def test_fast_on_real_data():
from pandas import read_table
from os import path as op
k = 200
subdir = "/home2/data/Projects/CWAS/share/nki/subinfo/40_Set1_N104"
ffile = op.join(subdir, "short_compcor_rois_random_k%04i.txt" % k)
fpaths = read_table(ffile, header=None)
fpath = fpaths.ix[0, 0]
import nibabel as nib
img = nib.load(fpath)
dat = img.get_data()
import numpy as np
from CPAC.cwas.subdist import norm_cols, ncor
norm_dat = norm_cols(dat)
corr_dat = norm_dat.T.dot(norm_dat)
ref = eigenvector_centrality(corr_dat)
comp = fast_eigenvector_centrality(norm_dat)
diff = np.abs(ref - comp).mean() # mean diff
print(diff)
ok_(diff < np.spacing(1e10)) # allow some differences
def test_fast_on_real_data():
from pandas import read_table
from os import path as op
k = 200
subdir = "/home2/data/Projects/CWAS/share/nki/subinfo/40_Set1_N104"
ffile = op.join(subdir, "short_compcor_rois_random_k%04i.txt" % k)
fpaths = read_table(ffile, header=None)
fpath = fpaths.ix[0,0]
import nibabel as nib
img = nib.load(fpath)
dat = img.get_data()
import numpy as np
from CPAC.cwas.subdist import norm_cols, ncor
norm_dat = norm_cols(dat)
corr_dat = norm_dat.T.dot(norm_dat)
ref = eigenvector_centrality(corr_dat)
comp = fast_eigenvector_centrality(norm_dat)
diff = np.abs(ref-comp).mean() # mean diff
print(diff)
ok_(diff < np.spacing(1e10)) # allow some differences
def test_fast_eigenvector_centrality(ntpts=100, nvoxs=1000):
print "testing fast_eigenvector_centrality"
# Simulate Data
import numpy as np
from CPAC.cwas.subdist import norm_cols
# Normalize Random Time-Series Data
m = np.random.random((ntpts,nvoxs))
m = norm_cols(m)
# Correlation Data with Range 0-1
mm = m.T.dot(m) # note that need to generate connectivity matrix here
# Execute
#from CPAC.network_centrality.core import fast_eigenvector_centrality,slow_eigenvector_centrality
ref = eigenvector_centrality(mm, verbose=False) # we need to transform mm to be a distance
comp = fast_eigenvector_centrality(m, verbose=False)
diff = np.abs(ref-comp).mean() # mean diff
print(diff)
ok_(diff < np.spacing(1e2)) # allow minimal difference
def test_fast_eigenvector_centrality(ntpts=100, nvoxs=1000):
print "testing fast_eigenvector_centrality"
# Simulate Data
import numpy as np
from CPAC.cwas.subdist import norm_cols
# Normalize Random Time-Series Data
m = np.random.random((ntpts, nvoxs))
m = norm_cols(m)
# Correlation Data with Range 0-1
mm = m.T.dot(m) # note that need to generate connectivity matrix here
# Execute
#from CPAC.network_centrality.core import fast_eigenvector_centrality,slow_eigenvector_centrality
ref = eigenvector_centrality(
mm, verbose=False) # we need to transform mm to be a distance
comp = fast_eigenvector_centrality(m, verbose=False)
diff = np.abs(ref - comp).mean() # mean diff
print(diff)
ok_(diff < np.spacing(1e2)) # allow minimal difference
def get_centrality_fast(timeseries,
method_options):
'''
Method to calculate degree and eigen vector centrality.
Relative to `get_centrality_opt`, it runs fast by not directly computing
the correlation matrix. As a consequence, there are several differences/
limitations from the standard approach:
1. Cannot specify a correlation threshold
2. As a consequence, the weighted dense matrix centrality is computed
3. No memory limit is specified since it is assumed to be ok
Note that the approach doesn't directly calculate the complete correlation
matrix.
Parameters
----------
timeseries_data : numpy array
timeseries of the input subject
method_options : string (list of boolean)
list of two booleans for binarize and weighted options respectively
Returns
-------
out_list : string (list of tuples)
list of tuple containing output name to be used to store nifti image
for centrality and centrality matrix. this will only be weighted since
the fast approaches are limited to this type of output.
Raises
------
Exception
'''
# Import packages
from CPAC.network_centrality import fast_degree_centrality,\
fast_eigenvector_centrality
from CPAC.cwas.subdist import norm_cols
try:
out_list = []
nvoxs = timeseries.shape[0]
ntpts = timeseries.shape[1]
# It's assumed that there is enough memory
# So a block size isn't set here
calc_degree = method_options[0]
calc_eigen = method_options[1]
print "Normalize Time-series"
timeseries = norm_cols(timeseries.T)
print "Computing centrality across %i voxels" % nvoxs
if calc_degree:
print "...calculating degree"
degree_weighted = fast_degree_centrality(timeseries)
out_list.append(('degree_centrality_weighted', degree_weighted))
if calc_eigen:
print "...calculating eigen"
eigen_weighted = fast_eigenvector_centrality(timeseries)
out_list.append(('eigenvector_centrality_weighted', eigen_weighted))
return out_list
except Exception:
print "Error in calcuating centrality"
raise
