def get_centrality_opt(timeseries_data,
method_options,
weight_options,
memory_allocated,
threshold,
scans,
r_value=None):
"""
Method to calculate degree and eigen vector centrality.
This method takes into consideration the amount of memory
allocated by the user to calculate degree centrality.
Parameters
----------
timeseries_data : numpy array
timeseries of the input subject
weight_options : string (list of boolean)
list of two booleans for binarize and weighted options respectively
method_options : string (list of boolean)
list of two booleans for binarize and weighted options respectively
memory_allocated : a string
amount of memory allocated to degree centrality
scans : an integer
number of scans in the subject
r_value :a float
threshold value
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
Raises
------
Exception
"""
import numpy as np
from CPAC.network_centrality import load_mat,\
calc_corrcoef,\
calc_blocksize,\
calc_eigenV,\
calc_threshold
#from scipy.sparse import dok_matrix
try:
out_list = []
timeseries = load_mat(timeseries_data)
shape = timeseries.shape
try:
block_size = calc_blocksize(shape, memory_allocated)
except:
raise Exception("Error in calculating block size")
r_matrix = None
if method_options[0]:
if weight_options[0]:
degree_mat_binarize = np.zeros(shape[0], dtype=np.float32)
out_list.append(
('degree_centrality_binarize', degree_mat_binarize))
if weight_options[1]:
degree_mat_weighted = np.zeros(shape[0], dtype=np.float32)
out_list.append(
('degree_centrality_weighted', degree_mat_weighted))
if method_options[1]:
r_matrix = np.zeros((shape[0], shape[0]), dtype=np.float32)
j = 0
i = block_size
while i <= timeseries.shape[0]:
print "running block -> ", i + j
try:
corr_matrix = np.nan_to_num(
calc_corrcoef(timeseries[j:i].T, timeseries.T))
except:
raise Exception(
"Error in calcuating block wise correlation for the block %,%"
% (j, i))
if r_value == None:
r_value = calc_threshold(1,
threshold,
scans,
corr_matrix,
full_matrix=False)
if method_options[1]:
r_matrix[j:i] = corr_matrix
if method_options[0]:
if weight_options[0]:
degree_mat_binarize[j:i] = np.sum(
(corr_matrix > r_value).astype(np.float32), axis=1) - 1
if weight_options[1]:
degree_mat_weighted[j:i] = np.sum(
corr_matrix *
(corr_matrix > r_value).astype(np.float32),
axis=1) - 1
j = i
if i == timeseries.shape[0]:
break
elif (i + block_size) > timeseries.shape[0]:
i = timeseries.shape[0]
else:
i += block_size
try:
if method_options[1]:
out_list.extend(calc_eigenV(r_matrix, r_value, weight_options))
except Exception:
print "Error in calcuating eigen vector centrality"
raise
return out_list
except Exception:
print "Error in calcuating Centrality"
raise
def get_centrality_opt(timeseries_data,
method_options,
weight_options,
memory_allocated,
threshold,
scans,
r_value = None):
"""
Method to calculate degree and eigen vector centrality.
This method takes into consideration the amount of memory
allocated by the user to calculate degree centrality.
Parameters
----------
timeseries_data : numpy array
timeseries of the input subject
weight_options : string (list of boolean)
list of two booleans for binarize and weighted options respectively
method_options : string (list of boolean)
list of two booleans for binarize and weighted options respectively
memory_allocated : a string
amount of memory allocated to degree centrality
scans : an integer
number of scans in the subject
r_value :a float
threshold value
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
Raises
------
Exception
"""
import numpy as np
from CPAC.network_centrality import load_mat,\
calc_corrcoef,\
calc_blocksize,\
calc_eigenV,\
calc_threshold
#from scipy.sparse import dok_matrix
try:
out_list =[]
timeseries = load_mat(timeseries_data)
shape = timeseries.shape
try:
block_size = calc_blocksize(shape, memory_allocated)
except:
raise Exception("Error in calculating block size")
r_matrix = None
if method_options[0]:
if weight_options[0]:
degree_mat_binarize = np.zeros(shape[0], dtype= np.float32)
out_list.append(('degree_centrality_binarize', degree_mat_binarize))
if weight_options[1]:
degree_mat_weighted = np.zeros(shape[0], dtype = np.float32)
out_list.append(('degree_centrality_weighted', degree_mat_weighted))
if method_options[1]:
r_matrix = np.zeros((shape[0], shape[0]), dtype = np.float32)
j=0
i = block_size
while i <= timeseries.shape[0]:
print "running block -> ", i + j
try:
corr_matrix = np.nan_to_num(calc_corrcoef(timeseries[j:i].T, timeseries.T))
except:
raise Exception("Error in calcuating block wise correlation for the block %,%"%(j,i))
if r_value == None:
r_value = calc_threshold(1, threshold, scans, corr_matrix, full_matrix = False)
if method_options[1]:
r_matrix[j:i] = corr_matrix
if method_options[0]:
if weight_options[0]:
