Merge branch 'environmentalStuff' into 'develop'

environment docs added - modified env func selection

See merge request !353

saqc/constants.py

@@ -12,45 +12,74 @@ __all__ = [
 
 
 import numpy as np
+import scipy.stats as st
 import saqc.lib.ts_operators as ts_ops
 
-
+#: Internal :py:mod:`flag level constant <saqc.constants>`.
+#: When returned by a test, it indicates, that the test did not consider to flag the respective value
 UNTOUCHED = np.nan
+
+#: A :py:mod:`flag level constant <saqc.constants>`
+#: , evaluating to the level, that indicates, no flag has been assigned to yet.
 UNFLAGGED = -np.inf
+
+#: A :py:mod:`flag level constant <saqc.constants>`
+#: , evaluating to the lowest level level of flagging, that is
+#: not :py:const:`UNFLAGGED <saqc.constants.UNFLAGGED>`.
 GOOD = 0
+
+#: A :py:mod:`flag level constant <saqc.constants>`
+#: , evaluating to a somewhat modest flag level.
 DOUBTFUL = 25.0
+
+#: A :py:mod:`flag level constant <saqc.constants>`
+#: , evaluating to the highest (internal) flag level available.
 BAD = 255.0
 
-# aliases
-DOUBT = DOUBTFUL
+DOUBT = DOUBTFUL  #: Alias for :py:const:`DOUBTFUL <saqc.constants.DOUBTFUL>`.
+
 
 ENVIRONMENT = {
+    # Not A number Constant.
     "NAN": np.nan,
+    # Pointwise absolute Value Function.
     "abs": np.abs,
+    # Maximum Value Function. Ignores NaN.
     "max": np.nanmax,
+    # Minimum Value Function. Ignores NaN.
     "min": np.nanmin,
+    # Mean Value Function. Ignores NaN.
     "mean": np.nanmean,
+    # Summation. Ignores NaN.
     "sum": np.nansum,
-    "std": np.nanstd,
+    # Standart Deviation. Ignores NaN.
     "len": len,
+    # Pointwise Exponential.
     "exp": np.exp,
+    # Pointwise Logarithm.
     "log": np.log,
+    # Logarithm, returning NaN for zero input, instead of -inf.
+    "nanLog": ts_ops.zeroLog,
+    # Standart Deviation. Ignores NaN.
+    "std": np.nanstd,
+    # Variance. Ignores NaN.
     "var": np.nanvar,
+    # Median. Ignores NaN.
     "median": np.nanmedian,
-    "first": ts_ops.first,
-    "last": ts_ops.last,
+    # Count Number of values. Ignores NaNs.
     "count": ts_ops.count,
-    "deltaT": ts_ops.deltaT,
+    # Identity.
     "id": ts_ops.identity,
+    # Returns a Series` diff.
     "diff": ts_ops.difference,
-    "relDiff": ts_ops.relativeDifference,
-    "deriv": ts_ops.derivative,
-    "rateOfChange": ts_ops.rateOfChange,
-    "scale": ts_ops.scale,
-    "normScale": ts_ops.normScale,
-    "meanStandardize": ts_ops.standardizeByMean,
-    "medianStandardize": ts_ops.standardizeByMedian,
-    "zLog": ts_ops.zeroLog,
+    # Scales data to [0,1] Interval.
+    "scale": ts_ops.normScale,
+    # Standardize with Standart Deviation.
+    "zScore": lambda x: st.zscore(x, nan_policy="omit"),
+    # Standardize with Median and MAD.
+    "madScore": ts_ops.standardizeByMedian,
+    # Standardize with Median and inter quantile range.
+    "iqsScore": ts_ops.standardizeByIQR,
     "GOOD": GOOD,
     "BAD": BAD,
     "UNFLAGGED": UNFLAGGED,

saqc/lib/ts_operators.py

@@ -111,7 +111,7 @@ def standardizeByMedian(ts):
 
 
 def standardizeByIQR(ts):
-    # standardization with median and interquartile range
+    # standardization with median and inter quantile range
     return (ts - np.median(ts)) / iqr(ts, nan_policy="omit")