf58+fr3r+4osv4sUX5SE7znJ7nlSr1YrCwkK8/PLL+O2337Bv3z7bAwBmzpxpmwA2ISEB+/fvx8WLF23rp6WlITg4WBqUyxirBISTjwqK+1HGmMuc7Ud9qC916kpqcnIyEhMTUbNmTeTk5GDx4sXYvHkz1q9fj6ioKPJXfM2aNW2VCbp37474+HgMGzYMb731FjIyMvDqq69i3Lhxrv3CVxRVZQlFSwygJn4tCCKZSikmBm7n5que6zPlwdhGogqVxShnBFmJQdtCQyQwUWP2qQQb4i9nJRKiCsPkla/GyRVKquSrq5tosuWqVIVV5MaZ/aQQhFZuh4VInLIEEhlGxN9Lk0ckNxRQg+CJmN0YfSqpiRo8T6HWpRKnHElqAugkKQq1HFU9pThIHRPh8ns6JChfihVbiPcwVbFHp34xzcQyzqisV1K9rh/1UuuOvCXFut8jj6m16IOkGNXvUYmaxf7ycoWh8oKdO8sTnG/aJM8n6ari2AgpVhRAVDWqIccK6sqJwIKojud3Wk668r8gt0VL5Cvpr6k7SX028X1ZSJXfI/rzbLkPUvLkhE4q6RlUWWM/oiSUlkhKDpKXKwiX/9ZX49T9WnCrv6RlHq/5kxQ7bZZLhF62yn+bOros1fMci7uJU+VzJdVTnDpJvXjxIoYPH47z588jJCQEzZo1w/r169GtWzeH1tdqtVi9ejXGjh2LhIQEBAQEICkpCVOmTHGp8Ywx5mu4H2WMMcc4dZI6d+5cpzYuiDkOYmNjsWbNGqe2wxirgCpp4hT3o4yxMlMOiVOe5PI8qYwx5h4FpcwCfIt1GGOMXedKP1qynvfjk1TGmGdU0iupjDFWZvhKqg8hKk8oVnlQMnX7TBQTg75z1CU0tHr55TKa5JjZj0qmIgZyE1VBhI5IpqIqMxFjrekEGzmWe4e8X7MpVPVcYw6VlsmPkNcrDpHba/YnGhdaLIX0Bvk1L6aqveQTf1diTL2WKCSmsU+cosb2O5oQRY1vd7RqFPFJo8atU39rqlpXcbjcGE2oOplBR1Q7KSyWG6LVytvy08svlF6jXk7RuDfgn09SmdN+PSKFAqreKcWKg+R+pFjOFyXfT1RfXVBV3l7nLkQy1Xe3Tqbq2vZfUuyvu+TGXaspN65qMznTqVWonNizN6OGFCs+Jx+DPlfeh+kvOSlKf1Wd2EQl1opsuXKduCZXcrRQFR8VudPUBMhlwzTBcpIclZxl8Ze/+KhqXX/dI/dhDZucVD2vFXhFWua3PHl+4eqGTCnW3E8uGxquVXfyOiLJyykV/CTV7SmoGGOMMcYYK2sV60oqY8x3CIW+nHyrdRhjjF3nSj9asp4P4JNUxphHCHH94ew6jDHGrnOlHy1ZzxfwSSpjzDN4TCpjjLmngo9J9emTVEWrhaL8PQiZqiTlaDIVVd1CFKoHkGty5EHgOn+5wovBn0imMlHJSkSMGNxvNTqWoKIxUxk7xPaIv3p+pF1biE2Z/YkkqSpEQpS/HINCVJwqJrKECuSYtlB+nXT5VEzenMa+KcRLqRCVuqjEKeruCFk1ihjIT/UHVHUxKhGtOJiokOZPvF/tVrUQlaQU4u9g1MsvSqBRTm4IMqhjxWaiNI0z+HY/c9KGwkVSrGfTV6VYdmy4FCuUQ+QHUy/n/5DJlUXBckfavt/bqudUElZWW7mTL7r7mhRrHSMn3eiJjuloplyt6lpGoBQLzZDbEvin/Dk3Hr8oxayX1clD5jw5cSrNukyKuaOH/zA5SPStxdWCpdilpiYplhUvv3ahNTOlWL5Z3TGfywuRlqkZIG8rXCv/Ddv6yd+Fxup/qp5rs7MByPtwGN/uZ4yxsqcI8rfLLddhjDF2nSv9aMl6voCz+xljjDHGmNfhK6mMMc/gMamMMeYeHpPKGGO3AY9JZYwx9/CYVC+mUdQDqaksFgeTqUh264oiOVFEyS2QYoZsubKF2V/eZ7G/nCRkDiCqZ/g5+GZytACQA5uz6okkqUD5tdSaiBJOBHMh8VbLISqgZMuvkz5HbrBeHqMObSGRAGU3bp0ah0N+VqkYlZfmYMwsj+NHUSjxGgdRpbSoCmnyTuwTpzREQhRFQ1SOMhAJGv469fu/WOdu4hT4Sipzm+XgYSlmathaiuXeQX1Y5Q+6tkh+kynUfD1UxTidOlgYJO8zt5b82YqvJleNMhMdyeFLkVLsyjE5I6zKQblxVfbLnabmYLoUW5v1mRTzBMUkd5pFdeXjv9BKXi6nvvy9pPjLsexr8rpmq/p11zlYWS9SJ2fcGaufcGhdt/CVVMYYuw34JJUxxtxTwU9SOXGKMcYYY4x5Hb6SyhjzDL6Syhhj7qngV1L5JJUx5hmcOMUYY+7hxCnfoVCVfrREVSOq4hQ1MN5uOVEkV4/Q5MmJU9ocOXHK6EckSREJURY/IiFGIy9HVSuiBvKTiT1UIo5d86j1FCJZx0IkPxGpP9Dky8evz5K3Z8iS1zXkyO3VyoVSoDFTWVFEY+wXod4iOuq9RKxLLFcsF3tBEVFJikpOU4qJ/VLHoLv1gSkaeRm9Xv7r+OnlhAKjTo6ZtOr3v1Yq5+UcnsyflQWq0lFi7WelmMVwhxQrDCU2SPSjZiOV+HrrKoJWuSAhdNfkbR06GU00RKY/K3+3VPtN/lCEbf9Tiq1Nf8+hfXhCj8AkKVZ8T0Mp9ldzPylWJBecgi5T7qwtZuJ7NFjuw+yXMunkZeqY5ES3OP1luSHloKJP5l+hTlIZYz6Eb/czxph7Kvjtfk6cYowxxhhjXodPUhljjDHGmNfx7dv9iub6w4YaDek6YT8YkJjMX+TlSzGNTh4PYyAKCFj1xPhTrTxuRiEGOFPjHi0GYnJ4fzlmCZBfJ/t9aPKJSa4LiPGSxfKxKhZi8n1iQn7jVSkEQzYxjjJfHkOsUH9qB8bkWonXlxp/S7Ho5XWLguTlzAHUZODychpi/Ck5bTQ1Fpa4V6PYjTV2dPxpoF5+X9tP3A8AAVp1TK91c0wqXBiT6tYeWWVBjb9MrDlRiuU2lceCFlSRP3DZtYhJ+WOJCePtxj2azsvbMmUQY1mz5MGrOjndAeGH5M+lcdNvUmxt4SJ5ZS/RTTNQiomEO6VYVh35NdEQtWOowi5UT2ExEd+tRAfkb1S/xnUCL0nLNPc7LcXqxpynGnLbudKPlqznC/hKKmPMM0qyUp19OOns2bN45JFHUKVKFZhMJjRt2hS7d+++DQfEGGPlzNV+lLP7GWPsJsohcerq1ato27YtOnXqhLVr1yIiIgJHjx5FWFiYkztmjDEvVMETp/gklTHmGeVwkvrmm28iJiYG8+bNs8Vq167t5E4ZY8xLVfCTVL7dzxjzOdnZ2apHYSExcS6AVatWoVWrVhg4cCAiIyPRokUL/Pvf/y7n1jLGGHNFxbqSSkx6r1Dz9hNJTLAQyUR2xQGERd6YyJcTpyDk5TRW+WeLUUdN3G+St0f9mYjxJNYwIuZHZBgZiOOwn6jfwWwiKvlHny3HTJeI48+U26HLJxKCLMRPPjJJijh+g12MeCmtREIUOVE3kawmiO3RCVFEAQUHhwRRxRcU4m+oM6izCkxGOcki2E/Oxggxyu/hEL0cC9apY4XEJNfOcGcy/5iYGFV80qRJSElJkZY/ceIEUlNT8eyzz+Kf//wndu3ahQkTJsBgMCApSZ5AnFVca0+/L8V6Vh8nxYo71JFiBRHy500fLn+Wiq+oJ5vXOJjgacyUPwhVDsjb/27LP4kNei8qSUrTTJ6kPz9ULlJgyKHSSImCMvZ9PABBFDIRxHdhUJD8GseHXVA97xx8SFqmo4lMcfUInsyfMcZuBzdu9585cwbBwX+XmjEaidI+AKxWK1q1aoXp06cDAFq0aIEDBw5g9uzZfJLKGPN9fLufMcZuA+HiA0BwcLDqUdpJavXq1REfH6+KNWrUCKdPy1PIMMaYz3G1H/WRk1S+ksoY8wh3bvc7qm3btjh8+LAqduTIEcTGxjq3IcYY80IV/XY/X0lljFVYzzzzDLZv347p06fj2LFjWLx4MebMmYNx4+SxiIwxxryLb19JVRR1spSVOud2sAqV4kAWC5GYI1WlAoACOtPYnpaoTGWk9kEkU1m18rpUApDQEgPNC+SYfYIZ9SuL+uGly5P36X9BXjLgglwqREtVkiJeT0H8bchjJV879XOqapTZTwrBSt89lihEBRQS9Umjkr/0xKtMJEnpTXLSUqC/Ogkg3F9OfqrilyvFwg15UixUJ8fC9Op1C4jqVU5xZUJpJ5e/++67sXLlSiQnJ2PKlCmoXbs23n//fQwdOtS5/bIKad35j6VYzyavSLGigCpSrOCCnElp/7HUELmFVE5q8En5s+RrSVIUXUwNKWYl+nN9DvFCafRSqDiASjaWV6Wq/vlXlfu0e6rLw34GVtmpet6zjpw45VVcnZifJ/NnjLGbKId5UgGgd+/e6N27t/MrMsaYt6vgiVN8ksoY84jyGJPKGGMVWUUfk8onqYwxzyinK6mMMVZhVfArqZw4xRhjjDHGvI5vX0lVFHXCE3XKTQ0OpioYUewrUxFVqUBUkhLUTxQqmSo7RwqRyVR6OSZ0coUORcjL6fKppCO5KY4UmNLIBYxguiwn9fhnyIPgtfmOJdkILZH8ZJCPi0p0o6o/2VcjscgvG4lKiCJz64imEX8GWKm2+RGVv0zye8wQIL/wYUFyEkBVf3ViU4TfNWmZcL2cOBWml7cVqJUrsfgp6r+rTuNu4pQLt5x85Nc/813rDkyTYt1Nj0gxbWSEFBN+t+5gRMZfUmx91mcOts63WK9clWIa4jtOCZQzVYv95S+loiC5Ey6oKncKujvkPq1dzAkp9ljED1Ls3tiTUsyruXi731f6Ut8+SWWM+S6+3c8YY+7h2/1/S01NRbNmzWxVXhISErB27VoAwJUrV/DUU08hLi4OJpMJNWvWxIQJE5CVlaXaxunTp9GrVy/4+/sjMjISL7zwAsxmN6/IMMZ8TwWuknIz3I8yxsoMV5z6W40aNfDGG2+gfv36EEJgwYIF6NevH/bu3QshBM6dO4d33nkH8fHxOHXqFMaMGYNz585h+fLlAACLxYJevXohKioKW7duxfnz5zF8+HDo9XpbbW3GWOVQWbP7uR9ljJUVzu6/QZ8+fVTPp02bhtTUVGzfvh2PPfYYVqxYYfu3unXrYtq0aXjkkUdgNpuh0+mwYcMGHDp0CBs3bkS1atXQvHlzTJ06FS+99BJSUlJgMDg4YJAxxnwU96OMMeYYl8ekWiwWLFu2DLm5uUhISCCXycrKQnBwMHS667vZtm0bmjZtimrVqtmW6dGjB8aOHYuDBw+iRYsW5HYKCwtRWPh34lF2dvb1/1E01x82ciKK+t//P0Qlu1A7tqq3p1DJOsSagkqwEkTb8uXkFE2OnNii08t/JiORxKMpkjOiLNny8Vt1t640oSmWj0ufKx+XPlNOCFOKiWOlEqJ0RPUQ4u9FJXVRx0BVk7Jf176yFgBoqD8XNRCG+O4nk7X85NfO4k8kSQXIOzYGya8nlSRVPSBbitXwz1Q9jzTIiXlhOvn9FaCR96lXbl2pTafl28vu8op+lN3ShvwvPN0En2TNlfsbjSInnBUFy99dedXkL+rcGLlv1daSE0S71j4ixcZFfC/FGtc8K8WYd3F6Cqr9+/cjMDAQRqMRY8aMwcqVKxEfHy8td+nSJUydOhWjRo2yxTIyMlQdKwDb84yMjFL3OWPGDISEhNgeMTExzjabMeZtKvA4qlvhfpQxViYq+JhUp09S4+LisG/fPuzYsQNjx45FUlISDh1S17bNzs5Gr169EB8fj5SUFLcbmZycjKysLNvjzJkzbm+TMeZZJWOpnH1UBNyPMsbKgqv9qK/0pU7f7jcYDKhXrx4AoGXLlti1axdmzZqFTz/9FACQk5ODnj17IigoCCtXroRe//dl/KioKOzcuVO1vQsXLtj+rTRGoxFGozyPGmPMx/lIR1nWuB9ljJWZCtyPul1xymq12sY5ZWdno3v37jAYDFi1ahX8/PxUyyYkJGD//v24ePGiLZaWlobg4GDyVhdjrAKrwLeonMX9KGPMJRX8dr9TV1KTk5ORmJiImjVrIicnB4sXL8bmzZuxfv16W8eal5eHL774AtnZ2baB+REREdBqtejevTvi4+MxbNgwvPXWW8jIyMCrr76KcePGlc0vfCLpBhriL2F1LJnKvsSQEMS2LEQmDoFMpiqWE09EXr7cDJ38ZyKKRkFbKL+GVj3xmhAJYIrdcSiFcnuV4lsn0wAgf/oIrXwMgkpEczTBiipCRfx57IsiUQlRFneqRhnlnZpNRDJdoPza+RFJUuFBcqJBTFCmFIv1vyLFahovq55H6bKkZYI08vvLTyNXCLMSL1Sm1V/1XHEguYrJvL4fZayMaPz9pZglPFCK5VWTO9fcO4jk3TpyMmhi7UNSbHxVuZJU3ZjzpbaTeS+nTlIvXryI4cOH4/z58wgJCUGzZs2wfv16dOvWDZs3b8aOHTsAwHYbq0R6ejpq1aoFrVaL1atXY+zYsUhISEBAQACSkpIwZcqUsjsixphPqKzzpHI/yhgrKzxP6g3mzp1b6r917NiRvtJoJzY2FmvWrHFmt4yxisiVW04+0rHeDPejjLEy4+qteyfXOXv2LF566SWsXbsWeXl5qFevHubNm4dWrVqVus7mzZvx7LPP4uDBg4iJicGrr76KESNGOLVft8ekMsaYKypyRipjjJWH8sjuv3r1Ktq2bQu9Xo+1a9fi0KFDePfddxEWFlbqOunp6ejVqxc6deqEffv2YeLEiXj88cexfv16p47P5cn8GWPMLZX0SipjjJWZcriS+uabbyImJgbz5s2zxWrXrn3TdWbPno3atWvj3XffBQA0atQIP/30E2bOnIkePXo4vG+fPklVtBoomr8vBgsqiYlIziGvHxPJVIBdYgi5LSIJSRDJP1RVIzORsFIor6vJlbN4qJpRmiJ5exoNlcUkvzvtE6coQktsSye3TWiorCbHXjt6x1R7idedSn6z3xRZvUqOWYj8E7OcAwBzAJUkJSfEUUlSVYgkqehAOdmpjv8lKVbX76IUq2X4S/U8QktUl1LktlHv/ALihbLYves0VKkuxhj7f0pMtBTLrh0gxXJqEd97dR1Lkno6Qk6SqlWDk6QcZV95jpqqbtWqVejRowcGDhyILVu24I477sCTTz6JJ554otTtbtu2DV27dlXFevTogYkTJzrVPr7dzxjzjAo8bQpjjJULN6egiomJUVWimzFjhrSLEydOIDU1FfXr18f69esxduxYTJgwAQsWLCi1WaVVxsvOzkZ+vjzLTGl8+koqY8x3VdbsfsYYKyvuZvefOXMGwcHBtjg1jZ3VakWrVq0wffp0AECLFi1w4MABzJ49G0lJSS6121F8JZUx5hl8JZUxxtzj5pXU4OBg1YM6Sa1evbpUKKRRo0Y4ffp0qc2KioqyVcIrceHCBQQHB8NkMjl8eHwllTHmGZw4xRhj7imHxKm2bdvi8OHDqtiRI0cQGxtb6joJCQnSNHlpaWlISEhwqpm+fZJq1AMag+2pYpYTOQSRTERmMVFJPPYJUEQyiaIlEoes1F+fypwikm6oKlTU+A1BJDoVGqSQQiU7kUlMdssRCVEOJzpR26dQCVFkkhQR0xGvHbFfi14ds8gvEcz+8npmeWw/ioPk19waTCRJBbueJOVIJSkAiNETMZ16AHwE8ffSK/JHvoD6PFjlYw3SFKieazSOVVsrDd/uZ6ziSKzznBTLuau6FLvSiPjOjJeTpPrV3y/FJlb5SYrdUcmTpMpjMv9nnnkGbdq0wfTp0zFo0CDs3LkTc+bMwZw5c2zLJCcn4+zZs/j8888BAGPGjMFHH32EF198EY8++ig2bdqEpUuX4ttvv3WqnXy7nzHGGGOMke6++26sXLkSX375JZo0aYKpU6fi/fffx9ChQ23LnD9/XnX7v3bt2vj222+RlpaGO++8E++++y7+85//ODX9FODrV1IZY76Lb/czxph7yuF2PwD07t0bvXv3LvXf58+fL8U6duyIvXv3OtkwNT5JZYx5BN/uZ4wx95TH7X5P4pNUxphn8JVUxhhzTzldSfUU3z5J1WjUCT/E0VApPKKICFqJ5BFFPWRXIX56UMk6ZBUqKsHK4ljFHmGWk3NQIIdAJF3BQGQK6eQXSsqnISpwKURCGPk+J5KfqEQcovgRXdWKYNURyU4mOVYcaPc8gFgvUAqhKJhIkgqVG+wfIie1RQZfk2J3BMhJUneYMqVYDcNVKRall9cN1ebJbbF7fwZq/KRl9ERZrkyz3N4sq16KlTk+SWXMJ/VslCzFslvJSVKXG8v9jaaZ3J8Nqb9Hir1QRU6cMlWv3ElSpAp+ksqJU4wxxhhjzOv49pVUxpjPUkDf6bjVOowxxq5zpR8tWc8X8EkqY8wz+HY/Y4y5p4Lf7ueTVMaYR3B2P2OMuYez+71ZUbFd4hRRJcnBxCaputT1oAPruVGFinqXEJWkhNmxdxN5+Z5KziKTk+za7Gh1KQuREEW95o5UuQLIUdJWoxws9pdjRUFE4pRdrChIfi2Lg4nXN0SuVBZIJElFBcuVUmoGyMlPtf0vSbEaBrm6VLhWTmIK0MgVrKzE+67Q7jCKiUpSVOySRU6Sumz1l2J6Rb1uAVX1zBnlcCU1JSUFkydPVsXi4uLwxx9/OLljxiqvHi1eVz2/endVaZkr8XL/G9BY7uMerbdNij3daKMbravk+EoqY4z5rsaNG2Pjxr+/BHXE7BaMMca8D/fWjDHPKYdf8zqdDlFRUbd/R4wx5gk+clXUFTwFFWPMI0rGUjn7AIDs7GzVo7BQHhJR4ujRo4iOjkadOnUwdOhQVX1pxhjzZa72o74yJpVPUhljniFcfACIiYlBSEiI7TFjxgxyF61bt8b8+fOxbt06pKamIj09He3bt0dOjjyWmDHGfI6r/aiPnKT69O1+UVAIofn7lVao6koEMrGHGKcmYFdhyOxgoghRXclhVLUmajEiEYtK2AIVI7enbrNCJESRyU9WImYhYnq5HVad/BvJ7CcvVxRExIJvnSQFAEUh6tepOIQ4rmA5SSooWE6Sqh6cLcViA+XEgHr+F6VYfeMFKRallSuv2CcnAUCukN/XmURiU7ZVXWGqQMjtDdHI29dI5cZo9slaVgffq6VxJ7v/zJkzCA4OtsWNRiO5fGJiou3/mzVrhtatWyM2NhZLly7FY4895nSbGavoetw1SYpdahmqen6lifzBrR4v93Gjav0gxUY0kBOnmOs4u58xxm4HN7L7g4ODVSepjgoNDUWDBg1w7Ngxp9dljDGvU8Gz+/l2P2Os0rh27RqOHz+O6tXlOuOMMca8C5+kMsY8ojwG+z///PPYsmULTp48ia1bt+LBBx+EVqvFkCFDbs9BMcZYOaroiVN8u58x5hnlMJn/n3/+iSFDhuDy5cuIiIhAu3btsH37dkRERDi5Y8YY80IV/Ha/b5+kms2A8vfFYEEk9ijUxN1UAhDFPumISiYCEaMqKRFJJgpR5UrYV34CoFDVnxxMiCIrTjlSKcjB14hKQhN6IgmNSpwikqTMAUSSVCCVECXHCkOJalKh6uPXhhZJy4QE5Umx6kGOVZKK9bssxeoY/pJiMTo5wSpUI7clV8ivXQFRESrXKicKaRX13/UvS4C0jAZyRSs98R72U+RkMmk9xfsrTi1ZssTJHTBWefRo/poU+6t1mBS70lz9WY9vLE/jNqGGXDWqZ51DbrSOOYRPUhljrOy5k93PGGOs4mf385hUxhhjjDHmdfhKKmPMM8rhdj9jjFVofLvfi2kU9djJInkcnVDki8WK1rELyPbjLQWxniLkmP3E+NS2Sm2bhppEn1iOGjNKjHsV1ITrFmJ8rF3hAur9SxYLoI6LWM7iJ7/VigPlWGGwfKwOjz8Nl8ff6kMLVM+rhORKy9wRKE+qX8M/U17OKI9JjdbLy4Vq5X1oiVc00ypP0n+ZGEeaYzVJMYrF7r1ITfivIcaRRhHtDSLGyxYI9d/V3TGpihBQnCwI4OzyjLHrejZ4UYr91SlKil2+2yzFOjb7Q/U8ufo6aZmGMefcaB1zlSv9aMl6vsC3T1IZY76Lr6Qyxph7+EoqY4yVPU6cYowx93DiFGOMMcYYY+WMr6QyxjyDb/czxph7+Ha/F9MZAM0NySeFhfIyVmIyeypxypHJ66nEITIxiUgooSbzJ3YhiAn+YXXw3UQkbJExar9QTxiv6InXiChSIHTyayKIJClzgBwrCpK3V+hoklSY/Hc1hudLseph2arntYLkSfVrmuRYpD5bioVq5Un/gzTyPimXiSSmAiFP0l9MTOZvId8pMvvkLPtEKgDIsfpJsQBFTpIK0shJiMV229O42cvx7X7Gbo+eEaOl2JXEBlLscku5H+1z1z4p9m717arnxuqcJOUtKvrtft8+SWWM+S6+ksoYY+6p4FdSnRqTmpqaimbNmiE4OBjBwcFISEjA2rVrbf9eUFCAcePGoUqVKggMDMSAAQNw4cIF1TZOnz6NXr16wd/fH5GRkXjhhRdgNstTXjDGKraSKwDOPnwd96OMsbLiaj/qK32pUyepNWrUwBtvvIE9e/Zg9+7d6Ny5M/r164eDBw8CAJ555hn873//w7Jly7BlyxacO3cO/fv3t61vsVjQq1cvFBUVYevWrViwYAHmz5+P119/vWyPijHm/YSLDx/H/ShjrMy42o/6SF/q1O3+Pn36qJ5PmzYNqamp2L59O2rUqIG5c+di8eLF6Ny5MwBg3rx5aNSoEbZv3457770XGzZswKFDh7Bx40ZUq1YNzZs3x9SpU/HSSy8hJSUFBoM8uTljjFUk3I8yxphjXB6TarFYsGzZMuTm5iIhIQF79uxBcXExunbtalumYcOGqFmzJrZt24Z7770X27ZtQ9OmTVGtWjXbMj169MDYsWNx8OBBtGjRgtxXYWEhCm9IisrOvp7Uohh0UDR/J58I6naXRR4YLogkKUUvJ7HYJwqRiU46+SVUiEQnR3+0KESiE5H/QleSohKshGOJU1I1KQ1xtETCmTDIrxtVXcocICdYFQXJ+ygOlndbHCofgz6sQIrdES5XjmoUor5NGuefIS1TjagaFaCRk/CoRCQtUXWJSoii1rUSNzKo5VxFtY1KzMokKlpR1aTsE7OoKlrO8pVbTreLN/SjrOLJa11Xil1qLi/XsfnvUuyD6F1STBN1oiyaxW6TityPOv2NuH//fgQGBsJoNGLMmDFYuXIl4uPjkZGRAYPBgNDQUNXy1apVQ0bG9RODjIwMVcda8u8l/1aaGTNmICQkxPaIiYlxttmMMW8jhGuPCoD7UcZYmXC1H/WRvtTpk9S4uDjs27cPO3bswNixY5GUlIRDhw7djrbZJCcnIysry/Y4c+bMbd0fY+z2q8iD/W+F+1HGWFmo6IlTTt/uNxgMqFevHgCgZcuW2LVrF2bNmoWHH34YRUVFyMzMVF0FuHDhAqKiogAAUVFR2Llzp2p7JVmrJctQjEYjjEajs01ljHkzVwbv+0jHeivcjzLGyoSrSVA+0pe6PQDOarWisLAQLVu2hF6vx3fffWf7t8OHD+P06dNISEgAACQkJGD//v24ePGibZm0tDQEBwcjPj7e3aYwxphP4n6UMcZkTl1JTU5ORmJiImrWrImcnBwsXrwYmzdvxvr16xESEoLHHnsMzz77LMLDwxEcHIynnnoKCQkJuPfeewEA3bt3R3x8PIYNG4a33noLGRkZePXVVzFu3DjXfuHrdYDmhkMgkphEgZxgo1jlajpCkc/XFfvNERWXFGJcB5lMJUUAQVWDovZBJVNR40mIJClBJI5R7NunUBW4qJiOSAgyyklSZj8iSSqAiAXJx6WEyhWRIkOvSbF6wX9JsUYB6soo9Q3ymD2qklSeVX4/Zgu5WpOjiU5UkpSjHE1QohKlHJFLHCslSKP+LMmfIuco1usPZ9fxdV7XjzKflljvBSl2eUC0FItqIvd9k6PXSjFN1PmyaRgrF670oyXr+QKnTlIvXryI4cOH4/z58wgJCUGzZs2wfv16dOvWDQAwc+ZMaDQaDBgwAIWFhejRowc++eQT2/parRarV6/G2LFjkZCQgICAACQlJWHKlClle1SMMe9XSW/3cz/KGCszFfx2v1MnqXPnzr3pv/v5+eHjjz/Gxx9/XOoysbGxWLNmjTO7ZYxVQK4M3veVwf43w/0oY6ysuJoE5St9qcvzpDLGmFtcmQbFR6ZNYYyxcuHqdFI+0peW3czhjDHGGGOMlRGfvpIqtFqIGyolKUT1I1iJxKFiojJVsZycA62cKCOhkomoak1kjPiN4GCiE1ldylFUsleROg1GFMtpMYqRKLdIbUsrH6tVJ8csRI6HOVA+/uAgOfmtZtBVKVbHdEmKxegvq55TSVJWIvkp2yr/7alqTRRXE5ic2Z4Gru2DSuCiYlQylX0CVx712XJCZb3dz1hZKrojTIrlxsqfzadit0uxWjU4ScrX8e1+xhi7HSpp4hRjjJUZTpxijLGyx1dSGWPMPXwllTHGbgdOnGKMMfdU8MQpPklljHkEX0lljDH38JVUb6bVXn+U0MmVjhS9nEwlqKQjImFJmNUJVlTVKIcRCVaKVk5YIatQUahELDfyWITZLnEqX05Woip6KcUmOWZ27N1PFmvSy+uGmOS21PK/LMWq6+VkKvuko0yLv7RMjlU+Bgvx16YSrPSKe8lD9hxNunKkghVVDYs6LoqBWKxIaO2eO7QpxthtZDHKn3NNmJwIPDDwWHk0h7Ey5dsnqYwx38WJU4wx5h5OnGKMsbLHt/sZY8w9fLufMcZuB6twfr5fd+YHZoyxisaVfrRkPR/AJ6mMMc/g2/2MMeYevt3vxbSa64//J/REYo+FSEQxExWnqEpP9lWYyDbIyVpuoapQEVNFKESClVDkdRWiecKBqlaiSB54rxDJVEqeHNMWyIlI2iI5gU2xOpbEY9LJ1a/C9LlSjKomZV8lKlcQZa4IVJKUxsGkJlerQZWGqnTlSAIUdQwU6riopKsCqP+GhQ5u35u88cYbSE5OxtNPP43333/f081hzG26POL7jEjUrXLH2XJoDWNly7dPUhljPkuBC2NS3djfrl278Omnn6JZs2ZubIUxxryHK/1oyXq+wPcuhTDGKoaSSaidfbjg2rVrGDp0KP79738jLEyudc4YYz7J1X7Uib40JSUFiqKoHg0bNix1+fnz50vL+/n5uXR4fCWVMeYR7mT3Z2dnq+JGoxFGY+lDOcaNG4devXqha9eu+Ne//uVsUxljzCuVV3Z/48aNsXHjRttzHTFv+o2Cg4Nx+PDhv/dHDEFxBJ+kMsY8w43EqZiYGFV40qRJSElJIVdZsmQJfvnlF+zatcvpJjLGmFcrp8QpnU6HqKgoh5dXFMWp5Uvdr9tb8CCh0UDckGikUElHVBUq4heAIBKspIpTVMIVhUhgcnw5IunG0eQs6vI9lSNF7Veo90slV1kLC6WYJjdfimmz5apO+lz5Kpcun0j0KpJjucUGKXbNIt86oBKMHKng5GiCEcXRClHUclRyksP7INYtFrd+n1BJUlqit6L2aV/lyurB9NAzZ84gODjY9ry0q6hnzpzB008/jbS0NJdvNzHmzfTpF6SY9a9aUuz0n9WlWM0a529Hk5gPcfSu1NGjRxEdHQ0/Pz8kJCRgxowZqFmzZqnbvXbtGmJjY2G1WnHXXXdh+vTpaNy4sdPt4zGpjDGPUIRw6QFcv5V046O0k9Q9e/bg4sWLuOuuu6DT6aDT6bBlyxZ88MEH0Ol0sDgw0wVjjHkrV/vRkr40JiYGISEhtseMGTOkfbRu3Rrz58/HunXrkJqaivT0dLRv3x45OTlkm+Li4vDZZ5/hv//9L7744gtYrVa0adMGf/75p9PH59NXUhljPswK8sbBLddxQpcuXbB//35VbOTIkWjYsCFeeuklaMt6CjnGGCtPrvSjJevBsbtSiYmJtv9v1qwZWrdujdjYWCxduhSPPfaYtHxCQgISEhJsz9u0aYNGjRrh008/xdSpU51qJp+kMsY84sZf886s44ygoCA0adJEFQsICECVKlWkOGOM+RpX+tGS9YC/70o5IzQ0FA0aNMCxY8ccWl6v16NFixYOL38jvt3PGPMM4eKDMcbYda72o270pdeuXcPx48dRvbo8zplisViwf/9+h5e/kW9fSdWpK07BSty6MxNjzqipE4jEKcVuvBqVXKVoib80NdMClaykISpJaYnliBq7nviuFsVy4pjIlSs/aXLkBBVDplyFypAl/7102fLxX84JkGKnQ8OlWHV9phSL0mepntNJQvJ7xNGEKIp9ghHgRPUn4r4NGXOgfVRVKkeTpBypmuV2ZS1X5j11cZ7UG23evNntbTDmLdae/VCKNX/yPSk2q317KfZujdvSJFaeXJ0/2ol1nn/+efTp0wexsbE4d+4cJk2aBK1WiyFDhgAAhg8fjjvuuMM2nnXKlCm49957Ua9ePWRmZuLtt9/GqVOn8PjjjzvdTN8+SWWMMcYYY7fNn3/+iSFDhuDy5cuIiIhAu3btsH37dkRERAAATp8+Dc0NsytdvXoVTzzxBDIyMhAWFoaWLVti69atiI+Pd3rffJLKGPMIdybzZ4wxVj6T+S9ZsuSm/25/d2rmzJmYOXOm840i8EkqY8wzPHS7nzHGKoxyuN3vSXySyhjzCMV6/eHsOowxxq5zpR8tWc8X+PRJqlAUiBvrweocrDhFTOBNJSwJ+zkUiYpToqhY3paO+IVCJURRtWz1cnUlWIn2yksBVqJqFpGwpWiI5awa+4C8fSJGJlNlX5Ni+sty4pQpXC/FCi7Lb8lrYfK6R4MipFikUZ5YOEhboHoeoJGrZhURlZosRMxAJFg5WjWKSmKitudowha1X2ldR5bxJL6SythtEfX9X1JsectWUuzZqnK29R1chcq3VPArqTwFFWOMMcYY8zo+fSWVMebDXJmrzzd+/DPGWPlwdc5TH+lL+SSVMeYR5VFxijHGKjJ3K055Oz5JZYx5Bo9JZYwx91TwMak+fZIq/YKgEpGIxClhlQ9bIapJSYlSco4QQCRhUX96hUqwsU/MAgAtlRJFLKchKmlR26PeiPZJUpCTqaREqlII6vjz86WY5mq2FPO7KCdE+YfIx1AcLMcyiFrDR/3lZKpqevV+Yw2XpGUcTYhyJ0nKUQ4lRDnI1UpSju7T7SQsAThdtMo3+lXGPGrd7zOkWOtH3pViT9QfKMXWcBUq3+JKP1qyng/gxCnGGGOMMeZ1fPpKKmPMd/GYVMYYcw+PSWWMsdtBwIUxqbelJYwx5ptc6UdL1vMBvn2SaraqJ5inBi9Q41SpsZt6apyq3WTzxLhVakwmiuUJ/qEQk/QTzaDGuIKYkJ+KKQ6OSRUg9mHXGIVYRlgde1cLap+5eVJM95c8TjUgSJ7gvyhIfu2yg/yk2HH/qlIsyqSe4D9EK7ejik4uPkChxppq3fikk2NcqeGsZdiZWIkPiaPjVMscJ04xVm5Clu6WYscb3CPF5kclSLERDbbdljaxMsCJU4wxdhtYUUrptFuswxhj7DpX+tGS9XyAU4lTM2bMwN13342goCBERkbigQcewOHDh1XLZGRkYNiwYYiKikJAQADuuusurFixQrXMlStXMHToUAQHByM0NBSPPfYYrl1z7GoWY6xiKBlL5ezD13E/yhgrK672o77Slzp1krplyxaMGzcO27dvR1paGoqLi9G9e3fk5ubalhk+fDgOHz6MVatWYf/+/ejfvz8GDRqEvXv32pYZOnQoDh48iLS0NKxevRo//PADRo0aVXZHxRhjXor7UcYYc4xTt/vXrVunej5//nxERkZiz549uO+++wAAW7duRWpqKu655/pYl1dffRUzZ87Enj170KJFC/z+++9Yt24ddu3ahVatWgEAPvzwQ9x///145513EB0dXRbHxRjzdpV0TCr3o4yxMsNjUkuXlZUFAAgPD7fF2rRpg6+++gq9evVCaGgoli5dioKCAnTs2BEAsG3bNoSGhto6VgDo2rUrNBoNduzYgQcffFDaT2FhIQoLC23Ps7OvJ9woxWYo1hsSfrRUIgoxWEMjxwSxrqKze3l0cjKRQk1mX1Qkx+wLAwBQiHaQSV3UeBP7tgHqJDLbuvLKChGTkp3IxCyiHdQ+iQQr6jVRMuXEKWOGUYoFBIVIseIguTHZgYFS7HiQOpmqtukvaZk79FelGPWaFwg5qcvRCf7dQSU7OYJKiKK2RW6f6L/cnrxf2kflPEm15+l+lFUOG4q+lGLd75ksxaY06C3FRjS4LU1iZaGCn6S6/A1rtVoxceJEtG3bFk2aNLHFly5diuLiYlSpUgVGoxGjR4/GypUrUa9ePQDXx1pFRkaqtqXT6RAeHo6MjAxyXzNmzEBISIjtERMT42qzGWPeoqRzdfZRgXA/yhhzi6v9qI/0pS6fpI4bNw4HDhzAkiVLVPHXXnsNmZmZ2LhxI3bv3o1nn30WgwYNwv79+11uZHJyMrKysmyPM2fOuLwtxpiXsLr4qEC4H2WMucXVftRH+lKXbvePHz/eNlC/Ro2/C/0eP34cH330EQ4cOIDGjRsDAO688078+OOP+PjjjzF79mxERUXh4sWLqu2ZzWZcuXIFUVFR5P6MRiOMRvk2MGOM+SruRxlj7OacupIqhMD48eOxcuVKbNq0CbVr11b9e17e9YnSNRr1ZrVaLazW66ftCQkJyMzMxJ49e2z/vmnTJlitVrRu3dqlg2CM+Z6KPG3KzXA/yhgrKxV9CiqnrqSOGzcOixcvxn//+18EBQXZxj6FhITAZDKhYcOGqFevHkaPHo133nkHVapUwTfffGObIgUAGjVqhJ49e+KJJ57A7NmzUVxcjPHjx2Pw4MFOZ6QqZnXilLAS59waB8/DycpU6nUVnZysI8xENpGjlZ+KiWQqsroUlRBFLEckUylUEpO8pvyGJfZJVZIii1cRyVSCWi4vXwppLsvJHP4B8tWfokB/KVYcJB//mbBQ9fOQcGmZOON5KRZKVKayWMkMNjlEJFNR1arc4VDCloO7pLZlIROs1H/XYuHmMVXSxClv60dZ5bVh5yQpdtcT70mxf8b3Vz2f3uzr29Ym5iROnPpbamoqsrKy0LFjR1SvXt32+OqrrwAAer0ea9asQUREBPr06YNmzZrh888/x4IFC3D//ffbtrNo0SI0bNgQXbp0wf3334927dphzpw5ZXtkjDHvZhWuPXwc96OMsTLjaj/qI32pU1dSyStpdurXry9VRrEXHh6OxYsXO7NrxlhFU0mvpHI/yhgrM3wllTHGGGOMsfLl1mT+jDHmOleuAPjGr3/GGCsfrs556ht9qW+fpBabAc3fSUoKlThFVVeiKlNR7NelEqIMchUihUocKiqW16WqVVGVqagkGaotGiKmJ5KpqIpbFrs2U5WqqOQvalwLVYWKek2IxDFrdo4U018wSLGAYPl1LwqRl8sKVydY/R4mT89Th6hC1dRPnkNST2SJUQlGVEzrYIdQJKiyXjJqe65WhHI4qcvufWhx93ZRJb3dz5g3i/z6Dym2qFWC6vn0ZuXVGnZLFfx2v2+fpDLGfJdVwOlf8z4y2J8xxsqFK/2obT3vxyepjDHPEFb6qvut1mGMMXadK/1oyXo+gE9SGWOewbf7GWPMPRX8dj9n9zPGGGOMMa/j21dSi83qilJWIumESgByMMHKoW0RFa0EldSkJS6tW4lYsZxgJUAkZzlamYpKpiLYV6Zy+DcWkYRFVqGikAlmRXIsS65C5ZdhkmL+ofLbuTBMHTsZUkVaZreplrx9Rf47xBguSzFKWVeXcpUGjt3OMRDNpY4/VndV9fyawc3bReUwJjU1NRWpqak4efIkAKBx48Z4/fXXkZiY6Nx+Gask1l2WC0K0e/Bt1fMHGj8pLfNNu09uW5vYTfCYVMYYuw3K4XZ/jRo18MYbb6B+/foQQmDBggXo168f9u7di8aNGzu3b8YY8zYV/HY/n6QyxjxDwIWTVOcW79Onj+r5tGnTkJqaiu3bt/NJKmPM97nSj5as5wP4JJUx5hluXEnNzlYPATEajTAajTdd1WKxYNmyZcjNzUVCQsJNl2WMMZ/AV1K9T0nta7PVbvwiOU6TGHDn6HL2qDGkVmJCfvt2ARCCmMyfilGINxNVMADkWFsiRrQZQj2xvhDUMsRk/uTE/Y4OSiUQ29NQr6elUIqZiwukmKVAffzWPHmZ4lx5+/k6udBAnp6YzJ94TQqo4gtlzJHJ/B0dk2oltmVV5GO9plNvL/fa9eeO1KIvazExMarnkyZNQkpKCrns/v37kZCQgIKCAgQGBmLlypWIj48vh1Z6N1HKCT9j9uz7VqrP5PeRa0peN0/0o77AJ09Sc3KuVyXa/NcCD7eknMjnVRWXfG4IyOejwBUitt+1XZ4kYstd21SllJOTg5CQEOdXtFoBB0+k1esAZ86cQXBwsC18s6uocXFx2LdvH7KysrB8+XIkJSVhy5Ytlf5EtaQftT/hZ+yWvpVDIZhb/u2oQMq1H7Wt5/188iQ1OjoaZ86cQVBQEBRHroCWo+zsbMTExEhfor6Ej8E7ePsxCCGQk5OD6OhoVzfg8u3+4OBgh18Tg8GAevXqAQBatmyJXbt2YdasWfj000+d23cFw/3o7VcRjoOP4fbySD9asp4P8MmTVI1Ggxo1ani6GTflzJeot+Jj8A7efAwu/fIv4aHJ/K1WKwoLqcvzlQv3o+WnIhwHH8PtU+79aMl6PsAnT1IZYxVAOcyTmpycjMTERNSsWRM5OTlYvHgxNm/ejPXr1zu3X8YY80Y8TypjjPmmixcvYvjw4Th//jxCQkLQrFkzrF+/Ht26dfN00xhjjN0Cn6SWMaPRiEmTJt1yOhxvxsfgHSrCMdyMEFZ6dohbrOOMuXM5mcMXVZT3fkU4Dj4G7+ZKP1qyni9QBM97wBgrR9nZ2QgJCUGX0OHQKQan1jWLInyX+TmysrK8cmwZY4yVB3f6UcB3+lK+ksoY8wzhwlgq/k3NGGN/c6Ufta3n/fgklTHmGVYroDh5y8lHblExxli5cKUfBXymL+WTVMaYZ/CVVMYYc08Fv5J6++s3MsYYY4wx5qRKd5KampqKZs2a2Sb1TUhIwNq1a23/npGRgWHDhiEqKgoBAQG46667sGLFCnJbhYWFaN68ORRFwb59+2zxlJQUKIoiPQICAlTrZ2ZmYty4cahevTqMRiMaNGiANWvWqJb5+OOPUatWLfj5+aF169bYuXOnVx3D+++/j7i4OJhMJsTExOCZZ55BQYG6jqunjgEA1q9fj3vvvRdBQUGIiIjAgAEDcPLkSdUymzdvxl133QWj0Yh69eph/vz50j68+Ri+/vprdOvWDREREbZ2UPOAUsfgScJqdenBPM+b+iDuRz3fBwHcj3qKq/2oz/SlopJZtWqV+Pbbb8WRI0fE4cOHxT//+U+h1+vFgQMHhBBCdOvWTdx9991ix44d4vjx42Lq1KlCo9GIX375RdrWhAkTRGJiogAg9u7da4vn5OSI8+fPqx7x8fEiKSnJtkxhYaFo1aqVuP/++8VPP/0k0tPTxebNm8W+fftsyyxZskQYDAbx2WefiYMHD4onnnhChIaGis8//9wrjmHRokXCaDSKRYsWifT0dLF+/XpRvXp18cwzz3jFMZw4cUIYjUaRnJwsjh07Jvbs2SPuu+8+0aJFC9Uy/v7+4tlnnxWHDh0SH374odBqtWLdunU+cwxPP/20ePPNN8XOnTvFkSNHRHJystDr9ar9lHYMFy5ckNpyu2VlZQkAorPpYdHdf5hTj86mhwUAkZWVVe7tZn/jfrTsjoH7Ue84hsrUj/pSX1rpTlIpYWFh4j//+Y8QQoiAgADx+eefq/49PDxc/Pvf/1bF1qxZIxo2bCgOHjwofSDs7du3TwAQP/zwgy2Wmpoq6tSpI4qKikpd75577hHjxo2zPbdYLCI6OlrMmDHDK45h3LhxonPnzqrlnn32WdG2bVuvOIZly5YJnU4nLBaLLbZq1SqhKIrtdX/xxRdF48aNVdt8+OGHRY8ePXzmGCjx8fFi8uTJLh3D7WbrXI2DRHe/R5x6dDYO8omOtTLifpT70RtxP3p7udOP+lJfWulu99/IYrFgyZIlyM3NRUJCAgCgTZs2+Oqrr3DlyhVYrVYsWbIEBQUF6Nixo229Cxcu4IknnsDChQvh7+9/y/385z//QYMGDdC+fXtbbNWqVUhISMC4ceNQrVo1NGnSBNOnT4fFYgEAFBUVYc+ePejatattHY1Gg65du2Lbtm1ecQxt2rTBnj17bLc7Tpw4gTVr1uD+++/3imNo2bIlNBoN5s2bB4vFgqysLCxcuBBdu3aFXq8HAGzbtk3VPgDo0aOHrX2+cAz2rFYrcnJyEB4e7tQxlDshrmeYOvXwjcH+lQn3o9yPcj/qa/2oD/Wlnj5L9oTffvtNBAQECK1WK0JCQsS3335r+7erV6+K7t27CwBCp9OJ4OBgsX79etu/W61W0bNnTzF16lQhhBDp6ek3/fWcn58vwsLCxJtvvqmKx8XFCaPRKB599FGxe/dusWTJEhEeHi5SUlKEEEKcPXtWABBbt25VrffCCy+Ie+65xyuOQQghZs2aJfR6vdDpdAKAGDNmjO3fvOEYNm/eLCIjI4VWqxUAREJCgrh69art3+vXry+mT5+uWufbb78VAEReXp5PHIO9N998U4SFhdluQd3qGMqb7QqAYaDobvyHU4/OhoE+8eu/MvCGPoj7Ue/og7gf9a1+1Jf60kp5JTUuLg779u3Djh07MHbsWCQlJeHQoUMAgNdeew2ZmZnYuHEjdu/ejWeffRaDBg3C/v37AQAffvghcnJykJyc7NC+Vq5ciZycHCQlJaniVqsVkZGRmDNnDlq2bImHH34Yr7zyCmbPnu0zx7B582ZMnz4dn3zyCX755Rd8/fXX+PbbbzF16lSvOIaMjAw88cQTSEpKwq5du7BlyxYYDAY89NBDEGX0K9LbjmHx4sWYPHkyli5disjIyDI5xttFWIVLD+YdvKEP4n7U+/ogV3jbMVSGftRn+lLPniN7hy5duohRo0aJY8eOCQC2Ads3/vvo0aOFEEL069dPaDQaodVqbQ8AQqvViuHDh0vb7ty5s3jggQek+H333Se6dOmiiq1Zs0YAEIWFhaKwsFBotVqxcuVK1TLDhw8Xffv29YpjaNeunXj++edVsYULFwqTySQsFovHj+HVV18VrVq1Um3jzJkzAoDYtm2bEEKI9u3bi6efflq1zGeffSaCg4OFEMInjqHEl19+KUwmk1i9erUq7uwx3G4lVwA6afuLbrqHnXp00vb3iV//lRH3o9yP3oj70dvLnX7Ul/rSSnkl1Z7VakVhYSHy8vIAXB9nciOtVgvr/0/X8MEHH+DXX3/Fvn37sG/fPttUJ1999RWmTZumWi89PR3ff/89HnvsMWmfbdu2xbFjx2zbBYAjR46gevXqMBgMMBgMaNmyJb777jtVO7/77jvbWB1PH0NeXh65HwAQQnj8GG7WvpLtJCQkqNoHAGlpabb2+cIxAMCXX36JkSNH4ssvv0SvXr1Uyzt7DOWlQv/6r4S4H+V+9Ebcj5YPvpJawbz88stiy5YtIj09Xfz222/i5ZdfFoqiiA0bNoiioiJRr1490b59e7Fjxw5x7Ngx8c477whFUVRjZG50s3FIr776qoiOjhZms1n6t9OnT4ugoCAxfvx4cfjwYbF69WoRGRkp/vWvf9mWWbJkiTAajWL+/Pni0KFDYtSoUSI0NFQ89dRTXnEMkyZNEkFBQeLLL78UJ06cEBs2bBB169YVgwYN8opj+O6774SiKGLy5MniyJEjYs+ePaJHjx4iNjZW5OXlCSH+njrlhRdeEL///rv4+OOPyalTvPkYFi1aJHQ6nfj4449VU91kZmbe8hgyMjLIttxOJVcAOqKf6Ko85NSjI/r5xK//io770bI7Bu5HveMYKlM/6kt9aaU7SX300UdFbGysMBgMIiIiQnTp0kVs2LDB9u9HjhwR/fv3F5GRkcLf3180a9ZMmv7iRqV1TBaLRdSoUUP885//LHXdrVu3itatWwuj0Sjq1Kkjpk2bJnViH374oahZs6YwGAzinnvuEdu3b/eaYyguLhYpKSmibt26ws/PT8TExIgnn3xSGozuyWP48ssvRYsWLURAQICIiIgQffv2Fb///rtqme+//140b95cGAwGUadOHTFv3jxp+958DB06dCipi6d63DgXY2nH4AklnWs73C86op9Tj3a43yc61orOW/ogIbgf9YY+SAjuR8ubO/2oL/WlihC+Mg8BY6wiKCgoQO3atZGRkeHS+lFRUUhPT4efn18Zt4wxxnyDu/0o4Bt9KZ+kMsbKXUFBAYqKilxa12AweHWnyhhj5cGdfhTwjb6UT1IZY4wxxpjX4ex+xhhjjDHmdfgktRylpKRAURRPN4Mxxnwa96WMVQ58kuqi+fPnQ1EU28PPzw/R0dHo0aMHPvjgA+Tk5JTJfs6dO4eUlBTs27evTLbnaVu3bkVKSgoyMzM93RSfUtHeB4yV4L7UNdyXuqaivQ8qOj5JddOUKVOwcOFCpKam4qmnngIATJw4EU2bNsVvv/2mWvbVV19Ffn6+U9s/d+4cJk+eXGE+UFu3bsXkyZO5Y3VSRXsfMGaP+1LncF/qmor2PqjodJ5ugK9LTExEq1atbM+Tk5OxadMm9O7dG3379sXvv/8Ok8kEANDpdNDp+CWvDPLy8uDv7+/pZjDmM7gvZRTuSys5T07S6svmzZsnAIhdu3aR/z59+nQBQMyZM8cWmzRpkrB/yTds2CDatm0rQkJCREBAgGjQoIFITk4WQlyfHBnE5MIlkyT/8MMP4qGHHhIxMTHCYDCIGjVqiIkTJ9oqaJRISkoSAQEB4s8//xT9+vUTAQEBomrVquK5556TJr22WCzi/fffF02aNBFGo1FUrVpV9OjRQzrOhQsXirvuukv4+fmJsLAw8fDDD4vTp0/f9DUrOX77R3p6uhDi+qTWU6ZMEXXq1BEGg0HExsaK5ORkUVBQcNPtunKMM2fOFPHx8cJoNIrIyEgxatQoceXKFdVy33zzjbj//vtF9erVbRNUT5kyRdpehw4dROPGjcXu3btF+/bthclkstWxLigoEK+//rqoW7eu7W/0wgsvSMfkzvuAMV/GfSn3pSW4L2X2+KfobTJs2DD885//xIYNG/DEE0+Qyxw8eBC9e/dGs2bNMGXKFBiNRhw7dgw///wzAKBRo0aYMmUKXn/9dYwaNQrt27cHALRp0wYAsGzZMuTl5WHs2LGoUqUKdu7ciQ8//BB//vknli1bptqXxWJBjx490Lp1a7zzzjvYuHEj3n33XdStWxdjx461LffYY49h/vz5SExMxOOPPw6z2Ywff/wR27dvt13lmDZtGl577TUMGjQIjz/+OP766y98+OGHuO+++7B3716EhoaSx9u/f38cOXIEX375JWbOnImqVasCACIiIgAAjz/+OBYsWICHHnoIzz33HHbs2IEZM2bg999/x8qVK2/5mjt6jKNHj8b8+fMxcuRITJgwAenp6fjoo4+wd+9e/Pzzz9Dr9QCuj5ULDAzEs88+i8DAQGzatAmvv/46srOz8fbbb6v2ffnyZSQmJmLw4MF45JFHUK1aNVitVvTt2xc//fQTRo0ahUaNGmH//v2YOXMmjhw5gm+++aZM3geMVWTcl8q4L+W+tNLw9Fmyr7rVr38hhAgJCREtWrSwPbf/9T9z5kwBQPz111+lbmPXrl2l/tKz/5UvhBAzZswQiqKIU6dO2WJJSUkCgJgyZYpq2RYtWoiWLVvanm/atEkAEBMmTJC2a7VahRBCnDx5Umi1WjFt2jTVv+/fv1/odDopbu/tt99W/eIvsW/fPgFAPP7446r4888/LwCITZs23XS7jh7jjz/+KACIRYsWqZZbt26dFKde39GjRwt/f3/Vr/eScnqzZ89WLbtw4UKh0WjEjz/+qIrPnj1bABA///yzEML99wFjvoz7Uu5LS3Bfyuxx4tRtFBgYeNPM1JJfyf/9739htVqd3n7J+CwAyM3NxaVLl9CmTRsIIbB3715p+TFjxqiet2/fHidOnLA9X7FiBRRFwaRJk6R1S6Z7+frrr2G1WjFo0CBcunTJ9oiKikL9+vXx/fffO30cALBmzRoAwLPPPquKP/fccwCAb7/91qHt3OoYly1bhpCQEHTr1k3V/pYtWyIwMFDV/htf35ycHFy6dAnt27dHXl4e/vjjD9V+jEYjRo4cqYotW7YMjRo1QsOGDVX76ty5MwDY9uXu+4Cxio77UsdxX8p9aUXCJ6m30bVr1xAUFFTqvz/88MNo27YtHn/8cVSrVg2DBw/G0qVLHf5wnT59GiNGjEB4eDgCAwMRERGBDh06AACysrJUy/r5+dluBZUICwvD1atXbc+PHz+O6OhohIeHl7rPo0ePQgiB+vXrIyIiQvX4/fffcfHiRYfabu/UqVPQaDSoV6+eKh4VFYXQ0FCcOnXqlttw5BiPHj2KrKwsREZGSu2/du2aqv0HDx7Egw8+iJCQEAQHByMiIgKPPPIIAPn1veOOO2AwGFSxo0eP4uDBg9J+GjRoAAC2fbn7PmCsouO+1HHcl3JfWpHwmNTb5M8//0RWVpbUUdzIZDLhhx9+wPfff49vv/0W69atw1dffYXOnTtjw4YN0Gq1pa5rsVjQrVs3XLlyBS+99BIaNmyIgIAAnD17FiNGjJA+lDfbljOsVisURcHatWvJbQYGBrq1fXcm6HbkGK1WKyIjI7Fo0SLy30s65szMTHTo0AHBwcGYMmUK6tatCz8/P/zyyy946aWXpNf3xisFN+6radOmeO+998h9xcTE2NZ19X3AWEXHfalruC/lvrQi4JPU22ThwoUAgB49etx0OY1Ggy5duqBLly547733MH36dLzyyiv4/vvv0bVr11I7mv379+PIkSNYsGABhg8fbounpaW53Oa6deti/fr1uHLlSqlXAOrWrQshBGrXrm37FeuM0o4nNjYWVqsVR48eRaNGjWzxCxcuIDMzE7GxsU7vi1K3bl1s3LgRbdu2JTvDEps3b8bly5fx9ddf47777rPF09PTndrXr7/+ii5dutzyC8PV9wFjFR33pTTuS2ncl1YsfLv/Nti0aROmTp2K2rVrY+jQoaUud+XKFSnWvHlzAEBhYSEAICAgAACkCZtLfhEKIWwxIQRmzZrlcrsHDBgAIQQmT54s/VvJfvr37w+tVovJkyer9l2yzOXLl2+6j9KO5/777wcAvP/++6p4yS/nXr16OXwcNzNo0CBYLBZMnTpV+jez2WxrF/X6FhUV4ZNPPnFqX2fPnsW///1v6d/y8/ORm5sLwL33AWMVGfelpeO+9DruSys2vpLqprVr1+KPP/6A2WzGhQsXsGnTJqSlpSE2NharVq2Cn59fqetOmTIFP/zwA3r16oXY2FhcvHgRn3zyCWrUqIF27doBuP4LMjQ0FLNnz0ZQUBACAgLQunVrNGzYEHXr1sXzzz+Ps2fPIjg4GCtWrFCNGXJWp06dMGzYMHzwwQc4evQoevbsCavVih9//BGdOnXC+PHjUbduXfzrX/9CcnIyTp48iQceeABBQUFIT0/HypUrMWrUKDz//POl7qNly5YAgFdeeQWDBw+GXq9Hnz59cOeddyIpKQlz5syx3R7auXMnFixYgAceeACdOnVy+bhu1KFDB4wePRozZszAvn370L17d+j1ehw9ehTLli3DrFmz8NBDD6FNmzYICwtDUlISJkyYAEVRsHDhQunL5GaGDRuGpUuXYsyYMfj+++/Rtm1bWCwW/PHHH1i6dCnWr1+PVq1aufU+qF27dpm8Lox5Gvel3JeWhvvSSqw8pxKoSEqmTSl5GAwGERUVJbp16yZmzZolsrOzpXXsp0357rvvRL9+/UR0dLQwGAwiOjpaDBkyRBw5ckS13n//+18RHx8vdDqdauqMQ4cOia5du4rAwEBRtWpV8cQTT4hff/1Vml6jZHLmW7VHCCHMZrN4++23RcOGDYXBYBAREREiMTFR7NmzR7XcihUrRLt27URAQIAICAgQDRs2FOPGjROHDx++5Ws3depUcccddwiNRiNNQD158mRRu3ZtodfrRUxMjNMTUDtyjEIIMWfOHNGyZUthMplEUFCQaNq0qXjxxRfFuXPnbMv8/PPP4t577xUmk0lER0eLF198Uaxfv14AEN9//71tuZIJqClFRUXizTffFI0bNxZGo1GEhYWJli1bismTJ4usrCwhhPvvA8Z8Gfel3JeW4L6U2VOEcOLnDGOMMcYYY+WAx6QyxhhjjDGvwyepjDHGGGPM6/BJKmOMMcYY8zp8ksoYY4wxxrwOn6QyxhhjjDGvwyepjDHGGGPM6/BJKqtwNm/eDEVRsHnzZk83hTHGfBL3o8wb8EkqY4wxxhjzOnySyhhjjDHGvA6fpDLGGGOMMa/DJ6mVzKlTp/Dkk08iLi4OJpMJVapUwcCBA3Hy5EnVcvPnz4eiKPj555/x7LPPIiIiAgEBAXjwwQfx119/Sdv95JNP0LhxYxiNRkRHR2PcuHHIzMxULdOxY0c0adIEv/32Gzp06AB/f3/Uq1cPy5cvBwBs2bIFrVu3hslkQlxcHDZu3OhS2+1NmjQJer2ebPeoUaMQGhqKgoKCW794jDEG7kftcT/Kbhc+Sa1kdu3aha1bt2Lw4MH44IMPMGbMGHz33Xfo2LEj8vLypOWfeuop/Prrr5g0aRLGjh2L//3vfxg/frxqmZSUFIwbNw7R0dF49913MWDAAHz66afo3r07iouLVctevXoVvXv3RuvWrfHWW2/BaDRi8ODB+OqrrzB48GDcf//9eOONN5Cbm4uHHnoIOTk5Lre9xLBhw2A2m/HVV1+p4kVFRVi+fDkGDBgAPz8/V15OxlglxP3o37gfZbeVYJVKXl6eFNu2bZsAID7//HNbbN68eQKA6Nq1q7Barbb4M888I7RarcjMzBRCCHHx4kVhMBhE9+7dhcVisS330UcfCQDis88+s8U6dOggAIjFixfbYn/88YcAIDQajdi+fbstvn79egFAzJs3z+m2f//99wKA+P77722xhIQE0bp1a9W6X3/9tbQcY4zdCvejf+N+lN1OfCW1kjGZTLb/Ly4uxuXLl1GvXj2Ehobil19+kZYfNWoUFEWxPW/fvj0sFgtOnToFANi4cSOKioowceJEaDR/v52eeOIJBAcH49tvv1VtLzAwEIMHD7Y9j4uLQ2hoKBo1aoTWrVvb4iX/f+LECZfbfqPhw4djx44dOH78uC22aNEixMTEoEOHDjddlzHGbsT9KPejrHzwSWolk5+fj9dffx0xMTEwGo2oWrUqIiIikJmZiaysLGn5mjVrqp6HhYUBuH67CYCtk42Li1MtZzAYUKdOHdu/l6hRo4aqswaAkJAQxMTESLEb9+NK22/08MMPw2g0YtGiRQCArKwsrF69GkOHDpXawxhjN8P9KPejrHzoPN0AVr6eeuopzJs3DxMnTkRCQgJCQkKgKAoGDx4Mq9UqLa/VasntCCFc2n9p23NkP862/UZhYWHo3bs3Fi1ahNdffx3Lly9HYWEhHnnkEZeOgzFWeXE/yv0oKx98klrJLF++HElJSXj33XdtsYKCAimD1FGxsbEAgMOHD6NOnTq2eFFREdLT09G1a1e32nsjd9s+fPhw9OvXD7t27cKiRYvQokULNG7cuMzaxxirHLgf5X6UlQ++3V/JaLVa6df7hx9+CIvF4tL2unbtCoPBgA8++EC13blz5yIrKwu9evVyq703crftiYmJqFq1Kt58801s2bKFf/0zxlzC/Sj3o6x88JXUSqZ3795YuHAhQkJCEB8fj23btmHjxo2oUqWKS9uLiIhAcnIyJk+ejJ49e6Jv3744fPgwPvnkE9x9991l2oG523a9Xo/Bgwfjo48+glarxZAhQ8qsbYyxyoP7Ue5HWfngk9RKZtasWdBqtVi0aBEKCgrQtm1bbNy4ET169HB5mykpKYiIiMBHH32EZ555BuHh4Rg1ahSmT58OvV7vVW0fPnw4PvroI3Tp0gXVq1cvs7YxxioP7ke5H2XlQxGujtxmzAf9+uuvaN68OT7//HMMGzbM081hjDGfw/0oKy88JpVVKv/+978RGBiI/v37e7opjDHmk7gfZeWFb/ezSuF///sfDh06hDlz5mD8+PEICAjwdJMYY8yncD/Kyhvf7meVQq1atXDhwgX06NEDCxcuRFBQkKebxBhjPoX7UVbe+CSVMcYYY4x5HR6TyhhjjDHGvA6fpDLGGGOMMa9T6U9S33rrLTRs2PCmNYtPnjwJRVEwf/58WywlJQWK8n/t3Xl4U1X6B/BvkjZpaWlLgVI6XUCKlIIsVoUiKrIvgygoOCIURFCmoIJrRxwRBNwGUYTizCCigiAi6ACyuADjsAjVCoIDoij8hFIVW9Zuuef3RyexN/eUJjdpk5t+P8+T5yEn99wlSV9Ozn3POaY6OEOqD8aMGYMWLVrU6jEWLVqE5ORklJaW1upxiLzhTkz2hDd/Wy1atMCYMWN8ch7+8vrrr8NkMuGHH37w2T6N9v/fxo0bERkZiZ9//tnfp0IeqteN1DNnzuDZZ5/Fo48+CrO59t+KEydOYPr06cjPz6/1Y5HvLVy4UPVDxVP+/vzHjBmDsrIyvPrqq345PlFN6jomk+/Mnj0ba9eu9fdpSPXv3x+pqamYM2eOv0+FPFSvo8Brr72GiooKXcu6TZs2DRcvXvSozokTJ/DUU0+xkWpQvmikVvf5/+Mf/8ChQ4f0n5wbwsLCkJWVhblz52rW7iYKBN7EZJIbNWoULl68iJSUFJ/tU/b/XyA3UgHgnnvuwauvvoqzZ8/6+1TIA/W6kbpkyRLcdNNNCAsL87huSEiIrnpEMqGhobDZbLV+nOHDh+PHH3/Ep59+WuvHIvKUNzE5GJw/f97n+7RYLAgLC/Pp7Xkj/v83bNgwlJaWYtWqVf4+FfJAvW2kHj16FPv27UPv3r1V5UVFRRgzZgyio6MRExODrKwsFBUVaerLcnK2bNmC7t27IyYmBpGRkWjTpg3+8pe/AAC2bt2Kq6++GgAwduxYmEwmVZ7rv//9b9x2221ITk6GzWZDUlISpkyZovm1OmbMGERGRuKnn37CzTffjMjISDRt2hQPPfQQ7Ha7altFUfDSSy/hiiuuQFhYGJo2bYr+/ftj7969qu3eeustZGRkIDw8HLGxsbj99ttx/Phxj9/ToqIiTJkyBS1atIDNZkNiYiJGjx6NX375xblNYWEhxo0bh2bNmiEsLAwdO3bE0qVLVftx5AC/8MILWLBgAS677DI0aNAAffv2xfHjxyGEwMyZM5GYmIjw8HAMGTIEp0+fVu2jRYsW+OMf/4jNmzejU6dOCAsLQ3p6Ot577z3VdtXlVrnmcbVo0QIHDhzAtm3bnJ9djx49AACnT5/GQw89hCuuuAKRkZGIiorCgAED8NVXXzn3V9PnL8ubO3/+PB588EEkJSXBZrOhTZs2eOGFFzS9oCaTCZMmTcLatWvRvn172Gw2tGvXDhs3btRcV0ZGBmJjY/H+++9rXiPyp+pi8q+//opRo0YhKirKGZO/+uorzTgBAM6/gbCwMLRv3x5r1qyRHuuFF15At27d0LhxY4SHhyMjIwPvvvuuT67D8fe4bNkytGnTBmFhYcjIyMD27dtV2zliz8GDB3HHHXegUaNG6N69OwCgoqICM2fORKtWrWCz2dCiRQv85S9/ceaTCyFw4403omnTpigsLHTus6ysDFdccQVatWrlbPDKclId8XHr1q246qqrEB4ejiuuuAJbt24FALz33nvO/zcyMjLw5ZdfSs+96jWfP38eS5cudca2MWPG4NNPP4XJZJJ+DsuXL4fJZMLOnTs1r/Xo0QPt27dHXl4eunXrhvDwcLRs2RKLFi1ybnPu3DlERETg/vvv19T/v//7P1gsFtXt/bi4OHTo0IGxz2hEPfXWW28JAGLfvn3OMkVRxPXXXy/MZrP485//LObPny969uwpOnToIACIJUuWOLd98sknRdW37+uvvxZWq1VcddVV4qWXXhKLFi0SDz30kLj++uuFEEIUFBSIGTNmCABiwoQJ4s033xRvvvmm+O6774QQQkyePFkMHDhQzJ49W7z66qti3LhxwmKxiFtvvVV13llZWSIsLEy0a9dO3HXXXSI3N1cMGzZMABALFy5UbTtmzBgBQAwYMEDMmzdPvPDCC2LIkCFi/vz5zm2efvppYTKZxIgRI8TChQvFU089JZo0aSJatGghfvvtN7ffz7Nnz4r27dsLi8Uixo8fL3Jzc8XMmTPF1VdfLb788kshhBAXLlwQbdu2FaGhoWLKlCni5ZdfFtddd50AIObNm+fc19GjRwUA0alTJ5Geni7mzp0rpk2bJqxWq+jatav4y1/+Irp16yZefvllcd999wmTySTGjh2rOp+UlBRx+eWXi5iYGPHYY4+JuXPniiuuuEKYzWaxefPmaj9HhyVLlggA4ujRo0IIIdasWSMSExNFWlqa87Nz7GfPnj2iVatW4rHHHhOvvvqqmDFjhvjDH/4goqOjxU8//eTW55+VlSVSUlKcx1cURfTs2VOYTCZx9913i1deeUUMHjxYABAPPPCA6lwBiI4dO4rmzZuLmTNninnz5onLLrtMNGjQQPzyyy+aa+vdu7fIyMhw85MlqhuymGy320VmZqawWCxi0qRJ4pVXXhF9+vQRHTt21MTkTZs2CbPZLNq3by/mzp0rHn/8cREdHS3atWun+tsSQojExETx5z//Wbzyyiti7ty54pprrhEAxLp161TbpaSkiKysLI+uA4Bo3769aNKkiZgxY4Z49tlnRUpKiggPDxf79+93bueIPenp6WLIkCFi4cKFYsGCBUKIyngAQNx6661iwYIFYvTo0QKAuPnmm531v//+exEZGSluueUWZ9ljjz0mTCaT2LZtm7PMNZY5rqtNmzaiefPmYvr06eLFF18Uf/jDH0RkZKR46623RHJysnjmmWfEM888I6Kjo0Vqaqqw2+2ac3d48803hc1mE9ddd50ztu3YsUMoiiKSkpLEsGHDNO/TwIEDRatWraTv4Q033CASEhJEXFycmDRpknj55ZdF9+7dBQCxePFi53YjR44UzZo1ExUVFar6zz33nDCZTOLHH39Uld99992iSZMm0mNSYKq3jdRp06YJAOLs2bPOsrVr1woA4rnnnnOWVVRUOBtSl2qkvvjiiwKA+Pnnn6s95p49ezT7cbhw4YKmbM6cOZo/NEfwmjFjhmrbzp07qxoen3zyiQAg7rvvPs1+FUURQgjxww8/CIvFImbNmqV6ff/+/SIkJERTfil//etfBQDx3nvvVXu8efPmCQDirbfecr5WVlYmMjMzRWRkpDhz5owQ4vdGatOmTUVRUZFz25ycHGeDrLy83Fn+pz/9SVitVlFSUuIsS0lJEQDE6tWrnWXFxcWiefPmonPnzs4ydxupQgjRrl07ccMNN2i2LSkpUQVwxzXYbDbV53Spz9+1ker4Lj799NOq7W699VZhMpnEkSNHnGUAhNVqVZV99dVXAoDqB4nDhAkTRHh4uKacyJ9kMXn16tWaH7F2u1307NlT87fUqVMn0bx5c1XM2Lx5swCgaaS6xtuysjLRvn170bNnT1W53kYqALF3715n2Y8//ijCwsJUDUpH7PnTn/6kqp+fny8AiLvvvltV/tBDDwkA4pNPPnGWvfrqq86YumvXLmGxWDQ/YqtrpAIQO3bscJZt2rRJABDh4eGq/3Mcx/j00081515VRESE9L3KyckRNptN9bkUFhaKkJAQ8eSTT2q2F6KykQpA/O1vf3OWlZaWik6dOom4uDhRVlamOucPP/xQVb9Dhw7SWD179mwBQJw6dUp6XAo89fZ2/6+//oqQkBBERkY6yzZs2ICQkBBMnDjRWWaxWDB58uQa9xcTEwMAeP/993VNnRIeHu789/nz5/HLL7+gW7duEEJobrUAwL333qt6ft111+H77793Pl+9ejVMJhOefPJJTV3HbZr33nsPiqJg+PDh+OWXX5yP+Ph4tG7d2qO8xdWrV6Njx4645ZZbqj3ehg0bEB8frxoUERoaivvuuw/nzp3Dtm3bVPVuu+02REdHO5936dIFAHDnnXciJCREVV5WVoaffvpJVT8hIUF1PlFRURg9ejS+/PJLFBQUuH1tNbHZbM6RyHa7Hb/++qsz3eOLL77Qtc8NGzbAYrHgvvvuU5U/+OCDEELgww8/VJX37t0brVq1cj7v0KEDoqKiVN8Jh0aNGuHixYu4cOGCrnMjqg2ymLxx40aEhoZi/PjxzjKz2Yzs7GxV3ZMnTyI/Px9ZWVmqmNGnTx+kp6drjlU13v72228oLi7Gddddp/vv1VVmZiYyMjKcz5OTkzFkyBBs2rRJk5blGss3bNgAAJg6daqq/MEHHwQArF+/3lk2YcIE9OvXD5MnT8aoUaPQqlUrzJ49261zTE9PR2ZmpvO5I7727NkTycnJmnJZLHHH6NGjUVpaqkqnWLlyJSoqKnDnnXdWWy8kJAT33HOP87nVasU999yDwsJC5OXlAaiMewkJCVi2bJlzu6+//hr79u2T7rtRo0YAoEpBo8BWbxupMj/++COaN2+uCpIA0KZNmxrrjhgxAtdeey3uvvtuNGvWDLfffjveeecdtxusx44dw5gxYxAbG+vMM73hhhsAAMXFxaptHfmlVTVq1Ai//fab8/l3332HhIQExMbGVnvMb7/9FkIItG7dGk2bNlU9vvnmG1WuU02+++47tG/f/pLb/Pjjj2jdurVmapm2bds6X6+qaqAE4PzPJykpSVpe9foBIDU1VZNvevnllwOAT+cMVBQFL774Ilq3bg2bzYYmTZqgadOm2Ldvn+azc9ePP/6IhIQEzdrY7r5XgPY74SD+l9NqpHkOqX5yxOQGDRqoylNTUzXbAUDr1q01+5DF73Xr1qFr164ICwtDbGwsmjZtitzcXN1/r65k53H55ZfjwoULmrk6W7ZsqXr+448/wmw2a64xPj4eMTExmr/9xYsX48KFC/j222/x+uuvqxrgl+JtfHVXWloarr76alVDctmyZejatavmGqtKSEhARESEqsw1fpvNZowcORJr1651/uhetmwZwsLCcNttt2n2ydhnPCE1bxKcGjdujIqKCpw9e1bTENAjPDwc27dvx6effor169dj48aNWLlyJXr27InNmzfDYrFUW9dut6NPnz44ffo0Hn30UaSlpSEiIgI//fQTxowZo2noXmpfnlAUBSaTCR9++KF0n66N9bpW3XVWV+4IQJ6oLli59nZcyuzZs/HEE0/grrvuwsyZMxEbGwuz2YwHHnjAZxOS18ST9+S3335DgwYN3P7PjKgu+DomV+ff//43brrpJlx//fVYuHAhmjdvjtDQUCxZsgTLly+vteNWp7q/Q3cbUlu3bnUOqNq/f7+qd/RS6iK+OowePRr3338//u///g+lpaXYtWsXXnnlFd37c933888/j7Vr1+JPf/oTli9fjj/+8Y+qHnUHR0O7SZMmPjk21b5620hNS0sDUDmitEOHDgCAlJQUfPzxxzh37pyqgebu/JVmsxm9evVCr169MHfuXMyePRuPP/44Pv30U/Tu3bvaoLN//34cPnwYS5cuxejRo53lW7Zs0Xt5aNWqFTZt2oTTp09X25vaqlUrCCHQsmVL5y9Ub4739ddfX3KblJQU7Nu3D4qiqHpT//vf/zpf96UjR45ACKF63w8fPgwAzpH0jts/RUVFzpQNQNtTCVT/n8a7776LG2+8EYsXL1aVFxUVqYKhJ7/eU1JS8NFHH2n+w/bFe3X06FFnjyxRoKguJn/66ae4cOGCqjf1yJEjqrqOv4dvv/1Ws1/X+L169WqEhYVh06ZNqmnflixZ4psLqeY8Dh8+jAYNGmjugrlKSUmBoij49ttvVX+np06dQlFRkepv/+TJk5g8eTL69u0Lq9WKhx56CP369fN5LHXHpeLb7bffjqlTp+Ltt9/GxYsXERoaihEjRlxyfydOnMD58+dVvamu8RsA2rdvj86dO2PZsmVITEzEsWPHMH/+fOk+jx496rzTRcZQb2/3O35tVp2OaeDAgaioqEBubq6zzG63V/uFr8p1CiQA6NSpEwA4f+U6/thcp7Ry/HKt+ktVCIGXXnrJjSuRGzZsGIQQeOqppzSvOY4zdOhQWCwWPPXUU5pfyUII/Prrrx4d76uvvpJONeLY98CBA1FQUICVK1c6X6uoqMD8+fMRGRnpTG/wlRMnTqjO58yZM3jjjTfQqVMnxMfHA4Azj7Pq9DCOqVRcRURESKcjs1gsmvdv1apVmhzZ6j5/mYEDB8Jut2t6G1588UWYTCYMGDCgxn1U54svvkC3bt101yeqDbKY3K9fP5SXl+Mf//iHs0xRFCxYsEBVt3nz5ujUqROWLl2qumW/ZcsWHDx4ULWtxWKByWRS3S354YcffDoR/c6dO1X5rcePH8f777+Pvn371ngnbODAgQCAefPmqcrnzp0LABg0aJCzbPz48VAUBYsXL8bf//53hISEYNy4cX5ZrKO6+AhU9lwOGDAAb731FpYtW4b+/fs7f8AfO3bM+eO7qoqKCtXqeI7V8po2barK9wUqFyzYvHkz5s2bh8aNG1cbH/Py8tzuaabAUG97Ui+77DK0b98eH330Ee666y4AwODBg3Httdfiscceww8//OCcV9OdPKUZM2Zg+/btGDRoEFJSUlBYWIiFCxciMTHROfddq1atEBMTg0WLFqFhw4aIiIhAly5dkJaWhlatWuGhhx7CTz/9hKioKKxevVp3DhAA3HjjjRg1ahRefvllfPvtt+jfvz8URcG///1v3HjjjZg0aRJatWqFp59+Gjk5Ofjhhx9w8803o2HDhjh69CjWrFmDCRMm4KGHHnLreA8//DDeffdd3HbbbbjrrruQkZGB06dP44MPPsCiRYvQsWNHTJgwAa+++irGjBmDvLw8tGjRAu+++y7+85//YN68eT6/xXf55Zdj3Lhx2LNnD5o1a4bXXnsNp06dUvWY9O3bF8nJyRg3bhwefvhhWCwWvPbaa2jatCmOHTum2l9GRgZyc3Px9NNPIzU1FXFxcejZsyf++Mc/YsaMGRg7diy6deuG/fv3Y9myZbjssstU9av7/F1z0oDK7+KNN96Ixx9/HD/88AM6duyIzZs34/3338cDDzygGiTliby8PJw+fRpDhgzRVZ+otshi8s0334xrrrkGDz74II4cOYK0tDR88MEHzk6Bqr13c+bMwaBBg9C9e3fcddddOH36NObPn4927drh3Llzzu0GDRqEuXPnon///rjjjjtQWFiIBQsWIDU1Ffv27fPJtbRv3x79+vXDfffdB5vNhoULFwKAtNPAVceOHZGVlYW///3vKCoqwg033IDPP/8cS5cuxc0334wbb7wRQGXP7/r16/H6668jMTERADB//nzceeedyM3NxZ///GefXIu7MjIy8NFHH2Hu3LlISEhAy5YtnYOugMrb8rfeeisAYObMmarybdu2aRrWCQkJePbZZ/HDDz/g8ssvx8qVK5Gfn4+///3vCA0NVW17xx134JFHHsGaNWswceJEzetA5Rzd+/bt0wy6owBXt5MJBJa5c+eKyMhI1XQkv/76qxg1apSIiooS0dHRYtSoUeLLL7+scQqqjz/+WAwZMkQkJCQIq9UqEhISxJ/+9Cdx+PBh1THff/99kZ6eLkJCQlT7PHjwoOjdu7eIjIwUTZo0EePHj3dOI1T1uFlZWSIiIkJzLbIpQSoqKsTzzz8v0tLShNVqFU2bNhUDBgwQeXl5qu1Wr14tunfvLiIiIkRERIRIS0sT2dnZ4tChQx69n7/++quYNGmS+MMf/iCsVqtITEwUWVlZqrk6T506JcaOHSuaNGkirFaruOKKKzRTMjmmoHr++edV5Z9++qkAIFatWqUqd0yxsmfPHmdZSkqKGDRokNi0aZPo0KGDsNlsIi0tTVNXCCHy8vJEly5dhNVqFcnJyWLu3LnSaVsKCgrEoEGDRMOGDQUA5xQnJSUl4sEHHxTNmzcX4eHh4tprrxU7d+4UN9xwg2YalOo+f9cpqISonHt2ypQpIiEhQYSGhorWrVuL559/3jmllwMAkZ2drbku2fQ5jz76qEhOTtbsgygQyGLyzz//LO644w7RsGFDER0dLcaMGSP+85//CABixYoVqvqrV68Wbdu2FTabTaSnp4v33ntP+re1ePFi0bp1a2dcWLJkiTSG6p2CKjs7W7z11lvOY3Tu3Fk1hZMQv8ds2bSF5eXl4qmnnhItW7YUoaGhIikpSeTk5Din2Tt+/LiIjo4WgwcP1tS95ZZbREREhPj++++FENVPQTVo0KBqz70qWTyWvVf//e9/xfXXXy/Cw8MFAM37VlpaKho1aiSio6PFxYsXneWO6aaquuGGG0S7du3E3r17RWZmpggLCxMpKSnilVde0Zyzw8CBAzXTalWVm5srGjRo4JzqkIyhXjdSi4qKRGxsrPjnP//p71MhH6suCNdnJSUlIj4+XjXnJFEgcTcmr1mzRgAQn332WR2dmfuq+9FY35WXl4umTZuKu+66q8ZtHY1UT9x8883VLg4gROU8uq5zyFLgq7c5qUDl1BqPPPIInn/++TobhU3kL0uWLEFoaKhmXkaiQCGLya5LQzvGCURFReHKK6/0x2mSDmvXrsXPP/+sGhzsKydPnsT69esxatQo6esbN27Et99+i5ycHJ8fm2qXSQg/ZFiTYVy8eLHGnNzY2FhYrdY6OiP3tGjRAu3bt8e6dev8fSokUVJSgrKyMl11rVYrwsLCfHxGFKjuvvtuXLx4EZmZmSgtLcV7772HHTt2YPbs2XXa6KhpAZDw8HBER0fDZDIhOzvbZ1MsGd3u3buxb98+zJw5E02aNHFrwYQePXrgl19+qXHGmKNHj+I///kP/vnPf2LPnj347rvvnINi6wNv4ihgjFhabwdOkXtWrlyJsWPHXnKbTz/9FD169KibEyLDKykpQcuUSBQUuj8XbVXx8fE4evRowAdX8o2ePXvib3/7G9atW4eSkhKkpqZi/vz5mDRpUp2eR/PmzS/5elZWFl5//fW6ORkDyc3NxVtvvYVOnTr5/P3Ztm0bxo4di+TkZCxdurTeNVC9iaOAMWIpe1Lpkk6ePIkDBw5ccpuMjAznfKNENTlz5gyio6NxNC8FUQ09yzg6c1ZBy4wfUVxcjKioqFo6QyKtjz766JKvJyQkSJdgJaoN3sRRwDixlD2pdEnNmzevsQeBSI+IyMqHJ+z8SU1+0rt3b3+fApGGnjgKGCeWspFKRH6hQECBZ5HS0+2JiIKZnjjqqGcEbKQSkV8oUODpnBqe1yAiCl564qijnhHU6ymoiIiIiCgw1btG6vr169GlSxeEh4ejUaNGuPnmm2us88033+Cmm25CdHQ0IiIicPXVVzuXzPzhhx9gMpmkj1WrVjn3cd999yEjIwM2mw2dOnXSff6XOhciI7ELoetB/sc4ShQY9MZRo8TSoGuk9ujRo9ppLlavXo1Ro0Zh7Nix+Oqrr/Cf//wHd9xxxyX3991336F79+5IS0vD1q1bsW/fPjzxxBPOKRuSkpJw8uRJ1eOpp55CZGQkBgwYoNrXXXfdhREjRui+tprOhchIHLlUnj6o9jGOEhmD3jhqlFhab3JSKyoqcP/99+P555/HuHHjnOU1TRny+OOPY+DAgXjuueecZa1atXL+22KxaOZmW7NmDYYPH47IyN+H3L388ssAgJ9//hn79u2THuuzzz5DTk4O9u7diyZNmuCWW27BnDlzEBER4da5EBmJAgE7B04ZCuMoUWDRE0cd9Ywg6HpSq/PFF1/gp59+gtlsRufOndG8eXMMGDDgkitaKIqC9evX4/LLL0e/fv0QFxeHLl26YO3atdXWycvLQ35+viqAu+O7775D//79MWzYMOzbtw8rV67EZ5995pywWs+5EAWyYP71H6wYR4kCS7D3pNabRur3338PAJg+fTqmTZuGdevWoVGjRujRowdOnz4trVNYWIhz587hmWeeQf/+/bF582bccsstGDp0KLZt2yats3jxYrRt2xbdunXz6PzmzJmDkSNH4oEHHkDr1q3RrVs3vPzyy3jjjTdQUlKi61yIiHyJcZSI6pLhb/fPnj0bs2fPdj6/ePEidu3apVoy7+DBg1CUyukWHn/8cQwbNgwAsGTJEiQmJmLVqlW45557NPt21BkyZAimTJkCAOjUqRN27NiBRYsW4YYbblBtf/HiRSxfvhxPPPGEx9fx1VdfYd++fVi2bJmzTAgBRVFw9OhRREdHe3QuRIFOT/K+UZL9jYZxlHGUjEnvICijxFLDN1LvvfdeDB8+3Pl85MiRGDZsGIYOHeosS0hIcK6aVDV3ymaz4bLLLqt2VGeTJk0QEhKiybdq27YtPvvsM8327777Li5cuIDRo0d7fB3nzp3DPffcg/vuu0/zWnJyMgB4dC5EgU7538PTOuR7jKOMo2RMeuKoo54RGL6RGhsbi9jYWOfz8PBwxMXFITU1VbWdY9qSQ4cOoXv37gCA8vJy/PDDD0hJSZHu22q14uqrr8ahQ4dU5YcPH5bWWbx4MW666SY0bdrU4+u48sorcfDgQc15V+XJuRAFOruOhH89AwSoZoyjjKNkTHriqKOeERi+kequqKgo3HvvvXjyySeRlJSElJQUPP/88wCA2267zbldWloa5syZg1tuuQUA8PDDD2PEiBG4/vrrceONN2Ljxo3417/+ha1bt6r2f+TIEWzfvh0bNmyQHv/IkSM4d+4cCgoKcPHiReTn5wOo7JGwWq149NFH0bVrV0yaNAl33303IiIicPDgQWzZsgWvvPKKR+dCZAR24fn60UZZbzpYMY4SBRY9cdRRzxBEkLnhhhvEkiVLpK+VlZWJBx98UMTFxYmGDRuK3r17i6+//lq1DQBN/cWLF4vU1FQRFhYmOnbsKNauXavZd05OjkhKShJ2u73a8wKgeRw9etS5zeeffy769OkjIiMjRUREhOjQoYOYNWuWx+dCFMiKi4sFAJF/ME58dzzeo0f+wTgBQBQXF/v7MoIa4yhRYPMmjhoplpqEMEj2LBEFhTNnziA6Ohr5B+PQsKFnE4ycPaugU3ohiouLERUVVUtnSEQU2LyJo4BxYmm9ud1PRIFFgQl2mDyuQ0RElfTEUUc9I2AjlYj8QhGVD0/rEBFRJT1x1FHPCAzZSFUUBSdOnEDDhg1hMhnj1wBRsBFC4OzZs0hISIDZ7PntJruOHgA9PQYkxzhK5H/+iKOOekZgyEbqiRMnkJSU5O/TICIAx48fR2Jiosf12Ej1L8ZRosBRl3HUUc8IDNlIbdiwIQAgcfo0mMPCnOXmMu2bbq7Q1jfJtivXbudaJt1Gsn9LqbYfPURSZpLUNVdI6pZop921lGrLzGXaMlOFZLtSu3a7crvLc+3JmcrKNGWwuzclsLhYoi0sl7yhXrCfOevT/bmyRDXUFoaGaopMkQ00ZUqEtsweadWUVURo/yQrIiyasvIG2l/cFQ1MLttov+cV4Zoi2G3aMiVM+z20h6k/a6WkBD/9dZbz75GMhXGUcVSGcZRxNJAYspHquDVlDgtTB1eze8FVup2kl921zJ1tAMAimTBBViY5DZhNkuAqCWAWSZlZkQRXyboS5gpJ4FRcgqui/YM2yU5YuBlczZLtfHyL0WTSBjpfspi0wRBmSXA1a6OVYtGWmUIkUS1E8icZqv0sFKv2iyes6vdTsWnfXyE5JMJkZdrvoQiXf9Z6bxUrwgRFeDhwysPtqXqMo4yjMoyjwR9HHfWMwJCNVCIyPt7uJyLyDm/3BzBLiQnmKm+09DaV27eWJNu53JWxlLl3q0m2new2lex2lrlcVibpAbioPbBZUuZ6+6myTLJdqcvFSnoYxMWLmjKU6b/VZD9zRnddf5Cdr0U2v5zkvTPJemckvUKSDiAp6XYuh5B+9yV/8ULbwQAh267c5e+rwrsgZ4cZdng2UED7bSZvMY66bMc4WqsYR/0fRyvrGYOhG6lEZFxCx20qYZBbVEREdUFPHHXUMwKPmt+5ubno0KEDoqKiEBUVhczMTHz44YfO1wsKCjBq1CjEx8cjIiICV155JVavXq3ax+nTpzFy5EhERUUhJiYG48aNw7lz53xzNUREAY5xlIjIPR41UhMTE/HMM88gLy8Pe/fuRc+ePTFkyBAcOHAAADB69GgcOnQIH3zwAfbv34+hQ4di+PDh+PLLL537GDlyJA4cOIAtW7Zg3bp12L59OyZMmODbqyKigOfIpfL0YXSMo0TkK3rjqFFiqUeN1MGDB2PgwIFo3bo1Lr/8csyaNQuRkZHYtWsXAGDHjh2YPHkyrrnmGlx22WWYNm0aYmJikJeXBwD45ptvsHHjRvzzn/9Ely5d0L17d8yfPx8rVqzAiRMnfH91RBSw7MKs62F0jKNE5Ct646hRYqnunFS73Y5Vq1bh/PnzyMzMBAB069YNK1euxKBBgxATE4N33nkHJSUl6NGjBwBg586diImJwVVXXeXcT+/evWE2m7F7927ccsst0mOVlpaitPT3jPwz/0u8NpeYYFEl/GvrujNvX3Vlrgn5sqR9+YACN+fok5S5PUdfiSTh/4J21IImkR9wL5nfzUR+oyXtG5FsEIhsUIkm5khikBIimU5FEgUUSTK/5YL6uanEu1/iCkxQPEz4V2CQtfzcxDh6iX0xjpIPMY661jNGLPW4kbp//35kZmaipKQEkZGRWLNmDdLT0wEA77zzDkaMGIHGjRsjJCQEDRo0wJo1a5CamgqgMtcqLi5OfQIhIYiNjUVBQUG1x5wzZw6eeuopT0+ViAJYfZ6CinGUiHwh2Keg8rj53aZNG+Tn52P37t2YOHEisrKycPDgQQDAE088gaKiInz00UfYu3cvpk6diuHDh2P//v1enWROTg6Ki4udj+PHj3u1PyLyv2C+RVUTxlEi8gXe7ndhtVqdv+gzMjKwZ88evPTSS3jkkUfwyiuv4Ouvv0a7du0AAB07dsS///1vLFiwAIsWLUJ8fDwKCwtV+6uoqMDp06cRHx9f7TFtNhtsNtkSD0RExsM4SkRUM6+b0oqioLS0FBcuVCZamF3Wt7NYLFD+NwFvZmYmioqKnAMAAOCTTz6Boijo0qWLt6dCRAZSmUvl+SMYMY4SkR5646hRYqlHPak5OTkYMGAAkpOTcfbsWSxfvhxbt27Fpk2bkJaWhtTUVNxzzz144YUX0LhxY6xdu9Y5RQoAtG3bFv3798f48eOxaNEilJeXY9KkSbj99tuRkJDg8clbygBLlffZndVOKuvpW/HEcKudABDnzmvK3EnmZyK/j8lWTynVfq4W2brekj9TIdlOsajLZEtwywaoKJLtLCWS03AJavZSbxP+PV8pxSjJ/pfCOMo4SjoxjmqPq3PFKaPEUo8aqYWFhRg9ejROnjyJ6OhodOjQAZs2bUKfPn0AABs2bMBjjz2GwYMH49y5c0hNTcXSpUsxcOBA5z6WLVuGSZMmoVevXjCbzRg2bBhefvll314VEQU8PXlRdskSiEbDOEpEvqI3v9QosdSjRurixYsv+Xrr1q01K6O4io2NxfLlyz05LBEFIQXmejkFFeMoEfmKnjhaWc8YsdQYw7uIiIiIqF7RPZk/EZE37MIEu/BwnlQPtyciCmZ64qijnhEYupFquQhYquRRu7PaCaB/xRPDrXYCSJP7mcxfu2Sfg8mivWnh7m0MIasbKilz+b5aJN9p6aoo5dpgJft7cGWSDLDxhF1Hwr/dILeojIRxVI1xNDAwjrpHTxytrGeMWGroRioRGZcizFA8TPhXDJLsT0RUF/TE0cp6xoilbKQSkV+wJ5WIyDvB3pPKgVNEREREFHDYk0pEfqHA8+R9bQYhEVH9pSeOOuoZgaEbqaEXBSz237us3VntBNC/4onRVjsBmNxf22TvryUqSlMm+wxlYcUUqv2TlA4WkX0Xy9R7VEK0R5CtnhJ6Qd9tH7Pkb8sT+uZJ5c0fX2McrRnjaO1iHNVP/zypxoilhm6kEpFx6VtxyhiBlYioLuhfccoYsZSNVCLyCwUmKNJ+kEvXISKiSnriqKOeEbCRSkR+wZ5UIiLvsCc1gIW45FK5M5E0oH8yaU4kTe5wN79K9vnL8uvMJRZNmQipeWJq2XffIvkaQlJmciNNyjXXkIyJcZQCEeMoAQZvpBKRcembJ9UYv/6JiOqC/nlSjRFL2UglIr9QhAmKp1NQGWS9aSKiuqAnjjrqGQEbqUTkF4qOHgCjTJtCRFQX9MRRRz0jMMZZElHQcaw57enDG8888wxMJhMeeOABZ1lJSQmys7PRuHFjREZGYtiwYTh16pSXV0dEVPv0xlFvYmldxlFD96RaSgVCFKF67kqWlKx3MmlOJE2+JBvcYbJoA4feialDSmRHlQwUsOtL3DdJ/o4C2Z49e/Dqq6+iQ4cOqvIpU6Zg/fr1WLVqFaKjozFp0iQMHToU//nPf/x0pnWLcZSMjHG0btV1HGVPKhH5hR0mXQ89zp07h5EjR+If//gHGjVq5CwvLi7G4sWLMXfuXPTs2RMZGRlYsmQJduzYgV27dvnqUomIaoXeOKonlvojjrKRSkR+4c0tqjNnzqgepaXa6Yqqys7OxqBBg9C7d29VeV5eHsrLy1XlaWlpSE5Oxs6dO31/0UREPuTt7X5PYqk/4igbqUTkF3bo6QWolJSUhOjoaOdjzpw51R5nxYoV+OKLL6TbFBQUwGq1IiYmRlXerFkzFBQU+O5iiYhqgb446nks9VccNXROKhEZl57kfcf2x48fR1SVib1tNpt0++PHj+P+++/Hli1bEBYWpv9kiYgCkN5BUJ7EUn/GUUM3UkMuKgipkvjszmongP4VT7jaCelVF6unaFOMJAMFtGNWpH8P7qyUYqqQ7KyOREVFqQJrdfLy8lBYWIgrr7zSWWa327F9+3a88sor2LRpE8rKylBUVKTqBTh16hTi4+Nr49QDDuMoGQXjqO+5E0v9GUcN3UglIuPSs+a0p9v36tUL+/fvV5WNHTsWaWlpePTRR5GUlITQ0FB8/PHHGDZsGADg0KFDOHbsGDIzMz06FhFRXdMTRx313OXPOMpGKhH5hYAJiocjTIWH2zds2BDt27dXlUVERKBx48bO8nHjxmHq1KmIjY1FVFQUJk+ejMzMTHTt2tWjYxER1TU9cdRRz13+jKNspBKRX9RFT6o7XnzxRZjNZgwbNgylpaXo168fFi5c6PPjEBH5Wl30pLqjtuIoG6lE5Bd61pz2xXrTW7duVT0PCwvDggULsGDBAq/3TURUl/TEUUc9b9RVHDV0I9VSqsBSJUHandVOAP0rnnC1E6pt7q6e4s5vYCGr5+YAGJNwI+O/wr3vfnXsOtac1rNGNV0a4ygFG8ZR9+oZgTHOkoiIiIjqFUP3pBKRcfnrdj8RUbDw1+3+usJGKhH5hQIzFA9v5ni6PRFRMNMTRx31jICNVCLyC7swwe7hr3lPtyciCmZ64qijnhEYupFquVgBS8jvifqy5H7X1U4AL1Y8qUernUhX8ZAI1uuvC+6uniIbaCILL6ZQ9Z+zuysCSQfAKNq6mvOya/+2PMHb/YGBcbT2MI7WPsbR4L7db4z+XiIiIiKqVwzdk0pExiWEGYqHE0qLWpjMn4jIqPTEUUc9I2AjlYj8wg4T7B4u5+fp9kREwUxPHHXUMwI2UonILxTheV6U4sbc2ERE9YWeOOqoZwSGbqSaS+wwW6ok/LuZ3K93xZN6n9xuDdUUyRLU6/375AV3BwHIBqi4rvZjOaut5s6KQNXtX1NP8Tbh3/PbVHpua9GlMY7WMcbRWsc46l49IzDGWRIRERFRveJRIzU3NxcdOnRAVFQUoqKikJmZiQ8//FC1zc6dO9GzZ09EREQgKioK119/PS5WmYLk9OnTGDlyJKKiohATE4Nx48bh3LlzvrkaIjIMBSZdD6NjHCUiX9EbR40SSz1qpCYmJuKZZ55BXl4e9u7di549e2LIkCE4cOAAgMrA2r9/f/Tt2xeff/459uzZg0mTJsFs/v0wI0eOxIEDB7BlyxasW7cO27dvx4QJE3x7VUQU8ByTUHv6MDrGUSLyFb1x1Cix1KOc1MGDB6uez5o1C7m5udi1axfatWuHKVOm4L777sNjjz3m3KZNmzbOf3/zzTfYuHEj9uzZg6uuugoAMH/+fAwcOBAvvPACEhISvLkWIjKQ+pqTyjhKRL4S7DmpugdO2e12rFq1CufPn0dmZiYKCwuxe/dujBw5Et26dcN3332HtLQ0zJo1C927dwdQ2UMQExPjDKwA0Lt3b5jNZuzevRu33HKL9FilpaUoLf09ufjM/5KiTeV2mJTfV3nwJrmfSeo6cRCAX2hW8YF29RSTxb0gpHcAjBCSgQIeUKBjxSmD3KJyF+MoAWAc9ZP6Gkcd9YzA46b0/v37ERkZCZvNhnvvvRdr1qxBeno6vv/+ewDA9OnTMX78eGzcuBFXXnklevXqhW+//RYAUFBQgLi4ONX+QkJCEBsbi4KCgmqPOWfOHERHRzsfSUlJnp42EQUYoSOHShgksNaEcZSIfEFPHDVSLPW4kdqmTRvk5+dj9+7dmDhxIrKysnDw4EEo/1uj9p577sHYsWPRuXNnvPjii2jTpg1ee+01r04yJycHxcXFzsfx48e92h8RkT8xjhIR1czj2/1WqxWpqakAgIyMDOzZswcvvfSSM38qPT1dtX3btm1x7NgxAEB8fDwKCwtVr1dUVOD06dOIj4+v9pg2mw02m83TUyWiAKYIHbf7DZLsXxPGUSLyBT1x1FHPCLzOnFUUBaWlpWjRogUSEhJw6NAh1euHDx9GSkoKACAzMxNFRUXIy8tzvv7JJ59AURR06dLF21MhIgNxJPx7+ghGjKNEpIfeOGqUWOpRT2pOTg4GDBiA5ORknD17FsuXL8fWrVuxadMmmEwmPPzww3jyySfRsWNHdOrUCUuXLsV///tfvPvuuwAqewP69++P8ePHY9GiRSgvL8ekSZNw++236xqRalIUmExVVnSQre7A5H5d3F6xg2qdu5+FcP2uSwZjSOn8G7GLmgcFXEp97UllHK0/GEcDB+Ootp4ReNRILSwsxOjRo3Hy5ElER0ejQ4cO2LRpE/r06QMAeOCBB1BSUoIpU6bg9OnT6NixI7Zs2YJWrVo597Fs2TJMmjQJvXr1gtlsxrBhw/Dyyy/79qqIKODpmVDaKCNSL4VxlIh8Re/E/EaJpR41UhcvXlzjNo899phqfj9XsbGxWL58uSeHJSIKGoyjRETu0T1PKhGRN+rr7X4iIl/h7X4iolrARioRkXfYSCW/8ybR3peDGzhQInC481m4+73x1+fKRirVJcZRclVf46ijnhGwkUpEfsFGKhGRd4K9kWqMibKIiIiIqF5hTyoR+YWA59OgiNo5FSIiQ9ITRx31jICNVCLyC97uJyLyTrDf7mcj1aBM0ZKVMi5e1JTJkr6ZuF8/BPrnzEYq+RvjKNUk0D9nNlKJiGoBG6lERN5hI5WIqBawkUpE5J1gb6RydD8RERERBRz2pBKRXwhhgvDw17yn2xMRBTM9cdRRzwgM3UgVZjOE+ffOYJNF0jFsDdUUBUMSvCy5n8hIFJg8njpFz1QrdGmMo0TGpSeOOuoZgaEbqURkXMxJJSLyTrDnpLKRSkR+wdv9RETeCfbb/Rw4RUREREQBx9A9qSLUAmGx/P7cZtVsI/utIM6d15QFcn6V7Dxk5+tu3UARDNdA+vF2f2BgHNVXN1AEwzWQfrzdT0RUC3i7n4jIO8F+u5+NVCLyC6GjB8AogZWIqC7oiaOOekbARioR+YUAIITndYiIqJKeOOqoZwQcOEVEREREAcfQPalKmAVKyKUvQdYKN9kVTZkoK/fRWdUNoyXBS5P7JROEw2CfA+mnwAQTJ/P3O8ZR42AcJVd64qijnhEYupFKRMbFgVNERN7hwCkiolqgCBNMnIKKiEg3PXHUUc8I2EglIr8QQsfAKaNk+xMR1QE9cdRRzwjYSCUiv+DtfiIi7/B2fwCzh4fAVDXhX/LLwFRu15ZZJMMAJMnnrknqRkuyJyKqCeMoEQUqQzdSici42JNKROQd9qQSEdUCDpwiIvIOB04REdUCDpwiIvIOB04REdWCyuDq6e3+WjoZIiID0hNHHfWMwNDLoiohZiihvz9EiOQRatE+bFbNwxQZoXnAGqp6WKKiNA8jCpjrKCvXPOxnzmgeRHrl5uaiQ4cOiIqKQlRUFDIzM/Hhhx86Xy8pKUF2djYaN26MyMhIDBs2DKdOnfLjGdc9xlF9AuY6GEeplvkzjhq6kUpExuVI+Pf04YnExEQ888wzyMvLw969e9GzZ08MGTIEBw4cAABMmTIF//rXv7Bq1Sps27YNJ06cwNChQ2vjcomIfE5vHPUklvozjvJ2PxH5hYB0tqMa6wDAGZeeIZvNBpvNptl+8ODBquezZs1Cbm4udu3ahcTERCxevBjLly9Hz549AQBLlixB27ZtsWvXLnTt2tXDsyMiqlt64qijHuBeLPVnHGVPKhH5hTe//pOSkhAdHe18zJkzp8bj2e12rFixAufPn0dmZiby8vJQXl6O3r17O7dJS0tDcnIydu7cWWvXTUTkK972pHoaS+s6jrInlYj8w4uu1OPHjyOqSg6grBfVYf/+/cjMzERJSQkiIyOxZs0apKenIz8/H1arFTExMartmzVrhoKCAg9PjIjID7zsSnU3lvorjhq6kaqEmqCE/p5XoVi1HcOmcPcuUdalbLIrqueirNyj8wsE0mR+N1aFcZe7CflM3Dc+1++IEGWAnz5WRwK/O9q0aYP8/HwUFxfj3XffRVZWFrZt21bLZ2gcjKM1YxwlXwmkOAq4H0v9FUcN3UglIgPTs1KKjqlWrFYrUlNTAQAZGRnYs2cPXnrpJYwYMQJlZWUoKipS9QKcOnUK8fHxHh+HiKjO6VxxytNY6q846lFOak3TEDgIITBgwACYTCasXbtW9dqxY8cwaNAgNGjQAHFxcXj44YdRUVHh1UUQkfE4JqH29OEtRVFQWlqKjIwMhIaG4uOPP3a+dujQIRw7dgyZmZneH6gajKNE5Ct646i3sbSu4qhHPamOaQhat24NIQSWLl2KIUOG4Msvv0S7du2c282bNw8mk7aVbrfbMWjQIMTHx2PHjh04efIkRo8ejdDQUMyePdvriyEi49AzpZSn2+fk5GDAgAFITk7G2bNnsXz5cmzduhWbNm1CdHQ0xo0bh6lTpyI2NhZRUVGYPHkyMjMza3VkP+MoEfmKnjjqqOcuf8ZRjxqpl5qGwBFc8/Pz8be//Q179+5F8+bNVdtv3rwZBw8exEcffYRmzZqhU6dOmDlzJh599FFMnz4dVqvVy8shIsMQJs9v33u4fWFhIUaPHo2TJ08iOjoaHTp0wKZNm9CnTx8AwIsvvgiz2Yxhw4ahtLQU/fr1w8KFCz07Jw8xjhKRz+iJo456bvJnHNWdk2q327Fq1SrnNAQAcOHCBdxxxx1YsGCBNBdh586duOKKK9CsWTNnWb9+/TBx4kQcOHAAnTt3lh6rtLQUpaWlzueOeb3sNhNMVRL+TYose0FyiZJublO5XVtmcdmfm4nyRkxuN0XXnDgtio13XeQ7rt9ru/BuAIyeW06ebr948eJLvh4WFoYFCxZgwYIFnu3YRxhHKzGOUn0RCHHUUc9d/oyjHs+Tun//fkRGRsJms+Hee+91TkMAVK460K1bNwwZMkRat6CgQBVYATifX2qqgjlz5qjm8UpKSvL0tImIAgbjKBFRzTzuSa1uGoIjR47gk08+wZdffunzk8zJycHUqVOdz8+cOcMAS2R03iw5ZXCMo0TkE94uORXgPG6kVjcNQXh4OL777jvNhK7Dhg3Dddddh61btyI+Ph6ff/656vVTp04BwCWnKqhuyUMiMq66GDgVqBhHicgX6mLglD95vSyqYxqCxx57DPv27UN+fr7zAVQm1C5ZsgQAkJmZif3796OwsNBZf8uWLYiKinLe6iKiekR4+AhSjKNEpJuncdRAsdSjntRLTUMQHx8v/RWfnJyMli1bAgD69u2L9PR0jBo1Cs899xwKCgowbdo0ZGdn6/qFb7eaAGuVXwNCslKKNo8f5hDtdiLUoi2zqUfJyn53iHPnNWWBNAhAdlzZ+RnoO0tBor72pDKOajGOEukT7D2pHjVSa5qGoCYWiwXr1q3DxIkTkZmZiYiICGRlZWHGjBm6Tp6IyGgYR4mI3ONRI7WmaQhcCckcBykpKdiwYYNH+yGiIFRPB04xjhKRz3DgFBFRbTBBfvO3pjpERFRJTxx11At8bKQSkX/U055UIiKfYU9q4HJN+DdXaLdRQrW/FhSrZGBAeM1vhWwqBJNd0ZSJMu9WkKht7g4CcKcekW5spAYExlF9GEcpIAR5I9XrKaiIiIiIiHzN0D2pRGRgwlT58LQOERFV0hNHHfUMgI1UIvILISofntYhIqJKeuKoo54RsJFKRP7BnFQiIu8EeU6qoRupIqTy4aCEarex27Rd2iZFlooreStcPkRTuXbZFZNFsi+r9kQCafUUmUA6l7rmzmAHTwTre+n6PglRBnhzqbzdHxAYR30nkM6lrjGOuicg4qijngEYupFKRMZlEpUPT+sQEVElPXHUUc8IOLqfiIiIiAIOe1KJyD+Yk0pE5B3mpBIR1QLmpBIReYc5qYHLdaUUaBctkf9aEJKVUrS5/DCHqLcToRbtrmxWyb6Mt3pKMNCduC8ZoGEKD9eUiYsXtXWD9HOVvpeu75Pi5U9x9qQGBMZRqopx1HcCNo466hmAoRupRGRgbKQSEXknyBupHDhFRERERAGHPalE5B/sSSUi8k6Q96SykUpE/sGBU0RE3uHAqcClhAKmqvn2kjfdJBkEYK6Q7UtbV7GqsyFMYdq3K5hXTwkUbifyu5m4rz2A9vMS585rt5Mk99enz8v1vZSvOOTB/jiZf0BgHK0fGEcDQyDEUUc9IzB0I5WIDIy3+4mIvBPkt/s5cIqIiIiIAg4bqUREREQUcAx9u99uA2CrUiDJmxKSOYIVbcpN5YTWLszl6ja866TUgAcTU2sPKc3Xqe/5VW5NfoxqcqRkOVEun4WptEy7TT3Pm3L3Pde8vyYvc6mgIyfVqyOSDONo8GEcrXtGiqOOekZg6EYqERkYR/cTEXmHo/uJiGoBB04REXknyAdOsZFKRP7BRioRkXeCvJHKgVNEREREFHAM3ZOqhACmKldgkiXySyaXNkkmoTZLBwao67pOSg0ApnD33kLZr4H6NAjAq4mkIyM0ZbJBFSJUMkm44jIKxC4ZFVKPkvvdJRtQ4fqeC7t3P8U5mX9gYBw1DsZRYwnUOOqoZwSGbqQSkYHxdj8RkXeC/HY/G6lE5B9spBIReYeNVCIi3+PtfiIi7wT77X4OnCIiIiKigGPonlQlRJ3kb5Yk8stWRXF79RSbOiXfpMja9PrfwmAdBFDbq50AgNLAJtlOu2qN5UxJNWcZ/LwZZCH9HFwGVAizXc9pVdkBJ/MPBIyjaoyjjKNVBWUcddQzAEM3UonIwJiTSkTkHeakEhH5HnNSiYi8E+w5qWykEpF/sCeViMg7Qd6TyoFTRERERBRwjN2TGiIgQn7/OaCESFZFkSXyWyUJw5IFNLS/NNxt09efQQBuJ/d7sdqJCNUm8iuSFWpMikF+GtYSzWfh40EWrp+DMGs/F4/ouU1Vvz/i2sE4qsI4Wr//yOpFHP1fPSMwdiOViIyLt/uJiLzD2/2/y83NRYcOHRAVFYWoqChkZmbiww8/BACcPn0akydPRps2bRAeHo7k5GTcd999KC4uVu3j2LFjGDRoEBo0aIC4uDg8/PDDqKiQzHlCRMFN6HwYHOMoEfmM3jhqkFjqUU9qYmIinnnmGbRu3RpCCCxduhRDhgzBl19+CSEETpw4gRdeeAHp6en48ccfce+99+LEiRN49913AQB2ux2DBg1CfHw8duzYgZMnT2L06NEIDQ3F7Nmza+UCiSgw1dfR/YyjROQrHN1fxeDBg1XPZ82ahdzcXOzatQvjxo3D6tWrna+1atUKs2bNwp133omKigqEhIRg8+bNOHjwID766CM0a9YMnTp1wsyZM/Hoo49i+vTpsFq1+RtERMGEcZSIyD26c1LtdjtWrVqF8+fPIzMzU7pNcXExoqKiEBJSeZidO3fiiiuuQLNmzZzb9OvXDxMnTsSBAwfQuXNn6X5KS0tRWlrqfH7mf0nsSoj6CmTJ/SbJYg6KbIEH3asvGG8QgE95kdwvW+1ECZMk/Ido3wHFqi0LOS9ZAidIuTPQorYHWSi8vew1xlEHxlFXjKO1j3E08Hk8BdX+/fsRGRkJm82Ge++9F2vWrEF6erpmu19++QUzZ87EhAkTnGUFBQWqwArA+bygoKDaY86ZMwfR0dHOR1JSkqenTUSBJojzqGrCOEpEPhHkOakeN1LbtGmD/Px87N69GxMnTkRWVhYOHjyo2ubMmTMYNGgQ0tPTMX36dK9PMicnB8XFxc7H8ePHvd4nEfmXI5fK00cwYBwlIl/QG0eNEks9vnditVqRmpoKAMjIyMCePXvw0ksv4dVXXwUAnD17Fv3790fDhg2xZs0ahIb+3nUeHx+Pzz//XLW/U6dOOV+rjs1mg82mvaVBRAZnkEDpa4yjROQzQRxHvV5xSlEUZ57TmTNn0LdvX1itVnzwwQcICwtTbZuZmYn9+/ejsLDQWbZlyxZERUVJb3URURAL4ltUnmIcJSJdgvx2v0c9qTk5ORgwYACSk5Nx9uxZLF++HFu3bsWmTZucgfXChQt46623cObMGWdiftOmTWGxWNC3b1+kp6dj1KhReO6551BQUIBp06YhOztb1y98xSoA6+/vtEmy2olJ0abGy9KU5W+E8QcBwC5bAsaH3FxlQ5rcL1ntxC4pkx72ovZTNJVKRnLU9vXXAb2r0dT2IAt7ReCvBTJnzhy89957+O9//4vw8HB069YNzz77LNq0aePcpqSkBA8++CBWrFiB0tJS9OvXDwsXLtTkffoK46i7GEdd+S2OBgHGUf38GUc96kktLCzE6NGj0aZNG/Tq1Qt79uzBpk2b0KdPH3zxxRfYvXs39u/fj9TUVDRv3tz5cOQ+WSwWrFu3DhaLBZmZmbjzzjsxevRozJgxw6uLICLjqYs8qm3btiE7Oxu7du3Cli1bUF5ejr59++L8+d9HcE+ZMgX/+te/sGrVKmzbtg0nTpzA0KFDfXy1v2McJSJfqYucVH/GUY+a8IsXL672tR49ekCImq86JSUFGzZs8OSwRBSM9Nxy8nD7jRs3qp6//vrriIuLQ15eHq6//noUFxdj8eLFWL58OXr27AkAWLJkCdq2bYtdu3aha9euHp5gzRhHichn9N6696COP+Oo1zmpRER6ePPr33Eb3PGoOv/npTiWF42NjQUA5OXloby8HL1793Zuk5aWhuTkZOzcudO3F0xE5GPe9qTqiaV1GUfZSCUi//Ai2T8pKUk15+ecOXNqPJyiKHjggQdw7bXXon379gAq5xW1Wq2IiYlRbdusWbNLzjlKRBQQvBw45Wksres4GvgZu5cgQgVElYR/RZbbLu3S1jsIQO8AAKAuBgGYJCtemKRviu8IsyTh341VNgDvkvvNsrIL2l+AptIy9bldvOjWMf1Fb3I/oE3w92aQhRIqSfgPVX//K8q1n3NdOX78OKKqvFfuDBjKzs7G119/jc8++6w2T81wGEfVGEcZR6sK5jgKeB5L6zqOGrqRSkQG5kVOalRUlCqw1mTSpElYt24dtm/fjsTERGd5fHw8ysrKUFRUpOoFOHXq1CXnHCUiCghe5qR6Ekv9EUd5u5+I/KIuRvcLITBp0iSsWbMGn3zyCVq2bKl6PSMjA6Ghofj444+dZYcOHcKxY8eQmZnpi8skIqo1dTG6359xlD2pROQfdTC6Pzs7G8uXL8f777+Phg0bOvOjoqOjER4ejujoaIwbNw5Tp05FbGwsoqKiMHnyZGRmZtbKyH4iIp+qg9H9/oyjbKQSkX/UQSM1NzcXQOXUTlUtWbIEY8aMAQC8+OKLMJvNGDZsmGoSaiKigFcHjVR/xlFDN1KFVYGw/p7QrghvshdqHgTg29VUAG8GAQjJCiWmCm1yv8mNORe9IUza63dnlY3q+DK5HwDEufPqgrJyzTb2/63oU9d8mdwPaBP83U3ut4dpP5sKSZkS4prw7122kJ7b93pu99ckLCwMCxYswIIFCzzbeZBgHHU5E8ZR7bkwjqoYPY466rnLn3GUOalEREREFHAM3ZNKRAZWB7f7iYiCWh3c7vcnNlKJyC/q4nY/EVEwq4vb/f7ERioR+Qd7UomIvMOe1MBlstlhstmdz2XvueJV2q1LgrNki7oYBCDM2v2ZJStZyBP+vTgVNwjJpcoS/s3l2nOr9eR+QJPgH6zJ/YA2wd/t5P5wSZlN+8Hareoye5k333OwkRogGEddjso4qj0ZxlF1mdHjqKOeAXDgFBEREREFHEP3pBKRcZngeV+Zl30ORERBRU8cddQzAjZSicg/eLufiMg7QX67n41UIvILju4nIvIOR/cHsFBrBcy23xPEtWtg+HoQQM2rqQBeDgKQbKZYJAn/FdorM9vrvgPfXK49D0tJYCT3A7Wf4C9N5Jep5eR+QJvg701yf0WYJOHfZTu75HvpEfakBgTGUZcyxlFNEeOomuHjqKOeAXDgFBEREREFHEP3pBKRwRnk1zwRUcAK4jjKRioR+QVzUomIvMOcVCKi2sCcVCIi7wR5TqqhG6lWWwUsNlma/+8CehCASZIwLTkNkzZXHOYKbV2Tor0yvb+WTHZtWegFSSJ/fU/ulyXyh4dLKktWwPFhcj+gTfD3ZXI/ACgup6b95D3DntTAwDjqsh3jqHY7H2IcdXmu2cIz7EklIqoN7EklIvJOkPekcnQ/EREREQUc9qQSkV/wdj8RkXd4u5+IqDbwdj8RkXeC/Ha/oRupEdYyWCSJyTUJlEEAZovkmCGSVVEkJ2y2S5L7Ff0rV4RcVO/PUipZiUWyKoq5TJv2Xa+S+91c7USEav/URKj2C6A3uR/QJvj7Mrm/sq5LPW8X5mEjNSAwjroclXHUZxhHNUWBEUcd9QzA0I1UIjIu3u4nIvJOsN/u58ApIiIiIgo47EklIv/g7X4iIu/wdn/girCWIcTqbUJHJXfyq9zOrZKckjBrC4Ukl0pIPhFFNgm1LJFF8qUza9OVNHlTgDZfy1Im20abN2WqkJSVa2ewrk95U9KJpMMkuVQh2u+TYtWWuZM3BWhzp3yZN1V5bi7PvZyF2iQETMKzSOnp9lQzxlHXypLtGEdrxDiqKQrYOOqoZwSGbqQSkYGxJ5WIyDvsSSUi8j0OnCIi8g4HThERERER1TH2pBKRf/B2PxGRd3i7P3BFhpYiNLT23mnX1HPpRNUmbWe0kPRPS8tkyf0VkkmoJTNY2yXJ1qEXtGWyyapl+zNXqLdze8LpEu3OTOVunnCwJve7OZG0EipJ+A91L0nfnQmmfZncX7k/9XdC8TLxnrf7AwPjqBrjaM0YRzVFhoqjjnpGYOhGKhEZGHtSiYi8E+Q9qR7lpObm5qJDhw6IiopCVFQUMjMz8eGHHzpfLykpQXZ2Nho3bozIyEgMGzYMp06dUu3j2LFjGDRoEBo0aIC4uDg8/PDDqKiQLXZHRMHM0QPg6cPoGEeJyFf0xlGjxFKPGqmJiYl45plnkJeXh71796Jnz54YMmQIDhw4AACYMmUK/vWvf2HVqlXYtm0bTpw4gaFDhzrr2+12DBo0CGVlZdixYweWLl2K119/HX/96199e1VEFPiEzofBMY4Skc/ojaMGiaUe3e4fPHiw6vmsWbOQm5uLXbt2ITExEYsXL8by5cvRs2dPAMCSJUvQtm1b7Nq1C127dsXmzZtx8OBBfPTRR2jWrBk6deqEmTNn4tFHH8X06dNhtUoSOIiIggjjKBGRe3TnpNrtdqxatQrnz59HZmYm8vLyUF5ejt69ezu3SUtLQ3JyMnbu3ImuXbti586duOKKK9CsWTPnNv369cPEiRNx4MABdO7cWXqs0tJSlJaWOp+f+V9SeEzoRVit2lU5akuFpH9cMWmXOxEWySAAiyT52s3kfiFb7eS8eyulmCR59iZFkszvMjBAugKKN6uiXLyoPZE6oEnw91Nyv3S1E0mZEiL5nkhWypEl/Lsm+Psyub9yf75N+AeMc8uptjCOVmIcZRx1xTjqvmCOox7Pk7p//35ERkbCZrPh3nvvxZo1a5Ceno6CggJYrVbExMSotm/WrBkKCgoAAAUFBarA6njd8Vp15syZg+joaOcjKSnJ09MmokAjhL5HEGAcJSKf0BtHDRJLPW6ktmnTBvn5+di9ezcmTpyIrKwsHDx4sDbOzSknJwfFxcXOx/Hjx2v1eERU+4I52b8mjKNE5AvBPnDK49v9VqsVqampAICMjAzs2bMHL730EkaMGIGysjIUFRWpegFOnTqF+Ph4AEB8fDw+//xz1f4co1Yd28jYbDbYbJI+dyIyLj3J+wYJrDVhHCUin9A7CMogsdTrZVEVRUFpaSkyMjIQGhqKjz/+2PnaoUOHcOzYMWRmZgIAMjMzsX//fhQWFjq32bJlC6KiopCenu7tqRARGRLjKBGRlkc9qTk5ORgwYACSk5Nx9uxZLF++HFu3bsWmTZsQHR2NcePGYerUqYiNjUVUVBQmT56MzMxMdO3aFQDQt29fpKenY9SoUXjuuedQUFCAadOmITs7W9cv/OjQi7CF1t7cgK4p1aWS/vEKSTNfkawyIh0EIEn4N12QLakiSe6XcfOXkTvd/LJtTJIcFpMiGVHgp1VR3FkFxW/J/eGSMlnSvlVbJltRR7ada+K+L5P7ZWWKZOCIJ0yKfEBKTXWMjnGUcVRTxjiqLWMcdYueOOqoZwQeNVILCwsxevRonDx5EtHR0ejQoQM2bdqEPn36AABefPFFmM1mDBs2DKWlpejXrx8WLlzorG+xWLBu3TpMnDgRmZmZiIiIQFZWFmbMmOHbqyKiwFdPb/czjhKRzwT57X6PGqmLFy++5OthYWFYsGABFixYUO02KSkp2LBhgyeHJaIgpCd53yjJ/pfCOEpEvqJ3EJRRYqnueVKJiLyiZxoUg0ybQkRUJ/ROJ2WQWOr1wCkiIiIiIl8zdE9qbOh5hIVKlpHQwSxJ0DC79IdbzNpM41KLtqzcol09pcKkfatFhaHffr9yJ7kf0Cb4B1Jyf0VYzaudAPKVUtwp82VyPwAIm/q7LmSDPTxQX2/3BxrG0fqLcbTmsmCMo456RsC/biLyj3o6cIqIyGc4cIqIyPfYk0pE5B32pBIR1QYOnCIi8k6QD5xiI5WI/II9qURE3mFPagBrFHIe4SG+uYQQSTK/yTXhX/KpWszasrN2TprgD6bwcE2Za4J/wCf3SxYMskvKFMnXXpvwL0nkl5W5kdwPACabXf1csWu2IeNhHKWqpHG0gXrZJcWmHXHEOMo4WhsM3UglIgPjwCkiIu9w4BQRke/xdj8RkXd4u5+IqDYoovLhaR0iIqqkJ4466hkAG6lE5B+83U9E5B3e7g9cTSzn0CBEuypJTVxXQAGAELM2eTnEpE56tgttcvfFct+s1ELVc3dVFFi0n48IVX/FlbDATu6vCNOWyVY8kSbuh9S8jZCW1ZzcDwChtgr1/u0Vmm3IeBhH6wev4qhZXcY4yjhaVzh8koj8woTf86ncfug4zvbt2zF48GAkJCTAZDJh7dq1qteFEPjrX/+K5s2bIzw8HL1798a3337ri0skIqpVuuKojljqrzjKRioR+YdjEmpPHx46f/48OnbsiAULFkhff+655/Dyyy9j0aJF2L17NyIiItCvXz+UlJR4e4VERLVLbxz1MJb6K44a+nY/ERlXXY3uHzBgAAYMGCB9TQiBefPmYdq0aRgyZAgA4I033kCzZs2wdu1a3H777Z4fkIiojtTV6H5/xVH2pBKRfwidDwBnzpxRPUpLS3WdwtGjR1FQUIDevXs7y6Kjo9GlSxfs3LlT54UREdURvXHUh7G0NuOooXtSYy3nEGG5dMK/LLk/1KxNVC4X2v2cLZdkX1Ods585oymTDgKwSxLXy9WftblE+zmLEO1vNSVU8vtN9stTltgjqeq6iglQzQookuR+u2QlE9nqJkqoukyW3I8Q7XtkliT3h1i1ZTZbufq8/Jjwn5SUpHr+5JNPYvr06R7vp6CgAADQrFkzVXmzZs2crwU7xtH6gXGUcVTGF7G0NuOooRupRGRcJiFg8jAvyrH98ePHEVXlP1ibTfI/FRFRkNMTRx31gMCPpbzdT0T+oeh8AIiKilI99AbW+Ph4AMCpU6dU5adOnXK+RkQUsPTGUR/G0tqMo2ykEpFfOHoAPH34UsuWLREfH4+PP/7YWXbmzBns3r0bmZmZPj0WEZGv6Y2jvoyltRlHebufiPyjjlacOnfuHI4cOeJ8fvToUeTn5yM2NhbJycl44IEH8PTTT6N169Zo2bIlnnjiCSQkJODmm2/2/GBERHWpjlac8lccNXQjtYnlAiKrrI5hNmmTmWWJ/MX2cE2ZBdq6rsxufqomd2fJ9dN2wo3tZNsIyYW5rkQCACbJiiWylU1kSfuy5H53iYsXazwXU6j2K2+qkCTBl2vLLGWS1U5CtGUmSXK/uVxbJhsEYJL8RcqmCpF+Pi4J/6YwbdK+OVR7XSGh2sR9m1Vb1sBWpnpuryjTbOMRPfOe6vj1v3fvXtx4443O51OnTgUAZGVl4fXXX8cjjzyC8+fPY8KECSgqKkL37t2xceNGhIXVjwE/jKP6tmMcddmGcVRTFrBx1FHPA/6Ko4ZupBIR1aRHjx4QlwjIJpMJM2bMwIwZM+rwrIiIjMNfcZSNVCLyi7qazJ+IKFjV1WT+/sJGKhH5Rx3d7iciClp1dLvfX9hIJSK/MCmVD0/rEBFRJT1x1FHPCAzdSA032dGgSp91kRtJ+wBgcTNxX7bKiiuTZBtZmVfJ+LLkbtnkYW5uJ8ySxHWLuky2eoi0LFSy8ohNu9yHSbKKiSiTZMG7Se/qKa4rpwDur55ilqyeYq7QftYWyWUJyV+aUi4ZQCHbrkK7nWwAgQhx5zOUDGSwaMtskkEAUVb1cnkV5fqWIv39ZNiTGggYR/VtxziqxjhqoDjqqGcAnCeViIiIiAKOoXtSicjA6mieVCKioFVH86T6CxupROQXelY98fWKU0RERqZ39SijxFI2UonIP5iTSkTknSDPSTV0I9Viqnw4n0v6r0NNktUiJMPaQsyS1TJctrNItpGVSQcBmLVlQlamzT2XJ/dLyhRJXddEfgBQQrTHdV3xQwmV1LNKVkUJk6w8Uq59zwNl9RTZeQT66iny5H5JXZeBAUIyUEBUaK+/okL7xamwa7crc/mCVci+cJ4QgJtjdNR1yKcYR9UYR7UYR13qGT2OOuoZAAdOEREREVHAMXRPKhEZF3NSiYi8w5xUIqLaIKAjJ7VWzoSIyJj0xFFHPQMwdCM19H8P53NJ3pRFkjcl205epq4ry7eySPKmZJP6miV5U4qkDNL8Klm+jqSqLL9Gkpsj3MjhsVtlEx9LJmE22sTUkmMGzcTULpchz6WSfJckeVMViras3O6SS2X3NpeKA6cCAeOoS1XGUU0Z42jNZYaKo456BmDoRioRGZgCt1cQUtUhIqJKeuKoo54BeDRwas6cObj66qvRsGFDxMXF4eabb8ahQ4dU2xQUFGDUqFGIj49HREQErrzySqxevVq1zenTpzFy5EhERUUhJiYG48aNw7lz57y/GiIyDEculacPo2McJSJf0RtHjRJLPWqkbtu2DdnZ2di1axe2bNmC8vJy9O3bF+fPn3duM3r0aBw6dAgffPAB9u/fj6FDh2L48OH48ssvnduMHDkSBw4cwJYtW7Bu3Tps374dEyZM8N1VEREFKMZRIiL3eHS7f+PGjarnr7/+OuLi4pCXl4frr78eALBjxw7k5ubimmuuAQBMmzYNL774IvLy8tC5c2d888032LhxI/bs2YOrrroKADB//nwMHDgQL7zwAhISEnxxXUQU6OppTirjKBH5DHNSq1dcXAwAiI2NdZZ169YNK1euxKBBgxATE4N33nkHJSUl6NGjBwBg586diImJcQZWAOjduzfMZjN2796NW265RXOc0tJSlJaWOp+f+V9id6jJhFDT78kYrgn6lWXaZG53E/5tFnVdq2tGNYBQi7aedEJrySAAk2QyaCGdIFpbJk3ud3PyY7tkgmnXt0k+QbLxJ6Z2nZS6uvMI+ImptV9FKK5vsSJJVJKUKZIyu2QQgF24TFQu9CRCVVFPG6muGEcZRxlHGUd1C/JGqu7J/BVFwQMPPIBrr70W7du3d5a/8847KC8vR+PGjWGz2XDPPfdgzZo1SE1NBVCZaxUXF6faV0hICGJjY1FQUCA91pw5cxAdHe18JCUl6T1tIgoUjuDq6SOIMI4SkVf0xlGDxFLdjdTs7Gx8/fXXWLFihar8iSeeQFFRET766CPs3bsXU6dOxfDhw7F//37dJ5mTk4Pi4mLn4/jx47r3RUQBQtH5CCKMo0TkFb1x1CCxVNft/kmTJjkT9RMTE53l3333HV555RV8/fXXaNeuHQCgY8eO+Pe//40FCxZg0aJFiI+PR2FhoWp/FRUVOH36NOLj46XHs9lssNlsek6ViCggMY4SEV2aRz2pQghMmjQJa9aswSeffIKWLVuqXr9w4ULlTs3q3VosFihKZbM9MzMTRUVFyMvLc77+ySefQFEUdOnSRddFEJHxBPO0KZfCOEpEvhLsU1B51JOanZ2N5cuX4/3330fDhg2duU/R0dEIDw9HWloaUlNTcc899+CFF15A48aNsXbtWucUKQDQtm1b9O/fH+PHj8eiRYtQXl6OSZMm4fbbb/d4RGqIyYxQ0++BPFTSfx1m0mZQh5nKtGWSTGvXBH+rWZu0LisLkST3h4Rot6sIcW81DvkgADcTyGWJ4W6sniLbpk5WT9EeFuLceU2Zu4MAXMtk9fy1eopZttqJXbKdXZKQL7lV4zreRTKGBUKS3C8tkyTz211WT7EL3dlCjoPUy4FTjKOMo5r9M45qt2McdQ8HTv0uNzcXxcXF6NGjB5o3b+58rFy5EgAQGhqKDRs2oGnTphg8eDA6dOiAN954A0uXLsXAgQOd+1m2bBnS0tLQq1cvDBw4EN27d8ff//53314ZEQU2Reh7GBzjKBH5jN44apBY6lFPqnCj5d26dWvNyiiuYmNjsXz5ck8OTUTBpp72pDKOEpHPsCeViIiIiKhueTWZPxGRfnp6AIzx65+IqG7onfPUGLHU0I3UMFMIwqok/IeZSiXbSBL+Jcn9NklZA7N6YEC4RVIvRJsYHiYpK7Vo32qLZBCAkCSLC0liuCJNINcU6V49RbLAjHerp4S791WTde2bJNntokxyMjr5evUU6YoqkqR9kyQnyCRb3UQSSySLAmm2M7kbgyTbyRL+Xctk23iknt7uDzSMo2qMo/owjkqKAjWOOuoZgKEbqURkYIqAx7/mDZLsT0RUJ/TEUWe9wMdGKhH5h1AqH57WISKiSnriqKOeAbCRSkT+wdv9RETeCfLb/RzdT0REREQBx9A9qTZTKGxVEv4jzNoVUCIUbWJ4hFk7MMA1uR8AGlhqTvhvEKKtdzFEm1FvC9Vm0FfYtb8R7BWBsXqK68opsm0AwG6TJbLLfvtIvmqyRPZy7SAIWfI9rNqTcWf1FNlqKm6vnqJIyiS/Rt1NtPcmIV/3dm7uq05+ZDMnNSAwjqoxjjKO+mpfARtHnfUCn6EbqURkYLzdT0TknSC/3c9GKhH5h4CORmqtnAkRkTHpiaOOegbARioR+Qd7UomIvMOe1MDjWPv6zDl1bstZSf7LOcmEwBfs2nydixXaBKOScnVCUVmpNsGovFybS1VRon1b7aXanCN7iSQf6qL2fBXJ/kwXJZMkl0hyjkokuU7aVDLApcxcJskRkpShXFYmmTRa8jkIyXsu7Nr306xoT1go2u1kCWB2WVKYazWh3ZdJdky7Ns/NLslLq6jQfr8qyiV1yyW5dGWS74lFVqY9rmIWl3wOAIpk9mqTZMZx2ftmh/p9sl+ofI/cWYueAg/jKOMo46j2uIyjgcWQjdSzZ88CAFKu/EHnHn7y2blQENCOAZCXkdTZs2cRHR3teUVFAeDhXH2SQRekD+Mo+RTjqFfqNI466wU+QzZSExIScPz4cTRs2BAmk5dLivnYmTNnkJSUhOPHjyNKNtrRAHgNgSHQr0EIgbNnzyIhIUHvDni7348YR2tfMFwHr6F2+SWOOuoZgCEbqWazGYmJif4+jUuKiooKuD8GT/EaAkMgX4OuX/4ObKT6FeNo3QmG6+A11J46j6OOegZgyEYqEQUBzpNKROSdIJ8nlStOEREREVHAYU+qj9lsNjz55JOw2Wz+PhXdeA2BIRiu4VKEUCCEZ8n7nm5PxhQs3/1guA5eQ2DTE0cd9YzAJDjvARHVoTNnziA6Ohq9YkYjxGT1qG6FKMPHRW+guLg4IHPLiIjqgjdxFDBOLGVPKhH5h9CRS8Xf1EREv9MTR531Ah8bqUTkH4oCSCbFviSD3KIiIqoTeuIoYJhYykYqEfkHe1KJiLwT5D2pHN1PRERERAGn3jVSc3Nz0aFDB+ekvpmZmfjwww+drxcUFGDUqFGIj49HREQErrzySqxevVq6r9LSUnTq1Akmkwn5+fnO8unTp8NkMmkeERERqvpFRUXIzs5G8+bNYbPZcPnll2PDhg2qbRYsWIAWLVogLCwMXbp0weeffx5Q1zBv3jy0adMG4eHhSEpKwpQpU1BSUhIQ1wAAmzZtQteuXdGwYUM0bdoUw4YNww8//KDaZuvWrbjyyiths9mQmpqK119/XXOMQL6G9957D3369EHTpk2d57Fp0ya3rsGfhKLoepD/BVIMYhz1fwwCGEf9RW8cNUwsFfXMBx98INavXy8OHz4sDh06JP7yl7+I0NBQ8fXXXwshhOjTp4+4+uqrxe7du8V3330nZs6cKcxms/jiiy80+7rvvvvEgAEDBADx5ZdfOsvPnj0rTp48qXqkp6eLrKws5zalpaXiqquuEgMHDhSfffaZOHr0qNi6davIz893brNixQphtVrFa6+9Jg4cOCDGjx8vUqhEpgAACbNJREFUYmJixBtvvBEQ17Bs2TJhs9nEsmXLxNGjR8WmTZtE8+bNxZQpUwLiGr7//nths9lETk6OOHLkiMjLyxPXX3+96Ny5s2qbBg0aiKlTp4qDBw+K+fPnC4vFIjZu3GiYa7j//vvFs88+Kz7//HNx+PBhkZOTI0JDQ1XHqe4aTp06pTmX2lZcXCwAiJ7hI0TfBqM8evQMHyEAiOLi4jo/b/od46jvroFxNDCuoT7FUSPF0nrXSJVp1KiR+Oc//ymEECIiIkK88cYbqtdjY2PFP/7xD1XZhg0bRFpamjhw4IDmD8JVfn6+ACC2b9/uLMvNzRWXXXaZKCsrq7beNddcI7Kzs53P7Xa7SEhIEHPmzAmIa8jOzhY9e/ZUbTd16lRx7bXXBsQ1rFq1SoSEhAi73e4s++CDD4TJZHK+74888oho166dap8jRowQ/fr1M8w1yKSnp4unnnpK1zXUNmdwtQ0XfcPu9OjR0zbcEIG1PmIcZRytinG0dnkTR40US+vd7f6q7HY7VqxYgfPnzyMzMxMA0K1bN6xcuRKnT5+GoihYsWIFSkpK0KNHD2e9U6dOYfz48XjzzTfRoEGDGo/zz3/+E5dffjmuu+46Z9kHH3yAzMxMZGdno1mzZmjfvj1mz54Nu90OACgrK0NeXh569+7trGM2m9G7d2/s3LkzIK6hW7duyMvLc97u+P7777FhwwYMHDgwIK4hIyMDZrMZS5Ysgd1uR3FxMd5880307t0boaGhAICdO3eqzg8A+vXr5zw/I1yDK0VRcPbsWcTGxnp0DXVOiMoRph49jJHsX58wjjKOMo4aLY4aKJb6u5XsD/v27RMRERHCYrGI6OhosX79eudrv/32m+jbt68AIEJCQkRUVJTYtGmT83VFUUT//v3FzJkzhRBCHD169JK/ni9evCgaNWoknn32WVV5mzZthM1mE3fddZfYu3evWLFihYiNjRXTp08XQgjx008/CQBix44dqnoPP/ywuOaaawLiGoQQ4qWXXhKhoaEiJCREABD33nuv87VAuIatW7eKuLg4YbFYBACRmZkpfvvtN+frrVu3FrNnz1bVWb9+vQAgLly4YIhrcPXss8+KRo0aOW9B1XQNdc3ZA2C9TfS13eHRo6f1NkP8+q8PAiEGMY4GRgxiHDVWHDVSLK2XPalt2rRBfn4+du/ejYkTJyIrKwsHDx4EADzxxBMoKirCRx99hL1792Lq1KkYPnw49u/fDwCYP38+zp49i5ycHLeOtWbNGpw9exZZWVmqckVREBcXh7///e/IyMjAiBEj8Pjjj2PRokWGuYatW7di9uzZWLhwIb744gu89957WL9+PWbOnBkQ11BQUIDx48cjKysLe/bswbZt22C1WnHrrbdC+OhXZKBdw/Lly/HUU0/hnXfeQVxcnE+usbYIReh6UGAIhBjEOBp4MUiPQLuG+hBHDRNL/dtGDgy9evUSEyZMEEeOHBEAnAnbVV+/5557hBBCDBkyRJjNZmGxWJwPAMJisYjRo0dr9t2zZ09x8803a8qvv/560atXL1XZhg0bBABRWloqSktLhcViEWvWrFFtM3r0aHHTTTcFxDV0795dPPTQQ6qyN998U4SHhwu73e73a5g2bZq46qqrVPs4fvy4ACB27twphBDiuuuuE/fff79qm9dee01ERUUJIYQhrsHh7bffFuHh4WLdunWqck+vobY5egButAwVfUJGePS40TJU16//V155RaSkpAibzSauueYasXv37lq6uvqLcZRxtCrG0drlTRzVG0v9EUfrZU+qK0VRUFpaigsXLgCozDOpymKxQPnfdA0vv/wyvvrqK+Tn5yM/P9851cnKlSsxa9YsVb2jR4/i008/xbhx4zTHvPbaa3HkyBHnfgHg8OHDaN68OaxWK6xWKzIyMvDxxx+rzvPjjz925ur4+xouXLggPQ4ACCH8fg2XOj/HfjIzM1XnBwBbtmxxnp8RrgEA3n77bYwdOxZvv/02Bg0apNre02uoK3X163/lypWYOnUqnnzySXzxxRfo2LEj+vXrh8LCwlq4qvqLcZRxtCrG0bpRVz2pfoujtd4MDjCPPfaY2LZtmzh69KjYt2+feOyxx4TJZBKbN28WZWVlIjU1VVx33XVi9+7d4siRI+KFF14QJpNJlSNT1aXykKZNmyYSEhJERUWF5rVjx46Jhg0bikmTJolDhw6JdevWibi4OPH00087t1mxYoWw2Wzi9ddfFwcPHhQTJkwQMTExYvLkyQFxDU8++aRo2LChePvtt8X3338vNm/eLFq1aiWGDx8eENfw8ccfC5PJJJ566ilx+PBhkZeXJ/r16ydSUlLEhQsXhBC/T53y8MMPi2+++UYsWLBAOnVKIF/DsmXLREhIiFiwYIFqqpuioqIar6GgoEB6LrXJ0QPQA0NEb9OtHj16YIjHv/4DaURusGAc9d01MI4GxjXUpziqJ5b6K47Wu0bqXXfdJVJSUoTVahVNmzYVvXr1Eps3b3a+fvjwYTF06FARFxcnGjRoIDp06KCZ/qKq6gKT3W4XiYmJ4i9/+Uu1dXfs2CG6dOkibDabuOyyy8SsWbM0QWz+/PkiOTlZWK1Wcc0114hdu3YFzDWUl5eL6dOni1atWomwsDCRlJQk/vznP2uS0f15DW+//bbo3LmziIiIEE2bNhU33XST+Oabb1TbfPrpp6JTp07CarWKyy67TCxZskSz/0C+hhtuuMGxLp7qUXUuxuquwR8cwbU7BooeGOLRozsGCgDi+PHjori42PkoKSmRHivQbtEFi0CJQUIwjgZCDBKCcbSueRNHPY2l/oyjJiGMMg8BEQWDkpIStGzZEgUFBbrqR0ZG4ty5c6qyJ598EtOnT9dse+LECfzhD3/Ajh07VLfkHnnkEWzbtg27d+/WdQ5ERP7kbRwF3I+l/oyjIbW2ZyIiibCwMBw9ehRlZWW66gshYDKZVGU2m80Xp0ZEZAjexlHAGLGUjVQiqnNhYWEICwur9eM0adIEFosFp06dUpWfOnUK8fHxtX58IqLaUh/iKEf3E1HQCtQRuURERuHPOMqeVCIKalOnTkVWVhauuuoqXHPNNZg3bx7Onz+PsWPH+vvUiIgMwV9xlI1UIgpqI0aMwM8//4y//vWvKCgoQKdOnbBx40Y0a9bM36dGRGQI/oqjHN1PRERERAGHOalEREREFHDYSCUiIiKigMNGKhEREREFHDZSiYiIiCjgsJFKRERERAGHjVQiIiIiCjhspBIRERFRwGEjlYiIiIgCDhupRERERBRw2EglIiIiooDDRioRERERBZz/B5Ndqn5zd2RjAAAAAElFTkSuQmCC", + "image/png": 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", "text/plain": [ - "
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" ] }, "metadata": {}, @@ -219,7 +213,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 7, "id": "de981707-328a-4280-9496-054ede1c2947", "metadata": { "tags": [] @@ -229,14 +223,15 @@ "name": "stdout", "output_type": "stream", "text": [ - "Several drivers matching tif extension. Using GTiff\n" + "The time taken by distance_to_anomaly function to calculate the distance to anomaly is = 0.518155574798584 seconds\n", + "The time taken by gdal.compute_proximity function to calculate the distance to anomaly is = 0.001996755599975586 seconds\n" ] }, { "data": { - "image/png": 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", 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", "text/plain": [ - "
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" ] }, "metadata": {}, @@ -252,7 +247,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "eis_toolkit_development_platform_eis_not_installed_py3.9", "language": "python", "name": "python3" }, @@ -266,7 +261,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.13" + "version": "3.9.18" } }, "nbformat": 4, diff --git a/notebooks/testing_idw.ipynb b/notebooks/testing_idw.ipynb index 89f16100..840c6a32 100644 --- a/notebooks/testing_idw.ipynb +++ b/notebooks/testing_idw.ipynb @@ -5,67 +5,68 @@ "execution_count": 1, "id": "b6eba8d9-924c-488c-bde5-56f91d7e8964", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/mika/.cache/pypoetry/virtualenvs/eis-toolkit-l5cKD1lZ-py3.10/lib/python3.10/site-packages/geopandas/_compat.py:112: UserWarning: The Shapely GEOS version (3.10.3-CAPI-1.16.1) is incompatible with the GEOS version PyGEOS was compiled with (3.10.4-CAPI-1.16.2). Conversions between both will be slow.\n", + " warnings.warn(\n" + ] + } + ], "source": [ "import sys\n", "sys.path.insert(0, \"..\")\n", "from eis_toolkit.vector_processing.idw_interpolation import idw\n", "\n", - "import numpy as np\n", "import matplotlib.pyplot as plt\n", - "from shapely.geometry import Point\n", "import geopandas as gpd\n", - "from pyproj import CRS\n", - "import rasterio" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "382080ca-3fd1-4f54-bae3-d272bd76d719", - "metadata": {}, - "outputs": [], - "source": [ - "data = {\n", - " 'random_number': [124, 248, 496, 992],\n", - " 'geometry': [Point(24.945831, 60.192059), Point(24.6559, 60.2055),\n", - " Point(25.0378, 60.2934), Point(24.7284, 60.2124)]\n", - " }\n", + "import rasterio\n", + "import time\n", "\n", - "gdf = gpd.GeoDataFrame(data)\n", + "SMALL_RASTER = \"../tests/data/remote/small_raster.tif\"\n", + "POINTS = \"../tests/data/remote/interpolating/interpolation_test_data_small.gpkg\"\n", "\n", - "crs = CRS.from_epsg(4326)\n", - "gdf.crs = crs" + "gdf = gpd.read_file(POINTS)\n", + "\n", + "with rasterio.open(SMALL_RASTER) as src:\n", + " raster_profile = src.profile" ] }, { "cell_type": "code", - "execution_count": 3, - "id": "fe9944d7-daf7-4e5f-a43a-870b1e2ef404", + "execution_count": 2, + "id": "382080ca-3fd1-4f54-bae3-d272bd76d719", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/mika/.cache/pypoetry/virtualenvs/eis-toolkit-l5cKD1lZ-py3.10/lib/python3.10/site-packages/geopandas/geoseries.py:643: FutureWarning: the convert_dtype parameter is deprecated and will be removed in a future version. Do ``ser.astype(object).apply()`` instead if you want ``convert_dtype=False``.\n", + " result = super().apply(func, convert_dtype=convert_dtype, args=args, **kwargs)\n" + ] + } + ], "source": [ - "interpolated_values, out_meta = idw(\n", + "interpolated_values = idw(\n", " geodataframe=gdf,\n", - " target_column='random_number',\n", - " #resolution=(0.005, 0.005),\n", - " resolution=(0.0049, 0.0047),\n", - " #output_resolution=(0.005, 0.005),\n", - " extent=(24.6558990000000016, 25.0378036000000002, 60.1920590000000004, 60.2934078769999999),\n", - " #extent=None,\n", + " target_column='value',\n", + " raster_profile=raster_profile,\n", " power=2\n", ")" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "id": "b6e5c929-dc18-4a21-a46f-114f5d21f77f", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -87,7 +88,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 4, "id": "d742b84b-60bd-495f-b0d0-3305a89b2ee2", "metadata": {}, "outputs": [ @@ -116,44 +117,38 @@ }, { "cell_type": "code", - "execution_count": 8, - "id": "6b9b4afb-1258-45ec-8896-41b3d4985533", - "metadata": {}, - "outputs": [], - "source": [ - "data = {\n", - " \"value1\": [1, 2, 3, 4, 5],\n", - " \"value2\": [5, 4, 3, 2, 1],\n", - " \"geometry\": [Point(0, 0), Point(1, 1), Point(2, 2), Point(3, 3), Point(4, 4)],\n", - "}\n", - "simple_gdf = gpd.GeoDataFrame(data)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "c9b8915f-785f-49af-8cbc-c24df8411033", + "execution_count": 5, + "id": "d3f4c60e", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/mika/.cache/pypoetry/virtualenvs/eis-toolkit-l5cKD1lZ-py3.10/lib/python3.10/site-packages/geopandas/geoseries.py:643: FutureWarning: the convert_dtype parameter is deprecated and will be removed in a future version. Do ``ser.astype(object).apply()`` instead if you want ``convert_dtype=False``.\n", + " result = super().apply(func, convert_dtype=convert_dtype, args=args, **kwargs)\n" + ] + } + ], "source": [ - "interpolated_values, out_meta = idw(\n", - " geodataframe=simple_gdf,\n", - " target_column='value1',\n", - " resolution=(1, 1),\n", - " extent=None,\n", - " power=2\n", + "interpolated_values_with_radius = idw(\n", + " geodataframe=gdf,\n", + " target_column='value',\n", + " raster_profile=raster_profile,\n", + " power=2,\n", + " search_radius=50,\n", ")" ] }, { "cell_type": "code", - "execution_count": 10, - "id": "d4180811-d210-4d80-8e0a-55456b01880a", + "execution_count": 6, + "id": "c4c20135", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -163,8 +158,9 @@ } ], "source": [ - "x_min, y_min, x_max, y_max = simple_gdf.geometry.total_bounds\n", - "plt.imshow(interpolated_values, cmap='jet', extent=[x_min, x_max, y_min, y_max])\n", + "# Plot the interpolated values\n", + "x_min, y_min, x_max, y_max = gdf.geometry.total_bounds\n", + "plt.imshow(interpolated_values_with_radius, cmap='jet', extent=[x_min, x_max, y_min, y_max])\n", "plt.colorbar()\n", "plt.xlabel('X')\n", "plt.ylabel('Y')\n", @@ -174,50 +170,37 @@ }, { "cell_type": "code", - "execution_count": 11, - "id": "9ec231e6-a072-4f95-a32e-a22801a05c5b", - "metadata": {}, - "outputs": [], - "source": [ - "interpolated_values, out_meta = idw(\n", - " geodataframe=simple_gdf,\n", - " target_column='value2',\n", - " resolution=(1, 1),\n", - " extent=None,\n", - " power=2\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "91fe2734-dddf-4049-bd41-1a5efd90ed59", + "execution_count": 7, + "id": "66a365f6", "metadata": {}, "outputs": [ { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" + "name": "stdout", + "output_type": "stream", + "text": [ + "Interpolated values saved as GeoTIFF: ../tests/data/local/results/idw_radius_test_output.tif\n" + ] } ], "source": [ - "plt.imshow(interpolated_values, cmap='jet', extent=[x_min, x_max, y_min, y_max])\n", - "plt.colorbar()\n", - "plt.xlabel('X')\n", - "plt.ylabel('Y')\n", - "plt.title('IDW Interpolation')\n", - "plt.show()" + "# Saves the output of the most recently executed interpolation\n", + "# As a GeoTIFF to the eis_toolkit/notebooks folder.\n", + "output_file = '../tests/data/local/results/idw_radius_test_output.tif'\n", + "\n", + "# Create a rasterio dataset for writing the GeoTIFF\n", + "with rasterio.open(output_file, 'w', driver='GTiff', width=interpolated_values_with_radius.shape[1],\n", + " height=interpolated_values_with_radius.shape[0], count=1, dtype=interpolated_values_with_radius.dtype,\n", + " crs=gdf.crs, transform=rasterio.transform.from_bounds(x_min, y_min, x_max, y_max, interpolated_values_with_radius.shape[1], interpolated_values_with_radius.shape[0])) as dst:\n", + " # Write the interpolated values to the GeoTIFF band\n", + " dst.write(interpolated_values_with_radius, 1)\n", + "\n", + "print(f\"Interpolated values saved as GeoTIFF: {output_file}\")" ] } ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "eis-toolkit-l5cKD1lZ-py3.10", "language": "python", "name": "python3" }, @@ -231,7 +214,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.12" + "version": "3.10.15" } }, "nbformat": 4, diff --git a/notebooks/testing_logratio_transformations.ipynb b/notebooks/testing_logratio_transformations.ipynb index 19f377aa..f8f62961 100644 --- a/notebooks/testing_logratio_transformations.ipynb +++ b/notebooks/testing_logratio_transformations.ipynb @@ -20,8 +20,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "/root/.cache/pypoetry/virtualenvs/eis-toolkit-QEzTY9B6-py3.10/lib/python3.10/site-packages/geopandas/_compat.py:112: UserWarning: The Shapely GEOS version (3.10.3-CAPI-1.16.1) is incompatible with the GEOS version PyGEOS was compiled with (3.10.4-CAPI-1.16.2). Conversions between both will be slow.\n", - " warnings.warn(\n" + "/root/.cache/pypoetry/virtualenvs/eis-toolkit-QEzTY9B6-py3.10/lib/python3.10/site-packages/beartype/_util/hint/pep/utilpeptest.py:347: BeartypeDecorHintPep585DeprecationWarning: PEP 484 type hint typing.Sequence[str] deprecated by PEP 585. This hint is scheduled for removal in the first Python version released after October 5th, 2025. To resolve this, import this hint from \"beartype.typing\" rather than \"typing\". For further commentary and alternatives, see also:\n", + " https://beartype.readthedocs.io/en/latest/api_roar/#pep-585-deprecations\n", + " warn(\n" ] } ], @@ -726,7 +727,7 @@ { "cell_type": "code", "execution_count": 21, - "id": "41bfcb78-bdfa-4c03-a9b4-45c2258c60a8", + "id": "e1bda63b-ab9b-4060-90d5-7520952f2e3a", "metadata": { "tags": [] }, @@ -756,6 +757,7 @@ " Ca_ppm_511\n", " Fe_ppm_511\n", " Mg_ppm_511\n", + " residual\n", " \n", " \n", " \n", @@ -765,6 +767,7 @@ " 40200.0\n", " 83200.0\n", " 17200.0\n", + " 831800.0\n", " \n", " \n", " 1\n", @@ -772,6 +775,7 @@ " 5000.0\n", " 28300.0\n", " 7520.0\n", + " 945080.0\n", " \n", " \n", " 2\n", @@ -779,6 +783,7 @@ " 3070.0\n", " 14500.0\n", " 4540.0\n", + " 970010.0\n", " \n", " \n", " 3\n", @@ -786,6 +791,7 @@ " 3290.0\n", " 14600.0\n", " 3240.0\n", + " 971570.0\n", " \n", " \n", " 4\n", @@ -793,18 +799,19 @@ " 3600.0\n", " 31500.0\n", " 8020.0\n", + " 944380.0\n", " \n", " \n", "\n", "" ], "text/plain": [ - " Al_ppm_511 Ca_ppm_511 Fe_ppm_511 Mg_ppm_511\n", - "0 27600.0 40200.0 83200.0 17200.0\n", - "1 14100.0 5000.0 28300.0 7520.0\n", - "2 7880.0 3070.0 14500.0 4540.0\n", - "3 7300.0 3290.0 14600.0 3240.0\n", - "4 12500.0 3600.0 31500.0 8020.0" + " Al_ppm_511 Ca_ppm_511 Fe_ppm_511 Mg_ppm_511 residual\n", + "0 27600.0 40200.0 83200.0 17200.0 831800.0\n", + "1 14100.0 5000.0 28300.0 7520.0 945080.0\n", + "2 7880.0 3070.0 14500.0 4540.0 970010.0\n", + "3 7300.0 3290.0 14600.0 3240.0 971570.0\n", + "4 12500.0 3600.0 31500.0 8020.0 944380.0" ] }, "execution_count": 21, @@ -818,12 +825,15 @@ "df = gpd.read_file(GEOCHEMICAL_DATA, include_fields=elements_to_analyze)\n", "df = pd.DataFrame(df.drop(columns='geometry'))\n", "\n", + "# Add a column for the residual\n", + "\n", + "df[\"residual\"] = million - np.sum(df, axis=1)\n", "df.head()" ] }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 24, "id": "75728aa4-5b2e-46b6-9511-1250bf4b13ae", "metadata": { "tags": [] @@ -833,6 +843,7 @@ "pair_Al_Ca = pairwise_logratio(df, \"Al_ppm_511\", \"Ca_ppm_511\")\n", "pair_Fe_Mg = pairwise_logratio(df, \"Fe_ppm_511\", \"Mg_ppm_511\")\n", "pair_Mg_Al = pairwise_logratio(df, \"Mg_ppm_511\", \"Al_ppm_511\")\n", + "pair_Mg_res = pairwise_logratio(df, \"Mg_ppm_511\", \"residual\")\n", "\n", "df_alr = alr_transform(df)\n", "df_alr_Mg = alr_transform(df, \"Mg_ppm_511\")\n", @@ -848,7 +859,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 25, "id": "e136d05d-671d-420f-95b9-5f350bc7a94c", "metadata": { "tags": [] @@ -865,7 +876,7 @@ "dtype: float64" ] }, - "execution_count": 23, + "execution_count": 25, "metadata": {}, "output_type": "execute_result" } @@ -876,7 +887,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 26, "id": "ad352680-433a-4026-b7b5-560b682dfb96", "metadata": { "tags": [] @@ -906,6 +917,7 @@ " V1\n", " V2\n", " V3\n", + " V4\n", " \n", " \n", " \n", @@ -914,45 +926,50 @@ " 0.472906\n", " 0.848958\n", " 1.576338\n", + " 3.878683\n", " \n", " \n", " 1\n", " 0.628609\n", " -0.408128\n", " 1.325296\n", + " 4.833703\n", " \n", " \n", " 2\n", " 0.551401\n", " -0.391249\n", " 1.161222\n", + " 5.364379\n", " \n", " \n", " 3\n", " 0.812301\n", " 0.015314\n", " 1.505448\n", + " 5.703340\n", " \n", " \n", " 4\n", " 0.443790\n", " -0.801005\n", " 1.368049\n", + " 4.768590\n", " \n", " \n", "\n", "" ], "text/plain": [ - " V1 V2 V3\n", - "0 0.472906 0.848958 1.576338\n", - "1 0.628609 -0.408128 1.325296\n", - "2 0.551401 -0.391249 1.161222\n", - "3 0.812301 0.015314 1.505448\n", - "4 0.443790 -0.801005 1.368049" + " V1 V2 V3 V4\n", + "0 0.472906 0.848958 1.576338 3.878683\n", + "1 0.628609 -0.408128 1.325296 4.833703\n", + "2 0.551401 -0.391249 1.161222 5.364379\n", + "3 0.812301 0.015314 1.505448 5.703340\n", + "4 0.443790 -0.801005 1.368049 4.768590" ] }, - "execution_count": 24, + "execution_count": 26, "metadata": {}, "output_type": "execute_result" } @@ -960,6 +977,14 @@ "source": [ "df_alr_Mg.head()" ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8b6a1929-51ef-4b7a-8621-f46bbe337e31", + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { diff --git a/notebooks/testing_points_to_raster.ipynb b/notebooks/testing_points_to_raster.ipynb new file mode 100644 index 00000000..b8707c1a --- /dev/null +++ b/notebooks/testing_points_to_raster.ipynb @@ -0,0 +1,243 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import sys\n", + "\n", + "sys.path.insert(0, \"..\")" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import geopandas as gpd\n", + "import rasterio\n", + "from rasterio import plot\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import textwrap\n", + "\n", + "from tests.raster_processing.clip_test import raster_path as SMALL_RASTER_PATH\n", + "from eis_toolkit.training_data_tools.points_to_raster import points_to_raster\n", + "\n", + "PATH_LABELS_GPKG = \"../tests/data/remote/interpolating/interpolation_test_data_small.gpkg\"\n", + "RASTER_PATH = \"../tests/data/remote/small_raster.tif\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Creating data points" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " id value geometry\n", + "0 1.0 10.0 POINT (384758.306 6671365.922)\n", + "1 2.0 20.0 POINT (384769.919 6671362.194)\n", + "2 3.0 30.0 POINT (384769.059 6671350.580)\n", + "3 4.0 40.0 POINT (384777.519 6671344.128)\n", + "4 5.0 35.0 POINT (384761.030 6671342.264)\n", + "5 6.0 25.0 POINT (384758.306 6671355.025)\n", + "6 7.0 50.0 POINT (384783.397 6671365.205)\n", + "7 8.0 100.0 POINT (384795.584 6671358.179)\n", + "8 9.0 120.0 POINT (384795.728 6671344.988)\n", + "9 11.0 79.0 POINT (384786.408 6671335.955)\n", + "10 12.0 60.0 POINT (384780.243 6671327.783)\n", + "11 13.0 50.0 POINT (384771.497 6671320.040)\n", + "12 NaN 40.0 POINT (384791.797 6671314.259)\n", + "13 NaN 30.0 POINT (384799.487 6671303.044)\n", + "14 NaN 20.0 POINT (384808.459 6671290.707)\n", + "15 NaN 30.0 POINT (384787.791 6671295.033)\n", + "16 NaN 30.0 POINT (384815.509 6671310.254)\n", + "17 NaN 20.0 POINT (384826.244 6671297.436)\n", + "18 NaN 15.0 POINT (384825.763 6671287.503)\n", + "19 NaN 10.0 POINT (384815.349 6671280.453)\n", + "20 NaN 13.0 POINT (384797.404 6671281.895)\n", + "21 NaN 32.0 POINT (384779.941 6671288.785)\n", + "22 NaN 35.0 POINT (384765.521 6671282.216)\n", + "23 NaN 45.0 POINT (384768.084 6671297.917)\n", + "24 NaN 50.0 POINT (384762.637 6671305.608)\n", + "25 NaN 55.0 POINT (384759.913 6671316.182)\n", + "26 NaN 60.0 POINT (384749.820 6671310.414)\n", + "27 NaN 65.0 POINT (384750.941 6671299.840)\n", + "28 NaN 40.0 POINT (384753.665 6671287.343)\n", + "29 NaN 100.0 POINT (384815.028 6671333.325)\n", + "30 NaN 90.0 POINT (384801.410 6671331.723)\n", + "31 NaN 70.0 POINT (384792.438 6671325.795)\n", + "32 NaN 120.0 POINT (384812.144 6671349.828)\n", + "33 NaN 100.0 POINT (384807.338 6671365.689)\n", + "34 NaN 80.0 POINT (384796.443 6671373.380)\n", + "35 NaN 30.0 POINT (384778.499 6671375.623)\n" + ] + } + ], + "source": [ + "gdf = gpd.read_file(PATH_LABELS_GPKG)\n", + "print(gdf)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Creating binary raster from template raster" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "def plot_binary_raster_from_template_raster(template_raster_path):\n", + "\n", + " attribute = 'value'\n", + " radius = 8\n", + " \n", + " with rasterio.open(template_raster_path) as temp_raster:\n", + "\n", + " raster_profile = temp_raster.profile\n", + " \n", + " outarray,outmeta = points_to_raster(geodataframe=gdf,\n", + " raster_profile=raster_profile)\n", + " \n", + " outarray2,outmeta2 = points_to_raster(geodataframe=gdf,\n", + " attribute=attribute,\n", + " raster_profile=raster_profile)\n", + " \n", + " outarray3,outmeta3 = points_to_raster(geodataframe=gdf,\n", + " raster_profile=raster_profile,\n", + " radius=radius)\n", + " \n", + " outarray4,outmeta4 = points_to_raster(geodataframe=gdf,\n", + " raster_profile=raster_profile,\n", + " attribute=attribute,\n", + " radius=radius)\n", + " \n", + " outarray5,outmeta5 = points_to_raster(geodataframe=gdf,\n", + " raster_profile=raster_profile,\n", + " attribute=attribute,\n", + " radius=radius,\n", + " buffer='avg')\n", + " \n", + " outarray6,outmeta6 = points_to_raster(geodataframe=gdf,\n", + " raster_profile=raster_profile,\n", + " attribute=attribute,\n", + " radius=radius,\n", + " buffer='min')\n", + " \n", + " outarray7,outmeta7 = points_to_raster(geodataframe=gdf,\n", + " raster_profile=raster_profile,\n", + " attribute=attribute,\n", + " radius=radius,\n", + " buffer='max')\n", + "\n", + " extent = [temp_raster.bounds.left, temp_raster.bounds.right,\n", + " temp_raster.bounds.bottom, temp_raster.bounds.top]\n", + "\n", + " fig,axes = plt.subplots(3,3,figsize=(20,15))\n", + " norm = plt.Normalize(vmax=np.nanmax(outarray), vmin=np.nanmin(outarray))\n", + " norm2 = plt.Normalize(vmax=np.nanmax(outarray2), vmin=np.nanmin(outarray2))\n", + "\n", + " plot.show(temp_raster, ax=axes[0][0])\n", + " axes[0][0].set_title('Template Raster with geodataframe.', fontsize=15)\n", + " points = gdf.cx[\n", + " temp_raster.bounds.left : temp_raster.bounds.right, # noqa: E203\n", + " temp_raster.bounds.bottom : temp_raster.bounds.top, # noqa: E203\n", + " ]\n", + " points.plot(ax=axes[0][0], facecolor='w', edgecolor='w')\n", + "\n", + " im1 = axes[0][1].imshow(outarray, cmap='gray', norm=norm, extent=extent)\n", + " axes[0][1].set_title(textwrap.fill('True geodataframe converted to raster.', width=30), fontsize=15)\n", + " fig.colorbar(im1, ax=axes[0][1], orientation='vertical')\n", + "\n", + " im2 = axes[0][2].imshow(outarray2, cmap='gray', norm=norm2, extent=extent)\n", + " axes[0][2].set_title(textwrap.fill('True geodataframe converted to raster with attribute.', width=30), fontsize=15)\n", + " fig.colorbar(im2, ax=axes[0][2], orientation='vertical')\n", + "\n", + " im3 = axes[1][0].imshow(outarray3, cmap='gray', norm=norm, extent=extent)\n", + " axes[1][0].set_title(textwrap.fill('True geodataframe converted to raster with radius.', width=30), fontsize=15)\n", + " fig.colorbar(im3, ax=axes[1][0], orientation='vertical')\n", + "\n", + " im4 = axes[1][1].imshow(outarray4, cmap='gray', norm=norm2, extent=extent)\n", + " axes[1][1].set_title(textwrap.fill('True geodataframe converted to raster with attribute and radius.', width=30), fontsize=15)\n", + " fig.colorbar(im4, ax=axes[1][1], orientation='vertical')\n", + "\n", + " im5 = axes[1][2].imshow(outarray5, cmap='gray', norm=norm2, extent=extent)\n", + " axes[1][2].set_title(textwrap.fill('True geodataframe converted to raster with attribute and radius and averaging buffer.', width=30), fontsize=15)\n", + " fig.colorbar(im5, ax=axes[1][2], orientation='vertical')\n", + " \n", + " im6 = axes[2][0].imshow(outarray6, cmap='gray', norm=norm2, extent=extent)\n", + " axes[2][0].set_title(textwrap.fill('True geodataframe converted to raster with attribute and radius and min buffer.', width=30), fontsize=15)\n", + " fig.colorbar(im6, ax=axes[2][0], orientation='vertical')\n", + "\n", + " im7 = axes[2][1].imshow(outarray7, cmap='gray', norm=norm2, extent=extent)\n", + " axes[2][1].set_title(textwrap.fill('True geodataframe converted to raster with attribute and radius and max buffer.', width=30), fontsize=15)\n", + " fig.colorbar(im7, ax=axes[2][1], orientation='vertical')\n", + "\n", + " axes[2][2].axis('off')\n", + "\n", + " plt.tight_layout()\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_binary_raster_from_template_raster(SMALL_RASTER_PATH)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "eis_toolkit_development_platform_eis_not_installed_py3.9", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.18" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/notebooks/testing_proximity_computation.ipynb b/notebooks/testing_proximity_computation.ipynb new file mode 100644 index 00000000..aebe1038 --- /dev/null +++ b/notebooks/testing_proximity_computation.ipynb @@ -0,0 +1,118 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import sys\n", + "\n", + "sys.path.append(\"..\")" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import rasterio\n", + "from rasterio import plot\n", + "\n", + "import geopandas as gpd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib\n", + "from textwrap import fill\n", + "from functools import partial\n", + "\n", + "from tests.raster_processing.clip_test import raster_path as SMALL_RASTER_PATH\n", + "from tests.raster_processing.clip_test import polygon_path as polygon_path\n", + "\n", + "from eis_toolkit.vector_processing.distance_computation import distance_computation\n", + "from eis_toolkit.vector_processing.proximity_computation import proximity_computation" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "def _plot_distance_matrix(ax, distance_array, title, transform):\n", + " plot.show(distance_array, transform=transform, ax=ax,cmap='gray')\n", + " ax.set_title(fill(title, width=30))\n", + " norm = plt.Normalize(vmax=np.nanmax(distance_array), vmin=np.nanmin(distance_array))\n", + " cmap = \"gray\"\n", + " plt.colorbar(matplotlib.cm.ScalarMappable(norm=norm, cmap=cmap),ax=ax)\n", + "\n", + "\n", + "def _plot_example(path_polygon,path_raster,maximum_distance):\n", + " fig, axes = plt.subplots(1,3, figsize=(15,10))\n", + "\n", + " gdf = gpd.read_file(path_polygon)\n", + " with rasterio.open(path_raster) as test_raster:\n", + " raster_profile = test_raster.profile\n", + " transform = test_raster.transform\n", + "\n", + " gdf = gpd.read_file(polygon_path)\n", + " _plot_image_with_transform = partial(_plot_distance_matrix,transform=transform)\n", + "\n", + " with rasterio.open(SMALL_RASTER_PATH) as test_raster:\n", + " raster_profile = test_raster.profile\n", + " transform = test_raster.transform\n", + "\n", + " distance_matrix=distance_computation(gdf,raster_profile) #Distance matrix from the distance computation function\n", + " _plot_image_with_transform(ax=axes[0], distance_array=distance_matrix,title=\"Distance Computation\") \n", + "\n", + " distance_matrix=distance_computation(gdf,raster_profile, maximum_distance) #Distance matrix from the distance computation function with maximum distance\n", + " _plot_image_with_transform(ax=axes[1], distance_array=distance_matrix,title=\"Distance Computation with maximum distance = 25\")\n", + "\n", + " output_mat2 = proximity_computation(gdf,raster_profile, maximum_distance, 'linear', (1,0)) #Output form the new proximity tool with maximum distance = 25\n", + " _plot_image_with_transform(ax=axes[2], distance_array=output_mat2,title=\"Inversion from Proximity_computation with maximum distance = 25\") " + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "_plot_example(polygon_path,SMALL_RASTER_PATH,25)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "eis_toolkit_development_platform_eis_not_installed_py3.9", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.18" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/notebooks/testing_random_sampling.ipynb b/notebooks/testing_random_sampling.ipynb new file mode 100644 index 00000000..9e629711 --- /dev/null +++ b/notebooks/testing_random_sampling.ipynb @@ -0,0 +1,183 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "import sys\n", + "\n", + "sys.path.insert(0, \"..\")" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import geopandas as gpd\n", + "import rasterio\n", + "from rasterio import plot\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import textwrap\n", + "\n", + "from tests.raster_processing.clip_test import raster_path as SMALL_RASTER_PATH\n", + "from eis_toolkit.training_data_tools.points_to_raster import points_to_raster\n", + "from eis_toolkit.training_data_tools.random_sampling import generate_negatives\n", + "\n", + "PATH_LABELS_GPKG = \"../tests/data/remote/interpolating/interpolation_test_data_small.gpkg\"\n", + "RASTER_PATH = \"../tests/data/remote/small_raster.tif\"" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " id value geometry\n", + "0 1.0 10.0 POINT (384758.306 6671365.922)\n", + "1 2.0 20.0 POINT (384769.919 6671362.194)\n", + "2 3.0 30.0 POINT (384769.059 6671350.580)\n", + "3 4.0 40.0 POINT (384777.519 6671344.128)\n", + "4 5.0 35.0 POINT (384761.030 6671342.264)\n", + "5 6.0 25.0 POINT (384758.306 6671355.025)\n", + "6 7.0 50.0 POINT (384783.397 6671365.205)\n", + "7 8.0 100.0 POINT (384795.584 6671358.179)\n", + "8 9.0 120.0 POINT (384795.728 6671344.988)\n", + "9 11.0 79.0 POINT (384786.408 6671335.955)\n", + "10 12.0 60.0 POINT (384780.243 6671327.783)\n", + "11 13.0 50.0 POINT (384771.497 6671320.040)\n", + "12 NaN 40.0 POINT (384791.797 6671314.259)\n", + "13 NaN 30.0 POINT (384799.487 6671303.044)\n", + "14 NaN 20.0 POINT (384808.459 6671290.707)\n", + "15 NaN 30.0 POINT (384787.791 6671295.033)\n", + "16 NaN 30.0 POINT (384815.509 6671310.254)\n", + "17 NaN 20.0 POINT (384826.244 6671297.436)\n", + "18 NaN 15.0 POINT (384825.763 6671287.503)\n", + "19 NaN 10.0 POINT (384815.349 6671280.453)\n", + "20 NaN 13.0 POINT (384797.404 6671281.895)\n", + "21 NaN 32.0 POINT (384779.941 6671288.785)\n", + "22 NaN 35.0 POINT (384765.521 6671282.216)\n", + "23 NaN 45.0 POINT (384768.084 6671297.917)\n", + "24 NaN 50.0 POINT (384762.637 6671305.608)\n", + "25 NaN 55.0 POINT (384759.913 6671316.182)\n", + "26 NaN 60.0 POINT (384749.820 6671310.414)\n", + "27 NaN 65.0 POINT (384750.941 6671299.840)\n", + "28 NaN 40.0 POINT (384753.665 6671287.343)\n", + "29 NaN 100.0 POINT (384815.028 6671333.325)\n", + "30 NaN 90.0 POINT (384801.410 6671331.723)\n", + "31 NaN 70.0 POINT (384792.438 6671325.795)\n", + "32 NaN 120.0 POINT (384812.144 6671349.828)\n", + "33 NaN 100.0 POINT (384807.338 6671365.689)\n", + "34 NaN 80.0 POINT (384796.443 6671373.380)\n", + "35 NaN 30.0 POINT (384778.499 6671375.623)\n" + ] + } + ], + "source": [ + "gdf = gpd.read_file(PATH_LABELS_GPKG)\n", + "print(gdf)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "def plot_samples(template_raster_path):\n", + " \n", + " with rasterio.open(template_raster_path) as temp_raster:\n", + " raster_profile = temp_raster.profile\n", + " \n", + " outarray,outmeta = points_to_raster(geodataframe=gdf,\n", + " raster_profile=raster_profile)\n", + " \n", + " sampled_negatives, outarray2, _ = generate_negatives(raster_array=outarray,\n", + " raster_profile=outmeta,\n", + " sample_number=10,\n", + " random_seed=30)\n", + "\n", + " extent = [temp_raster.bounds.left, temp_raster.bounds.right,\n", + " temp_raster.bounds.bottom, temp_raster.bounds.top]\n", + "\n", + " fig,axes = plt.subplots(2,2,figsize=(20,15))\n", + " norm = plt.Normalize(vmax=np.nanmax(outarray), vmin=np.nanmin(outarray))\n", + " norm2 = plt.Normalize(vmax=np.nanmax(outarray2), vmin=np.nanmin(outarray2))\n", + "\n", + " plot.show(temp_raster, ax=axes[0][0])\n", + " axes[0][0].set_title('Template Raster with positives', fontsize=15)\n", + " points = gdf.cx[\n", + " temp_raster.bounds.left : temp_raster.bounds.right, \n", + " temp_raster.bounds.bottom : temp_raster.bounds.top, \n", + " ]\n", + " points.plot(ax=axes[0][0], facecolor='w', edgecolor='w')\n", + "\n", + " im1 = axes[0][1].imshow(outarray, cmap='gray', norm=norm, extent=extent)\n", + " axes[0][1].set_title(textwrap.fill('True positives converted to raster.', width=30), fontsize=15)\n", + " fig.colorbar(im1, ax=axes[0][1], orientation='vertical')\n", + "\n", + " plot.show(temp_raster, ax=axes[1][0])\n", + " axes[1][0].set_title('Template Raster with positives and random negatives', fontsize=15)\n", + " sampled_negatives.plot(ax=axes[1][0], facecolor='r', edgecolor='r')\n", + " points.plot(ax=axes[1][0], facecolor='w', edgecolor='w')\n", + "\n", + " im1 = axes[1][1].imshow(outarray2, cmap='gray', norm=norm2, extent=extent)\n", + " axes[1][1].set_title(textwrap.fill('True positives and sampled negatives converted to raster.', width=30), fontsize=15)\n", + " fig.colorbar(im1, ax=axes[1][1], orientation='vertical')\n", + "\n", + " plt.tight_layout()\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_samples(SMALL_RASTER_PATH)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "eis_toolkit_development_platform_eis_not_installed_py3.9", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.18" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/notebooks/weights_of_evidence.ipynb b/notebooks/weights_of_evidence.ipynb index 62bddc71..c56ad0dc 100644 --- a/notebooks/weights_of_evidence.ipynb +++ b/notebooks/weights_of_evidence.ipynb @@ -3,8 +3,19 @@ { "cell_type": "code", "execution_count": 1, - "metadata": {}, - "outputs": [], + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/root/.cache/pypoetry/virtualenvs/eis-toolkit-QEzTY9B6-py3.10/lib/python3.10/site-packages/geopandas/_compat.py:112: UserWarning: The Shapely GEOS version (3.10.3-CAPI-1.16.1) is incompatible with the GEOS version PyGEOS was compiled with (3.10.4-CAPI-1.16.2). Conversions between both will be slow.\n", + " warnings.warn(\n" + ] + } + ], "source": [ "import rasterio\n", "from matplotlib import pyplot as plt\n", @@ -14,22 +25,16 @@ "import sys\n", "sys.path.insert(0, \"..\")\n", "\n", - "from eis_toolkit.prediction.weights_of_evidence import weights_of_evidence_calculate_weights, weights_of_evidence_calculate_responses" + "from eis_toolkit.prediction.weights_of_evidence import agterberg_cheng_CI_test, weights_of_evidence_calculate_weights, weights_of_evidence_calculate_responses" ] }, { "cell_type": "code", "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "108 1\n" - ] - } - ], + "metadata": { + "tags": [] + }, + "outputs": [], "source": [ "with rasterio.open(\"../tests/data/remote/wofe/wofe_evidence_raster.tif\") as evidence_raster:\n", " deposits = gpd.read_file(\"../tests/data/remote/wofe/wofe_deposits.shp\")\n", @@ -43,7 +48,31 @@ { "cell_type": "code", "execution_count": 3, - "metadata": {}, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "16" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "nr_of_deposits" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "tags": [] + }, "outputs": [ { "data": { @@ -209,7 +238,7 @@ "7 0.0000 0.0000 0.0000 " ] }, - "execution_count": 3, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -221,8 +250,10 @@ }, { "cell_type": "code", - "execution_count": 4, - "metadata": {}, + "execution_count": 5, + "metadata": { + "tags": [] + }, "outputs": [ { "data": { @@ -425,7 +456,7 @@ "7 -0.8055 1.0047 " ] }, - "execution_count": 4, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -437,8 +468,10 @@ }, { "cell_type": "code", - "execution_count": 5, - "metadata": {}, + "execution_count": 6, + "metadata": { + "tags": [] + }, "outputs": [ { "data": { @@ -641,7 +674,7 @@ "7 -0.3994 0.3806 " ] }, - "execution_count": 5, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -653,8 +686,10 @@ }, { "cell_type": "code", - "execution_count": 6, - "metadata": {}, + "execution_count": 7, + "metadata": { + "tags": [] + }, "outputs": [ { "data": { @@ -857,7 +892,7 @@ "7 -0.0501 0.2608 " ] }, - "execution_count": 6, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -869,8 +904,10 @@ }, { "cell_type": "code", - "execution_count": 7, - "metadata": {}, + "execution_count": 8, + "metadata": { + "tags": [] + }, "outputs": [], "source": [ "colormap_name = \"jet\"" @@ -878,8 +915,10 @@ }, { "cell_type": "code", - "execution_count": 8, - "metadata": {}, + "execution_count": 9, + "metadata": { + "tags": [] + }, "outputs": [ { "data": { @@ -887,13 +926,13 @@ "" ] }, - "execution_count": 8, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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" ] @@ -925,8 +964,10 @@ }, { "cell_type": "code", - "execution_count": 9, - "metadata": {}, + "execution_count": 10, + "metadata": { + "tags": [] + }, "outputs": [ { "data": { @@ -934,13 +975,13 @@ "" ] }, - "execution_count": 9, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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ODta7776ru+++W8XFxZo4caKk01Xxe++9V/Xr19fjjz8uSWratKkk6fjx4xowYIAOHDigCRMmqEWLFtq4caMmT56sgwcP6oUXXpB0+o3l2muv1X//+1/9+c9/1kUXXaR33nlHY8aMscS0Zs0aDR06VG3atFFSUpJOnDihl156SX379tWWLVssf/xHjBih9u3b6+mnn7ZcV+VsU6dOVVJSkuLj4zVt2jSFhITo888/19q1a3XllVdKOp0M1K9fXw899JDq16+vtWvX6qmnnlJubq5mzpxZoX1aon///po9e7Z27dqlSy65RNLp758HBQXp008/1X333edYJkl/+MMfHPvquuuu04YNG3TnnXeqY8eO2rFjh2bPnq1vv/1WqampZY554MABRxIzefJk1atXTwsWLCjzk7c9e/bopptu0h133KExY8Zo4cKFGjt2rHr27KmLL75Yf/jDH3TffffpxRdf1P/+7/86zgzp2LGjfv75Z1155ZVq0qSJHnvsMTVo0ED79+/XsmXL3NpP5/Ltt9/q22+/VWJiourXr1/hfq+//rrGjBmjIUOG6JlnntHx48c1b9489evXT1u3bnX6HSoqKtKQIUPUu3dvzZo1S2vWrNFzzz2ntm3b6s9//rOj3YQJE7Ro0SLdfvvtuu+++7Rv3z79/e9/19atW5WWluZ0avDu3bt1yy23aMKECRo/frw6dOggqWKvt4ro2LGjXn/9dadl2dnZeuihh5ySkYruh1WrVunGG29Up06dlJycrN9++023336701fKyhIWFqaePXtqw4YNjmU//PCDfvjhB8XHxys7O9vpU/wdO3YoNzeXhAaAT5AXkReRF5EXubsfzicvKrmW49KlSzVixAjVrVu3wvMorU2bNoqJiXHKsTZt2qSCggLFx8crPj5eaWlp+stf/iJJ2rhxoyS+/l/tGKAS5eTkGElm+PDhFe5z/Phxy7IhQ4aYNm3aOC27+OKLzYABAyxtp0+fburVq2e+/fZbp+WPPfaYqVWrlsnMzDTGGPP//t//M5LMCy+84GhTVFRkLr/8ciPJpKSkOJZ369bNREVFmd9++82x7MsvvzRBQUFm9OjRjmVTpkwxkswtt9xiiatkXYnvvvvOBAUFmeuvv94UFRU5tS0uLj7n/pgwYYKpW7euOXnypGPZmDFjTMuWLS1tz/bzzz8bSWbu3LnGGGOys7NNUFCQGTFihGnatKmj3X333WcaNmzoiOP11183QUFB5tNPP3Xa3ssvv2wkmbS0NMeyli1bmjFjxjie33vvvcZms5mtW7c6lv3222+mYcOGRpLZt2+fU19J5pNPPnGKOTQ01PzlL39xLFu6dKmRZNatW+cUz/Lly40ks2nTpnPuh/PxzjvvWH5vjDl9zH755Renn8LCQmOMMUePHjUNGjQw48ePd+qTlZVlIiIinJaPGTPGSDLTpk1zatu9e3fTs2dPx/NPP/3USDJvvPGGU7uVK1dalpfs15UrV1rmU9HX24ABA5xeb/v27bO8Tkrvj2uuucbUr1/f7Nq1y+390K1bN9OsWTOTnZ3tWLZq1Sojqdzfc2OMeeSRR4wk8+OPPxpjjHnrrbdMWFiYyc/PN++//76pVauWyc3NNcYY8/e//93ye+zKmDFjXP7NAYCKIi8ylnUlyIvIi8iLyt4P55sXjR492kgykZGR5vrrrzezZs0yX3/9dbn9XBkxYoSpU6eOKSgoMMYYk5ycbFq3bm2MMWbu3LkmKirK0fbhhx82ksyBAwfOuc0BAwY4vU5QtfH1NFSq3NxcSTrnd1hLO/v7xDk5Ofr11181YMAAff/998rJySm3/9KlS9W/f39FRkbq119/dfwMGjRIRUVF+uSTTyRJK1euVO3atTV+/HhH36CgIMunCAcPHtS2bds0duxYNWzY0LG8S5cuGjx4sN5//31LDHfddVe5caampqq4uFhPPfWU5QKJZ5+uffb+OHr0qH799Vf1799fx48f1zfffFPuOGdr0qSJLrroIsc+KLkw3SOPPKJDhw7pu+++k3T6E7V+/fo54li6dKk6duyoiy66yGmfXn755ZKkdevWlTnmypUrFRcXp27dujmWNWzYUKNGjXLZvlOnTurfv79TzB06dND3339f7vwaNGggSXrvvfdUWFhYbntPlPxOl/40LScnR02aNHH62bZtmyRp9erVys7O1i233OK0/2rVqqXevXu73H+lf4f69+/vtA+WLl2qiIgIDR482GmbPXv2VP369S3bbN26tYYMGWIZ53xfb2WZPn263nvvPS1atEidOnVyaz+UvObGjBnjdJHFwYMHO7ZVnpJPtEo+HU5LS1PPnj0VEhKiuLg4x1fSStaFhYXp0ksvdfQvLi52ivHXX39Vfn6+CgsLLcu99bsGoPohLyobeRF5EXmR9/KilJQU/f3vf1fr1q21fPlyPfzww+rYsaOuuOIKHThwwK259OvXTydOnNDmzZslnX7dlJw52bdvX/3888+O105aWppat26tmJgYR/+ycqn8/HzL8tJfoUXVUCO/nvbJJ59o5syZ2rx5sw4ePKjly5crISHBrW0YY/Tcc8/p1VdfVUZGhho3bqy7777bcYpwTWW32yWdflOvqLS0NE2ZMkXp6ek6fvy407rSV+p35bvvvtP27dvVpEkTl+t//vlnSVJGRoaaNWtmOUWzXbt2Ts8zMjIkyXHa6tk6duyoDz/80HFRxxKtW7c+Z4zS6VvtBgUFlfvHfteuXXriiSe0du1axxtzCU/evPr37+9I6D799FNdeumluvTSS9WwYUN9+umnatq0qb788kvdeuutjj7fffedvv7663L3qSsZGRku75ZQej+XaNGihWVZZGSk5fvorgwYMEA33nijpk6dqtmzZ2vgwIFKSEjQrbfees4LUebk5Djd3jgkJMQpET5bSaKfl5fntLx+/fpavXq1pNOnEJ99inzJG2dJMllayeukRFhYmGVfl94H3333nXJycsr8HnrpY1LW7+T5vt5cWblypaZOnarJkyfrxhtvdIpZKn8/lLzm2rdvb2nToUMHbdmypdwY+vbtK5vNprS0NN18881KS0vT4MGDJZ1Oojt16uRYlpaWpv/5n/9xug5DZmZmmfus9LFZt26d07VDgJqC/Ml95EVlIy8iLypBXnRaZeZFJQXgiRMn6rffflNaWppefvllffDBB7r55psdH7JVxNnXNerdu7c2btyov/71r5KkSy65RHa7XWlpaYqNjdXmzZv1xz/+0al/WlqaLrvsMst2N27cqCVLljgt27dvn9/uCIiy1cii0bFjx9S1a1eNGzdON9xwg0fbuP/++7Vq1SrNmjVLnTt31uHDh3X48OFKjjTw2O12xcTEaOfOnRVqv3fvXl1xxRW66KKL9Pzzzys2NlYhISF6//33NXv27ApVm4uLizV48GBNmjTJ5foLL7zQrTl44nzuvnC27OxsDRgwQHa7XdOmTVPbtm0VFhamLVu26NFHH/Wo+l5y14Pvv/9en376qfr37y+bzaZ+/frp008/VUxMjIqLi50+1SouLlbnzp31/PPPu9xmbGysx3MsrVatWi6Xm3NcA6GEzWbT22+/rc8++0zvvvuuPvzwQ40bN07PPfecPvvsszK/a3///ffrH//4h+P5gAEDyrxD1kUXXSRJlt/p4OBgDRo0SJL0448/Oq0rOU6vv/66oqOjLdssffeYsvZB6W1GRUXpjTfecLm+dHLl6neyMl5vpe3bt0+jRo3S4MGDHQnE2TFLFd8P56NRo0a66KKLtGHDBuXl5Wn79u2aMmWKY318fLw2bNigH3/8UZmZmZZPeKOjox3JbomZM2cqKytLzz33nNPyrl27VlrcQCAhf3IfedH5IS/6HXmRM/KiimvUqJGuu+46XXfddRo4cKA+/vhjZWRkOK59VJ6uXbsqPDxcGzZs0NVXX63Dhw87zjQKCgpS7969tWHDBrVt21YFBQWW6xl17drVkmP95S9/UXR0tB555BGn5a72C/yvRhaNhg4d6nTLwNLy8/P1+OOP66233lJ2drYuueQSPfPMM45Plr/++mvNmzdPO3fudHzqUpFPVGqKa665Rq+++qrS09NdfrJytnfffVf5+flasWKF0ycrrk5TLesuR23btlVeXp7jjaosLVu21Lp163T8+HGnT9X27NljaSedvmBead98840aN27s9GlaRbVt21bFxcX66quvnE5RPtv69ev122+/admyZY6LL0qn34A8VZL0rF69Wps2bdJjjz0m6fTFHefNm6eYmBjVq1dPPXv2dIr1yy+/1BVXXOH23aVatmxp2aeSdT+7o7wY+vTpoz59+uj//u//9Oabb2rUqFFasmSJEhMTXbafNGmS/vSnPzmeR0ZGlrntDh06qH379kpNTdULL7xQoWPftm1bSVJUVFS5v5cV1bZtW61Zs0Z9+/b1OBl35/VWESdOnNANN9ygBg0a6K233rJ8vaCi+6HkNVfyCdzZXL0Oy9KvXz8tXLhQq1atUlFRkdNFZ+Pj4/XWW285kuDSCU1YWJglxsWLFys/P7/SjiEQ6MifPENe5Bp5EXnR+SAv8syll16qjz/+WAcPHqxw0ahWrVrq06eP0tLStGHDBtntdnXu3NmxPj4+Xv/6178cZ8+VzrEiIyMt842MjFSzZs3IsQIE1zRy4Z577lF6erqWLFmi7du3a8SIEbrqqqscL9ySu2C89957at26tVq1aqXExMRq/UmZOyZNmqR69eopMTFRhw4dsqzfu3ev/va3v0n6/ZOEsz89ycnJUUpKiqVfvXr1lJ2dbVk+cuRIpaen68MPP7Ssy87O1qlTpyRJQ4YMUWFhoebPn+9YX1xcrDlz5jj1adasmbp166Z//OMfTuPt3LlTq1at0tVXX32O2ZctISFBQUFBmjZtmuWTi5L5u9ofBQUFmjt3rkdjSqcT8gsuuECzZ89WYWGh4/ab/fv31969e/X222+rT58+Tp9ujBw5UgcOHHDaVyVOnDihY8eOlTnekCFDlJ6e7vgeuyQdPny4zE+CKqIkISl9/I8cOWL55K0k8Sx9C9yzderUSYMGDXL8nJ0YupKUlKRff/1V48ePd3mNgNIxDBkyRHa7XU8//bTL9r/88ss5x3Nl5MiRKioq0vTp0y3rTp065fK1UZo7r7eKuOuuu/Ttt99q+fLlLhPMiu6Hs19zZ3/VYPXq1frqq68qHE+/fv1UVFSkWbNmqX379k6fMsbHxysvL09z585VUFBQhe9iBKDiyJ9cIy9yjbyIvKgEedFplZUXZWVluWxXUFCgjz76SEFBQWV+PbIs/fr10y+//KKUlBT17t3bqSAWHx+v3bt365133lGjRo0cd/RD9VEjzzQ6l8zMTKWkpCgzM9NxAa+HH35YK1euVEpKip5++ml9//33ysjI0NKlS/XPf/5TRUVFevDBB3XTTTdp7dq1fp6B/7Vt21Zvvvmm/vjHP6pjx44aPXq0LrnkEhUUFGjjxo1aunSpxo4dK0m68sorFRISomuvvVYTJkxQXl6e5s+fr6ioKB08eNBpuz179tS8efP017/+Ve3atVNUVJQuv/xyPfLII1qxYoWuueYaxy1Jjx07ph07dujtt9/W/v371bhxYyUkJKhXr176y1/+oj179uiiiy7SihUrHMnq2Z/azJw5U0OHDlVcXJzuuOMOx61lIyIilJSU5NF+adeunR5//HFNnz5d/fv31w033KDQ0FBt2rRJMTExSk5OVnx8vCIjIzVmzBjdd999stlsev311yt0SvK59O/fX0uWLFHnzp0db2I9evRQvXr19O233zp9b1+SbrvtNv373//WXXfdpXXr1qlv374qKirSN998o3//+9/68MMPnS4ifLZJkyZp8eLFGjx4sO69917HrWVbtGihw4cPu/0JnXQ64alVq5aeeeYZ5eTkKDQ0VJdffrnefPNNzZ07V9dff73atm2ro0ePav78+bLb7R4nsa7ceuut2rlzp5KTk/Xf//5XN998s1q3bq1jx45p586deuuttxQeHu7Yt3a7XfPmzdNtt92mHj166Oabb1aTJk2UmZmp//znP+rbt6/+/ve/uxXDgAEDNGHCBCUnJ2vbtm268sorVbt2bX333XdaunSp/va3v+mmm2465zbceb2V5z//+Y/++c9/6sYbb9T27du1fft2x7r69esrISHBrf2QnJysYcOGqV+/fho3bpwOHz6sl156SRdffLHlugllKflkKz093fE3psSFF16oxo0bKz09XZ07d3ZcLBRA5SB/Kht5kWvkReRF5EXeyYt+/PFH9erVS5dffrmuuOIKRUdH6+eff9Zbb72lL7/8Ug888IAaN27s1vzOzrFKv+b79Okjm82mzz77TNdee61Hv9Oo4nx9u7aqRpJZvny54/l7771nJJl69eo5/QQHB5uRI0caY4wZP368kWR2797t6Ld582YjyXzzzTe+nkKV9e2335rx48ebVq1amZCQEBMeHm769u1rXnrpJadbpK5YscJ06dLFhIWFmVatWplnnnnGLFy40HIb0qysLDNs2DATHh5uJDnd9vLo0aNm8uTJpl27diYkJMQ0btzYxMfHm1mzZjluD2mMMb/88ou59dZbTXh4uImIiDBjx441aWlpRpJZsmSJU/xr1qwxffv2NXXq1DF2u91ce+215quvvnJqU3L72F9++cUy/9K3li2xcOFC0717dxMaGmoiIyPNgAEDzOrVqx3r09LSTJ8+fUydOnVMTEyMmTRpkvnwww8tt1atyK1lS8yZM8dIMn/+85+dlg8aNMhIMh999JGlT0FBgXnmmWfMxRdf7Ii1Z8+eZurUqSYnJ8fRrvStZY0xZuvWraZ///4mNDTUNG/e3CQnJ5sXX3zRSDJZWVlOfYcNG2YZu/RtTY0xZv78+aZNmzamVq1ajn2xZcsWc8stt5gWLVqY0NBQExUVZa655hrzxRdfVGi/uGv9+vXmpptuMs2aNTO1a9c2drvdXHrppWbKlCnm4MGDlvbr1q0zQ4YMMRERESYsLMy0bdvWjB071im+MWPGmHr16ln6lvX78+qrr5qePXuaOnXqmPDwcNO5c2czadIk89NPPznalLVfjan46628W8umpKQYSS5/Sv9eVmQ/GHP69s8dO3Y0oaGhplOnTmbZsmVu/Z4bY0xMTIyRZF599VXLuuuuu87l66AsY8aMcXk7awDkT54gLyIvIi8iL6rofjDG87woNzfX/O1vfzNDhgwxzZs3N7Vr1zbh4eEmLi7OzJ8/3xQXF5+zvyvHjh0zwcHBRpJZtWqVZX2XLl2MJPPMM89UaHsDBgywvE5QddmMOc9SfYCz2WxOd//417/+pVGjRmnXrl2Wi7DVr19f0dHRmjJliuW0whMnTqhu3bpatWqV4449CAypqam6/vrrtWHDBscpyqh8DzzwgF555RXl5eVV6AKHAICqi/yp+iIv8g3yIgCBgq+nldK9e3cVFRXp559/drprwtn69u2rU6dOae/evY4Lmn377beSVOELisE/Tpw44XSxvKKiIr300kuy2+3q0aOHHyOrXkrv599++02vv/66+vXrR2IEANUQ+VNgIi/yDfIiAIGsRhaN8vLynO5YsG/fPm3btk0NGzbUhRdeqFGjRmn06NF67rnn1L17d/3yyy/66KOP1KVLFw0bNkyDBg1Sjx49NG7cOL3wwgsqLi7WxIkTNXjwYJ/cxhSeu/fee3XixAnFxcUpPz9fy5Yt08aNG/X0009X2u1hIcXFxWngwIHq2LGjDh06pNdee025ubl68skn/R0aAMBD5E/VD3mRb5AXoSrKyso65/o6deooIiLCR9GgSvP39+P8Yd26dS6/b1ryvcqCggLz1FNPmVatWpnatWubZs2ameuvv95s377dsY0DBw6YG264wdSvX980bdrUjB071vz2229+mhEq6o033jA9evQwdrvdhISEmE6dOpmXXnrJ32FVO5MnTzbt27c3derUMXXr1jX9+vVzuj4BACDwkD9VP+RFvkFehKrI1d9zV3/bgRp/TSMAAAAAAGqSNWvWnHN9TEyMOnXq5KNoUJVRNAIAAAAAAIBFkL8DAAAAAAAAQNVTYy6EXVxcrJ9++knh4eGy2Wz+DgcAAJTBGKOjR48qJiZGQUF8vuVP5E8AAAQGb+VPNaZo9NNPPyk2NtbfYQAAgAr64Ycf1Lx5c3+HUaORPwEAEFgqO3+qMUWj8PBwSad3oN1u93M0AACgLLm5uYqNjXW8d8N/yJ8AAAgM3sqfakzRqOSUarvdTtIDAEAA4OtQ/kf+BABAYKns/IkLBQAAAAAAAMCCohEAAAAAAAAsKBoBAAAAAADAgqIRAAAAAAAALCgaAQAAAAAAwIKiEQAAAAAAACwoGgEAAAAAAMCCohEAAAAAAAAsKBoBAAAAAADAItidxq1atVJGRoZl+d133605c+ZYli9atEi3336707LQ0FCdPHlSklRYWKgnnnhC77//vr7//ntFRERo0KBBmjFjhmJiYs45bnJysh577DF3wvcJm22zv0Pwr5d7en+Mu7y/j43xwTwAADUC+VP5bLbj/g7Bz571wRiTvD6CMXW9PgYAwLfcKhpt2rRJRUVFjuc7d+7U4MGDNWLEiDL72O127d692/HcZrM5/n38+HFt2bJFTz75pLp27aojR47o/vvv13XXXacvvvjCaTvTpk3T+PHjHc/Dw8PdCR0AAMAvyJ8AAECgcqto1KRJE6fnM2bMUNu2bTVgwIAy+9hsNkVHR7tcFxERodWrVzst+/vf/65evXopMzNTLVq0cCwPDw8vczsAAABVFfkTAAAIVB5f06igoECLFy/WuHHjnD79Ki0vL08tW7ZUbGyshg8frl27dp1zuzk5ObLZbGrQoIHT8hkzZqhRo0bq3r27Zs6cqVOnTp1zO/n5+crNzXX6AQAA8CfyJwAAEEjcOtPobKmpqcrOztbYsWPLbNOhQwctXLhQXbp0UU5OjmbNmqX4+Hjt2rVLzZs3t7Q/efKkHn30Ud1yyy2y2+2O5ffdd5969Oihhg0bauPGjZo8ebIOHjyo559/vsyxk5OTNXXqVE+nBwAAUOnInwAAQCCxGWOMJx2HDBmikJAQvfvuuxXuU1hYqI4dO+qWW27R9OnTLetuvPFG/fjjj1q/fr1T0lPawoULNWHCBOXl5Sk0NNRlm/z8fOXn5zue5+bmKjY2Vjk5Oefc9vniQthcCBsAcH5yc3MVERHh9fdsfyB/co0LYXMhbADA+fFW/uTRmUYZGRlas2aNli1b5la/2rVrq3v37tqzZ4/T8sLCQo0cOVIZGRlau3ZtuRPs3bu3Tp06pf3796tDhw4u24SGhpaZEAEAAPga+RMAAAg0Hl3TKCUlRVFRURo2bJhb/YqKirRjxw41a9bMsawk4fnuu++0Zs0aNWrUqNztbNu2TUFBQYqKinI7dgAAAH8gfwIAAIHG7TONiouLlZKSojFjxig42Ln76NGjdcEFFyg5OVnS6du89unTR+3atVN2drZmzpypjIwMJSYmSjqd8Nx0003asmWL3nvvPRUVFSkrK0uS1LBhQ4WEhCg9PV2ff/65LrvsMoWHhys9PV0PPvig/vSnPykyMvJ85w8AAOB15E8AACAQuV00WrNmjTIzMzVu3DjLuszMTAUF/X7y0pEjRzR+/HhlZWUpMjJSPXv21MaNG9WpUydJ0oEDB7RixQpJUrdu3Zy2tW7dOg0cOFChoaFasmSJkpKSlJ+fr9atW+vBBx/UQw895G7oAAAAfkH+BAAAApHHF8IONL66qCYXwuZC2ACA81OdL4QdaHyXP3EhbO/jQtgAUJ156z3bo2saAQAAAAAAoHrz6O5pOAdPzrS5K6nSw7B42Qdj+IpH+9i9s5Nq/BljPsDZXACA37l/ps1e84oX4nDW1jbB62P4jidnM7l3dhJnjHkfZ3MB8DXONAIAAAAAAIAFRSMAAAAAAABYUDQCAAAAAAC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" ] @@ -982,8 +1023,10 @@ }, { "cell_type": "code", - "execution_count": 10, - "metadata": {}, + "execution_count": 11, + "metadata": { + "tags": [] + }, "outputs": [ { "data": { @@ -991,13 +1034,13 @@ "" ] }, - "execution_count": 10, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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" ] @@ -1039,8 +1082,10 @@ }, { "cell_type": "code", - "execution_count": 11, - "metadata": {}, + "execution_count": 12, + "metadata": { + "tags": [] + }, "outputs": [ { "data": { @@ -1048,13 +1093,13 @@ "" ] }, - "execution_count": 11, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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hwwZ1795dwcHBstlsWr58udPbMAxDU6ZMUb169eTt7a1q1arptddey/9gUeRdvHhR5cuXlyQdPnxYhmFozJgx8vf3d3hlPb781KlTkq4+eSQpKUn16tVTkyZN9MILLzg8cSTrRKJx48Y59n369GklJSVp7ty5lv4GDBjg0F+WG0+0KlasKEnmF1HWF3fdunUd6nl6eurOO+/M1T7x8fGRv7+/pZ/rEwdxcXGqWrWq5SlcderUyVUfWSc833zzjVJTU7V79261b99eHTp0ME+OvvnmG/n6+ppTfbNONKKjoy3767333lNaWlqOX7xZ++XG+CpVqmTuwxvdal/fzK0+H9mx2+1KSEhweKWnp9+0Tbt27cx7F+3bt0/JycnmkzvatGmjX3/9VceOHTPvdXTjCc0bb7xh2ZfS1ZPG68vCwsJuOWYARRvnT8gNzousOC+6qjicF1WtWlWzZ8/WyZMndejQIb399tvy9/fX2LFjNX/+/FvGeb127dqZ9y7avHmzw/2S2rRpo507dyotLS3Hy/+HDRvmcEzuvvtuSVLPnj0dym/1gBLgdpXKp6elpqaqWbNmGjhwoP7yl7/kaRvDhw/XV199pSlTpqhJkyY6d+6czp07l8+Roqj75ZdflJycbH5hZv015vnnn1dUVFS2bbLqdujQQT///LM++eQTffXVV3rvvfc0bdo0zZkzR4MGDcpV/1n9Pf744zle8960aVOH5ZyefOHMX09upTCerhEcHKzQ0FBt2LBBtWrVkmEYioiIkL+/v4YPH664uDh98803atOmjdzcrubHs/bX5MmT1bx582y3m/UUsfxwO/s6L5+P48ePKzQ01KHsVk/YuP6aey8vL1WqVEkNGjSQJDVv3tx8ZOzRo0cd6mfp16+fpezee+/VCy+8oPvuu88sK41P0QFKGs6fcCucF2WP86KrisN5URabzaZ69eqpXr166tatm+rWrat///vfuf4sSlfPmd555x1t2rRJmzdvVpMmTcz92aZNG6WlpWn79u3auHGjPDw8LDfg/vvf/67HH3/cXE5MTNTjjz+uKVOmONz7KKckHZBfSmXSqGvXruratWuO69PS0vTSSy/pgw8+UFJSkho3bqzXX3/d/AFz4MABzZ49W/v27VP9+vUlyfIDCaVD1o3ysk6Esv7i5OnpqcjIyFu2r1SpkgYMGKABAwbo4sWL6tChg2JjYzVo0CBzW/v27cuxvb+/v8qXLy+73Z6r/nKjZs2akq7+9alz585meUZGho4ePZpvN+irWbOm1q1bp0uXLjn8VS27J5bkpH379tqwYYNCQ0PVvHlz8waCfn5+WrlypXbt2qVx48aZ9WvXri3p6hMwnN1fWfvl8OHDDv/fz549e1uXXl0/TftGN/t8ZCcoKMjyFJFbHa+7777bTAx5e3srIiLCjMnDw0N/+MMftGnTJh09elQBAQGqV6+eQ/s777wz27+0NmrUKN8+kwCKBs6fcCucF91eP5wXuf68KDt33nmnKlasqJMnTzrV7vo/zG3ZssWcyS1dTfLVrFlTmzZt0qZNmxQWFmaZZdaoUSM1atTIXM56cl2LFi1ylfgC8kupvDztVoYNG6YtW7ZoyZIl+v777/Xwww/r/vvvN6dwfvrpp7rzzjv12WefKTQ0VLVq1dKgQYP4S1kps3btWr3yyisKDQ01n7IREBCgTp066d133832i+X6x4qePXvWYV25cuVUp04dpaWlSbp64tOhQwctWLBA8fHxDnWz/iLj7u6uBx98UB9//HG2J1HX95dbLVu2lL+/v+bMmeMwhXfRokVKSkpyens5iYqKUkZGhubNm2eWZWZmaubMmbneRvv27XXs2DF9+OGH5rRsNzc3tWnTRlOnTlVGRobDE0JatGih2rVra8qUKbp48aJlezfbX126dJGHh4dmz57tUD5jxoxcx5udO+64I9v9eqvPR3Z8fHwUGRnp8LrVX588PDwUHh5unrRkXWufpU2bNtqwYYO+/fZbh5MdALgR50+lG+dFt4fzoqtceV60devWbJ/8tm3bNp09e/b/2bvv+Cjq/I/j7w1pQBolIQQCREAQBSkeIZEmRUBUsGFBqoAKKsKdCicIAidREFEPsZyE34knlkNEz0Y9BQIiRQGRk5YoEkAgCUEIKd/fH5CVzSQku2R3U17PxyOPZWe+3/mWmd398NnZGXuyu6Tyz/5auXKlvv3220JjrKVLl2r37t3cmRZlWqU80+hiUlJSlJiYqJSUFEVFRUk6d0rt559/rsTERD3zzDPat2+fkpOT9f777+uf//yncnNzNW7cON1+++1atWqVl0cAd/jss8/0448/KicnR4cPH9aqVau0fPlyNWzYUMuWLVNgYKC97Lx589SxY0e1bNlSI0eO1GWXXabDhw8rKSlJv/zyi7777jtJ57496Nq1q9q1a6eaNWvq22+/1QcffOBw69GXXnpJHTt2VNu2bTVq1CjFxMTowIED+s9//qNt27ZJOner2NWrVys2NlYjR45UixYtdPz4cW3ZskUrVqxwOhj38/PTjBkzdP/996tbt2668847tX//fiUmJpb4t/sl0b9/f7Vv315//vOftWfPHjVv3lzLli2z9/di3zTlyw98du/erWeeeca+vHPnzvrss88UEBCgP/3pT/blPj4++sc//qE+ffroyiuv1LBhw1SvXj0dPHhQq1evVkhIiMNtey9Up04djR07Vs8//7xuvvlm9e7dW999950+++wz1a5du0T9LUy7du00f/58zZgxQ02aNFFERIS6detWouOjtHTs2FGrV6+WJEtiKD4+XjNnzrSXA4DCED9VLsRFxEUVMS5666239Pbbb+uWW25Ru3bt5O/vr127dmnBggUKDAzUX//6V6e32bFjR/sZeIXFWO+88469HFBmefhubWWOJPPhhx/an3/yySdGkqlevbrDn6+vrxkwYIAxxpiRI0caSWb37t32eps3bzaSzI8//ujpIcCN8m8Rmv/n7+9vIiMjTc+ePc2LL75oMjIyCq23d+9eM3jwYBMZGWn8/PxMvXr1zI033mg++OADe5kZM2aY9u3bm7CwMFO1alXTvHlz87e//c2cPXvWYVs7duwwt9xyiwkLCzOBgYGmWbNmZvLkyQ5lDh8+bMaMGWOio6ONn5+fiYyMNN27dzevv/66vUz+rWXff/99h7qF3R7WGGNeeeUVExMTYwICAsw111xjvvrqK9OlS5cS3Vq2evXqljmZMmWKKfiWc/ToUXPPPfeY4OBgExoaaoYOHWrWrVtnJJnFixcXOrcFRUREGEnm8OHD9mVr1641kkynTp0KrbN161Zz6623mlq1apmAgADTsGFDM2DAALNy5Up7mYK3ljXGmJycHDN58mQTGRlpqlatarp162Z27dplatWqZR544AFL3YK3JM7fBxfeKjU1NdX07dvXBAcHO9y6t6THR2n44osvjCTj6+trTp065bAu//a8kszGjRtLtL3CjicAFQvxU+VEXERcVJHjou+//9489thjpm3btqZmzZrG19fX1K1b19xxxx1my5YtLm3ztddeM5JMvXr1LOu2bNlify1duL+Kkn98XThfgCfYjCnFq7yVQzabTR9++KH69+8vSXr33Xc1cOBA7dy503KxtqCgIEVGRmrKlCl65plnlJ2dbV93+vRpVatWTV9++aV69uzpySEAFcrSpUt1yy23aO3ateXi51BpaWmqUaOGZsyYoSeffNLb3QEAjyB+AjyDuAiAt/HztALatGmj3NxcHTlyxOE3vxe69tprlZOTo71799ovIPe///1P0h8XhQNQvNOnTzvcVSs3N1cvv/yyQkJC7LcVLUsK9leS5s6dK0lckBBApUb8BFw64iIAZVGlTBplZmY63Ilg//792rZtm2rWrKnLL79cAwcO1ODBg/X888+rTZs2Onr0qFauXKlWrVqpb9++6tGjh9q2bavhw4dr7ty5ysvL05gxY9SzZ0/LnYUAFO3hhx/W6dOnFRcXp6ysLC1ZskTr16/XM888UyZv0f7uu+9q4cKFuuGGGxQUFKS1a9fqnXfe0fXXX18uvv0DgEtB/AS4F3FR2Xf69Gmlp6dftEzNmjXl7+/voR4BHuDt38d5Q/7vZwv+DRkyxBhjzNmzZ81TTz1lGjVqZPz8/EzdunXNLbfcYr7//nv7Ng4ePGhuvfVWExQUZOrUqWOGDh1qjh075qURAeXT22+/bdq2bWtCQkKMv7+/adGihXn55Ze93a0ibd682XTv3t3UqlXL+Pn5mfr165uxY8eakydPertrAOB2xE+AexEXlX0Fr+tV2B/XHEJFU+mvaQQAAAAAQHEOHTqknTt3XrRMu3btVKNGDQ/1CHA/kkYAAAAAAACwqDTXNMrLy9Ovv/6q4OBg2Ww2b3cHAAAUwRijkydPKioqSj4+Pt7uTqVG/AQAQPngrvip0iSNfv31V0VHR3u7GwAAoIR+/vln1a9f39vdqNSInwAAKF9KO36qNEmj4OBgSecmMCQkxMu9AQAARcnIyFB0dLT9sxveQ/wEAED54K74qdIkjfJPqQ4JCSHoAQCgHODnUN5H/AQAQPlS2vETFwoAAAAAAACABUkjAAAAAAAAWJA0AgAAAAAAgAVJIwAAAAAAAFiQNAIAAAAAAIAFSSMAAAAAAABYkDQCAAAAAACABUkjAAAAAAAAWJA0AgAAAAAAgIWvM4UbNWqk5ORky/LRo0dr3rx5luULFy7UsGHDHJYFBATozJkzkqTs7GxNmjRJn376qfbt26fQ0FD16NFDCQkJioqKumi7M2fO1IQJE5zpvkfYbHO83QXv6jDe/W1scP8cG+OBcQAAKgXip+IFnzrq7S54VWZCuNvbCJrg/jk+Wd394wAAeJZTSaNNmzYpNzfX/nzHjh3q2bOn7rjjjiLrhISEaPfu3fbnNpvN/u/ff/9dW7Zs0eTJk3X11VfrxIkTGjt2rG6++WZ9++23DtuZNm2aRo4caX8eHBz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" ] @@ -1103,8 +1148,10 @@ }, { "cell_type": "code", - "execution_count": 12, - "metadata": {}, + "execution_count": 13, + "metadata": { + "tags": [] + }, "outputs": [], "source": [ "# Say we have 2 different weight calculations that can be used for posterior probabilities calculations\n", @@ -1118,8 +1165,10 @@ }, { "cell_type": "code", - "execution_count": 13, - "metadata": {}, + "execution_count": 14, + "metadata": { + "tags": [] + }, "outputs": [ { "data": { @@ -1127,13 +1176,13 @@ "" ] }, - "execution_count": 13, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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"text/plain": [ "
" ] @@ -1165,8 +1214,10 @@ }, { "cell_type": "code", - "execution_count": 14, - "metadata": {}, + "execution_count": 15, + "metadata": { + "tags": [] + }, "outputs": [], "source": [ "import numpy as np\n", @@ -1186,11 +1237,61 @@ " with rasterio.open(posterior_raster_conf, \"w\", **raster_meta) as dest:\n", " dest.write(posterior_confidence, 1)" ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " Results of conditional independence test:\n", + "\n", + "\n", + " Observed number of deposits, n: 16\n", + "\n", + " Expected number of deposits, T: 15.76794227613399\n", + "\n", + " Standard deviation of the expected number of deposits, s(T): 2.906223209503864\n", + "\n", + " T - n = -0.23205772386600998\n", + "\n", + " T / n = 0.9854963922583744\n", + "\n", + " Agterberg & Cheng CI test:\n", + "\n", + " Data satisfies condition T - n < 2.33 * s(T):\n", + "\n", + " -0.23205772386600998 < 6.771500078144003\n", + "\n", + " Data satisfies condition T - n < 1.645 * s(T):\n", + "\n", + " -0.23205772386600998 < 4.780737179633856\n", + "\n", + " \n", + " \n" + ] + } + ], + "source": [ + "ci_overall, ci_99, ci_95, ratio, summary = agterberg_cheng_CI_test(posterior_array, posterior_array_std, nr_of_deposits)\n", + "print(summary)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { "kernelspec": { - "display_name": "eis_toolkit", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -1205,9 +1306,8 @@ "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.12" - }, - "orig_nbformat": 4 + } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } diff --git a/poetry.lock b/poetry.lock index a75d0afd..275486db 100755 --- a/poetry.lock +++ b/poetry.lock @@ -1,4 +1,4 @@ -# This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand. +# This file is automatically @generated by Poetry 2.1.1 and should not be changed by hand. [[package]] name = "absl-py" @@ -6,6 +6,7 @@ version = "2.0.0" description = "Abseil Python Common Libraries, see https://github.com/abseil/abseil-py." optional = false python-versions = ">=3.7" +groups = ["main"] files = [ {file = "absl-py-2.0.0.tar.gz", hash = "sha256:d9690211c5fcfefcdd1a45470ac2b5c5acd45241c3af71eed96bc5441746c0d5"}, {file = "absl_py-2.0.0-py3-none-any.whl", hash = "sha256:9a28abb62774ae4e8edbe2dd4c49ffcd45a6a848952a5eccc6a49f3f0fc1e2f3"}, @@ -17,6 +18,7 @@ version = "1.1.9" description = "Calculate spatial accessibility metrics." optional = false python-versions = "*" +groups = ["main"] files = [ {file = "access-1.1.9-py3-none-any.whl", hash = "sha256:47e147aee8d44bbbad77ab45a8dbcd6bd39c038bca2f1c3613c07f9cb0ea9cec"}, {file = "access-1.1.9.tar.gz", hash = "sha256:89d0043e3596854701b72c77dfedb677839a5b416a18362c222b7d321ad02138"}, @@ -38,6 +40,7 @@ version = "2.4.0" description = "Matrices describing affine transformation of the plane" optional = false python-versions = ">=3.7" +groups = ["main"] files = [ {file = "affine-2.4.0-py3-none-any.whl", hash = "sha256:8a3df80e2b2378aef598a83c1392efd47967afec4242021a0b06b4c7cbc61a92"}, {file = "affine-2.4.0.tar.gz", hash = "sha256:a24d818d6a836c131976d22f8c27b8d3ca32d0af64c1d8d29deb7bafa4da1eea"}, @@ -53,6 +56,7 @@ version = "22.1.0" description = "File support for asyncio." optional = false python-versions = ">=3.7,<4.0" +groups = ["dev"] files = [ {file = "aiofiles-22.1.0-py3-none-any.whl", hash = "sha256:1142fa8e80dbae46bb6339573ad4c8c0841358f79c6eb50a493dceca14621bad"}, {file = "aiofiles-22.1.0.tar.gz", hash = "sha256:9107f1ca0b2a5553987a94a3c9959fe5b491fdf731389aa5b7b1bd0733e32de6"}, @@ -64,14 +68,15 @@ version = "0.19.0" description = "asyncio bridge to the standard sqlite3 module" optional = false python-versions = ">=3.7" +groups = ["dev"] files = [ {file = "aiosqlite-0.19.0-py3-none-any.whl", hash = "sha256:edba222e03453e094a3ce605db1b970c4b3376264e56f32e2a4959f948d66a96"}, {file = "aiosqlite-0.19.0.tar.gz", hash = "sha256:95ee77b91c8d2808bd08a59fbebf66270e9090c3d92ffbf260dc0db0b979577d"}, ] [package.extras] -dev = ["aiounittest (==1.4.1)", "attribution (==1.6.2)", "black (==23.3.0)", "coverage[toml] (==7.2.3)", "flake8 (==5.0.4)", "flake8-bugbear (==23.3.12)", "flit (==3.7.1)", "mypy (==1.2.0)", "ufmt (==2.1.0)", "usort (==1.0.6)"] -docs = ["sphinx (==6.1.3)", "sphinx-mdinclude (==0.5.3)"] +dev = ["aiounittest (==1.4.1) ; python_version < \"3.8\"", "attribution (==1.6.2)", "black (==23.3.0)", "coverage[toml] (==7.2.3)", "flake8 (==5.0.4)", "flake8-bugbear (==23.3.12)", "flit (==3.7.1)", "mypy (==1.2.0)", "ufmt (==2.1.0)", "usort (==1.0.6)"] +docs = ["sphinx (==6.1.3) ; python_version >= \"3.8\"", "sphinx-mdinclude (==0.5.3)"] [[package]] name = "anyio" @@ -79,6 +84,7 @@ version = "4.0.0" description = "High level compatibility layer for multiple asynchronous event loop implementations" optional = false python-versions = ">=3.8" +groups = ["dev"] files = [ {file 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"sha256:46e24f5412c948d81736509377e255f6040e94216bf1a9b5ea1eaa9d29f6ec1b"}, {file = "contourpy-1.1.1-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:0e48694d6a9c5a26ee85b10130c77a011a4fedf50a7279fa0bdaf44bafb4299d"}, @@ -816,6 +892,7 @@ version = "0.7.0" description = "CSS selectors for Python ElementTree" optional = false python-versions = ">=3.7" +groups = ["dev"] files = [ {file = "cssselect2-0.7.0-py3-none-any.whl", hash = "sha256:fd23a65bfd444595913f02fc71f6b286c29261e354c41d722ca7a261a49b5969"}, {file = "cssselect2-0.7.0.tar.gz", hash = "sha256:1ccd984dab89fc68955043aca4e1b03e0cf29cad9880f6e28e3ba7a74b14aa5a"}, @@ -835,6 +912,7 @@ version = "0.12.1" description = "Composable style cycles" optional = false python-versions = ">=3.8" +groups = ["main"] files = [ {file = "cycler-0.12.1-py3-none-any.whl", hash = "sha256:85cef7cff222d8644161529808465972e51340599459b8ac3ccbac5a854e0d30"}, {file = "cycler-0.12.1.tar.gz", hash = "sha256:88bb128f02ba341da8ef447245a9e138fae777f6a23943da4540077d3601eb1c"}, @@ -850,6 +928,7 @@ version = "1.8.0" description = "An implementation of the Debug Adapter Protocol for Python" optional = false python-versions = ">=3.8" +groups = ["dev"] files = [ {file = "debugpy-1.8.0-cp310-cp310-macosx_11_0_x86_64.whl", hash = "sha256:7fb95ca78f7ac43393cd0e0f2b6deda438ec7c5e47fa5d38553340897d2fbdfb"}, {file = "debugpy-1.8.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:ef9ab7df0b9a42ed9c878afd3eaaff471fce3fa73df96022e1f5c9f8f8c87ada"}, @@ -877,6 +956,7 @@ version = "5.1.1" description = "Decorators for Humans" optional = false python-versions = ">=3.5" +groups = ["dev"] files = [ {file = "decorator-5.1.1-py3-none-any.whl", hash = "sha256:b8c3f85900b9dc423225913c5aace94729fe1fa9763b38939a95226f02d37186"}, {file = "decorator-5.1.1.tar.gz", hash = "sha256:637996211036b6385ef91435e4fae22989472f9d571faba8927ba8253acbc330"}, @@ -888,6 +968,7 @@ version = "0.7.1" description = "XML bomb protection for Python stdlib modules" optional = false python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*" +groups = ["dev"] files = [ {file = "defusedxml-0.7.1-py2.py3-none-any.whl", hash = "sha256:a352e7e428770286cc899e2542b6cdaedb2b4953ff269a210103ec58f6198a61"}, {file = "defusedxml-0.7.1.tar.gz", hash = "sha256:1bb3032db185915b62d7c6209c5a8792be6a32ab2fedacc84e01b52c51aa3e69"}, @@ -899,6 +980,7 @@ version = "2.1.0" description = "A library to handle automated deprecations" optional = false python-versions = "*" +groups = ["main"] files = [ {file = "deprecation-2.1.0-py2.py3-none-any.whl", hash = "sha256:a10811591210e1fb0e768a8c25517cabeabcba6f0bf96564f8ff45189f90b14a"}, {file = "deprecation-2.1.0.tar.gz", hash = "sha256:72b3bde64e5d778694b0cf68178aed03d15e15477116add3fb773e581f9518ff"}, @@ -913,6 +995,7 @@ version = "0.4" description = "Discover and load entry points from installed packages." optional = false python-versions = ">=3.6" +groups = ["dev"] files = [ {file = "entrypoints-0.4-py3-none-any.whl", hash = "sha256:f174b5ff827504fd3cd97cc3f8649f3693f51538c7e4bdf3ef002c8429d42f9f"}, {file = "entrypoints-0.4.tar.gz", hash = "sha256:b706eddaa9218a19ebcd67b56818f05bb27589b1ca9e8d797b74affad4ccacd4"}, @@ -924,6 +1007,7 @@ version = "2.5.1" description = "Exploratory Spatial Data Analysis in PySAL" optional = false python-versions = ">=3.8" +groups = ["main"] files = [ {file = "esda-2.5.1-py3-none-any.whl", hash = "sha256:6fdbce49e8ef39feeca7ab0f92cd1d8f1ddda527425c42e61d0e8199b70bc8e8"}, {file = "esda-2.5.1.tar.gz", hash = "sha256:b1f0ac023b3a55dbdbad34a98eae6af2c7d2b3cc7a14ea884dfef51819c04c28"}, @@ -947,6 +1031,7 @@ version = "1.1.3" description = "Backport of PEP 654 (exception groups)" optional = false python-versions = ">=3.7" +groups = ["dev"] files = [ {file = "exceptiongroup-1.1.3-py3-none-any.whl", hash = "sha256:343280667a4585d195ca1cf9cef84a4e178c4b6cf2274caef9859782b567d5e3"}, {file = "exceptiongroup-1.1.3.tar.gz", hash = "sha256:097acd85d473d75af5bb98e41b61ff7fe35efe6675e4f9370ec6ec5126d160e9"}, @@ -961,13 +1046,14 @@ version = "2.0.0" description = "Get the currently executing AST node of a frame, and other information" optional = false python-versions = "*" +groups = ["dev"] files = [ {file = "executing-2.0.0-py2.py3-none-any.whl", hash = "sha256:06df6183df67389625f4e763921c6cf978944721abf3e714000200aab95b0657"}, {file = "executing-2.0.0.tar.gz", hash = "sha256:0ff053696fdeef426cda5bd18eacd94f82c91f49823a2e9090124212ceea9b08"}, ] [package.extras] -tests = ["asttokens (>=2.1.0)", "coverage", "coverage-enable-subprocess", "ipython", "littleutils", "pytest", "rich"] +tests = ["asttokens (>=2.1.0)", "coverage", "coverage-enable-subprocess", "ipython", "littleutils", "pytest", "rich ; python_version >= \"3.11\""] [[package]] name = "fastjsonschema" @@ -975,6 +1061,7 @@ version = "2.18.1" description = "Fastest Python implementation of JSON schema" optional = false python-versions = "*" +groups = ["dev"] files = [ {file = "fastjsonschema-2.18.1-py3-none-any.whl", hash = "sha256:aec6a19e9f66e9810ab371cc913ad5f4e9e479b63a7072a2cd060a9369e329a8"}, {file = "fastjsonschema-2.18.1.tar.gz", hash = "sha256:06dc8680d937628e993fa0cd278f196d20449a1adc087640710846b324d422ea"}, @@ -989,6 +1076,7 @@ version = "1.9.5" description = "Fiona reads and writes spatial data files" optional = false python-versions = ">=3.7" +groups = ["main"] files = [ {file = "fiona-1.9.5-cp310-cp310-macosx_10_15_x86_64.whl", hash = "sha256:5f40a40529ecfca5294260316cf987a0420c77a2f0cf0849f529d1afbccd093e"}, {file = "fiona-1.9.5-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:374efe749143ecb5cfdd79b585d83917d2bf8ecfbfc6953c819586b336ce9c63"}, @@ -1038,6 +1126,7 @@ version = "5.0.4" description = "the modular source code checker: pep8 pyflakes and co" optional = false python-versions = ">=3.6.1" +groups = ["dev"] files = [ {file = "flake8-5.0.4-py2.py3-none-any.whl", hash = "sha256:7a1cf6b73744f5806ab95e526f6f0d8c01c66d7bbe349562d22dfca20610b248"}, {file = "flake8-5.0.4.tar.gz", hash = "sha256:6fbe320aad8d6b95cec8b8e47bc933004678dc63095be98528b7bdd2a9f510db"}, @@ -1054,6 +1143,7 @@ version = "1.7.0" description = "Extension for flake8 which uses pydocstyle to check docstrings" optional = false python-versions = ">=3.7" +groups = ["dev"] files = [ {file = "flake8_docstrings-1.7.0-py2.py3-none-any.whl", hash = "sha256:51f2344026da083fc084166a9353f5082b01f72901df422f74b4d953ae88ac75"}, {file = "flake8_docstrings-1.7.0.tar.gz", hash = "sha256:4c8cc748dc16e6869728699e5d0d685da9a10b0ea718e090b1ba088e67a941af"}, @@ -1069,6 +1159,7 @@ version = "23.5.26" description = "The FlatBuffers serialization format for Python" optional = false python-versions = "*" +groups = ["main"] files = [ {file = "flatbuffers-23.5.26-py2.py3-none-any.whl", hash = "sha256:c0ff356da363087b915fde4b8b45bdda73432fc17cddb3c8157472eab1422ad1"}, {file = "flatbuffers-23.5.26.tar.gz", hash = "sha256:9ea1144cac05ce5d86e2859f431c6cd5e66cd9c78c558317c7955fb8d4c78d89"}, @@ -1080,6 +1171,7 @@ version = "4.43.1" description = "Tools to manipulate font files" optional = false python-versions = ">=3.8" +groups = ["main", "dev"] files = [ {file = "fonttools-4.43.1-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:bf11e2cca121df35e295bd34b309046c29476ee739753bc6bc9d5050de319273"}, {file = "fonttools-4.43.1-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:10b3922875ffcba636674f406f9ab9a559564fdbaa253d66222019d569db869c"}, @@ -1131,18 +1223,18 @@ brotlicffi = {version = ">=0.8.0", optional = true, markers = "platform_python_i zopfli = {version = ">=0.1.4", optional = true, markers = "extra == \"woff\""} [package.extras] -all = ["brotli (>=1.0.1)", "brotlicffi (>=0.8.0)", "fs (>=2.2.0,<3)", "lxml (>=4.0,<5)", "lz4 (>=1.7.4.2)", "matplotlib", "munkres", "scipy", "skia-pathops (>=0.5.0)", "sympy", "uharfbuzz (>=0.23.0)", "unicodedata2 (>=15.0.0)", "xattr", "zopfli (>=0.1.4)"] +all = ["brotli (>=1.0.1) ; platform_python_implementation == \"CPython\"", "brotlicffi (>=0.8.0) ; platform_python_implementation != \"CPython\"", "fs (>=2.2.0,<3)", "lxml (>=4.0,<5)", "lz4 (>=1.7.4.2)", "matplotlib", "munkres ; platform_python_implementation == \"PyPy\"", "scipy ; platform_python_implementation != \"PyPy\"", "skia-pathops (>=0.5.0)", "sympy", "uharfbuzz (>=0.23.0)", "unicodedata2 (>=15.0.0) ; python_version <= \"3.11\"", "xattr ; sys_platform == \"darwin\"", "zopfli (>=0.1.4)"] graphite = ["lz4 (>=1.7.4.2)"] -interpolatable = ["munkres", "scipy"] +interpolatable = ["munkres ; platform_python_implementation == \"PyPy\"", "scipy ; platform_python_implementation != \"PyPy\""] lxml = ["lxml (>=4.0,<5)"] pathops = ["skia-pathops (>=0.5.0)"] plot = ["matplotlib"] repacker = ["uharfbuzz (>=0.23.0)"] symfont = ["sympy"] -type1 = ["xattr"] +type1 = ["xattr ; sys_platform == \"darwin\""] ufo = ["fs (>=2.2.0,<3)"] -unicode = ["unicodedata2 (>=15.0.0)"] -woff = ["brotli (>=1.0.1)", "brotlicffi (>=0.8.0)", "zopfli (>=0.1.4)"] +unicode = ["unicodedata2 (>=15.0.0) ; python_version <= \"3.11\""] +woff = ["brotli (>=1.0.1) ; platform_python_implementation == \"CPython\"", "brotlicffi (>=0.8.0) ; platform_python_implementation != \"CPython\"", "zopfli (>=0.1.4)"] [[package]] name = "fqdn" @@ -1150,6 +1242,7 @@ version = "1.5.1" description = "Validates fully-qualified domain names against RFC 1123, so that they are acceptable to modern bowsers" optional = false python-versions = ">=2.7, !=3.0, !=3.1, !=3.2, !=3.3, !=3.4, <4" +groups = ["dev"] files = [ {file = "fqdn-1.5.1-py3-none-any.whl", hash = "sha256:3a179af3761e4df6eb2e026ff9e1a3033d3587bf980a0b1b2e1e5d08d7358014"}, {file = "fqdn-1.5.1.tar.gz", hash = "sha256:105ed3677e767fb5ca086a0c1f4bb66ebc3c100be518f0e0d755d9eae164d89f"}, @@ -1161,6 +1254,7 @@ version = "0.5.4" description = "Python AST that abstracts the underlying Python version" optional = false python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*" +groups = ["main"] files = [ {file = "gast-0.5.4-py3-none-any.whl", hash = "sha256:6fc4fa5fa10b72fb8aab4ae58bcb023058386e67b6fa2e3e34cec5c769360316"}, {file = "gast-0.5.4.tar.gz", hash = "sha256:9c270fe5f4b130969b54174de7db4e764b09b4f7f67ccfc32480e29f78348d97"}, @@ -1172,6 +1266,7 @@ version = "3.4.3" description = "GDAL: Geospatial Data Abstraction Library" optional = false python-versions = ">=3.6.0" +groups = ["main"] files = [ {file = "GDAL-3.4.3.tar.gz", hash = "sha256:f4f4b0bb34073b164c5a05a1b0eb44af47349e5606eb7b75244c2167ac462f36"}, ] @@ -1185,6 +1280,7 @@ version = "0.11.1" description = "Geographic pandas extensions" optional = false python-versions = ">=3.8" +groups = ["main"] files = [ {file = "geopandas-0.11.1-py3-none-any.whl", hash = "sha256:f3344937f3866e52996c7e505d56dae78be117dc840cd1c23507da0b33c0af71"}, {file = "geopandas-0.11.1.tar.gz", hash = "sha256:f0f0c8d0423d30cf81de2056d853145c4362739350a7f8f2d72cc7409ef1eca1"}, @@ -1203,6 +1299,7 @@ version = "2.1.0" description = "Copy your docs directly to the gh-pages branch." optional = false python-versions = "*" +groups = ["dev"] files = [ {file = "ghp-import-2.1.0.tar.gz", hash = "sha256:9c535c4c61193c2df8871222567d7fd7e5014d835f97dc7b7439069e2413d343"}, {file = "ghp_import-2.1.0-py3-none-any.whl", hash = "sha256:8337dd7b50877f163d4c0289bc1f1c7f127550241988d568c1db512c4324a619"}, @@ -1220,6 +1317,7 @@ version = "2.3.4" description = "GIDDY: GeospatIal Distribution DYnamics" optional = false python-versions = ">3.6" +groups = ["main"] files = [ {file = "giddy-2.3.4-py3-none-any.whl", hash = "sha256:248fcd04ed300fa7689f18eede4fbb0c7ad23fdb343ac28e37db5274f8e8e21c"}, {file = "giddy-2.3.4.tar.gz", hash = "sha256:854ca12fd09e96aac084fe114947a2151de19c0b546408509cb578345670efc1"}, @@ -1242,6 +1340,7 @@ version = "2.23.3" description = "Google Authentication Library" optional = false python-versions = ">=3.7" +groups = ["main"] files = [ {file = "google-auth-2.23.3.tar.gz", hash = "sha256:6864247895eea5d13b9c57c9e03abb49cb94ce2dc7c58e91cba3248c7477c9e3"}, {file = "google_auth-2.23.3-py2.py3-none-any.whl", hash = "sha256:a8f4608e65c244ead9e0538f181a96c6e11199ec114d41f1d7b1bffa96937bda"}, @@ -1265,6 +1364,7 @@ version = "1.0.0" description = "Google Authentication Library" optional = false python-versions = ">=3.6" +groups = ["main"] files = [ {file = "google-auth-oauthlib-1.0.0.tar.gz", hash = "sha256:e375064964820b47221a7e1b7ee1fd77051b6323c3f9e3e19785f78ab67ecfc5"}, {file = "google_auth_oauthlib-1.0.0-py2.py3-none-any.whl", hash = "sha256:95880ca704928c300f48194d1770cf5b1462835b6e49db61445a520f793fd5fb"}, @@ -1283,6 +1383,7 @@ version = "0.2.0" description = "pasta is an AST-based Python refactoring library" optional = false python-versions = "*" +groups = ["main"] files = [ {file = "google-pasta-0.2.0.tar.gz", hash = "sha256:c9f2c8dfc8f96d0d5808299920721be30c9eec37f2389f28904f454565c8a16e"}, {file = "google_pasta-0.2.0-py2-none-any.whl", hash = "sha256:4612951da876b1a10fe3960d7226f0c7682cf901e16ac06e473b267a5afa8954"}, @@ -1298,6 +1399,7 @@ version = "0.36.7" description = "Signatures for entire Python programs. Extract the structure, the frame, the skeleton of your project, to generate API documentation or find breaking changes in your API." optional = false python-versions = ">=3.8" +groups = ["dev"] files = [ {file = "griffe-0.36.7-py3-none-any.whl", hash = "sha256:7a09f8e9b97c7ebe227f6529a298bf7e0e742a9837ee261cc8260d50b4aa039f"}, {file = "griffe-0.36.7.tar.gz", hash = "sha256:a6fe60b16720ca0cf63c9e667a4c05eea40dfe4abcf114741885f945b74c7071"}, @@ -1312,6 +1414,7 @@ version = "1.59.0" description = "HTTP/2-based RPC framework" optional = false python-versions = ">=3.7" +groups = ["main"] files = [ {file = "grpcio-1.59.0-cp310-cp310-linux_armv7l.whl", hash = "sha256:225e5fa61c35eeaebb4e7491cd2d768cd8eb6ed00f2664fa83a58f29418b39fd"}, {file = "grpcio-1.59.0-cp310-cp310-macosx_12_0_universal2.whl", hash = "sha256:b95ec8ecc4f703f5caaa8d96e93e40c7f589bad299a2617bdb8becbcce525539"}, @@ -1378,6 +1481,7 @@ version = "3.10.0" description = "Read and write HDF5 files from Python" optional = false python-versions = ">=3.8" +groups = ["main"] files = [ {file = "h5py-3.10.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:b963fb772964fc1d1563c57e4e2e874022ce11f75ddc6df1a626f42bd49ab99f"}, {file = "h5py-3.10.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:012ab448590e3c4f5a8dd0f3533255bc57f80629bf7c5054cf4c87b30085063c"}, @@ -1415,6 +1519,7 @@ version = "1.1" description = "HTML parser based on the WHATWG HTML specification" optional = false python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*" +groups = ["dev"] files = [ {file = "html5lib-1.1-py2.py3-none-any.whl", hash = "sha256:0d78f8fde1c230e99fe37986a60526d7049ed4bf8a9fadbad5f00e22e58e041d"}, {file = "html5lib-1.1.tar.gz", hash = "sha256:b2e5b40261e20f354d198eae92afc10d750afb487ed5e50f9c4eaf07c184146f"}, @@ -1425,10 +1530,10 @@ six = ">=1.9" webencodings = "*" [package.extras] -all = ["chardet (>=2.2)", "genshi", "lxml"] +all = ["chardet (>=2.2)", "genshi", "lxml ; platform_python_implementation == \"CPython\""] chardet = ["chardet (>=2.2)"] genshi = ["genshi"] -lxml = ["lxml"] +lxml = ["lxml ; platform_python_implementation == \"CPython\""] [[package]] name = "idna" @@ -1436,6 +1541,7 @@ version = "3.4" description = "Internationalized Domain Names in Applications (IDNA)" optional = false python-versions = ">=3.5" +groups = ["main", "dev"] files = [ {file = "idna-3.4-py3-none-any.whl", hash = "sha256:90b77e79eaa3eba6de819a0c442c0b4ceefc341a7a2ab77d7562bf49f425c5c2"}, {file = "idna-3.4.tar.gz", hash = "sha256:814f528e8dead7d329833b91c5faa87d60bf71824cd12a7530b5526063d02cb4"}, @@ -1447,6 +1553,7 @@ version = "0.11.0" description = "Toolbox for imbalanced dataset in machine learning." optional = false python-versions = "*" +groups = ["main"] files = [ {file = "imbalanced-learn-0.11.0.tar.gz", hash = "sha256:7582ae8858e6db0b92fef97dd08660a18297ee128d78c2abdc006b8bd86b8fdc"}, {file = "imbalanced_learn-0.11.0-py3-none-any.whl", hash = "sha256:20dc7dee3c838b4d213f021bb2b4007862704160d06bd292a6bdf931590b2516"}, @@ -1471,6 +1578,8 @@ version = "6.8.0" description = "Read metadata from Python packages" optional = false python-versions = ">=3.8" +groups = ["main", "dev"] +markers = "python_version <= \"3.9\"" files = [ {file = "importlib_metadata-6.8.0-py3-none-any.whl", hash = "sha256:3ebb78df84a805d7698245025b975d9d67053cd94c79245ba4b3eb694abe68bb"}, {file = "importlib_metadata-6.8.0.tar.gz", hash = "sha256:dbace7892d8c0c4ac1ad096662232f831d4e64f4c4545bd53016a3e9d4654743"}, @@ -1482,7 +1591,7 @@ zipp = ">=0.5" [package.extras] docs = ["furo", "jaraco.packaging (>=9)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"] perf = ["ipython"] -testing = ["flufl.flake8", "importlib-resources (>=1.3)", "packaging", "pyfakefs", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-mypy (>=0.9.1)", "pytest-perf (>=0.9.2)", "pytest-ruff"] +testing = ["flufl.flake8", "importlib-resources (>=1.3) ; python_version < \"3.9\"", "packaging", "pyfakefs", "pytest (>=6)", "pytest-black (>=0.3.7) ; platform_python_implementation != \"PyPy\"", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-mypy (>=0.9.1) ; platform_python_implementation != \"PyPy\"", "pytest-perf (>=0.9.2)", "pytest-ruff"] [[package]] name = "importlib-resources" @@ -1490,6 +1599,8 @@ version = "6.1.0" description = "Read resources from Python packages" optional = false python-versions = ">=3.8" +groups = ["main"] +markers = "python_version <= \"3.9\"" files = [ {file = "importlib_resources-6.1.0-py3-none-any.whl", hash = "sha256:aa50258bbfa56d4e33fbd8aa3ef48ded10d1735f11532b8df95388cc6bdb7e83"}, {file = "importlib_resources-6.1.0.tar.gz", hash = "sha256:9d48dcccc213325e810fd723e7fbb45ccb39f6cf5c31f00cf2b965f5f10f3cb9"}, @@ -1500,7 +1611,7 @@ zipp = {version = ">=3.1.0", markers = "python_version < \"3.10\""} [package.extras] docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (<7.2.5)", "sphinx (>=3.5)", "sphinx-lint"] -testing = ["pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-mypy (>=0.9.1)", "pytest-ruff", "zipp (>=3.17)"] +testing = ["pytest (>=6)", "pytest-black (>=0.3.7) ; platform_python_implementation != \"PyPy\"", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-mypy (>=0.9.1) ; platform_python_implementation != \"PyPy\"", "pytest-ruff", "zipp (>=3.17)"] [[package]] name = "inequality" @@ -1508,6 +1619,7 @@ version = "1.0.0" description = "Spatial inequality analysis for PySAL A library of spatial analysis functions." optional = false python-versions = ">3.4" +groups = ["main"] files = [ {file = "inequality-1.0.0.tar.gz", hash = "sha256:e3ad59ece8d25ecfc7c7e94efe59ead0e4ffb76ff72befd18a60e10caba6fd54"}, ] @@ -1526,6 +1638,7 @@ version = "2.0.0" description = "brain-dead simple config-ini parsing" optional = false python-versions = ">=3.7" +groups = ["dev"] files = [ {file = "iniconfig-2.0.0-py3-none-any.whl", hash = "sha256:b6a85871a79d2e3b22d2d1b94ac2824226a63c6b741c88f7ae975f18b6778374"}, {file = "iniconfig-2.0.0.tar.gz", hash = "sha256:2d91e135bf72d31a410b17c16da610a82cb55f6b0477d1a902134b24a455b8b3"}, @@ -1537,6 +1650,7 @@ version = "1.7.3" description = "Pythonic task execution" optional = false python-versions = "*" +groups = ["dev"] files = [ {file = "invoke-1.7.3-py3-none-any.whl", hash = "sha256:d9694a865764dd3fd91f25f7e9a97fb41666e822bbb00e670091e3f43933574d"}, {file = "invoke-1.7.3.tar.gz", hash = "sha256:41b428342d466a82135d5ab37119685a989713742be46e42a3a399d685579314"}, @@ -1548,6 +1662,7 @@ version = "6.25.2" description = "IPython Kernel for Jupyter" optional = false python-versions = ">=3.8" +groups = ["dev"] files = [ {file = "ipykernel-6.25.2-py3-none-any.whl", hash = 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"sha256:eb2e116e75ecef9d4d228fdc66af54269afa26ab4463042e33785b887c628ba8"}, @@ -1632,6 +1749,7 @@ version = "20.11.0" description = "Operations with ISO 8601 durations" optional = false python-versions = ">=3.7" +groups = ["dev"] files = [ {file = "isoduration-20.11.0-py3-none-any.whl", hash = "sha256:b2904c2a4228c3d44f409c8ae8e2370eb21a26f7ac2ec5446df141dde3452042"}, {file = "isoduration-20.11.0.tar.gz", hash = "sha256:ac2f9015137935279eac671f94f89eb00584f940f5dc49462a0c4ee692ba1bd9"}, @@ -1646,6 +1764,7 @@ version = "5.12.0" description = "A Python utility / library to sort Python imports." optional = false python-versions = ">=3.8.0" +groups = ["dev"] files = [ {file = "isort-5.12.0-py3-none-any.whl", hash = "sha256:f84c2818376e66cf843d497486ea8fed8700b340f308f076c6fb1229dff318b6"}, {file = "isort-5.12.0.tar.gz", hash = "sha256:8bef7dde241278824a6d83f44a544709b065191b95b6e50894bdc722fcba0504"}, @@ -1663,6 +1782,7 @@ version = "0.19.1" description = "An autocompletion tool for Python that can be used for text editors." optional = false python-versions = ">=3.6" +groups = ["dev"] files = [ {file = "jedi-0.19.1-py2.py3-none-any.whl", hash = "sha256:e983c654fe5c02867aef4cdfce5a2fbb4a50adc0af145f70504238f18ef5e7e0"}, {file = "jedi-0.19.1.tar.gz", hash = "sha256:cf0496f3651bc65d7174ac1b7d043eff454892c708a87d1b683e57b569927ffd"}, @@ -1682,6 +1802,7 @@ version = "3.1.2" description = "A very fast and expressive template engine." optional = false python-versions = ">=3.7" +groups = ["main", "dev"] files = [ {file = "Jinja2-3.1.2-py3-none-any.whl", hash = "sha256:6088930bfe239f0e6710546ab9c19c9ef35e29792895fed6e6e31a023a182a61"}, {file = "Jinja2-3.1.2.tar.gz", hash = "sha256:31351a702a408a9e7595a8fc6150fc3f43bb6bf7e319770cbc0db9df9437e852"}, @@ -1699,6 +1820,7 @@ version = "1.3.2" description = "Lightweight pipelining with Python functions" optional = false python-versions = ">=3.7" +groups = ["main"] files = [ {file = "joblib-1.3.2-py3-none-any.whl", hash = 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"jsonpointer-2.4.tar.gz", hash = "sha256:585cee82b70211fa9e6043b7bb89db6e1aa49524340dde8ad6b63206ea689d88"}, @@ -1735,6 +1859,7 @@ version = "4.19.1" description = "An implementation of JSON Schema validation for Python" optional = false python-versions = ">=3.8" +groups = ["dev"] files = [ {file = "jsonschema-4.19.1-py3-none-any.whl", hash = "sha256:cd5f1f9ed9444e554b38ba003af06c0a8c2868131e56bfbef0550fb450c0330e"}, {file = "jsonschema-4.19.1.tar.gz", hash = "sha256:ec84cc37cfa703ef7cd4928db24f9cb31428a5d0fa77747b8b51a847458e0bbf"}, @@ -1764,6 +1889,7 @@ version = "2023.7.1" description = "The JSON Schema meta-schemas and vocabularies, exposed as a Registry" optional = false python-versions = ">=3.8" +groups = ["dev"] files = [ {file = "jsonschema_specifications-2023.7.1-py3-none-any.whl", hash = "sha256:05adf340b659828a004220a9613be00fa3f223f2b82002e273dee62fd50524b1"}, {file = "jsonschema_specifications-2023.7.1.tar.gz", hash = "sha256:c91a50404e88a1f6ba40636778e2ee08f6e24c5613fe4c53ac24578a5a7f72bb"}, @@ -1778,6 +1904,7 @@ version = "7.4.9" description = "Jupyter protocol implementation and client libraries" optional = false python-versions = ">=3.7" +groups = ["dev"] files = [ {file = "jupyter_client-7.4.9-py3-none-any.whl", hash = "sha256:214668aaea208195f4c13d28eb272ba79f945fc0cf3f11c7092c20b2ca1980e7"}, {file = "jupyter_client-7.4.9.tar.gz", hash = "sha256:52be28e04171f07aed8f20e1616a5a552ab9fee9cbbe6c1896ae170c3880d392"}, @@ -1802,6 +1929,7 @@ version = "5.4.0" description = "Jupyter core package. A base package on which Jupyter projects rely." optional = false python-versions = ">=3.8" +groups = ["dev"] files = [ {file = "jupyter_core-5.4.0-py3-none-any.whl", hash = "sha256:66e252f675ac04dcf2feb6ed4afb3cd7f68cf92f483607522dc251f32d471571"}, {file = "jupyter_core-5.4.0.tar.gz", hash = "sha256:e4b98344bb94ee2e3e6c4519a97d001656009f9cb2b7f2baf15b3c205770011d"}, @@ -1822,6 +1950,7 @@ version = "0.8.0" description = "Jupyter Event System library" optional = false python-versions = ">=3.8" +groups = ["dev"] files = [ {file = "jupyter_events-0.8.0-py3-none-any.whl", hash = "sha256:81f07375c7673ff298bfb9302b4a981864ec64edaed75ca0fe6f850b9b045525"}, {file = "jupyter_events-0.8.0.tar.gz", hash = "sha256:fda08f0defce5e16930542ce60634ba48e010830d50073c3dfd235759cee77bf"}, @@ -1847,6 +1976,7 @@ version = "2.8.0" description = "The backend—i.e. core services, APIs, and REST endpoints—to Jupyter web applications." optional = false python-versions = ">=3.8" +groups = ["dev"] files = [ {file = 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-1959,6 +2093,7 @@ version = "3.6.6" description = "JupyterLab computational environment" optional = false python-versions = ">=3.7" +groups = ["dev"] files = [ {file = "jupyterlab-3.6.6-py3-none-any.whl", hash = "sha256:2c1309e77135670233f1146aef88e2101002ff0dc5b9147c2b987807efbbca07"}, {file = "jupyterlab-3.6.6.tar.gz", hash = "sha256:0a47d7adb28bd5659d727783f4113537e54f8c66e0d6322d1d8f9edb081dc926"}, @@ -1987,6 +2122,7 @@ version = "0.2.2" description = "Pygments theme using JupyterLab CSS variables" optional = false python-versions = ">=3.7" +groups = ["dev"] files = [ {file = "jupyterlab_pygments-0.2.2-py2.py3-none-any.whl", hash = "sha256:2405800db07c9f770863bcf8049a529c3dd4d3e28536638bd7c1c01d2748309f"}, {file = "jupyterlab_pygments-0.2.2.tar.gz", hash = "sha256:7405d7fde60819d905a9fa8ce89e4cd830e318cdad22a0030f7a901da705585d"}, @@ -1998,6 +2134,7 @@ version = "2.25.0" description = "A set of server components for JupyterLab and JupyterLab like applications." optional = false 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platform_python_implementation != \"PyPy\""] tests = ["pytest (>=4.6)"] [[package]] @@ -2724,6 +2885,7 @@ version = "0.942" description = "Optional static typing for Python" optional = false python-versions = ">=3.6" +groups = ["dev"] files = [ {file = "mypy-0.942-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:5bf44840fb43ac4074636fd47ee476d73f0039f4f54e86d7265077dc199be24d"}, {file = "mypy-0.942-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:dcd955f36e0180258a96f880348fbca54ce092b40fbb4b37372ae3b25a0b0a46"}, @@ -2766,6 +2928,7 @@ version = "1.0.0" description = "Type system extensions for programs checked with the mypy type checker." optional = false python-versions = ">=3.5" +groups = ["dev"] files = [ {file = "mypy_extensions-1.0.0-py3-none-any.whl", hash = "sha256:4392f6c0eb8a5668a69e23d168ffa70f0be9ccfd32b5cc2d26a34ae5b844552d"}, {file = "mypy_extensions-1.0.0.tar.gz", hash = "sha256:75dbf8955dc00442a438fc4d0666508a9a97b6bd41aa2f0ffe9d2f2725af0782"}, @@ -2777,6 +2940,7 @@ version = "1.0.0" description = "Jupyter Notebook as a Jupyter Server extension." optional = false python-versions = ">=3.7" +groups = ["dev"] files = [ {file = "nbclassic-1.0.0-py3-none-any.whl", hash = "sha256:f99e4769b4750076cd4235c044b61232110733322384a94a63791d2e7beacc66"}, {file = "nbclassic-1.0.0.tar.gz", hash = "sha256:0ae11eb2319455d805596bf320336cda9554b41d99ab9a3c31bf8180bffa30e3"}, @@ -2804,7 +2968,7 @@ traitlets = ">=4.2.1" [package.extras] docs = ["myst-parser", "nbsphinx", "sphinx", "sphinx-rtd-theme", "sphinxcontrib-github-alt"] json-logging = ["json-logging"] -test = ["coverage", "nbval", "pytest", "pytest-cov", "pytest-jupyter", "pytest-playwright", "pytest-tornasync", "requests", "requests-unixsocket", "testpath"] +test = ["coverage", "nbval", "pytest", "pytest-cov", "pytest-jupyter", "pytest-playwright", "pytest-tornasync", "requests", "requests-unixsocket ; sys_platform != \"win32\"", "testpath"] [[package]] name = "nbclient" @@ -2812,6 +2976,7 @@ version = "0.8.0" description = "A client library for executing notebooks. Formerly nbconvert's ExecutePreprocessor." optional = false python-versions = ">=3.8.0" +groups = ["dev"] files = [ {file = "nbclient-0.8.0-py3-none-any.whl", hash = "sha256:25e861299e5303a0477568557c4045eccc7a34c17fc08e7959558707b9ebe548"}, {file = "nbclient-0.8.0.tar.gz", hash = "sha256:f9b179cd4b2d7bca965f900a2ebf0db4a12ebff2f36a711cb66861e4ae158e55"}, @@ -2834,6 +2999,7 @@ version = "7.9.2" description = "Converting Jupyter Notebooks" optional = false python-versions = ">=3.8" +groups = ["dev"] files = [ {file = "nbconvert-7.9.2-py3-none-any.whl", hash = "sha256:39fe4b8bdd1b0104fdd86fc8a43a9077ba64c720bda4c6132690d917a0a154ee"}, {file = "nbconvert-7.9.2.tar.gz", hash = "sha256:e56cc7588acc4f93e2bb5a34ec69028e4941797b2bfaf6462f18a41d1cc258c9"}, @@ -2872,6 +3038,7 @@ version = "5.9.2" description = "The Jupyter Notebook format" optional = false python-versions = ">=3.8" +groups = ["dev"] files = [ {file = "nbformat-5.9.2-py3-none-any.whl", hash = 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Prometheus monitoring system." optional = false python-versions = ">=3.6" +groups = ["dev"] files = [ {file = "prometheus_client-0.17.1-py3-none-any.whl", hash = "sha256:e537f37160f6807b8202a6fc4764cdd19bac5480ddd3e0d463c3002b34462101"}, {file = "prometheus_client-0.17.1.tar.gz", hash = "sha256:21e674f39831ae3f8acde238afd9a27a37d0d2fb5a28ea094f0ce25d2cbf2091"}, @@ -3392,6 +3583,7 @@ version = "3.0.39" description = "Library for building powerful interactive command lines in Python" optional = false python-versions = ">=3.7.0" +groups = ["dev"] files = [ {file = "prompt_toolkit-3.0.39-py3-none-any.whl", hash = "sha256:9dffbe1d8acf91e3de75f3b544e4842382fc06c6babe903ac9acb74dc6e08d88"}, {file = "prompt_toolkit-3.0.39.tar.gz", hash = "sha256:04505ade687dc26dc4284b1ad19a83be2f2afe83e7a828ace0c72f3a1df72aac"}, @@ -3406,6 +3598,7 @@ version = "4.24.4" description = "" optional = false python-versions = ">=3.7" +groups = ["main"] files = [ {file = "protobuf-4.24.4-cp310-abi3-win32.whl", hash = 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\"win32\"", "wmi ; sys_platform == \"win32\""] [[package]] name = "ptyprocess" @@ -3456,6 +3650,8 @@ version = "0.7.0" description = "Run a subprocess in a pseudo terminal" optional = false python-versions = "*" +groups = ["dev"] +markers = "sys_platform != \"win32\" or os_name != \"nt\"" files = [ {file = "ptyprocess-0.7.0-py2.py3-none-any.whl", hash = "sha256:4b41f3967fce3af57cc7e94b888626c18bf37a083e3651ca8feeb66d492fef35"}, {file = "ptyprocess-0.7.0.tar.gz", hash = "sha256:5c5d0a3b48ceee0b48485e0c26037c0acd7d29765ca3fbb5cb3831d347423220"}, @@ -3467,6 +3663,7 @@ version = "2.8.0" description = "PuLP is an LP modeler written in python. PuLP can generate MPS or LP files and call GLPK, COIN CLP/CBC, CPLEX, and GUROBI to solve linear problems." optional = false python-versions = ">=3.7" +groups = ["main"] files = [ {file = "PuLP-2.8.0-py3-none-any.whl", hash = "sha256:4a19814a5b0a4392d788ac2315263435293579b0583c3469943fe0c6a586f263"}, {file = "PuLP-2.8.0.tar.gz", hash = "sha256:4903bf96110bbab8ed2c68533f90565ebb76aa367d9e4df38e51bf727927c125"}, @@ -3478,6 +3675,7 @@ version = "0.2.2" description = "Safely evaluate AST nodes without side effects" optional = false python-versions = "*" +groups = ["dev"] files = [ {file = "pure_eval-0.2.2-py3-none-any.whl", hash = "sha256:01eaab343580944bc56080ebe0a674b39ec44a945e6d09ba7db3cb8cec289350"}, {file = "pure_eval-0.2.2.tar.gz", hash = "sha256:2b45320af6dfaa1750f543d714b6d1c520a1688dec6fd24d339063ce0aaa9ac3"}, @@ -3492,6 +3690,8 @@ version = "1.11.0" description = "library with cross-python path, ini-parsing, io, code, log facilities" optional = false python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*" +groups = ["dev"] +markers = "implementation_name == \"pypy\"" files = [ {file = "py-1.11.0-py2.py3-none-any.whl", hash = "sha256:607c53218732647dff4acdfcd50cb62615cedf612e72d1724fb1a0cc6405b378"}, {file = "py-1.11.0.tar.gz", hash = "sha256:51c75c4126074b472f746a24399ad32f6053d1b34b68d2fa41e558e6f4a98719"}, @@ -3503,6 +3703,7 @@ version = "0.5.0" description = "Pure-Python implementation of ASN.1 types and DER/BER/CER codecs (X.208)" optional = false python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,>=2.7" +groups = ["main"] files = [ {file = "pyasn1-0.5.0-py2.py3-none-any.whl", hash = "sha256:87a2121042a1ac9358cabcaf1d07680ff97ee6404333bacca15f76aa8ad01a57"}, {file = "pyasn1-0.5.0.tar.gz", hash = "sha256:97b7290ca68e62a832558ec3976f15cbf911bf5d7c7039d8b861c2a0ece69fde"}, @@ -3514,6 +3715,7 @@ version = "0.3.0" description = "A collection of ASN.1-based protocols modules" optional = false python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,>=2.7" +groups = ["main"] files = [ {file = "pyasn1_modules-0.3.0-py2.py3-none-any.whl", hash = "sha256:d3ccd6ed470d9ffbc716be08bd90efbd44d0734bc9303818f7336070984a162d"}, {file = "pyasn1_modules-0.3.0.tar.gz", hash = "sha256:5bd01446b736eb9d31512a30d46c1ac3395d676c6f3cafa4c03eb54b9925631c"}, @@ -3528,6 +3730,7 @@ version = "2.9.1" description = "Python style guide checker" optional = false python-versions = ">=3.6" +groups = ["dev"] files = [ {file = "pycodestyle-2.9.1-py2.py3-none-any.whl", hash = "sha256:d1735fc58b418fd7c5f658d28d943854f8a849b01a5d0a1e6f3f3fdd0166804b"}, {file = "pycodestyle-2.9.1.tar.gz", hash = "sha256:2c9607871d58c76354b697b42f5d57e1ada7d261c261efac224b664affdc5785"}, @@ -3539,6 +3742,7 @@ version = "2.21" description = "C parser in Python" optional = false python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*" +groups = ["dev"] files = [ {file = "pycparser-2.21-py2.py3-none-any.whl", hash = "sha256:8ee45429555515e1f6b185e78100aea234072576aa43ab53aefcae078162fca9"}, {file = "pycparser-2.21.tar.gz", hash = "sha256:e644fdec12f7872f86c58ff790da456218b10f863970249516d60a5eaca77206"}, @@ -3550,6 +3754,7 @@ version = "6.3.0" description = "Python docstring style checker" optional = false python-versions = ">=3.6" +groups = ["dev"] files = [ {file = "pydocstyle-6.3.0-py3-none-any.whl", hash = "sha256:118762d452a49d6b05e194ef344a55822987a462831ade91ec5c06fd2169d019"}, {file = "pydocstyle-6.3.0.tar.gz", hash = "sha256:7ce43f0c0ac87b07494eb9c0b462c0b73e6ff276807f204d6b53edc72b7e44e1"}, @@ -3559,7 +3764,7 @@ files = [ snowballstemmer = ">=2.2.0" [package.extras] -toml = ["tomli (>=1.2.3)"] +toml = ["tomli (>=1.2.3) ; python_version < \"3.11\""] [[package]] name = "pydyf" @@ -3567,6 +3772,7 @@ version = "0.8.0" description = "A low-level PDF generator." optional = false python-versions = ">=3.7" +groups = ["dev"] files = [ {file = "pydyf-0.8.0-py3-none-any.whl", hash = "sha256:901186a2e9f897108139426a6486f5225bdcc9b70be2ec965f25111e42f8ac5d"}, {file = "pydyf-0.8.0.tar.gz", hash = "sha256:b22b1ef016141b54941ad66ed4e036a7bdff39c0b360993b283875c3f854dd9a"}, @@ -3582,6 +3788,7 @@ version = "2.5.0" description = "passive checker of Python programs" optional = false python-versions = ">=3.6" +groups = ["dev"] files = [ {file = "pyflakes-2.5.0-py2.py3-none-any.whl", hash = "sha256:4579f67d887f804e67edb544428f264b7b24f435b263c4614f384135cea553d2"}, {file = "pyflakes-2.5.0.tar.gz", hash = "sha256:491feb020dca48ccc562a8c0cbe8df07ee13078df59813b83959cbdada312ea3"}, @@ -3593,6 +3800,7 @@ version = "0.14" description = "GEOS wrapped in numpy ufuncs" optional = false python-versions = ">=3.7" +groups = ["main"] files = [ {file = "pygeos-0.14-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:cfad06eae27e7236a9dc1a6d1c278525d4cf79422cd05ac6513d55032ff0b6b6"}, {file = "pygeos-0.14-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:bf00c2be6ea9816875636cfe139ff5ac53e9b99e0e7ce38c92cf67b69cf2d6be"}, @@ -3642,13 +3850,14 @@ version = "2.16.1" description = "Pygments is a syntax highlighting package written in Python." optional = false python-versions = ">=3.7" +groups = ["main", "dev"] files = [ {file = "Pygments-2.16.1-py3-none-any.whl", hash = "sha256:13fc09fa63bc8d8671a6d247e1eb303c4b343eaee81d861f3404db2935653692"}, {file = "Pygments-2.16.1.tar.gz", hash = "sha256:1daff0494820c69bc8941e407aa20f577374ee88364ee10a98fdbe0aece96e29"}, ] [package.extras] -plugins = ["importlib-metadata"] +plugins = ["importlib-metadata ; python_version < \"3.8\""] [[package]] name = "pykrige" @@ -3656,6 +3865,7 @@ version = "1.7.1" description = "Kriging Toolkit for Python." optional = false python-versions = ">=3.8" +groups = ["main"] files = [ {file = "PyKrige-1.7.1-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:61cf7dcc9bbbbb63c8d00c52d8dede545ea7403f179efe945cdfaca621f8a37b"}, {file = "PyKrige-1.7.1-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:300302067808a9fed5be9a34aac5e2ed6ec4a0e1de72d5ccf8fe991c71ba29ac"}, @@ -3705,6 +3915,7 @@ version = "10.3.1" description = "Extension pack for Python Markdown." optional = false python-versions = ">=3.8" +groups = ["dev"] files = [ {file = "pymdown_extensions-10.3.1-py3-none-any.whl", hash = "sha256:8cba67beb2a1318cdaf742d09dff7c0fc4cafcc290147ade0f8fb7b71522711a"}, {file = "pymdown_extensions-10.3.1.tar.gz", hash = "sha256:f6c79941498a458852853872e379e7bab63888361ba20992fc8b4f8a9b61735e"}, @@ -3723,6 +3934,7 @@ version = "3.1.1" description = "pyparsing module - Classes and methods to define and execute parsing grammars" optional = false python-versions = ">=3.6.8" +groups = ["main"] files = [ {file = "pyparsing-3.1.1-py3-none-any.whl", hash = "sha256:32c7c0b711493c72ff18a981d24f28aaf9c1fb7ed5e9667c9e84e3db623bdbfb"}, {file = "pyparsing-3.1.1.tar.gz", hash = "sha256:ede28a1a32462f5a9705e07aea48001a08f7cf81a021585011deba701581a0db"}, @@ -3737,6 +3949,7 @@ version = "0.14.0" description = "Pure Python module to hyphenate text" optional = false python-versions = ">=3.7" +groups = ["dev"] files = [ {file = "pyphen-0.14.0-py3-none-any.whl", hash = "sha256:414c9355958ca3c6a3ff233f65678c245b8ecb56418fb291e2b93499d61cd510"}, {file = "pyphen-0.14.0.tar.gz", hash = "sha256:596c8b3be1c1a70411ba5f6517d9ccfe3083c758ae2b94a45f2707346d8e66fa"}, @@ -3752,6 +3965,7 @@ version = "3.6.1" description = "Python interface to PROJ (cartographic projections and coordinate transformations library)" optional = false python-versions = ">=3.9" +groups = ["main"] files = [ {file = "pyproj-3.6.1-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:ab7aa4d9ff3c3acf60d4b285ccec134167a948df02347585fdd934ebad8811b4"}, {file = "pyproj-3.6.1-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:4bc0472302919e59114aa140fd7213c2370d848a7249d09704f10f5b062031fe"}, @@ -3791,6 +4005,7 @@ version = "23.7" description = "A library of spatial analysis functions." optional = false python-versions = ">3.6" +groups = ["main"] files = [ {file = "pysal-23.7-py3-none-any.whl", hash = "sha256:e55e5e20fc9cb3b8a6f1e7a78a7ab38aea97da36d7bd508918be2b526fddd9f1"}, {file = "pysal-23.7.tar.gz", hash = "sha256:0749fcaf4024e5bf49ee65df27eece86080836b75d7c2b12f2d4c611a41cbb1b"}, @@ -3827,6 +4042,7 @@ version = "7.4.2" description = "pytest: simple powerful testing with Python" optional = false python-versions = ">=3.7" +groups = ["dev"] files = [ {file = "pytest-7.4.2-py3-none-any.whl", hash = "sha256:1d881c6124e08ff0a1bb75ba3ec0bfd8b5354a01c194ddd5a0a870a48d99b002"}, {file = "pytest-7.4.2.tar.gz", hash = "sha256:a766259cfab564a2ad52cb1aae1b881a75c3eb7e34ca3779697c23ed47c47069"}, @@ -3849,6 +4065,7 @@ version = "2.8.2" description = "Extensions to the standard Python datetime module" optional = false python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,>=2.7" +groups = ["main", "dev"] files = [ {file = "python-dateutil-2.8.2.tar.gz", hash = "sha256:0123cacc1627ae19ddf3c27a5de5bd67ee4586fbdd6440d9748f8abb483d3e86"}, {file = "python_dateutil-2.8.2-py2.py3-none-any.whl", hash = "sha256:961d03dc3453ebbc59dbdea9e4e11c5651520a876d0f4db161e8674aae935da9"}, @@ -3863,6 +4080,7 @@ version = "2.0.7" description = "A python library adding a json log formatter" optional = false python-versions = ">=3.6" +groups = ["dev"] files = [ {file = "python-json-logger-2.0.7.tar.gz", hash = "sha256:23e7ec02d34237c5aa1e29a070193a4ea87583bb4e7f8fd06d3de8264c4b2e1c"}, {file = "python_json_logger-2.0.7-py3-none-any.whl", hash = "sha256:f380b826a991ebbe3de4d897aeec42760035ac760345e57b812938dc8b35e2bd"}, @@ -3874,6 +4092,7 @@ version = "2023.3.post1" description = "World timezone definitions, modern and historical" optional = false python-versions = "*" +groups = ["main"] files = [ {file = "pytz-2023.3.post1-py2.py3-none-any.whl", hash = "sha256:ce42d816b81b68506614c11e8937d3aa9e41007ceb50bfdcb0749b921bf646c7"}, {file = "pytz-2023.3.post1.tar.gz", hash = "sha256:7b4fddbeb94a1eba4b557da24f19fdf9db575192544270a9101d8509f9f43d7b"}, @@ -3885,6 +4104,8 @@ version = "306" description = "Python for Window Extensions" optional = false python-versions = "*" +groups = ["dev"] +markers = "sys_platform == \"win32\" and platform_python_implementation != \"PyPy\"" files = [ {file = "pywin32-306-cp310-cp310-win32.whl", hash = "sha256:06d3420a5155ba65f0b72f2699b5bacf3109f36acbe8923765c22938a69dfc8d"}, {file = "pywin32-306-cp310-cp310-win_amd64.whl", hash = "sha256:84f4471dbca1887ea3803d8848a1616429ac94a4a8d05f4bc9c5dcfd42ca99c8"}, @@ -3908,6 +4129,8 @@ version = "2.0.12" description = "Pseudo terminal support for Windows from Python." optional = false python-versions = ">=3.8" +groups = ["dev"] +markers = "os_name == \"nt\"" files = [ {file = "pywinpty-2.0.12-cp310-none-win_amd64.whl", hash = "sha256:21319cd1d7c8844fb2c970fb3a55a3db5543f112ff9cfcd623746b9c47501575"}, {file = "pywinpty-2.0.12-cp311-none-win_amd64.whl", hash = "sha256:853985a8f48f4731a716653170cd735da36ffbdc79dcb4c7b7140bce11d8c722"}, @@ -3923,6 +4146,7 @@ version = "6.0.1" description = "YAML parser and emitter for Python" optional = false python-versions = ">=3.6" +groups = ["dev"] files = [ {file = "PyYAML-6.0.1-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:d858aa552c999bc8a8d57426ed01e40bef403cd8ccdd0fc5f6f04a00414cac2a"}, {file = "PyYAML-6.0.1-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:fd66fc5d0da6d9815ba2cebeb4205f95818ff4b79c3ebe268e75d961704af52f"}, @@ -3983,6 +4207,7 @@ version = "0.1" description = "A custom YAML tag for referencing environment variables in YAML files. " optional = false python-versions = ">=3.6" +groups = ["dev"] files = [ {file = "pyyaml_env_tag-0.1-py3-none-any.whl", hash = "sha256:af31106dec8a4d68c60207c1886031cbf839b68aa7abccdb19868200532c2069"}, {file = "pyyaml_env_tag-0.1.tar.gz", hash = "sha256:70092675bda14fdec33b31ba77e7543de9ddc88f2e5b99160396572d11525bdb"}, @@ -3997,6 +4222,7 @@ version = "24.0.1" description = "Python bindings for 0MQ" optional = false python-versions = ">=3.6" +groups = ["dev"] files = [ {file = "pyzmq-24.0.1-cp310-cp310-macosx_10_15_universal2.whl", hash = "sha256:28b119ba97129d3001673a697b7cce47fe6de1f7255d104c2f01108a5179a066"}, {file = "pyzmq-24.0.1-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:bcbebd369493d68162cddb74a9c1fcebd139dfbb7ddb23d8f8e43e6c87bac3a6"}, @@ -4084,6 +4310,7 @@ version = "0.7.1" description = "Import the main names to top level." optional = false python-versions = ">=3.7" +groups = ["main"] files = [ {file = "quantecon-0.7.1-py3-none-any.whl", hash = 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blessed package to manage your versions by scm tags" optional = false python-versions = ">=3.8" +groups = ["main"] files = [ {file = "setuptools-scm-8.0.4.tar.gz", hash = "sha256:b5f43ff6800669595193fd09891564ee9d1d7dcb196cab4b2506d53a2e1c95c7"}, {file = "setuptools_scm-8.0.4-py3-none-any.whl", hash = "sha256:b47844cd2a84b83b3187a5782c71128c28b4c94cad8bfb871da2784a5cb54c4f"}, @@ -4710,6 +4956,7 @@ version = "1.8.5.post1" description = "Geometric objects, predicates, and operations" optional = false python-versions = ">=3.6" +groups = ["main"] files = [ {file = "Shapely-1.8.5.post1-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:d048f93e42ba578b82758c15d8ae037d08e69d91d9872bca5a1895b118f4e2b0"}, {file = "Shapely-1.8.5.post1-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:99ab0ddc05e44acabdbe657c599fdb9b2d82e86c5493bdae216c0c4018a82dee"}, @@ -4766,6 +5013,7 @@ version = "1.5.4" description = "Tool to Detect Surrounding Shell" optional = false python-versions = ">=3.7" +groups 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description = "Analysis of Network-constrained Spatial Data" optional = false python-versions = ">=3.8" +groups = ["main"] files = [ {file = "spaghetti-1.7.4-py3-none-any.whl", hash = "sha256:9a330c5fd851c2f9569bfc8c658d23c10afafaa3825cf97a79cdabd6c6a7c4da"}, {file = "spaghetti-1.7.4.tar.gz", hash = "sha256:9898ea86744b6ced9923f8bc213964045615f31ce14f304d20db0aecca40728d"}, @@ -4973,6 +5228,7 @@ version = "1.1.0" description = "Sparse Generalized Linear Models" optional = false python-versions = ">=3.9" +groups = ["main"] files = [ {file = "spglm-1.1.0-py3-none-any.whl", hash = "sha256:98240e3889c6672405ca90d9f9e7bd59bc86f8ecb69c03a14b87c38d8e6cf1c4"}, {file = "spglm-1.1.0.tar.gz", hash = "sha256:20519dc38be9d660a28109bb1b89d1068454e79f6413bab2e3987db5bf959327"}, @@ -4996,6 +5252,7 @@ version = "1.0.7" description = "SPatial INTeraction models" optional = false python-versions = ">3.5" +groups = ["main"] files = [ {file = "spint-1.0.7.tar.gz", hash = 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Python" optional = false python-versions = ">=3.8" +groups = ["main"] files = [ {file = "spreg-1.4.2-py3-none-any.whl", hash = "sha256:dc12426194f857b9d4ffbd7fc1e8ca5d6b64cccef0353b791b846af5c43cbd35"}, {file = "spreg-1.4.2.tar.gz", hash = "sha256:68927071005daf8ddaf0d75c53f3d6438e23d2cd863f1d4eb3725ded5e7df91a"}, @@ -5093,6 +5353,7 @@ version = "0.3.0" description = "Fit spatial multilevel models and diagnose convergence" optional = false python-versions = "*" +groups = ["main"] files = [ {file = "spvcm-0.3.0.tar.gz", hash = "sha256:ce331bd5d6bcb64a07c4393093f3978763cfc8764ad0737e1866f3905e6cceae"}, ] @@ -5111,6 +5372,7 @@ version = "0.6.3" description = "Extract data from python stack frames and tracebacks for informative displays" optional = false python-versions = "*" +groups = ["dev"] files = [ {file = "stack_data-0.6.3-py3-none-any.whl", hash = "sha256:d5558e0c25a4cb0853cddad3d77da9891a08cb85dd9f9f91b9f8cd66e511e695"}, {file = "stack_data-0.6.3.tar.gz", hash = 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= "sha256:3db108fb58f023b6439880e177743c5f1e703e9eeb5fb7d597871f949f85fd58"}, ] @@ -5221,6 +5486,7 @@ version = "0.7.2" description = "Fast data loading for TensorBoard" optional = false python-versions = ">=3.7" +groups = ["main"] files = [ {file = "tensorboard_data_server-0.7.2-py3-none-any.whl", hash = "sha256:7e0610d205889588983836ec05dc098e80f97b7e7bbff7e994ebb78f578d0ddb"}, {file = "tensorboard_data_server-0.7.2-py3-none-macosx_10_9_x86_64.whl", hash = "sha256:9fe5d24221b29625dbc7328b0436ca7fc1c23de4acf4d272f1180856e32f9f60"}, @@ -5233,6 +5499,7 @@ version = "2.14.0" description = "TensorFlow is an open source machine learning framework for everyone." optional = false python-versions = ">=3.9" +groups = ["main"] files = [ {file = "tensorflow-2.14.0-cp310-cp310-macosx_10_15_x86_64.whl", hash = "sha256:318b21b18312df6d11f511d0f205d55809d9ad0f46d5f9c13d8325ce4fe3b159"}, {file = "tensorflow-2.14.0-cp310-cp310-macosx_12_0_arm64.whl", hash = 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+groups = ["main"] files = [ {file = "termcolor-2.3.0-py3-none-any.whl", hash = "sha256:3afb05607b89aed0ffe25202399ee0867ad4d3cb4180d98aaf8eefa6a5f7d475"}, {file = "termcolor-2.3.0.tar.gz", hash = "sha256:b5b08f68937f138fe92f6c089b99f1e2da0ae56c52b78bf7075fd95420fd9a5a"}, @@ -5343,6 +5613,7 @@ version = "0.17.1" description = "Tornado websocket backend for the Xterm.js Javascript terminal emulator library." optional = false python-versions = ">=3.7" +groups = ["dev"] files = [ {file = "terminado-0.17.1-py3-none-any.whl", hash = "sha256:8650d44334eba354dd591129ca3124a6ba42c3d5b70df5051b6921d506fdaeae"}, {file = "terminado-0.17.1.tar.gz", hash = "sha256:6ccbbcd3a4f8a25a5ec04991f39a0b8db52dfcd487ea0e578d977e6752380333"}, @@ -5363,6 +5634,7 @@ version = "3.2.0" description = "threadpoolctl" optional = false python-versions = ">=3.8" +groups = ["main"] files = [ {file = "threadpoolctl-3.2.0-py3-none-any.whl", hash = "sha256:2b7818516e423bdaebb97c723f86a7c6b0a83d3f3b0970328d66f4d9104dc032"}, {file = "threadpoolctl-3.2.0.tar.gz", hash = "sha256:c96a0ba3bdddeaca37dc4cc7344aafad41cdb8c313f74fdfe387a867bba93355"}, @@ -5374,6 +5646,7 @@ version = "1.2.1" description = "A tiny CSS parser" optional = false python-versions = ">=3.7" +groups = ["dev"] files = [ {file = "tinycss2-1.2.1-py3-none-any.whl", hash = "sha256:2b80a96d41e7c3914b8cda8bc7f705a4d9c49275616e886103dd839dfc847847"}, {file = "tinycss2-1.2.1.tar.gz", hash = "sha256:8cff3a8f066c2ec677c06dbc7b45619804a6938478d9d73c284b29d14ecb0627"}, @@ -5392,6 +5665,7 @@ version = "0.9.0" description = "TOBLER: Areal Interpolation" optional = false python-versions = ">3.6" +groups = ["main"] files = [ {file = "tobler-0.9.0-py3-none-any.whl", hash = "sha256:bf9fa6adaa18353fb0d4da4e68f45fe688732852e2099149bcad9655980925ca"}, {file = "tobler-0.9.0.tar.gz", hash = "sha256:904b4b4de8d2ff0dfa5df4b80ad113b93207df952eee4b57a75474a41a583c0f"}, @@ -5420,6 +5694,7 @@ version = "2.0.1" description = "A lil' TOML parser" optional = false python-versions = ">=3.7" +groups = ["main", "dev"] files = [ {file = "tomli-2.0.1-py3-none-any.whl", hash = "sha256:939de3e7a6161af0c887ef91b7d41a53e7c5a1ca976325f429cb46ea9bc30ecc"}, {file = "tomli-2.0.1.tar.gz", hash = "sha256:de526c12914f0c550d15924c62d72abc48d6fe7364aa87328337a31007fe8a4f"}, @@ -5431,6 +5706,7 @@ version = "6.3.3" description = "Tornado is a Python web framework and asynchronous networking library, originally developed at FriendFeed." optional = false python-versions = ">= 3.8" +groups = ["dev"] files = [ {file = "tornado-6.3.3-cp38-abi3-macosx_10_9_universal2.whl", hash = "sha256:502fba735c84450974fec147340016ad928d29f1e91f49be168c0a4c18181e1d"}, {file = "tornado-6.3.3-cp38-abi3-macosx_10_9_x86_64.whl", hash = "sha256:805d507b1f588320c26f7f097108eb4023bbaa984d63176d1652e184ba24270a"}, @@ -5451,6 +5727,7 @@ version = "4.66.1" description = "Fast, Extensible Progress Meter" optional = false python-versions = ">=3.7" +groups = ["main"] files = [ {file = "tqdm-4.66.1-py3-none-any.whl", hash = "sha256:d302b3c5b53d47bce91fea46679d9c3c6508cf6332229aa1e7d8653723793386"}, {file = "tqdm-4.66.1.tar.gz", hash = "sha256:d88e651f9db8d8551a62556d3cff9e3034274ca5d66e93197cf2490e2dcb69c7"}, @@ -5471,6 +5748,7 @@ version = "5.11.2" description = "Traitlets Python configuration system" optional = false python-versions = ">=3.8" +groups = ["dev"] files = [ {file = "traitlets-5.11.2-py3-none-any.whl", hash = "sha256:98277f247f18b2c5cabaf4af369187754f4fb0e85911d473f72329db8a7f4fae"}, {file = "traitlets-5.11.2.tar.gz", hash = "sha256:7564b5bf8d38c40fa45498072bf4dc5e8346eb087bbf1e2ae2d8774f6a0f078e"}, @@ -5486,6 +5764,7 @@ version = "0.9.0" description = "Typer, build great CLIs. Easy to code. Based on Python type hints." optional = false python-versions = ">=3.6" +groups = ["main"] files = [ {file = "typer-0.9.0-py3-none-any.whl", hash = "sha256:5d96d986a21493606a358cae4461bd8cdf83cbf33a5aa950ae629ca3b51467ee"}, {file = "typer-0.9.0.tar.gz", hash = "sha256:50922fd79aea2f4751a8e0408ff10d2662bd0c8bbfa84755a699f3bada2978b2"}, @@ -5510,6 +5789,7 @@ version = "2.8.19.14" description = "Typing stubs for python-dateutil" optional = false python-versions = "*" +groups = ["dev"] files = [ {file = "types-python-dateutil-2.8.19.14.tar.gz", hash = "sha256:1f4f10ac98bb8b16ade9dbee3518d9ace017821d94b057a425b069f834737f4b"}, {file = "types_python_dateutil-2.8.19.14-py3-none-any.whl", hash = "sha256:f977b8de27787639986b4e28963263fd0e5158942b3ecef91b9335c130cb1ce9"}, @@ -5521,6 +5801,7 @@ version = "4.8.0" description = "Backported and Experimental Type Hints for Python 3.8+" optional = false python-versions = ">=3.8" +groups = ["main", "dev"] files = [ {file = "typing_extensions-4.8.0-py3-none-any.whl", hash = "sha256:8f92fc8806f9a6b641eaa5318da32b44d401efaac0f6678c9bc448ba3605faa0"}, {file = "typing_extensions-4.8.0.tar.gz", hash = "sha256:df8e4339e9cb77357558cbdbceca33c303714cf861d1eef15e1070055ae8b7ef"}, @@ -5532,6 +5813,7 @@ version = "2023.3" description = "Provider of IANA time zone data" optional = false python-versions = ">=2" +groups = ["main"] files = [ {file = "tzdata-2023.3-py2.py3-none-any.whl", hash = "sha256:7e65763eef3120314099b6939b5546db7adce1e7d6f2e179e3df563c70511eda"}, {file = "tzdata-2023.3.tar.gz", hash = "sha256:11ef1e08e54acb0d4f95bdb1be05da659673de4acbd21bf9c69e94cc5e907a3a"}, @@ -5543,6 +5825,7 @@ version = "1.3.0" description = "RFC 6570 URI Template Processor" optional = false python-versions = ">=3.7" +groups = ["dev"] files = [ {file = "uri-template-1.3.0.tar.gz", hash = "sha256:0e00f8eb65e18c7de20d595a14336e9f337ead580c70934141624b6d1ffdacc7"}, {file = "uri_template-1.3.0-py3-none-any.whl", hash = "sha256:a44a133ea12d44a0c0f06d7d42a52d71282e77e2f937d8abd5655b8d56fc1363"}, @@ -5557,13 +5840,14 @@ version = "2.0.7" description = "HTTP library with thread-safe connection pooling, file post, and more." optional = false python-versions = ">=3.7" +groups = ["main", "dev"] files = [ {file = "urllib3-2.0.7-py3-none-any.whl", hash = "sha256:fdb6d215c776278489906c2f8916e6e7d4f5a9b602ccbcfdf7f016fc8da0596e"}, {file = "urllib3-2.0.7.tar.gz", hash = "sha256:c97dfde1f7bd43a71c8d2a58e369e9b2bf692d1334ea9f9cae55add7d0dd0f84"}, ] [package.extras] -brotli = ["brotli (>=1.0.9)", "brotlicffi (>=0.8.0)"] +brotli = ["brotli (>=1.0.9) ; platform_python_implementation == \"CPython\"", "brotlicffi (>=0.8.0) ; platform_python_implementation != \"CPython\""] secure = ["certifi", "cryptography (>=1.9)", "idna (>=2.0.0)", "pyopenssl (>=17.1.0)", "urllib3-secure-extra"] socks = ["pysocks (>=1.5.6,!=1.5.7,<2.0)"] zstd = ["zstandard (>=0.18.0)"] @@ -5574,6 +5858,7 @@ version = "3.0.0" description = "Filesystem events monitoring" optional = false python-versions = ">=3.7" +groups = ["dev"] files = [ {file = "watchdog-3.0.0-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:336adfc6f5cc4e037d52db31194f7581ff744b67382eb6021c868322e32eef41"}, {file = "watchdog-3.0.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:a70a8dcde91be523c35b2bf96196edc5730edb347e374c7de7cd20c43ed95397"}, @@ -5613,6 +5898,7 @@ version = "0.2.8" description = "Measures the displayed width of unicode strings in a terminal" optional = false python-versions = "*" +groups = ["dev"] files = [ {file = "wcwidth-0.2.8-py2.py3-none-any.whl", hash = "sha256:77f719e01648ed600dfa5402c347481c0992263b81a027344f3e1ba25493a704"}, {file = "wcwidth-0.2.8.tar.gz", hash = "sha256:8705c569999ffbb4f6a87c6d1b80f324bd6db952f5eb0b95bc07517f4c1813d4"}, @@ -5624,6 +5910,7 @@ version = "60.1" description = "The Awesome Document Factory" optional = false python-versions = ">=3.7" +groups = ["dev"] files = [ {file = "weasyprint-60.1-py3-none-any.whl", hash = "sha256:55227e5e44f5f34bc9cec651329bd38d063ef7d29151d4b058d4af1ca943d4a7"}, {file = "weasyprint-60.1.tar.gz", hash = "sha256:56b9812280118357b0f63b1efe18199e08343d4a56a3393c1d475ab878cea26a"}, @@ -5649,6 +5936,7 @@ version = "1.13" description = "A library for working with the color formats defined by HTML and CSS." optional = false python-versions = ">=3.7" +groups = ["dev"] files = [ {file = "webcolors-1.13-py3-none-any.whl", hash = "sha256:29bc7e8752c0a1bd4a1f03c14d6e6a72e93d82193738fa860cbff59d0fcc11bf"}, {file = "webcolors-1.13.tar.gz", hash = "sha256:c225b674c83fa923be93d235330ce0300373d02885cef23238813b0d5668304a"}, @@ -5664,6 +5952,7 @@ version = "0.5.1" description = "Character encoding aliases for legacy web content" optional = false python-versions = "*" +groups = ["dev"] files = [ {file = "webencodings-0.5.1-py2.py3-none-any.whl", hash = "sha256:a0af1213f3c2226497a97e2b3aa01a7e4bee4f403f95be16fc9acd2947514a78"}, {file = "webencodings-0.5.1.tar.gz", hash = "sha256:b36a1c245f2d304965eb4e0a82848379241dc04b865afcc4aab16748587e1923"}, @@ -5675,6 +5964,7 @@ version = "1.6.4" description = "WebSocket client for Python with low level API options" optional = false python-versions = ">=3.8" +groups = ["dev"] files = [ {file = "websocket-client-1.6.4.tar.gz", hash = "sha256:b3324019b3c28572086c4a319f91d1dcd44e6e11cd340232978c684a7650d0df"}, {file = "websocket_client-1.6.4-py3-none-any.whl", hash = "sha256:084072e0a7f5f347ef2ac3d8698a5e0b4ffbfcab607628cadabc650fc9a83a24"}, @@ -5691,6 +5981,7 @@ version = "3.0.0" description = "The comprehensive WSGI web application library." optional = false python-versions = ">=3.8" +groups = ["main"] files = [ {file = "werkzeug-3.0.0-py3-none-any.whl", hash = "sha256:cbb2600f7eabe51dbc0502f58be0b3e1b96b893b05695ea2b35b43d4de2d9962"}, {file = "werkzeug-3.0.0.tar.gz", hash = "sha256:3ffff4dcc32db52ef3cc94dff3000a3c2846890f3a5a51800a27b909c5e770f0"}, @@ -5708,6 +5999,7 @@ version = "0.41.2" description = "A built-package format for Python" optional = false python-versions = ">=3.7" +groups = ["main"] files = [ {file = "wheel-0.41.2-py3-none-any.whl", hash = "sha256:75909db2664838d015e3d9139004ee16711748a52c8f336b52882266540215d8"}, {file = "wheel-0.41.2.tar.gz", hash = "sha256:0c5ac5ff2afb79ac23ab82bab027a0be7b5dbcf2e54dc50efe4bf507de1f7985"}, @@ -5722,6 +6014,7 @@ version = "1.14.1" description = "Module for decorators, wrappers and monkey patching." optional = false python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,>=2.7" +groups = ["main"] files = [ {file = "wrapt-1.14.1-cp27-cp27m-macosx_10_9_x86_64.whl", hash = "sha256:1b376b3f4896e7930f1f772ac4b064ac12598d1c38d04907e696cc4d794b43d3"}, {file = "wrapt-1.14.1-cp27-cp27m-manylinux1_i686.whl", hash = "sha256:903500616422a40a98a5a3c4ff4ed9d0066f3b4c951fa286018ecdf0750194ef"}, @@ -5805,6 +6098,7 @@ version = "0.6.2" description = "Python bindings for the Y-CRDT built from yrs (Rust)" optional = false python-versions = "*" +groups = ["dev"] files = [ {file = "y_py-0.6.2-cp310-cp310-macosx_10_7_x86_64.whl", hash = "sha256:c26bada6cd109095139237a46f50fc4308f861f0d304bc9e70acbc6c4503d158"}, {file = "y_py-0.6.2-cp310-cp310-macosx_10_9_x86_64.macosx_11_0_arm64.macosx_10_9_universal2.whl", hash = "sha256:bae1b1ad8d2b8cf938a60313f8f7461de609621c5dcae491b6e54975f76f83c5"}, @@ -5888,6 +6182,7 @@ version = "0.8.4" description = "WebSocket connector for Ypy" optional = false python-versions = ">=3.7" +groups = ["dev"] files = [ {file = "ypy_websocket-0.8.4-py3-none-any.whl", hash = "sha256:b1ba0dfcc9762f0ca168d2378062d3ca1299d39076b0f145d961359121042be5"}, {file = "ypy_websocket-0.8.4.tar.gz", hash = "sha256:43a001473f5c8abcf182f603049cf305cbc855ad8deaa9dfa0f3b5a7cea9d0ff"}, @@ -5907,6 +6202,8 @@ version = "3.17.0" description = "Backport of pathlib-compatible object wrapper for zip files" optional = false python-versions = ">=3.8" +groups = ["main", "dev"] +markers = "python_version <= \"3.9\"" files = [ {file = "zipp-3.17.0-py3-none-any.whl", hash = "sha256:0e923e726174922dce09c53c59ad483ff7bbb8e572e00c7f7c46b88556409f31"}, {file = "zipp-3.17.0.tar.gz", hash = "sha256:84e64a1c28cf7e91ed2078bb8cc8c259cb19b76942096c8d7b84947690cabaf0"}, @@ -5914,7 +6211,7 @@ files = [ [package.extras] docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (<7.2.5)", "sphinx (>=3.5)", "sphinx-lint"] -testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-ignore-flaky", "pytest-mypy (>=0.9.1)", "pytest-ruff"] +testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "pytest (>=6)", "pytest-black (>=0.3.7) ; platform_python_implementation != \"PyPy\"", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-ignore-flaky", "pytest-mypy (>=0.9.1) ; platform_python_implementation != \"PyPy\"", "pytest-ruff"] [[package]] name = "zopfli" @@ -5922,6 +6219,7 @@ version = "0.2.3" description = "Zopfli module for python" optional = false python-versions = ">=3.8" +groups = ["dev"] files = [ {file = "zopfli-0.2.3-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:52438999888715a378fc6fe1477ab7813e9e9b58a27a38d2ad7be0e396b1ab2e"}, {file = "zopfli-0.2.3-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:6020a3533c6c7be09db9e59c2a8f3f894bf5d8e95cc01890d82114c923317c57"}, @@ -5990,6 +6288,6 @@ files = [ test = ["pytest"] [metadata] -lock-version = "2.0" +lock-version = "2.1" python-versions = ">=3.9,<3.11" -content-hash = "6867c5a7fdce4247b374cfa2aef76f6f4c6dd5c67f0b4c70bc5049b1db00e5d9" +content-hash = "2662968a16c89a987cd6a2d78b5f805a30d71fd7ec4b974120554edf3949cda8" diff --git a/pyproject.toml b/pyproject.toml index 9445b3fd..0211221a 100755 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "eis_toolkit" -version = "1.0.1" +version = "1.1.5" description = "EIS Toolkit is a comprehensive collection of tools suitable for mineral prospectivity mapping. This toolkit has been developed as part of the Exploration Information System project which has been funded by European Union." authors = [] maintainers = ["Gispo Ltd. "] @@ -37,6 +37,9 @@ typer = {extras = ["all"], version = "^0.9.0"} imbalanced-learn = "^0.11.0" pysal = "^23.7" esda = "^2.5.1" +scipy = "1.11.4" +numba = "^0.60.0" +fiona = "<1.10.0" [tool.poetry.dev-dependencies] jupyterlab = "^3.4.5" diff --git a/tests/data/remote/interpolating/idw_radius_test_data.tif b/tests/data/remote/interpolating/idw_radius_test_data.tif new file mode 100644 index 00000000..8f34d6ce Binary files /dev/null and b/tests/data/remote/interpolating/idw_radius_test_data.tif differ diff --git a/tests/data/remote/interpolating/interpolation_test_data_small.gpkg b/tests/data/remote/interpolating/interpolation_test_data_small.gpkg index dbc9cf3c..7b741f58 100644 Binary files a/tests/data/remote/interpolating/interpolation_test_data_small.gpkg and b/tests/data/remote/interpolating/interpolation_test_data_small.gpkg differ diff --git a/tests/data/remote/interpolating/interpolation_test_data_small.gpkg-shm b/tests/data/remote/interpolating/interpolation_test_data_small.gpkg-shm deleted file mode 100644 index fe9ac284..00000000 Binary files a/tests/data/remote/interpolating/interpolation_test_data_small.gpkg-shm and /dev/null differ diff --git a/tests/data/remote/interpolating/interpolation_test_data_small.gpkg-wal b/tests/data/remote/interpolating/interpolation_test_data_small.gpkg-wal deleted file mode 100644 index e69de29b..00000000 diff --git a/tests/data/remote/nonsquared_pixelsize_raster.tif.aux.xml b/tests/data/remote/nonsquared_pixelsize_raster.tif.aux.xml new file mode 100644 index 00000000..e2093664 --- /dev/null +++ b/tests/data/remote/nonsquared_pixelsize_raster.tif.aux.xml @@ -0,0 +1,11 @@ + + + + 0.12201571464539 + 999.97509765625 + 494.99426819916 + 291.24802892273 + 100 + + + diff --git a/tests/eis_toolkit_test.py b/tests/eis_toolkit_test.py index 11b3688d..8b1034aa 100755 --- a/tests/eis_toolkit_test.py +++ b/tests/eis_toolkit_test.py @@ -3,4 +3,4 @@ def test_version(): """Tests that version number is correct.""" - assert __version__ == "0.1.0" + assert __version__ == "1.1.5" diff --git a/tests/exploratory_analyses/descriptive_statistics_test.py b/tests/exploratory_analyses/descriptive_statistics_test.py index ee8f4a69..b7aa634c 100644 --- a/tests/exploratory_analyses/descriptive_statistics_test.py +++ b/tests/exploratory_analyses/descriptive_statistics_test.py @@ -61,7 +61,7 @@ def test_descriptive_statistics_geodataframe(): def test_descriptive_statistics_raster(): """Checks that returned statistics are correct when using numpy.ndarray.""" - test = descriptive_statistics_raster(src_raster) + test = descriptive_statistics_raster(src_raster, 1) np.testing.assert_almost_equal(test["min"], 2.503) np.testing.assert_almost_equal(test["max"], 9.67) np.testing.assert_almost_equal(test["mean"], 5.1865644) diff --git a/tests/exploratory_analyses/feature_importance_test.py b/tests/exploratory_analyses/feature_importance_test.py index a5d81470..a90d8197 100644 --- a/tests/exploratory_analyses/feature_importance_test.py +++ b/tests/exploratory_analyses/feature_importance_test.py @@ -4,7 +4,11 @@ from sklearn.neural_network import MLPClassifier from sklearn.preprocessing import StandardScaler -from eis_toolkit.exceptions import InvalidDatasetException, InvalidParameterValueException +from eis_toolkit.exceptions import ( + InvalidDatasetException, + InvalidParameterValueException, + NonMatchingParameterLengthsException, +) from eis_toolkit.exploratory_analyses.feature_importance import evaluate_feature_importance feature_names = [ @@ -42,39 +46,33 @@ def test_empty_data(): empty_data = np.array([]) empty_labels = np.array([]) with pytest.raises(InvalidDatasetException): - _, _ = evaluate_feature_importance( - model=classifier, x_test=empty_data, y_test=labels, feature_names=feature_names - ) + _, _ = evaluate_feature_importance(model=classifier, X=empty_data, y=labels, feature_names=feature_names) with pytest.raises(InvalidDatasetException): - _, _ = evaluate_feature_importance( - model=classifier, x_test=data, y_test=empty_labels, feature_names=feature_names - ) + _, _ = evaluate_feature_importance(model=classifier, X=data, y=empty_labels, feature_names=feature_names) def test_invalid_n_repeats(): """Test that invalid value for 'n_repeats' raises exception.""" with pytest.raises(InvalidParameterValueException): - _, _ = evaluate_feature_importance( - model=classifier, x_test=data, y_test=labels, feature_names=feature_names, n_repeats=0 - ) + _, _ = evaluate_feature_importance(model=classifier, X=data, y=labels, feature_names=feature_names, n_repeats=0) def test_model_output(): """Test that function output is as expected.""" classifier.fit(data, labels.ravel()) feature_importance, importance_results = evaluate_feature_importance( - model=classifier, x_test=data, y_test=labels, feature_names=feature_names, random_state=0 + model=classifier, X=data, y=labels, feature_names=feature_names, n_repeats=50, random_state=0 ) np.testing.assert_almost_equal( feature_importance.loc[feature_importance["Feature"] == "EM_ratio", "Importance"].values[0], - desired=12.923077, + desired=0.129231, decimal=6, ) np.testing.assert_almost_equal( feature_importance.loc[feature_importance["Feature"] == "EM_Qd", "Importance"].values[0], - desired=4.461538, + desired=0.044615, decimal=6, ) np.testing.assert_equal(len(feature_importance), desired=len(feature_names)) @@ -82,3 +80,21 @@ def test_model_output(): tuple(importance_results.keys()), desired=("importances_mean", "importances_std", "importances"), ) + + +def test_invalid_input_lengths(): + """Test that non matcing X and y lengths raises an exception.""" + labels = np.random.randint(2, size=12) + with pytest.raises(NonMatchingParameterLengthsException): + _, _ = evaluate_feature_importance(model=classifier, X=data, y=labels, feature_names=feature_names) + + +def test_invalid_number_of_feature_names(): + """Test that invalid number of feature names raises an exception.""" + with pytest.raises(InvalidParameterValueException): + _, _ = evaluate_feature_importance( + model=classifier, + X=data, + y=labels, + feature_names=["a", "b", "c"], + ) diff --git a/tests/exploratory_analyses/local_morans_i_test.py b/tests/exploratory_analyses/local_morans_i_test.py index ed956e03..8a1fec85 100644 --- a/tests/exploratory_analyses/local_morans_i_test.py +++ b/tests/exploratory_analyses/local_morans_i_test.py @@ -146,7 +146,7 @@ def test_geodataframe_missing_column(): gdf = gpd.GeoDataFrame({"test_col": [1, 2, 3]}) - with pytest.raises(exceptions.InvalidParameterValueException): + with pytest.raises(exceptions.InvalidColumnException): local_morans_i(gdf, column="value", weight_type="queen", k=4, permutations=999) diff --git a/tests/exploratory_analyses/pca_test.py b/tests/exploratory_analyses/pca_test.py index 25063810..ac62df4f 100644 --- a/tests/exploratory_analyses/pca_test.py +++ b/tests/exploratory_analyses/pca_test.py @@ -1,122 +1,186 @@ import sys -from pathlib import Path import geopandas as gpd import numpy as np import pandas as pd import pytest +from beartype.typing import Optional from shapely.geometry import Point from eis_toolkit.exceptions import EmptyDataException, InvalidColumnException, InvalidParameterValueException from eis_toolkit.exploratory_analyses.pca import compute_pca -parent_dir = Path(__file__).parent -MULTIBAND_RASTER_PATH = parent_dir.joinpath("../data/remote/small_raster_multiband.tif") - DATA = np.array([[1, 1], [2, 2], [3, 3]]) +EXPECTED_DATA_PCA_VALUES = expected_pca_values = np.array( + [[-1.73205081, 1.11022302e-16], [0.0, 0.0], [1.73205081, 1.11022302e-16]] +) +EXPECTED_DATA_COMPONENT_VALUES = np.array([[0.70711, 0.70711], [0.70711, -0.70711]]) +EXPECTED_DATA_COMPONENT_VALUES_ALTERNATIVE = np.array([[0.70711, 0.70711], [-0.70711, 0.70711]]) +EXPECTED_DATA_EXPLAINED_VARIANCE_RATIOS_VALUES = [1.0, 4.10865055e-33] + +DATA_DF = pd.DataFrame(data=DATA, columns=["A", "B"]) +EXPECTED_DATA_DF_COLUMNS = ["principal_component_1", "principal_component_2"] + +DATA_GDF = gpd.GeoDataFrame( + data=DATA, columns=["A", "B"], geometry=[Point(1, 2), Point(2, 1), Point(3, 3)], crs="EPSG:4326" +) +EXPECTED_DATA_GDF_COLUMNS = ["principal_component_1", "principal_component_2", "geometry"] + +DATA_WITH_NAN = np.array([[1, 1], [2, np.nan], [3, 3]]) +DATA_WITH_NODATA = np.array([[1, 1], [2, -9999], [3, 3]]) + + +def _assert_expected_values( + pca_array: np.ndarray, + principal_components, + explained_variances, + explained_variance_ratios, + expected_pca_values=EXPECTED_DATA_PCA_VALUES, + expected_component_values=EXPECTED_DATA_COMPONENT_VALUES, + expected_component_values_alternative: Optional[np.ndarray] = None, + expected_explained_variance_ratios_values=EXPECTED_DATA_EXPLAINED_VARIANCE_RATIOS_VALUES, + decimal_accuracy: int = 5, + data_shape=DATA.shape, +): + np.testing.assert_equal(principal_components.size, 4) + np.testing.assert_equal(explained_variances.size, 2) + np.testing.assert_equal(explained_variance_ratios.size, 2) + np.testing.assert_equal(pca_array.shape, data_shape) + np.testing.assert_array_almost_equal(pca_array, expected_pca_values, decimal=decimal_accuracy) + + try: + np.testing.assert_array_almost_equal(principal_components, expected_component_values, decimal=decimal_accuracy) + except AssertionError: + # Both variations in the sign of the two last members of principal_components occurs + # depending on environment in nan and nodata tests + # Both are allowed for those + if expected_component_values_alternative is None: + # Deviations in order are not expected unless *_alternative array is passed as input + raise + np.testing.assert_array_almost_equal( + principal_components, expected_component_values_alternative, decimal=decimal_accuracy + ) + + np.testing.assert_array_almost_equal( + explained_variance_ratios, expected_explained_variance_ratios_values, decimal=decimal_accuracy + ) @pytest.mark.xfail(sys.platform == "win32", reason="Results deviate on Windows.", raises=AssertionError) def test_pca_numpy_array(): """Test that PCA function gives correct output for Numpy array input.""" - pca_array, explained_variances = compute_pca(DATA, 2) - - expected_pca_values = np.array([[-1.73205081, 1.11022302e-16], [0.0, 0.0], [1.73205081, 1.11022302e-16]]) - expected_explained_variances_values = [1.0, 4.10865055e-33] + pca_array, principal_components, explained_variances, explained_variance_ratios = compute_pca(DATA, 2) - np.testing.assert_equal(explained_variances.size, 2) - np.testing.assert_equal(pca_array.shape, DATA.shape) - - np.testing.assert_array_almost_equal(pca_array, expected_pca_values, decimal=5) - np.testing.assert_array_almost_equal(explained_variances, expected_explained_variances_values, decimal=5) + _assert_expected_values( + pca_array=pca_array, + principal_components=principal_components, + explained_variances=explained_variances, + explained_variance_ratios=explained_variance_ratios, + ) @pytest.mark.xfail(sys.platform == "win32", reason="Results deviate on Windows.", raises=AssertionError) def test_pca_df(): """Test that PCA function gives correct output for DF input.""" - data_df = pd.DataFrame(data=DATA, columns=["A", "B"]) - pca_df, explained_variances = compute_pca(data_df, 2) + pca_df, principal_components, explained_variances, explained_variance_ratios = compute_pca(DATA_DF, 2) - expected_columns = ["principal_component_1", "principal_component_2"] - expected_pca_values = np.array([[-1.73205081, 1.11022302e-16], [0.0, 0.0], [1.73205081, 1.11022302e-16]]) - expected_explained_variances_values = [1.0, 4.10865055e-33] - - np.testing.assert_equal(explained_variances.size, 2) - np.testing.assert_equal(list(pca_df.columns), expected_columns) - np.testing.assert_equal(pca_df.shape, data_df.shape) - - np.testing.assert_array_almost_equal(pca_df.values, expected_pca_values, decimal=5) - np.testing.assert_array_almost_equal(explained_variances, expected_explained_variances_values, decimal=5) + _assert_expected_values( + pca_array=pca_df.values, + principal_components=principal_components, + explained_variances=explained_variances, + explained_variance_ratios=explained_variance_ratios, + ) + np.testing.assert_equal(list(pca_df.columns), EXPECTED_DATA_DF_COLUMNS) + np.testing.assert_equal(pca_df.shape, DATA_DF.shape) @pytest.mark.xfail(sys.platform == "win32", reason="Results deviate on Windows.", raises=AssertionError) def test_pca_gdf(): """Test that PCA function gives correct output for GDF input.""" - data_gdf = gpd.GeoDataFrame( - data=DATA, columns=["A", "B"], geometry=[Point(1, 2), Point(2, 1), Point(3, 3)], crs="EPSG:4326" - ) - - pca_gdf, explained_variances = compute_pca(data_gdf, 2) - expected_columns = ["principal_component_1", "principal_component_2", "geometry"] - expected_pca_values = np.array([[-1.73205081, 1.11022302e-16], [0.0, 0.0], [1.73205081, 1.11022302e-16]]) - expected_explained_variances_values = [1.0, 4.10865055e-33] + pca_gdf, principal_components, explained_variances, explained_variance_ratios = compute_pca(DATA_GDF, 2) - np.testing.assert_equal(explained_variances.size, 2) - np.testing.assert_equal(list(pca_gdf.columns), expected_columns) - np.testing.assert_equal(pca_gdf.shape, data_gdf.shape) + _assert_expected_values( + pca_array=pca_gdf.drop(columns=["geometry"]).values, + principal_components=principal_components, + explained_variances=explained_variances, + explained_variance_ratios=explained_variance_ratios, + ) - np.testing.assert_array_almost_equal(pca_gdf.drop(columns=["geometry"]).values, expected_pca_values, decimal=5) - np.testing.assert_array_almost_equal(explained_variances, expected_explained_variances_values, decimal=5) + np.testing.assert_equal(list(pca_gdf.columns), EXPECTED_DATA_GDF_COLUMNS) + np.testing.assert_equal(pca_gdf.shape, DATA_GDF.shape) @pytest.mark.xfail(sys.platform == "win32", reason="Results deviate on Windows.", raises=AssertionError) def test_pca_with_nan_removal(): """Test that PCA function gives correct output for Numpy array input that has NaN values and remove strategy.""" - data = np.array([[1, 1], [2, np.nan], [3, 3]]) - pca_array, explained_variances = compute_pca(data, 2, nodata_handling="remove") + pca_array, principal_components, explained_variances, explained_variance_ratios = compute_pca( + DATA_WITH_NAN, 2, nodata_handling="remove" + ) expected_pca_values = np.array([[-1.414, 0.0], [np.nan, np.nan], [1.414, 0.0]]) - expected_explained_variances_values = [1.0, 0.0] - - np.testing.assert_equal(explained_variances.size, 2) - np.testing.assert_equal(pca_array.shape, DATA.shape) - - np.testing.assert_array_almost_equal(pca_array, expected_pca_values, decimal=3) - np.testing.assert_array_almost_equal(explained_variances, expected_explained_variances_values, decimal=3) + expected_explained_variance_ratios_values = [1.0, 0.0] + + _assert_expected_values( + pca_array=pca_array, + principal_components=principal_components, + explained_variances=explained_variances, + explained_variance_ratios=explained_variance_ratios, + expected_pca_values=expected_pca_values, + # Original implementation expected order as defined by EXPECTED_DATA_COMPONENT_VALUES_ALTERNATIVE + expected_component_values=EXPECTED_DATA_COMPONENT_VALUES_ALTERNATIVE, + expected_component_values_alternative=EXPECTED_DATA_COMPONENT_VALUES, + expected_explained_variance_ratios_values=expected_explained_variance_ratios_values, + decimal_accuracy=3, + data_shape=DATA_WITH_NAN.shape, + ) @pytest.mark.xfail(sys.platform == "win32", reason="Results deviate on Windows.", raises=AssertionError) def test_pca_with_nan_replace(): """Test that PCA function gives correct output for Numpy array input that has NaN values and replace strategy.""" - data = np.array([[1, 1], [2, np.nan], [3, 3]]) - pca_array, explained_variances = compute_pca(data, 2, nodata_handling="replace") - - expected_pca_values = np.array([[-1.73205, 1.11022e-16], [0, 0], [1.73205, 1.11022e-16]]) - expected_explained_variances_values = [1.0, 4.10865e-33] - - np.testing.assert_equal(explained_variances.size, 2) - np.testing.assert_equal(pca_array.shape, DATA.shape) + pca_array, principal_components, explained_variances, explained_variance_ratios = compute_pca( + DATA_WITH_NAN, 2, nodata_handling="replace" + ) - np.testing.assert_array_almost_equal(pca_array, expected_pca_values, decimal=3) - np.testing.assert_array_almost_equal(explained_variances, expected_explained_variances_values, decimal=3) + _assert_expected_values( + pca_array=pca_array, + principal_components=principal_components, + explained_variances=explained_variances, + explained_variance_ratios=explained_variance_ratios, + expected_pca_values=EXPECTED_DATA_PCA_VALUES, + expected_component_values=EXPECTED_DATA_COMPONENT_VALUES, + expected_component_values_alternative=EXPECTED_DATA_COMPONENT_VALUES_ALTERNATIVE, + expected_explained_variance_ratios_values=EXPECTED_DATA_EXPLAINED_VARIANCE_RATIOS_VALUES, + decimal_accuracy=3, + data_shape=DATA_WITH_NAN.shape, + ) @pytest.mark.xfail(sys.platform == "win32", reason="Results deviate on Windows.", raises=AssertionError) def test_pca_with_nodata_removal(): """Test that PCA function gives correct output for input that has specified nodata values and removal strategy.""" - data = np.array([[1, 1], [2, -9999], [3, 3]]) - pca_array, explained_variances = compute_pca(data, 2, nodata_handling="remove", nodata=-9999) + pca_array, principal_components, explained_variances, explained_variance_ratios = compute_pca( + DATA_WITH_NODATA, 2, nodata_handling="remove", nodata=-9999 + ) expected_pca_values = np.array([[-1.414, 0.0], [np.nan, np.nan], [1.414, 0.0]]) - expected_explained_variances_values = [1.0, 0.0] - - np.testing.assert_equal(explained_variances.size, 2) - np.testing.assert_equal(pca_array.shape, DATA.shape) - - np.testing.assert_array_almost_equal(pca_array, expected_pca_values, decimal=3) - np.testing.assert_array_almost_equal(explained_variances, expected_explained_variances_values, decimal=3) + expected_explained_variance_ratios_values = [1.0, 0.0] + + _assert_expected_values( + pca_array=pca_array, + principal_components=principal_components, + explained_variances=explained_variances, + explained_variance_ratios=explained_variance_ratios, + expected_pca_values=expected_pca_values, + # Original implementation expected order as defined by EXPECTED_DATA_COMPONENT_VALUES_ALTERNATIVE + expected_component_values=EXPECTED_DATA_COMPONENT_VALUES_ALTERNATIVE, + expected_component_values_alternative=EXPECTED_DATA_COMPONENT_VALUES, + expected_explained_variance_ratios_values=expected_explained_variance_ratios_values, + decimal_accuracy=3, + data_shape=DATA_WITH_NODATA.shape, + ) def test_pca_empty_data(): diff --git a/tests/prediction/weights_of_evidence_test.py b/tests/prediction/weights_of_evidence_test.py index 979aa60e..5e3ae592 100644 --- a/tests/prediction/weights_of_evidence_test.py +++ b/tests/prediction/weights_of_evidence_test.py @@ -2,11 +2,25 @@ import geopandas as gpd import numpy as np +import pandas as pd import pytest import rasterio -from eis_toolkit.exceptions import ClassificationFailedException, InvalidColumnException -from eis_toolkit.prediction.weights_of_evidence import weights_of_evidence_calculate_weights +from eis_toolkit.exceptions import InvalidColumnException +from eis_toolkit.prediction.weights_of_evidence import ( + CLASS_COLUMN, + CONTRAST_COLUMN, + GENERALIZED_CLASS_COLUMN, + S_CONTRAST_COLUMN, + STUDENTIZED_CONTRAST_COLUMN, + WEIGHT_MINUS_COLUMN, + WEIGHT_PLUS_COLUMN, + WEIGHT_S_MINUS_COLUMN, + WEIGHT_S_PLUS_COLUMN, + generalize_weights_cumulative, + weights_of_evidence_calculate_weights, +) +from eis_toolkit.warnings import ClassificationFailedWarning test_dir = Path(__file__).parent.parent EVIDENCE_PATH = test_dir.joinpath("../tests/data/remote/wofe/wofe_evidence_raster.tif") @@ -15,6 +29,30 @@ evidence_raster = rasterio.open(EVIDENCE_PATH) deposits = gpd.read_file(DEPOSIT_PATH) +weights_table = pd.DataFrame( + [ + [1.0, 0.5059, 0.4083, -0.0877, 0.1961, 0.5936, 0.4529, 1.3106], + [2.0, 0.4387, 0.3015, -0.1706, 0.2182, 0.6093, 0.3722, 1.6371], + [3.0, 0.3942, 0.2426, -0.3142, 0.2582, 0.7084, 0.3543, 1.9997], + [4.0, 0.2571, 0.2236, -0.3205, 0.2887, 0.5777, 0.3652, 1.5819], + [5.0, 0.2877, 0.1961, -0.7339, 0.4083, 1.0216, 0.4529, 2.2555], + [6.0, 0.2683, 0.1826, -1.5107, 0.7071, 1.7790, 0.7303, 2.4360], + [7.0, 0.1722, 0.1796, -1.7844, 1.0050, 1.9566, 1.0210, 1.9164], + [8.0, 0.0608, 0.1796, -1.0464, 1.0051, 1.1072, 1.0210, 1.0844], + [9.0, -0.0314, 0.1796, 9.4195, 1.4213, -9.4509, 1.4326, -6.5968], + ], + columns=[ + CLASS_COLUMN, + WEIGHT_PLUS_COLUMN, + WEIGHT_S_PLUS_COLUMN, + WEIGHT_MINUS_COLUMN, + WEIGHT_S_MINUS_COLUMN, + CONTRAST_COLUMN, + S_CONTRAST_COLUMN, + STUDENTIZED_CONTRAST_COLUMN, + ], +) + def test_weights_of_evidence(): """Test that weights of evidence works as intended.""" @@ -27,14 +65,103 @@ def test_weights_of_evidence(): def test_too_high_studentized_contrast_threshold(): - """Tests that too high studentized contrast threshold for reclassification raises the correct exception.""" - with pytest.raises(ClassificationFailedException): - weights_of_evidence_calculate_weights( + """Tests that too high studentized contrast threshold for reclassification results in warning.""" + with pytest.warns(ClassificationFailedWarning): + result, _, _, _, _ = weights_of_evidence_calculate_weights( evidence_raster, deposits, weights_type="ascending", studentized_contrast_threshold=2 ) + assert GENERALIZED_CLASS_COLUMN not in result.columns.values + def test_invalid_choice_in_rasters_to_generate(): """Tests that invalid metric/column in rasters to generate raises the correct exception.""" with pytest.raises(InvalidColumnException): weights_of_evidence_calculate_weights(evidence_raster, deposits, arrays_to_generate=["invalid_metric"]) + + +def test_cumulative_reclassification_manual(): + """Test generalizing cumulative weights with the 'manual' option.""" + df = weights_table.copy() + + result = generalize_weights_cumulative(df, "manual", manual_cutoff_index=4) + + assert GENERALIZED_CLASS_COLUMN in result.columns.values + + expected_output = pd.Series([2, 2, 2, 2, 2, 1, 1, 1, 1], dtype=np.int64) + pd.testing.assert_series_equal(expected_output, result[GENERALIZED_CLASS_COLUMN], check_names=False) + + with pytest.warns(ClassificationFailedWarning): + result = generalize_weights_cumulative(df, "manual", 8) + assert GENERALIZED_CLASS_COLUMN not in result.columns.values + + +def test_cumulative_reclassification_max_contrast(): + """Test generalizing cumulative weights with the 'max_contrast' option.""" + df = weights_table.copy() + + result = generalize_weights_cumulative(df, "max_contrast") + + assert GENERALIZED_CLASS_COLUMN in result.columns.values + + expected_output = pd.Series([2, 2, 2, 2, 2, 2, 2, 1, 1], dtype=np.int64) + pd.testing.assert_series_equal(expected_output, result[GENERALIZED_CLASS_COLUMN], check_names=False) + + with pytest.warns(ClassificationFailedWarning): + df = weights_table.copy() + df = df.head(3) # Last row has the highest contrast + + result = generalize_weights_cumulative(df, "max_contrast") + + assert GENERALIZED_CLASS_COLUMN not in result.columns.values + + +def test_cumulative_reclassification_max_contrast_if_feasible(): + """Test generalizing cumulative weights with the 'max_contrast_if_feasible' option.""" + df = weights_table.copy() + + result = generalize_weights_cumulative(df, "max_contrast_if_feasible", studentized_contrast_threshold=1) + + assert GENERALIZED_CLASS_COLUMN in result.columns.values + + expected_output = pd.Series([2, 2, 2, 2, 2, 2, 2, 1, 1], dtype=np.int64) + pd.testing.assert_series_equal(expected_output, result[GENERALIZED_CLASS_COLUMN], check_names=False) + + with pytest.warns(ClassificationFailedWarning): + result = generalize_weights_cumulative(df, "max_contrast_if_feasible", studentized_contrast_threshold=2) + assert GENERALIZED_CLASS_COLUMN not in result.columns.values + + +def test_cumulative_reclassification_max_feasible_contrast(): + """Test generalizing cumulative weights with the 'max_feasible_contrast' option.""" + df = weights_table.copy() + + result = generalize_weights_cumulative(df, "max_feasible_contrast", studentized_contrast_threshold=2) + + assert GENERALIZED_CLASS_COLUMN in result.columns.values + + expected_output = pd.Series([2, 2, 2, 2, 2, 2, 1, 1, 1], dtype=np.int64) + pd.testing.assert_series_equal(expected_output, result[GENERALIZED_CLASS_COLUMN], check_names=False) + + with pytest.warns(ClassificationFailedWarning): + result = generalize_weights_cumulative(df, "max_feasible_contrast", studentized_contrast_threshold=2.5) + assert GENERALIZED_CLASS_COLUMN not in result.columns.values + + +def test_cumulative_reclassification_max_studentized_contrast(): + """Test generalizing cumulative weights with the 'max_studentized_contrast' option.""" + df = weights_table.copy() + + result = generalize_weights_cumulative(df, "max_studentized_contrast") + + assert GENERALIZED_CLASS_COLUMN in result.columns.values + + expected_output = pd.Series([2, 2, 2, 2, 2, 2, 1, 1, 1], dtype=np.int64) + pd.testing.assert_series_equal(expected_output, result[GENERALIZED_CLASS_COLUMN], check_names=False) + + with pytest.warns(ClassificationFailedWarning): + df = weights_table.copy() + df = df.head(3) # Last row has the highest studentized contrast + + result = generalize_weights_cumulative(df, "max_studentized_contrast") + assert GENERALIZED_CLASS_COLUMN not in result.columns.values diff --git a/tests/raster_processing/test_distance_to_anomaly.py b/tests/raster_processing/test_distance_to_anomaly.py index 00adac8a..e93bbf3a 100644 --- a/tests/raster_processing/test_distance_to_anomaly.py +++ b/tests/raster_processing/test_distance_to_anomaly.py @@ -1,7 +1,5 @@ -import sys from contextlib import nullcontext from functools import partial -from pathlib import Path import numpy as np import pytest @@ -20,6 +18,45 @@ EXPECTED_SMALL_RASTER_SHAPE = SMALL_RASTER_PROFILE["height"], SMALL_RASTER_PROFILE["width"] +EXPECTED_PYTESTPARAMS = [ + pytest.param( + SMALL_RASTER_PROFILE, + SMALL_RASTER_DATA, + 5.0, + "lower", + EXPECTED_SMALL_RASTER_SHAPE, + 5.6953, + id="small_raster_lower", + ), + pytest.param( + SMALL_RASTER_PROFILE, + SMALL_RASTER_DATA, + 5.0, + "higher", + EXPECTED_SMALL_RASTER_SHAPE, + 6.4521, + id="small_raster_higher", + ), + pytest.param( + SMALL_RASTER_PROFILE, + SMALL_RASTER_DATA, + (2.5, 7.5), + "in_between", + EXPECTED_SMALL_RASTER_SHAPE, + 2.1145, + id="small_raster_in_between", + ), + pytest.param( + SMALL_RASTER_PROFILE, + SMALL_RASTER_DATA, + (2.5, 7.5), + "outside", + EXPECTED_SMALL_RASTER_SHAPE, + 32.4904, + id="small_raster_outside", + ), +] + def _check_result(out_image, out_profile): assert isinstance(out_image, np.ndarray) @@ -38,44 +75,7 @@ def _check_result(out_image, out_profile): "expected_mean", ] ), - [ - pytest.param( - SMALL_RASTER_PROFILE, - SMALL_RASTER_DATA, - 5.0, - "lower", - EXPECTED_SMALL_RASTER_SHAPE, - 5.694903, - id="small_raster_lower", - ), - pytest.param( - SMALL_RASTER_PROFILE, - SMALL_RASTER_DATA, - 5.0, - "higher", - EXPECTED_SMALL_RASTER_SHAPE, - 6.451948, - id="small_raster_higher", - ), - pytest.param( - SMALL_RASTER_PROFILE, - SMALL_RASTER_DATA, - (2.5, 7.5), - "in_between", - EXPECTED_SMALL_RASTER_SHAPE, - 2.114331, - id="small_raster_in_between", - ), - pytest.param( - SMALL_RASTER_PROFILE, - SMALL_RASTER_DATA, - (2.5, 7.5), - "outside", - EXPECTED_SMALL_RASTER_SHAPE, - 32.490106, - id="small_raster_outside", - ), - ], + EXPECTED_PYTESTPARAMS, ) def test_distance_to_anomaly_expected( anomaly_raster_profile, @@ -100,66 +100,7 @@ def test_distance_to_anomaly_expected( assert out_image.shape == expected_shape if expected_mean is not None: - assert np.isclose(np.mean(out_image), expected_mean) - - -@pytest.mark.parametrize( - ",".join( - [ - "anomaly_raster_profile", - "anomaly_raster_data", - "threshold_criteria_value", - "threshold_criteria", - "expected_shape", - "expected_mean", - ] - ), - [ - pytest.param( - SMALL_RASTER_PROFILE, - SMALL_RASTER_DATA, - 5.0, - "higher", - EXPECTED_SMALL_RASTER_SHAPE, - 6.452082, - id="small_raster_higher", - ), - ], -) -@pytest.mark.xfail( - sys.platform == "win32", reason="GDAL utilities are not available on Windows.", raises=ModuleNotFoundError -) -def test_distance_to_anomaly_gdal( - anomaly_raster_profile, - anomaly_raster_data, - threshold_criteria_value, - threshold_criteria, - expected_shape, - expected_mean, - tmp_path, -): - """Test distance_to_anomaly_gdal.""" - - output_path = tmp_path / "output.tif" - result = distance_to_anomaly.distance_to_anomaly_gdal( - anomaly_raster_profile=anomaly_raster_profile, - anomaly_raster_data=anomaly_raster_data, - threshold_criteria_value=threshold_criteria_value, - threshold_criteria=threshold_criteria, - output_path=output_path, - ) - - assert isinstance(result, Path) - assert result.is_file() - - with rasterio.open(result) as result_raster: - - assert result_raster.meta["dtype"] in {"float32", "float64"} - result_raster_data = result_raster.read(1) - - assert result_raster_data.shape == expected_shape - if expected_mean is not None: - assert np.isclose(np.mean(result_raster_data), expected_mean) + assert np.isclose(np.mean(out_image), expected_mean, atol=0.0001) @pytest.mark.parametrize( @@ -318,3 +259,211 @@ def test_distance_to_anomaly_nodata_handling( # Result should not be same as without nodata addition assert not np.isclose(np.mean(out_image), expected_mean_without_nodata) + + +# @pytest.mark.parametrize( +# ",".join( +# [ +# "anomaly_raster_profile", +# "anomaly_raster_data", +# "threshold_criteria_value", +# "threshold_criteria", +# "expected_shape", +# "expected_mean", +# ] +# ), +# EXPECTED_PYTESTPARAMS, +# ) +# @pytest.mark.xfail( +# sys.platform != "win32", reason="gdal_array available only on Windows.", raises=ModuleNotFoundError +# ) +# def test_distance_to_anomaly_gdal_expected( +# anomaly_raster_profile, +# anomaly_raster_data, +# threshold_criteria_value, +# threshold_criteria, +# expected_shape, +# expected_mean, +# ): +# """Test distance_to_anomaly_gdal with expected result.""" + +# assert not np.any(np.isnan(anomaly_raster_data)) +# out_image, out_profile = distance_to_anomaly.distance_to_anomaly_gdal( +# anomaly_raster_profile=anomaly_raster_profile, +# anomaly_raster_data=anomaly_raster_data, +# threshold_criteria_value=threshold_criteria_value, +# threshold_criteria=threshold_criteria, +# ) + +# _check_result(out_image=out_image, out_profile=out_profile) + +# assert out_image.shape == expected_shape +# if expected_mean is not None: +# # adding a relative tolerance and absolute tolerance to accomodate the very small error +# # that arises due to the floating point errors +# assert np.isclose(np.mean(out_image), expected_mean, rtol=1e-2, atol=1e-2) + + +# @pytest.mark.parametrize( +# ",".join( +# [ +# "anomaly_raster_profile", +# "anomaly_raster_data", +# "threshold_criteria_value", +# "threshold_criteria", +# "expected_shape", +# "expected_mean_without_nodata", +# "nodata_mask_value", +# ] +# ), +# [ +# pytest.param( +# SMALL_RASTER_PROFILE, +# SMALL_RASTER_DATA, +# 5.0, +# "higher", +# EXPECTED_SMALL_RASTER_SHAPE, +# 6.451948, +# # Part of values over 5 will now be masked as nodata +# 9.0, +# id="small_raster_with_inserted_nodata", +# ), +# ], +# ) +# @pytest.mark.xfail( +# sys.platform != "win32", reason="gdal_array available only on Windows.", raises=ModuleNotFoundError +# ) +# def test_distance_to_anomaly_gdal_nodata_handling( +# anomaly_raster_profile, +# anomaly_raster_data, +# threshold_criteria_value, +# threshold_criteria, +# expected_shape, +# expected_mean_without_nodata, +# nodata_mask_value, +# ): +# """Test distance_to_anomaly_gdal with expected result.""" + +# anomaly_raster_data_with_nodata = np.where(anomaly_raster_data > nodata_mask_value, np.nan, anomaly_raster_data) +# assert np.any(np.isnan(anomaly_raster_data_with_nodata)) + +# out_image, out_profile = distance_to_anomaly.distance_to_anomaly_gdal( +# anomaly_raster_profile=anomaly_raster_profile, +# anomaly_raster_data=anomaly_raster_data_with_nodata, +# threshold_criteria_value=threshold_criteria_value, +# threshold_criteria=threshold_criteria, +# ) + +# _check_result(out_image=out_image, out_profile=out_profile) + +# assert out_image.shape == expected_shape + +# # Result should not be same as without nodata addition +# assert not np.isclose(np.mean(out_image), expected_mean_without_nodata) + + +# @pytest.mark.parametrize( +# ",".join( +# [ +# "anomaly_raster_profile", +# "anomaly_raster_data", +# "threshold_criteria_value", +# "threshold_criteria", +# "profile_additions", +# "raises", +# ] +# ), +# [ +# pytest.param( +# SMALL_RASTER_PROFILE, +# SMALL_RASTER_DATA, +# (100.5, 122.5), +# "in_between", +# dict, +# partial(pytest.raises, EmptyDataException), +# id="expected_empty_data_due_to_threshold_range_outside_values2", +# ), +# pytest.param( +# SMALL_RASTER_PROFILE, +# SMALL_RASTER_DATA, +# 5.0, +# "lower", +# dict, +# nullcontext, +# id="no_expected_exception", +# ), +# pytest.param( +# SMALL_RASTER_PROFILE, +# SMALL_RASTER_DATA, +# 5.0, +# "higher", +# partial(dict, height=2.2), +# partial(pytest.raises, InvalidParameterValueException), +# id="expected_invalid_param_due_to_float_value_in_profile", +# ), +# pytest.param( +# SMALL_RASTER_PROFILE, +# SMALL_RASTER_DATA, +# 5.0, +# "higher", +# partial(dict, transform=None), +# partial(pytest.raises, InvalidParameterValueException), +# id="expected_invalid_param_due_to_none_transform_value", +# ), +# pytest.param( +# SMALL_RASTER_PROFILE, +# SMALL_RASTER_DATA, +# 5.0, +# "in_between", +# dict, +# partial(pytest.raises, InvalidParameterValueException), +# id="expected_invalid_param_due_to_number_rather_than_range", +# ), +# pytest.param( +# SMALL_RASTER_PROFILE, +# SMALL_RASTER_DATA, +# (7.5, 2.5), +# "in_between", +# dict, +# partial(pytest.raises, InvalidParameterValueException), +# id="expected_invalid_param_due_to_invalid_order_in_tuple", +# ), +# pytest.param( +# SMALL_RASTER_PROFILE, +# SMALL_RASTER_DATA, +# (1.5, 2.5, 7.5), +# "in_between", +# dict, +# partial(pytest.raises, BeartypeCallHintParamViolation), +# id="expected_invalid_param_due_to_tuple_of_length_three", +# ), +# ], +# ) +# @pytest.mark.xfail( +# sys.platform != "win32", reason="gdal_array available only on