climFIR data handler can create a sample plot if a path is provided.

mlair/data_handler/data_handler_with_filter.py

@@ -301,12 +301,13 @@ class DataHandlerClimateFirFilterSingleStation(DataHandlerFirFilterSingleStation
     _hash = DataHandlerFirFilterSingleStation._hash + ["apriori_type", "apriori_sel_opts"]
     _store_attributes = DataHandlerFirFilterSingleStation.store_attributes() + ["apriori"]
 
-    def __init__(self, *args, apriori=None, apriori_type=None, apriori_sel_opts=None, **kwargs):
+    def __init__(self, *args, apriori=None, apriori_type=None, apriori_sel_opts=None, plot_path=None, **kwargs):
         self.apriori_type = apriori_type
         self.climate_filter_coeff = None  # coefficents of the used FIR filter
         self.apriori = apriori  # exogenous apriori information or None to calculate from data (endogenous)
         self.all_apriori = None  # collection of all apriori information
         self.apriori_sel_opts = apriori_sel_opts  # ensure to separate exogenous and endogenous information
+        self.plot_path = plot_path  # use this path to create insight plots
         super().__init__(*args, **kwargs)
 
     @TimeTrackingWrapper
@@ -316,7 +317,7 @@ class DataHandlerClimateFirFilterSingleStation(DataHandlerFirFilterSingleStation
         climate_filter = ClimateFIRFilter(self.input_data, self.fs, self.filter_order, self.filter_cutoff_freq,
                                           self.filter_window_type, time_dim=self.time_dim, var_dim=self.target_dim,
                                           apriori_type=self.apriori_type, apriori=self.apriori,
-                                          sel_opts=self.apriori_sel_opts)
+                                          sel_opts=self.apriori_sel_opts, plot_path=self.plot_path, plot_name=str(self))
         self.climate_filter_coeff = climate_filter.filter_coefficients
 
         # store apriori information: store all if residuum_stat method was used, otherwise just store initial apriori

mlair/helpers/filter.py

@@ -2,6 +2,7 @@ import gc
 import warnings
 from typing import Union
 import logging
+import os
 
 import datetime
 import numpy as np
@@ -54,7 +55,7 @@ class FIRFilter:
 class ClimateFIRFilter:
 
     def __init__(self, data, fs, order, cutoff, window, time_dim, var_dim, apriori=None, apriori_type=None,
-                 sel_opts=None):
+                 sel_opts=None, plot_path=None, plot_name=None):
         """
         :param data: data to filter
         :param fs: sampling frequency in 1/days -> 1d: fs=1 -> 1H: fs=24
@@ -69,6 +70,8 @@ class ClimateFIRFilter:
             the residuum either the value zero is used (apriori_type is None or "zeros") or a climatology on the
             residua is used ("residuum_stats").
         """
+        self.plot_path = plot_path
+        self.plot_name = plot_name
         filtered = []
         h = []
         sel_opts = sel_opts if isinstance(sel_opts, dict) else {time_dim: sel_opts}
@@ -81,7 +84,7 @@ class ClimateFIRFilter:
             # calculate climatological filter
             fi, hi, apriori = self.clim_filter(input_data, fs, cutoff[i], order[i], apriori=apriori_list[i],
                                                sel_opts=sel_opts, sampling=sampling, time_dim=time_dim, window=window,
-                                               var_dim=var_dim)
+                                               var_dim=var_dim, plot_index=i)
             filtered.append(fi)
             h.append(hi)
 
@@ -194,7 +197,7 @@ class ClimateFIRFilter:
         return apriori
 
     def clim_filter(self, data, fs, cutoff_high, order, apriori=None, padlen=None, sel_opts=None, sampling="1d",
-                    time_dim="datetime", var_dim="variables", window="hamming"):
+                    time_dim="datetime", var_dim="variables", window="hamming", plot_index=None):
 
         # calculate apriori information from data if not given and extend its range if not sufficient long enough
         if apriori is None:
@@ -226,16 +229,28 @@ class ClimateFIRFilter:
                 tmp_filter, _ = fir_filter(tmp_comb, fs, cutoff_high=cutoff_high, order=order, causal=False,
                                            padlen=_padlen, dim=var_dim, window=window, h=h)
                 res.loc[{time_dim: t0}] = tmp_filter.loc[{time_dim: t0}]
+                if i == 720 and self.plot_path is not None:
+                    self.plot(data, tmp_comb, var_dim, time_dim, slice(dt[i_m], dt[i_p + 1]), t0, plot_index)
             except IndexError:
                 res.loc[{time_dim: t0}] = np.nan
-            # if i == 720:
-            #     for var in data.coords[var_dim]:
-            #         data.sel({var_dim: var, time_dim: slice(dt[i_m], dt[i_p+1])}).plot()
-            #         tmp_comb.sel({var_dim: var}).plot()
-            #         plt.title(var)
-            #         plt.show()
         return res, h, apriori
 
+    def plot(self, data, tmp_comb, var_dim, time_dim, time_dim_slice, t0, plot_index):
+        try:
+            plot_folder = os.path.join(os.path.abspath(self.plot_path), "climFIR")
+            if not os.path.exists(plot_folder):
+                os.makedirs(plot_folder)
+            for var in data.coords[var_dim]:
+                data.sel({var_dim: var, time_dim: time_dim_slice}).plot()
+                tmp_comb.sel({var_dim: var}).plot()
+                plt.axvline(t0, color="lightgrey")
+                plt.title(str(var.values))
+                plot_name = os.path.join(plot_folder, f"climFIR_{self.plot_name}_{str(var.values)}_{plot_index}.pdf")
+                plt.savefig(plot_name, dpi=300)
+                plt.close('all')
+        except:
+            pass
+
     @property
     def filter_coefficients(self):
         return self._h