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Adapted plots to correct expiration

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This commit is contained in:
Luis Aleixo 2022-02-25 17:32:56 +01:00
parent 98aba25bd0
commit 93a6913c8c
3 changed files with 89 additions and 58 deletions
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2
cara/models.py
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@ -1097,7 +1097,7 @@ class ShortRangeModel:
# Verifies if the given time falls within a short range interaction # Verifies if the given time falls within a short range interaction
if start < time <= finish: if start < time <= finish:
dilution = self.dilutions[index] dilution = self.dilutions[index]
jet_origin_concentration = concentration_model.infected.expiration.jet_origin_concentration() jet_origin_concentration = self.expirations[index].jet_origin_concentration()
# Long range concentration normalized by the virus viral load # Long range concentration normalized by the virus viral load
long_range_normed_concentration = concentration_model.concentration(time) / concentration_model.virus.viral_load_in_sputum long_range_normed_concentration = concentration_model.concentration(time) / concentration_model.virus.viral_load_in_sputum

70
cara/short_range_plots/results.py
View file

@ -39,44 +39,44 @@ from cara.monte_carlo.data import symptomatic_vl_frequencies
# thickness = [2, 2]) # thickness = [2, 2])
# print('\n<<<<<<<<<<< Peak viral concentration with short range interactions for baseline scenarios >>>>>>>>>>>') # print('\n<<<<<<<<<<< Peak viral concentration with short range interactions for baseline scenarios >>>>>>>>>>>')
# concentration_curve(models=[exposure_module_with_short_range( concentration_curve(models=[exposure_module_with_short_range(
# activity='Light activity', activity='Light activity',
# expiration={"Speaking": 1, "Breathing": 2}, expiration={"Speaking": 1, "Breathing": 2},
# mask='No mask', mask='No mask',
# sr_presence=[(10.5, 11.0), (15.0, 16.0)], sr_presence=[(10.5, 11.0)],
# sr_activities=['Breathing', 'Speaking']), sr_activities=['Speaking']),
# exposure_module_without_short_range( exposure_module_without_short_range(
# activity='Light activity', activity='Light activity',
# expiration={"Speaking": 1, "Breathing": 2}, expiration={"Speaking": 1, "Breathing": 2},
# mask='No mask',) mask='No mask',)
# ], ],
# labels = ['Concentration with short range interactions', 'Background (long-range) concentration'], labels = ['Concentration with short range interactions', 'Background (long-range) concentration'],
# labelsDose = ['Dose (full)', 'Dose (long-range)'], labelsDose = ['Dose (full)', 'Dose (long-range)'],
# colors = ['salmon', 'royalblue'], colors = ['salmon', 'royalblue'],
# linestyles = ['-', '--'], linestyles = ['-', '--'],
# thickness = [2, 2]) thickness = [2, 2])
print('\n<<<<<<<<<<< Dose vs SR exposure time >>>>>>>>>>>') print('\n<<<<<<<<<<< Dose vs SR exposure time >>>>>>>>>>>')
#Always assume 1h for the short range interactions. #Always assume 1h for the short range interactions.
#Always assume that in each model there is only ONE short range interaction. #Always assume that in each model there is only ONE short range interaction.
plot_vD_vs_exposure_time(exp_models = [ # plot_vD_vs_exposure_time(exp_models = [
baseline_model( # baseline_model(
activity='Light activity', # activity='Light activity',
expiration={"Speaking": 2, "Breathing": 1}, # expiration={"Speaking": 2, "Breathing": 1},
mask='No mask', # mask='No mask',
sr_presence=[(8.5, 9.5)], # sr_presence=[(8.5, 9.5)],
sr_activities=['Breathing']), # sr_activities=['Breathing']),
baseline_model( # baseline_model(
activity='Light activity', # activity='Light activity',
expiration={"Speaking": 2, "Breathing": 1}, # expiration={"Speaking": 2, "Breathing": 1},
mask='No mask', # mask='No mask',
sr_presence=[(8.5, 9.5)], # sr_presence=[(8.5, 9.5)],
sr_activities=['Speaking'])], # sr_activities=['Speaking'])],
labels = ['Breathing', 'Speaking'], # labels = ['Breathing', 'Speaking'],
colors=['royalblue', 'darkviolet'], # colors=['royalblue', 'darkviolet'],
linestyles=['solid', 'solid'], # linestyles=['solid', 'solid'],
points=20, # points=20,
time_in_minutes=True, # time_in_minutes=True,
normalize_y_axis=True) # normalize_y_axis=True)

