318 lines
10 KiB
Python
318 lines
10 KiB
Python
from __future__ import annotations
|
|
from dataclasses import dataclass, fields, field
|
|
import textwrap
|
|
from typing import Any, Callable, Union
|
|
from collections.abc import Generator
|
|
|
|
import numpy as np
|
|
import pandas as pd
|
|
import matplotlib as mpl
|
|
|
|
from numpy import ndarray
|
|
from pandas import DataFrame
|
|
from matplotlib.artist import Artist
|
|
|
|
from seaborn._core.scales import Scale
|
|
from seaborn._core.properties import (
|
|
PROPERTIES,
|
|
Property,
|
|
RGBATuple,
|
|
DashPattern,
|
|
DashPatternWithOffset,
|
|
)
|
|
from seaborn._core.exceptions import PlotSpecError
|
|
|
|
|
|
class Mappable:
|
|
def __init__(
|
|
self,
|
|
val: Any = None,
|
|
depend: str | None = None,
|
|
rc: str | None = None,
|
|
auto: bool = False,
|
|
grouping: bool = True,
|
|
):
|
|
"""
|
|
Property that can be mapped from data or set directly, with flexible defaults.
|
|
|
|
Parameters
|
|
----------
|
|
val : Any
|
|
Use this value as the default.
|
|
depend : str
|
|
Use the value of this feature as the default.
|
|
rc : str
|
|
Use the value of this rcParam as the default.
|
|
auto : bool
|
|
The default value will depend on other parameters at compile time.
|
|
grouping : bool
|
|
If True, use the mapped variable to define groups.
|
|
|
|
"""
|
|
if depend is not None:
|
|
assert depend in PROPERTIES
|
|
if rc is not None:
|
|
assert rc in mpl.rcParams
|
|
|
|
self._val = val
|
|
self._rc = rc
|
|
self._depend = depend
|
|
self._auto = auto
|
|
self._grouping = grouping
|
|
|
|
def __repr__(self):
|
|
"""Nice formatting for when object appears in Mark init signature."""
|
|
if self._val is not None:
|
|
s = f"<{repr(self._val)}>"
|
|
elif self._depend is not None:
|
|
s = f"<depend:{self._depend}>"
|
|
elif self._rc is not None:
|
|
s = f"<rc:{self._rc}>"
|
|
elif self._auto:
|
|
s = "<auto>"
|
|
else:
|
|
s = "<undefined>"
|
|
return s
|
|
|
|
@property
|
|
def depend(self) -> Any:
|
|
"""Return the name of the feature to source a default value from."""
|
|
return self._depend
|
|
|
|
@property
|
|
def grouping(self) -> bool:
|
|
return self._grouping
|
|
|
|
@property
|
|
def default(self) -> Any:
|
|
"""Get the default value for this feature, or access the relevant rcParam."""
|
|
if self._val is not None:
|
|
return self._val
|
|
elif self._rc is not None:
|
|
return mpl.rcParams.get(self._rc)
|
|
|
|
|
|
# TODO where is the right place to put this kind of type aliasing?
|
|
|
|
MappableBool = Union[bool, Mappable]
|
|
MappableString = Union[str, Mappable]
|
|
MappableFloat = Union[float, Mappable]
|
|
MappableColor = Union[str, tuple, Mappable]
|
|
MappableStyle = Union[str, DashPattern, DashPatternWithOffset, Mappable]
|
|
|
|
|
|
@dataclass
|
|
class Mark:
|
|
"""Base class for objects that visually represent data."""
|
|
|
|
artist_kws: dict = field(default_factory=dict)
|
|
|
|
@property
|
|
def _mappable_props(self):
|
|
return {
|
|
f.name: getattr(self, f.name) for f in fields(self)
|
|
if isinstance(f.default, Mappable)
|
|
}
|
|
|
|
@property
|
|
def _grouping_props(self):
|
|
# TODO does it make sense to have variation within a Mark's
|
|
# properties about whether they are grouping?
|
|
return [
|
|
f.name for f in fields(self)
|
|
if isinstance(f.default, Mappable) and f.default.grouping
|
|
]
|
|
|
|
# TODO make this method private? Would extender every need to call directly?
|
|
def _resolve(
|
|
self,
|
|
data: DataFrame | dict[str, Any],
|
|
name: str,
|
|
scales: dict[str, Scale] | None = None,
|
|
) -> Any:
|
|
"""Obtain default, specified, or mapped value for a named feature.
|
|
|
|
Parameters
|
|
----------
|
|
data : DataFrame or dict with scalar values
|
|
Container with data values for features that will be semantically mapped.
|
|
name : string
|
|
Identity of the feature / semantic.
|
|
scales: dict
|
|
Mapping from variable to corresponding scale object.
|
|
|
|
Returns
|
|
-------
|
|
value or array of values
|
|
Outer return type depends on whether `data` is a dict (implying that
|
|
we want a single value) or DataFrame (implying that we want an array
|
|
of values with matching length).
