updated changelog and docstrings

Author: ValentinGebhart

CHANGELOG.md

@@ -28,6 +28,7 @@ Removed:
 - World Bank indicator data is now downloaded directly from their API via the function `download_world_bank_indicator`, instead of relying on the `pandas-datareader` package [#1033](https://github.com/CLIMADA-project/climada_python/pull/1033)
 - `Exposures.write_hdf5` pickles geometry data in WKB format, which is faster and more sustainable. [#1051](https://github.com/CLIMADA-project/climada_python/pull/1051)
 - The online documentation has been completely overhauled, now uses PyData theme: [#977](https://github.com/CLIMADA-project/climada_python/pull/977)
+- The default parameters of `impact_yearset`, `sample_events`, and `impact_yearset_from_sampling_vect` functions of the `climada.util.yearsets` module have been changed to sampling with replacement and not applying the correction factor to the impact yearsets. The application of the correction factor and the frequency of the resulting yearly impact object are corrected. [#1075](https://github.com/CLIMADA-project/climada_python/pull/1075)
 
 ### Fixed
 

climada/util/yearsets.py

@@ -40,6 +40,14 @@ def impact_yearset(
             impact object containing impacts per event
         sampled_years : list
             A list of years that shall be covered by the resulting yimp.
+        lam: float, optional
+            The parameter (i.e., the mean) of the Poisson distribution to sample the number of
+            events per year. If no lambda value is given, the default lam = sum(imp.frequency)
+            is used. Default is None.
+        correction_fac : boolean, optional
+            If True a correction factor is applied to the resulting yimp. It is
+            scaled in such a way that the expected annual impact (eai) of the yimp
+            equals the eai of the input impact. Defaults to False.
         with_replacement : bool, optional
             If True, impact events are sampled with replacement. If False, events are sampled
             without replacement. Sampling without replacement can yield distorted samples if
@@ -48,15 +56,6 @@ def impact_yearset(
             seed for the default bit generator
             default: None
 
-    Optional parameters
-        lam: int
-            The applied Poisson distribution is centered around lam events per year.
-            If no lambda value is given, the default lam = sum(imp.frequency) is used.
-        correction_fac : boolean
-            If True a correction factor is applied to the resulting yimp. It is
-            scaled in such a way that the expected annual impact (eai) of the yimp
-            equals the eai of the input impact
-
     Returns
     -------
         yimp : climada.engine.Impact()
@@ -110,12 +109,10 @@ def impact_yearset_from_sampling_vect(
             i.e. [yimp, sampling_vect] = climada_yearsets.impact_yearset(...)
             and can then be provided in this function to obtain the exact same sampling
             (also for a different imp object)
-
-    Optional parameter
-        correction_fac : boolean
+        correction_fac : boolean, optional
             If True a correction factor is applied to the resulting yimp. It is
             scaled in such a way that the expected annual impact (eai) of the yimp
-            equals the eai of the input impact
+            equals the eai of the input impact. Defaults to False.
 
     Returns
     -------
@@ -156,7 +153,8 @@ def sample_from_poisson(n_sampled_years, lam, seed=None):
         n_sampled_years : int
             The target number of years the impact yearset shall contain.
         lam: float
-            the applied Poisson distribution is centered around lambda events per year
+            The parameter (i.e., the mean) of the Poisson distribution to sample the number of
+            events per year.
         seed : int, optional
             seed for numpy.random, will be set if not None
             default: None