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Modify CDF plot title & colors

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This commit is contained in:
Andrejh 2021-02-18 23:38:20 +01:00
parent 17340781f5
commit df090a7952
1 changed files with 39 additions and 7 deletions
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46
cara/montecarlo.py
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@ -358,7 +358,7 @@ class MCExposureModel:
exposure = np.zeros(self.concentration_model.infected.samples)
for start, stop in self.exposed.presence.boundaries():
times = np.arange(start, stop, 0.01)
times = np.arange(start, stop, 0.1)
concentrations = np.asarray([self.concentration_model.concentration(t) for t in times])
integrals = np.trapz(concentrations, times, axis=0)
exposure += integrals
@ -670,8 +670,8 @@ def generate_cdf_curves_vs_qr(masked: bool = False, samples: int = 200000, qid:
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')
plt.suptitle("$F(qR|qID=$" + str(qid) + "$)$")
fig.text(0.02, 0.5, 'Probability', va='center', rotation='vertical')
scenarios = [MCInfectedPopulation(
number=1,
@ -687,7 +687,7 @@ def generate_cdf_curves_vs_qr(masked: bool = False, samples: int = 200000, qid:
right = max(np.max(qrs) for qrs in qr_values)
# Colors can be changed here
colors = ['lightgreen', 'lightblue', 'pink']
colors = ['lightgrey', 'grey', 'black']
breathing_rates = ['Seated', 'Light activity', 'Heavy activity']
activities = ['Breathing', 'Speaking', 'Shouting']
@ -871,7 +871,7 @@ exposure_models = [MCExposureModel(
concentration_model=MCConcentrationModel(
room=models.Room(volume=45),
ventilation=models.SlidingWindow(
active=models.PeriodicInterval(period=120, duration=10),
active=models.PeriodicInterval(period=120, duration=120),
inside_temp=models.PiecewiseConstant((0, 24), (293,)),
outside_temp=models.PiecewiseConstant((0, 24), (283,)),
window_height=1.6, opening_length=0.6,
@ -884,6 +884,7 @@ exposure_models = [MCExposureModel(
expiratory_activity=1,
samples=2000000,
breathing_category=1,
expiratory_activity_weights=(0.7, 0.3, 0)
)
),
exposed=models.Population(
@ -894,9 +895,40 @@ exposure_models = [MCExposureModel(
)
) for qid in (100, 60)]
print(np.mean(large_population_baselines[0].infection_probability()))
exposure_models_2 = [MCExposureModel(
concentration_model=MCConcentrationModel(
room=models.Room(volume=33),
ventilation=models.SlidingWindow(
active=models.PeriodicInterval(period=120, duration=10),
inside_temp=models.PiecewiseConstant((0, 24), (293,)),
outside_temp=models.PiecewiseConstant((0, 24), (283,)),
window_height=1.6, opening_length=0.6,
),
infected=MCInfectedPopulation(
number=1,
presence=models.SpecificInterval(((0, 4), (5, 9))),
masked=True,
virus=MCVirus(halflife=1.1, qID=qid),
expiratory_activity=4,
samples=2000000,
breathing_category=1,
expiratory_activity_weights=(0.7, 0.1, 0)
)
),
exposed=models.Population(
number=2,
presence=models.SpecificInterval(((0, 4), (5, 9))),
activity=models.Activity.types['Seated'],
mask=models.Mask.types['FFP2']
)
) for qid in (100, 60)]
#generate_cdf_curves_vs_qr(masked=False)
#print(np.mean(exposure_models_2[1].infection_probability()))
#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'])
generate_cdf_curves_vs_qr(masked=False,qid=500)
# rs = [model.expected_new_cases() for model in large_population_baselines]
# print(f"R0 - original variant:\t{np.mean(rs[0])}")