diff --git a/.gitlab-ci.yml b/.gitlab-ci.yml
index 2f4d6604a73532a9f3df497e53424e34a08defcc..0cdb40fae3fb1a10f93047c2a5a5de695778ecb8 100644
--- a/.gitlab-ci.yml
+++ b/.gitlab-ci.yml
@@ -20,33 +20,27 @@ default:
# test saqc with python 3.7
python37:
stage: test
- except:
- - schedules
image: python:3.7
script:
- - pytest tests/core tests/funcs tests/integration dios/test
- - python -m saqc --config ressources/data/config_ci.csv --data ressources/data/data.csv --outfile /tmp/test.csv
+ - pytest tests/core tests/funcs tests/integration dios/test -Werror
+ - python -m saqc --config ressources/data/config.csv --data ressources/data/data.csv --outfile /tmp/test.csv
# test saqc with python 3.8
python38:
stage: test
- except:
- - schedules
script:
- - pytest tests/core tests/funcs tests/integration dios/test
- - python -m saqc --config ressources/data/config_ci.csv --data ressources/data/data.csv --outfile /tmp/test.csv
+ - pytest tests/core tests/funcs tests/integration dios/test -Werror
+ - python -m saqc --config ressources/data/config.csv --data ressources/data/data.csv --outfile /tmp/test.csv
# test saqc with python 3.9
python39:
stage: test
- except:
- - schedules
image: python:3.9
script:
- - pytest tests/core tests/funcs tests/integration
- - python -m saqc --config ressources/data/config_ci.csv --data ressources/data/data.csv --outfile /tmp/test.csv
+ - pytest tests/core tests/funcs tests/integration dios/test -Werror
+ - python -m saqc --config ressources/data/config.csv --data ressources/data/data.csv --outfile /tmp/test.csv
# check if everthing is properly formatted
@@ -60,12 +54,10 @@ black:
# make (visual) coverage in gitlab merge request diff's
coverage:
stage: test
- except:
- - schedules
allow_failure: true
script:
- pip install pytest-cov coverage
- - pytest --cov=saqc tests/core tests/funcs
+ - pytest --cov=saqc tests/core tests/funcs -Werror
after_script:
- coverage xml
@@ -105,7 +97,7 @@ fuzzy:
only:
- schedules
script:
- - pytest tests/fuzzy
+ - pytest tests/fuzzy -Werror
# test lib saqc
@@ -114,5 +106,5 @@ testLib:
only:
- schedules
script:
- - pytest tests/lib
+ - pytest tests/lib -Werror
diff --git a/README.md b/README.md
index 02262fd5cad0080554a16c15d59c9d418ca7216c..dce9fa2b991795df90649c93e5f6a9abd58b8720 100644
--- a/README.md
+++ b/README.md
@@ -1,61 +1,46 @@
-[](https://git.ufz.de/rdm-software/saqc/-/commits/develop)
-# System for automated Quality Control (SaQC)
-
-Quality Control of numerical data requires a significant amount of
-domain knowledge and practical experience. Finding a robust setup of
-quality tests that identifies as many suspicious values as possible, without
-removing valid data, is usually a time-consuming endeavor,
-even for experts.
-
-SaQC is both, a Python framework and a command line application, that
-addresses the exploratory nature of quality control by offering a
-continuously growing number of quality check routines through a flexible
-and simple configuration system.
+<img src="sphinx-doc/ressources/images/Representative/UFZLogo.jpg" width="400"/>
-Below its user interface, SaQC is highly customizable and extensible.
-A modular structure and well-defined interfaces make it easy to extend
-the system with custom quality checks. Furthermore, even core components like
-the flagging scheme are exchangeable.
+<img src="sphinx-doc/ressources/images/Representative/RDMlogo.jpg" align="right" width="180"/>
-
-## Why?
-During the implementation of data workflows in environmental sciences,
-our experience shows a significant knowledge gap between the people
-collecting data and those responsible for the processing and the
-quality-control of these datasets.
-While the former usually have a solid understanding of the underlying
-physical properties, measurement principles and the resulting errors,
-the latter are mostly software developers with expertise in
-data processing.
+# System for automated Quality Control (SaQC)
-The main objective of SaQC is to bridge this gap by allowing both
-parties to focus on their strengths: The data collector/owner should be
-able to express his/her ideas in an easy way, while the actual
-implementation of the algorithms is left to the respective developers.
+Anomalies and errors are the rule not the exception when working with
+time series data. This is especially true, if such data originates
+from in-situ measurements of environmental properties.
+Almost all applications, however, implicily rely on data, that complies
+with some definition of 'correct'.
+In order to infer reliable data products and tools, there is no alternative
+to quality control. SaQC provides all the building blocks to comfortably
+bridge the gap between 'usually faulty' and 'expected to be corrected' in
+a accessible, consistent, objective and reproducible way.
+For a (continously improving) overview of features, typical usage patterns,
+the specific system components and how to customize `SaQC` to your specific
+needs, please refer to our
+[online documentation](https://rdm-software.pages.ufz.de/saqc/index.html).
-## How?
-`SaQC` is both a command line application controlled by a text based configuration file and a python
-module with a simple API.
+## Installation
-While a good (but still growing) number of predefined and highly configurable
-[functions](docs/FunctionIndex.md) are included and ready to use, SaQC
-additionally ships with a python based
-[extension language](docs/GenericFunctions.md) for quality and general
-purpose data processing.
+SaQC is available on the Python Package Index ([PyPI](https://pypi.org/)) and
+can be installed using [pip](https://pip.pypa.io/en/stable/):
+```sh
+python -m pip install saqc
+```
+For a more detailed installion guide, see the [installation guide](https://rdm-software.pages.ufz.de/saqc/getting_started/InstallationGuide.html).
-For a more specific round trip to some of SaQC's possibilities, we refer to
-our [GettingStarted](docs/GettingStarted.md).
+## Usage
+`SaQC` is both, a command line application controlled by a text based configuration
+and a python module with a simple API.
### SaQC as a command line application
-Most of the magic is controlled by a
-[semicolon-separated text file](saqc/docs/ConfigurationFiles.md) listing the variables of the
-dataset and the routines to inspect, quality control and/or process them.
-The content of such a configuration could look like this:
+The command line application is controlled by a semicolon-separated text
+file listing the variables in the dataset and the routines to inspect,
+quality control and/or process them. The content of such a configuration
+could look like this:
```
varname ; test
@@ -66,8 +51,8 @@ SM2 ; flagMissing()
SM2 ; flagMad(window="30d", z=3.5)
```
-As soon as the basic inputs, a dataset and the configuration file are
-prepared, running SaQC is as simple as:
+As soon as the basic inputs, dataset and configuration file, are
+prepared, `SaQC` is run with:
```sh
saqc \
--config path_to_configuration.txt \
@@ -93,52 +78,6 @@ saqc = (SaQC(data)
data, flags = saqc.getResult()
```
-## Installation
-
-### Python Package Index
-SaQC is available on the Python Package Index ([PyPI](https://pypi.org/)) and
-can be installed using [pip](https://pip.pypa.io/en/stable/):
-```sh
-python -m pip install saqc
-```
-For a more detailed installion guide, see [GettingStarted](docs/GettingStarted.md).
-
-### Anaconda
-Currently we don't provide pre-build conda packages but the installing of `SaQC`
-using the [conda package manager](https://docs.conda.io/en/latest/) is
-straightforward:
-1. Create an anaconda environment including all the necessary dependencies with:
- ```sh
- conda env create -f environment.yml
- ```
-2. Load the freshly created environment with:
- ```sh
- conda activate saqc
- ```
-
-### Manual installation
-
-The latest development version is directly available from the
-[gitlab](https://git.ufz.de/rdm-software/saqc) server of the
-[Helmholtz Center for Environmental Research](https://www.ufz.de/index.php?en=33573).
-More details on how to install using the gitlab server are available
-[here](docs/GettingStarted.md).
-
-### Python version
-The minimum Python version required is 3.7.
-
-## License
-Copyright(c) 2019,
-Helmholtz Centre for Environmental Research - UFZ.
-All rights reserved.
-
-The "System for Automated Quality Control" is free software. You can
-redistribute it and/or modify it under the terms of the GNU General
-Public License as published by the free Software Foundation either
-version 3 of the License, or (at your option) any later version. See the
-[license](LICENSE.txt) for details.
-
-This program is distributed in the hope that it will be useful, but
-WITHOUT ANY WARRANTY; without even the implied warranty of
-MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
-See the GNU General Public License for more details.
+A more detailed description of the Python API is available in the
+[respective section](https://rdm-software.pages.ufz.de/saqc/getting_started/TutorialAPI.html)
+of the documentation.
diff --git a/dios/dios/base.py b/dios/dios/base.py
index 11392b4869beadbe04c0b20703140e7a42bbc556..dfa64defc3a9d6da63a5f930c6d878703f0edcd0 100644
--- a/dios/dios/base.py
+++ b/dios/dios/base.py
@@ -1,4 +1,8 @@
#!/usr/bin/env python
+from __future__ import annotations
+
+from copy import deepcopy, copy as shallowcopy
+from typing import Mapping, Hashable, Any, Sequence, overload
from . import operators as ops
from . import pandas_bridge as pdextra
@@ -23,8 +27,12 @@ __copyright__ = "Copyright 2018, Helmholtz-Zentrum für Umweltforschung GmbH - U
class _DiosBase:
@property
@abstractmethod
- def _constructor(self):
- pass
+ def _constructor(self) -> type[_DiosBase]:
+ raise NotImplementedError
+
+ def _finalize(self, other: _DiosBase):
+ self._attrs = other._attrs
+ return self
def __init__(
self,
@@ -36,8 +44,8 @@ class _DiosBase:
fastpath=False,
):
- # set via property
- self.cast_policy = cast_policy
+ self._attrs = {}
+ self.cast_policy = cast_policy # set via property
# we are called internally
if fastpath:
@@ -98,6 +106,9 @@ class _DiosBase:
if pdextra.is_iterator(data):
data = list(data)
+ if _is_dios_like(data) and not data.columns.is_unique:
+ raise ValueError("columns index must have unique values")
+
if _is_dios_like(data) or isinstance(data, dict):
if columns is None:
pass # data is dict-like
@@ -128,7 +139,8 @@ class _DiosBase:
raise TypeError("data type not understood")
for k in data:
- self._insert(k, pd.Series(data[k], index=index))
+ s = pd.Series(data[k], index=index, dtype=object).infer_objects()
+ self._insert(k, s)
# ----------------------------------------------------------------------
# checks
@@ -173,6 +185,22 @@ class _DiosBase:
val.name = col
self._data.at[col] = val.copy(deep=True)
+ @overload
+ def __getitem__(self, key: str | int) -> pd.Series:
+ ...
+
+ @overload
+ def __getitem__(self, key: slice) -> pd.Series:
+ ...
+
+ @overload
+ def __getitem__(self, key: "_DiosBase" | pd.DataFrame) -> "_DiosBase":
+ ...
+
+ @overload
+ def __getitem__(self, key: Sequence[str | int]) -> "_DiosBase":
+ ...
+
def __getitem__(self, key):
"""dios[key] -> dios/series"""
# scalar -> select a column
@@ -202,7 +230,7 @@ class _DiosBase:
if self._is_valid_columns_index(data):
return self._constructor(
data=data, itype=self.itype, cast_policy=self.cast_policy, fastpath=True
- )
+ )._finalize(self)
raise TypeError(f"cannot index columns with this type, {type(key)}")
@@ -331,6 +359,19 @@ class _DiosBase:
# ------------------------------------------------------------------------------
# Base properties and basic dunder magic
+ @property
+ def attrs(self) -> dict[Hashable, Any]:
+ """
+ Dictionary of global attributes of this dataset.
+ """
+ if self._attrs is None:
+ self._attrs = {}
+ return self._attrs
+
+ @attrs.setter
+ def attrs(self, value: Mapping[Hashable, Any]) -> None:
+ self._attrs = dict(value)
+
@property
def columns(self):
"""The column labels of the DictOfSeries"""
@@ -452,7 +493,7 @@ class _DiosBase:
return self.copy(deep=True)
def __copy__(self):
- return self.copy(deep=True)
+ return self.copy(deep=False)
def copy(self, deep=True):
"""Make a copy of this DictOfSeries' indices and data.
@@ -471,15 +512,20 @@ class _DiosBase:
--------
pandas.DataFrame.copy
"""
- data = self._data.copy()
+ data = self._data.copy() # always copy the outer hull series
if deep:
for c, series in self.items():
data.at[c] = series.copy()
- return self._constructor(
+ new = self._constructor(
data=data, itype=self.itype, cast_policy=self.cast_policy, fastpath=True
)
+ copyfunc = deepcopy if deep else shallowcopy
+ new._attrs = copyfunc(self._attrs)
+
+ return new
+
def copy_empty(self, columns=True):
"""
Return a new DictOfSeries object, with same properties than the original.
@@ -530,7 +576,7 @@ class _DiosBase:
return self._constructor(
data=data, itype=self.itype, cast_policy=self.cast_policy, fastpath=True
- )
+ )._finalize(self)
# ------------------------------------------------------------------------------
# Operators
@@ -544,7 +590,11 @@ class _DiosBase:
raise type(e)(f"'{ops.OP_MAP[op]} dios' failed: " + str(e)) from e
return new
- def _op2(self, op, other, align=True, inplace=False):
+ def _op2_inplace(self, op, other, align=True) -> None:
+ new = self._op2(op, other, align)
+ self._data = new._data
+
+ def _op2(self, op, other, align=True) -> "_DiosBase":
def raiseif(kself, kother, s):
if kself != kother:
raise ValueError(
@@ -583,10 +633,6 @@ class _DiosBase:
except Exception as e:
raise type(e)(f"'dios {ops.OP_MAP[op]} other' failed: " + str(e)) from e
- if inplace:
- self._data = new._data
- return None
-
return new
# unary
@@ -608,13 +654,13 @@ class _DiosBase:
__truediv__ = ftools.partialmethod(_op2, op.truediv)
__floordiv__ = ftools.partialmethod(_op2, op.floordiv)
__pow__ = ftools.partialmethod(_op2, op.pow)
- __iadd__ = ftools.partialmethod(_op2, op.add, inplace=True)
- __isub__ = ftools.partialmethod(_op2, op.sub, inplace=True)
- __imul__ = ftools.partialmethod(_op2, op.mul, inplace=True)
- __imod__ = ftools.partialmethod(_op2, op.mod, inplace=True)
- __itruediv__ = ftools.partialmethod(_op2, op.truediv, inplace=True)
- __ifloordiv__ = ftools.partialmethod(_op2, op.floordiv, inplace=True)
- __ipow__ = ftools.partialmethod(_op2, op.pow, inplace=True)
+ __iadd__ = ftools.partialmethod(_op2_inplace, op.add)
+ __isub__ = ftools.partialmethod(_op2_inplace, op.sub)
+ __imul__ = ftools.partialmethod(_op2_inplace, op.mul)
+ __imod__ = ftools.partialmethod(_op2_inplace, op.mod)
+ __itruediv__ = ftools.partialmethod(_op2_inplace, op.truediv)
+ __ifloordiv__ = ftools.partialmethod(_op2_inplace, op.floordiv)
+ __ipow__ = ftools.partialmethod(_op2_inplace, op.pow)
# bool
__and__ = ftools.partialmethod(_op2, op.and_)
__or__ = ftools.partialmethod(_op2, op.or_)
diff --git a/dios/dios/dios.py b/dios/dios/dios.py
index 2f908e8f280c2550056829f7283927f4d021cd70..da9f4ca1993a30630cb032a3a12ff410c43e797d 100644
--- a/dios/dios/dios.py
+++ b/dios/dios/dios.py
@@ -1,3 +1,6 @@
+from __future__ import annotations
+from typing import Any, Mapping, Hashable
+
from .base import _DiosBase, _is_dios_like
from .lib import Opts, OptsFields, dios_options
from .lib import _find_least_common_itype
@@ -42,26 +45,8 @@ class DictOfSeries(_DiosBase):
the ``itype``.
"""
- def __init__(
- self,
- data=None,
- columns=None,
- index=None,
- itype=None,
- cast_policy="save",
- fastpath=False,
- ):
- super().__init__(
- data=data,
- columns=columns,
- index=index,
- itype=itype,
- cast_policy=cast_policy,
- fastpath=fastpath,
- )
-
@property
- def _constructor(self):
+ def _constructor(self) -> type[DictOfSeries]:
"""Return the class. Useful for construction in the elder class.
A import of DictOfSeries would end up cyclic."""
return DictOfSeries
@@ -69,7 +54,7 @@ class DictOfSeries(_DiosBase):
def _construct_like_self(self, **kwargs):
kwargs.setdefault("itype", self.itype)
kwargs.setdefault("cast_policy", self.cast_policy)
- return self._constructor(**kwargs)
+ return self._constructor(**kwargs)._finalize(self)
@property
def indexes(self):
@@ -223,7 +208,9 @@ class DictOfSeries(_DiosBase):
yield from df.iterrows()
else:
for idx, row in df.iterrows():
- yield idx, self._constructor(data=row.to_dict(), index=[idx])
+ yield idx, self._constructor(
+ data=row.to_dict(), index=[idx]
+ )._finalize(self)
# ------------------------------------------------------------------------------
# Broadcasting and Reducing
@@ -460,6 +447,8 @@ class DictOfSeries(_DiosBase):
result.at[c] = pd.Series(val)
itype = _find_least_common_itype(result)
result = self._constructor(data=result, itype=itype, fastpath=True)
+ result._finalize(self)
+
return result
def reduce_columns(self, func, initial=None, skipna=False):
@@ -822,6 +811,8 @@ class DictOfSeries(_DiosBase):
# was set before. Missing locations are already
# nans, present locations are set.
df[c] = series.copy()
+
+ df.attrs = self.attrs
return df
@property
diff --git a/dios/dios/indexer.py b/dios/dios/indexer.py
index 8f325b282acb458f66a0170231f983ab905f009e..10ccbd304a918be0925f53956eb1bb7a34da22df 100644
--- a/dios/dios/indexer.py
+++ b/dios/dios/indexer.py
@@ -258,7 +258,9 @@ class _aLocIndexer(_Indexer):
if lowdim:
return data.squeeze()
else:
- return self.obj._constructor(data=data, fastpath=True, **kws)
+ return self.obj._constructor(data=data, fastpath=True, **kws)._finalize(
+ self.obj
+ )
def __setitem__(self, key, value):
rowkeys, colkeys, _ = self._unpack_key_aloc(key)
diff --git a/dios/test/test__ops__.py b/dios/test/test__ops__.py
index ef2d3074729c464fdbd43d2ad78965280f9b47ea..2ee9f579b86fa1d8257636d673a46d45790f336b 100644
--- a/dios/test/test__ops__.py
+++ b/dios/test/test__ops__.py
@@ -1,4 +1,6 @@
#!/usr/bin/env python
+import pytest
+
from .test_setup import *
@@ -21,6 +23,8 @@ def test__eq__(left, right):
assert res == exp
+@pytest.mark.filterwarnings("ignore: invalid value encountered in long_scalars")
+@pytest.mark.filterwarnings("ignore: divide by zero encountered in long_scalars")
@pytest.mark.parametrize("left", diosFromMatr(DATA_ALIGNED))
@pytest.mark.parametrize("right", diosFromMatr(DATA_ALIGNED))
@pytest.mark.parametrize("op", OP2)
@@ -39,6 +43,8 @@ def test__op2__aligningops(left, right, op):
assert res == exp
+@pytest.mark.filterwarnings("ignore: invalid value encountered in long_scalars")
+@pytest.mark.filterwarnings("ignore: divide by zero encountered in long_scalars")
@pytest.mark.parametrize("left", diosFromMatr(DATA_UNALIGNED))
@pytest.mark.parametrize("right", diosFromMatr(DATA_UNALIGNED))
@pytest.mark.parametrize("op", OPNOCOMP)
diff --git a/dios/test/test__setget__aloc.py b/dios/test/test__setget__aloc.py
index 3c76c82450ef9ba606b5434dc8fcb6915e2571d3..2c47ec19e0502c89bfb7f5c910df44e46e0f26a8 100644
--- a/dios/test/test__setget__aloc.py
+++ b/dios/test/test__setget__aloc.py
@@ -4,7 +4,9 @@ from pandas.core.dtypes.common import is_scalar
pytestmark = pytest.mark.skip
-@pytest.mark.parametrize(("idxer", "exp"), [("a", s1), ("c", s3), ("x", pd.Series())])
+@pytest.mark.parametrize(
+ ("idxer", "exp"), [("a", s1), ("c", s3), ("x", pd.Series(dtype=float))]
+)
def test__getitem_aloc_singleCol(dios_aligned, idxer, exp):
di = dios_aligned.aloc[:, idxer]
assert isinstance(di, pd.Series)
diff --git a/dios/test/test_setup.py b/dios/test/test_setup.py
index 2bd04548693cafd10eeb0d3fff0a1656871e2731..0a59193bfe53689dbe5645bd74268d7cf9f381de 100644
--- a/dios/test/test_setup.py
+++ b/dios/test/test_setup.py
@@ -24,10 +24,10 @@ except ModuleNotFoundError:
import pandas as pd
import numpy as np
-a = pd.Series(range(0, 70, 7))
-b = pd.Series(range(5, 15, 1))
-c = pd.Series(range(7, 107, 10))
-d = pd.Series(range(0, 10, 1))
+a = pd.Series(range(0, 70, 7), dtype=int)
+b = pd.Series(range(5, 15, 1), dtype=int)
+c = pd.Series(range(7, 107, 10), dtype=int)
+d = pd.Series(range(0, 10, 1), dtype=int)
s1, s2, s3, s4 = a, b, c, d
@@ -330,8 +330,7 @@ OP1 = list(_OP1_MAP)
def diosFromMatr(mlist):
l = []
for m in mlist:
- m = np.array(m)
- l.append(DictOfSeries(m.copy()))
+ l.append(DictOfSeries({i: li.copy() for i, li in enumerate(m)}))
return tuple(l)
diff --git a/environment.yml b/environment.yml
deleted file mode 100644
index aabe58efcf1dab30195217d3e75e53abe969cf63..0000000000000000000000000000000000000000
--- a/environment.yml
+++ /dev/null
@@ -1,19 +0,0 @@
-name: saqc
-channels:
- - defaults
-dependencies:
- - numpy
- - pandas
- - scipy
- - matplotlib
- - numba
- - click
- - scikit-learn
- - pyarrow
- - PyWavelets
- - pip
- - pip:
- - python-intervals
- - dtw
- - mlxtend
- - outlier-utils
diff --git a/requirements.txt b/requirements.txt
index f1f8206244a79cf318b3a9dbc50407362262ae3d..1f9510979613e3a9e7a33d24e12ed37b54412251 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -2,7 +2,6 @@ Click==8.0.1
dtw==1.4.0
hypothesis==6.23.1
matplotlib==3.4.3
-mlxtend==0.19.0
numba==0.54.0
numpy==1.20.3
outlier-utils==0.0.3
@@ -10,7 +9,7 @@ pyarrow==4.0.1
pandas==1.3.3
pytest==6.2.5
pytest-lazy-fixture==0.6.3
-PyWavelets==1.1.1
scikit-learn==1.0
scipy==1.7.1
typing_extensions==3.10.0.2
+seaborn==0.11.2
diff --git a/ressources/data/config.csv b/ressources/data/config.csv
index e23612fb9682a0a5e9344967f2b15f65f6d64ab3..1f11becdb3eada375a1b1ef8fa1ffd9917abbcc3 100644
--- a/ressources/data/config.csv
+++ b/ressources/data/config.csv
@@ -1,6 +1,8 @@
varname ; test
-#----------;---------------------------------------
+#----------; -----------------------------------------------------
SM2 ; shift(freq="15Min")
-SM2 ; flagMissing()
-'SM(1|2)+' ; flagRange(min=10, max=60)
+'SM(1|2)+' ; flagMissing()
+SM1 ; flagRange(min=10, max=60)
+SM2 ; flagRange(min=10, max=40)
SM2 ; flagMAD(window="30d", z=3.5)
+Dummy ; flagGeneric(field=["SM1", "SM2"], func=(isflagged(x) | isflagged(y)))
diff --git a/ressources/data/config_ci.csv b/ressources/data/config_ci.csv
deleted file mode 100644
index e5b883102b8a541fc9fc89e6e1ffb7c8f4e1ca55..0000000000000000000000000000000000000000
--- a/ressources/data/config_ci.csv
+++ /dev/null
@@ -1,8 +0,0 @@
-varname ; test
-#-------; -----------------------------------------------------
-SM2 ; shift(freq="15Min")
-'.*' ; flagRange(min=10, max=60)
-SM2 ; flagMissing()
-SM2 ; flagRange(min=10, max=60)
-SM2 ; flagMAD(window="30d", z=3.5)
-Dummy ; genericFlag(func=(isflagged(SM1) | isflagged(SM2)))
diff --git a/saqc/__init__.py b/saqc/__init__.py
index 34f673b943c96f4b085e3f7af3c7d2f8aa4c7921..7bd3bb9011a94d76edd502681c34855fee6c9d42 100644
--- a/saqc/__init__.py
+++ b/saqc/__init__.py
@@ -1,19 +1,19 @@
#! /usr/bin/env python
# -*- coding: utf-8 -*-
+"""The system for automated quality controll package."""
__version__ = "1.4"
# import order: from small to big
-from saqc.constants import *
+from saqc.constants import (
+ UNFLAGGED,
+ GOOD,
+ DOUBTFUL,
+ BAD,
+)
+
from saqc.core import (
- flagging,
- processing,
- initFlagsLike,
Flags,
- FloatTranslator,
- DmpTranslator,
- PositionalTranslator,
- SimpleTranslator,
SaQC,
fromConfig,
)
diff --git a/saqc/__main__.py b/saqc/__main__.py
index fbfa1a706ab2b7b4cbecbb4bb2e4c6dd397ffaca..1326bd0be4a96628804a86b826963aad6e7297a1 100644
--- a/saqc/__main__.py
+++ b/saqc/__main__.py
@@ -11,26 +11,13 @@ import numpy as np
import pandas as pd
import pyarrow as pa
-from saqc.core import (
- fromConfig,
- FloatTranslator,
- DmpTranslator,
- PositionalTranslator,
- SimpleTranslator,
-)
+from saqc.core.reader import fromConfig
+from saqc.core.core import TRANSLATION_SCHEMES
logger = logging.getLogger("SaQC")
-SCHEMES = {
- "simple": SimpleTranslator,
- "float": FloatTranslator,
- "positional": PositionalTranslator,
- "dmp": DmpTranslator,
-}
-
-
def _setupLogging(loglvl):
logger.setLevel(loglvl)
handler = logging.StreamHandler()
@@ -86,6 +73,7 @@ def writeData(writer_dict, df, fname):
"-d",
"--data",
type=click.Path(exists=True),
+ multiple=True,
required=True,
help="path to the data file",
)
@@ -95,7 +83,7 @@ def writeData(writer_dict, df, fname):
@click.option(
"--scheme",
default=None,
- type=click.Choice(SCHEMES.keys()),
+ type=click.Choice(tuple(TRANSLATION_SCHEMES.keys())),
help="the flagging scheme to use",
)
@click.option("--nodata", default=np.nan, help="nodata value")
@@ -106,19 +94,24 @@ def writeData(writer_dict, df, fname):
help="set output verbosity",
)
def main(config, data, scheme, outfile, nodata, log_level):
+ # data is always a list of data files
_setupLogging(log_level)
reader, writer = setupIO(nodata)
- data = readData(reader, data)
+ _data = []
+ for dfile in data:
+ df = readData(reader, dfile)
+ _data.append(df)
+ data = _data
saqc = fromConfig(
config,
data=data,
- scheme=SCHEMES[scheme or "simple"](),
+ scheme=TRANSLATION_SCHEMES[scheme or "simple"](),
)
- data_result, flags_result = saqc.getResult()
+ data_result, flags_result = saqc.result.data, saqc.result.flags
if outfile:
diff --git a/saqc/constants.py b/saqc/constants.py
index 7d4cb5f621c4eb0509325e09fcc0ae4ff9448de5..781de815580c477a125a5bacb6b4f10af93fac68 100644
--- a/saqc/constants.py
+++ b/saqc/constants.py
@@ -1,21 +1,110 @@
#!/usr/bin/env python
+"""
+The module comprises flag value constants in use throughout saqc.
+The constants order as follows (from "worse" to "best"):
+
+:py:const:`~saqc.constants.BAD` > :py:const:`~saqc.constants.DOUBTFUL` > :py:const:`~saqc.constants.GOOD` >
+:py:const:`~saqc.constants.UNFLAGGED`
+"""
__all__ = [
- "UNTOUCHED",
"UNFLAGGED",
"DOUBTFUL",
"BAD",
"GOOD",
- "DOUBT",
+ "ENVIRONMENT",
+ "FILTER_ALL",
+ "FILTER_NONE",
]
+
import numpy as np
+import scipy.stats as st
+import saqc.lib.ts_operators as ts_ops
+
+# ----------------------------------------------------------------------
+# global flag constants
+# ----------------------------------------------------------------------
-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
+
+# ----------------------------------------------------------------------
+# global dfilter constants
+# ----------------------------------------------------------------------
+
+#: A :py:mod:`dfilter constant <saqc.constants>`
+#: , mask/filter all flagged data.
+FILTER_ALL = -np.inf
+
+#: A :py:mod:`dfilter constant <saqc.constants>`
+#: , mask/filter no data at all.
+FILTER_NONE = np.inf
+
+
+# ----------------------------------------------------------------------
+# other
+# ----------------------------------------------------------------------
+
+#: A :py:mod:`flag level constant <saqc.constants>`
+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,
+ # 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,
+ # Count Number of values. Ignores NaNs.
+ "count": ts_ops.count,
+ # Identity.
+ "id": ts_ops.identity,
+ # Returns a Series` diff.
+ "diff": ts_ops.difference,
+ # 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,
+ "DOUBTFUL": DOUBTFUL,
+}
diff --git a/saqc/core/__init__.py b/saqc/core/__init__.py
index efb119fc34739b57589eccc03a2ea8e530f87642..cf86db46d2572cdca0c32b7ae2bc098bfa52ddef 100644
--- a/saqc/core/__init__.py
+++ b/saqc/core/__init__.py
@@ -1,13 +1,13 @@
#! /usr/bin/env python
# -*- coding: utf-8 -*-
-from saqc.core.register import flagging, processing
+from saqc.core.register import register, flagging, processing
from saqc.core.flags import Flags, initFlagsLike
from saqc.core.core import SaQC
-from saqc.core.translator import (
- FloatTranslator,
- DmpTranslator,
- PositionalTranslator,
- SimpleTranslator,
+from saqc.core.translation import (
+ FloatScheme,
+ DmpScheme,
+ PositionalScheme,
+ SimpleScheme,
)
from saqc.core.reader import fromConfig
diff --git a/saqc/core/core.py b/saqc/core/core.py
index 6b729178193a6193d11365db365f8a57c0ed6296..b7bb6050b0069252e7c63958e2d57bc744e1820f 100644
--- a/saqc/core/core.py
+++ b/saqc/core/core.py
@@ -2,253 +2,233 @@
# -*- coding: utf-8 -*-
from __future__ import annotations
-import inspect
import warnings
-import copy as stdcopy
-from typing import Any, Callable, Tuple, Union, Optional
+from typing import (
+ Any,
+ Callable,
+ List,
+ Sequence,
+ Hashable,
+ Tuple,
+)
+from copy import deepcopy, copy as shallowcopy
import pandas as pd
import numpy as np
from dios import DictOfSeries, to_dios
+from saqc.constants import BAD
-from saqc.core.flags import initFlagsLike, Flags
-from saqc.core.register import FUNC_MAP
from saqc.core.modules import FunctionsMixin
-from saqc.core.translator.basetranslator import Translator, FloatTranslator
-from saqc.lib.tools import toSequence
-from saqc.lib.types import (
- ExternalFlag,
- PandasLike,
+from saqc.core.flags import initFlagsLike, Flags
+from saqc.core.history import History
+from saqc.core.register import FUNC_MAP, FunctionWrapper
+from saqc.core.translation import (
+ TranslationScheme,
+ FloatScheme,
+ SimpleScheme,
+ PositionalScheme,
+ DmpScheme,
)
+from saqc.lib.tools import toSequence, concatDios
+from saqc.lib.types import ExternalFlag, OptionalNone
+# the import is needed to trigger the registration
+# of the built-in (test-)functions
+import saqc.funcs # noqa
-# TODO: shouldn't the code/function go to SaQC.__init__ ?
-def _prepInput(
- data: PandasLike, flags: Optional[Union[DictOfSeries, pd.DataFrame, Flags]]
-) -> Tuple[DictOfSeries, Optional[Flags]]:
- dios_like = (DictOfSeries, pd.DataFrame)
-
- data = stdcopy.deepcopy(data)
-
- if isinstance(data, pd.Series):
- data = data.to_frame()
-
- if not isinstance(data, dios_like):
- raise TypeError(
- "'data' must be of type pd.Series, pd.DataFrame or dios.DictOfSeries"
- )
-
- if isinstance(data, pd.DataFrame):
- if isinstance(data.index, pd.MultiIndex) or isinstance(
- data.columns, pd.MultiIndex
- ):
- raise TypeError("'data' should not use MultiIndex")
- data = to_dios(data)
-
- if not hasattr(data.columns, "str"):
- raise TypeError("expected dataframe columns of type string")
-
- if flags is not None:
-
- if isinstance(flags, pd.DataFrame):
- if isinstance(flags.index, pd.MultiIndex) or isinstance(
- flags.columns, pd.MultiIndex
- ):
- raise TypeError("'flags' should not use MultiIndex")
-
- if isinstance(flags, (DictOfSeries, pd.DataFrame, Flags)):
- # NOTE: only test common columns, data as well as flags could
- # have more columns than the respective other.
- cols = flags.columns.intersection(data.columns)
- for c in cols:
- if not flags[c].index.equals(data[c].index):
- raise ValueError(
- f"the index of 'flags' and 'data' missmatch in column {c}"
- )
-
- # this also ensures float dtype
- if not isinstance(flags, Flags):
- flags = Flags(flags, copy=True)
-
- return data, flags
-
-
-def _setup():
- # NOTE:
- # the import is needed to trigger the registration
- # of the built-in (test-)functions
- import saqc.funcs
-
- # warnings
- pd.set_option("mode.chained_assignment", "warn")
- np.seterr(invalid="ignore")
+# warnings
+pd.set_option("mode.chained_assignment", "warn")
+np.seterr(invalid="ignore")
-_setup()
-
-
-class Accessor:
- def __init__(self, obj: Union[DictOfSeries, pd.DataFrame, Flags]):
- self._obj = obj
-
- def __getitem__(self, key):
- return self._obj[key]
-
- @property
- def columns(self):
- return self._obj.columns
-
- def __len__(self):
- return len(self.columns)
-
- def __repr__(self):
- return self._obj.__repr__()
+TRANSLATION_SCHEMES = {
+ "float": FloatScheme,
+ "simple": SimpleScheme,
+ "dmp": DmpScheme,
+ "positional": PositionalScheme,
+}
class SaQC(FunctionsMixin):
+ _attributes = {
+ "_data",
+ "_flags",
+ "_scheme",
+ "_attrs",
+ "_called",
+ }
+
def __init__(
self,
- data,
+ data=None,
flags=None,
- scheme: Translator = None,
+ scheme: str | TranslationScheme = "float",
+ copy: bool = True,
):
- data, flags = _prepInput(data, flags)
- self._data = data
- self._flags = self._initFlags(data, flags)
- self._translator = scheme or FloatTranslator()
- self.called = []
-
- @staticmethod
- def _initFlags(data: DictOfSeries, flags: Optional[Flags]) -> Flags:
- """
- Init the internal Flags-object.
-
- Ensures that all data columns are present and user passed
- flags from a frame or an already initialised Flags-object
- are used.
- """
- if flags is None:
- return initFlagsLike(data)
-
- # add columns that are present in data but not in flags
- for c in data.columns.difference(flags.columns):
- flags[c] = initFlagsLike(data[c])
-
- return flags
-
- def _construct(self, **injectables) -> SaQC:
+ self._data = self._initData(data, copy)
+ self._flags = self._initFlags(flags, copy)
+ self._scheme = self._initTranslationScheme(scheme)
+ self._called = []
+ self._attrs = {}
+ self._validate(reason="init")
+
+ def _construct(self, **attributes) -> SaQC:
"""
Construct a new `SaQC`-Object from `self` and optionally inject
attributes with any chechking and overhead.
Parameters
----------
- **injectables: any of the `SaQC` data attributes with name and value
+ **attributes: any of the `SaQC` data attributes with name and value
Note
----
For internal usage only! Setting values through `injectables` has
the potential to mess up certain invariants of the constructed object.
"""
- out = SaQC(
- data=DictOfSeries(),
- flags=Flags(),
- scheme=self._translator,
- )
- for k, v in injectables.items():
- if not hasattr(out, k):
- raise AttributeError(f"failed to set unknown attribute: {k}")
+ out = SaQC(data=DictOfSeries(), flags=Flags(), scheme=self._scheme)
+ out.attrs = self._attrs
+ for k, v in attributes.items():
+ if k not in self._attributes:
+ raise AttributeError(f"SaQC has no attribute {repr(k)}")
setattr(out, k, v)
return out
+ def _validate(self, reason=None):
+ if not self._data.columns.equals(self._flags.columns):
+ msg = "Consistency broken. data and flags have not the same columns."
+ if reason:
+ msg += f" This was most likely caused by: {reason}"
+ raise RuntimeError(msg)
+
@property
- def data(self) -> Accessor:
- return Accessor(self._data)
+ def attrs(self) -> dict[Hashable, Any]:
+ """
+ Dictionary of global attributes of this dataset.
+ """
+ return self._attrs
+
+ @attrs.setter
+ def attrs(self, value: dict[Hashable, Any]) -> None:
+ self._attrs = dict(value)
@property
- def flags(self) -> Accessor:
- return Accessor(self._translator.backward(self._flags))
+ def data_raw(self) -> DictOfSeries:
+ return self._data
- def getResult(
- self, raw=False
- ) -> Union[Tuple[DictOfSeries, Flags], Tuple[pd.DataFrame, pd.DataFrame]]:
- """
- Realize the registered calculations and return the results
+ @property
+ def flags_raw(self) -> Flags:
+ return self._flags
- Returns
- -------
- data, flags: (DictOfSeries, DictOfSeries)
- """
+ @property
+ def data(self) -> pd.DataFrame:
+ data: pd.DataFrame = self._data.to_df()
+ data.attrs = self._attrs.copy()
+ return data
- data, flags = self._data, self._flags
+ @property
+ def flags(self) -> pd.DataFrame:
+ data: pd.DataFrame = self._scheme.backward(self._flags, attrs=self._attrs)
+ data.attrs = self._attrs.copy()
+ return data
- if raw:
- return data, flags
+ @property
+ def result(self) -> SaQCResult:
+ return SaQCResult(self._data, self._flags, self._attrs, self._scheme)
- return data.to_df(), self._translator.backward(flags)
+ def _expandFields(
+ self,
+ regex: bool,
+ multivariate: bool,
+ field: str | Sequence[str],
+ target: str | Sequence[str] = None,
+ ) -> Tuple[List[str], List[str]]:
+ """
+ check and expand `field` and `target`
+ """
- def _wrap(self, func: Callable):
- """Enrich a function by special saqc-functionality.
+ if regex and target is not None:
+ raise NotImplementedError(
+ "explicit `target` not supported with regular expressions"
+ )
+ # expand regular expressions
+ if regex:
+ fmask = self._data.columns.str.match(field)
+ fields = self._data.columns[fmask].tolist()
+ else:
+ fields = toSequence(field)
+
+ targets = fields if target is None else toSequence(target)
+
+ if multivariate:
+ # wrap again to generalize the down stream loop
+ fields, targets = [fields], [targets]
+ else:
+ if len(fields) != len(targets):
+ raise ValueError(
+ "expected the same number of 'field' and 'target' values"
+ )
+ return fields, targets
- For each saqc function this realize
- - the source-target workflow,
- - regex's in field,
- - use default of translator for ``to_mask`` if not specified by user,
- - translation of ``flag`` and
- - working inplace.
- Therefore it adds the following keywords to each saqc function:
- ``target``, ``regex`` and ``inplace``.
+ def _wrap(self, func: FunctionWrapper):
+ """
+ prepare user function input:
+ - expand fields and targets
+ - translate user given ``flag`` values or set the default ``BAD``
+ - translate user given ``dfilter`` values or set the scheme default
+ - dependeing on the workflow: initialize ``target`` variables
+
+ Here we add the following parameters to all registered functions, regardless
+ of their repsective definition:
+ - ``regex``
+ - ``target``
- The returned function returns a Saqc object.
"""
def inner(
- field: str,
+ field: str | Sequence[str],
*args,
- target: str = None,
+ target: str | Sequence[str] = None,
regex: bool = False,
- flag: ExternalFlag = None,
+ flag: ExternalFlag | OptionalNone = OptionalNone(),
**kwargs,
) -> SaQC:
- if regex and target is not None:
- raise ValueError("explicit `target` not supported with `regex=True`")
-
- kwargs.setdefault("to_mask", self._translator.TO_MASK)
+ kwargs.setdefault("dfilter", self._scheme.DFILTER_DEFAULT)
- # translation
- if flag is not None:
- kwargs["flag"] = self._translator(flag)
+ if not isinstance(flag, OptionalNone):
+ # translation schemes might want to use a flag,
+ # None so we introduce a special class here
+ kwargs["flag"] = self._scheme(flag)
- # expand regular expressions
- if regex:
- fields = self._data.columns.str.match(field)
- fields = self._data.columns[fields]
- targets = fields
- else:
- fields, targets = toSequence(field), toSequence(target, default=field)
+ fields, targets = self._expandFields(
+ regex=regex, multivariate=func.multivariate, field=field, target=target
+ )
out = self
for field, target in zip(fields, targets):
- if field != target:
- out = out._callFunction(
- FUNC_MAP["copyField"],
- data=out._data,
- flags=out._flags,
- field=field,
- new_field=target,
- )
- field = target
+
+ fkwargs = {
+ **kwargs,
+ "field": field,
+ "target": target,
+ }
+
+ if not func.handles_target and field != target:
+ if target not in self.data.columns:
+ out = out._callFunction(
+ FUNC_MAP["copyField"],
+ *args,
+ **fkwargs,
+ )
+ fkwargs["field"] = fkwargs.pop("target")
out = out._callFunction(
func,
- data=out._data,
- flags=out._flags,
- field=field,
*args,
- **kwargs,
+ **fkwargs,
)
return out
@@ -257,72 +237,223 @@ class SaQC(FunctionsMixin):
def _callFunction(
self,
function: Callable,
- data: DictOfSeries,
- flags: Flags,
- field: str,
+ field: str | Sequence[str],
*args: Any,
**kwargs: Any,
) -> SaQC:
- assert data.columns.difference(flags.columns).empty
+ res = function(data=self._data, flags=self._flags, field=field, *args, **kwargs)
- data, flags = function(data=data, flags=flags, field=field, *args, **kwargs)
- # we check the passed function-kwargs after the actual call,
- # because now "hard" errors would already have been raised
- # (eg. `TypeError: got multiple values for argument 'data'`,
- # when the user pass data=...)
- _warnForUnusedKwargs(function, kwargs, self._translator)
+ # keep consistence: if we modify data and flags inplace in a function,
+ # but data is the original and flags is a copy (as currently implemented),
+ # data and flags of the original saqc obj may change inconsistently.
+ self._data, self._flags = res
+ self._called += [(field, (function, args, kwargs))]
+ self._validate(reason=f"call to {repr(function.__name__)}")
- planned = self.called + [(field, (function, args, kwargs))]
-
- return self._construct(_data=data, _flags=flags, called=planned)
+ return self._construct(
+ _data=self._data, _flags=self._flags, _called=self._called
+ )
def __getattr__(self, key):
"""
- All failing attribute accesses are redirected to
- __getattr__. We use this mechanism to make the
- registered functions as `SaQC`-methods without
- actually implementing them.
+ All failing attribute accesses are redirected to __getattr__.
+ We use this mechanism to make the registered functions appear
+ as `SaQC`-methods without actually implementing them.
"""
if key not in FUNC_MAP:
- raise AttributeError(f"no such attribute: '{key}'")
+ raise AttributeError(f"SaQC has no attribute {repr(key)}")
return self._wrap(FUNC_MAP[key])
def copy(self, deep=True):
- if deep:
- return stdcopy.deepcopy(self)
- return stdcopy.copy(self)
-
-
-def _warnForUnusedKwargs(func, keywords, translator: Translator):
- """Warn for unused kwargs, passed to a SaQC.function.
-
- Parameters
- ----------
- func: SaqcFunction
- Saqc internal data structure that hold all function info.
-
- Returns
- -------
- None
-
- Notes
- -----
- A single warning is thrown, if any number of missing kws are detected, naming each missing kw.
- """
- sig_kws = inspect.signature(func).parameters
-
- # we need to ignore kws that are injected or by default hidden in ``**kwargs``
- ignore = ("to_mask",)
-
- missing = []
- for kw in keywords:
- # there is no need to check for
- # `kw in [KEYWORD_ONLY, VAR_KEYWORD or POSITIONAL_OR_KEYWORD]`
- # because this would have raised an error beforehand.
- if kw not in sig_kws and kw not in ignore and kw not in translator.ARGUMENTS:
- missing.append(kw)
-
- if missing:
- missing = ", ".join(missing)
- warnings.warn(f"Unused argument(s): {missing}")
+ copyfunc = deepcopy if deep else shallowcopy
+ new = self._construct()
+ for attr in self._attributes:
+ setattr(new, attr, copyfunc(getattr(self, attr)))
+ return new
+
+ def __copy__(self):
+ return self.copy(deep=False)
+
+ def __deepcopy__(self, memodict=None):
+ return self.copy(deep=True)
+
+ def _initTranslationScheme(
+ self, scheme: str | TranslationScheme
+ ) -> TranslationScheme:
+ if isinstance(scheme, str) and scheme in TRANSLATION_SCHEMES:
+ return TRANSLATION_SCHEMES[scheme]()
+ if isinstance(scheme, TranslationScheme):
+ return scheme
+ raise TypeError(
+ f"expected one of the following translation schemes '{TRANSLATION_SCHEMES.keys()} "
+ f"or an initialized Translator object, got '{scheme}'"
+ )
+
+ def _initData(self, data, copy: bool) -> DictOfSeries:
+
+ if data is None:
+ return DictOfSeries()
+
+ if isinstance(data, list):
+ results = []
+ for d in data:
+ results.append(self._castToDios(d, copy=copy))
+ return concatDios(results, warn=True, stacklevel=3)
+
+ if isinstance(data, (DictOfSeries, pd.DataFrame, pd.Series)):
+ return self._castToDios(data, copy)
+
+ raise TypeError(
+ "'data' must be of type pandas.Series, "
+ "pandas.DataFrame or dios.DictOfSeries or"
+ "a list of those."
