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Add shaded areas in Pi vs vl

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Andrejh 2021-02-22 00:21:30 +01:00
parent 184981c400
commit e5f4027f6a
1 changed files with 71 additions and 10 deletions
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81
cara/montecarlo.py
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@ -561,22 +561,31 @@ def plot_pi_vs_viral_load(baselines: typing.Union[MCExposureModel, typing.List[M
plt.ylabel('Probability of infection')
plt.xticks(ticks=[i for i in range(3, 13)], labels=['$10^{' + str(i) + '}$' for i in range(3, 13)])
plt.xlabel('Viral load')
# add vertical lines for the critical viral loads for which pi= 0 or 100
# 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=('lightgrey', 'grey'), linestyles='dotted')
plt.text(6.5, 0.80, '$vl_{crit1}$', fontsize=12,color='black')
plt.text(10.5, 0.80, '$vl_{crit2}$', fontsize=12,color='black')
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')
if labels is not None:
plt.legend(labels)
# this is an inset plot inside the main plot
a = plt.axes([.2, .25, .25, .2], facecolor='k')
a = plt.axes([.25, .25, .1, .3], facecolor='lightgrey')
#TODO - Markus can you plot the hist using the chosen model instead of hardcoding
# in the 'exposure_model' str?
plt.hist(shared_office_model[1].infection_probability()/100, bins=200)
plt.title('PDF',fontsize=10)
plt.xticks([0,0.5,1])
plt.yticks([])
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.show()
@ -1058,6 +1067,58 @@ shared_office_model = [MCExposureModel(
)
) for qid in (100, 60)]
shared_office_worst_model = [MCExposureModel(
concentration_model=MCConcentrationModel(
room=models.Room(volume=50),
ventilation=models.AirChange(
active=models.PeriodicInterval(period=120, duration=120),
air_exch=0.,
),
infected=MCInfectedPopulation(
number=1,
presence=models.SpecificInterval(((0, 2), (2.1, 4), (5, 7), (7.1, 9))),
masked=False,
virus=MCVirus(halflife=1.35, qID=qid),
expiratory_activity=4,
samples=200000,
breathing_category=1,
expiratory_activity_weights=(0.7, 0.3, 0)
)
),
exposed=models.Population(
number=3,
presence=models.SpecificInterval(((0, 2), (2.1, 4), (5, 7), (7.1, 9))),
activity=models.Activity.types['Seated'],
mask=models.Mask.types['No mask']
)
) for qid in (100, 60)]
shared_office_better_model = [MCExposureModel(
concentration_model=MCConcentrationModel(
room=models.Room(volume=50),
ventilation=models.AirChange(
active=models.PeriodicInterval(period=120, duration=120),
air_exch=5.,
),
infected=MCInfectedPopulation(
number=1,
presence=models.SpecificInterval(((0, 2), (2.1, 4), (5, 7), (7.1, 9))),
masked=True,
virus=MCVirus(halflife=1.35, qID=qid),
expiratory_activity=4,
samples=200000,
breathing_category=1,
expiratory_activity_weights=(0.7, 0.3, 0)
)
),
exposed=models.Population(
number=3,
presence=models.SpecificInterval(((0, 2), (2.1, 4), (5, 7), (7.1, 9))),
activity=models.Activity.types['Seated'],
mask=models.Mask.types['Type I']
)
) for qid in (100, 60)]
ski_cabin_model = MCExposureModel(
concentration_model=MCConcentrationModel(
room=models.Room(volume=10),
@ -1174,7 +1235,7 @@ 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, VOC'],title='$P(I|qID)$ - Shared office scenario')
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')
#generate_cdf_curves_vs_qr(masked=False,qid=1000)