diff --git a/0.5.1/.buildinfo b/0.5.1/.buildinfo deleted file mode 100644 index 7d1d6bce..00000000 --- a/0.5.1/.buildinfo +++ /dev/null @@ -1,4 +0,0 @@ -# Sphinx build info version 1 -# This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done. -config: 8c6cd37f9281cd48972238c0f5dfb631 -tags: 645f666f9bcd5a90fca523b33c5a78b7 diff --git a/0.5.1/.doctrees/environment.pickle b/0.5.1/.doctrees/environment.pickle deleted file mode 100644 index 0ae754c3..00000000 Binary files a/0.5.1/.doctrees/environment.pickle and /dev/null differ diff --git a/0.5.1/.doctrees/geocube.doctree b/0.5.1/.doctrees/geocube.doctree deleted file mode 100644 index 62b8addb..00000000 Binary files a/0.5.1/.doctrees/geocube.doctree and /dev/null differ diff --git a/0.5.1/.doctrees/history.doctree b/0.5.1/.doctrees/history.doctree deleted file mode 100644 index 3778749c..00000000 Binary files a/0.5.1/.doctrees/history.doctree and /dev/null differ diff --git a/0.5.1/_modules/geocube/_show_versions.html b/0.5.1/_modules/geocube/_show_versions.html deleted file mode 100644 index 93f13dba..00000000 --- a/0.5.1/_modules/geocube/_show_versions.html +++ /dev/null @@ -1,216 +0,0 @@ - - - - - - geocube._show_versions — geocube 0.5.1 documentation - - - - - - - - - - - - - - - - - - -
- - -
- -
-
-
- -
-
-
-
- -

Source code for geocube._show_versions

-"""
-Utility methods to print system info for debugging
-
-adapted from :func:`sklearn.utils._show_versions`
-which was adapted from :func:`pandas.show_versions`
-"""
-# pylint: disable=import-outside-toplevel
-import importlib.metadata
-import platform
-import sys
-
-
-def _get_sys_info():
-    """System information
-    Return
-    ------
-    sys_info : dict
-        system and Python version information
-    """
-    blob = [
-        ("python", sys.version.replace("\n", " ")),
-        ("executable", sys.executable),
-        ("machine", platform.platform()),
-    ]
-
-    return dict(blob)
-
-
-def _get_gdal_info():
-    """Get the GDAL dependency information.
-
-    Returns
-    -------
-    proj_info: dict
-        system GDAL information
-    """
-    import fiona
-    import rasterio
-
-    blob = [
-        ("fiona", importlib.metadata.version("fiona")),
-        ("GDAL[fiona]", fiona.__gdal_version__),
-        ("rasterio", importlib.metadata.version("rasterio")),
-        ("GDAL[rasterio]", rasterio.__gdal_version__),
-    ]
-
-    return dict(blob)
-
-
-def _get_deps_info():
-    """Overview of the installed version of dependencies
-    Returns
-    -------
-    deps_info: dict
-        version information on relevant Python libraries
-    """
-    deps = [
-        "appdirs",
-        "click",
-        "geopandas",
-        "odc_geo",
-        "rioxarray",
-        "pyproj",
-        "xarray",
-    ]
-
-    def get_version(module):
-        try:
-            return importlib.metadata.version(module)
-        except importlib.metadata.PackageNotFoundError:
-            return None
-
-    return {dep: get_version(dep) for dep in deps}
-
-
-def _print_info_dict(info_dict):
-    """Print the information dictionary"""
-    for key, stat in info_dict.items():
-        print(f"{key:>14}: {stat}")
-
-
-

-[docs]
-def show_versions():
-    """
-    .. versionadded:: 0.0.12
-
-    Print useful debugging information
-
-    Example
-    -------
-    > python -c "import geocube; geocube.show_versions()"
-
-    """
-    print(f"geocube v{importlib.metadata.version('geocube')}\n")
-    print("GDAL deps:")
-    _print_info_dict(_get_gdal_info())
-    print("\nPython deps:")
-    _print_info_dict(_get_deps_info())
-    print("\nSystem:")
-    _print_info_dict(_get_sys_info())

-
-
- -
-
- -
-
-
-
- - - - \ No newline at end of file diff --git a/0.5.1/_modules/geocube/api/core.html b/0.5.1/_modules/geocube/api/core.html deleted file mode 100644 index c9a0bf0d..00000000 --- a/0.5.1/_modules/geocube/api/core.html +++ /dev/null @@ -1,227 +0,0 @@ - - - - - - geocube.api.core — geocube 0.5.1 documentation - - - - - - - - - - - - - - - - - - -
- - -
- -
-
-
- -
-
-
-
- -

Source code for geocube.api.core

-"""
-GeoCube client core functionality
-"""
-import os
-from collections.abc import Iterable
-from typing import Any, Callable, Literal, Optional, Union
-
-import geopandas
-import numpy
-import shapely
-import xarray
-from odc.geo.geom import Geometry
-
-from geocube.geo_utils.geobox import GeoBoxMaker
-from geocube.vector_to_cube import VectorToCube
-
-
-

-[docs]
-def make_geocube(
-    vector_data: Union[str, os.PathLike, geopandas.GeoDataFrame],
-    measurements: Optional[list[str]] = None,
-    datetime_measurements: Optional[list[str]] = None,
-    output_crs: Any = None,
-    resolution: Optional[Union[float, Iterable[float]]] = None,
-    align: Optional[tuple[float, float]] = None,
-    geom: Optional[
-        Union[str, dict, shapely.geometry.base.BaseGeometry, Geometry]
-    ] = None,
-    like: Optional[Union[xarray.Dataset, xarray.DataArray]] = None,
-    fill: float = numpy.nan,
-    group_by: Optional[str] = None,
-    interpolate_na_method: Optional[Literal["linear", "nearest", "cubic"]] = None,
-    categorical_enums: Optional[dict[str, list]] = None,
-    rasterize_function: Optional[Callable[..., Optional[numpy.typing.NDArray]]] = None,
-) -> xarray.Dataset:
-    """
-    Rasterize vector data into an ``xarray`` object.  Each attribute will be a data
-    variable in the :class:`xarray.Dataset`.
-
-    Parameters
-    ----------
-    vector_data: str, path-like object or :obj:`geopandas.GeoDataFrame`
-        A file path to an OGR supported source or GeoDataFrame containing
-        the vector data.
-    measurements: list(str), optional
-        Attributes name or list of names to be included. If a list is specified,
-        the attributes will be returned in the order requested.
-        By default all available attributes are included.
-    datetime_measurements: list(str), optional
-        Attributes that are temporal in nature and should be converted to the datetime
-        format. These are only included if listed in 'measurements'.
-    output_crs: Any, optional
-        The CRS of the returned data. Can be anything accepted by :obj:`pyproj.CRS`.
-        If no CRS is supplied, the CRS of the stored data is used.
-    resolution: Union[float, Iterable[float]], optional
-        A tuple of the spatial resolution of the returned data (Y, X).
-        This includes the direction (as indicated by a positive or negative number).
-        Typically when using most CRSs, the first number would be negative.
-    align: tuple[float, float], optional
-        Load data such that point 'align' lies on the pixel boundary.
-        Units are in the co-ordinate space of the output CRS.
-        Default is (0,0)
-    geom: Union[str, dict, shapely.geometry.base.BaseGeometry, odc.geo.geom.Geometry], optional
-        A GeoJSON string for the bounding box of the data used to construct the
-        grid. It defaults to EPSG:4326 if a CRS is not provided.
-        Example of adding CRS::
-
-            {"type": "Polygon", "crs": {"properties": {"name": "EPSG:3857"}}}
-
-    like: :obj:`xarray.Dataset`, optional
-        Uses the output of a previous ``load()`` to form the basis of a request
-        for another product.
-        E.g.::
-
-            gcds = make_geocube(vector_data='my_vector.geopackage', like=other_gcds)
-
-    fill: float, optional
-        The value to fill in the grid with for nodata. Default is NaN.
-    group_by: str, optional
-        When specified, perform basic combining/reducing of the data on this column.
-    interpolate_na_method:  {'linear', 'nearest', 'cubic'}, optional
-        This is the method for interpolation to use to fill in the nodata with
-        :meth:`scipy.interpolate.griddata`.
-    categorical_enums: dict, optional
-        A dictionary of all categories for the table columns containing categorical
-        data. The categories will be made unique and sorted if they are not already.
-        E.g. {'column_name': ['a', 'b'], 'other_column': ['c', 'd']}
-    rasterize_function: function, optional
-        Function to rasterize geometries. Other options are available in
-        `geocube.rasterize`. Default is :func:`geocube.rasterize.rasterize_image`.
-
-    Returns
-    --------
-    :obj:`xarray.Dataset`:
-        Requested data in a :obj:`xarray.Dataset`.
-
-    """
-    geobox_maker = GeoBoxMaker(output_crs, resolution, align, geom, like)
-
-    return VectorToCube(
-        vector_data=vector_data,
-        geobox_maker=geobox_maker,
-        fill=fill,
-        categorical_enums=categorical_enums,
-        measurements=measurements,
-        group_by=group_by,
-        datetime_measurements=datetime_measurements,
-    ).make_geocube(
-        interpolate_na_method=interpolate_na_method,
-        rasterize_function=rasterize_function,
-    )

-
-
- -
-
- -
-
-
-
- - - - \ No newline at end of file diff --git a/0.5.1/_modules/geocube/exceptions.html b/0.5.1/_modules/geocube/exceptions.html deleted file mode 100644 index bb59deca..00000000 --- a/0.5.1/_modules/geocube/exceptions.html +++ /dev/null @@ -1,131 +0,0 @@ - - - - - - geocube.exceptions — geocube 0.5.1 documentation - - - - - - - - - - - - - - - - - - -
- - -
- -
-
-
- -
-
-
-
- -

Source code for geocube.exceptions

-"""
-This contains exceptions for GeoCube.
-"""
-
-
-

-[docs]
-class GeoCubeError(RuntimeError):
-    """Base GeoCube exception class."""

-
-
-
-

-[docs]
-class VectorDataError(GeoCubeError):
-    """This is for errors in the vector data passed into GeoCube."""

-
-
- -
-
- -
-
-
-
- - - - \ No newline at end of file diff --git a/0.5.1/_modules/geocube/rasterize.html b/0.5.1/_modules/geocube/rasterize.html deleted file mode 100644 index 8f224792..00000000 --- a/0.5.1/_modules/geocube/rasterize.html +++ /dev/null @@ -1,358 +0,0 @@ - - - - - - geocube.rasterize — geocube 0.5.1 documentation - - - - - - - - - - - - - - - - - - -
- - -
- -
-
-
- -
-
-
-
- -

Source code for geocube.rasterize

-"""
-This module contains tools for rasterizing vector data.
-"""
-from typing import Optional, Union
-
-import geopandas
-import numpy
-import odc.geo.geobox
-import pandas
-import rasterio
-import rasterio.features
-from numpy.typing import NDArray
-from packaging import version
-from rasterio.enums import MergeAlg
-from scipy.interpolate import Rbf, griddata
-
-_INT8_SUPPORTED = version.parse(rasterio.__gdal_version__) >= version.parse(
-    "3.7.0"
-) and version.parse(rasterio.__version__) >= version.parse("1.3.7")
-
-
-def _is_numeric(data_values: NDArray) -> bool:
-    """
-    Check if array data type is numeric.
-    """
-    return numpy.issubdtype(data_values.dtype.type, numpy.number)
-
-
-def _remove_missing_data(
-    data_values: NDArray,
-    geometry_array: geopandas.GeoSeries,
-) -> tuple[NDArray, geopandas.GeoSeries]:
-    """
-    Missing data causes issues with interpolation of point data
-    https://github.com/corteva/geocube/issues/9
-
-    This filters the data so those issues don't cause problems.
-    """
-    not_missing_data = ~pandas.isnull(data_values)
-    geometry_array = geometry_array[not_missing_data]
-    data_values = data_values[not_missing_data]
-    return data_values, geometry_array
-
-
-def _minimize_dtype(dtype: numpy.dtype, fill: float) -> numpy.dtype:
-    """
-    If int64, convert to float64:
-    https://github.com/OSGeo/gdal/issues/3325
-
-    Attempt to convert to float32 if fill is NaN and dtype is integer.
-    """
-    if numpy.issubdtype(dtype, numpy.integer):
-        if not _INT8_SUPPORTED and dtype.name == "int8":
-            # GDAL<3.7/rasterio<1.3.7 doesn't support int8
-            dtype = numpy.dtype("int16")
-        if numpy.isnan(fill):
-            dtype = (
-                numpy.dtype("float64") if dtype.itemsize > 2 else numpy.dtype("float32")  # type: ignore
-            )
-    elif not numpy.issubdtype(dtype, numpy.floating):
-        # default to float64 for non-integer/float types
-        dtype = numpy.dtype("float64")
-    return dtype
-
-
-

-[docs]
-def rasterize_image(
-    geometry_array: geopandas.GeoSeries,
-    data_values: Union[NDArray, pandas.arrays.IntegerArray],
-    geobox: odc.geo.geobox.GeoBox,
-    fill: float,
-    merge_alg: MergeAlg = MergeAlg.replace,
-    filter_nan: bool = False,
-    all_touched: bool = False,
-    **ignored_kwargs,
-) -> Optional[NDArray]:
-    """
-    Rasterize a list of shapes+values for a given GeoBox.
-
-    Parameters
-    -----------
-    geometry_array: geopandas.GeoSeries
-        A geometry array of points.
-    data_values: Union[NDArray, pandas.arrays.IntegerArray]
-        Data values associated with the list of geojson shapes
-    geobox: :obj:`odc.geo.geobox.GeoBox`
-        Transform of the resulting image.
-    fill: float
-        The value to fill in the grid with for nodata.
-    merge_alg: `rasterio.enums.MergeAlg`, optional
-        The algorithm for merging values into one cell. Default is `MergeAlg.replace`.
-    filter_nan: bool, optional
-        If True, will remove nodata values from the data before rasterization.
-        Default is False.
-    all_touched: bool, optional
-        Passed to rasterio.features.rasterize. If True, all pixels touched by
-        geometries will be burned in. If false, only pixels whose center is
-        within the polygon or that are selected by Bresenham’s line algorithm
-        will be burned in.
-    **ignored_kwargs:
-        These are there to be flexible with additional rasterization methods and
-        will be ignored.
-
-    Returns
-    -------
-    :obj:`numpy.ndarray` or None
-        The vector data in the rasterized format.
-
-    """
-    if not _is_numeric(data_values):
-        # only numbers can be rasterized
-        return None
-
-    if isinstance(data_values, pandas.arrays.IntegerArray):
-        data_values = data_values.to_numpy(
-            dtype=_minimize_dtype(data_values.dtype.numpy_dtype, fill),
-            na_value=fill,
-        )
-
-    if filter_nan:
-        data_values, geometry_array = _remove_missing_data(data_values, geometry_array)
-
-    image = rasterio.features.rasterize(
-        zip(geometry_array.values, data_values),
-        out_shape=(geobox.height, geobox.width),
-        transform=geobox.affine,
-        fill=fill,
-        all_touched=all_touched,
-        merge_alg=merge_alg,
-        dtype=_minimize_dtype(data_values.dtype, fill),
-    )
-    return image

-
-
-
-

-[docs]
-def rasterize_points_griddata(
-    geometry_array: geopandas.GeoSeries,
-    data_values: NDArray,
-    grid_coords: dict[str, NDArray],
-    fill: float,
-    method: str = "nearest",
-    rescale: bool = False,
-    filter_nan: bool = False,
-    **ignored_kwargs,
-) -> Optional[NDArray]:
-    """
-    This method uses scipy.interpolate.griddata to interpolate point data
-    to a grid.
-
-    Parameters
-    ----------
-    geometry_array: geopandas.GeoSeries
-        A geometry array of points.
-    data_values: list
-        Data values associated with the list of geojson shapes
-    grid_coords: dict
-        Output from `rioxarray.rioxarray.affine_to_coords`
-    fill: float
-        The value to fill in the grid with for nodata.
-    method: {'linear', 'nearest', 'cubic'}, optional
-        The method to use for interpolation in `scipy.interpolate.griddata`.
-    rescale: bool, optional
-        Rescale points to unit cube before performing interpolation. Default is false.
-    filter_nan: bool, optional
-        If True, will remove nodata values from the data before rasterization.
-        Default is False.
-    **ignored_kwargs:
-        These are there to be flexible with additional rasterization methods and
-        will be ignored.
-
-    Returns
-    -------
-    :class:`numpy.ndarray`: An interpolated :class:`numpy.ndarray`.
-
-    """
-    if not _is_numeric(data_values):
-        # only numbers can be rasterized
-        return None
-
-    if filter_nan:
-        data_values, geometry_array = _remove_missing_data(data_values, geometry_array)
-
-    return griddata(
-        points=(geometry_array.x, geometry_array.y),
-        values=data_values,
-        xi=tuple(numpy.meshgrid(grid_coords["x"], grid_coords["y"])),
-        method=method,
-        fill_value=fill,
-        rescale=rescale,
-    )

-
-
-
-

-[docs]
-def rasterize_points_radial(
-    geometry_array: geopandas.GeoSeries,
-    data_values: NDArray,
-    grid_coords: dict[str, NDArray],
-    method: str = "linear",
-    filter_nan=False,
-    **ignored_kwargs,
-) -> Optional[NDArray]:
-    """
-    This method uses scipy.interpolate.Rbf to interpolate point data
-    to a grid.
-
-    Parameters
-    ----------
-    geometry_array: geopandas.GeoSeries
-        A geometry array of points.
-    data_values: list
-        Data values associated with the list of geojson shapes
-    grid_coords: dict
-        Output from `rioxarray.rioxarray.affine_to_coords`
-    method: str, optional
-        The function to use for interpolation in `scipy.interpolate.Rbf`.
-        {'multiquadric', 'inverse', 'gaussian', 'linear',
-        'cubic', 'quintic', 'thin_plate'}
-    filter_nan: bool, optional
-        If True, will remove nodata values from the data before rasterization.
-        Default is False.
-    **ignored_kwargs:
-        These are there to be flexible with additional rasterization methods and
-        will be ignored.
-
-    Returns
-    -------
-    :class:`numpy.ndarray`: An interpolated :class:`numpy.ndarray`.
-
-    """
-    if not _is_numeric(data_values):
-        # only numbers can be rasterized
-        return None
-
-    if filter_nan:
-        data_values, geometry_array = _remove_missing_data(data_values, geometry_array)
-
-    interp = Rbf(geometry_array.x, geometry_array.y, data_values, function=method)
-    return interp(*numpy.meshgrid(grid_coords["x"], grid_coords["y"]))

-
-
- -
-
- -
-
-
-
- - - - \ No newline at end of file diff --git a/0.5.1/_modules/geocube/vector.html b/0.5.1/_modules/geocube/vector.html deleted file mode 100644 index 4b434f61..00000000 --- a/0.5.1/_modules/geocube/vector.html +++ /dev/null @@ -1,186 +0,0 @@ - - - - - - geocube.vector — geocube 0.5.1 documentation - - - - - - - - - - - - - - - - - - -
- - -
- -
-
-
- -
-
-
-
- -

Source code for geocube.vector

-"""
-Module for vector methods
-"""
-import warnings
-
-import geopandas
-import numpy
-import rasterio.features
-import rioxarray  # noqa: F401 pylint: disable=unused-import
-import shapely.geometry
-import xarray
-
-
-

-[docs]
-def vectorize(data_array: xarray.DataArray) -> geopandas.GeoDataFrame:
-    """
-    .. versionadded:: 0.4.0
-
-    Powered by: :func:`rasterio.features.shapes`
-
-    Convert 2D :class:`xarray.DataArray` into
-    a :class:`geopandas.GeoDataFrame`.
-
-    The ``nodata``,  ``CRS``, and ``transform`` of the :class:`xarray.DataArray`
-    are determined using ``rioxarray``.
-
-    Helpful references:
-
-    - https://corteva.github.io/rioxarray/stable/getting_started/crs_management.html
-    - https://corteva.github.io/rioxarray/stable/getting_started/nodata_management.html
-
-
-    Parameters
-    ----------
-    data_array: xarray.DataArray
-        Input 2D DataArray raster.
-
-    Returns
-    -------
-    geopandas.GeoDataFrame
-    """
-    # nodata mask
-    mask = None
-    if data_array.rio.nodata is not None:
-        if numpy.isnan(data_array.rio.nodata):
-            mask = ~data_array.isnull()
-        else:
-            mask = data_array != data_array.rio.nodata
-
-    # vectorize generator
-    vectorized_data = (
-        (value, shapely.geometry.shape(polygon))
-        for polygon, value in rasterio.features.shapes(
-            data_array,
-            transform=data_array.rio.transform(),
-            mask=mask,
-        )
-    )
-
-    if data_array.name:
-        name = data_array.name
-    else:
-        warnings.warn("The array has no name. Column name defaults to _data")
-        name = "_data"
-
-    return geopandas.GeoDataFrame(
-        vectorized_data,
-        columns=[name, "geometry"],
-        crs=data_array.rio.crs,
-    )

-
-
- -
-
- -
-
-
-
- - - - \ No newline at end of file diff --git a/0.5.1/_modules/index.html b/0.5.1/_modules/index.html deleted file mode 100644 index d53373b1..00000000 --- a/0.5.1/_modules/index.html +++ /dev/null @@ -1,118 +0,0 @@ - - - - - - Overview: module code — geocube 0.5.1 documentation - - - - - - - - - - - - - - - - - - -
- - -
- -
-
-
-
    -
    • -
  • Overview: module code
    • -
  • -
    • -
-
-
-
-
- -

All modules for which code is available

- - -
-
- -
-
-
-
- - - - \ No newline at end of file diff --git a/0.5.1/_sources/history.rst.txt b/0.5.1/_sources/history.rst.txt deleted file mode 100644 index f7ecee55..00000000 --- a/0.5.1/_sources/history.rst.txt +++ /dev/null @@ -1,131 +0,0 @@ -History -======= - -Latest ------- - -0.5.0 ------- -- BUG: Ignore categorical columns not in measurements (pull #161) -- ENH: Read subset of measurements for efficiency (pull #161) - -0.4.3 -------- -- DEP: Support Python 3.10+ (pull #156) -- BUG: Update minimize dtype for int64 & int8 support (issue #139) -- BUG: Support pandas IntegerArray (pull #158) - -0.4.2 -------- -- BUG: Fix conversion to datetime64[ns] for naive datetimes (pull #145) - -0.4.1 -------- -- BUG: Fix conversion to datetime64[ns] (pull #143) -- BUG: Fix :func:`geocube.vector.vectorize` when nodata is None (pull #142) - -0.4.0 -------- -- DEP: Drop Python 3.8 Support (issue #136) -- ENH: Add :func:`geocube.vector.vectorize` (issue #65) - -0.3.3 ------- -- BUG: Allow resolution in list format (pull #122) - -0.3.2 ------- -- MNT: corrections to setup.cfg; use "pytest"; add "make dist" target (pull #111) -- BUG: Write transform on output dataset (pull #112) - -0.3.1 ------- -- TYPE: Modified NDArray import for typing (pull #106) - -0.3.0 -------- -- BUG: Handle input CRS without EPSG (pull #105) -- TYPE: Added type hints (pull #101) - -0.2.0 -------- -- DEP: Replace datacube with odc-geo (issue #90) - -0.1.2 ------- -- BUG: Fix width and height order to the one used in rioxarray (issue #93) - -0.1.1 -------- -- DEP: Drop Python 3.7 Support (issue #86) -- BUG: Updated logic to skip rasterizing strings for numpy 1.22+ (issue #88) - -0.1.0 ------- -- ENH: Minimize dtype when possible in `geocube.rasterize.rasterize_image` (issue #72) -- REF: remove unnecessary shapely.geometry.mapping in rasterize_image (#80) -- ENH: `vector_data` to read path-like objects (pull #81) - -0.0.18 ------- -- DEP: Explicitly add scipy as dependency (pull #75) - -0.0.17 ------- -- DEP: Python 3.7+ (#67) -- REF: Write grid_mapping to encoding instead of attrs (#66) - -0.0.16 ------- -- BUG: Compatibility with rioxarray 0.3 (#57) - -0.0.15 ------- -- Address xarray & numpy deprecations (#43) - -0.0.14 ------- -- Add "all_touched" keyword argument to geocube.rasterize.rasterize_image (pull #40) - -0.0.13 ------- -- Address deprecation warnings from datacube and rioxarray (issue #29) - -0.0.12 ------- -- ENH: Added :func:`geocube.show_versions` and cli `geocube --show-versions` (pull #23) -- Add compatibility between datacube and geopandas CRS versions (pull #24) - -0.0.11 ------- -- Drop Python 3.5 Support (issue #12) -- ENH: Update to support geopandas with pyproj.CRS (pull #18) -- BUG: Update timestamp handling to ensure correct format for dtype (pull #18) - -0.0.10 ------- -- Added filter_nan kwarg to filter out missing data when rasterizing (issue #9) -- Change default fill value to NaN when rasterizing (pull #11) - -0.0.9 ------ -- Added `rescale` kwarg to `geocube.rasterize.rasterize_points_griddata`. (pull #8) -- Removed `fillna(numpy.nan)` in `geocube.geo_utils.geobox.load_vector_data` as not necessary - and for compatibility with `geopandas==0.6.0`. (pull #8) - -0.0.8 ------ -- Add merge algorithm option for rasterization (issue #5) -- Drop Python 2 support (issue #6) - -0.0.7 ------ -- Remove geocube pin (pull #4) - -0.0.6 ------ -- Added additional methods for resampling points to a 2D grid (pull #3) - -0.0.5 ------ -- Fix converting to another projection to ensure bounds are correctly accounted for (pull #2) diff --git a/0.5.1/_static/basic.css b/0.5.1/_static/basic.css deleted file mode 100644 index 30fee9d0..00000000 --- a/0.5.1/_static/basic.css +++ /dev/null @@ -1,925 +0,0 @@ -/* - * basic.css - * ~~~~~~~~~ - * - * Sphinx stylesheet -- basic theme. - * - * :copyright: Copyright 2007-2023 by the Sphinx team, see AUTHORS. - * :license: BSD, see LICENSE for details. - * - */ - -/* -- main layout ----------------------------------------------------------- */ - -div.clearer { - clear: both; -} - -div.section::after { - display: block; - content: ''; - clear: left; -} - -/* -- relbar ---------------------------------------------------------------- */ - -div.related { - width: 100%; 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- const toggledRows = document.querySelectorAll(`tr.cg-${idNumber}`); - if (el.src.substr(-9) === "minus.png") { - el.src = `${el.src.substr(0, el.src.length - 9)}plus.png`; - toggledRows.forEach((el) => (el.style.display = "none")); - } else { - el.src = `${el.src.substr(0, el.src.length - 8)}minus.png`; - toggledRows.forEach((el) => (el.style.display = "")); - } - }; - - const togglerElements = document.querySelectorAll("img.toggler"); - togglerElements.forEach((el) => - el.addEventListener("click", (event) => toggler(event.currentTarget)) - ); - togglerElements.forEach((el) => (el.style.display = "")); - if (DOCUMENTATION_OPTIONS.COLLAPSE_INDEX) togglerElements.forEach(toggler); - }, - - initOnKeyListeners: () => { - // only install a listener if it is really needed - if ( - !DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS && - !DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS - ) - return; - - document.addEventListener("keydown", (event) => { - // bail for input elements - if (BLACKLISTED_KEY_CONTROL_ELEMENTS.has(document.activeElement.tagName)) return; - // bail with special keys - if (event.altKey || event.ctrlKey || event.metaKey) return; - - if (!event.shiftKey) { - switch (event.key) { - case "ArrowLeft": - if (!DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) break; - - const prevLink = document.querySelector('link[rel="prev"]'); - if (prevLink && prevLink.href) { - window.location.href = prevLink.href; - event.preventDefault(); - } - break; - case "ArrowRight": - if (!DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) break; - - const nextLink = document.querySelector('link[rel="next"]'); - if (nextLink && nextLink.href) { - window.location.href = nextLink.href; - event.preventDefault(); - } - break; - } - } - - // some keyboard layouts may need Shift to get / - switch (event.key) { - case "/": - if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) break; - Documentation.focusSearchBar(); - event.preventDefault(); - } - }); - }, -}; - -// quick alias for translations -const _ = Documentation.gettext; - -_ready(Documentation.init); diff --git a/0.5.1/_static/documentation_options.js b/0.5.1/_static/documentation_options.js deleted file mode 100644 index 492efc9e..00000000 --- a/0.5.1/_static/documentation_options.js +++ /dev/null @@ -1,13 +0,0 @@ -const DOCUMENTATION_OPTIONS = { - VERSION: '0.5.1', - LANGUAGE: 'en', - COLLAPSE_INDEX: false, - BUILDER: 'html', - FILE_SUFFIX: '.html', - LINK_SUFFIX: '.html', - HAS_SOURCE: true, - SOURCELINK_SUFFIX: '.txt', - NAVIGATION_WITH_KEYS: false, - SHOW_SEARCH_SUMMARY: true, - ENABLE_SEARCH_SHORTCUTS: true, -}; \ No newline at end of file diff --git a/0.5.1/_static/language_data.js b/0.5.1/_static/language_data.js deleted file mode 100644 index 250f5665..00000000 --- a/0.5.1/_static/language_data.js +++ /dev/null @@ -1,199 +0,0 @@ -/* - * language_data.js - * ~~~~~~~~~~~~~~~~ - * - * This script contains the language-specific data used by searchtools.js, - * namely the list of stopwords, stemmer, scorer and splitter. - * - * :copyright: Copyright 2007-2023 by the Sphinx team, see AUTHORS. - * :license: BSD, see LICENSE for details. - * - */ - -var stopwords = ["a", "and", "are", "as", "at", "be", "but", "by", "for", "if", "in", "into", "is", "it", "near", "no", "not", "of", "on", "or", "such", "that", "the", "their", "then", "there", "these", "they", "this", "to", "was", "will", "with"]; - - -/* Non-minified version is copied as a separate JS file, is available */ - -/** - * Porter Stemmer - */ -var Stemmer = function() { - - var step2list = { - ational: 'ate', - tional: 'tion', - enci: 'ence', - anci: 'ance', - izer: 'ize', - bli: 'ble', - alli: 'al', - entli: 'ent', - eli: 'e', - ousli: 'ous', - ization: 'ize', - ation: 'ate', - ator: 'ate', - alism: 'al', - iveness: 'ive', - fulness: 'ful', - ousness: 'ous', - aliti: 'al', - iviti: 'ive', - biliti: 'ble', - logi: 'log' - }; - - var step3list = { - icate: 'ic', - ative: '', - alize: 'al', - iciti: 'ic', - ical: 'ic', - ful: '', - ness: '' - }; - - var c = "[^aeiou]"; // consonant - var v = "[aeiouy]"; // vowel - var C = c + "[^aeiouy]*"; // consonant sequence - var V = v + "[aeiou]*"; // vowel sequence - - var mgr0 = "^(" + C + ")?" + V + C; // [C]VC... is m>0 - var meq1 = "^(" + C + ")?" + V + C + "(" + V + ")?$"; // [C]VC[V] is m=1 - var mgr1 = "^(" + C + ")?" + V + C + V + C; // [C]VCVC... is m>1 - var s_v = "^(" + C + ")?" + v; // vowel in stem - - this.stemWord = function (w) { - var stem; - var suffix; - var firstch; - var origword = w; - - if (w.length < 3) - return w; - - var re; - var re2; - var re3; - var re4; - - firstch = w.substr(0,1); - if (firstch == "y") - w = firstch.toUpperCase() + w.substr(1); - - // Step 1a - re = /^(.+?)(ss|i)es$/; - re2 = /^(.+?)([^s])s$/; - - if (re.test(w)) - w = w.replace(re,"$1$2"); - else if (re2.test(w)) - w = w.replace(re2,"$1$2"); - - // Step 1b - re = /^(.+?)eed$/; - re2 = /^(.+?)(ed|ing)$/; - if (re.test(w)) { - var fp = re.exec(w); - re = new RegExp(mgr0); - if (re.test(fp[1])) { - re = /.$/; - w = w.replace(re,""); - } - } - else if (re2.test(w)) { - var fp = re2.exec(w); - stem = fp[1]; - re2 = new RegExp(s_v); - if (re2.test(stem)) { - w = stem; - re2 = /(at|bl|iz)$/; - re3 = new RegExp("([^aeiouylsz])\\1$"); - re4 = new RegExp("^" + C + v + "[^aeiouwxy]$"); - if (re2.test(w)) - w = w + "e"; - else if (re3.test(w)) { - re = /.$/; - w = w.replace(re,""); - } - else if (re4.test(w)) - w = w + "e"; - } - } - - // Step 1c - re = /^(.+?)y$/; - if (re.test(w)) { - var fp = re.exec(w); - stem = fp[1]; - re = new RegExp(s_v); - if (re.test(stem)) - w = stem + "i"; - } - - // Step 2 - re = /^(.+?)(ational|tional|enci|anci|izer|bli|alli|entli|eli|ousli|ization|ation|ator|alism|iveness|fulness|ousness|aliti|iviti|biliti|logi)$/; - if (re.test(w)) { - var fp = re.exec(w); - stem = fp[1]; - suffix = fp[2]; - re = new RegExp(mgr0); - if (re.test(stem)) - w = stem + step2list[suffix]; - } - - // Step 3 - re = /^(.+?)(icate|ative|alize|iciti|ical|ful|ness)$/; - if (re.test(w)) { - var fp = re.exec(w); - stem = fp[1]; - suffix = fp[2]; - re = new RegExp(mgr0); - if (re.test(stem)) - w = stem + step3list[suffix]; - } - - // Step 4 - re = /^(.+?)(al|ance|ence|er|ic|able|ible|ant|ement|ment|ent|ou|ism|ate|iti|ous|ive|ize)$/; - re2 = /^(.+?)(s|t)(ion)$/; - if (re.test(w)) { - var fp = re.exec(w); - stem = fp[1]; - re = new RegExp(mgr1); - if (re.test(stem)) - w = stem; - } - else if (re2.test(w)) { - var fp = re2.exec(w); - stem = fp[1] + fp[2]; - re2 = new RegExp(mgr1); - if (re2.test(stem)) - w = stem; - } - - // Step 5 - re = /^(.+?)e$/; - if (re.test(w)) { - var fp = re.exec(w); - stem = fp[1]; - re = new RegExp(mgr1); - re2 = new RegExp(meq1); - re3 = new RegExp("^" + C + v + "[^aeiouwxy]$"); - if (re.test(stem) || (re2.test(stem) && !(re3.test(stem)))) - w = stem; - } - re = /ll$/; - re2 = new RegExp(mgr1); - if (re.test(w) && re2.test(w)) { - re = /.$/; - w = w.replace(re,""); - } - - // and turn initial Y back to y - if (firstch == "y") - w = firstch.toLowerCase() + w.substr(1); - return w; - } -} - diff --git a/0.5.1/_static/searchtools.js b/0.5.1/_static/searchtools.js deleted file mode 100644 index 7918c3fa..00000000 --- a/0.5.1/_static/searchtools.js +++ /dev/null @@ -1,574 +0,0 @@ -/* - * searchtools.js - * ~~~~~~~~~~~~~~~~ - * - * Sphinx JavaScript utilities for the full-text search. - * - * :copyright: Copyright 2007-2023 by the Sphinx team, see AUTHORS. - * :license: BSD, see LICENSE for details. - * - */ -"use strict"; - -/** - * Simple result scoring code. - */ -if (typeof Scorer === "undefined") { - var Scorer = { - // Implement the following function to further tweak the score for each result - // The function takes a result array [docname, title, anchor, descr, score, filename] - // and returns the new score. - /* - score: result => { - const [docname, title, anchor, descr, score, filename] = result - return score - }, - */ - - // query matches the full name of an object - objNameMatch: 11, - // or matches in the last dotted part of the object name - objPartialMatch: 6, - // Additive scores depending on the priority of the object - objPrio: { - 0: 15, // used to be importantResults - 1: 5, // used to be objectResults - 2: -5, // used to be unimportantResults - }, - // Used when the priority is not in the mapping. - objPrioDefault: 0, - - // query found in title - title: 15, - partialTitle: 7, - // query found in terms - term: 5, - partialTerm: 2, - }; -} - -const _removeChildren = (element) => { - while (element && element.lastChild) element.removeChild(element.lastChild); -}; - -/** - * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Guide/Regular_Expressions#escaping - */ -const _escapeRegExp = (string) => - string.replace(/[.*+\-?^${}()|[\]\\]/g, "\\$&"); // $& means the whole matched string - -const _displayItem = (item, searchTerms, highlightTerms) => { - const docBuilder = DOCUMENTATION_OPTIONS.BUILDER; - const docFileSuffix = DOCUMENTATION_OPTIONS.FILE_SUFFIX; - const docLinkSuffix = DOCUMENTATION_OPTIONS.LINK_SUFFIX; - const showSearchSummary = DOCUMENTATION_OPTIONS.SHOW_SEARCH_SUMMARY; - const contentRoot = document.documentElement.dataset.content_root; - - const [docName, title, anchor, descr, score, _filename] = item; - - let listItem = document.createElement("li"); - let requestUrl; - let linkUrl; - if (docBuilder === "dirhtml") { - // dirhtml builder - let dirname = docName + "/"; - if (dirname.match(/\/index\/$/)) - dirname = dirname.substring(0, dirname.length - 6); - else if (dirname === "index/") dirname = ""; - requestUrl = contentRoot + dirname; - linkUrl = requestUrl; - } else { - // normal html builders - requestUrl = contentRoot + docName + docFileSuffix; - linkUrl = docName + docLinkSuffix; - } - let linkEl = listItem.appendChild(document.createElement("a")); - linkEl.href = linkUrl + anchor; - linkEl.dataset.score = score; - linkEl.innerHTML = title; - if (descr) { - listItem.appendChild(document.createElement("span")).innerHTML = - " (" + descr + ")"; - // highlight search terms in the description - if (SPHINX_HIGHLIGHT_ENABLED) // set in sphinx_highlight.js - highlightTerms.forEach((term) => _highlightText(listItem, term, "highlighted")); - } - else if (showSearchSummary) - fetch(requestUrl) - .then((responseData) => responseData.text()) - .then((data) => { - if (data) - listItem.appendChild( - Search.makeSearchSummary(data, searchTerms) - ); - // highlight search terms in the summary - if (SPHINX_HIGHLIGHT_ENABLED) // set in sphinx_highlight.js - highlightTerms.forEach((term) => _highlightText(listItem, term, "highlighted")); - }); - Search.output.appendChild(listItem); -}; -const _finishSearch = (resultCount) => { - Search.stopPulse(); - Search.title.innerText = _("Search Results"); - if (!resultCount) - Search.status.innerText = Documentation.gettext( - "Your search did not match any documents. Please make sure that all words are spelled correctly and that you've selected enough categories." - ); - else - Search.status.innerText = _( - `Search finished, found ${resultCount} page(s) matching the search query.` - ); -}; -const _displayNextItem = ( - results, - resultCount, - searchTerms, - highlightTerms, -) => { - // results left, load the summary and display it - // this is intended to be dynamic (don't sub resultsCount) - if (results.length) { - _displayItem(results.pop(), searchTerms, highlightTerms); - setTimeout( - () => _displayNextItem(results, resultCount, searchTerms, highlightTerms), - 5 - ); - } - // search finished, update title and status message - else _finishSearch(resultCount); -}; - -/** - * Default splitQuery function. Can be overridden in ``sphinx.search`` with a - * custom function per language. - * - * The regular expression works by splitting the string on consecutive characters - * that are not Unicode letters, numbers, underscores, or emoji characters. - * This is the same as ``\W+`` in Python, preserving the surrogate pair area. - */ -if (typeof splitQuery === "undefined") { - var splitQuery = (query) => query - .split(/[^\p{Letter}\p{Number}_\p{Emoji_Presentation}]+/gu) - .filter(term => term) // remove remaining empty strings -} - -/** - * Search Module - */ -const Search = { - _index: null, - _queued_query: null, - _pulse_status: -1, - - htmlToText: (htmlString) => { - const htmlElement = new DOMParser().parseFromString(htmlString, 'text/html'); - htmlElement.querySelectorAll(".headerlink").forEach((el) => { el.remove() }); - const docContent = htmlElement.querySelector('[role="main"]'); - if (docContent !== undefined) return docContent.textContent; - console.warn( - "Content block not found. Sphinx search tries to obtain it via '[role=main]'. Could you check your theme or template." - ); - return ""; - }, - - init: () => { - const query = new URLSearchParams(window.location.search).get("q"); - document - .querySelectorAll('input[name="q"]') - .forEach((el) => (el.value = query)); - if (query) Search.performSearch(query); - }, - - loadIndex: (url) => - (document.body.appendChild(document.createElement("script")).src = url), - - setIndex: (index) => { - Search._index = index; - if (Search._queued_query !== null) { - const query = Search._queued_query; - Search._queued_query = null; - Search.query(query); - } - }, - - hasIndex: () => Search._index !== null, - - deferQuery: (query) => (Search._queued_query = query), - - stopPulse: () => (Search._pulse_status = -1), - - startPulse: () => { - if (Search._pulse_status >= 0) return; - - const pulse = () => { - Search._pulse_status = (Search._pulse_status + 1) % 4; - Search.dots.innerText = ".".repeat(Search._pulse_status); - if (Search._pulse_status >= 0) window.setTimeout(pulse, 500); - }; - pulse(); - }, - - /** - * perform a search for something (or wait until index is loaded) - */ - performSearch: (query) => { - // create the required interface elements - const searchText = document.createElement("h2"); - searchText.textContent = _("Searching"); - const searchSummary = document.createElement("p"); - searchSummary.classList.add("search-summary"); - searchSummary.innerText = ""; - const searchList = document.createElement("ul"); - searchList.classList.add("search"); - - const out = document.getElementById("search-results"); - Search.title = out.appendChild(searchText); - Search.dots = Search.title.appendChild(document.createElement("span")); - Search.status = out.appendChild(searchSummary); - Search.output = out.appendChild(searchList); - - const searchProgress = document.getElementById("search-progress"); - // Some themes don't use the search progress node - if (searchProgress) { - searchProgress.innerText = _("Preparing search..."); - } - Search.startPulse(); - - // index already loaded, the browser was quick! - if (Search.hasIndex()) Search.query(query); - else Search.deferQuery(query); - }, - - /** - * execute search (requires search index to be loaded) - */ - query: (query) => { - const filenames = Search._index.filenames; - const docNames = Search._index.docnames; - const titles = Search._index.titles; - const allTitles = Search._index.alltitles; - const indexEntries = Search._index.indexentries; - - // stem the search terms and add them to the correct list - const stemmer = new Stemmer(); - const searchTerms = new Set(); - const excludedTerms = new Set(); - const highlightTerms = new Set(); - const objectTerms = new Set(splitQuery(query.toLowerCase().trim())); - splitQuery(query.trim()).forEach((queryTerm) => { - const queryTermLower = queryTerm.toLowerCase(); - - // maybe skip this "word" - // stopwords array is from language_data.js - if ( - stopwords.indexOf(queryTermLower) !== -1 || - queryTerm.match(/^\d+$/) - ) - return; - - // stem the word - let word = stemmer.stemWord(queryTermLower); - // select the correct list - if (word[0] === "-") excludedTerms.add(word.substr(1)); - else { - searchTerms.add(word); - highlightTerms.add(queryTermLower); - } - }); - - if (SPHINX_HIGHLIGHT_ENABLED) { // set in sphinx_highlight.js - localStorage.setItem("sphinx_highlight_terms", [...highlightTerms].join(" ")) - } - - // console.debug("SEARCH: searching for:"); - // console.info("required: ", [...searchTerms]); - // console.info("excluded: ", [...excludedTerms]); - - // array of [docname, title, anchor, descr, score, filename] - let results = []; - _removeChildren(document.getElementById("search-progress")); - - const queryLower = query.toLowerCase(); - for (const [title, foundTitles] of Object.entries(allTitles)) { - if (title.toLowerCase().includes(queryLower) && (queryLower.length >= title.length/2)) { - for (const [file, id] of foundTitles) { - let score = Math.round(100 * queryLower.length / title.length) - results.push([ - docNames[file], - titles[file] !== title ? `${titles[file]} > ${title}` : title, - id !== null ? "#" + id : "", - null, - score, - filenames[file], - ]); - } - } - } - - // search for explicit entries in index directives - for (const [entry, foundEntries] of Object.entries(indexEntries)) { - if (entry.includes(queryLower) && (queryLower.length >= entry.length/2)) { - for (const [file, id] of foundEntries) { - let score = Math.round(100 * queryLower.length / entry.length) - results.push([ - docNames[file], - titles[file], - id ? "#" + id : "", - null, - score, - filenames[file], - ]); - } - } - } - - // lookup as object - objectTerms.forEach((term) => - results.push(...Search.performObjectSearch(term, objectTerms)) - ); - - // lookup as search terms in fulltext - results.push(...Search.performTermsSearch(searchTerms, excludedTerms)); - - // let the scorer override scores with a custom scoring function - if (Scorer.score) results.forEach((item) => (item[4] = Scorer.score(item))); - - // now sort the results by score (in opposite order of appearance, since the - // display function below uses pop() to retrieve items) and then - // alphabetically - results.sort((a, b) => { - const leftScore = a[4]; - const rightScore = b[4]; - if (leftScore === rightScore) { - // same score: sort alphabetically - const leftTitle = a[1].toLowerCase(); - const rightTitle = b[1].toLowerCase(); - if (leftTitle === rightTitle) return 0; - return leftTitle > rightTitle ? -1 : 1; // inverted is intentional - } - return leftScore > rightScore ? 1 : -1; - }); - - // remove duplicate search results - // note the reversing of results, so that in the case of duplicates, the highest-scoring entry is kept - let seen = new Set(); - results = results.reverse().reduce((acc, result) => { - let resultStr = result.slice(0, 4).concat([result[5]]).map(v => String(v)).join(','); - if (!seen.has(resultStr)) { - acc.push(result); - seen.add(resultStr); - } - return acc; - }, []); - - results = results.reverse(); - - // for debugging - //Search.lastresults = results.slice(); // a copy - // console.info("search results:", Search.lastresults); - - // print the results - _displayNextItem(results, results.length, searchTerms, highlightTerms); - }, - - /** - * search for object names - */ - performObjectSearch: (object, objectTerms) => { - const filenames = Search._index.filenames; - const docNames = Search._index.docnames; - const objects = Search._index.objects; - const objNames = Search._index.objnames; - const titles = Search._index.titles; - - const results = []; - - const objectSearchCallback = (prefix, match) => { - const name = match[4] - const fullname = (prefix ? prefix + "." : "") + name; - const fullnameLower = fullname.toLowerCase(); - if (fullnameLower.indexOf(object) < 0) return; - - let score = 0; - const parts = fullnameLower.split("."); - - // check for different match types: exact matches of full name or - // "last name" (i.e. last dotted part) - if (fullnameLower === object || parts.slice(-1)[0] === object) - score += Scorer.objNameMatch; - else if (parts.slice(-1)[0].indexOf(object) > -1) - score += Scorer.objPartialMatch; // matches in last name - - const objName = objNames[match[1]][2]; - const title = titles[match[0]]; - - // If more than one term searched for, we require other words to be - // found in the name/title/description - const otherTerms = new Set(objectTerms); - otherTerms.delete(object); - if (otherTerms.size > 0) { - const haystack = `${prefix} ${name} ${objName} ${title}`.toLowerCase(); - if ( - [...otherTerms].some((otherTerm) => haystack.indexOf(otherTerm) < 0) - ) - return; - } - - let anchor = match[3]; - if (anchor === "") anchor = fullname; - else if (anchor === "-") anchor = objNames[match[1]][1] + "-" + fullname; - - const descr = objName + _(", in ") + title; - - // add custom score for some objects according to scorer - if (Scorer.objPrio.hasOwnProperty(match[2])) - score += Scorer.objPrio[match[2]]; - else score += Scorer.objPrioDefault; - - results.push([ - docNames[match[0]], - fullname, - "#" + anchor, - descr, - score, - filenames[match[0]], - ]); - }; - Object.keys(objects).forEach((prefix) => - objects[prefix].forEach((array) => - objectSearchCallback(prefix, array) - ) - ); - return results; - }, - - /** - * search for full-text terms in the index - */ - performTermsSearch: (searchTerms, excludedTerms) => { - // prepare search - const terms = Search._index.terms; - const titleTerms = Search._index.titleterms; - const filenames = Search._index.filenames; - const docNames = Search._index.docnames; - const titles = Search._index.titles; - - const scoreMap = new Map(); - const fileMap = new Map(); - - // perform the search on the required terms - searchTerms.forEach((word) => { - const files = []; - const arr = [ - { files: terms[word], score: Scorer.term }, - { files: titleTerms[word], score: Scorer.title }, - ]; - // add support for partial matches - if (word.length > 2) { - const escapedWord = _escapeRegExp(word); - Object.keys(terms).forEach((term) => { - if (term.match(escapedWord) && !terms[word]) - arr.push({ files: terms[term], score: Scorer.partialTerm }); - }); - Object.keys(titleTerms).forEach((term) => { - if (term.match(escapedWord) && !titleTerms[word]) - arr.push({ files: titleTerms[word], score: Scorer.partialTitle }); - }); - } - - // no match but word was a required one - if (arr.every((record) => record.files === undefined)) return; - - // found search word in contents - arr.forEach((record) => { - if (record.files === undefined) return; - - let recordFiles = record.files; - if (recordFiles.length === undefined) recordFiles = [recordFiles]; - files.push(...recordFiles); - - // set score for the word in each file - recordFiles.forEach((file) => { - if (!scoreMap.has(file)) scoreMap.set(file, {}); - scoreMap.get(file)[word] = record.score; - }); - }); - - // create the mapping - files.forEach((file) => { - if (fileMap.has(file) && fileMap.get(file).indexOf(word) === -1) - fileMap.get(file).push(word); - else fileMap.set(file, [word]); - }); - }); - - // now check if the files don't contain excluded terms - const results = []; - for (const [file, wordList] of fileMap) { - // check if all requirements are matched - - // as search terms with length < 3 are discarded - const filteredTermCount = [...searchTerms].filter( - (term) => term.length > 2 - ).length; - if ( - wordList.length !== searchTerms.size && - wordList.length !== filteredTermCount - ) - continue; - - // ensure that none of the excluded terms is in the search result - if ( - [...excludedTerms].some( - (term) => - terms[term] === file || - titleTerms[term] === file || - (terms[term] || []).includes(file) || - (titleTerms[term] || []).includes(file) - ) - ) - break; - - // select one (max) score for the file. - const score = Math.max(...wordList.map((w) => scoreMap.get(file)[w])); - // add result to the result list - results.push([ - docNames[file], - titles[file], - "", - null, - score, - filenames[file], - ]); - } - return results; - }, - - /** - * helper function to return a node containing the - * search summary for a given text. keywords is a list - * of stemmed words. - */ - makeSearchSummary: (htmlText, keywords) => { - const text = Search.htmlToText(htmlText); - if (text === "") return null; - - const textLower = text.toLowerCase(); - const actualStartPosition = [...keywords] - .map((k) => textLower.indexOf(k.toLowerCase())) - .filter((i) => i > -1) - .slice(-1)[0]; - const startWithContext = Math.max(actualStartPosition - 120, 0); - - const top = startWithContext === 0 ? "" : "..."; - const tail = startWithContext + 240 < text.length ? "..." : ""; - - let summary = document.createElement("p"); - summary.classList.add("context"); - summary.textContent = top + text.substr(startWithContext, 240).trim() + tail; - - return summary; - }, -}; - -_ready(Search.init); diff --git a/0.5.1/authors.html b/0.5.1/authors.html deleted file mode 100644 index d349e80c..00000000 --- a/0.5.1/authors.html +++ /dev/null @@ -1,150 +0,0 @@ - - - - - - - Contributors ✨ — geocube 0.5.1 documentation - - - - - - - - - - - - - - - - - - - - -
- - -
- -
-
-
- -
-
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- -
-

Contributors ✨

-

Thanks goes to these wonderful people (emoji key):

- - - - - - - - - - - - - - - - - - - - - -

Alan D. Snow
💻 🤔 💬 🐛 📖 💡 🚧 👀 ⚠️
Alfredo Delos Santos
🤔 👀
Justin Gruca
👀
Jordan Cook
📖
LiamRMoore
💻 ⚠️
Rambaud Pierrick
📖
Mike Taves
💻 📖 🚧

BENR0
🐛
Kirill Kouzoubov
👀
Sangzi Liang
👀
Ray Bell
📖
Mussab Abdalla
📖
Imanol
💻 ⚠️
- - - - -

This project follows the all-contributors specification. Contributions of any kind welcome!

-
- - -
-
- -
-
-
-
- - - - \ No newline at end of file diff --git a/0.5.1/cli.html b/0.5.1/cli.html deleted file mode 100644 index c829905a..00000000 --- a/0.5.1/cli.html +++ /dev/null @@ -1,238 +0,0 @@ - - - - - - - geocube.cli package — geocube 0.5.1 documentation - - - - - - - - - - - - - - - - - - - - -
- - -
- -
-
-
- -
-
-
-
- -
-

geocube.cli package

-
-

geocube

-

Top-level command and entry point into the GeoCube CLI

-
geocube [OPTIONS] COMMAND [ARGS]...
-
-
-

Options

-
-
--v, --version
-

Show the current version

-
- -
-
---show-versions
-

Show debugging version information

-
- -

Commands

-
-
-make-geocube
-

Utility to load vector data into the…

-
- -
-
-

geocube make_geocube

-

Utility to load vector data into the xarray raster format.

-
geocube make_geocube [OPTIONS] VECTOR_DATA OUTPUT_FILE
-
-
-

Options

-
-
--i, --interpolate-na-method <interpolate_na_method>
-

This is the method for interpolation to use to fill in the nodata.

-
-
Options:
-

linear | nearest | cubic

-
-
-
- -
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--gb, --group-by <group_by>
-

When specified, perform basic combining/reducing of the data on this column.

-
- -
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--f, --fill <fill>
-

The value to fill in the grid with for nodata. Default is NaN.

-
- -
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--l, --like <like>
-

Uses the netCDF file output of a previous load() to form the basis of a request for another product.

-
- -
-
--g, --geom <geom>
-

GeoJSON string for the bounding box of the data used to construct the grid.

-
- -
-
--a, --align <align>
-

Load data such that point ‘align’ lies on the pixel boundary. Default is (0, 0).

-
- -
-
--r, --resolution <resolution>
-

A tuple of the spatial resolution of the returned data. Ex. -r -10 10.

-
- -
-
--c, --output-crs <output_crs>
-

The CRS of the returned data. (e.g. epsg:4326). If no CRS is supplied, the CRS of the input data is used.

-
- -
-
--m, --measurements <measurements>
-

Measurement subset to load in.

-
- -

Arguments

-
-
-VECTOR_DATA
-

Required argument

-
- -
-
-OUTPUT_FILE
-

Required argument

-
- -
-
- - -
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- -
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-
-
- - - - \ No newline at end of file diff --git a/0.5.1/contributing.html b/0.5.1/contributing.html deleted file mode 100644 index 9ee5df75..00000000 --- a/0.5.1/contributing.html +++ /dev/null @@ -1,248 +0,0 @@ - - - - - - - Contributing — geocube 0.5.1 documentation - - - - - - - - - - - - - - - - - - - - -
- - -
- -
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- -
-
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- -
-

Contributing

-

Contributions are welcome, and they are greatly appreciated! Every little bit -helps, and credit will always be given.

-

You can contribute in many ways:

-
-

Types of Contributions

-
-

Report Bugs

-

Report bugs at https://github.com/corteva/geocube/issues.

-

If you are reporting a bug, please include:

-
    -

  • Your operating system name and version.

    • -

  • Any details about your local setup that might be helpful in troubleshooting.

    • -

  • Detailed steps to reproduce the bug.

    • -
-
-
-

Fix Bugs

-

Look through the GitLab issues for bugs. Anything tagged with “bug” and “help -wanted” is open to whoever wants to implement it.

-
-
-

Implement Features

-

Look through the GitLab issues for features. Anything tagged with “enhancement” -and “help wanted” is open to whoever wants to implement it.

-
-
-

Write Documentation

-

geocube could always use more documentation, whether as part of the -official geocube docs, in docstrings, or even on the web in blog posts, -articles, and such.

-
-
-

Submit Feedback

-

The best way to send feedback is to file an issue at https://github.com/corteva/geocube/issues.

-

If you are proposing a feature:

-
    -

  • Explain in detail how it would work.

    • -

  • Keep the scope as narrow as possible, to make it easier to implement.

    • -

  • Remember that this is a volunteer-driven project, and that contributions -are welcome :)

    • -
-
-
-
-

Get Started!

-

Ready to contribute? Here’s how to set up geocube for local development.

-
    -

  1. Fork the geocube repo on GitHub.

    2. -

  2. Clone your fork locally:

    -
    $ git clone git@github.com:your_name_here/geocube.git
-
    -
    -
    3. -

  3. Install your local copy into a virtualenv. Assuming you have virtualenvwrapper installed, this is how you set up your fork for local development:

    -
    $ python -m venv geocube_env
-$ cd geocube/
-$ pip install -e .[dev]
-
    -
    -
    4. -

  4. Setup pre-commit hooks:

    -
    $ pre-commit install
-$ pre-commit autoupdate
-
    -
    -
    5. -

  5. Create a branch for local development:

    -
    $ git checkout -b name-of-your-bugfix-or-feature
-
    -
    -

    Now you can make your changes locally.

    -
    6. -

  6. When you’re done making changes, check that your changes pass flake8, are black formatter, -and the tests pass:

    -
    $ make check
-$ make test
-
    -
    -

    Or, if you cannot run makefile commands:

    -
    $ flake8 geocube/ test/
-$ black --check .
-$ pytest
-
    -
    -
    7. -

  7. Commit your changes and push your branch to GitLab:

    -
    $ git add .
-$ git commit -m "Your detailed description of your changes."
-$ git push origin name-of-your-bugfix-or-feature
-
    -
    -
    8. -

  8. Submit a pull request through the GitHub website.

    9. -
-
-
-

Pull Request Guidelines

-

Before you submit a pull request, check that it meets these guidelines:

-
    -

  1. The pull request should include tests.

    2. -

  2. If the pull request adds functionality, the docs should be updated. Put -your new functionality into a function with a docstring, and add the -feature to the list in README.rst.

    3. -

  3. The pull request should work for Python 3.10-3.12.

    4. -
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-
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Tips

-

To run a subset of tests:

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$ pytest test/unit/cli/test_geocube.py::test_check_version
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- - - - \ No newline at end of file diff --git a/0.5.1/examples/categorical.html b/0.5.1/examples/categorical.html deleted file mode 100644 index 62ee7069..00000000 --- a/0.5.1/examples/categorical.html +++ /dev/null @@ -1,680 +0,0 @@ - - - - - - - Example - Categorical Data — geocube 0.5.1 documentation - - - - - - - - - - - - - - - - - - - - - - - -
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Example - Categorical Data

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-
[1]:
-
-
-
import geopandas
-import pandas
-
-from geocube.api.core import make_geocube
-
-%matplotlib inline
-
-
-
-
-

Load in soil data

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-
[2]:
-
-
-
ssurgo_data = geopandas.read_file("../../test/test_data/input/soil_data_group.geojson")
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-
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-
[3]:
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-
-
# original data
-ssurgo_data[ssurgo_data.hzdept_r==15].plot(column='sandtotal_r')
-
-
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-
[3]:
-
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-
-<matplotlib.axes._subplots.AxesSubplot at 0x7f9e210d3be0>
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-../_images/examples_categorical_4_1.png -
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-
-
-

Generate categories for categorical data

-

If your data is only a subset of all of the data, the list of categories you get will likely not be complete.

-

NOTE: The categories will be made unique and sorted internally if they are not already.

-
-
[4]:
-
-
-
# this is only a subset of all of the classes
-ssurgo_data.drclassdcd.drop_duplicates().values.tolist()
-
-
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-
[4]:
-
-
-
-
-['Somewhat poorly drained',
- 'Poorly drained',
- 'Well drained',
- 'Excessively drained']
-
-
-
-
[5]:
-
-
-
# complete list of categories
-drclasses_complete = [
-    'Poorly drained',
-    'Somewhat poorly drained',
-    'Excessively drained',
-    'Subaqueous',
-    'Well drained',
-    'Somewhat excessively drained',
-    'Very poorly drained',
-    'Moderately well drained'
-]
-
-
-
-
-
[6]:
-
-
-
categorical_enums = {'drclassdcd': drclasses_complete}
-
-
-
-
-
-

Convert data to grid

-

See docs for make_geocube

-
-
[7]:
-
-
-
out_grid = make_geocube(
-    vector_data=ssurgo_data,
-    output_crs="epsg:32615",
-    group_by='hzdept_r',
-    resolution=(-100, 100),
-    categorical_enums=categorical_enums
-)
-
-
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-
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[8]:
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out_grid
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[8]:
-
-
-
-
<xarray.Dataset>
-Dimensions:                (drclassdcd_categories: 9, hzdept_r: 11, x: 15, y: 19)
-Coordinates:
-  * y                      (y) float64 4.597e+06 4.597e+06 ... 4.595e+06
-  * x                      (x) float64 7.004e+05 7.004e+05 ... 7.018e+05
-  * hzdept_r               (hzdept_r) float64 0.0 5.0 15.0 ... 105.0 120.0 150.0
-  * drclassdcd_categories  (drclassdcd_categories) object 'Excessively drained' ... 'nodata'
-    spatial_ref            int64 0
-Data variables:
-    drclassdcd             (hzdept_r, y, x) float64 -1.0 -1.0 -1.0 ... 7.0 7.0
-    hzdepb_r               (hzdept_r, y, x) float64 nan nan nan ... 180.0 180.0
-    claytotal_r            (hzdept_r, y, x) float64 nan nan nan ... 21.0 21.0
-    sandtotal_r            (hzdept_r, y, x) float64 nan nan nan ... 10.0 10.0
-    silttotal_r            (hzdept_r, y, x) float64 nan nan nan ... 69.0 69.0
-Attributes:
-    grid_mapping:  spatial_ref
-
-
-
[9]:
-
-
-
# mask nodata and plot
-clay_slice = out_grid.claytotal_r.sel(hzdept_r=15)
-clay_slice.where(clay_slice!=out_grid.claytotal_r.rio.nodata).plot()
-
-
-
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[9]:
-
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-<matplotlib.collections.QuadMesh at 0x7f9e1e823748>
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-
-../_images/examples_categorical_13_1.png -
-
-
-
-

Dealing with categorical data

-

Because the data needs to be numerical for conversion from vector to raster, the code displays the categories as numbers. To convert back to strings, you will need to use the categories provided to convert back.

-
-
[10]:
-
-
-
drclassdcd_slice = out_grid.drclassdcd.sel(hzdept_r=15)
-drclassdcd_slice.where(drclassdcd_slice!=out_grid.drclassdcd.rio.nodata).plot()
-
-
-
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-
[10]:
-
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-
-
-<matplotlib.collections.QuadMesh at 0x7f9e1e76bc50>
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-../_images/examples_categorical_16_1.png -
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[11]:
-
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drclassdcd_string = out_grid['drclassdcd_categories'][out_grid['drclassdcd'].astype(int)]\
-    .drop('drclassdcd_categories')
-
-
-
-
-
[12]:
-
-
-
out_grid['drclassdcd'] = drclassdcd_string
-out_grid
-
-
-
-
-
[12]:
-
-
-
-
<xarray.Dataset>
-Dimensions:                (drclassdcd_categories: 9, hzdept_r: 11, x: 15, y: 19)
-Coordinates:
-  * y                      (y) float64 4.597e+06 4.597e+06 ... 4.595e+06
-  * x                      (x) float64 7.004e+05 7.004e+05 ... 7.018e+05
-  * hzdept_r               (hzdept_r) float64 0.0 5.0 15.0 ... 105.0 120.0 150.0
-  * drclassdcd_categories  (drclassdcd_categories) object 'Excessively drained' ... 'nodata'
-    spatial_ref            int64 0
-Data variables:
-    drclassdcd             (hzdept_r, y, x) object 'nodata' ... 'Well drained'
-    hzdepb_r               (hzdept_r, y, x) float64 nan nan nan ... 180.0 180.0
-    claytotal_r            (hzdept_r, y, x) float64 nan nan nan ... 21.0 21.0
-    sandtotal_r            (hzdept_r, y, x) float64 nan nan nan ... 10.0 10.0
-    silttotal_r            (hzdept_r, y, x) float64 nan nan nan ... 69.0 69.0
-Attributes:
-    grid_mapping:  spatial_ref
-
-
-
[13]:
-
-
-
pdf = out_grid.drop(['spatial_ref', 'drclassdcd_categories']).to_dataframe()
-pdf.head()
-
-
-
-
-
[13]:
-
-
-
-
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
drclassdcdhzdepb_rclaytotal_rsandtotal_rsilttotal_r
hzdept_rxy
0.0700350.04597050.0nodataNaNNaNNaNNaN
4596950.0Well drained5.026.038.036.0
4596850.0Well drained5.026.038.036.0
4596750.0Well drained5.026.038.036.0
4596650.0Well drained5.026.038.036.0
-
-
-
-

Make sure all categories are represented

-

To do this, convert the column type to categorical beforehand and make sure that you include all of the possible categories.

-
-
[14]:
-
-
-
cat_dtype = pandas.api.types.CategoricalDtype(out_grid.drclassdcd_categories.values)
-pdf['drclassdcd'] = pdf['drclassdcd'].astype(cat_dtype)
-
-
-
-
-
[15]:
-
-
-
training_df = pandas.get_dummies(pdf, columns=['drclassdcd'])
-training_df.head()
-
-
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-
[15]:
-
-
-
-
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
hzdepb_rclaytotal_rsandtotal_rsilttotal_rdrclassdcd_Excessively draineddrclassdcd_Moderately well draineddrclassdcd_Poorly draineddrclassdcd_Somewhat excessively draineddrclassdcd_Somewhat poorly draineddrclassdcd_Subaqueousdrclassdcd_Very poorly draineddrclassdcd_Well draineddrclassdcd_nodata
hzdept_rxy
0.0700350.04597050.0NaNNaNNaNNaN000000001
4596950.05.026.038.036.0000000010
4596850.05.026.038.036.0000000010
4596750.05.026.038.036.0000000010
4596650.05.026.038.036.0000000010
-
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[16]:
-
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training_df.columns
-
-
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-
[16]:
-
-
-
-
-Index(['hzdepb_r', 'claytotal_r', 'sandtotal_r', 'silttotal_r',
-       'drclassdcd_Excessively drained', 'drclassdcd_Moderately well drained',
-       'drclassdcd_Poorly drained', 'drclassdcd_Somewhat excessively drained',
-       'drclassdcd_Somewhat poorly drained', 'drclassdcd_Subaqueous',
-       'drclassdcd_Very poorly drained', 'drclassdcd_Well drained',
-       'drclassdcd_nodata'],
-      dtype='object')
-
-
-
-
-
- - -
-
- -
-
-
-
- - - - \ No newline at end of file diff --git a/0.5.1/examples/examples.html b/0.5.1/examples/examples.html deleted file mode 100644 index 6345f815..00000000 --- a/0.5.1/examples/examples.html +++ /dev/null @@ -1,157 +0,0 @@ - - - - - - - Usage Examples — geocube 0.5.1 documentation - - - - - - - - - - - - - - - - - - - - -
- - -
- -
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-
- -
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- -
- -
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- - - - \ No newline at end of file diff --git a/0.5.1/examples/grid_to_vector_map.html b/0.5.1/examples/grid_to_vector_map.html deleted file mode 100644 index c3495274..00000000 --- a/0.5.1/examples/grid_to_vector_map.html +++ /dev/null @@ -1,570 +0,0 @@ - - - - - - - Example - Mapping Grid Data to Vector Data — geocube 0.5.1 documentation - - - - - - - - - - - - - - - - - - - - - - - -
- - -
- -
-
-
- -
-
-
-
- -
-

Example - Mapping Grid Data to Vector Data

-

This is useful in the case where you want to get statistics for a specific raster over a certain region. In this example, the vector data is a random region with using SSURGO data.

-
-
[1]:
-
-
-
import json
-
-import geopandas
-
-from geocube.api.core import make_geocube
-
-%matplotlib inline
-
-
-
-
-
[2]:
-
-
-
ssurgo_data = geopandas.read_file("../../test/test_data/input/soil_data_group.geojson")
-
-
-
-
-
[3]:
-
-
-
ssurgo_data.head()
-
-
-
-
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[3]:
-
-
-
-
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
cokeymukeydrclassdcdhzdept_rchkeyhzdepb_rclaytotal_rsandtotal_rsilttotal_rgeometry
012577452271425Somewhat poorly drained0.01000340905.023.0676759.97833866.953987MULTIPOLYGON (((-90.59735 41.49255, -90.59730 ...
112577452271425Somewhat poorly drained5.010003409015.023.0676759.97833866.953987MULTIPOLYGON (((-90.59735 41.49255, -90.59730 ...
212577452271425Somewhat poorly drained15.010003409130.023.0676759.97833866.953987MULTIPOLYGON (((-90.59735 41.49255, -90.59730 ...
312577452271425Somewhat poorly drained30.010003409245.023.0676759.97833866.953987MULTIPOLYGON (((-90.59735 41.49255, -90.59730 ...
412577452271425Somewhat poorly drained45.010003409360.023.2316439.96194166.806416MULTIPOLYGON (((-90.59735 41.49255, -90.59730 ...
-
-
-
-
[4]:
-
-
-
# convert the key to group to the vector data to an integer as that is one of the
-# best data types for this type of mapping. If your data is not integer,
-# then consider using a mapping of your data to an integer with something
-# like a categorical dtype.
-ssurgo_data["mukey"] = ssurgo_data.mukey.astype(int)
-
-
-
-
-

Convert data to grid

-

See docs for make_geocube

-
-
[5]:
-
-
-
out_grid = make_geocube(
-    vector_data=ssurgo_data,
-    group_by='hzdept_r',
-    resolution=(-0.0001, 0.0001)
-)
-out_grid
-
-
-
-
-
[5]:
-
-
-
-
-<xarray.Dataset>
-Dimensions:      (hzdept_r: 11, x: 165, y: 165)
-Coordinates:
-  * y            (y) float64 41.5 41.5 41.5 41.5 ... 41.48 41.48 41.48 41.48
-  * x            (x) float64 -90.6 -90.6 -90.6 -90.6 ... -90.58 -90.58 -90.58
-  * hzdept_r     (hzdept_r) float64 0.0 5.0 15.0 30.0 ... 90.0 105.0 120.0 150.0
-    spatial_ref  int64 0
-Data variables:
-    mukey        (hzdept_r, y, x) float64 1.988e+05 1.988e+05 ... 1.987e+05
-    hzdepb_r     (hzdept_r, y, x) float64 5.0 5.0 5.0 5.0 ... 180.0 180.0 180.0
-    claytotal_r  (hzdept_r, y, x) float64 26.0 26.0 26.0 26.0 ... 21.0 21.0 21.0
-    sandtotal_r  (hzdept_r, y, x) float64 38.0 38.0 38.0 38.0 ... 10.0 10.0 10.0
-    silttotal_r  (hzdept_r, y, x) float64 36.0 36.0 36.0 36.0 ... 69.0 69.0 69.0
-Attributes:
-    grid_mapping:  spatial_ref
-
-
-
-

Get the mean/median of each region using the unique ID

-
-
[6]:
-
-
-
grid_mean = out_grid.sel(hzdept_r=15).groupby(out_grid.mukey.sel(hzdept_r=15)).mean()
-grid_mean.to_dataframe()
-
-
-
-
-
[6]:
-
-
-
-
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
hzdept_rspatial_refhzdepb_rclaytotal_rsandtotal_rsilttotal_r
mukey
198692.015.0030.023.0000007.00000070.000000
198714.015.0030.05.00000087.0000008.000000
198724.015.0030.021.00000010.00000069.000000
198750.015.0030.012.00000063.00000025.000000
198754.015.0030.026.00000038.00000036.000000
271425.015.0030.023.0676759.97833866.953987
271431.015.0030.014.00000055.00000031.000000
-
-
-
-
[7]:
-
-
-
grid_median = out_grid.sel(hzdept_r=75).groupby(out_grid.mukey.sel(hzdept_r=75)).median()
-grid_median.to_dataframe()
-
-
-
-
-
[7]:
-
-
-
-
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
hzdept_rspatial_refhzdepb_rclaytotal_rsandtotal_rsilttotal_r
mukey
198692.075.0090.023.0000007.00000070.000000
198714.075.0090.07.80000086.4666675.733333
198724.075.0090.021.00000010.00000069.000000
198750.075.0090.012.00000063.00000025.000000
198754.075.0090.026.00000038.00000036.000000
271425.075.0090.024.56496610.12049765.314537
271431.075.0090.08.33333374.66666717.000000
-
-
-
-
-
- - -
-
- -
-
-
-
- - - - \ No newline at end of file diff --git a/0.5.1/examples/rasterize_function.html b/0.5.1/examples/rasterize_function.html deleted file mode 100644 index e81bd7dc..00000000 --- a/0.5.1/examples/rasterize_function.html +++ /dev/null @@ -1,229 +0,0 @@ - - - - - - - Example - Use a rasterize function when creating a raster — geocube 0.5.1 documentation - - - - - - - - - - - - - - - - - - - - - - - -
- - -
- -
-
-
- -
-
-
-
- -
-

Example - Use a rasterize function when creating a raster

-
-
[18]:
-
-
-
import geopandas
-
-from functools import partial
-from geocube.rasterize import rasterize_image
-
-from geocube.api.core import make_geocube
-
-%matplotlib inline
-
-
-
-
-
[19]:
-
-
-
gdf = geopandas.read_file(geopandas.datasets.get_path('naturalearth_lowres'))
-
-
-
-
-
[22]:
-
-
-
geo_grid = make_geocube(
-    vector_data=gdf,
-    measurements=['pop_est'],
-    resolution=(-3.0, 3.0),
-)
-geo_grid["pop_est"].plot()
-
-
-
-
-
[22]:
-
-
-
-
-<matplotlib.collections.QuadMesh at 0x190f9eeb0>
-
-
-
-
-
-
-../_images/examples_rasterize_function_3_1.png -
-
-
-
[23]:
-
-
-
geo_grid = make_geocube(
-    vector_data=gdf,
-    measurements=['pop_est'],
-    resolution=(-3.0, 3.0),
-    rasterize_function=partial(rasterize_image, all_touched=True),
-)
-geo_grid["pop_est"].plot()
-
-
-
-
-
[23]:
-
-
-
-
-<matplotlib.collections.QuadMesh at 0x1910469d0>
-
-
-
-
-
-
-../_images/examples_rasterize_function_4_1.png -
-
-
-
[ ]:
-
-
-

-
-
-
-
- - -
-
- -
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-
-
- - - - \ No newline at end of file diff --git a/0.5.1/examples/rasterize_point_data.html b/0.5.1/examples/rasterize_point_data.html deleted file mode 100644 index c87a3036..00000000 --- a/0.5.1/examples/rasterize_point_data.html +++ /dev/null @@ -1,461 +0,0 @@ - - - - - - - Example - Rasterizing Point Data — geocube 0.5.1 documentation - - - - - - - - - - - - - - - - - - - - - - - -
- - -
- -
-
-
- -
-
-
-
- -
-

Example - Rasterizing Point Data

-
-
[1]:
-
-
-
import json
-from functools import partial
-
-import geopandas
-from shapely.geometry import box, mapping
-
-from geocube.api.core import make_geocube
-from geocube.rasterize import rasterize_points_griddata, rasterize_points_radial
-
-%matplotlib inline
-
-
-
-
-

Load in geopackage data and add CRS

-
-
[2]:
-
-
-
gdf = geopandas.read_file(
-    "../../test/test_data/input/time_vector_data.geojson",
-    crs="epsg:4326"
-)
-
-
-
-
-
[3]:
-
-
-
gdf.head()
-
-
-
-
-
[3]:
-
-
-
-
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
test_attrtest_str_attrtest_time_attrgeometry
01.3dcf866195/21/2016 10:09:21 AM -05:00POINT (-47.26681 44.21932)
11.3dcf866195/21/2016 10:09:21 AM -05:00POINT (-47.26680 44.21932)
21.9dcf866195/21/2016 10:09:21 AM -05:00POINT (-47.26681 44.21932)
31.3dcf866195/21/2016 10:09:21 AM -05:00POINT (-47.26680 44.21932)
41.3dcf866195/21/2016 10:09:21 AM -05:00POINT (-47.26679 44.21932)
-
-
-
-
-

Convert to raster with GeoCube

-

See docs for make_geocube

-
-
-

Load into grid with griddata nearest resampling

-
-
[4]:
-
-
-
geo_grid = make_geocube(
-    vector_data=gdf,
-    measurements=['test_attr'],
-    resolution=(-0.1, 0.00001),
-    rasterize_function=rasterize_points_griddata,
-)
-
-
-
-
-
[5]:
-
-
-
geo_grid
-
-
-
-
-
[5]:
-
-
-
-
<xarray.Dataset>
-Dimensions:      (x: 12, y: 11)
-Coordinates:
-  * y            (y) float64 45.25 45.15 45.05 44.95 ... 44.55 44.45 44.35 44.25
-  * x            (x) float64 -47.27 -47.27 -47.27 ... -47.27 -47.27 -47.27
-    spatial_ref  int64 0
-Data variables:
-    test_attr    (y, x) float64 1.3 1.3 1.3 1.2 1.3 1.3 ... 2.3 1.3 1.9 1.3 1.3
-Attributes:
-    grid_mapping:  spatial_ref
-
-
-
[6]:
-
-
-
# mask nodata and plot
-geo_grid.test_attr.where(geo_grid.test_attr!=geo_grid.test_attr.rio.nodata).plot()
-
-
-
-
-
[6]:
-
-
-
-
-<matplotlib.collections.QuadMesh at 0x7f91dda7e710>
-
-
-
-
-
-
-../_images/examples_rasterize_point_data_9_1.png -
-
-
-
-

Load into grid with griddata cubic resampling

-
-
[7]:
-
-
-
geo_grid = make_geocube(
-    vector_data=gdf,
-    measurements=['test_attr'],
-    resolution=(-0.1, 0.00001),
-    rasterize_function=partial(rasterize_points_griddata, method="cubic"),
-)
-
-
-
-
-
[8]:
-
-
-
# mask nodata and plot
-geo_grid.test_attr.where(geo_grid.test_attr!=geo_grid.test_attr.rio.nodata).plot()
-
-
-
-
-
[8]:
-
-
-
-
-<matplotlib.collections.QuadMesh at 0x7f91d9b1f2b0>
-
-
-
-
-
-
-../_images/examples_rasterize_point_data_12_1.png -
-
-
-
-

Load into user-defined grid with radial linear resampling

-
-
[9]:
-
-
-
geo_grid = make_geocube(
-    vector_data=gdf,
-    measurements=['test_attr'],
-    geom=json.dumps(mapping(box(-48, 44, -47, 45))),
-    output_crs="epsg:3857",
-    resolution=(-300, 300),
-    rasterize_function=rasterize_points_radial,
-)
-
-
-
-
-
[10]:
-
-
-
# mask nodata and plot
-geo_grid.test_attr.where(geo_grid.test_attr!=geo_grid.test_attr.rio.nodata).plot()
-
-
-
-
-
[10]:
-
-
-
-
-<matplotlib.collections.QuadMesh at 0x7f91d9ac8320>
-
-
-
-
-
-
-../_images/examples_rasterize_point_data_15_1.png -
-
-
-
-

Load into user-defined grid with radial cubic resampling

-

Note: This example is simply to demonstrate the option and and would need to be adjusted for better results. As seen in the plot below, the values swelled quite a bit due to the input generating an ill-conditioned matrix.

-
-
[11]:
-
-
-
geo_grid = make_geocube(
-    vector_data=gdf,
-    measurements=['test_attr'],
-    geom=json.dumps(mapping(box(-48, 44, -47, 45))),
-    output_crs="epsg:3857",
-    resolution=(-300, 300),
-    rasterize_function=partial(rasterize_points_radial, method="cubic", filter_nan=True),
-)
-
-
-
-
-
-
-
-
-/home/snowal/miniconda/envs/geocube/lib/python3.6/site-packages/scipy/interpolate/rbf.py:268: LinAlgWarning: Ill-conditioned matrix (rcond=3.16678e-18): result may not be accurate.
-  self.nodes = linalg.solve(self.A, self.di)
-
-
-
-
[12]:
-
-
-
# mask nodata and plot
-geo_grid.test_attr.where(geo_grid.test_attr!=geo_grid.test_attr.rio.nodata).plot()
-
-
-
-
-
[12]:
-
-
-
-
-<matplotlib.collections.QuadMesh at 0x7f91d99fcdd8>
-
-
-
-
-
-
-../_images/examples_rasterize_point_data_18_1.png -
-
-
-
- - -
-
- -
-
-
-
- - - - \ No newline at end of file diff --git a/0.5.1/examples/timestamp_missing_data.html b/0.5.1/examples/timestamp_missing_data.html deleted file mode 100644 index 53d088e6..00000000 --- a/0.5.1/examples/timestamp_missing_data.html +++ /dev/null @@ -1,441 +0,0 @@ - - - - - - - Example - Filling in missing data & dealing with timestamps — geocube 0.5.1 documentation - - - - - - - - - - - - - - - - - - - - - - - -
- - -
- -
-
-
- -
-
-
-
- -
-

Example - Filling in missing data & dealing with timestamps

-
-
[1]:
-
-
-
import geopandas
-
-from geocube.api.core import make_geocube
-
-%matplotlib inline
-
-
-
-
-

Load in geopackage data and add CRS

-
-
[2]:
-
-
-
gdf = geopandas.read_file(
-    "../../test/test_data/input/time_vector_data.geojson",
-    crs="epsg:4326",
-)
-
-
-
-
-
[3]:
-
-
-
gdf.head()
-
-
-
-
-
[3]:
-
-
-
-
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
test_attrtest_str_attrtest_time_attrgeometry
01.3dcf866195/21/2016 10:09:21 AM -05:00POINT (-47.26681 44.21932)
11.3dcf866195/21/2016 10:09:21 AM -05:00POINT (-47.26680 44.21932)
21.9dcf866195/21/2016 10:09:21 AM -05:00POINT (-47.26681 44.21932)
31.3dcf866195/21/2016 10:09:21 AM -05:00POINT (-47.26680 44.21932)
41.3dcf866195/21/2016 10:09:21 AM -05:00POINT (-47.26679 44.21932)
-
-
-
-
[4]:
-
-
-
gdf.head().plot(column='test_attr')
-
-
-
-
-
[4]:
-
-
-
-
-<matplotlib.axes._subplots.AxesSubplot at 0x7f2f1cd18240>
-
-
-
-
-
-
-../_images/examples_timestamp_missing_data_5_1.png -
-
-
-
-

Convert to raster with GeoCube

-

See docs for make_geocube

-
-

Load into grid basic

-
-
[5]:
-
-
-
geo_grid = make_geocube(
-    vector_data=gdf,
-    measurements=['test_attr', "test_time_attr"],
-    datetime_measurements=["test_time_attr"],
-    resolution=(-0.1, 0.00001),
-    group_by="test_time_attr",
-)
-
-
-
-
-
-
-
-
-/home/snowal/miniconda/envs/geocube/lib/python3.6/site-packages/xarray/core/variable.py:151: FutureWarning: Converting timezone-aware DatetimeArray to timezone-naive ndarray with 'datetime64[ns]' dtype. In the future, this will return an ndarray with 'object' dtype where each element is a 'pandas.Timestamp' with the correct 'tz'.
-        To accept the future behavior, pass 'dtype=object'.
-        To keep the old behavior, pass 'dtype="datetime64[ns]"'.
-  return np.asarray(pd.Series(values.ravel())).reshape(values.shape)
-
-
-
-
[6]:
-
-
-
geo_grid
-
-
-
-
-
[6]:
-
-
-
-
-<xarray.Dataset>
-Dimensions:         (test_time_attr: 2, x: 12, y: 11)
-Coordinates:
-  * y               (y) float64 45.25 45.15 45.05 44.95 ... 44.45 44.35 44.25
-  * x               (x) float64 -47.27 -47.27 -47.27 ... -47.27 -47.27 -47.27
-  * test_time_attr  (test_time_attr) datetime64[ns] 2016-05-21T15:09:21 2016-05-22T15:09:22
-    spatial_ref     int64 0
-Data variables:
-    test_attr       (test_time_attr, y, x) float64 nan nan nan ... nan nan nan
-Attributes:
-    grid_mapping:  spatial_ref
-
-
-
-
[7]:
-
-
-
# mask nodata and plot
-grid_time_1 = geo_grid.isel(test_time_attr=0)
-grid_time_1.test_attr.where(grid_time_1.test_attr!=grid_time_1.test_attr.rio.nodata).plot()
-
-
-
-
-
[7]:
-
-
-
-
-<matplotlib.collections.QuadMesh at 0x7f2f19274128>
-
-
-
-
-
-
-../_images/examples_timestamp_missing_data_10_1.png -
-
-
-
-

Load into UTM grid followed by interpolation for missing values

-

See docs for make_geocube

-
-
[8]:
-
-
-
geo_grid_interp = make_geocube(
-    vector_data=gdf,
-    measurements=['test_attr', "test_time_attr"],
-    datetime_measurements=["test_time_attr"],
-    resolution=(-0.1, 0.00001),
-    group_by="test_time_attr",
-    interpolate_na_method='nearest'
-)
-
-
-
-
-
-
-
-
-/home/snowal/miniconda/envs/geocube/lib/python3.6/site-packages/xarray/core/variable.py:151: FutureWarning: Converting timezone-aware DatetimeArray to timezone-naive ndarray with 'datetime64[ns]' dtype. In the future, this will return an ndarray with 'object' dtype where each element is a 'pandas.Timestamp' with the correct 'tz'.
-        To accept the future behavior, pass 'dtype=object'.
-        To keep the old behavior, pass 'dtype="datetime64[ns]"'.
-  return np.asarray(pd.Series(values.ravel())).reshape(values.shape)
-
-
-
-
[9]:
-
-
-
geo_grid_interp
-
-
-
-
-
[9]:
-
-
-
-
-<xarray.Dataset>
-Dimensions:         (test_time_attr: 2, x: 12, y: 11)
-Coordinates:
-  * y               (y) float64 45.25 45.15 45.05 44.95 ... 44.45 44.35 44.25
-  * x               (x) float64 -47.27 -47.27 -47.27 ... -47.27 -47.27 -47.27
-  * test_time_attr  (test_time_attr) datetime64[ns] 2016-05-21T15:09:21 2016-05-22T15:09:22
-    spatial_ref     int64 0
-Data variables:
-    test_attr       (test_time_attr, y, x) float64 1.3 1.3 1.3 ... 1.3 1.3 1.3
-Attributes:
-    grid_mapping:   spatial_ref
-    creation_date:  2019-12-12 19:57:17.046150
-
-
-
-
[10]:
-
-
-
geo_grid_interp.test_attr.isel(test_time_attr=0).plot()
-
-
-
-
-
[10]:
-
-
-
-
-<matplotlib.collections.QuadMesh at 0x7f2f191bc0f0>
-
-
-
-
-
-
-../_images/examples_timestamp_missing_data_14_1.png -
-
-
-
-
- - -
-
- -
-
-
-
- - - - \ No newline at end of file diff --git a/0.5.1/examples/vectorize.html b/0.5.1/examples/vectorize.html deleted file mode 100644 index 90f60c5b..00000000 --- a/0.5.1/examples/vectorize.html +++ /dev/null @@ -1,217 +0,0 @@ - - - - - - - Example - Raster DataArray to GeoDataFrame (vectorize) — geocube 0.5.1 documentation - - - - - - - - - - - - - - - - - - - - - - - -
- - -
- -
-
-
- -
-
-
-
- -
-

Example - Raster DataArray to GeoDataFrame (vectorize)

-
-
[1]:
-
-
-
import geopandas
-
-from geocube.api.core import make_geocube
-from geocube.vector import vectorize
-
-%matplotlib inline
-
-
-
-
-

Generate Raster DataArray

-
-
[2]:
-
-
-
gdf = geopandas.read_file(geopandas.datasets.get_path('naturalearth_lowres'))
-geo_grid = make_geocube(
-    vector_data=gdf,
-    measurements=['pop_est'],
-    resolution=(-3.0, 3.0),
-)
-geo_grid["pop_est"].plot()
-
-
-
-
-
[2]:
-
-
-
-
-<matplotlib.collections.QuadMesh at 0x7fd708913f40>
-
-
-
-
-
-
-../_images/examples_vectorize_3_1.png -
-
-
-
-

Convert to GeoDataFrame (vectorize)

-
-
[3]:
-
-
-
grid_gdf = vectorize(geo_grid.pop_est.astype("float32"))
-grid_gdf.plot(column="pop_est")
-
-
-
-
-
[3]:
-
-
-
-
-<AxesSubplot: >
-
-
-
-
-
-
-../_images/examples_vectorize_5_1.png -
-
-
-
- - -
-
- -
-
-
-
- - - - \ No newline at end of file diff --git a/0.5.1/examples/zonal_statistics.html b/0.5.1/examples/zonal_statistics.html deleted file mode 100644 index 2d501837..00000000 --- a/0.5.1/examples/zonal_statistics.html +++ /dev/null @@ -1,879 +0,0 @@ - - - - - - - Example - Zonal Statistics — geocube 0.5.1 documentation - - - - - - - - - - - - - - - - - - - - - - - -
- - -
- -
-
-
- -
-
-
-
- -
-

Example - Zonal Statistics

-

This is useful in the case where you want to get regional statistics for a raster.

