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
On 8 June 2015 at 03:27, Paul Hobson <[email protected]> wrote:
> (apologies if the list receives this twice)
>
> On Fri, Jun 5, 2015 at 9:14 AM, Juan Wu <[email protected]> wrote:
>>
>>> Hi, 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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