+ )
+
+ def _castToDios(self, data, copy: bool):
+ if isinstance(data, pd.Series):
+ if not isinstance(data.name, str):
+ raise ValueError(f"Cannot init from unnamed pd.Series")
+ data = data.to_frame()
+ if isinstance(data, pd.DataFrame):
+ for idx in [data.index, data.columns]:
+ if isinstance(idx, pd.MultiIndex):
+ raise TypeError("'data' should not have MultiIndex")
+ data = to_dios(data) # noop for DictOfSeries
+ for c in data.columns:
+ if not isinstance(c, str):
+ raise TypeError("columns labels must be of type string")
+ if copy:
+ data = data.copy()
+ return data
+
+ def _initFlags(self, flags, copy: bool) -> Flags:
+ if flags is None:
+ return initFlagsLike(self._data)
+
+ if isinstance(flags, list):
+ result = Flags()
+ for f in flags:
+ f = self._castToFlags(f, copy=copy)
+ for c in f.columns:
+ if c in result.columns:
+ warnings.warn(
+ f"Column {c} already exist. Data is overwritten. "
+ f"Avoid duplicate columns names over all inputs.",
+ stacklevel=2,
+ )
+ result.history[c] = f.history[c]
+ flags = result
+
+ elif isinstance(flags, (pd.DataFrame, DictOfSeries, Flags)):
+ flags = self._castToFlags(flags, copy=copy)
+
+ else:
+ raise TypeError(
+ "'flags' must be of type pandas.DataFrame, "
+ "dios.DictOfSeries or saqc.Flags or "
+ "a list of those."
+ )
+
+ # sanitize
+ # - if column is missing flags but present in data, add it
+ # - if column is present in both, the index must be equal
+ for c in self._data.columns:
+ if c not in flags.columns:
+ flags.history[c] = History(self._data[c].index)
+ else:
+ if not flags[c].index.equals(self._data[c].index):
+ raise ValueError(
+ f"The flags index of column {c} does not equals "
+ f"the index of the same column in data."
+ )
+ return flags
+
+ def _castToFlags(self, flags, copy):
+ if isinstance(flags, pd.DataFrame):
+ for idx in [flags.index, flags.columns]:
+ if isinstance(idx, pd.MultiIndex):
+ raise TypeError("'flags' should not have MultiIndex")
+ if not isinstance(flags, Flags):
+ flags = Flags(flags)
+ if copy:
+ flags = flags.copy()
+ return flags
+
+
+class SaQCResult:
+ def __init__(
+ self,
+ data: DictOfSeries,
+ flags: Flags,
+ attrs: dict,
+ scheme: TranslationScheme,
+ ):
+ assert isinstance(data, DictOfSeries)
+ assert isinstance(flags, Flags)
+ assert isinstance(attrs, dict)
+ assert isinstance(scheme, TranslationScheme)
+ self._data = data.copy()
+ self._flags = flags.copy()
+ self._attrs = attrs.copy()
+ self._scheme = scheme
+ self._validate()
+
+ try:
+ self._scheme.backward(self._flags, attrs=self._attrs)
+ except Exception as e:
+ raise RuntimeError("Translation of flags failed") from e
+
+ def _validate(self):
+ if not self._data.columns.equals(self._flags.columns):
+ raise AssertionError(
+ "Consistency broken. data and flags have not the same columns"
+ )
+
+ @property
+ def data(self) -> pd.DataFrame:
+ data: pd.DataFrame = self._data.copy().to_df()
+ data.attrs = self._attrs.copy()
+ return data
+
+ @property
+ def flags(self) -> pd.DataFrame:
+ data: pd.DataFrame = self._scheme.backward(self._flags, attrs=self._attrs)
+ data.attrs = self._attrs.copy()
+ return data
+
+ @property
+ def data_raw(self) -> DictOfSeries:
+ return self._data
+
+ @property
+ def flags_raw(self) -> Flags:
+ return self._flags
+
+ @property
+ def columns(self) -> DictOfSeries():
+ self._validate()
+ return self._data.columns
+
+ def __getitem__(self, key):
+ self._validate()
+ if key not in self.columns:
+ raise KeyError(key)
+ data_series = self._data[key].copy()
+ # slice flags to one column
+ flags = Flags({key: self._flags._data[key]}, copy=True)
+
+ df = self._scheme.backward(flags, attrs=self._attrs)
+ if isinstance(df.columns, pd.MultiIndex):
+ df = df.droplevel(level=0, axis=1)
+
+ if len(df.columns) == 1:
+ df.columns = ["flags"]
+
+ df.insert(0, column="data", value=data_series)
+ df.columns.name = None
+ df.index.name = None
+ return df
+
+ def __repr__(self):
+ return f"SaQCResult\nColumns: {self.columns.to_list()}"
diff --git a/saqc/core/flags.py b/saqc/core/flags.py
index a895e0fb0a00d3f2afa37012ad508b34e385331e..6b119d70491d63ac59b442ca7aaf5e87b80eb1e6 100644
--- a/saqc/core/flags.py
+++ b/saqc/core/flags.py
@@ -2,6 +2,7 @@
from __future__ import annotations
import pandas as pd
+import numpy as np
import dios
from typing import Mapping, Union, Dict, DefaultDict, Optional, Type, Tuple, Iterable
@@ -32,7 +33,7 @@ class _HistAccess:
self.obj = obj
def __getitem__(self, key: str) -> History:
- return self.obj._data[key].copy()
+ return self.obj._data[key]
def __setitem__(self, key: str, value: History):
if not isinstance(value, History):
@@ -66,13 +67,16 @@ class Flags:
Examples
--------
+
We create an empty instance, by calling ``Flags`` without any arguments and then add a column to it.
- >>> from saqc.constants import UNFLAGGED, BAD, DOUBT, UNTOUCHED
+ >>> from saqc.constants import UNFLAGGED, BAD, DOUBTFUL
>>> flags = Flags()
>>> flags
Empty Flags
Columns: []
+
+
>>> flags['v0'] = pd.Series([BAD,BAD,UNFLAGGED], dtype=float)
>>> flags
v0 |
@@ -83,6 +87,7 @@ class Flags:
Once the column exist, we cannot overwrite it anymore, with a different series.
+ >>> flags = Flags()
>>> flags['v0'] = pd.Series([666.], dtype=float)
Traceback (most recent call last):
some file path ...
@@ -91,7 +96,7 @@ class Flags:
But if we pass a series, which index match it will work,
because the series now is interpreted as value-to-set.
- >>> flags['v0'] = pd.Series([DOUBT,UNTOUCHED,DOUBT], dtype=float)
+ >>> flags['v0'] = pd.Series([DOUBT,np.nan,DOUBT], dtype=float)
>>> flags
v0 |
======== |
@@ -100,10 +105,11 @@ class Flags:
2 25.0 |
As we see above, the column now holds a combination from the values from the
- first and the second set. This is, because the special constant ``UNTOUCHED``,
- an alias for ``numpy.nan`` was used. We can inspect all the updates that was
+ first and the second set. This is, because ``numpy.nan`` was used.
+ We can inspect all the updates that was
made by looking in the history.
+ >>> flags['v0'] = pd.Series([DOUBTFUL, np.nan, DOUBTFUL], dtype=float)
>>> flags.history['v0']
0 1
0 (255.0) 25.0
@@ -297,7 +303,7 @@ class Flags:
)
mask, key = key
- tmp = pd.Series(UNTOUCHED, index=self._data[key].index, dtype=float)
+ tmp = pd.Series(np.nan, index=self._data[key].index, dtype=float)
# make a mask from an index, because it seems
# that passing an index is a very common workflow
@@ -359,9 +365,10 @@ class Flags:
"""
Accessor for the flags history.
- To get a copy of the current history use ``flags.history['var']``.
+ Access via ``flags.history['var']``.
To set a new history use ``flags.history['var'] = value``.
- The passed value must be a instance of History or must be convertible to a history.
+ The passed value must be a instance of History or must be convertible to a
+ history.
Returns
-------
@@ -392,11 +399,11 @@ class Flags:
copy of flags
"""
new = self._constructor()
- new._data = {c: h.copy() if deep else h for c, h in self._data.items()}
+ new._data = {c: h.copy(deep) for c, h in self._data.items()}
return new
- def __copy__(self, deep=True):
- return self.copy(deep=deep)
+ def __copy__(self):
+ return self.copy(deep=False)
def __deepcopy__(self, memo=None):
"""
diff --git a/saqc/core/history.py b/saqc/core/history.py
index a46c998f20a3bb2eb7dbad53e66b8569440bdc44..9aa1859bdc996d6bc0cba75d037271e8c054e062 100644
--- a/saqc/core/history.py
+++ b/saqc/core/history.py
@@ -1,7 +1,7 @@
#!/usr/bin/env python
from __future__ import annotations
-from copy import deepcopy, copy
+from copy import deepcopy, copy as shallowcopy
import itertools
from typing import Dict, Tuple, Type, Union, List, Any
@@ -161,7 +161,7 @@ class History:
raise ValueError("Index does not match")
self._insert(val, pos=len(self))
- self.meta.append(deepcopy(meta))
+ self.meta.append(meta.copy())
return self
def _appendHistory(self, value: History):
@@ -189,18 +189,19 @@ class History:
if not value.index.equals(self.index):
raise ValueError("Index does not match")
- n = len(self.columns)
- # don't overwrite the `.columns` of the input down the line
+ # we copy shallow because we only want to set new columns
+ # the actual data copy happens in calls to astype
value_hist = value.hist.copy(deep=False)
- value_meta = deepcopy(value.meta)
+ value_meta = value.meta.copy()
# rename columns, to avoid ``pd.DataFrame.loc`` become confused
+ n = len(self.columns)
columns = pd.Index(range(n, n + len(value_hist.columns)))
value_hist.columns = columns
hist = self.hist.astype(float)
hist.loc[:, columns] = value_hist.astype(float)
- self.hist = hist.astype("category", copy=True)
+ self.hist = hist.astype("category")
self.meta += value_meta
return self
@@ -214,7 +215,7 @@ class History:
"""
result = self.hist.astype(float)
if result.empty:
- result = pd.DataFrame(data=UNTOUCHED, index=self.hist.index, columns=[0])
+ result = pd.DataFrame(data=np.nan, index=self.hist.index, columns=[0])
result = result.ffill(axis=1).iloc[:, -1]
@@ -223,7 +224,9 @@ class History:
else:
return result.fillna(UNFLAGGED)
- def reindex(self, index: pd.Index, fill_value_last: float = UNFLAGGED) -> History:
+ def reindex(
+ self, index: pd.Index, fill_value_last: float = UNFLAGGED, copy: bool = True
+ ) -> History:
"""
Reindex the History. Be careful this alters the past.
@@ -231,24 +234,30 @@ class History:
----------
index : pd.Index
the index to reindex to.
+
fill_value_last : float, default UNFLAGGED
- value to fill nan's (UNTOUCHED) in the last column.
+ value to fill nan's in the last column.
Defaults to 0 (UNFLAGGED).
+ copy : bool, default True
+ If False, alter the underlying history, otherwise return a copy.
+
Returns
-------
History
"""
- hist = self.hist.astype(float).reindex(
+ out = self.copy() if copy else self
+
+ hist = out.hist.astype(float).reindex(
index=index, copy=False, fill_value=np.nan
)
# Note: all following code must handle empty frames
hist.iloc[:, -1:] = hist.iloc[:, -1:].fillna(fill_value_last)
- self.hist = hist.astype("category")
+ out.hist = hist.astype("category")
- return self
+ return out
def apply(
self,
@@ -279,10 +288,10 @@ class History:
because the initial history is empty. Then the altered empty history is
reindexed to this index.
- hist_func : callable
+ func : callable
function to apply on `History.hist` (flags DataFrame)
- hist_kws : dict
+ func_kws : dict
hist-function keywords dict
func_handle_df : bool, default False
@@ -300,10 +309,10 @@ class History:
"""
hist = pd.DataFrame(index=index)
+ # implicit copy by astype
+ # convert data to floats as functions may fail with categoricals
if func_handle_df:
- # we need to pass the data as floats as functions may fail with Categorical
hist = func(self.hist.astype(float), **func_kws)
-
else:
for pos in self.columns:
hist[pos] = func(self.hist[pos].astype(float), **func_kws)
@@ -312,7 +321,7 @@ class History:
if copy:
history = History(index=None) # noqa
- history.meta = deepcopy(self.meta)
+ history.meta = self.meta.copy()
else:
history = self
@@ -335,10 +344,23 @@ class History:
copy : History
the copied FH
"""
- if deep:
- return deepcopy(self)
- else:
- return copy(self)
+ copyfunc = deepcopy if deep else shallowcopy
+ new = History(self.index)
+ new.hist = self.hist.copy(deep)
+ new.meta = copyfunc(self.meta)
+ return new
+
+ def __copy__(self):
+ return self.copy(deep=False)
+
+ def __deepcopy__(self, memo=None):
+ """
+ Parameters
+ ----------
+ memo, default None
+ Standard signature. Unused
+ """
+ return self.copy(deep=True)
def __len__(self) -> int:
return len(self.hist.columns)
@@ -380,7 +402,7 @@ class History:
if not hist.empty and (
not hist.columns.equals(pd.Index(range(len(hist.columns))))
- or hist.columns.dtype != int
+ or not np.issubdtype(hist.columns.dtype, np.integer)
):
raise ValueError(
"column names must be continuous increasing int's, starting with 0."
diff --git a/saqc/core/modules/breaks.py b/saqc/core/modules/breaks.py
index 6def4f2da89f6a5234741b9c5b3fa13e0044c3b0..e76e783cd92bb440878958e12c0321b651545c0f 100644
--- a/saqc/core/modules/breaks.py
+++ b/saqc/core/modules/breaks.py
@@ -2,22 +2,21 @@
# -*- coding: utf-8 -*-
from __future__ import annotations
-from saqc.constants import BAD, UNFLAGGED
+from saqc.constants import BAD, FILTER_ALL
import saqc
-from saqc.lib.types import FreqString
class Breaks:
def flagMissing(
- self, field: str, flag: float = BAD, to_mask: float = UNFLAGGED, **kwargs
+ self, field: str, flag: float = BAD, dfilter: float = FILTER_ALL, **kwargs
) -> saqc.SaQC:
return self._defer("flagMissing", locals())
def flagIsolated(
self,
field: str,
- gap_window: FreqString,
- group_window: FreqString,
+ gap_window: str,
+ group_window: str,
flag: float = BAD,
**kwargs
) -> saqc.SaQC:
@@ -27,7 +26,7 @@ class Breaks:
self,
field: str,
thresh: float,
- window: FreqString,
+ window: str,
min_periods: int = 1,
flag: float = BAD,
**kwargs
diff --git a/saqc/core/modules/changepoints.py b/saqc/core/modules/changepoints.py
index a9a350bf3e12e44763b4ac6ad7cab4078171b37f..0fffc914caddff3f173fd2a7723520670c07d59c 100644
--- a/saqc/core/modules/changepoints.py
+++ b/saqc/core/modules/changepoints.py
@@ -9,7 +9,6 @@ from typing_extensions import Literal
from saqc.constants import BAD
import saqc
-from saqc.lib.types import FreqString
class ChangePoints:
@@ -18,10 +17,10 @@ class ChangePoints:
field: str,
stat_func: Callable[[np.ndarray, np.ndarray], float],
thresh_func: Callable[[np.ndarray, np.ndarray], float],
- window: FreqString | Tuple[FreqString, FreqString],
+ window: str | Tuple[str, str],
min_periods: int | Tuple[int, int],
closed: Literal["right", "left", "both", "neither"] = "both",
- reduce_window: FreqString = None,
+ reduce_window: str = None,
reduce_func: Callable[[np.ndarray, np.ndarray], int] = lambda x, _: x.argmax(),
flag: float = BAD,
**kwargs,
@@ -41,9 +40,6 @@ class ChangePoints:
[np.ndarray, np.ndarray], float
] = lambda x, _: x.argmax(),
model_by_resids: bool = False,
- set_flags: bool = False,
- assign_cluster: bool = True,
- flag: float = BAD,
**kwargs,
) -> saqc.SaQC:
return self._defer("assignChangePointCluster", locals())
diff --git a/saqc/core/modules/constants.py b/saqc/core/modules/constants.py
index b11c62cd3a00b1d6e9eef2524c716a9360c76bdf..035147e67dac5a7ca956bbe7b1c1d618ab7e912e 100644
--- a/saqc/core/modules/constants.py
+++ b/saqc/core/modules/constants.py
@@ -4,14 +4,13 @@ from __future__ import annotations
from saqc.constants import BAD
import saqc
-from saqc.lib.types import FreqString
class Constants:
def flagByVariance(
self,
field: str,
- window: FreqString = "12h",
+ window: str = "12h",
thresh: float = 0.0005,
maxna: int = None,
maxna_group: int = None,
@@ -21,6 +20,6 @@ class Constants:
return self._defer("flagByVariance", locals())
def flagConstants(
- self, field: str, thresh: float, window: FreqString, flag: float = BAD, **kwargs
+ self, field: str, thresh: float, window: str, flag: float = BAD, **kwargs
) -> saqc.SaQC:
return self._defer("flagConstants", locals())
diff --git a/saqc/core/modules/curvefit.py b/saqc/core/modules/curvefit.py
index 91a2cd6b7ea17ca43b0c99e492ad6b6a48e84741..ba595d0659e6e745396ee1cdd463ac6e776e8c16 100644
--- a/saqc/core/modules/curvefit.py
+++ b/saqc/core/modules/curvefit.py
@@ -16,10 +16,7 @@ class Curvefit:
field: str,
window: Union[int, str],
order: int,
- set_flags: bool = True,
min_periods: int = 0,
- return_residues: bool = False,
- flag: float = BAD,
**kwargs
) -> saqc.SaQC:
return self._defer("fitPolynomial", locals())
diff --git a/saqc/core/modules/drift.py b/saqc/core/modules/drift.py
index ed248633066cebbdf8f379625c0b51376802c2e4..e2b6c4f2aed1ed01af14792db6907074d748e1f8 100644
--- a/saqc/core/modules/drift.py
+++ b/saqc/core/modules/drift.py
@@ -2,7 +2,8 @@
# -*- coding: utf-8 -*-
from __future__ import annotations
-from typing import Sequence, Callable, Optional
+from typing import Sequence, Callable, Optional, Union
+from typing_extensions import Literal
import numpy as np
from scipy.spatial.distance import pdist
@@ -10,15 +11,14 @@ from scipy.spatial.distance import pdist
from saqc.constants import BAD
import saqc
from saqc.funcs import LinkageString
-from saqc.lib.types import FreqString, CurveFitter
+from saqc.lib.types import CurveFitter
class Drift:
def flagDriftFromNorm(
self,
- field: str,
- fields: Sequence[str],
- freq: FreqString,
+ field: Sequence[str],
+ freq: str,
spread: float,
frac: float = 0.5,
metric: Callable[[np.ndarray, np.ndarray], float] = lambda x, y: pdist(
@@ -33,14 +33,15 @@ class Drift:
def flagDriftFromReference(
self,
- field: str,
- fields: Sequence[str],
- freq: FreqString,
+ field: Sequence[str],
+ reference: str,
+ freq: str,
thresh: float,
metric: Callable[[np.ndarray, np.ndarray], float] = lambda x, y: pdist(
np.array([x, y]), metric="cityblock"
)
/ len(x),
+ target=None,
flag: float = BAD,
**kwargs
) -> saqc.SaQC:
@@ -51,7 +52,7 @@ class Drift:
field: str,
set_1: Sequence[str],
set_2: Sequence[str],
- freq: FreqString,
+ freq: str,
spread: float,
frac: float = 0.5,
metric: Callable[[np.ndarray, np.ndarray], float] = lambda x, y: pdist(
@@ -59,6 +60,7 @@ class Drift:
)
/ len(x),
method: LinkageString = "single",
+ target: str = None,
flag: float = BAD,
**kwargs
) -> saqc.SaQC:
@@ -68,9 +70,8 @@ class Drift:
self,
field: str,
maintenance_field: str,
- model: Callable[..., float],
+ model: Callable[..., float] | Literal["linear", "exponential"],
cal_range: int = 5,
- flag: float = BAD,
**kwargs
) -> saqc.SaQC:
return self._defer("correctDrift", locals())
@@ -80,7 +81,7 @@ class Drift:
field: str,
cluster_field: str,
model: CurveFitter,
- tolerance: Optional[FreqString] = None,
+ tolerance: Optional[str] = None,
epoch: bool = False,
**kwargs
) -> saqc.SaQC:
@@ -91,9 +92,9 @@ class Drift:
field: str,
max_jump: float,
spread: float,
- window: FreqString,
+ window: str,
min_periods: int,
- tolerance: Optional[FreqString] = None,
+ tolerance: Optional[str] = None,
**kwargs
) -> saqc.SaQC:
return self._defer("correctOffset", locals())
diff --git a/saqc/core/modules/flagtools.py b/saqc/core/modules/flagtools.py
index a782d9700c1e869a13d6d12d2a6d95ba4dd6c1b7..0df9dfc27d5b3e2c9761d40a2a6299f5f5fcbd47 100644
--- a/saqc/core/modules/flagtools.py
+++ b/saqc/core/modules/flagtools.py
@@ -5,6 +5,7 @@ from __future__ import annotations
from typing import Any, Union
import pandas as pd
+import numpy as np
from dios import DictOfSeries
from typing_extensions import Literal
@@ -28,9 +29,12 @@ class FlagTools:
def flagManual(
self,
field: str,
- mdata: Union[pd.Series, pd.DataFrame, DictOfSeries],
+ mdata: Union[pd.Series, pd.DataFrame, DictOfSeries, list, np.array],
+ method: Literal[
+ "left-open", "right-open", "closed", "plain", "ontime"
+ ] = "left-open",
+ mformat: Literal["start-end", "mflag"] = "start-end",
mflag: Any = 1,
- method: Literal["plain", "ontime", "left-open", "right-open"] = "plain",
flag: float = BAD,
**kwargs,
) -> saqc.SaQC:
diff --git a/saqc/core/modules/generic.py b/saqc/core/modules/generic.py
index 898be936416168d97c0480e7cd73a687532dfa7b..c0ed0a21ff0eec046d88c8bd56d46dec1c77e84d 100644
--- a/saqc/core/modules/generic.py
+++ b/saqc/core/modules/generic.py
@@ -2,31 +2,32 @@
# -*- coding: utf-8 -*-
from __future__ import annotations
-from typing import Callable
+from typing import Sequence, Union
-import numpy as np
-import pandas as pd
-
-from saqc.constants import UNFLAGGED, BAD
import saqc
+from saqc.constants import UNFLAGGED, BAD, FILTER_ALL
+from saqc.lib.types import GenericFunction
class Generic:
- def genericProcess(
+ def processGeneric(
self,
- field: str,
- func: Callable[[pd.Series], pd.Series],
- to_mask: float = UNFLAGGED,
+ field: str | Sequence[str],
+ func: GenericFunction,
+ target: str | Sequence[str] = None,
+ flag: float = UNFLAGGED,
+ dfilter: float = FILTER_ALL,
**kwargs,
) -> saqc.SaQC:
- return self._defer("genericProcess", locals())
+ return self._defer("processGeneric", locals())
- def genericFlag(
+ def flagGeneric(
self,
- field: str,
- func: Callable[[pd.Series], pd.Series],
+ field: Union[str, Sequence[str]],
+ func: GenericFunction,
+ target: Union[str, Sequence[str]] = None,
flag: float = BAD,
- to_mask: float = UNFLAGGED,
+ dfilter: float = FILTER_ALL,
**kwargs,
) -> saqc.SaQC:
- return self._defer("genericFlag", locals())
+ return self._defer("flagGeneric", locals())
diff --git a/saqc/core/modules/interpolation.py b/saqc/core/modules/interpolation.py
index 8787bd3250b3438702186cf1c7eb6c7b2200e169..3eed7ac7ed31df0b816239348d2e50b9464e0717 100644
--- a/saqc/core/modules/interpolation.py
+++ b/saqc/core/modules/interpolation.py
@@ -21,7 +21,7 @@ class Interpolation:
center: bool = True,
min_periods: int = 0,
flag: float = UNFLAGGED,
- **kwargs
+ **kwargs,
) -> saqc.SaQC:
return self._defer("interpolateByRolling", locals())
@@ -33,7 +33,7 @@ class Interpolation:
limit: int = 2,
downgrade: bool = False,
flag: float = UNFLAGGED,
- **kwargs
+ **kwargs,
) -> saqc.SaQC:
return self._defer("interpolateInvalid", locals())
@@ -45,6 +45,6 @@ class Interpolation:
order: int = 2,
limit: int = 2,
downgrade: bool = False,
- **kwargs
+ **kwargs,
) -> saqc.SaQC:
return self._defer("interpolateIndex", locals())
diff --git a/saqc/core/modules/noise.py b/saqc/core/modules/noise.py
index 8985f56f2eae96d36f8df749092f26c9ec54f54e..dfc734162930c8a4075c379b994de869be4966cf 100644
--- a/saqc/core/modules/noise.py
+++ b/saqc/core/modules/noise.py
@@ -8,17 +8,16 @@ from typing import Callable
from saqc.constants import BAD
import saqc
-from saqc.lib.types import FreqString
class Noise:
def flagByStatLowPass(
self,
field: str,
- func: Callable[[np.ndarray, pd.Series], float],
- window: FreqString,
+ func: Callable[[np.array, pd.Series], float],
+ window: str,
thresh: float,
- sub_window: FreqString = None,
+ sub_window: str = None,
sub_thresh: float = None,
min_periods: int = None,
flag: float = BAD,
diff --git a/saqc/core/modules/outliers.py b/saqc/core/modules/outliers.py
index 8e64e99d34a8ce0ee22b12b40b8394263d9cc4a6..f66e3b10134d30340096dcfac7f8f668c7e7e49c 100644
--- a/saqc/core/modules/outliers.py
+++ b/saqc/core/modules/outliers.py
@@ -10,14 +10,13 @@ from typing_extensions import Literal
from saqc.constants import BAD
import saqc
-from saqc.lib.types import FreqString
class Outliers:
def flagByStray(
self,
field: str,
- freq: Optional[Union[int, FreqString]] = None,
+ freq: Optional[Union[int, str]] = None,
min_periods: int = 11,
iter_start: float = 0.5,
alpha: float = 0.05,
@@ -28,19 +27,19 @@ class Outliers:
def flagMVScores(
self,
- field: str,
- fields: Sequence[str],
+ field: Sequence[str],
trafo: Callable[[pd.Series], pd.Series] = lambda x: x,
alpha: float = 0.05,
n: int = 10,
func: Callable[[pd.Series], float] = np.sum,
iter_start: float = 0.5,
- partition: Optional[Union[int, FreqString]] = None,
+ partition: Optional[Union[int, str]] = None,
partition_min: int = 11,
- stray_range: Optional[FreqString] = None,
+ stray_range: Optional[str] = None,
drop_flagged: bool = False, # TODO: still a case ?
thresh: float = 3.5,
min_periods: int = 1,
+ target: str = None,
flag: float = BAD,
**kwargs,
) -> saqc.SaQC:
@@ -50,9 +49,9 @@ class Outliers:
self,
field: str,
thresh: float,
- raise_window: FreqString,
- freq: FreqString,
- average_window: Optional[FreqString] = None,
+ raise_window: str,
+ freq: str,
+ average_window: Optional[str] = None,
raise_factor: float = 2.0,
slope: Optional[float] = None,
weight: float = 0.8,
@@ -64,7 +63,7 @@ class Outliers:
def flagMAD(
self,
field: str,
- window: FreqString,
+ window: str,
z: float = 3.5,
flag: float = BAD,
**kwargs,
@@ -76,7 +75,7 @@ class Outliers:
field: str,
thresh: float,
tolerance: float,
- window: Union[int, FreqString],
+ window: Union[int, str],
thresh_relative: Optional[float] = None,
flag: float = BAD,
**kwargs,
@@ -86,7 +85,7 @@ class Outliers:
def flagByGrubbs(
self,
field: str,
- window: Union[FreqString, int],
+ window: Union[str, int],
alpha: float = 0.05,
min_periods: int = 8,
pedantic: bool = False,
@@ -107,11 +106,11 @@ class Outliers:
def flagCrossStatistic(
self,
- field: str,
- fields: Sequence[str],
+ field: Sequence[str],
thresh: float,
method: Literal["modZscore", "Zscore"] = "modZscore",
flag: float = BAD,
+ target: str = None,
**kwargs,
) -> saqc.SaQC:
return self._defer("flagCrossStatistic", locals())
diff --git a/saqc/core/modules/pattern.py b/saqc/core/modules/pattern.py
index 2dbb020bec70fa15b20c44d21f2e8c92edbea4ff..f38de24416b852376ea85c356d81e1e34f2c30b9 100644
--- a/saqc/core/modules/pattern.py
+++ b/saqc/core/modules/pattern.py
@@ -2,8 +2,6 @@
# -*- coding: utf-8 -*-
from __future__ import annotations
-from typing import Sequence
-
from saqc.constants import BAD
import saqc
@@ -20,8 +18,3 @@ class Pattern:
**kwargs
) -> saqc.SaQC:
return self._defer("flagPatternByDTW", locals())
-
- def flagPatternByWavelet(
- self, field, reference, widths=(1, 2, 4, 8), waveform="mexh", flag=BAD, **kwargs
- ) -> saqc.SaQC:
- return self._defer("flagPatternByWavelet", locals())
diff --git a/saqc/core/modules/resampling.py b/saqc/core/modules/resampling.py
index 14350a1b9a34646528d5f6512819576035de8fea..1331647e5079cc4b366d2785f417622ac1f32ecf 100644
--- a/saqc/core/modules/resampling.py
+++ b/saqc/core/modules/resampling.py
@@ -14,7 +14,12 @@ from saqc.funcs.interpolation import _SUPPORTED_METHODS
class Resampling:
- def linear(self, field: str, freq: str, **kwargs) -> saqc.SaQC:
+ def linear(
+ self,
+ field: str,
+ freq: str,
+ **kwargs,
+ ) -> saqc.SaQC:
return self._defer("linear", locals())
def interpolate(
@@ -53,9 +58,10 @@ class Resampling:
) -> saqc.SaQC:
return self._defer("resample", locals())
- def reindexFlags(
+ def concatFlags(
self,
field: str,
+ target: str,
method: Literal[
"inverse_fagg",
"inverse_bagg",
@@ -65,9 +71,8 @@ class Resampling:
"inverse_nshift",
"inverse_interpolation",
],
- source: str,
freq: Optional[str] = None,
drop: Optional[bool] = False,
**kwargs,
) -> saqc.SaQC:
- return self._defer("reindexFlags", locals())
+ return self._defer("concatFlags", locals())
diff --git a/saqc/core/modules/residues.py b/saqc/core/modules/residues.py
index d2ee8db46a0217c386360ed0ed8831aa2efaa7d2..30503a46d2e79dd19e6e2d97b0f2cc79b5fdbc08 100644
--- a/saqc/core/modules/residues.py
+++ b/saqc/core/modules/residues.py
@@ -5,6 +5,7 @@ from __future__ import annotations
from typing import Optional, Union, Callable
import numpy as np
+import pandas as pd
from typing_extensions import Literal
from saqc.constants import BAD
@@ -17,9 +18,7 @@ class Residues:
field: str,
window: Union[str, int],
order: int,
- set_flags: bool = True, # TODO, not valid anymore, if still needed, maybe assign user-passed ``flag``?
min_periods: Optional[int] = 0,
- flag: float = BAD,
**kwargs
) -> saqc.SaQC:
return self._defer("calculatePolynomialResidues", locals())
@@ -28,11 +27,9 @@ class Residues:
self,
field: str,
window: Union[str, int],
- func: Callable[[np.ndarray], np.ndarray] = np.mean,
- set_flags: bool = True,
+ func: Callable[[pd.Series], np.ndarray] = np.mean,
min_periods: Optional[int] = 0,
center: bool = True,
- flag: float = BAD,
**kwargs
) -> saqc.SaQC:
return self._defer("calculateRollingResidues", locals())
diff --git a/saqc/core/modules/rolling.py b/saqc/core/modules/rolling.py
index 11131514b158ce4d62a83fe145b49a6e8ef864e3..67e0e10b103dbf6aee3b9e83c0036956af2b497d 100644
--- a/saqc/core/modules/rolling.py
+++ b/saqc/core/modules/rolling.py
@@ -14,12 +14,9 @@ class Rolling:
self,
field: str,
window: Union[str, int],
- func: Callable[[pd.Series], float] = np.mean,
- set_flags: bool = True, # TODO: not applicable anymore
+ func: Callable[[pd.Series], np.ndarray] = np.mean,
min_periods: int = 0,
center: bool = True,
- return_residues=False, # TODO: this should not be public, a wrapper would be better
- flag: float = BAD,
**kwargs
):
return self._defer("roll", locals())
diff --git a/saqc/core/modules/scores.py b/saqc/core/modules/scores.py
index cc015d98fc63a466f22346cfd174a3402362460d..feaa89b8a70d9c79a62ef202ebfd1b5d3cce41ac 100644
--- a/saqc/core/modules/scores.py
+++ b/saqc/core/modules/scores.py
@@ -14,9 +14,8 @@ import saqc
class Scores:
def assignKNNScore(
self,
- field: str,
- fields: Sequence[str],
- target: str = "kNNscores",
+ field: Sequence[str],
+ target: str,
n: int = 10,
func: Callable[[pd.Series], float] = np.sum,
freq: Union[float, str] = np.inf,
@@ -24,6 +23,6 @@ class Scores:
method: Literal["ball_tree", "kd_tree", "brute", "auto"] = "ball_tree",
metric: str = "minkowski",
p: int = 2,
- **kwargs
+ **kwargs,
) -> saqc.SaQC:
return self._defer("assignKNNScore", locals())
diff --git a/saqc/core/modules/tools.py b/saqc/core/modules/tools.py
index bd6f189fe7ff391e5a0215b1de7848464107c5d7..cf6fd5d0a09df53c973c27580d486aa399e114ab 100644
--- a/saqc/core/modules/tools.py
+++ b/saqc/core/modules/tools.py
@@ -6,11 +6,13 @@ from typing import Optional
from typing_extensions import Literal
import saqc
-from saqc.lib.types import FreqString
+import numpy as np
+
+from saqc.constants import FILTER_NONE
class Tools:
- def copyField(self, field: str, new_field: str, **kwargs) -> saqc.SaQC:
+ def copyField(self, field: str, target: str, **kwargs) -> saqc.SaQC:
return self._defer("copyField", locals())
def dropField(self, field: str, **kwargs) -> saqc.SaQC:
@@ -35,12 +37,14 @@ class Tools:
self,
field: str,
path: Optional[str] = None,
- max_gap: Optional[FreqString] = None,
+ max_gap: Optional[str] = None,
stats: bool = False,
- plot_kwargs: Optional[dict] = None,
- fig_kwargs: Optional[dict] = None,
+ history: Optional[Literal["valid", "complete", "clear"]] = "valid",
+ xscope: Optional[slice] = None,
+ phaseplot: Optional[str] = None,
stats_dict: Optional[dict] = None,
store_kwargs: Optional[dict] = None,
+ dfilter: Optional[float] = FILTER_NONE,
**kwargs,
) -> saqc.SaQC:
return self._defer("plot", locals())
diff --git a/saqc/core/reader.py b/saqc/core/reader.py
index 219f5b4c4a5fbaef7cd8bc3cb91d8974a385fd18..1f1a6113a691e1bd8c16cbed1ced26ec764bbd48 100644
--- a/saqc/core/reader.py
+++ b/saqc/core/reader.py
@@ -15,7 +15,7 @@ COMMENT = "#"
SEPARATOR = ";"
-def readFile(fname):
+def readFile(fname) -> pd.DataFrame:
fobj = (
io.open(fname, "r", encoding="utf-8")
@@ -37,15 +37,12 @@ def readFile(fname):
"for the variable name and one for the test to apply, but "
f"in line {i} we got: \n'{line}'"
)
- out.append(
- [
- i + 1,
- ]
- + parts
- )
-
- if isinstance(fname, str):
+ out.append([i + 1] + parts)
+
+ try:
fobj.close()
+ except AttributeError:
+ pass
df = pd.DataFrame(
out[1:],
@@ -65,12 +62,21 @@ def fromConfig(fname, *args, **kwargs):
regex = False
if isQuoted(field):
- field = field[1:-1]
+ fld = field[1:-1]
regex = True
-
- tree = ast.parse(expr, mode="eval")
- func, kwargs = ConfigFunctionParser().parse(tree.body)
-
- saqc = getattr(saqc, func)(field=field, regex=regex, **kwargs)
+ else:
+ fld = field
+
+ try:
+ tree = ast.parse(expr, mode="eval")
+ func_name, kwargs = ConfigFunctionParser().parse(tree.body)
+ except Exception as e:
+ raise type(e)(f"failed to parse: {field} ; {expr}") from e
+
+ kwargs["field" if "field" not in kwargs else "target"] = fld
+ try:
+ saqc = getattr(saqc, func_name)(regex=regex, **kwargs)
+ except Exception as e:
+ raise type(e)(f"failed to execute: {field} ; {expr}") from e
return saqc
diff --git a/saqc/core/register.py b/saqc/core/register.py
index 2ccb2b318cd7c53ae957fe6c3c1b79b8b650a2c3..8f7aec66886441b6eaa6dd524d9ed47d6b59eae1 100644
--- a/saqc/core/register.py
+++ b/saqc/core/register.py
@@ -1,416 +1,474 @@
#!/usr/bin/env python
-from typing import Dict, Optional, Union, Tuple, Callable, Sequence
-from typing_extensions import Literal
-from functools import wraps
-import dataclasses
+from __future__ import annotations
+
+import inspect
+import warnings
+from typing import Dict, Tuple, Callable, Sequence, Any
+import functools
import numpy as np
import pandas as pd
import dios
-from saqc.constants import *
-from saqc.core.flags import initFlagsLike, Flags, History
+from saqc.constants import UNFLAGGED, FILTER_ALL
+from saqc.core.flags import Flags, History
+from saqc.lib.tools import squeezeSequence, toSequence
# NOTE:
# the global SaQC function store,
# will be filled by calls to register
FUNC_MAP: Dict[str, Callable] = {}
-MaskingStrT = Literal["all", "field", "none"]
-FuncReturnT = Tuple[dios.DictOfSeries, Flags]
-
-
-@dataclasses.dataclass
-class CallState:
- func: Callable
- func_name: str
-
- flags: Flags
- field: str
-
- args: tuple
- kwargs: dict
-
- masking: MaskingStrT
- mthresh: float
- mask: dios.DictOfSeries
-
-
-def processing():
- # executed on module import
- def inner(func):
- @wraps(func)
- def callWrapper(data, field, flags, *args, **kwargs):
- kwargs["to_mask"] = _getMaskingThresh(kwargs)
- return func(data, field, flags, *args, **kwargs)
-
- FUNC_MAP[func.__name__] = callWrapper
- return callWrapper
-
- return inner
-
-
-def flagging(masking: MaskingStrT = "all"):
-
- # executed on module import
- if masking not in ("all", "field", "none"):
- raise ValueError(
- f"invalid masking argument '{masking}', choose one of ('all', 'field', 'none')"
+_is_list_like = pd.api.types.is_list_like
+
+
+class FunctionWrapper:
+ def __init__(
+ self,
+ func: Callable,
+ mask: list,
+ demask: list,
+ squeeze: list,
+ multivariate: bool = False,
+ handles_target: bool = False,
+ ):
+ # todo:
+ # - meta only is written with squeeze
+
+ self.func = func
+ self.func_name = func.__name__
+ self.func_signature = inspect.signature(func)
+
+ # ensure type and all elements exist in signature
+ self._checkDecoratorKeywords(mask, demask, squeeze)
+ if multivariate and not handles_target:
+ raise ValueError("multivariate=True requires handle_target=True")
+
+ self.decorator_mask = mask
+ self.decorator_demask = demask
+ self.decorator_squeeze = squeeze
+ self.multivariate = multivariate
+ self.handles_target = handles_target
+
+ # set in __call__
+ self.data = None
+ self.flags = None
+ self.fields = None
+ self.args = None
+ self.kwargs = None
+ self.mask_thresh = None
+ self.stored_data = None
+
+ # make ourself look like the wrapped function, especially the docstring
+ functools.update_wrapper(self, func)
+
+ def _checkDecoratorKeywords(self, mask, demask, squeeze):
+ params = self.func_signature.parameters.keys()
+ for dec_arg, name in zip(
+ [mask, demask, squeeze], ["mask", "demask", "squeeze"]
+ ):
+ typeerr = TypeError(
+ f"type of decorator argument '{name}' must "
+ f"be a list of strings, not {repr(type(dec_arg))}"
+ )
+ if not isinstance(dec_arg, list):
+ raise typeerr
+ for elem in dec_arg:
+ if not isinstance(elem, str):
+ raise typeerr
+ if elem not in params:
+ raise ValueError(
+ f"passed value {repr(elem)} in {repr(name)} is not an "
+ f"parameter in decorated function {repr(self.func_name)}"
+ )
+
+ @staticmethod
+ def _argnamesToColumns(names: list, values: dict):
+ clist = []
+ for name in names:
+ value = values[name] # eg. the value behind 'field'
+
+ # NOTE: do not change order of the tests
+ if value is None:
+ pass
+ elif isinstance(value, str):
+ clist.append(value)
+ # we ignore DataFrame, Series, DictOfSeries
+ # and high order types alike
+ elif hasattr(value, "columns"):
+ pass
+ elif _is_list_like(value) and all([isinstance(e, str) for e in value]):
+ clist += value
+ return pd.Index(clist)
+
+ @staticmethod
+ def _warn(missing, source):
+ if len(missing) == 0:
+ return
+ action = source + "ed"
+ obj = "flags" if source == "squeeze" else "data"
+ warnings.warn(
+ f"Column(s) {repr(missing)} cannot not be {action} "
+ f"because they are not present in {obj}. ",
+ RuntimeWarning,
)
- def inner(func):
- func_name = func.__name__
-
- # executed if a register-decorated function is called,
- # nevertheless if it is called plain or via `SaQC.func`.
- @wraps(func)
- def callWrapper(data, field, flags, *args, **kwargs):
- args = data, field, flags, *args
- args, kwargs, old_state = _preCall(func, args, kwargs, masking, func_name)
- result = func(*args, **kwargs)
- return _postCall(result, old_state)
-
- FUNC_MAP[func_name] = callWrapper
- callWrapper._masking = masking
-
- return callWrapper
+ def __call__(
+ self, data: dios.DictOfSeries, field: str, flags: Flags, *args, **kwargs
+ ) -> Tuple[dios.DictOfSeries, Flags]:
+ """
+ This wraps a call to a saqc function.
+
+ Before the saqc function call it copies flags and maybe mask data (inplace).
+ After the call it maybe squeezes modified histories and maybe reinsert the
+ masked data locations.
+
+ If the squeezing and/or the masking and/or the demasking will happen, depends
+ on the decorator keywords `handles` and `datamask`. See ``_determineActions``,
+ for that.
+ """
+ # keep this the original values
+ self.data = data
+ self.flags = flags
+ self.fields = toSequence(field)
+ self.args = args
+ self.kwargs = self._checkKwargs(kwargs)
+
+ self.mask_thresh = self._getMaskingThresh()
+
+ # skip (data, field, flags)
+ names = list(self.func_signature.parameters.keys())[3 : 3 + len(args)]
+ all_args = {"field": field, **dict(zip(names, args)), **kwargs}
+
+ # find columns that need masking
+ columns = self._argnamesToColumns(self.decorator_mask, all_args)
+ self._warn(columns.difference(self.data.columns).to_list(), source="mask")
+ columns = columns.intersection(self.data.columns)
+
+ masked, stored = self._maskData(
+ data=self.data,
+ flags=self.flags,
+ columns=columns,
+ thresh=self.mask_thresh,
+ )
+ self.data = masked
+ self.stored_data = stored
+
+ args, kwargs = self._prepareArgs()
+ data, flags = self.func(*args, **kwargs)
+
+ # find columns that need squeezing
+ columns = self._argnamesToColumns(self.decorator_squeeze, all_args)
+ self._warn(columns.difference(flags.columns).to_list(), source="squeeze")
+ columns = columns.intersection(flags.columns)
+
+ # if the function did not want to set any flags at all,
+ # we assume a processing function that altered the flags
+ # in an unpredictable manner or do nothing with the flags.
+ # in either case we take the returned flags as the new truth.
+ if columns.empty:
+ result_flags = flags
+ else:
+ # even if this looks like a noop for columns=[],
+ # it returns the old instead the new flags and
+ # therefore ignores any possible processing changes
+ result_flags = self._squeezeFlags(flags, columns)
+
+ # find columns that need demasking
+ columns = self._argnamesToColumns(self.decorator_demask, all_args)
+ self._warn(columns.difference(data.columns).to_list(), source="demask")
+ columns = columns.intersection(data.columns)
+
+ result_data = self._unmaskData(data, self.stored_data, columns=columns)
+
+ return result_data, result_flags
+
+ @staticmethod
+ def _checkKwargs(kwargs: dict) -> dict[str, Any]:
+ if "dfilter" in kwargs and not isinstance(
+ kwargs["dfilter"], (bool, float, int)
+ ):
+ raise TypeError(f"'dfilter' must be of type bool or float")
+ return kwargs
+
+ def _prepareArgs(self) -> Tuple[tuple, dict[str, Any]]:
+ """
+ Prepare the args and kwargs passed to the function
+ Returns
+ -------
+ args: tuple
+ arguments to be passed to the actual call
+ kwargs: dict
+ keyword-arguments to be passed to the actual call
+ """
+ kwargs = self.kwargs.copy()
+ kwargs["dfilter"] = self.mask_thresh
+
+ # always pass a list to multivariate functions and
+ # unpack single element lists for univariate functions
+ if self.multivariate:
+ field = self.fields
+ else:
+ field = squeezeSequence(self.fields)
+
+ args = self.data, field, self.flags.copy(), *self.args
+ return args, kwargs
+
+ def _getMaskingThresh(self) -> float:
+ """
+ Generate a float threshold by the value of the `dfilter` keyword
+
+ Returns
+ -------
+ threshold: float
+ All data gets masked, if the flags are equal or worse than the threshold.
