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plotting.py
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#!/usr/bin/python
# -*- coding: utf-8 -*-
import numpy as np
import matplotlib.pyplot as plt
## Custom colors for k = 9 solution used in publication
nine_colors = [(0.89411765336990356, 0.10196078568696976, 0.10980392247438431),
(0.65845446095747107, 0.34122261685483596, 0.1707958535236471),
(1.0, 0.50591311045721465, 0.0031372549487095253),
(0.21602460800432691, 0.49487120380588606, 0.71987698697576341),
(0.30426760128900115, 0.68329106055054012, 0.29293349969620797),
(0.400002384185791, 0.4000002384185791, 0.40000002384185791),
(0.60083047361934883, 0.30814303335021526, 0.63169552298153153),
(0.99850826852461868, 0.60846600392285513, 0.8492888871361229),
(0.99315647868549117, 0.9870049982678657, 0.19915417450315812)]
def plot_clf_polar(importances, palette=None, mask=None, **kwargs):
""" Make polar plot for classificaiton results.
importances - formatted importances
palette - Colors to use for each region
mask - List of which regions to include, by default uses all """
import pandas as pd
import seaborn as sns
if mask is not None:
importances = importances[importances.region.isin(mask)]
pplot = pd.pivot_table(importances, values='importance', index='feature', columns=['region'])
if palette is None:
palette = sns.color_palette('Set1', importances.region.unique().shape[0])
if mask is not None:
palette = [n[0] for n in sorted(zip(np.array(palette)[np.array(mask)-1], mask), key=lambda tup: tup[1])]
return plot_polar(pplot, overplot=True, palette=palette, **kwargs)
def plot_polar(data, n_top=3, selection='top', overplot=False, labels=None,
palette='husl', reorder=False, method='weighted', metric='correlation',
label_size=26, threshold=None, max_val=None,
alpha_level=1, legend=False, error_bars=None,):
""" Make a polar plot
data - Tabular data of shape features x classes
n_top - Number of features to select
selection - Selection method to use `
(top = M strongest for each class; std = N with greatest std across all)
overplot - Overlap plots for each class?
labels - Subset of features to use (overrides auto selection by n_top)
palette - Color palette to use (can be label or list of colors from seaborn)
reorder - If True, uses hierarchical clustering to reorder axis
method - Method to use for clustering
metric - Metric to use for clustering
label_size - X axis label size
threshold - Value to draw an optional line that denotes significance threshold
max_val - Maximum value of y axis
alpha_level - transparency value for lines
legend - Show legend?
error_bars - Option bootstrapped data to draw error bars """
n_panels = data.shape[1]
if labels is None:
if selection == 'top':
labels = []
for i in range(n_panels):
labels.extend(data.iloc[:, i].sort_values(ascending=False) \
.index[:n_top])
labels = np.unique(labels)
elif selection == 'std':
labels = data.T.std().sort_values(ascending=False).index[:n_top]
data = data.loc[labels,:]
else:
data = data.loc[labels,:]
if error_bars is not None:
error_bars = error_bars.loc[labels,:]
if reorder is True:
# Use hierarchical clustering to order
from scipy.spatial.distance import pdist
from scipy.cluster.hierarchy import linkage, leaves_list
dists = pdist(data, metric=metric)
pairs = linkage(dists, method=method)
pairs[pairs < 0] = 0
order = leaves_list(pairs)
data = data.iloc[order,:]
if error_bars is not None:
error_bars = error_bars.iloc[order,:]
labels = [labels[i] for i in order]
theta = np.linspace(0.0, 2 * np.pi, len(labels), endpoint=False)
## Add first
theta = np.concatenate([theta, [theta[0]]])
if overplot:
fig, ax = plt.subplots(1, 1, subplot_kw=dict(polar=True))
fig.set_size_inches(10, 10)
else:
fig, axes = plt.subplots(n_panels, 1, sharex=False, sharey=False,
subplot_kw=dict(polar=True))
fig.set_size_inches((6, 6 * n_panels))
if isinstance(palette, str):
from seaborn import color_palette
colors = color_palette(palette, n_panels)
else:
colors = palette
for i in range(n_panels):
if overplot:
alpha = 0.025
else:
ax = axes[i]
alpha = 0.8
if max_val is None:
if error_bars is not None:
max_val = data.values.max() + error_bars.values.max() + data.values.max() * .02
else:
max_val = data.values.max()
ax.set_ylim(data.values.min(), max_val)
d = data.iloc[:,i].values
d = np.concatenate([d, [d[0]]])
name = data.columns[i]
if error_bars is not None:
e = error_bars.iloc[:,i].values
e = np.concatenate([e, [e[0]]])
else:
e = None
if error_bars is not None:
ax.errorbar(theta, d, yerr=e, capsize=0, color=colors[i], elinewidth = 3, linewidth=0)
else:
ax.plot(theta, d, alpha=alpha_level - 0.1, color=colors[i], linewidth=8, label=name)
ax.fill(theta, d, ec='k', alpha=alpha, color=colors[i], linewidth=8)
ax.set_xticks(theta)
ax.set_rlabel_position(11.12)
ax.set_xticklabels(labels, fontsize=label_size)
[lab.set_fontsize(22) for lab in ax.get_yticklabels()]
if threshold is not None:
theta = np.linspace(0.0, 2 * np.pi, 999, endpoint=False)
theta = np.concatenate([theta, [theta[0]]])
d = np.array([threshold] * 1000)
ax.plot(theta, d, alpha=1, color='black', linewidth=2, linestyle='--')
if legend is True:
ax.legend(bbox_to_anchor=(1.15, 1.1))
plt.tight_layout()
return labels, data, fig
def make_thresholded_slices(regions, colors, display_mode='z', overplot=True, binarize=True, **kwargs):
""" Plots on axial slices numerous images
regions: Nibabel images
colors: List of colors (rgb tuples)
overplot: Overlay images?
binarize: Binarize images or plot full stat maps
"""
from matplotlib.colors import LinearSegmentedColormap
from nilearn import plotting as niplt
if binarize:
for reg in regions:
reg.get_data()[reg.get_data().nonzero()] = 1
for i, reg in enumerate(regions):
reg_color = LinearSegmentedColormap.from_list('reg1', [colors[i], colors[i]])
if i == 0:
plot = niplt.plot_stat_map(reg, draw_cross=False, display_mode=display_mode, cmap = reg_color, alpha=0.9, colorbar=False, **kwargs)
else:
if overplot:
plot.add_overlay(reg, cmap = reg_color, alpha=.72)
else:
plt.plot_stat_map(reg, draw_cross=False, display_mode=display_mode, cmap = reg_color, colorbar=False, **kwargs)
return plot