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add generate_cdf_curves_vs_qr

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markus 2021-02-17 16:49:33 +01:00
parent 5d675d1d1a
commit 473f50d1e4
1 changed files with 82 additions and 25 deletions
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107
cara/montecarlo.py
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@ -9,6 +9,7 @@ from scipy.integrate import quad
import typing import typing
import matplotlib.pyplot as plt import matplotlib.pyplot as plt
import matplotlib.patches as patches import matplotlib.patches as patches
import matplotlib.lines as mlines
from sklearn.neighbors import KernelDensity from sklearn.neighbors import KernelDensity
# This is a test comment # This is a test comment
@ -635,6 +636,61 @@ def generate_boxplot(masked: bool = False, samples: int = 200000, qid: int = 100
plt.show() plt.show()
def generate_cdf_curves_vs_qr(masked: bool = False, samples: int = 200000, qid: int = 100) -> None:
"""
Generates and displays a boxplot for comparing the qR-values in scenarios with various combinations of breathing
rates and expiratory activities
:param masked: A bool indicating whether or not the infected subject is wearing a mask
:param samples: The number of samples to use for the monte carlo simulation
:param qid: The number of "viral copies per quantum"
:return: Nothing, a graph is displayed
"""
fig, axs = plt.subplots(3, 1, sharex='all')
# TODO: Insert title and y-label
plt.suptitle("INSERT TITLE HERE")
fig.text(0.02, 0.5, 'INSERT Y-LABEL HERE', va='center', rotation='vertical')
scenarios = [MCInfectedPopulation(
number=1,
presence=models.SpecificInterval(((0, 4), (5, 9))),
masked=masked,
virus=MCVirus(halflife=1.1, qID=qid),
expiratory_activity=e,
samples=samples,
breathing_category=b,
) for e, b in product((1, 2, 3), (1, 3, 5))]
qr_values = [np.log10(scenario.emission_rate_when_present()) for scenario in scenarios]
left = min(np.min(qrs) for qrs in qr_values)
right = max(np.max(qrs) for qrs in qr_values)
# Colors can be changed here
colors = ['lightgreen', 'lightblue', 'pink']
breathing_rates = ['Seated', 'Light activity', 'Heavy activity']
activities = ['Breathing', 'Speaking', 'Shouting']
lines = [mlines.Line2D([], [], color=color, markersize=15, label=label)
for color, label in zip(colors, breathing_rates)]
for i in range(3):
axs[i].hist(qr_values[3 * i:3 * (i + 1)], bins=2000, histtype='step',
color=colors, cumulative=True, range=(left, right))
axs[i].set_xlim(left, right)
axs[i].set_yticks([0, samples / 2, samples])
axs[i].set_yticklabels(['0.0', '0.5', '1.0'])
axs[i].yaxis.set_label_position("right")
axs[i].set_ylabel(activities[i])
axs[i].legend(handles=lines, loc='upper left')
plt.xlabel('qR [$q\;h^{-1}$]')
tick_positions = np.arange(int(np.ceil(left)), int(np.ceil(right)), 2)
plt.xticks(ticks=tick_positions, labels=['$\;10^{' + str(i) + '}$' for i in tick_positions])
fig.set_figheight(8)
fig.set_figwidth(5)
plt.show()
def buonanno_exposure_model() -> MCExposureModel: def buonanno_exposure_model() -> MCExposureModel:
""" """
:return: An MCExposureModel object corresponding to one of the scenarios outlined in the paper of Buonanno et al. :return: An MCExposureModel object corresponding to one of the scenarios outlined in the paper of Buonanno et al.
@ -814,30 +870,31 @@ exposure_models = [MCExposureModel(
) )
) for qid in (100, 60)] ) for qid in (100, 60)]
generate_cdf_curves_vs_qr(masked=False)
rs = [model.expected_new_cases() for model in large_population_baselines] # rs = [model.expected_new_cases() for model in large_population_baselines]
#
print(f"R0 - original variant:\t{np.mean(rs[0])}") # print(f"R0 - original variant:\t{np.mean(rs[0])}")
print(f"R0 - english variant:\t{np.mean(rs[1])}") # print(f"R0 - english variant:\t{np.mean(rs[1])}")
print(f"Ratio between R0's:\t\t{np.mean(rs[1]) / np.mean(rs[0])}") # print(f"Ratio between R0's:\t\t{np.mean(rs[1]) / np.mean(rs[0])}")
#
# compare_infection_probabilities_vs_viral_loads(*exposure_models) # compare_infection_probabilities_vs_viral_loads(*exposure_models)
#
#
present_model(exposure_models[0].concentration_model) # present_model(exposure_models[0].concentration_model)
plot_pi_vs_qid(fixed_vl_exposure_models, labels=['Viral load = $10^{' + str(i) + '}$' for i in range(6, 11)], # plot_pi_vs_qid(fixed_vl_exposure_models, labels=['Viral load = $10^{' + str(i) + '}$' for i in range(6, 11)],
qid_min=5, qid_max=2000, qid_samples=200) # qid_min=5, qid_max=2000, qid_samples=200)
#
plot_pi_vs_qid(fixed_vl_exposure_models, labels=['Viral load = $10^{' + str(i) + '}$' for i in range(6, 11)], # plot_pi_vs_qid(fixed_vl_exposure_models, labels=['Viral load = $10^{' + str(i) + '}$' for i in range(6, 11)],
qid_min=100, qid_max=400, qid_samples=100) # qid_min=100, qid_max=400, qid_samples=100)
#
#
plot_pi_vs_viral_load(exposure_models, labels=['Without masks', 'With masks']) # plot_pi_vs_viral_load(exposure_models, labels=['Without masks', 'With masks'])
#
for model in exposure_models: # for model in exposure_models:
present_model(model.concentration_model, title=f'Model summary - {"English" if model.concentration_model.infected.qid == 60 else "Original"} variant') # present_model(model.concentration_model, title=f'Model summary - {"English" if model.concentration_model.infected.qid == 60 else "Original"} variant')
plt.hist(model.infection_probability(), bins=200) # plt.hist(model.infection_probability(), bins=200)
plt.xlabel('Percentage probability of infection') # plt.xlabel('Percentage probability of infection')
plt.title(f'Probability of infection in baseline case - {"English" if model.concentration_model.infected.qid == 60 else "Original"} variant') # plt.title(f'Probability of infection in baseline case - {"English" if model.concentration_model.infected.qid == 60 else "Original"} variant')
plt.yticks([], []) # plt.yticks([], [])
plt.show() # plt.show()