refactor data service
This commit is contained in:
Nicola Tarocco
parent
e0714d6aad
commit
035190564c
4 changed files with 530 additions and 426 deletions
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@ -1,319 +1,451 @@
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import asyncio
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import os
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from caimira.store.global_store import GlobalStore
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class Configuration:
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'''
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Configuration to handle data. Contains the default values used in the model.
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Might suffer update from the Data Service.
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'''
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"""Configuration singleton to cache data values."""
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def __init__(self):
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self.data_fetched = False
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self.BLOmodel = {
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'cn': {'B': 0.06,
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'L': 0.2,
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'O': 0.0010008,
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},
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'mu': {
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'B': 0.989541,
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'L': 1.38629,
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'O': 4.97673,
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},
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'sigma': {
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'B': 0.262364,
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'L': 0.506818,
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'O': 0.585005,
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},
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}
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self.activity_distributions = {
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'Seated': {
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'inhalation_rate': {
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'associated_distribution': 'Numpy Log-normal Distribution (random.lognormal)',
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'parameters': {'mean_gaussian': -0.6872121723362303, 'standard_deviation_gaussian': 0.10498338229297108},
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},
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'exhalation_rate': {
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'associated_distribution': 'Numpy Log-normal Distribution (random.lognormal)',
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'parameters': {'mean_gaussian': -0.6872121723362303, 'standard_deviation_gaussian': 0.10498338229297108},
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BLOmodel = {
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"cn": {
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"B": 0.06,
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"L": 0.2,
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"O": 0.0010008,
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},
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"mu": {
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"B": 0.989541,
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"L": 1.38629,
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"O": 4.97673,
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},
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"sigma": {
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"B": 0.262364,
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"L": 0.506818,
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"O": 0.585005,
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},
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}
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activity_distributions = {
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"Seated": {
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"inhalation_rate": {
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"associated_distribution": "Numpy Log-normal Distribution (random.lognormal)",
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"parameters": {
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"mean_gaussian": -0.6872121723362303,
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"standard_deviation_gaussian": 0.10498338229297108,
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},
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},
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'Standing': {
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'inhalation_rate': {
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'associated_distribution': 'Numpy Log-normal Distribution (random.lognormal)',
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'parameters': {'mean_gaussian': -0.5742377578494785, 'standard_deviation_gaussian': 0.09373162411398223},
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},
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'exhalation_rate': {
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'associated_distribution': 'Numpy Log-normal Distribution (random.lognormal)',
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'parameters': {'mean_gaussian': -0.5742377578494785, 'standard_deviation_gaussian': 0.09373162411398223},
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"exhalation_rate": {
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"associated_distribution": "Numpy Log-normal Distribution (random.lognormal)",
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"parameters": {
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"mean_gaussian": -0.6872121723362303,
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"standard_deviation_gaussian": 0.10498338229297108,
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},
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},
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'Light activity': {
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'inhalation_rate': {
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'associated_distribution': 'Numpy Log-normal Distribution (random.lognormal)',
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'parameters': {'mean_gaussian': 0.21380242785625422, 'standard_deviation_gaussian': 0.09435378091059601},
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},
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'exhalation_rate': {
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'associated_distribution': 'Numpy Log-normal Distribution (random.lognormal)',
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'parameters': {'mean_gaussian': 0.21380242785625422, 'standard_deviation_gaussian': 0.09435378091059601},
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},
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"Standing": {
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"inhalation_rate": {
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"associated_distribution": "Numpy Log-normal Distribution (random.lognormal)",
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"parameters": {
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"mean_gaussian": -0.5742377578494785,
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"standard_deviation_gaussian": 0.09373162411398223,
