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CL_calc_conf_Xmm.py
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CL_calc_conf_Xmm.py
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#!/usr/bin/env python
import numpy as np
import nibabel as nib
from dipy.tracking.metrics import downsample
from dipy.tracking.distances import bundles_distances_mdf
import matplotlib.pyplot as plt
from dipy.tracking.utils import length
import sys
import os
#in the quickbundles paper, the authors removed streamlines less than 40mm,
#which seems like as good a threshold as any
def makehist(x,name='none',saveloc=None, show=False):
num_bins = 50
# the histogram of the data
fig, ax = plt.subplots(nrows=1, ncols=1) # create figure & 1 axis
#ax.plot([0,1,2], [10,20,3])
n, bins, patches = ax.hist(x, num_bins, facecolor='green', alpha=0.5)
# add a 'best fit' line
#y = mlab.normpdf(bins, mu, sigma)
#plt.plot(bins, y, 'r--')
#plt.xlabel('Smarts')
#plt.ylabel('Probability')
ax.set_title(r'Histogram of Confidence: %s' % name)
#fig.savefig(saveloc,dpi=100)
#plt.close(fig)
# Tweak spacing to prevent clipping of ylabel
#plt.subplots_adjust(left=0.15)
if show:
plt.show()
if saveloc:
fig.savefig(saveloc)
def showsls(sls,values,outpath,show=False):
from dipy.viz import window, actor,fvtk
from dipy.data import fetch_bundles_2_subjects, read_bundles_2_subjects
from dipy.tracking.streamline import transform_streamlines
#renderer.clear()
from dipy.tracking.streamline import length
renderer=window.Renderer()
hue = [0.5, 1] # white to purple to red
saturation = [0.0, 1.0] # black to white
lut_cmap = actor.colormap_lookup_table(
scale_range=(values.min(), np.percentile(values,50)),
hue_range=hue,
saturation_range=saturation)
stream_actor5 = actor.line(sls, values, linewidth=0.1,
lookup_colormap=lut_cmap)
renderer.add(stream_actor5)
bar3 = actor.scalar_bar(lut_cmap)
renderer.add(bar3)
# window.show(renderer, size=(600, 600), reset_camera=False)
if outpath:
window.record(renderer, out_path=outpath, size=(600, 600))
if show:
fvtk.show(renderer)
def doit(basedir, rootname, trkloc, lenthr, subsampN=12, save_snapshot=False, debug=False):
savename = '%s/%s_mtrx.txt' % (basedir, rootname)
savetrk = '%s/%s.trk' % (basedir, rootname)
savetrkss = '%s/%s_SS.trk' % (basedir,rootname)
savetrkpic = savetrk.replace('.trk','.png')
savetrkpicss = savetrk.replace('.trk','SS.png')
savehist = '%s/%s_hist.png' % (basedir,rootname)
showhist=False
showsls_var=False
lenthr = 50
pow = 1
if debug:
subsampN=3
subset = 800
trk,hdr = nib.trackvis.read(trkloc)
sls = [item[0] for item in trk]
lengths = list(length(sls))
#print lengths
'''print("cheerio")
print(len(lengths))
print(len(sls))
print "two"
print lengths[0]'''
sls_long = []
for n,j in enumerate(sls):
if len(j) > 5:
if lengths[n]>lenthr:
print(lengths[n])
sls_long.append(j)
else:
print("BAD")
print(lengths[n])
print("CHECKME")
print(len(sls))
print(len(sls_long))
subsamp = [downsample(sl,subsampN) for sl in sls_long]
if debug:
subsamp = subsamp[0:subset]
sls_long = sls_long[0:subset]
score_mtrx = np.zeros([len(subsamp)])
print("what")
print(len(subsamp))
print(len(sls))
for i,sl in enumerate(subsamp):
print(str(i)+'/'+str(len(subsamp)))
mtrx2 = bundles_distances_mdf([sls_long[i]],sls_long)
#mtrx2_oi = np.all([(mtrx2>0) & (mtrx2<5) & (not np.isnan(mtrx2))],axis=0)
mtrx2_oi = (mtrx2>0) & (mtrx2<5) & ~np.isnan(mtrx2)
zoom = mtrx2[mtrx2_oi]
score = np.sum(np.divide(1,np.power(zoom,pow)))
#makehist(zoom.T,score)
score_mtrx[i] = score
#print score
#print score_mtrx
'''print savename
print basedir
print rootname'''
np.savetxt(savename,score_mtrx)
#makehist(score_mtrx,name=rootname,saveloc=savehist,show=showhist)
newhdr=hdr.copy()
newhdr['n_properties']=1
proplist=newhdr['property_name']
proplist[0]='Cluster Confidence'
newhdr['property_name']=proplist
#newtrkss = ((sl,None,score_mtrx[i]) for i,sl in enumerate(subsamp))
newtrk = ((sl, None, score_mtrx[i]) for i,sl in enumerate(sls_long))
#nib.trackvis.write(savetrkss, newtrkss, newhdr)
nib.trackvis.write(savetrk,newtrk,newhdr)
#showsls(subsamp,score_mtrx,savetrkpicss,show=showsls_var)
if save_snapshot:
showsls(sls_long, score_mtrx, savetrkpic,show=showsls_var)
if __name__ == '__main__':
trkloc = os.path.abspath(sys.argv[1])
lenthr = int(sys.argv[2])
putloc = os.path.join(os.path.dirname(trkloc),'cci'+str(lenthr))
if os.path.exists(trkloc):
if not os.path.exists(putloc):
os.mkdir(putloc)
doit(putloc, os.path.basename(trkloc).replace('.trk','_cci'), trkloc, lenthr, 5, False)