Beispiel #1
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    def test_interpolation_wrapper(self):
        """Interpolation library works for linear function
        """

        # Create test data
        lon_ul = 100  # Longitude of upper left corner
        lat_ul = 10   # Latitude of upper left corner
        numlon = 8    # Number of longitudes
        numlat = 5    # Number of latitudes
        dlon = 1
        dlat = -1

        # Define array where latitudes are rows and longitude columns
        A = numpy.zeros((numlat, numlon))

        # Establish coordinates for lower left corner
        lat_ll = lat_ul - numlat
        lon_ll = lon_ul

        # Define pixel centers along each direction
        longitudes = numpy.linspace(lon_ll + 0.5,
                                    lon_ll + numlon - 0.5, numlon)
        latitudes = numpy.linspace(lat_ll + 0.5,
                                   lat_ll + numlat - 0.5, numlat)

        # Define raster with latitudes going bottom-up (south to north).
        # Longitudes go left-right (west to east)
        for i in range(numlat):
            for j in range(numlon):
                A[numlat - 1 - i, j] = linear_function(longitudes[j],
                                                   latitudes[i])

        # Test first that original points are reproduced correctly
        for i, eta in enumerate(latitudes):
            for j, xi in enumerate(longitudes):

                val = interpolate_raster(longitudes, latitudes, A,
                                         [(xi, eta)], mode='linear')[0]
                assert numpy.allclose(val,
                                      linear_function(xi, eta),
                                      rtol=1e-12, atol=1e-12)

        # Then test that genuinly interpolated points are correct
        xis = numpy.linspace(lon_ll + 1, lon_ll + numlon - 1, 10 * numlon)
        etas = numpy.linspace(lat_ll + 1, lat_ll + numlat - 1, 10 * numlat)
        for xi in xis:
            for eta in etas:
                val = interpolate_raster(longitudes, latitudes, A,
                                         [(xi, eta)], mode='linear')[0]
                assert numpy.allclose(val,
                                      linear_function(xi, eta),
                                      rtol=1e-12, atol=1e-12)
Beispiel #2
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def interpolate_raster_vector_points(R, V, name=None):
    """Interpolate from raster layer to point data

    Input
        R: Raster data set (grid)
        V: Vector data set (points)
        name: Name for new attribute.
              If None (default) the name of R is used

    Output
        I: Vector data set; points located as V with values interpolated from R

    """

    msg = ('There are no data points to interpolate to. Perhaps zoom out '
           'and try again')
    assert len(V) > 0, msg

    # Input checks
    assert R.is_raster
    assert V.is_vector
    assert V.is_point_data

    # Get raster data and corresponding x and y axes
    A = R.get_data(nan=True)
    longitudes, latitudes = R.get_geometry()
    assert len(longitudes) == A.shape[1]
    assert len(latitudes) == A.shape[0]

    # Get vector point geometry as Nx2 array
    coordinates = numpy.array(V.get_geometry(), dtype='d', copy=False)

    # Interpolate and create new attribute
    N = len(V)
    attributes = []
    if name is None:
        name = R.get_name()

    values = interpolate_raster(longitudes,
                                latitudes,
                                A,
                                coordinates,
                                mode='linear')

    # Create list of dictionaries for this attribute and return
    for i in range(N):
        attributes.append({name: values[i]})

    return Vector(data=attributes,
                  projection=V.get_projection(),
                  geometry=coordinates)
Beispiel #3
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def interpolate_raster_vector_points(R, V, name=None):
    """Interpolate from raster layer to point data

    Input
        R: Raster data set (grid)
        V: Vector data set (points)
        name: Name for new attribute.
              If None (default) the name of R is used

    Output
        I: Vector data set; points located as V with values interpolated from R

    """

    msg = ('There are no data points to interpolate to. Perhaps zoom out '
           'and try again')
    assert len(V) > 0, msg

    # Input checks
    assert R.is_raster
    assert V.is_vector
    assert V.is_point_data

    # Get raster data and corresponding x and y axes
    A = R.get_data(nan=True)
    longitudes, latitudes = R.get_geometry()
    assert len(longitudes) == A.shape[1]
    assert len(latitudes) == A.shape[0]

