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wiheto/teneto | teneto/classes/network.py | TemporalNetwork.network_from_edgelist | def network_from_edgelist(self, edgelist):
"""
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 t... | python | def network_from_edgelist(self, edgelist):
"""
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 t... | [
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wiheto/teneto | teneto/classes/network.py | TemporalNetwork._drop_duplicate_ij | def _drop_duplicate_ij(self):
"""
Drops duplicate entries from the network dataframe.
"""
self.network['ij'] = list(map(lambda x: tuple(sorted(x)), list(
zip(*[self.network['i'].values, self.network['j'].values]))))
self.network.drop_duplicates(['ij', 't'], inplace=Tr... | python | def _drop_duplicate_ij(self):
"""
Drops duplicate entries from the network dataframe.
"""
self.network['ij'] = list(map(lambda x: tuple(sorted(x)), list(
zip(*[self.network['i'].values, self.network['j'].values]))))
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wiheto/teneto | teneto/classes/network.py | TemporalNetwork._drop_diagonal | def _drop_diagonal(self):
"""
Drops self-contacts from the network dataframe.
"""
self.network = self.network.where(
self.network['i'] != self.network['j']).dropna()
self.network.reset_index(inplace=True, drop=True) | python | def _drop_diagonal(self):
"""
Drops self-contacts from the network dataframe.
"""
self.network = self.network.where(
self.network['i'] != self.network['j']).dropna()
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wiheto/teneto | teneto/classes/network.py | TemporalNetwork.add_edge | def add_edge(self, edgelist):
"""
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
------... | python | def add_edge(self, edgelist):
"""
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
------... | [
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wiheto/teneto | teneto/classes/network.py | TemporalNetwork.drop_edge | def drop_edge(self, edgelist):
"""
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
--------
Updates TenetoBIDS.network dataframe
... | python | def drop_edge(self, edgelist):
"""
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
--------
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wiheto/teneto | teneto/classes/network.py | TemporalNetwork.calc_networkmeasure | def calc_networkmeasure(self, networkmeasure, **measureparams):
"""
Calculate network measure.
Parameters
-----------
networkmeasure : str
Function to call. Functions available are in teneto.networkmeasures
measureparams : kwargs
kwargs for tenet... | python | def calc_networkmeasure(self, networkmeasure, **measureparams):
"""
Calculate network measure.
Parameters
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networkmeasure : str
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wiheto/teneto | teneto/classes/network.py | TemporalNetwork.generatenetwork | def generatenetwork(self, networktype, **networkparams):
"""
Generate a network
Parameters
-----------
networktype : str
Function to call. Functions available are in teneto.generatenetwork
measureparams : kwargs
kwargs for teneto.generatenetwork.... | python | def generatenetwork(self, networktype, **networkparams):
"""
Generate a network
Parameters
-----------
networktype : str
Function to call. Functions available are in teneto.generatenetwork
measureparams : kwargs
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wiheto/teneto | teneto/classes/network.py | TemporalNetwork.save_aspickle | def save_aspickle(self, fname):
"""
Saves object as pickle.
fname : str
file path.
"""
if fname[-4:] != '.pkl':
fname += '.pkl'
with open(fname, 'wb') as f:
pickle.dump(self, f, pickle.HIGHEST_PROTOCOL) | python | def save_aspickle(self, fname):
"""
Saves object as pickle.
fname : str
file path.
"""
if fname[-4:] != '.pkl':
fname += '.pkl'
with open(fname, 'wb') as f:
pickle.dump(self, f, pickle.HIGHEST_PROTOCOL) | [
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wiheto/teneto | teneto/timeseries/postprocess.py | postpro_fisher | def postpro_fisher(data, report=None):
"""
Performs fisher transform on everything in data.
If report variable is passed, this is added to the report.
"""
if not report:
report = {}
# Due to rounding errors
data[data < -0.99999999999999] = -1
data[data > 0.99999999999999] = 1
... | python | def postpro_fisher(data, report=None):
"""
Performs fisher transform on everything in data.
If report variable is passed, this is added to the report.
"""
if not report:
report = {}
# Due to rounding errors
data[data < -0.99999999999999] = -1
data[data > 0.99999999999999] = 1
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wiheto/teneto | teneto/timeseries/postprocess.py | postpro_boxcox | def postpro_boxcox(data, report=None):
"""
Performs box cox transform on everything in data.
If report variable is passed, this is added to the report.
"""
if not report:
report = {}
# Note the min value of all time series will now be at least 1.
mindata = 1 - np.nanmin(data)
da... | python | def postpro_boxcox(data, report=None):
"""
Performs box cox transform on everything in data.
If report variable is passed, this is added to the report.
"""
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report = {}
# Note the min value of all time series will now be at least 1.
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wiheto/teneto | teneto/timeseries/postprocess.py | postpro_standardize | def postpro_standardize(data, report=None):
"""
Standardizes everything in data (along axis -1).
If report variable is passed, this is added to the report.
"""
if not report:
report = {}
# First make dim 1 = time.
data = np.transpose(data, [2, 0, 1])
standardized_data = (data - ... | python | def postpro_standardize(data, report=None):
"""
Standardizes everything in data (along axis -1).
If report variable is passed, this is added to the report.
"""
if not report:
report = {}
# First make dim 1 = time.
data = np.transpose(data, [2, 0, 1])
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wiheto/teneto | teneto/timeseries/derive.py | derive_temporalnetwork | def derive_temporalnetwork(data, params):
"""
Derives connectivity from the data. A lot of data is inherently built with edges
(e.g. communication between two individuals).
However other networks are derived from the covariance of time series
(e.g. brain networks between two regions).
Covaria... | python | def derive_temporalnetwork(data, params):
"""
Derives connectivity from the data. A lot of data is inherently built with edges
(e.g. communication between two individuals).
However other networks are derived from the covariance of time series
(e.g. brain networks between two regions).
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wiheto/teneto | teneto/timeseries/derive.py | _weightfun_jackknife | def _weightfun_jackknife(T, report):
"""
Creates the weights for the jackknife method. See func: teneto.derive.derive.
"""
weights = np.ones([T, T])
np.fill_diagonal(weights, 0)
report['method'] = 'jackknife'
report['jackknife'] = ''
return weights, report | python | def _weightfun_jackknife(T, report):
"""
Creates the weights for the jackknife method. See func: teneto.derive.derive.
"""
weights = np.ones([T, T])
np.fill_diagonal(weights, 0)
report['method'] = 'jackknife'
report['jackknife'] = ''
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wiheto/teneto | teneto/timeseries/derive.py | _weightfun_sliding_window | def _weightfun_sliding_window(T, params, report):
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"""
Creates the weights for the sliding window method. See func: teneto.derive.derive.
"""
weightat0 = np.zeros(T)
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wiheto/teneto | teneto/timeseries/derive.py | _weightfun_tapered_sliding_window | def _weightfun_tapered_sliding_window(T, params, report):
"""
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"""
x = np.arange(-(params['windowsize'] - 1) / 2, (params['windowsize']) / 2)
distribution_parameters = ','.join(map(str, params['distribution_params']))
tap... | python | def _weightfun_tapered_sliding_window(T, params, report):
"""
Creates the weights for the tapered method. See func: teneto.derive.derive.
"""
x = np.arange(-(params['windowsize'] - 1) / 2, (params['windowsize']) / 2)
distribution_parameters = ','.join(map(str, params['distribution_params']))
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wiheto/teneto | teneto/timeseries/derive.py | _weightfun_spatial_distance | def _weightfun_spatial_distance(data, params, report):
"""
Creates the weights for the spatial distance method. See func: teneto.derive.derive.
"""
distance = getDistanceFunction(params['distance'])
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"""
Creates the weights for the spatial distance method. See func: teneto.derive.derive.
"""
distance = getDistanceFunction(params['distance'])
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wiheto/teneto | teneto/timeseries/derive.py | _temporal_derivative | def _temporal_derivative(data, params, report):
"""
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"""
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# Normalize
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# Coupling
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"""
Performs mtd method. See func: teneto.derive.derive.
"""
# Data should be timexnode
report = {}
# Derivative
tdat = data[1:, :] - data[:-1, :]
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wiheto/teneto | teneto/utils/utils.py | binarize_percent | def binarize_percent(netin, level, sign='pos', axis='time'):
"""
Binarizes a network proprtionally. When axis='time' (only one available at the moment) then the top values for each edge time series are considered.
Parameters
----------
netin : array or dict
network (graphlet or contact rep... | python | def binarize_percent(netin, level, sign='pos', axis='time'):
"""
Binarizes a network proprtionally. When axis='time' (only one available at the moment) then the top values for each edge time series are considered.
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wiheto/teneto | teneto/utils/utils.py | binarize_rdp | def binarize_rdp(netin, level, sign='pos', axis='time'):
"""
Binarizes a network based on RDP compression.
Parameters
----------
netin : array or dict
Network (graphlet or contact representation),
level : float
Delta parameter which is the tolorated error in RDP compression.
... | python | def binarize_rdp(netin, level, sign='pos', axis='time'):
"""
Binarizes a network based on RDP compression.
Parameters
----------
netin : array or dict
Network (graphlet or contact representation),
level : float
Delta parameter which is the tolorated error in RDP compression.
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wiheto/teneto | teneto/utils/utils.py | binarize | def binarize(netin, threshold_type, threshold_level, sign='pos', axis='time'):
"""
Binarizes a network, returning the network. General wrapper function for different binarization functions.
Parameters
----------
netin : array or dict
Network (graphlet or contact representation),
thresh... | python | def binarize(netin, threshold_type, threshold_level, sign='pos', axis='time'):
"""
Binarizes a network, returning the network. General wrapper function for different binarization functions.
Parameters
----------
netin : array or dict
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wiheto/teneto | teneto/utils/utils.py | process_input | def process_input(netIn, allowedformats, outputformat='G'):
"""
Takes input network and checks what the input is.
Parameters
----------
netIn : array, dict, or TemporalNetwork
Network (graphlet, contact or object)
allowedformats : str
Which format of network objects that are al... | python | def process_input(netIn, allowedformats, outputformat='G'):
"""
Takes input network and checks what the input is.
Parameters
----------
netIn : array, dict, or TemporalNetwork
Network (graphlet, contact or object)
allowedformats : str
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netIn : array, dict, or TemporalNetwork
Network (graphlet, contact or object)
allowedformats : str
Which format of network objects that are allowed. Options: 'C', 'TN', 'G'.
outputformat: str, default=G
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wiheto/teneto | teneto/utils/utils.py | clean_community_indexes | def clean_community_indexes(communityID):
"""
Takes input of community assignments. Returns reindexed community assignment by using smallest numbers possible.
Parameters
----------
communityID : array-like
list or array of integers. Output from community detection algorithems.
Returns... | python | def clean_community_indexes(communityID):
"""
Takes input of community assignments. Returns reindexed community assignment by using smallest numbers possible.
Parameters
----------
communityID : array-like
list or array of integers. Output from community detection algorithems.
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wiheto/teneto | teneto/utils/utils.py | multiple_contacts_get_values | def multiple_contacts_get_values(C):
"""
Given an contact representation with repeated contacts, this function removes duplicates and creates a value
Parameters
----------
C : dict
contact representation with multiple repeated contacts.
Returns
-------
:C_out: dict
... | python | def multiple_contacts_get_values(C):
"""
Given an contact representation with repeated contacts, this function removes duplicates and creates a value
Parameters
----------
C : dict
contact representation with multiple repeated contacts.
Returns
-------
:C_out: dict
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wiheto/teneto | teneto/utils/utils.py | df_to_array | def df_to_array(df, netshape, nettype):
"""
Returns a numpy array (snapshot representation) from thedataframe contact list
Parameters:
df : pandas df
pandas df with columns, i,j,t.
netshape : tuple
network shape, format: (node, time)
nettype : str
... | python | def df_to_array(df, netshape, nettype):
"""
Returns a numpy array (snapshot representation) from thedataframe contact list
Parameters:
df : pandas df
pandas df with columns, i,j,t.
netshape : tuple
network shape, format: (node, time)
nettype : str
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wiheto/teneto | teneto/utils/utils.py | check_distance_funciton_input | def check_distance_funciton_input(distance_func_name, netinfo):
"""
Funciton checks distance_func_name, if it is specified as 'default'. Then given the type of the network selects a default distance function.
