xgi.stats.MultiDiNodeStat#

class MultiDiNodeStat(network, view, stats)[source]#

Bases: MultiIDStat

Multiple node-quantity mappings.

For more details, see the tutorial.

Attributes

name

Name of this stat.

Methods

asdict

Output the stats as a dict of collections.

aslist

Output the stats as a list of collections.

asnumpy

Output the stats as a numpy array.

aspandas

Output the stats as a pandas dataframe.

ashist

Return the distributions of a numpy array.
argmax()#

The ID corresponding to the maximum of the stat

When the maximal value is not unique, returns first ID corresponding to the maximal value.

Returns:

The ID to which the maximum value corresponds.

Return type:

hashable

argmin()#

The ID corresponding to the minimum of the stat

When the minimum value is not unique, returns first ID corresponding to the minimum value.

Returns:

The ID to which the minimum value corresponds.

Return type:

hashable

argsort(reverse=False)#

Get the list of IDs sorted by stat value.

When values are not unique, the order of the IDs is preserved.

Parameters:

reverse (bool) – Whether the sorting should be ascending or descending.

Returns:

The IDs sorted in ascending or descending order.

Return type:

list

asdict(inner=<class 'dict'>, transpose=False)#

Output the stats as a dict of collections.

Parameters:

  • inner (dict (default) or list) – The type of the inner collections. If dict (default), output a dict of dicts. If list, output a dict of lists.

  • transpose (bool (default False)) – By default, output a dict of dicts whose outer keys are the nodes and inner keys are the specified stats. If True, the outer and inner keys are reversed. Only used when inner is dict.

Examples

>>> import xgi
>>> H = xgi.Hypergraph([[1, 2, 3], [2, 3, 4, 5], [3, 4, 5]])
>>> m = H.nodes.multi(['degree', 'clustering_coefficient'])
>>> m.asdict() 
{1: {'degree': 1, 'clustering_coefficient': 1.0},
 2: {'degree': 2, 'clustering_coefficient': 0.6666666666666666},
 3: {'degree': 3, 'clustering_coefficient': 0.6666666666666666},
 4: {'degree': 2, 'clustering_coefficient': 1.0},
 5: {'degree': 2, 'clustering_coefficient': 1.0}}
>>> m.asdict(transpose=True) 
{'degree': {1: 1, 2: 2, 3: 3, 4: 2, 5: 2},
'clustering_coefficient': {1: 1.0,
2: 0.6666666666666666,
3: 0.6666666666666666,
4: 1.0,
5: 1.0}}
ashist(bins=10, bin_edges=False, density=False, log_binning=False)#

Return the distributions of a numpy array.

Parameters:

  • vals (Numpy array) – The array of values

  • bins (int, list, or Numpy array) – The number of bins or the bin edges.

  • bin_edges (bool) – Whether to also output the min and max of each bin, by default, False.

  • density (bool) – Whether to normalize the resulting distribution.

  • log_binning (bool) – Whether to bin the values with log-sized bins. By default, False.

Returns:

Each entry of the list is a two-column table with “bin_center” and “value” columns, where “value” is a count or a probability. If bin_edges is True, outputs two additional columns, bin_lo and bin_hi, which outputs the left and right bin edges respectively.

Return type:

list of Pandas DataFrames

Notes

Originally from jkbren/networks-and-dataviz

aslist(inner=<class 'list'>, transpose=False)#

Output the stats as a list of collections.

Parameters:

  • inner (list (default) or dict) – The type of the inner collections. If list (default), output a list of lists. If dict, output a list of dicts.

  • transpose (bool (default False)) – By default, output a list of lists where each inner list contains the stats of a single node. If True, each inner list contains the values of a single stat of all nodes.

Examples

>>> import xgi
>>> H = xgi.Hypergraph([[1, 2, 3], [2, 3, 4, 5], [3, 4, 5]])
>>> m = H.nodes.multi(['degree', 'clustering_coefficient'])
>>> m.aslist() # doctest:
[[1, 1.0], [2, 0.6666666666666666], [3, 0.6666666666666666], [2, 1.0], [2, 1.0]]
>>> m.aslist(transpose=True)
[[1, 2, 3, 2, 2], [1.0, 0.6666666666666666, 0.6666666666666666, 1.0, 1.0]]
asnumpy()#

Output the stats as a numpy array.

Notes

Equivalent to np.array(self.aslist(inner=list)).

Examples

>>> import xgi
>>> H = xgi.Hypergraph([[1, 2, 3], [2, 3, 4, 5], [3, 4, 5]])
>>> H.nodes.multi(['degree', 'clustering_coefficient']).asnumpy()
... 
array([[1.        , 1.        ],
       [2.        , 0.66666667],
       [3.        , 0.66666667],
       [2.        , 1.        ],
       [2.        , 1.        ]])
aspandas()#

Output the stats as a pandas dataframe.

Examples

>>> import xgi
>>> H = xgi.Hypergraph([[1, 2, 3], [2, 3, 4, 5], [3, 4, 5]])
>>> H.nodes.multi(['degree', 'clustering_coefficient']).aspandas()
... 
   degree  clustering_coefficient
1       1    1.000000
2       2    0.666667
3       3    0.666667
4       2    1.000000
5       2    1.000000
max()#

The maximum value of this stat.

mean()#

The arithmetic mean of this stat.

median()#

The median of this stat.

min()#

The minimum value of this stat.

moment(order=2, center=False)#

The statistical moments of this stat.

Parameters:

  • order (int (default 2)) – The order of the moment.

  • center (bool (default False)) – Whether to compute the centered (False) or uncentered/raw (True) moment.

property name#

Name of this stat.

The name of a stat is used to populate the keys of dictionaries in MultiStat objects, as well as the names of columns of pandas dataframes.

Examples

>>> import xgi
>>> H = xgi.Hypergraph([[1, 2, 3], [2, 3, 4, 5], [3, 4, 5]])
>>> da, d3 = H.nodes.degree, H.nodes.degree(order=3)
>>> da.name, d3.name
('degree', 'degree(order=3)')
>>> H.nodes.multi([da, d3]).asdict(transpose=True).keys()
dict_keys(['degree', 'degree(order=3)'])
>>> H.nodes.multi([da, d3]).aspandas().columns
Index(['degree', 'degree(order=3)'], dtype='object')
statsclass#

alias of DiNodeStat

statsmodule = <module 'xgi.stats.dinodestats' from '/home/docs/checkouts/readthedocs.org/user_builds/xgi/envs/stable/lib/python3.12/site-packages/xgi/stats/dinodestats.py'>#

Module in which to search for mappings.

std()#

The standard deviation of this stat.

Notes

This implementation calculates the standard deviation with N in the denominator (NumPy’s default). This is in contrast to the sample standard deviation which normalizes by N-1. See https://www.allendowney.com/blog/2024/06/08/which-standard-deviation/ for more details.

sum()#

The sum of this stat.

var()#

The variance of this stat.

Notes

This implementation calculates the variance with N in the denominator. (NumPy’s default) This is in contrast to the sample variation which normalizes by N-1. See https://www.allendowney.com/blog/2024/06/08/which-standard-deviation/ for more details.

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