lib5c.plotters.expected module

Module for visualization of one-dimensional distance-dependent expected models.

Two convenience functions are exposed:

• plot_bin_expected()

• plot_fragment_expected()

which are bin- and fragment-level wrappers around plot_log_log_expected(). The other functions are utility functions.

These functions are all overloaded so that an arg called distance_expected can be replaced with a dict of named distance expecteds. This will result in an overlayed comparison of all the expected models in the dict.

The other functions in this module are private helper functions.

lib5c.plotters.expected.plot_bin_expected(obs_matrix, distance_expected, hexbin=False, kde=False, color='r', semilog=False, linewidth=4, title='1-D expected model', ylabel='log counts', xlabel='log distance', legend=True, **kwargs)

Convenience function for plotting a visualization of a one-dimensional distance-dependent expected model defined over bin-level data.

Parameters

• obs_matrix (np.ndarray) – The matrix of real interaction data that the model will be compared to.

• distance_expected (List[float]) – The one-dimensional distance-dependence model. The i th element of the list should correspond to the expected value for interactions between loci separated by i bins. To compare multiple expected models to the same observed data, pass a dict or an OrderedDict whose keys are string names for the models.

• hexbin (bool) – Pass True to use a hexbin plot to represent the density of the real data.

• kde (bool) – Pass True to use a kernel density estimate to represent the density of the real data.

• color (str) – The color to draw the expected model line with. When comparing multiple models, this can be a dict or OrderedDict with the same keys as distance_expected.

• semilog (bool) – Pass True to leave the distance axis unlogged.

• linewidth (float) – Line width to draw the model with.

• kwargs (kwargs) – Typical plotter kwargs.

Returns

The axis plotted on.

Return type

pyplot axis

lib5c.plotters.expected.plot_fragment_expected(obs_matrix, distance_expected, distance_matrix, hexbin=False, kde=False, color='r', semilog=False, linewidth=4, title='1-D expected model', ylabel='log counts', xlabel='log distance', legend=True, **kwargs)

Convenience function for plotting a visualization of a one-dimensional distance-dependent expected model defined over fragment-level data.

Parameters

• obs_matrix (np.ndarray) – The matrix of real interaction data that the model will be compared to.

• distance_expected (Dict[int, float]) – A mapping from interaction distances in units of base pairs to the expected value at that distance. To compare multiple expected models to the same observed data, pass a dict or an OrderedDict whose keys are string names for the models.

• distance_matrix (np.ndarray) – The pairwise distance matrix for the fragments in this region.

• hexbin (bool) – Pass True to use a hexbin plot to represent the density of the real data.

• kde (bool) – Pass True to use a kernel density estimate to represent the density of the real data.

• color (str) – The color to draw the expected model line with. When comparing multiple models, this can be a dict or OrderedDict with the same keys as distance_expected.

• semilog (bool) – Pass True to leave the distance axis unlogged.

• linewidth (float) – Line width to draw the model with.

• kwargs (kwargs) – Typical plotter kwargs.

Returns

The axis plotted on.

Return type

pyplot axis

lib5c.plotters.expected.plot_log_log_expected(obs_distances, obs_values, exp_distances, exp_values, hexbin=False, kde=False, color='r', pseudocount=1, semilog=False, linewidth=4, title='1-D expected model', ylabel='log counts', xlabel='log distance', legend=True, **kwargs)

Plot a visualization of an expected model over real data.

Parameters

• obs_distances (np.ndarray) – Flat array of the distances of the obs_values.

• obs_values (np.ndarray) – Flat array of the real data values.

• exp_distances (np.ndarray) – Flat array of the distances of the exp_values.

• exp_values (np.ndarray) – Flat array of the expected data values predicted by the model. To compare multiple expected models to the same observed data, pass a dict or an OrderedDict whose keys are string names for the models.

• title (str) – Title to write on the plot.

• ylabel (str) – Label for the y-axis on the plot.

• xlabel (str) – Label for the x-axis on the plot.

• hexbin (bool) – Pass True to use a hexbin plot to represent the density of the real data.

• kde (bool) – Pass True to use a kernel density estimate to represent the density of the real data.

• color (str) – The color to draw the expected model line with. When comparing multiple models, this can be a dict or OrderedDict with the same keys as distance_expected.

• pseudocount (int) – Pseudocount to add to distances if called with semilog=True.

• semilog (bool) – Pass True to leave the distance axis unlogged.

• linewidth (float) – Line width to draw the model with.

• kwargs (kwargs) – Typical plotter kwargs.

Returns

The axis plotted on.

Return type

pyplot axis