Removed unused code and added line legend for cn (B and L)
This commit is contained in:
Luis Aleixo
parent
7be638c92f
commit
25f4981c96
1 changed files with 70 additions and 111 deletions
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@ -213,65 +213,39 @@ def exposure_model_from_vl_talking(viral_loads):
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def exposure_model_from_vl_talking_cn(viral_loads):
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n_lines = 5
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cns = np.linspace(0.1, 1, n_lines)
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norm = mpl.colors.Normalize(vmin=cns.min(), vmax=cns.max())
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cmap = mpl.cm.ScalarMappable(norm=norm, cmap=mpl.cm.jet)
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cmap.set_array([])
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n_lines = 30
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cns = np.linspace(0.01, 2, n_lines)
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cmap = define_colormap(cns)
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for cn in tqdm(cns):
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er_means = []
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er_medians = []
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lower_percentiles = []
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upper_percentiles = []
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for vl in viral_loads:
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exposure_mc = mc.ExposureModel(
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concentration_model=mc.ConcentrationModel(
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room=models.Room(volume=100, humidity=0.5),
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ventilation=models.AirChange(
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active=models.SpecificInterval(((0, 24),)),
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air_exch=0.25,
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),
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infected=mc.InfectedPopulation(
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number=1,
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virus=models.Virus(
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viral_load_in_sputum=10**vl,
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infectious_dose=50.,
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),
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presence=mc.SpecificInterval(((0, 2),)),
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mask=models.Mask.types["No mask"],
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activity=activity_distributions['Seated'],
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expiration=models.Expiration.types['Talking'],
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),
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),
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exposed=mc.Population(
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number=14,
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presence=mc.SpecificInterval(((0, 2),)),
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activity=models.Activity.types['Seated'],
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mask=models.Mask.types["No mask"],
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),
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)
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exposure_mc = model_scenario("Talking", vl)
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exposure_model = exposure_mc.build_model(size=SAMPLE_SIZE)
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# divide by 4 to have in 15min (quarter of an hour)
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emission_rate = exposure_model.concentration_model.infected.emission_rate_when_present(cn)/4
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emission_rate = exposure_model.concentration_model.infected.emission_rate_when_present(cn_B = 0.1, cn_L = cn) /4
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er_means.append(np.mean(emission_rate))
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er_medians.append(np.median(emission_rate))
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lower_percentiles.append(np.quantile(emission_rate, 0.01))
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upper_percentiles.append(np.quantile(emission_rate, 0.99))
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# divide by 4 to have in 15min (quarter of an hour)
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coleman_etal_er_talking_2 = [x/4 for x in coleman_etal_er_talking]
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coleman_etal_er_talking_2 = [x/4 for x in coleman_etal_er_talking]
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ax.plot(viral_loads, er_means, color=cmap.to_rgba(cn, alpha=0.75), linewidth=0.5)
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ax.plot(viral_loads, er_means, color=cmap.to_rgba(cn))
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#ax.fill_between(viral_loads, lower_percentiles,
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# upper_percentiles, alpha=0.2, color=cmap.to_rgba(cn))
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fig.colorbar(cmap, ticks=cns)
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er_means = []
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for vl in viral_loads:
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exposure_mc = model_scenario("Talking", vl)
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exposure_model = exposure_mc.build_model(size=SAMPLE_SIZE)
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# divide by 4 to have in 15min
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emission_rate = exposure_model.concentration_model.infected.emission_rate_when_present(cn_B = 0.1, cn_L = 0.2) / 4
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er_means.append(np.mean(emission_rate))
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ax.plot(viral_loads, er_means, color=cmap.to_rgba(cn, alpha=0.75), linewidth=1, ls='--')
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plt.text(viral_loads[int(len(viral_loads)*0.93)], 10**5.5, r"$\mathbf{C_{n,B}=0.2}$", color='black', size='small')
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fig.colorbar(cmap, ticks=[0.01, 0.5, 1.0, 2.0], label="Particle emission concentration for talking.")
