TemporalNetwork¶
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class
TemporalNetwork
(N=None, T=None, nettype=None, from_df=None, from_array=None, from_dict=None, from_edgelist=None, timetype=None, diagonal=False, timeunit=None, desc=None, starttime=None, nodelabels=None, timelabels=None, hdf5=False, hdf5path=None, forcesparse=False, dense_threshold=0.25)[source]¶ Bases:
object
A class for temporal networks.
This class allows to call different teneto functions within the class and store the network representation.
Parameters: - N (int) – number of nodes in network
- T (int) – number of time-points in network
- nettype (str) – description of network. Can be: bu, bd, wu, wd where the letters stand for binary, weighted, undirected and directed. Default is weighted and undirected.
- from_df (pandas df) – input data frame with i,j,t,[weight] columns
- from_array (array) – input data from an array with dimesnions node,node,time
- from_dict (dict) – input data is a contact sequence dictionary.
- from_edgelist (list) – input data is a list of lists where each item in main list consists of [i,j,t,[weight]].
- timetype (str) – discrete or continuous
- diagonal (bool) – if the diagonal should be included in the edge list.
- timeunit (str) – string (used in plots)
- desc (str) – string to describe network.
- startime (int) – integer represents time of first index.
- nodelabels (list) – list of labels for naming the nodes
- timelabels (list) – list of labels for time-points
- hdf5 (bool) – if true, pandas dataframe is stored and queried as a h5 file.
- hdf5path (str) – Where the h5 files is saved if hdf5 is True. If left unset, the default is ./teneto_temporalnetwork.h5
- forcesparse (bool) – When forsesparse if False (default), if importing array and if dense_threshold% (default%) edges are present, tnet.network is an array. If forsesparse is True, then this inhibts arrays being created.
- dense_threshold (float) – If forsesparse == False, what percentage (as a decimal) of edges need to be present in order for representation to be dense.
Methods Summary
add_edge
(edgelist)Adds an edge from network. binarize
(threshold_type, threshold_level, …)Binarizes the network. calc_networkmeasure
(networkmeasure, …)Calculate network measure. df_to_array
([start_at])Turns datafram to array. drop_edge
(edgelist)Removes an edge from network. generatenetwork
(networktype, **networkparams)Generate a network get_network_when
(**kwargs)hdf5_setup
(hdf5path)network_from_array
(array[, forcesparse, …])Defines a network from an array. network_from_df
(df)Defines a network from an array. network_from_dict
(contact)network_from_edgelist
(edgelist)Defines a network from an array. plot
(plottype[, ij, t, ax])Methods Documentation
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add_edge
(edgelist)[source]¶ Adds an edge from network.
Parameters: edgelist (list) – a list (or list of lists) containing the i,j and t indicies to be added. For weighted networks list should also contain a ‘weight’ key. Returns: Return type: Updates TenetoBIDS.network dataframe with new edge
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binarize
(threshold_type, threshold_level, **kwargs)[source]¶ Binarizes the network.
Parameters: - threshold_type (str) – What type of thresholds to make binarization. Options: ‘rdp’, ‘percent’, ‘magnitude’.
- threshold_level (str) – Paramter dependent on threshold type. If ‘rdp’, it is the delta (i.e. error allowed in compression). If ‘percent’, it is the percentage to keep (e.g. 0.1, means keep 10% of signal). If ‘magnitude’, it is the amplitude of signal to keep.
- teneto.utils.binarize for kwarg arguments. (See) –
Returns: Return type: Updates tnet.network to be binarized
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calc_networkmeasure
(networkmeasure, **measureparams)[source]¶ Calculate network measure.
Parameters: - networkmeasure (str) – Function to call. Functions available are in teneto.networkmeasures
- measureparams (kwargs) – kwargs for teneto.networkmeasure.[networkmeasure]
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df_to_array
(start_at='auto')[source]¶ Turns datafram to array. See teneto.utils.df_to_array for more information.
Parameters: start_at (str) – ‘min’ or ‘zero’. If auto, the 0th time-point is tnet.starttime. If min, the 0th time-point in the array is the minimum time-point found. If zero, the 0th time-point in the array is 0.
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drop_edge
(edgelist)[source]¶ Removes an edge from network.
Parameters: edgelist (list) – a list (or list of lists) containing the i,j and t indicies to be removes. Returns: Return type: Updates TenetoBIDS.network dataframe
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generatenetwork
(networktype, **networkparams)[source]¶ Generate a network
Parameters: - networktype (str) – Function to call. Functions available are in teneto.generatenetwork
- measureparams (kwargs) – kwargs for teneto.generatenetwork.[networktype]
Returns: Return type: TenetoBIDS.network is made with the generated network.
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network_from_array
(array, forcesparse=False, dense_threshold=0.25)[source]¶ Defines a network from an array.
Parameters: - array (array) – 3D numpy array.
- forcespace (bool) – If true, will always make the array sparse (can be slow). If false, dense form will be kept if more than dense_threshold% of edges are present.
- dense_threshold (float) – Threshold for when array representation is kept as an array instead of sparse. Only done if forcesparse is False.
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network_from_df
(df)[source]¶ Defines a network from an array.
Parameters: array (array) – Pandas dataframe. Should have columns: ‘i’, ‘j’, ‘t’ where i and j are node indicies and t is the temporal index. If weighted, should also include ‘weight’. Each row is an edge.
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network_from_edgelist
(edgelist)[source]¶ Defines a network from an array.
Parameters: edgelist (list of lists.) – A list of lists which are 3 or 4 in length. For binary networks each sublist should be [i, j ,t] where i and j are node indicies and t is the temporal index. For weighted networks each sublist should be [i, j, t, weight].