On Thu, 20 Oct 2011, Mike E. Klein wrote: > Thanks for the quick response. I actually don't know how to crossplot…
should be as easy as
import pylab as pl # not needed if you did import from mvpa.suite
pl.plot(np.ravel(s1_map), np.ravel(s2_map), '.')
pl.show() # your might not even need this one
alternatively -- here is a helper I hacked up/use -- probably should
place it under PyMVPA (see attached) -- just give it two nifti files ;-)
> histrograms are output from MRICron.
pl.hist
should become your friend
> I've tried to follow the plotting
> from the searchlight example from the manual, but haven't figured out
> to get my searchlight data in there (from nifti) without running the
> entire multi-hour script first.
just load using fmri_dataset with the original mask ;)
> As far as ds.summary, I've pasted below. It has been run averaged, but
> not detrended or zscored.
if I were you I would have detrended, zscored and only then averaged!
if averaging was done across multiple conditions spread through the
'run' detrending might be detremental
and I think I might know where other catch could be -- if you
zscore such a dataset as you have (2 samples total per chunk) with
zscoring per chunk -- do you know what values would you get? ;)
so -- how was zscoring done?
what about ds.summary() right before you fed it into searchlight?
--
=------------------------------------------------------------------=
Keep in touch www.onerussian.com
Yaroslav Halchenko www.ohloh.net/accounts/yarikoptic
#!/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 cross_plot.py @date Thu Jul 14 14:56:33 2011 @brief Yaroslav Halchenko Dartmouth web: http://www.onerussian.com College e-mail: [email protected] ICQ#: 60653192 DESCRIPTION (NOTES): COPYRIGHT: Yaroslav Halchenko 2011 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) 2011 Yaroslav Halchenko' __license__ = 'MIT' from mvpa.base import verbose import sys import pylab as pl import nibabel as nb import numpy as np files = sys.argv[1:] assert(len(files) == 2) # just 2 files if files[1].endswith('.nii.gz'): niftis = [nb.load(f) for f in files] datas = [n.get_data() for n in niftis] elif files[1].endswith('.hdf5'): from mvpa.base.hdf5 import h5load datas = [h5load(f).samples for f in files] data = np.asarray(datas).reshape((2, -1)) nz = (data != 0) #nz[:, 80000:] = False # for quick testing nzsum = np.sum(nz, axis=0) intersection = nzsum == 2 union = nzsum > 0 x, y = datainter = data[:, intersection] xnoty = (nz[0].astype(int) - nz[1].astype(int))>0 ynotx = (nz[1].astype(int) - nz[0].astype(int))>0 verbose(1, "total: %d union: %d intersection: %d x_only: %d y_only: %d" % (len(nzsum), np.sum(union), np.sum(intersection), np.sum(xnoty), np.sum(ynotx))) #fig=pl.figure() #pl.plot(datainter[0], datainter[1], '.') #fig.show() nullfmt = pl.NullFormatter() # no labels # definitions for the axes left, width = 0.1, 0.65 bottom, height = 0.1, 0.65 bottom_h = left_h = left+width+0.02 rect_scatter = [left, bottom, width, height] rect_histx = [left, bottom_h, width, 0.2] rect_histy = [left_h, bottom, 0.2, height] # start with a rectangular Figure pl.figure(1, figsize=(10,10)) axScatter = pl.axes(rect_scatter) axHistx = pl.axes(rect_histx) axHisty = pl.axes(rect_histy) # no labels axHistx.xaxis.set_major_formatter(nullfmt) axHisty.yaxis.set_major_formatter(nullfmt) # the scatter plot: axScatter.scatter(x, y, s=1, facecolors='none') #, '.') if np.sum(xnoty): axScatter.scatter(data[0, np.where(xnoty)[0]], data[1, np.where(xnoty)[0]], s=1, edgecolor='b',facecolors='none') if np.sum(ynotx): axScatter.scatter(data[0, np.where(ynotx)[0]], data[1, np.where(ynotx)[0]], s=1, edgecolor='g',facecolors='none') # pl.xlabel(files[0]) # pl.ylabel(files[1]) # now determine nice limits by hand: binwidth = np.max(datainter)/51. # 0.25 xymax = np.max( [np.max(x), np.max(y)] ) xymin = np.min( [np.min(x), np.min(y)] ) xyrange = xymax - xymin xyamax = np.max( [np.max(np.fabs(x)), np.max(np.fabs(y))] ) limn = -( int(-xymin/binwidth) + 1) * binwidth limp = ( int(xymax/binwidth) + 1) * binwidth axScatter.plot((limn*0.9, limp*0.9), (limn*0.9, limp*0.9), 'y--') axScatter.plot((np.min(x), np.max(x)), (0, 0), 'r', alpha=0.5) axScatter.plot((0,0), (np.min(y), np.max(y)), 'r', alpha=0.5) axScatter.set_xlim( (limn, limp) ) axScatter.set_ylim( (limn, limp) ) bins = np.arange(limn, limp + binwidth, binwidth) histx = axHistx.hist(x, bins=bins, facecolor='b') histy = axHisty.hist(y, bins=bins, orientation='horizontal', facecolor='g') axHistx.set_xlim( axScatter.get_xlim() ) axHistx.vlines(0, 0, 0.9*np.max(histx[0]), 'r') axHisty.set_ylim( axScatter.get_ylim() ) axHisty.hlines(0, 0, 0.9*np.max(histy[0]), 'r') rect_scatter = [left, bottom, width, height] abpx = pl.axes( [left, bottom+height * 0.9, width, height/10], axisbg='y' ) abpx.axis('off') bpx = abpx.boxplot(x, vert=0) #'r', 0) abpx.set_xlim(axScatter.get_xlim()) abpy = pl.axes( [left + width * 0.9, bottom, width/10, height], axisbg='y' ) abpy.axis('off') bpy = abpy.boxplot(y, sym='g+') abpy.set_ylim(axScatter.get_ylim()) pl.show()
signature.asc
Description: Digital signature
_______________________________________________ Pkg-ExpPsy-PyMVPA mailing list [email protected] http://lists.alioth.debian.org/cgi-bin/mailman/listinfo/pkg-exppsy-pymvpa
