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import pytest
import numpy as np
import pandas as pd
from datetime import datetime
from _plotly_utils.basevalidators import (
NumberValidator,
IntegerValidator,
DataArrayValidator,
ColorValidator,
)
@pytest.fixture
def data_array_validator(request):
return DataArrayValidator("prop", "parent")
@pytest.fixture
def integer_validator(request):
return IntegerValidator("prop", "parent", array_ok=True)
@pytest.fixture
def number_validator(request):
return NumberValidator("prop", "parent", array_ok=True)
@pytest.fixture
def color_validator(request):
return ColorValidator("prop", "parent", array_ok=True, colorscale_path="")
@pytest.fixture(
params=[
"int8",
"int16",
"int32",
"int64",
"uint8",
"uint16",
"uint32",
"uint64",
"float16",
"float32",
"float64",
]
)
def numeric_dtype(request):
return request.param
@pytest.fixture(params=[pd.Series, pd.Index])
def pandas_type(request):
return request.param
@pytest.fixture
def numeric_pandas(request, pandas_type, numeric_dtype):
return pandas_type(np.arange(10), dtype=numeric_dtype)
@pytest.fixture
def color_object_pandas(request, pandas_type):
return pandas_type(["blue", "green", "red"] * 3, dtype="object")
@pytest.fixture
def color_categorical_pandas(request, pandas_type):
return pandas_type(pd.Categorical(["blue", "green", "red"] * 3))
@pytest.fixture
def dates_array(request):
return np.array(
[
datetime(year=2013, month=10, day=10),
datetime(year=2013, month=11, day=10),
datetime(year=2013, month=12, day=10),
datetime(year=2014, month=1, day=10),
datetime(year=2014, month=2, day=10),
]
)
@pytest.fixture
def datetime_pandas(request, pandas_type, dates_array):
return pandas_type(dates_array)
def test_numeric_validator_numeric_pandas(number_validator, numeric_pandas):
res = number_validator.validate_coerce(numeric_pandas)
# Check type
assert isinstance(res, np.ndarray)
# Check dtype
assert res.dtype == numeric_pandas.dtype
# Check values
np.testing.assert_array_equal(res, numeric_pandas)
def test_integer_validator_numeric_pandas(integer_validator, numeric_pandas):
res = integer_validator.validate_coerce(numeric_pandas)
# Check type
assert isinstance(res, np.ndarray)
# Check dtype
if numeric_pandas.dtype.kind in ("u", "i"):
# Integer and unsigned integer dtype unchanged
assert res.dtype == numeric_pandas.dtype
else:
# Float datatypes converted to default integer type of int32
assert res.dtype == "int32"
# Check values
np.testing.assert_array_equal(res, numeric_pandas)
def test_data_array_validator(data_array_validator, numeric_pandas):
res = data_array_validator.validate_coerce(numeric_pandas)
# Check type
assert isinstance(res, np.ndarray)
# Check dtype
assert res.dtype == numeric_pandas.dtype
# Check values
np.testing.assert_array_equal(res, numeric_pandas)
def test_color_validator_numeric(color_validator, numeric_pandas):
res = color_validator.validate_coerce(numeric_pandas)
# Check type
assert isinstance(res, np.ndarray)
# Check dtype
assert res.dtype == numeric_pandas.dtype
# Check values
np.testing.assert_array_equal(res, numeric_pandas)
def test_color_validator_object(color_validator, color_object_pandas):
res = color_validator.validate_coerce(color_object_pandas)
# Check type
assert isinstance(res, np.ndarray)
# Check dtype
assert res.dtype == "object"
# Check values
np.testing.assert_array_equal(res, color_object_pandas)
def test_color_validator_categorical(color_validator, color_categorical_pandas):
res = color_validator.validate_coerce(color_categorical_pandas)
# Check type
assert color_categorical_pandas.dtype == "category"
assert isinstance(res, np.ndarray)
# Check dtype
assert res.dtype == "object"
# Check values
np.testing.assert_array_equal(res, np.array(color_categorical_pandas))
def test_data_array_validator_dates_series(
data_array_validator, datetime_pandas, dates_array
):
res = data_array_validator.validate_coerce(datetime_pandas)
# Check type
assert isinstance(res, np.ndarray)
# Check dtype
assert res.dtype == "object"
# Check values
np.testing.assert_array_equal(res, dates_array)
def test_data_array_validator_dates_dataframe(
data_array_validator, datetime_pandas, dates_array
):
df = pd.DataFrame({"d": datetime_pandas})
res = data_array_validator.validate_coerce(df)
# Check type
assert isinstance(res, np.ndarray)
# Check dtype
assert res.dtype == "object"
# Check values
np.testing.assert_array_equal(res, dates_array.reshape(len(dates_array), 1))
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