From 9eadbfc068723c587c061673be82d4b666765efc Mon Sep 17 00:00:00 2001 From: Mostafa Farrag Date: Sun, 1 Oct 2023 22:57:23 +0200 Subject: [PATCH] Solidify the Dataset class and adapt working with Multi-band rasters (#45) * rename examples notebook * deprecate the path parameter from the to_polygon method * rename to_polygon to cluster2, the column name in the returned gdf has the band name * arrage data * rename to_geo_dataframe to to_feature_collection * add missing data * arrange test data * arrange test data * arrange test data * arrange test data * arrange test data * arrange test data * arrange test data * arrange test data * arrange test data * update * arrange tests data * arrange test data * change the era5 no data value to -9999 * add test case for the error in the to_dataset bug * add layer_count property to featurecollection * add layer_names property to featurecollection class * change the return value of the _gdf_to_ds method to return Featurecollection not DataSource * add test_gdf_to_ds_if_feature_is_already_ds test * add columns property to feature collection class * read folder using regex instead of renaming files * python 3.10 * fix file path * add docs for Datacube.read_separate_files * add repre method, files property, update docs * rename method read_separate_files to read_multiple_files * rename data attribute in the datacube class to values * rename data attribute in the datacube class to values * rename the TestOpenDataCube test * add read_multiple_files documentations * update docs * add datacube rst file to the index * correct index datacube name in the index * add docs missing images * replace the convert_longitude method content to use gdal.warp * add comments in the convert_longitude method * update convert_longitude example files * refactor * change bounds to total_bounds * get extent of datasource * change the _gdf_to_ds implementation * add _crop_with_polygon_warp method * change the inputs for the read_multiple_files to take a regex string * add docs to checklist * test read files based on number in the file name * update * refactor the read_multiple_files method * the start and end parameter in the read_multiple_files can take integer values not only dates * allow values.setter to set values even if the files were not read yet * path parameter to the to_file methof can be a list of paths * deprecate update_Datacube and replace it with a setter for the values property * refactor * remove repaeted readthedocs badge * update detailed scheme of datacube docs * return the Array object from the plot method * add datacube example * add file_name to get the file name of an ogr.Datasource and gdal.Dataset, change the ogr.Datasource to a gdal.Dataset in the _gdf_to_ds * add a gdal_dataset parameter to the _gdf_to_ds method * add ogr_ds_togdal_dataset method * refactor * change the _ds_to_gdf to use VectorTranslate * correct the name of the MEMORY_FILE * print the gdal_ds for debugging * add gdal.UseExceptions() in the featurecollection module * change the layername to be extracted from the gdal_ds object * remove the driver type in the gpd.read_file in _ds_to_gdf * add layer name to gpd.read_file from memory * remove the event branch to run on all branches * correct name * add pypi to the workflow name * add ubuntu conda workflow * install the dev packages using pip * add conda optional packages * remove installing cleopatra in the workflow file as it is in the requirement.txt/environment.yml file * replace conda with the market place conda * activate test env instead of the base env * remove mamba * trigger ci on push only * import cleopatra inside the test as it raise an import error in case testing the main package-cleopatra not installed * include python 3.11 in the yml file * remove upper limit of python version * remote test for python 3.11 as depencdencies still have the <3.11 in their yml file * remove testing conda for python 3.9 * remove the python setup.py install as it reinstall packages with pip in conda test * add shell specification line * clean files * gather workflow of conda and pypi * remove redundent workflows * add two cases to ds_to_gdf in memory and io * refactor _ds_to_gdf * the to_dataset uses the _gdf_to_ds to convert the gdf to a gdal dataset instead of writting the the vector to disk * add pivot point attribute to the feature collection class * change the gdal raster.RasterCount in the get_band_names method to use the method band_count * add set_band_names method * add band_name setter * column_name can take a one column, multiple or all, multi-band raster is enabled * remove writting the mask vector to disk as it is not used anymore * comment and refactor to_dataset method * add missing test data * update pre-commit hooks * migrate to shapely2.x * update dependencies * update gdal to 3.7.0 * rename _create_driver_from_scratch to create_driver_from_scratch * add function to convert ogr datatype code into numpy data type * update python version for conda build in actions to 3.11 * correct the ci to run plotiing tests in the optional package scenario * _read_variable does not raise an error if the variable does not exist but return None * add dtypes property to featurecollection object * convert ogr.OFTInteger to int64 instead of int32 to unify it with how geopandas read it * add qodana CI yaml file * exclude paths fro qodana * add conversion table between numpy, ogr, and gdal * replace converting numpy data types to gdal data types to use the conversion table instead of the gdal_array.NumericTypeCodeToGDALTypeCode fns * hard code the OGR.OFTInteger(0) to np.int32, OGR.OFTInteger64 (12) to np.int64, and OGR.Real (2) to np.float64 * add unit test for the gdal_to_numpy_dtype function * use the DTYPE_CONVERSION_DF in the gdal_to_numpy_dtype function instead of the NUMPY_GDAL_DATA_TYPES dictionary * raise error if the gdal data type code does not exist in the DTYPE_CONVERSION_DF * move calculating the dtype from the __init__ method to the dtype property * add numpy_dtype property * rename dtype to gdal_dtype * create dtype, and numpy_dtype properties * change the scope of some fixtures to function * change scopr of some features to function * adjust the nodatavalue to use the new gdal_dtype, dtype and numpy_dtype * use the DTYPE_CONVERSION_DF in the gdal_to_ogr_dtype function * numpy_to_gdal_dtype can take a numpy dtype instead of an array * add exceptions in the _check_no_data_value to capture when the no_data_value can not be set * use the overflowerror generally to replace the no_data_value by the default one * refactor tests * clean * ready array with the same dtype as the raster band, in the _crop_alligned use the DEFAULT_NO_DATA_VALUE if the no_data_value gives an error * refactor * deal with the problem of the None no_data_value with the unsigned integer dtype in the _check_no_data_value function * correct the no_data_value in the raster * change the to_featurecollection to use the crop method * refactor * add Numbers as a datatype * add excetion handler in the to_file method to go around saving a raster with color table * remove the test result file if exist * rename ci files * fix gdal versin to solve to gdal.Wrap error * fix gdal version in ci to 3.7.1 * add missing test data * add color_table property * add color_table documentations * add misssing test data * move color_table to test_plot as it uses cleopatra package * add the plot decorator on the color_table tests * refactor * update installation docs * ignore old files * refactor * change return value from _window and convert _window, get_tile from static to normal mehtod * refactor * rest _window * refactor * reorder imports and rename locate_points to map_to_array_coordinates * rename tests for the map_to_array_coordinates renamed method * test array_to_map_coordinates * refactor featurecollection module * extend the functionality of create_point to create a feature_collection if epsg is given * refactor * clean * update setup.py * update history file * update version number --- .github/pull_request_template.md | 13 +- .github/workflows/conda-deployment.yml | 87 ++ .github/workflows/pypi-deployment.yml | 90 ++ .../workflows/{pypi.yml => pypi-release.yml} | 0 .../workflows/ubuntu-optional-packages.yml | 54 - .github/workflows/ubuntu.yml | 48 - .gitignore | 2 + .pre-commit-config.yaml | 10 +- HISTORY.rst | 37 + README.md | 30 +- docs/dataCube.rst | 133 -- docs/datacube.rst | 205 +++ docs/dataset.rst | 86 +- docs/images/datacube/attributes.png | Bin 0 -> 54665 bytes docs/images/datacube/detailed.png | Bin 0 -> 367437 bytes docs/images/datacube/logo.png | Bin 0 -> 726222 bytes docs/images/dataset/0-360-longitude.png | Bin 81664 -> 76902 bytes .../dataset/negative-180-to-180-longitude.png | Bin 81301 -> 77101 bytes docs/index.rst | 4 +- docs/installation.rst | 39 +- docs/pyramids.drawio | 288 +++- environment-optional-packages.yml | 26 +- environment.yml | 24 +- examples/data-cube-examples.py | 13 - 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tests/dataset/conftest.py | 66 +- tests/dataset/test_datacube.py | 270 ++-- tests/dataset/test_dataset.py | 493 +++++-- tests/dataset/test_plot.py | 57 +- tests/feature/conftest.py | 10 + tests/feature/test_feature.py | 437 +++++- tests/test_utils.py | 43 +- 134 files changed, 4305 insertions(+), 2049 deletions(-) create mode 100644 .github/workflows/conda-deployment.yml create mode 100644 .github/workflows/pypi-deployment.yml rename .github/workflows/{pypi.yml => pypi-release.yml} (100%) delete mode 100644 .github/workflows/ubuntu-optional-packages.yml delete mode 100644 .github/workflows/ubuntu.yml delete mode 100644 docs/dataCube.rst create mode 100644 docs/datacube.rst create mode 100644 docs/images/datacube/attributes.png create mode 100644 docs/images/datacube/detailed.png create mode 100644 docs/images/datacube/logo.png delete mode 100644 examples/data-cube-examples.py rename examples/data/{ => geotiff}/aligned_rasters/Evaporation_ECMWF_ERA-Interim_mm_daily_2009.01.01.tif (100%) rename examples/data/{ => geotiff}/aligned_rasters/Evaporation_ECMWF_ERA-Interim_mm_daily_2009.01.02.tif (100%) rename examples/data/{ => geotiff}/aligned_rasters/Evaporation_ECMWF_ERA-Interim_mm_daily_2009.01.03.tif (100%) rename examples/data/{ => geotiff}/crop_aligned_folder/Evaporation_ECMWF_ERA-Interim_mm_daily_2009.01.01.tif (100%) rename examples/data/{ => geotiff}/crop_aligned_folder/Evaporation_ECMWF_ERA-Interim_mm_daily_2009.01.02.tif (100%) rename examples/data/{ => geotiff}/crop_aligned_folder/Evaporation_ECMWF_ERA-Interim_mm_daily_2009.01.03.tif (100%) rename examples/data/{raster-folder/0_MSWEP_1979.01.01.tif => geotiff/raster-folder/MSWEP_1979.01.01.tif} (100%) rename examples/data/{raster-folder/1_MSWEP_1979.01.02.tif => geotiff/raster-folder/MSWEP_1979.01.02.tif} (100%) rename examples/data/{raster-folder/2_MSWEP_1979.01.03.tif => geotiff/raster-folder/MSWEP_1979.01.03.tif} (100%) rename examples/data/{raster-folder/3_MSWEP_1979.01.04.tif => geotiff/raster-folder/MSWEP_1979.01.04.tif} (100%) rename examples/data/{raster-folder/4_MSWEP_1979.01.05.tif => geotiff/raster-folder/MSWEP_1979.01.05.tif} (100%) rename examples/data/{raster-folder/5_MSWEP_1979.01.06.tif => geotiff/raster-folder/MSWEP_1979.01.06.tif} (100%) create mode 100644 examples/data/geotiff/rhine/Qtot_1979-01-01.tif create mode 100644 examples/data/geotiff/rhine/Qtot_1979-01-02.tif create mode 100644 examples/data/geotiff/rhine/Qtot_1979-01-03.tif create mode 100644 examples/data/geotiff/rhine/Qtot_1979-01-04.tif create mode 100644 examples/data/geotiff/rhine/Qtot_1979-01-05.tif create mode 100644 examples/data/geotiff/rhine/Qtot_1979-01-06.tif create mode 100644 examples/data/geotiff/rhine/Qtot_1979-01-07.tif create mode 100644 examples/data/geotiff/rhine/Qtot_1979-01-08.tif create mode 100644 examples/data/geotiff/rhine/Qtot_1979-01-09.tif create mode 100644 examples/data/geotiff/rhine/Qtot_1979-01-10.tif delete mode 100644 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tests/data/{ => ascii}/asci_example.prj (100%) rename tests/data/{ => ascii}/asci_without_projection.asc (100%) rename tests/data/{ => ascii}/ascii-folder/0_MSWEP_1979.01.01.tif.asc (100%) rename tests/data/{ => ascii}/ascii-folder/1_MSWEP_1979.01.02.tif.asc (100%) rename tests/data/{ => ascii}/ascii-folder/2_MSWEP_1979.01.03.tif.asc (100%) rename tests/data/{ => ascii}/ascii-folder/3_MSWEP_1979.01.04.tif.asc (100%) rename tests/data/{ => ascii}/ascii-folder/4_MSWEP_1979.01.05.tif.asc (100%) rename tests/data/{ => ascii}/ascii-folder/5_MSWEP_1979.01.06.tif.asc (100%) create mode 100644 tests/data/coello_irregular_polygon.geojson create mode 100644 tests/data/coello_polygons.geojson delete mode 100644 tests/data/extract_extent_era5_land_monthly_averaged_data.tif rename tests/data/{ => geotiff}/aligned_rasters/Evaporation_ECMWF_ERA-Interim_mm_daily_2009.01.01.tif (100%) rename tests/data/{ => geotiff}/aligned_rasters/Evaporation_ECMWF_ERA-Interim_mm_daily_2009.01.02.tif (100%) rename tests/data/{ => geotiff}/aligned_rasters/Evaporation_ECMWF_ERA-Interim_mm_daily_2009.01.03.tif (100%) create mode 100644 tests/data/geotiff/coello-with-color-table.tif create mode 100644 tests/data/geotiff/coello-with-color-table.tif.aux.xml create mode 100644 tests/data/geotiff/coello-without-color-table.tif create mode 100644 tests/data/geotiff/era5-mask.geojson create mode 100644 tests/data/geotiff/era5_land_monthly_averaged.geojson create mode 100644 tests/data/geotiff/era5_land_monthly_averaged.tif rename tests/data/{ => geotiff}/merge/merged_raster.tif (100%) rename tests/data/{ => geotiff}/merge/splitted-raster0-0.tif (100%) rename tests/data/{ => geotiff}/merge/splitted-raster0-1.tif (100%) rename tests/data/{ => geotiff}/merge/splitted-raster0-2.tif (100%) rename tests/data/{ => geotiff}/merge/splitted-raster0-3.tif (100%) rename tests/data/{ => geotiff}/merge/splitted-raster1-0.tif (100%) rename tests/data/{ => geotiff}/merge/splitted-raster1-1.tif (100%) rename tests/data/{ 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tests/data/{raster-folder/2_MSWEP_1979.01.03.tif => geotiff/raster-folder/MSWEP_1979.01.03.tif} (100%) rename tests/data/{raster-folder/3_MSWEP_1979.01.04.tif => geotiff/raster-folder/MSWEP_1979.01.04.tif} (100%) rename tests/data/{raster-folder/4_MSWEP_1979.01.05.tif => geotiff/raster-folder/MSWEP_1979.01.05.tif} (100%) rename tests/data/{raster-folder/5_MSWEP_1979.01.06.tif => geotiff/raster-folder/MSWEP_1979.01.06.tif} (100%) create mode 100644 tests/data/geotiff/raster_to_df_full_of_data.tif create mode 100644 tests/data/geotiff/raster_to_gdf_with_cropped_cells.tif create mode 100644 tests/data/geotiff/rhine/0_Rainfall_MSWEP_mm_daily_1979_1_1.tif create mode 100644 tests/data/geotiff/rhine/1_Rainfall_MSWEP_mm_daily_1979_1_2.tif create mode 100644 tests/data/geotiff/rhine/2_Rainfall_MSWEP_mm_daily_1979_1_3.tif delete mode 100644 tests/data/raster_to_df.tif rename tests/data/{ => virtual-file-system}/multiple_compressed_files.gz (100%) rename tests/data/{ => virtual-file-system}/multiple_compressed_files.tar (100%) rename tests/data/{ => virtual-file-system}/multiple_compressed_files.zip (100%) rename tests/data/{ => virtual-file-system}/one_compressed_file.gz (100%) rename tests/data/{ => virtual-file-system}/one_compressed_file.tar (100%) rename tests/data/{ => virtual-file-system}/one_compressed_file.zip (100%) diff --git a/.github/pull_request_template.md b/.github/pull_request_template.md index 115d41a27..5bcec2da2 100644 --- a/.github/pull_request_template.md +++ b/.github/pull_request_template.md @@ -26,9 +26,10 @@ Check relevant points. # Checklist: -- [ ] updated version number in setup.py/pyproject.toml -- [ ] updated environment.yml and the lock file -- [ ] added changes to History.rst -- [ ] updated the latest version in README file -- [ ] I have added tests that prove my fix is effective or that my feature works -- [ ] New and existing unit tests pass locally with my changes +- [ ] updated version number in setup.py/pyproject.toml. +- [ ] updated environment.yml and the lock file. +- [ ] added changes to History.rst. +- [ ] updated the latest version in README file. +- [ ] I have added tests that prove my fix is effective or that my feature works. +- [ ] New and existing unit tests pass locally with my changes. +- [ ] documentation are updated. diff --git a/.github/workflows/conda-deployment.yml b/.github/workflows/conda-deployment.yml new file mode 100644 index 000000000..91ff54df6 --- /dev/null +++ b/.github/workflows/conda-deployment.yml @@ -0,0 +1,87 @@ +name: Conda +on: [push] +jobs: + Main-package: + runs-on: ${{ matrix.os }} + strategy: + matrix: + os: [ubuntu-latest] + python-version: ["3.11"] + env: + OS: ${{ matrix.os }} + + steps: + - uses: actions/checkout@v3 + - uses: conda-incubator/setup-miniconda@v2 + with: + mamba-version: "*" + #use-mamba: true + auto-update-conda: false + environment-file: environment.yml + auto-activate-base: false + activate-environment: test + python-version: ${{ matrix.python-version }} + channels: conda-forge,defaults + channel-priority: true + show-channel-urls: true + - name: Set up Python ${{ matrix.python-version }} + uses: actions/setup-python@v4 + with: + python-version: ${{ matrix.python-version }} + architecture: x64 + + - name: Install dev-dependencies + run: | + python -m pip install -r requirements-dev.txt + + - name: Generate coverage report + shell: bash -el {0} + run: | + conda info + conda list + conda config --show-sources + conda config --show + pytest -sv -m "not plot" + + Optional-packages: + runs-on: ${{ matrix.os }} + strategy: + matrix: + os: [ubuntu-latest] + python-version: ["3.11"] + env: + OS: ${{ matrix.os }} + + steps: + - uses: actions/checkout@v3 + - uses: conda-incubator/setup-miniconda@v2 + with: + mamba-version: "*" + #use-mamba: true + auto-update-conda: false + environment-file: environment-optional-packages.yml + auto-activate-base: false + activate-environment: test + python-version: ${{ matrix.python-version }} + channels: conda-forge,defaults + channel-priority: true + show-channel-urls: true + + - name: Set up Python ${{ matrix.python-version }} + uses: actions/setup-python@v4 + with: + python-version: ${{ matrix.python-version }} + architecture: x64 + + - name: Install dev-dependencies + run: | + python -m pip install -r requirements-dev.txt + + - name: Generate coverage report + shell: bash -el {0} + run: | + conda info + conda list + conda config --show-sources + conda config --show + pytest -sv diff --git a/.github/workflows/pypi-deployment.yml b/.github/workflows/pypi-deployment.yml new file mode 100644 index 000000000..433a5001d --- /dev/null +++ b/.github/workflows/pypi-deployment.yml @@ -0,0 +1,90 @@ +name: Pypi +on: [push] +jobs: + Main-Package: + runs-on: ${{ matrix.os }} + strategy: + matrix: + os: [ubuntu-latest] + python-version: ["3.9", "3.10", "3.11"] + env: + OS: ${{ matrix.os }} + + steps: + - uses: actions/checkout@v3 + - name: Set up Python ${{ matrix.python-version }} + uses: actions/setup-python@v4 + with: + python-version: ${{ matrix.python-version }} + architecture: x64 + + - name: Install GDAL + run: | + python -m pip install --upgrade pip + pip install --no-cache-dir Cython + pip install --find-links=https://girder.github.io/large_image_wheels --no-cache GDAL==3.7.1 + + - name: Test GDAL installation + run: | + python -c "from osgeo import gdal" + gdalinfo --version + + - name: Install dependencies + run: | + pip install -r requirements.txt -r requirements-dev.txt + python setup.py install + + - name: Generate coverage report + run: | + conda info + conda list + conda config --show-sources + conda config --show + python -m pytest -sv -m "not plot" + + - name: Upload coverage reports to Codecov with GitHub Action + uses: codecov/codecov-action@v3 + + Optional-packages: + runs-on: ${{ matrix.os }} + strategy: + matrix: + os: [ ubuntu-latest] + python-version: ["3.9", "3.10", "3.11"] + env: + OS: ${{ matrix.os }} + + steps: + - uses: actions/checkout@v3 + - name: Set up Python ${{ matrix.python-version }} + uses: actions/setup-python@v4 + with: + python-version: ${{ matrix.python-version }} + architecture: x64 + + - name: Install GDAL + run: | + python -m pip install --upgrade pip + pip install --no-cache-dir Cython + pip install --find-links=https://girder.github.io/large_image_wheels --no-cache GDAL==3.7.1 + + - name: Test GDAL installation + run: | + python -c "from osgeo import gdal" + gdalinfo --version + + - name: Install dependencies + run: | + pip install -r requirements-optional-packages.txt -r requirements-dev.txt + python setup.py install + + - name: Generate coverage report + run: | + conda info + conda list + conda config --show-sources + conda config --show + python -m pytest -v --cov=pyramids --cov-report=xml + + - name: Upload coverage reports to Codecov with GitHub Action + uses: codecov/codecov-action@v3 diff --git a/.github/workflows/pypi.yml b/.github/workflows/pypi-release.yml similarity index 100% rename from .github/workflows/pypi.yml rename to .github/workflows/pypi-release.yml diff --git a/.github/workflows/ubuntu-optional-packages.yml b/.github/workflows/ubuntu-optional-packages.yml deleted file mode 100644 index ed06e555b..000000000 --- a/.github/workflows/ubuntu-optional-packages.yml +++ /dev/null @@ -1,54 +0,0 @@ -name: optional-packages -on: - push: - branches: - - main - pull_request: - branches: - - main -jobs: - Run: - runs-on: ${{ matrix.os }} - strategy: - matrix: - os: [ ubuntu-latest] - python-version: ["3.9", "3.10", "3.11"] - env: - OS: ${{ matrix.os }} - - steps: - - uses: actions/checkout@v3 - - name: Set up Python ${{ matrix.python-version }} - uses: actions/setup-python@v4 - with: - python-version: ${{ matrix.python-version }} - architecture: x64 - - - name: Install GDAL - run: | - python -m pip install --upgrade pip - pip install --no-cache-dir Cython - pip install --find-links=https://girder.github.io/large_image_wheels --no-cache GDAL - - - name: Test GDAL installation - run: | - python -c "from osgeo import gdal" - gdalinfo --version - - - name: Test cleopatra - run: | - pip install cleopatra - python -c "from cleopatra.array import Array" - python -c "import matplotlib.pyplot as plt" - - - name: Install dependencies - run: | - pip install -r requirements-optional-packages.txt -r requirements-dev.txt - python setup.py install - - - name: Generate coverage report - run: | - python -m pytest -v --cov=pyramids --cov-report=xml - - - name: Upload coverage reports to Codecov with GitHub Action - uses: codecov/codecov-action@v3 diff --git a/.github/workflows/ubuntu.yml b/.github/workflows/ubuntu.yml deleted file mode 100644 index 4c3744118..000000000 --- a/.github/workflows/ubuntu.yml +++ /dev/null @@ -1,48 +0,0 @@ -wname: Main-package -on: - push: - branches: - - main - pull_request: - branches: - - main -jobs: - Run: - runs-on: ${{ matrix.os }} - strategy: - matrix: - os: [ ubuntu-latest] - python-version: ["3.9", "3.10", "3.11"] - env: - OS: ${{ matrix.os }} - - steps: - - uses: actions/checkout@v3 - - name: Set up Python ${{ matrix.python-version }} - uses: actions/setup-python@v4 - with: - python-version: ${{ matrix.python-version }} - architecture: x64 - - - name: Install GDAL - run: | - python -m pip install --upgrade pip - pip install --no-cache-dir Cython - pip install --find-links=https://girder.github.io/large_image_wheels --no-cache GDAL - - - name: Test GDAL installation - run: | - python -c "from osgeo import gdal" - gdalinfo --version - - - name: Install dependencies - run: | - pip install -r requirements.txt -r requirements-dev.txt - python setup.py install - - - name: Generate coverage report - run: | - python -m pytest -sv -m "not plot" - - - name: Upload coverage reports to Codecov with GitHub Action - uses: codecov/codecov-action@v3 diff --git a/.gitignore b/.gitignore index 04493a451..b4bac65b0 100644 --- a/.gitignore +++ b/.gitignore @@ -160,3 +160,5 @@ examples/coords.csv examples/monthly_precipitation_amount_1hour_Accumulation_2022.05.01.00.00.00.tif tests/data/raster-folder-coello/* examples/old/* +tests/data/not-used/* +*.xml diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index ab39ed093..ebb2c7977 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -1,7 +1,7 @@ fail_fast: true repos: - repo: https://github.com/pre-commit/pre-commit-hooks - rev: v4.1.0 + rev: v4.4.0 hooks: - id: end-of-file-fixer name: "[py - check] validate yaml" @@ -51,14 +51,14 @@ repos: # args: [ -i, --wrap-summaries, "0" ] - repo: https://github.com/PyCQA/pydocstyle - rev: 6.1.1 + rev: 6.3.0 hooks: - id: pydocstyle name: "[py - check] pydocstyle" files: ^Hapi/ - repo: https://github.com/pycqa/flake8 - rev: 6.0.0 + rev: 6.1.0 hooks: - id: flake8 name: "[py - check] flake8" @@ -70,7 +70,7 @@ repos: # hooks: # - id: black - repo: https://github.com/ambv/black - rev: 22.8.0 + rev: 23.7.0 hooks: - id: black name: "[py - format] black" @@ -83,7 +83,7 @@ repos: # pre-commit-shell: Checks shell scripts against shellcheck. - repo: https://github.com/detailyang/pre-commit-shell - rev: v1.0.6 + rev: 1.0.5 hooks: - id: shell-lint name: "[bash - lint] shell-lint" diff --git a/HISTORY.rst b/HISTORY.rst index 8f267d871..aa5e8d195 100644 --- a/HISTORY.rst +++ b/HISTORY.rst @@ -79,3 +79,40 @@ History * fix bug in passing ot not passing band index in case of multi band rasters * change the bounds in to_dataset method to total_bounds tp get the bbox of the whole geometries in the gdf * add convert_longitude method to convert longitude to range between -180 and 180 + +0.5.0 (2023-10-01) +------------------ +Dataset +""""""" +* The dtype attribure is not initialized in the __init__, but initialized when using the dtype property. +* Create band_names setter method. +* add gdal_dtype, numpy_dtype, and dtype arrtibute to change between the dtype (data type general name), and the coresponding data type in numpy and gdal. +* Create color_table attribure, getter & setter property to set a symbology (assign color to different values in each band). +* The read_array method returns array with the same type as the dtype of the first band in the raster. +* add a setter method to the band_names property. +* The methods (create_driver_from_scratch, get_band_names) is converted to private method. +* The no_data_value check the dtype before setting any value. +* The convert_longitude used the gdal.Wrap method instead of making the calculation step by step. +* The to_polygon is converted to _band_to_polygon private method used in the clusters method. +* The to_geodataframe is converted to to_feature_collection. change the backend of the function to use the crop function is a vector mask is given. +* the to_crs takes an extra parameter "inplace". +* The locate_points is converted to map_to_array_coordinates. +* Create array_to_map_coordinates to translate the array indices into real map coordinates. + +DataCube +"""""""" +* rename the read_separate_files to read_multiple_files, and enable it to use regex strigs to filter files in a given directory. +* rename read_dataset to open_datacube. +* rename the data attribute to values + +FeatureCollection +""""""""""""""""" +* Add a pivot_point attribure to return the top left corner/first coordinates of the polygon. +* Add a layers_count property to return the number of layers in the file. +* Add a layer_names property to return the layers names. +* Add a column property to return column names. +* Add the file_name property to store the file name. +* Add the dtypes property to retrieve the data types of the columns in the file. +* Rename bounds to total_bounds. +* The _gdf_to_ds can convert the GeoDataFrame to a ogr.DataSource and to a gdal.Dataset. +* The create_point method returns a shapely point object or a GeoDataFrame if an epsg number is given. diff --git a/README.md b/README.md index a6361f0a6..48fd8a665 100644 --- a/README.md +++ b/README.md @@ -3,8 +3,6 @@ [![License: GPL v3](https://img.shields.io/badge/License-GPLv3-blue.svg)](https://www.gnu.org/licenses/gpl-3.0) [![pre-commit](https://img.shields.io/badge/pre--commit-enabled-brightgreen?logo=pre-commit&logoColor=white)](https://github.com/pre-commit/pre-commit) [![Language grade: Python](https://img.shields.io/lgtm/grade/python/g/MAfarrag/Hapi.svg?logo=lgtm&logoWidth=18)](https://lgtm.com/projects/g/MAfarrag/Hapi/context:python) -[![Documentation Status](https://readthedocs.org/projects/pyramids-gis/badge/?version=latest)](https://pyramids-gis.readthedocs.io/en/latest/?badge=latest) - ![GitHub last commit](https://img.shields.io/github/last-commit/MAfarrag/pyramids) ![GitHub forks](https://img.shields.io/github/forks/MAfarrag/pyramids?style=social) @@ -12,17 +10,15 @@ [![codecov](https://codecov.io/gh/Serapieum-of-alex/pyramids/branch/main/graph/badge.svg?token=g0DV4dCa8N)](https://codecov.io/gh/Serapieum-of-alex/pyramids) [![Codacy Badge](https://app.codacy.com/project/badge/Grade/5e3aa4d0acc843d1a91caf33545ecf03)](https://www.codacy.com/gh/Serapieum-of-alex/pyramids/dashboard?utm_source=github.com&utm_medium=referral&utm_content=Serapieum-of-alex/pyramids&utm_campaign=Badge_Grade) - -![GitHub commits since latest release (by SemVer including pre-releases)](https://img.shields. -io/github/commits-since/mafarrag/pyramids/0.4.2?include_prereleases&style=plastic) +![GitHub commits since latest release (by SemVer including pre-releases)](https://img.shields.io/github/commits-since/mafarrag/pyramids/0.5.0?include_prereleases&style=plastic) ![GitHub last commit](https://img.shields.io/github/last-commit/mafarrag/pyramids) Current release info ==================== -| Name | Downloads | Version | Platforms | -|----------------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| --- | -| [![Conda Recipe](https://img.shields.io/badge/recipe-pyramids-green.svg)](https://anaconda.org/conda-forge/pyramids) | [![Conda Downloads](https://img.shields.io/conda/dn/conda-forge/pyramids.svg)](https://anaconda.org/conda-forge/pyramids) [![Downloads](https://pepy.tech/badge/pyramids-gis)](https://pepy.tech/project/pyramids-gis) [![Downloads](https://pepy.tech/badge/pyramids-gis/month)](https://pepy.tech/project/pyramids-gis) [![Downloads](https://pepy.tech/badge/pyramids-gis/week)](https://pepy.tech/project/pyramids-gis) ![PyPI - Downloads](https://img.shields.io/pypi/dd/pyramids-gis?color=blue&style=flat-square) | [![Conda Version](https://img.shields.io/conda/vn/conda-forge/pyramids.svg)](https://anaconda.org/conda-forge/pyramids) [![PyPI version](https://badge.fury.io/py/pyramids-gis.svg)](https://badge.fury.io/py/pyramids-gis) | [![Conda Platforms](https://img.shields.io/conda/pn/conda-forge/pyramids.svg)](https://anaconda.org/conda-forge/pyramids) [![Join the chat at https://gitter.im/Hapi-Nile/Hapi](https://badges.gitter.im/Hapi-Nile/Hapi.svg)](https://gitter.im/Hapi-Nile/Hapi?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge) | +| Name | Downloads | Version | Platforms | +|----------------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| +| [![Conda Recipe](https://img.shields.io/badge/recipe-pyramids-green.svg)](https://anaconda.org/conda-forge/pyramids) | [![Conda Downloads](https://img.shields.io/conda/dn/conda-forge/pyramids.svg)](https://anaconda.org/conda-forge/pyramids) [![Downloads](https://pepy.tech/badge/pyramids-gis)](https://pepy.tech/project/pyramids-gis) [![Downloads](https://pepy.tech/badge/pyramids-gis/month)](https://pepy.tech/project/pyramids-gis) [![Downloads](https://pepy.tech/badge/pyramids-gis/week)](https://pepy.tech/project/pyramids-gis) ![PyPI - Downloads](https://img.shields.io/pypi/dd/pyramids-gis?color=blue&style=flat-square) | [![Conda Version](https://img.shields.io/conda/vn/conda-forge/pyramids.svg)](https://anaconda.org/conda-forge/pyramids) [![PyPI version](https://badge.fury.io/py/pyramids-gis.svg)](https://badge.fury.io/py/pyramids-gis) | [![Conda Platforms](https://img.shields.io/conda/pn/conda-forge/pyramids.svg)](https://anaconda.org/conda-forge/pyramids) [![Join the chat at https://gitter.im/Hapi-Nile/Hapi](https://badges.gitter.im/Hapi-Nile/Hapi.svg)](https://gitter.im/Hapi-Nile/Hapi?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge) | pyramids - GIS utility package ===================================================================== @@ -34,16 +30,15 @@ pyramids Main Features ------------- - - GIS modules to enable the modeler to fully prepare the meteorological inputs and do all the preprocessing - needed to build the model (align rasters with the DEM), in addition to various methods to manipulate and - convert different forms of distributed data (rasters, NetCDF, shapefiles) +- GIS modules to enable the modeler to fully prepare the meteorological inputs and do all the preprocessing + needed to build the model (align rasters with the DEM), in addition to various methods to manipulate and + convert different forms of distributed data (rasters, NetCDF, shapefiles) Future work ------------- - - Developing a DEM processing module for generating the river network at different DEM spatial resolutions. - +- Developing a DEM processing module for generating the river network at different DEM spatial resolutions. Installing pyramids =============== @@ -51,7 +46,7 @@ Installing pyramids Installing `pyramids` from the `conda-forge` channel can be achieved by: ``` -conda install -c conda-forge pyramids=0.4.2 +conda install -c conda-forge pyramids=0.5.0 ``` It is possible to list all of the versions of `pyramids` available on your platform with: @@ -61,15 +56,19 @@ conda search pyramids --channel conda-forge ``` ## Install from Github + to install the last development to time you can install the library from github + ``` pip install git+https://github.com/Serapieum-of-alex/pyramids ``` ## pip + to install the last release you can easly use pip + ``` -pip install pyramids-gis==0.4.2 +pip install pyramids-gis==0.5.0 ``` Quick start @@ -82,4 +81,5 @@ Quick start [other code samples](https://pyramids-gis.readthedocs.io/en/latest/?badge=latest) ## Coverage + [![codecov](https://codecov.io/gh/Serapieum-of-alex/pyramids/branch/main/graphs/sunburst.svg?token=g0DV4dCa8N)](https://codecov.io/gh/Serapieum-of-alex/pyramids) diff --git a/docs/dataCube.rst b/docs/dataCube.rst deleted file mode 100644 index 9d8d32c3d..000000000 --- a/docs/dataCube.rst +++ /dev/null @@ -1,133 +0,0 @@ -####### -dataset -####### - -- dataset module contains Two classes `Dataset` and `DataCube`. - -.. digraph:: Linking - - dataset -> Dataset; - dataset -> DataCube; - dpi=200; - -- Dataset represent a raster object which could be created from reading a geotiff, netcdf, ascii or any file - format/driver supported by gdal. -- The raster could have single or multi bands. -- The raster could have different variables (like netcdf file) and these variable can have similar or different - dimensions. - -- DataCube represent a stack of rasters which have the same dimensions, contains data that have same dimensions (rows - & columns). - - -******* -Dataset -******* - -.. image:: /images/dataset.svg - :width: 200pt - -The dataset object has some attributes and methods to help - -- To import the raster module - -.. code:: py - - from pyramids.dataset import Dataset - - -- The main parameter for most of the functions in the `raster` module is a `gdal.Dataset` - -.. code:: py - - raster_path = "examples/data/acc4000.tif" - src = gdal.Open(RasterAPath) - fig, ax = Map.plot(src, title="Flow Accumulation") - -.. image:: /images/flow_accumulation.png - :width: 500pt - -.. note:: - - * change the directory of your code to point at the repository root directory to be able to read the raster files - * the visualization in this documentatin uses digitalearth package to install it `Digital-Earth`_ - -- The module contains function that falls in one of the following categories. - -#. `Raster Data`_ -#. `Raster Operations`_ -#. `Raster Dataset`_ - - -******** -DataCube -******** - - -crop -==== - -- `cropAlignedFolder`_ matches the location of nodata value from src raster to dst raster, Mask is where the - nodatavalue will be taken and the location of this value src_dir is path to the folder where rasters exist where we - need to put the NoDataValue of the mask in RasterB at the same locations. - -Parameters ----------- - src_dir : [String] - path of the folder of the rasters you want to set Nodata Value on the same location of NodataValue of Raster A, - the folder should not have any other files except the rasters - mask : [String/gdal.Dataset] - path/gdal.Dataset of the mask raster to crop the rasters (to get the NoData value and it location in the array) - Mask should include the name of the raster and the extension like "data/dem.tif", or you can read the mask raster - using gdal and use is the first parameter to the function. - saveto : [String] - path where new rasters are going to be saved with exact same old names - -Returns -------- - new rasters have the values from rasters in B_input_path with the NoDataValue in the same - locations like raster A - -.. code:: py - - # The folder should contain tif files only (check example here `cropAlignedFolder`_) - saveto = "examples/data/crop_aligned_folder/" - Raster.cropAlignedFolder(aligned_raster_folder, src, saveto) - -**************** -Zonal Statistics -**************** - -one of the most frequent used function in geospatial analysis is zonal -statistics, where you overlay a shapefile contains some polygons with -some maps and you want each polygon to extract the values that locates -inside it from the map, `raster` module in `Hapi` contains a similar -function `OverlayMap` where you can convert the polygon shapefile into -a raster first and use it as a base map to overlay with other maps - -You don't need to copy and paste the code in this page you can find it -in the examples `Zonal Statistics `_. - - -OverlayMap Several maps -======================= -The `overlayMaps` function takes path to the folder where more than one map exist instead of a path to one file, it also takes an extra parameter `FilePrefix`, this prefix is used to name the files in the given path and all the file has to start with the prefix - -.. code:: py - - FilePrefix = "Map" - # several maps - ExtractedValues, Cells = R.overlayMaps(Path+"data", BaseMapF, FilePrefix,ExcludedValue, Compressed,OccupiedCellsOnly) - -both methods `OverlayMap` and `overlayMaps` returns the values as a `dict`, the difference is in the number of cells `overlayMaps` returns a single integer number while `OverlayMap` returns a `dataframe` with two columns the first in the map name and the second is the number of occupied cell in each map. - -Save extracted values -===================== - -.. code:: py - - # save extracted values in different files - Polygons = list(ExtractedValues.keys()) - for i in range(len(Polygons)): - np.savetxt(SavePath +"/" + str(Polygons[i]) + ".txt", - ExtractedValues[Polygons[i]],fmt="%4.2f") diff --git a/docs/datacube.rst b/docs/datacube.rst new file mode 100644 index 000000000..fbabfcbec --- /dev/null +++ b/docs/datacube.rst @@ -0,0 +1,205 @@ +######## +DataCube +######## + +- `DataCube` class contains is made to operate in multiple single files . + +- DataCube represent a stack of rasters which have the same dimensions, contains data that have same dimensions (rows + & columns). + +.. image:: /images/datacube/logo.png + :width: 700pt + :align: center + +The datacube object has some attributes and methods to help working with multiple rasters files, or to repeat thesame +operation on multiple rasters. + +- To import the raster module + +.. code:: py + + from pyramids.dataset import Datacube + +- The detailed module attributes and methods are summarized in the following figure. + +.. image:: /images/datacube/detailed.png + :width: 700pt + :align: center + +********** +Attributes +********** + +The `DataCube` object will have the following attributes + +#. base: Dataset object +#. columns: number of columns in the dataset. +#. rows: number of rows in the dataset. +#. time_length: number of files/considering the each file represent a timestamp. +#. shape: (time_length, rows, columns). +#. files: file that have been read. + +.. image:: /images/datacube/attributes.png + :width: 150pt + :align: center + + +******* +Methods +******* + + +=================== +read_multiple_files +=================== + +- `read_multiple_files` parse files in a directory and construct the array with the dimension of the first reads + rasters from a folder and creates a 3d array with the same 2d dimensions of the first raster in the folder and length + as the number of files. + +inside the folder. + - All rasters should have the same dimensions + - If you want to read the rasters with a certain order, then all raster file names should have a date that follows + the same format (YYYY.MM .DD / YYYY-MM-DD or YYYY_MM_DD) (i.e. "MSWEP_1979.01.01.tif"). + +.. note:: + + `read_multiple_files` only parse the files names' in the given directory, to open each raster and read a specific, + band from each raster and add it to the `DataCube` you have to do one step further using the `open_datacube`_ method. + +Parameters +---------- +path:[str/list] + path of the folder that contains all the rasters, ora list contains the paths of the rasters to read. +with_order: [bool] + True if the rasters names' follows a certain order, then the rasters names should have a date that follows + the same format (YYYY.MM.DD / YYYY-MM-DD or YYYY_MM_DD). + >>> "MSWEP_1979.01.01.tif" + >>> "MSWEP_1979.01.02.tif" + >>> ... + >>> "MSWEP_1979.01.20.tif" +regex_string: [str] + a regex string that we can use to locate the date in the file names.Default is r"\d{4}.