def test_link_communities():
base_dir = str(Path(__file__).parent/"examples")
in_mat = np.load(base_dir + '/997/997_Default_est_cov_0.1_4.npy')
start_time = time.time()
M = netstats.link_communities(in_mat, type_clustering='single')
print("%s%s%s" % ('thresh_and_fit (Functional, proportional thresholding) --> finished: ', str(np.round(time.time() - start_time, 1)), 's'))
assert M is not None
def plot_connectogram(conn_matrix, conn_model, atlas_name, dir_path, ID,
NETWORK, label_names):
import json
from pynets.thresholding import normalize
from pathlib import Path
from random import sample
from string import ascii_uppercase, ascii_lowercase
link_comm = True
conn_matrix = normalize(conn_matrix)
G = nx.from_numpy_matrix(conn_matrix)
def doClust(X, clust_levels):
##get the linkage diagram
Z = linkage(
X,
'ward',
)
##choose # cluster levels
cluster_levels = range(1, int(clust_levels))
##init array to store labels for each level
clust_levels_tmp = int(clust_levels) - 1
label_arr = np.zeros((int(clust_levels_tmp), int(X.shape[0])))
##iterate thru levels
for c in cluster_levels:
fl = fcluster(Z, c, criterion='maxclust')
#print(fl)
label_arr[c - 1, :] = fl
return label_arr, clust_levels_tmp
if NETWORK is not None:
clust_levels = 3
[label_arr, clust_levels_tmp] = doClust(conn_matrix, clust_levels)
else:
if link_comm == True:
from pynets.netstats import link_communities
#G_lin = nx.line_graph(G)
##Plot link communities
node_comm_aff_mat = link_communities(conn_matrix,
type_clustering='single')
clust_levels = len(node_comm_aff_mat)
clust_levels_tmp = int(clust_levels) - 1
mask_mat = np.squeeze(
np.array([node_comm_aff_mat == 0]).astype('int'))
label_arr = node_comm_aff_mat * np.expand_dims(
np.arange(1, clust_levels + 1), axis=1) + mask_mat
#else:
##Plot node communities
#from pynets.netstats import community_louvain
#[ci, q] = community_louvain(conn_matrix, gamma=0.75)
#clust_levels = len(np.unique(ci))
#clust_levels_tmp = int(clust_levels) - 1
def get_node_label(node_idx, labels, clust_levels_tmp):
def get_letters(n, random=False, uppercase=False):
"""Return n letters of the alphabet."""
letters = (ascii_uppercase if uppercase else ascii_lowercase)
return json.dumps(
(sample(letters, n) if random else list(letters[:n])))
abet = get_letters(clust_levels_tmp)
node_labels = labels[:, node_idx]
return ".".join([
"{}{}".format(abet[i], int(l)) for i, l in enumerate(node_labels)
]) + ".{}".format(label_names[node_idx])
output = []
for node_idx, connections in enumerate(G.adjacency_list()):
weight_vec = []
for i in connections:
wei = G.get_edge_data(node_idx, int(i))['weight']
#wei = G_lin.get_edge_data(node_idx,int(i))['weight']
weight_vec.append(wei)
entry = {}
nodes_label = get_node_label(node_idx, label_arr, clust_levels_tmp)
entry["name"] = nodes_label
entry["size"] = len(connections)
entry["imports"] = [
get_node_label(int(d) - 1, label_arr, clust_levels_tmp)
for d in connections
]
entry["weights"] = weight_vec
output.append(entry)
if NETWORK != None:
json_file_name = str(
ID
) + '_' + NETWORK + '_connectogram_' + conn_model + '_network.json'
connectogram_plot = dir_path + '/' + json_file_name
connectogram_js_sub = dir_path + '/' + str(
ID) + '_' + NETWORK + '_connectogram_' + conn_model + '_network.js'
connectogram_js_name = str(
ID) + '_' + NETWORK + '_connectogram_' + conn_model + '_network.js'
else:
json_file_name = str(ID) + '_connectogram_' + conn_model + '.json'
connectogram_plot = dir_path + '/' + json_file_name
connectogram_js_sub = dir_path + '/' + str(
ID) + '_connectogram_' + conn_model + '.js'
connectogram_js_name = str(ID) + '_connectogram_' + conn_model + '.js'
save_json(connectogram_plot, output)
##Copy index.html and json to dir_path
