tdm.plot.two_cells.phase_portrait.plot_growth_rate#
- tdm.plot.two_cells.phase_portrait.plot_growth_rate(ana: Analysis, kde_bw: float = 0.3, cell_a_rbf_gamma: float = 1.2, cell_b_rbf_gamma: float = 0.3, xlim: tuple[float, float] | None = None, ylim: tuple[float, float] | None = None, fixed_cell_counts: dict[str, float] | None = None, include_titles: bool = True, plot_nullclines: bool = True, streamplot_density: float = 0.6, _plot_contours: bool = True) Figure [source]#
- Display the growth rate of both cell-types over the phase-plane.
Plots 3 axes: growth rate for each cell-type and a phase-portrait.
Hint
How to interpret these plots?
The growth rate is computed by taking a smoothed mean of the binary cell-division events over the phase-plane. We then subtract the mean division rate so that areas with high proliferation are positive (plotted red) and areas with low proliferation are negative (plotted blue). See paper for more on why the mean division rate is a good approximation of the death rate.
For
cell_a
(x axis), regions with positive growth (red) correspond with flow to the right on the phase-portrait, and regions with negative growth (blue) correspond with flow to the left. Forcell_b
(y axis), positive and negative growth correspond with flow up or down respectively.Thus, this plot connects the measured division events directly with the modeled dynamics.
- Parameters:
ana (Analysis) – _description_
kde_bw (float, optional) – _description_. Defaults to 0.3.
cell_a_rbf_gamma (float, optional) – _description_. Defaults to 1.2.
ylim (tuple[float, float] | None, optional) – _description_. Defaults to None.
fixed_cell_counts (dict[str, float] | None, optional) – _description_. Defaults to None.
include_titles (bool, optional) – _description_. Defaults to True.
plot_nullclines (bool, optional) – _description_. Defaults to True.
streamplot_density (float, optional) – _description_. Defaults to 0.6.
_plot_contours (bool, optional) – _description_. Defaults to True.
- Returns:
_description_
- Return type:
Figure