From d7e9e7899f587ec7e4dcda71c06f34047661fc87 Mon Sep 17 00:00:00 2001
From: Peter Luenenschloss <peter.luenenschloss@ufz.de>
Date: Mon, 3 Aug 2020 16:14:00 +0200
Subject: [PATCH] functions module documented
---
saqc/funcs/functions.py | 52 +++++++++++++++++++++++++++++++++++++++--
1 file changed, 50 insertions(+), 2 deletions(-)
diff --git a/saqc/funcs/functions.py b/saqc/funcs/functions.py
index f446a3f95..2b454aaf7 100644
--- a/saqc/funcs/functions.py
+++ b/saqc/funcs/functions.py
@@ -595,12 +595,38 @@ def flagForceFail(data, field, flagger, **kwargs):
The fieldname of the column, holding the data-to-be-flagged.
flagger : saqc.flagger
A flagger object, holding flags and additional informations related to `data`.
+
"""
raise RuntimeError("Works as expected :D")
@register
def flagUnflagged(data, field, flagger, **kwargs):
+ """
+ Function sets the flagger.GOOD flag to all values flagged better then flagger.GOOD.
+ If there is an entry 'flag' in the kwargs dictionary passed, the
+ function sets the kwargs['flag'] flag to all values flagged better kwargs['flag']
+
+ Parameters
+ ----------
+ data : dios.DictOfSeries
+ A dictionary of pandas.Series, holding all the data.
+ field : str
+ The fieldname of the column, holding the data-to-be-flagged.
+ flagger : saqc.flagger
+ A flagger object, holding flags and additional informations related to `data`.
+ kwargs : Dict
+ If kwargs contains 'flag' entry, kwargs['flag] is set, if no entry 'flag' is present,
+ 'flagger.UNFLAGGED' is set.
+
+ Returns
+ -------
+ data : dios.DictOfSeries
+ A dictionary of pandas.Series, holding all the data.
+ flagger : saqc.flagger
+ The flagger object, holding flags and additional Informations related to `data`.
+ """
+
flag = kwargs.pop('flag', flagger.GOOD)
flagger = flagger.setFlags(field, flag=flag, **kwargs)
return data, flagger
@@ -608,8 +634,30 @@ def flagUnflagged(data, field, flagger, **kwargs):
@register
def flagGood(data, field, flagger, **kwargs):
+ """
+ Function sets the flagger.GOOD flag to all values flagged better then flagger.GOOD.
+
+ Parameters
+ ----------
+ Parameters
+ ----------
+ data : dios.DictOfSeries
+ A dictionary of pandas.Series, holding all the data.
+ field : str
+ The fieldname of the column, holding the data-to-be-flagged.
+ flagger : saqc.flagger
+ A flagger object, holding flags and additional informations related to `data`.
+
+ Returns
+ -------
+ data : dios.DictOfSeries
+ A dictionary of pandas.Series, holding all the data.
+ flagger : saqc.flagger
+ The flagger object, holding flags and additional Informations related to `data`.
+
+ """
kwargs.pop('flag', None)
- return flagUnflagged(data, field, flagger)
+ return flagUnflagged(data, field, flagger, **kwargs)
@register
@@ -747,7 +795,7 @@ def flagCrossScoring(data, field, flagger, fields, thresh, cross_stat='modZscore
For fields=[f_1,f_2,...,f_N] and timestamps [t_1,t_2,...,t_K], the following steps are taken for outlier detection:
1. All timestamps t_i, where there is one f_k, with data[f_K] having no entry at t_i, are excluded from the
- following process (inner merge of the f_i fields.)
+ following process (inner join of the f_i fields.)
2. for every 0 <= i <= K, the value m_j = median({data[f_1][t_i], data[f_2][t_i], ..., data[f_N][t_i]}) is
calculated
2. for every 0 <= i <= K, the set {data[f_1][t_i] - m_j, data[f_2][t_i] - m_j, ..., data[f_N][t_i] - m_j} is tested
--
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