handled way to visualize custom value types (namely constant values); handled display of conditional probability plot
This commit is contained in:
Luis Aleixo
parent
9bc6252a4b
commit
39fc9d8e96
4 changed files with 76 additions and 66 deletions
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@ -19,6 +19,7 @@ from caimira.store.data_registry import DataRegistry
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from ... import monte_carlo as mc
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from .model_generator import VirusFormData
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from ... import dataclass_utils
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from caimira.enums import ViralLoads
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def model_start_end(model: models.ExposureModel):
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@ -168,12 +169,27 @@ def calculate_report_data(form: VirusFormData, model: models.ExposureModel, exec
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prob_dist_count, prob_dist_bins = np.histogram(prob/100, bins=100, density=True)
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prob_probabilistic_exposure = np.array(model.total_probability_rule()).mean()
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expected_new_cases = np.array(model.expected_new_cases()).mean()
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uncertainties_plot_src = img2base64(_figure2bytes(uncertainties_plot(model, prob))) if form.conditional_probability_plot else None
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exposed_presence_intervals = [list(interval) for interval in model.exposed.presence_interval().boundaries()]
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conditional_probability_data = {key: value for key, value in
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zip(('viral_loads', 'pi_means', 'lower_percentiles', 'upper_percentiles'),
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manufacture_conditional_probability_data(model, prob))}
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if (model.data_registry.virological_data['virus_distributions'][form.virus_type]['viral_load_in_sputum'] == ViralLoads.COVID_OVERALL.value # type: ignore
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and form.conditional_probability_plot): # Only generate this data if covid_overall_vl_data is selected.
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viral_load_in_sputum: models._VectorisedFloat = model.concentration_model.infected.virus.viral_load_in_sputum
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viral_loads, pi_means, lower_percentiles, upper_percentiles = manufacture_conditional_probability_data(model, prob)
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uncertainties_plot_src = img2base64(_figure2bytes(uncertainties_plot(prob, viral_load_in_sputum, viral_loads,
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pi_means, lower_percentiles, upper_percentiles)))
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conditional_probability_data = {key: value for key, value in
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zip(('viral_loads', 'pi_means', 'lower_percentiles', 'upper_percentiles'),
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(viral_loads, pi_means, lower_percentiles, upper_percentiles))}
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vl_dist = list(np.log10(viral_load_in_sputum))
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else:
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uncertainties_plot_src = None
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conditional_probability_data = None
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vl = model.concentration_model.virus.viral_load_in_sputum
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if isinstance(vl, np.ndarray): vl_dist = list(np.log10(model.concentration_model.virus.viral_load_in_sputum))
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else: vl_dist = np.log10(model.concentration_model.virus.viral_load_in_sputum)
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return {
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"model_repr": repr(model),
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@ -194,7 +210,7 @@ def calculate_report_data(form: VirusFormData, model: models.ExposureModel, exec
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"expected_new_cases": expected_new_cases,
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"uncertainties_plot_src": uncertainties_plot_src,
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"CO2_concentrations": CO2_concentrations,
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"vl_dist": list(np.log10(model.concentration_model.virus.viral_load_in_sputum)),
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"vl_dist": vl_dist,
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"conditional_probability_data": conditional_probability_data,
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}
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@ -256,11 +272,12 @@ def manufacture_conditional_probability_data(
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return list(viral_loads), list(pi_means), list(lower_percentiles), list(upper_percentiles)
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def uncertainties_plot(exposure_model: models.ExposureModel, prob: models._VectorisedFloat):
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fig = plt.figure(figsize=(4, 7), dpi=110)
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infection_probability = prob / 100
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viral_loads, pi_means, lower_percentiles, upper_percentiles = manufacture_conditional_probability_data(exposure_model, infection_probability)
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def uncertainties_plot(infection_probability: models._VectorisedFloat,
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viral_load_in_sputum: models._VectorisedFloat,
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viral_loads: models._VectorisedFloat,
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pi_means: models._VectorisedFloat,
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lower_percentiles: models._VectorisedFloat,
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upper_percentiles: models._VectorisedFloat):
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fig, axs = plt.subplots(2, 3,
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gridspec_kw={'width_ratios': [5, 0.5] + [1],
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@ -273,8 +290,8 @@ def uncertainties_plot(exposure_model: models.ExposureModel, prob: models._Vecto
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axs[0, 1].set_visible(False)
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axs[0, 0].plot(viral_loads, pi_means, label='Predictive total probability')
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axs[0, 0].fill_between(viral_loads, lower_percentiles, upper_percentiles, alpha=0.1, label='5ᵗʰ and 95ᵗʰ percentile')
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axs[0, 0].plot(viral_loads, np.array(pi_means)/100, label='Predictive total probability')
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axs[0, 0].fill_between(viral_loads, np.array(lower_percentiles)/100, np.array(upper_percentiles)/100, alpha=0.1, label='5ᵗʰ and 95ᵗʰ percentile')
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axs[0, 2].hist(infection_probability, bins=30, orientation='horizontal')
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axs[0, 2].set_xticks([])
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@ -285,8 +302,8 @@ def uncertainties_plot(exposure_model: models.ExposureModel, prob: models._Vecto
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axs[0, 2].set_xlim(0, highest_bar)
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axs[0, 2].text(highest_bar * 0.5, 0.5,
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rf"$\bf{np.round(np.mean(infection_probability) * 100, 1)}$%", ha='center', va='center')
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axs[1, 0].hist(np.log10(exposure_model.concentration_model.infected.virus.viral_load_in_sputum),
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rf"$\bf{np.round(np.mean(infection_probability), 1)}$%", ha='center', va='center')
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axs[1, 0].hist(np.log10(viral_load_in_sputum),
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bins=150, range=(2, 10), color='grey')
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axs[1, 0].set_facecolor("lightgrey")
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axs[1, 0].set_yticks([])
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@ -214,11 +214,12 @@
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draw_histogram("prob_inf_hist", {{ prob_inf }}, {{ prob_inf_sd }});
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</script>
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<br>
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<div class="form-check">
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<input type="checkbox" id="conditional_probability_plot" class="tabbed form-check-input" name="conditional_probability_plot" value="1" onClick="conditional_probability_plot(this.checked, {{ form.conditional_probability_plot | int }});">
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<label id="label_conditional_probability_plot" for="conditional_probability_plot" class="form-check-label col-sm-12">Generate full uncertainty data (as function of the viral load)</label>
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</div>
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{% if model.data_registry.virological_data['virus_distributions'][form.virus_type]['viral_load_in_sputum'] == 'Ref: Viral load - covid_overal_vl_data' %}
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<div class="form-check">
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<input type="checkbox" id="conditional_probability_plot" class="tabbed form-check-input" name="conditional_probability_plot" value="1" onClick="conditional_probability_plot(this.checked, {{ form.conditional_probability_plot | int }});">
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<label id="label_conditional_probability_plot" for="conditional_probability_plot" class="form-check-label col-sm-12">Generate full uncertainty data (as function of the viral load)</label>
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</div>
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{% endif %}
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{% if form.conditional_probability_plot %}
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<div id="conditional_probability_div">
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<img src= "{{ uncertainties_plot_src }}" />
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@ -14,25 +14,31 @@ from caimira.monte_carlo.sampleable import LogCustom, LogNormal, Normal, LogCust
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from caimira.store.data_registry import DataRegistry
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def evaluate_vl(value, data_registry: DataRegistry):
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if value == ViralLoads.COVID_OVERALL.value:
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def evaluate_vl(root: typing.Dict, value: str, data_registry: DataRegistry):
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if root[value] == ViralLoads.COVID_OVERALL.value:
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return covid_overal_vl_data(data_registry)
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elif value == ViralLoads.SYMPTOMATIC_FREQUENCIES.value:
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elif root[value] == ViralLoads.SYMPTOMATIC_FREQUENCIES.value:
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return symptomatic_vl_frequencies
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elif root[value] == 'Custom':
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return param_evaluation(root, 'Viral load custom')
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else:
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raise ValueError(f"Invalid ViralLoads value {value}")
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def evaluate_infectd(value, data_registry: DataRegistry):
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if value == InfectiousDoses.DISTRIBUTION.value:
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def evaluate_infectd(root: typing.Dict, value: str, data_registry: DataRegistry):
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if root[value] == InfectiousDoses.DISTRIBUTION.value:
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return infectious_dose_distribution(data_registry)
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elif root[value] == "Custom":
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return param_evaluation(root, 'Infectious dose custom')
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else:
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raise ValueError(f"Invalid InfectiousDoses value {value}")
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def evaluate_vtrr(value, data_registry: DataRegistry):
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if value == ViableToRNARatios.DISTRIBUTION.value:
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def evaluate_vtrr(root: typing.Dict, value: str, data_registry: DataRegistry):
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if root[value] == ViableToRNARatios.DISTRIBUTION.value:
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return viable_to_RNA_ratio_distribution(data_registry)
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elif root[value] == "Custom":
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return param_evaluation(root, 'Viable to RNA ratio custom')
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else:
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raise ValueError(f"Invalid ViableToRNARatios value {value}")
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@ -60,7 +66,7 @@ def custom_value_type_lookup(dict: dict, key_part: str) -> typing.Any:
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return f"Key '{key_part}' not found."
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def evaluate_custom_value_type(dist: str, params: typing.Dict) -> typing.Any:
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def evaluate_custom_value_type(value_type: str, params: typing.Dict) -> typing.Any:
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"""
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Evaluate a custom value type.
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@ -75,13 +81,13 @@ def evaluate_custom_value_type(dist: str, params: typing.Dict) -> typing.Any:
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ValueError: If the value type is not recognized.
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"""
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if dist == 'Constant':
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if value_type == 'Constant value':
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return params
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elif dist == 'Normal distribution':
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elif value_type == 'Normal distribution':
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return Normal(params['normal_mean_gaussian'], params['normal_standard_deviation_gaussian'])
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elif dist == 'Log-normal distribution':
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elif value_type == 'Log-normal distribution':
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return LogNormal(params['lognormal_mean_gaussian'], params['lognormal_standard_deviation_gaussian'])
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elif dist == 'Uniform distribution':
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elif value_type == 'Uniform distribution':
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return Uniform(params['low'], params['high'])
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else:
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raise ValueError('Bad request - value type not found.')
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@ -104,17 +110,10 @@ def param_evaluation(root: typing.Dict, param: typing.Union[str, typing.Any]) ->
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"""
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value = root.get(param)
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if isinstance(value, str):
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if value == 'Custom':
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custom_value_type: typing.Dict = custom_value_type_lookup(
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root, 'custom distribution')
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for d, p in custom_value_type.items():
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return evaluate_custom_value_type(d, p)
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elif isinstance(value, dict):
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dist: str = root[param]['associated_value']
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if isinstance(value, dict):
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value_type: str = root[param]['associated_value']
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params: typing.Dict = root[param]['parameters']
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return evaluate_custom_value_type(dist, params)
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return evaluate_custom_value_type(value_type, params)
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elif isinstance(value, float) or isinstance(value, int):
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return value
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@ -290,39 +289,39 @@ def virus_distributions(data_registry):
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vd = data_registry.virological_data['virus_distributions']
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return {
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'SARS_CoV_2': mc.SARSCoV2(
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viral_load_in_sputum=evaluate_vl(vd['SARS_CoV_2']['viral_load_in_sputum'], data_registry),
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infectious_dose=evaluate_infectd(vd['SARS_CoV_2']['infectious_dose'], data_registry),
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viable_to_RNA_ratio=evaluate_vtrr(vd['SARS_CoV_2']['viable_to_RNA_ratio'], data_registry),
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viral_load_in_sputum=evaluate_vl(vd['SARS_CoV_2'], 'viral_load_in_sputum', data_registry),
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infectious_dose=evaluate_infectd(vd['SARS_CoV_2'], 'infectious_dose', data_registry),
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viable_to_RNA_ratio=evaluate_vtrr(vd['SARS_CoV_2'], 'viable_to_RNA_ratio', data_registry),
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transmissibility_factor=vd['SARS_CoV_2']['transmissibility_factor'],
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),
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'SARS_CoV_2_ALPHA': mc.SARSCoV2(
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viral_load_in_sputum=evaluate_vl(vd['SARS_CoV_2_ALPHA']['viral_load_in_sputum'], data_registry),
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infectious_dose=evaluate_infectd(vd['SARS_CoV_2_ALPHA']['infectious_dose'], data_registry),
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viable_to_RNA_ratio=evaluate_vtrr(vd['SARS_CoV_2_ALPHA']['viable_to_RNA_ratio'], data_registry),
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viral_load_in_sputum=evaluate_vl(vd['SARS_CoV_2_ALPHA'], 'viral_load_in_sputum', data_registry),
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infectious_dose=evaluate_infectd(vd['SARS_CoV_2_ALPHA'], 'infectious_dose', data_registry),
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viable_to_RNA_ratio=evaluate_vtrr(vd['SARS_CoV_2_ALPHA'], 'viable_to_RNA_ratio', data_registry),
