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Tryout Python 3.12 #392

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merged 6 commits into from
May 14, 2024
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Merged

Tryout Python 3.12 #392

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94b832b
update env
twallema May 14, 2024
c07d7fc
trim dependencies
twallema May 14, 2024
5884450
remove warning suppression in spatial model
twallema May 14, 2024
d0e15a3
chained assignment warning in VOC parameters
twallema May 14, 2024
5f02fae
unsuppress warnings in national model calibration
twallema May 14, 2024
b53f236
update vaccination incidences
twallema May 14, 2024
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Binary file modified data/covid19_DTM/interim/sciensano/vacc_incidence_national.pkl
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2 changes: 1 addition & 1 deletion environment.yml
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Original file line number Diff line number Diff line change
Expand Up @@ -3,7 +3,7 @@ channels:
- conda-forge
- defaults
dependencies:
- python=3.11.4
- python=3.12
- pandas
- numpy
- scipy
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6 changes: 1 addition & 5 deletions notebooks/calibration/calibrate_BASE-COVID19_SEIQRD_hybrid_vacc.py
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Expand Up @@ -35,10 +35,6 @@
from pySODM.optimization.mcmc import perturbate_theta, run_EnsembleSampler, emcee_sampler_to_dictionary
from pySODM.optimization.objective_functions import log_posterior_probability, log_prior_uniform, ll_negative_binomial, ll_poisson

# Suppress warnings
import warnings
warnings.filterwarnings("ignore")

#############################
## Handle script arguments ##
#############################
Expand Down Expand Up @@ -192,7 +188,7 @@
pars = pars1 + pars2 + pars3 + pars4
bounds = bounds1 + bounds2 + bounds3 + bounds4
# Define labels
labels = ['$\Omega$', '$\Psi$', 'k', '$K_{inf, abc}$', '$K_{inf, \\delta}$', '$A$', '$f_h$']
labels = [r'$\Omega$', r'$\Psi$', r'k', r'$K_{inf, abc}$', r'$K_{inf, \delta}$', r'$A$', r'$f_h$']
# Setup objective function without priors and with negative weights
objective_function = log_posterior_probability(model,pars,bounds,data,states,log_likelihood_fnc,log_likelihood_fnc_args,labels=labels)

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4 changes: 1 addition & 3 deletions notebooks/calibration/calibrate_BASE-COVID19_SEIQRD_spatial_hybrid_vacc.py
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Expand Up @@ -32,8 +32,6 @@
from pySODM.optimization.mcmc import perturbate_theta, run_EnsembleSampler, emcee_sampler_to_dictionary
from pySODM.optimization.objective_functions import log_posterior_probability, log_prior_uniform, ll_poisson, ll_negative_binomial

import warnings
warnings.filterwarnings("ignore")

####################################
## Public or private spatial data ##
Expand Down Expand Up @@ -237,7 +235,7 @@
# Join them together
pars = pars1 + pars2 + pars3 + pars4
bounds = bounds1 + bounds2 + bounds3 + bounds4
labels = ['$\\Omega$', '$\Psi$', '$k$', '$\Psi_{F}$', '$\Psi_{W}$', '$\Psi_{B}$', '$K_{inf, abc}$', '$K_{inf,\\delta}$', '$A$', '$f_h$']
labels = [r'$\Omega$', r'$\Psi$', '$k$', r'$\Psi_{F}$', r'$\Psi_{W}$', r'$\Psi_{B}$', r'$K_{inf, abc}$', r'$K_{inf,\delta}$', '$A$', '$f_h$']
# Setup objective function with uniform priors
objective_function = log_posterior_probability(model,pars,bounds,data,states,log_likelihood_fnc,log_likelihood_fnc_args,labels=labels, aggregation_function=aggregate_Brussels_Brabant_DataArray)

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10 changes: 2 additions & 8 deletions setup.py
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Expand Up @@ -13,15 +13,9 @@
'numpy',
'scipy',
'pandas',
'xlrd',
'openpyxl',
'zarr',
'emcee',
'xarray',
'rbfopt',
'openpyxl',
'numba',
'SAlib',
'h5py'
'pySODM',
],
extras_require={
"develop": ["pytest",
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8 changes: 4 additions & 4 deletions src/covid19_DTM/data/model_parameters.py
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Expand Up @@ -464,13 +464,13 @@ def get_COVID19_SEIQRD_VOC_parameters(VOCs=['WT', 'abc', 'delta', 'omicron'], pa
index=['WT', 'abc', 'delta', 'omicron'], columns=pd.MultiIndex.from_tuples(columns))

# Define logistic growth properties
VOC_parameters.loc['WT']['logistic_growth'] = [
VOC_parameters.loc['WT', 'logistic_growth'] = [
datetime(2019, 1, 1), datetime(2019, 2, 1), 0.20]
VOC_parameters.loc['abc']['logistic_growth'] = [
VOC_parameters.loc['abc', 'logistic_growth'] = [
datetime(2020, 12, 1), datetime(2021, 2, 14), 0.07]
VOC_parameters.loc['delta']['logistic_growth'] = [
VOC_parameters.loc['delta', 'logistic_growth'] = [
datetime(2021, 5, 1), datetime(2021, 6, 25), 0.11]
VOC_parameters.loc['omicron']['logistic_growth'] = [
VOC_parameters.loc['omicron', 'logistic_growth'] = [
datetime(2021, 11, 26), datetime(2021, 12, 24), 0.19]

# Define variant properties
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