Allow to set loglevel

CHANGELOG.md

@@ -58,7 +58,8 @@ Removed:
 - Reduce memory requirements of `TropCyclone.from_tracks` [#749](https://github.com/CLIMADA-project/climada_python/pull/749)
 - Support for different wind speed and pressure units in `TCTracks` when running `TropCyclone.from_tracks` [#749](https://github.com/CLIMADA-project/climada_python/pull/749)
 - Changed the parallel package from Pathos to Multiproess in the unsequa module [#763](https://github.com/CLIMADA-project/climada_python/pull/763)
-
+- Allow setting the loglevel in unsequa module [#765](https://github.com/CLIMADA-project/climada_python/pull/765)
+- 
 ### Fixed
 
 - `util.lines_polys_handler` solve polygon disaggregation issue in metre-based projection [#666](https://github.com/CLIMADA-project/climada_python/pull/666)

climada/engine/unsequa/calc_cost_benefit.py

@@ -129,7 +129,7 @@ def __init__(
 
 
 
-    def uncertainty(self, unc_data, processes=1, **cost_benefit_kwargs):
+    def uncertainty(self, unc_data, processes=1, loglevel='ERROR', **cost_benefit_kwargs):
         """
         Computes the cost benefit for each sample in unc_output.sample_df.
 
@@ -152,6 +152,9 @@ def uncertainty(self, unc_data, processes=1, **cost_benefit_kwargs):
         processes : int, optional
             Number of CPUs to use for parralel computations.
             The default is 1 (not parallel)
+        loglevel: str
+            Level of warning for the climada logger during the computation.
+            The default is "ERROR".
         cost_benefit_kwargs : keyword arguments
             Keyword arguments passed on to climada.engine.CostBenefit.calc()
 
@@ -170,7 +173,9 @@ def uncertainty(self, unc_data, processes=1, **cost_benefit_kwargs):
         See Also
         --------
         climada.engine.cost_benefit:
-            Compute risk and adptation option cost benefits.
+            compute risk and adptation option cost benefits.
+        climada.util.log_level:
+            set log level
 
         """
 
@@ -205,7 +210,7 @@ def uncertainty(self, unc_data, processes=1, **cost_benefit_kwargs):
         self.est_comp_time(unc_data.n_samples, elapsed_time, processes)
 
         #Compute impact distributions
-        with log_level(level='ERROR', name_prefix='climada'):
+        with log_level(level=loglevel, name_prefix='climada'):
             p_iterator = self._sample_parallel_iterator(
                 samples_df.iterrows(),
                 ent_input_var=self.ent_input_var,
@@ -227,7 +232,7 @@ def uncertainty(self, unc_data, processes=1, **cost_benefit_kwargs):
                     )
 
         #Perform the actual computation
-        with log_level(level='ERROR', name_prefix='climada'):
+        with log_level(level=loglevel, name_prefix='climada'):
             [imp_meas_present,
              imp_meas_future,
              tot_climate_risk,

climada/engine/unsequa/calc_impact.py

@@ -122,7 +122,8 @@ def uncertainty(self,
                     rp=None,
                     calc_eai_exp=False,
                     calc_at_event=False,
-                    processes=1
+                    processes=1,
+                    loglevel='ERROR'
                     ):
         """
         Computes the impact for each sample in unc_data.sample_df.
@@ -160,6 +161,9 @@ def uncertainty(self,
         processes : int, optional
             Number of CPUs to use for parralel computations.
             The default is 1 (not parallel)
+        loglevel: str
+            Level of warning for the climada logger during the computation.
+            The default is "ERROR".
 
         Returns
         -------
@@ -175,7 +179,10 @@ def uncertainty(self,
 
         See Also
         --------
-        climada.engine.impact: Compute risk.
+        climada.engine.impact:
+            compute impact and risk.
+        climada.util.log_level:
+            set log level
 
         """
 
@@ -212,7 +219,7 @@ def uncertainty(self,
         self.est_comp_time(unc_sample.n_samples, elapsed_time, processes)
 
         #Compute impact distributions
-        with log_level(level='ERROR', name_prefix='climada'):
+        with log_level(level=loglevel, name_prefix='climada'):
             p_iterator = self._sample_parallel_iterator(
                 samples_df.iterrows(),
                 exp_input_var=self.exp_input_var,
@@ -235,7 +242,7 @@ def uncertainty(self,
                     )
 
         #Perform the actual computation
-        with log_level(level='ERROR', name_prefix='climada'):
+        with log_level(level=loglevel, name_prefix='climada'):
             [aai_agg_list, freq_curve_list,
              eai_exp_list, at_event_list] = list(zip(*imp_metrics))