75
cara/short_range_plots/scripts.py
View file

@ -4,6 +4,7 @@ Date: 18/02/2021
Code version: 4.0.0 Code version: 4.0.0
""" """
from cara.monte_carlo.data import short_range_expiration_distributions, expiration_distributions, expiration_BLO_factors
from tqdm import tqdm from tqdm import tqdm
from matplotlib.patches import Rectangle, Patch from matplotlib.patches import Rectangle, Patch
from scipy.spatial import ConvexHull from scipy.spatial import ConvexHull
@ -73,13 +74,23 @@ def concentration_curve(models, labels, labelsDose, colors, linestyles, thicknes
concentrations = [[np.mean(model.concentration( concentrations = [[np.mean(model.concentration(
t)) for t in times] for model in tqdm(exp_models)] t)) for t in times] for model in tqdm(exp_models)]
fig, ax = plt.subplots(figsize=(8,6)) fig, (ax, ax2) = plt.subplots(2, 1, sharex=True, figsize=(8,6))
for c, color, linestyle, width in zip(concentrations, colors, linestyles, thickness): for c, color, linestyle, width in zip(concentrations, colors, linestyles, thickness):
ax.plot(times, c, color=color, ls=linestyle, lw=width) ax.plot(times, c, color=color, ls=linestyle, lw=width)
ax2.plot(times, c, color=color, ls=linestyle, lw=width)
# zoom-in / limit the view to different portions of the data
ax.set_ylim(ax.get_ylim()[1]*0.8, ax.get_ylim()[1]) # outliers only
ax2.set_ylim(0, max(concentrations[1])) # most of the data
ax.spines['bottom'].set_visible(False)
ax2.spines['top'].set_visible(False)
ax.xaxis.tick_top()
ax.tick_params(labeltop=False) # don't put tick labels at the top
ax2.xaxis.tick_bottom()
#ax.set_ylim(ax.get_ylim()[0], ax.get_ylim()[1] * 1.2) #ax.set_ylim(ax.get_ylim()[0], ax.get_ylim()[1] * 1.2)
ax.set_ylim(ax.get_ylim()[0], 90) # ax.set_ylim(ax.get_ylim()[0], 90)
ax.spines["right"].set_visible(False) ax.spines["right"].set_visible(False)
cumulative_doses = [np.cumsum([ cumulative_doses = [np.cumsum([
@ -88,26 +99,35 @@ def concentration_curve(models, labels, labelsDose, colors, linestyles, thicknes
for time1, time2 in tqdm(zip(times[:-1], times[1:])) for time1, time2 in tqdm(zip(times[:-1], times[1:]))
]) for model in exp_models] ]) for model in exp_models]
quantile_05 = [np.cumsum([ # quantile_05 = [np.cumsum([
np.quantile(np.array(model.deposited_exposure_between_bounds( # np.quantile(np.array(model.deposited_exposure_between_bounds(
float(time1), float(time2))), 0.05) # float(time1), float(time2))), 0.05)
for time1, time2 in tqdm(zip(times[:-1], times[1:])) # for time1, time2 in tqdm(zip(times[:-1], times[1:]))
]) for model in exp_models] # ]) for model in exp_models]
quantile_95 = [np.cumsum([ # quantile_95 = [np.cumsum([
np.quantile(np.array(model.deposited_exposure_between_bounds( # np.quantile(np.array(model.deposited_exposure_between_bounds(
float(time1), float(time2))), 0.95) # float(time1), float(time2))), 0.95)
for time1, time2 in tqdm(zip(times[:-1], times[1:])) # for time1, time2 in tqdm(zip(times[:-1], times[1:]))
]) for model in exp_models] # ]) for model in exp_models]
plt.xlabel("Time of day", fontsize=14) plt.xlabel("Time of day", fontsize=14)
plt.ylabel("Mean concentration\n(virions m$^{-3}$)", fontsize=14) plt.ylabel("Mean concentration\n(virions m$^{-3}$)", fontsize=14)
ax1 = ax.twinx() ax1 = ax.twinx()
ax11 = ax2.twinx()
for vd, color, width in tqdm(zip(cumulative_doses, colors, thickness)): for vd, color, width in tqdm(zip(cumulative_doses, colors, thickness)):