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ax.set_yscale('log')
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############# Coleman #############
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plt.scatter(coleman_etal_vl_talking, coleman_etal_er_talking_2, marker='x')
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plt.scatter(coleman_etal_vl_talking, coleman_etal_er_talking_2, marker='x', c = 'orange')
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x_hull, y_hull = get_enclosure_points(
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coleman_etal_vl_talking, coleman_etal_er_talking_2)
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# plot shape
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@ -438,50 +412,18 @@ def exposure_model_from_vl_breathing(viral_loads):
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def exposure_model_from_vl_breathing_cn(viral_loads):
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min_val, max_val = 0.25,0.85
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n = 10
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orig_cmap = plt.cm.Blues
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colors = orig_cmap(np.linspace(min_val, max_val, n))
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n_lines = 30
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cns = np.linspace(0.01, 0.5, n_lines)
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norm = mpl.colors.Normalize(vmin=cns.min(), vmax=cns.max())
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cmap = mpl.cm.ScalarMappable(norm=norm, cmap=mpl.colors.LinearSegmentedColormap.from_list("mycmap", colors))
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cmap.set_array([])
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cmap = define_colormap(cns)
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for cn in tqdm(cns):
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er_means = []
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for vl in viral_loads:
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exposure_mc = mc.ExposureModel(
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concentration_model=mc.ConcentrationModel(
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room=models.Room(volume=100, humidity=0.5),
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ventilation=models.AirChange(
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active=models.SpecificInterval(((0, 24),)),
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air_exch=0.25,
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),
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infected=mc.InfectedPopulation(
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number=1,
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virus=models.Virus(
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viral_load_in_sputum=10**vl,
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infectious_dose=50.,
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),
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presence=mc.SpecificInterval(((0, 2),)),
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mask=models.Mask.types["No mask"],
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activity=activity_distributions['Seated'],
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expiration=models.Expiration.types['Breathing'],
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),
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),
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exposed=mc.Population(
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number=14,
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presence=mc.SpecificInterval(((0, 2),)),
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activity=models.Activity.types['Seated'],
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mask=models.Mask.types["No mask"],
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),
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)
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exposure_mc = model_scenario("Breathing", vl)
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exposure_model = exposure_mc.build_model(size=SAMPLE_SIZE)
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# divide by 2 to have in 30min (half an hour)
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emission_rate = exposure_model.concentration_model.infected.emission_rate_when_present(cn_B = cn, cn_L = 1.0) / 2
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emission_rate = exposure_model.concentration_model.infected.emission_rate_when_present(cn_B = cn, cn_L = 0.2) / 2
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er_means.append(np.mean(emission_rate))
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# divide by 2 to have in 30min (half an hour)
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@ -492,40 +434,16 @@ def exposure_model_from_vl_breathing_cn(viral_loads):
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er_means = []
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for vl in viral_loads:
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exposure_mc = mc.ExposureModel(
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concentration_model=mc.ConcentrationModel(
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room=models.Room(volume=100, humidity=0.5),
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ventilation=models.AirChange(
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active=models.SpecificInterval(((0, 24),)),
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air_exch=0.25,
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),
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infected=mc.InfectedPopulation(
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number=1,
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virus=models.Virus(
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viral_load_in_sputum=10**vl,
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infectious_dose=50.,
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),
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presence=mc.SpecificInterval(((0, 2),)),
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mask=models.Mask.types["No mask"],
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activity=activity_distributions['Seated'],
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expiration=models.Expiration.types['Breathing'],
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),
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),
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exposed=mc.Population(
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number=14,
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presence=mc.SpecificInterval(((0, 2),)),
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activity=models.Activity.types['Seated'],
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mask=models.Mask.types["No mask"],
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),
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)
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exposure_mc = model_scenario("Breathing", vl)
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exposure_model = exposure_mc.build_model(size=SAMPLE_SIZE)
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# divide by 2 to have in 30min (half an hour)
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emission_rate = exposure_model.concentration_model.infected.emission_rate_when_present(cn_B = 0.1, cn_L = 1.0) / 2
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emission_rate = exposure_model.concentration_model.infected.emission_rate_when_present(cn_B = 0.1, cn_L = 0.2) / 2
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er_means.append(np.mean(emission_rate))
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ax.plot(viral_loads, er_means, color=cmap.to_rgba(cn, alpha=0.75), linewidth=1, ls='--')
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plt.text(viral_loads[int(len(viral_loads)*0.95)], er_means[-1], "cn_B=0.1", color='black', size='small')
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fig.colorbar(cmap, ticks=[0.01, 0.25, 0.5], label="Particle emission concentration for breathing.")
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ax.plot(viral_loads, er_means, color=cmap.to_rgba(cn, alpha=0.75), linewidth=1, ls='--')
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plt.text(viral_loads[int(len(viral_loads)*0.95)], 10**4.9, r"$\mathbf{C_{n,L}=0.1}$", color='black', size='small')
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fig.colorbar(cmap, ticks=[0.01, 0.1, 0.5], label="Particle emission concentration for breathing.")
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ax.set_yscale('log')
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############# Coleman #############
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@ -623,6 +541,47 @@ def get_enclosure_points(x_coordinates, y_coordinates):
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points[hull.vertices, 1][0])
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return x_hull, y_hull
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def define_colormap(cns):
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min_val, max_val = 0.25, 0.85
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n = 10
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orig_cmap = plt.cm.Blues
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colors = orig_cmap(np.linspace(min_val, max_val, n))
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norm = mpl.colors.Normalize(vmin=cns.min(), vmax=cns.max())
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cmap = mpl.cm.ScalarMappable(norm=norm, cmap=mpl.colors.LinearSegmentedColormap.from_list("mycmap", colors))
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cmap.set_array([])
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return cmap
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def model_scenario(activity, vl):
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exposure_mc = mc.ExposureModel(
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concentration_model=mc.ConcentrationModel(
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room=models.Room(volume=100, humidity=0.5),
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ventilation=models.AirChange(
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active=models.SpecificInterval(((0, 24),)),
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air_exch=0.25,
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),
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infected=mc.InfectedPopulation(
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number=1,
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virus=models.Virus(
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viral_load_in_sputum=10**vl,
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infectious_dose=50.,
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),
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presence=mc.SpecificInterval(((0, 2),)),
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mask=models.Mask.types["No mask"],
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activity=activity_distributions['Seated'],
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expiration=models.Expiration.types[activity],
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),
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),
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exposed=mc.Population(
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number=14,
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presence=mc.SpecificInterval(((0, 2),)),
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activity=models.Activity.types['Seated'],
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mask=models.Mask.types["No mask"],
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),
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)
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return exposure_mc
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# # Milton
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# boxes = [
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