From 1c22331b1c8c89307e80f7cb49b4047e161057f1 Mon Sep 17 00:00:00 2001
From: leufen1 <l.leufen@fz-juelich.de>
Date: Thu, 18 Feb 2021 13:37:24 +0100
Subject: [PATCH] added description, /close #275
---
mlair/helpers/statistics.py | 35 +++++++++++++++++++++++++++++++----
1 file changed, 31 insertions(+), 4 deletions(-)
diff --git a/mlair/helpers/statistics.py b/mlair/helpers/statistics.py
index 0a25644c..ea5a9f05 100644
--- a/mlair/helpers/statistics.py
+++ b/mlair/helpers/statistics.py
@@ -155,20 +155,47 @@ def min_max_apply(data: Data, min: Data, max: Data) -> Data:
def log(data: Data, dim: Union[str, int]) -> Tuple[Data, Dict[(str, Data)]]:
+ """
+ Apply logarithmic transformation (and standarization) to data. This method first uses the logarithm for
+ transformation and second applies the `standardise` method additionally. A logarithmic function numpy's log1p is
+ used (`res = log(1+x)`) instead of the pure logarithm to be applicable to values of 0 too.
+
+ :param data: transform this data
+ :param dim: name (xarray) or axis (pandas) of dimension which should be transformed
+ :return: transformed data, and option dictionary with keys method, mean, and std
+ """
transformed_standardized, opts = standardise(np.log1p(data), dim)
opts.update({"method": "log"})
return transformed_standardized, opts
-def log_apply(data: Data, mean: Data, std: Data) -> Data:
- return standardise_apply(np.log1p(data), mean, std)
-
-
def log_inverse(data: Data, mean: Data, std: Data) -> Data:
+ """
+ Apply inverse log transformation (therefore exponential transformation). Because `log` is using `np.log1p` this
+ method is based on the equivalent method `np.exp1m`. Data are first rescaled using `standardise_inverse` and then
+ given to the exponential function.
+
+ :param data: apply inverse log transformation on this data
+ :param mean: mean of the standarization
+ :param std: std of the standarization
+ :return: inverted data
+ """
data_rescaled = standardise_inverse(data, mean, std)
return np.expm1(data_rescaled)
+def log_apply(data: Data, mean: Data, std: Data) -> Data:
+ """
+ Apply numpy's log1p on given data. Further information can be found in description of `log` method.
+
+ :param data: transform this data
+ :param mean: mean of the standarization
+ :param std: std of the standarization
+ :return: transformed data
+ """
+ return standardise_apply(np.log1p(data), mean, std)
+
+
def mean_squared_error(a, b):
"""Calculate mean squared error."""
return np.square(a - b).mean()
--
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