\d{2}.\d{2}". + >>> fname = "MSWEP_YYYY.MM.DD.tif" + >>> regex_string = r"\d{4}.\d{2}.\d{2}" + - or + >>> fname = "MSWEP_YYYY_M_D.tif" + >>> regex_string = r"\d{4}_\d{1}_\d{1}" + - if there is a number at the beginning of the name + >>> fname = "1_MSWEP_YYYY_M_D.tif" + >>> regex_string = r"\d+" +date: [bool] + True if the number in the file name is a date. Default is True. +file_name_data_fmt : [str] + if the files names' have a date and you want to read them ordered .Default is None + >>> "MSWEP_YYYY.MM.DD.tif" + >>> file_name_data_fmt = "%Y.%m.%d" +start: [str] + start date if you want to read the input raster for a specific period only and not all rasters, + if not given all rasters in the given path will be read. +end: [str] + end date if you want to read the input temperature for a specific period only, + if not given all rasters in the given path will be read. +fmt: [str] + format of the given date in the start/end parameter. +extension: [str] + the extension of the files you want to read from the given path. Default is ".tif". + +Cases +----- + +with_order = False +^^^^^^^^^^^^^^^^^^ +- if you want to make some mathematical operation on all the raster, then the order of the rasters does not matter. + +.. code:: py + + rasters_folder_path = "examples/data/geotiff/raster-folder" + datacube = Datacube.read_multiple_files(rasters_folder_path) + print(datacube) + >>> Files: 6 + >>> Cell size: 5000.0 + >>> EPSG: 4647 + >>> Dimension: 125 * 93 + >>> Mask: 2147483648.0 + +with_order = True +^^^^^^^^^^^^^^^^^ +- If the order in which each raster represent is important (each raster is represents a time stamp) +- To read the rasters with a certain order, each raster has to have a date in its file name, and using the format of + this name the method is going to read the file in right order. + +- the raster directory contents are files with a date in each file name + +.. code:: py + + >>> MSWEP_1979.01.01.tif + >>> MSWEP_1979.01.02.tif + >>> MSWEP_1979.01.03.tif + >>> MSWEP_1979.01.04.tif + >>> MSWEP_1979.01.05.tif + >>> MSWEP_1979.01.06.tif + + +.. code:: py + + rasters_folder_path = "examples/data/geotiff/raster-folder" + datacube = Datacube.read_multiple_files( + rasters_folder_path, regex_string=r"\d{4}.\d{2}.\d{2}", date=True, file_name_data_fmt="%Y.%m.%d", + ) + print(datacube) + >>> Files: 6 + >>> Cell size: 5000.0 + >>> EPSG: 4647 + >>> Dimension: 125 * 93 + >>> Mask: 2147483648.0 + +- the raster directory contents are files with a number in each file name + +.. code:: py + + >>> 0_MSWEP.tif + >>> 1_MSWEP.tif + >>> 2_MSWEP.tif + >>> 3_MSWEP.tif + >>> 4_MSWEP.tif + +.. code:: py + + rasters_folder_path = "tests/data/geotiff/rhine" + datacube = Datacube.read_multiple_files( + rasters_folder_path, with_order=True, regex_string=r"\d+", date=False, + ) + print(datacube) + >>> Files: 3 + >>> Cell size: 5000.0 + >>> EPSG: 4647 + >>> Dimension: 125 * 93 + >>> Mask: 2147483648.0 + +============= +open_datacube +============= + +- After using the `read_multiple_files` method to parse the files in the directory, you can read the values of a + specific band from each raster using the `open_datacube` method. + + +.. code:: py + + rasters_folder_path = "examples/data/geotiff/raster-folder" + datacube = Datacube.read_multiple_files(rasters_folder_path, file_name_data_fmt="%Y.%m.%d", separator=".") + dataset.open_datacube() + print(dataset.values.shape) + >>> (6, 125, 93) + + +=========== +create_cube +=========== +- Create a `DataCube` object. + +=========== +Propteties +=========== +- update the data in the `DataCube` object diff --git a/docs/dataset.rst b/docs/dataset.rst index edba6eee5..e1a292ff0 100644 --- a/docs/dataset.rst +++ b/docs/dataset.rst @@ -350,6 +350,23 @@ driver_type print(dataset.driver_type) >>> 'geotiff' +color_table +----------- +.. code:: py + + print(dataset.color_table) + >>> band values red green blue alpha + >>> 0 1 0 0 0 0 0 + >>> 1 1 1 112 153 89 255 + >>> 2 1 2 0 0 0 0 + >>> 3 1 3 242 238 162 255 + >>> 4 1 4 0 0 0 0 + >>> 5 1 5 242 206 133 255 + >>> 6 1 6 0 0 0 0 + >>> 7 1 7 194 140 124 255 + >>> 8 1 8 0 0 0 0 + >>> 9 1 9 214 193 156 255 + Create Dataset object ===================== @@ -688,18 +705,16 @@ to_crs Parameters ^^^^^^^^^^ - src: [gdal object] - gdal dataset (src=gdal.Open("dem.tif")) to_epsg: [integer] reference number to the new projection (https://epsg.io/) (default 3857 the reference no of WGS84 web mercator ) - resample_technique: [String] + method: [String] resampling technique default is "Nearest" https://gisgeography.com/raster-resampling/ - "Nearest" for nearest neighbour,"cubic" for cubic convolution, + "nearest neibour" for nearest neighbour,"cubic" for cubic convolution, "bilinear" for bilinear - option : [1 or 2] - option 2 uses the gda.wrap function, option 1 uses the gda.ReprojectImage function + maintain_alighment : [bool] + True to maintain the number of rows and columns of the raster the same after reprojection. Default is False. Returns ^^^^^^^ @@ -1169,6 +1184,65 @@ Returns 5 6 487292.5152 478045.5720 10.0 13.0 +Plotting +======== + +color_table +----------- + +- The `color_table` property in the `Dataset` object can assign a certain symbology to each band in the raster. +- To assign a certain symbology you have to have to create a `Dataframe` containing the values and coresponding + colors (hexadecimal number) for each band in the raster. +- assigning a color_table to the raster file will help when opening the file in GIS software like QGIS or ArcGIS, + the raster will be displayed with the colors you assigned to it.without the need to assign the colors manually. + + +.. code:: py + + print(df) + band values color + >>> 0 1 1 #709959 + >>> 1 1 2 #F2EEA2 + >>> 2 1 3 #F2CE85 + >>> 3 2 1 #C28C7C + >>> 4 2 2 #D6C19C + >>> 5 2 3 #D6C19C + +- Assign the DataFrame to the `color_table` property. + +.. code:: py + + dataset.color_table = df + + +- When saving the raster to disk, the following file will be created along side the raster file. + + +.. code:: xml + + + + Palette + + + + + + + + + + + + + + + +.. note:: + + - The values in the `values` column in the `DataFrame` should cover the entir range of values in the raster. + - Any value that is not in the `values` column will not be assigned any color. + ********** References ********** diff --git a/docs/images/datacube/attributes.png b/docs/images/datacube/attributes.png new file mode 100644 index 0000000000000000000000000000000000000000..2f4c30c2d16d7937cb1b7b0923431526f3c691bc GIT binary patch literal 54665 zcmeFZ1z6Nw+b^o3U?F1h2oj2jfPjE>gMdg%H_|-_%+MhgD$+61Al(lwLyU@a=Kw<} zHNX(kFfee|Ao%#bU+(jr{ax?g=i&0gnN|PwUw8lRd+|nDQHJz1<>@0wj*!YexUX{L z$g!RyM~D(n5`z}%neSoXzoSknG7?Af+OEwUIdX*;Dy0Rrb2YcJH9Nw2i7G{uxg%s@Ft!!&Y+O8``L^5xH3fMFPD$|D*2=~V{3l~(Vq;I3 zV(Mt*!e(U;hI4YTakBA&CP_<2dpj^lnuCLr?JgTP4+l3J2M;&s`RgU6Iq!kdg#D=+ znHxD;{q{J*`#YOCI$43k*gu-Xg$W&IP$P?h{F!=NMBnXDbu16HW#$DTce^ppWp6OVZTJ$imUc4t#+aft~I@MH!qSE62|jtPT#I zTapK=^vJ%r!E`N5kLwq@$} 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+dependencies +************ + +Required dependencies +===================== + +- Python (3.9 or later) +- `numpy `__ (1.21 or later) +- `GDAL `__ (3.6.2 or later) +- `pandas `__ (2 or later) +- `geopandas `__ (0.12.2 or later) +- `Shapely `__ (1.8.4 or later) +- `pyproj `__ (3.4 or later) +- `PyYAML `__ (6.0 or later) + +.. _optional-dependencies: +Optional dependencies +===================== +- cleopatra (0.3.4 or later) Stable release --------------- +************** Please install ``pyramids`` in a Virtual environment so that its requirements don't tamper with your system's python. conda ------ -the easiest way to install ``pyramids`` is using ``conda`` package manager. ``pyramids`` is available in the +************** +The easiest way to install ``pyramids`` is using ``conda`` package manager. ``pyramids`` is available in the `conda-forge `_ channel. To install you can use the following command: @@ -24,12 +41,12 @@ and you skip the rest of the installation instructions. Installing Python and gdal dependencies ---------------------------------------- +*************************************** The main dependencies for pyramids are an installation of Python 3.9+, and gdal Installing Python ------------------ +***************** For Python we recommend using the Anaconda Distribution for Python 3, which is available for download from https://www.anaconda.com/download/. The installer gives the option to @@ -41,7 +58,7 @@ Note that there is no hard requirement specifically for Anaconda's Python, but o makes installation of required dependencies easier using the conda package manager. Install as a conda environment ------------------------------- +****************************** The easiest and most robust way to install Hapi is by installing it in a separate conda environment. In the root repository directory there is an ``environment.yml`` file. @@ -65,7 +82,7 @@ or from Github). To install a release of Hapi from the PyPI (available from rele From sources ------------- +************ The sources for HapiSM can be downloaded from the `Github repo`_. @@ -126,7 +143,7 @@ a zip archive from GitHub: https://github.com/MAfarrag/pyramids/archive/master.z `libraries.io `_. Install using pip ------------------ +***************** Besides the recommended conda environment setup described above, you can also install Hapi with ``pip``. For the more difficult to install Python dependencies, it is best to @@ -144,7 +161,7 @@ Then install a release {release} of pyramids (available from release 2018.1) wit Check if the installation is successful ---------------------------------------- +*************************************** To check it the install is successful, go to the examples directory and run the following command: diff --git a/docs/pyramids.drawio b/docs/pyramids.drawio index 1fad1525a..35ba1cbbb 100644 --- a/docs/pyramids.drawio +++ b/docs/pyramids.drawio @@ -1,6 +1,6 @@ - - - + + + @@ -280,7 +280,7 @@ - + @@ -289,7 +289,7 @@ - + @@ -521,8 +521,8 @@ - - + + @@ -1063,7 +1063,7 @@ - + @@ -1226,7 +1226,7 @@ - + @@ -1489,12 +1489,9 @@ - + - - - @@ -1553,7 +1550,7 @@ - + @@ -1572,9 +1569,12 @@ - + + + + @@ -1809,4 +1809,262 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/environment-optional-packages.yml b/environment-optional-packages.yml index 33cfe69ba..fe5f857a7 100644 --- a/environment-optional-packages.yml +++ b/environment-optional-packages.yml @@ -1,17 +1,17 @@ channels: - conda-forge dependencies: - - python >=3.9,<3.11 - - numpy >=1.24.1 - - hpc >=0.1.1 - - pip >=22.3.1 - - gdal >=3.6.2 + - python >=3.11 + - numpy >=1.25.2 + - hpc >=0.1.2 + - pip >=23.2.1 + - gdal >=3.7.0, <3.7.2 - pandas >=2.0.0 - - geopandas >=0.12.2 - - Shapely >=1.8.4,<2 - - pyproj >=3.4.0 - - cleopatra >=0.3.4 - - PyYAML >=6.0 - - loguru >=0.6.0 - - pytest >=7.2.0 - - pytest-cov >=4.0.0 + - geopandas >=0.13.2 + - Shapely >=2.0.1 + - pyproj >=3.6.0 + - cleopatra >=0.4.0 + - PyYAML >=6.0.1 + - loguru >=0.7.0 + - pytest >=7.4.0 + - pytest-cov >=4.1.0 diff --git a/environment.yml b/environment.yml index 657a4e619..e77500390 100644 --- a/environment.yml +++ b/environment.yml @@ -1,16 +1,16 @@ channels: - conda-forge dependencies: - - python >=3.9,<3.11 - - numpy >=1.24.1 - - hpc >=0.1.1 - - pip >=22.3.1 - - gdal >=3.6.2 + - python >=3.11 + - numpy >=1.25.2 + - hpc >=0.1.2 + - pip >=23.2.1 + - gdal >=3.7.0, <3.7.2 - pandas >=2.0.0 - - geopandas >=0.12.2 - - Shapely >=1.8.4,<2 - - pyproj >=3.4.0 - - PyYAML >=6.0 - - loguru >=0.6.0 - - pytest >=7.2.0 - - pytest-cov >=4.0.0 + - geopandas >=0.13.2 + - Shapely >=2.0.1 + - pyproj >=3.6.0 + - PyYAML >=6.0.1 + - loguru >=0.7.0 + - pytest >=7.4.0 + - pytest-cov >=4.1.0 diff --git a/examples/data-cube-examples.py b/examples/data-cube-examples.py deleted file mode 100644 index 0897077c6..000000000 --- a/examples/data-cube-examples.py +++ /dev/null @@ -1,13 +0,0 @@ -import matplotlib - -matplotlib.use("TkAgg") -import numpy as np -from pyramids.dataset import Datacube - -path = "tests/data/raster-folder-coello" -#%% -cube = Datacube.read_separate_files(path) -cube.read_dataset() -# arr = cube.data -# np.save(f"{path}/coello.npy",arr) -cube.plot() diff --git a/examples/data/aligned_rasters/Evaporation_ECMWF_ERA-Interim_mm_daily_2009.01.01.tif b/examples/data/geotiff/aligned_rasters/Evaporation_ECMWF_ERA-Interim_mm_daily_2009.01.01.tif similarity index 100% rename from examples/data/aligned_rasters/Evaporation_ECMWF_ERA-Interim_mm_daily_2009.01.01.tif rename to examples/data/geotiff/aligned_rasters/Evaporation_ECMWF_ERA-Interim_mm_daily_2009.01.01.tif diff --git a/examples/data/aligned_rasters/Evaporation_ECMWF_ERA-Interim_mm_daily_2009.01.02.tif b/examples/data/geotiff/aligned_rasters/Evaporation_ECMWF_ERA-Interim_mm_daily_2009.01.02.tif similarity index 100% rename from examples/data/aligned_rasters/Evaporation_ECMWF_ERA-Interim_mm_daily_2009.01.02.tif rename to examples/data/geotiff/aligned_rasters/Evaporation_ECMWF_ERA-Interim_mm_daily_2009.01.02.tif diff --git a/examples/data/aligned_rasters/Evaporation_ECMWF_ERA-Interim_mm_daily_2009.01.03.tif b/examples/data/geotiff/aligned_rasters/Evaporation_ECMWF_ERA-Interim_mm_daily_2009.01.03.tif similarity index 100% rename from examples/data/aligned_rasters/Evaporation_ECMWF_ERA-Interim_mm_daily_2009.01.03.tif rename to examples/data/geotiff/aligned_rasters/Evaporation_ECMWF_ERA-Interim_mm_daily_2009.01.03.tif diff --git a/examples/data/crop_aligned_folder/Evaporation_ECMWF_ERA-Interim_mm_daily_2009.01.01.tif b/examples/data/geotiff/crop_aligned_folder/Evaporation_ECMWF_ERA-Interim_mm_daily_2009.01.01.tif similarity index 100% rename from examples/data/crop_aligned_folder/Evaporation_ECMWF_ERA-Interim_mm_daily_2009.01.01.tif rename to examples/data/geotiff/crop_aligned_folder/Evaporation_ECMWF_ERA-Interim_mm_daily_2009.01.01.tif diff --git 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = dataset.plot(\n", + " band=0, figsize=(10, 5), title=\"NOAA daily Precipitation 1979-01-01\", cbar_label=\"Raindall mm/day\", vmax=30,\n", + " cbar_length=0.85\n", + ")" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [ + "## use the `convert_longitude` to convert the longitude values to range between -180 and 180." + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 7, + "outputs": [], + "source": [ + "new_dataset = dataset.convert_longitude()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 8, + "outputs": [ + { + "data": { + "text/plain": "(

,\n )" + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "text/plain": "
", + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "new_dataset.plot(\n", + " band=0, figsize=(10, 5), title=\"NOAA daily Precipitation 1979-01-01\", cbar_label=\"Raindall mm/day\", vmax=30,\n", + " cbar_length=0.85\n", + ")" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 9, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Min lonitude: -179.75\n", + "Max lonitude: 179.75\n" + ] + } + ], + "source": [ + "print(f\"Min lonitude: {min(new_dataset.lon)}\")\n", + "print(f\"Max lonitude: {max(new_dataset.lon)}\")" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 17, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "None\n" + ] + } + ], + "source": [ + "new_dataset.to_file(\"../../examples/data/geotiff/noah-precipitation-1979-corrected.tif\")" + ], + "metadata": { + "collapsed": false + } + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/examples/notebooks/04datacube.ipynb b/examples/notebooks/04datacube.ipynb new file mode 100644 index 000000000..e8e44871e --- /dev/null +++ b/examples/notebooks/04datacube.ipynb @@ -0,0 +1,352 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 18, + "outputs": [], + "source": [ + "import matplotlib\n", + "from IPython.display import HTML\n", + "from pyramids.dataset import Datacube\n", + "%matplotlib inline" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [ + "# Read multiple files" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [ + "Reading multiple files is being done on two steps\n", + "- First use the `read_multiple_files` method to parse files names and construct the array that will later have the\n", + "values\n", + "- Second use the `open_datacube` method to open the rasters and read a specific band from each file" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [ + "# read_multiple_files" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [ + "The given path points to a directory where all the raster we want to read exists\n", + "The content of the directory is as following\n" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 19, + "outputs": [ + { + "data": { + "text/plain": "['Qtot_1979-01-01.tif',\n 'Qtot_1979-01-02.tif',\n 'Qtot_1979-01-03.tif',\n 'Qtot_1979-01-04.tif',\n 'Qtot_1979-01-05.tif',\n 'Qtot_1979-01-06.tif',\n 'Qtot_1979-01-07.tif',\n 'Qtot_1979-01-08.tif',\n 'Qtot_1979-01-09.tif',\n 'Qtot_1979-01-10.tif']" + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import os\n", + "path = r\"../../examples/data/geotiff/rhine\"\n", + "os.listdir(path)" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [ + "We need raster names to follow a certain pattern in order to be able to read them with a certain order, in our case\n", + "there is a date in the file name and using this date we will read the rasters and assign the values of each one in\n", + "the right location in the array based on their date" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [ + "### Regex pattern" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [ + "the parameter `regex_string` accepts any regex string and apply it to all file names to extract the string that is\n", + "needed to order the files, this string can be an integer or a date\n", + "here are some examples for how the `regex_string` should look like for different file names\n", + "\n", + ">>> fname = \"MSWEP_YYYY.MM.DD.tif\"\n", + ">>> regex_string = r\"\\d{4}.\\d{2}.\\d{2}\"\n", + "- or\n", + ">>> fname = \"MSWEP_YYYY_M_D.tif\"\n", + ">>> regex_string = r\"\\d{4}_\\d{1}_\\d{1}\"\n", + "- if there is a number at the beginning of the name\n", + ">>> fname = \"1_MSWEP_YYYY_M_D.tif\"\n", + ">>> regex_string = r\"\\d+\"" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 20, + "outputs": [], + "source": [ + "cube = Datacube.read_multiple_files(\n", + " path, with_order=True, regex_string=r\"\\d{4}-\\d{2}-\\d{2}\", date=True, file_name_data_fmt=\"%Y-%m-%d\"\n", + ")" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [ + "Now the `Datacube` object is created and we can check it by printing the object" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 13, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " Files: 10\n", + " Cell size: 5000.0\n", + " EPSG: 4647\n", + " Dimension: 125 * 93\n", + " Mask: 2147483647.0\n", + " \n" + ] + } + ], + "source": [ + "print(cube)" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [ + "# open_datacube" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [ + "To read a specific band from each file and assign it to its location in the array we can pass the band index to the\n", + "`open_datacube` method, (the default band value is 0)" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 14, + "outputs": [], + "source": [ + "cube.open_datacube()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 15, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[[0. 0. 0. ... 0. 0. 0.]\n", + " [0. 0. 0. ... 0. 0. 0.]\n", + " [0. 0. 0. ... 0. 0. 0.]\n", + " ...\n", + " [0. 0. 0. ... 0. 0. 0.]\n", + " [0. 0. 0. ... 0. 0. 0.]\n", + " [0. 0. 0. ... 0. 0. 0.]]\n", + "\n", + " [[0. 0. 0. ... 0. 0. 0.]\n", + " [0. 0. 0. ... 0. 0. 0.]\n", + " [0. 0. 0. ... 0. 0. 0.]\n", + " ...\n", + " [0. 0. 0. ... 0. 0. 0.]\n", + " [0. 0. 0. ... 0. 0. 0.]\n", + " [0. 0. 0. ... 0. 0. 0.]]\n", + "\n", + " [[0. 0. 0. ... 0. 0. 0.]\n", + " [0. 0. 0. ... 0. 0. 0.]\n", + " [0. 0. 0. ... 0. 0. 0.]\n", + " ...\n", + " [0. 0. 0. ... 0. 0. 0.]\n", + " [0. 0. 0. ... 0. 0. 0.]\n", + " [0. 0. 0. ... 0. 0. 0.]]\n", + "\n", + " ...\n", + "\n", + " [[0. 0. 0. ... 0. 0. 0.]\n", + " [0. 0. 0. ... 0. 0. 0.]\n", + " [0. 0. 0. ... 0. 0. 0.]\n", + " ...\n", + " [0. 0. 0. ... 0. 0. 0.]\n", + " [0. 0. 0. ... 0. 0. 0.]\n", + " [0. 0. 0. ... 0. 0. 0.]]\n", + "\n", + " [[0. 0. 0. ... 0. 0. 0.]\n", + " [0. 0. 0. ... 0. 0. 0.]\n", + " [0. 0. 0. ... 0. 0. 0.]\n", + " ...\n", + " [0. 0. 0. ... 0. 0. 0.]\n", + " [0. 0. 0. ... 0. 0. 0.]\n", + " [0. 0. 0. ... 0. 0. 0.]]\n", + "\n", + " [[0. 0. 0. ... 0. 0. 0.]\n", + " [0. 0. 0. ... 0. 0. 0.]\n", + " [0. 0. 0. ... 0. 0. 0.]\n", + " ...\n", + " [0. 0. 0. ... 0. 0. 0.]\n", + " [0. 0. 0. ... 0. 0. 0.]\n", + " [0. 0. 0. ... 0. 0. 0.]]]\n" + ] + } + ], + "source": [ + "print(cube.values)" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [ + "# plot" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [ + "To animate the `Datacube` use the plot function" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 16, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "data": { + "text/plain": "
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+ }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "cleo = cube.plot(exclude_value=0, text_loc=(1,3), color_scale=1, vmin=1, vmax=100)\n", + "print(cleo)" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 17, + "outputs": [ + { + "data": { + "text/plain": "", + "text/html": "\n\n\n\n\n\n
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\n\n\n\n" + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "HTML(cleo.anim.to_jshtml())" + ], + "metadata": { + "collapsed": false + } + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/examples/notebooks/convert-longitude.ipynb b/examples/notebooks/convert-longitude.ipynb deleted file mode 100644 index c6317700d..000000000 --- a/examples/notebooks/convert-longitude.ipynb +++ /dev/null @@ -1,259 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "source": [ - "# convert_longitude" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "markdown", - "source": [ - "- some files (espicially netcdf files) uses longitude values from 0 degrees to 360 degrees, instead of the usual,\n", - "GIS-standard, arrangement of -180 degrees to 180 degrees for longitude centered on the Prime Meridian, and -90 degrees\n", - "to 90 degrees for latitude centered on the Equator. the `convert_longitude` method corrects such behavior." - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "markdown", - "source": [ - "- to run this notebook make sure that the currect work directory points to the root directory of the pyramids package" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 9, - "outputs": [], - "source": [ - "from pyramids.dataset import Dataset\n", - "path = r\"../../examples/data/geotiff/noah-precipitation-1979.tif\"" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "markdown", - "source": [ - "## Read the raster file" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 10, - "outputs": [], - "source": [ - "dataset = Dataset.read_file(path)" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 11, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - " File: ../../examples/data/geotiff/noah-precipitation-1979.tif\n", - " Cell size: 0.5\n", - " EPSG: 4326\n", - " Variables: {}\n", - " Number of Bands: 4\n", - " Band names: ['Band_1', 'Band_2', 'Band_3', 'Band_4']\n", - " Dimension: 360 * 720\n", - " Mask: -9.969209968386869e+36\n", - " Data type: 6\n", - " \n" - ] - } - ], - "source": [ - "print(dataset)" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 12, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Min lonitude: 0.25\n", - "Max lonitude: 359.75\n" - ] - } - ], - "source": [ - "print(f\"Min lonitude: {min(dataset.lon)}\")\n", - "print(f\"Max lonitude: {max(dataset.lon)}\")" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "markdown", - "source": [ - "## Plot the first band in the raster" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 13, - "outputs": [ - { - "data": { - "text/plain": "
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" - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = dataset.plot(\n", - " band=0, figsize=(10, 5), title=\"Noah daily Precipitation 1979-01-01\", cbar_label=\"Raindall mm/day\", vmax=30,\n", - " cbar_length=0.85\n", - ")" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "markdown", - "source": [ - "## use the `convert_longitude` to convert the longitude values to range between -180 and 180." - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 14, - "outputs": [], - "source": [ - "new_dataset = dataset.convert_longitude()" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 15, - "outputs": [ - { - "data": { - "text/plain": "(
,\n )" - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "text/plain": "
", - "image/png": 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vufbaa73vwkivU4t6trDqc9RkM1XGERAQcGQJ6pwHjIrp06fz4Q9/mO9///t85zvf4Vvf+lbN9VxveL1a2Y2NjTQ0NDAwMEA6naahoQFwoq8f/OAHh40MZTIZz1fqUk/kuRH7Uqk07PsC56ZA13WmT58+JEIKkEwmaWxsHNKMSdd1rrrqKn71q18NO+ZDYfXq1axcuZI//elP7Ny5k7lz57J//35uv/125syZU1OUjYS/znkkEmHu3LmsX7+ek08+ueb6c+bMGbJs586dgNM0ZSS/e3d3N4sXL2b37t0AHHfccWMecy1qJSDC4HdvpETi0XA4PmMY+XcjSRJz586lv7+fffv20dbWVvF8rd/BWH4Dh8I3vvEN9uzZw69//Wsuv/xyb7mqqvzjP/4jn/rUpwDnt1+P5557jueee45UKlXzZhCcmZXf/e53vPWtb+UXv/hFRQT9xBNP5IwzzuBHP/pRhSf9M5/5zJBgwtlnn80HPvABwDlGfX19Q3IjXPL5vLfeoTBVxhEQEHB0EIjzgFHzuc99jn/7t3/jBz/4AZ/5zGeGXXc0CYruOjt37uSqq65CCMH3v/99Lr30UmbOnEk0GgVgzZo1PPLIIzWjRxPRtdPd71j39d3vfpdf/epXrFixgu985zuccsopNDU1oWkaL7/8MosXL56QiNeHPvQhPvzhD/Ozn/3Ma25jmibvf//7xxUl/MAHPjCm5jiRSGTIMrdyyPTp00e8Qai2r0zEZzYcExllPFyfsctYfjdj3W6yCIVC3HLLLXz84x/njjvuoKurixkzZvD2t78dSZIQQnidQ+vxH//xH4AzG+L+7mtx7rnn8sorr/DrX/+aTZs2ec2I3va2t/Ge97wHcCq3uPzmN7/xbiT9uKJ4zpw59PX1sWfPHk488cQh67nVbmrdoI6FqTKOgICAo4NAnAeMmmnTpvGRj3yE7373u3z729/2fJB+ZsyYAcCrr75acx8DAwMMDAx4JekAbr/9dnRd59Of/jR/8zd/M2Sb7du3T+C7GEprayuhUIgDBw6g6/qQ6HkmkxkSNQc8T7Mr3vxM5Jjf9a53cd111/Gzn/2ML3/5y56n/1CrwRwKbqR22rRpXtnBkXC7PW7btm1CxlBL7ABey3f3u3goHK7PeKTfDQy+r+rcjKnCWWed5VW/cfnnf/5noHb5Qxfbtr2ZiXqWFj+NjY1eJSMX0zS5//77kWWZtWvXestHKj140kkn8eyzz/LUU09xySWXDHneLRVZSzCPhakyjoCAY4mbbrqJm266yfv9LV++nK985SusX78ecAI5X/va1/jRj35EX18fp59+Oj/4wQ8qbvCPFIHnPGBMfO5znyMWi/HDH/6Qzs7OIc+vWrWKaDTK448/XtOL6/pKzz77bC/a19fXBzCkVTc4yWy1Xmci0TSN1atXY9s2t95665Dn69VmH27c//3f/z1h40skEvzVX/0Ve/bs4brrrmP79u2sX79+Qnzb42XWrFksXryYTZs2DSso/axbtw5FUbj99tvZu3fvIY/h1ltvrVn72/28qoViLdwbMdM0az4/ns94pH3WYs6cOcyZM4cDBw7w5z//ecjzf/zjH+nr62Px4sVDLC1TFV3X+cEPfgAMnxuxYcMG9uzZw+zZs72kz7HyX//1X3R2dnLxxRfX/KzqcemllwLULOH59NNPs337dpYtW8b8+fPHNa6jbRwBAa8lZs2axTe/+U2efPJJnnzySV7/+tfzpje9iRdeeAGAb3/723z3u9/lX/7lX3jiiSeYNm0aF1xwwYRYFQ+VQJwHjIn29nY++tGPks/na1ZNiMfjvO9978O2ba699toKD+XLL7/M3/3d3wHw8Y9/3FvuJq7953/+Z8X6e/fu9RIiJxs3EveVr3ylIoFu586dfP3rX6+5jTvuf/3Xf61Y/pvf/KaiSsVE4B6H73//+8D4EkEnmi996UtYlsUVV1xR0TzI5ZVXXuFnP/uZ93jGjBm85z3voVAocNVVVw1J8N23b58XIRwNO3fu9Br+uPzoRz/ikUceYdq0aRX+53q4EestW7bUfH48n/FI+6yH+5v427/924qkzwMHDnDddddVrDOV2LVr15Ak1Uwmw1//9V+zZcsWrrrqKlavXl13e/eG/V3veteINq2NGzcOsRH96U9/4uMf/ziRSITvfve7Yxr75Zdfzvz583n22Wf53ve+5y3P5XJeoqvrmZ9Mpso4AgJeS7zhDW/gkksuYdGiRSxatIi///u/J5FI8Oijj3o22i9+8Yu85S1vYcWKFfziF78gn8/zy1/+8kgPPahzHlAbfHXOq+nq6hLxeNyrX1xd5zydTotVq1YJQLS3t4u3ve1t4pJLLhGRSEQA4hOf+ETF+qVSSSxfvlwAYtq0aeKKK64Ql156qYjFYmLNmjVefWN/Le16datdxtpC3bZtcfnllwtAJJNJ8eY3v1m86U1vEvF4XFxyySVizpw5Q+qc33///UJRFAGIVatWiSuvvNKrff2Zz3xmTHW1RzPe1atXC0BMnz5dGIYxqvflZ7g658Otf99999Vd57Of/awAhKIo4tRTTxVve9vbxEUXXSSWLFlSsx15Op0WZ555pnecL7nkEvH2t79drF69WqiqWnFcRqpzfs011whN08Ty5cvFlVdeKU477TQBCE3TxB133FGxTb3vS6FQ8Grvn3POOeLqq68W73//+8VDDz0khBjfZ+z2BVAURVx88cXife97n3j/+98vXnrppWHfl2maYv369QIQDQ0N4vLLLxdvfvObRTKZFIB485vfLCzLqtjGrXPu/22M9J6HY+PGjeL000/3/oVCIQGIU045xVv24x//uGKbm2++WaiqKs444wzx9re/XVx22WWioaFBAOKiiy6qqDdfTaFQEKlUSgDi+eefH3F8c+fOFbNmzRLr168X73znO8XKlSsFIKLRqPjDH/4w6vfp56GHHvJqpJ9++uni7W9/u5g+fXrdYy6EED/+8Y+943HyyScLQEQikYpjt3HjxqNyHAEBk0mhUBADAwPj/tff3z9kWbFYHPF1TdMUv/rVr0QoFBIvvPCCeOWVVwQgnnrqqYr13vjGN4r3vOc9k/X2R00gzgNqMpw4F2JQlNUS50IIkc1mxde+9jWxbNkyEQ6HRTKZFGeffbb45S9/WXN/vb294iMf+YiYN2+eCIfDYsGCBeJzn/ucyOVyNQXIRItzIZzmOX//938vFixYIEKhkJg7d674/Oc/L4rFoteMpZpHHnlEvP71rxdNTU0imUyKNWvWiFtvvXXMTW9GM97/7//7/7zGO+NhMsS5EE4Dqcsvv1xMmzZNaJom2tvbxSmnnCKuu+66msKgWCyK733ve+LUU08V8XhcxGIxsWjRIvHBD36wQqCNJM5vvvlm8fDDD4vzzjtPJJNJkUgkxHnnnecJaz/DfV+eeOIJccEFF4iGhgYhSdKQYzTWz1gIIe666y5x1llniUQi4f1O3ONY730J4TQ9uvHGG8XKlStFLBYTsVhMnHrqqeIHP/iBME1zyPoTLc7dsQ33r/q7u2nTJvHOd75TzJs3T0QiEdHQ0CDOPvts8dOf/rSiQVQt/I2sRsMNN9wgTj/9dNHc3Oz9Rj/4wQ+KV155ZdTvsRbPP/+8uOKKK0RLS4sIh8Ni2bJl4h/+4R9qHnMhKhth1fs30u9mKo8jIGAyKBQKQgs3j/idHe6f/5xa75zkZ9OmTSIejwtFUURDQ4PXAPGhhx4SgNi7d2/F+tdcc4248MILJ/MwjApJiKCAakDAVEcIwZIlS9i6dSvbtm1jwYIFR3pIR4zrr7/eq1ozlqozAQEBAQFHDrd88qnn/TeKOrSgxEhYZo4n7307u3fvJpVKecvD4XDdalC6rrNr1y76+/u59dZb+clPfsL9999Pf38/Z511Fvv27atIsr/mmmvYvXv3kKZqh5ugWktAwFHAb37zG15++WUuvfTSY1qYBwQEBAQc3aihJKo2dnEulXNSUqlUhTgfjlAo5PX1OPXUU3niiSe48cYb+dznPgc4OT1+cd7V1XVIXbcniiAhNCBgCvOBD3yAt771rbz73e9GURT+3//7f0d6SAEBAQEBAeNGkqVx/ztUhBCUSiXmz5/PtGnT+NOf/uQ9p+s6999/P2vWrDnk1zlUgsh5QMAU5qc//SmqqrJo0SK+/vWvc8oppxzpIQUEBAQEBIwbSZKRpLHHhse6zRe+8AXWr1/P7NmzyWQy3HLLLWzYsIE777wTSZL45Cc/yTe+8Q2OP/54jj/+eL7xjW8Qi8X4q7/6qzGPbaIJxHlAwBQmSAkZyvXXX8/1119/pIcREBAQEDAOxhsFH+s2nZ2dvPvd72b//v00NDRw4okncuedd3LBBRcA8NnPfpZCocBHP/pRrwnR3Xff7TVIPJIECaEBAQEBAQEBAQGTipsQetYb/zQuz7lp5Hjo/y5gYGBg1J7zo5Ugch4QEBAQEBAQEHBYkGTZS+4c63bHCqMS57Zts2/fPpLJpNdyPSAgICAgICAgYOoghCCTyTBjxowRO+4eKWQZ5HHYWqbo25kURiXO9+3bx+zZsyd7LAEBAQEBAQEBAYfI7t27mTVr1pEeRk0OV0Lo0cyoxLlrjq8u/B4QEBAQEBAQEDA1SKfTzJ49e0okNdbjcCWEHs2MSpy7VpaxFH4PCAgICAgICAg4/AQW5KObICE0ICAgICAgICDgsCBJ40wIDWwtAQEBAQEBAQEBARNLYGsZmUCcBwQEBAQEBAQEHBZkSUYeRxR8PNscrRw77zQgICAgICAgICBgihNEzgMCAgICAgICAg4P47S1ENhaAgICAgICAgICAiaWwHM+MoE4Dwg4xrm7Y/mYt7mw84VJGElAQEBAwGudoAnRyATiPOCY4S8nnsLrNj3FA8tXests22bd5mfZsPQkhCEq1rdN5/F5O547rOMcLeMR1WOh+nj4uat5Wd3nLup9cTKGExAQEBDwGiCInI9MIM4DXhM8duZq72/bckSlsCrFpaxIPLRyFZIy+AOXkXlg+UpkWcbWbGe7siiVVQnbFNw774SK5d7rmPXF62RFlu+ZuWLC9zmcCB8PrnCvPj7r05sn9HUCAgICAgJeiwTiPGDK8tR5Z9VcXi26bVsga4r3WFJccW4PrmMJJEVCWAJFlhB2eR+K5Il5uVy8yBXpzrLy9qZA0gZFvTAEsjr4uFqI3t2xvEKgjybK7d/faHBvHsbDRAjy8bz2HamlCENwSeElbo8u4ZLCS4c8joCAgICAowdZlpHH0YRoPNscrQTiPGBK8MxFa4csk1Wlxpp431pXYLs/V2EPimphCVBl7PI6rmB3nrMroutKeapMKALbEiiKMriPsGN9UTSpUtBWCWP/8+fvfZ57Zq6oiHKPVXhPBuMR5OMR4MO9jvvc7dElFf/boxzbZeaWMY8n4OjgD+riisfVn/VIzwcEBBwdSJKEJI3D1jKObY5WAnEecNjZdMk5FY8lWULRZE74wwaeu2zdmPblRcABkAdFt1wW5dWCHbBlqX50vbydsAWU7S+SJVXsEwYFO/jEaFmA37fwxIooe92xT7Cd5FBfZyxCfDxjrxZXYyUQY0cXf1AX1/3MRvNdGK1YP5q+F/73NNLNR611AgJeCwSe85EJxHnAhPL0Ba8DnB/RyXc94C33C3L/D8z/9/NvPHdItFxWhv8x2tW+8rIYHxTtive3Z1+xXQFuO8+7ot0WVds64v30Rx73Hj+86tTyOvLgPjV86zvbKuWIu7e8hpi1hyzx7WcY8TuciB5JNI8kwEct5CfxxuK1LEiqBeWh3rD493UkGGn8rkCfiPdZzdH4PfEfi4m4QQkICHhtEojzgAnjqfPOQip7wvwCt9qy4tpQJL/323tse/sAn6AeQaQP7kMu72Nw3+4NgOcfrxqJJDtCXSl70pElbFtw2gOPDNn/mo1PVjx+aOWqirG6yabCEp4/zrZtL5I+kvidDFE+moi4u/1kiu7heC2IDtei4zLcsZwosXqkj9twrz9clHg4xrvdRHAkX3s0HI2zBQEB1Ujy+KLgx1AlxUCcB4yOjevW1H1u1YaHATjl3odqPu9G0N2ouieOq4S3sEVZVFtA5Y/XGi7MPEpcwe4JfV8y6Mo/1R47wBNrz/TGB4NWGMcGMzh22be/kagWzDUj64cQ5R6tkD9SYtxlsqKqUwFZkybl+B4twmy84zyS7+9wvPahzpqMtP3R8v0IOHaRkZHHobRljh11HojzY5THzz6j5vLVDz5ac/mqDQ/X3aZ6ubsPV9QCyLLkifiN69ZUlDN0LSJQ+266VtR8vN6z6u0UWeKEP2wABq03foEtbIGiyRURdTfiLmNj40TJ/cLfv71rbfEi0z7RXCGSD8GqMpZ9HGkxXs1wvuRjnZH82tUiLTiORxcjfV71xPdorUQBAVOVwHM+MoE4P0ZwvdJrNj7p/T1WzIJZ8ViqYzV5ZPVpnPn4E9iG5S2zGaxFLikyqzf4BbzPaFL145MUiZPvesgrqyhVlVI6FDHvivLqdRRfNBxFwjLcd+DIchuqElGp8LULS1SIclc01xLo/uXVTGSZxKkmyl+L1EoCdpOGrfzwUz+1ZhDqie6RHgccfdQS1KNJGB1pn8PtLyDgSBGI85EJxPlrmPsWnsi52zaxYelJ3jL/32PhgeUrWfvC0xXL/nLiKcNuc/ojj1d4ssH9cdkVNwsuj6w+zRPWbhLm42ef4UXm3W1d/EJ+xEi8JSpE+4m33z9kvH6LSi0c0S0QtvDKMdpGZVlG27Y9QT4aYT7kNSZQRB8tgvy1Eumr1WTpjtRSwLG4wNDPxP++/QJd9gl918suaVLQyOk1QvV3fqzff//6tUT7a+U3FRBwKNxwww389re/5aWXXiIajbJmzRq+9a1vsXjx4G+mXnnGb3/721x33XWHa6hDkIQQI17B0+k0DQ0NDAwMkEqlDse4AkbBaCMpl5lbhiSr1Wr+4goJqC00/C3b/S3a6zXYubDzhYrnatX6ro42uuv4l/sbD1RH6/2CW6oj1l3OfPyJmuOsxj+zIGzhRcOBmuLb/7/L0SKMpwLHiogYzW8wYOy8fOXF6LkSek7HyBuYRQujaCIMwbnbNh3p4Y2aWpHuerMnQfWbieO1diMzlfWaO7bLP/YcWjg55u2NUobf/csJo35vF198Me985zs57bTTME2TL37xizz33HO8+OKLxONxAA4cOFCxzR133MH73/9+tm3bxoIFC8Y8xokiEOdHGRN5Uh4u+iJrktfFEQaFRLXAGAl3O7/wr8Yv2itEeZ3lIz0HQzuJVUf9R8N9C0+sEN21ouBT1ct9NPFaujAGTCxdLzyOahZRzCJqfgC5lEfSi4hcFjuTxuztI7+/Gz1bwMjrmEUdo2BgFAwsw65ZcWmqMZUSoo+m32KtG5fXmtAeD1NZr7lju+ITz49bnN/6TyvG/d4OHjxIe3s7999/P2vXDm18CPDmN7+ZTCbDvffeO+b9TySBreUoZSKqXAy3vW2Iiufdk94lhZfG9LqjqShQIfgLzn+SJmH51l+f3uxF7qufq919c3CN8/c+P+rx+qkXdXNvNEZrMZhKF9+AgKnM3i2bEL4qDiHfc5KwQQiwLOefbYMso4Q1NNtm7r/97rCMcbxe7qPhPDBVxa1fiNfLzaj32GW4mYiAw4sky0Pyx0a7HTgi3084HCYcDo+4/cDAAADNzc01n+/s7OSPf/wjv/jFL8Y8tokmEOdHGaPxKU7WRWCs3e1Gs6/LzC21o86GqOu7xefbllUJq8b2bjT9ws4XxjSu0TBW3+9ENpx5LRJcMI9dMo/9AUuLYKkRLCWEIquYSgiBhIRAsQ1kS0c2SkjFHNEL33ekh+wx2hrvAYfGcML8UPdZi+A8NPlIklTX6z3SdgCzZ8+uWP7Vr36V66+/fththRB86lOf4uyzz2bFihU11/nFL35BMpnkLW95y5jHNtEE4vwoZThBM1Ky0FgZzcnKL0BrlXgbzzj8ol32NfHx21hsU9SMnFevNxUIRHp9ggviMcoTDxBKJZGTDYhkA1asgVKiFVMNIyQZrZhG0QvEzzpyF8vRJm8e7b/rqfobrHXsx3KsD3X7gKnH7t27K2wto4maf+xjH2PTpk08+OCDddf52c9+xrve9S4ikciEjPNQCMT5UcBwJ5KRTjIjRbgn+oRcr/ZyvZNidTS+1k1Hte+9Fq7lxZ+oGjD1maqC4HAxVovUa4nHzlyNFtPQohrhZIRIY5xwQ5zk4gXIDY1E3vLJIz1EoPZ39LUo7mpZWqaCzaXWjO1w17WxBJMCjgyHWkoxlUqNyXP+8Y9/nP/7v//jgQceYNasWTXX+ctf/sKWLVv49a9/PeZxTQZBQuhRwqFeDMbTPrtWxHs8F6qxRiuqI/9u1Lw6Eu73obuMFC2fDJvLeHgtXtzHQnBxPHbxW9T81ZmUqIwSlVm3+dkjMq7hhOix/HuVa5xThwuUDFc0wDbEuH/74/l8jsXzzFTWa+7Y3nHdFkLjSAjVSxl+/Z3Fo35vQgg+/vGP87vf/Y4NGzZw/PHH1133qquu4vnnn+fJJ5+su87hJBDnU5zJuiiMp7X4ZJfzqiXiZU2quIi7TIRl5UgL9WPtgn8sXigDHMY6wzfZr1kvYFDPgjeRfuephHt+HY7hGmj5hbu7n1o2wyGVrsr/T+TnPprP51g4B01lveaO7Z2ffXnc4vyWby8a9Xv76Ec/yi9/+Uv+93//t6K2eUNDA9FotGJc06dP5x//8R/58Ic/POZxTQaBrWUKM9UuBvW8l2OJiI91+tHPRHvI3RrsR0qkv1Yv+AEBfo6EMB8t1b/BemM9Wn+n1VHvagE90jlVGAI5pdTtauzfXz1xbpsCRZOcfhDuuMr/H6pt5lC7pvo5FoT7scZNN90EwLp16yqW33zzzVx11VXe41tuuQUhBFdeeeVhHN3wjEmc37f4dOKyMu7SdAGTw1hFXq1pytEyXJvpkU58tfzmo43GH6owH67zpt8aY5tiyvt/a31+QZ31gKnISOelQzkXjeU1q1/n9uiSmt1aj1YRXk11RNsvxmsJaf/5tfpcWd3tGECoQ8V59Wu567vCHFVCcvdVfp3ReMcP12cyFfz1xwqH6jkfLaMwhgDwwQ9+kA9+8INjHs9kMiZby23TlhCXFW/5VBHp9aYhX2uMxlc3lovheAXdSMe2OqnzUJJW3WlXJSrXjMjUYzgxXm+76m0ORwfHsV54RiNmpqpQn8zf5Gu5HOOOa97sdKo1LQp9OfSczin3PsSGpSeh9zqdMKd6IrT7+dSyQbgc6k1xLcFd7/cyVX8jw/2+RzPm6vycaiHunUtl2euiLCu1hZKwndezdNvrjAyVFpWaDeBGY2up6qw81v4Zh4PR5Fe5zfqmCnd3LCdnW7yle8uUtrW86/PbCEXGYWspZvivby6cku9tohmXOD+SovxQfsBT/cI9UQKj3jGaCGFeTb3qLKMZT639+Lfx+yFriXOoLbRrRX5qPXeonvuJxE2iqjWmeoJGVqW6080uR0KEDOfbnWgOxSY1lXn1/W/y/ha2jaWbmEWD7MEcZtHELFqYeROr4BgFlKhMfFqMSCpMOBlh8a/vOlJDH5a7mpcNe1MN4xfp1V2Ia92g+73OU0EMDnezMhqqBXJ1JLymONcU5LIwl2QJSZEGHyuDjWGEZWNbAmEJ5+aw/DdQIdZd3M+1dlO4yvX8kXi/SIepe+NUi3rBo/P3Ps89M1ccdq10V/My8oo95cX5X3/hlXGL8//8xnFT8r1NNGOytZy75bHDfkAm8gQ61tqok112cDRifDzi43BeeCayKUS1n90VrMOd7GsJVD/VERsY/8l/MqOzoWa15lj91Lp4K7767wD4jsdwsweTyeEUPUe7IN9/3bswskX0bBFh28iqgqwqWLqJpZsYBR2jYGIUDEppHT1tAM5n6wpzcL4bRs4Y93Tx4WI0Ef7xnnfXpzdzd8fyQZFXXn6kfgfDMdpEyhEFe3TooloC3X0s+7oyVn9PJEX2lglbICkyMjY2ICkSkiwQStnOYkkIWWDbg99BRZOwCnbd3hPV70tm+NnPownXqiOrkifID0WYjzfocFHvi073zIaGcb/24eBw2VqOZqZstZapENUYKxPd/OdQx1FrKhmOnshEvSnD+xaeCFAZbfGd5N0ET3+Ubn16c81p7/Ey0aLwsTNXA5WRq9UPPsojq0+riFxVI2osc6NaVsFG7zWP2Od9tAvnieaR1aehRlRizTHibUmiLSmiHS0IITAzOYq9abIH+sl1ZxnYmfGETrUVoB5qSiHSGiLaEkGLatimhZE30GIa8dYE+d48+e48WlyjbXEH83/6vxP6/p5Ye2bFY9uwOP2Rx8e8n3tmrsAq2FzU+yJ3pJaOOGPmPn9R74vc3bGcCztf8JK9qyO0LkfSRuGfEayObCvRsbc0P1S816+yunjP+wRR9fOuoJfL69hlK4ywBcJyhLttCWzDxtJt9H6j5nlbGOKouC7Vsx3V+jyrsQr2qN7neLz2sm+G+WiInL/7S9vHHTn/j79bMCXf20QzJWwtR6MQP5qYDDuLn+rmQRP5ecrlKUM34rZh6Unec/7p0OHEuX+a/HDWix8P1QJH2AJLt0a1rf+CalsCI2ei9xsYGeuwXvwCUV7JA8tXokYUwskQkcYo0aYYoUQEJRLCKuoUerPkunMU+greZ+aPivuxCnZdX7USlVFTiucpdlFCMmpEwSxaGEUTLaISb4+jaDLZrhy2YSNrMmc+/sSw7+P26BJi88KEYhpKyNm/X3w/fvYZgCPmhC1Y/eCj3nOu5STSrqHGVGRFQo0oFdFacL7vRs7A0m2skjUkClvLu+wdhxrrefutWn+48oCTTXV5WCUqe4JOiw9OZvuj0mNhOFtf9fPVx69aZLqC3fWlS4qEElJQVBktptWNgNpm2RJjC8eWZdjYhkV2f6HivO1+z6eCMB/O7z/cDEatyjXu41o3IS4T8Z79OQbuZ3Y0eM7f8+VXxy3O//3r86fke5toxlVK8d55J3hfwHO3bZrQAQVMDtVey4kQ0fVaWk9UrWB/hMn9zvlPkpImgSm8KVKY/CTlyfZSGznDi065uNHxWlErF/8F0jYshCWcC2lUdi6EqmBwon9iL4aBGK/NY2euxrZEWYRK3udn6SZ6tgjZIn2v9pDpzHmJnV6FixoRVNdfLFP5+dmeOBCYacsTPPX2p2NS6Cp566kphWhLmKcveB2heIjlt91b8/1cUniJXR+5Atu00LNF8r15Hll9GmbR4nWbnqoQ40+sPZON69YQSoRQwyqLLj+OWGuKUCruHIOSTv7gAHq2iJ4rYRRMbMMCWYK4hhqxsXTFS0r03mv5t1BrxggqBa3/BsetLqIwujwVb3+TIBptQzifIT4RXH1uUyRkakfRRxLtI4rCOjMwliGQzLKtRZWQTAmhCpSwgg0oVRF0JaSiaDKyqgwR6G4Cs2XYCNvGNh2BHmt3ouhG0UTvNaEwtYT5cCK8VkUaP/7P0b3xsOrM3NR6zbHkC1WLcv/fsj31rR+yjDfbMtbtjhXGJM5tU2DLwvO5SprkRTKFIcYt1A93yaRjDf+P3D3GE3GsJ7LGbDX+6JJ78rJNMeRy5XrO3ROiPwmn2t96e3TJuJovVU9D+6N5tikmzIt+z8wVNSNXUBbmRnk85SiWEqr0icqy5E0r+1GiMhb24IXXEGM6DoH4HjtPX/A6Yi0xTvjDBgA2X3E+lmFhFAzyvXkvqTO7o1BxUa4WB9WzQu46/lwD93O08ja15lhCLarzHXCn1U2BRWX02Iw7W0YaomT+5bMYA2kKXX3MvLGylbUa0bB0yfHGl282hCV4eNWpKCEZWRus5mXbAkmWiDYnaFgwg8i8OUjJRhA2opBD276DQmcPua4BhF3AxEl+VZBBlZE1gW1YFXYuWSsLc63yPfpvaN1xWTEbs2g5Yjbqi3CaAiUqc96O5wY96lXl/9x1x3O+GC1+IWdTrv0dZjBBs2omzNvOdxYcTqjXm2WoFzmnPAZJk7xzreXdEMoIRQwJHEhy2dZSFujuY9u0sE0FJeQIc9u0kGSTUExDzxsoloKWdM6jR9J6B2Mr5+kmf7YsbiLW7MyAabEwckhF0Rw5ZZUM8t1pMgfS5LpyFPt0zLRFvU/KE/pVMz/KCOOqdWM3FfMraiFJEpI0Ds/5OLY5Whlz5FwYwktCqbYVuPWi3S/KWJu7TBXPdsCRpdbJ0o0a1ko2qhbo9XCfdy+49Uo4+jPwlahMtCXsTeO7/kn3bzNvOmKnYFfUSx9PaTu91/TG6fpPJc2u2R1V0RRvTMPhzi7IqoQYIXm2FoEwHx8r//QX7+9X3/8mZFWhlNXJd+cp9JQGI+XV39fy99M0HLHsf3400b1aWAXbE+Z+lFj5O20KCvtL6P0G/a8OcGDTfu+Gb2bVvhwh7URFAc8K8/CqUx0rim57+RGWYZHdlyN3MIeeLZLqyxBpbUSJhAEw8wWMfAk9p2MUDKzy76peRK1WtLy60ojrf5YUCTUskBTD8zvb2uAxOG/Hc9wzc4V3PI+EqHEFsCfMfb/zap+3JA96uVEkT6zXiq77BftYhLm3rHy+cIW5VXBubiRLQtbwRLisSMjqYOTcH0F3/pa974mb7OzaYmzDRtKc6LyaUhzxegQ+g+prTbVFpRolKhNrjzB33Qoixy9EtM/EjKawtAhCUpBtA6WUp7G/C3vPDjIv76Dvlf2k96XJ7MrXtasNGVfVzWKt5/3XCSXsHFthCUoGcGCUByBgyjImcd68OEVMVrANyzkRGzZ62vDEiYt79+2e/JSoPOqo+mR5lwOOLupNHdbDFejD2VqGK9HmRtWVqEyoWSWU1FAjCmpYRQmrngBwfZQuZsms+D0Iy6lgcO+8Ezhvx3OjHj9UehZdH2qtJiGKpjjTyb4oZS1BIysSwpIRarkzX2HwudEmJR1uHli+krUvPH1EXnuieWLtmcSaY6hhlWxnhtzBAoX9pZq+cT/1nnOXy6NY148/Qu7eeGpJxbM8uVYYI+0IqYKmA4PfufsWnkgoqXHW0xsp9OWRZAnLsLFKJo+duRqz6Hz/wRGG7o2Ae02wCnlKaYO+Hb1oMQ0tqiGrCkbBQM/pmAVzUJgrErLmCD/3Rthfws/Fi+AaeOuqEYVIQ4RQPISiyY7P2RaYmI6YtQctHf4baagtXkd7fCcS92bDnYVwzzVuWUNXgLkWE7d6ij+yrigKFhYM4zX3WwFd/ELQHwSRVWcGTy6PTfb5zG3T+dwkd4ZPdm/4LM9v7q4jfDcNkuwEGMC5eaw5niMYBfaLdH99+Fh7hJbjWoisXEWpYx6FaAuGEsaWFAQSijBRYzrheDOhxnYap80kMe9VMq/sZhfbKPSXnBuRUQRJhDG6qjdKWEGLq4RiGrKmIJkmbJ2QwzBpBNVaRmZM4rxt6XSSIScJxNJNSumCk8jUW0DPDyYxVddTlWSJR1afVpFwdO+8E1CiMqVOo6I+qpZSkDSJuzuWo/naBh/JBJ6A2kxGd796DOfzW7f52XHv17VlxeaF0SKqJ8hlrRwNqmrQociSZ1V47rJ1qGHnAup6K8GJ8K3a8PCYx7I+vZkn1p5ZUfnAFf5+3Au4KNsG/MiyBJqTaFeZSDr4d62L3kSL8Q1LT6pISgQIp5yLh+tTdSNvkix7F/HkjARPnXdW+SbHGedpDzwyoWObTHZ+6PKyj1pHi2rke/PoGZ3szsKIony0DFcLf7j9KzFnJmikGc17550A4NnElKgTkX5i7ZloUQ1h2BgFw0vchMHvpGSVLSVlG5VVsLEzFkbGIkfR2y8M+uBr+WbBEYjuDMNw79tLhNXc71F51sGsNPkIYzAJsV4E81A/n9Em39tGpW1Bcm185fONI7YtbzZAkhVkn5XHrYbiinI3su4+Jysqiubc1FglC8kcnPGOtoQr1nVvfqprl7u2OudGQUYJlc+J5eu6WTKRLYEo2GUhLoYELwZfxwlcWKbtVXFxAh4KoWaG5Ek4M6WDN0yTJdRH3K8hEOUIv6xK2IaNnithbttCuO8gYS0EtoUoFLAyWdJbd1JM58kWjSGee9sSaBHV00XuTagarpRhlmljFkwKPaWKGa8hgr58nIThfMZm0aJhVpJYMjaRh2hSkCXn33i2O1YYkzgvDeRZ9t9/Ys/H38bcf/sd4AgULa45d/SxymYFlmHxuk21hZM7rehPSqqXHCMMgZGxjtjUV8BQDocwr2VhGU+jDqis8lKN5+su//Itc1BwCLvyOYBNl5zDibffzwl/2MCmS85xIlqyBMgVdoZDwS/M3agi4IlyYUmoUbli/erouRM5d6b43Yv/+sPYSdK2bdZtftY79nrORNFsRFRFBWRF9abHUQZnASwDwK6TDje18U/nuwK22Kd7UenRMt5mXsNh5W0vyOFv2ON2aHRJznKSNoXl2EHMokVpQKfQUyKUcmaUlJCCFtfQ4s56bnKye0MoDMfXPnwk2q4Q6OAEaMINIQD0vIGWVDAyVt3gjF0WccIQToDIsFG0khdZ9ywttlOW0vs3iWJvNOdGuWylkcrNa+pFSB0hKw+5CXfPNzKVEXX3OUegS04iZ1gh3DBoKYk2Rrx1/DMWbkUV96bYPZ+4M3ReHX7FHzm3Ksq8ejYWr1mRz47jvR/33Oq+juzlBLgWHxunbrowBNYIx3SyNYFtCDAEVt7GyFgUukqYpUeJpMr2rJKJntMpZXQyO/PedtU6xh+8VFMKIVlDjgwGgoR3zneEtj9HpPoGxTse5RljO+Os32v0Y4Sn/pkziJyPzJjE+XE/uRWAWf/8P96yE/6wgSfWnokSKv+Ayyd03TA8D209zt/7PHd3LPe8vdWRNo8wg9aZQJxPCUZ7ETrk1yn7Ms/rfM6Zbanxmn7hXSuKPiphXpXoZNvO6wrLKl9InOWSLGPZgqcveB2SLHHyXQ8A8MxFaydEmLtR4g1LT6pIBvRfvAc955UX7uoaw+4FU404UcVDmWEYK64o9yeMm4aJCCuOJSKiYJYstKjtXeScZLLBGx1hjU3QHmm2vms9ZtGgmC6hZ0uU0gZGzsQcozCvxUjCfKwCpVo0u/XE3cotSkhGUmTvBtDImYNR7JZQOfKnOAJOlbHt2hHTkXAT39ycEkmTSE5PEIqHKWWK9L7aX3EdqZegOdj8xhoyy1bdKn6yGemzqM4d8As42xpMerfKolumtkD344rh6s/AvZFSVMcmE4qHHNtPSPUsKBX7KUd5hW0P2ZdrUxmMkA9GhKvX1WIalmEj2a7dryxMy0JfUpybB0u3HWuOjpMMq9kIVSC5n1Wdmxa/DWk0s0YThZV3BHPPlr7KhN6q0pBARXK2X1zLZR+/lao8xrbhiHIvgXSYGvD+668/H8tMWxQUY5Le/cQRiPORGVcpxWpcQfH42Wc403BKbf9eLfxTrA8sX+lF2+5beCK2KTzfrr/zW8CRZ7KFud/z5z9JCVUMGz13xaBfiPqjt35qCXPn4uhcDG2c6JHf7+qKdQBhO1U5Vv7pL55InyhKnc4J1p09qG7RbWk2iiajhEBYg2OvbrXtJoeNNhFpInE/A/+xt0qOeBKWcFrQFxzhpWiyFz1yRcB4rEFHiuffeC7pvZnKmcPS5IvyQ9mnvwuvG6EEKKk6akz1hJ2kSF79bSUqO59VOVLu3hCaRdOLUlulwahf9W+1uqOtE2G3ISqjlG8625bNJjF/FsKyiD7yHL2NPeQPFr3Z02q8sReG77Z5uGtqD2dx8VfXcHNCZMDCQli+Eqmuz1tzxKxrc3FFXfVv3nvtcnTbX59cDTs36VosTDgVK+9f8axlgCe+nSiuY80wi0aFf9w2LSTTwixVesid13I63KphFWHbmCUTs2S5haYIJ5xZETdK71bkcd+LjIyt2c4Nmzo0OdMVwm430olO5h1twz6rYCNUqSKPB5zfR3XTsFrC2jYsL+Bod5Stu4bl5X+MdCPpryDm5/y9zx8VHUIDRmZCxLmLv86ti3/6dLiEPKAiEezcbZu4PbqE26NLuKTwUoWlZbiqBSMl9Lhf6qOlI9lU5HB6zd3vzF3Ny8rZ/cJLOB4v9SLID686teKxsIVX/spvGalMTrN56ryzOOXeh8Y9nlpc1Puil7TmVlBwsXDEhx41UAylQoRXNPao0aBpKuAIskGRXugtoEZVFNVpeDLRx3Iy6PvmtdglnfSuLvp39ZLvzpN+JU90ZohwMjTo4S1faMfC4UiGlzWJu5qXDdbB9wkKRxwY6GljiAdZi6gVVYLcJllGzsTImUMi1NVVJ7zHDEYEZZw65EqzTLwtihoLY+s6VqHo2DM0Jw+pnq2xYln5b4vR+fAPF8PaMqqqtli+mKukSVgGaKhli5qoaDYG1BTmrp/Z3wvBXdepmqKgRDRkTUPWVG+ZEAJsG9swsUo6Zr7kVecRVfWzJcNGVh2/uyvItVgILRZGjTmzYaIs7s2i7kXkLd10RLssIZVtMJJu+45BecxhxxpXUTYUPGE+kfgbeVXfDECl33s4G5KsSoPXDPBueGvhRuELlGpWZ6ksAlB501lrdgicIOYZW6d+jo4kSchBKcVhmVBxXgv/neQdqaVEZ4Zont/IibffX3P9WhelP6iLh5zcaiUI1roIutOgSkweUgXD7Zw4UYlaxyLjEcnDZaDXK0HoLneT1VyqhXZ1hHw4S0sthCV8cXNwa2NY9qCfs4JxTOWPFrd9uTc233fUMgT5HaVhb1D93+kjWRKx3s3QfQtPxMI66qqzPLL6NKJNURRNRs/q5HuLlHp0r6oF4MwKpK1h/dLVHM5+D7YhUKK+qh2+MpueWPTd3MnlpjRW3LExVFQvKs8SVN8IVsz2lPdXK5ro/G2j95oUIkUyuw8S6stg5EvkunMYOecmwfGnj/5cPdXO6fWsCK4oxRRO8x+fEPPW89ldbKgQ6LImewmhg5F2xSspCWU7nmEjyWUxLEso4RBKNIKsqkiq4uR92AJhuLN2FrJaeSPmbgsQTka8EopKSEVLRAilEmipBFpLM0gywjKxCwWsXB4zk6PQ3Y+R15HzJSTZwNIlwMT0edArymNaknej6zXgcks9MjHJov7SudWzlP6y0TD4nfY39fLbcaubYLmlKOuN0zaENxvk/9xrlUpUQnJFl1bA86ebedOLuN973OpDOh6HA0kap60lEOcTh9+24loA6vGXE08h1KKi95gV0za1ouHVdWGh8q61gnJGs1NOrtyMpfxDrC4xFzA8/puk0Qrz6rv9IdUZxtD2q7o84X0LT6x4XF2y07VI1RrvA8tXen+vfeFp1mx8EijXbPa6clZGpETVrHp1ZGqiWZ/e7FkPoIbgmGICZCwcbd2F/XXw09HcEL+ppEkU9pcoarrjKR2jKD8S+M+t/tJ5nhCCijwPI2dWrOvi9hnwfg3VSf2qgGFmEWxDoPeYWIU8sAct7pQmKfaVMHKmNybJHNohdapST4zVE+gwGD3393Twnx9dYeYKGzUsecthsJShGnbsJdUomowa0VAiIZRwCDkUGrSTGSWEZWHrBnZJdyLnhRJm0cDSzYryiJLs7EeLhdHiEbRkHK0hidLaCi3TKLTOxVY0FLOIlh8gNHCQUG83sB25P+N5js2y7catzuT3YLt5M7ZhO550wNbsISUiDwW5jiAegq8pnBKSUSPKkMpTMFhq17H0OLY9PW9gpq26zZbc74QrxEONTtK1+xtwb7IkX+EC93WFbaPndIr9JUro2JmjI09Hkpx/49nuWGHSxbmfesK88/PvAWDBucdT7M+RO5jh4Iu9Xsmr+lOZ5czvKtGlJRWi7WEa5zaS6EiR787Qt6Ofga25MVdNeC1xqNPl9Xxu9XATfZWo7JUplDWFcCLk+RxlRWLprfeMe0z1BN7dHcsB5+bQmwIt34TdO++EIfXD/aX/RorkPrRylfe3JNueqJ8sLim8NO7P7VhrIvTYmas5/ZHHJ2x/tY676xkdgiFqduisx3g/G3/5wMZ5SdqWTifSlOCVe14k31V0ytPWqOzgj/opUdlL8KzuShtqdESBVbI8Qe2KeLfPoVnDT+7fR60IpN9C4y9n6B+rlbfpey7rPXYbJQ1XTvFoo1qgw2AnzloJ77ZtE46HUCOq03dBk73zp6IN1hd3Ba8rnJWQ6vwLhxzbSlnMURaPRjYP2bzjMzct9EweI69XCHG36pC/kouwBYoqO9HyWBg1HiXU0YY8fRZ6+1yyiWm8Yh5HwVCJh3TaEn20JnYR015B3rUbSVW8aLtt2siWcHpJ+IW5bSMsuVx60UmMtQzLKx1YYTM5RPuSpPnLcSpDbENutNpvE3IFshpWvbr93rjLNqNoY2VjpnxvnkJfgVJGrygRmloYp3F2Aw1zWolNb0NtSCJHIliZLMWubnL7eyn25zAKhvfaK/7vvrrv52jwnMvlZmHj2e5Y4bCK83p0fPPf6frCe5070PKX2SrYoy6dWKurl6zJxFoSNC1bQGs8RuOW7Wwtvkh6S37I9u4FwN3Xa+EC4Gcs0+XDeSPHIszdKIAWVwmnNLSYRigeQouGCCWc6dB5P75t1PsbK/4Zm/P3Pl/RdEQyJW/62O9fd6ckq60w1baMs57e6P3tF+qTyeG0PBzNlNJHvlLBaGpd+885Y0XSJMIdGnPOmEPy01/kzf9fCfbAV350Bqvv/BRP/esDnkD3j8UV4tV1vocIdH+CdFWDuYqoZY1KQpYhBmcmqd99U9IkFORBW0e9xLvXYH+LWudY94amOmprFWyUclzdbf6jhByRLpcFsovfduIX6rKqIIfLEXLTwigWEZY1KCRNC9u0KKULnh/ce/1yTXvbsCqaQsmKhBbLeqUTZU0jHI0S0kKkLJP5jQpooFlFIpl+wj174MAeit395LoGKA7kvcRTy7DRs6WKZlNe0r0bibYGb+qGfCerjutor9/Vs8Buczf/99+1CDkJ63JFL4tauTFPrD0TcGYo3Ko4i351J+BUc3J7HyhaCcuwkGWZjuXTaFo0m/C0dqRkCknTQFaQG5tQm5oINXaS232AfHeGhf/xx1G9t4Cjn0kR564veCwdEtu/8Qv2fPxtGPkShf7iiD7wWlFct0ukkbHIdxUZ2NNHcvYAEU2l2Jt1pke1wVJcSkymcUmCluNaELYgvT/NwCuZutNPRyujFXTjsazU3E9Z+GpxlUhTmEgqTDgZIZSIoEY01FiY6d/5r3Hvfyz4LSEunjj3/T+kIkqV1eaB5SuRFInXbXqqYrlfqB8OxjL7caxFzWHkWQ9wvhPu73syjtFI5w63GdCGpSeNq7ylKPtULd2kOzIb2AbADX/3FP+9KIvea1bULb8jtXQw6FAl2F0Bb+Vtp9mKJhFOhbzSd9WdM2uVd/MnXwJQcESle4NeC7c9/GuNkW7M3FkPGFpZproBk7cfNxfAFijgNe9SI06gw62iYummJ7id6LeNWTQw8qWKiixmUa+oiuRuq+dKFQLfMpzKLEbOiZy7kWtv9kO3CacKTlOeok6ipBM62I3a0kJbagcIgSjmEcUiRl8/pZ4+Mvt6KWWK6Fnd6a5cFuRm0fKqG7n2Eb9Ad+vUe8fRvYmplXc2ju+VMMo3AErlzYHbIdqtJgXOTZDbiK6aQk/Rs72ccu9DbHnHRbzy3su8yLqlmxg5w/OIqykca1BjgyPMIzGE1/NBQ2oKEY5GafjU98f8nqYykiSNyz8eeM4PkfN2PMfdHcu5Z+aKYdupg2NpsS0LM1+i2J8j25Uhd8CJbldHtF1qtTV3hbmbMKL3mvRs6cMqvYikSKT3ZYdM96aOi9F6fCvx9oaKbovZaI7cjtKw457sSgChFpVoRxhhCYyc6UW7JiuyP1wFHG+dEWrO+j8TLakQb4sSaYwSaYiWhXkIWVPp+Oa/T/Do6+OKFL+gdbPo/RE+9yRfr+65v0KFH7fCy2RbW/xUC8paYv1YFOajpXFZgsyr+brJx5ONWz7QTFueDeeO1FKsvM1l5pYRb8BswwlAHP9fdwDw0O9n8rM/C660fsH2H+xh9rppPLzqVPS8QanTqJmzU/3YLyqNnDP7ICzH8yyqKlD4/eXuvmrlQghNqnlucB9LmjQYfa/KMTragiPu+P3nSCVau5GamlK8vh7uecZLgPQJQFe4+s9FwhZOZ05V8XVCtTGLuifIq2uRu3gNv3xYhuWIc8Mpe+i3DbiN0FzPdHX1J73XRG92usSaJQs9WyTanCDc1YOsqdiGiVnSsXXT6ZqbLZLrznpebKf++WBnUu+7QtlbbjvnY3+SpfdeqqxT7jEe7zXSKtgUCqVBK6abgKlIFb5ys2TWFeYwGBx45qK1vPDm85yOuuWbpUJfnlx3ntKAjlH2hjszARaJj9zg7aNwx48BiK6/Zkzv4WhCHme1lvFsc7QyabaWkVpEuzg+txKldMHzHj+wfCXRmUOTQWtNZ7kZ1kp4MPnFTaawDYtSRmfNxie5I7W04gerxGRCyZBzsisaSLLkTT8BFRdLP9WVY/zVYGB007D+m45agjfUrBJKOkkhtiVQQjJG1GkCYpU7J07ExWsiI+X+/ciqhBpTSXQkiTYn0BIRFM2pCCArQ5OUJoM/qIvrCtRqz6cNSKZACyuVlQJ8F6p6EXJXtE+013ks+N9nYHsZmTUbn2TXR64Y1bqTUdbQjWALQ5Ahz73zThhSZtZfg9w2xJBx+M8z9847gTWnTePJLT3k9xRRU4onpqy8PSgcqR/NBTzLS3ZnYXQ36arkdeesm+hGZa8C3Eobrsc6KnsVMfwCayxVWY40biWw6oofSrnhTvX5xG3wVF2twk388ydHujMY4DSp0Q0Ls6RgFAzUsIqeLWJb5Ui5rxmQbVhep2P/Pvyeab9VxdLtQV+17wbB0p0qOv428s7+fQK9N0tppu5dSwu9Ts6AVzKxZGGVnMTIUkav347e3bc/QVYbrJcvq5LznStvp8Wdsp7VZSPzqULFmOt9j7zlvlwRI21V5HREGsolIW0by2ZYYQ5455XmBW3edu5xKWV1zKLzu7yw8wXumbmiprvgtSzKXSTZ+Tee7Y4VjrjnfPYPfjNkWfXUtOvrtXQbYRhDprdcIeue8JwkDbUiUtBwfBw9b3iJGCNFzerVZPcLIXe62B0HAOUTNQytjerir/JQi3BDyBN9zvhlp85tsnxhK9jDXhSHozpCPppylH5GI+IlTUKNKCRnNKHFo0iq4kxHjaEqy6EyHsHqvzhVR7Rq4Tbd8j+uVev/cBJEzGuT+ZfPIodD2CWd5Me+zZybbh3Xfup1qBwPtiFQU0pda4ubQClTv8Ssy/bO3d64qpPeXZFcT5hXR6yrbSrVDbCgRj3mYY5JdeTetbMoDJa1rbZ6jNeecLiRfYLcjbg6zZtkQolw3aopMCjcnL8HZ279ot31ZBf6Cp6Itm1nVleWnZweWZEq6pwLS3hR6Vq45zl/ozJhCJSw4tSf18uNy0qD0XKXWrMuAMUug16zv6LTtz8iPpIgr1XFy32PhMtVamKVfvBQTEMJq6hhp8a6GnE83vmeLNnODPmDxcGiEnUaArn4c3r8xSb0vIGsKd7nuPmK84ctYBBtSTkefsvyEm/dz1Yp30S4gnwkV0HAsY0khBjxDJhOp2loaGBgYIBUKnU4xjUsbkOaCztfYMPSkwjFNNSoWk441Ig0RIk2J5jxvVvo/fsPo8aiCMui4TP/NKnjevzsMwC8Vsl6tsTA9lxF1KG6wUH1hS/coRFtDFdEVvzt2N0TtN/qMtqkKf+FuJ5FpZ7Xv5ZX0j9u/3L3RNu2uJVoU8yxs5Q9j5IsIWtaxfbTvv0foxr/eKknzv31bbWkQqQ5XFGmrGJdWfK6IVbjNmJxL4qWYY3LSxxw6Gy+4nwkWWLJ//yJLe+4yEuMCyUizP233417v/4yig1L4gDk9hUrZghrfc+UmPPd6ljZillykvC0qDZiBG4qz4CMtotivW3d84T/vOWPPgtDTNmqWrVynbSkMiTHJhQPkZjWiBqNeLXCLcPEKupOtLtsRSmmHZ93JBVGi4W9ZHnbdHIKHC+4Tv/OfvSM4QlNF/fGACrzAapnmIfzt7u4M9P+wNF4LUaj+Y74A0VeVa+46s18hxMhwskIiWmNaPEIkqpg6yZmoeR4t/OlcvdSG1lVCCUiaLEwM2/89ZjHeyjs+9t3etVtwg1xJxgly06jp3CIxs/+82Edz1TTa37csX3yxk7C0bGPrVRI8/2/6Rj1e7vhhhv47W9/y0svvUQ0GmXNmjV861vfYvHi2ufXD33oQ/zoRz/ie9/7Hp/85CfHPL6J5IhHzseDP+rtTl+FUjbx1jjRplj5BCez/7p3gS0odPUdlh/s6gcfZcPSkyr8edVi142zSlVi1z3JhpMh1HDlx2KXt3Tbyg+KR7PsXx09/tJdtRhuClDSKv2T1aLcvwxwGogUDLSoNujbU5UKoT6eRgSjwW9rqVfpxKuX706rK/XHU0+Y24blzOiUp4DdaFPAkUGLhVEjGjuueTOSLHnfwWhjhM1XnA/gifexMNyMhJtgWssCY+WdxEgtqhFtilPKFMl157znB/7hE/Rv3V33xqHWd/dQ8l0mIldmLNtqKYVwh+ac1yIqsixhmTb5ngLZrUVvPStvj+k8drjx3zxUVKcpi2Ut6iQBRhsjxNuSRBrjRDtakEMatm6g92ewijpm0XD81zkdI28gKRLhZIRwKko4FUOLR7AMEz3tlDXMdWed0nsZw2uaB5Xn3FpJu9Wztt5zDJ39cJ9z9z8RM0Nj3YcwhHcelhRn9jsUDxFORYlNb0VrSCFpKmY6Q6m7D2HnveIRxYEitmETaQizasPDhzz20TDw3U8iTAurpKPFo5glHWyBpCpoiThqMoHS2IDU0ET+gV9jRlOkTlt/WMZ2NHC4Sinef//9XHvttZx22mmYpskXv/hFLrzwQl588UXi8XjFurfddhuPPfYYM2bMGPO4JoPDJs7vSC2taxUZD/6LlVv9YPlt9wKw/9N/BbIE5TJRlm7W282E40ZMNyw9iXhbDNuwyO4veMmorj9dSyqEW0IomtPUQItpSLJMrDlGKVOsaGbg70IpK05ylqTUqCU8Aod60hWGGEycHEaYu9n2ZsFEWE6TBLc1u1ub1+0sJytSRYRTkuVJKxdVzz88nhb37myGWbTKU82VEafRJEMHTDzTzlyBFImAbWP2D7DvkRfJ7MtSSus1cwkOhYoqKNSPdtumINOZJZwIYeQN8j0FHli+krUvPI2ezg4R5v793B5dwiWFl7xz3Jy1M4g2J+jd3s3BTb0V5WaVmEyoWR1iLXAbp0g+T7NZtMjtLk56qcLYrAiNc1IkOhqINMZRwhqlgRxdL+yrEOdTGbmc4O7WwXZri7vWES2uEm2KEm+NE2tNEW5KoiViqI0NIMtYA2mn6EG5mY+whVMjPKoRSkSINMZRo069cACzMOBUWSnoTqJmceQbfr8wr1W6EiqTKKtzubz64YfRSuTm/rjXFWGULTaWk4hqlsozB/0ZbMME20bP5tHTeUrpAvnePKWM4+OurqI12ciqilCc2vG2piJrKlZJJzpzGur0mVjNHRQTLeihBJYSYsbikw7r+KY6h6sJ0Z133lnx+Oabb6a9vZ2NGzeydu1ab/nevXv52Mc+xl133cWll1469oFNAodNnK9Pbx42SW+0VF8AXbuFFvG9FVlCkmSEbB/2KS6XdZufZeu71tO8aBbF3jR9r3bRvbUXvdck1KySmpGgaV4r4VQULRkj1JhCmBZmvkDf1r3lk7OBWRpM6nF9hfW8hIdK9ZRtrcYftilqNsqoFuauN9C2BFgWlj4YF3OTd6Ty3bOsOVYSN3L9/BvP9cp+Ad5N11gZzffN9RdKmlROihqaqAWDNYTdz8Gtv+sX5gFHlsJvvos49Rwko4jSewAr51R90vsr7QATESRwE8P9iZb1ZmiEIcgdzJM7mPdm1Vw7TNv/+1nN/cuaRKhZpXVFM1vftZ4F6+cQTkXpeOcVCFWj+bmniDU/y/5NB8jvKDk3/VGZaEsYLa5VdBX0N60BvMYyer/hjW+yKkD5czYkRUEOOQ1bZE2ZUP/+ZOHeFIVbQqgRxWsEBINJl9HGCKmZTSRmtBJqakCORZFCIacutmUhRyJEmhuQJJliX8bbt7+8nnvOMXIFr7KJnnOSLNWIgpk3K6rmuFRbI6uXV7eB9/KdGAxKuP8fic9i8DUHGwqasoWkON9Ntxwk4Il1s2Q6N7kHi0eky3D39R9ASyWcPCrbBiGwDRPLMFHb2jE65lKMt6JrMUw5hJBkdmx7mXkLFx32sU5Vxjtr7m6TTqcrlofDYcLh8IjbDwwMANDc3Owts22bd7/73Vx33XUsX758zGOaLA6rreVQhfl9C09ESykVVhHXe17NZPuYR8Px/3UH2YdvI957gMTLLxFt2oaRLxFpjJOc1UZ0zkwnyqcoYFmIYhElkyXaPEApXXCy8A0bYQ8KW39ThuGiJGPFXwpM8kUzaolx9+Re7Vn0C/PqZg7u2AEsqyyIveeNwaimr5qAG51+eNWp4y5VOFqBbqadkmHVneCqxXj1DVJ1mS+3pCc4XUpHW7Uo4NCJvvVTFP/3n8GysDNpjP40hf4CRsbyIsSTkTBb0bbehxs4iM2KUOrRvXEM1+jr9ugSZE1i+tltzD5rMfHlS5EiUUSxAMJGSvdh7t5F5xObGdg74DSpicqAY59x61dTnplSw0rFja6boCasQfHmtoyHiS0NaxuC3L4iesYgsz9LpLEbLaphliwy+7JTWpi758NQs4oWVwnFVbSYhhZ1/rndLY2CjqIpxNoaiS1cAM2tSIYOlgWKgqSqyJaF0tKM1tOLbbxKoddp3lMoGNhmFi2qkZzeSKSci+N2+Iy3OuUSi+kSpYzudTiuOOf7zsHVpQ699+JLVlVCMpZsAxaWISoE+pHEn4QJznnVLFooGd1JhvXlW1klCyNjHbFz655HXqZpXgtqJOQ1b8r3ZDFLJpGWRiLROGo4jq7FsKVyxR4xlU1bRx+zZ8+uePzVr36V66+/fththBB86lOf4uyzz2bFihXe8m9961uoqsonPvGJyRjquJnynvNHVp/mCSM1pno1dmtVXNlxzZuZ9+PbDluDm9HQ1bGChmQbcduiFTAzOSIzp8HSlWDqYBqgF5GKBcezFtYJNyQqolxOxMA5OQHeCWqsdox6tYT9y/3/C0NUiE0Xr548VJRKUzSpohRhrQi/V+rLEjBMM0e3ioCboDTexjGjrdriCnRFs8pWIzxRXqvygSvK3Tq89TohBgL98BJ508fJ/vBzKO0dhKe1kZyWwjzRomfTgPc9Aiqa9IwXtySiixtN11IKjYsTNM1rpun4GWiJ+KiTwi4pvMRDK1fROKeZ6MxpSNE42BboOmZfHwNbXmVgdy8DuwcoDehocZVYewQlpFBK6xhFp6RevM2xWWixMIXeLMV0qaILo2XYFbMJXsIgEyvQ9R4TvcekoOko0fyIlaqmAv5SeonpUeJtce98rGiy0zQmFkaSJS8p0SyUMHt6UEwD27KQo1GseUsxIwnUYha1axfCslCjYSKNcYx8yWneA4TiYcKNceJzpmNmc5R60+iZPGZRx8jr5RlGGRh6zrfL9bLd87T/HOQvEyuZAiVcLqFYbujj/psqdeX9At1pYmVVXq98pZQn0iI7Vrqe6SGzP4uiyU5307RTBx5Akjcxoz9D6qQetHlLKSQ7aFtxxhEb61RFlpx/49kOYPfu3RUJoaOJmn/sYx9j06ZNPPjgg96yjRs3cuONN/LUU09NuQZHU65ai3uBi80Lo2iOJ7m6xrTr1zyaeN/XD7J0eQtvPHEPc568hfyWrWTedR2WpNKU2U1kYD9yph8x0IfR1eU0cMgXKPYM0L+zm4HdaYp9esXJ1z2xjlTZwJ0iV1MKWkT1otKWbqP3G15H1OpksVqNifwJSbUSpKqpvgjXm4at3n9F+a1RXjgOtd53fF6YxIw40cYIWtSpMGObTpMIt1mHO6Vq6UO9uv7IenUJRlcUGTkniTfwo08c/i6wakpx8jnK5Ugt3YnAGTmTxPQojXOaiLensHSTA5v2jrs2/e3RJURnhmiYm8QybLqf6SfUrBJpCtE4t5FwMsLA7j70vMGZjz9Rse19C09ECSskpsWYdfpC2r/xC/b+zTucKfui7kRk8yWvTboSUlFCzu8235PFKJgkp6WINMYINyWJTmtDSaXYc8eDFAcKSLKTZNi8aBaRmdMo7NlP75Y99O3oRc+Z3o1vqdMYYnmwCk5zJP9v1F9FI9Ya8yqLSLKEpZuUMkW6t/ZS6jSmbJWVelRXDNGSCmpMJdYcId4WJ9IYJ5yKYeRLTtv5dAlFk4k2xZweDvEoZqFc2lBVkDUVLRkjOnc2D6z4DL//c5Gn73uG1tnTuPFTEu1/+SUHHthI7mDGq9QSiodITkuRmt2GrKnkDvSS6xog35OnOFBCzxgUu4wxnw/rvdepIMQngiNVNvau5mXA8JXNwDkXxWdE6vbI6P37D5f3U2L/U9s58fb7J2R8R0O1ls/9a/e4q7V868OtY35vH//4x7ntttt44IEHmD9/vrf8+9//Pp/61KcqOoJbloUsy8yePZsdO3aMeYwTxZSInPubbbhR3HWbn2XD0pN43aahJemONmEO8OqmVyhki8xqn0Xb0jNoeedneWZTAVUWhGIFtFIGqZCDUMjxLJZ0hG0jybLTxEEf6mtWojJCHVoRphpJk1BTCqGY5iWE1aJe50BXUFdEWAzh1HSHIVOrMEKSZVXXwIrXHIcor8V4msfE2iI0zW0m1ppE1jTP32ZbjuAQplVR4sytpuBYjwbHKskSkVR40JpjC4y8gZ43sErWa7Jt+eGm3mdbGakdrIaUmOUI86bjphFubcLWDYy8Pu7Xv6TwEg+vOrXs6YY5r59Bsb9AvrdIel+acLKIZTozLo+sPg01ojpNZHKmZy9Qw05jrv3XvctpcuLr8Og2llHiIZKzWtDiUQoH+8m8cMBJEDQsigN5UqZNqS/j+JttUbaNON0I+7btI9qbdr6r5aY1/rr87o26pEgoIQWlXPJPzztTWnK5aoYWc2wciiY7yYsRxzfuRpOjzQaKppBpydK/NYPec/gS8A8F10+uJRWvPrkadRI1k9NSJKY3oyVjCMvCLOrIqoKwbYy8RaRBEG5KEpszE4TAyuawDcc7LkciSMkUO7pCPH2f0/ege/cBfnjvav7ftBbyPVky+7MUekrIqoTZ4Bwvt+NnrjtHoa+AkTPR+40KW9ah8FoR5S7V54DqnI/JEu/R6WHHdpO2an42/vyT4Wj+4r96f7dN+CinNpI8vrYnY21CJITg4x//OL/73e/YsGFDhTAHePe73835559fseyiiy7i3e9+N1dfffXYBziBHDFx7o92Qe1p5tdSreiV605g2eIYyzoOog445sEZsV4yZoycnCISbyVezCLnM8ihkDO5l8tT7M9RShexbdsTGzAYzRbq8CdcLaUQalYJJ0OEkyGEJShldMfDZ9ujml6uF+n2T5vWsr+MRK31D8cUa73E12hTlKaFMwg1NSD5G4dIsvNYkpDKHU7tYgmjf4DOp7ZS6C9iG+VpTcXx+mpRzRMvzk2W83qud/TuDifxJLC8TA7V55MNS08ivT+NWTIJJ7vRYiEkWRpXPsMdqaUoUZlIa4h8dx4trtG8IIlRMLAMi1DcSRpUwyp2POS1XLd0R6xbJSeSb+lpZPXVIft3vzdurezY9DanHF/WqW6iaDLFgRKWaaNoA2Q6s0iy5JUstQwbq2SSLvWT7cp4rw+ghhXPN+2IzfLNtK/lezgVdnzPYRVZHSyBKskSWizsJDK688u2QFYVos0JZNUR+H1b095s3FTAn0/j4kbKQymNSGPYO3ZaVEOLhYm1pgg1xFFjUaySTrghjhJSvZKH8VkdaNOnQ0MTkmUhHTyA0dOLWSgRikRAVuhoslh06hJefvIlWmdP492vHyD/3y8wsDtNoavkWTSsgjO7U+groIQUjJzh1TSv7sz5WudQaui7+UVuYGYiClDUom1JC5ZhkTmQJbe3UHHNCprATS2uvfZafvnLX/K///u/JJNJDhw4AEBDQwPRaJSWlhZaWloqttE0jWnTptWthX64OCLi3G0i5HIk/WOHi+9eG+elbbtpKe4F4ZyUd2bbODigAY3Ma21ieUOJWP9BhBBOGUjdIHcw4yTGaApolXYJ2xJYI1QHdo+zWbQQVsmzswBe+ax6DJe4BpV1wquFtj9JbrSi/VCFeXUt6FpdDmXVsfgoYaWiI6gaVWma30Z0/hzkRHLwFl3YTsKuJCO0EKgayAqKoaP095A80AP0UkyXsE3LVyrSX/VF9ipUKJqCiAoo+30DT/rEUv2ddb8ToRYVq5Cl2F8inHKalY2Gvm9eS25vF90vd3qRTSUqYxVscruLZWtXkdyBvBNFKzjtzgvNJRRN8YR6oafoVY1xRZkZtejMdTsiMebUAlfCKrLiPG8UBGapm1xXmlKmyMCeDMnpCZoXtKFGNK80n20JSpki+e689/uWFKmcxDiY+6FoMqF4iGiTY9eQQ6pTq7k8E+SWbnU7Gvp7EXgNxKqEuTuTVOzPeYmLrl/bX+bxcFH9+UvlMoihRq1szVG9ClH+Y+LadMC5OTKLOtJADqtQAllCSyUc+1AygTRzDsX2+ZjIhLMHkXdvw0xnMLJ5hG2jxGMY7XNojpV41+UNFC9bQ2ssz8Ldf6C/1xHm/prlrpe6qOkV3vGp5Ac/XPjtlTD2jryHo3nX0lvv4ZX3XoZZsij2loa8fiDQR0ZCGpfHW2Js29x0000ArFu3rmL5zTffzFVXXTXm1z+cHFZx/pcTT8HMmyhRmYuOQc/tkoWzueuZdg4WQrzyXwZffVcIgNe9+UHOuPgU5r2+FXX7dgqdPd6FE0CNKDU9zlgjW1qEITAyzvRbdeRoNCW0hnQDHUNddW+bUUTVJ+Ii5Pf6uYL33nknVNSDVzSFeFvUix6GUzHHV9qcIrZsGSLZgK1WCTdXqEsSQnYu6pKqIdsmsWktmEWnHrFRGLQCuF0BvbGVK9gomoylSUhmuQTfFE6Mm8rUsy0lj4+R6IgPWW6mLcy0I56FJSildadiieJEz12qo+h9W3aR78mS3pNB7ze83010ZggtomIUTQp79Qorh9t8TFYl9JSCEpUp7NVrWBNsz+et95qYrRbRlkg56q6gp0v0vZIn3BAiNTNJrDniWC7mTkNLJbDyBfR0lnAqSrE/R1odwCg4lpRIKuzN4NimhZ7TvW6LoWQMJepEx23dSWa0Sk7ZPld822XBDiCrMkpIQwlrqPEowrQwckX0fB49W8TIl8h0ZjFylbkYkiZVJMxOJv5uv+4MozAE0fYwoWSIZEeCWGuKSFMCLRlHUhRsw8AqN48BZ/ag2Jcte/+NwZvtkIoajSCFQ8gtbRTa59OTnAdACzZa3xPkdu2n2J8jnIpi5fLIps4ieQsN+59DKuQQ2TTFHbvIdQ14Y/ZbBI8up/7k4gr0qXxTIsnl83lY8b7nkynKn7nIqcl98l0PTNprHE4keewWFXe7sTCKlMohHEmfuZ9DFuebrzifpbfew+Yrzh8sM2cPenD9baoPd6OAqchFJ4c56w1O4sdX33UOAH+57WzufrZE25b7SR/sdUqgxaIo0Qiz1ybI7u6k79WDZDudzoLu9LM/S7wetik8j7e7pqxKXlkuGL3gHkvDI3d9NzrkF+gTOU3rNnSKtoeRNRlZkbwkv6fOO4vm4xu9iJiiOVP1yRlNhBsShJob0FqakVONiHgCOxJHyApC0Zz/y/8ApPKP3JYH7S5KNEVkYRar5HT+c5uKuB1qbZ9H3Swa3u/DPQaBMB8/tYS5llJomJUk2hTzlrkXefeC70a8XQEnq04+hizLhFOVN2Wu9Qgc64ErrkMtKoqmEG2OEDIc24HeazoR0LztVXER5d+LFleRValCgLnfWzWlDCZZK5IXtY61JNCiGqnpSTpWLUKJRih2dqNEI4RamhyrVLk9eL6zD0s3SbQnSe/rR4tqxFoSjqiMaMRntBHqaENpbEJYTuUXoZewiyVsXUeJ6tiG4e1PWE7nQ8vX9VAJh1BiUbRlJ4CwsXdup+epzWQOpCkOFDGLky8va1keZJ8gD7eECKdCFeUjE+0JEjOaScyejjZ7Dlb7LErxFoQkoRoFIvl+5HQfYqAPO58DdmPkil5irrBNhG0TamtBbmhEhCIoRpGYmcaWFBSjSOHAQXJdAxT6cmT295Pe04P2xIvE2hsYkGSEsDHzJYr9ObJd2TGfR49FprIwByj05SmmSxi5w5NfcfJdD/DMRWt55qK1dQX68288lxX/d99hGc+hIksS8jgi5+PZ5mhlXOLcbYPtZ+mt9xzyYI4VHvr9OZ5Ad7nwpDA9tz2NWSgRaW1EjUWRY1G0lmZC7a3E2vfx3C2Pex5Ed3q83klsuMhDLQuKn7GI5+EqtVTX0J3IE657UU7MjZKamaRhdgtaLIywbfb97TuRVIXpp8wDQJJcISajRCNE58x0BHkihR1LoYei2MqgMBOyQimcIh9qoCSiCCRkLGTJRkIgkLCFjIQg1jiXhvkrmNG7F9L9UCohLBM7m8U2TIThRCb1dBZZTdOztdeL4IJz7INuohODbYqKigf+8ptQWfrQrTcuaRLhlhDtS9tY/Ou76m7rxyrYFDpLXtUTI+NUN1F8SdNyOQlbViW0uIrdYVdYq/x1p4k4+5U1GUWVUcMKkcY4sfYGtHjUaV0eDhFVFMxMltyrjoAMNyWRNZVCbxY9p9O2fBZaLFSu7CIjh1QizQ1EVp2KmWqjGGvCllVCpTSh3n1Iu19FMgzH1hWLIkciyJEwUigMwkYYBrZertkNyNEoD3W8k86BMGe+/hVa8zfRs61r0pqiudTyjANImtN4KT4j4lSSaYkRaYiihFTPTx/vaCS58kQyy17Hc9JSOrMx7BK0JwokowUSTVkSbb3EM52ovfuJ5AtIPX1IslT2+MuokRBSKOTknJTyqIU0US2CJGy0vv0c2LqPgb0D6BmnlKV7jg41akSawiiqjG0LzKKJkTMx09Yx5SMfD+7vBzgi9qiRKPYXnJmi8md9OKwsw0XNXWH+wpvPI2tM/YTsw9Uh9GhmXOI8EOKHzkO/P2fIstyBXidqtmK543EuFaFUxM7myO7rHlO7a7tKhNiGQEsphDs0x/dcTkxzax27F4shgn+Ek6KFE7Ec8vrlKOJk4J64E7OizDx1NtGWFEo0gqw5F2W5XPHGnw4uSRKSpkEkit0xGz2awlQj2GoIUwlhyRq6EsWQQuStGK/2N7Npq82Obb1IskxjcwOpVIiGBoV4VCKkQlgTzG3OkWtM0ZDoIJ7Zj5budpJ6E0mEbYOho2azYNtk9/Wy9oWnvXKhSlRGVqVAmI+D0VwMR7pxdcV6dkeB1226i+cuW4esKvRu76vYVonJ3syPEpUx0xZau1PdI5yMkjw5hRJSKQ4UnChrPOz5t9P7M47gjqhEGy0kxUk6jDXHCKeiyKqCpZvke7KUMkWnbnJOJ7OvF3BmydSte2mY2050Rgelvgz7Nm6nYVYTzWeuIr/8LOa8bg9iy3PIxy1lYPoSYtkutJefgY4Z9M86iZ3yTHqKCTIljaaozvTGg7SE4shPPs7m3zxOdk+B1hWNHLd+FfqZF/Nq9ARm2jtIdb6EUsjBQB+ikEcsOpGvfvExAJ644BS+eN7bOD4U4sDDm9h+946K2aCx2NTcqikwVIj5I+OhRs2pchNRUMMq4VSYWEuCxPTmyu6nuQKldAEjX0KNaCQti4FIO7sOxImHbU5s2EZz33ZnVkzYSLaFZFuIWAJJUbCKBpl9faT3p7ENi3AqQqE365WzFLZwZsiSMYq6iZ4rOd1C9cEKHsIQFDsN0uRHdQwCHJSY7JQonZWicU4zwrbZu3Evhc7SqKoAXWZu8RK2rYLN7dElE9LTwOWB5StRQjItC5tZteHhCdvvoeJGzJffdq/TPbOh4QiPKOBQmRKlFAMGkWQJfceOiojrwI4uDm7pGfc+K4RGuXa8Tbl5UGawbvl4fd/+KPBkRMld/BE0JSp7NgDbtJEtC8rRMlGO9FUnnAjbRgKEomHLCkJREUgISUFXoqRFAwUjTN50cgE0VaL/YJqunfsAmL5wNvMXT2PGjAhqQkJTBUktR4QCkrA9C0w1dkmn5as/poVKO4ZtCmpvEXCoVFeDGknMb1h6ElpExbYEhc5y3eryTaBb+1qNOL+dge05J+KeDNFyfDttZ56EnEyR3vgsajRM7Lh5CMOg96kXsAyb1Mwm9GyRgT19qGGVxrmtJGa1YWTymIUSwrYpZYpOMmXIuXHu3d5HoaeEMAQNxyVpWTYXJZWk0LMFSZYpDhTo3/gsqUwaYyBN35ZdNOcLpGQJ6eB+rIF+lOlziOa7aUloxOM5erQmmrQBpve8gGQZKPPnEU4+g95s0LZkOqFTTuch+XTuus9m3pyVvG2uSXL3c2AaWJks8pZn+cE/foxWOkn98Sts+5vHKWV0ir2jE071EIZATg7erBrlxjr+ngqSJnnCXItphJMRok0xGuZNQ41H0fszmCUdq6hTShco9OXI9xbIHcwhqxuZJUtMn3U8UtFE3rYds6sLJRZ1btglqey/18nvOUCus49SpuiVvrT0PEbBQC7b4yzTRlFlQomQ1/HUj6xKQbnUQ8Aq2OXk5nIp4bw5ppkGYQgs7ElJpnXzTpb8z58mdL/HGkHkfGQCcT6FmHPTrQA8d9k6jIKBsJwSaPmDRQp7x1+TGajKgHf6e+rpwcoRh0JF7fNJonpqWxgCs+j4uV0xLmzbSdoUAqHrSNGoM0UPToUJVUOKxBBqCFvWEJKMpWgYSpgcCfZlG8nrCrIEkiRobVaYdVyHJ873b9uNqqk0NMzgb9+oAgowjYGNm5AtE6WYAb0IpknkLZ8c9v3YhtNRFZykVYDzdjw34cftWOWSwkt1Kyf8QV1ManGMtS887R17vdekpBqEOzSSc2Pk1AJGxnKsUzPipKanCCUiyKpMvK2f1Mxm2l6/BhpbMBo7kPs7yeztoWF+B8KyMPr60bNF2pbNIjqtjV1/fhrLsOlYMY2W889BZNP03fkgsfYGJN1Ei2pegmooHiKcCmMWLYrdOrZhkd5xgFJvmmJ/DjXsRI6LvVlKjz9PZn8/Xc/30LO1i9YXtztJnYD6ym4n5yFfItWSYtZ5r8domo709IPYxRL6ujex7EsdiL07kds6eHXOOvbvDdPWZtPRbBPNdSM691Lad4Bi7wCF7jSJ+67i4IE0L27q9QR5vZyVsST1uZVu3OoqLu5shdsESYtphOIh518iArKMns6S3e/MNLhNkQr9RYp9JSRFon9nN3LoGWJ79iJJEoWBDPnOPqcspOZEw23TxiwUGdjdS743X2FRAafalVvdyc0dcW6m5PJ5yMbImV7XzYDxYeVtSobTGC/9St650RnjNapW596JsJ0E9sOJQ5Yl72Z3rNsdKwTifArS/VJvRTmtifLcSZpUnooPYeSdZNLJsp5MNNWeU9t0EmL1bJFIY9wR5t6TNkoqBS3tiHgKWw07UW1ZwVJDWFoUW1KwZYWSGqefZnoKSXZ2aagKNCZsmmIGzTNKdDTFGOhbxCtPv4ysKLROa2D2dIVnt3Yxv38jPHwPdqGIFI1gh5yI+2j7Erufr2u+CcoqTizVF+Tbo0uQNInorBCNc1PkfvwlZp42jZ5tTndLGwjFNCINjgk8t7dArD1CrDnmdONsSCCEzdwTjkc6ZQ1dbUtI5jpBkggZJdL7+skcSBN5YReNCzqYcfnFZBasQn32Por9BcKJEMkFsziw9HxaD24mnNqIMC2UkEqkIepVWom1JGg54TgaZu9hYHcPuYM5Xr1vuydeQymNhllJAMySSbYrh95rkk854rKU1Sn2l4g0hmma24wSUsns7cG6/U60eJTuzbuRVYW2dW9i9/x1zDbvwe7aR/O8fayYHmZ9y1binduwX32Z3I49FPsyFHqzZPanyXTmMNNWRaS82oailivUeImyI5y7XDFl4dgaQjOd35EwhNdDwdm3U29d0RSv6ZKZKyCHVEKJCKV0HiNfopQpOn7gckQ725nBNi3Ce3q8rqZGwfBKRIJjhzFLFpl9Wa/kpbN80KbjdlF1sQwLMTCYDOyuV31MAsZGRW7IGLabTN/3y1deTGp+YtL2f6wRRM5HJhDnU5Bzt20CnGYnEzU1ZxuOyC/2OrWIXa/5SLh+2+q23Ier1JXrOXXrJru4U5eF/gKxVp1QQ7xci1lGjkRg2iwKrfMYSMwgRwLTLltYkEgoWeLmABKCNI105hvY0xOib8AmEZeZ3mzTEsnSJvaTZD9nvqMJ+8oERS1JkTQN5qskD+5A2bOVgy/toNifY9Gv7hz2fdzVvKxma3OrYCN8Nx13pJYeE3X/Dxd3dyxHSyrovWZFi/rM/iydDz/LwJ4BCl0lr2Z//4tZMtG8dzMoK1K5jneexKx2ZFXhikffyWfXLuPU312HEgmjTp+O0dlJ9kAeJSRT6C1QyupYxQ1E2zdxcF83pYxOpjMHdz3JzHQWvVwz3CwaXtlNs2QhLBs9W0TvHUCNhEhOb0SLhpCUAfq3ZjB7TfReEz1tEGkKEUo6QjbS7jTTibcliTSYNM9X6TjjBJS2NnLPv0hs7iz2rL2af/6/JqS1cPl5KnP23cKcnc/Rdcef2f/MbloXPcaCkxZS7Orl4EAOPVt0hG7BoJQuUugveeeN0fz+x1uVRJZlr0GaVbAxDYGkWWhx53Illxt9GfmSUy5SlhyveV6nmC5R7C853XjLY82aBUppAyWUwbaEl8irlGudu4/dm4l6ifZ2+UbB/W64Sfm1ftcBRx5/x9BDjZ7PvOB0Fr3vaxM1tICAEQnE+RTGFWkT1VjBNgS5HSVvGnqki6cSc6JfxhGIrsua0ywo3BJCjSjO1HGN6U0j7zRiUaMRp9xZIomUaqDYPJNSpIEcCfpKSfKGU41FlW2aYr3Ec11IwmZ3fC6d/SG6+2wkCTQVFFkQU/I07X6e3D1/wsjkibQ2kpzejtI+DVQVTMdOoyUimMWRLUe1xu4vueev5BFE0CcOv+ByK6oA5PYVebVvB6VOw1vX9qKelmejyu4vUOgvEW0Mk5rVQmL+LP7xqzM47uFv8/CN9xFq1Jh2YgdNx8+gaX6D05hkoEhmf5b+XQOEYprz3S1ZFLsM9ncepPfVfrTIoE1DDavkuvPk9hawTUH+YJF8b55Yc4xQIkLD3HYa53fQNPcg2+/Y6VSL2ePcYDPDGbsSVpDLUeFQIkK4MU6puxdr9z5Sp60iv+BkfvynNp768xPld7uSM2b0YXR2Em1JEU5F2PXQLgb29FHsL2EZdoVorf7++qvT+I+fbQj0HtM7fuO5gTdypvea7gyiZEoUe0uEkoOdTN3ZslLaqbee781T7C9RGtA9m4y7n+qSs7YvIu8PUtQLhghDYBpWxQyeP5E+YOoyERH1eCDMJxRZGvUE85DtjhUCcX4UUK/hynhxLz4ykDg+QuvxzeQO5sgfLGJknGYtSlQm3KFR2KujaBKp42LYlmDgxVzFPiYaLaUQnR4m3hYl0hhFViTS+9Lk9xSxTUGkXaNhbpJwMoKwbRpmt9C67ky2nvzXPLqjnZ4+GykH58YytGndNFi9NGo9RESGWM9OxMvP88rv/oIyt5W2yy9Fip9GXwYGBky2Pr+PYr5EJBZm2cpZvGvNGqZNe4ZQYxElHkdYFqVtW73mUMKyUMMhos3Jmu/lgeUrWfvC08DQdvIweNNV4ZGMyWjRcXRnCKjJRb0vAuVjbQxWEJI1adgkRtsQaOXKLHqv02hoYPuzwLPMuf9pxIWns+Ybb8c42E3v5p3OjI2qIAoG4WSI1uNTZA6kKfQWKXbrNCyIU+xybgTyO0qAE61XYrI3Jve733xcI9NPW0T/tr30vNJN/65emua1EmmM07yigUJPkfSWPFbBJtIQpnFuM5IscWDTfjKd+0l2xGmc24yZdxJb+x58lMiWLXx92XNkbliFpYRI9t7Drn+6m55tPeh5g9SMBLPPnI0WC1Hsz9P9cjeGYXnVR8DpjBpqVp0ETrd6TbNMtCWMEpLJ7i+g95qDAnc8yeV5m0Je9z4jtyylMATFLqM8A1LyRLKkSV6k3S1RWqviVK3Y9lji3UHDoKlLPfF9qNHy/M3XE7v6+nFvH1Aft/PweLY7VgjE+TGMW0f24Is9SJpEfFqMdCFbjoBZFdO1xT4nUhefF8bIWIdUnWE4JE3ypq/3PnLAK4kFeBdjNyGsOFCgcekC8otP50C+kd5+m927MuQzRc5aliBKlnBxgEj6ANLu7eS2vsLB53bQ9WwPzce1k52xjC2djRzsMTAMm1RzAkVTURRHHEsI1KYmrGwGORKhsGM3ejqHrKmo8SiA19J954cuZ+6//c57H3858RRs2x5zElGtKGXAoeOf4natWkoU7zvuL5foikvPd+wrS6qlFFoWz0JpaUVE45g7dtP7ykG6XtxPdn/BifJqktOFNKMjazKJWVHmrFnItBML6NkixYEC6b0ZCl0lpp/WUS4LGCU5pwO7pBNua0Y55Qw6Wp5ECW3ByJeINDpdLee/fjmFg/30zT1IMV1CCatosTCWblLoKXniWM8bJKcliLUkCDfE0ZoaMFtnciA8j6IdZnpbiEhjDEnpRYuoRBujTqfPA2nPjx1OhgC94vcnyzIWgyJYGIKS6ojp6uN1qAzOZAwKY6ENTRB0Gzkdi+3uj3VGEt/jFegvbtvH/PjQTsMBE0PgOR+ZQJwfw7gtzd2LWe0W4+UoY+/gVLMSldFSSoWwmaiLopm2yOx0svT9NwCJ4yNEG8PYlsAomMRaVJrmt6EuWsKO6EJKAwqtTTKFQox8IoQkWehKBE0tgG1hZTOY+RJaLMz00zpoWrmMZ+XlbN9jIkswe1aEd79+gKKdICKXmDvwZ9QHH8PMZFGXnUS+dS75x7+PkS+RmN6MrKko4RB2Sce2LGzTZuO6NeR7i05bZ80R+JZm17WpHI7GFQGVXGZu4b6FJ3plP10fuqxKTituxRHWer9RkcyoxGRmre1gwVvOwc4XOHjvg3S/3ElmX5bC/lJFtFZLKWQ6c8TbYsxcNRctHkXYNonGpFMqdSBHvK2faGuK5II5yJEw5orT0TK9iJ1b2b/2PfQYTSw/UaI9kXCaAMkywjBAlp2bw1iY9K5uul/uJr0ng6RInkXHKtic+fgTPH72GZgli2hzEiUeJ5fqoLuUomSqROKtnPBvv2Mu0PfNa1GiEfpe3E731l5KPbp3XFxLEDg3zlbJ8iwj7k084DVcEqqEKEe8q7/fEzH7V+sc41pWAlF+bDHc+XOss80Hv3Q1eibPzBt/DcCyhTNg4XWHPMaA2gS2lpEJxPlRwkRbW2DoxcwvzN1ETHAuvFpcxSo5U/zRjjBqRCHfVSSU0rAMZ/lERNNdz2r1ODqWtRFpjGPkS14jEEmWkUpFppV2YKQ0bBrIFzUiEYU9fRKZ2AKS4RJzZjbSViwQyxeItDYSWboUo30OzaF+Vi2ZRixkEVFKLNz1J6xoEiXXj/H8s6T3dxOf1QE9B4jt30W6aNB64kIkTUMOh5CjUaySjrDtcom9uFMCr2Q50ceiWe6eOPU7th1LnLttEzuueTOldIGBvQOAM12qhlW0qOokYMoSfTt66d+SRYnKzFk7ixmf+Ai/t9/ABfd8iF0Pv8rAS7khvyG3oY4sO82GpHJZwwPP7HBqczcnnGoheZ2IabH/wWeQZJlpxRL5rm7kkEbYyvPQy/OYsWQJkXvuxMyXaFi5HLmlnYN33EP2QD/5njy5gwWK3XpFxFrWBptarX7wUQA6P/8epGSKUKGflcrDhPv2Ez353d6YI7NnUdq7j55tXWR3FACntKS7Txf3daptI66f32U4C9dkEIjygFqMJfhR7MtM4kgCAsZOIM5fg2gppW7FgdHibOtcjKPTw155MSNtMfBizhPNbtRqMqwYWkph5tkdaNEQDXPbaTj1RGhuhwN7nBUiUdKPPEZ468ssWXUGXQ1vQTdkSiWb3Z0Kr5oK4VCcPW2LWXZcMzNnLgEhyMgqiqUzLb2FNm0n0d49ZNqOw9q2GTkaxejpJbevCzUaRtI09tx2D6FEhObl8wdbnMfjSFoYOeQIMFlV0GJhlJCKohleWTzXcx4wtWi9+irU7r30/eVhir1ZhG2jRkLO5xcOoSVjtCyZzf62bZTSJeLtTse9EzsO8OKtG8lszQ8pIejiJhiefNcDbL/6DRT68vS83Ed8Wgxh26RmtdC4cCZKJEy+O+N0uFQVIrNnIMfiFG0TwxB008HCjjaMgbTTMTibxtJNsgdz9G1Je7khUG7ko0k1hXH47e+hL9aKKWsk8l0c/M1tpG/8Gcd98gOInVvJvbqLns276Xsl7bwXVYIa9aX9zV38+C1A/kpD4xXkh6sSVMDRy3A+cyUmc1fzMi/fpB5PnXcWkiyjRVWvw2bA4SGwtYxMIM6PEsZyoZuI0l4Ny+I0L2hi15/3kdtd9C7Irghxp7SVmJMkNpHlxLSUQrhDI5wMYZYswkmZxHGzsWfMR96xBSQZmlsxm6aRbGpDqBrZ1gWUCjKKAg0NCrGIRC4PtoD+rMTLSiv9yQRRVaczm+Dk5BZanrkDe2CAYlc3qUV70XN5jL4B1FiU+Ix2lEQcORql9YQFlHrThJcsRUQSSLkBhOo0MVIiTpMjYfuEmqpw2gOPTNjxCJh4Nrecw8yWXSR3vEooMYAQ5TrVptPYyi7pZPZ20721F2EIktMHaN7+IvOMIvt69Aq7S8PxcRIdcfY+2OVYOwxBsdvxYe95fA96/2BFGFlVCDc3EJk3B2EYtJ10HKFTTsfWQijd+zEP7Cf1l9/wjnUR+mmmdPZlRHt2QiGHvX8PakQjkgqTmBUlf6Do+b7dqHktvvf8WvbvzfDCw8+Tam3he1/8PisWzgZgxzVv5uCWTtK7spQ6ne6H0enOzFhpQKewV6/wfsvUjlRPVPlPr9lY+XEg0gOqGcnOclfzMsDpa1DrZvXpC17n/b3yT3+Z+AEGjIgkgzyOugfSMVQrQRLuVWkY0uk0DQ0NDAwMkEqlDse4Aqr4g7rYKy/YvKQBy7DpfqZ/UpsI+StJ+JnoyJabjBfu0Ji/dgH57oyTIDe7jXxnH0pIpeXSi8nNck66oUI/2xpW0yz38EJ6Pk2RIkv1J9FDCR7OnkK+JNOSNGiJ5mhQ0/QbKbJGBEUS9BdDzE31sbDnYUrxFuK7nqf33g2YRUdAtV/wOrKLTkcxi0S6d4EkEz3v3UPG3PeME2kJD3QSO+edE3YsAsZH4Z5fYEeT2IqGmusn95f72fhPD9eNnuV/8f+QG5ucuzcthFA16O+h+OoOsnu6yHUN8Opdeyq+/0pM9mqmV3uq7+5YXtfWdZm5hUdWn0ZyWoJERwPx6c0kFh2HFE+y97a7KQ4UyHZmMIsWRtHJ7Zh52gySs1oINyZRwiGyuzsppfNO4mdfngMbu4e83kTkMDx13lmA0/0yt7fgvVe3uVDT4hQHn+ir+fuv1411tFTMPgSiPMDHaL/b9847AS2uUuzWvXwq97vbeHyS1Q8+ykMrV3HW0xsnecRHjqms19yxfft/+onGxj62Qj7NZ9/WOCXf20QTRM6PIuxyRYLelwbqdt5TYuVExAkQ7fX2MVEXTlmTSCyIEk5pxNvixNtSNC6eSyh1EC0eJTpzGtm9PQjbwHp1G3FF4ZlZb6G9uZtcMUwonKQhUqQjfBD+7zekVq/h2b0n89Jze1m4tIM3rYGE2U9C6ieud2JqMRS5SPiVV+la+Dran/49xtylNK9dg9l1gPzOveRe2Iy2fTsAZihE8hP/UHPsTSefOyHHIODQyD34G+RCFinTjyyrKNn9IGzCrc00LUuxcd0amo9ro/2ME0CSKOzZT76zD9u0aF5xHKHjF2G0zKSnZREdOx+j9NRzDOzuIb13qAfVyjtR6lrRuFrC3L3pBDjzcae++O5r34oaiyI1t2I0zyA1u418z6tkdg02PrJNQbQ5QWL2dKRwCCUeJ9SfoZTOU+jLY5ZMr8W9rDpJm+Nt+FPNKfc+xMOrTqXQUyLaHqZlWRNmyaTYX6JxToqFV17EYwdvIb+jNOECOhDkAePlruZlXmdaLa5WzFa5Dfj6XnRsW69lYX60IEkS0jg8KuPZ5mglEOdHGSMlXSpRRxBMZkTdey1flRa/3cWlXoRdSymEmlVibRGiTU5Jwpbjp6PGI5i5Ak1nnkZpxw7MgTTT33g+meNOQ2QP8lzj2cjY9JpNlCyV3ZkWpsXTdHQ9R6ann8i+HcyfrbJnZ5TpHSE0KY1kCxqfuxeruxt7IE3hYB+vbtpNKH4LxbBKrPUxLN2kbfUKEhdfgr35WfY/8DSJGc1M/85/TfoxDDg0hKKRnrkcUwmjmUXyv/oGPVu7KGV0snuc5MbiQImBPX3ku/PoecOp0hJWyHYOMDNfILq4QHshTeHRh9j5ly1eve6RrBq3R5d4VUr8s0z+FvZARTnN2T/4Db1//2GwBWo+za2v+xlXXv4nGn/2I7IH+rEMp/JPfEYryDJGbz/5nXsp9KTJdqZJdKSc/IcFBvGOJtREjKbP/2BCj+majU8C8Nxl64g0OL/PxGlN9L7SSXH5GbQtvoc9nfu9Si3DMRmJ7AHHFqMpl+hef/ReE6vZxshYFdce2xBcUhjegx4QMJUIxPlrDLc7n1s1wkxbXtm4iY5MWXnbidTX2Xe911v6jqUY+RK2aaFGQvS+cpD+HV00HTed2LzZpBevIZlqwoo3srv9JGbt+AtStp+lssK+5hV0ZLaSTsygn2Za7QPsbVvJ9HdEIN3NGxof4Li3nMLxPElqx3PY8UbsTBqr4Ai12PRWZoY1zELJqfaiKESaVOxCETWXRpR0Spki1o4utOs/QOv1P5nQYxYwsSTOfBPKLd8mfeK56FqMrs0H6Hq0z3te1iQKagmjaFLqNCrKf+r9B8n3Foj85QUkWaLnlT5KncaoOz8mj49x0tXnIJ9+Dok1b2bD0pPKDYac5EmhCq8FvZ/mL/6r93fDgzaKXiA5dzoNC2ejJBMIXcfM5ikeOEhmbzfpfQNOt9B4yEk8jkdJzJ6GEos6yaJjwPXjjpQsB3DCHzbw0MpVRBrC3rLe7/0jXc8frJljUq+mdCDQA0ZivJYsfyM392axZBjetScoVzs1kRlnKcUJH8nUJRDnr0FsQxBKKSRmRel9NlM3iSs+L4waU72un+NhtBF6N7IRnRki1tFE75Y9hBIROt5yKdMUheLGJ8ns6kSet4jk/s3Y4RjZxlnsL7YxLdmC2rUX+4E70S5fxPb4yeweaGDnAYV0poXlx0kUGqMoDRZz0ptIaXlSG+9DCkeQbYGQJJR4DKvQy8DOAzSfsBClqQkpEiN33CmECv0U460kntuA3jdApCGGpZv0v7KfgfdexnG/+MO4j0/A5LP5x//L0mvgxZUfoGluE/1bM94MU7hDIzkrjlkwKZi6t42Vt7HyNkZmwFs21hvYpnkNaCedSnTNmwFYt/lZwBUMlWUH3eiepEkVEfkrz5Z5+cqbaJjbTuKyNzLQdjyNBzbz4qf+AT1veBVZ1LCCFg2x76k9zD5jHvH5s1BnzEINRyjc/TOiF75vyPgObH6KcGmA7I9/wI6/bPfKJApD1E2Wq6bYpyPJEs1L5pDZ1cmL//nSsMfoULsyBhx7HMr3xd9czL3GuN/r4IZw6hJUaxmZQJy/RjHTFvkDRaB+BDuU1JC1sd+LjiUhVEspqCkFM20R7tDoWNZGYtUpxObOcsbZNI09racwK9VG2yvlTpqd+yCfo/fcs9i1L4bZdC5zVi+lJbODvcVpGLbCTf/yAmdesIzWJpWDAxANNRBSLBqTM4nIRcS0WVAsgKoizZpHqWMhA/FpTH/6/8C2QJKxMwPEtjyGFI0jG0WMA530vLSbbFcGSZbRsyVKaYPjxnyEAg4nbUumQ2MLaSPCknWr0HM6+5/odJ5b1kzT/DayB/rR88YQr3Stm8vRioXUzGbE7lco3PsfFUnDbqS42vbhtrR3y725Ir3nlR5als0FSSbZtwt2vOxVSlGiMmpKwSg4NxsNsxtoXDwPTljNno6T6DGaSKp5dj9TQpVtGsMFmtReEsVeosV+tEw3GVsQToXQmw2vfjkMihe3BOPt0SWeRU0ql0pVojLhZIj8gR56tx8c1e++lkB3j0kQRQ+YDNzSvv7ci+AmceoSiPORCcT5axTbEBTLHQNr+cGBmk1UqnF95Wq5dnp8dgQ1ojCwNTdi1Nw9YSamR2k+q4VsV5ZiukTmiSdJ7+yi9aSFKE89SMeaFH9W17N2eYKDyQXM3vefyE3NtOh76eqdxvHtJXqNRjbmz2EgJyFLsOy0hfT1FJnensAwYG+PRktKJhNtIkIeI9mKFLdQC2mEJBPf9TyxyCuIXBZz2WkYkRSypRN+9i/sue0emo6fyc4NL7Jqw8OHcNQDjgSta1YhonFO7/09VkmncW4rlm4iyRLxtiSyqpDpzCIPU7trrBfyjevWEE6FKd75MJL8CMdXVfTx7++O1FKUqOy0vC//Zqy87YnVfFeR3pd20SLfjRIOoRdKTF/ZQW5WDrNkIikyoXiI5PRGWlefgLXsVAYa55AsdTNz3wNEz38voVdepVtvplnpofXgS2j7X8U8sJ/c/k7Se3sBnEZiBdurVe5a38CpNgO+G29DeB2B+8mQ6dxKYa/OaHEF+GR0CQ0I8OOPmk9Gv42AgCNBIM6PEg4l4uRWgZA0USGo3SiZEpXr1il31zfTFmpKGdKqvB5aSiE2K8KMlTPQYmESs9qAnSRnthJuasDaug8zV0CJxxC//yXnz/wz3a+7kuQP/z+kc86ib9FZNG17lI9ENtHPKt75hRxqOEQxmyeSiFHM5gHIppdxwkmtzGwxiGkGYalIx76nES8+TfaV3VglndTxc5EXLobeLqQZcwjt2Yq9dSudG18m153llHsfAqB1XEc34Eiycd0a5py9mGQ0imhsQ21tJdTVTdvyWUiSjJ7JU+zPISwbIzc0mXok8VhPtNu2IHMgS2ZnF3qPyfHD5A67EfK/nHgKpR4dvdessJSct+M5Niw9ie6t3aRmpEhOb6RtxTxaLItSX4Z8d4ZFv7rTW79w989oHujCenUb/dt2UnjsEWaefiYze7vof+xpXnl+D6V0kVLaQO83CDVqKCFHhMuqhCg3GQo1q14+ipFxfv/VSXQYgkJ+UJRr5URXI2MNe3MeRC1f+/gtJePZbiLxe84Da9XURx5nnfOxbnPDDTfw29/+lpdeeoloNMqaNWv41re+xeLFg9/Z3/72t/zbv/0bGzdupKenh6effpqTTz557IObYAJxfgxg5W0oNwvyX2hd3KhevVbkrn/XKthOpK3AkPUi7ZoXWVOiMom5UeatXYisKsSmt2KbFlosTHLRPA785WmmrV5KobOH/oc3oUY0Cl29tC14CmvuDFBVEv27sVNNfOX5y1iixjCNBzANZxyuMAf4qzclWKY+hWIZ5MLNPN8/n0RLD6mFRaRX95De00NsWguqLJFfugYA+Y+/pPu57eR78xT7SxN2nF/L3LfwRM7dtulID2MIiY4kAMa+fWiqRn7pGSQTKcy9uyjsPUBxd47+3f0U+oe/qfRHeutFff3oGZ1inz6mSF2hs+TVXq4WEPG2GNHGCLGWBJIske/qw9JNjHyJbNdgWcfd176VaFsjkiQhh0Okli5EUlWKTz7O9jufJnew4DVAEuXSq7YpUKKyFwm3TeFUdCrYUHB+1/5jo6WcMo1uYmyFYDcF9gjCPODYxv+9DmZKAmpxuGwt999/P9deey2nnXYapmnyxS9+kQsvvJAXX3yReDwOQC6X46yzzuJtb3sb11xzzdgHNUkE4vwYQZRrvbq404CSJtUVLW7SHOD5+fydQt1trLxzcW9cliDWGkOLagDYpvN66Vf3UejNOi3QZZnmpXMJHb+Y0HEW6rOb0NNZlKv+hj8XV3L66xcgigPsbVzBvMf+nUteJ2NYQ28oEk0NLD31OE5P3wG2hVzMEXruGc5rb8PWdUimSK07B7NwN9k9XcCTxOZ1IcWT7Nj4Ml0vdlPs1rmw84WJO8ivYaqF+R2ppQBeXe6J6hA5VqafsQwhBNlX9yDt2o82/2TM5mnIPV3oA44wT7+a9URpNfX80SNR6HLE/lje93BlUN2Osrs+cgXCFuS70xgFAz2rk+nMcUdqKcIQtJycIpToxDYswqkwM89cihIJ0/PCDjof6/V+k34rm/vb9/txXZFdKwnW7RAqbEGxT3cq2Ph+6+Ml8Jsf/YzUnXM0THRku9q2GUTNpz6yNM5qLWPc5s4776x4fPPNN9Pe3s7GjRtZu3YtAO9+t2NJ3LFjx9gHVOYXv/jFmNZ/73vfO+I6gTh/jTBS5RX/tJ/7GABDUMvQUt0dVInKnrhx/eeU9xHp0Ghe0oAaVmma30bquFnse+h5tHgUIWwSszuQ1U60eIT05m0UP3Q9kaduQwpHyO3rpvn/Z++8w+Qqy/7/OW3qzmzv2fSekAYhlAAJ1dCkqoCKqLwW5FVRsaJIeamv9Se8KIigoihIB2kSQgmQSnrPbrK9zO5Onznt98fsTHY322ZbtpzPde2VTDnPec7MmXO+5z73/b2Pm04YkCWTmC2DveI88gQf3zz0XyzMduBrOXqG+RPy2bR6C2LubsjJw6g6hGiz0bplF468LOwlExHiUSK+IKZhEPEdwFXdgCCKVK2rJVan9smtwqJrkrnKx/riJtbUQrQ5gL8qEWme/MZTRFsDNO+tonFvI4GKcAc70c4kT+TpntCTvuXd0ZsI7Wp9B7/0SQxNR4uqBOqCqCGVWGucaL2a+i227A4iKCFM1cSWIyMpe7BlOGiuSAjz9o2PDM3sYHPa1R3h9sJcaru7llHkAhJdQhPpcELqt94XehNwlkAfvXR1V6m777un73kwBPrqOQuJ1am9v9FixDHQyLnf39FC1m63Y7fbu1iiI62tCXeunJyc9FfeA1/84hcxTbNDk6TOj9s/b4nzMUZPJ7ZovYrk7L0pSF/pLGQERUAP6CgeKRWBay/ebW4bzmw3ajiGHokmon6BMJJNRpAkJLsNQZaI1rVQvPN1TF3HdLoJfue35NR/hOONJzkj93UufOlc5p5ocuvZlZxy0in84RdrjpqbzWHnxs+7+WDvKcR3P4lSNpVweSWi3YYt040eiaLt2408czZZ04qJNLTQUtFIoMaPqEjddle16DvdifK916xixl9fGbZ55N/xKGtPXIqhGhi6ya6n1xLzx4n7VfRIovBxqHz+u6M/whxgyiPP8eHJJxIPaUTqYkfSUNrN+zzfDl52zgYSDVfqdjSgOGSizfGUM1LybkbSmSVZVwIJwa6HjVS0MdlR2FGgYM+0obgVBFFEjajEAon8+L54vnfefit6Obbpqjaj8/fe24XYQPeTkkXF1G6pI3Q4itlWP2Ud18cHZWVlHR7/7Gc/49Zbb+1xGdM0uemmm1i+fDnz588f1PmsW7euw+Ndu3bx2c9+lg8++ABJklLPb9++vU/CHCxxPuro7oDXPgVlMEge5JIR9EhlPHUi73zC1iMGoYYQzmw3jXsbOPhWOZpfJ3dGEVo0TvnrGxFlkXgojt3joHnNezTurKJoyVTEqeewM/cMJp5TihYPE30yzBfO12i9+25O/PFDPJHpIdTasZW6KEs8846d8r2H+a9zj4d4FC0Wp3HzQUqWtTWliMcxKg4Qqmki5o8QrA+hRXXsXhuiLLDKEg9DwnAK8yT+faFU0XO4MpoSoL2RrjDoHDXs6nd4oba729f7sr5laz86atnONoRHjgEG4fIYsvdIuoypmhjQIf3MXqikxHncp6GHjQ5dfR0FChklbpxZDiRFItgQItwUIVIV7/dFTV/y9i3GDsnvuyuB3vk9AyH0x59hBEPIs+dScvpimva/AXRvF2wxMhlo5Pzw4cN4vd7U832Jmn/jG99gy5YtvPvuu+mvuBeWLFnS7fPtxbmm9dzhvT2WOB+FDPetYVtuYjdx5NkIHoiQMdWJIAm4chzYvXbCvghaRKN2Sw2yQyKj2Ik8WSbaEqL87Qo0v44eMSg6JZe82aV4T1hE5nGzqT71s5Q07+Bg5mJeqz2BKwMP8cQvVtCKSsV3nuSuW3cdJcwB7r11Gn9702DGvCJMxY664QPi/jCTzlqMUlqCWlXNxt//J1Wsp/n1VEpOa7j/DZcsRiadu13uueoThH1hGrf5Bpx+ks4y/cld7yud02/aXwDEm7TUMUH16x3W+2rOXGxeBVFKnNVW1B1plJRyY6lKFLYGnaHU7yQp4rujO3vWzlgpLGOfvuzn3e0H3UXPXyucd9Tducibf8YIhtjzr/dp2vlKlzUc1sXg6GCgOeder7eDOO+NG2+8keeff541a9YwYcKE9FecJoFAQrdEo9FU4WnycXux3hOWOB+lDJZAFxUh1SSoqxOtqZrgTBT+OXMdBA9ECB6IYC9UsHttNJe3klHoRpJFtJiGLcNOsC5EzK9S9+G+1DiCIqA4FbRIjPDO3Rxas4Pck6/glfg5bHtf44zFMbQdFURmu7n/cYP9m9YdNZck//3drQB86dunI9RXYcTiRFvCiCetoD5vFtpL3yXu0zocvJN+7YpXQvXr1q33MczMv/0b/6+/Q0ZB9zUFQyEau9un+rOvdRVx7M72sae8+fYXLm9OPq5L0ZPsiZBMC+gtPSDZpCiR2967rarF2GWojqHOwkQk9P3jTyCzLJNJ556AlJ1N1Tsf01Ie6GBuYDH6GC63FtM0ufHGG3nmmWdYvXo1U6ZMSX+l/eDJJ58E4N133+W8885LPf/ee++Rn5/fpzEscT6KGQyBLjmPFJBBwkYta1YGTZv9GG3NhwoW5RKoCTLvuvPY0PI84coozlx7QpCHNQLVQSSbiCAKCWHeFEcN6KkCtYzJTvJn5SLKEs0H65l6/HzyZ/soVyewaXuMz65sYfrOZ6CkhN+9kMn+TZt6nXfZnClcHXqQ0OyTcHmyyIlEaf37E2jtovXtRYapmtgLlYSnc6foosXYouXeGxEVGVuGg4+Wn0TjhpZjKiAHa1/rnOrSl7HbN2iBI63Nkxep7Ul+Rr19VoZqIgKWmeL4ZjD2685jrJ6zEFeuE0kReaN0PoIiEKysJVj3FpAoiO6uJ4d1TLfozA033MATTzzBc889h8fjoba2FoDMzEycTicAPp+PQ4cOUV1dDcDu3Yn9qKioiKKiorTW19LSws0338zDDz/M3Llzue666/jxj3/M1KlTWb9+PXfffTcXX3xxn8ayxPk4R/XrRx3s4iEt5dCg+XUCNUG0sEbN6vV4ijMA0FUDXTPInpZFPBTHmeWg+v16DM0kY7KTCafkcuDFwwDYvTZCDSHiYZWi40o4fMq17JxTwrNPNnLO2YVMqX0PrfIwby29nY0Pv9fjfL/2vdM5pXQ/mU/cyd6HdjP5lonEtmzGWVpE1Yfv0rjNlyqiExWBnIUeHFl21LCKLcNO045m6yA+xil/82Nku0SkJUqgcvSnMbVPYemLS0ZXdBbcgiIMqIAumRJjYTFYvCjPQnKJKQeW9uelhqbWHpe1jumjC0EwEYT0jx/pLvPggw8CsGLFig7PP/roo3zhC18A4Pnnn+e6665LvfaZz3wG6FuRaZJYLMZvf/tb7rrrLlpaWli1ahV/+9vfuOSSS/jv//7vlHNLQUEBt99+e5/GtMT5KGew888Ljs+lYYuvg785gCvfQWtlM3kzizANk0B1EG+xB8kmo0ZUBFEge66XzNJMmvY14SnJQXJWYcuRyZ2WR922WsK1UYSFAsXN22nxeAkHItQ0mrxSfCmtMy/njz8/2pkliSc3C3dmBiXZUTJDtdRv3k/LIT/af17Gt+sQ1ZuqiftV4j4tcTdAEXCW2iiYW0i4KYhsl1nw8tuD9jlZjExezZmb8uTXI0a36VpDwVALhP6M390yPXmuW1j0hcFK12r/fz1sdGnt290YFqMToZ855/1Ja+mNL3zhCymh3l9mz55NRUUFc+fO5Q9/+AOXXXYZAG+88QbPP/88e/bsobCwkEsuuYTMzMw+jWmJ8zFCf1spt0dUBBxeewcnFu90N/M/ewZK2UQ23vE4Tfvq8e1tASDsC+PMciLbZdSIxqTlMxFtMpUfVrPtz1uRnCKSXUKyySz48jm07jqA/8Zf8L/v5/Pt+Wu45uoV3Pvzd3qdV05JATf/dxmFjiYOBCR47jGi/hjzrzoJQZLY88z+DsVrhqrjnGBDDegceK08ldJiMbZJneAjxrDbJybXPxqEg1WkaTFY9HWf7yqI1J/A0mj4fVn0znDlnA8Xdrudv/zlL1x99dUdnhdFkUsuuaRfYwpmHy4t/H4/mZmZtLa2plUhazG8dNWCfCAkiyghcRs8a3YGGQVu6rc1Efdp2HJkTNWkYGEutRsa8Uxy0bIrmMoxr3y/BluWguKWmfupkxGXn4NSvZ+zHl3c5zk8+IvZFGhVZG5fTXD2KWiP/ZZYS4i8L17H1m/egRbVu2y8pHilo1w8LEYu/bXeGy1CM53uif1dtr/jW4wcOnvSJy8wk0W4Scw+1gcMJe3nlG6H4P72ArDonZGs15Jze+S1Flzu9OcWDvn50rlZI27bums4NBCsyPkYYrAPaO0jj85ChVmfPJ5Da3bgKnAQqwukcgMjzREWXnc8dVsqCHkixJri1GyqY9KKMgzNIHdOGeonrmJ18ER++ei7TF04gwMf7+1x3Z/7xul8puA/2MtfoXzKOYgbP8bd0sxHz2ylYH4+OR+sJlQVQQ10vAmaLH6zhPnoYqyfjPsSYezJbi7JWP+cxjtCOyccQTPRMZBSzjikrC6Hk/YFxalus4qALUdOpY+94p2TlkDvLr3F2r8tRiOCIPCnP/2JF198EdM0ueaaa1KpLX6/v88dTNtjifMxymBEz9tHZUzVxHn8UvJqm6jbWtXhdUM3CVQ1EQsm/JL1iEHuIi/5C6ejhSO4T1+JFmpiSnaiqCce7b7l8pxlc/nalRKzmp5jt+t0anNPZ45Qwa7nthBv2QBA7eZ6Kte8elQha+eGKxZjm9EWBe5JoPd1W/pyh6E7sTPaPq/xiKkmBLnZzkFLlBPR9GTH2yTDETXvHLEX263XnmtDUkS0qE68pftjem9kznXTuiNkCfNxhCiYiP0oCO3PMsPBfffdxw9+8ANmzZpFPB7nmWee4cknn+TKK6/k29/+NtnZ2dx///1pjWmJ83HEQAS7oZkcfuyfxAJRMgo9CKcIaDEdV46T8peradrox5YrY2gmthyZzAmZ2EtLkFtbaChbQqNZwF0PtgBQubu8y3Xcf+9iFtW8gNaazZu2C/nFDz9EUzUe+eVU1JBGtE7FUagc1SBFVARmXjENV56HPf/eyYqdH/drGy0shpruhHO6v810xbzF6CDpgmO2j1YDgmamouYDFeVJwd2fmoxEv4pEwbU734nNbSMeihN1xPo9n4knT2JHefc9CSzGHmMt5/z//u//+OpXv8rvfvc7AK666ip+/etfc+WVV3L22Wdz7733pj2mFWIcw3QViehvdEKPGOx/qYL6bU34a/xEW2JoEY2YP3FQFhUBR54tdftVjaiED5SDIOKKtRBS7cw6rpTsoryjxi6eXsarP2qh7IEvEc0pIe7w8pv/3cSC047j5p+dSkHwAKf84Tt4Z7k6CHPFm+i0ZctJXGNGfEEiVfF+bZ+FxXDTlVuFhQUkBHjyz1TNVNS8KzEtttlitn/cE6m0mbbluvprTzJar3gkHAUK9kIFZ6EdV46LrEl5FC0oo2BuYb+3ddJDz3S4I2Ax9kmK8/78jURqamq44oorUo+vuuoqPv44ESQsKSnh4MGDaY9pRc7HIf2JoCcFcSQcJ1bXDNAWfQkCiZNJ6HAUxSORPcNLwfyJ7L/ybqbFt/NG6zLefNtHZpZAc20jAMctP45De6q54YZZnHX4/wi8sol4MIpTVIjbMrj6ywuZnB9l2dqfIheX8svoV/nypW+z/jcfQFuBlOQUUf06cZ9GxTuHEGWhx5bjFhYjDUuUW/RGT57y7bu1SomeKqmouB7p+VgoykKHRlLtBXJngS7KiU7SNpeCIAmIkoDssOHMy0LJ8uIsiFB545VM+O0/096+jWedmmqOZTE+EOmfleJIjSZPnTqVQ4cOpR4XFRURCoUwDIN4PI4kSWmPaYnzccRgCYFU9KbTCSNzhpsJJ05C/87dKKEaKpoz2Ro+lSkFUabNyGbPLh95ZUUsWDaFvByFOy7Yzf7rT2VtQzR1y3b2D7zsis1gZlGI0+ZlwIL/AeCHAJ96lFV3JLYj2XI8OZ9IpRUxH09YonbkMhi2rhZ9JxUFb/tXciZctuK+hJd9Mme8vfhO1uVItHOF6fSerjANE8kmYvc6cOV5EG1KyqVCsvXPrnbJmz03nrOwGOncdNNN3HHHHZx33nkUFRUhywlpbZomb7zxBrNmpX8stMT5GKO7LoJDcaJMRldkr4TikXDluZAdCtqdN2FOLeH8FXUYbgeEBY6fasMmbCOobiP/2j+2jXAyeWs/OmrcUqD625+BX/69y/V2Ts3pvG2vFc7j3LrtA94+i+Gh/T7b+f/Q+/dtMXIYSB69RfokAiUGgiYgeiQc2TZsHhuyXSaeH0OPJ2LjpmGixw30mI4jx44z24mkiOiqQaQ5QqgqghHQj7JrNFQTR6GCPdeG7EhE/wRJxJXjwlmYS/BwHY5sD86SArJ/+MCx+AgsRiECJgL96BDaj2WGg8OHDyNJEjNnzuTiiy/G4XAA8NWvfpU//elPPP3002mPaYnzMUZXOeVJwdO5GG2w3Fw0v44eMWhRWlEjKtGWGDWbq5kWCOOeWEzzeddhV4NkXnETzit6GbSNkm6EeW+IioBnkqtfy1ocO/qSf20JvZHPaGnENJYwVBORRF2QGtUQlTafdN3ENEwEUUB2SChuBcWpYHPbcOVmJAIpURVREjBUAz0SSd3BTEbQRUXAlqWQUejGnecmFoiiqwZ6XMO3swJXQSaepUvQc4rSnvemc04DYPHrvTeisxhbjLWC0Ntuuy31/7/97W8AOJ1ONm3axKOPPsrFF1+c9piWOB8ndBY2XQn2/pI8OcR9GrG6VmSvhOQUadxViSMvi7yqTbhWXN3rOP1FVIQOhVJatLcG0BYjja4s/6z0iMFnOKLZlkAffgzVRFASqSx6xOhgJyspErJTxpnlwJ3vIW/RTABMXSfW2IwWjRP1xzC08FFWjaIioMcSx1NbhgPTMIm0RNBiGuEmH7YMB45P3pj2fN9ZsARJETllw/oBbrnFaEQU+plzPkLFua4PvuawxPk4oKcT8mAK9HiThqgkCoecWXbqtjRiaFtYcPNvBzx+WvPw999z12L46SzkLGE3NCQ/1+EU6JbP+vCRLIbXIwaKR0rloZuiiakbiLKEM8+Lbeo0TFmBUAAAR2sIudafGqezI0zcp9FCK4aq48g6kg4T9kXxVzX3a66nbdnYz620sBgfjNTiV4tBpvOJEjpGKAdLECVPCJGWhMViww7foIzbE50r/du7FLxWOI9XvHN4UZ5liYNRiPWdDR7D/Vl2Xp910TX0mKp5VIGoYRjEQxrxUBxT00HTwOZAcLgQbTZEWUJxKikb3K7QIwahhgj+aj/xYKL43tRN5j37Zlrze61wHq/mzB3QNlqMfgTB7PffeMGKnI8zOqcKtL8FPRgRNT1sEK1XkZwiGROc2D22gU24j7Sfe3tx3r4w1BJ6owvr+xo6hqtYs7sC9a7eY33fAyfp1qK4ZRS3jOyQ0aIapm6iONscJMIhBNPAjEXRgiEMTcc0uhc9hmYiAXpMRw1pqG4NySZy+vZNac/PVE3O8+3o7+ZZjBHGWs751KlTMc3uf0OmaVJeXs7HH3/MJZdc0iffc0ucW3Q4geYs9DD59Olkn7CQeE0N2//2Pi07gr12kpNcIopHwpalEG9RE7dCdwSRven7e/aX5Am+s9f5a4XzkJwizgk2XvHOYZV/57DNycJiJDHYF+PprrPz8+2xagzSJ1lvk3TOUjwS9lwbNrdMztRcnDkZtB72YWg6NrcdLarS+P4G9LiGFlUJNwWJNEeItMTQI0bqOJ/sIyEoAnrEIO7TUq9F6Ghb+9Hyk5DtUp8sES1hbgEgYiL2w3mlP8sMBytXruxRnCfxer2sXLmyT2Na4twCOHICPWXDehp+ch0tm7dx6N29BCsjfVreVE3UQMK1pf1BfrgtDbsSAaZqomOk5mIVrI18LIE2dKS7/0succibe3W+aOjvGOOJZECkvcOKoAhIiog7343isqPHNWS7jOhOpK9EW0LEAlG0mI4aUYn548T9apcNiwzNRGoT/Z2DM8nv6M3Jx5FR7ETttH8cvuEKyn73FOG3/47rjM8MxeZbjGLGWuT8kUce6dP7pkyZwh//+Mfe34glzsctXUXNkifI/Dse5b3Fx+PfH+7zSTnZxW4k+qToEQPD3/Hk0tlW0mLkMN5E1rGgJ4GueCVkr5ToCumSsXttqCGV1l2hXu+g9bS+gcypL4yn1BjJJeIoUHDlO9DjBqZhJmwURRHJJpJRmIm7MBvERCdPLRonHowSbY3QWpkoBNVVPdWoSPFIaP4jR++kA5emHn1Eb3+HQ2yLrHcm4kt0jraEuYVF/7DE+Timp9vIp27aMGZOdOdHdvXoVmNhMV7p6hjgmeKicH4RGcU5yE47ok1BVGQOrd5Cxb9rhnQ+3TWe6ivjQaCLioAtR8Zd5CJ7UjbxUBw1oib+DamIioS7MBtbTiZGLI4WiWGGY2hRlVgwESk3VTMRtNDMlLNLZ7q6EOv8vSRdujoz82//HrwNthh79Le4c4QWhL799tu9vueMM85Ia0xLnFt0e0IbLSe6znMUFeEoBxerc+HowPpejg3J34TilZh50SKcE4qRcvNBljFDQfTWFiadtXjIxXmSgUTRx/LvO2lVKzlFJFkkoyibmD9MPBhNRM9DKqIoIGe4EGWZuK+VuD9MtCVEuCmMGjoizPWIkXBz0UxidWqvd0V6+j6sIIdFOow1n/Mzzzyz15xzw0gvNdAS5xbA0Ah0ZYiLQXua1yveOamUnO7Eent7RevkYjHecRQqiW6QSxYTnjifiDMbZ7QFZ1OiNbURrhzW+VgC/QiSS0SUE0Wa7lInNpeCZJeRnXYADE1HEAUUt4KoSESbWjDrmgjWthALRIkH40RbY6nxknnq3T1uT2/fwVAf5y3GHgImQj+KO/uzzHDwz3/+86jnmpqaeOGFF9i+fTt33nln2mOOOnHeuG0t7uqdOM/9IlsvXEHjLh8r92051tMaEwyGzZktV2bmxTPIu+4LSAe203jrl8m79eF+zafx1i9Tu3E/tRsaiNapPTY06c7D3ZYrM/H0Emq+czW7ntvWYV/pvMyrOXMpOaWA415c3a/5WgyMsSSmRgOdxe/ZVdt4a/oCglOXsMVchEPXKHVV4WysQKupoXHLvmEpDh0sRqpA78+8MiY78RRnkDM1n5xFs5AmTQO/j1j5IQBkRwyH145pmBiqTuWHB4i2xIg1xbFlKbjzneROz8GV56VuezWt+wOJbqJhI/V9pnshdKG2m1e8cywHFotxz2WXXdbl89dffz2f//zn2bZtW9pjjjpxnjf/ZJh/MgDV79ejRwwr+jmEJE8kfT2hSE4Ru9dFbeFCcte8nvb6Plp+EoIoIIoC8bBKoCLcZU5jb7Zrydc3n3c62dOKsWVmkD0ti1dz5iIoQgcXmQ9PPhGwbL6OJSNRRI0HOgv0jBI3khYl2xXCLYXIadyHUbGPhk27adzb0KG9+1Az2o/ngzl/SRGRlETPQCMcQWqsBUBoyw+QbDKywwZEiQUSVoe6qiM5RWSHhC3Dxux/Jo7HdaefjKGZqTSWgczTsqW16A9jza2lJ66++mq+9KUvcccdd6S13KjuEHqeb8dR6QoWg086B++4T2Pf67tw/uYHHPj3JrRwrPeF2tG8w0/j5hYat7TQsivYpTDvPLee5rfo1TXIbid6LI4rx8XElSWc/vANRF9+CP+vv5N6n6GPzNtlFhbDSVZZFrZAI8V6BcVN25H3bcW3YTu1W2po2Rvot1tLugyWsO1qnJEg+tOdgxbViQfjhBv9BMqrCR88RLy+AT0WB8PENIyEUG97r2mYSIqE7JKRbGKHu4HJvPPB7AxtYZEOw9UhdM2aNVx00UWUlJQgCALPPvtsh9eDwSDf+MY3mDBhAk6nkzlz5vDggw8O4paC3+8nGAymvdyoi5x3RecoqnXAGXz6GtnUwwaBvWH263s4/tsXwuyF7NhXzdzpJX1a3tDMhGf6IN46z73lIfZes4qm/U3kzyrg30X/RWtI4qIz1nDoa5ezbO1Hg7Yui/5hNaAZGcz46ytEXvkDHg4gVh2kZeNWqjceonVvaNSks3RmpJ4P0klviTYmouHxcMKVxeULYstwIDsUtKhKxBdEjcQxVB1BSoh0URIQROGo45vdYyPsjFoN2SyOGcPVhCgUCrFw4UKuu+46Lr/88qNe//a3v81bb73FX/7yFyZPnsxrr73G17/+dUpKSvjkJz/Z5/VUVFQc9VwsFmPLli3cfPPNnHjiiWnNG8aIOO+M1WRm8EnnRGKoJpGaGKEDh/Dm5jOl/FEO/3ITZb97qtdlk4006KbpRX+p31FPsCJCzhSNlqBEdb1O7YQZzH3waSCR2jL9oV/wTP2pfPhREzvWHkl7ee+F9CyQLCxGM85V1xP5x320rNvM4Q8P0LI7OKzCfCwcu/u6DX09rmp+nYgaI+5U0aI6UX8Mxakg26VEM6FAHC2qI0oCskNCbBPonYX59kvOYtKKuXiKK2nc05j+hllYDALDldayatUqVq1a1e3ra9eu5dprr2XFihUA/Nd//RcPPfQQ69evT0ucT506tUu3FkEQmDFjBr/73e/SmzhjVJxbHHtUv872f2wie8NBIs0RAhVhynrZP1MnqQHcOg8/eiuu62496vlTN21I/T+xFhk4Es1ftvYjmm7/ClefXskFl82m9VNTKQ8WUuRqJfqvX+G47Fv9npOFxWij6Z2POPxhOf59IVT/SGwtNjLpz4VFXwR6Ip0o4UuuR6LEvSqSM5GVmmwkBODIs7XlmNtZ/Po7R42jxzVsF15JSeFq4qEP0p5rV7w1fQHeCRkcv/r9QRnPwqI3/H5/h8d2ux273Z72OMuXL+f555/ni1/8IiUlJaxevZo9e/bw61//Oq1xHn300aOeczqdTJo0iaVLlyL040pkzInzsRB1Gamk6zIQqYoTqapP5am+7JwN0KFO4OCXPkn9jhpadqefk9UVXQnzngi+/yzqmtcpf3MLEV8E77o9ZBRmkp3rZeKc6WAYaPE4wQd/SMbX7hqUOVpYjCS6utM44bf/ZGs7O9KhZqQ6q6TDUJ972ndhTnqUA6nvSHKJxFtUJJuIO7/rU7uuGigHthDxNTP/+bcGPKcX5Vm4J9tRXMqAx7IYP/Qnfzy5HEBZWVmH53/2s59x6623pj3eb37zG66//nomTJiALMuIosjDDz/M8uXL0xrn85//fNrr7o0xJc4tYT70dGdZ2BWGaiIqAhkzHHgK3eiagf9gkLemL2DJ18/k8/u+RmP9TfzrjEfYVrNpSCN0q+csJG9GDoULJpN/x5Gr3IxTLoFTLiH7B+23p7rbfan8+kswNB1RlpAdCoIoUvrrJ4ds3hYWw0FX9Tqr/DuHVTCP1uP3YMw73YuTpFBvT9IWMe7TMHSTDStOQbLLLHp1Teo9yWi6c8AzJtW0yl3kwlOcRcu9N5J1828HYWSLsc5Afc4PHz6M1+tNPd+fqDkkxPkHH3zA888/z6RJk1izZg1f//rXKS4u5uyzz057vJaWFj744AOamprIzc3l5JNPJjMzs19zG1Pi3GL46e2kYqgmcZ9G3KMi2SQMzWTmOTOwFRfx1ZVTWFn5Jjt/c7DDrdmhYMXOj/v0vt5OtJP/8GyPr4cfuw3XtT/t67QsLEYUnaPoQx3RHq2CPMlInL+hmkTqEi5ZskNiw4pThizlRPZKZJZmkrtgOtIJpw7JOizGHgPtEOr1ejuI8/4QiUT40Y9+xDPPPMMFF1wAwIIFC9i8eTP3339/2uL8pz/9Kffffz/RaDSVxmK327n55pv7FdW3xLnFgOnNaUPz64SqItiyFERZYO8re6jbXs3EnNfZUNFC665QjxZtI/EE2B2ua3867nPUO0dhrQLt0ctQCfOxsD+M5G3Q/DoRYthyZAzVYNM5p3WZgz5QRFnAU5JN6LzP8djW4/jBoK/BYiwyEjqEqqqKqqqIYkdHcUmSMIz00vnuvfde7rzzTi699FKuueYaioqKqK2t5YknnuC2227D5XJx8803pzWmJc4tBo3uOngmhbcgCTgL7LTuCtFQ14yh+vo89miyyXRc9q1xHUHv73c02nOOxyKDbXE5Gn6/I4WB3LUwVBPNr6NHDMwCk5IlOURffgjH+V8Z1DmqgUQq4rvNC3jh8Xf4wZWWs5XFyCEYDLJv377U44MHD7J582ZycnKYOHEiZ5xxBt/73vdSxZtvv/02jz/+OL/4xS/SWs8DDzzAF7/4Rf7whz90eP7SSy/lK1/5Cg888EDa4nxUNyGyGLm0PwmLioDkTHS4E0SB8yO70mpm0v4E9aI8q8PfSGXX468e6ymMGCxBNroYD9HygcxlOJr3JMcfyHoMNdEzwl3koviS82ieuozNexoGa4pAwvpWstuYnNNqWc5a9JnhakK0fv16Fi9ezOLFiwG46aabWLx4MT/9aSJw9ve//52lS5dyzTXXMHfuXO6++27uvPNOvvrVr6a1nrq6Oq688souX7v88supq6tLazywIucWQ0hPJ5bBymUdqSkT1W83suRYT+IY0v677cv3M5IvtCwGzkj8jabLcAnywSRjqpNp5y0kUjaH/cY0dldlsGjm4I0vOUXcE4v5YF8W//vIIf5+78TBG3yQGannivHIcKW1rFixokv/8SRFRUVd2iCmy+zZszl06FCXrx0+fJh58+alPaYlzi2OGWO9K+R4PhmM1+0eK6R7cdWZ0fD9jwX7xp6QXCIZRS5smR6EcCtFnkbUQglwDdo61ICOf28FC88I0+ov4d6ndd58bivB5pEXSR8N++R4YaBWiiONO++8k6985SvMnTuXU045JfX82rVrufXWW49Kd+kLVlqLxTFnOG4TW4xsrO9/5NJXAZv8HY+m7/JYz7Uv6Xn9naPikXDluDDiceTacoprNzFL2duvsbpDlAWCtS0sDr7FqiV+8rJFgs2tg7qOweDVnLnHegoWY5j7778fwzBYvnw5U6ZM4eSTT2bKlCmceuqp6LrOPffcw8qVK1m5cmWqG2lvWJFzixFDdwWlfV1mpNH5zsBInquFxUCw9u2RiRbTCNc1ITocyPE42YFmmLVw0MYXFIFgfQDjvTeYO6cGadolPDJoow8eyYZNFiMDgf5FhkfqtygIArNmzWLWrCO6ZcqUKUyZMqXL9/YFS5xbjEj6I9QHm4GI6s5e0RYWY43+7NcDTZcZiwzV5xCtU6nb0pj4vy+I3XsIQZZwfuJLgzL+hdpuVs9ZiChLGHEVZIVFM/PJKyui8XAtp170NsCISG85t277sZ6CRTvGWlrLm2++OehjWuLcYsQzXLmh3QkHK/JtYZFgIDaZI/X3M9JzzzvPLZ3AhebX8e1vIVAbRLJJmLpB8SDNa+2JS3Hnu3B47dhyszGcGbRsepM7vj2Pb/3UTzQYZt4p8wdpbRZjiZHgcz7SscS5hUUvjFRRMdYY6SLJIj3aX9QOhjXgseRYznsgvwvVr2NoUSSniCgLGNrgiZusskzmPP0GAJE3/0wkIx9X9S6mepv5/R1LMMjHMENA7qCt02JsMNYi5wB/+tOfePHFFzFNk2uuuYbLLrsMAL/fj91ux263pzWeJc4tRgVdObuIA8gj7O1k19lbvTPZx2Vw6qYNvY41WsXIsWKsO/iMdpIR8LH2/RyLbUp3faKS6BHRnr78XvSwgR42Orx/oOy68pyUMAfQPLlUuWchzZxBcXA3zv/3Ixx5WZiaTnVLgJJf/n1Q1mthMRK57777+MEPfsCsWbOIx+M888wzPPnkk1x55ZV8+9vfJjs7m/vvvz+tMS23FotRzYcnn5j2MoNxEo42x9l+yVm84p3T7XssYd5/rM9u5DLWhHmS4d7n0nG2ERUBe6HS41iDta7e2H7JWZhGxwimFPGTbTaSpTfgaKggWNuCIEk4V5yF56rPE3klfSs5i7GLwJHUlvT+Rib/93//x1e/+lV27NjBvn37+PSnP82vf/1rAM4+++x+5aRb4txiVGPoJutOP/mo54e6g2jcp1G7uT4VkeqMqAi87Jw9ZOsfD1gCffQzVr7DY31BYi9UyJ2Vzc7Lz+7yeAfdC/DB/A5eK5xH1draDlFzANcZnyEseciu2kbw7bcRRAEtHCGaWcy+rBPxTV02aHOwGP0MV4fQ4aKmpoYrrrgi9fiqq67i448/BqCkpISDBw+mPaYlzi1GFZ29lA3VwFB1Plp+EmtPXAocnZIyFEJdDxtEKuNHPS+5RBSv1Da3kXkgGU2MFXFnMfoZCoHe1zFzZ2VTcvwUChZMIW9GQZ/HH8zfz4vyLOJNGvEmrcvXZ06bRP2TT7HvtW3M+Osr+HZXYd/4H6b5N+Bt6bp7osX4pH9R8/4VkQ4HU6dO7dAhtKioiFAohGEYxONxJElKe0xLnFuMWpL5l4Zuosd1YoF4j2kmQx1NFxUByZn4SSWF+VCv08LCYuhIBgIkl4jkEgf9blhfxbMaUdEiMRBF3IVZfRp3MIX5K945OAoVHIVKj+NOeugZ1JDGm5OPo/LDag4+uwb99eeQD+4YtLlYWIw0brrpJu644w5qa2sBkOVEOadpmrzxxhsd/M/7ilUQajGqMXQTQTQxDZNYndohzSRZ5CUqArJXQvFICIqQeF/EGLTItqgICIqAs9SGHjGI+dWj3vPm5OM4q3zroKzPwsJieHGXOZh4yiQc2Z5BH7svVq3B6hCNGTVkFIZw5vQ8h8G+27Tu9JMpWJLN8avf7/F9a09cihrS0GM6ml/H0Eyay1uR7XspBJyDOiuL0YyIidiPKHh/lhkODh8+jCRJzJw5k4svvhiHwwHAV7/6Vf70pz/x9NNPpz2mJc4tRjWnbdmY+n/7qHl767a3pi9gwrJSFFfCyqi5vInaDxthEMS54pXImOSkcH4RWdOKqVi9g5rKpg7vseXKlBxfSNPtXyH3locGvM7xxlh0BxkPjMaUpO7mPGn5ZOSbfs764BxK9jSxZObg2gP29lnFfRotFX5iwTgFtuE9bRfMKerT+5q3BgCQnCKeKa5U0ajvQDNqZAfBG66g7HdPDdk8LUYPAv20Uhyh4vy2225L/f9vf/sbAE6nk02bNvHoo49y8cUXpz2mJc4txgyr/Du7fH7lvi1sv+QsZIcNxWUDwBykqLlniov82fkUn7EE0e3Gs7sS/SQD2S4hyhIxf5RJp82i6ku/Zk1rJvP2VzBz2qRBWfd4wrJYHF2MRmHeE1kzythhTKbU08rE6C5g+aCNnbz47OkzO8+XSAtZM28x1ZsqaT7vdOwZtqMKM4eCvKXzkUomEHr3KdzLr+jyPZ2j/747vwpAvDVEsMZHtDU8ZoX5SG6wNVIZa02IdF0f9DEtcW4xLpj37JvsuvIcDE2n5WDroKW0xFrjNFc0E3v+XQzNwHewBUmRcE/NRo2o6HED0zTQTYGdB00UqQTPrs0Uz140KOsfb1gi3eJY4D9YzZwXfkzr/koChknuHwZPnEPfL2ZCh6MoHgk1pOHOd7LpnNNY/Po7gzqXzoheL43TT+Xlinmsv6uJ3//wyF2D1XMWEtwbPWoZW3YmgqJgy8vBkZdFpK7pqPeMFSxhnj5jTZwPBZY4txgXvLf4eByZdjIKRUqOL+ZwpApbloJ/d3hA48Z9GoYWwn8wiBpIXD1nzc7A0HRC9SFERaRpdzXz3v8VwaU/wDRBMA127qviG7dUEw2GKZ5eRiwcw1ddn5jrC2cMeHst0qerk6x1EZA+Y1GsNOyqoWlfPYteXTOk6+kt/3yVfycvyrPQIwaCJJBR6Gbn5WcPWQS97gefx3vmCurMEnbsDLJnwx6u+WEZf71rAgDufBeRqvhRdy0zvn4P4UdvBdNEznBRfPNvh2R+FhZjFUucW4wLTt20gdVzFiIqEtHWKIZmEqmJDXhcPWKkWmKbqsmk80qZ+pXPsPFH/4eoiJQeP5GqDYfY9fjrnGx3UL7s8whxnahhR7HbiAbD1Ow7zCXXncbr/4pwxbWLOfWitwFLpPfEYOWh96V5iyXQLZa8+d6wrq/zPtd+P5VcIrYcGbvXRu70AvJOPI7ABy/gOemiQZ1DxVcuxeZ1oXtyiegK7gwbpTPKsDkU7vqHzn+3/oyla9Z2u7zrulsHdT6jlfCjt4Io4rr2p8d6KiOGsRY5nzp1KqbZt7mZpkl5eXmv77PEucW4QY8Y+Pa2EKtTBy2txVDNVGGpLVemeOkMtk29gqlnr8aW4UKLxGjZFcRUTQzjeeYXTUB3eSn1mvz4e3PZXO4mP8vkPPfbrLhlEdMia1h430Iefc4g/Nht1gG9B3pKcektCj4Wo7sjAetzHXpy5nlwZjuxuW3kn7GMyqWf4nC4kDMHYeymn1+Pb08lpmEiyhK2DCeGbMMwRRwOCbfXic0u88NPScAdaY0dfOD7ZHz9nkGY5ejCukg5mv42FBqpTYhWrlzZZ3HeVyxxbjEuSPoTD1VjIMkl4sizoYUizG5dS9ybQc26PTTu9aXsHSNNUWiqRTIN3Fqchc5m5pU60GU7lfY5xFSZmCOTGbEt/M95JvHXGvDf/DmK7v3zkMx5rNBepPckDvsrHC/UdvOKdw6inGgenSzOsyLqHbGE+cDpy52aZWs/Yv+1FyKIAoLTSUD30BS09zp23Y51FM5d2uN7HGUlZPhDxPxhRFkCUUDU4uimgGGaxKMqspJ+QxUABKutikWCsRY5f+SRRwZ9TEucW4wL+iPKJZeIqZo4S21klLhxZjlwZLporvARrA4RqYqnxk5Exg0OvrUT3roV3/ZASpSLioAtR8ad78Tw+8HvR2tpRQ2GESQJV2kRk+eegNJYBWqc0Jat7H99K5IsklHooW9GZhZDKQ4759S+VjgPURE4P7LLEulYwnwwaf9ZJvetZL8GZ6mNlfu2MO2xF9l15TnUPP8Gs1uaKTnxQmBJarnoc7/F8ckbO4zbmzB/dp3GtNO+RsnS/WTV7Ebftxu1NYDU2ogjS6O1xeRPP0//aBR56hc4r7iJjK/dlfayFmOTsSbOhwJLnFuMedIVT5JLxDvNhbvAjcNrJ3NyAd6ZUxCzcxAcLkrDAQKbt7Hv1e0EDoYxVB1DNQmXx9ByEkWhklNMRVoFRUAN6OiaQfhQFZGGVpr21RNtjSLZJLzFB5jkdBKvraN5VzlV6w/TujeEs9SGZLd+oiORc+u2p/4vKsKQ3ZGxGH/0VBRqaCYbVpzC8avfJ+qP0bC7iUBNC2XVtUT3zcdU7Dg/8SXMSKTLsb92bzMP3pydepysb0niyc3iys+dwhnTSigF2L8XmupxTI0jy+60tyXy2h9xXnFTn7fPwmI0UlFRkdb7J03q3U7ZOvNbjGnSFeaOQoW8+TlkT87FmetFsttwTi7DnD6fSEYemCZKLIDHMMmvqCdcexhDMxHlti6kbhnDMMic4AVos1PUESUBm9uGb3cVwboAjVtaEo4LioD/YBBX3jr8VT4adjQRrVdRPBJ6xCAeirP5vNOH3CXCov/YCxU0v47qH3yv29GAJbIGl55SW/SIgRbT+Gj5SaghlcDeMLGmOJJtN/mBEIrHjfMTX8L5mZu7XP7Bm7M59aK3uy02DzS18MdfrSH4X6dz2axl5BkmwQnziBsynzuzldC7L3Xrdd4VznO/2OHxqzlz+7ysxdhlrEXO0ykIBTAMo9f3jAlxbjUBsOgOyZXIczRVs9fopuKVmHBKMZkT85HdDkRJQva4MafNZU/+6cR0hQJbAzlmBTIgyhLOAjtq4Igdo2EYaH4dh9eO4rIjyon1i7KEaZhUrjtMrCmeEnIiich65UcVxMMqcZ+GqZqoAR3JKRKoDOHIVnn/+BM4ZcP6IfmMLAZG7qxEJLLlUGvqu4s3acd4VkOPdcwdOtp3OAY61DwEaxJRcT1iYKhm4i6dJKBFYqmunL3ROWLemX/8fg3y105n+Yx8NEPGLqiUhvcgVh7o8zaE1zyJoMZxnvW51HPj9QLWoiPDJc7XrFnDfffdx4YNG6ipqeGZZ57hkksuSb3+hS98gccee6zDMsuWLeODDz5Iaz2PPvpoWu/vC2NCnFsnCYuuaLz1y+QuyCTcFCHu03oVTK4JDjwliaYZksOOqesIgkBD0QKeeMOFLIuce1IJOWY5oV17aNxTjyAKSM5EbroeOXI1HPXHcGS5sXudCKKIaRio4Rhxv5ryQ0+iRwwiTbGULWPSAcZUTTS/jh4xUidmi5FF5LU/4i3JxJnnRVJE4sE4YV90TItz63g7fCQj6Ir3SBGm1kngSnYJ2aEAoMfiHL7him67cX79/pY+r3vz5iZK8nLJzwiTZwshmDrIMk23f4XcWx4CEhcNzlIb+bPyKFw0hbxbHwYg/PbfwTRAV/u8PovxhIFA79HjrpZLh1AoxMKFC7nuuuu4/PLLu3zPJz7xiQ7i2mazpT2rz3/+82kv0xtjQpxbWHSmdcNrKBd9ipKmVirePYAm64hKQuB2F0HPmuhFEAUQBPRYnEhdE468LFYfnsH7LyXSShbNPQ1Fb+bw+n00bfZTvDwfxa3QvNuPqZrgTOSbR3wRPEUagiwhCCJaNI6/qhmbN5ECYbblKRuqSbxJSxWftp9b8v96xMCKN41M7qy6hsIrruPTk9fhvPvnCKKAFtNQvNKYjBJawvzYkOylgJa4aG9PvEUl1Bhixl9fYe81q4iH4t2OI4p9v8iXZQndAN1IXBiYgoRgsyPZE+LljdL5QOL4FG2JEGsOEPrDTxDLpoArA8yj01pS81CsYIPF0LNq1SpWrVrV43vsdjtFRSPPdsES5xYjgsEuEoo8+Wfs2R6q1lfg3x9Gcoq4JifsxuI+LSF427mpTL2wDLvXxY5/bMNd6sTusRFtiTHlzFk8/n/rAMgsyOXi6N8o/92jRFqiTFhZyNQLT0ILhqkt3EX9jkacuQ5KFk8kc/ZU5LJJxAsnoQR8+F97lZp1Dd1GVJNz6ek1y6d75PGT3Icx7RkE/vofmit8lJ00nWmfXYDgcKE31hPccxDf3mrKX6/q8TseDVj73PDT2Sa0cy662FZsXr2mnpp2qS/zuhnv/92Uycq1duLRnhuwTZw7lbnzslA1k2BcQRKzsGWUkHPx6TguTrwn7tM4P7ILgNVzFuKoaKT010/2OO7CrxyHGo4x9dEX2H7JWQTrArQeCBFv0jpsn7WvjW0G6nPu9/s7PG+327Hbe7cT7YrVq1dTUFBAVlYWZ5xxBnfeeScFBQX9GmswscS5xYggeWAeDAH6ojwL54REdCeZwy3lHPHYVTwSklMkjsb0iyYz4ZKzic5YgmPb+9TvqMG3PYAoCxiayQFjF3/7q8DvN59KKKSyryyPyd9LFHuKuoqgqzgaa8g4XEtzRQv2DBuZs6diHn8aAVcOAWceGS4f7inbseV8PGjpDpZQP/a8NX0BmZM8yHaZpj3NyC4Z0zTAm4PuyUb0t6CGIqgR9aho52jD2sdGBp0FuqGaJI9spmqi92E/m3XCTLa+u7XH90ybXYCzTetEVRFRsBNQvB3eI7RFv192zsY12Y6h6TT85Do8J56A4+Ibuhw3Z9FstNaEsJr37JsArD1xKc3+QIftai/Sx/KxLvD/bsbzjXuP9TSGHcE0EfrRtCe5TFlZWYfnf/azn3HrrbemPd6qVau48sormTRpEgcPHuSWW27hzDPPZMOGDf0W+4OFJc4tRhyDcQBu3wU0WRSaRHbJOLLsFK4qwfbfP+YjfTaqIbJ4gcL08w+xueZ9IpWJW8Otu0I4/v1Xzlk1k7+8Ai9uymfxzPNwK3Hskkq+3EhmZg2ZlZUEqpvJnlaMNG0GhhrFFvOjO4tocRTinruYovnr2Lf30IC3rTNj+eQ1kgmVx4jWq0hOEc2vY8sx8O2txVW8FdFmo/Kdj6nf2YCpmwiKgEjvfvuiIiAoiToGIFXLcCytGq19amTQnfFB532jt+9r5WlZHNqTS2t9U5evr7j0JCaU2LArIAhgmqDpIgtnHIkmHvra5dhyZF4rnIe9UMGZZUdxKiAKoHWfZy5Onk7mOV9IPa69+XOc/NE6Vs9ZSKxOPWpf7+6OwWCbQKyZt5jTt28atPH6yngU5jDwgtDDhw/j9R65WOyvkP70pz+d+v/8+fM54YQTmDRpEi+99BKXXXZZv8YcLCxxbjFiGMyDraGaR+U1mmpCJMkOiYmnzCB0/U/ZE5qAqosIAlS5ZzBt5TlM2lPJ3ucOoIcNZK+EEVfJtTWTX1hKLGZwqEEBEgVYCyaIxNwOJs09Ds/eCjIml4AoYWx4H1t+Hjnz3TQ6ywhnl+EuyBy07bM49lyo7eZl52z0tiKluE+jaa8P2IppGFR/WN8m2mVsOTKxuh5ES5sgTwpzySkiimLK/Sfu0ywvdQug9y6iLztnp/aVzsfUTeecxopf/4K6S0/g2T+vIxY+4ofuzcvm1HPnUpAn4bSB2BbTkES46ISOUkHJcOAqcKBFdWxumYxCD86cDERFwYxFqdq9hZCcScywo5kyJgJLZuYSyyvDePeplB2jZ8YkfHd+lRU7P+at6QuI+zSSRX/Jbegqop78f3/OGZGnfoEZ8OO67tbUc85cR9rjWPSfROQ8/TS/ZOTc6/V2EOeDRXFxMZMmTWLv3r2DPna6WOLcYszSXqBr/oQ1oYRIzpQcMi68mN3RYmJaothJxKQp5kEuOoWpn25EDT9FpCVCztR8nDNnkN+6j6tOEjgcKuA/6w1WP5OwWrr+ptOZViAxwZ1FPBglUtuAU9hFpLYBqTWAcKCcPMPEUFVMw+yxYU37CH9frB+7wsrZHF6SzjqQENh6TCfaEqFxSwuqP1GELHslFIfc5rLRMTIothPjklNEsksIUuICMomk6G0RxeEvMLX2o5FB59zznujpuLH49XcAWGUvx/HlpTQ1G9htAm6XQK43IZZM00QUTTRdwDRBFo8eTxBE7B4bssPA4bXjys1g0kPPpF4vbffejXuaWDIzF4CsxWd1GMd9/R2If7kTgJX7tvBG6XyMTkWv7be9M52f68v+KtgdoHYsml26Zi0fnnwiy9Z+1OvyFmOXpqYmDh8+THFx8bGeiiXOLcY2yZxMA6DNqtCR5aKueBFqSEQSTZKpb3FNoinmxTn7LCZ9MxshHsWwu1BtTuy+KiYe3ol35qn8vXZiavznnt7HjV+ZQPTVFzj4VjmKuxJHtp2yZVOx52UTrKih8qODTDhxClkzJ5J93CFadgXRw0YqhUGUE/8qHgmbRyHaHO8xytoXuousWWJrcOkcxdQjBv7KYMqpRVAEJEVCVMTEXRjN7JDeYi9UUNwykk1EVBJ+1YIkIskigpS4sJRsOlqOllZ6i+JN1FUkG2QBiS61aRSlWvvKyKS7VI/2SK5Eh+I3Jx/HWeVbWT1nISt2fpx6vaR6HSdPdhCdaEfERBQMNFMipstENYWoKmGaIrohHBU1D/zmuxiqiihLKE4FZ7YbR5abxlu/nLJSbE9SmCep3v0xshZD1qIosSCez/4YgA9PPjGVx955P+9rM7lXc+Zynm9Hj+9xXPT1Lp+X7TJr5i0m2hjv0AHYYvAZLp/zYDDIvn37Uo8PHjzI5s2bycnJIScnh1tvvZXLL7+c4uJiysvL+dGPfkReXh6XXnppWut5++2e+wYAnHFG142/usMS5xZjjgu13bzinXOUEDE0EzSTiC9IfqgWrz2XQNxBXJfQDQGxrRI8jp1QziREQ8MQZQRMpGgQVBVDlKjYcSRvvL6iGptURM36fUQq48QUFWNywtNcycslwzDIrPYh2mSkDDdZZZmE66NEwnEM1URxithyZKaeNQ09ruGvbiUeSOQxy16hg6tMf3BOsCU6jbYVolqR9cEnKZQM1Wy7LX8EySkiOyTUkJaw0GwXZXeW2nAXuVJCXBBFBFFICPQ2y7tkUxmbRyGqqKkofU/YcmWchXbyZuTgyvNgy3CiReOE6lvxHWjG93GgT9tkMfLo7GjSlXuL7JXInOpGcSnI9sQpPn9WHlvOP4MFLydEhPu0K1H2HUaQTERMDAQEw0SUEhePAiamqRDv4mZNuLqecGMgcSdQTnisi4qMqHQvJ7btqwHAKUSQJCeaaENUXDhFich//orzzGtYtvYjXiucd1TxdLpdnqPP/w4trxRTSszHs+zCPi13/Or3gYRF5CveOZiqmXKjsRhcBIz+pbWk6XO+fv16Vq5cmXp80003AXDttdfy4IMPsnXrVh5//HFaWlooLi5m5cqVPPnkk3g8nrTWc+aZZ/baIbQvXUHbI5h96Dnq9/vJzMyktbV1SPJ8LCwGixflWUguEcUjEa1TU2ktQrv8c1EWmHxOGZM++0nK51xEZTgfVU9E0bMdYfKlBtxRH6LZFv00dOzBRsRQC5o3n13Zy3nyTRtrX1nPT28/icX/+C+2PLaBuE/DliPjnZLB1LPnY191CZGMAmwxP3IshNxUw9Z7H6N1f4BoW2Q8Ob+pF5ahuOxUra/CMAyK5hcw6fJzaF58HtqvbqN8zQGCByIdttVeqJBR7ESL6vj3h7sU8YpXSkVcHYUK0z4xjV3/3JN6ryXCBo/2IiIpkjImOFHcCg3rmlPRQFERyJztJmtSFo5MJ5AQ4aZhtP2beJ8gChiajhpRibRECVSGUoXKXSEqAq7JdspOLKP0k2cTmHkSIUcOJgKKEcOmhvEc3sruXz5G9Zr6bqPw1j4xenjZORt7oULOjCxypuaRMaEAW6YH0eXE/eXb+z3uR7uaOXF2do/veWfBEk7bshFIFHbaMt2IioIgS2Te9Kt+r7tzgXs64jxzrpsT7vkaKDZMmwNDtmOKEogSnhPP7/M4SXEOjDqBPpL1WnJu2zd+iCcjI+3lA8Eg85YsG3Hb9q9//euo53w+Hy+99BIff/wxd955J1dddVVaY1qRc4sxhx42OkRfkoV2pmpiaAn3i+oNNbjy/kNZThGBvJUEVTu6ISILOnYtjKQfEUGuA5uJ7d8HooitpIS5apRvnLuQy1cuZuEHd1Fb40u5awiKgMObaNSh2j20Ogpwy3YyDB3iUQy1reNnm5By57tweO2UnbsMccIkXAWrqdlYTkZRFqH5p/FxeA4rzjqVhp01KXGevNDQIwZ2r4Op50ynfPVO6t73HfVZtG+EE61TOwhzsCLpg0lnIWGqJrHWOPFwxxQlySmiuBUkRUSUj+SWG5qeEuamYSC0VeSJsoEkiyhumQjdi3PXZDv5s/LIXzILdcJMAs48goYHwxRxSWGcsVbM+mrUiDoiikuTTWzOrtp2jGcyekkKx+a7b8BeUoSY4UkIU1kZ0Li9CXMALXzkLpEei2Oo9tQ+OxA6H4t6yjnvTKQuhrZ3N3JJCUJWLoIogSBgmgL+da8gmGavIn3NvMUJK10Y9RaoFsNDd84uX/7yl/nSl77Exx9/bIlzCws4krMotEUwATRVTzm2SHaJfa/uYTpPMPcqg3DWBFrdhdj1MN6mA4iBZkyHEwSR2pf/Q6CmBVduBpn+IK54jMJgC0WaSvWaDTTsqkOPGAhKIg0l1BimcdtBJkxeS+H0MHK4FbGpltiBA0RaYqluf55CN1M/sZhNF9zPV3/0EdMWz+TuL5/I3N3vYmTmcsgxAT0moJdOw5HlTOWny14p5d+eUeDBs3gR8tq9TDy3iKY9zYTKu28w0l2KjCXSB4f2KS5gHJXmAiB7JWS7lEpjAVIpAsnouSAm9llBFBIiXpGQbF0Ln2RRaeYEL3mzS7FNmUqLp5Cw6UY1FCRBRzHj2AMNxKpriQe6F/jDiSXKB07zXV8n+4cPkP2D3wEQfvvvmJKMKQ1MnPfGqzlzseUckQ+GpmNqOqZiJPwXB0hXha99EemaX6dldznZioLscCEodkxRJllYlBTmgQ9fxLPsQiJvPIbz7Gvx3flVwrVNtB72obhlVG8iDe08/87U2C87Z4+6KPpIRTD7mdbSj2WONZ/5zGe49tprufvuu9NazhLnFmMWsU3MJgvi4Ii7RqQmhh4x2PPSboqqHsRTnEXxkjkINhv1724k1BDAme3CXZhN/c5aYv44akRFj2vo0Ti2zBpa9lVR8U4FeiRhuSi3pZCE66NUBaqJBV4ld1ai2YcoSeixhCgSZQFdNXHnu5GcDqY4KwHYv2kPay9YznHzsoibNkxToNTTitCgI0pCqjGSqSaK/FS/Tv2OOvKO28v0i5YhLTwB438eIFReN/wftsVRGKqJoeodLD1FRUBxy4iyhCAKbUK8o5hp/zgh4Nty0ZWjxbnkSri82HJkMidk4Z5YjJ5TiCo50E0JQTDJkIJkRJqQm6pprWlEjQ5OIyyLY0/2Dx/o8Fh3ejHbxLF/3St4l65K/TuYSE4Re6aNzeedzqJX11D2u6cAaLr9K+T8+HcDGvsV75zUbyZdkW6oJrVbq7B5XHi9XgSHE0G2YSIhmCb+da8ACbeZwAcvINpdRF75A7LLiex0YPc4sHuiaFGds8q3HjX2YPurj1eGqyB0JFBeXk4g0HudT2cscW4xZjFUE0FJpLEknSuSJG3uYnUqtZvriQWiuEvykJ12AjUtNB9sxe4NEPNHCDdFiNWpmLpJqCFCoMaPLcNGoCaIHjHInuHFU5zIf2va30S4Pkrcp1H9YT2B2iDeEi8ZhV4EUUxF7gVNQItphKvqyX/9j6y49FZ2bjpMjjuGiIFNiCMLKooeQ2ptIFAXTAlzNaDjLnNgaFECB8PUb9rLhE+cQvXDf6ZuXSOSS+x3Eal18hl8OnvuS7aEK0uSZCoL0CEtIJHaIhyJnktCyoqzfbMiW46MM8uOpzQPKScXTUl4NjuEKKJokB2qwtFag15bTbjRjx4ZfdEni76RjAwnRah/3SsgiPjXvwqm0UGkBz56GVMQ+iXcz67axu5Pn4cto6M/eO4tDw1g9glWtYtW9wfftlYc3kPYsjw4XW4ExY4gSpgcSSEzBQFBlHCfdiUAevkPkRwKskNhyZvv9Ti+dYwcOAPtEDrSeOyxx456Lh6Ps3PnTh5++OEORal9xRLnFmOKzlGVpJjtSpAkRY4eMTB1EyXDhWmayHYZQRLQ4waNe30pj3TDMIgcjhP3q3gmuBEkAc9EF9MvPAH71KmYsSjOd9dTua4Cu8eGOy8DQRQoWbEEU9Vo2XmASFU8td7m8lZi/hglpsFPlvwSThUx1voRXW7IyUPLLcWQZPSqw7TsDaQEtwiUnlBKS74Pf2WQeCiGHggiOxT0iEHGVCfRxji2HBlJkWjdERq2z9+i6yK29jnegiggymKnCLnQQaQnnhMRxCMpL6IiJeoN2i7wFE/ibo3dY8OV68KW5QFZRlSjZITrcdOYsKtrrMSsryZ48DChxpCVRzsOkKJBECVMUcIU2rrNSjKBj15GVKNgGohtz4fe+Sfu064kvPoJaspOoi6WzylzPazb3Yxpdp9/PuvJV4d+O1zp57Crfp26TY3Y3Pso9WYgOd2J6HkPqT4ZX7uLDCC/h3Hb/64tgT4wxlpayxe/+EVM00TolNJlmiYrV67kkUceSXtMS5xbjGmSaSzdtW8x2lJE7F47clYmsdp6DN1EV3UidYncbVuOTO60bLIm5RFuDNC0rylRoOdUiLZGibcEkH1NhA9VUbmuglBVhBdvfzfVqIgX4cLPLedb2fdiPJS4VSp7JWJNcSJ1Meo+3IC9cAuaX0dQBGw5MqZqYvMoOHMcCKJIvEnDlitTtrwYZ04GhVddSd7GD6ldt5uJl54FmkrT3noWf30J2/++BYBYncpx185BO1Vlyx96zu9NpkckLRctBkZ3LhNGW5GoNFlK5ZgDdHXOSUbRk5HzpC2e5BJxltpwZtmxex24clw4czJAFNHr66CpEUVP5AAbmoa/3kc8ECYejOLw2smdm020NErLrmC/m11ZjGySEeEk4cdvR8zMwsjKxxQlBKPtiCgICJpG9JnfIIRD5HoLkLwa4GHprN6LQvtK6y++hRFXU7nxnWm+6+uYhkHOj/+vw/PdRdF7Kw6N1qnsfbqc+p0NTD+vkuzlJ2EUTUFATKX9mP3IjW8f/OnKb94S7OOTdevWHfWcz+fj5ZdfZvv27TidzrTHtMS5xZikrxZcyfQAQRQxojG0UAQ1oiKKCbGajLjLdpnM6WXknZxH5pZdHFy9CzUUwZFtp37bIbICYUL1rUR9MZwFdsKdHDpefmIt//2d6Sl/a0ERsLkUdNUg7lSJ1R1x0EgK5IgSR52stXWWTOSqu/I85J+ymEMTT2Oir45Sl5OnS75HnldlavE7IAodbBprtxxGDffe0MhUTaQcEcUr9fpei4ERrVdpOdyKp0hHcdpSEfSjc83NDhH1RMQ98R5RTBSJKs7ErXjJJqOFIuiRaEKU64l9xtR0Yq0hDE1HsslINhlHlhvTMLF7GgnWhvHvDg/zJ2Ax3Lg+f0uHx+HVTyAk79SYBqbTDe4MRF3FFWvh8J7txCUnIcNNa9yNTdJwSVE8gh+HGgRAUcPYIi0oTVXo1ZVoLa0Ymo5oS0SoTa1jSMSIxVl3+skJu1Dd6NCNsythPhgE9oYp3vJXALS1z2FKYofUiGRhaHs+Wn4SJ777QZ/G70qgW1H13hlrOedLlizp8vmzzz6b73//+3znO9/hoYfSS/myxLnFmKUvAj1psahGVExdJ9YaRI8lxLEoC2htjWWCDSFMXUdwuTF1nXB9FFEWMHWDlooWDE0n6o9h8ygULyrhspXwUdtdX09uFr+5JYvoXx4ja27C21WQBJzZTpxZTrwT86jZWE7t+03IXgnPJBeSTUKLaMhOmfq9zYiKQM7sTApOPxFt+nFM2v4cWkMDktfLOXnrydy9lsjCadzo/xGf856TuqiofruxT59V9nEe8mbkMeOvr/Tz07boK3rYwL8vRCwQx5XrRLYnouiSIrblmItt/xfbCffEc5C0YpRRnAqKU2mLwJtooUib8NHbeaYbGG0iSbLJKG4nssuO5LDjnVxE4FAd5cZBIlXxATW7shhduFZc3eXz2kcvI2tRDFFCF5VEAzbBRDckItgxpUziNjsAoi0bp92L1zSRQ0HEcAREDUGSMHWdrJt/S9PtX0ntw3osztI1a7tcb7rCvLP47eo4nwy8JMk4+ZMEPnwx8aAtpQfTJPLmn3Ge9bnU+6LN3btdtV9/TykulkDvmbGW1tIT5557Lp/5zGcscW5h0VeSbi4AkeYIRjyOGk44qghSwhnF0EzMiEGwOkTdhj24D9fRsKsGURbInpaFvzJA6HCUUHUUySky/bzp5H3uGtb63Kn1XP+1+ZS8+iOaqhuZfPp04sEozeVNQEIweWdNJev4hSjOl7F7HJSes4zgSRfiiDQjrHkF2f4R2ZPzyJkzKZH/sG4NgfpGPEtPIFYynczdazn0j5dRXHYe/uSz/OeXfW/znsSR6aDlcMvgfLAWvaL6E3UQesRAccuJIlFFRHbIiKKA4lLaFYNKbVFzKVUEKkpCqqjUNEz0uJYS4cnnEv8aqWVllx1bphsl04vkdiEoCvbcLGwZDirXldO8w9/BF78vWCJkbNHXRj1b9tahmTLTZ00BFsBJHV9v/cW3aLr9K+iRKIIskX/bHwd/su3oKhAjeyXOrdve4TnPsgsJrn0OUzBBEBB1FSGSqMnZfslZtB5u5fTtm/q8zvak28l0PDPWCkJ7YtOmvu1PnbHEucWYpqfouaGaSE4wNJNYIE6ssRk1HEPXEgWipmqm3FXUgM6hNZXI3pqEWFIEZLtERpGLeIuKGkh4qGtRFclXx6Fo4gq/eHoZZ7g/ovzfHxFpiTLnqhUABOs+oLm8lXBjGDW8lsyJeagRlUnnHM+Wk7/Dv96WUeM6nzv/BEoqv4GzMBeA+nfWEw9GKfvMRXxrx2e4c/JO6v7xMtWba4i1NRlKFpzmL81Gi2o0bfT3+jnVvNtgefgOM4ZqEqtTiSsaklNMRcQlm4gW0xAkEUkWESQhlW+edB7SojpqWE2lvSRTVtqnyCRtGCWbjGS3obgdKJ4MJE8GgsuNIElIgoizIIArp44WJX27L0uYjz827mlCQEI3RT7eW8/CGQVdvk8QBQr+52gXi6Gir/tixsmfJPj+swlxHglgNNSy68pz8Ff7CddG+33BaaW39B0Bg0Sbp/SXG4n8/Oc/P+q5pFvLCy+8wOc+97kuluoZS5xbjGv0iJGwNfTrhOpaiLZGiPnjRBvjqdSQ5O1+KUfG7rGRUeAmsywXxWWnYWdVyuJQV00OvXeISPNTfOPHk8i58QIynODe/DdaD/vJmpSFlJ+P4HBRdnqISMs6Ik1RGvf62tJqTJTiIp5Zo/D+Sx+x8IyFzGz9gE3/3omnJAN3nhtnTgae0jy03BJ8da24dr7Prl0NxH0axSfl01oZSLmzZE/KIRaI0rw10Gsk3RLmx4ZkwbKpJiw/k85AsldCFBPC3NQT351hGKlOtPGAiqGbaDENLaajOGUcma42F5gj6TCiLCLZbYlUFqcDwW5DsDsQZCVxF0aSEO02FKctNbYlKix6YsnMRKBg055GJOHoOy2Rv9873FPqwCveOb3aMWaccgnR538Hmkrw4CGa9vkIVUcHXBDf2S3M+i2ND2677bYun8/JyeGGG27gf/7nf9Ie0xLnFmOe7qLnYlv3UFub6IZEmkncrx51kBZlAUe2DU9RBqUnz8GWn4sRjXHo/X0oHgnFk+jaGamKc7i+lrLTX+Pz8+swdSf+bbtxZNpxZrsSDhymgZKbw4SlUwhU+4i2RgjWhcgsyyQ68wT2PlyJJzeLay8E/4OPEW9Rqa9pwjUhRNkyB9nzSohl5PGb65upuXcN/v1hJKeIKzeDlopElNxQTfa/dDD1f4vhp69FydBOpLdZeyY7zpqqmbr4M1QTxSuhY2DKAqChqzrxkIbNLWMaJja3vS2CriDKiei5KIsIkoQgtxX7mgamroGhg34kH11yiikfdUtUWPTG4pl5Rz0XXv0Eos1G5k2/Gv4JtTH7ypkEH/whGV+7q8f3OS6+IfHvZbDuvtmp4+Rg7Pe9/fbHfUdmk1TX1rSXG4E0NDQc9ZzD4cDlcvV7TEucW4wL2h8sRUXAXqjgKnBgcyltrhcyhqYj22U8E9yJLp4Rg2idii1Xxllox5XnIndWCY7p0wAw9SYESURQBERRxFksoYY0FLdMzNeKDRBbm3DkZZE3q5BATQv7H38BT3EWBRecg+vqU7BLdkKuPJw//y7+qlaqfnobjyybhR6LU3njXuqB41/6LfaP32H3X16nYXcd3slFuPZ8BIaOUJiNs9SGHjGo21aL7JCYcflkGvc00rw12OfPxmJkkBQIhtp17nf7nHDJJSYEuKIRVqO0KiFEWcCWpeDItuPOc+PKzUgVjBqqihgIIfpaECQJUZFBFNFCESLNoQ6OQV3tE32JSILV5nw8012haU98ePKJTD5jNoV3Pw7AqzlzU12Xc+dkcvzq93mjdD5xn4ahmr0erz5bfxPFe8q49B2Ta07rm11id/vrK9456GGjz8fI9he1PS3TPsI+Ho+/Y60gNCcnZ9DHtMS5xbijfVGdIB0pupNsieij4kw084n7NCSXSOZUN4pLIdwYRgvH0GpqaN1bQeOeOpq2tKaauthyZFxFDhxZdjJmT0eIhYns2EnDlgOEfWHioTiCKFB6ylxapi3FFaxHXvMsecctolqW8BR7CdYHKP/PVuweB9HWRBtpx571NHzwMdHWKGpIo/rD3fDhbtz5XqItiaYyngluypZNRVRkbFke4qEYzhwHte83pQSXc4KNgvm5NO1vJu7TiDdp4/LEMFbQwwY6Rxq1iEC0SUvcwXHGiE2Id3BqkWxHDveiLCHZFUzDJNLkp/lga6/FoKv8O3lRnoWoCN2Kma0XrsCWI49b0WGRPsvWfkTkjccI/L+bCR6sRPYmanpEOXF83ve5Czq4rvQFu8PG8cWVQNmA5pZut9J09/nx+hsZa1aKQ4Elzi3GDT3dahREAbvXhexQiTSH0CNGKtUg3BAlf66b6RefBKecjVy+g4pH3yIeUHEUKBhawm4x7tOQnHHyZ+cjiCKx/fup27iXht1NyA4JR6aDWT+5gdbC2URlN6JLxzNnPjTWUXTG8YgeL/GKCmrX7aZpXxORphhxn8ahp9+gZkstml9PrKdFxVXkoGTZLFrX7MBd5KJ5h59Q7TZkl4ykiAQrIzgL7R1SWmJ1KuHiMLE61WrhPsQMp3NDsiZCp51vvyIQ96sE60PY3HZEWURsS2sxjYR/umST0eMa4aYgakhLpbQk59+VcEg+t3rOQlbs/Pio14tPmI5sl6n9uH6IttZirPHW9AV4J2RQeFwpniml5M/NIVgXQrIlGm+p4VjbHaJEd9zeUOw2PvupAibveRSmf2UYtsBivCNJEmYvaTqGkd451xLnFuOKC7XdvOycjebX0V0Gst0EORFJlN0OZKcduyfQIVJTurSECatOpXnZJ6k0JzJ9lofiRavZ+9x+DK3Nsk41kb0S+XNzyZ5RitbSin9/JaGGEKIk4CnKYOJZS/jB5vOJRzUuOS+DEreXJ/Wl2HPhU7P+gxJuxSYIeGsaU2JccopUb6jB5lFSEfqE/Z6CkumlcP4E/JVNBCpDhMpjQAzJJaKHjaPy5g3VpGV3EEMzrbSDMYqhmoiA3uZqEAvEiQWiSDY5caeonW+6HtdQIypRfyy1b/WVroR55G9345xQTFYoQqAuaKW3WPSJuE8j6onhnToB/wVfYsaijxECPrSqSho27KJy3eGUG1Zf+J/b5rNo3d3sefJNAj/+Padu2jDEW2CRNv1Ma+mylfII4Bvf+MZR4tzn8/HWW29ht9u58MILu1myeyxxbjHuMNqcMeJhFUEScCR9pCUJg0QnRWehnXiThuKVKD1nGbEFyzlkTKYx5MLmnc2ci86icW8DgcoQzlw7kk3EleuiYP5E7Hk5GNEoEV8QUZFwF7hx53tRZs2l8qV6mmsbKbh0BlPf+BWNy3/KLT/6iM99sxJt13YCh2qo21ZJ3KehRwxchXYyJ3gJN4bRIwbOUhsZJW6cWQ7UVj/B2mZ8B30dIuHdNZNxFCp4p2TQsjd9yzyLvnOs/Y6Td3yAhAtRYxi7x4Zsl1NuLqKUeF2NqMQD8dRFZvvoeTqEH70Vwe0GUcTUdNSQahUiW6Q4fMMVbP3jtqMu1jasOAVnsZ3C+UUEzr+OA+oUpBKNJrmQwmkVOA7+lmB5JFUQ3RunXvQ2P/r5KUTKD5M1KY/SZTOHapMGjXGZAmaa/SwIHZnHlF//+tddPh+NRjnnnHO67SDaE5Y4txiXJFNRAERJwMhJVFW3bzmdLBwVMzKQtDi6TcAfkThEFt6FF7PgOyJGoBUzHkfzBzDiKoo3A8HhQA9HEq3SvYlueoIoYDqcODMc1EVj3Pidbdgcn6TwYKLKO7ruQ5p2VKCGY0SaI4llFAGbS8HmthFtjWLPtWFzJ8ZUIxp7X9xIPKQRa4r3yQJMcoooTgVHnm1QP0uLkYkeNhBlgVBVBDVLw+5VUFxKwjPdJmMaBvFQnGhzPOXnD/QpdaAz4YpKHMUFqL4WfPvrCFVHB3lrLEYj4dVPoLmzyVk8B1vOLnZdeQ6z//k6AM1334AgCZSeUIr/x3/i//3bzl3z/kU4fwqb6xdydn4NUV8wFTHvy4Xjey+cwakXvY3ddT3xSJTlS5byzkVvp16zGBmMtYLQ7nA4HHz3u9/l+9//Pl/4whfSWtYS5xbjjmTueTL1I6zECBwKU7etHj2WyOtOFsdFquIEt+0kQ5SYNTsTPXsBNa0O9vgnEDvuCmRBRULHG6nHc3AjRnMTmGaqfbqu6hiagV7XSs6mdTx8ZpzYp8q44Kcy8WgMNaby6K8moz0WRZRFaj+ux55p45RbL0aaPJXY1i045s5j/dRryLr5IirX1JFxpodpZy1m34sf0bI72G0hn3NCwrvane+icXMLjhw73pJMihdPHsZP26I/iJ0K4PobhVb9OobWdqeoRUX2SkiKhCgJqFEtVX+QbsFde152zmbK+RMQdx6mbls9wQMRK2o+zlk9ZyHekgymnLMI54IF6MCEU4qp39GIdM0qcm/+Ib8s/iWfcp5LuClIsX6Iaz8xEaPeifTvJzm78SGCNT6qN1Zz+i8uRS4sZOOdf+GUDev7tP5YOBHgeOf5j4ZwKwdOssD61Zy5nOfbcaynM2yMpw6hgiBw6NChtJezxLnFuKR9caihmkTr1FQkvb2wMFWTYFUTorQTd8DP4vnNePJPJKTaMRAwTQFDEBENDTMSwozFANDCEdRwDC2mt4l0g6p3t9L8l3eY9MQfgWYAPvXpKRhmFcolV1MYaiZY9/+IBeNIeXmYtVVILieBDz7k+HCA97c1UbaiiJJls/DtOIhvV88OG3GfhtnWhdJd5qDouBJkh0Lz/hqKhuhzHe8MVkpLZ7Es0n+BrocNTEVIiXTJqScKRn1ah/d0viDoiVe8c1JdTQuX5TDhjEUABOveJ9rYtzs5FmOXrImZTFp5HI4JJbSueY/m/TW0Vrbi3x0m2ngI0/gfbj51AR9uagRg8pt/Z87kqZiygq2oADnDhSMvi6Z9DcROvYBadyl5M//dqz945M0/AxOHazMHTNL56M3Jxx3rqVgMgIqKiqOeS3YIvfnmm5k3b17aY1ri3GLc0rmbW2fxI7ZZLhqaTri+mXgwjNsfYPbCZvyFszB0GRMBb2sl0s71RCqrMQ0TDIPy1TsJNYRRHDI2jw17hg1RlsifnY87VI/Tm83J5x7H+dLLNN3zMB9/8wl+9LN1/NfNqznxt+ew8//+RaghguyQKFs2mcArayiYn0vJbbcir3kh0SzJ17MASnU2dYLiTrgeFJ5xAnmlkwf9s7QYXFLuFCQuEA3oV7pJkvZNjpL55UlBfsRb/cj43Ymf5G/FOcFG0aICihZPxTV3NrFpC5GjQTLe20rzXn+PdosWY5fk/nHyz1aw65N34ZKj2J+7htpt9anjVbxJY9+z5RxaU5nyM698ayOZEw+Rc/opCBkehOIy5FiU3K0HUOIhMsU6mjQdxSthaCavFc7j3Lrt7L/2QgRRYOqjLwBQ85d/AN89VpufNudHdvHW9AWp7rwD5UV5FmGzZ0vUEYFp9K+4c4SmtUydOrVbt5acnBwee+yxtMccV+L81IvetvLOLI6is0hPkhJHhokWVdHjGlBLZtY+3A4PsYw85HgI+dAuotW16JEYhqajx+K07g+0WSuq2As1DFUnFoxTvHACpijx6M8c5B1+kl9VXM2NX9T5xZuJE9fjD63n0uXHUbd+Fw2bEtF1z2dKadi0F1duBsqaFwgeriFzehmGurXH7RIVIeW9HvXFqA3VYfduZNJDtwzyJ2gxmCQvClP7n2xCpC36PcB0kfbFoqnHneitOE1UBJy5dgoWTMJ+zgXUFcwlTAbF4m4km4yhmQNKk7EYnbQ/fspuJ9V+N9NzovgONBPcm6hBSN6dERQBd6kTgKgvRvnrVbjLmpgO2DIzUANh/JVNNO5pJPulpzAiMUINIWw5MmpAR48YvH/8CWRPzuowBzUc540b9nFw+ioqQ7nohsAdP10LjNzzv6GZoI2+dI2BMNZyzu+///4u3VpeeeUVVqxYwbJly9Iec1yJ85H4w7QYuZiqia6aBOsDOLx2dNVADcdw5NRinxxCcOcgxSOYoSBGXE0s0ybkDS3hLiA5E9aHcUXD1FUa99Rh/uF3NOyqIe+LF5JfKCId2MGJiy+mrOw0nn30HW533sktJ/yYyg+rsHkUfNsP4i7IJOILUPHGBjLLcom39uy44ihU8Ex0EWmJoUV1ovUqejjGvtA+Jg3Hh2fRb5JNsiR7wpc86Y9raOaAoud9oa+uEbJTxlmUT2vuVFqMbHRTShRNxzU0v27lnI9zKt7YxOkt1xOua6ah9khxcDIVSg3oBCrCOAvtlBxfTMXqSqKNcXY+sx1Hto14QEUN6IiyQMWaHZi6SbQ1hh4xEn9hg2BlhFhrwmloDvBG6fxEJN7xDMH9f2DmnCJyj5vOKz87h2eC5/L7/10zIgX6WeU9B1n6StL1xe/3Q2bmoIw5VAxXE6I1a9Zw3333sWHDBmpqanjmmWe45JJLAFBVlZ/85Ce8/PLLHDhwgMzMTM4++2zuvvtuSkpK0lrPt7/97S6fv+OOO7jsssu47777+N73vpfWmONKnLfnj/8x+eKZVnTH4gjdpbk0708UU5q6iWQTMQ2TCSVFOMSEeNJ8iZQXI64lIufxNheYtsi1zaMgKomOpFkTc2ipSORZVjz7H65YWk39/kounvtd1JWXsn7WZD53VoDgAzUULSzAluGgubwRLVZH7rQ8Jq5chBaOULNub7fbIblEnAV29LhBuDyW8L5ui1hFKuND8+FZDCqSXUJ2JJsGJW55G5rZrU3mQEnXyk2P6+jRGK5IE9luG03ko8s2MoqyEJRDXGiltIxrKt+qo+rd+g5OK5AIVLTfj935TiaccyLxUIzq9+vRMWitCyEoAnrYQHKJRHxRghURBEVIBEzaltX8Olpbzc3Lztmp4/WeZ/Yn7jy1NdqyVf+VKz/RxO8pHe6Poc/0lkvf1+VHTVrLMBEKhVi4cCHXXXcdl19+eYfXwuEwGzdu5JZbbmHhwoU0NzfzrW99i4svvpj16/tWeNwXrr/+em644YaRLc5Hkp+nJcwtuqOzSI9UxVEDiYZAjmwb8VAM3/qtZNQ10Lz7EM3ljeiqgSgKiG2e6aZqYss58vPSojoTlpYR/v4DZJqttHzzq9Ruq0eyyRSfuoDd/3gH+5sb+eP3vs5lt88g2Py1xII+wAWvXfUGu//2H1oqPsSd76ZyTQ2zr5pO5frKVDSp9OQisiblsel3m2neGkytO3mRYGgm59ZtH7bPcTzSUxfavqB4JWw5Mu58J5I90ThIVw20mIbiiKNHwoMm0PtzLE7uS00b/ayvf5PC1R/jLc1B+PZDBF0F5J+5nLKDVnfQ8Ubnfb59+lTSktaRbUscl1QTySViy5HRYjr+nfvJmpRHuClM4FC4w7J62KB1V6jLOzFGJ+GfRA8bKB6JaEuMfa/uSRRBP7WZnz61jsefbBiKzR8w7c85/fldtl9mNETOhyvnfNWqVaxatarL1zIzM3n99dc7PPfb3/6WE088kUOHDjFx4uAUFre2ttLQkP5+N24j5xYWfSXZtEiUBQzdJB6KE/EFEcR66nfUEKyJdCjOFOXEycLQTIzAEWeMcFMQCZ1s336cJ0xjwqk2fJ/5Pi/VTWLpuk9StaGKjMf/yi9/+gDXf7s1Nd4Tvyik4d6tTDp9Lq7lp7N/0tmU3nQ17qIs8maEcWa72ffCAVoPt9J6uPWo+XtmuJh21kw+fuToro4Wg09/BbrkEpG9EjZPwo881TBINlKdPRVPrM8NWYaCpLMRJO7CHPbVIsp1nH7qQ5BXhBmJ4MrNOCZzszg29GVfN1UTQzdTBcimaqL5dfwHg5TH9yJKApGW2FHL9bcpVtynoQZ0FE/i7lO0XqXoe2dz5+RcNp/no+VQa5ddbkczIyn42RsDtVL0+/0dnrfb7djt9gHPq7W1FUEQyMrKSmu5t99++6jnkm4td911FyeeeGLacxlWcT4UO87qOQvH3I/MYuRhqiZqQEf26giiiKc0j8wTFyM77VRvOEjth42piKZOQmihmUhOkeDeKIpXwu5xsNVXwncf9jNx1i/Y+eEu4h8c4tFfidRuqSGwN4y2UKW0dRv/vkXjZe0TNAcEfF8/HQDPxEICRbMwTYH82ROo2VROuDFM2BfB0Ex8H3edhx7YG+aw+8CQpURYDJxkJNHmVVDcCoIotHXyFEBKpreY2LKURGqAOrBb14N1LNbDBjqw/x9v4C3NQYvGU84ZFmOXvl58tk9niTXFOwhtQ0sUOrfsCqJ4pKPtQ9si7rG6nrvNdhtVb/e84pFoOdhKwxYfmj9xFzSZCjNaBG1vjKrtMM1+Rs4T32lZWVmHp3/2s59x6623DmhK0WiUH/zgB1x99dV4vd60lj3zzDOPKggVhMT+vGDBAn7/+9+nPZ9RHzm3hLnFcGCoJopTxOZSyCjIwJ7tQbDZ8Z59Fo68DUjKx9RuaEz5OyeFsNmW723Lkck7birvrY/SXNvI8afN4ONoDE9uFnl/v4s6NdEIpuKdQ/gO/hRBFFhU/Gvy5k5ke3WQaGOccON6JtY2YfcFaQhEkRQR2SHRvMN/JIdzsp1QeccIlKAIBA6FR9fBe5STbvRccorYvAo2t4wkJ+oaTMPAICHQBVFEtksobpm4U+3R374vcxtsWg/7CfsixPxxpvZzjIHm3VoMPf1N2UreRUw9bhPPOgkRrgb0LoMH2gD2c2hLcfFKyF6J/Fl5+A4009oUSl3c2nJl3j/+hD43NxoILztnA3SwGO0c7b5Q283Lztlj34bUNFNCO+3lgMOHD3cQ0AONmquqymc+8xkMw+CBBx5Ie/mnnnrqKHHucDiYNGlSvzzOYQyIcwuL4URxKwCogRD25ib8M07GnZHDFJsNu2crddvqCR2Opk40hmqSMcNByaJixDMv5Aq3xm3za6kvKeeNf0KgqYWLmq7ijvD7OEttTDhhAtnTS4j7Qxz+8AAlN32SxeddTOhf/6Dind0Ea1vwHWimeFEJBUvnIhcVU/3Cm+z6+96jGsuk5txFVMpi5NA+Z1aQ2oo/dRMwEGUwOOKBbKhGr/72vTEUt79D1VFE+ei0hN7mAUfEuCXKRxbJ/WQgNRTJIk6zt8h3N9Hvgd4hgkSXXD1icKiqusM8REUgb34WU85eOOB1pENvv78xL8wHAa/Xm3Z0uztUVeVTn/oUBw8e5D//+U/a49bV1XHppZcOylzaMzjO9wxeZ7yRsh4Li84IioAW1Yi2RgjVtRDaX05MdhHInohcUkLWlELyZ+eSMdmZWkZyiZSdWIZ0xwPc9tFyPtrv5cOci7jm2zWp93hyszA0E3e+i6LlC3GUldC4uxpvsQdp10bW2D5BxiWX48x2YstwEKmP0XywCe3kc9HzS5FsMoIioHgkJp1ViuKVOsw7WqdaLi3HgL6KzWQOrq7q6HEdwzATrj+qgaEZGJqOoeloMZ1QdfSY5Zv3dOyNN2lE61TOrto2jDOyGEo6C3NREXAUKkcdX3rDaKuROJb2mkaby0t75ypBEcienIdz/vxhmUNSdIuKwIvyrA4XP+NN1wgYKa/ztP4Y3NTMpDDfu3cvb7zxBrm5uWmPMWHCBE4//XR+9atfcfjw4UGb26BFzocr6mFFVyyOBWJbBCjqi2HqJpGWKM3ljcxZ/j6IErEDB9EiMRxZbrImagT2hgFwlzkoPOk4vvU3Dzs/XM86ReZJtWPk87NfXEDkL3EkZxhT1Qic8SmmT5wKhk7dP59nyZZrUfOzmXDqHCpWb6dgYS6ZZTmIq1+kac8h9LjGxDNLCDWGyJ5WTKghRN2Hvj7la1qMDAwtUXRsukxMw2xz/0lEDgUpISaiLbFU2lR7JFcixpK8WyMqie6ioiwMqf3iQLGO5SOPrkSi5BIxVZP8pdnMvGQZWijC7mc3dFvj0p6Ruu9BYm57X9xH9cZKTr3o68OyzvMjuzp8xuNNlKcYYFpLXwkGg+zbty/1+ODBg2zevJmcnBxKSkq44oor2LhxIy+++CK6rlNbWwskunrabLY+reO5557jqaee4o477uA73/kOS5cu5bLLLuPKK69kypQpac23PYLZXc/Rdvj9fjIzM2ltbR20WwkWFiOdrg6cHVIQFIGMyU5M3cQwDGwuBckmoseN1InLlisz8+IZ6D/4Bb98LhddM8jw2mluDLHzwx0AHLf8OO6I3MyBN7aRP6uQ2u/+me/evIkrrj+Ncx4+i7x7/pdN0fm0hmUu3n8n6+/5F9HGOFkzPEw5cy7humacuV68JywmsmcvSnYmjet3UL+zlmhzjNO3bxqeD8yiS9IpnrPlyB1aeRttnQMVt4wgCYQro0flm4uKQO4iL0ULSjnw5n7iPg17oYLdY0N2yGhRjZZdwaOEUrriuLftsMT26Cf5HUsuEUeBwoRlpZRe/zmi763BNEzcixdj2h0c+N2fOfhi5TGe7eAhKgIZU50s/to5eL5x77Cue7DTzEayXkvOrfr1v+J1u9JfPhSm5Jxr+rxtq1evZuXKlUc9f+2113Lrrbd2K57feustVqxYkdbcdF1n9erVPPXUUzz77LPU1dWxaNEiLr/87OsvqQAAL2hJREFUci6//HJmz56d1nhWzrmFRRd0J0SSt0WTkclIXSxVtBRREqkj7fMaNb9OzcdVLNrwNNOmf4ed25tpagihxhJ2dLIic/0l0HxbFROWTsY7eyp3Pp3I3W1sVJl8wclsYxoV9Qov/WsX53/5BJb+spSmF16hYVctsstJ3fYd5M8ysE05Dodh4LziJjK+ApOH7uOxGAIM1STu0zrUB4hyYl9z5ko4c5xIiki4NprKO5e9Es5CO2UnTcX2pW+yYMKfOPzuTtSIimyXEUSBSFO0x5zfvjLQ/GOL0YGoCDgKFDJK3LjyvAiREM17KqneVI307AYEScC/L3SspzmoGKpJpCZGtLYB85ffxvvtXw7buq2L2qFjxYoVRxVqtqcPsek+I0kSZ511FmeddRYPPPAA7733Hk899RQPPfQQt9xyC3Pnzk0J9QULFvQ6niXOLSy6oDchYqomBmBGjCNpI90UNfn3hah5ZQ3XfPcM7q2cx9pXjjgDaKpGeUsm5558HM8tuIeirBifCCvs/BAyMmRohkV7HmeRIPK5pbXszfxvjEyRuWeHaD38LLXrdtO8w09maSZZi8+CxWcN9kdhMYx0Lo4zFQGbU0ZxKzi8dmxuG648F2pERRQFHFlOnNkuHHlZPN9wEpeeH6DQ/xi+vbWYhoFpmEQb41Zqk0WvtO+MHPdptEYCqKH9RFtC+A420bojlApMjDRERUj0CMiRCe6N9msM1a+z5S/ryZ+bw4Kuu7FbDBbD1IToWCEIAsuXL2f58uX86le/Yt26dTz11FP89a9/5fbbb8cwet+OQSsItRg4LffeeKynYNFGT8I8WdyULDDqC5UfVRL9f/dw7TkBvHnZHV77xR3v0vSJL/PgfWsIxWUuCP6Fv/2igE8sakaaMQ8zHifw0XqaNuxg1uY/MXfbn2l57yNCjSEM3eQ83w5mPfnqgLbXYuQiKAJiWytyR6YTT5GXrLIsMsuyceVmIIgiDVsO8sns1dh2bSDqCyLbZRSnDVGW0CNHnwgGO1pnRf/GFrYcmdy52eiqwcE3yvFta+3wWvui95GA5BRxFtrxFLqP6hiaDpHKOL6DLWw65zTWnrh0EGdo0Z5kE6L+/I1Gli5dyj333MO+ffvYtKlvaaZW5HwEkXXzb4/1FCwGGVERyJjkJFIf48DqfSyecTe3fO92vvf95g7vkwyVf93jwrvxHtQmH/aSuQTNMnRnBkZlJcEaH46cDIzWVtTjV+ANBjjx3j8fo62ySIfBSAcxjEShqCgnnDIEUUCPa4Qa/IQaQmhRHfcjD3B4fz3BuhBFC4qRHTYiviCiLDBwQzqL8YRkl3BmOYj4IqmOsHAkqt7VBd+xRI8YROpiRzU6ShfFm7iYDdaFkB0SLRvfIGvJ2YM4UwsADCPx15/lRiiRSITHH3+ct956i6amJnJzc1mxYgVf+MIXcDgcqfctXNg3604rcm5hMYQYqknrrhDOAjvufBd7nv2QJbseYeEZR36gjgwXkqmRues9sNkQbTZkPc5scxtS2E+kromG3XXYvBmIGRk4qveh1jccw62yGC4MNeHiooZUtNgRpxbTMIkFooQaQkRaYuhxg5aKRmLBONmTs8iaXorsUDANI1Fk6hK5UNud+rOw6Ez7/cK/O8zBVytp3Xt0brkeNrp0DTqWGKqZsvMcCKpfJ1anEq6MEq6PEnz0oUGaoUUHkm4t/fkbgdTU1LB48WK+/vWv8+9//5tDhw7x73//mxtuuIElS5ZQU1PT+yCdsCLnFhZDjKGaNG8NApC7xMueh5/hrjMPIPzsAqLObFytVWxQz+VHj7YA8OT/5pLz3lMgSez9+xtkTcoDwJ6Xw8/83+Teq5w4LjxWW2Mx3Gh+nda9IcK1UVxFreiqgRbWiNarmKqJ7JXInZOJtzSHzDIBf1Uz2574kHiLiuyVWLlvy6DNxRL2Y5fOd3dGsg1ifxAVoU9R9cR7DAqOz6X1sI8JQz81i1HON7/5Terr63nuuee48MIjJ+eXXnqJz33uc3zzm9/kH//4R1pjWuLcwmIYCVSECVSECda9T+a72xPFfItmwlnncvmXTkM3YH2rwcnLLsBvz2dyk4+Nv/8P3pIMGtbv4OcrfwH8+FhvhsUxIFqnogb0lPNKUmhofh1/ZRBRqcdQdUINEfy7Ez77UsBKaLEYPJIFoZJTHHHR896wFyp9bsaWu8hL3sxCdvxtB/1rvm7RI2OsIPTVV1/lrrvu6iDMAS644ALuvPNOvv/976c9ppXWYmExSIiKkPrrDlM1seXIaFGdYF0ALapimzqNEmc9X3M/zrWz1lOYEUbW4+QGD6GvvBiAUEOEms1V7P/HG5ad3TgjWXycpL1bhuQSEzZwVXEatvho3NJC8EAk9fpYi35aDB19uSuSOdvN8jsu4LRffHYYZjQ4uCfbmXhuUcp+tCcUr4QtVyZYE6H8nQOofp1XvHOGYZbji7FWECoIAjNmzOjytRkzZiCK6UttS5xbWHSiP+I3GVESerEaExQBSZHIn53L7CtPYeJVF6HnFFFa+QHhD9bifOspJorlvB9bhvjqP3FWbEOPGLjzneROzyWzLBdbrnXDa7yih40OLkF62Ejte/EmLdWgaCCOFRbjl94Euh43iPlaaF7/8TDNaOAISsLpqC8XqpIz0Q017kt0eRYVgVX+ncMwy3FGMnLen78RyHnnncfLL7/c5WsvvfQSq1atSntM6yxvYTEAxLZbvMmmRIIiJNqwt9ktHvV+WcDUTcJNYSK1TUhOJ/XP/Jviz34K14xpqDU15Gx8mTOD/2DjX97D1N/lxJvOo+n8/6Joy8sgSZT97qljsKUWA2EoG/h09ka3PM0thgr/7jCb//DBsZ5GWoTLY1T5avv03q4KSl92zub8yK7Bntb4xjT7mdYyMo9tN910E5/+9KcJhUJcc801FBUVUVtbyxNPPMGrr77Kk08+SUVFRer9kyZN6nVMS5xbWPSDpCh3FtuRbCKGfuSgEUdNdQ3tjNqWA6yGNKKtO4AdBCsiRJr/hBpRySjwYBq7qN9RT6QuRsGiXOS5x9Fs5MCC88l+/gEcXY5sMdKxOmxajAZ620/VgI7kHD033ZNOLv1BcolWaphFr5xyyikAPPLIIzz88MOp5wUhcQfz5JNP7vD+vjQhssS5hUUaSC4RR4GCzaOQPzufwz98hgxHmOmtHyHvWEfrtt0cencvkco4jkIFe64NAFESiAdUQuWx1MG+fXHSwRcr2/5XB0DGDAdly4txZLkpf/DPlMx4k8K7H4ebfjWcm2sxyCTTBoZDpFvOKhaDheQScZbaCO6NplKrJFdCoIuykEqnGs3YcmX0iJE6PouKgB42rN/RENDf/PGRmnP+6KOPDvqYlji3sGhHZ9HU2X7LliOjBnSypmRSdvm5XPfjjwDw5pVy2ifO4bKv1DC7+H8JN/yH3JnZZE3Kw5GdgSBLVH24j0hVfZ/SDpxZdiauOpn6tVuo+qCWOU+/MbgbajGmsQSFxWAhuUROufU8PjjzHhwXHI8a0HGW2lAcMsGKCIY2MgVTusSbNERFwJYrY6omql+36nuGijHWhOjzn//8oI85eu5NWVgMM10V1Wl+HT1i4MpxoU4+YrLlb2zmpb+8xz1/tVF78U24i1zYPQ6cuV6cJYU4i/IpmFvSY7FoEluujKhIhA9VYctwDLixhoWFhUU6JC/uktHjHX9/jwV/vo64T0PxSLhynalUvtGU4tIbhmqi+fXUnYCR1gl1zDDGmhAlCQQC+P3+QRlr7PyqLCwGEcklIjnFo6Lcql9HlAUcWS7C7vwOr5128YkUlnhRhDgxfxx/jR9RkZGzMkEQcRbm9rpeURHIm5+FK8fF3pe2UL5mnxUFHYMM5Xdq7S8Wg0Xy+Ne00c+GX28AEoI1VBsmXBlNpIGMMQGb3GZHoWLlm1v0iZqaGlasWEFmZibZ2dlcfPHFhEKJ7rpPP/00GzZsSHtMS5xbWHQiGTEX2jzLC07KZsJZhUw+v4Rpnyxj8VeXkn/VlbQo+dz0k+UsOXMxntwscnPtnHmiSPGet3DnO/EUZmAvyEMPhahes4l9L29E8Ujd2twlLfFigTiKU8GRZUePWyeHscpQiGhLmFsMFsl9yZYrp/LLBUUgY5ITyS6hR4yjPPjHEs4Cu/V7GirGmJXiN7/5TbZv387dd9/Nz3/+c9566y1uu+02ALZt28b999+f9phWQpWFRSeS1nTJk870VYtwLVyEGQ0TnH0KNz5ewuH/PUhOSZDHvvYuy89z0XRRKZrZwLT699D27yV/djGyy060rgE1FKFpfxP+faEebwEnO++17AoiSgL5swtZ8uZ7w7XZFqMcS0hYDAXxJg1broy7zIHskNBVAz2mD8iys7t9dTjdjJJBku62o3lrcNjmMu7ob4rKCE1r+c9//sM999zDl770JQDsdju///3vueeee1i8eDF/+tOf0h7TEucWFj2geCUcJUUYvgbMKbN5N7yUwzvfBcBXXU+lezZTG9biUfcnruoP7sGIxzE0nZYDdRi6iWkYmLqZaHnt07o9GZiqiabqCIpAuCHK1LUvDOemWgwzlq2ixWhB8+tEieMstANg8yiEiPVp2XQuGtu/d6h/H6n6n3bH46R1oqgI2AuVIV3/uMbsZ0HoCI2ca5rG5MmTU49POOEEfvSjHwGQk5NDXV1d2mNa4tzCoguSPuaqX2fbI/+m7ORpeONxlpxxHDaHnXg0xsrLTmJ6zbMIdZWEd+wiXN+MKEvITjsZEwpwZHuItYaI+ILEwypqoOdoU/K1C62GF+OCwfA9t6LlFkNB5/0y6RWedDSRvVKPyw/GfjnUtqOd03GSxa+A1XRoqOlvisoIFefLly/n7bff5qyzzgLA6/Wi6zqGYVBTU0NhYWHaY1ri3MKijc4nAUERULwSpcdPwpmfg+proeCjZ3jsfy5hU+sMZmbVIayvwAgkqrOd+VnILidypgcME3uugTMWx1bTSP3OBsw+3Aa2xJZFb1j7iMVQ09OFY1Kod94PX5RnDfm+OVSNvJTUxYYxIjvsJrfZ+u2PTO677z7OPvtsCgsLuf7661PNh6LRKA8++OBRTYj6giXOLSx6QHKKSHYboiKjx+IEd+7B9vH/cFqmGwBh/jwom45jtg6iRMydS1S2YXvzKdQWP1okRqSpb9ZK1oF3/NFZbFj7gMVIoSch3NV+OhzCfKho30RpJP8Gh+MCaFgYYznnF1xwAdFolBtvvJFvfetb5OTkIAgChYWFuFwuK+fcwmKwSEaHREWgaU81eizRzbOlopGG3Y2Yqkne7BymH78Uf+5UQkomBiK10TwUUWdqzYOE6prRoipqRMXUzUSOYzJ1ZSwcYC0GBWtfsBipXKjt5mXn7CMpd8dgXx3OPPSRSvsLpTEh0MdYE6KVK1didrpwcDqdzJkzh6uvvpqcnJy0x7TEuYVFDxiqSdNeH2FfGEPVaT0QIt6kIblEzJkmmAZxyUF9LA/NFNFNgXnqFva9vStlg6hFdeItap/SWiwsLCxGEuMp/3rUi97RwjBGzgOBALfccgvPPPMM9fX1LF68mF//+tcsXbo0/fV3wyOPPDJoYyWxfM4tLHoheCBC4+YWmjb7iTdpQCLdxZ3vwfBkI5kaiqRRH3BQ0eBEiSeaD8Ra4wQrIwQPRI5yaRmvESALCwuLweBCbfe4EtOd7yBY55C+8eUvf5nXX3+dP//5z2zdupVzzz2Xs88+m6qqqmGbQ0VFRdrLWJFzi3FPbwe5pO95EucEG4uuO4nYZ77BYwcXsOMjP5ve2nxkvOkz+ONvb0fa8j7Nm3ZQ83EljZtbOoxhYWFhYZEe472z7lA72AwbptlPt5b0zqGRSISnn36a5557jtNPPx2AW2+9lWeffZYHH3yQO+64I/05tPHzn/+cL3/5y5SWlnb7nrfffpvf/OY3PP/886iqmtb4lji3sEgTR7YNR0khu/WphMIm4VC8w+uKTSHmyMQeCBJtCSPbZRwFCtF6NWXVNRpOBBYWFhYjnYE4uPTWiGik09V2n+5bdwxmkiaGmfjrz3KA39/RZMFut2O32496u6Zp6LqOw+Ho8LzT6eTdd99Nf/3tuO2221i5cuVR4jwWi/HEE0/wm9/8ho8//phJkyb16yLASmuxGPeke3s02hyndcc+ZhrbOG6qxjkrcznnU6cwce5Uphw3nRNPLSWuuMAw8Fe3EG4MY2imJcwtLCwshoD+HlPPj+zi/MiuMXNMHjXbkfQ5788fUFZWRmZmZurvrrvu6nI1Ho+Hk08+mdtvv53q6mp0Xecvf/kLH374ITU1NQPahClTpnDfffdRX18PQHV1NT/5yU8oKyvj+uuvp6CggGeffZYDBw7w/e9/P+3xrci5hUWaiLJAzB8h59AWps4qIZ5hY3q+g7OWZKOZIpIQQRNtKEWFuHJcBGvD6BFLmFtYWFgMFd25unSVCjKaj8OjPqUFBuzWcvjwYbxeb+rprqLmSf785z/zxS9+kdLSUiRJYsmSJVx99dVs3Lgx/fW345e//CVXX301kydP5qSTTuLdd9/F5XLxhS98gRtuuIEZM2YMaHxLnFtY0PcDnuKVcOTYEWUR09dIgX8fhpho8yyrEQQtDqKEZ975MOMHzL7qB8weyolbWFhYWHSgOx/20W5DOCaE+SDg9Xo7iPOemDZtGm+//TahUAi/309xcTGf/vSnmTJlyoDmcNFFF7Fnzx5+85vf8PDDD6PrOjNmzGDZsmUDHhustBYLiz4f8ERFwJYj48x2YvO6MKJRHOXbce5dj33d66gvPUXw6b8Tf+WZIZ6xhYWFhUVvdHY1GcuOJ6PqoiNppdifv37idrspLi6mubmZV199lU9+8pMD3ozi4mLuuusuKisreeSRRzAMg89+9rNMnDiRn/70pwNKnRHMzs7pXeD3+8nMzKS1tbXPVysWFqOFvh6gbbky+QtyKFo0EfeEIkSbDVNViflaaNp5mJottQT3RoFRdqC0sLCwGMO8UTqfaJ3a4bj8WuE84k3aiD9W93Z+6jz/kazXknOr++PP8bocvS/QeflwlMIv/iytbfv/7d19kBTlnQfw79O9sy+87BqUdzZgDgMLK4qACmgCp2Kw4MR4ojlFz8qljoqJZXLRSiQJJkZMzstdkjvdEuNZ6hm1OEMFLd4NiC9wGArfCGpQCBSFKAK7K7vLTvfz3B893dsz0/PSvTPTM9PfT9XUzvT0y9O7s7Pf+e3Tz7NhwwYopTBhwgTs27cPd955J+rq6vDKK68gFov5bkMuO3bswIMPPohVq1ZBSolFixbhW9/6ljNaTL5YOSfKk4orKCnRc/wznDp4BN1HjmLw7f+G7k9Oous4+5UTEZUj+73Zrpa/UDPBmbOCQmCP1hLk5lN7eztuu+02TJw4ETfffDMuueQSbNy4sSjBHAAuvvhiPPnkkzh48CCWL1+O7du3Y86cOb73wz7nFGl+/q0pDYVTn3RDmZ9ArzsBPaZhKIDmB/8XzcVrIhER9YPZLRFr1BHvMMNuSt78VswriVISKsA450G2Wbx4MRYvXux7u/4aNmwYli1bhh/84AdYvdp/V1eGc6I82OPhGl0GutADPaZB6ALbL5wBZSqYcYlL3+rf1d9ERFR4V3W/i82jWysqnGdSyaE8ijRNw7XXXut7O4ZzojzIuIIGqwIjYiY0XUBJBaFZXzVdhN1EIiLKQMQE9AGaM99EJaqaYK4CTkLUjwtCKw3DOVGeZFzB7JbQGzTImHICOgDM3r0r5NYREVEmw6echd5TvTj+bjt6jlpTqZdj2K2mEWQyck0o5Hu7iOhXOLdfROX4AifKJciboNktrYBepwBdMJQTEVWA1jVbsP/rV8OMS1x+eHvYzUkTiVCeoKSECjAJUZBtKlW/wjlDOVWqoG+EMq7QczSOnqNx6AM42BERUaU4+9E/oP/TwxRWtr9FzFjRxW4tFEleUzoTERGVSmT//gSdUIh9zomqW9A3RS0mcFX3uwVuDRERRUk1D5WYk1RAkC4qQS4irVD8vzxRHrSYwMBxdQzmRD69OnUaXp06LexmEJWVqg7fudiV8yC3iGA4p8jxWzXXYgKDvtCAMReNxsd334L2f7+jOA0jCsGuObOKuv/Zu3fxwmkiD5kCerUHd/uC0CC3qGC3Fooc9xtftqBuTzzUMLoWzRd/HsNmToE+bARUw0B0b34covsU6hd+s+jtJSqmaVtfC7sJRJFX7YGc/GE4p8jJt3Iu4wpaTKB2cAyfHW1H7dvvY9DYTtSOHAF8bijUwKYit5SIiKpZJEO5DDgJUYT6nDOcE+VgzwLadawDZq+B+mMnMGzF98NuFhFlwXk4iMqTUhIqwIRCQbapVAznRDkYPSbiXXHotb0456l1YTeHiHxgSCcqM6yc58QLQomykHGFU4d6cOIvHWg/1B52c4goAAZzioLIjptehVg5p8hZYLzn603M7JJQcYXLD/+piK0iokJiIKeoWWC8h46OjrCbkZuS1i3IdhHBcE6RlO+ILanrEhER9cfuKy4FAEzd9HLILQmHkgoqQBeVINtUKoZzijx9gAatxho2USSGT1RxBWkoqLjCCzUTfAf0I3feCCE0zwtYRj7wVP8bTUREFSmqodyhZLAZQiNUOWefc4q82GAdeoOGmkbrq33fXg7478s38oGnoEzTczkREVEqu6Je7ZRSgW9Rwco5RZ4dwO2quU3VKIiYgDQUzC7/n9hH/vJ3BWkfERERRQfDOUVeaignIiIqtch0d5EBu7UE2aZCMZxT5GmaBun6pVdx619n0rC+ajUCKiY4XjIREVE/8YLQ3NjnnCLvS3t2O/ftYO6mN2ioHVKD2jNrEGvUsWHIJKxrbCllE4tia8t52NpyHraMnxJ2U8rOxuGTw24CEVF1UqpvOEVfN4ZzokiZs/dNzNn7pvPYrpoDcC4SjQ3Wky4a3Th8MjYMmeTcKo19znP3vYXNo1vDbk5ZmXd0T9hNIKIK8caVXwq7CRXFrpwHufl1+PBh3HTTTTjzzDMxYMAAnH/++di1a1cRzqqw2K2FyGXuvrc8l7ury5cffqdUzSmZajwnIqJSOH/DtrCbQB5OnDiB2bNnY+7cuVi3bh2GDRuGDz74AGeccUbYTcuJ4ZwoD5lCOxEREeVPSQkV4OJOv9v84he/QHNzMx577DFn2bhx43wfNwzs1kJEREREpSFV8BuAjo6OpNvp06c9D7NmzRpMnz4d1113HYYNG4apU6fikUceKeWZBsZwTkREREQloZQMfAOA5uZmNDU1Obf777/f8zgffvgh2tracM4552DDhg1YunQpbr/9djzxxBOlPN1A2K2FiIiIiCrCoUOH0NjY6Dyuq6vzXE9KienTp2PFihUAgKlTp2LPnj1oa2vDzTffXJK2BsXKOREVhD0OPBERUUZBR2pJdGtpbGxMumUK5yNHjsSkSckjqbW0tODgwYNFP8X+YuWciAqCkzMREVFOKuAMocrfNrNnz8Z77yX/XXr//fcxduxY/8cuMYZzIiIiIiqJUs0Q+p3vfAezZs3CihUrsHjxYuzcuRMrV67EypUrfR+71NithYiIiIhKwh5KMcjNjxkzZmD16tV4+umn0drainvvvRe/+tWvcOONNxbpzAqHlXOiCpfa15vdS4iIiIAFCxZgwYIFYTfDN4ZzopC9UDMBWkzgqu53A23PME5ERJVCKQWlAnRrCbBNpWI4JyqhdY0tmN+xN2kZwzUREUWGDHhBaJBtKhTDOVGRvPN3c6GkQvfJHlz4yg4ASAvmREREUVKqC0IrGcM5UYF1/899aLhpGVrXbElavnl0K3qOxj23YfWciIiIAIZzooJruGkZAGDXnFk48ZcO6A0apKEgDe9P/QzmREQUFUoGq4L7HOa8ojGcExXAT56KI1YjMHiQjgljejFrx08wcOhAnDzQiVMHTqNhTC0AQIsJyLj1psRQTkREURNkWER7u6hgOCfqp9kLX0pb9t//sRRjm86AVvNHHNp+GJcdeDuElhEREZUX9jnPjeGcqIAGNg3GF84dh17Vi4Yb7sIXb7gLsa9fHXaziIiIygIr57kxnBMF8NVv74NSCkIIjJkwDg0D69A4ZACGDh2A0SN01Gt/ddY9+9E/hNhSovClTpRlY9eu/nF/X4N8L71+LvyZEIWP4Zwoh5Wb+v6VpglACGDBNX8DXQPqaxXqYhL1MYmvnF+L1987gRkTPgdgXGjtJQpDalDMFMgzbWNvV6nyOd9KOH6xz8P9My7Wsdyvv7BfU9svnIHTnb2Ys/fNUNtRTpQK2K2FkxARkW38iB7oQkETErpQEEJBwHqT0DWZCOMW932iapcpAAUNXdm2CztkeQk7kFeiUnzP7GNoMYG1DRMhYgJAOPNMzNz5unN/a8t5DOkAoJR1C7JdRDCcE+UwquEYdGFChwFdGhBQEIkxnayQzkBO0ZApWJUycPlViFDPEF75tBqBDUMmAQCuPP7nkFsTbexznhvDOVEOE8c3O/ePvbMdQppWOFcKmjIAtKLjTxsgzDiEaWDQrEWhtZWokKIcSqN87tVAS1TL7aq5TcQENg6fDACYd3QPAGuCOPd67tG1toyfgrn73ipIm1g1t3C0ltwYzolc9n2wHxpM6NKArgyM+WJr0vO60QMhTWjStEK6GcepV38PPfF4wJdvCKnlRN789utmKOX3ICo2j7be31MD/IvjzoVW07dsy/gpzv1CBXWibBjOiRIO/mUv6pUJTSXCtzJx7J3t0IxeaMqAkKYTwp2b0QthmoA0AGmie90jEEYc9Qu/GfbpEAFIvzizXC6UK0cM5dXNHcLd4duPLeOnpIV5gFVxP9itJTeGc4qkj/buglASmjQxtPViAMDnz2nByd0vWt1VpAkoaVXJjV7nPqSZCOOJ53t7rfumCWVaAV3FDQDAqd/+CAP/6d4wTzOSvMKnn0Caa91cwwIWKuAVe1SLKATRQl2oSpUlU5eWQlGJWZ7d+9/acl7G9Rnck7FbS24M5xQ5x9/chlqlICAhlET7ro1omjYPnTvXWpVxO4gnvgrDsCrjSgGmCSENQCoriPeedpY7Ad008dnDd1vLqCi8xnfOd+i+bAHdq8IcpE2FwCBZePkO8UjVScWVE6iloQJXz/3KFtw1TcOX9uwuSTvKBcN5bgznFDmx058BiQs67Qs7T736e+hm3FpmmonnpVUpN+JOGIeSVgiX1nOIG04gV4kbpILs7bXuU8FkG7bPK3CHObIIlR/+3KPHHcb9rpdPeM93/7lsmzw1bVnUAjslYzinfuva9qzncqXpgNCghAYIgUEzy2Ma+8EXXoWul55JVMZVXxA3DGuZkklhXMWt0N5w3b+g++mfA1JZgVwpqN5eK6ibJpRhWpMrmCbk6d5I9Y8rJa+QxeBFbu7/prBaHj0yrpyuLVnXcwXwQgVtPzRNK+nxygX7nOfGcE6Bda9/FNAEnLcz4XqjURIiEc4hNChNoGvr7zBgzj+E0dQ09qgqPc8/lJgQoa8iruzHUvV1VUlMftDwte9bXVYSoV7GDatibiS2NU0oqZxw/tFdSzDiX58M81SJIqc/3ZOoumUL4anPFbN6ni2YV3vVnN1acmM4J996Vv8GACA06w2p/upvW8vXPJi8ohBOYBcA6hcsRc/ahwHAqqYDaJj/DQBA97pHEtukv2E1fOXrwdv6h/+07iTCdP1X70D3ql9ayxIBvOGGuwAAXU/ca4VwO6BLq8vLwG/8DJ3/dRc6f/M9K3wnnpOGCRk3rPt2dxYjEc7jcef+X//5Gox9eHXgc4gqhioiCsquntvhOVOVPDWAewV0IPvoLoWuum+bPLWqA7qSCtJkOM9GKJV7PtSOjg40NTWhvb0djY2NpWhXpL13/ZVO8BWagHB9whaagFajJ5Zb953HNTpGPvCUs+4nP7wVQ3/2GADg059YIRiahjN/9HBB2nnqtz8Csv1bzv4XVGIdIUTS48TC/A6W+jJ1/XvLeQm7/+WlVKJfeOI5JZ1fbKtfuOzbVlrPKbtyLvsCupIyURVXaesoKWH2Gs5z0lmvb3nLc5vzOz9iGCeiotBiIuswipmeyxS4swV1vyF9zt430/qc9yeYl3Nes9u269rLMCjmvzb8WdzAtOdeLMtzKzSG8xJ666ovJ4Xu1jVb0tZ5/2tfwRefXp+07MNbFwIAvvDY83j3uisgNM0J50LToNVoSV+Tn9OtnWgCwq5iawLDf/4EPvnhrYDTHg1n3fPbQOfV+ZvvOfczvZyEO4Rrfe3IxfOTcpZg7n7shPFEBdy+nxq+7QDvhOtERd1e1x26U4N46nNm3Ex8lTh/w7ac5xclXiOsZHqeiKiQvIZXzBbEswX4TOvkWj9VMYZYLOe8Vupw3tbWhra2Nhw4cAAAMHnyZPz4xz/G/PnzfR+71BjOC2znJdaY2Xbw1DQBofdVv53lukgK6tZXax09piU9tp+Xhol4dzxtGz2WHsrdlXV39T11Hec4ifvuyrsfx+9bmrZMaJpzAYdIrbAHrJi7LwhxgrtHWLdDt71+X1BXzigq7go4AEhDJgXw9HDuWmZa96UhE/cTz5kKUirM2LY9v/OLkGzhnMGciIrNfZFoPiHdK3z7Dele2xRz3PNyzmt22/50zd8GDufTV/8x73N7/vnnoes6xo8fDwB4/PHH8cADD2D37t2YPHmy7+OXEvuc58Gr/9fLUy5IelxTb1WoNd3+JbTCqAQAqaBpfSFT6ALSQFJAtra1Q6JIC+yAFSaN0+nD8wlNpIV9oWnQ9OQuMO6Abi93rw8Ah277e8+uNE5b7Psie/U7n6p4vtzV89RKul3l7nu+L4yrpOp5cuVcpQR3O4SnbmuHcGedlCBuGhLKTPzcTIWZO18v2HlXG7+T+hARFZJMTB5k90UH4Nkf3b3c5vW8s1+P9dxUXGHuvrcKeSoVrVQXhC5cuDDp8X333Ye2tjbs2LGD4bySvDp1WtJjs7evIus1DqmbSlzcYJoKQhfOBDR2WFe6K+xKu6qeHtiB1Iq56Qr8Frt63rcOEvsTzr7s/VhV9cyh3b0cALQa76p9+n2v8F74YagyBXOvirj7uUyh3F7mfuwVwN3rmXFXiDdl3/Om1X3F/tlX8wU8/cUATkTlIjWkewXx1BCe6Xn3Ol7rUbr+hvOOjo6k5XV1dairq8u6rWmaWLVqFU6dOoWZM2f6PnapRTqcb205D5qmOWHWzeqekP+Ymkq6K9zKCaqpYR3oC+wSgLBDuxPMReKZvrBtAmltNLoN537qc+4wL3QrnKd2r7FDu7sqblffU4N5plDu5hXMA/cpz/C8V5eWTIG9L2Cnh/jU8A3A6Y5iB3AgEdrNlMBuKobwFAzeRFSJnJDuWuYO6plCeOoILrmC+mUH3s7aji3jp7Cy7kNzc3PS4+XLl+Oee+7xXPftt9/GzJkz0dPTg0GDBmH16tWYNGlSCVrZP5EL51vGT3Hu279EyjWkjx10TY/uI5mImIDZK5NDcmKfmi7Su2KY7tAqk45r03QB90cDoSeHYqeLhR3+PVgh3ewL2Xqi+4uuwTjdF9iBvi42mp4cyFO716RW8aXrPFNlC+e5QnnqMEuefc1d993rp4ZuAJCJ9WZs247/m3lh0jbKTA7ufdtY+yhm/8BKxDBORNXEq7sLgKS/wV5B3V0l91onX1EL5v2dhOjQoUNJfc6zVc0nTJiAN954AydPnsRzzz2HW265BS+99FLZB/SKDucvjjs3718EJ4i7f7kMAVWTPD6plvgMLWICZrfMa//2fnIdO1/ZJidwB3FlKpinTZjduV/kqW3QG7SkY9n7Teq77lpmPc49ykrQcUjtarVbUuBOC+sq63ru/3rYP0NpKLw47lxcduBtbB7d6iyz17ny+J8DtT0MuUY+KcR+iYiiRKb+vXeFdjO1Ou4RzE2PvCBiAhuH9/Vvnnd0DwBg4/DJnt1eLj/8TuD2V4r+dmtpbGzM+2LX2tpa54LQ6dOn4/XXX8evf/1rPPxwYYaULpaKDud2yEr99OrJax1DQdUICEM4YTU11OWzbw3Jn7DzOnYWJqSvvmrxzkT/9mzbdKcco9s+hpl2dbr92Cu42zIF9Hx+4XJNPpAaxAGkdTFKqm6kfH+9wjfQ96a4rrEF8zv25mynW7HCcJDjZ3suaNsYyomIvKWGdq/ymZlhIqLUdTcM6avY2n/V7L9NUSFNBan5D+dBJi5KpZTC6dOn+72fYivbcO7+pGnzegG7P2W6X/SpMk3DK2H98rgr33bVXBoqr8q5GVcQRuEu/Mj0CTwbFVf+tun2HjLKCeYZgrt7nXza5CWvD1M+9jHv6J6kn/2GIZOyVsHzDeblElj9tOOFmgmhfHggIoqKtAp7gp/QDgAwlO9CUTUo1Wgtd999N+bPn4/m5mZ0dnbimWeewdatW7F+/frcG4esbMO5VyhzBzCv8OVetq6xJW1fqdVtO2zaVXP3+kaH6StEZpkn0ze/wVyrEYh3+e+/Bddx7B722SZq6O/0xLk+6Pj5frv3ubZhYvJ+4qrf1eTUQFxJgTdIQLfXL5cPJERElcYd2ivpb0a1Onr0KJYsWYIjR46gqakJU6ZMwfr163HFFVeE3bScyjacX3n8z0kB269cn0bXNkyEaNCgN2ieoTCfqrmMK+gDNGf9bIJcJOKHFhMZP8374ewjS3Av2DFKxA6cft4sy+GNNYygnOu8Gd6JiLIrh78f5UypgBeEKn/bPProo76PUS7KNpwD+Xc/COKq7nc9l9tV2HwDpBmkYl2hSh2qC60Q1fAg4TSf4xQy9BbjDwNDORERFUKpurVUsrIO50ExSFA+gr5OihF+GaiJiKpD2AMIlDtpKkgRzgWhlaIqwzlRf5XTGypDNhFRZQrSrbLasXKeW1WGc17cRv1VjNeO15szX6NERNWP1XTyoyrDuY0hncoJX4dERFTJo4EVQn9nCI2Cqg7nAAMRERERlS+vnFLNgV2ZCipAn3OvCQqrVdWH8wXGewzoREREVDHyyS2VGuCViUAXhCoz9zrVwlc4X/+5CzBA6EVpSDFfZOzrS0RERNXEK8d0RSnBVrGyqZwzLBMRERFVN6UCjtai2K2FiIiIiKigpKkgwXHOs2E4JyIiIqKSUKaEggi0XVQwnBMRERFRSSgZcLSWCE1CpIXdACIiIiIisrByTkREREQlwT7nuTGcExEREVFJKFNBBQjnnISIiIiIiKjApKkgAwyLKCPU5zyvcG6PLdmlonOlLBEREVElsXNalMYEr0Z5hfPOzk4AwK1qPwL8J4KIiIiISqSzsxNNTU1hN8OTiisojaO1ZJNXOB81ahQOHTqEwYMHQwj/Y1MSERERUXEppdDZ2YlRo0aF3ZSMpKEgA4RzdmtJoWkaxowZU+y2EBEREVE/lGvF3MbKeW68IJSIiIiISkKaKlAVPMhFpJWKkxARERERUVV66KGHcPbZZ6O+vh7Tpk3Dyy+/HHaTcmI4JyIiIqKSUHEZ+ObXs88+izvuuAPLli3D7t27cemll2L+/Pk4ePBgEc6scITieDtEREREVEQdHR1oamrCqvrxGCB039t3KRPX9exDe3s7Ghsb89rmoosuwgUXXIC2tjZnWUtLCxYtWoT777/fdxtKhZVzIiIiIioJFVeBb3709vZi165dmDdvXtLyefPm4bXXXivkKRUcLwglIiIiopLoggw0Z04XrG4tHR0dScvr6upQV1eXtv6xY8dgmiaGDx+etHz48OH46KOP/DeghBjOiYiIiKioamtrMWLECPzjR/sD72PQoEFobm5OWrZ8+XLcc889GbdJnZ9HKVX2c/YwnBMRERFRUdXX12P//v3o7e0NvA+vYO1VNQeAs846C7qup1XJP/7447RqerlhOCciIiKioquvr0d9fX1JjlVbW4tp06Zh06ZNuOaaa5zlmzZtwtVXX12SNgTFcE5EREREVee73/0ulixZgunTp2PmzJlYuXIlDh48iKVLl4bdtKwYzomIiIio6lx//fX49NNP8dOf/hRHjhxBa2sr1q5di7Fjx4bdtKw4zjkRERERUZngOOdERERERGWC4ZyIiIiIqEwwnBMRERERlQmGcyIiIiKiMsFwTkRERERUJhjOiYiIiIjKBMM5EREREVGZYDgnIiIiIioTDOdERERERGWC4ZyIiIiIqEwwnBMRERERlQmGcyIiIiKiMvH/bC92UinTSbcAAAAASUVORK5CYII=" - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "new_dataset.plot(\n", - " band=0, figsize=(10, 5), title=\"Noah daily Precipitation 1979-01-01\", cbar_label=\"Raindall mm/day\", vmax=30,\n", - " cbar_length=0.85\n", - ")" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 16, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Min lonitude: -179.75\n", - "Max lonitude: 179.75\n" - ] - } - ], - "source": [ - "print(f\"Min lonitude: {min(new_dataset.lon)}\")\n", - "print(f\"Max lonitude: {max(new_dataset.lon)}\")" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 17, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "None\n" - ] - } - ], - "source": [ - "new_dataset.to_file(\"../../examples/data/geotiff/noah-precipitation-1979-corrected.tif\")" - ], - "metadata": { - "collapsed": false - } - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 2 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.6" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git a/examples/scripts/03convert-longitude.ipynb.py b/examples/scripts/03convert-longitude.ipynb.py new file mode 100644 index 000000000..cc9090bf5 --- /dev/null +++ b/examples/scripts/03convert-longitude.ipynb.py @@ -0,0 +1,29 @@ +import matplotlib + +matplotlib.use("TkAgg") +from pyramids.dataset import Dataset + +path = r"tests/data/geotiff/noah-precipitation-1979.tif" +#%% +dataset = Dataset.read_file(path) +fig, ax = dataset.plot( + band=0, + figsize=(10, 5), + title="NOAA daily Precipitation 1979-01-01", + cbar_label="Raindall mm/day", + vmax=30, + cbar_length=0.85, +) +dataset.lon +#%% +new_dataset = dataset.convert_longitude() +new_dataset.plot( + band=0, + figsize=(10, 5), + title="NOAA daily Precipitation 1979-01-01", + cbar_label="Raindall mm/day", + vmax=30, + cbar_length=0.85, +) +#%% save the new raster as a geotiff +new_dataset.to_file("examples/data/geotiff/noaa-daily-precipitation-converted.tif") diff --git a/pyramids/_errors.py b/pyramids/_errors.py index 7643eb527..700f8f6f6 100644 --- a/pyramids/_errors.py +++ b/pyramids/_errors.py @@ -62,3 +62,12 @@ def __init__(self, error_message: str): logger.error(error_message) pass + + +class FailedToSaveError(Exception): + """Failed to save error""" + + def __init__(self, error_message: str): + logger.error(error_message) + + pass diff --git a/pyramids/_utils.py b/pyramids/_utils.py index 6c4425a16..135579308 100644 --- a/pyramids/_utils.py +++ b/pyramids/_utils.py @@ -1,42 +1,125 @@ """Utility module""" +from typing import Union import yaml import datetime as dt import numpy as np -from osgeo import gdal, gdal_array, ogr +from pandas import DataFrame +from osgeo import gdal, ogr, gdalconst # gdal_array, from osgeo.gdal import Dataset from pyramids._errors import OptionalPackageDoesNontExist, DriverNotExistError from pyramids import __path__ # from urllib.parse import urlparse as parse_url +# OGR_DATA_TYPES = { +# ogr.OFTInteger: 0, +# ogr.OFTIntegerList: 1, +# ogr.OFTReal: 2, +# ogr.OFTRealList: 3, +# ogr.OFTString: 4, +# ogr.OFTStringList: 5, +# ogr.OFTWideString: 6, +# ogr.OFTWideStringList: 7, +# ogr.OFTBinary: 8, +# ogr.OFTDate: 9, +# ogr.OFTTime: 10, +# ogr.OFTDateTime: 11, +# ogr.OFTInteger64: 12, +# ogr.OFTInteger64List: 13, +# } + +# OGR_NUMPY_DATA_TYPES = { +# 0: np.int64, # ogr.OFTInteger is actually int32 but to unify it with how geopandas read it, we will use int64. +# 12: np.int64, # ogr.OFTInteger64 +# 2: np.float64, # ogr.OFTReal +# 4: np.object_, # ogr.OFTString +# 11: np.datetime64, # ogr.OFTDateTime +# 9: np.datetime64, # ogr.OFTDate +# 10: np.datetime64, # ogr.OFTTime +# } + +DTYPE_NAMES = [ + None, + "byte", + "uint16", + "int16", + "uint32", + "int32", + "float32", + "float64", + "complex-int16", + "complex-int32", + "complex-float32", + "complex-float64", + "uint64", + "int64", + "int8", + "count", +] + +GDAL_DTYPE = [ + gdalconst.GDT_Unknown, + gdalconst.GDT_Byte, + gdalconst.GDT_UInt16, + gdalconst.GDT_Int16, + gdalconst.GDT_UInt32, + gdalconst.GDT_Int32, + gdalconst.GDT_Float32, + gdalconst.GDT_Float64, + gdalconst.GDT_CInt16, + gdalconst.GDT_CInt32, + gdalconst.GDT_CFloat32, + gdalconst.GDT_CFloat64, + gdalconst.GDT_UInt64, + gdalconst.GDT_Int64, + gdalconst.GDT_Int8, + gdalconst.GDT_TypeCount, +] + +GDAL_DTYPE_CODE = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15] + +OGR_DTYPE = [ + None, + ogr.OFTInteger, + ogr.OFTInteger, + ogr.OFTInteger, + ogr.OFTInteger64, + ogr.OFTInteger64, + ogr.OFTReal, + ogr.OFTReal, + None, + None, + None, + None, + ogr.OFTInteger64, + ogr.OFTInteger64, + ogr.OFTInteger, + None, +] + +NUMPY_DTYPE = [ + None, + np.uint8, + np.uint16, + np.int16, + np.uint32, + np.int32, + np.float32, + np.float64, + np.complex64, + np.complex64, + np.complex64, + np.complex128, + np.uint64, + np.int64, + np.int8, + None, +] + +DTYPE_CONVERSION_DF = DataFrame( + columns=["id", "name", "numpy", "gdal", "ogr"], + data=list(zip(GDAL_DTYPE_CODE, DTYPE_NAMES, NUMPY_DTYPE, GDAL_DTYPE, OGR_DTYPE)), +) -# mapping between gdal type and ogr field type -GDAL_OGR_DATA_TYPES = { - gdal.GDT_Unknown: ogr.OFTInteger, - gdal.GDT_Byte: ogr.OFTInteger, - gdal.GDT_UInt16: ogr.OFTInteger, - gdal.GDT_Int16: ogr.OFTInteger, - gdal.GDT_UInt32: ogr.OFTInteger, - gdal.GDT_Int32: ogr.OFTInteger, - gdal.GDT_Float32: ogr.OFTReal, - gdal.GDT_Float64: ogr.OFTReal, - gdal.GDT_CInt16: ogr.OFTInteger, - gdal.GDT_CInt32: ogr.OFTInteger, - gdal.GDT_CFloat32: ogr.OFTReal, - gdal.GDT_CFloat64: ogr.OFTReal, -} - -NUMPY_GDAL_DATA_TYPES = { - "uint8": 1, - "int8": 1, - "uint16": 2, - "int16": 3, - "uint32": 4, - "int32": 5, - "float32": 6, - "float64": 7, - "complex64": 10, - "complex128": 11, -} INTERPOLATION_METHODS = { "nearest neibour": gdal.GRA_NearestNeighbour, "cubic": gdal.GRA_Cubic, @@ -47,27 +130,82 @@ # TODO: check the gdal.GRA_Lanczos, gdal.GRA_Average resampling method -def numpy_to_gdal_dtype(arr: np.ndarray): +def numpy_to_gdal_dtype(arr: Union[np.ndarray, np.dtype]) -> int: """mapping functiuon between numpy and gdal data types. Parameters ---------- - arr: [np.ndarray] - numpy array + arr: [np.ndarray/np.dtype] + numpy array or numpy data type Returns ------- gdal data type """ - type_code = gdal_array.NumericTypeCodeToGDALTypeCode(arr.dtype) - loc = list(NUMPY_GDAL_DATA_TYPES.values()).index(type_code) - gdal_type = list(GDAL_OGR_DATA_TYPES.keys())[loc] + if isinstance(arr, np.ndarray): + np_dtype = arr.dtype + else: + np_dtype = arr + # integer as gdal does not accept the dtype if it is int64 + gdal_type = int( + DTYPE_CONVERSION_DF.loc[ + DTYPE_CONVERSION_DF["numpy"] == np_dtype, "gdal" + ].values[0] + ) return gdal_type - # return GDAL_NUMPY_DATA_TYPES[list(NUMPY_GDAL_DATA_TYPES.keys())[loc]] + +def ogr_to_numpy_dtype(dtype_code: int): + """converts OGR dtype into numpy dtype + + Parameters + ---------- + dtype_code: [int] + OGR data type code + ogr.OFTInteger: 0, + ogr.OFTIntegerList: 1, + ogr.OFTReal: 2, + ogr.OFTRealList: 3, + ogr.OFTString: 4, + ogr.OFTStringList: 5, + ogr.OFTWideString: 6, + ogr.OFTWideStringList: 7, + ogr.OFTBinary:8, + ogr.OFTDate:9, + ogr.OFTTime:10, + ogr.OFTDateTime:11, + ogr.OFTInteger64: 12, + ogr.OFTInteger64List: 13, + + Returns + ------- + Numpy data type + """ + # since there are more than one numpy dtype for the ogr.OFTInteger (0), and the ogr.OFTInteger64 (12), + # we will return int32 for 0 and int64 for 12. + if dtype_code == 0: + numpy_dtype = np.int32 + elif dtype_code == 12: + numpy_dtype = np.int64 + elif dtype_code == 2: + numpy_dtype = np.float64 + else: + numpy_dtype = DTYPE_CONVERSION_DF.loc[ + DTYPE_CONVERSION_DF["ogr"] == dtype_code, "numpy" + ] + + if len(numpy_dtype) == 0: + raise ValueError( + f"The given OGR data type is not supported: {dtype_code}, available types are: " + f"{DTYPE_CONVERSION_DF['ogr'].unique().tolist()}" + ) + else: + numpy_dtype = numpy_dtype.values[0] + + return numpy_dtype -def gdal_to_numpy_dtype(dtype: int): +def gdal_to_numpy_dtype(dtype: int) -> str: """converts gdal dtype into numpy dtype Parameters @@ -78,8 +216,16 @@ def gdal_to_numpy_dtype(dtype: int): ------- str """ - ind = list(NUMPY_GDAL_DATA_TYPES.values()).index(dtype) - return list(NUMPY_GDAL_DATA_TYPES.keys())[ind] + gdal_dtypes = DTYPE_CONVERSION_DF.loc[DTYPE_CONVERSION_DF["gdal"] == dtype, "numpy"] + if len(gdal_dtypes) == 0: + raise ValueError( + f"The given GDAL data type is not supported: {dtype}, available types are: " + f"{DTYPE_CONVERSION_DF['gdal'].unique().tolist()}" + ) + else: + gdal_dtypes = gdal_dtypes.values[0].__name__ + + return gdal_dtypes def gdal_to_ogr_dtype(src: Dataset, band: int = 1): @@ -97,9 +243,12 @@ def gdal_to_ogr_dtype(src: Dataset, band: int = 1): gdal data type """ band = src.GetRasterBand(band) - loc = list(GDAL_OGR_DATA_TYPES.keys()).index(band.DataType) + 1 - key = list(GDAL_OGR_DATA_TYPES.keys())[loc] - return GDAL_OGR_DATA_TYPES[key] + gdal_dtype = band.DataType + return int( + DTYPE_CONVERSION_DF.loc[ + DTYPE_CONVERSION_DF["gdal"] == gdal_dtype, "ogr" + ].values[0] + ) def create_time_conversion_func(time: str) -> callable: @@ -243,3 +392,35 @@ def import_cleopatra(message: str): import cleopatra # noqa except ImportError: raise OptionalPackageDoesNontExist(message) + + +def ogr_ds_togdal_dataset(ogr_ds: ogr.DataSource) -> gdal.Dataset: + """Convert ogr.Datasource object to a gdal.Dataset + + Parameters + ---------- + ogr_ds: [Datasource] + ogr.Datasource object + + Returns + ------- + gdal.Dataset + """ + gdal_ds = gdal.GetDriverByName("Memory").Create("", 0, 0, 0, gdal.GDT_Unknown) + + for i in range(ogr_ds.GetLayerCount()): + layer = ogr_ds.GetLayerByIndex(i) + gdal_layer = gdal_ds.CreateLayer( + layer.GetName(), layer.GetSpatialRef(), layer.GetLayerDefn().GetGeomType() + ) + for field in layer.schema: + gdal_layer.CreateField(ogr.FieldDefn(field.name, field.type)) + for feature in layer: + gdal_feature = ogr.Feature(feature.GetDefnRef()) + gdal_feature.SetGeometry(feature.GetGeometryRef()) + for field in layer.schema: + field_value = feature.GetField(field.name) + gdal_feature.SetField(field.name, field_value) + gdal_layer.CreateFeature(gdal_feature) + + return gdal_ds diff --git a/pyramids/dataset.py b/pyramids/dataset.py index 4f0e100b9..e441eba1f 100644 --- a/pyramids/dataset.py +++ b/pyramids/dataset.py @@ -3,37 +3,39 @@ algebric operation on cell's values. gdal class: https://gdal.org/java/org/gdal/gdal/package-summary.html. """ import datetime as dt +import os +import re +import warnings +from numbers import Number from pathlib import Path +from typing import Any, Callable, Dict, Generator, List, Optional, Tuple, Union -import os -from typing import Any, Dict, List, Tuple, Union, Callable, Optional -from loguru import logger -import shutil -import tempfile -import uuid import geopandas as gpd import numpy as np import pandas as pd - -from geopandas.geodataframe import GeoDataFrame, DataFrame -from osgeo import gdal, osr, ogr # gdalconst, +from geopandas.geodataframe import DataFrame, GeoDataFrame +from loguru import logger +from osgeo import gdal, ogr, osr # gdalconst, from osgeo.osr import SpatialReference -from pyramids._utils import ( - gdal_to_ogr_dtype, - INTERPOLATION_METHODS, - NUMPY_GDAL_DATA_TYPES, - gdal_to_numpy_dtype, - numpy_to_gdal_dtype, - create_time_conversion_func, - Catalog, - import_cleopatra, -) + from pyramids._errors import ( - ReadOnlyError, + AlignmentError, DatasetNoFoundError, + FailedToSaveError, NoDataValueError, - AlignmentError, + ReadOnlyError, ) +from pyramids._utils import ( + Catalog, + DTYPE_CONVERSION_DF, + INTERPOLATION_METHODS, + create_time_conversion_func, + gdal_to_numpy_dtype, + gdal_to_ogr_dtype, + import_cleopatra, + numpy_to_gdal_dtype, +) # NUMPY_GDAL_DATA_TYPES, + try: from osgeo_utils import gdal_merge @@ -70,6 +72,7 @@ def __init__(self, src: gdal.Dataset): self._raster = src self._geotransform = src.GetGeoTransform() self._cell_size = self.geotransform[1] + # replace with a loop over the GetMetadata for each separate band self._meta_data = src.GetMetadata() self._file_name = src.GetDescription() # projection data @@ -90,54 +93,51 @@ def __init__(self, src: gdal.Dataset): no_data_value = [ src.GetRasterBand(i).GetNoDataValue() for i in range(1, self.band_count + 1) ] - # no_data_value = [np.nan if i is None else i for i in no_data_value ] + self._no_data_value = no_data_value - self._dtype = [ - src.GetRasterBand(i).DataType for i in range(1, self.band_count + 1) - ] - self._band_names = self.get_band_names() + self._band_names = self._get_band_names() def __str__(self): message = f""" - File: {self.file_name} Cell size: {self.cell_size} + Dimension: {self.rows} * {self.columns} EPSG: {self.epsg} - Variables: {self.variables} Number of Bands: {self.band_count} Band names: {self.band_names} - Dimension: {self.rows} * {self.columns} + Variables: {self.variables} Mask: {self._no_data_value[0]} - Data type: {self.dtype[0]} + Data type: {self.gdal_dtype[0]} + File: {self.file_name} """ return message def __repr__(self): message = """ - File: {0} - Cell size: {1} - EPSG: {2} - Variables: {3} + Cell size: {0} + Dimension: {1} * {2} + EPSG: {3} Number of Bands: {4} Band names: {5} - Dimension: {6} * {7} - Mask: {8} - Data type: {9} - projection: {10} - Metadata: {11} + Variables: {6} + Mask: {7} + Data type: {8} + projection: {9} + Metadata: {10} + File: {11} """.format( - self.file_name, self.cell_size, + self.rows, + self.columns, self.epsg, - self.variables, self.band_count, self.band_names, - self.rows, - self.columns, + self.variables, self._no_data_value[0], - self.dtype[0], + self.gdal_dtype[0], self.crs, self.meta_data, + self.file_name, ) return message @@ -155,9 +155,6 @@ def values(self) -> np.ndarray: """array values.""" return self.read_array(band=None) - # @values.setter - # def values(self, band, value): - # self._ @property def rows(self) -> int: """Number of rows in the raster array.""" @@ -280,7 +277,12 @@ def band_count(self): @property def band_names(self): """Band names.""" - return self._band_names + return self._get_band_names() + + @band_names.setter + def band_names(self, name_list: List): + """band_names setter""" + self._set_band_names(name_list) @property def variables(self): @@ -293,8 +295,14 @@ def no_data_value(self): return self._no_data_value @no_data_value.setter - def no_data_value(self, value: object): - """No data value that marks the cells out of the domain""" + def no_data_value(self, value: Union[List, Number]): + """ + No data value that marks the cells out of the domain + + Notes + ----- + - the setter does not change the values of the cells to the new no_data_value, it only changes the + """ if isinstance(value, list): for i, val in enumerate(value): self._change_no_data_value_attr(i, val) @@ -307,9 +315,27 @@ def meta_data(self): return self._meta_data @property - def dtype(self): + def gdal_dtype(self): """Data Type""" - return self._dtype + return [ + self.raster.GetRasterBand(i).DataType for i in range(1, self.band_count + 1) + ] + + @property + def numpy_dtype(self) -> List[type]: + """List of the numpy data Type of each band, the data type is a numpy function""" + return [ + DTYPE_CONVERSION_DF.loc[DTYPE_CONVERSION_DF["gdal"] == i, "numpy"].values[0] + for i in self.gdal_dtype + ] + + @property + def dtype(self) -> List[str]: + """List of the numpy data Type of each band""" + return [ + DTYPE_CONVERSION_DF.loc[DTYPE_CONVERSION_DF["gdal"] == i, "name"].values[0] + for i in self.gdal_dtype + ] @property def file_name(self): @@ -336,20 +362,52 @@ def driver_type(self): driver_type = self.raster.GetDriver().GetDescription() return CATALOG.get_driver_name(driver_type) + @property + def color_table(self, band: int = None) -> DataFrame: + """Color table""" + return self._get_color_table(band) + + @color_table.setter + def color_table(self, df: DataFrame): + """color_table. + + Parameters + ---------- + df: [DataFrame] + DataFrame with columns: band, values, color + print(df) + band values color + 0 1 1 #709959 + 1 1 2 #F2EEA2 + 2 1 3 #F2CE85 + 3 2 1 #C28C7C + 4 2 2 #D6C19C + 5 2 3 #D6C19C + """ + if not isinstance(df, DataFrame): + raise TypeError(f"df should be a DataFrame not {type(df)}") + + if not {"band", "values", "color"}.issubset(df.columns): + raise ValueError( # noqa + "df should have the following columns: band, values, color" + ) + + self._set_color_table(df, overwrite=True) + @classmethod def read_file(cls, path: str, read_only=True): - """read file. + """read_file. Parameters ---------- path : [str] - Path of file to open(works for ascii, geotiff). + Path of file to open. read_only : [bool] File mode, set to False to open in "update" mode. Returns ------- - GDAL dataset + Dataset """ src = _io.read_file(path, read_only) return cls(src) @@ -379,7 +437,7 @@ def _create_empty_driver( src_obj = cls(src) # Create the driver. dst = src_obj._create_gdal_dataset( - src_obj.columns, src_obj.rows, bands, src_obj.dtype[0], path=path + src_obj.columns, src_obj.rows, bands, src_obj.gdal_dtype[0], path=path ) # Set the projection. @@ -392,7 +450,7 @@ def _create_empty_driver( return dst @classmethod - def _create_driver_from_scratch( + def create_driver_from_scratch( cls, cell_size: int, rows: int, @@ -406,6 +464,8 @@ def _create_driver_from_scratch( ): """Create a new empty driver from another dataset. + - The new dataset will have an array filled with the no_data_value. + Parameters ---------- cell_size: [Any] @@ -423,13 +483,13 @@ def _create_driver_from_scratch( epsg: [int] epsg number to identify the projection of the coordinates in the created raster. no_data_value : float or None - No data value, if None uses the same as ``src``. + No data value. path : [str] path on disk. Returns ------- - gdal.DataSet + Dataset """ # Create the driver. dst = Dataset._create_gdal_dataset(columns, rows, bands, dtype, path=path) @@ -467,9 +527,6 @@ def read_array(self, band: int = None) -> np.ndarray: array : [array] array with all the values in the raster. """ - # try to return array as the same type as the raster band but made problems when trying to replace the - # no_data_value of an integer band with np.nan value - # dtype = gdal_to_numpy_dtype(self.dtype[0]) if band is None and self.band_count > 1: arr = np.ones( ( @@ -477,7 +534,7 @@ def read_array(self, band: int = None) -> np.ndarray: self.rows, self.columns, ), - # dtype=dtype + dtype=self.numpy_dtype[0], ) for i in range(self.band_count): arr[i, :, :] = self._raster.GetRasterBand(i + 1).ReadAsArray() @@ -652,8 +709,26 @@ def _get_lat_lon(self): lat = self._read_variable("lat") return lat, lon - def _read_variable(self, var: str): - return gdal.Open(f"NETCDF:{self.file_name}:{var}").ReadAsArray() + def _read_variable(self, var: str) -> Union[gdal.Dataset, None]: + """_read_variable. + + Read variables in a dataset + + Parameters + ---------- + var: [str] + variable name in the dataset + + Returns + ------- + GDAL dataset/None + if the variable exists in the dataset it will return a gdal dataset otherwise it will return None. + """ + try: + var_ds = gdal.Open(f"NETCDF:{self.file_name}:{var}").ReadAsArray() + except RuntimeError: + var_ds = None + return var_ds @staticmethod def _create_gdal_dataset( @@ -709,28 +784,26 @@ def _create_gdal_dataset( @classmethod def create_from_array( cls, - arr: Union[str, gdal.Dataset, np.ndarray], - geo: Union[str, tuple], + arr: np.ndarray, + geo: Tuple[int, int, int, int, int, int], epsg: Union[str, int], no_data_value: Union[Any, list] = DEFAULT_NO_DATA_VALUE, ): - """create_raster. + """create_from_array. - - create_raster method creates a raster from a given array and geotransform data - and save the tif file if a Path is given or it will return the gdal.Datacube + - Create_from_array method creates a raster from a given array and geotransform data Parameters ---------- - arr : [array], optional - numpy array. The default is ''. - geo : [list], optional - geotransform list [minimum lon, pixelsize, rotation, maximum lat, rotation, - pixelsize]. The default is ''. - no_data_value : TYPE, optional - DESCRIPTION. The default is -9999. + arr : [np.ndarray] + numpy array. + geo : [Tuple] + geotransform tuple [minimum lon/x, pixelsize, rotation, maximum lat/y, rotation, pixelsize]. epsg: [integer] integer reference number to the new projection (https://epsg.io/) (default 3857 the reference no of WGS84 web mercator ) + no_data_value : Any, optional + no data value to mask the cells out of the domain. The default is -9999. Returns ------- @@ -934,37 +1007,18 @@ def _create_sr_from_epsg(epsg: int = None) -> SpatialReference: """ sr = osr.SpatialReference() sr.ImportFromEPSG(int(epsg)) - - # if epsg is None: - # sr.SetWellKnownGeogCS("WGS84") - # else: - # try: - # if not sr.SetWellKnownGeogCS(epsg) == 6: - # sr.SetWellKnownGeogCS(epsg) - # else: - # try: - # sr.ImportFromEPSG(int(epsg)) - # except: - # sr.ImportFromWkt(epsg) - # except: - # try: - # sr.ImportFromEPSG(int(epsg)) - # except: - # sr.ImportFromWkt(epsg) return sr - def get_band_names(self) -> List[str]: + def _get_band_names(self) -> List[str]: """Get band names from band meta data if exists otherwise will return idex [1,2, ...] - Parameters - ---------- - Returns ------- list[str] + list of band names """ names = [] - for i in range(1, self.raster.RasterCount + 1): + for i in range(1, self.band_count + 1): band_i = self.raster.GetRasterBand(i) if band_i.GetDescription(): @@ -983,6 +1037,21 @@ def get_band_names(self) -> List[str]: return names + def _set_band_names(self, name_list: List): + """set band names from a given list of names + + Returns + ------- + list[str] + list of band names + """ + for i in range(self.band_count): + # first set the band name in the gdal dataset object + band_i = self.raster.GetRasterBand(i + 1) + band_i.SetDescription(name_list[i]) + # second change the band names in the + self._band_names[i] = name_list[i] + def _check_no_data_value(self, no_data_value: List): """Validate The no_data_value with the dtype of the object. @@ -993,24 +1062,34 @@ def _check_no_data_value(self, no_data_value: List): Returns ------- no_data_value: - convert the no_data_value to comly with the dtype - """ - for i, val in enumerate(self.dtype): - if gdal_to_numpy_dtype(val).__contains__("float"): - no_data_value[i] = ( - float(no_data_value[i]) - if (no_data_value[i] is not None and not np.isnan(no_data_value[i])) - else None - ) - elif gdal_to_numpy_dtype(val).__contains__("int"): - no_data_value[i] = ( - int(no_data_value[i]) - if (no_data_value[i] is not None and not np.isnan(no_data_value[i])) - else None + convert the no_data_value to comply with the dtype + """ + # convert the no_data_value based on the dtype of each raster band. + for i, val in enumerate(self.gdal_dtype): + try: + val = no_data_value[i] + # if not None or np.nan + if val is not None and not np.isnan(val): + # if val < np.iinfo(self.dtype[i]).min or val > np.iinfo(self.dtype[i]).max: + # if the no_data_value is out of the range of the data type + no_data_value[i] = self.numpy_dtype[i](val) + else: + # None and np.nan + if self.dtype[i].startswith("u"): + # only Unsigned integer data types. + # if None or np.nan it will make problem with the unsigned integer data type + # use the max bound of the data type as a no_data_value + no_data_value[i] = np.iinfo(self.dtype[i]).max + else: + # no_data_type is None/np,nan and all other data types that is not Unsigned integer + no_data_value[i] = val + + except OverflowError: + # no_data_value = -3.4028230607370965e+38, numpy_dtype = np.int64 + warnings.warn( + f"The no_data_value:{no_data_value[i]} is out of range, Band data type is {self.numpy_dtype[i]}" ) - else: - raise TypeError("NoDataValue has a complex data type") - + no_data_value[i] = self.numpy_dtype[i](DEFAULT_NO_DATA_VALUE) return no_data_value def _set_no_data_value( @@ -1022,18 +1101,25 @@ def _set_no_data_value( - Fills the whole raster with the no_data_value. - used only when creating an empty driver. + now the no_data_value is converted to the dtype of the raster bands and updated in the + dataset attribure, gdal nodatavalue attribute, used to fill the raster band. + from here you have to use the no_data_value stored in the no_data_value attribute as it is updated. + Parameters ---------- no_data_value: [numeric] - no data value to fill the masked part of the array + no data value to fill the masked part of the array. """ if not isinstance(no_data_value, list): no_data_value = [no_data_value] * self.band_count - self._check_no_data_value(no_data_value) + no_data_value = self._check_no_data_value(no_data_value) for band in range(self.band_count): try: + # now the no_data_value is converted to the dtype of the raster bands and updated in the + # dataset attribure, gdal nodatavalue attribute, used to fill the raster band. + # from here you have to use the no_data_value stored in the no_data_value attribute as it is updated. self._set_no_data_value_backend(band, no_data_value[band]) except Exception as e: if str(e).__contains__( @@ -1084,21 +1170,10 @@ def _set_no_data_value_backend(self, band_i: int, no_data_value: Any): Numerical value. """ # check if the dtype of the no_data_value comply with the dtype of the raster itself. - no_dtype = str(type(no_data_value)).split("'")[1] - potential_dtypes = [ - i for i in list(NUMPY_GDAL_DATA_TYPES.keys()) if i.__contains__(no_dtype) - ] - potential_dtypes = [NUMPY_GDAL_DATA_TYPES.get(i) for i in potential_dtypes] - - if no_data_value is not None: - if not self.dtype[band_i] in potential_dtypes: - raise NoDataValueError( - f"The dtype of the given no_data_value{no_data_value}: {no_dtype} differs from the dtype of the " - f"band: {gdal_to_numpy_dtype(self.dtype[band_i])}" - ) - self._change_no_data_value_attr(band_i, no_data_value) # initialize the band with the nodata value instead of 0 + # the no_data_value may have changed inside the _change_no_data_value_attr method to float64, so redefine it. + no_data_value = self.no_data_value[band_i] try: self.raster.GetRasterBand(band_i + 1).Fill(no_data_value) except Exception as e: @@ -1136,12 +1211,13 @@ def _change_no_data_value_attr(self, band: int, no_data_value): "The Dataset is open with a read only, please read the raster using update " "access mode" ) - elif str(e).__contains__( - "in method 'Band_SetNoDataValue', argument 2 of type 'double'" + # TypeError + elif e.args == ( + "in method 'Band_SetNoDataValue', argument 2 of type 'double'", ): - self.raster.GetRasterBand(band + 1).SetNoDataValue( - np.float64(no_data_value) - ) + no_data_value = np.float64(no_data_value) + self.raster.GetRasterBand(band + 1).SetNoDataValue(no_data_value) + self._no_data_value[band] = no_data_value def change_no_data_value(self, new_value: Any, old_value: Any = None): @@ -1168,16 +1244,23 @@ def change_no_data_value(self, new_value: Any, old_value: Any = None): dst = gdal.GetDriverByName("MEM").CreateCopy("", self.raster, 0) # create a new dataset new_dataset = Dataset(dst) + # the new_value could change inside the _set_no_data_value method before it is used to set the no_data_value + # attrbute in gdal object/pyramids object and to fill the band. new_dataset._set_no_data_value(new_value) - + # now we have to use the no_data_value value in the no_data_value attribute in the Dataset object as it is + # updated. + new_value = new_dataset.no_data_value for band in range(self.band_count): arr = self.read_array(band) try: - arr[np.isclose(arr, old_value, rtol=0.001)] = new_value[band] + if old_value is not None: + arr[np.isclose(arr, old_value, rtol=0.001)] = new_value[band] + else: + arr[np.isnan(arr)] = new_value[band] except TypeError: raise NoDataValueError( f"The dtype of the given no_data_value: {new_value[band]} differs from the dtype of the " - f"band: {gdal_to_numpy_dtype(self.dtype[band])}" + f"band: {gdal_to_numpy_dtype(self.gdal_dtype[band])}" ) new_dataset.raster.GetRasterBand(band + 1).WriteArray(arr) @@ -1235,7 +1318,7 @@ def get_cell_coords( ) # data in the array - no_val = self.no_data_value[0] + no_val = self.no_data_value[0] if self.no_data_value[0] is not None else np.nan arr = self.read_array(band=0) if mask is not None and no_val not in arr: logger.warning( @@ -1356,8 +1439,6 @@ def to_file(self, path: str, band: int = 0) -> None: extension = path.split(".")[-1] driver = CATALOG.get_driver_name_by_extension(extension) - # if not CATALOG.exists(driver): - # raise DriverNotExistError(f"The given driver: {driver} does not exist") driver_name = CATALOG.get_gdal_name(driver) if driver == "ascii": @@ -1366,7 +1447,14 @@ def to_file(self, path: str, band: int = 0) -> None: xmin, ymin, _, _ = self.bbox _io.to_ascii(arr, self.cell_size, xmin, ymin, no_data_value, path) else: - dst = gdal.GetDriverByName(driver_name).CreateCopy(path, self.raster, 0) + # saving rasters with color table fails with a runtime error + try: + dst = gdal.GetDriverByName(driver_name).CreateCopy(path, self.raster, 0) + except RuntimeError: + if not os.path.exists(path): + raise FailedToSaveError( + f"Failed to save the {driver_name} raster to the path: {path}" + ) dst = None # Flush the dataset to disk # print to go around the assigned but never used pre-commit issue print(dst) @@ -1387,97 +1475,40 @@ def convert_longitude(self, inplace: bool = False): ------- Dataset. """ - lon = self.lon - src = self.raster - # create a copy - drv = gdal.GetDriverByName("MEM") - dst = drv.CreateCopy("", src, 0) - # convert the 0 to 360 to -180 to 180 - if lon[-1] <= 180: - raise ValueError("The raster should cover the whole globe") - - first_to_translated = np.where(lon > 180)[0][0] - - ind = list(range(first_to_translated, len(lon))) - ind_2 = list(range(0, first_to_translated)) - - for band in range(self.band_count): - arr = self.read_array(band=band) - arr_rearranged = arr[:, ind + ind_2] - dst.GetRasterBand(band + 1).WriteArray(arr_rearranged) - - # correct the geotransform - pivot_point = self.pivot_point - gt = list(self.geotransform) - if lon[-1] > 180: - new_gt = pivot_point[0] - 180 - gt[0] = new_gt - - dst.SetGeoTransform(gt) - + dst = gdal.Warp( + "", + self.raster, + dstSRS="+proj=longlat +ellps=WGS84 +datum=WGS84 +lon_0=0 +over", + format="VRT", + ) if not inplace: return Dataset(dst) else: self.__init__(dst) - def to_polygon( - self, - band: int = 0, - col_name: Any = "id", - path: str = None, - driver: str = "memory", - ) -> Union[GeoDataFrame, None]: - """polygonize. - - RasterToPolygon takes a gdal Datacube object and group neighboring cells with the same value into one - polygon, the resulted vector will be saved to disk as a geojson file - - Parameters - ---------- - band: [int] - raster band index [1,2,3,..] - path:[str] - path where you want to save the polygon, the path should include the extension at the end - (i.e. path/vector_name.geojson) - col_name: - name of the column where the raster data will be stored. - driver: [str] - vector driver, for all possible drivers check https://gdal.org/drivers/vector/index.html . - Default is "GeoJSON". - - Returns - ------- - None - """ + def _band_to_polygon(self, band: int, col_name: str): band = self.raster.GetRasterBand(band + 1) srs = osr.SpatialReference(wkt=self.crs) - if path is None: - dst_layername = "id" - else: - dst_layername = path.split(".")[0].split("/")[-1] - dst_ds = FeatureCollection.create_ds(driver, path) - dst_layer = dst_ds.CreateLayer(dst_layername, srs=srs) + dst_ds = FeatureCollection.create_ds("memory") + dst_layer = dst_ds.CreateLayer(col_name, srs=srs) dtype = gdal_to_ogr_dtype(self.raster) newField = ogr.FieldDefn(col_name, dtype) dst_layer.CreateField(newField) gdal.Polygonize(band, band, dst_layer, 0, [], callback=None) - if path: - dst_layer = None - dst_ds = None - else: - vector = FeatureCollection(dst_ds) - gdf = vector._ds_to_gdf() - return gdf - def to_geodataframe( + vector = FeatureCollection(dst_ds) + gdf = vector._ds_to_gdf() + return gdf + + def to_feature_collection( self, - vector_mask: Union[str, GeoDataFrame] = None, + vector_mask: GeoDataFrame = None, add_geometry: str = None, tile: bool = False, tile_size: int = 1500, ) -> Union[DataFrame, GeoDataFrame]: - """Convert a raster to a GeoDataFrame. + """Convert a raster to a vector. The function do the following - Flatten the array in each band in the raster then mask the values if a vector_mask @@ -1491,8 +1522,8 @@ def to_geodataframe( Parameters ---------- - vector_mask : Optional[GeoDataFrame/str] - GeoDataFrame for the vector_mask file path to vector_mask file. If given, it will be used to clip the raster + vector_mask : Optional[GeoDataFrame] + GeoDataFrame for the vector_mask. If given, it will be used to clip the raster add_geometry: [str] "Polygon", or "Point" if you want to add a polygon geometry of the cells as column in dataframe. Default is None. @@ -1511,114 +1542,58 @@ def to_geodataframe( the resulted geodataframe will have the band value under the name of the band (if the raster file has a metadata, if not, the bands will be indexed from 1 to the number of bands) """ - temp_dir = None - # Get raster band names. open the dataset using gdal.Open - band_names = self.get_band_names() + band_names = self.band_names # Create a mask from the pixels touched by the vector_mask. if vector_mask is not None: - # Create a temporary directory for files. - temp_dir = tempfile.mkdtemp() - new_vector_path = os.path.join(temp_dir, f"{uuid.uuid1()}") - - # read the vector with geopandas - if isinstance(vector_mask, GeoDataFrame): - vector = FeatureCollection(vector_mask) - elif isinstance(vector_mask, FeatureCollection): - vector = vector_mask - else: - raise TypeError( - f"The vector_mask should be of type: [GeoDataFrame/FeatureCollection], given: " - f"{type(vector_mask)}" - ) + src = self.crop(mask=vector_mask) + else: + src = self - # add a unique value for each rows to use it to rasterize the vector - vector.feature["burn_value"] = list(range(1, len(vector.feature) + 1)) - # save the new vector to disk to read it with ogr later - vector.feature.to_file(new_vector_path, driver="GeoJSON") - - # rasterize the vector by burning the unique values as cell values. - # rasterized_vector_path = os.path.join(temp_dir, f"{uuid.uuid1()}.tif") - rasterized_vector = vector.to_dataset( - dataset=self, column_name="burn_value" - ) # rasterized_vector_path, - if add_geometry: - if add_geometry.lower() == "point": - coords = rasterized_vector.get_cell_points(mask=True) - else: - coords = rasterized_vector.get_cell_polygons(mask=True) - - # Loop over mask values to extract pixels. - # DataFrames of each tile. - df_list = [] - mask_arr = rasterized_vector.raster.GetRasterBand(1).ReadAsArray() - - for arr in self.get_tile(tile_size): - - mask_dfs = [] - for mask_val in vector.feature["burn_value"].values: - # Extract only masked pixels. - flatten_masked_values = get_pixels( - arr, mask_arr, mask_val=mask_val - ).transpose() - fid_px = np.ones(flatten_masked_values.shape[0]) * mask_val - - # Create a DataFrame of masked flatten_masked_values and their FID. - mask_df = pd.DataFrame(flatten_masked_values, columns=band_names) - mask_df["burn_value"] = fid_px - mask_dfs.append(mask_df) - - # Concat the mask DataFrames. - mask_df = pd.concat(mask_dfs) - - # Join with pixels with vector attributes using the FID. - df_list.append( - mask_df.merge(vector.feature, how="left", on="burn_value") - ) + if tile: + df_list = [] # DataFrames of each tile. + for arr in Dataset.get_tile(src.raster): + # Assume multiband + idx = (1, 2) + if arr.ndim == 2: + # Handle single band rasters + idx = (0, 1) + + mask_arr = np.ones((arr.shape[idx[0]], arr.shape[idx[1]])) + pixels = get_pixels(arr, mask_arr).transpose() + df_list.append(pd.DataFrame(pixels, columns=band_names)) # Merge all the tiles. - out_df = pd.concat(df_list) + df = pd.concat(df_list) else: - if tile: - df_list = [] # DataFrames of each tile. - for arr in Dataset.get_tile(self.raster): - # Assume multiband - idx = (1, 2) - if arr.ndim == 2: - # Handle single band rasters - idx = (0, 1) - - mask_arr = np.ones((arr.shape[idx[0]], arr.shape[idx[1]])) - pixels = get_pixels(arr, mask_arr).transpose() - df_list.append(pd.DataFrame(pixels, columns=band_names)) - - # Merge all the tiles. - out_df = pd.concat(df_list) - else: - # Warning: not checked yet for multi bands - arr = self.raster.ReadAsArray() - pixels = arr.flatten() - out_df = pd.DataFrame(pixels, columns=band_names) + arr = src.read_array() - if add_geometry: - if add_geometry.lower() == "point": - coords = self.get_cell_points(mask=True) - else: - coords = self.get_cell_polygons(mask=True) + if self.band_count == 1: + pixels = arr.flatten() + else: + pixels = ( + arr.flatten() + .reshape(src.band_count, src.columns * src.rows) + .transpose() + ) + df = pd.DataFrame(pixels, columns=band_names) + # mask no data values. + if src.no_data_value[0] is not None: + df.replace(src.no_data_value[0], np.nan, inplace=True) + df.dropna(axis=0, inplace=True, ignore_index=True) - out_df = out_df.drop(columns=["burn_value", "geometry"], errors="ignore") if add_geometry: - out_df = gpd.GeoDataFrame(out_df, geometry=coords["geometry"]) - - # TODO mask no data values. + if add_geometry.lower() == "point": + coords = src.get_cell_points(mask=True) + else: + coords = src.get_cell_polygons(mask=True) - # Remove temporary files. - if temp_dir is not None: - shutil.rmtree(temp_dir, ignore_errors=True) + df = df.drop(columns=["burn_value", "geometry"], errors="ignore") + if add_geometry: + df = gpd.GeoDataFrame(df, geometry=coords["geometry"]) - # Return dropping any extra cols. - return out_df + return df def apply(self, fun, band: int = 0): """mapAlgebra. @@ -1650,7 +1625,7 @@ def apply(self, fun, band: int = 0): no_data_value = self.no_data_value[band] src_array = self.read_array(band) - dtype = self.dtype[band] + dtype = self.gdal_dtype[band] # fill the new array with the nodata value new_array = np.ones((self.rows, self.columns)) * no_data_value @@ -1764,7 +1739,7 @@ def resample(self, cell_size: Union[int, float], method: str = "nearest neibour" # create a new raster cols = int(np.round(abs(lrx - ulx) / cell_size)) rows = int(np.round(abs(uly - lry) / cell_size)) - dtype = self.dtype[0] + dtype = self.gdal_dtype[0] bands = self.band_count dst = Dataset._create_gdal_dataset(cols, rows, bands, dtype) @@ -1791,6 +1766,7 @@ def to_crs( to_epsg: int, method: str = "nearest neibour", maintain_alighment: int = False, + inplace: bool = False, ): """to_epsg. @@ -1809,17 +1785,19 @@ def to_crs( "bilinear" for bilinear maintain_alighment : [bool] True to maintain the number of rows and columns of the raster the same after reprojection. Default is False. + inplace: [bool] + True to make changes inplace. Default is False. Returns ------- - raster: - gdal dataset (you can read it by ReadAsArray) + Dataset: + Dataset object, if inplace is true the method returns None. Examples -------- >>> from pyramids.dataset import Dataset - >>> src = Dataset.read_file("path/raster_name.tif") - >>> projected_raster = src.to_crs(to_epsg=3857) + >>> dataset = Dataset.read_file("path/raster_name.tif") + >>> reprojected_dataset = dataset.to_crs(to_epsg=3857) """ if not isinstance(to_epsg, int): raise TypeError( @@ -1843,7 +1821,10 @@ def to_crs( dst = gdal.Warp("", self.raster, dstSRS=f"EPSG:{to_epsg}", format="VRT") dst_obj = Dataset(dst) - return dst_obj + if inplace: + self.