#conn_js_path = '/Users/PSYC-dap3463/Applications/PyNets/pynets/connectogram.js'
#index_html_path = '/Users/PSYC-dap3463/Applications/PyNets/pynets/index.html'
conn_js_path = Path(__file__).parent / "connectogram.js"
index_html_path = Path(__file__).parent / "index.html"
replacements_html = {'connectogram.js': str(connectogram_js_name)}
with open(index_html_path) as infile, open(str(dir_path + '/index.html'),
'w') as outfile:
for line in infile:
for src, target in replacements_html.items():
line = line.replace(src, target)
outfile.write(line)
replacements_js = {'template.json': str(json_file_name)}
with open(conn_js_path) as infile, open(connectogram_js_sub,
'w') as outfile:
for line in infile:
for src, target in replacements_js.items():
line = line.replace(src, target)
outfile.write(line)
def plot_connectogram(conn_matrix, conn_model, atlas_select, dir_path, ID,
network, label_names):
import json
from pathlib import Path
from networkx.readwrite import json_graph
from pynets.thresholding import normalize
from pynets.netstats import most_important
from scipy.cluster.hierarchy import linkage, fcluster
from nipype.utils.filemanip import save_json
# Advanced Settings
comm = 'nodes'
pruned = False
#color_scheme = 'interpolateCool'
#color_scheme = 'interpolateGnBu'
#color_scheme = 'interpolateOrRd'
#color_scheme = 'interpolatePuRd'
#color_scheme = 'interpolateYlOrRd'
#color_scheme = 'interpolateReds'
#color_scheme = 'interpolateGreens'
color_scheme = 'interpolateBlues'
# Advanced Settings
conn_matrix = normalize(conn_matrix)
G = nx.from_numpy_matrix(conn_matrix)
if pruned is True:
[G, pruned_nodes] = most_important(G)
conn_matrix = nx.to_numpy_array(G)
pruned_nodes.sort(reverse=True)
for j in pruned_nodes:
del label_names[label_names.index(label_names[j])]
def doClust(X, clust_levels):
# get the linkage diagram
Z = linkage(X, 'ward')
# choose # cluster levels
cluster_levels = range(1, int(clust_levels))
# init array to store labels for each level
clust_levels_tmp = int(clust_levels) - 1
label_arr = np.zeros((int(clust_levels_tmp), int(X.shape[0])))
# iterate thru levels
for c in cluster_levels:
fl = fcluster(Z, c, criterion='maxclust')
#print(fl)
label_arr[c - 1, :] = fl
return label_arr, clust_levels_tmp
if comm == 'nodes' and len(conn_matrix) > 40:
from pynets.netstats import modularity_louvain_und_sign
gamma = nx.density(nx.from_numpy_array(conn_matrix))
try:
[node_comm_aff_mat,
q] = modularity_louvain_und_sign(conn_matrix, gamma=float(gamma))
print("%s%s%s%s%s" %
('Found ', str(len(np.unique(node_comm_aff_mat))),
' communities with γ=', str(gamma), '...'))
except:
print(
'WARNING: Louvain community detection failed. Proceeding with single community affiliation vector...'
)
node_comm_aff_mat = np.ones(conn_matrix.shape[0]).astype('int')
clust_levels = len(node_comm_aff_mat)
clust_levels_tmp = int(clust_levels) - 1
mask_mat = np.squeeze(np.array([node_comm_aff_mat == 0]).astype('int'))
label_arr = node_comm_aff_mat * np.expand_dims(
np.arange(1, clust_levels + 1), axis=1) + mask_mat
elif comm == 'links' and len(conn_matrix) > 40:
from pynets.netstats import link_communities
# Plot link communities
link_comm_aff_mat = link_communities(conn_matrix,
type_clustering='single')
print("%s%s%s" %
('Found ', str(len(link_comm_aff_mat)), ' communities...'))
clust_levels = len(link_comm_aff_mat)
clust_levels_tmp = int(clust_levels) - 1
mask_mat = np.squeeze(np.array([link_comm_aff_mat == 0]).astype('int'))
label_arr = link_comm_aff_mat * np.expand_dims(
np.arange(1, clust_levels + 1), axis=1) + mask_mat
elif len(conn_matrix) > 20:
print(
'Graph too small for reliable plotting of communities. Plotting by fcluster instead...'