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},
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},
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'Moderate activity': {
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'inhalation_rate': {
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'associated_distribution': 'Numpy Log-normal Distribution (random.lognormal)',
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'parameters': {'mean_gaussian': 0.551771330362601, 'standard_deviation_gaussian': 0.1894616357138137},
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},
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'exhalation_rate': {
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'associated_distribution': 'Numpy Log-normal Distribution (random.lognormal)',
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'parameters': {'mean_gaussian': 0.551771330362601, 'standard_deviation_gaussian': 0.1894616357138137},
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"exhalation_rate": {
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"associated_distribution": "Numpy Log-normal Distribution (random.lognormal)",
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"parameters": {
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"mean_gaussian": -0.5742377578494785,
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"standard_deviation_gaussian": 0.09373162411398223,
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},
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},
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'Heavy exercise': {
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'inhalation_rate': {
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'associated_distribution': 'Numpy Log-normal Distribution (random.lognormal)',
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'parameters': {'mean_gaussian': 1.1644665696723049, 'standard_deviation_gaussian': 0.21744554768657565},
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},
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'exhalation_rate': {
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'associated_distribution': 'Numpy Log-normal Distribution (random.lognormal)',
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'parameters': {'mean_gaussian': 1.1644665696723049, 'standard_deviation_gaussian': 0.21744554768657565},
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},
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"Light activity": {
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"inhalation_rate": {
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"associated_distribution": "Numpy Log-normal Distribution (random.lognormal)",
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"parameters": {
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"mean_gaussian": 0.21380242785625422,
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"standard_deviation_gaussian": 0.09435378091059601,
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},
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},
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}
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self.symptomatic_vl_frequencies = {
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'log_variable': [2.46032, 2.67431, 2.85434, 3.06155, 3.25856, 3.47256, 3.66957, 3.85979, 4.09927, 4.27081,
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4.47631, 4.66653, 4.87204, 5.10302, 5.27456, 5.46478, 5.6533, 5.88428, 6.07281, 6.30549,
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6.48552, 6.64856, 6.85407, 7.10373, 7.30075, 7.47229, 7.66081, 7.85782, 8.05653, 8.27053,
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8.48453, 8.65607, 8.90573, 9.06878, 9.27429, 9.473, 9.66152, 9.87552],
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'frequencies': [0.001206885, 0.007851618, 0.008078144, 0.01502491, 0.013258014, 0.018528495, 0.020053765,
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0.021896167, 0.022047184, 0.018604005, 0.01547796, 0.018075445, 0.021503523, 0.022349217,
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0.025097721, 0.032875078, 0.030594727, 0.032573045, 0.034717482, 0.034792991,
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0.033267721, 0.042887485, 0.036846816, 0.03876473, 0.045016819, 0.040063473, 0.04883754,
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0.043944602, 0.048142864, 0.041588741, 0.048762031, 0.027921732, 0.033871788,
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0.022122693, 0.016927718, 0.008833228, 0.00478598, 0.002807662],
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'kernel_bandwidth': 0.1,
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}
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self.covid_overal_vl_data = {
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'shape_factor': 3.47,
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'scale_factor': 7.01,
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'start': 0.01,
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'stop': 0.99,
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'num': 30.0,
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'min_bound': 2,
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'max_bound': 10,
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'interpolation_fp_left': 0,
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'interpolation_fp_right': 0,
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'max_function': 0.2,
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}
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self.viable_to_RNA_ratio_distribution = {
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'low': 0.01,
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'high': 0.6,
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}
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self.infectious_dose_distribution = {
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'low': 10,
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'high': 100,
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}
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self.virus_distributions = {
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'SARS_CoV_2': {
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'viral_load_in_sputum': 'Ref: Viral load - covid_overal_vl_data',
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'infectious_dose': 'Ref: Infectious dose - infectious_dose_distribution',
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'viable_to_RNA_ratio': 'Ref: Viable to RNA ratio - viable_to_RNA_ratio_distribution',
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'transmissibility_factor': 1,
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'infectiousness_days': 14,
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},
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'SARS_CoV_2_ALPHA': {
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'viral_load_in_sputum': 'Ref: Viral load - covid_overal_vl_data',
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'infectious_dose': 'Ref: Infectious dose - infectious_dose_distribution',
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'viable_to_RNA_ratio': 'Ref: Viable to RNA ratio - viable_to_RNA_ratio_distribution',
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'transmissibility_factor': 0.78,
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'infectiousness_days': 14,
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},
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'SARS_CoV_2_BETA': {
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'viral_load_in_sputum': 'Ref: Viral load - covid_overal_vl_data',