ax1.plot(times[:-1], vd, ax1.plot(times[:-1], vd,
color=color, linestyle='dotted', lw=1) color=color, linestyle='dotted', lw=1)
ax11.plot(times[:-1], vd,
color=color, linestyle='dotted', lw=1)
ax1.scatter([times[-1]], [vd[-1]], marker='.', color=color) ax1.scatter([times[-1]], [vd[-1]], marker='.', color=color)
ax11.scatter([times[-1]], [vd[-1]], marker='.', color=color)
# zoom-in / limit the view to different portions of the data
ax1.set_ylim(ax1.get_ylim()[1]*0.8, ax1.get_ylim()[1]) # outliers only
ax11.set_ylim(0, max(cumulative_doses[1])) # most of the data
ax11.spines["bottom"].set_visible(False)
# # Plot presence of exposed person # # Plot presence of exposed person
# for i, model in enumerate(models): # for i, model in enumerate(models):
@ -129,10 +149,10 @@ def concentration_curve(models, labels, labelsDose, colors, linestyles, thicknes
# ) # )
ax1.spines["right"].set_linestyle((0, (1, 5))) # ax1.spines["right"].set_linestyle((0, (1, 5)))
ax1.set_ylabel('Mean cumulative dose\n(infectious virus)', fontsize=14) # ax1.set_ylabel('Mean cumulative dose\n(infectious virus)', fontsize=14)
#ax1.set_ylim(ax1.get_ylim()[0], ax1.get_ylim()[1] * 1.3) #ax1.set_ylim(ax1.get_ylim()[0], ax1.get_ylim()[1] * 1.3)
ax1.set_ylim(ax1.get_ylim()[0], 40) # ax1.set_ylim(ax1.get_ylim()[0], 40)
complete_labels = labels + [label for label in labelsDose] complete_labels = labels + [label for label in labelsDose]
complete_colors = colors + [color for color in colors] complete_colors = colors + [color for color in colors]
@ -141,14 +161,25 @@ def concentration_curve(models, labels, labelsDose, colors, linestyles, thicknes
labels_legend = [mlines.Line2D([], [], color=color, label=label, linestyle=linestyle) labels_legend = [mlines.Line2D([], [], color=color, label=label, linestyle=linestyle)
for color, label, linestyle in zip(complete_colors, complete_labels, complete_linestyles)] for color, label, linestyle in zip(complete_colors, complete_labels, complete_linestyles)]
for i in range(len(models)): # for i in range(len(models)):
print('Scenario: ', labels[i]) # print('Scenario: ', labels[i])
print( # print(
f"MEAN vD = {cumulative_doses[i][-1]}\n" # f"MEAN vD = {cumulative_doses[i][-1]}\n"
f"5th per = {quantile_05[i][-1]}\n" # f"5th per = {quantile_05[i][-1]}\n"
f"95th per = {quantile_95[i][-1]}\n") # f"95th per = {quantile_95[i][-1]}\n")
plt.legend(handles=labels_legend, loc='upper left') ax.legend(handles=labels_legend, loc='upper right')
d = .015 # how big to make the diagonal lines in axes coordinates
# arguments to pass to plot, just so we don't keep repeating them
kwargs = dict(transform=ax.transAxes, color='k', clip_on=False)
ax.plot((-d, +d), (-d, +d), **kwargs) # top-left diagonal
ax.plot((1 - d, 1 + d), (-d, +d), **kwargs) # top-right diagonal
kwargs.update(transform=ax2.transAxes) # switch to the bottom axes
ax2.plot((-d, +d), (1 - d, 1 + d), **kwargs) # bottom-left diagonal
ax2.plot((1 - d, 1 + d), (1 - d, 1 + d), **kwargs) # bottom-right diagonal
plt.show() plt.show()
def plot_vD_vs_exposure_time(exp_models: typing.List[mc.ExposureModel], labels, colors, linestyles, points: int = 20, time_in_minutes: bool = False, normalize_y_axis: bool = False) -> None: def plot_vD_vs_exposure_time(exp_models: typing.List[mc.ExposureModel], labels, colors, linestyles, points: int = 20, time_in_minutes: bool = False, normalize_y_axis: bool = False) -> None: