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| # This file is part of Hypothesis, which may be found at | ||
| # https://github.com/HypothesisWorks/hypothesis/ | ||
| # | ||
| # Most of this work is copyright (C) 2013-2020 David R. MacIver | ||
| # ([email protected]), but it contains contributions by others. See | ||
| # CONTRIBUTING.rst for a full list of people who may hold copyright, and | ||
| # consult the git log if you need to determine who owns an individual | ||
| # contribution. | ||
| # | ||
| # This Source Code Form is subject to the terms of the Mozilla Public License, | ||
| # v. 2.0. If a copy of the MPL was not distributed with this file, You can | ||
| # obtain one at https://mozilla.org/MPL/2.0/. | ||
| # | ||
| # END HEADER | ||
|
|
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| import numpy as np | ||
| import pytest | ||
| from tests.common.debug import find_any | ||
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| from hypothesis import assume, given, settings, strategies as st | ||
| from hypothesis.errors import InvalidArgument | ||
| from hypothesis.extra import numpy as nps | ||
| from hypothesis.strategies._internal import SearchStrategy | ||
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| STANDARD_TYPES = [ | ||
| np.dtype(t) | ||
| for t in ( | ||
| "int8", | ||
| "int16", | ||
| "int32", | ||
| "int64", | ||
| "uint8", | ||
| "uint16", | ||
| "uint32", | ||
| "uint64", | ||
| "float", | ||
| "float16", | ||
| "float32", | ||
| "float64", | ||
| "complex64", | ||
| "complex128", | ||
| "datetime64", | ||
| "timedelta64", | ||
| bool, | ||
| str, | ||
| bytes, | ||
| ) | ||
| ] | ||
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| @given(nps.nested_dtypes()) | ||
| def test_strategies_for_standard_dtypes_have_reusable_values(dtype): | ||
| assert nps.from_dtype(dtype).has_reusable_values | ||
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| @pytest.mark.parametrize("t", STANDARD_TYPES) | ||
| def test_produces_instances(t): | ||
| @given(nps.from_dtype(t)) | ||
| def test_is_t(x): | ||
| assert isinstance(x, t.type) | ||
| assert x.dtype.kind == t.kind | ||
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| test_is_t() | ||
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| @settings(max_examples=100) | ||
| @given(nps.nested_dtypes(max_itemsize=400), st.data()) | ||
| def test_infer_strategy_from_dtype(dtype, data): | ||
| # Given a dtype | ||
| assert isinstance(dtype, np.dtype) | ||
| # We can infer a strategy | ||
| strat = nps.from_dtype(dtype) | ||
| assert isinstance(strat, SearchStrategy) | ||
| # And use it to fill an array of that dtype | ||
| data.draw(nps.arrays(dtype, 10, elements=strat)) | ||
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| @given(st.data()) | ||
| def test_can_cast_for_scalars(data): | ||
| # Note: this only passes with castable datatypes, certain dtype | ||
| # combinations will result in an error if numpy is not able to cast them. | ||
| dt_elements = np.dtype(data.draw(st.sampled_from(["bool", "<i2", ">i2"]))) | ||
| dt_desired = np.dtype( | ||
| data.draw(st.sampled_from(["<i2", ">i2", "float32", "float64"])) | ||
| ) | ||
| result = data.draw( | ||
| nps.arrays(dtype=dt_desired, elements=nps.from_dtype(dt_elements), shape=()) | ||
| ) | ||
| assert isinstance(result, np.ndarray) | ||
| assert result.dtype == dt_desired | ||
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| @given(st.data()) | ||
| def test_unicode_string_dtypes_generate_unicode_strings(data): | ||
| dt = data.draw(nps.unicode_string_dtypes()) | ||
| result = data.draw(nps.from_dtype(dt)) | ||
| assert isinstance(result, str) | ||
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| @given(nps.arrays(dtype="U99", shape=(10,))) | ||
| def test_can_unicode_strings_without_decode_error(arr): | ||
| # See https://github.com/numpy/numpy/issues/15363 | ||
| pass | ||
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| @pytest.mark.xfail(strict=False, reason="mitigation for issue above") | ||
| def test_unicode_string_dtypes_need_not_be_utf8(): | ||
| def cannot_encode(string): | ||
| try: | ||
| string.encode("utf-8") | ||
| return False | ||
| except UnicodeEncodeError: | ||
| return True | ||
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| find_any(nps.from_dtype(np.dtype("U")), cannot_encode) | ||
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| @given(st.data()) | ||
| def test_byte_string_dtypes_generate_unicode_strings(data): | ||
| dt = data.draw(nps.byte_string_dtypes()) | ||
| result = data.draw(nps.from_dtype(dt)) | ||
| assert isinstance(result, bytes) | ||
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| @pytest.mark.parametrize("dtype", ["U", "S", "a"]) | ||
| def test_unsized_strings_length_gt_one(dtype): | ||
| # See https://github.com/HypothesisWorks/hypothesis/issues/2229 | ||
| find_any(nps.arrays(dtype=dtype, shape=1), lambda arr: len(arr[0]) >= 2) | ||
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| @given( | ||
| st.data(), | ||
| st.builds( | ||
| "{}[{}]".format, | ||
| st.sampled_from(("datetime64", "timedelta64")), | ||
| st.sampled_from(nps.TIME_RESOLUTIONS), | ||
| ).map(np.dtype), | ||
| ) | ||
| def test_inferring_from_time_dtypes_gives_same_dtype(data, dtype): | ||
| ex = data.draw(nps.from_dtype(dtype)) | ||
| assert dtype == ex.dtype | ||
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| @given(st.data(), nps.byte_string_dtypes() | nps.unicode_string_dtypes()) | ||
| def test_inferred_string_strategies_roundtrip(data, dtype): | ||
| # Check that we never generate too-long or nul-terminated strings, which | ||
| # cannot be read back out of an array. | ||
| arr = np.zeros(shape=1, dtype=dtype) | ||
| ex = data.draw(nps.from_dtype(arr.dtype)) | ||
| arr[0] = ex | ||
| assert arr[0] == ex | ||
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| @given(st.data(), nps.scalar_dtypes()) | ||
| def test_all_inferred_scalar_strategies_roundtrip(data, dtype): | ||
| # We only check scalars here, because record/compound/nested dtypes always | ||
| # give an array of np.void objects. We're interested in whether scalar | ||
| # values are safe, not known type coercion. | ||
| arr = np.zeros(shape=1, dtype=dtype) | ||
| ex = data.draw(nps.from_dtype(arr.dtype)) | ||
| assume(ex == ex) # If not, the roundtrip test *should* fail! (eg NaN) | ||
| arr[0] = ex | ||
| assert arr[0] == ex | ||
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| @pytest.mark.parametrize("dtype_str", ["m8", "M8"]) | ||
| @given(data=st.data()) | ||
| def test_from_dtype_works_without_time_unit(data, dtype_str): | ||
| arr = data.draw(nps.from_dtype(np.dtype(dtype_str))) | ||
| assert (dtype_str + "[") in arr.dtype.str | ||
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| @pytest.mark.parametrize("dtype_str", ["m8", "M8"]) | ||
| @given(data=st.data()) | ||
| def test_arrays_selects_consistent_time_unit(data, dtype_str): | ||
| arr = data.draw(nps.arrays(dtype_str, 10)) | ||
| assert (dtype_str + "[") in arr.dtype.str | ||
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| def test_arrays_gives_useful_error_on_inconsistent_time_unit(): | ||
| with pytest.raises(InvalidArgument, match="mismatch of time units in dtypes"): | ||
| nps.arrays("m8[Y]", 10, elements=nps.from_dtype(np.dtype("m8[D]"))).example() |
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