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'infectious_dose': 'Ref: Infectious dose - infectious_dose_distribution',
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'viable_to_RNA_ratio': 'Ref: Viable to RNA ratio - viable_to_RNA_ratio_distribution',
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'transmissibility_factor': 0.8,
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'infectiousness_days': 14
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},
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'SARS_CoV_2_GAMMA': {
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'viral_load_in_sputum': 'Ref: Viral load - covid_overal_vl_data',
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'infectious_dose': 'Ref: Infectious dose - infectious_dose_distribution',
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'viable_to_RNA_ratio': 'Ref: Viable to RNA ratio - viable_to_RNA_ratio_distribution',
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'transmissibility_factor': 0.72,
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'infectiousness_days': 14
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},
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'SARS_CoV_2_DELTA': {
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'viral_load_in_sputum': 'Ref: Viral load - covid_overal_vl_data',
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'infectious_dose': 'Ref: Infectious dose - infectious_dose_distribution',
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'viable_to_RNA_ratio': 'Ref: Viable to RNA ratio - viable_to_RNA_ratio_distribution',
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'transmissibility_factor': 0.51,
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'infectiousness_days': 14
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},
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'SARS_CoV_2_OMICRON': {
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'viral_load_in_sputum': 'Ref: Viral load - covid_overal_vl_data',
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'infectious_dose': 'Ref: Infectious dose - infectious_dose_distribution',
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'viable_to_RNA_ratio': 'Ref: Viable to RNA ratio - viable_to_RNA_ratio_distribution',
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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': 'Ref: Viral load - covid_overal_vl_data',
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'infectious_dose': 'Ref: Infectious dose - infectious_dose_distribution',
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'viable_to_RNA_ratio': 'Ref: Viable to RNA ratio - viable_to_RNA_ratio_distribution',
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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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self.mask_distributions = {
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'Type I': {
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'η_inhale': {
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'associated_distribution': 'Numpy Uniform Distribution (random.uniform)',
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'parameters': {
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'low': 0.25,
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'high': 0.80,
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},
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"exhalation_rate": {
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"associated_distribution": "Numpy Log-normal Distribution (random.lognormal)",
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"parameters": {
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"mean_gaussian": 0.21380242785625422,
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"standard_deviation_gaussian": 0.09435378091059601,
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},
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'Known filtration efficiency of masks when exhaling?': 'No',
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'factor_exhale': 1,
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},
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'FFP2': {
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'η_inhale': {
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'associated_distribution': 'Numpy Uniform Distribution (random.uniform)',
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'parameters': {
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'low': 0.83,
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'high': 0.91,
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},
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},
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"Moderate activity": {
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"inhalation_rate": {
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"associated_distribution": "Numpy Log-normal Distribution (random.lognormal)",
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"parameters": {
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"mean_gaussian": 0.551771330362601,
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"standard_deviation_gaussian": 0.1894616357138137,
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},
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'Known filtration efficiency of masks when exhaling?': 'No',
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'factor_exhale': 1,
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},
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'Cloth': {
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'η_inhale': {
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'associated_distribution': 'Numpy Uniform Distribution (random.uniform)',
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'parameters': {
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'low': 0.05,
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'high': 0.40,
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},
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"exhalation_rate": {
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"associated_distribution": "Numpy Log-normal Distribution (random.lognormal)",
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"parameters": {
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"mean_gaussian": 0.551771330362601,
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"standard_deviation_gaussian": 0.1894616357138137,
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},
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'Known filtration efficiency of masks when exhaling?': 'Yes',
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'η_exhale': {
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'associated_distribution': 'Numpy Uniform Distribution (random.uniform)',
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'parameters': {
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'low': 0.20,
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'high': 0.50,
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},
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},
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'factor_exhale': 1,
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},
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}
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self.expiration_BLO_factors = {
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'Breathing': {'B': 1., 'L': 0., 'O': 0., },
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'Speaking': {'B': 1., 'L': 1., 'O': 1., },
