KaneAI - World’s First E2E Software Testing Agent.Book a Demo 
How to use verify_dtype method in pandera
Best Python code snippet using pandera_python
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__})'...
Automation Testing Tutorials
Learn to execute automation testing from scratch with LambdaTest Learning Hub. Right from setting up the prerequisites to run your first automation test, to following best practices and diving deeper into advanced test scenarios. LambdaTest Learning Hubs compile a list of step-by-step guides to help you be proficient with different test automation frameworks i.e. Selenium, Cypress, TestNG etc.
LambdaTest Learning Hubs:
- JUnit Tutorial
- TestNG Tutorial
- Webdriver Tutorial
- WebDriverIO Tutorial
- Protractor Tutorial
- Selenium 4 Tutorial
- Jenkins Tutorial
- NUnit Tutorial
- Jest Tutorial
- Playwright Tutorial
- Cypress Tutorial
- PyTest Tutorial
YouTube
You could also refer to video tutorials over LambdaTest YouTube channel to get step by step demonstration from industry experts.
Run pandera automation tests on LambdaTest cloud grid
Perform automation testing on 3000+ real desktop and mobile devices online.
Try LambdaTest Now !!
Get 100 minutes of automation test minutes FREE!!
Was this article helpful?