+
+ Notes
+ -----
+ If ``dfilter`` is **not** in the kwargs, the threshold defaults to `FILTER_ALL`.
+ For any floatish value, it is taken as the threshold.
+ """
+ if "dfilter" not in self.kwargs:
+ return FILTER_ALL
+ return float(self.kwargs["dfilter"]) # handle int
+
+ def _createMeta(self) -> dict:
+ return {
+ "func": self.func_name,
+ "args": self.args,
+ "kwargs": self.kwargs,
+ }
+
+ def _squeezeFlags(self, flags: Flags, columns: pd.Index) -> Flags:
+ """
+ Generate flags from the temporary result-flags and the original flags.
+
+ Parameters
+ ----------
+ flags : Flags
+ The flags-frame, which is the result from a saqc-function
+
+ Returns
+ -------
+ Flags
+ """
+ out = self.flags.copy() # the old flags
+ meta = self._createMeta()
+ for col in columns:
+
+ # todo: shouldn't we fail or warn here or even have a explicit test upstream
+ # because the function should ensure consistence, especially because
+ # a empty history maybe issnt what is expected, but this happens silently
+ if col not in out: # ensure existence
+ out.history[col] = History(index=flags.history[col].index)
+
+ old_history = out.history[col]
+ new_history = flags.history[col]
+
+ # We only want to add new columns, that were appended during the last
+ # function call. If no such columns exist, we end up with an empty
+ # new_history.
+ start = len(old_history.columns)
+ new_history = self._sliceHistory(new_history, slice(start, None))
+
+ squeezed = new_history.max(raw=True)
+ out.history[col] = out.history[col].append(squeezed, meta=meta)
+
+ return out
+
+ @staticmethod
+ def _sliceHistory(history: History, sl: slice) -> History:
+ history.hist = history.hist.iloc[:, sl]
+ history.meta = history.meta[sl]
+ return history
+
+ @staticmethod
+ def _maskData(
+ data: dios.DictOfSeries, flags: Flags, columns: Sequence[str], thresh: float
+ ) -> Tuple[dios.DictOfSeries, dios.DictOfSeries]:
+ """
+ Mask data with Nans, if the flags are worse than a threshold.
+ - mask only passed `columns` (preselected by `datamask`-kw from decorator)
+
+ Returns
+ -------
+ masked : dios.DictOfSeries
+ masked data, same dim as original
+ mask : dios.DictOfSeries
+ dios holding iloc-data-pairs for every column in `data`
+ """
+ mask = dios.DictOfSeries(columns=columns)
+
+ # we use numpy here because it is faster
+ for c in columns:
+ col_mask = _isflagged(flags[c].to_numpy(), thresh)
+
+ if col_mask.any():
+ col_data = data[c].to_numpy(dtype=np.float64)
+
+ mask[c] = pd.Series(col_data[col_mask], index=np.where(col_mask)[0])
+
+ col_data[col_mask] = np.nan
+ data[c] = col_data
+
+ return data, mask
+
+ @staticmethod
+ def _unmaskData(
+ data: dios.DictOfSeries, mask: dios.DictOfSeries, columns: pd.Index = None
+ ) -> dios.DictOfSeries:
+ """
+ Restore the masked data.
+
+ Notes
+ -----
+ - Even if this returns data, it works inplace !
+ - `mask` is not a boolean mask, instead it holds the original values.
+ The index of mask is numeric and represent the integer location
+ in the original data.
+ """
+ if columns is None:
+ columns = data.columns # field was in old, is in mask and is in new
+ columns = mask.columns.intersection(columns)
+
+ for c in columns:
+
+ # ignore
+ if data[c].empty or mask[c].empty:
+ continue
+
+ # get the positions of values to unmask
+ candidates = mask[c]
+ # if the mask was removed during the function call, don't replace
+ unmask = candidates[data[c].iloc[candidates.index].isna().to_numpy()]
+ if unmask.empty:
+ continue
+ data[c].iloc[unmask.index] = unmask
- return inner
+ return data
-def _preCall(
- func: Callable, args: tuple, kwargs: dict, masking: MaskingStrT, fname: str
+def register(
+ mask: list[str],
+ demask: list[str],
+ squeeze: list[str],
+ multivariate: bool = False,
+ handles_target: bool = False,
):
"""
- Handler that runs before any call to a saqc-function.
+ Generalized decorator for any saqc functions.
- This is called before each call to a saqc-function, nevertheless if it is
- called via the SaQC-interface or plain by importing and direct calling.
+ Before the call of the decorated function:
+ - data gets masked by flags according to `dfilter`
- Parameters
- ----------
- func : callable
- the function, which is called after this returns. This is not called here!
-
- args : tuple
- args to the function
-
- kwargs : dict
- kwargs to the function
-
- masking : str
- a string indicating which columns in data need masking
-
- See Also
- --------
- _postCall: runs after a saqc-function call
-
- Returns
- -------
- args: tuple
- arguments to be passed to the actual call
- kwargs: dict
- keyword-arguments to be passed to the actual call
- state: CallState
- control keyword-arguments passed to `_postCall`
-
- """
- mthresh = _getMaskingThresh(kwargs)
- kwargs["to_mask"] = mthresh
-
- data, field, flags, *args = args
-
- # handle data - masking
- columns = _getMaskingColumns(data, field, masking)
- masked_data, mask = _maskData(data, flags, columns, mthresh)
-
- # store current state
- state = CallState(
- func=func,
- func_name=fname,
- flags=flags,
- field=field,
- args=args,
- kwargs=kwargs,
- masking=masking,
- mthresh=mthresh,
- mask=mask,
- )
-
- args = masked_data, field, flags.copy(), *args
- return args, kwargs, state
-
-
-def _postCall(result, old_state: CallState) -> FuncReturnT:
- """
- Handler that runs after any call to a saqc-function.
-
- This is called after a call to a saqc-function, nevertheless if it was
- called via the SaQC-interface or plain by importing and direct calling.
+ After the call of the decorated function:
+ - data gets demasked (original data is written back)
+ - flags gets squeezed (only one history column append per call)
Parameters
----------
- result : tuple
- the result from the called function, namely: data and flags
+ mask : list of string
+ A list of all parameter of the decorated function, that specify a column in
+ data, that is read by the function and therefore should be masked by flags.
+
+ The masking takes place before the call of the decorated function and
+ temporary sets data to `NaN` at flagged locations. It is undone by ``demask``.
+ The threshold of which data is considered to be flagged can be controlled
+ via ``dfilter``, a parameter each function takes.
+
+ demask : list of string
+ A list of all parameter of the decorated function, that specify a column in
+ data, that was masked (see ``mask``) and needs unmasking after the call.
+
+ The unmasking replace all remaining(!) ``NaN`` by its original values from
+ before the call of the decorated function.
+
+ squeeze : list of string
+ A list of all parameter of the decorated function, that specify a column in
+ flags, that is written by the function.
+
+ The squeezing combines multiple columns in the history of flags to one
+ single column. This is because, multiple writes to flags, (eg. using
+ ``flags[:,'a'] = 255`` twice) will result in multiple history columns,
+ but should considered as a single column, because only one function call
+ happened.
+
+ multivariate : bool, default False
+ If ``True``, the decorated function, process multiple data or flags
+ columns at once. Therefore the decorated function must handle a list
+ of columns in the parameter ``field``.
+
+ If ``False``, the decorated function must take a single column (``str``)
+ in ``field``.
+
+ handles_target : bool, default False
+ If ``True``, the decorated function, handles the target parameter by
+ itself. Mandatory for multivariate functions.
+ """
- old_state : dict
- control keywords from `_preCall`
+ def inner(func):
+ wrapper = FunctionWrapper(
+ func, mask, demask, squeeze, multivariate, handles_target
+ )
+ FUNC_MAP[wrapper.func_name] = wrapper
+ return wrapper
- Returns
- -------
- data, flags : dios.DictOfSeries, saqc.Flags
- """
- data, flags = result
- flags = _restoreFlags(flags, old_state)
- data = _unmaskData(data, old_state)
- return data, flags
+ return inner
-def _getMaskingColumns(data: dios.DictOfSeries, field: str, masking: MaskingStrT):
+def flagging(**kwargs):
"""
- Return columns to mask, by `masking` (decorator keyword)
+ Default decorator for univariate flagging functions.
- Depending on the `masking` kw, the following s returned:
- * 'all' : all columns from data
- * 'None' : empty pd.Index
- * 'field': single entry Index
+ Before the call of the decorated function:
+ - `data[field]` gets masked by `flags[field]` according to `dfilter`
+ After the call of the decorated function:
+ - `data[field]` gets demasked (original data is written back)
+ - `flags[field]` gets squeezed (only one history column append per call) if needed
- Returns
- -------
- columns: pd.Index
- Data columns that need to be masked.
+ Notes
+ -----
+ For full control over masking, demasking and squeezing or to implement
+ a multivariate function (multiple in- or outputs) use the `@register` decorator.
- Raises
- ------
- ValueError: if given masking literal is not supported
+ See Also
+ --------
+ resister: generalization of of this function
"""
- if masking == "all":
- return data.columns
- if masking == "none":
- return pd.Index([])
- if masking == "field":
- return pd.Index([field])
-
- raise ValueError(f"wrong use of `register(masking={masking})`")
+ if kwargs:
+ raise ValueError("use '@register' to pass keywords")
+ return register(mask=["field"], demask=["field"], squeeze=["field"])
-def _getMaskingThresh(kwargs):
+def processing(**kwargs):
"""
- Check the correct usage of the `to_mask` keyword, iff passed, otherwise return a default.
+ Default decorator for univariate processing functions.
- Parameters
- ----------
- kwargs : dict
- The kwargs that will be passed to the saqc-function, possibly contain ``to_mask``.
-
- Returns
- -------
- threshold: float
- All data gets masked, if the flags are equal or worse than the threshold.
+ - no masking of data
+ - no demasking of data
+ - no squeezing of flags
Notes
-----
- If ``to_mask`` is **not** in the kwargs, the threshold defaults to
- - ``-np.inf``
- If boolean ``to_mask`` is found in the kwargs, the threshold defaults to
- - ``-np.inf``, if ``True``
- - ``+np.inf``, if ``False``
- If a floatish ``to_mask`` is found in the kwargs, this value is taken as the threshold.
- """
- if "to_mask" not in kwargs:
- return UNFLAGGED
-
- thresh = kwargs["to_mask"]
+ For full control over masking, demasking and squeezing or to implement
+ a multivariate function (multiple in- or outputs) use the `@register` decorator.
- if not isinstance(thresh, (bool, float, int)):
- raise TypeError(f"'to_mask' must be of type bool or float")
-
- if thresh is True: # masking ON
- thresh = UNFLAGGED
-
- if thresh is False: # masking OFF
- thresh = np.inf
-
- thresh = float(thresh) # handle int
-
- return thresh
+ See Also
+ --------
+ resister: generalization of of this function
+ """
+ if kwargs:
+ raise ValueError("use '@register' to pass keywords")
+ return register(mask=[], demask=[], squeeze=[])
-def _isflagged(
- flagscol: Union[np.array, pd.Series], thresh: float
-) -> Union[np.array, pd.Series]:
+def _isflagged(flagscol: np.ndarray | pd.Series, thresh: float) -> np.array | pd.Series:
"""
Return a mask of flags accordingly to `thresh`. Return type is same as flags.
"""
- if thresh == UNFLAGGED:
+ if not isinstance(thresh, (float, int)):
+ raise TypeError(f"thresh must be of type float, not {repr(type(thresh))}")
+
+ if thresh == FILTER_ALL:
return flagscol > UNFLAGGED
return flagscol >= thresh
-
-
-def _restoreFlags(flags: Flags, old_state: CallState):
- """
- Generate flags from the temporary result-flags and the original flags.
-
- Parameters
- ----------
- flags : Flags
- The flags-frame, which is the result from a saqc-function
-
- old_state : CallState
- The state before the saqc-function was called
-
- Returns
- -------
- Flags
- """
- out = old_state.flags.copy()
- meta = {
- "func": old_state.func_name,
- "args": old_state.args,
- "keywords": old_state.kwargs,
- }
- new_columns = flags.columns.difference(old_state.flags.columns)
-
- # masking == 'none'
- # - no data was masked (no relevance here, but help understanding)
- # - the saqc-function got a copy of the whole flags frame with all full histories
- # (but is not allowed to change them; we have -> @processing for this case)
- # - the saqc-function appended none or some columns to each history
- #
- # masking == 'all'
- # - all data was masked by flags (no relevance here, but help understanding)
- # - the saqc-function got a complete new flags frame, with empty Histories
- # - the saqc-function appended none or some columns to the each history
- #
- # masking == 'field'
- # - the column `field` was masked by flags (no relevance here)
- # - the saqc-function got a complete new flags frame, with empty `History`s
- # - the saqc-function appended none or some columns to none or some `History`s
- #
- # NOTE:
- # Actually the flags SHOULD have been cleared only at the `field` (as the
- # masking-parameter implies) but the current implementation in `_prepareFlags`
- # clears all columns. Nevertheless the following code only update the `field`
- # (and new columns) and not all columns.
-
- if old_state.masking in ("none", "all"):
- columns = flags.columns
- else: # field
- columns = pd.Index([old_state.field])
-
- for col in columns.union(new_columns):
-
- if col not in out: # ensure existence
- out.history[col] = History(index=flags.history[col].index)
-
- old_history = out.history[col]
- new_history = flags.history[col]
-
- # We only want to add new columns, that were appended during the last function
- # call. If no such columns exist, we end up with an empty new_history.
- start = len(old_history.columns)
- new_history = _sliceHistory(new_history, slice(start, None))
-
- # NOTE:
- # Nothing to update -> i.e. a function did not set any flags at all.
- # This has implications for function writers: early returns out of
- # functions before `flags.__getitem__` was called once, make the
- # function call invisable to the flags/history machinery and likely
- # break translation schemes such as the `PositionalTranslator`
- if new_history.empty:
- continue
-
- squeezed = new_history.max(raw=True)
- out.history[col] = out.history[col].append(squeezed, meta=meta)
-
- return out
-
-
-def _maskData(
- data: dios.DictOfSeries, flags: Flags, columns: Sequence[str], thresh: float
-) -> Tuple[dios.DictOfSeries, dios.DictOfSeries]:
- """
- Mask data with Nans, if the flags are worse than a threshold.
- - mask only passed `columns` (preselected by `masking`-kw from decorator)
-
- Returns
- -------
- masked : dios.DictOfSeries
- masked data, same dim as original
- mask : dios.DictOfSeries
- dios holding iloc-data-pairs for every column in `data`
- """
- mask = dios.DictOfSeries(columns=columns)
-
- # we use numpy here because it is faster
- for c in columns:
- col_mask = _isflagged(flags[c].to_numpy(), thresh)
-
- if col_mask.any():
- col_data = data[c].to_numpy(dtype=np.float64)
-
- mask[c] = pd.Series(col_data[col_mask], index=np.where(col_mask)[0])
-
- col_data[col_mask] = np.nan
- data[c] = col_data
-
- return data, mask
-
-
-def _unmaskData(data: dios.DictOfSeries, old_state: CallState) -> dios.DictOfSeries:
- """
- Restore the masked data.
-
- Notes
- -----
- Even if this returns data, it work inplace !
- """
- if old_state.masking == "none":
- return data
-
- # we have two options to implement this:
- #
- # =================================
- # set new data on old
- # =================================
- # col in old, in masked, in new:
- # index differ : old <- new (replace column)
- # else : old <- new (set on masked: ``old[masked & new.notna()] = new``)
- # col in new only : old <- new (create column)
- # col in old only : old (delete column)
- #
- #
- # =================================
- # set old data on new (implemented)
- # =================================
- # col in old, in masked, in new :
- # index differ : new (keep column)
- # else : new <- old (set on masked, ``new[masked & new.isna()] = old``)
- # col in new only : new (keep column)
- # col in old only : new (ignore, was deleted)
-
- columns = old_state.mask.columns.intersection(
- data.columns
- ) # in old, in masked, in new
-
- for c in columns:
-
- # ignore
- if data[c].empty or old_state.mask[c].empty:
- continue
-
- # get the positions of values to unmask
- candidates = old_state.mask[c]
- # if the mask was removed during the function call, don't replace
- unmask = candidates[data[c].iloc[candidates.index].isna().to_numpy()]
- if unmask.empty:
- continue
- data[c].iloc[unmask.index] = unmask
-
- return data
-
-
-def _sliceHistory(history: History, sl: slice) -> History:
- history.hist = history.hist.iloc[:, sl]
- history.meta = history.meta[sl]
- return history
diff --git a/saqc/core/translation/__init__.py b/saqc/core/translation/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..b5acd9cf759f5ab036c86d275404c9ee44efed36
--- /dev/null
+++ b/saqc/core/translation/__init__.py
@@ -0,0 +1,9 @@
+#! /usr/bin/env python
+# -*- coding: utf-8 -*-
+from saqc.core.translation.basescheme import (
+ TranslationScheme,
+ FloatScheme,
+ SimpleScheme,
+)
+from saqc.core.translation.positionalscheme import PositionalScheme
+from saqc.core.translation.dmpscheme import DmpScheme
diff --git a/saqc/core/translator/basetranslator.py b/saqc/core/translation/basescheme.py
similarity index 88%
rename from saqc/core/translator/basetranslator.py
rename to saqc/core/translation/basescheme.py
index b6bec9146327a4372be7dd19e8f3312d5de22f39..ecfeff35f07c511a353e0450394416a0abed11d6 100644
--- a/saqc/core/translator/basetranslator.py
+++ b/saqc/core/translation/basescheme.py
@@ -9,14 +9,14 @@ import numpy as np
import pandas as pd
from dios import DictOfSeries
-
-from saqc.core.flags import (
- Flags,
+from saqc.constants import (
+ FILTER_ALL,
UNFLAGGED,
- UNTOUCHED,
BAD,
GOOD,
)
+
+from saqc.core.flags import Flags
from saqc.lib.types import ExternalFlag
@@ -24,7 +24,7 @@ ForwardMap = Dict[ExternalFlag, float]
BackwardMap = Dict[float, ExternalFlag]
-class Translator:
+class TranslationScheme:
"""
This class provides the basic translation mechanism and should serve as
a base class for every other translation scheme.
@@ -49,7 +49,7 @@ class Translator:
"""
# (internal) threshold flag above which values will be masked
- TO_MASK: Union[float, bool] = True
+ DFILTER_DEFAULT: float = FILTER_ALL
# additional arguments and default values the translation scheme accepts
ARGUMENTS: Dict[str, Any] = {}
@@ -71,7 +71,8 @@ class Translator:
"""
if UNFLAGGED not in backward or BAD not in backward:
raise ValueError(
- f"need translations for the special flags `UNFLAGGED` ({UNFLAGGED}) and `BAD` ({BAD})"
+ f"need translations for the special flags `UNFLAGGED` ({UNFLAGGED})"
+ f" and `BAD` ({BAD})"
)
self._forward = forward
self._backward = backward
@@ -143,7 +144,11 @@ class Translator:
return Flags(self._translate(flags, self._forward))
def backward(
- self, flags: Flags, raw: bool = False
+ self,
+ flags: Flags,
+ raw: bool = False,
+ attrs: dict | None = None,
+ **kwargs,
) -> Union[pd.DataFrame, DictOfSeries]:
"""
Translate from 'internal flags' to 'external flags'
@@ -153,17 +158,24 @@ class Translator:
flags : pd.DataFrame
The external flags to translate
+ raw: bool, default False
+ if True return data as DictOfSeries, otherwise as pandas DataFrame.
+
+ attrs : dict or None, default None
+ global meta information of saqc-object
+
Returns
-------
pd.DataFrame
"""
out = self._translate(flags, self._backward)
+ out.attrs = attrs or {}
if not raw:
out = out.to_df()
return out
-class FloatTranslator(Translator):
+class FloatScheme(TranslationScheme):
"""
Acts as the default Translator, provides a changeable subset of the
@@ -179,7 +191,7 @@ class FloatTranslator(Translator):
super().__init__(self._MAP, self._MAP)
-class SimpleTranslator(Translator):
+class SimpleScheme(TranslationScheme):
"""
Acts as the default Translator, provides a changeable subset of the
@@ -187,14 +199,14 @@ class SimpleTranslator(Translator):
"""
_FORWARD = {
- "UNFLAGGED": -np.inf,
+ "UNFLAGGED": UNFLAGGED,
"BAD": BAD,
"OK": GOOD,
}
_BACKWARD = {
UNFLAGGED: "UNFLAGGED",
- UNTOUCHED: "UNFLAGGED",
+ np.nan: "UNFLAGGED",
BAD: "BAD",
GOOD: "OK",
}
diff --git a/saqc/core/translator/dmptranslator.py b/saqc/core/translation/dmpscheme.py
similarity index 89%
rename from saqc/core/translator/dmptranslator.py
rename to saqc/core/translation/dmpscheme.py
index 8544a770e197f34e56daca88e54eb30f752baf50..aefad4018dc4c2c13ebff6ddbfb4482980b2abf6 100644
--- a/saqc/core/translator/dmptranslator.py
+++ b/saqc/core/translation/dmpscheme.py
@@ -14,12 +14,15 @@ from saqc.core.history import History
from saqc.core.flags import (
Flags,
UNFLAGGED,
- UNTOUCHED,
GOOD,
DOUBTFUL,
BAD,
)
-from saqc.core.translator.basetranslator import BackwardMap, Translator, ForwardMap
+from saqc.core.translation.basescheme import (
+ BackwardMap,
+ TranslationScheme,
+ ForwardMap,
+)
_QUALITY_CAUSES = [
@@ -43,7 +46,7 @@ _QUALITY_LABELS = [
]
-class DmpTranslator(Translator):
+class DmpScheme(TranslationScheme):
"""
Implements the translation from and to the flagging scheme implemented in
@@ -61,7 +64,7 @@ class DmpTranslator(Translator):
_BACKWARD: BackwardMap = {
UNFLAGGED: "NIL",
- UNTOUCHED: "NIL",
+ np.nan: "NIL",
GOOD: "OK",
DOUBTFUL: "DOUBTFUL",
BAD: "BAD",
@@ -99,12 +102,12 @@ class DmpTranslator(Translator):
except json.decoder.JSONDecodeError:
comment = {"test": "unknown", "comment": ""}
- histcol = pd.Series(UNTOUCHED, index=field_flags.index)
+ histcol = pd.Series(np.nan, index=field_flags.index)
histcol.loc[values.index] = self(flag)
meta = {
"func": comment["test"],
- "keywords": {"comment": comment["comment"], "cause": cause},
+ "kwargs": {"comment": comment["comment"], "cause": cause},
}
field_history.append(histcol, meta=meta)
@@ -112,7 +115,9 @@ class DmpTranslator(Translator):
return Flags(data)
- def backward(self, flags: Flags) -> pd.DataFrame:
+ def backward(
+ self, flags: Flags, attrs: dict | None = None, **kwargs
+ ) -> pd.DataFrame:
"""
Translate from 'internal flags' to 'external flags'
@@ -120,11 +125,14 @@ class DmpTranslator(Translator):
----------
flags : The external flags to translate
+ attrs : dict or None, default None
+ global meta information of saqc-object
+
Returns
-------
translated flags
"""
- tflags = super().backward(flags, raw=True)
+ tflags = super().backward(flags, raw=True, attrs=attrs)
out = pd.DataFrame(
index=reduce(lambda x, y: x.union(y), tflags.indexes).sort_values(),
@@ -148,7 +156,7 @@ class DmpTranslator(Translator):
# extract from meta
meta = history.meta[col]
- keywords = meta.get("keywords", {})
+ keywords = meta.get("kwargs", {})
comment = json.dumps(
{
"test": meta.get("func", "unknown"),
diff --git a/saqc/core/translator/positionaltranslator.py b/saqc/core/translation/positionalscheme.py
similarity index 89%
rename from saqc/core/translator/positionaltranslator.py
rename to saqc/core/translation/positionalscheme.py
index 9a8f085f8c4f522726331a049cf48f4078f7392b..d2e7623dad980c5011c6c3d7c548276c19fe4064 100644
--- a/saqc/core/translator/positionaltranslator.py
+++ b/saqc/core/translation/positionalscheme.py
@@ -8,22 +8,25 @@ import pandas as pd
from saqc.core.flags import (
Flags,
History,
- UNTOUCHED,
UNFLAGGED,
GOOD,
DOUBTFUL,
BAD,
)
-from saqc.core.translator.basetranslator import Translator, ForwardMap, BackwardMap
+from saqc.core.translation.basescheme import (
+ TranslationScheme,
+ ForwardMap,
+ BackwardMap,
+)
-class PositionalTranslator(Translator):
+class PositionalScheme(TranslationScheme):
"""
Implements the translation from and to the flagging scheme implemented by CHS
"""
- TO_MASK = DOUBTFUL + 1
+ DFILTER_DEFAULT = DOUBTFUL + 1
_FORWARD: ForwardMap = {
-6: UNFLAGGED,
@@ -34,7 +37,7 @@ class PositionalTranslator(Translator):
2: BAD,
}
_BACKWARD: BackwardMap = {
- UNTOUCHED: 0,
+ np.nan: 0,
UNFLAGGED: 0,
GOOD: 0,
DOUBTFUL: 1,
@@ -76,7 +79,7 @@ class PositionalTranslator(Translator):
return Flags(data)
- def backward(self, flags: Flags) -> pd.DataFrame:
+ def backward(self, flags: Flags, **kwargs) -> pd.DataFrame:
"""
Translate from 'internal flags' to 'external flags'
diff --git a/saqc/core/translator/__init__.py b/saqc/core/translator/__init__.py
deleted file mode 100644
index 17a0490bb274c77fd847a11cde24a80efd3ca020..0000000000000000000000000000000000000000
--- a/saqc/core/translator/__init__.py
+++ /dev/null
@@ -1,9 +0,0 @@
-#! /usr/bin/env python
-# -*- coding: utf-8 -*-
-from saqc.core.translator.basetranslator import (
- Translator,
- FloatTranslator,
- SimpleTranslator,
-)
-from saqc.core.translator.positionaltranslator import PositionalTranslator
-from saqc.core.translator.dmptranslator import DmpTranslator
diff --git a/saqc/core/visitor.py b/saqc/core/visitor.py
index e4b768f4d0b427e55b03831a7fafddffa4da4e18..1ec26b48817f9e5e7e3bff9a87858c5dafb560aa 100644
--- a/saqc/core/visitor.py
+++ b/saqc/core/visitor.py
@@ -2,59 +2,21 @@
# -*- coding: utf-8 -*-
import ast
-import numpy as np
-import pandas as pd
-from saqc.constants import *
+from saqc.constants import ENVIRONMENT
from saqc.core.register import FUNC_MAP
-import saqc.lib.ts_operators as ts_ops
-
-
-ENVIRONMENT = {
- "NAN": np.nan,
- "abs": np.abs,
- "max": np.nanmax,
- "min": np.nanmin,
- "mean": np.nanmean,
- "sum": np.nansum,
- "std": np.nanstd,
- "len": len,
- "exp": np.exp,
- "log": np.log,
- "var": np.nanvar,
- "median": np.nanmedian,
- "first": ts_ops.first,
- "last": ts_ops.last,
- "count": ts_ops.count,
- "deltaT": ts_ops.deltaT,
- "id": ts_ops.identity,
- "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,
-}
-
-# TODO:
-# get from saqc.constants
-RESERVED = {"GOOD", "BAD", "UNFLAGGED"}
class ConfigExpressionParser(ast.NodeVisitor):
"""
Generic configuration functions will be rewritten as lambda functions
- and variables that need a look up in `data` will act as arguments, e.g.:
- `flagGeneric(func=(x != 4) & (y < 3))`
- will be rewritten to
- `lambda x, y: (x != 4) & (y < 3)`
-
- The main purpose of this class is to identify all such lambda arguments
- and check the given expression for accordance with the restrictions
- imposed onto generic functions.
+ and all defined variables will act as arguments, e.g.:
+ ``flagGeneric(func=(x != 4) & (y < 3))`` will be rewritten to
+ ``lambda x, y: (x != 4) & (y < 3)``
+
+ The main purpose of this class is to identify all variables used in
+ a given generic function and to check that it does not violate the
+ restrictions imposed onto generic functions.
"""
SUPPORTED = (
@@ -88,11 +50,6 @@ class ConfigExpressionParser(ast.NodeVisitor):
def __init__(self, node):
self._args = []
self.visit(node)
- if not self._args:
- # NOTE:
- # we assume, that we are not dealing with an
- # expression as we couldn't find any arguments
- raise TypeError("not a valid expression")
@property
def args(self):
@@ -106,10 +63,9 @@ class ConfigExpressionParser(ast.NodeVisitor):
def visit_Name(self, node):
# NOTE:
- # the assumption is, that anything not in
- # ENVIRONMENT + RESERVED needs a lookup in `data`
+ # anything not in ENVIRONMENT should be an argument of the generic function
name = node.id
- if name not in ENVIRONMENT and name not in RESERVED:
+ if name not in ENVIRONMENT:
self._args.append(name)
self.generic_visit(node)
@@ -139,20 +95,14 @@ class ConfigFunctionParser(ast.NodeVisitor):
)
def __init__(self):
-
self.kwargs = {}
- self.environment = {
- "GOOD": GOOD,
- "BAD": BAD,
- "UNFLAGGED": UNFLAGGED,
- **ENVIRONMENT,
- }
def parse(self, node):
func = self.visit_Call(node)
return func, self.kwargs
def visit_Call(self, node):
+
if not isinstance(node, ast.Call):
raise TypeError("expected function call")
@@ -172,13 +122,11 @@ class ConfigFunctionParser(ast.NodeVisitor):
def visit_keyword(self, node):
- k, v = node.arg, node.value
+ key, value = node.arg, node.value
check_tree = True
- # NOTE: `node` is not a constant or a variable,
- # so it should be a function call
- try:
- visitor = ConfigExpressionParser(v)
+ if key == "func":
+ visitor = ConfigExpressionParser(value)
args = ast.arguments(
posonlyargs=[],
kwonlyargs=[],
@@ -188,34 +136,33 @@ class ConfigFunctionParser(ast.NodeVisitor):
kwarg=None,
vararg=None,
)
- v = ast.Lambda(args=args, body=v)
+ value = ast.Lambda(args=args, body=value)
# NOTE:
# don't pass the generated functions down
# to the checks implemented in this class...
check_tree = False
- except TypeError:
- pass
- vnode = ast.Assign(targets=[ast.Name(id=k, ctx=ast.Store())], value=v)
+ vnode = ast.Assign(targets=[ast.Name(id=key, ctx=ast.Store())], value=value)
# NOTE:
# in order to get concrete values out of the AST
- # we compile and evaluate the keyword (e.g. max=100)
+ # we compile and evaluate every keyword (e.g. max=100)
# into the dictionary `self.kwargs`
# -> after all keywords where visited we end up with
- # a dictionary holding all the passed arguments as
- # real python objects
+ # a dictionary holding all the passed arguments as
+ # real python objects
co = compile(
ast.fix_missing_locations(ast.Interactive(body=[vnode])),
"<ast>",
mode="single",
)
- # NOTE: only pass a copy to not clutter the self.environment
- exec(co, {**self.environment}, self.kwargs)
+ # NOTE: only pass a copy to not clutter the ENVIRONMENT
+ # try:
+ exec(co, {**ENVIRONMENT}, self.kwargs)
# let's do some more validity checks
if check_tree:
- self.generic_visit(v)
+ self.generic_visit(value)
def generic_visit(self, node):
if not isinstance(node, self.SUPPORTED_NODES):
diff --git a/saqc/funcs/__init__.py b/saqc/funcs/__init__.py
index 006f061ab0cd5bbd2110d69906a5f05ef59c7c99..d4fabccfc98765f48221838d54f21f9db73f5301 100644
--- a/saqc/funcs/__init__.py
+++ b/saqc/funcs/__init__.py
@@ -2,7 +2,7 @@
# -*- coding: utf-8 -*-
# imports needed to make the functions register themself
-from saqc.core.register import flagging
+from saqc.core.register import register
from saqc.funcs.breaks import *
from saqc.funcs.changepoints import *
from saqc.funcs.constants import *
diff --git a/saqc/funcs/breaks.py b/saqc/funcs/breaks.py
index e139b886153ddc0e4a52ef7510a4f32f4402b96b..92da3e89a6b4d0b4ff4a17260df9c1ee5e230c34 100644
--- a/saqc/funcs/breaks.py
+++ b/saqc/funcs/breaks.py
@@ -18,24 +18,20 @@ from dios import DictOfSeries
from saqc.constants import *
from saqc.lib.tools import groupConsecutives
-from saqc.lib.types import FreqString
-from saqc.funcs.changepoints import assignChangePointCluster
+from saqc.funcs.changepoints import _assignChangePointCluster
from saqc.core.flags import Flags
from saqc.core.history import History
-from saqc.core.register import _isflagged, flagging
+from saqc.core.register import _isflagged, register, flagging
-# NOTE:
-# masking="none" as we otherwise might interprete
-# the masked values as missing data
-@flagging(masking="none")
+@register(mask=[], demask=[], squeeze=["field"])
def flagMissing(
data: DictOfSeries,
field: str,
flags: Flags,
flag: float = BAD,
- to_mask: float = UNFLAGGED,
- **kwargs
+ dfilter: float = FILTER_ALL,
+ **kwargs,
) -> Tuple[DictOfSeries, Flags]:
"""
The function flags all values indicating missing data.
@@ -62,21 +58,21 @@ def flagMissing(
datacol = data[field]
mask = datacol.isna()
- mask = ~_isflagged(flags[field], to_mask) & mask
+ mask = ~_isflagged(flags[field], dfilter) & mask
flags[mask, field] = flag
return data, flags
-@flagging(masking="field")
+@flagging()
def flagIsolated(
data: DictOfSeries,
field: str,
flags: Flags,
- gap_window: FreqString,
- group_window: FreqString,
+ gap_window: str,
+ group_window: str,
flag: float = BAD,
- **kwargs
+ **kwargs,
) -> Tuple[DictOfSeries, Flags]:
"""
The function flags arbitrary large groups of values, if they are surrounded by sufficiently
@@ -147,19 +143,19 @@ def flagIsolated(
return data, flags
-@flagging(masking="field")
+@flagging()
def flagJumps(
data: DictOfSeries,
field: str,
flags: Flags,
thresh: float,
- window: FreqString,
+ window: str,
min_periods: int = 1,
flag: float = BAD,
- **kwargs
+ **kwargs,
) -> Tuple[DictOfSeries, Flags]:
"""
- Flag datapoints, where the mean of the values significantly changes (where the value course "jumps").
+ Flag where the mean of the values significantly changes (the data "jumps").
Parameters
----------
@@ -180,7 +176,7 @@ def flagJumps(
flag : float, default BAD
flag to set.
"""
- return assignChangePointCluster(
+ return _assignChangePointCluster(
data,
field,
flags,
@@ -192,5 +188,5 @@ def flagJumps(
model_by_resids=False,
assign_cluster=False,
flag=flag,
- **kwargs
+ **kwargs,
)
diff --git a/saqc/funcs/changepoints.py b/saqc/funcs/changepoints.py
index 58af0ad4c1f54e2eb053271c08ed76e933e04a4a..1cd87eff083fcb3cfaaaa4b22305a2dc944f28bb 100644
--- a/saqc/funcs/changepoints.py
+++ b/saqc/funcs/changepoints.py
@@ -12,22 +12,22 @@ from typing_extensions import Literal
from dios import DictOfSeries
from saqc.constants import *
-from saqc.lib.tools import customRoller
-from saqc.core import flagging, Flags
-from saqc.lib.types import FreqString
+from saqc.core.register import flagging
+from saqc.lib.tools import customRoller, filterKwargs
+from saqc.core import register, Flags
-@flagging(masking="field")
+@flagging()
def flagChangePoints(
data: DictOfSeries,
field: str,
flags: Flags,
stat_func: Callable[[np.ndarray, np.ndarray], float],
thresh_func: Callable[[np.ndarray, np.ndarray], float],
- window: FreqString | Tuple[FreqString, FreqString],
+ window: str | Tuple[str, str],
min_periods: int | Tuple[int, int],
closed: Literal["right", "left", "both", "neither"] = "both",
- reduce_window: FreqString = None,
+ reduce_window: str = None,
reduce_func: Callable[[np.ndarray, np.ndarray], int] = lambda x, _: x.argmax(),
flag: float = BAD,
**kwargs,
@@ -93,7 +93,7 @@ def flagChangePoints(
Returns
-------
"""
- return assignChangePointCluster(
+ return _assignChangePointCluster(
data,
field,
flags,
@@ -112,7 +112,7 @@ def flagChangePoints(
)
-@flagging(masking="field")
+@register(mask=["field"], demask=[], squeeze=[])
def assignChangePointCluster(
data: DictOfSeries,
field: str,
@@ -125,18 +125,15 @@ def assignChangePointCluster(
reduce_window: str = None,
reduce_func: Callable[[np.ndarray, np.ndarray], float] = lambda x, _: x.argmax(),
model_by_resids: bool = False,
- set_flags: bool = False,
- assign_cluster: bool = True,
- flag: float = BAD,
**kwargs,
-) -> Tuple[DictOfSeries, Flags]:
+):
"""
- Assigns label to the data, aiming to reflect continous regimes of the processes the data is assumed to be
- generated by.
- The regime change points detection is based on a sliding window search.
+ Assigns label to the data, aiming to reflect continuous regimes of the processes
+ the data is assumed to be generated by. The regime change points detection is
+ based on a sliding window search.
- Note, that the cluster labels will be stored to the `field` field of the input data, so that the data that is
- clustered gets overridden.
+ Note, that the cluster labels will be stored to the `field` field of the input
+ data, so that the data that is clustered gets overridden.
Parameters
----------
@@ -150,12 +147,13 @@ def assignChangePointCluster(
A flags object, holding flags and additional informations related to `data`.
stat_func : Callable[[numpy.array, numpy.array], float]
- A function that assigns a value to every twin window. Left window content will be passed to first variable,
+ A function that assigns a value to every twin window. Left window content will
+ be passed to first variable,
right window content will be passed to the second.
thresh_func : Callable[numpy.array, numpy.array], float]
- A function that determines the value level, exceeding wich qualifies a timestamps func func value as denoting a
- changepoint.
+ A function that determines the value level, exceeding wich qualifies a
+ timestamps func func value as denoting a changepoint.
window : str, tuple of string
Size of the rolling windows the calculation is performed in. If it is a single
@@ -173,35 +171,65 @@ def assignChangePointCluster(
Determines the closure of the sliding windows.
reduce_window : {None, str}, default None
- The sliding window search method is not an exact CP search method and usually there wont be
- detected a single changepoint, but a "region" of change around a changepoint.
- If `reduce_window` is given, for every window of size `reduce_window`, there
- will be selected the value with index `reduce_func(x, y)` and the others will be dropped.
- If `reduce_window` is None, the reduction window size equals the
- twin window size, the changepoints have been detected with.
-
- reduce_func : Callable[[numpy.array, numpy.array], numpy.array], default lambda x, y: x.argmax()
+ The sliding window search method is not an exact CP search method and usually
+ there wont be detected a single changepoint, but a "region" of change around
+ a changepoint. If `reduce_window` is given, for every window of size
+ `reduce_window`, there will be selected the value with index `reduce_func(x,
+ y)` and the others will be dropped. If `reduce_window` is None, the reduction
+ window size equals the twin window size, the changepoints have been detected
+ with.
+
+ reduce_func : callable, default lambda x,y: x.argmax()
A function that must return an index value upon input of two arrays x and y.
- First input parameter will hold the result from the stat_func evaluation for every
- reduction window. Second input parameter holds the result from the thresh_func evaluation.
- The default reduction function just selects the value that maximizes the stat_func.
-
- set_flags : bool, default False
- If true, the points, where there is a change in data modelling regime detected gets flagged.
+ First input parameter will hold the result from the stat_func evaluation for
+ every reduction window. Second input parameter holds the result from the
+ thresh_func evaluation. The default reduction function just selects the value
+ that maximizes the stat_func.
model_by_resids : bool, default False
- If True, the data is replaced by the stat_funcs results instead of regime labels.
-
- assign_cluster : bool, default True
- Is set to False, if called by function that oly wants to calculate flags.
-
- flag : float, default BAD
- flag to set.
+ If True, the stat_funcs results are written, otherwise the regime labels.
Returns
-------
"""
- data = data.copy()
+ reserved = ["assign_cluster", "set_flags", "flag"]
+ kwargs = filterKwargs(kwargs, reserved)
+ return _assignChangePointCluster(
+ data=data,
+ field=field,
+ flags=flags,
+ stat_func=stat_func,
+ thresh_func=thresh_func,
+ window=window,
+ min_periods=min_periods,
+ closed=closed,
+ reduce_window=reduce_window,
+ reduce_func=reduce_func,
+ model_by_resids=model_by_resids,
+ **kwargs,
+ # control args
+ assign_cluster=True,
+ set_flags=False,
+ )
+
+
+def _assignChangePointCluster(
+ data: DictOfSeries,
+ field: str,
+ flags: Flags,
+ stat_func: Callable[[np.array, np.array], float],
+ thresh_func: Callable[[np.array, np.array], float],
+ window: str | Tuple[str, str],
+ min_periods: int | Tuple[int, int],
+ closed: Literal["right", "left", "both", "neither"] = "both",
+ reduce_window: str = None,
+ reduce_func: Callable[[np.ndarray, np.ndarray], float] = lambda x, _: x.argmax(),
+ model_by_resids: bool = False,
+ set_flags: bool = False,
+ assign_cluster: bool = True,
+ flag: float = BAD,
+ **kwargs,
+) -> Tuple[DictOfSeries, Flags]:
data_ser = data[field].dropna()
if isinstance(window, (list, tuple)):
bwd_window, fwd_window = window
@@ -220,12 +248,14 @@ def assignChangePointCluster(
)
reduce_window = f"{s}s"
- roller = customRoller(data_ser, window=bwd_window)
+ roller = customRoller(data_ser, window=bwd_window, min_periods=bwd_min_periods)
bwd_start, bwd_end = roller.window_indexer.get_window_bounds(
len(data_ser), min_periods=bwd_min_periods, closed=closed
)
- roller = customRoller(data_ser, window=fwd_window, forward=True)
+ roller = customRoller(
+ data_ser, window=fwd_window, forward=True, min_periods=fwd_min_periods
+ )
fwd_start, fwd_end = roller.window_indexer.get_window_bounds(
len(data_ser), min_periods=fwd_min_periods, closed=closed
)
@@ -272,7 +302,7 @@ def assignChangePointCluster(
detected = pd.Series(True, index=det_index)
if reduce_window:
l = detected.shape[0]
- roller = customRoller(detected, window=reduce_window)
+ roller = customRoller(detected, window=reduce_window, min_periods=1)
start, end = roller.window_indexer.get_window_bounds(
num_values=l, min_periods=1, closed="both", center=True
)
diff --git a/saqc/funcs/constants.py b/saqc/funcs/constants.py
index 395f0c808dc21e01557d104696e2219d99e9def1..18bf035497c79cbfc708f29753803707af0b5245 100644
--- a/saqc/funcs/constants.py
+++ b/saqc/funcs/constants.py
@@ -11,21 +11,21 @@ import operator
from dios import DictOfSeries
from saqc.constants import *
-from saqc.core import flagging, Flags
+from saqc.core import register, Flags
+from saqc.core.register import flagging
from saqc.lib.ts_operators import varQC
from saqc.lib.tools import customRoller, getFreqDelta, statPass
-from saqc.lib.types import FreqString
-@flagging(masking="field")
+@flagging()
def flagConstants(
data: DictOfSeries,
field: str,
flags: Flags,
thresh: float,
- window: FreqString,
+ window: str,
flag: float = BAD,
- **kwargs
+ **kwargs,
) -> Tuple[DictOfSeries, Flags]:
"""
This functions flags plateaus/series of constant values of length `window` if
@@ -34,7 +34,7 @@ def flagConstants(
Function flags plateaus/series of constant values. Any interval of values y(t),..y(t+n) is flagged, if:
(1) n > `window`
- (2) |(y(t + i) - (t + j)| < `thresh`, for all i,j in [0, 1, ..., n]
+ (2) `|(y(t + i) - (t + j)|` < `thresh`, for all i,j in [0, 1, ..., n]
Flag values are (semi-)constant.
@@ -61,8 +61,6 @@ def flagConstants(
The flags object, holding flags and additional informations related to `data`.
Flags values may have changed, relatively to the flags input.
"""
- if not isinstance(window, str):
- raise TypeError("window must be offset string.")
d = data[field]
# min_periods=2 ensures that at least two non-nan values are present
@@ -81,17 +79,17 @@ def flagConstants(
return data, flags
-@flagging(masking="field")
+@flagging()
def flagByVariance(
data: DictOfSeries,
field: str,
flags: Flags,
- window: FreqString = "12h",
+ window: str = "12h",
thresh: float = 0.0005,
maxna: int = None,
maxna_group: int = None,
flag: float = BAD,
- **kwargs
+ **kwargs,
) -> Tuple[DictOfSeries, Flags]:
"""
Function flags plateaus/series of constant values. Any interval of values y(t),..y(t+n) is flagged, if:
diff --git a/saqc/funcs/curvefit.py b/saqc/funcs/curvefit.py
index c29d2bbb4afda3ace36e582c53df0a333baa13b1..a49cf98eb1e3b7df85c56fa7dc3fe6312d509999 100644
--- a/saqc/funcs/curvefit.py
+++ b/saqc/funcs/curvefit.py
@@ -9,8 +9,8 @@ import pandas as pd
from dios import DictOfSeries
from saqc.constants import *
-from saqc.core import flagging, Flags
-from saqc.lib.tools import getFreqDelta
+from saqc.core import register, Flags
+from saqc.lib.tools import getFreqDelta, filterKwargs
from saqc.lib.ts_operators import (
polyRollerIrregular,
polyRollerNumba,
@@ -20,85 +20,103 @@ from saqc.lib.ts_operators import (
)
-@flagging(masking="field")
+@register(mask=["field"], demask=[], squeeze=[])
def fitPolynomial(
data: DictOfSeries,
field: str,
flags: Flags,
window: Union[int, str],
order: int,
- set_flags: bool = True,
min_periods: int = 0,
- return_residues: bool = False,
- flag: float = BAD,
**kwargs
) -> Tuple[DictOfSeries, Flags]:
"""
- Function fits a polynomial model to the data and returns the fitted data curve.