-
-
[1]:
-
-
-
import geopandas
-import numpy
-import rioxarray
-import xarray
-
-from geocube.api.core import make_geocube
-
-%matplotlib inline
-
-
-
-
-

Create the data mask by rasterizing the unique ID of the vector data

-

See docs for make_geocube

-
-
[2]:
-
-
-
# This assumes you are running this example from a clone of
-# https://github.com/corteva/geocube/
-# You could also use the full path:
-# https://raw.githubusercontent.com/corteva/geocube/master/test/test_data/input/soil_data_group.geojson
-ssurgo_data = geopandas.read_file("../../test/test_data/input/soil_data_group.geojson")
-ssurgo_data = ssurgo_data.loc[ssurgo_data.hzdept_r==0]
-# convert the key to group to the vector data to an integer as that is one of the
-# best data types for this type of mapping. If your data is not integer,
-# then consider using a mapping of your data to an integer with something
-# like a categorical dtype.
-ssurgo_data["mukey"] = ssurgo_data.mukey.astype(int)
-
-
-
-
-
[3]:
-
-
-
# load in source elevation data subset relevant for the vector data
-elevation = rioxarray.open_rasterio(
-    "https://prd-tnm.s3.amazonaws.com/StagedProducts/Elevation/13/TIFF/current/n42w091/USGS_13_n42w091.tif", masked=True
-).rio.clip(
-    ssurgo_data.geometry.values, ssurgo_data.crs, from_disk=True
-).sel(band=1).drop("band")
-elevation.name = "elevation"
-
-
-
-
-
[4]:
-
-
-
out_grid = make_geocube(
-    vector_data=ssurgo_data,
-    measurements=["mukey"],
-    like=elevation, # ensure the data are on the same grid
-)
-
-
-
-
-
[5]:
-
-
-
# merge the two together
-out_grid["elevation"] = (elevation.dims, elevation.values, elevation.attrs, elevation.encoding)
-out_grid
-
-
-
-
-
[5]:
-
-
-
-
- - - - - - - - - - - - - - -
<xarray.Dataset>
-Dimensions:      (y: 178, x: 178)
-Coordinates:
-  * y            (y) float64 41.5 41.5 41.5 41.5 ... 41.48 41.48 41.48 41.48
-  * x            (x) float64 -90.6 -90.6 -90.6 -90.6 ... -90.58 -90.58 -90.58
-    spatial_ref  int64 0
-Data variables:
-    mukey        (y, x) float64 1.988e+05 1.988e+05 ... 1.987e+05 1.987e+05
-    elevation    (y, x) float64 173.7 172.0 171.1 170.6 ... 181.0 181.2 181.4
-
-
-
[6]:
-
-
-
out_grid.mukey.plot.imshow()
-
-
-
-
-
[6]:
-
-
-
-
-<matplotlib.image.AxesImage at 0x7f2dd5398550>
-
-
-
-
-
-
-../_images/examples_zonal_statistics_7_1.png -
-
-
-
[7]:
-
-
-
out_grid.elevation.plot()
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[7]:
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-<matplotlib.collections.QuadMesh at 0x7f2dd5282ed0>
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-../_images/examples_zonal_statistics_8_1.png -
-
-
-

Get the elevation statistics of each region using the mask

-
-
[8]:
-
-
-
grouped_elevation = out_grid.drop("spatial_ref").groupby(out_grid.mukey)
-grid_mean = grouped_elevation.mean().rename({"elevation": "elevation_mean"})
-grid_min = grouped_elevation.min().rename({"elevation": "elevation_min"})
-grid_max = grouped_elevation.max().rename({"elevation": "elevation_max"})
-grid_std = grouped_elevation.std().rename({"elevation": "elevation_std"})
-
-
-
-
-
[9]:
-
-
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zonal_stats = xarray.merge([grid_mean, grid_min, grid_max, grid_std]).to_dataframe()
-zonal_stats
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[9]:
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elevation_meanelevation_minelevation_maxelevation_std
mukey
198692.0173.130925169.394562188.4425053.307044
198714.0175.045866170.214157179.7166752.150987
198724.0179.931131178.237244181.4903870.630699
198750.0176.187118167.951233190.1387633.815206
198754.0171.633084167.610321181.6112982.997591
271425.0167.974433167.951233168.6530150.079769
271431.0176.718101170.258133180.4602202.731732
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[10]:
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ssurgo_data = ssurgo_data.merge(zonal_stats, on="mukey")
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[11]:
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ssurgo_data.plot(column="elevation_mean", legend=True)
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[11]:
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-<AxesSubplot:>
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[12]:
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ssurgo_data.plot(column="elevation_max", legend=True)
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- - -
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  • Index
    • -
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Index

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- Symbols - | G - | M - | O - | R - | S - | V - -
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Symbols

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M

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Core

-
-

make_geocube

-
-
-geocube.api.core.make_geocube(vector_data: str | PathLike | GeoDataFrame, measurements: list[str] | None = None, datetime_measurements: list[str] | None = None, output_crs: Any | None = None, resolution: float | Iterable[float] | None = None, align: tuple[float, float] | None = None, geom: str | dict | BaseGeometry | Geometry | None = None, like: Dataset | DataArray | None = None, fill: float = nan, group_by: str | None = None, interpolate_na_method: Literal['linear', 'nearest', 'cubic'] | None = None, categorical_enums: dict[str, list] | None = None, rasterize_function: Callable[[...], ndarray[Any, dtype[_ScalarType_co]] | None] | None = None) → Dataset[source]
-

Rasterize vector data into an xarray object. Each attribute will be a data -variable in the xarray.Dataset.

-
-
Parameters:
-
    -

  • vector_data (str, path-like object or geopandas.GeoDataFrame) – A file path to an OGR supported source or GeoDataFrame containing -the vector data.

    • -

  • measurements (list(str), optional) – Attributes name or list of names to be included. If a list is specified, -the attributes will be returned in the order requested. -By default all available attributes are included.

    • -

  • datetime_measurements (list(str), optional) – Attributes that are temporal in nature and should be converted to the datetime -format. These are only included if listed in ‘measurements’.

    • -

  • output_crs (Any, optional) – The CRS of the returned data. Can be anything accepted by pyproj.CRS. -If no CRS is supplied, the CRS of the stored data is used.

    • -

  • resolution (Union[float, Iterable[float]], optional) – A tuple of the spatial resolution of the returned data (Y, X). -This includes the direction (as indicated by a positive or negative number). -Typically when using most CRSs, the first number would be negative.

    • -

  • align (tuple[float, float], optional) – Load data such that point ‘align’ lies on the pixel boundary. -Units are in the co-ordinate space of the output CRS. -Default is (0,0)

    • -

  • geom (Union[str, dict, shapely.geometry.base.BaseGeometry, odc.geo.geom.Geometry], optional) –

    A GeoJSON string for the bounding box of the data used to construct the -grid. It defaults to EPSG:4326 if a CRS is not provided. -Example of adding CRS:

    -
    {"type": "Polygon", "crs": {"properties": {"name": "EPSG:3857"}}}
-
    -
    -

    • -

  • like (xarray.Dataset, optional) –

    Uses the output of a previous load() to form the basis of a request -for another product. -E.g.:

    -
    gcds = make_geocube(vector_data='my_vector.geopackage', like=other_gcds)
-
    -
    -

    • -

  • fill (float, optional) – The value to fill in the grid with for nodata. Default is NaN.

    • -

  • group_by (str, optional) – When specified, perform basic combining/reducing of the data on this column.

    • -

  • interpolate_na_method ({'linear', 'nearest', 'cubic'}, optional) – This is the method for interpolation to use to fill in the nodata with -scipy.interpolate.griddata().

    • -

  • categorical_enums (dict, optional) – A dictionary of all categories for the table columns containing categorical -data. The categories will be made unique and sorted if they are not already. -E.g. {‘column_name’: [‘a’, ‘b’], ‘other_column’: [‘c’, ‘d’]}

    • -

  • rasterize_function (function, optional) – Function to rasterize geometries. Other options are available in -geocube.rasterize. Default is geocube.rasterize.rasterize_image().

    • -
-
-
Returns:
-

Requested data in a xarray.Dataset.

-
-
Return type:
-

xarray.Dataset

-
-
-
- -
-
-

rasterize

-
-
-geocube.rasterize.rasterize_image(geometry_array: GeoSeries, data_values: ndarray[Any, dtype[_ScalarType_co]] | IntegerArray, geobox: GeoBox, fill: float, merge_alg: MergeAlg = MergeAlg.replace, filter_nan: bool = False, all_touched: bool = False, **ignored_kwargs) → ndarray[Any, dtype[_ScalarType_co]] | None[source]
-

Rasterize a list of shapes+values for a given GeoBox.

-
-
Parameters:
-
    -

  • geometry_array (geopandas.GeoSeries) – A geometry array of points.

    • -

  • data_values (Union[NDArray, pandas.arrays.IntegerArray]) – Data values associated with the list of geojson shapes

    • -

  • geobox (odc.geo.geobox.GeoBox) – Transform of the resulting image.

    • -

  • fill (float) – The value to fill in the grid with for nodata.

    • -

  • merge_alg (rasterio.enums.MergeAlg, optional) – The algorithm for merging values into one cell. Default is MergeAlg.replace.

    • -

  • filter_nan (bool, optional) – If True, will remove nodata values from the data before rasterization. -Default is False.

    • -

  • all_touched (bool, optional) – Passed to rasterio.features.rasterize. If True, all pixels touched by -geometries will be burned in. If false, only pixels whose center is -within the polygon or that are selected by Bresenham’s line algorithm -will be burned in.

    • -

  • **ignored_kwargs – These are there to be flexible with additional rasterization methods and -will be ignored.

    • -
-
-
Returns:
-

The vector data in the rasterized format.

-
-
Return type:
-

numpy.ndarray or None

-
-
-
- -
-
-geocube.rasterize.rasterize_points_griddata(geometry_array: GeoSeries, data_values: ndarray[Any, dtype[_ScalarType_co]], grid_coords: dict[str, ndarray[Any, dtype[_ScalarType_co]]], fill: float, method: str = 'nearest', rescale: bool = False, filter_nan: bool = False, **ignored_kwargs) → ndarray[Any, dtype[_ScalarType_co]] | None[source]
-

This method uses scipy.interpolate.griddata to interpolate point data -to a grid.

-
-
Parameters:
-
    -

  • geometry_array (geopandas.GeoSeries) – A geometry array of points.

    • -

  • data_values (list) – Data values associated with the list of geojson shapes

    • -

  • grid_coords (dict) – Output from rioxarray.rioxarray.affine_to_coords

    • -

  • fill (float) – The value to fill in the grid with for nodata.

    • -

  • method ({'linear', 'nearest', 'cubic'}, optional) – The method to use for interpolation in scipy.interpolate.griddata.

    • -

  • rescale (bool, optional) – Rescale points to unit cube before performing interpolation. Default is false.

    • -

  • filter_nan (bool, optional) – If True, will remove nodata values from the data before rasterization. -Default is False.

    • -

  • **ignored_kwargs – These are there to be flexible with additional rasterization methods and -will be ignored.

    • -
-
-
Returns:
-

:class:`numpy.ndarray`

-
-
Return type:
-

An interpolated numpy.ndarray.

-
-
-
- -
-
-geocube.rasterize.rasterize_points_radial(geometry_array: GeoSeries, data_values: ndarray[Any, dtype[_ScalarType_co]], grid_coords: dict[str, ndarray[Any, dtype[_ScalarType_co]]], method: str = 'linear', filter_nan=False, **ignored_kwargs) → ndarray[Any, dtype[_ScalarType_co]] | None[source]
-

This method uses scipy.interpolate.Rbf to interpolate point data -to a grid.

-
-
Parameters:
-
    -

  • geometry_array (geopandas.GeoSeries) – A geometry array of points.

    • -

  • data_values (list) – Data values associated with the list of geojson shapes

    • -

  • grid_coords (dict) – Output from rioxarray.rioxarray.affine_to_coords

    • -

  • method (str, optional) – The function to use for interpolation in scipy.interpolate.Rbf. -{‘multiquadric’, ‘inverse’, ‘gaussian’, ‘linear’, -‘cubic’, ‘quintic’, ‘thin_plate’}

    • -

  • filter_nan (bool, optional) – If True, will remove nodata values from the data before rasterization. -Default is False.

    • -

  • **ignored_kwargs – These are there to be flexible with additional rasterization methods and -will be ignored.

    • -
-
-
Returns:
-

:class:`numpy.ndarray`

-
-
Return type:
-

An interpolated numpy.ndarray.

-
-
-
- -
-
-

vectorize

-
-
-geocube.vector.vectorize(data_array: DataArray) → GeoDataFrame[source]
-
-

New in version 0.4.0.

-
-

Powered by: rasterio.features.shapes()

-

Convert 2D xarray.DataArray into -a geopandas.GeoDataFrame.

-

The nodata, CRS, and transform of the xarray.DataArray -are determined using rioxarray.

-

Helpful references:

- -
-
Parameters:
-

data_array (xarray.DataArray) – Input 2D DataArray raster.

-
-
Return type:
-

geopandas.GeoDataFrame

-
-
-
- -
-
-

show_versions

-
-
-geocube.show_versions()[source]
-
-

New in version 0.0.12.

-
-

Print useful debugging information

-

Example

-

> python -c “import geocube; geocube.show_versions()”

-
- -
-
-

exceptions

-

This contains exceptions for GeoCube.

-
-
-exception geocube.exceptions.GeoCubeError[source]
-

Bases: RuntimeError

-

Base GeoCube exception class.

-
- -
-
-exception geocube.exceptions.VectorDataError[source]
-

Bases: GeoCubeError

-

This is for errors in the vector data passed into GeoCube.

-
- -
-
- - -
-
- -
-
-
-
- - - - \ No newline at end of file diff --git a/0.5.1/getting_started.html b/0.5.1/getting_started.html deleted file mode 100644 index 01515148..00000000 --- a/0.5.1/getting_started.html +++ /dev/null @@ -1,155 +0,0 @@ - - - - - - - Getting Started — geocube 0.5.1 documentation - - - - - - - - - - - - - - - - - - - - -
- - -
- -
-
-
- -
-
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- -
-

Getting Started

-

geocube combines the interfaces of:

- -

and is powered by GDAL using:

- -

When getting started, the API documentation to start reading would be geocube.api.core.make_geocube().

-

The simplest example would be to rasterize a single column:

-
from geocube.api.core import make_geocube
-
-out_grid = make_geocube(
-    vector_data="path_to_file.gpkg",
-    measurements=["column_name"],
-    resolution=(-0.0001, 0.0001),
-)
-out_grid["column_name"].rio.to_raster("my_rasterized_column.tif")
-
-
-

You can also rasterize a GeoDataFrame -directly in the vector_data argument. This enables you to load in subsets of data -or perform various operations before rasterization.

-

Once finished, you can write to anything supported by rasterio -using rioxarray’s rio.to_raster() method. -You can also write to a netCDF file using xarray’s to_netcdf().

-

However, life is only this simple when your data is perfectly clean, geospatially unique, and numeric. -The good news is that geocube supports a variety of use cases and custom rasterization functions if your dataset -does not meet these criteria (see: Usage Examples).

-
- - -
-
- -
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- - - - \ No newline at end of file diff --git a/0.5.1/history.html b/0.5.1/history.html deleted file mode 100644 index f386eddf..00000000 --- a/0.5.1/history.html +++ /dev/null @@ -1,333 +0,0 @@ - - - - - - - History — geocube 0.5.1 documentation - - - - - - - - - - - - - - - - - - - -
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History

-
-

Latest

-
-
-

0.5.0

-
    -

  • BUG: Ignore categorical columns not in measurements (pull #161)

    • -

  • ENH: Read subset of measurements for efficiency (pull #161)

    • -
-
-
-

0.4.3

-
    -

  • DEP: Support Python 3.10+ (pull #156)

    • -

  • BUG: Update minimize dtype for int64 & int8 support (issue #139)

    • -

  • BUG: Support pandas IntegerArray (pull #158)

    • -
-
-
-

0.4.2

-
    -

  • BUG: Fix conversion to datetime64[ns] for naive datetimes (pull #145)

    • -
-
-
-

0.4.1

- -
-
-

0.4.0

- -
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0.3.3

-
    -

  • BUG: Allow resolution in list format (pull #122)

    • -
-
-
-

0.3.2

-
    -

  • MNT: corrections to setup.cfg; use “pytest”; add “make dist” target (pull #111)

    • -

  • BUG: Write transform on output dataset (pull #112)

    • -
-
-
-

0.3.1

-
    -

  • TYPE: Modified NDArray import for typing (pull #106)

    • -
-
-
-

0.3.0

-
    -

  • BUG: Handle input CRS without EPSG (pull #105)

    • -

  • TYPE: Added type hints (pull #101)

    • -
-
-
-

0.2.0

-
    -

  • DEP: Replace datacube with odc-geo (issue #90)

    • -
-
-
-

0.1.2

-
    -

  • BUG: Fix width and height order to the one used in rioxarray (issue #93)

    • -
-
-
-

0.1.1

-
    -

  • DEP: Drop Python 3.7 Support (issue #86)

    • -

  • BUG: Updated logic to skip rasterizing strings for numpy 1.22+ (issue #88)

    • -
-
-
-

0.1.0

-
    -

  • ENH: Minimize dtype when possible in geocube.rasterize.rasterize_image (issue #72)

    • -

  • REF: remove unnecessary shapely.geometry.mapping in rasterize_image (#80)

    • -

  • ENH: vector_data to read path-like objects (pull #81)

    • -
-
-
-

0.0.18

-
    -

  • DEP: Explicitly add scipy as dependency (pull #75)

    • -
-
-
-

0.0.17

-
    -

  • DEP: Python 3.7+ (#67)

    • -

  • REF: Write grid_mapping to encoding instead of attrs (#66)

    • -
-
-
-

0.0.16

-
    -

  • BUG: Compatibility with rioxarray 0.3 (#57)

    • -
-
-
-

0.0.15

-
    -

  • Address xarray & numpy deprecations (#43)

    • -
-
-
-

0.0.14

-
    -

  • Add “all_touched” keyword argument to geocube.rasterize.rasterize_image (pull #40)

    • -
-
-
-

0.0.13

-
    -

  • Address deprecation warnings from datacube and rioxarray (issue #29)

    • -
-
-
-

0.0.12

-
    -

  • ENH: Added geocube.show_versions() and cli geocube –show-versions (pull #23)

    • -

  • Add compatibility between datacube and geopandas CRS versions (pull #24)

    • -
-
-
-

0.0.11

-
    -

  • Drop Python 3.5 Support (issue #12)

    • -

  • ENH: Update to support geopandas with pyproj.CRS (pull #18)

    • -

  • BUG: Update timestamp handling to ensure correct format for dtype (pull #18)

    • -
-
-
-

0.0.10

-
    -

  • Added filter_nan kwarg to filter out missing data when rasterizing (issue #9)

    • -

  • Change default fill value to NaN when rasterizing (pull #11)

    • -
-
-
-

0.0.9

-
    -

  • Added rescale kwarg to geocube.rasterize.rasterize_points_griddata. (pull #8)

    • -

  • Removed fillna(numpy.nan) in geocube.geo_utils.geobox.load_vector_data as not necessary -and for compatibility with geopandas==0.6.0. (pull #8)

    • -
-
-
-

0.0.8

-
    -

  • Add merge algorithm option for rasterization (issue #5)

    • -

  • Drop Python 2 support (issue #6)

    • -
-
-
-

0.0.7

-
    -

  • Remove geocube pin (pull #4)

    • -
-
-
-

0.0.6

-
    -

  • Added additional methods for resampling points to a 2D grid (pull #3)

    • -
-
-
-

0.0.5

-
    -

  • Fix converting to another projection to ensure bounds are correctly accounted for (pull #2)

    • -
-
-
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-
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- - - - \ No newline at end of file diff --git a/0.5.1/index.html b/0.5.1/index.html deleted file mode 100644 index fe97c65b..00000000 --- a/0.5.1/index.html +++ /dev/null @@ -1,142 +0,0 @@ - - - - - - - Welcome to geocube’s documentation! — geocube 0.5.1 documentation - - - - - - - - - - - - - - - - - - - -
- - -
- -
-
-
- -
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Welcome to geocube’s documentation!

-

GitHub: http://github.com/corteva/geocube

-
-

Contents:

- -
-
-
-

Indices and tables

- -
- - -
-
- -
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- - - - \ No newline at end of file diff --git a/0.5.1/installation.html b/0.5.1/installation.html deleted file mode 100644 index 8a450cec..00000000 --- a/0.5.1/installation.html +++ /dev/null @@ -1,172 +0,0 @@ - - - - - - - Installation — geocube 0.5.1 documentation - - - - - - - - - - - - - - - - - - - - -
- - -
- -
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- -
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- -
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Installation

-
-

Stable release

-

Use pip to install from PyPI:

-

Install from pip:

-
python -m pip install geocube
-
-
-

Use conda with the conda-forge channel:

-
conda config --prepend channels conda-forge
-conda config --set channel_priority strict
-conda create -n geocube_env geocube
-conda activate geocube_env
-
-
- -
-

Note

-

“… we recommend always installing your packages inside a -new environment instead of the base environment from -anaconda/miniconda. Using envs make it easier to -debug problems with packages and ensure the stability -of your root env.” -– https://conda-forge.org/docs/user/tipsandtricks.html

-
-
-

Warning

-

Avoid using pip install with a conda environment. If you encounter -a python package that isn’t in conda-forge, consider submitting a -recipe: https://github.com/conda-forge/staged-recipes/

-
-
-
-

From source

-

The source for geocube can be installed from the GitHub repo.

-
python -m pip install git+git://github.com/corteva/geocube.git#egg=geocube
-
-
-

To install for local development:

-
git clone git@github.com:corteva/geocube.git
-cd geocube
-python -m pip install -e .[dev]
-
-
-
-
- - -
-
- -
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- - - - \ No newline at end of file diff --git a/0.5.1/modules.html b/0.5.1/modules.html deleted file mode 100644 index ba1ff532..00000000 --- a/0.5.1/modules.html +++ /dev/null @@ -1,163 +0,0 @@ - - - - - - - API Documentation — geocube 0.5.1 documentation - - - - - - - - - - - - - - - - - - - - -
- - -
- -
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- -
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- -
-

API Documentation

-

This page serves as the index for the lower level per-module documentation tree.

-
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Contents:

- -
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-
- -
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Tool to convert geopandas vector data into rasterized xarray data.

