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Copy pathfig_S2ab_plot_two_compartment_differing_phi.py
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142 lines (113 loc) · 6.04 KB
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from typing import List, Iterable
import numpy
from matplotlib import pyplot as plt
from matplotlib.axes import Axes
from numpy import ndarray
from stem_cell_model import tools, sweeper
from stem_cell_model.parameters import SimulationParameters
from stem_cell_model.results import MultiRunStats
class _SimulationForPoint:
alpha_n: float
alpha_m: float
phi_n: ndarray # Linearly increasing phi_n
phi_m: ndarray # Linearly increasing phi_m
cov_of_variation_d: ndarray # Indexed as phi_n, phi_m
d_mean: ndarray # Indexed as phi_h, phi_m
f_collapse: ndarray # Indexed as phi_h, phi_m
def __init__(self, *, alpha_n: float, alpha_m: float, steps_along_axis: int = 40):
self.phi_n = numpy.linspace(25, 1000, steps_along_axis) * 0.001
self.phi_m = numpy.linspace(25, 1000, steps_along_axis) * 0.001
self.cov_of_variation_d = numpy.full((self.phi_n.shape[0], self.phi_m.shape[0]), numpy.nan)
self.f_collapse = numpy.copy(self.cov_of_variation_d)
self.d_mean = numpy.copy(self.cov_of_variation_d)
self.alpha_n = alpha_n
self.alpha_m = alpha_m
def offer_data_point(self, params: SimulationParameters, multi_run_stats: MultiRunStats):
if abs(params.alpha[0] - self.alpha_n) > 0.001:
return
if abs(params.alpha[1] - self.alpha_m) > 0.001:
return
stats = tools.get_single_parameter_set_statistics(multi_run_stats)
phi_n_index = numpy.argmin(numpy.abs(self.phi_n - params.phi[0]))
phi_m_index = numpy.argmin(numpy.abs(self.phi_m - params.phi[1]))
self.cov_of_variation_d[phi_n_index, phi_m_index] = stats.d_coeff_var
self.f_collapse[phi_n_index, phi_m_index] = stats.f_collapse
self.d_mean[phi_n_index, phi_m_index] = stats.d_mean
def __repr__(self):
return f"_SimulationsForPoint(alpha_n={self.alpha_n}, alpha_m={self.alpha_m})"
def _find_point(points: Iterable[_SimulationForPoint], alpha_n: float, alpha_m: float) -> _SimulationForPoint:
for point in points:
if point.alpha_n == alpha_n and point.alpha_m == alpha_m:
return point
raise ValueError(f"Point with alpha_n={alpha_n} and alpha_m={alpha_m} not found")
def main():
points = [
# alpha_n, alpha_m
_SimulationForPoint(alpha_n=0.2, alpha_m=-0.7),
_SimulationForPoint(alpha_n=0.2, alpha_m=-0.2),
_SimulationForPoint(alpha_n=0.95, alpha_m=-0.2),
_SimulationForPoint(alpha_n=0.95, alpha_m=-0.7)
]
for params, multi_run_stats in sweeper.load_sweep_results("two_comp_sweep_data_fixed_D_differing_phi"):
for point in points:
point.offer_data_point(params, multi_run_stats)
#_plot_d_mean(points)
_plot_cov_of_variation_d(points)
def _plot_d_mean(points: List[_SimulationForPoint]):
fig = plt.figure()
axes = fig.subplots(nrows=2, ncols=2, sharex="all", sharey="all")
for ax, point in zip(numpy.array(axes).flatten(), points):
ax: Axes
mappable = ax.imshow(point.d_mean, interpolation="nearest",
cmap="gnuplot", vmin=20, vmax=50,
extent=(point.phi_m[0], point.phi_m[-1], point.phi_n[-1], point.phi_n[0]))
if point.alpha_n > 0.9:
ax.set_xlabel("phi_m") # We're on the last row
ax.set_ylabel("phi_n")
ax.set_title(f"a_n={point.alpha_n}, a_m={point.alpha_m}")
ax.invert_yaxis()
plt.suptitle("<D(t)>")
plt.colorbar(mappable, ax=axes[:, 1], shrink=0.6)
plt.show()
def _plot_cov_of_variation_d(points: List[_SimulationForPoint]):
fig = plt.figure()
axes = fig.subplots(nrows=3, ncols=2, sharex="all")
for ax, point in zip(numpy.array(axes).flatten()[0:4], points):
ax: Axes
mappable = ax.imshow(point.cov_of_variation_d, interpolation="nearest",
cmap="gnuplot", vmin=0, vmax=0.6,
extent=(point.phi_m[0], point.phi_m[-1], point.phi_n[-1], point.phi_n[0]))
if point.alpha_n > 0.9:
ax.set_xlabel("phi_m") # We're on the last row
ax.set_ylabel("phi_n")
ax.set_title(f"a_n={point.alpha_n}, a_m={point.alpha_m}")
ax.invert_yaxis()
ax_bottom_left = axes[2, 0]
point = _find_point(points, alpha_n=0.2, alpha_m=-0.2)
ax_bottom_left.plot(point.phi_n, point.cov_of_variation_d[:, numpy.searchsorted(point.phi_m, 0.25)],
label="alpha_n=0.2, alpha_m=-0.2, phi_m=0.25")
ax_bottom_left.plot(point.phi_n, point.cov_of_variation_d[:, numpy.searchsorted(point.phi_m, 0.95)],
label="alpha_n=0.2, alpha_m=-0.2, phi_m=0.95")
point = _find_point(points, alpha_n=0.95, alpha_m=-0.7)
ax_bottom_left.plot(point.phi_n, point.cov_of_variation_d[:, numpy.searchsorted(point.phi_m, 0.95)],
label="alpha_n=0.95, alpha_m=-0.7, phi_m=0.95")
ax_bottom_left.set_ylim(0, 0.6)
ax_bottom_left.set_aspect("equal")
ax_bottom_left.set_ylabel("CoV in D")
ax_bottom_right: Axes = axes[2, 1]
point = _find_point(points, alpha_n=0.2, alpha_m=-0.2)
ax_bottom_right.plot(point.phi_m, point.cov_of_variation_d[numpy.searchsorted(point.phi_n, 0.25)], label="alpha_n=0.2, alpha_m=-0.2, phi_n=0.25")
ax_bottom_right.plot(point.phi_m, point.cov_of_variation_d[numpy.searchsorted(point.phi_n, 0.95)], label="alpha_n=0.2, alpha_m=-0.2, phi_n=0.95")
point = _find_point(points, alpha_n=0.95, alpha_m=-0.7)
ax_bottom_right.plot(point.phi_m, point.cov_of_variation_d[numpy.searchsorted(point.phi_n, 0.95)], label="alpha_n=0.95, alpha_m=-0.7, phi_n=0.95")
point = _find_point(points, alpha_n=0.95, alpha_m=-0.2)
ax_bottom_right.plot(point.phi_m, point.cov_of_variation_d[numpy.searchsorted(point.phi_n, 0.95)], label="alpha_n=0.95, alpha_m=-0.2, phi_n=0.95")
ax_bottom_right.set_ylim(0, 0.6)
ax_bottom_right.set_aspect("equal")
ax_bottom_right.set_ylabel("CoV in D")
ax_bottom_right.legend()
plt.suptitle("Coefficient of variation")
plt.colorbar(mappable, ax=axes[:, 1], shrink=0.6)
plt.show()
if __name__ == '__main__':
main()