+ Fits a polynomial model to the data.
The fit is calculated by fitting a polynomial of degree `order` to a data slice
of size `window`, that has x at its center.
- Note, that the resulting fit is stored to the `field` field of the input data, so that the original data, the
- polynomial is fitted to, gets overridden.
-
- Note, that, if data[field] is not alligned to an equidistant frequency grid, the window size passed,
- has to be an offset string.
-
- Note, that calculating the residues tends to be quite costy, because a function fitting is perfomed for every
- sample. To improve performance, consider the following possibillities:
+ Note that the result is stored in `field` and overwrite it unless a
+ `target` is given.
In case your data is sampled at an equidistant frequency grid:
- (1) If you know your data to have no significant number of missing values, or if you do not want to
- calculate residues for windows containing missing values any way, performance can be increased by setting
- min_periods=window.
+ (1) If you know your data to have no significant number of missing values,
+ or if you do not want to calculate residues for windows containing missing values
+ any way, performance can be increased by setting min_periods=window.
+
+ Note, that the initial and final window/2 values do not get fitted.
- Note, that in the current implementation, the initial and final window/2 values do not get fitted.
+ Each residual gets assigned the worst flag present in the interval of
+ the original data.
Parameters
----------
data : dios.DictOfSeries
- A dictionary of pandas.Series, holding all the data.
+ The data.
+
field : str
- The fieldname of the column, holding the data-to-be-modelled.
+ The column, holding the data-to-be-modelled.
+
flags : saqc.Flags
Container to store quality flags to data.
+
window : {str, int}
- The size of the window you want to use for fitting. If an integer is passed, the size
- refers to the number of periods for every fitting window. If an offset string is passed,
- the size refers to the total temporal extension. The window will be centered around the vaule-to-be-fitted.
- For regularly sampled timeseries the period number will be casted down to an odd number if
- even.
+ The size of the window you want to use for fitting. If an integer is passed,
+ the size refers to the number of periods for every fitting window. If an
+ offset string is passed, the size refers to the total temporal extension. The
+ window will be centered around the vaule-to-be-fitted. For regularly sampled
+ timeseries the period number will be casted down to an odd number if even.
+
order : int
The degree of the polynomial used for fitting
- set_flags : bool, default True
- Whether or not to assign new flags to the calculated residuals. If True, a residual gets assigned the worst
- flag present in the interval, the data for its calculation was obtained from.
- min_periods : {int, None}, default 0
- The minimum number of periods, that has to be available in every values fitting surrounding for the polynomial
- fit to be performed. If there are not enough values, np.nan gets assigned. Default (0) results in fitting
- regardless of the number of values present (results in overfitting for too sparse intervals). To automatically
- set the minimum number of periods to the number of values in an offset defined window size, pass np.nan.
- return_residues : bool, default False
- Internal parameter. Makes the method return the residues instead of the fit.
- flag : float, default BAD
- flag to set.
+
+ min_periods : int or None, default 0
+ The minimum number of periods, that has to be available in every values
+ fitting surrounding for the polynomial fit to be performed. If there are not
+ enough values, np.nan gets assigned. Default (0) results in fitting
+ regardless of the number of values present (results in overfitting for too
+ sparse intervals). To automatically set the minimum number of periods to the
+ number of values in an offset defined window size, pass np.nan.
Returns
-------
data : dios.DictOfSeries
- A dictionary of pandas.Series, holding all the data.
- Data values may have changed relatively to the data input.
flags : saqc.Flags
- The quality flags of data
"""
- # TODO: some (rater large) parts are functional similar to saqc.funcs.rolling.roll
+ reserved = ["residues", "set_flags"]
+ filterKwargs(kwargs, reserved)
+ return _fitPolynomial(
+ data=data,
+ field=field,
+ flags=flags,
+ window=window,
+ order=order,
+ min_periods=min_periods,
+ **kwargs,
+ # ctrl args
+ return_residues=False,
+ set_flags=True,
+ )
+
+
+def _fitPolynomial(
+ data: DictOfSeries,
+ field: str,
+ flags: Flags,
+ window: Union[int, str],
+ order: int,
+ set_flags: bool = True,
+ min_periods: int = 0,
+ return_residues: bool = False,
+ **kwargs
+) -> Tuple[DictOfSeries, Flags]:
+
+ # TODO: some (rather large) parts are functional similar to saqc.funcs.rolling.roll
if data[field].empty:
- flags[:, field] = UNTOUCHED
return data, flags
- data = data.copy()
- to_fit = data[field]
+
+ to_fit = data[field].copy()
regular = getFreqDelta(to_fit.index)
if not regular:
if isinstance(window, int):
@@ -166,7 +184,8 @@ def fitPolynomial(
engine="numba",
engine_kwargs={"no_python": True},
)
- # due to a tiny bug - rolling with center=True doesnt work when using numba engine.
+ # due to a tiny bug - rolling with center=True doesnt work
+ # when using numba engine.
residues = residues.shift(-int(center_index))
else:
residues = to_fit.rolling(window, center=True).apply(
@@ -185,7 +204,8 @@ def fitPolynomial(
engine_kwargs={"no_python": True},
raw=True,
)
- # due to a tiny bug - rolling with center=True doesnt work when using numba engine.
+ # due to a tiny bug - rolling with center=True doesnt work
+ # when using numba engine.
residues = residues.shift(-int(center_index))
else:
residues = to_fit.rolling(window, center=True).apply(
diff --git a/saqc/funcs/drift.py b/saqc/funcs/drift.py
index 54e658eec8a00703a23ba18f88c84ad5e6abe81d..98a1c9e380cbddf41d93d6d8b051f2d788fccca2 100644
--- a/saqc/funcs/drift.py
+++ b/saqc/funcs/drift.py
@@ -1,39 +1,44 @@
#! /usr/bin/env python
# -*- coding: utf-8 -*-
+from __future__ import annotations
+import functools
from typing import Optional, Tuple, Sequence, Callable, Optional
from typing_extensions import Literal
import numpy as np
import pandas as pd
-import functools
from scipy import stats
from scipy.optimize import curve_fit
from scipy.spatial.distance import pdist
from dios import DictOfSeries
-from saqc.constants import *
-from saqc.core.register import flagging
+from saqc.constants import BAD
+from saqc.core.register import register, flagging
from saqc.core import Flags
-from saqc.funcs.changepoints import assignChangePointCluster
+from saqc.funcs.changepoints import _assignChangePointCluster
from saqc.funcs.tools import dropField, copyField
-from saqc.lib.tools import detectDeviants
-from saqc.lib.types import FreqString, CurveFitter
+
+from saqc.lib.tools import detectDeviants, toSequence, filterKwargs
+from saqc.lib.ts_operators import linearDriftModel, expDriftModel
+from saqc.lib.types import CurveFitter
+from saqc.lib.ts_operators import linearDriftModel, expDriftModel
LinkageString = Literal[
"single", "complete", "average", "weighted", "centroid", "median", "ward"
]
+MODELDICT = {"linear": linearDriftModel, "exponential": expDriftModel}
+
-@flagging(masking="all")
+@flagging()
def flagDriftFromNorm(
data: DictOfSeries,
- field: str,
+ field: Sequence[str],
flags: Flags,
- fields: Sequence[str],
- freq: FreqString,
+ freq: str,
spread: float,
frac: float = 0.5,
metric: Callable[[np.ndarray, np.ndarray], float] = lambda x, y: pdist(
@@ -42,14 +47,14 @@ def flagDriftFromNorm(
/ len(x),
method: LinkageString = "single",
flag: float = BAD,
- **kwargs
+ **kwargs,
) -> Tuple[DictOfSeries, Flags]:
"""
The function flags value courses that significantly deviate from a group of normal value courses.
"Normality" is determined in terms of a maximum spreading distance, that members of a normal group must not exceed.
In addition, only a group is considered "normal" if it contains more then `frac` percent of the
- variables in "fields".
+ variables in "field".
See the Notes section for a more detailed presentation of the algorithm
@@ -57,12 +62,10 @@ def flagDriftFromNorm(
----------
data : dios.DictOfSeries
A dictionary of pandas.Series, holding all the data.
- field : str
- A dummy parameter.
+ field : list of str
+ List of fieldnames in data, determining which variables are to be included into the flagging process.
flags : saqc.Flags
A flags object, holding flags and additional informations related to `data`.
- fields : str
- List of fieldnames in data, determining which variables are to be included into the flagging process.
freq : str
An offset string, determining the size of the seperate datachunks that the algorihm is to be piecewise
applied on.
@@ -99,12 +102,12 @@ def flagDriftFromNorm(
following steps are performed for every data "segment" of length `freq` in order to find the
"abnormal" data:
- 1. Calculate the distances :math:`d(x_i,x_j)` for all :math:`x_i` in parameter `fields`. (with :math:`d`
+ 1. Calculate the distances :math:`d(x_i,x_j)` for all :math:`x_i` in parameter `field`. (with :math:`d`
denoting the distance function
passed to the parameter `metric`.
2. Calculate a dendogram with a hierarchical linkage algorithm, specified by the parameter `method`.
3. Flatten the dendogram at the level, the agglomeration costs exceed the value given by the parameter `spread`
- 4. check if there is a cluster containing more than `frac` percentage of the variables in fields.
+ 4. check if there is a cluster containing more than `frac` percentage of the variables in field.
1. if yes: flag all the variables that are not in that cluster (inside the segment)
2. if no: flag nothing
@@ -130,6 +133,7 @@ def flagDriftFromNorm(
Introduction to Hierarchical clustering:
[2] https://en.wikipedia.org/wiki/Hierarchical_clustering
"""
+ fields = toSequence(field)
data_to_flag = data[fields].to_df()
data_to_flag.dropna(inplace=True)
@@ -147,20 +151,28 @@ def flagDriftFromNorm(
return data, flags
-@flagging(masking="all")
+# default multivariate flagging
+@register(
+ mask=["field", "reference"],
+ demask=["field", "reference"],
+ squeeze=["field", "reference"], # reference is written !
+ multivariate=True,
+ handles_target=True,
+)
def flagDriftFromReference(
data: DictOfSeries,
- field: str,
+ field: Sequence[str],
flags: Flags,
- fields: Sequence[str],
- freq: FreqString,
+ reference: str,
+ freq: str,
thresh: float,
metric: Callable[[np.ndarray, np.ndarray], float] = lambda x, y: pdist(
np.array([x, y]), metric="cityblock"
)
/ len(x),
+ target=None,
flag: float = BAD,
- **kwargs
+ **kwargs,
) -> Tuple[DictOfSeries, Flags]:
"""
The function flags value courses that deviate from a reference course by a margin exceeding a certain threshold.
@@ -171,12 +183,12 @@ def flagDriftFromReference(
----------
data : dios.DictOfSeries
A dictionary of pandas.Series, holding all the data.
- field : str
- The reference variable, the deviation from wich determines the flagging.
+ field : list of str
+ List of fieldnames in data, determining wich variables are to be included into the flagging process.
flags : saqc.Flags
A flags object, holding flags and additional informations related to `data`.
- fields : str
- List of fieldnames in data, determining wich variables are to be included into the flagging process.
+ reference : str
+ The reference variable, the deviation from wich determines the flagging.
freq : str
An offset string, determining the size of the seperate datachunks that the algorihm is to be piecewise
applied on.
@@ -186,6 +198,8 @@ def flagDriftFromReference(
A distance function. It should be a function of 2 1-dimensional arrays and return a float scalar value.
This value is interpreted as the distance of the two input arrays. The default is the averaged manhatten metric.
See the Notes section to get an idea of why this could be a good choice.
+ target : None
+ ignored.
flag : float, default BAD
flag to set.
@@ -204,14 +218,15 @@ def flagDriftFromReference(
That is, why, the "averaged manhatten metric" is set as the metric default, since it corresponds to the
averaged value distance, two timeseries have (as opposed by euclidean, for example).
"""
- data_to_flag = data[fields].to_df()
- data_to_flag.dropna(inplace=True)
+ if target:
+ raise NotImplementedError("target is not implemented for this function")
- fields = list(fields)
- if field not in fields:
- fields.append(field)
+ fields = toSequence(field)
+ data_to_flag = data[fields].to_df().dropna()
- var_num = len(fields)
+ fields = list(fields)
+ if reference not in fields:
+ fields.append(reference)
segments = data_to_flag.groupby(pd.Grouper(freq=freq))
for segment in segments:
@@ -219,8 +234,10 @@ def flagDriftFromReference(
if segment[1].shape[0] <= 1:
continue
- for i in range(var_num):
- dist = metric(segment[1].iloc[:, i].values, segment[1].loc[:, field].values)
+ for i in range(len(fields)):
+ dist = metric(
+ segment[1].iloc[:, i].values, segment[1].loc[:, reference].values
+ )
if dist > thresh:
flags[segment[1].index, fields[i]] = flag
@@ -228,14 +245,21 @@ def flagDriftFromReference(
return data, flags
-@flagging(masking="all")
+# default multivariate flagging
+@register(
+ mask=["set_1", "set_2"],
+ demask=["set_1", "set_2"],
+ squeeze=["set_1", "set_2"],
+ multivariate=True,
+ handles_target=True,
+)
def flagDriftFromScaledNorm(
data: DictOfSeries,
field: str,
flags: Flags,
set_1: Sequence[str],
set_2: Sequence[str],
- freq: FreqString,
+ freq: str,
spread: float,
frac: float = 0.5,
metric: Callable[[np.ndarray, np.ndarray], float] = lambda x, y: pdist(
@@ -243,8 +267,9 @@ def flagDriftFromScaledNorm(
)
/ len(x),
method: LinkageString = "single",
+ target: str = None,
flag: float = BAD,
- **kwargs
+ **kwargs,
) -> Tuple[DictOfSeries, Flags]:
"""
The function linearly rescales one set of variables to another set of variables
@@ -307,6 +332,9 @@ def flagDriftFromScaledNorm(
different keywords (References [1]). See wikipedia for an introduction to
hierarchical clustering (References [2]).
+ target : None
+ ignored
+
flag : float, default BAD
flag to set.
@@ -325,9 +353,11 @@ def flagDriftFromScaledNorm(
Introduction to Hierarchical clustering:
[2] https://en.wikipedia.org/wiki/Hierarchical_clustering
"""
+ if target:
+ raise NotImplementedError("target is not implemented for this function")
+
fields = list(set_1) + list(set_2)
- data_to_flag = data[fields].to_df()
- data_to_flag.dropna(inplace=True)
+ data_to_flag = data[fields].to_df().dropna()
convert_slope = []
convert_intercept = []
@@ -365,16 +395,15 @@ def flagDriftFromScaledNorm(
return data, flags
-@flagging(masking="all")
+@register(mask=["field"], demask=[], squeeze=[])
def correctDrift(
data: DictOfSeries,
field: str,
flags: Flags,
maintenance_field: str,
- model: Callable[..., float],
+ model: Callable[..., float] | Literal["linear", "exponential"],
cal_range: int = 5,
- flag: float = BAD,
- **kwargs
+ **kwargs,
) -> Tuple[DictOfSeries, Flags]:
"""
The function corrects drifting behavior.
@@ -394,8 +423,9 @@ def correctDrift(
The maint data is to expected to have following form:
The series' timestamp itself represents the beginning of a
maintenance event, wheras the values represent the endings of the maintenance intervals.
- model : Callable
+ model : Callable or {'exponential', 'linear'}
A modelfunction describing the drift behavior, that is to be corrected.
+ Either use built-in exponential or linear drift model by passing a string, or pass a custom callable.
The model function must always contain the keyword parameters 'origin' and 'target'.
The starting parameter must always be the parameter, by wich the data is passed to the model.
After the data parameter, there can occure an arbitrary number of model calibration arguments in
@@ -404,8 +434,6 @@ def correctDrift(
cal_range : int, default 5
The number of values the mean is computed over, for obtaining the value level directly after and
directly before maintenance event. This values are needed for shift calibration. (see above description)
- flag : float, default BAD
- flag to set.
Returns
-------
@@ -418,26 +446,26 @@ def correctDrift(
Notes
-----
It is assumed, that between support points, there is a drift effect shifting the meassurements in a way, that
- can be described, by a model function M(t, *p, origin, target). (With 0<=t<=1, p being a parameter set, and origin,
- target being floats).
+ can be described, by a model function ``M(t, *p, origin, target)``. (With ``0<=t<=1``, ``p`` being a parameter
+ set, and ``origin``, ``target`` being floats).
Note, that its possible for the model to have no free parameters p at all. (linear drift mainly)
- The drift model, directly after the last support point (t=0),
- should evaluate to the origin - calibration level (origin), and directly before the next support point
- (t=1), it should evaluate to the target calibration level (target).
+ The drift model, directly after the last support point (``t=0``),
+ should evaluate to the origin - calibration level (``origin``), and directly before the next support point
+ (``t=1``), it should evaluate to the target calibration level (``target``).::
- M(0, *p, origin, target) = origin
- M(1, *p, origin, target) = target
+ M(0, *p, origin, target) = origin
+ M(1, *p, origin, target) = target
- The model is than fitted to any data chunk in between support points, by optimizing the parameters p*, and
- thus, obtaining optimal parameterset P*.
+ The model is than fitted to any data chunk in between support points, by optimizing the parameters ``p*``, and
+ thus, obtaining optimal parameterset ``P*``.
- The new values at t are computed via:
+ The new values at t are computed via:::
- new_vals(t) = old_vals(t) + M(t, *P, origin, target) - M_drift(t, *P, origin, new_target)
+ new_vals(t) = old_vals(t) + M(t, *P, origin, target) - M_drift(t, *P, origin, new_target)
- Wheras new_target represents the value level immediately after the nex support point.
+ Wheras ``new_target`` represents the value level immediately after the next support point.
Examples
--------
@@ -445,25 +473,31 @@ def correctDrift(
Linear drift modell (no free parameters).
- >>> M = lambda t, origin, target: origin + t*target
+ >>> M = lambda t, origin, target: origin + t * target
exponential drift model (exponential raise!)
>>> expFunc = lambda t, a, b, c: a + b * (np.exp(c * x) - 1)
>>> M = lambda t, p, origin, target: expFunc(t, (target - origin) / (np.exp(abs(c)) - 1), abs(c))
- Exponential and linear driftmodels are part of the ts_operators library, under the names
- expDriftModel and linearDriftModel.
+ Exponential and linear driftmodels are part of the ``ts_operators`` library, under the names
+ ``expDriftModel`` and ``linearDriftModel``.
"""
+ # extract model func:
+ if isinstance(model, str):
+ if model not in MODELDICT:
+ raise ValueError(
+ f"invalid model '{model}', choose one of '{MODELDICT.keys()}'"
+ )
+ model = MODELDICT[model]
+
# 1: extract fit intervals:
if data[maintenance_field].empty:
- flags[:, field] = UNTOUCHED
return data, flags
- data = data.copy()
- to_correct = data[field]
- maint_data = data[maintenance_field]
+ to_correct = data[field].copy()
+ maint_data = data[maintenance_field].copy()
to_correct_clean = to_correct.dropna()
d = {"drift_group": np.nan, to_correct.name: to_correct_clean.values}
@@ -497,16 +531,16 @@ def correctDrift(
return data, flags
-@flagging(masking="all")
+@register(mask=["field", "cluster_field"], demask=["cluster_field"], squeeze=[])
def correctRegimeAnomaly(
data: DictOfSeries,
field: str,
flags: Flags,
cluster_field: str,
model: CurveFitter,
- tolerance: Optional[FreqString] = None,
+ tolerance: Optional[str] = None,
epoch: bool = False,
- **kwargs
+ **kwargs,
) -> Tuple[DictOfSeries, Flags]:
"""
Function fits the passed model to the different regimes in data[field] and tries to correct
@@ -616,17 +650,17 @@ def correctRegimeAnomaly(
return data, flags
-@flagging(masking="all")
+@register(mask=["field"], demask=[], squeeze=[])
def correctOffset(
data: DictOfSeries,
field: str,
flags: Flags,
max_jump: float,
spread: float,
- window: FreqString,
+ window: str,
min_periods: int,
- tolerance: Optional[FreqString] = None,
- **kwargs
+ tolerance: Optional[str] = None,
+ **kwargs,
) -> Tuple[DictOfSeries, Flags]:
"""
Parameters
@@ -662,7 +696,7 @@ def correctOffset(
The quality flags of data
"""
data, flags = copyField(data, field, flags, field + "_CPcluster")
- data, flags = assignChangePointCluster(
+ data, flags = _assignChangePointCluster(
data,
field + "_CPcluster",
flags,
@@ -671,7 +705,7 @@ def correctOffset(
window=window,
min_periods=min_periods,
)
- data, flags = assignRegimeAnomaly(data, field, flags, field + "_CPcluster", spread)
+ data, flags = _assignRegimeAnomaly(data, field, flags, field + "_CPcluster", spread)
data, flags = correctRegimeAnomaly(
data,
field,
@@ -714,7 +748,7 @@ def _driftFit(x, shift_target, cal_mean, driftModel):
return data_fit, data_shift
-@flagging(masking="all")
+@flagging()
def flagRegimeAnomaly(
data: DictOfSeries,
field: str,
@@ -727,39 +761,41 @@ def flagRegimeAnomaly(
),
frac: float = 0.5,
flag: float = BAD,
- **kwargs
+ **kwargs,
) -> Tuple[DictOfSeries, Flags]:
"""
- A function to flag values belonging to an anomalous regime regarding modelling regimes of field.
+ Flags anomalous regimes regarding to modelling regimes of field.
- "Normality" is determined in terms of a maximum spreading distance, regimes must not exceed in respect
- to a certain metric and linkage method.
+ "Normality" is determined in terms of a maximum spreading distance,
+ regimes must not exceed in respect to a certain metric and linkage method.
- In addition, only a range of regimes is considered "normal", if it models more then `frac` percentage of
- the valid samples in "field".
+ In addition, only a range of regimes is considered "normal", if it models
+ more then `frac` percentage of the valid samples in "field".
Note, that you must detect the regime changepoints prior to calling this function.
- Note, that it is possible to perform hypothesis tests for regime equality by passing the metric
- a function for p-value calculation and selecting linkage method "complete".
+ Note, that it is possible to perform hypothesis tests for regime equality
+ by passing the metric a function for p-value calculation and selecting linkage
+ method "complete".
Parameters
----------
data : dios.DictOfSeries
- A dictionary of pandas.Series, holding all the data.
+ Data to process
field : str
- The fieldname of the column, holding the data-to-be-flagged.
+ Name of the column to process
flags : saqc.Flags
Container to store flags of the data.
cluster_field : str
- The name of the column in data, holding the cluster labels for the samples in field. (has to be indexed
- equal to field)
+ Column in data, holding the cluster labels for the samples in field.
+ (has to be indexed equal to field)
spread : float
- A threshold denoting the valuelevel, up to wich clusters a agglomerated.
+ A threshold denoting the value level, up to wich clusters a agglomerated.
method : {"single", "complete", "average", "weighted", "centroid", "median", "ward"}, default "single"
- The linkage method used for hierarchical (agglomerative) clustering of the variables.
- metric : Callable[[numpy.array, numpy.array], float], default lambda x, y: np.abs(np.nanmean(x) - np.nanmean(y))
- A metric function for calculating the dissimilarity between 2 regimes. Defaults to just the difference in mean.
+ The linkage method for hierarchical (agglomerative) clustering of the variables.
+ metric : Callable, default lambda x,y: np.abs(np.nanmean(x) - np.nanmean(y))
+ A metric function for calculating the dissimilarity between 2 regimes.
+ Defaults to the difference in mean.
frac : float
Has to be in [0,1]. Determines the minimum percentage of samples,
the "normal" group has to comprise to be the normal group actually.
@@ -775,23 +811,25 @@ def flagRegimeAnomaly(
The flags object, holding flags and additional informations related to `data`.
Flags values may have changed, relatively to the flags input.
"""
- return assignRegimeAnomaly(
- data,
- field,
- flags,
- cluster_field,
- spread,
+ reserverd = ["set_cluster", "set_flags"]
+ kwargs = filterKwargs(kwargs, reserverd)
+ return _assignRegimeAnomaly(
+ data=data,
+ field=field,
+ flags=flags,
+ cluster_field=cluster_field,
+ spread=spread,
method=method,
metric=metric,
frac=frac,
+ flag=flag,
+ **kwargs,
set_cluster=False,
set_flags=True,
- flag=flag,
- **kwargs
)
-@flagging(masking="all")
+@register(mask=["field", "cluster_field"], demask=["cluster_field"], squeeze=[])
def assignRegimeAnomaly(
data: DictOfSeries,
field: str,
@@ -803,10 +841,7 @@ def assignRegimeAnomaly(
np.nanmean(x) - np.nanmean(y)
),
frac: float = 0.5,
- set_cluster: bool = True, # todo: hide by a wrapper
- set_flags: bool = False, # todo: hide by a wrapper
- flag: float = BAD,
- **kwargs
+ **kwargs,
) -> Tuple[DictOfSeries, Flags]:
"""
A function to detect values belonging to an anomalous regime regarding modelling
@@ -827,34 +862,24 @@ def assignRegimeAnomaly(
Parameters
----------
data : dios.DictOfSeries
- A dictionary of pandas.Series, holding all the data.
+ Data to process
field : str
- The fieldname of the column, holding the data-to-be-flagged.
+ Name of the column to process
flags : saqc.Flags
Container to store flags of the data.
cluster_field : str
- The name of the column in data, holding the cluster labels for the samples in field. (has to be indexed
- equal to field)
+ Column in data, holding the cluster labels for the samples in field.
+ (has to be indexed equal to field)
spread : float
- A threshold denoting the valuelevel, up to wich clusters a agglomerated.
- method : str, default "single"
- The linkage method used for hierarchical (agglomerative) clustering of the
- variables.
- metric : Callable[[numpy.array, numpy.array], float], default lambda x, y: np.abs(np.nanmean(x) - np.nanmean(y))
- A metric function for calculating the dissimilarity between 2 regimes. Defaults
- to just the difference in mean.
+ A threshold denoting the value level, up to wich clusters a agglomerated.
+ method : {"single", "complete", "average", "weighted", "centroid", "median", "ward"}, default "single"
+ The linkage method for hierarchical (agglomerative) clustering of the variables.
+ metric : Callable, default lambda x,y: np.abs(np.nanmean(x) - np.nanmean(y))
+ A metric function for calculating the dissimilarity between 2 regimes.
+ Defaults to the difference in mean.
frac : float
Has to be in [0,1]. Determines the minimum percentage of samples,
the "normal" group has to comprise to be the normal group actually.
- set_cluster : bool, default False
- If True, all data, considered "anormal", gets assigned a negative clusterlabel.
- This option is present for further use (correction) of the anomaly information.
- set_flags : bool, default True
- Whether or not to flag abnormal values (do not flag them, if you want to
- correct them afterwards, because flagged values usually are not visible in
- further tests).
- flag : float, default BAD
- flag to set.
Returns
-------
@@ -864,6 +889,40 @@ def assignRegimeAnomaly(
The flags object, holding flags and additional informations related to `data`.
Flags values may have changed, relatively to the flags input.
"""
+ reserverd = ["set_cluster", "set_flags", "flag"]
+ kwargs = filterKwargs(kwargs, reserverd)
+ return _assignRegimeAnomaly(
+ data=data,
+ field=field,
+ flags=flags,
+ cluster_field=cluster_field,
+ spread=spread,
+ method=method,
+ metric=metric,
+ frac=frac,
+ **kwargs,
+ # control args
+ set_cluster=True,
+ set_flags=False,
+ )
+
+
+def _assignRegimeAnomaly(
+ data: DictOfSeries,
+ field: str,
+ flags: Flags,
+ cluster_field: str,
+ spread: float,
+ method: LinkageString = "single",
+ metric: Callable[[np.array, np.array], float] = lambda x, y: np.abs(
+ np.nanmean(x) - np.nanmean(y)
+ ),
+ frac: float = 0.5,
+ set_cluster: bool = True,
+ set_flags: bool = False,
+ flag: float = BAD,
+ **kwargs,
+) -> Tuple[DictOfSeries, Flags]:
series = data[cluster_field]
cluster = np.unique(series)
cluster_dios = DictOfSeries({i: data[field][series == i] for i in cluster})
diff --git a/saqc/funcs/flagtools.py b/saqc/funcs/flagtools.py
index 30924b46bda36e2e9ef24d7e4b8599caecf892d9..12bea62a516241f287aa7f6bcd7ae793b854474a 100644
--- a/saqc/funcs/flagtools.py
+++ b/saqc/funcs/flagtools.py
@@ -3,14 +3,18 @@
from typing import Any, Tuple, Union
from typing_extensions import Literal
import pandas as pd
+from dateutil.parser import ParserError
+import numpy as np
from dios import DictOfSeries
from saqc.constants import BAD, UNFLAGGED
-from saqc.core import flagging, processing, Flags
+from saqc.core import register, Flags
import warnings
+from saqc.core.register import flagging
-@flagging(masking="field")
+
+@register(mask=[], demask=[], squeeze=["field"])
def forceFlags(
data: DictOfSeries, field: str, flags: Flags, flag: float = BAD, **kwargs
) -> Tuple[DictOfSeries, Flags]:
@@ -44,8 +48,7 @@ def forceFlags(
return data, flags
-# masking='none' is sufficient because call is redirected
-@flagging(masking="none")
+@register(mask=[], demask=[], squeeze=["field"])
def clearFlags(
data: DictOfSeries, field: str, flags: Flags, **kwargs
) -> Tuple[DictOfSeries, Flags]:
@@ -70,7 +73,7 @@ def clearFlags(
Notes
-----
- This function ignores the ``to_mask`` keyword, because the data is not relevant
+ This function ignores the ``dfilter`` keyword, because the data is not relevant
for processing.
A warning is triggered if the ``flag`` keyword is given, because the flags are
always set to `UNFLAGGED`.
@@ -90,7 +93,7 @@ def clearFlags(
return forceFlags(data, field, flags, flag=UNFLAGGED, **kwargs)
-@flagging(masking="none")
+@register(mask=[], demask=[], squeeze=["field"])
def flagUnflagged(
data: DictOfSeries, field: str, flags: Flags, flag: float = BAD, **kwargs
) -> Tuple[DictOfSeries, Flags]:
@@ -119,7 +122,7 @@ def flagUnflagged(
Notes
-----
- This function ignores the ``to_mask`` keyword, because the data is not relevant
+ This function ignores the ``dfilter`` keyword, because the data is not relevant
for processing.
See Also
@@ -132,14 +135,17 @@ def flagUnflagged(
return data, flags
-@flagging(masking="field")
+@register(mask=["field"], demask=["field"], squeeze=["field"])
def flagManual(
data: DictOfSeries,
field: str,
flags: Flags,
- mdata: Union[pd.Series, pd.DataFrame, DictOfSeries],
+ mdata: Union[pd.Series, pd.DataFrame, DictOfSeries, list, np.array],
+ method: Literal[
+ "left-open", "right-open", "closed", "plain", "ontime"
+ ] = "left-open",
+ mformat: Literal["start-end", "mflag"] = "start-end",
mflag: Any = 1,
- method: Literal["plain", "ontime", "left-open", "right-open"] = "plain",
flag: float = BAD,
**kwargs,
) -> Tuple[DictOfSeries, Flags]:
@@ -159,22 +165,35 @@ def flagManual(
The fieldname of the column, holding the data-to-be-flagged.
flags : saqc.Flags
A flags object, holding flags and additional informations related to `data`.
- mdata : {pd.Series, pd.Dataframe, DictOfSeries}
- The "manually generated" data
- mflag : scalar
- The flag that indicates data points in `mdata`, of wich the projection in data should be flagged.
-
- method : {'plain', 'ontime', 'left-open', 'right-open'}, default plain
+ mdata : pd.Series, pd.DataFrame, DictOfSeries, str, list or np.ndarray
+ The Data determining, wich intervals are to be flagged, or a string, denoting under which field the data is
+ accessable.
+ method : {'plain', 'ontime', 'left-open', 'right-open', 'closed'}, default 'plain'
Defines how mdata is projected on data. Except for the 'plain' method, the methods assume mdata to have an
index.
* 'plain': mdata must have the same length as data and is projected one-to-one on data.
* 'ontime': works only with indexed mdata. mdata entries are matched with data entries that have the same index.
* 'right-open': mdata defines intervals, values are to be projected on.
- The intervals are defined by any two consecutive timestamps t_1 and 1_2 in mdata.
- the value at t_1 gets projected onto all data timestamps t with t_1 <= t < t_2.
+ The intervals are defined,
+
+ (1) Either, by any two consecutive timestamps t_1 and 1_2 where t_1 is valued with mflag, or by a series,
+ (2) Or, a Series, where the index contains in the t1 timestamps nd the values the respective t2 stamps.
+
+ The value at t_1 gets projected onto all data timestamps t with t_1 <= t < t_2.
+
* 'left-open': like 'right-open', but the projected interval now covers all t with t_1 < t <= t_2.
+ * 'closed': like 'right-open', but the projected interval now covers all t with t_1 <= t <= t_2.
+
+ mformat : {"start-end", "mflag"}, default "start-end"
+
+ * "start-end": mdata is a Series, where every entry indicates an interval to-flag. The index defines the left
+ bound, the value defines the right bound.
+ * "mflag": mdata is an array like, with entries containing 'mflag',where flags shall be set. See documentation
+ for examples.
+ mflag : scalar
+ The flag that indicates data points in `mdata`, of wich the projection in data should be flagged.
flag : float, default BAD
flag to set.
@@ -186,6 +205,7 @@ def flagManual(
Examples
--------
An example for mdata
+
>>> mdata = pd.Series([1,0,1], index=pd.to_datetime(['2000-02', '2000-03', '2001-05']))
>>> mdata
2000-02-01 1
@@ -196,6 +216,7 @@ def flagManual(
On *dayly* data, with the 'ontime' method, only the provided timestamnps are used.
Bear in mind that only exact timestamps apply, any offset will result in ignoring
the timestamp.
+
>>> _, fl = flagManual(data, field, flags, mdata, mflag=1, method='ontime')
>>> fl[field] > UNFLAGGED
2000-01-31 False
@@ -209,6 +230,7 @@ def flagManual(
Freq: D, dtype: bool
With the 'right-open' method, the mdata is forward fill:
+
>>> _, fl = flagManual(data, field, flags, mdata, mflag=1, method='right-open')
>>> fl[field] > UNFLAGGED
2000-01-31 False
@@ -221,6 +243,7 @@ def flagManual(
Freq: D, dtype: bool
With the 'left-open' method, backward filling is used:
+
>>> _, fl = flagManual(data, field, flags, mdata, mflag=1, method='left-open')
>>> fl[field] > UNFLAGGED
2000-01-31 False
@@ -233,39 +256,62 @@ def flagManual(
Freq: D, dtype: bool
"""
dat = data[field]
+ # internal not-mflag-value -> cant go for np.nan
+ not_mflag = -1 if mflag == 0 else 0
+ if isinstance(mdata, str):
+ mdata = data[mdata]
if isinstance(mdata, (pd.DataFrame, DictOfSeries)):
mdata = mdata[field]
hasindex = isinstance(mdata, (pd.Series, pd.DataFrame, DictOfSeries))
- if not hasindex and method != "plain":
- raise ValueError("mdata has no index")
-
+ if not hasindex:
+ if method != "plain":
+ raise ValueError("mdata has no index")
+ else:
+ mdata = pd.Series(mdata, index=dat.index)
+
+ # check, if intervals where passed in format (index:start-time, data:end-time)
+ if mformat == "start-end":
+ if method in ["plain", "ontime"]:
+ raise ValueError(
+ "'Start-End' formatting not compatible to 'plain' or 'ontime' methods"
+ )
+ else:
+ mdata = pd.Series(
+ not_mflag,
+ index=mdata.index.join(pd.DatetimeIndex(mdata.values), how="outer"),
+ )
+ mdata[::2] = mflag
+
+ # get rid of values that are neither mflag nor not_mflag (for bw-compatibillity mainly)
+ mdata[mdata != mflag] = not_mflag
+
+ # evaluate methods
if method == "plain":
-
- if hasindex:
- mdata = mdata.to_numpy()
-
- if len(mdata) != len(dat):
- raise ValueError("mdata must have same length then data")
-
- mdata = pd.Series(mdata, index=dat.index)
-
+ pass
# reindex will do the job later
elif method == "ontime":
pass
- elif method in ["left-open", "right-open"]:
+ elif method in ["left-open", "right-open", "closed"]:
+ mdata = mdata.drop(mdata.index[mdata.diff() == 0])
+ app_entry = pd.Series(mdata[-1], dat.index.shift(freq="1min")[-1:])
mdata = mdata.reindex(dat.index.union(mdata.index))
- # -->)[t0-->)[t1--> (ffill)
if method == "right-open":
mdata = mdata.ffill()
- # <--t0](<--t1](<-- (bfill)
if method == "left-open":
- mdata = mdata.bfill()
-
+ mdata = (
+ mdata.replace({mflag: not_mflag, not_mflag: mflag})
+ .append(app_entry)
+ .bfill()
+ )
+
+ if method == "closed":
+ mdata[mdata.ffill() == mflag] = mflag
+ mdata.replace({not_mflag: mflag}, inplace=True)
else:
raise ValueError(method)
diff --git a/saqc/funcs/generic.py b/saqc/funcs/generic.py
index 99e460bc7d95b3af4146f8cc30e5f061d1774907..5f7977b684c0ae382b95f3d83370a213a2541c53 100644
--- a/saqc/funcs/generic.py
+++ b/saqc/funcs/generic.py
@@ -1,100 +1,72 @@
#! /usr/bin/env python
# -*- coding: utf-8 -*-
+from __future__ import annotations
-from functools import partial
-from inspect import signature
-from typing import Tuple, Union, Callable
+from typing import Sequence, Tuple, Union
import numpy as np
import pandas as pd
from dios import DictOfSeries
-from saqc.constants import GOOD, BAD, UNFLAGGED
-from saqc.core.flags import initFlagsLike, Flags
-from saqc.core.register import flagging, processing, _maskData, _isflagged
-from saqc.core.visitor import ENVIRONMENT
+from saqc.constants import BAD, UNFLAGGED, ENVIRONMENT, FILTER_ALL
+from saqc.core.history import History
+from saqc.lib.tools import toSequence
+from saqc.lib.types import GenericFunction, PandasLike
+from saqc.core.flags import Flags
+from saqc.core.register import register, _isflagged, FunctionWrapper
-import operator as op
-_OP = {"<": op.lt, "<=": op.le, "==": op.eq, "!=": op.ne, ">": op.gt, ">=": op.ge}
-
-
-def _dslIsFlagged(
- flags: Flags, var: pd.Series, flag: float = None, comparator: str = None
-) -> Union[pd.Series, DictOfSeries]:
- """
- helper function for `flag`
-
- Param Combinations
- ------------------
- - ``isflagged('var')`` : show me (anything) flagged
- - ``isflagged('var', DOUBT)`` : show me ``flags >= DOUBT``
- - ``isflagged('var', DOUBT, '==')`` : show me ``flags == DOUBT``
-
- Raises
- ------
- ValueError: if `comparator` is passed but no `flag` vaule. Eg. ``isflagged('var', comparator='>=')``
- """
- if flag is None:
- if comparator is not None:
- raise ValueError("if `comparator` is used, explicitly pass a `flag` level.")
- flag = UNFLAGGED
- comparator = ">"
-
- # default
- if comparator is None:
- comparator = ">="
-
- _op = _OP[comparator]
- return _op(flags[var.name], flag)
+def _prepare(
+ data: DictOfSeries, flags: Flags, columns: Sequence[str], dfilter: float
+) -> Tuple[DictOfSeries, Flags]:
+ fchunk = Flags({f: flags[f] for f in columns})
+ dchunk, _ = FunctionWrapper._maskData(
+ data=data.loc[:, columns].copy(), flags=fchunk, columns=columns, thresh=dfilter
+ )
+ return dchunk, fchunk.copy()
def _execGeneric(
flags: Flags,
- data: DictOfSeries,
- func: Callable[[pd.Series], pd.Series],
- field: str,
-) -> pd.Series:
- # TODO:
- # - check series.index compatibility
- # - field is only needed to translate 'this' parameters
- # -> maybe we could do the translation on the tree instead
-
- sig = signature(func)
- args = []
- for k, v in sig.parameters.items():
- k = field if k == "this" else k
- if k not in data:
- raise NameError(f"variable '{k}' not found")
- args.append(data[k])
+ data: PandasLike,
+ func: GenericFunction,
+ dfilter: float = FILTER_ALL,
+) -> DictOfSeries:
globs = {
- "isflagged": partial(_dslIsFlagged, flags),
- "ismissing": lambda var: pd.isnull(var),
- "this": field,
- "GOOD": GOOD,
- "BAD": BAD,
- "UNFLAGGED": UNFLAGGED,
+ "isflagged": lambda data: _isflagged(flags[data.name], thresh=dfilter),
**ENVIRONMENT,
}
+
func.__globals__.update(globs)
- return func(*args)
+ if isinstance(data, pd.Series):
+ data = data.to_frame()
+
+ out = func(*[data[c] for c in data.columns])
+ return DictOfSeries(out)
-@processing()
-def genericProcess(
+
+@register(
+ mask=[],
+ demask=[],
+ squeeze=[],
+ multivariate=True,
+ handles_target=True,
+)
+def processGeneric(
data: DictOfSeries,
- field: str,
+ field: str | Sequence[str],
flags: Flags,
- func: Callable[[pd.Series], pd.Series],
- to_mask: float = UNFLAGGED,
+ func: GenericFunction,
+ target: str | Sequence[str] = None,
+ flag: float = UNFLAGGED,
+ dfilter: float = FILTER_ALL,
**kwargs,
) -> Tuple[DictOfSeries, Flags]:
"""
- generate/process data with generically defined functions.
-
- The functions can depend on on any of the fields present in data.
+ Generate/process data with user defined functions.
Formally, what the function does, is the following:
@@ -102,21 +74,34 @@ def genericProcess(
Than, for every timestamp t_i that occurs in at least one of the timeseries data[f_j] (outer join),
The value v_i is computed via:
v_i = data([f_1][t_i], data[f_2][t_i], ..., data[f_K][t_i]), if all data[f_j][t_i] do exist
- v_i = `np.nan`, if at least one of the data[f_j][t_i] is missing.
- 2. The result is stored to data[field] (gets generated if not present)
+ v_i = ``np.nan``, if at least one of the data[f_j][t_i] is missing.
+ 2. The result is stored to ``data[target]``, if ``target`` is given or to ``data[field]`` otherwise
Parameters
----------
data : dios.DictOfSeries
A dictionary of pandas.Series, holding all the data.
- field : str
- The fieldname of the column, where you want the result from the generic expressions processing to be written to.
+ field : str or list of str
+ The variable(s) passed to func.
flags : saqc.Flags
- Container to store quality flags to data.
- func : Callable
- The data processing function with parameter names that will be
- interpreted as data column entries.
- See the examples section to learn more.
+ Container to store flags of the data.
+ func : callable
+ Function to call on the variables given in ``field``. The return value will be written
+ to ``target`` or ``field`` if the former is not given. This implies, that the function
+ needs to accept the same number of arguments (of type pandas.Series) as variables given
+ in ``field`` and should return an iterable of array-like objects with the same number
+ of elements as given in ``target`` (or ``field`` if ``target`` is not specified).
+ target: str or list of str
+ The variable(s) to write the result of ``func`` to. If not given, the variable(s)
+ specified in ``field`` will be overwritten. If a ``target`` is not given, it will be
+ created.
+ flag: float, default ``UNFLAGGED``
+ The quality flag to set. The default ``UNFLAGGED`` states the general idea, that
+ ``processGeneric`` generates 'new' data without direct relation to the potentially
+ already present flags.
+ dfilter: float, default ``FILTER_ALL``
+ Threshold flag. Flag values greater than ``dfilter`` indicate that the associated
+ data value is inappropiate for further usage.
Returns
-------
@@ -127,73 +112,106 @@ def genericProcess(
The quality flags of data
The flags shape may have changed relatively to the input flags.
+ Note
+ -----
+ All the numpy functions are available within the generic expressions.
+
Examples
--------
- Some examples on what to pass to the func parameter:
- To compute the sum of the variables "temperature" and "uncertainty", you would pass the function:
+ Compute the sum of the variables 'rainfall' and 'snowfall' and save the result to
+ a (new) variable 'precipitation'
- >>> lambda temperature, uncertainty: temperature + uncertainty
+ >>> saqc.genericProcess(field=["rainfall", "snowfall"], target="precipitation'", func=lambda x, y: x + y)
+ """
- You also can pass numpy and pandas functions:
+ fields = toSequence(field)
+ targets = fields if target is None else toSequence(target)
+
+ dchunk, fchunk = _prepare(data, flags, fields, dfilter)
+ result = _execGeneric(fchunk, dchunk, func, dfilter=dfilter)
+
+ meta = {
+ "func": "procGeneric",
+ "args": (),
+ "kwargs": {
+ "field": field,
+ "func": func.__name__,
+ "target": target,
+ "flag": flag,
+ "dfilter": dfilter,
+ },
+ }
- >>> lambda temperature, uncertainty: np.round(temperature) * np.sqrt(uncertainty)
- """
+ # update data & flags
+ for i, col in enumerate(targets):
+
+ datacol = result.iloc[:, i]
+ data[col] = datacol
- data_masked, _ = _maskData(data.copy(), flags, data.columns, to_mask)
- data[field] = _execGeneric(flags, data_masked, func, field).squeeze()
+ if col not in flags:
+ flags.history[col] = History(datacol.index)
- if field in flags:
- flags.drop(field)
+ if not flags[col].index.equals(datacol.index):
+ raise ValueError(
+ f"cannot assign function result to the existing variable {repr(col)} "
+ "because of incompatible indices, please choose another 'target'"
+ )
- flags[field] = initFlagsLike(data[field])[field]
+ flags.history[col].append(pd.Series(flag, index=datacol.index), meta)
return data, flags
-@flagging(masking="all")
-def genericFlag(
+@register(
+ mask=[],
+ demask=[],
+ squeeze=[],
+ multivariate=True,
+ handles_target=True,
+)
+def flagGeneric(
data: DictOfSeries,
- field: str,
+ field: Union[str, Sequence[str]],
flags: Flags,
- func: Callable[[pd.Series], pd.Series],
+ func: GenericFunction,
+ target: Union[str, Sequence[str]] = None,
flag: float = BAD,
- to_mask: float = UNFLAGGED,
+ dfilter: float = FILTER_ALL,
**kwargs,
) -> Tuple[DictOfSeries, Flags]:
- # TODO : fix docstring, check if all still works
"""
- a function to flag a data column by evaluation of a generic expression.
