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How to use verify_dtype method in pandera

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test_proximity.py

Source:test_proximity.py

1import dask.array as da2import numpy as np3import pytest4import xarray as xr5from xrspatial import allocation, direction, euclidean_distance, great_circle_distance, proximity6from xrspatial.proximity import _calc_direction7from xrspatial.tests.general_checks import general_output_checks8def test_great_circle_distance():9    # invalid x_coord10    ys = [0, 0, -91, 91]11    xs = [-181, 181, 0, 0]12    for x, y in zip(xs, ys):13        with pytest.raises(Exception) as e_info:14            great_circle_distance(x1=0, x2=x, y1=0, y2=y)15            assert e_info16@pytest.fixture17def test_raster(backend):18    height, width = 4, 619    # create test raster, all non-zero cells are unique,20    # this is to test allocation and direction against corresponding proximity21    data = np.asarray([[0., 0., 0., 0., 0., 2.],22                       [0., 0., 1., 0., 0., 0.],23                       [0., np.inf, 3., 0., 0., 0.],24                       [4., 0., 0., 0., np.nan, 0.]])25    _lon = np.linspace(-20, 20, width)26    _lat = np.linspace(20, -20, height)27    raster = xr.DataArray(data, dims=['lat', 'lon'])28    raster['lon'] = _lon29    raster['lat'] = _lat30    if 'dask' in backend:31        raster.data = da.from_array(data, chunks=(4, 3))32    return raster33@pytest.fixture34def result_default_proximity():35    # DEFAULT SETTINGS36    expected_result = np.array([37        [20.82733247, 15.54920505, 13.33333333, 15.54920505,  8., 0.],38        [16., 8., 0., 8., 15.54920505, 13.33333333],39        [13.33333333, 8., 0., 8., 16., 24.],40        [0., 8., 13.33333333, 15.54920505, 20.82733247, 27.45501371]41    ], dtype=np.float32)42    return expected_result43@pytest.fixture44def result_target_proximity():45    target_values = [2, 3]46    expected_result = np.array([47        [31.09841011, 27.84081736, 24., 16., 8., 0.],48        [20.82733247, 15.54920505, 13.33333333, 15.54920505, 15.54920505, 13.33333333],49        [16., 8., 0., 8., 16., 24.],50        [20.82733247, 15.54920505, 13.33333333, 15.54920505, 20.82733247, 27.45501371]51    ], dtype=np.float32)52    return target_values, expected_result53@pytest.fixture54def result_manhattan_proximity():55    # distance_metric SETTING: MANHATTAN56    expected_result = np.array([57        [29.33333333, 21.33333333, 13.33333333, 16., 8., 0.],58        [16., 8., 0., 8., 16., 13.33333333],59        [13.33333333, 8., 0., 8., 16., 24.],60        [0., 8., 13.33333333, 21.33333333, 29.33333333, 37.33333333]61    ], dtype=np.float32)62    return expected_result63@pytest.fixture64def result_great_circle_proximity():65    # distance_metric SETTING: GREAT_CIRCLE66    expected_result = np.array([67        [2278099.27025501, 1717528.97437217, 1484259.87724365, 1673057.17235307, 836769.1780019, 0],68        [1768990.54084204, 884524.60324856, 0, 884524.60324856, 1717528.97437217, 1484259.87724365],69        [1484259.87724365, 884524.60324856, 0, 884524.60324856, 1768990.54084204, 2653336.85436932],70        [0, 836769.1780019, 1484259.87724365, 1717528.97437217, 2278099.27025501, 2986647.12982316]71    ], dtype=np.float32)72    return expected_result73@pytest.fixture74def result_max_distance_proximity():75    # max_distance setting76    max_distance = 1077    expected_result = np.array([78        [np.nan, np.nan, np.nan, np.nan, 8., 0.],79        [np.nan, 8., 0., 8., np.nan, np.nan],80        [np.nan, 8., 0., 8., np.nan, np.nan],81        [0., 8., np.nan, np.nan, np.nan, np.nan]82    ], dtype=np.float32)83    return max_distance, expected_result84@pytest.fixture85def result_default_allocation():86    expected_result = np.array([87        [1., 1., 1., 1., 2., 2.],88        [1., 1., 1., 1., 2., 2.],89        [4., 3., 3., 3., 3., 3.],90        [4., 4., 3., 3., 3., 3.]91    ], dtype=np.float32)92    return expected_result93@pytest.fixture94def result_max_distance_allocation():95    # max_distance setting96    max_distance = 1097    expected_result = np.array([98        [np.nan, np.nan, np.nan, np.nan, 2., 2.],99        [np.nan, 1., 1., 1., np.nan, np.nan],100        [np.nan, 3., 3., 3., np.nan, np.nan],101        [4., 4., np.nan, np.nan, np.nan, np.nan]102    ], dtype=np.float32)103    return max_distance, expected_result104@pytest.fixture105def result_default_direction():106    expected_result = np.array([107        [50.194427, 30.963757, 360., 329.03625, 90., 0.],108        [90., 90., 0., 270., 149.03624, 180.],109        [360., 90., 0., 270., 270., 270.],110        [0., 270., 180., 210.96376, 230.19443, 240.9454]111    ], dtype=np.float32)112    return expected_result113@pytest.fixture114def result_max_distance_direction():115    # max_distance setting116    max_distance = 10117    expected_result = np.array([118        [np.nan, np.nan, np.nan, np.nan, 90., 0.],119        [np.nan, 90., 0., 270., np.nan, np.nan],120        [np.nan, 90., 0., 270., np.nan, np.nan],121        [0., 270., np.nan, np.nan, np.nan, np.nan]122    ], dtype=np.float32)123    return max_distance, expected_result124@pytest.mark.parametrize("backend", ['numpy', 'dask+numpy'])125def test_default_proximity(test_raster, result_default_proximity):126    default_prox = proximity(test_raster, x='lon', y='lat')127    general_output_checks(test_raster, default_prox, result_default_proximity, verify_dtype=True)128@pytest.mark.parametrize("backend", ['numpy', 'dask+numpy'])129def test_target_proximity(test_raster, result_target_proximity):130    target_values, expected_result = result_target_proximity131    target_prox = proximity(test_raster, x='lon', y='lat', target_values=target_values)132    general_output_checks(test_raster, target_prox, expected_result, verify_dtype=True)133@pytest.mark.parametrize("backend", ['numpy', 'dask+numpy'])134def test_manhattan_proximity(test_raster, result_manhattan_proximity):135    manhattan_prox = proximity(test_raster, x='lon', y='lat', distance_metric='MANHATTAN')136    general_output_checks(137        test_raster, manhattan_prox, result_manhattan_proximity, verify_dtype=True138    )139@pytest.mark.parametrize("backend", ['numpy', 'dask+numpy'])140def test_great_circle_proximity(test_raster, result_great_circle_proximity):141    great_circle_prox = proximity(test_raster, x='lon', y='lat', distance_metric='GREAT_CIRCLE')142    general_output_checks(143        test_raster, great_circle_prox, result_great_circle_proximity, verify_dtype=True144    )145@pytest.mark.parametrize("backend", ['numpy', 'dask+numpy'])146def test_max_distance_proximity(test_raster, result_max_distance_proximity):147    max_distance, expected_result = result_max_distance_proximity148    max_distance_prox = proximity(test_raster, x='lon', y='lat', max_distance=max_distance)149    general_output_checks(test_raster, max_distance_prox, expected_result, verify_dtype=True)150@pytest.mark.parametrize("backend", ['numpy', 'dask+numpy'])151def test_default_allocation(test_raster, result_default_allocation):152    allocation_agg = allocation(test_raster, x='lon', y='lat')153    general_output_checks(test_raster, allocation_agg, result_default_allocation, verify_dtype=True)154@pytest.mark.parametrize("backend", ['numpy'])155def test_default_allocation_against_proximity(test_raster, result_default_proximity):156    allocation_agg = allocation(test_raster, x='lon', y='lat')157    # check against corresponding proximity158    xcoords = allocation_agg['lon'].data159    ycoords = allocation_agg['lat'].data160    for y in range(test_raster.shape[0]):161        for x in range(test_raster.shape[1]):162            a = allocation_agg.data[y, x]163            py, px = np.where(test_raster.data == a)164            # non-zero cells in raster are unique, thus len(px)=len(py)=1165            d = euclidean_distance(xcoords[x], xcoords[px[0]], ycoords[y], ycoords[py[0]])166            np.testing.assert_allclose(result_default_proximity[y, x], d)167@pytest.mark.parametrize("backend", ['numpy', 'dask+numpy'])168def test_max_distance_allocation(test_raster, result_max_distance_allocation):169    max_distance, expected_result = result_max_distance_allocation170    max_distance_alloc = allocation(test_raster, x='lon', y='lat', max_distance=max_distance)171    general_output_checks(test_raster, max_distance_alloc, expected_result, verify_dtype=True)172def test_calc_direction():173    n = 3174    x1, y1 = 1, 1175    output = np.zeros(shape=(n, n))176    for y2 in range(n):177        for x2 in range(n):178            output[y2, x2] = _calc_direction(x2, x1, y2, y1)179    expected_output = np.asarray([[135, 180, 225],180                                  [90,  0,   270],181                                  [45,  360, 315]])182    # set a tolerance of 1e-5183    tolerance = 1e-5184    assert (abs(output-expected_output) <= tolerance).all()185@pytest.mark.parametrize("backend", ['numpy', 'dask+numpy'])186def test_default_direction(test_raster, result_default_direction):187    direction_agg = direction(test_raster, x='lon', y='lat')188    general_output_checks(test_raster, direction_agg, result_default_direction)189@pytest.mark.parametrize("backend", ['numpy'])190def test_default_direction_against_allocation(test_raster, result_default_allocation):191    direction_agg = direction(test_raster, x='lon', y='lat')192    xcoords = direction_agg['lon'].data193    ycoords = direction_agg['lat'].data194    for y in range(test_raster.shape[0]):195        for x in range(test_raster.shape[1]):196            a = result_default_allocation.data[y, x]197            py, px = np.where(test_raster.data == a)198            # non-zero cells in raster are unique, thus len(px)=len(py)=1199            d = _calc_direction(xcoords[x], xcoords[px[0]], ycoords[y], ycoords[py[0]])200            np.testing.assert_allclose(direction_agg.data[y, x], d)201@pytest.mark.parametrize("backend", ['numpy', 'dask+numpy'])202def test_max_distance_direction(test_raster, result_max_distance_direction):203    max_distance, expected_result = result_max_distance_direction204    max_distance_direction = direction(test_raster, x='lon', y='lat', max_distance=max_distance)...

test_classify.py

Source:test_classify.py

1import numpy as np2import pytest3import xarray as xr4from xrspatial import binary, equal_interval, natural_breaks, quantile, reclassify5from xrspatial.tests.general_checks import (create_test_raster, cuda_and_cupy_available,6                                            general_output_checks)7def input_data(backend='numpy'):8    elevation = np.array([9        [-np.inf,  2.,  3.,  4., np.nan],10        [5.,  6.,  7.,  8.,  9.],11        [10., 11., 12., 13., 14.],12        [15., 16., 17., 18., np.inf],13    ])14    raster = create_test_raster(elevation, backend, attrs={'res': (10.0, 10.0)})15    return raster16@pytest.fixture17def result_binary():18    values = [1, 2, 3]19    expected_result = np.asarray([20        [0, 1, 1, 0, 0],21        [0, 0, 0, 0, 0],22        [0, 0, 0, 0, 0],23        [0, 0, 0, 0, 0]24    ], dtype=np.float32)25    return values, expected_result26def test_binary_numpy(result_binary):27    values, expected_result = result_binary28    numpy_agg = input_data()29    numpy_result = binary(numpy_agg, values)30    general_output_checks(numpy_agg, numpy_result, expected_result)31def test_binary_dask_numpy(result_binary):32    values, expected_result = result_binary33    dask_agg = input_data(backend='dask')34    dask_result = binary(dask_agg, values)35    general_output_checks(dask_agg, dask_result, expected_result)36@cuda_and_cupy_available37def test_binary_cupy(result_binary):38    values, expected_result = result_binary39    cupy_agg = input_data(backend='cupy')40    cupy_result = binary(cupy_agg, values)41    general_output_checks(cupy_agg, cupy_result, expected_result)42@cuda_and_cupy_available43def test_binary_dask_cupy(result_binary):44    values, expected_result = result_binary45    dask_cupy_agg = input_data(backend='dask+cupy')46    dask_cupy_result = binary(dask_cupy_agg, values)47    general_output_checks(dask_cupy_agg, dask_cupy_result, expected_result)48@pytest.fixture49def result_reclassify():50    bins = [10, 15, np.inf]51    new_values = [1, 2, 3]52    expected_result = np.asarray([53        [np.nan, 1., 1., 1., np.nan],54        [1., 1., 1., 1., 1.],55        [1., 2., 2., 2., 2.],56        [2., 3., 3., 3., np.nan]57    ], dtype=np.float32)58    return bins, new_values, expected_result59def test_reclassify_numpy_mismatch_length():60    bins = [10]61    new_values = [1, 2, 3]62    numpy_agg = input_data()63    msg = 'bins and new_values mismatch. Should have same length.'64    with pytest.raises(ValueError, match=msg):65        reclassify(numpy_agg, bins, new_values)66def test_reclassify_numpy(result_reclassify):67    bins, new_values, expected_result = result_reclassify68    numpy_agg = input_data()69    numpy_result = reclassify(numpy_agg, bins=bins, new_values=new_values)70    general_output_checks(numpy_agg, numpy_result, expected_result, verify_dtype=True)71def test_reclassify_dask_numpy(result_reclassify):72    bins, new_values, expected_result = result_reclassify73    dask_agg = input_data(backend='dask')74    dask_result = reclassify(dask_agg, bins=bins, new_values=new_values)75    general_output_checks(dask_agg, dask_result, expected_result, verify_dtype=True)76@cuda_and_cupy_available77def test_reclassify_cupy(result_reclassify):78    bins, new_values, expected_result = result_reclassify79    cupy_agg = input_data(backend='cupy')80    cupy_result = reclassify(cupy_agg, bins=bins, new_values=new_values)81    general_output_checks(cupy_agg, cupy_result, expected_result, verify_dtype=True)82@cuda_and_cupy_available83def test_reclassify_dask_cupy(result_reclassify):84    bins, new_values, expected_result = result_reclassify85    dask_cupy_agg = input_data(backend='dask+cupy')86    dask_cupy_result = reclassify(dask_cupy_agg, bins=bins, new_values=new_values)87    general_output_checks(dask_cupy_agg, dask_cupy_result, expected_result, verify_dtype=True)88@pytest.fixture89def result_quantile():90    k = 591    expected_result = np.asarray([92        [np.nan, 0., 0., 0., np.nan],93        [0., 1., 1., 1., 2.],94        [2., 2., 3., 3., 3.],95        [4., 4., 4., 4., np.nan]96    ], dtype=np.float32)97    return k, expected_result98def test_quantile_not_enough_unique_values():99    agg = input_data()100    n_uniques = np.isfinite(agg.data).sum()101    k = n_uniques + 1102    result_quantile = quantile(agg, k=k)103    n_uniques_result = np.isfinite(result_quantile.data).sum()104    np.testing.assert_allclose(n_uniques_result, n_uniques)105def test_quantile_numpy(result_quantile):106    k, expected_result = result_quantile107    numpy_agg = input_data()108    numpy_quantile = quantile(numpy_agg, k=k)109    general_output_checks(numpy_agg, numpy_quantile, expected_result, verify_dtype=True)110def test_quantile_dask_numpy(result_quantile):111    #     Note that dask's percentile algorithm is112    #     approximate, while numpy's is exact.113    #     This may cause some differences between114    #     results of vanilla numpy and115    #     dask version of the input agg.116    #     https://github.com/dask/dask/issues/3099117    dask_numpy_agg = input_data('dask+numpy')118    k, expected_result = result_quantile119    