From c04eca58bbbd7db6133f603bd50efd3b29d73bd8 Mon Sep 17 00:00:00 2001
From: Peter Luenenschloss <peter.luenenschloss@ufz.de>
Date: Tue, 28 Apr 2020 08:30:06 +0200
Subject: [PATCH] tiny refactoring of interpolateNANs method
---
saqc/funcs/harm_functions.py | 2 +-
saqc/lib/ts_operators.py | 26 ++++++++++++++------------
2 files changed, 15 insertions(+), 13 deletions(-)
diff --git a/saqc/funcs/harm_functions.py b/saqc/funcs/harm_functions.py
index b58070ec8..e67fe5157 100644
--- a/saqc/funcs/harm_functions.py
+++ b/saqc/funcs/harm_functions.py
@@ -364,7 +364,7 @@ def _interpolateGrid(
data = _insertGrid(data, freq)
data, chunk_bounds = interpolateNANs(
- data, method, order=order, inter_limit=2, downcast_interpolation=downcast_interpolation,
+ data, method, order=order, inter_limit=2, downgrade_interpolation=downcast_interpolation,
return_chunk_bounds=True
)
diff --git a/saqc/lib/ts_operators.py b/saqc/lib/ts_operators.py
index abb2ca499..c8bae5f5d 100644
--- a/saqc/lib/ts_operators.py
+++ b/saqc/lib/ts_operators.py
@@ -165,29 +165,31 @@ def meanQC(data, max_nan_total=np.inf, max_nan_consec=np.inf):
return np.nan
-def interpolateNANs(data, method, order=2, inter_limit=2, downcast_interpolation=False, return_chunk_bounds=False):
+def interpolateNANs(data, method, order=2, inter_limit=2, downgrade_interpolation=False, return_chunk_bounds=False):
"""
The function interpolates nan-values (and nan-grids) in timeseries data. It can be passed all the method keywords
from the pd.Series.interpolate method and will than apply this very methods. Note, that the inter_limit keyword
really restricts the interpolation to chunks, not containing more than "inter_limit" nan entries
- (thereby opposing the limit keyword of pd.Series.interpolate).
+ (thereby not being identical to the "limit" keyword of pd.Series.interpolate).
- :param data: pd.Series. The data series to be interpolated
+ :param data: pd.Series or np.array. The data series to be interpolated
:param method: String. Method keyword designating interpolation method to use.
:param order: Integer. If your desired interpolation method needs an order to be passed -
here you pass it.
- :param inter_limit: Integer. Default = 2. Limit up to whitch nan - gaps in the data get interpolated.
- Its default value suits an interpolation that only will apply on an inserted
- frequency grid. (regularization by interpolation)
- :param downcast_interpolation: Boolean. Default False. If True:
+ :param inter_limit: Integer. Default = 2. Limit up to which consecutive nan - values in the data get
+ replaced by interpolation.
+ Its default value suits an interpolation that only will apply to points of an
+ inserted frequency grid. (regularization by interpolation)
+ Gaps wider than "inter_limit" will NOT be interpolated at all.
+ :param downgrade_interpolation: Boolean. Default False. If True:
If a data chunk not contains enough values for interpolation of the order "order",
- the highest order possible will be selected for that chunks interpolation."
+ the highest order possible will be selected for that chunks interpolation.
:param return_chunk_bounds: Boolean. Default False. If True:
Additionally to the interpolated data, the start and ending points of data chunks
not containing no series consisting of more then "inter_limit" nan values,
are calculated and returned.
- (This option fits requirements of the "_interpolate" functions use in the context of
- saqc harmonization mainly.)
+ (This option fits requirements of the "interpolateNANs" functions use in the
+ context of saqc harmonization mainly.)
:return:
"""
@@ -227,7 +229,7 @@ def interpolateNANs(data, method, order=2, inter_limit=2, downcast_interpolation
except (NotImplementedError, ValueError):
logger.warning(
"Interpolation with method {} is not supported at order {}. "
- "Interpolation will be performed with order {}".format(
+ "Interpolation will be performed at order {}".format(
method, str(wrap_order), str(wrap_order - 1)
)
)
@@ -235,7 +237,7 @@ def interpolateNANs(data, method, order=2, inter_limit=2, downcast_interpolation
elif x.size < 3:
return x
else:
- if downcast_interpolation:
+ if downgrade_interpolation:
return _interpolWrapper(x, int(x.count() - 1), wrap_method)
else:
return x
--
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