Address warnings + remove warning suppression (#69)

twallema · May 12, 2024 · 035d025 · 035d025
1 parent e344a09
commit 035d025
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Showing 3 changed files with 3 additions and 14 deletions.
diff --git a/tutorials/SIR/workflow_tutorial.py b/tutorials/SIR/workflow_tutorial.py
@@ -20,10 +20,6 @@
 from pySODM.optimization.mcmc import perturbate_theta, run_EnsembleSampler, emcee_sampler_to_dictionary
 from pySODM.optimization.objective_functions import log_posterior_probability, ll_negative_binomial
 
-# Suppress warnings
-import warnings
-warnings.filterwarnings("ignore")
-
 ######################################################
 ## Generate a synthetic dataset with overdispersion ##
 ######################################################
@@ -107,7 +103,7 @@ def integrate(t, S, I, R, beta, gamma):
     # Calibated parameters and bounds
     pars = ['beta',]
     labels = ['$\\beta$',]
-    bounds = [(1e-6,1),]
+    bounds = [(0,10),]
     # Setup objective function (no priors --> uniform priors based on bounds)
     objective_function = log_posterior_probability(model, pars, bounds, data, states, log_likelihood_fnc, log_likelihood_fnc_args, labels=labels)
     # Extract start- and enddate

diff --git a/tutorials/SIR_SI/calibration.py b/tutorials/SIR_SI/calibration.py
@@ -21,10 +21,6 @@
 from pySODM.optimization.mcmc import perturbate_theta, run_EnsembleSampler, emcee_sampler_to_dictionary
 from pySODM.optimization.objective_functions import log_posterior_probability, ll_negative_binomial, ll_poisson
 
-# Suppress warnings
-import warnings
-warnings.filterwarnings("ignore")
-
 ##################
 ## Define model ##
 ##################

diff --git a/tutorials/influenza_1718/calibration.py b/tutorials/influenza_1718/calibration.py
@@ -25,9 +25,6 @@
 from pySODM.optimization.objective_functions import log_posterior_probability, ll_negative_binomial
 # pySODM dependecies
 import corner
-# Suppress warnings
-import warnings
-warnings.filterwarnings("ignore")
 
 ##############
 ## Settings ##
@@ -52,10 +49,10 @@
 ###############
 
 # Load case data
-data = pd.read_csv(os.path.join(os.path.dirname(__file__),'data/interim/data_influenza_1718_format.csv'), index_col=[0,1], parse_dates=True)
+data = pd.read_csv(os.path.join(os.path.dirname(__file__),'data/interim/data_influenza_1718_format.csv'), index_col=[0,1], parse_dates=True, date_format='%Y-%m-%d')
 data = data.squeeze()
 # Load case data per 100K
-data_100K = pd.read_csv(os.path.join(os.path.dirname(__file__),'data/interim/data_influenza_1718_format_100K.csv'), index_col=[0,1], parse_dates=True)
+data_100K = pd.read_csv(os.path.join(os.path.dirname(__file__),'data/interim/data_influenza_1718_format_100K.csv'), index_col=[0,1], parse_dates=True, date_format='%Y-%m-%d')
 data_100K = data_100K.squeeze()
 # Re-insert pd.IntervalIndex (pd.IntervalIndex is always loaded as a string..)
 age_groups = pd.IntervalIndex.from_tuples([(0,5),(5,15),(15,65),(65,120)], closed='left')