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compare_viruses_vr plot

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This commit is contained in:
Luis Aleixo 2021-09-16 17:29:50 +02:00
parent 0c992e459f
commit adb8dccbad
3 changed files with 168 additions and 12 deletions
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83
cara/model_scenarios_paper.py
View file

@ -603,4 +603,85 @@ def office_model_no_mask_windows_open_alltimes():
mask=models.Mask.types['No mask']
)
)
return office_model_no_vent
return office_model_no_vent
######### Standard exposure models ###########
######### Breathing model ###########
def breathing_exposure(activity: str, mask: str):
exposure_mc = mc.ExposureModel(
concentration_model=mc.ConcentrationModel(
room=models.Room(volume=100, humidity=0.5),
ventilation=models.AirChange(
active=models.SpecificInterval(((0, 24),)),
air_exch=0.25,
),
infected=mc.InfectedPopulation(
number=1,
virus=mc.Virus(
viral_load_in_sputum=symptomatic_vl_frequencies,
infectious_dose=infectious_dose_distribution,
viable_to_RNA=infectious_virus_distribution
),
presence=mc.SpecificInterval(((0, 2),)),
mask=models.Mask.types[mask],
activity=activity_distributions[activity],
expiration=models.Expiration.types['Breathing'],
),
),
exposed=mc.Population(
number=14,
presence=mc.SpecificInterval(((0, 2),)),
activity=models.Activity.types[activity],
mask=models.Mask.types[mask],
),
)
return exposure_mc
######### Speaking model ###########
def speaking_exposure(activity: str, mask: str):
exposure_mc = mc.ExposureModel(
concentration_model=mc.ConcentrationModel(
room=models.Room(volume=100, humidity=0.5),
ventilation=models.AirChange(
active=models.SpecificInterval(((0, 24),)),
air_exch=0.25,
),
infected=mc.InfectedPopulation(
number=1,
virus=mc.Virus(
viral_load_in_sputum=symptomatic_vl_frequencies,
infectious_dose=infectious_dose_distribution,
viable_to_RNA=infectious_virus_distribution,
),
presence=mc.SpecificInterval(((0, 2),)),
mask=models.Mask.types[mask],
activity=activity_distributions[activity],
expiration=models.Expiration.types['Talking'],
),
),
exposed=mc.Population(
number=14,
presence=mc.SpecificInterval(((0, 2),)),
activity=models.Activity.types[activity],
mask=models.Mask.types[mask],
),
)
return exposure_mc
######### Infected Population model ###########
def infected_model(mask: str, activity: str, expiratory_activity: str):
infected=mc.InfectedPopulation(
number=1,
virus=mc.Virus(
viral_load_in_sputum=symptomatic_vl_frequencies,
infectious_dose=infectious_dose_distribution,
viable_to_RNA=infectious_virus_distribution,
),
presence=mc.SpecificInterval(((0, 2),)),
mask=models.Mask.types[mask],
activity=activity_distributions[activity],
expiration=models.Expiration.types[expiratory_activity])
return infected

14
cara/plot_output.py
View file

@ -46,16 +46,10 @@ from dataclasses import dataclass
#calculate_deposition_factor()
############ Comparison between concentration curves ############
# compare_concentration_curves_virus([classroom_model_IGH_no_mask_windows_closed[0],classroom_model_IGH_no_mask_windows_open_breaks[0],
# classroom_model_IGH_no_mask_windows_open_alltimes[0]],
# labels=['Windows closed', 'Window open during breaks', 'Window open at all times'],
# colors=['tomato', 'lightskyblue', 'limegreen', '#1f77b4', 'seagreen', 'lightskyblue', 'deepskyblue'],
# title='No mask - Spring/Summer period'
# )
# print_qd_info(classroom_model_IGH_no_mask_windows_closed[0])
# print_qd_info(classroom_model_IGH_no_mask_windows_open_breaks[0])
# print_qd_info(classroom_model_IGH_no_mask_windows_open_alltimes[0])
compare_concentration_curves()
#compare_concentration_curves()
############ Emission Rate Violin plot ############
compare_viruses_vr()
############ Used for testing ############
#exposure_model_from_vl_talking_new_points()

