Adapted plots to correct expiration
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Luis Aleixo
parent
98aba25bd0
commit
93a6913c8c
3 changed files with 89 additions and 58 deletions
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@ -1097,7 +1097,7 @@ class ShortRangeModel:
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# Verifies if the given time falls within a short range interaction
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if start < time <= finish:
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dilution = self.dilutions[index]
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jet_origin_concentration = concentration_model.infected.expiration.jet_origin_concentration()
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jet_origin_concentration = self.expirations[index].jet_origin_concentration()
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# Long range concentration normalized by the virus viral load
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long_range_normed_concentration = concentration_model.concentration(time) / concentration_model.virus.viral_load_in_sputum
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@ -39,44 +39,44 @@ from cara.monte_carlo.data import symptomatic_vl_frequencies
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# thickness = [2, 2])
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# print('\n<<<<<<<<<<< Peak viral concentration with short range interactions for baseline scenarios >>>>>>>>>>>')
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# concentration_curve(models=[exposure_module_with_short_range(
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# activity='Light activity',
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# expiration={"Speaking": 1, "Breathing": 2},
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# mask='No mask',
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# sr_presence=[(10.5, 11.0), (15.0, 16.0)],
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# sr_activities=['Breathing', 'Speaking']),
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# exposure_module_without_short_range(
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# activity='Light activity',
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# expiration={"Speaking": 1, "Breathing": 2},
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# mask='No mask',)
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# ],
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# labels = ['Concentration with short range interactions', 'Background (long-range) concentration'],
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# labelsDose = ['Dose (full)', 'Dose (long-range)'],
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# colors = ['salmon', 'royalblue'],
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# linestyles = ['-', '--'],
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# thickness = [2, 2])
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concentration_curve(models=[exposure_module_with_short_range(
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activity='Light activity',
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expiration={"Speaking": 1, "Breathing": 2},
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mask='No mask',
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sr_presence=[(10.5, 11.0)],
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sr_activities=['Speaking']),
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exposure_module_without_short_range(
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activity='Light activity',
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expiration={"Speaking": 1, "Breathing": 2},
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mask='No mask',)
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],
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labels = ['Concentration with short range interactions', 'Background (long-range) concentration'],
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labelsDose = ['Dose (full)', 'Dose (long-range)'],
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colors = ['salmon', 'royalblue'],
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linestyles = ['-', '--'],
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thickness = [2, 2])
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print('\n<<<<<<<<<<< Dose vs SR exposure time >>>>>>>>>>>')
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#Always assume 1h for the short range interactions.
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#Always assume that in each model there is only ONE short range interaction.
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plot_vD_vs_exposure_time(exp_models = [
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baseline_model(
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activity='Light activity',
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expiration={"Speaking": 2, "Breathing": 1},
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mask='No mask',
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sr_presence=[(8.5, 9.5)],
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sr_activities=['Breathing']),
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baseline_model(
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activity='Light activity',
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expiration={"Speaking": 2, "Breathing": 1},
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mask='No mask',
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sr_presence=[(8.5, 9.5)],
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sr_activities=['Speaking'])],
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labels = ['Breathing', 'Speaking'],
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colors=['royalblue', 'darkviolet'],
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linestyles=['solid', 'solid'],
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points=20,
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time_in_minutes=True,
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normalize_y_axis=True)
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# plot_vD_vs_exposure_time(exp_models = [
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# baseline_model(
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# activity='Light activity',
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# expiration={"Speaking": 2, "Breathing": 1},
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# mask='No mask',
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# sr_presence=[(8.5, 9.5)],
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# sr_activities=['Breathing']),
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# baseline_model(
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# activity='Light activity',
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# expiration={"Speaking": 2, "Breathing": 1},
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# mask='No mask',
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# sr_presence=[(8.5, 9.5)],
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# sr_activities=['Speaking'])],
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# labels = ['Breathing', 'Speaking'],
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# colors=['royalblue', 'darkviolet'],
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# linestyles=['solid', 'solid'],
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# points=20,
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# time_in_minutes=True,
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# normalize_y_axis=True)
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@ -4,6 +4,7 @@ Date: 18/02/2021
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Code version: 4.0.0
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"""
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from cara.monte_carlo.data import short_range_expiration_distributions, expiration_distributions, expiration_BLO_factors
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from tqdm import tqdm
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from matplotlib.patches import Rectangle, Patch
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from scipy.spatial import ConvexHull
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@ -73,13 +74,23 @@ def concentration_curve(models, labels, labelsDose, colors, linestyles, thicknes
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concentrations = [[np.mean(model.concentration(
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t)) for t in times] for model in tqdm(exp_models)]
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fig, ax = plt.subplots(figsize=(8,6))
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fig, (ax, ax2) = plt.subplots(2, 1, sharex=True, figsize=(8,6))
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for c, color, linestyle, width in zip(concentrations, colors, linestyles, thickness):
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ax.plot(times, c, color=color, ls=linestyle, lw=width)