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a/refs/tags/0.5.2/.doctrees/nbsphinx/examples/categorical.ipynb +++ /dev/null @@ -1,783 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Example - Categorical Data" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import geopandas\n", - "import pandas\n", - "\n", - "from geocube.api.core import make_geocube\n", - "\n", - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Load in soil data" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "ssurgo_data = geopandas.read_file(\"../../test/test_data/input/soil_data_group.geojson\")" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - 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", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# original data\n", - "ssurgo_data[ssurgo_data.hzdept_r==15].plot(column='sandtotal_r')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Generate categories for categorical data" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If your data is only a subset of all of the data, the list of categories you get will likely not be complete.\n", - "\n", - "NOTE: The categories will be made unique and sorted internally if they are not already." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['Somewhat poorly drained',\n", - " 'Poorly drained',\n", - " 'Well drained',\n", - " 'Excessively drained']" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# this is only a subset of all of the classes\n", - "ssurgo_data.drclassdcd.drop_duplicates().values.tolist()" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "# complete list of categories\n", - "drclasses_complete = [\n", - " 'Poorly drained',\n", - " 'Somewhat poorly drained',\n", - " 'Excessively drained',\n", - " 'Subaqueous',\n", - " 'Well drained',\n", - " 'Somewhat excessively drained',\n", - " 'Very poorly drained',\n", - " 'Moderately well drained'\n", - "]" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "categorical_enums = {'drclassdcd': drclasses_complete}" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Convert data to grid\n", - "\n", - "See docs for [make_geocube](../geocube.rst#make-geocube)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "out_grid = make_geocube(\n", - " vector_data=ssurgo_data,\n", - " output_crs=\"epsg:32615\",\n", - " group_by='hzdept_r',\n", - " resolution=(-100, 100),\n", - " categorical_enums=categorical_enums\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
<xarray.Dataset>\n",
-       "Dimensions:                (drclassdcd_categories: 9, hzdept_r: 11, x: 15, y: 19)\n",
-       "Coordinates:\n",
-       "  * y                      (y) float64 4.597e+06 4.597e+06 ... 4.595e+06\n",
-       "  * x                      (x) float64 7.004e+05 7.004e+05 ... 7.018e+05\n",
-       "  * hzdept_r               (hzdept_r) float64 0.0 5.0 15.0 ... 105.0 120.0 150.0\n",
-       "  * drclassdcd_categories  (drclassdcd_categories) object 'Excessively drained' ... 'nodata'\n",
-       "    spatial_ref            int64 0\n",
-       "Data variables:\n",
-       "    drclassdcd             (hzdept_r, y, x) float64 -1.0 -1.0 -1.0 ... 7.0 7.0\n",
-       "    hzdepb_r               (hzdept_r, y, x) float64 nan nan nan ... 180.0 180.0\n",
-       "    claytotal_r            (hzdept_r, y, x) float64 nan nan nan ... 21.0 21.0\n",
-       "    sandtotal_r            (hzdept_r, y, x) float64 nan nan nan ... 10.0 10.0\n",
-       "    silttotal_r            (hzdept_r, y, x) float64 nan nan nan ... 69.0 69.0\n",
-       "Attributes:\n",
-       "    grid_mapping:  spatial_ref
" - ], - "text/plain": [ - "\n", - "Dimensions: (drclassdcd_categories: 9, hzdept_r: 11, x: 15, y: 19)\n", - "Coordinates:\n", - " * y (y) float64 4.597e+06 4.597e+06 ... 4.595e+06\n", - " * x (x) float64 7.004e+05 7.004e+05 ... 7.018e+05\n", - " * hzdept_r (hzdept_r) float64 0.0 5.0 15.0 ... 105.0 120.0 150.0\n", - " * drclassdcd_categories (drclassdcd_categories) object 'Excessively drained' ... 'nodata'\n", - " spatial_ref int64 0\n", - "Data variables:\n", - " drclassdcd (hzdept_r, y, x) float64 -1.0 -1.0 -1.0 ... 7.0 7.0\n", - " hzdepb_r (hzdept_r, y, x) float64 nan nan nan ... 180.0 180.0\n", - " claytotal_r (hzdept_r, y, x) float64 nan nan nan ... 21.0 21.0\n", - " sandtotal_r (hzdept_r, y, x) float64 nan nan nan ... 10.0 10.0\n", - " silttotal_r (hzdept_r, y, x) float64 nan nan nan ... 69.0 69.0\n", - "Attributes:\n", - " grid_mapping: spatial_ref" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "out_grid" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# mask nodata and plot\n", - "clay_slice = out_grid.claytotal_r.sel(hzdept_r=15)\n", - "clay_slice.where(clay_slice!=out_grid.claytotal_r.rio.nodata).plot()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Dealing with categorical data" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Because the data needs to be numerical for conversion from vector to raster, the code displays the categories as numbers. To convert back to strings, you will need to use the categories provided to convert back." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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Llzf0RkMIoTeW59JHd1uraFrmIelDwALbs+qcdgUw2vYY4DrgXADbM4CrgFtJJYjbgNq3+VDg5TxI5pfAWR3i/DBwS4cqq91sbwfsB3xJ0u6dJcb2NNvjbI8bOXxgLUYfQhi4bEXJo2BXYIKkR4ELgfGSziueYHux7VfyyzOB7QvHptoea3tvQMAD+dB84Pd5/1JgTIf7HkKHKivbT+R/F+RrduzdWwshhOqkBvPBpbZW0bTMw/YU26NsjyZ9od9ge2LxHEkbF15OIDWs13poDc/7Y0gZxIx83mWkaiyAPViRqdQmBNsDuLwQtrakdWv7wD7AvIreZgghVCCtYV5maxV93u9L0slAm+3pwNGSJgDLgCXkWSCBIcDNkgCWAhNtL8vHvkMa/HIs8AJwRCH6jwIzOkwAthFwaY5rDeAC29c0472FEEJPpAbz1mnPKKNPMg/bM4GZef+EQvgUYEon579M6nHVWVzPknpwdXbsHOCcDmEPA9v2JN0hhNBXKh5h3nStM+IkhBBWU1WPMO8LkXmEEEILaF/VSh6SPgacAryF1OtJgG13NWI8hBBCA2x4rX0VyzyA7wIftn1vsxMTQgiro1RtteplHv+IjCOEEJqrlUaPl1Em82iTdBFpfEVtQB+2f9/1JSGEEMpaVbvqDgNeIg2uqzErRnmHEELolVWw2sp25StQhRBCWFnJ9clbRrdZnaRRki6VtCBvv8tTrYcQQqhA6m01uNTWKsqUk84GpgNvy9sVOSyEEEIFaoMEy2ytokzmMdL22XlhpmV5CpCRTU5XCCGsVtpRqa1VlGkwXyxpIiumOT8UWNy8JIXQ9z74ttaa/uzaJ+f0dxJCHxqIva3KlDw+C3wSeBp4Cvg4EI3oIYRQoYG2GFSZ3laPkdbaCCGE0AS2WFZhxpAX4XuetALrsrzyaqW6zDwkfdX2dyX9hFSqWonto6tOTAghrK6aUG21l+1FVUdaU6/kUZuSpK1ZNw8hhDAw2zy6zDxsX5F3X7J9cfGYpE80NVUhhLCaaSDzGCGp+KN+mu1pHc4xMEOSgV90crzXyvS2mgJcXCIshBBCDzS4GNSiEm0Yu9l+QtJbgOsk3Wf7pt6lcmX12jz2A/YHNpH048KhYaQ1x0MIIVSkyjEctp/I/y6QdCmwI9A3mQfwJKm9YwIwqxD+PHBslYkIIYTVmQ3LKloMStLawCDbz+f9fYCTK4m8oMvU2p5j+1zgX4HzbJ+bX19OYWr27kgaLOlOSVd2cmySpIWSZuftiMKxUyTNy9vBhXBJmirpAUn3Sjo6h+8p6blCXCcUrtlX0v2SHpJ0fNm0hxBCX6lwepKNgD9JmgP8BfiD7WuqTm+ZNo8ZwL8BL+TXb85h7yt5j8mknltdLVt7ke2jigGSDgC2A8YCQ4GZkq62vRSYBGwKbG27Pdfp1dxs+0Md4hoM/BTYG5gP3CFpuu17SqY/hBCaqsE2j/px2Q8DTZ8yoUw5aU3btYyDvL9Wmcjz7LsHAGc2mK5tgJvyXFovAnOBffOxLwIn227P6VnQTVw7Ag/Zftj2q8CFwIENpieEEJrKVqmtVZQpebwoaTvbfwWQtD3wz5LxnwZ8FVi3zjkHSdodeAA41vbjwBzgREnfJ2VUewG1ksLmwMGSPgosBI62/WA+tksuqj0JfMX23cAmwOOF+80HduosIZKOBI4E2GyTMo8mhJXFnFShp1pp0sMyynxDHgNcLOlJQMBbgYPrXwKSPgQssD1L0p5dnHYF8Bvbr0j6PHAuMN72DEk7ALeSMojbSMPsIVVjvWx7nKSPAWcB7wf+Crzd9guS9ictm7tFiff3utwXehrAuG3XfMOo+hBCaAZ74A0S7LbayvYdwNak6qIvAP9ie1b9qwDYFZiQ51i5EBgv6bwOcS+2XWt8PxPYvnBsqu2xtvcmZVoP5EPzWbEE7qXAmHz+0lr1mu2rgCGSRgBPkNpIakblsBBCaBFiefugUlurKLOS4FrA14DJtucBo3Opoi7bU2yPsj0aOAS4wfbEDnFvXHg5gTwlSu6hNTzvjyFlEDPyeZeRqrEA9iBnKpLeKkl5f8f83hYDdwBbSHqHpDfltEzvLv0hhNCXVsU2j7NJ4zx2ya+fII0uf0PX2zIknQy02Z4OHC1pAmnQ4RJSTyqAIcDNOS9YCky0XRuY+B3gfEnHknqA1br3fhz4oqRlpDaZQ2wbWCbpKOBaYDBwVm4LCSGElrBKzW1VsLntgyUdCmD7pdov/LJszwRm5v0TCuFTSFOddDz/ZVKPq87iepbUg6tj+OnA6V1ccxVwVSNpDiGEPuPU7jGQlMk8XpX0ZvK07JI2p4FBgiGEELq3Kva2OhG4BthU0vmkhvBJzUxUCCGsTpwbzAeSMisJXifpr8DOpF5Pk5u5wEgIIayOVplqK0lb275P0nY56Kn872aSNgWW5CVqQwgh9FIr9aQqo17J4z9Jo62/38Xx4ZLm2P5U9ckKIYTVh70KZR62j8z/7tXVOZJmdHUshIEkphUJ/W2V66oraQhpdPnuOWgmaVnD12zv08S0hRDCamOVafMo+Dlp0N7P8utP5bAjurwihBBCaUa0r2q9rYAdbBfnhr8hz1wbQgihIgOs4FFqPY/leWAgAJLeyYoZbkMIIfSWV825rb4C3CjpYdI4j7cDn2lqqkIIYXUzwIoedTOPvITrtqR1MbbKwfcXplEPIYRQgb4sVUj6CXWyK9tHdxdH3Wor28uBQ22/Yntu3iLjCCGEChlob1eprSJtpNnS1wS2Ax7M21jgTWUiKFNtdYuk04GLgBdrgbVlaUMIIfSSgT4sedg+F0DSF4HdakteSDoDuLlMHGUyj7H535OL9wbGl09qCCGEevppnMcGwDDSekoA6+SwbpWZGLHLEeYhhBAqUmHmkdur24AnbNdb+fU7wJ2SbiR1iNod+GaZe5QZYT6cNC37bqS39yfgZNuLy9wghGb64Nu27f6kEmJ6ktC/Ku+GO5m0rPeweifZPlvS1cBOOehrtp8uc4My4zwuBBYCB5GWel1Iav8IIYRQFZfcuiFpFGm11TNLnPtR4J+2L7d9OfCypI+USW6ZzGNj29+y/Ujevg1sVCbyEEIIJRjcrlJbCacBXwXaS5x7ou3nXk9GWub7xDI3KZN5zJB0iKRBefskcG2ZyCHVvUm6U9KVnRybJGmhpNl5O6Jw7BRJ8/J2cCFckqZKekDSvZKOzuGHSZor6S5Jt0ratnDNozl8tqS2smkPIYS+o5IbIyS1FbYjX49B+hCwwPaskjftLA8o05Gq1En/BzgG+HV+PRh4UdLnAduuW6dG93VvF9k+qhgg6QBS3+OxwFBgpqSrbS8lLYG7KbC17XZJb8mXPQLsYfsZSfsB01hRjwewV6yAGEJoWeUbzBfZHtfFsV2BCZL2J43hGCbpPNsTuzi/TdIPgJ/m118ijf/oVrclD9vr2h5ke0jeBuWwdbvLOBqpe+tgG+Am28tsvwjMBfbNx75IarBvz+lbkP+91fYz+ZzbgVEN3jOEEPpPBW0etqfYHmV7NHAIcEOdjAPgy8CrpHbsi4BXSBlIt5o9B3CZureDcnXTJXl5W4A5wL6S1pI0AtiLVNoA2Bw4OBfXrpa0RSdxfg64uvDapOq3WcUiXgghtITaIMEyW5W3tV+0fbztcXmbkn+wd6tU3VZPFOveJO3ZxWlXAL+x/UquBjsXGG97hqQdgFtJvbtuY8VMvkOBl22Pk/Qx4Czg/YX77kXKPHYr3Gc320/kKq7rJN1n+6ZO0nwkaeldNtukaY8mhBDeoOpBgrZnkhbvewNJV1B/bqsJ3cXfZclD0ju6T15dtbq3R0ndfcdLOq9DAhcX5so6E9i+cGyq7bG29ya1Ej2QD80Hfp/3LwXGFNI8JsdzYHEciu0n8r8L8jU7dpZg29NqOfDI4YN79q5DCKEn2lVuq8b3gO+T2or/Cfwyby8AfysTQb1qq0sAJF3fk5SVqXuTtHHh5QRSw3qth9bwvD+GlEHU1ku/jFSNBbAHOVORtBkpU/mU7VpGg6S1Ja1b2wf2Aeb15D2FEEKzyOW2Ktj+o+0/ArvaPtj2FXn7dwo1OfXUq5sZJOm/gC0l/WcnN/9BTxIt6WSgzfZ04GhJE4BlpLlVJuXThgA3SwJYCkysTdxFGk5/vqRjSblkrXvvCcBw4Gf5umW5R8JGwKU5bA3gAtvX9CTtIYTQFCUHADbB2pLeaftheL3Gae0yF9bLPA4BPpLPWbc3qSvWvdk+oRA+BZjSyfkvk3pcdRbXs6QeXB3Dj6CTddXzQ6lmDosQQmiK6hvDSzqWNBSiuNhfqU5FXWYetu8HTpE01/bVXZ0Xmu+dMz5bSTwP73NWJfG0kqrmpIo5skK/64eSh+1rco/VrXPQfWXXbCrTpejWPIhk9/z6j6RxFs/VuWbgG/IeBr21d4PR25/esqLEhBBWeWUmE6mYpE8A19ieI+nrwImSvl1mvaYy4zzOAp4HPpm3pcDZvUlwCCGEgn4a5wF8w/bzknYDPgD8D/DzMheWKXlsbvugwutvSprdg0SGEELoQlU9qRpUGz93APBL23+Q9O0yF5Ypefwz50oASNqV1C84hBBCVSqakr1BT0j6BXAwcJWkoZSceaRMyeMLwK8krZdfPwN8ukfJDCGE0Eo+SZo38Hu2n81j744rc2GZZWjnANtKGpZfL+1NSkMIIbxRP1VbbQz8IU8RtSdpQPavylxYemJE20sj4wghhCYwfT09Sc3vgOWS3kVaxmJT4IIyFzZ7Vt0QQghl9E+bR3ueveNjwE9sH0cqjXQrpo4NIYQW0E/VVq9JOhQ4HPhwDhtS5sJuSx55TY1vSPplfr1Fnm49hBBCVfqn5PEZYBdgqu1H8txWv+7mGqBcyeNs0rKEu+TXTwAXA29YkzysrKppRULzxbQiod/1z/Qk9wBHF14/ApxS5toybR6b2/4u8FqO/CXyKuwhhBB6r+x07FVXbeWapEsk3SPp4dpW5toymcerkt5MzhclbU5a5zaEEEJV+qe31dmk6UiWkdZJ+hVwXt0rsjKZx0nANcCmks4Hrge+1qNkhhBC6FR/lDyAN9u+HpDtx2yfRCdLXnSmzCDBGZJmATuTqqsm217Um9SGEELooH96W70iaRDwoKSjSG3a65S5sExvq+vzWuN/sH2l7UU9XZo2hBBCJ/qpzQOYDKxFajTfHvgUJaef6rLkIWnNHOkISRuwopF8GLBJb1IbQgihg/7pbXVH3n2B1G23tHrVVp8HjgHeRuqqW8s8lgKnN5jGEEIIdaiixaDyD/+bgKGk7/hLbJ/Y4ZwrqJNd2Z7Q3X3qLUP7I+BHkr5s+ydlEx5CCKFfvQKMt/2CpCHAnyRdbfv2wjnf6+1NyjSY/0TSe4BtgDUL4aVmXpQ0GGgDnrD9oQ7HJgGnkhppAE63fWY+dgorWv2/ZfuiHC7g28AnSAuZ/Nz2j3P4j4D9gZeASbWlFCV9Gvh6juvbts8tk/YQQugzFVVb2TapGgrSVCNDOsZu+48AktYG/mm7Pb8eTCqxdKvbzEPSicCepMzjKmA/4E+UnLaX1CBzL6mtpDMX2T6qwz0PALYDxpLeyMyccy4FJpFmftzadrukt+TL9gO2yNtOpL7LO0naEDgRGEd6gLMkTbf9TMn0hxBCc1XcGJ4zgVnAu4Cf2v5zF6deD/wbKzKbNwMzgPd1d48y4zw+Tlrb9mnbnwG2Bdarf0kiaRSp9HBmmfMLtgFusr3M9ovAXNKCJQBfBE6u5ZS2F+TwA4FfObkdWD8vbPJB4DrbS3KGcV0hrhBCaA3l57YaIamtsB35hqjs5bbHAqOAHXPtUWfWtP1C4boXSB2lulVmbqt/5l/4y/KCUAtIv/zLOA34KrBunXMOkrQ78ABwrO3HgTnAiZK+T3ojewH35PM3Bw6W9FFgIXC07QdJPcAeL8Q7P4d1Ff4G+UM4EmCzzTYr+RbDquCDb9u2knhijqzQY+VLHotsjysVZVod8EbSD+Z5nZzyoqTtClX821NymfEyJY82SesDvyQVg/4K3NbdRXnm3QW2Z9U57QpgtO0xpBLBuZAGJpKqyG4FfpPvV1uofSjwcn54vwTOKvEeSrE9zfY423jdmicAABlBSURBVONGjhxZVbQhhFCXSL2tymzdxiWNzN/Z5Kml9gbu6+L0ycDFkm6W9CfgIuCoLs5dSZkG8//Iu2dIugYYZntuibh3BSZI2p/U0D5M0nm2JxbiXlw4/0zgu4VjU4GpAJIuIJVMIJUcfp/3LyXNzQKp0b1YIhqVw54gtdkUw2eWSH8IIfSNats8NgbOze0eg4Df2n7DLOj5+PuBrYGtcvD9tl8rc5NSKwlK2kTS+4DNSG0Ju3d3je0ptkfZHg0cAtxQzDhyvMUVqyaQGtaRNFjS8Lw/hrSu7ox83mWkaiyAPViRqUwHDleyM/Cc7aeAa4F9JG2QBzvuk8NCCKF1VLSeh+25tt9re4zt99g+uYvzlgOH2n7N9ry8lco4oFxvq1OAg0ltDrWqI5MGoTRM0slAm+3pwNGSJpBmdFxC6kkFqWvZzan3LUuBiXmpRIDvAOdLOpbUQ+CIHH4VqZvuQ6Suup8BsL1E0reA2kjKk20v6UnaQwihafpnbqtbJJ1Oqq568fWk5DaQeso0mH8E2Mp2j6dhtz2TXFVk+4RC+BRgSifnv0zqcdVZXM/SyayPuW/zl7q45iwqbBsJIYSq9dMytGPzv9+sJYOUjY3v7sIymcfDpJJArOERQgjN0oeZh6T/zLtX5jsXFwoplZIymcdLwOw8k+7rGYjto7u+JIQQQmmubm6rkmrDJ7YCdgAuJ2UgHwb+UiaCMpnH9LyFEEJolj4sedj+JoCkm4DtbD+fX58E/KFMHGW66sY8UCGE0GT91OaxEfBq4fWrOaxb9dbz+K3tT0q6i07yxDywL4QQQhX6J/P4FfAXSZfm1x8BzilzYb2Sx+T874fqnBNCj+xy3Bcqiee2U8+oJJ4Q+lXJMRyV39aeKulq0mBBgM/YvrPMtfXW83gq//tY75MYQgihK6Lfqq1qYzq6HdfRUb1qq+epv9JUV1OshxBCaFB/ZR49Va/ksS5AHp39FPBrUgZ5GGnulBBCCFVZVTKPggm2i/NV/1zSHOCEri4IIYTQoAGWeZSZGPFFSYflyQoHSTqMwhwoIYQQeinPqltmaxVlMo9/Bz4J/CNvn8hhIYQQqlLRrLp9pW61VZ7v/SjbB/ZRekIIYbXUx9OT9FrdzMP2ckm79VViQghhddVKVVJllGkwv1PSdOBiVp7v/fddXxJCCKG0FquSKqNM5rEmsJiV53c3K5aCDSGE0FurWuZh+zN9kZAQQlhd9ecI854qswztKOAnwK456GZgsu35zUxYWOHhfWIRxGa79sk5/Z2EsJpT+8DKPcp01T2btJ7H2/J2RQ4LIYRQhbLddFsofymTeYy0fbbtZXk7BxjZ5HSFEMJqZVUcJLhY0sQ8wnywpImkBvRS8jV3Srqyk2OTJC2UNDtvRxSOnSJpXt4OLoSfI+mRwjVjc/hxhbB5kpZL2jAfe1TSXflYW9m0hxBCnxlgJY8yva0+S2rz+GF+fQvQSCP6ZOBeoKtZeC+yfVQxQNIBwHbAWGAoMFPS1baX5lOOs31J8RrbpwKn5us/DBxre0nhlL1sL2og3SGE0GeqKlVI2pS0yNNGpOxmmu0fVRP7Ct2WPGw/ZnuC7ZF5+4jtv5eJPDe2HwCc2WC6tgFuytVkLwJzgX0buP5Q4DcN3jOEEPpPdSWPZcD/tb0NsDPwJUnbVJ3cbjMPSaMkXSppQd5+lzOFMk4DvgrUG3h/kKS5ki7JOSbAHGBfSWtJGgHsBWxauGZqvuaHkoZ2SO9apIzmd4VgAzMkzZJ0ZJ33eqSkNkltCxcuLPkWQwihl5ymJymzdRuV/VRe4Anbz5NqfjapOslN620l6UPAAtuz6px2BTA6r4d+HXAugO0ZwFXAraQSxG3A8nzNFGBrYAdgQ+BrHeL8MHBLhyqr3WxvB+xHyoV37ywxtqfZHmd73MiR0ScghNA3auM8SjaYj6j9yM1bvR/Eo4H3An+uOs3N7G21KzBB0qPAhcB4SecVT7C92PYr+eWZwPaFY1Ntj7W9N+nZPpDDn3LyCikT27HDfQ+hQ5WV7SfyvwuASzu5JoQQ+pddboNFtR+5eZvWWXSS1iHVwBxTaC+uTNN6W9meYnuU7dGkL/QbbE8sniOpuCLhBFLxqtZDa3jeHwOMAWYUr5Ek4CPAvEJ86wF7AJcXwtaWVFsVcW1gn+I1IYTQCqrsqitpCCnjOL9Z8xA22tvKpKqkHk9ZIulkoM32dOBoSRNIDTxLgEn5tCHAzSl/YCkw0fayfOx8SSNJpZHZwBcK0X8UmJEb2Ws2Ai7Nca0BXGD7mp6mP4QQKldhN9z8w/p/gHtt/6CaWDu5j91CHYdbyLhx49zW1rshIe1Pb1lRakJXPvi2bbs/qYSYnmT1NOitD/Q6DkmzbI/rTRzrbLipx+x9TKlzb/vtV+reLy+jcTNwFys6K/2X7at6k8aOysxtdS5pLqtn8+sNgO/b/myVCQkhhNVZVYtB2f4TqWamqcpUW42pZRwAtp+R9N4mpimEEFYvptYYPmCUaTAflEsbAOQpP8pkOiGEEEoaaHNblckEvg/cJuni/PoTwNTmJSmEEFZDLZQxlFFmMahf5ckEaysJfsz2Pc1NVgghrD5WycWgAHJmERlGCCE0gz3gFoOKtosQQmgFAyvviMwjhBBawUCrtiozq+6Xi72tQgghVMxAu8ttLaJMV92NgDsk/VbSvnnoewghhCoNsJUEyywG9XVgC9JcKZOAByX9t6TNm5y2EEJYbQy0cR5lSh44TYD1dN6WARsAl0j6bhPTFkIIqw21u9TWKsrMbTUZOBxYRFpz4zjbr0kaBDxIWikwhBBCT7VYlVQZZXpbbUgaGPhYMdB2e14tMIQQQi+kQYIDK/coM8L8xDrH7q02OSGEsJqqaFbdvhLjPEIIoQWsciWPEEIITbaKtnmEEEJoqtbqSVVGZB4hhNAKBli1ValxHr0habCkOyVd2cmxSZIWSpqdtyMKx06RNC9vBxfCz5H0SOGasTl8T0nPFcJPKFyzr6T7JT0k6fhmv+cQQmiI0zK0ZbZW0Rclj8nAvcCwLo5fZPuoYoCkA4DtgLHAUGCmpKttL82nHGf7kk7iutn2St2HJQ0GfgrsDcwnTbUyPdYkCSG0lCh5rCBpFHAAaXBhI7YBbrK9zPaLwFxg3x4mY0fgIdsP234VuBA4sIdxhRBCcwywua2aXfI4jTQCfd065xwkaXfgAeBY248Dc4ATJX0fWAvYi5UXo5qaq6WuB463/UoO30XSHOBJ4Cu27wY2AR4vXDsf2KmzhEg6EjgSYLPNNmvojYb+ce2Tc/o7CSFUQu0tVCdVQtNKHnn0+QLbs+qcdgUw2vYY4DrgXADbM4CrgFuB3wC3AcvzNVOArYEdSKPfv5bD/wq83fa2wE+AyxpNs+1ptsfZHjdy5MhGLw8hhJ4xaZBgma0bks6StEDSvGYlF5pbbbUrMEHSo6SqovGSziueYHtxodRwJrB94dhU22Nt700avf9ADn/KySvA2aRqKWwvtf1C3r8KGCJpBPAEsGnhtqNyWAghtARh5HJbCefQ82r+0pqWedieYnuU7dHAIcANticWz5G0ceHlBFLDeq2H1vC8PwYYA8woXpPXFfkIMC+/fmttrRFJO+b3thi4A9hC0jskvSmnZXpT3nQIIfSUXW7rNhrfBCxpdnL7fJyHpJOBNtvTgaMlTSBN876EtF4IwBDg5pwXLAUm2l6Wj50vaSSpNDIb+EIO/zjwRUnLgH8Ch+Sp5JdJOgq4FhgMnJXbQkIIoXWU7201QlJb4fU029OakKK6+iTzsD0TmJn3TyiETyG1YXQ8/2VSj6vO4hrfRfjpwOldHLuK1IYSQgitp9bmUc4i2+Oal5hyYoR5CCG0gIHW2yoyjxBC6Hfl2jNaSdOnJwkhhNANU1mDuaTa8IatJM2X9LlmJDlKHiGE0AoqqrWyfWg1MdUXmUcIIbSAWAwqhBBC4yLzCCGE0BAblkdvqxBCCI2KkkcIIYSGReYRQgihIQZiDfMQQgiNMTjaPEIIITTCRIN5CCGEHog2jxBCCA2LzCOEEEJjBt7EiJF5hBBCfzMQU7KHEEJoWJQ8QghhYGh/esv+TkIW05OEEEJolMExziOEEELDBtgI86avJChpsKQ7JV3ZybFJkhZKmp23IwrHTpE0L28HF8LPkfRI4ZqxOfwwSXMl3SXpVknbFq55NIfPltTW7PccQggNq2glwb7SFyWPycC9wLAujl9k+6higKQDgO2AscBQYKakq20vzaccZ/uSDvE8Auxh+xlJ+wHTgJ0Kx/eyvaiX7yWEEKpnD7jeVk0teUgaBRwAnNngpdsAN9leZvtFYC6wb70LbN9q+5n88nZgVKPpDSGEfjPASh7NrrY6Dfgq9VfnPShXN10iadMcNgfYV9JakkYAewGbFq6Zmq/5oaShncT5OeDqwmsDMyTNknRkVwmRdKSkNkltCxcuLPP+QgihAsbLl5faWkXTMg9JHwIW2J5V57QrgNG2xwDXAecC2J4BXAXcCvwGuA2oPbUpwNbADsCGwNc63HcvUuZRDN/N9nbAfsCXJO3eWWJsT7M9zva4kSNHNvJ2Qwih52pTspfZWkQzSx67AhMkPQpcCIyXdF7xBNuLbb+SX54JbF84NtX2WNt7AwIeyOFPOXkFOBvYsXaNpDE5ngNtLy7E9UT+dwFwafGaEEJoCW4vt7WIpmUetqfYHmV7NHAIcIPticVzJG1ceDmB1LBe66E1PO+PAcYAM4rXSBLwEWBefr0Z8HvgU7YfKNxjbUnr1vaBfWrXhBBCKzDgdpfaypC0r6T7JT0k6fhmpLnPx3lIOhlosz0dOFrSBGAZsASYlE8bAtyc8geWAhNtL8vHzpc0klQamQ18IYefAAwHfpavW2Z7HLARcGkOWwO4wPY1TX2TIYTQCFe3GJSkwcBPgb2B+cAdkqbbvqeSG2R9knnYngnMzPsnFMKnkNowOp7/MqnHVWdxje8i/AjgiE7CHwa2feMVIYTQOipsDN8ReCh/9yHpQuBAYOBlHgPRrFmzFkl6rJfRjABaaWxJpKe+VkpPK6UFIj31bNXbCJ7nmWv/15eMKHn6mh0GO0+zPa3wehPg8cLr+aw85q0SkXl0wXavu1tJastVZy0h0lNfK6WnldICkZ56qpi1wnbdcWytqOnTk4QQQuhTT7DyuLhROaxSkXmEEMKq5Q5gC0nvkPQmUm/X6VXfJKqtmmta96f0qUhPfa2UnlZKC0R66mmltGB7maSjgGuBwcBZtu+u+j5yC82VEkIIYWCIaqsQQggNi8wjhBBC42zH1mEj9dueXdiWAseQJmK8Dngw/7tBPl/Aj4GHSNPHb9chvmGkvtanF8K2B+7K1/yYFVWIb7hHlekBNiNN9XIvadDQ6Bz+DuDP+ZqLgDfl8KH59UP5+OiK0/Nd4O6cnuJzaObz2Zo02eYrwFc6fFb7Avfn+x5fCC/7fMZXkRZSb5kb82d0NzC5cKz0c67y2eTjg4E7gSt78GxGV/xZrQ9cAtxH+vvZpcHn89EK03Js/pzmkSZzXbPRZ9Pf33sNf0/2dwJafcv/WZ4G3k76ojs+hx8PnJL39ydNAS9gZ+DPHeL4EXABK2cef8nnKl+7Xw7v9B5VpYc00n/vvL8OsFbe/y1wSN4/A/hi3v8P4Iy8fwhp8a5K0gO8D7glxzE4/8fcsw+ez1tIszJPZeUv7MHA34B3Am8iLQ2wTU+fTy/TsjE5kwXWJU0Muk29Z1Dvc+9tegpx/Cfpb7mYeTTzb6fL9JBm4T4i778JWL+nz6eXn9UmpMXo3lx4HpN682wGwtbvCWj1jTSR4i15/35g47y/MXB/3v8FcGjhmuJ525NmFZ5EzjzytfcVzj8U+EW9e1SRHtKUL3/q5D2KNFp3jfx6F+DavH8tK37RrZHPU0Xp2QWYBbwZWAtoA/6l2c+ncO5JHb4EXn/f+XVt+pwePZ/epKWTz+hyVmT6Df8dVpEe0niB60mlqyv74m+nzme1HukLW508q578P+1NWmojujfM7/PKHF+Pn81A2KLNo3uHkIqhABvZfirvP02adBE6nw5gE0mDgO8DX+kQ5yb5nJXO7+YevU4PsCXwrKTf53XlT82TqA0HnvWKySeL6Xk9rnz8uXx+r9Nj+zZS1cxTebvW9r198Hy60tVz6+nz6U1aXidpNPBeUvVGvbi6Sn9Nb9PT2eJuzf7b6co7gIXA2flv+cw8a3a9uOo9nx6nxWnJh+8Bfyf9HT/ntCZRb55Ny4vMo448wGYCcHHHY04/GdxNFP8BXGV7fjfndarjPSpIzxrA+0mZ2Q6k6plJPUlbFemR9C5SSWMU6T/TeEnvL3v/JjyfylSVFknrAL8DjrG9tKdxVfBZlVncrbSK/pa3A35u+73Ai6QqpobjquDZbECaePAdwNuAtSVNrHfNqiAyj/r2A/5q+x/59T8K64lsDCzI4V1NB7ALcFReEOt7wOGSvpOPjerk/Hr3qCI984HZth/Ov3YuI/0HXAysL2mNDuevFFc+vl4+v4r0fBS43fYLtl8g1Ufv0gfPpytdpbMnz6e3aUHSEFLGcb7t3xcONfqcqSA9XS3u1uy/na7MB+bbrpXGLiH9LdeLq6vn09u0/BvwiO2Ftl8jrSv0Pnr+bAaEyDzqO5QVRVlIQ/w/nfc/TaqHroUfrmRnUrH1KduH2d7MaUGsrwC/sn18LhIvlbRzXtTq8A5xdXaPXqeHNG3B+nk9FEh11/fkX1c3Ah/vIq7aPT5OWtSr9kust+n5O7CHpDXyF+UewL198Hy60um0Dj18Pr1KS37f/0N6Hj/ocLjR50xv0+MuFnfrg7+drtLzNPC4pNqMth9gxZTjjT6f3v7d/B3YWdJa+XP7AOlz6+mzGRj6qnFloG3A2qRfAusVwoaTGgwfBP4X2DCHi7T4yt9I3UvHdRLfJFbubTWO1K3vb8DprGhk7eoelaSHtEDM3Bx+Diu6Dr6T1MPpIVLxfWgOXzO/figff2dV6SH1cPkFK7oN/6CPns9bSb9clwLP5v1h+dj+pJ5NfwP+XyGu0s+nirQAu5GqS+ayoivp/j18zpU8m8K1e7Jyb6tm/u3U+6zGkjpZzCWVojdo9PlUmJZvkroMzwN+XXgGDT2bgbTF9CQhhBAaFtVWIYQQGhaZRwghhIZF5hFCCKFhkXmEEEJoWGQeIYQQGhaZRxiwJL2Q/32bpEt6Ec8xktaqKE1bS5qdp8zYvIo4C3GfKWmbHlw3VtL+hdcTJL1hNHYIjYiuumFAkLSGV8wRVAt7wfY6FcT9KGlMxKIK4jqeNBHet3t4/RveZwVpmkR6f0dVGW9YvUXJIzRE0g6S5kpaU9Laku6W9J5Ozjs8nzdH0q9z2GhJN+Tw6yVt1k34OZLOkPRn4Lt55Pdtku6S9O3CvUZLmpf3JylN/HiNpAclfbdw3s8lteU0fzOHHU2aj+hGSTfmsH3yff4q6WKl+aU6vr+xkm7Pab5U0gb51/0xwBdrcXW45gVJP8z3v7420l/STEmnSWoDJkv6QC653CXpLElDC+eNq5fG/Pncmp/7XyStB5wMHJxLRAfnZ3R6iWf/4xzXw5I+3vH9hNVcf49SjG3gbcC3SXN1/RSY0snxd5NGaY/Ir2sjdK8APp33Pwtc1k34OaTprQfn19OBw/P+l4AX8v5oYF7enwQ8TJoraE3gMWDTDukYTFrXZEx+/WghrSOAm4C18+uvASd08h7nAnvk/ZOB0/L+SXQxtTppxPhhef8EVkzRPxP4Wd5fkzTb6pb59a9IkyLWzhvXVRpJa1o8DOyQw4eRJhCcxMqzG7z+uptnfzHpB+Y2wEP9/XcXW2ttUfIIPXEyaZqTcaSFczoaD1zsXA1ke0kO34W0kBCkKRx26yacHM/yvL8rK+Yg+nWd9F1v+znbL5OmPXl7Dv+kpL+SVsJ7N+lLsaOdc/gtkmaT5h96e/GE/Gt+fdt/zEHnArvXSU9NO2n1OIDzWPl91sK3Ik2y90CduLtK41bAU7bvALC91N1XgdV79pfZbrd9Dw1MIR9WD2t0f0oIbzCctArhENIv5RebeK+OcZdppHulsL8cWEPSO8hT0dt+RtI5pLR3JOA624f2JLENKr6XRp5hp2mU9K+VpGqF4nNUxXGHAS5KHqEnfgF8AzgfOKWT4zcAn5A0HEDShjn8VtKMrACHATd3E97RLR3Oa8Qw0hf0c5I2Ik3DXfM8aalXgNuBXZXWGiG362xZjMj2c8AzWrH2yKeAP9K9QayYYfXfgT91cs79wOja/buIu6s03g9sLGmHHL6u0nTfxffXUdlnH8JKIvMIDZF0OPCa7QuA7wA7SBpfPMf23aR1nv8oaQ5Qm1L8y8BnJM0lfSlO7ia8o8nAlyTdxcor5HXL9hxSddV9pGqaWwqHpwHXSLrR9kJSm8BvcnpuA7buJMpPA6fmc8aSqvK68yKwY27cH9/ZNbmq7TPAxfl9tpPWvi6c0nkabb8KHAz8JD/360ilqxuBbWoN5h1uWfbZh7CS6KobQh9RL7sW58xkgu1HKkxWCD0SJY8QBgBJ1wF3RcYRWkWUPEIIITQsSh4hhBAaFplHCCGEhkXmEUIIoWGReYQQQmhYZB4hhBAa9v8DuOz0KKXwwvsAAAAASUVORK5CYII=", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "drclassdcd_slice = out_grid.drclassdcd.sel(hzdept_r=15)\n", - "drclassdcd_slice.where(drclassdcd_slice!=out_grid.drclassdcd.rio.nodata).plot()" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "drclassdcd_string = out_grid['drclassdcd_categories'][out_grid['drclassdcd'].astype(int)]\\\n", - " .drop('drclassdcd_categories')" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
<xarray.Dataset>\n",
-       "Dimensions:                (drclassdcd_categories: 9, hzdept_r: 11, x: 15, y: 19)\n",
-       "Coordinates:\n",
-       "  * y                      (y) float64 4.597e+06 4.597e+06 ... 4.595e+06\n",
-       "  * x                      (x) float64 7.004e+05 7.004e+05 ... 7.018e+05\n",
-       "  * hzdept_r               (hzdept_r) float64 0.0 5.0 15.0 ... 105.0 120.0 150.0\n",
-       "  * drclassdcd_categories  (drclassdcd_categories) object 'Excessively drained' ... 'nodata'\n",
-       "    spatial_ref            int64 0\n",
-       "Data variables:\n",
-       "    drclassdcd             (hzdept_r, y, x) object 'nodata' ... 'Well drained'\n",
-       "    hzdepb_r               (hzdept_r, y, x) float64 nan nan nan ... 180.0 180.0\n",
-       "    claytotal_r            (hzdept_r, y, x) float64 nan nan nan ... 21.0 21.0\n",
-       "    sandtotal_r            (hzdept_r, y, x) float64 nan nan nan ... 10.0 10.0\n",
-       "    silttotal_r            (hzdept_r, y, x) float64 nan nan nan ... 69.0 69.0\n",
-       "Attributes:\n",
-       "    grid_mapping:  spatial_ref
" - ], - "text/plain": [ - "\n", - "Dimensions: (drclassdcd_categories: 9, hzdept_r: 11, x: 15, y: 19)\n", - "Coordinates:\n", - " * y (y) float64 4.597e+06 4.597e+06 ... 4.595e+06\n", - " * x (x) float64 7.004e+05 7.004e+05 ... 7.018e+05\n", - " * hzdept_r (hzdept_r) float64 0.0 5.0 15.0 ... 105.0 120.0 150.0\n", - " * drclassdcd_categories (drclassdcd_categories) object 'Excessively drained' ... 'nodata'\n", - " spatial_ref int64 0\n", - "Data variables:\n", - " drclassdcd (hzdept_r, y, x) object 'nodata' ... 'Well drained'\n", - " hzdepb_r (hzdept_r, y, x) float64 nan nan nan ... 180.0 180.0\n", - " claytotal_r (hzdept_r, y, x) float64 nan nan nan ... 21.0 21.0\n", - " sandtotal_r (hzdept_r, y, x) float64 nan nan nan ... 10.0 10.0\n", - " silttotal_r (hzdept_r, y, x) float64 nan nan nan ... 69.0 69.0\n", - "Attributes:\n", - " grid_mapping: spatial_ref" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "out_grid['drclassdcd'] = drclassdcd_string\n", - "out_grid" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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drclassdcdhzdepb_rclaytotal_rsandtotal_rsilttotal_r
hzdept_rxy
0.0700350.04597050.0nodataNaNNaNNaNNaN
4596950.0Well drained5.026.038.036.0
4596850.0Well drained5.026.038.036.0
4596750.0Well drained5.026.038.036.0
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" - ], - "text/plain": [ - " drclassdcd hzdepb_r claytotal_r sandtotal_r \\\n", - "hzdept_r x y \n", - "0.0 700350.0 4597050.0 nodata NaN NaN NaN \n", - " 4596950.0 Well drained 5.0 26.0 38.0 \n", - " 4596850.0 Well drained 5.0 26.0 38.0 \n", - " 4596750.0 Well drained 5.0 26.0 38.0 \n", - " 4596650.0 Well drained 5.0 26.0 38.0 \n", - "\n", - " silttotal_r \n", - "hzdept_r x y \n", - "0.0 700350.0 4597050.0 NaN \n", - " 4596950.0 36.0 \n", - " 4596850.0 36.0 \n", - " 4596750.0 36.0 \n", - " 4596650.0 36.0 " - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pdf = out_grid.drop(['spatial_ref', 'drclassdcd_categories']).to_dataframe()\n", - "pdf.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Make sure all categories are represented\n", - "\n", - "To do this, convert the column type to categorical beforehand and make sure that\n", - "you include all of the possible categories." - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "cat_dtype = pandas.api.types.CategoricalDtype(out_grid.drclassdcd_categories.values)\n", - "pdf['drclassdcd'] = pdf['drclassdcd'].astype(cat_dtype)" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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hzdepb_rclaytotal_rsandtotal_rsilttotal_rdrclassdcd_Excessively draineddrclassdcd_Moderately well draineddrclassdcd_Poorly draineddrclassdcd_Somewhat excessively draineddrclassdcd_Somewhat poorly draineddrclassdcd_Subaqueousdrclassdcd_Very poorly draineddrclassdcd_Well draineddrclassdcd_nodata
hzdept_rxy
0.0700350.04597050.0NaNNaNNaNNaN000000001
4596950.05.026.038.036.0000000010
4596850.05.026.038.036.0000000010
4596750.05.026.038.036.0000000010
4596650.05.026.038.036.0000000010
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" - ], - "text/plain": [ - " hzdepb_r claytotal_r sandtotal_r silttotal_r \\\n", - "hzdept_r x y \n", - "0.0 700350.0 4597050.0 NaN NaN NaN NaN \n", - " 4596950.0 5.0 26.0 38.0 36.0 \n", - " 4596850.0 5.0 26.0 38.0 36.0 \n", - " 4596750.0 5.0 26.0 38.0 36.0 \n", - " 4596650.0 5.0 26.0 38.0 36.0 \n", - "\n", - " drclassdcd_Excessively drained \\\n", - "hzdept_r x y \n", - "0.0 700350.0 4597050.0 0 \n", - " 4596950.0 0 \n", - " 4596850.0 0 \n", - " 4596750.0 0 \n", - " 4596650.0 0 \n", - "\n", - " drclassdcd_Moderately well drained \\\n", - "hzdept_r x y \n", - "0.0 700350.0 4597050.0 0 \n", - " 4596950.0 0 \n", - " 4596850.0 0 \n", - " 4596750.0 0 \n", - " 4596650.0 0 \n", - "\n", - " drclassdcd_Poorly drained \\\n", - "hzdept_r x y \n", - "0.0 700350.0 4597050.0 0 \n", - " 4596950.0 0 \n", - " 4596850.0 0 \n", - " 4596750.0 0 \n", - " 4596650.0 0 \n", - "\n", - " drclassdcd_Somewhat excessively drained \\\n", - "hzdept_r x y \n", - "0.0 700350.0 4597050.0 0 \n", - " 4596950.0 0 \n", - " 4596850.0 0 \n", - " 4596750.0 0 \n", - " 4596650.0 0 \n", - "\n", - " drclassdcd_Somewhat poorly drained \\\n", - "hzdept_r x y \n", - "0.0 700350.0 4597050.0 0 \n", - " 4596950.0 0 \n", - " 4596850.0 0 \n", - " 4596750.0 0 \n", - " 4596650.0 0 \n", - "\n", - " drclassdcd_Subaqueous \\\n", - "hzdept_r x y \n", - "0.0 700350.0 4597050.0 0 \n", - " 4596950.0 0 \n", - " 4596850.0 0 \n", - " 4596750.0 0 \n", - " 4596650.0 0 \n", - "\n", - " drclassdcd_Very poorly drained \\\n", - "hzdept_r x y \n", - "0.0 700350.0 4597050.0 0 \n", - " 4596950.0 0 \n", - " 4596850.0 0 \n", - " 4596750.0 0 \n", - " 4596650.0 0 \n", - "\n", - " drclassdcd_Well drained drclassdcd_nodata \n", - "hzdept_r x y \n", - "0.0 700350.0 4597050.0 0 1 \n", - " 4596950.0 1 0 \n", - " 4596850.0 1 0 \n", - " 4596750.0 1 0 \n", - " 4596650.0 1 0 " - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "training_df = pandas.get_dummies(pdf, columns=['drclassdcd'])\n", - "training_df.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Index(['hzdepb_r', 'claytotal_r', 'sandtotal_r', 'silttotal_r',\n", - " 'drclassdcd_Excessively drained', 'drclassdcd_Moderately well drained',\n", - " 'drclassdcd_Poorly drained', 'drclassdcd_Somewhat excessively drained',\n", - " 'drclassdcd_Somewhat poorly drained', 'drclassdcd_Subaqueous',\n", - " 'drclassdcd_Very poorly drained', 'drclassdcd_Well drained',\n", - " 'drclassdcd_nodata'],\n", - " dtype='object')" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "training_df.columns" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.10" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/refs/tags/0.5.2/.doctrees/nbsphinx/examples/grid_to_vector_map.ipynb b/refs/tags/0.5.2/.doctrees/nbsphinx/examples/grid_to_vector_map.ipynb deleted file mode 100644 index a22d938b..00000000 --- a/refs/tags/0.5.2/.doctrees/nbsphinx/examples/grid_to_vector_map.ipynb +++ /dev/null @@ -1,559 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Example - Mapping Grid Data to Vector Data\n", - "\n", - "This is useful in the case where you want to get statistics for a specific raster\n", - "over a certain region. In this example, the vector data is a random region with\n", - "using SSURGO data." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import json\n", - "\n", - "import geopandas\n", - "\n", - "from geocube.api.core import make_geocube\n", - "\n", - "%matplotlib inline" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "ssurgo_data = geopandas.read_file(\"../../test/test_data/input/soil_data_group.geojson\")" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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cokeymukeydrclassdcdhzdept_rchkeyhzdepb_rclaytotal_rsandtotal_rsilttotal_rgeometry
012577452271425Somewhat poorly drained0.01000340905.023.0676759.97833866.953987MULTIPOLYGON (((-90.59735 41.49255, -90.59730 ...
112577452271425Somewhat poorly drained5.010003409015.023.0676759.97833866.953987MULTIPOLYGON (((-90.59735 41.49255, -90.59730 ...
212577452271425Somewhat poorly drained15.010003409130.023.0676759.97833866.953987MULTIPOLYGON (((-90.59735 41.49255, -90.59730 ...
312577452271425Somewhat poorly drained30.010003409245.023.0676759.97833866.953987MULTIPOLYGON (((-90.59735 41.49255, -90.59730 ...
412577452271425Somewhat poorly drained45.010003409360.023.2316439.96194166.806416MULTIPOLYGON (((-90.59735 41.49255, -90.59730 ...
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" - ], - "text/plain": [ - " cokey mukey drclassdcd hzdept_r chkey hzdepb_r \\\n", - "0 12577452 271425 Somewhat poorly drained 0.0 100034090 5.0 \n", - "1 12577452 271425 Somewhat poorly drained 5.0 100034090 15.0 \n", - "2 12577452 271425 Somewhat poorly drained 15.0 100034091 30.0 \n", - "3 12577452 271425 Somewhat poorly drained 30.0 100034092 45.0 \n", - "4 12577452 271425 Somewhat poorly drained 45.0 100034093 60.0 \n", - "\n", - " claytotal_r sandtotal_r silttotal_r \\\n", - "0 23.067675 9.978338 66.953987 \n", - "1 23.067675 9.978338 66.953987 \n", - "2 23.067675 9.978338 66.953987 \n", - "3 23.067675 9.978338 66.953987 \n", - "4 23.231643 9.961941 66.806416 \n", - "\n", - " geometry \n", - "0 MULTIPOLYGON (((-90.59735 41.49255, -90.59730 ... \n", - "1 MULTIPOLYGON (((-90.59735 41.49255, -90.59730 ... \n", - "2 MULTIPOLYGON (((-90.59735 41.49255, -90.59730 ... \n", - "3 MULTIPOLYGON (((-90.59735 41.49255, -90.59730 ... \n", - "4 MULTIPOLYGON (((-90.59735 41.49255, -90.59730 ... " - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ssurgo_data.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "# convert the key to group to the vector data to an integer as that is one of the\n", - "# best data types for this type of mapping. If your data is not integer,\n", - "# then consider using a mapping of your data to an integer with something\n", - "# like a categorical dtype.\n", - "ssurgo_data[\"mukey\"] = ssurgo_data.mukey.astype(int)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Convert data to grid\n", - "\n", - "See docs for [make_geocube](../geocube.rst#make-geocube)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "\n", - "Dimensions: (hzdept_r: 11, x: 165, y: 165)\n", - "Coordinates:\n", - " * y (y) float64 41.5 41.5 41.5 41.5 ... 41.48 41.48 41.48 41.48\n", - " * x (x) float64 -90.6 -90.6 -90.6 -90.6 ... -90.58 -90.58 -90.58\n", - " * hzdept_r (hzdept_r) float64 0.0 5.0 15.0 30.0 ... 90.0 105.0 120.0 150.0\n", - " spatial_ref int64 0\n", - "Data variables:\n", - " mukey (hzdept_r, y, x) float64 1.988e+05 1.988e+05 ... 1.987e+05\n", - " hzdepb_r (hzdept_r, y, x) float64 5.0 5.0 5.0 5.0 ... 180.0 180.0 180.0\n", - " claytotal_r (hzdept_r, y, x) float64 26.0 26.0 26.0 26.0 ... 21.0 21.0 21.0\n", - " sandtotal_r (hzdept_r, y, x) float64 38.0 38.0 38.0 38.0 ... 10.0 10.0 10.0\n", - " silttotal_r (hzdept_r, y, x) float64 36.0 36.0 36.0 36.0 ... 69.0 69.0 69.0\n", - "Attributes:\n", - " grid_mapping: spatial_ref" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "out_grid = make_geocube(\n", - " vector_data=ssurgo_data,\n", - " group_by='hzdept_r',\n", - " resolution=(-0.0001, 0.0001)\n", - ")\n", - "out_grid" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Get the mean/median of each region using the unique ID" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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hzdept_rspatial_refhzdepb_rclaytotal_rsandtotal_rsilttotal_r
mukey
198692.015.0030.023.0000007.00000070.000000
198714.015.0030.05.00000087.0000008.000000
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198750.015.0030.012.00000063.00000025.000000
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" - ], - "text/plain": [ - " hzdept_r spatial_ref hzdepb_r claytotal_r sandtotal_r \\\n", - "mukey \n", - "198692.0 15.0 0 30.0 23.000000 7.000000 \n", - "198714.0 15.0 0 30.0 5.000000 87.000000 \n", - "198724.0 15.0 0 30.0 21.000000 10.000000 \n", - "198750.0 15.0 0 30.0 12.000000 63.000000 \n", - "198754.0 15.0 0 30.0 26.000000 38.000000 \n", - "271425.0 15.0 0 30.0 23.067675 9.978338 \n", - "271431.0 15.0 0 30.0 14.000000 55.000000 \n", - "\n", - " silttotal_r \n", - "mukey \n", - "198692.0 70.000000 \n", - "198714.0 8.000000 \n", - "198724.0 69.000000 \n", - "198750.0 25.000000 \n", - "198754.0 36.000000 \n", - "271425.0 66.953987 \n", - "271431.0 31.000000 " - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "grid_mean = out_grid.sel(hzdept_r=15).groupby(out_grid.mukey.sel(hzdept_r=15)).mean()\n", - "grid_mean.to_dataframe()" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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hzdept_rspatial_refhzdepb_rclaytotal_rsandtotal_rsilttotal_r
mukey
198692.075.0090.023.0000007.00000070.000000
198714.075.0090.07.80000086.4666675.733333
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198750.075.0090.012.00000063.00000025.000000
198754.075.0090.026.00000038.00000036.000000
271425.075.0090.024.56496610.12049765.314537
271431.075.0090.08.33333374.66666717.000000
\n", - "
" - ], - "text/plain": [ - " hzdept_r spatial_ref hzdepb_r claytotal_r sandtotal_r \\\n", - "mukey \n", - "198692.0 75.0 0 90.0 23.000000 7.000000 \n", - "198714.0 75.0 0 90.0 7.800000 86.466667 \n", - "198724.0 75.0 0 90.0 21.000000 10.000000 \n", - "198750.0 75.0 0 90.0 12.000000 63.000000 \n", - "198754.0 75.0 0 90.0 26.000000 38.000000 \n", - "271425.0 75.0 0 90.0 24.564966 10.120497 \n", - "271431.0 75.0 0 90.0 8.333333 74.666667 \n", - "\n", - " silttotal_r \n", - "mukey \n", - "198692.0 70.000000 \n", - "198714.0 5.733333 \n", - "198724.0 69.000000 \n", - "198750.0 25.000000 \n", - "198754.0 36.000000 \n", - "271425.0 65.314537 \n", - "271431.0 17.000000 " - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "grid_median = out_grid.sel(hzdept_r=75).groupby(out_grid.mukey.sel(hzdept_r=75)).median()\n", - "grid_median.to_dataframe()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3.10.6 64-bit", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.6" - }, - "vscode": { - "interpreter": { - "hash": "e7370f93d1d0cde622a1f8e1c04877d8463912d04d973331ad4851f04de6915a" - } - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/refs/tags/0.5.2/.doctrees/nbsphinx/examples/rasterize_function.ipynb b/refs/tags/0.5.2/.doctrees/nbsphinx/examples/rasterize_function.ipynb deleted file mode 100644 index 53b7229b..00000000 --- a/refs/tags/0.5.2/.doctrees/nbsphinx/examples/rasterize_function.ipynb +++ /dev/null @@ -1,144 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Example - Use a rasterize function when creating a raster" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [], - "source": [ - "import geopandas\n", - "\n", - "from functools import partial\n", - "from geocube.rasterize import rasterize_image\n", - "\n", - "from geocube.api.core import make_geocube\n", - "\n", - "%matplotlib inline" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [], - "source": [ - "gdf = geopandas.read_file(geopandas.datasets.get_path('naturalearth_lowres'))" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "geo_grid = make_geocube(\n", - " vector_data=gdf,\n", - " measurements=['pop_est'],\n", - " resolution=(-3.0, 3.0),\n", - ")\n", - "geo_grid[\"pop_est\"].plot()" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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" - ], - "text/plain": [ - " test_attr test_str_attr test_time_attr \\\n", - "0 1.3 dcf86619 5/21/2016 10:09:21 AM -05:00 \n", - "1 1.3 dcf86619 5/21/2016 10:09:21 AM -05:00 \n", - "2 1.9 dcf86619 5/21/2016 10:09:21 AM -05:00 \n", - "3 1.3 dcf86619 5/21/2016 10:09:21 AM -05:00 \n", - "4 1.3 dcf86619 5/21/2016 10:09:21 AM -05:00 \n", - "\n", - " geometry \n", - "0 POINT (-47.26681 44.21932) \n", - "1 POINT (-47.26680 44.21932) \n", - "2 POINT (-47.26681 44.21932) \n", - "3 POINT (-47.26680 44.21932) \n", - "4 POINT (-47.26679 44.21932) " - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "gdf.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Convert to raster with GeoCube\n", - "\n", - "See docs for [make_geocube](../geocube.rst#make-geocube)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Load into grid with griddata nearest resampling" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "geo_grid = make_geocube(\n", - " vector_data=gdf,\n", - " measurements=['test_attr'],\n", - " resolution=(-0.1, 0.00001),\n", - " rasterize_function=rasterize_points_griddata,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
<xarray.Dataset>\n",
-       "Dimensions:      (x: 12, y: 11)\n",
-       "Coordinates:\n",
-       "  * y            (y) float64 45.25 45.15 45.05 44.95 ... 44.55 44.45 44.35 44.25\n",
-       "  * x            (x) float64 -47.27 -47.27 -47.27 ... -47.27 -47.27 -47.27\n",
-       "    spatial_ref  int64 0\n",
-       "Data variables:\n",
-       "    test_attr    (y, x) float64 1.3 1.3 1.3 1.2 1.3 1.3 ... 2.3 1.3 1.9 1.3 1.3\n",
-       "Attributes:\n",
-       "    grid_mapping:  spatial_ref
" - ], - "text/plain": [ - "\n", - "Dimensions: (x: 12, y: 11)\n", - "Coordinates:\n", - " * y (y) float64 45.25 45.15 45.05 44.95 ... 44.55 44.45 44.35 44.25\n", - " * x (x) float64 -47.27 -47.27 -47.27 ... -47.27 -47.27 -47.27\n", - " spatial_ref int64 0\n", - "Data variables:\n", - " test_attr (y, x) float64 1.3 1.3 1.3 1.2 1.3 1.3 ... 2.3 1.3 1.9 1.3 1.3\n", - "Attributes:\n", - " grid_mapping: spatial_ref" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "geo_grid" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# mask nodata and plot\n", - "geo_grid.test_attr.where(geo_grid.test_attr!=geo_grid.test_attr.rio.nodata).plot()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Load into grid with griddata cubic resampling" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "geo_grid = make_geocube(\n", - " vector_data=gdf,\n", - " measurements=['test_attr'],\n", - " resolution=(-0.1, 0.00001),\n", - " rasterize_function=partial(rasterize_points_griddata, method=\"cubic\"),\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# mask nodata and plot\n", - "geo_grid.test_attr.where(geo_grid.test_attr!=geo_grid.test_attr.rio.nodata).plot()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Load into user-defined grid with radial linear resampling" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "geo_grid = make_geocube(\n", - " vector_data=gdf,\n", - " measurements=['test_attr'],\n", - " geom=json.dumps(mapping(box(-48, 44, -47, 45))),\n", - " output_crs=\"epsg:3857\",\n", - " resolution=(-300, 