)
if len(conn_matrix) >= 250:
clust_levels = 7
elif len(conn_matrix) >= 200:
clust_levels = 6
elif len(conn_matrix) >= 150:
clust_levels = 5
elif len(conn_matrix) >= 100:
clust_levels = 4
elif len(conn_matrix) >= 50:
clust_levels = 3
else:
clust_levels = 2
[label_arr, clust_levels_tmp] = doClust(conn_matrix, clust_levels)
def get_node_label(node_idx, labels, clust_levels_tmp):
from collections import OrderedDict
def write_roman(num):
roman = OrderedDict()
roman[1000] = "M"
roman[900] = "CM"
roman[500] = "D"
roman[400] = "CD"
roman[100] = "C"
roman[90] = "XC"
roman[50] = "L"
roman[40] = "XL"
roman[10] = "X"
roman[9] = "IX"
roman[5] = "V"
roman[4] = "IV"
roman[1] = "I"
def roman_num(num):
for r in roman.keys():
x, y = divmod(num, r)
yield roman[r] * x
num -= (r * x)
if num > 0:
roman_num(num)
else:
break
return "".join([a for a in roman_num(num)])
rn_list = []
node_idx = node_idx - 1
node_labels = labels[:, node_idx]
for k in [int(l) for i, l in enumerate(node_labels)]:
rn_list.append(json.dumps(write_roman(k)))
abet = rn_list
node_lab_alph = ".".join([
"{}{}".format(abet[i], int(l)) for i, l in enumerate(node_labels)
]) + ".{}".format(label_names[node_idx])
return node_lab_alph
output = []
adj_dict = {}
for i in list(G.adjacency()):
source = list(i)[0]
target = list(list(i)[1])
adj_dict[source] = target
for node_idx, connections in adj_dict.items():
weight_vec = []
for i in connections:
wei = G.get_edge_data(node_idx, int(i))['weight']
weight_vec.append(wei)
entry = {}
nodes_label = get_node_label(node_idx, label_arr, clust_levels_tmp)
entry["name"] = nodes_label
entry["size"] = len(connections)
entry["imports"] = [
get_node_label(int(d) - 1, label_arr, clust_levels_tmp)
for d in connections
]
entry["weights"] = weight_vec
output.append(entry)
if network:
json_file_name = "%s%s%s%s%s%s" % (str(ID), '_', network,
'_connectogram_', conn_model,
'_network.json')
json_fdg_file_name = "%s%s%s%s%s%s" % (str(ID), '_', network, '_fdg_',
conn_model, '_network.json')
connectogram_plot = "%s%s%s" % (dir_path, '/', json_file_name)
fdg_js_sub = "%s%s%s%s%s%s%s%s" % (dir_path, '/', str(ID), '_',
network, '_fdg_', conn_model,
'_network.js')
fdg_js_sub_name = "%s%s%s%s%s%s" % (str(ID), '_', network, '_fdg_',
conn_model, '_network.js')
connectogram_js_sub = "%s%s%s%s%s%s%s%s" % (dir_path, '/', str(
ID), '_', network, '_connectogram_', conn_model, '_network.js')
connectogram_js_name = "%s%s%s%s%s%s" % (
str(ID), '_', network, '_connectogram_', conn_model, '_network.js')
else:
json_file_name = "%s%s%s%s" % (str(ID), '_connectogram_', conn_model,
'.json')
json_fdg_file_name = "%s%s%s%s" % (str(ID), '_fdg_', conn_model,
'.json')
connectogram_plot = "%s%s%s" % (dir_path, '/', json_file_name)
connectogram_js_sub = "%s%s%s%s%s%s" % (
dir_path, '/', str(ID), '_connectogram_', conn_model, '.js')
fdg_js_sub = "%s%s%s%s%s%s" % (dir_path, '/', str(ID), '_fdg_',
conn_model, '.js')
fdg_js_sub_name = "%s%s%s%s" % (str(ID), '_fdg_', conn_model, '.js')
connectogram_js_name = "%s%s%s%s" % (str(ID), '_connectogram_',
conn_model, '.js')
save_json(connectogram_plot, output)
# Force-directed graphing
G = nx.from_numpy_matrix(np.round(conn_matrix.astype('float64'), 6))
data = json_graph.node_link_data(G)
data.pop('directed', None)
data.pop('graph', None)
data.pop('multigraph', None)
for k in range(len(data['links'])):
data['links'][k]['value'] = data['links'][k].pop('weight')
for k in range(len(data['nodes'])):
data['nodes'][k]['id'] = str(data['nodes'][k]['id'])
for k in range(len(data['links'])):
data['links'][k]['source'] = str(data['links'][k]['source'])
data['links'][k]['target'] = str(data['links'][k]['target'])
# Add community structure
for k in range(len(data['nodes'])):
data['nodes'][k]['group'] = str(label_arr[0][k])
# Add node labels
for k in range(len(data['nodes'])):
data['nodes'][k]['name'] = str(label_names[k])
out_file = "%s%s%s" % (dir_path, '/', str(json_fdg_file_name))
save_json(out_file, data)
# Copy index.html and json to dir_path
#conn_js_path = '/Users/PSYC-dap3463/Applications/PyNets/pynets/connectogram.js'
#index_html_path = '/Users/PSYC-dap3463/Applications/PyNets/pynets/index.html'
conn_js_path = str(Path(__file__).parent / "connectogram.js")
index_html_path = str(Path(__file__).parent / "index.html")
fdg_replacements_js = {"FD_graph.json": str(json_fdg_file_name)}
replacements_html = {
'connectogram.js': str(connectogram_js_name),
'fdg.js': str(fdg_js_sub_name)
}
fdg_js_path = str(Path(__file__).parent / "fdg.js")
with open(index_html_path) as infile, open(str(dir_path + '/index.html'),
'w') as outfile:
for line in infile:
for src, target in replacements_html.items():
line = line.replace(src, target)
outfile.write(line)
replacements_js = {
'template.json': str(json_file_name),
'interpolateCool': str(color_scheme)
}
with open(conn_js_path) as infile, open(connectogram_js_sub,
'w') as outfile:
for line in infile:
for src, target in replacements_js.items():
line = line.replace(src, target)
outfile.write(line)
with open(fdg_js_path) as infile, open(fdg_js_sub, 'w') as outfile:
for line in infile:
for src, target in fdg_replacements_js.items():
line = line.replace(src, target)
outfile.write(line)
return