# -*- coding: utf-8 -*-
"""
PlotUtils Module
================
Plotting routines for ClearMap based on matplotlib.
Note
----
This module is using matplotlib.
"""
__author__ = 'Christoph Kirst <christoph.kirst.ck@gmail.com>'
__license__ = 'GPLv3 - GNU General Pulic License v3 (see LICENSE)'
__copyright__ = 'Copyright © 2020 by Christoph Kirst'
__webpage__ = 'http://idisco.info'
__download__ = 'http://www.github.com/ChristophKirst/ClearMap2'
import math
import numpy as np
import matplotlib as mpl; # analysis:ignore
import matplotlib.pyplot as plt
import scipy.stats as st
###############################################################################
### Density and Countour plots
###############################################################################
[docs]def plot_density(points, plot_points=True, plot_contour=True, plot_contour_lines=10, n_bins = 100, color = None, cmap = plt.cm.gray, xlim = None, ylim = None, verbose = False):
"""Plot point distributions."""
if xlim is None:
xlim = (np.min(points[:,0]), np.max(points[:,0]));
if ylim is None:
ylim = (np.min(points[:,1]), np.max(points[:,1]));
xmin,xmax = xlim;
ymin,ymax = ylim;
# kernel density estimates
dx = float(xmax-xmin) / n_bins;
dy = float(ymax-ymin) / n_bins;
xx, yy = np.mgrid[xmin:xmax:dx, ymin:ymax:dy]
positions = np.vstack([xx.ravel(), yy.ravel()])
kernel = st.gaussian_kde(points.T);
if verbose:
print('plot_density: kernel estimation done.');
density = kernel(positions)
density = np.reshape(density, xx.shape)
if verbose:
print('plot_density: density estimation done.');
# figure setup
ax = plt.gca()
ax.set_xlim(xmin, xmax)
ax.set_ylim(ymin, ymax)
# contour plot
if plot_contour:
cfset = ax.contourf(xx, yy, density, cmap=cmap);
if plot_points:
plt.scatter(points[:, 0], points[:, 1], c=color, cmap=cmap, s=1)
if plot_contour_lines is not None:
cset = ax.contour(xx, yy, density, plot_contour_lines, cmap=cmap)
return (kernel, (xx,yy,density))
[docs]def plot_curve(coordinates, **kwargs):
"""Plot a curve in 3d.
Arguments
---------
coordinates : nx3 array.
The coordinates of the curve.
kwargs
Matplotlib parameter.
Returns
-------
ax : ax
3d axes object.
"""
from mpl_toolkits.mplot3d import Axes3D # analysis:ignore
ax = plt.gca(projection='3d');
x,y,z = coordinates.T;
ax.plot(x, y, z, **kwargs)
return ax;
[docs]def subplot_tiling(n, tiling = None):
"""Finds a good tiling to arrange subplots.
Arguments
---------
n : int
Number of subplots.
tiling : None, 'automatic, int or tuple
The tiling to use. If None or 'automatic' calculate automatically.
If number use this for the number of subplots along the horizontal axis.
If tuple, (nx,ny) nx and ny can be numbes or None to indicate the number
of sub-plots in each axis. Iif one of them is None, it will be
determined automatically to fit the total number of plots.
Returns
-------
tiling : tuple of int
The subplot tiling.
"""
if tiling is None:
tiling = 'automatic';
if tiling == "automatic":
nx = math.floor(math.sqrt(n));
ny = int(math.ceil(n / nx));
nx = int(nx);
else:
if not isinstance(tiling, tuple):
tiling = (tiling,None);
if tiling[0] is None:
ny = tiling[1];
if ny is None:
return subplot_tiling(n);
nx = int(math.ceil(n / ny));
if tiling[1] is None:
nx = tiling[0];
if nx is None:
return subplot_tiling(n);
ny = int(math.ceil(n / nx));
return (nx,ny);
Source
