import numpy as np import pandas as pd import pytest from pytest_regressions.testing import check_regression_fixture_workflow @pytest.fixture def no_regen(dataframe_regression, request): if dataframe_regression._force_regen or request.config.getoption("force_regen"): pytest.fail("--force-regen should not be used on this test.") def test_usage_workflow(testdir, monkeypatch): """ :type testdir: _pytest.pytester.TmpTestdir :type monkeypatch: _pytest.monkeypatch.monkeypatch """ import sys monkeypatch.setattr( sys, "testing_get_data", lambda: {"data": 1.1 * np.ones(50)}, raising=False ) source = """ import sys import pandas as pd def test_1(dataframe_regression): contents = sys.testing_get_data() dataframe_regression.check(pd.DataFrame.from_dict(contents)) """ def get_csv_contents(): filename = testdir.tmpdir / "test_file" / "test_1.csv" frame = pd.read_csv(str(filename)) return {"data": frame["data"].values} def compare_arrays(obtained, expected): assert (obtained["data"] == expected["data"]).all() check_regression_fixture_workflow( testdir, source=source, data_getter=get_csv_contents, data_modifier=lambda: monkeypatch.setattr( sys, "testing_get_data", lambda: {"data": 1.2 * np.ones(50)}, raising=False ), expected_data_1={"data": 1.1 * np.ones(50)}, expected_data_2={"data": 1.2 * np.ones(50)}, compare_fn=compare_arrays, ) def test_common_cases(dataframe_regression, no_regen): # Most common case: Data is valid, is present and should pass data1 = 1.1 * np.ones(5000) data2 = 2.2 * np.ones(5000) dataframe_regression.check(pd.DataFrame.from_dict({"data1": data1, "data2": data2})) # Assertion error case 1: Data has one invalid place data1 = 1.1 * np.ones(5000) data2 = 2.2 * np.ones(5000) data1[500] += 0.1 with pytest.raises(AssertionError) as excinfo: dataframe_regression.check( pd.DataFrame.from_dict({"data1": data1, "data2": data2}) ) obtained_error_msg = str(excinfo.value) expected = "\n".join( [ "Values are not sufficiently close.", "To update values, use --force-regen option.", ] ) assert expected in obtained_error_msg expected = "\n".join( [ "data1:", " obtained_data1 expected_data1 diff", "500 1.20000000000000018 1.10000000000000009 0.10000000000000009", ] ) assert expected in obtained_error_msg # Assertion error case 2: More than one invalid data data1 = 1.1 * np.ones(5000) data2 = 2.2 * np.ones(5000) data1[500] += 0.1 data1[600] += 0.2 data2[700] += 0.3 with pytest.raises(AssertionError) as excinfo: dataframe_regression.check( pd.DataFrame.from_dict({"data1": data1, "data2": data2}) ) obtained_error_msg = str(excinfo.value) expected = "\n".join( [ "Values are not sufficiently close.", "To update values, use --force-regen option.", ] ) assert expected in obtained_error_msg expected = "\n".join( [ "data1:", " obtained_data1 expected_data1 diff", "500 1.20000000000000018 1.10000000000000009 0.10000000000000009", "600 1.30000000000000004 1.10000000000000009 0.19999999999999996", ] ) assert expected in obtained_error_msg expected = "\n".join( [ "data2:", " obtained_data2 expected_data2 diff", "700 2.5 2.20000000000000018 0.29999999999999982", ] ) assert expected in obtained_error_msg # Assertion error case 3: More than one invalid data data1 = 1.1 * np.ones(5000) data2 = 2.2 * np.ones(5000) data1[500] += 0.01 data2[500] += 0.01 with pytest.raises(AssertionError) as excinfo: dataframe_regression.check( pd.DataFrame.from_dict({"data1": data1, "data2": data2}), tolerances={ "data1": dict(atol=1e-1, rtol=1e-17), "data2": dict(atol=1e-17, rtol=1e-17), }, ) obtained_error_msg = str(excinfo.value) assert " data1:" not in obtained_error_msg assert ( "\n".join( [ "Values are not sufficiently close.", "To update values, use --force-regen option.", ] ) in obtained_error_msg ) assert ( "\n".join( [ "data2:", " obtained_data2 expected_data2 diff", "500 2.20999999999999996 2.20000000000000018 0.00999999999999979", ] ) in obtained_error_msg ) def test_different_data_types(dataframe_regression, no_regen): # Original CSV file contains integer data data1 = np.array([True] * 10) with pytest.raises( AssertionError, match="Data type for data data1 of obtained and expected are not the same.", ): dataframe_regression.check(pd.DataFrame.from_dict({"data1": data1})) class Foo: def __init__(self, bar): self.bar = bar @pytest.mark.parametrize( "array", [[np.random.randint(10, 99, 6)] * 6, [Foo(i) for i in range(4)]] ) def test_non_numeric_data(dataframe_regression, array, no_regen): data1 = pd.DataFrame() data1["data1"] = array with pytest.raises( AssertionError, match="Only numeric data is supported on dataframe_regression fixture.\n" " Array with type '%s' was given." % (str(data1["data1"].dtype),), ): dataframe_regression.check(data1) def test_arrays_with_different_sizes(dataframe_regression, no_regen): data1 = np.ones(10, dtype=np.float64) with pytest.raises( AssertionError, match="Obtained and expected data shape are not the same." ): dataframe_regression.check(pd.DataFrame.from_dict({"data1": data1})) def test_integer_values_smoke_test(dataframe_regression, no_regen): data1 = np.ones(11, dtype=np.int) dataframe_regression.check(pd.DataFrame.from_dict({"data1": data1})) def test_number_formats(dataframe_regression, no_regen): data1 = np.array([1.2345678e50, 1.2345678e-50, 0.0]) dataframe_regression.check(pd.DataFrame.from_dict({"data1": data1})) def test_bool_array(dataframe_regression, no_regen): data1 = np.array([True, True, True], dtype=np.bool) with pytest.raises(AssertionError) as excinfo: dataframe_regression.check(pd.DataFrame.from_dict({"data1": data1})) obtained_error_msg = str(excinfo.value) expected = "\n".join( [ "Values are not sufficiently close.", "To update values, use --force-regen option.", ] ) assert expected in obtained_error_msg expected = "\n".join( [ "data1:", " obtained_data1 expected_data1 diff", "0 True False True", "1 True False True", "2 True False True", ] ) assert expected in obtained_error_msg def test_arrays_of_same_size(dataframe_regression): same_size_int_arrays = { "hello": np.zeros((1,), dtype=np.int), "world": np.zeros((1,), dtype=np.int), } dataframe_regression.check(pd.DataFrame.from_dict(same_size_int_arrays)) def test_string_array(dataframe_regression): data1 = {"potato": ["delicious", "nutritive", "yummy"]} dataframe_regression.check(pd.DataFrame.from_dict(data1)) def test_non_pandas_dataframe(dataframe_regression): data = np.ones(shape=(10, 10)) with pytest.raises( AssertionError, match="Only pandas DataFrames are supported on on dataframe_regression fixture.\n" " Object with type '%s' was given." % (str(type(data)),), ): dataframe_regression.check(data)