300),\n", - " rasterize_function=rasterize_points_radial,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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0x42pLmudNzbi+TZwts5jqsv6mmMCwY2YjW/vLR2hDaMde7uehef+aS2ci/P62S/mYGpeD80tNSfjsx3aiBlrL6h3aG4N2e7vLPQskmxE2B68DwhvxuwblwIinvvz3r/C9tp6Szkj1abOlcFjK9Baeja+5nG7SNJjgJdSVUrcCXxI0gfM7GpJR1C1YjjEzO6Q9OBoq8s2/17ZoXDZumjz78qWyMnkyo4MlaXy/byB59fiF/WihnTR5cXOMytclqs3xm4BWhYIVLSew2j/CbjIzG4HkPT3wDOAU4BjgZPN7A4AM7sxSmN7MSgdLgudr1EJc9DOSJCpxoVs48h05znc2HF4oV4XOZlMHWm5l5BsHtCUzMkkhctCugd4JUA0SfcEmGAmdtjW8FyGaC2fwnGSPi3pLEkP9Fy/HHiSpAfVLbKPBA6orx1cX7tI0t9LOixkRNIxi35Dd+78DoPhrZi+ZaFwWfvwyGb1LcsMl7X7h3l1Cedw7PX0LYsJl7Xn6bPXF27qhDqcZz8UIjHnXlJCa0G9K6tHbLgsGL4Jzjek1+VHhssSbGfras8NknuXxYb6xoTLQrqD1yegnWjw2Ao0mWcj6aPAQz2XXkPVPO4kqr/Pk6iax73IFTKzqyS9Afhb4FvAZVSN5aCa915ULRgOA86VdKBZ98URZnYGcAbA/Xd7iEXlZHZdT9j8GeEZTd1WpvhmzCFPxuGV2PE/5MnMEi5ry0d6MiXCZSH+sDeYoittPoMeScYcxtiL9pAC42LCZRbg51BVILCeA0jz0WRPwcyeHCPXah7X1vF26vYJkn6PqgU29c/za3D5pKSdwN7A1yIMrp6vh5YygfOSLWU6MvJcH1u+3LI9urps7jb/ru6JmmM2+FGyQ88ww24kv29xLv7q5QJgka2XNJlUWhQILGmNcjY9zePacg82sxslPYwqX/PE+tL7gCOACyUdDOwG3BQ9gbGJ/1yQSfFiIsaNzcdAHsgs+5algkYqf1qQyfaK8MsW9ZoSdI/SmzC3sbSjpKu0gWmt/LtTfM3jJO0LvM3p3XOepAcBdwG/ZGbfqPlnAWdJupyqUu0FvhBah8RqDmUXb4bqslyQCciPbSkDfpAJLeQlPZmhUFmfTFa4bCqQ6Z1bpGzouRZYqEsvzqPDZWOvR/Jjw2VzgAyw7CDg0JqAjZkdHeBfR1UIsPj8pIDcncDzsozHejIz5GOKtJQJ6fMtlIX7lk3WHNORmcqTmaXNv2svOD4BZDIX9+zQFH5+1r0WANAGPxWE+q6HxheinctqNGB9lz5PR+u1zX9g3Fq1+W/oLh0ug97Ef4mczFSeTPFw2ZDuUTa6vKKeTCEQyfEGS4fLpsrZLD2birYY2IhdJcy7WP2gEP1GzD4dGTmZ2dv8OzrWW5v/bNm2fGTivxyY+HhDzzDeRukcSdHmmCGZAvcXGyobM7dSHo4h7lq2qwG2GtiI/hLmOTyZAuGyhswQyBQIl4XsTZaTmcqTGQNUOQtVCsi0+KO9qJT54pedyl42gMU8t6HrKc+1AJmx3NRZ09YCG4j3ZFr8aLAY4QEt2/yPBCQfwIywl7IQjg6VRdho8AsszGv16uUkvbEyPXpHeWejaets2hyirQc24F/sp2opExo7FmTaMmOry7ZSm39Hz1Q5mck8mUKL86w5mUwQHbyO53rq3IK2h4tbY8hYejYL2npgI/k9mQI5mVmaY87hyYxY6HOqy4p5PTnhsszFKMWTCenIt+HnZ4XLcu0VAKGxobIY22n3VAZg2rQsEKhoi4GNAzQFN2POEi5ry2TkZIpvxvSATPFwmSM/S04mG3xGgswI24MLK35+bmhw7P2VCJcN2U4HavPLjCRDy5en1bTFwIbxnkzJUFmfjG+x3ept/h359dDmv+LLw/PLFvWiYnTh4Y8AurEeSTZ44bmeazsGYIqCDdy17I0GbDWwaS1Ec4TLgvIzhMtCttdFm/8Rtidv8x8lOwDULX6fviKLs6sPv2yuJ1fCI4kGiRSQidCR5MkUBBnX6PJ9NhVtLbABkLwLcJGkf0h+qo2YHdv985y6OWaUvgHZQYApZi/M6+cPgEyBhbCoJ+OcFw9pTQVeDpUOQy5AZugZlyJj2UFgQVsObCZp8184XAYJIbPQQjhHdVkmaE3lyZQIl4Xn7AN1//jSobrBRR2/7FSe01iQ6ZXJ0TE434hwWUhHAVp6NhVtObDZKm3+N0pLmaDuOXIyUbI+UPfL5ntL6fqKv3p5qvtLAJhc20mhsoDehkxBbDDT0rOpaes9BWnwaLwRcfG2ycV1muedt1Ku4NXVfktlQ8cKzeoy55u6qW27Ohp6HduNuS9subpCNlx5z1sxm7YTdPnuQ8NvxRzUEbAX1Ovj98659TsLyXps9D973z3F6xt6I2ZIb+i5xMyzdz5j9DqU9SyC8zXccFmM3vbfRSnAqQoEtg0eQyTpAEkXSrpS0hWSjq/5J0q6VtJl9XFkzf95h3eZpJ11p/22Xu/4KWhreTa7/pCcv6SERH62J6PA9Rmqy0aHyjq2/fOLDa0NhsrG6PDwx4bK+nTk2B4VesPDjxnnW0CHxqXojdEdAJjo8TH8BE8mxnYZUqlNnXcDrzCzSyXtAVwi6SP1tVPN7I2usJmdDZwNIOn7gfeZ2WUB3Z3xU9DWAhtEw0sgDjRS5NdDZVlDX0KoLCSTHS4bGypLthfSMTQ+D2RKhOkGx+GXzbrXAgA6SneO7ag5D4BMAvCW3thZFQiMR7D6ZZPX1+e3SboK2C9y+FHAOaMnMZK2Zhitpqh9Monhsmoc44HG0TEqXOa1sbhnNXIyoVBP91nEhIsGZGNyMtHhudAcPPfRsdcFmmLhsvY9ufyYcXRlh+61NxTWN5+QTN/zjnz2LmU/C+89ecJlrkyE3ijQGkk7WBk8gL0lbXeOY0L6JD0ceCxwUc06TtKnJZ0l6YGeIc8G/rJnikPji9DWAxtaC7UHOLz5loUcjmxbV+MfwZFZ5CGCOQvXLvWi4bc9CDIReZEhkPHODb+u0H30gUw7J5Nvz3P/oefSsOfPo+UuptHzjNC3yMkY/nEhvb0g0zf/Ad2h550CMtnPwjvOdh25ervnHn2FaNFBYOgAbjKzxznHGT59ku4HnAecYGa3AqcBjwQOpfJ83tSSfwJwu5ldHphi7/iStCZgE5uUknS8pMvrhNgJDv9QSf9aj90u6fGxtoPeCc3z5JyMnOshkKH9x+/axvkH6drO9mTcuUWCTPdZxHxjD+ttPotS9jx833Np2It5hhk2ImRjQcY7Lvb+Xf7As4gFUZ+9WJBp6Eh5FiG9iTmZXtse0JqKdrIyeMSQpHtSAc3ZZnY+gJndYGY7zGwncCbQXgufQ49XEzG+GA3mbCQ9A3gD8GBWf1VmZnuOtN2blJL0GOClVDd/J/AhSR8ws6uBU4DfMbO/qYHqFODHBi2KyV9Ylhwq8+ldby1lUmy7cpu9zf+Qvhhd+Pm59zr2/sY8l97ruaAJwyDj/dtM1OWTLUBmcNfO8d/pJQl4O3CVmb3Z4e9T53MAng5c7lxbAZ4FPKlHb3B8aYopEDgF+P/M7KqpJhGg/wRcZGa3A0j6e+AZ9XwMWIDd/YHrorWmJP4TQAbIao4Zsj1H4j9FXzIgbeY2/xGy2UAzsHCOAoOcZzsWZCJ1+EFioKVM4nNJAZpSnk4VRisSQDocOBr4jKRFVdmrgaPqkmYDvgS8zBnzI8BXzOyLriJJbwNON7PtwCk944tSDNjcMBHQHCfp+cB2qpK+W1rXLwdeL+lBwLeBI2tZgBOAD0t6I9Xy/F+SLPtAZLO0lIE1a/M/VUuZdNkEkMm24ecPghN+2VyQndqTGQKY4rbH7PaPBaw2+eZTkEp0EDCzT+D/DVzQM+bjwBM9/Jc450ePnlwkxYDNdknvBt4H3LFgLmKGIZL0UeChnkuvoUpKnUT1v3cSVVLqRa6QmV0l6Q3A3wLfAi4DdtSXjwV+2czOk/QsKvfyyYF5HAMcA7D7bvfvAkmJljJzhMtCdjwgUzxc5sgPejIl7GUu9uuhzX/o+rp+9XIqwOGRKQKcBffJ5ILMOi193gwUAzZ7ArcDP+nwDOgFGzPzLv5tknQm8IGAjrdTAQmSfg+4pr70AuD4+vw9wNt65nEGcAbAnvfdz8aCTMmWMiF7SYt0Zk5mFk8mGdAS+L5nG7Wg5dro15ebk5nKkyntkbk0VU6mpCczBDBdXeaVGU/LdjULGgQbM3thaaOxSSlJDzazGyU9jCpfs3AJrwN+FPg48OPA55MmsEXa/CcBR6LtOT2ZIuGyKH3puoJzxi9b1F4JAB/Si+f6GNuBBX50TibBk+nqs17bY2lnWfTasBRTjbY/8IdUCSqAfwSON7NrwqMGyZuUkrQv8DYzW5RCn1fnbO4CfsnMvlHzXwq8RdI9gO9Qh8kGSayWO9eU01KmGtflJXkyKYt0AsCk6kvyQiaqLkv3bgZAJgUUxi7MbT5+fg7QTempDXpIDpX3ouI8mZh7LhIuGwDZMVRVow33PtsKFBNGewfwLuCZ9efn1byn5BoNJaXM7DqqQoDFZ2/JXp0s+6Es4znJ//VQwgzj2/xnAgT4gSY/PJfHnyMnk+tZbMY2/yHd+banqS7zgkxQVyBcFvEscmj5WuhVigGb7zKzdzif/9TdYLnRaHJPpkTSvz2/ZZv/7txyQSFzcRv0ZAqBbOz9FdfrUI6OJIAJ6A3KxOgb1DXgyYRkC9AyjFZRDNjcLOl5rO5CPQq4ebopTUwJIDOUj5mjsqw7LlJXyHaJcNnY8uVofveLQXZ4K3NxG52PccflLrYDOop7Ma5M9j1lbMTs6IjQ1zufRC9mgiKBZTXaKsWAzYuocjanUj27fwZeOOWkJqeJNmJ2+D3AkBQq69iO15ckuw48mTna/IfmE3Uf+PlT2RvryQRt54yPtj1jTiY4nwGg8clOhAnLarSKYqrRvgz8zAxzmYek6TZiOrINfSNDZX1zy/JkUgAm1Z7DT1v880BmsnyMOw6/7Fh70yz0A9cjx4dkJvNkUgCmV19kuCwARqWdEDNx9xJsgB6wkfTrZnaKpD+k9f8GYGb/c9KZTUTrIifjAZnuOI++ucNljp4sryGa7wN2v2xR2zHjeuYQZS8TLEI6svUSIRP7bFNAJkLvaE+mULgsCFojaRlGq6jPs1m0qNneI7OxSJT1ZFIApiW/Vi1lwA8yKQBZJoQ2ANRFbPhlszyZEfc/1iMZpXfk3MaGymJlYjdjJoXKAjaSvKKRtMzZrFIQbMzs/9Snt5vZe9xrkp7pGbJhaAhkKpn6wxyezBjgSAGJkhsxQ/zBxSvmGY6zEZLNAplUey6/JIjGjA2saSUBPLu6zPt8CofLGrr7ZacGGZeWYFNRTIHAb1C1hBnibQgyMQwwkA0y3n/Awm3+k2TnaI4ZnJsr232GpXMWo0NlifMoETYqHoYbuj74ewqNWwc5mRRPJiVU5v5pFsaF5T6bVerL2fxXqg2W+0n6386lPYG7p57YZLTOWsqEdI4FGYgAmoItZcJzjgFq37gUG/26evXh4Wfe/2SeWgLIhPTme2frpM3/UEuZgI0UT0YhmZG03GdTUZ9ncx1VvuZngEsc/m3AL085qelIjR9z5GSKhL9SZCfyZNLDagkgUxDcYmTWW5v/Bj/lOp7ribaHPJkiwFqypUxD77BsEshMkbMxuLvAy9M2A/XlbD4FfErSXwHfMrMdAJK2AfeaaX5lSdUxWbgMpsvJzNAcMztP09Ax8AwL2Ajyh0DLGTIZGAzNp4TeFuUAVXa4LEJ3Dsh07U3ryQS9mEIg49IyjFZRTM7mb6neFfPN+vO9a17aC8vWCbUT/3O0lGnwN2JLmaBul6ewXIufb6NfX25OpojnNJXetkykjiI5mUxgnWwjZsiO9/eRGCqLAPUcWuZsVikGbHY3swXQYGbflHSfCec0La3jzZhFQSZCR4OfudBPVl1WYHGepa1MCQAvCDLptmdsjtmrL9KTKREuC8k05lwuZ2NLsAHiwOZbkn7QzC4FkPRDVK9p3rC0Vi1lGvwB2cFQWTF7YV4/f+gZxusrvTCPfpfMiMV2rXIyuZ5MCWAtWl0WYSMFZNI8mbINOBe0LBCoKGyptVsAACAASURBVAZsTgDeI+k6ql/LQ4FnTzqricioF8mURRqGgSYXOBJtb+o2/wUW9XXR5j/CXoon05Ap8gzXxpMpEi4LzW80yMCuv57CuGC2zNksKKY32sWSvhd4VM36nJndNe20JiLRu0h3+fXPqVrKhOQHAGacvXTZit8FmdLftrNDb7FjEu2NDgUmjnNp9LMdApiYuYVk5myOGaPP/dMcuO4FmI6Okh6O2LGsRgPi3tR5H+BXgO82s5dKOkjSo8zsA9NPrzzl5GNCMmNDZX22t2Sb/9RxHt6k3kbWQh+hlwiZHttJXkyC3mh9HtneUFmkjSyQSfFiOjqm8m4KK9ygFPumzkuA/1x/vpaqe8CGBBtgcKGe7F0yjvzYUFm8PQ9/kOf+Bw7rLWk7CWQCY0vngHI9tSxPptCXhNhw2WQg0xqX48nE6CoRLpsKZBbWlmG0imL8u0ea2SnAXQBmdjuFfi2SXi7ps5KukHRKQOapkj4n6WpJr3L4j5B0Uc1/t6TdIo1iYtfB4ljRrsN73eXVelzgaPxDO/8slQ7tOnyytrJ6lLHX1WEDvGq8mp5MWy9N3b36Em0P3gerh28O3efVr29o7kl6W/zBe6U5PupZRN2T7TqG7mto7kFdLgXnaTS8j+BhzhGam0cXjX8Hr47G4f711DzJdh1NvQ6/BBlYxLEVKAZs7pR0bxb/59IjgTvGGpZ0BPA04BAzezTwRo/MNuCtwH8Fvg84StL31ZffAJxqZt8D3AK8OMau9x8sNSfTs+g3/8n7ZcHvyXTnEWvP0duzSPntqcPvXZAGbETb7hu3mJszLHZBjl3IO3rbtmPvL7hYtnS39ebabtzT6oI89DsL6e3o882TrqwXGLwLfthG+O/en/gPgkzbxurXkw6gdPU6ABP4neXSTjR4DJGkAyRdKOnK+sv58TX/REnXSrqsPo6s+T/v8C6TtFPSoR69e0n6iKTP1z8fWO7OmxQDNq8FPgQcIOls4GPArxewfSxwspndAWBmN3pkHg9cbWZfNLM7gXOAp0kS8OPAe2u5dwL/Pcqq+4e08GaI+WZe/ZUX83p6PJmgNxSxoEUvvGKXJ7OrQq9P98D1FO+mvQh59UGvJxP3e3B+1wP2hu4l9z5cGtSRYhvwgkyC3u79B0CLpmxTl8c76Xn2Pk9lUJ+cf6kYey0vphrv8WQcfltHqYacRlUgMHRE0N3AK8zs+4AnAr/kfPE+1cwOrY8LAMzs7AUPOBr4dzO7zKP3VcDHzOwgqrX9VR6ZIhRTjfYRSZdS3aCA483spgK2DwaeJOn1wHeAXzWzi1sy+wFfcT5fAzwBeBDwDTO72+Hv5zMi6RjgGIB77f6AijlDm/+gbMk2/0Ed/fw5mmMG+UNzo8ub0l623pBMYJHyXk+x3bieWV0WlB25EbNXd1g2Rlf2RsyGDr8nM2ivEJUIk5nZ9cD19fltkq4isOZ56CiqL+o+ehrwY/X5O4GPA6/MnmgP9XV9/l4z+6ykH6xZ19c/HybpAODr9SujgyTpo1T7ctr0mtr2XlQgdhhwrqQDzcpGMM3sDOAMgD0esL8tcjKrk3RkU0AjASDGvhFzFL/BU1iuxc8Hsn59QV34+VPZy1/oh8cNXs8E79HVZTG6XBoCmYhnm/PCsuykf0NHfMXZVN2ed80u+IAbtLck90WVZ9RrV4ckPRx4LHARcDhwnKTnUzVOfoWZ3dIa8mwqUPHRQ2ogA/gq8JCYyeZQn2fzK1QewZsC1x8k6VNmdnRIgZk9OXRN0rHA+TW4fFLSTmBv4GuO2LXAAc7n/WvezcADJN2j9m4W/EEa68kkeyEzejLh8Qrw/fLRtkuBFh7+SJCJkSnqIbVotCfTsB1XWdY7t0ygGQ0yoflN5MkMgkykvVIFAlUBQBTY3GRmjxsSknQ/4DzgBDO7VdJpwElUD+MkqvX6RY78E6hegHn58FzNVHaTUYP6uj4fU/88IiQj6W9H2H4fcARwoaSDgd2AdnjuYuAgSY+gApPnAM+tH8qFwM9RuYcvAP46ymrKou/9J0kAmFR7jnwamIT4CSCTDWR+/iA44Zcda28qTyYJYIrZ9oS3cn8PJVvKRNhI8WTA+XdIaSkTBSbx9tQ4pxiVKn2WdE8qoDnbzM4HMLMbnOtn0t2S8hzgL3vU3iBpHzO7XtI+gC93XoQGM1OS7inpf0p6b30cV980ZvaTI2yfBRwo6XJqwKhBZF9JiyTX3cBxwIeBq4BzzeyKevwrgV+RdDVVDuftsYYbyc++xL9P1tGxiwaAJtqe2rJNHV573oVxoITZd3TmmWDbke21B259UO+47j3F2evMM/L+2vOMuX+Xom17ftdd281Evdd2wu+hN+kfmqczLnRPPhuubEdXXwlz4HrjPDEn0078OzU3DXuubKParRCVKH2ui6LeDlxlZm92+Ps4Yk8HLneurQDPIpyvAXg/1Zd1SPnSnkExmzpPA+4J/HH9+eia95Ixhuvqsud5+NdRvSF08fkC4AKP3BepqtXS7Io40Gjw++VnafMfJasOP/dbcWnPIidUFmOvIVPwPnplInWk2y4YLptqI2ZozkP6GuCQaG+inIzPkykdRTLEzjLtag6nWns/I2lRVfZqqu0gh1I9pC8BL3PG/AjwlXqt3EWS3gacbmbbgZOp8uUvBr5MBU6TUAzYHGZmhzif/07Sp6aa0OSUlCPxyLpyc7T5d/hzhMtS5hM1Dr9s7r2OBdFRuiPHJ9seSvwn/h42XZt/H5hE87v2QuGykI6xVAK+zOwT+GfV+SLujPk4VQFWm/8S5/xm4CcKTHGQYsBmh6RHmtkXACQdCOyYdloTkQpVoq0DT6Z0S5mcPEbvOPz86HvNBIreOcXqDiw068KTSdHVJq++Ac8iAkCGQCa7zX8KyEQA2iDIFASYXRRfILDpKQZsfpUqif9Fql/HdwMvnHRWE1EjV7DgeUCmy695E71LJmgvKDsANCVBJlUfHtnM+0+VyfGQemXwyBR4tmvlyRQJl4XmN1EJc6UnxcPp2ksBmUmKsSar79pY1As2dbuYQ4CDaL5iYHS7mjUjZYS5Ru6TyfZuOvwuyJT+tj06JzOTvRxvMEqvQ6NtpwBMSEdIZo6cTIon41vwQzpSACaCXyJU1pCnLC09m4p6wcbMdkg6ysxOBT4905wmpaRQV04H5lFg4uN1ASaoI9P2qNBU7LiUhTRCpnTYz6WyHlQ8yKR4MR15j2xWPqZlIwtkSngxDn+qfEwIYEp6Nwbs3LkEG4gLo/2TpD8C3g18a8FcvCZ6Q1Js4j/4jxYBVCMX38k2YkbIFgWZGP5EIBot075e4p7ADwyZv4fRGzFDulNAJjfp3z6PbPM/ZiOmt7osoFseXjEyen5hW4tiwGbRKfR3HZ5RNcLcWCQIFQnMnY8J6+iCTJGkeIK+QYAZM4+M+UTpTdUdOT7Kdgwo5Nx3btK/NdYPLB7ZFF1EgIwPYBz+lBsxh3Q3/627XlFJ2iqvEBiimEacR8wxkbmo15Nx+NmeTDYAdEGmT0d0OClCNsmTcfmZ919CJjuxToRMxrOdqjlmR9/gfCYKlzkyXpAJPu/pq8vGgkz3nLK0CcCm3lS6v5l9ZVA4QDGvhX4Q1WsGfpjqsX0C+N26Pnvj0WIhmciTKZH0D8oXBLfosT3jittLuZdU2yPnNhQqKwWssSCTnfTvkY8Ol021ETNkz7E5NlTW1eEHmZViXo42RYFA3d3lAuD7c3XEbG09h6o55s9S9SL7GlX+ZsNSbEuZBl8h2Sa/rTfEL9ZSpsdGkD9gz1g9XNncex18dpHPdigU5xvnUtbcCOiV5QFN4Hku9C3m0Fmfgs8+sbrM9RwcW9XfQPc6NP4dIuzt+stp6fCDT+hdMu0XpIVayuQCjU/fimzXUZQs4tgYdKmkw3IHx+Rs9jGzk5zPr5P07FyDa027Xli2+OwCTJtHQDaV3+ApLNfi59vo15ebkyniySToGOUtRM4t/Z5G5mRidLkUnFNGuGxIlyMzyx6ZBs9vr2ROpnHdEVgJ2CtCBrZ5qtGeAPy8pC9TFYuJyun5gZjBMWDzt5KeA5xbf/45qsaYG4+C3w4TgCZzgZwsJ1MKtDLGzRIuC+ieKlwW5A+BTOLvYTO3+a90zFddVipcNl2hwKYBm58aMzgGbF4KnAD8ef15G/AtSS+jQrU9x0xgbvKBTFr4K1624itatqjtFICJmccAb0pPbfA6nuuJtmfJyeR6MqmhssG5JSz6XnuZSf8Ie7m7/YdAJsaTmaxIYOOEyYbode33l0n6c6oGoYMUU422R+bE1i/N+VbMCNl8G37ZwYUVv+xknlP2Qh9hmwGZ7HuaprosO1zmykTMOcuTKREuc/hzlDC77CHgmB1kFrR5wObR7oe6w8wPxQ6O8Ww2F8U04xwFPF2QKR1Kyl38ckJlY+xNBl5EXB8AsnXhyaQATO95V34o9DZLm/+GrN/eWnky01eh1WT0fMPYGCTpN6heZ3BvSbcu2MCdwJmxerYU2DR+75mL7GiQKW07Rhd+/liPo3heJEZ35Pgo24EFsGROZjJPpnS4LGRvxhLmkI7cnEyqJzNVocBG39RpZr8P/L6k3zez38jVEwQbSY8ws3/PVbxuKXMxygqVtfj5QJauC4ZBpnQOqKgnE1ikyxcceEAmE1gnqy6LAJAkkEnxZKLApGB1WcCefLxE0BiSd6+vlIx9bZ5qtM7LKiV9zMyi3ofTt8/mvQtlmRPrJUkvl/RZSVdIOiUg81RJn5N0taRXOfyza/7lks5avKY6hkyrB87h5TvylWFXj1Z5bdnAMWQ7qM+13Xdd+PfI5Npr2fZez3mGsffqUK/tHhth20ZnX0vP3HZRj66OPVqynWdkzhHS3bXh/5vz6BK79pC0X4UctNfeJwONPSmrep29L3JlaYKas5C397WEdTiy7jR7dMHqPhkFj1V5d0+Nq28FKws0zmPoO9YzSdq93ty/t6QHStqrPh4O7Berpy+MtiLp1cDBkn6lfdF9D3YqSToCeBpwiJndIenBHpltwFuBpwDXABdLer+ZXQmczeorpd9F9Yrq04YNt36SGsZStGxzXLyNBj91HB7+hPaGdGR7SETIxNpujMlM+odk5sjJZHoys7ywLMJeTkuZzjQnysn4PJlJWtWsczCJoJdRVSTvC1zC6q/nVuCPYpX0gc1zgP9ey5SuSDsWOHnxXhwzu9Ej83jg6sX7syWdQwVQV5rZrlehSvoksH+05fY3z6jFOAFkBgErzXYSyMxkL1p3ql48MkWAOh5kioTLvPNJAJge+aHQ21SVZQ2+z55jc6qNmG19sSAzBDB9esdTe8HZeGRmbwHeIunlZvaHuXqCYGNmnwPeIOnTZvY3uQYCdDDwJEmvB74D/KqZXdyS2Q9wm75dQ7WDdRfV4bOjgeNDhiQdAxwDsNt9H1jWk8ldCGP4Q9/cnSFF5jH2eg8/BWhKP9vsljI9ujr6BudT0JNJSML32ovMyZTYiNngB+zJxxsJMiFZ8ANN8Uq0BW18zwYAM/tDSY8Bvg/Y3eH/Wcz4mGq0f5b0ZuBH6s9/T9WI8z/6Bkn6KPBQz6XX1Hb3Ap4IHAacK+lAs+S6jT8G/sHM/jEkYGZnAGcA3HfvAzpfoQbzMW25Fj93Icz2GvDzx9pryOTe31iASZzbEMDE6B4C1iJJ/4Btr51EXetht/9atpQpES7zFQgUpZ3TqJ2bJL0W+DEqsLkA+K9UjZmLgc1ZwOXAs+rPRwPvAJ7RN8jMnhy6JulY4PwaXD4paSewN1WTzwVdCxzgfN6/5i10vBb4Lqp4YjypULgsJJO9QA7L5ORkiuQmSLiXBJAZo8M/buZwWVBfd/EuEi5zZIqCTIgfAWpjczIlqstSKsuiAKkk6BgbPozm0M8BhwD/ZmYvlPQQ4C9iB8eAzSPN7Gedz78j6bLESbbpfcARwIWSDgZ2A25qyVwMHCTpEVQg8xzguQCSXkLVp+cnzCzpe0OvJ+Pwcxfp3BBTridT2l5RTyYFYEbZzgiV9cisVU4myZPJzMlMtUemRNK/bW+OnMyuKrSAjhK03qvNEujbZrZT0t2S9gRupOkQ9FLMKwa+LemHFx8kHQ58O32eDToLOFDS5VSvMHhB/b6Efet3JmBmdwPHUTX9vAo418yuqMefDjwE+BdJl0n67WjLwvnnDrf5b/Pa130yIfCJ0dcZRyvxP7G92PszEf9cPJRj2xSybeOBxqOvYY/WGO88E3MyrufQ+ZvzX9+Kbf595ctt26k5mRWPbR+vKFnEsTFou6QHUHUNuAS4FPiX2MExns0vAn8m6f7151uAF6TO0iUzu5PV0mWXfx1wpPP5AqrYYFsur/OBAzJdXlcu5vpknkxhew2ZAvcXJYNHpojtgXBZyvxzczK5nkxwbhmhsvb5Jmzzn7sRM9eTmaxIYCRJOoAqN/IQql/0GWb2FkknUjVLXqQgXr2o1pX0A8CfAHtSZY4OM7PvtPQGx7fJzP5HfXq6pA8Be5rZpx1dj3Ycgg7FNOL8FHBI7TZhZrcODFnX1E7+b/bEf+n7i9IdOT7Pdo8nkwiSRavLImykgNZ6aCnTll+rNv+DwJGZkwmBzBSbOgvQ3cArzOxSSXsAl0j6SH3tVDN7Y8OmdA+qfMrRZvapelPmXQHdnfFDZGZf8rD/HPjB0JhoD2Gjg8wuGrkIl67ISgKZAvbGglfQNp7riTqSPJmU+04BmOA8BzyLFIDpkVm2+R/nycR4LDEgUyycZhRpV2Nm1wPX1+e3SbqK/t37Pwl8unYWMLObR09imHpvNCZns+koOifjHIN5DCBlAfTmZDz22nmKXHsN2557ic31RANNgo6w7WZOZigUF7S9S9ajy6XgPLs5jSEbbX50TmZxXRa2N6a6rMdeX04mZZ9MKCezIF+OpC8n09dSJip/E8jTrLT0tXNDxagvV7O6EOwtabtzHBNSV7eJeSxwUc06TtKn69ZdD6x5BwMm6cOSLpX06z0z9I3Pod4HtzW7PgcWqzYvmR8zzsOb0l6+NzFw3aGitofyMTE6YvQN6orIkQzIe/U1FutEexuszb+rb6qNmCH5IeAJeTFT5GwiVd5kZo8b1CXdDzgPOMHMbpV0GnAS1R/HScCbgBdRre0/TLWP8XbgY5IuMbN2r8vQ+OI06NlIuo+k35J0Zv35IEk/PcVkZqHAt+3OdfB+U/Z+gx66jseTGWOPyOshmR57Mc/FHZ/sJfaOGwCa2OeiWleMJ+O151nA+2y4Xog8XtFooHH+emob4SqycHVZ216uJ9N4FAPeSagCLDUnE/Jk2lVrfRVnKUBTfq/NwBFBdceU84Czzex8ADO7wcx21FtAzmS1M/M1VBvebzKz26kKrTr5lJ7xOXRn38WYMNo7gDuA/1x/vhZ43YgJrR15FoRQuCy4QPoO/Itf428pMDY6XBYYN3Q99v6iFnIinkXqPYEfGIaec/CIBJmOPj9wDNkI319XxtuBuc/e4q9Hro4BkNGwvWiQcexJtqq6tcCvHqv6+kAmJlwmmRdkgt2aB0BmoasdLlvpOYpRAbCRJODtwFVuE2RJ+zhiT6fagA/VlpHvr52FewA/Clzp0Rsa75tD5w0ALs/Mnth3D7GbOp8t6aha4e31jW9Iyg6XJVxv/O1MZc/lp+hNHOdSbq7IPy4zXBaUtaZcm4LzTAiXeWRjdK3nvmUxYTj5eAmeSYlwWW75cqoXs+LRPYbc7xwj6XCq7i2fcTbVvxo4StKhVH80X6LuqGJmt9Rtxi6ur11gZh8EkPQ24HQz2w6c4hvfuAdpd+A+1K8YYPVPYk8KvWJgQXdKujeLL+jSI6k8nY1LQ2CRsvi1+T47E9obkikKMgHbaQCXCTINHS4voG9Q18CiHyE7pG+WljIdfteeL0mfAjIdvkdfTkuZtmzJ6rIhkBkCm6JUphrtE/j/Q717Yuoxf4GnnYyZvcQ5PzrC/OSvGFjQicCHgAMknU2FsC+MNbDuSOneTVFPpoC9yTwkIq6X9GRG/B7WqqVMd34+L6T/ei7IpHgyg0n/gL0hgGnrm7OlTAzfBzIxwLIB9tmsGU3+igHH0N9KuoSqQ7OA482s3cdsw9CWDJc559meTEmQCcwtrCNCX5+u1rh1Fy5z+FOFyxr8BE9mPYTLXH7pcJl77uoonrPZHPRVSXvU+3x+k6rg4HVmdmnM4EGwcd4x/UEPb+NRgYXZPLwSQNCQSZnPCC/EpbK240FmcP5DANM7p4Hw1YAXkg0yuZ5MlBfSnU+uJ+Oyx4bKYuX72vy7siVyMimezCQhNNv4no1Dv2Vm76l7ZT4Z+AOq0ukn9A+rKAg2pZJC65FmCZc5/FnCZYGxs4TLXP4QyCSCZArQDIW3ioTLHJn11ubflZkjXNbQNwQchcNlrs6xINMnU4Q2D9jsqH/+N6rebB+UFF2Z3OfZFEkKrSsSq2WzDm9BSUn/0NgCAFI0DDfkxSTaHvJkShU7ZFWX5eZk5vBkEr2NIXslQ2V9+tZDm/8hT6ZEqGxKsNEmeXkacK2kPwGeQvUW53uR0IWm77XQRZJC645SFlbfuALeRozMaA+pLcOAzEiQSZ5fSZBpzSM6XBacWwLIhOxN5MmUCJe5/CGQKREuc+WnysmU8GQmCaNtLnoW8FTgjWb2jXqPzq/FDo4pEBj13ul1RymeTK634fJzQajAOJeKli9DWU9mbKisx/YQyGRVlvXZGJmTWbb5T2spkwsys4LNJsGweo/ljVRtcD5P1Yn687HjYwoEXsuI906vR5osXBYYVz4vEmE7cm4hmWxPJhEkZ/VkUnSRCjQ+4BgfLmvLz5mTSZGdss3/ENCUCJdNVvpszT+djUw1FjwOeBRVZ5l7Uu3jOTxmfMw+m1HvnV5v1AaLJICJ4RcAiqKeTIl7Aj/IZAJrkeqygWebm/SHwKI/5MkkeBtBeymeTEDvWnkypZP+IZlBsMkEmcnKnmHTeDZU7WweS/WGTszsuvrdOlEU9Vrouklb1nun+0jSyyV9VtIVkk4JyDxV0uckXS3pVZ7r/1vSN+ONEg00ptbC2scfut6yEfImguPaMu51hzo6PPeXZrv6ara43rHdmpvPXhLQ1Pqa9sw5/LaTgGahq6Ujvc1/HNBIA69edubjyk7RHHNBK97r+W3+23rbzyIURltp6ZP6+5Z1xiUATVAHXV5RsohjY9CdZrZrxpLumzI4xrNpv3f6myS8dzpEko4AngYcYmZ3SHqwR2Yb8Faq6odrgIslvd/MrqyvPw5Ifv+CF2QGvIZSVVbRumP0EiETazsGEHLvOwZgvLr8oDFkI0XfVH3LQvyp+pZ1dXT1TbURMyQ/R06mtCezUrh0TLCZqtHOravRHiDppVSvIjgzdnBMgUDve6dH0LHAyWZ2R23nRo/M44GrzeyLAJLOoQKoK2sg+gPguVTu3SCtQrLDjFg0x+ZkJqla67s+6p7igWbI9mbfjDlLuCygewhk5t6MWTpc5p6nbMbM3cTpgkxR78aaf34bnL4LeC/V9pdHAb9NtbkziqJeniZpP+C7F/KSfsTM/iF5qk06GHiSpNcD3wF+1cwubsnsB3zF+XwNq7tVjwPeb2bXRzehbodEahq9l8XlTwleDhXNyeR6MqmANTifgp5MCsD02ZuxA3OMbnnkcjdiuvIly5dD/Dk8mfSyZz/IlC4S2CT0FDN7JfCRBUPSm4BXxgyOqUZ7A/BsqnchLHaQGjAINpI+CjzUc+k1te29qHquHUbloh1YxwSH9O4LPJOqSm5I9hjgGIB77vHA6byNgcW4+B6ZBCCYZY9MaS+mobtfdo7KskpHOZAp2VKmrW/OPTKlvRj3PKd8OWZcdb4zWnY0bXCwkXQs8D+AAyW5Ua09gH+K1RPj2fx34FGLcFcKmVnQxapv4PwaXD4paSewN/A1R+xamsUI+9e8xwLfA1xdezX3kXS1mX2PZw5nAGcA3PuhBxiEE+RtXvu8rDcxPG7w+lSeTCJIbvmWMhH2xoJMU0eeJ1Oyb1ksf46WMjk5mVlApqZNEEZ7F/A3wO8DbpHWbWb29VglMWDzRap66tLvsHkfcARwoaSDgd2Adjfpi4GDJD2CCmSeAzzXzK7A8ZgkfdMHND7aVU3lfgZSF/GiYbghL6aYbRutd3S4LCVUFpCfIx9T6eiOS/FkxobKujriQWZsqKxXPiNcNmavy1hwigmV+WSL0QYHGzP7D+A/gKPG6IkBm9uBy+rXf+4CHDP7n2MMA2cBZ0m6nOrd1S8wM6tDZG8zsyPN7G5Jx1G94nQbcFYNNPmU48m4/IHrxcNlQ7ajAGLAk4nRMwRaLiV4HsHzIU9mk4fLmjr6waB0uCyke6gYYKPkZGJkt5VyR4zNVI02imLA5v31UZTM7E7geR7+dcCRzucL6HkbXS1zv2i7GQASlCnkAcXqKFJZluvJJITKmvam92Q2eksZl70eKstc2RI5mak8mTFJf598MYBp0wb3bEpRTOnzO+eYyKxU2CNZFzmZFE8mESSLVpclgExDX64nUxBk+uzF7pMpHS4LyXuBY4ZwWXtcvkcyzpNJCZdtC8iWoqkwbKNR3/tszjWzZ0n6DB5sNrMfmHRmU5Cco6apypcbMgFPYJY9MkkgNaCrTTmeTArA9MjE52SGvYrJcjIDXkxXR3xIK0V2PbT5L5H0D40tESpryheOey3BBuj3bI6vf/70HBOZjUZ6MnOEy9JtZ4BMj8xaJf5j9Pk2RmaHy0L8AXslwmUuf46czJiWMm1doXElqsuGxpUIl4EfaCbZ2GkswaamvvfZXF///PJ805meRudkMgGmvO0AIOSCTMlQWe95d0EfKocukZMpWb7clinpyYwNlUFZT2Yt2/yXyMmkeDKTlD3DMoxWU18Y7TZ6MNnM9pxkRnNQkZBVhEzf9VG2B4BmSEeKF9Orz7OADwBZkXBZyN4Mbf5LtpRpL+SFGgAAIABJREFU6xvrycwRLuvI9AFH4XBZdR4XMksNl7k2thV2RZZgU1GfZ7MHgKSTgOuBP6f6//l5YJ9ZZjcFiSIgU3S3f7Rti5pbjO3RGzFDuiNkJ/NkEoAgBWDaMrGJ/9JJf1d+yJPJTfp39YXBxD0vkZPJ9WRKhMq2BUCoCC3BBiDqFQM/Y2Z/bGa3mdmtZnYaVTPMjUkDi7qptVC3zoPXHerIjLZtuG3+Q3OLte2CVjBk1rlXawJDaB4tG+H7a+lbsFPsedr8S6F2/qEW/d05u7K9bf7VknWnmQgc6rG9otUW++71dpv/HKBZ6HX1Sd02/x1ZTd/mf0U7GyGu9jy7NlZLmLd5ZfuBxrVXlCziGCBJB0i6UNKV9StZjq/5J0q6VtJl9XGkM+YHJP1LLf8ZSbt79O4l6SOSPl//TO6iH0sx+2y+JenngXOoHstRwLemmtDUtK7Llxu2A55HytxSPJkhzyNgO9uTcWSK7vaP5nfnU3IjZve8+jnkyUy1ETPEn6pvWZSOAi1lcsNlPk9mipxNwa7PdwOvMLNL6xeWXSJp0RDzVDN7oyss6R5UL7k82sw+JelBwF0eva8CPmZmJ9fvC3sVkY01UykGbJ4LvKU+jKrx2nOnmMzklLFQl0j8zxIuc/kpIBOcp3+RHQSasSDTZyNnn8yAF9OQbcmMzcls9Db/KbmV3JxMieqyEuGyhkxp0Cmgri7YWhRt3SbpKqqu+CH6SeDTZvapeszNAbmnsdrQ+J3Ax1kLsKnfGXOcmW3csFmb2qGjVCBgQManIwFg+mX6dQ8Clkspnkzo3CNbJOnfPo/MyZTYiNngJ3gyuTmZkh2YQ/zcpH/ofKoOzDHyPk8mNek/BDIrFH6BWpy6vSVtdz6fUTcR7uqTHk7VjPgi4HDgOEnPB7ZTeT+3UL3CxSR9mOo9NOeYme9tyA9ZVB4DXwUeEjXbDOoFGzPbIemHpzI+NxnjS5hHly93xnlAJtkT6tHVJu88M70Yh58EMiW8GIefEyprXO/V0QWZkuXLffKbuc1/iT0yOaGytt6mVzTNC9QiVd1kZo8b1CXdDzgPOMHMbpV0GnAS1T/MScCbqN6geQ/gh6le33I78DFJl5jZx0K6696Uhd26VYoJo/2bpPcD78HJ1ZjZ+VNNanJKAYOh6xE6NkRLmUyQaejL9WSmApmWPd9iXxJkYLwnU7qljKtzqo2YofM5cjK5nswcIANEFwDEkKR7UgHN2Yv118xucK6fCXyg/ngN8A9mdlN97QLgB4E22NwgaZ/6JZT7AL43JhehGLDZHbgZ+HGHZ8DGBBsxDAR4rrsyKQDT1p3jyeQCTFDfgGeRAjA9MuuhpYwrP0dLmaa9ftncjZhBHQOeTJm8yjSeTImkPwwn/n2hsub49fmKAVUv7no7cJWZvdnh7+OEwZ4OXF6ffxj4dUn3oeqq/6PAqR7V7wdeAJxc//zr8bP1U0wjzhdOZXxNyBv26YptlJYyc4fLYvQtW8okAkdGddnc4bKgbIJ3MocnMyZctm0CD0cQG0YbosOBo4HPSLqs5r0aOErSoVT/SF8CXgZgZrdIejPVO8EMuMDMPggg6W3A6Wa2nQpkzpX0YuDLwLOKzNZDMa+F3h/4Q6qbBfhH4Hgzu2aqSU1GyvRkWudbpqWMw5+jpUylp9/jWA2h+e1NVVnm6svNyeR6MmNDZdHna9RSpi3fl5MZU1nmA5kQwGwruN9GO8ejjZl9Av+qFXz9ipn9BVX5c5v/Euf8ZuAnRk8wgmLCaO+gei3oM+vPz6t5T5lqUpNTrCdTOlyWortEuKw11q+7Ry5GF6lA4wOOVO/Gby8nJ1MiXNbQNwQcBXIyuZ7MWJCJGVedz9tSJicnMwfIAEVzNhudYsDmu8zsHc7nP5V0wlQTmpxai3XRljIJnkyRpH9Q38CCnxIqC8jM0lImwl7J8mX3fCO2lHFlxobKes/XKCeTW74cEypbgMzyfTbTUQzY3CzpecBf1p+PoioY2NA0VQlzkXBZSJ9LOSATsJGqr2hOJtOTmSNcBnk5mRKJf5c/VeI/Rj4FODZKTibkybhzLurhLMEGiAObF1HlbBaVDP8EvHCsYUkvB34J2AF80Mx+3SPzVKrOBduAt5nZyTVfwOuoQns7gNPM7H/H2B3tyZQMlcXqG5ynZ4FNAJgYfVOVL4f4Jbsvt6eXk5MpnfTvyPuAyhOCKpGTSRk3R3NMV6ZkqKzSMRwuW8yjeAitpqVnU1FMNdqXgZ8paVTSEVRtEg4xszskPdgjsw14K1Vu6BrgYknvN7MrgV8ADgC+18x2+sb3T6D1k+nCZWEdLm8AZBz5jdhSZpZwWUD3HOGytsycLWXc85Ig07adU11WIiczVeI/5MVM1ohzSWtWjXYscLKZ3QFgZr6NRI8HrjazL9bzOIcKoK6sxz/XzHb2jPeTGPRYZmkpkwAwXXsJIDM2VBZrb61aygR0yyOXm/R35VM8mRI5mVxPZmyorCs7zpMp3VKm0unzhvJCZY05l0QHI7ZdzaanmFcMvINq48++9fF/at4YOhh4kqSLJP29pMM8MvsBX3E+X8Nq47lHAs+WtF3S30g6KGRI0jG13PYd3/pWY8HNzck0xrUOc44GsDXOM4FG1vROeg+PbOf+Bhb+gI7Vc1s9nOtSuM1/rz1nTq6ORfv8BU+yxr02ZN1pZgKNz/aKVlvcu9dz2/w3dMjfPt97PeY8UF0WOt8li+96fJt/V7bd5n91PvFt/l17C30r7Fw93OvsHA002zCvRzWG3H/FvmMr0GTVaJI+CjzUc+k1td29gCdS9e45V9KBZhb72O8FfMfMHifpGcBZwJN8gnUzuzMAdt/vABudk0kJlcXoc2nIk0m0kaJvlpxMgiczx0bMrkzXdk6orCNb0JMpkfRv6JthI2ZbZhUg+u2VyMmkeDKThNAAope1zU2TVaOZ2ZND1yQdC5xfg8snJe0E9ga+5ohdS5WXWdD+NQ8qL2fRLueviPW0Wgtn8XCZx0avvkFd/kV2dE6mRLjM4ZcEmbZMbLisqyMeDDbLZsw5wmUh+Snb/E+VkwnNY/la6GkotRrNgH9mfDXa+4AjgAslHQzsBtzUkrkYOEjSI6hA5jmsvkdnMf7fqXr+/N9oy0oEmQKezLpuKePKJABMpSMFeLr2cpP+nalGejIlkv4xuoeKAaZq89+4nuwBrX1OJteTSQ2Vee8jwB9NxrJAoKY1qUajCnudJelyqiZxL6jbW+9LVeJ8pJndLek4qoZy24CzzOyKevzJwNmSfhn4JvASjw0v7cqlLKiEF9M57wGZ0l6Mw5+jsqzSEwcypSvLXHZuIj8FZOZojunK5FaI5Xs/G6OlTKUnDmTGeDFNfsl2NcVUbWiKqUZ7J1X12Tfqzw8E3mRmL8o1amZ3UrW9afOvA450Pl+Ap/dPPZf/lmU8BWi8nkKiroDtdddSZmyoLNKeb7EfGypr20sBmpIgE+JP1VImSsdEOZn10lImN1zm5020z2YJNkBcGO0HFkADLLqJPnbCOU1LCZ7MsqVMGshsxpYyQdkI4BnryWzEljJQts1/CsikeDIhL8a1V4SMZYFATTFgsyLpgfWrRpG0V+S4dUvLljKt85I5mZnDZeAHgznCZQ0dM4TLonSsUUuZKcNl1dhw4r9EuCw0hxJUGr82KsWAxpuAf5H0nvrzM4HXTzel6ci7tyYTYDryHtlZWsoUDJVVOsp5MpuppUzJUFnveWROZu7KMvd6iZxMriczNlTWaw8XZAsXCSwpqkDgzyRtZ/VNnc+oW8ZsTErxZOYIl4XszBEua+hIBZ+uvVxPZizI9NkeAprN3lKmYXsgtLYe2/yXri4b8mRK90cTS89mQVHhsBpcNi7AtMm3kHvBJh5gOjJDi37JUFmMvUqrR0cPL9Le2D0ypZP+rvyQJ5Ob9O/qiwOL3KR/aGyKJ5Obk5nDkymR9Hf5Q55MCGCKv2LArMjL0zYDbejcSxapULisJd+7r6VEuKwlE5+TSfVY4oEmtrKsI0uAnwA0KV7PRmzzP0dOZo7mmCUS/405TxguW75iYFracmAzuoQ5IYcyS04mIVRW6RjgJ3gyG6WljCuf68nk5mRKeDKbvaWMO4+UljIlQCYIbiX32SzBBojbZ/Ny4C8W1WibgnJyMrnhsoa9AZApHS5z+CVBpi0zdp/MRixhLhP2yh23fhP/rsxcLWViczJRHlTpvTYGLMNoQJxn8xCqd8lcSrXz/8MJDTPXHwkvyMzdUiY7J1PAkxkbKuvXMc6TKZH0d+WHwKRE0j90Pna3f1cmHmTWQ5t/lz/VRsygvcRQ2QpdQC5GG3e1LEox1Wi/Kem3gJ+k6on2R5LOBd5uZl+YeoJFyQWaHJBxxjVkcsNlRTyZeJBp8Et4MjOEy1z5OcJlXX1xIFE68d/UHVdZ1padKicz92bMucJl2wI6xtIU+LURKbYazSR9FfgqcDfwQOC9kj7ie53zuqaxIDM2VNYjk7VPpnDSvy2/Vi1l5tiI2eb3dWAukfQPnc+Rk1nPzTHdeZQuX04Bmena1SzRBuJyNscDz6fqyvw24NfM7C5JK8DngY0FNiTkZAa9ivZ5AtD4Fv0YexNtxiwdLnPPcyrLQrKQBjQ5lWUdmWyPJNcDWr85mRRPZo5wWZsf68mEvJji77QxlmG0mmI8m72oNnJ+2WWa2U5JPz3NtKajDtAk5FBK5mSKd2BO8WRSQmU9uuXjDQDHVBsxO7Iee7kgk+vJ5OZk5vBk5ujA7PJLdWCOTfynhsom6/gMqECKW9IBwJ9R5dANOMPM3iLpROClrL4L7NVmdoGkhwNXAZ+r+f9qZr/o0esdP3rCHorJ2by259pVZaczE40NlbmyMfocma3eUiZku4QX4/JLli93zjMS/6W9GOhP/JeuLKv0+Eqf08uXY/i5e2RSQCYEMMWLBMpg193AK8zsUkl7AJdI+kh97VQze6NnzBfM7NAI3aHxRWnL7bMZ7clMBTLt85zy5Qh7uZ5MSZBp2x6bk8lN+ndkIj2SEuBUnccl/jd6Sxl3HlOBjMvP9WQmqUSjjGdjZtcD19fnt0m6CthvtOIZacuBzbKlzDDIjA2VpcrPXVnmyqyHyjJXtkRl2Xpujunyx4bKuvx4kPF5MsX32MAkOZs6RPZY4CLgcOA4Sc8HtlN5P4s9kY+Q9G/ArcBvmtk/BlSGxhellSmUrnuSNUNmoSMkS0ZLmT57u/4irWFDstFA4+qQFoet8p15NGTdR5AIHOqxvSLbdbjXV7BGTiYHaBZ6XX3uva54js64TKDx2W7a2tkIcbVlu3qHczI+fduGrssai2sf0DTH7Wwk39tA49rbhnnn0eYvbHntOfwYT8ZN/G9r6VjMYUU7W3rNCzTuvY6nqjfa0AHsLWm7cxzj0ybpfsB5wAlmditwGvBI4FAqz+dNtej1wMPM7LHArwDvkrSnR2VofHFaU8+m7k7wS8AO4IO+MmpJTwXeAmyjemX0yTX/J4A/oALMbwK/YGZX9xukXvhbPO+5f0HP2iOTm/QP8RM8mblbypSuLEvxZKbaiDlu3DSeTMlQWaVjnCdTOukfZTuhsiyUk2mDjE9+NMWF0W4ys8f1CUi6JxXQnG1m51eq7Qbn+pnAB2r+HcAd9fklkr4AHEzlvThT84+fgtYMbCQdATwNOMTM7pD0YI/MNuCtwFOAa6g6Gby/7kJ9GvA0M7tK0v8AfhP4hWHDnvOpQCZkr9LY4c+yETNgbygnUyJc5srM0VLGlUkJe80RLuvq6y6cU4XLmjqmD5d1+WFvqnS4rM0PeTJtuWJkFHkttCQBbweuMrM3O/x96nwOwNOBy2v+dwFfN7Mdkg4EDgK+6NHrHT8FraVncyxwco3AmNmNHpnHA1eb2RcBJJ1DBVBXUq3WC7fw/sB1UVYTPJl1/UZMx+ZGLF925VNDZX5d03gyJZL+rvyQJzN3S5kSSf8Qf6qWMu37S6kuW/GAWmNuU2yKKdPd63DgaOAzki6rea8GjpJ0KNXi8iXgZfW1HwF+V9JdVPVwv2hmXweQ9DbgdDPbDpwSGF+c1hJsDgaeJOn1wHeAXzWzi1sy+wFfcT5fAzyhPn8JcIGkb1MlwJ7oM1LHPo8B2LbXA2qmKzASZHpkVs/jQabBL+HJTBQuC8nPES7r6kv3SDZi4h/8C7kPZEq3lHFtzt3mvyTItO3t0hG45yJUAGvM7BO0vrrW5N0TY2bnUYXcfNde4pwfPX52cTQp2Ej6KPBQz6XX1Lb3ogKJw4BzJR2Y0OTzl4EjzewiSb8GvJkKgBpkZmcAZwDc67sPsF15m12TdGRLgIzPk0n0NkrmZHL3yMAwyIztvtyRTwCZsaGyaPlBcOp6CCX6lg15MimhskpHP8hM1VLGtVe6pcxQ+XJMPiZke1tBD0c7JwjPbUCaFGzM7Mmha5KOBc6vweWTknYCe7O6kxXgWuAA5/P+wLV1PPIQM7uo5r8b+FDUpFKAZoZwWYM/Q7isqycOOEon/l35OcJlrp5BYCkQLgvJl+xb1tWXnpMp3eZ/juaYuZ7M3CAD1K8YKKtyo9JahtHeBxwBXCjpYGA3qv5rLl0MHCTpEVTA8xzgucAtwP0lHWxm/5eqgCCim4EleTJbqaVMiaQ/uJ5HhOxACG09t5Rp6EjwZHJzMiU8mbGhsn5+/zymbCmTk5MJAUzpvTbCimzq3Ay0lmBzFnCWpMuBO4EX1N2l96UqcT7SzO6WdBzwYWAbcJaZXQEg6aXAebVHdAvwokGLJXMyU4FMy55vsR8CmdLhMle+JMj08XNyMiXCZe7YkiDTlfd5IfGezBzhshB/7s2YOZVlnblFeDIhPaNpCTbAGoKNmd0JPM/Dvw440vl8AZ4kmJn9FfBXyXZ7cjYbsaVMbk6mhCezlVrKpBQIzNFSpnTSPzSPqVrKuPfi3uMcOZkYgCkaTluCDbAF29UUDZeVSPxvcpBx+XPskQnJlKws6+obFy5bdmBe23DZMmczD209sIHBRT8rVBbN79rLCZW1pzd2n8yUHZhdfmxlWfT5SJApkfSH+MR/6Q7MDd0zg8zczTFTPJkUkNkW+P2WomU1WkVbD2wU8GQKVJZNVb7c5pfcI7NsKTMMMhslH+POY+6XlQVtz9RSpg9kYryY0P2NJ1uG0WracmATX8Kc6clkJv1DuufYiOnKz70R05XJT97neUApnswcGzErff0eREpOZqqWMiHbJcJlpcuXU4CmcX+lwmnGEmxq2lpg4xYHzOHJZAJMR96rIx4ISuyRGRsq6xs31pPJTfqH5EvmY3plPAurb3EvnfQP8edoKROyl9tSZghkUjyZ4vmaBS2jaMBWA5sF5XgyM5cvd6ZZ0JOZCmTa/PXQUmbucBnEezLLljL54TJXZizI9MmUoOU+m4q2HtgIvydTIukP8TmZwkn/kPzcLWXmqCwLnU9VWdaVj8vJlEj6V2PTczKl2/wPeTJzVJZ1ZbqgnRsqC3mlRWgJNsBWBRtg2VImDWjmCJe5egaBpUC4LCQ/d0uZSk84ZFY6XBa0V7CljGtvypYyKdVlQ0AzSRjNDHYs42iwFcFmpCdTIunv8ufeIwOr/8TruaXMOA+ou6DP0VLGlZmjpYw7j7mbY673PTJDIOP7XU6Ws1l6NsCWBBsmy8lMBTKQBjQlQaaPn7NPpkS4zB1bEmS68nHhsrbutSphdvlThcvc+3N1zBEu684jzpOJCZc1ZShLS7ABtiLYKOSx5AFMWyanfNk9n/uNmA3ZBIAJyZQMlfXZ9gHH2FCZe709bqwnU7qlTMnuy722I1vKlE76d+6rJ/GfmvQfApmyrWqAnUuwga0GNi7QZHoyU4FMW59v4Z8KZII6ZgiXRemYKFzWlfflMcaBTKWjH2jmCJe1+XMm/tu6+15YVjpc5srMCjK7yMCWORvYamADaSAzMlTWYieBTK7XM1XfshJJ/5CMl1c46Q9+kNnsLWVc/hx9y6ZqKdOR8XhZKZ6MGypr2i5MxrJAoKatBzaEvBtXYBgUxm7GLB0uAxcY+mVLhMtC53OEy+LsTBMuc3XmVJa151EyJ1M6XNbW0xcymyNcFrrHEuEyF2S2LXM2k9AWAxurFs4NuBHTlU/xZEpsxHRlBsGmQNK/aS/ekynd5j/Fk5lqI2aIP8dGzO48fACeHirr3FNBTyYm6R8DMtsoiDhLsAG2HNhAXwnz3HtkmtfzvJgY+ZL5mN7zyBLmEl5MSL5kPqarL92TmXKPjGtzqj0yLn9ukCmZj+naXth1r6tzfTzZEmxq2nJgs1neiBklm+DJ5ILMZm8pU+nrD1OleDJzlC8HbW/SljI5OZnpQaYmA5avGADWEGwkvRz4JWAH8EEz+3WPzFnATwM3mtljHP5ewLuBhwNfAp5lZrfEG0/3ZEpXlrnnc7eUcXWOLV+Olh8Ep+6CvmwpkwYyY8uXu3r7QWaqljKdeXg9nXhPJiZUtgpCpRM2LD2bmtYEbCQdATwNOMTM7pD04IDonwJ/BPxZi/8q4GNmdrKkV9WfXzlsGJDNHi4rkfSP0T13uGwwRDZzuGzKljIly5fj+WGPqnTSv6m7HMiEdLgyc4TLKtuL635PxgWalWI5G1tWo9W0Vp7NscDJZnYHgJnd6BMys3+Q9HDPpacBP1afvxP4OBFgI/ygMLT3Zu6WMlOVL/fKZIDFOA+ou3gvW8rE52N6bc/YUsaVmaOlTPM63uspnkwlo5o3hVcDttxnAzSf+Zx0MPAkSRdJ+ntJhyWOf4iZXV+ffxV4SEhQ0jGStkvavuPWbyE1czJ9QCOH7co2z/uBpk92xSPjli8Pyrb4CwqFy3zjFuc+3mK868nEAk143M5GCGtFFiW7TRYNNNuwxgLftrfQV+na2cjJuAv8Nh8/IifjVpe1gSY0t6C9AaDZpp2NnEwf0ITsNZ/tzg7QNObm/K5c2zEtZbZ5fseNedQyzeur45o6WLURyMm4nswCaFbqY5u061hBu4BmIVu0Gm2nDR8DJOkASRdKulLSFZKOr/knSrpW0mX1cWTNf7ikbzv80wN695L0EUmfr38+sNyNN2kyz0bSR4GHei69pra7F/BE4DDgXEkHmqUHN83M1Ih/da6fAZwBsPsj97Nqbo54T06mdNLflV/P5cuujtwQW1cmPSezEcuX3XlspZYyJUNlbX5OTiY1VNaUL+zhlMnZ3A28wswulbQHcImkj9TXTjWzN3rGfMHMDh3Qm5eSyKDJwMbMnhy6JulY4PwaXD4paSewN/C1SPU3SNrHzK6XtA/gDcP5bVuRcBmEPZk+2dKJ/znCZVE6Zk78zxEuq8am52Q2euLflV/PLWVyw2UVvws0DV6pQgGzItVodSTn+vr8NklXAfuNVpyZksihtcrZvA84ArhQ0sHAbsBNCePfD7wAOLn++dfRI0dWlw15MnO3lJlyj0zDzoDtoXxMrHxKqGzIXsmczJR7ZFz+HC1lXD1zt/kf8mRKbMTM9WQmqUSDWM9mb0nbnc9n1FGZDtV57McCFwGHA8dJej6wncr7WVTmPkLSvwG3Ar9pZv/oURedkhhLawU2ZwFnSbocuBN4QR0O2xd4m5kt4o5/SYW6e0u6Bnitmb2dCmTOlfRi4MvAs1KMx+6TWbaUyQGnfqCZKlzWkdkFWv2Le4lwWYi/2VvKuDJTtZTp2l7Yda/3lzDHhMua8iVT2Ybt2BEjeJOZPW5ISNL9gPOAE8zsVkmnASdR7eg5CXgT8CIqL+hh9v+3d+axelRlHH5+XQShLbS0IRCEArbIommAErRsFlEExWCAWhF6ERcEEUJYDRC8QtBqFBGwLEplKTFFqFCxUAtUadkKbblAaYG2asWwlM2CIJbXP875uHO/5d5vmfnW90km98w5Z8785tyZ78xZ5n3N1kvaC5gjaXcze7Ok0gGmJGqlIY2Nmf0X+FqR+BeAwxL7U0scvx44uOITK06UJ6PKHP4qzFNbT6aSobL8+GKNTCWNRaublKn3h5jJc7ajSZnkueuxfDkZX4+eTKUNTKpzNkZqLgYkDSU0NDeb2W0AZvZiIv1aYG6MfxfIrfZ9TNLzhIVZS/KKrXpKolI6z4JA7m+KPZl6DJcVlpd2j2SA8mpsZPLzV/OdTNrDZeHY8uZk6jFcVhhf2HC0yhLmLI1jZtWTyWTpM6TiYkCSgF8DK8zsZ4n4bRLDYEcCT8b4McCrZrZR0k7AOGB1kaKrn5KokI5qbIRl3pOp99f+pcLVTvqXOrbWoTKorCeT5lBZ4XED9CZqHCrrT0ezmZRJ0/py/3mKvTxQGEciLoVJ/4L4D76pKezJDFb6X4IYYOn0bCYBxwE9kpbFuO8DUyVNiKdaC3w7ph0AdEt6D3gfOMnMXgWQdB0ww8yWUOOURCV0VGOTo9yGI+2v/ZP5s2pkSpWRjM+qkSmVvx7DZYVl1DYnk5VxzFLnbkVfMsmy6zFcVpinyCqyAVaWlRouSzY0qc7ZmKXSszGzB6Bo1+uuEvl/TxhyK5b2jUS4uimJKui4xiZ83Ni7n8bEf6t88V/bcYU/plkNl5XKU+8lzMn4egyX5V9jrb2ZZvYlU3junLZkenYT/8UamnQXBvRS5gKBtkdVfEfZskh6mdBVTDKaypZdNxuuv7G4/saSpf4dzGxMLQVImkfQOBCvmNmhtZyr2emoxqYYkpaUs+SwWXH9jcX1N5ZW199JNMo2muM4jtNBeGPjOI7jZI43NtFIZwvj+huL628sra6/Y+j4ORvHcRwne7xn4ziO42SONzaO4zhO5rR9YyNpsKSlkuYWSdtB0gJJT0i6X9J2ibRp0Xvds5Km1Vd1H40V65c0QdKD0aPfE5Km1F/5Bxqrqv+YPkLSOklX1E9xgcZq75/tJd0jaUX0rji2nroTOqrVPz3ePyskXR5tc9UVSWsl9URPk/kGJFHgcknPxWvYM5HWFM+vk8DM2nrORJLYAAAIMUlEQVQDzgBmAXOLpM0muDcAmAzcGMOjCEbrRgEjY3hkC+kfD4yL4W0J5sa3bBX9ifRfxGOvaKX7J+7fDxwSw8OAzVpFP/ApYBEwOG4PAgc1QPtaYHQ/6YcBfyKYcdkXeDjGN83z61vv1tY9m/imdjhwXYksuwH3xvB9BK91AJ8D5pvZqxYcEc0H6v51b7X6zWyVmT0bwy8QzIbX9CV0NdRQ/0QfHFsD92SpsT+q1S9pN2CImc0HMLMNZvZ2xnILqKH+DdiU4NRwE2Ao8GLB0Y3nS8ANFngI2DKayW+K59fpS1s3NsBlwNmQMBLVl+XAl2P4SGC4pK0I7lb/kci3jnRcsFZKtfo/QNI+hB+N57MS2Q9V6Zc0iOAE6szsJfZLtfU/Hnhd0m1xCOsnkgZnL7eAqvSb2YOExifnivhuM1uRtdgiGHCPpMckfatIeqnntFmeXydB2zY2kr4AvGRmj/WT7UzgQAXXqQcC/wSawmpeGvrjW96NwAlmKZierYAa9Z8M3GVm67JXWpwa9Q8B9o/pE4GdgK5MBedRi35JHwV2BbYj/EhPlrR/1pqLsJ+Z7Ql8HjhF0gEN0OCkRaPH8bLagEsJbzRrCb613wZu6if/MGBdDE8Frk6kXQ1MbRX9cX8E8DhwVAvW/83A3+OxrxB8qP+ohfTvCyxMpB0HXNlC+s8CLkikXQic3Yj7KKHhIuDMvLg+zyWwEtimGZ5f34r8DxstoC4XCQdRfIJ0NDAohi8BumN4FLCGMLk4MoZHtZD+DwELCH7KW67+8/J00cAFAlXW/2DCENWYuH89cEoL6Z8C/JnQQxsa76Uv1lnz5sDwRHgxcGhensPpu0DgkRjfVM+vb2Fr22G0UkjqlnRE3D0IWClpFWEy+hIACx7tfgg8GrfuGNdwytFP8LZ3ANAVl40uU/Dm13DK1N+0lHn/bCQMUS2Q1EP4Mby2AXILKLP+byXM8fUQGs3lZnZnnaVuDTwgaTnwCPBHM5sn6SRJJ8U8dxFWmj1HqN+Tobmf307GzdU4juM4mdNxPRvHcRyn/nhj4ziO42SONzaO4zhO5nhj4ziO42SONzaO47QVkn4j6SVJT5aZ/5hoLPUpSbOy1tepeGPjNBWSNsS/20q6tYZyTpe0WUqaPhaXjy+VtHMaZSbKvi7aUqv0uAmSDkvsHyHp3DS1tTAzKdMWmqRxwHnAJDPbHTg9Q10djS99dhqGpCFm9r+8uA1mNiyFstcCe5vZKymUdS7BsObFVR5fcJ0paOoiXN930yy3XYguHeaa2R5xf2fgSoJB2reBb5rZM5KmA6vMrJSxUiclvGfjIGli9AeyqaTN43DCHkXyHR/zLZd0Y4wbK+neGL9A0vYDxM+UNEPSw8B0STsq+N7pkXRx4lxjc8MgkrqiUct50T/J9ES+X0laEjX/IMZ9j+Ba4T5J98W4z8bzPC5ptqSCBi32Fh6Kmm+XNDL2Hk4HvpMrK++YDZJ+Hs+/QNKYGH+/pMsU/LCcJung2DPqicM8myTy7d2fxvj/WRzr/RFJWwDdwJTY45oS6+iKMur+8ljWaklHVXKftDjXAKea2V6ED26vivHjgfGSFsX/vVuHzopGmzDwrTk24GLgp4S3v/OKpO8OrCL6FyGa/wDupNcnyteBOQPEzwTmAoPj/h3A8TF8CrAhhscCT8ZwF+FL8S0Ipu//BnwkT8dggg+ZT8T9tQmto4G/AJvH/XOAC4tc4xPAgTHcDVwWwxeRZ5crcYwBx8bwhUTTOlHLVTG8KcEK8fi4fwPRlFDMt3cpjQTTQ6uBiTF+BMGMTBcJMz7J/QHqfjbhJXM34LlG33cZ3s/J+2cY8B9gWWJbEdPmArcTzPLsGP9PDfH91O6b92ycHN3AIYQfvulF0icDsy0OS1mv+Y9PEpxzQbAwvd8A8cRyctapJwG3JPKVYoGZvWFm7wBPAzvE+GMkPQ4sJTSIxeY/9o3xiyQtA6Yljgcg9ha2NLOFMeq3BJM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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# mask nodata and plot\n", - "geo_grid.test_attr.where(geo_grid.test_attr!=geo_grid.test_attr.rio.nodata).plot()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Load into user-defined grid with radial cubic resampling\n", - "\n", - "Note: This example is simply to demonstrate the option and and\n", - "would need to be adjusted for better results. As seen in the plot below,\n", - "the values swelled quite a bit due to the input generating an \n", - "ill-conditioned matrix." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/snowal/miniconda/envs/geocube/lib/python3.6/site-packages/scipy/interpolate/rbf.py:268: LinAlgWarning: Ill-conditioned matrix (rcond=3.16678e-18): result may not be accurate.\n", - " self.nodes = linalg.solve(self.A, self.di)\n" - ] - } - ], - "source": [ - "geo_grid = make_geocube(\n", - " vector_data=gdf,\n", - " measurements=['test_attr'],\n", - " geom=json.dumps(mapping(box(-48, 44, -47, 45))),\n", - " output_crs=\"epsg:3857\",\n", - " resolution=(-300, 300),\n", - " rasterize_function=partial(rasterize_points_radial, method=\"cubic\", filter_nan=True),\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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test_attrtest_str_attrtest_time_attrgeometry
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" - ], - "text/plain": [ - " test_attr test_str_attr test_time_attr \\\n", - "0 1.3 dcf86619 5/21/2016 10:09:21 AM -05:00 \n", - "1 1.3 dcf86619 5/21/2016 10:09:21 AM -05:00 \n", - "2 1.9 dcf86619 5/21/2016 10:09:21 AM -05:00 \n", - "3 1.3 dcf86619 5/21/2016 10:09:21 AM -05:00 \n", - "4 1.3 dcf86619 5/21/2016 10:09:21 AM -05:00 \n", - "\n", - " geometry \n", - "0 POINT (-47.26681 44.21932) \n", - "1 POINT (-47.26680 44.21932) \n", - "2 POINT (-47.26681 44.21932) \n", - "3 POINT (-47.26680 44.21932) \n", - "4 POINT (-47.26679 44.21932) " - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "gdf.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "gdf.head().plot(column='test_attr')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Convert to raster with GeoCube\n", - "\n", - "See docs for [make_geocube](../geocube.rst#make-geocube)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Load into grid basic" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/snowal/miniconda/envs/geocube/lib/python3.6/site-packages/xarray/core/variable.py:151: FutureWarning: Converting timezone-aware DatetimeArray to timezone-naive ndarray with 'datetime64[ns]' dtype. In the future, this will return an ndarray with 'object' dtype where each element is a 'pandas.Timestamp' with the correct 'tz'.\n", - "\tTo accept the future behavior, pass 'dtype=object'.\n", - "\tTo keep the old behavior, pass 'dtype=\"datetime64[ns]\"'.\n", - " return np.asarray(pd.Series(values.ravel())).reshape(values.shape)\n" - ] - } - ], - "source": [ - "geo_grid = make_geocube(\n", - " vector_data=gdf,\n", - " measurements=['test_attr', \"test_time_attr\"],\n", - " datetime_measurements=[\"test_time_attr\"],\n", - " resolution=(-0.1, 0.00001),\n", - " group_by=\"test_time_attr\",\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "\n", - "Dimensions: (test_time_attr: 2, x: 12, y: 11)\n", - "Coordinates:\n", - " * y (y) float64 45.25 45.15 45.05 44.95 ... 44.45 44.35 44.25\n", - " * x (x) float64 -47.27 -47.27 -47.27 ... -47.27 -47.27 -47.27\n", - " * test_time_attr (test_time_attr) datetime64[ns] 2016-05-21T15:09:21 2016-05-22T15:09:22\n", - " spatial_ref int64 0\n", - "Data variables:\n", - " test_attr (test_time_attr, y, x) float64 nan nan nan ... nan nan nan\n", - "Attributes:\n", - " grid_mapping: spatial_ref" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "geo_grid" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# mask nodata and plot\n", - "grid_time_1 = geo_grid.isel(test_time_attr=0)\n", - "grid_time_1.test_attr.where(grid_time_1.test_attr!=grid_time_1.test_attr.rio.nodata).plot()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Load into UTM grid followed by interpolation for missing values\n", - "\n", - "See docs for [make_geocube](../geocube.rst#make-geocube)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/snowal/miniconda/envs/geocube/lib/python3.6/site-packages/xarray/core/variable.py:151: FutureWarning: Converting timezone-aware DatetimeArray to timezone-naive ndarray with 'datetime64[ns]' dtype. In the future, this will return an ndarray with 'object' dtype where each element is a 'pandas.Timestamp' with the correct 'tz'.\n", - "\tTo accept the future behavior, pass 'dtype=object'.\n", - "\tTo keep the old behavior, pass 'dtype=\"datetime64[ns]\"'.\n", - " return np.asarray(pd.Series(values.ravel())).reshape(values.shape)\n" - ] - } - ], - "source": [ - "geo_grid_interp = make_geocube(\n", - " vector_data=gdf,\n", - " measurements=['test_attr', \"test_time_attr\"],\n", - " datetime_measurements=[\"test_time_attr\"],\n", - " resolution=(-0.1, 0.00001),\n", - " group_by=\"test_time_attr\",\n", - " interpolate_na_method='nearest'\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "\n", - "Dimensions: (test_time_attr: 2, x: 12, y: 11)\n", - "Coordinates:\n", - " * y (y) float64 45.25 45.15 45.05 44.95 ... 44.45 44.35 44.25\n", - " * x (x) float64 -47.27 -47.27 -47.27 ... -47.27 -47.27 -47.27\n", - " * test_time_attr (test_time_attr) datetime64[ns] 2016-05-21T15:09:21 2016-05-22T15:09:22\n", - " spatial_ref int64 0\n", - "Data variables:\n", - " test_attr (test_time_attr, y, x) float64 1.3 1.3 1.3 ... 1.3 1.3 1.3\n", - "Attributes:\n", - " grid_mapping: spatial_ref\n", - " creation_date: 2019-12-12 19:57:17.046150" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "geo_grid_interp" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "geo_grid_interp.test_attr.isel(test_time_attr=0).plot()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.10" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/refs/tags/0.5.2/.doctrees/nbsphinx/examples/vectorize.ipynb b/refs/tags/0.5.2/.doctrees/nbsphinx/examples/vectorize.ipynb deleted file mode 100644 index 331ba07a..00000000 --- a/refs/tags/0.5.2/.doctrees/nbsphinx/examples/vectorize.ipynb +++ /dev/null @@ -1,132 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Example - Raster DataArray to GeoDataFrame (vectorize)" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import geopandas\n", - "\n", - "from geocube.api.core import make_geocube\n", - "from geocube.vector import vectorize\n", - "\n", - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Generate Raster DataArray" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "gdf = geopandas.read_file(geopandas.datasets.get_path('naturalearth_lowres'))\n", - "geo_grid = make_geocube(\n", - " vector_data=gdf,\n", - " measurements=['pop_est'],\n", - " resolution=(-3.0, 3.0),\n", - ")\n", - "geo_grid[\"pop_est\"].plot()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Convert to GeoDataFrame (vectorize)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "grid_gdf = vectorize(geo_grid.pop_est.astype(\"float32\"))\n", - "grid_gdf.plot(column=\"pop_est\")" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.15" - }, - "vscode": { - "interpreter": { - "hash": "f7286e25aede83087e9621ce0e75f07eda2aa917ee0205c1a0b96e7ba4f20042" - } - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/refs/tags/0.5.2/.doctrees/nbsphinx/examples/zonal_statistics.ipynb b/refs/tags/0.5.2/.doctrees/nbsphinx/examples/zonal_statistics.ipynb deleted file mode 100644 index 6e348407..00000000 --- a/refs/tags/0.5.2/.doctrees/nbsphinx/examples/zonal_statistics.ipynb +++ /dev/null @@ -1,845 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Example - Zonal Statistics\n", - "This is useful in the case where you want to get regional statistics for a raster." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import geopandas\n", - "import numpy\n", - "import rioxarray\n", - "import xarray\n", - "\n", - "from geocube.api.core import make_geocube\n", - "\n", - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Create the data mask by rasterizing the unique ID of the vector data\n", - "\n", - "See docs for [make_geocube](../geocube.rst#make-geocube)" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "# This assumes you are running this example from a clone of\n", - "# https://github.com/corteva/geocube/\n", - "# You could also use the full path: \n", - "# https://raw.githubusercontent.com/corteva/geocube/master/test/test_data/input/soil_data_group.geojson\n", - "ssurgo_data = geopandas.read_file(\"../../test/test_data/input/soil_data_group.geojson\")\n", - "ssurgo_data = ssurgo_data.loc[ssurgo_data.hzdept_r==0]\n", - "# convert the key to group to the vector data to an integer as that is one of the\n", - "# best data types for this type of mapping. If your data is not integer,\n", - "# then consider using a mapping of your data to an integer with something\n", - "# like a categorical dtype.\n", - "ssurgo_data[\"mukey\"] = ssurgo_data.mukey.astype(int)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "# load in source elevation data subset relevant for the vector data\n", - "elevation = rioxarray.open_rasterio(\n", - " \"https://prd-tnm.s3.amazonaws.com/StagedProducts/Elevation/13/TIFF/current/n42w091/USGS_13_n42w091.tif\", masked=True\n", - ").rio.clip(\n", - " ssurgo_data.geometry.values, ssurgo_data.crs, from_disk=True\n", - ").sel(band=1).drop(\"band\")\n", - "elevation.name = \"elevation\"" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "out_grid = make_geocube(\n", - " vector_data=ssurgo_data,\n", - " measurements=[\"mukey\"],\n", - " like=elevation, # ensure the data are on the same grid\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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-       "    spatial_ref  int64 0\n",
-       "Data variables:\n",
-       "    mukey        (y, x) float64 1.988e+05 1.988e+05 ... 1.987e+05 1.987e+05\n",
-       "    elevation    (y, x) float64 173.7 172.0 171.1 170.6 ... 181.0 181.2 181.4
" - ], - "text/plain": [ - "\n", - "Dimensions: (y: 178, x: 178)\n", - "Coordinates:\n", - " * y (y) float64 41.5 41.5 41.5 41.5 ... 41.48 41.48 41.48 41.48\n", - " * x (x) float64 -90.6 -90.6 -90.6 -90.6 ... -90.58 -90.58 -90.58\n", - " spatial_ref int64 0\n", - "Data variables:\n", - " mukey (y, x) float64 1.988e+05 1.988e+05 ... 1.987e+05 1.987e+05\n", - " elevation (y, x) float64 173.7 172.0 171.1 170.6 ... 181.0 181.2 181.4" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# merge the two together\n", - "out_grid[\"elevation\"] = (elevation.dims, elevation.values, elevation.attrs, elevation.encoding)\n", - "out_grid" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", 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", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "out_grid.elevation.plot()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Get the elevation statistics of each region using the mask" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "grouped_elevation = out_grid.drop(\"spatial_ref\").groupby(out_grid.mukey)\n", - "grid_mean = grouped_elevation.mean().rename({\"elevation\": \"elevation_mean\"})\n", - "grid_min = grouped_elevation.min().rename({\"elevation\": \"elevation_min\"})\n", - "grid_max = grouped_elevation.max().rename({\"elevation\": \"elevation_max\"})\n", - "grid_std = grouped_elevation.std().rename({\"elevation\": \"elevation_std\"})" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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elevation_meanelevation_minelevation_maxelevation_std
mukey
198692.0173.130925169.394562188.4425053.307044
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198724.0179.931131178.237244181.4903870.630699
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" - ], - "text/plain": [ - " elevation_mean elevation_min elevation_max elevation_std\n", - "mukey \n", - "198692.0 173.130925 169.394562 188.442505 3.307044\n", - "198714.0 175.045866 170.214157 179.716675 2.150987\n", - "198724.0 179.931131 178.237244 181.490387 0.630699\n", - "198750.0 176.187118 167.951233 190.138763 3.815206\n", - "198754.0 171.633084 167.610321 181.611298 2.997591\n", - "271425.0 167.974433 167.951233 168.653015 0.079769\n", - "271431.0 176.718101 170.258133 180.460220 2.731732" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "zonal_stats = xarray.merge([grid_mean, grid_min, grid_max, grid_std]).to_dataframe()\n", - "zonal_stats" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "ssurgo_data = ssurgo_data.merge(zonal_stats, on=\"mukey\")" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", 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Tfu1kYqyCuS0dMRI0MaGqdqlqt/l6OeAJEoa5Dag043sBNAL7I13QUUA5ypttiTFC/2hrVXQNFFw27Izf3Ck0/Wkh7w+dF7lyhuJ3RLTDBhSCoIkJRWSCaePBXBlzYSicEdmMjeMvA9eYRTcSJomhH8cPKEdpHRR8UmNrZtQ+jg+bx73IncdJFfUUSB4DQ146+/s51NNN//Aw1YWFVBYUUpKfj3GvK6rgdrko9Ljp12E2dxxmwBc6kL4ifOgvs3j9w33UuVfZ9rnSCbvS8kSZmPAa4HMiMgz0Adf5I1WMSUz4FeBJEfkWsJaRKVxIHAWUw/ToJMqwTwG93LpgXFmZJ5+TyusZGPSypbWN9d3Bc9F39PfT0R8+PlF9SQkFJW7e7wkdeH7IJzy8fQ7/dmL2KSA7N6NGk5hQVR8CHgpSd2xiwp1YyAcfiDMFy2FahsJmU4qKXk7kX94yVnA94mZB1UQWVkxmsBvW7DvIO83NDIUZvVihuaeHoX5fxBhGT+x0oyRnJS7Z2OWImC5kjqQOtvNeT7Ut/Sguvrz+LKaV1LCoagpFw/lsONDM+kOH4lY6YznU3c2JFeEVZ59PaPNlX+goVWFYXZaOTCFzJHWwnRdbSmzpp3n4DLa1NbCzuZPVBw7QPZjYbBvFeZH3kaw6Mi2hMqSKBBuhk46jgHKY5w8rSkHM7YelgZ/s+gQf+N08th9JTj72Yo+HHV2R7VY/2FKOkl3hS+30hE4XHCN0DjPgE7o5njLeiVw5ACWftzov4bOvNtCZgBjP4Ti5oZ6VbXsi1tvUIRz2Lsq6cK2ZpFys4IyAcpzNvdOjqt+js7nhtRv5+AsTkqZ8SjweZlVV09Q4iZWtkZWPn2W7TkigVMknCX5ASccZAeU4926t4dcL3Mi4tGyjUVys6vgIn3qpngEbYtK4RagqKqLAnUe+202+243H7cLtclHgduMTpds7yOH+o7T19XK0v9/wTInit/XIljzuPH4CeZo9gcvs8gNKFxwFlOO806Uc9J7JJPfrIev4pIpvbryKx96LLYhQRUEhk8rKyM9z43G7GWSY7V0tNA8fhWGIGBk2xt+cIuwbmM30/OxQQKownKsByUTELSJrReRZ8/1tZuAhDbIvZGzbchHZJyIPBZRdKyIbRGSTiNwXUH6TiLQEBD+6OeDcjSKyzTxujO6jOoTif3bPDHmuj+P52MvXxKR8ZlVV0zRlEv2eATb1HGJt535WHdnDuiMH6B4eikdky6xsm5CU6ySLbJuCRaNO7wACc6C8DlwIvG+h7TeBFf43IlIDfAdYoqpzgQkiEhhX4amA4EePmm2qMdzFz8Dwtvy6mS3VIU5+tR965aRx5fuHzuasZ85nbav12+TEmlrmT26gvrqYrf0trGrdS583caE/IvH0+8Upu7bdZKMNyNKdJSKNwGXAo/4yVV2rqrsttD0NaACeDyieCWxT1Rbz/V+AqyN0dRHwgqoeUdV24AXGR3RziAnht80Ljr1ThFfb/o5zfz8nKkPzwokT2dLbzNojBzjUdzQBckbPmjahh9mpFsM2VMXSkSlYfbQ9iJGGOaoweiLiAu4H7hxzajtwoohMNze+XQVMCTh/tTk9e1pE/OWTgb0BdYIGPBKRW/xBmFpaWsaedgjBt7d48EoDSjEPbvkkn365jmiMLwsmTGBtx358ycj1HCUvtmSPV3QU8YAygogKSEQuB5pVdU0M/d8KLFfVfYGF5gjmc8BTwGvAbji2DPMHYLqqnoIxyvl5NBdU1Uf8QZjq6upiEDk36fMJyw5dwA+3X8NDGwujartgwgTWdR5IS+UD8Pu9ZakWwRZUs88GZGUV7CzgChG5FCgEykXkcVW9wULbxcA5InIrUArki0i3qt6lqn/AUDaY4SK9AKqj4kM8ykhg7P0Y4QP8NGLmqXewh/u2eDiJqYD1keP8hvRWPgDrj2TODzI8gjfXVsFU9W5VbVTV6cB1wEsWlQ+qer2qTjXb3gk8pqp3AYhIvfm3CmOk5Dc2Twzo4gpGDN/PAR8WkSqzzYfNMgcbKSqxfoPPratnQ9fBtFY+AJUJzACSbHLVBjQOEbndDGTUCGwQEb8CafK/jsD3RORdjNW0e1V1q1l+u7k0vx64HbgJQFWPYKymvWUe3zDLHGykZdharq2KggIODHXijc4smBJOqU5/Ga2Q83vBVPUVzGmPqi4Flgapsxq4OUj5MoxcRP73QQMiqerdjI7GH3jup8BPo5HZITr29rdTUVBJ58BA2HrTaipY134wSVLFxydnBA+ClnGoYQeyAxH5KeC3755sli0AHsYwtQwDt6rqKhG5HiPaoQBHgc+p6vogfS7DCOnqf4rdpKrrwsmRXRNKh7gRgWlVlWHrnDppUsYon9oCZX7pa6kWwzZsXAVbhvXEhLuA81R1HsYsJFyGjS8F+PCtiySEsxXDYRxFhaGfSw0lpWw+mjlbG761sBPR9PBJihe10QitqitEZPq4SwRJTKiqfwuosxLD7GILzgjIYRzDEtpzuaqsgJ4kbaOIl+I8ZUntX1Mthq2oWjti5F8InZjQz2eAP4Xp4x7Th+8BEYkYbMpRQGnA1GJ4+9w1rD9vJZc0pFoaGNTgCmhR42Te7WxOsjSxc39TV1bthIeoVsFiyYwaKjEhACJyPoYC+kqI9ncDJwGnA9Vh6h3DmYKlmLNr4Acnv06h7gCFB046xN9P/iDv9xXzrc15eFPwjAimgKZVVLLmSMQ8c2lDbYHy4doXIgawzySM0Y3lFa5WVW2K8hI3Yuz5BCMx4bHVbBE5xXx/yRhfvQD51G8YHBCRnzF+B8Q4nBFQirlyUp+hfExc2sXZpc9wfd2TrD53DfWxR0yNmVLXaE/osvwChjxDDKm9AeYTydJFrbi0I9Vi2E6KEhNOxciY+okAd5lx+H34zCSGV0HkUJvOCChFFLiUyycKS6o2haxTwmaeaKpjyevTkyaXKhztGvGb8bhcTKouYXNn5uyrK85Tzqh4OatGP35sXIaPJjHh14Aa4AdmgtRh/+hqTGLC/xUR/ybCdcBnI8nhKKAk88ApvSyu2EWVbEbojVh/insFn5k+lZ/sTs5gdWHpNNbsGrHzzJtUz+q2zJl6AfzzSYO4tCvVYtiOIvjsWwWLJjHhzQTx7TPPBSYmvCBaOZwpWBL56klDXFb5NNWyxpLy8fOZKVsSKNUIBZLHnsPdx94vnDgx45QPwEcbt6VahIShFo9MwVFASUJQ/qEhtiXhWnmLGUmIqzWvcBotvYZiLMsvYGevfWmbk8X0UqXevTrVYiQGdfaCOcTILTMUj+6NXDEEH5mUWANwvaeMdftGtiycVF9D+2BfQq+ZCP676RASMch0BpNlQyBHASWJGxvjm0bNK+uxSZLgTNBaBryGkivKy2NDR2ZstQhkbqWyoOQvqRYjoTgjIIeoOb0KaiS+acGUQmu71GPhhKIJrD0w4rA3q7ompXGcY6EqX3nqrL8hhN9Em8ko4POJpSNTcBRQwlEePPk9JM5xcVVeghSQwkDX6NugMD+zUhoXuJQXlqynZFTOhCxEARVrR4bgKKAEc+/J/dTJqrj7cUtibECnlk5nZ3v7qLIjQ5lj+xGU5y/cTo0rSw3PY0jwXrCk4/gBJZAil3Jlzau2GAVdCTCslrgL2H5w/Miqd3jQ9mslit+ef4CpnldTLUbyyCDlYgVnBJRA/m32AG61Z/OmKwEjoNmeRtr7+8eV92bIbvefndXG/OJwG7OzDWsG6EwyQjsjoATyoRr7HAhdEXK3R8uk/ErW7Au+U7x7KL1HQILy2w8eZH7J8lSLknyybATkKKAE0VAA5YTe5xUteXRHrmQVhZKBMoZ9reNOuUQY8g3HnI890dQXKr8+dyuNnuyJcmgZBc2gFS4rOAooQdw0fchWhzi3tmI8/uK/AZtKZvLm7uB+Pj5VagtLaB1IrN9RtBS4lC+dPMBNU5/DrZmzMdZ+HAXkYIFzquz9kQhDTCt28X5vfGPwGUW1rNkTPkj7xOKypCsgQTmzDuZXD1Nb6KU6f4hKzxDlnkEmF7XR4N6I0J11U5CoybLPb1kBiYgbWA3sV9XLReQ2jBCOxwF1qjp+PD/Sthx4F/idqt5mll0LfBVwA8+q6lfM8i9i7LwdxsiQ92lVfd885wU2mt3uUdUrovisSaUu3/59VCeWKe9b38MalBqtYLsvvHIpEk98FwmKcvFkZXFdPz4Ver0uppf2c2JpGxMKDlLMboTMWf5PGbmqgDAipW1mJGj168CzWMtO+k1ghf+NiNQA3wFOU9UWEfm5iCxR1ReBtUCTqvaKyOcwIvNfazbtMyP2pz2l0mL7zXJciRdDX8fO4HDkHFnbWttwucS2hIOn1yo/PWMlpTbaxHISvyNiFmFpGV5EGoHLCAjRqKprVXW3hbanAQ3A8wHFM4Ftqscm838Brjb7fVlV/c95WyPwJ4PFA3Vce7iIPLU/jMXUoviXxz15kf/LOwcGOL260RYFevFkH0+e+WtH+dhEtjkiWvUDehD4MkSXBlNEXMD9jI8Nux04UUSmm9HXrgKmBOlibAT+QjPA9koRuSrENW/xB+JuaUmusfJ0bx0Hb/oLq+54g2FvsI8THxMLx/vsRIvPbW05/639BzijZio1BbHHAZlUpCxd+HxWhkZNGT6xdkRARH4qIs0i8k5A2QLzt7XO/A0tMstFRJaKyHYz48WpIfo8TUQ2mvWWmqFZwxJRAYmIP3vimoifajy3AstVdV9goaq2Y0Tgfwp4DdgNox1dROQGoAljquZnmhkK8h+AB0XkuLEXVNVHVLVJVZvq6upiENka560r4ry1I7GTF0odbZ96CVEAYaC/zPZrVubFv9Gyy2fdiPTW/v10dAwwp6SBRTVTolZGPzjjIJ7R//UOcSJq7bDAMqwnJrwEON48bgF+GKLPH2KEdfXXHdv/OKzYgM4CrhCRSzFStpaLyOOqeoOFtouBc0TkVqAUyBeRblW9S1X/APwBjFELAQpIRC7EMFCfp6rHfnWqxrxGVXeKyCvAQmAkonuSmFZQwZ57DZPWuV84HSl0s+e+F8Fn/M+LKMUl9k/B7Mhwvq//CHmuAoZ91npT4L1WY31BgDm1DRQV5rG27UBYG9GkIuWUkhzaIpEMbIz1E01iQuBK4DFVVWCliFSKyMSALBj+gPTlqrrSfP8YxswmrKt6RAUUmKtdRD4I3GlR+aCq1wcIeBOGcfku8329qjaLSBXGSOljZvlC4EfAxaoj+xjMer2qOiAitRiK8dukgKn78/A/1/c+8Na484svrcTFBtuva4eL/YAOM6+hkXUHo8+XHqiM6kvKqC4rDJkn7K55RxHSy5co80n4Tvd/AZ4Tke9izI4+YJZPBgKj6e0zywKdySab5WPrhCXmvWAicrsZTb8R2CAij5rlTf7XEfieiLyLsZp2b0C6j+9gjJZ+Zc5FnzHLZwOrRWQ98LLZ5t1Y5Y8H95HwWxXOvzoRy9jgs2nrXpu7A48rvtW05p4e9rUdDTktO682y0NjpArrERFtT0yYCKJyRFTVVzCX3VV1KbA0SJ3VBImgr6rLMOad/vdBo/Kr6oUhyv8GzItG3kSheeGfQnOaEhO7Z9imp9+BwQ5OnzqTlSG8oa3SPTTIaaUTaRsYbVeaW6mUjdg2HezE+jzczsSE+xm9SNRolgWyn9Er1sHqjMPZDR8L7vCKoKIqMemLmweKbOvr7d7dTK+sjLsfd5Bb6O+m9ccdgM0hCIkPSBY0MSHwDPBJczXsTKAz0P4Dx7KidonImebq1yeB30e6oLMVIxbaw0/B3O7EjIB29ebb1pcXH57yYaQjPrtmr3f8dzG5OHvDoqYaiytckfuJLjHhcuBSDPeZXuBTAf2sC3AOvhVjllOEYXyOGCvFUUAxcPTV0EvLeR5wkRgFtLXb3v+unf0tLJp6HG/uORC5chDqikt4p2O8MbsmP35/JYcQ2LcKFk1iQgU+H6KfBQGvVwMnRyOHMwWLklOlnvY3QiugycfZN00ay7oO+/t8Z2APE0pKY2o7vboi6FL8zu7Y+stk3upZnGoRMhJHAUWJ52c7w56fPMu+aVIgPczmQAIGFv06REN9QdTtTps0iTdbg+c5e/FgErIophFv93yAL24c5xObEGx0REwLnClYFMx319H85/Vh60yckZgl+Nc65ySkX4CtfYcA63LPratnTfu+kKFpXj4kKEU5sbt9e/9cblk/z/aIlUFRLG2zyCScEVAUFP8icmbThkb7v1KfVPCNzYkbVbijCHI1u7aWHf0tYb2gh3xCtyZnRJBKhnx5fHHzEjoHk2h0dzKj5ibz8mo59NvIMZ6r6+3YMDGaN7vPo3UwcU++fJe1gfBJtbVs7W2ldzhypMfdfRkVxCAmfnzoJjZ3diX1mtk2BXMUkAXm5dXi/cL4LRfByC+MXCcafFLJ3Zsq7e10DB6J7BWd73bT4etjSK1NNdYcqYlXrLRmW9/JPLQ9Bb90ZwSUW5y3pZTua16kb6+1J53Lbe///q9bP5wQ43MgHcN9EbdmLJg4gQN91p/2v9xdiGaxifGeXRcy5EuC3WcsjgLKLYZ3RDfEjhwBxTrdzOWrm6JfoYqWYfVy6pSGsHX290fn27S5U9g39IHIFTOQ7f1zeb25I+nXtTr9cqZgWYRrcXQxhQ7tsS+v+i8OzSdZWRAGXaG9uxvLytnbHb1z5X9tmhWPSGnLqx2np+7iNgUkSxccBRSBrQ0DUW2tefdNe5L6+aSc72+3T5lFYmd/C+4Qw7dJFWUx6cE/7XfR7gsaPC+j2dVnf7A5qzgjoByjebCbognWPXvXvGRPAsEhahmw+CS7eZYLV5wjpW5vP1NDbE49MhR7Ko4f7UyLAAa2Up/KrSaODSi3+EBPHf0HrSuVjtZhvBrenmKFAS2xVO/aacUsqX2EU2sq4r5mMBVWW1TM1s6QGZci8qMteQxJdi3JV3pSpIAcG1BucarUceAfX4y6XU93/Aqo3xfZ8XB2RQWXN/wIgCnF8Wdh7ewf/8OaVlUZpxlK2D+QXbagQ/0p3OvmjIBygxPzqzn6jyuOxXmOhpaD8Y9Gil1Hw5/P8/D5WcvJcxleuHWF8Yc/HQoSJ9plw7Le9p7quPtIJw4NJGa/nxXEZ+3IFBwFFISKvEI8d65luCs2g/LOd+NfOi9hM9PCDIL++YRWajwjYU9rbcjEOqWifFyZHespBa4M+kVYIIMGGGmPo4CCsGC10LOzI+b2v/1BN0p8LtGC8n+ODz6quWxyOQvKfzWqrDo/tpg+gZQWjXcc7LOw7SISxe74+0gnXKlc5XamYNlNiTufPd9+M64+drzTx59/dXbcslxUsZwPjNnRML20jI9N/vG4upV54cOEWOF9b8s4j+j3Wlooy4tvylHliTOhfZqRsnCzjhE6+zmO8pjsPmN58I5munvmxtWH0MePT36RRVUjZZ+f9Sb5QexDxa5mivPiCwXSMniUhY2jDehDPh8nVdTH1e+EAvtzpKWSCCHBE4tNI6AQmVGfMjPRrBOR3SKyziy/PqB8nYj4RGRBkD7/XUT2B9S7NJIclhWQiLhFZK2IPGu+v81Mwapmnq5wbctFZJ+IPBRQdq2Z5nWTiNwXUF5gfhHbReTNwORpInK3Wb5FRC6yKrtVClx5FD66y6behMHB+G1BHt3PY6f8kY9MhPMnVNJY+LfgVxOYVBx/NMbNg3uZVT3aaNw/EPuep9oCpYj4R2fpREqf2vZNwZYxJnOpql6rqgvMMKu/Bn5jlv9vQPkngF2qui5Evw/466rq8khCRPNd3gEEJnt6HbgQeN9C228CK/xvRKQGI//XElWdC0wQkSXm6c8A7ao6C3gAuM9sMwe4DpiL8cX9QMTCNu4oOPNtDy3P2fNjEVEqKu158ru0jfuPf4wHZ/8VF6GnQxOL4v9p9PoGaSloG5UxY3NrC1X5sSm3L8ztQxiKW650QlI0xxHsWwVT1RXAkaDXMbJafAx4IsjpjwNPxv4pRmPpjhWRRuAyRvIEoaprVXW3hbanAQ3A8wHFM4Ftqtpivv8LcLX5+krg5+brp4El5hdyJfCkqg6o6i6MCP2LrMgfimpPEdMKKjj7aB0f+M0ge+5dGU93o7jiH2txS/TZR8NR5H2becUnkSfBnRQbCu3ZBtLt7edocQd1xcYynE+V48vDDnJDcknDe7bIlE6kbAYWnQ0olsSEfs4BDqvqtiDnriW4YvJzmzmz+amZzTgsVuMlPAh8GYhqE4yIuID7gRswRkt+tgMnmtOrfRg5pP2P9mNpYFV1WEQ6gRqzPFBDBE39an7RtwBMnTo1pGx5hwcpv2Ylohayp8XARz6VGG/ZvOH3mFc4g80DzfT72kedqy/oxq4JQsdwH6c01NGyyzAgd/dGr9xmVyiVrnW2yJNOvN+bvD1647A++IolMaGfjxNEyYjIGRjp0UNlnfwhxmxHzb/3A58Od6GId6uIXA40q+qaSHWDcCuwXFVHpZFQ1XaMNLBPAa8Bu8GeoLqq+oiqNqlqU11d6J3s4tOErRaUlLtpnGZ/bng/Lu8u5uSXk+8a7bdTkx/7lolgbO87dGyD6nttrdQXWtse4ucLczqQKFJ5ZgLNQ5NZ3ZaYtEuWSPAyvJkT7O8wfptjuY4wox9VPayqXlX1AT/GwgzFyuPyLOAKEdmNMfe7QEQet9AOYDHGkGw38F2M7Ir3msL+QVXPUNXFwBbAnxv+WBpY88uoANqwlh42Lbj+y9UI9mxKDYX4DnJ84ehA9dUee7+OXt8gjeUjSm5qScQR9ShOr8ou4zPAL1suwaupW+dOwjL8hcB7YwcN5mzmY4Sx/4jIxIC3HwUi5ueOqIBU9W5VbVTV6Rga8CVVvSFSO7Pt9ao61Wx7J/CYqt5lCltv/q3CGCn57UvPYOSoBrjGvJ6a5deZq2QzgOOBVVbkSDbnfzSobc92Coffpi5/9rH3VZ5tttsnXK6RWyRUuI5gCEqFbI1cMYM46q1g2e4UTr/AzmX4J4A3MEwh+0TkM+apUKOcc4G9qjrqqSIij4qIf6r3bRHZKCIbgPOBL0SSI+aYmSJyO4ZdaAKwQUSWq+rNpjCfVdWbI3TxPRGZb77+hqr679afAP9PRLZjWOmvA1DVTSLyS+BdYBj4vKrFAMVJZNLMAqqq3k7a9RrdQ/gt+QWuLk4or2BLl31TBG/A/jBfFE/+ynwQ4t+flk78/PDfJzcDxljUvn1eoTKjqupNIcpfAc4MUn5zwOtPRCtHVArIFOIV8/VSYGmQOquBccpHVZdh+B7434f6AvqBvw9x7h7gnmhkTjaX3lSOkLytBy7vHiYUzOPQwEYA5lUNscWmRA2q0DUw8oPzqvW7v284c6LyWaFruIqf7XJDEv9vg5JBXs5WcDyhbebEBckflFW7R8JDzCq1zw7UWFhJR0CIjq4h60//fp+iYXyWMo2XOpdE9fkThbMVwyEsk2eED6ORCAp0xN+o1hM6b320THCP9ohu7ovGsC4cHE5h7GSb2dwT33YU23A2ozqEo6LqYNKv6fLuo8htOApWe7bb1m93z8h0oyy/gK7h6EYAv9yTPdlRd/WkwWjOqvJxFFBukucBtyv+sBixUOuZAUCRu41yT/x70Ipd+WxtG4kx1FASnQ8QwMNbPPgkO4KRpTQEh4ngTMEcwjBhWmHKQjWUBaRXnlwcf3rWWUUNDAesgJUVRq/UBnzCpp64dsukDUU2J5yMFUcBOYRkwvT4wmHEQx4jWyUmFdvwuB4YvUDqccd2qzy8bVL8sqQBiypbIldKBs4UzCEU005KnZ3ArSMG4gmF8e1Dq/IUs/FQ86iylsHYfHqW73PRT+bbgi6ueoE8SYOfi6OAHEJx7W3xx2WOFQnYmFpX0BFXX8d5JoyaftUVl7CjK/bP9sv94/zXMo6qvBbOqR8fMzupWJx+OVOwHEPyhOvvnU5F+frUyaAdx2IF1ebHHgbEhbDj8GhP6ulxpub593WFDMjM2DtIEy5v2JtqEZwRkMNo6i6aSekTS1j4sdQ7qeW5DONztWdXzH2cXDKF1r7RMZx7ffF9NkX4xd7FcfWRDiypeJ5Cd8y7l2zBScvjcIxJN53Cmk+Xs8HbwoSC9sgNEohKGYM+Yw9Ged6umH8ofd2jH5/FHg+bO+M3wH53UyE+qYi7n1RS4j7KhROid0ewE2cK5gCAp6KALR8pORaaocJlb/TDaPG5Rjx13eJldkX02TunFFTzXsvomEIn1NQwHMUesFD0DgtbejPfFnR5fbAggUnCcUR08KOqtA4ZUxU3Pjya2tBEwzLaQDqnIvqVsHrGx/sRG2ccP9mR+Uvy55Y9z4zSqAKD2oujgBwA1Kvkmzm0Fla5Uh54fYDRcWqOK7GSK2A0zUdH237qikvY1GHfyO537wsqKfzx2oDHNcwdx6XGGO14QjscY9K3zmLQZ+x8n1+Reqtf5/Dolauphaujap8nbvZ1jY7jMbmqjH6vfeEnvLjo0SmRK6Y555a/nLLA9OJTS0em4CigGJh6/3msmDhidJ5YmOq4aB5ah0bbJkryDjKlxLodaEpB9Sjfn7l19bzdZv+0sn0odJzuTKHM3ckJ5SnwCbLRBhRlYsLpItIXcO7hEH1Wi8gLIrLN/Bsxhq+jgKJAgSnfv4BXJ3eMKq/JT+30y+ueiU/HZ62YW2H9v7fKPbK643G5aNeehOSfOdCf2SthfqaXpOanY+MUbBkWExOa7AhIOPjZEH3eBbyoqscDL5rvw+IoIIsoMOXhC1hRN94juNpjTz6uWOkNYVc5vsxalgwXQmfXyFRr4aSJ7OuxKaziGHYcTe0ytl3UFqQoMqJNI6A4EhOG40pGcvr9HCPdVlgcBWQBBSb/eAmvVQbfjlCRl1onxI7h4DGgpxVtsdR+YeHMY6E36ktKWNueuBW9bm+Kg7rbRHleaka9STJCB0tMOMNMzf6qiJwTol2DqvoDYh3CSEgaFkcBWWDa/efxepjRRFleXxKlGUveOPuPn7r8iFlRmFfcyKo9IzGMJlSWMuBLnE3rpLLsCFR/dDhFHtHWR0DxZEYdm5jwIDBVVRcCXwR+ISJhjWBmJpuIqjC1fuUZwNQvLRpn8xlLvqTOBuR1z8Q3GFzR5Lt6aCwuZV9v8FCqE/Ir2H5gJIRsfXEJ69oOJMT242dqcUfiOk8iHUMp+OloVNssYsqMGpCY8LRjl1UdAAbM12tEZAdwAjB2qfWwiExU1YNmjrBmImB5BCQibnMI9qz5/jYR2S4iKiJhE4eLSLmZe+ihgLKP+3MIicif/X3Ea4m3k4kfn8vrTVaUS+qWPfvCP4iYXhrih6JQ2FtK9+CI/WpqVUVClQ9AbX7EezIjaB9M/kJ8kvyAxiUmFJE6EXGbr2di5OQLlnUyMKffjcDvI10sminYHcDmgPevm8Ja8Xj7JrDC/8bUst8DzlfVU4ANwG1giyXeFmrOncqGq4stbUNIZbTO9uHwxuKivOB344LSaew4MtoG6Yoi8WCsFEtqQtbaSedwNWuOJDbzbUhUrR0RiDIx4bkYuf/WAU9j5P07YvYTmJjwXuBDIrINQzfcG0kOS+NIEWkELsPIyfVFAFVda56L1PY0DGPUnwG/oGIeJSLSBpQD28e081viL7Aio52Uza1j779MoXvQ6k2WqhFQHq1DobOPqsKGI+NHcC6Eg63j7VbeBOdxbyxWXNoeuWKa87sjl9vqoBkNdnk5R5OYUFV/jTEYCFY/MDFhG7AkGjmsjoAexMiCGtUdauaTvh8jLfMxVHUI+BywETgAzMHIiBpITJZ4EbnFb3hraYl+F3fVGZM5/M0TOGxZ+UASBg5BCeX/42dv/9kc7u8dV76gZBoHjo5PHySa2A+yuD5zPHRDoQpP7E2RL1MWbkaNOAISkcuBZtP49MEo+78VWK6q+wJHSiLiwVBACzHmkt8H7ga+FdA2lCW+zRxV/U5E5qrqqDmIqj4CPALQ1NQU1X9FQUMJzV+ZyeHBaH1gUvM/Hmj/aR+axXs9H2DH0VoGfMKgT3irtZuxmTzzxMX7zcGV6+4jHcZHSZAeml8VX6jYdGBV93nsCKK8k0UmxfqxgpUp2FnAFSJyKVAIlIvI46p6g4W2i4FzRORWoBTIF5FuzOGcqu4AMHO+H/OajNMSHxsuoeChxbw3GP2oKVWZMDpM+8+hgdP40rrTTHtVeOU5v3gaqw4fCnquta+XmXXV7OwO6p8WNzNKM38J/sf7FgIdKbt+timgiFMwVb1bVRtVdTqGgeoli8oHVb1eVaeabe8EHlPVu4D9wBwR8W8M+hCjDdzxWOJjovH757PeG1vgrdQooDzaTP+fDV2LLBnL81157DgU3GnRT31h9HGErFKal1qP8Xh58+h5vHq4I3UCKLYZodOFmB0RReR2EdkHNGJYyB81y5v8r0OhqgeA/wBWiMgGYAHwnwFVorLEx8uU25t4rSZ1AeVjoT/vFIbV8MB2W3wsnlI0lba+8E6T7T2JmyYVuzNbAX1v98JUi5B14Tii8qZS1VeAV8zXS4GlQeqsBm4OUr4MYwOc//3DQFBfnmgt8bGg5lNiyj+fxlvnKMTh/OuW5O6GV6lmR//I6pfLwvWLXfm8dyDyCtS2I21UVRTRPmi/d/egL3P9Xg8PNfJWW0eqxcgoA7MVMveOiJOhiQW0Pr2IXcMDcSkfgCLpTNqNoVLNdq+Hfl9wO04o5hZOYeWANR+cxuLyhCig1oEiKLK926SwovPsVItwzBExm8hZBSQiHB22ZxNpPonPmqm46GE2WwcK8OoqBn3VDGgl/b4y3umoJJxhtMRdwDv7re2MByhyJybBYtdw6jLHxsuhwTJIcdRLNLOCjVkhZxWQXVR4QDT6ZdkD3rN4bP8M1na42NBh2HAK3UKR2/hb6BKK8pRCFxS4YE8f7Dnm0nPymN46wl6rsaCKd4fCG5+TQWleikJY2MDRYQ8pV0DgTMEcRjMnysB4Sj7PtF/JlzYWMuJwY6wF9HiNw18zVRzqT4yfy3CCHR0TSVcqNp8GwZmCOYzi+FLrjhlDMoU73zufPyU5g0/H0Hhv6FDMrq1lU09iBNzVXUyQxBsZQedQGkSuUcCZgjkEMqPY2rTCKzVcteYCtnUn/wZqHjpKnqt4VMznYBTm5dEriVsq39LpgQyNSd+ZBrMvIOumYGmg1jObxsLIfjOKm3t2XZoS5WNcX2koiRwK9aSGGnZ1J26zqCdD77YBXwHvdqaHF3e2+QFl6C2RPtTnR57evNJ1BY/vSa39o74svAJa1DiZt48kNlTGjLJ0GUZEx587LqdnOD1kz7a0PM4ULE6qPeENts2+M/indYnb3mCV/ILQCnBefQNvtu5JeGCjxTXWXQHShd0DJ/L1d6tJmxWwzNEtlnAUUJyUujtC3hS9chJXrTqB1IYsM2j3Bd8B31hezva+FpKxQHVc8e7EX8Rmlredkz6jH0AyaJ+XFZwpWJwUEnwP2aBM54q3FtGagtCdwdjd10qBe3RGilJPPj6PNyk/MDc+itiR8OvYzZrO1I9eR+GzeEQgysSEHxKRNWYI5TUiEjRIoIj8u4jsD+jj0khyOCOgOHDjw6XjFdCwTOC6teexpzd9nlZefBxfXcV7LcY0SIBpdRVsbI9uS0esLKgRJB2mMVHgVRdvH7HuwpAMbBwBLQMeAh7zF6jqtceuI3I/4PdebQU+oqoHRORk4Dlgcoh+H1DV71oVwlFAcXBCmRsZs5FMpYyb3/kw73Slj/LxU140shXi9MbJrGzbk7RrN9Vmnhd0u7eB7qE02sFvow1IVVeIyPRg58aGQ/aHXzbZBBSJSIEZoysunClYHMwqHX83/KL5Mv6WppE9xPzfPn3yZFa2Jk/5AMytSGXutNiozTvI5OJ0yuRqbQXMhlWwYOGQ/VwNvB1G+dxmZrr5qZMbPsG4x5h3DvsW8x+b03fDZZuvi7mT6njzSOJXvMbSVL0vcqU05IyaNMvkaj0gmZ2JCQEQkbnAfcA/hWj3Q+A4jPheBzHiwYfFmYLFwVDAg8YnVdy49gTSeZ30qK+fre2Jyfkejqp8ZYL77aRf1w4WVx7gN3vTxBCdosSEZnkj8Fvgk/5QyuPEUz0cUP/HwLORrueMgOKgPcA88OjBS9jZk77KB+DocGqCwv/TiQMImRmQ/uLKP1JXmEZBjBIfkjVYOORK4I/AXar6eqiGZjZUPx8FIuYGdxRQHPytDQ77zuTtvkv57tb0H0wO+IZpKE7+0/zMmsQEuU8Ghe4+bpmRHtswANvS8kSZmPA2YBbwtYAl9nqzn8DEhN/2ZzsGzge+EEmO9P/VpDnn/PWEVIsQFQ0lJRwOkSs+UUwrPpjU69nNdbW/4kcFN9E6kHpDukTYUGyVKBMTfovRKbMCzwUmJvxEtHI4I6AcoyQ/2c8cpdyVeQ6IgRS6+5hdUZBqMcxwHBaPDMEZAeUY3b7kPsWvmebDpck3fNtNTX7q7XuC5u5WDBFxm2mRnzXf3yYi20VERaQ2Qttyc575UEDZx/3zRRH5s7+PcO7cInK3ec0tInJR9B/XYU/fkSSuwCv/OjczV7/GUpOfJl7cOZwX7A5GJw98HcNi/r6Ftt8EVvjfmEt93wPOV9VTgA0Yhi4/D6jqAvNYbraZg2EgmwtcDPzAn6jQwTq93kGmllcm5Vq3zR6iQtYn5VqJpjo/TVbxclEBmT4AlwHHEg6q6lpV3W2h7WlAA/B8YLF5lJhu3+VApGA0VwJPquqAqu4CtgOLrMjvMJr6kuKEX8ONj8/PDLlim3GUutNAAWWhDcjqCOhB4MtE+dFExIXhDXlnYLmqDgGfAzZiKJ45wE8CqgRz554M7A2os48gG+JE5Ba/92dLS+LT5WQiLlfin5DfbuqhMAN3v4did19lqkUAjFUwK0emEFEBicjlQLOqromh/1uB5YFOTWafHgwFtBCYhDEFu9s8HbU7dyCq+oiqNqlqU11dXeQGOUjrUGKyXvhZMtHHRxt+n9BrJJPt/XN5em86bKa1OP3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index 