From 50bc7fe7b33e14672ab44c6e660f6a574dd420cc Mon Sep 17 00:00:00 2001 From: Simon <31246246+SimonMolinsky@users.noreply.github.com> Date: Fri, 20 Jan 2023 17:49:16 +0200 Subject: [PATCH 01/29] Updated tests and functional test for smoothing blocks --- tests/{dataviz => dev_tests}/__init__.py | 0 .../dataviz}/__init__.py | 0 tests/{ => dev_tests}/dataviz/aggregated.py | 0 .../dataviz/interpolate_raster.py | 0 .../dataviz/select_triangles.py | 0 .../dataviz/variogram_clouds.py | 0 .../debug_sandbox}/__init__.py | 0 .../analyze_area_to_area_pk_errors.py | 2 +- tests/{ => dev_tests}/debug_sandbox/angles.py | 0 .../debug_sandbox/ata_pk_from_cov.py | 0 .../debug_sandbox/ata_pk_from_cov_up.py | 2 +- .../debug_sandbox/ata_pk_to_atp_pk.py | 2 +- .../debug_sandbox/check_atp_pk}/__init__.py | 0 .../check_atp_pk/check_atp_from_cov.py | 2 +- .../debug_sandbox/check_cent_pk}/__init__.py | 0 .../debug_sandbox/check_cent_pk/check.py | 2 +- .../debug_sandbox/directional_ok.py | 2 +- ...rectional_variogram_div_by_zero_warning.py | 0 .../debug_sandbox/kriging_from_cov.py | 2 +- .../test_ata_pk_data/compare_ata_atp.csv | 0 .../test_ata_pk_data/compare_ata_atp2.csv | 0 .../test_ata_pk_data/compare_ata_atp3.csv | 0 .../test_ata_pk_data/project.qgz | Bin .../test_ata_pk_data/wrong_areas.cpg | 0 .../test_ata_pk_data/wrong_areas.dbf | Bin .../test_ata_pk_data/wrong_areas.prj | 0 .../test_ata_pk_data/wrong_areas.qmd | 0 .../test_ata_pk_data/wrong_areas.shp | Bin .../test_ata_pk_data/wrong_areas.shx | Bin .../debug_sandbox/test_ok_data}/__init__.py | 0 .../test_ok_data/check_results.csv | 0 .../test_ok_data/check_results.ipynb | 0 .../debug_sandbox/test_ok_data/data_test.py | 0 .../debug_sandbox/test_ok_data/k.csv | 0 .../debug_sandbox/test_ok_data/weights.csv | 0 .../debug_sandbox/test_ok_data/weights2.csv | 0 .../point => dev_tests/profile}/__init__.py | 0 .../profile/kriging}/__init__.py | 0 .../profile/kriging/block}/__init__.py | 0 .../kriging/block/ataprofile_v0.3.0.profile | Bin .../kriging/block/atpprofile_v0.3.0.profile | Bin .../kriging/block/cbprofile_v0.3.0.profile | Bin .../profile/kriging/block/profile_ata_pk.py | 0 .../profile/kriging/block/profile_atp_pk.py | 0 .../profile/kriging/block/profile_cb_pk.py | 0 .../profile/kriging/point/__init__.py | 0 .../point/kmultprofile_v0.3.0.clean.profile | Bin .../point/kmultprofile_v0.3.0.dask.profile | Bin .../kriging/point/okprofile_v0.3.0.profile | Bin .../profile/kriging/point/profile_kriging.py | 0 .../kriging/point/profile_ordinary_kriging.py | 0 .../kriging/point/profile_simple_kriging.py | 0 .../kriging/point/skprofile_v0.3.0.profile | Bin tests/dev_tests/profile/pipelines/__init__.py | 0 .../profile/pipelines/air_quality_sample.csv | 0 .../pipelines/get_air_quality_v0.3.0.profile | Bin .../pipelines/profile_download_air_quality.py | 0 .../profile/samples/cancer_data.gpkg | Bin .../profile/samples/pl_dem_epsg2180.txt | 0 .../samples/regularized_variogram.json | 0 .../profile/semivariance/__init__.py | 0 .../circular_power_changed_v0.3.0.profile | Bin .../semivariance/circular_v0.3.0.profile | Bin .../profile/semivariance/cubic_v0.3.0.profile | Bin .../profile/semivariance/decon_v0.3.0.profile | Bin .../semivariance/profile_circular_model.py | 0 .../semivariance/profile_cubic_model.py | 0 ...file_directional_variogram_ellipse.profile | Bin .../profile_directional_variogram_methods.py | 0 ...ile_directional_variogram_triangle.profile | Bin .../profile_inblock_semivariance.py | 0 .../profile_semivarogram_regularization.py | 0 tests/test_pipelines/test_smooth_areas.py | 42 ++++++++++++++++++ 73 files changed, 49 insertions(+), 7 deletions(-) rename tests/{dataviz => dev_tests}/__init__.py (100%) rename tests/{debug_sandbox => dev_tests/dataviz}/__init__.py (100%) rename tests/{ => dev_tests}/dataviz/aggregated.py (100%) rename tests/{ => dev_tests}/dataviz/interpolate_raster.py (100%) rename tests/{ => dev_tests}/dataviz/select_triangles.py (100%) rename tests/{ => dev_tests}/dataviz/variogram_clouds.py (100%) rename tests/{debug_sandbox/test_ok_data => dev_tests/debug_sandbox}/__init__.py (100%) rename tests/{ => dev_tests}/debug_sandbox/analyze_area_to_area_pk_errors.py (97%) rename tests/{ => dev_tests}/debug_sandbox/angles.py (100%) rename tests/{ => dev_tests}/debug_sandbox/ata_pk_from_cov.py (100%) rename tests/{ => dev_tests}/debug_sandbox/ata_pk_from_cov_up.py (99%) rename tests/{ => dev_tests}/debug_sandbox/ata_pk_to_atp_pk.py (98%) rename tests/{profile => dev_tests/debug_sandbox/check_atp_pk}/__init__.py (100%) rename tests/{ => dev_tests}/debug_sandbox/check_atp_pk/check_atp_from_cov.py (99%) rename tests/{profile/kriging => dev_tests/debug_sandbox/check_cent_pk}/__init__.py (100%) rename tests/{ => dev_tests}/debug_sandbox/check_cent_pk/check.py (99%) rename tests/{ => dev_tests}/debug_sandbox/directional_ok.py (99%) rename tests/{ => dev_tests}/debug_sandbox/directional_variogram_div_by_zero_warning.py (100%) rename tests/{ => dev_tests}/debug_sandbox/kriging_from_cov.py (98%) rename tests/{ => dev_tests}/debug_sandbox/test_ata_pk_data/compare_ata_atp.csv (100%) rename tests/{ => dev_tests}/debug_sandbox/test_ata_pk_data/compare_ata_atp2.csv (100%) rename tests/{ => dev_tests}/debug_sandbox/test_ata_pk_data/compare_ata_atp3.csv (100%) rename tests/{ => dev_tests}/debug_sandbox/test_ata_pk_data/project.qgz (100%) rename tests/{ => dev_tests}/debug_sandbox/test_ata_pk_data/wrong_areas.cpg (100%) rename tests/{ => dev_tests}/debug_sandbox/test_ata_pk_data/wrong_areas.dbf (100%) rename tests/{ => dev_tests}/debug_sandbox/test_ata_pk_data/wrong_areas.prj (100%) rename tests/{ => dev_tests}/debug_sandbox/test_ata_pk_data/wrong_areas.qmd (100%) rename tests/{ => dev_tests}/debug_sandbox/test_ata_pk_data/wrong_areas.shp (100%) rename tests/{ => dev_tests}/debug_sandbox/test_ata_pk_data/wrong_areas.shx (100%) rename tests/{profile/kriging/block => dev_tests/debug_sandbox/test_ok_data}/__init__.py (100%) rename tests/{ => dev_tests}/debug_sandbox/test_ok_data/check_results.csv (100%) rename tests/{ => dev_tests}/debug_sandbox/test_ok_data/check_results.ipynb (100%) rename tests/{ => dev_tests}/debug_sandbox/test_ok_data/data_test.py (100%) rename tests/{ => dev_tests}/debug_sandbox/test_ok_data/k.csv (100%) rename tests/{ => dev_tests}/debug_sandbox/test_ok_data/weights.csv (100%) rename tests/{ => dev_tests}/debug_sandbox/test_ok_data/weights2.csv (100%) rename tests/{profile/kriging/point => dev_tests/profile}/__init__.py (100%) rename tests/{profile/pipelines => dev_tests/profile/kriging}/__init__.py (100%) rename tests/{profile/semivariance => dev_tests/profile/kriging/block}/__init__.py (100%) rename tests/{ => dev_tests}/profile/kriging/block/ataprofile_v0.3.0.profile (100%) rename tests/{ => dev_tests}/profile/kriging/block/atpprofile_v0.3.0.profile (100%) rename tests/{ => dev_tests}/profile/kriging/block/cbprofile_v0.3.0.profile (100%) rename tests/{ => dev_tests}/profile/kriging/block/profile_ata_pk.py (100%) rename tests/{ => dev_tests}/profile/kriging/block/profile_atp_pk.py (100%) rename tests/{ => dev_tests}/profile/kriging/block/profile_cb_pk.py (100%) create mode 100644 tests/dev_tests/profile/kriging/point/__init__.py rename tests/{ => dev_tests}/profile/kriging/point/kmultprofile_v0.3.0.clean.profile (100%) rename tests/{ => dev_tests}/profile/kriging/point/kmultprofile_v0.3.0.dask.profile (100%) rename tests/{ => dev_tests}/profile/kriging/point/okprofile_v0.3.0.profile (100%) rename tests/{ => dev_tests}/profile/kriging/point/profile_kriging.py (100%) rename tests/{ => dev_tests}/profile/kriging/point/profile_ordinary_kriging.py (100%) rename tests/{ => dev_tests}/profile/kriging/point/profile_simple_kriging.py (100%) rename tests/{ => dev_tests}/profile/kriging/point/skprofile_v0.3.0.profile (100%) create mode 100644 tests/dev_tests/profile/pipelines/__init__.py rename tests/{ => dev_tests}/profile/pipelines/air_quality_sample.csv (100%) rename tests/{ => dev_tests}/profile/pipelines/get_air_quality_v0.3.0.profile (100%) rename tests/{ => dev_tests}/profile/pipelines/profile_download_air_quality.py (100%) rename tests/{ => dev_tests}/profile/samples/cancer_data.gpkg (100%) rename tests/{ => dev_tests}/profile/samples/pl_dem_epsg2180.txt (100%) rename tests/{ => dev_tests}/profile/samples/regularized_variogram.json (100%) create mode 100644 tests/dev_tests/profile/semivariance/__init__.py rename tests/{ => dev_tests}/profile/semivariance/circular_power_changed_v0.3.0.profile (100%) rename tests/{ => dev_tests}/profile/semivariance/circular_v0.3.0.profile (100%) rename tests/{ => dev_tests}/profile/semivariance/cubic_v0.3.0.profile (100%) rename tests/{ => dev_tests}/profile/semivariance/decon_v0.3.0.profile (100%) rename tests/{ => dev_tests}/profile/semivariance/profile_circular_model.py (100%) rename tests/{ => dev_tests}/profile/semivariance/profile_cubic_model.py (100%) rename tests/{ => dev_tests}/profile/semivariance/profile_directional_variogram_ellipse.profile (100%) rename tests/{ => dev_tests}/profile/semivariance/profile_directional_variogram_methods.py (100%) rename tests/{ => dev_tests}/profile/semivariance/profile_directional_variogram_triangle.profile (100%) rename tests/{ => dev_tests}/profile/semivariance/profile_inblock_semivariance.py (100%) rename tests/{ => dev_tests}/profile/semivariance/profile_semivarogram_regularization.py (100%) create mode 100644 tests/test_pipelines/test_smooth_areas.py diff --git a/tests/dataviz/__init__.py b/tests/dev_tests/__init__.py similarity index 100% rename from tests/dataviz/__init__.py rename to tests/dev_tests/__init__.py diff --git a/tests/debug_sandbox/__init__.py b/tests/dev_tests/dataviz/__init__.py similarity index 100% rename from tests/debug_sandbox/__init__.py rename to tests/dev_tests/dataviz/__init__.py diff --git a/tests/dataviz/aggregated.py b/tests/dev_tests/dataviz/aggregated.py similarity index 100% rename from tests/dataviz/aggregated.py rename to tests/dev_tests/dataviz/aggregated.py diff --git a/tests/dataviz/interpolate_raster.py b/tests/dev_tests/dataviz/interpolate_raster.py similarity index 100% rename from tests/dataviz/interpolate_raster.py rename to tests/dev_tests/dataviz/interpolate_raster.py diff --git a/tests/dataviz/select_triangles.py b/tests/dev_tests/dataviz/select_triangles.py similarity index 100% rename from tests/dataviz/select_triangles.py rename to tests/dev_tests/dataviz/select_triangles.py diff --git a/tests/dataviz/variogram_clouds.py b/tests/dev_tests/dataviz/variogram_clouds.py similarity index 100% rename from tests/dataviz/variogram_clouds.py rename to tests/dev_tests/dataviz/variogram_clouds.py diff --git a/tests/debug_sandbox/test_ok_data/__init__.py b/tests/dev_tests/debug_sandbox/__init__.py similarity index 100% rename from tests/debug_sandbox/test_ok_data/__init__.py rename to tests/dev_tests/debug_sandbox/__init__.py diff --git a/tests/debug_sandbox/analyze_area_to_area_pk_errors.py b/tests/dev_tests/debug_sandbox/analyze_area_to_area_pk_errors.py similarity index 97% rename from tests/debug_sandbox/analyze_area_to_area_pk_errors.py rename to tests/dev_tests/debug_sandbox/analyze_area_to_area_pk_errors.py index e2af1d79..88fd55cc 100644 --- a/tests/debug_sandbox/analyze_area_to_area_pk_errors.py +++ b/tests/dev_tests/debug_sandbox/analyze_area_to_area_pk_errors.py @@ -18,7 +18,7 @@ format=LOGGING_FORMAT) DATASET = '../samples/regularization/cancer_data.gpkg' -VARIOGRAM_MODEL_FILE = '../samples/regularization/regularized_variogram.json' +VARIOGRAM_MODEL_FILE = '../../samples/regularization/regularized_variogram.json' POLYGON_LAYER = 'areas' POPULATION_LAYER = 'points' POP10 = 'POP10' diff --git a/tests/debug_sandbox/angles.py b/tests/dev_tests/debug_sandbox/angles.py similarity index 100% rename from tests/debug_sandbox/angles.py rename to tests/dev_tests/debug_sandbox/angles.py diff --git a/tests/debug_sandbox/ata_pk_from_cov.py b/tests/dev_tests/debug_sandbox/ata_pk_from_cov.py similarity index 100% rename from tests/debug_sandbox/ata_pk_from_cov.py rename to tests/dev_tests/debug_sandbox/ata_pk_from_cov.py diff --git a/tests/debug_sandbox/ata_pk_from_cov_up.py b/tests/dev_tests/debug_sandbox/ata_pk_from_cov_up.py similarity index 99% rename from tests/debug_sandbox/ata_pk_from_cov_up.py rename to tests/dev_tests/debug_sandbox/ata_pk_from_cov_up.py index 99874bad..1a6035a1 100644 --- a/tests/debug_sandbox/ata_pk_from_cov_up.py +++ b/tests/dev_tests/debug_sandbox/ata_pk_from_cov_up.py @@ -25,7 +25,7 @@ format=LOGGING_FORMAT) DATASET = '../samples/regularization/cancer_data.gpkg' -VARIOGRAM_MODEL_FILE = '../samples/regularization/regularized_variogram.json' +VARIOGRAM_MODEL_FILE = '../../samples/regularization/regularized_variogram.json' POLYGON_LAYER = 'areas' POPULATION_LAYER = 'points' POP10 = 'POP10' diff --git a/tests/debug_sandbox/ata_pk_to_atp_pk.py b/tests/dev_tests/debug_sandbox/ata_pk_to_atp_pk.py similarity index 98% rename from tests/debug_sandbox/ata_pk_to_atp_pk.py rename to tests/dev_tests/debug_sandbox/ata_pk_to_atp_pk.py index 611b070f..f9ff9602 100644 --- a/tests/debug_sandbox/ata_pk_to_atp_pk.py +++ b/tests/dev_tests/debug_sandbox/ata_pk_to_atp_pk.py @@ -28,7 +28,7 @@ format=LOGGING_FORMAT) DATASET = '../samples/regularization/cancer_data.gpkg' -VARIOGRAM_MODEL_FILE = '../samples/regularization/regularized_variogram.json' +VARIOGRAM_MODEL_FILE = '../../samples/regularization/regularized_variogram.json' POLYGON_LAYER = 'areas' POPULATION_LAYER = 'points' POP10 = 'POP10' diff --git a/tests/profile/__init__.py b/tests/dev_tests/debug_sandbox/check_atp_pk/__init__.py similarity index 100% rename from tests/profile/__init__.py rename to tests/dev_tests/debug_sandbox/check_atp_pk/__init__.py diff --git a/tests/debug_sandbox/check_atp_pk/check_atp_from_cov.py b/tests/dev_tests/debug_sandbox/check_atp_pk/check_atp_from_cov.py similarity index 99% rename from tests/debug_sandbox/check_atp_pk/check_atp_from_cov.py rename to tests/dev_tests/debug_sandbox/check_atp_pk/check_atp_from_cov.py index 2de52593..9963e530 100644 --- a/tests/debug_sandbox/check_atp_pk/check_atp_from_cov.py +++ b/tests/dev_tests/debug_sandbox/check_atp_pk/check_atp_from_cov.py @@ -29,7 +29,7 @@ format=LOGGING_FORMAT) DATASET = '../../samples/regularization/cancer_data.gpkg' -VARIOGRAM_MODEL_FILE = '../../samples/regularization/regularized_variogram.json' +VARIOGRAM_MODEL_FILE = '../../../samples/regularization/regularized_variogram.json' POLYGON_LAYER = 'areas' POPULATION_LAYER = 'points' POP10 = 'POP10' diff --git a/tests/profile/kriging/__init__.py b/tests/dev_tests/debug_sandbox/check_cent_pk/__init__.py similarity index 100% rename from tests/profile/kriging/__init__.py rename to tests/dev_tests/debug_sandbox/check_cent_pk/__init__.py diff --git a/tests/debug_sandbox/check_cent_pk/check.py b/tests/dev_tests/debug_sandbox/check_cent_pk/check.py similarity index 99% rename from tests/debug_sandbox/check_cent_pk/check.py rename to tests/dev_tests/debug_sandbox/check_cent_pk/check.py index cc8ad928..8756a16e 100644 --- a/tests/debug_sandbox/check_cent_pk/check.py +++ b/tests/dev_tests/debug_sandbox/check_cent_pk/check.py @@ -30,7 +30,7 @@ format=LOGGING_FORMAT) DATASET = '../../samples/regularization/cancer_data.gpkg' -VARIOGRAM_MODEL_FILE = '../../samples/regularization/regularized_variogram.json' +VARIOGRAM_MODEL_FILE = '../../../samples/regularization/regularized_variogram.json' POLYGON_LAYER = 'areas' POPULATION_LAYER = 'points' POP10 = 'POP10' diff --git a/tests/debug_sandbox/directional_ok.py b/tests/dev_tests/debug_sandbox/directional_ok.py similarity index 99% rename from tests/debug_sandbox/directional_ok.py rename to tests/dev_tests/debug_sandbox/directional_ok.py index 1d776228..46de5b59 100644 --- a/tests/debug_sandbox/directional_ok.py +++ b/tests/dev_tests/debug_sandbox/directional_ok.py @@ -15,7 +15,7 @@ from pyinterpolate import TheoreticalVariogram # Read data -dem = read_txt('../samples/point_data/txt/pl_dem_epsg2180.txt') +dem = read_txt('../../samples/point_data/txt/pl_dem_epsg2180.txt') def ordinary_kriging2( diff --git a/tests/debug_sandbox/directional_variogram_div_by_zero_warning.py b/tests/dev_tests/debug_sandbox/directional_variogram_div_by_zero_warning.py similarity index 100% rename from tests/debug_sandbox/directional_variogram_div_by_zero_warning.py rename to tests/dev_tests/debug_sandbox/directional_variogram_div_by_zero_warning.py diff --git a/tests/debug_sandbox/kriging_from_cov.py b/tests/dev_tests/debug_sandbox/kriging_from_cov.py similarity index 98% rename from tests/debug_sandbox/kriging_from_cov.py rename to tests/dev_tests/debug_sandbox/kriging_from_cov.py index 3f1a0603..e083adb7 100644 --- a/tests/debug_sandbox/kriging_from_cov.py +++ b/tests/dev_tests/debug_sandbox/kriging_from_cov.py @@ -8,7 +8,7 @@ from pyinterpolate import TheoreticalVariogram # Read data -dem = read_txt('../samples/point_data/txt/pl_dem_epsg2180.txt') +dem = read_txt('../../samples/point_data/txt/pl_dem_epsg2180.txt') def create_model_validation_sets(dataset: np.array, frac=0.1): diff --git a/tests/debug_sandbox/test_ata_pk_data/compare_ata_atp.csv b/tests/dev_tests/debug_sandbox/test_ata_pk_data/compare_ata_atp.csv similarity index 100% rename from tests/debug_sandbox/test_ata_pk_data/compare_ata_atp.csv rename to tests/dev_tests/debug_sandbox/test_ata_pk_data/compare_ata_atp.csv diff --git a/tests/debug_sandbox/test_ata_pk_data/compare_ata_atp2.csv b/tests/dev_tests/debug_sandbox/test_ata_pk_data/compare_ata_atp2.csv similarity index 100% rename from tests/debug_sandbox/test_ata_pk_data/compare_ata_atp2.csv rename to tests/dev_tests/debug_sandbox/test_ata_pk_data/compare_ata_atp2.csv diff --git a/tests/debug_sandbox/test_ata_pk_data/compare_ata_atp3.csv b/tests/dev_tests/debug_sandbox/test_ata_pk_data/compare_ata_atp3.csv similarity index 100% rename from tests/debug_sandbox/test_ata_pk_data/compare_ata_atp3.csv rename to tests/dev_tests/debug_sandbox/test_ata_pk_data/compare_ata_atp3.csv diff --git a/tests/debug_sandbox/test_ata_pk_data/project.qgz b/tests/dev_tests/debug_sandbox/test_ata_pk_data/project.qgz similarity index 100% rename from tests/debug_sandbox/test_ata_pk_data/project.qgz rename to tests/dev_tests/debug_sandbox/test_ata_pk_data/project.qgz diff --git a/tests/debug_sandbox/test_ata_pk_data/wrong_areas.cpg b/tests/dev_tests/debug_sandbox/test_ata_pk_data/wrong_areas.cpg similarity index 100% rename from tests/debug_sandbox/test_ata_pk_data/wrong_areas.cpg rename to tests/dev_tests/debug_sandbox/test_ata_pk_data/wrong_areas.cpg diff --git a/tests/debug_sandbox/test_ata_pk_data/wrong_areas.dbf b/tests/dev_tests/debug_sandbox/test_ata_pk_data/wrong_areas.dbf similarity index 100% rename from tests/debug_sandbox/test_ata_pk_data/wrong_areas.dbf rename to tests/dev_tests/debug_sandbox/test_ata_pk_data/wrong_areas.dbf diff --git a/tests/debug_sandbox/test_ata_pk_data/wrong_areas.prj b/tests/dev_tests/debug_sandbox/test_ata_pk_data/wrong_areas.prj similarity index 100% rename from tests/debug_sandbox/test_ata_pk_data/wrong_areas.prj rename to tests/dev_tests/debug_sandbox/test_ata_pk_data/wrong_areas.prj diff --git a/tests/debug_sandbox/test_ata_pk_data/wrong_areas.qmd b/tests/dev_tests/debug_sandbox/test_ata_pk_data/wrong_areas.qmd similarity index 100% rename from tests/debug_sandbox/test_ata_pk_data/wrong_areas.qmd rename to tests/dev_tests/debug_sandbox/test_ata_pk_data/wrong_areas.qmd diff --git a/tests/debug_sandbox/test_ata_pk_data/wrong_areas.shp b/tests/dev_tests/debug_sandbox/test_ata_pk_data/wrong_areas.shp similarity index 100% rename from tests/debug_sandbox/test_ata_pk_data/wrong_areas.shp rename to tests/dev_tests/debug_sandbox/test_ata_pk_data/wrong_areas.shp diff --git a/tests/debug_sandbox/test_ata_pk_data/wrong_areas.shx b/tests/dev_tests/debug_sandbox/test_ata_pk_data/wrong_areas.shx similarity index 100% rename from tests/debug_sandbox/test_ata_pk_data/wrong_areas.shx rename to tests/dev_tests/debug_sandbox/test_ata_pk_data/wrong_areas.shx diff --git a/tests/profile/kriging/block/__init__.py b/tests/dev_tests/debug_sandbox/test_ok_data/__init__.py similarity index 100% rename from tests/profile/kriging/block/__init__.py rename to tests/dev_tests/debug_sandbox/test_ok_data/__init__.py diff --git a/tests/debug_sandbox/test_ok_data/check_results.csv b/tests/dev_tests/debug_sandbox/test_ok_data/check_results.csv similarity index 100% rename from tests/debug_sandbox/test_ok_data/check_results.csv rename to tests/dev_tests/debug_sandbox/test_ok_data/check_results.csv diff --git a/tests/debug_sandbox/test_ok_data/check_results.ipynb b/tests/dev_tests/debug_sandbox/test_ok_data/check_results.ipynb similarity index 100% rename from tests/debug_sandbox/test_ok_data/check_results.ipynb rename to tests/dev_tests/debug_sandbox/test_ok_data/check_results.ipynb diff --git a/tests/debug_sandbox/test_ok_data/data_test.py b/tests/dev_tests/debug_sandbox/test_ok_data/data_test.py similarity index 100% rename from tests/debug_sandbox/test_ok_data/data_test.py rename to tests/dev_tests/debug_sandbox/test_ok_data/data_test.py diff --git a/tests/debug_sandbox/test_ok_data/k.csv b/tests/dev_tests/debug_sandbox/test_ok_data/k.csv similarity index 100% rename from tests/debug_sandbox/test_ok_data/k.csv rename to tests/dev_tests/debug_sandbox/test_ok_data/k.csv diff --git a/tests/debug_sandbox/test_ok_data/weights.csv b/tests/dev_tests/debug_sandbox/test_ok_data/weights.csv similarity index 100% rename from tests/debug_sandbox/test_ok_data/weights.csv rename to 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b/tests/dev_tests/profile/semivariance/profile_inblock_semivariance.py similarity index 100% rename from tests/profile/semivariance/profile_inblock_semivariance.py rename to tests/dev_tests/profile/semivariance/profile_inblock_semivariance.py diff --git a/tests/profile/semivariance/profile_semivarogram_regularization.py b/tests/dev_tests/profile/semivariance/profile_semivarogram_regularization.py similarity index 100% rename from tests/profile/semivariance/profile_semivarogram_regularization.py rename to tests/dev_tests/profile/semivariance/profile_semivarogram_regularization.py diff --git a/tests/test_pipelines/test_smooth_areas.py b/tests/test_pipelines/test_smooth_areas.py new file mode 100644 index 00000000..fad06a8c --- /dev/null +++ b/tests/test_pipelines/test_smooth_areas.py @@ -0,0 +1,42 @@ +import unittest +import geopandas as gpd +from pyinterpolate import smooth_blocks, Blocks, PointSupport, TheoreticalVariogram + + +DATASET = 'samples/regularization/cancer_data.gpkg' +VARIOGRAM_MODEL_FILE = 'samples/regularization/regularized_variogram.json' +POLYGON_LAYER = 'areas' +POPULATION_LAYER = 'points' +POP10 = 'POP10' +GEOMETRY_COL = 'geometry' +POLYGON_ID = 'FIPS' +POLYGON_VALUE = 'rate' +NN = 8 + +AREAL_INPUT = Blocks() +AREAL_INPUT.from_file(DATASET, value_col=POLYGON_VALUE, index_col=POLYGON_ID, layer_name=POLYGON_LAYER) +POINT_SUPPORT_INPUT = PointSupport() +POINT_SUPPORT_INPUT.from_files(point_support_data_file=DATASET, + blocks_file=DATASET, + point_support_geometry_col=GEOMETRY_COL, + point_support_val_col=POP10, + blocks_geometry_col=GEOMETRY_COL, + blocks_index_col=POLYGON_ID, + use_point_support_crs=True, + point_support_layer_name=POPULATION_LAYER, + blocks_layer_name=POLYGON_LAYER) + +THEORETICAL_VARIOGRAM = TheoreticalVariogram() +THEORETICAL_VARIOGRAM.from_json(VARIOGRAM_MODEL_FILE) + + +class TestSmoothBlocks(unittest.TestCase): + + def test_real_data(self): + + output_results = smooth_blocks(semivariogram_model=THEORETICAL_VARIOGRAM, + blocks=AREAL_INPUT, + point_support=POINT_SUPPORT_INPUT, + number_of_neighbors=NN) + + self.assertIsInstance(output_results, gpd.GeoDataFrame) From 11bedbd74818c59cafc4d02238b4913b56c8c870 Mon Sep 17 00:00:00 2001 From: Simon <31246246+SimonMolinsky@users.noreply.github.com> Date: Fri, 20 Jan 2023 17:52:00 +0200 Subject: [PATCH 02/29] Update changelog.rst --- changelog.rst | 1 + 1 file changed, 1 insertion(+) diff --git a/changelog.rst b/changelog.rst index 88407a7a..6ecae42d 100755 --- a/changelog.rst +++ b/changelog.rst @@ -12,6 +12,7 @@ Changes by date **version 0.3.7** * (enhancement) added logging to Poisson Kriging ATP process, +* (test) added functional test for `smooth_blocks` function, 2023-01-16 ---------- From 74c87ffa2b02c8a137354c7ee0a2e25f5dfc894a Mon Sep 17 00:00:00 2001 From: Simon <31246246+SimonMolinsky@users.noreply.github.com> Date: Fri, 20 Jan 2023 18:13:20 +0200 Subject: [PATCH 03/29] Update samples.py --- pyinterpolate/pipelines/samples.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyinterpolate/pipelines/samples.py b/pyinterpolate/pipelines/samples.py index 87dd6253..ceeec4ac 100644 --- a/pyinterpolate/pipelines/samples.py +++ b/pyinterpolate/pipelines/samples.py @@ -72,7 +72,7 @@ def download_air_quality_poland(dataset: str, export=False, export_path='air_qua api_output = requests.get(data_url + str(i)).json()['values'] try: values_df.append([i, pd.DataFrame.from_dict(api_output).dropna()['value'].values[0]]) - except Exception as _: + except KeyError: values_df.append([i, np.nan]) values_df = pd.DataFrame(data=values_df, columns=['id', dataset]) From a3cd1005e82c51fd778f126df70f99e00473d67a Mon Sep 17 00:00:00 2001 From: Simon <31246246+SimonMolinsky@users.noreply.github.com> Date: Fri, 20 Jan 2023 18:17:06 +0200 Subject: [PATCH 04/29] Update changelog.rst --- changelog.rst | 2 ++ 1 file changed, 2 insertions(+) diff --git a/changelog.rst b/changelog.rst index 6ecae42d..e8c8e1f6 100755 --- a/changelog.rst +++ b/changelog.rst @@ -13,6 +13,8 @@ Changes by date * (enhancement) added logging to Poisson Kriging ATP process, * (test) added functional test for `smooth_blocks` function, +* (debug) too broad exception in `download_air_quality_poland` is narrowed to `KeyError`, +* 2023-01-16 ---------- From 32903a3e992b330fdac8b386e7c25d6e43c769f2 Mon Sep 17 00:00:00 2001 From: Simon <31246246+SimonMolinsky@users.noreply.github.com> Date: Sat, 21 Jan 2023 01:16:00 +0200 Subject: [PATCH 05/29] Updated logging and docs --- changelog.rst | 3 +- docs/build/doctrees/api/api.doctree | Bin 2910 -> 2869 bytes .../build/doctrees/api/datatypes/core.doctree | Bin 64852 -> 70422 bytes .../doctrees/api/distance/distance.doctree | Bin 18127 -> 17741 bytes docs/build/doctrees/api/idw/idw.doctree | Bin 16910 -> 16687 bytes docs/build/doctrees/api/io/io.doctree | Bin 38292 -> 37818 bytes .../api/kriging/block/block_kriging.doctree | Bin 72723 -> 73502 bytes .../doctrees/api/kriging/kriging.doctree | Bin 2829 -> 2788 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Kriging interpolation (Intermediate).html | 20 +- ...ional Ordinary Kriging (Intermediate).html | 10 +- .../Directional Semivariograms (Basic).html | 26 +- .../Directional Semivariograms (Basic).ipynb | 2 +- ...test it against IDW algorithm (Basic).html | 10 +- .../Ordinary and Simple Kriging (Basic).html | 12 +- ...nce on the Final Model (Intermediate).html | 20 +- ...son Kriging - Area to Area (Advanced).html | 14 +- ...on Kriging - Area to Point (Advanced).html | 10 +- ...n Kriging - Centroid Based (Advanced).html | 14 +- .../Semivariogram Estimation (Basic).html | 26 +- ...riogram Regularization (Intermediate).html | 18 +- .../tutorials/Theoretical Models (Basic).html | 30 +- .../Variogram Point Cloud (Basic).html | 16 +- docs/requirements.txt | 2 +- .../processing/preprocessing/blocks.py | 42 +- tests/test_processing/test_blocks.py | 11 + 125 files changed, 1876 insertions(+), 1819 deletions(-) diff --git a/changelog.rst b/changelog.rst index e8c8e1f6..6f1f3012 100755 --- a/changelog.rst +++ b/changelog.rst @@ -14,7 +14,8 @@ Changes by date * (enhancement) added logging to Poisson Kriging ATP process, * (test) added functional test for `smooth_blocks` function, * (debug) too broad exception in `download_air_quality_poland` is narrowed to `KeyError`, -* +* (enhancement) log points that cannot be assigned to any area in `PointSupport` class, +* 2023-01-16 ---------- diff --git a/docs/build/doctrees/api/api.doctree b/docs/build/doctrees/api/api.doctree index ef6f330e2ab5387387c5df47193b6f64881c72fe..d6ef403530d661cfabbda370cfad8425f3bf4dd0 100644 GIT binary patch delta 52 zcmca7wpEO!fpx0ZMivW3MK}G>;?$yI{o<<1-2A-!VttqV{Nl`#{G!a%V*P^3%)FA+qJsRK#FA9q)V#9HqWnCNp3Sk0H#h-a9wY1k diff --git a/docs/build/doctrees/api/datatypes/core.doctree b/docs/build/doctrees/api/datatypes/core.doctree index 2f57d5aa8981d84a82b9dbc7330c7edbf210e6eb..055a00fae65553825ee8b673f415261f383a3116 100644 GIT binary patch literal 70422 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All modules for which code is available

-Created using Sphinx 5.3.0.
+Created using Sphinx 5.0.2.

diff --git a/docs/build/html/_modules/pyinterpolate/distance/distance.html b/docs/build/html/_modules/pyinterpolate/distance/distance.html index e28909b2..572d88d6 100644 --- a/docs/build/html/_modules/pyinterpolate/distance/distance.html +++ b/docs/build/html/_modules/pyinterpolate/distance/distance.html @@ -37,7 +37,6 @@ - @@ -388,7 +387,7 @@

Source code for pyinterpolate.distance.distance

< def _calc_b2b_dist_from_array(blocks: np.ndarray) -> Dict: - """Function calculates distances between blocks. + """Function calculates distances between blocks. Parameters ---------- @@ -420,7 +419,7 @@

Source code for pyinterpolate.distance.distance

< def _calc_b2b_dist_from_dataframe(blocks: Union[pd.DataFrame, gpd.GeoDataFrame]) -> Dict: - """Function calculates distances between blocks. + """Function calculates distances between blocks. Parameters ---------- @@ -448,7 +447,7 @@

Source code for pyinterpolate.distance.distance

< def _calc_b2b_dist_from_dict(blocks: Dict) -> Dict: - """Function calculates distances between blocks. + """Function calculates distances between blocks. Parameters ---------- @@ -480,7 +479,7 @@

Source code for pyinterpolate.distance.distance

< def _calc_b2b_dist_from_ps(blocks: PointSupport) -> Dict: - """Function calculates distances between blocks. + """Function calculates distances between blocks. Parameters ---------- @@ -498,7 +497,7 @@

Source code for pyinterpolate.distance.distance

<
[docs]def calc_block_to_block_distance(blocks: Union[Dict, np.ndarray, gpd.GeoDataFrame, pd.DataFrame, PointSupport]) -> Dict: - """Function calculates distances between blocks. + """Function calculates distances between blocks. Parameters ---------- @@ -537,7 +536,7 @@

Source code for pyinterpolate.distance.distance

< def _calculate_block_to_block_distance(block_1: np.ndarray, block_2: np.ndarray) -> float: - """Function calculates distance between two blocks based on how they are divided (into the point support grid). + """Function calculates distance between two blocks based on how they are divided (into the point support grid). Parameters ---------- @@ -600,7 +599,7 @@

Source code for pyinterpolate.distance.distance

< def calc_angles(points_b: Iterable, point_a: Iterable = None, origin: Iterable = None): - """ + """ Function calculates angles between points and origin or between vectors from origin to points and a vector from a specific point to origin. @@ -643,7 +642,7 @@

Source code for pyinterpolate.distance.distance

<
[docs]def calc_point_to_point_distance(points_a, points_b=None): - """Function calculates distances between two group of points of a single group to itself. + """Function calculates distances between two group of points of a single group to itself. Parameters ---------- @@ -669,7 +668,7 @@

Source code for pyinterpolate.distance.distance

< def calculate_angular_distance(angles: np.ndarray, expected_direction: float) -> np.ndarray: - """ + """ Function calculates minimal direction between one vector and other vectors. Parameters @@ -774,7 +773,7 @@

Source code for pyinterpolate.distance.distance

<

-Created using Sphinx 5.3.0.
+Created using Sphinx 5.0.2.

diff --git a/docs/build/html/_modules/pyinterpolate/kriging/models/block/area_to_area_poisson_kriging.html b/docs/build/html/_modules/pyinterpolate/kriging/models/block/area_to_area_poisson_kriging.html index 3e7ff4c8..8f314ca8 100644 --- a/docs/build/html/_modules/pyinterpolate/kriging/models/block/area_to_area_poisson_kriging.html +++ b/docs/build/html/_modules/pyinterpolate/kriging/models/block/area_to_area_poisson_kriging.html @@ -37,7 +37,6 @@ - @@ -373,11 +372,8 @@

Source code for pyinterpolate.kriging.models.block.area_to_area_poisson_krig ------- 1. Szymon Moliński | @SimonMolinsky -TODO ----- -* log errors -* control negative predictions and errors """ +import logging from typing import Dict, Union import geopandas as gpd @@ -388,19 +384,20 @@

Source code for pyinterpolate.kriging.models.block.area_to_area_poisson_krig from pyinterpolate.processing.preprocessing.blocks import Blocks, PointSupport from pyinterpolate.processing.select_values import select_poisson_kriging_data, prepare_pk_known_areas,\ get_aggregated_point_support_values, get_distances_within_unknown -from pyinterpolate.processing.transform.transform import get_areal_values_from_agg, transform_ps_to_dict +from pyinterpolate.processing.transform.transform import get_areal_values_from_agg, transform_ps_to_dict, sem_to_cov from pyinterpolate.variogram import TheoreticalVariogram
[docs]def area_to_area_pk(semivariogram_model: TheoreticalVariogram, - blocks: Union[Blocks, gpd.GeoDataFrame, pd.DataFrame, np.ndarray], - point_support: Union[Dict, np.ndarray, gpd.GeoDataFrame, pd.DataFrame, PointSupport], - unknown_block: np.ndarray, - unknown_block_point_support: np.ndarray, - number_of_neighbors: int, - raise_when_negative_prediction=True, - raise_when_negative_error=True): - """ + blocks: Union[Blocks, gpd.GeoDataFrame, pd.DataFrame, np.ndarray], + point_support: Union[Dict, np.ndarray, gpd.GeoDataFrame, pd.DataFrame, PointSupport], + unknown_block: np.ndarray, + unknown_block_point_support: np.ndarray, + number_of_neighbors: int, + raise_when_negative_prediction=True, + raise_when_negative_error=True, + log_process=True): + """ Function predicts areal value in a unknown location based on the area-to-area Poisson Kriging Parameters @@ -437,6 +434,9 @@

Source code for pyinterpolate.kriging.models.block.area_to_area_poisson_krig raise_when_negative_error : bool, default=True Raise error when prediction error is negative. + log_process : bool, default=True + Log process info and debug info. + Returns ------- results : List @@ -455,11 +455,14 @@

Source code for pyinterpolate.kriging.models.block.area_to_area_poisson_krig if isinstance(point_support, Dict): dps = point_support else: + if log_process: + logging.info('Point support is transformed to dictionary') dps = transform_ps_to_dict(point_support) - # Check ids - + # Get c0 + sill = semivariogram_model.sill + # Check ids kriging_data = select_poisson_kriging_data( u_block_centroid=unknown_block, u_point_support=unknown_block_point_support, @@ -472,8 +475,9 @@

Source code for pyinterpolate.kriging.models.block.area_to_area_poisson_krig avg_semivariances = b2b_semivariance.calculate_average_semivariance(kriging_data) k = np.array(list(avg_semivariances.values())) + k = sem_to_cov(k, sill) k = k.T - k_ones = np.ones(len(k)+1) + k_ones = np.ones(len(k) + 1) k_ones[:-1] = k # Prepare blocks for calculation @@ -494,6 +498,7 @@

Source code for pyinterpolate.kriging.models.block.area_to_area_poisson_krig predicted.append(row) predicted = np.array(predicted) + predicted = sem_to_cov(predicted, sill) # Add diagonal weights values = get_areal_values_from_agg(blocks, prepared_ids) @@ -511,17 +516,14 @@

Source code for pyinterpolate.kriging.models.block.area_to_area_poisson_krig try: w = np.linalg.solve(weights, k_ones) except TypeError: + if log_process: + logging.debug('Wrong dtypes used for np.linalg.solve, casting to float.') weights = weights.astype(np.float) k_ones = k_ones.astype(np.float) w = np.linalg.solve(weights, k_ones) zhat = values.dot(w[:-1]) - if raise_when_negative_prediction: - if zhat < 0: - raise ValueError(f'Predicted value is {zhat} and it should not be lower than 0. Check your sampling ' - f'grid, samples, number of neighbors or semivariogram model type.') - # Calculate prediction error if isinstance(unknown_block[0], np.ndarray): @@ -529,17 +531,29 @@

Source code for pyinterpolate.kriging.models.block.area_to_area_poisson_krig else: u_idx = unknown_block[0] - distances_within_unknown_block = get_distances_within_unknown(unknown_block_point_support) + if zhat < 0: + if log_process: + logging.debug(f'Prediction below 0 for area {u_idx}') + if raise_when_negative_prediction: + raise ValueError(f'Predicted value is {zhat} and it should not be lower than 0. Check your sampling ' + f'grid, samples, number of neighbors or semivariogram model type.') + + # TODO test - come back here when covariance terms are used + # TODO - probably it should be without subtraction - those are semivariance terms, not covariances. + sigmasq = np.matmul(w.T, k_ones) + distances_within_unknown_block = get_distances_within_unknown(unknown_block_point_support) semivariance_within_unknown = b2b_semivariance.calculate_average_semivariance({ u_idx: distances_within_unknown_block })[u_idx] - sig_base = np.matmul(w.T, k_ones) + covariance_within_unknown = sem_to_cov([semivariance_within_unknown], sill)[0] - sigmasq = semivariance_within_unknown - sig_base + sigmasq = covariance_within_unknown - sigmasq if sigmasq < 0: + if log_process: + logging.debug(f'Variance Error below 0 for area {u_idx}') if raise_when_negative_error: raise ValueError(f'Predicted error value is {sigmasq} and it should not be lower than 0. ' f'Check your sampling grid, samples, number of neighbors or semivariogram model type.') @@ -625,7 +639,7 @@

Source code for pyinterpolate.kriging.models.block.area_to_area_poisson_krig

-Created using Sphinx 5.3.0.
+Created using Sphinx 5.0.2.

diff --git a/docs/build/html/_modules/pyinterpolate/kriging/models/block/area_to_point_poisson_kriging.html b/docs/build/html/_modules/pyinterpolate/kriging/models/block/area_to_point_poisson_kriging.html index 37abd773..c264aa15 100644 --- a/docs/build/html/_modules/pyinterpolate/kriging/models/block/area_to_point_poisson_kriging.html +++ b/docs/build/html/_modules/pyinterpolate/kriging/models/block/area_to_point_poisson_kriging.html @@ -37,7 +37,6 @@ - @@ -372,13 +371,9 @@

Source code for pyinterpolate.kriging.models.block.area_to_point_poisson_kri Authors ------- 1. Szymon Moliński | @SimonMolinsky - -TODO ----- -* log errors -* control negative predictions and errors """ from typing import Union, Dict +import logging import geopandas as gpd import numpy as np @@ -389,7 +384,7 @@

Source code for pyinterpolate.kriging.models.block.area_to_point_poisson_kri from pyinterpolate.processing.preprocessing.blocks import Blocks, PointSupport from pyinterpolate.processing.select_values import select_poisson_kriging_data, prepare_pk_known_areas, \ get_aggregated_point_support_values -from pyinterpolate.processing.transform.transform import transform_ps_to_dict, get_areal_values_from_agg +from pyinterpolate.processing.transform.transform import transform_ps_to_dict, get_areal_values_from_agg, sem_to_cov from pyinterpolate.variogram import TheoreticalVariogram @@ -403,7 +398,7 @@

Source code for pyinterpolate.kriging.models.block.area_to_point_poisson_kri raise_when_negative_prediction=True, raise_when_negative_error=True, err_to_nan=True): - """ + """ Function predicts areal value in the unknown location based on the area-to-area Poisson Kriging Parameters @@ -458,6 +453,7 @@

Source code for pyinterpolate.kriging.models.block.area_to_point_poisson_kri ValueError Prediction or prediction error are negative. """ + logging.info("POISSON KRIGING: AREA-TO-POINT | Operation starts") # Get total point-support value of the unknown area tot_unknown_value = np.sum(unknown_block_point_support[:, -1]) @@ -475,6 +471,9 @@

Source code for pyinterpolate.kriging.models.block.area_to_point_poisson_kri else: rng = max_range + # Get sill to calc cov + sill = semivariogram_model.sill + kriging_data = select_poisson_kriging_data( u_block_centroid=unknown_block, u_point_support=unknown_block_point_support, @@ -484,12 +483,15 @@

Source code for pyinterpolate.kriging.models.block.area_to_point_poisson_kri ) prepared_ids = list(kriging_data.keys()) + logging.info("POISSON KRIGING: AREA-TO-POINT | Kriging data has been prepared") # Get block to block weighted semivariances b2b_semivariance = WeightedBlock2BlockSemivariance(semivariance_model=semivariogram_model) + logging.info("POISSON KRIGING: AREA-TO-POINT | Block to block semivariances are calculated") # Get block to point weighted semivariances b2p_semivariance = WeightedBlock2PointSemivariance(semivariance_model=semivariogram_model) + logging.info("POISSON KRIGING: AREA-TO-POINT | Block to point semivariances are calculated") # array( # [unknown point 1 (n) semivariance against point support from block 1, # unknown point 2 (n+1) semivariance against point support from block 1, @@ -498,14 +500,21 @@

Source code for pyinterpolate.kriging.models.block.area_to_point_poisson_kri # unknown point 2 (n+1) semivariance against point support from block 2, # ...], # ) - avg_b2p_semivariances = add_ones(b2p_semivariance.calculate_average_semivariance(kriging_data)) + # Transform to covariances + avg_b2p_covariances = add_ones( + sem_to_cov( + b2p_semivariance.calculate_average_semivariance(kriging_data), + sill + ) + ) # {(known block id a, known block id b): [pt a val, pt b val, distance between points]} distances_between_known_areas = prepare_pk_known_areas(dps, prepared_ids) semivariances_between_known_areas = b2b_semivariance.calculate_average_semivariance( distances_between_known_areas) - + logging.info("POISSON KRIGING: AREA-TO-POINT | Average semivariance between areas is estimated") + logging.info("POISSON KRIGING: AREA-TO-POINT | Kriging system weights preparation") # Create array predicted = [] for idx_a in prepared_ids: @@ -516,6 +525,9 @@

Source code for pyinterpolate.kriging.models.block.area_to_point_poisson_kri predicted = np.array(predicted) + # Transform to covariances + predicted = sem_to_cov(predicted, sill) + # Add diagonal weights values = get_areal_values_from_agg(blocks, prepared_ids) aggregated_ps = get_aggregated_point_support_values(dps, prepared_ids) @@ -533,7 +545,8 @@

Source code for pyinterpolate.kriging.models.block.area_to_point_poisson_kri # Each point of unknown area represents different kriging system # Transpose points matrix - transposed = avg_b2p_semivariances.T + transposed = avg_b2p_covariances.T + logging.info("POISSON KRIGING: AREA-TO-POINT | Kriging system weights has been prepared") predicted_points = [] @@ -552,7 +565,9 @@

Source code for pyinterpolate.kriging.models.block.area_to_point_poisson_kri if zhat < 0: if raise_when_negative_prediction: + logging.info(f"POISSON KRIGING: AREA-TO-POINT | Negative prediction for point {point}") if err_to_nan: + logging.info(f"POISSON KRIGING: AREA-TO-POINT | Negative prediction - NaN zhat and error") predicted_points.append([(analyzed_pts[0], analyzed_pts[1]), np.nan, np.nan]) continue else: @@ -566,7 +581,9 @@

Source code for pyinterpolate.kriging.models.block.area_to_point_poisson_kri sigmasq = np.matmul(w.T, point) if sigmasq < 0: if raise_when_negative_error: + logging.info(f"POISSON KRIGING: AREA-TO-POINT | Negative variance error for point {point}") if err_to_nan: + logging.info(f"POISSON KRIGING: AREA-TO-POINT | Negative error - NaN error") predicted_points.append([(analyzed_pts[0], analyzed_pts[1]), zhat, np.nan]) continue else: @@ -580,6 +597,8 @@

Source code for pyinterpolate.kriging.models.block.area_to_point_poisson_kri predicted_points.append([(analyzed_pts[0], analyzed_pts[1]), zhat, sigma]) + logging.info(f"POISSON KRIGING: AREA-TO-POINT | Process ends") + return predicted_points

@@ -657,7 +676,7 @@

Source code for pyinterpolate.kriging.models.block.area_to_point_poisson_kri

-Created using Sphinx 5.3.0.
+Created using Sphinx 5.0.2.

diff --git a/docs/build/html/_modules/pyinterpolate/kriging/models/block/centroid_based_poisson_kriging.html b/docs/build/html/_modules/pyinterpolate/kriging/models/block/centroid_based_poisson_kriging.html index 6132fbf2..74998f1a 100644 --- a/docs/build/html/_modules/pyinterpolate/kriging/models/block/centroid_based_poisson_kriging.html +++ b/docs/build/html/_modules/pyinterpolate/kriging/models/block/centroid_based_poisson_kriging.html @@ -37,7 +37,6 @@ - @@ -384,7 +383,7 @@

Source code for pyinterpolate.kriging.models.block.centroid_based_poisson_kr from pyinterpolate.kriging.utils.process import solve_weights from pyinterpolate.processing.preprocessing.blocks import Blocks, PointSupport from pyinterpolate.processing.select_values import select_centroid_poisson_kriging_data -from pyinterpolate.processing.transform.transform import transform_ps_to_dict +from pyinterpolate.processing.transform.transform import transform_ps_to_dict, sem_to_cov from pyinterpolate.variogram import TheoreticalVariogram @@ -398,7 +397,7 @@

Source code for pyinterpolate.kriging.models.block.centroid_based_poisson_kr raise_when_negative_prediction=True, raise_when_negative_error=True, allow_approximate_solutions=False) -> List: - """ + """ Function performs centroid-based Poisson Kriging of blocks (areal) data. Parameters @@ -471,6 +470,7 @@

Source code for pyinterpolate.kriging.models.block.centroid_based_poisson_kr weighted=is_weighted_by_point_support, direction=semivariogram_model.direction ) + sill = semivariogram_model.sill distances_column_index = 3 values_column_index = 2 @@ -481,15 +481,17 @@

Source code for pyinterpolate.kriging.models.block.centroid_based_poisson_kr values = kriging_data[:, values_column_index] partial_semivars = semivariogram_model.predict(distances) - semivars = np.ones(len(partial_semivars) + 1) - semivars[:-1] = partial_semivars - semivars = semivars.transpose() + pcovars = sem_to_cov(partial_semivars, sill) + covars = np.ones(len(pcovars) + 1) + covars[:-1] = pcovars + covars = covars.transpose() # Distances between known blocks coordinates = kriging_data[:, :values_column_index] block_distances = calc_point_to_point_distance(coordinates).flatten() known_blocks_semivars = semivariogram_model.predict(block_distances) predicted = np.array(known_blocks_semivars.reshape(n, n)) + predicted = sem_to_cov(predicted, sill) # Add diagonal weights to predicted semivars array weights = weights_array(predicted.shape, values, kriging_data[:, weights_column_index]) @@ -504,7 +506,7 @@

Source code for pyinterpolate.kriging.models.block.centroid_based_poisson_kr # Solve Kriging system try: - output_weights = solve_weights(kriging_weights, semivars, allow_approximate_solutions) + output_weights = solve_weights(kriging_weights, covars, allow_approximate_solutions) except np.linalg.LinAlgError as _: msg = 'Singular matrix in Kriging system detected, check if you have duplicated coordinates ' \ 'in the ``known_locations`` variable.' @@ -517,7 +519,7 @@

Source code for pyinterpolate.kriging.models.block.centroid_based_poisson_kr raise ValueError(f'Predicted value is {zhat} and it should not be lower than 0. Check your sampling ' f'grid, samples, number of neighbors or semivariogram model type.') - sigmasq = np.matmul(output_weights.T, semivars) + sigmasq = np.matmul(output_weights.T, covars) if sigmasq < 0: if raise_when_negative_error: @@ -611,7 +613,7 @@

Source code for pyinterpolate.kriging.models.block.centroid_based_poisson_kr

-Created using Sphinx 5.3.0.
+Created using Sphinx 5.0.2.

diff --git a/docs/build/html/_modules/pyinterpolate/kriging/models/point/ordinary_kriging.html b/docs/build/html/_modules/pyinterpolate/kriging/models/point/ordinary_kriging.html index 939e7684..fc1af5c3 100644 --- a/docs/build/html/_modules/pyinterpolate/kriging/models/point/ordinary_kriging.html +++ b/docs/build/html/_modules/pyinterpolate/kriging/models/point/ordinary_kriging.html @@ -1,172 +1,369 @@ + - - - - - - -Pyinterpolate - pyinterpolate.kriging.models.point.ordinary_kriging - - - - - - - - - - - - - - - - - - - - + + + + + + pyinterpolate.kriging.models.point.ordinary_kriging — Pyinterpolate 0.3.5 documentation + + + + + + + + + + + + + + + + + + + + + + + + + - - - - - - - - -
-
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-

pyinterpolate.kriging.models.point.ordinary_kriging

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pyinterpolate.kriging.models.point.ordinary_kriging
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Source code for pyinterpolate.kriging.models.point.ordinary_kriging

 """
 Perform point ordinary kriging.
@@ -183,6 +380,7 @@ 
Source code for pyinterpolate.kriging.models.point.ordinary_kriging
# Pyinterpolate
 from pyinterpolate.kriging.utils.process import get_predictions, solve_weights
+from pyinterpolate.processing.transform.transform import sem_to_cov
 from pyinterpolate.variogram import TheoreticalVariogram
 
 
@@ -195,7 +393,7 @@ 
Source code for pyinterpolate.kriging.models.point.ordinary_kriging
use_all_neighbors_in_range=False,
         allow_approximate_solutions=False
 ) -> List:
-    """
+    """
     Function predicts value at unknown location with Ordinary Kriging technique.
 
     Parameters
@@ -267,360 +465,181 @@ 
Source code for pyinterpolate.kriging.models.point.ordinary_kriging
return [zhat, np.nan, unknown_location[0], unknown_location[1]]
 
     return [zhat, sigma, unknown_location[0], unknown_location[1]]

-
- -
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- - - - + - var clipboard = new ClipboardJS('.code-block-caption .fa-clipboard', { - 'text': function(trigger) { - return $(trigger).data('clip-text'); - } - }); +
+ +
+ +

- clipboard.on('success', function(e) { - var $copied = $(e.trigger).prev(); + © Copyright 2022, Szymon Moliński.
- $copied.removeClass('hidden'); +

- setTimeout(function() {$copied.addClass('hidden'); }, 1000); - }); +
+ +
+

+ Built with the + + PyData Sphinx Theme + + 0.12.0. +

+
+ +
+ +

+Created using Sphinx 5.0.2.
+

- if (sizzle.on_load) { - sizzle.on_load.forEach(function(f) { - f(); - }); - } -}); - - +
+ +
+
+ \ No newline at end of file diff --git a/docs/build/html/_modules/pyinterpolate/kriging/models/point/simple_kriging.html b/docs/build/html/_modules/pyinterpolate/kriging/models/point/simple_kriging.html index 633626e1..0d8dbf5e 100644 --- a/docs/build/html/_modules/pyinterpolate/kriging/models/point/simple_kriging.html +++ b/docs/build/html/_modules/pyinterpolate/kriging/models/point/simple_kriging.html @@ -1,172 +1,369 @@ + - - - - - - -Pyinterpolate - pyinterpolate.kriging.models.point.simple_kriging - - - - - - - - - - - - - - - - - - - - + + + + + + pyinterpolate.kriging.models.point.simple_kriging — Pyinterpolate 0.3.5 documentation + + + + + + + + + + + + + + + + + + + + + + + + + - - - - - - - - -
-
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pyinterpolate.kriging.models.point.simple_kriging

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pyinterpolate.kriging.models.point.simple_kriging
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Source code for pyinterpolate.kriging.models.point.simple_kriging

 """
 Perform point simple kriging.
@@ -201,7 +398,7 @@ 
Source code for pyinterpolate.kriging.models.point.simple_kriging
allow_approximate_solutions=False,
         err_to_nan=False
 ) -> List:
-    """
+    """
     Function predicts value at unknown location with Ordinary Kriging technique.
 
     Parameters
@@ -273,358 +470,86 @@ 
Source code for pyinterpolate.kriging.models.point.simple_kriging
return [zhat, sigma, unknown_location[0], unknown_location[1]]
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2) Detect and remove outliers

-../../_images/usage_tutorials_Blocks_to_points_Ordinary_Kriging_interpolation_%28Intermediate%29_11_5.png +../../_images/usage_tutorials_Blocks_to_points_Ordinary_Kriging_interpolation_(Intermediate)_11_5.png
@@ -1488,7 +1488,7 @@

6. Show a map of interpolated values with a choropleth map of the breast can
-../../_images/usage_tutorials_Blocks_to_points_Ordinary_Kriging_interpolation_%28Intermediate%29_25_0.png +../../_images/usage_tutorials_Blocks_to_points_Ordinary_Kriging_interpolation_(Intermediate)_25_0.png

Visual inspection shows that:

@@ -1700,7 +1700,7 @@

6. Show a map of interpolated values with a choropleth map of the breast can

-Created using Sphinx 5.3.0.
+Created using Sphinx 5.0.2.

diff --git a/docs/build/html/usage/tutorials/Directional Ordinary Kriging (Intermediate).html b/docs/build/html/usage/tutorials/Directional Ordinary Kriging (Intermediate).html index 391a4964..8445bef1 100644 --- a/docs/build/html/usage/tutorials/Directional Ordinary Kriging (Intermediate).html +++ b/docs/build/html/usage/tutorials/Directional Ordinary Kriging (Intermediate).html @@ -38,7 +38,6 @@ - @@ -511,10 +510,11 @@ } } -/* disable scrollbars on prompts */ +/* disable scrollbars and line breaks on prompts */ div.nbinput.container div.prompt pre, div.nboutput.container div.prompt pre { overflow: hidden; + white-space: pre; } /* input/output area */ @@ -969,7 +969,7 @@

4. Compare interpolated results

-../../_images/usage_tutorials_Directional_Ordinary_Kriging_%28Intermediate%29_33_0.png +../../_images/usage_tutorials_Directional_Ordinary_Kriging_(Intermediate)_33_0.png

Directional Kriging clearly shows the leading direction, and it’s even more pronounced in the variance error maps. At the end, let’s compare the mean of directional interpolations to the isotropic interpolation result:

@@ -1010,7 +1010,7 @@

4. Compare interpolated results

-../../_images/usage_tutorials_Directional_Ordinary_Kriging_%28Intermediate%29_37_0.png +../../_images/usage_tutorials_Directional_Ordinary_Kriging_(Intermediate)_37_0.png
@@ -1173,7 +1173,7 @@

4. Compare interpolated results

-Created using Sphinx 5.3.0.
+Created using Sphinx 5.0.2.

diff --git a/docs/build/html/usage/tutorials/Directional Semivariograms (Basic).html b/docs/build/html/usage/tutorials/Directional Semivariograms (Basic).html index 24b7e3b5..f7f67d05 100644 --- a/docs/build/html/usage/tutorials/Directional Semivariograms (Basic).html +++ b/docs/build/html/usage/tutorials/Directional Semivariograms (Basic).html @@ -38,7 +38,6 @@ - @@ -511,10 +510,11 @@ } } -/* disable scrollbars on prompts */ +/* disable scrollbars and line breaks on prompts */ div.nbinput.container div.prompt pre, div.nboutput.container div.prompt pre { overflow: hidden; + white-space: pre; } /* input/output area */ @@ -836,7 +836,7 @@

1) Read and show point data
def df2gdf(df, lon_col='x', lat_col='y', epsg=4326, crs=None):
-    """
+    """
     Function transforms DataFrame into GeoDataFrame.
 
     Parameters
@@ -1012,7 +1012,7 @@ 
1) Read and show point data
-../../_images/usage_tutorials_Directional_Semivariograms_%28Basic%29_9_1.png +../../_images/usage_tutorials_Directional_Semivariograms_(Basic)_9_1.png

@@ -1108,7 +1108,7 @@

Case 1: W-E Direction

-../../_images/usage_tutorials_Directional_Semivariograms_%28Basic%29_16_0.png +../../_images/usage_tutorials_Directional_Semivariograms_(Basic)_16_0.png
@@ -1135,7 +1135,7 @@

Case 2: N-S Direction

-../../_images/usage_tutorials_Directional_Semivariograms_%28Basic%29_18_0.png +../../_images/usage_tutorials_Directional_Semivariograms_(Basic)_18_0.png
@@ -1162,7 +1162,7 @@

Case 3: NW-SE Direction

-../../_images/usage_tutorials_Directional_Semivariograms_%28Basic%29_20_0.png +../../_images/usage_tutorials_Directional_Semivariograms_(Basic)_20_0.png
@@ -1189,7 +1189,7 @@

Case 4: NE-SW Direction

-../../_images/usage_tutorials_Directional_Semivariograms_%28Basic%29_22_0.png +../../_images/usage_tutorials_Directional_Semivariograms_(Basic)_22_0.png
@@ -1213,7 +1213,7 @@

Case 5: Isotropic Variogram

-../../_images/usage_tutorials_Directional_Semivariograms_%28Basic%29_24_0.png +../../_images/usage_tutorials_Directional_Semivariograms_(Basic)_24_0.png
@@ -1261,7 +1261,7 @@

3) Compare semivariograms
-../../_images/usage_tutorials_Directional_Semivariograms_%28Basic%29_26_0.png +../../_images/usage_tutorials_Directional_Semivariograms_(Basic)_26_0.png

What do you think about this comparison? Do you agree, that the NW-SE variogram is the best fit for a data?

@@ -1323,7 +1323,7 @@

4) Bonus: Compare calculation times and results
[20]:
-
msg = f'The triangular method is {tfin_e / tfin_t} times faster than the elliptical selection.'
+
msg = f'The triangular method is {tfin_e / tfin_t} times faster than the elliptical selection.'
 
 print(msg)
 

@@ -1362,7 +1362,7 @@ 
4) Bonus: Compare calculation times and results
 

 

-../../_images/usage_tutorials_Directional_Semivariograms_%28Basic%29_34_0.png
+../../_images/usage_tutorials_Directional_Semivariograms_(Basic)_34_0.png

The general shape is the same but variance of a semivariogram from a triangular selection is lower; it is harder to distinguish the covariance between neighboring points, and it could be dangerously close to the nugget effect. You should consider this (especially) in automatic calculations to avoid a strange results or models that are not better than a simple IDW technique.

@@ -1557,7 +1557,7 @@

4) Bonus: Compare calculation times and results

-Created using Sphinx 5.3.0.
+Created using Sphinx 5.0.2.

diff --git a/docs/build/html/usage/tutorials/Directional Semivariograms (Basic).ipynb b/docs/build/html/usage/tutorials/Directional Semivariograms (Basic).ipynb index 4c7d9ef5..2f4b576b 100644 --- a/docs/build/html/usage/tutorials/Directional Semivariograms (Basic).ipynb +++ b/docs/build/html/usage/tutorials/Directional Semivariograms (Basic).ipynb @@ -1015,7 +1015,7 @@ }, { "cell_type": "markdown", - "id": "3d90c342", + "id": "d3da8c90", "metadata": { "collapsed": false, "pycharm": { diff --git a/docs/build/html/usage/tutorials/How good is our Kriging model - test it against IDW algorithm (Basic).html b/docs/build/html/usage/tutorials/How good is our Kriging model - test it against IDW algorithm (Basic).html index fbe40ba3..1ab2bf66 100644 --- a/docs/build/html/usage/tutorials/How good is our Kriging model - test it against IDW algorithm (Basic).html +++ b/docs/build/html/usage/tutorials/How good is our Kriging model - test it against IDW algorithm (Basic).html @@ -38,7 +38,6 @@ - @@ -511,10 +510,11 @@ } } -/* disable scrollbars on prompts */ +/* disable scrollbars and line breaks on prompts */ div.nbinput.container div.prompt pre, div.nboutput.container div.prompt pre { overflow: hidden; + white-space: pre; } /* input/output area */ @@ -929,7 +929,7 @@

4) Perform variogram modeling on the modeling set
-../../_images/usage_tutorials_How_good_is_our_Kriging_model_-_test_it_against_IDW_algorithm_%28Basic%29_16_0.png +../../_images/usage_tutorials_How_good_is_our_Kriging_model_-_test_it_against_IDW_algorithm_(Basic)_16_0.png
@@ -1133,7 +1133,7 @@

6) Bonus scenario: only 2% of values are known!
-../../_images/usage_tutorials_How_good_is_our_Kriging_model_-_test_it_against_IDW_algorithm_%28Basic%29_26_0.png +../../_images/usage_tutorials_How_good_is_our_Kriging_model_-_test_it_against_IDW_algorithm_(Basic)_26_0.png
@@ -1366,7 +1366,7 @@

6) Bonus scenario: only 2% of values are known!

-Created using Sphinx 5.3.0.
+Created using Sphinx 5.0.2.

diff --git a/docs/build/html/usage/tutorials/Ordinary and Simple Kriging (Basic).html b/docs/build/html/usage/tutorials/Ordinary and Simple Kriging (Basic).html index c35724bc..f0e8e231 100644 --- a/docs/build/html/usage/tutorials/Ordinary and Simple Kriging (Basic).html +++ b/docs/build/html/usage/tutorials/Ordinary and Simple Kriging (Basic).html @@ -38,7 +38,6 @@ - @@ -511,10 +510,11 @@ } } -/* disable scrollbars on prompts */ +/* disable scrollbars and line breaks on prompts */ div.nbinput.container div.prompt pre, div.nboutput.container div.prompt pre { overflow: hidden; + white-space: pre; } /* input/output area */ @@ -765,7 +765,7 @@

1) Read point data
def create_train_test(dataset: np.ndarray, training_set_ratio=0.3):
-    """
+    """
     Function divides base dataset into a training and a test set.
 
     Parameters
@@ -832,7 +832,7 @@ 
2) Set Semivariogram model
-../../_images/usage_tutorials_Ordinary_and_Simple_Kriging_%28Basic%29_10_0.png +../../_images/usage_tutorials_Ordinary_and_Simple_Kriging_(Basic)_10_0.png
@@ -857,7 +857,7 @@

2) Set Semivariogram model

-../../_images/usage_tutorials_Ordinary_and_Simple_Kriging_%28Basic%29_12_0.png +../../_images/usage_tutorials_Ordinary_and_Simple_Kriging_(Basic)_12_0.png
@@ -1513,7 +1513,7 @@

4) Predict values at unknown locations

-Created using Sphinx 5.3.0.
+Created using Sphinx 5.0.2.

diff --git a/docs/build/html/usage/tutorials/Outliers and Their Influence on the Final Model (Intermediate).html b/docs/build/html/usage/tutorials/Outliers and Their Influence on the Final Model (Intermediate).html index d7472fba..2b36c549 100644 --- a/docs/build/html/usage/tutorials/Outliers and Their Influence on the Final Model (Intermediate).html +++ b/docs/build/html/usage/tutorials/Outliers and Their Influence on the Final Model (Intermediate).html @@ -38,7 +38,6 @@ - @@ -511,10 +510,11 @@ } } -/* disable scrollbars on prompts */ +/* disable scrollbars and line breaks on prompts */ div.nbinput.container div.prompt pre, div.nboutput.container div.prompt pre { overflow: hidden; + white-space: pre; } /* input/output area */ @@ -819,7 +819,7 @@

2) Check outliers: analyze the distribution of the values
-../../_images/usage_tutorials_Outliers_and_Their_Influence_on_the_Final_Model_%28Intermediate%29_9_0.png +../../_images/usage_tutorials_Outliers_and_Their_Influence_on_the_Final_Model_(Intermediate)_9_0.png
@@ -848,7 +848,7 @@

2) Check outliers: analyze the distribution of the values
-../../_images/usage_tutorials_Outliers_and_Their_Influence_on_the_Final_Model_%28Intermediate%29_11_0.png +../../_images/usage_tutorials_Outliers_and_Their_Influence_on_the_Final_Model_(Intermediate)_11_0.png

Boxplot is a unique and handy data visualization tool. Let’s analyze this plot from the bottom up to the top.

@@ -915,7 +915,7 @@

2) Check outliers: analyze the distribution of the values
-../../_images/usage_tutorials_Outliers_and_Their_Influence_on_the_Final_Model_%28Intermediate%29_15_0.png +../../_images/usage_tutorials_Outliers_and_Their_Influence_on_the_Final_Model_(Intermediate)_15_0.png

Clarification: We have cut some records from the baseline training dataset. The distribution plot (violinplot) has a shorter tail and ends more abruptly upwards. The boxplot of the new data doesn’t have any outliers. The one crucial thing to notice is that the observations are still skewed, but this is not a problem for this concrete tutorial.

@@ -962,7 +962,7 @@

3) Create the Variogram Point Cloud model for datasets A and B
-../../_images/usage_tutorials_Outliers_and_Their_Influence_on_the_Final_Model_%28Intermediate%29_20_0.png +../../_images/usage_tutorials_Outliers_and_Their_Influence_on_the_Final_Model_(Intermediate)_20_0.png
@@ -979,7 +979,7 @@

3) Create the Variogram Point Cloud model for datasets A and B

-../../_images/usage_tutorials_Outliers_and_Their_Influence_on_the_Final_Model_%28Intermediate%29_21_0.png +../../_images/usage_tutorials_Outliers_and_Their_Influence_on_the_Final_Model_(Intermediate)_21_0.png

Clarification: a quick look into the results shows that calculated semivariances are skewed into significant positive values for each lag, but most profoundly within middle lags (~15:21 km). The processed dataset has lower semivariances than the raw readings, and both variograms have a similar shape.

@@ -1110,7 +1110,7 @@

4) Remove outliers from the variograms
-../../_images/usage_tutorials_Outliers_and_Their_Influence_on_the_Final_Model_%28Intermediate%29_28_0.png +../../_images/usage_tutorials_Outliers_and_Their_Influence_on_the_Final_Model_(Intermediate)_28_0.png

Clarification: Comparison of multiple variogram clouds could be hard. We see that the largest semivariances are present in the raw data. Heavily processed data has the lowest number of outliers. The medians in each dataset are distributed over a similar pattern. How is it similar? We can check if we transform the variogram point cloud into the experimental semivariogram. Pyinterpolate has a method for it: .calculate_experimental_variogram(). We use it and compare four plots of @@ -1147,7 +1147,7 @@

4) Remove outliers from the variograms
-../../_images/usage_tutorials_Outliers_and_Their_Influence_on_the_Final_Model_%28Intermediate%29_31_0.png +../../_images/usage_tutorials_Outliers_and_Their_Influence_on_the_Final_Model_(Intermediate)_31_0.png

Clarification: An understanding of those plots is not an easy task. Let’s divide reasoning into multiple points:

@@ -1548,7 +1548,7 @@

5) Create Four Ordinary Kriging models based on the four Variogram Point Clo

-Created using Sphinx 5.3.0.
+Created using Sphinx 5.0.2.

diff --git a/docs/build/html/usage/tutorials/Poisson Kriging - Area to Area (Advanced).html b/docs/build/html/usage/tutorials/Poisson Kriging - Area to Area (Advanced).html index 76f1fb2a..05182a00 100644 --- a/docs/build/html/usage/tutorials/Poisson Kriging - Area to Area (Advanced).html +++ b/docs/build/html/usage/tutorials/Poisson Kriging - Area to Area (Advanced).html @@ -38,7 +38,6 @@ - @@ -511,10 +510,11 @@ } } -/* disable scrollbars on prompts */ +/* disable scrollbars and line breaks on prompts */ div.nbinput.container div.prompt pre, div.nboutput.container div.prompt pre { overflow: hidden; + white-space: pre; } /* input/output area */ @@ -802,7 +802,7 @@

1) Read and explore data
-../../_images/usage_tutorials_Poisson_Kriging_-_Area_to_Area_%28Advanced%29_4_1.png +../../_images/usage_tutorials_Poisson_Kriging_-_Area_to_Area_(Advanced)_4_1.png

Clarification: It is a good idea to look into the spatial patterns in a dataset and to visually check if our data do not have any NaN values. We use the geopandas GeoDataFrame.plot() function with a color map that diverges regions based on the cancer incidence rates. The output choropleth map is not ideal, and (probably) it has a few unreliable results, for example:

@@ -836,7 +836,7 @@

3) Prepare training and test data.
# Remove 40% of rows (values) to test our model
 
 def create_test_areal_set(areal_dataset: Blocks, points_dataset: PointSupport, frac=0.4):
-    """
+    """
 
     Parameters
     ----------
@@ -979,7 +979,7 @@ 
5) Analyze Forecast Bias and Root Mean Squared Error of prediction
-../../_images/usage_tutorials_Poisson_Kriging_-_Area_to_Area_%28Advanced%29_15_1.png +../../_images/usage_tutorials_Poisson_Kriging_-_Area_to_Area_(Advanced)_15_1.png
@@ -1003,7 +1003,7 @@

5) Analyze Forecast Bias and Root Mean Squared Error of prediction

-../../_images/usage_tutorials_Poisson_Kriging_-_Area_to_Area_%28Advanced%29_16_1.png +../../_images/usage_tutorials_Poisson_Kriging_-_Area_to_Area_(Advanced)_16_1.png
@@ -1269,7 +1269,7 @@

5) Analyze Forecast Bias and Root Mean Squared Error of prediction

-Created using Sphinx 5.3.0.
+Created using Sphinx 5.0.2.

diff --git a/docs/build/html/usage/tutorials/Poisson Kriging - Area to Point (Advanced).html b/docs/build/html/usage/tutorials/Poisson Kriging - Area to Point (Advanced).html index e62b5c4e..d6cb53c8 100644 --- a/docs/build/html/usage/tutorials/Poisson Kriging - Area to Point (Advanced).html +++ b/docs/build/html/usage/tutorials/Poisson Kriging - Area to Point (Advanced).html @@ -38,7 +38,6 @@ - @@ -511,10 +510,11 @@ } } -/* disable scrollbars on prompts */ +/* disable scrollbars and line breaks on prompts */ div.nbinput.container div.prompt pre, div.nboutput.container div.prompt pre { overflow: hidden; + white-space: pre; } /* input/output area */ @@ -791,7 +791,7 @@

1) Read and explore data
-../../_images/usage_tutorials_Poisson_Kriging_-_Area_to_Point_%28Advanced%29_3_1.png +../../_images/usage_tutorials_Poisson_Kriging_-_Area_to_Point_(Advanced)_3_1.png

Clarification: It is a good idea to look into the spatial patterns in a dataset and to visually check if our data do not have any NaN values. We use the geopandas GeoDataFrame.plot() function with a color map that diverges regions based on the cancer incidence rates. The output choropleth map is not ideal, and (probably) it has a few unreliable results, for example:

@@ -951,7 +951,7 @@

4) Visualize data
-../../_images/usage_tutorials_Poisson_Kriging_-_Area_to_Point_%28Advanced%29_12_1.png +../../_images/usage_tutorials_Poisson_Kriging_-_Area_to_Point_(Advanced)_12_1.png
@@ -1105,7 +1105,7 @@

4) Visualize data

-Created using Sphinx 5.3.0.
+Created using Sphinx 5.0.2.

diff --git a/docs/build/html/usage/tutorials/Poisson Kriging - Centroid Based (Advanced).html b/docs/build/html/usage/tutorials/Poisson Kriging - Centroid Based (Advanced).html index 0f4264b3..76f4be02 100644 --- a/docs/build/html/usage/tutorials/Poisson Kriging - Centroid Based (Advanced).html +++ b/docs/build/html/usage/tutorials/Poisson Kriging - Centroid Based (Advanced).html @@ -38,7 +38,6 @@ - @@ -511,10 +510,11 @@ } } -/* disable scrollbars on prompts */ +/* disable scrollbars and line breaks on prompts */ div.nbinput.container div.prompt pre, div.nboutput.container div.prompt pre { overflow: hidden; + white-space: pre; } /* input/output area */ @@ -806,7 +806,7 @@

1) Read and explore data
-../../_images/usage_tutorials_Poisson_Kriging_-_Centroid_Based_%28Advanced%29_4_1.png +../../_images/usage_tutorials_Poisson_Kriging_-_Centroid_Based_(Advanced)_4_1.png

Clarification: It is a good idea to look into the spatial patterns in a dataset and to visually check if our data do not have any NaN values. We use the geopandas GeoDataFrame.plot() function with a color map that diverges regions based on the cancer incidence rates. The output choropleth map is not ideal, and (probably) it has a few unreliable results, for example:

@@ -840,7 +840,7 @@

3) Prepare training and test data.
# Remove 40% of rows (values) to test our model
 
 def create_test_areal_set(areal_dataset: Blocks, points_dataset: PointSupport, frac=0.4):
-    """
+    """
 
     Parameters
     ----------
@@ -982,7 +982,7 @@ 
5) Analyze Forecast Bias and Root Mean Squared Error of prediction
-../../_images/usage_tutorials_Poisson_Kriging_-_Centroid_Based_%28Advanced%29_15_1.png +../../_images/usage_tutorials_Poisson_Kriging_-_Centroid_Based_(Advanced)_15_1.png
@@ -1006,7 +1006,7 @@

5) Analyze Forecast Bias and Root Mean Squared Error of prediction

-../../_images/usage_tutorials_Poisson_Kriging_-_Centroid_Based_%28Advanced%29_16_1.png +../../_images/usage_tutorials_Poisson_Kriging_-_Centroid_Based_(Advanced)_16_1.png
@@ -1272,7 +1272,7 @@

5) Analyze Forecast Bias and Root Mean Squared Error of prediction

-Created using Sphinx 5.3.0.
+Created using Sphinx 5.0.2.

diff --git a/docs/build/html/usage/tutorials/Semivariogram Estimation (Basic).html b/docs/build/html/usage/tutorials/Semivariogram Estimation (Basic).html index 79380a0f..05c3b25d 100644 --- a/docs/build/html/usage/tutorials/Semivariogram Estimation (Basic).html +++ b/docs/build/html/usage/tutorials/Semivariogram Estimation (Basic).html @@ -38,7 +38,6 @@ - @@ -511,10 +510,11 @@ } } -/* disable scrollbars on prompts */ +/* disable scrollbars and line breaks on prompts */ div.nbinput.container div.prompt pre, div.nboutput.container div.prompt pre { overflow: hidden; + white-space: pre; } /* input/output area */ @@ -902,7 +902,7 @@

2) Create the experimental variogram
-../../_images/usage_tutorials_Semivariogram_Estimation_%28Basic%29_12_0.png +../../_images/usage_tutorials_Semivariogram_Estimation_(Basic)_12_0.png

Our plot shows three objects:

@@ -1030,7 +1030,7 @@

Models:
-../../_images/usage_tutorials_Semivariogram_Estimation_%28Basic%29_19_0.png +../../_images/usage_tutorials_Semivariogram_Estimation_(Basic)_19_0.png
@@ -1105,7 +1105,7 @@

Models:

-../../_images/usage_tutorials_Semivariogram_Estimation_%28Basic%29_22_0.png +../../_images/usage_tutorials_Semivariogram_Estimation_(Basic)_22_0.png
@@ -1173,7 +1173,7 @@

Models:

-../../_images/usage_tutorials_Semivariogram_Estimation_%28Basic%29_24_0.png +../../_images/usage_tutorials_Semivariogram_Estimation_(Basic)_24_0.png
@@ -1241,7 +1241,7 @@

Models:

-../../_images/usage_tutorials_Semivariogram_Estimation_%28Basic%29_26_0.png +../../_images/usage_tutorials_Semivariogram_Estimation_(Basic)_26_0.png
@@ -1309,7 +1309,7 @@

Models:

-../../_images/usage_tutorials_Semivariogram_Estimation_%28Basic%29_28_0.png +../../_images/usage_tutorials_Semivariogram_Estimation_(Basic)_28_0.png
@@ -1377,7 +1377,7 @@

Models:

-../../_images/usage_tutorials_Semivariogram_Estimation_%28Basic%29_30_0.png +../../_images/usage_tutorials_Semivariogram_Estimation_(Basic)_30_0.png
@@ -1446,7 +1446,7 @@

Models:

-../../_images/usage_tutorials_Semivariogram_Estimation_%28Basic%29_32_0.png +../../_images/usage_tutorials_Semivariogram_Estimation_(Basic)_32_0.png

Quick look into plots, and we can bet that the best model is linear. We can read each table printed with a print() method, but instead, we will read Root Mean Squared Error of each model and select the model with the lowest value of RMSE:

@@ -1669,7 +1669,7 @@

4) Set automatically semivariogram model
-../../_images/usage_tutorials_Semivariogram_Estimation_%28Basic%29_44_0.png +../../_images/usage_tutorials_Semivariogram_Estimation_(Basic)_44_0.png

Compare this model to the linear model fitted before:

@@ -1697,7 +1697,7 @@

4) Set automatically semivariogram model
-../../_images/usage_tutorials_Semivariogram_Estimation_%28Basic%29_46_0.png +../../_images/usage_tutorials_Semivariogram_Estimation_(Basic)_46_0.png
@@ -1988,7 +1988,7 @@

6) Import model

-Created using Sphinx 5.3.0.
+Created using Sphinx 5.0.2.

diff --git a/docs/build/html/usage/tutorials/Semivariogram Regularization (Intermediate).html b/docs/build/html/usage/tutorials/Semivariogram Regularization (Intermediate).html index 623e2ce5..739a4498 100644 --- a/docs/build/html/usage/tutorials/Semivariogram Regularization (Intermediate).html +++ b/docs/build/html/usage/tutorials/Semivariogram Regularization (Intermediate).html @@ -38,7 +38,6 @@ - @@ -511,10 +510,11 @@ } } -/* disable scrollbars on prompts */ +/* disable scrollbars and line breaks on prompts */ div.nbinput.container div.prompt pre, div.nboutput.container div.prompt pre { overflow: hidden; + white-space: pre; } /* input/output area */ @@ -820,7 +820,7 @@

2) Set semivariogram parameters.
-../../_images/usage_tutorials_Semivariogram_Regularization_%28Intermediate%29_7_0.png +../../_images/usage_tutorials_Semivariogram_Regularization_(Intermediate)_7_0.png
@@ -847,7 +847,7 @@

2) Set semivariogram parameters.

-../../_images/usage_tutorials_Semivariogram_Regularization_%28Intermediate%29_8_0.png +../../_images/usage_tutorials_Semivariogram_Regularization_(Intermediate)_8_0.png

We see that block and point support variograms follow a spatial-dependency pattern. In this case, we can move to the next step - deconvolution. The next step is to create the Deconvolution object. We have multiple parameters to choose from, and it is hard to find the best fit at the beginning, so try to avoid multiple loops because it is time-consuming.

@@ -905,7 +905,7 @@

3) Regularize semivariogram
-../../_images/usage_tutorials_Semivariogram_Regularization_%28Intermediate%29_12_0.png +../../_images/usage_tutorials_Semivariogram_Regularization_(Intermediate)_12_0.png

After the first step, we see that variogram can be regularized. There is a big difference between the theoretical curve and the experimental values. Let’s run the transformation process.

@@ -951,7 +951,7 @@

4) Visualize and check semivariogram
-../../_images/usage_tutorials_Semivariogram_Regularization_%28Intermediate%29_16_0.png +../../_images/usage_tutorials_Semivariogram_Regularization_(Intermediate)_16_0.png

Clarification: Deviation between regularized semivariogram and a theoretical model is smaller in each step, but the significant improvement can be seen after the first step, and then it slows down. That’s why semivariogram regularization usually does not require many steps. However, if there are many lags, optimization may require more steps to achieve a meaningful result.

@@ -970,7 +970,7 @@

4) Visualize and check semivariogram
-../../_images/usage_tutorials_Semivariogram_Regularization_%28Intermediate%29_18_0.png +../../_images/usage_tutorials_Semivariogram_Regularization_(Intermediate)_18_0.png

NOTE: Weights are smaller with each iteration. It is the expected behavior of the algorithm. The general trend goes downward, and small oscillations may occur due to the optimization process.

@@ -987,7 +987,7 @@

4) Visualize and check semivariogram
-../../_images/usage_tutorials_Semivariogram_Regularization_%28Intermediate%29_20_0.png +../../_images/usage_tutorials_Semivariogram_Regularization_(Intermediate)_20_0.png

The regularized model works well for our dataset and follows general trends. For closest lags, it assigns smaller weights, and for larger lags, weights are bigger. It is related to the semivariogram weighting method - we penalize more models that are poorly performing for the shortest distances from the origin.

@@ -1167,7 +1167,7 @@

5) Export semivariogram to text file

-Created using Sphinx 5.3.0.
+Created using Sphinx 5.0.2.

diff --git a/docs/build/html/usage/tutorials/Theoretical Models (Basic).html b/docs/build/html/usage/tutorials/Theoretical Models (Basic).html index 1f35a843..64c9281c 100644 --- a/docs/build/html/usage/tutorials/Theoretical Models (Basic).html +++ b/docs/build/html/usage/tutorials/Theoretical Models (Basic).html @@ -38,7 +38,6 @@ - @@ -511,10 +510,11 @@ } } -/* disable scrollbars on prompts */ +/* disable scrollbars and line breaks on prompts */ div.nbinput.container div.prompt pre, div.nboutput.container div.prompt pre { overflow: hidden; + white-space: pre; } /* input/output area */ @@ -771,7 +771,7 @@

1) Create a random surface
-../../_images/usage_tutorials_Theoretical_Models_%28Basic%29_7_0.png +../../_images/usage_tutorials_Theoretical_Models_(Basic)_7_0.png

Let’s reshape this signal into a 2-D matrix:

@@ -799,7 +799,7 @@

1) Create a random surface
-../../_images/usage_tutorials_Theoretical_Models_%28Basic%29_10_0.png +../../_images/usage_tutorials_Theoretical_Models_(Basic)_10_0.png

The spatial correlation of this structure is very weak, we can change it with a simple blur filter. It’s size will be our range parameter!

