Beispiel #1
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    def __eq__(self, other, rtol=1.0e-5, atol=1.0e-8):
        """Override '==' to allow comparison with other raster objecs

        Args:
           * other: Raster instance to compare to
           * rtol, atol: Relative and absolute tolerance.
                         See numpy.allclose for details
        """

        # Check type
        if not isinstance(other, Raster):
            msg = ('Raster instance cannot be compared to %s'
                   ' as its type is %s ' % (str(other), type(other)))
            raise TypeError(msg)

        # Check projection
        if self.projection != other.projection:
            return False

        # Check geotransform
        if self.get_geotransform() != other.get_geotransform():
            return False

        # Check data
        if not nanallclose(
                self.get_data(), other.get_data(), rtol=rtol, atol=atol):
            return False

        # Check keywords
        if self.keywords != other.keywords:
            return False

        # Raster layers are identical up to the specified tolerance
        return True
Beispiel #2
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    def test_linear_interpolation_nan_array(self):
        """Interpolation library works (linear mode) with grid points being NaN
        """

        # Define pixel centers along each direction
        x = [0.0, 1.0, 2.0, 3.0, 4.0, 5.0]
        y = [4.0, 5.0, 7.0, 9.0, 11.0, 13.0]

        # Define ny by nx array with corresponding values
        A = numpy.zeros((len(x), len(y)))

        # Define values for each x, y pair as a linear function
        for i in range(len(x)):
            for j in range(len(y)):
                A[i, j] = linear_function(x[i], y[j])
        A[2, 3] = numpy.nan  # (x=2.0, y=9.0): NaN

        # Then test that interpolated points can contain NaN
        xis = numpy.linspace(x[0], x[-1], 12)
        etas = numpy.linspace(y[0], y[-1], 10)
        points = combine_coordinates(xis, etas)

        vals = interpolate2d(x, y, A, points, mode='linear')
        refs = linear_function(points[:, 0], points[:, 1])

        # Set reference result with expected NaNs and compare
        for i, (xi, eta) in enumerate(points):
            if (1.0 < xi <= 3.0) and (7.0 < eta <= 11.0):
                refs[i] = numpy.nan

        assert nanallclose(vals, refs, rtol=1e-12, atol=1e-12)
Beispiel #3
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    def test_linear_interpolation_nan_points(self):
        """Interpolation library works with interpolation points being NaN

        This is was the reason for bug reported in:
        https://github.com/AIFDR/riab/issues/155
        """

        # Define pixel centers along each direction
        x = [1.0, 2.0, 4.0]
        y = [5.0, 9.0]

        # Define ny by nx array with corresponding values
        A = numpy.zeros((len(x), len(y)))

        # Define values for each x, y pair as a linear function
        for i in range(len(x)):
            for j in range(len(y)):
                A[i, j] = linear_function(x[i], y[j])

        # Then test that interpolated points can contain NaN
        xis = numpy.linspace(x[0], x[-1], 10)
        etas = numpy.linspace(y[0], y[-1], 10)
        xis[6:7] = numpy.nan
        etas[3] = numpy.nan
        points = combine_coordinates(xis, etas)

        vals = interpolate2d(x, y, A, points, mode='linear')
        refs = linear_function(points[:, 0], points[:, 1])
        assert nanallclose(vals, refs, rtol=1e-12, atol=1e-12)
Beispiel #4
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    def __eq__(self, other, rtol=1.0e-5, atol=1.0e-8):
        """Override '==' to allow comparison with other raster objecs

        Args:
           * other: Raster instance to compare to
           * rtol, atol: Relative and absolute tolerance.
                         See numpy.allclose for details
        """

        # Check type
        if not isinstance(other, Raster):
            msg = ('Raster instance cannot be compared to %s'
                   ' as its type is %s ' % (str(other), type(other)))
            raise TypeError(msg)

        # Check projection
        if self.projection != other.projection:
            return False

        # Check geotransform
        if self.get_geotransform() != other.get_geotransform():
            return False

        # Check data
        if not nanallclose(self.get_data(),
                           other.get_data(),
                           rtol=rtol, atol=atol):
            return False