Parameters
----------
distance_func_name : str
distance function name.
netin... | python | def check_distance_funciton_input(distance_func_name, netinfo):
"""
Funciton checks distance_func_name, if it is specified as 'default'. Then given the type of the network selects a default distance function.
Parameters
----------
distance_func_name : str
distance function name.
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wiheto/teneto | teneto/utils/utils.py | load_parcellation_coords | def load_parcellation_coords(parcellation_name):
"""
Loads coordinates of included parcellations.
Parameters
----------
parcellation_name : str
options: 'gordon2014_333', 'power2012_264', 'shen2013_278'.
Returns
-------
parc : array
parcellation cordinates
"""
... | python | def load_parcellation_coords(parcellation_name):
"""
Loads coordinates of included parcellations.
Parameters
----------
parcellation_name : str
options: 'gordon2014_333', 'power2012_264', 'shen2013_278'.
Returns
-------
parc : array
parcellation cordinates
"""
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wiheto/teneto | teneto/utils/utils.py | make_parcellation | def make_parcellation(data_path, parcellation, parc_type=None, parc_params=None):
"""
Performs a parcellation which reduces voxel space to regions of interest (brain data).
Parameters
----------
data_path : str
Path to .nii image.
parcellation : str
Specify which parcellation t... | python | def make_parcellation(data_path, parcellation, parc_type=None, parc_params=None):
"""
Performs a parcellation which reduces voxel space to regions of interest (brain data).
Parameters
----------
data_path : str
Path to .nii image.
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wiheto/teneto | teneto/utils/utils.py | create_traj_ranges | def create_traj_ranges(start, stop, N):
"""
Fills in the trajectory range.
# Adapted from https://stackoverflow.com/a/40624614
"""
steps = (1.0/(N-1)) * (stop - start)
if np.isscalar(steps):
return steps*np.arange(N) + start
else:
return steps[:, None]*np.arange(N) + start[:... | python | def create_traj_ranges(start, stop, N):
"""
Fills in the trajectory range.
# Adapted from https://stackoverflow.com/a/40624614
"""
steps = (1.0/(N-1)) * (stop - start)
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wiheto/teneto | teneto/utils/utils.py | get_dimord | def get_dimord(measure, calc=None, community=None):
"""
Get the dimension order of a network measure.
Parameters
----------
measure : str
Name of funciton in teneto.networkmeasures.
calc : str, default=None
Calc parameter for the function
community : bool, default=None
... | python | def get_dimord(measure, calc=None, community=None):
"""
Get the dimension order of a network measure.
Parameters
----------
measure : str
Name of funciton in teneto.networkmeasures.
calc : str, default=None
Calc parameter for the function
community : bool, default=None
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wiheto/teneto | teneto/utils/utils.py | get_network_when | def get_network_when(tnet, i=None, j=None, t=None, ij=None, logic='and', copy=False, asarray=False):
"""
Returns subset of dataframe that matches index
Parameters
----------
tnet : df or TemporalNetwork
TemporalNetwork object or pandas dataframe edgelist
i : list or int
get node... | python | def get_network_when(tnet, i=None, j=None, t=None, ij=None, logic='and', copy=False, asarray=False):
"""
Returns subset of dataframe that matches index
Parameters
----------
tnet : df or TemporalNetwork
TemporalNetwork object or pandas dataframe edgelist
i : list or int
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wiheto/teneto | teneto/utils/utils.py | create_supraadjacency_matrix | def create_supraadjacency_matrix(tnet, intersliceweight=1):
"""
Returns a supraadjacency matrix from a temporal network structure
Parameters
--------
tnet : TemporalNetwork
Temporal network (any network type)
intersliceweight : int
Weight that links the same node from adjacent t... | python | def create_supraadjacency_matrix(tnet, intersliceweight=1):
"""
Returns a supraadjacency matrix from a temporal network structure
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--------
tnet : TemporalNetwork
Temporal network (any network type)
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wiheto/teneto | teneto/utils/io.py | tnet_to_nx | def tnet_to_nx(df, t=None):
"""
Creates undirected networkx object
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if t is not None:
df = get_network_when(df, t=t)
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else:
nxobj = nx.from_pandas_edg... | python | def tnet_to_nx(df, t=None):
"""
Creates undirected networkx object
"""
if t is not None:
df = get_network_when(df, t=t)
if 'weight' in df.columns:
nxobj = nx.from_pandas_edgelist(
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wiheto/teneto | teneto/communitydetection/louvain.py | temporal_louvain | def temporal_louvain(tnet, resolution=1, intersliceweight=1, n_iter=100, negativeedge='ignore', randomseed=None, consensus_threshold=0.5, temporal_consensus=True, njobs=1):
r"""
Louvain clustering for a temporal network.
Parameters
-----------
tnet : array, dict, TemporalNetwork
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r"""
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wiheto/teneto | teneto/communitydetection/louvain.py | make_consensus_matrix | def make_consensus_matrix(com_membership, th=0.5):
r"""
Makes the consensus matrix
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Parameters
----------
com_membership : array
Shape should be node, time, iteration.
th : float
threshold to cancel noisey edges
Returns
-------
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consensus matrix
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r"""
Makes the consensus matrix
.
Parameters
----------
com_membership : array
Shape should be node, time, iteration.
th : float
threshold to cancel noisey edges
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-------
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consensus matrix
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wiheto/teneto | teneto/communitydetection/louvain.py | make_temporal_consensus | def make_temporal_consensus(com_membership):
r"""
Matches community labels accross time-points
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Parameters
----------
com_membership : array
Shape should be node, time.
Returns... | python | def make_temporal_consensus(com_membership):
r"""
Matches community labels accross time-points
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com_membership : array
Shape should be node, time.
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wiheto/teneto | teneto/temporalcommunity/flexibility.py | flexibility | def flexibility(communities):
"""
Amount a node changes community
Parameters
----------
communities : array
Community array of shape (node,time)
Returns
--------
flex : array
Size with the flexibility of each node.
Notes
-----
Flexbility calculates the numb... | python | def flexibility(communities):
"""
Amount a node changes community
Parameters
----------
communities : array
Community array of shape (node,time)
Returns
--------
flex : array
Size with the flexibility of each node.