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transmissibility_factor=vd['SARS_CoV_2_ALPHA']['transmissibility_factor'],
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),
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'SARS_CoV_2_BETA': mc.SARSCoV2(
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viral_load_in_sputum=evaluate_vl(vd['SARS_CoV_2_BETA']['viral_load_in_sputum'], data_registry),
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infectious_dose=evaluate_infectd(vd['SARS_CoV_2_BETA']['infectious_dose'], data_registry),
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viable_to_RNA_ratio=evaluate_vtrr(vd['SARS_CoV_2_BETA']['viable_to_RNA_ratio'], data_registry),
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viral_load_in_sputum=evaluate_vl(vd['SARS_CoV_2_BETA'], 'viral_load_in_sputum', data_registry),
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infectious_dose=evaluate_infectd(vd['SARS_CoV_2_BETA'], 'infectious_dose', data_registry),
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viable_to_RNA_ratio=evaluate_vtrr(vd['SARS_CoV_2_BETA'], 'viable_to_RNA_ratio', data_registry),
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transmissibility_factor=vd['SARS_CoV_2_BETA']['transmissibility_factor'],
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),
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'SARS_CoV_2_GAMMA': mc.SARSCoV2(
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viral_load_in_sputum=evaluate_vl(vd['SARS_CoV_2_GAMMA']['viral_load_in_sputum'], data_registry),
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infectious_dose=evaluate_infectd(vd['SARS_CoV_2_GAMMA']['infectious_dose'], data_registry),
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viable_to_RNA_ratio=evaluate_vtrr(vd['SARS_CoV_2_GAMMA']['viable_to_RNA_ratio'], data_registry),
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viral_load_in_sputum=evaluate_vl(vd['SARS_CoV_2_GAMMA'], 'viral_load_in_sputum', data_registry),
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infectious_dose=evaluate_infectd(vd['SARS_CoV_2_GAMMA'], 'infectious_dose', data_registry),
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viable_to_RNA_ratio=evaluate_vtrr(vd['SARS_CoV_2_GAMMA'], 'viable_to_RNA_ratio', data_registry),
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transmissibility_factor=vd['SARS_CoV_2_GAMMA']['transmissibility_factor'],
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),
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'SARS_CoV_2_DELTA': mc.SARSCoV2(
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viral_load_in_sputum=evaluate_vl(vd['SARS_CoV_2_DELTA']['viral_load_in_sputum'], data_registry),
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infectious_dose=evaluate_infectd(vd['SARS_CoV_2_DELTA']['infectious_dose'], data_registry),
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viable_to_RNA_ratio=evaluate_vtrr(vd['SARS_CoV_2_DELTA']['viable_to_RNA_ratio'], data_registry),
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viral_load_in_sputum=evaluate_vl(vd['SARS_CoV_2_DELTA'], 'viral_load_in_sputum', data_registry),
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infectious_dose=evaluate_infectd(vd['SARS_CoV_2_DELTA'], 'infectious_dose', data_registry),
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viable_to_RNA_ratio=evaluate_vtrr(vd['SARS_CoV_2_DELTA'], 'viable_to_RNA_ratio', data_registry),
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transmissibility_factor=vd['SARS_CoV_2_DELTA']['transmissibility_factor'],
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),
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'SARS_CoV_2_OMICRON': mc.SARSCoV2(
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viral_load_in_sputum=evaluate_vl(vd['SARS_CoV_2_OMICRON']['viral_load_in_sputum'], data_registry),
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infectious_dose=evaluate_infectd(vd['SARS_CoV_2_OMICRON']['infectious_dose'], data_registry),
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viable_to_RNA_ratio=evaluate_vtrr(vd['SARS_CoV_2_OMICRON']['viable_to_RNA_ratio'], data_registry),
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viral_load_in_sputum=evaluate_vl(vd['SARS_CoV_2_OMICRON'], 'viral_load_in_sputum', data_registry),
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infectious_dose=evaluate_infectd(vd['SARS_CoV_2_OMICRON'], 'infectious_dose', data_registry),
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viable_to_RNA_ratio=evaluate_vtrr(vd['SARS_CoV_2_OMICRON'], 'viable_to_RNA_ratio', data_registry),
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transmissibility_factor=vd['SARS_CoV_2_OMICRON']['transmissibility_factor'],
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),
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}
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@ -261,13 +261,6 @@ class DataRegistry:
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"transmissibility_factor": 0.2,
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"infectiousness_days": 14,
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},
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"SARS_CoV_2_Other": {
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"viral_load_in_sputum": ViralLoads.COVID_OVERALL.value,
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"infectious_dose": InfectiousDoses.DISTRIBUTION.value,
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"viable_to_RNA_ratio": ViableToRNARatios.DISTRIBUTION.value,
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"transmissibility_factor": 0.1,
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"infectiousness_days": 14,
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},
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},
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}
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