        # Check keywords
        if self.keywords != other.keywords:
            return False

        # Raster layers are identical up to the specified tolerance
        return True
Beispiel #5
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    def test_linear_interpolation_nan_array(self):
        """Interpolation library works (linear mode) with grid points being NaN
        """

        # Define pixel centers along each direction
        x = [0.0, 1.0, 2.0, 3.0, 4.0, 5.0]
        y = [4.0, 5.0, 7.0, 9.0, 11.0, 13.0]

        # Define ny by nx array with corresponding values
        A = numpy.zeros((len(x), len(y)))

        # Define values for each x, y pair as a linear function
        for i in range(len(x)):
            for j in range(len(y)):
                A[i, j] = linear_function(x[i], y[j])
        A[2, 3] = numpy.nan  # (x=2.0, y=9.0): NaN

        # Then test that interpolated points can contain NaN
        xis = numpy.linspace(x[0], x[-1], 12)
        etas = numpy.linspace(y[0], y[-1], 10)
        points = combine_coordinates(xis, etas)

        vals = interpolate2d(x, y, A, points, mode='linear')
        refs = linear_function(points[:, 0], points[:, 1])

        # Set reference result with expected NaNs and compare
        for i, (xi, eta) in enumerate(points):
            if (1.0 < xi <= 3.0) and (7.0 < eta <= 11.0):
                refs[i] = numpy.nan

        assert nanallclose(vals, refs, rtol=1e-12, atol=1e-12)
Beispiel #6
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    def test_linear_interpolation_nan_points(self):
        """Interpolation library works with interpolation points being NaN

        This is was the reason for bug reported in:
        https://github.com/AIFDR/riab/issues/155
        """

        # Define pixel centers along each direction
        x = [1.0, 2.0, 4.0]
        y = [5.0, 9.0]

        # Define ny by nx array with corresponding values
        A = numpy.zeros((len(x), len(y)))

        # Define values for each x, y pair as a linear function
        for i in range(len(x)):
            for j in range(len(y)):
                A[i, j] = linear_function(x[i], y[j])

        # Then test that interpolated points can contain NaN
        xis = numpy.linspace(x[0], x[-1], 10)
        etas = numpy.linspace(y[0], y[-1], 10)
        xis[6:7] = numpy.nan
        etas[3] = numpy.nan
        points = combine_coordinates(xis, etas)

        vals = interpolate2d(x, y, A, points, mode='linear')
        refs = linear_function(points[:, 0], points[:, 1])
        assert nanallclose(vals, refs, rtol=1e-12, atol=1e-12)
Beispiel #7
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    def test_interpolation_random_array_and_nan(self):
        """Interpolation library (constant and linear) works with NaN
        """

        # Define pixel centers along each direction
        x = numpy.arange(20) * 1.0
        y = numpy.arange(25) * 1.0

        # Define ny by nx array with corresponding values
        A = numpy.zeros((len(x), len(y)))

        # Define arbitrary values for each x, y pair
        numpy.random.seed(17)
        A = numpy.random.random((len(x), len(y))) * 10

        # Create islands of NaN
        A[5, 13] = numpy.nan
        A[6, 14] = A[6, 18] = numpy.nan
        A[7, 14:18] = numpy.nan
        A[8, 13:18] = numpy.nan
        A[9, 12:19] = numpy.nan
        A[10, 14:17] = numpy.nan
        A[11, 15] = numpy.nan

        A[15, 5:6] = numpy.nan

        # Creat interpolation points
        xis = numpy.linspace(x[0], x[-1], 39)  # Hit all mid points
        etas = numpy.linspace(y[0], y[-1], 73)  # Hit thirds
        points = combine_coordinates(xis, etas)

        for mode in ['linear', 'constant']:
            vals = interpolate2d(x, y, A, points, mode=mode)

            # Calculate reference result with expected NaNs and compare
            i = j = 0
            for k, (xi, eta) in enumerate(points):

                # Find indices of nearest higher value in x and y
                i = numpy.searchsorted(x, xi)
                j = numpy.searchsorted(y, eta)

                if i > 0 and j > 0:

                    # Get four neigbours
                    A00 = A[i - 1, j - 1]
                    A01 = A[i - 1, j]
                    A10 = A[i, j - 1]
                    A11 = A[i, j]

                    if numpy.allclose(xi, x[i]):
                        alpha = 1.0
                    else:
                        alpha = 0.5

                    if numpy.allclose(eta, y[j]):
                        beta = 1.0
                    else:
                        beta = eta - y[j - 1]

                    if mode == 'linear':
                        if numpy.any(numpy.isnan([A00, A01, A10, A11])):
                            ref = numpy.nan
                        else:
                            ref = (A00 * (1 - alpha) * (1 - beta) + A01 *
                                   (1 - alpha) * beta + A10 * alpha *
                                   (1 - beta) + A11 * alpha * beta)
                    elif mode == 'constant':
                        assert alpha >= 0.5  # Only case in this test

                        if beta < 0.5:
                            ref = A10
                        else:
                            ref = A11
                    else:
                        msg = 'Unknown mode: %s' % mode
                        raise Exception(msg)

                    #print i, j, xi, eta, alpha, beta, vals[k], ref
                    assert nanallclose(vals[k], ref, rtol=1e-12, atol=1e-12)
Beispiel #8
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    def test_interpolation_random_array_and_nan(self):
        """Interpolation library (constant and linear) works with NaN
        """

        # Define pixel centers along each direction
        x = numpy.arange(20) * 1.0
        y = numpy.arange(25) * 1.0

        # Define ny by nx array with corresponding values
        A = numpy.zeros((len(x), len(y)))

        # Define arbitrary values for each x, y pair
        numpy.random.seed(17)
        A = numpy.random.random((len(x), len(y))) * 10

        # Create islands of NaN
        A[5, 13] = numpy.nan
        A[6, 14] = A[6, 18] = numpy.nan
        A[7, 14:18] = numpy.nan
        A[8, 13:18] = numpy.nan
        A[9, 12:19] = numpy.nan
        A[10, 14:17] = numpy.nan
        A[11, 15] = numpy.nan

        A[15, 5:6] = numpy.nan

        # Creat interpolation points
        xis = numpy.linspace(x[0], x[-1], 39)   # Hit all mid points
        etas = numpy.linspace(y[0], y[-1], 73)  # Hit thirds
        points = combine_coordinates(xis, etas)

        for mode in ['linear', 'constant']:
            vals = interpolate2d(x, y, A, points, mode=mode)

            # Calculate reference result with expected NaNs and compare
            i = j = 0
            for k, (xi, eta) in enumerate(points):

                # Find indices of nearest higher value in x and y
                i = numpy.searchsorted(x, xi)
                j = numpy.searchsorted(y, eta)

                if i > 0 and j > 0:

                    # Get four neigbours
                    A00 = A[i - 1, j - 1]
                    A01 = A[i - 1, j]
                    A10 = A[i, j - 1]
                    A11 = A[i, j]

                    if numpy.allclose(xi, x[i]):
                        alpha = 1.0
                    else:
                        alpha = 0.5

                    if numpy.allclose(eta, y[j]):
                        beta = 1.0
                    else:
                        beta = eta - y[j - 1]

                    if mode == 'linear':
                        if numpy.any(numpy.isnan([A00, A01, A10, A11])):
                            ref = numpy.nan
                        else:
                            ref = (A00 * (1 - alpha) * (1 - beta) +
                                   A01 * (1 - alpha) * beta +
                                   A10 * alpha * (1 - beta) +
                                   A11 * alpha * beta)
                    elif mode == 'constant':
                        assert alpha >= 0.5  # Only case in this test

                        if beta < 0.5:
                            ref = A10
                        else:
                            ref = A11
                    else:
                        msg = 'Unknown mode: %s' % mode
                        raise Exception(msg)

                    #print i, j, xi, eta, alpha, beta, vals[k], ref
                    assert nanallclose(vals[k], ref, rtol=1e-12, atol=1e-12)