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# This file is part of Hypothesis, which may be found at
# https://github.com/HypothesisWorks/hypothesis/
#
# Copyright the Hypothesis Authors.
# Individual contributors are listed in AUTHORS.rst and the git log.
#
# 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/.
import sys
import numpy as np
import pandas
import pytest
from hypothesis import HealthCheck, given, reject, settings, strategies as st
from hypothesis.errors import Unsatisfiable
from hypothesis.extra import numpy as npst, pandas as pdst
from tests.common.debug import check_can_generate_examples
from tests.pandas.helpers import supported_by_pandas
# https://pandas.pydata.org/docs/whatsnew/v2.0.0.html#index-can-now-hold-numpy-numeric-dtypes
@given(pdst.indexes(dtype=int, max_size=0))
def test_gets_right_dtype_for_empty_indices(ix):
is_32bit = sys.maxsize == 2**31 - 1
pandas2 = pandas.__version__.startswith("2.")
numpy1 = np.__version__.startswith("1.")
windows = sys.platform == "win32" # including 64-bit windows, confusingly
if pandas2 and (is_32bit or (windows and numpy1)):
# No, I don't know what this is int32 on 64-bit windows until Numpy 2.0
assert ix.dtype == np.dtype("int32")
else:
assert ix.dtype == np.dtype("int64")
@given(pdst.indexes(elements=st.integers(0, sys.maxsize), max_size=0))
def test_gets_right_dtype_for_empty_indices_with_elements(ix):
assert ix.dtype == np.dtype("int64")
def test_does_not_generate_impossible_conditions():
with pytest.raises(Unsatisfiable):
check_can_generate_examples(pdst.indexes(min_size=3, max_size=3, dtype=bool))
@given(pdst.indexes(dtype=bool, unique=True))
def test_unique_indexes_of_small_values(ix):
assert len(ix) <= 2
assert len(set(ix)) == len(ix)
@given(pdst.indexes(dtype=bool, min_size=2, unique=True))
def test_unique_indexes_of_many_small_values(ix):
assert len(ix) == 2
assert len(set(ix)) == len(ix)
@given(pdst.indexes(dtype="int8", name=st.just("test_name")))
def test_name_passed_on_indexes(s):
assert s.name == "test_name"
# Sizes that fit into an int64 without overflow
range_sizes = st.integers(0, 2**63 - 1)
@given(range_sizes, range_sizes | st.none(), st.data())
def test_arbitrary_range_index(i, j, data):
if j is not None:
i, j = sorted((i, j))
data.draw(pdst.range_indexes(i, j))
@given(pdst.range_indexes(name=st.just("test_name")))
def test_name_passed_on_range_indexes(s):
assert s.name == "test_name"
@given(pdst.range_indexes())
def test_basic_range_indexes(ix):
assert isinstance(ix, pandas.RangeIndex)
@settings(suppress_health_check=[HealthCheck.too_slow])
@given(st.data())
def test_generate_arbitrary_indices(data):
min_size = data.draw(st.integers(0, 10), "min_size")
max_size = data.draw(st.none() | st.integers(min_size, min_size + 10), "max_size")
unique = data.draw(st.booleans(), "unique")
dtype = data.draw(
st.one_of(
npst.boolean_dtypes(),
npst.integer_dtypes(endianness="="),
npst.floating_dtypes(endianness="=", sizes=(32, 64)),
npst.datetime64_dtypes(endianness="="),
npst.timedelta64_dtypes(endianness="="),
).filter(supported_by_pandas),
"dtype",
)
pass_elements = data.draw(st.booleans(), "pass_elements")
converted_dtype = pandas.Index([], dtype=dtype).dtype
try:
inferred_dtype = pandas.Index([data.draw(npst.from_dtype(dtype))]).dtype
if pass_elements:
elements = npst.from_dtype(dtype)
dtype = None
else:
elements = None
index = data.draw(
pdst.indexes(
elements=elements,
dtype=dtype,
min_size=min_size,
max_size=max_size,
unique=unique,
)
)
except Exception as e:
if type(e).__name__ == "OutOfBoundsDatetime":
# See https://github.com/HypothesisWorks/hypothesis-python/pull/826
reject()
else:
raise
if dtype is None:
assert index.dtype == inferred_dtype
else:
assert index.dtype == converted_dtype
if unique:
assert len(set(index.values)) == len(index)
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