03c71df1..00000000 --- a/refs/tags/0.5.2/_sources/cli.rst.txt +++ /dev/null @@ -1,10 +0,0 @@ -geocube\.cli package -======================= - -.. click:: geocube.cli.geocube:geocube - :prog: geocube - - -.. click:: geocube.cli.commands.make_geocube:make_geocube - :prog: geocube make_geocube - :show-nested: diff --git a/refs/tags/0.5.2/_sources/contributing.rst.txt b/refs/tags/0.5.2/_sources/contributing.rst.txt deleted file mode 100644 index e582053e..00000000 --- a/refs/tags/0.5.2/_sources/contributing.rst.txt +++ /dev/null @@ -1 +0,0 @@ -.. include:: ../CONTRIBUTING.rst diff --git a/refs/tags/0.5.2/_sources/examples/categorical.ipynb.txt b/refs/tags/0.5.2/_sources/examples/categorical.ipynb.txt deleted file mode 100644 index 580303f0..00000000 --- a/refs/tags/0.5.2/_sources/examples/categorical.ipynb.txt +++ /dev/null @@ -1,783 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Example - Categorical Data" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import geopandas\n", - "import pandas\n", - "\n", - "from geocube.api.core import make_geocube\n", - "\n", - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Load in soil data" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "ssurgo_data = geopandas.read_file(\"../../test/test_data/input/soil_data_group.geojson\")" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# original data\n", - "ssurgo_data[ssurgo_data.hzdept_r==15].plot(column='sandtotal_r')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Generate categories for categorical data" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If your data is only a subset of all of the data, the list of categories you get will likely not be complete.\n", - "\n", - "NOTE: The categories will be made unique and sorted internally if they are not already." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['Somewhat poorly drained',\n", - " 'Poorly drained',\n", - " 'Well drained',\n", - " 'Excessively drained']" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# this is only a subset of all of the classes\n", - "ssurgo_data.drclassdcd.drop_duplicates().values.tolist()" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "# complete list of categories\n", - "drclasses_complete = [\n", - " 'Poorly drained',\n", - " 'Somewhat poorly drained',\n", - " 'Excessively drained',\n", - " 'Subaqueous',\n", - " 'Well drained',\n", - " 'Somewhat excessively drained',\n", - " 'Very poorly drained',\n", - " 'Moderately well drained'\n", - "]" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "categorical_enums = {'drclassdcd': drclasses_complete}" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Convert data to grid\n", - "\n", - "See docs for [make_geocube](../geocube.rst#make-geocube)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "out_grid = make_geocube(\n", - " vector_data=ssurgo_data,\n", - " output_crs=\"epsg:32615\",\n", - " group_by='hzdept_r',\n", - " resolution=(-100, 100),\n", - " categorical_enums=categorical_enums\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
<xarray.Dataset>\n",
-       "Dimensions:                (drclassdcd_categories: 9, hzdept_r: 11, x: 15, y: 19)\n",
-       "Coordinates:\n",
-       "  * y                      (y) float64 4.597e+06 4.597e+06 ... 4.595e+06\n",
-       "  * x                      (x) float64 7.004e+05 7.004e+05 ... 7.018e+05\n",
-       "  * hzdept_r               (hzdept_r) float64 0.0 5.0 15.0 ... 105.0 120.0 150.0\n",
-       "  * drclassdcd_categories  (drclassdcd_categories) object 'Excessively drained' ... 'nodata'\n",
-       "    spatial_ref            int64 0\n",
-       "Data variables:\n",
-       "    drclassdcd             (hzdept_r, y, x) float64 -1.0 -1.0 -1.0 ... 7.0 7.0\n",
-       "    hzdepb_r               (hzdept_r, y, x) float64 nan nan nan ... 180.0 180.0\n",
-       "    claytotal_r            (hzdept_r, y, x) float64 nan nan nan ... 21.0 21.0\n",
-       "    sandtotal_r            (hzdept_r, y, x) float64 nan nan nan ... 10.0 10.0\n",
-       "    silttotal_r            (hzdept_r, y, x) float64 nan nan nan ... 69.0 69.0\n",
-       "Attributes:\n",
-       "    grid_mapping:  spatial_ref
" - ], - "text/plain": [ - "\n", - "Dimensions: (drclassdcd_categories: 9, hzdept_r: 11, x: 15, y: 19)\n", - "Coordinates:\n", - " * y (y) float64 4.597e+06 4.597e+06 ... 4.595e+06\n", - " * x (x) float64 7.004e+05 7.004e+05 ... 7.018e+05\n", - " * hzdept_r (hzdept_r) float64 0.0 5.0 15.0 ... 105.0 120.0 150.0\n", - " * drclassdcd_categories (drclassdcd_categories) object 'Excessively drained' ... 'nodata'\n", - " spatial_ref int64 0\n", - "Data variables:\n", - " drclassdcd (hzdept_r, y, x) float64 -1.0 -1.0 -1.0 ... 7.0 7.0\n", - " hzdepb_r (hzdept_r, y, x) float64 nan nan nan ... 180.0 180.0\n", - " claytotal_r (hzdept_r, y, x) float64 nan nan nan ... 21.0 21.0\n", - " sandtotal_r (hzdept_r, y, x) float64 nan nan nan ... 10.0 10.0\n", - " silttotal_r (hzdept_r, y, x) float64 nan nan nan ... 69.0 69.0\n", - "Attributes:\n", - " grid_mapping: spatial_ref" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "out_grid" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# mask nodata and plot\n", - "clay_slice = out_grid.claytotal_r.sel(hzdept_r=15)\n", - "clay_slice.where(clay_slice!=out_grid.claytotal_r.rio.nodata).plot()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Dealing with categorical data" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Because the data needs to be numerical for conversion from vector to raster, the code displays the categories as numbers. To convert back to strings, you will need to use the categories provided to convert back." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "drclassdcd_slice = out_grid.drclassdcd.sel(hzdept_r=15)\n", - "drclassdcd_slice.where(drclassdcd_slice!=out_grid.drclassdcd.rio.nodata).plot()" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "drclassdcd_string = out_grid['drclassdcd_categories'][out_grid['drclassdcd'].astype(int)]\\\n", - " .drop('drclassdcd_categories')" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
<xarray.Dataset>\n",
-       "Dimensions:                (drclassdcd_categories: 9, hzdept_r: 11, x: 15, y: 19)\n",
-       "Coordinates:\n",
-       "  * y                      (y) float64 4.597e+06 4.597e+06 ... 4.595e+06\n",
-       "  * x                      (x) float64 7.004e+05 7.004e+05 ... 7.018e+05\n",
-       "  * hzdept_r               (hzdept_r) float64 0.0 5.0 15.0 ... 105.0 120.0 150.0\n",
-       "  * drclassdcd_categories  (drclassdcd_categories) object 'Excessively drained' ... 'nodata'\n",
-       "    spatial_ref            int64 0\n",
-       "Data variables:\n",
-       "    drclassdcd             (hzdept_r, y, x) object 'nodata' ... 'Well drained'\n",
-       "    hzdepb_r               (hzdept_r, y, x) float64 nan nan nan ... 180.0 180.0\n",
-       "    claytotal_r            (hzdept_r, y, x) float64 nan nan nan ... 21.0 21.0\n",
-       "    sandtotal_r            (hzdept_r, y, x) float64 nan nan nan ... 10.0 10.0\n",
-       "    silttotal_r            (hzdept_r, y, x) float64 nan nan nan ... 69.0 69.0\n",
-       "Attributes:\n",
-       "    grid_mapping:  spatial_ref
" - ], - "text/plain": [ - "\n", - "Dimensions: (drclassdcd_categories: 9, hzdept_r: 11, x: 15, y: 19)\n", - "Coordinates:\n", - " * y (y) float64 4.597e+06 4.597e+06 ... 4.595e+06\n", - " * x (x) float64 7.004e+05 7.004e+05 ... 7.018e+05\n", - " * hzdept_r (hzdept_r) float64 0.0 5.0 15.0 ... 105.0 120.0 150.0\n", - " * drclassdcd_categories (drclassdcd_categories) object 'Excessively drained' ... 'nodata'\n", - " spatial_ref int64 0\n", - "Data variables:\n", - " drclassdcd (hzdept_r, y, x) object 'nodata' ... 'Well drained'\n", - " hzdepb_r (hzdept_r, y, x) float64 nan nan nan ... 180.0 180.0\n", - " claytotal_r (hzdept_r, y, x) float64 nan nan nan ... 21.0 21.0\n", - " sandtotal_r (hzdept_r, y, x) float64 nan nan nan ... 10.0 10.0\n", - " silttotal_r (hzdept_r, y, x) float64 nan nan nan ... 69.0 69.0\n", - "Attributes:\n", - " grid_mapping: spatial_ref" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "out_grid['drclassdcd'] = drclassdcd_string\n", - "out_grid" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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drclassdcdhzdepb_rclaytotal_rsandtotal_rsilttotal_r
hzdept_rxy
0.0700350.04597050.0nodataNaNNaNNaNNaN
4596950.0Well drained5.026.038.036.0
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" - ], - "text/plain": [ - " drclassdcd hzdepb_r claytotal_r sandtotal_r \\\n", - "hzdept_r x y \n", - "0.0 700350.0 4597050.0 nodata NaN NaN NaN \n", - " 4596950.0 Well drained 5.0 26.0 38.0 \n", - " 4596850.0 Well drained 5.0 26.0 38.0 \n", - " 4596750.0 Well drained 5.0 26.0 38.0 \n", - " 4596650.0 Well drained 5.0 26.0 38.0 \n", - "\n", - " silttotal_r \n", - "hzdept_r x y \n", - "0.0 700350.0 4597050.0 NaN \n", - " 4596950.0 36.0 \n", - " 4596850.0 36.0 \n", - " 4596750.0 36.0 \n", - " 4596650.0 36.0 " - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pdf = out_grid.drop(['spatial_ref', 'drclassdcd_categories']).to_dataframe()\n", - "pdf.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Make sure all categories are represented\n", - "\n", - "To do this, convert the column type to categorical beforehand and make sure that\n", - "you include all of the possible categories." - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "cat_dtype = pandas.api.types.CategoricalDtype(out_grid.drclassdcd_categories.values)\n", - "pdf['drclassdcd'] = pdf['drclassdcd'].astype(cat_dtype)" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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hzdepb_rclaytotal_rsandtotal_rsilttotal_rdrclassdcd_Excessively draineddrclassdcd_Moderately well draineddrclassdcd_Poorly draineddrclassdcd_Somewhat excessively draineddrclassdcd_Somewhat poorly draineddrclassdcd_Subaqueousdrclassdcd_Very poorly draineddrclassdcd_Well draineddrclassdcd_nodata
hzdept_rxy
0.0700350.04597050.0NaNNaNNaNNaN000000001
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" - ], - "text/plain": [ - " hzdepb_r claytotal_r sandtotal_r silttotal_r \\\n", - "hzdept_r x y \n", - "0.0 700350.0 4597050.0 NaN NaN NaN NaN \n", - " 4596950.0 5.0 26.0 38.0 36.0 \n", - " 4596850.0 5.0 26.0 38.0 36.0 \n", - " 4596750.0 5.0 26.0 38.0 36.0 \n", - " 4596650.0 5.0 26.0 38.0 36.0 \n", - "\n", - " drclassdcd_Excessively drained \\\n", - "hzdept_r x y \n", - "0.0 700350.0 4597050.0 0 \n", - " 4596950.0 0 \n", - " 4596850.0 0 \n", - " 4596750.0 0 \n", - " 4596650.0 0 \n", - "\n", - " drclassdcd_Moderately well drained \\\n", - "hzdept_r x y \n", - "0.0 700350.0 4597050.0 0 \n", - " 4596950.0 0 \n", - " 4596850.0 0 \n", - " 4596750.0 0 \n", - " 4596650.0 0 \n", - "\n", - " drclassdcd_Poorly drained \\\n", - "hzdept_r x y \n", - "0.0 700350.0 4597050.0 0 \n", - " 4596950.0 0 \n", - " 4596850.0 0 \n", - " 4596750.0 0 \n", - " 4596650.0 0 \n", - "\n", - " drclassdcd_Somewhat excessively drained \\\n", - "hzdept_r x y \n", - "0.0 700350.0 4597050.0 0 \n", - " 4596950.0 0 \n", - " 4596850.0 0 \n", - " 4596750.0 0 \n", - " 4596650.0 0 \n", - "\n", - " drclassdcd_Somewhat poorly drained \\\n", - "hzdept_r x y \n", - "0.0 700350.0 4597050.0 0 \n", - " 4596950.0 0 \n", - " 4596850.0 0 \n", - " 4596750.0 0 \n", - " 4596650.0 0 \n", - "\n", - " drclassdcd_Subaqueous \\\n", - "hzdept_r x y \n", - "0.0 700350.0 4597050.0 0 \n", - " 4596950.0 0 \n", - " 4596850.0 0 \n", - " 4596750.0 0 \n", - " 4596650.0 0 \n", - "\n", - " drclassdcd_Very poorly drained \\\n", - "hzdept_r x y \n", - "0.0 700350.0 4597050.0 0 \n", - " 4596950.0 0 \n", - " 4596850.0 0 \n", - " 4596750.0 0 \n", - " 4596650.0 0 \n", - "\n", - " drclassdcd_Well drained drclassdcd_nodata \n", - "hzdept_r x y \n", - "0.0 700350.0 4597050.0 0 1 \n", - " 4596950.0 1 0 \n", - " 4596850.0 1 0 \n", - " 4596750.0 1 0 \n", - " 4596650.0 1 0 " - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "training_df = pandas.get_dummies(pdf, columns=['drclassdcd'])\n", - "training_df.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Index(['hzdepb_r', 'claytotal_r', 'sandtotal_r', 'silttotal_r',\n", - " 'drclassdcd_Excessively drained', 'drclassdcd_Moderately well drained',\n", - " 'drclassdcd_Poorly drained', 'drclassdcd_Somewhat excessively drained',\n", - " 'drclassdcd_Somewhat poorly drained', 'drclassdcd_Subaqueous',\n", - " 'drclassdcd_Very poorly drained', 'drclassdcd_Well drained',\n", - " 'drclassdcd_nodata'],\n", - " dtype='object')" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "training_df.columns" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.10" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/refs/tags/0.5.2/_sources/examples/examples.rst.txt b/refs/tags/0.5.2/_sources/examples/examples.rst.txt deleted file mode 100644 index c9ebc33e..00000000 --- a/refs/tags/0.5.2/_sources/examples/examples.rst.txt +++ /dev/null @@ -1,28 +0,0 @@ -.. _usage_examples: - -Usage Examples -============== - -This page contains links to a collection of examples of how to use GeoCube. - -.. toctree:: - :maxdepth: 1 - :caption: notebooks: - - timestamp_missing_data.ipynb - categorical.ipynb - rasterize_point_data.ipynb - grid_to_vector_map.ipynb - zonal_statistics.ipynb - rasterize_function.ipynb - vectorize.ipynb - - -.. toctree:: - :maxdepth: 1 - :caption: stackexchange: - - How to convert vector format (geopackage, shapefile) to raster format (geotiff) using rasterio without template raster - Rasterizing polygon grid in Python (geopandas & rasterio) - Rasterising multiple shapefiles into a single raster - Extract population count from GeoTIFF based on mask layer diff --git a/refs/tags/0.5.2/_sources/examples/grid_to_vector_map.ipynb.txt b/refs/tags/0.5.2/_sources/examples/grid_to_vector_map.ipynb.txt deleted file mode 100644 index a22d938b..00000000 --- a/refs/tags/0.5.2/_sources/examples/grid_to_vector_map.ipynb.txt +++ /dev/null @@ -1,559 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Example - Mapping Grid Data to Vector Data\n", - "\n", - "This is useful in the case where you want to get statistics for a specific raster\n", - "over a certain region. In this example, the vector data is a random region with\n", - "using SSURGO data." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import json\n", - "\n", - "import geopandas\n", - "\n", - "from geocube.api.core import make_geocube\n", - "\n", - "%matplotlib inline" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "ssurgo_data = geopandas.read_file(\"../../test/test_data/input/soil_data_group.geojson\")" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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cokeymukeydrclassdcdhzdept_rchkeyhzdepb_rclaytotal_rsandtotal_rsilttotal_rgeometry
012577452271425Somewhat poorly drained0.01000340905.023.0676759.97833866.953987MULTIPOLYGON (((-90.59735 41.49255, -90.59730 ...
112577452271425Somewhat poorly drained5.010003409015.023.0676759.97833866.953987MULTIPOLYGON (((-90.59735 41.49255, -90.59730 ...
212577452271425Somewhat poorly drained15.010003409130.023.0676759.97833866.953987MULTIPOLYGON (((-90.59735 41.49255, -90.59730 ...
312577452271425Somewhat poorly drained30.010003409245.023.0676759.97833866.953987MULTIPOLYGON (((-90.59735 41.49255, -90.59730 ...
412577452271425Somewhat poorly drained45.010003409360.023.2316439.96194166.806416MULTIPOLYGON (((-90.59735 41.49255, -90.59730 ...
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" - ], - "text/plain": [ - " hzdept_r spatial_ref hzdepb_r claytotal_r sandtotal_r \\\n", - "mukey \n", - "198692.0 15.0 0 30.0 23.000000 7.000000 \n", - "198714.0 15.0 0 30.0 5.000000 87.000000 \n", - "198724.0 15.0 0 30.0 21.000000 10.000000 \n", - "198750.0 15.0 0 30.0 12.000000 63.000000 \n", - "198754.0 15.0 0 30.0 26.000000 38.000000 \n", - "271425.0 15.0 0 30.0 23.067675 9.978338 \n", - "271431.0 15.0 0 30.0 14.000000 55.000000 \n", - "\n", - " silttotal_r \n", - "mukey \n", - "198692.0 70.000000 \n", - "198714.0 8.000000 \n", - "198724.0 69.000000 \n", - "198750.0 25.000000 \n", - "198754.0 36.000000 \n", - "271425.0 66.953987 \n", - "271431.0 31.000000 " - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "grid_mean = out_grid.sel(hzdept_r=15).groupby(out_grid.mukey.sel(hzdept_r=15)).mean()\n", - "grid_mean.to_dataframe()" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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hzdept_rspatial_refhzdepb_rclaytotal_rsandtotal_rsilttotal_r
mukey
198692.075.0090.023.0000007.00000070.000000
198714.075.0090.07.80000086.4666675.733333
198724.075.0090.021.00000010.00000069.000000
198750.075.0090.012.00000063.00000025.000000
198754.075.0090.026.00000038.00000036.000000
271425.075.0090.024.56496610.12049765.314537
271431.075.0090.08.33333374.66666717.000000
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" - ], - "text/plain": [ - " hzdept_r spatial_ref hzdepb_r claytotal_r sandtotal_r \\\n", - "mukey \n", - "198692.0 75.0 0 90.0 23.000000 7.000000 \n", - "198714.0 75.0 0 90.0 7.800000 86.466667 \n", - "198724.0 75.0 0 90.0 21.000000 10.000000 \n", - "198750.0 75.0 0 90.0 12.000000 63.000000 \n", - "198754.0 75.0 0 90.0 26.000000 38.000000 \n", - "271425.0 75.0 0 90.0 24.564966 10.120497 \n", - "271431.0 75.0 0 90.0 8.333333 74.666667 \n", - "\n", - " silttotal_r \n", - "mukey \n", - "198692.0 70.000000 \n", - "198714.0 5.733333 \n", - "198724.0 69.000000 \n", - "198750.0 25.000000 \n", - "198754.0 36.000000 \n", - "271425.0 65.314537 \n", - "271431.0 17.000000 " - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "grid_median = out_grid.sel(hzdept_r=75).groupby(out_grid.mukey.sel(hzdept_r=75)).median()\n", - "grid_median.to_dataframe()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3.10.6 64-bit", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.6" - }, - "vscode": { - "interpreter": { - "hash": "e7370f93d1d0cde622a1f8e1c04877d8463912d04d973331ad4851f04de6915a" - } - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/refs/tags/0.5.2/_sources/examples/rasterize_function.ipynb.txt b/refs/tags/0.5.2/_sources/examples/rasterize_function.ipynb.txt deleted file mode 100644 index fe3bfbde..00000000 --- a/refs/tags/0.5.2/_sources/examples/rasterize_function.ipynb.txt +++ /dev/null @@ -1,145 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Example - Use a rasterize function when creating a raster" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [], - "source": [ - "import geopandas\n", - "\n", - "from functools import partial\n", - "from geocube.rasterize import rasterize_image\n", - "\n", - "from geocube.api.core import make_geocube\n", - "\n", - "%matplotlib inline" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [], - "source": [ - "gdf = geopandas.read_file(geopandas.datasets.get_path('naturalearth_lowres'))" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "geo_grid = make_geocube(\n", - " vector_data=gdf,\n", - " measurements=['pop_est'],\n", - " resolution=(-3.0, 3.0),\n", - ")\n", - "geo_grid[\"pop_est\"].plot()" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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" - ], - "text/plain": [ - " test_attr test_str_attr test_time_attr \\\n", - "0 1.3 dcf86619 5/21/2016 10:09:21 AM -05:00 \n", - "1 1.3 dcf86619 5/21/2016 10:09:21 AM -05:00 \n", - "2 1.9 dcf86619 5/21/2016 10:09:21 AM -05:00 \n", - "3 1.3 dcf86619 5/21/2016 10:09:21 AM -05:00 \n", - "4 1.3 dcf86619 5/21/2016 10:09:21 AM -05:00 \n", - "\n", - " geometry \n", - "0 POINT (-47.26681 44.21932) \n", - "1 POINT (-47.26680 44.21932) \n", - "2 POINT (-47.26681 44.21932) \n", - "3 POINT (-47.26680 44.21932) \n", - "4 POINT (-47.26679 44.21932) " - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "gdf.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Convert to raster with GeoCube\n", - "\n", - "See docs for [make_geocube](../geocube.rst#make-geocube)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Load into grid with griddata nearest resampling" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "geo_grid = make_geocube(\n", - " vector_data=gdf,\n", - " measurements=['test_attr'],\n", - " resolution=(-0.1, 0.00001),\n", - " rasterize_function=rasterize_points_griddata,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
<xarray.Dataset>\n",
-       "Dimensions:      (x: 12, y: 11)\n",
-       "Coordinates:\n",
-       "  * y            (y) float64 45.25 45.15 45.05 44.95 ... 44.55 44.45 44.35 44.25\n",
-       "  * x            (x) float64 -47.27 -47.27 -47.27 ... -47.27 -47.27 -47.27\n",
-       "    spatial_ref  int64 0\n",
-       "Data variables:\n",
-       "    test_attr    (y, x) float64 1.3 1.3 1.3 1.2 1.3 1.3 ... 2.3 1.3 1.9 1.3 1.3\n",
-       "Attributes:\n",
-       "    grid_mapping:  spatial_ref
" - ], - "text/plain": [ - "\n", - "Dimensions: (x: 12, y: 11)\n", - "Coordinates:\n", - " * y (y) float64 45.25 45.15 45.05 44.95 ... 44.55 44.45 44.35 44.25\n", - " * x (x) float64 -47.27 -47.27 -47.27 ... -47.27 -47.27 -47.27\n", - " spatial_ref int64 0\n", - "Data variables:\n", - " test_attr (y, x) float64 1.3 1.3 1.3 1.2 1.3 1.3 ... 2.3 1.3 1.9 1.3 1.3\n", - "Attributes:\n", - " grid_mapping: spatial_ref" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "geo_grid" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# mask nodata and plot\n", - "geo_grid.test_attr.where(geo_grid.test_attr!=geo_grid.test_attr.rio.nodata).plot()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Load into grid with griddata cubic resampling" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "geo_grid = make_geocube(\n", - " vector_data=gdf,\n", - " measurements=['test_attr'],\n", - " resolution=(-0.1, 0.00001),\n", - " rasterize_function=partial(rasterize_points_griddata, method=\"cubic\"),\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# mask nodata and plot\n", - "geo_grid.test_attr.where(geo_grid.test_attr!=geo_grid.test_attr.rio.nodata).plot()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Load into user-defined grid with radial linear resampling" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "geo_grid = make_geocube(\n", - " vector_data=gdf,\n", - " measurements=['test_attr'],\n", - " geom=json.dumps(mapping(box(-48, 44, -47, 45))),\n", - " output_crs=\"epsg:3857\",\n", - " resolution=(-300, 300),\n", - " rasterize_function=rasterize_points_radial,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# mask nodata and plot\n", - "geo_grid.test_attr.where(geo_grid.test_attr!=geo_grid.test_attr.rio.nodata).plot()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Load into user-defined grid with radial cubic resampling\n", - "\n", - "Note: This example is simply to demonstrate the option and and\n", - "would need to be adjusted for better results. As seen in the plot below,\n", - "the values swelled quite a bit due to the input generating an \n", - "ill-conditioned matrix." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/snowal/miniconda/envs/geocube/lib/python3.6/site-packages/scipy/interpolate/rbf.py:268: LinAlgWarning: Ill-conditioned matrix (rcond=3.16678e-18): result may not be accurate.\n", - " self.nodes = linalg.solve(self.A, self.di)\n" - ] - } - ], - "source": [ - "geo_grid = make_geocube(\n", - " vector_data=gdf,\n", - " measurements=['test_attr'],\n", - " geom=json.dumps(mapping(box(-48, 44, -47, 45))),\n", - " output_crs=\"epsg:3857\",\n", - " resolution=(-300, 300),\n", - " rasterize_function=partial(rasterize_points_radial, method=\"cubic\", filter_nan=True),\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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ZabPs5gYg/noHww5GBlx7vDDlGIKKB4FE+RcgSVfAl9/RKikWzZJF0uQRQ/oMjBowafMvxj08HxVQ74BDdHFl+desL5GAglnRSppcV07mvfA7ggNaZZyOiKf1KlFmwUsBbVCaVVq2PmSjv6Mc3RlhEcBGxbkfJw04ZaoXWYaYSUchx3pjLCnWUdbfrLVIMrdU9WiIss3MMVentBoDEE+yboEM7UH1pzsdL1ApQBD9+ZiBpKv8/X8VKymUrQkp+c1mILH9Op9X+tOCR+GiKnm9+8THSZamBbu0g8RLAGHF9PijvZ65uAbe43dcZxLrLjy8ZSIUzJqR/USb/vXMzaMsBcArt5lvNiU2W2eiZE95CIApeSdLyHVTm98BylK7pWuzlkemOVkaq1DEVCpeMSHrx5Cxcq+V/KJ9WZzmcERcexTBmpOYjheoADmgxK1NYl5lmaj4DFAF91s+m/nB3RDz6sCQT9YvlLy3DOLeWpmSV9bEQYLvJX8GNI3CCGAiLSRR/8gns7yMHLpv0pIasKSSqu7BTOnoOLQl8lVxmjT+wLlMlEfFSrJRtAK/BiiytiigJrAfrMSmP3pgE69lQOF5KYXu+UT+wt1W8V6yhHqz3cSsvMPSEKjfgcrpTdkK+AICamsTTz7NrLUi4jYqS6P3mEfVnVhP0vWk1qkEfn5K8cC/UzegwScFLPddxQ5aGZdiJtWIuYllOOCJdVf5IN08A0+2eRIeOQDm+ea0wC+uMWFwzQCIgOhdR1OW2C6f5pRvzFPArpkajFkGVZ9ydS3FPVLLxMuT9WebVTwHiRzlWwTg233iat7NvTgEGbCzVI4DyZiJjIOEWWHeJVXyREAp31nMRAFEz0XVWf0urYve7DNXXu4C7NqXblc/5tNKnVV/qIWLjeL1e0M5RZWtcVm3PqKOmVSKPdmXqxn9x7YIRdmsfJ/6Dik1q98TUMqsq8ztlYGicnHNvASoet6BXxM3EYDVtWRGoOptTAk4HCjPxFogCXwaSyi2rSPzNo2L/WNmqWwpHHWKEB2QeKukByjRMol56XhFHgDkWhNfNrrNPDCFRZWVol2Y3ut5Gwnb42zZdO66bTatQvdWRozBALMW6K01UZZTBKZR0Sk5p/SizBQITPL5ehz/jLLrjcxz3mFW1QwmBRCqWUjONWeb+n+RfY53zLJn8meWlAKUio9/LJI1E1N5oWyrmMxm/m4sFgUoMb25b+F607g6f+aebCgBw6o+B4xNf20JB4qlsvRZIpJnkXwTyXeQvJjkj4g8TyH5TpIXkvwTknd2184dy72L5M+Ta1woJ0bHzFLpKX/3O1v97oGgt9dWNmtsL6SpeAxZ5TNveawIok/l1EhfTQ2m2wwylgkus7lMuB6Vxtg2P4uriZlEXBIWmNoHrBotix2DGxmViyvKHNsD1Eo61F1ZJ8otFtsW1trG+nouOhWbYJQPuux8TdynLH/o60jKYuvGrGJbYh2qjLvuy8lYUsnv08QzpyZCKJnlvTgEGYhrtzN2vwrAg83sCpJnAngDydeY2fkuz9sA3NvMriT5BADnAngkyS8CcH8AnzPmewOABwL4s20IFumYgcpIauS8dhuVlN8GsH3I6cGKh5dB7RjcyNeRv4pTtMo5lT/Wl4jenR7s8yWgJ2MnSb3ZlNbmZETXj714ia+/siI6ymNKE3GJNfGVaraRsqgEdV1X2fRoFfOoguyUlokEP6WAPU+iBYVIQpY0fpENAsL3YsBf9c0Sb8UH4jnZEm3D/WVmBuCK8e+Z48dCnte7v+cDeFS5BOAsADfA0NozAfzzoYVK6PiBSjXyT+IPHeUr4yVlWrBapxL/RyAJ19P9stS04lDH4iLE6JrK6nJuszlNXO8E7Sd3EKxuq3cblXhNkMecdaF4Vv+LbIbBwgu7RPdG9BasGJ+/tWyWgaSKH7nrqWVS8opRueet2qrrIUBU29GUNOlSy8DKgVbpD7UHV+qKiuDq0yCoc48UUFekZnMlfVjl67U7A80TIAPxiWiqaroVyQvc//PM7DyfgeQegLcA+AwAzzOzN3b4fQeA1wCAmf01ydcD+EcMrftFM3vXAZpxILpeYiokn0nyAyTfPn4emuR7EsmLRl/gk8O17yX57vHauasr32zmz8zMKftNneZBIJ5jUqyS6O4KmzA2v+VUUw9cQuY44iSH+McYA+megRJiIeYBaI9VvorfxAc1n41QaphdIbVvmvXoObZ9g7a+bhtEHaUdgDtsqw8oyvXUzS9lQ9UX0UIxuvaGeEnXjaTk8GmJEqdZs79Z5sZq1nKoPBUA13Km8YdyfeOuKSVf0sPEhZ4FMckt+qEXE/JkoU+WgPGwZAD2sVn8ALjMzO7tPuc1vMyuNbN7AbgDgPuSvIeqk+SjANwbwE+P/z8DwGeN5W4P4MEkH7CdFrZ0fVoqzzWz52QXxw57HID7AvgEgNeSfLWZvZfkgwA8HMA9zewqkrdZVaNQKt51VKXJNSHOKgHWxWc8r+R432ZGVuRR/mfuMW8x+EOxqtlL9XcTX2nqq+uuRuKN/JpHc46Jk1WCVrLOpBszEeTBoS3HOh/Quqhcvl692d5cTX9IV6qwhhyfND7DmE/0Y5TNX0tALVI/9tTmzywXGTMRs7myyQZNfZzzTdZesnalN8MMI+56q03OStsCbXtKsZldPloeDwFwkb9G8ssB/DCAB5rZVWPy1wA438yuGPO8BsAXAvjLrQo20sk8++uzALzRzK40s2sA/DmAR4zXngDgp0qnmdmlB+IcrZDMIqnyOKukWCZMPpFnob1NpeTXWhhVGgIwbNDmqZRxmE21QbWP2CTHhtOZG7L9qBVVGYVXCjScR67WQXQpyVqNQjnK72bw2Z6znsY8s+XRtmXaCysoItXX9dqRuR+qkbjvkwAo08jey7ZplZ6fahw3WKysgygz2rTGihDKuVmHYVrWql1Nf9UyRjBpAEXIHtufttX9rlyVAlAm3t5lFtsS+mMJzE+UzIhrbbP4WSKStyZ58/H3jQB8BYB3hzyfC+AFAB4WdOL7ATyQ5BljkP+BAE4v99dITxynvr2I5C3E9YsAPIDkLUl+EoCHArjjeO2c8dobSf45yftklZB8PMkLSF7wif2P58oe0DO6gBlE/CevEJN14vNG15OfrkvOij6634DKJWV7rMEkAZTGxeXlG8lUTClaG3tjnQJkqjKTAseoHGpZC59KLqdgJ1eRA7hKwQFVf1bui0RRRZr4YS4z1SP+SzCI5V0bfB8pRTvwD7+jwguAMtU15XH31/VFVxk7fqUN+3tzm6Issv2BV6WEQ31yWrAHHNW3Ufb4afp6/Dgwqvowtjvh75+/St4t0j64+FlBtwXwepIXAngzgNeZ2atJ/ijJh415fhrATQC8Ygwr/P6Y/koAfwvg/wPwDgDvMLP/vtVGOjoy9xfJPwbwqeLSDwP4ZQDPwvD8PQvAzwD4dp/JzN5F8tkA/gjARwG8HcC14+UzAHwygPsBuA+Al5P89HGGBAKf8wCcBwA3u8GnWKOEywr4zN1V8gR3Up1v4aGI1yOgKH6cz0FpyiV1pNvbx+tVOVe3m+I8B5HrNk/KM5YH5LYkQ5kkP9Gu90j2/FJnmaRTTEsbivLorf4G2vtTylRtC9cjH+FWi4ot1tG4fbIZThj7sPwW/SzPNJnyz31W2uUD7zLO4+UPvBSlU4PL9URpr3bDKb5BbuX2rLa2F+A28Ykz6bZEQ6D+8GrWzC4E8Lki/enu95cnZa8F8F2HFmIlHRmoZA2MRPKFAF6d8PhVAL865vsJAJeMly4B8LsjiLyJ5D6AWwH40CrhPKAUqyECSaHOfluVMurN5kqUfDqbywHAtEmkmmnlRq3T1FEPWE1+zC/P9JIlYDLJ0co28fJtLLSZ5alXb6OdTTWCSAUu6sVesE4qYELIk86sYpvm2pUdjBV5LMVF5M63Hmzi/yWQjrzLSN3HFkS75KJDfz1Jj3VW/0We6Xc2e0reCy2rKlsNKjKgWpoRhvB8xPJbBJcSqD9OdL0E6kne1sz+cfz7NQjBJpfvNmZ2Kck7YYin3G+89CoAD8JgDp6DYf71Zasq95s9TuCyh2aNiQioV9N6q72zakXfBKYjKEz1tg+bDMALiyYqbrUPmNx1tnwXRb1xiU7Rez4VZWAyylnJ0yiLAKSNBeRkG+9Rpqy9vKutBAckctv3LLiNVtE0CzsdgNXAMKdXfIWlIPtWyTCW705fXijbXXuSgTGhdHDTLuX264JJB8ji/6bPMf+vrLXQBp+vV8+2YyoAcO02UeoUoOtr9te5JO+F4ba/D6NpRvJ2AH7FzMoU498heUsAVwP4j2Z2+Zj+IgAvInkRhplhj1Gur4bKvRUnIw4HdzmwEVN4m6nCQvlKUNgT/AqpRYulTCN/XcfiupSSh/n1Ji397fgVcoDZyJ8pZDW6J5q1JdxQKuTMcmoUbidftrJe/VcA1riLRuVMzMqO+0Pcoii65oRBzrLU9a1YsChAaklZLrnyIgj7tgyFgjzlxyYkdABC9nkGYEruUF5aYxEcY74EbKZ8W9b/W1xRf8rQ9QIqZvboJP2DGALy5b+cS21mn8C8WvRgVMUtQswkKmcVmC75Ha9VW8irI4uBfBuWIHPjUvJllByJUh3qFPKhHdEXPjIWU9wp08aUkCN1zy8q4ZlXDSgxbyZb6kIJeZdG+Cr4DjiF756ZTPHEKbGVu4l522tXDav+lgo21J+675K+SRV7kC2eS9J1V8U+iUCRWWtw10vairYtySPL+nys73G3T7ZA+ytmd51OdMxW1JcnKcz2iu6YbPX60nYqCpQaEQIA+HhLlMWsAp3u9OMqLci4QbNb8Mwz4eNxJYJZHOkqV47jmVr/ycr5fNQ655dTfZO6G7fISNXIVsheKf81iisBRJmfs1Uid3wOPFXdzSr3tB60I/iFMv5bTg1eoJ4rcamfJC2AZfkvAS7j5wcCC/f6RMmAnaVy2tMSoEz5vBLVAFEp2jJiL2esBF4lb7NxI1md01UF3z2PxAJavS9XccE1o/ccTLL9wKJLrR35z+euV4rP0Lj1dDyhBo3qHPdQ56JVNOb1Ms6LOOe6F+MumRJOAbBuR2bhyG1UfH0d5d8LSFdgWvoyWB+KpwcSJu1qFgquBKgUuNLJFAnPci22NevDKFvs2+weboEMxNXrtmk5beh4gUp5aOKW93G3YDXN2JFUttmOwyV/yaZiJbGcqkfFSqaycKNQV1cyZTlzc0nevboQlUABzuBGkXWgq4xsIyyFdNTZ0QZK2bt3vKdIusHgzNWHtkzbTuZt6fER16IV0vTZUvAdIb/6n7SnP6W7rTPKvgZQDh77akVRsauYtym/HKFdRWZYtbjxdKLjBSpAc0YJgNmyKGASZod19+Xy5QtlgfR9QCn5xTpiTMU/+B4ogdnVlVoE449pxOlk9G64GHz3o0JEBSCmHrsyKmYy8YoWjZdRkAKxyKuSV7i9Mj49q2mKjZR0H3zvyljSWP2X9U3TvEOeCA7hWnNPeko8a6fKX35L92DgJeoBkv5XfAJA9dbdTLwXgMTLH7dzSZ+XDs8To9WLG08bOn6gopS6PwI4cXUN1+J/DSZAUMhlSvEaF5ZYx9JM0y0vR1ybsam/hzKiTvfiTEAVT3es2gXh8tBAUb0/CZi0ilHMqOrkzaYRN5aFdN0VvjOQdGeDRb6ElFWO/DdhIBAtvARMvGKdKFHqaf3+OUHbPjkQD4p4qjNTvFGOJaXfk3lxlp/g2QF1GUeLbtpeu7ZEhp2lcjxIgUaxYNRhVbFcEn9ZnAWm8iV5S0xFxkwUmEj3Wai/uta2sRpNR4W0Rvmn7kIsvrjVCLXkb5TwACgqxgI6K83HSxD6QcSV/Ii/IcknSZsUsQM8D6yZbkncSPngoA8wjZWW5RPp6Rbzvb7JwEAo7bXHHk+/1XOYyJTFm1bfQ8FzG7QL1J/WRDTrS4Ba2a8FlEZBhXJiRD2xyjZazCwktnlWzeSyNi6UbfBYpRerJJkxJuULcai+0svbmVkdc7td+/2ss039PbfLN1JNVHBVCKDK8maWyZTP6m8ZE1oAsaaeHjAn1k5VLqtLKf0gSwX0K8G165ZK/ivZ1lgQPcDJrJsmfaGbfRAAACAASURBVLOlQIqXCzx2Z9QfM1BBPesrTNntTtf1gDOSLFtZK/PU5FVrWQrfaUYQmzxdl86UYJW8mWXU25Nrqitzc5UyHkTiJASp4BQIorVKsryer5pUM8nugFXFfHrKtweISXoE5cpaYEjPwCEDrQ64Tf97AKjqSOrysQ05oyqsOZEyZWtcltodwW1hgN+zbnpgVV0rQLIwYDhRMgBXb2Hvr1OJjldriaCgF0Ah/h9BSJ7nXvJmm076evZRrbJfs2jREgtIuriqFySAmQ/GB2pf9gQAJn7i7QsvZ7OAMNaTjUg7FgWg3RvSKokz0pTCCKCWUVZebknTUfyy/liuyNT0S/I7lo38DzSpIOGn7pXvO/c7rgFKSbRh6UyTpf7I6u7FzabrfgCwFeLWz1M52el4gYojtVeW/D3+r/zkvbJxsSLQ5h1H2BOoRaWWWSbOckn9/+plcGBSrYBHbkXMbXC8J34BIDoKNU4Nbnh1ZGhGuEmMoBszCco5PQwqAy+R3lus2Ci8cE9UX/SUZM9t1ozoSztF3ghqB7LQFkBPyuzkaXhn8mR5Vd2RV8hjI87reuuXpOG7BIQHIMNuRf1pT9mCvoZ8wLzaPLLNo/L39uVa3IIe6MdMZP1hxDwCyQQiwlUkrRxZD6oZcj330BRQ91n8GooOmJU0ryjb2E6/DU0QP1snoYAm8qzq6WuZXoC6Gdk3sjtZs2uRT6/eyHPtrCcPFJEf6vsH1HwX3YsJiFXrfsS96g5IFsBQ1y0AxQO/AvFD0s5SOd2pjNrjjCl/DnxQ/s0KeE8xXgLU29Uz2berKN84PTZd7Kjq9tdbELO9sJPxpnmnupbBRCVussd8mqubHLAY8BX1+7yN4uwF39ECSQ5eLs9GpKWKkU1aJUtH8UWl1Y2jeODsKPdGvoRfUZDqfjQKXQBhJW8iQwZIKchmAB7SUndl9hwsAqR1y3UtuUOQGXeWymlP5BCci/t7ZbsFKyUfXWEln1/r4YLW7d5ZLa8DbfIItAqvGY0WvrXSjuDUszrUTC65ilpabOgogATI5KyzwEPGrUbFminzhfSuoheurgO7iQAZA8ryTr87ij2dyeXrVO1GeBZE2a7V5fg1e4+xve/RKlyyBNTAINafuRCbxajEJOTaNm2bhkD9bpuW05/8rCoFEIWSbU6a/BudX677CFbJtPpdBd9jnQ5I1LTkbPt57c5xeeLLGMGk+0IrmVU9c750PyyvgLM6037S7SrKXLlZdPm2D5WLbJViSmTq5Y3l1Kr9xjJRMgjgbKzHLJCtZBJ5FuMgGaAGMKqspoxHAiSZbKllEvi25UTeQxF3ix9Pa/IgolxbLn367SyYJlbiLJNmVhggRk1RYRE2TWl0irITc6lOhMScNsV9Ogq9TsPwcherSa0xCby6M7l6yinks6jwiuLfn69np0RWyjAqRRewTi0lASb+3jIeUuZ59+IN8Xrsh1j/CiXp08ylTQmZUnf9mu7DBqSLLqdr0cLIQGoNUCWKu5TvWV6LcZWpL6x6UFfV7dO3DigYA/VHwPgkpuMFKsAMFBCjUgDZlGAZfM9cXNXDWtehdvRt5Jt+Q8spVsE3OwcHQFkcZftZWsmLl74bUiH2gUeNYiurIgLi5KZs5V8cYXcU7ywjJ4CRPEuZlcouxlK6PBU5RUuVNwKCUOhex8Z4TQQc+UxEgA/92AAK2jxZ+5r+CaC7auV9KbtxDSmAogYBvbiWTzNsdSHkbkX9aU7V9ifFXdVZWyItk8zFFSnGS8a0ngL2vNLtYsq1dNQVgCy+NGv35Ar8um6KhCRQLcUXKgtRK5jF2IgE2Dn/4rTgJcDoXcvaGutbAXbV9SWATxS6Ao5mO5xOP8iBRQIasu9WyNwAVm+Rb/kOYJLKFnn6Njf30rZqsRzHFfXXC4SSfCbJD5B8+/h5aJLvSSQvInkxySe79HuRPH8sewHJ+x6k/mpacTwzPYuxiA0X02mmjs+0Kj3wlmnAuK7DTRhwq9/9Z0ibPxVffy3EJ04UUKprbsRXWRTuU19z5ffQvuyTrKzvxdQnbV4vd0lTh1Y1CnYjJmPE9sV+UXVz/sj7EOsWH1VmKhsT0vsj5PNtoOi/zZhW0sWUY9XnindVpqM7u66mpKwFmQdZbVT8xSKx+TpCW0pb1bPo34vy4Di32TZxYB+bxc/pRIuWCslHAHg2gNtgfhzMzM4+ZN3PNbPndOq9B4DHAbgvhnPoX0vy1Wb2XgDnAvgRM3vNCEjnAvjSVbV6S0McqNWsQO8Fw71CDbRqb66ioD1L4WZTAf/ZakhAwssHVNOBpf89acuidRIARE1TzQKwqs7MnSMtlaQNmevFNoR3tyy6QZJ6mvTsWq+fYxmR5l1VDflnQPFU7cgALAEJJeeqPAu8s76SwNzRtz3LZNF9GV8Gb+lskcyAq/dPL9BYojXur3MB/B9m9q6jFibQZwF4o5ldCQAk/xzAI0Z5DEABtZsB+OAqjkRrLYy0eGZKzON5+BlJEojGrAEEikxrYibTi+JfyGRX4JJ/4MW57rUKMpDcDyoAQUmv1n/0tlLJXFxeNgEo3iJR23noEe9CP6kZVq6eSiYIOWO9ARhl/MLHWjK5sKJsBAsFXlEmR+os+qzdcVAj83jeSVl5XVEEAFVGPStKruq343cEm0h6GtxfO1CJ9M9HBChPJPmtAC4A8P1m9pFw/SIAP07ylgA+BuChY14AeDKAPyT5HAwq+ItW1zrNJAo3Olkpn20HX+XZYwokPm872m7BpN42PbzgsR2AVHADr/x6KlvFH1KBZJZRC3h1vXrkGACPYvQt5PYg11Nu8oRFBt7d8rp+KVemIBswHbuVAUz89dAHzdog9a1iS4kCrtohrEH1LExpYpZbw9PL5EEr9n0PoHpbqfh4SLDcKx6l/ol3wjPWncSYTpR2K+pbuoDkywC8CsBVJdHMfrdXiOQfA/hUcemHAfwygGdhuH3PAvAzAL7dZzKzd5F8NoA/AvBRAG8HcO14+QkAvs/MfofkNwD4VQBfnsjxeACPB4CzbnCzFlC8m8riQxcehgAoiwsWyzX/sEa+nbn5alptCgKA2OYezf9VJyw6ZabyS6XTCUj3XGsx7yKPmKUCpDDtO1N8mTIBJEB03YUdftEKqIDDT4uO+YQcPWvOxkdsFZgIAFqyYksIIz1rxZfzCnlpYOEBdMo7C5NZZ03cJLavlAkAlVpGS2B8CNpNKdZ0NoArAXylSzMAXVAxM6nkI5F8IYBXJzx+FQNggORPALhkvPQYAE8af78CwK905DgPwHkAcPaNb28ToCgw8dNzC22CMhcWjRr5z/+dLP7oXi9jtIYyns5VNNEe83NMQv1q5lk2yp5HyQIIqjZhWl/SX6fBOr1bZ1B2a8EBtXUiR7eeGGWs5ZtW9gcXlly5PQngeHrlHQUVijcq/ShbbLd/FhqF6kUS6VXZAmphcWhVvqMXp+vZverJM91j10MqvuH7ptOuRUsn8ozvmzzf+jC0c381ZGaP3XalJG9rZv84/v0aDK4ule82ZnYpyTthiKfcb7z0QQAPBPBnAB4M4G8OJEAzqhZnwCeuri4dYI1JtdBOBbU7o3oj6llce1x0GUleIS3bcjxT8LaHSeF62bpHDgvF1uyyG5S7ikvUe321/S4pU25JOaXEgTiwEG3xwJDV74CntQgT2Zzir1xiS2Ci6hYg1a1b0NLIfnGAvuRGW5JvhZxL1knNb7wJGxOjgBOnbZxRT/IsAH8B4IYY9PYrzewZIc9TAHwngGsAfAjAt5vZ34/X7oRh8H1HDK17qJm979CCCVoz++sOAH4BwP3HpL8E8CQzuyQvtUjnkrwXhsa9D8B3jXXdDsCvmFmZYvw7Y0zlagD/0cwuH9MfB+DnSJ4B4OMY3VuLROSbPNJbDFyn5KtZZFGDOH4q8A5nBQSllbqZvFUyfkvT3/PvbWsyKcEhXwoEvh/CbyV7VW5B4a0ZjTdKEMB+tX9a4Bv7TdStgtHKtVTx9L78rC0eZJQMSlYC+w6YVy8I7PVtByRlIJ6hnYKv59GtP87+k2UOEDNRPKpBX2tdrHZ1Tek2X0/670RomP21lb2/rgLwYDO7guSZAN5A8jVmdr7L8zYA9zazK0k+AcOkpkeO134dwI+b2etI3gTdk5UOR2vcXy8G8FsAvn78/6gx7StOtFIze3SS/kEMAfny/wFJvjcA+PwTqnwKDgerxF0DMLt1gHF/rjHdP2xFYTdTVftgUvIVficyNTieiyLLLcVgKtCodyDWINWWm/MK0FX1UPzPZFp6uTM+lcKpvwvPJeBLR8MZAIXqpzzBumjcVUWXmcsf5TZRTQSAyFMpdQF+1XXU+eR1ofwbQFDuu4Qk7xjcV5MDCESXmWyLy1/X5W5EsUziM7QF2tbiRzMzAFeMf88cPxbyvN79PR+DrgbJzwZwhpm9bsx3BY6Q1jj7bm1mLzaza8bPSwDc+iiFOkqqFhAWZeiU4rQwDJhmddmeW4vhF/mVvaiAaV+qSdlnAfii1KKVMX5XL2j5bKhPWcRcbijDWbmXRYilPVO9qBeECTCoFr5t6j5BkDW+L7INmOuMijD2TWZ5Tfdtw7mvvQxVf4W66dqlZHV1q3xKvuhyN/ep2h77bVP3L2IbYt9zGNBU/dcrH2RtgLnXz+IR61omCjjoZK36waqFi1V7RD9V8kfZCr+erL6tm9gOq+XCWL/v1y2GQfbBxQ+AW40Lucun8b6Q3CP5dgCXAnidmb2xU+13AHjN+PscAJeT/F2SbyP50ySPbOvkNZbKh0k+CsBvj/+/CcCHj0qgIyevgEfymzNm7p8sruKtEttnxU/NRFrk6ZPUtODoTx/5RL6Ny6ZT96RgNvW3kim66Bq+Y/7GXeNHrz3ZOCpmX6fa4FEoyjSPB7IUtNo2yH7D4KoqSRWIiN9KKTeKMrrVMmUqQLFbn78mYjA9a1IueF0qJy2KjvJPZG0AqcNz0dKNMmT5qzqT4OIJkGH17K/LzOzeXV5m1wK4F8mbA/g9kvcwsyYePerre2OIOwODnn8AgM8F8H4ALwPwbRgnQW2b1uDxtwP4BgD/BOAfAXwdgMcehTDXGcUtQAQ1o/9p1ON+x5Xve/XD4/NOPMfRdvnfjLYLZZZJouhSXiWtbIHirJnKKgnAouqLgNJYKlO/oPH/N6PFTltK35rrq9hOxE8vj+8Hd62xtkL+wqta/8AOoAg5It/mPgO1dZLIHq/HPLIdwmKrBW9Jn5cjeABt3THOUS0y1M+mf2aiVV0BCq3lufZ5Cm2YqHnWHf8tLorct83i5yA0xpZfD+Ah8RrJL8ewbONhZlaWgFwC4O1m9ndmdg2G5SGfd6hGdWjN7K+/B/CwoxLgOqfRJeT/A0ERuvRCfnv5yjoR+Yby5b/jN5aXU2UNU+BdzeRqX6A5QY58S3rPovDVH2D1e8xX8SlKYuQZ2+KVtFKw6RqTOO01lRXwVpGqHwhA4cn99+AzTX0tSk7FPuD6MfKJ/FdYHRKkfN0Z76z+5P4313vPnurXqkwSgFf8V8hWAEXWGfkWfjQY2MZKIlA5/qp92yAz4potTCkmeWsAV5vZ5SRvhCGm/eyQ53MBvADAQ8zsUnfpzQBuTvLWZvYhDDNmL8ARUQoqJP9vMzuX5C9AjG3M7P86KqGOmujPbnckAYUzoCydU65fQqfUw2yvidxoPFub0cjVyFzL0bW4/UuYKMFhLcYMhDGQnyr98lJ6+UK/VG6SygWmha7aEl0hEH3AUFdolxwxR3ePqkO137VtWiQYFZfi2Qmkx9/NACPOFFsql1ADxEs8IqA05eZgeTbDTMqVAUsIvvcskxbkVgTfY6Gl9+YEaUuLH28L4NfGWMgGwMvN7NUkfxTABWb2+wB+GsBNALyCw7v0fjN7mJldS/KpAP6Ew4W3AHjhNoRS1LNUytYsR4Zo1zmxmNhxXUf9YAEtkKTWiVJIjmf6Aq3cLbjMDEtHahVAEN0pxPB5dX1FdtU3Eex6yl6BSVXfpCCp2yyAoipX8DmNGdX8pFzjDD9j6B4vj0jLFH6j/BJl2cgZR84ij+yXGIsRbYzpvcWSjWWZyNOAb3BzYRTNWwZqLU5Fkywd91ZWdmqPBV4Jf98RG2EFFUtnC3SAmEqfj9mFGGIiMf3p7ne64Hyc+fU5hxZkBaWgYmb/ffx5pZm9wl8j+fWiyKlBjCPuFlAK9dxcU34PKNlWKlFx9KYPI77Aw5/GHFfKJ54Lk5BfoNdz+/WUk5ehkaO0NxJbsFZ8m4BvpvSiIk+Ud6bkfTBant8h5K+ur/VqKKvFU7DyYl0VMPrrGRgndUlQcnX3Fr+mDGMe5VZUsjWgsh5MvPKfEsqMLmW5KQslUP0eUeY5Udpt09LSD2LYCmUp7ZSg4cFugWRSdnucz04RZ6ikLgs/lRfixYhB/N4otrOavknr1emVdEgrv9PV7+FlzJRWamWFOpuY1BKPZAGdmhasSLrYFupMQUSVk/dNlI9gkfHtrQHqXPc8e7prkk/0x1Ld07XeWpDsOVD9Vf2ekWzJZdu4twpNR3JD9m11PbVsg5WzrZgKjt8hXb2YyldjWIh4e5I/7y6djWEbgFOX/Ig++vTHeEsBmGqRZCnugaQXwO5t8HiCANFQFofYw7R4c0191bVEGS65tFLX1fjbwKYdPaUjt32Pda9IT89iZ6I7ovKNZTtpWRuafB3Fu/beZwC3dK2XT8cngjjKhVbyqUkEzXdwQam9vjLZowWxpK8r3yc0+CqrZIs4sI1tWk4l6lkqH8QQT3kYhsBOoX8H8H1HKdSRkZ8ePFJ2IFY8+719EDl9py+g2uix+/KKvF6xhZjJ1IZksV61yA55G6QllChDBTr1Plyxzhq8pcUTZKz4RcskrrZO+qqRGWjWXlSqJALg+JisGwiE8qjbtwga6loHOKd2JG611Goq1537U8nUxgeFhZLdOwjZqnIjr96UXdWXybWqDoOeCly1zQFaBMsIelsgM+Ca3SFdA5nZOwC8g+TvAfjouPAG4+yDG15H8m2XglVCs/Z0RU/y5R+D5vvWzuZSsRI/egsvsNwBOIrs4zVjmSbwrPKL+io+nRdVKXQ5yk+AZJZFt6/h5e5LGowu5TLrIQl2UwDQEpgoOdIRfE8BijoyPql15/6XSQly8gDEfVgjW0yPivUAgHJCvNDmt6Rc84x1Funqvgn8/HPHkHkXUzlhWhNT+SMMZ5WU/WJuNKatPxjrZCHWlkmZ4aF2uR0eQqLdFn+8ngCKeh5bN04HfMSLKtfSqJFlpkRW7MvVG5FXIOYtGAUkYp3Jkl+9aw3EtgpwWzvqrrp3oQ9K3RFYmrrRuYdJH5ji5duiKAOeyL8XP+r1rRrle7dUUncKsh4YIi+EMoqfK9cDiyqvu96Ad+AX5tS0de1iKidMa0DlLL8B2bhL5icdoUxHTy4InwLKlCYAwOVVK9/lTKIFQNEuizavtEyKMlP1KTky0EhecF+mWaMw5emAiU9nMloX9dfCoK9clIJ3RSuZOiC0CGBBFql03f3oum6yNPTvS1NvBLOe7Ah9UGgzWxG9uFgjn687Kmglb08mX66Xf6GeOuaTx2AYry/dqxMk24FKQx8l+Xlm9lYAIPn5GI73PSVpWvRYtlNrlEMCIoUCmGTWw1S+N5NLuX3UVFsVVxByLVomGTBFoFmaYVX4bBYmMoT8vdGzUmayb1eUm+rw9RvyExZdvq5SLWUyt0vWh+zz8+mZlSXzm/ss9H/qvhJTeWV/I7SBmA9K3Tg+YwemAwfHs5VzIWDugcfYWFazF2EBSGJdVVs9Um6HdoH6lp6MYYXmBzH09qdi3qP/1KSg0CtXV7GJvW3sgSScYwKgCh5nVk51bXr4o1w5oE0L9Xqurt6K+0QGlSdNn/rLydlbxBlGsI1rcAHcJKAIK6EXtJ7qz/re8UyVas8yKbI7t6BS4JWCDS6yBjxFW+Q9Gtvt3asqhiQtnt66EAWksd2ZFZXxdeUkmCw8O5H34roW0Y8VoMTrFY/tAYrZLqbSkJm9meRnArjbmPQeM7v6aMU6GorKohnZ+7iJAhSg3d5FAcSCy0y/BKGMerHDZtUVGPZmcnWURqokoxJOFol23Tsi0L82+C/5+fJFKTheLDxTZeHqjYMCnzfyVut8hNLqutb8twDZzDpQ8lbyCYDNBxIrgu/hd9cNdgAXV23lJC6nirdP0/mzdk1AXt03l6e5J8462br+J67dzf6qaYyfPAXAnc3scSTvSvJuZibPlT/ZqVVk7bntrXLsK+zhmawLKTeXsmK6Cj9ZtBjBsNl2xsmlRphd144AtIPESyIvPwJf6ueMZzPidnmqeEkiT2YB9NrQAI0KgGf3JwOhWCey/kZOol8k/6avRyDZCMWZPTcp6CyASQaspWwA7Za/qyMF6cBgdHk1gXeRZ+YZnp7ynG1x5heAXUxF0IsxrFP5wvH/BzCspj8lQWWi6CpaAyQKHMY8/mVs3A1rAMW9aL3t52feNY9UOcmRoqsrUxzIwUS7U0TdQYbMx94dDcd8AUjWyBCvLwFiOuLOdEO4/z3eSh5PPeuh1x45gMiUf+IqNNU/jRwLbqdyTa11mUYHkCC/ZLWk9U1ggMFo9zGV5B1TAfxtgwnG6o6b+2uNXXYXMzsXwznxMLMrkb8mByKS30vy3SQvJnlukuchJN9D8r0kn+bSP43kG8f0l5G8wcpKpw0ay2dqzXjCYnFxKddFZSX4D5zS28x5LZQxBVZjmXLC3/TSlXRiOg+lnHxYyU5RX0ir2hF/h//VyZhO1sqF1alb9kM4K6M6XwMtv1JfPCVxLTV8fXqop6pPlYttdG3xJxg2vNV9ETI1dSf9kS5GRbwnNmX2MjfPVXhOq/oUvzEA38ga5G2vjYIXK8naMvMJjKWu8Ey5UyOnacHu2SgzuVjqaD7eNWZV/dzYACjq2TgsjWHapc/pRGtA5RPj/v0GACTvAuCqfpFlIvkgAA8HcE8zuzuA54g8ewCeB+CrAXw2gG8az1sGhrMEnmtmnwHgIxiOz1wk+eIC7fbzwf0RXU6en2UPoo/VsD0+eOK9dBcWNomUSgGireO3Bp94zPIsWzMKVooa4Xq85iyjCsgTfmoGmiHI4RW7A6nm4LFMccp71n4aJarjxTUvda9FfbI/Nmhl39QfoH1Gi0ASxEIbi25OrbLQwBRIZFuc8i9KPg4OvLKfFLrVAK3k8s9JwYYGNDLydeW5SNuq1bLyOOHThta4v54B4LUA7kjyNwHcH8NRlIelJwD4qXI6WThUptB9AbzXzP4OAEi+FMDDSb4Lw0Ez3zzm+zUAzwTwy6tqFmepAOIlDIphXvDIZruQlgfrMhB5F7afb9KETGkb1uRfSGtOASQqhbpKrlFBSD99Z0+uqaxPi/3twT/k68U1evKneeQ9EuV92d56JU9R2Yp6Js+RBQWfTKvNZJMDgMZdKwL5Sl7Efg4uLpEfMb/ga2yvc4PZteU7Q9VVudVaV1gFGPH13HY8BbtAfUNm9jqSbwVwPwy34ElmdtkW6j4HwANI/jiAjwN4qpm9OeS5PYB/cP8vAfAFAG4J4PLxaMySfntVCcnHA3g8ANzwrJvrl7+nOAYmbb4Y9M2O/418e1tL+PrcjK4Y7K5GmUIWz7fk9+nNljSx3R3Ft3iQVUl3MmWxhiWgawLv2fqZTP61YBLklWDTqbMpU64nsQtVXo7OhTzZ9OlU5h6gxMkHS2DS5efcVx0ZFoFE5YF7HXrCNfemlUUBCldZOSdOp5t7a4l6uxR/ppm9m2Q5y/gfx+87kbwjgH8ZjxpOieQfY1jXEumHx7o/GQNY3QfAy0l+utl2b4GZnQfgPAC46c3vYE0AHhAjJKfUzfQLW5SA38ol47u0WHFKc7KJIHlVhwKqkmZCEU1l/RsWeAoZm3qzskuKfam+0leq7hUDgQMp1TX3qHkm0CrzrM+6Fqwuu8rCnMA1sU4S/nKgUNLWTgtOJqmssxbyvJJfBQKhbKyjum85YJixAg/SmjRf3za10G7210xPwTDC/5nk+i1JvsPMHp0x6J1ERvIJAH53BJE3kdwHcCsAH3LZPgDgju7/Hca0D2M4c/mM0Vop6YvUVYy1gLNyT5T1kGZzXlWf2BF2qk9sQLRKPvVyxzoDj+XtaAKT3gg3prl8TYwhjobhlFxvJK9kQgRIkT8o9G4cYCGPBJQMDFYCrn+Wqvuo+FblVgCJkj3KL+pq8iuKz1wEiFjfpPRbRV+5Qz2vwGeKlUSXmrn2WClPUd6BiFiD0rNQFsKYq2kIxO9ABQBgZo8fvx+U5SH5R4eo+1UAHgTg9STPAXADANGt