dask_quantile = quantile(dask_numpy_agg, k=k)120    general_output_checks(dask_numpy_agg, dask_quantile)121    dask_quantile = dask_quantile.compute()122    unique_elements = np.unique(123        dask_quantile.data[np.isfinite(dask_quantile.data)]124    )125    assert len(unique_elements) == k126@cuda_and_cupy_available127def test_quantile_cupy(result_quantile):128    k, expected_result = result_quantile129    cupy_agg = input_data('cupy')130    cupy_result = quantile(cupy_agg, k=k)131    general_output_checks(cupy_agg, cupy_result, expected_result, verify_dtype=True)132@pytest.fixture133def result_natural_breaks():134    k = 5135    expected_result = np.asarray([136        [np.nan, 0., 0., 0., np.nan],137        [1., 1., 1., 2., 2.],138        [2., 3., 3., 3., 3.],139        [4., 4., 4., 4., np.nan]140    ], dtype=np.float32)141    return k, expected_result142@pytest.fixture143def result_natural_breaks_num_sample():144    k = 5145    num_sample = 8146    expected_result = np.asarray([147        [np.nan, 0., 0., 0., np.nan],148        [0., 1., 1., 1., 2.],149        [2., 3., 3., 3., 3.],150        [4., 4., 4., 4., np.nan]151    ], dtype=np.float32)152    return k, num_sample, expected_result153def test_natural_breaks_not_enough_unique_values():154    agg = input_data()155    n_uniques = np.isfinite(agg.data).sum()156    k = n_uniques + 1157    result_natural_breaks = natural_breaks(agg, k=k)158    n_uniques_result = np.isfinite(result_natural_breaks.data).sum()159    np.testing.assert_allclose(n_uniques_result, n_uniques)160def test_natural_breaks_numpy(result_natural_breaks):161    numpy_agg = input_data()162    k, expected_result = result_natural_breaks163    numpy_natural_breaks = natural_breaks(numpy_agg, k=k)164    general_output_checks(numpy_agg, numpy_natural_breaks, expected_result, verify_dtype=True)165def test_natural_breaks_numpy_num_sample(result_natural_breaks_num_sample):166    numpy_agg = input_data()167    k, num_sample, expected_result = result_natural_breaks_num_sample168    numpy_natural_breaks = natural_breaks(numpy_agg, k=k, num_sample=num_sample)169    general_output_checks(numpy_agg, numpy_natural_breaks, expected_result, verify_dtype=True)170def test_natural_breaks_cpu_deterministic():171    results = []172    elevation = np.arange(100).reshape(10, 10)173    agg = xr.DataArray(elevation, attrs={'res': (10.0, 10.0)})174    k = 5175    numIters = 3176    for i in range(numIters):177        # vanilla numpy178        numpy_natural_breaks = natural_breaks(agg, k=k)179        general_output_checks(agg, numpy_natural_breaks)180        unique_elements = np.unique(181            numpy_natural_breaks.data[np.isfinite(numpy_natural_breaks.data)]182        )183        assert len(unique_elements) == k184        results.append(numpy_natural_breaks)185    # Check that the code is deterministic.186    # Multiple runs on same data should produce same results187    for i in range(numIters-1):188        np.testing.assert_allclose(189            results[i].data, results[i+1].data, equal_nan=True190        )191@pytest.fixture192def result_equal_interval():193    k = 3194    expected_result = np.asarray([195        [np.nan, 0., 0., 0., np.nan],196        [0., 0., 0., 1., 1.],197        [1., 1., 1., 2., 2.],198        [2., 2., 2., 2., np.nan]199    ], dtype=np.float32)200    return k, expected_result201def test_equal_interval_numpy(result_equal_interval):202    k, expected_result = result_equal_interval203    numpy_agg = input_data('numpy')204    numpy_result = equal_interval(numpy_agg, k=k)205    general_output_checks(numpy_agg, numpy_result, expected_result, verify_dtype=True)206def test_equal_interval_dask_numpy(result_equal_interval):207    k, expected_result = result_equal_interval208    dask_agg = input_data('dask+numpy')209    dask_numpy_result = equal_interval(dask_agg, k=k)210    general_output_checks(dask_agg, dask_numpy_result, expected_result, verify_dtype=True)211@cuda_and_cupy_available212def test_equal_interval_cupy(result_equal_interval):213    k, expected_result = result_equal_interval214    cupy_agg = input_data(backend='cupy')215    cupy_result = equal_interval(cupy_agg, k=k)...

features.py

Source:features.py

...16        return cls(**f)17    def validate(self, example: "Features"):18        assert example.keys() == self.keys()19        def verify_shape(key): return self[key].shape == example[key].shape20        def verify_dtype(key): return self[key].dtype == example[key].dtype21        return all(filter(verify_shape, filter(verify_dtype, self.keys())))22    def reshape(self, example: dict):23        example = OrderedDict(example)24        for key, value in self.items():25            example[key] = example[key] \26                .reshape(value.shape)   \27                .astype(value.dtype)28        return example29    def __hash__(self):30        return int(self.md5, 16)31    def __repr__(self):32        __features_repr__ = ", ".join(f'{key}={value}'33                                      for key, value in self.items())34        return f'{self.__class__.__name__}({__features_repr__})'...

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