I just found some code (http://www.onerussian.com/tmp/plots.py and
pasted below for review/feedback) laying around which I wrote around
matplotlib for plotting primarily pair-wise stats results. Here is a
demonstration:
http://nbviewer.ipython.org/url/www.onerussian.com/tmp/run_plots.ipynb
I wonder if there is a need/place for it in matplotlib and what changes would
you advise. Sorry for the lack of documentation -- I guess I have not finished
it at that point (scipy dependency can easily be dropped, used only for
standard error function iirc):
#!/usr/bin/python
#emacs: -*- mode: python-mode; py-indent-offset: 4; tab-width: 4; indent-tabs-mode: nil -*-
#ex: set sts=4 ts=4 sw=4 noet:
#------------------------- =+- Python script -+= -------------------------
"""
@file paired-plots.py
@date Fri Jan 13 11:48:00 2012
@brief
Yaroslav Halchenko Dartmouth
web: http://www.onerussian.com College
e-mail: yoh@...825... ICQ#: 60653192
DESCRIPTION (NOTES):
COPYRIGHT: Yaroslav Halchenko 2012
LICENSE: MIT
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in
all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
THE SOFTWARE.
"""
#-----------------\____________________________________/------------------
__author__ = 'Yaroslav Halchenko'
__revision__ = '$Revision: $'
__date__ = '$Date: $'
__copyright__ = 'Copyright (c) 2012 Yaroslav Halchenko'
__license__ = 'MIT'
import numpy as np
import pylab as pl
import scipy.stats as ss
def plot_boxplot_enhanced(
v,
contrast_labels=None,
condition_labels=None,
ccolors=['y', 'b'],
rand_offsets=None,
grid=True,
xticks_rotation=0,
**bp_kwargs):
width = bp_kwargs.get('width', 0.5)
pl.boxplot(v, **bp_kwargs)
if v.ndim < 2: v = v[:, None]
ncol = v.shape[1]
eff = np.mean(v, axis=0) # effect sizes
sem = ss.sem(v, axis=0)
if rand_offsets is None:
rand_offsets = np.random.randn(len(v)) * 0.02
pl.plot((np.arange(ncol) + 1)[:, None] + rand_offsets,
v.T, '.', color='k', markerfacecolor='k')
for i in range(ncol):
lw = 2
pl.plot([1 - width/2. + i, 1+i],
[0, 0],
'--', color=ccolors[0], linewidth=lw) # first condition
pl.plot([1+i, 1 + width/2. +i],
[eff[i]]*2,
'--', color=ccolors[1], linewidth=lw)
# place ste
pl.errorbar(i+1 + 1.1*width/2.,
eff[i],
sem[i],
elinewidth=2, linewidth=0,
color='r', ecolor='r')
if contrast_labels and not i: # only for the first one
pl.text(1 - 1.1*width/2 + i, 0.1, contrast_labels[0],
verticalalignment='bottom',
horizontalalignment='right')
pl.text(1 + 1.2*width/2 + i, eff[i], contrast_labels[1],
verticalalignment='bottom', horizontalalignment='left')
ax = pl.gca()
if condition_labels:
ax.set_xticklabels(condition_labels, rotation=xticks_rotation)
else:
# hide it
ax.axes.xaxis.set_visible(False)
if grid:
ax.grid()
return ax
def plot_paired_stats(
v0, v1, contrast_labels,
condition_labels=None,
style=['barplot_effect',
'boxplot_raw',
'boxplot_effect'],
ccolors=['y', 'g'],
xticks_rotation=0,
grid=False,
fig=None,
bottom_adjust=None,
bp_kwargs={}):
if isinstance(style, str):
style = [style]
nplots = len(style) # how many subplots will be needed
# assure having 2nd dimension
if v0.ndim < 2: v0 = v0[:, None]
if v1.ndim < 2: v1 = v1[:, None]
assert(v0.shape == v1.shape)
ncol = v0.shape[1]
v10 = (v1 - v0) # differences
mv0 = np.mean(v0, axis=0) # means
mv1 = np.mean(v1, axis=0)
eff = np.mean(v10, axis=0) # effect sizes
sem = ss.sem(v10, axis=0)
