add present_model

cara/montecarlo.py

@@ -5,6 +5,7 @@ import numpy as np
 import scipy.stats as sct
 import typing
 import matplotlib.pyplot as plt
+import matplotlib.patches as patches
 from sklearn.neighbors import KernelDensity
 
 USE_SCOEH = False
@@ -327,6 +328,48 @@ def print_qr_info(qr_values: np.ndarray) -> None:
         print(f"qR_{quantile} = {np.quantile(qr_values, quantile)}")
 
 
+def present_model(model: MCConcentrationModel, bins: int = 30) -> None:
+    fig, axs = plt.subplots(2, 2, sharex=False, sharey=False)
+    fig.set_figheight(8)
+    fig.set_figwidth(10)
+    fig.suptitle('Summary of model parameters')
+    plt.tight_layout()
+    plt.subplots_adjust(hspace=0.2)
+    fig.set_figheight(10)
+
+    for x, y in ((0, 0), (0, 1), (1, 0), (1, 1)):
+        axs[x, y].set_yticklabels([])
+        axs[x, y].set_yticks([])
+
+    for data, (x, y) in zip((model.infected._generate_viral_loads(),
+                           model.infected._generate_breathing_rates(),
+                           np.log10(model.infected.emission_rate_when_present())),
+                          ((0, 0), (1, 0), (1, 1))):
+        axs[x, y].hist(data, bins=bins)
+        top = axs[x, y].get_ylim()[1]
+        mean, median, std = np.mean(data), np.median(data), np.std(data)
+        axs[x, y].vlines(x=(mean, median, mean - std, mean + std), ymin=0, ymax=top,
+                         colors=('red', 'green', 'pink', 'pink'))
+
+    axs[0, 0].set_title('Viral load in sputum')
+    axs[0, 0].set_xlabel('Viral load [log10(RNA copies / mL)]')
+
+    categories = ("seated", "standing", "light exercise", "moderate exercise", "heavy exercise")
+    axs[1, 0].set_title(f'Breathing rate - '
+                        f'{categories[model.infected.breathing_category - 1]}')
+    axs[1, 0].set_xlabel('Breathing rate [m^3 / h]')
+
+    axs[1, 1].set_title('qR')
+    axs[1, 1].set_xlabel('qR [log10(RNA copies / h)]')
+
+    mean_patch = patches.Patch(color='red', label='Mean')
+    median_patch = patches.Patch(color='green', label='Median')
+    std_patch = patches.Patch(color='pink', label='Standard deviations')
+    fig.legend(handles=(mean_patch, median_patch, std_patch))
+
+    plt.show()
+
+
 def buaonanno_exposure_model():
     return MCExposureModel(
         concentration_model=MCConcentrationModel(