@@ -830,7 +830,7 @@

1) Create a random surface
-../../_images/usage_tutorials_Theoretical_Models_%28Basic%29_14_0.png +../../_images/usage_tutorials_Theoretical_Models_(Basic)_14_0.png
@@ -914,7 +914,7 @@

2) Create the experimental variogram
-../../_images/usage_tutorials_Theoretical_Models_%28Basic%29_24_0.png +../../_images/usage_tutorials_Theoretical_Models_(Basic)_24_0.png

We read a plot and decide to set:

@@ -1028,7 +1028,7 @@

3) Set all variogram models
-../../_images/usage_tutorials_Theoretical_Models_%28Basic%29_32_0.png +../../_images/usage_tutorials_Theoretical_Models_(Basic)_32_0.png
@@ -1051,7 +1051,7 @@

3) Set all variogram models

-../../_images/usage_tutorials_Theoretical_Models_%28Basic%29_33_0.png +../../_images/usage_tutorials_Theoretical_Models_(Basic)_33_0.png
@@ -1074,7 +1074,7 @@

3) Set all variogram models

-../../_images/usage_tutorials_Theoretical_Models_%28Basic%29_34_0.png +../../_images/usage_tutorials_Theoretical_Models_(Basic)_34_0.png
@@ -1097,7 +1097,7 @@

3) Set all variogram models

-../../_images/usage_tutorials_Theoretical_Models_%28Basic%29_35_0.png +../../_images/usage_tutorials_Theoretical_Models_(Basic)_35_0.png
@@ -1120,7 +1120,7 @@

3) Set all variogram models

-../../_images/usage_tutorials_Theoretical_Models_%28Basic%29_36_0.png +../../_images/usage_tutorials_Theoretical_Models_(Basic)_36_0.png
@@ -1143,7 +1143,7 @@

3) Set all variogram models

-../../_images/usage_tutorials_Theoretical_Models_%28Basic%29_37_0.png +../../_images/usage_tutorials_Theoretical_Models_(Basic)_37_0.png
@@ -1166,7 +1166,7 @@

3) Set all variogram models

-../../_images/usage_tutorials_Theoretical_Models_%28Basic%29_38_0.png +../../_images/usage_tutorials_Theoretical_Models_(Basic)_38_0.png

What was your guess?

@@ -1211,7 +1211,7 @@

4) Compare variogram models
-../../_images/usage_tutorials_Theoretical_Models_%28Basic%29_41_0.png +../../_images/usage_tutorials_Theoretical_Models_(Basic)_41_0.png

We observe that the lowest distance from a modeled value to the experimental curve at a small distance has…

@@ -1371,7 +1371,7 @@

4) Compare variogram models

-Created using Sphinx 5.3.0.
+Created using Sphinx 5.0.2.

diff --git a/docs/build/html/usage/tutorials/Variogram Point Cloud (Basic).html b/docs/build/html/usage/tutorials/Variogram Point Cloud (Basic).html index 38f52c2d..5040dd3b 100644 --- a/docs/build/html/usage/tutorials/Variogram Point Cloud (Basic).html +++ b/docs/build/html/usage/tutorials/Variogram Point Cloud (Basic).html @@ -38,7 +38,6 @@ - @@ -511,10 +510,11 @@ } } -/* disable scrollbars on prompts */ +/* disable scrollbars and line breaks on prompts */ div.nbinput.container div.prompt pre, div.nboutput.container div.prompt pre { overflow: hidden; + white-space: pre; } /* input/output area */ @@ -864,7 +864,7 @@

2) Set proper lag size with variogram cloud histogram
-../../_images/usage_tutorials_Variogram_Point_Cloud_%28Basic%29_11_0.png +../../_images/usage_tutorials_Variogram_Point_Cloud_(Basic)_11_0.png

The output per lag is dense, and we cannot distinguish a single value. But, we can follow a general trend and see maxima on the plot. We can make it better if we use a box plot instead:

@@ -880,7 +880,7 @@

2) Set proper lag size with variogram cloud histogram
-../../_images/usage_tutorials_Variogram_Point_Cloud_%28Basic%29_13_0.png +../../_images/usage_tutorials_Variogram_Point_Cloud_(Basic)_13_0.png

The orange line in the middle of each box represents the median (50%) value per lag. The trend is more visible if we look into it. What else can be seen here?

@@ -903,7 +903,7 @@

2) Set proper lag size with variogram cloud histogram
-../../_images/usage_tutorials_Variogram_Point_Cloud_%28Basic%29_15_0.png +../../_images/usage_tutorials_Variogram_Point_Cloud_(Basic)_15_0.png

The violin plot supports our earlier assumptions, but we can learn more from it. For distant lags, a distribution starts to be multimodal. We see one mode around very low values of semivariance and another mode close to the 1st quartile. Multimodality may affect our outcomes, and maybe it tells us that there is more than one level of spatial dependency?

@@ -1054,7 +1054,7 @@

3) Detect and remove outliers
-../../_images/usage_tutorials_Variogram_Point_Cloud_%28Basic%29_21_0.png +../../_images/usage_tutorials_Variogram_Point_Cloud_(Basic)_21_0.png
@@ -1112,7 +1112,7 @@

3) Detect and remove outliers

-../../_images/usage_tutorials_Variogram_Point_Cloud_%28Basic%29_25_0.png +../../_images/usage_tutorials_Variogram_Point_Cloud_(Basic)_25_0.png

When we compare both figures - before and after data cleaning - we see that the y-axis of the second plot is 6-7 times smaller than the y-axis of the first plot! We’ve cleaned our data from the extreme values.

@@ -1303,7 +1303,7 @@

4) Calculate experimental semivariogram from point cloud

-Created using Sphinx 5.3.0.
+Created using Sphinx 5.0.2.

diff --git a/docs/requirements.txt b/docs/requirements.txt index e276445c..beadbe4c 100644 --- a/docs/requirements.txt +++ b/docs/requirements.txt @@ -13,7 +13,7 @@ prettytable pandas dask requests -sphinx +sphinx<6 nbsphinx numpydoc pydata-sphinx-theme diff --git a/pyinterpolate/processing/preprocessing/blocks.py b/pyinterpolate/processing/preprocessing/blocks.py index 96b77df0..54ae1396 100644 --- a/pyinterpolate/processing/preprocessing/blocks.py +++ b/pyinterpolate/processing/preprocessing/blocks.py @@ -4,11 +4,8 @@ Authors ------- 1. Szymon Moliński | @SimonMolinsky - -TODO ----- -* PointSupport | Fn skips points that are not assigned to any area, maybe log it somewhere... """ +import logging from typing import Union import geopandas as gpd @@ -212,11 +209,34 @@ def from_geodataframe(self, class PointSupport: """Class prepares the point support data in relation to block dataset. + Parameters + ---------- + log_not_used_points : bool, default=False + Should dropped points be logged? + Attributes ---------- point_support : gpd.GeoDataFrame Dataset with point support values and indexes of blocks (where points fall into). + value_column : str + The value column name + + geometry_column : str + The geometry column name. + + block_index_column : str + The area index. + + x_col : str, default = "x_col" + Longitude column name. + + y_col : str, default = "y_col" + Latitude column name. + + log_dropped : bool + See log_not_used_points parameter. + Methods ------- from_files() @@ -264,13 +284,14 @@ class PointSupport: ... blocks_index_col=POLYGON_ID) """ - def __init__(self): + def __init__(self, log_not_used_points=False): self.point_support = None self.value_column = None self.geometry_column = None self.block_index_column = None self.x_col = 'x_col' self.y_col = 'y_col' + self.log_dropped = log_not_used_points def from_files(self, point_support_data_file: str, @@ -348,7 +369,6 @@ def from_geodataframes(self, ---------- point_support_dataframe : GeoDataFrame or GeoSeries - blocks_dataframe : GeoDataFrame Block data with block indexes and geometries. @@ -384,6 +404,16 @@ def from_geodataframes(self, # Merge data joined = gpd.sjoin(point_support, blocks, how='left') + # Check which points weren't joined + if self.log_dropped: + is_na = joined.isna().any(axis=1) + not_joined_points = joined[is_na]['geometry'] + if len(not_joined_points) > 0: + logging.info('POINT SUPPORT : Dropped points:') + for pt in not_joined_points: + msg = '({}, {})'.format(pt.x, pt.y) + logging.info(msg) + # Clean data joined.dropna(inplace=True) joined = joined[['index_right', point_support_geometry_col, point_support_val_col]] diff --git a/tests/test_processing/test_blocks.py b/tests/test_processing/test_blocks.py index 5524c4dc..a42aba86 100644 --- a/tests/test_processing/test_blocks.py +++ b/tests/test_processing/test_blocks.py @@ -79,3 +79,14 @@ def test_get_from_geodataframes_fn(self): out_keys = set(ps.point_support.keys()) self.assertEqual(expected_keys, out_keys) + def test_get_from_geodataframes_fn_with_logging(self): + gdf_points = gpd.read_file(DATASET, layer=POPULATION_LAYER) + gdf_polygons = gpd.read_file(DATASET, layer=POLYGON_LAYER) + ps = PointSupport(log_not_used_points=True) + ps.from_geodataframes(gdf_points, + gdf_polygons, + point_support_geometry_col=GEOMETRY_COL, + point_support_val_col=POP10, + blocks_geometry_col=GEOMETRY_COL, + blocks_index_col=POLYGON_ID) + self.assertTrue(not ps.point_support.empty) From a98da0e9a0d68730ee9a57f0650c9173fb666186 Mon Sep 17 00:00:00 2001 From: Simon <31246246+SimonMolinsky@users.noreply.github.com> Date: Sat, 21 Jan 2023 18:21:37 +0200 Subject: [PATCH 06/29] Added possibility to use own columns by the user --- .../build/doctrees/api/datatypes/core.doctree | Bin 70422 -> 70535 bytes .../doctrees/api/distance/distance.doctree | Bin 17741 -> 17922 bytes docs/build/doctrees/api/idw/idw.doctree | Bin 16687 -> 16787 bytes docs/build/doctrees/api/io/io.doctree | Bin 37818 -> 38005 bytes .../api/kriging/block/block_kriging.doctree | Bin 73502 -> 73718 bytes .../api/kriging/point/point_kriging.doctree | Bin 58411 -> 58609 bytes .../api/pipelines/data/download.doctree | Bin 12411 -> 12515 bytes .../kriging_based_processes.doctree | Bin 102904 -> 103232 bytes .../api/variogram/block/block.doctree | Bin 90716 -> 91266 bytes .../deconvolution/deconvolution.doctree | Bin 123030 -> 123376 bytes .../experimental/experimental.doctree | Bin 157228 -> 157634 bytes .../variogram/theoretical/theoretical.doctree | Bin 142471 -> 143341 bytes docs/build/doctrees/api/viz/viz.doctree | Bin 21741 -> 21836 bytes docs/build/doctrees/environment.pickle | Bin 464044 -> 436516 bytes docs/build/doctrees/index.doctree | Bin 14111 -> 14111 bytes .../Directional Semivariograms (Basic).ipynb | 2 +- ...al Ordinary Kriging (Intermediate).doctree | Bin 42677 -> 42677 bytes ...Directional Semivariograms (Basic).doctree | Bin 88128 -> 88128 bytes docs/build/html/.buildinfo | 2 +- docs/build/html/_modules/index.html | 7 +- .../processing/preprocessing/blocks.html | 7 +- docs/build/html/_sources/index.rst.txt | 2 +- docs/build/html/_static/basic.css | 30 ---- docs/build/html/_static/doctools.js | 130 ++---------------- .../html/_static/documentation_options.js | 4 +- docs/build/html/_static/searchtools.js | 91 ++++++++---- docs/build/html/api/api.html | 7 +- docs/build/html/api/datatypes/core.html | 7 +- docs/build/html/api/distance/distance.html | 7 +- docs/build/html/api/idw/idw.html | 9 +- docs/build/html/api/io/io.html | 7 +- .../html/api/kriging/block/block_kriging.html | 7 +- docs/build/html/api/kriging/kriging.html | 7 +- .../html/api/kriging/point/point_kriging.html | 7 +- .../html/api/pipelines/data/download.html | 7 +- .../kriging_based_processes.html | 7 +- docs/build/html/api/pipelines/pipelines.html | 7 +- .../build/html/api/variogram/block/block.html | 7 +- .../deconvolution/deconvolution.html | 7 +- .../variogram/experimental/experimental.html | 9 +- .../variogram/theoretical/theoretical.html | 7 +- docs/build/html/api/variogram/variogram.html | 7 +- docs/build/html/api/viz/viz.html | 7 +- docs/build/html/community/community.html | 7 +- .../community/doc_parts/contributors.html | 7 +- .../build/html/community/doc_parts/forum.html | 7 +- .../html/community/doc_parts/use_cases.html | 7 +- docs/build/html/developer/dev.html | 7 +- docs/build/html/developer/doc_parts/bugs.html | 7 +- .../html/developer/doc_parts/development.html | 7 +- .../html/developer/doc_parts/package.html | 7 +- docs/build/html/developer/doc_parts/reqs.html | 7 +- .../doc_parts/tests_and_contribution.html | 7 +- docs/build/html/genindex.html | 7 +- docs/build/html/index.html | 9 +- docs/build/html/science/biblio.html | 7 +- docs/build/html/science/cite.html | 7 +- docs/build/html/search.html | 7 +- docs/build/html/searchindex.js | 2 +- docs/build/html/setup/setup.html | 7 +- docs/build/html/usage/good_practices.html | 7 +- docs/build/html/usage/quickstart.html | 7 +- docs/build/html/usage/tutorials.html | 7 +- ... 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"collapsed": false, "pycharm": { diff --git a/docs/build/doctrees/usage/tutorials/Directional Ordinary Kriging (Intermediate).doctree b/docs/build/doctrees/usage/tutorials/Directional Ordinary Kriging (Intermediate).doctree index eb9d06f1afc0c366772432587da5c3fc13b23a70..51ec64a1f1487c1c32f5ed9c58b65e6549cc4380 100644 GIT binary patch delta 34 jcmdmbmTBu*rVVjijJ}iOxjY$9fyoRonYY=NyJ9u~-$o1X delta 34 jcmdmbmTBu*rVVjij9ioBxjY$V!6YA;Y_wWvl=b=`jHS delta 162 zcmX@Gf%U)!)(vX=6f6x)4NT3_j7*Jj5l$}V>tnIUc7?-gE00(X^Jpcdz diff --git a/docs/build/html/.buildinfo b/docs/build/html/.buildinfo index 6f2d4067..65240432 100644 --- a/docs/build/html/.buildinfo +++ b/docs/build/html/.buildinfo @@ -1,4 +1,4 @@ # Sphinx build info version 1 # This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done. -config: b6ff2721fc32f5ea6338e058f2e17684 +config: 682585118dfff1c264aafc5d2ebde2a7 tags: 645f666f9bcd5a90fca523b33c5a78b7 diff --git a/docs/build/html/_modules/index.html b/docs/build/html/_modules/index.html index b8dc4a18..28ce9fa8 100644 --- a/docs/build/html/_modules/index.html +++ b/docs/build/html/_modules/index.html @@ -5,7 +5,7 @@ - Overview: module code — Pyinterpolate 0.3.5 documentation + Overview: module code — Pyinterpolate 0.3.6 documentation @@ -37,6 +37,7 @@ + @@ -98,7 +99,7 @@ -

Pyinterpolate 0.3.5 documentation

+

Pyinterpolate 0.3.6 documentation

 @@ -460,7 +461,7 @@

All modules for which code is available

-Created using Sphinx 5.0.2.
+Created using Sphinx 5.3.0.

diff --git a/docs/build/html/_modules/pyinterpolate/processing/preprocessing/blocks.html b/docs/build/html/_modules/pyinterpolate/processing/preprocessing/blocks.html index 50bd2cd4..93de868a 100644 --- a/docs/build/html/_modules/pyinterpolate/processing/preprocessing/blocks.html +++ b/docs/build/html/_modules/pyinterpolate/processing/preprocessing/blocks.html @@ -5,7 +5,7 @@ - pyinterpolate.processing.preprocessing.blocks — Pyinterpolate 0.3.5 documentation + pyinterpolate.processing.preprocessing.blocks — Pyinterpolate 0.3.6 documentation @@ -37,6 +37,7 @@ + @@ -98,7 +99,7 @@ -

Pyinterpolate 0.3.5 documentation

+

Pyinterpolate 0.3.6 documentation

@@ -926,7 +927,7 @@

Source code for pyinterpolate.processing.preprocessing.blocks

-Created using Sphinx 5.0.2.
+Created using Sphinx 5.3.0.

diff --git a/docs/build/html/_sources/index.rst.txt b/docs/build/html/_sources/index.rst.txt index 2887b5a8..18c25124 100644 --- a/docs/build/html/_sources/index.rst.txt +++ b/docs/build/html/_sources/index.rst.txt @@ -14,7 +14,7 @@ Pyinterpolate :alt: The pyinterpolate logo with the name Kyiv and the version of package. .. note:: - The last documentation update: *2022-10-31* + The last documentation update: *2023-01-21* **Pyinterpolate** is the Python library for **geostatistics**. The package provides access to spatial statistics tools used in various studies. This package helps you **interpolate spatial data** with the *Kriging* technique. diff --git a/docs/build/html/_static/basic.css b/docs/build/html/_static/basic.css index 9e364ed3..18495ea0 100644 --- a/docs/build/html/_static/basic.css +++ b/docs/build/html/_static/basic.css @@ -326,7 +326,6 @@ p.sidebar-title { } nav.contents, aside.topic, - div.admonition, div.topic, blockquote { clear: left; } @@ -334,7 +333,6 @@ div.admonition, div.topic, blockquote { /* -- topics ---------------------------------------------------------------- */ nav.contents, aside.topic, - div.topic { border: 1px solid #ccc; padding: 7px; @@ -375,7 +373,6 @@ div.sidebar > :last-child, aside.sidebar > :last-child, nav.contents > :last-child, aside.topic > :last-child, - div.topic > :last-child, div.admonition > :last-child { margin-bottom: 0; @@ -385,7 +382,6 @@ div.sidebar::after, aside.sidebar::after, nav.contents::after, aside.topic::after, - div.topic::after, div.admonition::after, blockquote::after { @@ -610,26 +606,6 @@ ol.simple p, ul.simple p { margin-bottom: 0; } - -/* Docutils 0.17 and older (footnotes & citations) */ -dl.footnote > dt, -dl.citation > dt { - float: left; - margin-right: 0.5em; -} - -dl.footnote > dd, -dl.citation > dd { - margin-bottom: 0em; -} - -dl.footnote > dd:after, -dl.citation > dd:after { - content: ""; - clear: both; -} - -/* Docutils 0.18+ (footnotes & citations) */ aside.footnote > span, div.citation > span { float: left; @@ -654,8 +630,6 @@ div.citation > p:last-of-type:after { clear: both; } -/* Footnotes & citations ends */ - dl.field-list { display: grid; grid-template-columns: fit-content(30%) auto; @@ -668,10 +642,6 @@ dl.field-list > dt { padding-right: 5px; } -dl.field-list > dt:after { - content: ":"; -} - dl.field-list > dd { padding-left: 0.5em; margin-top: 0em; diff --git a/docs/build/html/_static/doctools.js b/docs/build/html/_static/doctools.js index c3db08d1..527b876c 100644 --- a/docs/build/html/_static/doctools.js +++ b/docs/build/html/_static/doctools.js @@ -10,6 +10,13 @@ */ "use strict"; +const BLACKLISTED_KEY_CONTROL_ELEMENTS = new Set([ + "TEXTAREA", + "INPUT", + "SELECT", + "BUTTON", +]); + const _ready = (callback) => { if (document.readyState !== "loading") { callback(); @@ -18,73 +25,11 @@ const _ready = (callback) => { } }; -/** - * highlight a given string on a node by wrapping it in - * span elements with the given class name. - */ -const _highlight = (node, addItems, text, className) => { - if (node.nodeType === Node.TEXT_NODE) { - const val = node.nodeValue; - const parent = node.parentNode; - const pos = val.toLowerCase().indexOf(text); - if ( - pos >= 0 && - !parent.classList.contains(className) && - !parent.classList.contains("nohighlight") - ) { - let span; - - const closestNode = parent.closest("body, svg, foreignObject"); - const isInSVG = closestNode && closestNode.matches("svg"); - if (isInSVG) { - span = document.createElementNS("http://www.w3.org/2000/svg", "tspan"); - } else { - span = document.createElement("span"); - span.classList.add(className); - } - - span.appendChild(document.createTextNode(val.substr(pos, text.length))); - parent.insertBefore( - span, - parent.insertBefore( - document.createTextNode(val.substr(pos + text.length)), - node.nextSibling - ) - ); - node.nodeValue = val.substr(0, pos); - - if (isInSVG) { - const rect = document.createElementNS( - "http://www.w3.org/2000/svg", - "rect" - ); - const bbox = parent.getBBox(); - rect.x.baseVal.value = bbox.x; - rect.y.baseVal.value = bbox.y; - rect.width.baseVal.value = bbox.width; - rect.height.baseVal.value = bbox.height; - rect.setAttribute("class", className); - addItems.push({ parent: parent, target: rect }); - } - } - } else if (node.matches && !node.matches("button, select, textarea")) { - node.childNodes.forEach((el) => _highlight(el, addItems, text, className)); - } -}; -const _highlightText = (thisNode, text, className) => { - let addItems = []; - _highlight(thisNode, addItems, text, className); - addItems.forEach((obj) => - obj.parent.insertAdjacentElement("beforebegin", obj.target) - ); -}; - /** * Small JavaScript module for the documentation. */ const Documentation = { init: () => { - Documentation.highlightSearchWords(); Documentation.initDomainIndexTable(); Documentation.initOnKeyListeners(); }, @@ -126,51 +71,6 @@ const Documentation = { Documentation.LOCALE = catalog.locale; }, - /** - * highlight the search words provided in the url in the text - */ - highlightSearchWords: () => { - const highlight = - new URLSearchParams(window.location.search).get("highlight") || ""; - const terms = highlight.toLowerCase().split(/\s+/).filter(x => x); - if (terms.length === 0) return; // nothing to do - - // There should never be more than one element matching "div.body" - const divBody = document.querySelectorAll("div.body"); - const body = divBody.length ? divBody[0] : document.querySelector("body"); - window.setTimeout(() => { - terms.forEach((term) => _highlightText(body, term, "highlighted")); - }, 10); - - const searchBox = document.getElementById("searchbox"); - if (searchBox === null) return; - searchBox.appendChild( - document - .createRange() - .createContextualFragment( - '

' + - '' + - Documentation.gettext("Hide Search Matches") + - "

" - ) - ); - }, - - /** - * helper function to hide the search marks again - */ - hideSearchWords: () => { - document - .querySelectorAll("#searchbox .highlight-link") - .forEach((el) => el.remove()); - document - .querySelectorAll("span.highlighted") - .forEach((el) => el.classList.remove("highlighted")); - const url = new URL(window.location); - url.searchParams.delete("highlight"); - window.history.replaceState({}, "", url); - }, - /** * helper function to focus on search bar */ @@ -210,15 +110,11 @@ const Documentation = { ) return; - const blacklistedElements = new Set([ - "TEXTAREA", - "INPUT", - "SELECT", - "BUTTON", - ]); document.addEventListener("keydown", (event) => { - if (blacklistedElements.has(document.activeElement.tagName)) return; // bail for input elements - if (event.altKey || event.ctrlKey || event.metaKey) return; // bail with special keys + // bail for input elements + if (BLACKLISTED_KEY_CONTROL_ELEMENTS.has(document.activeElement.tagName)) return; + // bail with special keys + if (event.altKey || event.ctrlKey || event.metaKey) return; if (!event.shiftKey) { switch (event.key) { @@ -240,10 +136,6 @@ const Documentation = { event.preventDefault(); } break; - case "Escape": - if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) break; - Documentation.hideSearchWords(); - event.preventDefault(); } } diff --git a/docs/build/html/_static/documentation_options.js b/docs/build/html/_static/documentation_options.js index 221be5bc..db494a19 100644 --- a/docs/build/html/_static/documentation_options.js +++ b/docs/build/html/_static/documentation_options.js @@ -1,6 +1,6 @@ var DOCUMENTATION_OPTIONS = { URL_ROOT: document.getElementById("documentation_options").getAttribute('data-url_root'), - VERSION: '0.3.5', + VERSION: '0.3.6', LANGUAGE: 'en', COLLAPSE_INDEX: false, BUILDER: 'html', @@ -10,5 +10,5 @@ var DOCUMENTATION_OPTIONS = { SOURCELINK_SUFFIX: '.txt', NAVIGATION_WITH_KEYS: true, SHOW_SEARCH_SUMMARY: true, - ENABLE_SEARCH_SHORTCUTS: false, + ENABLE_SEARCH_SHORTCUTS: true, }; \ No newline at end of file diff --git a/docs/build/html/_static/searchtools.js b/docs/build/html/_static/searchtools.js index ac4d5861..e89e34d4 100644 --- a/docs/build/html/_static/searchtools.js +++ b/docs/build/html/_static/searchtools.js @@ -57,14 +57,14 @@ const _removeChildren = (element) => { const _escapeRegExp = (string) => string.replace(/[.*+\-?^${}()|[\]\\]/g, "\\$&"); // $& means the whole matched string -const _displayItem = (item, highlightTerms, searchTerms) => { +const _displayItem = (item, searchTerms) => { const docBuilder = DOCUMENTATION_OPTIONS.BUILDER; const docUrlRoot = DOCUMENTATION_OPTIONS.URL_ROOT; const docFileSuffix = DOCUMENTATION_OPTIONS.FILE_SUFFIX; const docLinkSuffix = DOCUMENTATION_OPTIONS.LINK_SUFFIX; const showSearchSummary = DOCUMENTATION_OPTIONS.SHOW_SEARCH_SUMMARY; - const [docName, title, anchor, descr] = item; + const [docName, title, anchor, descr, score, _filename] = item; let listItem = document.createElement("li"); let requestUrl; @@ -82,13 +82,12 @@ const _displayItem = (item, highlightTerms, searchTerms) => { requestUrl = docUrlRoot + docName + docFileSuffix; linkUrl = docName + docLinkSuffix; } - const params = new URLSearchParams(); - params.set("highlight", [...highlightTerms].join(" ")); let linkEl = listItem.appendChild(document.createElement("a")); - linkEl.href = linkUrl + "?" + params.toString() + anchor; + linkEl.href = linkUrl + anchor; + linkEl.dataset.score = score; linkEl.innerHTML = title; if (descr) - listItem.appendChild(document.createElement("span")).innerText = + listItem.appendChild(document.createElement("span")).innerHTML = " (" + descr + ")"; else if (showSearchSummary) fetch(requestUrl) @@ -96,7 +95,7 @@ const _displayItem = (item, highlightTerms, searchTerms) => { .then((data) => { if (data) listItem.appendChild( - Search.makeSearchSummary(data, searchTerms, highlightTerms) + Search.makeSearchSummary(data, searchTerms) ); }); Search.output.appendChild(listItem); @@ -116,15 +115,14 @@ const _finishSearch = (resultCount) => { const _displayNextItem = ( results, resultCount, - highlightTerms, searchTerms ) => { // results left, load the summary and display it // this is intended to be dynamic (don't sub resultsCount) if (results.length) { - _displayItem(results.pop(), highlightTerms, searchTerms); + _displayItem(results.pop(), searchTerms); setTimeout( - () => _displayNextItem(results, resultCount, highlightTerms, searchTerms), + () => _displayNextItem(results, resultCount, searchTerms), 5 ); } @@ -155,10 +153,8 @@ const Search = { _pulse_status: -1, htmlToText: (htmlString) => { - const htmlElement = document - .createRange() - .createContextualFragment(htmlString); - _removeChildren(htmlElement.querySelectorAll(".headerlink")); + const htmlElement = new DOMParser().parseFromString(htmlString, 'text/html'); + htmlElement.querySelectorAll(".headerlink").forEach((el) => { el.remove() }); const docContent = htmlElement.querySelector('[role="main"]'); if (docContent !== undefined) return docContent.textContent; console.warn( @@ -239,6 +235,12 @@ const Search = { * execute search (requires search index to be loaded) */ query: (query) => { + const filenames = Search._index.filenames; + const docNames = Search._index.docnames; + const titles = Search._index.titles; + const allTitles = Search._index.alltitles; + const indexEntries = Search._index.indexentries; + // stem the search terms and add them to the correct list const stemmer = new Stemmer(); const searchTerms = new Set(); @@ -266,6 +268,10 @@ const Search = { } }); + if (SPHINX_HIGHLIGHT_ENABLED) { // set in sphinx_highlight.js + localStorage.setItem("sphinx_highlight_terms", [...highlightTerms].join(" ")) + } + // console.debug("SEARCH: searching for:"); // console.info("required: ", [...searchTerms]); // console.info("excluded: ", [...excludedTerms]); @@ -274,6 +280,40 @@ const Search = { let results = []; _removeChildren(document.getElementById("search-progress")); + const queryLower = query.toLowerCase(); + for (const [title, foundTitles] of Object.entries(allTitles)) { + if (title.toLowerCase().includes(queryLower) && (queryLower.length >= title.length/2)) { + for (const [file, id] of foundTitles) { + let score = Math.round(100 * queryLower.length / title.length) + results.push([ + docNames[file], + titles[file] !== title ? `${titles[file]} > ${title}` : title, + id !== null ? "#" + id : "", + null, + score, + filenames[file], + ]); + } + } + } + + // search for explicit entries in index directives + for (const [entry, foundEntries] of Object.entries(indexEntries)) { + if (entry.includes(queryLower) && (queryLower.length >= entry.length/2)) { + for (const [file, id] of foundEntries) { + let score = Math.round(100 * queryLower.length / entry.length) + results.push([ + docNames[file], + titles[file], + id ? "#" + id : "", + null, + score, + filenames[file], + ]); + } + } + } + // lookup as object objectTerms.forEach((term) => results.push(...Search.performObjectSearch(term, objectTerms)) @@ -320,7 +360,7 @@ const Search = { // console.info("search results:", Search.lastresults); // print the results - _displayNextItem(results, results.length, highlightTerms, searchTerms); + _displayNextItem(results, results.length, searchTerms); }, /** @@ -401,8 +441,8 @@ const Search = { // prepare search const terms = Search._index.terms; const titleTerms = Search._index.titleterms; - const docNames = Search._index.docnames; const filenames = Search._index.filenames; + const docNames = Search._index.docnames; const titles = Search._index.titles; const scoreMap = new Map(); @@ -499,16 +539,15 @@ const Search = { /** * helper function to return a node containing the * search summary for a given text. keywords is a list - * of stemmed words, highlightWords is the list of normal, unstemmed - * words. the first one is used to find the occurrence, the - * latter for highlighting it. + * of stemmed words. */ - makeSearchSummary: (htmlText, keywords, highlightWords) => { - const text = Search.htmlToText(htmlText).toLowerCase(); + makeSearchSummary: (htmlText, keywords) => { + const text = Search.htmlToText(htmlText); if (text === "") return null; + const textLower = text.toLowerCase(); const actualStartPosition = [...keywords] - .map((k) => text.indexOf(k.toLowerCase())) + .map((k) => textLower.indexOf(k.toLowerCase())) .filter((i) => i > -1) .slice(-1)[0]; const startWithContext = Math.max(actualStartPosition - 120, 0); @@ -516,13 +555,9 @@ const Search = { const top = startWithContext === 0 ? "" : "..."; const tail = startWithContext + 240 < text.length ? "..." : ""; - let summary = document.createElement("div"); + let summary = document.createElement("p"); summary.classList.add("context"); - summary.innerText = top + text.substr(startWithContext, 240).trim() + tail; - - highlightWords.forEach((highlightWord) => - _highlightText(summary, highlightWord, "highlighted") - ); + summary.textContent = top + text.substr(startWithContext, 240).trim() + tail; return summary; }, diff --git a/docs/build/html/api/api.html b/docs/build/html/api/api.html index 79e35889..cea6b793 100644 --- a/docs/build/html/api/api.html +++ b/docs/build/html/api/api.html @@ -6,7 +6,7 @@ - API — Pyinterpolate 0.3.5 documentation + API — Pyinterpolate 0.3.6 documentation @@ -38,6 +38,7 @@ + @@ -101,7 +102,7 @@ -

Pyinterpolate 0.3.5 documentation

+

Pyinterpolate 0.3.6 documentation

@@ -528,7 +529,7 @@

API#

-Created using Sphinx 5.0.2.
+Created using Sphinx 5.3.0.

diff --git a/docs/build/html/api/datatypes/core.html b/docs/build/html/api/datatypes/core.html index 02e47130..37380ccd 100644 --- a/docs/build/html/api/datatypes/core.html +++ b/docs/build/html/api/datatypes/core.html @@ -6,7 +6,7 @@ - Core data structures — Pyinterpolate 0.3.5 documentation + Core data structures — Pyinterpolate 0.3.6 documentation @@ -38,6 +38,7 @@ + @@ -101,7 +102,7 @@ -

Pyinterpolate 0.3.5 documentation

+

Pyinterpolate 0.3.6 documentation

@@ -734,7 +735,7 @@

Core data structures

-Created using Sphinx 5.0.2.
+Created using Sphinx 5.3.0.

diff --git a/docs/build/html/api/distance/distance.html b/docs/build/html/api/distance/distance.html index accbcbfd..7bb70d7a 100644 --- a/docs/build/html/api/distance/distance.html +++ b/docs/build/html/api/distance/distance.html @@ -6,7 +6,7 @@ - Distance calculations — Pyinterpolate 0.3.5 documentation + Distance calculations — Pyinterpolate 0.3.6 documentation @@ -38,6 +38,7 @@ + @@ -101,7 +102,7 @@ -

Pyinterpolate 0.3.5 documentation

+

Pyinterpolate 0.3.6 documentation

@@ -561,7 +562,7 @@

Distance calculations

-Created using Sphinx 5.0.2.
+Created using Sphinx 5.3.0.

diff --git a/docs/build/html/api/idw/idw.html b/docs/build/html/api/idw/idw.html index f4d2b412..547dafef 100644 --- a/docs/build/html/api/idw/idw.html +++ b/docs/build/html/api/idw/idw.html @@ -6,7 +6,7 @@ - Inverse Distance Weighting — Pyinterpolate 0.3.5 documentation + Inverse Distance Weighting — Pyinterpolate 0.3.6 documentation @@ -38,6 +38,7 @@ + @@ -101,7 +102,7 @@ -

Pyinterpolate 0.3.5 documentation

+

Pyinterpolate 0.3.6 documentation

@@ -407,7 +408,7 @@

Inverse Distance Weighting#

-inverse_distance_weighting(known_points, unknown_location, number_of_neighbours=- 1, power=2.0)[source]
+inverse_distance_weighting(known_points, unknown_location, number_of_neighbours=-1, power=2.0)[source]

Inverse Distance Weighting with a given set of points and an unknown location.

Parameters:
@@ -542,7 +543,7 @@

Inverse Distance Weighting

-Created using Sphinx 5.0.2.
+Created using Sphinx 5.3.0.

diff --git a/docs/build/html/api/io/io.html b/docs/build/html/api/io/io.html index 401af7c1..5b7bd3a5 100644 --- a/docs/build/html/api/io/io.html +++ b/docs/build/html/api/io/io.html @@ -6,7 +6,7 @@ - Input / Output — Pyinterpolate 0.3.5 documentation + Input / Output — Pyinterpolate 0.3.6 documentation @@ -38,6 +38,7 @@ + @@ -101,7 +102,7 @@ -

Pyinterpolate 0.3.5 documentation

+

Pyinterpolate 0.3.6 documentation

@@ -629,7 +630,7 @@

Input / Output

-Created using Sphinx 5.0.2.
+Created using Sphinx 5.3.0.

diff --git a/docs/build/html/api/kriging/block/block_kriging.html b/docs/build/html/api/kriging/block/block_kriging.html index b532aa03..890288d8 100644 --- a/docs/build/html/api/kriging/block/block_kriging.html +++ b/docs/build/html/api/kriging/block/block_kriging.html @@ -6,7 +6,7 @@ - Block - Poisson Kriging — Pyinterpolate 0.3.5 documentation + Block - Poisson Kriging — Pyinterpolate 0.3.6 documentation @@ -38,6 +38,7 @@ + @@ -101,7 +102,7 @@ -

Pyinterpolate 0.3.5 documentation

+

Pyinterpolate 0.3.6 documentation

@@ -685,7 +686,7 @@

Block - Poisson Kriging

-Created using Sphinx 5.0.2.
+Created using Sphinx 5.3.0.

diff --git a/docs/build/html/api/kriging/kriging.html b/docs/build/html/api/kriging/kriging.html index dda978d5..cd0d5b24 100644 --- a/docs/build/html/api/kriging/kriging.html +++ b/docs/build/html/api/kriging/kriging.html @@ -6,7 +6,7 @@ - Kriging — Pyinterpolate 0.3.5 documentation + Kriging — Pyinterpolate 0.3.6 documentation @@ -38,6 +38,7 @@ + @@ -101,7 +102,7 @@ -

Pyinterpolate 0.3.5 documentation

+

Pyinterpolate 0.3.6 documentation

@@ -508,7 +509,7 @@

Kriging

-Created using Sphinx 5.0.2.
+Created using Sphinx 5.3.0.

diff --git a/docs/build/html/api/kriging/point/point_kriging.html b/docs/build/html/api/kriging/point/point_kriging.html index c3c061af..9a609646 100644 --- a/docs/build/html/api/kriging/point/point_kriging.html +++ b/docs/build/html/api/kriging/point/point_kriging.html @@ -6,7 +6,7 @@ - Point Kriging — Pyinterpolate 0.3.5 documentation + Point Kriging — Pyinterpolate 0.3.6 documentation @@ -38,6 +38,7 @@ + @@ -101,7 +102,7 @@ -

Pyinterpolate 0.3.5 documentation

+

Pyinterpolate 0.3.6 documentation

@@ -643,7 +644,7 @@

Point Kriging

-Created using Sphinx 5.0.2.
+Created using Sphinx 5.3.0.

diff --git a/docs/build/html/api/pipelines/data/download.html b/docs/build/html/api/pipelines/data/download.html index b4ea1243..00a4c35e 100644 --- a/docs/build/html/api/pipelines/data/download.html +++ b/docs/build/html/api/pipelines/data/download.html @@ -6,7 +6,7 @@ - Data download — Pyinterpolate 0.3.5 documentation + Data download — Pyinterpolate 0.3.6 documentation @@ -38,6 +38,7 @@ + @@ -101,7 +102,7 @@ -

Pyinterpolate 0.3.5 documentation

+

Pyinterpolate 0.3.6 documentation

@@ -536,7 +537,7 @@

Data download

-Created using Sphinx 5.0.2.
+Created using Sphinx 5.3.0.

diff --git a/docs/build/html/api/pipelines/kriging_based/kriging_based_processes.html b/docs/build/html/api/pipelines/kriging_based/kriging_based_processes.html index 798f177c..65100fb8 100644 --- a/docs/build/html/api/pipelines/kriging_based/kriging_based_processes.html +++ b/docs/build/html/api/pipelines/kriging_based/kriging_based_processes.html @@ -6,7 +6,7 @@ - Kriging-based processes — Pyinterpolate 0.3.5 documentation + Kriging-based processes — Pyinterpolate 0.3.6 documentation @@ -38,6 +38,7 @@ + @@ -101,7 +102,7 @@ -

Pyinterpolate 0.3.5 documentation

+

Pyinterpolate 0.3.6 documentation

@@ -765,7 +766,7 @@

Kriging-based processes

-Created using Sphinx 5.0.2.
+Created using Sphinx 5.3.0.

diff --git a/docs/build/html/api/pipelines/pipelines.html b/docs/build/html/api/pipelines/pipelines.html index 5cae6936..046a18de 100644 --- a/docs/build/html/api/pipelines/pipelines.html +++ b/docs/build/html/api/pipelines/pipelines.html @@ -6,7 +6,7 @@ - Pipelines — Pyinterpolate 0.3.5 documentation + Pipelines — Pyinterpolate 0.3.6 documentation @@ -38,6 +38,7 @@ + @@ -101,7 +102,7 @@ -

Pyinterpolate 0.3.5 documentation

+

Pyinterpolate 0.3.6 documentation

@@ -508,7 +509,7 @@

Pipelines

-Created using Sphinx 5.0.2.
+Created using Sphinx 5.3.0.

diff --git a/docs/build/html/api/variogram/block/block.html b/docs/build/html/api/variogram/block/block.html index 90ba95cc..df9b4901 100644 --- a/docs/build/html/api/variogram/block/block.html +++ b/docs/build/html/api/variogram/block/block.html @@ -6,7 +6,7 @@ - Block — Pyinterpolate 0.3.5 documentation + Block — Pyinterpolate 0.3.6 documentation @@ -38,6 +38,7 @@ + @@ -103,7 +104,7 @@ -

Pyinterpolate 0.3.5 documentation

+

Pyinterpolate 0.3.6 documentation

@@ -778,7 +779,7 @@

Block#

-Created using Sphinx 5.0.2.
+Created using Sphinx 5.3.0.

diff --git a/docs/build/html/api/variogram/deconvolution/deconvolution.html b/docs/build/html/api/variogram/deconvolution/deconvolution.html index c339d6c5..e5b1fa3a 100644 --- a/docs/build/html/api/variogram/deconvolution/deconvolution.html +++ b/docs/build/html/api/variogram/deconvolution/deconvolution.html @@ -6,7 +6,7 @@ - Deconvolution — Pyinterpolate 0.3.5 documentation + Deconvolution — Pyinterpolate 0.3.6 documentation @@ -38,6 +38,7 @@ + @@ -101,7 +102,7 @@ -

Pyinterpolate 0.3.5 documentation

+

Pyinterpolate 0.3.6 documentation

@@ -852,7 +853,7 @@

Deconvolution

-Created using Sphinx 5.0.2.
+Created using Sphinx 5.3.0.

diff --git a/docs/build/html/api/variogram/experimental/experimental.html b/docs/build/html/api/variogram/experimental/experimental.html index 3c13e5cc..79e4030f 100644 --- a/docs/build/html/api/variogram/experimental/experimental.html +++ b/docs/build/html/api/variogram/experimental/experimental.html @@ -6,7 +6,7 @@ - Experimental — Pyinterpolate 0.3.5 documentation + Experimental — Pyinterpolate 0.3.6 documentation @@ -38,6 +38,7 @@ + @@ -103,7 +104,7 @@ -

Pyinterpolate 0.3.5 documentation

+

Pyinterpolate 0.3.6 documentation

@@ -912,7 +913,7 @@

Experimental
-remove_outliers(method='zscore', z_lower_limit=- 3, z_upper_limit=3, iqr_lower_limit=1.5, iqr_upper_limit=1.5, inplace=False)[source]
+remove_outliers(method='zscore', z_lower_limit=-3, z_upper_limit=3, iqr_lower_limit=1.5, iqr_upper_limit=1.5, inplace=False)[source]
Parameters:
@@ -1047,7 +1048,7 @@

Experimental

-Created using Sphinx 5.0.2.
+Created using Sphinx 5.3.0.

diff --git a/docs/build/html/api/variogram/theoretical/theoretical.html b/docs/build/html/api/variogram/theoretical/theoretical.html index 4d431fe0..af79a11d 100644 --- a/docs/build/html/api/variogram/theoretical/theoretical.html +++ b/docs/build/html/api/variogram/theoretical/theoretical.html @@ -6,7 +6,7 @@ - Theoretical — Pyinterpolate 0.3.5 documentation + Theoretical — Pyinterpolate 0.3.6 documentation @@ -38,6 +38,7 @@ + @@ -101,7 +102,7 @@ -

Pyinterpolate 0.3.5 documentation

+

Pyinterpolate 0.3.6 documentation

@@ -978,7 +979,7 @@

Theoretical

-Created using Sphinx 5.0.2.
+Created using Sphinx 5.3.0.

diff --git a/docs/build/html/api/variogram/variogram.html b/docs/build/html/api/variogram/variogram.html index 45840653..cd8f3f5d 100644 --- a/docs/build/html/api/variogram/variogram.html +++ b/docs/build/html/api/variogram/variogram.html @@ -6,7 +6,7 @@ - Variogram — Pyinterpolate 0.3.5 documentation + Variogram — Pyinterpolate 0.3.6 documentation @@ -38,6 +38,7 @@ + @@ -101,7 +102,7 @@ -

Pyinterpolate 0.3.5 documentation

+

Pyinterpolate 0.3.6 documentation

@@ -510,7 +511,7 @@

Variogram

-Created using Sphinx 5.0.2.
+Created using Sphinx 5.3.0.

diff --git a/docs/build/html/api/viz/viz.html b/docs/build/html/api/viz/viz.html index 5640460e..e4d98465 100644 --- a/docs/build/html/api/viz/viz.html +++ b/docs/build/html/api/viz/viz.html @@ -6,7 +6,7 @@ - Visualization — Pyinterpolate 0.3.5 documentation + Visualization — Pyinterpolate 0.3.6 documentation @@ -38,6 +38,7 @@ + @@ -101,7 +102,7 @@ -

Pyinterpolate 0.3.5 documentation

+

Pyinterpolate 0.3.6 documentation

@@ -562,7 +563,7 @@

Visualization

-Created using Sphinx 5.0.2.
+Created using Sphinx 5.3.0.

diff --git a/docs/build/html/community/community.html b/docs/build/html/community/community.html index 1927d0bb..e585df3d 100644 --- a/docs/build/html/community/community.html +++ b/docs/build/html/community/community.html @@ -6,7 +6,7 @@ - Community — Pyinterpolate 0.3.5 documentation + Community — Pyinterpolate 0.3.6 documentation @@ -38,6 +38,7 @@ + @@ -101,7 +102,7 @@ -

Pyinterpolate 0.3.5 documentation

+

Pyinterpolate 0.3.6 documentation

@@ -490,7 +491,7 @@

Community

-Created using Sphinx 5.0.2.
+Created using Sphinx 5.3.0.

diff --git a/docs/build/html/community/doc_parts/contributors.html b/docs/build/html/community/doc_parts/contributors.html index 90db778d..3d6e3d17 100644 --- a/docs/build/html/community/doc_parts/contributors.html +++ b/docs/build/html/community/doc_parts/contributors.html @@ -6,7 +6,7 @@ - Contributors — Pyinterpolate 0.3.5 documentation + Contributors — Pyinterpolate 0.3.6 documentation @@ -38,6 +38,7 @@ + @@ -101,7 +102,7 @@ -

Pyinterpolate 0.3.5 documentation

+

Pyinterpolate 0.3.6 documentation

@@ -545,7 +546,7 @@

Reviewers (JOSS)

-Created using Sphinx 5.0.2.
+Created using Sphinx 5.3.0.

diff --git a/docs/build/html/community/doc_parts/forum.html b/docs/build/html/community/doc_parts/forum.html index c5f26c8a..a9f6d743 100644 --- a/docs/build/html/community/doc_parts/forum.html +++ b/docs/build/html/community/doc_parts/forum.html @@ -6,7 +6,7 @@ - Network — Pyinterpolate 0.3.5 documentation + Network — Pyinterpolate 0.3.6 documentation @@ -38,6 +38,7 @@ + @@ -101,7 +102,7 @@ -

Pyinterpolate 0.3.5 documentation

+

Pyinterpolate 0.3.6 documentation

@@ -484,7 +485,7 @@

Network

-Created using Sphinx 5.0.2.
+Created using Sphinx 5.3.0.

diff --git a/docs/build/html/community/doc_parts/use_cases.html b/docs/build/html/community/doc_parts/use_cases.html index 1920a00b..770f3bd5 100644 --- a/docs/build/html/community/doc_parts/use_cases.html +++ b/docs/build/html/community/doc_parts/use_cases.html @@ -6,7 +6,7 @@ - Use Cases — Pyinterpolate 0.3.5 documentation + Use Cases — Pyinterpolate 0.3.6 documentation @@ -38,6 +38,7 @@ + @@ -101,7 +102,7 @@ -

Pyinterpolate 0.3.5 documentation

+

Pyinterpolate 0.3.6 documentation

@@ -490,7 +491,7 @@

Use Cases

-Created using Sphinx 5.0.2.
+Created using Sphinx 5.3.0.

diff --git a/docs/build/html/developer/dev.html b/docs/build/html/developer/dev.html index ac18b250..6fa072d9 100644 --- a/docs/build/html/developer/dev.html +++ b/docs/build/html/developer/dev.html @@ -6,7 +6,7 @@ - Development — Pyinterpolate 0.3.5 documentation + Development — Pyinterpolate 0.3.6 documentation @@ -38,6 +38,7 @@ + @@ -101,7 +102,7 @@ -

Pyinterpolate 0.3.5 documentation

+

Pyinterpolate 0.3.6 documentation

@@ -494,7 +495,7 @@

Development

-Created using Sphinx 5.0.2.
+Created using Sphinx 5.3.0.

diff --git a/docs/build/html/developer/doc_parts/bugs.html b/docs/build/html/developer/doc_parts/bugs.html index 5772ea84..0bb54f03 100644 --- a/docs/build/html/developer/doc_parts/bugs.html +++ b/docs/build/html/developer/doc_parts/bugs.html @@ -6,7 +6,7 @@ - Known Bugs — Pyinterpolate 0.3.5 documentation + Known Bugs — Pyinterpolate 0.3.6 documentation @@ -38,6 +38,7 @@ + @@ -101,7 +102,7 @@ -

Pyinterpolate 0.3.5 documentation

+

Pyinterpolate 0.3.6 documentation

@@ -488,7 +489,7 @@

Known Bugs

-Created using Sphinx 5.0.2.
+Created using Sphinx 5.3.0.

diff --git a/docs/build/html/developer/doc_parts/development.html b/docs/build/html/developer/doc_parts/development.html index ca432058..e8e4b61f 100644 --- a/docs/build/html/developer/doc_parts/development.html +++ b/docs/build/html/developer/doc_parts/development.html @@ -6,7 +6,7 @@ - Development — Pyinterpolate 0.3.5 documentation + Development — Pyinterpolate 0.3.6 documentation @@ -38,6 +38,7 @@ + @@ -101,7 +102,7 @@ -

Pyinterpolate 0.3.5 documentation

+

Pyinterpolate 0.3.6 documentation

@@ -490,7 +491,7 @@

Development

-Created using Sphinx 5.0.2.
+Created using Sphinx 5.3.0.

diff --git a/docs/build/html/developer/doc_parts/package.html b/docs/build/html/developer/doc_parts/package.html index 65f017f3..4d14e630 100644 --- a/docs/build/html/developer/doc_parts/package.html +++ b/docs/build/html/developer/doc_parts/package.html @@ -6,7 +6,7 @@ - Package structure — Pyinterpolate 0.3.5 documentation + Package structure — Pyinterpolate 0.3.6 documentation @@ -38,6 +38,7 @@ + @@ -101,7 +102,7 @@ -

Pyinterpolate 0.3.5 documentation

+

Pyinterpolate 0.3.6 documentation

@@ -503,7 +504,7 @@

Package structure

-Created using Sphinx 5.0.2.
+Created using Sphinx 5.3.0.

diff --git a/docs/build/html/developer/doc_parts/reqs.html b/docs/build/html/developer/doc_parts/reqs.html index 760fad4c..3e6ba6fb 100644 --- a/docs/build/html/developer/doc_parts/reqs.html +++ b/docs/build/html/developer/doc_parts/reqs.html @@ -6,7 +6,7 @@ - Requirements and dependencies (v 0.3.0) — Pyinterpolate 0.3.5 documentation + Requirements and dependencies (v 0.3.0) — Pyinterpolate 0.3.6 documentation @@ -38,6 +38,7 @@ + @@ -101,7 +102,7 @@ -

Pyinterpolate 0.3.5 documentation

+

Pyinterpolate 0.3.6 documentation

@@ -504,7 +505,7 @@

Requirements and dependencies (v 0.3.0)

-Created using Sphinx 5.0.2.
+Created using Sphinx 5.3.0.

diff --git a/docs/build/html/developer/doc_parts/tests_and_contribution.html b/docs/build/html/developer/doc_parts/tests_and_contribution.html index 04f9eaca..24f2acbc 100644 --- a/docs/build/html/developer/doc_parts/tests_and_contribution.html +++ b/docs/build/html/developer/doc_parts/tests_and_contribution.html @@ -6,7 +6,7 @@ - Tests and contribution — Pyinterpolate 0.3.5 documentation + Tests and contribution — Pyinterpolate 0.3.6 documentation @@ -38,6 +38,7 @@ + @@ -101,7 +102,7 @@ -

Pyinterpolate 0.3.5 documentation

+

Pyinterpolate 0.3.6 documentation

@@ -486,7 +487,7 @@

Tests and contribution

-Created using Sphinx 5.0.2.
+Created using Sphinx 5.3.0.

diff --git a/docs/build/html/genindex.html b/docs/build/html/genindex.html index 38435ea7..250cde1a 100644 --- a/docs/build/html/genindex.html +++ b/docs/build/html/genindex.html @@ -5,7 +5,7 @@ - Index — Pyinterpolate 0.3.5 documentation + Index — Pyinterpolate 0.3.6 documentation @@ -37,6 +37,7 @@ + @@ -98,7 +99,7 @@ -

Pyinterpolate 0.3.5 documentation

+

Pyinterpolate 0.3.6 documentation

@@ -446,7 +447,7 @@

Index

-Created using Sphinx 5.0.2.
+Created using Sphinx 5.3.0.

diff --git a/docs/build/html/index.html b/docs/build/html/index.html index 617dc76e..6acf8c70 100644 --- a/docs/build/html/index.html +++ b/docs/build/html/index.html @@ -6,7 +6,7 @@ - Pyinterpolate — Pyinterpolate 0.3.5 documentation + Pyinterpolate — Pyinterpolate 0.3.6 documentation @@ -38,6 +38,7 @@ + @@ -100,7 +101,7 @@ -

Pyinterpolate 0.3.5 documentation

+

Pyinterpolate 0.3.6 documentation

@@ -373,7 +374,7 @@

version 0.3.6 - KyivThe pyinterpolate logo with the name Kyiv and the version of package.

Note

-

The last documentation update: 2022-10-31

+

The last documentation update: 2023-01-21

Pyinterpolate is the Python library for geostatistics. The package provides access to spatial statistics tools used in various studies. This package helps you interpolate spatial data with the Kriging technique.

If you’re:

@@ -545,7 +546,7 @@

How to cite

-Created using Sphinx 5.0.2.
+Created using Sphinx 5.3.0.

diff --git a/docs/build/html/science/biblio.html b/docs/build/html/science/biblio.html index 190f4a45..989a7d7e 100644 --- a/docs/build/html/science/biblio.html +++ b/docs/build/html/science/biblio.html @@ -6,7 +6,7 @@ - Bibliography — Pyinterpolate 0.3.5 documentation + Bibliography — Pyinterpolate 0.3.6 documentation @@ -38,6 +38,7 @@ + @@ -100,7 +101,7 @@ -

Pyinterpolate 0.3.5 documentation

+

Pyinterpolate 0.3.6 documentation

@@ -466,7 +467,7 @@

Bibliography

-Created using Sphinx 5.0.2.
+Created using Sphinx 5.3.0.

diff --git a/docs/build/html/science/cite.html b/docs/build/html/science/cite.html index 06aad131..5b2f57ae 100644 --- a/docs/build/html/science/cite.html +++ b/docs/build/html/science/cite.html @@ -6,7 +6,7 @@ - How to cite — Pyinterpolate 0.3.5 documentation + How to cite — Pyinterpolate 0.3.6 documentation @@ -38,6 +38,7 @@ + @@ -101,7 +102,7 @@ -

Pyinterpolate 0.3.5 documentation

+

Pyinterpolate 0.3.6 documentation

@@ -467,7 +468,7 @@

How to cite

-Created using Sphinx 5.0.2.
+Created using Sphinx 5.3.0.

diff --git a/docs/build/html/search.html b/docs/build/html/search.html index 2650e20e..df4ad7b1 100644 --- a/docs/build/html/search.html +++ b/docs/build/html/search.html @@ -4,7 +4,7 @@ - Search - Pyinterpolate 0.3.5 documentation + Search - Pyinterpolate 0.3.6 documentation @@ -36,6 +36,7 @@ + @@ -100,7 +101,7 @@ -

Pyinterpolate 0.3.5 documentation

+

Pyinterpolate 0.3.6 documentation

@@ -468,7 +469,7 @@

Search

-Created using Sphinx 5.0.2.
+Created using Sphinx 5.3.0.

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"tests-and-contribution"]], "Pyinterpolate": [[27, "pyinterpolate"]], "version 0.3.6 - Kyiv": [[27, "version-0-3-6-kyiv"]], "Contents": [[27, "contents"]], "How to cite": [[27, "how-to-cite"], [29, "how-to-cite"]], "Bibliography": [[28, "bibliography"]], "Setup": [[30, "setup"]], "Installation guidelines": [[30, "installation-guidelines"]], "Conda": [[30, "conda"]], "pip": [[30, "pip"]], "Installation - additional topics": [[30, "installation-additional-topics"]], "Working with Notebooks": [[30, "working-with-notebooks"]], "The libspatialindex_c.so dependency error": [[30, "the-libspatialindex-c-so-dependency-error"]], "Good practices": [[31, "good-practices"]], "Quickstart": [[32, "quickstart"]], "Installation": [[32, "installation"]], "Ordinary Kriging": [[32, "ordinary-kriging"]], "Tutorials": [[33, "tutorials"]], "Beginner": [[33, "beginner"]], "Intermediate": [[33, "intermediate"]], "Advanced": [[33, "advanced"]], "Blocks to points Ordinary Kriging interpolation": [[34, 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"Introduction"], [42, "Introduction"], [43, "Introduction"], [44, "Introduction"], [45, "Introduction"], [46, "Introduction"]], "Import packages": [[34, "Import-packages"], [36, "Import-packages"], [37, "Import-packages"], [38, "Import-packages"], [39, "Import-packages"], [43, "Import-packages"], [44, "Import-packages"], [45, "Import-packages"], [46, "Import-packages"]], "1) Read areal data": [[34, "1)-Read-areal-data"]], "2) Detect and remove outliers": [[34, "2)-Detect-and-remove-outliers"]], "3. 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Pyinterpolate 0.3.5 documentation

+

Pyinterpolate 0.3.6 documentation

@@ -562,7 +563,7 @@

The libspatialindex_c.so dependency error

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+Created using Sphinx 5.3.0.

diff --git a/docs/build/html/usage/good_practices.html b/docs/build/html/usage/good_practices.html index 53000228..3a6f8ad8 100644 --- a/docs/build/html/usage/good_practices.html +++ b/docs/build/html/usage/good_practices.html @@ -6,7 +6,7 @@ - Good practices — Pyinterpolate 0.3.5 documentation + Good practices — Pyinterpolate 0.3.6 documentation @@ -38,6 +38,7 @@ + @@ -99,7 +100,7 @@ -

Pyinterpolate 0.3.5 documentation

+

Pyinterpolate 0.3.6 documentation

@@ -451,7 +452,7 @@

Good practices

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+Created using Sphinx 5.3.0.

diff --git a/docs/build/html/usage/quickstart.html b/docs/build/html/usage/quickstart.html index 5bae13c7..6a0d07e4 100644 --- a/docs/build/html/usage/quickstart.html +++ b/docs/build/html/usage/quickstart.html @@ -6,7 +6,7 @@ - Quickstart — Pyinterpolate 0.3.5 documentation + Quickstart — Pyinterpolate 0.3.6 documentation @@ -38,6 +38,7 @@ + @@ -101,7 +102,7 @@ -

Pyinterpolate 0.3.5 documentation

+

Pyinterpolate 0.3.6 documentation

@@ -576,7 +577,7 @@

Ordinary Kriging

-Created using Sphinx 5.0.2.
+Created using Sphinx 5.3.0.

diff --git a/docs/build/html/usage/tutorials.html b/docs/build/html/usage/tutorials.html index 8c8855e8..cc1bedfe 100644 --- a/docs/build/html/usage/tutorials.html +++ b/docs/build/html/usage/tutorials.html @@ -6,7 +6,7 @@ - Tutorials — Pyinterpolate 0.3.5 documentation + Tutorials — Pyinterpolate 0.3.6 documentation @@ -38,6 +38,7 @@ + @@ -101,7 +102,7 @@ -

Pyinterpolate 0.3.5 documentation

+

Pyinterpolate 0.3.6 documentation

@@ -559,7 +560,7 @@

Advanced

-Created using Sphinx 5.0.2.
+Created using Sphinx 5.3.0.

diff --git a/docs/build/html/usage/tutorials/Blocks to points Ordinary Kriging interpolation (Intermediate).html b/docs/build/html/usage/tutorials/Blocks to points Ordinary Kriging interpolation (Intermediate).html index bcb8fa4d..a2606bc2 100644 --- a/docs/build/html/usage/tutorials/Blocks to points Ordinary Kriging interpolation (Intermediate).html +++ b/docs/build/html/usage/tutorials/Blocks to points Ordinary Kriging interpolation (Intermediate).html @@ -6,7 +6,7 @@ - Blocks to points Ordinary Kriging interpolation — Pyinterpolate 0.3.5 documentation + Blocks to points Ordinary Kriging interpolation — Pyinterpolate 0.3.6 documentation @@ -38,6 +38,7 @@ + @@ -103,7 +104,7 @@ -

Pyinterpolate 0.3.5 documentation

+

Pyinterpolate 0.3.6 documentation

@@ -943,7 +944,7 @@

2) Detect and remove outliers
-../../_images/usage_tutorials_Blocks_to_points_Ordinary_Kriging_interpolation_(Intermediate)_8_1.png +../../_images/usage_tutorials_Blocks_to_points_Ordinary_Kriging_interpolation_%28Intermediate%29_8_1.png
@@ -962,7 +963,7 @@

2) Detect and remove outliers

-../../_images/usage_tutorials_Blocks_to_points_Ordinary_Kriging_interpolation_(Intermediate)_8_3.png +../../_images/usage_tutorials_Blocks_to_points_Ordinary_Kriging_interpolation_%28Intermediate%29_8_3.png
@@ -981,7 +982,7 @@

2) Detect and remove outliers

-../../_images/usage_tutorials_Blocks_to_points_Ordinary_Kriging_interpolation_(Intermediate)_8_5.png +../../_images/usage_tutorials_Blocks_to_points_Ordinary_Kriging_interpolation_%28Intermediate%29_8_5.png
@@ -1044,7 +1045,7 @@

2) Detect and remove outliers

-../../_images/usage_tutorials_Blocks_to_points_Ordinary_Kriging_interpolation_(Intermediate)_11_1.png +../../_images/usage_tutorials_Blocks_to_points_Ordinary_Kriging_interpolation_%28Intermediate%29_11_1.png
@@ -1063,7 +1064,7 @@

2) Detect and remove outliers

-../../_images/usage_tutorials_Blocks_to_points_Ordinary_Kriging_interpolation_(Intermediate)_11_3.png +../../_images/usage_tutorials_Blocks_to_points_Ordinary_Kriging_interpolation_%28Intermediate%29_11_3.png
@@ -1082,7 +1083,7 @@

2) Detect and remove outliers

-../../_images/usage_tutorials_Blocks_to_points_Ordinary_Kriging_interpolation_(Intermediate)_11_5.png +../../_images/usage_tutorials_Blocks_to_points_Ordinary_Kriging_interpolation_%28Intermediate%29_11_5.png
@@ -1488,7 +1489,7 @@

6. Show a map of interpolated values with a choropleth map of the breast can
-../../_images/usage_tutorials_Blocks_to_points_Ordinary_Kriging_interpolation_(Intermediate)_25_0.png +../../_images/usage_tutorials_Blocks_to_points_Ordinary_Kriging_interpolation_%28Intermediate%29_25_0.png

Visual inspection shows that:

@@ -1700,7 +1701,7 @@

6. Show a map of interpolated values with a choropleth map of the breast can

-Created using Sphinx 5.0.2.
+Created using Sphinx 5.3.0.

diff --git a/docs/build/html/usage/tutorials/Directional Ordinary Kriging (Intermediate).html b/docs/build/html/usage/tutorials/Directional Ordinary Kriging (Intermediate).html index 8445bef1..8f6e54f3 100644 --- a/docs/build/html/usage/tutorials/Directional Ordinary Kriging (Intermediate).html +++ b/docs/build/html/usage/tutorials/Directional Ordinary Kriging (Intermediate).html @@ -6,7 +6,7 @@ - Directional Ordinary Kriging — Pyinterpolate 0.3.5 documentation + Directional Ordinary Kriging — Pyinterpolate 0.3.6 documentation @@ -38,6 +38,7 @@ + @@ -103,7 +104,7 @@ -

Pyinterpolate 0.3.5 documentation

+

Pyinterpolate 0.3.6 documentation

@@ -969,7 +970,7 @@

4. Compare interpolated results
-../../_images/usage_tutorials_Directional_Ordinary_Kriging_(Intermediate)_33_0.png +../../_images/usage_tutorials_Directional_Ordinary_Kriging_%28Intermediate%29_33_0.png

Directional Kriging clearly shows the leading direction, and it’s even more pronounced in the variance error maps. At the end, let’s compare the mean of directional interpolations to the isotropic interpolation result:

@@ -1010,7 +1011,7 @@

4. Compare interpolated results
-../../_images/usage_tutorials_Directional_Ordinary_Kriging_(Intermediate)_37_0.png +../../_images/usage_tutorials_Directional_Ordinary_Kriging_%28Intermediate%29_37_0.png
@@ -1173,7 +1174,7 @@

4. Compare interpolated results

-Created using Sphinx 5.0.2.
+Created using Sphinx 5.3.0.

diff --git a/docs/build/html/usage/tutorials/Directional Semivariograms (Basic).html b/docs/build/html/usage/tutorials/Directional Semivariograms (Basic).html index f7f67d05..064158c4 100644 --- a/docs/build/html/usage/tutorials/Directional Semivariograms (Basic).html +++ b/docs/build/html/usage/tutorials/Directional Semivariograms (Basic).html @@ -6,7 +6,7 @@ - Directional semivariograms — Pyinterpolate 0.3.5 documentation + Directional semivariograms — Pyinterpolate 0.3.6 documentation @@ -38,6 +38,7 @@ + @@ -103,7 +104,7 @@ -

Pyinterpolate 0.3.5 documentation

+

Pyinterpolate 0.3.6 documentation

@@ -1012,7 +1013,7 @@

1) Read and show point data
-../../_images/usage_tutorials_Directional_Semivariograms_(Basic)_9_1.png +../../_images/usage_tutorials_Directional_Semivariograms_%28Basic%29_9_1.png
@@ -1108,7 +1109,7 @@

Case 1: W-E Direction
-../../_images/usage_tutorials_Directional_Semivariograms_(Basic)_16_0.png +../../_images/usage_tutorials_Directional_Semivariograms_%28Basic%29_16_0.png
@@ -1135,7 +1136,7 @@

Case 2: N-S Direction
-../../_images/usage_tutorials_Directional_Semivariograms_(Basic)_18_0.png +../../_images/usage_tutorials_Directional_Semivariograms_%28Basic%29_18_0.png
@@ -1162,7 +1163,7 @@

Case 3: NW-SE Direction
-../../_images/usage_tutorials_Directional_Semivariograms_(Basic)_20_0.png +../../_images/usage_tutorials_Directional_Semivariograms_%28Basic%29_20_0.png
@@ -1189,7 +1190,7 @@

Case 4: NE-SW Direction
-../../_images/usage_tutorials_Directional_Semivariograms_(Basic)_22_0.png +../../_images/usage_tutorials_Directional_Semivariograms_%28Basic%29_22_0.png
@@ -1213,7 +1214,7 @@

Case 5: Isotropic Variogram
-../../_images/usage_tutorials_Directional_Semivariograms_(Basic)_24_0.png +../../_images/usage_tutorials_Directional_Semivariograms_%28Basic%29_24_0.png
@@ -1261,7 +1262,7 @@

3) Compare semivariograms
-../../_images/usage_tutorials_Directional_Semivariograms_(Basic)_26_0.png +../../_images/usage_tutorials_Directional_Semivariograms_%28Basic%29_26_0.png

What do you think about this comparison? Do you agree, that the NW-SE variogram is the best fit for a data?

@@ -1362,7 +1363,7 @@

4) Bonus: Compare calculation times and results
-../../_images/usage_tutorials_Directional_Semivariograms_(Basic)_34_0.png +../../_images/usage_tutorials_Directional_Semivariograms_%28Basic%29_34_0.png

The general shape is the same but variance of a semivariogram from a triangular selection is lower; it is harder to distinguish the covariance between neighboring points, and it could be dangerously close to the nugget effect. You should consider this (especially) in automatic calculations to avoid a strange results or models that are not better than a simple IDW technique.

@@ -1557,7 +1558,7 @@

4) Bonus: Compare calculation times and results

-Created using Sphinx 5.0.2.
+Created using Sphinx 5.3.0.

diff --git a/docs/build/html/usage/tutorials/Directional Semivariograms (Basic).ipynb b/docs/build/html/usage/tutorials/Directional Semivariograms (Basic).ipynb index 2f4b576b..29d6f634 100644 --- a/docs/build/html/usage/tutorials/Directional Semivariograms (Basic).ipynb +++ b/docs/build/html/usage/tutorials/Directional Semivariograms (Basic).ipynb @@ -1015,7 +1015,7 @@ }, { "cell_type": "markdown", - "id": "d3da8c90", + "id": "700f83ad", "metadata": { "collapsed": false, "pycharm": { diff --git a/docs/build/html/usage/tutorials/How good is our Kriging model - test it against IDW algorithm (Basic).html b/docs/build/html/usage/tutorials/How good is our Kriging model - test it against IDW algorithm (Basic).html index 1ab2bf66..c2287e68 100644 --- a/docs/build/html/usage/tutorials/How good is our Kriging model - test it against IDW algorithm (Basic).html +++ b/docs/build/html/usage/tutorials/How good is our Kriging model - test it against IDW algorithm (Basic).html @@ -6,7 +6,7 @@ - How good is my Kriging model? - tests with IDW algorithm — Pyinterpolate 0.3.5 documentation + How good is my Kriging model? - tests with IDW algorithm — Pyinterpolate 0.3.6 documentation @@ -38,6 +38,7 @@ + @@ -103,7 +104,7 @@ -

Pyinterpolate 0.3.5 documentation

+

Pyinterpolate 0.3.6 documentation

@@ -929,7 +930,7 @@

4) Perform variogram modeling on the modeling set
-../../_images/usage_tutorials_How_good_is_our_Kriging_model_-_test_it_against_IDW_algorithm_(Basic)_16_0.png +../../_images/usage_tutorials_How_good_is_our_Kriging_model_-_test_it_against_IDW_algorithm_%28Basic%29_16_0.png
@@ -1133,7 +1134,7 @@

6) Bonus scenario: only 2% of values are known!
-../../_images/usage_tutorials_How_good_is_our_Kriging_model_-_test_it_against_IDW_algorithm_(Basic)_26_0.png +../../_images/usage_tutorials_How_good_is_our_Kriging_model_-_test_it_against_IDW_algorithm_%28Basic%29_26_0.png
@@ -1366,7 +1367,7 @@

6) Bonus scenario: only 2% of values are known!

-Created using Sphinx 5.0.2.
+Created using Sphinx 5.3.0.

diff --git a/docs/build/html/usage/tutorials/Ordinary and Simple Kriging (Basic).html b/docs/build/html/usage/tutorials/Ordinary and Simple Kriging (Basic).html index f0e8e231..bd269b34 100644 --- a/docs/build/html/usage/tutorials/Ordinary and Simple Kriging (Basic).html +++ b/docs/build/html/usage/tutorials/Ordinary and Simple Kriging (Basic).html @@ -6,7 +6,7 @@ - Ordinary and Simple Kriging — Pyinterpolate 0.3.5 documentation + Ordinary and Simple Kriging — Pyinterpolate 0.3.6 documentation @@ -38,6 +38,7 @@ + @@ -103,7 +104,7 @@ -

Pyinterpolate 0.3.5 documentation

+

Pyinterpolate 0.3.6 documentation

@@ -832,7 +833,7 @@

2) Set Semivariogram model
-../../_images/usage_tutorials_Ordinary_and_Simple_Kriging_(Basic)_10_0.png +../../_images/usage_tutorials_Ordinary_and_Simple_Kriging_%28Basic%29_10_0.png
@@ -857,7 +858,7 @@

2) Set Semivariogram model

-../../_images/usage_tutorials_Ordinary_and_Simple_Kriging_(Basic)_12_0.png +../../_images/usage_tutorials_Ordinary_and_Simple_Kriging_%28Basic%29_12_0.png
@@ -1513,7 +1514,7 @@

4) Predict values at unknown locations

-Created using Sphinx 5.0.2.
+Created using Sphinx 5.3.0.

diff --git a/docs/build/html/usage/tutorials/Outliers and Their Influence on the Final Model (Intermediate).html b/docs/build/html/usage/tutorials/Outliers and Their Influence on the Final Model (Intermediate).html index 2b36c549..3e559b1b 100644 --- a/docs/build/html/usage/tutorials/Outliers and Their Influence on the Final Model (Intermediate).html +++ b/docs/build/html/usage/tutorials/Outliers and Their Influence on the Final Model (Intermediate).html @@ -6,7 +6,7 @@ - Outliers and Their Influence on the Final Model — Pyinterpolate 0.3.5 documentation + Outliers and Their Influence on the Final Model — Pyinterpolate 0.3.6 documentation @@ -38,6 +38,7 @@ + @@ -103,7 +104,7 @@ -

Pyinterpolate 0.3.5 documentation

+

Pyinterpolate 0.3.6 documentation

@@ -819,7 +820,7 @@

2) Check outliers: analyze the distribution of the values
-../../_images/usage_tutorials_Outliers_and_Their_Influence_on_the_Final_Model_(Intermediate)_9_0.png +../../_images/usage_tutorials_Outliers_and_Their_Influence_on_the_Final_Model_%28Intermediate%29_9_0.png
@@ -848,7 +849,7 @@

2) Check outliers: analyze the distribution of the values
-../../_images/usage_tutorials_Outliers_and_Their_Influence_on_the_Final_Model_(Intermediate)_11_0.png +../../_images/usage_tutorials_Outliers_and_Their_Influence_on_the_Final_Model_%28Intermediate%29_11_0.png

Boxplot is a unique and handy data visualization tool. Let’s analyze this plot from the bottom up to the top.

@@ -915,7 +916,7 @@

2) Check outliers: analyze the distribution of the values
-../../_images/usage_tutorials_Outliers_and_Their_Influence_on_the_Final_Model_(Intermediate)_15_0.png +../../_images/usage_tutorials_Outliers_and_Their_Influence_on_the_Final_Model_%28Intermediate%29_15_0.png

Clarification: We have cut some records from the baseline training dataset. The distribution plot (violinplot) has a shorter tail and ends more abruptly upwards. The boxplot of the new data doesn’t have any outliers. The one crucial thing to notice is that the observations are still skewed, but this is not a problem for this concrete tutorial.

@@ -962,7 +963,7 @@

3) Create the Variogram Point Cloud model for datasets A and B
-../../_images/usage_tutorials_Outliers_and_Their_Influence_on_the_Final_Model_(Intermediate)_20_0.png +../../_images/usage_tutorials_Outliers_and_Their_Influence_on_the_Final_Model_%28Intermediate%29_20_0.png
@@ -979,7 +980,7 @@

3) Create the Variogram Point Cloud model for datasets A and B

-../../_images/usage_tutorials_Outliers_and_Their_Influence_on_the_Final_Model_(Intermediate)_21_0.png +../../_images/usage_tutorials_Outliers_and_Their_Influence_on_the_Final_Model_%28Intermediate%29_21_0.png

Clarification: a quick look into the results shows that calculated semivariances are skewed into significant positive values for each lag, but most profoundly within middle lags (~15:21 km). The processed dataset has lower semivariances than the raw readings, and both variograms have a similar shape.

@@ -1110,7 +1111,7 @@

4) Remove outliers from the variograms
-../../_images/usage_tutorials_Outliers_and_Their_Influence_on_the_Final_Model_(Intermediate)_28_0.png +../../_images/usage_tutorials_Outliers_and_Their_Influence_on_the_Final_Model_%28Intermediate%29_28_0.png

Clarification: Comparison of multiple variogram clouds could be hard. We see that the largest semivariances are present in the raw data. Heavily processed data has the lowest number of outliers. The medians in each dataset are distributed over a similar pattern. How is it similar? We can check if we transform the variogram point cloud into the experimental semivariogram. Pyinterpolate has a method for it: .calculate_experimental_variogram(). We use it and compare four plots of @@ -1147,7 +1148,7 @@

4) Remove outliers from the variograms
-../../_images/usage_tutorials_Outliers_and_Their_Influence_on_the_Final_Model_(Intermediate)_31_0.png +../../_images/usage_tutorials_Outliers_and_Their_Influence_on_the_Final_Model_%28Intermediate%29_31_0.png

Clarification: An understanding of those plots is not an easy task. Let’s divide reasoning into multiple points:

@@ -1548,7 +1549,7 @@

5) Create Four Ordinary Kriging models based on the four Variogram Point Clo

-Created using Sphinx 5.0.2.
+Created using Sphinx 5.3.0.

diff --git a/docs/build/html/usage/tutorials/Poisson Kriging - Area to Area (Advanced).html b/docs/build/html/usage/tutorials/Poisson Kriging - Area to Area (Advanced).html index 05182a00..b587ff04 100644 --- a/docs/build/html/usage/tutorials/Poisson Kriging - Area to Area (Advanced).html +++ b/docs/build/html/usage/tutorials/Poisson Kriging - Area to Area (Advanced).html @@ -6,7 +6,7 @@ - Poisson Kriging - Area to Area Kriging — Pyinterpolate 0.3.5 documentation + Poisson Kriging - Area to Area Kriging — Pyinterpolate 0.3.6 documentation @@ -38,6 +38,7 @@ + @@ -103,7 +104,7 @@ -

Pyinterpolate 0.3.5 documentation

+

Pyinterpolate 0.3.6 documentation

@@ -802,7 +803,7 @@

1) Read and explore data
-../../_images/usage_tutorials_Poisson_Kriging_-_Area_to_Area_(Advanced)_4_1.png +../../_images/usage_tutorials_Poisson_Kriging_-_Area_to_Area_%28Advanced%29_4_1.png

Clarification: It is a good idea to look into the spatial patterns in a dataset and to visually check if our data do not have any NaN values. We use the geopandas GeoDataFrame.plot() function with a color map that diverges regions based on the cancer incidence rates. The output choropleth map is not ideal, and (probably) it has a few unreliable results, for example:

@@ -979,7 +980,7 @@

5) Analyze Forecast Bias and Root Mean Squared Error of prediction
-../../_images/usage_tutorials_Poisson_Kriging_-_Area_to_Area_(Advanced)_15_1.png +../../_images/usage_tutorials_Poisson_Kriging_-_Area_to_Area_%28Advanced%29_15_1.png
@@ -1003,7 +1004,7 @@

5) Analyze Forecast Bias and Root Mean Squared Error of prediction

-../../_images/usage_tutorials_Poisson_Kriging_-_Area_to_Area_(Advanced)_16_1.png +../../_images/usage_tutorials_Poisson_Kriging_-_Area_to_Area_%28Advanced%29_16_1.png
@@ -1269,7 +1270,7 @@

5) Analyze Forecast Bias and Root Mean Squared Error of prediction

-Created using Sphinx 5.0.2.
+Created using Sphinx 5.3.0.

diff --git a/docs/build/html/usage/tutorials/Poisson Kriging - Area to Point (Advanced).html b/docs/build/html/usage/tutorials/Poisson Kriging - Area to Point (Advanced).html index d6cb53c8..6b5a3649 100644 --- a/docs/build/html/usage/tutorials/Poisson Kriging - Area to Point (Advanced).html +++ b/docs/build/html/usage/tutorials/Poisson Kriging - Area to Point (Advanced).html @@ -6,7 +6,7 @@ - Poisson Kriging - Area to Point Kriging — Pyinterpolate 0.3.5 documentation + Poisson Kriging - Area to Point Kriging — Pyinterpolate 0.3.6 documentation @@ -38,6 +38,7 @@ + @@ -103,7 +104,7 @@ -

Pyinterpolate 0.3.5 documentation

+

Pyinterpolate 0.3.6 documentation

@@ -791,7 +792,7 @@

1) Read and explore data
-../../_images/usage_tutorials_Poisson_Kriging_-_Area_to_Point_(Advanced)_3_1.png +../../_images/usage_tutorials_Poisson_Kriging_-_Area_to_Point_%28Advanced%29_3_1.png

Clarification: It is a good idea to look into the spatial patterns in a dataset and to visually check if our data do not have any NaN values. We use the geopandas GeoDataFrame.plot() function with a color map that diverges regions based on the cancer incidence rates. The output choropleth map is not ideal, and (probably) it has a few unreliable results, for example:

@@ -951,7 +952,7 @@

4) Visualize data
-../../_images/usage_tutorials_Poisson_Kriging_-_Area_to_Point_(Advanced)_12_1.png +../../_images/usage_tutorials_Poisson_Kriging_-_Area_to_Point_%28Advanced%29_12_1.png
@@ -1105,7 +1106,7 @@

4) Visualize data

-Created using Sphinx 5.0.2.
+Created using Sphinx 5.3.0.

diff --git a/docs/build/html/usage/tutorials/Poisson Kriging - Centroid Based (Advanced).html b/docs/build/html/usage/tutorials/Poisson Kriging - Centroid Based (Advanced).html index 76f4be02..fb201d67 100644 --- a/docs/build/html/usage/tutorials/Poisson Kriging - Centroid Based (Advanced).html +++ b/docs/build/html/usage/tutorials/Poisson Kriging - Centroid Based (Advanced).html @@ -6,7 +6,7 @@ - Poisson Kriging - centroid based approach — Pyinterpolate 0.3.5 documentation + Poisson Kriging - centroid based approach — Pyinterpolate 0.3.6 documentation @@ -38,6 +38,7 @@ + @@ -103,7 +104,7 @@ -

Pyinterpolate 0.3.5 documentation

+

Pyinterpolate 0.3.6 documentation

@@ -806,7 +807,7 @@

1) Read and explore data
-../../_images/usage_tutorials_Poisson_Kriging_-_Centroid_Based_(Advanced)_4_1.png +../../_images/usage_tutorials_Poisson_Kriging_-_Centroid_Based_%28Advanced%29_4_1.png

Clarification: It is a good idea to look into the spatial patterns in a dataset and to visually check if our data do not have any NaN values. We use the geopandas GeoDataFrame.plot() function with a color map that diverges regions based on the cancer incidence rates. The output choropleth map is not ideal, and (probably) it has a few unreliable results, for example:

@@ -982,7 +983,7 @@

5) Analyze Forecast Bias and Root Mean Squared Error of prediction
-../../_images/usage_tutorials_Poisson_Kriging_-_Centroid_Based_(Advanced)_15_1.png +../../_images/usage_tutorials_Poisson_Kriging_-_Centroid_Based_%28Advanced%29_15_1.png
@@ -1006,7 +1007,7 @@

5) Analyze Forecast Bias and Root Mean Squared Error of prediction

-../../_images/usage_tutorials_Poisson_Kriging_-_Centroid_Based_(Advanced)_16_1.png +../../_images/usage_tutorials_Poisson_Kriging_-_Centroid_Based_%28Advanced%29_16_1.png
@@ -1272,7 +1273,7 @@

5) Analyze Forecast Bias and Root Mean Squared Error of prediction

-Created using Sphinx 5.0.2.
+Created using Sphinx 5.3.0.

diff --git a/docs/build/html/usage/tutorials/Semivariogram Estimation (Basic).html b/docs/build/html/usage/tutorials/Semivariogram Estimation (Basic).html index 05c3b25d..65573d54 100644 --- a/docs/build/html/usage/tutorials/Semivariogram Estimation (Basic).html +++ b/docs/build/html/usage/tutorials/Semivariogram Estimation (Basic).html @@ -6,7 +6,7 @@ - Semivariogram Estimation — Pyinterpolate 0.3.5 documentation + Semivariogram Estimation — Pyinterpolate 0.3.6 documentation @@ -38,6 +38,7 @@ + @@ -103,7 +104,7 @@ -

Pyinterpolate 0.3.5 documentation

+

Pyinterpolate 0.3.6 documentation

@@ -902,7 +903,7 @@

2) Create the experimental variogram
-../../_images/usage_tutorials_Semivariogram_Estimation_(Basic)_12_0.png +../../_images/usage_tutorials_Semivariogram_Estimation_%28Basic%29_12_0.png

Our plot shows three objects:

@@ -1030,7 +1031,7 @@

Models:
-../../_images/usage_tutorials_Semivariogram_Estimation_(Basic)_19_0.png +../../_images/usage_tutorials_Semivariogram_Estimation_%28Basic%29_19_0.png
@@ -1105,7 +1106,7 @@

Models:

-../../_images/usage_tutorials_Semivariogram_Estimation_(Basic)_22_0.png +../../_images/usage_tutorials_Semivariogram_Estimation_%28Basic%29_22_0.png
@@ -1173,7 +1174,7 @@

Models:

-../../_images/usage_tutorials_Semivariogram_Estimation_(Basic)_24_0.png +../../_images/usage_tutorials_Semivariogram_Estimation_%28Basic%29_24_0.png
@@ -1241,7 +1242,7 @@

Models:

-../../_images/usage_tutorials_Semivariogram_Estimation_(Basic)_26_0.png +../../_images/usage_tutorials_Semivariogram_Estimation_%28Basic%29_26_0.png
@@ -1309,7 +1310,7 @@

Models:

-../../_images/usage_tutorials_Semivariogram_Estimation_(Basic)_28_0.png +../../_images/usage_tutorials_Semivariogram_Estimation_%28Basic%29_28_0.png
@@ -1377,7 +1378,7 @@

Models:

-../../_images/usage_tutorials_Semivariogram_Estimation_(Basic)_30_0.png +../../_images/usage_tutorials_Semivariogram_Estimation_%28Basic%29_30_0.png
@@ -1446,7 +1447,7 @@

Models:

-../../_images/usage_tutorials_Semivariogram_Estimation_(Basic)_32_0.png +../../_images/usage_tutorials_Semivariogram_Estimation_%28Basic%29_32_0.png

Quick look into plots, and we can bet that the best model is linear. We can read each table printed with a print() method, but instead, we will read Root Mean Squared Error of each model and select the model with the lowest value of RMSE:

@@ -1669,7 +1670,7 @@

4) Set automatically semivariogram model
-../../_images/usage_tutorials_Semivariogram_Estimation_(Basic)_44_0.png +../../_images/usage_tutorials_Semivariogram_Estimation_%28Basic%29_44_0.png

Compare this model to the linear model fitted before:

@@ -1697,7 +1698,7 @@

4) Set automatically semivariogram model
-../../_images/usage_tutorials_Semivariogram_Estimation_(Basic)_46_0.png +../../_images/usage_tutorials_Semivariogram_Estimation_%28Basic%29_46_0.png
@@ -1988,7 +1989,7 @@

6) Import model

-Created using Sphinx 5.0.2.
+Created using Sphinx 5.3.0.

diff --git a/docs/build/html/usage/tutorials/Semivariogram Regularization (Intermediate).html b/docs/build/html/usage/tutorials/Semivariogram Regularization (Intermediate).html index 739a4498..b0ccbde8 100644 --- a/docs/build/html/usage/tutorials/Semivariogram Regularization (Intermediate).html +++ b/docs/build/html/usage/tutorials/Semivariogram Regularization (Intermediate).html @@ -6,7 +6,7 @@ - Semivariogram regularization — Pyinterpolate 0.3.5 documentation + Semivariogram regularization — Pyinterpolate 0.3.6 documentation @@ -38,6 +38,7 @@ + @@ -103,7 +104,7 @@ -

Pyinterpolate 0.3.5 documentation

+

Pyinterpolate 0.3.6 documentation

@@ -820,7 +821,7 @@

2) Set semivariogram parameters.
-../../_images/usage_tutorials_Semivariogram_Regularization_(Intermediate)_7_0.png +../../_images/usage_tutorials_Semivariogram_Regularization_%28Intermediate%29_7_0.png
@@ -847,7 +848,7 @@

2) Set semivariogram parameters.

-../../_images/usage_tutorials_Semivariogram_Regularization_(Intermediate)_8_0.png +../../_images/usage_tutorials_Semivariogram_Regularization_%28Intermediate%29_8_0.png

We see that block and point support variograms follow a spatial-dependency pattern. In this case, we can move to the next step - deconvolution. The next step is to create the Deconvolution object. We have multiple parameters to choose from, and it is hard to find the best fit at the beginning, so try to avoid multiple loops because it is time-consuming.

@@ -905,7 +906,7 @@

3) Regularize semivariogram
-../../_images/usage_tutorials_Semivariogram_Regularization_(Intermediate)_12_0.png +../../_images/usage_tutorials_Semivariogram_Regularization_%28Intermediate%29_12_0.png

After the first step, we see that variogram can be regularized. There is a big difference between the theoretical curve and the experimental values. Let’s run the transformation process.

@@ -951,7 +952,7 @@

4) Visualize and check semivariogram
-../../_images/usage_tutorials_Semivariogram_Regularization_(Intermediate)_16_0.png +../../_images/usage_tutorials_Semivariogram_Regularization_%28Intermediate%29_16_0.png

Clarification: Deviation between regularized semivariogram and a theoretical model is smaller in each step, but the significant improvement can be seen after the first step, and then it slows down. That’s why semivariogram regularization usually does not require many steps. However, if there are many lags, optimization may require more steps to achieve a meaningful result.

@@ -970,7 +971,7 @@

4) Visualize and check semivariogram
-../../_images/usage_tutorials_Semivariogram_Regularization_(Intermediate)_18_0.png +../../_images/usage_tutorials_Semivariogram_Regularization_%28Intermediate%29_18_0.png

NOTE: Weights are smaller with each iteration. It is the expected behavior of the algorithm. The general trend goes downward, and small oscillations may occur due to the optimization process.

@@ -987,7 +988,7 @@

4) Visualize and check semivariogram
-../../_images/usage_tutorials_Semivariogram_Regularization_(Intermediate)_20_0.png +../../_images/usage_tutorials_Semivariogram_Regularization_%28Intermediate%29_20_0.png

The regularized model works well for our dataset and follows general trends. For closest lags, it assigns smaller weights, and for larger lags, weights are bigger. It is related to the semivariogram weighting method - we penalize more models that are poorly performing for the shortest distances from the origin.

@@ -1167,7 +1168,7 @@

5) Export semivariogram to text file

-Created using Sphinx 5.0.2.
+Created using Sphinx 5.3.0.

diff --git a/docs/build/html/usage/tutorials/Theoretical Models (Basic).html b/docs/build/html/usage/tutorials/Theoretical Models (Basic).html index 64c9281c..d00f6e23 100644 --- a/docs/build/html/usage/tutorials/Theoretical Models (Basic).html +++ b/docs/build/html/usage/tutorials/Theoretical Models (Basic).html @@ -6,7 +6,7 @@ - Semivariogram models — Pyinterpolate 0.3.5 documentation + Semivariogram models — Pyinterpolate 0.3.6 documentation @@ -38,6 +38,7 @@ + @@ -103,7 +104,7 @@ -

Pyinterpolate 0.3.5 documentation

+

Pyinterpolate 0.3.6 documentation

@@ -771,7 +772,7 @@

1) Create a random surface
-../../_images/usage_tutorials_Theoretical_Models_(Basic)_7_0.png +../../_images/usage_tutorials_Theoretical_Models_%28Basic%29_7_0.png

Let’s reshape this signal into a 2-D matrix:

@@ -799,7 +800,7 @@

1) Create a random surface
-../../_images/usage_tutorials_Theoretical_Models_(Basic)_10_0.png +../../_images/usage_tutorials_Theoretical_Models_%28Basic%29_10_0.png

The spatial correlation of this structure is very weak, we can change it with a simple blur filter. It’s size will be our range parameter!

@@ -830,7 +831,7 @@

1) Create a random surface
-../../_images/usage_tutorials_Theoretical_Models_(Basic)_14_0.png +../../_images/usage_tutorials_Theoretical_Models_%28Basic%29_14_0.png
@@ -914,7 +915,7 @@

2) Create the experimental variogram
-../../_images/usage_tutorials_Theoretical_Models_(Basic)_24_0.png +../../_images/usage_tutorials_Theoretical_Models_%28Basic%29_24_0.png

We read a plot and decide to set:

@@ -1028,7 +1029,7 @@

3) Set all variogram models
-../../_images/usage_tutorials_Theoretical_Models_(Basic)_32_0.png +../../_images/usage_tutorials_Theoretical_Models_%28Basic%29_32_0.png
@@ -1051,7 +1052,7 @@

3) Set all variogram models

-../../_images/usage_tutorials_Theoretical_Models_(Basic)_33_0.png +../../_images/usage_tutorials_Theoretical_Models_%28Basic%29_33_0.png
@@ -1074,7 +1075,7 @@

3) Set all variogram models

-../../_images/usage_tutorials_Theoretical_Models_(Basic)_34_0.png +../../_images/usage_tutorials_Theoretical_Models_%28Basic%29_34_0.png
@@ -1097,7 +1098,7 @@

3) Set all variogram models

-../../_images/usage_tutorials_Theoretical_Models_(Basic)_35_0.png +../../_images/usage_tutorials_Theoretical_Models_%28Basic%29_35_0.png
@@ -1120,7 +1121,7 @@

3) Set all variogram models

-../../_images/usage_tutorials_Theoretical_Models_(Basic)_36_0.png +../../_images/usage_tutorials_Theoretical_Models_%28Basic%29_36_0.png
@@ -1143,7 +1144,7 @@

3) Set all variogram models

-../../_images/usage_tutorials_Theoretical_Models_(Basic)_37_0.png +../../_images/usage_tutorials_Theoretical_Models_%28Basic%29_37_0.png
@@ -1166,7 +1167,7 @@

3) Set all variogram models

-../../_images/usage_tutorials_Theoretical_Models_(Basic)_38_0.png +../../_images/usage_tutorials_Theoretical_Models_%28Basic%29_38_0.png

What was your guess?

@@ -1211,7 +1212,7 @@

4) Compare variogram models
-../../_images/usage_tutorials_Theoretical_Models_(Basic)_41_0.png +../../_images/usage_tutorials_Theoretical_Models_%28Basic%29_41_0.png

We observe that the lowest distance from a modeled value to the experimental curve at a small distance has…

@@ -1371,7 +1372,7 @@

4) Compare variogram models

-Created using Sphinx 5.0.2.
+Created using Sphinx 5.3.0.

diff --git a/docs/build/html/usage/tutorials/Variogram Point Cloud (Basic).html b/docs/build/html/usage/tutorials/Variogram Point Cloud (Basic).html index 5040dd3b..0a307688 100644 --- a/docs/build/html/usage/tutorials/Variogram Point Cloud (Basic).html +++ b/docs/build/html/usage/tutorials/Variogram Point Cloud (Basic).html @@ -6,7 +6,7 @@ - Variogram Point Cloud — Pyinterpolate 0.3.5 documentation + Variogram Point Cloud — Pyinterpolate 0.3.6 documentation @@ -38,6 +38,7 @@ + @@ -103,7 +104,7 @@ -

Pyinterpolate 0.3.5 documentation

+

Pyinterpolate 0.3.6 documentation

@@ -864,7 +865,7 @@

2) Set proper lag size with variogram cloud histogram
-../../_images/usage_tutorials_Variogram_Point_Cloud_(Basic)_11_0.png +../../_images/usage_tutorials_Variogram_Point_Cloud_%28Basic%29_11_0.png

The output per lag is dense, and we cannot distinguish a single value. But, we can follow a general trend and see maxima on the plot. We can make it better if we use a box plot instead:

@@ -880,7 +881,7 @@

2) Set proper lag size with variogram cloud histogram
-../../_images/usage_tutorials_Variogram_Point_Cloud_(Basic)_13_0.png +../../_images/usage_tutorials_Variogram_Point_Cloud_%28Basic%29_13_0.png

The orange line in the middle of each box represents the median (50%) value per lag. The trend is more visible if we look into it. What else can be seen here?

@@ -903,7 +904,7 @@

2) Set proper lag size with variogram cloud histogram
-../../_images/usage_tutorials_Variogram_Point_Cloud_(Basic)_15_0.png +../../_images/usage_tutorials_Variogram_Point_Cloud_%28Basic%29_15_0.png

The violin plot supports our earlier assumptions, but we can learn more from it. For distant lags, a distribution starts to be multimodal. We see one mode around very low values of semivariance and another mode close to the 1st quartile. Multimodality may affect our outcomes, and maybe it tells us that there is more than one level of spatial dependency?

@@ -1054,7 +1055,7 @@

3) Detect and remove outliers
-../../_images/usage_tutorials_Variogram_Point_Cloud_(Basic)_21_0.png +../../_images/usage_tutorials_Variogram_Point_Cloud_%28Basic%29_21_0.png
@@ -1112,7 +1113,7 @@

3) Detect and remove outliers

-../../_images/usage_tutorials_Variogram_Point_Cloud_(Basic)_25_0.png +../../_images/usage_tutorials_Variogram_Point_Cloud_%28Basic%29_25_0.png

When we compare both figures - before and after data cleaning - we see that the y-axis of the second plot is 6-7 times smaller than the y-axis of the first plot! We’ve cleaned our data from the extreme values.

@@ -1303,7 +1304,7 @@

4) Calculate experimental semivariogram from point cloud

-Created using Sphinx 5.0.2.
+Created using Sphinx 5.3.0.

diff --git a/docs/source/conf.py b/docs/source/conf.py index ad12a397..949bf207 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -15,7 +15,7 @@ project = 'Pyinterpolate' copyright = '2022, Szymon Moliński' author = 'Szymon Moliński' -release = '0.3.5' +release = '0.3.6' # -- General configuration --------------------------------------------------- # https://www.sphinx-doc.org/en/master/usage/configuration.html#general-configuration diff --git a/docs/source/index.rst b/docs/source/index.rst index 2887b5a8..18c25124 100644 --- a/docs/source/index.rst +++ b/docs/source/index.rst @@ -14,7 +14,7 @@ Pyinterpolate :alt: The pyinterpolate logo with the name Kyiv and the version of package. .. note:: - The last documentation update: *2022-10-31* + The last documentation update: *2023-01-21* **Pyinterpolate** is the Python library for **geostatistics**. The package provides access to spatial statistics tools used in various studies. This package helps you **interpolate spatial data** with the *Kriging* technique. diff --git a/pyinterpolate/processing/transform/transform.py b/pyinterpolate/processing/transform/transform.py index 28e3f77e..6660e624 100644 --- a/pyinterpolate/processing/transform/transform.py +++ b/pyinterpolate/processing/transform/transform.py @@ -222,7 +222,11 @@ def sem_to_cov(semivariances, sill) -> np.ndarray: return sill - np.asarray(semivariances) -def transform_ps_to_dict(ps: Union[Dict, np.ndarray, gpd.GeoDataFrame, pd.DataFrame, PointSupport]) -> Dict: +def transform_ps_to_dict(ps: Union[Dict, np.ndarray, gpd.GeoDataFrame, pd.DataFrame, PointSupport], + df_x_col_name='x', + df_y_col_name='y', + df_value_col_name='ds', + df_index_col_name='index') -> Dict: """ Parameters @@ -233,23 +237,33 @@ def transform_ps_to_dict(ps: Union[Dict, np.ndarray, gpd.GeoDataFrame, pd.DataFr * DataFrame and GeoDataFrame: columns={x, y, ds, index} * PointSupport + df_x_col_name : str, default='x' + If DataFrame or GeoDataFrame is passed then this parameter represents the name of a column x (longitude). + + df_y_col_name : str, default='y' + If DataFrame or GeoDataFrame is passed then this parameter represents the name of a column y (latitude). + + df_value_col_name : str, default='ds' + If DataFrame or GeoDataFrame is passed then this parameter represents the name of a column with point support + values. + + df_index_col_name : str, default='index' + If DataFrame or GeoDataFrame is passed then this parameter represents the name of a column that can represent + the point support index. + Returns ------- : Dict Point Support as a Dict: {block id: [[point x, point y, value]]} - - TODO - ---- - Allow user to pass any column names for DF and GDF data types. """ if isinstance(ps, PointSupport): return point_support_to_dict(ps) elif isinstance(ps, pd.DataFrame) or isinstance(ps, gpd.GeoDataFrame): - expected_cols = {'x', 'y', 'ds', 'index'} + expected_cols = {df_x_col_name, df_y_col_name, df_value_col_name, df_index_col_name} if not expected_cols.issubset(set(ps.columns)): raise KeyError(f'Given dataframe doesnt have all expected columns {expected_cols}. ' - f'It has {ps.columns} instead.') + f'It has {ps.columns} instead. Set those columns as function parameters.') return block_dataframe_to_dict(ps) elif isinstance(ps, np.ndarray): return block_arr_to_dict(ps) diff --git a/requirements.txt b/requirements.txt index 1d8a3ecf..414f3ab0 100644 --- a/requirements.txt +++ b/requirements.txt @@ -6,7 +6,7 @@ tqdm pyproj scipy pyogrio -shapely +shapely<2 fiona rtree nbsphinx From 64ba317ffb89d19059cee7c09cf89ade28ec64b9 Mon Sep 17 00:00:00 2001 From: Simon <31246246+SimonMolinsky@users.noreply.github.com> Date: Sat, 21 Jan 2023 18:24:09 +0200 Subject: [PATCH 07/29] Update changelog.rst --- changelog.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/changelog.rst b/changelog.rst index 6f1f3012..21093c2c 100755 --- a/changelog.rst +++ b/changelog.rst @@ -15,7 +15,7 @@ Changes by date * (test) added functional test for `smooth_blocks` function, * (debug) too broad exception in `download_air_quality_poland` is narrowed to `KeyError`, * (enhancement) log points that cannot be assigned to any area in `PointSupport` class, -* +* (enhancement) `transform_ps_to_dict()` function takes custom parameters for lon, lat, value and index, 2023-01-16 ---------- From abec022a731654ac30b6c70a0800c11c5b17d960 Mon Sep 17 00:00:00 2001 From: Simon <31246246+SimonMolinsky@users.noreply.github.com> Date: Sat, 21 Jan 2023 19:54:21 +0200 Subject: [PATCH 08/29] Added tests to function check_limits --- changelog.rst | 1 + pyinterpolate/processing/checks.py | 10 ++--- tests/test_processing/test_check_limits.py | 46 ++++++++++++++++++++++ 3 files changed, 52 insertions(+), 5 deletions(-) create mode 100644 tests/test_processing/test_check_limits.py diff --git a/changelog.rst b/changelog.rst index 21093c2c..91e299f9 100755 --- a/changelog.rst +++ b/changelog.rst @@ -16,6 +16,7 @@ Changes by date * (debug) too broad exception in `download_air_quality_poland` is narrowed to `KeyError`, * (enhancement) log points that cannot be assigned to any area in `PointSupport` class, * (enhancement) `transform_ps_to_dict()` function takes custom parameters for lon, lat, value and index, +* (test) `check_limits()` function tests, 2023-01-16 ---------- diff --git a/pyinterpolate/processing/checks.py b/pyinterpolate/processing/checks.py index f4586515..2b8eb8a7 100644 --- a/pyinterpolate/processing/checks.py +++ b/pyinterpolate/processing/checks.py @@ -34,14 +34,14 @@ def check_limits(value: float, lower_limit=0, upper_limit=1, exclusive_lower=Tru ------ ValueError Value is outside given limits. - - TODO - ---- - Tests """ msg = f'Value {value} is outside the limits {lower_limit}:{upper_limit}. Lower limit is excluded: ' \ - f'{exclusive_lower}, Upper limit is excluded" {exclusive_upper}.' + f'{exclusive_lower}, Upper limit is excluded: {exclusive_upper}.' + + if value == lower_limit: + if lower_limit == upper_limit: + raise ValueError('Provided value, lower and upper limits are the same') # <= if exclusive_lower: diff --git a/tests/test_processing/test_check_limits.py b/tests/test_processing/test_check_limits.py new file mode 100644 index 00000000..c7df017d --- /dev/null +++ b/tests/test_processing/test_check_limits.py @@ -0,0 +1,46 @@ +import unittest + +from pyinterpolate.processing.checks import check_limits + + +VALUE = 10 + + +class TestCheckLimits(unittest.TestCase): + + def test_case_1(self): + check_limits( + value=VALUE, lower_limit=9, upper_limit=11, exclusive_lower=True, exclusive_upper=True + ) + + self.assertTrue(1) + + def test_case_2(self): + with self.assertRaises(ValueError): + check_limits(VALUE, lower_limit=10, upper_limit=11, exclusive_lower=True, exclusive_upper=True) + + def test_case_3(self): + with self.assertRaises(ValueError): + check_limits(VALUE, lower_limit=9, upper_limit=10, exclusive_lower=True, exclusive_upper=True) + + def test_case_4(self): + check_limits( + value=VALUE, lower_limit=10, upper_limit=11, exclusive_lower=False, exclusive_upper=True + ) + + self.assertTrue(1) + + def test_case_5(self): + check_limits( + value=VALUE, lower_limit=9, upper_limit=10, exclusive_lower=True, exclusive_upper=False + ) + + self.assertTrue(1) + + def test_case_6(self): + with self.assertRaises(ValueError): + check_limits(VALUE, lower_limit=10, upper_limit=10, exclusive_lower=True, exclusive_upper=True) + + def test_case_7(self): + with self.assertRaises(ValueError): + check_limits(VALUE, lower_limit=10, upper_limit=10, exclusive_lower=False, exclusive_upper=False) From 3c3a20d5df1c4f76fd69b400ea1aca1edfc1e548 Mon Sep 17 00:00:00 2001 From: Simon <31246246+SimonMolinsky@users.noreply.github.com> Date: Mon, 23 Jan 2023 17:21:57 +0200 Subject: [PATCH 09/29] added matplotlib tests for variogram cloud class --- changelog.rst | 2 ++ .../experimental/experimental.doctree | Bin 157634 -> 158557 bytes docs/build/doctrees/environment.pickle | Bin 436516 -> 437443 bytes .../variogram/empirical/cloud.html | 16 ++++++++++++---- .../variogram/experimental/experimental.html | 6 ++++++ docs/build/html/searchindex.js | 2 +- pyinterpolate/variogram/empirical/cloud.py | 7 +++++++ .../empirical/test_variogram_cloud_class.py | 11 ++++++++++- 8 files changed, 38 insertions(+), 6 deletions(-) diff --git a/changelog.rst b/changelog.rst index 91e299f9..6e66d077 100755 --- a/changelog.rst +++ b/changelog.rst @@ -17,6 +17,8 @@ Changes by date * (enhancement) log points that cannot be assigned to any area in `PointSupport` class, * (enhancement) `transform_ps_to_dict()` function takes custom parameters for lon, lat, value and index, * (test) `check_limits()` function tests, +* (test) plotting function of the `VariogramCloud()` class is tested and slightly changed to return `True` if everything has worked fine, +* 2023-01-16 ---------- diff --git a/docs/build/doctrees/api/variogram/experimental/experimental.doctree b/docs/build/doctrees/api/variogram/experimental/experimental.doctree index 3502813e82e8e86f8f6f3c0b0b0ac075bfbdc238..549acf310c7aebd6c399cd91eddd16619173e79a 100644 GIT binary patch delta 2477 zcmai$e@s(X6vsISrIa5c3RMP_R{0@)Z68n+on~qjBg6SC;tw{ZGLeF)L~sTcooqT? zLO}O$Cz2RZ=P=WS5aT+xrIAgSnJqz+WlP)-r_p5ofmxGnS>iuC_uW1o4J`JL*V}u} z_uS8U_uPB>)v)!~AFYRvKo4f%qFvWAu{D?0Hoz*nmx~*qn$$e3riWFS4;#fej~?b> zF+JQ3X;hkzU&qA?*QWImpOP^YpW>7UYC|y?% z7ag^G_ckRs_~Z9)*tfsV?{_rpbgXEo-@mWUabSCkqq%8sYir#OuXd)-SKy2IKhM{3 z&N(|y`t}4YTYCbwPUGj%o{pY_J#(1zs>C^wZJ|H&(Gr)za|15AyBhNHbDpz;9(4|l zbbyNAy0mkreBPU`n3(;wywZ&S$)Lwv`ld@edD@qp!;*l#GMN`1a#2Sy6e0tJpw4p1Q`?b_N;Y*0?lc~}6O#7rrcD|FP0rBFxCxmYQFaz1wL#_l9ss*q1bKO8c( z?NZyRYoVR;i?9%SrL;X-dbZw4WtbEHTC79DnB91t7v>X1_ZDL}gd=9v 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z0&M;42@{S0f1F@_r0eS}HRW|vD*wVyBs(O8wmo5L8J?S4VdG@dBd?oqJURcm2}jb{ zlO`NCCY@xdjVDbwc|Uj3gu~iTCrvo9cYMQy1LD*-O#QeOKOU0(bO@-d8GY&QkY=H5 zFg^N)31|NcZ@^bJ(&$sB_)xqSK--;wR&1o>PJ!T!bk!+STZj&vf;O|LEnV@t$qb(_ zo?_2Moi-(@Uu;3go;F$Bl4X#DXIU@3;( - pyinterpolate.variogram.empirical.cloud — Pyinterpolate 0.3.5 documentation + pyinterpolate.variogram.empirical.cloud — Pyinterpolate 0.3.6 documentation @@ -37,6 +37,7 @@ + @@ -98,7 +99,7 @@ -

Pyinterpolate 0.3.5 documentation

+

Pyinterpolate 0.3.6 documentation

@@ -827,6 +828,11 @@

Source code for pyinterpolate.variogram.empirical.cloud

---------- kind : string, default='scatter' available plot types: 'scatter', 'box', 'violin' + + Returns + ------- + : bool + ``True`` if Figure was plotted. """ if self.experimental_point_cloud is None: @@ -841,7 +847,9 @@

Source code for pyinterpolate.variogram.empirical.cloud

self._violin_plot() else: msg = f'Plot kind {kind} is not available. Use "scatter", "box" or "violin" instead.' - raise KeyError(msg)
+ raise KeyError(msg) + return True
+
[docs] def remove_outliers(self, method='zscore', z_lower_limit=-3, @@ -1088,7 +1096,7 @@

Source code for pyinterpolate.variogram.empirical.cloud

-Created using Sphinx 5.0.2.
+Created using Sphinx 5.3.0.

diff --git a/docs/build/html/api/variogram/experimental/experimental.html b/docs/build/html/api/variogram/experimental/experimental.html index 79e4030f..23588644 100644 --- a/docs/build/html/api/variogram/experimental/experimental.html +++ b/docs/build/html/api/variogram/experimental/experimental.html @@ -908,6 +908,12 @@

ExperimentalReturns: +
+
: bool

True if Figure was plotted.

+
+
+

diff --git a/docs/build/html/searchindex.js b/docs/build/html/searchindex.js index 51a85bd9..927daf09 100644 --- a/docs/build/html/searchindex.js +++ b/docs/build/html/searchindex.js @@ -1 +1 @@ -Search.setIndex({"docnames": ["api/api", "api/datatypes/core", "api/distance/distance", "api/idw/idw", "api/io/io", "api/kriging/block/block_kriging", "api/kriging/kriging", "api/kriging/point/point_kriging", "api/pipelines/data/download", "api/pipelines/kriging_based/kriging_based_processes", "api/pipelines/pipelines", "api/variogram/block/block", "api/variogram/deconvolution/deconvolution", "api/variogram/experimental/experimental", "api/variogram/theoretical/theoretical", "api/variogram/variogram", "api/viz/viz", "community/community", "community/doc_parts/contributors", "community/doc_parts/forum", "community/doc_parts/use_cases", "developer/dev", "developer/doc_parts/bugs", "developer/doc_parts/development", "developer/doc_parts/package", "developer/doc_parts/reqs", 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"alltitles": {"API": [[0, "api"]], "Core data structures": [[1, "core-data-structures"]], "Distance calculations": [[2, "distance-calculations"]], "Inverse Distance Weighting": [[3, "inverse-distance-weighting"]], "Input / Output": [[4, "input-output"]], "Block - Poisson Kriging": [[5, "block-poisson-kriging"]], "Kriging": [[6, "kriging"]], "Point Kriging": [[7, "point-kriging"]], "Data download": [[8, "data-download"]], "Kriging-based processes": [[9, "kriging-based-processes"]], "Pipelines": [[10, "pipelines"]], "Block": [[11, "block"]], "Deconvolution": [[12, "deconvolution"]], "Experimental": [[13, "experimental"]], "Theoretical": [[14, "theoretical"]], "Variogram": [[15, "variogram"]], "Visualization": [[16, "visualization"]], "Community": [[17, "community"]], "Contributors": [[18, "contributors"], [18, "id1"]], "Author(s)": [[18, "author-s"]], "Maintainer(s)": [[18, "maintainer-s"]], "Reviewers (JOSS)": [[18, "reviewers-joss"]], "Network": [[19, "network"]], "Use Cases": [[20, "use-cases"]], "Development": [[21, "development"], [23, "development"]], "Known Bugs": [[22, "known-bugs"]], "Package structure": [[24, "package-structure"]], "Requirements and dependencies (v 0.3.0)": [[25, "requirements-and-dependencies-v-0-3-0"]], "Tests and contribution": [[26, "tests-and-contribution"]], "Pyinterpolate": [[27, "pyinterpolate"]], "version 0.3.6 - Kyiv": [[27, "version-0-3-6-kyiv"]], "Contents": [[27, "contents"]], "How to cite": [[27, "how-to-cite"], [29, "how-to-cite"]], "Bibliography": [[28, "bibliography"]], "Setup": [[30, "setup"]], "Installation guidelines": [[30, "installation-guidelines"]], "Conda": [[30, "conda"]], "pip": [[30, "pip"]], "Installation - additional topics": [[30, "installation-additional-topics"]], "Working with Notebooks": [[30, "working-with-notebooks"]], "The libspatialindex_c.so dependency error": [[30, "the-libspatialindex-c-so-dependency-error"]], "Good practices": [[31, "good-practices"]], "Quickstart": [[32, "quickstart"]], "Installation": [[32, "installation"]], "Ordinary Kriging": [[32, "ordinary-kriging"]], "Tutorials": [[33, "tutorials"]], "Beginner": [[33, "beginner"]], "Intermediate": [[33, "intermediate"]], "Advanced": [[33, "advanced"]], "Blocks to points Ordinary Kriging interpolation": [[34, "Blocks-to-points-Ordinary-Kriging-interpolation"]], "Table of Contents:": [[34, "Table-of-Contents:"], [35, "Table-of-Contents:"], [36, "Table-of-Contents:"], [37, "Table-of-Contents:"], [38, "Table-of-Contents:"], [39, "Table-of-Contents:"], [40, "Table-of-Contents:"], [41, "Table-of-Contents:"], [42, "Table-of-Contents:"], [43, "Table-of-Contents:"], [44, "Table-of-Contents:"], [45, "Table-of-Contents:"], [46, "Table-of-Contents:"]], "Level: Intermediate": [[34, "Level:-Intermediate"], [39, "Level:-Intermediate"], [44, "Level:-Intermediate"]], "Changelog": [[34, "Changelog"], [35, "Changelog"], [36, "Changelog"], [37, "Changelog"], [38, "Changelog"], [39, "Changelog"], [40, "Changelog"], [41, "Changelog"], [42, "Changelog"], [43, "Changelog"], [44, "Changelog"], [45, "Changelog"], [46, "Changelog"]], "Introduction": [[34, "Introduction"], [35, "Introduction"], [36, "Introduction"], [37, "Introduction"], [38, "Introduction"], [39, "Introduction"], [40, "Introduction"], [41, "Introduction"], [42, "Introduction"], [43, "Introduction"], [44, "Introduction"], [45, "Introduction"], [46, "Introduction"]], "Import packages": [[34, "Import-packages"], [36, "Import-packages"], [37, "Import-packages"], [38, "Import-packages"], [39, "Import-packages"], [43, "Import-packages"], [44, "Import-packages"], [45, "Import-packages"], [46, "Import-packages"]], "1) Read areal data": [[34, "1)-Read-areal-data"]], "2) Detect and remove outliers": [[34, "2)-Detect-and-remove-outliers"]], "3. Create a theoretical semivariogram model": [[34, "3.-Create-a-theoretical-semivariogram-model"]], "4. Read point data canvas": [[34, "4.-Read-point-data-canvas"]], "5. Build a map of interpolated values": [[34, "5.-Build-a-map-of-interpolated-values"]], "6. Show a map of interpolated values with a choropleth map of the breast cancer rates": [[34, "6.-Show-a-map-of-interpolated-values-with-a-choropleth-map-of-the-breast-cancer-rates"]], "Directional Ordinary Kriging": [[35, "Directional-Ordinary-Kriging"]], "Level: Basic": [[35, "Level:-Basic"], [36, "Level:-Basic"], [37, "Level:-Basic"], [38, "Level:-Basic"], [43, "Level:-Basic"], [45, "Level:-Basic"], [46, "Level:-Basic"]], "1. Read meuse dataset and transform data": [[35, "1.-Read-meuse-dataset-and-transform-data"]], "2. Create directional variograms": [[35, "2.-Create-directional-variograms"]], "3. Create directional Ordinary Kriging models": [[35, "3.-Create-directional-Ordinary-Kriging-models"]], "4. Compare interpolated results": [[35, "4.-Compare-interpolated-results"], [35, "id1"]], "Directional semivariograms": [[36, "Directional-semivariograms"]], "A directional proces": [[36, "A-directional-proces"]], "1) Read and show point data": [[36, "1)-Read-and-show-point-data"]], "2) Create the experimental semivariogram": [[36, "2)-Create-the-experimental-semivariogram"]], "Case 1: W-E Direction": [[36, "Case-1:-W-E-Direction"]], "Case 2: N-S Direction": [[36, "Case-2:-N-S-Direction"]], "Case 3: NW-SE Direction": [[36, "Case-3:-NW-SE-Direction"]], "Case 4: NE-SW Direction": [[36, "Case-4:-NE-SW-Direction"]], "Case 5: Isotropic Variogram": [[36, "Case-5:-Isotropic-Variogram"]], "3) Compare semivariograms": [[36, "3)-Compare-semivariograms"]], "4) Bonus: Compare calculation times and results": [[36, "4)-Bonus:-Compare-calculation-times-and-results"]], "How good is my Kriging model? - tests with IDW algorithm": [[37, "How-good-is-my-Kriging-model?---tests-with-IDW-algorithm"]], "1) Read point data": [[37, "1)-Read-point-data"], [38, "1)-Read-point-data"], [43, "1)-Read-point-data"], [46, "1)-Read-point-data"]], "2) Divide the dataset into two sets: modeling and validation set": [[37, "2)-Divide-the-dataset-into-two-sets:-modeling-and-validation-set"]], "3) Perform IDW and evaluate it": [[37, "3)-Perform-IDW-and-evaluate-it"]], "4) Perform variogram modeling on the modeling set": [[37, "4)-Perform-variogram-modeling-on-the-modeling-set"]], "5) Validate Kriging and compare Kriging and IDW validation results": [[37, "5)-Validate-Kriging-and-compare-Kriging-and-IDW-validation-results"]], "6) Bonus scenario: only 2% of values are known!": [[37, "6)-Bonus-scenario:-only-2%-of-values-are-known!"]], "Ordinary and Simple Kriging": [[38, "Ordinary-and-Simple-Kriging"]], "2) Set Semivariogram model": [[38, "2)-Set-Semivariogram-model"]], "3) Set Ordinary Kriging and Simple Kriging models": [[38, "3)-Set-Ordinary-Kriging-and-Simple-Kriging-models"]], "4) Predict values at unknown locations": [[38, "4)-Predict-values-at-unknown-locations"]], "Outliers and Their Influence on the Final Model": [[39, "Outliers-and-Their-Influence-on-the-Final-Model"]], "1) Read point data and divide it into training and test set": [[39, "1)-Read-point-data-and-divide-it-into-training-and-test-set"]], "2) Check outliers: analyze the distribution of the values": [[39, "2)-Check-outliers:-analyze-the-distribution-of-the-values"]], "3) Create the Variogram Point Cloud model for datasets A and B": [[39, "3)-Create-the-Variogram-Point-Cloud-model-for-datasets-A-and-B"]], "4) Remove outliers from the variograms": [[39, "4)-Remove-outliers-from-the-variograms"]], "5) Create Four Ordinary Kriging models based on the four Variogram Point Clouds and compare their performance": [[39, "5)-Create-Four-Ordinary-Kriging-models-based-on-the-four-Variogram-Point-Clouds-and-compare-their-performance"]], "Poisson Kriging - Area to Area Kriging": [[40, "Poisson-Kriging---Area-to-Area-Kriging"]], "Level: Advanced": [[40, "Level:-Advanced"], [41, "Level:-Advanced"], [42, "Level:-Advanced"]], "1) Read and explore data": [[40, "1)-Read-and-explore-data"], [41, "1)-Read-and-explore-data"], [42, "1)-Read-and-explore-data"]], "2) Load a semivariogram model": [[40, "2)-Load-a-semivariogram-model"]], "3) Prepare training and test data.": [[40, "3)-Prepare-training-and-test-data."], [42, "3)-Prepare-training-and-test-data."]], "4) Predict values at unknown locations and calculate forecast bias and root mean squared error.": [[40, "4)-Predict-values-at-unknown-locations-and-calculate-forecast-bias-and-root-mean-squared-error."], [42, "4)-Predict-values-at-unknown-locations-and-calculate-forecast-bias-and-root-mean-squared-error."]], "5) Analyze Forecast Bias and Root Mean Squared Error of prediction": [[40, "5)-Analyze-Forecast-Bias-and-Root-Mean-Squared-Error-of-prediction"], [42, "5)-Analyze-Forecast-Bias-and-Root-Mean-Squared-Error-of-prediction"]], "Poisson Kriging - Area to Point Kriging": [[41, "Poisson-Kriging---Area-to-Point-Kriging"]], "2) Load semivariogram model": [[41, "2)-Load-semivariogram-model"], [42, "2)-Load-semivariogram-model"]], "3) Perform Area to Point smoothing of areal data.": [[41, "3)-Perform-Area-to-Point-smoothing-of-areal-data."]], "4) Visualize data": [[41, "4)-Visualize-data"]], "Poisson Kriging - centroid based approach": [[42, "Poisson-Kriging---centroid-based-approach"]], "Semivariogram Estimation": [[43, "Semivariogram-Estimation"]], "2) Create the experimental variogram": [[43, "2)-Create-the-experimental-variogram"], [45, "2)-Create-the-experimental-variogram"]], "3. 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Compare interpolated results": [[35, "4.-Compare-interpolated-results"], [35, "id1"]], "Directional semivariograms": [[36, "Directional-semivariograms"]], "A directional proces": [[36, "A-directional-proces"]], "1) Read and show point data": [[36, "1)-Read-and-show-point-data"]], "2) Create the experimental semivariogram": [[36, "2)-Create-the-experimental-semivariogram"]], "Case 1: W-E Direction": [[36, "Case-1:-W-E-Direction"]], "Case 2: N-S Direction": [[36, "Case-2:-N-S-Direction"]], "Case 3: NW-SE Direction": [[36, "Case-3:-NW-SE-Direction"]], "Case 4: NE-SW Direction": [[36, "Case-4:-NE-SW-Direction"]], "Case 5: Isotropic Variogram": [[36, "Case-5:-Isotropic-Variogram"]], "3) Compare semivariograms": [[36, "3)-Compare-semivariograms"]], "4) Bonus: Compare calculation times and results": [[36, "4)-Bonus:-Compare-calculation-times-and-results"]], "How good is my Kriging model? - tests with IDW algorithm": [[37, "How-good-is-my-Kriging-model?---tests-with-IDW-algorithm"]], "1) Read point data": [[37, "1)-Read-point-data"], [38, "1)-Read-point-data"], [43, "1)-Read-point-data"], [46, "1)-Read-point-data"]], "2) Divide the dataset into two sets: modeling and validation set": [[37, "2)-Divide-the-dataset-into-two-sets:-modeling-and-validation-set"]], "3) Perform IDW and evaluate it": [[37, "3)-Perform-IDW-and-evaluate-it"]], "4) Perform variogram modeling on the modeling set": [[37, "4)-Perform-variogram-modeling-on-the-modeling-set"]], "5) Validate Kriging and compare Kriging and IDW validation results": [[37, "5)-Validate-Kriging-and-compare-Kriging-and-IDW-validation-results"]], "6) Bonus scenario: only 2% of values are known!": [[37, "6)-Bonus-scenario:-only-2%-of-values-are-known!"]], "Ordinary and Simple Kriging": [[38, "Ordinary-and-Simple-Kriging"]], "2) Set Semivariogram model": [[38, "2)-Set-Semivariogram-model"]], "3) Set Ordinary Kriging and Simple Kriging models": [[38, "3)-Set-Ordinary-Kriging-and-Simple-Kriging-models"]], "4) Predict values at unknown locations": [[38, "4)-Predict-values-at-unknown-locations"]], "Outliers and Their Influence on the Final Model": [[39, "Outliers-and-Their-Influence-on-the-Final-Model"]], "1) Read point data and divide it into training and test set": [[39, "1)-Read-point-data-and-divide-it-into-training-and-test-set"]], "2) Check outliers: analyze the distribution of the values": [[39, "2)-Check-outliers:-analyze-the-distribution-of-the-values"]], "3) Create the Variogram Point Cloud model for datasets A and B": [[39, "3)-Create-the-Variogram-Point-Cloud-model-for-datasets-A-and-B"]], "4) Remove outliers from the variograms": [[39, "4)-Remove-outliers-from-the-variograms"]], "5) Create Four Ordinary Kriging models based on the four Variogram Point Clouds and compare their performance": [[39, "5)-Create-Four-Ordinary-Kriging-models-based-on-the-four-Variogram-Point-Clouds-and-compare-their-performance"]], "Poisson Kriging - Area to Area Kriging": [[40, "Poisson-Kriging---Area-to-Area-Kriging"]], "Level: Advanced": [[40, "Level:-Advanced"], [41, "Level:-Advanced"], [42, "Level:-Advanced"]], "1) Read and explore data": [[40, "1)-Read-and-explore-data"], [41, "1)-Read-and-explore-data"], [42, "1)-Read-and-explore-data"]], "2) Load a semivariogram model": [[40, "2)-Load-a-semivariogram-model"]], "3) Prepare training and test data.": [[40, "3)-Prepare-training-and-test-data."], [42, "3)-Prepare-training-and-test-data."]], "4) Predict values at unknown locations and calculate forecast bias and root mean squared error.": [[40, "4)-Predict-values-at-unknown-locations-and-calculate-forecast-bias-and-root-mean-squared-error."], [42, "4)-Predict-values-at-unknown-locations-and-calculate-forecast-bias-and-root-mean-squared-error."]], "5) Analyze Forecast Bias and Root Mean Squared Error of prediction": [[40, "5)-Analyze-Forecast-Bias-and-Root-Mean-Squared-Error-of-prediction"], [42, "5)-Analyze-Forecast-Bias-and-Root-Mean-Squared-Error-of-prediction"]], "Poisson Kriging - Area to Point Kriging": [[41, "Poisson-Kriging---Area-to-Point-Kriging"]], "2) Load semivariogram model": [[41, "2)-Load-semivariogram-model"], [42, "2)-Load-semivariogram-model"]], "3) Perform Area to Point smoothing of areal data.": [[41, "3)-Perform-Area-to-Point-smoothing-of-areal-data."]], "4) Visualize data": [[41, "4)-Visualize-data"]], "Poisson Kriging - centroid based approach": [[42, "Poisson-Kriging---centroid-based-approach"]], "Semivariogram Estimation": [[43, "Semivariogram-Estimation"]], "2) Create the experimental variogram": [[43, "2)-Create-the-experimental-variogram"], [45, "2)-Create-the-experimental-variogram"]], "3. Set manually different semivariogram models": [[43, "3.-Set-manually-different-semivariogram-models"]], "Models:": [[43, "Models:"]], "4) Set automatically semivariogram model": [[43, "4)-Set-automatically-semivariogram-model"]], "5) Export model": [[43, "5)-Export-model"]], "6) Import model": [[43, "6)-Import-model"]], "Semivariogram regularization": [[44, "Semivariogram-regularization"]], "1) Prepare areal and point data": [[44, "1)-Prepare-areal-and-point-data"]], "2) Set semivariogram parameters.": [[44, "2)-Set-semivariogram-parameters."]], "3) Regularize semivariogram": [[44, "3)-Regularize-semivariogram"]], "4) Visualize and check semivariogram": [[44, "4)-Visualize-and-check-semivariogram"]], "5) Export semivariogram to text file": [[44, "5)-Export-semivariogram-to-text-file"]], "Semivariogram models": [[45, "Semivariogram-models"]], "1) Create a random surface": [[45, "1)-Create-a-random-surface"]], "3) Set all variogram models": [[45, "3)-Set-all-variogram-models"]], "4) Compare variogram models": [[45, "4)-Compare-variogram-models"]], "Variogram Point Cloud": [[46, "Variogram-Point-Cloud"]], "2) Set proper lag size with variogram cloud histogram": [[46, "2)-Set-proper-lag-size-with-variogram-cloud-histogram"]], "3) Detect and remove outliers": [[46, "3)-Detect-and-remove-outliers"]], "4) Calculate experimental semivariogram from point cloud": [[46, "4)-Calculate-experimental-semivariogram-from-point-cloud"]], "Experimental": [[13, "experimental"]], "Pyinterpolate": [[27, "pyinterpolate"]], "version 0.3.6 - Kyiv": [[27, "version-0-3-6-kyiv"]], "Contents": [[27, "contents"]]}, "indexentries": {}}) \ No newline at end of file diff --git a/pyinterpolate/variogram/empirical/cloud.py b/pyinterpolate/variogram/empirical/cloud.py index b42e6594..f319c5ac 100644 --- a/pyinterpolate/variogram/empirical/cloud.py +++ b/pyinterpolate/variogram/empirical/cloud.py @@ -460,6 +460,11 @@ def plot(self, kind='scatter'): ---------- kind : string, default='scatter' available plot types: 'scatter', 'box', 'violin' + + Returns + ------- + : bool + ``True`` if Figure was plotted. """ if self.experimental_point_cloud is None: @@ -475,6 +480,8 @@ def plot(self, kind='scatter'): else: msg = f'Plot kind {kind} is not available. Use "scatter", "box" or "violin" instead.' raise KeyError(msg) + return True + def remove_outliers(self, method='zscore', z_lower_limit=-3, diff --git a/tests/test_variogram/empirical/test_variogram_cloud_class.py b/tests/test_variogram/empirical/test_variogram_cloud_class.py index 3a90f1f8..949ad541 100644 --- a/tests/test_variogram/empirical/test_variogram_cloud_class.py +++ b/tests/test_variogram/empirical/test_variogram_cloud_class.py @@ -1,6 +1,7 @@ # Core modules import unittest # Math modules +import matplotlib.pyplot as plt import numpy as np # Package modules from pyinterpolate.variogram.empirical.semivariance import calculate_semivariance @@ -134,4 +135,12 @@ def test_raises_value_error(self): self.assertRaises(ValueError, VariogramCloud, **kwargs) - # TODO: test plots (future) + def test_plots_figure(self): + variogram_cloud = VariogramCloud( + input_array=armstrong_arr, + step_size=gen_data.param_step_size, + max_range=gen_data.param_max_range + ) + was_plotted = variogram_cloud.plot(kind='scatter') + msg = "Figure wasn't plotted" + self.assertTrue(was_plotted, msg) From 4d261023b94393782e6c5cb36b0e12dd00d56688 Mon Sep 17 00:00:00 2001 From: Simon <31246246+SimonMolinsky@users.noreply.github.com> Date: Tue, 24 Jan 2023 18:31:04 +0200 Subject: [PATCH 10/29] The first update of the new tutorial --- ...ariogram Point Cloud classes (Basic).ipynb | 411 ++++++++++++++++++ tutorials/Variogram Point Cloud (Basic).ipynb | 148 ++++++- 2 files changed, 538 insertions(+), 21 deletions(-) create mode 100644 tutorials/Experimental Variogram and Variogram Point Cloud classes (Basic).ipynb diff --git a/tutorials/Experimental Variogram and Variogram Point Cloud classes (Basic).ipynb b/tutorials/Experimental Variogram and Variogram Point Cloud classes (Basic).ipynb new file mode 100644 index 00000000..56cea047 --- /dev/null +++ b/tutorials/Experimental Variogram and Variogram Point Cloud classes (Basic).ipynb @@ -0,0 +1,411 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "c18b3146", + "metadata": {}, + "source": [ + "# Experimental Variogram and Variogram Cloud classes\n", + "\n", + "## Table of Contents:\n", + "\n", + "1. Create Experimental Variogram with the `ExperimentalVariogram` class.\n", + "2. Create Experimental Variogram Point Cloud with the `VariogramCloud` class.\n", + "\n", + "\n", + "## Level: Basic\n", + "\n", + "## Changelog\n", + "\n", + "| Date | Change description | Author |\n", + "|------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------|----------------------------|\n", + "| 2023-01-23 | The first release of the tutorial | @SimonMolinsky |\n", + "\n", + "\n", + "## Introduction\n", + "\n", + "Geostatistical analysis starts with a dissimilarity estimation. We must know if process is spatially depended and at which distances points tend to be related to each other. Thus, we start from the experimental variogram estimation.\n", + "\n", + "In this tutorial, we will take a closer look into the API, and we will learn what can be done with two basic experimental semivariogram estimation functionalities. One is `ExperimentalVariogram` class and another is `VariogramCloud` class. The first is a foundation of every other complex function, from semivariogram modeling up to Poisson Kriging. The latter is used for thorough analysis of relations between points and their dispersion.\n", + "\n", + "To learn more about variogram and related concepts you can start from those tutorials:\n", + "\n", + "- [Semivariogram Estimation](https://github.com/DataverseLabs/pyinterpolate/blob/main/tutorials/Semivariogram%20Estimation%20(Basic).ipynb)\n", + "- [Directional Semivariograms](https://github.com/DataverseLabs/pyinterpolate/blob/main/tutorials/Directional%20Semivariograms%20(Basic).ipynb)\n", + "- [Variogram Point Cloud](https://github.com/DataverseLabs/pyinterpolate/blob/main/tutorials/Variogram%20Point%20Cloud%20(Basic).ipynb)\n", + "\n", + "We will use:\n", + "\n", + "- **DEM data** stored in a file `samples/point_data/txt/pl_dem_epsg2180.txt`" + ] + }, + { + "cell_type": "markdown", + "id": "f5e96c24", + "metadata": {}, + "source": [ + "## Import packages & read data" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "3ffc9a4e", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from pyinterpolate import read_txt, ExperimentalVariogram, VariogramCloud" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "5e8e320a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[2.42207337e+05, 5.46881660e+05, 5.03191910e+01],\n", + " [2.50856917e+05, 5.51689039e+05, 6.96442337e+01],\n", + " [2.43882223e+05, 5.53252314e+05, 4.52078705e+01],\n", + " [2.53679240e+05, 5.30855626e+05, 5.43067894e+01],\n", + " [2.52375160e+05, 5.34773476e+05, 4.86135292e+01],\n", + " [2.44833732e+05, 5.28542218e+05, 5.09161682e+01],\n", + " [2.45380349e+05, 5.30266794e+05, 5.63001442e+01],\n", + " [2.48391707e+05, 5.46418696e+05, 1.87953167e+01],\n", + " [2.46936777e+05, 5.36967503e+05, 2.04066505e+01],\n", + " [2.44688507e+05, 5.33463225e+05, 5.17071571e+01]])" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dem = read_txt('samples/point_data/txt/pl_dem_epsg2180.txt')\n", + "\n", + "# Take a sample\n", + "sample_size = int(0.05 * len(dem))\n", + "indices = np.random.choice(len(dem), sample_size, replace=False)\n", + "dem = dem[indices]\n", + "\n", + "# Look into a first few lines of data\n", + "dem[:10, :]" + ] + }, + { + "cell_type": "markdown", + "id": "2faec977", + "metadata": {}, + "source": [ + "## 1. Experimental Variogram class" + ] + }, + { + "cell_type": "markdown", + "id": "eb950327", + "metadata": {}, + "source": [ + "Let's start from the `ExperimentalVariogram` class.\n", + "\n", + "```python\n", + "\n", + "class ExperimentalVariogram(\n", + " input_array,\n", + " step_size,\n", + " max_range,\n", + " weights=None,\n", + " direction=None,\n", + " tolerance=1.0,\n", + " method='t',\n", + " is_semivariance=True,\n", + " is_covariance=True,\n", + " is_variance=True)\n", + "\n", + "```\n", + "\n", + "The class has three required parameters and seven optional.\n", + "\n", + "- `input_array` is a list of coordinates and values in a form `(x, y, value)`. Those are our observations.\n", + "- `step_size` parameter which describes distance between lags, and `max_range` which describes maximum range of analysis are hard to guess at a first run, so probably we will change those to find semivariogram features. Let's experiment with those parameters!\n", + "\n", + "What we know at the beginning? That our coordinate reference system is metric, it is [EPSG:2180](https://epsg.io/2180). We don't know the limits of the region of interest, and we will check it:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "785e9110", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Max lon distance: 18055.017384361156\n", + "Max lat distance: 25230.138770977966\n" + ] + } + ], + "source": [ + "# Get max distances for a region\n", + "\n", + "minx = np.min(dem[:, 0])\n", + "maxx = np.max(dem[:, 0])\n", + "\n", + "miny = np.min(dem[:, 1])\n", + "maxy = np.max(dem[:, 1])\n", + "\n", + "x_max_distance = maxx - minx\n", + "y_max_distance = maxy - miny\n", + "\n", + "print('Max lon distance:', x_max_distance)\n", + "print('Max lat distance:', y_max_distance)" + ] + }, + { + "cell_type": "markdown", + "id": "2dc49a0e", + "metadata": {}, + "source": [ + "We can assume, that the maximum distance `max_range` parameter shouldn't be larger than the maximum distance in a lower dimension (**18055** m). In reality, the `max_range` parameter is rather close to the halved value of this distance, and we can check it on a variogram.\n", + "\n", + "But we don't know the `step_size` parameter... Let's assume that it is 100 meters and we will see if we've estimated it correctly." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "3fbc21c8", + "metadata": {}, + "outputs": [], + "source": [ + "max_range = 18055\n", + "step_size = 100\n", + "\n", + "experimental_variogram = ExperimentalVariogram(\n", + " input_array=dem,\n", + " step_size=step_size,\n", + " max_range=max_range\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "b1923982", + "metadata": {}, + "source": [ + "We will visually inspect semivariogram with the `.plot()` method of `ExperimentalVariogram` class: " + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "db85fb2b", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "experimental_variogram.plot()" + ] + }, + { + "cell_type": "markdown", + "id": "8efa683d", + "metadata": {}, + "source": [ + "Variogram looks nice, but it introduces a lot of variance. We can see a trend, but points are oscillating around it. This piece of information tells us that `step_size` should be larger.\n", + "\n", + "What with `max_range`? It is a tricky problem because variogram seems to be right up to the end of its range. But we should be aware, that this could be an effect of sampling points in the North-South axis (with a greater range).\n", + "\n", + "We can check how many point pairs we have for each distance to make a better decision. The `ExperimentalVariogram` class stores information about points within its attribute `.experimental_semivariance_array`. It is `numpy` array with three columns: `[lag, semivariance, number of point pairs]` and we will use the last column to decide where to cut `max_range`." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "b662300d", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "_ds = experimental_variogram.experimental_semivariance_array\n", + "plt.figure()\n", + "plt.scatter(x=_ds[:, 0], y=_ds[:, -1])\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "704f8733", + "metadata": {}, + "source": [ + "We clearly see that after 12 kilometers number of point pairs is rapidly falling, so we can set `max_range` to **12000**." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "da5aae7e", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "max_range = 12500\n", + "step_size = 250\n", + "\n", + "experimental_variogram = ExperimentalVariogram(\n", + " input_array=dem,\n", + " step_size=step_size,\n", + " max_range=max_range\n", + ")\n", + "\n", + "experimental_variogram.plot()" + ] + }, + { + "cell_type": "markdown", + "id": "35d71c3e", + "metadata": {}, + "source": [ + "This time variogram looks cleaner. However, it still has a little too much variance. Let's set `step_size` to 500 meters:" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "78a330bd", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "max_range = 12500\n", + "step_size = 500\n", + "\n", + "experimental_variogram = ExperimentalVariogram(\n", + " input_array=dem,\n", + " step_size=step_size,\n", + " max_range=max_range\n", + ")\n", + "\n", + "experimental_variogram.plot()" + ] + }, + { + "cell_type": "markdown", + "id": "848d992d", + "metadata": {}, + "source": [ + "We can use this variogram for further processing. With this in mind, we will make a use from two other parameters: `is_variance` and `is_covariance`. Both are set to `True` by default. What do they mean?\n", + "\n", + "- if we want to know variance of a dataset (variance at lag 0) then we should set `is_variance` parameter to `True`. This value can be treated as the **sill** of variogram that we will use in semivariogram modeling and kriging.\n", + "- if we want to know covariance of a dataset then we should set `is_covariance` parameter to `True`. Covariance is a measure of spatial similarity, so it is opposite to semivariance. But we can use it in kriging systems instead of semivariance.\n", + "\n", + "`ExperimentalVariogram` plotting function `.plot()` has additional parameters:\n", + "\n", + "- `plot_semivariance` (boolean, set to `True` by default),\n", + "- `plot_covariance` (boolean, set to `False` by default),\n", + "- `plot_variance` (boolean, set to `False` by default).\n", + "\n", + "We will set all those parameters to `True` and compare plot semivariance with covariance and variance:" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "12241262", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "experimental_variogram.plot(\n", + " plot_semivariance=True,\n", + " plot_covariance=True,\n", + " plot_variance=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4a29a218", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.9" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/tutorials/Variogram Point Cloud (Basic).ipynb b/tutorials/Variogram Point Cloud (Basic).ipynb index 4d64a063..5af0e6dc 100755 --- a/tutorials/Variogram Point Cloud (Basic).ipynb +++ b/tutorials/Variogram Point Cloud (Basic).ipynb @@ -108,7 +108,18 @@ "outputs": [ { "data": { - "text/plain": "array([[2.42769460e+05, 5.30657496e+05, 4.16058350e+01],\n [2.51626284e+05, 5.43737929e+05, 2.04681377e+01],\n [2.49607562e+05, 5.47163152e+05, 2.28987751e+01],\n [2.39932922e+05, 5.36875213e+05, 1.63156300e+01],\n [2.39496018e+05, 5.41235762e+05, 1.72428761e+01],\n [2.41583412e+05, 5.40178522e+05, 1.78676319e+01],\n [2.49883911e+05, 5.46295438e+05, 1.96465473e+01],\n [2.51152700e+05, 5.42332084e+05, 2.03913765e+01],\n [2.41198900e+05, 5.34454688e+05, 3.00375862e+01],\n [2.38489710e+05, 5.48418692e+05, 9.50086288e+01]])" + "text/plain": [ + "array([[2.42769460e+05, 5.30657496e+05, 4.16058350e+01],\n", + " [2.51626284e+05, 5.43737929e+05, 2.04681377e+01],\n", + " [2.49607562e+05, 5.47163152e+05, 2.28987751e+01],\n", + " [2.39932922e+05, 5.36875213e+05, 1.63156300e+01],\n", + " [2.39496018e+05, 5.41235762e+05, 1.72428761e+01],\n", + " [2.41583412e+05, 5.40178522e+05, 1.78676319e+01],\n", + " [2.49883911e+05, 5.46295438e+05, 1.96465473e+01],\n", + " [2.51152700e+05, 5.42332084e+05, 2.03913765e+01],\n", + " [2.41198900e+05, 5.34454688e+05, 3.00375862e+01],\n", + " [2.38489710e+05, 5.48418692e+05, 9.50086288e+01]])" + ] }, "execution_count": 3, "metadata": {}, @@ -238,8 +249,10 @@ "outputs": [ { "data": { - "text/plain": "
", - "image/png": 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+ "text/plain": [ + "
" + ] }, "metadata": {}, "output_type": "display_data" @@ -271,8 +284,10 @@ "outputs": [ { "data": { - "text/plain": "
", - "image/png": 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\n" 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\n", + "text/plain": [ + "
" + ] }, "metadata": {}, "output_type": "display_data" @@ -311,8 +326,10 @@ "outputs": [ { "data": { - "text/plain": "
", - "image/png": 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\n" 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FIPSgeometryratecentroid_xcentroid_y
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FIPSgeometryratecentroid_xcentroid_y
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\n" 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\n", + "text/plain": [ + "
" + ] }, "metadata": {}, "output_type": "display_data" @@ -530,8 +633,10 @@ "outputs": [ { "data": { - "text/plain": "
", - "image/png": 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7tioqKvTkk0/q97//vbq7uzVx4kQdffTRuuSSS1RaWhp9/l133aXvfve7OuywwyRJ8+bN07XXXmv7BwQAAAAKAXEfAPmELEX/sB34mT17tswUy1qpPidJEydO1LPPPpv2+9TX12vp0qV2Lw8AACDrGNwiG9KNswHAr+i+csv1Gj8AAADFhgEtAADwK8+OcwdQXHp6enJ9CQnV1tbm+hIAFAHiPvDK0Cwf2hm8RL0fuI1FEf8g8APAFSNGjMj1JSREWjyAbKCvQVbQzAAADrDVC4BvBMorNemChzTpgocUKK/M9eUAgGXEfeCVoW2LWlJwG0k+yBb6r9wi4weAK7q7uzN+jd6BiPa/8jlJ0qZNraqpKE3zFQAAFDamSgCATBH4AeAKN2rpBMrDQ16vRjUVdFEA8gMZP8gG2hmAfEKX5R9s9QIAAAB8Kqa4M7MoAHmK/iu3CPwAAABkiNoF8AotCwCQKfZRAAB8r6enJ9eXkJAbWxxRGFjJRLaYpqkA524DyAOceOkfBH4AAL43YsSIXF9CQgxoMIiWAK8M72ZMUyLuAwCwg61eAICiECiv1KQLHtKkCx5SoLwy15cDAJawjRAAkCkyfgAAvtfd3Z3xa/QORLT/lc9JkjZtalVNRWnGrwkMYnIOr8Rl/OTmMgDANvor/yDwAwDwPTdq6QTKw0Ner0Y1FdwC4R4Gt8iW7VtM2esFIL+wOz632OoFAACQIQa0yBaaGgDALgI/AAAAgE8RVASQr4b2X2yJzi0CPwAAABljQAtvDJ8sEQgCANhF4AcAACBDTMbhlfjizjQ2APnik/6K+2RuEfgBAADIEONZZAuTJwCAXQR+AAAAAJ8izgMgX8XW+EEuEfgB4EsmS5oA8snQwS39F1xEewIAZIrADwBfYpwLAED8Kjn3RwD5Ymh3RRA7twj8APAlbg0A8olJAUt4hOLOAPIV90P/IPADwJcM7hQA8gh1DAAASI57Y24R+AHgS8R9AABQ3GyJ+yOAfDF0exd9V24R+AHgS6SyA8gnJsWdkSW0LgD5gv7KPwj8APAl5k0AACSo8cMNEkBeou/KJQI/AAAAGYop7pzD60DhiTvVKydXgUJFe4KXYrNhc3cdIPADwKe4OcBthkGjgncY3MIrwzN8aF/wCm0LbmNRxD8I/ADwJWr8wG20KAD5KK7vojMDkC9YFPENAj8AfImbA9xmxJwsQQODdwhcw01xNX5oXwDyBL2VfxD4AeBLBhNzuMzgSFF4iK1e8MrwQA/tC26iPcFLnHjpHwR+APgStwa4beh4g8AigLwRl/EDAPmBGj/+QeAHgC+ZRq6vAIUmNvCTu+tAYWL7DbwSd6oXgWsAeYiuK7cI/AAAikLsqhOjD7iLAS28QtuCp2hf8NLQrV40tpwi8APAl8jIgNsMarAgS2hf8BL3R3iFvgtuM5N+gGwj8APAl0hlh9tMijsjS1jVhJviijvTvgDkiZixVw6vAwR+APgUK5pwmxFT44cGBnfRouCVuPshjQ0eIagIt3HipX8Q+AHgSww+4Doz4X8BV5CliGyhpQHIF9RX9A8CPwB8iTkU3MZx7sgWmhfcZAxL+aF9wU1kZMBLMW2K9pVTBH4A+BITc7jNoMYPPMTYFtnCqjncRGuCl1h08w8CPwB8iXsD3MbJEgDy0fD7IfdHuIniu/DK8C3QtK/cIvADwJdYFYDbyPiBl2K3S9DA4J74U70Ab9B3wU1xzYnmlVMEfgD4EmMPuI50YwB5KD7jh/4L7mGbKrwyfKzFNtXcIvADwJeYmMNtDG7hLbZLwBtxi+Y0MLiI4s7wyvDmZBg5uQz8B4EfAL4x9OQSg8EHXMbgFl6iTcErrJLDSybLIvBIovsiGYu5Q+AHgG8MzfIxGHzAZTE1fmhf8BDjWrgpbtWcBgYXsSgCryTqq2hjuUPgB4BvxKw5cWOAy2hfAPIRp3rBSzGBn9xdBooEgevcIfADwDdiT13ixgB3xRxZS/OCy8ykHwCZ4lQveGdoBqzBPnu4KFGQhyaWOwR+APgGq07wUmz7ooXBXQQT4ZXhEyUWRuAmg7EXPJIoyMP4K3cI/ADwDTJ+4KWhTYoVJwB5Y3jgJzdXgQJFNiy8kmgsTxvLHQI/AHwjZmLOkY9w2dBVJgKLcBttCl4ZvkJOU4ObYos707jgnkSLbNT4yR0CPwB8I/ZAUW4McBcnlyBbaF9wU3x7ooHBPWTDwitk/PgLgR8AvmGQbgwP0aTgJTPJ/4FMcaoXvBQz9qL3govI+PEXAj8AfIOMDHgpdqtXDi8EAGyI2+qVo+tAYYoJWtO44CJO9fIXAj8A/INTl+AhthLCS9TJgFfI+IGXjKHHudO44KJE7Yn7Y+4Q+AHgS9wX4DYyyuAlgonIFibncFPsqV60Lbgo4Vav7F8GtiPwA8A3YibmubsMFCwzwf8A99G+4CYyfuAlg0UReIQaP/5C4AeAb8TuM+fGAHfRpOApJk/wCNlk8JI57GANxl9wSyRhjR/aV64Q+AHgGzGDjxxeBwoTNX7gJdoXvDJ8nsTECW4yjGEf07zgEiNBYxre3pA9BH4A+AZjDWQLbQ1uYy4Or9C04KXhgUQCi3BLoqZE+8odAj8AfIn7AlzHVhxkC+0LLho+UaL/gpuGt6cIKT9wSeLj3GlfuULgB4BvxN4LuDEAyB8mxcORJWwlhJuG12FhXg63JD7OPQcXAkkEfgAARcIcVoUFcBUZZfAIp3rBS2z1glfI+PEXAj8AfINVTHiJsQa8RPOCV4afskRbg5uGty8m5nBL4uPcs38d2I7ADwBfYtwBL9G+4LbYUwlpYPAOx23DTcMn4py6BLckCiJSQyp3CPwAAIoCcyVkC20NbhrenGhecNPwiTgZP3BLoiAigevcIfADACg6DDvgNipIwSvGsIk58ya4iRo/8EriGj85uBBIchD4+de//qVjjz1WEyZMUCAQ0AMPPBDzedM0tXjxYk2YMEHV1dWaPXu23nrrrZjnBINBnXPOORo3bpxqa2s1b948bdy4MeY5bW1tmj9/vurq6lRXV6f58+ervb3d9g8IAADguZjizoxs4Z4E5+Lk4CpQqIZnZQw/5Qtwiq1e/mI78NPT06O9995b1157bcLPX3XVVfrtb3+ra6+9VsuXL1djY6Pmzp2rrq6u6HPOPfdc3X///brnnnv0/PPPq7u7W8ccc4wikUj0OaeeeqpWrlypZcuWadmyZVq5cqXmz5/v4EcEAADwVsxx7oxr4SJO9YKX4oo7U+MHLklc3JkOLFfK7H7BkUceqSOPPDLh50zT1NVXX62LLrpIxx9/vCTp9ttv1/jx43X33Xfrf/7nf9TR0aFbbrlFd955pw499FBJ0tKlSzVx4kQ98cQTOvzww/XOO+9o2bJleumll3TAAQdIkm666SbNmDFDq1ev1mc+8xmnPy8AoEhRcBdeYiwLrwzvu2hqcBNbveCVRNk9NK/ccbXGz9q1a9XS0qLDDjss+lhlZaVmzZqlF154QZK0YsUKhUKhmOdMmDBBU6ZMiT7nxRdfVF1dXTToI0kHHnig6urqos8ZLhgMqrOzM+YfAAAJMfCAy0wz8f+BjJHxAw8R+IFXEm17pn3ljquBn5aWFknS+PHjYx4fP3589HMtLS2qqKjQmDFjUj6noaEh7vUbGhqizxnuiiuuiNYDqqur08SJEzP+eQAAANIZPrgluwxuijvVi4kTXDR8axdbveCWRFu9qCGVO56c6hUIBGI+Nk0z7rHhhj8n0fNTvc6FF16ojo6O6L8NGzY4uHIAAAB7qMECL8W1r9xcBgrQ8BPjJDIy4J7EW71oX7niauCnsbFRkuKyclpbW6NZQI2NjRoYGFBbW1vK52zatCnu9Tdv3hyXTTSosrJSo0aNivkHAMAgxhrwSlxGRk6uAoUqrsYPDQwuSZR9QUYG3JJ4q1cOLgSSXA78TJ48WY2NjXr88cejjw0MDOjZZ5/VzJkzJUnTp09XeXl5zHOam5u1atWq6HNmzJihjo4OvfLKK9HnvPzyy+ro6Ig+BwAAwA/Y6gVPxWX80L7gjkTZPWRkwC0Jt3oR+ckZ26d6dXd36/33349+vHbtWq1cuVL19fXaeeedde655+ryyy/Xbrvtpt12202XX365ampqdOqpp0qS6urqtHDhQp1//vkaO3as6uvrtWjRIk2dOjV6ytcee+yhI444QmeeeaZuuOEGSdJZZ52lY445hhO9AAAZY+IEN5HxAy/FtScaGFySKMbDvBxuSRRYZCth7tgO/Lz66quaM2dO9OPzzjtPkrRgwQItWbJEP/zhD9XX16ezzz5bbW1tOuCAA/TYY49p5MiR0a/53e9+p7KyMp100knq6+vTIYccoiVLlqi0tDT6nLvuukvf/e53o6d/zZs3T9dee63jHxTb9fT05PoS4tTW1ub6EuAX3AsA5CFq/MBL8RllgDuYmMNLCdsSzStnbAd+Zs+enTIFMBAIaPHixVq8eHHS51RVVemaa67RNddck/Q59fX1Wrp0qd3LQxojRozI9SXEIaUUAJDP4jPIuK/BPfGneuXkMlCAEm274VQvuCVRX0UNqdzx5FQvFK5AeaUmXfCQJl3wkALllbm+HABwhHEH3DS8PbFVAq6ixg88kqivIuMHbkmcUZaDC4EkBxk/yG/d3d0ZfX3vQET7X/mcJGn12o2aMLrajcsCAABAApzqBa8kPnWJBobMmaaZsK9ip0fuEPgpMpnW0wmUh6P/r6quUW1tTaaXBCTEbQFAvogbx9KBwUVxNaRycxkoQImyL5iXww3J2hHtK3fY6gXHWBEAkE+Gdll0X3DT8IwM7o9wU3zxcNoX3JGor6IGC9yQ7D7I/TF3CPzAMYq/wW3cCgDko+Gr5oxr4ZZEQR6aF9ySaBJO/wU3JGtGtK/cIfADx1gRAJBPKIgKr8Qft01bgzsSDrVoXnAJNVjglVSZPQYVnnOCwA8c400LLzHugNtitnoxc4KLOG4b2UT/Bbdw6hK8kuo+yHav3CDwA8ci3BngMu4DyBbaGtxEe4JXEib80N7gkkRtiUk53JCqGdHCcoPADxyL8LYFkEfoseAZavzAIwlr/NC+4JLEGT80MGQuVTuiieUGgR84xlYveIk95nDb0DZF84Kbhm+9YSsO3EJLgpeoIQWvpGpGBBdzg8APHOM9CyCfmDH/pwODe+KP287NdaB4sDgCNyTe6pX960DhoY/yHwI/cIwaP3Abk3F4Kaa4M00NLoor7pyTq0AhStZX0YfBDWz1gldSTRNpY7lB4AeO8aaFl2hdcFvMVq8cXgcKT9xx7twf4RIWROClRGN5Whxckaq4M40sJwj8wDEyfuA2bgTw0tDmReAabiLjB15JmvGT3ctAgUp8ahytC5lLFbSmheUGgR84FuHGACCPsNULXqHGD4B8lKivov+CG1Ie504jywkCP3CMNy3cFlN8l+YFl8Vm+dDA4J74lU3aF7zFGAxuSNSO2F4IN6Q8zj2L14FPEPiBYxEj11eAQsbAA24bOghhpypcRcYPsowmBjckakcG43t4jHtkbhD4gWPU+IHbYlaeaF5w25A2ZdB/wUXk+8ArTJDgpWRZGWSUIVMph1k0r5wg8APHuCkAyCdDByHEfeAmavwg22hjcEOy7B7ukchU6uLONLBcIPADxyjuDLfRpOClmOPcaWxw0fBBLO0LbmGCBC8la1/0YfASzSs3CPzAMbZKwEu0LriNGj/wSlzGT24uAwBsSTYBpw9DplKe6pW9y8AQBH5gixkzceJtC3dx3Da8ZJDxA4/QmuCV5BNzWh0yl6x9McZHpjjO3X8I/MCWoQWdDYM3Ltw1dCDLoBZuG9pdMaiFl2heAPJB8q1eWb4QFBWaV24Q+IEtw+v6sF0CboppXrQtuMgwTAI/8MzwRRAC1/AaXRjckGwcT/tCplIWd6Z95QSBH9gy/I3Kke5wE3EfeGV4oCfZSSaAE3GDWDowuISmBC8ly9wneI1Mpa7xQ/vKBQI/sGV4oIdVc7gpZqsXbQsuSnQKIQXq4RVaFoB8kKyv4vaITKVsQrSvnCDwA1vit3rxzoV7YjJ+aFpwUaL2lCgYBDgRd6oXTQsuYREEXkqa8UO7Q4ZStSFaV24Q+IEt5rDtEWz1gpuG3iRIA4WbEgWpCVzDK/RfAPJB8lO9snsdKDypT/XK3nXgEwR+YMvwGwE3BrgppvguNVjgokRBatoY3EKgB0A+SjqOp0uDh7hn5gaBH9gSXyCVNy7cE1cflSUBuCRRV0XGD9xCcWdkG90X3JDsPsj9EZki48d/CPzAluGBHm4McFN8+8rRhaDgJApSU+MHbiHuA6/QluClZLdB2h0ylfI49yxeBz5B4Ae2DJ87MXGCm4Y3JwKLcEuitjS8Zhng1PDsRLIV4TW2SsANyfoqxl/IVKrFW+6RuUHgB7YMD/TwvoWb4rYS0sDgkkRBagLXcAsZP/AK3RS8lLTED+0OGUp5qhftKycI/MCW4dslONULbuJIZHglUSFnAotwCzV+AOSjZPdBMjKQqVQtiOaVGwR+YAsZGfDS8AwM2hfcwnHu8NawbFgiP3BLshosNDG4gOPc4ZWUxZ25R+YEgR/YMvxGwMADbooPLOboQlBwEgZ+qPEDl5CtCCAfJc34YWKOjLHVy28I/MAWY9ibmK1ecBNbCeEVtnrBS3E7vWhacEmyCThNDG5I1o4YfiFTKYs7Z+8yMASBH9gyfM8vEye4KT6jjPYFdyTqqwgswi2JM8poX/AO90e4IVk7on0hU6maEPPH3CDwA1uGj2MZ18JNwyfiTMzhloTHudO84JJEbYnmBTfQT8FLydoX7Q6ZShXcoX3lBoEf2DJ8BZMVAbhp+E2C47bhlkQxRFac4JaEgR/aF1yQ9LjtrF4FCpFpmgR+4JmUxZ1pYDlB4Ae2xBWwzM1loEANDyxSfBduSTTIoP+CWxKfGpeDC0HBSb4VJ8sXgoLDVhx4KVWBcFpXbhD4gS0c5w4vDc/wIeMHbknUkthKCC9xKg48RfNChlJuxcnidaAwEVj0HwI/sGX4+5SMDLgpPuOHGwPckXiQQfuCO6ghBa8k3+pFA0NmUrUgJubIFDV+/IfAD2wh4wdeMYz4veZkZMAtiY9zz/51oDAlrvGT/etA4aEdwStMzOElMn78h8APbOFtCq8k2tYVZmYOlyRaHWfcAbckrvFDA0PmkmX20LyQKYrvwksEFv2HwA9sGX4jYGALtyTK7qF9wS2Jj9umfcEdnBoHzyQ7dSm7V4EClDLwk73LQIFK1Ya4PeYGgR/YMvx9yhsXbkk0SWKrF7xE/wW3cGocvJK0xg8dGDKUavGDwDUylaqPon3lBoEf2DL8TczbFm5JtK2LwA/cQvFdeClh6XAOP4ALkvVTdF/IVKohFvdHZCpV+yLwkxsEfmDL8PcpK05wS6ITvAj8wC2JuioGHnBLov6L9gU3UOMHXiEjA15KdTIvrSs3CPzAlritXjm5ChSiREGeRAWfAcBvqPEDryTP+KF9ITPUYIGXUrcvGlguEPiBLfEZP7m5DhSehMWdyfiBS2hJ8FKiQSzdF9yQtBnRvpAhxvDwUqrFD+6PuUHgB7bED25558IdHOcOL7G6BC8l6qpoc3BDsnZE60KmUvVRdF/IVKo2xMJubhD4gS2c6gWvkPGDbKP/glsSrWzSfcENyfopthIiUykn5rQvZChVG6J15QaBHwC+YCQ4AYcaP3BLwlOXGHrAJYn6KvovuCFpjR+aFzKUsgZL1q4ChYrAov8Q+IEtcTV+cnMZKEAJJ04smQPIAwlr/NB/wQVJT/XK8nWg8KTe6kULQ2ZSBndoXjlB4Ae2DB+AcF+AWxJu9aKBAcgDiTIW6b7ghmTxQwKLyFSqJkTrQqZSZ/xk7zrwCQI/yAgrAnBLwhoZBm0M7qAZwSvJJuBs9YIbuAfCK6m2O9PskKlU7YuF3dwg8AN7eJ/CI8m2dbHdC4CfJQvwMLCFG5Jm/NC+kKmUTYj2hcwkyoSNfo7+KycI/MAWDnOHV5IFeIj7APCzZANYMjXgDu6N8EaqJkT7QibSbUWleeUGgR8AvpBs8sSqAAA/S37cdnavA4UpWTsisIhMpay9S/NCBtI1H/qv3CDwA1viTvXifQuXkM6ObKNpwQ3J+ii2qcINyRdFsnwhKDipa/zQwOBcurF7qm1g8A6BH9gSd6oXyXpwCZMnAPko+TZV+i5kLtkEiYk5MsWpXvCKle6JPiz7CPwgM7xn4ZJk+4GJ+wDws2QTc1Y04QYyfuCVVBNv5uTIhJWFD/qw7CPwA3t4k8IjSbd6cWcA4GPUJ4OXkteQon0hM6lr/NC+4BwZP/5E4Ae2cKoXvMLkCUA+Snace7LHATu4N8IrKQM/2bsMFCArpUBY180+Aj+wZXh0lnEH3JL0SOQsXwcA2JF0Ys6oFi5Iem+keSFDqSbnBBaRCSu3P9pY9hH4QUYo7gy3kM4OIB8lq+VDYXq4gYMP4JWUxZ1pXsgA27j8yfXAzy677KJAIBD379vf/rYk6fTTT4/73IEHHhjzGsFgUOecc47GjRun2tpazZs3Txs3bnT7UuFA3FYv3tdwCauaAPJRsi1dzMvhhmTtiK2EyFSqyTmLbsgEGT/+5HrgZ/ny5Wpubo7+e/zxxyVJJ554YvQ5RxxxRMxz/vnPf8a8xrnnnqv7779f99xzj55//nl1d3frmGOOUSQScftyYZeZ8kPAsWT9P/cFuIOGBG8kP5GQNofMJcvsYUUdmeI4d3jFSv9EF5Z9ZW6/4A477BDz8S9/+UvtuuuumjVrVvSxyspKNTY2Jvz6jo4O3XLLLbrzzjt16KGHSpKWLl2qiRMn6oknntDhhx/u9iXDhuFbuxh4wC3JWhKTJ3iFlgU3JJuYsxUHbkgWWIwk2WIIWJfqOHf6Lzhnpfkwvs8+T2v8DAwMaOnSpfr617+uQCAQffyZZ55RQ0ODdt99d5155plqbW2Nfm7FihUKhUI67LDDoo9NmDBBU6ZM0QsvvODl5cKC4e9R3rJwC8WdAeQjTvWCl5K2LwKLyFCqJpSsdhlghZWgDj1Y9rme8TPUAw88oPb2dp1++unRx4488kideOKJmjRpktauXauLL75YBx98sFasWKHKykq1tLSooqJCY8aMiXmt8ePHq6WlJen3CgaDCgaD0Y87Oztd/3mQIILLuxZuSbrVi0aGzCVqRrQtuCHpVi8m5nBBsgAPq+XIVLo2ZBimSkoCKZ8DJGKldzIJLmadp4GfW265RUceeaQmTJgQfewrX/lK9P9TpkzRfvvtp0mTJunhhx/W8ccfn/S1TNOMyRoa7oorrtCll17qzoUjoUSTJE71gluStSXGtgD8LFlGhmkycULmONULXkmX1UMLg1PWMn5oYdnm2VavDz/8UE888YS+8Y1vpHxeU1OTJk2apPfee0+S1NjYqIGBAbW1tcU8r7W1VePHj0/6OhdeeKE6Ojqi/zZs2JD5D4EYiVfMs38dKEy0JXgpUfOiycENqSbgbPdCJkzTTDo5J/CDTKXN+KH/glOWavx4fxmI5Vng57bbblNDQ4OOPvrolM/bunWrNmzYoKamJknS9OnTVV5eHj0NTJKam5u1atUqzZw5M+nrVFZWatSoUTH/4K5ENwDuCXALp3rBSwSu4ZVUq+ZMzpGJcKqgIm0LGUp3D6SNwSkrTYft9tnnyVYvwzB02223acGCBSor++RbdHd3a/Hixfryl7+spqYmrVu3Tj/+8Y81btw4HXfccZKkuro6LVy4UOeff77Gjh2r+vp6LVq0SFOnTo2e8oXcSPb2TLcND8gEqaBwQ+IBBm0LmUuV1cOKOTKRbuIdMUyVspUQDqXrn+i+4JSVsTtxxezzJPDzxBNPaP369fr6178e83hpaanefPNN3XHHHWpvb1dTU5PmzJmjv/zlLxo5cmT0eb/73e9UVlamk046SX19fTrkkEO0ZMkSlZaWenG5sChVRgZxH2SCqD+8lmiAQbODG1Ju9WJkiwykaz9hw1BpCWNjOJNuKyqBazhlKeOHxbes8yTwc9hhhyWcyFVXV+vRRx9N+/VVVVW65pprdM0113hxeXAo2RvUME2ViMgPvMG4A25I1H8xJ4cbCPzAK6m2ekm0L2Qm3cmD1CiDU1YWdGle2edZjR8UnqQZP9m9DBSgVJ0/7Quu4Dh3eITAD7ySPuOH9gXn0jWfdIEhIBkrwyuGYNlH4AeWUXwXQL5KNH5lTAs3pNoOwYo5MhFOc952OEL7gnPpAovcI+GUlVsfWwmzj8APLEu21Ys9mgD8LvGphPRdyBwnL8Er6QI76QJDQCrpJt70X3DKSlCHwE/2EfiBZWT8AMhXiQYYjGnhhlTbIZg4IRNpAz9k/CADVk6NA5ywEtRh/ph9BH5gGe9PAPkq0QCD1SZkyjRNavzAM2z1gpfSBn64R8IhKy2H5pV9BH5gWbJtEUyeAPhdogEufRcyxYo5vJSueDNbvZCJtDV+6L/gkKVTvUgpyDoCP7CMtyeAfJRs8ErgB5niuG14KRRJk/FD+4JDVvom2hecstJ0aF7ZR+AHlplJxh+8cQH4WbJ0dcOgwDMyk7Y4Ku0LGUi3lStdYAhIxkrgh8A1nOJUL38i8APLkp7qxRsXgI9RgwVeSbsVhxosyAA1fuAVAj/wEsWd/YnADyxLeqpXdi8DAGxJNQAhIwOZiKSZeDNxQiZCHOcOj1hpO7QvOGVlaEXiQPYR+IFlyd6evG8B+FmqrAzGtchEusAhgUVkIt1WroEw7QvOkPEDL1nJ+KF5ZR+BH1iWNDLLGxeAj6U6mYQVTWSCU73gpXRbuei/4JSVws0Ud4ZT1PjxJwI/sCxpxg+RH2SIFgQvpRq8MjFHJgj8wCuGYaZtP9T4gVNW2g79F5yixo8/EfiBZUkTfnjjAvAxijvDKwR+4JWQhWyeiGGmzGgEkrGSLcapcXDK2lYv+q5sI/ADy5Jl9vDGhZco/oZMkfEDr6Q91Yv2BYesZvNYCRABw5HxAy9ZaTkM77OPwA+s4w0KIA+lOnmJiTkykW5iRDYGnLKabZHu5C8gEUs1fmhbcMjKoi2JA9lH4AeWJa/xAwD+lSqlnYEtMmFlqxdZi3DCakAnzHYcOGBlqxf9F5yysuZB4Cf7CPzAsmTvT964yBQDC3gp1eScU3GQCY5Ehles9k1k/MAJy1sJaV9wwEq2Ky0r+wj8wDJO70Iu0OqQqVQDVyblyISV9sN2QjhhdWJO8BpOWO2XaF9wwlLGD/fGrCPwA8uSJmXwvkWGaELwUqrJOauZyESEOgbwiNUaP2xXhRNWtwgSuIYTVjL5aVnZR+AHliV7g3JPgJeYMyFTqU69IeMHmYhYrJMB2GV1ws2R23DC6qJHKEz7gn1k/PgTgR9YRh0WeIWmBS+lzPghjR0ZsDLnJvADJyxn/NC+4IDVLVz0X3DCSnkQmlb2EfiBZckm59T+QaZStyHaFzKTaisEg1pkwurJOIBd1ovvEryGfZbbF/0XHLBy3yOhIPsI/CBj3BOQqVR9P/cFZCrV5JxJEzJhZdHcSh0gYDjLxXep8QObDMO0HJBmqxecsNK6mD9mH4EfWMbYFblAs0OmUk2gyMZAJqwEdWhjcILiu/CKnS3OtC84YSWbh4MPso/ADyxL9galOBcyRcYPvGKapiIpVsQZ1CITVu5/BH7ghNV2Q/uCXXayxMiKhV2maVrKhiXwk30EfmAZb094JVWNH24MyES6wE4kYrLPHI6YprXtEtQPhxNWa6tYzQwCBtkJ/LCVEHZZHVIx9Mo+Aj+wLNnkiDcuMkXGD7xiZWJO1g+csNpsqPEDJyIWI4b0X7DLzlYvTr6EXVbveSzsZh+BH1iW7O3JqV7IVKrOnxsDMmFlUsRWCTjBVhx4xTCsbZWQaF+wj4wfeMnquN00Odkr2wj8wLJk703GHMgUbQheSVXfZxAr5nCCwA+8YidLLGKwXRX22Knbw1ZC2GWnO+L2mF0EfmBZ8q1evGuRoRRNiIwfZCLVUe6DrASHgOGsTs4J/MAuu22GNgY77Cx2sDACu+yM2xnjZxeBH1iW7K3JPQGZSr3VK4sXgoJjrcYPK5qwz+pkm4Et7LId+KGNwQar9aMk7o+wz073RdA6uwj8wLLkg1fetMgMNX7gFSsTIiZNcMLKUe4SA1vYZzfLgjYGO0I2slwNw3pfB0j2xu0Mv7KLwA8sSxb0536ATKVqQ9wUkAkrhSlZ0IQTlrd60YnBJrsTbQI/sMNuwWZO9oIddvovFnezi8APLEv25uRNi0ylqhNFDSlkwkr/RCo7nLA6uGW1HHbZDRbShcEOu/c8Aouww05zYQ6ZXQR+YFnSU70YcCBDqW4SDDiQCSvthz4MTlDcGV6xnfHD5Ak22O2TKPAMO2wVd2b8lVUEfmAZGT/wSqo2ROtCJqz0T0ya4ATHucMrdpsMbQx22A3k2N0ahuLGqV7+ReAHliV7c/KeRaZSFndmQIsMRCysJjFpghNWVyoJLMIu21u9aGOwwW4gh+3QsMNOc6Hvyi4CP7As2ZuTQS0ylaoJMSdHJixt9aIPgwNWJ0MEFmEXxZ3hJWr8wEt2xlTMIbOLwA8sS9bvM2lCpjjOHV6xtNWLQS0csNo30YfBLrt9Em0MdthtL2z1gh0c5+5fBH5gWbKBCFtxkKlUTYgBLTJhpf3QxuCElW2E0va0d04nhB12+yR24sAqwzBttxfukbCDrV7+ReAHliUL8LBajkylakPcE5AJK/0TbQxO2Ln3cZ+EHbYDP3RisMjJ1hpO9YIdtrZ60bayisAPLEt2s2B/JjKVajWcmwIyYaX50MbghJ12w8QJdtg+1YtxGCxycr8jsx922OmPyFbMLgI/sCzZzYJJEzKVrgkx6IBTVrbYsFoOJ2wNbmljsMHuuIqthLDKyZidwDXssDNm596YXQR+YBmBH3glXRvixgCnrHRPdGFwgq1e8Ird9mK13hTgJDuM/gt22GkuZCtmF4EfWGIYZtI6GNwQkKl0gR1uDHDKStCQ1XI4QeAHXrHbJdG+YJWTDGpukbDDTn/E+Cu7CPzAklRpnqbJVhxkJt3knPsCnLLSN9F9wQkCP/CK3cUOsmJhlZO+iMU32GGvuLOHF4I4BH5gSbobRYjqXMhAuvbFpAlOWdvqRfuCfbYGt7Qx2MCpXvCKk+EUYzDYwale/kXgB5aE0wR2eOMiE2z1gldMUdwZ3rBT8JS1EdhhN4uaMRiscnK/YzsO7LDTHzH+yi4CP7AkHEn9xqTiPzKRLtWTrYRwysoAhHEHnLDTL6VbPAGGshvI4RYJq5xMtAkswg4yfvyLwA8sSRfYiaQJDAGpsNULXrEy/mDFCXaZpmlvVZO4D2ygxg+84mQ4xRAMdqRLFhiKjP7sIvADS6jxAy+l3erFqAMOWZkQ0bxgl90+iYwf2GG3uXCPhFXOTvWifcE6O8EcMvqzi8APLAml2YvDoAOZSJtRxqADDpimScYPPEFGBrxku30xBoNFTroimhfssBO4plRIdhH4gSXp3ph20vqA4dINWmlfcMLqeILVTNhlP+OHNgbrIjZTfmhfsMrJQhqBa9hBxo9/EfiBJekGIekygoBkrNTKYNABJ6y2G8Mg+AN77AZ+yIqFVaZp2t/qRf8Fi5yMpxiDwQ47gWv6ruwi8ANLQmkyLhjUwikrK5Xp2h+QiJ3BKl0Y7CDwA684aSscsAGrnMyzmZvDKruBa7IVs4vADyxJt9WGiTmcsjLIZdIEJ+wMPljRhB0EfuAVpxMh2hiscJLdaoq2BWvs9l9s9couAj+wJN2JJAw44JSVbYKciAMn7ARz6MNgB4EfeMVpEJr7JKxwdJw7TQsW2b3XmSb3x2wi8ANLOM4dXrHS4VPcGU7YKjBIxg9ssFuXgIEtrCLjB15yeq8jMwNWOOmHCFpnD4EfWEKNH3jFyjZBbgpwws5AlS4MdtgNRnOPhFVO6/XQxmCF0zUOFkdghZPANX1X9hD4gSXp3pRkZMApK0EdakjBCTuDCQYesMPuJIiTS2CV0wxqxmGwwmkAh9YFK5xl/NC6soXADyyhxg+8Egqz1QvesLXViz4MNtgdqHKPhFVO2wqTJ1hBxg+85CRDn1MJs4fADyxJ1+GzFQdOWVndtFIAGhjOTrdERgbssBsoJPADq5wudNDGYIXTE7q4RcIKJ/0XQevsIfCDtAzDTDuBYiUATlm5SUQMk4wM2GYnmMOkCXbYHaiaJlllsMZpX8QCCaxw2g0xzIcVTvovxl/ZQ+AHaVmZPBmGZHJXgANWB6ucHAe77KQPM/CAHdQxgFecZlDTh8EKx6d6McaHBU7ucwSts4fAD9KyOphg0AEnBqwGftgDDJvI+IFXnLQXJk6wwmmAkMAirKDGD7wUdhDEYfyVPQR+kJbVvp73LZwIhS0Gfiw+DxjEqV7wipOaUEzMYYXTGj/UWoQVTrPz6b5ghZNFWvqu7HE98LN48WIFAoGYf42NjdHPm6apxYsXa8KECaqurtbs2bP11ltvxbxGMBjUOeeco3Hjxqm2tlbz5s3Txo0b3b5UWGQ1ys9qAJywOhliqxfsshPMYVIOOxzVMSBrERY43fbA6ZewwnmNH9oX0nNybySjP3s8yfjZc8891dzcHP335ptvRj931VVX6be//a2uvfZaLV++XI2NjZo7d666urqizzn33HN1//3365577tHzzz+v7u5uHXPMMYpEIl5cLtIg8AMvsdULXrGzikT/BTscBX5oY7CA4s7wktN7Hd0XrHCySEvQOnvKPHnRsrKYLJ9Bpmnq6quv1kUXXaTjjz9eknT77bdr/Pjxuvvuu/U///M/6ujo0C233KI777xThx56qCRp6dKlmjhxop544gkdfvjhXlwyUrA6BuGmALsMw7S8Cj7AVi/YZCvjh4EHbHBW3Jk+DOk5XeQgaxFWUNwZXnIyliKjP3s8yfh57733NGHCBE2ePFknn3yyPvjgA0nS2rVr1dLSosMOOyz63MrKSs2aNUsvvPCCJGnFihUKhUIxz5kwYYKmTJkSfQ6yy2p6JzcF2GU120diNRP22ZkIUeMHdjgq7kwXBgucBggJXsMKp0N1MhZhBcWd/c31jJ8DDjhAd9xxh3bffXdt2rRJl112mWbOnKm33npLLS0tkqTx48fHfM348eP14YcfSpJaWlpUUVGhMWPGxD1n8OsTCQaDCgaD0Y87Ozvd+pGKntW+nrct7LIzMSfwA7sMWzV+aF+wjowfeIXizvASW73gpZCTGj9k9GeN64GfI488Mvr/qVOnasaMGdp11111++2368ADD5QkBQKBmK8xTTPuseHSPeeKK67QpZdemsGVIxmrb2FuCrDLTmdPjR/YRcYPvGCaJhk/8IRhOGtbEhk/sMZp+yKrH+mYpuko48dJsAjOeH6ce21traZOnar33nsvWvdneOZOa2trNAuosbFRAwMDamtrS/qcRC688EJ1dHRE/23YsMHln6R4Wa7kz/sWNtnJ4nFyM0Fx41QveMHxxJzID9LIpNZFxDBtZTmiODmN39C0kE7YMB21L8b32eN54CcYDOqdd95RU1OTJk+erMbGRj3++OPRzw8MDOjZZ5/VzJkzJUnTp09XeXl5zHOam5u1atWq6HMSqays1KhRo2L+IbtMIj+wyU6U3049IEAi4wfecBokZMUc6WSatUORVKTjOOOHeyTScNp/mSbBn2xxfavXokWLdOyxx2rnnXdWa2urLrvsMnV2dmrBggUKBAI699xzdfnll2u33XbTbrvtpssvv1w1NTU69dRTJUl1dXVauHChzj//fI0dO1b19fVatGiRpk6dGj3lC9lFVw+v2OnoSWOHXREbkyAyfmCV0wAObQzpZHqfC0dMVXpyXi8KQSbBGwLXSCeTwHMoYqqs1MWLQUKu3x42btyoU045RVu2bNEOO+ygAw88UC+99JImTZokSfrhD3+ovr4+nX322Wpra9MBBxygxx57TCNHjoy+xu9+9zuVlZXppJNOUl9fnw455BAtWbJEpaW0CKCQ2KnbwzYJ2GEYpq2aKnaCRChu1GCBVzLN2KGNIZVMTuYiKxbpZFKkOWQYqhbzfK+5Hvi55557Un4+EAho8eLFWrx4cdLnVFVV6ZprrtE111zj8tUB8BM7wRzD2D7wKC1JXQgekOwPcA3D2kEDAMVR4RW2esFLmfRB9F9IJ5OsVk72yg7Pa/wAQDJ2B7kc6Q6rnB23zcAW6TltJ7QvpJNpZisZP0glk+ZF94V0BjLJ+KHvygoCPwByxu5EiBUnWOVkkk0qO6xwWieD4qhIJ9PJD4sjSCWTrV4EFZFORhk/9F1ZQeAHabHxAV6xW1eFFXNY5SSIQ+AHVpDxA69kWmuMNoZUMrnHsfCGdDIJ3hD4yQ4CPwByxu4KEitOsIqtXvAKxyHDK5lm/FCkHqlwqhe8xFYv/yPwg7R4K8IrdidQZGTAKie1MpiYwwrHp3rRvpBGxsWdmTwhhYy2etF/IQ0yfvyPwA+AnLF98hIrTrDIycI3A1tY4XTylMmkC8WB49zhpYy2enF/RBqZBJ4J/GQHgR+4hjEt7CLjB15xMsmmfcEKp+0kwqQcaXCcO7yUyT2OhRGkE84o44f2lQ0EfpAWAR14xW4GDxNzWOVkkk1GBqzIpB+iD0MqmUyctn897QvJkfEDLw2w1cv3CPwgLdNilR9uCbDDNE3b23HY6gWrnARxGNjCisxWzRncIrlQhn1QpoEjFLaMgtaMv5CCYZgZBZ4zCRrBOgI/cI3JTQE2OGkuzMthlZPTbUhlhxWZrZq7eCEoOJkGbjINHKGwZXKP4/6IVDLdZhqJmCy+ZQGBH6RldYLO2xV2UIMFXnIyf6J9wYrMJk9EfpBYOGJkvLWejB+kklHGT8RkgRdJuVGjh6wf7xH4QVqWAz/cD2CDk21bDDpglaPj3GlfsCCTdkLcB8m4kVFhmgR/kFymgWcWR5BMKJx5v0OdH+8R+EFaVge5TMphB1u94CVHx7lTGBUWZNJOyPhBMm5NetiSg2QyDdzQtpCMG/0XJ3t5j8AP0rIa+OF+ADucrJqTkQGrnEywWc2EFZn0QxRIRTJuBZ5ZNUcymQZuuEciGTe2adF3eY/AD9KyOk5lQAs7nDQXmhiscjJAJRsDVmQyeWLihGQyLY46iMxFJJNp26BtIRlXavy4sF0MqRH4QVpWB6pUY4cdZPzAS04m50zKYYWTE+M++VraGBJza5sDq+ZIJvNT42hbSMydrV60L68R+EFaVjN5mJTDDifzH5oYrHKW8UMDQ2qmaWZUoJnAD5JxqygzR7ojmUzvcWT8IBk3snWo8eM9Aj9Iy+pAlUkT7HBSDJzgIqwi4wdeyHjiRBtDEm5NejjVC8lkup2Z7dBIhho/+YHAD9KyGuFn0gQ7HGX8uH8ZKFBOtuMwKUc6md7n2BKNZNya9DB5QiKGkVm2okTGD5Jz4zh3N4JHSI3AD9KynPHDDQE2OMneIbgIKyIOB7islCMdjkOGV9w71Ys2hnhuTKrJ+EEyFHfODwR+kJbVjp5JOexwEvhxsj0Mxcfpirdp0o8hNY5DhlfcWu0m4weJuBF0HgjTfyGxgUgk49eg7/IegR+kZfVmQbV/2OGkuTBnghWZTK4ZeCCVTLdqkfGDZFwr7kwfhgTc2IpDxg8SCUeMjLcRSvRd2UDgB2lZfSOy1Qt2sNULXslk8MDAA6mQ8QOvuHecO20M8dxYnOX+iETc6nMMgzbmNQI/SMtqQIf6GLDDyQSIrV6wIpMtEwSwkUqmgRsCP0iG4s7wkhuTc4KKSMTNosz0X94i8IO0rL4JqcYOO5xk/LBNAlZkErxh0IFUOA4ZXjAM07WgIH0YEnFjqxdtC4m4WZQ5RB0pTxH4QUqhiCGr8/NwxOSoWljmJIgTIeMHFmQyOCWAjVTI+IEX3Ox3DIN2hnhuBG0I/CARN9tF0IUi0UiOwA9SsvtmpsAzrHISJIyQZgwLMll9IpUdqWSadUjWIhJxe0LNBB3DuRFcpAYLEnE144cxmKcI/CAluyl3br75UdicToBYyUQ6mQxw6cOQSsYZPxGTWmWI4/Zkh8xFDOfWvY3AD4Zzs00wBvMWgR+kZDfljkgtrHJah4VBB9LJLOOH9oXk3Cj+TfAaw7me8cPkCcO4dmocNVgwTNDF/obAj7cI/CAluzcK3rCwymmRUyZNSCeTfsjNAQwKjxvFmdnuheHc3+pFG0Mst9oYNVgwHKd65Q8CP0gpGLLXwRP4gVVOJz8ct410MhmEBMMMapGcG0EbAj8Yzu1ADZMnDOfeVi/6L8Ryc+7H4pu3CPwgJbsTqAFWAmCR04EpBcSRimmaGQ1CCF4jFTcCz2Em5RiG4s7wUjhiuJYtbXdBGIWPGj/5g8APUrL7BiRSC6ucrhoxoEUqAxFDmdTODUdMthMiKTeCNmT8YDi3JztkZWAoN7fiUDgcQ2W62DYc7ctbBH6Qkt1ADoEfWGGapuMJFIUFkYobAxBWnJCMK1u9mJRjGDJ+4CVXJ+bcHzFEKGJmtNgW93phQwaLI54h8IOU7Hbw3BBgRSY3ClYDkEp/KPP2QZ0fJONGcWcm5RiO49zhJU5dgle8GC/Rf3mHwA9SsvuGJuMHVmQy8WHQgVTcGIS4ETxC4YkYptwoMcZWLwzndt0nssowFMV34RUvxuQEfrxD4AdJhSKG7UEuKXqwIqPiu9wQkIIbg1IyfpCIW5k6FHfGcG7f18gqw1Bu3tO4P2IoL8bkQRbfPEPgB0k5nUAxMUc6mUzOyfhBKm4MGFjRRCJuZepQeBdDba95x1YveMfNLFbDILCIT3gRpKH/8g6BHyTl9MhGIrVIJ5PgDatNSKXfhfZBH4ZEQi4FBJk0YSgvAoGRiEn2NaLcXsxgcQSDvMn4YZzvFQI/SMppx87EHOkMRJy3kYGwIdPNIwRQUNwI2rgRPELhCblR4EcEfhDLjYLhiV+X+yS2c3tczsQcg7xYKCOw6B0CP0jKeeCHNyxSyyTt2DRJA0Vy7hR3ZlCLeG5lZtB/YSivtv55FVBC/iHjB17xYrGf9uUdAj9IyumbmYwfpJNpp06dHyQSjhiu1MogqwyJuLXVixOXMJRXGWChMO0M2++LEZf7HCbmGORFWyCjzDsEfpCU0/Q9jkJGOpl26rQxJOLWAMQ0GdginlsT9FCEwCI+4VUgkMwySN7cy8iKxSAyfvILgR8kxVYveCXTNkJWGRJxczBKgWcM52ZgkforGORVxk+ENgZ5E6RhnA9pe9/lxY7SgbBBcXqPEPhBUmz1ghdCESPjASkZP0ik38XBKAWeMZybGRRsV8Ugr4KAFBGHxFYceMfLzC8yFr1B4AcJmabpeGDKSgBSceNGQXARibg5GCXjB8O5VeNHIvCDT4Q9muCQVQbJo61e9F+Qt4uwjMG8QeAHCQXDhpyWIIhETM8GMsh/bgxCyPhBIm62C4KLGM7VjB/ukfgPr071IuMHkjdZGaFw5pnbyH9ejpPIuvYGgR8klHkNFgYcSMyVjB/SjJGAm4MQgosYKpMs2ETI+MEgr45dJ/ADybvtOCyOgIyf/EPgBwll2qET+EEybtwoWAlAIm72OwxqMdRAxHkWbCK0LwzyKuOHjAxI3k3OWRwBGT/5h8APEso00sqgFsm4sfpkGKyYI56rp3rRvjCE2/0N7QuDvNoa71VACfnFq/E4R7rDyzZA+/IGgR8klPFWL1YCkIRbgxBWAzBUxDAVdnGiEwxHZLqZ4oG85naghsAPBnlVhJlaiwhHDFfvi0MxMYeXWV99A7QvLxD4QUJs9YJX3LpRMOjAUG6vahoGBXjxCdcDPyyO4D+8qsXDqV7w8vQttnoVN9M0vc34YR7pCQI/SCjTQS6TciTi5o2if4CbAj7hxUSa7YQY5PY9jYxFDPKqFg/FneFl1gR9WHHL5PRnKzg5zhsEfpBQpoNcMn6QiJs3CgYdGMqLPocVTQxyO/ATiZhMzKGQy0XDhwpHTLarFjlPMzLYilPUsrHATxKB+wj8IKHMj3PnzYp4bnbi7P/FUF70OWz1wiAvAosskMCr+ivR12fFvKhx6hK8ko2FsT4CP64j8IM44YihSIaDkWDIkMGAA8O4eaNgJQBDeZPxQxvDdl60BdoXQoa3kyevA0vwNy8n54bBIm8x6x0Ie/49WOB1X1muLwD+49YEaiBiqKqk1JXXQmFwM3rPSgCG8qIeDzV+MMiLIA2DWngdmAkZhqrFOKxYeT1O6h8wVFlG+ypo4Z6ED/f3d6ok0m/pJUqGBLhLjF6VRKzlnfT3m1I4SR9ZVmvpNRCLwA/iuDXADYYMVZVzQ8An3JzohCOmwhFDZaUkLsKblUe24kDa3ra8SMxgtRxeH7keog8ral4Hl/tCEdWp3NPvgRy7d0TChz/3n39W9BqVkv4mSZr11hTVlAQzv65TyWZ0gsAP4rh1hF5/mBsCYrm9+tQXimgkgR/Im1O9gmSVQd6dINjHyYTFI8mqeSjYq5JI4s8N5XTFPDxQJoWTbMlgxbwwJGlbEcNUONhlqaaH44yMvoBUm+Q+SfsCfIfAD+K4NdmhfgGGc7tN9IUiGllFcBEU34V3vNouwXbVIpJk1XzH//xLx/GK+ZspPseKeWFI0rZKJR1s8SXIyEBSJ3XHPWQYpp5Z3Wr5JYJhQ1q1WpL07J6rVFlmLbBYWhLQrM80WP4+SI/AD+K4VQyOo5AxlGGY7gd+qJEB/acgvQfF5LcftWwqEAi4/trIH14FaLJRHBMAAMcSZG71BsMySq1ndBnGJ/dQo6RGRqm1MiCGpKCqqCPlIgI/iOPWEY1k/GCo/nBEpstz814CP5B3mTmmuf21qVVW3HqC3gRowhFToYihcrarFr4Eq+aS9NbHHdrUkb5AqtMV853qa7T7+JHWrxP5J0nb2ritV+9u6rL0Ek7bV3VFqWbsOs7adaJgZHPRojcYIfDjIgI/iONacWe2SWAIL4I0bJWA5O3pWwMRAj/FzstFjN6BiOqqCfwUvCT1TgY0YGn12+mKedCsotZKoUvy9+0xDBml1u6NTttXr7H9+SUlZMUWk95g9sbevaGIxmTtuxU+RhuI41aRVCblGMqLG0U2bz7wLy+DzF4UjUZ+6fEws5DtqsXNy6C1tD1wjeKUjawM03RvlwDyRzaz7Xs9yrgtVgR+EGMg7F6tjJCLr4X81xtyv/PuD0VoY/D0WGyO3C5uoYjh6ZHYPdT5KWpeZ0bTfxWvbAWV2XJffLK61Yv25SoCP4jhduSeOj8Y1ONRdg4FUuFpxg9bVoua11mFZC0WL9M0Pc/4of8qToZhZi3rnqzF4uNlFmzc9yLjx1UEfhCj3+U3M4EfDPIqQMNqALzcjsVWr+LmdUYOGT/FKxtBmUjEJCu2CHlxmEYy9GHFJRiOeJoFO1xfKCKDPsw1BH4Qw+0j2PtZbYK2b5fwagLdzWpA0WOrF7zidUZh70BYZrZmaPCVbGXj0IcVH68yrHP9vZB72f57mybBRTe5Hvi54oortP/++2vkyJFqaGjQl770Ja1evTrmOaeffroCgUDMvwMPPDDmOcFgUOecc47GjRun2tpazZs3Txs3bnT7cjGM26mhpIBC8nY7A2mgYKsXMhbuSfivt7dTJZEea/+M3ujLlRi9lr5GoR719XUm/f4oXNkKyJC1WHyyW4OFMVgxycWYm+Cie1w/zv3ZZ5/Vt7/9be2///4Kh8O66KKLdNhhh+ntt99Wbe0nRw4eccQRuu2226IfV1RUxLzOueeeq3/84x+65557NHbsWJ1//vk65phjtGLFCpVaPGYQ9rkd+GGrFySp28OBARk/xc00TU8nUPRhReLeEQkf3svGS/QalZL+Jkma9dYU1ZQErX3hmyk+dyrZQIWqfyA7AZk+jkMuOtkcFwVDhsIRQ2WlbCIpBrkYczPOd4/rgZ9ly5bFfHzbbbepoaFBK1as0Be/+MXo45WVlWpsbEz4Gh0dHbrlllt055136tBDD5UkLV26VBMnTtQTTzyhww8/3O3Lxn+4naHDke6QpO5+7zrtvoHtJ3uVlgQ8+x7wr4GIIcPD+VP4PzUyaF8A3JS14ruMw4pOtmsf9gxEVFdN4KcY5Cbjh8CPW1wP/AzX0dEhSaqvr495/JlnnlFDQ4NGjx6tWbNm6Re/+IUaGhokSStWrFAoFNJhhx0Wff6ECRM0ZcoUvfDCCwkDP8FgUMHgJ6trnZ2dXvw4Bc/t1W22ekGSuoMhz157cP/vqKpyz74H/MvtumSJv0dEtZWe3y6RSyd1xz3U0RfSinXbLL9EMGxIq7ZvbX92z1WqLLM2ERpfV6U9J9RZ/j4oDJy6BK9ke6LcEwyrrpoxWDEg4ye/eTqSNU1T5513nv7rv/5LU6ZMiT5+5JFH6sQTT9SkSZO0du1aXXzxxTr44IO1YsUKVVZWqqWlRRUVFRozJjY5dfz48WppaUn4va644gpdeumlXv44BS8Yjrh++sNAmBRQSF0eZvwMvj6Bn+KUja1YBH6KQFlt3EPdkT4ZpfGPJ2MYn7RFo6RGhsVt6Z2hsoTfH4UtWwEZtqsWl/5QROFIdreIkpFRHPoGst+2Br9vKGKonLlkxjwdyX7nO9/RG2+8oeeffz7m8a985SvR/0+ZMkX77befJk2apIcffljHH3980tczTVOBQOJ0+wsvvFDnnXde9OPOzk5NnDgxw5+guHg1COkLRTSSN2vRysaNwsutZPC3bExq2CpRnLLVr/QOhGUYpkrYTlg0TNPMWkAm29t+kFteL7Ql/J4EfopCV7932fvpv3dY9bUV6Z+IlDybjZ9zzjl68MEH9fTTT2unnXZK+dympiZNmjRJ7733niSpsbFRAwMDamtri3lea2urxo8fn/A1KisrNWrUqJh/sMeryQ1pxsUtGzeKXN6MkFvZ2erFqTjFKFv9iml6WwAf/tMfMlzPsE5mIGwoFKEPKxY52YrD4ltR6Mzh35lxvjtcD/yYpqnvfOc7uu+++/TUU09p8uTJab9m69at2rBhg5qamiRJ06dPV3l5uR5//PHoc5qbm7Vq1SrNnDnT7UvGf3i1KsRqU3HLxo2iqz8s0+T0m4KW5Ljr/r4OT4/bLon0qJ/jtouOaZpZXcXOxSo9cifbk3O24hSPXPytB8KGp6drwh86c5zxg8y5vtXr29/+tu6++279/e9/18iRI6M1eerq6lRdXa3u7m4tXrxYX/7yl9XU1KR169bpxz/+scaNG6fjjjsu+tyFCxfq/PPP19ixY1VfX69FixZp6tSp0VO+4D6vMnMI/BS3bNwoIoapnoGIRlCHpXAlOW57bxsv4fi47VQ4brsg9YUiimSxlsH21czqrH0/5FZvljO8egYiGl2T1W+JHMnV5Ly7P6zKEdbqmiE/5TL40tlHxo8bXJ8lXXfddZKk2bNnxzx+22236fTTT1dpaanefPNN3XHHHWpvb1dTU5PmzJmjv/zlLxo5cmT0+b/73e9UVlamk046SX19fTrkkEO0ZMkSlVoslgj7vFol6AsRpS1m2eqsO/tCBH4AuCLbA1xWM4sLGT/wQjhiqDeYm8XWzv6wxo6ozMn3hvd6B8IKhXO3ZbR3IKKBsKEKi6dlIjHXZ0nptltUV1fr0UcfTfs6VVVVuuaaa3TNNde4dWlIo9ejGj89OboJIcsSbHvpHQjLGOi2vKe0xDCG/L9XJRHrHXxnt6EJIxLclDgtpzAkOG67byCiF9dssfwSTo/blqT/2m0HBhxFJNv1BLr6QykPsEBhyfa4iOOQiwMZGfBKhw/+vh19Ie0wkuBiJlgeh6TtJ+N4ldY+WFiQY/gKXIKtODWSDrbxEmzFQVKJjtvuC2bluG1pe9usKONEiWLR0ZfdCZRhbJ+cj6wqz+r3RfYZhqnuYHYnUUzKi0Mua7Dk8nvDe+29uf/7dvQNEPjJEDNxSPI+DThXqacAClc262T0UKusaJimmZNJTC5PTEH2dA+EZWR5x0Q4Yma9rhCyrzPLAeuhgiFD/R7tHEDu+SHw44dryHdk/ECS92nH3QNh1dWwklnQEmzFWb52q63U40y24kjSvjuP0ZhasjKKRTa3S1Ajo3j0DmS3sPOgzr6QdhxNgedCl6vsm86+sGoqGPYXslxn3XT2h1RVTi3WQhOKGL4YA3X2h2QYpkpK2BLtFHcASPJ+/7cfOgx4bNhWnHDEUGe4Umap9bTMTLbiSFJ7qEJjqOlTNHqyuIJNjYzikataBn6ooQDv5Soro7M/pMa6qpx8b3ivPxTx7HReqzr7QmoYSRsrNH7JtDGM7f3Y6BoWeJ1iqxckeT+BYtJUfDr7w0pT69117b0D2f2GyBnTNNWdxa0x2fxeyK1cBWB6gmGFI7k7NQXZkav2RZ2fwuaHwHGbTwIEcFebj8bW23r8cy35iMAPsjKBYtJUfHIRhOnoC6U9WRCFoS8UUcTI3t96IGwoGKZ+QTHI1QTKNKnzU+gGwrnbMtHZH8pqn4ns8sPkvIs2VpC2dLtw0IpLCPxkhsAPsjKBGghT9K3Y5GLlJxwxyS4rErkIJhPALnzhHNcy8MOqPbzT3pe7SYth0L4KmR+249DGCk9/KOKrA3o6+kIKkRnrGIEfZG2/uZ0iv8hvhmHmLK3cD4MfeI9Tl+CF7VmDufv+bFctbG09ub0/+SErBO4LRQzfLEzQhxUWv2XYmCb9WCYI/EBdWZpAZev7IPdymVLODaE4dOSgQCo1MgpfrmtUtLNdtaDl+v7U5rNJHNzhp79rrts43OW3wI/kz2vKFwR+kLVVbFbLi0cuJ09tvUycCp1pmjnK+CHwU+hyvVodiZjqYrtqQRoI5z4rgzo/hWlLt38mwu29bMUpFIZh+qq+z6AtXf5p7/mG49yLTbgn5kPTNNXd3a4SiwOBEsMY8v9elUSsxw67uvukcHn8Jzh+u+DkMhofChvqDoY1sipBW0NB6B2IKBLJ/uQlGNpeq6yqvDTr3xveM4zcBBSHa+8JaRT9V8HZ2pP7CZRhbL8/7zCyMteXAhf5oW0NMs3tGUgNozjWPd+19Q4onIOxVjr9oYg6+kKqq+Y+aReBn2Jz74iYDwOSZtn48l6jUtLfJEmz3pqimhKbN5v/S/DYqf7rVOBcxDDVkcMCltL2OgoEfgpXLotHdvaFCPwUqPa+kAwfLFRv6x3QzmNrcn0ZcNnmLn9Mzjd3BQn8FJCu/pCCIR90XENs6SbwUwhafdJnJbK5q5/AjwNs9QLgqg4fTJ78tPoF9+WygHc7dX4Kll/qBrT1DshgO05BMQxTW33SvrZ0B9kOXUC2+mib1yDGYPnPNE1fB35aO/17bX5Gxk+xOak75sM3P2rXZhtvnmDYkFatliQ9u+cqVZbZix02jq7S55rqbH0N8ss2H9zw23tDMgxTJSWBXF8KPJDLOiycGle4/BL4iUS2bzkbXVOR60uBS9p6B3KyPTWRgbChzv4wq+UFwo81WIIhQ539bFnNZ+29IYXC/sokG6p3IKKufrL77SLwU2yG1dPZFuyVUWq9GRhG5JP/l9TIKLW35aEtWEpNnwLnhyKDEcNUe19I9bVMnApNMBxR70Ak/RM90tUfUjhiqKyUhNlCEooYvjp5clvPAIGfAuKH++JQm7uCBH4KQH8o4tvFiNbOfgI/eWxTV3+uLyGt1q4ggR+bGLkWsa7+7Edz+wYi6svhpA3e6g9Fcn5qyaCtPlwFQ+baenI7yDVNtnsVoraeAflp94tfso+QOdM0tanTX5OoVp9dD5zx83aXTT6+NqRmGKZaOvzfRzS397Nt1SYCP0UsVwNL9v4WLj9NVvy2wgp3+KGN+eEa4K7NPgsUd/SFNODjNHtYt61nwHd/y96BSE6L5MMdfs7K6KON5a3N3UFfnuY1XH8ownjMJgI/RSxXE2M/FqKDO/z0t+0JhtUfIrus0LTlsL7PIAYahcU0Td8Fik2TRZJC0ezTlfN8WNFHcv2hiDp8us1rEJll+emj9r5cX4JlH7fTxuwg8FOkwhEjZ0dub+vhxJJCZBimtvhsouKX43PhDr9sFe3uDysYzv11wB2dfWFfFrHc0uWvYBTsiximb+9DLZ39jMXymN+2DyayqZMT5PJN30BE23y2EJLK5u5+32VU+hmBnyK1pXsgZ0duR3x0rCncs81Hp5YM8tv2DWTGT6eXkPVTOPzaT2zpCTIxz3Obu4KK+PRvGAobjMXyWD5kZbAVJ/983OH/djWUYZC9aAeBnyLVmuN9wbn+/nCfH1c123oGFIqwElAo/BT4IRujcPh1O0IkwiJJvvuovTfXl5DSx3kQPEC8tp4B9QbzI+t0YxttLF8YhpmXfUI+BEH9gsBPEYoYZs5rsWzuYiWzkJimP9PZTdNfwQI4FzFMX9T3GbS1hxT2QtDZH1KvD7YPJpMP2zmQWFd/KOenEKazpTtILbw8lE8TXdpY/mjtCioYyr/F0p5gmLG+RQR+ipAfUo/DEf/Vg4Fzbb3+PYGGFNDCsLUnmLPtqYmEI6bafV5YE+n5Ndtn0OZuFknyVT5kOpimtLHN31lJiBUMR/Iqa940ySzLF+u29uT6Ehz7cCv9mBVlub4AZJ9f9m+2dPSrYWRVri8DLvBzcGXwKN2KMuLc+ay103+B4tauoMbUVuT6MpCBTT5sV0NF/rNIwr0yv4QiRsx9sa83+YSqtLRUFZVVKZ87EDZkDPRLgUDM46let6SkRJVV1dGP+/t6Y7IUq2tqJW0PUE0eN0KlJYG414D/NLf3xyyCZNq2pP+0r1BQJeWVll43XduSPmlf0vYMpcnjahUI0Mb8alvPgLr7w3GP50P7krZvf+zsD2lUVXnS1wWBn6LTH4qozSc1A7Z0B5mQFwDDMH29+mSa22tK7TSmJteXAocMw/RlGm9rV792Hz+CwWyeausZ8MUpcek0t7NIkm+a2/tjMqvn7b9r0ud+/ouH6BfX3RX9+KQvTlF/X+IFusqJU6TTHo1+PP+w/dXRti3hc3ffc2/98d5PnvuNeV/Upo83Rj9+/K0WSduzF1s6+7Xj6Oq414C/GIapDcMytNxsW42n/jL6cSZtS/qkfUlSMGSotSuo8aPox/zqwyTZPvnQvgat39qrKTvWJb1esNWr6HzU3ie/lKUwDKnZJ9lHcG5Ld1Bhn53mNZyfM5KQ3rbeAV+2sWDIUEcf273yVb7UyaBGRn4xDFPrt+XXtoP1W+NX1OE/LZ39eVmDRZLWbsnfbUSFrqs/lPPar27Y1NnPvTINMn6KiGGY+shne843tvVp5/oaVszzWD5Mntp7Q+oOhjWiki4vH/k5cNfS2a/RNWz3yjehiBGTqejndHbTlJo7+jV5XO3wl4APNSeYfDy4fE3S55eWlsZ8fO+/VsU9ZyBs6Pv3vh631evOx5Ynfd2Skti13Zsf/FfS4E5PMExGhs+Zpql1CYInmbYt6T/t669vxDzmVtsa1N2/vQDvuBGVKZ+H7EtVHydf2pe0PcN/3dYefbZxVNrnFitmQUWktSvouwK8fQMRbe6mfkG+6g9F8maV4OP2Pu0+fmSuLwM2hSOGL0+MG9TS0a/dG0aqhPoYeaWlI7ZOht/T2T9u79MuY1kk8TvDSDw5H16PIpVEzy0JRVRSET9OsvO6VdWptzt/sLlHDSMraWM+1doVTHgCYaZtS/pP+yqvtPTcRNK1rUHrtvQQ+PGZ7mA45eJaPrUvafBeWauq8tL0Ty5CbPUqIn6t1r6eSux5Kx+yfQZ93N7H6Th5aJMPTiFMJRzxZ/0hJGea8XUy/K5vIKKtPqnPh+SaO/vzom5UIj3BsK+D7MUuUUAx37T3htTeSz/mJx9s7s71JbjKMLYHsZEYGT9FYnNXMGG1dj9o7w1pW8+A6jkdJ6/4cetgKoMFLCdQwDKvNP8nuOjnrTgftfepgS0SeWNzd1C9QXe34kjep7N/uJXVcj9Llu2TTz7Y0qMdyPrxnc1dQXX5dAxv15rNPZo+ifG+H3T1h3x5Ymqmmju2nyJXXUHWz3AEfoqE34uqrd3SQ+AnzzR39vtu62A6H27tJfCTR7r6Q2rv3V482c9bcbZ2D6h3IKyaCm6p+SBRlmk+pLO39YTU0RdSXTXH1frRxx19eZvtM6i7n1o/fmOapt5vLZysjLaeAW3tDmosQeycK9TMGNOUPtjSrT0ncMLXcIxSi0BrV786fX7yDDeC/GKaZtzRj37OyBjUE6S4YD7Jp5NxNrZRQyoftPcORIOJ+ejDrT3aa6fRub4MDBOOGAUziXq/tVs7jKikbplPNHf0qydYGNk+g95v7VZ9bQWZZTnU0Rcq6K2dLf85EIEFuVj8NgqcYZh6f1N+rBS8x40gb2zpHojbKuHnjIyhPtzaS+AnDwTDEW3q/KTgoN+34nzU3qdPjatVWSml8/xsXZ7XlGvtDKonGFYtJxT6yrqtvXmXAZtM30BEG9p6NWksp8jlWsQwtabAarBIUld/WJs6g2qsI7MsVwopiywR05TWtPZo6k5k/QzFyKHAfdTel/AUAD/q7g+ruYMaLPnA71sHU2nrGVBbz4DGsLXQ1za29cWcuuT3rTiRiKmP2vuYLPlYe++AthTACueazd1k/fhIfyii9dvy956YyAdbetRUV62KMgLZubSxrVfBUGEEFIdbs7lbDSPJLMuFrd1BtRXBYQGbOvu1c18N26OHIPBTwAbCRt6tFLzf2q0dRlaqnFVz30q2ddDvGRlDrdncrf1q69M+D7kxEDbyapvXoHVbe7Xj6GqyfnyqUFY4WzuD1Prxkfdbu2OC1IUgEjH1wZZufbZxVK4vpWiFIkZeL7Kl0zcQ0ca2Pu081vpR3cicaZp6r0DuhVa839ql6ZMY7w8i8FPA3t3UpXDEv8cgJzIQNvR+a7f2aGKw4UemaWpNa+KBiN8zMoZq7w1R68fH1m/rUSTP+i5JCoUNbWjbfpoE/GVzVzCva/sM935rt6ZPGpPryyh6Hb0htXT0p39iHvqorU87janRCLYV5sQHm3vybgxv1wdbutVYV0VmWRZt6vTvKc9eaOthvD8U77QCtbU7mLeDkY/a+tTeW/gpiPmopbNwigyuae22lB2E7AqGI9qwLXH9p3zw4dYehSIFtvyf50yz8OpkDB6IgNwxTVP/bunM9WV4xjSl1QX88/lZTzCsjW35l/VqVzhiFnRWk98YBVozKp33Ge9HEfgpQOGIoX+3dOX6MjLydnOnIgZvUj8JR4yC2SohbS8u+HGeBkcL2Qebe/L6vR+OmFrHQNZXPmrvK8gVznc3dcvI4/dKvtvY1qeuAmxXQ7X1FG5Gk5+9u6lLxTJP3djWq+4CWVD0u4/a+9SXJ3Vf3dTdH1ZLJ/2YROCnIL27qTvv39i9wUhRRqX9bN3WnoIrMvh+azfZGT7S0RfSR235m+0zaP02BrJ+Mbh9uBBtzwrI//dLPsrHGopOvdfapTD3yazZ0h3U1u7iyXo3ze2BLngrYhR3dtXazT0slIjAT8Fp7erXx+2FMRBcv7VX24qg6nw+6AmG87LYbjqhsKEPNhfvjdBPTNPU6jzPVBzEFgn/eL+1u6DrZKzZ0q3+UH4v9OSjQm9XQwVDhj4o4gljNhmGWZRBkG3dA9pcACcu+tmGbb0aCBdvALd3IKJmsn4o7lxI+kMRvdOc+obR15v85l1aWqqKyqqUzx0IGzIG+qVA7PGLqV63pKRElVWfHNHe39cbs9cyVfHetz7u0AGTx1L4LcdWb+oquFNLBm1s69WE0VUaWcUJObn0UXtfwtPi8tXgFonGuqr0T4YnOvpCBbMQkkwkYur91m5N2bEu15dSNDp6C79dDbdhW68mjK6m0LPHPmrvU2+wOAO577V2aWxtBce7eyAcMbRuK8HbtZt71DSqqqjbGD14gTBNU2993KlQmmjuvP13Tfq5z3/xEP3iuruiH5/0xSnq70s8uKmcOEU67dHox/MP218dbdsSPnf3PffWH+/95LnfmPdFbfp4Y/Tjx99qSXpNwZCht5s7tc/E0UmfA281d/RpWwGnHZum9PbHnfr85HoFAsV7M8il/lCkII8XXb2pS2Nqy1VZVprrSyk6pmnq383FkXXV0tGvCaOrVV9bketLKXiFXtA5me1ZjF2cJOehUKS4M6t6gxF91N6nifUc7+629dt6iyZDMZX+EG2MNIoCsXZLj9oKdFvUlq6g1m8tvG1G+SAYjhTM9ptUuvoLcytbPhgMWufj8e3phMKG/p0mCxPeWL+tt+AL7w71bw5EyIqPO/qLql0N1dYzoE1slfDMh1t70i7eFroPtvRQT8ploYihDxnfRq3dkt8HiGSKjJ8C0NYzYLlg14PL1yT9XGlp7Kr0vf9aFfecgbCh79/7etxWrzsfW570dUtKYuOLNz/4L9vH6r2/uUt1NeWqq2Y7Tja9t6l46hh8sLlHO4ysVE0F3WI2bWzrK9igtSRt7gqquaNPTXXV6Z8MV/QNRIqudlfvQERrt/To0w0jcn0pBStUYCdbOvHepm6NG1Gp0iLeKuGF/lCExSdtXyxZt7WXfsxFG9v6CnJhzamBsKHmjj7tNKY4s36Y4eS5YDiiNz/qsHzsY6p6OlaeWxKKqKQivmaFndetqrb/ZjMM6c2NHTrgU/UqLyVRLRs2dwWL6hjXiGHqnWZS2bOpdyBcFBOpf7d0aUxNharK2fKVDe+0FGf2y4dbezR+VCX1yjzywWYyMvpDBBi98H5rd8HWUbRr/bYe7TSmmvulC0zTLIiTUt22YVvxBn6YQecx0zS16qPOoqnS3h+K6K2PO21nC8G+UMQoyjoGbT0D2tjGqls2GIapNzd2FMUEPRIxteqjDvquLGjp6C/ommSpmKb0TnMX7cwD3cEw94b/WL+tR30DxVmA2As9wXBRLbKlYxiiELFLNncHOfUxgZ5guKAzzVMh8JPHPijguj7JbOkK6kPq/XjuvU3dCoaKI6A43HutHI+cDWs2dxdVrYz23pDlLblwJhQxivIo5KE6+0L6qMhOnMqGDzZ3W86sLnSGIfoyFzGmjdfc3l80i9pe2ki2T1LF+rsh8JOntvUMaG2R1TAYtGZztzp6C+fYZ7/Z2h0suqNqh4pETL1dJKcB5cqW7uIM4K7d0qP23uIK1mfTB5t7mCxo+7YRfg/u6eoPqbUzmOvL8JXmjj6yflwQDEfU0lm8461kIoZJhl2GeoLhos1+taK1q78oF3kJ/OShgbChtz7uyPVl5IxpSqs+7lCIyv+uG6xzU+y2dQ+ouYPBmBeC4Yje/rg4A2umKa36qJO+ywOd/SEmCv8Rjph6r5V+3C3rttCuhjNNsn7csGFbH7V9ktjQ1lcUW8G9QuZnaqYpNRfhFksCP3no7ebOot2GM6hvIMIxyR5Yu6WnKCPgiby3qZsJugfeae4q6myE/lBEq1vou9y2uqWLrThDNLf3k13mgu5gmCPMk2ju6GO8kIFwxCBYnUIobBR19nmmtpLtk9bW7uLL5CTwk2c+bu/Tlq7ia6iJbOrsVysDMtf0BMNav40VvEEDYaPojoT22kf0X5K2FyBmMume1q5+tv8msIb+K2PryGpJiqyfzLR2BRXmmO2UyFpxZiBsqCdYPDUUnersD8kosqwyAj95JBiOFH3hyuH+3dJFVoZLVm/qIuV4mI1tversZ0Lphr4B+q+h3mnuZLXcJcVa7y6dtp4Bsn4yEDFMtXYRoE1lU2d/0U2c3LKtyA5ncaK7P6xgmPukXe19tC0rDENFN8Yn8JNHVrd0sTowzECYU1zcsLU7SBG4BExze6FUZO7t5g5F6L+iwhFT/2bLV8a2dAeL6nQ4uz4gI8OxrT1BFkPSCEdMdfQV18TJDaZpaiuBH0vaemhfdpEBa11bkf2uCPzkiY5eTpVIprm9X92kNGZkXRGesGTVtu4BdRXZioDbWrv6GbwlsKUryKpvhthqktq27gEmAQ5tZluqJZuLsE5GprqDYYWKuNadHVt7aF92tROMtazYsmIJ/OSJNVvIOkjlg838fpzq7A+pjclnSsV49LhbDMPU+5t4fybz7qYumVQldqSrP0RQw4KN7fRfdpmmqS1kwVpCgMw+Av7W8buyr5ssWMuKLXGAwE8eaO8dYBtOGq2dQbIyHPqQo2rT2tTZr74B9pk7sbGtT7387pLq7g8X5ZGibiCLzBomTvZ19IXIyLCobyBSdJOnTLUTsLYsGDIYf8EzAQVyfQlZReAnDzApsKaF35NthmFqC2naaZmm+D05YJqmPuSkuLTWbeV35ARbAKwJhjjhxa4+Cq/bwsTcnghZnrYY/L7sKa5YRkYCRfa7IvCTB5hwWsM+c/u6gmFFOJHDEgpY2tfZF1YwxKp5Or3BCBNzmwzDZNXcBrJ+7OG2aA/bVe0psrlmxoptcp4pfl3WFdvvisCPz3X2h5g4WdQbjKh3gMmTHdTHsI5Jpn0EY62jToY93QMEre0gcG0PgQx7eCsC/lFCpMy6IvtVEfjxud4g6bN29PD7sqWTukiW9Yci6if935bWLrZfWkWQzB4Gtvbw+7KHuI89pviFAX5Bd28dNX7gK+wDtocVYHiJm6k9QYqjWkZQ0Z6yEt6MdpSV8vuyg5oi9jD0sifAYMKWYpucZ6q2sizXl5A3RhTZ74rAj89FItxN7SBQZk9FGV2AHRWl/L7sKGVwaxm/K3tKCfzYwu/LnuqK0lxfQl6pLuf3Zcfo6vJcX0LeqCwvUVU5Yy87dhhRmetLyBs7jCyu3xXvJJ+rpLOzpZJAhi3lBDIsKy8rYZXOJiab1pXwu7KlrCRABp4N5SX09XaMra2k/7KorDSgMTUEMuxoGFVck81M7DCykrGXTcUWzHAqEJDGjqjI9WVkFSMBn6tjVcAWfl/2sEpnXRVBRduYOFnH1iV7AoGAxo+qyvVl5IVAgImmXaUlAdXXFteEwCkm5vbVVJRpRFVxbTFxqmEk/bxdVeWlGkn7SmtMbUXRLYAX10+bh6rKS1VDyrElI6rKiu4NnKkdRlZS+8GiCaOrc30JeYeJuXX8ruzbaQzvSSsaRlapiiC/bayaW8PvyRl+b+mVl5WQTeYQ7Su9YtwSxyw5DzTWMSGwYkIdkwC7SksC2mlMTa4vw/fKSgMEfhzYcXQ1WT8WlJUG1EQ/b9vomgpWNS2YWE/f5cS4EZVsJ0yjtCSgsbXFN3lyQwMT87R2GEE2mVMNLCalFAgUZ3CMwE8e2Lm+hiK8aVSVl7L669BOY6pF+YfUdhpTQwDDgYqyEgLXFuw0plplZCs6MrGewHUqI6rKNLqGLUtOVJSVkImXxgSC+46NrCrXuCKceFpVUiLtPJb+3akRlWWMv1KYWF9TlJmwvh9p/ulPf9LkyZNVVVWl6dOn67nnnsv1JWVdWWmJJo+rzfVl+NquDbUUR3WoqrxUO47m5ppMWWmAFfMMTBpL0CyVslKy7jLRVFelMdRiSaikRNqjcVSuLyOv7T5+pMpZeEuoqrxUu+7A2DQTn20cyf0xiUn/v717j4qyWv8A/p1BGAYYRhEGHCB0aZqlaNk5Bp3CUFGXCtY5ZeohPZqpxclWukrLjpezjK6ap05lqZRlaeVltY6GwcFLJilyMaRUSrxxyfICCAoIz+8Pf7yHl5lhLoDS+P2sNX/wvvvd735nHvbes2fv9+3qe8M9arut3RzsBw/ezsGCV6cb93t1h27NNmzYgKeeegrPP/88cnNzcc8992DUqFE4efLk9S7aNRfaWY/OXOdqVYCfF0L4q1yr3Gzyg5HxZUGjAfqHGqHrdOP9KtBWfLw64TYzv3xa0xhfN+KvTm1Fo9HwPbShd7CB9XoreXXS4pYQw/UuRofUt5uBMxVb6ergmd/1LkaH46PzQI+uN+YX87ak6+SBnoGMr+Z6mfxu2HvCduih1GXLlmHatGl49NFHAQBvvPEGtm/fjnfeeQfJycnXuXTXllarQf8wI/YXnUNNXYPL+VyqrmpVOWqu/O/cly5Vo6Gu9f84eh/XK3e9lwf6hxq5BriVtNqrX572F51D7RXX4qu1sQV0vPjqZfJD1xvw5m9tzeTvjZ6mevx85qLLebhjfPUONjC+2oBXJy0iw404cPwcGlxsHt0tvsyd9ZxJ1kaC/b1RZriMXytrXM7D3fpe3Tp7s+5qI+EBepRVXEbFpTqXjne3ugu4OlORs/jbRlgXPU5fqEZ1Tb1Lx7tbfBl9PG/oeyp22IGf2tpaZGdnY968eartcXFx2Lt3r0X6mpoa1NT8r1GuqKho9zJea7pOHogM64zsE653buP/0LNVZdB46nDT0xsBAA/d2w9S53pHqFFaQZlLx3loNRgQ3vmGHbVta96eHogMMyL7xHmIOH98a2ML6FjxFezvjQj+4tRmegT6oqrmCsrKL7t0vLvFV1iAnvenaUP+3p7o280fBcWutf3uFF9GH0/OUmljfUIMOF9diyv1LjSOcK++l1cnLXoHM77aikajQd9uBuwvOse+F64OWnP5btvRajW4JcQfOSfOu3S8u8VXnxDDDT1ZQCPiSjXT/kpKShAaGopvv/0W0dHRyvYXX3wRH374IY4cOaJKv2jRIixevNgin/Lycvj7u9cyg8rLdS7Pygg0dLxRzt8qXfsiqPP04PrfdlBxuQ51LsRXR4wtwPX46uzjxbX3bayhQXC+utalY90tvrr4ePEXzXZwoboW9Q3Od2vcKb78vDtxeWo7YN/rKm9PD/iy79XmyqvrcMWFX3U7YmwBrseXv96TP+i2g/NVtWhw4Su/O8WXh1bjlg87qKiogNFodGjMo8PX3M1H5UTE6kjd/Pnz8fTTTyt/V1RUIDw8vN3Ldz0YvF1fs3/xoutLLdqLLx8F2qH4uxhfHTG2AMZXR6LValxeHsD4Ike42qljfJE97HtRe3L1flwdMbYAxldH4+osKsaXe+mwAz+BgYHw8PBAWZl6KteZM2cQHBxskV6n00GnYxDY4+vLpSvUPhhb1J4YX9SeGF/Unhhf1F4YW9SeGF/upcPOpfPy8sKgQYOQlpam2p6WlqZa+kVERERERERERNZ12Bk/APD0008jMTERd955J6KiovDee+/h5MmTmDlz5vUuGhERERERERFRh9ehB37Gjx+Ps2fPYsmSJSgtLUW/fv2wbds2REREXO+iERERERERERF1eB32qV6t5cwdromIiIiIiIiIfi+cGfPosPf4ISIiIiIiIiKi1uHADxERERERERGRm+LADxERERERERGRm+LADxERERERERGRm+LADxERERERERGRm+LADxERERERERGRm+LADxERERERERGRm+LADxERERERERGRm+LADxERERERERGRm+LADxERERERERGRm+LADxERERERERGRm+LADxERERERERGRm+LADxERERERERGRm+LADxERERERERGRm+LADxERERERERGRm+LADxERERERERGRm+LADxERERERERGRm+LADxERERERERGRm+LADxERERERERGRm+LADxERERERERGRm+p0vQvQXkQEAFBRUXGdS0JERERERERE1HYaxzoaxz5a4rYDP5WVlQCA8PDw61wSIiIiIiIiIqK2V1lZCaPR2GIajTgyPPQ71NDQgJKSEhgMBmg0mutdHLdSUVGB8PBwnDp1Cv7+/te7OORmGF/Unhhf1J4YX9ReGFvUnhhf1J4YX+1HRFBZWQmz2QyttuW7+LjtjB+tVouwsLDrXQy35u/vz39eajeML2pPjC9qT4wvai+MLWpPjC9qT4yv9mFvpk8j3tyZiIiIiIiIiMhNceCHiIiIiIiIiMhNceCHnKbT6bBw4ULodLrrXRRyQ4wvak+ML2pPjC9qL4wtak+ML2pPjK+OwW1v7kxEREREREREdKPjjB8iIiIiIiIiIjfFgR8iIiIiIiIiIjfFgR8iIiIiIiIiIjfFgR8iIiIiIiIiIjfFgZ/fqe7du0Oj0Vi8nnjiCQDAlClTLPbdddddqjxmzJiBnj17Qq/XIygoCAkJCTh8+LAqzfnz55GYmAij0Qij0YjExERcuHBB2X/27FmMHDkSZrMZOp0O4eHhSEpKQkVFRYvlHzJkiEX5Hn74YWX/zp07rV6fRqNBVlZWK989am737t0YO3YszGYzNBoNtmzZotrvSDzV1NTg73//OwIDA+Hr64v4+HicPn3a4lxbt27F4MGDodfrERgYiAceeEC1/+TJkxg7dix8fX0RGBiIJ598ErW1tcr+RYsWWY0LX1/fFq9x6dKliI6Oho+PDzp37myx39VYppYlJyfjD3/4AwwGA0wmE8aNG4cjR44o++vq6vDss8+if//+8PX1hdlsxiOPPIKSkhJVPvbqDADIycnB8OHD0blzZ3Tt2hWPPfYYLl68qEoze/ZsDBo0CDqdDgMHDrRa5vz8fMTExECv1yM0NBRLlixBS89BcLS+shfb5Lx33nkHkZGR8Pf3h7+/P6KiovDVV18p+0UEixYtgtlshl6vx5AhQ1BQUKDK47333sOQIUPg7+8PjUajauMA4Pjx45g2bRp69OgBvV6Pnj17YuHChRafnSOx1einn36CwWCwWhc5e+4PPvjAZvydOXOmxfypZfbiy9b7/uqrr6ryyczMRGxsLHx9fdG5c2cMGTIEly5dUvZb69PNmzdPlYe9+HK1bTx69CgSEhIQGBgIf39/3H333dixY4eyn23jtZGcnAyNRoOnnnpK2eZI38te/QXY/4wPHjyICRMmIDw8HHq9Hn379sWKFStUeVy+fBlTpkxB//790alTJ4wbN86p66upqcHAgQOh0WiQl5en2se2se1Zqw9CQkIAONbvOn78uM367fPPP1fS2etbOxJbTTnaNgL2+3xsGy1x4Od3KisrC6WlpcorLS0NAPDggw8qaUaOHKlKs23bNlUegwYNQkpKCn788Uds374dIoK4uDjU19craSZOnIi8vDykpqYiNTUVeXl5SExMVPZrtVokJCTgyy+/xNGjR/HBBx8gPT0dM2fOtHsN06dPV5Vv5cqVyr7o6GjVvtLSUjz66KPo3r077rzzTpffN7KuqqoKAwYMwFtvvWUzjb14euqpp7B582asX78ee/bswcWLFzFmzBhVPG3cuBGJiYn429/+hoMHD+Lbb7/FxIkTlf319fUYPXo0qqqqsGfPHqxfvx4bN27EnDlzlDRz5861iI1bb71VFfvW1NbW4sEHH8SsWbOs7m9NLJNtu3btwhNPPIHvvvsOaWlpuHLlCuLi4lBVVQUAqK6uRk5ODl544QXk5ORg06ZNOHr0KOLj4y3yaqnOKCkpwbBhw9CrVy/s27cPqampKCgowJQpU1R5iAimTp2K8ePHWy1vRUUFhg8fDrPZjKysLLz55pt47bXXsGzZMpvX6Eh95Uhsk/PCwsLw0ksv4cCBAzhw4ABiY2ORkJCgDO688sorWLZsGd566y1kZWUhJCQEw4cPR2VlpZJHdXU1Ro4cieeee87qOQ4fPoyGhgasXLkSBQUFWL58Od59912L9PZiq1FdXR0mTJiAe+65x+71OXLu8ePHW8TfiBEjEBMTA5PJZPccZJu9+Gr+vq9ZswYajQZ//vOflTwyMzMxcuRIxMXFYf/+/cjKykJSUhK0WnUXfMmSJaq8FixYoNpvL75cbRtHjx6NK1euICMjA9nZ2Rg4cCDGjBmDsrIyAGwbr4WsrCy89957iIyMtNhnr+9lr/4C7H/G2dnZCAoKwscff4yCggI8//zzmD9/vqpPWF9fD71ejyeffBLDhg1z+hqfeeYZmM1mi+1sG9vPbbfdpoqd/Px8AI71u8LDwy3qk8WLF8PX1xejRo1S0tnrWzsSW42caRsd6fOxbbRCyC3Mnj1bevbsKQ0NDSIiMnnyZElISHAqj4MHDwoA+emnn0RE5IcffhAA8t133ylpMjMzBYAcPnzYZj4rVqyQsLCwFs8VExMjs2fPdrhstbW1YjKZZMmSJQ4fQ64BIJs3b1ZtsxdPFy5cEE9PT1m/fr2yrbi4WLRaraSmpoqISF1dnYSGhsqqVats5rNt2zbRarVSXFysbPv0009Fp9NJeXm51WPy8vIEgOzevduBqxNJSUkRo9HoUFpHYpmcc+bMGQEgu3btsplm//79AkBOnDihbLNXZ6xcuVJMJpPU19cr23JzcwWAFBYWWqRfuHChDBgwwGL722+/LUajUS5fvqxsS05OFrPZrNSv9lirr1yJbXJNly5dZNWqVdLQ0CAhISHy0ksvKfsuX74sRqNR3n33XYvjduzYIQDk/Pnzds/xyiuvSI8ePazusxVbjZ555hn561//6lRd5Oi5Ra7+j3l6esratWudzpvsa4wvaxISEiQ2Nla1bfDgwbJgwYIW84yIiJDly5c7dH578dXIkbbx119/tUhTUVEhACQ9Pd3mcWwb205lZaXcfPPNkpaWZtHOOdOXt1V/ufoZP/7443LfffdZ3efsd4xt27bJLbfcIgUFBQJAcnNzVfvYNrY9R+uJRtb6Xc0NHDhQpk6danWfM+2Zrdhypm10ts8nwrZRRIQzftxAbW0tPv74Y0ydOhUajUbZvnPnTphMJvTu3RvTp09vcVpbVVUVUlJS0KNHD4SHhwO4+iuV0WjE4MGDlXR33XUXjEYj9u7dazWfkpISbNq0CTExMXbLvW7dOgQGBuK2227D3LlzVb/ANvfll1/it99+s/j1nq6dluIpOzsbdXV1iIuLU7aZzWb069dPiZWcnBwUFxdDq9Xi9ttvR7du3TBq1CjVsovMzEz069dP9avQiBEjUFNTg+zsbKvlWrVqFXr37u3QLwTOcCaWyXHl5eUAgICAgBbTaDQai6m+LdUZNTU18PLyUv2KrtfrAQB79uxxuHyZmZmIiYmBTqdTto0YMQIlJSU4fvy4Q3lYq69ciW1yTn19PdavX4+qqipERUWhqKgIZWVlqnpJp9MhJibGZhvmqPLy8hZj2JaMjAx8/vnn+Pe//91u5167di18fHzwl7/8xeVzkKXm8dXcL7/8gq1bt2LatGnKtjNnzmDfvn0wmUyIjo5GcHAwYmJirNZJL7/8Mrp27YqBAwdi6dKlrV7q4kjb2LVrV/Tt2xdr165FVVUVrly5gpUrVyI4OBiDBg2yegzbxrb1xBNPYPTo0TZn0TjTl7fGlc8YcL2Oa+6XX37B9OnT8dFHH8HHx8diP9vG9lNYWAiz2YwePXrg4YcfxrFjx2ymtdXvapSdnY28vDxV/eYqa7HlbNvoSp+PbSOXermFLVu24MKFC6ovGaNGjcK6deuQkZGB119/HVlZWYiNjUVNTY3q2Lfffht+fn7w8/NDamoq0tLS4OXlBQAoKyuzOhXOZDIp00MbTZgwAT4+PggNDYW/vz9WrVrVYpknTZqETz/9FDt37sQLL7yAjRs3WtzrpanVq1djxIgRyqAUXVv24qmsrAxeXl7o0qWL6rjg4GAlVhobnEWLFmHBggX4z3/+gy5duiAmJgbnzp1T8gkODlbl0aVLF3h5eVnEHHC14l+3bl2bNESNnI1lcpyI4Omnn8af/vQn9OvXz2qay5cvY968eZg4cSL8/f2V7fbqjNjYWJSVleHVV19FbW0tzp8/r0x9Ly0tdbiM1mKw8W9rMWiNtfrK2dgmx+Xn58PPzw86nQ4zZ87E5s2bceuttyrvq7XPszXv+c8//4w333zT6aUuZ8+exZQpU/DBBx+oYrutz71mzRpMnDhR6QRT69iKr+Y+/PBDGAwGVb3UtN2bPn06UlNTcccdd2Do0KEoLCxU0s2ePRvr16/Hjh07kJSUhDfeeAOPP/64y2V2tG3UaDRIS0tDbm4uDAYDvL29sXz5cqSmplp8AWTb2PbWr1+PnJwcJCcnW93vaF++Jc58xo0yMzPx2WefYcaMGa5clkJEMGXKFMycOdPmbRrYNraPwYMHY+3atdi+fTvef/99lJWVITo6GmfPnrVIa6vf1dTq1avRt29fREdHt6pc1mLLlbbRlT4f20ZwqZc7iIuLkzFjxrSYpqSkRDw9PWXjxo2q7RcuXJCjR4/Krl27ZOzYsXLHHXfIpUuXRERk6dKl0rt3b4u8evXqJcnJyaptpaWl8uOPP8qWLVvk1ltvlVmzZjl1DQcOHBAAkp2dbbHv1KlTotVq5YsvvnAqT3INrCz1aq55PK1bt068vLws0g0bNkxmzJihpAEgK1euVPZfvnxZAgMDlWUX06dPl7i4OIt8PD095dNPP7XY/sknn0inTp2ktLTU4euzN4W0tbFMtj3++OMSEREhp06dsrq/trZWEhIS5Pbbb7c7xdtanbFu3ToJDg4WDw8P8fLykrlz50pwcLC8/PLLFsfbmgY9fPhweeyxx1TbTp8+LQAkMzPT7jXaqq+cjW1yXE1NjRQWFkpWVpbMmzdPAgMDpaCgQL799lsBICUlJar0jz76qIwYMcIiH0eWehUXF0uvXr1k2rRpNtPYiq37779fnn32WeVvZ5d6OXLuvXv3CgA5cOCAw/lSy2zFV3N9+vSRpKQk1bbGGJw/f75qe//+/WXevHk2z/nFF18IAPntt98s9jmyhMPRtrGhoUHi4+Nl1KhRsmfPHsnOzpZZs2ZJaGioxf8N28a2dfLkSTGZTJKXl6dss7ek2VZfXsR2/eXMZywicujQIQkKCpJ//vOfNsvh6FKvFStWSHR0tFy5ckVERIqKiiyWerFtvDYuXrwowcHB8vrrr6u2O9Lvqq6uFqPRKK+99prN/B1pz2zFlqttozN9PraNV3Hg53fu+PHjotVqZcuWLXbT9urVS3Wvg+ZqamrEx8dHPvnkExERWb16tdV/PKPRKGvWrLGZzzfffGO1s92ShoYGi3vENFqyZIkEBQVJbW2tw/mR6xwZ+BFRx9N///tfASDnzp1TpYmMjJR//OMfIiKSkZEhAOSbb75RpfnjH/8ozz33nIiIvPDCCxIZGanaf+7cOQEgGRkZFmWIjY2VcePGOXxtIs592XIllsm6pKQkCQsLk2PHjlndX1tbK+PGjZPIyEirX3aaa6nOKCsrk8rKSrl48aJotVr57LPPLNLY+vKUmJgo8fHxqm05OTkCwGbZm7JVXzkb2+S6oUOHymOPPSY///yzAJCcnBzV/vj4eHnkkUcsjrM38FNcXCy9e/eWxMRE1X0FmrMVW0ajUTw8PJSXVqsVAOLh4SGrV69u8ZocPffUqVNl4MCBLeZFrdMYX03t3r1bAKi+xIuIHDt2TADIRx99pNr+0EMPycSJE22eo3Gwuek9Fhs5MvDjaNuYnp4uWq3W4guftR/4mmLb2HqbN29W/v8bXwBEo9GIh4eHMljSnK2+vK36y5nPuKCgQEwmk9Ins8XRgZ+EhATRarUW1+jh4aHUwWwbr51hw4bJzJkzlb8d7XetXbtWPD095cyZMzbT2OtbtxRbrWkbRRzr87FtvIpLvX7nUlJSYDKZMHr06BbTnT17FqdOnUK3bt1aTCciyhTSqKgolJeXY//+/cr+ffv2oby8vMWpfvL/jz12ZipqQUEB6urqLMonIkhJScEjjzwCT09Ph/Oj9tU8ngYNGgRPT0/l6XLA1amWhw4dUmKl8TG0zR/lffz4cURERAC4GnOHDh1STdP8+uuvodPpLNaiFxUVYceOHW26zKs5V2KZ1EQESUlJ2LRpEzIyMtCjRw+LNHV1dXjooYdQWFiI9PR0dO3a1W6+tuoM4OpSHj8/P2zYsAHe3t4YPny4w+WNiorC7t27VffX+Prrr2E2m9G9e/cWj22pvnImtql1GtuxHj16ICQkRFUv1dbWYteuXU5PVy8uLsaQIUNwxx13ICUlxeKJTI7IzMxEXl6e8lqyZAkMBgPy8vJw//33t/rcFy9exGeffdaudSKp+0mNVq9ejUGDBmHAgAGq7d27d4fZbFa1e8DVx2s3tnvW5ObmAoDdPps1zrSN1dXVAGARU1qtFg0NDTaPY9vYekOHDkV+fr6qTrjzzjsxadIk5OXlwcPDw+IYR/vyTTn6GRcUFOC+++7D5MmTsXTpUhevSu1f//oXDh48qFxf4xPJNmzYoJyDbeO1UVNTgx9//FGJHWf6XatXr0Z8fDyCgoJcOre92HK1bWxkr8/HtrGJ6zbkRK1WX18vN910k2p6nMjVJwTMmTNH9u7dK0VFRbJjxw6JioqS0NBQqaioEBGRn3/+WV588UU5cOCAnDhxQvbu3SsJCQkSEBAgv/zyi5LXyJEjJTIyUjIzMyUzM1P69++vWla2detWWbNmjeTn50tRUZFs3bpVbrvtNrn77ruVNKdPn5Y+ffrIvn37RETkp59+ksWLF0tWVpZyzC233CK33367xS8c6enpAkB++OGHNn//6H8qKyslNzdXuSP+smXLJDc3V06cOOFQPImIzJw5U8LCwiQ9PV1ycnIkNjZWBgwYoPpMZ8+eLaGhobJ9+3Y5fPiwTJs2TUwmkzJT6MqVK9KvXz8ZOnSo5OTkSHp6uoSFhVlMnxcRWbBggZjNZqu/iu3bt0/69Okjp0+fVradOHFCcnNzZfHixeLn56dcb2VlpYg4FsvkvFmzZonRaJSdO3dKaWmp8qqurhaRq097i4+Pl7CwMMnLy1OlqampERHH64w333xTsrOz5ciRI/LWW2+JXq+XFStWqMpTWFgoubm5MmPGDOndu7cSB43nunDhggQHB8uECRMkPz9fNm3aJP7+/qopztbiS6Tl+sqZ2CbHzZ8/X3bv3i1FRUXy/fffy3PPPSdarVa+/vprERF56aWXxGg0yqZNmyQ/P18mTJgg3bp1U9VdpaWlkpubK++//77y9Jvc3Fw5e/asiPxviVVsbKycPn1aFaNN2Yut5qz9Qto8thw9t4jIqlWrxNvb22LmJbnOXnyJiJSXl4uPj4+88847VvNYvny5+Pv7y+effy6FhYWyYMEC8fb2Vp6gunfvXqXNPXbsmGzYsEHMZrPFzENH48uZtvHXX3+Vrl27ygMPPCB5eXly5MgRmTt3rnh6eiqzl9g2XjtNl3o52veyV3858hk3LsGZNGmSqo5pPsOjoKBAcnNzZezYsTJkyBAlBhvZahsbWVvqxbaxfcyZM0d27twpx44dk++++07GjBkjBoNBjh8/7lC/q1FhYaFoNBr56quvrJ7HXt/a0dhqypG2UcSxPp8I28amOPDzO7Z9+3YBIEeOHFFtr66ulri4OAkKChJPT0+56aabZPLkyXLy5EklTXFxsYwaNUpMJpN4enpKWFiYTJw40eIx7WfPnpVJkyaJwWAQg8EgkyZNUk0jzcjIkKioKDEajeLt7S0333yzPPvss6o0jRX9jh07ROTquuZ7771XAgICxMvLS3r27ClPPvmk0kg1NWHCBImOjm79m0Utapwi3Pw1efJkh+JJROTSpUuSlJQkAQEBotfrZcyYMRZpamtrZc6cOWIymcRgMMiwYcPk0KFDqjQnTpyQ0aNHi16vl4CAAElKSlI9Wlvk6qBnWFiYzenIjddTVFSkbJs8ebLVa2yMS0dimZxn7T0HICkpKSLyv/qhpc/G0TojMTFRSRMZGWn1kZ0xMTFWz9U0Vr7//nu55557RKfTSUhIiCxatEj1KHdr8SViv75yJLbJOVOnTpWIiAjx8vKSoKAgGTp0qOpLeUNDgyxcuFBCQkJEp9PJvffeK/n5+ao8Fi5c2GKMpqSk2IzRphyJraasdW6bx5aj5xYRiYqKanH5EDnPXnyJXH2ssF6vlwsXLtjMJzk5WcLCwsTHx0eioqJUS56zs7Nl8ODBStvTp08fWbhwoVRVVanycCS+XGkbs7KyJC4uTgICAsRgMMhdd90l27ZtU/azbbx2mg78ONr3sld/idj/jG3lERERoTpXREREi3WRrbaxkbWBHxG2je1h/Pjx0q1bN/H09BSz2SwPPPCAcm8yR/pdjebPny9hYWE2lxjb61s7GltNOdI2ijjW5xNh29iURuT/52sSEREREREREZFb4T1+iIiIiIiIiIjcFAd+iIiIiIiIiIjcFAd+iIiIiIiIiIjcFAd+iIiIiIiIiIjcFAd+iIiIiIiIiIjcFAd+iIiIiIiIiIjcFAd+iIiIiIiIiIjcFAd+iIiIiIiIiIjcFAd+iIiIiIiIiIjcFAd+iIiIiIiIiIjcFAd+iIiIiIiIiIjcFAd+iIiIiIiIiIjc1P8BoGYNdTIg2UsAAAAASUVORK5CYII=\n" 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\n" 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\n", 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" + ] }, "metadata": {}, "output_type": "display_data" @@ -400,8 +561,10 @@ "outputs": [ { "data": { - "text/plain": "
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\n" + "image/png": 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\n", + "text/plain": [ + "
" + ] }, "metadata": {}, "output_type": "display_data" @@ -444,8 +607,10 @@ "outputs": [ { "data": { - "text/plain": "
", - "image/png": 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\n" + "image/png": 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W7NmztXXrVh0+fFgrVqzQhAkT1KNHD4WGhlbr92ez2fTLX/5Ss2bNUlpamp544gn3vHr16qlBgwaaM2eO/vnPf+qTTz5RUlLSNW2nMr63QYMGKSIiQg899JA2bNiggwcPKiUlRZs2bZIkvfzyy1q0aJEmTpyor7/+WpmZmfrv//5v/eEPf5D00xPx58+frz179ujgwYN677335HA41LRp02vaJwAAAMCXLBHSjxw5ovT0dD399NNX7BcREaGcnByPttzcXNWqVUsNGjQocxm73a7Q0FCPqaoE+PkrwhEqP5X9BwM/2RThCFWAn3+Z83+uVatWKTIy0mO66667JElvvPGGDh8+7H71VUREhObNm6c//OEP2rFjh3sdQ4cOVUFBgf7t3/5Nzz//vF544QX9+te/ds9fuHChhg4dqt/+9rdq2bKlHnjgAX3xxReKjo6ukn26VFRUlDZs2KCSkhIlJiaqTZs2Gj16tJxOp8fVAFczdepUpaWlKTo6Wu3bt5ckvf3226pXr566dOmifv36KTEx0euzsImJiXr33XfVq1cv3X777XrhhReUmJioDz74wN2nOr+/wYMHa+/evbrpppvUtWtXd7ufn5+WLFmirVu3qk2bNnrppZf0pz/96Zq2URnfW2BgoNasWaOwsDD16dNHbdu21Ztvvum+JSAxMVEff/yx0tLS1KlTJ3Xu3FnTpk1zh/C6detq7ty56tq1q2JjY7V27Vp99NFH5R4TAAAAACuzGeP7dylMnDhRs2fP1tGjR694GfO4ceP00Ucfae/eve62Z599Vjt27HCfdbua/Px8OZ1O5eXllQrs58+f16FDh9SsWTMFBQVd075cfNDG5ffx+MmmhkF1NKZdTzkDr3wPrq/cfffduuOOO8p8oBxwI6iMn3EAAG5kLmM0aety5RacLnVPuvTT77RhjhDuSQe8dKUcejmfn0l3uVxauHChhg0bViqgjx8/XkOHDnV/HjFihI4cOaKkpCRlZmZqwYIFmj9/vsaMGVPdZZfLGejQmHY9FeYI8WgPc4RYOqADAAAAfjabkmIT1DCoTqmrQy+edEqKTSCgA1XI5097SE9PV1ZWloYPH15qXnZ2tsernJo1a6YVK1bopZde0n/9138pKipKM2bM0COPPFKdJV+VM9ChVzrer2LX/90nHeDnz8EMAAAAlnfxpNO0XWs9HiIX5ghRUmwCJ52AKmaJy92rU1Vf7g7AuvgZBwCg4lzGcNIJqCTeXO7u8zPpAAAAAKzHz2bjNWuAD/j8nnQAAAAAAPATQnoZatgdAECNwc82AAAArI6QfomAgABJ0rlz53xcCYCqcPFn++LPOgAAAGA13GRyCX9/f9WtW1e5ubmSpNq1a8vGwzGA654xRufOnVNubq7q1q0rf39/X5cEAAAAlImQfpmIiAhJcgd1ADeOunXrun/GAQAAACsipF/GZrMpMjJSYWFhKi4u9nU5ACpJQEAAZ9ABAABgeYT0cvj7+/MLPQAAAACgWvHgOAAAAAAALIKQDgAAAACARRDSAQAAAACwCEI6AAAAAAAWQUgHAAAAAMAiCOkAAAAAAFgEIR0AAAAAAIsgpAMAAAAAYBGEdAAAAAAALIKQDgAAAACARRDSAQAAAACwCEI6AAAAAAAWQUgHAAAAAMAiCOkAAAAAAFgEIR0AAAAAAIsgpAMAAAAAYBGEdAAAAAAALIKQDgAAAACARRDSAQAAAACwCEI6AAAAAAAWQUgHAAAAAMAiCOkAAAAAAFgEIR0AAAAAAIsgpAMAAAAAYBGEdAAAAAAALIKQDgAAAACARRDSAQAAAACwCEI6AAAAAAAWQUgHAAAAAMAiCOkAAAAAAFgEIR0AAAAAAIsgpAMAAAAAYBE+D+nHjh3TE088oQYNGqh27dq64447tHXr1nL7Z2RkyGazlZq++eabaqwaAAAAAOBLLmNUWHLBPbmM8XVJlaKWLzf+448/qmvXrurRo4dWrlypsLAwHThwQHXr1r3qsvv27VNoaKj7c6NGjaqwUgAAAACAVeQVFWjarrXKKch3t0U4QpUUmyBnoMOHlf18Pg3pb731lqKjo7Vw4UJ3W0xMTIWWDQsLq1CYBwAAAADcOPKKCjRlZ7pOnD/j0Z5bcFpTdqZrTLue13VQ9+nl7qmpqYqLi9OAAQMUFham9u3ba+7cuRVatn379oqMjFRCQoLWrVtXxZUCAAAAAHzNZYym7VqrE+fPyCXPy9tdMjpx/oym7Vp7XV/67tOQfvDgQc2cOVO33HKLVq9erREjRmjUqFFatGhRuctERkZqzpw5SklJ0dKlS9WyZUslJCTo008/LbN/YWGh8vPzPSYAAAAAwPWn2FWinIL8UgH9IpeMcgryVewqqebKKo/NGN/9iSEwMFBxcXHauHGju23UqFHasmWLNm3aVOH19OvXTzabTampqaXmTZw4UZMmTSrVnpeX53FPOwAAAADA2gpLLmjUxg+u2m9Gl4Gy+/v07m4P+fn5cjqdFcqhPj2THhkZqVatWnm03X777crKyvJqPZ07d9b+/fvLnDd+/Hjl5eW5p6NHj15zvQAAAAAAVCWf/mmha9eu2rdvn0fbt99+q6ZNm3q1nu3btysyMrLMeXa7XXa7/ZprBAAAAABYQ4CfvyIcocotOF3mJe9+sinMEaIAP38fVFc5fBrSX3rpJXXp0kWTJ0/WwIED9eWXX2rOnDmaM2eOu8/48eN17Ngx933q06dPV0xMjFq3bq2ioiItXrxYKSkpSklJ8dVuAAAAAACqgZ/NpqTYBPfT3S8N6n6yqWFQHSXFJsjPZvNhlT+PTy9379Spk5YtW6a//e1vatOmjV577TVNnz5dgwcPdvfJzs72uPy9qKhIY8aMUWxsrLp166bPP/9cy5cvV//+/X2xCwAAAACAauQMdGhMu54Kc4R4tIc5Qq77169JPn5wnC94c8M+AAAAAMCaXMZ4PMU9wM/fsmfQvcmh1nncHQAAAAAAFeRns1nqCe6VxaeXuwMAAAAAgP9DSAcAAAAAwCII6QAAAAAAWAQhHQAAAAAAiyCkAwAAAABgEYR0AAAAAAAsgpAOAAAAAIBFENIBAAAAALAIQjoAAAAAABZBSAcAAAAAwCII6QAAAAAAWAQhHQAAAAAAiyCkAwAAAABgEYR0AAAAAAAsgpAOAAAAAIBFENIBAAAAALAIQjoAAAAAABZBSAcAAAAAwCII6QAAAAAAWAQhHQAAAAAAiyCkAwAAAABgEYR0AAAAAAAsgpAOAAAAAIBFENIBAAAAALAIQjoAAAAAABZBSAcAAAAAwCII6QAAAAAAWAQhHQAAAAAAiyCkAwAAAABgEYR0AAAAAAAsgpAOAAAAAIBFENIBAAAAALAIQjoAAAAAABZBSAcAAAAAwCII6QAAAAAAWAQhHQAAAAAAiyCkAwAAAABgEYR0AAAAAAAsgpAOAAAAAIBFENIBAAAAALAIn4f0Y8eO6YknnlCDBg1Uu3Zt3XHHHdq6desVl1m/fr06duyooKAgNW/eXLNmzaqmagEAAAAAqDq1fLnxH3/8UV27dlWPHj20cuVKhYWF6cCBA6pbt265yxw6dEh9+vTRM888o8WLF2vDhg167rnn1KhRIz3yyCPVVzwAAAAAAJXMZowxvtr47373O23YsEGfffZZhZcZN26cUlNTlZmZ6W4bMWKEdu7cqU2bNl11+fz8fDmdTuXl5Sk0NPSa6gYAAAAAoKK8yaE+vdw9NTVVcXFxGjBggMLCwtS+fXvNnTv3ists2rRJvXr18mhLTEzUV199peLi4lL9CwsLlZ+f7zEBAAAAAGBFPg3pBw8e1MyZM3XLLbdo9erVGjFihEaNGqVFixaVu0xOTo7Cw8M92sLDw3XhwgWdOHGiVP/k5GQ5nU73FB0dXen7AQAAAABAZfBpSHe5XOrQoYMmT56s9u3b6ze/+Y2eeeYZzZw584rL2Ww2j88Xr9i/vF2Sxo8fr7y8PPd09OjRytsBAAAAAAAqkU9DemRkpFq1auXRdvvttysrK6vcZSIiIpSTk+PRlpubq1q1aqlBgwal+tvtdoWGhnpMAAAAAABYkU9DeteuXbVv3z6Ptm+//VZNmzYtd5n4+HilpaV5tK1Zs0ZxcXEKCAiokjoBAAAAAKgOPg3pL730kjZv3qzJkyfrn//8p/76179qzpw5ev755919xo8fr6FDh7o/jxgxQkeOHFFSUpIyMzO1YMECzZ8/X2PGjPHFLgAAAAAAUGl8GtI7deqkZcuW6W9/+5vatGmj1157TdOnT9fgwYPdfbKzsz0uf2/WrJlWrFihjIwM3XHHHXrttdc0Y8YM3pEOAAAAALju+fQ96b7Ae9IBAAAAANXpunlPOgAAAAAA+D+EdAAAAAAALIKQDgAAAACARRDSAQAAAACwCEI6AAAAAAAWQUgHAAAAAMAiCOkAAAAAAFgEIR0AAAAAAIsgpAMAAAAAYBGEdAAAAAAALIKQDgAAAACARRDSAQAAAACwCEI6AAAAAAAWQUgHAAAAAMAiCOkAAAAAAFgEIR0AAAAAAIsgpAMAAAAAYBGEdAAAAAAALIKQDgAAAACARRDSAQAAAACwCEI6AAAAAAAWQUgHAAAAAMAiCOkAAAAAAFgEIR0AAAAAAIsgpAMAAAAAYBGEdAAAAAAALIKQDgAAAACARRDSAQAAAACwCEI6AAAAAAAWQUgHAAAAAMAiavm6AAAAgJrIZYyKXSXuzwF+/vKz2XxYEQDACgjpAAAA1SyvqEDTdq1VTkG+uy3CEaqk2AQ5Ax0+rAwA4Gtc7g4AAFCN8ooKNGVnunILTnu05xac1pSd6corKvBRZQAAKyCkAwAAVBOXMZq2a61OnD8jl4znPBmdOH9G03atlcuYctYAALjREdIBAACqSbGrRDkF+aUC+kUuGeUU5Hvcqw4AqFkI6QAAAAAAWAQhHQAAAAAAiyCkAwAAVJMAP39FOELlp7JfteYnmyIcoQrw86/mygAAVkFIBwAAqCZ+NpuSYhPUMKhOqaDuJ5saBtVRUmwC70sHgBqMkA4AAFCNnIEOjWnXU2GOEI/2MEeIxrTryXvSAaCGq+XLjU+cOFGTJk3yaAsPD1dOTk6Z/TMyMtSjR49S7ZmZmbrtttuqpEYAAIDK5gx06JWO93s8xT3Az58z6AAA34Z0SWrdurXS09Pdn/39r34P1r59+xQaGur+3KhRoyqpDQAAoKr42Wyy+/v8VzEAgMX4/P8MtWrVUkREhFfLhIWFqW7dulVTEAAAAAAAPuLze9L379+vqKgoNWvWTI8//rgOHjx41WXat2+vyMhIJSQkaN26ddVQJQAAAAAAVc+nIf3OO+/UokWLtHr1as2dO1c5OTnq0qWLTp48WWb/yMhIzZkzRykpKVq6dKlatmyphIQEffrpp+Vuo7CwUPn5+R4TAAAAAABWZDPGGF8XcdHZs2d18803a+zYsUpKSqrQMv369ZPNZlNqamqZ88t6OJ0k5eXledzXDgAAAABAVcjPz5fT6axQDvX55e6XCg4OVtu2bbV///4KL9O5c+cr9h8/frzy8vLc09GjRyujVAAAAAAAKp3PHxx3qcLCQmVmZqpbt24VXmb79u2KjIwsd77dbpfdbq+M8gAAAAAAqFI+DeljxoxRv3791KRJE+Xm5ur1119Xfn6+hg0bJumns+DHjh3TokWLJEnTp09XTEyMWrduraKiIi1evFgpKSlKSUnx5W4AAAAAAFApfBrSv/vuOw0aNEgnTpxQo0aN1LlzZ23evFlNmzaVJGVnZysrK8vdv6ioSGPGjNGxY8fkcDjUunVrLV++XH369PHVLgAAAAAAUGks9eC46uDNDfsAAAAAAPxc1+2D4wAAAAAAqMkI6QAAAAAAWAQhHQAAAAAAiyCkAwAAAABgEYR0AAAAAAAsgpAOAAAAAIBFENIBAAAAALAIQjoAAAAAABZBSAcAAAAAwCII6QAAAAAAWAQhHQAAAAAAiyCkAwAAAABgEYR0AAAAAAAsgpAOAAAAAIBFENIBAAAAALAIQjoAAAAAABZBSAcAAAAAwCII6QAAAAAAWAQhHQAAAAAAiyCkAwAAAABgEYR0AAAAAAAsgpAOAAAAAIBFXFNIv3DhgtLT0zV79mydPn1aknT8+HGdOXOmUosDAAAAAKAmqeXtAkeOHFHv3r2VlZWlwsJC3XvvvQoJCdF//Md/6Pz585o1a1ZV1AkAAAAAwA3P6zPpo0ePVlxcnH788Uc5HA53+8MPP6y1a9dWanEAAAAAANQkXp9J//zzz7VhwwYFBgZ6tDdt2lTHjh2rtMIAAAAAAKhpvD6T7nK5VFJSUqr9u+++U0hISKUUBQAAAABATeR1SL/33ns1ffp092ebzaYzZ87olVdeUZ8+fSqzNgAAAAAAahSbMcZ4s8Dx48fVo0cP+fv7a//+/YqLi9P+/fvVsGFDffrppwoLC6uqWitFfn6+nE6n8vLyFBoa6utyAAAAAAA3OG9yqNf3pEdFRWnHjh1asmSJtm7dKpfLpV/96lcaPHiwx4PkAAAAAACAd7w+k36940w6AAAAAKA6eZNDvb4nPTk5WQsWLCjVvmDBAr311lverg4AAAAAAPwvr0P67Nmzddttt5Vqb926tWbNmlUpRQEAAAAAUBN5HdJzcnIUGRlZqr1Ro0bKzs6ulKIAAAAAAKiJvA7p0dHR2rBhQ6n2DRs2KCoqqlKKAgAAAACgJvL66e5PP/20XnzxRRUXF+uee+6RJK1du1Zjx47Vb3/720ovEAAAAACAmsLrkD527Fj98MMPeu6551RUVCRJCgoK0rhx4zR+/PhKLxAAAAAAgJriml/BdubMGWVmZsrhcOiWW26R3W6v7NqqBK9gAwAAAABUJ29yqNdn0i+qU6eOOnXqdK2LAwAAAACAy3gd0s+ePas333xTa9euVW5urlwul8f8gwcPVlpxAAAAAADUJNf04Lj169dryJAhioyMlM1mq4q6AAAAAACocbwO6StXrtTy5cvVtWvXn73xiRMnatKkSR5t4eHhysnJKXeZ9evXKykpSV9//bWioqI0duxYjRgx4mfXAgAAAACAr3kd0uvVq6f69etXWgGtW7dWenq6+7O/v3+5fQ8dOqQ+ffromWee0eLFi7VhwwY999xzatSokR555JFKqwkAAAAAAF/wOqS/9tprevnll/Xuu++qdu3aP7+AWrUUERFRob6zZs1SkyZNNH36dEnS7bffrq+++kpTpkwhpAMAAAAArnteh/SpU6fqwIEDCg8PV0xMjAICAjzmb9u2zav17d+/X1FRUbLb7brzzjs1efJkNW/evMy+mzZtUq9evTzaEhMTNX/+fBUXF5eqRZIKCwtVWFjo/pyfn+9VfQAAAAAAVBevQ/pDDz1UaRu/8847tWjRIt166636/vvv9frrr6tLly76+uuv1aBBg1L9c3JyFB4e7tEWHh6uCxcu6MSJE4qMjCy1THJycqn73gEAAAAAsCKvQ/orr7xSaRu/77773P/dtm1bxcfH6+abb9a7776rpKSkMpe5/Gnyxpgy2y8aP368x7ry8/MVHR39c0sHAAAAAKDSeR3Sq1JwcLDatm2r/fv3lzk/IiKi1JPfc3NzVatWrTLPvEuS3W6X3W6v9FoBAAAAAKhsft4uUFJSoilTpujf/u3fFBERofr163tMP0dhYaEyMzPLvGxdkuLj45WWlubRtmbNGsXFxZV5PzoAAAAAANcTr0P6pEmTNG3aNA0cOFB5eXlKSkpS//795efnp4kTJ3q1rjFjxmj9+vU6dOiQvvjiCz366KPKz8/XsGHDJP10qfrQoUPd/UeMGKEjR44oKSlJmZmZWrBggebPn68xY8Z4uxsAAAAAAFiO1yH9/fff19y5czVmzBjVqlVLgwYN0rx58/Tyyy9r8+bNXq3ru+++06BBg9SyZUv1799fgYGB2rx5s5o2bSpJys7OVlZWlrt/s2bNtGLFCmVkZOiOO+7Qa6+9phkzZvD6NQAAAADADcFmLj55rYKCg4OVmZmpJk2aKDIyUsuXL1eHDh108OBBtW/fXnl5eVVVa6XIz8+X0+lUXl6eQkNDfV0OAAAAAOAG500O9fpMeuPGjZWdnS1JatGihdasWSNJ2rJlCw9oAwAAAADgZ/A6pD/88MNau3atJGn06NH64x//qFtuuUVDhw7V8OHDK71AAAAAAABqCq8vd7/c5s2btXHjRrVo0UIPPPBAZdVVZbjcHQAAAABQnbzJoT/7PemdO3dW586df+5qAAAAAACo8SoU0lNTU3XfffcpICBAqampV+x7PZxNBwAAAADAiip0ubufn59ycnIUFhYmP7/yb2O32WwqKSmp1AIrG5e7AwAAAACqU6Vf7u5yucr8bwAAAAAAUHm8erp7cXGxevTooW+//baq6gEAAAAAoMbyKqQHBARoz549stlsVVUPAAAAAAA1ltfvSR86dKjmz59fFbUAAAAAAFCjef0KtqKiIs2bN09paWmKi4tTcHCwx/xp06ZVWnEAAAAAANQkXof0PXv2qEOHDpJU6t50LoMHAAAAAODaeR3S161bVxV1AAAAAABQ43l9TzoAAAAAAKgaXp9Jl6QtW7bof/7nf5SVlaWioiKPeUuXLq2UwgAAAAAAqGm8PpO+ZMkSde3aVXv37tWyZctUXFysvXv36pNPPpHT6ayKGgEAAAAAqBG8DumTJ0/W22+/rY8//liBgYH685//rMzMTA0cOFBNmjSpihoBAAAAAKgRvA7pBw4c0P333y9JstvtOnv2rGw2m1566SXNmTOn0gsEAAAAAKCm8Dqk169fX6dPn5Yk3XTTTdqzZ48k6dSpUzp37lzlVgcAAAAAQA1S4ZC+Y8cOSVK3bt2UlpYmSRo4cKBGjx6tZ555RoMGDVJCQkKVFAkAAAAAQE1Q4ae7d+jQQe3bt9dDDz2kQYMGSZLGjx+vgIAAff755+rfv7/++Mc/VlmhAAAAAADc6GzGGFORjps2bdKCBQv0wQcfqLi4WP3799evfvUr9ejRo6prrFT5+flyOp3Ky8tTaGior8sBAAAAANzgvMmhFb7cPT4+XnPnzlVOTo5mzpyp7777Tj179tTNN9+sN954Q999993PLhwAAAAAgJrM6wfHORwODRs2TBkZGfr22281aNAgzZ49W82aNVOfPn2qokYAAAAAAGqECl/uXp4zZ87o/fff14QJE3Tq1CmVlJRUVm1VgsvdAQAAAADVyZscWuEHx11u/fr1WrBggVJSUuTv76+BAwfqV7/61bWuDgAAAACAGs+rkH706FG98847euedd3To0CF16dJFf/nLXzRw4EAFBwdXVY0AAAAAANQIFQ7p9957r9atW6dGjRpp6NChGj58uFq2bFmVtQEAAAAAUKNUOKQ7HA6lpKSob9++8vf3r8qaAAAAAACokSoc0lNTU6uyDgAAAAAAajyvX8EGAAAAAACqBiEdAAAAAACLIKQDAAAAAGARhHQAAAAAACyCkA4AAAAAgEUQ0gEAAAAAsAhCOgAAAAAAFkFIBwAAAADAIgjpAAAAAABYBCEdAAAAAACLsExIT05Ols1m04svvlhun4yMDNlstlLTN998U32FAgAAAABQRWr5ugBJ2rJli+bMmaPY2NgK9d+3b59CQ0Pdnxs1alRVpQEAAAAAUG18fib9zJkzGjx4sObOnat69epVaJmwsDBFRES4J39//yquEgAAAACAqufzkP7888/r/vvvV8+ePSu8TPv27RUZGamEhAStW7euCqsDAAAAAKD6+PRy9yVLlmjbtm3asmVLhfpHRkZqzpw56tixowoLC/Xee+8pISFBGRkZ+sUvflHmMoWFhSosLHR/zs/Pr5TaAQAAAACobD4L6UePHtXo0aO1Zs0aBQUFVWiZli1bqmXLlu7P8fHxOnr0qKZMmVJuSE9OTtakSZMqpWYAAAAAAKqSzRhjfLHhDz/8UA8//LDH/eQlJSWy2Wzy8/NTYWFhhe41f+ONN7R48WJlZmaWOb+sM+nR0dHKy8vzePgcAAAAAABVIT8/X06ns0I51Gdn0hMSErR7926Ptqeeekq33Xabxo0bV+GHwW3fvl2RkZHlzrfb7bLb7T+rVgAAAAAAqoPPQnpISIjatGnj0RYcHKwGDRq428ePH69jx45p0aJFkqTp06crJiZGrVu3VlFRkRYvXqyUlBSlpKRUe/0AAAAAAFQ2S7wnvTzZ2dnKyspyfy4qKtKYMWN07NgxORwOtW7dWsuXL1efPn18WCUAAAAAAJXDZ/ek+4o39wIAAAAAAPBzeZNDff6edAAAAAAA8BNCOgAAAAAAFkFIBwAAAADAIgjpAAAAAABYBCEdAAAAAACLIKQDAAAAAGARhHQAAAAAACyCkA4AAAAAgEUQ0gEAAAAAsAhCOgAAAAAAFkFIBwAAAADAIgjpAAAAAABYBCEdAAAAAACLIKQDAAAAAGARhHQAAAAAACyCkA4AAAAAgEUQ0gEAAAAAsAhCOgAAAAAAFkFIBwAAAADAIgjpAAAAAABYBCEdAAAAAACLIKQDAAAAAGARhHQAAAAAACyCkA4AAAAAgEUQ0gEAAAAAsAhCOgAAAAAAFkFIBwAAAADAIgjpAAAAAABYBCEdAAAAAACLIKQDAAAAAGARhHQAAAAAACyCkA4AAAAAgEUQ0gEAAAAAsAhCOgAAAAAAFkFIBwAAAADAIgjpAAAAAABYBCEdAAAAAACLIKQDAAAAAGARhHQAAAAAACyCkA4AAAAAgEUQ0gEAAAAAsAjLhPTk5GTZbDa9+OKLV+y3fv16dezYUUFBQWrevLlmzZpVPQUCAAAAAFDFLBHSt2zZojlz5ig2NvaK/Q4dOqQ+ffqoW7du2r59uyZMmKBRo0YpJSWlmioFAAAAAKDq+DyknzlzRoMHD9bcuXNVr169K/adNWuWmjRpounTp+v222/X008/reHDh2vKlCnVVC0AAAAAAFXH5yH9+eef1/3336+ePXtete+mTZvUq1cvj7bExER99dVXKi4uLnOZwsJC5efne0wAAAAAAFiRT0P6kiVLtG3bNiUnJ1eof05OjsLDwz3awsPDdeHCBZ04caLMZZKTk+V0Ot1TdHT0z64bAAAAAICq4LOQfvToUY0ePVqLFy9WUFBQhZez2Wwen40xZbZfNH78eOXl5bmno0ePXnvRAAAAAABUoVq+2vDWrVuVm5urjh07uttKSkr06aef6j//8z9VWFgof39/j2UiIiKUk5Pj0Zabm6tatWqpQYMGZW7HbrfLbrdX/g4AACzNZYyKXSXuzwF+/vIr5w+6AAAAVuGzkJ6QkKDdu3d7tD311FO67bbbNG7cuFIBXZLi4+P10UcfebStWbNGcXFxCggIqNJ6AQDXj7yiAk3btVY5Bf/3HJIIR6iSYhPkDHT4sDIAAIAr89nl7iEhIWrTpo3HFBwcrAYNGqhNmzaSfrpUfejQoe5lRowYoSNHjigpKUmZmZlasGCB5s+frzFjxvhqNwAAFpNXVKApO9OVW3Daoz234LSm7ExXXlGBjyoDAAC4Op8/3f1KsrOzlZWV5f7crFkzrVixQhkZGbrjjjv02muvacaMGXrkkUd8WCUAwCpcxmjarrU6cf6MXDKe82R04vwZTdu1Vi5jylkDAACAb9mMqVm/qeTn58vpdCovL0+hoaG+LgcAUIkKSy5o1MYPrtpvRpeBsvv77I4vAABQw3iTQy19Jh0AAAAAgJqEkA4AAAAAgEUQ0gEAN4wAP39FOELlp7JfteYnmyIcoQrwK/0GEQAAACsgpAMAbhh+NpuSYhPUMKhOqaDuJ5saBtVRUmwC70sHAACWRUgHANxQnIEOjWnXU2GOEI/2MEeIxrTryXvSAQCApfFoWwDADccZ6NArHe9XsavE3Rbg588ZdAAAYHmEdADADcnPZuM1awAA4LrD5e4AAAAAAFgEIR0AAAAAAIsgpAMAAAAAYBGEdAAAAAAALIKQDgAAAACARRDSAQAAAACwCEI6AAAAAAAWQUgHAAAAAMAiavm6AADXJ5cxKnaVuD8H+PnLz2bzYUUAAADA9Y+QDsBreUUFmrZrrXIK8t1tEY5QJcUmyBno8GFlAAAAwPWNy90BeCWvqEBTdqYrt+C0R3tuwWlN2ZmuvKICH1UGAAAAXP8I6QAqzGWMpu1aqxPnz8gl4zlPRifOn9G0XWvlMqacNQAAAAC4EkI6gAordpUopyC/VEC/yCWjnIJ8j3vVAQAAAFQcIR0AAAAAAIsgpAMAAAAAYBGEdAAVFuDnrwhHqPxU9qvW/GRThCNUAX7+1VwZAAAAcGMgpAOoMD+bTUmxCWoYVKdUUPeTTQ2D6igpNoH3pQMAAADXiJAOwCvOQIfGtOupMEeIR3uYI0Rj2vXkPekAAADAz1DL1wUAuP44Ax16peP9Hk9xD/Dz5ww6AAAA8DMR0gFcEz+bTXZ/DiEAAABAZeJydwAAAAAALIKQDgAAAACARRDSAQAAAACwCEI6AAAAAAAWQUgHAAAAAMAiCOkAAAAAAFgEIR0AAAAAAIsgpAMAAAAAYBGEdAAAAAAALIKQDgAAAACARRDSAQAAAACwCEI6AAAAAAAWQUgHAAAAAMAifBrSZ86cqdjYWIWGhio0NFTx8fFauXJluf0zMjJks9lKTd988001Vg0AAAAAQNWo5cuNN27cWG+++aZatGghSXr33Xf14IMPavv27WrdunW5y+3bt0+hoaHuz40aNaryWgEAAAAAqGo+Den9+vXz+PzGG29o5syZ2rx58xVDelhYmOrWrVvF1QEAAAAAUL0sc096SUmJlixZorNnzyo+Pv6Kfdu3b6/IyEglJCRo3bp11VQhAAAAAABVy6dn0iVp9+7dio+P1/nz51WnTh0tW7ZMrVq1KrNvZGSk5syZo44dO6qwsFDvvfeeEhISlJGRoV/84hdlLlNYWKjCwkL35/z8/CrZDwAAAAAAfi6bMcb4soCioiJlZWXp1KlTSklJ0bx587R+/fpyg/rl+vXrJ5vNptTU1DLnT5w4UZMmTSrVnpeX53FfOwAAAAAAVSE/P19Op7NCOdTnIf1yPXv21M0336zZs2dXqP8bb7yhxYsXKzMzs8z5ZZ1Jj46OJqQDAAAAAKqFNyHd55e7X84Y4xGqr2b79u2KjIwsd77dbpfdbq+M0gAAAAAAqFI+DekTJkzQfffdp+joaJ0+fVpLlixRRkaGVq1aJUkaP368jh07pkWLFkmSpk+frpiYGLVu3VpFRUVavHixUlJSlJKS4svdAAAAAACgUvg0pH///fcaMmSIsrOz5XQ6FRsbq1WrVunee++VJGVnZysrK8vdv6ioSGPGjNGxY8fkcDjUunVrLV++XH369PHVLgAAAAAAUGksd096VfPmXgAAAAAAAH4ub3KoZd6TDgAAAABATUdIBwAAAADAIgjpAAAAAABYBCEdAAAAAACLIKQDAAAAAGARhHQAAAAAACyCkA4AAAAAgEUQ0gEAAAAAsAhCOgAAAAAAFkFIBwAAAADAIgjpAAAAAABYBCEdAAAAAACLIKQDAAAAAGARhHQAAAAAACyCkA4AAAAAgEUQ0gEAAAAAsAhCOgAAAAAAFkFIBwAAAADAIgjpAAAAAABYBCEdAAAAAACLIKQDAAAAAGARhHQAAAAAACyCkA4AAAAAgEUQ0gEAAAAAsAhCOgAAAAAAFkFIBwAAAADAIgjpAAAAAABYBCEdAAAAAACLIKQDAAAAAGARhHQAAAAAACyCkA4AAAAAgEUQ0gEAAAAAsAhCOgAAAAAAFkFIBwAAAADAIgjpAAAAAABYBCEdAAAAAACLIKQDAAAAAGARhHQAAAAAACyCkA4AAAAAgEUQ0gEAAAAAsAhCOgAAAAAAFuHTkD5z5kzFxsYqNDRUoaGhio+P18qVK6+4zPr169WxY0cFBQWpefPmmjVrVjVVCwAAAABA1fJpSG/cuLHefPNNffXVV/rqq690zz336MEHH9TXX39dZv9Dhw6pT58+6tatm7Zv364JEyZo1KhRSklJqebKAQAAAACofDZjjPF1EZeqX7++/vSnP+lXv/pVqXnjxo1TamqqMjMz3W0jRozQzp07tWnTpgqtPz8/X06nU3l5eQoNDa20ugEAAAAAKIs3OdQy96SXlJRoyZIlOnv2rOLj48vss2nTJvXq1cujLTExUV999ZWKi4vLXKawsFD5+fkeEwAAAAAAVuTzkL57927VqVNHdrtdI0aM0LJly9SqVasy++bk5Cg8PNyjLTw8XBcuXNCJEyfKXCY5OVlOp9M9RUdHV/o+AAAAAABQGXwe0lu2bKkdO3Zo8+bNevbZZzVs2DDt3bu33P42m83j88Wr9S9vv2j8+PHKy8tzT0ePHq284gEAAAAAqES1fF1AYGCgWrRoIUmKi4vTli1b9Oc//1mzZ88u1TciIkI5OTkebbm5uapVq5YaNGhQ5vrtdrvsdnvlFw4AAAAAQCXz+Zn0yxljVFhYWOa8+Ph4paWlebStWbNGcXFxCggIqI7yAAAAAACoMj4N6RMmTNBnn32mw4cPa/fu3fr973+vjIwMDR48WNJPl6oPHTrU3X/EiBE6cuSIkpKSlJmZqQULFmj+/PkaM2aMr3ahyriMUWHJBffkstZD+AEAAAAAVcCnl7t///33GjJkiLKzs+V0OhUbG6tVq1bp3nvvlSRlZ2crKyvL3b9Zs2ZasWKFXnrpJf3Xf/2XoqKiNGPGDD3yyCO+2oUqkVdUoGm71iqn4P+eRB/hCFVSbIKcgQ4fVgYAAAAAqEqWe096VbP6e9Lzigo0ZWe6Tpw/I5f+75/GTzY1DKqjMe16EtQBAAAA4DpyXb4nHT9d4j5t19pSAV2SXDI6cf6Mpu1ay6XvAAAAAHCDIqRbSLGrRDkF+aUC+kUuGeUU5KvYVVLNlQEAAAAAqgMhHQAAAAAAiyCkAwAAAABgEYR0Cwnw81eEI1R+spU53082RThCFeDnX82VAQAAAACqAyHdQvxsNiXFJqhhUJ1SQf3i092TYhPkZys7xAMAAAAArm+EdItxBjo0pl1PhTlCPNrDHCG8fg0AAAAAbnC1fF0ASnMGOvRKx/s9nuIe4OfPGXQAAAAAuMER0i3Kz2aT3Z9/HgAAAACoSbjcHQAAAAAAiyCkAwAAAABgEYR0AAAAAAAsgpAOAAAAAIBFENIBAAAAALAIQjoAAAAAABZBSAcAAAAAwCII6QAAAAAAWAQhHQAAAAAAiyCkAwAAAABgEYR0AAAAAAAsgpAOAAAAAIBFENIBAAAAALCIWr4uoLoZYyRJ+fn5Pq4EAAAAAFATXMyfF/PoldS4kH769GlJUnR0tI8rAQAAAADUJKdPn5bT6bxiH5upSJS/gbhcLh0/flwhISGy2WwVXi4/P1/R0dE6evSoQkNDq7BC3CgYM/AWYwbeYszAW4wZeIsxA28xZspmjNHp06cVFRUlP78r33Ve486k+/n5qXHjxte8fGhoKIMNXmHMwFuMGXiLMQNvMWbgLcYMvMWYKe1qZ9Av4sFxAAAAAABYBCEdAAAAAACLIKRXkN1u1yuvvCK73e7rUnCdYMzAW4wZeIsxA28xZuAtxgy8xZj5+Wrcg+MAAAAAALAqzqQDAAAAAGARhHQAAAAAACyCkA4AAAAAgEUQ0gEAAAAAsIgbNqRPnDhRNpvNY4qIiHDPN8Zo4sSJioqKksPh0N13362vv/7aYx2FhYV64YUX1LBhQwUHB+uBBx7Qd99959Hnxx9/1JAhQ+R0OuV0OjVkyBCdOnXKo09WVpb69eun4OBgNWzYUKNGjVJRUVGV7Tsq5tNPP1W/fv0UFRUlm82mDz/80GO+1cbI7t271b17dzkcDt1000169dVXxXMfq9fVxsyTTz5Z6rjTuXNnjz6MmZolOTlZnTp1UkhIiMLCwvTQQw9p3759Hn041uBSFRkzHGtw0cyZMxUbG6vQ0FCFhoYqPj5eK1eudM/n+ILLXW3McHyxCHODeuWVV0zr1q1Ndna2e8rNzXXPf/PNN01ISIhJSUkxu3fvNo899piJjIw0+fn57j4jRowwN910k0lLSzPbtm0zPXr0MO3atTMXLlxw9+ndu7dp06aN2bhxo9m4caNp06aN6du3r3v+hQsXTJs2bUyPHj3Mtm3bTFpamomKijIjR46sni8C5VqxYoX5/e9/b1JSUowks2zZMo/5VhojeXl5Jjw83Dz++ONm9+7dJiUlxYSEhJgpU6ZU3ReEUq42ZoYNG2Z69+7tcdw5efKkRx/GTM2SmJhoFi5caPbs2WN27Nhh7r//ftOkSRNz5swZdx+ONbhURcYMxxpclJqaapYvX2727dtn9u3bZyZMmGACAgLMnj17jDEcX1Da1cYMxxdruKFDert27cqc53K5TEREhHnzzTfdbefPnzdOp9PMmjXLGGPMqVOnTEBAgFmyZIm7z7Fjx4yfn59ZtWqVMcaYvXv3Gklm8+bN7j6bNm0yksw333xjjPnpl3o/Pz9z7Ngxd5+//e1vxm63m7y8vErbX/w8lwcuq42R//f//p9xOp3m/Pnz7j7JyckmKirKuFyuSvwmUFHlhfQHH3yw3GUYM8jNzTWSzPr1640xHGtwdZePGWM41uDK6tWrZ+bNm8fxBRV2ccwYw/HFKm7Yy90laf/+/YqKilKzZs30+OOP6+DBg5KkQ4cOKScnR7169XL3tdvt6t69uzZu3ChJ2rp1q4qLiz36REVFqU2bNu4+mzZtktPp1J133unu07lzZzmdTo8+bdq0UVRUlLtPYmKiCgsLtXXr1qrbefwsVhsjmzZtUvfu3WW32z36HD9+XIcPH678LwDXLCMjQ2FhYbr11lv1zDPPKDc31z2PMYO8vDxJUv369SVxrMHVXT5mLuJYg8uVlJRoyZIlOnv2rOLj4zm+4KouHzMXcXzxvRs2pN95551atGiRVq9erblz5yonJ0ddunTRyZMnlZOTI0kKDw/3WCY8PNw9LycnR4GBgapXr94V+4SFhZXadlhYmEefy7dTr149BQYGuvvAeqw2Rsrqc/Ez48g67rvvPr3//vv65JNPNHXqVG3ZskX33HOPCgsLJTFmajpjjJKSknTXXXepTZs2kjjW4MrKGjMSxxp42r17t+rUqSO73a4RI0Zo2bJlatWqFccXlKu8MSNxfLGKWr4uoKrcd9997v9u27at4uPjdfPNN+vdd991P/zAZrN5LGOMKdV2ucv7lNX/WvrAmqw0Rsqqpbxl4RuPPfaY+7/btGmjuLg4NW3aVMuXL1f//v3LXY4xUzOMHDlSu3bt0ueff15qHscalKW8McOxBpdq2bKlduzYoVOnTiklJUXDhg3T+vXr3fM5vuBy5Y2ZVq1acXyxiBv2TPrlgoOD1bZtW+3fv9/9lPfL/wKTm5vr/utMRESEioqK9OOPP16xz/fff19qW//61788+ly+nR9//FHFxcWl/jIE67DaGCmrz8VLjxhH1hUZGammTZtq//79khgzNdkLL7yg1NRUrVu3To0bN3a3c6xBecobM2XhWFOzBQYGqkWLFoqLi1NycrLatWunP//5zxxfUK7yxkxZOL74Ro0J6YWFhcrMzFRkZKSaNWumiIgIpaWluecXFRVp/fr16tKliySpY8eOCggI8OiTnZ2tPXv2uPvEx8crLy9PX375pbvPF198oby8PI8+e/bsUXZ2trvPmjVrZLfb1bFjxyrdZ1w7q42R+Ph4ffrppx6vpVizZo2ioqIUExNT+V8AKsXJkyd19OhRRUZGSmLM1ETGGI0cOVJLly7VJ598ombNmnnM51iDy11tzJSFYw0uZYxRYWEhxxdU2MUxUxaOLz5SpY+l86Hf/va3JiMjwxw8eNBs3rzZ9O3b14SEhJjDhw8bY356JYXT6TRLly41u3fvNoMGDSrzlRSNGzc26enpZtu2beaee+4p8/UCsbGxZtOmTWbTpk2mbdu2Zb5eICEhwWzbts2kp6ebxo0b8wo2Czh9+rTZvn272b59u5Fkpk2bZrZv326OHDlijLHWGDl16pQJDw83gwYNMrt37zZLly41oaGhNeIVFFZypTFz+vRp89vf/tZs3LjRHDp0yKxbt87Ex8ebm266iTFTgz377LPG6XSajIwMj9fZnDt3zt2HYw0udbUxw7EGlxo/frz59NNPzaFDh8yuXbvMhAkTjJ+fn1mzZo0xhuMLSrvSmOH4Yh03bEi/+B7IgIAAExUVZfr372++/vpr93yXy2VeeeUVExERYex2u/nFL35hdu/e7bGOgoICM3LkSFO/fn3jcDhM3759TVZWlkefkydPmsGDB5uQkBATEhJiBg8ebH788UePPkeOHDH333+/cTgcpn79+mbkyJEerxKAb6xbt85IKjUNGzbMGGO9MbJr1y7TrVs3Y7fbTUREhJk4ceIN//oJq7nSmDl37pzp1auXadSokQkICDBNmjQxw4YNKzUeGDM1S1njRZJZuHChuw/HGlzqamOGYw0uNXz4cNO0aVMTGBhoGjVqZBISEtwB3RiOLyjtSmOG44t12Iz537vvAQAAAACAT9WYe9IBAAAAALA6QjoAAAAAABZBSAcAAAAAwCII6QAAAAAAWAQhHQAAAAAAiyCkAwAAAABgEYR0AAAAAAAsgpAOAMANzmaz6cMPP/R1GQAAoAII6QAAXKeefPJJ2Ww22Ww2BQQEKDw8XPfee68WLFggl8vl7pedna377ruvQusk0AMA4FuEdAAArmO9e/dWdna2Dh8+rJUrV6pHjx4aPXq0+vbtqwsXLkiSIiIiZLfbfVwpAACoCEI6AADXMbvdroiICN10003q0KGDJkyYoH/84x9auXKl3nnnHUmeZ8eLioo0cuRIRUZGKigoSDExMUpOTpYkxcTESJIefvhh2Ww29+cDBw7owQcfVHh4uOrUqaNOnTopPT3do46YmBhNnjxZw4cPV0hIiJo0aaI5c+Z49Pnuu+/0+OOPq379+goODlZcXJy++OIL9/yPPvpIHTt2VFBQkJo3b65Jkya5/9AAAEBNQUgHAOAGc88996hdu3ZaunRpqXkzZsxQamqqPvjgA+3bt0+LFy92h/EtW7ZIkhYuXKjs7Gz35zNnzqhPnz5KT0/X9u3blZiYqH79+ikrK8tj3VOnTlVcXJy2b9+u5557Ts8++6y++eYb9zq6d++u48ePKzU1VTt37tTYsWPdl+WvXr1aTzzxhEaNGqW9e/dq9uzZeuedd/TGG29U1dcEAIAl1fJ1AQAAoPLddttt2rVrV6n2rKws3XLLLbrrrrtks9nUtGlT97xGjRpJkurWrauIiAh3e7t27dSuXTv359dff13Lli1TamqqRo4c6W7v06ePnnvuOUnSuHHj9PbbbysjI0O33Xab/vrXv+pf//qXtmzZovr160uSWrRo4V72jTfe0O9+9zsNGzZMktS8eXO99tprGjt2rF555ZXK+EoAALguENIBALgBGWNks9lKtT/55JO699571bJlS/Xu3Vt9+/ZVr169rrius2fPatKkSfr44491/PhxXbhwQQUFBaXOpMfGxrr/22azKSIiQrm5uZKkHTt2qH379u6AfrmtW7dqy5YtHmfOS0pKdP78eZ07d061a9eu8L4DAHA9I6QDAHADyszMVLNmzUq1d+jQQYcOHdLKlSuVnp6ugQMHqmfPnvr73/9e7rr+/d//XatXr9aUKVPUokULORwOPfrooyoqKvLoFxAQ4PHZZrO5L2d3OBxXrNflcmnSpEnq379/qXlBQUFXXBYAgBsJIR0AgBvMJ598ot27d+ull14qc35oaKgee+wxPfbYY3r00UfVu3dv/fDDD6pfv74CAgJUUlLi0f+zzz7Tk08+qYcffljST/eXHz582KuaYmNjNW/ePPd2LtehQwft27fP4xJ4AABqIkI6AADXscLCQuXk5KikpETff/+9Vq1apeTkZPXt21dDhw4t1f/tt99WZGSk7rjjDvn5+el//ud/FBERobp160r66Snta9euVdeuXWW321WvXj21aNFCS5cuVb9+/WSz2fTHP/7R4z3sFTFo0CBNnjxZDz30kJKTkxUZGant27crKipK8fHxevnll9W3b19FR0drwIAB8vPz065du7R79269/vrrlfFVAQBwXeDp7gAAXMdWrVqlyMhIxcTEqHfv3lq3bp1mzJihf/zjH/L39y/Vv06dOnrrrbcUFxenTp066fDhw1qxYoX8/H76lWDq1KlKS0tTdHS02rdvL+mnYF+vXj116dJF/fr1U2Jiojp06OBVnYGBgVqzZo3CwsLUp08ftW3bVm+++aa7xsTERH388cdKS0tTp06d1LlzZ02bNs3jwXYAANQENmOM8XURAAAAAACAM+kAAAAAAFgGIR0AAAAAAIsgpAMAAAAAYBGEdAAAAAAALIKQDgAAAACARRDSAQAAAACwCEI6AAAAAAAWQUgHAAAAAMAiCOkAAAAAAFgEIR0AAAAAAIsgpAMAAAAAYBGEdAAAAAAALOL/A2KxhmvFDpMPAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] }, "metadata": {}, "output_type": "display_data" @@ -488,8 +653,10 @@ "outputs": [ { "data": { - "text/plain": "
", - "image/png": 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\n" + "image/png": 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\n", + "text/plain": [ + "
" + ] }, "metadata": {}, "output_type": "display_data" @@ -532,8 +699,10 @@ "outputs": [ { "data": { - "text/plain": "
", - "image/png": 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\n" + "image/png": 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\n", + "text/plain": [ + "
" + ] }, "metadata": {}, "output_type": "display_data" @@ -576,8 +745,10 @@ "outputs": [ { "data": { - "text/plain": "
", - "image/png": 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\n" + "image/png": 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\n", + "text/plain": [ + "
" + ] }, "metadata": {}, "output_type": "display_data" @@ -626,8 +797,10 @@ "outputs": [ { "data": { - "text/plain": "
", - "image/png": 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+ "text/plain": [ + "
" + ] }, "metadata": {}, "output_type": "display_data" @@ -802,8 +975,10 @@ "outputs": [ { "data": { - "text/plain": "
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+ "text/plain": [ + "
" + ] }, "metadata": {}, "output_type": "display_data" @@ -840,15 +1015,15 @@ }, { "cell_type": "markdown", - "source": [ - "---" - ], + "id": "2518be43", "metadata": { - "collapsed": false, "pycharm": { "name": "#%% md\n" } - } + }, + "source": [ + "---" + ] } ], "metadata": { @@ -867,9 +1042,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.6" + "version": "3.10.9" } }, "nbformat": 4, "nbformat_minor": 5 -} \ No newline at end of file +} diff --git a/tutorials/Experimental Variogram and Variogram Point Cloud classes (Basic).ipynb b/tutorials/Experimental Variogram and Variogram Point Cloud classes (Basic).ipynb index 56cea047..70561abc 100644 --- a/tutorials/Experimental Variogram and Variogram Point Cloud classes (Basic).ipynb +++ b/tutorials/Experimental Variogram and Variogram Point Cloud classes (Basic).ipynb @@ -49,38 +49,39 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 27, "id": "3ffc9a4e", "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", + "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "from pyinterpolate import read_txt, ExperimentalVariogram, VariogramCloud" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 2, "id": "5e8e320a", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([[2.42207337e+05, 5.46881660e+05, 5.03191910e+01],\n", - " [2.50856917e+05, 5.51689039e+05, 6.96442337e+01],\n", - " [2.43882223e+05, 5.53252314e+05, 4.52078705e+01],\n", - " [2.53679240e+05, 5.30855626e+05, 5.43067894e+01],\n", - " [2.52375160e+05, 5.34773476e+05, 4.86135292e+01],\n", - " [2.44833732e+05, 5.28542218e+05, 5.09161682e+01],\n", - " [2.45380349e+05, 5.30266794e+05, 5.63001442e+01],\n", - " [2.48391707e+05, 5.46418696e+05, 1.87953167e+01],\n", - " [2.46936777e+05, 5.36967503e+05, 2.04066505e+01],\n", - " [2.44688507e+05, 5.33463225e+05, 5.17071571e+01]])" + "array([[2.50619963e+05, 5.51239899e+05, 7.45311050e+01],\n", + " [2.48382967e+05, 5.41598154e+05, 1.73819008e+01],\n", + " [2.38841957e+05, 5.52967791e+05, 6.44929733e+01],\n", + " [2.50322081e+05, 5.43366530e+05, 2.12877045e+01],\n", + " [2.39793019e+05, 5.49271428e+05, 8.65349960e+01],\n", + " [2.49195403e+05, 5.37611815e+05, 3.85723991e+01],\n", + " [2.46386924e+05, 5.28968757e+05, 6.23634529e+01],\n", + " [2.52703627e+05, 5.50164362e+05, 7.46828461e+01],\n", + " [2.38252113e+05, 5.49469713e+05, 1.04557449e+02],\n", + " [2.47506851e+05, 5.34515887e+05, 5.37074432e+01]])" ] }, - "execution_count": 5, + "execution_count": 2, "metadata": {}, "output_type": "execute_result" } @@ -114,17 +115,20 @@ "\n", "```python\n", "\n", - "class ExperimentalVariogram(\n", - " input_array,\n", - " step_size,\n", - " max_range,\n", - " weights=None,\n", - " direction=None,\n", - " tolerance=1.0,\n", - " method='t',\n", - " is_semivariance=True,\n", - " is_covariance=True,\n", - " is_variance=True)\n", + "class ExperimentalVariogram:\n", + " \n", + " def __init__(self,\n", + " input_array,\n", + " step_size,\n", + " max_range,\n", + " weights=None,\n", + " direction=None,\n", + " tolerance=1.0,\n", + " method='t',\n", + " is_semivariance=True,\n", + " is_covariance=True,\n", + " is_variance=True):\n", + " ...\n", "\n", "```\n", "\n", @@ -138,7 +142,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 3, "id": "785e9110", "metadata": {}, "outputs": [ @@ -146,8 +150,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "Max lon distance: 18055.017384361156\n", - "Max lat distance: 25230.138770977966\n" + "Max lon distance: 17935.773493349872\n", + "Max lat distance: 25187.335448016413\n" ] } ], @@ -172,19 +176,19 @@ "id": "2dc49a0e", "metadata": {}, "source": [ - "We can assume, that the maximum distance `max_range` parameter shouldn't be larger than the maximum distance in a lower dimension (**18055** m). In reality, the `max_range` parameter is rather close to the halved value of this distance, and we can check it on a variogram.\n", + "We can assume, that the maximum distance `max_range` parameter shouldn't be larger than the maximum distance in a lower dimension (**~18000** m). In reality, the `max_range` parameter is rather close to the halved value of this distance, and we can check it on a variogram.\n", "\n", "But we don't know the `step_size` parameter... Let's assume that it is 100 meters and we will see if we've estimated it correctly." ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 4, "id": "3fbc21c8", "metadata": {}, "outputs": [], "source": [ - "max_range = 18055\n", + "max_range = 18000\n", "step_size = 100\n", "\n", "experimental_variogram = ExperimentalVariogram(\n", @@ -204,13 +208,13 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 5, "id": "db85fb2b", "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -237,13 +241,13 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 6, "id": "b662300d", "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -269,13 +273,13 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 7, "id": "da5aae7e", "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -307,13 +311,13 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 8, "id": "78a330bd", "metadata": {}, "outputs": [ { "data": { - "image/png": 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TJikajerb3/62pkyZomnTpumqq66Kb1+0aJEmTZqk6667Tscff7zOPfdcrV69Wn369GmRa/q6Xr166c0331Rtba1GjRqlgQMHavr06erSpUvCaIFvcscdd2j58uXq3bu3Bg8eLEkqKipS165ddcopp2js2LEqKCho8l3agoICPfTQQxo1apROOOEEXXPNNSooKNBjjz0W36c1P78JEybo3Xff1RFHHKFTTz013m7btpYsWaI1a9bopJNO0rXXXqvf/va3h3SO5vjcPB6PXnrpJWVlZemss87SwIEDdfvtt8cfISgoKNCzzz6rl156ScOHD9fIkSO1cOHCeCjv0qWL7r//fp166qnKyclRcXGxnnnmGXXv3v2QrgkAAABIJsuY9jIP/qGLRCLy+/0Kh8Py+XwJ26qqqvTRRx/p6KOPVlpaWpOPXTcpwv7PWNiylJmWrhmD8uX3HPwZ3mT57ne/q5NPPjm+3jfQ3hzuv28AAAAkV8wYzV3znMqjFfWeaZe+yl1Z3gxHPtN+sBz6ddxpb2F+j1czBuUry5uR0J7lzXB0YAcAAAAAp7MtS4U5ecpMS683wrnuRmlhTp7jAntTtM0n8dsYv8erOUPHqCb23+es3barTXccAAAAAHCCuhulRWXBhEnpsrwZKszJa/M3SgntrcS2rDY3W+Grr76a7BIAAAAA4Bu15xulbStFAgAAAADQgLZ4o7QxeKYdAAAAAACHIrQ3EpPsA+0P/64BAADgdIT2b1C3NnR1dXWSKwHQ3Or+Xdf9OwcAAACcpv0N+G9mKSkp6tSpk3bs2CG32y3b5vccQHsQi8W0Y8cOderUSSkpfCsEAACAM/GT6jewLEs9e/bURx99pE8++STZ5QBoRrZtq0+fPrLawayiAAAAaJ8I7Y3g8XjUr18/hsgD7YzH42H0DAAAAByN0N5Itm0rLS0t2WUAAAAAADoQbjEBAAAAAOBQhHYAAAAAAByK0A4AAAAAgEMR2gEAAAAAcChCOwAAAAAADkVoBwAAAADAoQjtAAAAAAA4FKEdAAAAAACHSkl2AQAAAACAg4sZo5pYbfy923bJtqwkVoTWQmgHAAAAAAcLV0dVVBZUKBqJtwW8PhXm5Mnv8SaxMrQGhscDAAAAgEOFq6NaUFqs8mhFQnt5tEILSosVro4mqTK0FkI7AAAAADhQzBgVlQW1s6pSMZnEbTLaWVWporKgYsYc4AhoDwjtAAAAAOBANbFahaKReoG9TkxGoWgk4Vl3tD+EdgAAAAAAHIrQDgAAAACAQxHaAQAAAMCB3LZLAa9Pthpe2s2WpYDXJ7ftauXK0JoI7QAAAADgQLZlqTAnT5lp6fWCuy1LmWnpKszJY732do7QDgAAAAAO5fd4NWNQvrK8GQntWd4MzRiUzzrtHUBKsgsAAAAAAByY3+PVnKFjEmaJd9su7rB3EIR2AAAAAHA427KU6iK+dUT8rQMAAAAAmixmDHf/WwGhHQAAAADQJOHqqIrKggpFI/G2gNenwpw8nrNvZkxEBwAAAABotHB1VAtKi1UerUhoL49WaEFpscLV0SRV1j4R2gEAAAAAjRIzRkVlQe2sqlRMJnGbjHZWVaqoLKiYMQc4ApqK0A4AAAAAaJSaWK1C0Ui9wF4nJqNQNJLwrDsOD6EdAAAAAACHIrQDAAAAAOBQSQ3tv/rVr2RZVsKrf//+8e1VVVWaMmWKunfvrvT0dI0bN07bt29POMaWLVs0ZswYderUSVlZWbr++uu1b9++1r4UAAAAAGj33LZLAa9Pthpe2s2WpYDXJ7ftauXK2q+k32k/8cQTtW3btvjrjTfeiG+79tpr9cwzz+jxxx/XihUrtHXrVp1//vnx7bW1tRozZoyqq6u1cuVKPfTQQ1q8eLFmz56djEsBAAAAgHbNtiwV5uQpMy29XnC3ZSkzLV2FOXms196Mkh7aU1JSFAgE4q/MzExJUjgc1p/+9CcVFRXp+9//voYOHapFixZp5cqVeuuttyRJL730kt5991395S9/0cknn6wzzzxTt9xyi+6++25VV1cn87IAAAAAoF3ye7yaMShfWd6MhPYsb4ZmDMpnnfZmlpLsAjZv3qxevXopLS1Nubm5mjdvnvr06aM1a9aopqZG+fn58X379++vPn36qKSkRCNHjlRJSYkGDhyo7Ozs+D4FBQW6+uqrtXHjRg0ePDgZlwQAAAAA7Zrf49WcoWMSZol32y7usLeApIb2ESNGaPHixTr++OO1bds2zZ07V6effro2bNigUCgkj8ejLl26JHxNdna2QqGQJCkUCiUE9rrtddsOZO/evdq7d2/8fSQSaaYrAgAAANDWxYwhjDaCbVlKdSX9PnC7l9RP+Mwzz4z/OScnRyNGjNBRRx2lxx57TF5vyw2pmDdvnubOndtixwcAAADQNoWroyoqCyoU/e+NvYDXp8KcPIZ9IymS/kz713Xp0kXHHXec3n//fQUCAVVXV2v37t0J+2zfvl2BQECSFAgE6s0mX/e+bp+GzJo1S+FwOP769NNPm/dCAAAAALQ54eqoFpQWqzxakdBeHq3QgtJihaujSaoMHZmjQntlZaU++OAD9ezZU0OHDpXb7VYwGIxv37Rpk7Zs2aLc3FxJUm5urtavX6/y8vL4PsuXL5fP59OAAQMOeJ7U1FT5fL6EFwAAAICOK2aMisqC2llVqZhM4jYZ7ayqVFFZUDFjDnAEoGUkdXj8jBkzNHbsWB111FHaunWr5syZI5fLpfHjx8vv9+uKK65QYWGhunXrJp/Pp2uuuUa5ubkaOXKkJGnUqFEaMGCAJk6cqPnz5ysUCunGG2/UlClTlJqamsxLAwAAANCG1MRqE4bE7y8mo1A0oppYLc9xo1Ultbd99tlnGj9+vL744gv16NFDp512mt566y316NFDkrRw4ULZtq1x48Zp7969Kigo0B/+8If417tcLj377LO6+uqrlZubq86dO2vy5Mm6+eabk3VJAAAAAAA0G8sYxndEIhH5/X6Fw2GGygMAAAAd0N7affr5yse+cb87T7mIO+1oFo3NoY56ph0AAAAAksFtuxTw+mSr4aXdbFkKeH1y265WrgwdHaEdAAAAQIdnW5YKc/KUmZZeL7jbspSZlq7CnDzWa0erI7QDAAAAgCS/x6sZg/KV5c1IaM/yZmjGoHzWaUdS8DAGAAAAAPyH3+PVnKFjVBOrjbe5bRd32JE0hHYAAAAASRMzxnEB2bYsJpuDY9ATAQAAACRFuDqqorJgwvroAa9PhTl5DEUH/oNn2gEAAAC0unB1VAtKi1UerUhoL49WaEFpscLV0SRVBjgLoR0AAABAq4oZo6KyoHZWVSomk7hNRjurKlVUFlTMmAMcAeg4CO0AAAAAWlVNrFahaKReYK8Tk1EoGkl41h3oqAjtAAAAAAA4FKEdAAAAAACHIrQDAAAAaFVu26WA1ydbDS/tZstSwOuT23a1cmWA8xDaAQAAALQq27JUmJOnzLT0esHdlqXMtHQV5uQlfb12wAkI7QAAAABand/j1YxB+cryZiS0Z3kzNGNQPuu0A/+RkuwCAAAAAHRMfo9Xc4aOSZgl3m27uMMOfA2hHQAAAEDS2JalVBexBDgQhscDAAAAAOBQhHYAAAAAAByK0A4AAAAAgEMR2gEAAAAAcChCOwAAAAAADkVoBwAAAADAoQjtAAAAAAA4FAsiAgAAoF2IGaOaWG38vdt2ybasJFYEAIeP0A4AAIA2L1wdVVFZUKFoJN4W8PpUmJMnv8ebxMqch19uAG0LoR0AAABtWrg6qgWlxdpZVZnQXh6t0ILSYs0YlJ+04O60gMwvN4C2xzLGmGQXkWyRSER+v1/hcFg+ny/Z5QAAAKCRYsZo7prnVB6tUEz1f6y1ZSnLm6E5Q8e0elh2WkD++i83vv5Z2bKUmZae1F9uAB1RY3MoE9EBAACgzaqJ1SoUjTQY2CUpJqNQNJJwt7s11AXk8mhFQnvd3f9wdbRV64kZo6KyYL3ALn31Ge2sqlRRWVAx7ucBjkNoBwAAAJqREwOyU3+5AeCbEdoBAACAZkRABtCcCO0AAABos9y2SwGvT7Yafl7dlqWA1ye37WrlygCgeRDaAQAA0GbZlqXCnDxlpqXXC+51E6wV5uR1+CXN+OUG0HYR2gEAANCm+T1ezRiUryxvRkJ7ljcjKTOiOzEg88sNoO1iyTex5BsAAEB74KQ10Z26vJrTlqEDOrLG5lBCuwjtAAAAaH5ODchO+uUG0JE1NoemtGJNAAAAQIfh93g1Z+gYxwVk27KU6iIGAG0F/1oBAACAFkJABnC4mIgOAAAAAACHIrQDAAAAAOBQhHYAAAAAAByK0A4AAAAAgEMR2gEAAAAAcChCOwAAAAAADkVoBwAAAADAoQjtAAAAAAA4FKEdAAAAAACHIrQDAAAAAOBQhHYAAAAAAByK0A4AAAAAgEMR2gEAAAAAcKiUZBcAAACAbxYzRjWx2vh7t+2SbVlJrAgA0BoI7QAAAA4Xro6qqCyoUDQSbwt4fSrMyZPf401iZQCAlsbweAAAAAcLV0e1oLRY5dGKhPbyaIUWlBYrXB1NUmUAgNZAaAcAAHComDEqKgtqZ1WlYjKJ22S0s6pSRWVBxYw5wBEAAG0doR0AAMChamK1CkUj9QJ7nZiMQtFIwrPuAID2hdAOAAAAAIBDMREdAAAAmozZ7AGgdRDaAQAAHMptuxTw+lQerWhwiLwtS1neDLltV6vWxWz2ANB6GB4PAADgULZlqTAnT5lp6bKVeBfblqXMtHQV5uS16h1uZrMHgNZFaAcAAHAwv8erGYPyleXNSGjP8mZoxqD8Vr2zzWz2AND6GB4PAADgcH6PV3OGjkn6M+R1s9kfyNdns0918WMmADQHvpsCAAC0AbZlEYQBoANieDwAAAAAAA5FaAcAAECj1M1mv/+keHVsWQp4fa0+mz0AtGeEdgAAADSKE2ezB4D2jtAOAACARnPSbPYA0BE4JrTffvvtsixL06dPj7dVVVVpypQp6t69u9LT0zVu3Dht37494eu2bNmiMWPGqFOnTsrKytL111+vffv2tXL1AAAAHUfdbPZ3nnJR/DVn6BgCOwC0AEeE9tWrV+vee+9VTk5OQvu1116rZ555Ro8//rhWrFihrVu36vzzz49vr62t1ZgxY1RdXa2VK1fqoYce0uLFizV79uzWvgQAAIAOpW42+7oXQ+IBoGUkPbRXVlZqwoQJuv/++9W1a9d4ezgc1p/+9CcVFRXp+9//voYOHapFixZp5cqVeuuttyRJL730kt5991395S9/0cknn6wzzzxTt9xyi+6++25VV1cn65IAAAAAAGgWSQ/tU6ZM0ZgxY5Sfn5/QvmbNGtXU1CS09+/fX3369FFJSYkkqaSkRAMHDlR2dnZ8n4KCAkUiEW3cuPGA59y7d68ikUjCCwAAAAAAp0lJ5smXLFmif/zjH1q9enW9baFQSB6PR126dEloz87OVigUiu/z9cBet71u24HMmzdPc+fOPczqAQAAAABoWUm70/7pp59q2rRpevjhh5WWltaq5541a5bC4XD89emnn7bq+QEAAAAAaIykhfY1a9aovLxcQ4YMUUpKilJSUrRixQrdeeedSklJUXZ2tqqrq7V79+6Er9u+fbsCgYAkKRAI1JtNvu593T4NSU1Nlc/nS3gBAAAAAOA0SQvteXl5Wr9+vdatWxd/DRs2TBMmTIj/2e12KxgMxr9m06ZN2rJli3JzcyVJubm5Wr9+vcrLy+P7LF++XD6fTwMGDGj1awIAAAAAoDkl7Zn2jIwMnXTSSQltnTt3Vvfu3ePtV1xxhQoLC9WtWzf5fD5dc801ys3N1ciRIyVJo0aN0oABAzRx4kTNnz9foVBIN954o6ZMmaLU1NRWvyYAAAAAAJpTUiei+yYLFy6UbdsaN26c9u7dq4KCAv3hD3+Ib3e5XHr22Wd19dVXKzc3V507d9bkyZN18803J7FqAAAAAACah2WMMckuItkikYj8fr/C4TDPtwMAAAAAWlxjc6ij77QDAAAkQ8wY1cRq4+/dtku2ZSWxIgBAR0VoBwAA+JpwdVRFZUGFopF4W8DrU2FOnvwebxIrAwB0REmbPR4AAMBpwtVRLSgtVnm0IqG9PFqhBaXFCldHk1QZAKCjIrQDAADoqyHxRWVB7ayqVEyJU/7EZLSzqlJFZUHFmA4IANCKCO0AAACSamK1CkUj9QJ7nZiMQtFIwrPuAAC0NEI7AAAAAAAORWgHAAAAAMChCO0AAAD6alm3gNcnWw0v7WbLUsDrk9t2tXJlAICOjNAOAAAgybYsFebkKTMtvV5wt2UpMy1dhTl5rNcOAGhVhHYAAID/8Hu8mjEoX1nejIT2LG+GZgzKZ512AECrS0l2AQAAAE7i93g1Z+iYhFni3baLO+wAgKQgtAMAAOzHtiyluvgxCQCQfAyPBwAAAADAofgVMgAASKqYMQxFBwDgAAjtAAAgacLVURWVBRWKRuJtAa9PhTl5TPoGAIAYHg8AAJIkXB3VgtJilUcrEtrLoxVaUFqscHU0SZUBAOAchHYAANDqYsaoqCyonVWViskkbpPRzqpKFZUFFTPmAEcAAKBjILQDAIBWVxOrVSgaqRfY68RkFIpGEp51BwCgIyK0AwAAAADgUIR2AAAAAAAcitAOAABandt2KeD1yVbDS7vZshTw+uS2Xa1cGQAAzkJoBwAArc62LBXm5CkzLb1ecLdlKTMtXYU5eazXDgDo8AjtAAAgKfwer2YMyleWNyOhPcuboRmD8lmnHQAASSnJLgAAAHRcfo9Xc4aOSZgl3m27uMMOAMB/ENoBAEBS2ZalVBc/kgAA0BCGxwMAAAAA4FCEdgAAAAAAHIrQDgAAAACAQxHaAQAAAABwKEI7AAAAAAAORWgHAAAAAMChCO0AAAAAADgUoR0AAAAAAIcitAMAAAAA4FCEdgAAAAAAHIrQDgAAAACAQxHaAQAAAABwKEI7AAAAAAAORWgHAAAAAMChCO0AAAAAADgUoR0AAAAAAIcitAMAAAAA4FCEdgAAAAAAHIrQDgAAAACAQxHaAQAAAABwKEI7AAAAAAAORWgHAAAAAMChCO0AAAAAADgUoR0AAAAAAIcitAMAAAAA4FCEdgAAAAAAHIrQDgAAAACAQ6UkuwAAANB6YsaoJlYbf++2XbItK4kVAQCAgzmk0L5v3z69+uqr+uCDD/TjH/9YGRkZ2rp1q3w+n9LT05u7RgAA0AzC1VEVlQUVikbibQGvT4U5efJ7vEmsDAAAHEiTh8d/8sknGjhwoM455xxNmTJFO3bskCT95je/0YwZM5q9QAAAcPjC1VEtKC1WebQiob08WqEFpcUKV0eTVBkAADiYJof2adOmadiwYfr3v/8tr/e/v5U/77zzFAwGm7U4AABw+GLGqKgsqJ1VlYrJJG6T0c6qShWVBRUz5gBHAAAAydLk4fGvv/66Vq5cKY/Hk9Det29fff75581WGAAAaB41sdqEIfH7i8koFI2oJlarVBfT3QAA4CRNvtMei8VUW1tbr/2zzz5TRkZGsxQFAAAAAAAOIbSPGjVKv/vd7+LvLctSZWWl5syZo7POOqs5awMAAAAAoENr8hi4O+64QwUFBRowYICqqqr04x//WJs3b1ZmZqb++te/tkSNAADgMLhtlwJen8qjFfWeaZckW5ayvBly264kVAcAAA7GMqbps87s27dPjz76qEpLS1VZWakhQ4ZowoQJCRPTtSWRSER+v1/hcFg+ny/Z5QAA0OzqZo/ffzI6W5Yy09I1Y1A+y74BANCKGptDDym0tzeEdgBAR8A67QAAOEdjc2iTh8fPmzdP2dnZuvzyyxPaH3zwQe3YsUMzZ85serUAAKDF+T1ezRk6RjWx/04o67Zdsi0riVUBAICDafJEdPfee6/69+9fr/3EE0/UPffc0yxFAQCAlmFbllJdKfEXgR0AAGdrcmgPhULq2bNnvfYePXpo27ZtzVIUAAAAAAA4hNDeu3dvvfnmm/Xa33zzTfXq1atJx/rjH/+onJwc+Xw++Xw+5ebm6oUXXohvr6qq0pQpU9S9e3elp6dr3Lhx2r59e8IxtmzZojFjxqhTp07KysrS9ddfr3379jX1sgAAaBExY7S3dl/8FWMqGQAA0ARNfqb9yiuv1PTp01VTU6Pvf//7kqRgMKgbbrhB1113XZOOdeSRR+r2229Xv379ZIzRQw89pHPOOUdr167ViSeeqGuvvVbPPfecHn/8cfn9fk2dOlXnn39+/JcGtbW1GjNmjAKBgFauXKlt27Zp0qRJcrvduu2225p6aQAANCsmfgMAAIerybPHG2P0i1/8Qnfeeaeqq6slSWlpaZo5c6Zmz5592AV169ZNv/3tb3XBBReoR48eeuSRR3TBBRdIkv75z3/qhBNOUElJiUaOHKkXXnhBZ599trZu3ars7GxJ0j333KOZM2dqx44d8ng8jTons8cDAJobS6wBAICDaWwObfLweMuy9Jvf/EY7duzQW2+9pdLSUu3ateuwA3ttba2WLFmiL7/8Urm5uVqzZo1qamqUn58f36d///7q06ePSkpKJEklJSUaOHBgPLBLUkFBgSKRiDZu3HjAc+3du1eRSCThBQBAc4kZo6KyYL3ALkkxGe2sqlRRWZCh8gAA4Bs1eXh8nfT0dA0fPvywC1i/fr1yc3NVVVWl9PR0PfnkkxowYIDWrVsnj8ejLl26JOyfnZ2tUCgk6atJ8b4e2Ou21207kHnz5mnu3LmHXTsAAA2pidUmDInfX0xGoWhENbFapboO+b9iAADQATT5J4Uvv/xSt99+u4LBoMrLyxWLxRK2f/jhh0063vHHH69169YpHA7rb3/7myZPnqwVK1Y0tawmmTVrlgoLC+PvI5GIevfu3aLnBAAAAACgqZoc2v/nf/5HK1as0MSJE9WzZ09Zh7m+q8fj0bHHHitJGjp0qFavXq3f//73uvjii1VdXa3du3cn3G3fvn27AoGAJCkQCOjtt99OOF7d7PJ1+zQkNTVVqamph1U3AAAAAAAtrcmh/YUXXtBzzz2nU089tSXqUSwW0969ezV06FC53W4Fg0GNGzdOkrRp0yZt2bJFubm5kqTc3FzdeuutKi8vV1ZWliRp+fLl8vl8GjBgQIvUBwDAN3HbLgW8PpVHK+o90y59NRldljdDbtuVhOoAAEBb0uTQ3rVrV3Xr1q1ZTj5r1iydeeaZ6tOnjyoqKvTII4/o1Vdf1Ysvvii/368rrrhChYWF6tatm3w+n6655hrl5uZq5MiRkqRRo0ZpwIABmjhxoubPn69QKKQbb7xRU6ZM4U46ACBpbMtSYU7eQWePL8zJk32Yo9UAAED71+TZ42+55RbNnj1be/bsOeyTl5eXa9KkSTr++OOVl5en1atX68UXX9QPfvADSdLChQt19tlna9y4cfrOd76jQCCgJ554Iv71LpdLzz77rFwul3Jzc3XJJZdo0qRJuvnmmw+7NgAADoff49WMQfnK8mYktGd5M1juDQAANFqT12kfPHiwPvjgAxlj1LdvX7nd7oTt//jHP5q1wNbAOu0AgJYSM0Y1sdr4e7ft4g47AABodA5t8vD4c88993DqAgCgQ7Eti2XdAADAIWvynfb2iDvtAAAAAIDW1Ngc2uRn2gEAAAAAQOto8ni92tpaLVy4UI899pi2bNmi6urqhO27du1qtuIAAAAAAOjImnynfe7cuSoqKtLFF1+scDiswsJCnX/++bJtW7/61a9aoEQAAAAAADqmJof2hx9+WPfff7+uu+46paSkaPz48XrggQc0e/ZsvfXWWy1RIwAAAAAAHVKTQ3soFNLAgQMlSenp6QqHw5Kks88+W88991zzVgcAAAAAQAfW5NB+5JFHatu2bZKkb33rW3rppZckSatXr1ZqamrzVgcAAAAAQAfW5NB+3nnnKRgMSpKuueYa3XTTTerXr58mTZqkyy+/vNkLBAAAAACgozrsddpLSkpUUlKifv36aezYsc1VV6tinXYAAAAAQGtqbA5t8pJv+8vNzVVubu7hHgYAAAAAAOynUaF96dKlOvPMM+V2u7V06dKD7vvDH/6wWQoDAAAAAKCja9TweNu2FQqFlJWVJds+8GPwlmWptra2WQtsDQyPBwAAAAC0pmYdHh+LxRr8MwAAAAAAaDlNmj2+pqZGeXl52rx5c0vVAwAAAAAA/qNJod3tdqusrKylagEAAAAAAF/T5HXaL7nkEv3pT39qiVoAAAAAAMDXNHnJt3379unBBx9UcXGxhg4dqs6dOydsLyoqarbiAABoipgxqon9d0JUt+2SbVlJrAgAAODwNDm0b9iwQUOGDJEk/etf/0rYZvGDEQAgScLVURWVBRWKRuJtAa9PhTl58nu8SawMAADg0DVqybf2jiXfAKBtC1dHtaC0WDurKhXTf/9bs2UpMy1dMwblE9wBAICjNDaHNvmZdgAAnCRmjIrKgvUCuyTFZLSzqlJFZUHF+B01AABog5o8PF6S3nnnHT322GPasmWLqqurE7Y98cQTzVIYAACNUROrTRgSv7+YjELRiGpitUp1HdJ/ewAAAEnT5DvtS5Ys0SmnnKL33ntPTz75pGpqarRx40a9/PLL8vv9LVEjAAAAAAAdUpND+2233aaFCxfqmWeekcfj0e9//3v985//1EUXXaQ+ffq0RI0AAAAAAHRITQ7tH3zwgcaMGSNJ8ng8+vLLL2VZlq699lrdd999zV4gAAAH47ZdCnh9stXwCia2LAW8PrltVytXBgAAcPiaHNq7du2qiooKSdIRRxyhDRs2SJJ2796tPXv2NG91AAB8A9uyVJiTp8y09HrBvW72+MKcPNZrBwAAbVKjQ3tdOP/Od76j5cuXS5IuvPBCTZs2TVdeeaXGjx+vvLy8lqkSAICD8Hu8mjEoX1nejIT2LG8Gy70BAIA2rdHrtNu2reHDh+vcc8/VJZdcot69eysWi2n+/PlauXKl+vXrpxtvvFFdu3Zt6ZqbHeu0A0D7EDNGNbHa+Hu37eIOOwAAcKTG5tBGh/bXX39dixYt0t/+9jfFYjGNGzdO//M//6PTTz+92YpOFkI7AAAAAKA1NTaHNnp4/Omnn64HH3xQ27Zt01133aWPP/5YZ5xxho477jj95je/USgUapbCAQAAAADAV5o8EV3nzp112WWXacWKFfrXv/6lCy+8UHfffbf69OmjH/7why1RIwAAAAAAHVKjh8cfyJdffqmHH35Ys2bN0u7du1VbW/vNX+QwDI8HAAAAALSmxubQlEM9wWuvvaYHH3xQf//732Xbti666CJdccUVh3o4AAAAAACwnyaF9q1bt2rx4sVavHix3n//fZ1yyim68847ddFFF6lz584tVSMAAAAAAB1So0P7mWeeqeLiYmVmZmrSpEm6/PLLdfzxx7dkbQAAAAAAdGiNDu1ut1t/+9vfdPbZZ8vlcrVkTQAAAAAAQE0I7UuXLm3JOgAAAAAAwH6avOQbAAAAAABoHYR2AAAAAAAcitAOAAAAAIBDEdoBAAAAAHAoQjsAAAAAAA5FaAcAAAAAwKEI7QAAAAAAOBShHQAAAAAAhyK0AwAAAADgUIR2AAAAAAAcitAOAAAAAIBDEdoBAAAAAHAoQjsAAAAAAA5FaAcAAAAAwKFSkl0AAKBtihmjmlht/L3bdsm2rCRWBAAA0P4Q2gEATRaujqqoLKhQNBJvC3h9KszJk9/jTWJlAAAA7QvD4wEATRKujmpBabHKoxUJ7eXRCi0oLVa4OpqkygAAANofQjsAoNFixqioLKidVZWKySRuk9HOqkoVlQUVM+YARwAAAEBTENoBAI1WE6tVKBqpF9jrxGQUikYSnnUHAADAoSO0AwAAAADgUIR2AAAAAAAcitAOAGg0t+1SwOuTrYaXdrNlKeD1yW27WrkyAACA9onQDgBoNNuyVJiTp8y09HrB3ZalzLR0FebksV47AABAMyG0AwCaxO/xasagfGV5MxLas7wZmjEon3XaAQAAmlFKsgsAALQ9fo9Xc4aOSZgl3m27uMMOAADQzAjtAIBDYluWUl38NwIAANCSGB4PAAAAAIBDEdoBAAAAAHCopIb2efPmafjw4crIyFBWVpbOPfdcbdq0KWGfqqoqTZkyRd27d1d6errGjRun7du3J+yzZcsWjRkzRp06dVJWVpauv/567du3rzUvBQAAAACAZpfU0L5ixQpNmTJFb731lpYvX66amhqNGjVKX375ZXyfa6+9Vs8884wef/xxrVixQlu3btX5558f315bW6sxY8aourpaK1eu1EMPPaTFixdr9uzZybgkAAAAAACajWWMMckuos6OHTuUlZWlFStW6Dvf+Y7C4bB69OihRx55RBdccIEk6Z///KdOOOEElZSUaOTIkXrhhRd09tlna+vWrcrOzpYk3XPPPZo5c6Z27Nghj8fzjeeNRCLy+/0Kh8Py+Xwteo0AAAAAADQ2hzrqmfZwOCxJ6tatmyRpzZo1qqmpUX5+fnyf/v37q0+fPiopKZEklZSUaODAgfHALkkFBQWKRCLauHFjK1YPAAAAAEDzcsxaPbFYTNOnT9epp56qk046SZIUCoXk8XjUpUuXhH2zs7MVCoXi+3w9sNdtr9vWkL1792rv3r3x95FIpLkuAwAAAACAZuOYO+1TpkzRhg0btGTJkhY/17x58+T3++Ov3r17t/g5AQAAAABoKkeE9qlTp+rZZ5/VK6+8oiOPPDLeHggEVF1drd27dyfsv337dgUCgfg++88mX/e+bp/9zZo1S+FwOP769NNPm/FqAAAAAABoHkkN7cYYTZ06VU8++aRefvllHX300Qnbhw4dKrfbrWAwGG/btGmTtmzZotzcXElSbm6u1q9fr/Ly8vg+y5cvl8/n04ABAxo8b2pqqnw+X8ILAAAAAACnSeoz7VOmTNEjjzyip59+WhkZGfFn0P1+v7xer/x+v6644goVFhaqW7du8vl8uuaaa5Sbm6uRI0dKkkaNGqUBAwZo4sSJmj9/vkKhkG688UZNmTJFqampybw8AAAAAAAOS1KXfLMsq8H2RYsW6dJLL5UkVVVV6brrrtNf//pX7d27VwUFBfrDH/6QMPT9k08+0dVXX61XX31VnTt31uTJk3X77bcrJaVxv5NgyTcAAAAAQGtqbA511DrtyUJoBwAAAAC0pja5TjsAAAAAAPgvQjsAAAAAAA6V1InoAACNEzNGNbHa+Hu37ZJ9gHlBAAAA0H4Q2gHA4cLVURWVBRWKRuJtAa9PhTl58nu8SawMAAAALY3h8QDgYOHqqBaUFqs8WpHQXh6t0ILSYoWro0mqDAAAAK2B0A4ADhUzRkVlQe2sqlRMiQt9xGS0s6pSRWVBxVgEBAAAoN0itAOAQ9XEahWKRuoF9joxGYWikYRn3QEAANC+ENoBAAAAAHAoQjsAAAAAAA5FaAcAh3LbLgW8PtlqeGk3W5YCXp/ctquVKwMAAEBrIbQDgEPZlqXCnDxlpqXXC+62LGWmpaswJ4/12gEAANoxQjsAOJjf49WMQfnK8mYktGd5MzRjUD7rtAMAALRzKckuAABwcH6PV3OGjkmYJd5tu7jDDgAA0AEQ2gGgDbAtS6kuvmUDAAB0NAyPBwAAAADAoQjtAAAAAAA4FKEdAAAAAACHIrQDAAAAAOBQzGoEAPuJGcNM7QAAAHAEQjsAfE24OqqisqBC0Ui8LeD1qTAnjzXRAQAA0OoYHg8A/xGujmpBabHKoxUJ7eXRCi0oLVa4OpqkygAAANBREdoBQF8NiS8qC2pnVaViMonbZLSzqlJFZUHFjDnAEQAAAIDmR2gHAEk1sVqFopF6gb1OTEahaCThWXcAAACgpRHaAQAAAABwKEI7AAAAAAAORWgHAH21rFvA65Othpd2s2Up4PXJbbtauTIAAAB0ZIR2AJBkW5YKc/KUmZZeL7jbspSZlq7CnDzWawcAAECrIrQDwH/4PV7NGJSvLG9GQnuWN0MzBuWzTjsAAABaXUqyCwAAJ/F7vJozdEzCLPFu28UddgAAACQFoR0A9mNbllJdfHsEAABA8jE8HgAAAAAAhyK0AwAAAADgUIR2AAAAAAAcitAOAAAAAIBDEdoBAAAAAHAoQjsAAAAAAA5FaAcAAAAAwKEI7QAAAAAAOFRKsgsAgJgxqonVxt+7bZdsy0piRQAAAIAzENoBJFW4OqqisqBC0Ui8LeD1qTAnT36PN4mVAQAAAMnH8HgASROujmpBabHKoxUJ7eXRCi0oLVa4OpqkygAAAABnILQDSIqYMSoqC2pnVaViMonbZLSzqlJFZUHFjDnAEQAAAID2j9AOIClqYrUKRSP1AnudmIxC0UjCs+4AAABAR0NoBwAAAADAoQjtAAAAAAA4FKEdQFK4bZcCXp9sNby0my1LAa9PbtvVypUBAAAAzkFoB5AUtmWpMCdPmWnp9YK7LUuZaekqzMljvXYAAAB0aIR2AEnj93g1Y1C+srwZCe1Z3gzNGJTPOu0AAADo8FKSXQCAjs3v8WrO0DEJs8S7bRd32AEAAAAR2gE4gG1ZSnXx7QgAAADYH8PjAQAAAABwKG5tAR1MzBiGogMAAABtBKEd6EDC1VEVlQUVikbibQGvT4U5eUz6BgAAADgQw+OBDiJcHdWC0mKVRysS2sujFVpQWqxwdTRJlQEAAAA4EEI70AHEjFFRWVA7qyoVk0ncJqOdVZUqKgsqZswBjgAAAAAgGQjtQAdQE6tVKBqpF9jrxGQUikYSnnUHAAAAkHyEdgAAAAAAHIrQDgAAAACAQxHagQ7AbbsU8Ppkq+Gl3WxZCnh9ctuuVq4MAAAAwMEQ2oEOwLYsFebkKTMtvV5wt2UpMy1dhTl5rNcOAAAAOAyhHegg/B6vZgzKV5Y3I6E9y5uhGYPyWacdAAAAcKCUZBcAoPX4PV7NGTomYZZ4t+3iDjsAAADgUIR2oIOxLUupLv7pAwAAAG0Bw+MBAAAAAHAoQjsAAAAAAA5FaAcAAAAAwKGSGtpfe+01jR07Vr169ZJlWXrqqacSthtjNHv2bPXs2VNer1f5+fnavHlzwj67du3ShAkT5PP51KVLF11xxRWqrKxsxasAAAAAAKBlJDW0f/nllxo0aJDuvvvuBrfPnz9fd955p+655x6tWrVKnTt3VkFBgaqqquL7TJgwQRs3btTy5cv17LPP6rXXXtNVV13VWpcAAAAAAECLsYwxJtlFSJJlWXryySd17rnnSvrqLnuvXr103XXXacaMGZKkcDis7OxsLV68WD/60Y/03nvvacCAAVq9erWGDRsmSVq2bJnOOussffbZZ+rVq1ejzh2JROT3+xUOh+Xz+Vrk+tAxxYxheTUAAAAA9TQ2hzp23aePPvpIoVBI+fn58Ta/368RI0aopKREP/rRj1RSUqIuXbrEA7sk5efny7ZtrVq1Suedd16Dx967d6/27t0bfx+JRFruQtBhhaujKioLKhT9b/8KeH0qzMmT3+NNYmUAAAAA2grHTkQXCoUkSdnZ2Qnt2dnZ8W2hUEhZWVkJ21NSUtStW7f4Pg2ZN2+e/H5//NW7d+9mrh4dXbg6qgWlxSqPViS0l0crtKC0WOHqaJIqAwAAANCWODa0t6RZs2YpHA7HX59++mmyS0I7EjNGRWVB7ayqVEyJT5/EZLSzqlJFZUHFnPFkCgAAAAAHc2xoDwQCkqTt27cntG/fvj2+LRAIqLy8PGH7vn37tGvXrvg+DUlNTZXP50t4Ac2lJlarUDRSL7DXickoFI0kPOsOAAAAAA1xbGg/+uijFQgEFAwG422RSESrVq1Sbm6uJCk3N1e7d+/WmjVr4vu8/PLLisViGjFiRKvXDAAAAABAc0rqRHSVlZV6//334+8/+ugjrVu3Tt26dVOfPn00ffp0/frXv1a/fv109NFH66abblKvXr3iM8yfcMIJGj16tK688krdc889qqmp0dSpU/WjH/2o0TPHAwAAAADgVEkN7e+8846+973vxd8XFhZKkiZPnqzFixfrhhtu0JdffqmrrrpKu3fv1mmnnaZly5YpLS0t/jUPP/ywpk6dqry8PNm2rXHjxunOO+9s9WsB6rhtlwJen8qjFQ0OkbdlKcubIbftSkJ1AAAAANoSx6zTnkys047mVjd7/P6T0dmylJmWrhmD8ln2DQAAAOjAGptDHftMO9CW+T1ezRiUryxvRkJ7ljeDwA4AAACg0ZI6PB5oz/wer+YMHZMwS7zbdsm2rCRWBQAAAKAtIbQDLci2LKW6+GcGAAAA4NAwPB4AAAAAAIcitAMAAAAA4FCEdgAAAAAAHIrQDgAAAACAQxHaAQAAAABwKEI7AAAAAAAORWgHAAAAAMChCO0AAAAAADgUoR0AAAAAAIcitAMAAAAA4FCEdgAAAAAAHIrQDgAAAACAQxHaAQAAAABwKEI7AAAAAAAORWgHAAAAAMChCO0AAAAAADhUSrILAJpLzBjVxGrj7922S7ZlJbEiAAAAADg8hHa0C+HqqIrKggpFI/G2gNenwpw8+T3eJFYGAAAAAIeO4fFo88LVUS0oLVZ5tCKhvTxaoQWlxQpXR5NUGQAAAAAcHkI72rSYMSoqC2pnVaViMonbZLSzqlJFZUHFjDnAEQAAAADAuQjtaNNqYrUKRSP1AnudmIxC0UjCs+4AAAAA0FYQ2gEAAAAAcChCOwAAAAAADkVoR5vmtl0KeH2y1fDSbrYsBbw+uW1XK1cGAAAAAIeP0I42zbYsFebkKTMtvV5wt2UpMy1dhTl5rNcOAAAAoE0itKPN83u8mjEoX1nejIT2LG+GZgzKZ512AAAAAG1WSrILAJqD3+PVnKFjEmaJd9su7rADAAAAaNMI7Wg3bMtSqosuDQAAAKD9YHg8AAAAAAAORWgHAAAAAMChCO0AAAAAADgUoR0AAAAAAIcitAMAAAAA4FCEdgAAAAAAHIrQDgAAAACAQxHaAQAAAABwqJRkF4C2K2aMamK18fdu2yXbspJYEQAAAAC0L4R2HJJwdVRFZUGFopF4W8DrU2FOnvwebxIrAwAAAID2g+HxaLJwdVQLSotVHq1IaC+PVmhBabHC1dEkVQYAAAAA7QuhHU0SM0ZFZUHtrKpUTCZxm4x2VlWqqCyomDEHOAIAAAAAoLEI7WiSmlitQtFIvcBeJyajUDSS8Kw7AAAAAODQENoBAAAAAHAoQjsAAAAAAA5FaEeTuG2XAl6fbDW8tJstSwGvT27b1cqVAQAAAED7Q2hHk9iWpcKcPGWmpdcL7rYsZaalqzAnj/XaAQAAAKAZENrRZH6PVzMG5SvLm5HQnuXN0IxB+azTDgAAAADNJCXZBaBt8nu8mjN0TMIs8W7bxR12AAAAAGhGhHYcMtuylOqiCwEAAABAS2F4PAAAAAAADkVoBwAAAADAoQjtAAAAAAA4FKEdAAAAAACHIrQDAAAAAOBQhHYAAAAAAByK9braiJgxrIkOAAAAAB0Mob0NCFdHVVQWVCgaibcFvD4V5uTJ7/EmsTIAAAAAQEtieLzDhaujWlBarPJoRUJ7ebRCC0qLFa6OJqkyAAAAAEBLI7Q7WMwYFZUFtbOqUjGZxG0y2llVqaKyoGLGHOAIAAAAAIC2jNDuYDWxWoWikXqBvU5MRqFoJOFZdwAAAABA+0FoBwAAAADAoQjtAAAAAAA4FKHdwdy2SwGvT7YaXtrNlqWA1ye37WrlygAAAAAAraHdhPa7775bffv2VVpamkaMGKG333472SUdNtuyVJiTp8y09HrB3ZalzLR0FebksV47AAAAALRT7SK0P/rooyosLNScOXP0j3/8Q4MGDVJBQYHKy8uTXdph83u8mjEoX1nejIT2LG+GZgzKZ512AAAAAGjHLGPa/nphI0aM0PDhw/X//t//kyTFYjH17t1b11xzjX7xi19849dHIhH5/X6Fw2H5fL6WLveQxIxJmCXebbu4ww4AAAAAbVRjc2ibv9NeXV2tNWvWKD8/P95m27by8/NVUlLS4Nfs3btXkUgk4eV0tmUp1ZUSfxHYAQAAAKD9a/OhfefOnaqtrVV2dnZCe3Z2tkKhUINfM2/ePPn9/vird+/erVEqAAAAAABN0uZD+6GYNWuWwuFw/PXpp58muyQAAAAAAOpJSXYBhyszM1Mul0vbt29PaN++fbsCgUCDX5OamqrU1NTWKA8AAAAAgEPW5u+0ezweDR06VMFgMN4Wi8UUDAaVm5ubxMoAAAAAADg8bf5OuyQVFhZq8uTJGjZsmL797W/rd7/7nb788ktddtllyS4NAAAAAIBD1i5C+8UXX6wdO3Zo9uzZCoVCOvnkk7Vs2bJ6k9MBAAAAANCWtIt12g9XW1inHQAAAADQfnSYddoBAAAAAGivCO0AAAAAADgUoR0AAAAAAIcitAMAAAAA4FCEdgAAAAAAHIrQDgAAAACAQ7WLddoPV92qd5FIJMmVAAAAAAA6grr8+U2rsBPaJVVUVEiSevfuneRKAAAAAAAdSUVFhfx+/wG3W+abYn0HEIvFtHXrVmVkZMiyLElf/dajd+/e+vTTTw+60D3wTehLaC70JTQX+hKaC30JzYW+hObSlvqSMUYVFRXq1auXbPvAT65zp12Sbds68sgjG9zm8/kc/5eNtoG+hOZCX0JzoS+hudCX0FzoS2gubaUvHewOex0mogMAAAAAwKEI7QAAAAAAOBSh/QBSU1M1Z84cpaamJrsUtHH0JTQX+hKaC30JzYW+hOZCX0JzaY99iYnoAAAAAABwKO60AwAAAADgUIR2AAAAAAAcitAOAAAAAIBDEdoBAAAAAHAoQvsB3H333erbt6/S0tI0YsQIvf3228kuCUk0b948DR8+XBkZGcrKytK5556rTZs2JexTVVWlKVOmqHv37kpPT9e4ceO0ffv2hH22bNmiMWPGqFOnTsrKytL111+vffv2Jezz6quvasiQIUpNTdWxxx6rxYsXt/TlIUluv/12WZal6dOnx9voR2iKzz//XJdccom6d+8ur9ergQMH6p133olvN8Zo9uzZ6tmzp7xer/Lz87V58+aEY+zatUsTJkyQz+dTly5ddMUVV6iysjJhn7KyMp1++ulKS0tT7969NX/+/Fa5PrSO2tpa3XTTTTr66KPl9Xr1rW99S7fccou+PlcxfQkNee211zR27Fj16tVLlmXpqaeeStjemv3m8ccfV//+/ZWWlqaBAwfq+eefb/brRcs5WF+qqanRzJkzNXDgQHXu3Fm9evXSpEmTtHXr1oRjtOu+ZFDPkiVLjMfjMQ8++KDZuHGjufLKK02XLl3M9u3bk10akqSgoMAsWrTIbNiwwaxbt86cddZZpk+fPqaysjK+z09/+lPTu3dvEwwGzTvvvGNGjhxpTjnllPj2ffv2mZNOOsnk5+ebtWvXmueff95kZmaaWbNmxff58MMPTadOnUxhYaF59913zV133WVcLpdZtmxZq14vWt7bb79t+vbta3Jycsy0adPi7fQjNNauXbvMUUcdZS699FKzatUq8+GHH5oXX3zRvP/++/F9br/9duP3+81TTz1lSktLzQ9/+ENz9NFHm2g0Gt9n9OjRZtCgQeatt94yr7/+ujn22GPN+PHj49vD4bDJzs42EyZMMBs2bDB//etfjdfrNffee2+rXi9azq233mq6d+9unn32WfPRRx+Zxx9/3KSnp5vf//738X3oS2jI888/b/73f//XPPHEE0aSefLJJxO2t1a/efPNN43L5TLz58837777rrnxxhuN2+0269evb/HPAM3jYH1p9+7dJj8/3zz66KPmn//8pykpKTHf/va3zdChQxOO0Z77EqG9Ad/+9rfNlClT4u9ra2tNr169zLx585JYFZykvLzcSDIrVqwwxnz1zcTtdpvHH388vs97771nJJmSkhJjzFffjGzbNqFQKL7PH//4R+Pz+czevXuNMcbccMMN5sQTT0w418UXX2wKCgpa+pLQiioqKky/fv3M8uXLzRlnnBEP7fQjNMXMmTPNaaeddsDtsVjMBAIB89vf/jbetnv3bpOammr++te/GmOMeffdd40ks3r16vg+L7zwgrEsy3z++efGGGP+8Ic/mK5du8b7V925jz/++Oa+JCTJmDFjzOWXX57Qdv7555sJEyYYY+hLaJz9g1Zr9puLLrrIjBkzJqGeESNGmJ/85CfNeo1oHQ39Amh/b7/9tpFkPvnkE2NM++9LDI/fT3V1tdasWaP8/Px4m23bys/PV0lJSRIrg5OEw2FJUrdu3SRJa9asUU1NTUK/6d+/v/r06RPvNyUlJRo4cKCys7Pj+xQUFCgSiWjjxo3xfb5+jLp96Hvty5QpUzRmzJh6f9f0IzTF0qVLNWzYMF144YXKysrS4MGDdf/998e3f/TRRwqFQgl9we/3a8SIEQn9qUuXLho2bFh8n/z8fNm2rVWrVsX3+c53viOPxxPfp6CgQJs2bdK///3vlr5MtIJTTjlFwWBQ//rXvyRJpaWleuONN3TmmWdKoi/h0LRmv+H/vY4nHA7Lsix16dJFUvvvS4T2/ezcuVO1tbUJPxBLUnZ2tkKhUJKqgpPEYjFNnz5dp556qk466SRJUigUksfjiX/jqPP1fhMKhRrsV3XbDrZPJBJRNBptictBK1uyZIn+8Y9/aN68efW20Y/QFB9++KH++Mc/ql+/fnrxxRd19dVX6+c//7keeughSf/tDwf7/ywUCikrKythe0pKirp169akPoe27Re/+IV+9KMfqX///nK73Ro8eLCmT5+uCRMmSKIv4dC0Zr850D70q/apqqpKM2fO1Pjx4+Xz+SS1/76UktSzA23QlClTtGHDBr3xxhvJLgVtzKeffqpp06Zp+fLlSktLS3Y5aONisZiGDRum2267TZI0ePBgbdiwQffcc48mT56c5OrQljz22GN6+OGH9cgjj+jEE0/UunXrNH36dPXq1Yu+BMBRampqdNFFF8kYoz/+8Y/JLqfVcKd9P5mZmXK5XPVma96+fbsCgUCSqoJTTJ06Vc8++6xeeeUVHXnkkfH2QCCg6upq7d69O2H/r/ebQCDQYL+q23awfXw+n7xeb3NfDlrZmjVrVF5eriFDhiglJUUpKSlasWKF7rzzTqWkpCg7O5t+hEbr2bOnBgwYkNB2wgknaMuWLZL+2x8O9v9ZIBBQeXl5wvZ9+/Zp165dTepzaNuuv/76+N32gQMHauLEibr22mvjI4LoSzgUrdlvDrQP/ap9qQvsn3zyiZYvXx6/yy61/75EaN+Px+PR0KFDFQwG422xWEzBYFC5ublJrAzJZIzR1KlT9eSTT+rll1/W0UcfnbB96NChcrvdCf1m06ZN2rJlS7zf5Obmav369QnfUOq+4dT94J2bm5twjLp96HvtQ15entavX69169bFX8OGDdOECRPif6YfobFOPfXUektP/utf/9JRRx0lSTr66KMVCAQS+kIkEtGqVasS+tPu3bu1Zs2a+D4vv/yyYrGYRowYEd/ntddeU01NTXyf5cuX6/jjj1fXrl1b7PrQevbs2SPbTvyR0OVyKRaLSaIv4dC0Zr/h/732ry6wb968WcXFxerevXvC9nbfl5I6DZ5DLVmyxKSmpprFixebd99911x11VWmS5cuCbM1o2O5+uqrjd/vN6+++qrZtm1b/LVnz574Pj/96U9Nnz59zMsvv2zeeecdk5uba3Jzc+Pb65bqGjVqlFm3bp1ZtmyZ6dGjR4NLdV1//fXmvffeM3fffTdLdbVzX5893hj6ERrv7bffNikpKebWW281mzdvNg8//LDp1KmT+ctf/hLf5/bbbzddunQxTz/9tCkrKzPnnHNOg8stDR482Kxatcq88cYbpl+/fglL5OzevdtkZ2ebiRMnmg0bNpglS5aYTp06sUxXOzJ58mRzxBFHxJd8e+KJJ0xmZqa54YYb4vvQl9CQiooKs3btWrN27VojyRQVFZm1a9fGZ/RurX7z5ptvmpSUFLNgwQLz3nvvmTlz5jhimS403sH6UnV1tfnhD39ojjzySLNu3bqEn8W/PhN8e+5LhPYDuOuuu0yfPn2Mx+Mx3/72t81bb72V7JKQRJIafC1atCi+TzQaNT/72c9M165dTadOncx5551ntm3blnCcjz/+2Jx55pnG6/WazMxMc91115mampqEfV555RVz8sknG4/HY4455piEc6D92T+004/QFM8884w56aSTTGpqqunfv7+57777ErbHYjFz0003mezsbJOammry8vLMpk2bEvb54osvzPjx4016errx+XzmsssuMxUVFQn7lJaWmtNOO82kpqaaI444wtx+++0tfm1oPZFIxEybNs306dPHpKWlmWOOOcb87//+b8IPw/QlNOSVV15p8OejyZMnG2Nat9889thj5rjjjjMej8eceOKJ5rnnnmux60bzO1hf+uijjw74s/grr7wSP0Z77kuWMca03n19AAAAAADQWDzTDgAAAACAQxHaAQAAAABwKEI7AAAAAAAORWgHAAAAAMChCO0AAAAAADgUoR0AAAAAAIcitAMAAAAA4FCEdgAAOiDLsvTUU08luwwAAPANCO0AALQjl156qSzLkmVZcrvdys7O1g9+8AM9+OCDisVi8f22bdumM888s1HHJOADAJA8hHYAANqZ0aNHa9u2bfr444/1wgsv6Hvf+56mTZums88+W/v27ZMkBQIBpaamJrlSAADwTQjtAAC0M6mpqQoEAjriiCM0ZMgQ/fKXv9TTTz+tF154QYsXL5aUePe8urpaU6dOVc+ePZWWlqajjjpK8+bNkyT17dtXknTeeefJsqz4+w8++EDnnHOOsrOzlZ6eruHDh6u4uDihjr59++q2227T5ZdfroyMDPXp00f33Xdfwj6fffaZxo8fr27duqlz584aNmyYVq1aFd/+9NNPa8iQIUpLS9MxxxyjuXPnxn/xAABAR0BoBwCgA/j+97+vQYMG6Yknnqi37c4779TSpUv12GOPadOmTXr44Yfj4Xz16tWSpEWLFmnbtm3x95WVlTrrrLMUDAa1du1ajR49WmPHjtWWLVsSjn3HHXdo2LBhWrt2rX72s5/p6quv1qZNm+LHOOOMM/T5559r6dKlKi0t1Q033BAfxv/6669r0qRJmjZtmt59913de++9Wrx4sW699daW+pgAAHCclGQXAAAAWkf//v1VVlZWr33Lli3q16+fTjvtNFmWpaOOOiq+rUePHpKkLl26KBAIxNsHDRqkQYMGxd/fcsstevLJJ7V06VJNnTo13n7WWWfpZz/7mSRp5syZWrhwoV555RUdf/zxeuSRR7Rjxw6tXr1a3bp1kyQde+yx8a+dO3eufvGLX2jy5MmSpGOOOUa33HKLbrjhBs2ZM6c5PhIAAByP0A4AQAdhjJFlWfXaL730Uv3gBz/Q8ccfr9GjR+vss8/WqFGjDnqsyspK/epXv9Jzzz2nbdu2ad++fYpGo/XutOfk5MT/bFmWAoGAysvLJUnr1q3T4MGD44F9f6WlpXrzzTcT7qzX1taqqqpKe/bsUadOnRp97QAAtFWEdgAAOoj33ntPRx99dL32IUOG6KOPPtILL7yg4uJiXXTRRcrPz9ff/va3Ax5rxowZWr58uRYsWKBjjz1WXq9XF1xwgaqrqxP2c7vdCe8ty4oPf/d6vQett7KyUnPnztX5559fb1taWtpBvxYAgPaC0A4AQAfw8ssva/369br22msb3O7z+XTxxRfr4osv1gUXXKDRo0dr165d6tatm9xut2praxP2f/PNN3XppZfqvPPOk/RVwP7444+bVFNOTo4eeOCB+Hn2N2TIEG3atClhyDwAAB0NoR0AgHZm7969CoVCqq2t1fbt27Vs2TLNmzdPZ599tiZNmlRv/6KiIvXs2VODBw+Wbdt6/PHHFQgE1KVLF0lfzQIfDAZ16qmnKjU1VV27dlW/fv30xBNPaOzYsbIsSzfddFPCOvCNMX78eN12220699xzNW/ePPXs2VNr165Vr169lJubq9mzZ+vss89Wnz59dMEFF8i2bZWWlmrDhg369a9/3RwfFQAAjsfs8QAAtDPLli1Tz5491bdvX40ePVqvvPKK7rzzTj399NNyuVz19s/IyND8+fM1bNgwDR8+XB9//LGef/552fZXPybccccdWr58uXr37q3BgwdL+irod+3aVaeccorGjh2rgoICDRkypEl1ejwevfTSS8rKytJZZ52lgQMH6vbbb4/XWFBQoGeffVYvvfSShg8frpEjR2rhwoUJE+UBANDeWcYYk+wiAAAAAABAfdxpBwAAAADAoQjtAAAAAAA4FKEdAAAAAACHIrQDAAAAAOBQhHYAAAAAAByK0A4AAAAAgEMR2gEAAAAAcChCOwAAAAAADkVoBwAAAADAoQjtAAAAAAA4FKEdAAAAAACHIrQDAAAAAOBQ/x+46hg6twD9HwAAAABJRU5ErkJggg==\n", 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W8uXL1a1bt4jtvPPOkyTdd999+uKLL/T4449LOnQ7+RNPPKE777xTRUVF4X2MHTtWwWBQP/7xjzVhwgRNmjRJN954Y7h/4cKFGjt2rG677TaddtppuvTSS7Vu3Tr16NGjWY7p+9LS0vTee++ppqZGw4YNU79+/TR58mQlJSVF3C3wQx544AGtWLFC3bt314ABAyRJeXl56tSpk84991yNGDFC2dnZjb5Km52draefflrDhg3T6aefrptvvlnZ2dl6/vnnw2Na8vMbPXq0tm7dqhNOOEFDhw4Nt9vtdi1evFjr16/XmWeeqVtvvVV/+ctfjuk9muJzi42N1ZtvvqmUlBRdfPHF6tevn+bMmRP+JUx2draWLl2qN998U2effbbOOecczZs3LxzKk5KStGDBAg0dOlTp6enKz8/Xq6++qi5duhzTMQEAACBSW3+G3GpsxrSVefCPXSAQkMfjkd/vl9vtjuirqKjQZ599pl69eik+Pr7R+649oQ9/xsIum5LjEzSlf5ZlfxP105/+VGeddVZ4vW+grTnef98AAADtTcgYzVz/mkqDZXWeIZcO5ZwUV2KLPUNutXoa42g59Pu40t7MPLEuTemfpRRXYkR7iivR0oEdAAAAAA5X+wx5fQFZinyGvCXYbTblpmcqOT6hzh3OtRdKc9MzLRfYG4Nn2luAJ9alGYNyoj5JAwAAAAC0NbUXSg9/xj7FldgmnrEntLcQu83W6mYrXLlyZbRLAAAAAIAf1JYvlHJ7PAAAAACgQaI92fbR1F4ord3aQmCXCO0AAAAAgAZqD8+QWw2hvYGYZB9oe/h3DQAA0HhMtt2yWtdD1lFQuzZ0VVWVXC5OPqAtqaqqkvTdv3MAAAA0TFt+htxqCO0/ICYmRh06dNDXX38tp9Mpu52bE4C2IBQK6euvv1aHDh0UE8O3QgAAgMZqjZNtt0ZR/4S/+uorTZ06VcuWLdOBAwd0yimnaOHChRo8eLCkQ7evzpgxQwsWLND+/fs1dOhQPfroo+rdu3d4H/v27dPNN9+sV199VXa7XSNHjtSDDz6ohISE467PZrOpW7du+uyzz/TFF18c9/4AWIfdblePHj1k4zfCAAAAsKiohvb//Oc/Gjp0qH72s59p2bJl6tq1q3bs2KFOnTqFx9x///2aP3++nn76afXq1Ut33XWXsrOztXXrVsXHx0uSRo8erd27d2vFihWqrq7WtddeqxtvvFHPPvtsk9QZGxur3r17h2+lBdA2xMbGcvcMAAAALM1mojgT0+9//3u99957evfdd+vtN8YoLS1Nt912m6ZMmSJJ8vv9Sk1N1aJFi/SrX/1K27ZtU9++fbVu3brw1fnly5fr4osv1pdffqm0tLQfrCMQCMjj8cjv98vtdjfdAQIAAAAAUI+G5tCoXmJasmSJBg8erCuvvFIpKSkaMGCAFixYEO7/7LPP5PP5lJWVFW7zeDwaMmSICgsLJUmFhYVKSkoKB3ZJysrKkt1u15o1a+p938rKSgUCgYgNAAAAAACriWpo//TTT8PPp7/xxhsaP368brnlFj399NOSJJ/PJ0lKTU2N+LrU1NRwn8/nU0pKSkR/TEyMOnfuHB5zuNmzZ8vj8YS37t27N/WhAQAAAABw3KIa2kOhkAYOHKhZs2ZpwIABuvHGG3XDDTfosccea9b3nTZtmvx+f3jbuXNns74fAAAAAADHIqqhvVu3burbt29E2+mnn66SkhJJktfrlSTt2bMnYsyePXvCfV6vV6WlpRH9Bw8e1L59+8JjDhcXFye32x2xAQAAAABgNVEN7UOHDtX27dsj2j766COddNJJkqRevXrJ6/WqoKAg3B8IBLRmzRplZGRIkjIyMrR//36tX78+POatt95SKBTSkCFDWuAoAAAAAABoHlFd8u3WW2/Vueeeq1mzZumqq67S2rVr9cQTT+iJJ56QdGiN9MmTJ+tPf/qTevfuHV7yLS0tTZdeeqmkQ1fmhw8fHr6tvrq6WhMnTtSvfvWrBs0cDwAAAACAVUV1yTdJWrp0qaZNm6YdO3aoV69eys3N1Q033BDuN8ZoxowZeuKJJ7R//36dd955euSRR3TqqaeGx+zbt08TJ07Uq6++KrvdrpEjR2r+/PlKSEhoUA0s+QYAAAAAaEkNzaFRD+1WQGgHAAAAALSkVrFOOwAAAAAAODJCOwAAAAAAFkVoBwAAAADAogjtAAAAAABYFKEdAAAAAACLiuo67QAAAACAHxYyRtWhmvBrp90hu80WxYrQUgjtAAAAAGBh/qqg8ooL5AsGwm1el1u56ZnyxLqiWBlaArfHAwAAAIiakDGqrDkY3kLGRLskS/FXBTW3KF+lwbKI9tJgmeYW5ctfFYxSZWgpXGkHAAAAEBVcQT66kDHKKy7Q3opyhRT5y4yQjPZWlCuvuEAzBuVwq3wbxpV2AAAAAC2OK8g/rDpUI18wUCew1wrJyBcMRDzrjraH0A4AAACgRTX0CjK3ygOEdgAAAAAtjCvIQMMR2gEAAADAgpx2h7wut+yq/3l1u2zyutxy2h0tXBlaEqEdAAAAACzIbrMpNz1TyfEJdYK7XTYlxycoNz2TSejaOEI7AAAAgBbFFeSG88S6NKV/llJciRHtKa5ETemfxSz77YDNGGZ3CAQC8ng88vv9crvd0S4HAAAAaPNqZ48/fDK62ivIBNJIIWMinvF32h1cYW/lGppDudIOAAAAoMVxBblx7Dab4hwx4Y3A3n7ERLsAAAAAAO2TJ9alGYNyuIIMHAWhHQAAAEDU1F5BBlA/bo8HAAAAAMCiCO0AAAAAAFgUoR0AAAAAAIsitAMAAAAAYFGEdgAAAAAALIrQDgAAAACARRHaAQAAAACwKBZEBAAAAIDvCRmj6lBN+LXT7pDdZotiRWjPCO0AAAAA8F/+qqDyigvkCwbCbV6XW7npmfLEuqJYGdorbo8HAAAAAB0K7HOL8lUaLItoLw2WaW5RvvxVwShVhvaM0A4AAACg3QsZo7ziAu2tKFdIJrJPRnsrypVXXKCQMUfYA9A8CO0AAAAA2r3qUI18wUCdwF4rJCNfMBDxrDvQEgjtAAAAAABYFKEdAAAAAACLIrQDAAAAaPecdoe8Lrfsqn9pN7ts8rrcctodLVwZ2jtCOwAAAIB2z26zKTc9U8nxCXWCu102JccnKDc9k/Xa0eII7QAAAAAgyRPr0pT+WUpxJUa0p7gSNaV/Fuu0Iypiol0AAAAAAFiFJ9alGYNyImaJd9odXGFH1BDaAQAAAOB77Dab4hxEJVgDZyIAAADQjoSM4Soy0IoQ2gEAAIB2wl8VVF5xgXzBQLjN63IrNz2T57UBi2IiOgAAAKAd8FcFNbcoX6XBsoj20mCZ5hbly18VjFJlAI6G0A4AAAC0cSFjlFdcoL0V5QrJRPbJaG9FufKKCxQy5gh7ABAthHYAAACgjasO1cgXDNQJ7LVCMvIFAxHPugOwBp5pBwAAAJoJk74BOF6EdgAAALQJVgvITPoGoCkQ2gEAANDqWS0g1076treiPKK9dtK3Kf2zWrQup90hr8ut0mBZvbfI22VTiitRTrujxWoC0DA80w4AAIBWzWqzoltx0je7zabc9EwlxyfIrsi7D+yyKTk+Qbnpmdy6D1gQoR0AAACtlhUDslUnffPEujSlf5ZSXIkR7SmuxBa/8g+g4bg9HgAAAK1WbUA+ku8H5DgHP/p6Yl2aMSjHUs/+Azg6vnMBAAAA7YjdZuMXGEArwu3xAAAAQBOqnfTt8GfHa9llk9flZtI3AA1CaAcAAECrZcWAzKRvAJoSoR0AAACtllUDMpO+AWgqNmNacCpNiwoEAvJ4PPL7/XK73dEuBwAAAI1ktXXaa4WMYdI3APVqaA5lBgoAAAC0eladFZ1J3wAcL76DAAAAoE0gIANoi3imHQAAAAAAiyK0AwAAAABgUVEN7XfffbdsNlvE1qdPn3B/RUWFJkyYoC5duighIUEjR47Unj17IvZRUlKinJwcdejQQSkpKbr99tt18ODBlj4UAAAAAACaXNQf+jnjjDOUn58ffh0T811Jt956q1577TW98MIL8ng8mjhxoi6//HK99957kqSamhrl5OTI6/Vq9erV2r17t8aOHSun06lZs2a1+LEAAAAAANCUoh7aY2Ji5PV667T7/X79z//8j5599lldeOGFkqSFCxfq9NNP1/vvv69zzjlHb775prZu3ar8/HylpqbqrLPO0r333qupU6fq7rvvVmxsbEsfDgAAAAAATSbqz7Tv2LFDaWlpOvnkkzV69GiVlJRIktavX6/q6mplZWWFx/bp00c9evRQYWGhJKmwsFD9+vVTampqeEx2drYCgYC2bNnSsgcCAAAAAEATi+qV9iFDhmjRokU67bTTtHv3bs2cOVPnn3++Nm/eLJ/Pp9jYWCUlJUV8TWpqqnw+nyTJ5/NFBPba/tq+I6msrFRlZWX4dSAQaKIjAgAAAACg6UQ1tF900UXhP6enp2vIkCE66aST9Pzzz8vlcjXb+86ePVszZ85stv0DAAAAANAUon57/PclJSXp1FNP1ccffyyv16uqqirt378/YsyePXvCz8B7vd46s8nXvq7vOfla06ZNk9/vD287d+5s2gMBAAAAAKAJWCq0l5eX65NPPlG3bt00aNAgOZ1OFRQUhPu3b9+ukpISZWRkSJIyMjK0adMmlZaWhsesWLFCbrdbffv2PeL7xMXFye12R2wAAAAAAFhNVG+PnzJlikaMGKGTTjpJu3bt0owZM+RwODRq1Ch5PB5df/31ys3NVefOneV2u3XzzTcrIyND55xzjiRp2LBh6tu3r8aMGaP7779fPp9Pd955pyZMmKC4uLhoHhoAAAAAAMctqqH9yy+/1KhRo/TNN9+oa9euOu+88/T++++ra9eukqR58+bJbrdr5MiRqqysVHZ2th555JHw1zscDi1dulTjx49XRkaGOnbsqHHjxumee+6J1iEBAAAAANBkbMYYE+0ioi0QCMjj8cjv93OrPAAAAACg2TU0h1rqmXYAAAAAAPCdqN4eDwAAgNYpZIyqQzXh1067Q3abLYoVAUDbRGgHAABAo/irgsorLpAvGAi3eV1u5aZnyhPrimJlAND2cHs8AAAAGsxfFdTconyVBssi2kuDZZpblC9/VTBKlQFA20RoBwAAQIOEjFFecYH2VpQrpMi5jEMy2ltRrrziAoWY5xgAmgyhHQAAAA1SHaqRLxioE9hrhWTkCwYinnUHABwfQjsAAAAAABZFaAcAAAAAwKII7QAAAGgQp90hr8stu+pf2s0um7wut5x2RwtXBgBtF6EdAAAADWK32ZSbnqnk+IQ6wd0um5LjE5Sbnsl67QDQhAjtAAAAaDBPrEtT+mcpxZUY0Z7iStSU/lms0w4ATSwm2gUAAACgdfHEujRjUE7ELPFOu4Mr7ADQDAjtAAAAaDS7zaY4Bz9KAkBz4/Z4AAAAAAAsitAOAAAAAIBFEdoBAAAAALAoQjsAAAAAABZFaAcAAAAAwKII7QAAAAAAWBShHQAAAAAAiyK0AwAAAABgUYR2AAAAAAAsKibaBQAAAOCHhYxRdagm/Nppd8hus0WxIgBASyC0AwAAWJy/Kqi84gL5goFwm9flVm56pjyxrihWBgBobtweDwAAYGH+qqDmFuWrNFgW0V4aLNPconz5q4JRqgwA0BII7QAAABYVMkZ5xQXaW1GukExkn4z2VpQrr7hAIWOOsAcAQGtHaAcAALCo6lCNfMFAncBeKyQjXzAQ8aw7AKBtIbQDAAAAAGBRhHYAAAAAACyK0A4AAGBRTrtDXpdbdtW/tJtdNnldbjntjhauDADQUgjtAAAAFmW32ZSbnqnk+IQ6wd0um5LjE5Sbnsl67QDQhhHaAQAALMwT69KU/llKcSVGtKe4EjWlfxbrtANAGxcT7QIAAABwdJ5Yl2YMyomYJd5pd3CFHQDaAUI7AABAK2C32RTn4Ec3AGhvuD0eAAAAAACL4te1AAAAhwkZw63oAABLILQDAAB8j78qqLziAvmCgXCb1+VWbnomk74BAFoct8cDAAD8l78qqLlF+SoNlkW0lwbLNLcoX/6qYJQqAwC0V4R2AAAAHbolPq+4QHsryhWSieyT0d6KcuUVFyhkzBH2AABA0yO0AwAASKoO1cgXDNQJ7LVCMvIFAxHPugMA0NwI7QAAAAAAWBShHQAAAAAAiyK0AwAA6NCybl6XW3bVv7SbXTZ5XW457Y4WrgwA0J4R2gEAACTZbTblpmcqOT6hTnC3y6bk+ATlpmeyXjsAoEUR2gEAAP7LE+vSlP5ZSnElRrSnuBI1pX8W67QDAFpcTLQLAAAAsBJPrEszBuVEzBLvtDu4wg4AiApCOwAAwGHsNpviHPyYBACIPm6PBwAAAADAogjtAAAAAABYFKEdAAAAAACLIrQDAAAAAGBRhHYAAAAAACyK0A4AAAAAgEUR2gEAAAAAsChCOwAAAAAAFkVoBwAAAADAogjtAAAAAABYFKEdAAAAAACLiol2AQAAoH0LGaPqUE34tdPukN1mi2JFAABYh2WutM+ZM0c2m02TJ08Ot1VUVGjChAnq0qWLEhISNHLkSO3Zsyfi60pKSpSTk6MOHTooJSVFt99+uw4ePNjC1QMAgGPhrwpq5vrXdMvq58PbzPWvyV8VjHZpAABYgiVC+7p16/T4448rPT09ov3WW2/Vq6++qhdeeEGrVq3Srl27dPnll4f7a2pqlJOTo6qqKq1evVpPP/20Fi1apOnTp7f0IQAAgEbyVwU1tyhfpcGyiPbSYJnmFuUT3AEAkAVCe3l5uUaPHq0FCxaoU6dO4Xa/36//+Z//UV5eni688EINGjRICxcu1OrVq/X+++9Lkt58801t3bpVf/vb33TWWWfpoosu0r333quHH35YVVVV0TokAADwA0LGKK+4QHsryhWSieyT0d6KcuUVFyhkzBH2AABA+xD10D5hwgTl5OQoKysron39+vWqrq6OaO/Tp4969OihwsJCSVJhYaH69eun1NTU8Jjs7GwFAgFt2bLliO9ZWVmpQCAQsQEAgJZTHaqRLxioE9hrhWTkCwYinnUHAKA9iupEdIsXL9aHH36odevW1enz+XyKjY1VUlJSRHtqaqp8Pl94zPcDe21/bd+RzJ49WzNnzjzO6gEAAAAAaF5Ru9K+c+dOTZo0Sc8884zi4+Nb9L2nTZsmv98f3nbu3Nmi7w8AAAAAQENELbSvX79epaWlGjhwoGJiYhQTE6NVq1Zp/vz5iomJUWpqqqqqqrR///6Ir9uzZ4+8Xq8kyev11plNvvZ17Zj6xMXFye12R2wAAKDlOO0OeV1u2VX/0m522eR1ueW0O1q4MgAArCVqoT0zM1ObNm3Sxo0bw9vgwYM1evTo8J+dTqcKCgrCX7N9+3aVlJQoIyNDkpSRkaFNmzaptLQ0PGbFihVyu93q27dvix8TAABoGLvNptz0TCXHJ9QJ7nbZlByfoNz0TNZrBwC0e1F7pj0xMVFnnnlmRFvHjh3VpUuXcPv111+v3Nxcde7cWW63WzfffLMyMjJ0zjnnSJKGDRumvn37asyYMbr//vvl8/l05513asKECYqLi2vxYwIAAA3niXVpSv8s5RUXyBf8blLYFFeictMz5Yl1RbE6AACsIaoT0f2QefPmyW63a+TIkaqsrFR2drYeeeSRcL/D4dDSpUs1fvx4ZWRkqGPHjho3bpzuueeeKFYNAAAayhPr0oxBORGzxDvtDq6wAwDwXzZjWAA1EAjI4/HI7/fzfDsAAAAAoNk1NIdGfZ12AAAAAABQP0I7AAAAAAAWRWgHAAAAAMCiCO0AAAAAAFgUoR0AAAAAAIsitAMAAAAAYFGEdgAAAAAALIrQDgAAAACARRHaAQAAAACwKEI7AAAAAAAWRWgHAAAAAMCiCO0AAAAAAFgUoR0AAAAAAIsitAMAAAAAYFGEdgAAAAAALIrQDgAAAACARRHaAQAAAACwKEI7AAAAAAAWRWgHAAAAAMCiCO0AAAAAAFgUoR0AAAAAAIs6ptB+8OBB5efn6/HHH1dZWZkkadeuXSovL2/S4gAAAAAAaM9iGvsFX3zxhYYPH66SkhJVVlbq5z//uRITE/XnP/9ZlZWVeuyxx5qjTgAAAAAA2p1GX2mfNGmSBg8erP/85z9yuVzh9ssuu0wFBQVNWhwAAAAAAO1Zo6+0v/vuu1q9erViY2Mj2nv27KmvvvqqyQoDAAAAAKC9a3RoD4VCqqmpqdP+5ZdfKjExsUmKAgAAzSNkjKpD3/0/7rQ7ZLfZolgRAAA4mkaH9mHDhumvf/2rnnjiCUmSzWZTeXm5ZsyYoYsvvrjJCwQAAE3DXxVUXnGBfMFAuM3rcis3PVOeWNdRvhIAAESLzRhjGvMFX375pbKzs2WM0Y4dOzR48GDt2LFDycnJeuedd5SSktJctTabQCAgj8cjv98vt9sd7XIAAGhy/qqg5hbla29FuUL67r9+u2xKjk/QlP5ZBHcAAFpQQ3Noo0O7dGjJt+eee05FRUUqLy/XwIEDNXr06IiJ6VoTQjsAoC0LGaOZ619TabAsIrDXssumFFeiZgzK4VZ5AABaSENzaKNvj5ekmJgYjR49WqNHjz7mAgEAQMuoDtVE3BJ/uJCMfMGAqkM1inMc048GAACgmTR6ybfZs2frqaeeqtP+1FNP6c9//nOTFAUAAAAAAI4htD/++OPq06dPnfYzzjhDjz32WJMUBQAAAAAAjiG0+3w+devWrU57165dtXv37iYpCgAANB2n3SGvyy276n9e3S6bvC63nHZHC1cGAAB+SKNDe/fu3fXee+/VaX/vvfeUlpbWJEUBAICmY7fZlJueqeT4hDrBvXb2+Nz0TCahAwDAgho928wNN9ygyZMnq7q6WhdeeKEkqaCgQHfccYduu+22Ji8QAAAcP0+sS1P6Z9VZpz3Flcg67QAAWFijQ/vtt9+ub775RjfddJOqqqokSfHx8Zo6daqmTZvW5AUCAICm4Yl1acagHFWHasJtTruDK+wAAFjYMa3TLknl5eXatm2bXC6Xevfurbi4uKaurcWwTjsAAAAAoCU16zrtkpSQkKCzzz77WL8cAAAAAAD8gEaH9m+//VZz5sxRQUGBSktLFQqFIvo//fTTJisOAAAAAID2rNGh/Te/+Y1WrVqlMWPGqFu3brLxHBwAAAAAAM2i0aF92bJleu211zR06NDmqAcAAAAAAPxXo9dp79Spkzp37twctQAAAAAAgO9pdGi/9957NX36dB04cKA56gEAAAAAAP/V6NvjH3jgAX3yySdKTU1Vz5495XQ6I/o//PDDJisOAAAAAID2rNGh/dJLL22GMgAAAAAAwOFsxhgT7SKiraGL2gMAAAAA0BQamkMb/Uw7AAAAAABoGY2+Pb6mpkbz5s3T888/r5KSElVVVUX079u3r8mKAwAAAACgPWv0lfaZM2cqLy9PV199tfx+v3Jzc3X55ZfLbrfr7rvvboYSAQAAAABonxod2p955hktWLBAt912m2JiYjRq1Cg9+eSTmj59ut5///3mqBEAAAAAgHap0aHd5/OpX79+kqSEhAT5/X5J0iWXXKLXXnutaasDAAAAAKAda3RoP/HEE7V7925J0o9+9CO9+eabkqR169YpLi6uaasDAAAAAKAda3Rov+yyy1RQUCBJuvnmm3XXXXepd+/eGjt2rK677romLxAAAAAAgPbquNdpLywsVGFhoXr37q0RI0Y0VV0tinXaAQAAAAAtqaE5tNFLvh0uIyNDGRkZx7sbAADapJAxqg7VhF877Q7ZbbYoVgQAAFqTBoX2JUuW6KKLLpLT6dSSJUuOOvYXv/hFkxQGAEBr568KKq+4QL5gINzmdbmVm54pT6wripUBAIDWokG3x9vtdvl8PqWkpMhuP/Jj8DabTTU1NUfstypujwcANDV/VVBzi/K1t6JcIX33X61dNiXHJ2hK/yyCOwAA7VhDc2iDJqILhUJKSUkJ//lIW2sM7AAANLWQMcorLqgT2CUpJKO9FeXKKy5Q6PimlQEAAO1Ao2aPr66uVmZmpnbs2NEkb/7oo48qPT1dbrdbbrdbGRkZWrZsWbi/oqJCEyZMUJcuXZSQkKCRI0dqz549EfsoKSlRTk6OOnTooJSUFN1+++06ePBgk9QHAMCxqA7VyBcM1AnstUIy8gUDEc+6AwAA1KdRod3pdKq4uLjJ3vzEE0/UnDlztH79en3wwQe68MIL9ctf/lJbtmyRJN1666169dVX9cILL2jVqlXatWuXLr/88vDX19TUKCcnR1VVVVq9erWefvppLVq0SNOnT2+yGgEAAAAAiJZGL/l26623Ki4uTnPmzGmWgjp37qy//OUvuuKKK9S1a1c9++yzuuKKKyRJ//73v3X66aersLBQ55xzjpYtW6ZLLrlEu3btUmpqqiTpscce09SpU/X1118rNja2Qe/JM+0AgKZUWXNQt6x+/gfHzT/3KsU5jnshFwAA0Ao125JvBw8e1FNPPaX8/HwNGjRIHTt2jOjPy8trfLU6dNX8hRde0LfffquMjAytX79e1dXVysrKCo/p06ePevToEQ7thYWF6tevXziwS1J2drbGjx+vLVu2aMCAAfW+V2VlpSorK8OvA4FAveMAADgWTrtDXpdbpcGyem+Rt8umFFeinHZHFKoDAACtSaND++bNmzVw4EBJ0kcffRTRZzuGdWc3bdqkjIwMVVRUKCEhQS+99JL69u2rjRs3KjY2VklJSRHjU1NT5fP5JEk+ny8isNf21/YdyezZszVz5sxG1woAQEPYbTblpmcedfb43PRM1msHAAA/qNGh/e23327SAk477TRt3LhRfr9f//jHPzRu3DitWrWqSd/jcNOmTVNubm74dSAQUPfu3Zv1PQEA7Ysn1qUp/bPqrNOe4kpknXYAANBgUX+QLjY2VqeccookadCgQVq3bp0efPBBXX311aqqqtL+/fsjrrbv2bNHXq9XkuT1erV27dqI/dXOLl87pj5xcXGKi4tr4iMBACCSJ9alGYNyImaJd9odXGEHAAANdkyh/YMPPtDzzz+vkpISVVVVRfS9+OKLx1VQKBRSZWWlBg0aJKfTqYKCAo0cOVKStH37dpWUlCgjI0OSlJGRofvuu0+lpaXhdeRXrFght9utvn37HlcdAAA0BbvNxmRzAADgmDVqyTdJWrx4sc4991xt27ZNL730kqqrq7Vlyxa99dZb8ng8jdrXtGnT9M477+jzzz/Xpk2bNG3aNK1cuVKjR4+Wx+PR9ddfr9zcXL399ttav369rr32WmVkZOicc86RJA0bNkx9+/bVmDFjVFRUpDfeeEN33nmnJkyYwJV0AAAAAECr1+hf/c+aNUvz5s3ThAkTlJiYqAcffFC9evXSb3/7W3Xr1q1R+yotLdXYsWO1e/dueTwepaen64033tDPf/5zSdK8efNkt9s1cuRIVVZWKjs7W4888kj46x0Oh5YuXarx48crIyNDHTt21Lhx43TPPfc09rAAAAAAALCcRq/T3rFjR23ZskU9e/ZUly5dtHLlSvXr10/btm3ThRdeqN27dzdXrc2GddoBAAAAAC2poTm00bfHd+rUSWVlZZKkE044QZs3b5Yk7d+/XwcOHDjGcgEAAAAAwOEaHNprw/lPfvITrVixQpJ05ZVXatKkSbrhhhs0atQoZWZmNk+VAAAAAAC0Qw1+pj09PV1nn322Lr30Ul155ZWSpD/+8Y9yOp1avXq1Ro4cqTvvvLPZCgUAAAAAoL1p8DPt7777rhYuXKh//OMfCoVCGjlypH7zm9/o/PPPb+4amx3PtAMAAAAAWlKTP9N+/vnn66mnntLu3bv10EMP6fPPP9cFF1ygU089VX/+85/l8/mapHAAAAAAAHBIoyei69ixo6699lqtWrVKH330ka688ko9/PDD6tGjh37xi180R40AAAAAALRLjV7y7XDffvutnnnmGU2bNk379+9XTU1NU9XWYrg9HgAAAADQkhqaQxs8Ed3h3nnnHT311FP65z//KbvdrquuukrXX3/9se4OAAAAAAAcplGhfdeuXVq0aJEWLVqkjz/+WOeee67mz5+vq666Sh07dmyuGgEAAAAAaJcaHNovuugi5efnKzk5WWPHjtV1112n0047rTlrAwAAAACgXWtwaHc6nfrHP/6hSy65RA6HozlrAgAAAAAAakRoX7JkSXPWAQAAAAAADtPoJd8AAAAAAEDLILQDAAAAAGBRhHYAAAAAACzqmNdpBwDAakLGqDpUE37ttDtkt9miWBEAAMDxIbQDANoEf1VQecUF8gUD4Tavy63c9Ex5Yl1RrAwAAODYcXs8AKDV81cFNbcoX6XBsoj20mCZ5hbly18VjFJlAAAAx4fQDgBo1ULGKK+4QHsryhWSieyT0d6KcuUVFyhkzBH2AAAAYF2EdgBAq1YdqpEvGKgT2GuFZOQLBiKedQcAAGgtCO0AAAAAAFgUoR0AAAAAAIsitAMAWjWn3SGvyy276l/azS6bvC63nHZHC1cGAABw/AjtAIBWzW6zKTc9U8nxCXWCu102JccnKDc9k/XaAQBAq0RoBwC0ep5Yl6b0z1KKKzGiPcWVqCn9s1inHQAAtFox0S4AAICm4Il1acagnIhZ4p12B1fYAQBAq0ZoBwC0GXabTXEO/msDAABtB7fHAwAAAABgUVyOAAAck5Ax3IoOAADQzAjtAIBG81cFlVdcIF8wEG7zutzKTc9k0jcAAIAmxO3xAIBG8VcFNbcoX6XBsoj20mCZ5hbly18VjFJlAAAAbQ+hHQDQYCFjlFdcoL0V5QrJRPbJaG9FufKKCxQy5gh7AAAAQGMQ2gEADVYdqpEvGKgT2GuFZOQLBiKedQcAAMCxI7QDAAAAAGBRhHYAAAAAACyK0A4AaDCn3SGvyy276l/azS6bvC63nHZHC1cGAADQNhHaAQANZrfZlJueqeT4hDrB3S6bkuMTlJueyXrtAAAATYTQDgBoFE+sS1P6ZynFlRjRnuJK1JT+WazTDgAA0IRiol0AAKD18cS6NGNQTsQs8U67gyvsAAAATYzQDgA4JnabTXEO/hsBAABoTtweDwAAAACARRHaAQAAAACwKEI7AAAAAAAWRWgHAAAAAMCiCO0AAAAAAFgUoR0AAAAAAIsitAMAAAAAYFGEdgAAAAAALIrQDgAAAACARRHaAQAAAACwKEI7AAAAAAAWRWgHAAAAAMCiCO0AAAAAAFgUoR0AAAAAAIsitAMAAAAAYFGEdgAAAAAALIrQDgAAAACARRHaAQAAAACwqKiG9tmzZ+vss89WYmKiUlJSdOmll2r79u0RYyoqKjRhwgR16dJFCQkJGjlypPbs2RMxpqSkRDk5OerQoYNSUlJ0++236+DBgy15KAAAAAAANLmohvZVq1ZpwoQJev/997VixQpVV1dr2LBh+vbbb8Njbr31Vr366qt64YUXtGrVKu3atUuXX355uL+mpkY5OTmqqqrS6tWr9fTTT2vRokWaPn16NA4JAAAAAIAmYzPGmGgXUevrr79WSkqKVq1apZ/85Cfy+/3q2rWrnn32WV1xxRWSpH//+986/fTTVVhYqHPOOUfLli3TJZdcol27dik1NVWS9Nhjj2nq1Kn6+uuvFRsb+4PvGwgE5PF45Pf75Xa7m/UYAQAAAABoaA611DPtfr9fktS5c2dJ0vr161VdXa2srKzwmD59+qhHjx4qLCyUJBUWFqpfv37hwC5J2dnZCgQC2rJlS73vU1lZqUAgELEBAAAAAGA1lgntoVBIkydP1tChQ3XmmWdKknw+n2JjY5WUlBQxNjU1VT6fLzzm+4G9tr+2rz6zZ8+Wx+MJb927d2/iowEAAAAA4PhZJrRPmDBBmzdv1uLFi5v9vaZNmya/3x/edu7c2ezvCQAAAABAY8VEuwBJmjhxopYuXap33nlHJ554Yrjd6/WqqqpK+/fvj7javmfPHnm93vCYtWvXRuyvdnb52jGHi4uLU1xcXBMfBQA0n5Axqg7VhF877Q7ZbbYoVgQAAICWENXQbozRzTffrJdeekkrV65Ur169IvoHDRokp9OpgoICjRw5UpK0fft2lZSUKCMjQ5KUkZGh++67T6WlpUpJSZEkrVixQm63W3379m3ZAwKAZuCvCiqvuEC+4Hfzb3hdbuWmZ8oT64piZQAAAGhuUZ09/qabbtKzzz6rV155Raeddlq43ePxyOU69IPo+PHj9frrr2vRokVyu926+eabJUmrV6+WdGjJt7POOktpaWm6//775fP5NGbMGP3mN7/RrFmzGlQHs8cDsCp/VVBzi/K1t6JcIX337doum5LjEzSlfxbBHQAAoBVqaA6Nami3HeHWzoULF+qaa66RJFVUVOi2227T3//+d1VWVio7O1uPPPJIxK3vX3zxhcaPH6+VK1eqY8eOGjdunObMmaOYmIbdSEBoB2BFIWM0c/1rKg2WRQT2WnbZlOJK1IxBOdwqDwAA0Mq0itBuFYR2AFZUWXNQt6x+/gfHzT/3KsU5LDFFCQAAABqoVa7TDgAAAAAAvkNoBwAAAADAogjtAGBRTrtDXpdbdtX/vLpdNnldbjntjhauDAAAAC2F0A4AFmW32ZSbnqnk+IQ6wb129vjc9EwmoQMAAGjDCO0AYGGeWJem9M9Siisxoj3FlchybwAAAO0A0w0DgMV5Yl2aMShH1aGacJvT7uAKOwAAQDtAaAeAVsBus7GsGwAAQDvE7fEAAAAAAFgUoR0AAAAAAIsitAMAAAAAYFGEdgAAAAAALIrQDgAAAACARRHaAQAAAACwKEI7AAAAAAAWRWgHAAAAAMCiCO0AAAAAAFgUoR0AAAAAAIsitAMAAAAAYFGEdgAAAAAALIrQDgAAAACARRHaAQAAAACwKEI7AAAAAAAWRWgHAAAAAMCiCO0AAAAAAFgUoR0AAAAAAIsitAMAAAAAYFEx0S4AAKwmZIyqQzXh1067Q3abLYoVAQAAoL0itAPA9/irgsorLpAvGAi3eV1u5aZnyhPrimJlAAAAaI+4PR4A/stfFdTconyVBssi2kuDZZpblC9/VTBKlQEAAKC9IrQDgA7dEp9XXKC9FeUKyUT2yWhvRbnyigsUMuYIewAAAACaHqEdACRVh2rkCwbqBPZaIRn5goGIZ90BAACA5kZoBwAAAADAogjtAAAAAABYFKEdAHRoWTevyy276l/azS6bvC63nHZHC1cGAACA9ozQDgCS7DabctMzlRyfUCe422VTcnyCctMzWa8dAAAALYrQDgD/5Yl1aUr/LKW4EiPaU1yJmtI/i3XaAQAA0OJiol0AAFiJJ9alGYNyImaJd9odXGEHAABAVBDaAeAwdptNcQ6+PQIAACD6uD0eAAAAAACL4lISgKgLGcPt6AAAAEA9CO0AospfFVRecYF8wUC4zetyKzc9k4nfAAAA0O5xezyAqPFXBTW3KF+lwbKI9tJgmeYW5ctfFYxSZQAAAIA1ENoBREXIGOUVF2hvRblCMpF9MtpbUa684gKFjDnCHgAAAIC2j9AOICqqQzXyBQN1AnutkIx8wUDEs+4AAABAe0NoBwAAAADAogjtAAAAAABYFKEdQFQ47Q55XW7ZVf/SbnbZ5HW55bQ7WrgyAAAAwDoI7QCiwm6zKTc9U8nxCXWCu102JccnKDc9k/XaAQAA0K4R2gFEjSfWpSn9s5TiSoxoT3Elakr/LNZpBwAAQLsXE+0CALRvnliXZgzKiZgl3ml3cIUdAAAAEKEdgAXYbTbFOfh2BAAAAByO2+MBAAAAALAoQjsAAAAAABZFaAcAAAAAwKII7QAAAAAAWBShHQAAAAAAiyK0AwAAAABgUYR2AAAAAAAsKqqh/Z133tGIESOUlpYmm82ml19+OaLfGKPp06erW7ducrlcysrK0o4dOyLG7Nu3T6NHj5bb7VZSUpKuv/56lZeXt+BRAAAAAADQPKIa2r/99lv1799fDz/8cL39999/v+bPn6/HHntMa9asUceOHZWdna2KiorwmNGjR2vLli1asWKFli5dqnfeeUc33nhjSx0CAAAAAADNxmaMMdEuQpJsNpteeuklXXrppZIOXWVPS0vTbbfdpilTpkiS/H6/UlNTtWjRIv3qV7/Stm3b1LdvX61bt06DBw+WJC1fvlwXX3yxvvzyS6WlpTXovQOBgDwej/x+v9xud7McHwAAAAAAtRqaQy37TPtnn30mn8+nrKyscJvH49GQIUNUWFgoSSosLFRSUlI4sEtSVlaW7Ha71qxZ0+I1AwAAAADQlGKiXcCR+Hw+SVJqampEe2pqarjP5/MpJSUloj8mJkadO3cOj6lPZWWlKisrw68DgUBTlQ0AAAAAQJOx7JX25jR79mx5PJ7w1r1792iXBAAAAABAHZYN7V6vV5K0Z8+eiPY9e/aE+7xer0pLSyP6Dx48qH379oXH1GfatGny+/3hbefOnU1cPQAAAAAAx8+yob1Xr17yer0qKCgItwUCAa1Zs0YZGRmSpIyMDO3fv1/r168Pj3nrrbcUCoU0ZMiQI+47Li5Obrc7YgPai5Axqqw5GN5C1piLEgAAAEA9ovpMe3l5uT7++OPw688++0wbN25U586d1aNHD02ePFl/+tOf1Lt3b/Xq1Ut33XWX0tLSwjPMn3766Ro+fLhuuOEGPfbYY6qurtbEiRP1q1/9qsEzxwPtib8qqLziAvmC383j4HW5lZueKU+sK4qVAQAAAKhPVJd8W7lypX72s5/VaR83bpwWLVokY4xmzJihJ554Qvv379d5552nRx55RKeeemp47L59+zRx4kS9+uqrstvtGjlypObPn6+EhIQG18GSb2gP/FVBzS3K196KcoX03T97u2xKjk/QlP5ZBHcAAACghTQ0h1pmnfZoIrSjrQsZo5nrX1NpsCwisNeyy6YUV6JmDMqR3WaLQoUAAABA+9Lq12kH0HSqQzXyBQP1BnZJCsnIFwyoOlTTwpUBAAAAOBpCOwAAAAAAFkVoBwAAAADAogjtQDvgtDvkdbllV/3Pq9tlk9flltPuaOHKAAAAABwNoR1oB+w2m3LTM5Ucn1AnuNfOHp+bnskkdAAAAIDFENqBZhQyRpU1B8NbKIqLNXhiXZrSP0sprsSI9hRXIsu9AQAAABYVE+0CgLbKXxVUXnGBfMFAuM3rcis3PTNqAdkT69KMQTkRs8Q77Q6usAMAAAAWxZV2oBn4q4KaW5Sv0mBZRHtpsExzi/LlrwpGqbJDt8rHOWLCG4EdAAAAsC5CO9DEQsYor7hAeyvK66yLHpLR3opy5RUXRPVWeQAAAACtA6EdaGLVoRr5goE6gb1WSEa+YCDiFnUAAAAAqA+hHQAAAAAAiyK0AwAAAABgUYR2oIk57Q55Xe4666HXsssmr8stp93RwpUBAAAAaG0I7UATs9tsyk3PVHJ8Qp3gbpdNyfEJyk3PZNZ2AAAAAD+I0A40A0+sS1P6ZynFlRjRnuJK1JT+WVFbpx0AAABA6xIT7QKAtsoT69KMQTkRs8Q77Q6usAMAAABoMEI70IzsNpviHPwzAwAAAHBsuD0eAAAAAACLIrQDAAAAAGBRhHYAAAAAACyK0A4AAAAAgEUR2gEAAAAAsChCOwAAAAAAFkVoBwAAAADAogjtAAAAAABYFKEdAAAAAACLIrQDAAAAAGBRMdEuAGgqIWNUHaoJv3baHbLbbFGsCAAAAACOD6EdbYK/Kqi84gL5goFwm9flVm56pjyxrihWBgAAAADHjtvj0er5q4KaW5Sv0mBZRHtpsExzi/LlrwpGqTIAAAAAOD6EdrRqIWOUV1ygvRXlCslE9slob0W58ooLFDLmCHsAAAAAAOsitKNVqw7VyBcM1AnstUIy8gUDEc+6AwAAAEBrQWgHAAAAAMCiCO0AAAAAAFgUoR2tmtPukNflll31L+1ml01el1tOu6OFKwMAAACA40doR6tmt9mUm56p5PiEOsHdLpuS4xOUm57Jeu0AAAAAWiVCO1o9T6xLU/pnKcWVGNGe4krUlP5ZrNMOAAAAoNWKiXYBQFPwxLo0Y1BOxCzxTruDK+wAAAAAWjVCO9oMu82mOAenNAAAAIC2g4SDYxYyhivbAAAAANCMCO04Jv6qoPKKC+QLBsJtXpdbuemZPEMOAAAAAE2EiejQaP6qoOYW5as0WBbRXhos09yifPmrglGqDAAAAADaFkI7GiVkjPKKC7S3olwhmcg+Ge2tKFdecYFCxhxhDwAAAACAhiK0o1GqQzXyBQN1AnutkIx8wUDEs+4AAAAAgGNDaAcAAAAAwKII7QAAAAAAWBShHY3itDvkdbllV/1Lu9llk9flltPuaOHKAAAAAKDtIbS3EiFjVFlzMLxFa6I3u82m3PRMJccn1AnudtmUHJ+g3PRM1msHAAAAgCbAOu2tgNXWRPfEujSlf1admlJciazTDgAAAABNyGYMa3MFAgF5PB75/X653e5olxOhdk30w5dYq72qPaV/VtRCcsiYiFninXYHV9gBAAAAoAEamkO5Pd7CrL4mut1mU5wjJrwR2AEAAACgaRHaLYw10QEAAACgfSO0AwAAAABgUYR2AAAAAAAsitBuYayJDgAAAADtG6HdwlgTHQAAAADaN0K7xdWuiZ7iSoxoT3ElRnW5NwAAAABA84uJdgH4YZ5Yl2YMymFNdAAAAABoZwjtrUTtmugAAAAAgPajzdwe//DDD6tnz56Kj4/XkCFDtHbt2miXBAAAAADAcWkTof25555Tbm6uZsyYoQ8//FD9+/dXdna2SktLo10aAAAAAADHrE2E9ry8PN1www269tpr1bdvXz322GPq0KGDnnrqqWiXBgAAAADAMWv1ob2qqkrr169XVlZWuM1utysrK0uFhYX1fk1lZaUCgUDEBgAAAACA1bT60L53717V1NQoNTU1oj01NVU+n6/er5k9e7Y8Hk946969e0uUCgAAAABAo7T60H4spk2bJr/fH9527twZ7ZIAAAAAAKij1a8hlpycLIfDoT179kS079mzR16vt96viYuLU1xcXEuUBwAAAADAMWv1V9pjY2M1aNAgFRQUhNtCoZAKCgqUkZERxcoAAAAAADg+rf5KuyTl5uZq3LhxGjx4sH784x/rr3/9q7799ltde+210S4NAAAAAIBj1iZC+9VXX62vv/5a06dPl8/n01lnnaXly5fXmZwOAAAAAIDWxGaMMdEuItoCgYA8Ho/8fr/cbne0ywEAAAAAtHENzaGt/pl2AAAAAADaKkI7AAAAAAAW1SaeaT9etU8IBAKBKFcCAAAAAGgPavPnDz2xTmiXVFZWJknq3r17lCsBAAAAALQnZWVl8ng8R+xnIjodWtd9165dSkxMlM1mk3Totx7du3fXzp07mZwOx4VzCU2FcwlNhXMJTYVzCU2FcwlNpTWdS8YYlZWVKS0tTXb7kZ9c50q7JLvdrhNPPLHePrfbbfm/bLQOnEtoKpxLaCqcS2gqnEtoKpxLaCqt5Vw62hX2WkxEBwAAAACARRHaAQAAAACwKEL7EcTFxWnGjBmKi4uLdilo5TiX0FQ4l9BUOJfQVDiX0FQ4l9BU2uK5xER0AAAAAABYFFfaAQAAAACwKEI7AAAAAAAWRWgHAAAAAMCiCO0AAAAAAFgUof0IHn74YfXs2VPx8fEaMmSI1q5dG+2SEEWzZ8/W2WefrcTERKWkpOjSSy/V9u3bI8ZUVFRowoQJ6tKlixISEjRy5Ejt2bMnYkxJSYlycnLUoUMHpaSk6Pbbb9fBgwcjxqxcuVIDBw5UXFycTjnlFC1atKi5Dw9RMmfOHNlsNk2ePDncxnmExvjqq6/061//Wl26dJHL5VK/fv30wQcfhPuNMZo+fbq6desml8ulrKws7dixI2If+/bt0+jRo+V2u5WUlKTrr79e5eXlEWOKi4t1/vnnKz4+Xt27d9f999/fIseHllFTU6O77rpLvXr1ksvl0o9+9CPde++9+v5cxZxLqM8777yjESNGKC0tTTabTS+//HJEf0ueNy+88IL69Omj+Ph49evXT6+//nqTHy+az9HOperqak2dOlX9+vVTx44dlZaWprFjx2rXrl0R+2jT55JBHYsXLzaxsbHmqaeeMlu2bDE33HCDSUpKMnv27Il2aYiS7Oxss3DhQrN582azceNGc/HFF5sePXqY8vLy8Jjf/e53pnv37qagoMB88MEH5pxzzjHnnntuuP/gwYPmzDPPNFlZWWbDhg3m9ddfN8nJyWbatGnhMZ9++qnp0KGDyc3NNVu3bjUPPfSQcTgcZvny5S16vGh+a9euNT179jTp6elm0qRJ4XbOIzTUvn37zEknnWSuueYas2bNGvPpp5+aN954w3z88cfhMXPmzDEej8e8/PLLpqioyPziF78wvXr1MsFgMDxm+PDhpn///ub999837777rjnllFPMqFGjwv1+v9+kpqaa0aNHm82bN5u///3vxuVymccff7xFjxfN57777jNdunQxS5cuNZ999pl54YUXTEJCgnnwwQfDYziXUJ/XX3/d/PGPfzQvvviikWReeumliP6WOm/ee+8943A4zP3332+2bt1q7rzzTuN0Os2mTZua/TNA0zjaubR//36TlZVlnnvuOfPvf//bFBYWmh//+Mdm0KBBEftoy+cSob0eP/7xj82ECRPCr2tqakxaWpqZPXt2FKuClZSWlhpJZtWqVcaYQ99MnE6neeGFF8Jjtm3bZiSZwsJCY8yhb0Z2u934fL7wmEcffdS43W5TWVlpjDHmjjvuMGeccUbEe1199dUmOzu7uQ8JLaisrMz07t3brFixwlxwwQXh0M55hMaYOnWqOe+8847YHwqFjNfrNX/5y1/Cbfv37zdxcXHm73//uzHGmK1btxpJZt26deExy5YtMzabzXz11VfGGGMeeeQR06lTp/D5Vfvep512WlMfEqIkJyfHXHfddRFtl19+uRk9erQxhnMJDXN40GrJ8+aqq64yOTk5EfUMGTLE/Pa3v23SY0TLqO8XQIdbu3atkWS++OILY0zbP5e4Pf4wVVVVWr9+vbKyssJtdrtdWVlZKiwsjGJlsBK/3y9J6ty5syRp/fr1qq6ujjhv+vTpox49eoTPm8LCQvXr10+pqanhMdnZ2QoEAtqyZUt4zPf3UTuGc69tmTBhgnJycur8XXMeoTGWLFmiwYMH68orr1RKSooGDBigBQsWhPs/++wz+Xy+iHPB4/FoyJAhEedTUlKSBg8eHB6TlZUlu92uNWvWhMf85Cc/UWxsbHhMdna2tm/frv/85z/NfZhoAeeee64KCgr00UcfSZKKior0r3/9SxdddJEkziUcm5Y8b/h/r/3x+/2y2WxKSkqS1PbPJUL7Yfbu3auampqIH4glKTU1VT6fL0pVwUpCoZAmT56soUOH6swzz5Qk+Xw+xcbGhr9x1Pr+eePz+eo9r2r7jjYmEAgoGAw2x+GghS1evFgffvihZs+eXaeP8wiN8emnn+rRRx9V79699cYbb2j8+PG65ZZb9PTTT0v67nw42v9nPp9PKSkpEf0xMTHq3Llzo845tG6///3v9atf/Up9+vSR0+nUgAEDNHnyZI0ePVoS5xKOTUueN0caw3nVNlVUVGjq1KkaNWqU3G63pLZ/LsVE9d2BVmjChAnavHmz/vWvf0W7FLQyO3fu1KRJk7RixQrFx8dHuxy0cqFQSIMHD9asWbMkSQMGDNDmzZv12GOPady4cVGuDq3J888/r2eeeUbPPvuszjjjDG3cuFGTJ09WWloa5xIAS6murtZVV10lY4weffTRaJfTYrjSfpjk5GQ5HI46szXv2bNHXq83SlXBKiZOnKilS5fq7bff1oknnhhu93q9qqqq0v79+yPGf/+88Xq99Z5XtX1HG+N2u+VyuZr6cNDC1q9fr9LSUg0cOFAxMTGKiYnRqlWrNH/+fMXExCg1NZXzCA3WrVs39e3bN6Lt9NNPV0lJiaTvzoej/X/m9XpVWloa0X/w4EHt27evUeccWrfbb789fLW9X79+GjNmjG699dbwHUGcSzgWLXneHGkM51XbUhvYv/jiC61YsSJ8lV1q++cSof0wsbGxGjRokAoKCsJtoVBIBQUFysjIiGJliCZjjCZOnKiXXnpJb731lnr16hXRP2jQIDmdzojzZvv27SopKQmfNxkZGdq0aVPEN5Tabzi1P3hnZGRE7KN2DOde25CZmalNmzZp48aN4W3w4MEaPXp0+M+cR2iooUOH1ll68qOPPtJJJ50kSerVq5e8Xm/EuRAIBLRmzZqI82n//v1av359eMxbb72lUCikIUOGhMe88847qq6uDo9ZsWKFTjvtNHXq1KnZjg8t58CBA7LbI38kdDgcCoVCkjiXcGxa8rzh/722rzaw79ixQ/n5+erSpUtEf5s/l6I6DZ5FLV682MTFxZlFixaZrVu3mhtvvNEkJSVFzNaM9mX8+PHG4/GYlStXmt27d4e3AwcOhMf87ne/Mz169DBvvfWW+eCDD0xGRobJyMgI99cu1TVs2DCzceNGs3z5ctO1a9d6l+q6/fbbzbZt28zDDz/MUl1t3PdnjzeG8wgNt3btWhMTE2Puu+8+s2PHDvPMM8+YDh06mL/97W/hMXPmzDFJSUnmlVdeMcXFxeaXv/xlvcstDRgwwKxZs8b861//Mr17945YImf//v0mNTXVjBkzxmzevNksXrzYdOjQgWW62pBx48aZE044Ibzk24svvmiSk5PNHXfcER7DuYT6lJWVmQ0bNpgNGzYYSSYvL89s2LAhPKN3S5037733nomJiTFz584127ZtMzNmzLDEMl1ouKOdS1VVVeYXv/iFOfHEE83GjRsjfhb//kzwbflcIrQfwUMPPWR69OhhYmNjzY9//GPz/vvvR7skRJGkereFCxeGxwSDQXPTTTeZTp06mQ4dOpjLLrvM7N69O2I/n3/+ubnooouMy+UyycnJ5rbbbjPV1dURY95++21z1llnmdjYWHPyySdHvAfansNDO+cRGuPVV181Z555pomLizN9+vQxTzzxRER/KBQyd911l0lNTTVxcXEmMzPTbN++PWLMN998Y0aNGmUSEhKM2+021157rSkrK4sYU1RUZM477zwTFxdnTjjhBDNnzpxmPza0nEAgYCZNmmR69Ohh4uPjzcknn2z++Mc/RvwwzLmE+rz99tv1/nw0btw4Y0zLnjfPP/+8OfXUU01sbKw544wzzGuvvdZsx42md7Rz6bPPPjviz+Jvv/12eB9t+VyyGWNMy13XBwAAAAAADcUz7QAAAAAAWBShHQAAAAAAiyK0AwAAAABgUYR2AAAAAAAsitAOAAAAAIBFEdoBAAAAALAoQjsAAAAAABZFaAcAoB2y2Wx6+eWXo10GAAD4AYR2AADakGuuuUY2m002m01Op1Opqan6+c9/rqeeekqhUCg8bvfu3brooosatE8CPgAA0UNoBwCgjRk+fLh2796tzz//XMuWLdPPfvYzTZo0SZdccokOHjwoSfJ6vYqLi4typQAA4IcQ2gEAaGPi4uLk9Xp1wgknaODAgfrDH/6gV155RcuWLdOiRYskRV49r6qq0sSJE9WtWzfFx8frpJNO0uzZsyVJPXv2lCRddtllstls4deffPKJfvnLXyo1NVUJCQk6++yzlZ+fH1FHz549NWvWLF133XVKTExUjx499MQTT0SM+fLLLzVq1Ch17txZHTt21ODBg7VmzZpw/yuvvKKBAwcqPj5eJ598smbOnBn+xQMAAO0BoR0AgHbgwgsvVP/+/fXiiy/W6Zs/f76WLFmi559/Xtu3b9czzzwTDufr1q2TJC1cuFC7d+8Ovy4vL9fFF1+sgoICbdiwQcOHD9eIESNUUlISse8HHnhAgwcP1oYNG3TTTTdp/Pjx2r59e3gfF1xwgb766istWbJERUVFuuOOO8K38b/77rsaO3asJk2apK1bt+rxxx/XokWLdN999zXXxwQAgOXERLsAAADQMvr06aPi4uI67SUlJerdu7fOO+882Ww2nXTSSeG+rl27SpKSkpLk9XrD7f3791f//v3Dr++991699NJLWrJkiSZOnBhuv/jii3XTTTdJkqZOnap58+bp7bff1mmnnaZnn31WX3/9tdatW6fOnTtLkk455ZTw186cOVO///3vNW7cOEnSySefrHvvvVd33HGHZsyY0RQfCQAAlkdoBwCgnTDGyGaz1Wm/5ppr9POf/1ynnXaahg8frksuuUTDhg076r7Ky8t1991367XXXtPu3bt18OBBBYPBOlfa09PTw3+22Wzyer0qLS2VJG3cuFEDBgwIB/bDFRUV6b333ou4sl5TU6OKigodOHBAHTp0aPCxAwDQWhHaAQBoJ7Zt26ZevXrVaR84cKA+++wzLVu2TPn5+brqqquUlZWlf/zjH0fc15QpU7RixQrNnTtXp5xyilwul6644gpVVVVFjHM6nRGvbTZb+PZ3l8t11HrLy8s1c+ZMXX755XX64uPjj/q1AAC0FYR2AADagbfeekubNm3SrbfeWm+/2+3W1VdfrauvvlpXXHGFhg8frn379qlz585yOp2qqamJGP/ee+/pmmuu0WWXXSbpUMD+/PPPG1VTenq6nnzyyfD7HG7gwIHavn17xC3zAAC0N4R2AADamMrKSvl8PtXU1GjPnj1avny5Zs+erUsuuURjx46tMz4vL0/dunXTgAEDZLfb9cILL8jr9SopKUnSoVngCwoKNHToUMXFxalTp07q3bu3XnzxRY0YMUI2m0133XVXxDrwDTFq1CjNmjVLl156qWbPnq1u3bppw4YNSktLU0ZGhqZPn65LLrlEPXr00BVXXCG73a6ioiJt3rxZf/rTn5riowIAwPKYPR4AgDZm+fLl6tatm3r27Knhw4fr7bff1vz58/XKK6/I4XDUGZ+YmKj7779fgwcP1tlnn63PP/9cr7/+uuz2Qz8mPPDAA1qxYoW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" ] @@ -356,13 +360,13 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 9, "id": "12241262", "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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" ] @@ -378,10 +382,780 @@ " plot_variance=True)" ] }, + { + "cell_type": "markdown", + "id": "8b04c2d3", + "metadata": {}, + "source": [ + "Two important things that we can read from this plot:\n", + "\n", + "1. Experimental covariance is a mirrored version of experimental semivariance and we can use it to solve kriging system instead of semivariance. It is a measure of similarity between point pairs, the highest similarity can be noticed on the first lags and it decreases with a rising distance.\n", + "2. Variance calculated from a data is used later as the **sill** in kriging models, and it can be used to transform semivariances into covariances.\n", + "\n", + "This plot summarizes basic steps that we can done with the `ExperimentalVariogram` class. We left many parameters untouched but only for a moment, now we are going to explore all capabilities of the class." + ] + }, + { + "cell_type": "markdown", + "id": "95b7f9f0", + "metadata": {}, + "source": [ + "### Optional parameter: `weights`\n", + "\n", + "We won't use this parameter in our tutorial, but we should learn from where it comes from. It is a part of **Poisson Kriging** operations. **Poisson Kriging** algorithms uses `weights` to weight semivariance between blocks by in-block population.\n", + "\n", + "\n", + "### Optional parameters: `direction, tolerance, method`\n", + "\n", + "Those three parameters are used to define directional variogram. We will check how variogram behaves in the Weast-East axis (0 degrees)." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "3b45c1e9", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "direction = 0\n", + "tolerance = 0.1\n", + "method = 'e'\n", + "\n", + "\n", + "experimental_variogram = ExperimentalVariogram(\n", + " input_array=dem,\n", + " step_size=step_size,\n", + " max_range=max_range,\n", + " direction=direction,\n", + " tolerance=tolerance,\n", + " method=method\n", + ")\n", + "\n", + "experimental_variogram.plot(\n", + " plot_semivariance=True,\n", + " plot_covariance=True,\n", + " plot_variance=True)" + ] + }, + { + "cell_type": "markdown", + "id": "97921175", + "metadata": {}, + "source": [ + "Let's look into the North-South axis too:" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "5e4ca777", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "direction = 90\n", + "\n", + "experimental_variogram = ExperimentalVariogram(\n", + " input_array=dem,\n", + " step_size=step_size,\n", + " max_range=max_range,\n", + " direction=direction,\n", + " tolerance=tolerance,\n", + " method=method\n", + ")\n", + "\n", + "experimental_variogram.plot(\n", + " plot_semivariance=True,\n", + " plot_covariance=True,\n", + " plot_variance=True)" + ] + }, + { + "cell_type": "markdown", + "id": "9f4806b9", + "metadata": {}, + "source": [ + "What can we learn from those plots? We see that variability along W-E axis is lower than along N-S axis. It may be an indicator that we have spatial structures that are continuous along W-E axis (for example a river plain)." + ] + }, + { + "cell_type": "markdown", + "id": "e651b931", + "metadata": {}, + "source": [ + "At this point, we are ready for modeling. However, if we want to be precise we should take a look into outliers and analyze `VariogramCloud`." + ] + }, + { + "cell_type": "markdown", + "id": "c4fc177f", + "metadata": {}, + "source": [ + "## 2. (Experimental) Variogram Cloud class\n", + "\n", + "A classic variogram analysis tells us almost everything about spatial dependencies and dissimilarities in data. But variogram is not an ideal tool. We know nothing about distribution of variances within a single lag and it is a valuable information. Semivariogram shows us averaged semivariances. If we want to dig deeper, then we should use `VariogramCloud` class and plot... the variogram cloud.\n", + "\n", + "The `VariogramCloud` class definition API is:\n", + "\n", + "```python\n", + "\n", + "class VariogramCloud:\n", + "\n", + " def __init__(self,\n", + " input_array,\n", + " step_size,\n", + " max_range,\n", + " direction=0,\n", + " tolerance=1,\n", + " calculate_on_creation=True):\n", + "\n", + "```\n", + "\n", + "We pass the same parameters to the class as for the `ExperimentalVariogram` class. The only difference is `calculate_on_creation` boolean value. Variogram point cloud calculation is a slow operation and that's why we have an opportunity to start it during or after the class initialization. If we set `calculate_on_creation` to `False` then we must run `.calculate_experimental_variogram()` method later.\n", + "\n", + "`VariogramCloud` has more methods than `ExperimentalVariogram`:\n", + "\n", + "- `calculate_experimental_variogram()` to obtain the variogram cloud,\n", + "- `describe()` to get lag-statistics,\n", + "- `plot()` to plot variogram cloud,\n", + "- `remove_outliers()` to clean variogram.\n", + "\n", + "We will start from calculation and plotting variogram cloud." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "03c92a1c", + "metadata": {}, + "outputs": [], + "source": [ + "variogram_cloud = VariogramCloud(\n", + " input_array=dem,\n", + " step_size=step_size,\n", + " max_range=max_range\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "9b3b2a74", + "metadata": {}, + "source": [ + "We can plot three different kinds of distribution plots: `'scatter', 'box', 'violin'`. We will choose `box` because it clearly shows data distribution in contrast to `scatter` and it's easier to interpret than `violin` plot." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "0c305795", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "variogram_cloud.plot(kind='box')" + ] + }, + { + "cell_type": "markdown", + "id": "110b3e9a", + "metadata": {}, + "source": [ + "The general shape is similar to the shape of experimental variogram. We can see that semivariances between point pairs contain outliers, especially for the lags close to our maximum range. To be sure about outliers existence, let's look into more complex violinplot:" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "b72a1f3e", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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pMOrLL7/UXnvt1eLySK+lSLeVlPTtb775Zn344Yfq27evfvrTn+raa6/Vv//97ziq/lE87w+/iooKFRUVhVy29957S1K7zgUT6/05YMAAfffdd6qrqwu5vLXX3v/7f/9PRxxxhC666CKVl5fr7LPP1hNPPBF3ABTpmO29996qr6/Xt99+q2+//VZbt27VnDlz1KNHj5Cf8ePHS1KL+csSORbhduzYoenTpwfm3/J/Lm7dujXkM+XLL79M6PMLAJBZmNMHAOCqoUOHat9999X//d//6be//a3+7//+T47jhHwLfeONN+r3v/+9LrzwQl133XXq2rWrsrOzNWXKlIgNtmS/OU+VSI//yCOP6IILLtBpp52mK6+8Uj179lROTo5mzpwZmFcnmD/wivdyJ2yi5WiOPvrowOpd7aGt9YXbd999JUlr167VUUcdlXRdiSxHH87r9SZ921Q766yzdNRRR+mZZ57RkiVLdMstt+imm27S008/rRNPPDGu+0j1+yPavk33fmvttVdQUKBXX31VL7/8shYtWqQXXnhBjz/+uI499lgtWbIk6u3j5f8s+sUvfqFx48ZF3GbIkCEhv7flWEyePFkPPvigpkyZosrKSpWWliorK0tnn312Uj2ZAACZidAHAOC6sWPH6ve//70++OADLViwQHvttZcOOeSQwPV/+9vfdMwxx2ju3Lkht9u6dWvSAUa/fv0kSevXrw+sYOW3fv36wPX+fz/77LMW9xHpsmj+9re/affdd9fTTz8d0kj+wx/+kHDt7aVfv35atmyZtm/fHtLb55NPPglcnwqJBDCnnHKKZs6cqUceeSSp0Kdfv37y+Xz617/+FeixJO2ccHfr1q0hz2mXXXbR1q1bQ27f1NSkb775psV9/utf/2rxWOvXr4+rHklJ316SevfurV/+8pf65S9/qerqah100EG64YYbAqFPWwKucF9//bXq6upCevt8+umnkhSYSNnfoyZ830XqRRVvbcHvz3CffPKJunfv3qIHUjyys7N13HHH6bjjjtPtt9+uG2+8Ub/73e/08ssva8SIETFvG+mYffrppyosLAwM5+zSpYu8Xm+r95UKf/vb3zRu3DjddtttgcsaGhpaHId+/fq1+fMLAGAvhncBAFzn79Uzffp0rVmzpsVcEzk5OS16ijz55JNxz2MTycEHH6yePXtq9uzZIUt3P//88/r44481evRoSTuHtwwaNEgPPfSQamtrA9u98sorWrt2bdyP5+9FEPw8Vq5cqRUrViT9HFLtpJNOktfr1Z///OeQy2fNmqWsrKy4e5K0pqioqEXDNJrKykqdcMIJeuCBB/Tss8+2uL6pqUlXXHFF1NufdNJJknauGhbs9ttvl6TAcZakPfbYo8US3HPmzGnRY+Wkk07SW2+9pbfffjtw2bfffqtHH3201efTu3dvHXDAAZo/f37IEJylS5fqo48+inlbr9fbYihgz549VVFREfIaLioqijhkMBkej0d/+ctfAr83NTXpL3/5i3r06BEYFrjHHntIUsi+83q9mjNnTov7i7e24P0U/Fr58MMPtWTJksBxTcTmzZtbXOaf5yZ4/0WzYsWKkLlyvvrqKz333HMaOXKkcnJylJOTozFjxuipp57Shx9+2OL23377bcI1xxLpc/Huu+9u8XodNWqUVqxYoTVr1gQu27x5c8TX6zfffKNPPvlEzc3NKa0VAOAeevoAAFzXv39/HX744XruueckqUXoc/LJJ2vGjBkaP368Dj/8cK1du1aPPvqodt9996QfMzc3VzfddJPGjx+vYcOG6Zxzzgks2b7bbrtp6tSpgW1vvPFGnXrqqTriiCM0fvx4bdmyRX/+8581aNCgkCAolpNPPllPP/20Tj/9dI0ePVobNmzQ7NmzNXDgwLjvo72dcsopOuaYY/S73/1OX3zxhfbff38tWbJEzz33nKZMmRJo3LfV0KFDdd999+n666/XnnvuqZ49e7bobRXsoYce0siRI3XGGWfolFNO0XHHHaeioiL961//0mOPPaZvvvlGt956a8Tb7r///ho3bpzmzJmjrVu3atiwYXr77bc1f/58nXbaaTrmmGMC21500UW69NJLNWbMGB1//PF6//33tXjx4ha9ya666io9/PDDOuGEE/S///u/gSXb+/Xrpw8++KDV5z9z5kyNHj1aRx55pC688EJt3rxZd999t/bbb7+Yr4Xt27erT58++vnPf679999fxcXFWrZsmVatWhXS22Po0KF6/PHHdfnll+uQQw5RcXGxTjnllFbriqSiokI33XSTvvjiC+299956/PHHtWbNGs2ZM0e5ubmSpP3220+HHXaYpk2bps2bN6tr16567LHH5PF4WtxfIrXdcsstOvHEE1VZWakJEyYElmwvLS3Vtddem/BzmTFjhl599VWNHj1a/fr1U3V1te6991716dNHRx55ZKu3HzRokEaNGhWyZLsk/fGPfwxs86c//Ukvv/yyDj30UF188cUaOHCgNm/erHfffVfLli2LGDwl6+STT9bDDz+s0tJSDRw4UCtWrNCyZcvUrVu3kO2uuuoqPfLIIzr++OM1efLkwJLtu+66qzZv3hzS+2ratGmaP3++NmzYEOjJBQCwnFvLhgEAEOyee+5xJDk//elPW1zX0NDg/PrXv3Z69+7tFBQUOEcccYSzYsUKZ9iwYSFLf/uXQX7yySdb3Eek5aMdx3Eef/xx58ADD3Ty8/Odrl27OmPHjnX+85//tLj9Y4895uy7775Ofn6+M2jQIOfvf/+7M2bMGGffffcNbONfdvqWW25pcXufz+fceOONTr9+/Zz8/HznwAMPdBYuXOiMGzcu4rLv4fcR7bn5l8tetWpVi8cM5l/WurXlqrdv3+5MnTrVqaiocHJzc5299trLueWWW0KWYXec6Eu2h9cRab9v2rTJGT16tNOlSxdHUlzLt9fX1zu33nqrc8ghhzjFxcVOXl6es9deezmTJ08OWUo+fMl2x3Gc5uZm549//KPTv39/Jzc31+nbt68zbdq0Fsvce71e5+qrr3a6d+/uFBYWOqNGjXI+++yzFs/VcRzngw8+cIYNG+Z07tzZ+clPfuJcd911zty5c+NeAvupp55yBgwY4OTn5zsDBw50nn766RavBccJXaa7sbHRufLKK53999/f6dKli1NUVOTsv//+zr333htym9raWufcc891ysrKQpaBT/T9MWzYMGe//fZz3nnnHaeystLp3Lmz069fP+fPf/5zi9t//vnnzogRI5z8/HynvLzc+e1vf+ssXbq0xX1Gqy3Sku2O4zjLli1zjjjiCKegoMApKSlxTjnlFOejjz4K2Sbaazt8KfkXX3zROfXUU52KigonLy/PqaiocM455xzn008/bfF8wklyJk6c6DzyyCPOXnvtFXgPh3+eOI7jVFVVORMnTnT69u3r5ObmOr169XKOO+44Z86cOS32d6RjEauG4CXbt2zZ4owfP97p3r27U1xc7IwaNcr55JNPIr5e33vvPeeoo45y8vPznT59+jgzZ8507rrrLkeSs2nTpsB248aNYxl3AMgwWY6T5MyKAAB0cAcccIB69OihpUuXul0KgHaUlZWliRMnthj6aLMpU6boL3/5i2pra9s8iTUAwFzM6QMAQCuam5tbDFVZvny53n//fQ0fPtydogAgTjt27Aj5/fvvv9fDDz+sI488ksAHADIcc/oAANCK//73vxoxYoR+8YtfqKKiQp988olmz56tXr166dJLL3W7PACIqbKyUsOHD9eAAQNUVVWluXPnqqamRr///e/dLg0A0M4IfQAAaMUuu+yioUOH6oEHHtC3336roqIijR49Wn/6059aTJoKAKY56aST9Le//U1z5sxRVlaWDjroIM2dO1dHH32026UBANoZc/oAAAAAAABkIOb0AQAAAAAAyECEPgAAAAAAABkoY+f08fl8+vrrr9WlSxdlZWW5XQ4AAAAAAEBKOI6j7du3q6KiQtnZ0fvzZGzo8/XXX6tv375ulwEAAAAAANAuvvrqK/Xp0yfq9Rkb+nTp0kXSzh1QUlLicjUAAAAAAACpUVNTo759+wayj2gyNvTxD+kqKSkh9AEAAAAAABmntelsmMgZAAAAAAAgAxH6AAAAAAAAZCBCHwAAAAAAgAyUsXP6xMvr9aq5udntMtDOcnNzlZOT43YZAAAAAACkTYcNfRzH0aZNm7R161a3S0GalJWVqVevXq1OdAUAAAAAQCbosKGPP/Dp2bOnCgsLCQIymOM4qq+vV3V1tSSpd+/eLlcEAAAAAED7Syj08Xq9uvbaa/XII49o06ZNqqio0AUXXKBrrrkmEJo4jqM//OEPuv/++7V161YdccQRuu+++7TXXnsF7mfz5s2aPHmy/vGPfyg7O1tjxozRnXfeqeLi4sA2H3zwgSZOnKhVq1apR48emjx5sq666qqUPGmv1xsIfLp165aS+4TZCgoKJEnV1dXq2bMnQ70AAAAAABkvoYmcb7rpJt13333685//rI8//lg33XSTbr75Zt19992BbW6++Wbdddddmj17tlauXKmioiKNGjVKDQ0NgW3Gjh2rdevWaenSpVq4cKFeffVVXXLJJYHra2pqNHLkSPXr10+rV6/WLbfcomuvvVZz5sxJwVNWYA6fwsLClNwf7OA/3szhBAAAAADoCLIcx3Hi3fjkk09WeXm55s6dG7hszJgxKigo0COPPCLHcVRRUaFf//rXuuKKKyRJ27ZtU3l5uebNm6ezzz5bH3/8sQYOHKhVq1bp4IMPliS98MILOumkk/Sf//xHFRUVuu+++/S73/1OmzZtUl5eniTpN7/5jZ599ll98skncdVaU1Oj0tJSbdu2TSUlJSHXNTQ0aMOGDerfv786d+4c79OH5TjuAAAAAIBMECvzCJZQT5/DDz9cL774oj799FNJ0vvvv6/XX39dJ554oiRpw4YN2rRpk0aMGBG4TWlpqQ499FCtWLFCkrR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\n", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "variogram_cloud.plot(kind='violin')" + ] + }, + { + "cell_type": "markdown", + "id": "b6871569", + "metadata": {}, + "source": [ + "Extreme outliers are more visible. Another thing is a difference between mean and median. Distribution are highly skewed toward large values. If graphical representation is not enough we may describe statistics with method `.describe()`:" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "e01f9bc5", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{500: {'count': 212,\n", + " 'avg semivariance': 16.20556312961057,\n", + " 'std': 62.35368205266852,\n", + " 'min': 0.0,\n", + " 'max': 349.4402585342177,\n", + " '25%': 0.7014918734930689,\n", + " 'median': 6.171842484269291,\n", + " '75%': 34.700226907167234,\n", + " 'skewness': 3.0530584703741126,\n", + " 'kurtosis': 9.723029758212611,\n", + " 'lag': 500},\n", + " 1000: {'count': 630,\n", + " 'avg semivariance': 27.56975361181044,\n", + " 'std': 159.65149662070695,\n", + " 'min': 1.0373914847150445e-06,\n", + " 'max': 1279.6886120000854,\n", + " '25%': 0.654255814658427,\n", + " 'median': 5.782991647720337,\n", + " '75%': 32.85118472040631,\n", + " 'skewness': 5.225033413826659,\n", + " 'kurtosis': 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let's convert it to dataframe:" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "d6706e29", + "metadata": {}, + "outputs": [], + "source": [ + "desc = pd.DataFrame(variogram_cloud.describe())" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "ac7f4d1a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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We see that variability along W-E axis is lower than along N-S axis. It may be an indicator that we have spatial structures that are continuous along W-E axis (for example a river plain)." @@ -501,7 +501,7 @@ }, { "cell_type": "markdown", - "id": "e651b931", + "id": "0fe801d3", "metadata": {}, "source": [ "At this point, we are ready for modeling. However, if we want to be precise we should take a look into outliers and analyze `VariogramCloud`." @@ -509,7 +509,7 @@ }, { "cell_type": "markdown", - "id": "c4fc177f", + "id": "7aa38fec", "metadata": {}, "source": [ "## 2. (Experimental) Variogram Cloud class\n", @@ -536,7 +536,7 @@ "\n", "`VariogramCloud` has more methods than `ExperimentalVariogram`:\n", "\n", - "- `calculate_experimental_variogram()` to obtain the variogram cloud,\n", + "- `calculate_experimental_variogram()` to obtain the variogram (similar to output from `ExperimentalVariogram` class,\n", "- `describe()` to get lag-statistics,\n", "- `plot()` to plot variogram cloud,\n", "- `remove_outliers()` to clean variogram.\n", @@ -547,7 +547,7 @@ { "cell_type": "code", "execution_count": 18, - "id": "03c92a1c", + "id": "f565ede3", "metadata": {}, "outputs": [], "source": [ @@ -560,7 +560,7 @@ }, { "cell_type": "markdown", - "id": "9b3b2a74", + "id": "afe591d4", "metadata": {}, "source": [ "We can plot three different kinds of distribution plots: `'scatter', 'box', 'violin'`. We will choose `box` because it clearly shows data distribution in contrast to `scatter` and it's easier to interpret than `violin` plot." @@ -569,7 +569,7 @@ { "cell_type": "code", "execution_count": 21, - "id": "0c305795", + "id": "1a9edb5e", "metadata": {}, "outputs": [ { @@ -589,7 +589,7 @@ }, { "cell_type": "markdown", - "id": "110b3e9a", + "id": "fcae8d56", "metadata": {}, "source": [ "The general shape is similar to the shape of experimental variogram. We can see that semivariances between point pairs contain outliers, especially for the lags close to our maximum range. To be sure about outliers existence, let's look into more complex violinplot:" @@ -598,7 +598,7 @@ { "cell_type": "code", "execution_count": 22, - "id": "b72a1f3e", + "id": "0825b30e", "metadata": {}, "outputs": [ { @@ -618,7 +618,7 @@ }, { "cell_type": "markdown", - "id": "b6871569", + "id": "098668ec", "metadata": {}, "source": [ "Extreme outliers are more visible. Another thing is a difference between mean and median. Distribution are highly skewed toward large values. If graphical representation is not enough we may describe statistics with method `.describe()`:" @@ -627,7 +627,7 @@ { "cell_type": "code", "execution_count": 26, - "id": "e01f9bc5", + "id": "0c11c36f", "metadata": { "scrolled": true }, @@ -912,7 +912,7 @@ }, { "cell_type": "markdown", - "id": "c03c9e6b", + "id": "3366be34", "metadata": {}, "source": [ "We get `dict` as an output but it is not especially readable, let's convert it to dataframe:" @@ -921,7 +921,7 @@ { "cell_type": "code", "execution_count": 28, - "id": "d6706e29", + "id": "9584d193", "metadata": {}, "outputs": [], "source": [ @@ -930,8 +930,8 @@ }, { "cell_type": "code", - "execution_count": 29, - "id": "ac7f4d1a", + "execution_count": 30, + "id": "69508402", "metadata": {}, "outputs": [ { @@ -1099,9 +1099,153 @@ " 7599.070801\n", " 7590.695166\n", " \n", + " \n", + " 25%\n", + " 0.701492\n", + " 0.654256\n", + " 1.004204\n", + " 1.511615\n", + " 1.396649\n", + " 2.325767\n", + " 3.143072\n", + " 3.944196\n", + " 6.187897e+00\n", + " 4.957852\n", + " ...\n", + " 19.416636\n", + " 28.750957\n", + " 55.088435\n", + " 5.803509e+01\n", + " 74.651666\n", + " 127.516748\n", + " 102.017401\n", + " 144.585433\n", + " 212.192972\n", + " 239.073972\n", + " \n", + " \n", + " median\n", + " 6.171842\n", + " 5.782992\n", + " 9.299424\n", + " 20.100519\n", + " 20.559582\n", + " 27.694637\n", + " 42.710316\n", + " 66.112301\n", + " 7.583133e+01\n", + " 102.866010\n", + " ...\n", + " 400.087667\n", + " 494.391264\n", + " 622.193252\n", + " 6.432937e+02\n", + " 624.708877\n", + " 728.441314\n", + " 732.929563\n", + " 739.591246\n", + " 825.188326\n", + " 845.767093\n", + " \n", + " \n", + " 75%\n", + " 34.700227\n", + " 32.851185\n", + " 64.594637\n", + " 133.282116\n", + " 137.811204\n", + " 201.408542\n", + " 249.434189\n", + " 341.526456\n", + " 4.856856e+02\n", + " 503.167120\n", + " ...\n", + " 1197.456227\n", + " 1296.464351\n", + " 1461.236619\n", + " 1.419800e+03\n", + " 1493.148607\n", + " 1696.896446\n", + " 1501.099059\n", + " 1625.474747\n", + " 1742.442064\n", + " 1877.968433\n", + " \n", + " \n", + " skewness\n", + " 3.053058\n", + " 5.225033\n", + " 6.063957\n", + " 5.830170\n", + " 4.145943\n", + " 3.954366\n", + " 4.129248\n", + " 2.961544\n", + " 2.872795e+00\n", + " 2.712578\n", + " ...\n", + " 1.765235\n", + " 1.815569\n", + " 1.640250\n", + " 1.803398e+00\n", + " 1.761560\n", + " 1.494611\n", + " 1.618052\n", + " 1.602935\n", + " 1.547272\n", + " 1.408764\n", + " \n", + " \n", + " kurtosis\n", + " 9.723030\n", + " 31.431383\n", + " 43.380043\n", + " 47.220256\n", + " 18.510835\n", + " 19.066822\n", + " 21.057248\n", + " 9.547389\n", + " 9.292135e+00\n", + " 8.061868\n", + " ...\n", + " 2.804334\n", + " 3.409597\n", + " 2.821448\n", + " 3.812818e+00\n", + " 3.588097\n", + " 2.053842\n", + " 2.822236\n", + " 2.631514\n", + " 2.464061\n", + " 1.833263\n", + " \n", + " \n", + " lag\n", + " 500.000000\n", + " 1000.000000\n", + " 1500.000000\n", + " 2000.000000\n", + " 2500.000000\n", + " 3000.000000\n", + " 3500.000000\n", + " 4000.000000\n", + " 4.500000e+03\n", + " 5000.000000\n", + " ...\n", + " 7500.000000\n", + " 8000.000000\n", + " 8500.000000\n", + " 9.000000e+03\n", + " 9500.000000\n", + " 10000.000000\n", + " 10500.000000\n", + " 11000.000000\n", + " 11500.000000\n", + " 12000.000000\n", + " \n", " \n", "\n", - "