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'Singing': {'B': 1., 'L': 5., 'O': 5., },
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'Shouting': {'B': 1., 'L': 5., 'O': 5., },
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}
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self.long_range_expiration_distributions = {
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'minimum_diameter': 0.1,
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'maximum_diameter': 30,
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}
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self.short_range_expiration_distributions = {
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'minimum_diameter': 0.1,
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'maximum_diameter': 100,
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}
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self.short_range_distances = {
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'minimum_distance': 0.5,
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'maximum_distance': 2.,
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}
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},
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"Heavy exercise": {
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"inhalation_rate": {
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"associated_distribution": "Numpy Log-normal Distribution (random.lognormal)",
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"parameters": {
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"mean_gaussian": 1.1644665696723049,
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"standard_deviation_gaussian": 0.21744554768657565,
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},
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},
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"exhalation_rate": {
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"associated_distribution": "Numpy Log-normal Distribution (random.lognormal)",
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"parameters": {
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"mean_gaussian": 1.1644665696723049,
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"standard_deviation_gaussian": 0.21744554768657565,
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},
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},
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},
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}
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symptomatic_vl_frequencies = {
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"log_variable": [
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2.46032,
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2.67431,
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2.85434,
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3.06155,
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3.25856,
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3.47256,
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3.66957,
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3.85979,
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4.09927,
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4.27081,
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4.47631,
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4.66653,
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4.87204,
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5.10302,
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5.27456,
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5.46478,
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5.6533,
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5.88428,
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6.07281,
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6.30549,
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6.48552,
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6.64856,
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6.85407,
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7.10373,
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7.30075,
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7.47229,
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7.66081,
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7.85782,
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8.05653,
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8.27053,
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8.48453,
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8.65607,
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8.90573,
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9.06878,
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9.27429,
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9.473,
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9.66152,
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9.87552,
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],
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"frequencies": [
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0.001206885,
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0.007851618,
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0.008078144,
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0.01502491,
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0.013258014,
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0.018528495,
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0.020053765,
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0.021896167,
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0.022047184,
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0.018604005,
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0.01547796,
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0.018075445,
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0.021503523,
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0.022349217,
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0.025097721,
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0.032875078,
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0.030594727,
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0.032573045,
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0.034717482,
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0.034792991,
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0.033267721,
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0.042887485,
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0.036846816,
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0.03876473,
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0.045016819,
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0.040063473,
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0.04883754,
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0.043944602,
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0.048142864,
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0.041588741,
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0.048762031,
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0.027921732,
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0.033871788,
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0.022122693,
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0.016927718,
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0.008833228,
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0.00478598,
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0.002807662,
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],
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"kernel_bandwidth": 0.1,
