HELPpy.visualization package

Submodules

HELPpy.visualization.plot module

HELPpy.visualization.plot.svenn_intesect(sets: List[set], labels: List[str], figsize=(10, 20), fontsize=10, ylabel='EG', xlabel='no. Genes', saveflag: bool = False) → None[source]

Generate a Supervenn diagram to visualize the intersection of multiple sets.

Parameters:

  • sets (List[set]) – List of sets to be visualized.

  • labels (List[str]) – List of labels corresponding to each set.

  • figsize (tuple) – Figure size in inches, as a tuple (width, height) (default is (10, 20)).

  • fontsize (int) – Font size for labels (default is 10).

  • xlabel (str) – label for x axis (default is ‘no. Genes’)

  • ylabel (str) – label for y axis (default is ‘EG’)

  • saveflag (bool) – Whether to save the generated diagram as an image (default is False).

Returns:

None

The function generates a Supervenn diagram using the ‘supervenn’ library and Matplotlib. The diagram visualizes the intersection of multiple sets with labeled areas.

Example:

# Usage example:
set1 = {1, 2, 3, 4, 5}
set2 = {3, 4, 5, 6, 7}
set3 = {5, 6, 7, 8, 9}
svenn_intesect([set1, set2, set3], ["Set A", "Set B", "Set C"], saveflag=True)

HELPpy.visualization.ui module

class HELPpy.visualization.ui.Help_Dashboard(verbose: bool = False)[source]

Bases: object

labelling(path: str = '/Users/maurizio/HELP/docs', filename: str = '', modelname: str = '', rows: int = 5, minlines=10, percent=100.0, line_group='OncotreeLineage', line_col='ModelID', verbose=False)[source]

Generate an interactive widget for labeling cell lines based on specified criteria.

Parameters:

  • path (str) – path for input file loading.

  • filename (str) – name of CRISPR effect input file.

  • modelname (str) – name of Model input file.

  • rows (int, optional) – The number of rows to display in the widget for selecting tissues (default is 5).

  • minlines (int, optional) – Minimum number of cell lines for tissue/lineage to be considered (default is 1).

  • line_group (str, optional) – The column in ‘df_map’ to use for tissue selection (default is ‘OncotreeLineage’).

  • line_col (str, optional) – The column in ‘df_map’ to use for line selection (default is ‘ModelID’).

Returns:

Widget containing the labeled cell lines.

Return type:

ipywidgets.ValueWidget

process_features(label_path: str = '.', feature_path: str = '.', rows: int = 5)[source]

Create an interactive widget for processing features.

Parameters:

  • label_path (str, optional) – Path to the label file (default is “.”).

  • feature_path (str, optional) – Path to the feature files (default is “.”).

  • rows (int, optional) – Number of rows to display in the widget (default is 5).

Returns:

Widget containing the assembled features and labels DataFrames.

Return type:

ipywidgets.ValueWidget

select_cell_lines(df: DataFrame, df_map: DataFrame, outvar: object, rows: int = 5, minlines=1, line_group='OncotreeLineage', line_col='ModelID', verbose=False)[source]

Generate an interactive widget for labeling cell lines based on specified criteria.

Parameters:

  • df (pd.DataFrame) – The main DataFrame containing the data.

  • df_map (pd.DataFrame) – A DataFrame used for mapping data.

  • rows (int, optional) – The number of rows to display in the widget for selecting tissues (default is 5).

  • minlines (int, optional) – Minimum number of cell lines for tissue/lineage to be considered (default is 1).

  • line_group (str, optional) – The column in ‘df_map’ to use for tissue selection (default is ‘OncotreeLineage’).

  • line_col (str, optional) – The column in ‘df_map’ to use for line selection (default is ‘ModelID’).

Returns:

Widget containing the labeled cell lines.

Return type:

ipywidgets.ValueWidget

HELPpy.visualization.ui.cstr(s, color='black')[source]
HELPpy.visualization.ui.print_color(t)[source]

Module contents