Sébastien / Seb-bubuntu wrote:
Hi folks,
I have a few plots which I would like to stack in a single figure. When
I first came with this problem (back to matplotlib version 0.93 or
older), the most efficient way (at least in my case) of doing this
consisted in playing with transformations as you can see in the cookbook
(see:
http://www.scipy.org/Cookbook/Matplotlib/MultilinePlots#head-b5f2ad87ab7ec637a0fc63ec85281469d9aeeb46).
Unfortunately, this example (and, of course, the script I wrote back
then) doesn't work anymore with with the newest version of Matplotlib
(0.98).
So I started to hack the "mri_with_eeg.py" example to achieve what I
want (since the way EEGs are stacked!)... with no luck!
I probably don't understand well how the transformation works... That's
why I decided to beg for a friendly help! :)
Here is the code from the example (I focused on the lines I haven't
completely figured):
boxin = Bbox.from_extents(ax.viewLim.x0, -20, ax.viewLim.x1, 20)
height = ax.bbox.height
boxout = Bbox.from_extents(ax.bbox.x0, -1.0 * height,
ax.bbox.x1, 1.0 * height)
transOffset = BboxTransformTo(
Bbox.from_extents(0.0, ax.bbox.y0, 1.0, ax.bbox.y1))
for i in range(numRows):
# effectively a copy of transData
trans = BboxTransform(boxin, boxout)
offset = (i+1)/(numRows+1)
trans += Affine2D().translate(*transOffset.transform_point((0,
offset)))
thisLine = Line2D(
t, data[:,i]-data[0,i],
)
thisLine.set_transform(trans)
ax.add_line(thisLine)
ticklocs.append(offset)
ax being a AxesSubplot instance
as far as I understood, 20 and -20 (see line where boxin is defined)
refer to min and max values (on Y axis) to be plotted within the space
allocated to each plot (thanks to the tranformation named trans).
In my case Y values can be much higher (roughly from -1e3 to 5e7), so I
should change those values according to mine in order to get the
tranformation working for me. Obviously something is wrong in my
understanding since it doesn't work!
Hence my first question: Why is it not working?
As a bonus, I also have another question: Is there a "new" (i.e.
provided by the new API) way that can be used to stack plots composed by
many (>5k) points?
I think a LineCollection may provide a very easy way to do what you
want, if I understand correctly. I recently changed mri_with_eeg.py to
use a LineCollection; maybe you are looking at an older version?
Attached is the current version from svn.
Eric
Thanks
Sébastien
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#!/usr/bin/env python
"""
This now uses the imshow command instead of pcolor which *is much
faster*
"""
from __future__ import division
import numpy as np
from matplotlib.pyplot import *
from matplotlib.collections import LineCollection
# I use if 1 to break up the different regions of code visually
if 1: # load the data
# data are 256x256 16 bit integers
dfile = '../data/s1045.ima'
im = np.fromstring(file(dfile, 'rb').read(), np.uint16).astype(float)
im.shape = 256, 256
if 1: # plot the MRI in pcolor
subplot(221)
imshow(im, cmap=cm.jet)
axis('off')
if 1: # plot the histogram of MRI intensity
subplot(222)
im = np.ravel(im)
im = im[np.nonzero(im)] # ignore the background
im = im/(2.0**15) # normalize
hist(im, 100)
xticks([-1, -.5, 0, .5, 1])
yticks([])
xlabel('intensity')
ylabel('MRI density')
if 1: # plot the EEG
# load the data
numSamples, numRows = 800,4
data = np.fromstring(file('../data/eeg.dat', 'rb').read(), float)
data.shape = numSamples, numRows
t = 10.0 * np.arange(numSamples, dtype=float)/numSamples
ticklocs = []
ax = subplot(212)
xlim(0,10)
xticks(np.arange(10))
dmin = data.min()
dmax = data.max()
dr = (dmax - dmin)*0.7 # Crowd them a bit.
y0 = dmin
y1 = (numRows-1) * dr + dmax
ylim(y0, y1)
segs = []
for i in range(numRows):
segs.append(np.hstack((t[:,np.newaxis], data[:,i,np.newaxis])))
ticklocs.append(i*dr)
offsets = np.zeros((numRows,2), dtype=float)
offsets[:,1] = ticklocs
lines = LineCollection(segs, offsets=offsets,
transOffset=None,
)
ax.add_collection(lines)
# set the yticks to use axes coords on the y axis
ax.set_yticks(ticklocs)
ax.set_yticklabels(['PG3', 'PG5', 'PG7', 'PG9'])
xlabel('time (s)')
show()
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