diff --git a/mlair/data_handler/data_handler_with_filter.py b/mlair/data_handler/data_handler_with_filter.py
index 097c0da73ba5f4759be7e63e771e7cea8d450e10..c33da2a9337642d891918462722d30f7fd12b772 100644
--- a/mlair/data_handler/data_handler_with_filter.py
+++ b/mlair/data_handler/data_handler_with_filter.py
@@ -9,7 +9,7 @@ import pandas as pd
import xarray as xr
from typing import List, Union, Tuple, Optional
from functools import partial
-
+import logging
from mlair.data_handler.data_handler_single_station import DataHandlerSingleStation
from mlair.data_handler import DefaultDataHandler
from mlair.helpers import remove_items, to_list, TimeTrackingWrapper
@@ -318,6 +318,7 @@ class DataHandlerClimateFirFilterSingleStation(DataHandlerFirFilterSingleStation
def apply_filter(self):
"""Apply FIR filter only on inputs."""
self.apriori = self.apriori.get(str(self)) if isinstance(self.apriori, dict) else self.apriori
+ logging.info(f"{self.station}: call ClimateFIRFilter")
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,
diff --git a/mlair/helpers/filter.py b/mlair/helpers/filter.py
index 9f7b5a6ebcb62b87b9eb059027bab4844fa2e67a..21dfcbeebe49119d4fb503f64c2e0435f306fe18 100644
--- a/mlair/helpers/filter.py
+++ b/mlair/helpers/filter.py
@@ -75,23 +75,27 @@ class ClimateFIRFilter:
:param apriori_diurnal: Use diurnal cycle as additional apriori information (only applicable for hourly
resoluted data). The mean anomaly of each hour is added to the apriori_type information.
"""
+ logging.info(f"{plot_name}: start init ClimateFIRFilter")
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}
sampling = {1: "1d", 24: "1H"}.get(int(fs))
+ logging.info(f"{plot_name}: create diurnal_anomalies")
if apriori_diurnal is True and sampling == "1H":
diurnal_anomalies = self.create_hourly_mean(data, sel_opts=sel_opts, sampling=sampling, time_dim=time_dim,
as_anomaly=True)
else:
diurnal_anomalies = 0
+ logging.info(f"{plot_name}: create monthly apriori")
if apriori is None:
apriori = self.create_monthly_mean(data, sel_opts=sel_opts, sampling=sampling,
time_dim=time_dim) + diurnal_anomalies
apriori_list = to_list(apriori)
input_data = data.__deepcopy__()
for i in range(len(order)):
+ logging.info(f"{plot_name}: start filter for order {order[i]}")
# calculate climatological filter
# clim_filter: Callable = {True: self.clim_filter_vectorized, False: self.clim_filter}[vectorized]
clim_filter: Callable = {True: self.clim_filter_vectorized_less_memory, False: self.clim_filter}[vectorized]
@@ -99,20 +103,25 @@ class ClimateFIRFilter:
apriori=apriori_list[i],
sel_opts=sel_opts, sampling=sampling, time_dim=time_dim, window=window,
var_dim=var_dim, plot_index=i, padlen_factor=padlen_factor)
+
+ logging.info(f"{plot_name}: finished clim_filter calculation")
filtered.append(fi)
h.append(hi)
gc.collect()
# calculate residuum
+ logging.info(f"{plot_name}: calculate residuum")
input_data = input_data - fi
# create new apriori information for next iteration if no further apriori is provided
if len(apriori_list) <= i + 1:
+ logging.info(f"{plot_name}: create diurnal_anomalies")
if apriori_diurnal is True and sampling == "1H":
diurnal_anomalies = self.create_hourly_mean(input_data, sel_opts=sel_opts, sampling=sampling,
time_dim=time_dim, as_anomaly=True)
else:
diurnal_anomalies = 0
+ logging.info(f"{plot_name}: create monthly apriori")
if apriori_type is None or apriori_type == "zeros": # zero version
apriori_list.append(xr.zeros_like(apriori_list[i]) + diurnal_anomalies)
elif apriori_type == "residuum_stats": # calculate monthly statistic on residuum
@@ -361,6 +370,8 @@ class ClimateFIRFilter:
sampling="1d", time_dim="datetime", var_dim="variables", window="hamming",
plot_index=None):
+ logging.info(f"{data.coords['Stations'].values[0]}: extend apriori")
+
# calculate apriori information from data if not given and extend its range if not sufficient long enough
if apriori is None:
apriori = self.create_monthly_mean(data, sel_opts=sel_opts, sampling=sampling, time_dim=time_dim)
@@ -376,12 +387,16 @@ class ClimateFIRFilter:
coll = []
for var in data.coords[var_dim].values:
+ logging.info(f"{data.coords['Stations'].values[0]} ({var}): sel data")
d = data.sel({var_dim: [var]})
a = apriori.sel({var_dim: [var]})
# combine historical data / observation [t0-length,t0] and climatological statistics [t0+1,t0+length]
+ logging.info(f"{data.coords['Stations'].values[0]} ({var}): history")
history = self._shift_data(d, range(int(-(length - 1) / 2), 1), time_dim, var_dim, new_dim)
+ logging.info(f"{data.coords['Stations'].values[0]} ({var}): future")
future = self._shift_data(a, range(1, int((length - 1) / 2) + 1), time_dim, var_dim, new_dim)
+ logging.info(f"{data.coords['Stations'].values[0]} ({var}): concat to filter input")
filter_input_data = xr.concat([history.dropna(time_dim), future], dim=new_dim, join="left")
# filter_input_data = history.combine_first(future)
# history.sel(datetime=slice("2010-11-01", "2011-04-01"),variables="o3").plot()
@@ -397,6 +412,7 @@ class ClimateFIRFilter:
# filt = xr.apply_ufunc(fir_filter_vectorized, filter_input_data, time_axis,
# input_core_dims=[[new_dim], []], output_core_dims=[[new_dim]], vectorize=True,
# kwargs=kwargs)
+ logging.info(f"{data.coords['Stations'].values[0]} ({var}): start filter convolve")
with TimeTracking(name="convolve"):
slicer = slice(int(-(length - 1) / 2), int((length - 1) / 2))
filt = xr.apply_ufunc(fir_filter_convolve_vectorized, filter_input_data.sel({new_dim: slicer}),
@@ -422,8 +438,10 @@ class ClimateFIRFilter:
# select only values at tmp dimension 0 at each point in time
coll.append(filt.sel({new_dim: 0}, drop=True))
+ logging.info(f"{data.coords['Stations'].values[0]}: concat all variables")
res = xr.concat(coll, var_dim)
# create result array with same shape like input data, gabs are filled by nans
+ logging.info(f"{data.coords['Stations'].values[0]}: create res_full")
res_full = xr.ones_like(data) * np.nan
res_full.loc[res.coords] = res
return res_full, h, apriori