5 rows × 24 columns

\n", + "

11 rows × 24 columns

\n", "" ], "text/plain": [ @@ -1111,6 +1255,12 @@ "std 62.353682 159.651497 251.355086 390.472410 \n", "min 0.000000 0.000001 0.000007 0.000039 \n", "max 349.440259 1279.688612 2377.416349 4426.619403 \n", + "25% 0.701492 0.654256 1.004204 1.511615 \n", + "median 6.171842 5.782992 9.299424 20.100519 \n", + "75% 34.700227 32.851185 64.594637 133.282116 \n", + "skewness 3.053058 5.225033 6.063957 5.830170 \n", + "kurtosis 9.723030 31.431383 43.380043 47.220256 \n", + "lag 500.000000 1000.000000 1500.000000 2000.000000 \n", "\n", " 2500 3000 3500 4000 \\\n", "count 1700.000000 1950.000000 2140.000000 2506.000000 \n", @@ -1118,6 +1268,12 @@ "std 438.202078 593.275581 662.483431 740.710483 \n", "min 0.000112 0.000014 0.000012 0.000009 \n", "max 3043.143446 5619.733232 5642.480677 5251.904118 \n", + "25% 1.396649 2.325767 3.143072 3.944196 \n", + "median 20.559582 27.694637 42.710316 66.112301 \n", + "75% 137.811204 201.408542 249.434189 341.526456 \n", + "skewness 4.145943 3.954366 4.129248 2.961544 \n", + "kurtosis 18.510835 19.066822 21.057248 9.547389 \n", + "lag 2500.000000 3000.000000 3500.000000 4000.000000 \n", "\n", " 4500 5000 ... 7500 8000 \\\n", "count 2.630000e+03 2882.000000 ... 3742.000000 3764.000000 \n", @@ -1125,40 +1281,232 @@ "std 9.048100e+02 969.759977 ... 1229.990401 1251.298017 \n", "min 3.842615e-07 0.000003 ... 0.000165 0.000157 \n", "max 5.870231e+03 7408.018253 ... 7445.123252 7696.494923 \n", + "25% 6.187897e+00 4.957852 ... 19.416636 28.750957 \n", + "median 7.583133e+01 102.866010 ... 400.087667 494.391264 \n", + "75% 4.856856e+02 503.167120 ... 1197.456227 1296.464351 \n", + "skewness 2.872795e+00 2.712578 ... 1.765235 1.815569 \n", + "kurtosis 9.292135e+00 8.061868 ... 2.804334 3.409597 \n", + "lag 4.500000e+03 5000.000000 ... 7500.000000 8000.000000 \n", "\n", - " 8500 9000 9500 10000 \\\n", - "count 3694.000000 3.778000e+03 3834.000000 3784.000000 \n", - "avg semivariance 520.134150 5.209362e+02 528.493360 573.742860 \n", - "std 1240.405905 1.259410e+03 1255.845201 1277.460075 \n", - "min 0.000269 2.310262e-07 0.000008 0.001874 \n", - "max 7585.617314 7.725139e+03 7680.213529 7725.679306 \n", + " 8500 9000 9500 10000 \\\n", + "count 3694.000000 3.778000e+03 3834.000000 3784.000000 \n", + "avg semivariance 520.134150 5.209362e+02 528.493360 573.742860 \n", + "std 1240.405905 1.259410e+03 1255.845201 1277.460075 \n", + "min 0.000269 2.310262e-07 0.000008 0.001874 \n", + "max 7585.617314 7.725139e+03 7680.213529 7725.679306 \n", + "25% 55.088435 5.803509e+01 74.651666 127.516748 \n", + "median 622.193252 6.432937e+02 624.708877 728.441314 \n", + "75% 1461.236619 1.419800e+03 1493.148607 1696.896446 \n", + "skewness 1.640250 1.803398e+00 1.761560 1.494611 \n", + "kurtosis 2.821448 3.812818e+00 3.588097 2.053842 \n", + "lag 8500.000000 9.000000e+03 9500.000000 10000.000000 \n", "\n", - " 10500 11000 11500 12000 \n", - "count 3966.000000 3850.000000 3900.000000 3690.000000 \n", - "avg semivariance 546.144646 576.447162 616.466762 622.677236 \n", - "std 1220.792230 1289.503589 1318.705100 1282.670287 \n", - "min 0.000494 0.000056 0.000750 0.017062 \n", - "max 7687.258344 7790.453214 7599.070801 7590.695166 \n", + " 10500 11000 11500 12000 \n", + "count 3966.000000 3850.000000 3900.000000 3690.000000 \n", + "avg semivariance 546.144646 576.447162 616.466762 622.677236 \n", + "std 1220.792230 1289.503589 1318.705100 1282.670287 \n", + "min 0.000494 0.000056 0.000750 0.017062 \n", + "max 7687.258344 7790.453214 7599.070801 7590.695166 \n", + "25% 102.017401 144.585433 212.192972 239.073972 \n", + "median 732.929563 739.591246 825.188326 845.767093 \n", + "75% 1501.099059 1625.474747 1742.442064 1877.968433 \n", + "skewness 1.618052 1.602935 1.547272 1.408764 \n", + "kurtosis 2.822236 2.631514 2.464061 1.833263 \n", + "lag 10500.000000 11000.000000 11500.000000 12000.000000 \n", "\n", - "[5 rows x 24 columns]" + "[11 rows x 24 columns]" ] }, - "execution_count": 29, + "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "desc.head()" + "desc" + ] + }, + { + "cell_type": "markdown", + "id": "852dfb92", + "metadata": {}, + "source": [ + "With this detailed table, we can analyze variogram in details and decide:\n", + "- if lags are correctly placed,\n", + "- if there are enough points per lag,\n", + "- if maximum range is too low or too high,\n", + "- is distribution close to normal,\n", + "\n", + "and based on the answers for those question, we can change semivariogram parameters slightly.\n", + "\n", + "The interesting row is `avg semivariance`. Let's plot it against lags, and let's plot the output from `ExperimentalVariogram` in the same figure:" ] }, { "cell_type": "code", - "execution_count": null, - "id": "832f41a0", + "execution_count": 31, + "id": "560b7e1f", "metadata": {}, "outputs": [], - "source": [] + "source": [ + "# Re-calculate experimental variogram\n", + "\n", + "max_range = 12500\n", + "step_size = 500\n", + "\n", + "experimental_variogram = ExperimentalVariogram(\n", + " input_array=dem,\n", + " step_size=step_size,\n", + " max_range=max_range\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "id": "09f11c4c", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot\n", + "\n", + "plt.figure()\n", + "plt.plot(experimental_variogram.experimental_semivariance_array[:, 0], \n", + " experimental_variogram.experimental_semivariance_array[:, 1])\n", + "plt.plot(desc.loc['avg semivariance'])\n", + "plt.legend(['ExperimentalVariogram output', 'Average semivariance from VariogramCloud'])\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "5dbdb816", + "metadata": {}, + "source": [ + "The result is the same and it shouldn't be any surprise. Experimental Variogram averages all semivariances per lag, we took a step back in computations and focus on all point pairs within a lag.\n", + "\n", + "There is one last method within `VariogramCloud` class: `.remove_outliers()`. It cleans our variogram from anomalous readings. Cleaning base on the z-score or the inter-quartile range analysis, and we can cut outliers from the largest or the lowest values. For data that deviaties greately from normal distribution it is better to use inter-quartile range for cleaning:" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "id": "8133b1da", + "metadata": {}, + "outputs": [], + "source": [ + "new_variogram_cloud = variogram_cloud.remove_outliers(method='iqr', iqr_lower_limit=3, iqr_upper_limit=2)" + ] + }, + { + "cell_type": "markdown", + "id": "b3761852", + "metadata": {}, + "source": [ + "Now we can check what has happend with a variogram:" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "id": "e6ff8126", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "new_variogram_cloud.plot(kind='violin')" + ] + }, + { + "cell_type": "markdown", + "id": "3ff45d41", + "metadata": {}, + "source": [ + "Maximum semivariances are much lower than before. How looks averaged semivariogram? Now we will use `.calculate_experimental_variogram()` method to calculate variogram directly and compare it to the experimental variogram retrieved from `ExperimentalVariogram` class." + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "id": "238e891f", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot\n", + "exp_var_from_point_cloud = new_variogram_cloud.calculate_experimental_variogram()\n", + "\n", + "plt.figure()\n", + "plt.plot(experimental_variogram.experimental_semivariance_array[:, 0], \n", + " experimental_variogram.experimental_semivariance_array[:, 1])\n", + "plt.plot(exp_var_from_point_cloud[:, 0], \n", + " exp_var_from_point_cloud[:, 1])\n", + "plt.legend(['ExperimentalVariogram output', 'Average semivariance from VariogramCloud'])\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "30eff7c5", + "metadata": {}, + "source": [ + "Now we see that the shape of variogram persisted, but maximum values per lag are lower. For some models it could be a useful step, especially if we get rid of outliers that have extreme values." + ] + }, + { + "cell_type": "markdown", + "id": "b1ca16d9", + "metadata": {}, + "source": [ + "## Summary\n", + "\n", + "After all those steps you have a better insights into `ExperimentalVariogram` and `VariogramCloud` classes. You may check your own dataset and compare results to those in tutorial.\n", + "\n", + "The main takeaways from this tutorial are:\n", + "\n", + "- API of `ExperimentalVariogram` and `VariogramCloud` classes,\n", + "- differences and similarities between `ExperimentalVariogram` and `VariogramCloud` classes,\n", + "- full variogram analysis and preparation before spatial interpolation." + ] + }, + { + "cell_type": "markdown", + "id": "84c66d91", + "metadata": {}, + "source": [ + "---" + ] } ], "metadata": { From a88d433ea2641103886c451557d29617dca55156 Mon Sep 17 00:00:00 2001 From: Simon <31246246+SimonMolinsky@users.noreply.github.com> Date: Fri, 27 Jan 2023 15:30:13 +0200 Subject: [PATCH 13/29] Update Experimental Variogram and Variogram Point Cloud classes (Basic).ipynb --- ...ariogram Point Cloud classes (Basic).ipynb | 135 +++++++++--------- 1 file changed, 66 insertions(+), 69 deletions(-) diff --git a/tutorials/Experimental Variogram and Variogram Point Cloud classes (Basic).ipynb b/tutorials/Experimental Variogram and Variogram Point Cloud classes (Basic).ipynb index a824b47b..d85eb398 100644 --- a/tutorials/Experimental Variogram and Variogram Point Cloud classes (Basic).ipynb +++ b/tutorials/Experimental Variogram and Variogram Point Cloud classes (Basic).ipynb @@ -24,11 +24,10 @@ "\n", "## Introduction\n", "\n", - "Geostatistical analysis starts with a dissimilarity estimation. We must know if process is spatially depended and at which distances points tend to be related to each other. Thus, we start from the experimental variogram estimation.\n", + "The geostatistical analysis starts with a dissimilarity estimation. We must know if a process is spatially dependent and at which distances points tend to be related to each other. Thus, we start with the experimental variogram estimation.\n", + "In this tutorial, we will take a closer look into the API, and we will learn what can be done with two basic experimental semivariogram estimation functionalities. One is `ExperimentalVariogram` class, and another is `VariogramCloud` class. The first is a foundation of every other complex function, from semivariogram modeling to Poisson Kriging. The latter is used for a thorough analysis of relations between points and their dispersion.\n", + "To learn more about variograms and related concepts you can start with those tutorials:\n", "\n", - "In this tutorial, we will take a closer look into the API, and we will learn what can be done with two basic experimental semivariogram estimation functionalities. One is `ExperimentalVariogram` class and another is `VariogramCloud` class. The first is a foundation of every other complex function, from semivariogram modeling up to Poisson Kriging. The latter is used for thorough analysis of relations between points and their dispersion.\n", - "\n", - "To learn more about variogram and related concepts you can start from those tutorials:\n", "\n", "- [Semivariogram Estimation](https://github.com/DataverseLabs/pyinterpolate/blob/main/tutorials/Semivariogram%20Estimation%20(Basic).ipynb)\n", "- [Directional Semivariograms](https://github.com/DataverseLabs/pyinterpolate/blob/main/tutorials/Directional%20Semivariograms%20(Basic).ipynb)\n", @@ -134,10 +133,10 @@ "\n", "The class has three required parameters and seven optional.\n", "\n", - "- `input_array` is a list of coordinates and values in a form `(x, y, value)`. Those are our observations.\n", - "- `step_size` parameter which describes distance between lags, and `max_range` which describes maximum range of analysis are hard to guess at a first run, so probably we will change those to find semivariogram features. Let's experiment with those parameters!\n", + "- `input_array` is a list of coordinates and values in the form `(x, y, value)`. Those are our observations.\n", + "- `step_size` parameter which describes the distance between lags, and `max_range` which describes the maximum range of analysis are hard to guess at the first run, so we will probably change those to find semivariogram features. Let's experiment with those parameters!\n", "\n", - "What we know at the beginning? That our coordinate reference system is metric, it is [EPSG:2180](https://epsg.io/2180). We don't know the limits of the region of interest, and we will check it:" + "What do we know at the beginning? Our coordinate reference system is metric, it is [EPSG:2180](https://epsg.io/2180). We don't know the limits of the region of interest, and we will check it:" ] }, { @@ -176,9 +175,9 @@ "id": "2dc49a0e", "metadata": {}, "source": [ - "We can assume, that the maximum distance `max_range` parameter shouldn't be larger than the maximum distance in a lower dimension (**~18000** m). In reality, the `max_range` parameter is rather close to the halved value of this distance, and we can check it on a variogram.\n", + "We can assume that the maximum distance `max_range` parameter shouldn't be larger than the maximum distance in a lower dimension (**~18000** m). In reality, the `max_range` parameter is relatively close to the halved value of this distance, and we can check it on a variogram.\n", "\n", - "But we don't know the `step_size` parameter... Let's assume that it is 100 meters and we will see if we've estimated it correctly." + "But we don't know the `step_size` parameter... Let's assume it is 100 meters, and we will see if we've estimated it correctly." ] }, { @@ -232,11 +231,11 @@ "id": "8efa683d", "metadata": {}, "source": [ - "Variogram looks nice, but it introduces a lot of variance. We can see a trend, but points are oscillating around it. This piece of information tells us that `step_size` should be larger.\n", + "Variogram looks nice, but it introduces much variance. We can see a trend, but points are oscillating around it. This piece of information tells us that `step_size` should be larger.\n", "\n", - "What with `max_range`? It is a tricky problem because variogram seems to be right up to the end of its range. But we should be aware, that this could be an effect of sampling points in the North-South axis (with a greater range).\n", + "What with `max_range`? It is a tricky problem because the variogram seems right up to the end of its range. But we should be aware that this could affect sampling points in the North-South axis (with a greater range).\n", "\n", - "We can check how many point pairs we have for each distance to make a better decision. The `ExperimentalVariogram` class stores information about points within its attribute `.experimental_semivariance_array`. It is `numpy` array with three columns: `[lag, semivariance, number of point pairs]` and we will use the last column to decide where to cut `max_range`." + "We can check how many point pairs we have for each distance to make a better decision. The `ExperimentalVariogram` class stores information about points within its attribute `.experimental_semivariance_array`. It is a `numpy` array with three columns: `[lag, semivariance, number of point pairs]`, and we will use the last column to decide where to cut `max_range`.\n" ] }, { @@ -268,7 +267,7 @@ "id": "704f8733", "metadata": {}, "source": [ - "We clearly see that after 12 kilometers number of point pairs is rapidly falling, so we can set `max_range` to **12000**." + "We see that after 12 kilometers number of point pairs is rapidly falling, so we can set `max_range` to **12000**." ] }, { @@ -344,10 +343,10 @@ "id": "848d992d", "metadata": {}, "source": [ - "We can use this variogram for further processing. With this in mind, we will make a use from two other parameters: `is_variance` and `is_covariance`. Both are set to `True` by default. What do they mean?\n", + "We can use this variogram for further processing. With this in mind, we will use two other parameters: `is_variance` and `is_covariance`. Both are set to `True` by default. What do they mean?\n", "\n", - "- if we want to know variance of a dataset (variance at lag 0) then we should set `is_variance` parameter to `True`. This value can be treated as the **sill** of variogram that we will use in semivariogram modeling and kriging.\n", - "- if we want to know covariance of a dataset then we should set `is_covariance` parameter to `True`. Covariance is a measure of spatial similarity, so it is opposite to semivariance. But we can use it in kriging systems instead of semivariance.\n", + "- if we want to know the variance of a dataset (variance at lag 0), then we should set the `is_variance` parameter to `True`. This value can be treated as the **sill** of the variogram we will use in semivariogram modeling and kriging.\n", + "- if we want to know the covariance of a dataset, then we should set the `is_covariance` parameter to `True`. Covariance is a measure of spatial similarity, opposite to semivariance. But we can use it in kriging systems instead of semivariance.\n", "\n", "`ExperimentalVariogram` plotting function `.plot()` has additional parameters:\n", "\n", @@ -389,10 +388,10 @@ "source": [ "Two important things that we can read from this plot:\n", "\n", - "1. Experimental covariance is a mirrored version of experimental semivariance and we can use it to solve kriging system instead of semivariance. It is a measure of similarity between point pairs, the highest similarity can be noticed on the first lags and it decreases with a rising distance.\n", - "2. Variance calculated from a data is used later as the **sill** in kriging models, and it can be used to transform semivariances into covariances.\n", + "1. Experimental covariance is a mirrored version of experimental semivariance, and we can use it to solve the kriging system instead of semivariance. It is a measure of similarity between point pairs, the highest similarity can be noticed on the first lags, and it decreases with a rising distance.\n", + "2. Variance calculated from data is used later as the **sill** in kriging models, and it can be used to transform semivariances into covariances.\n", "\n", - "This plot summarizes basic steps that we can done with the `ExperimentalVariogram` class. We left many parameters untouched but only for a moment, now we are going to explore all capabilities of the class." + "This plot summarizes the basic steps we can do with the `ExperimentalVariogram` class. We left many parameters untouched but only for a moment, now we are going to explore all capabilities of the class." ] }, { @@ -402,12 +401,12 @@ "source": [ "### Optional parameter: `weights`\n", "\n", - "We won't use this parameter in our tutorial, but we should learn from where it comes from. It is a part of **Poisson Kriging** operations. **Poisson Kriging** algorithms uses `weights` to weight semivariance between blocks by in-block population.\n", + "We won't use this parameter in our tutorial, but we should learn where it comes from. It is a part of **Poisson Kriging** operations. **Poisson Kriging** algorithms use `weights` to weight the semivariance between blocks by in-block population.\n", "\n", "\n", "### Optional parameters: `direction, tolerance, method`\n", "\n", - "Those three parameters are used to define directional variogram. We will check how variogram behaves in the Weast-East axis (0 degrees)." + "Those three parameters are used to define a directional variogram. We will check how the variogram behaves in the Weast-East axis (0 degrees)." ] }, { @@ -450,7 +449,7 @@ }, { "cell_type": "markdown", - "id": "3ce49546", + "id": "0d71880e", "metadata": {}, "source": [ "Let's look into the North-South axis too:" @@ -459,7 +458,7 @@ { "cell_type": "code", "execution_count": 17, - "id": "56e49c1d", + "id": "33c97d92", "metadata": {}, "outputs": [ { @@ -493,28 +492,26 @@ }, { "cell_type": "markdown", - "id": "35d06782", + "id": "d3b758e2", "metadata": {}, "source": [ - "What can we learn from those plots? We see that variability along W-E axis is lower than along N-S axis. It may be an indicator that we have spatial structures that are continuous along W-E axis (for example a river plain)." + "What can we learn from those plots? We see that variability along the W-E axis is lower than along the N-S axis. It may indicate that we have continuous spatial structures along the W-E axis (for example, a river plain)." ] }, { "cell_type": "markdown", - "id": "0fe801d3", + "id": "c6f49aa8", "metadata": {}, "source": [ - "At this point, we are ready for modeling. However, if we want to be precise we should take a look into outliers and analyze `VariogramCloud`." + "At this point, we are ready for modeling. However, if we want to be precise, we should look into outliers and analyze `VariogramCloud`." ] }, { "cell_type": "markdown", - "id": "7aa38fec", + "id": "e2eb825a", "metadata": {}, "source": [ - "## 2. (Experimental) Variogram Cloud class\n", - "\n", - "A classic variogram analysis tells us almost everything about spatial dependencies and dissimilarities in data. But variogram is not an ideal tool. We know nothing about distribution of variances within a single lag and it is a valuable information. Semivariogram shows us averaged semivariances. If we want to dig deeper, then we should use `VariogramCloud` class and plot... the variogram cloud.\n", + "A classic variogram analysis tells us almost everything about spatial dependencies and dissimilarities in data. But variogram is not an ideal tool. We need to learn about the distribution of variances within a single lag, which is valuable information. Semivariogram shows us averaged semivariances. If we want to dig deeper, we should use the `VariogramCloud` class and plot... the variogram cloud.\n", "\n", "The `VariogramCloud` class definition API is:\n", "\n", @@ -532,22 +529,22 @@ "\n", "```\n", "\n", - "We pass the same parameters to the class as for the `ExperimentalVariogram` class. The only difference is `calculate_on_creation` boolean value. Variogram point cloud calculation is a slow operation and that's why we have an opportunity to start it during or after the class initialization. If we set `calculate_on_creation` to `False` then we must run `.calculate_experimental_variogram()` method later.\n", + "We pass the same parameters to the class as for the `ExperimentalVariogram` class, and the only difference is the `calculate_on_creation` boolean value. Variogram point cloud calculation is a slow operation, and that's why we can start it during or after the class initialization. If we set `calculate_on_creation` to `False`, we must run the `.calculate_experimental_variogram()` method later.\n", "\n", "`VariogramCloud` has more methods than `ExperimentalVariogram`:\n", "\n", - "- `calculate_experimental_variogram()` to obtain the variogram (similar to output from `ExperimentalVariogram` class,\n", - "- `describe()` to get lag-statistics,\n", + "- `calculate_experimental_variogram()` to obtain the variogram (similar to the output from the `ExperimentalVariogram` class,\n", + "- `describe()` to get lag statistics,\n", "- `plot()` to plot variogram cloud,\n", - "- `remove_outliers()` to clean variogram.\n", + "- `remove_outliers()` to clean the variogram.\n", "\n", - "We will start from calculation and plotting variogram cloud." + "We will start with calculation and plotting variogram cloud." ] }, { "cell_type": "code", "execution_count": 18, - "id": "f565ede3", + "id": "5f668759", "metadata": {}, "outputs": [], "source": [ @@ -560,7 +557,7 @@ }, { "cell_type": "markdown", - "id": "afe591d4", + "id": "23fa47b9", "metadata": {}, "source": [ "We can plot three different kinds of distribution plots: `'scatter', 'box', 'violin'`. We will choose `box` because it clearly shows data distribution in contrast to `scatter` and it's easier to interpret than `violin` plot." @@ -569,7 +566,7 @@ { "cell_type": "code", "execution_count": 21, - "id": "1a9edb5e", + "id": "9a8fc9f3", "metadata": {}, "outputs": [ { @@ -589,16 +586,16 @@ }, { "cell_type": "markdown", - "id": "fcae8d56", + "id": "dd7f1785", "metadata": {}, "source": [ - "The general shape is similar to the shape of experimental variogram. We can see that semivariances between point pairs contain outliers, especially for the lags close to our maximum range. To be sure about outliers existence, let's look into more complex violinplot:" + "The general shape is similar to the shape of the experimental variogram. We can see that semivariances between point pairs contain outliers, especially for the lags close to our maximum range. To be sure about outliers' existence, let's look into a more complex violin plot:" ] }, { "cell_type": "code", "execution_count": 22, - "id": "0825b30e", + "id": "00a75c70", "metadata": {}, "outputs": [ { @@ -618,16 +615,16 @@ }, { "cell_type": "markdown", - "id": "098668ec", + "id": "4cff4162", "metadata": {}, "source": [ - "Extreme outliers are more visible. Another thing is a difference between mean and median. Distribution are highly skewed toward large values. If graphical representation is not enough we may describe statistics with method `.describe()`:" + "Extreme outliers are more visible. Another thing is the difference between the mean and median. Distribution is highly skewed toward large values. If graphical representation is not enough we may describe statistics with the method `.describe()`:" ] }, { "cell_type": "code", "execution_count": 26, - "id": "0c11c36f", + "id": "a9f0d3fc", "metadata": { "scrolled": true }, @@ -912,16 +909,16 @@ }, { "cell_type": "markdown", - "id": "3366be34", + "id": "bbd72bd5", "metadata": {}, "source": [ - "We get `dict` as an output but it is not especially readable, let's convert it to dataframe:" + "We get `dict` as an output, but it is not especially readable; let's convert it to a `DataFrame`:" ] }, { "cell_type": "code", "execution_count": 28, - "id": "9584d193", + "id": "087961af", "metadata": {}, "outputs": [], "source": [ @@ -931,7 +928,7 @@ { "cell_type": "code", "execution_count": 30, - "id": "69508402", + "id": "c28c7cea", "metadata": {}, "outputs": [ { @@ -1328,16 +1325,16 @@ }, { "cell_type": "markdown", - "id": "852dfb92", + "id": "b90dfd29", "metadata": {}, "source": [ - "With this detailed table, we can analyze variogram in details and decide:\n", + "With this detailed table, we can analyze the variogram in detail and decide:\n", "- if lags are correctly placed,\n", "- if there are enough points per lag,\n", - "- if maximum range is too low or too high,\n", + "- if the maximum range is too low or too high,\n", "- is distribution close to normal,\n", "\n", - "and based on the answers for those question, we can change semivariogram parameters slightly.\n", + "and based on the answers to those questions, we can change semivariogram parameters slightly.\n", "\n", "The interesting row is `avg semivariance`. Let's plot it against lags, and let's plot the output from `ExperimentalVariogram` in the same figure:" ] @@ -1345,7 +1342,7 @@ { "cell_type": "code", "execution_count": 31, - "id": "560b7e1f", + "id": "0a6d4290", "metadata": {}, "outputs": [], "source": [ @@ -1364,7 +1361,7 @@ { "cell_type": "code", "execution_count": 42, - "id": "09f11c4c", + "id": "0b00b899", "metadata": {}, "outputs": [ { @@ -1391,18 +1388,18 @@ }, { "cell_type": "markdown", - "id": "5dbdb816", + "id": "97a7bbc7", "metadata": {}, "source": [ - "The result is the same and it shouldn't be any surprise. Experimental Variogram averages all semivariances per lag, we took a step back in computations and focus on all point pairs within a lag.\n", + "The result is the same and it shouldn't be a surprise. Experimental Variogram averages all semivariances per lag, we took a step back in computations and focus on all point pairs within a lag.\n", "\n", - "There is one last method within `VariogramCloud` class: `.remove_outliers()`. It cleans our variogram from anomalous readings. Cleaning base on the z-score or the inter-quartile range analysis, and we can cut outliers from the largest or the lowest values. For data that deviaties greately from normal distribution it is better to use inter-quartile range for cleaning:" + "There is one last method within the `VariogramCloud` class: `.remove_outliers()`. It cleans our variogram from anomalous readings. Cleaning default algorithm is to use the z-score, but we can use the inter-quartile range analysis too. Both methods cut outliers from the largest or the lowest intervals. For data that deviaties greately from normal distribution it is better to use inter-quartile range for cleaning:" ] }, { "cell_type": "code", "execution_count": 43, - "id": "8133b1da", + "id": "055301a7", "metadata": {}, "outputs": [], "source": [ @@ -1411,7 +1408,7 @@ }, { "cell_type": "markdown", - "id": "b3761852", + "id": "fc4591db", "metadata": {}, "source": [ "Now we can check what has happend with a variogram:" @@ -1420,7 +1417,7 @@ { "cell_type": "code", "execution_count": 45, - "id": "e6ff8126", + "id": "06a98d0f", "metadata": {}, "outputs": [ { @@ -1440,16 +1437,16 @@ }, { "cell_type": "markdown", - "id": "3ff45d41", + "id": "82fc481e", "metadata": {}, "source": [ - "Maximum semivariances are much lower than before. How looks averaged semivariogram? Now we will use `.calculate_experimental_variogram()` method to calculate variogram directly and compare it to the experimental variogram retrieved from `ExperimentalVariogram` class." + "Maximum semivariances are much lower than before. How looks averaged semivariogram? Now we will use the `.calculate_experimental_variogram()` method to calculate the variogram directly and compare it to the experimental variogram retrieved from the `ExperimentalVariogram` class." ] }, { "cell_type": "code", "execution_count": 50, - "id": "238e891f", + "id": "fb848ef5", "metadata": {}, "outputs": [ { @@ -1478,20 +1475,20 @@ }, { "cell_type": "markdown", - "id": "30eff7c5", + "id": "5bab466d", "metadata": {}, "source": [ - "Now we see that the shape of variogram persisted, but maximum values per lag are lower. For some models it could be a useful step, especially if we get rid of outliers that have extreme values." + "Now we see that the shape of the variogram persisted, but maximum values per lag are lower. For some models, it could be a useful step, especially if we get rid of outliers that have extreme values." ] }, { "cell_type": "markdown", - "id": "b1ca16d9", + "id": "d983a6e6", "metadata": {}, "source": [ "## Summary\n", "\n", - "After all those steps you have a better insights into `ExperimentalVariogram` and `VariogramCloud` classes. You may check your own dataset and compare results to those in tutorial.\n", + "After all those steps you have better insights into the `ExperimentalVariogram` and `VariogramCloud` classes. You may check your own dataset and compare results to those in the tutorial.\n", "\n", "The main takeaways from this tutorial are:\n", "\n", @@ -1502,7 +1499,7 @@ }, { "cell_type": "markdown", - "id": "84c66d91", + "id": "c08c8f08", "metadata": {}, "source": [ "---" From 5fd25a636a1f6cd8348d3ca4598fe74cd0021a0a Mon Sep 17 00:00:00 2001 From: Simon <31246246+SimonMolinsky@users.noreply.github.com> Date: Sat, 28 Jan 2023 17:27:47 +0200 Subject: [PATCH 14/29] Updated tests for calculate_average_semivariance function --- changelog.rst | 4 ++- .../block/avg_inblock_semivariances.py | 1 - .../test_avg_inblock_semivariance.py | 27 +++++++++++++++++++ 3 files changed, 30 insertions(+), 2 deletions(-) diff --git a/changelog.rst b/changelog.rst index 6e66d077..e261b12b 100755 --- a/changelog.rst +++ b/changelog.rst @@ -18,7 +18,9 @@ Changes by date * (enhancement) `transform_ps_to_dict()` function takes custom parameters for lon, lat, value and index, * (test) `check_limits()` function tests, * (test) plotting function of the `VariogramCloud()` class is tested and slightly changed to return `True` if everything has worked fine, -* +* (tutorials) new tutorial about `ExperimentalVariogram` and `VariogramCloud` classes, +* (test) new tests for `calculate_average_semivariance()` function from `block` module, +* 2023-01-16 ---------- diff --git a/pyinterpolate/variogram/regularization/block/avg_inblock_semivariances.py b/pyinterpolate/variogram/regularization/block/avg_inblock_semivariances.py index 191efec3..b4f0189a 100644 --- a/pyinterpolate/variogram/regularization/block/avg_inblock_semivariances.py +++ b/pyinterpolate/variogram/regularization/block/avg_inblock_semivariances.py @@ -104,7 +104,6 @@ def calculate_average_semivariance(block_to_block_distances: Dict, partial_neighbors = [x for x in block_neighbors if x != block_name] n_len = len(partial_neighbors) if n_len > 0: - # TODO: check if it is valid if there are no neighbors n_semivariances = [inblock_semivariances[bid] for bid in partial_neighbors] average_semivariance.extend(n_semivariances) number_of_blocks_per_lag.append(no_of_areas) diff --git a/tests/test_variogram/regularize/test_avg_inblock_semivariance.py b/tests/test_variogram/regularize/test_avg_inblock_semivariance.py index 3cb235d7..0aa02582 100644 --- a/tests/test_variogram/regularize/test_avg_inblock_semivariance.py +++ b/tests/test_variogram/regularize/test_avg_inblock_semivariance.py @@ -65,3 +65,30 @@ def test_avg_from_inblock_real_world_data(self): MAX_RANGE) self.assertIsInstance(avg_semivariance, np.ndarray) + + def test_average_semivariance_no_neighbors(self): + + distances_between_blocks = { + 'a': [0, 2, 99], + 'b': [2, 0, 300], + 'c': [99, 300, 0] + } + + inblock_semivariances = {'a': 10, 'b': 20, 'c': 900} + + step_size = 1 + max_range = 3 + + # Avg semi + avg_semivariance = calculate_average_semivariance(distances_between_blocks, + inblock_semivariances, + step_size, + max_range) + + arrays_equal = np.array_equal(avg_semivariance, + np.array([ + [1, 0, 0], + [2, 7.5, 1] + ])) + + self.assertTrue(arrays_equal) From b76ac1287144c193d976cbd9a409c00019a04bc8 Mon Sep 17 00:00:00 2001 From: Simon <31246246+SimonMolinsky@users.noreply.github.com> Date: Sat, 28 Jan 2023 22:36:10 +0200 Subject: [PATCH 15/29] inblock semivariance tests --- changelog.rst | 2 +- .../block/inblock_semivariance.py | 5 - .../inblock_semivariances_optimization.py | 62 + .../semivariance/inblock_times_integer.csv | 2401 +++++++++++++++++ 4 files changed, 2464 insertions(+), 6 deletions(-) create mode 100644 tests/dev_tests/profile/semivariance/inblock_semivariances_optimization.py create mode 100644 tests/dev_tests/profile/semivariance/inblock_times_integer.csv diff --git a/changelog.rst b/changelog.rst index e261b12b..0c19dba2 100755 --- a/changelog.rst +++ b/changelog.rst @@ -20,7 +20,7 @@ Changes by date * (test) plotting function of the `VariogramCloud()` class is tested and slightly changed to return `True` if everything has worked fine, * (tutorials) new tutorial about `ExperimentalVariogram` and `VariogramCloud` classes, * (test) new tests for `calculate_average_semivariance()` function from `block` module, -* +* (enhancement) function `inblock_semivariance` has been optimized, 2023-01-16 ---------- diff --git a/pyinterpolate/variogram/regularization/block/inblock_semivariance.py b/pyinterpolate/variogram/regularization/block/inblock_semivariance.py index 4f47cdbe..a2bbb473 100644 --- a/pyinterpolate/variogram/regularization/block/inblock_semivariance.py +++ b/pyinterpolate/variogram/regularization/block/inblock_semivariance.py @@ -38,11 +38,6 @@ def inblock_semivariance(points_of_block: np.ndarray, variogram_model: Theoretic flattened_distances = distances_between_points.flatten() semivariances = variogram_model.predict(flattened_distances) - # TODO: part below to test with very large datasets - # unique_distances, uniq_count = np.unique(distances_between_points, return_counts=True) # Array is flattened here - # semivariances = variogram_model.predict(unique_distances) - # multiplied_semivariances = semivariances * uniq_count - average_block_semivariance = np.sum(semivariances) / p return average_block_semivariance diff --git a/tests/dev_tests/profile/semivariance/inblock_semivariances_optimization.py b/tests/dev_tests/profile/semivariance/inblock_semivariances_optimization.py new file mode 100644 index 00000000..0b19995c --- /dev/null +++ b/tests/dev_tests/profile/semivariance/inblock_semivariances_optimization.py @@ -0,0 +1,62 @@ +import numpy as np + + +def some_model(x): + return (100 + 9.8*x) + + +def inblock_semivariance(distances_between_points) -> float: + semivariances = some_model(distances_between_points) + + average_block_semivariance = np.sum(semivariances) / len(semivariances) + return average_block_semivariance + + +def inblock_semivariance_unique(distances_between_points) -> float: + + # TODO: part below to test with very large datasets + unique_distances, uniq_count = np.unique(distances_between_points, return_counts=True) # Array is flattened here + semivariances = some_model(unique_distances) + multiplied_semivariances = semivariances * uniq_count + + average_block_semivariance = np.sum(multiplied_semivariances) / len(semivariances) + return average_block_semivariance + + +if __name__ == '__main__': + import pandas as pd + from datetime import datetime + + outputs = [] + + for idx in range(0, 20): + for arr_size in np.logspace(1, 7, 20): + for x_limit in [10, 100, 1000, 5000, 20000, 100000]: + arr_size = int(arr_size) + x_limit = int(x_limit) + + print(idx, arr_size, x_limit) + + arr = np.random.randint(0, x_limit, arr_size) + + t_start_0 = datetime.now() + _ = inblock_semivariance(arr) + secs_0 = (datetime.now() - t_start_0).total_seconds() + + t_start_1 = datetime.now() + _ = inblock_semivariance_unique(arr) + secs_1 = (datetime.now() - t_start_1).total_seconds() + + d = { + 'idx': idx, + 'arr_size': arr_size, + 'max_element': x_limit, + 'plain time': secs_0, + 'uniq time': secs_1, + 'experiment': 'integer random numbers' + } + + outputs.append(d) + + df = pd.DataFrame(outputs) + df.to_csv('inblock_times_integer.csv') diff --git a/tests/dev_tests/profile/semivariance/inblock_times_integer.csv b/tests/dev_tests/profile/semivariance/inblock_times_integer.csv new file mode 100644 index 00000000..bf3124b3 --- /dev/null +++ b/tests/dev_tests/profile/semivariance/inblock_times_integer.csv @@ -0,0 +1,2401 @@ +,idx,arr_size,max_element,plain time,uniq time,experiment +0,0,10,10,2.6e-05,7.8e-05,integer random numbers +1,0,10,100,1.4e-05,4e-05,integer random numbers +2,0,10,1000,1.2e-05,3.7e-05,integer random numbers +3,0,10,5000,1.3e-05,3.5e-05,integer random numbers +4,0,10,20000,1.2e-05,3.3e-05,integer random numbers +5,0,10,100000,1.2e-05,3.3e-05,integer random numbers +6,0,20,10,1.2e-05,3.9e-05,integer random numbers +7,0,20,100,1.2e-05,3.5e-05,integer random numbers 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numbers +2389,19,4832930,100,0.012438,0.152874,integer random numbers +2390,19,4832930,1000,0.013095,0.19711,integer random numbers +2391,19,4832930,5000,0.012629,0.2226,integer random numbers +2392,19,4832930,20000,0.012509,0.245686,integer random numbers +2393,19,4832930,100000,0.012509,0.28181,integer random numbers +2394,19,10000000,10,0.047687,0.277821,integer random numbers +2395,19,10000000,100,0.026902,0.330724,integer random numbers +2396,19,10000000,1000,0.026687,0.412594,integer random numbers +2397,19,10000000,5000,0.026529,0.454678,integer random numbers +2398,19,10000000,20000,0.026642,0.52492,integer random numbers +2399,19,10000000,100000,0.026436,0.581916,integer random numbers From 0b3d5b42d06aeaea547fc9e77bd5152963400987 Mon Sep 17 00:00:00 2001 From: Simon <31246246+SimonMolinsky@users.noreply.github.com> Date: Sat, 28 Jan 2023 23:34:11 +0200 Subject: [PATCH 16/29] Updated module desc --- changelog.rst | 1 + pyinterpolate/variogram/empirical/__init__.py | 4 --- .../variogram/theoretical/__init__.py | 32 ++++++++++++++++--- 3 files changed, 28 insertions(+), 9 deletions(-) diff --git a/changelog.rst b/changelog.rst index 0c19dba2..704e19b6 100755 --- a/changelog.rst +++ b/changelog.rst @@ -21,6 +21,7 @@ Changes by date * (tutorials) new tutorial about `ExperimentalVariogram` and `VariogramCloud` classes, * (test) new tests for `calculate_average_semivariance()` function from `block` module, * (enhancement) function `inblock_semivariance` has been optimized, +* (docs) updated `__init__.py` of `variogram.theoretical` module, 2023-01-16 ---------- diff --git a/pyinterpolate/variogram/empirical/__init__.py b/pyinterpolate/variogram/empirical/__init__.py index 699f52fb..8591faed 100644 --- a/pyinterpolate/variogram/empirical/__init__.py +++ b/pyinterpolate/variogram/empirical/__init__.py @@ -33,10 +33,6 @@ [2] Oliver, M. and Webster, R. Basic Steps in Geostatistics: The Variogram and Kriging. Springer 2015. doi:10.1007/978-3-319-15865-5 -TODO ----- -- Tutorial for experimental semivariance (Basics) - """ diff --git a/pyinterpolate/variogram/theoretical/__init__.py b/pyinterpolate/variogram/theoretical/__init__.py index 89403ca6..320f993f 100644 --- a/pyinterpolate/variogram/theoretical/__init__.py +++ b/pyinterpolate/variogram/theoretical/__init__.py @@ -24,10 +24,30 @@ brings information about the modeled data. This module contains class and methods to fit experimental variances into theoretical functions. It can be done manully or automatically. -Module contains: -TODO: list of fn and classes +Module contains +--------------- -Flow of analysis: +############# +# functions # +############# + +- build_theoretical_variogram() : Function is a wrapper into ``TheoreticalVariogram`` class and its ``fit()`` method. +- circular_model() : Function calculates circular model of semivariogram. +- cubic_model() : Function calculates cubic model of semivariogram. +- exponential_model() : Function calculates exponential model of semivariogram. +- gaussian_model() : Function calculates gaussian model of semivariogram. +- linear_model() : Function calculates linear model of semivariogram. +- power_model() : Function calculates power model of semivariogram. +- spherical_model() : Function calculates spherical model of semivariogram. + +############# +# classes # +############# + +- TheoreticalVariogram : Theoretical model of a spatial dissimilarity. + +Flow of analysis +---------------- Experimental Variogram -> Theoretical Variogram -> Kriging ...................... --------------------- ....... @@ -48,7 +68,7 @@ nugget (float). It is used later for Kriging. Variogram Models: -- circular variogram [4], +- circular [4], cubic, exponential, gaussian, linear, power, spherical [3]. Changelog @@ -57,6 +77,7 @@ | Date | Change description | Author | |------------|------------------------------|----------------| | 2022-02-16 | First release of v0.3 module | @SimonMolinsky | +| 2023-01-28 | Added description of functions and classes | @SimonMolinsky | Authors ------- @@ -69,7 +90,8 @@ References ---------- -TODO +* variogram.empirical : Experimental Variogram and Covariogram calculations. +* variogram.regularization : Block variograms and semivariogram regularization. Bibliography ------------ From 5d45c808a4148df6c17448910bd0913a6ce56793 Mon Sep 17 00:00:00 2001 From: Simon <31246246+SimonMolinsky@users.noreply.github.com> Date: Wed, 1 Feb 2023 00:46:31 +0200 Subject: [PATCH 17/29] Updated scatterplot --- changelog.rst | 2 + pyinterpolate/variogram/empirical/cloud.py | 18 +- pyinterpolate/variogram/utils/plots.py | 87 ++++++ pyinterpolate/viz/__init__.py | 2 +- tests/dev_tests/dataviz/beeswarm.py | 109 +++++++ tests/dev_tests/dataviz/variogram_clouds.py | 14 +- ...ariogram Point Cloud classes (Basic).ipynb | 295 +++++++++++++++--- tutorials/Variogram Point Cloud (Basic).ipynb | 66 ++-- 8 files changed, 492 insertions(+), 101 deletions(-) create mode 100644 pyinterpolate/variogram/utils/plots.py create mode 100644 tests/dev_tests/dataviz/beeswarm.py diff --git a/changelog.rst b/changelog.rst index 704e19b6..b26bea05 100755 --- a/changelog.rst +++ b/changelog.rst @@ -22,6 +22,8 @@ Changes by date * (test) new tests for `calculate_average_semivariance()` function from `block` module, * (enhancement) function `inblock_semivariance` has been optimized, * (docs) updated `__init__.py` of `variogram.theoretical` module, +* (enhancement) scatter plot represented as a swarm plot in `VariogramCloud`, +* 2023-01-16 ---------- diff --git a/pyinterpolate/variogram/empirical/cloud.py b/pyinterpolate/variogram/empirical/cloud.py index f319c5ac..76f5d4db 100644 --- a/pyinterpolate/variogram/empirical/cloud.py +++ b/pyinterpolate/variogram/empirical/cloud.py @@ -19,6 +19,7 @@ from pyinterpolate.processing.select_values import select_points_within_ellipse, select_values_in_range from pyinterpolate.processing.transform.statistics import remove_outliers from pyinterpolate.variogram.utils.exceptions import validate_direction, validate_points, validate_tolerance +from pyinterpolate.variogram.utils.plots import build_swarmplot_input def omnidirectional_point_cloud(input_array: np.array, @@ -565,12 +566,14 @@ def _box_plot(self): self.__dist_plots(title_box, 'box') def _scatter_plot(self): + ds = self.__prep_scatterplot_data() plt.figure(figsize=(14, 8)) xs = ds[:, 0] ys = ds[:, 1] plt.scatter(x=xs, - y=ys) + y=ys, + s=0.2) plt.title('Variogram Point Cloud per lag.') plt.show() @@ -640,14 +643,11 @@ def __prep_distplot_data(self): return data, x_tick_labels def __prep_scatterplot_data(self) -> np.array: - ds = [] - for idx, values in self.experimental_point_cloud.items(): - vals_l = len(values) - idxs = np.ones(vals_l) * idx - elems = list(zip(idxs, values)) - ds.extend(elems) - - return np.array(ds) + + ds = build_swarmplot_input(data=self.experimental_point_cloud, + step_size=self.step) + + return ds @staticmethod def __str_empty(): diff --git a/pyinterpolate/variogram/utils/plots.py b/pyinterpolate/variogram/utils/plots.py new file mode 100644 index 00000000..4a2b98b1 --- /dev/null +++ b/pyinterpolate/variogram/utils/plots.py @@ -0,0 +1,87 @@ +from typing import Dict +import numpy as np + + +def _get_bin_width(no, max_no, max_width=0.3): + """ + Function gets bin width on a plot. + + Parameters + ---------- + no : int + The number of points within bin. + + max_no : int + The maximum number of points within all bins. + + max_width : float + The maximum deviation from the lag center in percent. + + Returns + ------- + : float + The deviation from the lag center. + """ + + bin_width = (no * max_width) / max_no + return bin_width + + +def build_swarmplot_input(data: Dict, step_size: float, bins=None): + """ + Function prepares data for lagged beeswarm plot. + + Parameters + ---------- + data : Dict + ``{lag: [values]}`` + + step_size : float + The step between lags. + + bins : int, optional + If ``None`` given then number of bins per lag is chosen automatically. + + Returns + ------- + : numpy array + [lags (x-coordinates), values] + """ + + xs_arr = [] + ys_arr = [] + + for lag, values in data.items(): + center = lag + # bin values + # TODO: set max points per level + if bins is None: + histogram, bin_edges = np.histogram(values, bins='auto') + else: + histogram, bin_edges = np.histogram(values, bins=bins) + + # Now prepare x-coordinates per lag + x_indexes = [] + y_values = [] + + # Define limits + max_no = np.max(histogram) + + limits = [ + step_size * _get_bin_width(x, max_no) for x in histogram + ] + + lower_limits = [center - l for l in limits] + upper_limits = [center + l for l in limits] + + for idx, no_points in enumerate(histogram): + xc = np.linspace(lower_limits[idx], upper_limits[idx], no_points) + yc = [bin_edges[idx+1] for _ in xc] + x_indexes.extend(xc) + y_values.extend(yc) + + xs_arr.extend(x_indexes) + ys_arr.extend(y_values) + + arr = np.array([xs_arr, ys_arr]).transpose() + return arr diff --git a/pyinterpolate/viz/__init__.py b/pyinterpolate/viz/__init__.py index e1322fca..0c2a6347 100644 --- a/pyinterpolate/viz/__init__.py +++ b/pyinterpolate/viz/__init__.py @@ -1 +1 @@ -from pyinterpolate.viz.raster import interpolate_raster +from pyinterpolate.viz.raster import interpolate_raster \ No newline at end of file diff --git a/tests/dev_tests/dataviz/beeswarm.py b/tests/dev_tests/dataviz/beeswarm.py new file mode 100644 index 00000000..40ff1978 --- /dev/null +++ b/tests/dev_tests/dataviz/beeswarm.py @@ -0,0 +1,109 @@ +from typing import Dict +import numpy as np + +from pyinterpolate import VariogramCloud + + +def _get_bin_width(no, max_no, max_width=0.3): + """ + Function gets bin width on a plot. + + Parameters + ---------- + no : int + The number of points within bin. + + max_no : int + The maximum number of points within all bins. + + max_width : float + The maximum deviation from the lag center in percent. + + Returns + ------- + : float + The deviation from the lag center. + """ + + bin_width = (no * max_width) / max_no + return bin_width + + +def build_swarmplot_input(data: Dict, step_size: float, bins=None): + """ + Function prepares data for lagged beeswarm plot. + + Parameters + ---------- + data : Dict + ``{lag: [values]}`` + + step_size : float + The step between lags. + + bins : int, optional + If ``None`` given then number of bins per lag is chosen automatically. + + Returns + ------- + : numpy array + [lags (x-coordinates), values] + """ + + xs_arr = [] + ys_arr = [] + + for lag, values in data.items(): + center = lag + # bin values + # TODO: set max points per level + if bins is None: + histogram, bin_edges = np.histogram(values, bins='auto') + else: + histogram, bin_edges = np.histogram(values, bins=bins) + + # Now prepare x-coordinates per lag + x_indexes = [] + y_values = [] + + # Define limits + max_no = np.max(histogram) + + limits = [ + step_size * _get_bin_width(x, max_no) for x in histogram + ] + + lower_limits = [center - l for l in limits] + upper_limits = [center + l for l in limits] + + for idx, no_points in enumerate(histogram): + xc = np.linspace(lower_limits[idx], upper_limits[idx], no_points) + yc = [bin_edges[idx+1] for _ in xc] + x_indexes.extend(xc) + y_values.extend(yc) + + xs_arr.extend(x_indexes) + ys_arr.extend(y_values) + + arr = np.array([xs_arr, ys_arr]).transpose() + return arr + + +if __name__ == '__main__': + import os + + def get_armstrong_data(): + my_dir = os.path.dirname(__file__) + filename = 'armstrong_data.npy' + filepath = f'../../samples/point_data/numpy/{filename}' + path_to_the_data = os.path.abspath(os.path.join(my_dir, filepath)) + arr = np.load(path_to_the_data) + return arr + + ss = 1.5 + rng = 7 + _data = get_armstrong_data() + vc = VariogramCloud(input_array=_data, step_size=ss, max_range=rng) + ds = vc.experimental_point_cloud.copy() + swarmplot_data = build_swarmplot_input(ds, step_size=ss, bins=10) + print(swarmplot_data[0]) diff --git a/tests/dev_tests/dataviz/variogram_clouds.py b/tests/dev_tests/dataviz/variogram_clouds.py index 6db96904..610a8115 100644 --- a/tests/dev_tests/dataviz/variogram_clouds.py +++ b/tests/dev_tests/dataviz/variogram_clouds.py @@ -1,9 +1,7 @@ import os import numpy as np -from pyinterpolate.distance.distance import calc_point_to_point_distance -from pyinterpolate.io import read_txt -from pyinterpolate.variogram import VariogramCloud +from pyinterpolate.variogram.empirical.cloud import VariogramCloud def test_with_armstrong_data(): @@ -11,7 +9,7 @@ def test_with_armstrong_data(): def get_armstrong_data(): my_dir = os.path.dirname(__file__) filename = 'armstrong_data.npy' - filepath = f'../samples/point_data/numpy/{filename}' + filepath = f'../../samples/point_data/numpy/{filename}' path_to_the_data = os.path.abspath(os.path.join(my_dir, filepath)) arr = np.load(path_to_the_data) return arr @@ -20,6 +18,10 @@ def get_armstrong_data(): rng = 7 data = get_armstrong_data() vc = VariogramCloud(input_array=data, step_size=ss, max_range=rng) - vc.plot('box') + # vc.plot('box') vc.plot('scatter') - vc.plot('violin') + # vc.plot('violin') + + +if __name__ == '__main__': + test_with_armstrong_data() diff --git a/tutorials/Experimental Variogram and Variogram Point Cloud classes (Basic).ipynb b/tutorials/Experimental Variogram and Variogram Point Cloud classes (Basic).ipynb index d85eb398..d2a929ac 100644 --- a/tutorials/Experimental Variogram and Variogram Point Cloud classes (Basic).ipynb +++ b/tutorials/Experimental Variogram and Variogram Point Cloud classes (Basic).ipynb @@ -3,7 +3,11 @@ { "cell_type": "markdown", "id": "c18b3146", - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, "source": [ "# Experimental Variogram and Variogram Cloud classes\n", "\n", @@ -41,7 +45,11 @@ { "cell_type": "markdown", "id": "f5e96c24", - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, "source": [ "## Import packages & read data" ] @@ -50,7 +58,11 @@ "cell_type": "code", "execution_count": 27, "id": "3ffc9a4e", - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, "outputs": [], "source": [ "import numpy as np\n", @@ -63,7 +75,11 @@ "cell_type": "code", "execution_count": 2, "id": "5e8e320a", - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, "outputs": [ { "data": { @@ -100,7 +116,11 @@ { "cell_type": "markdown", "id": "2faec977", - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, "source": [ "## 1. Experimental Variogram class" ] @@ -108,7 +128,11 @@ { "cell_type": "markdown", "id": "eb950327", - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, "source": [ "Let's start from the `ExperimentalVariogram` class.\n", "\n", @@ -143,7 +167,11 @@ "cell_type": "code", "execution_count": 3, "id": "785e9110", - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, "outputs": [ { "name": "stdout", @@ -173,7 +201,11 @@ { "cell_type": "markdown", "id": "2dc49a0e", - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, "source": [ "We can assume that the maximum distance `max_range` parameter shouldn't be larger than the maximum distance in a lower dimension (**~18000** m). In reality, the `max_range` parameter is relatively close to the halved value of this distance, and we can check it on a variogram.\n", "\n", @@ -184,7 +216,11 @@ "cell_type": "code", "execution_count": 4, "id": "3fbc21c8", - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, "outputs": [], "source": [ "max_range = 18000\n", @@ -200,7 +236,11 @@ { "cell_type": "markdown", "id": "b1923982", - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, "source": [ "We will visually inspect semivariogram with the `.plot()` method of `ExperimentalVariogram` class: " ] @@ -209,7 +249,11 @@ "cell_type": "code", "execution_count": 5, "id": "db85fb2b", - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, "outputs": [ { "data": { @@ -229,7 +273,11 @@ { "cell_type": "markdown", "id": "8efa683d", - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, "source": [ "Variogram looks nice, but it introduces much variance. We can see a trend, but points are oscillating around it. This piece of information tells us that `step_size` should be larger.\n", "\n", @@ -242,7 +290,11 @@ "cell_type": "code", "execution_count": 6, "id": "b662300d", - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, "outputs": [ { "data": { @@ -265,7 +317,11 @@ { "cell_type": "markdown", "id": "704f8733", - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, "source": [ "We see that after 12 kilometers number of point pairs is rapidly falling, so we can set `max_range` to **12000**." ] @@ -274,7 +330,11 @@ "cell_type": "code", "execution_count": 7, "id": "da5aae7e", - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, "outputs": [ { "data": { @@ -303,7 +363,11 @@ { "cell_type": "markdown", "id": "35d71c3e", - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, "source": [ "This time variogram looks cleaner. However, it still has a little too much variance. Let's set `step_size` to 500 meters:" ] @@ -312,7 +376,11 @@ "cell_type": "code", "execution_count": 8, "id": "78a330bd", - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, "outputs": [ { "data": { @@ -341,7 +409,11 @@ { "cell_type": "markdown", "id": "848d992d", - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, "source": [ "We can use this variogram for further processing. With this in mind, we will use two other parameters: `is_variance` and `is_covariance`. Both are set to `True` by default. What do they mean?\n", "\n", @@ -361,7 +433,11 @@ "cell_type": "code", "execution_count": 9, "id": "12241262", - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, "outputs": [ { "data": { @@ -384,7 +460,11 @@ { "cell_type": "markdown", "id": "8b04c2d3", - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, "source": [ "Two important things that we can read from this plot:\n", "\n", @@ -397,7 +477,11 @@ { "cell_type": "markdown", "id": "95b7f9f0", - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, "source": [ "### Optional parameter: `weights`\n", "\n", @@ -413,7 +497,11 @@ "cell_type": "code", "execution_count": 16, "id": "3b45c1e9", - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, "outputs": [ { "data": { @@ -450,7 +538,11 @@ { "cell_type": "markdown", "id": "0d71880e", - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, "source": [ "Let's look into the North-South axis too:" ] @@ -459,7 +551,11 @@ "cell_type": "code", "execution_count": 17, "id": "33c97d92", - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, "outputs": [ { "data": { @@ -493,7 +589,11 @@ { "cell_type": "markdown", "id": "d3b758e2", - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, "source": [ "What can we learn from those plots? We see that variability along the W-E axis is lower than along the N-S axis. It may indicate that we have continuous spatial structures along the W-E axis (for example, a river plain)." ] @@ -501,7 +601,11 @@ { "cell_type": "markdown", "id": "c6f49aa8", - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, "source": [ "At this point, we are ready for modeling. However, if we want to be precise, we should look into outliers and analyze `VariogramCloud`." ] @@ -509,7 +613,11 @@ { "cell_type": "markdown", "id": "e2eb825a", - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, "source": [ "A classic variogram analysis tells us almost everything about spatial dependencies and dissimilarities in data. But variogram is not an ideal tool. We need to learn about the distribution of variances within a single lag, which is valuable information. Semivariogram shows us averaged semivariances. If we want to dig deeper, we should use the `VariogramCloud` class and plot... the variogram cloud.\n", "\n", @@ -545,7 +653,11 @@ "cell_type": "code", "execution_count": 18, "id": "5f668759", - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, "outputs": [], "source": [ "variogram_cloud = VariogramCloud(\n", @@ -558,7 +670,11 @@ { "cell_type": "markdown", "id": "23fa47b9", - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, "source": [ "We can plot three different kinds of distribution plots: `'scatter', 'box', 'violin'`. We will choose `box` because it clearly shows data distribution in contrast to `scatter` and it's easier to interpret than `violin` plot." ] @@ -567,7 +683,11 @@ "cell_type": "code", "execution_count": 21, "id": "9a8fc9f3", - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, "outputs": [ { "data": { @@ -587,7 +707,11 @@ { "cell_type": "markdown", "id": "dd7f1785", - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, "source": [ "The general shape is similar to the shape of the experimental variogram. We can see that semivariances between point pairs contain outliers, especially for the lags close to our maximum range. To be sure about outliers' existence, let's look into a more complex violin plot:" ] @@ -596,7 +720,11 @@ "cell_type": "code", "execution_count": 22, "id": "00a75c70", - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, "outputs": [ { "data": { @@ -616,7 +744,11 @@ { "cell_type": "markdown", "id": "4cff4162", - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, "source": [ "Extreme outliers are more visible. Another thing is the difference between the mean and median. Distribution is highly skewed toward large values. If graphical representation is not enough we may describe statistics with the method `.describe()`:" ] @@ -626,7 +758,10 @@ "execution_count": 26, "id": "a9f0d3fc", "metadata": { - "scrolled": true + "scrolled": true, + "pycharm": { + "name": "#%%\n" + } }, "outputs": [ { @@ -910,7 +1045,11 @@ { "cell_type": "markdown", "id": "bbd72bd5", - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, "source": [ "We get `dict` as an output, but it is not especially readable; let's convert it to a `DataFrame`:" ] @@ -919,7 +1058,11 @@ "cell_type": "code", "execution_count": 28, "id": "087961af", - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, "outputs": [], "source": [ "desc = pd.DataFrame(variogram_cloud.describe())" @@ -929,7 +1072,11 @@ "cell_type": "code", "execution_count": 30, "id": "c28c7cea", - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, "outputs": [ { "data": { @@ -1326,7 +1473,11 @@ { "cell_type": "markdown", "id": "b90dfd29", - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, "source": [ "With this detailed table, we can analyze the variogram in detail and decide:\n", "- if lags are correctly placed,\n", @@ -1343,7 +1494,11 @@ "cell_type": "code", "execution_count": 31, "id": "0a6d4290", - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, "outputs": [], "source": [ "# Re-calculate experimental variogram\n", @@ -1362,7 +1517,11 @@ "cell_type": "code", "execution_count": 42, "id": "0b00b899", - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, "outputs": [ { "data": { @@ -1389,7 +1548,11 @@ { "cell_type": "markdown", "id": "97a7bbc7", - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, "source": [ "The result is the same and it shouldn't be a surprise. Experimental Variogram averages all semivariances per lag, we took a step back in computations and focus on all point pairs within a lag.\n", "\n", @@ -1400,7 +1563,11 @@ "cell_type": "code", "execution_count": 43, "id": "055301a7", - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, "outputs": [], "source": [ "new_variogram_cloud = variogram_cloud.remove_outliers(method='iqr', iqr_lower_limit=3, iqr_upper_limit=2)" @@ -1409,7 +1576,11 @@ { "cell_type": "markdown", "id": "fc4591db", - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, "source": [ "Now we can check what has happend with a variogram:" ] @@ -1418,7 +1589,11 @@ "cell_type": "code", "execution_count": 45, "id": "06a98d0f", - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, "outputs": [ { "data": { @@ -1438,7 +1613,11 @@ { "cell_type": "markdown", "id": "82fc481e", - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, "source": [ "Maximum semivariances are much lower than before. How looks averaged semivariogram? Now we will use the `.calculate_experimental_variogram()` method to calculate the variogram directly and compare it to the experimental variogram retrieved from the `ExperimentalVariogram` class." ] @@ -1447,7 +1626,11 @@ "cell_type": "code", "execution_count": 50, "id": "fb848ef5", - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, "outputs": [ { "data": { @@ -1476,7 +1659,11 @@ { "cell_type": "markdown", "id": "5bab466d", - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, "source": [ "Now we see that the shape of the variogram persisted, but maximum values per lag are lower. For some models, it could be a useful step, especially if we get rid of outliers that have extreme values." ] @@ -1484,7 +1671,11 @@ { "cell_type": "markdown", "id": "d983a6e6", - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, "source": [ "## Summary\n", "\n", @@ -1500,7 +1691,11 @@ { "cell_type": "markdown", "id": "c08c8f08", - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, "source": [ "---" ] @@ -1527,4 +1722,4 @@ }, "nbformat": 4, "nbformat_minor": 5 -} +} \ No newline at end of file diff --git a/tutorials/Variogram Point Cloud (Basic).ipynb b/tutorials/Variogram Point Cloud (Basic).ipynb index 5af0e6dc..c5e7ea32 100755 --- a/tutorials/Variogram Point Cloud (Basic).ipynb +++ b/tutorials/Variogram Point Cloud (Basic).ipynb @@ -23,6 +23,7 @@ "\n", "| Date | Change description | Author |\n", "|------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------|----------------------------|\n", + "| 2023-02-01 | Tutorial updated for the version 0.3.7 of the package | @SimonMolinsky |\n", "| 2022-11-05 | Tutorial updated for the 0.3.5 version of the package | @SimonMolinsky |\n", "| 2022-10-21 | Updated to the version 0.3.4 | @SimonMolinsky |\n", "| 2022-10-17 | Corrected data selection, now tutorial will run faster | @SimonMolinsky |\n", @@ -108,18 +109,7 @@ "outputs": [ { "data": { - "text/plain": [ - "array([[2.42769460e+05, 5.30657496e+05, 4.16058350e+01],\n", - " [2.51626284e+05, 5.43737929e+05, 2.04681377e+01],\n", - " [2.49607562e+05, 5.47163152e+05, 2.28987751e+01],\n", - " [2.39932922e+05, 5.36875213e+05, 1.63156300e+01],\n", - " [2.39496018e+05, 5.41235762e+05, 1.72428761e+01],\n", - " [2.41583412e+05, 5.40178522e+05, 1.78676319e+01],\n", - " [2.49883911e+05, 5.46295438e+05, 1.96465473e+01],\n", - " [2.51152700e+05, 5.42332084e+05, 2.03913765e+01],\n", - " [2.41198900e+05, 5.34454688e+05, 3.00375862e+01],\n", - " [2.38489710e+05, 5.48418692e+05, 9.50086288e+01]])" - ] + "text/plain": "array([[2.38021426e+05, 5.50151241e+05, 1.06866257e+02],\n [2.38826239e+05, 5.39241490e+05, 1.84549274e+01],\n [2.51375565e+05, 5.30949430e+05, 4.87319641e+01],\n [2.46801816e+05, 5.43640990e+05, 1.88564606e+01],\n [2.51547499e+05, 5.35899682e+05, 3.67753220e+01],\n [2.46691296e+05, 5.50751631e+05, 5.52004814e+01],\n [2.53616703e+05, 5.40222972e+05, 2.14738140e+01],\n [2.45014169e+05, 5.34045938e+05, 5.11259308e+01],\n [2.49423055e+05, 5.42028826e+05, 2.04576187e+01],\n [2.53275034e+05, 5.39825126e+05, 2.29381065e+01]])" }, "execution_count": 3, "metadata": {}, @@ -195,22 +185,22 @@ "name": "stdout", "output_type": "stream", "text": [ - "Lag 876.7006794496056 has 654 point pairs.\n", - "Lag 1753.4013588992111 has 1820 point pairs.\n", - "Lag 2630.1020383488167 has 2822 point pairs.\n", - "Lag 3506.8027177984222 has 3898 point pairs.\n", - "Lag 4383.503397248028 has 4566 point pairs.\n", - "Lag 5260.204076697633 has 5212 point pairs.\n", - "Lag 6136.904756147239 has 5708 point pairs.\n", - "Lag 7013.6054355968445 has 6076 point pairs.\n", - "Lag 7890.30611504645 has 6234 point pairs.\n", - "Lag 8767.006794496056 has 6410 point pairs.\n", - "Lag 9643.707473945662 has 6848 point pairs.\n", - "Lag 10520.408153395267 has 6950 point pairs.\n", - "Lag 11397.108832844871 has 6812 point pairs.\n", - "Lag 12273.809512294476 has 6768 point pairs.\n", - "Lag 13150.510191744084 has 6432 point pairs.\n", - "Lag 14027.210871193689 has 5848 point pairs.\n" + "Lag 883.7145107499646 has 686 point pairs.\n", + "Lag 1767.4290214999291 has 1904 point pairs.\n", + "Lag 2651.1435322498937 has 3046 point pairs.\n", + "Lag 3534.8580429998583 has 3848 point pairs.\n", + "Lag 4418.572553749823 has 4654 point pairs.\n", + "Lag 5302.287064499787 has 5408 point pairs.\n", + "Lag 6186.001575249752 has 5952 point pairs.\n", + "Lag 7069.716085999717 has 6438 point pairs.\n", + "Lag 7953.430596749681 has 6538 point pairs.\n", + "Lag 8837.145107499646 has 6768 point pairs.\n", + "Lag 9720.859618249611 has 7020 point pairs.\n", + "Lag 10604.574128999575 has 6842 point pairs.\n", + "Lag 11488.288639749539 has 6628 point pairs.\n", + "Lag 12372.003150499502 has 6300 point pairs.\n", + "Lag 13255.71766124947 has 6382 point pairs.\n", + "Lag 14139.432171999433 has 6266 point pairs.\n" ] } ], @@ -235,7 +225,7 @@ "2. *Box plot* - an excellent tool for outliers detection. It shows dispersion and quartiles of semivariances per lag. We can check distribution differences and their deviation from normality or skewness.\n", "3. *Violin plot*. It is a box plot on steroids. We can read all information like from the box plot, plus we see kernel density plots.\n", "\n", - "Let's plot them all. The first is a scatter plot. It can take a while (we have thousands of point pairs), so now is a good time to prepare a beverage." + "Let's plot them all. The first is a scatter plot." ] }, { @@ -249,13 +239,19 @@ "outputs": [ { "data": { - "image/png": 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\n" }, "metadata": {}, "output_type": "display_data" + }, + { + "data": { + "text/plain": "True" + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ @@ -270,7 +266,7 @@ } }, "source": [ - "The output per lag is dense, and we cannot distinguish a single value. But, we can follow a general trend and see maxima on the plot. We can make it better if we use a box plot instead:" + "The output per lag is dense, and distributions are hard to read. But, we can follow a general trend, and see maxima on the plot. We can make it better if we use a box plot instead:" ] }, { @@ -752,4 +748,4 @@ }, "nbformat": 4, "nbformat_minor": 4 -} +} \ No newline at end of file From ef60d737b5812eeae0675498535bc0a0e06f0567 Mon Sep 17 00:00:00 2001 From: Simon <31246246+SimonMolinsky@users.noreply.github.com> Date: Sat, 4 Feb 2023 12:30:26 +0200 Subject: [PATCH 18/29] Updated directional ata and atp --- pyinterpolate/distance/distance.py | 31 +++ .../block/area_to_area_poisson_kriging.py | 31 +-- .../block/centroid_based_poisson_kriging.py | 1 + pyinterpolate/kriging/models/block/weight.py | 13 +- pyinterpolate/processing/select_values.py | 91 ++++++++- .../debug_sandbox/directional_ata.py | 179 ++++++++++++++++++ .../compare_ata_atp_directional.csv | 9 + .../test_ata_pk_data/smoothed_directional.cpg | 1 + .../test_ata_pk_data/smoothed_directional.dbf | Bin 0 -> 362210 bytes .../test_ata_pk_data/smoothed_directional.shp | Bin 0 -> 138980 bytes .../test_ata_pk_data/smoothed_directional.shx | Bin 0 -> 39780 bytes .../regularized_directional_variogram.json | 1 + 12 files changed, 337 insertions(+), 20 deletions(-) create mode 100644 tests/dev_tests/debug_sandbox/directional_ata.py create mode 100644 tests/dev_tests/debug_sandbox/test_ata_pk_data/compare_ata_atp_directional.csv create mode 100644 tests/dev_tests/debug_sandbox/test_ata_pk_data/smoothed_directional.cpg create mode 100644 tests/dev_tests/debug_sandbox/test_ata_pk_data/smoothed_directional.dbf create mode 100644 tests/dev_tests/debug_sandbox/test_ata_pk_data/smoothed_directional.shp create mode 100644 tests/dev_tests/debug_sandbox/test_ata_pk_data/smoothed_directional.shx create mode 100644 tests/samples/regularization/regularized_directional_variogram.json diff --git a/pyinterpolate/distance/distance.py b/pyinterpolate/distance/distance.py index bc154154..7a1b3ab4 100644 --- a/pyinterpolate/distance/distance.py +++ b/pyinterpolate/distance/distance.py @@ -231,6 +231,37 @@ def _calc_angle_from_origin(vec, origin): return np.rad2deg(ang % (2 * np.pi)) +def calc_angles_between_points(vec1, vec2, origin=None): + """ + Function calculates distances between two groups of points as their cross product. + + Parameters + ---------- + vec1 : numpy array + The first set of coordinates. + + vec2 : numpy array + The second set of coordinates. + + origin : Iterable, optional + The coordinates (x, y) of origin. + + Returns + ------- + angles : numpy array + An array with angles between all points from ``vec1`` to all points from ``vec2``, where rows are angles between + points from ``vec1`` to points from ``vec2`` (columns). + """ + angles = [] + + for point in vec1: + row = calc_angles(vec2, origin=point) + angles.append(row.flatten()) + + angles = np.array(angles).flatten() + return angles + + def calc_angles(points_b: Iterable, point_a: Iterable = None, origin: Iterable = None): """ Function calculates angles between points and origin or between vectors from origin to points and a vector from diff --git a/pyinterpolate/kriging/models/block/area_to_area_poisson_kriging.py b/pyinterpolate/kriging/models/block/area_to_area_poisson_kriging.py index a0c66c33..275aba52 100644 --- a/pyinterpolate/kriging/models/block/area_to_area_poisson_kriging.py +++ b/pyinterpolate/kriging/models/block/area_to_area_poisson_kriging.py @@ -15,23 +15,23 @@ from pyinterpolate.kriging.models.block.weight import weights_array, WeightedBlock2BlockSemivariance from pyinterpolate.processing.preprocessing.blocks import Blocks, PointSupport -from pyinterpolate.processing.select_values import select_poisson_kriging_data, prepare_pk_known_areas,\ +from pyinterpolate.processing.select_values import select_poisson_kriging_data, prepare_pk_known_areas, \ get_aggregated_point_support_values, get_distances_within_unknown from pyinterpolate.processing.transform.transform import get_areal_values_from_agg, transform_ps_to_dict, sem_to_cov from pyinterpolate.variogram import TheoreticalVariogram def area_to_area_pk(semivariogram_model: TheoreticalVariogram, - blocks: Union[Blocks, gpd.GeoDataFrame, pd.DataFrame, np.ndarray], - point_support: Union[Dict, np.ndarray, gpd.GeoDataFrame, pd.DataFrame, PointSupport], - unknown_block: np.ndarray, - unknown_block_point_support: np.ndarray, - number_of_neighbors: int, - raise_when_negative_prediction=True, - raise_when_negative_error=True, - log_process=True): + blocks: Union[Blocks, gpd.GeoDataFrame, pd.DataFrame, np.ndarray], + point_support: Union[Dict, np.ndarray, gpd.GeoDataFrame, pd.DataFrame, PointSupport], + unknown_block: np.ndarray, + unknown_block_point_support: np.ndarray, + number_of_neighbors: int, + raise_when_negative_prediction=True, + raise_when_negative_error=True, + log_process=True): """ - Function predicts areal value in a unknown location based on the area-to-area Poisson Kriging + Function predicts areal value in an unknown location based on the area-to-area Poisson Kriging Parameters ---------- @@ -101,7 +101,9 @@ def area_to_area_pk(semivariogram_model: TheoreticalVariogram, u_point_support=unknown_block_point_support, k_point_support_dict=dps, nn=number_of_neighbors, - max_range=semivariogram_model.rang + max_range=semivariogram_model.rang, + direction=semivariogram_model.direction, + angular_tolerance=5 ) b2b_semivariance = WeightedBlock2BlockSemivariance(semivariance_model=semivariogram_model) @@ -136,6 +138,8 @@ def area_to_area_pk(semivariogram_model: TheoreticalVariogram, # Add diagonal weights values = get_areal_values_from_agg(blocks, prepared_ids) aggregated_ps = get_aggregated_point_support_values(dps, prepared_ids) + if predicted.shape == (0, ): + print('STOP') weights = weights_array(predicted.shape, values, aggregated_ps) weighted_and_predicted = predicted + weights @@ -171,10 +175,9 @@ def area_to_area_pk(semivariogram_model: TheoreticalVariogram, raise ValueError(f'Predicted value is {zhat} and it should not be lower than 0. Check your sampling ' f'grid, samples, number of neighbors or semivariogram model type.') - # TODO test - come back here when covariance terms are used - # TODO - probably it should be without subtraction - those are semivariance terms, not covariances. sigmasq = np.matmul(w.T, k_ones) + distances_within_unknown_block = get_distances_within_unknown(unknown_block_point_support) semivariance_within_unknown = b2b_semivariance.calculate_average_semivariance({ u_idx: distances_within_unknown_block @@ -195,4 +198,4 @@ def area_to_area_pk(semivariogram_model: TheoreticalVariogram, sigma = np.sqrt(sigmasq) results = [u_idx, zhat, sigma] - return results \ No newline at end of file + return results diff --git a/pyinterpolate/kriging/models/block/centroid_based_poisson_kriging.py b/pyinterpolate/kriging/models/block/centroid_based_poisson_kriging.py index 589e2b24..dada9efe 100644 --- a/pyinterpolate/kriging/models/block/centroid_based_poisson_kriging.py +++ b/pyinterpolate/kriging/models/block/centroid_based_poisson_kriging.py @@ -103,6 +103,7 @@ def centroid_poisson_kriging(semivariogram_model: TheoreticalVariogram, weighted=is_weighted_by_point_support, direction=semivariogram_model.direction ) + sill = semivariogram_model.sill distances_column_index = 3 diff --git a/pyinterpolate/kriging/models/block/weight.py b/pyinterpolate/kriging/models/block/weight.py index cebb73f1..4f0ecaf0 100644 --- a/pyinterpolate/kriging/models/block/weight.py +++ b/pyinterpolate/kriging/models/block/weight.py @@ -70,7 +70,11 @@ def _avg_smv(self, datarows: np.ndarray) -> Tuple: : Tuple[float, float] [Weighted sum of semivariances, Weights sum] """ + if datarows.ndim == 1: + datarows = datarows[np.newaxis, :] + predictions = self.semivariance_model.predict(datarows[:, -1]) + weights = datarows[:, 0] * datarows[:, 1] summed_weights = np.sum(weights) summed_semivariances = np.sum( @@ -96,10 +100,15 @@ def calculate_average_semivariance(self, data_points: Dict) -> Dict: k = {} for idx, prediction_input in data_points.items(): + _input = prediction_input + if isinstance(prediction_input[0][0], Tuple): _input = prediction_input[1] - else: - _input = prediction_input + + if len(prediction_input) == 2: + if isinstance(prediction_input[0], np.ndarray) and isinstance(prediction_input[1], np.ndarray): + _input = prediction_input[1] + w_sem = self._avg_smv(_input) w_sem_sum = w_sem[0] diff --git a/pyinterpolate/processing/select_values.py b/pyinterpolate/processing/select_values.py index 9f53eebd..41430aa1 100644 --- a/pyinterpolate/processing/select_values.py +++ b/pyinterpolate/processing/select_values.py @@ -14,7 +14,7 @@ from scipy.linalg import fractional_matrix_power from pyinterpolate.distance.distance import calc_point_to_point_distance, calc_block_to_block_distance, \ - calc_angles, calculate_angular_distance + calc_angles, calculate_angular_distance, calc_angles_between_points from pyinterpolate.processing.preprocessing.blocks import Blocks from pyinterpolate.processing.transform.transform import get_areal_centroids_from_agg, transform_ps_to_dict @@ -622,11 +622,66 @@ def select_kriging_data(unknown_position: Iterable, return sorted_neighbors_and_dists[:number_of_neighbors] +def _select_datasets_based_on_angular_distances(dataset: Dict, + angular_distances: Dict, + angle_tol: float, + min_no_neighbors=1): + """ + Function checks if angular data is within given angle + Parameters + ---------- + dataset : Dict + ``{known block id: [(unknown x, unknown y), [unknown val, known val, distance between points]]}`` + + angular_distances : Dict + ``{known block id: angle for each point to unknown area points}`` + + angle_tol : float + Tolerance of angle. + + min_no_neighbors : int, default = 1 + Minimum number of neighbors that algorithm selects for further processing. + + Returns + ------- + : Dict + Cleaned dataset. + """ + + new_dataset = {} + + while len(new_dataset) < min_no_neighbors: + for nn_key in list(dataset.keys()): + + if nn_key in new_dataset: + continue + else: + angle_diffs = angular_distances[nn_key] + u_coordinates_arr, out_arr = dataset[nn_key] + + new_coordinates_arr = [] + new_out_arr = [] + + for angle_idx, _angle in enumerate(angle_diffs): + if abs(_angle) <= angle_tol: + new_coordinates_arr.append(u_coordinates_arr[angle_idx]) + new_out_arr.append(out_arr[angle_idx]) + + if len(new_coordinates_arr) > 0: + new_dataset[nn_key] = (np.array(new_coordinates_arr), np.array(out_arr)) + + angle_tol = angle_tol + 2 + + return new_dataset + + def select_poisson_kriging_data(u_block_centroid: np.ndarray, u_point_support: np.ndarray, k_point_support_dict: Dict, nn: int, - max_range: float) -> Dict: + max_range: float, + direction=None, + angular_tolerance=5) -> Dict: """ Function prepares data for the centroid-based Poisson Kriging Process. @@ -647,6 +702,12 @@ def select_poisson_kriging_data(u_block_centroid: np.ndarray, max_range : float The maximum range of influence (it should be set to semivariogram range). + direction : float, default = None + The direction of a directional variogram. + + angular_tolerance : float + How many degrees of deviation from the angle is respected. + Returns ------- datasets : Dict @@ -680,6 +741,7 @@ def select_poisson_kriging_data(u_block_centroid: np.ndarray, max_search_pos = np.argmax(sorted_kdata > max_range) idxs = sorted_kindex[:max_search_pos] + angle_diffs_dict = {} if len(idxs) < nn: idxs = sorted_kindex[:nn] @@ -689,18 +751,39 @@ def select_poisson_kriging_data(u_block_centroid: np.ndarray, for idx in idxs: point_s = k_point_support_dict[idx] + + # Distances between points distances = calc_point_to_point_distance(u_point_support[:, :-1], point_s[:, :-1]) fdistances = distances.flatten() ldist = len(fdistances) + u_coordinates_arr = [(uc[0], uc[1]) for uc in u_point_support[:, :-1]] u_values_arr = np.resize(u_point_support[:, -1], ldist) k_values_arr = np.resize(point_s[:, -1], ldist) u_coordinates_arr = u_coordinates_arr * int(ldist / len(u_coordinates_arr)) out_arr = list(zip(u_values_arr, k_values_arr, fdistances)) - datasets[idx] = [u_coordinates_arr, np.array(out_arr)] - return datasets + # Get angles if angular + if direction is not None: + angles = calc_angles_between_points(u_point_support[:, :-1], point_s) + angle_diffs = calculate_angular_distance(angles, direction) + angle_diffs_dict[idx] = angle_diffs + + datasets[idx] = (u_coordinates_arr, np.array(out_arr)) + + # Clean output + if direction is None: + return datasets + else: + datasets = _select_datasets_based_on_angular_distances( + dataset=datasets, + angular_distances=angle_diffs_dict, + angle_tol=angular_tolerance, + min_no_neighbors=2 + ) + + return datasets def select_neighbors_pk_centroid_with_angle(indexes, diff --git a/tests/dev_tests/debug_sandbox/directional_ata.py b/tests/dev_tests/debug_sandbox/directional_ata.py new file mode 100644 index 00000000..34ac5c66 --- /dev/null +++ b/tests/dev_tests/debug_sandbox/directional_ata.py @@ -0,0 +1,179 @@ +import numpy as np +import pandas as pd +from tqdm import tqdm + +from pyinterpolate import build_experimental_variogram, Blocks, PointSupport, Deconvolution, TheoreticalVariogram, \ + area_to_area_pk, area_to_point_pk +from pyinterpolate.pipelines.deconvolution import smooth_area_to_point_pk + +DATASET = '../../samples/regularization/cancer_data.gpkg' +OUTPUT = '../../samples/regularization/regularized_directional_variogram.json' +POLYGON_LAYER = 'areas' +POPULATION_LAYER = 'points' +POP10 = 'POP10' +GEOMETRY_COL = 'geometry' +POLYGON_ID = 'FIPS' +POLYGON_VALUE = 'rate' +NN = 8 + + +blocks = Blocks() +blocks.from_file(DATASET, value_col=POLYGON_VALUE, index_col=POLYGON_ID, layer_name=POLYGON_LAYER) + +point_support = PointSupport() +point_support.from_files(point_support_data_file=DATASET, + blocks_file=DATASET, + point_support_geometry_col=GEOMETRY_COL, + point_support_val_col=POP10, + blocks_geometry_col=GEOMETRY_COL, + blocks_index_col=POLYGON_ID, + use_point_support_crs=True, + point_support_layer_name=POPULATION_LAYER, + blocks_layer_name=POLYGON_LAYER) + + +# Check experimental semivariogram of areal data - this cell may be run multiple times +# before you find optimal parameters + +maximum_range = 400000 +step_size = 40000 + +# dt = blocks.data[[blocks.cx, blocks.cy, blocks.value_column_name]] # x, y, val +# exp_semivar = build_experimental_variogram(input_array=dt, step_size=step_size, max_range=maximum_range, +# direction=45, tolerance=0.1) +# +# # Plot experimental semivariogram +# +# exp_semivar.plot() +# +# reg_mod = Deconvolution(verbose=True) +# +# reg_mod.fit(agg_dataset=blocks, +# point_support_dataset=point_support, +# agg_step_size=step_size, +# agg_max_range=maximum_range, +# agg_direction=45, +# agg_tolerance=0.1, +# variogram_weighting_method='closest', +# model_types='basic') +# +# reg_mod.transform(max_iters=15) +# +# reg_mod.plot_deviations() +# reg_mod.plot_weights() +# reg_mod.plot_variograms() +# reg_mod.export_model(OUTPUT) + +def select_unknown_blocks_and_ps(areal_input, point_support, block_id): + ar_x = areal_input.cx + ar_y = areal_input.cy + ar_val = areal_input.value_column_name + ps_val = point_support.value_column + ps_x = point_support.x_col + ps_y = point_support.y_col + idx_col = areal_input.index_column_name + + areal_input = areal_input.data.copy() + point_support = point_support.point_support.copy() + + sample_key = np.random.choice(list(point_support[block_id].unique())) + + unkn_ps = point_support[point_support[block_id] == sample_key][[ps_x, ps_y, ps_val]].values + known_poses = point_support[point_support[block_id] != sample_key] + known_poses.rename(columns={ + ps_x: 'x', ps_y: 'y', ps_val: 'ds', idx_col: 'index' + }, inplace=True) + + unkn_area = areal_input[areal_input[block_id] == sample_key][[idx_col, ar_x, ar_y, ar_val]].values + known_areas = areal_input[areal_input[block_id] != sample_key] + known_areas.rename(columns={ + ar_x: 'centroid.x', ar_y: 'centroid.y', ar_val: 'ds', idx_col: 'index' + }, inplace=True) + + return known_areas, known_poses, unkn_area, unkn_ps + +# Read semivariogram +variogram = TheoreticalVariogram() +variogram.from_json(OUTPUT) + +# Perform ATA +areas = [] + +rmse_ata = [] +rmse_atp = [] +rmse_centroid = [] +err_ata = [] +err_atp = [] +err_centroid = [] +diff_ata_atp = [] +diff_err_ata_atp = [] + +# for _ in tqdm(range(200)): +# +# AREAL_INP, PS_INP, UNKN_AREA, UNKN_PS = select_unknown_blocks_and_ps(blocks, +# point_support, +# POLYGON_ID) +# +# if UNKN_AREA[0][0] in areas: +# continue +# else: +# areas.append(UNKN_AREA[0][0]) +# +# uar = UNKN_AREA[0][:-1] +# uvar = UNKN_AREA[0][-1] +# +# pk_output_ata = area_to_area_pk(semivariogram_model=variogram, +# blocks=AREAL_INP, +# point_support=PS_INP, +# unknown_block=uar, +# unknown_block_point_support=UNKN_PS, +# number_of_neighbors=NN, +# raise_when_negative_error=False, +# raise_when_negative_prediction=False, +# log_process=False) +# +# pk_output_atp = area_to_point_pk(semivariogram_model=variogram, +# blocks=AREAL_INP, +# point_support=PS_INP, +# unknown_block=uar, +# unknown_block_point_support=UNKN_PS, +# number_of_neighbors=NN, +# raise_when_negative_error=False, +# raise_when_negative_prediction=False) +# +# ata_pred = pk_output_ata[1] +# ata_err = pk_output_ata[2] +# +# atp_pred = np.sum([x[1] for x in pk_output_atp]) +# atp_err = np.mean([x[2] for x in pk_output_atp]) +# +# rmse_ata.append(np.sqrt((ata_pred - uvar)**2)) +# rmse_atp.append(np.sqrt((atp_pred - uvar)**2)) +# err_ata.append(ata_err) +# err_atp.append(atp_err) +# diff_ata_atp.append(ata_pred - atp_pred) +# diff_err_ata_atp.append(ata_err - atp_err) +# +# +# df = pd.DataFrame( +# data={ +# 'rmse ata': rmse_ata, +# 'rmse atp': rmse_atp, +# 'err ata': err_ata, +# 'err atp': err_atp, +# 'ata-atp': diff_ata_atp, +# 'err ata - err atp': diff_err_ata_atp +# } +# ) +# +# df.describe().to_csv('test_ata_pk_data/compare_ata_atp_directional.csv') + +smoothed = smooth_area_to_point_pk( + semivariogram_model=variogram, + blocks=blocks, + point_support=point_support, + number_of_neighbors=8, + max_range=maximum_range +) + +smoothed.to_file('test_ata_pk_data/smoothed_directional.shp') diff --git a/tests/dev_tests/debug_sandbox/test_ata_pk_data/compare_ata_atp_directional.csv b/tests/dev_tests/debug_sandbox/test_ata_pk_data/compare_ata_atp_directional.csv new file mode 100644 index 00000000..33d8ea8b --- /dev/null +++ b/tests/dev_tests/debug_sandbox/test_ata_pk_data/compare_ata_atp_directional.csv @@ -0,0 +1,9 @@ +,rmse ata,rmse atp,err ata,err atp,ata-atp,err ata - err atp +count,136.0,136.0,136.0,136.0,136.0,136.0 +mean,9.05046386692267,8.681941352307643,7.630792280264821,9.31305043089112,-0.19887729612537725,-1.6822581506262984 +std,7.196357756979615,7.5454768115501745,1.382751876076442,0.9838672897489383,5.0936877071849835,1.920958873322928 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z&T*L=+~Wx^c^`Nd!8j%}i-jy_EnC>d0giEoi(KOlk9f}8!2dOjF-&9z^I6JjHnD?! z9N}BO=SObwGr#hhkAde=e9koH@&#YAfv@A#=NEqC_rU)xlFyjJY!)FO04sn8WT;>M%c*0BG z2mbF7jAJshSjckLvV~n7;23AP$TjZpi08Zw{1(O-CNhKhEM+yD*ug%I@Gal-Be(dO zUwO^Pz`qm4=S*WRU+^Ux_=>OjhLe295B$V^e&IKM54?)xGo~<`MXX>QTiMM)j&qhv zT<0#2dBMBD|0A5SOkySrSjHMQvy=TCg!G#9waZ65NBH-UfeiO|meUNjS!&ODZ|ij8b%FNZnBd9HAi z2R!8!9|G^6nnGRo@l0h7i&@Efwy}ppoZuXnxxqc2@RIj||91rAn9M8|vYfSSVHXED z#u+YhjXONzId22MhcSkU%wRrCS-#Uh^^V|B2#rrZJZ<_>v8L z#n*hpNxtI;e&RmA@EgAeK1A{vQ<%*nR zV-1_x$$pMjo?r@Z1r;NO30 z3U&Scc&0Lk#jIpK+t|Y)PH>LP+~6Khc**;J;17Qg!8j%}i-jy_EnC>d0giEoi(KOl zk9f}8z#oP&hKbBzK1*56CU&roBYeyE{KzeS=2u?xF)%EO&zZ(tzTitX@D*S44JY}I zANYy;{K9Yi9{3ZHe8v=JvxpU}V=KEk$Z^hciR;|uF)w%*7#_}8CNYx*EMpCu*~xy6 za+(WV zS;=~~v4=yP;2f8^!9AYvlJ|i>6~Q$#xRi? z%x5X9*~AX^afEOAo*%iz&-}`3J_i1D6rVGVxqQKwY~U-t<{M7(9Y63B_xXk2_&xAP zk$lD!X0wPDtYa&?ImmI&a*6BQ Date: Sat, 4 Feb 2023 12:47:14 +0200 Subject: [PATCH 19/29] Added tests (passing) --- tests/test_kriging/test_ata_pk.py | 20 +++++++++++++++++--- tests/test_kriging/test_atp_pk.py | 15 +++++++++++++++ tests/test_pipelines/test_smooth_areas.py | 11 +++++++++++ 3 files changed, 43 insertions(+), 3 deletions(-) diff --git a/tests/test_kriging/test_ata_pk.py b/tests/test_kriging/test_ata_pk.py index 1844a1c6..2a240e8c 100644 --- a/tests/test_kriging/test_ata_pk.py +++ b/tests/test_kriging/test_ata_pk.py @@ -9,6 +9,11 @@ DATASET = 'samples/regularization/cancer_data.gpkg' VARIOGRAM_MODEL_FILE = 'samples/regularization/regularized_variogram.json' +THEORETICAL_VARIOGRAM = TheoreticalVariogram() +THEORETICAL_VARIOGRAM.from_json(VARIOGRAM_MODEL_FILE) +DIRECTIONAL_VARIOGRAM_MODEL_FILE = 'samples/regularization/regularized_directional_variogram.json' +DIRECTIONAL_VARIOGRAM = TheoreticalVariogram() +DIRECTIONAL_VARIOGRAM.from_json(DIRECTIONAL_VARIOGRAM_MODEL_FILE) POLYGON_LAYER = 'areas' POPULATION_LAYER = 'points' POP10 = 'POP10' @@ -62,9 +67,6 @@ def select_unknown_blocks_and_ps(areal_input, point_support, block_id): AREAL_INP, PS_INP, UNKN_AREA, UNKN_PS = select_unknown_blocks_and_ps(AREAL_INPUT, POINT_SUPPORT_INPUT, POLYGON_ID) -THEORETICAL_VARIOGRAM = TheoreticalVariogram() -THEORETICAL_VARIOGRAM.from_json(VARIOGRAM_MODEL_FILE) - class TestATAPK(unittest.TestCase): @@ -130,3 +132,15 @@ def test_flow_2(self): number_of_neighbors=8, raise_when_negative_error=False) self.assertTrue(np.array_equal([int(x) for x in pk_model], [6, 340, 8])) + + def test_flow_3(self): + pk_output = area_to_area_pk(semivariogram_model=DIRECTIONAL_VARIOGRAM, + blocks=AREAL_INP, + point_support=PS_INP, + unknown_block=UNKN_AREA, + unknown_block_point_support=UNKN_PS, + number_of_neighbors=NN, + raise_when_negative_error=False, + raise_when_negative_prediction=False) + + self.assertTrue(pk_output) diff --git a/tests/test_kriging/test_atp_pk.py b/tests/test_kriging/test_atp_pk.py index fdafd580..20669860 100644 --- a/tests/test_kriging/test_atp_pk.py +++ b/tests/test_kriging/test_atp_pk.py @@ -17,6 +17,9 @@ POLYGON_ID = 'FIPS' POLYGON_VALUE = 'rate' NN = 4 +DIRECTIONAL_VARIOGRAM_MODEL_FILE = 'samples/regularization/regularized_directional_variogram.json' +DIRECTIONAL_VARIOGRAM = TheoreticalVariogram() +DIRECTIONAL_VARIOGRAM.from_json(DIRECTIONAL_VARIOGRAM_MODEL_FILE) def select_unknown_blocks_and_ps(areal_input, point_support, block_id): @@ -141,3 +144,15 @@ def test_flow_2(self): self.assertEqual(pk_model[0][0], expected_output[0][0]) self.assertAlmostEqual(pk_model[0][1], expected_output[0][1], places=2) self.assertAlmostEqual(pk_model[1][1], expected_output[1][1], places=2) + + def test_flow_3(self): + pk_output = area_to_point_pk(semivariogram_model=DIRECTIONAL_VARIOGRAM, + blocks=AREAL_INP, + point_support=PS_INP, + unknown_block=UNKN_AREA, + unknown_block_point_support=UNKN_PS, + number_of_neighbors=NN, + raise_when_negative_error=False, + raise_when_negative_prediction=False) + + self.assertTrue(pk_output) diff --git a/tests/test_pipelines/test_smooth_areas.py b/tests/test_pipelines/test_smooth_areas.py index fad06a8c..077462d2 100644 --- a/tests/test_pipelines/test_smooth_areas.py +++ b/tests/test_pipelines/test_smooth_areas.py @@ -28,6 +28,9 @@ THEORETICAL_VARIOGRAM = TheoreticalVariogram() THEORETICAL_VARIOGRAM.from_json(VARIOGRAM_MODEL_FILE) +DIRECTIONAL_VARIOGRAM_MODEL_FILE = 'samples/regularization/regularized_directional_variogram.json' +DIRECTIONAL_VARIOGRAM = TheoreticalVariogram() +DIRECTIONAL_VARIOGRAM.from_json(DIRECTIONAL_VARIOGRAM_MODEL_FILE) class TestSmoothBlocks(unittest.TestCase): @@ -40,3 +43,11 @@ def test_real_data(self): number_of_neighbors=NN) self.assertIsInstance(output_results, gpd.GeoDataFrame) + + def test_directional(self): + output_results = smooth_blocks(semivariogram_model=DIRECTIONAL_VARIOGRAM, + blocks=AREAL_INPUT, + point_support=POINT_SUPPORT_INPUT, + number_of_neighbors=NN) + + self.assertIsInstance(output_results, gpd.GeoDataFrame) From ec2f9dc2e492eea31f0b2d0cd3b13f69eede9109 Mon Sep 17 00:00:00 2001 From: Simon <31246246+SimonMolinsky@users.noreply.github.com> Date: Sun, 5 Feb 2023 00:27:30 +0200 Subject: [PATCH 20/29] updated warnings --- .../models/block/area_to_area_poisson_kriging.py | 15 ++++++++++++--- .../models/block/area_to_point_poisson_kriging.py | 13 +++++++++++++ .../block/centroid_based_poisson_kriging.py | 13 +++++++++++++ pyinterpolate/kriging/utils/kwarnings.py | 12 ++++++++++++ 4 files changed, 50 insertions(+), 3 deletions(-) diff --git a/pyinterpolate/kriging/models/block/area_to_area_poisson_kriging.py b/pyinterpolate/kriging/models/block/area_to_area_poisson_kriging.py index 275aba52..1de4a3f6 100644 --- a/pyinterpolate/kriging/models/block/area_to_area_poisson_kriging.py +++ b/pyinterpolate/kriging/models/block/area_to_area_poisson_kriging.py @@ -7,6 +7,7 @@ """ import logging +import warnings from typing import Dict, Union import geopandas as gpd @@ -14,6 +15,7 @@ import pandas as pd from pyinterpolate.kriging.models.block.weight import weights_array, WeightedBlock2BlockSemivariance +from pyinterpolate.kriging.utils.kwarnings import ExperimentalFeatureWarning from pyinterpolate.processing.preprocessing.blocks import Blocks, PointSupport from pyinterpolate.processing.select_values import select_poisson_kriging_data, prepare_pk_known_areas, \ get_aggregated_point_support_values, get_distances_within_unknown @@ -80,10 +82,19 @@ def area_to_area_pk(semivariogram_model: TheoreticalVariogram, ValueError Prediction or prediction error are negative. + Warns + ----- + ExperimentalFeatureWarning + Directional Kriging is in early-phase and may contain bugs. + """ + # Warnings area + if semivariogram_model.direction is not None: + exp_warning_msg = 'Directional Poisson Kriging is an experimental feature. Use it at your own responsibility!' + warnings.warn(ExperimentalFeatureWarning(exp_warning_msg).__str__()) + # Prepare Kriging Data # {known block id: [(unknown x, unknown y), [unknown val, known val, distance between points]]} - # Transform point support to dict if isinstance(point_support, Dict): dps = point_support @@ -138,8 +149,6 @@ def area_to_area_pk(semivariogram_model: TheoreticalVariogram, # Add diagonal weights values = get_areal_values_from_agg(blocks, prepared_ids) aggregated_ps = get_aggregated_point_support_values(dps, prepared_ids) - if predicted.shape == (0, ): - print('STOP') weights = weights_array(predicted.shape, values, aggregated_ps) weighted_and_predicted = predicted + weights diff --git a/pyinterpolate/kriging/models/block/area_to_point_poisson_kriging.py b/pyinterpolate/kriging/models/block/area_to_point_poisson_kriging.py index 4fec5eff..a1fcd19f 100644 --- a/pyinterpolate/kriging/models/block/area_to_point_poisson_kriging.py +++ b/pyinterpolate/kriging/models/block/area_to_point_poisson_kriging.py @@ -5,6 +5,7 @@ ------- 1. Szymon Moliński | @SimonMolinsky """ +import warnings from typing import Union, Dict import logging @@ -14,6 +15,7 @@ from pyinterpolate.kriging.models.block.weight import WeightedBlock2BlockSemivariance,\ WeightedBlock2PointSemivariance, add_ones, weights_array +from pyinterpolate.kriging.utils.kwarnings import ExperimentalFeatureWarning from pyinterpolate.processing.preprocessing.blocks import Blocks, PointSupport from pyinterpolate.processing.select_values import select_poisson_kriging_data, prepare_pk_known_areas, \ get_aggregated_point_support_values @@ -85,7 +87,18 @@ def area_to_point_pk(semivariogram_model: TheoreticalVariogram, ------ ValueError Prediction or prediction error are negative. + + Warns + ----- + ExperimentalFeatureWarning + Directional Kriging is in early-phase and may contain bugs. + """ + # Warnings area + if semivariogram_model.direction is not None: + exp_warning_msg = 'Directional Poisson Kriging is an experimental feature. Use it at your own responsibility!' + warnings.warn(ExperimentalFeatureWarning(exp_warning_msg).__str__()) + logging.info("POISSON KRIGING: AREA-TO-POINT | Operation starts") # Get total point-support value of the unknown area tot_unknown_value = np.sum(unknown_block_point_support[:, -1]) diff --git a/pyinterpolate/kriging/models/block/centroid_based_poisson_kriging.py b/pyinterpolate/kriging/models/block/centroid_based_poisson_kriging.py index dada9efe..9343e3e7 100644 --- a/pyinterpolate/kriging/models/block/centroid_based_poisson_kriging.py +++ b/pyinterpolate/kriging/models/block/centroid_based_poisson_kriging.py @@ -5,6 +5,7 @@ ------- 1. Szymon Moliński | @SimonMolinsky """ +import warnings from typing import Dict, List, Union import geopandas as gpd @@ -13,6 +14,7 @@ from pyinterpolate.distance.distance import calc_point_to_point_distance from pyinterpolate.kriging.models.block.weight import weights_array +from pyinterpolate.kriging.utils.kwarnings import ExperimentalFeatureWarning from pyinterpolate.kriging.utils.process import solve_weights from pyinterpolate.processing.preprocessing.blocks import Blocks, PointSupport from pyinterpolate.processing.select_values import select_centroid_poisson_kriging_data @@ -84,7 +86,18 @@ def centroid_poisson_kriging(semivariogram_model: TheoreticalVariogram, ------ ValueError Prediction or prediction error are negative. + + Warns + ----- + ExperimentalFeatureWarning + Directional Kriging is in early-phase and may contain bugs. + """ + # Warnings area + if semivariogram_model.direction is not None: + exp_warning_msg = 'Directional Poisson Kriging is an experimental feature. Use it at your own responsibility!' + warnings.warn(ExperimentalFeatureWarning(exp_warning_msg).__str__()) + # Get data: [block id, cx, cy, value, distance to unknown, aggregated point support sum] if isinstance(point_support, Dict): dps = point_support diff --git a/pyinterpolate/kriging/utils/kwarnings.py b/pyinterpolate/kriging/utils/kwarnings.py index 7cb44fd8..80bb511a 100644 --- a/pyinterpolate/kriging/utils/kwarnings.py +++ b/pyinterpolate/kriging/utils/kwarnings.py @@ -28,3 +28,15 @@ def __init__(self): def __str__(self): return repr(self.message) + + +class ExperimentalFeatureWarning(Warning): + """ + Class describes experimental feature warnings. + """ + + def __init__(self, msg: str): + self.msg = msg + + def __str__(self): + return repr(self.msg) From aa129cce63dd6de851b6e050247da64daec3bde5 Mon Sep 17 00:00:00 2001 From: Simon <31246246+SimonMolinsky@users.noreply.github.com> Date: Sun, 5 Feb 2023 00:29:05 +0200 Subject: [PATCH 21/29] Update changelog.rst --- changelog.rst | 2 ++ 1 file changed, 2 insertions(+) diff --git a/changelog.rst b/changelog.rst index b26bea05..a6388306 100755 --- a/changelog.rst +++ b/changelog.rst @@ -23,6 +23,8 @@ Changes by date * (enhancement) function `inblock_semivariance` has been optimized, * (docs) updated `__init__.py` of `variogram.theoretical` module, * (enhancement) scatter plot represented as a swarm plot in `VariogramCloud`, +* (enahncement) added directional kriging for ATA and ATP Poisson Kriging, +* (debug) warning for directional kriging functions, * 2023-01-16 From 44e90647d87882cffa8486e5ee58cbf706c1c2b3 Mon Sep 17 00:00:00 2001 From: Simon <31246246+SimonMolinsky@users.noreply.github.com> Date: Sun, 5 Feb 2023 10:29:07 +0200 Subject: [PATCH 22/29] Updated documentaion --- docs/source/index.rst | 3 ++- docs/source/usage/learning_materials.rst | 22 ++++++++++++++++++++++ 2 files changed, 24 insertions(+), 1 deletion(-) create mode 100644 docs/source/usage/learning_materials.rst diff --git a/docs/source/index.rst b/docs/source/index.rst index 18c25124..3c988be7 100644 --- a/docs/source/index.rst +++ b/docs/source/index.rst @@ -14,7 +14,7 @@ Pyinterpolate :alt: The pyinterpolate logo with the name Kyiv and the version of package. .. note:: - The last documentation update: *2023-01-21* + The last documentation update: *2023-02-05* **Pyinterpolate** is the Python library for **geostatistics**. The package provides access to spatial statistics tools used in various studies. This package helps you **interpolate spatial data** with the *Kriging* technique. @@ -61,6 +61,7 @@ Contents api/api developer/dev community/community + usage/learning_materials science/biblio How to cite diff --git a/docs/source/usage/learning_materials.rst b/docs/source/usage/learning_materials.rst new file mode 100644 index 00000000..52012710 --- /dev/null +++ b/docs/source/usage/learning_materials.rst @@ -0,0 +1,22 @@ +Learning Materials +================== + +Publications +------------ + +- 💡 - (2022) `URL `_ Pyinterpolate: Spatial interpolation in Python for point measurements and aggregated datasets + + +Blog posts +---------- + +- 📜 - (2022) `URL `_ Interpolate Air Quality Measurements with Python, +- 📜 - (2022) `URL `_ Get More from Crime Rate Data and Other Socio-Economic Indicators with Pyinterpolate, +- 📜 - (2022) `URL `_ Geostatistics: Theoretical Variogram Models +- 📜 - (2022) `URL `_ Interpolate Air Pollution with Pyinterpolate + + +Presentations & Workshops +------------------------- + +- 🎙️ - (2022) 🇵🇱 `URL `_ Wzbogacanie agregowanych danych publicznych z Pythonem i Geostatystyką \ No newline at end of file From 6ceae674afccd38df0d14399d1297a3de11d27ad Mon Sep 17 00:00:00 2001 From: Simon <31246246+SimonMolinsky@users.noreply.github.com> Date: Sun, 5 Feb 2023 10:33:35 +0200 Subject: [PATCH 23/29] new doc files --- .../api/kriging/block/block_kriging.doctree | Bin 73718 -> 76753 bytes docs/build/doctrees/environment.pickle | Bin 437443 -> 442372 bytes docs/build/doctrees/index.doctree | Bin 14111 -> 14146 bytes .../doctrees/usage/learning_materials.doctree | Bin 0 -> 10744 bytes docs/build/html/_modules/index.html | 14 + .../pyinterpolate/distance/distance.html | 52 +- .../block/area_to_area_poisson_kriging.html | 61 +- .../block/area_to_point_poisson_kriging.html | 34 +- .../block/centroid_based_poisson_kriging.html | 35 +- .../variogram/empirical/cloud.html | 32 +- docs/build/html/_sources/index.rst.txt | 3 +- .../_sources/usage/learning_materials.rst.txt | 22 + docs/build/html/api/api.html | 14 + docs/build/html/api/distance/distance.html | 14 + .../html/api/kriging/block/block_kriging.html | 34 +- docs/build/html/api/kriging/kriging.html | 14 + .../variogram/experimental/experimental.html | 14 + docs/build/html/api/variogram/variogram.html | 14 + docs/build/html/genindex.html | 14 + docs/build/html/index.html | 17 +- docs/build/html/objects.inv | Bin 6384 -> 6510 bytes docs/build/html/search.html | 14 + docs/build/html/searchindex.js | 2 +- docs/build/html/usage/learning_materials.html | 540 ++++++++++++++++++ 24 files changed, 905 insertions(+), 39 deletions(-) create mode 100644 docs/build/doctrees/usage/learning_materials.doctree create mode 100644 docs/build/html/_sources/usage/learning_materials.rst.txt create mode 100644 docs/build/html/usage/learning_materials.html diff --git a/docs/build/doctrees/api/kriging/block/block_kriging.doctree 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  • + + Learning Materials + +
    • + +
  • Bibliography @@ -303,6 +310,13 @@
    • +
  • + + Learning Materials + +
    • + +
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    Pyinterpolate 0.3.5 documentation

