From a5d409c7fc1f3a423da81c77025a6a056e804399 Mon Sep 17 00:00:00 2001
From: Peter Luenenschloss <peter.luenenschloss@ufz.de>
Date: Wed, 27 Jan 2021 14:12:50 +0100
Subject: [PATCH] small fix
---
saqc/funcs/outliers.py | 11 +++++++++--
1 file changed, 9 insertions(+), 2 deletions(-)
diff --git a/saqc/funcs/outliers.py b/saqc/funcs/outliers.py
index df617514a..ee5e91633 100644
--- a/saqc/funcs/outliers.py
+++ b/saqc/funcs/outliers.py
@@ -717,8 +717,9 @@ def flagOffset(data, field, flagger, thresh, tolerance, window, numba_kickin=200
Minimum difference between to values, to consider the latter one as a spike. See condition (1)
tolerance : float, default 0
Maximum difference between pre-spike and post-spike values. See condition (2)
- window : str, default '15min'
- Maximum length of "spiky" value courses. See condition (3)
+ window : {str, int}, default '15min'
+ Maximum length of "spiky" value courses. See condition (3). Integer defined window length are only allowed for
+ regularly sampled timeseries.
numba_kickin : int, default 200000
When there are detected more than `numba_kickin` incidents of potential spikes,
the pandas.rolling - part of computation gets "jitted" with numba.
@@ -743,6 +744,12 @@ def flagOffset(data, field, flagger, thresh, tolerance, window, numba_kickin=200
"""
dataseries = data[field].dropna()
+ if isinstance(window, int):
+ delta = getFreqDelta(dataseries.index)
+ if not delta:
+ raise TypeError('Only offset string defined window sizes allowed for irrgegularly sampled timeseries')
+ window = delta * window
+
# get all the entries preceding a significant jump
post_jumps = dataseries.diff().abs() > thresh
post_jumps = post_jumps[post_jumps]
--
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