-
- The expression can depend on any of the fields present in data.
+ Flag data with user defined functions.
Formally, what the function does, is the following:
-
- Let X be an expression, depending on fields f_1, f_2,...f_K, (X = X(f_1, f_2,...f_K))
+ Let X be a Callable, depending on fields f_1, f_2,...f_K, (X = X(f_1, f_2,...f_K))
Than for every timestamp t_i in data[field]:
data[field][t_i] is flagged if X(data[f_1][t_i], data[f_2][t_i], ..., data[f_K][t_i]) is True.
- Note, that all value series included in the expression to evaluate must be labeled identically to field.
-
- Note, that the expression is passed in the form of a Callable and that this callables variable names are
- interpreted as actual names in the data header. See the examples section to get an idea.
-
- Note, that all the numpy functions are available within the generic expressions.
-
Parameters
----------
data : dios.DictOfSeries
A dictionary of pandas.Series, holding all the data.
- field : str
- The fieldname of the column, where you want the result from the generic expressions evaluation to be projected
- to.
+ field : str or list of str
+ The variable(s) passed to func.
flags : saqc.Flags
Container to store flags of the data.
- func : Callable
- The expression that is to be evaluated is passed in form of a callable, with parameter names that will be
- interpreted as data column entries. The Callable must return an boolen array like.
- See the examples section to learn more.
- flag : float, default BAD
- flag to set.
+ func : callable
+ Function to call on the variables given in ``field``. The function needs to accept the same
+ number of arguments (of type pandas.Series) as variables given in ``field`` and return an
+ iterable of array-like objects of with dtype bool and with the same number of elements as
+ given in ``target`` (or ``field`` if ``target`` is not specified). The function output
+ determines the values to flag.
+ target: str or list of str
+ The variable(s) to write the result of ``func`` to. If not given, the variable(s)
+ specified in ``field`` will be overwritten. If a ``target`` is not given, it will be
+ created.
+ flag: float, default ``UNFLAGGED``
+ The quality flag to set. The default ``UNFLAGGED`` states the general idea, that
+ ``processGeneric`` generates 'new' data without direct relation to the potentially
+ already present flags.
+ dfilter: float, default ``FILTER_ALL``
+ Threshold flag. Flag values greater than ``dfilter`` indicate that the associated
+ data value is inappropiate for further usage.
Returns
-------
@@ -203,53 +221,75 @@ def genericFlag(
The quality flags of data
Flags values may have changed relatively to the flags input.
+ Note
+ -----
+ All the numpy functions are available within the generic expressions.
+
Examples
--------
- Some examples on what to pass to the func parameter:
- To flag the variable `field`, if the sum of the variables
- "temperature" and "uncertainty" is below zero, you would pass the function:
-
- >>> lambda temperature, uncertainty: temperature + uncertainty < 0
- There is the reserved name 'This', that always refers to `field`. So, to flag field if field is negative, you can
- also pass:
+ 1. Flag the variable 'rainfall', if the sum of the variables 'temperature' and 'uncertainty' is below zero:
- >>> lambda this: this < 0
+ >>> saqc.flagGeneric(field=["temperature", "uncertainty"], target="rainfall", func= lambda x, y: temperature + uncertainty < 0
- If you want to make dependent the flagging from flags already present in the data, you can use the built-in
- ``isflagged`` method. For example, to flag the 'temperature', if 'level' is flagged, you would use:
+ 2. Flag the variable 'temperature', where the variable 'fan' is flagged:
- >>> lambda level: isflagged(level)
+ >>> saqc.flagGeneric(field="fan", target="temperature", func=lambda x: isflagged(x))
- You can furthermore specify a flagging level, you want to compare the flags to. For example, for flagging
- 'temperature', if 'level' is flagged at a level named DOUBTFUL or worse, use:
+ 3. The generic functions also support all pandas and numpy functions:
- >>> lambda level: isflagged(level, flag=DOUBTFUL, comparator='>')
+ >>> saqc.flagGeneric(field="fan", target="temperature", func=lambda x: np.sqrt(x) < 7)
+ """
- If you are unsure about the used flaggers flagging level names, you can use the reserved key words BAD, UNFLAGGED
- and GOOD, to refer to the worst (BAD), best(GOOD) or unflagged (UNFLAGGED) flagging levels. For example.
+ fields = toSequence(field)
+ targets = fields if target is None else toSequence(target)
- >>> lambda level: isflagged(level, flag=UNFLAGGED, comparator='==')
+ dchunk, fchunk = _prepare(data, flags, fields, dfilter)
+ result = _execGeneric(fchunk, dchunk, func, dfilter=dfilter)
- Your expression also is allowed to include pandas and numpy functions
+ if len(targets) != len(result.columns):
+ raise ValueError(
+ f"the generic function returned {len(result.columns)} field(s), but only {len(targets)} target(s) were given"
+ )
- >>> lambda level: np.sqrt(level) > 7
- """
- # we get the data unmasked, in order to also receive flags,
- # so let's do to the masking manually
- # data_masked, _ = _maskData(data, flags, data.columns, to_mask)
-
- mask = _execGeneric(flags, data, func, field).squeeze()
- if np.isscalar(mask):
- raise TypeError(f"generic expression does not return an array")
- if not np.issubdtype(mask.dtype, np.bool_):
+ if not (result.dtypes == bool).all():
raise TypeError(f"generic expression does not return a boolean array")
- if field not in flags:
- flags[field] = pd.Series(data=UNFLAGGED, index=mask.index, name=field)
+ meta = {
+ "func": "flagGeneric",
+ "args": (),
+ "kwargs": {
+ "field": field,
+ "func": func.__name__,
+ "target": target,
+ "flag": flag,
+ "dfilter": dfilter,
+ },
+ }
+
+ # update flags & data
+ for i, col in enumerate(targets):
+
+ maskcol = result.iloc[:, i]
+
+ # make sure the column exists
+ if col not in flags:
+ flags.history[col] = History(maskcol.index)
+
+ # dummy column to ensure consistency between flags and data
+ if col not in data:
+ data[col] = pd.Series(np.nan, index=maskcol.index)
+
+ maskcol = maskcol & ~_isflagged(flags[col], dfilter)
+ flagcol = maskcol.replace({False: np.nan, True: flag}).astype(float)
- mask = ~_isflagged(flags[field], to_mask) & mask
+ # we need equal indices to work on
+ if not flags[col].index.equals(maskcol.index):
+ raise ValueError(
+ f"cannot assign function result to the existing variable {repr(col)} "
+ "because of incompatible indices, please choose another 'target'"
+ )
- flags[mask, field] = flag
+ flags.history[col].append(flagcol, meta)
return data, flags
diff --git a/saqc/funcs/interpolation.py b/saqc/funcs/interpolation.py
index 53e4f1e6e341c896d65a2dd6bc482fd5052dc237..6076ba88ce053d4c7b236e1d6f48049008905b98 100644
--- a/saqc/funcs/interpolation.py
+++ b/saqc/funcs/interpolation.py
@@ -7,8 +7,8 @@ import pandas as pd
from dios import DictOfSeries
from saqc.constants import *
-from saqc.core import flagging, processing, Flags
-from saqc.core.register import _isflagged
+from saqc.core import register, Flags
+from saqc.core.register import _isflagged, processing
from saqc.lib.ts_operators import interpolateNANs
_SUPPORTED_METHODS = Literal[
@@ -30,7 +30,11 @@ _SUPPORTED_METHODS = Literal[
]
-@flagging(masking="field")
+@register(
+ mask=["field"],
+ demask=["field"],
+ squeeze=[], # func handles history by itself
+)
def interpolateByRolling(
data: DictOfSeries,
field: str,
@@ -40,7 +44,7 @@ def interpolateByRolling(
center: bool = True,
min_periods: int = 0,
flag: float = UNFLAGGED,
- **kwargs
+ **kwargs,
) -> Tuple[DictOfSeries, Flags]:
"""
Interpolates nan-values in the data by assigning them the aggregation result of the window surrounding them.
@@ -72,7 +76,8 @@ def interpolateByRolling(
computed.
flag : float or None, default UNFLAGGED
- Flag that is to be inserted for the interpolated values. If ``None`` no flags are set.
+ Flag that is to be inserted for the interpolated values.
+ If `None` the old flags are kept, even if the data is valid now.
Returns
-------
@@ -82,8 +87,6 @@ def interpolateByRolling(
flags : saqc.Flags
The quality flags of data
"""
-
- data = data.copy()
datcol = data[field]
roller = datcol.rolling(window=window, center=center, min_periods=min_periods)
try:
@@ -99,13 +102,22 @@ def interpolateByRolling(
datcol[na_mask] = rolled[na_mask]
data[field] = datcol
- if flag is not None:
- flags[interpolated, field] = flag
+ new_col = pd.Series(np.nan, index=flags[field].index)
+ new_col.loc[interpolated] = np.nan if flag is None else flag
+
+ # todo kwargs must have all passed args except data,field,flags
+ flags.history[field].append(
+ new_col, {"func": "interpolateByRolling", "args": (), "kwargs": kwargs}
+ )
return data, flags
-@flagging(masking="field")
+@register(
+ mask=["field"],
+ demask=["field"],
+ squeeze=[], # func handles history by itself
+)
def interpolateInvalid(
data: DictOfSeries,
field: str,
@@ -115,7 +127,7 @@ def interpolateInvalid(
limit: int = 2,
downgrade: bool = False,
flag: float = UNFLAGGED,
- **kwargs
+ **kwargs,
) -> Tuple[DictOfSeries, Flags]:
"""
Function to interpolate nan values in the data.
@@ -169,12 +181,17 @@ def interpolateInvalid(
inter_limit=limit,
downgrade_interpolation=downgrade,
)
+ data[field] = inter_data
+
interpolated = data[field].isna() & inter_data.notna()
+ new_col = pd.Series(np.nan, index=flags[field].index)
+ new_col.loc[interpolated] = np.nan if flag is None else flag
- if flag is not None:
- flags[interpolated, field] = flag
+ # todo kwargs must have all passed args except data,field,flags
+ flags.history[field].append(
+ new_col, {"func": "interpolateInvalid", "args": (), "kwargs": kwargs}
+ )
- data[field] = inter_data
return data, flags
@@ -199,7 +216,7 @@ def interpolateIndex(
order: int = 2,
limit: int = 2,
downgrade: bool = False,
- **kwargs
+ **kwargs,
) -> Tuple[DictOfSeries, Flags]:
"""
Function to interpolate the data at regular (equidistant) timestamps (or Grid points).
@@ -257,7 +274,10 @@ def interpolateIndex(
start, end = datcol.index[0].floor(freq), datcol.index[-1].ceil(freq)
grid_index = pd.date_range(start=start, end=end, freq=freq, name=datcol.index.name)
- flagged = _isflagged(flags[field], kwargs["to_mask"])
+ # todo:
+ # in future we could use `register(mask=[field], [], [])`
+ # and dont handle masking manually here
+ flagged = _isflagged(flags[field], kwargs["dfilter"])
# drop all points that hold no relevant grid information
datcol = datcol[~flagged].dropna()
@@ -290,7 +310,6 @@ def interpolateIndex(
index=data[field].index,
func=_resampleOverlapping,
func_kws=dict(freq=freq, fill_value=UNFLAGGED),
- copy=False,
)
flags.history[field] = history
diff --git a/saqc/funcs/noise.py b/saqc/funcs/noise.py
index fb70835d98f8e64b56e1ffe4e0a0839159f81c36..d9e808561e45becf21c301c90874d25f71ba1141 100644
--- a/saqc/funcs/noise.py
+++ b/saqc/funcs/noise.py
@@ -6,24 +6,24 @@ import operator
from dios import DictOfSeries
from typing import Callable
from saqc.constants import *
-from saqc.core import flagging, Flags
-from saqc.lib.types import FreqString
+from saqc.core import register, Flags
+from saqc.core.register import flagging
from saqc.lib.tools import statPass
-@flagging(masking="field")
+@flagging()
def flagByStatLowPass(
data: DictOfSeries,
field: str,
flags: Flags,
func: Callable[[np.array, pd.Series], float],
- window: FreqString,
+ window: str,
thresh: float,
- sub_window: FreqString = None,
+ sub_window: str = None,
sub_thresh: float = None,
min_periods: int = None,
flag: float = BAD,
- **kwargs
+ **kwargs,
):
"""
Flag *chunks* of length, `window`:
@@ -42,15 +42,17 @@ def flagByStatLowPass(
Container to store quality flags to data.
func: Callable[[np.array, pd.Series], float]
Function to aggregate chunk contnent with.
- window: FreqString
+ window: str
Temporal extension of the chunks to test
thresh: float
Threshold, that triggers flagging, if exceeded by stat value.
- sub_window: FreqString, default None,
+ sub_window: str, default None,
Window size of the sub chunks, that are additionally tested for exceeding
`sub_thresh` with respect to `stat`.
sub_thresh: float, default None
min_periods: int, default None
+ flag : float, default BAD
+ flag to set
Returns
-------
diff --git a/saqc/funcs/outliers.py b/saqc/funcs/outliers.py
index 667fcc81a9811629308bcdb845ab0c4c5e065e2d..adeb2091302651e449d4cd0f6b5086131e0add69 100644
--- a/saqc/funcs/outliers.py
+++ b/saqc/funcs/outliers.py
@@ -1,6 +1,6 @@
#! /usr/bin/env python
# -*- coding: utf-8 -*-
-
+import warnings
from typing import Optional, Union, Tuple, Sequence, Callable
from typing_extensions import Literal
@@ -14,21 +14,21 @@ from outliers import smirnov_grubbs
from scipy.optimize import curve_fit
from saqc.constants import *
-from saqc.core import flagging, Flags
-from saqc.lib.types import FreqString
-from saqc.lib.tools import customRoller, findIndex, getFreqDelta
+from saqc.core import register, Flags
+from saqc.core.register import flagging
+from saqc.lib.tools import customRoller, findIndex, getFreqDelta, toSequence
from saqc.funcs.scores import assignKNNScore
from saqc.funcs.tools import copyField, dropField
from saqc.funcs.transformation import transform
import saqc.lib.ts_operators as ts_ops
-@flagging(masking="field")
+@flagging()
def flagByStray(
data: DictOfSeries,
field: str,
flags: Flags,
- freq: Optional[Union[int, FreqString]] = None,
+ freq: Optional[Union[int, str]] = None,
min_periods: int = 11,
iter_start: float = 0.5,
alpha: float = 0.05,
@@ -84,7 +84,6 @@ def flagByStray(
scores = data[field].dropna()
if scores.empty:
- flags[:, field] = UNTOUCHED
return data, flags
if not freq:
@@ -159,7 +158,7 @@ def _evalStrayLabels(
flags : saqc.Flags
Container to store quality flags to data.
- fields : list[str]
+ fields : list of str
A list of strings, holding the column names of the variables, the stray labels
shall be projected onto.
@@ -399,23 +398,29 @@ def _expFit(
return val_frame.index[sorted_i[iter_index:]]
-@flagging(masking="all")
+@register(
+ mask=["field"],
+ demask=["field"],
+ squeeze=["field"],
+ multivariate=True,
+ handles_target=True,
+)
def flagMVScores(
data: DictOfSeries,
- field: str,
+ field: Sequence[str],
flags: Flags,
- fields: Sequence[str],
trafo: Callable[[pd.Series], pd.Series] = lambda x: x,
alpha: float = 0.05,
n: int = 10,
func: Callable[[pd.Series], float] = np.sum,
iter_start: float = 0.5,
- partition: Optional[Union[int, FreqString]] = None,
+ partition: Optional[Union[int, str]] = None,
partition_min: int = 11,
- stray_range: Optional[FreqString] = None,
+ stray_range: Optional[str] = None,
drop_flagged: bool = False, # TODO: still a case ?
thresh: float = 3.5,
min_periods: int = 1,
+ target: str = None,
flag: float = BAD,
**kwargs,
) -> Tuple[DictOfSeries, Flags]:
@@ -432,17 +437,13 @@ def flagMVScores(
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. (Here a dummy,
- for structural reasons)
+ field : list of str
+ List of fieldnames, corresponding to the variables that are to be included
+ into the flagging process.
flags : saqc.Flags
Container to store quality flags to data.
- fields : List[str]
- List of fieldnames, corresponding to the variables that are to be included
- into the flagging process.
-
trafo : callable, default lambda x:x
Transformation to be applied onto every column before scoring. Will likely
get deprecated soon. Its better to transform the data in a processing step,
@@ -505,6 +506,9 @@ def flagMVScores(
necessarily present in a reduction interval for reduction actually to be
performed.
+ target : None
+ ignored.
+
flag : float, default BAD
flag to set.
@@ -554,16 +558,18 @@ def flagMVScores(
more details. Although [2] gives a fully detailed overview over the `stray`
algorithm.
"""
+ if target:
+ raise NotImplementedError("target is not implemented for this function")
+ fields = toSequence(field)
for f in fields:
data, flags = copyField(data, f, flags, f"trafo_{f}")
data, flags = transform(data, f"trafo_{f}", flags, func=trafo, freq=partition)
data, flags = assignKNNScore(
- data,
- "dummy",
- flags,
- fields=[f"trafo_{f}" for f in fields],
+ data=data,
+ field=[f"trafo_{f}" for f in fields],
+ flags=flags,
target="kNN",
n=n,
func=func,
@@ -586,10 +592,10 @@ def flagMVScores(
)
data, flags = _evalStrayLabels(
- data,
- "kNN",
- flags,
+ data=data,
+ field="kNN",
fields=fields,
+ flags=flags,
reduction_range=stray_range,
reduction_drop_flagged=drop_flagged,
reduction_thresh=thresh,
@@ -604,15 +610,15 @@ def flagMVScores(
return data, flags
-@flagging(masking="field")
+@flagging()
def flagRaise(
data: DictOfSeries,
field: str,
flags: Flags,
thresh: float,
- raise_window: FreqString,
- freq: FreqString,
- average_window: Optional[FreqString] = None,
+ raise_window: str,
+ freq: str,
+ average_window: Optional[str] = None,
raise_factor: float = 2.0,
slope: Optional[float] = None,
weight: float = 0.8,
@@ -673,7 +679,7 @@ def flagRaise(
timestamps :math:`t_i`, is flagged a raise, if:
* There is any value :math:`x_{s}`, preceeding :math:`x_{k}` within `raise_window`
- range, so that:
+ range, so that:
* :math:`M = |x_k - x_s | >` `thresh` :math:`> 0`
@@ -684,8 +690,8 @@ def flagRaise(
* :math:`x_k > \\mu^* + ( M` / `mean_raise_factor` :math:`)`
- * Additionally, if `min_slope` is not `None`, :math:`x_{k}` is checked for being
- sufficiently divergent from its very predecessor :max:`x_{k-1}`$, meaning that, it
+ * Additionally, if ``min_slope`` is not `None`, :math:`x_{k}` is checked for being
+ sufficiently divergent from its very predecessor :math:`x_{k-1}`, meaning that, it
is additionally checked if:
* :math:`x_k - x_{k-1} >` `min_slope`
@@ -731,7 +737,6 @@ def flagRaise(
raise_series = raise_series.apply(raise_check, args=(thresh,), raw=True)
if raise_series.isna().all():
- flags[:, field] = UNTOUCHED
return data, flags
# "unflag" values of insufficient deviation to their predecessors
@@ -792,12 +797,12 @@ def flagRaise(
return data, flags
-@flagging(masking="field")
+@flagging()
def flagMAD(
data: DictOfSeries,
field: str,
flags: Flags,
- window: FreqString,
+ window: str,
z: float = 3.5,
flag: float = BAD,
**kwargs,
@@ -838,7 +843,6 @@ def flagMAD(
"""
d = data[field]
if d.empty:
- flags[:, field] = UNTOUCHED
return data, flags
median = d.rolling(window=window, closed="both").median()
@@ -849,9 +853,9 @@ def flagMAD(
# In pandas <= 0.25.3, the window size is not fixed if the
# window-argument to rolling is a frequency. That implies,
# that during the first iterations the window has a size of
- # 1, 2, 3, ... until it eventually covers the disered time
- # span. For stuff the calculation of median, that is rather
- # unfortunate, as the size of calculation base might differ
+ # 1, 2, 3, ... until it eventually covers the desired time
+ # span. For stuff like the calculation of median, that is rather
+ # unfortunate, as the size of the calculation base might differ
# heavily. So don't flag something until, the window reaches
# its target size
if not isinstance(window, int):
@@ -862,14 +866,14 @@ def flagMAD(
return data, flags
-@flagging(masking="field")
+@flagging()
def flagOffset(
data: DictOfSeries,
field: str,
flags: Flags,
thresh: float,
tolerance: float,
- window: Union[int, FreqString],
+ window: Union[int, str],
thresh_relative: Optional[float] = None,
flag: float = BAD,
**kwargs,
@@ -930,7 +934,6 @@ def flagOffset(
"""
dataseries = data[field].dropna()
if dataseries.empty:
- flags[:, field] = UNTOUCHED
return data, flags
# using reverted series - because ... long story.
@@ -963,7 +966,6 @@ def flagOffset(
post_jumps = post_jumps[post_jumps]
if post_jumps.empty:
- flags[:, field] = UNTOUCHED
return data, flags
# get all the entries preceding a significant jump
@@ -1030,12 +1032,12 @@ def flagOffset(
return data, flags
-@flagging(masking="field")
+@flagging()
def flagByGrubbs(
data: DictOfSeries,
field: str,
flags: Flags,
- window: Union[FreqString, int],
+ window: Union[str, int],
alpha: float = 0.05,
min_periods: int = 8,
pedantic: bool = False,
@@ -1100,8 +1102,7 @@ def flagByGrubbs(
[1] https://en.wikipedia.org/wiki/Grubbs%27s_test_for_outliers
"""
- data = data.copy()
- datcol = data[field]
+ datcol = data[field].copy()
rate = getFreqDelta(datcol.index)
# if timeseries that is analyzed, is regular,
@@ -1153,7 +1154,7 @@ def flagByGrubbs(
return data, flags
-@flagging(masking="field")
+@flagging()
def flagRange(
data: DictOfSeries,
field: str,
@@ -1196,15 +1197,21 @@ def flagRange(
return data, flags
-@flagging(masking="all")
+@register(
+ mask=["field"],
+ demask=["field"],
+ squeeze=["field"],
+ multivariate=True,
+ handles_target=True,
+)
def flagCrossStatistic(
data: DictOfSeries,
- field: str,
+ field: Sequence[str],
flags: Flags,
- fields: Sequence[str],
thresh: float,
method: Literal["modZscore", "Zscore"] = "modZscore",
flag: float = BAD,
+ target: str = None,
**kwargs,
) -> Tuple[DictOfSeries, Flags]:
"""
@@ -1217,7 +1224,7 @@ def flagCrossStatistic(
:math:`t_i`, are excluded from the following process (inner join of the :math:`f_i` fields.)
2. for every :math:`0 <= i <= K`, the value
:math:`m_j = median(\\{data[f_1][t_i], data[f_2][t_i], ..., data[f_N][t_i]\\})` is calculated
- 2. for every :math:`0 <= i <= K`, the set
+ 3. for every :math:`0 <= i <= K`, the set
:math:`\\{data[f_1][t_i] - m_j, data[f_2][t_i] - m_j, ..., data[f_N][t_i] - m_j\\}` is tested for outliers with the
specified method (`cross_stat` parameter).
@@ -1225,12 +1232,10 @@ def flagCrossStatistic(
----------
data : dios.DictOfSeries
A dictionary of pandas.Series, holding all the data.
- field : str
- A dummy parameter.
+ field : list of str
+ List of fieldnames in data, determining wich variables are to be included into the flagging process.
flags : saqc.Flags
A flags object, holding flags and additional informations related to `data`.
- fields : str
- List of fieldnames in data, determining wich variables are to be included into the flagging process.
thresh : float
Threshold which the outlier score of an value must exceed, for being flagged an outlier.
method : {'modZscore', 'Zscore'}, default 'modZscore'
@@ -1243,6 +1248,10 @@ def flagCrossStatistic(
flag : float, default BAD
flag to set.
+ target : None
+ ignored
+
+
Returns
-------
data : dios.DictOfSeries
@@ -1255,7 +1264,10 @@ def flagCrossStatistic(
----------
[1] https://www.itl.nist.gov/div898/handbook/eda/section3/eda35h.htm
"""
+ if target:
+ raise NotImplementedError("target is not implemented for this function")
+ fields = toSequence(field)
df = data[fields].loc[data[fields].index_of("shared")].to_df()
if isinstance(method, str):
@@ -1289,7 +1301,6 @@ def flagCrossStatistic(
mask = diff_scores > thresh
if mask.empty:
- flags[:, field] = UNTOUCHED
return data, flags
for var in fields:
diff --git a/saqc/funcs/pattern.py b/saqc/funcs/pattern.py
index a5c23e0323dcebc4c88cc4c735b7d20c7d292b49..f1d3c86926f8eab5e698b268fd231885736567a9 100644
--- a/saqc/funcs/pattern.py
+++ b/saqc/funcs/pattern.py
@@ -1,18 +1,16 @@
#! /usr/bin/env python
# -*- coding: utf-8 -*-
-import numpy as np
import pandas as pd
import dtw
-import pywt
-from mlxtend.evaluate import permutation_test
-from saqc.constants import *
+from saqc.constants import BAD
from saqc.core.register import flagging
from saqc.lib.tools import customRoller
-@flagging(masking="field")
+# todo should we mask `reference` even if the func fail if reference has NaNs
+@flagging()
def flagPatternByWavelet(
data,
field,
@@ -21,7 +19,7 @@ def flagPatternByWavelet(
widths=(1, 2, 4, 8),
waveform="mexh",
flag=BAD,
- **kwargs
+ **kwargs,
):
"""
Pattern recognition via wavelets.
@@ -42,16 +40,16 @@ def flagPatternByWavelet(
The fieldname of the data column, you want to correct.
flags : saqc.Flags
- The flags belongiong to `data`.
+ The flags belonging to ``data``.
reference: str
- The fieldname in `data' which holds the pattern.
+ The fieldname in ``data`' which holds the pattern.
widths: tuple of int
Widths for wavelet decomposition. [1] recommends a dyadic scale.
Default: (1,2,4,8)
- waveform: str.
+ waveform: str
Wavelet to be used for decomposition. Default: 'mexh'. See [2] for a list.
Returns
@@ -180,7 +178,8 @@ def calculateDistanceByDTW(
return distances.reindex(index=data.index) # reinsert NaNs
-@flagging(masking="field")
+# todo should we mask `reference` even if the func fail if reference has NaNs
+@flagging()
def flagPatternByDTW(
data,
field,
@@ -190,7 +189,7 @@ def flagPatternByDTW(
normalize=True,
plot=False,
flag=BAD,
- **kwargs
+ **kwargs,
):
"""Pattern Recognition via Dynamic Time Warping.
@@ -231,11 +230,12 @@ def flagPatternByDTW(
Show a calibration plot, which can be quite helpful to find the right threshold
for `max_distance`. It works best with `normalize=True`. Do not use in automatic
setups / pipelines. The plot show three lines:
- - data: the data the function was called on
- - distances: the calculated distances by the algorithm
- - indicator: have to distinct levels: `0` and the value of `max_distance`.
- If `max_distance` is `0.0` it defaults to `1`. Everywhere where the
- indicator is not `0` the data will be flagged.
+
+ - data: the data the function was called on
+ - distances: the calculated distances by the algorithm
+ - indicator: have to distinct levels: `0` and the value of `max_distance`.
+ If `max_distance` is `0.0` it defaults to `1`. Everywhere where the
+ indicator is not `0` the data will be flagged.
Returns
-------
@@ -267,12 +267,12 @@ def flagPatternByDTW(
distances = distances.fillna(max_distance + 1)
# find minima filter by threshold
- fw = customRoller(distances, window=winsz, forward=True, closed="both")
- bw = customRoller(distances, window=winsz, closed="both")
+ fw = customRoller(distances, window=winsz, forward=True, closed="both", expand=True)
+ bw = customRoller(distances, window=winsz, closed="both", expand=True)
minima = (fw.min() == bw.min()) & (distances <= max_distance)
# Propagate True's to size of pattern.
- rolling = customRoller(minima, window=winsz, closed="both")
+ rolling = customRoller(minima, window=winsz, closed="both", expand=True)
mask = rolling.sum() > 0
if plot:
diff --git a/saqc/funcs/resampling.py b/saqc/funcs/resampling.py
index d617798384dac1b6bef059c6e59c3d0d3cd47871..35a1ce6626aa069d48ada12faeedb6bbf201a9d7 100644
--- a/saqc/funcs/resampling.py
+++ b/saqc/funcs/resampling.py
@@ -8,9 +8,9 @@ import pandas as pd
from dios import DictOfSeries
from saqc.constants import *
-from saqc.core import processing, Flags
-from saqc.core.register import _isflagged
-from saqc.lib.tools import evalFreqStr, getFreqDelta
+from saqc.core import register, Flags
+from saqc.core.register import _isflagged, processing
+from saqc.lib.tools import evalFreqStr, getFreqDelta, filterKwargs
from saqc.lib.ts_operators import shift2Freq, aggregate2Freq
from saqc.funcs.interpolation import interpolateIndex, _SUPPORTED_METHODS
import saqc.funcs.tools as tools
@@ -29,7 +29,11 @@ METHOD2ARGS = {
@processing()
def linear(
- data: DictOfSeries, field: str, flags: Flags, freq: str, **kwargs
+ data: DictOfSeries,
+ field: str,
+ flags: Flags,
+ freq: str,
+ **kwargs,
) -> Tuple[DictOfSeries, Flags]:
"""
A method to "regularize" data by interpolating linearly the data at regular timestamp.
@@ -66,7 +70,8 @@ def linear(
The quality flags of data
Flags values and shape may have changed relatively to the flags input.
"""
-
+ reserved = ["method", "order", "limit", "downgrade"]
+ kwargs = filterKwargs(kwargs, reserved)
return interpolateIndex(data, field, flags, freq, "time", **kwargs)
@@ -129,13 +134,15 @@ def interpolate(
The quality flags of data
Flags values and shape may have changed relatively to the flags input.
"""
-
+ reserved = ["limit", "downgrade"]
+ kwargs = filterKwargs(kwargs, reserved)
return interpolateIndex(
data, field, flags, freq, method=method, order=order, **kwargs
)
-@processing()
+# for @processing this would need to handle dfilter
+@register(mask=["field"], demask=[], squeeze=[])
def shift(
data: DictOfSeries,
field: str,
@@ -192,23 +199,19 @@ def shift(
if datcol.empty:
return data, flags
- flagged = _isflagged(flags[field], kwargs["to_mask"])
- datcol[flagged] = np.nan
freq = evalFreqStr(freq, freq_check, datcol.index)
# do the shift
datcol = shift2Freq(datcol, method, freq, fill_value=np.nan)
# do the shift on the history
- history = flags.history[field]
-
kws = dict(method=method, freq=freq)
- history = history.apply(
+
+ history = flags.history[field].apply(
index=datcol.index,
func_handle_df=True,
- copy=False,
func=shift2Freq,
- func_kws={**kws, "fill_value": UNTOUCHED},
+ func_kws={**kws, "fill_value": np.nan},
)
flags.history[field] = history
@@ -216,7 +219,8 @@ def shift(
return data, flags
-@processing()
+# for @processing this would need to handle dfilter
+@register(mask=["field"], demask=[], squeeze=[])
def resample(
data: DictOfSeries,
field: str,
@@ -318,9 +322,8 @@ def resample(
The quality flags of data
Flags values and shape may have changed relatively to the flags input.
"""
- flagged = _isflagged(flags[field], kwargs["to_mask"])
+
datcol = data[field]
- datcol[flagged] = np.nan
freq = evalFreqStr(freq, freq_check, datcol.index)
datcol = aggregate2Freq(
@@ -337,7 +340,7 @@ def resample(
method=method,
freq=freq,
agg_func=flag_func,
- fill_value=UNTOUCHED,
+ fill_value=np.nan,
max_invalid_total=maxna_flags,
max_invalid_consec=maxna_group_flags,
)
@@ -346,7 +349,6 @@ def resample(
index=datcol.index,
func=aggregate2Freq,
func_kws=kws,
- copy=False,
)
data[field] = datcol
@@ -412,11 +414,17 @@ def _inverseShift(
return source.fillna(fill_value).astype(dtype, copy=False)
-@processing()
-def reindexFlags(
+@register(
+ mask=[],
+ demask=[],
+ squeeze=[],
+ handles_target=True, # target is mandatory in func, so its allowed
+)
+def concatFlags(
data: DictOfSeries,
field: str,
flags: Flags,
+ target: str,
method: Literal[
"inverse_fagg",
"inverse_bagg",
@@ -426,39 +434,35 @@ def reindexFlags(
"inverse_nshift",
"inverse_interpolation",
],
- source: str,
freq: Optional[str] = None,
drop: Optional[bool] = False,
**kwargs,
) -> Tuple[DictOfSeries, Flags]:
"""
- The Function projects flags of "source" onto flags of "field". Wherever the "field" flags are "better" then the
- source flags projected on them, they get overridden with this associated source flag value.
-
- Which "field"-flags are to be projected on which source flags, is controlled by the "method" and "freq"
- parameters.
+ The Function appends flags history of ``fields`` to flags history of ``target``.
+ Before Appending, columns in ``field`` history are projected onto the target index via ``method``
method: (field_flag in associated with "field", source_flags associated with "source")
- 'inverse_nagg' - all field_flags within the range +/- freq/2 of a source_flag, get assigned this source flags value.
- (if source_flag > field_flag)
- 'inverse_bagg' - all field_flags succeeding a source_flag within the range of "freq", get assigned this source flags
- value. (if source_flag > field_flag)
- 'inverse_fagg' - all field_flags preceeding a source_flag within the range of "freq", get assigned this source flags
- value. (if source_flag > field_flag)
+ 'inverse_nagg' - all target_flags within the range +/- freq/2 of a field_flag, get assigned this field flags value.
+ (if field_flag > target_flag)
+ 'inverse_bagg' - all target_flags succeeding a field_flag within the range of "freq", get assigned this field flags
+ value. (if field_flag > target_flag)
+ 'inverse_fagg' - all target_flags preceeding a field_flag within the range of "freq", get assigned this field flags
+ value. (if field_flag > target_flag)
- 'inverse_interpolation' - all field_flags within the range +/- freq of a source_flag, get assigned this source flags value.
- (if source_flag > field_flag)
+ 'inverse_interpolation' - all target_flags within the range +/- freq of a field_flag, get assigned this source flags value.
+ (if field_flag > target_flag)
- 'inverse_nshift' - That field_flag within the range +/- freq/2, that is nearest to a source_flag, gets the source
- flags value. (if source_flag > field_flag)
- 'inverse_bshift' - That field_flag succeeding a source flag within the range freq, that is nearest to a
- source_flag, gets assigned this source flags value. (if source_flag > field_flag)
- 'inverse_nshift' - That field_flag preceeding a source flag within the range freq, that is nearest to a
- source_flag, gets assigned this source flags value. (if source_flag > field_flag)
+ 'inverse_nshift' - That target_flag within the range +/- freq/2, that is nearest to a field_flag, gets the source
+ flags value. (if field_flag > target_flag)
+ 'inverse_bshift' - That target_flag succeeding a field flag within the range freq, that is nearest to a
+ field_flag, gets assigned this field flags value. (if field_flag > target_flag)
+ 'inverse_nshift' - That target_flag preceeding a field flag within the range freq, that is nearest to a
+ field_flag, gets assigned this field flags value. (if field_flag > target_flag)
- 'match' - any field_flag with a timestamp matching a source_flags timestamp gets this source_flags value
- (if source_flag > field_flag)
+ 'match' - any target_flag with a timestamp matching a field_flags timestamp gets this field_flags value
+ (if field_flag > target_flag)
Note, to undo or backtrack a resampling/shifting/interpolation that has been performed with a certain method,
you can just pass the associated "inverse" method. Also you should pass the same drop flags keyword.
@@ -469,23 +473,24 @@ def reindexFlags(
A dictionary of pandas.Series, holding all the data.
field : str
- The fieldname of the data column, you want to project the source-flags onto.
+ Fieldname of flags history to append.
flags : saqc.Flags
Container to store flags of the data.
- method : {'inverse_fagg', 'inverse_bagg', 'inverse_nagg', 'inverse_fshift', 'inverse_bshift', 'inverse_nshift'}
- The method used for projection of source flags onto field flags. See description above for more details.
+ target : str
+ Field name of flags history to append to.
- source : str
- The source source of flags projection.
+ method : {'inverse_fagg', 'inverse_bagg', 'inverse_nagg', 'inverse_fshift', 'inverse_bshift', 'inverse_nshift',
+ 'match'}
+ The method used for projection of ``field`` flags onto ``target`` flags. See description above for more details.
freq : {None, str},default None
- The freq determines the projection range for the projection method. See above description for more details.
- Defaultly (None), the sampling frequency of source is used.
+ The ``freq`` determines the projection range for the projection method. See above description for more details.
+ Defaultly (None), the sampling frequency of ``field`` is used.
drop : default False
- If set to `True`, the `source` column will be removed
+ If set to `True`, the `field` column will be removed after processing
Returns
-------
@@ -495,9 +500,9 @@ def reindexFlags(
The quality flags of data
Flags values and shape may have changed relatively to the flags input.
"""
- flagscol = flags[source]
- target_datcol = data[field]
- target_flagscol = flags[field]
+ flagscol = flags[field]
+ target_datcol = data[target]
+ target_flagscol = flags[target]
if target_datcol.empty or flagscol.empty:
return data, flags
@@ -517,7 +522,7 @@ def reindexFlags(
func = _inverseInterpolation
func_kws = dict(freq=freq, chunk_bounds=ignore, target=dummy)
- elif method[-3:] == "agg" or method == "match":
+ elif method[-3:] == "agg":
projection_method = METHOD2ARGS[method][0]
tolerance = METHOD2ARGS[method][1](freq)
func = _inverseAggregation
@@ -525,7 +530,7 @@ def reindexFlags(
elif method[-5:] == "shift":
drop_mask = target_datcol.isna() | _isflagged(
- target_flagscol, kwargs["to_mask"]
+ target_flagscol, kwargs["dfilter"]
)
projection_method = METHOD2ARGS[method][0]
tolerance = METHOD2ARGS[method][1](freq)
@@ -533,15 +538,19 @@ def reindexFlags(
kws = dict(
freq=tolerance, method=projection_method, drop_mask=drop_mask, target=dummy
)
- func_kws = {**kws, "fill_value": UNTOUCHED}
+ func_kws = {**kws, "fill_value": np.nan}
+
+ elif method == "match":
+ func = lambda x: x
+ func_kws = {}
else:
raise ValueError(f"unknown method {method}")
- history = flags.history[source].apply(dummy.index, func, func_kws, copy=False)
- flags.history[field] = flags.history[field].append(history)
+ history = flags.history[field].apply(dummy.index, func, func_kws)
+ flags.history[target].append(history)
if drop:
- data, flags = tools.dropField(data=data, flags=flags, field=source)
+ data, flags = tools.dropField(data=data, flags=flags, field=field)
return data, flags
diff --git a/saqc/funcs/residues.py b/saqc/funcs/residues.py
index bc21514671c6a6bb3bbf9c57aace11b484ac5c91..3adea11ed60bd7b1137846602f5df60b488b3bde 100644
--- a/saqc/funcs/residues.py
+++ b/saqc/funcs/residues.py
@@ -2,137 +2,157 @@
# -*- coding: utf-8 -*-
from typing import Tuple, Union, Optional, Callable
+
+import pandas as pd
from typing_extensions import Literal
import numpy as np
from dios import DictOfSeries
from saqc.constants import *
-from saqc.core import flagging, Flags
-from saqc.funcs.rolling import roll
-from saqc.funcs.curvefit import fitPolynomial
+from saqc.core import register, Flags
+from saqc.funcs.rolling import _roll
+from saqc.funcs.curvefit import _fitPolynomial
+from saqc.lib.tools import filterKwargs
-@flagging(masking="field")
+@register(mask=["field"], demask=[], squeeze=[])
def calculatePolynomialResidues(
data: DictOfSeries,
field: str,
flags: Flags,
window: Union[str, int],
order: int,
- set_flags: bool = True, # TODO, not valid anymore, if still needed, maybe assign user-passed ``flag``?
min_periods: Optional[int] = 0,
- flag: float = BAD,
**kwargs
) -> Tuple[DictOfSeries, Flags]:
"""
- Function fits a polynomial model to the data and returns the residues.
-
- The residue for value x is calculated by fitting a polynomial of degree "order" to a data slice
- of size "window", wich has x at its center.
-
- Note, that the residues will be stored to the `field` field of the input data, so that the original data, the
- polynomial is fitted to, gets overridden.
+ Fits a polynomial model to the data and calculate the residues.
- Note, that, if data[field] is not alligned to an equidistant frequency grid, the window size passed,
- has to be an offset string. Also numba boost options don`t apply for irregularly sampled
- timeseries.
+ The residue is calculated by fitting a polynomial of degree `order` to a data
+ slice of size `window`, that has x at its center.
- Note, that calculating the residues tends to be quite costy, because a function fitting is perfomed for every
- sample. To improve performance, consider the following possibillities:
+ Note, that calculating the residues tends to be quite costy, because a function
+ fitting is performed for every sample. To improve performance, consider the
+ following possibilities:
In case your data is sampled at an equidistant frequency grid:
- (1) If you know your data to have no significant number of missing values, or if you do not want to
- calculate residues for windows containing missing values any way, performance can be increased by setting
- min_periods=window.
-
- (2) If your data consists of more then around 200000 samples, setting numba=True, will boost the
- calculations up to a factor of 5 (for samplesize > 300000) - however for lower sample sizes,
- numba will slow down the calculations, also, up to a factor of 5, for sample_size < 50000.
- By default (numba='auto'), numba is set to true, if the data sample size exceeds 200000.
-
- in case your data is not sampled at an equidistant frequency grid:
+ (1) If you know your data to have no significant number of missing values,
+ or if you do not want to calculate residues for windows containing missing values
+ any way, performance can be increased by setting min_periods=window.
- (1) Harmonization/resampling of your data will have a noticable impact on polyfittings performance - since
- numba_boost doesnt apply for irregularly sampled data in the current implementation.
+ Note, that the initial and final window/2 values do not get fitted.
- Note, that in the current implementation, the initial and final window/2 values do not get fitted.
+ Each residual gets assigned the worst flag present in the interval of
+ the original data.
Parameters
----------
data : dios.DictOfSeries
- A dictionary of pandas.Series, holding all the data.
+ The data.
+
field : str
- The fieldname of the column, holding the data-to-be-modelled.
+ The column, holding the data-to-be-modelled.
+
flags : saqc.Flags
Container to store quality flags to data.
+
window : {str, int}
- The size of the window you want to use for fitting. If an integer is passed, the size
- refers to the number of periods for every fitting window. If an offset string is passed,
- the size refers to the total temporal extension. The window will be centered around the vaule-to-be-fitted.
- For regularly sampled timeseries the period number will be casted down to an odd number if
- even.
+ The size of the window you want to use for fitting. If an integer is passed,
+ the size refers to the number of periods for every fitting window. If an
+ offset string is passed, the size refers to the total temporal extension. The
+ window will be centered around the vaule-to-be-fitted. For regularly sampled
+ timeseries the period number will be casted down to an odd number if even.
+
order : int
The degree of the polynomial used for fitting
- set_flags : bool, default True
- Wheather or not to assign new flags to the calculated residuals. If True, a residual gets assigned the worst
- flag present in the interval, the data for its calculation was obtained from.
- min_periods : {int, None}, default 0
- The minimum number of periods, that has to be available in every values fitting surrounding for the polynomial
- fit to be performed. If there are not enough values, np.nan gets assigned. Default (0) results in fitting
- regardless of the number of values present (results in overfitting for too sparse intervals). To automatically
- set the minimum number of periods to the number of values in an offset defined window size, pass np.nan.
- flag : float, default BAD
- flag to set.
+
+ min_periods : int or None, default 0
+ The minimum number of periods, that has to be available in every values
+ fitting surrounding for the polynomial fit to be performed. If there are not
+ enough values, np.nan gets assigned. Default (0) results in fitting
+ regardless of the number of values present (results in overfitting for too
+ sparse intervals). To automatically set the minimum number of periods to the
+ number of values in an offset defined window size, pass np.nan.
Returns
-------
data : dios.DictOfSeries
- A dictionary of pandas.Series, holding all the data.
- Data values may have changed relatively to the data input.
flags : saqc.Flags
- The quality flags of data
- Flags values may have changed relatively to the flags input.
-
"""
- return fitPolynomial(
- data,
- field,
- flags,
+ reserved = ["residues", "set_flags"]
+ filterKwargs(kwargs, reserved)
+ return _fitPolynomial(
+ data=data,
+ field=field,
+ flags=flags,
window=window,
order=order,
- set_flags=set_flags,
min_periods=min_periods,
+ **kwargs,
+ # ctrl args
return_residues=True,
- flag=flag,
- **kwargs
+ set_flags=True,
)
-@flagging(masking="field")
+@register(mask=["field"], demask=[], squeeze=[])
def calculateRollingResidues(
data: DictOfSeries,
field: str,
flags: Flags,
window: Union[str, int],
- func: Callable[[np.ndarray], np.ndarray] = np.mean,
- set_flags: bool = True,
+ func: Callable[[pd.Series], np.ndarray] = np.mean,
min_periods: Optional[int] = 0,
center: bool = True,
- flag: float = BAD,
**kwargs
) -> Tuple[DictOfSeries, Flags]:
- """TODO: docstring needed"""
- return roll(
- data,
- field,
- flags,
+ """
+ Calculate the diff of a rolling-window function and the data.
+
+ Note, that the data gets assigned the worst flag present in the original data.