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ax2.plot(times, c, color=color, ls=linestyle, lw=width)
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# zoom-in / limit the view to different portions of the data
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ax.set_ylim(ax.get_ylim()[1]*0.8, ax.get_ylim()[1]) # outliers only
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ax2.set_ylim(0, max(concentrations[1])) # most of the data
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ax.spines['bottom'].set_visible(False)
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ax2.spines['top'].set_visible(False)
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ax.xaxis.tick_top()
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ax.tick_params(labeltop=False) # don't put tick labels at the top
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ax2.xaxis.tick_bottom()
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#ax.set_ylim(ax.get_ylim()[0], ax.get_ylim()[1] * 1.2)
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ax.set_ylim(ax.get_ylim()[0], 90)
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# ax.set_ylim(ax.get_ylim()[0], 90)
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ax.spines["right"].set_visible(False)
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cumulative_doses = [np.cumsum([
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@ -88,26 +99,35 @@ def concentration_curve(models, labels, labelsDose, colors, linestyles, thicknes
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for time1, time2 in tqdm(zip(times[:-1], times[1:]))
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]) for model in exp_models]
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quantile_05 = [np.cumsum([
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np.quantile(np.array(model.deposited_exposure_between_bounds(
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float(time1), float(time2))), 0.05)
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for time1, time2 in tqdm(zip(times[:-1], times[1:]))
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]) for model in exp_models]
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# quantile_05 = [np.cumsum([
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# np.quantile(np.array(model.deposited_exposure_between_bounds(
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# float(time1), float(time2))), 0.05)
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# for time1, time2 in tqdm(zip(times[:-1], times[1:]))
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# ]) for model in exp_models]
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quantile_95 = [np.cumsum([
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np.quantile(np.array(model.deposited_exposure_between_bounds(
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float(time1), float(time2))), 0.95)
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for time1, time2 in tqdm(zip(times[:-1], times[1:]))
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]) for model in exp_models]
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# quantile_95 = [np.cumsum([
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# np.quantile(np.array(model.deposited_exposure_between_bounds(
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# float(time1), float(time2))), 0.95)
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# for time1, time2 in tqdm(zip(times[:-1], times[1:]))
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# ]) for model in exp_models]
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plt.xlabel("Time of day", fontsize=14)
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plt.ylabel("Mean concentration\n(virions m$^{-3}$)", fontsize=14)
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ax1 = ax.twinx()
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ax11 = ax2.twinx()
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for vd, color, width in tqdm(zip(cumulative_doses, colors, thickness)):
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ax1.plot(times[:-1], vd,
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color=color, linestyle='dotted', lw=1)
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ax11.plot(times[:-1], vd,
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color=color, linestyle='dotted', lw=1)
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ax1.scatter([times[-1]], [vd[-1]], marker='.', color=color)
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ax11.scatter([times[-1]], [vd[-1]], marker='.', color=color)
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# zoom-in / limit the view to different portions of the data
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ax1.set_ylim(ax1.get_ylim()[1]*0.8, ax1.get_ylim()[1]) # outliers only
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ax11.set_ylim(0, max(cumulative_doses[1])) # most of the data
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ax11.spines["bottom"].set_visible(False)
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# # Plot presence of exposed person
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# for i, model in enumerate(models):
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@ -129,10 +149,10 @@ def concentration_curve(models, labels, labelsDose, colors, linestyles, thicknes
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# )
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ax1.spines["right"].set_linestyle((0, (1, 5)))
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ax1.set_ylabel('Mean cumulative dose\n(infectious virus)', fontsize=14)
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# ax1.spines["right"].set_linestyle((0, (1, 5)))
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# ax1.set_ylabel('Mean cumulative dose\n(infectious virus)', fontsize=14)
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#ax1.set_ylim(ax1.get_ylim()[0], ax1.get_ylim()[1] * 1.3)
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ax1.set_ylim(ax1.get_ylim()[0], 40)
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# ax1.set_ylim(ax1.get_ylim()[0], 40)
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complete_labels = labels + [label for label in labelsDose]
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complete_colors = colors + [color for color in colors]
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@ -141,14 +161,25 @@ def concentration_curve(models, labels, labelsDose, colors, linestyles, thicknes
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labels_legend = [mlines.Line2D([], [], color=color, label=label, linestyle=linestyle)
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for color, label, linestyle in zip(complete_colors, complete_labels, complete_linestyles)]
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for i in range(len(models)):
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print('Scenario: ', labels[i])
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print(
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f"MEAN vD = {cumulative_doses[i][-1]}\n"
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f"5th per = {quantile_05[i][-1]}\n"
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f"95th per = {quantile_95[i][-1]}\n")
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# for i in range(len(models)):
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# print('Scenario: ', labels[i])
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# print(
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# f"MEAN vD = {cumulative_doses[i][-1]}\n"
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# f"5th per = {quantile_05[i][-1]}\n"
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# f"95th per = {quantile_95[i][-1]}\n")
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plt.legend(handles=labels_legend, loc='upper left')
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ax.legend(handles=labels_legend, loc='upper right')
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d = .015 # how big to make the diagonal lines in axes coordinates
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# arguments to pass to plot, just so we don't keep repeating them
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kwargs = dict(transform=ax.transAxes, color='k', clip_on=False)
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ax.plot((-d, +d), (-d, +d), **kwargs) # top-left diagonal
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ax.plot((1 - d, 1 + d), (-d, +d), **kwargs) # top-right diagonal
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kwargs.update(transform=ax2.transAxes) # switch to the bottom axes
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ax2.plot((-d, +d), (1 - d, 1 + d), **kwargs) # bottom-left diagonal
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ax2.plot((1 - d, 1 + d), (1 - d, 1 + d), **kwargs) # bottom-right diagonal
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plt.show()
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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:
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