def _run_interface(self, runtime):
if not have_cv:
raise ImportError("cviewer library is not available")
THRESH = self.inputs.threshold
K = self.inputs.number_of_permutations
TAIL = self.inputs.t_tail
edge_key = self.inputs.edge_key
details = edge_key + '-thresh-' + str(THRESH) + '-k-' + str(
K) + '-tail-' + TAIL + '.pck'
# Fill in the data from the networks
X = ntwks_to_matrices(self.inputs.in_group1, edge_key)
Y = ntwks_to_matrices(self.inputs.in_group2, edge_key)
PVAL, ADJ, _ = nbs.compute_nbs(X, Y, THRESH, K, TAIL)
iflogger.info('p-values:')
iflogger.info(PVAL)
pADJ = ADJ.copy()
for idx, _ in enumerate(PVAL):
x, y = np.where(ADJ == idx + 1)
pADJ[x, y] = PVAL[idx]
# Create networkx graphs from the adjacency matrix
nbsgraph = nx.from_numpy_matrix(ADJ)
nbs_pval_graph = nx.from_numpy_matrix(pADJ)
# Relabel nodes because they should not start at zero for our convention
nbsgraph = nx.relabel_nodes(nbsgraph, lambda x: x + 1)
nbs_pval_graph = nx.relabel_nodes(nbs_pval_graph, lambda x: x + 1)
if isdefined(self.inputs.node_position_network):
node_ntwk_name = self.inputs.node_position_network
else:
node_ntwk_name = self.inputs.in_group1[0]
node_network = nx.read_gpickle(node_ntwk_name)
iflogger.info(
'Populating node dictionaries with attributes from {node}'.format(
node=node_ntwk_name))
for nid, ndata in node_network.nodes(data=True):
nbsgraph.nodes[nid] = ndata
nbs_pval_graph.nodes[nid] = ndata
path = op.abspath('NBS_Result_' + details)
iflogger.info(path)
nx.write_gpickle(nbsgraph, path)
iflogger.info(
'Saving output NBS edge network as {out}'.format(out=path))
pval_path = op.abspath('NBS_P_vals_' + details)
iflogger.info(pval_path)
nx.write_gpickle(nbs_pval_graph, pval_path)
iflogger.info(
'Saving output p-value network as {out}'.format(out=pval_path))
return runtime
def _run_interface(self, runtime):
if not have_cv:
raise ImportError("cviewer library is not available")
THRESH = self.inputs.threshold
K = self.inputs.number_of_permutations
TAIL = self.inputs.t_tail
edge_key = self.inputs.edge_key
details = edge_key + '-thresh-' + str(THRESH) + '-k-' + str(K) + '-tail-' + TAIL + '.pck'
# Fill in the data from the networks
X = ntwks_to_matrices(self.inputs.in_group1, edge_key)
Y = ntwks_to_matrices(self.inputs.in_group2, edge_key)
PVAL, ADJ, _ = nbs.compute_nbs(X, Y, THRESH, K, TAIL)
iflogger.info('p-values:')
iflogger.info(PVAL)
pADJ = ADJ.copy()
for idx, _ in enumerate(PVAL):
x, y = np.where(ADJ == idx + 1)
pADJ[x, y] = PVAL[idx]
# Create networkx graphs from the adjacency matrix
nbsgraph = nx.from_numpy_matrix(ADJ)
nbs_pval_graph = nx.from_numpy_matrix(pADJ)
# Relabel nodes because they should not start at zero for our convention
nbsgraph = nx.relabel_nodes(nbsgraph, lambda x: x + 1)
nbs_pval_graph = nx.relabel_nodes(nbs_pval_graph, lambda x: x + 1)
if isdefined(self.inputs.node_position_network):
node_ntwk_name = self.inputs.node_position_network
else:
node_ntwk_name = self.inputs.in_group1[0]
node_network = nx.read_gpickle(node_ntwk_name)
iflogger.info('Populating node dictionaries with attributes from %s',
node_ntwk_name)
for nid, ndata in node_network.nodes(data=True):
nbsgraph.nodes[nid] = ndata
nbs_pval_graph.nodes[nid] = ndata
path = op.abspath('NBS_Result_' + details)
iflogger.info(path)
nx.write_gpickle(nbsgraph, path)
iflogger.info('Saving output NBS edge network as %s', path)
pval_path = op.abspath('NBS_P_vals_' + details)
iflogger.info(pval_path)
nx.write_gpickle(nbs_pval_graph, pval_path)
iflogger.info('Saving output p-value network as %s', pval_path)
return runtime
def _run_interface(self, runtime):
from cviewer.libs.pyconto.groupstatistics import nbs
THRESH = self.inputs.threshold
K = self.inputs.number_of_permutations
TAIL = self.inputs.t_tail
edge_key = self.inputs.edge_key
details = (edge_key + "-thresh-" + str(THRESH) + "-k-" + str(K) +
"-tail-" + TAIL + ".pck")
# Fill in the data from the networks
X = ntwks_to_matrices(self.inputs.in_group1, edge_key)
Y = ntwks_to_matrices(self.inputs.in_group2, edge_key)
PVAL, ADJ, _ = nbs.compute_nbs(X, Y, THRESH, K, TAIL)
iflogger.info("p-values:")
iflogger.info(PVAL)
pADJ = ADJ.copy()
for idx, _ in enumerate(PVAL):
x, y = np.where(ADJ == idx + 1)
pADJ[x, y] = PVAL[idx]
# Create networkx graphs from the adjacency matrix
nbsgraph = nx.from_numpy_matrix(ADJ)
nbs_pval_graph = nx.from_numpy_matrix(pADJ)
# Relabel nodes because they should not start at zero for our convention
nbsgraph = nx.relabel_nodes(nbsgraph, lambda x: x + 1)
nbs_pval_graph = nx.relabel_nodes(nbs_pval_graph, lambda x: x + 1)
if isdefined(self.inputs.node_position_network):
node_ntwk_name = self.inputs.node_position_network
else:
node_ntwk_name = self.inputs.in_group1[0]
node_network = nx.read_gpickle(node_ntwk_name)
iflogger.info("Populating node dictionaries with attributes from %s",
node_ntwk_name)
for nid, ndata in node_network.nodes(data=True):
nbsgraph.nodes[nid] = ndata
nbs_pval_graph.nodes[nid] = ndata
path = op.abspath("NBS_Result_" + details)
iflogger.info(path)
nx.write_gpickle(nbsgraph, path)
iflogger.info("Saving output NBS edge network as %s", path)
pval_path = op.abspath("NBS_P_vals_" + details)
iflogger.info(pval_path)
nx.write_gpickle(nbs_pval_graph, pval_path)
iflogger.info("Saving output p-value network as %s", pval_path)
return runtime
# Run the NBS with the following parameter options:
# Set an appropriate threshold. It is difficult to provide a rule of thumb
# to guide the choice of this threshold. Trial-and-error is always an option
# with the number of permutations generated per trial set low.
THRESH = 3
# Generate 100 permutations. Many more permutations are required in practice
# to yield a reliable estimate.
K = 100
# Set TAIL to left, and thus test the alternative hypothesis that mean of
# population X < mean of population Y
TAIL = 'left'
# Run the NBS
PVAL, ADJ, NULL = nbs.compute_nbs(X, Y, THRESH, K, TAIL)
print "pval", PVAL
print "null", NULL
imshow(ADJ, interpolation='nearest')
title('Edges identified by the NBS')
show()
# Index of true positives
#ind_tp=[ind_set1;ind_set2];
ind_tp = np.vstack((set1, set2))
# Index of positives idenfitied by the NBS
#ind_obs=find(adj);
ind_obs = np.array(np.where(np.triu(ADJ))).T
# Run the NBS with the following parameter options:
# Set an appropriate threshold. It is difficult to provide a rule of thumb
# to guide the choice of this threshold. Trial-and-error is always an option
# with the number of permutations generated per trial set low.
THRESH=3
# Generate 100 permutations. Many more permutations are required in practice
# to yield a reliable estimate.
K=100
# Set TAIL to left, and thus test the alternative hypothesis that mean of
# population X < mean of population Y
TAIL='left'
# Run the NBS
PVAL, ADJ, NULL = nbs.compute_nbs(X,Y,THRESH,K,TAIL);
print "pval", PVAL
print "null", NULL
imshow(ADJ, interpolation='nearest')
title('Edges identified by the NBS')
show()
# Index of true positives
#ind_tp=[ind_set1;ind_set2];
ind_tp = np.vstack( (set1, set2) )
# Index of positives idenfitied by the NBS
#ind_obs=find(adj);
ind_obs = np.array(np.where(np.triu(ADJ))).T