I put the data into a list of lists of numpy arrays. The following script
generated a plot similar to what Juan attached:
import matplotlib.pyplot as plt
import numpy as np
def model(t, ii, jj):
"""
Returns some numbers according to the independent variable, t, and
parameters of the model, ii and jj.
"""
return (jj*6+ii)*np.ones_like(t)
### generate data (i.e. model + some random values)
t = np.linspace(-2.5, 2.5, 11)
data = [
[model(t, ii, jj) + np.random.rand(len(t)) - .5
for ii in range(6)] # different sessions
for jj in range(2)] # accuracy/speed
### do the plotting
colors = ['b', 'r']
titles = ['Accuracy emphasis', 'Speed emphasis']
fig, big_axes = plt.subplots(figsize=(16, 6), nrows=2, ncols=1, sharey=True)
for row, big_ax in enumerate(big_axes):
big_ax.set_ylabel('RT distributions')
big_ax.set_xlabel('Response time [s]')
big_ax.set_title(titles[row])
big_ax.tick_params(labelcolor=(1.,1.,1.,0.), top='off', bottom='off',
left='off', right='off')
big_ax._frameon = False
for jj, emph_data in enumerate(data):
for ii, iidata in enumerate(emph_data):
ax = fig.add_subplot(len(data), len(emph_data),
jj*len(emph_data)+ii+1)
# plot bars:
ax.bar(t, iidata,
color=colors[jj],
width=(max(t)-min(t))/(len(t)-1), # for non-overlapping bars
linewidth=0) # no outlines
# plot models:
ax.plot(t, model(t, ii, jj), 'k', linewidth=2)
# annotate axes etc.
ax.set_xticks((-2.5, 0., 2.5))
ax.set_ylim((-.1, 12.8))
ax.annotate('Session {}'.format(ii+1), xy=(-2.4, 11.8))
plt.tight_layout()
plt.show()
How to add the centered titles for each row is described here :
http://stackoverflow.com/questions/27426668/row-titles-for-matplotlib-subplot
Jan
Post by Paul Hobson(apologies if the list receives this twice)
Post by Juan WuHi, Experts,
My colleagues and I have a question, how we can make a plot via python
like below. According to a guy's original paper, "Each panel shows the
normalized histograms of the observed data (bar plots) and the model
prediction (black lines) ".
I believe that people can make it with Matplotlib. Any code suggestion
(with simple example data) would be much appreciated.
(I am more comfortable with Matlab, but now the python code is preferred).
J
Juan,
It is, of course, very difficult to give any concrete advice without
knowing how your data are stored.
In any case, seaborn builds on matplotlib to provide some very advanced
visualization with a very concise API.
I recommend you look into the seaborn.distplot function and
seaborn.FacetGrid class.
http://web.stanford.edu/~mwaskom/software/seaborn/
-Paul
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