Windows.", raises=ModuleNotFoundError +# ) +# def test_distance_to_anomaly_gdal_expected_check( +# anomaly_raster_profile, +# anomaly_raster_data, +# threshold_criteria_value, +# threshold_criteria, +# profile_additions, +# raises, +# ): +# """Test distance_to_anomaly_gdal checks.""" + +# anomaly_raster_profile.update(profile_additions()) +# anomaly_raster_profile_with_additions = anomaly_raster_profile +# with raises() as exc_info: +# out_image, out_profile = distance_to_anomaly.distance_to_anomaly_gdal( +# anomaly_raster_profile=anomaly_raster_profile_with_additions, +# anomaly_raster_data=anomaly_raster_data, +# threshold_criteria_value=threshold_criteria_value, +# threshold_criteria=threshold_criteria, +# ) + +# if exc_info is not None: +# # Expected error +# return + +# _check_result(out_image=out_image, out_profile=out_profile) diff --git a/tests/training_data_tools/points_to_raster_test.py b/tests/training_data_tools/points_to_raster_test.py new file mode 100644 index 00000000..3853d124 --- /dev/null +++ b/tests/training_data_tools/points_to_raster_test.py @@ -0,0 +1,88 @@ +from pathlib import Path + +import geopandas as gpd +import pytest +import rasterio + +from eis_toolkit.exceptions import ( + EmptyDataFrameException, + InvalidColumnException, + NonMatchingCrsException, + NonNumericDataException, +) +from eis_toolkit.training_data_tools.points_to_raster import points_to_raster +from tests.raster_processing.clip_test import raster_path as SMALL_RASTER_PATH + +test_dir = Path(__file__).parent.parent +PATH_LABELS_GPKG = test_dir.joinpath("data/remote/interpolating/interpolation_test_data_small.gpkg") + +geodataframe = gpd.read_file(PATH_LABELS_GPKG) +gdf = gpd.GeoDataFrame() + + +@pytest.mark.parametrize("geodataframe", [geodataframe]) # Case where CRS matches +def test_points_to_raster(geodataframe): + """Test that points_to_raster function works as expected.""" + with rasterio.open(SMALL_RASTER_PATH) as temp_raster: + + raster_profile = temp_raster.profile + + outarray, outmeta = points_to_raster( + geodataframe=geodataframe, attribute="value", raster_profile=raster_profile + ) + + assert outarray.shape == ( + temp_raster.height, + temp_raster.width, + ), f"Expected output array shape {(temp_raster.height,temp_raster.width)} but got {outarray.shape}" + + +@pytest.mark.parametrize("geodataframe", [geodataframe]) +def test_InvalidColumnException(geodataframe): + """Test that incorrect attribute raises InvalidColumnException.""" + with pytest.raises(InvalidColumnException): + with rasterio.open(SMALL_RASTER_PATH) as temp_raster: + + raster_profile = temp_raster.profile + + outarray, outmeta = points_to_raster( + geodataframe=geodataframe, attribute="data", raster_profile=raster_profile + ) + + +@pytest.mark.parametrize("geodataframe", [geodataframe]) +def test_Nonnumeric_Data(geodataframe): + """Test that non numeric values in attribute column raises NonNumericDataException.""" + with pytest.raises(NonNumericDataException): + with rasterio.open(SMALL_RASTER_PATH) as temp_raster: + + raster_profile = temp_raster.profile + + outarray, outmeta = points_to_raster( + geodataframe=geodataframe, attribute="id", raster_profile=raster_profile + ) + + +@pytest.mark.parametrize("geodataframe", [gdf]) +def test_Empty_Dataframe(geodataframe): + """Test that empty geodataframe raises EmptyDataFrameException.""" + with pytest.raises(EmptyDataFrameException): + with rasterio.open(SMALL_RASTER_PATH) as temp_raster: + + raster_profile = temp_raster.profile + + outarray, outmeta = points_to_raster( + geodataframe=geodataframe, attribute="id", raster_profile=raster_profile + ) + + +@pytest.mark.parametrize("geodataframe", [geodataframe.to_crs(epsg=4326)]) # Case where CRS do not matches +def test_non_matching_crs_error(geodataframe): + """Test that different crs raises NonMatchingCrsException.""" + + with pytest.raises(NonMatchingCrsException): + with rasterio.open(SMALL_RASTER_PATH) as temp_raster: + raster_profile = temp_raster.profile + outarray, outmeta = points_to_raster( + geodataframe=geodataframe, attribute="value", raster_profile=raster_profile + ) diff --git a/tests/training_data_tools/random_sampling_test.py b/tests/training_data_tools/random_sampling_test.py new file mode 100644 index 00000000..16e89b01 --- /dev/null +++ b/tests/training_data_tools/random_sampling_test.py @@ -0,0 +1,76 @@ +from pathlib import Path + +import geopandas as gpd +import numpy as np +import pytest +import rasterio + +from eis_toolkit.exceptions import EmptyDataFrameException, NumericValueSignException +from eis_toolkit.training_data_tools.points_to_raster import points_to_raster +from eis_toolkit.training_data_tools.random_sampling import generate_negatives +from tests.raster_processing.clip_test import raster_path as SMALL_RASTER_PATH + +test_dir = Path(__file__).parent.parent +PATH_LABELS_GPKG = test_dir.joinpath("data/remote/interpolating/interpolation_test_data_small.gpkg") + +gdf = gpd.read_file(PATH_LABELS_GPKG) + + +@pytest.mark.parametrize("geodataframe, sample_number, random_seed", [(gdf, 10, 30)]) +def test_points_to_raster(geodataframe, sample_number, random_seed): + """Test that generate_negatives function works as expected.""" + with rasterio.open(SMALL_RASTER_PATH) as temp_raster: + raster_profile = temp_raster.profile + + outarray, outmeta = points_to_raster( + geodataframe=geodataframe, attribute="value", raster_profile=raster_profile + ) + + sampled_negatives, outarray2, outmeta2 = generate_negatives( + raster_array=outarray, raster_profile=outmeta, sample_number=sample_number, random_seed=random_seed + ) + + row, col = rasterio.transform.rowcol( + outmeta2["transform"], sampled_negatives.geometry.x, sampled_negatives.geometry.y + ) + + assert outarray2.shape == ( + temp_raster.height, + temp_raster.width, + ), f"Expected output array shape {(temp_raster.height, temp_raster.width)} but got {outarray2.shape}" + + assert (outarray2[row, col] == -1).all() + + +@pytest.mark.parametrize("geodataframe, sample_number, random_seed", [(gdf, 10, 30)]) +def test_Empty_Data_Frame_exception(geodataframe, sample_number, random_seed): + """Test that generate_negatives function raises EmptyDataFrameException for an empty raster array.""" + with pytest.raises(EmptyDataFrameException): + with rasterio.open(SMALL_RASTER_PATH) as temp_raster: + raster_profile = temp_raster.profile + + outarray, outmeta = points_to_raster( + geodataframe=geodataframe, attribute="value", raster_profile=raster_profile + ) + + outarray = np.array([]) + + sampled_negatives, outarray2, outmeta2 = generate_negatives( + raster_array=outarray, raster_profile=outmeta, sample_number=sample_number, random_seed=random_seed + ) + + +@pytest.mark.parametrize("geodataframe, sample_number, random_seed", [(gdf, -10, 30), (gdf, 0, 30)]) +def test_Numeric_value_sign_exception(geodataframe, sample_number, random_seed): + """Test that generate_negatives function raises NumericValueSignException for negative and zero sample number.""" + with pytest.raises(NumericValueSignException): + with rasterio.open(SMALL_RASTER_PATH) as temp_raster: + raster_profile = temp_raster.profile + + outarray, outmeta = points_to_raster( + geodataframe=geodataframe, attribute="value", raster_profile=raster_profile + ) + + sampled_negatives, outarray2, outmeta2 = generate_negatives( + raster_array=outarray, raster_profile=outmeta, sample_number=sample_number, random_seed=random_seed + ) diff --git a/tests/transformations/coda/alr_test.py b/tests/transformations/coda/alr_test.py index 90049350..f6a41965 100644 --- a/tests/transformations/coda/alr_test.py +++ b/tests/transformations/coda/alr_test.py @@ -9,34 +9,23 @@ SAMPLE_DATAFRAME = pd.DataFrame(sample_array, columns=["a", "b", "c", "d"]) -def test_alr_transform_simple(): - """Test ALR transformation core functionality.""" - ones_df_4x4 = pd.DataFrame(np.ones((4, 4)), columns=["a", "b", "c", "d"]) - zeros_df_4x4 = pd.DataFrame(np.zeros((4, 3)), columns=["V1", "V2", "V3"]) - result = alr_transform(ones_df_4x4) - pd.testing.assert_frame_equal(result, zeros_df_4x4) - - def test_alr_transform(): """Test ALR transformation core functionality.""" - arr = np.array([[1, 4, 1, 1], [2, 1, 2, 2]]) + arr = np.random.dirichlet(np.ones(4), size=4) df = pd.DataFrame(arr, columns=["a", "b", "c", "d"], dtype=np.float64) result = alr_transform(df, column="b", keep_denominator_column=True) expected = pd.DataFrame( - { - "V1": [np.log(0.25), np.log(2)], - "V2": [0, 0], - "V3": [np.log(0.25), np.log(2)], - "V4": [np.log(0.25), np.log(2)], - }, + np.log(arr / arr[:, 1, None]), + columns=["V1", "V2", "V3", "V4"], dtype=np.float64, ) pd.testing.assert_frame_equal(result, expected) result = alr_transform(df, column="b") expected = pd.DataFrame( - {"V1": [np.log(0.25), np.log(2)], "V2": [np.log(0.25), np.log(2)], "V3": [np.log(0.25), np.log(2)]}, + np.log(np.delete(arr, 1, axis=1) / arr[:, 1, None]), + columns=["V1", "V2", "V3"], dtype=np.float64, ) pd.testing.assert_frame_equal(result, expected) diff --git a/tests/transformations/coda/clr_test.py b/tests/transformations/coda/clr_test.py index 41459035..03eb9815 100644 --- a/tests/transformations/coda/clr_test.py +++ b/tests/transformations/coda/clr_test.py @@ -9,19 +9,16 @@ SAMPLE_DATAFRAME = pd.DataFrame(sample_array, columns=["a", "b", "c", "d"]) -def test_clr_transform_simple(): - """Test CLR transform core functionality.""" - ones_df_4x4 = pd.DataFrame(np.ones((4, 4)), columns=["a", "b", "c", "d"]) - zeros_df_4x4 = pd.DataFrame(np.zeros((4, 4)), columns=["V1", "V2", "V3", "V4"]) - result = clr_transform(ones_df_4x4) - pd.testing.assert_frame_equal(result, zeros_df_4x4) - - def test_clr_transform(): """Test CLR transform core functionality.""" - result = clr_transform(SAMPLE_DATAFRAME) + arr = np.random.dirichlet(np.ones(4), size=4) + df = pd.DataFrame(arr, columns=["a", "b", "c", "d"], dtype=np.float64) + result = clr_transform(df) + geometric_means = np.prod(arr, axis=1) ** (1 / arr.shape[1]) expected = pd.DataFrame( - {"V1": [1.38, 1.29], "V2": [-0.30, -0.08], "V3": [0.10, -0.15], "V4": [-1.18, -1.06]}, dtype=np.float64 + np.log(arr / geometric_means[:, None]), + columns=["V1", "V2", "V3", "V4"], + dtype=np.float64, ) pd.testing.assert_frame_equal(result, expected, atol=1e-2) diff --git a/tests/transformations/linear_test.py b/tests/transformations/linear_test.py index 26c207c6..8bd14dab 100644 --- a/tests/transformations/linear_test.py +++ b/tests/transformations/linear_test.py @@ -4,11 +4,7 @@ import pytest import rasterio -from eis_toolkit.exceptions import ( - InvalidParameterValueException, - InvalidRasterBandException, - NonMatchingParameterLengthsException, -) +from eis_toolkit.exceptions import InvalidRasterBandException, NonMatchingParameterLengthsException from eis_toolkit.transformations.linear import ( _min_max_scaling, _z_score_normalization, @@ -106,12 +102,3 @@ def test_linear_parameter_length(): # Invalid new_range min_max_scaling(raster=raster, bands=[1, 2, 3], new_range=[(0, 1), (0, 2)], nodata=None) min_max_scaling(raster=raster, bands=[1, 2], new_range=[(0, 1), (0, 2), (0, 3)], nodata=None) - - -def test_linear_min_max_position(): - """Tests that invalid min-max positions for provided parameters raises the correct exception.""" - with pytest.raises(InvalidParameterValueException): - with rasterio.open(raster_path) as raster: - # Invalid position of minimum and maximum values for new_range - min_max_scaling(raster=raster, bands=None, new_range=[(1, 0)], nodata=None) - min_max_scaling(raster=raster, bands=[1, 2, 3], new_range=[(0, 0), (1, 0), (2, 0)], nodata=None) diff --git a/tests/utilities/compositional_test.py b/tests/utilities/compositional_test.py index ec655e5c..d7613f7e 100644 --- a/tests/utilities/compositional_test.py +++ b/tests/utilities/compositional_test.py @@ -1,83 +1,111 @@ -import numpy as np -import pandas as pd -import pytest - -from eis_toolkit.exceptions import InvalidCompositionException, NumericValueSignException -from eis_toolkit.transformations.coda.alr import alr_transform -from eis_toolkit.transformations.coda.clr import clr_transform -from eis_toolkit.transformations.coda.ilr import single_ilr_transform -from eis_toolkit.transformations.coda.plr import plr_transform, single_plr_transform -from eis_toolkit.utilities.checks.compositional import check_in_simplex_sample_space - - -def test_compositional_data_has_zeros(): - """Test that performing logratio transforms for data containing zeros raises the correct exception.""" - arr = np.array([[80, 0, 5], [75, 18, 7]]) - df = pd.DataFrame(arr, columns=["a", "b", "c"]) - with pytest.raises(NumericValueSignException): - alr_transform(df) - with pytest.raises(NumericValueSignException): - clr_transform(df) - with pytest.raises(NumericValueSignException): - single_ilr_transform(df, ["a"], ["b"]) - with pytest.raises(NumericValueSignException): - plr_transform(df) - with pytest.raises(NumericValueSignException): - single_plr_transform(df, "b") - - -def test_compositional_data_has_negatives(): - """Test that performing logratio transforms for data containing negative values raises the correct exception.""" - arr = np.array([[80, 25, -5], [75, 32, -7]]) - df = pd.DataFrame(arr, columns=["a", "b", "c"]) - with pytest.raises(NumericValueSignException): - alr_transform(df) - with pytest.raises(NumericValueSignException): - clr_transform(df) - with pytest.raises(NumericValueSignException): - single_ilr_transform(df, ["a"], ["b"]) - with pytest.raises(NumericValueSignException): - plr_transform(df) - with pytest.raises(NumericValueSignException): - single_plr_transform(df, "b") - - -def test_compositional_data_has_nans(): - """Test that performing logratio transforms for data containing NaN values raises the correct exception.""" - df = pd.DataFrame(np.ones((3, 3)), columns=["a", "b", "c"]) - df.iloc[:, 0] = np.NaN - with pytest.raises(InvalidCompositionException): - alr_transform(df) - with pytest.raises(InvalidCompositionException): - clr_transform(df) - with pytest.raises(InvalidCompositionException): - single_ilr_transform(df, ["a"], ["b"]) - with pytest.raises(InvalidCompositionException): - plr_transform(df) - with pytest.raises(InvalidCompositionException): - single_plr_transform(df, "b") - - -def test_check_for_simplex_sample_space(): - """Test whether or not a dataframe belongs to a simplex sample space is correctly identified.""" - unit_simplex_df = pd.DataFrame([[0.1, 0.2, 0.3, 0.4], [0.2, 0.3, 0.2, 0.3]]) - simplex_df = pd.DataFrame([[1, 2, 3, 4], [2, 3, 2, 3]], columns=["a", "b", "c", "d"]) - non_simplex_positive_df = pd.DataFrame([1, 2, 3, 4], [5, 6, 7, 8]) - non_positive_df = pd.DataFrame([-1, 2, 3, 4], [1, 2, 3, 4]) - - with pytest.raises(InvalidCompositionException): - check_in_simplex_sample_space(non_simplex_positive_df) - - with pytest.raises(NumericValueSignException): - check_in_simplex_sample_space(non_positive_df) - - with pytest.raises(InvalidCompositionException): - check_in_simplex_sample_space(simplex_df, np.float64(100)) - - # Valid cases - assert no exception is raised - try: - check_in_simplex_sample_space(simplex_df) - check_in_simplex_sample_space(simplex_df, np.float64(10)) - check_in_simplex_sample_space(unit_simplex_df, np.float64(1.0)) - except Exception as ex: - assert False, f"{type(ex)}: {ex}" +import numpy as np +import pandas as pd +import pytest + +from eis_toolkit.exceptions import InvalidCompositionException, NumericValueSignException +from eis_toolkit.transformations.coda.alr import alr_transform +from eis_toolkit.transformations.coda.clr import clr_transform +from eis_toolkit.transformations.coda.ilr import single_ilr_transform +from eis_toolkit.transformations.coda.plr import plr_transform, single_plr_transform +from eis_toolkit.utilities.checks.compositional import check_in_simplex_sample_space + + +def test_compositional_data_has_zeros(): + """Test that performing logratio transforms for data containing zeros raises the correct exception.""" + arr = np.array([[80, 0, 5], [75, 18, 7]]) + df = pd.DataFrame(arr, columns=["a", "b", "c"]) + with pytest.raises(NumericValueSignException): + alr_transform(df) + with pytest.raises(NumericValueSignException): + clr_transform(df) + with pytest.raises(NumericValueSignException): + single_ilr_transform(df, ["a"], ["b"]) + with pytest.raises(NumericValueSignException): + plr_transform(df) + with pytest.raises(NumericValueSignException): + single_plr_transform(df, "b") + + +def test_compositional_data_has_negatives(): + """Test that performing logratio transforms for data containing negative values raises the correct exception.""" + arr = np.array([[80, 25, -5], [75, 32, -7]]) + df = pd.DataFrame(arr, columns=["a", "b", "c"]) + with pytest.raises(NumericValueSignException): + alr_transform(df) + with pytest.raises(NumericValueSignException): + clr_transform(df) + with pytest.raises(NumericValueSignException): + single_ilr_transform(df, ["a"], ["b"]) + with pytest.raises(NumericValueSignException): + plr_transform(df) + with pytest.raises(NumericValueSignException): + single_plr_transform(df, "b") + + +def test_compositional_data_has_nans(): + """Test that performing logratio transforms for data containing NaN values raises the correct exception.""" + df = pd.DataFrame(np.ones((3, 3)), columns=["a", "b", "c"]) + df.iloc[:, 0] = np.NaN + with pytest.raises(InvalidCompositionException): + alr_transform(df) + with pytest.raises(InvalidCompositionException): + clr_transform(df) + with pytest.raises(InvalidCompositionException): + single_ilr_transform(df, ["a"], ["b"]) + with pytest.raises(InvalidCompositionException): + plr_transform(df) + with pytest.raises(InvalidCompositionException): + single_plr_transform(df, "b") + + +def test_compositional_data_invalid(): + """Test that input data that does not belong to a simplex sample space raises the correct exception.""" + arr = np.array([[1, 1, 1], [2, 2, 2]]) + df = pd.DataFrame(arr, columns=["a", "b", "c"]) + with pytest.raises(InvalidCompositionException): + alr_transform(df) + with pytest.raises(InvalidCompositionException): + clr_transform(df) + with pytest.raises(InvalidCompositionException): + single_ilr_transform(df, ["a"], ["b"]) + with pytest.raises(InvalidCompositionException): + plr_transform(df) + with pytest.raises(InvalidCompositionException): + single_plr_transform(df, "b") + + +def test_check_for_simplex_sample_space(): + """Test whether or not a dataframe belongs to a simplex sample space is correctly identified.""" + unit_simplex_df = pd.DataFrame([[0.1, 0.2, 0.3, 0.4], [0.2, 0.3, 0.2, 0.3]]) + closed_to_hundred_df = unit_simplex_df * 100 + closed_to_million_df = unit_simplex_df * 1e6 + closed_to_billion_df = unit_simplex_df * 1e9 + non_simplex_positive_df = pd.DataFrame([1, 2, 3, 4], [5, 6, 7, 8]) + non_positive_df = pd.DataFrame([-1, 2, 3, 4], [1, 2, 3, 4]) + + with pytest.raises(InvalidCompositionException): + check_in_simplex_sample_space(non_simplex_positive_df) + + with pytest.raises(NumericValueSignException): + check_in_simplex_sample_space(non_positive_df) + + # Valid cases - assert no exception is raised + try: + check_in_simplex_sample_space(unit_simplex_df) + except Exception as ex: + assert False, f"{type(ex)}: {ex}" + + try: + check_in_simplex_sample_space(closed_to_hundred_df) + except Exception as ex: + assert False, f"{type(ex)}: {ex}" + + try: + check_in_simplex_sample_space(closed_to_million_df) + except Exception as ex: + assert False, f"{type(ex)}: {ex}" + + try: + check_in_simplex_sample_space(closed_to_billion_df) + except Exception as ex: + assert False, f"{type(ex)}: {ex}" diff --git a/tests/utilities/nodata_test.py b/tests/utilities/nodata_test.py index be5d5179..aaa88c25 100644 --- a/tests/utilities/nodata_test.py +++ b/tests/utilities/nodata_test.py @@ -8,6 +8,7 @@ handle_nodata_as_nan, nan_to_nodata, nodata_to_nan, + replace_with_nodata, set_raster_nodata, unify_raster_nodata, ) @@ -38,6 +39,76 @@ def test_convert_raster_nodata(): assert out_meta["nodata"] == -999 +def test_replace_with_nodata(): + """Test that replacing raster pixel values with nodata works as expected.""" + target_value = 2.705 + nodata_value = -999 + + with rasterio.open(SMALL_RASTER_PATH) as raster: + raster_data = raster.read() + nr_of_pixels = np.count_nonzero(raster_data == target_value) + assert nr_of_pixels > 0 + assert np.count_nonzero(raster_data == nodata_value) == 0 + + replace_condition = "equal" + out_image, out_meta = replace_with_nodata(raster, target_value, nodata_value, replace_condition) + assert np.count_nonzero(out_image == nodata_value) > 0 + assert np.count_nonzero(out_image == target_value) == 0 + assert out_meta["nodata"] == -999 + + with rasterio.open(SMALL_RASTER_PATH) as raster: + raster_data = raster.read() + # Ensure some pixels exist that are less than the target value + nr_of_pixels_less_than_target = np.count_nonzero(raster_data < target_value) + assert nr_of_pixels_less_than_target > 0 + + replace_condition = "less_than" + out_image, out_meta = replace_with_nodata(raster, target_value, nodata_value, replace_condition) + + assert np.count_nonzero(out_image == nodata_value) == nr_of_pixels_less_than_target + assert np.count_nonzero((out_image < target_value) & (out_image != nodata_value)) == 0 + assert out_meta["nodata"] == -999 + + with rasterio.open(SMALL_RASTER_PATH) as raster: + raster_data = raster.read() + # Ensure some pixels exist that are greater than the target value + nr_of_pixels_greater_than_target = np.count_nonzero(raster_data > target_value) + assert nr_of_pixels_greater_than_target > 0 + + replace_condition = "greater_than" + out_image, out_meta = replace_with_nodata(raster, target_value, nodata_value, replace_condition) + + assert np.count_nonzero(out_image == nodata_value) == nr_of_pixels_greater_than_target + assert np.count_nonzero(out_image > target_value) == 0 + assert out_meta["nodata"] == -999 + + with rasterio.open(SMALL_RASTER_PATH) as raster: + raster_data = raster.read() + # Ensure some pixels exist that are less than or equal to the target value + nr_of_pixels_less_than_or_equal = np.count_nonzero(raster_data <= target_value) + assert nr_of_pixels_less_than_or_equal > 0 + + replace_condition = "less_than_or_equal" + out_image, out_meta = replace_with_nodata(raster, target_value, nodata_value, replace_condition) + + assert np.count_nonzero(out_image == nodata_value) == nr_of_pixels_less_than_or_equal + assert np.count_nonzero((out_image <= target_value) & (out_image != nodata_value)) == 0 + assert out_meta["nodata"] == -999 + + with rasterio.open(SMALL_RASTER_PATH) as raster: + raster_data = raster.read() + # Ensure some pixels exist that are greater than or equal to the target value + nr_of_pixels_greater_than_or_equal = np.count_nonzero(raster_data >= target_value) + assert nr_of_pixels_greater_than_or_equal > 0 + + replace_condition = "greater_than_or_equal" + out_image, out_meta = replace_with_nodata(raster, target_value, nodata_value, replace_condition) + + assert np.count_nonzero(out_image == nodata_value) == nr_of_pixels_greater_than_or_equal + assert np.count_nonzero(out_image >= target_value) == 0 + assert out_meta["nodata"] == -999 + + def test_unify_raster_nodata(): """Test that unifying raster nodata for multiple rasters works as expected.""" with rasterio.open(SMALL_RASTER_PATH) as raster: diff --git a/tests/utilities/raster_test.py b/tests/utilities/raster_test.py index becf1fc4..95c7330d 100644 --- a/tests/utilities/raster_test.py +++ b/tests/utilities/raster_test.py @@ -1,3 +1,5 @@ +from pathlib import Path + import numpy as np import rasterio from rasterio import profiles @@ -8,9 +10,11 @@ split_raster_bands, stack_raster_arrays, ) -from tests.exploratory_analyses.pca_test import MULTIBAND_RASTER_PATH from tests.raster_processing.clip_test import raster_path as SMALL_RASTER_PATH +TESTS_DIR = Path(__file__).parent.parent +MULTIBAND_RASTER_PATH = TESTS_DIR.joinpath("data/remote/small_raster_multiband.tif") + def test_split_raster_bands(): """Test that splitting a multiband raster into singleband raster works as expected.""" diff --git a/tests/vector_processing/distance_computation_test.py b/tests/vector_processing/distance_computation_test.py index 5e0e85f5..57c9febc 100644 --- a/tests/vector_processing/distance_computation_test.py +++ b/tests/vector_processing/distance_computation_test.py @@ -47,7 +47,7 @@ POINT_GEOMETRIES_WITHIN_SMALL_RASTER, EXPECTED_SMALL_RASTER_SHAPE, 0.0, - 107.83784122468327, + 107.51744, id="point_geometries_within_small_raster", ), pytest.param( @@ -55,7 +55,7 @@ LINE_GEOMETRIES_WITHIN_SMALL_RASTER, EXPECTED_SMALL_RASTER_SHAPE, 0.0, - 107.83784122468327, + 107.51744, id="line_geometries_within_small_raster", ), pytest.param( @@ -63,7 +63,7 @@ POLYGON_GEOMETRIES_WITHIN_SMALL_RASTER, EXPECTED_SMALL_RASTER_SHAPE, 0.0, - 91.0, + 92.0, id="polygon_geometries_within_small_raster", ), ], @@ -73,12 +73,12 @@ def test_distance_computation_with_expected_results( ): """Test distance_computation.""" - result = distance_computation(raster_profile=raster_profile, geodataframe=geodataframe) + out_image, _ = distance_computation(raster_profile=raster_profile, geodataframe=geodataframe) - assert isinstance(result, np.ndarray) - assert result.shape == expected_shape - assert np.isclose(result.min(), expected_min) - assert np.isclose(result.max(), expected_max) + assert isinstance(out_image, np.ndarray) + assert out_image.shape == expected_shape + assert np.isclose(out_image.min(), expected_min, atol=0.0001) + assert np.isclose(out_image.max(), expected_max, atol=0.0001) @pytest.mark.parametrize( @@ -115,6 +115,6 @@ def test_distance_computation(raster_profile, geodataframe, expected_error): """Test distance_computation.""" with expected_error: - result = distance_computation(raster_profile=raster_profile, geodataframe=geodataframe) - assert isinstance(result, np.ndarray) - assert len(result.shape) == 2 + out_image, _ = distance_computation(raster_profile=raster_profile, geodataframe=geodataframe) + assert isinstance(out_image, np.ndarray) + assert len(out_image.shape) == 2 diff --git a/tests/vector_processing/idw_interpolation_test.py b/tests/vector_processing/idw_interpolation_test.py index e5911198..aed9ec52 100644 --- a/tests/vector_processing/idw_interpolation_test.py +++ b/tests/vector_processing/idw_interpolation_test.py @@ -13,6 +13,7 @@ test_dir = Path(__file__).parent.parent idw_test_data = test_dir.joinpath("data/remote/interpolating/idw_test_data.tif") +idw_radius_test_data = test_dir.joinpath("data/remote/interpolating/idw_radius_test_data.tif") @pytest.fixture @@ -74,6 +75,24 @@ def test_validated_points_with_extent(validated_points, raster_profile): np.testing.assert_almost_equal(interpolated_values, external_values, decimal=2) +def test_validated_points_with_radius(validated_points, raster_profile): + """Test IDW with search radius.""" + target_column = "random_number" + interpolated_values = idw( + geodataframe=validated_points, + target_column=target_column, + raster_profile=raster_profile, + power=2, + search_radius=0.5, + ) + assert target_column in validated_points.columns + + with rasterio.open(idw_radius_test_data) as src: + external_values = src.read(1) + + np.testing.assert_almost_equal(interpolated_values, external_values, decimal=2) + + def test_invalid_column(test_points, raster_profile): """Test invalid column GeoDataFrame.""" target_column = "not-in-data-column" diff --git a/tests/vector_processing/proximity_computation_test.py b/tests/vector_processing/proximity_computation_test.py new file mode 100644 index 00000000..7af782a3 --- /dev/null +++ b/tests/vector_processing/proximity_computation_test.py @@ -0,0 +1,93 @@ +import sys + +import geopandas as gpd +import numpy as np +import pytest +import rasterio + +from eis_toolkit.exceptions import NumericValueSignException +from eis_toolkit.vector_processing.proximity_computation import proximity_computation +from tests.raster_processing.clip_test import polygon_path as polygon_path +from tests.raster_processing.clip_test import raster_path as SMALL_RASTER_PATH + +sys.path.append("../..") +gdf = gpd.read_file(polygon_path) + +with rasterio.open(SMALL_RASTER_PATH) as test_raster: + raster_profile = test_raster.profile + +EXPECTED_SMALL_RASTER_SHAPE = raster_profile["height"], raster_profile["width"] + + +@pytest.mark.parametrize( + "geodataframe,raster_profile,expected_shape,maximum_distance,scaling_range", + [ + pytest.param( + gdf, + raster_profile, + EXPECTED_SMALL_RASTER_SHAPE, + 25, + (1, 0), + id="Inversion_and_scaling_between_1_and_0", + ), + pytest.param( + gdf, + raster_profile, + EXPECTED_SMALL_RASTER_SHAPE, + 25, + (2, 1), + id="Inversion_and_scaling_between_2_and_1", + ), + ], +) +def test_proximity_computation_inversion_with_expected_result( + geodataframe, raster_profile, expected_shape, maximum_distance, scaling_range +): + """Tests if the enteries in the output matrix are between the minimum and maximum value.""" + + out_image, _ = proximity_computation(geodataframe, raster_profile, maximum_distance, scaling_range) + + assert out_image.shape == expected_shape + # Assert that all values in result within scaling_range + assert np.all((out_image >= scaling_range[1]) & (out_image <= scaling_range[0])), "Scaling out of scaling_range" + + +@pytest.mark.parametrize( + "geodataframe,raster_profile,expected_shape,maximum_distance,scaling_range", + [ + pytest.param( + gdf, + raster_profile, + EXPECTED_SMALL_RASTER_SHAPE, + 25, + (0, 1), + id="Scaling_between_0_and_1", + ), + pytest.param( + gdf, + raster_profile, + EXPECTED_SMALL_RASTER_SHAPE, + 25, + (1, 2), + id="Scaling_between_1_and_2", + ), + ], +) +def test_proximity_computation_with_expected_result( + geodataframe, raster_profile, expected_shape, maximum_distance, scaling_range +): + """Tests if the enteries in the output matrix are between the minimum and maximum value.""" + + out_image, _ = proximity_computation(geodataframe, raster_profile, maximum_distance, scaling_range) + + assert out_image.shape == expected_shape + # Assert that all values in result within scaling_range + assert np.all((out_image <= scaling_range[1]) & (out_image >= scaling_range[0])), "Scaling out of scaling_range" + + +def test_proximity_computation_with_expected_error(): + """Tests if an exception is raised for a negative maximum distance.""" + + with pytest.raises(NumericValueSignException, match="Expected max distance to be a positive number."): + out_image, _ = proximity_computation(gdf, raster_profile, -25, (1, 0)) + assert np.all((out_image >= 0) & (out_image <= 1)), "Scaling out of scaling_range"