interfaces.cmtk.nx

AverageNetworks

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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]
group_id: (a unicode string, nipype default value: group1)
        ID for group
ignore_exception: (a boolean, nipype default value: False)
        Print an error message instead of throwing an exception in case the
        interface fails to run
out_gexf_groupavg: (a file name)
        Average network saved as a .gexf file
out_gpickled_groupavg: (a file name)
        Average network saved as a NetworkX .pck
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.

Outputs:

gexf_groupavg: (a file name)
        Average network saved as a .gexf file
gpickled_groupavg: (a file name)
        Average network saved as a NetworkX .pck
matlab_groupavgs: (a list of items which are a file name)

NetworkXMetrics

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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]
compute_clique_related_measures: (a boolean, nipype default value:
         False)
        Computing clique-related measures (e.g. node clique number) can be
        very time consuming
ignore_exception: (a boolean, nipype default value: False)
        Print an error message instead of throwing an exception in case the
        interface fails to run
out_edge_metrics_matlab: (a file name)
        Output edge metrics in MATLAB .mat format
out_global_metrics_matlab: (a file name)
        Output node metrics in MATLAB .mat format
out_k_core: (a file name, nipype default value: k_core)
        Computed k-core 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.
out_k_shell: (a file name, nipype default value: k_shell)
        Computed k-shell network stored as a NetworkX pickle.
out_node_metrics_matlab: (a file name)
        Output node 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.
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

Outputs:

edge_measure_networks: (a list of items which are a file name)
edge_measures_matlab: (a file name)
        Output edge metrics in MATLAB .mat format
global_measures_matlab: (a file name)
        Output global metrics in MATLAB .mat format
gpickled_network_files: (a list of items which are a file name)
k_core: (a file name)
        Computed k-core network stored as a NetworkX pickle.
k_crust: (a file name)
        Computed k-crust network stored as a NetworkX pickle.
k_networks: (a list of items which are a file name)
k_shell: (a file name)
        Computed k-shell network stored as a NetworkX pickle.
matlab_dict_measures: (a list of items which are a file name)
matlab_matrix_files: (a list of items which are a file name)
node_measure_networks: (a list of items which are a file name)
node_measures_matlab: (a file name)
        Output node metrics in MATLAB .mat format
pickled_extra_measures: (a file name)
        Network measures for the group that return dictionaries, stored as a
        Pickle.

add_dicts_by_key()

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Combines two dictionaries and adds the values for those keys that are shared

add_edge_data()

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add_node_data()

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average_networks()

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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_dict_measures()

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Returns a dictionary

compute_edge_measures()

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These return edge-based measures

compute_network_measures()

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compute_node_measures()

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These return node-based measures

compute_singlevalued_measures()

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Returns a single value per network

fix_keys_for_gexf()

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GEXF Networks can be read in Gephi, however, the keys for the node and edge IDs must be converted to strings

read_unknown_ntwk()

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remove_all_edges()

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