+
+ Parameters
+ ----------
+ data : dios.DictOfSeries
+ The data.
+ field : str
+ The column to calculate on.
+ flags : saqc.Flags
+ Container to store quality flags to data.
+ window : {int, str}
+ The size of the window you want to roll with. If an integer is passed, the size
+ refers to the number of periods for every fitting window. If an offset string
+ is passed, the size refers to the total temporal extension. For regularly
+ sampled timeseries, the period number will be casted down to an odd number if
+ ``center=True``.
+ func : Callable, default np.mean
+ Function to roll with.
+ min_periods : int, default 0
+ The minimum number of periods to get a valid value
+ center : bool, default True
+ If True, center the rolling window.
+
+ Returns
+ -------
+ data : dios.DictOfSeries
+ A dictionary of pandas.Series, holding all the data.
+ Data values may have changed relatively to the data input.
+ flags : saqc.Flags
+ The quality flags of data
+ """
+ reserved = ["return_residues", "set_flags"]
+ kwargs = filterKwargs(kwargs, reserved)
+ return _roll(
+ data=data,
+ field=field,
+ flags=flags,
window=window,
func=func,
- set_flags=set_flags,
min_periods=min_periods,
center=center,
+ **kwargs,
+ # ctrl args
+ set_flags=True,
return_residues=True,
- flag=flag,
- **kwargs
)
diff --git a/saqc/funcs/rolling.py b/saqc/funcs/rolling.py
index 5eb13520115314e2735234c1aeb2feccee23bb92..ff73d7887838f9d532cfcbaf4e6eac742dc84c21 100644
--- a/saqc/funcs/rolling.py
+++ b/saqc/funcs/rolling.py
@@ -1,65 +1,52 @@
#! /usr/bin/env python
# -*- coding: utf-8 -*-
-from typing import Union, Callable
+from typing import Union, Callable, Tuple
import numpy as np
import pandas as pd
from dios import DictOfSeries
from saqc.constants import *
-from saqc.core import flagging, Flags
-from saqc.lib.tools import getFreqDelta
+from saqc.core import register, Flags
+from saqc.lib.tools import getFreqDelta, filterKwargs
-@flagging(masking="field")
+@register(mask=["field"], demask=[], squeeze=[])
def roll(
data: DictOfSeries,
field: str,
flags: Flags,
window: Union[str, int],
- func: Callable[[pd.Series], float] = np.mean,
- set_flags: bool = True, # TODO: not applicable anymore
+ func: Callable[[pd.Series], np.ndarray] = np.mean,
min_periods: int = 0,
center: bool = True,
- return_residues=False, # TODO: this should not be public, a wrapper would be better
- flag: float = BAD,
**kwargs
-):
+) -> Tuple[DictOfSeries, Flags]:
"""
- Models the data with the rolling mean and returns the residues.
+ Calculate a rolling-window function on the data.
- Note, that the residues will be stored to the `field` field of the input data, so that the data that is modelled
- gets overridden.
+ Note, that the data gets assigned the worst flag present in the original data.
Parameters
----------
data : dios.DictOfSeries
- A dictionary of pandas.Series, holding all the data.
+ The data.
field : str
- The fieldname of the column, holding the data-to-be-modelled.
+ The column to calculate on.
flags : saqc.Flags
Container to store quality flags to data.
window : {int, str}
The size of the window you want to roll with. If an integer is passed, the size
- refers to the number of periods for every fitting window. If an offset string is passed,
- the size refers to the total temporal extension.
- For regularly sampled timeseries, the period number will be casted down to an odd number if
- center = True.
- func : Callable[np.array, float], default np.mean
- Function to apply on the rolling window and obtain the curve fit value.
- set_flags : bool, default True
- Wheather or not to assign new flags to the calculated residuals. If True, a residual gets assigned the worst
- flag present in the interval, the data for its calculation was obtained from.
- Currently not implemented in combination with not-harmonized timeseries.
+ refers to the number of periods for every fitting window. If an offset string
+ is passed, the size refers to the total temporal extension. For regularly
+ sampled timeseries, the period number will be casted down to an odd number if
+ ``center=True``.
+ func : Callable, default np.mean
+ Function to roll with.
min_periods : int, default 0
- The minimum number of periods, that has to be available in every values fitting surrounding for the mean
- fitting to be performed. If there are not enough values, np.nan gets assigned. Default (0) results in fitting
- regardless of the number of values present.
+ The minimum number of periods to get a valid value
center : bool, default True
- Wheather or not to center the window the mean is calculated of around the reference value. If False,
- the reference value is placed to the right of the window (classic rolling mean with lag.)
- flag : float, default BAD
- flag to set.
+ If True, center the rolling window.
Returns
-------
@@ -69,14 +56,42 @@ def roll(
flags : saqc.Flags
The quality flags of data
"""
- data = data.copy()
- to_fit = data[field]
+ reserved = ["return_residues", "set_flags"]
+ kwargs = filterKwargs(kwargs, reserved)
+ return _roll(
+ data=data,
+ field=field,
+ flags=flags,
+ window=window,
+ func=func,
+ min_periods=min_periods,
+ center=center,
+ **kwargs,
+ # ctrl args
+ set_flags=True,
+ return_residues=False,
+ )
+
+
+def _roll(
+ data: DictOfSeries,
+ field: str,
+ flags: Flags,
+ window: Union[str, int],
+ func: Callable[[pd.Series], np.ndarray] = np.mean,
+ set_flags: bool = True,
+ min_periods: int = 0,
+ center: bool = True,
+ return_residues=False,
+ **kwargs
+):
+ to_fit = data[field].copy()
if to_fit.empty:
- flags[:, field] = UNTOUCHED
return data, flags
regular = getFreqDelta(to_fit.index)
- # starting with the annoying case: finding the rolling interval centers of not-harmonized input time series:
+ # starting with the annoying case: finding the rolling interval
+ # centers of not-harmonized input time series:
if center and not regular:
if isinstance(window, int):
raise NotImplementedError(
@@ -138,7 +153,6 @@ def roll(
data[field] = means
if set_flags:
- # TODO: we does not get any flags here, because of masking=field
worst = flags[field].rolling(window, center=True, min_periods=min_periods).max()
flags[field] = worst
diff --git a/saqc/funcs/scores.py b/saqc/funcs/scores.py
index dba04b3302cad157a5d489f7fdeb9212c503c9d2..5c41170b68f3decd4aabd9e81e05737ab8e080eb 100644
--- a/saqc/funcs/scores.py
+++ b/saqc/funcs/scores.py
@@ -7,18 +7,23 @@ import pandas as pd
from dios import DictOfSeries
from saqc.constants import *
-from saqc.core import flagging, Flags
+from saqc.core import register, Flags
from saqc.lib.tools import toSequence
import saqc.lib.ts_operators as ts_ops
-@flagging(masking="all")
+@register(
+ mask=["field"],
+ demask=[],
+ squeeze=["target"],
+ multivariate=True,
+ handles_target=True,
+)
def assignKNNScore(
data: DictOfSeries,
- field: str,
+ field: Sequence[str],
flags: Flags,
- fields: Sequence[str],
- target: str = "kNNscores",
+ target: str,
n: int = 10,
func: Callable[[pd.Series], float] = np.sum,
freq: Union[float, str] = np.inf,
@@ -26,7 +31,7 @@ def assignKNNScore(
method: Literal["ball_tree", "kd_tree", "brute", "auto"] = "ball_tree",
metric: str = "minkowski",
p: int = 2,
- **kwargs
+ **kwargs,
) -> Tuple[DictOfSeries, Flags]:
"""
TODO: docstring need a rework
@@ -36,8 +41,9 @@ def assignKNNScore(
The steps taken to calculate the scores are as follows:
- 1. All the timeseries, named fields, are combined to one feature space by an *inner* join on their date time indexes.
- thus, only samples, that share timestamps across all fields will be included in the feature space
+ 1. All the timeseries, given through ``field``, are combined to one feature space by an *inner* join on their
+ date time indexes. thus, only samples, that share timestamps across all ``field`` will be included in the
+ feature space.
2. Any datapoint/sample, where one ore more of the features is invalid (=np.nan) will get excluded.
3. For every data point, the distance to its `n` nearest neighbors is calculated by applying the
metric `metric` at grade `p` onto the feature space. The defaults lead to the euclidian to be applied.
@@ -53,10 +59,12 @@ def assignKNNScore(
----------
data : dios.DictOfSeries
A dictionary of pandas.Series, holding all the data.
- field : str
- Dummy Variable.
+ field : list of str
+ input variable names.
flags : saqc.flags
- A flags object, holding flags and additional informations related to `data`.fields
+ A flags object, holding flags and additional informations related to `data`.
+ target : str, default "kNNscores"
+ A new Column name, where the result is stored.
n : int, default 10
The number of nearest neighbors to which the distance is comprised in every datapoints scoring calculation.
func : Callable[numpy.array, float], default np.sum
@@ -88,31 +96,22 @@ def assignKNNScore(
The grade of the metrice specified by parameter `metric`.
The keyword just gets passed on to the underlying sklearn method.
See reference [1] for more information on the algorithm.
- radius : {None, float}, default None
- If the radius is not None, only the distance to neighbors that ly within the range specified by `radius`
- are comprised in the scoring aggregation.
- The scoring method passed must be capable of handling np.nan values - since, for every point missing
- within `radius` range to make complete the list of the distances to the `n` nearest neighbors,
- one np.nan value gets appended to the list passed to the scoring method.
- The keyword just gets passed on to the underlying sklearn method.
- See reference [1] for more information on the algorithm.
References
----------
[1] https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.NearestNeighbors.html
"""
- data = data.copy()
- fields = toSequence(fields)
-
- val_frame = data[fields]
+ if target in data.columns:
+ raise ValueError(f"'target' must not exist.")
+ fields = toSequence(field)
+ val_frame = data[fields].copy()
score_index = val_frame.index_of("shared")
- score_ser = pd.Series(np.nan, index=score_index, name=field)
+ score_ser = pd.Series(np.nan, index=score_index, name=target)
val_frame = val_frame.loc[val_frame.index_of("shared")].to_df()
val_frame.dropna(inplace=True)
if val_frame.empty:
- flags[:, target] = UNTOUCHED
return data, flags
# partitioning
@@ -146,7 +145,6 @@ def assignKNNScore(
score_ser[partition.index] = resids
flags[target] = pd.Series(UNFLAGGED, index=score_ser.index, dtype=float)
-
data[target] = score_ser
return data, flags
diff --git a/saqc/funcs/tools.py b/saqc/funcs/tools.py
index c8cace05fc624445cd2a2e61f410780eefc5b2fd..9bd2ee6d668dc842c97d8f66f2adc764b462e1e9 100644
--- a/saqc/funcs/tools.py
+++ b/saqc/funcs/tools.py
@@ -12,21 +12,20 @@ import matplotlib.pyplot as plt
import pickle
from saqc.constants import *
-from saqc.lib.types import FreqString
-from saqc.core import processing, Flags
-from saqc.lib.tools import periodicMask
+from saqc.core.register import processing
+from saqc.core import register, Flags
+from saqc.lib.tools import periodicMask, filterKwargs
from saqc.lib.plotting import makeFig
_MPL_DEFAULT_BACKEND = mpl.get_backend()
-@processing()
+@register(mask=[], demask=[], squeeze=[], handles_target=True)
def copyField(
- data: DictOfSeries, field: str, flags: Flags, new_field: str, **kwargs
+ data: DictOfSeries, field: str, flags: Flags, target: str, **kwargs
) -> Tuple[DictOfSeries, Flags]:
"""
- The function generates a copy of the data "field" and inserts it under the name field + suffix into the existing
- data.
+ Copy data and flags to a new name (preserve flags history).
Parameters
----------
@@ -36,7 +35,7 @@ def copyField(
The fieldname of the data column, you want to fork (copy).
flags : saqc.Flags
Container to store quality flags to data.
- new_field: str
+ target: str
Target name.
Returns
@@ -48,12 +47,12 @@ def copyField(
The quality flags of data
Flags shape may have changed relatively to the flags input.
"""
- if new_field in flags.columns.union(data.columns):
+ if target in flags.columns.union(data.columns):
raise ValueError(f"{field}: field already exist")
- data[new_field] = data[field].copy()
- # implicit copy in history access
- flags.history[new_field] = flags.history[field]
+ data[target] = data[field].copy()
+ flags.history[target] = flags.history[field].copy()
+
return data, flags
@@ -62,7 +61,7 @@ def dropField(
data: DictOfSeries, field: str, flags: Flags, **kwargs
) -> Tuple[DictOfSeries, Flags]:
"""
- The function drops field from the data dios and the flags.
+ Drops field from the data and flags.
Parameters
----------
@@ -92,7 +91,7 @@ def renameField(
data: DictOfSeries, field: str, flags: Flags, new_name: str, **kwargs
) -> Tuple[DictOfSeries, Flags]:
"""
- The function renames field to new name (in both, the flags and the data).
+ Rename field in data and flags.
Parameters
----------
@@ -119,7 +118,7 @@ def renameField(
return data, flags
-@processing()
+@register(mask=[], demask=[], squeeze=["field"])
def maskTime(
data: DictOfSeries,
field: str,
@@ -132,7 +131,7 @@ def maskTime(
**kwargs,
) -> Tuple[DictOfSeries, Flags]:
"""
- This function realizes masking within saqc.
+ Realizes masking within saqc.
Due to some inner saqc mechanics, it is not straight forwardly possible to exclude
values or datachunks from flagging routines. This function replaces flags with UNFLAGGED
@@ -141,12 +140,12 @@ def maskTime(
Here comes a recipe on how to apply a flagging function only on a masked chunk of the variable field:
- 1. dublicate "field" in the input data (copy)
- 2. mask the dublicated data (mask)
- 3. apply the tests you only want to be applied onto the masked data chunks (saqc_tests)
+ 1. dublicate "field" in the input data (`copyField`)
+ 2. mask the dublicated data (this, `maskTime`)
+ 3. apply the tests you only want to be applied onto the masked data chunks (a saqc function)
4. project the flags, calculated on the dublicated and masked data onto the original field data
- (projectFlags or flagGeneric)
- 5. drop the dublicated data (drop)
+ (`concateFlags` or `flagGeneric`)
+ 5. drop the dublicated data (`dropField`)
To see an implemented example, checkout flagSeasonalRange in the saqc.functions module
@@ -164,9 +163,9 @@ def maskTime(
- "mask_var": data[mask_var] is expected to be a boolean valued timeseries and is used as mask.
mask_field : {None, str}, default None
Only effective if mode == "mask_var"
- Fieldname of the column, holding the data that is to be used as mask. (must be moolean series)
+ Fieldname of the column, holding the data that is to be used as mask. (must be boolean series)
Neither the series` length nor its labels have to match data[field]`s index and length. An inner join of the
- indices will be calculated and values get masked where the values of the inner join are "True".
+ indices will be calculated and values get masked where the values of the inner join are ``True``.
start : {None, str}, default None
Only effective if mode == "seasonal"
String denoting starting point of every period. Formally, it has to be a truncated instance of "mm-ddTHH:MM:SS".
@@ -228,42 +227,43 @@ def maskTime(
When inclusive_selection="season", all above examples work the same way, only that you now
determine wich values NOT TO mask (=wich values are to constitute the "seasons").
"""
- data = data.copy()
datcol_idx = data[field].index
if mode == "periodic":
- to_mask = periodicMask(datcol_idx, start, end, closed)
+ mask = periodicMask(datcol_idx, start, end, closed)
elif mode == "mask_field":
idx = data[mask_field].index.intersection(datcol_idx)
- to_mask = data.loc[idx, mask_field]
+ mask = data.loc[idx, mask_field]
else:
raise ValueError("Keyword passed as masking mode is unknown ({})!".format(mode))
- data.aloc[to_mask, field] = np.nan
- flags[to_mask, field] = UNFLAGGED
+ data.aloc[mask, field] = np.nan
+ flags[mask, field] = UNFLAGGED
return data, flags
-@processing()
+@register(mask=[], demask=[], squeeze=[])
def plot(
data: DictOfSeries,
field: str,
flags: Flags,
path: Optional[str] = None,
- max_gap: Optional[FreqString] = None,
+ max_gap: Optional[str] = None,
stats: bool = False,
- plot_kwargs: Optional[dict] = None,
- fig_kwargs: Optional[dict] = None,
+ history: Optional[Literal["valid", "complete", "clear"]] = "valid",
+ xscope: Optional[slice] = None,
+ phaseplot: Optional[str] = None,
stats_dict: Optional[dict] = None,
store_kwargs: Optional[dict] = None,
+ dfilter: float = FILTER_ALL,
**kwargs,
):
"""
- Stores or shows a figure object, containing data graph with flag marks for field.
+ Plot data and flags or store plot to file.
- There are two modes, 'interactive' and 'store' mode, wich is determind via the
+ There are two modes, 'interactive' and 'store', which are determind through the
``save_path`` keyword. In interactive mode (default) the plot is shown at runtime
- and the execution stops until the plot window is closed manually by a user. In
+ and the program execution stops until the plot window is closed manually. In
store mode the generated plot is stored to disk and no manually interaction is
needed.
@@ -287,31 +287,27 @@ def plot(
max_gap : str, default None
If None, all the points in the data will be connected, resulting in long linear
lines, where continous chunks of data is missing. Nans in the data get dropped
- before plotting. If an Offset string is passed, only points that have a distance
+ before plotting. If an offset string is passed, only points that have a distance
below `max_gap` get connected via the plotting line.
stats : bool, default False
Whether to include statistics table in plot.
- plot_kwargs : dict, default None
- Keyword arguments controlling plot generation. Will be passed on to the
- ``Matplotlib.axes.Axes.set()`` property batch setter for the axes showing the
- data plot. The most relevant of those properties might be "ylabel", "title" and
- "ylim". In Addition, following options are available:
-
- * {'slice': s} property, that determines a chunk of the data to be plotted /
- processed. `s` can be anything, that is a valid argument to the
- ``pandas.Series.__getitem__`` method.
- * {'history': str}
- * str="all": All the flags are plotted with colored dots, refering to the
- tests they originate from
- * str="valid": - same as 'all' - but only plots those flags, that are not
- removed by later tests
-
- fig_kwargs : dict, default None
- Keyword arguments controlling figure generation. In interactive mode,
- ``None`` defaults to ``{"figsize": (16, 9)}`` to ensure a proper figure size
- in store-mode.
+ history : {"valid", "complete", None}, default "valid"
+ Discriminate the plotted flags with respect to the tests they originate from.
+
+ * "valid" - Only plot those flags, that do not get altered or "unflagged" by subsequent tests. Only list tests
+ in the legend, that actually contributed flags to the overall resault.
+ * "complete" - plot all the flags set and list all the tests ran on a variable. Suitable for debugging/tracking.
+ * "clear" - clear plot from all the flagged values
+ * None - just plot the resulting flags for one variable, without any historical meta information.
+
+ xscope : slice or Offset, default None
+ Parameter, that determines a chunk of the data to be plotted
+ processed. `xscope` can be anything, that is a valid argument to the ``pandas.Series.__getitem__`` method.
+
+ phaseplot : str or None, default None
+ If a string is passed, plot ``field`` in the phase space it forms together with the Variable ``phaseplot``.
store_kwargs : dict, default {}
Keywords to be passed on to the ``matplotlib.pyplot.savefig`` method, handling
@@ -320,11 +316,11 @@ def plot(
Reopen with: ``pickle.load(open(savepath,'w')).show()``
stats_dict: dict, default None
- (Only relevant if `stats`=True)
+ (Only relevant if ``stats = True``)
Dictionary of additional statisticts to write to the statistics table
accompanying the data plot. An entry to the stats_dict has to be of the form:
- * {"stat_name": lambda x, y, z: func(x, y, z)}
+ * ``{"stat_name": lambda x, y, z: func(x, y, z)}``
The lambda args ``x``,``y``,``z`` will be fed by:
@@ -349,7 +345,13 @@ def plot(
>>> func = lambda x, y, z: round((x.isna().sum()) / len(x), 2)
"""
+
interactive = path is None
+ level = kwargs.get("flag", BAD)
+
+ if dfilter < np.inf:
+ data = data.copy()
+ data.loc[flags[field] >= dfilter, field] = np.nan
if store_kwargs is None:
store_kwargs = {}
@@ -359,19 +361,17 @@ def plot(
else:
mpl.use("Agg")
- # ensure a proper size in stored plot
- if fig_kwargs is None:
- fig_kwargs = {"figsize": (16, 9)}
fig = makeFig(
data=data,
field=field,
flags=flags,
- level=kwargs.get("flag", BAD),
+ level=level,
max_gap=max_gap,
stats=stats,
- plot_kwargs=plot_kwargs,
- fig_kwargs=fig_kwargs,
+ history=history,
+ xscope=xscope,
+ phaseplot=phaseplot,
stats_dict=stats_dict,
)
diff --git a/saqc/funcs/transformation.py b/saqc/funcs/transformation.py
index 357e948cdbbf47a945b2a8f1e16ce5e1fa064197..560dc4bb143b9d5473f7c3a7798fe77475111c61 100644
--- a/saqc/funcs/transformation.py
+++ b/saqc/funcs/transformation.py
@@ -6,10 +6,10 @@ import numpy as np
import pandas as pd
from dios import DictOfSeries
-from saqc.core import flagging, Flags
+from saqc.core import register, Flags
-@flagging(masking="field")
+@register(mask=["field"], demask=[], squeeze=[])
def transform(
data: DictOfSeries,
field: str,
@@ -49,9 +49,7 @@ def transform(
flags : saqc.Flags
The quality flags of data
"""
-
- data = data.copy()
- val_ser = data[field]
+ val_ser = data[field].copy()
# partitioning
if not freq:
freq = val_ser.shape[0]
diff --git a/saqc/lib/plotting.py b/saqc/lib/plotting.py
index b9cab2c6bec2530fcc2bc4ac9a0263a7a0896e89..08305933f2d25838cc8f4cb627fbaa7d46740078 100644
--- a/saqc/lib/plotting.py
+++ b/saqc/lib/plotting.py
@@ -2,13 +2,17 @@
# -*- coding: utf-8 -*-
from typing import Optional
-
+from typing_extensions import Literal
+from saqc.lib.tools import toSequence
import pandas as pd
+import numpy as np
import matplotlib as mpl
-
+import itertools
+import matplotlib.pyplot as plt
+import seaborn
from saqc.constants import *
from saqc.core import Flags
-from saqc.lib.types import DiosLikeT, FreqString
+from saqc.lib.types import DiosLikeT
STATSDICT = {
@@ -19,16 +23,29 @@ STATSDICT = {
"flagged percentage": lambda x, y, z: round(((y >= z).sum()) / len(x), 2),
}
+PLOT_KWARGS = {"alpha": 0.8, "linewidth": 1}
+AX_KWARGS = {}
+FIG_KWARGS = {"figsize": (16, 9)}
+SCATTER_KWARGS = {
+ "marker": ["s", "D", "^", "o"],
+ "color": seaborn.color_palette("bright"),
+ "alpha": 0.7,
+ "zorder": 10,
+ "edgecolors": "black",
+ "s": 70,
+}
+
def makeFig(
data: DiosLikeT,
field: str,
flags: Flags,
level: float,
- max_gap: Optional[FreqString] = None,
+ max_gap: Optional[str] = None,
stats: bool = False,
- plot_kwargs: Optional[dict] = None,
- fig_kwargs: Optional[dict] = None,
+ history: Optional[Literal["valid", "complete"]] = "valid",
+ xscope: Optional[slice] = None,
+ phaseplot: Optional[str] = None,
stats_dict: Optional[dict] = None,
):
"""
@@ -57,24 +74,19 @@ def makeFig(
stats : bool, default False
Whether to include statistics table in plot.
- plot_kwargs : dict, default None
- Keyword arguments controlling plot generation. Will be passed on to the
- ``Matplotlib.axes.Axes.set()`` property batch setter for the axes showing the
- data plot. The most relevant of those properties might be "ylabel",
- "title" and "ylim".
- In Addition, following options are available:
-
- * {'slice': s} property, that determines a chunk of the data to be plotted /
- processed. `s` can be anything,
- that is a valid argument to the ``pandas.Series.__getitem__`` method.
- * {'history': str}
- * str="all": All the flags are plotted with colored dots, refering to the
- tests they originate from
- * str="valid": - same as 'all' - but only plots those flags, that are not
- removed by later tests
- fig_kwargs : dict, default None
- Keyword arguments controlling figure generation. None defaults to
- {"figsize": (16, 9)}
+ history : {"valid", "complete", None}, default "valid"
+ Discriminate the plotted flags with respect to the tests they originate from.
+ * "valid" - Only plot those flags, that do not get altered or "unflagged" by subsequent tests. Only list tests
+ in the legend, that actually contributed flags to the overall resault.
+ * "complete" - plot all the flags set and list all the tests ran on a variable. Suitable for debugging/tracking.
+ * "clear" - clear plot from all the flagged values
+ * None - just plot the resulting flags for one variable, without any historical meta information.
+
+ xscope : slice or Offset, default None
+ Parameter, that determines a chunk of the data to be plotted /
+ processed. `s` can be anything, that is a valid argument to the ``pandas.Series.__getitem__`` method.
+
+ phaseplot :
stats_dict: dict, default None
(Only relevant if `stats`=True).
@@ -111,33 +123,45 @@ def makeFig(
>>> func = lambda x, y, z: round((x.isna().sum()) / len(x), 2)
"""
- if plot_kwargs is None:
- plot_kwargs = {"history": False}
- if fig_kwargs is None:
- fig_kwargs = {}
+
if stats_dict is None:
stats_dict = {}
# data retrieval
d = data[field]
# data slicing:
- s = plot_kwargs.pop("slice", slice(None))
- d = d[s]
- flags_vals = flags[field][s]
- flags_hist = flags.history[field].hist.loc[s]
+ xscope = xscope or slice(xscope)
+ d = d[xscope]
+ flags_vals = flags[field][xscope]
+ flags_hist = flags.history[field].hist.loc[xscope]
+ flags_meta = flags.history[field].meta
if stats:
stats_dict.update(STATSDICT)
stats_dict = _evalStatsDict(stats_dict, d, flags_vals, level)
na_mask = d.isna()
d = d[~na_mask]
+ if phaseplot:
+ flags_vals = flags_vals.copy()
+ flags_hist = flags_hist.copy()
+ phase_index = data[phaseplot][xscope].values
+ phase_index_d = phase_index[~na_mask]
+ na_mask.index = phase_index
+ d.index = phase_index_d
+ flags_vals.index = phase_index
+ flags_hist.index = phase_index
+ plot_kwargs = {**PLOT_KWARGS, **{"marker": "o", "linewidth": 0}}
+ ax_kwargs = {**{"xlabel": phaseplot, "ylabel": d.name}, **AX_KWARGS}
+ else:
+ plot_kwargs = PLOT_KWARGS
+ ax_kwargs = AX_KWARGS
# insert nans between values mutually spaced > max_gap
- if max_gap:
+ if max_gap and not d.empty:
d = _insertBlockingNaNs(d, max_gap)
# figure composition
- fig = mpl.pyplot.figure(constrained_layout=True, **fig_kwargs)
+ fig = mpl.pyplot.figure(constrained_layout=True, **FIG_KWARGS)
grid = fig.add_gridspec()
if stats:
plot_gs, tab_gs = grid[0].subgridspec(ncols=2, nrows=1, width_ratios=[5, 1])
@@ -147,7 +171,19 @@ def makeFig(
else:
ax = fig.add_subplot(grid[0])
- _plotVarWithFlags(ax, d, flags_vals, flags_hist, level, plot_kwargs, na_mask)
+ _plotVarWithFlags(
+ ax,
+ d,
+ flags_vals,
+ flags_hist,
+ flags_meta,
+ history,
+ level,
+ na_mask,
+ plot_kwargs,
+ ax_kwargs,
+ SCATTER_KWARGS,
+ )
return fig
@@ -173,34 +209,76 @@ def _plotStatsTable(ax, stats_dict):
tab_obj.set_fontsize(10)
-def _plotVarWithFlags(ax, datser, flags_vals, flags_hist, level, plot_kwargs, na_mask):
+def _plotVarWithFlags(
+ ax,
+ datser,
+ flags_vals,
+ flags_hist,
+ flags_meta,
+ history,
+ level,
+ na_mask,
+ plot_kwargs,
+ ax_kwargs,
+ scatter_kwargs,
+):
+ scatter_kwargs = scatter_kwargs.copy()
ax.set_title(datser.name)
- ax.plot(datser)
- history = plot_kwargs.pop("history", False)
- ax.set(**plot_kwargs)
+ ax.plot(datser, color="black", **plot_kwargs)
+ ax.set(**ax_kwargs)
+ shape_cycle = scatter_kwargs.get("marker", "o")
+ shape_cycle = itertools.cycle(toSequence(shape_cycle))
+ color_cycle = scatter_kwargs.get(
+ "color", plt.rcParams["axes.prop_cycle"].by_key()["color"]
+ )
+ color_cycle = itertools.cycle(toSequence(color_cycle))
if history:
for i in flags_hist.columns:
- if history == "all":
- _plotFlags(
- ax,
- datser,
- flags_hist[i],
- na_mask,
- level,
- {"label": "test " + str(i)},
+ # catch empty but existing history case (flags_meta={})
+ if len(flags_meta[i]) == 0:
+ continue
+ label = (
+ flags_meta[i]["kwargs"].get("label", None)
+ or flags_meta[i]["func"].split(".")[-1]
+ )
+ scatter_kwargs.update({"label": label})
+ flags_i = flags_hist[i].astype(float)
+ if history == "complete":
+ scatter_kwargs.update(
+ {"color": next(color_cycle), "marker": next(shape_cycle)}
)
+ _plotFlags(ax, datser, flags_i, na_mask, level, scatter_kwargs)
if history == "valid":
+ # only plot those flags, that do not get altered later on:
+ mask = flags_i.eq(flags_vals)
+ flags_i[~mask] = np.nan
+ # Skip plot, if the test did not have no effect on the all over flagging result. This avoids
+ # legend overflow
+ if ~(flags_i >= level).any():
+ continue
+
+ # Also skip plot, if all flagged values are np.nans (to catch flag missing and masked results mainly)
+ temp_i = datser.index.join(flags_i.index, how="inner")
+ if datser[temp_i][flags_i[temp_i].notna()].isna().all() or (
+ "flagMissing" in flags_meta[i]["func"]
+ ):
+ continue
+
+ scatter_kwargs.update(
+ {"color": next(color_cycle), "marker": next(shape_cycle)}
+ )
_plotFlags(
ax,
datser,
- flags_hist[i].combine(flags_vals, min),
+ flags_i,
na_mask,
level,
- {"label": "test " + str(i)},
+ scatter_kwargs,
)
ax.legend()
else:
- _plotFlags(ax, datser, flags_vals, na_mask, level, {"color": "r"})
+ scatter_kwargs.update({"color": next(color_cycle), "marker": next(shape_cycle)})
+ _plotFlags(ax, datser, flags_vals, na_mask, level, scatter_kwargs)
def _plotFlags(ax, datser, flags, na_mask, level, scatter_kwargs):
diff --git a/saqc/lib/rolling.py b/saqc/lib/rolling.py
index 535b08ff29dde07122f3b118a437a2de71b4f2ef..4e12f07fca3636f5c54e688de2aa24bc07de9a56 100644
--- a/saqc/lib/rolling.py
+++ b/saqc/lib/rolling.py
@@ -14,15 +14,9 @@ else:
import pandas.core.indexers.objects as indexers
-class CustomNumericalIndexer(indexers.FixedWindowIndexer):
- def __init__(
- self,
- index_array: np.ndarray | None = None,
- window_size: int = 0,
- forward: bool = False,
- **kwargs,
- ):
- super().__init__(index_array, window_size, **kwargs)
+class ForwardMixin:
+ def __init__(self, *args, forward: bool = False, **kwargs):
+ super().__init__(*args, **kwargs)
self.forward = forward
def get_window_bounds(
@@ -37,12 +31,21 @@ class CustomNumericalIndexer(indexers.FixedWindowIndexer):
closed = "right"
if self.forward:
+
+ # this is only set with variable window indexer
+ if self.index_array is not None: # noqa
+ self.index_array = self.index_array[::-1] # noqa
+
+ # closed 'both' and 'neither' works the same
+ # on forward and backward windows by definition
if closed == "left":
closed = "right"
elif closed == "right":
closed = "left"
- start, end = super().get_window_bounds(num_values, min_periods, center, closed)
+ start, end = super().get_window_bounds( # noqa
+ num_values, min_periods, center, closed
+ )
if self.forward:
start, end = end, start
@@ -52,47 +55,13 @@ class CustomNumericalIndexer(indexers.FixedWindowIndexer):
return start, end
-class CustomDatetimeIndexer(indexers.VariableWindowIndexer):
- def __init__(
- self,
- index_array: np.ndarray | None = None,
- window_size: int = 0,
- forward: bool = False,
- **kwargs,
- ):
- super().__init__(index_array, window_size, **kwargs)
- self.forward = forward
+CustomFixedIndexer = type(
+ "CustomFixedIndexer", (ForwardMixin, indexers.FixedWindowIndexer), {}
+)
- def get_window_bounds(
- self,
- num_values: int = 0,
- min_periods: int | None = None,
- center: bool | None = None,
- closed: str | None = None,
- ) -> tuple[np.ndarray, np.ndarray]:
-
- # set the default
- if closed is None and self.forward:
- closed = "left"
- elif closed is None:
- closed = "right"
-
- if self.forward:
- self.index_array = self.index_array[::-1]
- # swap left / right because we inverted the array
- if closed == "left":
- closed = "right"
- elif closed == "right":
- closed = "left"
-
- start, end = super().get_window_bounds(num_values, min_periods, center, closed)
-
- if self.forward:
- start, end = end, start
- start = num_values - start[::-1]
- end = num_values - end[::-1]
-
- return start, end
+CustomVariableIndexer = type(
+ "CustomVariableIndexer", (ForwardMixin, indexers.VariableWindowIndexer), {}
+)
class AttrWrapper(object):
@@ -221,9 +190,7 @@ class CustomRoller:
self._expand = expand
# dummy roller.
- # 1. This lets pandas do all the checks.
- # 2. After the call, all the attributes (public and private)
- # of `_roller` are accessible on self
+ # This lets pandas do all the checks.
verified = obj.rolling(
window=window,
min_periods=min_periods,
@@ -245,27 +212,33 @@ class CustomRoller:
self._index_array = verified._index_array
if self._dtlike_window:
- self.window_indexer = CustomDatetimeIndexer(
+ self.window_indexer = CustomVariableIndexer(
index_array=verified._index_array,
window_size=verified._win_freq_i8,
center=verified.center,
forward=self._forward,
)
else:
- self.window_indexer = CustomNumericalIndexer(
+ self.window_indexer = CustomFixedIndexer(
window_size=verified.window,
forward=self._forward,
)
+ # set the default
+ if self._forward and closed is None:
+ closed = "left"
+
# create the real roller with a custom Indexer
# from the attributes of the old roller.
+ # After next line, all the attributes (public and private)
+ # of `_roller` are accessible on self
self._roller = obj.rolling(
window=self.window_indexer,
min_periods=verified.min_periods, # roller.min_periods
win_type=verified._win_type, # use private here to silence warning
on=verified.on,
center=verified.center,
- closed=verified.closed,
+ closed=closed,
axis=verified.axis,
)
diff --git a/saqc/lib/tools.py b/saqc/lib/tools.py
index b5dc0037da21c180a714cf52a2ad216909cffe11..94205e5a5863a873f1682d0103b721fae8bb4995 100644
--- a/saqc/lib/tools.py
+++ b/saqc/lib/tools.py
@@ -1,11 +1,12 @@
#! /usr/bin/env python
# -*- coding: utf-8 -*-
+from __future__ import annotations
import re
import datetime
import itertools
import warnings
-from typing import Sequence, Union, Any, Iterator, Callable
+from typing import List, Sequence, TypeVar, Union, Any, Iterator, Callable, Collection
import numpy as np
import numba as nb
@@ -16,13 +17,25 @@ import dios
import collections
from scipy.cluster.hierarchy import linkage, fcluster
-from saqc.lib.types import T
-
# keep this for external imports
# TODO: fix the external imports
from saqc.lib.rolling import customRoller
+T = TypeVar("T", str, float, int)
+
+
+def _swapToTarget(field, target, flags):
+ if target is None:
+ return field, flags
+ if not isinstance(target, str):
+ raise TypeError(f"target must be of type string, not {repr(type(target))}")
+ if target in flags.columns:
+ raise ValueError(f"cannot create target {repr(target)}, it already exists.")
+ flags.history[target] = flags.history[field].copy()
+ return target, flags
+
+
def assertScalar(name, value, optional=False):
if (not np.isscalar(value)) and (value is not None) and (optional is True):
raise ValueError(f"'{name}' needs to be a scalar or 'None'")
@@ -30,13 +43,15 @@ def assertScalar(name, value, optional=False):
raise ValueError(f"'{name}' needs to be a scalar")
-def toSequence(
- value: Union[T, Sequence[T]], default: Union[T, Sequence[T]] = None
-) -> Sequence[T]:
- if value is None:
- value = default
- if np.isscalar(value):
- value = [value]
+def toSequence(value: T | Sequence[T]) -> List[T]:
+ if isinstance(value, (str, int, float)):
+ return [value]
+ return list(value)
+
+
+def squeezeSequence(value: Sequence[T]) -> Union[T, Sequence[T]]:
+ if len(value) == 1:
+ return value[0]
return value
@@ -160,7 +175,7 @@ def periodicMask(dtindex, season_start, season_end, include_bounds):
Returns
-------
- to_mask : pandas.Series[bool]
+ dfilter : pandas.Series[bool]
A series, indexed with the input index and having value `True` for all the values that are to be masked.
Examples
@@ -279,6 +294,26 @@ def groupConsecutives(series: pd.Series) -> Iterator[pd.Series]:
start = stop
+def concatDios(data: List[dios.DictOfSeries], warn: bool = True, stacklevel: int = 2):
+ # fast path for most common case
+ if len(data) == 1 and data[0].columns.is_unique:
+ return data[0]
+
+ result = dios.DictOfSeries()
+ for di in data:
+ for c in di.columns:
+ if c in result.columns:
+ if warn:
+ warnings.warn(
+ f"Column {c} already exist. Data is overwritten. "
+ f"Avoid duplicate columns names over all inputs.",
+ stacklevel=stacklevel,
+ )
+ result[c] = di[c]
+
+ return result
+
+
def mergeDios(left, right, subset=None, join="merge"):
# use dios.merge() as soon as it implemented
# see https://git.ufz.de/rdm/dios/issues/15
@@ -623,3 +658,54 @@ def statPass(
to_set[start:end] = True
return to_set
+
+
+def filterKwargs(
+ kwargs: dict,
+ reserved: Collection,
+ inplace: bool = True,
+ warn: bool = True,
+ msg: str = "",
+ stacklevel: int = 3,
+) -> dict:
+ """
+ Filter kwargs (or any dict) by a list of reserved keys.
+
+ Parameters
+ ----------
+ kwargs : dict
+ The dict to filter.
+
+ reserved : list-like
+ A list of reserved keywords.
+
+ inplace : bool, default False
+ If `False` a copy is returned, otherwise the modified original kwargs.
+
+ warn : bool, default True
+ Throw a `RuntimeWarning` with the following text:
+
+ msg : str, default ""
+ A text to append to the warnings message.
+
+ stacklevel : int, default 3
+ The stacklevel this warning will refer to.
+ - `2` : warn at the location this function is called
+ - `3` : warn for the function that calls this function (default)
+
+ Returns
+ -------
+ kwargs: dict
+ the modified kwargs or a copy
+ """
+ if not inplace:
+ kwargs = kwargs.copy()
+ for key in reserved:
+ if warn and key in kwargs:
+ warnings.warn(
+ f"The keyword {repr(key)} is reserved and will be ignored {msg}",
+ RuntimeWarning,
+ stacklevel=stacklevel,
+ )
+ kwargs.pop(key, None)
+ return kwargs
diff --git a/saqc/lib/ts_operators.py b/saqc/lib/ts_operators.py
index 8bac0949edcce3c40aa3ab0bb5076dcd1d45f722..151b2e77e50916856f7038e9826389400f0896b7 100644
--- a/saqc/lib/ts_operators.py
+++ b/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")
@@ -217,7 +217,7 @@ def interpolateNANs(
:return:
"""
inter_limit = int(inter_limit)
- data = pd.Series(data).copy()
+ data = pd.Series(data, copy=True)
gap_mask = data.isna().rolling(inter_limit, min_periods=0).sum() != inter_limit
if inter_limit == 2:
@@ -287,9 +287,10 @@ def aggregate2Freq(
also serves as a replacement for "invalid" intervals.
"""
methods = {
- "nagg": lambda seconds_total: (seconds_total / 2, "left", "left"),
- "bagg": lambda _: (0, "left", "left"),
- "fagg": lambda _: (0, "right", "right"),
+ # offset, closed, label
+ "nagg": lambda f: (f / 2, "left", "left"),
+ "bagg": lambda _: (pd.Timedelta(0), "left", "left"),
+ "fagg": lambda _: (pd.Timedelta(0), "right", "right"),
}
# filter data for invalid patterns (since filtering is expensive we pre-check if
@@ -307,40 +308,35 @@ def aggregate2Freq(
)
data[temp_mask] = fill_value
- seconds_total = pd.Timedelta(freq).total_seconds()
- base, label, closed = methods[method](seconds_total)
+ freq = pd.Timedelta(freq)
+ offset, closed, label = methods[method](freq)
- # In the following, we check for empty intervals outside resample.apply, because:
- # - resample AND groupBy do insert value zero for empty intervals if resampling
- # with any kind of "sum" application - we want "fill_value" to be inserted - we
- # are aggregating data and flags with this function and empty intervals usually
- # would get assigned BAD flag (where resample inserts np.nan or 0)
-
- data_resampler = data.resample(
- f"{seconds_total:.0f}s", base=base, closed=closed, label=label
+ resampler = data.resample(
+ freq, closed=closed, label=label, origin="start_day", offset=offset
)
- empty_intervals = data_resampler.count() == 0
- # great performance gain can be achieved, when avoiding .apply and using
- # pd.resampler methods instead. (this covers all the basic func aggregations,
- # such as median, mean, sum, count, ...)
+ # count valid values
+ counts = resampler.count()
+
+ # native methods of resampling (median, mean, sum, ..) are much faster than apply
try:
check_name = re.sub("^nan", "", agg_func.__name__)
# a nasty special case: if function "count" was passed, we not want empty
- # intervals to be replaced by nan:
+ # intervals to be replaced by fill_value:
if check_name == "count":
- empty_intervals[:] = False
- data = getattr(data_resampler, check_name)()
+ data = counts.copy()
+ counts[:] = np.nan
+ else:
+ data = getattr(resampler, check_name)()
except AttributeError:
- data = data_resampler.apply(agg_func)
+ data = resampler.apply(agg_func)
+
+ # we custom fill bins that have no value
+ data[counts == 0] = fill_value
- # since loffset keyword of pandas.resample "discharges" after one use of the
- # resampler (pandas logic), we correct the resampled labels offset manually,
- # if necessary.
+ # undo the temporary shift, to mimic centering the frequency
if method == "nagg":
- data.index = data.index.shift(freq=pd.Timedelta(freq) / 2)
- empty_intervals.index = empty_intervals.index.shift(freq=pd.Timedelta(freq) / 2)
- data[empty_intervals] = fill_value
+ data.index += offset
return data
diff --git a/saqc/lib/types.py b/saqc/lib/types.py
index d4e76adf73526eadaf4ec09bfbfaa517f0eba4e2..55fe7392a6cf49b2483ce69771b2eda7b1ce7ca6 100644
--- a/saqc/lib/types.py
+++ b/saqc/lib/types.py
@@ -5,16 +5,12 @@ __all__ = [
"ArrayLike",
"PandasLike",
"DiosLikeT",
- "FreqString",
- "IntegerWindow",
- "Timestampstr",
"CurveFitter",
"ExternalFlag",
- "PositiveFloat",
- "PositiveInt",
+ "OptionalNone",
]
-from typing import TypeVar, Union, NewType, List, Tuple
+from typing import Any, TypeVar, Union, Dict
from typing_extensions import Protocol, Literal
import numpy as np
import pandas as pd
@@ -27,17 +23,21 @@ DiosLikeT = Union[DictOfSeries, pd.DataFrame]
ExternalFlag = Union[str, float, int]
-# we only support fixed length offsets
-FreqString = Literal["D", "H", "T", "min", "S", "L", "ms", "U", "us", "N"]
-
-# # we define a bunch of type aliases, mostly needed to generate appropiate fuzzy data through hypothesis
-# ColumnName = NewType("ColumnName", str)
-# IntegerWindow = NewType("IntegerWindow", int)
-# Timestampstr = TypeVar("Timestampstr", bound=str)
-# PositiveFloat = NewType("PositiveFloat", float)
-# PositiveInt = NewType("PositiveInt", int)
# needed for deeper type hinting magic
class CurveFitter(Protocol):
def __call__(self, data: np.ndarray, *params: float) -> np.ndarray:
...
+
+
+class GenericFunction(Protocol):
+
+ __name__: str
+ __globals__: Dict[str, Any]
+
+ def __call__(self, *args: pd.Series) -> PandasLike:
+ ...
+
+
+class OptionalNone:
+ pass
diff --git a/setup.py b/setup.py
index 22722f0d74c48da012ed6946a1f59179ab7cbb0b..2ea09a26956e2bbd508bf54ac5e82fb0633dc3eb 100644
--- a/setup.py
+++ b/setup.py
@@ -5,7 +5,7 @@ with open("README.md", "r") as fh:
setup(
name="saqc",
- version="1.4",
+ version="2.0.0",
author="Bert Palm, David Schaefer, Peter Luenenschloss, Lennard Schmidt",
author_email="david.schaefer@ufz.de",
description="Data quality checking and processing tool/framework",
@@ -15,19 +15,18 @@ setup(
packages=find_packages(exclude=("tests",)),
python_requires=">=3.7, <3.10",
install_requires=[
- "numpy",
- "pandas",
- "scipy",
- "scikit-learn",
- "numba",
- "matplotlib",
- "click",
- "pyarrow",
- "typing_extensions",
- "outlier-utils",
- "dtw",
- "PyWavelets",
- "mlxtend",
+ "numpy==1.20.*",
+ "pandas==1.3.*",
+ "scipy==1.7.*",
+ "scikit-learn==1.0.*",
+ "numba==0.54.*",
+ "matplotlib==3.4.*",
+ "Click==8.0.*",
+ "pyarrow==4.0.*",
+ "typing_extensions==3.10.*",
+ "outlier-utils==0.0.3",
+ "dtw==1.4.*",
+ "seaborn==0.11.*",
],
license_files=("LICENSE.md",),
entry_points={
diff --git a/sphinx-doc/Makefile b/sphinx-doc/Makefile
index 992c9c8b91567974b9d6467826b5777cccd1e28f..f3db1da13588941376be9065785a5055e0a97e63 100644
--- a/sphinx-doc/Makefile
+++ b/sphinx-doc/Makefile
@@ -8,40 +8,43 @@ SPHINXBUILD ?= sphinx-build
SOURCEDIR = .