# so that data points have are distinguishable
rand_offsets = np.random.randn(len(v10)) * 0.02
# interleaved combination for some plots
v_ = np.hstack((v0, v1))
v = np.zeros(v_.shape, dtype=v_.dtype)
v[:, np.hstack((np.arange(0, ncol*2, 2),
np.arange(1, ncol*2, 2)))] = v_
#print v.shape
#print np.mean(v0, axis=0), np.mean(v1, axis=0)
#print np.min(v10, axis=0), np.max(v10, axis=0), \
# np.mean(v10, axis=0), ss.sem(v10, axis=0)
#pl.boxplot(v10 + np.mean(v1), notch=1, widths=0.05)
#print v0.shape, v1.shape, np.hstack([v0, v1]).shape
if fig is None:
fig = pl.figure()
bwidth = 0.5
plot = 1
if condition_labels:
xlabels = [ '%s:%s' % (cond, contr)
for cond in condition_labels
for contr in contrast_labels ]
else:
xlabels = contrast_labels
bp_kwargs_ = {
#'bootstrap': 0,
'notch' : 1
}
bp_kwargs_.update(bp_kwargs)
def plot_grid(ax):
if grid:
ax.grid()
if 'barplot_effect' in style:
if len(style) > 1:
pl.subplot(1, nplots, plot)
plot += 1
# The simplest one
pl.bar(np.arange(1, ncol*2+1) - bwidth/2,
np.mean(v, axis=0),
color=ccolors*ncol,
edgecolor=ccolors*ncol,
alpha=0.8,
width=bwidth)
#pl.minorticks_off()
pl.tick_params('x', direction='out', length=6, width=1,
top=False)
ax = pl.gca()
pl.xlim(0.5, ncol*2+0.5)
ax.set_xticks(np.arange(1, ncol*2+1))
ax.set_xticklabels(xlabels, rotation=xticks_rotation)
# place ste for effect size into the 2nd column
pl.errorbar(np.arange(ncol)*2+2,
mv1,
sem, elinewidth=2, linewidth=0,
color='g', ecolor='r')
plot_grid(ax)
if 'boxplot_raw' in style:
if len(style) > 1:
pl.subplot(1, nplots, plot)
plot += 1
# Figure 1 -- "raw" data
# plot "connections" between boxplots
for i in range(ncol):
pargs = (np.array([i*2+1, i*2+2])[:, None] + rand_offsets,
np.array([v0[:,i], v1[:,i]]))
pl.plot(*(pargs+('-',)), color='k', alpha=0.5, linewidth=0.25)
pl.plot(*(pargs+('.',)), color='k', alpha=0.9)
# boxplot of "raw" data
bp1 = pl.boxplot(v, widths=bwidth, **bp_kwargs_)
for i in range(ncol):
for c in xrange(2):
b = bp1['boxes'][2*i+c]
b.set_color(ccolors[c])
b.set_linewidth(2)
ax = pl.gca()
ax.set_xticklabels(xlabels, rotation=xticks_rotation)
plot_grid(ax)
if 'boxplot_effect' in style:
if len(style) > 1:
pl.subplot(1, nplots, plot)
plot += 1
plot_boxplot_enhanced(v10,
contrast_labels=contrast_labels,
condition_labels=condition_labels,
widths=bwidth,
rand_offsets=rand_offsets, # reuse them
grid=grid,
**bp_kwargs_)
if bottom_adjust:
fig.subplots_adjust(bottom=bottom_adjust)
pl.draw_if_interactive()
return fig
if __name__ == '__main__':
if True:
v = np.random.normal(size=(50,8)) * 20 + 120
if False:
v[:, 1] += 40
v[:, 3] -= 30
v[:, 5] += 60
v[:, 6] -= 60
else:
v -= np.arange(v.shape[1])*10
v /= 10
v0 = v[:, ::2]
v1 = v[:, 1::2]
d = v1 - v0
print np.mean(d, axis=0)
styles = ['barplot_effect',
'boxplot_raw',
'boxplot_effect'
]
styles = styles + [styles]
pl.close('all')
if False:
f = plot_boxplot_enhanced((v1-v0)[:,0],
grid=True, xticks_rotation=30, notch=1)
for s in styles:
fig = pl.figure(figsize=(12,6))
f = plot_paired_stats(v0, v1, ['cont1', 'cont2'],
style=s, fig=fig,
condition_labels=['exp1', 'exp2', 'exp3', 'exp4'],
grid=True, xticks_rotation=30)
pl.show()
···
--
Yaroslav O. Halchenko
Postdoctoral Fellow, Department of Psychological and Brain Sciences
Dartmouth College, 419 Moore Hall, Hinman Box 6207, Hanover, NH 03755
Phone: +1 (603) 646-9834 Fax: +1 (603) 646-1419
WWW: http://www.linkedin.com/in/yarik