Utilities

This module contains functions useful throughout PyBEL Tools

pybel_tools.utils.pairwise(iterable)

Iterate over pairs in list s -> (s0,s1), (s1,s2), (s2, s3), …

pybel_tools.utils.graph_edge_data_iter(graph)

Iterate over the edge data dictionaries.

Returns:An iterator over the edge dictionaries in the graph Return type:iter[dict]

pybel_tools.utils.count_defaultdict(dict_of_lists)

Takes a dictionary and applies a counter to each list

Parameters:dict_of_lists (dict or collections.defaultdict) – A dictionary of lists Returns:A dictionary of {key: Counter(values)} Return type:dict

pybel_tools.utils.count_dict_values(dict_of_counters)

Counts the number of elements in each value (can be list, Counter, etc)

Parameters:dict_of_counters (dict or collections.defaultdict) – A dictionary of things whose lengths can be measured (lists, Counters, dicts) Returns:A Counter with the same keys as the input but the count of the length of the values list/tuple/set/Counter Return type:collections.Counter

pybel_tools.utils.set_percentage(x, y)

What percentage of x is contained within y?

Parameters:

• x (set) – A set
• y (set) – Another set

Returns:

The percentage of x contained within y

Return type:

float

pybel_tools.utils.tanimoto_set_similarity(x, y)

Calculates the tanimoto set similarity

Parameters:

• x (set) – A set
• y (set) – Another set

Returns:

The similarity between

Return type:

float

pybel_tools.utils.min_tanimoto_set_similarity(x, y)

Calculates the tanimoto set similarity using the minimum size

Parameters:

• x (set) – A set
• y (set) – Another set

Returns:

The similarity between

Return type:

float

pybel_tools.utils.calculate_single_tanimoto_set_distances(target, dict_of_sets)

Returns a dictionary of distances keyed by the keys in the given dict. Distances are calculated based on pairwise tanimoto similarity of the sets contained

Parameters:

• target (set) – A set
• dict_of_sets (dict) – A dict of {x: set of y}

Returns:

A similarity dicationary based on the set overlap (tanimoto) score between the target set and the sets in dos

Return type:

dict

pybel_tools.utils.calculate_tanimoto_set_distances(dict_of_sets)

Returns a distance matrix keyed by the keys in the given dict. Distances are calculated based on pairwise tanimoto similarity of the sets contained

Parameters:dict_of_sets (dict) – A dict of {x: set of y} Returns:A similarity matrix based on the set overlap (tanimoto) score between each x as a dict of dicts Return type:dict

pybel_tools.utils.calculate_global_tanimoto_set_distances(dict_of_sets)

Calculates an alternative distance matrix based on the following equation:

\[distance(A, B)=1- \|A \cup B\| / \| \cup_{s \in S} s\|\]

Parameters:dict_of_sets (dict) – A dict of {x: set of y} Returns:A similarity matrix based on the alternative tanimoto distance as a dict of dicts Return type:dict

pybel_tools.utils.barh(d, plt, title=None)

A convenience function for plotting a horizontal bar plot from a Counter

pybel_tools.utils.barv(d, plt, title=None, rotation='vertical')

A convenience function for plotting a vertical bar plot from a Counter

pybel_tools.utils.safe_add_edge(graph, u, v, key, attr_dict, **attr)

Adds an edge while preserving negative keys, and paying no respect to positive ones

Parameters:

• graph (pybel.BELGraph) – A BEL Graph
• u (tuple) – The source BEL node
• v (tuple) – The target BEL node
• key (int) – The edge key. If less than zero, corresponds to an unqualified edge, else is disregarded
• attr_dict (dict) – The edge data dictionary
• attr (dict) – Edge data to assign via keyword arguments

pybel_tools.utils.prepare_c3(data, y_axis_label='y', x_axis_label='x')

Prepares C3 JSON for making a bar chart from a Counter

Parameters:

• data (Counter or dict or collections.defaultdict) – A dictionary of {str: int} to display as bar chart
• y_axis_label (str) – The Y axis label
• x_axis_label (str) – X axis internal label. Should be left as default ‘x’)

Returns:

A JSON dictionary for making a C3 bar chart

Return type:

dict

pybel_tools.utils.prepare_c3_time_series(data, y_axis_label='y', x_axis_label='x')

Prepares C3 JSON for making a time series

Parameters:

• data (list) – A list of tuples [(year, count)]
• y_axis_label (str) – The Y axis label
• x_axis_label (str) – X axis internal label. Should be left as default ‘x’)

Returns:

A JSON dictionary for making a C3 bar chart

Return type:

dict

pybel_tools.utils.build_template_environment(here)

Builds a custom templating enviroment so Flask apps can get data from lots of different places

Parameters:here (str) – Give this the result of os.path.dirname(os.path.abspath(__file__)) Return type:jinja2.Environment

pybel_tools.utils.build_template_renderer(file)

In your file, give this function the current file

Parameters:file (str) – The location of the current file. Pass it __file__ like in the example below.

>>> render_template = build_template_renderer(__file__)

pybel_tools.utils.calculate_betweenness_centality(graph, k=200)

Calculates the betweenness centrality over nodes in the graph. Tries to do it with a certain number of samples, but then tries a complete approach if it fails.

Parameters:

• graph (pybel.BELGraph) – A BEL graph
• k (int) – The number of samples to use

Return type:

collections.Counter[tuple,float]

pybel_tools.utils.get_circulations(t)

Iterate over all possible circulations of an ordered collection (tuple or list)

Parameters:or list t (tuple) – Return type:iter

pybel_tools.utils.canonical_circulation(t, key=None)

Get get a canonical representation of the ordered collection by finding its minimum circulation with the given sort key

Parameters:

• or list t (tuple) –
• key – A function for sort

Returns:

The

pybel_tools.utils.get_version()

Gets the current PyBEL Tools version

Returns:The current PyBEL Tools version Return type:str