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wiheto/teneto | teneto/plot/slice_plot.py | slice_plot | def slice_plot(netin, ax, nodelabels=None, timelabels=None, communities=None, plotedgeweights=False, edgeweightscalar=1, timeunit='', linestyle='k-', cmap=None, nodesize=100, nodekwargs=None, edgekwargs=None):
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Fuction draws "slice graph" and exports axis handles
Parameters
----------
netin ... | python | def slice_plot(netin, ax, nodelabels=None, timelabels=None, communities=None, plotedgeweights=False, edgeweightscalar=1, timeunit='', linestyle='k-', cmap=None, nodesize=100, nodekwargs=None, edgekwargs=None):
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Fuction draws "slice graph" and exports axis handles
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wiheto/teneto | teneto/networkmeasures/local_variation.py | local_variation | def local_variation(data):
r"""
Calculates the local variaiont of inter-contact times. [LV-1]_, [LV-2]_
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----------
data : array, dict
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Calculates the local variaiont of inter-contact times. [LV-1]_, [LV-2]_
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data : array, dict
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wiheto/teneto | teneto/utils/bidsutils.py | drop_bids_suffix | def drop_bids_suffix(fname):
"""
Given a filename sub-01_run-01_preproc.nii.gz, it will return ['sub-01_run-01', '.nii.gz']
Parameters
----------
fname : str
BIDS filename with suffice. Directories should not be included.
Returns
-------
fname_head : str
BIDS filename ... | python | def drop_bids_suffix(fname):
"""
Given a filename sub-01_run-01_preproc.nii.gz, it will return ['sub-01_run-01', '.nii.gz']
Parameters
----------
fname : str
BIDS filename with suffice. Directories should not be included.
Returns
-------
fname_head : str
BIDS filename ... | [
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wiheto/teneto | teneto/utils/bidsutils.py | load_tabular_file | def load_tabular_file(fname, return_meta=False, header=True, index_col=True):
"""
Given a file name loads as a pandas data frame
Parameters
----------
fname : str
file name and path. Must be tsv.
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header : bool (default True)
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"""
Given a file name loads as a pandas data frame
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----------
fname : str
file name and path. Must be tsv.
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wiheto/teneto | teneto/utils/bidsutils.py | get_sidecar | def get_sidecar(fname, allowedfileformats='default'):
"""
Loads sidecar or creates one
"""
if allowedfileformats == 'default':
allowedfileformats = ['.tsv', '.nii.gz']
for f in allowedfileformats:
fname = fname.split(f)[0]
fname += '.json'
if os.path.exists(fname):
wi... | python | def get_sidecar(fname, allowedfileformats='default'):
"""
Loads sidecar or creates one
"""
if allowedfileformats == 'default':
allowedfileformats = ['.tsv', '.nii.gz']
for f in allowedfileformats:
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wiheto/teneto | teneto/utils/bidsutils.py | process_exclusion_criteria | def process_exclusion_criteria(exclusion_criteria):
"""
Parses an exclusion critera string to get the function and threshold.
Parameters
----------
exclusion_criteria : list
list of strings where each string is of the format [relation][threshold]. E.g. \'<0.5\' or \'>=1\'
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Parses an exclusion critera string to get the function and threshold.
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wiheto/teneto | teneto/networkmeasures/reachability_latency.py | reachability_latency | def reachability_latency(tnet=None, paths=None, rratio=1, calc='global'):
"""
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Parameters
---------
data : array or dict
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"""
Reachability latency. This is the r-th longest temporal path.
Parameters
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data : array or dict
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wiheto/teneto | teneto/networkmeasures/fluctuability.py | fluctuability | def fluctuability(netin, calc='global'):
r"""
Fluctuability of temporal networks. This is the variation of the network's edges over time. [fluct-1]_
This is the unique number of edges through time divided by the overall number of edges.
Parameters
----------
netin : array or dict
Temp... | python | def fluctuability(netin, calc='global'):
r"""
Fluctuability of temporal networks. This is the variation of the network's edges over time. [fluct-1]_
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wiheto/teneto | teneto/networkmeasures/topological_overlap.py | topological_overlap | def topological_overlap(tnet, calc='time'):
r"""
Topological overlap quantifies the persistency of edges through time. If two consequtive time-points have similar edges, this becomes high (max 1). If there is high change, this becomes 0.
References: [topo-1]_, [topo-2]_
Parameters
----------
t... | python | def topological_overlap(tnet, calc='time'):
r"""
Topological overlap quantifies the persistency of edges through time. If two consequtive time-points have similar edges, this becomes high (max 1). If there is high change, this becomes 0.
References: [topo-1]_, [topo-2]_
Parameters
----------
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wiheto/teneto | teneto/temporalcommunity/recruitment.py | recruitment | def recruitment(temporalcommunities, staticcommunities):
"""
Calculates recruitment coefficient for each node. Recruitment coefficient is the average probability of nodes from the
same static communities being in the same temporal communities at other time-points or during different tasks.
Parameters... | python | def recruitment(temporalcommunities, staticcommunities):
"""
Calculates recruitment coefficient for each node. Recruitment coefficient is the average probability of nodes from the
same static communities being in the same temporal communities at other time-points or during different tasks.
Parameters... | [
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wiheto/teneto | teneto/plot/circle_plot.py | circle_plot | def circle_plot(netIn, ax, nodelabels=None, linestyle='k-', nodesize=1000, cmap='Set2'):
r'''
Function draws "circle plot" and exports axis handles
Parameters
-------------
netIn : temporal network input (graphlet or contact)
ax : matplotlib ax handles.
nodelabels : list
nodes labe... | python | def circle_plot(netIn, ax, nodelabels=None, linestyle='k-', nodesize=1000, cmap='Set2'):
r'''
Function draws "circle plot" and exports axis handles
Parameters
-------------
netIn : temporal network input (graphlet or contact)
ax : matplotlib ax handles.
nodelabels : list
nodes labe... | [
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wiheto/teneto | teneto/temporalcommunity/integration.py | integration | def integration(temporalcommunities, staticcommunities):
"""
Calculates the integration coefficient for each node. Measures the average probability
that a node is in the same community as nodes from other systems.