BUILDDIR = _build
+# sphinx build controll:
+# Function documentations will be acessible through: FUNCTIONS.saqc
+FUNCTIONS = Functions
+CORE = Core
+# List of folders containing markdown content to include into the build
+MDLIST = getting_started_md how_to_doc_md cook_books_md misc_md
# Put it first so that "make" without argument is like "make help".
help:
@$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
.PHONY: help Makefile clean
+test:
+ for k in $(MDLIST); do echo sphinx-doc/"$$k"; done
+
# clean sphinx generated stuff
clean:
- rm -rf _build _static _api
- rm -f *.automodsumm
- rm -f func_modules/*.automodsumm
- rm -f intro_modules/*.automodsumm
- rm -rf ../docs
+ rm -rf _build _static
+ rm -rf ../$(FUNCTIONS)
+ rm -rf ../$(CORE)
mkdir _static
+ # for k in $(MDLIST); do rm -r "$$k"_m2r; done
+ rm -f *.automodsumm
+ rm -f *.automodapi
+ rm -f moduleAPIs/*.automodsumm
+ rm -f moduleAPIs/*.automodapi
# trigger (saqc) customized documentation pipeline
doc:
- # generate fake modules to b documented by sphinx
- python make_doc_module.py -p "saqc/funcs" -t "docs/intro_modules" -sr ".." -mo "intro_doc"
- python make_doc_module.py -p "saqc/funcs" -t "docs/func_modules" -sr ".." -mo "registered_doc"
- # make rest files from fake modules
- python make_doc_rst.py -p "docs/intro_modules" -t "sphinx-doc/intro_modules" -sr ".."
- python make_doc_rst.py -p "docs/func_modules" -t "sphinx-doc/func_modules" -sr ".."
+ # generate parent fake module for the functions to be documented
+ python scripts/make_doc_module.py -p "saqc/funcs" -t "$(FUNCTIONS)" -sr ".."
+ # generate environment table from dictionary
+ python scripts/make_env_tab.py
# make rest folders from markdown folders
- python make_md_to_rst.py -p "sphinx-doc/getting_started_md" -sr ".."
- python make_md_to_rst.py -p "sphinx-doc/how_to_doc_md" -sr ".."
+ # for k in $(MDLIST); do python scripts/make_md_to_rst.py -p sphinx-doc/"$$k" -sr ".."; done
# make the html build
@$(SPHINXBUILD) -M html "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
- # format docstring module domain strings to correct adresses
- python make_html_headings_proppa.py -b "sphinx-doc/_build/html/_api" -p "docs/func_modules" -sr ".."
- python make_html_headings_proppa.py -b "sphinx-doc/_build/html/_api" -p "docs/intro_modules" -sr ".."
- # clear fake modules/intermediate rest files
- rm -r getting_started_md_m2r
- rm -r how_to_doc_md_m2r
# Catch-all target: route all unknown targets to Sphinx using the new
# "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS).
diff --git a/sphinx-doc/conf.py b/sphinx-doc/conf.py
index e2792427e056fd82816a1ab08312e23a9d0bd111..bed7ae63676e534c3b34c3d404bbdb1f1ed96059 100644
--- a/sphinx-doc/conf.py
+++ b/sphinx-doc/conf.py
@@ -14,6 +14,8 @@ import os
import sys
sys.path.insert(0, os.path.abspath(".."))
+package_path = os.path.abspath("..")
+os.environ["PYTHONPATH"] = ":".join((package_path, os.environ.get("PYTHONPATH", "")))
# -- Project information -----------------------------------------------------
@@ -25,7 +27,7 @@ copyright = (
author = "Bert Palm, David Schäfer, Peter Lünenschloß, Lennart Schmidt, Juliane Geller"
# The full version, including alpha/beta/rc tags
-release = "develop"
+release = f"2.0"
# -- General configuration ---------------------------------------------------
@@ -36,7 +38,7 @@ release = "develop"
extensions = [
"sphinx.ext.autodoc",
"sphinx.ext.autosummary",
- # "sphinx.ext.doctest",
+ "sphinx.ext.doctest",
# "sphinx.ext.extlinks",
# "sphinx.ext.todo",
# "sphinx.ext.intersphinx",
@@ -59,24 +61,34 @@ extensions = [
"recommonmark",
# https://github.com/ryanfox/sphinx-markdown-tables
"sphinx_markdown_tables",
+ # snippet plotting
+ "matplotlib.sphinxext.plot_directive",
+ # jupyter code execution
+ "jupyter_sphinx",
+ # "sphinx_autodoc_typehints",
+ # "numpydoc"
]
# -- Params of the extensions ------------------------------------------------
-
+add_module_names = False
numpydoc_show_class_members = False
automodsumm_inherited_members = True
# write out the files generated by automodapi, mainly for debugging
automodsumm_writereprocessed = True
-
+automodapi_writereprocessed = True
automodapi_inheritance_diagram = False
automodapi_toctreedirnm = "_api"
autosectionlabel_prefix_document = True
autodoc_typehints = "none"
-
+doctest_global_setup = """
+import saqc
+import pandas as pd
+import numpy as np
+"""
# -- Other options -----------------------------------------------------------
# Add any paths that contain templates here, relative to this directory.
@@ -109,4 +121,18 @@ html_theme = "nature"
# Add any paths that contain custom static files (such as style sheets) here,
# relative to this directory. They are copied after the builtin static files,
# so a file named "default.css" will overwrite the builtin "default.css".
-html_static_path = ["_static"]
+html_static_path = ["_static", "css"]
+
+# -- RST options -------
+rst_prolog = """
+.. |ufzLogo| image:: /ressources/images/Representative/UFZLogo.jpg
+ :width: 40 %
+ :target: https://www.ufz.de/
+
+.. |rdmLogo| image:: /ressources/images/Representative/RDMlogo.jpg
+ :width: 18 %
+ :target: https://www.ufz.de/index.php?de=45348
+ :class: align-right
+
+|ufzLogo| |rdmLogo|
+"""
diff --git a/sphinx-doc/cook_books/DataRegularisation.rst b/sphinx-doc/cook_books/DataRegularisation.rst
new file mode 100644
index 0000000000000000000000000000000000000000..3bf9fd5c7107cb258c986e0938686f31cf2df807
--- /dev/null
+++ b/sphinx-doc/cook_books/DataRegularisation.rst
@@ -0,0 +1,490 @@
+Data Regularisation
+===================
+
+The tutorial aims to introduce the usage of ``SaQC`` methods, in order to obtain regularly sampled data derivatives
+from given time series data input. Regularly sampled time series data, is data, that exhibits a constant temporal
+spacing in between subsequent data points.
+
+In the following steps, the tutorial guides through the usage of the *SaQC* :py:mod:`resampling <saqc.resampling>`
+library.
+
+#. Initially, we introduce and motivate regularisation techniques and we do import the tutorial data.
+
+ * :ref:`Why Regularisation <cook_books/DataRegularisation:Why Regularisation>`
+ * :ref:`Tutorial Data <cook_books/DataRegularisation:Tutorial Data>`
+
+#. We will get an overview over the main :ref:`Regularisation <cook_books/DataRegularisation:regularisations>` methods, starting with the shift.
+
+ * :ref:`Shift <cook_books/DataRegularisation:shift>`
+ * :ref:`Target Parameter <cook_books/DataRegularisation:target parameter>`
+
+ * :ref:`Freq Parameter <cook_books/DataRegularisation:freq parameter>`
+ * :ref:`Method Parameter <cook_books/DataRegularisation:shifting method>`
+ * :ref:`Valid Data <cook_books/DataRegularisation:Valid Data>`
+
+#. We introduce the notion of *valid* data and see how sparse intervals and those with multiple values interact with
+ regularisation.
+
+
+ * :ref:`Data Loss and Empty Intervals <cook_books/DataRegularisation:data loss and empty intervals>`
+
+ * :ref:`Empty Intervals <cook_books/DataRegularisation:empty intervals>`
+
+ * :ref:`Valid Data <cook_books/DataRegularisation:Valid Data>`
+ * :ref:`Data Reduction <cook_books/DataRegularisation:data reduction>`
+ * :ref:`Minimize Shifting <cook_books/DataRegularisation:minimize shifting distance>`
+
+#. We use the Aggregation and the Interpolation method.
+
+
+ * :ref:`Aggregation <cook_books/DataRegularisation:aggregation>`
+
+ * :ref:`Function Parameter <cook_books/DataRegularisation:aggregation functions>`
+ * :ref:`Method Parameter <cook_books/DataRegularisation:shifting method>`
+
+ * :ref:`Interpolation <cook_books/DataRegularisation:interpolation>`
+
+ * :ref:`Representing Data Sparsity <cook_books/DataRegularisation:interpolation and data sparsity>`
+
+#. We see how regularisation interacts with Flags.
+
+ * :ref:`Flags and Regularisation <cook_books/DataRegularisation:flags and regularisation>`
+
+Why Regularisation
+------------------
+
+Often, measurement data does not come in regularly sampled time series. The reasons, why one usually would
+like to have time series data, that exhibits a constant temporal gap size
+in between subsequent measurements, are manifold.
+
+The 2 foremost important ones, may be, that statistics, such as *mean* and *standard deviation*
+usually presuppose the set of data points, they are computed of, to
+be equally weighted.
+
+The second reason, is, that, relating data of different sources to another, is impossible, if one
+has not a mapping at hand, that relates the different date time indices to each other. One easy and intuitive
+way of constructing such a mapping, is to just resample all data at the same (regular) timestamp.
+
+Tutorial Data
+-------------
+
+The following `dataset <../ressources/data/SoilMoisture.csv>`_ of Soil Moisture measurements may serve as
+example data set:
+
+
+.. image:: ../ressources/images/cbooks_SoilMoisture.png
+ :target: ../ressources/images/cbooks_SoilMoisture.png
+ :alt:
+
+
+Lets import it and check out the first and last lines.
+
+>>> import pandas as pd
+>>> data_path = './ressources/data/SoilMoisture.csv'
+>>> data = pd.read_csv(data_path, index_col=0)
+>>> data.index = pd.DatetimeIndex(data.index)
+>>> data
+ SoilMoisture
+2021-01-01 00:09:07 23.429701
+2021-01-01 00:18:55 23.431900
+2021-01-01 00:28:42 23.343100
+2021-01-01 00:38:30 23.476400
+2021-01-01 00:48:18 23.343100
+... ...
+2021-03-20 07:13:49 152.883102
+2021-03-20 07:26:16 156.587906
+2021-03-20 07:40:37 166.146194
+2021-03-20 07:54:59 164.690598
+2021-03-20 08:40:41 155.318893
+<BLANKLINE>
+[10607 rows x 1 columns]
+
+.. testsetup::
+
+ data_path = './ressources/data/SoilMoisture.csv'
+ data = pd.read_csv(data_path, index_col=0)
+ data.index = pd.DatetimeIndex(data.index)
+
+
+The data series seems to start with a sampling rate of roughly *10* minutes.
+Somewhere the sampling rate changes, and at the end it seems to exhibit an intended sampling
+rate of *15* minutes.
+
+Finding out about the proper sampling a series should be regularized to, is a subject on its own and wont be covered
+here. Usually, the intended sampling rate of sensor data is known from the specification of the sensor.
+
+If that is not the case, and if there seem to be more than one candidates for a rate regularisation, a rough rule of
+thumb, aiming at minimisation of data loss and data manipulation, may be,
+to go for the smallest rate seemingly present in the data.
+
+Regularisations
+---------------
+
+So lets transform the measurements timestamps to have a regular *10* minutes frequency. In order to do so,
+we have to decide what to do with each time stamps associated data, when we alter the timestamps value.
+
+Basically, there are three types of :doc:`regularisations <../moduleAPIs/Functionsresampling>` methods:
+
+
+#. We could keep the values as they are, and thus,
+ just :ref:`shift <cook_books/DataRegularisation:Shift>` them in time to match the equidistant *10* minutes frequency grid, we want the data to exhibit.
+#. We could calculate new, synthetic data values for the regular timestamps, via an :ref:`interpolation <cook_books/DataRegularisation:Interpolation>` method.
+#. We could apply some :ref:`aggregation <cook_books/DataRegularisation:Aggregation>` to up- or down sample the data.
+
+Shift
+-----
+
+Lets apply a simple shift via the :py:func:`shift <Functions.saqc.shift>` method.
+
+>>> import saqc
+>>> qc = saqc.SaQC(data)
+>>> qc = qc.shift('SoilMoisture', target='SoilMoisture_bshift', freq='10min', method='bshift')
+
+.. testsetup::
+
+ qc = saqc.SaQC(data)
+ qc = qc.shift('SoilMoisture', target='SoilMoisture_bshift', freq='10min', method='bshift')
+
+Target parameter
+^^^^^^^^^^^^^^^^
+
+We selected a new ``target`` field, to store the shifted data to a new field, so that our original data wouldnt be
+overridden.
+
+Freq parameter
+^^^^^^^^^^^^^^
+
+We passed the ``freq`` keyword of the intended sampling frequency in terms of a
+`date alias <https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#offset-aliases>`_ string. All of
+the :doc:`regularisations <../moduleAPIs/Functionsresampling>` methods have such a frequency keyword,
+and it just determines the sampling rate, the resulting regular timeseries will have.
+
+Shifting Method
+^^^^^^^^^^^^^^^
+
+With the ``method`` keyword, we determined the direction of the shift. We passed it the string ``bshift`` -
+which applies a *backwards* shift, so data points get shifted *backwards*\ , until they match a timestamp
+that is a multiple of *10* minutes. (See :py:func:`saqc.shift <Functions.saqc.shift>` documentation for more
+details on the keywords.)
+
+Lets see, how the data is now sampled. Therefore, we use the ``data_raw`` Atribute from the
+:py:class:`SaQC <saqc.core.core.SaQC>` object. This will prevent the methods output from
+being merged to a ``pandas.DataFrame`` object, and the changes from the resampling will be easier
+comprehensible from one look.
+
+Shifted data
+^^^^^^^^^^^^
+
+ >>> qc.data_raw
+ SoilMoisture | SoilMoisture_bshift |
+ ================================ | ======================================= |
+ 2021-01-01 00:09:07 23.429701 | 2021-01-01 00:00:00 23.429701 |
+ 2021-01-01 00:18:55 23.431900 | 2021-01-01 00:10:00 23.431900 |
+ 2021-01-01 00:28:42 23.343100 | 2021-01-01 00:20:00 23.343100 |
+ 2021-01-01 00:38:30 23.476400 | 2021-01-01 00:30:00 23.476400 |
+ 2021-01-01 00:48:18 23.343100 | 2021-01-01 00:40:00 23.343100 |
+ ... | ... ... |
+ 2021-03-20 07:13:49 152.883102 | 2021-03-20 08:10:00 NaN |
+ 2021-03-20 07:26:16 156.587906 | 2021-03-20 08:20:00 NaN |
+ 2021-03-20 07:40:37 166.146194 | 2021-03-20 08:30:00 NaN |
+ 2021-03-20 07:54:59 164.690598 | 2021-03-20 08:40:00 155.318893 |
+ 2021-03-20 08:40:41 155.318893 | 2021-03-20 08:50:00 NaN |
+ [10607] [11286]
+ <BLANKLINE>
+ max: [11286 rows x 2 columns]
+ <BLANKLINE>
+
+
+We see, the first and last *10* datapoints of both, the original data time series and the shifted one.
+
+Obveously, the shifted data series now exhibits a regular sampling rate of *10* minutes, with the index
+ranging from the latest timestamp, that is a multiple of *10* minutes and preceeds the initial timestamp
+of the original data, up to the first *10* minutes multiple, that succeeds the last original datas timestamp.
+This is default behavior to all the :doc:`regularisations <../moduleAPIs/Functionsresampling>` provided by ``saqc``.
+
+Data Loss and Empty Intervals
+-----------------------------
+
+The number of datapoints (displayed at the bottom of the table columns) has changed through the
+transformation as well. That change stems from 2 sources mainly:
+
+Empty Intervals
+^^^^^^^^^^^^^^^
+
+If there is no :ref:`valid <cook_books/DataRegularisation:valid data>` data point available within an interval of the passed frequency,
+that could be shifted to match a multiple of the frequency, a ``NaN`` value gets inserted to represent the fact,
+that in the interval that is represented by that date time index, there was data missing.
+
+Valid Data
+^^^^^^^^^^
+
+Data points are referred to, as *valid*\ , in context of a regularisation, if:
+
+
+#.
+ the data points value is not ``NaN``
+
+#.
+ the *flag* of that datapoint has a value lower than the value passed to the methods
+ ``to_mask`` keyword - since this keyword defaults to the highest flag level available,
+ defaultly, all data flagged :py:const:`~saqc.constants.BAD`, is considered invalid by that method.
+
+Note, that, from point *2* above, it follows, that flagging data values
+before regularisation, will effectively exclude them from the regularistaion process. See chapter
+:ref:`flagging and resampling <cook_books/DataRegularisation:flags and regularisation>` for an example of this effect and how it can help
+control :ref:`data reduction <cook_books/DataRegularisation:data reduction>`.
+
+data reduction
+^^^^^^^^^^^^^^
+
+If there are multiple values present within an interval with size according to the passed frequency alias passed to
+``freq``\ , this values get reduced to one single value, that will get assigned to the timestamp associated with the
+interval.
+
+This reduction depends on the selected :doc:`regularisation <../moduleAPIs/Functionsresampling>` method.
+
+For example, :ref:`above <cook_books/DataRegularisation:shift>`\ , we applied a backwards :py:func:`shift <Functions.saqc.shift>` with a *10* minutes frequency.
+As a result, the first value, encountered after any multiple of *10* minutes, gets shifted backwards to be aligned with
+the desired frequency and any other value in that *10* minutes interval just gets discarded.
+
+See the below chunk of our processed *SoilMoisture* data set to get an idea of the effect. There are 2 measurements
+within the *10* minutes interval ranging from ``2021-01-01 07:30:00`` to ``2021-01-01 07:40:00`` present
+in the original data - and only the first of the two reappears in the shifted data set, as representation
+for that interval.
+
+ >>> qc.data_raw["2021-01-01 07:00:00":"2021-01-01 08:00:00"]
+ SoilMoisture_bshift | SoilMoisture |
+ ================================ | ========================================= |
+ Date Time | Date Time |
+ 2021-01-01 07:00:00 23.3431 | 2021-01-01 07:00:41 23.3431 |
+ 2021-01-01 07:10:00 23.3431 | 2021-01-01 07:10:29 23.3431 |
+ 2021-01-01 07:20:00 23.2988 | 2021-01-01 07:20:17 23.2988 |
+ 2021-01-01 07:30:00 23.3874 | 2021-01-01 07:30:05 23.3874 |
+ 2021-01-01 07:40:00 23.3431 | 2021-01-01 07:39:53 23.3853 |
+ 2021-01-01 07:50:00 23.3874 | 2021-01-01 07:49:41 23.3431 |
+
+Minimize Shifting Distance
+^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+Notice, how, for example, the data point for ``2021-01-01 07:49:41`` gets shifted all the way back, to
+``2021-01-01 07:40:00`` - although, shifting it forward to ``07:40:00`` would be less a manipulation, since this timestamp
+appears to be closer to the original one.
+
+To shift to any frequncy aligned timestamp the value that is closest to that timestamp, we
+can perform a *nearest shift* instead of a simple *back shift*\ , by using the shift method ``"nshift"``\ :
+
+ >>> qc = qc.shift('SoilMoisture', target='SoilMoisture_nshift', freq='10min', method='nshift')
+ >>> qc.data_raw['2021-01-01T07:00:00':'2021-01-01T08:00:00']
+ SoilMoisture_nshift | SoilMoisture |
+ ================================ | ========================================= |
+ Date Time | Date Time |
+ 2021-01-01 07:00:00 23.3431 | 2021-01-01 07:00:41 23.3431 |
+ 2021-01-01 07:10:00 23.3431 | 2021-01-01 07:10:29 23.3431 |
+ 2021-01-01 07:20:00 23.2988 | 2021-01-01 07:20:17 23.2988 |
+ 2021-01-01 07:30:00 23.3874 | 2021-01-01 07:30:05 23.3874 |
+ 2021-01-01 07:40:00 23.3853 | 2021-01-01 07:39:53 23.3853 |
+ 2021-01-01 07:50:00 23.3431 | 2021-01-01 07:49:41 23.3431 |
+
+Now, any timestamp got assigned, the value that is nearest to it, *if* there is one valid data value available in the
+interval surrounding that timestamp with a range of half the frequency. In our example, this would mean, the regular
+timestamp would get assigned the nearest value of all the values, that preceed or succeed it by less than *5* minutes.
+
+Maybe check out, what happens with the chunk of the final 2 hours of our shifted *Soil Moisture* dataset, to get an idea.
+
+ >>> qc.data_raw['2021-03-20 07:00:00':]
+ SoilMoisture_nshift | SoilMoisture |
+ ================================ | ========================================= |
+ Date Time | Date Time |
+ 2021-03-20 07:00:00 145.027496 | 2021-03-20 07:13:49 152.883102 |
+ 2021-03-20 07:10:00 152.883102 | 2021-03-20 07:26:16 156.587906 |
+ 2021-03-20 07:20:00 NaN | 2021-03-20 07:40:37 166.146194 |
+ 2021-03-20 07:30:00 156.587906 | 2021-03-20 07:54:59 164.690598 |
+ 2021-03-20 07:40:00 166.146194 | 2021-03-20 08:40:41 155.318893 |
+ 2021-03-20 07:50:00 164.690598 | 2021-03-20 08:40:41 155.318893 |
+ 2021-03-20 08:00:00 NaN | |
+ 2021-03-20 08:10:00 NaN | |
+ 2021-03-20 08:20:00 NaN | |
+ 2021-03-20 08:30:00 NaN | |
+ 2021-03-20 08:40:00 155.318893 | |
+ 2021-03-20 08:50:00 NaN | |
+
+
+Since there is no valid data available, for example, in the interval from ``2021-03-20 07:55:00`` to ``2021-03-20 08:05:00`` - the new value
+for the regular timestamp ``2021-03-20 08:00:00``\ , that lies in the center of this interval, is ``NaN``.
+
+Aggregation
+-----------
+
+If we want to comprise several values by aggregation and assign the result to the new regular timestamp, instead of
+selecting a single one, we can do this, with the :py:func:`saqc.resample <Functions.saqc.resample>` method.
+Lets resample the *SoilMoisture* data to have a *20* minutes sample rate by aggregating every *20* minutes intervals
+content with the arithmetic mean (which is implemented by numpies ``numpy.mean`` function for example).
+
+ >>> import numpy as np
+ >>> qc = qc.resample('SoilMoisture', target='SoilMoisture_mean', freq='20min', method='bagg', agg_func=np.mean)
+ >>> qc.data_raw
+ SoilMoisture | SoilMoisture_mean |
+ ================================ | ===================================== |
+ Date Time | Date Time |
+ 2021-01-01 00:09:07 23.429701 | 2021-01-01 00:00:00 23.430800 |
+ 2021-01-01 00:18:55 23.431900 | 2021-01-01 00:20:00 23.409750 |
+ 2021-01-01 00:28:42 23.343100 | 2021-01-01 00:40:00 23.320950 |
+ 2021-01-01 00:38:30 23.476400 | 2021-01-01 01:00:00 23.365250 |
+ 2021-01-01 00:48:18 23.343100 | 2021-01-01 01:20:00 23.320950 |
+ 2021-01-01 00:58:06 23.298800 | 2021-01-01 01:40:00 23.343100 |
+ 2021-01-01 01:07:54 23.387400 | 2021-01-01 02:00:00 23.320950 |
+ 2021-01-01 01:17:41 23.343100 | 2021-01-01 02:20:00 23.343100 |
+ 2021-01-01 01:27:29 23.298800 | 2021-01-01 02:40:00 23.343100 |
+ 2021-01-01 01:37:17 23.343100 | 2021-01-01 03:00:00 23.343100 |
+ ... | ... ... |
+ 2021-03-20 05:07:02 137.271500 | 2021-03-20 05:40:00 154.116806 |
+ 2021-03-20 05:21:35 138.194107 | 2021-03-20 06:00:00 150.567505 |
+ 2021-03-20 05:41:59 154.116806 | 2021-03-20 06:20:00 NaN |
+ 2021-03-20 06:03:09 150.567505 | 2021-03-20 06:40:00 145.027496 |
+ 2021-03-20 06:58:10 145.027496 | 2021-03-20 07:00:00 152.883102 |
+ 2021-03-20 07:13:49 152.883102 | 2021-03-20 07:20:00 156.587906 |
+ 2021-03-20 07:26:16 156.587906 | 2021-03-20 07:40:00 165.418396 |
+ 2021-03-20 07:40:37 166.146194 | 2021-03-20 08:00:00 NaN |
+ 2021-03-20 07:54:59 164.690598 | 2021-03-20 08:20:00 NaN |
+ 2021-03-20 08:40:41 155.318893 | 2021-03-20 08:40:00 155.318893 |
+ [10607] [5643]
+
+Aggregation functions
+^^^^^^^^^^^^^^^^^^^^^
+
+You can pass arbitrary function objects to the ``agg_func`` parameter, to be applied to calculate every intervals result,
+as long as this function returns a scalar *float* value upon an array-like input. (So ``np.median`` would be propper
+for calculating the median, ``sum``\ , for assigning the value sum, and so on.)
+
+Aggregation method
+^^^^^^^^^^^^^^^^^^
+
+As it is with the :ref:`shift <cook_books/DataRegularisation:Shift>` functionality, a ``method`` keyword controlls, weather the
+aggregation result for the interval in between 2 regular timestamps gets assigned to the left (=\ ``bagg``\ ) or to the
+right (\ ``fagg``\ ) boundary timestamp.
+
+
+* Also, analogous to to the shift functionality, intervals of size ``freq``\ , that do
+ not contain any :ref:`valid <cook_books/DataRegularisation:valid data>` data, that could be aggregated, get ``ǹp.nan`` assigned.
+
+Interpolation
+-------------
+
+Another common way of obtaining regular timestamps, is, the interpolation of data at regular timestamps.
+
+In the pool of py:mod:`regularisation <Functions.saqc.resampling>` methods, is available the
+:py:func:`saqc.interpolate <Functions.saqc.interpolate>` method.
+
+Lets apply a linear interpolation onto the dataset. To access
+linear interpolation, we pass the ``method`` parameter the string ``"time"``. This
+applies an interpolation, that is sensitive to the difference in temporal gaps
+(as opposed by ``"linear"``\ , wich expects all the gaps to be equal). Get an overview
+of the possible interpolation methods in the :py:func:`saqc.interpolate <Functions.saqc.interpolate>`
+documentation. Lets check the results:
+
+ >>> qc = qc.interpolate('SoilMoisture', target='SoilMoisture_linear', freq='10min', method='time')
+ >>> qc.data_raw
+ SoilMoisture | SoilMoisture_linear |
+ ================================ | ========================================= |
+ Date Time | Date Time |
+ 2021-01-01 00:00:00 NaN | 2021-01-01 00:09:07 23.429701 |
+ 2021-01-01 00:10:00 23.429899 | 2021-01-01 00:18:55 23.431900 |
+ 2021-01-01 00:20:00 23.422067 | 2021-01-01 00:28:42 23.343100 |
+ 2021-01-01 00:30:00 23.360782 | 2021-01-01 00:38:30 23.476400 |
+ 2021-01-01 00:40:00 23.455997 | 2021-01-01 00:48:18 23.343100 |
+ 2021-01-01 00:50:00 23.335415 | 2021-01-01 00:58:06 23.298800 |
+ 2021-01-01 01:00:00 23.315977 | 2021-01-01 01:07:54 23.387400 |
+ 2021-01-01 01:10:00 23.377891 | 2021-01-01 01:17:41 23.343100 |
+ 2021-01-01 01:20:00 23.332627 | 2021-01-01 01:27:29 23.298800 |
+ 2021-01-01 01:30:00 23.310176 | 2021-01-01 01:37:17 23.343100 |
+ ... | ... ... |
+ 2021-03-20 07:20:00 154.723105 | 2021-03-20 05:07:02 137.271500 |
+ 2021-03-20 07:30:00 NaN | 2021-03-20 05:21:35 138.194107 |
+ 2021-03-20 07:40:00 NaN | 2021-03-20 05:41:59 154.116806 |
+ 2021-03-20 07:50:00 165.195497 | 2021-03-20 06:03:09 150.567505 |
+ 2021-03-20 08:00:00 NaN | 2021-03-20 06:58:10 145.027496 |
+ 2021-03-20 08:10:00 NaN | 2021-03-20 07:13:49 152.883102 |
+ 2021-03-20 08:20:00 NaN | 2021-03-20 07:26:16 156.587906 |
+ 2021-03-20 08:30:00 NaN | 2021-03-20 07:40:37 166.146194 |
+ 2021-03-20 08:40:00 NaN | 2021-03-20 07:54:59 164.690598 |
+ 2021-03-20 08:50:00 NaN | 2021-03-20 08:40:41 155.318893 |
+ [11286] [10607]
+
+Interpolation and Data Sparsity
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+The regularisation by interpolation is strict in the sense, that regular timestamps *only* get
+interpolated, if they have at least one :ref:`valid <cook_books/DataRegularisation:valid data>` data value preceeding them *and* one
+succeeding them *within* the given frequency range (wich is controlled by the ``freq`` keyword.).
+
+Thats, why, you have no interpolation value at ``2021-03-20 07:30:00`` - bacause it is preceeded
+by a :ref:`valid <cook_books/DataRegularisation:valid data>` value at ``2021-03-20 07:26:16``\ , but there is no :ref:`valid <cook_books/DataRegularisation:valid data>` value
+available in between the succeeding *10* minutes interval from ``2021-03-20 07:30:00`` to ``2021-03-20 07:30:00``.
+
+On the other hand, there is an interpolated value assigned to ``2021-03-20 07:50:00``\ , it is preceeded by
+a :ref:`valid <cook_books/DataRegularisation:valid data>` value at ``2021-03-20 07:40:37`` and one succeeding at ``2021-03-20 07:54:59``.
+
+This behavior is intended to reflect the sparsity of the original data in the
+regularized data set. The behavior can be circumvented by applying the more general
+:py:func:`saqc.interpolateIndex <Functions.saqc.interpolateIndex>`.
+
+Linear Interpolation
+~~~~~~~~~~~~~~~~~~~~
+
+Note, that there is a wrapper available for linear interpolation: :py:func:`saqc.linear <Functions.saqc.linear>`.
+
+Flags and Regularisation
+------------------------
+
+Since data, that is flagged by a level higher or equal to the passed ``to_mask`` value
+(default=:py:const:~saqc.constants.BAD), is not regarded :ref:`valid <cook_books/DataRegularisation:valid data>` by the applied function,
+it can be of advantage, to flag data before regularisation in order to effectively exclude it
+from the resulting regularly sampled data set. Lets see an example for the *SoilMoisture* data set.
+
+ >>> qc = qc.linear('SoilMoisture', target='SoilMoisture_linear', freq='10min')
+ >>> qc.data_raw['2021-01-01 15:00:00':'2021-01-01 16:00:00']
+ SoilMoisture_linear | SoilMoisture |
+ ================================ | ========================================= |
+ Date Time | Date Time |
+ 2021-01-01 15:00:00 23.341182 | 2021-01-01 15:00:51 23.3410 |
+ 2021-01-01 15:10:00 23.342964 | 2021-01-01 15:10:38 23.3431 |
+ 2021-01-01 15:20:00 23.341092 | 2021-01-01 15:20:26 23.3410 |
+ 2021-01-01 15:30:00 23.341000 | 2021-01-01 15:30:14 23.3410 |
+ 2021-01-01 15:40:00 -119.512446 | 2021-01-01 15:40:02 -120.0000 |
+ 2021-01-01 15:50:00 23.299553 | 2021-01-01 15:49:50 23.2988 |
+
+At ``2021-01-01 15:40:02`` the original data exhibits a measurement value
+of ``-120`` - which is obviously not a valid data point, regarding the fact, that *SoilMoisture* measurements
+should be percentage values in between *0* and *100*.
+
+Since we dont exclude the value from interpolation, it gets included in the interpolation
+process for the regular timstamp at ``2021-01-01 15:40:00`` - wich, as a result, also exhibits
+a non - sence value of *-119.512446*. We could now flag the resulting regular dataset and
+exclude this calculated non sence value from further processing and analysis.
+
+But, this would mean, that we would have a small data gap at this point.
+
+We can circumvent having that gap, by flagging that value before interpolation. This
+works, because there is actually another, now valid value, available in the interval
+in between ``2021-01-01 15:40:00`` and ``2021-01-01 15:50:00``\ , that can serve as right pillow point
+for the interpolation at ``2021-01-01 15:40:00``. So lets flag all the values smaller than *0*
+with the :py:func:`saqc.flagRange <Functions.saqc.flagRange>` method and after this,
+do the interpolation.
+
+ >>> qc = qc.flagRange('SoilMoisture', min=0)
+ >>> qc = qc.interpolate('SoilMoisture', freq='10min', method='time')
+ >>> qc.data_raw['2021-01-01T07:00:00':'2021-01-01T08:00:00']
+ SoilMoisture | SoilMoisture_original |
+ ================================ | ========================================= |
+ Date Time | Date Time |
+ 2021-01-01 15:00:00 23.341182 | 2021-01-01 15:00:51 23.3410 |
+ 2021-01-01 15:10:00 23.342964 | 2021-01-01 15:10:38 23.3431 |
+ 2021-01-01 15:20:00 23.341092 | 2021-01-01 15:20:26 23.3410 |
+ 2021-01-01 15:30:00 23.341000 | 2021-01-01 15:30:14 23.3410 |
+ 2021-01-01 15:40:00 23.319971 | 2021-01-01 15:40:02 -120.0000 |
+ 2021-01-01 15:50:00 23.299553 | 2021-01-01 15:49:50 23.2988 |
+
+back projection of flags
+------------------------
+
+TODO
diff --git a/sphinx-doc/cook_books/MultivariateFlagging.rst b/sphinx-doc/cook_books/MultivariateFlagging.rst
new file mode 100644
index 0000000000000000000000000000000000000000..c7ba19919e41d7ec4c2e2d6ad718d3ef977ff391
--- /dev/null
+++ b/sphinx-doc/cook_books/MultivariateFlagging.rst
@@ -0,0 +1,67 @@
+
+Multivariate Flagging
+=====================
+
+The tutorial aims to introduce the usage of SaQC in the context of some more complex flagging and processing techniques.
+Mainly we will see how to apply Drift Corrections onto the data and how to perform multivariate flagging.
+
+
+#. :ref:`Data Preparation <cook_books/MultivariateFlagging:Data Preparation>`
+
+#. :ref:`Drift Correction <cook_books/MultivariateFlagging:Drift Correction>`
+
+#. `Multivariate Flagging (odd Water) <#Multivariate-Flagging>`_
+
+Data preparation
+----------------
+
+
+* Flagging missing values via :py:func:`flagMissing <Functions.saqc.flagMissing>`.
+* Flagging out of range values via :py:func:`flagRange <Functions.saqc.flagRange>`.
+* Flagging values, where the Specific Conductance (\ *K25*\ ) drops down to near zero. (via :py:func:`flagGeneric <Functions.saqc.flag>`)
+* Resampling the data via linear Interpolation (:py:func:`linear <Functions.saqc.linear>`).
+
+Drift Correction
+----------------
+
+Exponential Drift
+^^^^^^^^^^^^^^^^^
+
+
+* The variables *SAK254* and *Turbidity* show drifting behavior originating from dirt, that accumulates on the light sensitive sensor surfaces over time.
+* The effect, the dirt accumulation has on the measurement values, is assumed to be properly described by an exponential model.
+* The Sensors are cleaned periodocally, resulting in a periodical reset of the drifting effect.
+* The Dates and Times of the maintenance events are input to the :py:func:`correctDrift <Functions.saqc.correctDrift>`, that will correct the data in between any two such maintenance intervals. (Find some formal description of the process :doc:`here <../misc/ExponentialModel>`.)
+
+Linear Long Time Drift
+^^^^^^^^^^^^^^^^^^^^^^
+
+
+* Afterwards, there remains a long time linear Drift in the *SAK254* and *Turbidity* measurements, originating from scratches, that accumule on the sensors glass lenses over time
+* The lenses are replaced periodically, resulting in a periodical reset of that long time drifting effect
+* The Dates and Times of the lenses replacements are input to the :py:func:`correctDrift <Functions.saqc.correctDrift>`, that will correct the data in between any two such maintenance intervals according to the assumption of a linearly increasing bias.
+
+Maintenance Intervals Flagging
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+
+* The *SAK254* and *Turbidity* values, obtained while maintenance, are, of course not trustworthy, thus, all the values obtained while maintenance get flagged via the :py:func:`flagManual <Functions.saqc.flagManual>` method.
+* When maintaining the *SAK254* and *Turbidity* sensors, also the *NO3* sensors get removed from the water - thus, they also have to be flagged via the :py:func:`flagManual <Functions.saqc.flagManual>` method.
+
+Multivariate Flagging
+---------------------
+
+Basically following the *oddWater* procedure, as suggested in *Talagala, P.D. et al (2019): A Feature-Based Procedure for Detecting Technical Outliers in Water-Quality Data From In Situ Sensors. Water Ressources Research, 55(11), 8547-8568.*
+
+
+* Variables *SAK254*\ , *Turbidity*\ , *Pegel*\ , *NO3N*\ , *WaterTemp* and *pH* get transformed to comparable scales
+* We are obtaining nearest neighbor scores and assigign those to a new variable, via :py:func:`assignKNNScores <Functions.saqc.assignKNNScores>`.
+* We are applying the *STRAY* Algorithm to find the cut_off points for the scores, above which values qualify as outliers. (:py:func:`flagByStray <Functions.saqc.flagByStray>`)
+* We project the calculated flags onto the input variables via :py:func:`assignKNNScore <Functions.saqc.assignKNNScore>`.
+
+Postprocessing
+--------------
+
+
+* (Flags reduction onto subspaces)
+* Back projection of calculated flags from resampled Data onto original data via :py:func: ``mapToOriginal <Functions.saqc.mapToOriginal>``
diff --git a/sphinx-doc/cook_books/OutlierDetection.rst b/sphinx-doc/cook_books/OutlierDetection.rst
new file mode 100644
index 0000000000000000000000000000000000000000..a464b67e5fe408096fd7ad89fa3aa18160878d8c
--- /dev/null
+++ b/sphinx-doc/cook_books/OutlierDetection.rst
@@ -0,0 +1,436 @@
+
+Outlier Detection and Flagging
+==============================
+
+The tutorial aims to introduce the usage of ``saqc`` methods in order to detect outliers in an uni-variate set up.
+The tutorial guides through the following steps:
+
+
+#. We checkout and load the example data set. Subsequently, we initialise an :py:class:`SaQC <saqc.core.core.SaQC>` object.
+
+ * :ref:`Preparation <cook_books/OutlierDetection:Preparation>`
+
+ * :ref:`Data <cook_books/OutlierDetection:Data>`
+ * :ref:`Initialisation <cook_books/OutlierDetection:Initialisation>`
+
+#.
+ We will see how to apply different smoothing methods and models to the data in order to obtain usefull residue
+ variables.
+
+
+ * :ref:`Modelling <cook_books/OutlierDetection:Modelling>`
+
+ * :ref:`Rolling Mean <cook_books/OutlierDetection:Rolling Mean>`
+ * :ref:`Rolling Median <cook_books/OutlierDetection:Rolling Median>`
+ * :ref:`Polynomial Fit <cook_books/OutlierDetection:Polynomial Fit>`
+ * :ref:`Custom Models <cook_books/OutlierDetection:Custom Models>`
+
+ * :ref:`Evaluation and Visualisation <cook_books/OutlierDetection:Visualisation>`
+
+#.
+ We will see how we can obtain residues and scores from the calculated model curves.
+
+
+ * :ref:`Residues and Scores <cook_books/OutlierDetection:Residues and Scores>`
+
+ * :ref:`Residues <cook_books/OutlierDetection:Residues>`
+ * :ref:`Scores <cook_books/OutlierDetection:Scores>`
+ * :ref:`Optimization by Decomposition <cook_books/OutlierDetection:Optimization by Decomposition>`
+
+#.
+ Finally, we will see how to derive flags from the scores itself and impose additional conditions, functioning as
+ correctives.
+
+
+ * :ref:`Setting and Correcting Flags <cook_books/OutlierDetection:Setting and Correcting Flags>`
+
+ * :ref:`Flagging the Scores <cook_books/OutlierDetection:Flagging the Scores>`
+ * `Additional Conditions ("unflagging") <#Additional-Conditions>`_
+ * :ref:`Including Multiple Conditions <cook_books/OutlierDetection:Including Multiple Conditions>`
+
+Preparation
+-----------
+
+Data
+^^^^
+
+The example `data set <https://git.ufz.de/rdm-software/saqc/-/blob/cookBux/sphinx-doc/ressources/data/incidentsLKG.csv>`_
+is selected to be small, comprehendable and its single anomalous outlier
+can be identified easily visually:
+
+
+.. image:: ../ressources/images/cbooks_incidents1.png
+ :target: ../ressources/images/cbooks_incidents1.png
+ :alt:
+
+
+It can be downloaded from the saqc git `repository <https://git.ufz.de/rdm-software/saqc/-/blob/cookBux/sphinx-doc/ressources/data/incidentsLKG.csv>`_.
+
+The data represents incidents of SARS-CoV-2 infections, on a daily basis, as reported by the
+`RKI <https://www.rki.de/DE/Home/homepage_node.html>`_ in 2020.
+
+In June, an extreme spike can be observed. This spike relates to an incidence of so called "superspreading" in a local
+`meat factory <https://www.heise.de/tp/features/Superspreader-bei-Toennies-identifiziert-4852400.html>`_.
+
+For the sake of modelling the spread of Covid, it can be of advantage, to filter the data for such extreme events, since
+they may not be consistent with underlying distributional assumptions and thus interfere with the parameter learning
+process of the modelling. Also it can help to learn about the conditions severely facilitating infection rates.
+
+To introduce into some basic ``saqc`` workflows, we will concentrate on classic variance based outlier detection approaches.
+
+Initialisation
+^^^^^^^^^^^^^^
+
+We initially want to import the data into our workspace. Therefore we import the `pandas <https://pandas.pydata.org/>`_
+library and use its csv file parser `pd.read_csv <https://pandas.pydata.org/docs/reference/api/pandas.read_csv.html>`_.
+
+.. code-block:: python
+
+ import pandas as pd
+ data = pd.read_csv(data_path, index_col=0)
+
+The resulting ``data`` variable is a pandas `data frame <https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html>`_
+object. We can generate an :py:class:`SaQC <saqc.core.core.SaQC>` object directly from that. Beforehand we have to make sure, the index
+of ``data`` is of the right type.
+
+.. code-block:: python
+
+ data.index = pd.DatetimeIndex(data.index)
+
+Now we do load the saqc package into the workspace and generate an instance of :py:class:`SaQC <saqc.core.core.SaQC>` object,
+that refers to the loaded data.
+
+.. code-block:: python
+
+ import saqc
+ saqc = saqc.SaQC(data)
+
+The only timeseries have here, is the *incidents* dataset. We can have a look at the data and obtain the above plot through
+the method :py:meth:`plot <Functions.saqc.plot>`:
+
+.. code-block:: python
+
+ >>> saqc.plot('incidents')
+
+Modelling
+---------
+
+First, we want to model our data in order to obtain a stationary, residuish variable with zero mean.
+
+Rolling Mean
+^^^^^^^^^^^^
+
+Easiest thing to do, would be, to apply some rolling mean
+model via the method :py:meth:`roll <Functions.saqc.roll>`.
+
+.. code-block:: python
+
+ >>> import numpy as np
+ >>> saqc = saqc.roll(field='incidents', target='incidents_mean', func=np.mean, window='13D')
+
+The :py:attr:`field` parameter is passed the variable name, we want to calculate the rolling mean of.
+The :py:attr:`target` parameter holds the name, we want to store the results of the calculation to.
+The :py:attr:`window` parameter controlls the size of the rolling window. It can be fed any so called `date alias <https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#offset-aliases>`_ string. We chose the rolling window to have a 13 days span.
+
+Rolling Median
+^^^^^^^^^^^^^^
+
+You can pass arbitrary function objects to the :py:attr:`func` parameter, to be applied to calculate every single windows "score".
+For example, you could go for the *median* instead of the *mean*. The numpy library provides a `median <https://numpy.org/doc/stable/reference/generated/numpy.median.html>`_ function
+under the name ``ǹp.median``. We just calculate another model curve for the ``"incidents"`` data with the ``np.median`` function from the ``numpy`` library.
+
+.. code-block:: python
+
+ >>> saqc = saqc.roll(field='incidents', target='incidents_median', func=np.median, window='13D')
+
+We chose another :py:attr:`target` value for the rolling *median* calculation, in order to not override our results from
+the previous rolling *mean* calculation.
+The :py:attr:`target` parameter can be passed to any call of a function from the
+saqc functions pool and will determine the result of the function to be written to the
+data, under the fieldname specified by it. If there already exists a field with the name passed to ``target``\ ,
+the data stored to this field will be overridden.
+
+We will evaluate and visualize the different model curves :ref:`later <cook_books/OutlierDetection:Visualisation>`.
+Beforehand, we will generate some more model data.
+
+Polynomial Fit
+^^^^^^^^^^^^^^
+
+Another common approach, is, to fit polynomials of certain degrees to the data.