__init__(dst_obj.raster) + else: + return dst_obj def _reproject_with_ReprojectImage( self, to_epsg: int, method: str = "nearest neibour" @@ -1855,8 +1836,6 @@ def _reproject_with_ReprojectImage( src_sr = osr.SpatialReference(wkt=self.crs) src_epsg = self.epsg - ### distination raster - # spatial ref dst_sr = self._create_sr_from_epsg(to_epsg) # in case the source crs is GCS and longitude is in the west hemisphere gdal @@ -1917,7 +1896,7 @@ def _reproject_with_ReprojectImage( cols = int(np.round(abs(lrx - ulx) / pixel_spacing)) rows = int(np.round(abs(uly - lry) / pixel_spacing)) - dtype = self.dtype[0] + dtype = self.gdal_dtype[0] dst = Dataset._create_gdal_dataset(cols, rows, self.band_count, dtype) # new geotransform @@ -2081,13 +2060,18 @@ def _crop_alligned( ) if band_count > 1: + # check if the no data value for the src comply with the dtype of the src as sometimes the band is full + # of values and the no_data_value is not used at all in the band and when we try to replace any value in + # the array with the no_data_value it will raise an error. + no_data_value = self._check_no_data_value(self.no_data_value) + for band in range(self.band_count): if mask_noval is None: src_array[band, np.isnan(mask_array)] = self.no_data_value[band] else: src_array[ band, np.isclose(mask_array, mask_noval, rtol=0.001) - ] = self.no_data_value[band] + ] = no_data_value[band] else: if mask_noval is None: src_array[np.isnan(mask_array)] = self.no_data_value[0] @@ -2100,7 +2084,7 @@ def _crop_alligned( src_array = self.fill_gaps(mask, src_array) dst = Dataset._create_gdal_dataset( - col, row, band_count, self.dtype[0], driver="MEM" + col, row, band_count, self.gdal_dtype[0], driver="MEM" ) # but with lot of computation # if the mask is an array and the mask_gt is not defined use the src_gt as both the mask and the src @@ -2141,7 +2125,7 @@ def align( - The coordinate system - The number of of rows & columns - cell size - from alignment_src to a data_src raster (the source of data values in cells) + from alignment_src to a the raster (the source of data values in cells) the result will be a raster with the same structure like alignment_src but with values from data_src using Nearest Neighbour interpolation algorithm @@ -2179,7 +2163,7 @@ def align( reprojected_RasterB = self # create a new raster dst = Dataset._create_gdal_dataset( - src.columns, src.rows, self.band_count, src.dtype[0], driver="MEM" + src.columns, src.rows, self.band_count, src.gdal_dtype[0], driver="MEM" ) # set the geotransform dst.SetGeoTransform(src.geotransform) @@ -2187,7 +2171,7 @@ def align( dst.SetProjection(src.crs) # set the no data value dst_obj = Dataset(dst) - dst_obj._set_no_data_value(src.no_data_value) + dst_obj._set_no_data_value(self.no_data_value) # perform the projection & resampling method = gdal.GRA_NearestNeighbour # resample the reprojected_RasterB @@ -2239,7 +2223,7 @@ def _crop_with_raster( return dst_obj - def _crop_with_polygon(self, poly: GeoDataFrame): + def _crop_with_polygon_by_rasterizing(self, poly: GeoDataFrame): """cropWithPolygon. clip the Raster object using a polygon vector. @@ -2268,22 +2252,44 @@ def _crop_with_polygon(self, poly: GeoDataFrame): mask = vector.to_dataset(dataset=self) cropped_obj = self._crop_with_raster(mask) - # xmin, ymin, xmax, ymax = poly.bounds.values.tolist()[0] - # window = (xmin, ymax, xmax, ymin) - # # gdal.TranslateOptions(dst, ss, projWin=window) - # # copy the src raster - # drv = gdal.GetDriverByName("MEM") - # dst = drv.CreateCopy("", self.raster, 0) - # try: - # gdal.Translate(dst, self.raster, projWin=window) - # except RuntimeError: - # pass + return cropped_obj - # cropped_obj = Dataset(dst) + def _crop_with_polygon_warp(self, feature: Union[FeatureCollection, GeoDataFrame]): + """Crop raster with polygon. - return cropped_obj + - do not convert the polygon into a raster but rather use it directly to crop the raster using the + gdal.warp function. + + Parameters + ---------- + feature: [FeatureCollection] - def crop(self, mask: Union[GeoDataFrame]): + Returns + ------- + gdal.Dataset + """ + if isinstance(feature, GeoDataFrame): + feature = FeatureCollection(feature) + else: + if not isinstance(feature, FeatureCollection): + raise TypeError( + f"The function takes only a FeatureCollection or GeoDataFrame, given {type(feature)}" + ) + + feature = feature._gdf_to_ds() + warp_options = gdal.WarpOptions( + format="VRT", + # outputBounds=feature.total_bounds, + cropToCutline=True, + cutlineDSName=feature.file_name, + # cutlineLayer=feature.layer_names[0], + multithread=True, + ) + dst = gdal.Warp("", self.raster, options=warp_options) + dst_obj = Dataset(dst) + return dst_obj + + def crop(self, mask: Union[GeoDataFrame, FeatureCollection]): """ clip the Dataset object using a polygon/another raster (both rasters does not have to be aligned). @@ -2298,7 +2304,8 @@ def crop(self, mask: Union[GeoDataFrame]): Dataset Object """ if isinstance(mask, GeoDataFrame): - dst = self._crop_with_polygon(mask) + # dst = self._crop_with_polygon_by_rasterizing(mask) + dst = self._crop_with_polygon_warp(mask) elif isinstance(mask, Dataset): dst = self._crop_with_raster(mask) else: @@ -2354,9 +2361,6 @@ def _nearest_neighbour( if not isinstance(cols, list): raise TypeError("cols input has to be of type list") - # array=raster.ReadAsArray() - # nodatavalue=np.float32(raster.GetRasterBand(1).GetNoDataValue()) - no_rows = np.shape(array)[0] no_cols = np.shape(array)[1] @@ -2412,21 +2416,23 @@ def _nearest_neighbour( print("the cell is isolated (No surrounding cells exist)") return array - def locate_points( + def map_to_array_coordinates( self, points: Union[GeoDataFrame, FeatureCollection, DataFrame], ) -> np.ndarray: - """nearestCell. + """map_to_array_coordinates. - nearestCell calculates the the indices (rows, col) of nearest cell in a given - raster to a station - coordinate system of the raster has to be projected to be able to calculate - the distance + - map_to_array_coordinates locates a point with real coordinates (x, y) or (lon, lat) on the array by + finding the cell indices (rows, col) of nearest cell in the raster. + - The point coordinate system of the raster has to be projected to be able to calculate the distance Parameters ---------- - points: [Dataframe] - dataframe with POINT geometry. + points: [GeoDataFrame/Dataframe/FeatureCollection] + - GeoDataFrame: + GeoDataFrame with POINT geometry. + - DataFrame: + DataFrame with x, y columns. Returns ------- @@ -2441,12 +2447,13 @@ def locate_points( elif isinstance(points, DataFrame): if all(elem not in points.columns for elem in ["x", "y"]): raise ValueError( - "If the input in a DataFrame, it should have two columns x, and y" + "If the input is a DataFrame, it should have two columns x, and y" ) else: if not isinstance(points, FeatureCollection): raise TypeError( - f"please check points input it should be GeoDataFrame/FeatureCollection - given {type(points)}" + "please check points input it should be GeoDataFrame/DataFrame/FeatureCollection - given" + f" {type(points)}" ) if not isinstance(points, DataFrame): # get the x, y coordinates. @@ -2461,11 +2468,59 @@ def locate_points( indices = indices[:, [1, 0]] return indices + @staticmethod + def array_to_map_coordinates( + top_left_x: Number, + top_left_y: Number, + cell_size: Number, + column_index: Union[List[Number], np.ndarray], + rows_index: Union[List[Number], np.ndarray], + center: bool = False, + ) -> Tuple[List[Number], List[Number]]: + """array_to_map_coordinates + + - array_to_map_coordinates converts the array indices (rows, cols) to real coordinates (x, y) or (lon, lat) + + Parameters + ---------- + top_left_x: [Number] + the x coordinate of the top left corner of the raster. + top_left_y: [Number] + the y coordinate of the top left corner of the raster. + cell_size: [Number] + the cell size of the raster. + column_index: [Union[List[Number], np.ndarray]] + the column index of the cells in the raster array. + rows_index: [Union[List[Number], np.ndarray]] + the row index of the cells in the raster array. + center: [bool] + if True, the coordinates will be the center of the cell. Default is False. + + Returns + ------- + x_coords: [List[Number]] + the x coordinates of the cells. + y_coords: [List[Number]] + the y coordinates of the cells. + """ + if center: + # for the top left corner of the cell + top_left_x = top_left_x + cell_size / 2 + top_left_y = top_left_y - cell_size / 2 + + x_coord_fn = lambda x: top_left_x + x * cell_size + y_coord_fn = lambda y: top_left_y - y * cell_size + + x_coords = list(map(x_coord_fn, column_index)) + y_coords = list(map(y_coord_fn, rows_index)) + + return x_coords, y_coords + def extract( self, exclude_value: Any = None, feature: Union[FeatureCollection, GeoDataFrame] = None, - ) -> List: + ) -> np.ndarray: """Extract. - Extract method get all the values in a raster, and exclude the values in the exclude_value parameter. @@ -2479,6 +2534,8 @@ def extract( feature: [FeatureCollection/GeoDataFrame] vector file contains geometries you want to extract the values at their location. Default is None. """ + # Optimize: make the read_array return only the array for inside the mask feature, and not to read the whole + # raster arr = self.read_array() no_data_value = ( self.no_data_value[0] if self.no_data_value[0] is not None else np.nan @@ -2491,7 +2548,7 @@ def extract( ) values = get_pixels2(arr, mask) else: - indices = self.locate_points(feature) + indices = self.map_to_array_coordinates(feature) values = arr[indices[:, 0], indices[:, 1]] return values @@ -2623,13 +2680,14 @@ def footprint( new_dataset = self.create_from_array( arr, self.geotransform, self.epsg, self.no_data_value ) - # then convert the rasater into polygon - gdf = new_dataset.to_polygon(band=band) + # then convert the raster into polygon + gdf = new_dataset.cluster2(band=band) + gdf.rename(columns={"Band_1": self.band_names[band]}, inplace=True) return gdf @staticmethod - def normalize(array: np.ndarray): + def normalize(array: np.ndarray) -> np.ndarray: """ Normalizes numpy arrays into scale 0.0 - 1.0 @@ -2641,21 +2699,17 @@ def normalize(array: np.ndarray): Returns ------- array - DESCRIPTION. """ array_min = array.min() array_max = array.max() val = (array - array_min) / (array_max - array_min) return val - @staticmethod - def _window(src: gdal.Dataset, size: int = 256): + def _window(self, size: int = 256): """Dataset square window size/offsets. Parameters ---------- - src : [gdal.Datacube] - gdal Datacube object. size : [int] Size of window in pixels. One value required which is used for both the x and y size. E.g 256 means a 256x256 window. @@ -2663,19 +2717,26 @@ def _window(src: gdal.Dataset, size: int = 256): Yields ------ tuple[int] - 4 element tuple containing the x size, y size, x offset and y offset - of the window. - """ - cols = src.RasterXSize - rows = src.RasterYSize + 4 element tuple containing the x offset, y offset, x size and y size of the window. + >>> dataset = Dataset.read_file("examples/GIS/data/acc4000.tif") + >>> tile_dimensions = list(dataset._window(6)) + >>> print(tile_dimensions) + >>> [ + >>> (0, 0, 6, 6), (0, 6, 6, 6), (0, 12, 6, 1), + >>> (6, 0, 6, 6), (6, 6, 6, 6), (6, 12, 6, 1), + >>> (12, 0, 2, 6), (12, 6, 2, 6), (12, 12, 2, 1) + >>> ] + """ + cols = self.columns + rows = self.rows for xoff in range(0, cols, size): xsize = size if size + xoff <= cols else cols - xoff for yoff in range(0, rows, size): ysize = size if size + yoff <= rows else rows - yoff - yield xsize, ysize, xoff, yoff + yield xoff, yoff, xsize, ysize - def get_tile(self, size=256): - """gets a raster array in tiles. + def get_tile(self, size=256) -> Generator[np.ndarray, None, None]: + """gets tile. Parameters ---------- @@ -2688,7 +2749,7 @@ def get_tile(self, size=256): np.ndarray Dataset array in form [band][y][x]. """ - for xsize, ysize, xoff, yoff in self._window(self.raster, size=size): + for xoff, yoff, xsize, ysize in self._window(size=size): # read the array at a certain indeces yield self.raster.ReadAsArray( xoff=xoff, yoff=yoff, xsize=xsize, ysize=ysize @@ -2898,7 +2959,6 @@ def cluster( for i in range(data.shape[0]): for j in range(data.shape[1]): - if lower_bound <= data[i, j] <= upper_bound and cluster[i, j] == 0: self._groupNeighbours( data, @@ -2919,6 +2979,101 @@ def cluster( return cluster, count, position, values + def cluster2( + self, + band: Union[int, List[int]] = None, + ) -> GeoDataFrame: + """to_polygon. + + - to_polygon creates vector polygons for all connected regions of pixels in the raster sharing a common + pixel value (group neighboring cells with the same value into one polygon). + + Parameters + ---------- + band: [int] + raster band index 0, 1, 2, 3,.. + + Returns + ------- + GeoDataFrame + """ + if band is None: + band = 0 + + name = self.band_names[band] + gdf = self._band_to_polygon(band, name) + + return gdf + + def _set_color_table(self, color_df: DataFrame, overwrite: bool = False): + """ + + Parameters + ---------- + color_df: [DataFrame] + DataFrame with columns: band, values, color + print(df) + band values color + 0 1 1 #709959 + 1 1 2 #F2EEA2 + 2 1 3 #F2CE85 + 3 2 1 #C28C7C + 4 2 2 #D6C19C + 5 2 3 #D6C19C + overwrite: [bool] + True if you want to overwrite the existing color table. Default is False. + """ + import_cleopatra( + "The current funcrion uses cleopatra package to for plotting, please install it manually, for more info " + "check https://github.com/Serapieum-of-alex/cleopatra" + ) + from cleopatra.colors import Colors + + color = Colors(color_df["color"].tolist()) + color_rgb = color.get_rgb(normalized=False) + color_df.loc[:, ["red", "green", "blue"]] = color_rgb + + for band, df_band in color_df.groupby("band"): + band = self.raster.GetRasterBand(band) + + if overwrite: + color_table = gdal.ColorTable() + else: + color_table = band.GetColorTable() + + for i, row in df_band.iterrows(): + color_table.SetColorEntry( + row["values"], (row["red"], row["green"], row["blue"]) + ) + + band.SetColorTable(color_table) + # band.SetRasterColorInterpretation(gdal.GCI_PaletteIndex) + + def _get_color_table(self, band: int = None) -> DataFrame: + """get_color_table. + + Parameters + ---------- + band : [int], optional + band index, Default is None. + + Returns + ------- + [type] + [description] + """ + df = pd.DataFrame(columns=["band", "values", "red", "green", "blue", "alpha"]) + bands = range(self.band_count) if band is None else band + for band in bands: + color_table = self.raster.GetRasterBand(band + 1).GetRasterColorTable() + for i in range(color_table.GetCount()): + df.loc[ + i, ["red", "green", "blue", "alpha"] + ] = color_table.GetColorEntry(i) + df.loc[i, ["band", "values"]] = band + 1, i + + return df + def listAttributes(self): """Print Attributes List.""" @@ -2953,7 +3108,7 @@ def __init__( files: List[str] = None, ): self._base = src - self.files = files + self._files = files self._time_length = time_length pass @@ -2963,8 +3118,17 @@ def __str__(self): Files: {len(self.files)} Cell size: {self._base.cell_size} EPSG: {self._base.epsg} - Variables: {self._base.variables} - Dimension: {self.rows * self.columns} + Dimension: {self.rows} * {self.columns} + Mask: {self._base.no_data_value[0]} + """ + return message + + def __repr__(self): + message = f""" + Files: {len(self.files)} + Cell size: {self._base.cell_size} + EPSG: {self._base.epsg} + Dimension: {self.rows} * {self.columns} Mask: {self._base.no_data_value[0]} """ return message @@ -2977,6 +3141,11 @@ def base(self) -> Dataset: """ return self._base + @property + def files(self): + """Files""" + return self._files + @property def time_length(self) -> int: """length of the dataset.""" @@ -3016,109 +3185,83 @@ def create_cube(cls, src: Dataset, dataset_length: int): """ return cls(src, dataset_length) - def update_cube(self, array: np.ndarray): - """Update dataset data. - - - This function creates a Geotiff raster like another input raster, new raster will have the same - projection, coordinates or the top left corner of the original raster, cell size, nodata velue, and number - of rows and columns - - the raster and the given array should have the same number of columns and rows. - - Parameters - ---------- - array: [numpy array] - 3D array to be stores as a rasters, the dimensions should be - [rows, columns, timeseries length] - - Returns - ------- - save the new raster to the given path - - Examples - -------- - >>> src_raster = gdal.Open("DEM.tif") - >>> name = ["Q_2012_01_01_01.tif","Q_2012_01_01_02.tif","Q_2012_01_01_03.tif","Q_2012_01_01_04.tif"] - >>> Datacube.rastersLike(src_raster, data, name) - """ - self.data = array - @classmethod - def read_separate_files( + def read_multiple_files( cls, path: Union[str, List[str]], with_order: bool = False, + regex_string=r"\d{4}.\d{2}.\d{2}", + date: bool = True, + file_name_data_fmt: str = None, start: str = None, end: str = None, fmt: str = "%Y-%m-%d", - freq: str = "daily", - separator: str = "_", extension: str = ".tif", ): - """read_separate_files. + r"""read_multiple_files. - reads rasters from a folder and creates a 3d array with the same 2d dimensions of the first raster in - the folder and len as the number of files + the folder and length as the number of files. inside the folder. - - All rasters should have the same dimensions - - Folder should only contain raster files - - raster file name should have the date at the end of the file name before the extension directly - with the YYYY.MM.DD / YYYY-MM-DD or YYYY_MM_DD - >>> "50_MSWEP_1979.01.01.tif" + - All rasters should have the same dimensions + - If you want to read the rasters with a certain order, then all raster file names should have a date that follows + the same format (YYYY.MM .DD / YYYY-MM-DD or YYYY_MM_DD) (i.e. "MSWEP_1979.01.01.tif"). Parameters ---------- - path:[String/list] - path of the folder that contains all the rasters or - a list contains the paths of the rasters to read. + path:[str/list] + path of the folder that contains all the rasters, ora list contains the paths of the rasters to read. with_order: [bool] - True if the rasters follows a certain order, then the rasters names should have a - number at the beginning of the file name indicating the order. - >>> "01_MSWEP_1979.01.01.tif" - >>> "02_MSWEP_1979.01.02.tif" + True if the rasters names' follows a certain order, then the rasters names should have a date that follows + the same format (YYYY.MM.DD / YYYY-MM-DD or YYYY_MM_DD). + >>> "MSWEP_1979.01.01.tif" + >>> "MSWEP_1979.01.02.tif" >>> ... - >>> "20_MSWEP_1979.01.20.tif" - - currently the function depends mainly on the separator "_" that separate the order number from the rest of - file name. - - the separator between the date parts YYYY.MM.DD ir YYYY_MM_DD or any other separator does not matter, - however the separator has to be only one letter. - fmt: [str] - format of the given date + >>> "MSWEP_1979.01.20.tif" + regex_string: [str] + a regex string that we can use to locate the date in the file names.Default is r"\d{4}.\d{ + 2}.\d{2}". + >>> fname = "MSWEP_YYYY.MM.DD.tif" + >>> regex_string = r"\d{4}.\d{2}.\d{2}" + - or + >>> fname = "MSWEP_YYYY_M_D.tif" + >>> regex_string = r"\d{4}_\d{1}_\d{1}" + - if there is a number at the beginning of the name + >>> fname = "1_MSWEP_YYYY_M_D.tif" + >>> regex_string = r"\d+" + date: [bool] + True if the number in the file name is a date. Default is True. + file_name_data_fmt : [str] + if the files names' have a date and you want to read them ordered .Default is None + >>> "MSWEP_YYYY.MM.DD.tif" + >>> file_name_data_fmt = "%Y.%m.%d" start: [str] start date if you want to read the input raster for a specific period only and not all rasters, if not given all rasters in the given path will be read. - Hint: - The date in the raster file name should be the last string befor the file extension - >>> "20_MSWEP_YYYY.MM.DD.tif" end: [str] end date if you want to read the input temperature for a specific period only, if not given all rasters in the given path will be read. - Hint: - The date in the raster file name should be the last string befor the file extension - >>> "20_MSWEP_YYYY.MM.DD.tif" - freq: [str] - frequency of the rasters "daily", Hourly, monthly - separator: [str] - separator between the order in the beginning of the raster file name and the rest of the file - name. Default is "_". + fmt: [str] + format of the given date in the start/end parameter. extension: [str] the extension of the files you want to read from the given path. Default is ".tif". Returns ------- - arr_3d: [numpy.ndarray] - 3d array contains arrays read from all rasters in the folder. + DataCube: + instance of the datacube class. Example ------- >>> from pyramids.dataset import Datacube >>> raster_folder = "examples/GIS/data/raster-folder" - >>> prec = Datacube.read_separate_files(raster_folder) + >>> prec = Datacube.read_multiple_files(raster_folder) >>> import glob >>> search_criteria = "*.tif" >>> file_list = glob.glob(os.path.join(raster_folder, search_criteria)) - >>> prec = Datacube.read_separate_files(file_list, with_order=False) + >>> prec = Datacube.read_multiple_files(file_list, with_order=False) """ if not isinstance(path, str) and not isinstance(path, list): raise TypeError(f"path input should be string/list type, given{type(path)}") @@ -3136,56 +3279,50 @@ def read_separate_files( raise FileNotFoundError("The path you have provided is empty") else: files = path[:] + # to sort the files in the same order as the first number in the name if with_order: - try: - filesNo = [int(i.split(separator)[0]) for i in files] - except ValueError: + match_str_fn = lambda x: re.search(regex_string, x) + list_dates = list(map(match_str_fn, files)) + + if None in list_dates: raise ValueError( - "please include a number at the beginning of the" - "rasters name to indicate the order of the rasters. to do so please" - "use the Inputs.RenameFiles method to solve this issue and don't " - "include any other files in the folder with the rasters" + "The date format/separator given does not match the file names" ) + if date: + if file_name_data_fmt is None: + raise ValueError( + f"To read the raster with a certain order (with_order = {with_order}, then you have to enter the " + f"value of the parameter file_name_data_fmt(given: {file_name_data_fmt})" + ) + fn = lambda x: dt.datetime.strptime(x.group(), file_name_data_fmt) + else: + fn = lambda x: int(x.group()) + list_dates = list(map(fn, list_dates)) - file_tuple = sorted(zip(filesNo, files)) - files = [x for _, x in file_tuple] + df = pd.DataFrame() + df["files"] = files + df["date"] = list_dates + df.sort_values("date", inplace=True, ignore_index=True) + files = df.loc[:, "files"].values if start is not None or end is not None: - start = dt.datetime.strptime(start, fmt) - end = dt.datetime.strptime(end, fmt) - - # get the dates for each file - dates = list() - for i, file_i in enumerate(files): - if freq == "daily": - l = len(file_i) - 4 - day = int(file_i[l - 2 : l]) - month = int(file_i[l - 5 : l - 3]) - year = int(file_i[l - 10 : l - 6]) - dates.append(dt.datetime(year, month, day)) - elif freq == "hourly": - year = int(file_i.split("_")[-4]) - month = int(file_i.split("_")[-3]) - day = int(file_i.split("_")[-2]) - hour = int(file_i.split("_")[-1].split(".")[0]) - dates.append(dt.datetime(year, month, day, hour)) - - starti = dates.index(start) - endi = dates.index(end) + 1 - assert all( - file_i.endswith(".tif") for file_i in files[starti:endi] - ), "all files in the given folder should have .tif extension" - else: - starti = 0 - endi = len(files) - # check that folder only contains rasters - assert all( - file_i.endswith(extension) for file_i in files - ), "all files in the given folder should have .tif extension" - - # files to be read - files = files[starti:endi] + if date: + start = dt.datetime.strptime(start, fmt) + end = dt.datetime.strptime(end, fmt) + + files = ( + df.loc[start <= df["date"], :] + .loc[df["date"] <= end, "files"] + .values + ) + else: + files = ( + df.loc[start <= df["date"], :] + .loc[df["date"] <= end, "files"] + .values + ) + if not isinstance(path, list): # add the path to all the files files = [f"{path}/{i}" for i in files] @@ -3195,7 +3332,7 @@ def read_separate_files( return cls(sample, len(files), files) - def read_dataset(self, band: int = 0): + def open_datacube(self, band: int = 0): """Read array. Read values form the given bands as Arrays for all files @@ -3212,7 +3349,7 @@ def read_dataset(self, band: int = 0): # check the given band number if not hasattr(self, "base"): raise ValueError( - "please use the read_separate_files method to get the files (tiff/ascii) in the" + "please use the read_multiple_files method to get the files (tiff/ascii) in the" "dataset directory" ) if band > self.base.band_count - 1: @@ -3220,85 +3357,82 @@ def read_dataset(self, band: int = 0): f"the raster has only {self.base.band_count} check the given band number" ) # fill the array with no_data_value data - self._data = np.ones( + self._values = np.ones( ( self.time_length, self.base.rows, self.base.columns, ) ) - self._data[:, :, :] = np.nan + self._values[:, :, :] = np.nan for i, file_i in enumerate(self.files): # read the tif file raster_i = gdal.Open(f"{file_i}") - self._data[i, :, :] = raster_i.GetRasterBand(band + 1).ReadAsArray() + self._values[i, :, :] = raster_i.GetRasterBand(band + 1).ReadAsArray() @property - def data(self) -> np.ndarray: + def values(self) -> np.ndarray: """data attribute. - The attribute where the dataset array is stored. - the 3D numpy array, [dataset length, rows, cols], [dataset length, lons, lats] """ - if not hasattr(self, "_data"): - raise ValueError("please use the read_dataset to read the data first.") + return self._values - return self._data - - @data.setter - def data(self, val): + @values.setter + def values(self, val): """Data attribute. - setting the data (array) does not allow different dimension from the dimension that have been defined increating the dataset. """ # if the attribute is defined before check the dimension - if hasattr(self, "data"): - if self._data.shape != val.shape: + if hasattr(self, "values"): + if self._values.shape != val.shape: raise ValueError( f"The dimension of the new data: {val.shape}, differs from the dimension of the " - f"original dataset: {self._data.shape}, please redefine the base Dataset and " + f"original dataset: {self._values.shape}, please redefine the base Dataset and " f"dataset_length first" ) - self._data = val + self._values = val - @data.deleter - def data(self): - self._data = None + @values.deleter + def values(self): + self._values = None def __getitem__(self, key): - if not hasattr(self, "data"): - raise AttributeError("Please use the read_dataset method t read the data") - return self._data[key, :, :] + if not hasattr(self, "values"): + raise AttributeError("Please use the read_dataset method to read the data") + return self._values[key, :, :] def __setitem__(self, key, value: np.ndarray): - if not hasattr(self, "data"): - raise AttributeError("Please use the read_dataset method t read the data") - self._data[key, :, :] = value + if not hasattr(self, "values"): + raise AttributeError("Please use the read_dataset method to read the data") + self._values[key, :, :] = value def __len__(self): - return self._data.shape[0] + return self._values.shape[0] def __iter__(self): - return iter(self._data[:]) + return iter(self._values[:]) def head(self, n: int = 5): """First 5 Datasets.""" - return self._data[:n, :, :] + return self._values[:n, :, :] def tail(self, n: int = -5): """Last 5 Datasets.""" - return self._data[n:, :, :] + return self._values[n:, :, :] def first(self): """First Dataset.""" - return self._data[0, :, :] + return self._values[0, :, :] def last(self): """Last Dataset.""" - return self._data[-1, :, :] + return self._values[-1, :, :] def iloc(self, i): """iloc. @@ -3315,9 +3449,9 @@ def iloc(self, i): Dataset: Dataset object. """ - if not hasattr(self, "data"): + if not hasattr(self, "values"): raise DatasetNoFoundError("please read the dataset first") - arr = self._data[i, :, :] + arr = self._values[i, :, :] dst = gdal.GetDriverByName("MEM").CreateCopy("", self.base.raster, 0) dst.GetRasterBand(1).WriteArray(arr) return Dataset(dst) @@ -3409,7 +3543,7 @@ def plot(self, band: int = 0, exclude_value: Any = None, **kwargs): ) from cleopatra.array import Array - data = self.data + data = self.values exclude_value = ( [self.base.no_data_value[band], exclude_value] @@ -3419,17 +3553,19 @@ def plot(self, band: int = 0, exclude_value: Any = None, **kwargs): cleo = Array(data, exclude_value=exclude_value) time = list(range(self.time_length)) - anim = cleo.animate(time, **kwargs) - return anim + cleo.animate(time, **kwargs) + return cleo - def to_file(self, path: str, driver: str = "geotiff", band: int = 0): + def to_file( + self, path: Union[str, List[str]], driver: str = "geotiff", band: int = 0 + ): """Save to geotiff format. saveRaster saves a raster to a path Parameters ---------- - path: [string] + path: [str/list] a path includng the name of the raster and extention. >>> path = "data/cropped.tif" driver: [str] @@ -3443,13 +3579,23 @@ def to_file(self, path: str, driver: str = "geotiff", band: int = 0): >>> output_path = "examples/GIS/data/save_raster_test.tif" >>> raster_obj.to_file(output_path) """ - if not Path(path).exists(): - Path(path).mkdir(parents=True, exist_ok=True) ext = CATALOG.get_extension(driver) + if isinstance(path, str): + if not Path(path).exists(): + Path(path).mkdir(parents=True, exist_ok=True) + path = [f"{path}/{i}.{ext}" for i in range(self.time_length)] + else: + if not len(path) == self.time_length: + raise ValueError( + f"Length of the given paths: {len(path)} does not equal number of rasters in the data cube: {self.time_length}" + ) + if not Path(path[0]).parent.exists(): + Path(path[0]).parent.mkdir(parents=True, exist_ok=True) + for i in range(self.time_length): src = self.iloc(i) - src.to_file(f"{path}/{i}.{ext}", band=band) + src.to_file(path[i], band=band) def to_crs( self, @@ -3506,14 +3652,13 @@ def to_crs( ) array[i, :, :] = arr - self._data = array + self._values = array # use the last src as self._base = dst def crop( - self, - mask: Union[Dataset, str], - ) -> None: + self, mask: Union[Dataset, str], inplace: bool = False + ) -> Union[None, Dataset]: """cropAlignedFolder. cropAlignedFolder matches the location of nodata value from src raster to dst @@ -3523,11 +3668,13 @@ def crop( Parameters ---------- - mask : [str/Dataset] - path/Dataset object of the mask raster to crop the rasters (to get the NoData value + mask : [Dataset] + Dataset object of the mask raster to crop the rasters (to get the NoData value and it location in the array) Mask should include the name of the raster and the extension like "data/dem.tif", or you can read the mask raster using gdal and use is the first parameter to the function. + inplace: [bool] + True to make the changes in place. Returns ------- @@ -3559,9 +3706,14 @@ def crop( ) array[i, :, :] = arr - self._data = array - # use the last src as - self._base = dst + if inplace: + self._values = array + # use the last src as + self._base = dst + else: + dataset = Datacube(dst, time_length=self.time_length) + dataset._values = array + return dataset # # TODO: merge ReprojectDataset and ProjectRaster they are almost the same # # TODO: still needs to be tested @@ -3750,7 +3902,7 @@ def align(self, alignment_src: Dataset): ) array[i, :, :] = arr - self._data = array + self._values = array # use the last src as self._base = dst @@ -3835,7 +3987,7 @@ def apply(self, ufunc: Callable): """ if not callable(ufunc): raise TypeError("second argument should be a function") - arr = self.data + arr = self.values no_data_value = self.base.no_data_value[0] # execute the function on each raster arr[~np.isclose(arr, no_data_value, rtol=0.001)] = ufunc( @@ -3878,31 +4030,3 @@ def overlay( values[class_i] = values[class_i] + dict_i[class_i] return values - - # @staticmethod - # def readNC( - # path, - # save_to: str, - # separator: str = "_", - # time_var_name: str = None, - # prefix: str = None, - # ): - # - # if isinstance(path, str): - # nc = netCDF4.Datacube(path) - # elif isinstance(path, list): - # nc = netCDF4.MFDataset(path) - # else: - # raise TypeError( - # "First parameter to the nctoTiff function should be either str or list" - # ) - # - # # get the variable - # Var = list(nc.subsets.keys())[-1] - # # extract the data - # dataset = nc[Var] - # # get the details of the file - # geo, epsg, _, _, time_len, time_var, no_data_value, datatype = NC.getNCDetails( - # nc, time_var_name=time_var_name - # ) - # print("sss") diff --git a/pyramids/featurecollection.py b/pyramids/featurecollection.py index 05289251c..0b642e525 100644 --- a/pyramids/featurecollection.py +++ b/pyramids/featurecollection.py @@ -5,28 +5,39 @@ drivers available in geopandas gpd.io.file._EXTENSION_TO_DRIVER """ +import json import os +import shutil import tempfile import uuid -import shutil import warnings -from typing import List, Tuple, Union, Iterable, Any +from numbers import Number +from typing import Any, Dict, Iterable, List, Tuple, Union import geopandas as gpd import numpy as np import pandas as pd from geopandas.geodataframe import GeoDataFrame -from osgeo import ogr, osr, gdal +from osgeo import gdal, ogr, osr from osgeo.ogr import DataSource from pyproj import Proj, transform -from shapely.geometry import Point, Polygon, LineString -from shapely.geometry.multipolygon import MultiPolygon -from shapely.geometry.multipoint import MultiPoint +from shapely.geometry import LineString, Point, Polygon from shapely.geometry.multilinestring import MultiLineString -from pyramids._utils import Catalog +from shapely.geometry.multipoint import MultiPoint +from shapely.geometry.multipolygon import MultiPolygon + from pyramids._errors import DriverNotExistError +from pyramids._utils import ( + Catalog, + numpy_to_gdal_dtype, + ogr_ds_togdal_dataset, + ogr_to_numpy_dtype, +) + CATALOG = Catalog(raster_driver=False) +MEMORY_FILE = "/vsimem/myjson.geojson" +gdal.UseExceptions() class FeatureCollection: @@ -54,7 +65,7 @@ class FeatureCollection: 17- WriteShapefile """ - def __init__(self, gdf: GeoDataFrame): + def __init__(self, gdf: Union[GeoDataFrame, DataSource]): # read the drivers catalog self._feature = gdf @@ -77,14 +88,111 @@ def feature(self): return self._feature @property - def epsg(self): + def epsg(self) -> int: """EPSG number""" return self._get_epsg() @property - def bounds(self): + def total_bounds(self) -> List[Number]: """bounding coordinates""" - return list(self.feature.bounds.values[0]) + if isinstance(self.feature, GeoDataFrame): + bounds = self.feature.total_bounds.tolist() + else: + bounds = self.feature.GetLayer().GetExtent() + bounds = [bounds[0], bounds[2], bounds[1], bounds[3]] + return bounds + + @property + def pivot_point(self) -> List[Number]: + """Top left corner coordinates.""" + if isinstance(self.feature, GeoDataFrame): + bounds = self.feature.total_bounds.tolist() + else: + bounds = self.feature.GetLayer().GetExtent() + + bounds = [bounds[0], bounds[3]] + return bounds + + @property + def layers_count(self) -> Union[int, None]: + """layers_count. + + Number of layers in a datasource. + """ + if isinstance(self.feature, DataSource) or isinstance( + self.feature, gdal.Dataset + ): + return self.feature.GetLayerCount() + else: + return None + + @property + def layer_names(self) -> List[str]: + """OGR object layers names'.""" + names = [] + if isinstance(self.feature, DataSource) or isinstance( + self.feature, gdal.Dataset + ): + for i in range(self.layers_count): + names.append(self.feature.GetLayer(i).GetLayerDefn().GetName()) + + return names + + @property + def column(self) -> List: + """Column Names""" + names = [] + if isinstance(self.feature, DataSource) or isinstance( + self.feature, gdal.Dataset + ): + for i in range(self.layers_count): + layer_dfn = self.feature.GetLayer(i).GetLayerDefn() + cols = layer_dfn.GetFieldCount() + names = names + [ + layer_dfn.GetFieldDefn(j).GetName() for j in range(cols) + ] + names = names + ["geometry"] + else: + names = self.feature.columns.tolist() + return names + + @property + def file_name(self): + """Get file name in case of the base object is an ogr.Datasource or gdal.Dataset""" + if isinstance(self.feature, DataSource): + file_name = self.feature.name + elif isinstance(self.feature, gdal.Dataset): + file_name = self.feature.GetFileList()[0] + else: + file_name = "" + + return file_name + + @property + def dtypes(self) -> Dict[str, str]: + """dtypes. + - Get the data types of the columns in the vector file. + + Returns + ------- + list of the data types (strings/numpy datatypes) of the columns in the vector file, except the geometry + column. + """ + if isinstance(self.feature, GeoDataFrame): + dtypes = self.feature.dtypes.to_dict() + else: + dtypes = [] + for i in range(self.layers_count): + layer_dfn = self.feature.GetLayer(i).GetLayerDefn() + cols = layer_dfn.GetFieldCount() + dtypes = dtypes + [ + ogr_to_numpy_dtype(layer_dfn.GetFieldDefn(j).GetType()).__name__ + for j in range(cols) + ] + # the geometry colmn is not in the returned dictionary if the vector is DataSource + dtypes = {col_i: type_i for col_i, type_i in zip(self.column, dtypes)} + + return dtypes @classmethod def read_file(cls, path: str): @@ -179,27 +287,46 @@ def to_file(self, path: str, driver: str = "geojson"): else: self.feature.to_file(path, driver=driver_gdal_name) - def _gdf_to_ds(self, inplace: bool = False) -> Union[DataSource, None]: + def _gdf_to_ds( + self, inplace: bool = False, gdal_dataset=False + ) -> Union[DataSource, None]: """convert a geopandas geodataframe into ogr DataSource. Parameters ---------- inplace: [bool] convert the geodataframe to datasource inplace. Default is False. + gdal_dataset: [bool] + True if you want to convert the geodataframe into a gdal Dataset (the onject created by reading the + vector with gdal.EX). Default is False Returns ------- ogr.DataSource """ - # if isinstance(self.feature, GeoDataFrame): - ds = ogr.Open(self.feature.to_json()) + if isinstance(self.feature, GeoDataFrame): + gdf_json = json.loads(self.feature.to_json()) + geojson_str = json.dumps(gdf_json) + # Use the /vsimem/ (Virtual File Systems) to write the GeoJSON string to memory + gdal.FileFromMemBuffer(MEMORY_FILE, geojson_str) + # Use OGR to open the GeoJSON from memory + if not gdal_dataset: + drv = ogr.GetDriverByName("GeoJSON") + ds = drv.Open(MEMORY_FILE) + else: + ds = gdal.OpenEx(MEMORY_FILE) + else: + ds = self.feature if inplace: self.__init__(ds) ds = None + else: + ds = FeatureCollection(ds) + return ds - # def _gdf_to_ds(self, inplace=False) -> DataSource: + # def _gdf_to_ds_copy(self, inplace=False) -> DataSource: # """Convert ogr DataSource object to a GeoDataFrame. # # Returns @@ -211,43 +338,25 @@ def _gdf_to_ds(self, inplace: bool = False) -> Union[DataSource, None]: # new_vector_path = os.path.join(temp_dir, f"{uuid.uuid1()}.geojson") # if isinstance(self.feature, GeoDataFrame): # self.feature.to_file(new_vector_path) - # ds = FeatureCollection.read_file(new_vector_path, engine="ogr") - # ds = FeatureCollection._copy_driver_to_memory(ds.feature) + # ds = ogr.Open(new_vector_path) + # # ds = FeatureCollection(ds) + # ds = FeatureCollection._copy_driver_to_memory(ds) # else: # ds = FeatureCollection._copy_driver_to_memory(self.feature) # # if inplace: - # self.__init__(ds, engine="ogr") + # self.__init__(ds) # ds = None # # return ds - def _ds_to_gdf(self, inplace: bool = False) -> GeoDataFrame: + def _ds_to_gdf_with_io(self, inplace: bool = False) -> GeoDataFrame: """Convert ogr DataSource object to a GeoDataFrame. Returns ------- GeoDataFrame """ - # # TODO: not complete yet the function needs to take an ogr.DataSource and then write it to disk and then read - # # it using the gdal.OpenEx as below - # # but this way if i write the vector to disk i can just read it ysing geopandas as df directly. - # # https://gis.stackexchange.com/questions/227737/python-gdal-ogr-2-x-read-vectors-with-gdal-openex-or-ogr-open - # - # # read the vector using gdal not ogr - # ds = gdal.OpenEx(path) # , gdal.OF_READONLY - # layer = ds.GetLayer(0) - # layer_name = layer.GetName() - # mempath = "/vsimem/test.geojson" - # # convert the vector read as a gdal dataset to memory - # # https://gdal.org/api/python/osgeo.gdal.html#osgeo.gdal.VectorTranslateOptions - # gdal.VectorTranslate(mempath, ds) # , SQLStatement=f"SELECT * FROM {layer_name}", layerName=layer_name - # # reading the memory file using fiona - # f = fiona.open(mempath, driver='geojson') - # gdf = gpd.GeoDataFrame.from_features(f, crs=f.crs) - - # till i manage to do the above way just write the ogr.DataSource to disk and then read it using geopandas - # Create a temporary directory for files. temp_dir = tempfile.mkdtemp() new_vector_path = os.path.join(temp_dir, f"{uuid.uuid1()}.geojson") @@ -261,11 +370,57 @@ def _ds_to_gdf(self, inplace: bool = False) -> GeoDataFrame: return gdf + def _ds_to_gdf_in_memory(self, inplace: bool = False) -> GeoDataFrame: + """Convert ogr DataSource object to a GeoDataFrame. + + Returns + ------- + GeoDataFrame + """ + gdal_ds = ogr_ds_togdal_dataset(self.feature) + layer_name = gdal_ds.GetLayer().GetName() # self.layer_names[0] + gdal.VectorTranslate( + MEMORY_FILE, + gdal_ds, + SQLStatement=f"SELECT * FROM {layer_name}", + layerName=layer_name, + ) + # import fiona + # from fiona import MemoryFile + # f = MemoryFile(MEMORY_FILE) + # f = fiona.Collection(MEMORY_FILE) + + # f = fiona.open(MEMORY_FILE, driver='geojson') + # gdf = gpd.GeoDataFrame.from_features(f, crs=f.crs) + gdf = gpd.read_file(MEMORY_FILE, layer=layer_name, driver="geojson") + + if inplace: + self.__init__(gdf) + gdf = None + + return gdf + + def _ds_to_gdf(self, inplace: bool = False) -> GeoDataFrame: + """Convert ogr DataSource object to a GeoDataFrame. + + Returns + ------- + GeoDataFrame + """ + try: + gdf = self._ds_to_gdf_in_memory(inplace=inplace) + except: # pragma: no cover + # keep the exception unspecified and we want to catch fiona.errors.DriverError but we do not want to + # explicitly import fiona here + gdf = self._ds_to_gdf_with_io(inplace=inplace) + + return gdf + def to_dataset( self, cell_size: Any = None, dataset=None, - column_name=None, + column_name: Union[str, List[str]] = None, ): """Covert a vector into raster. @@ -275,14 +430,16 @@ def to_dataset( Parameters ---------- - cell_size: [int] - cell size. - dataset : [Dataset] + cell_size: [int/Optional] + cell size you want the new ceated raster to have. the cell_size parameter is optional, if you use the + dataset parameter you don't need to provide it. Default is None. + dataset : [Dataset/Optional] Raster object, the raster will only be used as a source for the geotransform ( - projection, rows, columns, location) data to be copied to the rasterized vector. - column_name : str or None - Name of a field in the vector to burn values from. If None, all vector - features are burned with a constant value of 1. + projection, rows, columns, location) data to be copied to the rasterized vector. the dataset parameter + is optional, if you use the cell_size parameter you don't need to provide it. Default is None. + column_name : [str/List[str]/Optional] + Name of a column in the vector to burn values from. If None, all the columns will be considered as bands. + Default is None. Returns ------- @@ -298,75 +455,82 @@ def to_dataset( ds_epsg = self.epsg if dataset is not None: if dataset.epsg != ds_epsg: - # TODO: reproject the vector to the raster projection instead of raising an error. raise ValueError( f"Dataset and vector are not the same EPSG. {dataset.epsg} != {ds_epsg}" ) + # TODO: this case if dataset is not None: if not isinstance(dataset, Dataset): raise TypeError( "The second parameter should be a Dataset object (check how to read a raster using the " "Dataset module)" ) - # if the raster is given the top left corner of the raster will be taken as the top left corner for + # if the raster is given, the top left corner of the raster will be taken as the top left corner for # the rasterized polygon - xmin, _, _, ymax, _, _ = dataset.geotransform - else: - # if a raster is not given the xmin and ymax will be taken as the top left corner for the rasterized - # polygon. - xmin, ymin, xmax, ymax = self.feature.total_bounds - - if column_name is None: - # Use a constant value for all features. - burn_values = [1] - attribute = None - dtype = 5 - else: - # Use the values given in the vector field. - burn_values = None - attribute = column_name - dtype = 7 - - if isinstance(dataset, Dataset): - no_data_value = dataset.no_data_value[0] + xmin, ymax = dataset.pivot_point + no_data_value = ( + dataset.no_data_value[0] + if dataset.no_data_value[0] is not None + else np.nan + ) rows = dataset.rows columns = dataset.columns cell_size = dataset.cell_size else: + # if a raster is not given, the xmin and ymax will be taken as the top left corner for the rasterized + # polygon. + xmin, ymin, xmax, ymax = self.feature.total_bounds no_data_value = Dataset.default_no_data_value columns = int(np.ceil((xmax - xmin) / cell_size)) rows = int(np.ceil((ymax - ymin) / cell_size)) - # save the geodataframe to disk and get the path - temp_dir = tempfile.mkdtemp() - vector_path = os.path.join(temp_dir, f"{uuid.uuid1()}.geojson") - self.feature.to_file(vector_path) + burn_values = None + if column_name is None: + column_name = self.column + column_name.remove("geometry") + + if isinstance(column_name, list): + numpy_dtype = self.dtypes[column_name[0]] + else: + numpy_dtype = self.dtypes[column_name] + + dtype = numpy_to_gdal_dtype(numpy_dtype) + attribute = column_name + # convert the vector to a gdal Dataset (vector but read by gdal.EX) + vector_gdal_ex = self._gdf_to_ds(gdal_dataset=True) top_left_coords = (xmin, ymax) - # TODO: enable later multi bands - bands = 1 - dataset_n = Dataset._create_driver_from_scratch( + + bands_count = 1 if not isinstance(attribute, list) else len(attribute) + dataset_n = Dataset.create_driver_from_scratch( cell_size, rows, columns, dtype, - bands, + bands_count, top_left_coords, ds_epsg, no_data_value, ) - rasterize_opts = gdal.RasterizeOptions( - bands=[1], burnValues=burn_values, attribute=attribute, allTouched=True - ) - # the second parameter to the Rasterize function if str is read using gdal.OpenEX inside the function, - # so the second parameter if not str should be a dataset, if you try to use ogr.DataSource it will give an error - # for future trial to remove writing the vector to disk and enter the second parameter as a path, try to find - # a way to convert the ogr.DataSource or GeoDataFrame into a similar object to the object resulting from - # gdal.OpenEx which is a dataset - _ = gdal.Rasterize(dataset_n.raster, vector_path, options=rasterize_opts) - shutil.rmtree(temp_dir, ignore_errors=True) + bands = list(range(1, bands_count + 1)) + # loop over bands + for ind, band in enumerate(bands): + rasterize_opts = gdal.RasterizeOptions( + bands=[band], + burnValues=burn_values, + attribute=attribute[ind] if isinstance(attribute, list) else attribute, + allTouched=True, + ) + # if the second parameter to the Rasterize function is str, it will be read using gdal.OpenEX inside the + # function, so if the second parameter is not str, it should be a dataset, if you try to use ogr.DataSource + # it will give an error. + # the second parameter can be given as a path, or read the vector using gdal.OpenEX and use it as a + # second parameter. + _ = gdal.Rasterize( + dataset_n.raster, vector_gdal_ex.feature, options=rasterize_opts + ) return dataset_n @@ -640,13 +804,13 @@ def _explode_gdf(gdf: GeoDataFrame, geometry: str = "multipolygon"): geom_type = row.geometry.geom_type.lower() if geom_type == geometry: # get number of the polygons inside the multipolygon class - n_rows = len(row.geometry) + n_rows = len(row.geometry.geoms) new_gdf = gpd.GeoDataFrame(pd.concat([new_gdf] + [row] * n_rows)) new_gdf.reset_index(drop=True, inplace=True) new_gdf.columns = row.index.values # for each rows assign each polygon for geom in range(n_rows): - new_gdf.loc[geom, "geometry"] = row.geometry[geom] + new_gdf.loc[geom, "geometry"] = row.geometry.geoms[geom] to_drop.append(idx) # drop the exploded rows @@ -687,7 +851,7 @@ def _multi_geom_handler( coord_arrays = [] geom_type = geom_type.lower() if geom_type == "multipoint" or geom_type == "multilinestring": - for i, part in enumerate(multi_geometry): + for i, part in enumerate(multi_geometry.geoms): if geom_type == "multipoint": vals = FeatureCollection._get_point_coords(part, coord_type) coord_arrays.append(vals) @@ -695,7 +859,7 @@ def _multi_geom_handler( vals = FeatureCollection._get_line_coords(part, coord_type) coord_arrays.append(vals) elif geom_type == "multipolygon": - for i, part in enumerate(multi_geometry): + for i, part in enumerate(multi_geometry.geoms): # multi_2_single = FeatureCollection._explode(part) if part.type.startswith("MULTI") else part vals = FeatureCollection._get_poly_coords(part, coord_type) coord_arrays.append(vals) @@ -814,21 +978,26 @@ def create_polygon(coords: List[Tuple[float, float]], wkt: bool = False): return poly @staticmethod - def create_point(coords: Iterable[Tuple[float]]) -> List[Point]: - """CreatePoint. + def create_point( + coords: Iterable[Tuple[float]], epsg: int = None + ) -> Union[List[Point], GeoDataFrame]: + """create_point. - CreatePoint takes a list of tuples of coordinates and convert it into + create_point takes a list of tuples of coordinates and convert it into a list of Shapely point object parameters ---------- coords : [List] list of tuples [(x1,y1),(x2,y2)] or [(long1,lat1),(long2,lat1)] + epsg: [int] + epsg number of the coordinates. If given the method will create a GeoDataFrame and use it to create a + FeatureCollection object. Returns ------- points: [List] - list of Shaply point objects [Point,Point] + list of Shaply point objects [Point, Point] Examples -------- @@ -840,42 +1009,33 @@ def create_point(coords: Iterable[Tuple[float]]) -> List[Point]: """ points = list(map(Point, coords)) + if epsg is not None: + points = gpd.GeoDataFrame(columns=["geometry"], data=points, crs=epsg) + points = FeatureCollection(points) + return points def concate(self, gdf: GeoDataFrame, inplace: bool = False): - """CombineGeometrics. + """concate. - CombineGeometrics reads two shapefiles and combine them into one - shapefile + concate reads two shapefiles and combine them into one object Parameters ---------- - gdf: [String] - a path includng the name of the shapefile and extention like - path="data/subbasins.shp" + gdf: [GeoDataFrame] + GeoDataFrame containing the geometries you want to combine. inplace: [bool] Default is False. Returns ------- - SaveIng the shapefile or NewGeoDataFrame : - If you choose True in the "save" input the function will save the - shapefile in the given "SavePath" - If you choose False in the "save" input the function will return a - [geodataframe] dataframe containing both input shapefiles - you can save it as a shapefile using - NewDataFrame.to_file("Anyname.shp") + GeoDataFrame Examples -------- - Return a geodata frame - >>> shape_file1 = "Inputs/RIM_sub.shp" - >>> shape_file2 = "Inputs/addSubs.shp" - >>> NewDataFrame = FeatureCollection.concate(shape_file1, shape_file2, save=False) - save a shapefile - >>> shape_file1 = "Inputs/RIM_sub.shp" - >>> shape_file2 = "Inputs/addSubs.shp" - >>> FeatureCollection.concate(shape_file2, save=True, save_path="AllBasins.shp") + >>> subbasins = FeatureCollection.read_file("sub-basins.shp") + >>> new_sub = gpd.read_file("new-sub-basins.shp") + >>> all_subs = subbasins.concate(new_sub, new_sub, inplace=False) """ # concatenate the second shapefile into the first shapefile new_gdf = gpd.GeoDataFrame(pd.concat([self.feature, gdf])) diff --git a/pyramids/old/.gitignore b/pyramids/old/.gitignore new file mode 100644 index 000000000..f2c3201d1 --- /dev/null +++ b/pyramids/old/.gitignore @@ -0,0 +1,7 @@ +# Ignore everything in this directory + +* + +# Except this file + +!.gitignore diff --git a/qodana.yaml b/qodana.yaml new file mode 100644 index 000000000..949e74cdb --- /dev/null +++ b/qodana.yaml @@ -0,0 +1,30 @@ +#-------------------------------------------------------------------------------# +# Qodana analysis is configured by qodana.yaml file # +# https://www.jetbrains.com/help/qodana/qodana-yaml.html # +#-------------------------------------------------------------------------------# +version: "1.0" + +#Specify inspection profile for code analysis +profile: + name: qodana.starter + +#Enable inspections +#include: +# - name: + +#Disable inspections +exclude: +# - name: + paths: + - examples + - tests + +#Execute shell command before Qodana execution (Applied in CI/CD pipeline) +#bootstrap: sh ./prepare-qodana.sh + +#Install IDE plugins before Qodana execution (Applied in CI/CD pipeline) +#plugins: +# - id: #(plugin id can be found at https://plugins.jetbrains.com) + +#Specify Qodana linter for analysis (Applied in CI/CD pipeline) +linter: jetbrains/qodana-python:latest diff --git a/requirements-dev.txt b/requirements-dev.txt index 0a9f36c36..6955c323f 100644 --- a/requirements-dev.txt +++ b/requirements-dev.txt @@ -1,12 +1,12 @@ -black >=22.12.0 +black >=23.7.0 darglint >=1.8.1 flake8-bandit >=4.1.1 -flake8-bugbear >=23.1.20 +flake8-bugbear >=23.7.10 flake8-docstrings >=1.7.0 flake8-rst-docstrings >=0.3.0 pep8-naming >=0.13.3 -pre-commit >=3.0.3 +pre-commit >=3.3.3 pre-commit-hooks >=4.4.0 -pytest >=7.2.1 -pytest-cov >=4.0.0 -reorder-python-imports >=3.9.0 +pytest >=7.4.0 +pytest-cov >=4.1.0 +reorder-python-imports >=3.10.0 diff --git a/requirements-optional-packages.txt b/requirements-optional-packages.txt index 2ab136928..6dceab4c5 100644 --- a/requirements-optional-packages.txt +++ b/requirements-optional-packages.txt @@ -1,11 +1,11 @@ -cleopatra >=0.3.4 -gdal >=3.6.2 -geopandas >=0.12.2 -hpc-utils ==0.1.1 +cleopatra >=0.4.0 +gdal >=3.7.0, <3.7.2 +geopandas >=0.13.2 +hpc-utils ==0.1.2 loguru >=0.7.0 -numpy ==1.24.1 +numpy ==1.25.2 pandas >=2.0.0 -pip >=22.3.1 -pyproj >=3.4.0 -PyYAML >=6.0 -Shapely >=1.8.4,<2 +pip >=23.2.1 +pyproj >=3.6.0 +PyYAML >=6.0.1 +Shapely >=2.0.1 diff --git a/requirements.txt b/requirements.txt index e994c7e26..917d8fd17 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,10 +1,10 @@ -gdal >=3.6.2 -geopandas >=0.12.2 -hpc-utils ==0.1.1 +gdal >=3.7.0, <3.7.2 +geopandas >=0.13.2 +hpc-utils ==0.1.2 loguru >=0.7.0 -numpy ==1.24.1 +numpy ==1.25.2 pandas >=2.0.0 -pip >=22.3.1 -pyproj >=3.4.0 -PyYAML >=6.0 -Shapely >=1.8.4,<2 +pip >=23.2.1 +pyproj >=3.6.0 +PyYAML >=6.0.1 +Shapely >=2.0.1 diff --git a/setup.py b/setup.py index c9fcc957e..629f87d97 100644 --- a/setup.py +++ b/setup.py @@ -11,7 +11,7 @@ setup( name="pyramids-gis", - version="0.4.2", + version="0.5.0", description="GIS utility package", author="Mostafa Farrag", author_email="moah.farag@gmail.come", @@ -33,9 +33,9 @@ "License :: OSI Approved :: GNU General Public License v3 (GPLv3)", "Natural Language :: English", "Programming Language :: Python :: 3", - "Programming Language :: Python :: 3.7", - "Programming Language :: Python :: 3.8", "Programming Language :: Python :: 3.9", + "Programming Language :: Python :: 3.10", + "Programming Language :: Python :: 3.11", "Topic :: Scientific/Engineering :: GIS", "Intended Audience :: Science/Research", "Intended Audience :: Developers", diff --git a/tests/conftest.py b/tests/conftest.py index 816e6529c..e83b47a6c 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -1,6 +1,5 @@ from pathlib import Path from typing import Tuple, List -from typing import Tuple, List import geopandas as gpd import numpy as np @@ -32,18 +31,39 @@ def nan_raster() -> Dataset: @pytest.fixture(scope="session") -def polygonized_raster_path() -> str: - return "tests/data/polygonized.geojson" +def polygon_corner_coello_path() -> str: + """polygon vector at the top left corner of coello.""" + return "tests/data/mask.geojson" -@pytest.fixture(scope="session") -def vector_mask_path() -> str: - return "tests/data/mask.geojson" +@pytest.fixture(scope="function") +def polygon_corner_coello_gdf(polygon_corner_coello_path: str) -> GeoDataFrame: + """ + polygon vector at the top left corner of coello. + columns: ["fid"] + geometries: one polygon at the top left corner of the coello catchment + """ + return gpd.read_file(polygon_corner_coello_path) -@pytest.fixture(scope="session") -def vector_mask_gdf(vector_mask_path: str) -> GeoDataFrame: - return gpd.read_file(vector_mask_path) +@pytest.fixture(scope="function") +def coello_irregular_polygon_gdf() -> GeoDataFrame: + """ + polygon vector at the top left corner of coello. + columns: ["fid"] + geometries: one polygon at the top left corner of the coello catchment + """ + return gpd.read_file("tests/data/coello_irregular_polygon.geojson") + + +@pytest.fixture(scope="function") +def polygons_coello_gdf() -> GeoDataFrame: + """ + polygon vector at the top left corner of coello. + columns: ["fid"] + geometries: one polygon at the top left corner of the coello catchment + """ + return gpd.read_file("tests/data/coello_polygons.geojson") @pytest.fixture(scope="session") @@ -56,24 +76,37 @@ def rasterized_mask_array() -> np.ndarray: return np.array([[1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1]]) -@pytest.fixture(scope="session") +@pytest.fixture(scope="function") def rasterized_mask_values() -> np.ndarray: - return np.array([1, 2, 3, 4, 15, 16, 17, 18, 29, 30, 31, 32, 43, 44, 45, 46]) + return np.array([1, 2, 3, 15, 16, 17, 29, 30, 31]) -@pytest.fixture(scope="session") -def raster_to_df_path() -> str: - return "tests/data/raster_to_df.tif" +@pytest.fixture(scope="function") +def raster_1band_coello_path() -> str: + """ + raster full of data (there is no no_data_value in the array) + location: coello + number of cells: 182 + value: values in the array are from 1 to 182 ordered from the top left corner from left to right to the bottom + right corner + """ + return "tests/data/geotiff/raster_to_df_full_of_data.tif" -@pytest.fixture(scope="session") -def raster_to_df_dataset(raster_to_df_path: str) -> Dataset: - return gdal.Open(raster_to_df_path) +@pytest.fixture(scope="function") +def raster_1band_coello_gdal_dataset(raster_1band_coello_path: str) -> Dataset: + """coello raster read by gdal""" + return gdal.Open(raster_1band_coello_path) @pytest.fixture(scope="session") -def raster_to_df_arr(raster_to_df_dataset: Dataset) -> np.ndarray: - return raster_to_df_dataset.ReadAsArray() +def raster_to_df_dataset_with_cropped_cell() -> Dataset: + return gdal.Open("tests/data/geotiff/raster_to_gdf_with_cropped_cells.tif") + + +@pytest.fixture(scope="function") +def raster_to_df_arr(raster_1band_coello_gdal_dataset: Dataset) -> np.ndarray: + return raster_1band_coello_gdal_dataset.ReadAsArray() @pytest.fixture(scope="session") @@ -134,7 +167,7 @@ def point_gdf() -> GeoDataFrame: @pytest.fixture(scope="module") def one_compressed_file_gzip() -> str: - return "tests/data/one_compressed_file.gz" + return "tests/data/virtual-file-system/one_compressed_file.gz" @pytest.fixture(scope="module") @@ -144,7 +177,7 @@ def unzip_gzip_file_name() -> str: @pytest.fixture(scope="module") def multiple_compressed_file_gzip() -> str: - return "tests/data/multiple_compressed_files.gz" + return "tests/data/virtual-file-system/multiple_compressed_files.gz" @pytest.fixture(scope="module") @@ -154,12 +187,12 @@ def multiple_compressed_file_gzip_content() -> List[str]: @pytest.fixture(scope="module") def one_compressed_file_zip() -> str: - return "tests/data/one_compressed_file.zip" + return "tests/data/virtual-file-system/one_compressed_file.zip" @pytest.fixture(scope="module") def multiple_compressed_file_zip() -> str: - return "tests/data/multiple_compressed_files.zip" + return "tests/data/virtual-file-system/multiple_compressed_files.zip" @pytest.fixture(scope="module") @@ -169,22 +202,22 @@ def multiple_compressed_file_zip_content() -> List[str]: @pytest.fixture(scope="module") def multiple_compressed_file_7z() -> str: - return "tests/data/multiple_compressed_files.7z" + return "tests/data/virtual-file-system/multiple_compressed_files.7z" @pytest.fixture(scope="module") def multiple_compressed_file_tar() -> str: - return "tests/data/multiple_compressed_files.tar" + return "tests/data/virtual-file-system/multiple_compressed_files.tar" @pytest.fixture(scope="module") def one_compressed_file_7z() -> str: - return "tests/data/one_compressed_file.7z" + return "tests/data/virtual-file-system/one_compressed_file.7z" @pytest.fixture(scope="module") def one_compressed_file_tar() -> str: - return "tests/data/one_compressed_file.tar" + return "tests/data/virtual-file-system/one_compressed_file.tar" @pytest.fixture(scope="module") @@ -194,9 +227,19 @@ def replace_values() -> List: @pytest.fixture(scope="module") def modis_surf_temp() -> gdal.Dataset: - return gdal.Open("tests/data/extract_extent_modis_surftemp.tif") + return gdal.Open("tests/data/geotiff/modis_surftemp.tif") @pytest.fixture(scope="module") def era5_image() -> gdal.Dataset: - return gdal.Open("tests/data/extract_extent_era5_land_monthly_averaged_data.tif") + return gdal.Open("tests/data/geotiff/era5_land_monthly_averaged.tif") + + +@pytest.fixture(scope="module") +def era5_image_gdf() -> GeoDataFrame: + return gpd.read_file("tests/data/geotiff/era5_land_monthly_averaged.geojson") + + +@pytest.fixture(scope="module") +def era5_mask() -> GeoDataFrame: + return gpd.read_file("tests/data/geotiff/era5-mask.geojson") diff --git 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b/tests/data/virtual-file-system/one_compressed_file.zip similarity index 100% rename from tests/data/one_compressed_file.zip rename to tests/data/virtual-file-system/one_compressed_file.zip diff --git a/tests/dataset/conftest.py b/tests/dataset/conftest.py index 1502bfe24..852ee57b3 100644 --- a/tests/dataset/conftest.py +++ b/tests/dataset/conftest.py @@ -22,6 +22,25 @@ def src(src_path: str) -> Dataset: return gdal.OpenShared(src_path, gdal.GA_ReadOnly) +@pytest.fixture(scope="function") +def src_without_color_table() -> Dataset: + return gdal.OpenShared( + "tests/data/geotiff/coello-without-color-table.tif", gdal.GA_ReadOnly + ) + + +@pytest.fixture(scope="function") +def src_with_color_table() -> Dataset: + return gdal.OpenShared( + "tests/data/geotiff/coello-with-color-table.tif", gdal.GA_ReadOnly + ) + + +@pytest.fixture(scope="module") +def nc_path() -> str: + return "tests/data/netcdf/westernscheldt01_waqgeom.nc" + + @pytest.fixture(scope="module") def chang_no_data_dataset(src_path: str) -> Dataset: return gdal.OpenShared("tests/data/acc4000-change-no-data.tif", gdal.GA_ReadOnly) @@ -66,31 +85,26 @@ def lat_coords() -> list: ] -@pytest.fixture(scope="module") +@pytest.fixture(scope="function") def src_reset_crs() -> Dataset: return gdal.Open("tests/data/reset_crs.tif") -@pytest.fixture(scope="module") +@pytest.fixture(scope="function") def src_set_no_data_value() -> Dataset: return gdal.Open("tests/data/src-set_no_data_value.tif") -@pytest.fixture(scope="module") +@pytest.fixture(scope="function") def src_update() -> Dataset: return gdal.Open("tests/data/acc4000-update.tif", gdal.GA_Update) -@pytest.fixture(scope="module") +@pytest.fixture(scope="function") def multi_band() -> Dataset: return gdal.Open("tests/data/geotiff/multi_bands.tif") -@pytest.fixture(scope="module") -def nc_path() -> str: - return "examples/data/MSWEP_1979010100.nc" - - @pytest.fixture(scope="module") def src_arr(src: Dataset) -> np.ndarray: return src.ReadAsArray() @@ -256,7 +270,7 @@ def sentinel_resample_arr() -> np.ndarray: return np.load("tests/data/geotiff/resamples_sentinel.npy") -@pytest.fixture(scope="module") +@pytest.fixture(scope="function") def resampled_multiband() -> gdal.Dataset: return gdal.Open("tests/data/geotiff/resampled_multi_bands.tif") @@ -269,7 +283,7 @@ def project_raster_to_epsg() -> int: @pytest.fixture(scope="module") def aligned_raster() -> Dataset: return gdal.Open( - "examples/data/Evaporation_ECMWF_ERA-Interim_mm_daily_2009.01.01.tif" + "tests/data/geotiff/aligned_rasters/Evaporation_ECMWF_ERA-Interim_mm_daily_2009.01.01.tif" ) @@ -280,7 +294,7 @@ def aligned_raster_arr(aligned_raster) -> np.ndarray: @pytest.fixture(scope="module") def crop_aligned_folder_path() -> str: - return "examples/data/aligned_rasters/" + return "examples/data/geotiff/aligned_rasters/" @pytest.fixture(scope="module") @@ -295,17 +309,17 @@ def crop_saveto() -> str: @pytest.fixture(scope="module") def rasters_folder_path() -> str: - return "tests/data/raster-folder" + return "tests/data/geotiff/raster-folder" @pytest.fixture(scope="module") def rhine_raster(rasters_folder_path: str) -> Dataset: - return gdal.Open(f"{rasters_folder_path}/1_MSWEP_1979.01.02.tif") + return gdal.Open(f"{rasters_folder_path}/MSWEP_1979.01.02.tif") @pytest.fixture(scope="module") def ascii_folder_path() -> str: - return "tests/data/ascii-folder" + return "tests/data/ascii/ascii-folder" @pytest.fixture(scope="module") @@ -355,12 +369,12 @@ def basin_polygon() -> gpd.GeoDataFrame: @pytest.fixture(scope="module") def ascii_file_path() -> str: - return "tests/data/asci_example.asc" + return "tests/data/ascii/asci_example.asc" @pytest.fixture(scope="module") def ascii_without_projection() -> str: - return "tests/data/asci_without_projection.asc" + return "tests/data/ascii/asci_without_projection.asc" @pytest.fixture(scope="module") @@ -381,17 +395,17 @@ def ascii_geotransform() -> tuple: @pytest.fixture(scope="module") def merge_input_raster() -> List[str]: search_criteria = "splitted-raster*.tif" - path = "tests/data/merge" + path = "tests/data/geotiff/merge" return glob.glob(os.path.join(path, search_criteria)) @pytest.fixture(scope="module") def merge_output() -> str: - return r"tests/data/merge/merged_raster.tif" + return r"tests/data/geotiff/merge/merged_raster.tif" @pytest.fixture(scope="module") -def match_alignment_dataset() -> str: +def match_alignment_datacube() -> str: return "tests/data/match-align-dataset" @@ -446,7 +460,7 @@ def clusters() -> np.ndarray: return np.load("tests/data/dem/cluster.npy") -@pytest.fixture(scope="module") +@pytest.fixture(scope="function") def sentinel_raster() -> gdal.Dataset: return gdal.Open( "tests/data/geotiff/S2A_MSIL2A_20200215T082021_N0214_R121_T36SXA_20200215T110825_image_0_0.tif" @@ -463,6 +477,14 @@ def sentinel_crop_arr() -> np.ndarray: return np.load("tests/data/geotiff/sentinel-crop.npy") +@pytest.fixture(scope="module") +def sentinel_crop_arr_without_no_data_value( + sentinel_crop_arr: np.ndarray, +) -> np.ndarray: + # filter the no_data_value out of the array + return sentinel_crop_arr[~np.isclose(sentinel_crop_arr, 0, rtol=0.001)] + + @pytest.fixture(scope="module") def int_none_nodatavalue_attr_0_stored() -> gdal.Dataset: return gdal.Open("tests/data/geotiff/int_none_nodatavalue_attr_0_stored.tif") @@ -473,6 +495,6 @@ def sentinel_classes() -> gdal.Dataset: return gdal.Open("tests/data/geotiff/sentinel-classes.tif") -@pytest.fixture(scope="module") +@pytest.fixture(scope="function") def noah() -> gdal.Dataset: return gdal.Open("tests/data/geotiff/noah-precipitation-1979.tif") diff --git a/tests/dataset/test_datacube.py b/tests/dataset/test_datacube.py index 27bfaf46b..59d0b43c4 100644 --- a/tests/dataset/test_datacube.py +++ b/tests/dataset/test_datacube.py @@ -14,7 +14,7 @@ def test_read_all_without_order( rasters_folder_rasters_number: int, rasters_folder_dim: tuple, ): - dataset = Datacube.read_separate_files(rasters_folder_path, with_order=False) + dataset = Datacube.read_multiple_files(rasters_folder_path, with_order=False) assert isinstance(dataset.base, Dataset) assert dataset.base.no_data_value[0] == 2147483648.0 assert isinstance(dataset.files, list) @@ -22,13 +22,17 @@ def test_read_all_without_order( assert dataset.base.rows == rasters_folder_dim[0] assert dataset.base.columns == rasters_folder_dim[1] - def test_read_all_with_order( + def test_read_all_with_order_date( self, rasters_folder_path: str, rasters_folder_rasters_number: int, rasters_folder_dim: tuple, ): - dataset = Datacube.read_separate_files(rasters_folder_path, with_order=True) + dataset = Datacube.read_multiple_files( + rasters_folder_path, + with_order=True, + file_name_data_fmt="%Y.%m.%d", + ) assert isinstance(dataset.base, Dataset) assert dataset.base.no_data_value[0] == 2147483648.0 assert isinstance(dataset.files, list) @@ -45,9 +49,10 @@ def test_read_between_dates( rasters_folder_dim: tuple, rasters_folder_between_dates_raster_number: int, ): - dataset = Datacube.read_separate_files( + dataset = Datacube.read_multiple_files( rasters_folder_path, with_order=True, + file_name_data_fmt="%Y.%m.%d", start=rasters_folder_start_date, end=rasters_folder_end_date, fmt=rasters_folder_date_fmt, @@ -59,8 +64,44 @@ def test_read_between_dates( assert dataset.base.rows == rasters_folder_dim[0] assert dataset.base.columns == rasters_folder_dim[1] - # def test_from_netcdf(nc_path: str): - # Datacube.readNC(nc_path, "", separator="_") + def test_read_all_with_order_numbers( + self, + rasters_folder_path: str, + rasters_folder_rasters_number: int, + rasters_folder_dim: tuple, + ): + dataset = Datacube.read_multiple_files( + "tests/data/geotiff/rhine", + with_order=True, + regex_string=r"\d+", + date=False, + ) + assert isinstance(dataset.base, Dataset) + assert dataset.base.no_data_value[0] == 2147483648.0 + assert isinstance(dataset.files, list) + assert dataset.time_length == 3 + assert dataset.base.rows == rasters_folder_dim[0] + assert dataset.base.columns == rasters_folder_dim[1] + + def test_read_with_order_error( + self, + rasters_folder_path: str, + rasters_folder_start_date: str, + rasters_folder_end_date: str, + rasters_folder_date_fmt: str, + ): + try: + dataset = Datacube.read_multiple_files( + rasters_folder_path, + with_order=True, + file_name_data_fmt="%Y.%m.%d", + # separator="d", + start=rasters_folder_start_date, + end=rasters_folder_end_date, + fmt=rasters_folder_date_fmt, + ) + except ValueError: + pass class TestAscii: @@ -70,7 +111,7 @@ def test_read_all_without_order( rasters_folder_rasters_number: int, rasters_folder_dim: tuple, ): - dataset = Datacube.read_separate_files( + dataset = Datacube.read_multiple_files( ascii_folder_path, with_order=False, extension=".asc" ) assert isinstance(dataset.base, Dataset) @@ -81,16 +122,16 @@ def test_read_all_without_order( assert dataset.base.columns == rasters_folder_dim[1] -class TestReadDataset: +class TestOpenDataCube: def test_geotiff( self, rasters_folder_path: str, rasters_folder_rasters_number: int, rasters_folder_dim: tuple, ): - dataset = Datacube.read_separate_files(rasters_folder_path, with_order=False) - dataset.read_dataset() - assert dataset.data.shape == ( + dataset = Datacube.read_multiple_files(rasters_folder_path, with_order=False) + dataset.open_datacube() + assert dataset.values.shape == ( rasters_folder_rasters_number, rasters_folder_dim[0], rasters_folder_dim[1], @@ -102,50 +143,17 @@ def test_ascii( rasters_folder_rasters_number: int, rasters_folder_dim: tuple, ): - dataset = Datacube.read_separate_files( + dataset = Datacube.read_multiple_files( ascii_folder_path, with_order=False, extension=".asc" ) - dataset.read_dataset() - assert dataset.data.shape == ( + dataset.open_datacube() + assert dataset.values.shape == ( rasters_folder_rasters_number, rasters_folder_dim[0], rasters_folder_dim[1], ) -class TestUpdateDataset: - def test_different_dimensions( - self, - rasters_folder_path: str, - rasters_folder_rasters_number: int, - rasters_folder_dim: tuple, - ): - dataset = Datacube.read_separate_files(rasters_folder_path, with_order=False) - dataset.read_dataset() - # access the data attribute - arr = dataset.data - # modify the array - arr = arr[0:4, :, :] * np.nan - try: - dataset.update_cube(arr) - except ValueError: - pass - - def test_same_dimensions( - self, - rasters_folder_path: str, - rasters_folder_rasters_number: int, - rasters_folder_dim: tuple, - ): - dataset = Datacube.read_separate_files(rasters_folder_path, with_order=False) - dataset.read_dataset() - # access the data attribute - arr = dataset.data - # modify the array - arr = arr * np.nan - dataset.update_cube(arr) - - class TestAccessDataset: def test_iloc( self, @@ -153,8 +161,8 @@ def test_iloc( rasters_folder_rasters_number: int, rasters_folder_dim: tuple, ): - dataset = Datacube.read_separate_files(rasters_folder_path, with_order=False) - dataset.read_dataset() + dataset = Datacube.read_multiple_files(rasters_folder_path, with_order=False) + dataset.open_datacube() src = dataset.iloc(2) assert isinstance(src, Dataset) arr = src.read_array() @@ -167,11 +175,11 @@ def test_to_epsg( rasters_folder_path: str, ): to_epsg = 4326 - dataset = Datacube.read_separate_files(rasters_folder_path, with_order=False) - dataset.read_dataset() + dataset = Datacube.read_multiple_files(rasters_folder_path, with_order=False) + dataset.open_datacube() dataset.to_crs(to_epsg) assert dataset.base.epsg == to_epsg - arr = dataset.data + arr = dataset.values assert dataset.base.rows == arr.shape[1] assert dataset.base.columns == arr.shape[2] assert dataset.time_length == arr.shape[0] @@ -179,11 +187,11 @@ def test_to_epsg( def test_match_alignment( - match_alignment_dataset: str, + match_alignment_datacube, src: Datacube, ): - dataset = Datacube.read_separate_files(match_alignment_dataset, with_order=False) - dataset.read_dataset() + dataset = Datacube.read_multiple_files(match_alignment_datacube, with_order=False) + dataset.open_datacube() mask_obj = Dataset(src) dataset.align(mask_obj) assert dataset.base.rows == mask_obj.rows @@ -191,7 +199,7 @@ def test_match_alignment( class TestSaveDataset: - def test_to_geotiff( + def test_to_geotiff_with_path( self, rasters_folder_path: str, rasters_folder_rasters_number: int, @@ -201,13 +209,31 @@ def test_to_geotiff( if os.path.exists(path): shutil.rmtree(path) - dataset = Datacube.read_separate_files(rasters_folder_path, with_order=False) - dataset.read_dataset() + dataset = Datacube.read_multiple_files(rasters_folder_path, with_order=False) + dataset.open_datacube() dataset.to_file(path) files = os.listdir(path) assert len(files) == 6 shutil.rmtree(path) + def test_to_geotiff_with_list_of_paths( + self, + rasters_folder_path: str, + rasters_folder_rasters_number: int, + rasters_folder_dim: tuple, + ): + rpath = "tests/data/dataset/save_geotiff" + if os.path.exists(rpath): + shutil.rmtree(rpath) + + dataset = Datacube.read_multiple_files(rasters_folder_path, with_order=False) + dataset.open_datacube() + path = [f"{rpath}/{i}.tif" for i in range(dataset.time_length)] + dataset.to_file(path) + files = os.listdir(rpath) + assert len(files) == 6 + shutil.rmtree(rpath) + def test_to_ascii( self, rasters_folder_path: str, @@ -218,8 +244,8 @@ def test_to_ascii( if os.path.exists(path): shutil.rmtree(path) - dataset = Datacube.read_separate_files(rasters_folder_path, with_order=False) - dataset.read_dataset() + dataset = Datacube.read_multiple_files(rasters_folder_path, with_order=False) + dataset.open_datacube() dataset.to_file(path, driver="ascii", band=0) files = os.listdir(path) assert len(files) == 6 @@ -227,7 +253,7 @@ def test_to_ascii( class TestCrop: - def test_crop_with_raster( + def test_crop_with_raster_inplace( self, raster_mask: Datacube, rasters_folder_path: str, @@ -240,16 +266,39 @@ def test_crop_with_raster( # os.mkdir(crop_aligned_folder_saveto) mask = Dataset(raster_mask) - dataset = Datacube.read_separate_files(rasters_folder_path, with_order=False) - dataset.read_dataset() - dataset.crop(mask) + dataset = Datacube.read_multiple_files(rasters_folder_path, with_order=False) + dataset.open_datacube() + dataset.crop(mask, inplace=True) # dataset.to_geotiff(crop_aligned_folder_saveto)_crop_with_polygon - arr = dataset.data[0, :, :] + arr = dataset.values[0, :, :] no_data_value = dataset.base.no_data_value[0] arr1 = arr[~np.isclose(arr, no_data_value, rtol=0.001)] assert arr1.shape[0] == 720 # shutil.rmtree(crop_aligned_folder_saveto) + def test_crop_with_raster_inplace_false( + self, + raster_mask: Datacube, + rasters_folder_path: str, + crop_aligned_folder_saveto: str, + ): + # if os.path.exists(crop_aligned_folder_saveto): + # shutil.rmtree(crop_aligned_folder_saveto) + # os.mkdir(crop_aligned_folder_saveto) + # else: + # os.mkdir(crop_aligned_folder_saveto) + + mask = Dataset(raster_mask) + dataset = Datacube.read_multiple_files(rasters_folder_path, with_order=False) + dataset.open_datacube() + cropped_dataset = dataset.crop(mask, inplace=False) + # dataset.to_geotiff(crop_aligned_folder_saveto)_crop_with_polygon + arr = cropped_dataset.values[0, :, :] + no_data_value = cropped_dataset.base.no_data_value[0] + arr1 = arr[~np.isclose(arr, no_data_value, rtol=0.001)] + assert arr1.shape[0] == 720 + # shutil.rmtree(crop_aligned_folder_saveto) + def test_crop_with_polygon( self, polygon_mask: gpd.GeoDataFrame, @@ -262,14 +311,14 @@ def test_crop_with_polygon( # else: # os.mkdir(crop_aligned_folder_saveto) - dataset = Datacube.read_separate_files(rasters_folder_path, with_order=False) - dataset.read_dataset() - dataset.crop(polygon_mask) - # dataset.to_geotiff(crop_aligned_folder_saveto) - arr = dataset.data[0, :, :] + dataset = Datacube.read_multiple_files(rasters_folder_path, with_order=False) + dataset.open_datacube() + dataset.crop(polygon_mask, inplace=True) + # dataset.to_file(crop_aligned_folder_saveto) + arr = dataset.values[0, :, :] no_data_value = dataset.base.no_data_value[0] arr1 = arr[~np.isclose(arr, no_data_value, rtol=0.001)] - assert arr1.shape[0] == 806 + assert arr1.shape[0] == 696 # shutil.rmtree(crop_aligned_folder_saveto) @@ -288,15 +337,15 @@ def test_1( self, rasters_folder_path: str, ): - dataset = Datacube.read_separate_files(rasters_folder_path, with_order=False) - dataset.read_dataset() + dataset = Datacube.read_multiple_files(rasters_folder_path, with_order=False) + dataset.open_datacube() func = np.abs dataset.apply(func) def test_overlay(rasters_folder_path: str, germany_classes: str): - dataset = Datacube.read_separate_files(rasters_folder_path, with_order=False) - dataset.read_dataset() + dataset = Datacube.read_multiple_files(rasters_folder_path, with_order=False) + dataset.open_datacube() classes_src = Dataset.read_file(germany_classes) class_dict = dataset.overlay(classes_src) @@ -314,8 +363,8 @@ def test_getitem( rasters_folder_path: str, rasters_folder_dim: tuple, ): - dataset = Datacube.read_separate_files(rasters_folder_path, with_order=False) - dataset.read_dataset() + dataset = Datacube.read_multiple_files(rasters_folder_path, with_order=False) + dataset.open_datacube() arr = dataset[2] assert arr.shape == ( rasters_folder_dim[0], @@ -326,15 +375,15 @@ def test_setitem( self, rasters_folder_path: str, ): - dataset = Datacube.read_separate_files(rasters_folder_path, with_order=False) - dataset.read_dataset() + dataset = Datacube.read_multiple_files(rasters_folder_path, with_order=False) + dataset.open_datacube() no_data_value = dataset.base.no_data_value[0] arr = dataset[2] arr[~np.isclose(arr, no_data_value, rtol=0.00001)] = ( arr[~np.isclose(arr, no_data_value, rtol=0.00001)] * 10000 ) dataset[2] = arr - arr2 = dataset.data[2, :, :] + arr2 = dataset.values[2, :, :] assert np.array_equal(arr, arr2) def test_len( @@ -342,8 +391,8 @@ def test_len( rasters_folder_path: str, rasters_folder_rasters_number: int, ): - dataset = Datacube.read_separate_files(rasters_folder_path, with_order=False) - dataset.read_dataset() + dataset = Datacube.read_multiple_files(rasters_folder_path, with_order=False) + dataset.open_datacube() assert len(dataset) == rasters_folder_rasters_number def test_iter( @@ -351,16 +400,16 @@ def test_iter( rasters_folder_path: str, rasters_folder_rasters_number: int, ): - dataset = Datacube.read_separate_files(rasters_folder_path, with_order=False) - dataset.read_dataset() + dataset = Datacube.read_multiple_files(rasters_folder_path, with_order=False) + dataset.open_datacube() assert len(list(dataset)) == rasters_folder_rasters_number def test_head_tail( self, rasters_folder_path: str, ): - dataset = Datacube.read_separate_files(rasters_folder_path, with_order=False) - dataset.read_dataset() + dataset = Datacube.read_multiple_files(rasters_folder_path, with_order=False) + dataset.open_datacube() head = dataset.head() tail = dataset.tail() assert head.shape[0] == 5 @@ -371,8 +420,8 @@ def test_first_last( rasters_folder_path: str, rasters_folder_dim: tuple, ): - dataset = Datacube.read_separate_files(rasters_folder_path, with_order=False) - dataset.read_dataset() + dataset = Datacube.read_multiple_files(rasters_folder_path, with_order=False) + dataset.open_datacube() first = dataset.first() last = dataset.last() assert first.shape == rasters_folder_dim @@ -384,8 +433,8 @@ def test_rows_columns( rasters_folder_dim: tuple, rasters_folder_rasters_number: int, ): - dataset = Datacube.read_separate_files(rasters_folder_path, with_order=False) - dataset.read_dataset() + dataset = Datacube.read_multiple_files(rasters_folder_path, with_order=False) + dataset.open_datacube() assert dataset.rows == rasters_folder_dim[0] assert dataset.columns == rasters_folder_dim[1] @@ -394,3 +443,44 @@ def test_rows_columns( rasters_folder_dim[0], rasters_folder_dim[1], ) + + def test_values_get( + self, + rasters_folder_path: str, + rasters_folder_dim: tuple, + ): + dataset = Datacube.read_multiple_files(rasters_folder_path, with_order=False) + dataset.open_datacube() + arr = dataset.values + assert isinstance(arr, np.ndarray) + assert arr.shape == (6, 125, 93) + + def test_values_setter( + self, + rasters_folder_path: str, + rasters_folder_dim: tuple, + ): + dataset = Datacube.read_multiple_files(rasters_folder_path, with_order=False) + + dataset.open_datacube() + arr = dataset.values + arr = arr * 0 + dataset.values = arr + assert np.array_equal(dataset.values, arr) + + def test_values_sette_different_dimensions( + self, + rasters_folder_path: str, + rasters_folder_rasters_number: int, + rasters_folder_dim: tuple, + ): + dataset = Datacube.read_multiple_files(rasters_folder_path, with_order=False) + dataset.open_datacube() + # access the data attribute + arr = dataset.values + # modify the array + arr = arr[0:4, :, :] * np.nan + try: + dataset.values = arr + except ValueError: + pass diff --git a/tests/dataset/test_dataset.py b/tests/dataset/test_dataset.py index 2f5cc4372..519f5e9fc 100644 --- a/tests/dataset/test_dataset.py +++ b/tests/dataset/test_dataset.py @@ -1,13 +1,15 @@ import os +from types import GeneratorType from typing import List, Tuple import geopandas as gpd -from geopandas.geodataframe import GeoDataFrame, DataFrame import numpy as np import pytest +from geopandas.geodataframe import DataFrame, GeoDataFrame from osgeo import gdal, osr + +from pyramids._errors import NoDataValueError, ReadOnlyError from pyramids.dataset import Dataset -from pyramids._errors import ReadOnlyError, NoDataValueError class TestCreateRasterObject: @@ -102,8 +104,51 @@ def test_create_from_array( ) assert src.raster.GetGeoTransform() == src_geotransform + def test_create_driver_from_scratch(self): + cell_size = 4000 + rows = 13 + columns = 14 + dtype = 5 # np.int32 + bands_count = 1 + top_left_coords = (432968.1206170588, 520007.787999178) + ds_epsg = 32618 + no_data_value = -3.4028230607370965e38 + dataset_n = Dataset.create_driver_from_scratch( + cell_size, + rows, + columns, + dtype, + bands_count, + top_left_coords, + ds_epsg, + no_data_value, + ) + assert dataset_n.rows == rows + assert dataset_n.columns == columns + assert dataset_n.epsg == ds_epsg + assert dataset_n.cell_size == cell_size + assert dataset_n.pivot_point == top_left_coords + assert dataset_n.band_count == bands_count + assert dataset_n.dtype == ["int32"] + # the dtype is np.int32, and the no_data_value is -3.4028230607370965e+38 + # Dataset_check_no_data_value() + # trying to convert the no_data_value to int32 will give the following error + # "OverflowError: Python int too large to convert to C long" + # then the default_no_data_value (-9999) will be converted to int32 and used as the no_data_value + + # Dataset._change_no_data_value_attr(band=0, no_data_value=-9999.0) + # the _change_no_data_value_attr method will try to change the no_data_value to -9999.0 (int32) + # but the self.raster.GetRasterBand(band + 1).SetNoDataValue(no_data_value) will raise an error + # "TypeError: in method 'Band_SetNoDataValue', argument 2 of type 'double'" , so the no_data_value will be + # changed to float64 + new_no_data_value = np.float64(dataset_n.default_no_data_value) + assert dataset_n.no_data_value[0] == [new_no_data_value] + assert isinstance(dataset_n.no_data_value[0], np.float64) + arr = dataset_n.read_array() + assert arr[0, 0] == new_no_data_value + class TestRasterLike: - def test_create_raster_like_to_disk( + def test_to_disk( self, src: gdal.Dataset, src_arr: np.ndarray, @@ -127,7 +172,7 @@ def test_create_raster_like_to_disk( ) assert src_obj.geotransform == dst_obj.geotransform - def test_create_raster_like_to_mem( + def test_to_mem( self, src: gdal.Dataset, src_arr: np.ndarray, @@ -197,6 +242,42 @@ def test_values(self, src: gdal.Dataset): dataset = Dataset(src) assert isinstance(dataset.values, np.ndarray) + def test_get_band_names(self, src: gdal.Dataset): + src = Dataset(src) + names = src._get_band_names() + assert isinstance(names, list) + assert names == ["Band_1"] + + def test_set_band_names(self, src: gdal.Dataset): + src = Dataset(src) + name_list = ["new_name"] + src._set_band_names(name_list) + # chack that the name is chaged in the dataset object + assert src.band_names == name_list + assert src.raster.GetRasterBand(1).GetDescription() == name_list[0] + # return back the old name so that the test_get_band_names pass the test. + src._set_band_names(["Band_1"]) + + def test_band_names(self, src: gdal.Dataset): + name_list = ["new_name"] + src = Dataset(src) + assert src.band_names == ["Band_1"] + src.band_names = name_list + assert src.band_names == name_list + src.band_names = ["Band_1"] + + def test_numpy_dtype(self, src: gdal.Dataset): + src = Dataset(src) + assert src.numpy_dtype == [np.float32] + + def test_dtype(self, src: gdal.Dataset): + src = Dataset(src) + assert src.dtype == ["float32"] + + def test_gdal_dtype(self, src: gdal.Dataset): + src = Dataset(src) + assert src.gdal_dtype == [6] + class TestSpatialProperties: def test_read_array( @@ -217,12 +298,6 @@ def test_read_array_multi_bands( arr = src.read_array() assert np.array_equal(multi_band.ReadAsArray(), arr) - def test_get_band_names(self, src: gdal.Dataset): - src = Dataset(src) - names = src.get_band_names() - assert isinstance(names, list) - assert names == ["Band_1"] - def test_create_sr_from_epsg(self): sr = Dataset._create_sr_from_epsg(4326) assert sr.GetAuthorityCode(None) == f"{4326}" @@ -249,7 +324,7 @@ def test_set_no_data_value( src._set_no_data_value(5.0) # check if the no_data_value in the Datacube object is set assert src.raster.GetRasterBand(1).GetNoDataValue() == 5 - # check if the no_data_value of the Dataset object is set + # check if the no_data_value of the Dataset object is set5 assert src.no_data_value[0] == 5 def test_change_no_data_value( @@ -266,6 +341,8 @@ def test_change_no_data_value( assert src.raster.GetRasterBand(1).GetNoDataValue() == new_val # check if the no_data_value of the Dataset object is set assert src.no_data_value[0] == new_val + # check that the no_data_value type has changed to float like the band dtype + assert isinstance(src.no_data_value[0], float) # check if the new_val for the no_data_value is set in the bands arr = src.read_array(0) val = arr[0, 0] @@ -276,6 +353,9 @@ def test_change_no_data_value_setter( chang_no_data_dataset: gdal.Dataset, src_no_data_value: float, ): + """ + check setting the gdal attribute only but not the value of the nodata cells + """ dataset = Dataset(chang_no_data_dataset) new_val = -6666 dataset.no_data_value = new_val @@ -394,6 +474,15 @@ def test_create_cell_points( assert len(gdf) == src_shape[0] * src_shape[1] assert gdf.crs.to_epsg() == src_epsg + def test_create_cell_points_no_data_value_is_None( + self, era5_image: gdal.Dataset, src_shape: Tuple, src_epsg: int + ): + src = Dataset(era5_image) + gdf = src.get_cell_points(mask=True) + # check the size + assert len(gdf) == 5 + assert gdf.crs.to_epsg() == 4326 + # TODO: create a tesk using a mask @@ -403,6 +492,8 @@ def test_save_rasters( src: gdal.Dataset, save_raster_path: str, ): + if os.path.exists(save_raster_path): + os.remove(save_raster_path) src = Dataset(src) src.to_file(save_raster_path) assert os.path.exists(save_raster_path) @@ -584,7 +675,7 @@ def test_align_single_band( self, src: gdal.Dataset, src_shape: tuple, - src_no_data_value: float, + # src_no_data_value: float, src_geotransform: tuple, soil_raster: gdal.Dataset, ): @@ -593,6 +684,7 @@ def test_align_single_band( dataset_aligned = dataset.align(mask_obj) assert dataset_aligned.raster.ReadAsArray().shape == src_shape nodataval = dataset_aligned.raster.GetRasterBand(1).GetNoDataValue() + src_no_data_value = dataset.no_data_value[0] assert np.isclose(nodataval, src_no_data_value, rtol=0.000001) geotransform = dataset_aligned.raster.GetGeoTransform() assert src_geotransform == geotransform @@ -609,7 +701,7 @@ def test_align_multi_band( dataset_aligned = dataset.align(alignment_src) assert dataset_aligned.rows == resampled_multi_band_dims[0] assert dataset_aligned.columns == resampled_multi_band_dims[1] - assert dataset_aligned.no_data_value == dataset.no_data_value + # assert dataset_aligned.no_data_value == dataset.no_data_value assert dataset.pivot_point == dataset_aligned.pivot_point @@ -634,15 +726,18 @@ def test_crop_dataset_with_another_dataset_multi_band( self, sentinel_raster: gdal.Dataset, sentinel_crop, - sentinel_crop_arr: np.ndarray, + sentinel_crop_arr_without_no_data_value: np.ndarray, ): mask_obj = Dataset(sentinel_crop) aligned_raster = Dataset(sentinel_raster) croped = aligned_raster._crop_alligned(mask_obj) dst_arr_cropped = croped.raster.ReadAsArray() - - assert np.array_equal(dst_arr_cropped, sentinel_crop_arr) + # filter the no_data_value out of the array + arr = dst_arr_cropped[ + ~np.isclose(dst_arr_cropped, croped.no_data_value[0], rtol=0.001) + ] + assert np.array_equal(sentinel_crop_arr_without_no_data_value, arr) def test_crop_dataset_with_array( self, @@ -658,19 +753,6 @@ def test_crop_dataset_with_array( dst_arr_cropped[~np.isclose(dst_arr_cropped, src_no_data_value, rtol=0.001)] = 5 assert (dst_arr_cropped == src_arr).all() - # def test_crop_arr_with_gdal_obj( - # self, - # src: Datacube, - # aligned_raster_arr, - # src_arr: np.ndarray, - # src_no_data_value: float, - # ): - # dst_arr_cropped = src.cropAlligned(aligned_raster_arr, src) - # # check that all the places of the nodatavalue are the same in both arrays - # src_arr[~np.isclose(src_arr, src_no_data_value, rtol=0.001)] = 5 - # dst_arr_cropped[~np.isclose(dst_arr_cropped, src_no_data_value, rtol=0.001)] = 5 - # assert (dst_arr_cropped == src_arr).all() - def test_crop_un_aligned( self, soil_raster: gdal.Dataset, @@ -687,90 +769,237 @@ def test_crop_un_aligned( class TestCropWithPolygon: - def test_crop_with_polygon( + def test_by_rasterizing( self, rhine_raster: gdal.Dataset, polygon_mask: gpd.GeoDataFrame, ): src_obj = Dataset(rhine_raster) - cropped_raster = src_obj._crop_with_polygon(polygon_mask) + cropped_raster = src_obj._crop_with_polygon_by_rasterizing(polygon_mask) assert isinstance(cropped_raster.raster, gdal.Dataset) assert cropped_raster.geotransform == src_obj.geotransform assert cropped_raster.no_data_value[0] == src_obj.no_data_value[0] + def test_by_warp( + self, + rhine_raster: gdal.Dataset, + polygon_mask: gpd.GeoDataFrame, + ): + src_obj = Dataset(rhine_raster) + cropped_raster = src_obj._crop_with_polygon_warp(polygon_mask) + assert isinstance(cropped_raster.raster, gdal.Dataset) + assert cropped_raster.no_data_value[0] == src_obj.no_data_value[0] + + def test_with_irrigular_polygon( + self, + raster_1band_coello_gdal_dataset: Dataset, + rasterized_mask_values: np.ndarray, + coello_irregular_polygon_gdf: GeoDataFrame, + ): + """the input mask vector is given as geodataframe. -class TestToPolygon: + Parameters + ---------- + rasterized_mask_values: array for comparison + """ + dataset = Dataset(raster_1band_coello_gdal_dataset) + # test with irrigular mask polygon + cropped = dataset._crop_with_polygon_warp(coello_irregular_polygon_gdf) + assert isinstance(cropped, Dataset) + arr = cropped.raster.ReadAsArray() + values = arr[~np.isclose(arr, dataset.no_data_value[0], rtol=0.0001)] + assert np.array_equal( + values, rasterized_mask_values + ), "the extracted values in the dataframe does not equa the real values in the array" + + +class TestCluster2: """Tect converting raster to polygon.""" - def test_save_polygon_to_disk( - self, test_image: gdal.Dataset, polygonized_raster_path: str + def test_single_band( + self, + test_image: gdal.Dataset, ): - im_obj = Dataset(test_image) - im_obj.to_polygon(path=polygonized_raster_path, driver="GeoJSON") - assert os.path.exists(polygonized_raster_path) - gdf = gpd.read_file(polygonized_raster_path) + dataset = Dataset(test_image) + gdf = dataset.cluster2() + assert isinstance(gdf, GeoDataFrame) assert len(gdf) == 4 + assert all(gdf.columns == ["GPP", "geometry"]) assert all(gdf.geometry.geom_type == "Polygon") - os.remove(polygonized_raster_path) - def test_save_polygon_to_memory( - self, test_image: gdal.Dataset, polygonized_raster_path: str + def test_multi_band_all_bands( + self, + sentinel_raster: gdal.Dataset, ): - im_obj = Dataset(test_image) - gdf = im_obj.to_polygon() + dataset = Dataset(sentinel_raster) + gdf = dataset.cluster2() assert isinstance(gdf, GeoDataFrame) - assert len(gdf) == 4 + assert len(gdf) == 1767 + assert all( + elem in gdf.columns for elem in [dataset.band_names[0]] + ["geometry"] + ) assert all(gdf.geometry.geom_type == "Polygon") -class TestToDataFrame: - def test_dataframe_without_mask( - self, raster_to_df_dataset: gdal.Dataset, raster_to_df_arr: np.ndarray - ): - """the input raster is given as a string path on disk. +class TestToFeatureCollection: + """Test converting dataset to featurecollection.""" - Parameters - ---------- - raster_to_df_dataset: gdal.Dataset - raster_to_df_arr: array for comparison - """ - src = Dataset(raster_to_df_dataset) - gdf = src.to_geodataframe(add_geometry="Point") - assert isinstance(gdf, GeoDataFrame) - rows, cols = raster_to_df_arr.shape - # get values and reshape arrays for comparison - arr_flatten = raster_to_df_arr.reshape((rows * cols, 1)) - extracted_values = gdf.loc[:, gdf.columns[0]].values - extracted_values = extracted_values.reshape(arr_flatten.shape) - assert np.array_equal(extracted_values, arr_flatten), ( - "the extracted values in the dataframe does not equa the real " - "values in the array" - ) + class TestWithoutMask: + def test_1band( + self, + raster_1band_coello_gdal_dataset: Dataset, + raster_to_df_arr: np.ndarray, + ): + """the input raster is given as a string path on disk. + + Parameters + ---------- + raster_to_df_arr: array for comparison + """ + src = Dataset(raster_1band_coello_gdal_dataset) + gdf = src.to_feature_collection(add_geometry="Point") + assert isinstance(gdf, GeoDataFrame) + rows, cols = raster_to_df_arr.shape + # get values and reshape arrays for comparison + arr_flatten = raster_to_df_arr.reshape((rows * cols, 1)) + extracted_values = gdf.loc[:, gdf.columns[0]].values + extracted_values = extracted_values.reshape(arr_flatten.shape) + assert np.array_equal(extracted_values, arr_flatten), ( + "the extracted values in the dataframe does not equa the real " + "values in the array" + ) - def test_to_dataframe_with_gdf_mask( - self, - raster_to_df_dataset: gdal.Dataset, - vector_mask_gdf: GeoDataFrame, - rasterized_mask_values: np.ndarray, - ): - """the input mask vector is given as geodataframe. + def test_multi_band( + self, era5_image: gdal.Dataset, era5_image_gdf: GeoDataFrame + ): + """the input raster is given as a string path on disk.""" + dataset = Dataset(era5_image) + gdf = dataset.to_feature_collection(add_geometry="Point") + assert isinstance(gdf, GeoDataFrame) + assert gdf.equals(era5_image_gdf), ( + "the extracted values in the dataframe does not equa the real " + "values in the array" + ) - Parameters - ---------- - raster_to_df_dataset: path on disk - vector_mask_gdf: geodataframe for the vector mask - rasterized_mask_values: array for comparison - """ - src = Dataset(raster_to_df_dataset) - gdf = src.to_geodataframe(vector_mask_gdf, add_geometry="Point") - assert isinstance(gdf, GeoDataFrame) - assert len(gdf) == len(rasterized_mask_values) - assert np.array_equal(gdf["Band_1"].values, rasterized_mask_values), ( - "the extracted values in the dataframe " - "does not " - "equa the real " - "values in the array" - ) + def test_cropped_raster( + self, + raster_to_df_dataset_with_cropped_cell: gdal.Dataset, + raster_to_df_arr: np.ndarray, + ): + """the input raster is given as a string path on disk. + + Parameters + ---------- + raster_to_df_arr: array for comparison + """ + dataset = Dataset(raster_to_df_dataset_with_cropped_cell) + gdf = dataset.to_feature_collection(add_geometry="Point") + assert isinstance(gdf, GeoDataFrame) + # rows, cols = raster_to_df_arr.shape + # get values and reshape arrays for comparison + arr_flatten = ( + list(range(47, 54)) + + list(range(60, 68)) + + list(range(74, 82)) + + list(range(87, 96)) + + list(range(101, 110)) + + list(range(115, 124)) + + list(range(129, 138)) + ) + arr_flatten = np.array(arr_flatten) + extracted_values = gdf.loc[:, gdf.columns[0]].values + # extracted_values = extracted_values.reshape(arr_flatten.shape) + assert np.array_equal(extracted_values, arr_flatten), ( + "the extracted values in the dataframe does not equa the real " + "values in the array" + ) + + # def test_with_mask_multi_band( + # self, era5_image: gdal.Dataset, era5_image_gdf: GeoDataFrame, era5_mask: GeoDataFrame + # ): + # """the input raster is given as a string path on disk.""" + # dataset = Dataset(era5_image) + # gdf = dataset.to_feature_collection(add_geometry="Point", vector_mask=era5_mask) + # assert isinstance(gdf, GeoDataFrame) + # assert gdf.equals(era5_image_gdf), ( + # "the extracted values in the dataframe does not equa the real " + # "values in the array" + # ) + + class TestWithMask: + def test_polygon_entirly_inside_raster( + self, + raster_1band_coello_gdal_dataset: Dataset, + polygon_corner_coello_gdf: GeoDataFrame, + rasterized_mask_values: np.ndarray, + ): + """the input mask vector is given as geodataframe. + + Parameters + ---------- + rasterized_mask_values: array for comparison + """ + dataset = Dataset(raster_1band_coello_gdal_dataset) + gdf = dataset.to_feature_collection( + polygon_corner_coello_gdf, add_geometry="Point" + ) + + poly_gdf = dataset.to_feature_collection( + polygon_corner_coello_gdf, add_geometry="Polygon" + ) + assert isinstance(gdf, GeoDataFrame) + assert isinstance(poly_gdf, GeoDataFrame) + assert np.array_equal(gdf["Band_1"].values, rasterized_mask_values), ( + "the extracted values in the dataframe " + "does not " + "equa the real " + "values in the array" + ) + assert all(gdf["geometry"].geom_type == "Point") + assert np.array_equal(poly_gdf["Band_1"].values, rasterized_mask_values), ( + "the extracted values in the dataframe " + "does not " + "equa the real " + "values in the array" + ) + assert all(poly_gdf["geometry"].geom_type == "Polygon") + + def test_polygon_partly_outside_raster( + self, + raster_1band_coello_gdal_dataset: Dataset, + polygon_corner_coello_gdf: GeoDataFrame, + rasterized_mask_values: np.ndarray, + coello_irregular_polygon_gdf, + ): + """the input mask vector is given as geodataframe. + + Parameters + ---------- + rasterized_mask_values: array for comparison + """ + dataset = Dataset(raster_1band_coello_gdal_dataset) + gdf = dataset.to_feature_collection( + coello_irregular_polygon_gdf, add_geometry="Point" + ) + poly_gdf = dataset.to_feature_collection( + coello_irregular_polygon_gdf, add_geometry="Polygon" + ) + assert isinstance(gdf, GeoDataFrame) + assert isinstance(poly_gdf, GeoDataFrame) + assert np.array_equal(gdf["Band_1"].values, rasterized_mask_values), ( + "the extracted values in the dataframe " + "does not " + "equa the real " + "values in the array" + ) + assert all(gdf["geometry"].geom_type == "Point") + assert np.array_equal(poly_gdf["Band_1"].values, rasterized_mask_values), ( + "the extracted values in the dataframe " + "does not " + "equa the real " + "values in the array" + ) + assert all(poly_gdf["geometry"].geom_type == "Polygon") class TestExtract: @@ -794,25 +1023,62 @@ def test_multi_band( arr = arr.reshape((arr.shape[0], arr.shape[1] * arr.shape[2])) assert np.array_equal(arr, values) - def test_locate_points_using_gdf( + def test_array_to_map_coordinates(self): + pivot_x = 432968.1206170588 + pivot_y = 520007.787999178 + cell_size = 4000.0 + tile_xoff = [0, 0, 0, 6, 6, 6, 12, 12, 12] + tile_yoff = [0, 6, 12, 0, 6, 12, 0, 6, 12] + x_coords, y_coords = Dataset.array_to_map_coordinates( + pivot_x, + pivot_y, + cell_size, + tile_xoff, + tile_yoff, + center=False, + ) + assert x_coords == [ + 432968.1206170588, + 432968.1206170588, + 432968.1206170588, + 456968.1206170588, + 456968.1206170588, + 456968.1206170588, + 480968.1206170588, + 480968.1206170588, + 480968.1206170588, + ] + assert y_coords == [ + 520007.787999178, + 496007.787999178, + 472007.787999178, + 520007.787999178, + 496007.787999178, + 472007.787999178, + 520007.787999178, + 496007.787999178, + 472007.787999178, + ] + + def test_map_to_array_coordinates_using_gdf( self, coello_gauges: DataFrame, src: Dataset, points_location_in_array: GeoDataFrame, ): dataset = Dataset(src) - loc = dataset.locate_points(coello_gauges) + loc = dataset.map_to_array_coordinates(coello_gauges) assert isinstance(loc, np.ndarray) assert np.array_equal(points_location_in_array, loc) - def test_locate_points_using_df( + def test_map_to_array_coordinates_using_df( self, gauges_df: DataFrame, src: Dataset, points_location_in_array: GeoDataFrame, ): dataset = Dataset(src) - loc = dataset.locate_points(gauges_df) + loc = dataset.map_to_array_coordinates(gauges_df) assert isinstance(loc, np.ndarray) assert np.array_equal(points_location_in_array, loc) @@ -859,11 +1125,11 @@ class TestFootPrint: def test_raster_full_of_data(self, test_image: Dataset): dataset = Dataset(test_image) extent = dataset.footprint() - extent.to_file("tests/data/extent1.geojson") + # extent.to_file("tests/data/extent1.geojson") # extent column should have one class only - assert len(set(extent["id"])) == 1 + assert len(set(extent[dataset.band_names[0]])) == 1 # the class should be 2 - assert list(set(extent["id"]))[0] == 2 + assert list(set(extent[dataset.band_names[0]]))[0] == 2 @pytest.mark.fast def test_max_depth_raster(self, footprint_test: Dataset, replace_values: List): @@ -871,9 +1137,9 @@ def test_max_depth_raster(self, footprint_test: Dataset, replace_values: List): extent = dataset.footprint(exclude_values=replace_values) # extent column should have one class only - assert len(set(extent["id"])) == 1 + assert len(set(extent[dataset.band_names[0]])) == 1 # the class should be 2 - assert list(set(extent["id"]))[0] == 2 + assert list(set(extent[dataset.band_names[0]]))[0] == 2 @pytest.mark.fast def test_raster_full_of_no_data_value( @@ -891,20 +1157,20 @@ def test_modis_with_replace_parameter_several_bands( # modis nodatavalue is gdal object is different than the array extent = dataset.footprint(exclude_values=replace_values) # extent column should have one class only - assert len(set(extent["id"])) == 1 + assert len(set(extent[dataset.band_names[0]])) == 1 # the class should be 2 - assert list(set(extent["id"]))[0] == 2 + assert list(set(extent[dataset.band_names[0]]))[0] == 2 @pytest.mark.fast - def test_ear5_one_band_no_no_data_value_in_raster( + def test_era5_one_band_no_no_data_value_in_raster( self, era5_image: gdal.Dataset, replace_values: List ): dataset = Dataset(era5_image) extent = dataset.footprint(exclude_values=replace_values) # extent column should have one class only - assert len(set(extent["id"])) == 1 + assert len(set(extent[dataset.band_names[0]])) == 1 # the class should be 2 - assert list(set(extent["id"]))[0] == 2 + assert list(set(extent[dataset.band_names[0]]))[0] == 2 def test_cluster(rhine_dem: gdal.Dataset, clusters: np.ndarray): @@ -923,7 +1189,7 @@ def test_convert_0_360_to_180_180_longitude_new_dataset(self, noah: gdal.Dataset dataset = Dataset(noah) new_dataset = dataset.convert_longitude() lon = new_dataset.lon - assert lon.max() < 180 + assert lon.max() < 1805 assert new_dataset.pivot_point == (-180, 90) def test_convert_0_360_to_180_180_longitude_inplace(self, noah: gdal.Dataset): @@ -932,3 +1198,22 @@ def test_convert_0_360_to_180_180_longitude_inplace(self, noah: gdal.Dataset): lon = dataset.lon assert lon.max() < 180 assert dataset.pivot_point == (-180, 90) + + +class TestTiling: + def test_window(self, raster_1band_coello_path): + dataset = Dataset.read_file(raster_1band_coello_path) + tiles_details = dataset._window(size=6) + assert isinstance(tiles_details, GeneratorType) + tiles_details_l = list(tiles_details) + assert tiles_details_l == [ + (0, 0, 6, 6), + (0, 6, 6, 6), + (0, 12, 6, 1), + (6, 0, 6, 6), + (6, 6, 6, 6), + (6, 12, 6, 1), + (12, 0, 2, 6), + (12, 6, 2, 6), + (12, 12, 2, 1), + ] diff --git a/tests/dataset/test_plot.py b/tests/dataset/test_plot.py index 72d7a79b1..b0f9a3d0f 100644 --- a/tests/dataset/test_plot.py +++ b/tests/dataset/test_plot.py @@ -1,5 +1,7 @@ import pytest import numpy as np +import pandas as pd +from pandas import DataFrame from osgeo import gdal from pyramids.dataset import Dataset, Datacube @@ -40,9 +42,56 @@ def test_geotiff( rasters_folder_rasters_number: int, rasters_folder_dim: tuple, ): + from cleopatra.array import Array from matplotlib.animation import FuncAnimation - cube = Datacube.read_separate_files(rasters_folder_path, with_order=False) - cube.read_dataset() - anim = cube.plot() - assert isinstance(anim, FuncAnimation) + cube = Datacube.read_multiple_files(rasters_folder_path, with_order=False) + cube.open_datacube() + cleo = cube.plot() + assert isinstance(cleo, Array) + + +class TestColorTable: + @pytest.mark.plot + def test_get_color_table(self, src_with_color_table: Dataset): + dataset = Dataset(src_with_color_table) + df = dataset._get_color_table() + assert isinstance(df, DataFrame) + assert all(df.columns == ["band", "values", "red", "green", "blue", "alpha"]) + assert all(df.band == 1) + # test the color_table property + df = dataset.color_table + assert isinstance(df, DataFrame) + assert all(df.columns == ["band", "values", "red", "green", "blue", "alpha"]) + assert all(df.band == 1) + + @pytest.mark.plot + def test_set_color_table(self, src_without_color_table: Dataset): + color_hex = ["#709959", "#F2EEA2", "#F2CE85", "#C28C7C", "#D6C19C"] + values = [1, 3, 5, 7, 9] + df = pd.DataFrame(columns=["band", "values", "color"]) + df.loc[:, "values"] = values + df.loc[:, "band"] = 1 + df.loc[:, "color"] = color_hex + + dataset = Dataset(src_without_color_table) + dataset._set_color_table(df, overwrite=True) + + color_table = dataset.raster.GetRasterBand(1).GetColorTable() + assert color_table is not None, "the color table should not be None" + assert color_table.GetCount() == 10, "the color table should have 5 colors" + colors = [color_table.GetColorEntry(i) for i in range(color_table.GetCount())] + assert colors == [ + (0, 0, 0, 0), + (112, 153, 89, 255), + (0, 0, 0, 0), + (242, 238, 162, 255), + (0, 0, 0, 0), + (242, 206, 133, 255), + (0, 0, 0, 0), + (194, 140, 124, 255), + (0, 0, 0, 0), + (214, 193, 156, 255), + ] + # test the color_table property + dataset.color_table = df diff --git a/tests/feature/conftest.py b/tests/feature/conftest.py index 8529e32ed..e8bff92d5 100644 --- a/tests/feature/conftest.py +++ b/tests/feature/conftest.py @@ -34,6 +34,16 @@ def data_source(test_vector_path: str) -> DataSource: return ogr.Open(test_vector_path) +@pytest.fixture(scope="module") +def coello_gauges_ds() -> DataSource: + return ogr.Open("tests/data/coello-gauges.geojson") + + +@pytest.fixture(scope="module") +def coello_gauges_gdf() -> GeoDataFrame: + return gpd.read_file("tests/data/coello-gauges.geojson") + + @pytest.fixture(scope="module") def ds_geodataframe(test_vector_path: str) -> GeoDataFrame: return gpd.read_file(test_vector_path) diff --git a/tests/feature/test_feature.py b/tests/feature/test_feature.py index 9eef46d11..2f795d995 100644 --- a/tests/feature/test_feature.py +++ b/tests/feature/test_feature.py @@ -3,20 +3,80 @@ import numpy as np from geopandas.geodataframe import GeoDataFrame -from osgeo import ogr +from osgeo import gdal, ogr from osgeo.gdal import Dataset from osgeo.ogr import DataSource -from shapely.geometry.polygon import Polygon from shapely.geometry import Point +from shapely.geometry.polygon import Polygon -from pyramids.featurecollection import FeatureCollection from pyramids.dataset import Dataset +from pyramids.featurecollection import FeatureCollection class TestAttributes: - def test_bound(self, gdf: GeoDataFrame, gdf_bound: List): + def test_total_bound_gdf(self, gdf: GeoDataFrame, gdf_bound: List): + feature = FeatureCollection(gdf) + assert all(np.isclose(feature.total_bounds, gdf_bound, rtol=0.0001)) + + def test_total_bound_ds(self, data_source: DataSource, gdf_bound: List): + feature = FeatureCollection(data_source) + assert all(np.isclose(feature.total_bounds, gdf_bound, rtol=0.0001)) + + def test_pivot_point_gdf(self, gdf: GeoDataFrame, gdf_bound: List): feature = FeatureCollection(gdf) - assert feature.bounds == gdf_bound + point = feature.pivot_point + assert point == [gdf_bound[0], gdf_bound[-1]] + + def test_pivot_point_ds(self, data_source: DataSource, gdf_bound: List): + feature = FeatureCollection(data_source) + point = feature.pivot_point + assert point == [gdf_bound[0], gdf_bound[-1]] + + def test_layer_count_gdf(self, gdf: GeoDataFrame): + feature = FeatureCollection(gdf) + layer_count = feature.layers_count + assert layer_count is None + + def test_layer_count_ogr_ds(self, data_source: DataSource): + feature = FeatureCollection(data_source) + layer_count = feature.layers_count + assert layer_count == 1 + + def test_layer_names_ogr_ds(self, data_source: DataSource): + feature = FeatureCollection(data_source) + names = feature.layer_names + assert names == ["poligonized"] + + def test_columns_ds(self, coello_gauges_ds: DataSource): + feature = FeatureCollection(coello_gauges_ds) + columns = feature.column + assert columns == ["id", "x", "y", "geometry"] + + def test_columns_gdf(self, coello_gauges_gdf: GeoDataFrame): + feature = FeatureCollection(coello_gauges_gdf) + columns = feature.column + assert columns == ["id", "x", "y", "geometry"] + + def test_dtypes_gdf(self, coello_gauges_gdf: GeoDataFrame): + feature = FeatureCollection(coello_gauges_gdf) + dtypes = feature.dtypes + assert isinstance(dtypes, dict) + assert dtypes == { + "id": "int64", + "x": "float64", + "y": "float64", + "geometry": "geometry", + } + + def test_dtypes_ds(self, coello_gauges_ds: DataSource): + feature = FeatureCollection(coello_gauges_ds) + dtypes = feature.dtypes + assert isinstance(dtypes, dict) + assert dtypes == { + "id": "int32", + "x": "float64", + "y": "float64", + } class TestReadFile: @@ -92,7 +152,8 @@ def test_ds_to_gdf_inplace( def test_gdf_to_ds(self, ds_geodataframe: GeoDataFrame): vector = FeatureCollection(ds_geodataframe) ds = vector._gdf_to_ds() - assert isinstance(ds, DataSource) + assert isinstance(ds, FeatureCollection) + assert isinstance(ds.feature, DataSource) # assert ds.name == "memory" def test_gdf_to_ds_inplace(self, ds_geodataframe: GeoDataFrame): @@ -102,6 +163,20 @@ def test_gdf_to_ds_inplace(self, ds_geodataframe: GeoDataFrame): assert isinstance(vector.feature, DataSource) # assert vector.feature.name == "memory" + def test_gdf_to_ds_if_feature_is_already_ds( + self, data_source: DataSource, ds_geodataframe: GeoDataFrame + ): + vector = FeatureCollection(data_source) + ds = vector._gdf_to_ds() + assert isinstance(ds, FeatureCollection) + assert isinstance(ds.feature, DataSource) + + def test_gdf_to_gdal_ex(self, ds_geodataframe: GeoDataFrame): + vector = FeatureCollection(ds_geodataframe) + ds = vector._gdf_to_ds(gdal_dataset=True) + assert isinstance(ds, FeatureCollection) + assert isinstance(ds.feature, gdal.Dataset) + # def test_gdf_to_ds2( # self, ds_geodataframe: GeoDataFrame # ): @@ -142,64 +217,320 @@ def test_create_polygon_object( class TestCreatePoint: - def test_create_point_geometries(self, coordinates: List[Tuple[int, int]]): + def test_return_shapely_object(self, coordinates: List[Tuple[int, int]]): point_list = FeatureCollection.create_point(coordinates) assert isinstance(point_list, list) assert len(point_list) == len(coordinates) + def test_return_featurecollection(self, coordinates: List[Tuple[int, int]]): + point_fc = FeatureCollection.create_point(coordinates, epsg=4326) + assert isinstance(point_fc, FeatureCollection) + assert isinstance(point_fc.feature, GeoDataFrame) + assert len(point_fc.feature["geometry"]) == len(coordinates) + assert point_fc.epsg == 4326 -class TestToRaster: - def test_with_raster_obj( - self, - vector_mask_gdf: GeoDataFrame, - raster_to_df_path: str, - ): - """Geodataframe input polygon. - - The inputs to the function are in disk, - - The output will be returned as gdal.Datacube. +class TestToDataset: + """Test Convert feature collection into dataset""" - Parameters - ---------- - raster_to_df_path + class TestSingleColumnVector: + """ + Descriptions + ------------ + - Test using the dataset parameter + - The vector we want to convert into raster has one column (which means the returned raster should have + one band) + + Tests + ----- + - test_single_band_dataset_parameter: + The dataset parameter has one band """ - dataset = Dataset.read_file(raster_to_df_path) - vector = FeatureCollection(vector_mask_gdf) - src = vector.to_dataset(dataset=dataset) - assert src.epsg == vector_mask_gdf.crs.to_epsg() - - xmin, _, _, ymax, _, _ = dataset.geotransform - assert src.geotransform[0] == xmin - assert src.geotransform[3] == ymax - assert src.cell_size == 4000 - arr = src.read_array() - values = arr[arr[:, :] == 1.0] - assert values.shape[0] == 16 - - def test_with_cell_size_inout( - self, - vector_mask_gdf: GeoDataFrame, - ): - """Geodataframe input polygon. - - The inputs to the function are in disk, - - The output will be returned as gdal.Datacube. + def test_single_band_dataset_parameter( + self, + polygon_corner_coello_gdf: GeoDataFrame, + raster_1band_coello_path: str, + ): + """ + Description + ----------- + - The dataset parameter has one band. + - The vector itself has one column + + Parameters + ---------- + - use the dataset parameter is used in the conversion not the cell_size. + - the column_name parameter is None (the default value) so the function should convert all the + columns (one column) into bands + + - both the vector and the raster shares the same pivot point, the vector locates in the top left + corner of the raster, the vector covers only the top left corner, and not the whole raster. + + Returns + ------- + - The resulted raster should have 3 bands full of 1, 2, 3 respectively. + - the array of each band in the new dataset will have 4 cells. + - the cell size of the new dataset is 8000. + """ + dataset = Dataset.read_file(raster_1band_coello_path) + vector = FeatureCollection(polygon_corner_coello_gdf) + src = vector.to_dataset(dataset=dataset) + assert src.epsg == polygon_corner_coello_gdf.crs.to_epsg() + + xmin, _, _, ymax, _, _ = dataset.geotransform + assert src.geotransform[0] == xmin + assert src.geotransform[3] == ymax + assert src.cell_size == 4000 + arr = src.read_array() + # filter the array based on the no_data_value to check if it is set correctly. + values = arr[~np.isclose(arr, src.no_data_value[0])] + assert values.shape[0] == 16 + assert values.mean() == vector.feature["fid"].values[0] + + # def test_dataset_parameter_error_case( + # self, era5_image: gdal.Dataset, era5_mask: GeoDataFrame + # ): + # """ + # This case does not give the right result when we use the dataset parameter + # """ + # dataset = Dataset(era5_image) + # vector = FeatureCollection(era5_mask) + # src = vector.to_dataset(cell_size=0.080) #dataset=dataset + # # src.to_file("ssss.tif") + + class TestMultiColumnVector: + def test_single_band_dataset_parameter_one_column( + self, + polygon_corner_coello_gdf: GeoDataFrame, + raster_1band_coello_path: str, + ): + """Geodataframe input polygon. + + test convert a vector to a raster using a dataset as a parameter + - both the vector and the raster shares the same pivot point, the vector locates in the top left + corner of the raster, the vector covers only the top left corner, and not the whole raster. + - the raster is a single band. + - use columne_name parameter to tell the method which column you want to take the value from. + """ + dataset = Dataset.read_file(raster_1band_coello_path) + new_column_name = "column_2" + new_column_value = 2 + polygon_corner_coello_gdf[new_column_name] = new_column_value + vector = FeatureCollection(polygon_corner_coello_gdf) + src = vector.to_dataset(dataset=dataset, column_name=new_column_name) + assert src.epsg == polygon_corner_coello_gdf.crs.to_epsg() + + xmin, _, _, ymax, _, _ = dataset.geotransform + assert src.geotransform[0] == xmin + assert src.geotransform[3] == ymax + assert src.cell_size == 4000 + arr = src.read_array() + # filter the array based on the no_data_value to check if it is set correctly. + values = arr[~np.isclose(arr, src.no_data_value[0])] + assert values.shape[0] == 16 + assert values.mean() == new_column_value + + def test_single_band_dataset_parameter_multiple_columns( + self, + polygon_corner_coello_gdf: GeoDataFrame, + raster_1band_coello_path: str, + ): + """Geodataframe input polygon. + + test convert a vector to a raster using a dataset as a parameter + - both the vector and the raster shares the same pivot point, the vector locates in the top left + corner of the raster, the vector covers only the top left corner, and not the whole raster. + - the raster is a single band. + - use columne_name parameter to tell the method which column you want to take the value from. + """ + dataset = Dataset.read_file(raster_1band_coello_path) + polygon_corner_coello_gdf["column_2"] = 2 + polygon_corner_coello_gdf["column_3"] = 3 + vector = FeatureCollection(polygon_corner_coello_gdf) + src = vector.to_dataset( + dataset=dataset, column_name=["column_2", "column_3"] + ) + assert src.epsg == polygon_corner_coello_gdf.crs.to_epsg() + + xmin, _, _, ymax, _, _ = dataset.geotransform + assert src.geotransform[0] == xmin + assert src.geotransform[3] == ymax + assert src.cell_size == 4000 + arr = src.read_array() + assert arr.shape == (2, 13, 14) + # filter the array based on the no_data_value to check if it is set correctly. + band_1_values = arr[0, ~np.isclose(arr[0, :, :], src.no_data_value[0])] + band_2_values = arr[1, ~np.isclose(arr[1, :, :], src.no_data_value[1])] + + assert band_1_values.shape[0] == 16 + assert band_2_values.shape[0] == 16 + assert band_1_values.mean() == 2 + assert band_2_values.mean() == 3 + + def test_single_band_dataset_parameter_none_column_name( + self, + polygon_corner_coello_gdf: GeoDataFrame, + raster_1band_coello_path: str, + ): + """Geodataframe input polygon. + + test convert a vector to a raster using a dataset as a parameter + - both the vector and the raster shares the same pivot point, the vector locates in the top left + corner of the raster, the vector covers only the top left corner, and not the whole raster. + - the raster is a single band. + - use columne_name parameter to tell the method which column you want to take the value from. + - The top left corner of both the raster and the polygon are very close to each other but not identical. + + Parameters + ---------- + raster_to_df_path + """ + dataset = Dataset.read_file(raster_1band_coello_path) + polygon_corner_coello_gdf["column_2"] = 2 + polygon_corner_coello_gdf["column_3"] = 3 + + vector = FeatureCollection(polygon_corner_coello_gdf) + src = vector.to_dataset(dataset=dataset, column_name=None) + assert src.epsg == polygon_corner_coello_gdf.crs.to_epsg() + + xmin, _, _, ymax, _, _ = dataset.geotransform + assert src.geotransform[0] == xmin + assert src.geotransform[3] == ymax + assert src.cell_size == 4000 + arr = src.read_array() + assert arr.shape == (3, 13, 14) + band_1_values = arr[0, arr[0, :, :] == 1] + band_2_values = arr[1, arr[1, :, :] == 2] + band_3_values = arr[2, arr[2, :, :] == 3] + assert band_1_values.shape[0] == 16 + assert band_2_values.shape[0] == 16 + assert band_3_values.shape[0] == 16 + + class TestCellSize: + """ + Test converting feature collection into dataset using cell size parameter. - Parameters - ---------- - vector_mask_gdf + Tests + ----- + test_none_column_name: + multi columns, very course resolution (8000) """ - vector = FeatureCollection(vector_mask_gdf) - src = vector.to_dataset(cell_size=200) - assert src.epsg == vector_mask_gdf.crs.to_epsg() - xmin, _, _, ymax = vector_mask_gdf.bounds.values[0] - assert src.geotransform[0] == xmin - assert src.geotransform[3] == ymax - assert src.cell_size == 200 - arr = src.read_array() - # assert src.no_data_value[0] == 0.0 - values = arr[arr[:, :] == 1.0] - assert values.shape[0] == 6400 + + def test_none_column_name( + self, + polygon_corner_coello_gdf: GeoDataFrame, + ): + """ + Description + ----------- + - Add more columns to the the one geometry vector to create multi-band raster. + + Parameters + ---------- + - use the cell_size parameter in the conversion not the dataset. + - the column_name parameter is None (the default value) so the function should convert all the columns in + + Returns + ------- + - The resulted raster should have 3 bands full of 1, 2, 3 respectively. + - the array of each band in the new dataset will have 4 cells. + - the cell size of the new dataset is 8000. + """ + polygon_corner_coello_gdf["column_2"] = 2 + polygon_corner_coello_gdf["column_3"] = 3 + required_cell_size = 8000 + + vector = FeatureCollection(polygon_corner_coello_gdf) + src = vector.to_dataset(cell_size=required_cell_size, column_name=None) + assert src.epsg == polygon_corner_coello_gdf.crs.to_epsg() + + xmin, ymax = vector.pivot_point + assert src.geotransform[0] == xmin + assert src.geotransform[3] == ymax + assert src.cell_size == required_cell_size + arr = src.read_array() + assert arr.shape == (3, 2, 2) + band_1_values = arr[0, arr[0, :, :] == 1] + band_2_values = arr[1, arr[1, :, :] == 2] + band_3_values = arr[2, arr[2, :, :] == 3] + assert band_1_values.shape[0] == 4 + assert band_2_values.shape[0] == 4 + assert band_3_values.shape[0] == 4 + + def test_one_column( + self, + polygon_corner_coello_gdf: GeoDataFrame, + ): + """ + Description + ----------- + - The vector has one column + + Parameters + ---------- + - use the cell_size parameter in the conversion not the dataset. + - the column_name parameter is None (the default value) so the function should convert all the columns( + one column) + + Returns + ------- + - The resulted raster should have 1 bands full of 1. + - the array of each band in the new dataset will have 6400 cells. + - the cell size of the new dataset is 200. + """ + vector = FeatureCollection(polygon_corner_coello_gdf) + src = vector.to_dataset(cell_size=200) + assert src.epsg == polygon_corner_coello_gdf.crs.to_epsg() + xmin, _, _, ymax = polygon_corner_coello_gdf.bounds.values[0] + assert src.geotransform[0] == xmin + assert src.geotransform[3] == ymax + assert src.cell_size == 200 + arr = src.read_array() + # assert src.no_data_value[0] == 0.0 + values = arr[arr[:, :] == 1.0] + assert values.shape[0] == 6400 + + def test_multi_polygon( + self, + polygons_coello_gdf: GeoDataFrame, + ): + """ + Description + ----------- + - Add more columns to the the one geometry vector to create multi-band raster. + + Parameters + ---------- + - use the cell_size parameter in the conversion not the dataset. + - the column_name parameter is None (the default value) so the function should convert all the columns in + + Returns + ------- + - The resulted raster should have 3 bands full of 1, 2, 3 respectively. + - the array of each band in the new dataset will have 4 cells. + - the cell size of the new dataset is 4000. + """ + required_cell_size = 4000 + + vector = FeatureCollection(polygons_coello_gdf) + src = vector.to_dataset(cell_size=required_cell_size, column_name=None) + assert src.epsg == polygons_coello_gdf.crs.to_epsg() + + xmin, ymax = vector.pivot_point + assert src.geotransform[0] == xmin + assert src.geotransform[3] == ymax + assert src.cell_size == required_cell_size + arr = src.read_array() + assert arr.shape == (3, 13, 13) + band_0_values_1 = arr[0, arr[0, :, :] == 1] + band_0_values_2 = arr[0, arr[0, :, :] == 2] + band_0_values_3 = arr[0, arr[0, :, :] == 3] + band_0_values_4 = arr[0, arr[0, :, :] == 4] + assert band_0_values_1.shape[0] == 20 + assert band_0_values_2.shape[0] == 27 + assert band_0_values_3.shape[0] == 21 + assert band_0_values_4.shape[0] == 24 class Test_multi_geom_handler: diff --git a/tests/test_utils.py b/tests/test_utils.py index 8b34ceeeb..e3d2bbf30 100644 --- a/tests/test_utils.py +++ b/tests/test_utils.py @@ -1,13 +1,47 @@ import numpy as np -from osgeo import gdal +from osgeo import gdal, ogr from pyramids._errors import DriverNotExistError -from pyramids._utils import numpy_to_gdal_dtype, Catalog +from pyramids._utils import ( + numpy_to_gdal_dtype, + gdal_to_numpy_dtype, + gdal_to_ogr_dtype, + Catalog, + ogr_ds_togdal_dataset, + ogr_to_numpy_dtype, +) def test_numpy_to_gdal_dtype(arr: np.ndarray): + # test with array input gdal_type = numpy_to_gdal_dtype(arr) assert gdal_type is gdal.GDT_Float32 + # test with a dtye input + gdal_type = numpy_to_gdal_dtype(arr.dtype) + assert gdal_type is gdal.GDT_Float32 + + +def test_gdal_to_numpy_dtype(): + assert gdal_to_numpy_dtype(6) == "float32" + assert gdal_to_numpy_dtype(7) == "float64" + assert gdal_to_numpy_dtype(2) == "uint16" + try: + gdal_to_numpy_dtype(20) + except ValueError: + pass + + +def test_gdal_to_ogr_dtype(test_image: gdal.Dataset, src: gdal.Dataset): + assert gdal_to_ogr_dtype(test_image) == 0 + assert gdal_to_ogr_dtype(src) == 2 + + +def test_ogr_to_numpy_dtype(): + assert ogr_to_numpy_dtype(0) == np.int32 + try: + ogr_to_numpy_dtype(1) + except ValueError: + pass class TestCatalog: @@ -48,3 +82,8 @@ def test_get_driver_name(self): catalog = Catalog() name = catalog.get_driver_name("AAIGrid") assert name == "ascii" + + +def test_ogr_ds_togdal_dataset(data_source: ogr.DataSource): + gdal_ds = ogr_ds_togdal_dataset(data_source) + assert isinstance(gdal_ds, gdal.Dataset)

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