interfaces.cmtk.nx¶
AverageNetworks¶
Calculates and outputs the average network given a set of input NetworkX gpickle files
This interface will only keep an edge in the averaged network if that edge is present in at least half of the input networks.
Example¶
>>> import nipype.interfaces.cmtk as cmtk
>>> avg = cmtk.AverageNetworks()
>>> avg.inputs.in_files = ['subj1.pck', 'subj2.pck']
>>> avg.run()
Inputs:
[Mandatory]
in_files: (a list of items which are an existing file name)
Networks for a group of subjects
[Optional]
resolution_network_file: (an existing file name)
Parcellation files from Connectome Mapping Toolkit. This is not
necessary, but if included, the interface will output the
statistical maps as networkx graphs.
group_id: (a unicode string, nipype default value: group1)
ID for group
out_gpickled_groupavg: (a file name)
Average network saved as a NetworkX .pck
out_gexf_groupavg: (a file name)
Average network saved as a .gexf file
Outputs:
gpickled_groupavg: (a file name)
Average network saved as a NetworkX .pck
gexf_groupavg: (a file name)
Average network saved as a .gexf file
matlab_groupavgs: (a list of items which are a file name)
NetworkXMetrics¶
Calculates and outputs NetworkX-based measures for an input network
Example¶
>>> import nipype.interfaces.cmtk as cmtk
>>> nxmetrics = cmtk.NetworkXMetrics()
>>> nxmetrics.inputs.in_file = 'subj1.pck'
>>> nxmetrics.run()
Inputs:
[Mandatory]
in_file: (an existing file name)
Input network
[Optional]
out_k_core: (a file name, nipype default value: k_core)
Computed k-core network stored as a NetworkX pickle.
out_k_shell: (a file name, nipype default value: k_shell)
Computed k-shell network stored as a NetworkX pickle.
out_k_crust: (a file name, nipype default value: k_crust)
Computed k-crust network stored as a NetworkX pickle.
treat_as_weighted_graph: (a boolean, nipype default value: True)
Some network metrics can be calculated while considering only a
binarized version of the graph
compute_clique_related_measures: (a boolean, nipype default value:
False)
Computing clique-related measures (e.g. node clique number) can be
very time consuming
out_global_metrics_matlab: (a file name)
Output node metrics in MATLAB .mat format
out_node_metrics_matlab: (a file name)
Output node metrics in MATLAB .mat format
out_edge_metrics_matlab: (a file name)
Output edge metrics in MATLAB .mat format
out_pickled_extra_measures: (a file name, nipype default value:
extra_measures)
Network measures for group 1 that return dictionaries stored as a
Pickle.
Outputs:
gpickled_network_files: (a list of items which are a file name)
matlab_matrix_files: (a list of items which are a file name)
global_measures_matlab: (a file name)
Output global metrics in MATLAB .mat format
node_measures_matlab: (a file name)
Output node metrics in MATLAB .mat format
edge_measures_matlab: (a file name)
Output edge metrics in MATLAB .mat format
node_measure_networks: (a list of items which are a file name)
edge_measure_networks: (a list of items which are a file name)
k_networks: (a list of items which are a file name)
k_core: (a file name)
Computed k-core network stored as a NetworkX pickle.
k_shell: (a file name)
Computed k-shell network stored as a NetworkX pickle.
k_crust: (a file name)
Computed k-crust network stored as a NetworkX pickle.
pickled_extra_measures: (a file name)
Network measures for the group that return dictionaries, stored as a
Pickle.
matlab_dict_measures: (a list of items which are a file name)
add_dicts_by_key()¶
Combines two dictionaries and adds the values for those keys that are shared
add_edge_data()¶
add_node_data()¶
average_networks()¶
Sums the edges of input networks and divides by the number of networks Writes the average network as .pck and .gexf and returns the name of the written networks
compute_network_measures()¶
fix_keys_for_gexf()¶
GEXF Networks can be read in Gephi, however, the keys for the node and edge IDs must be converted to strings