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}
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covid_overal_vl_data = {
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"shape_factor": 3.47,
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"scale_factor": 7.01,
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"start": 0.01,
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"stop": 0.99,
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"num": 30.0,
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"min_bound": 2,
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"max_bound": 10,
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"interpolation_fp_left": 0,
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"interpolation_fp_right": 0,
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"max_function": 0.2,
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}
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viable_to_RNA_ratio_distribution = {
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"low": 0.01,
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"high": 0.6,
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}
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infectious_dose_distribution = {
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"low": 10,
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"high": 100,
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}
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virus_distributions = {
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"SARS_CoV_2": {
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"viral_load_in_sputum": "Ref: Viral load - covid_overal_vl_data",
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"infectious_dose": "Ref: Infectious dose - infectious_dose_distribution",
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"viable_to_RNA_ratio": "Ref: Viable to RNA ratio - viable_to_RNA_ratio_distribution",
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"transmissibility_factor": 1,
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"infectiousness_days": 14,
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},
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"SARS_CoV_2_ALPHA": {
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"viral_load_in_sputum": "Ref: Viral load - covid_overal_vl_data",
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"infectious_dose": "Ref: Infectious dose - infectious_dose_distribution",
|
||||
"viable_to_RNA_ratio": "Ref: Viable to RNA ratio - viable_to_RNA_ratio_distribution",
|
||||
"transmissibility_factor": 0.78,
|
||||
"infectiousness_days": 14,
|
||||
},
|
||||
"SARS_CoV_2_BETA": {
|
||||
"viral_load_in_sputum": "Ref: Viral load - covid_overal_vl_data",
|
||||
"infectious_dose": "Ref: Infectious dose - infectious_dose_distribution",
|
||||
"viable_to_RNA_ratio": "Ref: Viable to RNA ratio - viable_to_RNA_ratio_distribution",
|
||||
"transmissibility_factor": 0.8,
|
||||
"infectiousness_days": 14,
|
||||
},
|
||||
"SARS_CoV_2_GAMMA": {
|
||||
"viral_load_in_sputum": "Ref: Viral load - covid_overal_vl_data",
|
||||
"infectious_dose": "Ref: Infectious dose - infectious_dose_distribution",
|
||||
"viable_to_RNA_ratio": "Ref: Viable to RNA ratio - viable_to_RNA_ratio_distribution",
|
||||
"transmissibility_factor": 0.72,
|
||||
"infectiousness_days": 14,
|
||||
},
|
||||
"SARS_CoV_2_DELTA": {
|
||||
"viral_load_in_sputum": "Ref: Viral load - covid_overal_vl_data",
|
||||
"infectious_dose": "Ref: Infectious dose - infectious_dose_distribution",
|
||||
"viable_to_RNA_ratio": "Ref: Viable to RNA ratio - viable_to_RNA_ratio_distribution",
|
||||
"transmissibility_factor": 0.51,
|
||||
"infectiousness_days": 14,
|
||||
},
|
||||
"SARS_CoV_2_OMICRON": {
|
||||
"viral_load_in_sputum": "Ref: Viral load - covid_overal_vl_data",
|
||||
"infectious_dose": "Ref: Infectious dose - infectious_dose_distribution",
|
||||
"viable_to_RNA_ratio": "Ref: Viable to RNA ratio - viable_to_RNA_ratio_distribution",
|
||||
"transmissibility_factor": 0.2,
|
||||
"infectiousness_days": 14,
|
||||
},
|
||||
"SARS_CoV_2_Other": {
|
||||
"viral_load_in_sputum": "Ref: Viral load - covid_overal_vl_data",
|
||||
"infectious_dose": "Ref: Infectious dose - infectious_dose_distribution",
|
||||
"viable_to_RNA_ratio": "Ref: Viable to RNA ratio - viable_to_RNA_ratio_distribution",
|
||||
"transmissibility_factor": 0.1,
|
||||
"infectiousness_days": 14,
|
||||
},
|
||||
}
|
||||
mask_distributions = {
|
||||
"Type I": {
|
||||
"η_inhale": {
|
||||
"associated_distribution": "Numpy Uniform Distribution (random.uniform)",
|
||||
"parameters": {
|
||||
"low": 0.25,
|
||||
"high": 0.80,
|
||||
},
|
||||
},
|
||||
"Known filtration efficiency of masks when exhaling?": "No",
|
||||
"factor_exhale": 1,
|
||||
},
|
||||
"FFP2": {
|
||||
"η_inhale": {
|
||||
"associated_distribution": "Numpy Uniform Distribution (random.uniform)",
|
||||
"parameters": {
|
||||
"low": 0.83,
|
||||
"high": 0.91,
|
||||
},
|
||||
},
|
||||
"Known filtration efficiency of masks when exhaling?": "No",
|
||||
"factor_exhale": 1,
|
||||
},
|
||||
"Cloth": {
|
||||
"η_inhale": {
|
||||
"associated_distribution": "Numpy Uniform Distribution (random.uniform)",
|
||||
"parameters": {
|
||||
"low": 0.05,
|
||||
"high": 0.40,
|
||||
},
|
||||
},
|
||||
"Known filtration efficiency of masks when exhaling?": "Yes",
|
||||
"η_exhale": {
|
||||
"associated_distribution": "Numpy Uniform Distribution (random.uniform)",
|
||||
"parameters": {
|
||||
"low": 0.20,
|
||||
"high": 0.50,
|
||||
},
|
||||
},
|
||||
"factor_exhale": 1,
|
||||
},
|
||||
}
|
||||
expiration_BLO_factors = {
|
||||
"Breathing": {
|
||||
"B": 1.0,
|
||||
"L": 0.0,
|
||||
"O": 0.0,
|
||||
},
|
||||
"Speaking": {
|
||||
"B": 1.0,
|
||||
"L": 1.0,
|
||||
"O": 1.0,
|
||||
},
|
||||
"Singing": {
|
||||
"B": 1.0,
|
||||
"L": 5.0,
|
||||
"O": 5.0,
|
||||
},
|
||||
"Shouting": {
|
||||
"B": 1.0,
|
||||
"L": 5.0,
|
||||
"O": 5.0,
|
||||
},
|
||||
}
|
||||
long_range_expiration_distributions = {
|
||||
"minimum_diameter": 0.1,
|
||||
"maximum_diameter": 30,
|
||||
}
|
||||
short_range_expiration_distributions = {
|
||||
"minimum_diameter": 0.1,
|
||||
"maximum_diameter": 100,
|
||||
}
|
||||
short_range_distances = {
|
||||
"minimum_distance": 0.5,
|
||||
"maximum_distance": 2.0,
|
||||
}
|
||||
|
||||
####################################
|
||||
####################################
|
||||
|
||||
self.room = {
|
||||
'defaults': {
|
||||
'inside_temp': 293,
|
||||
'humidity_with_heating': 0.3,
|
||||
'humidity_without_heating': 0.5,
|
||||
room = {
|
||||
"defaults": {
|
||||
"inside_temp": 293,
|
||||
"humidity_with_heating": 0.3,
|
||||
"humidity_without_heating": 0.5,
|
||||
},
|
||||
}
|
||||
ventilation = {
|
||||
"natural": {
|
||||
"discharge_factor": {
|
||||
"sliding": 0.6,
|
||||
},
|
||||
}
|
||||
self.ventilation = {
|
||||
'natural': {
|
||||
'discharge_factor': {
|
||||
'sliding': 0.6,
|
||||
},
|
||||
},
|
||||
"infiltration_ventilation": 0.25,
|
||||
}
|
||||
particle = {
|
||||
"evaporation_factor": 0.3,
|
||||
}
|
||||
population_with_virus = {
|
||||
"fraction_of_infectious_virus": 1,
|
||||
}
|
||||
concentration_model = {
|
||||
"min_background_concentration": 0.0,
|
||||
"CO2_concentration_model": {
|
||||
"CO2_atmosphere_concentration": 440.44,
|
||||
"CO2_fraction_exhaled": 0.042,
|
||||
},
|
||||
}
|
||||
short_range_model = {
|
||||
"dilution_factor": {
|
||||
"mouth_diameter": 0.02,
|
||||
"exhalation_coefficient": 2,
|
||||
"tstar": 2,
|
||||
"penetration_coefficients": {
|
||||
"𝛽r1": 0.18,
|
||||
"𝛽r2": 0.2,
|
||||
"𝛽x1": 2.4,
|
||||
},
|
||||
'infiltration_ventilation': 0.25,
|
||||
}
|
||||
self.particle = {
|
||||
'evaporation_factor': 0.3,
|
||||
}
|
||||
