From c7b75f3e6a0b018b6cb711f09a4bfc8bb1a3c774 Mon Sep 17 00:00:00 2001
From: leufen1 <l.leufen@fz-juelich.de>
Date: Tue, 18 May 2021 14:28:26 +0200
Subject: [PATCH] filter plots different seasons, filter returns data with
missing values now (not squeezed version)
---
mlair/helpers/filter.py | 36 ++++++++++++++++++++++--------------
1 file changed, 22 insertions(+), 14 deletions(-)
diff --git a/mlair/helpers/filter.py b/mlair/helpers/filter.py
index b26b616f..b77a5910 100644
--- a/mlair/helpers/filter.py
+++ b/mlair/helpers/filter.py
@@ -230,13 +230,16 @@ class ClimateFIRFilter:
# create new time axis
factor = 1 if td_type == "D" else 24
start = coords[time_dim][-1].values.astype("datetime64[%s]" % td_type)
- end = coords[time_dim][-1].values.astype("datetime64[%s]" % td_type) + np.timedelta64(extend_range * factor,
- td_type)
+ end = coords[time_dim][-1].values.astype("datetime64[%s]" % td_type) + np.timedelta64(
+ extend_range * factor + 1,
+ td_type)
new_time_axis = np.arange(start, end).astype("datetime64[ns]")
# extract old values to use with new axis
start = coords[time_dim][-1].values.astype("datetime64[%s]" % td_type) - np.timedelta64(
- extend_range * factor - 1, td_type)
+ extend_range * factor, td_type)
+ # start = coords[time_dim][-1].values.astype("datetime64[%s]" % td_type) - np.timedelta64(
+ # extend_range * factor, td_type)
end = coords[time_dim][-1].values.astype("datetime64[%s]" % td_type)
new_values = apriori.sel({time_dim: slice(start, end)})
new_values.coords[time_dim] = new_time_axis
@@ -331,19 +334,24 @@ class ClimateFIRFilter:
# plot
if self.plot_path is not None:
- try:
- pos = 720 * fs
- filter_example = filter_input_data.isel({time_dim: pos})
- t0 = filter_example.coords[time_dim].values
- t_slice = filter_input_data.isel(
- {time_dim: slice(pos - int((length - 1) / 2), pos + int((length - 1) / 2) + 1)}).coords[
- time_dim].values
- self.plot(data, filter_example, var_dim, time_dim, t_slice, t0, plot_index)
- except IndexError:
- pass
+ for i, time_pos in enumerate([0.25, 1.5, 2.75, 4]): # [0.25, 1.5, 2.75, 4] x 365 days
+ try:
+ pos = int(time_pos * 365 * fs)
+ filter_example = filter_input_data.isel({time_dim: pos})
+ t0 = filter_example.coords[time_dim].values
+ t_slice = filter_input_data.isel(
+ {time_dim: slice(pos - int((length - 1) / 2), pos + int((length - 1) / 2) + 1)}).coords[
+ time_dim].values
+ self.plot(data, filter_example, var_dim, time_dim, t_slice, t0, f"{plot_index}_{i}")
+ except IndexError:
+ pass
# select only values at tmp dimension 0 at each point in time
- return filt.sel({new_dim: 0}, drop=True), h, apriori
+ res = filt.sel({new_dim: 0}, drop=True)
+ # create result array with same shape like input data, gabs are filled by nans
+ res_full = xr.ones_like(data) * np.nan
+ res_full.loc[res.coords] = res
+ return res_full, h, apriori
@staticmethod
def _create_tmp_dimension(data):
--
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