|
|
|
|
"""
|
|
feature = self._mappable_props[name]
|
|
prop = PROPERTIES.get(name, Property(name))
|
|
directly_specified = not isinstance(feature, Mappable)
|
|
return_multiple = isinstance(data, pd.DataFrame)
|
|
return_array = return_multiple and not name.endswith("style")
|
|
|
|
# Special case width because it needs to be resolved and added to the dataframe
|
|
# during layer prep (so the Move operations use it properly).
|
|
# TODO how does width *scaling* work, e.g. for violin width by count?
|
|
if name == "width":
|
|
directly_specified = directly_specified and name not in data
|
|
|
|
if directly_specified:
|
|
feature = prop.standardize(feature)
|
|
if return_multiple:
|
|
feature = [feature] * len(data)
|
|
if return_array:
|
|
feature = np.array(feature)
|
|
return feature
|
|
|
|
if name in data:
|
|
if scales is None or name not in scales:
|
|
# TODO Might this obviate the identity scale? Just don't add a scale?
|
|
feature = data[name]
|
|
else:
|
|
scale = scales[name]
|
|
value = data[name]
|
|
try:
|
|
feature = scale(value)
|
|
except Exception as err:
|
|
raise PlotSpecError._during("Scaling operation", name) from err
|
|
|
|
if return_array:
|
|
feature = np.asarray(feature)
|
|
return feature
|
|
|
|
if feature.depend is not None:
|
|
# TODO add source_func or similar to transform the source value?
|
|
# e.g. set linewidth as a proportion of pointsize?
|
|
return self._resolve(data, feature.depend, scales)
|
|
|
|
default = prop.standardize(feature.default)
|
|
if return_multiple:
|
|
default = [default] * len(data)
|
|
if return_array:
|
|
default = np.array(default)
|
|
return default
|
|
|
|
def _infer_orient(self, scales: dict) -> str: # TODO type scales
|
|
|
|
# TODO The original version of this (in seaborn._base) did more checking.
|
|
# Paring that down here for the prototype to see what restrictions make sense.
|
|
|
|
# TODO rethink this to map from scale type to "DV priority" and use that?
|
|
# e.g. Nominal > Discrete > Continuous
|
|
|
|
x = 0 if "x" not in scales else scales["x"]._priority
|
|
y = 0 if "y" not in scales else scales["y"]._priority
|
|
|
|
if y > x:
|
|
return "y"
|
|
else:
|
|
return "x"
|
|
|
|
def _plot(
|
|
self,
|
|
split_generator: Callable[[], Generator],
|
|
scales: dict[str, Scale],
|
|
orient: str,
|
|
) -> None:
|
|
"""Main interface for creating a plot."""
|
|
raise NotImplementedError()
|
|
|
|
def _legend_artist(
|
|
self, variables: list[str], value: Any, scales: dict[str, Scale],
|
|
) -> Artist | None:
|
|
|
|
return None
|
|
|
|
|
|
def resolve_properties(
|
|
mark: Mark, data: DataFrame, scales: dict[str, Scale]
|
|
) -> dict[str, Any]:
|
|
|
|
props = {
|
|
name: mark._resolve(data, name, scales) for name in mark._mappable_props
|
|
}
|
|
return props
|
|
|
|
|
|
def resolve_color(
|
|
mark: Mark,
|
|
data: DataFrame | dict,
|
|
prefix: str = "",
|
|
scales: dict[str, Scale] | None = None,
|
|
) -> RGBATuple | ndarray:
|
|
"""
|
|
Obtain a default, specified, or mapped value for a color feature.
|
|
|
|
This method exists separately to support the relationship between a
|
|
color and its corresponding alpha. We want to respect alpha values that
|
|
are passed in specified (or mapped) color values but also make use of a
|
|
separate `alpha` variable, which can be mapped. This approach may also
|
|
be extended to support mapping of specific color channels (i.e.
|
|
luminance, chroma) in the future.
|
|
|
|
Parameters
|
|
----------
|
|
mark :
|
|
Mark with the color property.
|
|
data :
|
|
Container with data values for features that will be semantically mapped.
|
|
prefix :
|
|
Support "color", "fillcolor", etc.
|
|
|
|
"""
|
|
color = mark._resolve(data, f"{prefix}color", scales)
|
|
|
|
if f"{prefix}alpha" in mark._mappable_props:
|
|
alpha = mark._resolve(data, f"{prefix}alpha", scales)
|
|
else:
|
|
alpha = mark._resolve(data, "alpha", scales)
|
|
|
|
def visible(x, axis=None):
|
|
"""Detect "invisible" colors to set alpha appropriately."""
|
|
# TODO First clause only needed to handle non-rgba arrays,
|
|
# which we are trying to handle upstream
|
|
return np.array(x).dtype.kind != "f" or np.isfinite(x).all(axis)
|
|
|
|
# Second check here catches vectors of strings with identity scale
|
|
# It could probably be handled better upstream. This is a tricky problem
|
|
if np.ndim(color) < 2 and all(isinstance(x, float) for x in color):
|
|
if len(color) == 4:
|
|
return mpl.colors.to_rgba(color)
|
|
alpha = alpha if visible(color) else np.nan
|
|
return mpl.colors.to_rgba(color, alpha)
|
|
else:
|
|
if np.ndim(color) == 2 and color.shape[1] == 4:
|
|
return mpl.colors.to_rgba_array(color)
|
|
alpha = np.where(visible(color, axis=1), alpha, np.nan)
|
|
return mpl.colors.to_rgba_array(color, alpha)
|
|
|
|
# TODO should we be implementing fill here too?
|
|
# (i.e. set fillalpha to 0 when fill=False)
|
|
|
|
|
|
def document_properties(mark):
|
|
|
|
properties = [f.name for f in fields(mark) if isinstance(f.default, Mappable)]
|
|
text = [
|
|
"",
|
|
" This mark defines the following properties:",
|
|
textwrap.fill(
|
|
", ".join([f"|{p}|" for p in properties]),
|
|
width=78, initial_indent=" " * 8, subsequent_indent=" " * 8,
|
|
),
|
|
]
|
|
|
|
docstring_lines = mark.__doc__.split("\n")
|
|
new_docstring = "\n".join([
|
|
*docstring_lines[:2],
|
|
*text,
|
|
*docstring_lines[2:],
|
|
])
|
|
mark.__doc__ = new_docstring
|
|
return mark
|