self.population_with_virus = {
|
||||
'fraction_of_infectious_virus': 1,
|
||||
}
|
||||
self.concentration_model = {
|
||||
'min_background_concentration': 0.,
|
||||
'CO2_concentration_model': {
|
||||
'CO2_atmosphere_concentration': 440.44,
|
||||
'CO2_fraction_exhaled': 0.042,
|
||||
},
|
||||
}
|
||||
self.short_range_model = {
|
||||
'dilution_factor': {
|
||||
'mouth_diameter': 0.02,
|
||||
'exhalation_coefficient': 2,
|
||||
'tstar': 2,
|
||||
'penetration_coefficients': {
|
||||
'𝛽r1': 0.18, '𝛽r2': .2, '𝛽x1': 2.4,
|
||||
},
|
||||
},
|
||||
}
|
||||
self.exposure_model = {
|
||||
'repeats': 1,
|
||||
}
|
||||
self.conditional_prob_inf_given_viral_load = {
|
||||
'lower_percentile': 0.05,
|
||||
'upper_percentile': 0.95,
|
||||
'min_vl': 2,
|
||||
'max_vl': 10,
|
||||
}
|
||||
self.monte_carlo_sample_size = 250000
|
||||
self.population_scenario_activity = {
|
||||
'office': {'activity': 'Seated', 'expiration': {'Speaking': 1, 'Breathing': 2}},
|
||||
'smallmeeting': {'activity': 'Seated', 'expiration': {'Speaking': 1, 'Breathing': None}},
|
||||
'largemeeting': {'activity': 'Standing', 'expiration': {'Speaking': 1, 'Breathing': 2}},
|
||||
'callcenter': {'activity': 'Seated', 'expiration': {'Speaking': 1}},
|
||||
'controlroom-day': {'activity': 'Seated', 'expiration': {'Speaking': 1, 'Breathing': 1}},
|
||||
'controlroom-night': {'activity': 'Seated', 'expiration': {'Speaking': 1, 'Breathing': 9}},
|
||||
'library': {'activity': 'Seated', 'expiration': {'Breathing': 1}},
|
||||
'lab': {'activity': 'Light activity', 'expiration': {'Speaking': 1, 'Breathing': 1}},
|
||||
'workshop': {'activity': 'Moderate activity', 'expiration': {'Speaking': 1, 'Breathing': 1}},
|
||||
'training': {'activity': 'Standing', 'expiration': {'Speaking': 1}},
|
||||
'training_attendee': {'activity': 'Seated', 'expiration': {'Breathing': 1}},
|
||||
'gym': {'activity': 'Heavy exercise', 'expiration': {'Breathing': 1}},
|
||||
'household-day': {'activity': 'Light activity', 'expiration': {'Breathing': 5, 'Speaking': 5}},
|
||||
'household-night': {'activity': 'Seated', 'expiration': {'Breathing': 7, 'Speaking': 3}},
|
||||
'primary-school': {'activity': 'Light activity', 'expiration': {'Breathing': 5, 'Speaking': 5}},
|
||||
'secondary-school': {'activity': 'Light activity', 'expiration': {'Breathing': 7, 'Speaking': 3}},
|
||||
'university': {'activity': 'Seated', 'expiration': {'Breathing': 9, 'Speaking': 1}},
|
||||
'restaurant': {'activity': 'Seated', 'expiration': {'Breathing': 1, 'Speaking': 9}},
|
||||
'precise': {'activity': None, 'expiration': None},
|
||||
}
|
||||
},
|
||||
}
|
||||
exposure_model = {
|
||||
"repeats": 1,
|
||||
}
|
||||
conditional_prob_inf_given_viral_load = {
|
||||
"lower_percentile": 0.05,
|
||||
"upper_percentile": 0.95,
|
||||
"min_vl": 2,
|
||||
"max_vl": 10,
|
||||
}
|
||||
monte_carlo_sample_size = 250000
|
||||
population_scenario_activity = {
|
||||
"office": {"activity": "Seated", "expiration": {"Speaking": 1, "Breathing": 2}},
|
||||
"smallmeeting": {
|
||||
"activity": "Seated",
|
||||
"expiration": {"Speaking": 1, "Breathing": None},
|
||||
},
|
||||
"largemeeting": {
|
||||
"activity": "Standing",
|
||||
"expiration": {"Speaking": 1, "Breathing": 2},
|
||||
},
|
||||
"callcenter": {"activity": "Seated", "expiration": {"Speaking": 1}},
|
||||
"controlroom-day": {
|
||||
"activity": "Seated",
|
||||
"expiration": {"Speaking": 1, "Breathing": 1},
|
||||
},
|
||||
"controlroom-night": {
|
||||
"activity": "Seated",
|
||||
"expiration": {"Speaking": 1, "Breathing": 9},
|
||||
},
|
||||
"library": {"activity": "Seated", "expiration": {"Breathing": 1}},
|
||||
"lab": {
|
||||
"activity": "Light activity",
|
||||
"expiration": {"Speaking": 1, "Breathing": 1},
|
||||
},
|
||||
"workshop": {
|
||||
"activity": "Moderate activity",
|
||||
"expiration": {"Speaking": 1, "Breathing": 1},
|
||||
},
|
||||
"training": {"activity": "Standing", "expiration": {"Speaking": 1}},
|
||||
"training_attendee": {"activity": "Seated", "expiration": {"Breathing": 1}},
|
||||
"gym": {"activity": "Heavy exercise", "expiration": {"Breathing": 1}},
|
||||
"household-day": {
|
||||
"activity": "Light activity",
|
||||
"expiration": {"Breathing": 5, "Speaking": 5},
|
||||
},
|
||||
"household-night": {
|
||||
"activity": "Seated",
|
||||
"expiration": {"Breathing": 7, "Speaking": 3},
|
||||
},
|
||||
"primary-school": {
|
||||
"activity": "Light activity",
|
||||
"expiration": {"Breathing": 5, "Speaking": 5},
|
||||
},
|
||||
"secondary-school": {
|
||||
"activity": "Light activity",
|
||||
"expiration": {"Breathing": 7, "Speaking": 3},
|
||||
},
|
||||
"university": {
|
||||
"activity": "Seated",
|
||||
"expiration": {"Breathing": 9, "Speaking": 1},
|
||||
},
|
||||
"restaurant": {
|
||||
"activity": "Seated",
|
||||
"expiration": {"Breathing": 1, "Speaking": 9},
|
||||
},
|
||||
"precise": {"activity": None, "expiration": None},
|
||||
}
|
||||
|
||||
async def populate_data(self):
|
||||
"""
|
||||
Fetches data from the API and populates the configuration object.
|
||||
"""
|
||||
if not self.data_fetched and os.environ.get('DATA_SERVICE_ENABLED', 'False').lower() == 'true':
|
||||
# Fetch and populate data from API
|
||||
await GlobalStore.populate_from_api()
|
||||
data = GlobalStore.get_data()['data']
|
||||
|
||||
# Dynamically set attributes based on the data fetched from the API
|
||||
for attr_name, value in data.items():
|
||||
setattr(self, attr_name, value)
|
||||
|
||||
self.data_fetched = True
|
||||
return
|
||||
def update(self, data):
|
||||
"""Update local cache with data provided as argument."""
|
||||
for attr_name, value in data.items():
|
||||
setattr(self, attr_name, value)
|
||||
|
||||
|
||||
# module-level variable as a form of singleton
|
||||
config = Configuration()
|
||||
|
||||
asyncio.run(config.populate_data())
|
||||
|
|
|
|||
|
|
@ -1,74 +1,95 @@
|
|||
import dataclasses
|
||||
import json
|
||||
import logging
|
||||
import os
|
||||
import typing
|
||||
|
||||
from tornado.httpclient import AsyncHTTPClient, HTTPRequest
|
||||
import requests
|
||||
|
||||
LOG = logging.getLogger(__name__)
|
||||
from .configuration import config
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
@dataclasses.dataclass
|
||||
class DataService():
|
||||
'''
|
||||
Responsible for establishing a connection to a
|
||||
database through a REST API by handling authentication
|
||||
and fetching data. It utilizes the Tornado web framework
|
||||
for asynchronous HTTP requests.
|
||||
'''
|
||||
# Credentials used for authentication
|
||||
credentials: dict
|
||||
|
||||
# Host URL for the CAiMIRA Data Service API
|
||||
host: str = 'https://caimira-data-api.app.cern.ch'
|
||||
class DataService:
|
||||
"""Responsible for fetching data from the data service endpoint."""
|
||||
|
||||
# Cached access token
|
||||
_access_token: typing.Optional[str] = None
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
credentials: typing.Dict[str, str],
|
||||
host: str = "https://caimira-data-api.app.cern.ch",
|
||||
):
|
||||
self._credentials = credentials
|
||||
self._host = host
|
||||
|
||||
def _is_valid(self, access_token):
|
||||
# decode access_token
|
||||
# check validity
|
||||
return False
|
||||
|
||||
async def _login(self):
|
||||
def _login(self):
|
||||
if self._is_valid(self._access_token):
|
||||
return self._access_token
|
||||
|
||||
# invalid access_token, fetch it again
|
||||
client_email = self.credentials["data_service_client_email"]
|
||||
client_password = self.credentials['data_service_client_password']
|
||||
client_email = self._credentials["email"]
|
||||
client_password = self._credentials["password"]
|
||||
|
||||
if (client_email == None or client_password == None):
|
||||
if client_email == None or client_password == None:
|
||||
# If the credentials are not defined, an exception is raised.
|
||||
raise Exception("DataService credentials not set")
|
||||
|
||||
http_client = AsyncHTTPClient()
|
||||
headers = {'Content-type': 'application/json'}
|
||||
json_body = {"email": f"{client_email}",
|
||||
"password": f"{client_password}"}
|
||||
url = f"{self._host}/login"
|
||||
headers = {"Content-Type": "application/json"}
|
||||
json_body = dict(email=client_email, password=client_password)
|
||||
|
||||
response = await http_client.fetch(HTTPRequest(
|
||||
url=self.host + '/login',
|
||||
method='POST',
|
||||
headers=headers,
|
||||
body=json.dumps(json_body),
|
||||
),
|
||||
raise_error=True)
|
||||
try:
|
||||
response = requests.post(url, json=json_body, headers=headers)
|
||||
response.raise_for_status()
|
||||
if response.status_code == 200:
|
||||
self._access_token = response.json()["access_token"]
|
||||
return self._access_token
|
||||
else:
|
||||
logger.error(
|
||||
f"Unexpected error on login. Response status code: {response.status_code}, body: f{response.text}"
|
||||
)
|
||||
except requests.exceptions.RequestException as e:
|
||||
logger.exception(e)
|
||||
|
||||
self._access_token = json.loads(response.body)['access_token']
|
||||
return self._access_token
|
||||
def fetch(self):
|
||||
access_token = self._login()
|
||||
|
||||
async def fetch(self):
|
||||
access_token = await self._login()
|
||||
headers = {
|
||||
"Authorization": f"Bearer {access_token}",
|
||||
"Content-Type": "application/json",
|
||||
}
|
||||
url = f"{self._host}/data"
|
||||
|
||||
http_client = AsyncHTTPClient()
|
||||
headers = {'Authorization': f'Bearer {access_token}'}
|
||||
try:
|
||||
response = requests.get(url, headers=headers)
|
||||
response.raise_for_status()
|
||||
if response.status_code == 200:
|
||||
return response.json()
|
||||
else:
|
||||
logger.error(
|
||||
f"Unexpected error when fetching data. Response status code: {response.status_code}, body: f{response.text}"
|
||||
)
|
||||
except requests.exceptions.RequestException as e:
|
||||
logger.exception(e)
|
||||
|
||||
response = await http_client.fetch(HTTPRequest(
|
||||
url=self.host + '/data',
|
||||
method='GET',
|
||||
headers=headers,
|
||||
),
|
||||
raise_error=True)
|
||||
|
||||
return json.loads(response.body)
|
||||
def update_configuration():
|
||||
data_service_enabled = os.environ.get("DATA_SERVICE_ENABLED", "False")
|
||||
is_enabled = data_service_enabled.lower() == "true"
|
||||
if is_enabled:
|
||||
credentials = {
|
||||
"email": os.environ.get("DATA_SERVICE_CLIENT_EMAIL", None),
|
||||
"password": os.environ.get("DATA_SERVICE_CLIENT_PASSWORD", None),
|
||||
}
|
||||
data_service = DataService(credentials)
|
||||
data = data_service.fetch()
|
||||
if data:
|
||||
config.update(data["data"])
|
||||
else:
|
||||
logger.error("Could not fetch fresh data from the data service.")
|
||||
|
|
|
|||
|
|
@ -1,43 +0,0 @@
|
|||
import os
|
||||
import logging
|
||||
|
||||
from caimira.store.data_service import DataService
|
||||
|
||||
LOG = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class GlobalStore:
|
||||
'''
|
||||
Singleton pattern - ensure that there's only one instance of
|
||||
GlobalStore throughout the application
|
||||
'''
|
||||
|
||||
_instance = None
|
||||
|
||||
def __new__(self):
|
||||
if self._instance is None:
|
||||
self._instance = super().__new__(self)
|
||||
self._instance = {}
|
||||
|
||||
return self._instance
|
||||
|
||||
@classmethod
|
||||
async def populate_from_api(self):
|
||||
data_service_credentials = {
|
||||
'data_service_client_email': os.environ.get('DATA_SERVICE_CLIENT_EMAIL', None),
|
||||
'data_service_client_password': os.environ.get('DATA_SERVICE_CLIENT_PASSWORD', None),
|
||||
}
|
||||
data_service = None
|
||||
data_service_enabled = os.environ.get(
|
||||
'DATA_SERVICE_ENABLED', 'False').lower() == 'true'
|
||||
if data_service_enabled:
|
||||
try:
|
||||
data_service = DataService(data_service_credentials)
|
||||
self._instance = await data_service.fetch()
|
||||
except Exception as err:
|
||||
error_message = f"Something went wrong with the data service: {str(err)}"
|
||||
LOG.error(error_message, exc_info=True)
|
||||
|
||||
@classmethod
|
||||
def get_data(self):
|
||||
return self._instance
|
||||
|
|
@ -1,87 +1,81 @@
|
|||
from dataclasses import dataclass
|
||||
|
||||
import unittest
|
||||
from unittest.mock import patch, MagicMock
|
||||
from tornado.httpclient import HTTPError
|
||||
from unittest.mock import Mock, patch
|
||||
|
||||
from caimira.store.data_service import DataService
|
||||
|
||||
@dataclass
|
||||
class MockResponse:
|
||||
body: str
|
||||
|
||||
class DataServiceTests(unittest.TestCase):
|
||||
def setUp(self):
|
||||
# Set up any necessary test data or configurations
|
||||
self.credentials = {
|
||||
"data_service_client_email": "test@example.com",
|
||||
"data_service_client_password": "password123"
|
||||
}
|
||||
self.credentials = {"email": "test@example.com", "password": "password123"}
|
||||
self.data_service = DataService(self.credentials)
|
||||
|
||||
@patch('caimira.apps.calculator.data_service.AsyncHTTPClient')
|
||||
async def test_login_successful(self, mock_http_client):
|
||||
@patch("requests.post")
|
||||
def test_login_successful(self, mock_post):
|
||||
# Mock successful login response
|
||||
mock_response = MockResponse('{"access_token": "dummy_token"}')
|
||||
mock_fetch = MagicMock(return_value=mock_response)
|
||||
mock_http_client.return_value.fetch = mock_fetch
|
||||
mock_response = Mock()
|
||||
mock_response.status_code = 200
|
||||
mock_response.json.return_value = {"access_token": "dummy_token"}
|
||||
mock_post.return_value = mock_response
|
||||
|
||||
# Call the login method
|
||||
access_token = await self.data_service._login()
|
||||
access_token = self.data_service._login()
|
||||
|
||||
# Assert that the access token is returned correctly
|
||||
self.assertEqual(access_token, "dummy_token")
|
||||
|
||||
# Verify that the fetch method was called with the expected arguments
|
||||
mock_fetch.assert_called_once_with(
|
||||
url='https://caimira-data-api.app.cern.ch/login',
|
||||
method='POST',
|
||||
headers={'Content-type': 'application/json'},
|
||||
body='{"email": "test@example.com", "password": "password123"}'
|
||||
mock_post.assert_called_once_with(
|
||||
"https://caimira-data-api.app.cern.ch/login",
|
||||
json=dict(email="test@example.com", password="password123"),
|
||||
headers={"Content-Type": "application/json"},
|
||||
)
|
||||
|
||||
@patch('caimira.apps.calculator.data_service.AsyncHTTPClient')
|
||||
async def test_login_error(self, mock_http_client):
|
||||
@patch("requests.post")
|
||||
def test_login_error(self, mock_post):
|
||||
# Mock login error response
|
||||
mock_fetch = MagicMock(side_effect=HTTPError(500))
|
||||
mock_http_client.return_value.fetch = mock_fetch
|
||||
mock_post.return_value = Mock()
|
||||
mock_post.return_value.status_code = 500
|
||||
|
||||
# Call the login method
|
||||
access_token = await self.data_service.login()
|
||||
access_token = self.data_service._login()
|
||||
|
||||
# Assert that the login method returns None in case of an error
|
||||
self.assertIsNone(access_token)
|
||||
|
||||
@patch('caimira.apps.calculator.data_service.AsyncHTTPClient')
|
||||
async def test_fetch_successful(self, mock_http_client):
|
||||
@patch("requests.get")
|
||||
@patch.object(DataService, "_login")
|
||||
def test_fetch_successful(self, mock_login, mock_get):
|
||||
# Mock successful fetch response
|
||||
mock_response = MockResponse('{"data": "dummy_data"}')
|
||||
mock_fetch = MagicMock(return_value=mock_response)
|
||||
mock_http_client.return_value.fetch = mock_fetch
|
||||
|
||||
mock_get.return_value = Mock()
|
||||
mock_get.return_value.status_code = 200
|
||||
mock_get.return_value.json.return_value = {"data": "dummy_data"}
|
||||
# Call the fetch method with a mock access token
|
||||
self.data_service._access_token = "dummy_token"
|
||||
data = await self.data_service.fetch()
|
||||
mock_login.return_value = "dummy_token"
|
||||
data = self.data_service.fetch()
|
||||
|
||||
# Assert that the data is returned correctly
|
||||
self.assertEqual(data, {"data": "dummy_data"})
|
||||
|
||||
# Verify that the fetch method was called with the expected arguments
|
||||
mock_fetch.assert_called_once_with(
|
||||
url='https://caimira-data-api.app.cern.ch/data',
|
||||
method='GET',
|
||||
headers={'Authorization': 'Bearer dummy_token'}
|
||||
mock_get.assert_called_once_with(
|
||||
"https://caimira-data-api.app.cern.ch/data",
|
||||
headers={
|
||||
"Authorization": "Bearer dummy_token",
|
||||
"Content-Type": "application/json",
|
||||
},
|
||||
)
|
||||
|
||||
@patch('caimira.apps.calculator.data_service.AsyncHTTPClient')
|
||||
async def test_fetch_error(self, mock_http_client):
|
||||
@patch("requests.get")
|
||||
@patch.object(DataService, "_login")
|
||||
def test_fetch_error(self, mock_login, mock_get):
|
||||
# Mock fetch error response
|
||||
mock_fetch = MagicMock(side_effect=HTTPError(404))
|
||||
mock_http_client.return_value.fetch = mock_fetch
|
||||
mock_get.return_value = Mock()
|
||||
mock_get.return_value.status_code = 500
|
||||
|
||||
# Call the fetch method with a mock access token
|
||||
self.data_service._access_token = "dummy_token"
|
||||
data = await self.data_service.fetch()
|
||||
mock_login.return_value = "dummy_token"
|
||||
data = self.data_service.fetch()
|
||||
|
||||
# Assert that the fetch method returns None in case of an error
|
||||
self.assertIsNone(data)
|
||||
|
|
|
|||
Loading…
Reference in a new issue