Tryout Python 3.12 (#392)

UGentBiomath · May 14, 2024 · ef6087f · ef6087f
1 parent c1692cd
commit ef6087f
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Showing 7 changed files with 9 additions and 21 deletions.
diff --git a/data/covid19_DTM/interim/sciensano/vacc_incidence_national.pkl b/data/covid19_DTM/interim/sciensano/vacc_incidence_national.pkl
diff --git a/data/covid19_DTM/interim/sciensano/vacc_incidence_prov.pkl b/data/covid19_DTM/interim/sciensano/vacc_incidence_prov.pkl
diff --git a/environment.yml b/environment.yml
@@ -3,7 +3,7 @@ channels:
   - conda-forge
   - defaults
 dependencies:
-  - python=3.11.4
+  - python=3.12
   - pandas  
   - numpy  
   - scipy  

diff --git a/notebooks/calibration/calibrate_BASE-COVID19_SEIQRD_hybrid_vacc.py b/notebooks/calibration/calibrate_BASE-COVID19_SEIQRD_hybrid_vacc.py
@@ -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 ##
 #############################
@@ -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)
 

diff --git a/notebooks/calibration/calibrate_BASE-COVID19_SEIQRD_spatial_hybrid_vacc.py b/notebooks/calibration/calibrate_BASE-COVID19_SEIQRD_spatial_hybrid_vacc.py
@@ -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 ##
@@ -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)
 

diff --git a/setup.py b/setup.py
@@ -13,15 +13,9 @@
         'numpy',
         'scipy',
         'pandas',
-        'xlrd',
-	    'openpyxl',
-        'zarr',
-        'emcee',
-        'xarray',
-        'rbfopt',
+        'openpyxl',
         'numba',
-        'SAlib',
-        'h5py'
+        'pySODM',
     ],
     extras_require={
         "develop":  ["pytest",

diff --git a/src/covid19_DTM/data/model_parameters.py b/src/covid19_DTM/data/model_parameters.py
@@ -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