Parameters:
------------
temporalcommunities : array
temporal co... | python | def integration(temporalcommunities, staticcommunities):
"""
Calculates the integration coefficient for each node. Measures the average probability
that a node is in the same community as nodes from other systems.
Parameters:
------------
temporalcommunities : array
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wiheto/teneto | teneto/networkmeasures/intercontacttimes.py | intercontacttimes | def intercontacttimes(tnet):
"""
Calculates the intercontacttimes of each edge in a network.
Parameters
-----------
tnet : array, dict
Temporal network (craphlet or contact). Nettype: 'bu', 'bd'
Returns
---------
contacts : dict
Intercontact times as numpy array in di... | python | def intercontacttimes(tnet):
"""
Calculates the intercontacttimes of each edge in a network.
Parameters
-----------
tnet : array, dict
Temporal network (craphlet or contact). Nettype: 'bu', 'bd'
Returns
---------
contacts : dict
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wiheto/teneto | teneto/timeseries/report.py | gen_report | def gen_report(report, sdir='./', report_name='report.html'):
"""
Generates report of derivation and postprocess steps in teneto.derive
"""
# Create report directory
if not os.path.exists(sdir):
os.makedirs(sdir)
# Add a slash to file directory if not included to avoid DirNameFleName
... | python | def gen_report(report, sdir='./', report_name='report.html'):
"""
Generates report of derivation and postprocess steps in teneto.derive
"""
# Create report directory
if not os.path.exists(sdir):
os.makedirs(sdir)
# Add a slash to file directory if not included to avoid DirNameFleName
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wiheto/teneto | teneto/classes/bids.py | TenetoBIDS.add_history | def add_history(self, fname, fargs, init=0):
"""
Adds a processing step to TenetoBIDS.history.
"""
if init == 1:
self.history = []
self.history.append([fname, fargs]) | python | def add_history(self, fname, fargs, init=0):
"""
Adds a processing step to TenetoBIDS.history.
"""
if init == 1:
self.history = []
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wiheto/teneto | teneto/classes/bids.py | TenetoBIDS.export_history | def export_history(self, dirname):
"""
Exports TenetoBIDShistory.py, tenetoinfo.json, requirements.txt (modules currently imported) to dirname
Parameters
---------
dirname : str
directory to export entire TenetoBIDS history.
"""
mods = [(m.__name__, ... | python | def export_history(self, dirname):
"""
Exports TenetoBIDShistory.py, tenetoinfo.json, requirements.txt (modules currently imported) to dirname
Parameters
---------
dirname : str
directory to export entire TenetoBIDS history.
"""
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wiheto/teneto | teneto/classes/bids.py | TenetoBIDS.derive_temporalnetwork | def derive_temporalnetwork(self, params, update_pipeline=True, tag=None, njobs=1, confound_corr_report=True):
"""
Derive time-varying connectivity on the selected files.
Parameters
----------
params : dict.
See teneto.timeseries.derive_temporalnetwork for the structu... | python | def derive_temporalnetwork(self, params, update_pipeline=True, tag=None, njobs=1, confound_corr_report=True):
"""
Derive time-varying connectivity on the selected files.
Parameters
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params : dict.
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wiheto/teneto | teneto/classes/bids.py | TenetoBIDS._derive_temporalnetwork | def _derive_temporalnetwork(self, f, i, tag, params, confounds_exist, confound_files):
"""
Funciton called by TenetoBIDS.derive_temporalnetwork for concurrent processing.
"""
data = load_tabular_file(f, index_col=True, header=True)
fs, _ = drop_bids_suffix(f)
save_name, ... | python | def _derive_temporalnetwork(self, f, i, tag, params, confounds_exist, confound_files):
"""
Funciton called by TenetoBIDS.derive_temporalnetwork for concurrent processing.
"""
data = load_tabular_file(f, index_col=True, header=True)
fs, _ = drop_bids_suffix(f)
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wiheto/teneto | teneto/classes/bids.py | TenetoBIDS.make_functional_connectivity | def make_functional_connectivity(self, njobs=None, returngroup=False, file_hdr=None, file_idx=None):
"""
Makes connectivity matrix for each of the subjects.
Parameters
----------
returngroup : bool, default=False
If true, returns the group average connectivity matrix... | python | def make_functional_connectivity(self, njobs=None, returngroup=False, file_hdr=None, file_idx=None):
"""
Makes connectivity matrix for each of the subjects.
Parameters
----------
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wiheto/teneto | teneto/classes/bids.py | TenetoBIDS._save_namepaths_bids_derivatives | def _save_namepaths_bids_derivatives(self, f, tag, save_directory, suffix=None):
"""
Creates output directory and output name
Paramters
---------
f : str
input files, includes the file bids_suffix
tag : str
what should be added to f in the output ... | python | def _save_namepaths_bids_derivatives(self, f, tag, save_directory, suffix=None):
"""
Creates output directory and output name
Paramters
---------
f : str
input files, includes the file bids_suffix
tag : str
what should be added to f in the output ... | [
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wiheto/teneto | teneto/classes/bids.py | TenetoBIDS.get_tags | def get_tags(self, tag, quiet=1):
"""
Returns which tag alternatives can be identified in the BIDS derivatives structure.
"""
if not self.pipeline:
print('Please set pipeline first.')
self.get_pipeline_alternatives(quiet)
else:
if tag == 'sub':... | python | def get_tags(self, tag, quiet=1):
"""
Returns which tag alternatives can be identified in the BIDS derivatives structure.
"""
if not self.pipeline:
print('Please set pipeline first.')
self.get_pipeline_alternatives(quiet)
else:
if tag == 'sub':... | [
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wiheto/teneto | teneto/classes/bids.py | TenetoBIDS.get_pipeline_alternatives | def get_pipeline_alternatives(self, quiet=0):
"""
The pipeline are the different outputs that are placed in the ./derivatives directory.
get_pipeline_alternatives gets those which are found in the specified BIDS directory structure.
"""
if not os.path.exists(self.BIDS_dir + '/de... | python | def get_pipeline_alternatives(self, quiet=0):
"""
The pipeline are the different outputs that are placed in the ./derivatives directory.
get_pipeline_alternatives gets those which are found in the specified BIDS directory structure.
"""
if not os.path.exists(self.BIDS_dir + '/de... | [
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wiheto/teneto | teneto/classes/bids.py | TenetoBIDS.get_pipeline_subdir_alternatives | def get_pipeline_subdir_alternatives(self, quiet=0):
"""
Note
-----
This function currently returns the wrong folders and will be fixed in the future.
This function should return ./derivatives/pipeline/sub-xx/[ses-yy/][func/]/pipeline_subdir
But it does not care about s... | python | def get_pipeline_subdir_alternatives(self, quiet=0):
"""
Note
-----
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wiheto/teneto | teneto/classes/bids.py | TenetoBIDS.get_selected_files | def get_selected_files(self, pipeline='pipeline', forfile=None, quiet=0, allowedfileformats='default'):
"""
Parameters
----------
pipeline : string
can be \'pipeline\' (main analysis pipeline, self in tnet.set_pipeline) or \'confound\' (where confound files are, set in tnet.s... | python | def get_selected_files(self, pipeline='pipeline', forfile=None, quiet=0, allowedfileformats='default'):
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Parameters
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wiheto/teneto | teneto/classes/bids.py | TenetoBIDS.set_exclusion_file | def set_exclusion_file(self, confound, exclusion_criteria, confound_stat='mean'):
"""
Excludes subjects given a certain exclusion criteria.
Parameters
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confound : str or list
string or list of confound name(s) from confound files
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"""
Excludes subjects given a certain exclusion criteria.
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confound : str or list
string or list of confound name(s) from confound files
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wiheto/teneto | teneto/classes/bids.py | TenetoBIDS.set_exclusion_timepoint | def set_exclusion_timepoint(self, confound, exclusion_criteria, replace_with, tol=1, overwrite=True, desc=None):
"""
Excludes subjects given a certain exclusion criteria. Does not work on nifti files, only csv, numpy or tsc. Assumes data is node,time
Parameters
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co... | python | def set_exclusion_timepoint(self, confound, exclusion_criteria, replace_with, tol=1, overwrite=True, desc=None):
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Excludes subjects given a certain exclusion criteria. Does not work on nifti files, only csv, numpy or tsc. Assumes data is node,time
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wiheto/teneto | teneto/classes/bids.py | TenetoBIDS.make_parcellation | def make_parcellation(self, parcellation, parc_type=None, parc_params=None, network='defaults', update_pipeline=True, removeconfounds=False, tag=None, njobs=None, clean_params=None, yeonetworkn=None):
"""
Reduces the data from voxel to parcellation space. Files get saved in a teneto folder in the deriva... | python | def make_parcellation(self, parcellation, parc_type=None, parc_params=None, network='defaults', update_pipeline=True, removeconfounds=False, tag=None, njobs=None, clean_params=None, yeonetworkn=None):
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wiheto/teneto | teneto/classes/bids.py | TenetoBIDS.communitydetection | def communitydetection(self, community_detection_params, community_type='temporal', tag=None, file_hdr=False, file_idx=False, njobs=None):
"""
Calls temporal_louvain_with_consensus on connectivity data
Parameters
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kwargs f... | python | def communitydetection(self, community_detection_params, community_type='temporal', tag=None, file_hdr=False, file_idx=False, njobs=None):
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Calls temporal_louvain_with_consensus on connectivity data
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wiheto/teneto | teneto/classes/bids.py | TenetoBIDS.removeconfounds | def removeconfounds(self, confounds=None, clean_params=None, transpose=None, njobs=None, update_pipeline=True, overwrite=True, tag=None):
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Removes specified confounds using nilearn.signal.clean
Parameters
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wiheto/teneto | teneto/classes/bids.py | TenetoBIDS.networkmeasures | def networkmeasures(self, measure=None, measure_params=None, tag=None, njobs=None):
"""
Calculates a network measure
For available funcitons see: teneto.networkmeasures
Parameters
----------
measure : str or list
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"""
Calculates a network measure
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measure : str or list
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wiheto/teneto | teneto/classes/bids.py | TenetoBIDS.set_confound_pipeline | def set_confound_pipeline(self, confound_pipeline):
"""
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"""
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wiheto/teneto | teneto/classes/bids.py | TenetoBIDS.set_bids_suffix | def set_bids_suffix(self, bids_suffix):
"""
The last analysis step is the final tag that is present in files.
"""
self.add_history(inspect.stack()[0][3], locals(), 1)
self.bids_suffix = bids_suffix | python | def set_bids_suffix(self, bids_suffix):
"""
The last analysis step is the final tag that is present in files.
"""
self.add_history(inspect.stack()[0][3], locals(), 1)
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wiheto/teneto | teneto/classes/bids.py | TenetoBIDS.set_pipeline | def set_pipeline(self, pipeline):
"""
Specify the pipeline. See get_pipeline_alternatives to see what are avaialble. Input should be a string.
"""
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wiheto/teneto | teneto/classes/bids.py | TenetoBIDS.print_dataset_summary | def print_dataset_summary(self):
"""
Prints information about the the BIDS data and the files currently selected.
"""
print('--- DATASET INFORMATION ---')
print('--- Subjects ---')
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print('... | python | def print_dataset_summary(self):
"""
Prints information about the the BIDS data and the files currently selected.
"""
print('--- DATASET INFORMATION ---')
print('--- Subjects ---')
if self.raw_data_exists:
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wiheto/teneto | teneto/classes/bids.py | TenetoBIDS.load_frompickle | def load_frompickle(cls, fname, reload_object=False):
"""
Loaded saved instance of
fname : str
path to pickle object (output of TenetoBIDS.save_aspickle)
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Loaded saved instance of
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wiheto/teneto | teneto/classes/bids.py | TenetoBIDS.load_data | def load_data(self, datatype='tvc', tag=None, measure=''):
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wiheto/teneto | teneto/networkmeasures/temporal_closeness_centrality.py | temporal_closeness_centrality | def temporal_closeness_centrality(tnet=None, paths=None):
'''
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Parameters
-----------
Input should be *either* tnet or paths.
data : array or dict
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paths : pandas dat... | python | def temporal_closeness_centrality(tnet=None, paths=None):
'''
Returns temporal closeness centrality per node.
