mushi.histories.mu

class mu(change_points, Z, mutation_types)[source]

Mutation spectrum history (MuSH) \(\boldsymbol\mu(t)\).

Parameters:

change_points (ndarray) – epoch change points (times)
Z (ndarray) – matrix of constant values for each epoch (rows) in each mutation type (columns)
mutation_types (List[str]) – list of mutation type names (optional None element)

Example

>>> import mushi
>>> import numpy as np

>>> mu = mushi.mu(np.array([1, 10, 100, 1000]),
...               np.ones((5, 2)),
...               ['TCC>TTC', 'GAA>GGA'])

Methods

`arrays`	Return time grid and values in each cell.
`check_grid`	Test if time grid is the same as another instance.
`clustermap`	Clustermap of compositionally centralized MuSH.
`epochs`	Generator yielding epochs as tuples: (start_time, end_time, value)
`plot`	Plot history.
`plot_cumulative`	Plot the cumulative mutation rate, like a Muller plot.

Attributes

`Z`	Read-only alias to vals attribute in base class.
`mutation_types`

check_grid(other)

Test if time grid is the same as another instance.

epochs(): Generator yielding epochs as tuples: (start_time, end_time, value)

plot(types=None, clr=False, **kwargs)[source]

Plot history.

Parameters:

types (List[str]) – list of mutation types to plot (default all)
clr (bool) – flag to normalize to total mutation intensity and display as centered log ratio transform

Return type:

None

plot_cumulative(t_gen=None, clr=False, **kwargs)[source]

Plot the cumulative mutation rate, like a Muller plot.

Parameters:

t_gen (float) – generation time in years (optional)
clr (bool) – flag to normalize to total mutation intensity and display as centered log ratio transform
kwargs – key word arguments passed to plt.fill_between

Return type:

None

clustermap(t_gen=None, **kwargs)[source]

Clustermap of compositionally centralized MuSH.

Parameters:

Return type:

None