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statistical data on emission rate

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This commit is contained in:
Luis Aleixo 2021-09-06 09:37:18 +02:00
parent b00af20e82
commit c4afd66747
1 changed files with 30 additions and 11 deletions
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41
cara/montecarlo.py
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@ -86,20 +86,20 @@ def exposure_model_from_vl_breathing_cn():
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
n_lines = 30
n_lines = 1
cns = np.linspace(0.01, 0.5, n_lines)
cmap = define_colormap(cns)
for cn in tqdm(cns):
er_means = []
er_means = np.array([])
for vl in viral_loads:
exposure_mc = breathing_exposure_vl(vl)
exposure_model = exposure_mc.build_model(size=SAMPLE_SIZE)
# divide by 2 to have in 30min (half an hour)
emission_rate = exposure_model.concentration_model.infected.emission_rate_when_present(
cn_B=cn, cn_L=0.2) / 2
er_means.append(np.mean(emission_rate))
er_means = np.append(er_means, np.mean(emission_rate))
# divide by 2 to have in 30min (half an hour)
coleman_etal_er_breathing_2 = [x/2 for x in coleman_etal_er_breathing]
@ -109,15 +109,15 @@ def exposure_model_from_vl_breathing_cn():
cn, alpha=0.75), linewidth=0.5)
# The dashed line for the chosen Cn,B
er_means = []
er_means = np.array([])
for vl in viral_loads:
exposure_mc = breathing_exposure_vl(vl)
exposure_model = exposure_mc.build_model(size=SAMPLE_SIZE)
# divide by 2 to have in 30min (half an hour)
emission_rate = exposure_model.concentration_model.infected.emission_rate_when_present(
cn_B=0.06, cn_L=0.2) / 2
er_means.append(np.mean(emission_rate))
er_means = np.append(er_means, np.mean(emission_rate))
print_er_info(er_means)
ax.plot(viral_loads, er_means, color=cmap.to_rgba(
cn, alpha=0.75), linewidth=1, ls='--')
plt.text(viral_loads[int(len(viral_loads)*0.9)], 10**4.2,
@ -200,19 +200,19 @@ def exposure_model_from_vl_talking_cn():
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
n_lines = 30
n_lines = 1
cns = np.linspace(0.01, 2, n_lines)
cmap = define_colormap(cns)
for cn in tqdm(cns):
er_means = []
er_means = np.array([])
for vl in viral_loads:
exposure_mc = talking_exposure_vl(vl)
exposure_model = exposure_mc.build_model(size=SAMPLE_SIZE)
# divide by 4 to have in 15min (quarter of an hour)
emission_rate = exposure_model.concentration_model.infected.emission_rate_when_present(
cn_B=0.1, cn_L=cn) / 4
er_means.append(np.mean(emission_rate))
er_means = np.append(er_means, np.mean(emission_rate))
# divide by 4 to have in 15min (quarter of an hour)
coleman_etal_er_talking_2 = [x/4 for x in coleman_etal_er_talking]
@ -220,16 +220,18 @@ def exposure_model_from_vl_talking_cn():
cn, alpha=0.75), linewidth=0.5)
# The dashed line for the chosen Cn,L
er_means = []
er_means = np.array([])
for vl in viral_loads:
exposure_mc = talking_exposure_vl(vl)
exposure_model = exposure_mc.build_model(size=SAMPLE_SIZE)
# divide by 4 to have in 15min
emission_rate = exposure_model.concentration_model.infected.emission_rate_when_present(
cn_B=0.06, cn_L=0.2) / 4
er_means.append(np.mean(emission_rate))
er_means = np.append(er_means, np.mean(emission_rate))
ax.plot(viral_loads, er_means, color=cmap.to_rgba(
cn, alpha=0.75), linewidth=1, ls='--')
'''Statistical Data'''
print_er_info(er_means)
plt.text(viral_loads[int(len(viral_loads)*0.93)], 10**5.5,
r"$\mathbf{c_{n,L}=0.2}$", color=cmap.to_rgba(cn), size='small')
@ -370,3 +372,20 @@ def build_breathing_legend(fig):
width = item.get_window_extent(fig.canvas.get_renderer()).width
label.set_ha('left')
label.set_position((-3*width, 0))
def print_er_info(er: np.array):
"""
Prints statistical parameters of a given distribution of ER-values
:param er: A numpy-array of the ER-values
:return: Nothing, parameters are printed
"""
print(f"MEAN of ER = {np.mean(er)}\n"
f"SD of ER = {np.std(er)}\n"
f"Median of qR = {np.quantile(er, 0.5)}\n")
print(f"Percentiles of ER:")
for quantile in (0.01, 0.05, 0.25, 0.50, 0.55, 0.65, 0.75, 0.95, 0.99):
print(f"ER_{quantile} = {np.quantile(er, quantile)}")
return