def __init__(
self,
data: typing.Union[DataFrame, DataSource],
layout: typing.Union[str, typing.List[typing.List[str]]] = "default",
categorical_columns: typing.Union[typing.List[str], None] = None,
row_height: typing.Union[int, typing.List[int]] = 400,
sample: typing.Union[float, int, None] = None,
select_color: typing.Union[str, typing.Tuple[int, int, int]] = "#323EEC",
deselect_color: typing.Union[str, typing.Tuple[int, int, int]] = "#8A8C93",
alpha: float = 0.75,
seed: typing.Union[int, None] = None,
):
"""
:param data: A pandas.DataFrame object or a :class:`DataSource`.
:param layout: Layout specification name or explicit definition of widget names in rows.
Those columns have to include all columns of the DataFrame
which have type `object`, `str`, `bool` or `category`.
This means it can only add columns which do not have the aforementioned types.
Defaults to 'default'.
:param categorical_columns: If given, specifies which columns are to be interpreted as categorical.
Defaults to None.
:param row_height: Height in pixels each row should have. If given an integer, each row has the height
specified by that value, if given a list of integers, each value in the list specifies the height of
the corresponding row.
Defaults to 400.
:param sample: Int or float value specifying if the DataFrame should be sub-sampled.
When an int is given, the DataFrame will be limited to that number of rows given by the value.
When a float is given, the DataFrame will include the fraction of rows given by the value.
Defaults to None.
:param select_color: RGB tuple or hex color specifying the color display selected data points.
Values in the tuple have to be between 0 and 255 inclusive or a hex string that converts to
such RGB values.
Defaults to '#323EEC'.
:param deselect_color: RGB tuple or hex color specifying the color display deselected data points.
Values in the tuple have to be between 0 and 255 inclusive or a hex string that converts to
such RGB values.
Defaults to '#8A8C93'.
:param alpha: Opacity of data points when applicable ranging from 0.0 to 1.0 inclusive. Defaults to 0.75.
:param seed: Random seed used for sampling the data.
Values can be any integer between 0 and 2**32 - 1 inclusive or None.
Defaults to None.
"""
super().__init__()
validate.validate_data(data)
validate.validate_alpha(alpha)
validate.validate_color(select_color)
validate.validate_color(deselect_color)
if isinstance(select_color, str):
self.select_color: typing.Tuple[int, int, int] = hex_to_rgb(select_color)
elif isinstance(select_color, tuple):
self.select_color: typing.Tuple[int, int, int] = select_color
if isinstance(deselect_color, str):
self.deselect_color: typing.Tuple[int, int, int] = hex_to_rgb(
deselect_color
)
elif isinstance(deselect_color, tuple):
self.deselect_color: typing.Tuple[int, int, int] = deselect_color
self.alpha = alpha
self.color_scale = [
[0, "rgb(%d,%d,%d)" % self.deselect_color],
[1, "rgb(%d,%d,%d)" % self.select_color],
]
config = Config()
config["alpha"] = self.alpha
config["select_color"] = self.select_color
config["deselect_color"] = self.deselect_color
config["color_scale"] = self.color_scale
if isinstance(data, DataFrame):
self.data_source = DataSource(
df=data,
categorical_columns=categorical_columns,
sample=sample,
seed=seed,
)
elif isinstance(data, DataSource):
self.data_source = data
self.layout = AnalysisLayout(
layout=layout, row_height=row_height, data_source=self.data_source
)
if self.data_source.few_num_cols and len(self._check_numerical_plots()) != 0:
warnings.warn(
"The passed DataFrame only has %d NUMERICAL column, which is insufficient for some plots "
"like Parallel Coordinates. These plots will not be displayed."
% len(self.data_source.numerical_columns)
)
if self.data_source.few_cat_cols and len(self._check_categorical_plots()) != 0:
warnings.warn(
"The passed DataFrame only has %d CATEGORICAL column, which is insufficient for some plots "
"like Parallel Categories. These plots will not be displayed."
% len(self.data_source.numerical_columns)
)
def test_hex_to_rgb_error_random_input():
with pytest.raises(TypeError):
hex_to_rgb(2)
def test_hex_to_rgb_error_none():
with pytest.raises(TypeError):
hex_to_rgb(None)
def test_hex_to_rgb_error_non_hashtag():
with pytest.raises(ValueError):
hex_to_rgb("ffffff")
def test_hex_to_rgb_error_non_hex():
with pytest.raises(ValueError):
hex_to_rgb("#ff3X45")
def test_hex_to_rgb_error_some_text():
with pytest.raises(ValueError):
hex_to_rgb("asdfefasdffe")
def test_hex_to_rgb_uppercase():
assert hex_to_rgb("#F245E1") == (242, 69, 225)
def test_hex_to_rgb():
assert hex_to_rgb("#ffffff") == (255, 255, 255)