Parameters
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Input should be *either* tnet or paths.
data : array or dict
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wiheto/teneto | teneto/networkmeasures/sid.py | sid | def sid(tnet, communities, axis=0, calc='global', decay=0):
r"""
Segregation integration difference (SID). An estimation of each community or global difference of within versus between community strength.[sid-1]_
Parameters
----------
tnet: array, dict
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r"""
Segregation integration difference (SID). An estimation of each community or global difference of within versus between community strength.[sid-1]_
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wiheto/teneto | teneto/networkmeasures/bursty_coeff.py | bursty_coeff | def bursty_coeff(data, calc='edge', nodes='all', communities=None, threshold_type=None, threshold_level=None, threshold_params=None):
r"""
Calculates the bursty coefficient.[1][2]
Parameters
----------
data : array, dict
This is either (1) temporal network input (graphlet or contact) with ... | python | def bursty_coeff(data, calc='edge', nodes='all', communities=None, threshold_type=None, threshold_level=None, threshold_params=None):
r"""
Calculates the bursty coefficient.[1][2]
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ianlini/flatten-dict | flatten_dict/flatten_dict.py | flatten | def flatten(d, reducer='tuple', inverse=False):
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salu133445/pypianoroll | pypianoroll/plot.py | plot_pianoroll | def plot_pianoroll(ax, pianoroll, is_drum=False, beat_resolution=None,
downbeats=None, preset='default', cmap='Blues', xtick='auto',
ytick='octave', xticklabel=True, yticklabel='auto',
tick_loc=None, tick_direction='in', label='both',
grid='bot... | python | def plot_pianoroll(ax, pianoroll, is_drum=False, beat_resolution=None,
downbeats=None, preset='default', cmap='Blues', xtick='auto',
ytick='octave', xticklabel=True, yticklabel='auto',
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salu133445/pypianoroll | pypianoroll/plot.py | plot_track | def plot_track(track, filename=None, beat_resolution=None, downbeats=None,
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xticklabel=True, yticklabel='auto', tick_loc=None,
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salu133445/pypianoroll | pypianoroll/plot.py | plot_multitrack | def plot_multitrack(multitrack, filename=None, mode='separate',
track_label='name', preset='default', cmaps=None,
xtick='auto', ytick='octave', xticklabel=True,
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label='both', grid='both... | python | def plot_multitrack(multitrack, filename=None, mode='separate',
track_label='name', preset='default', cmaps=None,
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salu133445/pypianoroll | pypianoroll/plot.py | save_animation | def save_animation(filename, pianoroll, window, hop=1, fps=None, is_drum=False,
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... | python | def save_animation(filename, pianoroll, window, hop=1, fps=None, is_drum=False,
beat_resolution=None, downbeats=None, preset='default',
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salu133445/pypianoroll | pypianoroll/multitrack.py | Multitrack.append_track | def append_track(self, track=None, pianoroll=None, program=0, is_drum=False,
name='unknown'):
"""
Append a multitrack.Track instance to the track list or create a new
multitrack.Track object and append it to the track list.
Parameters
----------
trac... | python | def append_track(self, track=None, pianoroll=None, program=0, is_drum=False,
name='unknown'):
"""
Append a multitrack.Track instance to the track list or create a new
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salu133445/pypianoroll | pypianoroll/multitrack.py | Multitrack.check_validity | def check_validity(self):
"""
Raise an error if any invalid attribute found.
Raises
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Raise an error if any invalid attribute found.
Raises
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If an attribute has an invalid type.
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salu133445/pypianoroll | pypianoroll/multitrack.py | Multitrack.clip | def clip(self, lower=0, upper=127):
"""
Clip the pianorolls of all tracks by the given lower and upper bounds.
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lower : int or float
The lower bound to clip the pianorolls. Defaults to 0.
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"""
Clip the pianorolls of all tracks by the given lower and upper bounds.
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The lower bound to clip the pianorolls. Defaults to 0.
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salu133445/pypianoroll | pypianoroll/multitrack.py | Multitrack.get_active_length | def get_active_length(self):
"""
Return the maximum active length (i.e., without trailing silence) among
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Returns
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"""
Return the maximum active length (i.e., without trailing silence) among
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salu133445/pypianoroll | pypianoroll/multitrack.py | Multitrack.get_active_pitch_range | def get_active_pitch_range(self):
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Return the active pitch range of the pianorolls of all tracks as a tuple
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Returns
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The lowest active pitch among the pianorolls of all tracks.
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"""
Return the active pitch range of the pianorolls of all tracks as a tuple
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lowest : int
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salu133445/pypianoroll | pypianoroll/multitrack.py | Multitrack.get_downbeat_steps | def get_downbeat_steps(self):
"""
Return the indices of time steps that contain downbeats.
Returns
-------
downbeat_steps : list
The indices of time steps that contain downbeats.
"""
if self.downbeat is None:
return []
downbeat_st... | python | def get_downbeat_steps(self):
"""
Return the indices of time steps that contain downbeats.
Returns
-------
downbeat_steps : list
The indices of time steps that contain downbeats.
"""
if self.downbeat is None:
return []
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salu133445/pypianoroll | pypianoroll/multitrack.py | Multitrack.get_empty_tracks | def get_empty_tracks(self):
"""
Return the indices of tracks with empty pianorolls.
Returns
-------
empty_track_indices : list
The indices of tracks with empty pianorolls.
"""
empty_track_indices = [idx for idx, track in enumerate(self.tracks)
... | python | def get_empty_tracks(self):
"""
Return the indices of tracks with empty pianorolls.
Returns
-------
empty_track_indices : list
The indices of tracks with empty pianorolls.
"""
empty_track_indices = [idx for idx, track in enumerate(self.tracks)
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salu133445/pypianoroll | pypianoroll/multitrack.py | Multitrack.get_max_length | def get_max_length(self):
"""
Return the maximum length of the pianorolls along the time axis (in
time step).
Returns
-------
max_length : int
The maximum length of the pianorolls along the time axis (in time
step).
"""
max_length... | python | def get_max_length(self):
"""
Return the maximum length of the pianorolls along the time axis (in
time step).
Returns
-------
max_length : int
The maximum length of the pianorolls along the time axis (in time
step).