    # Get vector point geometry as Nx2 array
    coordinates = numpy.array(V.get_geometry(),
                              dtype='d',
                              copy=False)

    # Interpolate and create new attribute
    N = len(V)
    attributes = []
    if name is None:
        name = R.get_name()

    values = interpolate_raster(longitudes, latitudes, A,
                                coordinates, mode='linear')

    # Create list of dictionaries for this attribute and return
    for i in range(N):
        attributes.append({name: values[i]})

    return Vector(data=attributes, projection=V.get_projection(),
                  geometry=coordinates)
Beispiel #4
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    def test_interpolation_raster_data(self):
        """Interpolation library works for raster data

        This shows interpolation of data arranged with
        latitudes bottom - up and
        longitudes left - right
        """

        # Create test data
        lon_ul = 100  # Longitude of upper left corner
        lat_ul = 10  # Latitude of upper left corner
        numlon = 8  # Number of longitudes
        numlat = 5  # Number of latitudes
        dlon = 1
        dlat = -1

        # Define array where latitudes are rows and longitude columns
        A = numpy.zeros((numlat, numlon))

        # Establish coordinates for lower left corner
        lat_ll = lat_ul - numlat
        lon_ll = lon_ul

        # Define pixel centers along each direction
        longitudes = numpy.linspace(lon_ll + 0.5, lon_ll + numlon - 0.5,
                                    numlon)
        latitudes = numpy.linspace(lat_ll + 0.5, lat_ll + numlat - 0.5, numlat)

        # Define raster with latitudes going bottom-up (south to north).
        # Longitudes go left-right (west to east)
        for i in range(numlat):
            for j in range(numlon):
                A[numlat - 1 - i, j] = linear_function(longitudes[j],
                                                       latitudes[i])

        # Then test that interpolated points are correct
        xis = numpy.linspace(lon_ll + 1, lon_ll + numlon - 1, 100)
        etas = numpy.linspace(lat_ll + 1, lat_ll + numlat - 1, 100)
        points = combine_coordinates(xis, etas)

        vals = interpolate_raster(longitudes,
                                  latitudes,
                                  A,
                                  points,
                                  mode='linear')
        refs = linear_function(points[:, 0], points[:, 1])

        assert numpy.allclose(vals, refs, rtol=1e-12, atol=1e-12)
Beispiel #5
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    def test_interpolation_raster_data(self):
        """Interpolation library works for raster data

        This shows interpolation of data arranged with
        latitudes bottom - up and
        longitudes left - right
        """

        # Create test data
        lon_ul = 100  # Longitude of upper left corner
        lat_ul = 10   # Latitude of upper left corner
        numlon = 8    # Number of longitudes
        numlat = 5    # Number of latitudes
        dlon = 1
        dlat = -1

        # Define array where latitudes are rows and longitude columns
        A = numpy.zeros((numlat, numlon))

        # Establish coordinates for lower left corner
        lat_ll = lat_ul - numlat
        lon_ll = lon_ul

        # Define pixel centers along each direction
        longitudes = numpy.linspace(lon_ll + 0.5,
                                    lon_ll + numlon - 0.5, numlon)
        latitudes = numpy.linspace(lat_ll + 0.5,
                                   lat_ll + numlat - 0.5, numlat)

        # Define raster with latitudes going bottom-up (south to north).
        # Longitudes go left-right (west to east)
        for i in range(numlat):
            for j in range(numlon):
                A[numlat - 1 - i, j] = linear_function(longitudes[j],
                                                       latitudes[i])

        # Then test that interpolated points are correct
        xis = numpy.linspace(lon_ll + 1, lon_ll + numlon - 1, 100)
        etas = numpy.linspace(lat_ll + 1, lat_ll + numlat - 1, 100)
        points = combine_coordinates(xis, etas)

        vals = interpolate_raster(longitudes, latitudes, A, points,
                                  mode='linear')
        refs = linear_function(points[:, 0], points[:, 1])

        assert numpy.allclose(vals, refs, rtol=1e-12, atol=1e-12)