degree_mat_binarize[j:i] = np.sum((corr_matrix > r_value).astype(np.float32), axis = 1) -1
if weight_options[1]:
degree_mat_weighted[j:i] = np.sum(corr_matrix*(corr_matrix > r_value).astype(np.float32), axis = 1) -1
j = i
if i == timeseries.shape[0]:
break
elif (i+block_size) > timeseries.shape[0]:
i = timeseries.shape[0]
else:
i += block_size
try:
if method_options[1]:
out_list.extend(calc_eigenV(r_matrix, r_value, weight_options))
except Exception:
print "Error in calcuating eigen vector centrality"
raise
return out_list
except Exception:
print "Error in calcuating Centrality"
raise
def get_centrality(timeseries_data, method_options, weight_options, threshold,
option, scans, memory_allocated):
"""
Method to calculate degree and eigen vector centrality
Parameters
----------
weight_options : string (list of boolean)
list of two booleans for binarize and weighted options respectively
method_options : string (list of boolean)
list of two booleans for binarize and weighted options respectively
threshold_matrix : string (numpy npy file)
path to file containing thresholded correlation matrix
correlation_matrix : string (numpy npy file)
path to file containing correlation matrix
template_data : string (numpy npy file)
path to file containing mask or parcellation unit data
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
Raises
------
Exception
"""
import numpy as np
from CPAC.network_centrality import load_mat,\
calc_corrcoef,\
calc_blocksize,\
calc_threshold,\
calc_eigenV
out_list = []
try:
timeseries = load_mat(timeseries_data)
shape = timeseries.shape
block_size = calc_blocksize(shape, memory_allocated)
corr_matrix = np.zeros((shape[0], shape[0]), dtype=np.float16)
j = 0
i = block_size
while i <= timeseries.shape[0]:
print "block -> ", i + j
temp_matrix = np.nan_to_num(
calc_corrcoef(timeseries[j:i].T, timeseries.T))
corr_matrix[j:i] = temp_matrix
j = i
if i == timeseries.shape[0]:
break
elif (i + block_size) > timeseries.shape[0]:
i = timeseries.shape[0]
else:
i += block_size
r_value = calc_threshold(option,
threshold,
scans,
corr_matrix,
full_matrix=True)
print "r_value -> ", r_value
if method_options[0]:
print "calculating binarize degree centrality matrix..."
degree_matrix = np.sum(corr_matrix > r_value, axis=1) - 1
out_list.append(('degree_centrality_binarize', degree_matrix))
print "calculating weighted degree centrality matrix..."
degree_matrix = np.sum(corr_matrix * (corr_matrix > r_value),
axis=1) - 1
out_list.append(('degree_centrality_weighted', degree_matrix))
if method_options[1]:
out_list.extend(calc_eigenV(corr_matrix, r_value, weight_options))
except Exception:
print "Error while calculating centrality"
raise
return out_list
def get_centrality(timeseries_data,
method_options,
weight_options,
threshold,
option,
scans,
memory_allocated):
"""
Method to calculate degree and eigen vector centrality
Parameters
----------
weight_options : string (list of boolean)
list of two booleans for binarize and weighted options respectively
method_options : string (list of boolean)
list of two booleans for binarize and weighted options respectively
threshold_matrix : string (numpy npy file)
path to file containing thresholded correlation matrix
correlation_matrix : string (numpy npy file)
path to file containing correlation matrix
template_data : string (numpy npy file)
path to file containing mask or parcellation unit data
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
Raises
------
Exception
"""
import numpy as np
from CPAC.network_centrality import load_mat,\
calc_corrcoef,\
calc_blocksize,\
calc_threshold,\
calc_eigenV
out_list=[]
try:
timeseries = load_mat(timeseries_data)
shape = timeseries.shape
block_size = calc_blocksize(shape, memory_allocated)
corr_matrix = np.zeros((shape[0], shape[0]), dtype = np.float16)
j=0
i = block_size
while i <= timeseries.shape[0]:
print "block -> ", i + j
temp_matrix = np.nan_to_num(calc_corrcoef(timeseries[j:i].T, timeseries.T))
corr_matrix[j:i] = temp_matrix
j = i
if i == timeseries.shape[0]:
break
elif (i+block_size) > timeseries.shape[0]:
i = timeseries.shape[0]
else:
i += block_size
r_value = calc_threshold(option,
threshold,
scans,
corr_matrix,
full_matrix = True)
print "r_value -> ", r_value
if method_options[0]:
print "calculating binarize degree centrality matrix..."
degree_matrix = np.sum( corr_matrix > r_value , axis = 1) -1
out_list.append(('degree_centrality_binarize', degree_matrix))
print "calculating weighted degree centrality matrix..."
degree_matrix = np.sum( corr_matrix*(corr_matrix > r_value), axis= 1) -1
out_list.append(('degree_centrality_weighted', degree_matrix))
if method_options[1]:
out_list.extend(calc_eigenV(corr_matrix,
r_value,
weight_options))
except Exception:
print "Error while calculating centrality"
raise
return out_list