def ints_floats_datetimes_and_timedeltas(draw):
dtypes = (
unsigned_integer_dtypes(endianness="="),
integer_dtypes(endianness="="),
floating_dtypes(endianness="="),
datetime64_dtypes(endianness="="),
timedelta64_dtypes(endianness="="),
)
return draw(one_of(dtypes))
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="="),
npst.complex_number_dtypes(endianness="="),
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)
test_set = load_by_id(datasaver.run_id)
expec_data = np.array([item for item in list_of_strings])
actual_data = test_set.get_parameter_data()["p"]["p"]
try:
np.testing.assert_array_equal(actual_data, expec_data)
finally:
test_set.conn.close()
@settings(suppress_health_check=(HealthCheck.function_scoped_fixture, ),
deadline=None)
@given(p_values=hypnumpy.arrays(dtype=hst.sampled_from(
(hypnumpy.unicode_string_dtypes(), hypnumpy.byte_string_dtypes(),
hypnumpy.timedelta64_dtypes(), hypnumpy.datetime64_dtypes())),
shape=hypnumpy.array_shapes()))
def test_string_and_date_data_in_array(experiment, p_values):
p = qc.Parameter('p',
label='String parameter',
unit='',
get_cmd=None,
set_cmd=None,
initial_value=p_values)
meas = Measurement(experiment)
meas.register_parameter(p, paramtype='array')
with meas.run() as datasaver:
datasaver.add_result((p, p.get()))
actual_data = datasaver.dataset.get_parameter_data()["p"]["p"]
id="unsigned_integer_dtype",
),
pytest.param(
byte_string_dtypes(endianness="="), None, id="byte_string_dtype"),
pytest.param(
datetime64_dtypes(endianness="="),
None,
id="datetime64_dtype",
marks=[
pytest.mark.xfail(
reason="RIP-375 - Categorical unsupported dtypes"),
pytest.mark.skip,
],
),
pytest.param(
timedelta64_dtypes(endianness="="),
None,
id="timedelta64_dtype",
marks=[
pytest.mark.xfail(
reason="RIP-375 - Categorical unsupported dtypes"),
pytest.mark.skip,
],
),
pytest.param(
unicode_string_dtypes(endianness="="),
None,
id="unicode_string_dtype",
marks=[
pytest.mark.xfail(
reason="RIP-375 - Categorical unsupported dtypes"),
expec_data = np.array([item for item in list_of_strings])
actual_data = test_set.get_parameter_data()["p"]["p"]
try:
np.testing.assert_array_equal(actual_data, expec_data)
finally:
test_set.conn.close()
@settings(suppress_health_check=(HealthCheck.function_scoped_fixture,))
@given(
p_values=hypnumpy.arrays(
dtype=hst.sampled_from(
(hypnumpy.unicode_string_dtypes(),
hypnumpy.byte_string_dtypes(),
hypnumpy.timedelta64_dtypes(),
hypnumpy.datetime64_dtypes())
),
shape=hypnumpy.array_shapes()
)
)
def test_string_and_date_data_in_array(experiment, p_values):
p = qc.Parameter('p', label='String parameter', unit='', get_cmd=None,
set_cmd=None, initial_value=p_values)
meas = Measurement(experiment)
meas.register_parameter(p, paramtype='array')
with meas.run() as datasaver:
datasaver.add_result((p, p.get()))
actual_data = datasaver.dataset.get_parameter_data()["p"]["p"]