add option to give already resamples ams to the ams_analysis function

statista/eva.py

@@ -12,10 +12,11 @@
 
 def ams_analysis(
     time_series_df: DataFrame,
+    ams: bool = False,
     ams_start: str = "A-OCT",
     save_plots: bool = False,
     save_to: str = None,
-    filter_out: Union[bool, float, int] = False,
+    filter_out: Union[float, int] = None,
     distribution: str = "GEV",
     method: str = "lmoments",
     estimate_parameters: bool = False,
@@ -31,6 +32,8 @@ def ams_analysis(
     ----------
     time_series_df : [DataFrame]
         DataFrame containing multiple time series to do the statistical analysis on.
+    ams: [bool]
+        True if the the given time series is annual mean series. Default is False.
     ams_start: [str]
         The beginning of the year which is used to resample the time series to get the annual maximum series.
         Default is"A-OCT".
@@ -129,30 +132,32 @@ def ams_analysis(
 
     for i in gauges:
         QTS = time_series_df.loc[:, i]
-        # The time series is resampled to the annual maxima, and turned into a
-        # numpy array.
+        # The time series is resampled to the annual maxima, and turned into a numpy array.
         # The hydrological year is 1-Nov/31-Oct (from Petrow and Merz, 2009, JoH).
-        ams = QTS.resample(ams_start).max().values
+        if not ams:
+            ams_df = QTS.resample(ams_start).max().values
+        else:
+            ams_df = QTS.values
 
-        if not isinstance(filter_out, bool):
-            ams = ams[ams != filter_out]
+        if filter_out is not None:
+            ams_df = ams_df[ams_df != filter_out]
 
         if estimate_parameters:
             # TODO: still to be tested and prepared for GEV
             # estimate the parameters through an optimization
-            # alpha = (np.sqrt(6) / np.pi) * ams.std()
-            # beta = ams.mean() - 0.5772 * alpha
+            # alpha = (np.sqrt(6) / np.pi) * ams_df.std()
+            # beta = ams_df.mean() - 0.5772 * alpha
             # param_dist = [beta, alpha]
-            threshold = np.quantile(ams, quartile)
+            threshold = np.quantile(ams_df, quartile)
             if distribution == "GEV":
-                dist = GEV(ams)
+                dist = GEV(ams_df)
                 param_dist = dist.estimateParameter(
                     method="optimization",
                     ObjFunc=Gumbel.ObjectiveFn,
                     threshold=threshold,
                 )
             else:
-                dist = Gumbel(ams)
+                dist = Gumbel(ams_df)
                 param_dist = dist.estimateParameter(
                     method="optimization",
                     ObjFunc=Gumbel.ObjectiveFn,
@@ -162,12 +167,12 @@ def ams_analysis(
             # estimate the parameters through maximum liklehood method
             try:
                 if distribution == "GEV":
-                    dist = GEV(ams)
+                    dist = GEV(ams_df)
                     # defult parameter estimation method is maximum liklihood method
                     param_dist = dist.estimateParameter(method=method)
                 else:
                     # A gumbel distribution is fitted to the annual maxima
-                    dist = Gumbel(ams)
+                    dist = Gumbel(ams_df)
                     # defult parameter estimation method is maximum liklihood method
                     param_dist = dist.estimateParameter(method=method)
             except Exception as e:
@@ -198,10 +203,10 @@ def ams_analysis(
             Qrp = dist.theporeticalEstimate(param_dist[0], param_dist[1], F)
 
         # to get the Non Exceedance probability for a specific Value
-        # sort the ams
-        ams.sort()
+        # sort the ams_df
+        ams_df.sort()
         # calculate the F (Exceedence probability based on weibul)
-        cdf_Weibul = PlottingPosition.weibul(ams)
+        cdf_Weibul = PlottingPosition.weibul(ams_df)
         # Gumbel.probapilityPlot method calculates the theoretical values
         # based on the Gumbel distribution
         # parameters, theoretical cdf (or weibul), and calculate the confidence interval
@@ -240,10 +245,12 @@ def ams_analysis(
         statistical_properties.loc[i, "max"] = QTS.max()
         statistical_properties.loc[i, "t_beg"] = QTS.index.min()
         statistical_properties.loc[i, "t_end"] = QTS.index.max()
-        statistical_properties.loc[i, "nyr"] = (
-            statistical_properties.loc[i, "t_end"]
-            - statistical_properties.loc[i, "t_beg"]
-        ).days / 365.25
+        if not ams:
+            statistical_properties.loc[i, "nyr"] = (
+                statistical_properties.loc[i, "t_end"]
+                - statistical_properties.loc[i, "t_beg"]
+            ).days / 365.25
+
         for irp, irp_name in zip(Qrp, rp_name):
             statistical_properties.loc[i, irp_name] = irp