83
cara/results_paper.py
View file

@ -10,8 +10,9 @@ import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import matplotlib.lines as mlines
import matplotlib.patches as patches
import matplotlib as mpl
import math
from random import randrange
######### Plot material #########
@ -568,6 +569,86 @@ def compare_concentration_curves():
plt.show()
def compare_viruses_vr():
# Represented as tuples of three numbers on the interval [0, 1] (e.g. (1, 0, 0)) (R, G, B)
colors = [(0, 0.5, 0), (0, 0, 0.5), (0.5, 0, 0)]
# The colors of the borders surrounding the violin plots
border_colors = [(0, 0, 0), (0, 0, 0), (0, 0, 0)]
whisker_width = 0.8
positions = [1, 2, 3, 12]
infected_populations = [infected_model('No mask', 'Light activity', activity).build_model(size=SAMPLE_SIZE) for activity in ('Breathing', 'Talking', 'Shouting')]
vrs = [np.log10(pop.emission_rate_when_present(cn_B=0.06, cn_L=0.2)) for pop in infected_populations]
fig, ax = plt.subplots()
ax.set_xlim((0, 13))
parts = ax.violinplot(vrs, quantiles=[(0.05, 0.95) for _ in vrs], showextrema=False)
means = [np.log10(np.mean(10 ** vr)) for vr in vrs]
ax.hlines(y=means,
xmin=[pos - whisker_width / 2 for pos in positions[:3]],
xmax=[pos + whisker_width / 2 for pos in positions[:3]],
colors=colors)
for pc, color, bc in zip(parts['bodies'], colors, border_colors):
pc.set_facecolor(color)
pc.set_edgecolor(bc)
parts['cquantiles'].set_color([c for c in colors[:3] for _ in range(2)])
######### SARS-CoV-2 #########
lower_bound = [290, 150, 150, 360,450, 610, 620, 1160, 1300, 1330, 1390, 1390, 1520, 2320, 6830, 9700, 42130]
higher_bound = [2900, 1500, 1500, 3600, 4500, 6100, 6200, 11600, 13000, 13300, 13900, 13900, 15200, 23200, 68300, 97000, 421300]
for i in range(len(lower_bound)):
data = np.random.uniform(lower_bound[i], higher_bound[i], size=200000)
ax.boxplot(np.log10(data), positions=[np.random.uniform(5.5, 12.5)], medianprops=dict(color=colors[0]+ (0.5,), linewidth=5), whiskerprops=dict(color=colors[0]+ (0.5,)), boxprops=dict(color=colors[0]+ (0.5,)))
######### Measles #########
lower_bound = [180, 6000, 27650, 86400]
higher_bound = [1800, 60000, 276500, 864000]
for i in range(len(lower_bound)):
data = np.random.uniform(lower_bound[i], higher_bound[i], size=200000)
ax.boxplot(np.log10(data), positions=[np.random.uniform(5.5, 12.5)], medianprops=dict(color=colors[1]+ (0.5,), linewidth=5), whiskerprops=dict(color=colors[1]+ (0.5,)), boxprops=dict(color=colors[1]+ (0.5,)))
######### Influenza #########
lower_bound = [1.1, 79.5, 790]
higher_bound = [11, 795, 7900]
for i in range(len(lower_bound)):
data = np.random.uniform(lower_bound[i], higher_bound[i], size=200000)
ax.boxplot(np.log10(data), positions=[np.random.uniform(5.5, 12.5)], medianprops=dict(color=colors[2]+ (0.5,), linewidth=5), whiskerprops=dict(color=colors[2]+ (0.5,)), boxprops=dict(color=colors[2]+ (0.5,)))
######### Rhinovirus #########
lower_bound = [31]
higher_bound = [310]
for i in range(len(lower_bound)):
data = np.random.uniform(lower_bound[i], higher_bound[i], size=200000)
ax.boxplot(np.log10(data), positions=[np.random.uniform(5.5, 12.5)], medianprops=dict(color=(0.5, 0.5, 0.5, 0.5, ), linewidth=5), whiskerprops=dict(color=(0.5, 0.5, 0.5, 0.5,)), boxprops=dict(color=(0.5, 0.5, 0.5, 0.5,)))
handles = [patches.Patch(color=c, label=l) for c, l in zip([p + (0.5,) for p in [(0, 0.5, 0), (0, 0, 0.5), (0.5, 0, 0), (0.5, 0.5, 0.5)]], ('SARS-CoV-2', 'Measles', 'Influenza', 'Rhinovirus'))]
boxplot_legend = plt.legend(handles=handles, loc='lower right')
handles = [patches.Patch(color=c, label=l) for c, l in zip([p + (0.3,) for p in colors], ('Breathing', 'Speaking', 'Shouting'))]
plt.legend(handles=handles, loc='lower left', bbox_to_anchor=(0.15, 0.))
plt.gca().add_artist(boxplot_legend)
ax.vlines(x=5, ymin=ax.get_ylim()[0], ymax=ax.get_ylim()[1], colors=(0.8, 0.8, 0.8))
ax.set_yticks([i for i in range(-4, 7, 2)])
ax.set_yticklabels(['$10^{' + str(i) + '}$' for i in range(-4, 7, 2)])
ax.set_xticks([2.5, 9])
ax.set_xticklabels(['SARS-CoV-2', 'Experimental Results'])
ax.set_ylabel('Emission rate (virions h$^{-1}$)', fontsize=12)
plt.tight_layout()
plt.show()
######### Auxiliar functions #########
def get_enclosure_points(x_coordinates, y_coordinates):