    +

    Pyinterpolate 0.3.6 documentation

     @@ -169,6 +170,13 @@
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    Source code for pyinterpolate.distance.distance

    < return np.rad2deg(ang % (2 * np.pi)) +def calc_angles_between_points(vec1, vec2, origin=None): + """ + Function calculates distances between two groups of points as their cross product. + + Parameters + ---------- + vec1 : numpy array + The first set of coordinates. + + vec2 : numpy array + The second set of coordinates. + + origin : Iterable, optional + The coordinates (x, y) of origin. + + Returns + ------- + angles : numpy array + An array with angles between all points from ``vec1`` to all points from ``vec2``, where rows are angles between + points from ``vec1`` to points from ``vec2`` (columns). + """ + angles = [] + + for point in vec1: + row = calc_angles(vec2, origin=point) + angles.append(row.flatten()) + + angles = np.array(angles).flatten() + return angles + + def calc_angles(points_b: Iterable, point_a: Iterable = None, origin: Iterable = None): """ Function calculates angles between points and origin or between vectors from origin to points and a vector from @@ -773,7 +819,7 @@

    Source code for pyinterpolate.distance.distance

    <

    -Created using Sphinx 5.0.2.
    +Created using Sphinx 5.3.0.

    diff --git a/docs/build/html/_modules/pyinterpolate/kriging/models/block/area_to_area_poisson_kriging.html b/docs/build/html/_modules/pyinterpolate/kriging/models/block/area_to_area_poisson_kriging.html index 8f314ca8..3b6adf6e 100644 --- a/docs/build/html/_modules/pyinterpolate/kriging/models/block/area_to_area_poisson_kriging.html +++ b/docs/build/html/_modules/pyinterpolate/kriging/models/block/area_to_area_poisson_kriging.html @@ -5,7 +5,7 @@ - pyinterpolate.kriging.models.block.area_to_area_poisson_kriging — Pyinterpolate 0.3.5 documentation + pyinterpolate.kriging.models.block.area_to_area_poisson_kriging — Pyinterpolate 0.3.6 documentation @@ -37,6 +37,7 @@ + @@ -98,7 +99,7 @@ -

    Pyinterpolate 0.3.5 documentation

    +

    Pyinterpolate 0.3.6 documentation

    @@ -169,6 +170,13 @@
    • +
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  • Bibliography @@ -302,6 +310,13 @@
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    Source code for pyinterpolate.kriging.models.block.area_to_area_poisson_krig """ import logging +import warnings from typing import Dict, Union import geopandas as gpd @@ -381,24 +397,25 @@

    Source code for pyinterpolate.kriging.models.block.area_to_area_poisson_krig import pandas as pd from pyinterpolate.kriging.models.block.weight import weights_array, WeightedBlock2BlockSemivariance +from pyinterpolate.kriging.utils.kwarnings import ExperimentalFeatureWarning from pyinterpolate.processing.preprocessing.blocks import Blocks, PointSupport -from pyinterpolate.processing.select_values import select_poisson_kriging_data, prepare_pk_known_areas,\ +from pyinterpolate.processing.select_values import select_poisson_kriging_data, prepare_pk_known_areas, \ get_aggregated_point_support_values, get_distances_within_unknown from pyinterpolate.processing.transform.transform import get_areal_values_from_agg, transform_ps_to_dict, sem_to_cov from pyinterpolate.variogram import TheoreticalVariogram
    [docs]def area_to_area_pk(semivariogram_model: TheoreticalVariogram, - blocks: Union[Blocks, gpd.GeoDataFrame, pd.DataFrame, np.ndarray], - point_support: Union[Dict, np.ndarray, gpd.GeoDataFrame, pd.DataFrame, PointSupport], - unknown_block: np.ndarray, - unknown_block_point_support: np.ndarray, - number_of_neighbors: int, - raise_when_negative_prediction=True, - raise_when_negative_error=True, - log_process=True): + blocks: Union[Blocks, gpd.GeoDataFrame, pd.DataFrame, np.ndarray], + point_support: Union[Dict, np.ndarray, gpd.GeoDataFrame, pd.DataFrame, PointSupport], + unknown_block: np.ndarray, + unknown_block_point_support: np.ndarray, + number_of_neighbors: int, + raise_when_negative_prediction=True, + raise_when_negative_error=True, + log_process=True): """ - Function predicts areal value in a unknown location based on the area-to-area Poisson Kriging + Function predicts areal value in an unknown location based on the area-to-area Poisson Kriging Parameters ---------- @@ -447,10 +464,19 @@

    Source code for pyinterpolate.kriging.models.block.area_to_area_poisson_krig ValueError Prediction or prediction error are negative. + Warns + ----- + ExperimentalFeatureWarning + Directional Kriging is in early-phase and may contain bugs. + """ + # Warnings area + if semivariogram_model.direction is not None: + exp_warning_msg = 'Directional Poisson Kriging is an experimental feature. Use it at your own responsibility!' + warnings.warn(ExperimentalFeatureWarning(exp_warning_msg).__str__()) + # Prepare Kriging Data # {known block id: [(unknown x, unknown y), [unknown val, known val, distance between points]]} - # Transform point support to dict if isinstance(point_support, Dict): dps = point_support @@ -468,7 +494,9 @@

    Source code for pyinterpolate.kriging.models.block.area_to_area_poisson_krig u_point_support=unknown_block_point_support, k_point_support_dict=dps, nn=number_of_neighbors, - max_range=semivariogram_model.rang + max_range=semivariogram_model.rang, + direction=semivariogram_model.direction, + angular_tolerance=5 ) b2b_semivariance = WeightedBlock2BlockSemivariance(semivariance_model=semivariogram_model) @@ -538,10 +566,9 @@

    Source code for pyinterpolate.kriging.models.block.area_to_area_poisson_krig raise ValueError(f'Predicted value is {zhat} and it should not be lower than 0. Check your sampling ' f'grid, samples, number of neighbors or semivariogram model type.') - # TODO test - come back here when covariance terms are used - # TODO - probably it should be without subtraction - those are semivariance terms, not covariances. sigmasq = np.matmul(w.T, k_ones) + distances_within_unknown_block = get_distances_within_unknown(unknown_block_point_support) semivariance_within_unknown = b2b_semivariance.calculate_average_semivariance({ u_idx: distances_within_unknown_block @@ -639,7 +666,7 @@

    Source code for pyinterpolate.kriging.models.block.area_to_area_poisson_krig

    -Created using Sphinx 5.0.2.
    +Created using Sphinx 5.3.0.

    diff --git a/docs/build/html/_modules/pyinterpolate/kriging/models/block/area_to_point_poisson_kriging.html b/docs/build/html/_modules/pyinterpolate/kriging/models/block/area_to_point_poisson_kriging.html index c264aa15..f44e87da 100644 --- a/docs/build/html/_modules/pyinterpolate/kriging/models/block/area_to_point_poisson_kriging.html +++ b/docs/build/html/_modules/pyinterpolate/kriging/models/block/area_to_point_poisson_kriging.html @@ -5,7 +5,7 @@ - pyinterpolate.kriging.models.block.area_to_point_poisson_kriging — Pyinterpolate 0.3.5 documentation + pyinterpolate.kriging.models.block.area_to_point_poisson_kriging — Pyinterpolate 0.3.6 documentation @@ -37,6 +37,7 @@ + @@ -98,7 +99,7 @@ -

    Pyinterpolate 0.3.5 documentation

    +

    Pyinterpolate 0.3.6 documentation

    @@ -169,6 +170,13 @@

    • +
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    Source code for pyinterpolate.kriging.models.block.area_to_point_poisson_kri ------- 1. Szymon Moliński | @SimonMolinsky """ +import warnings from typing import Union, Dict import logging @@ -381,6 +397,7 @@

    Source code for pyinterpolate.kriging.models.block.area_to_point_poisson_kri from pyinterpolate.kriging.models.block.weight import WeightedBlock2BlockSemivariance,\ WeightedBlock2PointSemivariance, add_ones, weights_array +from pyinterpolate.kriging.utils.kwarnings import ExperimentalFeatureWarning from pyinterpolate.processing.preprocessing.blocks import Blocks, PointSupport from pyinterpolate.processing.select_values import select_poisson_kriging_data, prepare_pk_known_areas, \ get_aggregated_point_support_values @@ -452,7 +469,18 @@

    Source code for pyinterpolate.kriging.models.block.area_to_point_poisson_kri ------ ValueError Prediction or prediction error are negative. + + Warns + ----- + ExperimentalFeatureWarning + Directional Kriging is in early-phase and may contain bugs. + """ + # Warnings area + if semivariogram_model.direction is not None: + exp_warning_msg = 'Directional Poisson Kriging is an experimental feature. Use it at your own responsibility!' + warnings.warn(ExperimentalFeatureWarning(exp_warning_msg).__str__()) + logging.info("POISSON KRIGING: AREA-TO-POINT | Operation starts") # Get total point-support value of the unknown area tot_unknown_value = np.sum(unknown_block_point_support[:, -1]) @@ -676,7 +704,7 @@

    Source code for pyinterpolate.kriging.models.block.area_to_point_poisson_kri

    -Created using Sphinx 5.0.2.
    +Created using Sphinx 5.3.0.

    diff --git a/docs/build/html/_modules/pyinterpolate/kriging/models/block/centroid_based_poisson_kriging.html b/docs/build/html/_modules/pyinterpolate/kriging/models/block/centroid_based_poisson_kriging.html index 74998f1a..097aa04a 100644 --- a/docs/build/html/_modules/pyinterpolate/kriging/models/block/centroid_based_poisson_kriging.html +++ b/docs/build/html/_modules/pyinterpolate/kriging/models/block/centroid_based_poisson_kriging.html @@ -5,7 +5,7 @@ - pyinterpolate.kriging.models.block.centroid_based_poisson_kriging — Pyinterpolate 0.3.5 documentation + pyinterpolate.kriging.models.block.centroid_based_poisson_kriging — Pyinterpolate 0.3.6 documentation @@ -37,6 +37,7 @@ + @@ -98,7 +99,7 @@ -

    Pyinterpolate 0.3.5 documentation

    +

    Pyinterpolate 0.3.6 documentation

    @@ -169,6 +170,13 @@

    • +
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    Source code for pyinterpolate.kriging.models.block.centroid_based_poisson_kr ------- 1. Szymon Moliński | @SimonMolinsky """ +import warnings from typing import Dict, List, Union import geopandas as gpd @@ -380,6 +396,7 @@

    Source code for pyinterpolate.kriging.models.block.centroid_based_poisson_kr from pyinterpolate.distance.distance import calc_point_to_point_distance from pyinterpolate.kriging.models.block.weight import weights_array +from pyinterpolate.kriging.utils.kwarnings import ExperimentalFeatureWarning from pyinterpolate.kriging.utils.process import solve_weights from pyinterpolate.processing.preprocessing.blocks import Blocks, PointSupport from pyinterpolate.processing.select_values import select_centroid_poisson_kriging_data @@ -451,7 +468,18 @@

    Source code for pyinterpolate.kriging.models.block.centroid_based_poisson_kr ------ ValueError Prediction or prediction error are negative. + + Warns + ----- + ExperimentalFeatureWarning + Directional Kriging is in early-phase and may contain bugs. + """ + # Warnings area + if semivariogram_model.direction is not None: + exp_warning_msg = 'Directional Poisson Kriging is an experimental feature. Use it at your own responsibility!' + warnings.warn(ExperimentalFeatureWarning(exp_warning_msg).__str__()) + # Get data: [block id, cx, cy, value, distance to unknown, aggregated point support sum] if isinstance(point_support, Dict): dps = point_support @@ -470,6 +498,7 @@

    Source code for pyinterpolate.kriging.models.block.centroid_based_poisson_kr weighted=is_weighted_by_point_support, direction=semivariogram_model.direction ) + sill = semivariogram_model.sill distances_column_index = 3 @@ -613,7 +642,7 @@

    Source code for pyinterpolate.kriging.models.block.centroid_based_poisson_kr

    -Created using Sphinx 5.0.2.
    +Created using Sphinx 5.3.0.

    diff --git a/docs/build/html/_modules/pyinterpolate/variogram/empirical/cloud.html b/docs/build/html/_modules/pyinterpolate/variogram/empirical/cloud.html index 6286e9e0..700ca9fc 100644 --- a/docs/build/html/_modules/pyinterpolate/variogram/empirical/cloud.html +++ b/docs/build/html/_modules/pyinterpolate/variogram/empirical/cloud.html @@ -170,6 +170,13 @@

    • +
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    Source code for pyinterpolate.variogram.empirical.cloud

    from pyinterpolate.processing.select_values import select_points_within_ellipse, select_values_in_range from pyinterpolate.processing.transform.statistics import remove_outliers from pyinterpolate.variogram.utils.exceptions import validate_direction, validate_points, validate_tolerance +from pyinterpolate.variogram.utils.plots import build_swarmplot_input def omnidirectional_point_cloud(input_array: np.array, @@ -933,12 +948,14 @@

    Source code for pyinterpolate.variogram.empirical.cloud

    self.__dist_plots(title_box, 'box') def _scatter_plot(self): + ds = self.__prep_scatterplot_data() plt.figure(figsize=(14, 8)) xs = ds[:, 0] ys = ds[:, 1] plt.scatter(x=xs, - y=ys) + y=ys, + s=0.2) plt.title('Variogram Point Cloud per lag.') plt.show() @@ -1008,14 +1025,11 @@

    Source code for pyinterpolate.variogram.empirical.cloud

    return data, x_tick_labels def __prep_scatterplot_data(self) -> np.array: - ds = [] - for idx, values in self.experimental_point_cloud.items(): - vals_l = len(values) - idxs = np.ones(vals_l) * idx - elems = list(zip(idxs, values)) - ds.extend(elems) - - return np.array(ds) + + ds = build_swarmplot_input(data=self.experimental_point_cloud, + step_size=self.step) + + return ds @staticmethod def __str_empty(): diff --git a/docs/build/html/_sources/index.rst.txt b/docs/build/html/_sources/index.rst.txt index 18c25124..3c988be7 100644 --- a/docs/build/html/_sources/index.rst.txt +++ b/docs/build/html/_sources/index.rst.txt @@ -14,7 +14,7 @@ Pyinterpolate :alt: The pyinterpolate logo with the name Kyiv and the version of package. .. note:: - The last documentation update: *2023-01-21* + The last documentation update: *2023-02-05* **Pyinterpolate** is the Python library for **geostatistics**. The package provides access to spatial statistics tools used in various studies. This package helps you **interpolate spatial data** with the *Kriging* technique. @@ -61,6 +61,7 @@ Contents api/api developer/dev community/community + usage/learning_materials science/biblio How to cite diff --git a/docs/build/html/_sources/usage/learning_materials.rst.txt b/docs/build/html/_sources/usage/learning_materials.rst.txt new file mode 100644 index 00000000..52012710 --- /dev/null +++ b/docs/build/html/_sources/usage/learning_materials.rst.txt @@ -0,0 +1,22 @@ +Learning Materials +================== + +Publications +------------ + +- 💡 - (2022) `URL `_ Pyinterpolate: Spatial interpolation in Python for point measurements and aggregated datasets + + +Blog posts +---------- + +- 📜 - (2022) `URL `_ Interpolate Air Quality Measurements with Python, +- 📜 - (2022) `URL `_ Get More from Crime Rate Data and Other Socio-Economic Indicators with Pyinterpolate, +- 📜 - (2022) `URL `_ Geostatistics: Theoretical Variogram Models +- 📜 - (2022) `URL `_ Interpolate Air Pollution with Pyinterpolate + + +Presentations & Workshops +------------------------- + +- 🎙️ - (2022) 🇵🇱 `URL `_ Wzbogacanie agregowanych danych publicznych z Pythonem i Geostatystyką \ No newline at end of file diff --git a/docs/build/html/api/api.html b/docs/build/html/api/api.html index cea6b793..67a1a259 100644 --- a/docs/build/html/api/api.html +++ b/docs/build/html/api/api.html @@ -173,6 +173,13 @@
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    Block - Poisson Kriging

    • +
    Warns:
    +
    +
    ExperimentalFeatureWarning

    Directional Kriging is in early-phase and may contain bugs.

    +
    +
    +
    @@ -472,7 +492,7 @@

    Block - Poisson Kriging
    area_to_area_pk(semivariogram_model, blocks, point_support, unknown_block, unknown_block_point_support, number_of_neighbors, raise_when_negative_prediction=True, raise_when_negative_error=True, log_process=True)[source]
    -

    Function predicts areal value in a unknown location based on the area-to-area Poisson Kriging

    +

    Function predicts areal value in an unknown location based on the area-to-area Poisson Kriging

    Parameters:
    @@ -524,6 +544,12 @@

    Block - Poisson Kriging

    +
    Warns:
    +
    +
    ExperimentalFeatureWarning

    Directional Kriging is in early-phase and may contain bugs.

    +
    +
    +
    @@ -586,6 +612,12 @@

    Block - Poisson Kriging +
    Warns:
    +
    +
    ExperimentalFeatureWarning

    Directional Kriging is in early-phase and may contain bugs.

    +
    +
    +
    diff --git a/docs/build/html/api/kriging/kriging.html b/docs/build/html/api/kriging/kriging.html index cd0d5b24..a91efa39 100644 --- a/docs/build/html/api/kriging/kriging.html +++ b/docs/build/html/api/kriging/kriging.html @@ -173,6 +173,13 @@ +
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    version 0.3.6 - KyivThe pyinterpolate logo with the name Kyiv and the version of package.

    Note

    -

    The last documentation update: 2023-01-21

    +

    The last documentation update: 2023-02-05

    Pyinterpolate is the Python library for geostatistics. The package provides access to spatial statistics tools used in various studies. This package helps you interpolate spatial data with the Kriging technique.

    If you’re:

    @@ -415,6 +429,7 @@

    ContentsAPI

  • Development
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  • Bibliography
    • diff --git a/docs/build/html/objects.inv b/docs/build/html/objects.inv index a21f018b841fc6395e6b2d1ba8838a5945239d3d..3d3b8dee080121d974d28617e352d296dd3b0160 100644 GIT binary patch delta 6442 zcmV+_8P(?SG43*ueSh6k+&H#>&tIWiU2`iu2ojp4?((t)B(uY2lCWWR@<+Asj_RFd!RlPXQlWUd}`j)7Q zyLy?ElZ*3L2L|a3{`ZsfTFtUZ>ZE>Jv+5u%O6FG7qTxEv;D3l<1(27UwBD4g@=2WG z0mX9K5C^Gob9U&?sybPvOdog*+9KmNeUN7)P4aY;Cv{e=x&+VE2{jo))p@u(KxB(Y z{Bzr+D!C=+s|QwAtP%E|Ww&>Awz}PtG|L1n)o>maZMTvx#$Npe{PWnstE3g{O-&BS z*G&xvElyAmg@3f*bzNd?LYK_@GP?z62iJL#-izNWS?0#r#lVrDViBR_qR6VMSdmYn zGDzKwm(aCfrLra-ghx&gv>@v(i~GdPdhM{VD{7}$Flkkb->~cCV&JGRuqaB&jFKscP|dDwd{S zT_+V=*k@PkvPfA~v8vD7Fd=5kq`Z>o%E7Qy+Y|z+t{VrgS9+is`l!!?4WYOb)M=-{ znLY~_ZSb^|JS1gS+?L7Ge1^GkyGfg8ZI3|lEQ}E=Ih?{5EGp^y6PYzVn!zpK+ufiry;F52K}E6M)CKMzt_ zDbQurCdsqE5~Ha%%(Dlxx?5C>wk0?%mdnj5tAC%Gqiu99BAI>3Q?Hbs(()c|!3?+HJ6UF`)bH4BC)w)h7a zoh{xDQHyK?v@H_|M&XYQbhE|(S)YWvyJQa;dxXr9{%=tYg3yxiO`Ve0|NMG+>A`VR zlz*FLSW_0HybXV0^xvD%vJ&27~Q(54jm#tIT2 zati0&GH3TlNbxj6yvfz4$uKp{2$P}H?|(iOL07`7F7Dc_lk`5hWnq>nVv*~Jlcx8u zSHVmkH@%Q?DjocRAWHT}U<>4w+`ez@k4;uGbX`S~)dI-ZY_(vkG=m%Q^#ggcKi_|| z*R2p9rHvE{Qm83~KF~LGPTxep3KX~#_?lI9bp>2k`qkh}$|870G>&x~jae6qVSmP} zxanrqPSAWZeTXw~<)7OQ5yj%}c!C!^gV#akuJ47Y09ubY*?_tKR!MV)zuN+`RRxK?7;T}+Vv*rNk^|&*mR3EIdzjCS zYG@`60TBc}Xl^&z!qw;24a4BF^f%?(YS-DiM+H*A-!>~h>R}c%o-0`2F@Kt8*VP(e zLYj;>9!+UgG%txK%F41Rdz6IB`R$I87UfmCUlq*@=ZSF2ZLk=WV_)oJR^QQ8QM2o! 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  • + + Learning Materials + +
    • + +
  • Bibliography @@ -305,6 +312,13 @@
    • +
  • + + Learning Materials + +
    • + +
  • Bibliography diff --git a/docs/build/html/searchindex.js b/docs/build/html/searchindex.js index 927daf09..ddf60371 100644 --- a/docs/build/html/searchindex.js +++ b/docs/build/html/searchindex.js @@ -1 +1 @@ -Search.setIndex({"docnames": ["api/api", "api/datatypes/core", "api/distance/distance", "api/idw/idw", "api/io/io", "api/kriging/block/block_kriging", "api/kriging/kriging", "api/kriging/point/point_kriging", "api/pipelines/data/download", "api/pipelines/kriging_based/kriging_based_processes", "api/pipelines/pipelines", "api/variogram/block/block", "api/variogram/deconvolution/deconvolution", "api/variogram/experimental/experimental", "api/variogram/theoretical/theoretical", "api/variogram/variogram", "api/viz/viz", "community/community", "community/doc_parts/contributors", "community/doc_parts/forum", "community/doc_parts/use_cases", "developer/dev", "developer/doc_parts/bugs", "developer/doc_parts/development", "developer/doc_parts/package", "developer/doc_parts/reqs", 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"Introduction"], [37, "Introduction"], [38, "Introduction"], [39, "Introduction"], [40, "Introduction"], [41, "Introduction"], [42, "Introduction"], [43, "Introduction"], [44, "Introduction"], [45, "Introduction"], [46, "Introduction"]], "Import packages": [[34, "Import-packages"], [36, "Import-packages"], [37, "Import-packages"], [38, "Import-packages"], [39, "Import-packages"], [43, "Import-packages"], [44, "Import-packages"], [45, "Import-packages"], [46, "Import-packages"]], "1) Read areal data": [[34, "1)-Read-areal-data"]], "2) Detect and remove outliers": [[34, "2)-Detect-and-remove-outliers"]], "3. Create a theoretical semivariogram model": [[34, "3.-Create-a-theoretical-semivariogram-model"]], "4. Read point data canvas": [[34, "4.-Read-point-data-canvas"]], "5. Build a map of interpolated values": [[34, "5.-Build-a-map-of-interpolated-values"]], "6. Show a map of interpolated values with a choropleth map of the breast cancer rates": [[34, "6.-Show-a-map-of-interpolated-values-with-a-choropleth-map-of-the-breast-cancer-rates"]], "Directional Ordinary Kriging": [[35, "Directional-Ordinary-Kriging"]], "Level: Basic": [[35, "Level:-Basic"], [36, "Level:-Basic"], [37, "Level:-Basic"], [38, "Level:-Basic"], [43, "Level:-Basic"], [45, "Level:-Basic"], [46, "Level:-Basic"]], "1. Read meuse dataset and transform data": [[35, "1.-Read-meuse-dataset-and-transform-data"]], "2. Create directional variograms": [[35, "2.-Create-directional-variograms"]], "3. Create directional Ordinary Kriging models": [[35, "3.-Create-directional-Ordinary-Kriging-models"]], "4. Compare interpolated results": [[35, "4.-Compare-interpolated-results"], [35, "id1"]], "Directional semivariograms": [[36, "Directional-semivariograms"]], "A directional proces": [[36, "A-directional-proces"]], "1) Read and show point data": [[36, "1)-Read-and-show-point-data"]], "2) Create the experimental semivariogram": [[36, "2)-Create-the-experimental-semivariogram"]], "Case 1: W-E Direction": [[36, "Case-1:-W-E-Direction"]], "Case 2: N-S Direction": [[36, "Case-2:-N-S-Direction"]], "Case 3: NW-SE Direction": [[36, "Case-3:-NW-SE-Direction"]], "Case 4: NE-SW Direction": [[36, "Case-4:-NE-SW-Direction"]], "Case 5: Isotropic Variogram": [[36, "Case-5:-Isotropic-Variogram"]], "3) Compare semivariograms": [[36, "3)-Compare-semivariograms"]], "4) Bonus: Compare calculation times and results": [[36, "4)-Bonus:-Compare-calculation-times-and-results"]], "How good is my Kriging model? - tests with IDW algorithm": [[37, "How-good-is-my-Kriging-model?---tests-with-IDW-algorithm"]], "1) Read point data": [[37, "1)-Read-point-data"], [38, "1)-Read-point-data"], [43, "1)-Read-point-data"], [46, "1)-Read-point-data"]], "2) Divide the dataset into two sets: modeling and validation set": [[37, "2)-Divide-the-dataset-into-two-sets:-modeling-and-validation-set"]], "3) Perform IDW and evaluate it": [[37, "3)-Perform-IDW-and-evaluate-it"]], "4) Perform variogram modeling on the modeling set": [[37, "4)-Perform-variogram-modeling-on-the-modeling-set"]], "5) Validate Kriging and compare Kriging and IDW validation results": [[37, "5)-Validate-Kriging-and-compare-Kriging-and-IDW-validation-results"]], "6) Bonus scenario: only 2% of values are known!": [[37, "6)-Bonus-scenario:-only-2%-of-values-are-known!"]], "Ordinary and Simple Kriging": [[38, "Ordinary-and-Simple-Kriging"]], "2) Set Semivariogram model": [[38, "2)-Set-Semivariogram-model"]], "3) Set Ordinary Kriging and Simple Kriging models": [[38, "3)-Set-Ordinary-Kriging-and-Simple-Kriging-models"]], "4) Predict values at unknown locations": [[38, "4)-Predict-values-at-unknown-locations"]], "Outliers and Their Influence on the Final Model": [[39, "Outliers-and-Their-Influence-on-the-Final-Model"]], "1) Read point data and divide it into training and test set": [[39, "1)-Read-point-data-and-divide-it-into-training-and-test-set"]], "2) Check outliers: analyze the distribution of the values": [[39, "2)-Check-outliers:-analyze-the-distribution-of-the-values"]], "3) Create the Variogram Point Cloud model for datasets A and B": [[39, "3)-Create-the-Variogram-Point-Cloud-model-for-datasets-A-and-B"]], "4) Remove outliers from the variograms": [[39, "4)-Remove-outliers-from-the-variograms"]], "5) Create Four Ordinary Kriging models based on the four Variogram Point Clouds and compare their performance": [[39, "5)-Create-Four-Ordinary-Kriging-models-based-on-the-four-Variogram-Point-Clouds-and-compare-their-performance"]], "Poisson Kriging - Area to Area Kriging": [[40, "Poisson-Kriging---Area-to-Area-Kriging"]], "Level: Advanced": [[40, "Level:-Advanced"], [41, "Level:-Advanced"], [42, "Level:-Advanced"]], "1) Read and explore data": [[40, "1)-Read-and-explore-data"], [41, "1)-Read-and-explore-data"], [42, "1)-Read-and-explore-data"]], "2) Load a semivariogram model": [[40, "2)-Load-a-semivariogram-model"]], "3) Prepare training and test data.": [[40, "3)-Prepare-training-and-test-data."], [42, "3)-Prepare-training-and-test-data."]], "4) Predict values at unknown locations and calculate forecast bias and root mean squared error.": [[40, "4)-Predict-values-at-unknown-locations-and-calculate-forecast-bias-and-root-mean-squared-error."], [42, "4)-Predict-values-at-unknown-locations-and-calculate-forecast-bias-and-root-mean-squared-error."]], "5) Analyze Forecast Bias and Root Mean Squared Error of prediction": [[40, "5)-Analyze-Forecast-Bias-and-Root-Mean-Squared-Error-of-prediction"], [42, "5)-Analyze-Forecast-Bias-and-Root-Mean-Squared-Error-of-prediction"]], "Poisson Kriging - Area to Point Kriging": [[41, "Poisson-Kriging---Area-to-Point-Kriging"]], "2) Load semivariogram model": [[41, "2)-Load-semivariogram-model"], [42, "2)-Load-semivariogram-model"]], "3) Perform Area to Point smoothing of areal data.": [[41, "3)-Perform-Area-to-Point-smoothing-of-areal-data."]], "4) Visualize data": [[41, "4)-Visualize-data"]], "Poisson Kriging - centroid based approach": [[42, "Poisson-Kriging---centroid-based-approach"]], "Semivariogram Estimation": [[43, "Semivariogram-Estimation"]], "2) Create the experimental variogram": [[43, "2)-Create-the-experimental-variogram"], [45, "2)-Create-the-experimental-variogram"]], "3. Set manually different semivariogram models": [[43, "3.-Set-manually-different-semivariogram-models"]], "Models:": [[43, "Models:"]], "4) Set automatically semivariogram model": [[43, "4)-Set-automatically-semivariogram-model"]], "5) Export model": [[43, "5)-Export-model"]], "6) Import model": [[43, "6)-Import-model"]], "Semivariogram regularization": [[44, "Semivariogram-regularization"]], "1) Prepare areal and point data": [[44, "1)-Prepare-areal-and-point-data"]], "2) Set semivariogram parameters.": [[44, "2)-Set-semivariogram-parameters."]], "3) Regularize semivariogram": [[44, "3)-Regularize-semivariogram"]], "4) Visualize and check semivariogram": [[44, "4)-Visualize-and-check-semivariogram"]], "5) Export semivariogram to text file": [[44, "5)-Export-semivariogram-to-text-file"]], "Semivariogram models": [[45, "Semivariogram-models"]], "1) Create a random surface": [[45, "1)-Create-a-random-surface"]], "3) Set all variogram models": [[45, "3)-Set-all-variogram-models"]], "4) 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"Blocks to points Ordinary Kriging interpolation": [[35, "Blocks-to-points-Ordinary-Kriging-interpolation"]], "Table of Contents:": [[35, "Table-of-Contents:"], [36, "Table-of-Contents:"], [37, "Table-of-Contents:"], [38, "Table-of-Contents:"], [39, "Table-of-Contents:"], [40, "Table-of-Contents:"], [41, "Table-of-Contents:"], [42, "Table-of-Contents:"], [43, "Table-of-Contents:"], [44, "Table-of-Contents:"], [45, "Table-of-Contents:"], [46, "Table-of-Contents:"], [47, "Table-of-Contents:"]], "Level: Intermediate": [[35, "Level:-Intermediate"], [40, "Level:-Intermediate"], [45, "Level:-Intermediate"]], "Changelog": [[35, "Changelog"], [36, "Changelog"], [37, "Changelog"], [38, "Changelog"], [39, "Changelog"], [40, "Changelog"], [41, "Changelog"], [42, "Changelog"], [43, "Changelog"], [44, "Changelog"], [45, "Changelog"], [46, "Changelog"], [47, "Changelog"]], "Introduction": [[35, "Introduction"], [36, "Introduction"], [37, "Introduction"], [38, "Introduction"], [39, "Introduction"], [40, "Introduction"], [41, "Introduction"], [42, "Introduction"], [43, "Introduction"], [44, "Introduction"], [45, "Introduction"], [46, "Introduction"], [47, "Introduction"]], "Import packages": [[35, "Import-packages"], [37, "Import-packages"], [38, "Import-packages"], [39, "Import-packages"], [40, "Import-packages"], [44, "Import-packages"], [45, "Import-packages"], [46, "Import-packages"], [47, "Import-packages"]], "1) Read areal data": [[35, "1)-Read-areal-data"]], "2) Detect and remove outliers": [[35, "2)-Detect-and-remove-outliers"]], "3. Create a theoretical semivariogram model": [[35, "3.-Create-a-theoretical-semivariogram-model"]], "4. Read point data canvas": [[35, "4.-Read-point-data-canvas"]], "5. Build a map of interpolated values": [[35, "5.-Build-a-map-of-interpolated-values"]], "6. Show a map of interpolated values with a choropleth map of the breast cancer rates": [[35, "6.-Show-a-map-of-interpolated-values-with-a-choropleth-map-of-the-breast-cancer-rates"]], "Directional Ordinary Kriging": [[36, "Directional-Ordinary-Kriging"]], "Level: Basic": [[36, "Level:-Basic"], [37, "Level:-Basic"], [38, "Level:-Basic"], [39, "Level:-Basic"], [44, "Level:-Basic"], [46, "Level:-Basic"], [47, "Level:-Basic"]], "1. Read meuse dataset and transform data": [[36, "1.-Read-meuse-dataset-and-transform-data"]], "2. Create directional variograms": [[36, "2.-Create-directional-variograms"]], "3. Create directional Ordinary Kriging models": [[36, "3.-Create-directional-Ordinary-Kriging-models"]], "4. Compare interpolated results": [[36, "4.-Compare-interpolated-results"], [36, "id1"]], "Directional semivariograms": [[37, "Directional-semivariograms"]], "A directional proces": [[37, "A-directional-proces"]], "1) Read and show point data": [[37, "1)-Read-and-show-point-data"]], "2) Create the experimental semivariogram": [[37, "2)-Create-the-experimental-semivariogram"]], "Case 1: W-E Direction": [[37, "Case-1:-W-E-Direction"]], "Case 2: N-S Direction": [[37, "Case-2:-N-S-Direction"]], "Case 3: NW-SE Direction": [[37, "Case-3:-NW-SE-Direction"]], "Case 4: NE-SW Direction": [[37, "Case-4:-NE-SW-Direction"]], "Case 5: Isotropic Variogram": [[37, "Case-5:-Isotropic-Variogram"]], "3) Compare semivariograms": [[37, "3)-Compare-semivariograms"]], "4) Bonus: Compare calculation times and results": [[37, "4)-Bonus:-Compare-calculation-times-and-results"]], "How good is my Kriging model? - tests with IDW algorithm": [[38, "How-good-is-my-Kriging-model?---tests-with-IDW-algorithm"]], "1) Read point data": [[38, "1)-Read-point-data"], [39, "1)-Read-point-data"], [44, "1)-Read-point-data"], [47, "1)-Read-point-data"]], "2) Divide the dataset into two sets: modeling and validation set": [[38, "2)-Divide-the-dataset-into-two-sets:-modeling-and-validation-set"]], "3) Perform IDW and evaluate it": [[38, "3)-Perform-IDW-and-evaluate-it"]], "4) Perform variogram modeling on the modeling set": [[38, "4)-Perform-variogram-modeling-on-the-modeling-set"]], "5) Validate Kriging and compare Kriging and IDW validation results": [[38, "5)-Validate-Kriging-and-compare-Kriging-and-IDW-validation-results"]], "6) Bonus scenario: only 2% of values are known!": [[38, "6)-Bonus-scenario:-only-2%-of-values-are-known!"]], "Ordinary and Simple Kriging": [[39, "Ordinary-and-Simple-Kriging"]], "2) Set Semivariogram model": [[39, "2)-Set-Semivariogram-model"]], "3) Set Ordinary Kriging and Simple Kriging models": [[39, "3)-Set-Ordinary-Kriging-and-Simple-Kriging-models"]], "4) Predict values at unknown locations": [[39, "4)-Predict-values-at-unknown-locations"]], "Outliers and Their Influence on the Final Model": [[40, "Outliers-and-Their-Influence-on-the-Final-Model"]], "1) Read point data and divide it into training and test set": [[40, "1)-Read-point-data-and-divide-it-into-training-and-test-set"]], "2) Check outliers: analyze the distribution of the values": [[40, "2)-Check-outliers:-analyze-the-distribution-of-the-values"]], "3) Create the Variogram Point Cloud model for datasets A and B": [[40, "3)-Create-the-Variogram-Point-Cloud-model-for-datasets-A-and-B"]], "4) Remove outliers from the variograms": [[40, "4)-Remove-outliers-from-the-variograms"]], "5) Create Four Ordinary Kriging models based on the four Variogram Point Clouds and compare their performance": [[40, "5)-Create-Four-Ordinary-Kriging-models-based-on-the-four-Variogram-Point-Clouds-and-compare-their-performance"]], "Poisson Kriging - Area to Area Kriging": [[41, "Poisson-Kriging---Area-to-Area-Kriging"]], "Level: Advanced": [[41, "Level:-Advanced"], [42, "Level:-Advanced"], [43, "Level:-Advanced"]], "1) Read and explore data": [[41, "1)-Read-and-explore-data"], [42, "1)-Read-and-explore-data"], [43, "1)-Read-and-explore-data"]], "2) Load a semivariogram model": [[41, "2)-Load-a-semivariogram-model"]], "3) Prepare training and test data.": [[41, "3)-Prepare-training-and-test-data."], [43, "3)-Prepare-training-and-test-data."]], "4) Predict values at unknown locations and calculate forecast bias and root mean squared error.": [[41, "4)-Predict-values-at-unknown-locations-and-calculate-forecast-bias-and-root-mean-squared-error."], [43, "4)-Predict-values-at-unknown-locations-and-calculate-forecast-bias-and-root-mean-squared-error."]], "5) Analyze Forecast Bias and Root Mean Squared Error of prediction": [[41, "5)-Analyze-Forecast-Bias-and-Root-Mean-Squared-Error-of-prediction"], [43, "5)-Analyze-Forecast-Bias-and-Root-Mean-Squared-Error-of-prediction"]], "Poisson Kriging - Area to Point Kriging": [[42, "Poisson-Kriging---Area-to-Point-Kriging"]], "2) Load semivariogram model": [[42, "2)-Load-semivariogram-model"], [43, "2)-Load-semivariogram-model"]], "3) Perform Area to Point smoothing of areal data.": [[42, "3)-Perform-Area-to-Point-smoothing-of-areal-data."]], "4) Visualize data": [[42, "4)-Visualize-data"]], "Poisson Kriging - centroid based approach": [[43, "Poisson-Kriging---centroid-based-approach"]], "Semivariogram Estimation": [[44, "Semivariogram-Estimation"]], "2) Create the experimental variogram": [[44, "2)-Create-the-experimental-variogram"], [46, "2)-Create-the-experimental-variogram"]], "3. Set manually different semivariogram models": [[44, "3.-Set-manually-different-semivariogram-models"]], "Models:": [[44, "Models:"]], "4) Set automatically semivariogram model": [[44, "4)-Set-automatically-semivariogram-model"]], "5) Export model": [[44, "5)-Export-model"]], "6) Import model": [[44, "6)-Import-model"]], "Semivariogram regularization": [[45, "Semivariogram-regularization"]], "1) Prepare areal and point data": [[45, "1)-Prepare-areal-and-point-data"]], "2) Set semivariogram parameters.": [[45, "2)-Set-semivariogram-parameters."]], "3) Regularize semivariogram": [[45, "3)-Regularize-semivariogram"]], "4) Visualize and check semivariogram": [[45, "4)-Visualize-and-check-semivariogram"]], "5) Export semivariogram to text file": [[45, "5)-Export-semivariogram-to-text-file"]], "Semivariogram models": [[46, "Semivariogram-models"]], "1) Create a random surface": [[46, "1)-Create-a-random-surface"]], "3) Set all variogram models": [[46, "3)-Set-all-variogram-models"]], "4) Compare variogram models": [[46, "4)-Compare-variogram-models"]], "Variogram Point Cloud": [[47, "Variogram-Point-Cloud"]], "2) Set proper lag size with variogram cloud histogram": [[47, "2)-Set-proper-lag-size-with-variogram-cloud-histogram"]], "3) Detect and remove outliers": [[47, "3)-Detect-and-remove-outliers"]], "4) Calculate experimental semivariogram from point cloud": [[47, "4)-Calculate-experimental-semivariogram-from-point-cloud"]], "Distance calculations": [[2, "distance-calculations"]], "Block - Poisson Kriging": [[5, "block-poisson-kriging"]], "Experimental": [[13, "experimental"]], "Pyinterpolate": [[27, "pyinterpolate"]], "version 0.3.6 - Kyiv": [[27, "version-0-3-6-kyiv"]], "Contents": [[27, "contents"]], "Learning Materials": [[32, "learning-materials"]], "Publications": [[32, "publications"]], "Blog posts": [[32, "blog-posts"]], "Presentations & Workshops": [[32, "presentations-workshops"]]}, "indexentries": {}}) \ No newline at end of file diff --git a/docs/build/html/usage/learning_materials.html b/docs/build/html/usage/learning_materials.html new file mode 100644 index 00000000..7d3d39f8 --- /dev/null +++ b/docs/build/html/usage/learning_materials.html @@ -0,0 +1,540 @@ + + + + + + + + + Learning Materials — Pyinterpolate 0.3.6 documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    • 💡 - (2022) URL Pyinterpolate: Spatial interpolation in Python for point measurements and aggregated datasets

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    + +
    +

    + Built with the + + PyData Sphinx Theme + + 0.12.0. +

    +
    + +
    + +

    +Created using Sphinx 5.3.0.
    +

    + +
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    +
    + + \ No newline at end of file From 5e93287b73a6e556b3625637ba237c7e3a952e92 Mon Sep 17 00:00:00 2001 From: Simon <31246246+SimonMolinsky@users.noreply.github.com> Date: Sun, 5 Feb 2023 10:39:26 +0200 Subject: [PATCH 24/29] Update setup.cfg --- setup.cfg | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/setup.cfg b/setup.cfg index 5ad85836..cef93e16 100644 --- a/setup.cfg +++ b/setup.cfg @@ -43,7 +43,7 @@ install_requires = numpy==1.21.5; python_version=='3.7' and sys_platform=='darwin' scipy>=1.9.0; python_version>='3.8' scipy==1.7.3; python_version=='3.7' - matplotlib>='3.6' + matplotlib>='3.6'; python_version>='3.7' dask==2022.2.1; python_version=='3.8' and sys_platform=='darwin' dask==2022.8.0; python_version>='3.9' and sys_platform=='darwin' dask==2022.8.0; python_version>='3.8' and sys_platform=='linux' From 64d3b959c3d74bc06c66357e861db55cff05c605 Mon Sep 17 00:00:00 2001 From: Simon <31246246+SimonMolinsky@users.noreply.github.com> Date: Sun, 5 Feb 2023 10:42:12 +0200 Subject: [PATCH 25/29] Update setup.cfg --- setup.cfg | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/setup.cfg b/setup.cfg index cef93e16..23d45693 100644 --- a/setup.cfg +++ b/setup.cfg @@ -43,7 +43,7 @@ install_requires = numpy==1.21.5; python_version=='3.7' and sys_platform=='darwin' scipy>=1.9.0; python_version>='3.8' scipy==1.7.3; python_version=='3.7' - matplotlib>='3.6'; python_version>='3.7' + matplotlib>=3.6 dask==2022.2.1; python_version=='3.8' and sys_platform=='darwin' dask==2022.8.0; python_version>='3.9' and sys_platform=='darwin' dask==2022.8.0; python_version>='3.8' and sys_platform=='linux' From e550c17fbf310fdad7d169be9438994a378d1802 Mon Sep 17 00:00:00 2001 From: Simon <31246246+SimonMolinsky@users.noreply.github.com> Date: Mon, 6 Feb 2023 23:45:44 +0200 Subject: [PATCH 26/29] updated and initialized Kriging dataclass --- changelog.rst | 2 +- .../kriging/models/point/simple_kriging.py | 5 ++-- .../kriging/models/structures/__init__.py | 0 .../models/structures/point_kriging.py | 28 +++++++++++++++++++ 4 files changed, 31 insertions(+), 4 deletions(-) create mode 100644 pyinterpolate/kriging/models/structures/__init__.py create mode 100644 pyinterpolate/kriging/models/structures/point_kriging.py diff --git a/changelog.rst b/changelog.rst index a6388306..f0535930 100755 --- a/changelog.rst +++ b/changelog.rst @@ -25,7 +25,7 @@ Changes by date * (enhancement) scatter plot represented as a swarm plot in `VariogramCloud`, * (enahncement) added directional kriging for ATA and ATP Poisson Kriging, * (debug) warning for directional kriging functions, -* +* (enhancement) initialization of `KrigingObject` dataclass. 2023-01-16 ---------- diff --git a/pyinterpolate/kriging/models/point/simple_kriging.py b/pyinterpolate/kriging/models/point/simple_kriging.py index 5a4d46e9..b2415816 100644 --- a/pyinterpolate/kriging/models/point/simple_kriging.py +++ b/pyinterpolate/kriging/models/point/simple_kriging.py @@ -28,8 +28,7 @@ def simple_kriging( neighbors_range=None, no_neighbors=1, use_all_neighbors_in_range=False, - allow_approximate_solutions=False, - err_to_nan=False + allow_approximate_solutions=False ) -> List: """ Function predicts value at unknown location with Ordinary Kriging technique. @@ -61,7 +60,7 @@ def simple_kriging( ``True``: if the real number of neighbors within the ``neighbors_range`` is greater than the ``number_of_neighbors`` parameter then take all of them anyway. - allow_approx_solutions : bool, default=False + allow_approximate_solutions : bool, default=False Allows the approximation of kriging weights based on the OLS algorithm. We don't recommend set it to ``True`` if you don't know what are you doing. This parameter can be useful when you have clusters in your dataset, that can lead to singular or near-singular matrix creation. diff --git a/pyinterpolate/kriging/models/structures/__init__.py b/pyinterpolate/kriging/models/structures/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/pyinterpolate/kriging/models/structures/point_kriging.py b/pyinterpolate/kriging/models/structures/point_kriging.py new file mode 100644 index 00000000..3fd21267 --- /dev/null +++ b/pyinterpolate/kriging/models/structures/point_kriging.py @@ -0,0 +1,28 @@ +from dataclasses import dataclass +from enum import Enum +from typing import Union, List, Tuple, Optional + +import numpy as np + +from pyinterpolate import TheoreticalVariogram + + +class KrigingType(Enum): + ORDINARY = 0 + SIMPLE = 1 + + +@dataclass +class KrigingObject: + """ + Representation of Kriging object that can be stored and reused. + """ + type: KrigingType + theoretical_model: TheoreticalVariogram + known_locations: np.ndarray + unknown_location: Union[List, Tuple, np.ndarray] + no_neighbors: int + neighbors_range: Optional[float] + process_mean: Optional[float] + use_all_neighbors_in_range: Optional[bool] + allow_approximate_solutions: Optional[bool] \ No newline at end of file From 199f3b0ed5148c86509e93ac76aaac9d79c57c07 Mon Sep 17 00:00:00 2001 From: Simon <31246246+SimonMolinsky@users.noreply.github.com> Date: Thu, 9 Feb 2023 10:42:09 +0200 Subject: [PATCH 27/29] Update before publication 0.3.7 --- README.md | 2 +- changelog.rst | 4 +-- .../api/kriging/point/point_kriging.doctree | Bin 58609 -> 58192 bytes docs/build/doctrees/environment.pickle | Bin 442372 -> 442059 bytes docs/build/doctrees/index.doctree | Bin 14146 -> 14146 bytes .../Directional Semivariograms (Basic).ipynb | 2 +- .../doctrees/usage/learning_materials.doctree | Bin 10744 -> 10146 bytes ...Directional Semivariograms (Basic).doctree | Bin 88128 -> 88128 bytes docs/build/html/.buildinfo | 2 +- docs/build/html/_modules/index.html | 4 +-- .../kriging/models/point/simple_kriging.html | 26 ++++++++++++++---- docs/build/html/_sources/index.rst.txt | 2 +- .../_sources/usage/learning_materials.rst.txt | 12 ++++---- .../html/_static/documentation_options.js | 2 +- docs/build/html/api/api.html | 4 +-- docs/build/html/api/datatypes/core.html | 18 ++++++++++-- docs/build/html/api/distance/distance.html | 4 +-- docs/build/html/api/idw/idw.html | 18 ++++++++++-- docs/build/html/api/io/io.html | 18 ++++++++++-- .../html/api/kriging/block/block_kriging.html | 4 +-- docs/build/html/api/kriging/kriging.html | 4 +-- .../html/api/kriging/point/point_kriging.html | 22 ++++++++++++--- .../html/api/pipelines/data/download.html | 18 ++++++++++-- .../kriging_based_processes.html | 18 ++++++++++-- docs/build/html/api/pipelines/pipelines.html | 18 ++++++++++-- .../build/html/api/variogram/block/block.html | 18 ++++++++++-- .../deconvolution/deconvolution.html | 18 ++++++++++-- .../variogram/experimental/experimental.html | 4 +-- .../variogram/theoretical/theoretical.html | 18 ++++++++++-- docs/build/html/api/variogram/variogram.html | 4 +-- docs/build/html/api/viz/viz.html | 18 ++++++++++-- docs/build/html/community/community.html | 18 ++++++++++-- .../community/doc_parts/contributors.html | 18 ++++++++++-- .../build/html/community/doc_parts/forum.html | 18 ++++++++++-- .../html/community/doc_parts/use_cases.html | 24 ++++++++++++---- docs/build/html/developer/dev.html | 18 ++++++++++-- docs/build/html/developer/doc_parts/bugs.html | 18 ++++++++++-- .../html/developer/doc_parts/development.html | 18 ++++++++++-- .../html/developer/doc_parts/package.html | 18 ++++++++++-- docs/build/html/developer/doc_parts/reqs.html | 18 ++++++++++-- .../doc_parts/tests_and_contribution.html | 18 ++++++++++-- docs/build/html/genindex.html | 4 +-- docs/build/html/index.html | 12 ++++---- docs/build/html/objects.inv | Bin 6510 -> 6511 bytes docs/build/html/science/biblio.html | 24 ++++++++++++---- docs/build/html/science/cite.html | 18 ++++++++++-- docs/build/html/search.html | 4 +-- docs/build/html/searchindex.js | 2 +- docs/build/html/setup/setup.html | 18 ++++++++++-- docs/build/html/usage/good_practices.html | 18 ++++++++++-- docs/build/html/usage/learning_materials.html | 16 +++++------ docs/build/html/usage/quickstart.html | 18 ++++++++++-- docs/build/html/usage/tutorials.html | 18 ++++++++++-- ... Kriging interpolation (Intermediate).html | 18 ++++++++++-- ...ional Ordinary Kriging (Intermediate).html | 18 ++++++++++-- .../Directional Semivariograms (Basic).html | 18 ++++++++++-- .../Directional Semivariograms (Basic).ipynb | 2 +- ...test it against IDW algorithm (Basic).html | 18 ++++++++++-- .../Ordinary and Simple Kriging (Basic).html | 18 ++++++++++-- ...nce on the Final Model (Intermediate).html | 18 ++++++++++-- ...son Kriging - Area to Area (Advanced).html | 18 ++++++++++-- ...on Kriging - Area to Point (Advanced).html | 18 ++++++++++-- ...n Kriging - Centroid Based (Advanced).html | 18 ++++++++++-- .../Semivariogram Estimation (Basic).html | 18 ++++++++++-- ...riogram Regularization (Intermediate).html | 18 ++++++++++-- .../tutorials/Theoretical Models (Basic).html | 18 ++++++++++-- .../Variogram Point Cloud (Basic).html | 18 ++++++++++-- docs/source/conf.py | 2 +- docs/source/index.rst | 2 +- docs/source/usage/learning_materials.rst | 12 ++++---- license.txt | 2 +- pyinterpolate/__init__.py | 2 +- setup.cfg | 1 + 73 files changed, 726 insertions(+), 151 deletions(-) diff --git a/README.md b/README.md index d833c0b5..ebcfbd93 100755 --- a/README.md +++ b/README.md @@ -4,7 +4,7 @@ # Pyinterpolate -**version 0.3.6** - *Kyiv* +**version 0.3.7** - *Kyiv* --- diff --git a/changelog.rst b/changelog.rst index f0535930..cf95737a 100755 --- a/changelog.rst +++ b/changelog.rst @@ -6,7 +6,7 @@ Pyinterpolate is the Python library for **geostatistics** and **spatial statisti Changes by date =============== -2023-02-XX +2023-02-09 ---------- **version 0.3.7** @@ -23,7 +23,7 @@ Changes by date * (enhancement) function `inblock_semivariance` has been optimized, * (docs) updated `__init__.py` of `variogram.theoretical` module, * (enhancement) scatter plot represented as a swarm plot in `VariogramCloud`, -* (enahncement) added directional kriging for ATA and ATP Poisson Kriging, +* (enhancement) added directional kriging for ATA and ATP Poisson Kriging, * (debug) 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    neighbors_range=None, no_neighbors=1, use_all_neighbors_in_range=False, - allow_approximate_solutions=False, - err_to_nan=False + allow_approximate_solutions=False ) -> List: """ Function predicts value at unknown location with Ordinary Kriging technique. @@ -428,7 +442,7 @@

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    ``True``: if the real number of neighbors within the ``neighbors_range`` is greater than the ``number_of_neighbors`` parameter then take all of them anyway. - allow_approx_solutions : bool, default=False + allow_approximate_solutions : bool, default=False Allows the approximation of kriging weights based on the OLS algorithm. We don't recommend set it to ``True`` if you don't know what are you doing. This parameter can be useful when you have clusters in your dataset, that can lead to singular or near-singular matrix creation. @@ -544,7 +558,7 @@

    Source code for pyinterpolate.kriging.models.point.simple_kriging

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    diff --git a/docs/build/html/_sources/index.rst.txt b/docs/build/html/_sources/index.rst.txt index 3c988be7..65f37137 100644 --- a/docs/build/html/_sources/index.rst.txt +++ b/docs/build/html/_sources/index.rst.txt @@ -6,7 +6,7 @@ Pyinterpolate ============= -**version 0.3.6** - *Kyiv* +**version 0.3.7** - *Kyiv* --------------------------- .. image:: imgs/logov03.jpg diff --git a/docs/build/html/_sources/usage/learning_materials.rst.txt b/docs/build/html/_sources/usage/learning_materials.rst.txt index 52012710..103b65ed 100644 --- a/docs/build/html/_sources/usage/learning_materials.rst.txt +++ b/docs/build/html/_sources/usage/learning_materials.rst.txt @@ -4,19 +4,19 @@ Learning Materials Publications ------------ -- 💡 - (2022) `URL `_ Pyinterpolate: Spatial interpolation in Python for point measurements and aggregated datasets +- 💡 - (2022) `URL (JOSS) `_ Pyinterpolate: Spatial interpolation in Python for point measurements and aggregated datasets Blog posts ---------- -- 📜 - (2022) `URL `_ Interpolate Air Quality Measurements with Python, -- 📜 - (2022) `URL `_ Get More from Crime Rate Data and Other Socio-Economic Indicators with Pyinterpolate, -- 📜 - (2022) `URL `_ Geostatistics: Theoretical Variogram Models -- 📜 - (2022) `URL `_ Interpolate Air Pollution with Pyinterpolate +- 📜 - (2022) `URL (Ordinary Kriging) `_ Interpolate Air Quality Measurements with Python, +- 📜 - (2022) `URL (Area-to-Point Poisson Kriging) `_ Get More from Crime Rate Data and Other Socio-Economic Indicators with Pyinterpolate, +- 📜 - (2022) `URL (Theoretical Variograms) `_ Geostatistics: Theoretical Variogram Models +- 📜 - (2022) `URL (Kriging - General) `_ Interpolate Air Pollution with Pyinterpolate Presentations & Workshops ------------------------- -- 🎙️ - (2022) 🇵🇱 `URL `_ Wzbogacanie agregowanych danych publicznych z Pythonem i Geostatystyką \ No newline at end of file +- 🎙️ - (2022) 🇵🇱 `URL (Poisson Kriging) `_ Wzbogacanie agregowanych danych publicznych z Pythonem i Geostatystyką \ No newline at end of file diff --git a/docs/build/html/_static/documentation_options.js b/docs/build/html/_static/documentation_options.js index db494a19..2d82d373 100644 --- a/docs/build/html/_static/documentation_options.js +++ b/docs/build/html/_static/documentation_options.js @@ -1,6 +1,6 @@ var DOCUMENTATION_OPTIONS = { URL_ROOT: document.getElementById("documentation_options").getAttribute('data-url_root'), - VERSION: '0.3.6', + VERSION: '0.3.7', LANGUAGE: 'en', COLLAPSE_INDEX: false, BUILDER: 'html', diff --git a/docs/build/html/api/api.html b/docs/build/html/api/api.html index 67a1a259..d9c5eb6f 100644 --- a/docs/build/html/api/api.html +++ b/docs/build/html/api/api.html @@ -6,7 +6,7 @@ - API — Pyinterpolate 0.3.6 documentation + API — Pyinterpolate 0.3.7 documentation @@ -102,7 +102,7 @@ -

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    +

    Pyinterpolate 0.3.7 documentation

    diff --git a/docs/build/html/api/kriging/point/point_kriging.html b/docs/build/html/api/kriging/point/point_kriging.html index 9a609646..b69c2e05 100644 --- a/docs/build/html/api/kriging/point/point_kriging.html +++ b/docs/build/html/api/kriging/point/point_kriging.html @@ -6,7 +6,7 @@ - Point Kriging — Pyinterpolate 0.3.6 documentation + Point Kriging — Pyinterpolate 0.3.7 documentation @@ -102,7 +102,7 @@ -

    Pyinterpolate 0.3.6 documentation

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    Point Kriging
    -simple_kriging(theoretical_model, known_locations, unknown_location, process_mean, neighbors_range=None, no_neighbors=1, use_all_neighbors_in_range=False, allow_approximate_solutions=False, err_to_nan=False)[source]
    +simple_kriging(theoretical_model, known_locations, unknown_location, process_mean, neighbors_range=None, no_neighbors=1, use_all_neighbors_in_range=False, allow_approximate_solutions=False)[source]

    Function predicts value at unknown location with Ordinary Kriging technique.

    Parameters:
    @@ -526,7 +540,7 @@

    Point Krigingbool, default = False

    True: if the real number of neighbors within the neighbors_range is greater than the number_of_neighbors parameter then take all of them anyway.

    -
    allow_approx_solutionsbool, default=False

    Allows the approximation of kriging weights based on the OLS algorithm. We don’t recommend set it to True +

    allow_approximate_solutionsbool, default=False

    Allows the approximation of kriging weights based on the OLS algorithm. We don’t recommend set it to True if you don’t know what are you doing. This parameter can be useful when you have clusters in your dataset, that can lead to singular or near-singular matrix creation.

    diff --git a/docs/build/html/api/pipelines/data/download.html b/docs/build/html/api/pipelines/data/download.html index 00a4c35e..7ac29c67 100644 --- a/docs/build/html/api/pipelines/data/download.html +++ b/docs/build/html/api/pipelines/data/download.html @@ -6,7 +6,7 @@ - Data download — Pyinterpolate 0.3.6 documentation + Data download — Pyinterpolate 0.3.7 documentation @@ -102,7 +102,7 @@ -

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    @@ -383,8 +383,8 @@

    Pyinterpolate#

    -
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    version 0.3.6 - Kyiv#

    +
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    version 0.3.7 - Kyiv#

    The pyinterpolate logo with the name Kyiv and the version of package.

    Note

    @@ -472,9 +472,9 @@

    How to cite
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"Level:-Intermediate"], [40, "Level:-Intermediate"], [45, "Level:-Intermediate"]], "Changelog": [[35, "Changelog"], [36, "Changelog"], [37, "Changelog"], [38, "Changelog"], [39, "Changelog"], [40, "Changelog"], [41, "Changelog"], [42, "Changelog"], [43, "Changelog"], [44, "Changelog"], [45, "Changelog"], [46, "Changelog"], [47, "Changelog"]], "Introduction": [[35, "Introduction"], [36, "Introduction"], [37, "Introduction"], [38, "Introduction"], [39, "Introduction"], [40, "Introduction"], [41, "Introduction"], [42, "Introduction"], [43, "Introduction"], [44, "Introduction"], [45, "Introduction"], [46, "Introduction"], [47, "Introduction"]], "Import packages": [[35, "Import-packages"], [37, "Import-packages"], [38, "Import-packages"], [39, "Import-packages"], [40, "Import-packages"], [44, "Import-packages"], [45, "Import-packages"], [46, "Import-packages"], [47, "Import-packages"]], "1) Read areal data": [[35, "1)-Read-areal-data"]], "2) Detect and remove outliers": [[35, 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Compare interpolated results": [[36, "4.-Compare-interpolated-results"], [36, "id1"]], "Directional semivariograms": [[37, "Directional-semivariograms"]], "A directional proces": [[37, "A-directional-proces"]], "1) Read and show point data": [[37, "1)-Read-and-show-point-data"]], "2) Create the experimental semivariogram": [[37, "2)-Create-the-experimental-semivariogram"]], "Case 1: W-E Direction": [[37, "Case-1:-W-E-Direction"]], "Case 2: N-S Direction": [[37, "Case-2:-N-S-Direction"]], "Case 3: NW-SE Direction": [[37, "Case-3:-NW-SE-Direction"]], "Case 4: NE-SW Direction": [[37, "Case-4:-NE-SW-Direction"]], "Case 5: Isotropic Variogram": [[37, "Case-5:-Isotropic-Variogram"]], "3) Compare semivariograms": [[37, "3)-Compare-semivariograms"]], "4) Bonus: Compare calculation times and results": [[37, "4)-Bonus:-Compare-calculation-times-and-results"]], "How good is my Kriging model? - tests with IDW algorithm": [[38, "How-good-is-my-Kriging-model?---tests-with-IDW-algorithm"]], "1) Read point data": [[38, "1)-Read-point-data"], [39, "1)-Read-point-data"], [44, "1)-Read-point-data"], [47, "1)-Read-point-data"]], "2) Divide the dataset into two sets: modeling and validation set": [[38, "2)-Divide-the-dataset-into-two-sets:-modeling-and-validation-set"]], "3) Perform IDW and evaluate it": [[38, "3)-Perform-IDW-and-evaluate-it"]], "4) Perform variogram modeling on the modeling set": [[38, "4)-Perform-variogram-modeling-on-the-modeling-set"]], "5) Validate Kriging and compare Kriging and IDW validation results": [[38, "5)-Validate-Kriging-and-compare-Kriging-and-IDW-validation-results"]], "6) Bonus scenario: only 2% of values are known!": [[38, "6)-Bonus-scenario:-only-2%-of-values-are-known!"]], "Ordinary and Simple Kriging": [[39, "Ordinary-and-Simple-Kriging"]], "2) Set Semivariogram model": [[39, "2)-Set-Semivariogram-model"]], "3) Set Ordinary Kriging and Simple Kriging models": [[39, "3)-Set-Ordinary-Kriging-and-Simple-Kriging-models"]], "4) Predict values at unknown locations": [[39, "4)-Predict-values-at-unknown-locations"]], "Outliers and Their Influence on the Final Model": [[40, "Outliers-and-Their-Influence-on-the-Final-Model"]], "1) Read point data and divide it into training and test set": [[40, "1)-Read-point-data-and-divide-it-into-training-and-test-set"]], "2) Check outliers: analyze the distribution of the values": [[40, "2)-Check-outliers:-analyze-the-distribution-of-the-values"]], "3) Create the Variogram Point Cloud model for datasets A and B": [[40, "3)-Create-the-Variogram-Point-Cloud-model-for-datasets-A-and-B"]], "4) Remove outliers from the variograms": [[40, "4)-Remove-outliers-from-the-variograms"]], "5) Create Four Ordinary Kriging models based on the four Variogram Point Clouds and compare their performance": [[40, "5)-Create-Four-Ordinary-Kriging-models-based-on-the-four-Variogram-Point-Clouds-and-compare-their-performance"]], "Poisson Kriging - Area to Area Kriging": [[41, "Poisson-Kriging---Area-to-Area-Kriging"]], "Level: Advanced": [[41, "Level:-Advanced"], [42, "Level:-Advanced"], [43, "Level:-Advanced"]], "1) Read and explore data": [[41, "1)-Read-and-explore-data"], [42, "1)-Read-and-explore-data"], [43, "1)-Read-and-explore-data"]], "2) Load a semivariogram model": [[41, "2)-Load-a-semivariogram-model"]], "3) Prepare training and test data.": [[41, "3)-Prepare-training-and-test-data."], [43, "3)-Prepare-training-and-test-data."]], "4) Predict values at unknown locations and calculate forecast bias and root mean squared error.": [[41, "4)-Predict-values-at-unknown-locations-and-calculate-forecast-bias-and-root-mean-squared-error."], [43, "4)-Predict-values-at-unknown-locations-and-calculate-forecast-bias-and-root-mean-squared-error."]], "5) Analyze Forecast Bias and Root Mean Squared Error of prediction": [[41, "5)-Analyze-Forecast-Bias-and-Root-Mean-Squared-Error-of-prediction"], [43, "5)-Analyze-Forecast-Bias-and-Root-Mean-Squared-Error-of-prediction"]], "Poisson Kriging - Area to Point Kriging": [[42, "Poisson-Kriging---Area-to-Point-Kriging"]], "2) Load semivariogram model": [[42, "2)-Load-semivariogram-model"], [43, "2)-Load-semivariogram-model"]], "3) Perform Area to Point smoothing of areal data.": [[42, "3)-Perform-Area-to-Point-smoothing-of-areal-data."]], "4) Visualize data": [[42, "4)-Visualize-data"]], "Poisson Kriging - centroid based approach": [[43, "Poisson-Kriging---centroid-based-approach"]], "Semivariogram Estimation": [[44, "Semivariogram-Estimation"]], "2) Create the experimental variogram": [[44, "2)-Create-the-experimental-variogram"], [46, "2)-Create-the-experimental-variogram"]], "3. 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"Import-packages"], [47, "Import-packages"]], "1) Read areal data": [[35, "1)-Read-areal-data"]], "2) Detect and remove outliers": [[35, "2)-Detect-and-remove-outliers"]], "3. 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Compare interpolated results": [[36, "4.-Compare-interpolated-results"], [36, "id1"]], "Directional semivariograms": [[37, "Directional-semivariograms"]], "A directional proces": [[37, "A-directional-proces"]], "1) Read and show point data": [[37, "1)-Read-and-show-point-data"]], "2) Create the experimental semivariogram": [[37, "2)-Create-the-experimental-semivariogram"]], "Case 1: W-E Direction": [[37, "Case-1:-W-E-Direction"]], "Case 2: N-S Direction": [[37, "Case-2:-N-S-Direction"]], "Case 3: NW-SE Direction": [[37, "Case-3:-NW-SE-Direction"]], "Case 4: NE-SW Direction": [[37, "Case-4:-NE-SW-Direction"]], "Case 5: Isotropic Variogram": [[37, "Case-5:-Isotropic-Variogram"]], "3) Compare semivariograms": [[37, "3)-Compare-semivariograms"]], "4) Bonus: Compare calculation times and results": [[37, "4)-Bonus:-Compare-calculation-times-and-results"]], "How good is my Kriging model? - tests with IDW algorithm": [[38, "How-good-is-my-Kriging-model?---tests-with-IDW-algorithm"]], "1) Read point data": [[38, "1)-Read-point-data"], [39, "1)-Read-point-data"], [44, "1)-Read-point-data"], [47, "1)-Read-point-data"]], "2) Divide the dataset into two sets: modeling and validation set": [[38, "2)-Divide-the-dataset-into-two-sets:-modeling-and-validation-set"]], "3) Perform IDW and evaluate it": [[38, "3)-Perform-IDW-and-evaluate-it"]], "4) Perform variogram modeling on the modeling set": [[38, "4)-Perform-variogram-modeling-on-the-modeling-set"]], "5) Validate Kriging and compare Kriging and IDW validation results": [[38, "5)-Validate-Kriging-and-compare-Kriging-and-IDW-validation-results"]], "6) Bonus scenario: only 2% of values are known!": [[38, "6)-Bonus-scenario:-only-2%-of-values-are-known!"]], "Ordinary and Simple Kriging": [[39, "Ordinary-and-Simple-Kriging"]], "2) Set Semivariogram model": [[39, "2)-Set-Semivariogram-model"]], "3) Set Ordinary Kriging and Simple Kriging models": [[39, "3)-Set-Ordinary-Kriging-and-Simple-Kriging-models"]], "4) Predict values at unknown locations": [[39, "4)-Predict-values-at-unknown-locations"]], "Outliers and Their Influence on the Final Model": [[40, "Outliers-and-Their-Influence-on-the-Final-Model"]], "1) Read point data and divide it into training and test set": [[40, "1)-Read-point-data-and-divide-it-into-training-and-test-set"]], "2) Check outliers: analyze the distribution of the values": [[40, "2)-Check-outliers:-analyze-the-distribution-of-the-values"]], "3) Create the Variogram Point Cloud model for datasets A and B": [[40, "3)-Create-the-Variogram-Point-Cloud-model-for-datasets-A-and-B"]], "4) Remove outliers from the variograms": [[40, "4)-Remove-outliers-from-the-variograms"]], "5) Create Four Ordinary Kriging models based on the four Variogram Point Clouds and compare their performance": [[40, "5)-Create-Four-Ordinary-Kriging-models-based-on-the-four-Variogram-Point-Clouds-and-compare-their-performance"]], "Poisson Kriging - Area to Area Kriging": [[41, "Poisson-Kriging---Area-to-Area-Kriging"]], "Level: Advanced": [[41, "Level:-Advanced"], [42, "Level:-Advanced"], [43, "Level:-Advanced"]], "1) Read and explore data": [[41, "1)-Read-and-explore-data"], [42, "1)-Read-and-explore-data"], [43, "1)-Read-and-explore-data"]], "2) Load a semivariogram model": [[41, "2)-Load-a-semivariogram-model"]], "3) Prepare training and test data.": [[41, "3)-Prepare-training-and-test-data."], [43, "3)-Prepare-training-and-test-data."]], "4) Predict values at unknown locations and calculate forecast bias and root mean squared error.": [[41, "4)-Predict-values-at-unknown-locations-and-calculate-forecast-bias-and-root-mean-squared-error."], [43, "4)-Predict-values-at-unknown-locations-and-calculate-forecast-bias-and-root-mean-squared-error."]], "5) Analyze Forecast Bias and Root Mean Squared Error of prediction": [[41, "5)-Analyze-Forecast-Bias-and-Root-Mean-Squared-Error-of-prediction"], [43, "5)-Analyze-Forecast-Bias-and-Root-Mean-Squared-Error-of-prediction"]], "Poisson Kriging - Area to Point Kriging": [[42, "Poisson-Kriging---Area-to-Point-Kriging"]], "2) Load semivariogram model": [[42, "2)-Load-semivariogram-model"], [43, "2)-Load-semivariogram-model"]], "3) Perform Area to Point smoothing of areal data.": [[42, "3)-Perform-Area-to-Point-smoothing-of-areal-data."]], "4) Visualize data": [[42, "4)-Visualize-data"]], "Poisson Kriging - centroid based approach": [[43, "Poisson-Kriging---centroid-based-approach"]], "Semivariogram Estimation": [[44, "Semivariogram-Estimation"]], "2) Create the experimental variogram": [[44, "2)-Create-the-experimental-variogram"], [46, "2)-Create-the-experimental-variogram"]], "3. 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      Learning Materials

      Publications#

        -

      • 💡 - (2022) URL Pyinterpolate: Spatial interpolation in Python for point measurements and aggregated datasets

        • +

      • 💡 - (2022) URL (JOSS) Pyinterpolate: Spatial interpolation in Python for point measurements and aggregated datasets

    Blog posts#

      -

    • 📜 - (2022) URL Interpolate Air Quality Measurements with Python,

      • -

    • 📜 - (2022) URL Get More from Crime Rate Data and Other Socio-Economic Indicators with Pyinterpolate,

      • -

    • 📜 - (2022) URL Geostatistics: Theoretical Variogram Models

      • -

    • 📜 - (2022) URL Interpolate Air Pollution with Pyinterpolate

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    • 📜 - (2022) URL (Ordinary Kriging) Interpolate Air Quality Measurements with Python,

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    • 📜 - (2022) URL (Area-to-Point Poisson Kriging) Get More from Crime Rate Data and Other Socio-Economic Indicators with Pyinterpolate,

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    • 📜 - (2022) URL (Theoretical Variograms) Geostatistics: Theoretical Variogram Models

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    • 📜 - (2022) URL (Kriging - General) Interpolate Air Pollution with Pyinterpolate

    Presentations & Workshops#

      -

    • 🎙️ - (2022) 🇵🇱 URL Wzbogacanie agregowanych danych publicznych z Pythonem i Geostatystyką

      • +

    • 🎙️ - (2022) 🇵🇱 URL (Poisson Kriging) Wzbogacanie agregowanych danych publicznych z Pythonem i Geostatystyką

    diff --git a/docs/build/html/usage/quickstart.html b/docs/build/html/usage/quickstart.html index 6a0d07e4..1ac77a46 100644 --- a/docs/build/html/usage/quickstart.html +++ b/docs/build/html/usage/quickstart.html @@ -6,7 +6,7 @@ - Quickstart — Pyinterpolate 0.3.6 documentation + Quickstart — Pyinterpolate 0.3.7 documentation @@ -102,7 +102,7 @@ -

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Redistribution and use in source and binary forms, with or without modification, diff --git a/pyinterpolate/__init__.py b/pyinterpolate/__init__.py index 954893b3..328d1a20 100755 --- a/pyinterpolate/__init__.py +++ b/pyinterpolate/__init__.py @@ -38,4 +38,4 @@ from pyinterpolate.viz import interpolate_raster -__version__ = "0.3.6" +__version__ = "0.3.7" diff --git a/setup.cfg b/setup.cfg index 23d45693..041c6139 100644 --- a/setup.cfg +++ b/setup.cfg @@ -52,6 +52,7 @@ install_requires = requests pyogrio rtree==1.0.0 + shapely<2 geopandas>=0.11.1; python_version>='3.8' geopandas==0.10.2; python_version=='3.7' From 17a70813b17e3ebbe93e7a04f15291673d63ba74 Mon Sep 17 00:00:00 2001 From: Simon <31246246+SimonMolinsky@users.noreply.github.com> Date: Thu, 9 Feb 2023 10:46:27 +0200 Subject: [PATCH 28/29] New workflows --- .../python-package-install-linux-check.yml | 31 +++++++++++++++++++ .../python-package-install-macos-check.yml | 31 +++++++++++++++++++ changelog.rst | 3 +- 3 files changed, 64 insertions(+), 1 deletion(-) create mode 100644 .github/workflows/python-package-install-linux-check.yml create mode 100644 .github/workflows/python-package-install-macos-check.yml diff --git a/.github/workflows/python-package-install-linux-check.yml b/.github/workflows/python-package-install-linux-check.yml new file mode 100644 index 00000000..9cd16e91 --- /dev/null +++ b/.github/workflows/python-package-install-linux-check.yml @@ -0,0 +1,31 @@ +# This workflow will install Python dependencies, run tests and lint with a variety of Python versions +# For more information see: https://docs.github.com/en/actions/automating-builds-and-tests/building-and-testing-python + +name: Pyinterpolate pip dependencies installation test on windows + +on: + push: + branches: [ "main", "dev", "dependencies" ] + pull_request: + branches: [ "main", "dev", "dependencies" ] + +jobs: + build: + + runs-on: ubuntu-latest + strategy: + fail-fast: false + matrix: + python-version: ["3.8", "3.9", "3.10", "3.11"] + + steps: + - uses: actions/checkout@v3 + - name: Set up Python ${{ matrix.python-version }} + uses: actions/setup-python@v3 + with: + python-version: ${{ matrix.python-version }} + - name: Install dependencies + run: | + python -m pip install --upgrade pip + python -m pip install flake8 pytest + pip install -r requirements.txt \ No newline at end of file diff --git a/.github/workflows/python-package-install-macos-check.yml b/.github/workflows/python-package-install-macos-check.yml new file mode 100644 index 00000000..7c273618 --- /dev/null +++ b/.github/workflows/python-package-install-macos-check.yml @@ -0,0 +1,31 @@ +# This workflow will install Python dependencies, run tests and lint with a variety of Python versions +# For more information see: https://docs.github.com/en/actions/automating-builds-and-tests/building-and-testing-python + +name: Pyinterpolate pip dependencies installation test on windows + +on: + push: + branches: [ "main", "dev", "dependencies" ] + pull_request: + branches: [ "main", "dev", "dependencies" ] + +jobs: + build: + + runs-on: macos-latest + strategy: + fail-fast: false + matrix: + python-version: ["3.8", "3.9", "3.10", "3.11"] + + steps: + - uses: actions/checkout@v3 + - name: Set up Python ${{ matrix.python-version }} + uses: actions/setup-python@v3 + with: + python-version: ${{ matrix.python-version }} + - name: Install dependencies + run: | + python -m pip install --upgrade pip + python -m pip install flake8 pytest + pip install -r requirements.txt \ No newline at end of file diff --git a/changelog.rst b/changelog.rst index cf95737a..b53b44a4 100755 --- a/changelog.rst +++ b/changelog.rst @@ -25,7 +25,8 @@ Changes by date * (enhancement) scatter plot represented as a swarm plot in `VariogramCloud`, * (enhancement) added directional kriging for ATA and ATP Poisson Kriging, * (debug) warning for directional kriging functions, -* (enhancement) initialization of `KrigingObject` dataclass. +* (enhancement) initialization of `KrigingObject` dataclass, +* (ci/cd) added new workflow tests for MacOS and Ubuntu. 2023-01-16 ---------- From f7617b4c55e4e7a5dbd4d0a024726982fa74f16e Mon Sep 17 00:00:00 2001 From: Simon <31246246+SimonMolinsky@users.noreply.github.com> Date: Thu, 9 Feb 2023 10:59:11 +0200 Subject: [PATCH 29/29] Update setup.cfg --- setup.cfg | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/setup.cfg b/setup.cfg index 041c6139..65f461c4 100644 --- a/setup.cfg +++ b/setup.cfg @@ -3,7 +3,7 @@ name = pyinterpolate description = Spatial Interpolation in Python long_description = file: README.md long_description_content_type = text/markdown; charset=UTF-8 -version = 0.3.6 +version = 0.3.7 url = https://github.com/DataverseLabs/pyinterpolate download_url = https://github.com/DataverseLabs/pyinterpolate/archive/v0.3.tar.gz author = Szymon Moliński