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various improvements to concentration plot

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markus 2021-02-22 16:45:38 +01:00
parent dea98d3c6f
commit 5c9181d798
1 changed files with 10 additions and 8 deletions
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18
cara/montecarlo.py
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@ -734,7 +734,7 @@ def generate_cdf_curves_vs_qr(masked: bool = False, samples: int = 200000, qid:
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))
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'])
@ -863,8 +863,8 @@ def plot_concentration_curve(model: MCExposureModel):
lower = [np.quantile(c, 0.5) for c in concentrations]
fig = plt.figure()
plt.plot(times, upper, color=color, linestyle='dashed', label='95th percentile')
plt.plot(times, lower, color=color, linestyle='dashed')
plt.plot(times, upper, color='red', linestyle='dashed', label='95th percentile')
plt.plot(times, lower, color='green', linestyle='dashed', label='5th percentile')
plt.plot(times, means, color=color, label='Mean concentration')
# Plot presence of exposed person
@ -879,8 +879,8 @@ def plot_concentration_curve(model: MCExposureModel):
upper_threshold = round(np.max(means) * 1.1, 1)
lower_threshold = round(np.max(lower) * 1.1, 1)
top = round(np.max(upper) * 1.1, 1)
upper_scaling_factor = 200
lower_scaling_factor = 20
upper_scaling_factor = np.max(upper) / np.max(lower)
lower_scaling_factor = np.max(means) * 0.5 / np.max(lower)
def forward(x: np.ndarray) -> np.ndarray:
high_indexes = x >= upper_threshold
@ -912,7 +912,9 @@ def plot_concentration_curve(model: MCExposureModel):
plt.yscale('function', functions=(forward, inverse))
plt.legend(bbox_to_anchor=(1.05, 1), loc='upper left')
plt.yticks([i for i in np.linspace(0, upper_threshold, 5)][:-1] + [i for i in np.linspace(upper_threshold, top, 5)])
plt.yticks([i for i in np.linspace(0, lower_threshold, 3)][:-1] +
[i for i in np.linspace(lower_threshold, upper_threshold, 6)][:-1] +
[i for i in np.linspace(upper_threshold, top, 3)])
fig.set_figwidth(10)
plt.tight_layout()
plt.show()
@ -1242,7 +1244,7 @@ chorale_model = MCExposureModel(
)
)
#plot_concentration_curve(classroom_model)
plot_concentration_curve(classroom_model)
#print(np.mean(chorale_model.infection_probability()))
#print(np.mean(chorale_model.infection_probability())+np.std(chorale_model.infection_probability()))
@ -1254,7 +1256,7 @@ chorale_model = MCExposureModel(
#print(np.mean(exposure_models_2[1].infection_probability()))
#plot_pi_vs_viral_load([exposure_models[1],exposure_models_2[1]], labels=['B.1.1.7 - Guideline', 'B.1.1.7 - w/o masks'])
plot_pi_vs_viral_load([shared_office_model[1]], labels=['Baseline, qID=60', 'HEPA, qID=60', 'No mask + windows closed, qID=60'],title='$P(I|qID)$ - Shared office scenario')
# plot_pi_vs_viral_load([shared_office_model[1]], labels=['Baseline, qID=60', 'HEPA, qID=60', 'No mask + windows closed, qID=60'],title='$P(I|qID)$ - Shared office scenario')
#generate_cdf_curves_vs_qr(masked=False,qid=1000)