# -*- coding: utf-8 -*-
from dataclasses import dataclass
from typing import List
from typing import Optional
import matplotlib.colors as clr
import numpy as np
from nata.plots.types import BasePlot
@dataclass
class ScatterPlot(BasePlot):
"""Color plot class.
Parameters
----------
s: ``float``, optional
Marker size in in points**2. If not provided, defaults to ``0.1``
c: ``str``, optional
Color of the markers. See :mod:`matplotlib.colors` for available
options.
marker: ``str``, optional
Marker style. See :mod:`matplotlib.markers` for available options.
alpha: ``float``, optional
Marker alpha value. Must be between ``0`` and ``1``.
vmin: ``float``, optional
Minimum of the colorbar axis. If not provided, it is
inferred from the dataset represented in the plot.
vmax: ``float``, optional
Same as ``vmin`` for the maximum of the colorbar axis.
cb_title: ``str``, optional
Colorbar title. If not provided, it is inferred from the dataset
represented in the plot.
cb_scale: ``{'linear','log', 'symlog'}``, optional
Scale of the colorbar. If not provided, defaults to ``'linear'``.
cb_map: ``str``, optional
Colormap used to represent the data. See
:func:`matplotlib.pyplot.colormaps` for available options. If not
provided, defaults to ``rainbow``.
cb_linthresh: ``float``, optional
Range within which the colorbar axis is linear. Applicable only when
``cb_scale`` is set to ``'symlog'``. If not provided, defaults to
``1e-5``.
Notes
-----
All colorbar parameters are only applicable if the dataset represented in
the plot has a quantity to be represented in color. In this case, ``c`` is
overriden if set.
"""
s: Optional[float] = 0.1
c: Optional[str] = None
marker: Optional[str] = None
alpha: Optional[float] = None
vmin: Optional[float] = None
vmax: Optional[float] = None
cb_map: Optional[str] = "rainbow"
cb_scale: Optional[str] = "linear"
cb_linthresh: Optional[float] = 1e-5
cb_title: Optional[str] = None
def __post_init__(self):
if self.cb_title is None and len(self.data.axes) > 2:
self.cb_title = self.data.axes[2].get_label(units=True)
if self.has_cb:
self.c = self.data.data[2]
super().__post_init__()
@property
def has_cb(self):
return len(self.data.axes) > 2
def _default_xlim(self):
return (np.min(self.data.data[0]), np.max(self.data.data[0]))
def _default_ylim(self):
return (np.min(self.data.data[1]), np.max(self.data.data[1]))
def _default_xlabel(self, units=True):
return self.data.axes[0].get_label(units)
def _default_ylabel(self, units=True):
return self.data.axes[1].get_label(units)
def _default_title(self):
return self.data.get_time_label()
def _default_label(self):
return self.data.get_label(units=False)
def _xunits(self):
return f"${self.data.axes[0].units}$" if self.data.axes[0].units else ""
def _yunits(self):
return f"${self.data.axes[1].units}$" if self.data.axes[1].units else ""
def build_canvas(self):
# get plot axes and data
x = self.data.data[0]
y = self.data.data[1]
# build color map norm
if self.cb_scale == "log":
self.cb_norm = clr.LogNorm(vmin=self.vmin, vmax=self.vmax)
elif self.cb_scale == "symlog":
self.cb_norm = clr.SymLogNorm(
vmin=self.vmin,
vmax=self.vmax,
linthresh=self.cb_linthresh,
base=10,
)
else:
self.cb_norm = clr.Normalize(vmin=self.vmin, vmax=self.vmax)
# build plot
self.h = self.axes.ax.scatter(
x,
y,
s=self.s,
c=self.c,
marker=self.marker,
alpha=self.alpha,
label=self.label,
cmap=self.cb_map,
norm=self.cb_norm
# antialiased=self.antialiased,
)
if self.has_cb:
self.axes.init_colorbar(plot=self)
@classmethod
def style_attrs(cls) -> List[str]:
return [
"s",
"c",
"marker",
"alpha",
"vmin",
"vmax",
"cb_map",
"cb_scale",
"cb_linthresh",
"cb_title",
] + BasePlot.style_attrs()
