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pi vs vl plot additions

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Andrejh 2021-02-22 11:57:08 +01:00
parent e5f4027f6a
commit 83eadc5280
1 changed files with 17 additions and 15 deletions
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32
cara/montecarlo.py
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@ -564,28 +564,30 @@ def plot_pi_vs_viral_load(baselines: typing.Union[MCExposureModel, typing.List[M
# add vertical lines for the critical viral loads for which pi= 5 or 95
# TODO Insert viral_load(Pi = 5) and viral_load(Pi = 95) instead of hard coded values
# 7.8 and 9.5
plt.vlines(x=(7.8, 9.5), ymin=0, ymax=1,
colors=('grey', 'grey'), linestyles='dotted')
plt.text(6.7, 0.80, '$vl_{crit1}$', fontsize=12,color='black')
plt.text(9.6, 0.80, '$vl_{crit2}$', fontsize=12,color='black')
#plt.vlines(x=(7.8, 9.5), ymin=0, ymax=1,
# colors=('grey', 'grey'), linestyles='dotted')
#plt.text(6.7, 0.80, '$vl_{crit1}$', fontsize=12,color='black')
#plt.text(9.6, 0.80, '$vl_{crit2}$', fontsize=12,color='black')
# add 3 shaded areas
plt.axvspan(3, 7.8, alpha=0.1, color='limegreen')
plt.axvspan(7.8, 9.5, alpha=0.1, color='orange')
plt.axvspan(9.5, 12, alpha=0.1, color='tomato')
#plt.axvspan(3, 7.8, alpha=0.1, color='limegreen')
#plt.axvspan(7.8, 9.5, alpha=0.1, color='orange')
#plt.axvspan(9.5, 12, alpha=0.1, color='tomato')
if labels is not None:
plt.legend(labels)
# this is an inset plot inside the main plot
a = plt.axes([.25, .25, .1, .3], facecolor='lightgrey')
a = plt.axes([.2, .25, .3, .2], facecolor='lightgrey')
#TODO - Markus can you plot the hist using the chosen model instead of hardcoding
# in the 'exposure_model' str?
plt.title('Histogram',fontsize=10)
#choose between hist or violin plot
#plt.hist(shared_office_model[1].infection_probability()/100, bins=200)
#plt.xticks([0,0.5,1])
#plt.yticks([])
plt.violinplot(shared_office_model[1].infection_probability()/100, showmeans=True, showmedians=True)
plt.xticks([])
plt.yticks([0,0.5,1], fontsize=8)
plt.hist(shared_office_model[1].infection_probability()/100, bins=50)
plt.xticks([0,0.5,1])
plt.yticks([])
#parts = plt.violinplot((shared_office_worst_model[1].infection_probability()/100, shared_office_model[1].infection_probability()/100, shared_office_better_model[1].infection_probability()/100), positions = (1, 2, 3), showmeans=True, showmedians=True)
#for pc in parts['bodies']:
# pc.set_facecolor('#D43F3A')
#plt.xticks([])
#plt.yticks([0,0.5,1], fontsize=8)
plt.show()
@ -1235,7 +1237,7 @@ 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], shared_office_better_model[1],shared_office_worst_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)