From 3df1c16f9a9133df3eb88a2010c1e1157c25b6ec Mon Sep 17 00:00:00 2001
From: David Schaefer <david.schaefer@ufz.de>
Date: Wed, 27 Nov 2019 09:15:34 +0100
Subject: [PATCH] a small cleanup
---
saqc/funcs/functions.py | 16 ++++++++--------
1 file changed, 8 insertions(+), 8 deletions(-)
diff --git a/saqc/funcs/functions.py b/saqc/funcs/functions.py
index c0909c0f7..4c7a30a28 100644
--- a/saqc/funcs/functions.py
+++ b/saqc/funcs/functions.py
@@ -137,22 +137,20 @@ def flagIsolated(
dat_col = data[field][~drop_mask]
dat_col.dropna(inplace=True)
+ gap_check = dat_col.rolling(isolation_range).count()
+ gap_check = gap_check[(gap_check.index[0] + pd.Timedelta(isolation_range)):]
+
if max_isolated_group_size == 1:
# isolated single values are much easier to identify:
- gap_check = dat_col.rolling(isolation_range).count()
# exclude series initials:
- gap_check = gap_check[(gap_check.index[0] + pd.Timedelta(isolation_range)) :]
# reverse rolling trick:
isolated_indices = gap_check[
(gap_check[::-1].rolling(2).sum() == 2)[::-1].values
].index
else:
- gap_check = dat_col.rolling(isolation_range).count()
# check, which groups are centered enough for being isolated
continuation_check = gap_check.rolling(continuation_range).count()
- # exclude series initials:
- gap_check = gap_check[(gap_check.index[0] + pd.Timedelta(isolation_range)) :]
# check which values are sparsely enough surrounded
gap_check = (
gap_check[::-1]
@@ -168,9 +166,11 @@ def flagIsolated(
isolated_indices = isolated_indices[
continuation_check[isolated_indices] <= max_isolated_group_size
]
- # propagate True value onto entire isolated group (will not work with bfill method, because its not sure the
- # frequencie grid is actually equidistant - so here comes rolling reverse trick for offset defined windows
- # again):
+ # propagate True value onto entire isolated group
+ # NOTE:
+ # will not work with bfill method, because its not sure the frequency
+ # grid is actually equidistant - so here comes the rolling reverse
+ # trick for offset defined windows again
gap_check[:] = np.nan
gap_check.loc[isolated_indices] = True
original_index = gap_check.index
--
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