9mYAdyX5aRhA4xsBfLOZGcnXA/g6AC8F8BgA/211zakyYZ0eX3SxwFEq0ph39PGrwGgWc2lfPCFzpogqV04Nij03S/m96JYS/WNePn9d8LNRpDXB2KZdnoeKqfTiNL02LPWNArMesHf6sOkj65QBqgdset4SIJFTi738a9aYAAezTNT/mBaeXyuAIfI3Cn1pwaKTK7NAinXSresIphQDx29K8ZrFj2diCKp/yZj0ZwBeYGZXm9lXHqLuFwF4EcmLAHwCwGNGsLgdgF8xs4ea2TUknwjgDzHs1vUiM7t4LP8DAF5K8scAvA3Ar66qVb4o4Qz43khOrHw3YDoUazHmEMr3gKMLKFI230ZW79ycHgq79nbBBqiVe1BkKn/JF9tYZfMyBllULMYofoe6paspTjAQbU2BKvIN9a3Ko9pV0rJnJsY4fH4BsE1eJeOYedHdWfo3Ufxt/zkBHAikoBT4dYFE1u0BpfCYgWWNVaJca0eyVmUXU2nolwGcCeCXxv+PHtO+8zAVm9knADxKpH8Qw4mT5f8fAPgDke/vMMwOO1i9GTigVSZV3s4Jj2X7lLWHYk3XRkDqKcTM9ZQHv1n9X2OZZPVEWb3ytyjzCnBq3q2SMf0OtgAAIABJREFUL+n3ZvaZkC3t7wAove1dfJm0/wOYKnll3TGPq0PL24nPGRo5pIWI+LuzwWOQcQBVC6MFzPc33sQFMJBuLqXYRVnJf2XwPUvPgMRbN9tyfQ3SEvu72V8N3cfM7un+/ynJdxyVQEdOa7ZHIZonqzk/BX2rJNZX1RkUdCOPAo/0RfVyCxljvkwpCmXl65EuKwU+kRygNOXLdeVqUUo95G+uZ/GbMGNqcQYeW50q8yn5wu9Fa1Mof3l9DR+hMEvedE1KkHPa4VfFTJrAuKivAmZXaZhcsMrttKDc14JJE7oUlsmwNkWU3wIdM0NlFahcS/IuZva3AEDy0wFce7RiHREllgYQFVoAFDFKW9we3tUnQSAq0SRNrhWp6uU6Zefk93VM749X7Ini6u1FVrm4/TWX35KyylXTBcOo9H2eNco7pgWQT0f/mWzu91K90sW6pFRLexfqrgRcCuwrN19mmcRpzKURwbIw1aaSp7hCs3an7ZnBTSr7CAQVuKBZINnEXFDLRC/vNsiwC9QLeiqGYPrfYejuOwN47JFKdURUPSeNYmuBZPKvF2UcdgmOfIb/7QPk3TlLLohUviZtPA455lE8EkCqFKg4ayTlK66bAJRGjvJbWSaxvuTFru5Hb0NHIUumkFMrbG1/lLQIqOp+J0p1DVBJkBP80nsWeM8A54TMnpmm3gRQlAsLqHcMBgbLpYmlJfGbUkcBgwqkQpkAXt466bm5mjjMNnHgmJkqXVAZt0m5J4C7ot76/tDbtFxv5Ea5vfgKgMoPH+MlMW86LRhIXj6dts73zTEvZznVqD1QV9lGl1JUqq5spljTmIn7rXz/aSwklou83bU4AFi0HJTCFjJnFqKsQyj9pm1O8a8ZYKT1L4FTkldbEzUPbR3WyryVb0W8xJN3VSqlO4GCiosYLHaIBxFvSWEuU76X3Fy7XYoPR11QMbNrSX6TmT0XwIXXkUxHTvMILQGC+GKLc0wGdw+r/7mLqk7vKio4MKsUfRhJet5xxGfhuq+7E0+QLqcOGEzlsrYkICUPlQr1lf/yvvS2P8nKRJk797tcT3WL57O0FYtXusbqugKjfswFThmvA5L22dSuq/x3DRapyywDB8FDWj0ufWkbFTPWOxFP12qemYsLwLjdS82/yi8bcnAyAPv7O1CJ9FckfxHAywB8tCSW44VPKSoKRx2IVUbhG7RTgx2V8k28YSWgZNTyIwYXRXtcbyX3lAD94gflEtNTGRdGkEgup7zdtWoCQCJrI0dob6Use/WtkNGTRV6ib5o6YnzCK3CfLS4E9G3ryeiv91bTx3IxT5GntzU8XN6Mj+fllXLZnyuAnpLJcV24Pl72Cn8jwEJaJYM8G3W8sOv3KX9fhBMjwwEeytOD1oDKvcbvH3VphmFDx1OLykg5U1pAc2Rvrbzqh0ONSLtpC8pWijy5uhbyJwF9DybpavQMELOYRZQj9GHjjir9iDpNlZNEnWdxyrHq/44S78U1pusZuEcQzgLlQhYJkEqBQ1genrJ+7E3VVXmD3I2lk/UjDWxMi5hPg0h3T66QP926xZUpeSSgIAATwq3buvvr8DxIngXgLwDcEIPefqWZPSPkeQqGpR7XYNiZ5Nv9VlokzwbwTgCvMrMnHl4qTWs2lHzQUVV+nRMxnksy/i/KNgbggxL3gNJ1k60ZfQqZ5vJM0kUZUY8f/TfWgHKJpXJ0rqn0OFL3PFxfVrEbDzQ9wOwAw+yGXNmW5L70QGlx4KDqXTM1OPDP1tEoN1cmr7QkSp74vMS8Rm0FqbYA7a7BS1YQsvQVgALU04JXgEnFQ6W5ZI7t91vebzUOsh2MugrAg83sinFB+htIvsbMznd53gbg3mZ25bgN1rkAHumuPwsDMEkiSQB3MLN/yPKsocVVOSRvSfLnSb6V5FtI/hzJWx6m0uuV3Es7HYpV0uOoKjmES7lishG0PNtjOgeErsygYWnWKmL/CedSGOv65fkn7rsZDbuDmtT1Kb10W5SHaC0aBxqN/D2FHOvehL7l3H+p+8z3M+v06r4Q7f3x+RJQaHTNdCiUTZ8mViL6s+IVv/2BVLEtjmJ7QBvK+p2CfVuqZ72uo5SZ+1VowrEtzcwq39ZWSlTWSVDwZdZVOVyrOsuk1Ldx9TVgY9hszL+mLQ+XxlA28inUWDgnTITZ8meJbKArxr9njh8LeV4/HqIIAOdj2BNxkIL8fACfAiDdWmvch7FZaH5QWrPU86UYTKmvxbDX1ocwxFdOWVJg4pVqOWVxyi9e6kqZU/PplQcwxUuMnIFuw1phxhe3V/cmlAvKswEUxy+6VGwWqQYTUValpa6k2E+qjQIQJuVgLl18SyBDXbbhgfZ/wwfhHpb0sfOaewLMHejaVPo5BdvMbRZkTKenl3on+UT5oLRjP1iUxYFJfSBWsE6q9jgwcXIDWuk3+Xx9pa5qBlcMxFsFUjOPGUz8o7JJeFV1botsxQe4FckL3OfxkQ3JPZJvB3ApgNeZ2Rs7tX4HgNeM5TYYNgZ+6gpp30ryPmubpmhNTOW2ZvYs9//HSD4yzX2qUPMwEwZDBBOfd1IKcYW2z5elA9j3m0cGBQZo3iqvClDHvNJF4ka2Mc7iLRLFT/IWbWjKjP9Td5BBb9Yo+MjNIy0oWNEnEuBCHVkeKY+aeaWAZPxW/Z3xlCCR8J62UylKvVz3dSQbScp7FxW9oV1fEgGlKhsFneuKgXa97fyo2L3MrvMWXVxOhBgzifmzmWGb2E+HJQNs3eyvy8zs3l1WZtcCuBfJmwP4PZL3MLOLYj6SjwJwbwAPHJO+B8AfmNklXEbMLwDwLST/HsPELA5V2+esaQSwDlT+iOQ3Anj5+P/rMGzweOoRMe/TNQEE4ZfdpiNDDygh3f+XSr1cKjO5EmpcceNLEi2JbJV9qrhVXf79j4CyFqxQK8yqfxb4TO2LgJKOmkM51fYEUKa+iyP4eG8XBgUzMgwFutYY6r5amh3W9GPCuwIAX0jJAZfXkT7fp1WmXAKkpr+DayrGV0r3Wb2iPSUjuNmveToAmtxGQZ4CDNVjsgAmvtz2N5VcBSqrycwuH3dpfwiAClRIfjmG86oe6NYTfiGGAxG/B8BNANyA5BVm9jTB/qsOK98aUHkcgCcD+I3x/x6Aj5L8LgwIdvZhhbiuaVbO0xOajpCjPz+mSd4yvX4BtCsFjWIo/KQLLfzvxVIauUNd1WvkFEBjZQh+q0BOKfPOti9N+ljWOP7MFGjkkcnPFfd2AqFgmfRmU0XZyrWg5H0ej1VzHVl7vKspWgux3lbOhk+Qsz3DRIDWxDO3flQavZssPH9xRpcK1FfpjABhjQjK6lBjuggmFWBtg7aAUSRvDeDqEVBuBOArADw75PlcAC8A8BB/PLuZfYvL820YgvkKUADgx+IZWSR/A8NGwqtozeyvm65ldirQ8MJy+p2NChdBJvlfXwsjqUqG8gdSgSt/+aKsa9rhB9tACyaZMl6qM/5Wac7ymtqzqQfcPb6L257E8hbyxTp6bZruyVChDFyrehPZG1eOtzyTewRA7xi8hgqgKMuryreCh8xbg41XxHH/LDk1eAKJUJ//G59/BeZjOn2eQJuNNdN6NwqQKvfaFq2V7bC6LYBfG3c52QB4uZm9muSPArjAzH4fwE9jsEReMbq53m9mDztgPXf3f8b6Pv8gDNZYKqcXuU0egVrBZyCzWgnRAYnL342DbPTLLteURF5CpopHoqDNX/cuoVjOlW3ATrRFWiYdebz1lVon4Vq21UzDQ8jQWxTa3BsHIrYRymwBdCUxOcckyqTiGL26splaSs7AK52qW/I1QOLRIlQZYyRj7KTNF+QZb0LZQqXI0wCV/56lGMQUeVWd3iopbjgdq2lYnBgZ+iPPtWzMLgTwuSL96e53etKuy/MSAC+J6SR/EMAPAbgRyX8ryRjOunrhQWQ9VqDi729UIt6VExVojBWsGqWPT71UmiH/IsCFvEtxBmnNuGy1QhLtimUzBQWRp5TNZFIUywaLRuZR9fprK/pdPgsx/qBk80kdkF01k0u11ZeNdapnrzDvubuAehNHzIqzUaDJ5qIjxxpXRLylWf1uJb0k1iajipuoA7c8XzWuiDPCfNs28prJfMB2T2s8FQ7pMrOfBPCTJH/SzH7wMLxSUCH5aWb2vw7D/KSlRGFGmmIFC3mzeMmUrfzoKVmRfhAgy2YVmftvSjlFWVb0TdcNlcmk2p2B4iboY3VvFgBHgUH3hMVRKcuYiucbeOj74pSf0k3dwYiwAsRecE3eEpxOwHG2HpLrqVyt6yq6oSaLxF9zssY65/27ZjBhwntKC2JlAFEsnb3EXblkxZTre9sM1p9ae381Bx+S/BMz+7K1DHrrVF5ZGJ6AYItE8ntJvpvkxSTPTfI8hOR7SL6X5NNc+m+O6ReRfNG4wnRlxcOXCYVg1Iq8WuMx5eX0Aec8DSj4OpSry/OMs7+CHLIMQ5q/FnltUC2Gm+R1n5KWrR9ZU+90XZSZ3tUEVKfyYpZdI1vs30S2eF/ndRVOEUdAgeO/pBO8Inc8e4Pduj1juU0AlNiHe05uAT6lHVkAX7p0Yh0VmMwusBJkbxcmtm4kYACSuM6kdjXNCxYbQHFWkAaBGsT8zsNZOzcCwPY2hr2NYUPD3mZ/qmuzzXgK5lvS+1zfRPKscVH7rUjeguQnj5//AOD2B+HVc39tSP4QgHPGPWUqMrOfPUhFnkg+CMDDAdzTzK4ieRuRZw/A8zDMcrgEwJtJ/r6ZvRPAb2I+ivi3MOx388tr6o4j2MXRdgCSalqwf5CzkTvakXe6B5crd5CReImNLLmCZJ2qvrg/VyznvzP3jaqPLQ+Lfbhk3VSMBd9QNk0ryt+vJC8ff68Y7q3PQ0BphNRFqGIl3qwVllHDOACGJTI0VgJCPnEPR45VnkqBh51953UlQ3q1oWSos3soFur/Bnfmicin4iAbGvbBJmZSg4lVgFLVOQJjAZStAcuIzacAfReGWb63A/AWzF3/bwB+8SCMeqDyjQD+zzHPtmeAPQHAT5V51H76m6P7AnjveBY9SL4UAxC9czy3HmP6m+C2I+iSG7nKa2iv2aYGEr+1yvC/U5fPp0bSvbw+PVPsXqlGXj0gKWUWADWVx/1u9qxioohR56usFiVPVnenX5syQVa5wLDTB03MJfS559cMKhzPpq1FFg8IsQ4FFs2zqStNFyxGHlV7nRzeFWnuYCxXplkRP6XX9akg+JQuwMWM3QWIGTjtj+X89QIo2eLGyjJJYjGHp4UR10lCZvZzAH6O5Pea2S8chlcKKmb2HgDPJnmhmb3mMJUIOgfDYpwfB/BxAE81szeHPLcH4Dc2uwTDas+JRrfXowE8Kato3O7g8QBwgxvfIlys/yolSLNxgWSSv/Fzh+s9hRcUbDoajrIpMBHtyepLAQWjPJ29r7oAl11L+kT1T1M2oUpRq36K5VcCynQv4iyzqj0rrZOm3R1LAajuqyRvyU1K3XR+hgdrAjHHLJplTqZ0GxUoQNDidgElIbX2BJinBS8BRC9QH+vx1sjWt2bxdGpYKgAAM/sFkvcA8NkAznLpv76Wx5rZX/8vyZ8F8CXj/z8H8KNm9q+9QiT/GMCniks/PNb7yQDuB+A+AF5O8tPHDc0OQr8E4C/M7C+zDGZ2HoDzAODGt7qj+YV66WgaaIPv2fYaPs+CEozlugp2VCCNjAUUMjkS0EhH/BR9EXj18k0yCosl7QMlu88TdJ0c7I1pKUhMadYHCt9fCsDL9xKQZIMAuLI+T7nu/yfbtAPBKkksj2Z2VXFXVTGkqOF9+bZ+ucZE1SV4MJZ3PFTwvS1fXFg1oPSmBTfyYQ7AFzfeHmcLRvHdOu0vZzlZiOQzAHwpBlD5AwBfDeANALYKKi/CsBXAN4z/Hw3gxQAe0SvUmzM9bsv8uyOIvInkPoBbYdisstAHANzR/b/DmFZ4PAPArTH4Ag9HlaKnVIyTMk2UWKb0KmUGkT9RtI3rpbBQ9Qclr+RLyQIoRL49wFRK2ClJrwMXrRLFO+v/Ne9+sEwyUK3arSyTnrwJ8EYZKpn9s9AMJFiX8fWVH+LEQgCN20laWF4QAkA4t73hqTs6rrpXyr8MD3t7fFXiJeA08J4V/95mP5wzn5/wqLZdydqkgGorZOiPNk8++joMR8i/zcweS/JTAPzXgzBYAyp3MbOvdf9/ZNwp8zD0KgAPAvB6kucAuAGAy0KeNwO4K8lPwwAm3wjgmwGA5Hdi2KPmy8zsQOMAr5jqgHl54jF9L8Ucugom5hEgEXlXloAbyVZWSawjUex+csAqMEx4NnEPCAvKl3d9WvKkcSchj5Qv/CeU5SjcWx3eGRBOI3thFTS8KysnsSQKqZl90TWF8AwkvNLgu2vffM01kDUgZQo0zuJqgu+Of+RRrBP6/g08p24Q9UbrwdPeFCOx8D3nUS4xYI6bRL57YVbbhqbv3yHoqAygI6KPmdk+yWvGQ70uRT24X6Q1W99/jOQXlz8k7w/gYweTs6EXAfh0khdh2Fr/MWZmJG9H8g8AwMyuAfBEDJtXvgvDtgQXj+Wfj+FsgL8m+XaST2+rWE8VoHhFXEgAxdLIXW4/XykU90GwajgDSc9t1biABIBVo/s1A6agdH1bPEBUwWvRlqoPHHl5Jdhl/RVkyqyV7gusAMV/AzOgjMqlkiXIWQ08IqCIfpH9H+MTEaxDnemph8298IAy5+cmTO0t55j4dN8+peCDUi/nmUiZXb6QvIo2bKf8ljoH3nU+sm7fBnPsJIJPtGSOzP1lKz4nD10w7oL8QgyzwN4K4K8PwmCNpfLdAH6d5M3G/x8B8JiDVBLJzD6BeUqwT/8ggIe6/38AcWiMmZ34TgBFsfgTILGgvGMedK5FxSXyyjwCqCbl2Ssb8qTW1dRucS3WKWROQbbXh4Gvf2cXN5Nc0e+2sbg4uy67dC+qMlb9VlZa08/R3RRdXPEexXqCDNk+X82CRQ9gEZgrDRUAK42P1OnefcUw9Xb+HZrR8NCWSXRdRV6bCLKJNROnEMe8Ckgm/oJvM3PsFDMvtkVm9j3jz+eTfC2As8ctYgAAJO/uBveS1mwo+Q4A9xxNIZjZvy0UOXmJzipJrmeB3CpPoEWXaa98VOox3QOCUlJQrqD29xq3rsqzBCbSZdfjfxDQWFD+qXtP8Qjp83etPCzKNfb15HZLyrVyhm9xzK62SKzK0whX3DPhGZCyFFYCTOZV7dElVn9PxQQATOnqubF6rUkvBhKtoRiA97z2NjZtoaIAYoPWhbahTVOOm/zCWtnmFi2AflROBTKz94nk3wDweb1yq0f8pzSYRFKKA+IbYmSP4Kqy9loFRpkl4Msksi0p3p6LbtFy6cnj2xdJKHxT9SvearS/VuaMd+Tv8yye7WJajlhX5fITo/3eQEGAxGSMJMCUxS+aOgk0yOKthPgsYlbY5XdT5zhCoAuIx23gG5Ay1GenuEvAsF6kXgFf54txjVJmSGsVfgYmJX/53gTwyNxcdK6wcn2rix9PrW1almixMcdqQ0kAOaC461LJKPAA+mfCZ8q7jLCVXJnizW5lIr+UKTYpAT3p1+8BUQZ+qn62fdFMKvBlDM356Rl/7xVqpjhX5Sxvp+Dnyy3eBwUW6rnp9W8ZZUcgqZ5BXUcza2ks47dA8daFXP3uAuJxWrCaFcWNVd3MkDcG8r010NvoUU313QubT/bcXN7KMVdfbEcFKDAJpIeiU9RSSWixNccKVAy5QpRKJipooYCB3P3UKNhMcUY5kjzpc56AXe96SVftzqyzRmaI67HO2L9LfeVHzE5GyTu53uQPsYolxV73Uecdivn8zQsA1LjNJgR0LDyQRP6Am1ajXWSrpgVHMGHdN16hzweiapnIGkw2bNeNzP/7YJLFV0r+slhxfxSguLlivgga04yxMBXZ560XQdbltkGnqvvrRGkRVEh+EoDvB3AnM3scybsCuJuZvfrIpTsKShS5/05dVj2ecYCYjNjl86XqCzwbUOvJmADS9HB7xZBZWbHuDq2Ju0zfzjKRSj2LcUTZFu5ZC04dV1em/H3mhf6t6wq81PUQV6h4B6u4rqeuY1XwHZimBk/1eeUbZJlH+MFKCWfL+27IYyZ6ZliMl8zftTts43jvgxUAFIsuusTUVi9e7h6YHMXW96eZpfKJpQxrphS/GMBVGM45BoY1Iz92CKFODoojXM4fcx+ZvkE1ZRiY0/x5LKW8Yf6A4gNXH9z/TZDDy4o6b9UGl8eX9/L5tkh5Nsl11O1TdVdTkMN1efQwMVgTTvH3ZKt4xzZPH6s2i4z9m99vG2XB/O35N7PnRrmVbNX1wAtFgbs8JS6xCWX9EzTyqJS34xunBXODafZYVR8CYDACSju92JepupIWPjVPP323TA3eOOAggTP29qddg6spzw4AinVSPmWH4Qgg0coo/HzeCCibcP2MLVoqp9KUYrUrvU8zs/st8Vi7+PGRJL9pZHoleaQ75RwdOaU2/Q8AECl127jyjXnL+ctCWszTc6up0fnSSFuC5RI5WaTlImSrJilQXBfg1tTpOm6N+2z67y2uplxi6fiyqUyt+6iRJeZR91G4nFTsouE//ba5QJBn7bTgtg0tkAz/Wx4x6F7cXLKucp1AvfI9q68GneK68tZGITWTC9Cr3+VGkYKfakOTb4v+qmAInrRE8iwAn4Rx63vMT9HZ2OLW94U+QfJGKANt8i4YLJdTkiql6RV1VFaOunEAzhZI0VcW8/ZAwxauZYDSAynBuyobZq1FCymVN9a9Wc63xsUleWdgaCJPD0gUiwUgmngL0GzkdPyGwUer3BtAETGXto4OwI1y5QDT1heDz+mUYX+McohBFB7tNik1z7Xbo3jrZUqrFH8rq4rFnAigLAHJVtepnBqzv66Tre8LPRPAawHckeRvArg/gMcepJKTibx7aiI/+vZphnavJmgQKj/NKyNXZroW6qwUb/KtFbNuW8yXWWCSdw9MEiWb9uWoN+d+nNE8BSkvcyJTZpk0VlYiUwNEGf+St2NVWODlnwu5/Xy3bl1P5eIqP9NTDUWdQDObq+Kr6hqTPHDEQ7Findmiwl5ef90H4bMtWrJAewGJYu0UXvPMsARoFtK3RaeCpXKdbH3vKvsjkm/BsKMwATzJzOI+XacMZcqnHm2OieL0wYpKuvs9lcvcLAdVmj49WB9R9rUB7DXuMVm+AyjKPdiu3nfKJAOAhokvX/OJ91K1q04LilRZpr4OXzj2W+RZKX2004K7NPOIi/08byZWUOUeklZCnacCqpg3Nq+ySPoAAaCZ/eXz+vweTKZrqGeJxd2FM6vEUzxkawlQltK2QqcAqDj6J5I3NbN/J/n/YFjo+GNm9ta1DBYD9eP5xB82s/9hZq82s8uO6ojh64p8oD0eFZweYxuCxob5o5XSXLYqxzpPVS4o7GpCQMhniexZgNsHz6trvl+8zKEtFX/Bp3ofI28ROzEC0/HGpb/isc0lDzEF8i2sWZEut2YyQFDGJT0eFxzzF+siAxN3ncR0hC7gAEXe67GsC76XvbeGsk7pj0AzAUp8TlYASvleY52UNP+Z0zAG0sfuC4Cz2exjE/bo8oF6vzdXXFw4TSLAzNvXrQLy3tLYoL3ezPYaeZyx2a++YwD/DO5vdfEjV3xOIvrPI6B8MYAvB/CrWHmqbqHUUtlm4OZkpO6NjAprHCkap59Vvso1FMsBcgQtgUiUTWVy6dLKEqRG2tJy8/K46b9VvqStM48AJOV66SdRR8VHKXIIWb3MJvJEgIhtrICzHsGj6Zf2ofHrOZp8AZQnppPpMRdZ2iK+Tnf1GCf3lpdp/i0UbQCYCCb1NN9B5mwn4E2n7qV9ufaEFRHdW3FqbwSKM1jHY9a4uvaNOGOzX7nLlMxbo5MLNJbo2vH7fwdwnpn9D5IHmu3bc39tLXBz0lAZcZeXXLhWGldVycr5vywnFgwqfup36mqKfLK08aG1vVk2GfBPqLt3mJOvPipAMApAIgEgCe7L/uvx6/WfGJHX1xO5Y7tXHhUtV783ef11w7SyXZVBAIMwipeyO1lSF1rkq6ue+GAc/cez3/P8A/VOWcw2eIzkg/hqhhfQurgivwgkcbpwAaSszLaIp9AhXQA+QPIFAL4Cw8m/N8S6pScT9Y4T3lrg5mSiJv4QwEQpwubxCuWy5++E4xoLgNMN3EfeAlBSyynk7cYoggJXINIAYe9apMxltpaWgNordGllmry56VkmnXvo04oCNJFh0TIJ9c7uIVGVmBZcWBrq1e8xT++kxYafAJMMeDLKrIvo/gLWLVhUgHBGMsPM03HdmTjQNwB4CIDnmNnlJG8L4D8dhMGaQP2hzyw+qahnmcD542OZjrK1jN8SIKwBE5Geubm6s6qgAcr/726euWRFwQGFcGUttju80KmLS11PTkNMLZJpBJHIF/NHSyGCiRe9WCJC9kYpR6vC1SH35CqAZPPWJ1UVwn2WrYD3+T2vTeAxX/eyz7EcuWZETAsutLT3VmxLte8X640pIxXgUJbH9B81317erdEphFXjOsRLAXwxgL8BcM34vZrWbNPyDBzyzOKTjigGoV5hxbT48hrqkXZUqoLndL3DV+YRaRHEmnfhgCP6hoeyWDIFjaQ/lgAyKk1nKqYWSQTTxjQKdWeWyRjPqACzYyVUMZMIKFN9vtOEhSOAaFLEjVVR1xcV6GYTZkVlZ57Eet3/bHaWz9ebyRV3AfY8y3TebIaYB4x9ARJxenBWNvKOMZi4UWSWPwbrff5Dk4n38ySmUd/fG8DdMOymciaG44Tvv5bHGl/Z1wH4MgD/ZGaPxXB+8c36RU5i8jrIn86I+VMpC6e844wrC58MLCpQ6IBGjFk0s6wKbdpycduYyMOIebaV49lYGa6NOtZjk+Kf+iPy7MwSmz828wOGWV0KUALfetoM5q1NCGAv1OFl9u35PhDUAAAgAElEQVTw7d6It56oZiNNMRO/bYuflTc9NIVX3LZktkKmExbLivMKENx/GuhmUjHINKRZvmYFIV/zqfltOJ+uqKYRxy1X9qq1LzZto7KBYU9YPr5utW1LyVuA5AzuY4/7jRzVTC2R5uMlRZ4zWM/yijzKLLBCfs3MVshWfBaI5Fkk30TyHSQvJvkjIs9TSL6T5IUk/4Tkncf0e5H867HchSQf2anqawA8DMBHAZSDE296kOauOk54PAf+hM8szojk95J899jYc5M8DyH5HpLvJfk0cf3nSV6xts4IANU9DUq0UozhegMUEUjCvl3ZHloRUCYlrWQKZavrop1Neeu0K0mb9uQqH6AGCtXeKHslv+PTmx6s+qmYFQ1o1O2b0x1gcKjPONcrwcRNC654ZO0agYSb/VmGTDYFmHDKvShgMS13/l3v+RWV9dTNEdTGtMjPT/VVdUQgK9e9q8tbJQeJsUigQJQ1B5GlIH/8HdtcPpV1gxnYtkZbABUMu5g82MzuCeBeAB5CMu7D9TYA9zazzwHwSgBFp14J4FvN7O4Y4iX/ZTwyWNEnzGweX5M3XiWdozUr6uOZxVfggGcWKyL5IAAPB3BPM7uK5G1Enj0Az8MwE+ESAG8m+ftm9s7x+r0B3OJgFQ9fDZAU8mslHMmddQPINNdLHqccqzyuXCZnAyLAvFI90KJCR9u22gWEWhjHUCp8c//9NS9fEtjOgu+6n52lUeInvt4s7yhH695jBRRyZpW6KRFM/D/lito4F9WeVf2SzebSriZXF2el7sXy0igX1yZYNCoWEmmzyRcbxtiJmiWWzdraC9N5Z3dZK0u0QGL6hoMl4rfEX1romMVZIt9tEIGtzP4aFX0ZPJ85fizkeb37ez7GI9vN7H+6PB8cYya3BnC5qOrl4+yvm5N8HIBvx6D7V9OaQH33zOJD0BMA/JSZXTXWc6nIc18A7zWzvwMAki/FAETvHAHnpwF8MwaTbRVVdyEZOar/leJN8vdG2p4HDe30XFF3L1gv64x5I48iQ4efRxmLrpWl/hL1T5X25M14qRc7AdQqf6h/6G8T6CKUe+RRpQf1rZR+KNudGsxapmhJVODhqlYg0LMKlvbOWoqvRMoC8VEGv6dXJq93M6VThwPIZLGPycoI+bPNKjX/fez3FnsdlCwZNLZ0K5IXuP/nmdl5PsOo894C4DMAPM/M3tjh9x0AXhMTSd4XwA0A/G1S7tYYrJx/wxBXeTqGRZCradUhXSRvD+DOJT/JLzGzvzhIRYLOAfAAkj8O4OMAnmpmbw55bg/gH9z/SwB8wfj7iQB+38z+ce2myVGZwv2vlL0b4cZ8sUxzPYzUo1Xi03TMQgi+pnmh/mpBocuTuqii4g/KMMqxGHxXPDOZQ72pa68B7VopNydIluvFulHnsat2Zv+rtSaFx5zWB5fI26bvEuMway0Kv9I+ylOmBfvjelV9GeBEEFMKeHJRddaZ7CfWRVU+8M52DFY8MuuhCr6bABxn5fQWOm4YraD9pq5D0TpWl5nZ/9/el0dZc1X1/nbd734EZUpMhDDIHCeegiY8FCEkgvICJk+XECZJAM0zDIIuJhcIGsgixscTFVTyMBJE0CSPYAgBDBhAhkDCmAGIMQwGokwBDEjId+9+f5xzqvbZZ+9T1d11u7/urr3WXbfq1Bl2Vd/ev9rjObw6DfMCwH2j5eg8IroPM1+h+xHRExCc7Ueq9kMR9pg/Ibo0LHoYMz8PwEVi3MsBPG/QXWBY9NcfAjgewFXosi0ZQC+oENE7AdzBuPSCuPZBCDXFjkBQu+4R1by+ee8I4FEIUWl9fU8CcBIAzG8tLGXGW7z0a9iTIfNLtG3Ij4sMcXFHrVbC6huG4DUEsdunBno1EAQ6odwDor19tJDv0UoKE1fylxh9i2M5Tmokafn0B5COfEaXZ+JqJOlggPZBnTZRhvPGWbgEL08zaBSv7XNiIyzYMV/1ZbRb/WRfa8fDGXV5K9acQzLjAbRmKi/XxNIwJCAAnTaiwc8KJ9ZrW8+lMdpH9acAQ0Fl+HQhf+RiBP9IBipE9FAE+XpksgLF9tsAeCuAFzDzJXpOIjoZwFMB3IOIpCXq1gDevxb+hmgq/xNhp8c1l7tnZldtijfxpggiHyaiJYCDAXxFdPsi8qCAO8e2+yGogNdELeX7iOgaZr6XwcMZAM4AgFve4S4sBZo0Q7lv0lqgaiHuCFxL+FrmsRaAohDJtKU+sniQxSzF/ZVjOgns+oH6AE/22SigyO/K/VlgwOqeuCn7UPa3Mx5uxq94gC1IrE0yJCHcRnlp8Ik8udqNuKaBBOpcF4zs00wkeVpJKfwVmwPNb1KYEzFmRmivPB8CFFZ/dz7t9xE/vD3NIkuYHB1MIo2h9BDRIQBujoByS8SMd9XnfgBeDeDh0p1ARHsBnAfgdcx8rrPEGxDMZS8DIAOi/pOZv74WXoeAyrUITqGx91B5M4CjAFxMRIch2Pl09eNLAdybiO6OACaPAfA4Zr4SQgMiohstQLFIAkM1Wkmdm9FUet7aOGeNJItbkFH9auMLp7b4tiK0igRDQ/ia86i2fL4OSNwcEwE+GQD0rN32k74Q+VKwRBdmrO4xqxSczc/qHuRFYx7DPGYKayqFu56jcfJJqnOjAyQLlLItgmGXiNdrt22avwgmRGiFv9VPt2dzOBLUzUNR5rFEWqtZK7B4Zq5GAW9aq8b7hmmcaQ8FcFb0qzQAzmbmC4joFACXMfP5CD7mWwE4J75sf4GZj0XIkn8wgB8gohPjfCcy88dbFpm/CeCbAB67UUaHgMp3AHw8ViZugYWZf2uDa58J4EwiugJh3+MTmJmjaes1zHwMM+8joqcDeAdCFsKZEVDGJUewDfEbZELXe7M3+qzJX+PwaAKKJi2ULd6MsYPmFd+9monj4zGPNSMOAJWAEr/U97CM+3weT5D748WahhD2fCZ6veyRSCHpFG709iipOd8tMGmvKQko52Umc6te3W/IDosymiuZuGRbApQ+p71u0+sHbaRpzVyaTyusOPQbqWAXY6zor08iWGh0+4vEsWkZYubXIyQwbgoNAZXz42dUYubvIYa8qfYvAThGnF+IkMlfm+tWgxf2BPoQzaOmgei5Pc3F2ghMkakdcEUz8Xj2BL8jwKtAJufz+quxpc+KC/AtAFdGaXnP23jL97QE817acxusWrOSUz9LglerKRhaSCHMGz3emV9QcsR3fcR8hqagTWLZ+gJs9KZYlvlMztfnL0lz6vupRWK1EWDKPyK1EzlGj7Uc/5a/RAKKBZrheoN5E1zGC6bWTzQKrUgB2l9pSEjxWZvByGZRzZyzHrAposb0GEO4uoBTE/oVAW5qSzXNRB33Ov8tYeeNkZqYRECdX1K7xwQoplYnBLPSPtxorqJvHUzcdrlOOhY7WmoyNRN5X1EL0tqJLI+SIsMk6VyQRpm8OlOYvdkVUJZSGQIolgmrPVdJi5aGkbQR05TlhPjKc52rYjn+uzFlXov0FZVrdP1HBRSY/z47mmr7qZzNzI8mosthYG3M2txeJIVZH5goqglmdtpdHsScrdx1BWAOGsXbfzY2DpSaieZJzN2nVaU5pQWqeA56Pg0mQBeBZVUKaPnO20veDYFPjmZSvS8u7rHqN1FC1+LZ28+knKM7L6YTgrvmUC99JeVWwbL/rMmLMOrQYEtbGGLiAjpfRM3XkoXzVsBER3ulNsvctWTha0ECnLqZS/NsgYgVoDAKTaDS0jPj9yM3g5FNo6FgUrwhG+MyzaBcqqZ5JKFZorXdz2w3+KLYsdWcNH/WfTFygQ/kQtN/Gbe1HWm+sl7ThgBK5TqghHwfoEjNRHY1gCo53U2zZDIRmWYnY17kbRyFK8Puq/0gnsnJW6Mv6ssDFCAHEgkwBWAYJi69vpxXAkrfplg6fFj2aYHG0DL2RLOVzi/RgNXNF+51ZvVtx4znU5lAJRIzXx+/P7957GwOrdlUpd/SZR8V/tun9bRv/ZW3aQ1apsYBwDJxeeY9Nyor3UPbJrQcjwpgikwOcYjrzHaPP1PgG2smEMj6qv/iQrvh7Nh0xlO5ns50J9Wm18tAAp0WYv4pFDjV/CXdjox5X23iGpr5rv0TfT4T97phHmvDgqOGqAGlBAg7n8TKM6nlmOh7SsCjTVs5Px0oacf9esl7r9rJVDN//ScqGMvMt1kJRysmlm+hNcHbBzZwrleApNVOKsAjASQdEyN38DNK4azns4S7cV/hO58j49fhsx2XOmkQqDyPeikUm0zNRPorsrnEH9nSINS6rXYS10kaSU0zMaO4KmRiLZWJi552kkeClRqB7NcHJnq+pYjqykCJuDAJSZ690OBEGaAgF+C1Wl5tm+N8T2VUaiHAnQnN9pVoMNE0ZnjxBCqRmPnWAEBELwFwPUJ6PwF4PELM9Pakvjd4T8gnqSCEtllN2CGd0KhDbNN1K/S2SGCM1XZruR69zve23dZM9H2bmkTiwdK8aoBRaFUDtBLrZaA99jUTN7kwXTPWTEBibc3rmrkc7cTiQc5Vc4YX81H5Bi779G3ZW4vkSuHCSxhv/cj9PDPhbB8S4uv1S8eWVpBnzuf1uGTSouQzzZf6azNXDUga4iw3Z1SaQKWgY2O55UR/QUSfQCg0tnNI/K6ziK5EqgAkpTGORuBGkgkBXfhLahqT97rTA4rF+mmueJN9Wr5ZskYJ40EOeHGNNR/pWnyWZik3+Yzavp461i/wU3vVAS+EcQEmchzn/Wukt+oFfI0jfMPsb+WYyDBi1yTm8Jg0FZ0Bn8YkIZ78HjqvRM9d27K3avIqBH/THmsw8sODgXmzwEL4YWThSstsBgALkMj4H3Fj+QlUCvo2ET0ewN8hPJ7HIm7gsi2pYn4qTE9eH3ndE8qWdqPHCUFZCHdlXjEByNIuNM+tNkFKWzB+6RafVn9XG1Fr1cDWMGFldbmq/pJ0L+KBCODIKvtabR4WmYCSn+t503nxKMhOQGQOob6yX64JlWP0tr2A7y/RfeXctXDhITssak0kc6ALIV+0V7QZbV7LnfxlNJeXnGmZuWaRpyaWXrAKRc4UiHma07qJ/ffBnUpDQOVxAP4kfhihuNjjVsnUKsn7vbRv5Frg0AAA0XOnb6+umOjX67fQ4w1eeudQDNRMfoWbpmJiy47lIAsQofooQViuZ2gDrXooGrUGkb6l4CeApBB29njxkhY1oLT9VB9v/3Q538zwxWiNSBZvtPJMPDOXVaJFJyBaYCLf4mV/d2teC2DE8/UiuORxLXFxaJFHq79FEpwkaUCxeB2FJlDpKNaZeTozH7dJ/KyWlEA2AUaZe7ICjbI9Cl/zeg18ejQht8CjMW+p3ShexD9Hr5lLgInrrxlyj96zVfNl+SU6Giy7B6AazZWaEnhYAsHSTCqagW53wT5e8nJMahFXdV9N13+WRU/loCOpFrnlJiRGX8kMXJRg6aKmVJVgYw6z3egvAUJrOxIgamY07bQHOo1Et1tjZuD69RWoFWNa0rYDVUGFmRdE9HObxcymEAkZ5gjvTCDWAEUL+x7toTB91YS/I8D7AgvatRq2ExaNMbmPxxDgrfTU89iCt6aZ2MmK8bjJ+9Z47vWZaE2o0B5KbSIBEy/JXS+BiMx619qBzGQ350gCtLIbY41McxhVHPiGkK4FBPgCv9Qaag749C21Iat0fQr3zZ5FRXsBbOe7NxcA01eiwaSBrD023kZdK8Cp/ZqGmL8+RkTnAzgHwpfCzG9aGVcrIkbnmyiiumADQTFHK7RFP28OoFpJ2K2lVQMk2e6AU+H3kWPUDzy/Z7bBTANK6qcA15xL9OnNfs/mMzQlCOE3oN3ThqqRXGwL3OJRpmftaBvZnMb5IE3D8JvUgMe7ZoUFW+Nq2oWcy4vusnwt+l48IBni5C/WFEBh7daoo7lqgFLw4u5htUZiTOYvgw4A8DUAR4s2BrDtQAVALoyhwKFCFgi55jQDWMIBfK1EzVuaoxzGVL6Ka+ZKcwL2NsFaI6tpJhpY0zXlXCeK0/aBiZ5ftddMURmYpENrTsc0ZYOR6GespefRPhDA3krXSmzUffI927vrpqA2QEsL56QhzJRA1c50PZ/lSK+FClvffbssWkBSmtGWg/JLNEglM5f2p7Rmxfb+vM27RkSCCVRyYuYnbQYjm0bUI6QjVf0anmajBbOzdqa5GPO7mobsB3RAEk1dfQ741DdjQtblSvx7YCLmzExlbWQVF2MLQCk0IeM/bqDAL/rrXR0Lp3XujyhCg9X8FM+lqcuep/SB1HwrllNfCsuZI9y0z0M68RMVmfFO9FefMz1rRxnBVYQFx+KOcoycUwt2WXbFWn8tIb3aZyLHy4iuebNsKxA3WGYmrnyO8Zwg8t9kt9CQ7YTvDODPADwwNv0zgGcy83WrZGwl1Cek+8b1jA/7e1h98l9VBmqe+chaz/iFmgDkgVIa24jvBCSMIkihWkolA5pcOzHnaPnPuM/mD+Pt/8C+sGAyKgxQK+C7flb5+dp2vem4zFXJBZHur9cFckAhKvMsvP6JpDOdiM1tfHXm+0yH5CpwkBnxtfBea46hTnu9FlCWXRmSsBj6hn5SM7GiuDQP8/jc5rRorzeQc3TjLMDdCNFyd6HKEG/UXyPsp3LH+HlLbNuW1Ap0R1thEuBg9GXqPpkZzTU7CaGggaRHq8mOE6AwuZqMBLXSv2H8sKVm0qi+AwCF5HMy70HMUfTxTUDymVPDWWRX0kbabyq1IQkCUitpNRNHO5GAIhMGNaA0xToagErtRAJEEvQapHR/SQ0xZs2y0DpqvgjZV/s8ZHRXOrYApe9b89gdL7MPEICkQZ7PosfMJFg6YGEBilyniXOkeVogEcAhtREJNLOo2czWoClViQd+dhAN8akcwswSRF5LRM9aFUMrJy2ITW0AthNagoj2xWgzDwwQSceMMlPd0DQy8xLlpVks01vEHNGutIRifqevuNauWTFxAUqwW2uF2Yo1pMNb9jf9KEobqu2yWMxhzJ2xaWgmci6tlcyMdTWZkVjIv2thx3qOYq6oXXhO+77ILMlHtY8xl19pOPdteNsCe5qJ5kuawW7R7MMi+YiM60CnkSTSvhMJKAlM9D2MBiiRRlR6tgUNAZWvEdETALwxnj8WwXG/c8gxa5nRYKTAggGZ7OeGGwuq7uBYA5QK7yVI5ZpGe+yZpwSI5Lxx9y1AtRXcltPfOnaiudquQhuRWgLiW39t7/duDog92wW/BpgwjLUqZPkvLCDT5hMNJkN2WZTtXshwLRnQa9fOfrtgY3ncajJM2X7uVjl5c57CJ9MBSl/VYDmXLKOi12z7MJlO+Np9AiqQYWzVYZeByhDz15MBPBrAv8fPrwJ40kYXJqJnENGniehKIjrd6fNwIvoMEV1DRM8X7UREpxLR1UT0KSL6rbWs3ZqwHDNXZt5Sgrhw8hs7/7UCXmozYu5snuKT/wK54Rx85FyN4BVxLHHgSfZN96kBwMl8b7UTqXWIb2q4uy41jKIGWNTtqeNHmoxI8ZlFV6nn3BZ5VI7wZGpqopks85kwWvNZCguWgrLcYTGMn4mPNB1J/sJ6Yn35phvNVBJQdFZ9Mmel+bSpSpvV2mvotBM9To63TF5AZ4LaE808tU82bxwjaU+zwJ5mgUbN1QKPASYJSObNIjNztZ/YJ8zLmNMSMzDmtGgjurpPt+aMQp85LdBgmWkmc1q0Gko6bv8WtAwmr7j+nBYr0VT6PjuJhkR/fR7AsWMuSkRHATgOwE8y801E9INGnxmAVwF4GIDrAFxKROcz81UATgRwFwA/wsxLa7xHfX4N78W1GCfAhJXztz3mvK2mvWTkaSZqPj2m1EK84/ItvtRM0Nl72zd9Y37i/E2sBZOS75oTvj1u8jd55o4nKdTNopNKI2nPBUdZLSxhKgnb8nZ81DQTTyuR/S3zlgafNKfWVixnsbVdrxcS3OdIl/dX00z0OG26sqjdidFwxAP9FYMzgb/GPJMlmgAoJhgLPwwtseAmM3+ldut4w7TDQKOPtir662QApzHzTQDAzF82+twfwDXMfG3k4+8QgOiqOP5xzCFDyRnvkxLuVr5INRIr/mizfA8lwE3TGcPWDcU/QV+eSdtfRjq54FHOL8Gn12RGQiiSf6/deaklMFOMzMrHSfOUrsVFhPb+mqbMWHd9JrHNiuayhLmcTwv3Wmiw1U+Sl2OSgY4CEmuPkjSXd68aFOS8FiANARDPB6L3J5HlVKys9m7PE3+XxXy+fI45LdooL12DLJ8rHz/DEgs0blhwAgsLULKilGMhAWPXlWnZquivwwA8iIg+RETvIaIjjD53AvBv4vy62AYA9wRwPBFdRkRvI6J7ewsR0Umx32WLb3+7MwVpM1Q7QE+QPkFPTeYozyTVvh1bv0klPDmZkSB4KW4AJUjVAEXx4c1VmMySkBcmHVszQffmVdO4pOCXeCTf/hOAc+cL6TqWJVBaM1cj5qAAXrItmbooHjfKzNRGYUUTlhTOltkprQ1U/BbChGM53z1ASZT2KEkmIxklZUVxWY70dlMsNb7gtQJQ3h7tlpkr3bfmbdlWBO6iuSwTl5xvhu7TRWPl+9XPG50IWQKSBJTONKbCibOcoCXmzb4CUBbVH/dwEqJjMn8JWlf0FxG9E8AdjEsviOseBOABAI4AcDYR3YO5EC0e3QLAd5n5cCL6FQBnAniQ1ZGZzwBwBgAccKe7MABzO2Fzwy2lEbiRYmktT7uxtBxvnNae5DgmtLklpsZT8uyauQQvhe8iUdLEpJbR3rP6UzmmJ8v0lUxcxZpCsLcsWDssKq1Hs8FqjjSnZTLTlX0TkOl9T7QprOXPMHOl/sW9GG2yvZyzX8PQe5FYvhBrXCKZtGhlzfdVDNY+E0m60KM1X2HmU5pL6ci3NRsgaCnaX5JIAspMaFv5eO7aHfPeumiwWNsZtLLoL2Z+qHeNiE4G8KYIIh8moiWAgwF8RXT7IoLfJNGdYxsQtJZUJuY8DNWc0hs6q/N4nPfl9rsQ+oZwMs1dbX//RzUw+EjxpNv1dS54yDLp0xhG7sPQgGTdjwUmXArKFkyM6LCiyq7QShIls1fbRd17BlKqTxIseh95SQlM5LjO15IDRU07kf10375ornIeG2y89fUmWX3jLHDSpq7O16Kc8msMDe4DkyTAZ2Ie7151FFciberSgKKTOfNSNZFfV5sbMat+d2HKIFB5MoJP5Y8RRMUHsPHorzcDOArAxUR0GIC9AL6q+lwK4N5EdHcEMHkMun1c0vjPAjgSwNVrWr2mDSC/1jYZ4OOFHFvzmevpsbKPBSC9AJg3uyazNEwkEGZ9PIA0woJJ9SfDB+I7+nNhKgEgm8MAEs2a1jQ6U1e4roWVVWXYC/PVGfC1sGDJi+WAlyRrc/VqL1HgF6XoC+FdB5SGLO2hE+yNuCcvWEA7yWfKP2HlmKRxSYgvQIVfQ5JVgj5FcEkgkXNYYcFzWmDBTaGdSBNXH8BsiBiTo17TKqK/EMxVZxLRFQC+B+AEZmYiuiOA1zDzMcy8j4ieDuAdAGYAzmTmK+P40wD8LRH9NoAbAfz64JXJAIP0hh5Jai+WJjMUTIaASL5eKYiHrGNRbU674KLHnw1YmcDn/Hor4FUkl6UJ6YiqQuPR60KdG3MlTUNqB9bmVYmkiU2PAeoZ8EPNXJZwTsdWWLAeo9u9rHRzPamBmE57v6hi+7bvaDFdLa1SM9F8ZdnvxnpzWrQRXNb9W0ACdNFc6ZoEkXRd+0zkbpCaN29Tr/XSbnPUD4n+Ogsh2usb8fxAAC9n5ievd1Fm/h6AJxjtXwJwjDi/EMCFRr9vAHjEutaW5qtM4Kp8EPl7Nv0RzgJ9uq4HTk6f3JdhzF3TcuJ1+WYtQ3TNNbJL9r1o7STzmbRaT+qrxiqTjbsxlprXelTabOWZoaztevuis1Jf7XjXc3tgwmwUbFxjJJe1XrY3SWz3NtGSx3uUeamvWnDWN7bdYrYvSy4s+w8LDS6up/tw4oZkJJflMwm+oFxjscBB8jFD0LCSxiT7zmkfFoNimIbRGKBCRAcAeC+CP3kPgHOZ+cWqz+8gvGDvQ3AlPDkqBSCiEwC8MHZ9KTOftXGubBpi/vqJBCgAwMw3ENH9VsXQykkJ3povJMt8rwGKmEspPUV/11Eu3/ZNUPEBpc/5DqD1mZB7L5GBApB8kLRMXN1aJc9SoDPDdsKrdUmMlT6Q0hHfHVuaQg14iHSYarnDYg1ItD+o6NMThZVATa8jhbosP285311gMXwg1hift044LxKPGT9LEe1VaiaD9zAx8ku0430pNMcFN8VznTf7Yr9Oa+nWzkEmZeenPnPal89VMc2tiRhjOepvAnA0M99IRHMA7yOitzHzJaLPxwAczszfiX7r0xGiZA8C8GIAh0eOPhJz/m4YgzFNQ0ClIaIDEwORwSHj9k8iI7FQC1p0gtqP6GIzMqzmeM9Ax9IW0kcCDERfY25zbxSUGoIus9L2zdYq12nf8lXf0t9R8mZRygkxw5WRC2NCCSYpKsta04rQsvJMUt90vVY12PKv6DkSzZrSpq95cKOwmmUBLLIsii6Psh6fiSbd14v8mot6XglIpOlIAkqfVgLYe5mk+53TIvOZpHsNWknTZb9HoW8lKWpAkHxaNcN0TosE+TGoz3gxhGJQ043xdB4/rPpcLE4vQWcN+kUAFzHz1wGAiC4C8HB0wVej0hBweDmADxLROfH8UQBOXQUzm0GFj6Qw+YiQY09jiJMUAl33zdYL4ziVJjH6hgFqHW1q0nPKezBtRAooCk3Fn0OauTKTl9ZK5FxMIBWmK/vVfCYpvyRbX31bgDJEM3E1DXD7DHRfKVgkDxK8kgCyAEX7SpKAXDK1TndJyQSWNIG9zta40lTmmdgsM5fu37eJFlAmLUoBrecr9zIRm5b1aERZBBcLkDLCgtvzZl/VxGW1J0JLWlsAACAASURBVI3FihyTbbPxMGWoo/5gIrpMnJ8R0yFailVGPgLgXgBexcwfqsz3FABvi8e1nL/RaYij/nXxZtPOj78SS6VsOyrMWIlqWoYJOsavpCbY0+TaXKSn0RqMONbgVGgepAS8zjHJ+LJ/5eRVCCjuxbiehG5KwItzJROSHQ3Guqlwqmtg0uaQTMAbWowU4pYAlkUo5bwznWinxmblXoSZSt5DpnGIEvMWKBRZ6dRFeXl+Enkf8roEw2WmUZSaiSy5MiQsWAOk5eDW/OqERU9z0vdoRXPJtrYoZSV82d6bPjnnS20lAMqITnoM1lS+ysyH1zow8wLAfYnodgDOI6L7MPMVxZohBeRwhMjYTadBZqwIItsSSKpENfOWPh7wy9DgZAhSAEDShLLdFjlnxtN2NHhp81NFEyja4zyWeSvM6YOMLjtfhBFDmbnUvFkOilqvMfIvJKCk8XJ/EcD3gWTAY+SWWGYuS/DVckx0n1lTgoJnsrLOLTDRgKqd7+G4Cwu2eNXms74cEy8Kam60F1v6tvP155l0Jq7ymWoh74UA19r1WpKH2lobJubRN+li5m8Q0cUIJqwMVIjooQgJ5kemMlgIKRkPEd3uDODdozIlaMS00W1CFMxbqcJvJpx11WIAso4CeyAkwGRQeLBs09qFmi+bi7p+ck+RrGqwBsW2jcUHOaCI8zCncD4bYGLtYyLNTUkzyZzX6nEkU1d7rPuL9VJVX7mOLgaZ+uZjuzF52ZNOO9Fg4gUmeHkmclzqk/plZVOUltIInnQmvNYGrHV1xeAwVhdx7EDC0lDkxlZ2v5z/NJ8soyLLrczUJ8xZ8qQ3yErzJue73JZY5pe0pV8Uj/I+ZHBA17/TQrS5Ta4jqxWPWv6eB3x6iIgOiRoKiOiWCIV2P6363A/AqwEcq+ohvgPALxDRgTF69xdi20po+zrcN0BaKfD8DFVfnQYQad5Kb/3aN+OBhEEpA741taksb33sR3SJb8uvYbXF9lbTMIDSir4yTVfK9CUBpdz7Pb9HbcopEgSjuUvnpci+Vo5JOpeCXIOGpZWkc8sJ72kdEljaa0ZYsLmOAgZr/r7QYM/5DgzTTCyfiewnr5fhxqUQ7+4nd7SnZzKjJWbIKQGJ3E9Fgkc6vplnbZuM5tK/I23qktFso2sqKI0H66RDAZwV/SoNgLOZ+QIiOgXAZcx8PoA/AnArAOdQ+MF/gZmPZeavE9FLEBLKAeCU5LRfBQ3JU3kGgNevKvxss6nQTiStFUyMsSWAGOtIABKgU2gmyndTOMU9UNGvPho4qOxi90cJmLqrmjsDEMG/NHFpMNFzAV0ZlfStTWJWyG8BOoapq71W0Uo80j4TDUSFn4T8jbXKvJHumowC02/uvf4I1T+71grcvOSKvGat4TngLX9JjU/pK0kJijV+5ZpeBr4EunQ9aSTy2euossRHx/tqAAUMYATzFzN/EkCRysHMLxLHbmksZj4TIel85TREU7k9wl4mH0Vg6h1rKPy4/5GlNVSEph43JCekuoa0CDlagJ6zWn4+Awhb2zBJX5NAIrsRKw1FXTP4lddIn1fAxBOYElCyvo6WNTRh0WtL/gpZaLG4L6VJWOukc78AY+fjkKYuDULa1FWYuaJglptj6X6ATDKUIOWXUvGuA2Hb3gUTGizNLHgvYbHjZdnxGQW69NN45VykxlIWhAx+kwz0LGe8WLvw17R9RhRx21darouGRH+9kIh+D8EO9yQArySiswH8FTP/66oZHJUcM5eZuxKp2PpXjh3qK5FrpXlJzOuAU+9eJl6bBSYeGIhr1nq69Lwc7znnXbaorLOVyPJtWCHJScPQxSCHlnmXTn7Nc6ZhCECx8kzScaGNGCYoa5z8TiYyKzeidMI79wnNh2/mskJ+gc75rhMF9ZqtkI/F45KZKwl1zfuSqch16Xgro7kk6Sz4bP22vdNMZlgWIcF9AQA6kGBG3CZ6jkFj4tN2oKHRX0xEaTvhfQAOBHAuEV3EzM9dJYOjkvzjJsFYEdJFQqOnKeg2uYZqkyDCUH6YNMzzvSStpOCZy2tGX52wWKwlNDYrA70AkMbQOlRfbbrSWfTaAe+tranINRH9C/ONKsKYqiJbfds3WJGMKPt5EVlaQNtl5H1gaefLnPZ+Haq+Gl5eKRUA7eZXabztKzHyUNT97Mk0CMMZHs1cCVCk30TzG8bq57VsNZIyHyXXhEzNxAhJ1tca2M9s71gZ9cDo0V/7Ow3xqTwTwBMRqgi/BsBzmPlmImoA/AuA7QMqUaBX81EsMxeAdh+TAcLbzF7vmxMVMMn4LLUMIgYvyTRpFX4UQhb9lE2lzExp86xUhj7LrocBMno+8v0Wnk9DazHybdfLWE/8WkRKk9A8WPOlMQCKqsAWX63G4TnNLU1IzWmBhJn3YWhkci75ll36Y/yExXCvi4IvGcXlbderBbwu8ChJaktLp6BjeX+d9mOBiVynNGfZWpHkSwYYBFD0t0teMzEm85dBByEkPH5eNsa94R+5GrZWR2tywluaAqnztr0Egnzjr1LLKetw9WhCjgM+hf7WBL6eS4YF6/5ZuHLSGtroM9EPuUlLzhXacmc6YNvXPc1EajFtVVvH+S77WuYtK1mxHQvDyV8BIenzkDW5auBjzdWX0T408msuqywrDWmPKJaYtBIZRdVk5qcSUDLnO5aZw9vK+0jk7f/eGIJ9WEZ8DhaJDw0mOpIrgVuuteRz7a3UCNsohX+z3YUqQ3wqL65c+9S47GwiSeHfJ9jTcc1PYs2Xrqt8D7dasLVmmMC8rs1ZlnkrrWfxbQOJDVxFfzjthvnMcoh761vmMKATCrOmLL5Y9DX8JTJSy6oWLEFBayGW1gHkG1wBuVZTNX0JraTmv7BDj3NzjbT962iuboz2QehM+lIz8XgP4+PcRjkVy8w0tIyKBhFrr/m0nrxmaSft74WWromrC3POkyNHjwJbQVDZ/ky7Mk/FdL73aglw/BkoNRxNTVn11s0xac/TYuwKdnNs20+ecOEvsRMNldah1pWWvho4pPuU5olMgBtb9Wo+2r7I5x5SWVfO42klXmFIqY1Y/g5vLT1Ob6jljfXMLDIyyrtfIGkTpaDuorBszSTwqWppFYUYc+d7kWtCdha8FspL7jQKWWo+8Zf4kuViNKDIqLKaiSutr6PSPDCRtJcWLUiPGf01aSo7nNL/cCtTGgaW5Ah2dL4IXRdLAIzpyBdzFEDiaBQ1TciP2OLC12HNofcxkQ5zObYR2+zKddvH5QCAVfYkma3ycOK8j5XcqIGk5dl5Hp7GYpmpTM1FgIAFFjoqy+rjaTOenyWc64RA24ymy6h0cy9MU40EFKBexl36TDwTXm3vdwtMarW3mtimd4tMfTwgkZRyXKwNudK9pG/9zJLPxNOEtMlvw8SYfCq7itIPLpVHaV/DkWslllZRA5SkYMhrqaNTqj7OZrTVzFoJMDrBnu03r8dDC/9y3sI/YsyRzmuVgq29T8waW6hpTvncFh8eaVPXTAhwKwRYR3K186Bs15qIt/4Qn0lryot9rfmSYJQO5T3ibd8CA3/v9TxhMcxD7do6gsr0LQmQDvzJ/rZAtoo/Wj4TLwhAa0QygTK0l+CgAcNa00veHM8ENn7tr/2dthRUYrb+0wAsALzVCk8moocD+BMAM4Sthk+L7T+PUJagQdhn4ERmvqZ/UUNAZw53eazABk6+ipjDKqDY9iuupYlLnmqaScG3WINkmLLqX/hNHD+K5S+p+T+WRr6KlQFvURony6l4pjHLV6Iz2719TORcFoDUIqwsLSTlltT2jK8BSQKdkg8/LFj3yf0mZWhwIaANASrL8eebWumxnVCf0RJQGkINMAA7ussDEnmtFhY8p32ZiUvep2fiykOOOy1N3+eoNJm/NoeI6CgAxwH4SWa+iYh+0OgzA/AqhOJp1yFk9p8fqyb/BYDjmPlTRPRUhK0yT6wv6nzrY0lcueZoEMV1b00FKJ4pywMSU1ArzSC7JDUCA1BI9asBCiDebAsAsddPJVekmcuKEJtp0xxyYaz9OfpNvSxBYgl6w6ylxgzZphewMt618M/nsopBSip3RSyFtdZKvKglWU7FKqOSzEAeoKzF1CV9N9n9iLbgX8m3681Coh0w8TSTZOJKWlyuZfnRYxpQWsd+PB9tO2HGtEf9JtLJAE5L5ZlVVc1E9wdwDTNfCwBE9HcIQHQVgjS+Tex3WwBfGrSqBywSIDzNRAn3QiA62kVxrHMsGpEhnnJXMBBMDK1kSMJim9ku+mUCXwFAzalu55t0xxIISJmF9Ny10N4EYFaOR+vDIQaJt2+5EVYh3I0S+1UNRl+rajR5qG6fb0UKRj23l9leOt+7fmm+uXGPmrTG0LePSd9+JZbGon0Ysk5Xxosyc6VkSQ0oSwWEnnbSp5mEefYVfecYL/lx0lQ2jw4D8CAiOhXAdwE8m5kvVX2sHcv+ezz+dQAXEtF/AfgWgAdYixDRSQBOAoDZQbeLjRDgobQN7+/vaCVmRnoBJixObC1EfjPIBBQLTDwfQ9VM5ozr85vU++ZRX16/vrpcNdKJjNrUxUyZiPIAoWbqsvpq/0lfkUfLAS9rc8k+iWTCYm0uHc2l+c6inCraice757uw+NahySU/tunJNHc5Icl6D/kuSTEHJampeVpJmL/UTLLnsQqv+u7ClNWCChG9E8AdjEsviGsfhAAGRwA4m4jusYZilb8N4Bhm/hARPQfA/0EAmozilpxnAMAt7noXzrRaYwfH9v9DggRQFeztdVNT4aJ/5tMQWlHg1zJ/Gesp8vwl3fXczOWZuGo+jQz4mLLQ4EbwXMt+79sGuBbhlWeOl2/gaU0rGmvfsun8HwNCg+WxZbaqAYQFenvIeIsutC1fc5IaQYNyR8dsjBDe2rSUzDpDwCTRvOnKyHslXaw8E+s+0rUhYcH6fmTWez637T+Z06L1uWhtR86zsgrFkWi5u+xfKwWVWilmIjoZwJsiiHyYiJYADgbwFdHtiwDuIs7vDOCLRHQIgi8m7dH89wDePpgx4y2+MHP1AYoab7eVAr4Q+vqaWMszrXk+Do8nHRZctBcAUweTvK1cvhXu6PpYWswQ0kJeCp/CSS/66iTE1rFumKEy57kDcF6bL9gV3waY6Ciq2jqaUu0u/Ra+VKAhASU8vy4DfRHzR2TxzFokV5+pS5IZrivOc3OW1K76c0wWymQl82i0iasRACa1E/3sEz+yOvIoxJiSHzeR3gzgKAAXE9FhAPYi1BeTdCmAexPR3REA5jEAHgfgBgC3JaLDmPlqBEf+gOx+Ns1Ypt+kUWYay0xGUJLaMCk5+S2m2Yq0P0StjRJQfL9LCSSdhiGjqLr6Xp6WoBMX22PiApCAHEyKe1FzWj4aOX8+nxpjzLGWaC7ZR/te9Fjp2K/tPQLk+8un/hpI5Px9Nbms9UpnfidEQyTYAF+KjuiS87UCuA4kntlJRpQt0GCv2DQrrVnyIUARy5CMiLLul/aBFJqL4ey3TFy6pL4cP9aWuASekh83kc4EcCYRXQHgewBOiNWQ74gQOnwMM+8joqcjbH05A3AmM18JAET0GwD+X9RwbgDw5N4VFQhU63x5JMGk1ldqIpy3+1FeyLUkpiJp0S2nAkPjQNnX2/9dz5NrJeXteYCShJ38P1qrmas9NsDEywuRY/tCfOWxqa04IKT3YLeCBeQ1K2lRvh33OeBlH/eeDT+FZ3oasge83k2x5VvlmeQ1vBRgUB6iXAp9G1DSd5uXA51VX/pLwhycaTczWhYajF7bM3eNmviYaAKVzSFm/h6AJxjtXwJwjDi/EMCFRr/zAJy3rrUtH0VfdJXrfLfGi2ustJXY1zUfRUAJgJP3MYHEKSVv9bfMZjrMN/Wz9j2xwCFFc+nKx54JrerEN4BAU19UVgPGkqTgtvtZ4cJ7VG5HZxIzTGbC+V7LgNe1udK1IKT1Nrc22MjkR+kn8IAka6f6Los6NLgM/120pVXW5jMp15brhedhO9T7woIliMh5tO9E+kz0/XZzsjl2VJpAZWdTu+e7EcXFGgAMLcMkS+j39jV+a6bQNsDEiuZq+9tC2/JrLJlMQLHWNLWNSmiw7LseE5f0l2gQKMYK7aIhBtgGE2ts2uHRqrElNRNTK+GuX7HfhwAEy8TVUL6fiVdYUmo5bVVfA1B0rkkY25mBZC0xXeQxzFFqIFaZF70RV3cPGiCW5dgKoMj5Pco1vjzpMYvmcsx6RR00NX4Wk0o8P9G6iDH5VHYjlbW5kEcBe34TR4NIeSeteYuRgYnsl8bnZU0kbx4ICiAw7ynnSScYEnXhpzWnezJztec9kVz6fjzTl2dqSiSjx/qSEPt8JnqMXtvKVtfzWU51qcXMG6OWlSGoJc1V7S6dBW/lVcj7kSG6c7o5K/Oe+E31sdr2HjCplcbXOSapLfN7CADL1qFlcR/WHiZ9OSbpeSSw0FpJuO6DggcoxXXjRWS9NEV/7XQiW3gW+SUNVLhvKdxrmknmL3HWs7SKmmZi+Ur6woItYW/1B+oaBWADimviotLn4kVyuT4VcWyVQ/E0DAtM5Jr+9WXR1zovBLMBuklISm3HEtSmk16ASelvsfcxsaKpdMHFvg2xWj9KRbjL/VTkdTPEuwIkel4LUGRCqKfZ7I3Z+fL3oHnRiYyuCczhbWPEk/lrp1MShlnhRddUJYBEy3lj3FoTFi1w85z4mo3c79FtiqXvU1Ny1Pflo3DM5+iq+6p5FGh4b3b6mpWwWPBoAIvloE/+DD1Gnte0jPCdCw8ZcuyBFtD5S+aGMO3W6IAwaQC6HMrSyHr38kzatZ23bMDeHKuWAW8nUebCXYJYAShKM5HbBWsaWkZF+qI885n0lzRYFH/f9nk7mfGlRrYKQEGsUjyBys6lBCIASGsilkYizVZOdeFWsKrrXsLioEx2o5QKx36ZMiUAwnrbt5zvFo/axAVIe7wtrIGyorFesxbJ1V6n3GfigoNyjOsNsTznO6AjruT63du+LKVimdUsf8nc0DjSeEtYz4UfJc2jt+tNVYObdo3+Io81XoG6ZmLeH0qgqm2IJdtn0KalzsQl1+r2Uyk1maCh2ffpRXLNjHuai5yc8n5ZgVDeZ7Q8FWDyqexoYohwXXT+EpYdxKGTB+KZkcJxuWwpeFW78/uVIDLkJ26ZoiwTWyrYmDLfW/9HvP8UIbTe7Hd5LYGB5VPxeHcd8wIYrOt7VBa/1jS8ootDTWJ9dbkkT0koWRtfaZLRS7KPVdZkaIHHWjmXejXhkofCMa80v5pmYpHOa6mVUZE8pbXldVlCRvtL3LBh8SJh5ap4z229NOWp7GQioN07pSV17gBJeyqFq3K681I22NWCi7m5vKZ9JkkjsbLepSCztaD83DItyX8+ovKNzixtL+7P3UVRCABtJtvTlAJTnuvwXn1dApMuDCmjs1JkVje+po2Ub/R9FYMlH32RXGk+yz+gzS+eVmI54Auejagt2e5pJRrMEqXExbS21AyG7GOy1mrBC0eLkdWKu/GxRIwZCVY3cc2M33q7zqgRYBOo7GwqIrmiZDed6eKwCgYIiYrCvJZd0+PldW0209eVNmCVnjfnbQW+7bBPNCMuIsOAHExmVP5TSpo1ZdmLwt9izOGZvVqNqcj/yPvpTbL8KLBlMb9lEhsCJl50mBZCNUCR+RWWLV8+F1uIhjd0WZOrmyPnMwlWyZeXAZ94qO3/rqPJ5L0t2Ponyu8t9U33n/WJPPUlacpyKnoeberSfxdp5krPJf8b5JrmhokZWOwu+9fuA5XC467e8muhuw3Ay+Tgd8YbVDOhcUXTWEuor5WoaO0/ApT/rN68yYGsC0SyOpfzZoDV2I506QPZ09ghqNocVdNodFs4rv8jywKPUquwIrlSP60Z2sUVuZ0nL2XfXW/rbzmRXEAeojynRQEuGlA0L/JcZscnoe05/72kwXQt8KPWjnPtzfw/3Xq15yHnrZEEC0vT02BSc7x3mlyp3Y2qoSQaQVMhogMAvBfALRDk9rnM/GLV58EAXgHgJwA8hpnPFddOB/AIAA2AiwA8cw3Fe9dEuxBUInkvIoxM8Ou+JM3Lrpagxos5PJ9EPt4WsvrNPZnGkvlJaxyWU11GcqWf1MzIl9FrZ/M5phmLPEe65s8a4/XzTFee2cnTYBLNm4UIXbWefX2HRTlniuayQmEtQScBpdw3xDGBUZmg1x8unJ9bWwfrNaWZzdq2VzvhC4AutJ4cULxorvKa7R/yQoM1JR76tBMNxKPROLL7JgBHM/ONRDQH8D4iehszXyL6fAHAiQCeLQcS0c8CeCAC2ADA+wAcCeDdYzCmafeBipMHop3vVsZ9OLDNSF7BRLmeFNhk9LXm08LVyjGx8kCKhEVDgEuzWDJTLSvzWAmLlq/FdbQLsJDRXOnaepMcw3En2BKP0lGvCzwmqkVyDckzkXzIaK4uiqnUCLznE/jII5bydWq+inIdXfvK8pnUHP9tJBcBsiaXXCPxkfxWWovZS4vW5CeTFvUccn0rGVFGcpUl7PPfjDZxyetNBUBSxJk2GW6IGMAIe9RHreLGeDqPH1Z9PgcAsR6i5uIAhKK9FMf+x4aZcmj3gQpQgEch1EWOSZbPIiiZm6R2UI63hWABYMhBIvVNpiNS4yRvkmoRU7Xsd9mmTXGWr0Pfm2eqck1XbZROCVh6nJWf4pm5NPjI+WUpFc/EJecLANiXuMiFWSfM2fWRlXo9QPH2MrFMeOW+JfazKIsxloBi0SBTlJFrUovmmtES4FIzcX0mjr9E9vMy32uA0vWxfGD6HsfSVDjYzEeguL36RwDcC8CrxNYfdQ6YP0hEFwO4HkE6vZKZB1R1Xx/tLlAhdNv11kxcKqpL9suTJ3V5/Hw5vWWvnEdXC/b8JsUttFpPqc1o7cSL/OoLC7bWXUv2u84jSeP9fBG7zar8G74rQFLRSqxQ4tSetJJC4DtgksBO+wlKv0vyQwgfiawpZrydyzf/NLbMOLfBRK95M88KM5e3u6Nr4uJl6y+xTFypn+UzyZ3rNa3Ir8uVzWNEa+XZ/d31uTFGUkNBiWh0FJjZe53EGOqoP5iILhPnZ8QNBrupmBcA7ktEtwNwHhHdh5mv6JuYiO4F4EcR9qMCgIuI6EHM/M+D7mGNtLtAJZEBKNrPAKAtO6+1g7CnCBlRYPaPt5YgaAFDuRGWr21YTngvm77GWw1IzHXI93sUGkecx9sgC8gz5rWJrRPUttkJ6BIcLUDp00ykVqIz5LWAT3PPm1IAyz6mliA1CW4KU5cGwNrbsszOtyoGW0UmZU2wImmRlgIIOPJI2XnOmzCvKZOfJiuZUpuwwjivSKT9O7PAJDyXLkCkNAum3wba75UBSqJhPpWvMvPhw6bjb0TN4+EAekEFwC8DuISZbwQAInobgJ8BMIHKxokLIMnMWyozPm2ZCyGo0yZVpKodF8ABDR45kNR2QkylV3R/z9cBlGYu+cbcFxzgaRNuQqMyT2mA0TwBRu0u79sI89W+Da9vd107hrWW1L1te1pJrdBjEtR6l8UwRxhjCfSc54UJJpYDfgbGTGgKei1PO9I1ubRZsM/5Pmt/S/maLcjwMivFL+9VOt6t3S11WHDio+Z8t35jVpZ8eF5cXA99ctOYBJGZeBmbjZlRP0701yEAbo6AckuEjQn/cODwLwD4DSJ6GYJoOhIhSmwltMtAJZI2dbWAku8tIqsX92khlokrux6BYeaU3m/E2vYWvaWg1/+oMiEyjFnCS0y0HPBpTh0FVnMsu/6SmubijCn720Ldi9CS5OWZJBOX5YCX86R+Vl2u7lmFPpafQmohVrFDuypvyQfQaSVJYwr7nJQbY1nl582se+E4T88qma4sf4mXY5Le9jUvkrzM+6HVgu0Clp7m0gFJnptT8jUvQC4AyQI8LqCAx4r+OhTAWdGv0gA4m5kvIKJTAFzGzOcT0REIe0wdCOCXiOgPmPnHAZwL4GgAlweG8HZmfssYTFm060CFGnvvd2rraDml54ESCByzlt6yt7sOU3uwHPe1+YFSM5H90/wNMaw8Ggk82tTUlXLJ+bcy4LWg134VqcHUxpXX7Tdwax3ZX5u5uugvez7pq7A0l3lW9sU268yEZti2GyHByeQjzWkyDLi/JH33Fq/L7OemqG4eaWaSOS4aUGZYAtQJ7noVZs/XYZgBtYmtAiRdFn3+e1groAw519pJApHQTmgwYrkuBjBC6Xtm/iSA+xntLxLHl6Lzm8g+CwD/a8NMDKQtAxUiegaApwFYAHgrMz/X6HMmgEcC+DIz30e0HwTg7wHcDcDnADyamW8Ysq7eiEtrEkkrKYS8Y+bS8wBB6/Ay1JPA12sks5prtnJARNbvqoUW64TFPWQISVWkUfMv/R96vKWNmNpKxZxhJRhK0PPABMhNXe01K2eFylIq4Vrc290RoHptqy4XEIFKvZ2ntebNPuXktn9LXuiyXE/ngOhSKtpv0e7r7owvEhpNkChDg7XzXd/7ghs3mks66LtAAKFpo3xOnmYSruX8mhWYkZu5rOvyexSayrSsnojoKADHAfhJZr6JiH7Q6fpaAK8E8DrV/nwA72Lm04jo+fH8eYPWbsoM82KTLkRhrMxe2sRV25tEUgIIvSlWk/lvSqe9Ren3KfvqDbHkGolmrWO51H608NfnxZ7vAyLVPC2jHGcLTlnc0tJias737j6SI7tMntOaSUpYDPP5AQG1bXs9gZ74lr4Rfe8SSEyBbvBQK6Ui2+X9tKVUKIU4aw2pNInJrXYtU1f2oiBMfJkvyAGT1E+CZRL6Vg261J7+ntLMlSK5irHtusCC03Uyro9p9krEU5mWTaKTAZzGzDcBADN/2erEzO8lorsZl44D8JB4fBZCZmg/qBQJiumbzevuNC0oLFV7+K4Jdy9bvthrPjNJ1YFmpmpgaVOXBSTyeu04M2GpKC4TOMS4nptGCQAAEkFJREFUtFVvGmuBQ7Gu4Xi1gKfIDVE8JZJvs1b0mN6u1zLh1aoFF0EASkPRfEheh+SYeJqJZWKzQKS71s2j15Pt2meSwESCZC37Xd6X9t2kvvJvknwmGQ+ujy064ZG0TQDMmYYine8ZiMQ+e0mautI1QhN/p0twezwKMcAj5alsF9oqUDkMwIOI6FQA3wXw7GgPHEq3Z+br4/G/A7i915GITgJwEgDsOfi2mUbhlSUJ17n4aXmZ714kl1Wby8svsfcmsTVnXYsrzbWRhMV0nHJM0jp6Dk19PpM0vuYz8eb1bPtewmIiLzs+BwUb3IFOgPcBCtA5orVW0vZVEWVzpan0FXeUtFc755WZSZO3y6IVQeaVqbfAJK2dtDJ9r+F6B3BeaHABlA6YWA74ResvkyAWfCFL5NqJdMBrf4nWThrQyI56jJJRv51oZaBCRO8EcAfj0gviugcBeACAIwCcTUT3WE+BM2Zm8hwd4foZAM4AgAPueScOvOUAke0pjy79xMoZ6e4vP7a0E8skNlMaySy+zad/vMLxrrSphhjkFKG0hHmtTx8g6E2ranNk80kfCMt1cp+Jl9yY+trZ6rmQlgCV5yrkJe+zaK9svqUpxAtNQURSedvWemCSaG6EBVtRXIlqmolMWGyEGSs9s+Sj6DL5bYe6m1vi9M/NW929DinyqO8xtCsnvgKSRHNiLDl8t3wYJi7pM5HgkAAlzNVpJvJ6fp/7V0jxdqKVgQozP9S7RkQnA3hTBJEPx1o1BwP4ysDp/4OIDmXm64noUACm+cwi6ceAEuAsmtpvAyDSb8QS7FZf7VCXfXQMfwo79t7gvf1TLO2kredVMVHVxlf7UO64t8xs+Vq+09vrp4s8ao1kj4qCSpSEt77ulVy3eMnyPpK2k96OK4BiVQvWpedbfoykwJxf2w+mr8uy8xJQ0hq10GBrPr2fiWVia3nGos3XkX1q+SW6uKNVLVifS3+JbJcaST6O1HXpQ/EBZVQwAcI/9AjRX9uJtsr89WYARwG4mIgOQyh09tU1jD8fwAkATovf/7CWxZP/oqi/BdsUpcOMZ0b0mD7Xzn+tyVg5JrKfNGdZ43WbZeZK13XioV5Hj0+AkcbUEhzb/iiv1/wm+bzW23MunLX5qu1r+CU8M1dr4xdry2RFOTZULm6yaK7WWW3sYwKgMAVZuz/2JSwGnnMzk91ePrMhJi7L+a5rcnm1yNJ9aV9J+t6rQNEz48m5gLJmXf43LdvCmvLe5ZydiUsCSqO0lkQL4UNZjaMek6aySXQmgDOJ6AoA3wNwQjRj3RHAa5j5GAAgojciOOQPJqLrALyYmf8KAUzOJqKnAPg8gEcPXplETS7iwtQlrwUe8uGWlmCVXZF9ZR9p5qqVqq+FB6d2rSFUTWFiPs/nIf+5i7DeAVpNvl4p4K0kR222scrPe0mCub+kM62ViY/5xlhpbW3uSkAiS5csW4ET2mRYsI5g0qQ1D3nfct32mPJESdkueTe1DMMBb2kmkqd0r7Xy99KElWlcyiznm7i8YIXOzJUiubwij6nPHFz4S8L1EgyGhARnQJNpMmMGFDN4Yf8OdiptCagw8/cAPMFo/xKAY8T5Y53xXwPw82temEoHvDyXYKJNUFpzyMY7wJOu60iuRGkNy+mexlobYkmfRZ/zHbA1lfaa0ia0llLMXTFrFDkhOjLK0E6k873mzLdqcsnjhkIklwUowbezLIBE8prXwDIEujTpiGPLUe2VUpGUhc4qzSDLvFdawV4jWko/Ew0meqvewnSn/k5WNFcjjFxW5nvHgwbrerXg9K1zTQqfijB3aVOX1kwAYE5G4EJrEsvNXbM2Iqxp25aV4JQ1EWNy1O8WIiW4c6d86FNzvst+sm+R1Ii0r7evOVjtct0iT2QNZAIEur1MZLSXxVfN5NUdl/uGWEUkAdsP4hV5tHaNTNd1u+UvkW/LM+NvNcTMJNdrr0PupFgCYVpTr2dd80BM3kuDZeEnsfizwoItf4c5RwVQ7PG+ZqK1Gv1CkECkrb9V+EPkb1WunfebZ2sQFsymCcsCkzBfCSbdfDOMRlNI8c4lEm/DEkQaUVfLAhLpXK+FBQNRYFP3T0qkBY8NPumaZeJKgtkbo48tf4k2qaQ59zhZ9DWzlecg1309MDCz3xXNwG20RB6Ga+1HUgKfZX4xAwQcrUSbuHQWfsujuje5pq2d2ECW81SarXQUlyYvNLg0U9n7mCy4iVpQKrSYj+vWKQM0LBNX6mdVC877oI3qykvfJ97k2lLDkPwn31VT9Ov6EzwTV+dTGb9GMQPgSVPZ2dQJZiBVH84KPYp+iZq2Llg4t/omwZ1X+c3XTqCR9qVfwtaGMq1EzaEjuiSAaK0jkdRKLODx/CzW/ie18vN95VSkv2SpbqzmhNfZ78nUJWkPLTIhpx3vYX17u14rz0MDitWe3VsloszLfM/60BJ9zndv7uSP0hWDS0AwwJtyJ3tISNzXPo/O79I9A/nb0zkmlubdp5lIk1cfmAAScGwNRBaFrIGJNXZ0Yp40ld1AXoSWFcllJUdatbk8MJFAote3/DTS8Q5IjScX/NpkNaQkihzjAZAcu0e8uZv9Co2jAx5vl0UZlmuasZT2UrztG2/iGih1EcJEXV6HAWAkQ2+763qDrKSxtILNWMfy3Vgais41se7TuxetmcxFORQPTGrVgtNYj2ftF9E7LJp7yAuNRK/l1T0L66Y+eVjwEsHMJanVMgSIyKz4IdrJgpetljKukz7QbnPU0zryDbctEdFXEKLFJB2MtYUz72808b+1NPG/tbRK/u/KzIdsZAIiejsCj330VWZ++EbW2l9oV4GKRUR02dAd1/ZHmvjfWpr431ra7vzvRFrJ7pkTTTTRRBPtTppAZaKJJppootFoApVYbHIb08T/1tLE/9bSdud/x9Gu96lMNNFEE000Hk2aykQTTTTRRKPRBCoTTTTRRBONRjseVIhoRkQfI6ILjGt3JaJ3EdEniejdRHRnce0EIvqX+Dlhc7nOeFwz/0R0XyL6IBFdGa8dv/mctzyu6/nH67chouuI6JWbx3HB43p/Pz9ERP9IRJ8ioqucbbFXThvg//T4+/kUEf0p0arqwvtERJ8josuJ6ONEdJlxnSJv18R7+Clxbb/4/92VxMw7+gPgdwC8AcAFxrVzEMruA8DRAP4mHh8E4Nr4fWA8PnAb8X8YgHvH4zsCuB7A7bYL/+L6n8Sxr9xOv594/m4AD4vHtwLwfduFfwA/C+D9AGbx80EAD9kC3j8H4ODK9WMAvA2hmNEDAHwotu83/7+78bOjNZX45vUIAK9xuvwYgH+KxxcDOC4e/yKAi5j568x8A4CLAGx6tut6+Wfmq5n5X+LxlxB2xtxQZvB6aAPPH0T00wBuD+AfV8ljjdbLPxH9GIA9zHwRADDzjcz8nRWzW9AGnj8DOABh87xbAJgD+I/VcbpuOg7A6zjQJQBuF3eC3S/+f3cr7WhQAfAKAM8F3Kp8nwDwK/H4lwHcmoh+AMCdAPyb6HddbNtsWi//LRHR/RGEw7+uiskKrYt/ImoAvBzAs1fPYpXW+/wPA/ANInpTND39EdGYtdQH07r4Z+YPIoDM9fHzDmb+1KqZNYgB/CMRfYSITjKue/+n+8v/766kHQsqRPRIAF9m5o9Uuj0bwJFE9DEARwL4IoD9ovrbGPzHt7a/AfAk5s0tlbpB/p8K4EJmvm71nNq0Qf73AHhQvH4EgHsAOHGlDCvaCP9EdC8APwrgzgjC+GgietCqeTbo55j5pwD8DwBPI6IHbwEPE62Vttr+tqoPgJchvKF8DsC/A/gOgNdX+t8KwHXx+LEAXi2uvRrAY7cL//H8NgA+CuBXt+Hz/1sAX4hjvwrgWwBO20b8PwDAe8S1XwPwqm3E/3MA/J649iIAz92K35Hg4fcBPFu1Zf+XAD4D4ND94f93N3+2nIFNuUngIbAdlQcDaOLxqQBOiccHAfgsgpPvwHh80Dbify+AdwF41lY/+/Xwr/qciC101K/z+c8QTEuHxPO/BvC0bcT/8QDeiaBxzeNv6Zc2mefvB3BrcfwBAA9XfR6B3FH/4di+X/3/7rbPjjV/eUREpxDRsfH0IQA+Q0RXIziFTwUAZv46gJcAuDR+ToltW05D+AfwaAAPBnBiDMf8OBHdd/O5LWkg//stDfz9LBBMS+8iossRhN7/3QJ2Cxr4/M9F8MFdjgCOn2Dmt2wyq7cH8D4i+gSADwN4KzO/nYh+k4h+M/a5ECGy6xqE5/tUYP/+/90NNJVpmWiiiSaaaDTadZrKRBNNNNFEq6MJVCaaaKKJJhqNJlCZaKKJJppoNJpAZaKJJppootFoApWJJppoRxERnUlEXyaiKwb2f3Qs+nklEb1h1fztdJpAZaL9iojoxvh9RyI6dwPzPIuIvm8knn4khmV/jIjuOcacYu7XxFphax13XyI6RpwfS0TPH5O3bUyvxcBaX0R0bwC/C+CBzPzjAJ61Qr52BU0hxRNtGRHRHmbep9puZOZbjTD35wAczsxfHWGu5yMUiHzpOscX9zkCTyci3N/Tx5x3p1DcauACZr5PPL8ngFchFFb9DoDfYOZPE9HpAK5mZq/o5kRrpElTmQhEdETcj+IAIvr+aAa4j9HvibHfJ4job2Lb3Yjon2L7u4joh3raX0tEf0lEHwJwOhHdncLeL5cT0UvFWndL5gsiOjEWZ3x73B/jdNHvL4jossjzH8S230Io+X8xEV0c234hrvNRIjqHiArgim//l0SezyOiA6M28CwAJ6e51JgbieiP4/rvIqJDYvu7iegVFPYBeSYR/XzUdC6P5plbiH6H13iMf58PxOf+YSK6LYBTABwfNajj4zN65YBn/6dxrmuJ6FfX8jvZ5nQGgGcw808jJKb+eWw/DMBhRPT++LefqhlvlLY6pX/67B8fAC8F8L8R3uZ+17j+4wCuRtzfArHsBYC3oNuT48kA3tzT/loAFwCYxfPzATwxHj8NwI3x+G4ArojHJyJkTt8WoST75wHcRfExQ9jD5Cfi+ecErwcDeC+A74/nzwPwIuMePwngyHh8CoBXxOPfh6o7JcYwgMfH4xchlpSJvPx5PD4AoWruYfH8dYgldGK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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# mask nodata and plot\n", - "geo_grid.test_attr.where(geo_grid.test_attr!=geo_grid.test_attr.rio.nodata).plot()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.10" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/refs/tags/0.5.2/_sources/examples/timestamp_missing_data.ipynb.txt b/refs/tags/0.5.2/_sources/examples/timestamp_missing_data.ipynb.txt deleted file mode 100644 index 3a97693b..00000000 --- a/refs/tags/0.5.2/_sources/examples/timestamp_missing_data.ipynb.txt +++ /dev/null @@ -1,398 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Example - Filling in missing data & dealing with timestamps" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import geopandas\n", - "\n", - "from geocube.api.core import make_geocube\n", - "\n", - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Load in geopackage data and add CRS" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "gdf = geopandas.read_file(\n", - " \"../../test/test_data/input/time_vector_data.geojson\",\n", - " crs=\"epsg:4326\",\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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test_attrtest_str_attrtest_time_attrgeometry
