add composite_plot_pi_vs_viral_load

cara/montecarlo.py

@@ -608,6 +608,92 @@ def plot_pi_vs_viral_load(baselines: typing.Union[MCExposureModel, typing.List[M
     plt.show()
 
 
+def composite_plot_pi_vs_viral_load(baselines: typing.List[MCExposureModel], labels: typing.List[str],
+                                    colors: typing.List[str], samples_per_vl: int = 2000, vl_points: int = 200,
+                                    title: str = 'Probability of infection vs viral load', show_lines: bool = True) -> None:
+    viral_loads = np.linspace(1, 12, vl_points)
+    lines, lowers, uppers = [], [], []
+    for baseline in baselines:
+        infected = baseline.concentration_model.infected
+        pi_means = []
+        lower_percentiles = []
+        upper_percentiles = []
+        for viral_load in tqdm(viral_loads):
+            model = MCExposureModel(concentration_model=MCConcentrationModel(
+                room=baseline.concentration_model.room,
+                ventilation=baseline.concentration_model.ventilation,
+                infected=MCInfectedPopulation(
+                    number=infected.number,
+                    presence=infected.presence,
+                    masked=infected.masked,
+                    expiratory_activity=infected.expiratory_activity,
+                    breathing_category=infected.breathing_category,
+                    virus=infected.virus,
+                    samples=samples_per_vl,
+                    viral_load=viral_load,
+                    expiratory_activity_weights=infected.expiratory_activity_weights
+                )
+            ),
+                exposed=baseline.exposed)
+
+            infection_probabilities = model.infection_probability()/100
+            pi_means.append(np.mean(infection_probabilities))
+            lower_percentiles.append(np.quantile(infection_probabilities, 0.01))
+            upper_percentiles.append(np.quantile(infection_probabilities, 0.99))
+
+        lines.append(pi_means)
+        uppers.append(upper_percentiles)
+        lowers.append(lower_percentiles)
+
+    histogram_data = [model.infection_probability() / 100 for model in baselines]
+
+    fig, axs = plt.subplots(2, 2 + len(baselines), gridspec_kw={'width_ratios': [5, 0.5] + [1] * len(baselines),
+                                                                'height_ratios': [3, 1], 'wspace': 0},
+                            sharey='row', sharex='col')
+
+    for y, x in [(0, 1)] + [(1, i + 1) for i in range(len(baselines) + 1)]:
+        axs[y, x].axis('off')
+
+    for x in range(len(baselines) - 1):
+        axs[0, x + 3].tick_params(axis='y', which='both', left='off')
+
+    axs[0, 1].set_visible(False)
+
+    for line, upper, lower, label, color in zip(lines, uppers, lowers, labels, colors):
+        axs[0, 0].plot(viral_loads, line, label=label, color=color)
+        axs[0, 0].fill_between(viral_loads, lower, upper, alpha=0.2, color=color)
+
+    for i, (data, color) in enumerate(zip(histogram_data, colors)):
+        axs[0, i + 2].hist(data, bins=30, orientation='horizontal', color=color)
+        axs[0, i + 2].set_xticks([])
+        axs[0, i + 2].set_xticklabels([])
+
+    axs[1, 0].hist(baselines[0].concentration_model.infected._generate_viral_loads(), bins=30, range=(1, 12))
+    axs[1, 0].set_yticks([])
+    axs[1, 0].set_yticklabels([])
+    axs[1, 0].set_xticks([i for i in range(1, 13, 2)])
+    axs[1, 0].set_xticklabels(['$10^{' + str(i) + '}$' for i in range(1, 13, 2)])
+    axs[1, 0].set_xlabel('Viral load')
+    axs[0, 0].set_ylabel('Probability of infection')
+    plt.suptitle(title)
+
+    if show_lines:
+        middle_positions = []
+        for line in lines:
+            for i, point in enumerate(line):
+                if point > 0.5:
+                    middle_positions.append(viral_loads[i])
+                    break
+
+        axs[0, 0].vlines(middle_positions, colors=colors, linestyles=['dashed']*2, ymin=axs[0, 0].get_ylim()[0],
+                         ymax=axs[0, 0].get_ylim()[1])
+        axs[1, 0].vlines(middle_positions, colors=colors, linestyles=['dashed']*2, ymin=0, ymax=axs[1, 0].get_ylim()[1])
+
+    axs[0, 0].legend()
+    plt.show()
+
+
+
 def plot_pi_vs_qid(baselines: typing.Union[MCExposureModel, typing.List[MCExposureModel]], samples_per_qid: int = 20000,
                    title: str = 'Probability of infection vs qID', labels: typing.List[str] = None, qid_min: float = 5,
                    qid_max: float = 2000, qid_samples: int = 200) -> None: