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