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" - ], - "text/plain": [ - " test_attr test_str_attr test_time_attr \\\n", - "0 1.3 dcf86619 5/21/2016 10:09:21 AM -05:00 \n", - "1 1.3 dcf86619 5/21/2016 10:09:21 AM -05:00 \n", - "2 1.9 dcf86619 5/21/2016 10:09:21 AM -05:00 \n", - "3 1.3 dcf86619 5/21/2016 10:09:21 AM -05:00 \n", - "4 1.3 dcf86619 5/21/2016 10:09:21 AM -05:00 \n", - "\n", - " geometry \n", - "0 POINT (-47.26681 44.21932) \n", - "1 POINT (-47.26680 44.21932) \n", - "2 POINT (-47.26681 44.21932) \n", - "3 POINT (-47.26680 44.21932) \n", - "4 POINT (-47.26679 44.21932) " - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "gdf.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "gdf.head().plot(column='test_attr')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Convert to raster with GeoCube\n", - "\n", - "See docs for [make_geocube](../geocube.rst#make-geocube)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Load into grid basic" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/snowal/miniconda/envs/geocube/lib/python3.6/site-packages/xarray/core/variable.py:151: FutureWarning: Converting timezone-aware DatetimeArray to timezone-naive ndarray with 'datetime64[ns]' dtype. In the future, this will return an ndarray with 'object' dtype where each element is a 'pandas.Timestamp' with the correct 'tz'.\n", - "\tTo accept the future behavior, pass 'dtype=object'.\n", - "\tTo keep the old behavior, pass 'dtype=\"datetime64[ns]\"'.\n", - " return np.asarray(pd.Series(values.ravel())).reshape(values.shape)\n" - ] - } - ], - "source": [ - "geo_grid = make_geocube(\n", - " vector_data=gdf,\n", - " measurements=['test_attr', \"test_time_attr\"],\n", - " datetime_measurements=[\"test_time_attr\"],\n", - " resolution=(-0.1, 0.00001),\n", - " group_by=\"test_time_attr\",\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "\n", - "Dimensions: (test_time_attr: 2, x: 12, y: 11)\n", - "Coordinates:\n", - " * y (y) float64 45.25 45.15 45.05 44.95 ... 44.45 44.35 44.25\n", - " * x (x) float64 -47.27 -47.27 -47.27 ... -47.27 -47.27 -47.27\n", - " * test_time_attr (test_time_attr) datetime64[ns] 2016-05-21T15:09:21 2016-05-22T15:09:22\n", - " spatial_ref int64 0\n", - "Data variables:\n", - " test_attr (test_time_attr, y, x) float64 nan nan nan ... nan nan nan\n", - "Attributes:\n", - " grid_mapping: spatial_ref" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "geo_grid" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# mask nodata and plot\n", - "grid_time_1 = geo_grid.isel(test_time_attr=0)\n", - "grid_time_1.test_attr.where(grid_time_1.test_attr!=grid_time_1.test_attr.rio.nodata).plot()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Load into UTM grid followed by interpolation for missing values\n", - "\n", - "See docs for [make_geocube](../geocube.rst#make-geocube)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/snowal/miniconda/envs/geocube/lib/python3.6/site-packages/xarray/core/variable.py:151: FutureWarning: Converting timezone-aware DatetimeArray to timezone-naive ndarray with 'datetime64[ns]' dtype. In the future, this will return an ndarray with 'object' dtype where each element is a 'pandas.Timestamp' with the correct 'tz'.\n", - "\tTo accept the future behavior, pass 'dtype=object'.\n", - "\tTo keep the old behavior, pass 'dtype=\"datetime64[ns]\"'.\n", - " return np.asarray(pd.Series(values.ravel())).reshape(values.shape)\n" - ] - } - ], - "source": [ - "geo_grid_interp = make_geocube(\n", - " vector_data=gdf,\n", - " measurements=['test_attr', \"test_time_attr\"],\n", - " datetime_measurements=[\"test_time_attr\"],\n", - " resolution=(-0.1, 0.00001),\n", - " group_by=\"test_time_attr\",\n", - " interpolate_na_method='nearest'\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "\n", - "Dimensions: (test_time_attr: 2, x: 12, y: 11)\n", - "Coordinates:\n", - " * y (y) float64 45.25 45.15 45.05 44.95 ... 44.45 44.35 44.25\n", - " * x (x) float64 -47.27 -47.27 -47.27 ... -47.27 -47.27 -47.27\n", - " * test_time_attr (test_time_attr) datetime64[ns] 2016-05-21T15:09:21 2016-05-22T15:09:22\n", - " spatial_ref int64 0\n", - "Data variables:\n", - " test_attr (test_time_attr, y, x) float64 1.3 1.3 1.3 ... 1.3 1.3 1.3\n", - "Attributes:\n", - " grid_mapping: spatial_ref\n", - " creation_date: 2019-12-12 19:57:17.046150" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "geo_grid_interp" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "geo_grid_interp.test_attr.isel(test_time_attr=0).plot()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.10" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/refs/tags/0.5.2/_sources/examples/vectorize.ipynb.txt b/refs/tags/0.5.2/_sources/examples/vectorize.ipynb.txt deleted file mode 100644 index 331ba07a..00000000 --- a/refs/tags/0.5.2/_sources/examples/vectorize.ipynb.txt +++ /dev/null @@ -1,132 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Example - Raster DataArray to GeoDataFrame (vectorize)" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import geopandas\n", - "\n", - "from geocube.api.core import make_geocube\n", - "from geocube.vector import vectorize\n", - "\n", - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Generate Raster DataArray" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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DpKQk6XhBQQEOHDiANm3alGusREREZBmspkcnOjoa8fHxqF27Npo0aYKUlBTMnz8fAwcOBPBoyCo2NhYzZsxA/fr1Ub9+fcyYMQPOzs7o1auXmaMnIiLSnzGGnmy1R8dqEp2FCxfi448/RkxMDLKysuDn54ehQ4di8uTJ0jnjx4/HgwcPEBMTg+zsbLRu3Rp79+412RokFZGmUu+ySrRVrzHGeYbStm1rLSEvi6WVZWsbj6XFrS/VMu/yLO3W9rM0laHr2x6VH2NUTbHqysK5uroiMTERiYmJGs+RyWSIi4tDXFxcucVFRERkasp/N0PbsEVWM0eHiIiISFdW06NDRERkqxRGqLoy9HprxUSHiIjIwikEjLB6uXFisTYcuiIiIqIKiz06ZDDVKid9KrD0+Rx921ClT6VPRazUotLpU0FlaLWStlVRpqZNHMXv1dgVZ+aqYLNUnIysPyY6REREFk4JGRTQvJyRtm3YIg5dERERUYXFHh0iIiILpxSPNkPbsEVMdIiIiCycwghDV4Zeb604dEVEREQVlkwIG138QoPc3Fy4u7sjJycHbm5uRmnz5S5zdL7m4A/jjPLZpmKMqilNbWmq4jI2Y6y3ZY4qrIqyLlRZNH1dLf3ey6pWUq0cspTqKlthyqotU/zOKK39I2d94eJqWN/E3Twl2jTJMFmslopDV0RERBZOKWRQCgOrrgy83lox0SEiIrJwnKOjP87RISIiogqLPTpEREQWTgE7KAzsm1AYKRZrw0SHiIjIwgkjzNERNjpHh0NXREREVGGxR0eDt4Ino5K9XK+yRH3KyfVtw+mn0wZ/VnnRtlTc0NJ1bcvVy/ocY5SeG8rSS6n1EW7fQ7sTZaX/DaZPWXZZ/w+zzNs2mPL7XKTIN1nbqjgZWX9MdIiIiCycQthBIQyco2OjT83j0BURERFVWOzRISIisnBKyKA0sG9CCdvs0mGiQ0REZOE4R0d/HLoiIiKiCouLehZjKYt6asuaqq5MydCFQMtzgU9TVlOVFYM5qrhKxKOhmipJsUn9Og3VWZXq1TFGWEQGUa3kK69FPbf+tz6quNob1Na9PAXeaP4nF/UkIiIiy/Jojo6Bi3ra6NAVEx0iIiILpzTCEhC2OhmZc3SIiIiowmKPDhERkYUzzgMDbbNHh4kOERGRhVPCjs/R0ROHroiIiKjCYo+OCZiynNwWmHJBTVO2rVq+rW2puSljKE8a71dDOTmgXlKu9WKfRFpSLQEvvqhnWce0ac8cFEIGhTDwgYEGXm+tmOgQERFZOIURqq4UHLqyfOnp6XjnnXfg6ekJZ2dntGjRAsnJydJxIQTi4uLg5+cHJycnhIWF4ezZs2aMmIiIiMzJahKd7OxstG3bFpUrV8auXbtw7tw5zJs3D1WrVpXOmT17NubPn49Fixbh+PHj8PHxQXh4OPLy8swXOBERkYGUws4omy4OHjyI6Oho+Pn5QSaT4fvvvy/z/C1btiA8PBzVq1eHm5sbQkNDsWfPHgPu2jisJtGZNWsW/P39sWrVKjz//POoU6cOXn31VdSrVw/Ao96cxMRETJo0CW+++SaCgoKwZs0a3L9/H+vXrzdz9ERERPp7PHRl6KaLe/fuoXnz5li0aJFW5x88eBDh4eHYuXMnkpOT0b59e0RHRyMlJUWfWzYaq5mjs23bNkRGRqJbt244cOAAatasiZiYGAwZMgQAcOXKFWRmZiIiIkK6Ri6Xo127djhy5AiGDh1aarv5+fnIz8+X3ufm5pr2RoiIiKxAVFQUoqKitD4/MTFR7f2MGTPwww8/4Mcff0RwcLCRo9Oe1SQ6ly9fxpIlSzB69Gj83//9H37//XeMHDkScrkcffv2RWZmJgDA29tb7Tpvb29cu3ZNY7sJCQmYOnWqUWM9+MM4rc4rXp2l7XXatMfFPkunuuBnWQuBGlqdZa7qJ2Po6PWu9Hr3zeU6X69WfaZlNVWJRT1VqrgqPVNX5xjI+pVXlZO5q6m0pYThVVPKf/9b/A96uVwOuVxuUNulfp5Siby8PFSrVs3obevCaoaulEolWrZsiRkzZiA4OBhDhw7FkCFDsGTJErXzZDL1HwQhRIl9qiZOnIicnBxpS0tLM0n8RERE+nr8wEBDNwDw9/eHu7u7tCUkJJgk5nnz5uHevXvo3r27SdrXltX06Pj6+qJx48Zq+xo1aoTvvvsOAODj4wMAyMzMhK+vr3ROVlZWiV4eVabKZImIiIzFOEtAPLo+LS0Nbm5u0n5T/A7csGED4uLi8MMPP6BGjRpGb18XVtOj07ZtW1y4cEFt38WLFxEQEAAACAwMhI+PD5KSkqTjBQUFOHDgANq0aVOusRIREVkqNzc3tc3Yic6mTZswaNAgfPPNN+jQoYNR29aH1fTofPDBB2jTpg1mzJiB7t274/fff8fy5cuxfPmjOQQymQyxsbGYMWMG6tevj/r162PGjBlwdnZGr169zBw9ERGR/pSQQQlD5+iY/snIGzZswMCBA7FhwwZ06tTJ5J+nDatJdJ577jls3boVEydOxLRp0xAYGIjExET07t1bOmf8+PF48OABYmJikJ2djdatW2Pv3r1wdXU1Y+RERESGMebQlbbu3r2LS5cuSe+vXLmCU6dOoVq1aqhduzYmTpyI9PR0rF37qHhjw4YN6Nu3Lz799FO88MILUpGQk5MT3N3dDYrdEDIhbHTddg1yc3Ph7u6OnJwctTFMa1cRKrK0rYQqXkFVXp9bURhadaWqeNVV8eoq6byy1gZTWS+rUr06BsVDlsWUFU9lrWFlzM819e+Mx+0vONEGTi6G9U08uFuED1od0TrW/fv3o3379iX29+vXD6tXr0b//v1x9epV7N+/HwAQFhaGAwcOaDzfXKymR4eIiMhWGWetK92uDwsLQ1l9IcWTl8cJj6VhokNERGThlEIGpaHP0bHR1cutpuqKiIiISFfs0SEiIrJwSiMMXSlttG+DiQ4REZGF02f18dLasEW2eddERERkE9ijYyNUFww1dvl1eSkrblOWgGtaCLSiKF7arboop2qpeXHK3LvSa/tqVUs9p1J1T7X3mr6Hdk5O6m0/zC/1vKK/rqq3b4Zyc8VV466HZ1/H36jtlZcdB7dqPNbp5Te0aqOsEnBVhpaDW8vCnWVRQAaFgQ/8M/R6a8VEh4iIyMJx6Ep/THSIiIgsnAKG98gojBOK1bHN9I6IiIhsAnt0iIiILByHrvTHRIeIiMjCmWNRz4qCiY4NKqtyqCJWZGmjIlZTaUu1ykoXdm4uTz2n6J9bmq93lGs8Zl/FWasYRGZWqftlPjW0ut7YFVT60DYG1eqsokuXjRpDpWfqajym+ll7rv9Xq/ZUK7K0rcAytopQaUXGwUSHiIjIwgnIoDRwMrJgeTkRERFZIg5d6c8275qIiIhsAnt0iIiILJxSyKAUhg09GXq9tWKiQ0REZOEURli93NDrrRUTHVLzoENTc4egNaefTps7BPqXanWVvbtbqa8BQHHnjvRadT0rmb292nmisOjJ66JC6XXxCrGObgOk17tzV0mvtV1DSdXegvVq7yMceunchrHtTj2h8u7J60i/5kb9HGNXcanSdk2s4lVS+nwPiUrDRIeIiMjCcehKf0x0iIiILJwSdlAaOPRk6PXWiokOERGRhVMIGRQG9sgYer21ss30joiIiGwCe3SIiIgsHOfo6I+JDhERkYUTRli9XNjok5GZ6JDVUi2FVy0117ZE/uAP44wekzVSLdEG1Mu0tZWk2PSkvWpDdL5eKBSa2y5j0VF9YlWlWlJeVjm5rLJ2/1SqlsXrQ72cXLOyFtc0Rum5tot36qOsknI1RZp/Joh0wUSHiIjIwikgg8LARTkNvd5aMdEhIiKycEph+BwbpTBSMFbGNgfsiIiIyCawR4eIiMjCKY0wGdnQ660VEx0iIiILp4QMSgPn2Bh6vbWy2kQnISEB//d//4dRo0YhMTERACCEwNSpU7F8+XJkZ2ejdevW+Pzzz9GkSRPzBksmV1alFauryqa4d1/tffEqrMe0rXDafXuFxmOqFVmqC3xCpv6XpmoVl6oonxitYiiL6gKk4fY9NH6m6rGyqsLsHOXS67Kqs1QXMVW1Jz1Fc7B6MGXFlCo7n4tq75WZDaTXUXWeexLPw3Vq5+1Sv0yjXZfn6h9cBcQnI+vPKvuxjh8/juXLl6NZs2Zq+2fPno358+dj0aJFOH78OHx8fBAeHo68vDwzRUpERETmZHWJzt27d9G7d2+sWLECHh4e0n4hBBITEzFp0iS8+eabCAoKwpo1a3D//n2sX7++jBaJiIgs2+M5OoZutsjq7vr9999Hp06d0KFDB7X9V65cQWZmJiIiIqR9crkc7dq1w5EjRzS2l5+fj9zcXLWNiIjIkighk5aB0HvjHB3Lt3HjRpw8eRLHjx8vcSwzMxMA4O3trbbf29sb165d09hmQkICpk6datxAiYiIyCJYTY9OWloaRo0aha+//hqOjo4az5PJ1DNWIUSJfaomTpyInJwcaUtLSzNazERERMYg/q26MmQT7NGxbMnJycjKykJISIi0T6FQ4ODBg1i0aBEuXLgA4FHPjq+vr3ROVlZWiV4eVXK5HHK5XONxIiIic+Pq5fqzmkTn1VdfxenTp9X2DRgwAA0bNsSECRNQt25d+Pj4ICkpCcHBwQCAgoICHDhwALNmlbFwHBGZTPEFPjWVnquWchdnjJJyVZWqexp0nmp5OqBeNq5aal4WY5eUm4NqOTmgXm4ukw/Uub2oumPV3rO8nIzFahIdV1dXBAUFqe2rUqUKPD09pf2xsbGYMWMG6tevj/r162PGjBlwdnZGr16aVyUmIiKydHwysv6sJtHRxvjx4/HgwQPExMRIDwzcu3cvXF1dzR0aERGR3jh0pT+rTnT279+v9l4mkyEuLg5xcXFmiYeIiIgsi1UnOkRERLaAa13pzzYH7IiIiKyIwQ8L1GPo6+DBg4iOjoafnx9kMhm+//77p15z4MABhISEwNHREXXr1sXSpUv1vGPjYY8OqVFdAPPlLnPMGIn+uIinboovZhlZpe+TNyrPoIp06adVe3vurpFel7XAZ1kxqNqVuVirNiIcSi86kDlUVntv51Kl1PO0re7StrKqRBwhTxYXvqs8Kr12sdP8XDBLVnxRT1W7c77UeKx4ddVjrLIqmznm6Ny7dw/NmzfHgAED8NZbbz31/CtXruC1117DkCFD8PXXX+PXX39FTEwMqlevrtX1pqJVotOyZUudGpXJZNi2bRtq1qypV1BERERkXlFRUYiKitL6/KVLl6J27dpITEwEADRq1AgnTpzA3LlzLT/ROXXqFMaMGQMXF5enniuEwMyZM5Gfn//Uc4mIiOjpjNmjU3xNR2M9OPfo0aNq600CQGRkJFauXInCwkJUrlxZw5WmpfXQ1bhx41CjRg2tzp03b57eAREREZE6YyY6/v7+avunTJlilGrlzMzMUtebLCoqws2bN9VWLShPWiU6V65cQfXq1bVu9Ny5c/Dz89M7KCIiIjKNtLQ0uLm5Se+NuQxSaetNlra/PGmV6AQEBOjUaPFskYiIiPQnYHh5uPj3v25ubmqJjrH4+PggMzNTbV9WVhYqVaoET0/tll4xBb2qru7cuYPff/8dWVlZUCqVasf69u2r4SqyNqrVS5oqJQDgXtMnXZVVTt8odb+psdJKf2pVVkZQ1rpVmqqril9TyffJz86uvz/T6nNllbX750x59570WlMFljGIJs+YrG1LUNZaV6q4hpVxWMOTkUNDQ/Hjjz+q7du7dy9atWpltvk5gB6Jzo8//ojevXvj3r17cHV1VeuOkslkTHSIiIgqgLt37+LSpUvS+ytXruDUqVOoVq0aateujYkTJyI9PR1r164FAAwbNgyLFi3C6NGjMWTIEBw9ehQrV67Ehg0bzHULAPR4YOCYMWMwcOBA5OXl4c6dO8jOzpa227dvmyJGIiIim2aOBwaeOHECwcHBCA4OBgCMHj0awcHBmDx5MgAgIyMDqamp0vmBgYHYuXMn9u/fjxYtWuCTTz7BZ599ZtbSckCPHp309HSMHDkSzs7OpoiHiIiIijHH0FVYWJg0mbg0q1evLrGvXbt2OHnypK6hmZTOPTqRkZE4ceKEKWIhIiIiMiqtenS2bdsmve7UqRPGjRuHc+fOoWnTpiUmGHXu3Nm4ERIREdk4a5iMbKm0SnS6du1aYt+0adNK7JPJZFAoFAYHRURERE8IIYMwMFEx9HprpVWiU7yEnGxPWSWhmkrPVUvNi1MtPde3NNxaFx21BJoW7jS1skrPNYmqNVLjMa1Kz8uYY6C6YKjWi3q6qi+Fo6jto9V1337/Ran77yofSq+71WunHt+V37RqW7UNS1sklOXkxqGEzODn6Bh6vbXSeY7O2rVrS13HqqCgQCoxIyIiIrIEOic6AwYMQE5OTon9eXl5GDBggFGCIiIioifMUV5eUehcXi6EKHXNir///hvu7u5GCYqIiIie4Bwd/Wmd6AQHB0Mmk0Emk+HVV19FpUpPLlUoFLhy5Qo6duxokiCJiIiI9KF1ovO48urUqVOIjIyEi8uTCXkODg6oU6eO2Z9+SEREVBGxvFx/Wic6U6ZMgUKhQEBAACIjI+Hr62vKuMiKqFZVlLX4p6qyKrK0xYU8daNaaaV88EDjeXZW9NTzsiqytKH6NdlzT7tiCm2rs/Sx+a8Dau/v6lHw+tqzL0qvd144bGhIGmlaxJNMg0NX+tNpMrK9vT2GDRuGhw8fPv1kIiIiIjPTueqqadOmuHz5siliISIiolIII1RcsUdHS/Hx8Rg7diy2b9+OjIwM5Obmqm1ERERkXAKPnn1p0GbumzATncvLH1dWde7cWa3M/HHZOZeAICIiIkuhc6Lzyy+/mCIOIiIi0kAJGWRcAkIvOic67dq1e/pJZLO0rcAy9vo3EZV7Sq/3Fm40atsVRVmVVqpEQWGp+2UOlbW63q6y5n9WhEqPr1AatyNdtWpKbS0vLRW/RtPXK0m5Wav2cq/X1jkGbbn5paq97+g+UKvrOoVElrp/R/IejdewusoysOpKfzonOgBw584drFy5EufPn4dMJkPjxo0xcOBAPhmZiIjIBJRCBhmfo6MXnScjnzhxAvXq1cOCBQtw+/Zt3Lx5E/Pnz0e9evVw8uRJU8RIREREpBede3Q++OADdO7cGStWrJCWgSgqKsLgwYMRGxuLgwcPGj1IIiIiW/a4csrQNmyRzonOiRMn1JIcAKhUqRLGjx+PVq1aGTU4IiIi4hwdQ+g8dOXm5obU1NQS+9PS0uDq6mqUoEqTkJCA5557Dq6urqhRowa6du2KCxcuqJ0jhEBcXBz8/Pzg5OSEsLAwnD171mQxERERkWXTOdHp0aMHBg0ahE2bNiEtLQ1///03Nm7ciMGDB+Ptt982RYwAgAMHDuD999/HsWPHkJSUhKKiIkRERODevXvSObNnz8b8+fOxaNEiHD9+HD4+PggPD0deXp7J4iIiIjK1xz06hm62SOehq7lz50Imk6Fv374oKioCAFSuXBnvvfceZs6cafQAH9u9e7fa+1WrVqFGjRpITk7Gyy+/DCEEEhMTMWnSJLz55psAgDVr1sDb2xvr16/H0KFDTRYb6U619LysUnNtz9O2pDzcvof0OkmxSatr6JHiZedq5eb//lugC5md5n90lXdydG5PlaGl5sVpW1KuqngJuD7l5sXb0NVrQWFq7xXZWdJr1Z9/ZWYDtfNYUm55WHWlP517dBwcHPDpp58iOzsbp06dQkpKCm7fvo0FCxZALpebIsZS5eQ8+oewWrVqAIArV64gMzMTERER0jlyuRzt2rXDkSNHNLaTn5/PZSyIiIgqKL2eowMAzs7OaNq0qTFj0ZoQAqNHj8aLL76IoKAgAEBmZiYAwNvbW+1cb29vXLt2TWNbCQkJmDp1qumCJSIiMhCrrvSnc6Jz7949zJw5Ez///DOysrKgVCrVjpfHyubDhw/H//73Pxw+fLjEMdX1t4Ana3BpMnHiRIwePVp6n5ubC39/f+MFS0REZKBHiY6hVVdGCsbK6JzoDB48GAcOHECfPn3g6+tbZhJhCiNGjMC2bdtw8OBB1KpVS9rv4+MD4FHPjq+vr7Q/KyurRC+PKrlcXq5DbkRERFR+dE50du3ahR07dqBt27amiEcjIQRGjBiBrVu3Yv/+/QgMDFQ7HhgYCB8fHyQlJSE4OBgAUFBQgAMHDmDWrFnlGisREZEx8Tk6+tM50fHw8JAmAJen999/H+vXr8cPP/wAV1dXaU6Ou7s7nJycIJPJEBsbixkzZqB+/fqoX78+ZsyYAWdnZ/Tq1avc4yXtq6nKWvyz6Krm+VX6sKVKq3C7bqb9AD0qrUxJY0WdnouH6lNpZShDq6yext7jyXqE6l8v3ausovxHqb3flfap/oHRU4l/N0PbsEU6JzqffPIJJk+ejDVr1sDZ2dkUMZVqyZIlAICwsDC1/atWrUL//v0BAOPHj8eDBw8QExOD7OxstG7dGnv37jXpgwyJiIhMjT06+tM50Zk3bx7++usveHt7o06dOqhcubLacVMt7Cm0mEUlk8kQFxeHuLg4k8RARERE1kXnRKdr164mCIOIiIg04tiV3nROdKZMmaLVeRs2bEDnzp1RpUoVnYMiIiIiFcZYwsFGh650fjKytoYOHYobN26YqnkiIiKip9L7ychPo82cGiIiIno6PhlZfyZLdIjKUlbpuSrVEljV14BtlYobg8zeXuWN7p25JRbhbPLMk9dnL+kZ1b9tG+GhnZVqPnlQaFTtWK2u2fPgK4M/V1uqpeP6LPBZlt05X5a6v6PXu2rvlbl3pddqPw96YDl5+WLVlf5MNnRFREREZG7s0SEiIrJ0Qmb4ZGIb7dFhokNERGThOEdHfyYbugoICCjxMEEiIiKi8qRzj05aWhpkMpm0cvjvv/+O9evXo3Hjxnj33ScT386cOWO8KImIiGwZHxioN50TnV69euHdd99Fnz59kJmZifDwcDRp0gRff/01MjMzMXnyZFPESTZKtbJK30UqO7oPlF6LgsJSzylefaP6WeZY3NEYiscdUbnnkzdC+eR1GRVYqpU5okj9a6c22q9agaVKy2os5f37au/ttFxHz66ax9Pbzn+o8ZjGhUAriN03l2s8FlVzRDlGQoZi1ZX+dB66OnPmDJ5//nkAwDfffIOgoCAcOXIE69evx+rVq40dHxEREQFPenX03fSwePFiBAYGwtHRESEhITh06FCZ569btw7NmzeHs7MzfH19MWDAANy6dUu/DzcSnROdwsJCyP995sVPP/2Ezp07AwAaNmyIjIwM40ZHREREZrFp0ybExsZi0qRJSElJwUsvvYSoqCikpqaWev7hw4fRt29fDBo0CGfPnsXmzZtx/PhxDB48uJwjV6dzotOkSRMsXboUhw4dQlJSEjp27AgAuH79Ojw9PY0eIBERka17PHRl6KaL+fPnY9CgQRg8eDAaNWqExMRE+Pv7Y8mSJaWef+zYMdSpUwcjR45EYGAgXnzxRQwdOhQnTpwwxpdAbzonOrNmzcKyZcsQFhaGt99+G82bNwcAbNu2TRrSIiIiIiMydNhKZfgqNzdXbcvPzy/xcQUFBUhOTkZERITa/oiICBw5cqTUENu0aYO///4bO3fuhBACN27cwLfffotOnToZevcG0XkyclhYGG7evInc3Fx4eDyZCPjuu+/CWcsJhERERGQe/v7+au+nTJmCuLg4tX03b96EQqGAt7e32n5vb29kZmaW2m6bNm2wbt069OjRAw8fPkRRURE6d+6MhQsXGjV+Xen1wEAhBJKTk/HXX3+hV69ecHV1hYODAxMdMiltq59Uq6y0FenUR+29ndxR5zYs3d7CjdJr1aoyOwft1jwSSvXZjPrUbwiFQrvzSvkLU1+VatXUeKwo3TzzClXXvTKXXenG/eUT5T/qSdtcB8sEZNDv/7ribTx6TIybm5u0V17GWnMymfpnCiFK7Hvs3LlzGDlyJCZPnozIyEhkZGRg3LhxGDZsGFauXGlg7PrTOdG5du0aOnbsiNTUVOTn5yM8PByurq6YPXs2Hj58iKVLl5oiTiIiIttlxOfouLm5qSU6pfHy8oK9vX2J3pusrKwSvTyPJSQkoG3bthg3bhwAoFmzZqhSpQpeeuklTJ8+Hb6+vqVeZ2o6z9EZNWoUWrVqhezsbDg5OUn733jjDfz8889GDY6IiIjKn4ODA0JCQpCUlKS2PykpCW3atCn1mvv378POTj2tsP/3WVxCh/Un7O3tkZWVVWL/rVu3pPZ0oXOPzuHDh/Hrr7/CwcFBbX9AQADS09N1DoCIiIiewgxPRh49ejT69OmDVq1aITQ0FMuXL0dqaiqGDRsGAJg4cSLS09Oxdu1aAEB0dDSGDBmCJUuWSENXsbGxeP755+Hn56d9mBqSovz8/BK5hzZ0TnSUSiUUpYyz//3333B1ddU5ACIiInoKM6xe3qNHD9y6dQvTpk1DRkYGgoKCsHPnTgQEBAAAMjIy1J6p079/f+Tl5WHRokUYM2YMqlatildeeQWzZs3S6vM+++wzAI/mBX3xxRdwcXGRjikUChw8eBANGzbU6R4APRKd8PBwJCYmYvny5VJAd+/exZQpU/Daa6/pHAARERFZppiYGMTExJR6rLTVEEaMGIERI/RbXmTBggUAHvXoLF26VG2YysHBAXXq1NFrHrDOic6CBQvQvn17NG7cGA8fPkSvXr3w559/wsvLCxs2bNA5ACIiIiqbEI82Q9uwZFeuXAEAtG/fHlu2bFF7hI0hdE50/Pz8cOrUKWzYsAEnT56EUqnEoEGD0Lt3b7XJyUSWSOZQWXqtaYHP4spaTNReZVHJshZQtDSqpfqRjr3Vju15uE7n9iJDpjx5o+VCnpVqVJdeF2X9o3bM3rOa9Fpx67bGNkROrvRa5l56FUlZJeQVcSHPV19JkF7/vG+iwe0Vf/TCY8UXwmVJuYnZ0Orlv/zyi9p7hUKB06dPIyAgQK/kR6/n6Dg5OWHgwIEYOFD355UQERGRjswwR8dcYmNj0bRpUwwaNAgKhQIvv/wyjh49CmdnZ2zfvh1hYWE6tadzeTkAfPXVV3jxxRfh5+eHa9euAXg0pPXDDz/o0xwRERERAGDz5s3S8lI//vgjrl69ij/++ENaYFRXOic6S5YswejRoxEVFYXs7GypAsvDwwOJiYk6B0BERERlkwnjbNbg1q1b8PHxAQDs3LkT3bp1Q4MGDTBo0CCcPn1a5/Z0TnQWLlyIFStWYNKkSahU6cnIV6tWrfQKgIiIiJ7CiIt6Wjpvb2+cO3cOCoUCu3fvRocOHQA8eiBhuTww8MqVKwgODi6xXy6X4969ezoHQERERPTYgAED0L17d/j6+kImkyE8PBwA8Ntvv5XPc3QCAwNx6tQp6YFBj+3atQuNGzfWOQAiswl65snrM5c0H0s+I720CwlSO81K/kAqkz5VViXaSJ4qvS6rSk0T1Qqs4lQrsMT9B+oHVeplVSuwylIRK61UqVZaqVZgAUClo+ek16pVU2WdVxT65N91Y1RxkZ5saDJyXFwcgoKCkJaWhm7dukmLjtrb2+PDDz/UuT2dE51x48bh/fffx8OHDyGEwO+//44NGzYgISEBX3zxhc4BEBER0VPYUHk5APznP/8BADx8+FDa169fP73a0nmOzoABAzBlyhSMHz8e9+/fR69evbB06VJ8+umn6Nmzp15BGNvixYsRGBgIR0dHhISE4NChQ+YOiYiIiLSgUCjwySefoGbNmnBxccHly5cBAB9//DFWrlypc3s6JTpFRUVYs2YNoqOjce3aNWRlZSEzMxNpaWkYNGiQzh9uCps2bZJK0FJSUvDSSy8hKipKbT0OIiIiq2JDk5Hj4+OxevVqzJ49W20Rz6ZNm+o1cqRTolOpUiW89957yM/PBwB4eXmhRo0aOn+oKc2fPx+DBg3C4MGD0ahRIyQmJsLf3x9Lliwxd2hERET6saFEZ+3atVi+fDl69+6tVmXVrFkz/PHHHzq3p/PQVevWrZGSkqLzB5WHgoICJCcnIyIiQm1/REQEjhw5Uuo1+fn5yM3NVduIiIjIPNLT0/HMM8+U2K9UKlFYqN3SPap0nowcExODMWPG4O+//0ZISAiqVKmidrxZs2Y6B2EsN2/ehEKhgLe3t9p+b29vZGZmlnpNQkICpk6dWuoxsh4RDr00HrNzcnx6A0El/6eSri9WaaVJ5HOaf472HJ+i8RjpoKxnaCiVpe6u6FVWZSleJVW8ukrX88o6R9uKLGOvxWUzbKjqqkmTJjh06FCJ6u7NmzeX+nibp9E50enRowcAYOTIkdI+mUwGIQRkMpn0pGRzksnUv5mPYyvNxIkTMXr0aOl9bm4u/P39TRofERGRLozxZGNreTLylClT0KdPH6Snp0OpVGLLli24cOEC1q5di+3bt+vcnl4PDLRUXl5esLe3L9F7k5WVVaKX5zG5XC7V6BMREVkkGyovj46OxqZNmzBjxgzIZDJMnjwZLVu2xI8//ig9PFAXOic6xbuSLImDgwNCQkKQlJSEN954Q9qflJSELl26mDEyIiIi0lZkZCQiIyPLPGfDhg3o3LlziSk0xemc6Gzbtq3U/TKZDI6OjnjmmWcQGBioa7NGM3r0aPTp0wetWrVCaGgoli9fjtTUVAwbNsxsMREREZFxDR06FK1bt0bdunXLPE/nRKdr167SnBxVqvN0XnzxRXz//ffw8PDQtXmD9ejRA7du3cK0adOQkZGBoKAg7Ny506J7ooiIiMoigxHm6BglEstRPA/RROfy8qSkJDz33HNISkpCTk4OcnJykJSUhOeffx7bt2/HwYMHcevWLYwdO1bnoI0lJiYGV69eRX5+PpKTk/Hyyy+bLRYiIiIyH517dEaNGoXly5ejTZs20r5XX30Vjo6OePfdd3H27FkkJiZi4MCBRg2USFuyYiXIokDluQtllJGbkqbS84pYdp6k3Cy9Lr7AZ1mLdxrM7snfbbuzue6eIbQp+9a3NLysRUdV2R04Jb225UcESGyovNzYdE50/vrrL7i5uZXY7+bmJq1HUb9+fdy8edPw6IiIiMimqq6MTeehq5CQEIwbNw7//POPtO+ff/7B+PHj8dxzzwEA/vzzT9SqVct4URIRERHpQecenZUrV6JLly6oVasW/P39IZPJkJqairp16+KHH34AANy9excff/yx0YMlIiKySezRKSEgIACVK1d+6nk6JzrPPvsszp8/jz179uDixYsQQqBhw4YIDw+H3b9j5F27dtU5YCIiIiqdLT0Z+bETJ07g/PnzkMlkaNiwIVq1aqV2/MyZM1q1o3OiAzwqJe/YsSPCwsIgl8s1Lq9AREREpIu///4bb7/9Nn799VdUrVoVAHDnzh20adMGGzZs0HmZJp0THaVSifj4eCxduhQ3btzAxYsXUbduXXz88ceoU6cOBg0apGuTRAYrXmmlxkyVVtooXo1V0aqw7OTqC6oqc/JKP8/dtTzCsXmaKqXKqn4y5cKb2lZghdv3kF7bbAWWDQ1dDRw4EIWFhTh//jyeffZZAMCFCxcwcOBADBo0CHv37tWpPZ0nI0+fPh2rV6/G7Nmz4eDgIO1v2rQpvviCJZ1ERERGJ4y0WYFDhw5hyZIlUpIDPJo2s3DhQhw6dEjn9nROdNauXYvly5ejd+/esFf5K7pZs2b4448/dA6AiIiIyvZ4jo6hmzWoXbs2CgsLS+wvKipCzZo1dW5P50QnPT0dzzxTcihAqVSWGhgRERGRtmbPno0RI0bgxIkT0jIPJ06cwKhRozB37lyd29N5jk6TJk1w6NChEmtHbd68GcHBwToHQERERE9hQ09G7t+/P+7fv4/WrVujUqVHaUpRUREqVaqEgQMHqq28cPv27ae2p3OiM2XKFPTp0wfp6elQKpXYsmULLly4gLVr12L79u26NkdERERPY0OTkRMTE43ans6JTnR0NDZt2oQZM2ZAJpNh8uTJaNmyJX788UeEh4cbNTgiIiKyLf369TNqe3o9RycyMhKRkZFGDYTIEHsefCW9jnTqo37wzKUnr1VKzcWp89JrWYtGJouNjEd5777ae7sqzmaKpGIxZQm5NcVgyWztgYEKhQLff/+99MDAxo0bo3PnzmpFUNrSK9EhIiKicmRDQ1eXLl3Ca6+9hvT0dDz77LMQQuDixYvw9/fHjh07UK9ePZ3a0yrR8fDw0Prpx9pMDCIiIiIqzciRI1GvXj0cO3YM1apVAwDcunUL77zzDkaOHIkdO3bo1J5WiY7qxKBbt25h+vTpiIyMRGhoKADg6NGj2LNnDxfyJCIiMgVjPAfHSnp0Dhw4oJbkAICnpydmzpyJtm3b6tyeVomO6sSgt956C9OmTcPw4cOlfSNHjsSiRYvw008/4YMPPtA5CCIiIiqDDQ1dyeVy5OWVXC7m7t27aisyaEvnBwbu2bMHHTt2LLE/MjISP/30k84BEBERET32+uuv491338Vvv/0GIQSEEDh27BiGDRuGzp0769yezpORPT09sXXrVowbN05t//fffw9PT0+dAyAyNtUKLKBYFZZqBZYK1Qqs4kxZkVXRFvHUl+L2Hb2uU63CUl3YVfV7XvznQVWkY+8n5z1cp1cMVDbVr3FxZX3N+b0pxoZ6dD777DP069cPoaGhqFy5MgCgsLAQXbp0waeffqpzezonOlOnTsWgQYOwf/9+aY7OsWPHsHv3bi7qSUREZAK2VF5etWpV/PDDD7h06RLOnTsHAGjcuHGpy09pQ+dEp3///mjUqBE+++wzbNmyBUIING7cGL/++itat26tVxBEREREj61cuRILFizAn3/+CQCoX78+YmNjMXjwYJ3b0nmODgC0bt0a69atw8mTJ5GSkoJ169YxySEiIqpgFi9ejMDAQDg6OiIkJASHDh0q8/z8/HxMmjQJAQEBkMvlqFevHr788kudPvPjjz/GqFGjEB0djc2bN2Pz5s2Ijo7GBx98gI8++kjne9CqRyc3Nxdubm5aN5qXlwdXV1edgyEiIqJSmGGOzqZNmxAbG4vFixejbdu2WLZsGaKionDu3DnUrl271Gu6d++OGzduYOXKlXjmmWeQlZWFoqIinT53yZIlWLFiBd5++21pX+fOndGsWTOMGDEC06dP16k9rR8YmJGRgRo1amjVaM2aNXHq1CnUrVtXp2CIiIioJHPM0Zk/fz4GDRokDRclJiZiz549WLJkCRISEkqcv3v3bhw4cACXL1+WnoFTp04dneNUKBRo1apVif0hISE6J02AlomOEAJffPEFXFxctGq0sLBQ50CITKXMdbC0wDWxzEwoNR6SVapc6n5tK63IePT5umpbWcUKLOPKzc1Vey+XyyGXy9X2FRQUIDk5GR9++KHa/oiICBw5cqTUdrdt24ZWrVph9uzZ+Oqrr1ClShV07twZn3zyCZycnLSO75133sGSJUswf/58tf3Lly9H7966/5xplejUrl0bK1as0LpRHx8fqSSMiIiIjMBIVVP+/v5q76dMmYK4uDi1fTdv3oRCoYC3t7fafm9vb2RmZpba7uXLl3H48GE4Ojpi69atuHnzJmJiYnD79m2d5+msXLkSe/fuxQsvvADgUXV3Wloa+vbti9GjR0vnFU+GSqNVonP16lWdAiQiIiIjMuIcnbS0NLV5t8V7c1QVX+dSCKFx7UulUgmZTIZ169bB3d0dwKNE5D//+Q8+//xzrXt1zpw5g5YtWwIA/vrrLwBA9erVUb16dZw5c0ZjbJpw9XIiIiIb4ubm9tQCIy8vL9jb25fovcnKyirRy/OYr68vatasKSU5ANCoUSMIIfD333+jfv36WsX3yy+/aHWetvQqLyciIqLy83gysqGbthwcHBASEoKkpCS1/UlJSWjTpk2p17Rt2xbXr1/H3bt3pX0XL16EnZ0datWqpdd9GwMTHSIiIksnjLTpYPTo0fjiiy/w5Zdf4vz58/jggw+QmpqKYcOGAQAmTpyIvn37Suf36tULnp6eGDBgAM6dO4eDBw9i3LhxGDhwoE6TkY2NQ1dERERUQo8ePXDr1i1MmzYNGRkZCAoKws6dOxEQEAAAyMjIQGpqqnS+i4sLkpKSMGLECLRq1Qqenp7o3r27zs+9MTarSHSuXr2KTz75BPv27UNmZib8/PzwzjvvYNKkSWpLtqempuL999/Hvn374OTkhF69emHu3Ll6LetOFZNq2XFE5Z5mjMQ2KPMfGtyG6mKdZSmrpFzjNSxV1puxy/RZ9l82c611FRMTg5iYmFKPrV69usS+hg0blhjuMje9Ep1Dhw5h2bJl+Ouvv/Dtt9+iZs2a+OqrrxAYGIgXX3zR2DHijz/+gFKpxLJly/DMM8/gzJkzGDJkCO7du4e5c+cCePSAoU6dOqF69eo4fPgwbt26hX79+kEIgYULFxo9JiIionJjQ6uXG5vOc3S+++47REZGwsnJCSkpKcjPzwfwaNmHGTNmGD1AAOjYsSNWrVqFiIgI1K1bF507d8bYsWOxZcsW6Zy9e/fi3Llz+PrrrxEcHIwOHTpg3rx5WLFiRYmHIxEREVkVM8zRqSh0TnSmT5+OpUuXYsWKFWoPBWzTpg1Onjxp1ODKkpOTIz1iGgCOHj2KoKAg+Pn5SfsiIyORn5+P5ORkje3k5+cjNzdXbSMiIqKKQedE58KFC3j55ZdL7Hdzc8OdO3eMEdNT/fXXX1i4cKE08xsAMjMzS9T2e3h4wMHBQeNTHAEgISEB7u7u0lb8iZFERETmVt7l5RWJzomOr68vLl26VGL/4cOHdV7EMy4uDjKZrMztxIkTatdcv34dHTt2RLdu3aSFxh4r7SmJZT3FEXhUHpeTkyNtaWlpOt0DERGRyXHoSm86T0YeOnQoRo0ahS+//BIymQzXr1/H0aNHMXbsWEyePFmntoYPH46ePcuufFFd+fT69eto3749QkNDsXz5crXzfHx88Ntvv6nty87ORmFhocanOAKlL2ZGJJSl/4ug3QPHy7bn+BQjtGIdkpSb1d6H23XTuQ2hUEiv7eSOOl/Pah7DWNrXr3g8rJyjp9E50Rk/fjxycnLQvn17PHz4EC+//DLkcjnGjh2L4cOH69SWl5cXvLy8tDo3PT0d7du3R0hICFatWgU7O/XOqNDQUMTHxyMjIwO+vr4AHk1QlsvlCAkJ0SkuIiIii8KqK73pVV4eHx+PSZMm4dy5c1AqlWjcuDFcXFyMHZvk+vXrCAsLQ+3atTF37lz8888/0jEfHx8Aj5aOb9y4Mfr06YM5c+bg9u3bGDt2LIYMGfLUNT2IiIgsmbmeo1MR6P3AQGdnZ7Rq1cqYsWi0d+9eXLp0CZcuXSqxXoYQj75z9vb22LFjB2JiYtC2bVu1BwYSERGRbdIq0XnzzTe1blD12TbG0r9/f/Tv3/+p59WuXRvbt283+ucTERGZFYeu9KZVoqO65LoQAlu3boW7u7vUo5OcnIw7d+7olBARERGRdjh0pT+tEp1Vq1ZJrydMmIDu3btj6dKlsP93DRqFQoGYmBjOhSGLF+HQS3ptp8dquoqT59Te27VsXOp5tlRZZWr2Kn9oFbc7+4unXs+qnIpNtQqL32sqjc7P0fnyyy8xduxYKckBHs2PGT16NL788kujBkdERETgc3QMoHOiU1RUhPPnz5fYf/78eSiVSqMERURERCqY6OhN56qrAQMGYODAgbh06RJeeOEFAMCxY8cwc+ZMDBgwwOgBEhER2ToZDH9gqTEeeGqNdE505s6dCx8fHyxYsAAZGRkAHi0LMX78eIwZM8boARIRERHpS+dEx87ODuPHj8f48eOllb45CZmIiMiEWF6uN70fGAgwwSEiIioPLC/Xn86JTmBgYJmrgV++fNmggIgsmaZyctKOaqm4Iien1P1ERMakc6ITGxur9r6wsBApKSnYvXs3xo0bZ6y4iIiI6DEOXelN50Rn1KhRpe7//PPPceLECYMDIiIiolLYaKJiKJ2fo6NJVFQUvvvuO2M1R0RERGQwgyYjq/r2229RrVo1YzVHRERE/+JkZP3pnOgEBwerTUYWQiAzMxP//PMPFi9ebNTgiIiICJyjYwCdE50uXbqoJTp2dnaoXr06wsLC0LBhQ6MGR2RplMln1N7bhQRJr7mQ59OpLsLZ0WOwQddT+VBdKFN1AU0ia6FzohMXF2eCMIiIiEgTDl3pT+fJyPb29sjKyiqx/9atW2ormhMREZGRcFFPvencoyNE6V+p/Px8ODg4GBwQERERqWOPjv60TnQ+++wzAIBMJsMXX3wBFxcX6ZhCocDBgwc5R4eIiIgsitaJzoIFCwA86tFZunSp2jCVg4MD6tSpg6VLlxo/QiIiIlvHqiu9aZ3oXLlyBQDQvn17bNmyBR4eHiYLioiIiFQw0dGbznN0fvnlF1PEQWSVWFJuHCwbtz6qZefGoG/purHjoIpHq0Rn9OjR+OSTT1ClShWMHj26zHPnz59vlMCIiIjoEU5G1p9WiU5KSgoKCwsBACdPnlR7YCARERGZGIeu9KZVoqM6XLV//35TxUJERERkVDo/MHDgwIHIy8srsf/evXsYOHCgUYIiIiKiJ2RCGGWzRTonOmvWrMGDBw9K7H/w4AHWrl1rlKCIiIhIBZ+MrDetq65yc3MhhIAQAnl5eXB0dJSOKRQK7Ny5EzVq1DBJkESGiHDoJb22c5Qb1FaScrOh4dC/WGllfUxZ4VS8bUtbQDTcrpv0mv8OWBetE52qVatCJpNBJpOhQYMGJY7LZDJMnTrVqMERERERq64MoXWi88svv0AIgVdeeQXfffcdqlWrJh1zcHBAQEAA/Pz8TBIkERGRTWPVld60TnTatWsH4NETkv39/WFnp/P0HiIiItIDe3T0p3O2EhAQADs7O9y/fx9//PEH/ve//6ltppafn48WLVpAJpPh1KlTasdSU1MRHR2NKlWqwMvLCyNHjkRBQYHJYyIiIiLLpHOi888//+D111+Hq6srmjRpguDgYLXN1MaPH1/qEJlCoUCnTp1w7949HD58GBs3bsR3332HMWPGmDwmIiIikzJT1dXixYsRGBgIR0dHhISE4NChQ1pd9+uvv6JSpUpo0aKF7h9qZDqvdRUbG4vs7GwcO3YM7du3x9atW3Hjxg1Mnz4d8+bNM0WMkl27dmHv3r347rvvsGvXLrVje/fuxblz55CWliYlQvPmzUP//v0RHx8PNzc3k8ZGFcvu3FXmDoHIpnENK3XmGLratGkTYmNjsXjxYrRt2xbLli1DVFQUzp07h9q1a2u8LicnB3379sWrr76KGzduGBa0Eejco7Nv3z4sWLAAzz33HOzs7BAQEIB33nkHs2fPRkJCgiliBADcuHEDQ4YMwVdffQVnZ+cSx48ePYqgoCC13p7IyEjk5+cjOTlZY7v5+fnIzc1V24iIiGzd/PnzMWjQIAwePBiNGjVCYmIi/P39sWTJkjKvGzp0KHr16oXQ0NByirRsOic69+7dk56XU61aNfzzzz8AgKZNm+LkyZPGje5fQgj0798fw4YNQ6tWrUo9JzMzE97e3mr7PDw84ODggMzMTI1tJyQkwN3dXdr8/f2NGjsREZHBjDh0VfyP+/z8/BIfV1BQgOTkZERERKjtj4iIwJEjRzSGuWrVKvz111+YMmWKIXdrVDonOs8++ywuXLgAAGjRogWWLVuG9PR0LF26FL6+vjq1FRcXJz2bR9N24sQJLFy4ELm5uZg4cWKZ7ZW22KgQosxFSCdOnIicnBxpS0tL0+keiIiIysPj4St9t8f8/f3V/sAvbTTm5s2bUCgUJToQvL29NXYe/Pnnn/jwww+xbt06VKqk88wYk9Frjk5GRgYAYMqUKYiMjMS6devg4OCA1atX69TW8OHD0bNnzzLPqVOnDqZPn45jx45BLld/qm2rVq3Qu3dvrFmzBj4+Pvjtt9/UjmdnZ6OwsLDEN0qVXC4v0S4REVFFlZaWpjZvtazfgcU7CjR1HigUCvTq1QtTp04t9aHC5qRzotO795PHcgcHB+Pq1av4448/ULt2bXh5eenUlpeXl1bXfPbZZ5g+fbr0/vr164iMjMSmTZvQunVrAEBoaCji4+ORkZEh9Szt3bsXcrkcISEhOsVFRERkUYR4tBnaBgA3N7enFuh4eXnB3t6+RO9NVlZWqZ0HeXl5OHHiBFJSUjB8+HAAgFKphBAClSpVwt69e/HKK68YFr+eDO5bcnZ2RsuWLY0Ri0bFZ3e7uLgAAOrVq4datWoBeDRu2LhxY/Tp0wdz5szB7du3MXbsWAwZMoQVV0REZNXKu+rKwcEBISEhSEpKwhtvvCHtT0pKQpcuXUqc7+bmhtOnT6vtW7x4Mfbt24dvv/0WgYGBesdtKK0SndGjR2vd4Pz58/UOxhD29vbYsWMHYmJi0LZtWzg5OaFXr16YO3euWeIhy7G3YL30uqPbgFLPYTk5ERVn6wt5jh49Gn369EGrVq0QGhqK5cuXIzU1FcOGDQPwaI5reno61q5dCzs7OwQFBaldX6NGDTg6OpbYX960SnRSUlK0aqysSb/GVKdOHYhSuvBq166N7du3l0sMRERE5cYMa1316NEDt27dwrRp05CRkYGgoCDs3LkTAQEBAICMjAykpqYaGJTpyURpGYMNy83Nhbu7O3JycjjkVQGxR4eItKVNj46pf2c8bv+5N6ajUmVHg9oqKnyI41s/srnfb5ZT/0VERESl4+rleuMS5ERERFRhsUeHiIjIwpljrauKgokOVXgRDr2k13aOfDgkEZVOdU4OYGGVVkZ8jo6t4dAVERERVVjs0SEiIrJwHLrSHxMdIiIiS8eqK71x6IqIiIgqLPboEBERWTgOXemPiQ7ZLD4NmYhUWVSVVXGsutIbh66IiIiowmKPDhERkYXj0JX+mOgQERFZOlZd6Y2JDhERkYVjj47+OEeHiIiIKiz26BAREVk6pXi0GdqGDWKiQzZF+TDf3CEQEemOc3T0xqErIiIiqrDYo0NERGThZDDCZGSjRGJ9mOgQERFZOj4ZWW8cuiIiIqIKiz06REREFo7P0dEfEx2q8PYWrDd3CEREhmHVld44dEVEREQVFnt0iIiILJxMCMgMnExs6PXWiokOERGRpVP+uxnahg1iokNERGTh2KOjP87RISIiogqLPTpERESWjlVXemOiQ0REZOn4ZGS9ceiKiIiIKiyrSnR27NiB1q1bw8nJCV5eXnjzzTfVjqempiI6OhpVqlSBl5cXRo4ciYKCAjNFS0REZByPn4xs6GaLrGbo6rvvvsOQIUMwY8YMvPLKKxBC4PTp09JxhUKBTp06oXr16jh8+DBu3bqFfv36QQiBhQsXmjFyIiIiA3HoSm9WkegUFRVh1KhRmDNnDgYNGiTtf/bZZ6XXe/fuxblz55CWlgY/Pz8AwLx589C/f3/Ex8fDzc2t3OMmIiIi87KKoauTJ08iPT0ddnZ2CA4Ohq+vL6KionD27FnpnKNHjyIoKEhKcgAgMjIS+fn5SE5O1th2fn4+cnNz1TYiIiJLIlMaZ7NFVpHoXL58GQAQFxeHjz76CNu3b4eHhwfatWuH27dvAwAyMzPh7e2tdp2HhwccHByQmZmpse2EhAS4u7tLm7+/v+luhIiISB+Ph64M3WyQWROduLg4yGSyMrcTJ05AqXyUhk6aNAlvvfUWQkJCsGrVKshkMmzevFlqTyaTlfgMIUSp+x+bOHEicnJypC0tLc34N0pERERmYdY5OsOHD0fPnj3LPKdOnTrIy8sDADRu3FjaL5fLUbduXaSmpgIAfHx88Ntvv6ldm52djcLCwhI9Parkcjnkcrm+t0BERGR6fGCg3sya6Hh5ecHLy+up54WEhEAul+PChQt48cUXAQCFhYW4evUqAgICAAChoaGIj49HRkYGfH19ATyaoCyXyxESEmK6myAiIjIxrnWlP6uounJzc8OwYcMwZcoU+Pv7IyAgAHPmzAEAdOvWDQAQERGBxo0bo0+fPpgzZw5u376NsWPHYsiQIay4IiIi68bycr1ZRaIDAHPmzEGlSpXQp08fPHjwAK1bt8a+ffvg4eEBALC3t8eOHTsQExODtm3bwsnJCb169cLcuXPNHDkRERGZi0wIG03xNMjNzYW7uztycnLYE2TDwu26Sa+TlJvLOJOIbJmpf2c8br99y4moZO9oUFtFiof45WSCzf1+s4ryciIiIlv2eI6OoZuuFi9ejMDAQDg6OiIkJASHDh3SeO6WLVsQHh6O6tWrw83NDaGhodizZ48ht20UTHSIiIiohE2bNiE2NhaTJk1CSkoKXnrpJURFRUnVzsUdPHgQ4eHh2LlzJ5KTk9G+fXtER0cjJSWlnCNXx6GrYjh0RQCHrohIO+U1dPVKiw9Ryd6wR6EUKfKx79RMrWNt3bo1WrZsiSVLlkj7GjVqhK5duyIhIUGrz2zSpAl69OiByZMn6x23odijQ0REZOmM+GTk4sse5efnl/i4goICJCcnIyIiQm1/REQEjhw5olXISqUSeXl5qFatmuH3bwAmOkRERDbE399fbemj0npnbt68CYVCUeKBu97e3mUuq6Rq3rx5uHfvHrp3726UuPVlNeXlRERENksJQPNqRtq3ASAtLU1t6Kqs1QGKL6H0tGWVHtuwYQPi4uLwww8/oEaNGvrFayRMdMhmlTUPh/NybIPqz4C2TPmzoU88tsZW/9805pOR3dzcnjpHx8vLC/b29iV6b7KysspcVgl4NIl50KBB2Lx5Mzp06GBQzMbAoSsiIiJLV86rlzs4OCAkJARJSUlq+5OSktCmTRuN123YsAH9+/fH+vXr0alTJ71v15jYo0NEREQljB49Gn369EGrVq0QGhqK5cuXIzU1FcOGDQMATJw4Eenp6Vi7di2AR0lO37598emnn+KFF16QeoOcnJzg7u5utvtgokNERGTpzLDWVY8ePXDr1i1MmzYNGRkZCAoKws6dO6XFtDMyMtSeqbNs2TIUFRXh/fffx/vvvy/t79evH1avXm1Y7AZgokNERGTpzLSoZ0xMDGJiYko9Vjx52b9/vx5BmR7n6BAREVGFxR4dsimaqlqK77fVyg59aapg07aKqPjX25KrjyriPVkTY3wdrfL/byOWl9saJjpEREQWzpjl5baGQ1dERERUYbFHh4iIyNKZaTJyRcBEh4iIyNIpBSAzMFFR2maiw6ErIiIiqrDYo0Nqylr/yVRt61tFoakNY8Rtyq+DuZRX1Y8+n1MRK5Iq4j1VFJq+Nxb9/zqHrvTGRIeIiMjiGSHRARMdIiIiskTs0dEb5+gQERFRhcUeHSIiIkunFDB46MlGq66Y6BAREVk6oXy0GdqGDeLQFREREVVY7NGxAuYqUzXl5xqjbW0X6NTEoktJy8CyZSLjsZp/BzgZWW9MdIiIiCwd5+jojUNXREREVGGxR4eIiMjScehKb0x0iIiILJ2AERIdo0RidTh0RURERBWW1fToXLx4EePGjcOvv/6KgoICNG3aFNOnT0f79u2lc1JTU/H+++9j3759cHJyQq9evTB37lw4ODiYMXL9FohkZY3+zFVFYcpqL/48EBmP1VRaqeLQld6spkenU6dOKCoqwr59+5CcnIwWLVrg9ddfR2ZmJgBAoVCgU6dOuHfvHg4fPoyNGzfiu+++w5gxY8wcORERkYGUSuNsNsgqEp2bN2/i0qVL+PDDD9GsWTPUr18fM2fOxP3793H27FkAwN69e3Hu3Dl8/fXXCA4ORocOHTBv3jysWLECubm5Zr4DIiIiAzzu0TF0s0FWkeh4enqiUaNGWLt2Le7du4eioiIsW7YM3t7eCAkJAQAcPXoUQUFB8PPzk66LjIxEfn4+kpOTNbadn5+P3NxctY2IiIgqBquYoyOTyZCUlIQuXbrA1dUVdnZ28Pb2xu7du1G1alUAQGZmJry9vdWu8/DwgIODgzS8VZqEhARMnTrVlOETEREZhnN09GbWHp24uDjIZLIytxMnTkAIgZiYGNSoUQOHDh3C77//ji5duuD1119HRkaG1J5MJivxGUKIUvc/NnHiROTk5EhbWlqaSe6ViIhIb0phnM0GmbVHZ/jw4ejZs2eZ59SpUwf79u3D9u3bkZ2dDTc3NwDA4sWLkZSUhDVr1uDDDz+Ej48PfvvtN7Vrs7OzUVhYWKKnR5VcLodcLjf8ZsrASqvyZeyvY1nfP30+i99nIvPSpxKWrJdZEx0vLy94eXk99bz79+8DAOzs1Dug7OzsoPx3FnloaCji4+ORkZEBX19fAI8mKMvlcmkeDxERkTUSQgkhDKuaMvR6a2UVk5FDQ0Ph4eGBfv364b///a/0TJ0rV66gU6dOAICIiAg0btwYffr0QUpKCn7++WeMHTsWQ4YMkXqBiIiIrJIwwrAV5+hYLi8vL+zevRt3797FK6+8glatWuHw4cP44Ycf0Lx5cwCAvb09duzYAUdHR7Rt2xbdu3dH165dMXfuXDNHT0REROZiFVVXANCqVSvs2bOnzHNq166N7du3l1NERERE5UQIGLxYlY326FhNokNERGSzlEpAZuAcG87RISIiIqpY2KOjQRf3vqgkq2y2z1cteWQ5snnx609UcRn6/3eRKDRSJE/BoSu9MdEhIiKycEKphDBw6MpWy8uZ6BAREVk69ujojXN0iIiIqMJijw4REZGlUwpAxh4dfTDRISIisnRCADC0vJyJDlkQVvoQEREZjokOERGRhRNKAWHg0JVgjw4RERFZJKGE4UNXtllezqorIiIiqrDYo0NERGThOHSlPyY6RERElo5DV3pjolPM44y3CIUGP4SSiIgqtiI8WuvK1L0lxvid9DhWW8NEp5i8vDwAwGHsNHMkRERkLfLy8uDu7m70dh0cHODj44PDmcb5neTj4wMHBwejtGUtZMJWB+00UCqVuH79OlxdXSGTycwSQ25uLvz9/ZGWlgY3NzezxGAqvDfrxHuzThX53gDLuD8hBPLy8uDn5wc7O9PU9zx8+BAFBQVGacvBwQGOjo5GactasEenGDs7O9SqVcvcYQAA3NzcKuQ/TgDvzVrx3qxTRb43wPz3Z4qeHFWOjo42l5wYE8vLiYiIqMJiokNEREQVFhMdCySXyzFlyhTI5XJzh2J0vDfrxHuzThX53oCKf39kHJyMTERERBUWe3SIiIiowmKiQ0RERBUWEx0iIiKqsJjoEBERUYXFRMeM4uPj0aZNGzg7O6Nq1aqlniOTyUpsS5cuVTvn9OnTaNeuHZycnFCzZk1MmzbN7KvUanNvqampiI6ORpUqVeDl5YWRI0eWePqnJd5baerUqVPi+/Thhx+qnaPN/VqqxYsXIzAwEI6OjggJCcGhQ4fMHZJO4uLiSnx/fHx8pONCCMTFxcHPzw9OTk4ICwvD2bNnzRhx2Q4ePIjo6Gj4+flBJpPh+++/Vzuuzf3k5+djxIgR8PLyQpUqVdC5c2f8/fff5XgXpXvavfXv37/E9/KFF15QO8dS743Mg4mOGRUUFKBbt2547733yjxv1apVyMjIkLZ+/fpJx3JzcxEeHg4/Pz8cP34cCxcuxNy5czF//nxTh1+mp92bQqFAp06dcO/ePRw+fBgbN27Ed999hzFjxkjnWOq9aTJt2jS179NHH30kHdPmfi3Vpk2bEBsbi0mTJiElJQUvvfQSoqKikJqaau7QdNKkSRO178/p06elY7Nnz8b8+fOxaNEiHD9+HD4+PggPD5fWvrM09+7dQ/PmzbFo0aJSj2tzP7Gxsdi6dSs2btyIw4cP4+7du3j99dehUCjK6zZK9bR7A4COHTuqfS937lRfB8pS743MRJDZrVq1Sri7u5d6DIDYunWrxmsXL14s3N3dxcOHD6V9CQkJws/PTyiVSiNHqjtN97Zz505hZ2cn0tPTpX0bNmwQcrlc5OTkCCEs/95UBQQEiAULFmg8rs39Wqrnn39eDBs2TG1fw4YNxYcffmimiHQ3ZcoU0bx581KPKZVK4ePjI2bOnCnte/jwoXB3dxdLly4tpwj1V/zfCG3u586dO6Jy5cpi48aN0jnp6enCzs5O7N69u9xif5rS/v3r16+f6NKli8ZrrOXeqPywR8cKDB8+HF5eXnjuueewdOlSKJVK6djRo0fRrl07tQdmRUZG4vr167h69aoZotXO0aNHERQUBD8/P2lfZGQk8vPzkZycLJ1jTfc2a9YseHp6okWLFoiPj1cbltLmfi1RQUEBkpOTERERobY/IiICR44cMVNU+vnzzz/h5+eHwMBA9OzZE5cvXwYAXLlyBZmZmWr3KJfL0a5dO6u7R0C7+0lOTkZhYaHaOX5+fggKCrKKe96/fz9q1KiBBg0aYMiQIcjKypKOWfu9kfFxUU8L98knn+DVV1+Fk5MTfv75Z4wZMwY3b96UhkUyMzNRp04dtWu8vb2lY4GBgeUdslYyMzOlOB/z8PCAg4MDMjMzpXOs5d5GjRqFli1bwsPDA7///jsmTpyIK1eu4IsvvgCg3f1aops3b0KhUJSI3dvb26LjLq5169ZYu3YtGjRogBs3bmD69Olo06YNzp49K91Hafd47do1c4RrEG3uJzMzEw4ODvDw8ChxjqV/X6OiotCtWzcEBATgypUr+Pjjj/HKK68gOTkZcrncqu+NTIM9OkZW2qTH4tuJEye0bu+jjz5CaGgoWrRogTFjxmDatGmYM2eO2jkymUztvfh3sm7x/YYy9r2VFp8QQm1/ed1baXS53w8++ADt2rVDs2bNMHjwYCxduhQrV67ErVu3NN7L4/spj3sxVGnfB2uI+7GoqCi89dZbaNq0KTp06IAdO3YAANasWSOdY+33WJw+92MN99yjRw906tQJQUFBiI6Oxq5du3Dx4kXpe6qJNdwbmQZ7dIxs+PDh6NmzZ5nnFO+l0MULL7yA3Nxc3LhxA97e3vDx8SnxV8rjbtzif9EZypj35uPjg99++01tX3Z2NgoLC6W4y/PeSmPI/T6uArl06RI8PT21ul9L5OXlBXt7+1K/D5Yc99NUqVIFTZs2xZ9//omuXbsCeNTL4evrK51jrff4uJqsrPvx8fFBQUEBsrOz1Xo+srKy0KZNm/IN2EC+vr4ICAjAn3/+CaBi3RsZB3t0jMzLywsNGzYsc3N0dNS7/ZSUFDg6Okol26GhoTh48KDafJC9e/fCz8/PoISqNMa8t9DQUJw5cwYZGRlqccvlcoSEhJT7vZXGkPtNSUkBAOkXjTb3a4kcHBwQEhKCpKQktf1JSUlW/UsjPz8f58+fh6+vLwIDA+Hj46N2jwUFBThw4IBV3qM29xMSEoLKlSurnZORkYEzZ85Y3T3funULaWlp0v9rFeneyEjMNg2axLVr10RKSoqYOnWqcHFxESkpKSIlJUXk5eUJIYTYtm2bWL58uTh9+rS4dOmSWLFihXBzcxMjR46U2rhz547w9vYWb7/9tjh9+rTYsmWLcHNzE3PnzjXXbQkhnn5vRUVFIigoSLz66qvi5MmT4qeffhK1atUSw4cPl9qw1Hsr7siRI2L+/PkiJSVFXL58WWzatEn4+fmJzp07S+doc7+WauPGjaJy5cpi5cqV4ty5cyI2NlZUqVJFXL161dyhaW3MmDFi//794vLly+LYsWPi9ddfF66urtI9zJw5U7i7u4stW7aI06dPi7ffflv4+vqK3NxcM0deury8POn/KQDSz9+1a9eEENrdz7Bhw0StWrXETz/9JE6ePCleeeUV0bx5c1FUVGSu2xJClH1veXl5YsyYMeLIkSPiypUr4pdffhGhoaGiZs2aVnFvZB5MdMyoX79+AkCJ7ZdffhFCCLFr1y7RokUL4eLiIpydnUVQUJBITEwUhYWFau3873//Ey+99JKQy+XCx8dHxMXFmb38+mn3JsSjZKhTp07CyclJVKtWTQwfPlytlFwIy7y34pKTk0Xr1q2Fu7u7cHR0FM8++6yYMmWKuHfvntp52tyvpfr8889FQECAcHBwEC1bthQHDhwwd0g66dGjh/D19RWVK1cWfn5+4s033xRnz56VjiuVSjFlyhTh4+Mj5HK5ePnll8Xp06fNGHHZfvnll1L//+rXr58QQrv7efDggRg+fLioVq2acHJyEq+//rpITU01w92oK+ve7t+/LyIiIkT16tVF5cqVRe3atUW/fv1KxG2p90bmIRPCAh8zS0RERGQEnKNDREREFRYTHSIiIqqwmOgQERFRhcVEh4iIiCosJjpERERUYTHRISIiogqLiQ4RERFVWEx0qMIJCwtDbGxshfrc/v37S2sy6atOnTrSYqR37tzReN7q1aulJUbI+Pr37y99H77//ntzh0NU4THRITKSLVu24JNPPpHe16lTB4mJieYLqBTTpk1DRkYG3N3dzR1Khbd///5Sk8pPP/1Ubc0zIjItrl5OZCTVqlUzdwhP5erqKq1ubW6FhYWoXLmyucMod+7u7kw0icoRe3SowsvOzkbfvn3h4eEBZ2dnREVF4c8//5SOPx6q2bNnDxo1agQXFxd07NhR7a/uoqIijBw5ElWrVoWnpycmTJiAfv36qQ0nqQ5dhYWF4dq1a/jggw+kYQoAiIuLQ4sWLdTiS0xMVFuNXaFQYPTo0dJnjR8/HsVXahFCYPbs2ahbty6cnJzQvHlzfPvtt3p9fVavXo3atWvD2dkZb7zxBm7dulXinB9//BEhISFwdHRE3bp1MXXqVBQVFUnH//jjD7z44otwdHRE48aN8dNPP6kNzVy9ehUymQzffPMNwsLC4OjoiK+//hoAsGrVKjRq1AiOjo5o2LAhFi9erPbZ6enp6NGjBzw8PODp6YkuXbrg6tWr0vH9+/fj+eefR5UqVVC1alW0bdsW165d0+ren3Zf8+fPR9OmTVGlShX4+/sjJiYGd+/elY5fu3YN0dHR8PDwQJUqVdCkSRPs3LkTV69eRfv27QEAHh4ekMlk6N+/v1YxEZFxMdGhCq9///44ceIEtm3bhqNHj0IIgddeew2FhYXSOffv38fcuXPx1Vdf4eDBg0hNTcXYsWOl47NmzcK6deuwatUq/Prrr8jNzS1zfsWWLVtQq1YtaahIl6GKefPm4csvv8TKlStx+PBh3L59G1u3blU756OPPsKqVauwZMkSnD17Fh988AHeeecdHDhwQPsvDIDffvsNAwcORExMDE6dOoX27dtj+vTpaufs2bMH77zzDkaOHIlz585h2bJlWL16NeLj4wEASqUSXbt2hbOzM3777TcsX74ckyZNKvXzJkyYgJEjR+L8+fOIjIzEihUrMGnSJMTHx+P8+fOYMWMGPv74Y6xZswbAo+9L+/bt4eLigoMHD+Lw4cNSIlpQUICioiJ07doV7dq1w//+9z8cPXoU7777rpRYluVp9wUAdnZ2+Oyzz3DmzBmsWbMG+/btw/jx46Xj77//PvLz83Hw4EGcPn0as2bNgouLC/z9/fHdd98BAC5cuICMjAx8+umnOn1viMhIzLqkKJEJtGvXTowaNUoIIcTFixcFAPHrr79Kx2/evCmcnJzEN998I4QQYtWqVQKAuHTpknTO559/Lry9vaX33t7eYs6cOdL7oqIiUbt2bdGlS5dSP1cIIQICAsSCBQvUYpsyZYpo3ry52r4FCxaIgIAA6b2vr6+YOXOm9L6wsFDUqlVL+qy7d+8KR0dHceTIEbV2Bg0aJN5++22NX5fS4nn77bdFx44d1fb16NFDuLu7S+9feuklMWPGDLVzvvrqK+Hr6yuEEGLXrl2iUqVKIiMjQzqelJQkAIitW7cKIYS4cuWKACASExPV2vH39xfr169X2/fJJ5+I0NBQIYQQK1euFM8++6zaivX5+fnCyclJ7NmzR9y6dUsAEPv379d435o87b5K88033whPT0/pfdOmTUVcXFyp5z5ehTs7O7vU46pfHyIyHc7RoQrt/PnzqFSpElq3bi3t8/T0xLPPPovz589L+5ydnVGvXj3pva+vL7KysgAAOTk5uHHjBp5//nnpuL29PUJCQqBUKo0ab05ODjIyMhAaGirtq1SpElq1aiUNX507dw4PHz5EeHi42rUFBQUIDg7W6fPOnz+PN954Q21faGgodu/eLb1PTk7G8ePH1Xo6FAoFHj58iPv37+PChQvw9/dXm/uj+rVS1apVK+n1P//8g7S0NAwaNAhDhgyR9hcVFUlzWJKTk3Hp0iW4urqqtfPw4UP89ddfiIiIQP/+/REZGYnw8HB06NAB3bt3h6+v71Pv/Wn35ezsjF9++QUzZszAuXPnkJubi6KiIjx8+BD37t1DlSpVMHLkSLz33nvYu3cvOnTogLfeegvNmjV76mcTUflhokMVmig2t0V1v+rwRvFJsTKZrMS1xYdDNLVdFjs7uxLXqQ6haeNxcrVjxw7UrFlT7ZhcLtepLW3uQalUYurUqXjzzTdLHHN0dCzxtSxLlSpV1NoFgBUrVqglosCjRPLxOSEhIVi3bl2JtqpXrw7g0RyfkSNHYvfu3di0aRM++ugjJCUl4YUXXjDovq5du4bXXnsNw4YNwyeffIJq1arh8OHDGDRokPQ9Gzx4MCIjI7Fjxw7s3bsXCQkJmDdvHkaMGKHV14OITI+JDlVojRs3RlFREX777Te0adMGAHDr1i1cvHgRjRo10qoNd3d3eHt74/fff8dLL70E4NFf/ikpKSUmFqtycHCAQqFQ21e9enVkZmaqJQenTp1S+yxfX18cO3YML7/8MoBHPRzJyclo2bKldE9yuRypqalo166dVvegSePGjXHs2DG1fcXft2zZEhcuXMAzzzxTahsNGzZEamoqbty4AW9vbwDA8ePHn/rZ3t7eqFmzJi5fvozevXuXek7Lli2xadMm1KhRA25ubhrbCg4ORnBwMCZOnIjQ0FCsX7/+qYnO0+7rxIkTKCoqwrx582Bn92g64zfffFPiPH9/fwwbNgzDhg3DxIkTsWLFCowYMQIODg4AUOJngIjKFxMdqtDq16+PLl26YMiQIVi2bBlcXV3x4YcfombNmujSpYvW7YwYMQIJCQl45pln0LBhQyxcuBDZ2dll9mTUqVMHBw8eRM+ePSGXy+Hl5YWwsDD8888/mD17Nv7zn/9g9+7d2LVrl9ov8VGjRmHmzJmoX78+GjVqhPnz56s9i8XV1RVjx47FBx98AKVSiRdffBG5ubk4cuQIXFxc0K9fP63va+TIkWjTpg1mz56Nrl27Yu/evWrDVgAwefJkvP766/D390e3bt1gZ2eH//3vfzh9+jSmT5+O8PBw1KtXD/369cPs2bORl5cnTUZ+Wk9PXFwcRo4cCTc3N0RFRSE/Px8nTpxAdnY2Ro8ejd69e2POnDno0qULpk2bhlq1aiE1NRVbtmzBuHHjUFhYiOXLl6Nz587w8/PDhQsXcPHiRfTt2/ep9/60+6pXrx6KioqwcOFCREdH49dff8XSpUvV2oiNjUVUVBQaNGiA7Oxs7Nu3T0qgAwICIJPJsH37drz22mtwcnKCi4uL1t8bIjISs80OIjKR4pOCb9++Lfr06SPc3d2Fk5OTiIyMFBcvXpSOr1q1Sm3yrRBCbN26Vaj+71FYWCiGDx8u3NzchIeHh5gwYYLo1q2b6Nmzp8bPPXr0qGjWrJmQy+VqbS1ZskT4+/uLKlWqiL59+4r4+Hi1yciFhYVi1KhRws3NTVStWlWMHj1a9O3bV23is1KpFJ9++ql49tlnReXKlUX16tVFZGSkOHDggMavS2mTkYV4NOG3Vq1awsnJSURHR4u5c+eW+Hrs3r1btGnTRjg5OQk3Nzfx/PPPi+XLl0vHz58/L9q2bSscHBxEw4YNxY8//igAiN27dwshnkxGTklJKfH569atEy1atBAODg7Cw8NDvPzyy2LLli3S8YyMDNG3b1/h5eUl5HK5qFu3rhgyZIjIyckRmZmZomvXrsLX11c4ODiIgIAAMXnyZKFQKDR+HXS5r/nz5wtfX1/p52bt2rVqE4yHDx8u6tWrJ+Ryuahevbro06ePuHnzpnT9tGnThI+Pj5DJZKJfv35qnw1ORiYqFzIh9JhoQGTjlEolGjVqhO7du6s9DdmS1alTB7GxseWyPMavv/6KF198EZcuXVKb5E1PyGQybN261eClPYiobHyODpEWrl27hhUrVuDixYs4ffo03nvvPVy5cgW9evUyd2g6mTBhAlxcXJCTk2PUdrdu3YqkpCRcvXoVP/30E9599120bduWSU4phg0bxiEsonLEHh0iLaSlpaFnz544c+YMhBAICgrCzJkzpQnD1uDatWtStVDdunWlCbbGsHbtWnzyySdIS0uDl5cXOnTogHnz5sHT09Non6GrJk2aaHxC8rJlyzROgDa1rKws5ObmAnj0GAPVSjQiMj4mOkRUIakmdsV5e3uXeDYPEVVMTHSIiIiowuIcHSIiIqqwmOgQERFRhcVEh4iIiCosJjpERERUYTHRISIiogqLiQ4RERFVWEx0iIiIqMJiokNEREQV1v8DfbDKusPqH5IAAAAASUVORK5CYII=\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "gdf = geopandas.read_file(geopandas.datasets.get_path('naturalearth_lowres'))\n", - "geo_grid = make_geocube(\n", - " vector_data=gdf,\n", - " measurements=['pop_est'],\n", - " resolution=(-3.0, 3.0),\n", - ")\n", - "geo_grid[\"pop_est\"].plot()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Convert to GeoDataFrame (vectorize)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "grid_gdf = vectorize(geo_grid.pop_est.astype(\"float32\"))\n", - "grid_gdf.plot(column=\"pop_est\")" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.15" - }, - "vscode": { - "interpreter": { - "hash": "f7286e25aede83087e9621ce0e75f07eda2aa917ee0205c1a0b96e7ba4f20042" - } - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/refs/tags/0.5.2/_sources/examples/zonal_statistics.ipynb.txt b/refs/tags/0.5.2/_sources/examples/zonal_statistics.ipynb.txt deleted file mode 100644 index 6e348407..00000000 --- a/refs/tags/0.5.2/_sources/examples/zonal_statistics.ipynb.txt +++ /dev/null @@ -1,845 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Example - Zonal Statistics\n", - "This is useful in the case where you want to get regional statistics for a raster." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import geopandas\n", - "import numpy\n", - "import rioxarray\n", - "import xarray\n", - "\n", - "from geocube.api.core import make_geocube\n", - "\n", - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Create the data mask by rasterizing the unique ID of the vector data\n", - "\n", - "See docs for [make_geocube](../geocube.rst#make-geocube)" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "# This assumes you are running this example from a clone of\n", - "# https://github.com/corteva/geocube/\n", - "# You could also use the full path: \n", - "# https://raw.githubusercontent.com/corteva/geocube/master/test/test_data/input/soil_data_group.geojson\n", - "ssurgo_data = geopandas.read_file(\"../../test/test_data/input/soil_data_group.geojson\")\n", - "ssurgo_data = ssurgo_data.loc[ssurgo_data.hzdept_r==0]\n", - "# convert the key to group to the vector data to an integer as that is one of the\n", - "# best data types for this type of mapping. If your data is not integer,\n", - "# then consider using a mapping of your data to an integer with something\n", - "# like a categorical dtype.\n", - "ssurgo_data[\"mukey\"] = ssurgo_data.mukey.astype(int)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "# load in source elevation data subset relevant for the vector data\n", - "elevation = rioxarray.open_rasterio(\n", - " \"https://prd-tnm.s3.amazonaws.com/StagedProducts/Elevation/13/TIFF/current/n42w091/USGS_13_n42w091.tif\", masked=True\n", - ").rio.clip(\n", - " ssurgo_data.geometry.values, ssurgo_data.crs, from_disk=True\n", - ").sel(band=1).drop(\"band\")\n", - "elevation.name = \"elevation\"" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "out_grid = make_geocube(\n", - " vector_data=ssurgo_data,\n", - " measurements=[\"mukey\"],\n", - " like=elevation, # ensure the data are on the same grid\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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-       "Data variables:\n",
-       "    mukey        (y, x) float64 1.988e+05 1.988e+05 ... 1.987e+05 1.987e+05\n",
-       "    elevation    (y, x) float64 173.7 172.0 171.1 170.6 ... 181.0 181.2 181.4
" - ], - "text/plain": [ - "\n", - "Dimensions: (y: 178, x: 178)\n", - "Coordinates:\n", - " * y (y) float64 41.5 41.5 41.5 41.5 ... 41.48 41.48 41.48 41.48\n", - " * x (x) float64 -90.6 -90.6 -90.6 -90.6 ... -90.58 -90.58 -90.58\n", - " spatial_ref int64 0\n", - "Data variables:\n", - " mukey (y, x) float64 1.988e+05 1.988e+05 ... 1.987e+05 1.987e+05\n", - " elevation (y, x) float64 173.7 172.0 171.1 170.6 ... 181.0 181.2 181.4" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# merge the two together\n", - "out_grid[\"elevation\"] = (elevation.dims, elevation.values, elevation.attrs, elevation.encoding)\n", - "out_grid" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", 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", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "out_grid.elevation.plot()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Get the elevation statistics of each region using the mask" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "grouped_elevation = out_grid.drop(\"spatial_ref\").groupby(out_grid.mukey)\n", - "grid_mean = grouped_elevation.mean().rename({\"elevation\": \"elevation_mean\"})\n", - "grid_min = grouped_elevation.min().rename({\"elevation\": \"elevation_min\"})\n", - "grid_max = grouped_elevation.max().rename({\"elevation\": \"elevation_max\"})\n", - "grid_std = grouped_elevation.std().rename({\"elevation\": \"elevation_std\"})" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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elevation_meanelevation_minelevation_maxelevation_std
mukey
198692.0173.130925169.394562188.4425053.307044
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198724.0179.931131178.237244181.4903870.630699
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" - ], - "text/plain": [ - " elevation_mean elevation_min elevation_max elevation_std\n", - "mukey \n", - "198692.0 173.130925 169.394562 188.442505 3.307044\n", - "198714.0 175.045866 170.214157 179.716675 2.150987\n", - "198724.0 179.931131 178.237244 181.490387 0.630699\n", - "198750.0 176.187118 167.951233 190.138763 3.815206\n", - "198754.0 171.633084 167.610321 181.611298 2.997591\n", - "271425.0 167.974433 167.951233 168.653015 0.079769\n", - "271431.0 176.718101 170.258133 180.460220 2.731732" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "zonal_stats = xarray.merge([grid_mean, grid_min, grid_max, grid_std]).to_dataframe()\n", - "zonal_stats" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "ssurgo_data = ssurgo_data.merge(zonal_stats, on=\"mukey\")" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "ssurgo_data.plot(column=\"elevation_max\", legend=True)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.10" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/refs/tags/0.5.2/_sources/geocube.rst.txt b/refs/tags/0.5.2/_sources/geocube.rst.txt deleted file mode 100644 index 12ae28df..00000000 --- a/refs/tags/0.5.2/_sources/geocube.rst.txt +++ /dev/null @@ -1,38 +0,0 @@ -Core -================== - -make_geocube -------------------------- - -.. autofunction:: geocube.api.core.make_geocube - - -rasterize -------------------------- - -.. autofunction:: geocube.rasterize.rasterize_image - -.. autofunction:: geocube.rasterize.rasterize_points_griddata - -.. autofunction:: geocube.rasterize.rasterize_points_radial - - -vectorize -------------------------- - -.. autofunction:: geocube.vector.vectorize - - -show_versions -------------------------- - -.. autofunction:: geocube.show_versions - - -exceptions -------------------------- - -.. automodule:: geocube.exceptions - :members: - :undoc-members: - :show-inheritance: diff --git a/refs/tags/0.5.2/_sources/getting_started.rst.txt b/refs/tags/0.5.2/_sources/getting_started.rst.txt deleted file mode 100644 index 585d0257..00000000 --- a/refs/tags/0.5.2/_sources/getting_started.rst.txt +++ /dev/null @@ -1,43 +0,0 @@ -Getting Started -================ - -`geocube` combines the interfaces of: - -- `geopandas `__ -- `xarray `__ -- `rioxarray `__ - -and is powered by `GDAL `__ using: - -- `rasterio `__ -- `fiona `__ -- `odc-geo `__ - - -When getting started, the API documentation to start reading would be :func:`geocube.api.core.make_geocube`. - -The simplest example would be to rasterize a single column: - -.. code-block:: python - - from geocube.api.core import make_geocube - - out_grid = make_geocube( - vector_data="path_to_file.gpkg", - measurements=["column_name"], - resolution=(-0.0001, 0.0001), - ) - out_grid["column_name"].rio.to_raster("my_rasterized_column.tif") - - -You can also rasterize a `GeoDataFrame `__ -directly in the `vector_data` argument. This enables you to `load in subsets of data `__ -or perform various operations before rasterization. - -Once finished, you can write to anything supported by `rasterio `__ -using `rioxarray`'s `rio.to_raster() `__ method. -You can also write to a netCDF file using `xarray`'s `to_netcdf() `__. - -However, life is only this simple when your data is perfectly clean, geospatially unique, and numeric. -The good news is that geocube supports a variety of use cases and custom rasterization functions if your dataset -does not meet these criteria (see: :ref:`usage_examples`). diff --git a/refs/tags/0.5.2/_sources/index.rst.txt b/refs/tags/0.5.2/_sources/index.rst.txt deleted file mode 100644 index 7d106db8..00000000 --- a/refs/tags/0.5.2/_sources/index.rst.txt +++ /dev/null @@ -1,23 +0,0 @@ -Welcome to geocube's documentation! -====================================== - -GitHub: http://github.com/corteva/geocube - -.. toctree:: - :maxdepth: 1 - :caption: Contents: - - readme - installation - getting_started - examples/examples - modules - contributing - authors - history - -Indices and tables -================== -* :ref:`genindex` -* :ref:`modindex` -* :ref:`search` diff --git a/refs/tags/0.5.2/_sources/installation.rst.txt b/refs/tags/0.5.2/_sources/installation.rst.txt deleted file mode 100644 index 4e549c87..00000000 --- a/refs/tags/0.5.2/_sources/installation.rst.txt +++ /dev/null @@ -1,65 +0,0 @@ -.. highlight:: shell - -============ -Installation -============ - - -Stable release --------------- - -Use pip to install from `PyPI `__: - - -Install from pip: - -.. code-block:: bash - - python -m pip install geocube - - -Use `conda `__ with the `conda-forge `__ channel: - -.. code-block:: bash - - conda config --prepend channels conda-forge - conda config --set channel_priority strict - conda create -n geocube_env geocube - conda activate geocube_env - -- `geocube` `conda-forge repository `__ - -.. note:: - "... we recommend always installing your packages inside a - new environment instead of the base environment from - anaconda/miniconda. Using envs make it easier to - debug problems with packages and ensure the stability - of your root env." - -- https://conda-forge.org/docs/user/tipsandtricks.html - -.. warning:: - Avoid using `pip install` with a conda environment. If you encounter - a python package that isn't in conda-forge, consider submitting a - recipe: https://github.com/conda-forge/staged-recipes/ - - -From source ------------ - -The source for geocube can be installed from the `GitHub repo`_. - -.. code-block:: bash - - python -m pip install git+git://github.com/corteva/geocube.git#egg=geocube - - -To install for local development: - -.. code-block:: bash - - git clone git@github.com:corteva/geocube.git - cd geocube - python -m pip install -e .[dev] - - -.. _GitHub repo: https://github.com/corteva/geocube diff --git a/refs/tags/0.5.2/_sources/modules.rst.txt b/refs/tags/0.5.2/_sources/modules.rst.txt deleted file mode 100644 index 1461fbbe..00000000 --- a/refs/tags/0.5.2/_sources/modules.rst.txt +++ /dev/null @@ -1,11 +0,0 @@ -API Documentation -================= - -This page serves as the index for the lower level per-module documentation tree. - -.. toctree:: - :maxdepth: 6 - :caption: Contents: - - geocube - cli diff --git a/refs/tags/0.5.2/_sources/readme.rst.txt b/refs/tags/0.5.2/_sources/readme.rst.txt deleted file mode 100644 index 72a33558..00000000 --- a/refs/tags/0.5.2/_sources/readme.rst.txt +++ /dev/null @@ -1 +0,0 @@ -.. include:: ../README.rst diff --git a/refs/tags/0.5.2/_static/_sphinx_javascript_frameworks_compat.js b/refs/tags/0.5.2/_static/_sphinx_javascript_frameworks_compat.js deleted file mode 100644 index 81415803..00000000 --- a/refs/tags/0.5.2/_static/_sphinx_javascript_frameworks_compat.js +++ /dev/null @@ -1,123 +0,0 @@ -/* Compatability shim for jQuery and underscores.js. - * - * Copyright Sphinx contributors - * Released under the two clause BSD licence - */ - -/** - * small helper function to urldecode strings - * - * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/decodeURIComponent#Decoding_query_parameters_from_a_URL - */ -jQuery.urldecode = function(x) { - if (!x) { - return x - } - return decodeURIComponent(x.replace(/\+/g, ' ')); -}; - -/** - * small helper function to urlencode strings - */ -jQuery.urlencode = encodeURIComponent; - -/** - * This function returns the parsed url parameters of the - * current request. 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- -const SPHINX_HIGHLIGHT_ENABLED = true - -/** - * highlight a given string on a node by wrapping it in - * span elements with the given class name. - */ -const _highlight = (node, addItems, text, className) => { - if (node.nodeType === Node.TEXT_NODE) { - const val = node.nodeValue; - const parent = node.parentNode; - const pos = val.toLowerCase().indexOf(text); - if ( - pos >= 0 && - !parent.classList.contains(className) && - !parent.classList.contains("nohighlight") - ) { - let span; - - const closestNode = parent.closest("body, svg, foreignObject"); - const isInSVG = closestNode && closestNode.matches("svg"); - if (isInSVG) { - span = document.createElementNS("http://www.w3.org/2000/svg", "tspan"); - } else { - span = document.createElement("span"); - span.classList.add(className); - } - - span.appendChild(document.createTextNode(val.substr(pos, text.length))); - const rest = document.createTextNode(val.substr(pos + text.length)); - parent.insertBefore( - span, - parent.insertBefore( - rest, - node.nextSibling - ) - ); 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' + - '' + - _("Hide Search Matches") + - "

" - ) - ); - }, - - /** - * helper function to hide the search marks again - */ - hideSearchWords: () => { - document - .querySelectorAll("#searchbox .highlight-link") - .forEach((el) => el.remove()); - document - .querySelectorAll("span.highlighted") - .forEach((el) => el.classList.remove("highlighted")); - localStorage.removeItem("sphinx_highlight_terms") - }, - - initEscapeListener: () => { - // only install a listener if it is really needed - if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) return; - - document.addEventListener("keydown", (event) => { - // bail for input elements - if (BLACKLISTED_KEY_CONTROL_ELEMENTS.has(document.activeElement.tagName)) return; - // bail with special keys - if (event.shiftKey || event.altKey || event.ctrlKey || event.metaKey) return; - if (DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS && (event.key === "Escape")) { - SphinxHighlight.hideSearchWords(); - event.preventDefault(); - } - }); - }, -}; - -_ready(() => { - /* Do not call highlightSearchWords() when we are on the search page. - * It will highlight words from the *previous* search query. - */ - if (typeof Search === "undefined") SphinxHighlight.highlightSearchWords(); - SphinxHighlight.initEscapeListener(); -}); diff --git a/refs/tags/0.5.2/examples/categorical.ipynb b/refs/tags/0.5.2/examples/categorical.ipynb deleted file mode 100644 index 580303f0..00000000 --- a/refs/tags/0.5.2/examples/categorical.ipynb +++ /dev/null @@ -1,783 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Example - Categorical Data" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import geopandas\n", - "import pandas\n", - "\n", - "from geocube.api.core import make_geocube\n", - "\n", - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Load in soil data" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "ssurgo_data = geopandas.read_file(\"../../test/test_data/input/soil_data_group.geojson\")" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# original data\n", - "ssurgo_data[ssurgo_data.hzdept_r==15].plot(column='sandtotal_r')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Generate categories for categorical data" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If your data is only a subset of all of the data, the list of categories you get will likely not be complete.\n", - "\n", - "NOTE: The categories will be made unique and sorted internally if they are not already." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['Somewhat poorly drained',\n", - " 'Poorly drained',\n", - " 'Well drained',\n", - " 'Excessively drained']" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# this is only a subset of all of the classes\n", - "ssurgo_data.drclassdcd.drop_duplicates().values.tolist()" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "# complete list of categories\n", - "drclasses_complete = [\n", - " 'Poorly drained',\n", - " 'Somewhat poorly drained',\n", - " 'Excessively drained',\n", - " 'Subaqueous',\n", - " 'Well drained',\n", - " 'Somewhat excessively drained',\n", - " 'Very poorly drained',\n", - " 'Moderately well drained'\n", - "]" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "categorical_enums = {'drclassdcd': drclasses_complete}" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Convert data to grid\n", - "\n", - "See docs for [make_geocube](../geocube.rst#make-geocube)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "out_grid = make_geocube(\n", - " vector_data=ssurgo_data,\n", - " output_crs=\"epsg:32615\",\n", - " group_by='hzdept_r',\n", - " resolution=(-100, 100),\n", - " categorical_enums=categorical_enums\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
<xarray.Dataset>\n",
-       "Dimensions:                (drclassdcd_categories: 9, hzdept_r: 11, x: 15, y: 19)\n",
-       "Coordinates:\n",
-       "  * y                      (y) float64 4.597e+06 4.597e+06 ... 4.595e+06\n",
-       "  * x                      (x) float64 7.004e+05 7.004e+05 ... 7.018e+05\n",
-       "  * hzdept_r               (hzdept_r) float64 0.0 5.0 15.0 ... 105.0 120.0 150.0\n",
-       "  * drclassdcd_categories  (drclassdcd_categories) object 'Excessively drained' ... 'nodata'\n",
-       "    spatial_ref            int64 0\n",
-       "Data variables:\n",
-       "    drclassdcd             (hzdept_r, y, x) float64 -1.0 -1.0 -1.0 ... 7.0 7.0\n",
-       "    hzdepb_r               (hzdept_r, y, x) float64 nan nan nan ... 180.0 180.0\n",
-       "    claytotal_r            (hzdept_r, y, x) float64 nan nan nan ... 21.0 21.0\n",
-       "    sandtotal_r            (hzdept_r, y, x) float64 nan nan nan ... 10.0 10.0\n",
-       "    silttotal_r            (hzdept_r, y, x) float64 nan nan nan ... 69.0 69.0\n",
-       "Attributes:\n",
-       "    grid_mapping:  spatial_ref
" - ], - "text/plain": [ - "\n", - "Dimensions: (drclassdcd_categories: 9, hzdept_r: 11, x: 15, y: 19)\n", - "Coordinates:\n", - " * y (y) float64 4.597e+06 4.597e+06 ... 4.595e+06\n", - " * x (x) float64 7.004e+05 7.004e+05 ... 7.018e+05\n", - " * hzdept_r (hzdept_r) float64 0.0 5.0 15.0 ... 105.0 120.0 150.0\n", - " * drclassdcd_categories (drclassdcd_categories) object 'Excessively drained' ... 'nodata'\n", - " spatial_ref int64 0\n", - "Data variables:\n", - " drclassdcd (hzdept_r, y, x) float64 -1.0 -1.0 -1.0 ... 7.0 7.0\n", - " hzdepb_r (hzdept_r, y, x) float64 nan nan nan ... 180.0 180.0\n", - " claytotal_r (hzdept_r, y, x) float64 nan nan nan ... 21.0 21.0\n", - " sandtotal_r (hzdept_r, y, x) float64 nan nan nan ... 10.0 10.0\n", - " silttotal_r (hzdept_r, y, x) float64 nan nan nan ... 69.0 69.0\n", - "Attributes:\n", - " grid_mapping: spatial_ref" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "out_grid" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# mask nodata and plot\n", - "clay_slice = out_grid.claytotal_r.sel(hzdept_r=15)\n", - "clay_slice.where(clay_slice!=out_grid.claytotal_r.rio.nodata).plot()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Dealing with categorical data" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Because the data needs to be numerical for conversion from vector to raster, the code displays the categories as numbers. To convert back to strings, you will need to use the categories provided to convert back." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "drclassdcd_slice = out_grid.drclassdcd.sel(hzdept_r=15)\n", - "drclassdcd_slice.where(drclassdcd_slice!=out_grid.drclassdcd.rio.nodata).plot()" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "drclassdcd_string = out_grid['drclassdcd_categories'][out_grid['drclassdcd'].astype(int)]\\\n", - " .drop('drclassdcd_categories')" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
<xarray.Dataset>\n",
-       "Dimensions:                (drclassdcd_categories: 9, hzdept_r: 11, x: 15, y: 19)\n",
-       "Coordinates:\n",
-       "  * y                      (y) float64 4.597e+06 4.597e+06 ... 4.595e+06\n",
-       "  * x                      (x) float64 7.004e+05 7.004e+05 ... 7.018e+05\n",
-       "  * hzdept_r               (hzdept_r) float64 0.0 5.0 15.0 ... 105.0 120.0 150.0\n",
-       "  * drclassdcd_categories  (drclassdcd_categories) object 'Excessively drained' ... 'nodata'\n",
-       "    spatial_ref            int64 0\n",
-       "Data variables:\n",
-       "    drclassdcd             (hzdept_r, y, x) object 'nodata' ... 'Well drained'\n",
-       "    hzdepb_r               (hzdept_r, y, x) float64 nan nan nan ... 180.0 180.0\n",
-       "    claytotal_r            (hzdept_r, y, x) float64 nan nan nan ... 21.0 21.0\n",
-       "    sandtotal_r            (hzdept_r, y, x) float64 nan nan nan ... 10.0 10.0\n",
-       "    silttotal_r            (hzdept_r, y, x) float64 nan nan nan ... 69.0 69.0\n",
-       "Attributes:\n",
-       "    grid_mapping:  spatial_ref
" - ], - "text/plain": [ - "\n", - "Dimensions: (drclassdcd_categories: 9, hzdept_r: 11, x: 15, y: 19)\n", - "Coordinates:\n", - " * y (y) float64 4.597e+06 4.597e+06 ... 4.595e+06\n", - " * x (x) float64 7.004e+05 7.004e+05 ... 7.018e+05\n", - " * hzdept_r (hzdept_r) float64 0.0 5.0 15.0 ... 105.0 120.0 150.0\n", - " * drclassdcd_categories (drclassdcd_categories) object 'Excessively drained' ... 'nodata'\n", - " spatial_ref int64 0\n", - "Data variables:\n", - " drclassdcd (hzdept_r, y, x) object 'nodata' ... 'Well drained'\n", - " hzdepb_r (hzdept_r, y, x) float64 nan nan nan ... 180.0 180.0\n", - " claytotal_r (hzdept_r, y, x) float64 nan nan nan ... 21.0 21.0\n", - " sandtotal_r (hzdept_r, y, x) float64 nan nan nan ... 10.0 10.0\n", - " silttotal_r (hzdept_r, y, x) float64 nan nan nan ... 69.0 69.0\n", - "Attributes:\n", - " grid_mapping: spatial_ref" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "out_grid['drclassdcd'] = drclassdcd_string\n", - "out_grid" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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drclassdcdhzdepb_rclaytotal_rsandtotal_rsilttotal_r
hzdept_rxy
0.0700350.04597050.0nodataNaNNaNNaNNaN
4596950.0Well drained5.026.038.036.0
4596850.0Well drained5.026.038.036.0
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" - ], - "text/plain": [ - " drclassdcd hzdepb_r claytotal_r sandtotal_r \\\n", - "hzdept_r x y \n", - "0.0 700350.0 4597050.0 nodata NaN NaN NaN \n", - " 4596950.0 Well drained 5.0 26.0 38.0 \n", - " 4596850.0 Well drained 5.0 26.0 38.0 \n", - " 4596750.0 Well drained 5.0 26.0 38.0 \n", - " 4596650.0 Well drained 5.0 26.0 38.0 \n", - "\n", - " silttotal_r \n", - "hzdept_r x y \n", - "0.0 700350.0 4597050.0 NaN \n", - " 4596950.0 36.0 \n", - " 4596850.0 36.0 \n", - " 4596750.0 36.0 \n", - " 4596650.0 36.0 " - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pdf = out_grid.drop(['spatial_ref', 'drclassdcd_categories']).to_dataframe()\n", - "pdf.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Make sure all categories are represented\n", - "\n", - "To do this, convert the column type to categorical beforehand and make sure that\n", - "you include all of the possible categories." - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "cat_dtype = pandas.api.types.CategoricalDtype(out_grid.drclassdcd_categories.values)\n", - "pdf['drclassdcd'] = pdf['drclassdcd'].astype(cat_dtype)" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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hzdepb_rclaytotal_rsandtotal_rsilttotal_rdrclassdcd_Excessively draineddrclassdcd_Moderately well draineddrclassdcd_Poorly draineddrclassdcd_Somewhat excessively draineddrclassdcd_Somewhat poorly draineddrclassdcd_Subaqueousdrclassdcd_Very poorly draineddrclassdcd_Well draineddrclassdcd_nodata
hzdept_rxy
0.0700350.04597050.0NaNNaNNaNNaN000000001
4596950.05.026.038.036.0000000010
4596850.05.026.038.036.0000000010
4596750.05.026.038.036.0000000010
4596650.05.026.038.036.0000000010
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" - ], - "text/plain": [ - " hzdepb_r claytotal_r sandtotal_r silttotal_r \\\n", - "hzdept_r x y \n", - "0.0 700350.0 4597050.0 NaN NaN NaN NaN \n", - " 4596950.0 5.0 26.0 38.0 36.0 \n", - " 4596850.0 5.0 26.0 38.0 36.0 \n", - " 4596750.0 5.0 26.0 38.0 36.0 \n", - " 4596650.0 5.0 26.0 38.0 36.0 \n", - "\n", - " drclassdcd_Excessively drained \\\n", - "hzdept_r x y \n", - "0.0 700350.0 4597050.0 0 \n", - " 4596950.0 0 \n", - " 4596850.0 0 \n", - " 4596750.0 0 \n", - " 4596650.0 0 \n", - "\n", - " drclassdcd_Moderately well drained \\\n", - "hzdept_r x y \n", - "0.0 700350.0 4597050.0 0 \n", - " 4596950.0 0 \n", - " 4596850.0 0 \n", - " 4596750.0 0 \n", - " 4596650.0 0 \n", - "\n", - " drclassdcd_Poorly drained \\\n", - "hzdept_r x y \n", - "0.0 700350.0 4597050.0 0 \n", - " 4596950.0 0 \n", - " 4596850.0 0 \n", - " 4596750.0 0 \n", - " 4596650.0 0 \n", - "\n", - " drclassdcd_Somewhat excessively drained \\\n", - "hzdept_r x y \n", - "0.0 700350.0 4597050.0 0 \n", - " 4596950.0 0 \n", - " 4596850.0 0 \n", - " 4596750.0 0 \n", - " 4596650.0 0 \n", - "\n", - " drclassdcd_Somewhat poorly drained \\\n", - "hzdept_r x y \n", - "0.0 700350.0 4597050.0 0 \n", - " 4596950.0 0 \n", - " 4596850.0 0 \n", - " 4596750.0 0 \n", - " 4596650.0 0 \n", - "\n", - " drclassdcd_Subaqueous \\\n", - "hzdept_r x y \n", - "0.0 700350.0 4597050.0 0 \n", - " 4596950.0 0 \n", - " 4596850.0 0 \n", - " 4596750.0 0 \n", - " 4596650.0 0 \n", - "\n", - " drclassdcd_Very poorly drained \\\n", - "hzdept_r x y \n", - "0.0 700350.0 4597050.0 0 \n", - " 4596950.0 0 \n", - " 4596850.0 0 \n", - " 4596750.0 0 \n", - " 4596650.0 0 \n", - "\n", - " drclassdcd_Well drained drclassdcd_nodata \n", - "hzdept_r x y \n", - "0.0 700350.0 4597050.0 0 1 \n", - " 4596950.0 1 0 \n", - " 4596850.0 1 0 \n", - " 4596750.0 1 0 \n", - " 4596650.0 1 0 " - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "training_df = pandas.get_dummies(pdf, columns=['drclassdcd'])\n", - "training_df.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Index(['hzdepb_r', 'claytotal_r', 'sandtotal_r', 'silttotal_r',\n", - " 'drclassdcd_Excessively drained', 'drclassdcd_Moderately well drained',\n", - " 'drclassdcd_Poorly drained', 'drclassdcd_Somewhat excessively drained',\n", - " 'drclassdcd_Somewhat poorly drained', 'drclassdcd_Subaqueous',\n", - " 'drclassdcd_Very poorly drained', 'drclassdcd_Well drained',\n", - " 'drclassdcd_nodata'],\n", - " dtype='object')" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "training_df.columns" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.10" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/refs/tags/0.5.2/examples/grid_to_vector_map.ipynb b/refs/tags/0.5.2/examples/grid_to_vector_map.ipynb deleted file mode 100644 index a22d938b..00000000 --- a/refs/tags/0.5.2/examples/grid_to_vector_map.ipynb +++ /dev/null @@ -1,559 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Example - Mapping Grid Data to Vector Data\n", - "\n", - "This is useful in the case where you want to get statistics for a specific raster\n", - "over a certain region. In this example, the vector data is a random region with\n", - "using SSURGO data." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import json\n", - "\n", - "import geopandas\n", - "\n", - "from geocube.api.core import make_geocube\n", - "\n", - "%matplotlib inline" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "ssurgo_data = geopandas.read_file(\"../../test/test_data/input/soil_data_group.geojson\")" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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cokeymukeydrclassdcdhzdept_rchkeyhzdepb_rclaytotal_rsandtotal_rsilttotal_rgeometry
012577452271425Somewhat poorly drained0.01000340905.023.0676759.97833866.953987MULTIPOLYGON (((-90.59735 41.49255, -90.59730 ...
112577452271425Somewhat poorly drained5.010003409015.023.0676759.97833866.953987MULTIPOLYGON (((-90.59735 41.49255, -90.59730 ...
212577452271425Somewhat poorly drained15.010003409130.023.0676759.97833866.953987MULTIPOLYGON (((-90.59735 41.49255, -90.59730 ...
312577452271425Somewhat poorly drained30.010003409245.023.0676759.97833866.953987MULTIPOLYGON (((-90.59735 41.49255, -90.59730 ...
412577452271425Somewhat poorly drained45.010003409360.023.2316439.96194166.806416MULTIPOLYGON (((-90.59735 41.49255, -90.59730 ...
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" - ], - "text/plain": [ - " cokey mukey drclassdcd hzdept_r chkey hzdepb_r \\\n", - "0 12577452 271425 Somewhat poorly drained 0.0 100034090 5.0 \n", - "1 12577452 271425 Somewhat poorly drained 5.0 100034090 15.0 \n", - "2 12577452 271425 Somewhat poorly drained 15.0 100034091 30.0 \n", - "3 12577452 271425 Somewhat poorly drained 30.0 100034092 45.0 \n", - "4 12577452 271425 Somewhat poorly drained 45.0 100034093 60.0 \n", - "\n", - " claytotal_r sandtotal_r silttotal_r \\\n", - "0 23.067675 9.978338 66.953987 \n", - "1 23.067675 9.978338 66.953987 \n", - "2 23.067675 9.978338 66.953987 \n", - "3 23.067675 9.978338 66.953987 \n", - "4 23.231643 9.961941 66.806416 \n", - "\n", - " geometry \n", - "0 MULTIPOLYGON (((-90.59735 41.49255, -90.59730 ... \n", - "1 MULTIPOLYGON (((-90.59735 41.49255, -90.59730 ... \n", - "2 MULTIPOLYGON (((-90.59735 41.49255, -90.59730 ... \n", - "3 MULTIPOLYGON (((-90.59735 41.49255, -90.59730 ... \n", - "4 MULTIPOLYGON (((-90.59735 41.49255, -90.59730 ... " - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ssurgo_data.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "# convert the key to group to the vector data to an integer as that is one of the\n", - "# best data types for this type of mapping. If your data is not integer,\n", - "# then consider using a mapping of your data to an integer with something\n", - "# like a categorical dtype.\n", - "ssurgo_data[\"mukey\"] = ssurgo_data.mukey.astype(int)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Convert data to grid\n", - "\n", - "See docs for [make_geocube](../geocube.rst#make-geocube)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "\n", - "Dimensions: (hzdept_r: 11, x: 165, y: 165)\n", - "Coordinates:\n", - " * y (y) float64 41.5 41.5 41.5 41.5 ... 41.48 41.48 41.48 41.48\n", - " * x (x) float64 -90.6 -90.6 -90.6 -90.6 ... -90.58 -90.58 -90.58\n", - " * hzdept_r (hzdept_r) float64 0.0 5.0 15.0 30.0 ... 90.0 105.0 120.0 150.0\n", - " spatial_ref int64 0\n", - "Data variables:\n", - " mukey (hzdept_r, y, x) float64 1.988e+05 1.988e+05 ... 1.987e+05\n", - " hzdepb_r (hzdept_r, y, x) float64 5.0 5.0 5.0 5.0 ... 180.0 180.0 180.0\n", - " claytotal_r (hzdept_r, y, x) float64 26.0 26.0 26.0 26.0 ... 21.0 21.0 21.0\n", - " sandtotal_r (hzdept_r, y, x) float64 38.0 38.0 38.0 38.0 ... 10.0 10.0 10.0\n", - " silttotal_r (hzdept_r, y, x) float64 36.0 36.0 36.0 36.0 ... 69.0 69.0 69.0\n", - "Attributes:\n", - " grid_mapping: spatial_ref" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "out_grid = make_geocube(\n", - " vector_data=ssurgo_data,\n", - " group_by='hzdept_r',\n", - " resolution=(-0.0001, 0.0001)\n", - ")\n", - "out_grid" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Get the mean/median of each region using the unique ID" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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hzdept_rspatial_refhzdepb_rclaytotal_rsandtotal_rsilttotal_r
mukey
198692.015.0030.023.0000007.00000070.000000
198714.015.0030.05.00000087.0000008.000000
198724.015.0030.021.00000010.00000069.000000
198750.015.0030.012.00000063.00000025.000000
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" - ], - "text/plain": [ - " hzdept_r spatial_ref hzdepb_r claytotal_r sandtotal_r \\\n", - "mukey \n", - "198692.0 15.0 0 30.0 23.000000 7.000000 \n", - "198714.0 15.0 0 30.0 5.000000 87.000000 \n", - "198724.0 15.0 0 30.0 21.000000 10.000000 \n", - "198750.0 15.0 0 30.0 12.000000 63.000000 \n", - "198754.0 15.0 0 30.0 26.000000 38.000000 \n", - "271425.0 15.0 0 30.0 23.067675 9.978338 \n", - "271431.0 15.0 0 30.0 14.000000 55.000000 \n", - "\n", - " silttotal_r \n", - "mukey \n", - "198692.0 70.000000 \n", - "198714.0 8.000000 \n", - "198724.0 69.000000 \n", - "198750.0 25.000000 \n", - "198754.0 36.000000 \n", - "271425.0 66.953987 \n", - "271431.0 31.000000 " - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "grid_mean = out_grid.sel(hzdept_r=15).groupby(out_grid.mukey.sel(hzdept_r=15)).mean()\n", - "grid_mean.to_dataframe()" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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hzdept_rspatial_refhzdepb_rclaytotal_rsandtotal_rsilttotal_r
mukey
198692.075.0090.023.0000007.00000070.000000
198714.075.0090.07.80000086.4666675.733333
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198754.075.0090.026.00000038.00000036.000000
271425.075.0090.024.56496610.12049765.314537
271431.075.0090.08.33333374.66666717.000000
\n", - "
" - ], - "text/plain": [ - " hzdept_r spatial_ref hzdepb_r claytotal_r sandtotal_r \\\n", - "mukey \n", - "198692.0 75.0 0 90.0 23.000000 7.000000 \n", - "198714.0 75.0 0 90.0 7.800000 86.466667 \n", - "198724.0 75.0 0 90.0 21.000000 10.000000 \n", - "198750.0 75.0 0 90.0 12.000000 63.000000 \n", - "198754.0 75.0 0 90.0 26.000000 38.000000 \n", - "271425.0 75.0 0 90.0 24.564966 10.120497 \n", - "271431.0 75.0 0 90.0 8.333333 74.666667 \n", - "\n", - " silttotal_r \n", - "mukey \n", - "198692.0 70.000000 \n", - "198714.0 5.733333 \n", - "198724.0 69.000000 \n", - "198750.0 25.000000 \n", - "198754.0 36.000000 \n", - "271425.0 65.314537 \n", - "271431.0 17.000000 " - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "grid_median = out_grid.sel(hzdept_r=75).groupby(out_grid.mukey.sel(hzdept_r=75)).median()\n", - "grid_median.to_dataframe()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3.10.6 64-bit", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.6" - }, - "vscode": { - "interpreter": { - "hash": "e7370f93d1d0cde622a1f8e1c04877d8463912d04d973331ad4851f04de6915a" - } - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/refs/tags/0.5.2/examples/rasterize_function.ipynb b/refs/tags/0.5.2/examples/rasterize_function.ipynb deleted file mode 100644 index 53b7229b..00000000 --- a/refs/tags/0.5.2/examples/rasterize_function.ipynb +++ /dev/null @@ -1,144 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Example - Use a rasterize function when creating a raster" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [], - "source": [ - "import geopandas\n", - "\n", - "from functools import partial\n", - "from geocube.rasterize import rasterize_image\n", - "\n", - "from geocube.api.core import make_geocube\n", - "\n", - "%matplotlib inline" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [], - "source": [ - "gdf = geopandas.read_file(geopandas.datasets.get_path('naturalearth_lowres'))" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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LgffUcw/nnGuEfuvJ9Go3mYLPkqYDnwdemz7HzPbLoQzfAb4AbJjatqWZLY/3WC6paFYySbOAWQBTpkzJoSjOOZdNnnM+t5qsvZKuAM4Dzgdy61sm6V+AFWa2QNK+1Z5vZrOB2QC9vb2WV7mccy6Lbo0xJNaa2Q8acP+3AYdKOgQYA4yX9HPgCUkT49PCRGBF2as4V4cktlAYc4DBMQCoPraQSKYPzbI9iUckcYTCFOBptcYW8pTEFWBdvEGjP1bxvJnbfA5o4xgDdGyvpLKNX5I2jT2E/p+kT0qamGyL2+tiZqea2WQzmwocCfzBzD4MzAGOjocdDVxV772ccy5XFpqSsrzaTaUnhgWEijF5Z+kuLAZsM+SMfJwFXC7pOOAR4PAKxzvn3LDKc6IeSRcASdP6zkX2CzgHOAR4CTjGzO7I5ebFymNWuWle0hgze6XStmbq7e21+fNLpwNoRXsfdnbT7t0J3VSLGZihTpX/w8574cKKx1SjMM2F1hs1sNwzbmzm6/Q//0LFY2yn7QD45ZWhM9+4njGZr5+3dHfVUpJmo7RmNiFJWmBmvfVcY5PXbWH7/jjb36xXvv3csveTtDfwAnBRiYrhEODThIphT+AcM9uzpoJnkLUf1V8ybnPOua6QTNSTR1OSmd0MPFXmkMMIlYaZ2a3AxjH+2hCVUmJsBUwC1pf0JtY1KY0HSk+C65xzHS6kxMg8RmGCpHSTxuzYqzKrSUB6Yu9lcdvyKq6RWaUYwwzgGGAy8C3WVQzPAV9sRIGcc65dVBFjWFVn01WxGzWsi37ZisHMLpT0M+AoM7u4UYXoVkk7f7H21xffsCUAY+95YtB6XvfsJANxhSoVdkUtTHuRPmbkxPDzv2bZd0ter1TabID+F14Eqos1FEriCq2mWHfVRLt3SS3LhnU+hmXA1qn1ycDjjbpZxecgM+sHPtGoAjjnXDvKM8aQwRzgowreAjybZIdohKwD3K6V9DlCxtMXk41mVi5Y4pxzHS2vJwZJlwD7EmIRy4AvA6MAzOw84GpCj6QlhO6qxxa/Uj6yVgzJMMYTU9saOY7BOedaWp65kszsqAr7jcHfvw2VqWIws2mNLkg3K9b+Whh3SGINiXTMIUvcoJljJhqlmjELWRTGHIqZOfnfh2wrGXcoMkbomn+eG65TJrVGz4bjAOibslXJY5LxC4kX+sOQosO33WfdvR7+a9Fzk2Oh8eMfOjK2kNLXhplTs8iaXXUUcAKwd9x0I/BDM1vToHI551xLs+ENPg+rrE1JPyC0d50b1z8St5Wb3c055zqadXnFsLuZ7Zpa/4Ok+qd+cs65ttWeCfKyyFox9Ena1sweBJC0DTnOy+CGStpmi41xgKExh0o6afxCElvof/nlIft6Nhj+AfnF4g6lJGWf9+JFpa9XY2pvgCseXDcT7wv9lY8/ZIe3A3D14j/VfM8suZI6Vbc/MXweuEHSQ4QReK+lwd2lnHOulSXjGDpR1l5J10vaHtiBUDH8zcxWVzjNOec6l0FfN1cM0W7A1HjOrpIws9LPwy4XpZqUau0GeNCoIwH4/ZpL6ytYExVrQkrYq4M7yqVTXxfqKUhhYX3rWketv/o0NEnzUJYUHeWaw67tv6LoOc89Xt+85uNf8wgAB29Uena1d+02Y8i23y2YN2i9m5uO0owub0qK+ZK2Be5iXWzBAK8YnHNdyoPPvcCOlmVWnypI2ppQuWwF9BNS0Z4Tpw29jPCEshT4gJk9nee9nXOuXvl+I7aOrMP27iV8eedtLfAfZvZ64C3AiZJ2BE4Brjez7YHr47pzzrUUM2V6tZusTwwTgPsk3QYMBJ3N7NB6bh6zAy6Py89Lup8w+cRhhIRSABcSRlqfXM+9Ok065lAYbyiX6rhcbCFJCVEs/XS7SmIOg2INa9dWPE89g/8z9z/zbOZ7VhNrSCsVW0gkMQLIFm9IH1/JITvvC0Df0ytCWVKfgSSttscWBjODvuwT9bSVrBXD6Y0sBICkqcCbgL8CWyYpZc1suaQtSpwzC5gFMGVKfYE555yrVqc2JWXtrnpTuf2SbjGzvWothKRxwK+Az5jZc8qYFC1OjTcboLe3t0N/Rc65VtWOzURZ5PUcVHOKxpig71fAxWb267j5iWSi6/jvivqL6Jxz+TGyxRfasfKoZhxDOTX9ta7waPBj4H4z+5/UrjnA0cBZ8d+r6i5hmysVRyhcBli79B813aPVYwsH9hxe+8kZ4gr1GhKjyTAWolJcoVrVxBWKGbHJRsDgFOTX9pWPLczc+qSB5WsePaeu+7ebTm2myKtiqNXbCJla75F0V9z2RUKFcLmk44BHgDq+EZxzrgGsc5uS8qoYavrpmNmfypy7f+3Fcc65xrP+zqwYMsUYJI2V1BOXp0s6NMYGEh9pSOmcc66FmWV7tZusTww3A++QtAlhwNl84AjgQwBmdm9jiudKKZcrKWkfHtxO3Nrxg2poxIi4UPnvmoHxCDttt27joiXZ7zV6dOZjR06aCMDMKZ8pecy8l3+W+XrlJLGEWvInzX32giHbDp4wC4D+514AUj/jDLotrpDo5FxJWXslycxeAv4V+F8zey+wY+OK5ZxzLc4AU7ZXm8lcMUjai/CE8Lu4rdmBa+eca6pub0r6DHAq8BszWxRncLuhYaVydUmajbJ070ynYC5MWZ00e6Svk3f3ymok905Sh2NxirIiTUpJU4itjSkx0jvTzUpQtmmp/6WXgPIzw/Vsuknxc1e/MmRbq6Ydmbtq9qD1mZM+3aSStBPlFnyWdDBwDjAC+JGZnVWwfyPg58AUwvf2N83sJ7ncvIhqRj7fJGlsXH8IyD6foXPOdaIcngYkjQC+DxwILANulzTHzO5LHXYicJ+ZvVvS5sBiSReb2av1l2CorL2S9pJ0H3B/XN9V0rmNKJBzzrUFyy276h7AEjN7KH7RX0pIJFpwNzaMg4LHAU8RslMXJemkLNtKyRpj+A4wA3gSwMzuBvbOehPnnOtIlvEFEyTNT71mpa4yCXg0tb4sbkv7HvB64HHgHuAks6Qttaiji2w7JtubqiKAbGaPFiS36yt1rGsN5eIB5aZ3TMxYPwxP6RldcyqshkhShyexj571SnetTKbozNISnJ7ac8i+1dVPcT5ycuH/bVj72PKqr1NOvSkwSrnmsf+t6bwkPUb3dGHNHGNYZWa9VVyksJFqBmEGzf0Is2leK+mPZvbcoAtJRwEfBKZJmpPaNZ74h30WWSuGRyW9FTBJ6xHiC/dnvYlzznWkfHocLQO2Tq1PJjwZpB0LnBVn0Vwi6WHgdcBtBcf9hTDHzQTgW6ntzwMLsxYoa1PS8YTgxyTCm3hjXHfOue5kQL+yvcq7Hdhe0rT4h/eRhESiaY8Q0wRJ2hLYAXhoSJHM/mFmNwIHAH+MHYeWEyqbzI83WXslrSKOcnbOORfkMUbBzNZK+hQwj9Bd9YI4LOD4uP884Azgp5LuIXzBnxy/l0spm62ikkwVg6TpwA8IM6vtLGkX4FAz+2qW8117SKa/LBzPkFY4NmJE7MNf2A9+OCQxlBlj1n3W571yccXzZuz25bBQZvzCyC02B2DtipUAjNhsUwD6nnxqyLH2bGjm1UbjB20vFk9otfEL++/33wBc/4dTM5+TxJ4S6TQf3RNbiHIavGZmVwNXF2w7L7X8OHBQFZeUmb0UM1T/r5l9Q9KdWU/O2pR0PmGA25pYyIWExx3nnOterZsSo65sFVkP3MDMbivoldT4mU+cc66FqXXTXXyGOrJVZK0YVknalvjgJOn9hICGc851p3VjFFpOvdkqslYMJwKzgddJegx4mAYHoyvlDnENtHPMJXTvksHrAAtChvWe3XYGWuP/RZa4wqDjF3wFyJZLKok1JJJYg7308rqNMQKZxBqKabXYQiKJLSSxhpG3hCwM6bhB4b61e+046NzulanHUVPEZqQfE0ZJT5G0K/AJM/tklvMrVgwxj8cJZnZArH16zOz5egqd8Z6Vcoc451xztcJfRsV9hzAobg6EbBWSMmerqBh8NrM+YLe4/GKjK4UoS+4Q55xrruwpMYa/aGaPFmzKnK0ia1PSnXF49RXAi6kb/zrrjapULHfInoUHxXwjswCmTKl+JqtudNB6Hxy03rN+mXQXO283ZFPShFRoxu5fGbJt3u1frq5w7aTYDGf9g1PXtGrzUTGFTUpZ9xVuL9W8lOWYtpNM1NOa6spWkbVi2JSQZ2O/1DYDGlUxZMkdgpnNJsQ+6O3tbd2HOudcR2rhXknHE2K0SbaK31NFtoqsI5+PralotcuSO8Q555qrRSuGStkqJJ1qZsUfAck+8vm7RTY/C8w3s6uyXKNKA7lDgMcIg+k+WP4U55wbXi38xFDJ4UB9FQMwhpDJL8nj/D5gEXCcpHea2WfqKWGhUrlD8rxHtxuY+jKd/qJITKEehXGHVos5JCk1km6rhV1Tq9YT+nLMffpH9V2nRZWKDWSJGaSPKYxV9Nx0F9BeMZkBrRtjqKRswbNWDNsB+5nZWgBJPyC0WR1ImDQid8VyhzjnXMto4QFuGZQtedZcSZOAsan1scBrYlfW6mcwcc65TtDC3VUryOWJ4RvAXZJujBfcG/haHPB2XV3Fc865NtXGMYbS0zuSvVfSjyVdTRh4JuCLMQ0swOfrK58bTirsf59zXKGcJObQarGGZOrS/meHjt3s2WjD4S5OUxWLFxTGBOodh1BqXMSBI44YWG6beEO5WZebKCbNOwfYi1DKW4DPxpxJmNnXyp2fqSlJIa3q/sCuZnYlMFLSHnWU2znn2pos+6sJfgFcDmwFvIbwhHBJ1pOzxhjOJdQ8R8X15wm5jJxzrnu18HwMZvYzM1sbXz+nimhH1hjDnmb25mQGIDN7Og6zds657tW6MYYbJJ1CyDNnhGk9fydpUwAzGzoVYUrWimFNzHiazMewOS3buubKSdIpD0zPeG9qessYb7C7QkoVvfH1w1q2dtL/4ksDyz1jN2hiSRqvUbmNOiFnUgsHn5OAzScKtn+M8D2+TbmTs1YM3wV+A2wh6Uzg/cB/VlFI55zrPC1aMZjZtHrOz9or6WJJCwgBaAHvMbPMmfqcc67jGKhF200kjQJOIAwtALgR+KGZrSl5UkrZiiFpj4pWkIpqS9q0UjuVa11DmpRgcLMS65qU0uppXmq1bqpZ9D31TMVjkmalpCtw8jNNz4KWmDEm5DWrdta5dpW837TC997WP5MWfWIAfgCMInQcAvhI3PbxLCdXemJYQHjrAqYAT8fljYFHgLoeV5xzrp3lFWPIMpWxpH0JM7ONAlaZ2T5lLrm7me2aWv+DpLuzlqdsd1Uzm2Zm2xCS2b3bzCaY2WbAv9C4uRicc65rpKYyngnsCBwlaceCYzYm/PV/qJntRMiOWk6fpG1T529DA2Zw293Mjk9WzOwaSWdkvYlzznWkfJ4YBqYyBpCUTGWcnuP+g8CvzewRADNbUeGanyd0WX0ork8FMs+rk7ViWCXpP4FkkMSHCTO6uTaXbgcfFG8ooeu7strQaKNGjhq0Xi620OmyvM9SMYX0uW0Rb8gv+JxlKuPpwKiYr25D4Bwzu6jMNf8M/JDQYYi4fEvWAmWtGI4CvkzosmrAzawbBe2cc90p+xPDBEnzU+uz49TEkG0q45HAboQv+vWBWyTdamZ/L3G/i4DngKRl5yjgZ1Rughq4WUWx99FJWY51zrluIKoKPq8ys94S+7JMZbwsXuNF4EVJNwO7AqUqhh0Kgs835BZ8lnR6pQtkOabEeWdL+pukhZJ+E4Mryb5TJS2RtFjSjFqu75xzDZfPfAwDUxnHVENHAnMKjrkKeIekkZI2IDQ1lRtLdqektyQrkvYkNC9lUumJ4eOSniuzX4Q3cXrWG6ZcC5wap/H8OnAqcHKMxh8J7ETICnidpOlxUiDXQEnb+EGjjmxySYZX/+pXMh87JG15SrHYwpBj2qHtvEq1xk/aPu6SU+bUUlMZSzo+7j/PzO6XNBdYSEhH9CMzu7fMZfcEPirpkbg+Bbhf0j3hkrZLuTJVqhjOJwQ6Kh1TNTP7fWr1VkKaDQjR+EvNbDXwsKQlhKh95sCJc84Ni5zGMRSbytjMzitYPxs4O+MlD66nPGUrBjP7Srn9OfoYkMzMMYlQUSSWxW1DSJoFzAKYMmVKI8vnnHNDtGpKDDP7Rz3nZ+2VVBNJ1xEmiih0mpldFY85DVgLJM/YWSL0YWOI6s8G6O3tbd3B6c65ztSh3zoNrRjM7IBy+yUdTRhFvb+ZJT/iLBF6N4ysf+inv5qpR1o9R9K1/WH62wN7Kvfks74Q6kqmAy2n7dvQSxiO99UW+ZOyBZbbUtYZ3HIXc4OcTBji/VJq1xzgSEmjJU0Dtgdua0YZnXOunBae2rMuWed8ni7pekn3xvVd4kjoenyPENi+VtJdks4DMLNFhLlK7wPmAid6jyTnXEvKp7tqy8nalHQ+IffGDwHMbKGkXwBfrfXGZrZdmX1nAmfWem1Xm4PW+yAAPeuvX/HYvjtCGpeeNw/K9dXyzUb1GrHRRkO2zX36R0WPbelmkDbR6k1K7fg0kEXWimEDM7tNGtSyvLYB5XHOufZgdOwEx9Uk0duWdXM+vx9Y3rBSOedcixPVdcJoJ1krhhMJ3UJfJ+kx4GFChlXnnOte3dyUFPOEHyBpLNBjZs83tliu1RXGFjpBOn7Q9+yzQ7Y5V6grYwyS/k+J7QCY2f80oEzOOdceurFiYF2epB2A3VmX8e/dhDkZnHOuO+U3UU/LyZQrSdLvgTcnTUgx1fYVDS+dc861si59YkhMAV5Nrb9KmEPUdan+BSHjb89uOwOdMX4hPR7h4E0+nvnYbpOMKejUlB/V6MoYQ8rPgNskJVN7vpcwdZxzznWvbq4YzOxMSdcA74ibjjWzOxtXLOeca31d/cQgaQqwCvhNepuZPVL6LOec62Btmgcpi6xNSb9j3Y9gfWAasJgw/abrYp0QWyinm2MJWdWaxyhLjKJVcyRBHPncjb2SEmb2hvS6pDcDn2hIiZxzrl10+RPDIGZ2h6Td8y6Mc861E1ln1gxZYwzpEdA9wJuBlQ0pkRtWSaptgJ4xozOfl8x61qm8Camyept5hrPbazI7X66fW48xDIyAhpBu+3fAr/IvjnPOtY9O7ZWUdWrP+8zsK/F1ppldTEiLUTdJn5Nkkiaktp0qaYmkxZJm5HEf55zLm/qzvSpeRzo4ft8tkXRKmeN2l9QXpz5omKwVw6kZt1VF0tbAgcAjqW07AkcSejwdDJwraUS993LOudzlMLVn/H77PjAT2BE4Kn4PFjvu68C83MpfQqXsqjOBQ4BJkr6b2jWefGZw+zbwBeCq1LbDgEvNbDXwsKQlwB7ALTncz9Vg7nM/aXYRXAdr5S6pZVluTUl7AEvi9AZIupTwPXhfwXGfJjThN7zjT6UnhseB+cArwILUaw5QVxOPpEOBx8zs7oJdk4BHU+vL4rZi15glab6k+StXeizcOTfMcnhiIMN3nqRJhFRE5+VQ6ooqZVe9G7hb0sVmVvUTgqTrgK2K7DoN+CJwULHTihWlRPlmE2aWo7e3t0PDQM65ViSqemKYIGl+an12/P5KLlWo8MrfAU42s75kPpxGqtSUdLmZfQC4Uxr6IzCzXcqdb2YHlLjuGwijp++Ob3IycIekPQi15dapwycTnlycc661ZB/HsMrMekvsy/Kd1wtcGr8vJwCHSFprZldmL2x2lbqrnhT//Zc8b2pm9wBbJOuSlgK9ZrZK0hzgF5L+B3gNsD1wW573d+v8/tVfDCwfPP7YQfs8tuDaWTJ2ARo07ia/iXpuB7aXNA14jND55oPpA8xsWrIs6afAbxtVKUCFGIOZLY+LnzSzf6RfwCcbUSAzWwRcTgi8zAVONLO+RtzLOefqkUd31dhM/ylCb6P7gcvNbJGk4yUd3/h3MVTWAW4HAicXbJtZZFtNzGxqwfqZwJl5XNs55xomp8immV0NXF2wrWig2cyOyeeupVWKMZxAeDLYRtLC1K4NgT83smDOOdfqOnXkc6Unhl8A1wD/DaRH4z1vZk81rFRu2NSaK8m5VtWQvEjFGNUEn9tKpe6qzwLPAkcBSNoCGAOMkzTOJ+pxznWzTn1iyJQSQ9K7JT0APAzcBCwlPEk451xXSibqySNXUqvJGnz+KvAW4Doze5OkdxKfIlzn8W6qrp0NW0p4s45tSsqaRG+NmT0J9EjqMbMbgDc2rljOOdf6ZNle7SbrE8MzksYBNwMXS1pBPkn0nHOufbXhl34WWZ8YDgNeBj5LGHT2IDnNx+Ccc+2qq58YzOzF1OqFDSqLa7L+V1Y3uwjOtQ8D+trwWz+DSgPcnqf4w5IAM7PxDSmVc861gXZ8Gsii0jiGDcvtd865rtahvZKyBp+dc84V6MonBtf50mm3nXNVyDY7W1vyisE552oQZnDrzJrBKwbnnKuRurFXknPOuRI6uCkp6wC3hpD0aUmLJS2S9I3U9lMlLYn7ZjSzjM45V5yty5dU6dVmmvbEEBPxHQbsYmarY0pvJO1ImPN0J8Kcz9dJmu7TezrnWk2n9kpq5hPDCcBZZrYawMxWxO2HAZea2WozexhYAuzRpDI651xpHfrE0MyKYTrwDkl/lXSTpN3j9knAo6njlsVtQ0iaJWm+pPkrV65scHGdcy7FfD6Gmki6DtiqyK7T4r03IczzsDtwuaRtCL3AChWtcs1sNjAboLe3t/2qZedce+vvzK+dhlYMZnZAqX2STgB+bWYG3CapH5hAeELYOnXoZODxRpbTOedq0anjGJrZlHQlsB+ApOnAesAqYA5wpKTRkqYB2wO3NauQzjlXUofGGJo5juEC4AJJ9wKvAkfHp4dFki4H7iNMBnSi90hqrgN7Dh9YHrZpE51rdQbkFD+QdDBwDjAC+JGZnVWw/0PAyXH1BeAEM7s7n7sP1bSKwcxeBT5cYt+ZwJnDWyLnnMtOWC5NSZJGAN8HDiQ0pd8uaY6Z3Zc67GFgHzN7WtJMQmx1z7pvXoKPfHbOuVr15/LIsAewxMweApB0KaHb/kDFYGZ/SR1/KyH22jBNHfnsnHNtK2lKyvKCCUnX+vialbpS5i760XHANTm9i6L8icENkcQUkniCxxXyk47XlFLPzzvL9dtBu3zmqmhKWmVmvaUuU2Rb0QvHjBHHAW/PeuNaeMXgnHO1yqfHUaYu+pJ2AX4EzDSzJ/O4cSnelOScczXJLYne7cD2kqZJWo+QK25O+gBJU4BfAx8xs7835O2k+BODc87VwsjlicHM1kr6FDCP0F31AjNbJOn4uP884EvAZsC5kgDWlmmaqptXDG5AYft0YayhUxS+r3Lt8lmOyVupe6V/D50SSyilmvfXzM9nXhP1mNnVwNUF285LLX8c+HguN8vAKwbnnKtVG45qzsIrBuecq4XhSfRcfuppoil2bjVdIGu5d6ulxMirGSXLdVqpyaaVytJKCn8uw/cZbc88SFl4xeCcc7XyisE559wgXjE455wbYAZ9nZn42SuGKuTdxlvP9ao9t1RX1GKaGUfwdnRXq6Z8bv2JwTnn3ADvleScc26IDn1iaFquJElvlHSrpLtiGto9UvtOlbRE0mJJM5pVRuecK8un9szdN4CvmNk1kg6J6/tK2pGQRGon4DXAdZKmN2p6zyz9+jux3Tvv9th6Yxad+DN2w6NpMbEODj43M7uqAePj8kasSzN7GHCpma02s4eBJYQZjpxzrrX4E0PuPgPMk/RNQgX11rh9EmHqukTJ2YziLEizAKZMmdKwgjrnXFFt+KWfRUMrBknXAVsV2XUasD/wWTP7laQPAD8GDqCK2YzMbDZhUmx6e3s78zfknGtR5r2SamFmB5TaJ+ki4KS4egVhZiLIOJtRXkq1T3Z6m3et76/w59Vu+YZc52laengDs/7hvecwaWaM4XFgn7i8H/BAXJ4DHClptKRpwPbAbU0on3POlddv2V5tppkxhn8DzpE0EniFGCuIMxddDtwHrAVObFSPJOecq1kH90qSdUjwZLw2tT21f0Ou3YxZvJxzjXOd/XJBvVNjbjRigu019t2Zjp33/E/rvt9w8pHPzjlXI+vvzBiDVwzOOVeT9hyjkIVXDM45VwtPotfdPLbgnCtkgHVo8NkrBuecq4UZdOg4Bq8YnHOuRuZNSc455wbp0CeGjhnHIGkl8I8GXX4CsKpB166VlykbL1N2rViuRpXptWa2eT0XkDSXUL4sVpnZwfXcbzh1TMXQSJLmt9rgFC9TNl6m7FqxXK1Ypm7QzFxJzjnnWpBXDM455wbxiiGb2c0uQBFepmy8TNm1YrlasUwdz2MMzjnnBvEnBuecc4N4xeCcc24QrxhSJB0uaZGkfkm9qe1TJb0s6a74Oi+1bzdJ90haIum7korNWZ17meK+U+N9F0uaMVxlKijD6ZIeS/1sDqlUvuEi6eB47yWSThnu+6fKsTT+Pu6SND9u21TStZIeiP9u0uAyXCBphaR7U9tKlmE4fnclytSyn6euYmb+ii/g9cAOwI1Ab2r7VODeEufcBuwFCLgGmDlMZdoRuBsYDUwDHgRGDEeZCsp3OvC5IttLlm+Yfpcj4j23AdaLZdmxSZ+rpcCEgm3fAE6Jy6cAX29wGfYG3pz+HJcqw3D97kqUqSU/T9328ieGFDO738wWZz1e0kRgvJndYuHTexHwnmEq02HApWa22sweBpYAewxHmTIqWr5hvP8ewBIze8jMXgUujWVqFYcBF8blC2nw78jMbgaeyliGYfndlShTKc3+PHUVrxiymybpTkk3SXpH3DYJWJY6ZlncNhwmAY8WuXczyvQpSQtj00DSHFGqfMOl2fdPM+D3khZImhW3bWlmywHiv1s0oVylytDsn10rfp66Stcl0ZN0HbBVkV2nmdlVJU5bDkwxsycl7QZcKWknQlNNoar7/9ZYplL3zqVMg25UpnzAD4Az4j3OAL4FfKwR5ahSs++f9jYze1zSFsC1kv7WpHJk1cyfXat+nrpK11UMZnZADeesBlbH5QWSHgSmE/5qmZw6dDLw+HCUKd576yL3zqVMaVnLJ+l84LcVyjdcmn3/AWb2ePx3haTfEJpAnpA00cyWx+a/FU0oWqkyNO1nZ2ZPJMst9nnqKt6UlIGkzSWNiMvbANsDD8XH7+clvSX2/PkoUOov/LzNAY6UNFrStFim24a7TPELJfFeIOlhUrR8jSpHEbcD20uaJmk94MhYpmElaaykDZNl4CDCz2gOcHQ87GiG73OTVqoMTfvdtfDnqbs0O/rdSi/CB3EZ4engCWBe3P4+YBGhV8QdwLtT5/QSPrwPAt8jjiZvdJnivtPifReT6nnU6DIVlO9nwD3AQsJ/3omVyjeMv89DgL/HMpzWpM/UNvFzc3f8DJ0Wt28GXA88EP/dtMHluITQJLomfp6OK1eG4fjdlShTy36euunlKTGcc84N4k1JzjnnBvGKwTnn3CBeMTjnnBvEKwbnnHODeMXgnHNuEK8YnHPODeIVQxeR9EIDrnloktJa0nsk7VjDNW4sTCme4fjFkg4tsm9qOo1zp5P0xdTy+jFV9auSJjSzXK69ecXg6mJmc8zsrLj6HkJ65OHwITNr6EjmZLR7ixuoGMzsZTN7I54qwtXJK4YupOBsSffGCWSOiNv3jX+N/1LS3yRdHNNqIOmQuO1PCpP//DZuP0bS9yS9FTgUODv+1bpt+klA0gRJS+Py+pIujRk0LwPWT5XtIEm3SLpD0hWSxmV4P7tJulvSLcCJqe0j4vu8Pd7rE3F7j6RzFSZA+q2kqyW9P+5bKulLkv4EHF6qPPGeN8WMqfOSVA6S/l3SffF+l5Yp81iF7KG3K2TtPSxunyrpj/F+d8SfK5ImSro5/mzvlfQOSWcByVPCxZl++c5l0eyh1/4avhfwQvz3fcC1hMlstgQeASYC+wLPEhKU9QC3AG8HxhBSHk+L518C/DYuHwN8Ly7/FHh/6n43EicXAiYAS+Py/wEuiMu7AGsJaTwmADcDY+O+k4EvFXkfA9eN6wuBfeLy2cSJX4BZwH/G5dHAfMIkL+8Hro7vcSvg6aTchEl1vpAq85DyAKOAvwCbx+1HpN7P48DouLxxmd/F14APJ8cRUneMBTYAxsTt2wPz4/J/sC6dxghgw/TvtODaSymYGMhf/qrm1XXZVR0QvuwvMbM+QobNm4DdgecIifiWAUi6izB73QuEpIEPx/MvIXzp1mpv4LsAZrZQ0sK4/S2Epqg/xweV9QiVU0mSNiJ8Ad8UN/0MmBmXDwJ2SZ4GgI0IX7ZvB64ws37gn5JuKLjsZRXKswOwMyGFNoQv6uXxnIXAxZKuBK4sU/SDgEMlfS6ujwGmECqW70l6I9BHyOILISngBZJGAVea2V1lru1cXbxi6E7l5oBenVruI3xGap0zei3rmivHFOwrlqRLwLVmdlQV91CJayX7Pm1m8wZtlN5V4ZovliuPpDcAi8xsryLnvotQ8R0K/JekncxsbYmyvc8KZueTdDohWeKuhJ/dKxBmO5O0d7z+zySdbWYXVXgfztXEYwzd6WbgiNgGvznhi6xcCuO/AdtImhrXjyhx3PPAhqn1pcBucfn9qe03Ax8CkLQzoTkJ4FbgbZK2i/s2kDSdMszsGeBZSW+Pmz6U2j0POCH+lY2k6Qqpr/8EvC/GGrYkNKEVU6o8i4HNJe0Vt4+StJOkHmBrM7sB+AKhiahUjGQe8OlUDOdNcftGwPL4NPMRwtMIkl4LrDCz84EfE+ZKBliTvD/n8uIVQ3f6DaHJ427gD4Q29X+WOtjMXgY+CcyNQdknCLGIQpcCn4/B1G2BbxK+mP9CaK9P/AAYF5uQvkCslMxsJSFmcUncdyvwugzv51jg+zH4/HJq+4+A+4A7FLqw/pDwBPQrQprnZNtfi72fUuWxMIf0+4GvS7obuAt4K+FL/OeS7gHuBL4dK65iziDEKhbGsp0Rt58LHC3pVkIzUvL0si9wl6Q7CTGic+L22fEaHnx2ufG02y4TSePM7IX4F+73gQfM7NtNKsuNwOfMbH4d10jez2aEiult5SrHdhJ7f/Wa2apml8W1J39icFn9WwxGLyI0d/ywiWV5Cvipigxwq8Jv4/v5I3BGJ1QKigPcCE8i/U0ujmtj/sTgXANJOhY4qWDzn83sxGLHO9cKvGJwzjk3iDclOeecG8QrBuecc4N4xeCcc24Qrxicc84N8v8B5OE01LsLFq0AAAAASUVORK5CYII=", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "geo_grid = make_geocube(\n", - " vector_data=gdf,\n", - " measurements=['pop_est'],\n", - " resolution=(-3.0, 3.0),\n", - ")\n", - "geo_grid[\"pop_est\"].plot()" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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" - ], - "text/plain": [ - " test_attr test_str_attr test_time_attr \\\n", - "0 1.3 dcf86619 5/21/2016 10:09:21 AM -05:00 \n", - "1 1.3 dcf86619 5/21/2016 10:09:21 AM -05:00 \n", - "2 1.9 dcf86619 5/21/2016 10:09:21 AM -05:00 \n", - "3 1.3 dcf86619 5/21/2016 10:09:21 AM -05:00 \n", - "4 1.3 dcf86619 5/21/2016 10:09:21 AM -05:00 \n", - "\n", - " geometry \n", - "0 POINT (-47.26681 44.21932) \n", - "1 POINT (-47.26680 44.21932) \n", - "2 POINT (-47.26681 44.21932) \n", - "3 POINT (-47.26680 44.21932) \n", - "4 POINT (-47.26679 44.21932) " - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "gdf.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Convert to raster with GeoCube\n", - "\n", - "See docs for [make_geocube](../geocube.rst#make-geocube)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Load into grid with griddata nearest resampling" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "geo_grid = make_geocube(\n", - " vector_data=gdf,\n", - " measurements=['test_attr'],\n", - " resolution=(-0.1, 0.00001),\n", - " rasterize_function=rasterize_points_griddata,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
<xarray.Dataset>\n",
-       "Dimensions:      (x: 12, y: 11)\n",
-       "Coordinates:\n",
-       "  * y            (y) float64 45.25 45.15 45.05 44.95 ... 44.55 44.45 44.35 44.25\n",
-       "  * x            (x) float64 -47.27 -47.27 -47.27 ... -47.27 -47.27 -47.27\n",
-       "    spatial_ref  int64 0\n",
-       "Data variables:\n",
-       "    test_attr    (y, x) float64 1.3 1.3 1.3 1.2 1.3 1.3 ... 2.3 1.3 1.9 1.3 1.3\n",
-       "Attributes:\n",
-       "    grid_mapping:  spatial_ref
" - ], - "text/plain": [ - "\n", - "Dimensions: (x: 12, y: 11)\n", - "Coordinates:\n", - " * y (y) float64 45.25 45.15 45.05 44.95 ... 44.55 44.45 44.35 44.25\n", - " * x (x) float64 -47.27 -47.27 -47.27 ... -47.27 -47.27 -47.27\n", - " spatial_ref int64 0\n", - "Data variables:\n", - " test_attr (y, x) float64 1.3 1.3 1.3 1.2 1.3 1.3 ... 2.3 1.3 1.9 1.3 1.3\n", - "Attributes:\n", - " grid_mapping: spatial_ref" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "geo_grid" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# mask nodata and plot\n", - "geo_grid.test_attr.where(geo_grid.test_attr!=geo_grid.test_attr.rio.nodata).plot()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Load into grid with griddata cubic resampling" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "geo_grid = make_geocube(\n", - " vector_data=gdf,\n", - " measurements=['test_attr'],\n", - " resolution=(-0.1, 0.00001),\n", - " rasterize_function=partial(rasterize_points_griddata, method=\"cubic\"),\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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pecCyJoOKiOh747toe7fthZ2ekPQm4E7bSyRt26vwOqmT8PcDfibpBqp++M8B9m4yqIiIgdCbNvxtgJ0k7QjMAuZIOs72Hj2pvc2oCb9cNd4ceD5PHR45QytExLTXi26Ztg8EDgQoZ/j7NZHsYYyLtmXi8t1tP2b70rIk2UfEtGdgaEi1ln5Rp0nnPElHAt8DHmkV2r6wsagiIvqdgR7feGX7HKqu742ok/C3KD8PaSszsF3vw4mIGBxTZiydltHutI2ImNYmMeGrGjNlnu2bu61jzDsCJK0t6SuSLpS0RNIRktbu9oAREVODsOstvWDbwP9MpI46d9p+F7gLeBuwc3n8vYkcNCJiSnDNpXculLRVtzvXacNfz/Zn2tYPlbRrtweMiJgSDJ78HjgvA94p6SaqTjSiOvl/SZ2d6yT8n0raDfh+Wd8ZOL2bSCMippZJT/ivm8jOdZp03gecQDWP7WNUTTzvl/SQpAcncvCIiIE2+U06h9q+qX0BDq2785gJ3/Zs2zNsr1iWGaVstu0xh/GUNFPSRZJOHVb+FUkPj7DPipK+KekySVdJOrDuC4qImDSTn/A3a18poyFsWXfnyZiBtzXG859IWgjMHWWfXYCVbb+Y6sW8X9KCpgKMiBi31o1XdZYJknSgpIeAl0h6sCwPAXcCp9Stp9GE32mM5/KJ9AWqoUBHYmA1SSsAqwCPA2k+ioi+Uk1zOPYy8eP4X2zPBr5ge05ZZtte2/YBdesZbRLz59q+cYJxdhrj+YPAKbZvH2Xu1ZOAN1NNnL4q8FHb93aIcR+qeXeZP3/+BEOdQlZoaL7VJm8rXPpEI9UOrdnM5EFDKzZ3rrTyXc38/92y2lqN1Lvg/mk8Wvrk99J56fACSWfZ3r7OzqP10jkJ2HI8lQ0L4mljPEtan6q5Ztsxdn8p1Zj761M1/fyfpDNt39C+UZlAYBHAwoULB+wmZzh9yacbqfcNL/yHRuqNiKfSJGUdSbOA1agmUpnLk92D5gAb1K1ntIQ/Q9I/ABtL+tjwJ21/aYy6nzbGM3AFVU+f68rZ/aqSrrO90bB93wGcZnspcKek84CFwA1ERPSD3l+QHc37gY9QnQQv4cmE/yBwZN1KRvteuhvVWfYKVE0yw5dR2T7Q9jzbC0pdZ9uea/tZtheU8kc7JHuA31EGZ5O0GrA1cHXdFxUR0byaF2x7M2b+EbafSzVW/vNsP7csm9uunfBHPMO3fQ3weUmX2v7JhCMeg6SdgIW2DwL+DThG0hVUn2TH2L606RgiIsZlkhuSbX9V0ouAF1K1nLTKv1Vn/zp32v5C0peAV5X1/wUOsf3AOII8hw5jPNteve3xKZTuRbYfpmrrj4joX0OTezhJn6K6BvpCqoHU3gD8HKiV8Ot0NfgG8BDw9rI8CBzTRawREVPHJPbDb7MzsD1wh+29qaagXaPuznXO8De0/ba29U9Lunh8MUZETD2T1UunzR9sD0l6QtIcqhuvnl135zpn+H+Q9IrWiqRtgD+MP86IiClm8odWWCxpTeDrVL11LgTOr7tznTP8DwDfktT62nAf8O7xRhkRERNj+2/Lw6MknQbMae/QImkz21eMtH+dKQ4vATYvXx+wnSEOIiJYLk06f2L7tx2Kvw38+Uj71DnDb1WeRB8R0WKWx9AKYxk1oNoJPyIihum/AV1GjSgJPyKiS8uzSacbY/bSkbSqpE9K+npZf34ZGC0iYnqb/F46Y3l8tCfrdMs8hmrAs5eX9VsZx5RaERFT1iQnfElnjVZme+vR9q9749WuknYvFT6qUQayj4iYDuRJHx55VRocHrnlcUmrUD6nJG1IdcYfETG9TV4vnZ4Mj1wn4R8MnAY8W9LxVOPc7z2eSCMipqLJOsO3fQRwhKQP2f5qt/XUufHqp5KWUI1JL2Bf23d3e8BonldoZvq9Rs9lHl/aSLXLZg1eR7RZ9zRT77JZKzVS74oPPtpIvQNh8nvp3CFptu2HJP0T1U1Wh9q+sM7OdXrpnGX7Hts/tn2q7bs7XTiIiJhW/GQ7/lhLD32yJPtXAH8B/CfwH3V3HjHhS5olaS3KRQJJa5VlAeO4SBARMWVNfrfM1ozxbwQW2f4xUPur22jfd3tykSAiYqpSjyZAKb1wzgVWpsrLJ9n+VIdNb5X0NeC1VDMSrky97vXA6FMc9uQiQUREjOkxYDvbD0taEfi5pJ/YvmDYdm8HXg/8q+37Ja0HfLzuQepctJ3QHIoREVNWj5prbBt4uKyuWJan1V7ug7oTeAVwLfBE+VnLmAl/onMoRkRMSeO7ILuOpMVt64tsL2rfQNJMqubzjYB/s/3L4ZWUfLwQ2IRqFIQVgeOousuPqU6ftZ2p5k28yPbekp5ZDhARMb3VT/h32144alX2MmCLMqPVyZJeZPvyYZv9FfBnVDNdYfs2SbPrBlFrikPbQ0BXcyhGRExZDfTSsX0/8DOqtvrhHi/NP62RD1YbT911Ev6E5lCMiJiKRNVLp84yZl3SuiXPUoayeS1wdYdNv1966awp6X3AmVS5uZY6F21HnUMxImJa6u1NVesB3yzt+DOA79s+tcN26wInUXWP3wQ4iOoGrFpq3XcuaQPgOa3tJb3K9rl1DxIRMSX1rpfOpVRt82N5re39gTNaBZK+COxf5zh1eul8HtgVuJIn7/Iy1U0CERHT1+QNj/w3wN8Cz5PU3sIyGzivbj11zvDfAmxiO0MiR0S0mcQpDk8AfgL8C3BAW/lDtu+tW0mdhH8DVV/PJPyIiHaTNzzyA8ADwO4TqadOwn8UuLiMkPmnpG/7wxM5cETEQHPvxtKZLHUS/illiYiIdpM/Hv6E1OmW+c3JCCQiYtBMYht+T4w2Hv73y8/LJF06fKl7AEkzJV0k6dRh5V+R9PAo+71E0vmSrigxzBpp24iI5WLyx8OfkNHO8PctP980wWPsC1xFNbs6AJIWAnNH2kHSClTj9bzL9iWS1gaamQMvIqIbfZbM6xhtPPzby8+buq1c0jyqmVk+C3yslM0EvgC8g2ogoE52AC61fUmJoaFZPqcmz5zZSL2Nzmm7tJnP86EVm5nft0mz7mkmiwyt0Mz/4IxHp+e5mBi8Jp0RE76khxjl88v2nJGea3M48AmqmwNaPgicYvt2acRfwI0BSzqd6lbi79o+rEOM+wD7AMyfP79GOBERvTNlEr7t2QCSPgPcDnyb6kPtnVTjPoxK0puAO20vkbRtKVsf2IVqfP2x4noFsBVVt9CzJC2x/ZTJ08t40osAFi5cOGBvfUQMvAHLOnW6Ze5ke/O29f+QdAnVoD2j2QbYSdKOVDNlzQGuoOrLf105u19V0nW2Nxq27y3AubbvBpD0P8CfA2cREdEvBizh12ngfETSO0tvmxmS3gk8MtZOtg+0Pc/2AmA34Gzbc20/y/aCUv5oh2QPcDrwYkmrlgu4r6Yayycioj+U0TLrLP2iTsJ/B9XEub8vyy6lrKck7STpEADb9wFfAn4NXAxcaPvHvT5mRMSETKFuma0eNR+0/eaJHMT2OcA5HcpXb3v8lDt6bR9HplKMiD42pYZWsL1M0ismK5iIiEHST801ddS5aHuRpFOAE2lru7f9w8aiiojod33WXFNHnYQ/C7gH2K6tzEASfkRMb1Mt4dveezICiYgYJIN4p+2YvXQkzZN0sqQ7y/KDMmRCRMS0piHXWvpFnW6Zx1D1nlm/LD8qZRER01fdLpn9k+9rJfx1bR9j+4myHEs1vk1ExLQ2FW+8ukfSHuVO25mS9qC6iBsRMb1NwTP891DdaXtHWXYGciE3Iqa9QTvDr9NL5yZgp0mIJSJisPRRMq8jvXQiIrrhamiFOku/SC+diIgutPrhD1KTTnrpRER0y6639Ik6QyvcU3rmfKes70566UxPI09JOWFuaE7bpibidYMT/M6654mGaq7z5z5+erypePtfP52911HnN+A9wFeBL1NdovgF6aXT106/6NON1PuGeR9upN6IgdRnXS7rSC+diIgu9dMF2Trq9NL5pqQ129bnSvpGs2FFRPS/qdhL5yW272+tlOkH/6y5kCIiBoDp2UVbSc+W9DNJV0q6QtK+TYRcpw1/hqS5JdEjaa2a+0VETGk9vGj7BPD3ti+UNBtYIukM21f27AjUS9xfBM6XdGJZ3wX4bC+DiIgYSD1K+LZvB24vjx+SdBWwATC5Cd/2tyQt5skZr97a60+diIhBM84JUNYpebRlke1FHeuVFlA1m/9yAuF1VKtppiT4JPmIiBaPa3KTu20vHGsjSasDPwA+YvvBiYTXSdriIyK61cN++JJWpEr2x9tuZM7wJPyIiC716qKtJAH/CVxl+0u9qfXp6vTD/5CkuU0FEBExkAwMud4ytm2AdwHbSbq4LDv2OuQ6Z/jPBH4t6ULgG8Dpdh+NBhQRsbz0rpfOz2ls5KcnjXmGb/ufgOdTfd3YC7hW0j9L2rDh2CIi+tpUHB6ZckbfmuLwCWAucJKkwxqMLSKir2nItZZ+MWaTTrnFd0/gbuBo4OO2l0qaAVwLfKLZECMi+tBUHC0TWIvqZqub2gttD0l6UzNhRUT0t+rGq8HK+HXa8D81PNm3PXfVWPtLminpIkmnDiv/iqSHx9h3vqSHJe031nEiIibdUM2lT0xGP/x9gauAOa0CSQuprgOM5UvATxqKKyJiQqbcGf5ESJoHvJGq7b9VNhP4AmO0/Ut6C3AjcEWTMUZEdMXjWPpE02f4h1Ml9tltZR8ETrF9u0aYI7WMJ7E/8FpgxOYcSfsA+wDMnz+/RyHHiGY0eH6wtKF5URv6Y/MKzXWZXvnexxqpd+bSZtoWNNRHbRaTqr964NTR2F9wuaB7p+0lbWXrUw2v/NUxdj8Y+LLtUdv4bS+yvdD2wnXXXXeiIUdEjE+PJkCZLE2e4W8D7FRuD55F1YZ/BfAYcF05u19V0nW2Nxq278uAnUs//zWBIUl/tH1kg/FGRNTn/pq+sI7GEr7tA4EDASRtC+xn+yndOCU93CHZY/uVbdscDDycZB8RfaePzt7raPSi7XhI2knSIcs7joiI2nLR9ulsnwOc06F89bbHpwCndNjm4AZDi4jo2qBdsM54+BER3TB9dVNVHUn4ERFdEB64G6+S8CMiupWEHxExTSThR0RMA2nDj4iYPtJLJyJiWuivYRPqSMKPiOiGScKPiJg2BqtFJwk/IqJb6YcfETFdJOFHREwDNiwbrDadJPyIiG7lDD8iYppIwo8pa4Xmfl28bFkj9WpZM3+QntXcVBIz7xl1Zs+uzXh0pUbqZcDmde0ZM3CvPQk/IqIrBqcNPyJi6jO5aBsRMW2kDT8iYpoYsITfN5OYR0QMljJ4Wp1lDJK+IelOSZc3GXESfkRENwwMDdVbxnYs8PpG4yVNOhER3etRk47tcyUt6Ello0jCj4joyriGVlhH0uK29UW2FzUQ1KiS8CMiumFw/X74d9te2GQ4dSThR0R0K3faRkRME+mWGRExDdg966Uj6TvA+cAmkm6R9NdNhJwz/IiIbvWul87uPaloDEn4ERFdcWOjvDYlCT8iohsZHjkiYhoZsOGRG79oK2mmpIsknTqs/CuSOs70IOm1kpZIuqz83K7pOCMixsOAh1xr6ReTcYa/L3AVMKdVIGkhMHeUfe4G/tL2bZJeBJwObNBolBER4+HBmwCl0TN8SfOANwJHt5XNBL4AfGKk/WxfZPu2snoFsIqklZuMNSJivLxsWa2lXzR9hn84VWKf3Vb2QeAU27dLqlPH24ALbT82/AlJ+wD7lNXHmh5atAHrUH2bGSSDFfPZAxZvJTEX0ud7XWXLJhOt4CHuO/1Mn7ROzc374v+zsYQv6U3AnbaXSNq2lK0P7AJsW7OOzYDPAzt0er4MPrSobLu4H8aqGI/E3LxBixcS82QYNpBZV2w3PpxxrzV5hr8NsJOkHYFZVG34VwCPAdeVs/tVJV1ne6PhO5fmoJOBPW1f32CcERHTQmNt+LYPtD3P9gJgN+Bs23NtP8v2glL+6AjJfk3gx8ABts9rKsaIiOmkb8bSkbSTpEPK6geBjYCDJF1clmeMUcWkjy3dA4m5eYMWLyTmyTBo8faEPGCjvUVERHf65gw/IiKalYQfETFd2O6LBYRGKRQAAAvCSURBVFgLOAO4tvycO8J27y7bXAu8u618S+Ay4DrgKzzZXNWxXqo7fU8GLgV+Bbyora7XA9eUug7oo5jXAH4EXELV42nvtroOK2VXtdfVx/HOB35a4r0SWNDv73F5fg5wC3BkP8cLbEE1vvoVVL/juw7I73HHY/RBzB8HLi7L5cAyYK3y3EfL67gc+A4wa7z5b7KW5R5A23/AYZTkChwAfH6E/+Qbys+55XHrP+RXwNaAgJ8AbxitXqq7fT9VHm8KnFUezwSuB54HrFR+KV/YJzH/Q9vjdYF7S4z/DzivxD6T6g99236Nt6yfA7y2PF4dWLWf3+O2Yx0BnMDICb8v4gU2Bp5fytcHbgfW7POYRzzG8o55WL1/SdXrEKohX24EVinr3wf26lVe7PWy3ANoexOvAdYrj9cDrumwze7A19rWv1bK1gOu7rTdSPVSdft8Zds+1wPPBF4OnN5WfiBwYJ/EfCDw7+WX9LlUZyczSsxLgFWAVYHFwAv6ON4XAj/v09+LjjGX57YEvgvsxcgJv2/iHXbMSygfAP0a80jH6IeYh9V7AvC+8ngD4GaqD5UVgFOBHer8bi+PpZ/a8J9p+/by+A6q5Dtc681tuaWUbVAeDy8frd5LgLcCSHop8Bxg3ijH6IeYjwReANxG9XV0X9tDts8HfkZ1Fnc71QfWVf0aL9XZ5/2SflhGUv1CGWOpk76IWdIM4IvAfiPE2Vfxth+s/H6vRHVS088x9/PfHgCSVqVq8v0BgO1bgX8Ffkf1t/eA7Z+OEPNyN6nj4Us6E3hWh6f+sX3FtiW518cfVu/ngCMkXUz1S3cRVbvccPsD67WGh2izPGJ+HVUb4nbAhsAZkv4PeAbVH9A8qrbRj0raHXi0T+NdAXgl8GdUfyi3Au+QdN+w6vop5j2B/7F9i6SPA8/q598J2w8CSFoP+DbwAHBph/Gr+ibmDrvtRXU3/l7DypdHzC1/CZxn+14ASXOBN1N9U7kfOFHSHraP63U8vTCpCd/2X4z0nKTfS1rP1aBq6wF3dtjsVp46Ds88qrbgW8vj9vJby+OO9ZY/iL3LsUXVDncDVbPIs9vqOr5s/y/LO+YS7+dcfZe8TtKNVNcfXg1cYPth4DWSDgL+aPuwPo33FuBi2zeUuPYDtrb9d8OD6aOYXw68UtLfUl1zWBE41fYBfRrvryTNoWq6/EfbJ3WIo99iHn6MM4FzbH+nD2Ju2Y3qwmzLXwA32r6rxPVDqmtqfZnw+6lJ5xSqK+qUn//dYZvTgR0kzS2frDtQNV/cDjwoaeuSvPds279jvZLWlLRSKX8vcG75EPg18HxJzy3P71bqWO4xU50Nb1/ifybViH83lPJXS1pB0opUHwCdmnT6Jd5fA2tKWrdstx1VT51O+iJm2++0Pd/VkCD7Ad8anuz7Kd7yu3tyiXPEZN9PMY90jD6JGUlrUP1ttR/rd8DWklYtdW1P57+9/tD0RYK6C7A2cBZVd6gzebLL00Lg6Lbt3kN1kec6ntqdayFVt6jrqdoINUa9Lwd+Q3WR5oe09QYAdizPXU91dtQvMa9P1ZXxsrLfHqV8JtUFqVYXxy/1c7zluddSdRe8DDiWtp4w/RpzW517MfJF276IF9gDWMqTXQkvBrbo55hHO8byjrnt//27HWL5NHB1qe/bwMrLK4+OtWRohYiIaaKfmnQiIqJBSfgREdNEEn5ExDSRhB8RMU0k4UfEhEj6e0mW9LQJvSW9Rk9OYnSxpD9Kekt57nhJ10i6XNI3Spfi1n7blu2vkPS/beVrSjpJ0tWSrpL08jFie5WkCyU9IWnnXr7uQZSEH5NG0sPl5/qSxuobPlo9H1F1i3svYtq0JJaLJG3Yizrb6j5a0gu72G8LVXNBt9Z3ktSpz/9yJ+nZVP3bf9fpeds/s72F7S2o7rd4lKpLJlQ3NW4KvJjqhsf3ljrXpBprZyfbmwG7tFV5BHCa7U2BzRm7z/vvqLpTnjDuFzcFJeFHIySNeBe37dtsT+Rs6yNUg8T1wluAk2z/me2RxpoZ0Riv8722R7qhbDRbUN0L0qrnFNuf66KeyfBl4BNAnf7dOwM/sf0ogO3/cUE1emXr7td3AD+0/buy3Z3wpxufXgX8Zyl/3Pb95bkNJZ0maYmk/5O0adnmt7YvBZ4yvtB0lYQ/xUnaStKlkmZJWq18RX5Rh+32LNtdIunbpWyBpLNL+VmS5o9RfqykoyT9Ejis3K18vqTLJB3adqwFki4vj/dSNYjaaZKulXRY23b/IWlxifnTpezDVDfu/EzSz0rZDuU4F0o6UdLqHV7fFpIuKDGfXO6+3JHqw+NvWnUN2+dhSV8uxz9L5c5gSedIOlzSYmBfSduXbwiXlaaJldu2WzhajOX/5xflff9VSWqHALuWbx67lvfoyBrv/VdKXTdMRvOFpDcDt9q+pOYuw4claNWzIvAu4LRStDEwt7x/SyTtWcqfC9wFHFPe76MlrVaeWwR8yPaWVHdC/3t3r2qKW953fmVpfgEOpRrR79/oMNQzsBnVncXrlPXWXYs/okwaQXXH4n+NUX4s1fCwM8v6KcCe5fHfAQ+XxwuAy8vjvahuq18DmAXcBDx7WBwzqcZAeUlZ/21brOsA5wKrlfX9gYM6vMZLgVeXx4cAh5fHBwP7jfC+GXhneXwQ5e7aEsu/l8ezqEZk3Lisfwv4SNt2C0eKkWoEyxuArUr5HKrxrfai7U7e9vUx3vsTeXL46et69LtzJtUdpMOXNwO/BNYY/n8yQj3rUSXrFTs89/XW/0dZPxK4AFitvHfXUn0ILASeAF5WtjsC+AzV2EZ/4Kl3FV817BjHAjsv77/F5b1M6uBpsdwcQjV+zR+BD3d4fjvgRNt3A7iMBEg1/MRby+NvU00OMVo5pZ7WqKPbAG9r2+7zI8R3lu0HACRdSTVU9c3A2yXtQ5UE16NKZJcO23frUn6eqpEgV6KaAOZPylnzmrZbF/++SZUcxzIEfK88Po5qCI6WVvkmVINn/aat7r8DDq8R4ybA7bZ/DX8a0A89fUTLdqO99//lapjhK1WNUTNhHmHAQ0kvpjrjvqTEOw+4UNJLbd/RYZe3AyfbXjqsnk9RTYLy/rbiW4B7bD8CPCLpXKr2+v8DbrH9y7LdSVSTlMwA7nd1nSBGkYQ/PazNkyM8zgIeafBYw+uu07b7WNvjZcAKkp5L9dV8K9v3STqWKvbhBJxhe/dugh2n9tcynvewY4wlafZS+/s46qfGRNm+jGpY7upg0m+Bha2Thg52p5r45E8kvZdqqOTt/dQx/P8bOLJcH1kJeBnwZdt3SLpZ0ia2r6EaqOxK2w9KulHSLrZPVPUJ9BLXb2qaNtKGPz18DfgkVa+ITmfZZwO7SFobQNJapfwXVO2uAO+kOsMarXy484ZtNx5zqJLqA+Vs9Q1tzz0EzC6PLwC2kbRRiX01SRu3V1S+Pdwn6ZWl6F3A/zK2GVQXGqG6kPjzDttcAyxoHX+EukeK8RqquRa2KuWzS5Jrf33D1X3vlxtJCyUd3ba+gGrI8eHvy1FUE4ycX65XHATgavKe03hyvumjbV9e9vkQcLykS6kubv9zKX8n8NeSWvPkvrkceytJt1D19PmapCt6/HIHSs7wp7hywWup7RNUzSr1C0nb2T67tY3tKyR9FvhfScuoJoPZi+qP6xhVE37cRZk/YJTy4fYFTpC0P52Hrx2R7UskXUQ1CuHNVB8eLYuA0yTdZvs1qibI+E7rYinwT1TXJNq9GzhKVXfOG0aJud0jwEsl/RPVuOi7dojzj5L2ppr4YgWqprOjnrqJ7+oUo+3fSNoV+KqkVajaof+CavayA1RNzjN8Hoa67/2kcjVsdOvxYkoXy7L+WzrMXGV7tB5OX6Cad3p4+cVUbfnDy2+kmolqePmveerY99NaRsuMGIGkh20/rcfPOPa/jKov+Y09DCuia2nSiWiApDOAy5Lso5/kDD8iYprIGX5ExDSRhB8RMU0k4UdETBNJ+BER00QSfkTENPH/AaG8OGW1mBUsAAAAAElFTkSuQmCC", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# mask nodata and plot\n", - "geo_grid.test_attr.where(geo_grid.test_attr!=geo_grid.test_attr.rio.nodata).plot()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Load into user-defined grid with radial linear resampling" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "geo_grid = make_geocube(\n", - " vector_data=gdf,\n", - " measurements=['test_attr'],\n", - " geom=json.dumps(mapping(box(-48, 44, -47, 45))),\n", - " output_crs=\"epsg:3857\",\n", - " resolution=(-300, 300),\n", - " rasterize_function=rasterize_points_radial,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# mask nodata and plot\n", - "geo_grid.test_attr.where(geo_grid.test_attr!=geo_grid.test_attr.rio.nodata).plot()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Load into user-defined grid with radial cubic resampling\n", - "\n", - "Note: This example is simply to demonstrate the option and and\n", - "would need to be adjusted for better results. As seen in the plot below,\n", - "the values swelled quite a bit due to the input generating an \n", - "ill-conditioned matrix." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/snowal/miniconda/envs/geocube/lib/python3.6/site-packages/scipy/interpolate/rbf.py:268: LinAlgWarning: Ill-conditioned matrix (rcond=3.16678e-18): result may not be accurate.\n", - " self.nodes = linalg.solve(self.A, self.di)\n" - ] - } - ], - "source": [ - "geo_grid = make_geocube(\n", - " vector_data=gdf,\n", - " measurements=['test_attr'],\n", - " geom=json.dumps(mapping(box(-48, 44, -47, 45))),\n", - " output_crs=\"epsg:3857\",\n", - " resolution=(-300, 300),\n", - " rasterize_function=partial(rasterize_points_radial, method=\"cubic\", filter_nan=True),\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# mask nodata and plot\n", - "geo_grid.test_attr.where(geo_grid.test_attr!=geo_grid.test_attr.rio.nodata).plot()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.10" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/refs/tags/0.5.2/examples/timestamp_missing_data.ipynb b/refs/tags/0.5.2/examples/timestamp_missing_data.ipynb deleted file mode 100644 index 3a97693b..00000000 --- a/refs/tags/0.5.2/examples/timestamp_missing_data.ipynb +++ /dev/null @@ -1,398 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Example - Filling in missing data & dealing with timestamps" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import geopandas\n", - "\n", - "from geocube.api.core import make_geocube\n", - "\n", - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Load in geopackage data and add CRS" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "gdf = geopandas.read_file(\n", - " \"../../test/test_data/input/time_vector_data.geojson\",\n", - " crs=\"epsg:4326\",\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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test_attrtest_str_attrtest_time_attrgeometry
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\n", - "
" - ], - "text/plain": [ - " test_attr test_str_attr test_time_attr \\\n", - "0 1.3 dcf86619 5/21/2016 10:09:21 AM -05:00 \n", - "1 1.3 dcf86619 5/21/2016 10:09:21 AM -05:00 \n", - "2 1.9 dcf86619 5/21/2016 10:09:21 AM -05:00 \n", - "3 1.3 dcf86619 5/21/2016 10:09:21 AM -05:00 \n", - "4 1.3 dcf86619 5/21/2016 10:09:21 AM -05:00 \n", - "\n", - " geometry \n", - "0 POINT (-47.26681 44.21932) \n", - "1 POINT (-47.26680 44.21932) \n", - "2 POINT (-47.26681 44.21932) \n", - "3 POINT (-47.26680 44.21932) \n", - "4 POINT (-47.26679 44.21932) " - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "gdf.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "gdf.head().plot(column='test_attr')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Convert to raster with GeoCube\n", - "\n", - "See docs for [make_geocube](../geocube.rst#make-geocube)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Load into grid basic" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/snowal/miniconda/envs/geocube/lib/python3.6/site-packages/xarray/core/variable.py:151: FutureWarning: Converting timezone-aware DatetimeArray to timezone-naive ndarray with 'datetime64[ns]' dtype. In the future, this will return an ndarray with 'object' dtype where each element is a 'pandas.Timestamp' with the correct 'tz'.\n", - "\tTo accept the future behavior, pass 'dtype=object'.\n", - "\tTo keep the old behavior, pass 'dtype=\"datetime64[ns]\"'.\n", - " return np.asarray(pd.Series(values.ravel())).reshape(values.shape)\n" - ] - } - ], - "source": [ - "geo_grid = make_geocube(\n", - " vector_data=gdf,\n", - " measurements=['test_attr', \"test_time_attr\"],\n", - " datetime_measurements=[\"test_time_attr\"],\n", - " resolution=(-0.1, 0.00001),\n", - " group_by=\"test_time_attr\",\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "\n", - "Dimensions: (test_time_attr: 2, x: 12, y: 11)\n", - "Coordinates:\n", - " * y (y) float64 45.25 45.15 45.05 44.95 ... 44.45 44.35 44.25\n", - " * x (x) float64 -47.27 -47.27 -47.27 ... -47.27 -47.27 -47.27\n", - " * test_time_attr (test_time_attr) datetime64[ns] 2016-05-21T15:09:21 2016-05-22T15:09:22\n", - " spatial_ref int64 0\n", - "Data variables:\n", - " test_attr (test_time_attr, y, x) float64 nan nan nan ... nan nan nan\n", - "Attributes:\n", - " grid_mapping: spatial_ref" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "geo_grid" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# mask nodata and plot\n", - "grid_time_1 = geo_grid.isel(test_time_attr=0)\n", - "grid_time_1.test_attr.where(grid_time_1.test_attr!=grid_time_1.test_attr.rio.nodata).plot()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Load into UTM grid followed by interpolation for missing values\n", - "\n", - "See docs for [make_geocube](../geocube.rst#make-geocube)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/snowal/miniconda/envs/geocube/lib/python3.6/site-packages/xarray/core/variable.py:151: FutureWarning: Converting timezone-aware DatetimeArray to timezone-naive ndarray with 'datetime64[ns]' dtype. In the future, this will return an ndarray with 'object' dtype where each element is a 'pandas.Timestamp' with the correct 'tz'.\n", - "\tTo accept the future behavior, pass 'dtype=object'.\n", - "\tTo keep the old behavior, pass 'dtype=\"datetime64[ns]\"'.\n", - " return np.asarray(pd.Series(values.ravel())).reshape(values.shape)\n" - ] - } - ], - "source": [ - "geo_grid_interp = make_geocube(\n", - " vector_data=gdf,\n", - " measurements=['test_attr', \"test_time_attr\"],\n", - " datetime_measurements=[\"test_time_attr\"],\n", - " resolution=(-0.1, 0.00001),\n", - " group_by=\"test_time_attr\",\n", - " interpolate_na_method='nearest'\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "\n", - "Dimensions: (test_time_attr: 2, x: 12, y: 11)\n", - "Coordinates:\n", - " * y (y) float64 45.25 45.15 45.05 44.95 ... 44.45 44.35 44.25\n", - " * x (x) float64 -47.27 -47.27 -47.27 ... -47.27 -47.27 -47.27\n", - " * test_time_attr (test_time_attr) datetime64[ns] 2016-05-21T15:09:21 2016-05-22T15:09:22\n", - " spatial_ref int64 0\n", - "Data variables:\n", - " test_attr (test_time_attr, y, x) float64 1.3 1.3 1.3 ... 1.3 1.3 1.3\n", - "Attributes:\n", - " grid_mapping: spatial_ref\n", - " creation_date: 2019-12-12 19:57:17.046150" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "geo_grid_interp" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "geo_grid_interp.test_attr.isel(test_time_attr=0).plot()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.10" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/refs/tags/0.5.2/examples/vectorize.ipynb b/refs/tags/0.5.2/examples/vectorize.ipynb deleted file mode 100644 index 331ba07a..00000000 --- a/refs/tags/0.5.2/examples/vectorize.ipynb +++ /dev/null @@ -1,132 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Example - Raster DataArray to GeoDataFrame (vectorize)" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import geopandas\n", - "\n", - "from geocube.api.core import make_geocube\n", - "from geocube.vector import vectorize\n", - "\n", - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Generate Raster DataArray" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "gdf = geopandas.read_file(geopandas.datasets.get_path('naturalearth_lowres'))\n", - "geo_grid = make_geocube(\n", - " vector_data=gdf,\n", - " measurements=['pop_est'],\n", - " resolution=(-3.0, 3.0),\n", - ")\n", - "geo_grid[\"pop_est\"].plot()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Convert to GeoDataFrame (vectorize)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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+fbsOHDigLVu2aMKECSooKNDtt9/uZNMAAECacDRnxdPOlto1NTWaOnWqzpw5o9tuu021tbU6ceKEioqKNGbMGD322GNRJ86ydLlzimUpc7gcFns9icx3GdtlkuV4Y/PahNWd7hKZs9KqrD2HJUKeSkv2nJWw94ljunn9qZfCXo8nh6VlzkqsOSrhxJO/Ys9JiSRczoqyrQP+zccbLcct/50FGy61XCNHJb24JmclUhyUm5ur9evDJ0gBAIDMxncDAQAAVyNYAQAArpaUpctArGLJQwmX33L+wMGEtcmOHBWrWPNUOiyGHJVUiZjPFGZ/l0gSmacSj1jzVDqqS+98y3HL13Zjc8dzVCoHPmQ5frvuf3W4LjiPkRUAAOBqBCsAAMDVCFYAAICrJe27gZzCPisIx547YEfeSeK0zFnxdOlivejp+P+LPFm2/Zou/5H1+NN9UdcVbp+VePZVscvq0zvs9eC3x9q9tv7M/0lYO+IRad8Vp3JWKgrusxwH/afaLbuh6VVH2oDkiOXvNyMrAADA1QhWAACAq7F0GZ2afZonnuW19i3CTdO5dsvah/Lt93XL8tNEatmn8uzJ1osmaD0OMy1kn0Iy562vc6sv8bBPC7UUwxRR8LvvLMdZ3btH/dxI0z6t7hU42+61TP8ah3eOrgp7vfLiB5LUErgJIysAAMDVCFYAAICrEawAAABXI2cFGSWWXJGwX2Mfwbjcuy3HWd5uHa4rHW04t8ZybM/ZycqxLW0Ow9i2pm+VsxKDcEuXW5UNBOK4U3hdB1zc7rXzX9c7dt9YJGs7/Vi9/fWzjtTL9vvuxsgKAABwNYIVAADgagQrAADA1chZATrIk5NtOQ6374pduP1eutj27Ii070Q6sOcKjev2d5bj9Wdf6XDd44Yv+OEghn1VJKlr/36hn88f+dZyrUvfPpbj5mN/abce0+i3HHvyw28dHi4vJdP2VbnxhiWW43ffm5uwuu25Yy3Z90IiR8XdGFkBAACuRrACAABcjWAFAAC4mityVlasWKGlS5eqvr5el19+uZ555hlde+21qW4WEJvSFt9Rs2df+9ckadcey2HW8NLQz9ZdRTqneHJUWtW1a2Ho53i++6ll/kpb7Dks5rszLQ6s75o9hyWcTMtRsbPnqNhzWLpu/8xy3DLXJFLZ82U/DXsvpI+Uj6ysXbtWs2bN0rx581RbW6trr71WlZWVOnTInRsSAQCA5Ep5sLJ8+XLde++9mjZtmi677DI988wzGjhwoJ5//vk2ywcCAfn9fssDAAB0XimdBmpqatKuXbv06KOPWs6Xl5dr27ZtbT5nyZIlWrhwYZvXgHiV59zV7rWs3Bi2zLdP+9jrajHtE8m4a8L/vq/fuSDsdTikS5ivDAgGwz4106d+wok0LdTRsnbxLJl2crk12pbSkZWjR4+qublZhYWFlvOFhYVqaGho8zlz585VY2Nj6FFXV5eMpgIAgBRxRYKtx2P9ajJjTKtzF3i9Xnm93mQ0CwAAuEBKR1YKCgrUpUuXVqMoR44caTXaAgAAMlNKR1ZycnI0fPhwbdy4Ubfffnvo/MaNG3XrrbemsGWA5LHlJLTaTj9CXkqyhMtpybR8Fvu2/valzJGWJydMlvX/ge8cfzE5981wseSOxJNnEmuuTNbW/wz9TL5Sx6R8Gmj27Nm6++67dfXVV6usrEyrVq3SoUOHdP/996e6aQAAwAVSHqxMmjRJx44d06JFi1RfX6/S0lKtW7dOJSUlqW4aAABwgZQHK5I0Y8YMzZgxI9XNAAAALuSKYAVwC3ueioVLclRiYc9nybQcliyvdW+cYOPJ9svm93S6OeiAcLklkXJFkrX/SSw5LGO7TLIck8MSnZTvYAsAABAOwQoAAHA1ghUAAOBq5KwALbT8+vlxuXdbL+7ZZz225bCY//w89LNn2GUJbxvcJXj6u9DPWT26p7Almcut38nj1nalM0ZWAACAqxGsAAAAVyNYAQAAruYxxphUNyIefr9f+fn5amxsVF5eXqqbg06sVQ6LjTl/Luz1lpKV05Jp+6rYRXrPWjLNzQm7b9j9emTNjbIb1+3vrGXPvpKQNiE+9velpUjvUcvn8n7+IJa/34ysAAAAVyNYAQAArsbSZSBK9qH7WKYY7Fouc5ZY6pwWTLDdS56u2WGfGsu0D1Ijnvchlqk7pvk6hpEVAADgagQrAADA1QhWAACAq5GzAnSQPQ+hPHtyilqC9gQDZxNWV6TlyC2Fy1GJ+FxyGJLCyVwh8pASj5EVAADgagQrAADA1QhWAACAq5GzAiSBCYb/VgtPgu6T6dvr220M/t5yPDZrYofrsm/Hn+Xt1uG6yGlIjnR4ndl3JTqOjawcOHBA9957r4YMGaLc3FxdcsklWrBggZqamizlPB5Pq8fKlSudahYAAEgzjo2s/PGPf1QwGNRvf/tb/ehHP9KePXs0ffp0nT59WsuWLbOUrampUUVFReg4Pz/fqWYBAIA041iwUlFRYQlAhg4dqi+++ELPP/98q2ClV69e8vl8TjUFAACksaTmrDQ2NqpPnz6tzldVVWnatGkaMmSI7r33Xt13333Kymp7hioQCCgQCISO/X6/Y+0FwinPuctynJWb2+G6mj/57Id6rvpp2LLkpaRGlzAjvu8cfzGmushLQHvIYWlb0oKVP//5z3r22Wf19NNPW84/9thjuvHGG5Wbm6t3331Xc+bM0dGjRzV//vw261myZIkWLlyYjCYDAAAXiDnBtrq6us2k2JaPjz/+2PKcw4cPq6KiQhMnTtS0adMs1+bPn6+ysjINGzZMc+bM0aJFi7R06dJ27z937lw1NjaGHnV1dbF2AQAApJGYR1aqqqo0eXL4bcUHDx4c+vnw4cMaM2aMysrKtGrVqoj1jxw5Un6/X998840KCwtbXfd6vfJ6vbE2G3C1SFM/SAz7VE5zY2O71wC4R8zBSkFBgQoKCqIq+/XXX2vMmDEaPny4ampq2s1Daam2tlbdunVTr169Ym0aAADohBzLWTl8+LCuv/56DRo0SMuWLdO3334bunZh5c8f/vAHNTQ0qKysTLm5udq8ebPmzZun++67j9ETAAAgycFgZcOGDdq3b5/27dunAQMGWK4Z8/1untnZ2VqxYoVmz56tYDCooUOHatGiRZo5c6ZTzQIAAGnGYy5EDmnK7/crPz9fjY2NysvLS3VzkEFaLV3u1vHRwOZTp36oZ3ip5RpLlZ1T0Xta5ELtiHW5MlIvHbbft+vMS5dj+fvNFxkCAABXI1gBAACuRrACAABcLanb7QOIjByV1CAHJbM4mQsST25MZ85RiQcjKwAAwNUIVgAAgKsRrAAAAFcjZwWIUiL3VbHbGPx9wupC9MhTySzJygex3ycd9ncZmzXRcuy2zyRGVgAAgKsRrAAAAFdjGghIgnf8NaluAoAUYTly/BhZAQAArkawAgAAXI1gBQAAuBo5K0CUNjS9ajmuyPv7dsuSowLAzdy+VNmOkRUAAOBqBCsAAMDVCFYAAICrkbMCRMnJ7fYBwGkt81TcnqNix8gKAABwNUeDlcGDB8vj8Vgejz76qKXMoUOHNGHCBPXo0UMFBQV68MEH1dTU5GSzAABAGnF8GmjRokWaPn166Piiiy4K/dzc3Kzx48erX79++uCDD3Ts2DFNmTJFxhg9++yzTjcNAACkAceDlZ49e8rn87V5bcOGDfrss89UV1en4uJiSdLTTz+tqVOnavHixcrLy3O6eUDCsLcKADdLtzyVlhzPWXnqqafUt29fDRs2TIsXL7ZM8Wzfvl2lpaWhQEWSxo0bp0AgoF27drVZXyAQkN/vtzwAAEDn5ejIykMPPaSrrrpKvXv31kcffaS5c+dq//79evHFFyVJDQ0NKiwstDynd+/eysnJUUNDQ5t1LlmyRAsXLnSy2QAAwEViHlmprq5ulTRrf3z88ceSpIcfflijR4/Wz372M02bNk0rV67U6tWrdezYsVB9Ho+n1T2MMW2el6S5c+eqsbEx9Kirq4u1C0BCBM8GLA8AgDNiHlmpqqrS5MmTw5YZPHhwm+dHjhwpSdq3b5/69u0rn8+nDz/80FLm+PHjOnfuXKsRlwu8Xq+8Xva3AAAgU8QcrBQUFKigoKBDN6utrZUkFRUVSZLKysq0ePFi1dfXh85t2LBBXq9Xw4cP79A9AABA5+JYzsr27du1Y8cOjRkzRvn5+dq5c6cefvhh3XLLLRo0aJAkqby8XD/96U919913a+nSpfrLX/6iRx55RNOnT2clEAAAkCR5jDHGiYo/+eQTzZgxQ3/84x8VCARUUlKiyZMn65e//KW6d+8eKnfo0CHNmDFD7733nnJzc3XXXXdp2bJlUU/1+P1+5efnq7GxkQAHAIA0Ecvfb8eClWQhWAEAIP3E8veb7wYCAACuRrACAABcjWAFAAC4GsEKAABwNYIVAADgagQrAADA1QhWAACAqxGsAAAAVyNYAQAArkawAgAAXM2xLzIE8IOxWRMtxxuDv09RSwAg/TCyAgAAXI1gBQAAuBrTQECCtJzqsU/zMO2DaNmnDGORzN+zeNqJH/DZEB1GVgAAgKsRrAAAAFcjWAEAAK7mMcaYVDciHn6/X/n5+WpsbFReXl6qm4MMEsucPfPSzgmXKxTPe5QJORkt+5wJ/e3s0u1zJpa/34ysAAAAVyNYAQAArkawAgAAXM2xnJUtW7ZozJgxbV776KOPdM0113zfAI+n1fXnn39e999/f1T3IWcldZK1hXyk+yRyX4pw+Q+R2hXPfTsb8h8A93Hb504sf78d2xRu1KhRqq+vt5z71a9+pU2bNunqq6+2nK+pqVFFRUXoOD8/36lmAQCANONYsJKTkyOfzxc6PnfunN58801VVVW1Gk3p1auXpSwAAMAFSctZefPNN3X06FFNnTq11bWqqioVFBTommuu0cqVKxUMBtutJxAIyO/3Wx4AAKDzSto+K3/7t38rSVq3bp3l/OOPP64bb7xRubm5evfdd/XP//zPmjt3rubPn99mPdXV1Vq4cGGr85mcs0J+gDu4bT44XvxeAenN7Z9Jju6zUl1dLY/HE/bx8ccfW57z1Vdfaf369br33ntb1Td//nyVlZVp2LBhmjNnjhYtWqSlS5e2e/+5c+eqsbEx9Kirq4u1CwAAII3EnLNSVVWlyZMnhy0zePBgy3FNTY369u2rW265JWL9I0eOlN/v1zfffKPCwsJW171er7xeb0xtBgAA6SvmYKWgoEAFBQVRlzfGqKamRvfcc4+ys7Mjlq+trVW3bt3Uq1evWJuWVuJZ9svwfHK4ZQg1mdv687sFpC+3fGY5wbHVQBe899572r9/f5tTQH/4wx/U0NCgsrIy5ebmavPmzZo3b57uu+8+Rk8AAICkJAQrq1ev1qhRo3TZZZe1upadna0VK1Zo9uzZCgaDGjp0qBYtWqSZM2c63SwAAJAmHA9WXn311XavVVRUWDaDAwAAsEva0mWnZMJ2++QRdD7h5pZ5vwEkgttzWBxdugwAAJBMBCsAAMDVCFYAAICrOZ5gm+7cmD9gn4d0YxsRHu8ZAKcl8nMm1fkvjKwAAABXI1gBAACuRrACAABcjZyVNES+AwAgkzCyAgAAXI1gBQAAuBrTQBGkerkWAACZjpEVAADgagQrAADA1QhWAACAqxGsAAAAVyNYAQAArkawAgAAXI1gBQAAuBrBCgAAcDWCFQAA4Gppv4OtMUaS5Pf7U9wSAAAQrQt/ty/8HQ8n7YOVkydPSpIGDhyY4pYAAIBYnTx5Uvn5+WHLeEw0IY2LBYNBHT58WD179pTH40l1cxzn9/s1cOBA1dXVKS8vL9XNSapM7Xum9lui75nY90ztt5R5fTfG6OTJkyouLlZWVvislLQfWcnKytKAAQNS3Yyky8vLy4hf5rZkat8ztd8Sfc/Evmdqv6XM6nukEZULSLAFAACuRrACAABcjWAlzXi9Xi1YsEBerzfVTUm6TO17pvZbou+Z2PdM7beU2X2PJO0TbAEAQOfGyAoAAHA1ghUAAOBqBCsAAMDVCFYAAICrEawAAABXI1hxscWLF2vUqFHq3r27evXq1WYZj8fT6rFy5UpLmd27d2v06NHKzc3VxRdfrEWLFkX1xVGpEk2/Dx06pAkTJqhHjx4qKCjQgw8+qKamJkuZdOt3WwYPHtzq/X300UctZaJ5LdLVihUrNGTIEHXr1k3Dhw/Xf/zHf6S6SQlVXV3d6v31+Xyh68YYVVdXq7i4WLm5ubr++uv16aefprDFHff+++9rwoQJKi4ulsfj0RtvvGG5Hk1fA4GAHnjgARUUFKhHjx665ZZb9NVXXyWxF7GL1O+pU6e2+h0YOXKkpUw69jvRCFZcrKmpSRMnTtQ//uM/hi1XU1Oj+vr60GPKlCmha36/X2PHjlVxcbF27typZ599VsuWLdPy5cudbn6HRep3c3Ozxo8fr9OnT+uDDz7QmjVr9G//9m+aM2dOqEw69rs9ixYtsry/8+fPD12L5rVIV2vXrtWsWbM0b9481dbW6tprr1VlZaUOHTqU6qYl1OWXX255f3fv3h269utf/1rLly/Xc889p507d8rn82ns2LGhL3BNJ6dPn9aVV16p5557rs3r0fR11qxZev3117VmzRp98MEHOnXqlG6++WY1Nzcnqxsxi9RvSaqoqLD8Dqxbt85yPR37nXAGrldTU2Py8/PbvCbJvP766+0+d8WKFSY/P9+cPXs2dG7JkiWmuLjYBIPBBLc0sdrr97p160xWVpb5+uuvQ+f+9V//1Xi9XtPY2GiMSe9+t1RSUmL+5V/+pd3r0bwW6eqv//qvzf33328595Of/MQ8+uijKWpR4i1YsMBceeWVbV4LBoPG5/OZJ598MnTu7NmzJj8/36xcuTJJLXSG/XMrmr6eOHHCZGdnmzVr1oTKfP311yYrK8u88847SWt7PNr6vJ4yZYq59dZb231OZ+h3IjCy0glUVVWpoKBA11xzjVauXKlgMBi6tn37do0ePdqyI+K4ceN0+PBhHThwIAWtjd/27dtVWlqq4uLi0Llx48YpEAho165doTKdpd9PPfWU+vbtq2HDhmnx4sWWKZ5oXot01NTUpF27dqm8vNxyvry8XNu2bUtRq5yxd+9eFRcXa8iQIZo8ebK+/PJLSdL+/fvV0NBgeQ28Xq9Gjx7d6V6DaPq6a9cunTt3zlKmuLhYpaWlaf96bNmyRf3799ell16q6dOn68iRI6FrnbnfsUj7b13OdI899phuvPFG5ebm6t1339WcOXN09OjR0FRBQ0ODBg8ebHlOYWFh6NqQIUOS3eS4NTQ0hPpwQe/evZWTk6OGhoZQmc7Q74ceekhXXXWVevfurY8++khz587V/v379eKLL0qK7rVIR0ePHlVzc3OrvhUWFqZ1v+xGjBihl19+WZdeeqm++eYbPf744xo1apQ+/fTTUD/beg0OHjyYiuY6Jpq+NjQ0KCcnR717925VJp1/JyorKzVx4kSVlJRo//79+tWvfqUbbrhBu3btktfr7bT9jhUjK0nWVkKd/fHxxx9HXd/8+fNVVlamYcOGac6cOVq0aJGWLl1qKePxeCzH5r+TTO3nnZTofrfVdmOM5bwb+t2WWF6Lhx9+WKNHj9bPfvYzTZs2TStXrtTq1at17NixUH3RvBbpqq33sDP064LKykr9/Oc/1xVXXKGbbrpJb731liTppZdeCpXp7K9BSx3pa7q/HpMmTdL48eNVWlqqCRMm6O2339af/vSn0O9Ce9K937FiZCXJqqqqNHny5LBl7CMCsRg5cqT8fr+++eYbFRYWyufztYq+Lwwx2v8X46RE9tvn8+nDDz+0nDt+/LjOnTsX6pNb+t2WeF6LC6sE9u3bp759+0b1WqSjgoICdenSpc33MJ37FUmPHj10xRVXaO/evbrtttskfT+iUFRUFCrTGV+DCyugwvXV5/OpqalJx48ft4wyHDlyRKNGjUpugx1UVFSkkpIS7d27V1Lm9DsSRlaSrKCgQD/5yU/CPrp169bh+mtra9WtW7fQkt+ysjK9//77ljyHDRs2qLi4OK6gKFaJ7HdZWZn27Nmj+vr60LkNGzbI6/Vq+PDhoTJu6Hdb4nktamtrJSn0gR7Na5GOcnJyNHz4cG3cuNFyfuPGjZ36AzoQCOjzzz9XUVGRhgwZIp/PZ3kNmpqatHXr1k73GkTT1+HDhys7O9tSpr6+Xnv27OlUr8exY8dUV1cX+jeeKf2OKGWpvYjo4MGDpra21ixcuNBcdNFFpra21tTW1pqTJ08aY4x58803zapVq8zu3bvNvn37zAsvvGDy8vLMgw8+GKrjxIkTprCw0Nx5551m9+7d5rXXXjN5eXlm2bJlqepWRJH6ff78eVNaWmpuvPFG88knn5hNmzaZAQMGmKqqqlAd6dhvu23btpnly5eb2tpa8+WXX5q1a9ea4uJic8stt4TKRPNapKs1a9aY7Oxss3r1avPZZ5+ZWbNmmR49epgDBw6kumkJM2fOHLNlyxbz5Zdfmh07dpibb77Z9OzZM9THJ5980uTn55vXXnvN7N6929x5552mqKjI+P3+FLc8didPngz9W5YU+t0+ePCgMSa6vt5///1mwIABZtOmTeaTTz4xN9xwg7nyyivN+fPnU9WtiML1++TJk2bOnDlm27ZtZv/+/Wbz5s2mrKzMXHzxxWnf70QjWHGxKVOmGEmtHps3bzbGGPP222+bYcOGmYsuush0797dlJaWmmeeecacO3fOUs9//dd/mWuvvdZ4vV7j8/lMdXW1q5fvRuq3Md8HNOPHjze5ubmmT58+pqqqyrJM2Zj067fdrl27zIgRI0x+fr7p1q2b+au/+iuzYMECc/r0aUu5aF6LdPWb3/zGlJSUmJycHHPVVVeZrVu3prpJCTVp0iRTVFRksrOzTXFxsbnjjjvMp59+GroeDAbNggULjM/nM16v11x33XVm9+7dKWxxx23evLnNf9dTpkwxxkTX1zNnzpiqqirTp08fk5uba26++WZz6NChFPQmeuH6/d1335ny8nLTr18/k52dbQYNGmSmTJnSqk/p2O9E8xiTZlt6AgCAjELOCgAAcDWCFQAA4GoEKwAAwNUIVgAAgKsRrAAAAFcjWAEAAK5GsAIAAFyNYAUAALgawQoAAHA1ghUAAOBqBCsAAMDV/j+uelwyo3kt/AAAAABJRU5ErkJggg==\n", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "grid_gdf = vectorize(geo_grid.pop_est.astype(\"float32\"))\n", - "grid_gdf.plot(column=\"pop_est\")" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.15" - }, - "vscode": { - "interpreter": { - "hash": "f7286e25aede83087e9621ce0e75f07eda2aa917ee0205c1a0b96e7ba4f20042" - } - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/refs/tags/0.5.2/examples/zonal_statistics.ipynb b/refs/tags/0.5.2/examples/zonal_statistics.ipynb deleted file mode 100644 index 6e348407..00000000 --- a/refs/tags/0.5.2/examples/zonal_statistics.ipynb +++ /dev/null @@ -1,845 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Example - Zonal Statistics\n", - "This is useful in the case where you want to get regional statistics for a raster." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import geopandas\n", - "import numpy\n", - "import rioxarray\n", - "import xarray\n", - "\n", - "from geocube.api.core import make_geocube\n", - "\n", - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Create the data mask by rasterizing the unique ID of the vector data\n", - "\n", - "See docs for [make_geocube](../geocube.rst#make-geocube)" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "# This assumes you are running this example from a clone of\n", - "# https://github.com/corteva/geocube/\n", - "# You could also use the full path: \n", - "# https://raw.githubusercontent.com/corteva/geocube/master/test/test_data/input/soil_data_group.geojson\n", - "ssurgo_data = geopandas.read_file(\"../../test/test_data/input/soil_data_group.geojson\")\n", - "ssurgo_data = ssurgo_data.loc[ssurgo_data.hzdept_r==0]\n", - "# convert the key to group to the vector data to an integer as that is one of the\n", - "# best data types for this type of mapping. If your data is not integer,\n", - "# then consider using a mapping of your data to an integer with something\n", - "# like a categorical dtype.\n", - "ssurgo_data[\"mukey\"] = ssurgo_data.mukey.astype(int)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "# load in source elevation data subset relevant for the vector data\n", - "elevation = rioxarray.open_rasterio(\n", - " \"https://prd-tnm.s3.amazonaws.com/StagedProducts/Elevation/13/TIFF/current/n42w091/USGS_13_n42w091.tif\", masked=True\n", - ").rio.clip(\n", - " ssurgo_data.geometry.values, ssurgo_data.crs, from_disk=True\n", - ").sel(band=1).drop(\"band\")\n", - "elevation.name = \"elevation\"" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "out_grid = make_geocube(\n", - " vector_data=ssurgo_data,\n", - " measurements=[\"mukey\"],\n", - " like=elevation, # ensure the data are on the same grid\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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-       "Data variables:\n",
-       "    mukey        (y, x) float64 1.988e+05 1.988e+05 ... 1.987e+05 1.987e+05\n",
-       "    elevation    (y, x) float64 173.7 172.0 171.1 170.6 ... 181.0 181.2 181.4
" - ], - "text/plain": [ - "\n", - "Dimensions: (y: 178, x: 178)\n", - "Coordinates:\n", - " * y (y) float64 41.5 41.5 41.5 41.5 ... 41.48 41.48 41.48 41.48\n", - " * x (x) float64 -90.6 -90.6 -90.6 -90.6 ... -90.58 -90.58 -90.58\n", - " spatial_ref int64 0\n", - "Data variables:\n", - " mukey (y, x) float64 1.988e+05 1.988e+05 ... 1.987e+05 1.987e+05\n", - " elevation (y, x) float64 173.7 172.0 171.1 170.6 ... 181.0 181.2 181.4" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# merge the two together\n", - "out_grid[\"elevation\"] = (elevation.dims, elevation.values, elevation.attrs, elevation.encoding)\n", - "out_grid" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", 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", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "out_grid.elevation.plot()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Get the elevation statistics of each region using the mask" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "grouped_elevation = out_grid.drop(\"spatial_ref\").groupby(out_grid.mukey)\n", - "grid_mean = grouped_elevation.mean().rename({\"elevation\": \"elevation_mean\"})\n", - "grid_min = grouped_elevation.min().rename({\"elevation\": \"elevation_min\"})\n", - "grid_max = grouped_elevation.max().rename({\"elevation\": \"elevation_max\"})\n", - "grid_std = grouped_elevation.std().rename({\"elevation\": \"elevation_std\"})" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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elevation_meanelevation_minelevation_maxelevation_std
mukey
198692.0173.130925169.394562188.4425053.307044
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198724.0179.931131178.237244181.4903870.630699
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" - ], - "text/plain": [ - " elevation_mean elevation_min elevation_max elevation_std\n", - "mukey \n", - "198692.0 173.130925 169.394562 188.442505 3.307044\n", - "198714.0 175.045866 170.214157 179.716675 2.150987\n", - "198724.0 179.931131 178.237244 181.490387 0.630699\n", - "198750.0 176.187118 167.951233 190.138763 3.815206\n", - "198754.0 171.633084 167.610321 181.611298 2.997591\n", - "271425.0 167.974433 167.951233 168.653015 0.079769\n", - "271431.0 176.718101 170.258133 180.460220 2.731732" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "zonal_stats = xarray.merge([grid_mean, grid_min, grid_max, grid_std]).to_dataframe()\n", - "zonal_stats" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "ssurgo_data = ssurgo_data.merge(zonal_stats, on=\"mukey\")" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", 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Tfu1kYqyCuS0dMRI0MaGqdqlqt/l6OeAJEoa5Dag043sBNAL7I13QUUA5ypttiTFC/2hrVXQNFFw27Izf3Ck0/Wkh7w+dF7lyhuJ3RLTDBhSCoIkJRWSCaePBXBlzYSicEdmMjeMvA9eYRTcSJomhH8cPKEdpHRR8UmNrZtQ+jg+bx73IncdJFfUUSB4DQ146+/s51NNN//Aw1YWFVBYUUpKfj3GvK6rgdrko9Ljp12E2dxxmwBc6kL4ifOgvs3j9w33UuVfZ9rnSCbvS8kSZmPAa4HMiMgz0Adf5I1WMSUz4FeBJEfkWsJaRKVxIHAWUw/ToJMqwTwG93LpgXFmZJ5+TyusZGPSypbWN9d3Bc9F39PfT0R8+PlF9SQkFJW7e7wkdeH7IJzy8fQ7/dmL2KSA7N6NGk5hQVR8CHgpSd2xiwp1YyAcfiDMFy2FahsJmU4qKXk7kX94yVnA94mZB1UQWVkxmsBvW7DvIO83NDIUZvVihuaeHoX5fxBhGT+x0oyRnJS7Z2OWImC5kjqQOtvNeT7Ut/Sguvrz+LKaV1LCoagpFw/lsONDM+kOH4lY6YznU3c2JFeEVZ59PaPNlX+goVWFYXZaOTCFzJHWwnRdbSmzpp3n4DLa1NbCzuZPVBw7QPZjYbBvFeZH3kaw6Mi2hMqSKBBuhk46jgHKY5w8rSkHM7YelgZ/s+gQf+N08th9JTj72Yo+HHV2R7VY/2FKOkl3hS+30hE4XHCN0DjPgE7o5njLeiVw5ACWftzov4bOvNtCZgBjP4Ti5oZ6VbXsi1tvUIRz2Lsq6cK2ZpFys4IyAcpzNvdOjqt+js7nhtRv5+AsTkqZ8SjweZlVV09Q4iZWtkZWPn2W7TkigVMknCX5ASccZAeU4926t4dcL3Mi4tGyjUVys6vgIn3qpngEbYtK4RagqKqLAnUe+202+243H7cLtclHgduMTpds7yOH+o7T19XK0v9/wTInit/XIljzuPH4CeZo9gcvs8gNKFxwFlOO806Uc9J7JJPfrIev4pIpvbryKx96LLYhQRUEhk8rKyM9z43G7GWSY7V0tNA8fhWGIGBk2xt+cIuwbmM30/OxQQKownKsByUTELSJrReRZ8/1tZuAhDbIvZGzbchHZJyIPBZRdKyIbRGSTiNwXUH6TiLQEBD+6OeDcjSKyzTxujO6jOoTif3bPDHmuj+P52MvXxKR8ZlVV0zRlEv2eATb1HGJt535WHdnDuiMH6B4eikdky6xsm5CU6ySLbJuCRaNO7wACc6C8DlwIvG+h7TeBFf43IlIDfAdYoqpzgQkiEhhX4amA4EePmm2qMdzFz8Dwtvy6mS3VIU5+tR965aRx5fuHzuasZ85nbav12+TEmlrmT26gvrqYrf0trGrdS583caE/IvH0+8Upu7bdZKMNyNKdJSKNwGXAo/4yVV2rqrsttD0NaACeDyieCWxT1Rbz/V+AqyN0dRHwgqoeUdV24AXGR3RziAnht80Ljr1ThFfb/o5zfz8nKkPzwokT2dLbzNojBzjUdzQBckbPmjahh9mpFsM2VMXSkSlYfbQ9iJGGOaoweiLiAu4H7hxzajtwoohMNze+XQVMCTh/tTk9e1pE/OWTgb0BdYIGPBKRW/xBmFpaWsaedgjBt7d48EoDSjEPbvkkn365jmiMLwsmTGBtx358ycj1HCUvtmSPV3QU8YAygogKSEQuB5pVdU0M/d8KLFfVfYGF5gjmc8BTwGvAbji2DPMHYLqqnoIxyvl5NBdU1Uf8QZjq6upiEDk36fMJyw5dwA+3X8NDGwujartgwgTWdR5IS+UD8Pu9ZakWwRZUs88GZGUV7CzgChG5FCgEykXkcVW9wULbxcA5InIrUArki0i3qt6lqn/AUDaY4SK9AKqj4kM8ykhg7P0Y4QP8NGLmqXewh/u2eDiJqYD1keP8hvRWPgDrj2TODzI8gjfXVsFU9W5VbVTV6cB1wEsWlQ+qer2qTjXb3gk8pqp3AYhIvfm3CmOk5Dc2Twzo4gpGDN/PAR8WkSqzzYfNMgcbKSqxfoPPratnQ9fBtFY+AJUJzACSbHLVBjQOEbndDGTUCGwQEb8CafK/jsD3RORdjNW0e1V1q1l+u7k0vx64HbgJQFWPYKymvWUe3zDLHGykZdharq2KggIODHXijc4smBJOqU5/Ga2Q83vBVPUVzGmPqi4Flgapsxq4OUj5MoxcRP73QQMiqerdjI7GH3jup8BPo5HZITr29rdTUVBJ58BA2HrTaipY134wSVLFxydnBA+ClnGoYQeyAxH5KeC3755sli0AHsYwtQwDt6rqKhG5HiPaoQBHgc+p6vogfS7DCOnqf4rdpKrrwsmRXRNKh7gRgWlVlWHrnDppUsYon9oCZX7pa6kWwzZsXAVbhvXEhLuA81R1HsYsJFyGjS8F+PCtiySEsxXDYRxFhaGfSw0lpWw+mjlbG761sBPR9PBJihe10QitqitEZPq4SwRJTKiqfwuosxLD7GILzgjIYRzDEtpzuaqsgJ4kbaOIl+I8ZUntX1Mthq2oWjti5F8InZjQz2eAP4Xp4x7Th+8BEYkYbMpRQGnA1GJ4+9w1rD9vJZc0pFoaGNTgCmhR42Te7WxOsjSxc39TV1bthIeoVsFiyYwaKjEhACJyPoYC+kqI9ncDJwGnA9Vh6h3DmYKlmLNr4Acnv06h7gCFB046xN9P/iDv9xXzrc15eFPwjAimgKZVVLLmSMQ8c2lDbYHy4doXIgawzySM0Y3lFa5WVW2K8hI3Yuz5BCMx4bHVbBE5xXx/yRhfvQD51G8YHBCRnzF+B8Q4nBFQirlyUp+hfExc2sXZpc9wfd2TrD53DfWxR0yNmVLXaE/osvwChjxDDKm9AeYTydJFrbi0I9Vi2E6KEhNOxciY+okAd5lx+H34zCSGV0HkUJvOCChFFLiUyycKS6o2haxTwmaeaKpjyevTkyaXKhztGvGb8bhcTKouYXNn5uyrK85Tzqh4OatGP35sXIaPJjHh14Aa4AdmgtRh/+hqTGLC/xUR/ybCdcBnI8nhKKAk88ApvSyu2EWVbEbojVh/insFn5k+lZ/sTs5gdWHpNNbsGrHzzJtUz+q2zJl6AfzzSYO4tCvVYtiOIvjsWwWLJjHhzQTx7TPPBSYmvCBaOZwpWBL56klDXFb5NNWyxpLy8fOZKVsSKNUIBZLHnsPdx94vnDgx45QPwEcbt6VahIShFo9MwVFASUJQ/qEhtiXhWnmLGUmIqzWvcBotvYZiLMsvYGevfWmbk8X0UqXevTrVYiQGdfaCOcTILTMUj+6NXDEEH5mUWANwvaeMdftGtiycVF9D+2BfQq+ZCP676RASMch0BpNlQyBHASWJGxvjm0bNK+uxSZLgTNBaBryGkivKy2NDR2ZstQhkbqWyoOQvqRYjoTgjIIeoOb0KaiS+acGUQmu71GPhhKIJrD0w4rA3q7ompXGcY6EqX3nqrL8hhN9Em8ko4POJpSNTcBRQwlEePPk9JM5xcVVeghSQwkDX6NugMD+zUhoXuJQXlqynZFTOhCxEARVrR4bgKKAEc+/J/dTJqrj7cUtibECnlk5nZ3v7qLIjQ5lj+xGU5y/cTo0rSw3PY0jwXrCk4/gBJZAil3Jlzau2GAVdCTCslrgL2H5w/Miqd3jQ9mslit+ef4CpnldTLUbyyCDlYgVnBJRA/m32AG61Z/OmKwEjoNmeRtr7+8eV92bIbvefndXG/OJwG7OzDWsG6EwyQjsjoATyoRr7HAhdEXK3R8uk/ErW7Au+U7x7KL1HQILy2w8eZH7J8lSLknyybATkKKAE0VAA5YTe5xUteXRHrmQVhZKBMoZ9reNOuUQY8g3HnI890dQXKr8+dyuNnuyJcmgZBc2gFS4rOAooQdw0fchWhzi3tmI8/uK/AZtKZvLm7uB+Pj5VagtLaB1IrN9RtBS4lC+dPMBNU5/DrZmzMdZ+HAXkYIFzquz9kQhDTCt28X5vfGPwGUW1rNkTPkj7xOKypCsgQTmzDuZXD1Nb6KU6f4hKzxDlnkEmF7XR4N6I0J11U5CoybLPb1kBiYgbWA3sV9XLReQ2jBCOxwF1qjp+PD/Sthx4F/idqt5mll0LfBVwA8+q6lfM8i9i7LwdxsiQ92lVfd885wU2mt3uUdUrovisSaUu3/59VCeWKe9b38MalBqtYLsvvHIpEk98FwmKcvFkZXFdPz4Ver0uppf2c2JpGxMKDlLMboTMWf5PGbmqgDAipW1mJGj168CzWMtO+k1ghf+NiNQA3wFOU9UWEfm5iCxR1ReBtUCTqvaKyOcwIvNfazbtMyP2pz2l0mL7zXJciRdDX8fO4HDkHFnbWttwucS2hIOn1yo/PWMlpTbaxHISvyNiFmFpGV5EGoHLCAjRqKprVXW3hbanAQ3A8wHFM4Ftqscm838Brjb7fVlV/c95WyPwJ4PFA3Vce7iIPLU/jMXUoviXxz15kf/LOwcGOL260RYFevFkH0+e+WtH+dhEtjkiWvUDehD4MkSXBlNEXMD9jI8Nux04UUSmm9HXrgKmBOlibAT+QjPA9koRuSrENW/xB+JuaUmusfJ0bx0Hb/oLq+54g2FvsI8THxMLx/vsRIvPbW05/639BzijZio1BbHHAZlUpCxd+HxWhkZNGT6xdkRARH4qIs0i8k5A2QLzt7XO/A0tMstFRJaKyHYz48WpIfo8TUQ2mvWWmqFZwxJRAYmIP3vimoifajy3AstVdV9goaq2Y0Tgfwp4DdgNox1dROQGoAljquZnmhkK8h+AB0XkuLEXVNVHVLVJVZvq6upiENka560r4ry1I7GTF0odbZ96CVEAYaC/zPZrVubFv9Gyy2fdiPTW/v10dAwwp6SBRTVTolZGPzjjIJ7R//UOcSJq7bDAMqwnJrwEON48bgF+GKLPH2KEdfXXHdv/OKzYgM4CrhCRSzFStpaLyOOqeoOFtouBc0TkVqAUyBeRblW9S1X/APwBjFELAQpIRC7EMFCfp6rHfnWqxrxGVXeKyCvAQmAkonuSmFZQwZ57DZPWuV84HSl0s+e+F8Fn/M+LKMUl9k/B7Mhwvq//CHmuAoZ91npT4L1WY31BgDm1DRQV5rG27UBYG9GkIuWUkhzaIpEMbIz1E01iQuBK4DFVVWCliFSKyMSALBj+gPTlqrrSfP8YxswmrKt6RAUUmKtdRD4I3GlR+aCq1wcIeBOGcfku8329qjaLSBXGSOljZvlC4EfAxaoj+xjMer2qOiAitRiK8dukgKn78/A/1/c+8Na484svrcTFBtuva4eL/YAOM6+hkXUHo8+XHqiM6kvKqC4rDJkn7K55RxHSy5co80n4Tvd/AZ4Tke9izI4+YJZPBgKj6e0zywKdySab5WPrhCXmvWAicrsZTb8R2CAij5rlTf7XEfieiLyLsZp2b0C6j+9gjJZ+Zc5FnzHLZwOrRWQ98LLZ5t1Y5Y8H95HwWxXOvzoRy9jgs2nrXpu7A48rvtW05p4e9rUdDTktO682y0NjpArrERFtT0yYCKJyRFTVVzCX3VV1KbA0SJ3VBImgr6rLMOad/vdBo/Kr6oUhyv8GzItG3kSheeGfQnOaEhO7Z9imp9+BwQ5OnzqTlSG8oa3SPTTIaaUTaRsYbVeaW6mUjdg2HezE+jzczsSE+xm9SNRolgWyn9Er1sHqjMPZDR8L7vCKoKIqMemLmweKbOvr7d7dTK+sjLsfd5Bb6O+m9ccdgM0hCIkPSBY0MSHwDPBJczXsTKAz0P4Dx7KidonImebq1yeB30e6oLMVIxbaw0/B3O7EjIB29ebb1pcXH57yYaQjPrtmr3f8dzG5OHvDoqYaiytckfuJLjHhcuBSDPeZXuBTAf2sC3AOvhVjllOEYXyOGCvFUUAxcPTV0EvLeR5wkRgFtLXb3v+unf0tLJp6HG/uORC5chDqikt4p2O8MbsmP35/JYcQ2LcKFk1iQgU+H6KfBQGvVwMnRyOHMwWLklOlnvY3QiugycfZN00ay7oO+/t8Z2APE0pKY2o7vboi6FL8zu7Y+stk3upZnGoRMhJHAUWJ52c7w56fPMu+aVIgPczmQAIGFv06REN9QdTtTps0iTdbg+c5e/FgErIophFv93yAL24c5xObEGx0REwLnClYFMx319H85/Vh60yckZgl+Nc65ySkX4CtfYcA63LPratnTfu+kKFpXj4kKEU5sbt9e/9cblk/z/aIlUFRLG2zyCScEVAUFP8icmbThkb7v1KfVPCNzYkbVbijCHI1u7aWHf0tYb2gh3xCtyZnRJBKhnx5fHHzEjoHk2h0dzKj5ibz8mo59NvIMZ6r6+3YMDGaN7vPo3UwcU++fJe1gfBJtbVs7W2ldzhypMfdfRkVxCAmfnzoJjZ3diX1mtk2BXMUkAXm5dXi/cL4LRfByC+MXCcafFLJ3Zsq7e10DB6J7BWd73bT4etjSK1NNdYcqYlXrLRmW9/JPLQ9Bb90ZwSUW5y3pZTua16kb6+1J53Lbe///q9bP5wQ43MgHcN9EbdmLJg4gQN91p/2v9xdiGaxifGeXRcy5EuC3WcsjgLKLYZ3RDfEjhwBxTrdzOWrm6JfoYqWYfVy6pSGsHX290fn27S5U9g39IHIFTOQ7f1zeb25I+nXtTr9cqZgWYRrcXQxhQ7tsS+v+i8OzSdZWRAGXaG9uxvLytnbHb1z5X9tmhWPSGnLqx2np+7iNgUkSxccBRSBrQ0DUW2tefdNe5L6+aSc72+3T5lFYmd/C+4Qw7dJFWUx6cE/7XfR7gsaPC+j2dVnf7A5qzgjoByjebCbognWPXvXvGRPAsEhahmw+CS7eZYLV5wjpW5vP1NDbE49MhR7Ko4f7UyLAAa2Up/KrSaODSi3+EBPHf0HrSuVjtZhvBrenmKFAS2xVO/aacUsqX2EU2sq4r5mMBVWW1TM1s6QGZci8qMteQxJdi3JV3pSpIAcG1BucarUceAfX4y6XU93/Aqo3xfZ8XB2RQWXN/wIgCnF8Wdh7ewf/8OaVlUZpxlK2D+QXbagQ/0p3OvmjIBygxPzqzn6jyuOxXmOhpaD8Y9Gil1Hw5/P8/D5WcvJcxleuHWF8Yc/HQoSJ9plw7Le9p7quPtIJw4NJGa/nxXEZ+3IFBwFFISKvEI8d65luCs2g/LOd+NfOi9hM9PCDIL++YRWajwjYU9rbcjEOqWifFyZHespBa4M+kVYIIMGGGmPo4CCsGC10LOzI+b2v/1BN0p8LtGC8n+ODz6quWxyOQvKfzWqrDo/tpg+gZQWjXcc7LOw7SISxe74+0gnXKlc5XamYNlNiTufPd9+M64+drzTx59/dXbcslxUsZwPjNnRML20jI9N/vG4upV54cOEWOF9b8s4j+j3Wlooy4tvylHliTOhfZqRsnCzjhE6+zmO8pjsPmN58I5munvmxtWH0MePT36RRVUjZZ+f9Sb5QexDxa5mivPiCwXSMniUhY2jDehDPh8nVdTH1e+EAvtzpKWSCCHBE4tNI6AQmVGfMjPRrBOR3SKyziy/PqB8nYj4RGRBkD7/XUT2B9S7NJIclhWQiLhFZK2IPGu+v81Mwapmnq5wbctFZJ+IPBRQdq2Z5nWTiNwXUF5gfhHbReTNwORpInK3Wb5FRC6yKrtVClx5FD66y6behMHB+G1BHt3PY6f8kY9MhPMnVNJY+LfgVxOYVBx/NMbNg3uZVT3aaNw/EPuep9oCpYj4R2fpREqf2vZNwZYxJnOpql6rqgvMMKu/Bn5jlv9vQPkngF2qui5Evw/466rq8khCRPNd3gEEJnt6HbgQeN9C228CK/xvRKQGI//XElWdC0wQkSXm6c8A7ao6C3gAuM9sMwe4DpiL8cX9QMTCNu4oOPNtDy3P2fNjEVEqKu158ru0jfuPf4wHZ/8VF6GnQxOL4v9p9PoGaSloG5UxY3NrC1X5sSm3L8ztQxiKW650QlI0xxHsWwVT1RXAkaDXMbJafAx4IsjpjwNPxv4pRmPpjhWRRuAyRvIEoaprVXW3hbanAQ3A8wHFM4Ftqtpivv8LcLX5+krg5+brp4El5hdyJfCkqg6o6i6MCP2LrMgfimpPEdMKKjj7aB0f+M0ge+5dGU93o7jiH2txS/TZR8NR5H2becUnkSfBnRQbCu3ZBtLt7edocQd1xcYynE+V48vDDnJDcknDe7bIlE6kbAYWnQ0olsSEfs4BDqvqtiDnriW4YvJzmzmz+amZzTgsVuMlPAh8GYhqE4yIuID7gRswRkt+tgMnmtOrfRg5pP2P9mNpYFV1WEQ6gRqzPFBDBE39an7RtwBMnTo1pGx5hwcpv2Ylohayp8XARz6VGG/ZvOH3mFc4g80DzfT72kedqy/oxq4JQsdwH6c01NGyyzAgd/dGr9xmVyiVrnW2yJNOvN+bvD1647A++IolMaGfjxNEyYjIGRjp0UNlnfwhxmxHzb/3A58Od6GId6uIXA40q+qaSHWDcCuwXFVHpZFQ1XaMNLBPAa8Bu8GeoLqq+oiqNqlqU11d6J3s4tOErRaUlLtpnGZ/bng/Lu8u5uSXk+8a7bdTkx/7lolgbO87dGyD6nttrdQXWtse4ucLczqQKFJ5ZgLNQ5NZ3ZaYtEuWSPAyvJkT7O8wfptjuY4wox9VPayqXlX1AT/GwgzFyuPyLOAKEdmNMfe7QEQet9AOYDHGkGw38F2M7Ir3msL+QVXPUNXFwBbAnxv+WBpY88uoANqwlh42Lbj+y9UI9mxKDYX4DnJ84ehA9dUee7+OXt8gjeUjSm5qScQR9ShOr8ou4zPAL1suwaupW+dOwjL8hcB7YwcN5mzmY4Sx/4jIxIC3HwUi5ueOqIBU9W5VbVTV6Rga8CVVvSFSO7Pt9ao61Wx7J/CYqt5lCltv/q3CGCn57UvPYOSoBrjGvJ6a5deZq2QzgOOBVVbkSDbnfzSobc92Coffpi5/9rH3VZ5tttsnXK6RWyRUuI5gCEqFbI1cMYM46q1g2e4UTr/AzmX4J4A3MEwh+0TkM+apUKOcc4G9qjrqqSIij4qIf6r3bRHZKCIbgPOBL0SSI+aYmSJyO4ZdaAKwQUSWq+rNpjCfVdWbI3TxPRGZb77+hqr679afAP9PRLZjWOmvA1DVTSLyS+BdYBj4vKrFAMVJZNLMAqqq3k7a9RrdQ/gt+QWuLk4or2BLl31TBG/A/jBfFE/+ynwQ4t+flk78/PDfJzcDxljUvn1eoTKjqupNIcpfAc4MUn5zwOtPRCtHVArIFOIV8/VSYGmQOquBccpHVZdh+B7434f6AvqBvw9x7h7gnmhkTjaX3lSOkLytBy7vHiYUzOPQwEYA5lUNscWmRA2q0DUw8oPzqvW7v284c6LyWaFruIqf7XJDEv9vg5JBXs5WcDyhbebEBckflFW7R8JDzCq1zw7UWFhJR0CIjq4h60//fp+iYXyWMo2XOpdE9fkThbMVwyEsk2eED6ORCAp0xN+o1hM6b320THCP9ohu7ovGsC4cHE5h7GSb2dwT33YU23A2ozqEo6LqYNKv6fLuo8htOApWe7bb1m93z8h0oyy/gK7h6EYAv9yTPdlRd/WkwWjOqvJxFFBukucBtyv+sBixUOuZAUCRu41yT/x70Ipd+WxtG4kx1FASnQ8QwMNbPPgkO4KRpTQEh4ngTMEcwjBhWmHKQjWUBaRXnlwcf3rWWUUNDAesgJUVRq/UBnzCpp64dsukDUU2J5yMFUcBOYRkwvT4wmHEQx4jWyUmFdvwuB4YvUDqccd2qzy8bVL8sqQBiypbIldKBs4UzCEU005KnZ3ArSMG4gmF8e1Dq/IUs/FQ86iylsHYfHqW73PRT+bbgi6ueoE8SYOfi6OAHEJx7W3xx2WOFQnYmFpX0BFXX8d5JoyaftUVl7CjK/bP9sv94/zXMo6qvBbOqR8fMzupWJx+OVOwHEPyhOvvnU5F+frUyaAdx2IF1ebHHgbEhbDj8GhP6ulxpub593WFDMjM2DtIEy5v2JtqEZwRkMNo6i6aSekTS1j4sdQ7qeW5DONztWdXzH2cXDKF1r7RMZx7ffF9NkX4xd7FcfWRDiypeJ5Cd8y7l2zBScvjcIxJN53Cmk+Xs8HbwoSC9sgNEohKGYM+Yw9Ged6umH8ofd2jH5/FHg+bO+M3wH53UyE+qYi7n1RS4j7KhROid0ewE2cK5gCAp6KALR8pORaaocJlb/TDaPG5Rjx13eJldkX02TunFFTzXsvomEIn1NQwHMUesFD0DgtbejPfFnR5fbAggUnCcUR08KOqtA4ZUxU3Pjya2tBEwzLaQDqnIvqVsHrGx/sRG2ccP9mR+Uvy55Y9z4zSqAKD2oujgBwA1Kvkmzm0Fla5Uh54fYDRcWqOK7GSK2A0zUdH237qikvY1GHfyO537wsqKfzx2oDHNcwdx6XGGO14QjscY9K3zmLQZ+x8n1+Reqtf5/Dolauphaujap8nbvZ1jY7jMbmqjH6vfeEnvLjo0SmRK6Y555a/nLLA9OJTS0em4CigGJh6/3msmDhidJ5YmOq4aB5ah0bbJkryDjKlxLodaEpB9Sjfn7l19bzdZv+0sn0odJzuTKHM3ckJ5SnwCbLRBhRlYsLpItIXcO7hEH1Wi8gLIrLN/Bsxhq+jgKJAgSnfv4BXJ3eMKq/JT+30y+ueiU/HZ62YW2H9v7fKPbK643G5aNeehOSfOdCf2SthfqaXpOanY+MUbBkWExOa7AhIOPjZEH3eBbyoqscDL5rvw+IoIIsoMOXhC1hRN94juNpjTz6uWOkNYVc5vsxalgwXQmfXyFRr4aSJ7OuxKaziGHYcTe0ytl3UFqQoMqJNI6A4EhOG40pGcvr9HCPdVlgcBWQBBSb/eAmvVQbfjlCRl1onxI7h4DGgpxVtsdR+YeHMY6E36ktKWNueuBW9bm+Kg7rbRHleaka9STJCB0tMOMNMzf6qiJwTol2DqvoDYh3CSEgaFkcBWWDa/efxepjRRFleXxKlGUveOPuPn7r8iFlRmFfcyKo9IzGMJlSWMuBLnE3rpLLsCFR/dDhFHtHWR0DxZEYdm5jwIDBVVRcCXwR+ISJhjWBmJpuIqjC1fuUZwNQvLRpn8xlLvqTOBuR1z8Q3GFzR5Lt6aCwuZV9v8FCqE/Ir2H5gJIRsfXEJ69oOJMT242dqcUfiOk8iHUMp+OloVNssYsqMGpCY8LRjl1UdAAbM12tEZAdwAjB2qfWwiExU1YNmjrBmImB5BCQibnMI9qz5/jYR2S4iKiJhE4eLSLmZe+ihgLKP+3MIicif/X3Ea4m3k4kfn8vrTVaUS+qWPfvCP4iYXhrih6JQ2FtK9+CI/WpqVUVClQ9AbX7EezIjaB9M/kJ8kvyAxiUmFJE6EXGbr2di5OQLlnUyMKffjcDvI10sminYHcDmgPevm8Ja8Xj7JrDC/8bUst8DzlfVU4ANwG1giyXeFmrOncqGq4stbUNIZbTO9uHwxuKivOB344LSaew4MtoG6Yoi8WCsFEtqQtbaSedwNWuOJDbzbUhUrR0RiDIx4bkYuf/WAU9j5P07YvYTmJjwXuBDIrINQzfcG0kOS+NIEWkELsPIyfVFAFVda56L1PY0DGPUnwG/oGIeJSLSBpQD28e081viL7Aio52Uza1j779MoXvQ6k2WqhFQHq1DobOPqsKGI+NHcC6Eg63j7VbeBOdxbyxWXNoeuWKa87sjl9vqoBkNdnk5R5OYUFV/jTEYCFY/MDFhG7AkGjmsjoAexMiCGtUdauaTvh8jLfMxVHUI+BywETgAzMHIiBpITJZ4EbnFb3hraYl+F3fVGZM5/M0TOGxZ+UASBg5BCeX/42dv/9kc7u8dV76gZBoHjo5PHySa2A+yuD5zPHRDoQpP7E2RL1MWbkaNOAISkcuBZtP49MEo+78VWK6q+wJHSiLiwVBACzHmkt8H7ga+FdA2lCW+zRxV/U5E5qrqqDmIqj4CPALQ1NQU1X9FQUMJzV+ZyeHBaH1gUvM/Hmj/aR+axXs9H2DH0VoGfMKgT3irtZuxmTzzxMX7zcGV6+4jHcZHSZAeml8VX6jYdGBV93nsCKK8k0UmxfqxgpUp2FnAFSJyKVAIlIvI46p6g4W2i4FzRORWoBTIF5FuzOGcqu4AMHO+H/OajNMSHxsuoeChxbw3GP2oKVWZMDpM+8+hgdP40rrTTHtVeOU5v3gaqw4fCnquta+XmXXV7OwO6p8WNzNKM38J/sf7FgIdKbt+timgiFMwVb1bVRtVdTqGgeoli8oHVb1eVaeabe8EHlPVu4D9wBwR8W8M+hCjDdzxWOJjovH757PeG1vgrdQooDzaTP+fDV2LLBnL81157DgU3GnRT31h9HGErFKal1qP8Xh58+h5vHq4I3UCKLYZodOFmB0RReR2EdkHNGJYyB81y5v8r0OhqgeA/wBWiMgGYAHwnwFVorLEx8uU25t4rSZ1AeVjoT/vFIbV8MB2W3wsnlI0lba+8E6T7T2JmyYVuzNbAX1v98JUi5B14Tii8qZS1VeAV8zXS4GlQeqsBm4OUr4MYwOc//3DQFBfnmgt8bGg5lNiyj+fxlvnKMTh/OuW5O6GV6lmR//I6pfLwvWLXfm8dyDyCtS2I21UVRTRPmi/d/egL3P9Xg8PNfJWW0eqxcgoA7MVMveOiJOhiQW0Pr2IXcMDcSkfgCLpTNqNoVLNdq+Hfl9wO04o5hZOYeWANR+cxuLyhCig1oEiKLK926SwovPsVItwzBExm8hZBSQiHB22ZxNpPonPmqm46GE2WwcK8OoqBn3VDGgl/b4y3umoJJxhtMRdwDv7re2MByhyJybBYtdw6jLHxsuhwTJIcdRLNLOCjVkhZxWQXVR4QDT6ZdkD3rN4bP8M1na42NBh2HAK3UKR2/hb6BKK8pRCFxS4YE8f7Dnm0nPymN46wl6rsaCKd4fCG5+TQWleikJY2MDRYQ8pV0DgTMEcRjMnysB4Sj7PtF/JlzYWMuJwY6wF9HiNw18zVRzqT4yfy3CCHR0TSVcqNp8GwZmCOYzi+FLrjhlDMoU73zufPyU5g0/H0Hhv6FDMrq1lU09iBNzVXUyQxBsZQedQGkSuUcCZgjkEMqPY2rTCKzVcteYCtnUn/wZqHjpKnqt4VMznYBTm5dEriVsq39LpgQyNSd+ZBrMvIOumYGmg1jObxsLIfjOKm3t2XZoS5WNcX2koiRwK9aSGGnZ1J26zqCdD77YBXwHvdqaHF3e2+QFl6C2RPtTnR57evNJ1BY/vSa39o74svAJa1DiZt48kNlTGjLJ0GUZEx587LqdnOD1kz7a0PM4ULE6qPeENts2+M/indYnb3mCV/ILQCnBefQNvtu5JeGCjxTXWXQHShd0DJ/L1d6tJmxWwzNEtlnAUUJyUujtC3hS9chJXrTqB1IYsM2j3Bd8B31hezva+FpKxQHVc8e7EX8Rmlredkz6jH0AyaJ+XFZwpWJwUEnwP2aBM54q3FtGagtCdwdjd10qBe3RGilJPPj6PNyk/MDc+itiR8OvYzZrO1I9eR+GzeEQgysSEHxKRNWYI5TUiEjRIoIj8u4jsD+jj0khyOCOgOHDjw6XjFdCwTOC6teexpzd9nlZefBxfXcV7LcY0SIBpdRVsbI9uS0esLKgRJB2mMVHgVRdvH7HuwpAMbBwBLQMeAh7zF6jqtceuI3I/4PdebQU+oqoHRORk4Dlgcoh+H1DV71oVwlFAcXBCmRsZs5FMpYyb3/kw73Slj/LxU140shXi9MbJrGzbk7RrN9Vmnhd0u7eB7qE02sFvow1IVVeIyPRg58aGQ/aHXzbZBBSJSIEZoysunClYHMwqHX83/KL5Mv6WppE9xPzfPn3yZFa2Jk/5AMytSGXutNiozTvI5OJ0yuRqbQXMhlWwYOGQ/VwNvB1G+dxmZrr5qZMbPsG4x5h3DvsW8x+b03fDZZuvi7mT6njzSOJXvMbSVL0vcqU05IyaNMvkaj0gmZ2JCQEQkbnAfcA/hWj3Q+A4jPheBzHiwYfFmYLFwVDAg8YnVdy49gTSeZ30qK+fre2Jyfkejqp8ZYL77aRf1w4WVx7gN3vTxBCdosSEZnkj8Fvgk/5QyuPEUz0cUP/HwLORrueMgOKgPcA88OjBS9jZk77KB+DocGqCwv/TiQMImRmQ/uLKP1JXmEZBjBIfkjVYOORK4I/AXar6eqiGZjZUPx8FIuYGdxRQHPytDQ77zuTtvkv57tb0H0wO+IZpKE7+0/zMmsQEuU8Ghe4+bpmRHtswANvS8kSZmPA2YBbwtYAl9nqzn8DEhN/2ZzsGzge+EEmO9P/VpDnn/PWEVIsQFQ0lJRwOkSs+UUwrPpjU69nNdbW/4kcFN9E6kHpDukTYUGyVKBMTfovRKbMCzwUmJvxEtHI4I6AcoyQ/2c8cpdyVeQ6IgRS6+5hdUZBqMcxwHBaPDMEZAeUY3b7kPsWvmebDpck3fNtNTX7q7XuC5u5WDBFxm2mRnzXf3yYi20VERaQ2Qttyc575UEDZx/3zRRH5s7+PcO7cInK3ec0tInJR9B/XYU/fkSSuwCv/OjczV7/GUpOfJl7cOZwX7A5GJw98HcNi/r6Ftt8EVvjfmEt93wPOV9VTgA0Yhi4/D6jqAvNYbraZg2EgmwtcDPzAn6jQwTq93kGmllcm5Vq3zR6iQtYn5VqJpjo/TVbxclEBmT4AlwHHEg6q6lpV3W2h7WlAA/B8YLF5lJhu3+VApGA0VwJPquqAqu4CtgOLrMjvMJr6kuKEX8ONj8/PDLlim3GUutNAAWWhDcjqCOhB4MtE+dFExIXhDXlnYLmqDgGfAzZiKJ45wE8CqgRz554M7A2os48gG+JE5Ba/92dLS+LT5WQiLlfin5DfbuqhMAN3v4did19lqkUAjFUwK0emEFEBicjlQLOqromh/1uB5YFOTWafHgwFtBCYhDEFu9s8HbU7dyCq+oiqNqlqU11dXeQGOUjrUGKyXvhZMtHHRxt+n9BrJJPt/XN5em86bKa1OP3KoCmYlVWws4ArTGNwIVAuIo+r6g0W2i4GzhGRW4FSIF9EujFTLPtdukXkl8BdZlkod+79jA5p3miWOUTJnt4jFOWV0JeAOEAVHuV/TnsJ0TSYssTJu30L+c8d5/FmaweQBrvilYxSLlaIOAJS1btVtVFVp2MYgV+yqHxQ1etVdarZ9k7gMVW9C0NxzBER/xDlQ5gG7jDu3M8A14lIgYjMAI4HVlmRw2EMAlPLKhLS9W8+uJOCyKbBtOeFjsu5etVCU/mkETlqAxqHiNwuIvswRiIbRORRs7zJ/zoUqnoA+A9ghem2vQD4T/N0UHduVd0E/BJ4F/gz8HlVjTOre+7icdvvg/r9MzqZmf+y7f0mm429p/PFjRMY1vT7JYuqpSNTiMoRUVVfAV4xXy8Flgapsxq4OUj5MowobP73DwMPB6kX0p1bVe8B7olGZofglOTbGzbkP0/t4fLaX9naZyo4MDidf9rQRL839dsugpJBysUKjid0jqJRxHWIxN2n9HPdxKds6y9VHPVW8I/vXEFLf5p6bquCN/1GZfHgKKAcpcsbf6xjQfnhmZ18uOZ3SCYZHoIw5Mvj9i2fYmtXR6pFCY8zAnLIBvb3deAWF94Yb+jTa5VHFq2h0kickNGowjff/yx/be5ItSiRcRSQQzbQ7xtiZuVEtrVHF8B6eqnyQNMh5pe8hJCmdpIo+X8tn+CJ95MboiQmFMigrKdWcBRQDlNTXMg2i6ngJxQpDy06zKmlL2aN4gE4MlTPf28tJC0yn0ZEIQ1X5uLBUUA5TK/PmrPgxZN9LF34PJ7RDu1ZwQ8PXEPPcHrl/gqJknVGaCcgWQ5zaKAzYp17FvbwgwW/yErls39gBv/7ftyprZKLTVsxosmMap6LGApHRGaIyJtmvadEJD+SHI4CymGODof/8T15bjP/MOmJjA0oH4nv7/sIQ74M82W1by/YMoywNgFd67X+MDgY26V+A1GFwrkPI5TOLKAd+EyQOqNwFFAOM6xejquoDnruPxb0ckbZM0mWKHn4FF44lGGjHxs3o6rqCiBotoCAzKj+4PQRQ+GYbS4AnjaLfg5cFUkORwHlOHWl41POfHSal09MfjpI7exhR/88uoYyTAEp4PNZO+JLTDg2M6qVUDg1QIeqDoepMw7HCJ3jjI0RPbtC+c68ZxFNg93fCWRtz9xUixAb1v2AYkpMaBI0M2oicBRQjrOjp4VCdwH93mHKPcqvzv4bbs3+QG5HhwtTLUIMJH4rRojMqFZC4bQBlSKSZ46CLIXLyVkF5BKhqiDxoUkzgRklE9jW3sZT5+ymRA4BVRHbZDoTC4epLiizrb+K/CRkT1XQxPsBjcuMihEK5xci8t8YAQTHhcJRVRWRl4FrgCeBG4GIUelyVgFNK63mrSu/mGoxHFLE5Q1w+UmpliIGbPKENjOjfhDDVrQP+Lqq/oQgmVFVdZMZNPBdYJiAUDgishy42Qyx8xXgSRH5FrCW0WGWg5KzCsjBISOxaS9YNJlRzfKgoXBU9dKA1zuJMlGEo4AcHDIFVf8KV9bgKCAHh0zC2Q3v4OCQGhT1ZpjndgQcBeTgkCk44TgcHBxSihOOw8HBIRUooM4IyMHBISWoE5DMwcEhhWSbEVo0y5b1AhGRFuD9EKdrgdYkimMHjszJIREyT1PVusjVQiMif8aQzQqtqnpx5GqpJasVUDhEZHUcu4VTgiNzcshEmTMVJx6Qg4NDynAUkIODQ8rIZQX0SKoFiAFH5uSQiTJnJDlrA3JwcEg9uTwCcnBwSDFZoYBEZL6IvCEiG0XkDyJSHnDOSj4jEZF7RGSriGwWkdsDypea7TeIyKkBbW4UkW3mcWOS5V0mIrsCcjgtMMurROS3pqyrROTkgDa7zeutE5HV0cibQpkvNvvcLiJ3pZHMFWZ/60Vkk4h8KqCNN6B+9qYVsQtVzfgDeAs4z3z9aeCb5us5wHqgAJgB7ADcQdp/CngMcJnv682/lwJ/AgQ4E3jTLK8Gdpp/q8zXVUmUdxlwTZDy72BEtgM4CXgx4NxuoDaF33FUMgNus6+ZQL55jTlpIvP/Be4zX9dhpLfJN993p/r3kElHVoyAgBOAFebrF4CrzdcR8xmZfA74hpoBd1W1OaD9Y2qwEiPo9kTgIuAFVT2iqu3mNaNx+opX3lDMAV4yP8N7wHQRaYiifTiSLfMiYLuq7lTVQYw4w1emicwKlJm5sEoxFNBw+CYOwcgWBbSJkZvz7xmJ4G8lnxHAccC1Zv6kP4nI8RHaW+03UfIC3GNOWx4QkQKzbD1GRgNEZBEwDSM7ARg/mudFZI1ElyMqVTLH+x0nUuaHgNnAAWAjcIeORIsvNO+jlSJyVZTy5hwZo4BE5C8i8k6Q40qM4fWtIrIGKAOiTWpVAPSr4f36Y+CnaS7v3RjTldMxpoFfMcvvxRilrQP+GSMwuH/z0NmqeipwCfB5ETk3A2SOSIpkvghYh5EhYgHwUIB9aZp5H/0D8KCIHBflNXOKjNmMqqoXRqjyYQAROQG4zCyzks8IjCfgb8zXvwV+FqH9foyMAoHlryRLXlU9aL4cEJGfAXea5V0Y9ix/qtxdGPYpVHW/+bdZRH6LMeVYMabfdJK5yGK/SZfZlPdeNYw+20VkF4aiWhXwPe8UkVeAhRg2JocgZMwIKBwiUm/+dQH/CjxsnnoGuE5ECkRkBkHyGZn8DjjffH0esDWg/SfF4Eyg07wpnwM+bK7gVGHc5M8lS17TDuX/wV4FvGO+rxSRfLPazcAKVe0SkRIRKTPrlJjyvmNV3lTIjGFAPl5EZpjnrzOvlXKZgT3AEvNcA3AisNO8HwrM8lrgLIxUNg6hSLUV3I4DuANDaWzFGNJLwLmvYjyBtgCXBJQvByaZryuBP2LM598A5pvlAvyP2X4j0BTQ/tMYxsvtwKeSLO9LpjzvAI8DpWb5YrPPLRgjuiqzfCaGrWU9hl3kqyn4jqOS2Tx3qXluR5rJPAl4PuDcDWb5B8yy9ebfz6T6t5Huh+MJ7eDgkDKyYgrm4OCQmTgKyMHBIWU4CsjBwSFlOArIwcEhZTgKyMHBIWU4CsjBwSFlOArIwcEhZTgKyMHBIWX8f1w3dacQmFJYAAAAAElFTkSuQmCC", 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