"""
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salu133445/pypianoroll | pypianoroll/multitrack.py | Multitrack.get_merged_pianoroll | def get_merged_pianoroll(self, mode='sum'):
"""
Return the merged pianoroll.
Parameters
----------
mode : {'sum', 'max', 'any'}
A string that indicates the merging strategy to apply along the
track axis. Default to 'sum'.
- In 'sum' mode, the... | python | def get_merged_pianoroll(self, mode='sum'):
"""
Return the merged pianoroll.
Parameters
----------
mode : {'sum', 'max', 'any'}
A string that indicates the merging strategy to apply along the
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salu133445/pypianoroll | pypianoroll/multitrack.py | Multitrack.get_stacked_pianoroll | def get_stacked_pianoroll(self):
"""
Return a stacked multitrack pianoroll. The shape of the return array is
(n_time_steps, 128, n_tracks).
Returns
-------
stacked : np.ndarray, shape=(n_time_steps, 128, n_tracks)
The stacked pianoroll.
"""
m... | python | def get_stacked_pianoroll(self):
"""
Return a stacked multitrack pianoroll. The shape of the return array is
(n_time_steps, 128, n_tracks).
Returns
-------
stacked : np.ndarray, shape=(n_time_steps, 128, n_tracks)
The stacked pianoroll.
"""
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salu133445/pypianoroll | pypianoroll/multitrack.py | Multitrack.load | def load(self, filename):
"""
Load a npz file. Supports only files previously saved by
:meth:`pypianoroll.Multitrack.save`.
Notes
-----
Attribute values will all be overwritten.
Parameters
----------
filename : str
The name of the npz... | python | def load(self, filename):
"""
Load a npz file. Supports only files previously saved by
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Notes
-----
Attribute values will all be overwritten.
Parameters
----------
filename : str
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salu133445/pypianoroll | pypianoroll/multitrack.py | Multitrack.merge_tracks | def merge_tracks(self, track_indices=None, mode='sum', program=0,
is_drum=False, name='merged', remove_merged=False):
"""
Merge pianorolls of the tracks specified by `track_indices`. The merged
track will have program number as given by `program` and drum indicator
a... | python | def merge_tracks(self, track_indices=None, mode='sum', program=0,
is_drum=False, name='merged', remove_merged=False):
"""
Merge pianorolls of the tracks specified by `track_indices`. The merged
track will have program number as given by `program` and drum indicator
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salu133445/pypianoroll | pypianoroll/multitrack.py | Multitrack.pad_to_same | def pad_to_same(self):
"""Pad shorter pianorolls with zeros at the end along the time axis to
make the resulting pianoroll lengths the same as the maximum pianoroll
length among all the tracks."""
max_length = self.get_max_length()
for track in self.tracks:
if track.p... | python | def pad_to_same(self):
"""Pad shorter pianorolls with zeros at the end along the time axis to
make the resulting pianoroll lengths the same as the maximum pianoroll
length among all the tracks."""
max_length = self.get_max_length()
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if track.p... | [
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salu133445/pypianoroll | pypianoroll/multitrack.py | Multitrack.parse_midi | def parse_midi(self, filename, **kwargs):
"""
Parse a MIDI file.
Parameters
----------
filename : str
The name of the MIDI file to be parsed.
**kwargs:
See :meth:`pypianoroll.Multitrack.parse_pretty_midi` for full
documentation.
... | python | def parse_midi(self, filename, **kwargs):
"""
Parse a MIDI file.
Parameters
----------
filename : str
The name of the MIDI file to be parsed.
**kwargs:
See :meth:`pypianoroll.Multitrack.parse_pretty_midi` for full
documentation.
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salu133445/pypianoroll | pypianoroll/multitrack.py | Multitrack.parse_pretty_midi | def parse_pretty_midi(self, pm, mode='max', algorithm='normal',
binarized=False, skip_empty_tracks=True,
collect_onsets_only=False, threshold=0,
first_beat_time=None):
"""
Parse a :class:`pretty_midi.PrettyMIDI` object. The da... | python | def parse_pretty_midi(self, pm, mode='max', algorithm='normal',
binarized=False, skip_empty_tracks=True,
collect_onsets_only=False, threshold=0,
first_beat_time=None):
"""
Parse a :class:`pretty_midi.PrettyMIDI` object. The da... | [
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salu133445/pypianoroll | pypianoroll/multitrack.py | Multitrack.remove_tracks | def remove_tracks(self, track_indices):
"""
Remove tracks specified by `track_indices`.
Parameters
----------
track_indices : list
The indices of the tracks to be removed.
"""
if isinstance(track_indices, int):
track_indices = [track_indi... | python | def remove_tracks(self, track_indices):
"""
Remove tracks specified by `track_indices`.
Parameters
----------
track_indices : list
The indices of the tracks to be removed.
"""
if isinstance(track_indices, int):
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salu133445/pypianoroll | pypianoroll/multitrack.py | Multitrack.save | def save(self, filename, compressed=True):
"""
Save the multitrack pianoroll to a (compressed) npz file, which can be
later loaded by :meth:`pypianoroll.Multitrack.load`.
Notes
-----
To reduce the file size, the pianorolls are first converted to instances
of scip... | python | def save(self, filename, compressed=True):
"""
Save the multitrack pianoroll to a (compressed) npz file, which can be
later loaded by :meth:`pypianoroll.Multitrack.load`.
Notes
-----
To reduce the file size, the pianorolls are first converted to instances
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To reduce the file size, the pianorolls are first converted to instances
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salu133445/pypianoroll | pypianoroll/multitrack.py | Multitrack.to_pretty_midi | def to_pretty_midi(self, constant_tempo=None, constant_velocity=100):
"""
Convert to a :class:`pretty_midi.PrettyMIDI` instance.
Notes
-----
- Only constant tempo is supported by now.
- The velocities of the converted pianorolls are clipped to [0, 127],
i.e. va... | python | def to_pretty_midi(self, constant_tempo=None, constant_velocity=100):
"""
Convert to a :class:`pretty_midi.PrettyMIDI` instance.
Notes
-----
- Only constant tempo is supported by now.
- The velocities of the converted pianorolls are clipped to [0, 127],
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