+:py:class:`SaQC <saqc.core.core.SaQC>` provides the polynomial fit function :py:meth:`fitPolynomial <Functions.saqc.fitPolynomial>`:
+
+.. code-block:: python
+
+ >>> saqc = saqc.fitPolynomial(field='incidents', target='incidents_polynomial', order=2, window='13D')
+
+
+It also takes a :py:attr:`window` parameter, determining the size of the fitting window.
+The parameter, :py:attr:`order` refers to the size of the rolling window, the polynomials get fitted to.
+
+Custom Models
+^^^^^^^^^^^^^
+
+If you want to apply a completely arbitrary function to your data, without pre-chunking it by a rolling window,
+you can make use of the more general :py:meth:`processGeneric <Functions.saqc.process>` function.
+
+Lets apply a smoothing filter from the `scipy.signal <https://docs.scipy.org/doc/scipy/reference/signal.html>`_
+module. We wrap the filter generator up into a function first:
+
+.. code-block:: python
+
+ from scipy.signal import filtfilt, butter
+
+ def butterFilter(x, filter_order, nyq, cutoff, filter_type="lowpass"):
+ b, a = butter(N=filter_order, Wn=cutoff / nyq, btype=filter_type)
+ return pd.Series(filtfilt(b, a, x), index=x.index)
+
+
+This function object, we can pass on to the :py:meth:`processGeneric <Functions.saqc.process>` methods :py:attr:`func` argument.
+
+.. code-block:: python
+
+ saqc = saqc.processGeneric(field='incidents', target='incidents_lowPass', func=lambda x: butterFilter(x, cutoff=0.1, nyq=0.5, filter_order=2))
+
+
+Visualisation
+-------------
+
+We can obtain those updated informations by generating a `pandas dataframe <https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html>`_
+representation of it, with the :py:attr:`data <saqc.core.core.SaQC.data>` method:
+
+.. code-block:: python
+
+ >>> data = saqc.data
+
+To see all the results obtained so far, plotted in one figure window, we make use of the dataframes `plot <https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.plot.html>`_ method.
+
+.. code-block:: python
+
+ >>> data.plot()
+
+
+.. image:: ../ressources/images/cbooks_incidents2.png
+ :target: ../ressources/images/cbooks_incidents2.png
+ :alt:
+
+
+Residues and Scores
+-------------------
+
+Residues
+^^^^^^^^
+
+We want to evaluate the residues of one of our models model, in order to score the outlierish-nes of every point.
+Therefor we just stick to the initially calculated rolling mean curve.
+
+First, we retrieve the residues via the :py:meth:`processGeneric <Functions.saqc.process>` method.
+This method always comes into play, when we want to obtain variables, resulting from basic algebraic
+manipulations of one or more input variables.
+
+For obtaining the models residues, we just subtract the model data from the original data and assign the result
+of this operation to a new variable, called ``incidents_residues``. This Assignment, we, as usual,
+control via the :py:attr:`target` parameter.
+
+.. code-block:: python
+
+ saqc = saqc.processGeneric(['incidents', 'incidents_mean'], target='incidents_residues', func=lambda x, y: x - y)
+
+
+Scores
+^^^^^^
+
+Next, we score the residues simply by computing their `Z-scores <https://en.wikipedia.org/wiki/Standard_score>`_.
+The Z-score of a point $\ ``x``\ $, relative to its surrounding $\ ``D``\ $, evaluates to $\ ``Z(x) = \frac{x - \mu(D)}{\sigma(D)}``\ $.
+
+So, if we would like to roll with a window of a fixed size of *27* periods through the data and calculate the *Z*\ -score
+for the point lying in the center of every window, we would define our function ``z_score``\ :
+
+.. code-block:: python
+
+ z_score = lambda D: abs((D[14] - np.mean(D)) / np.std(D))
+
+And subsequently, use the :py:meth:`roll <Functions.saqc.roll>` method to make a rolling window application with the scoring
+function:
+
+.. code-block:: python
+
+ saqc = saqc.roll(field='incidents_residues', target='incidents_scores', func=z_score, window='27D')
+
+
+Optimization by Decomposition
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+There are 2 problems with the attempt presented :ref:`above <cook_books/OutlierDetection:Scores>`.
+
+First, the rolling application of the customly
+defined function, might get really slow for large data sets, because our function ``z_scores`` does not get decomposed into optimized building blocks.
+
+Second, and maybe more important, it relies heavily on every window having a fixed number of values and a fixed temporal extension.
+Otherwise, ``D[14]`` might not always be the value in the middle of the window, or it might not even exist,
+and an error will be thrown.
+
+So the attempt works fine, only because our data set is small and strictly regularily sampled.
+Meaning that it has constant temporal distances between subsequent meassurements.
+
+In order to tweak our calculations and make them much more stable, it might be useful to decompose the scoring
+into seperate calls to the :py:meth:`roll <Functions.saqc.roll>` function, by calculating the series of the
+residues *mean* and *standard deviation* seperately:
+
+.. code-block:: python
+
+ saqc = saqc.roll(field='incidents_residues', target='residues_mean', window='27D', func=np.mean)
+ saqc = saqc.roll(field='incidents_residues', target='residues_std', window='27D', func=np.std)
+ saqc = saqc.processGeneric(field=['incidents_scores', "residues_mean", "residues_std"], target="residues_norm", func=lambda this, mean, std: (this - mean) / std)
+
+With huge datasets, this will be noticably faster, compared to the method presented :ref:`initially <cook_books/OutlierDetection:Scores>`\ ,
+because ``saqc`` dispatches the rolling with the basic numpy statistic methods to an optimized pandas built-in.
+
+Also, as a result of the :py:func: ``saqc.roll <Functions.saqc.roll>`` assigning its results to the center of every window,
+all the values are centered and we dont have to care about window center indices when we are generating
+the *Z*\ -Scores from the two series.
+
+We simply combine them via the
+:py:meth:`processGeneric <Functions.saqc.generic>` method, in order to obtain the scores:
+
+.. code-block:: python
+
+ saqc = saqc.processGeneric(field=['incidents_residues','incidents_mean','incidents_median'], target='incidents_scores', func=lambda x,y,z: abs((x-y) / z))
+
+Let's have a look at the resulting scores:
+
+.. code-block:: python
+
+ saqc.plot('incidents_scores')
+
+
+.. image:: ../ressources/images/cbook_incidents_scoresUnflagged.png
+ :target: ../ressources/images/cbook_incidents_scoresUnflagged.png
+ :alt:
+
+
+Setting and correcting Flags
+----------------------------
+
+Flagging the Scores
+^^^^^^^^^^^^^^^^^^^
+
+We can now implement the common `rule of thumb <https://en.wikipedia.org/wiki/68%E2%80%9395%E2%80%9399.7_rule>`_\ ,
+that any *Z*\ -score value above *3* may indicate an outlierish data point,
+by applying the :py:meth:`flagRange <Functions.saqc.flagRange>` method with a :py:attr:`max` value of *3*.
+
+.. code-block:: python
+
+ saqc = saqc.flagRange('incidents_scores', max=3)
+
+Now flags have been calculated for the scores:
+
+.. code-block:: python
+
+ >>> saqc.plot('incidents_scores')
+
+
+.. image:: ../ressources/images/cbooks_incidents_scores.png
+ :target: ../ressources/images/cbooks_incidents_scores.png
+ :alt:
+
+
+Projecting Flags
+^^^^^^^^^^^^^^^^
+
+We now can project those flags onto our original incidents timeseries:
+
+.. code-block:: python
+
+ >>> saqc = saqc.flagGeneric(field=['incidents_scores'], target='incidents', func=lambda x: isflagged(x))
+
+Note, that we could have skipped the :ref:`range flagging step <cook_books/OutlierDetection:Flagging the scores>`\ , by including the cutting off in our
+generic expression:
+
+.. code-block:: python
+
+ >>> saqc = saqc.flagGeneric(field=['incidents_scores'], target='incidents', func=lambda x: x > 3)
+
+Lets check out the results:
+
+.. code-block:: python
+
+ >>> saqc.plot('incidents')
+
+
+.. image:: ../ressources/images/cbooks_incidentsOverflagged.png
+ :target: ../ressources/images/cbooks_incidentsOverflagged.png
+ :alt:
+
+
+Obveously, there are some flags set, that, relative to their 13 days surrounding, might relate to minor incidents spikes,
+but may not relate to superspreading events we are looking for.
+
+Especially the left most flag seems not to relate to an extreme event at all.
+This overflagging stems from those values having a surrounding with very low data variance, and thus, evaluate to a relatively high Z-score.
+
+There are a lot of possibilities to tackle the issue. In the next section, we will see how we can improve the flagging results
+by incorporating additional domain knowledge.
+
+Additional Conditions
+---------------------
+
+In order to improve our flagging result, we could additionally assume, that the events we are interested in,
+are those with an incidents count that is deviating by a margin of more than
+*20* from the 2 week average.
+
+This is equivalent to imposing the additional condition, that an outlier must relate to a sufficiently large residue.
+
+Unflagging
+^^^^^^^^^^
+
+We can do that posterior to the preceeding flagging step, by *removing*
+some flags based on some condition.
+
+In orer want to *unflag* those values, that do not relate to
+sufficiently large residues, we assign them the :py:const:`unflagged <saqc.constants.UNFLAGGED>` flag.
+
+Therefore, we make use of the :py:meth:`flagGeneric <Functions.saqc.flag>` method.
+This method usually comes into play, when we want to assign flags based on the evaluation of logical expressions.
+
+So, we check out, which residues evaluate to a level below *20*\ , and assign the
+flag value for :py:const:`unflagged <saqc.constants.UNFLAGGED>`. This value defaults to
+to ``-np.inf`` in the default translation scheme, wich we selected implicitly by not specifying any special scheme in the
+generation of the :py:class:`SaQC <saqc.core.core.SaQC>` object in the :ref:`beginning <cook_books/OutlierDetection:Initialisation>`.
+
+.. code-block:: python
+
+ >>> saqc = saqc.flagGeneric(field=['incidents','incidents_residues'], target="incidents", func=lambda x,y: isflagged(x) & (y < 50), flag=-np.inf)
+
+Notice, that we passed the desired flag level to the :py:attr:`flag` keyword in order to perform an
+"unflagging" instead of the usual flagging. The :py:attr:`flag` keyword can be passed to all the functions
+and defaults to the selected translation schemes :py:const:`BAD <saqc.constants.BAD>` flag level.
+
+Plotting proofs the tweaking did in deed improve the flagging result:
+
+.. code-block:: python
+
+ >>> saqc.plot("incidents")
+
+
+.. image:: ../ressources/images/cbooks_incidents_correctFlagged.png
+ :target: ../ressources/images/cbooks_incidents_correctFlagged.png
+ :alt:
+
+
+Including multiple conditions
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+If we do not want to first set flags, only to remove the majority of them in the next step, we also
+could circumvent the :ref:`unflagging <cook_books/OutlierDetection:Unflagging>` step, by adding to the call to
+:py:meth:`flagRange <Functions.saqc.flagRange>` the condition for the residues having to be above *20*
+
+.. code-block:: python
+
+ >>> saqc = saqc.flagGeneric(field=['incidents_scores', 'incidents_residues'], target='incidents', func=lambda x, y: (x > 3) & (y > 20))
+ >>> saqc.plot("incidents")
+
+
+.. image:: ../ressources/images/cbooks_incidents_correctFlagged.png
+ :target: ../ressources/images/cbooks_incidents_correctFlagged.png
+ :alt:
+
diff --git a/sphinx-doc/cook_books_md/OutlierDetection.md b/sphinx-doc/cook_books_md/OutlierDetection.md
deleted file mode 100644
index e521eb948cd4f9eab38dfe59498d8e89308e655a..0000000000000000000000000000000000000000
--- a/sphinx-doc/cook_books_md/OutlierDetection.md
+++ /dev/null
@@ -1,151 +0,0 @@
-# Basic Outlier Detection Workflows
-
-## Data
-
-The [data set](https://git.ufz.de/rdm-software/saqc/-/blob/cookBux/sphinx-doc/ressources/data/incidentsLKG.csv) can be
-downloaded from the saqc git repository.
-
-The data represents incidents of SARS-CoV-2 infections, on a daily basis, as reported by the
-[RKI](https://www.rki.de/DE/Home/homepage_node.html) in 2020.
-
-
-
-## Outlier
-
-In June, an extreme spike can be observed. This spike relates to an incidence of so called "superspreading" in a local
-[meat factory](https://www.heise.de/tp/features/Superspreader-bei-Toennies-identifiziert-4852400.html).
-
-For the sake of modelling the Corona disease, it can be of advantage, to filter the data for such extreme events, since
-they may not be consistent with underlying distributional assumptions and thus interfere with the parameter learning
-process of the modelling.
-
-To just introduce into some basic `SaQC` workflows, we will concentrate on classic variance based outlier detection
-approaches.
-
-## Preparation
-We, initially want to import the relevant packages.
-
-```python
-import saqc
-import pandas
-import numpy as np
-from scipy.signal import filtfilt, butter
-import matplotlib.pyplot as plt
-```
-
-We include the data via pandas [csv file parser](https://pandas.pydata.org/docs/reference/api/pandas.read_csv.html).
-This will give us a [data frame](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html) object,
-that we can directly feed into SaQC, in order to generate an SaQC object.
-
-```python
-import pandas as pd
-# data path variable should point to where you have the incidents data set stored.
-i_data = pd.read_csv(data_path)
-i_data.index = pd.DatetimeIndex(i_data.index)
-i_saqc = saqc.SaQC(data=i_data)
-```
-
-## Modelling
-
-First, we want to model our data, to obtain a stationary, residuish variable with zero mean.
-In SaQC, the results of data processing functions, defaultly overrides the processed data column.
-So, if we want to transform our input data and reuse the original data later on, we need to duplicate
-it first, with the :py:func:`saqc.tools.copy <docs.func_modules.outliers.flagRange>` method:
-
-```python
-i_saqc = i_saqc.tools.copy(field='incidents', new_field='incidents_model')
-```
-
-The copy method has 2 parameters - the `field` parameter controlls the name of the variable to
-copy, the `new_field` parameter holds the new column name of the duplicated variable.
-
-Easiest thing to do, would be to apply some rolling mean
-model via the :py:func:`saqc.rolling.roll <docs.func_modules.rolling.roll>` method.
-
-```python
-i_saqc = i_saqc.rolling.roll(field='incidents_model', func=np.mean, window='13D')
-```
-
-We chose the rolling window to have a sice of 13 days.
-You can pass arbitrary functions to the rolling method. for example, you could go for the
-`median` instead. this would be done via:
-
-```python
-i_saqc = i_saqc.tools.copy(field='incidents', new_field='incidents_median')
-i_saqc = i_saqc.rolling.roll(field='incidents_median', func=np.median, window='13D')
-```
-
-Another common approach, is, to fit polynomials of certain degrees to the data. This could, of course, also be applied
-via a functions passed to the rolling method - since this can get computationally expensive easily, for greater data sets,
-SaQC offers a build-in polynomial fit function
-:py:func:`saqc.curvefit.fitPolynomial <docs.func_modules.curvefit.fitPolynomial>`:
-
-```python
-i_saqc = i_saqc.tools.copy(field='incidents', new_field='incidents_polynomial')
-i_saqc = i_saqc.curvefit.fitPolynomial(field='incidents_polynomial', order=2,
- winsz='13D')
-```
-
-If you want to apply a completely arbitrary function to your data, without rolling, for example
-a smoothing filter from the [scipy.signal](https://docs.scipy.org/doc/scipy/reference/signal.html)
-module, you would simply have to wrap the desired function up into a function of a single
-array-like variable. To wrap the scipy butterworth filter into a forward-backward application,
-you would need to define a function:
-
-```python
-def butterFilter(x, filter_order, nyq, cutoff, filter_type):
- b, a = butter(N=filter_order, Wn=cutoff / nyq, btype=filter_type)
- return filtfilt(b, a, x)
-```
-
-Than you can wrap it up with a lambda function and pass it to the
-:py:func:`saqc.transformation.transform <docs.func_modules.transformation.transform>`
-methods func argument.
-
-```python
-func=lambda x: butterFilter(x, cutoff=0.1, nyq=0.5, filter_order=2)
-i_saqc = i_saqc.tools.copy(field='incidents', new_field='incidents_lowPass')
-i_saqc = i_saqc.transformation.transform(field='incidents_lowPass',func=func)
-```
-
-You can check out the modelling results. Therefor we evaluate the qeued manipualations to the saqc object and return the
-results.
-
-```python
-i_saqc = i_saqc.evaluate()
-result_data, _ saqc.getResult()
-result_data.plot()
-```
-
-
-
-## Residues calculation
-
-We want to evaluate the residues of the model, in order to score the outlierish-nes of every point.
-First, we retrieve the residues via the :py:func:`saqc.genericProcess <docs.func_modules.genericProcess>` method.
-The method generates a new variable, resulting from the processing of other variables. It automatically
-generates the field name it gets passed - so we do not have to generate new variable beforehand. The function we apply
-is just the computation of the variables difference for any timestep.
-
-```python
-i_saqc = i_saqc.genericProcess('incidents_residues', func=lambda incidents, incidents_model:incidents - incidents_model)
-```
-
-Next, we score the residues simply by computing their [Z-scores](https://en.wikipedia.org/wiki/Standard_score).
-
-```python
-i_saqc = i_saqc.rolling.roll(field='incidents_residues', target='residues_mean',
- window='27D',
- func=np.mean)
-i_saqc = i_saqc.rolling.roll(field='incidents_residues', target='residues_std',
- window='27D',
- func=np.std)
-i_saqc = i_saqc.genericProcess(field='incidents_scores',
- func=lambda This, residues_mean, residues_std: (
- This - residues_mean) / residues_std)
-```
-
-
-
-
-
diff --git a/sphinx-doc/documentation/FlaggingTranslation.rst b/sphinx-doc/documentation/FlaggingTranslation.rst
new file mode 100644
index 0000000000000000000000000000000000000000..84548d8358d01f7db300a40edfa433b741650872
--- /dev/null
+++ b/sphinx-doc/documentation/FlaggingTranslation.rst
@@ -0,0 +1,4 @@
+Flags, History and Translations
+===============================
+
+TODO
diff --git a/sphinx-doc/documentation/GenericFunctions.rst b/sphinx-doc/documentation/GenericFunctions.rst
new file mode 100644
index 0000000000000000000000000000000000000000..b630a24832881431393c8f8de938ce5a8da9077e
--- /dev/null
+++ b/sphinx-doc/documentation/GenericFunctions.rst
@@ -0,0 +1,4 @@
+Generic Functions
+=================
+
+TODO
diff --git a/sphinx-doc/documentation/SourceTarget.rst b/sphinx-doc/documentation/SourceTarget.rst
new file mode 100644
index 0000000000000000000000000000000000000000..06a4e466459f824d9d554d4cd34af54f61882c01
--- /dev/null
+++ b/sphinx-doc/documentation/SourceTarget.rst
@@ -0,0 +1,4 @@
+Assigning results to new variables
+==================================
+
+TODO
diff --git a/sphinx-doc/environment/configEnv.rst b/sphinx-doc/environment/configEnv.rst
new file mode 100644
index 0000000000000000000000000000000000000000..b765aee302cb14e46ab48c13ef05a32ec97acb7f
--- /dev/null
+++ b/sphinx-doc/environment/configEnv.rst
@@ -0,0 +1,56 @@
+Config file Environment
+=======================
+
+.. list-table::
+ :header-rows: 1
+
+ * - Env-Value
+ - Env-Doc
+ * - "NAN"
+ - Pointwise absolute Value Function.
+ * - "abs"
+ - Maximum Value Function. Ignores NaN.
+ * - "max"
+ - Minimum Value Function. Ignores NaN.
+ * - "min"
+ - Mean Value Function. Ignores NaN.
+ * - "mean"
+ - Summation. Ignores NaN.
+ * - "sum"
+ - Standart Deviation. Ignores NaN.
+ * - "len"
+ - Pointwise Exponential.
+ * - "exp"
+ - Pointwise Logarithm.
+ * - "log"
+ - Logarithm, returning NaN for zero input, instead of -inf.
+ * - "nanLog"
+ - Standart Deviation. Ignores NaN.
+ * - "std"
+ - Variance. Ignores NaN.
+ * - "var"
+ - Median. Ignores NaN.
+ * - "median"
+ - Count Number of values. Ignores NaNs.
+ * - "count"
+ - Identity.
+ * - "id"
+ - Returns a Series` diff.
+ * - "diff"
+ - Scales data to [0,1] Interval.
+ * - "scale"
+ - Standardize with Standart Deviation.
+ * - "zScore"
+ - Standardize with Median and MAD.
+ * - "madScore"
+ - Standardize with Median and inter quantile range.
+ * - "iqsScore"
+ -
+ * - "GOOD"
+ -
+ * - "BAD"
+ -
+ * - "UNFLAGGED"
+ -
+ * - "DOUBTFUL"
+ -
diff --git a/sphinx-doc/environment/freqAliases.rst b/sphinx-doc/environment/freqAliases.rst
new file mode 100644
index 0000000000000000000000000000000000000000..f33af8a092ca93ab3d729ff9bb010f2ee2c3bcee
--- /dev/null
+++ b/sphinx-doc/environment/freqAliases.rst
@@ -0,0 +1,18 @@
+.. list-table::
+ :header-rows: 1
+
+ * - Alias Example
+ - Freq
+ * - "s"
+ - 1 second
+ * - "7min"
+ - 7 minutes
+ * - "3h"
+ - 3 hours
+ * - "1d"
+ - 1 days
+ * - "3M"
+ - 3 Months (only for resampling)
+ * - "8Y"
+ - 8 Years (only for resampling)
+
diff --git a/sphinx-doc/flags.rst b/sphinx-doc/flags.rst
deleted file mode 100644
index 28e8d605c0bd27917148c3c30f7a8815d6a4671d..0000000000000000000000000000000000000000
--- a/sphinx-doc/flags.rst
+++ /dev/null
@@ -1,11 +0,0 @@
-
-Flags
-=======
-
-.. automodapi:: saqc.core.flags
- :include-all-objects:
- :no-heading:
-
-
-
-
diff --git a/sphinx-doc/func_modules/breaks.rst b/sphinx-doc/func_modules/breaks.rst
deleted file mode 100644
index 309876aa1e9b54b24d9fb95d5ba2b61a8731c64b..0000000000000000000000000000000000000000
--- a/sphinx-doc/func_modules/breaks.rst
+++ /dev/null
@@ -1,6 +0,0 @@
-
-breaks
-======
-
-.. automodapi:: docs.func_modules.breaks
- :no-heading:
diff --git a/sphinx-doc/func_modules/changepoints.rst b/sphinx-doc/func_modules/changepoints.rst
deleted file mode 100644
index 0c1baba6ff9c5633e02192684f323d49461d1ac1..0000000000000000000000000000000000000000
--- a/sphinx-doc/func_modules/changepoints.rst
+++ /dev/null
@@ -1,6 +0,0 @@
-
-changepoints
-============
-
-.. automodapi:: docs.func_modules.changepoints
- :no-heading:
diff --git a/sphinx-doc/func_modules/constants.rst b/sphinx-doc/func_modules/constants.rst
deleted file mode 100644
index 02d8041d170702f7d7589222351e9a47a3997bc7..0000000000000000000000000000000000000000
--- a/sphinx-doc/func_modules/constants.rst
+++ /dev/null
@@ -1,6 +0,0 @@
-
-constants
-=========
-
-.. automodapi:: docs.func_modules.constants
- :no-heading:
diff --git a/sphinx-doc/func_modules/curvefit.rst b/sphinx-doc/func_modules/curvefit.rst
deleted file mode 100644
index 3f77cf70eff8b8d2ebf7c82338c7f4fe31946636..0000000000000000000000000000000000000000
--- a/sphinx-doc/func_modules/curvefit.rst
+++ /dev/null
@@ -1,6 +0,0 @@
-
-curvefit
-========
-
-.. automodapi:: docs.func_modules.curvefit
- :no-heading:
diff --git a/sphinx-doc/func_modules/drift.rst b/sphinx-doc/func_modules/drift.rst
deleted file mode 100644
index eaab918ea2eef26e858a75cd09c923d4c2311d2f..0000000000000000000000000000000000000000
--- a/sphinx-doc/func_modules/drift.rst
+++ /dev/null
@@ -1,6 +0,0 @@
-
-drift
-=====
-
-.. automodapi:: docs.func_modules.drift
- :no-heading:
diff --git a/sphinx-doc/func_modules/flagtools.rst b/sphinx-doc/func_modules/flagtools.rst
deleted file mode 100644
index e249cad636b8f020da8e0674a10f42577a5ff038..0000000000000000000000000000000000000000
--- a/sphinx-doc/func_modules/flagtools.rst
+++ /dev/null
@@ -1,6 +0,0 @@
-
-flagtools
-=========
-
-.. automodapi:: docs.func_modules.flagtools
- :no-heading:
diff --git a/sphinx-doc/func_modules/generic.rst b/sphinx-doc/func_modules/generic.rst
deleted file mode 100644
index 7774afe1e77b7690f55e34ee7bae55dbdf1c4802..0000000000000000000000000000000000000000
--- a/sphinx-doc/func_modules/generic.rst
+++ /dev/null
@@ -1,6 +0,0 @@
-
-generic
-=======
-
-.. automodapi:: docs.func_modules.generic
- :no-heading:
diff --git a/sphinx-doc/func_modules/interpolation.rst b/sphinx-doc/func_modules/interpolation.rst
deleted file mode 100644
index 3d59ea4d5397bad85e30e60b799ae8d7f3b9c01e..0000000000000000000000000000000000000000
--- a/sphinx-doc/func_modules/interpolation.rst
+++ /dev/null
@@ -1,6 +0,0 @@
-
-interpolation
-=============
-
-.. automodapi:: docs.func_modules.interpolation
- :no-heading:
diff --git a/sphinx-doc/func_modules/outliers.rst b/sphinx-doc/func_modules/outliers.rst
deleted file mode 100644
index a4a888a57af550a3aaf7d5012c632a8b4da6bebe..0000000000000000000000000000000000000000
--- a/sphinx-doc/func_modules/outliers.rst
+++ /dev/null
@@ -1,6 +0,0 @@
-
-outliers
-========
-
-.. automodapi:: docs.func_modules.outliers
- :no-heading:
diff --git a/sphinx-doc/func_modules/pattern.rst b/sphinx-doc/func_modules/pattern.rst
deleted file mode 100644
index c8d13fb5565d6bacb844684e66471d81141dc8a3..0000000000000000000000000000000000000000
--- a/sphinx-doc/func_modules/pattern.rst
+++ /dev/null
@@ -1,6 +0,0 @@
-
-pattern
-=======
-
-.. automodapi:: docs.func_modules.pattern
- :no-heading:
diff --git a/sphinx-doc/func_modules/resampling.rst b/sphinx-doc/func_modules/resampling.rst
deleted file mode 100644
index b348b2849c2e60a9dea0bd39a88fd78311e7ff0f..0000000000000000000000000000000000000000
--- a/sphinx-doc/func_modules/resampling.rst
+++ /dev/null
@@ -1,6 +0,0 @@
-
-resampling
-==========
-
-.. automodapi:: docs.func_modules.resampling
- :no-heading:
diff --git a/sphinx-doc/func_modules/residues.rst b/sphinx-doc/func_modules/residues.rst
deleted file mode 100644
index 5e7133cf67c6cdbd5c51c0f933a32b0d3c355cb1..0000000000000000000000000000000000000000
--- a/sphinx-doc/func_modules/residues.rst
+++ /dev/null
@@ -1,6 +0,0 @@
-
-residues
-========
-
-.. automodapi:: docs.func_modules.residues
- :no-heading:
diff --git a/sphinx-doc/func_modules/rolling.rst b/sphinx-doc/func_modules/rolling.rst
deleted file mode 100644
index 17425a73909505ecf5f6e1c84d684b1eeab546c6..0000000000000000000000000000000000000000
--- a/sphinx-doc/func_modules/rolling.rst
+++ /dev/null
@@ -1,6 +0,0 @@
-
-rolling
-=======
-
-.. automodapi:: docs.func_modules.rolling
- :no-heading:
diff --git a/sphinx-doc/func_modules/scores.rst b/sphinx-doc/func_modules/scores.rst
deleted file mode 100644
index 7be28b825177fd78ad20e9799637731a13bbaada..0000000000000000000000000000000000000000
--- a/sphinx-doc/func_modules/scores.rst
+++ /dev/null
@@ -1,6 +0,0 @@
-
-scores
-======
-
-.. automodapi:: docs.func_modules.scores
- :no-heading:
diff --git a/sphinx-doc/func_modules/tools.rst b/sphinx-doc/func_modules/tools.rst
deleted file mode 100644
index 79c6408a4a8d878fd92b1169620cbba93d647c09..0000000000000000000000000000000000000000
--- a/sphinx-doc/func_modules/tools.rst
+++ /dev/null
@@ -1,6 +0,0 @@
-
-tools
-=====
-
-.. automodapi:: docs.func_modules.tools
- :no-heading:
diff --git a/sphinx-doc/func_modules/transformation.rst b/sphinx-doc/func_modules/transformation.rst
deleted file mode 100644
index cec1d2ea5654414175581f498505ea04ee6fec49..0000000000000000000000000000000000000000
--- a/sphinx-doc/func_modules/transformation.rst
+++ /dev/null
@@ -1,6 +0,0 @@
-
-transformation
-==============
-
-.. automodapi:: docs.func_modules.transformation
- :no-heading:
diff --git a/sphinx-doc/getting_started/ConfigurationFiles.rst b/sphinx-doc/getting_started/ConfigurationFiles.rst
new file mode 100644
index 0000000000000000000000000000000000000000..e530d39dcb8f591246655656e074a8d08d1a1b09
--- /dev/null
+++ b/sphinx-doc/getting_started/ConfigurationFiles.rst
@@ -0,0 +1,133 @@
+
+Configuration Files
+===================
+
+The behaviour of SaQC can be completely controlled by a text based configuration file.
+
+Format
+------
+
+SaQC expects configuration files to be semicolon-separated text files with a
+fixed header. Each row of the configuration file lists
+one variable and one or several test functions that are applied on the given variable.
+
+Header names
+^^^^^^^^^^^^
+
+The header names are basically fixed, but if you really insist in custom
+configuration headers have a look `here <saqc/core/config.py>`_.
+
+.. list-table::
+ :header-rows: 1
+
+ * - Name
+ - Data Type
+ - Description
+ - Required
+ * - varname
+ - string
+ - name of a variable
+ - yes
+ * - test
+ - :ref:`function notation <getting_started_md_m2r/ConfigurationFiles:test function notation>`
+ - test function
+ - yes
+ * - plot
+ - boolean (\ ``True``\ /\ ``False``\ )
+ - plot the test's result
+ - no
+
+
+Test function notation
+^^^^^^^^^^^^^^^^^^^^^^
+
+The notation of test functions follows the function call notation of Python and
+many other programming languages and looks like this:
+
+.. code-block::
+
+ flagRange(min=0, max=100)
+
+Here the function ``flagRange`` is called and the values ``0`` and ``100`` are passed
+to the parameters ``min`` and ``max`` respectively. As we value readablity
+of the configuration more than conciseness of the extrension language, only
+keyword arguments are supported. That means that the notation ``flagRange(0, 100)``
+is not a valid replacement for the above example.
+
+Examples
+--------
+
+Single Test
+^^^^^^^^^^^
+
+Every row lists one test per variable. If you want to call multiple tests on
+a specific variable (and you probably want to), list them in separate rows:
+
+.. code-block::
+
+ varname | test
+ #-------|----------------------------------
+ x | flagMissing()
+ x | flagRange(min=0, max=100)
+ x | constants_flagBasic(window="3h")
+ y | flagRange(min=-10, max=40)
+
+Multiple Tests
+^^^^^^^^^^^^^^
+
+A row lists multiple tests for a specific variable in separate columns. All test
+columns need to share the common prefix ``test``\ :
+
+.. code-block::
+
+ varname ; test_1 ; test_2 ; test_3
+ #-------;----------------------------;---------------------------;---------------------------------
+ x ; flagMissing() ; flagRange(min=0, max=100) ; constants_flagBasic(window="3h")
+ y ; flagRange(min=-10, max=40) ; ;
+
+The evaluation of such a configuration is in columns-major order, so the given
+example is identical to the following:
+
+.. code-block::
+
+ varname ; test_1
+ #-------;---------------------------------
+ x ; flagMissing()
+ y ; flagRange(min=-10, max=40)
+ x ; flagRange(min=0, max=100)
+ x ; constants_flagBasic(window="3h")
+
+Plotting
+^^^^^^^^
+
+As the process of finding a good quality check setup is somewhat experimental, SaQC
+provides a possibility to plot the results of the test functions. To use this feature, add the optional column ``plot`` and set it
+to ``True`` for all results you want to plot. These plots are
+meant to provide a quick and easy visual evaluation of the test.
+
+.. code-block::
+
+ varname ; test ; plot
+ #-------;----------------------------------;-----
+ x ; flagMissing() ;
+ x ; flagRange(min=0, max=100) ; False
+ x ; constants_flagBasic(window="3h") ; True
+ y ; flagRange(min=-10, max=40)` ;
+
+Regular Expressions in ``varname`` column
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+Some of the tests (e.g. checks for missing values, range tests or interpolation
+functions) are very likely to be used on all or at least several variables of
+the processed dataset. As it becomes quite cumbersome to list all these
+variables seperately, only to call the same functions with the same
+parameters, SaQC supports regular expressions
+within the ``varname`` column. Please not that a ``varname`` needs to be quoted
+(with ``'`` or ``"``\ ) in order to be interpreted as a regular expression.
+
+.. code-block::
+
+ varname ; test
+ #----------;------------------------------
+ '.*' ; harm_shift2Grid(freq="15Min")
+ '(x \| y)' ; flagMissing()
diff --git a/sphinx-doc/getting_started/Customizations.rst b/sphinx-doc/getting_started/Customizations.rst
new file mode 100644
index 0000000000000000000000000000000000000000..f5c624e3160cb5614ecd7a3ff5c2320aa9853224
--- /dev/null
+++ b/sphinx-doc/getting_started/Customizations.rst
@@ -0,0 +1,93 @@
+
+Customizations
+==============
+
+SaQC comes with a continuously growing number of pre-implemented
+:doc:`quality check and processing routines <FunctionIndex>` and
+flagging schemes.
+For any sufficiently large use case however it is very likely that the
+functions provided won't fulfill all your needs and requirements.
+
+Acknowledging the impossibility to address all imaginable use cases, we
+designed the system to allow for extensions and costumizations. The main extensions options, namely
+:ref:`quality check routines <getting_started_md_m2r/Customizations:custom quality check routines>`
+and the :ref:`flagging scheme <getting_started_md_m2r/Customizations:custom flagging schemes>`
+are described within this documents.
+
+Custom quality check routines
+-----------------------------
+
+In case you are missing quality check routines, you are of course very
+welcome to file a feature request issue on the project's
+`gitlab repository <https://git.ufz.de/rdm-software/saqc>`_. However, if
+you are more the "no-way-I-get-this-done-by-myself" type of person,
+SaQC provides two ways to integrate custom routines into the system:
+
+
+#. The :doc:`extension language <GenericFunctions>`
+#. An :ref:`interface <getting_started_md_m2r/Customizations:interface>` to the evaluation machinery
+
+Interface
+^^^^^^^^^
+
+In order to make a function usable within the evaluation framework of SaQC the following interface is needed:
+
+.. code-block:: python
+
+ import pandas
+ import dios
+ import saqc
+
+ def yourTestFunction(
+ data: pandas.DataFrame,
+ field: str,
+ flags: saqc.Flags,
+ *args,
+ **kwargs
+ ) -> (dios.DictOfSeries, saqc.Flags)
+
+Argument Descriptions
+~~~~~~~~~~~~~~~~~~~~~
+
+.. list-table::
+ :header-rows: 1
+
+ * - Name
+ - Description
+ * - ``data``
+ - The actual dataset.
+ * - ``field``
+ - The field/column within ``data``\ , that function is processing.
+ * - ``flags``
+ - An instance of Flags, responsible for the translation of test results into quality attributes.
+ * - ``args``
+ - Any other arguments needed to parameterize the function.
+ * - ``kwargs``
+ - Any other keyword arguments needed to parameterize the function.
+
+
+Integrate into SaQC
+^^^^^^^^^^^^^^^^^^^
+
+In order make your function available to the system it needs to be registered. We provide a decorator
+`\ ``register`` <saqc/functions/register.py>`_ with saqc, to integrate your
+test functions into SaQC. Here is a complete dummy example:
+
+.. code-block:: python
+
+ from saqc import register
+
+ @register()
+ def yourTestFunction(data, field, flags, *args, **kwargs):
+ return data, flags
+
+Example
+^^^^^^^
+
+The function `\ ``flagRange`` <saqc/funcs/functions.py>`_ provides a simple, yet complete implementation of
+a quality check routine. You might want to look into its implementation as a reference for your own.
+
+Custom flagging schemes
+-----------------------
+
+Sorry for the inconvenience! Coming soon...
diff --git a/sphinx-doc/getting_started/FlaggingSchemes.rst b/sphinx-doc/getting_started/FlaggingSchemes.rst
new file mode 100644
index 0000000000000000000000000000000000000000..4162147258a7c962c735227dc0bb7c50217ca5d1
--- /dev/null
+++ b/sphinx-doc/getting_started/FlaggingSchemes.rst
@@ -0,0 +1,13 @@
+
+DMP flagging scheme
+===================
+
+Possible flags
+--------------
+
+The DMP scheme produces the following flag constants:
+
+
+* "ok"
+* "doubtfull"
+* "bad"
diff --git a/sphinx-doc/getting_started/GenericFunctions.rst b/sphinx-doc/getting_started/GenericFunctions.rst
new file mode 100644
index 0000000000000000000000000000000000000000..6907ecc97a18045b679ff5bc960d5add1aff7e98
--- /dev/null
+++ b/sphinx-doc/getting_started/GenericFunctions.rst
@@ -0,0 +1,322 @@
+
+Generic Functions
+=================
+
+Generic Flagging Functions
+--------------------------
+
+Generic flagging functions provide for cross-variable quality
+constraints and to implement simple quality checks directly within the configuration.
+
+Why?
+^^^^
+
+In most real world datasets many errors
+can be explained by the dataset itself. Think of a an active, fan-cooled
+measurement device: no matter how precise the instrument may work, problems
+are to be expected when the fan stops working or the power supply
+drops below a certain threshold. While these dependencies are easy to
+:ref:`formalize <getting_started_md_m2r/GenericFunctions:a real world example>` on a per dataset basis, it is quite
+challenging to translate them into generic source code.
+
+Specification
+^^^^^^^^^^^^^
+
+Generic flagging functions are used in the same manner as their
+:doc:`non-generic counterparts <FunctionIndex>`. The basic
+signature looks like that:
+
+.. code-block:: sh
+
+ flagGeneric(func=<expression>, flag=<flagging_constant>)
+
+where ``<expression>`` is composed of the :ref:`supported constructs <getting_started_md_m2r/GenericFunctions:supported constructs>`
+and ``<flag_constant>`` is one of the predefined
+:ref:`flagging constants <getting_started_md_m2r/ParameterDescriptions:flagging constants>` (default: ``BAD``\ ).
+Generic flagging functions are expected to return a boolean value, i.e. ``True`` or ``False``. All other expressions will
+fail during the runtime of ``SaQC``.
+
+Examples
+^^^^^^^^
+
+Simple comparisons
+~~~~~~~~~~~~~~~~~~
+
+Task
+""""
+
+Flag all values of ``x`` where ``y`` falls below 0.
+
+Configuration file
+""""""""""""""""""
+
+.. code-block::
+
+ varname ; test
+ #-------;------------------------
+ x ; flagGeneric(func=y < 0)
+
+Calculations
+~~~~~~~~~~~~
+
+Task
+""""
+
+Flag all values of ``x`` that exceed 3 standard deviations of ``y``.
+
+Configuration file
+""""""""""""""""""
+
+.. code-block::
+
+ varname ; test
+ #-------;---------------------------------
+ x ; flagGeneric(func=x > std(y) * 3)
+
+Special functions
+~~~~~~~~~~~~~~~~~
+
+Task
+""""
+
+Flag all values of ``x`` where: ``y`` is flagged and ``z`` has missing values.
+
+Configuration file
+""""""""""""""""""
+
+.. code-block::
+
+ varname ; test
+ #-------;----------------------------------------------
+ x ; flagGeneric(func=isflagged(y) & ismissing(z))
+
+A real world example
+~~~~~~~~~~~~~~~~~~~~
+
+Let's consider the following dataset:
+
+.. list-table::
+ :header-rows: 1
+
+ * - date
+ - meas
+ - fan
+ - volt
+ * - 2018-06-01 12:00
+ - 3.56
+ - 1
+ - 12.1
+ * - 2018-06-01 12:10
+ - 4.7
+ - 0
+ - 12.0
+ * - 2018-06-01 12:20
+ - 0.1
+ - 1
+ - 11.5
+ * - 2018-06-01 12:30
+ - 3.62
+ - 1
+ - 12.1
+ * - ...
+ -
+ -
+ -
+
+
+Task
+""""
+
+Flag ``meas`` where ``fan`` equals 0 and ``volt``
+is lower than ``12.0``.
+
+Configuration file
+""""""""""""""""""
+
+There are various options. We can directly implement the condition as follows:
+
+.. code-block::
+
+ varname ; test
+ #-------;-----------------------------------------------
+ meas ; flagGeneric(func=(fan == 0) \| (volt < 12.0))
+
+But we could also quality check our independent variables first
+and than leverage this information later on:
+
+.. code-block::
+
+ varname ; test
+ #-------;----------------------------------------------------
+ '.*' ; flagMissing()
+ fan ; flagGeneric(func=fan == 0)
+ volt ; flagGeneric(func=volt < 12.0)
+ meas ; flagGeneric(func=isflagged(fan) \| isflagged(volt))
+
+Generic Processing
+------------------
+
+Generic processing functions provide a way to evaluate mathmetical operations
+and functions on the variables of a given dataset.
+
+Why
+^^^
+
+In many real-world use cases, quality control is embedded into a larger data
+processing pipeline and it is not unusual to even have certain processing
+requirements as a part of the quality control itself. Generic processing
+functions make it easy to enrich a dataset through the evaluation of a
+given expression.
+
+Specification
+^^^^^^^^^^^^^
+
+The basic signature looks like that:
+
+.. code-block:: sh
+
+ procGeneric(func=<expression>)
+
+where ``<expression>`` is composed of the :ref:`supported constructs <getting_started_md_m2r/GenericFunctions:supported constructs>`.
+
+Variable References
+-------------------
+
+All variables of the processed dataset are available within generic functions,
+so arbitrary cross references are possible. The variable of interest
+is furthermore available with the special reference ``this``\ , so the second
+:ref:`example <getting_started_md_m2r/GenericFunctions:calculations>` could be rewritten as:
+
+.. code-block::
+
+ varname ; test
+ #-------;------------------------------------
+ x ; flagGeneric(func=this > std(y) * 3)
+
+When referencing other variables, their flags will be respected during evaluation
+of the generic expression. So, in the example above only values of ``x`` and ``y``\ , that
+are not already flagged with ``BAD`` will be used the avaluation of ``x > std(y)*3``.
+
+Supported constructs
+--------------------
+
+Operators
+^^^^^^^^^
+
+Comparison
+~~~~~~~~~~
+
+The following comparison operators are available:
+
+.. list-table::
+ :header-rows: 1
+
+ * - Operator
+ - Description
+ * - ``==``
+ - ``True`` if the values of the operands are equal
+ * - ``!=``
+ - ``True`` if the values of the operands are not equal
+ * - ``>``
+ - ``True`` if the values of the left operand are greater than the values of the right operand
+ * - ``<``
+ - ``True`` if the values of the left operand are smaller than the values of the right operand
+ * - ``>=``
+ - ``True`` if the values of the left operand are greater or equal than the values of the right operand
+ * - ``<=``
+ - ``True`` if the values of the left operand are smaller or equal than the values of the right operand
+
+
+Arithmetics
+~~~~~~~~~~~
+
+The following arithmetic operators are supported:
+
+.. list-table::
+ :header-rows: 1
+
+ * - Operator
+ - Description
+ * - ``+``
+ - addition
+ * - ``-``
+ - subtraction
+ * - ``*``
+ - multiplication
+ * - ``/``
+ - division
+ * - ``**``
+ - exponentiation
+ * - ``%``
+ - modulus
+
+
+Bitwise
+~~~~~~~
+
+The bitwise operators also act as logical operators in comparison chains
+
+.. list-table::
+ :header-rows: 1
+
+ * - Operator
+ - Description
+ * - ``&``
+ - binary and
+ * - ``|``
+ - binary or
+ * - ``^``
+ - binary xor
+ * - ``~``
+ - binary complement
+
+
+Functions
+^^^^^^^^^
+
+All functions expect a :ref:`variable reference <getting_started_md_m2r/GenericFunctions:variable references>`
+as the only non-keyword argument (see :ref:`here <getting_started_md_m2r/GenericFunctions:special functions>`\ )
+
+Mathematical Functions
+~~~~~~~~~~~~~~~~~~~~~~
+
+.. list-table::
+ :header-rows: 1
+
+ * - Name
+ - Description
+ * - ``abs``
+ - absolute values of a variable
+ * - ``max``
+ - maximum value of a variable
+ * - ``min``
+ - minimum value of a variable
+ * - ``mean``
+ - mean value of a variable
+ * - ``sum``
+ - sum of a variable
+ * - ``std``
+ - standard deviation of a variable
+ * - ``len``
+ - the number of values for variable
+
+
+Special Functions
+~~~~~~~~~~~~~~~~~
+
+.. list-table::
+ :header-rows: 1
+
+ * - Name
+ - Description
+ * - ``ismissing``
+ - check for missing values
+ * - ``isflagged``
+ - check for flags
+
+
+Constants
+^^^^^^^^^
+
+Generic functions support the same constants as normal functions, a detailed
+list is available :ref:`here <getting_started_md_m2r/ParameterDescriptions:constants>`.
diff --git a/sphinx-doc/getting_started/InstallationGuide.rst b/sphinx-doc/getting_started/InstallationGuide.rst
new file mode 100644
index 0000000000000000000000000000000000000000..4a91cd591efc0f73b1a7f790f0e93678db77085c
--- /dev/null
+++ b/sphinx-doc/getting_started/InstallationGuide.rst
@@ -0,0 +1,85 @@
+Installation Guide
+==================
+
+SaQC is written in Python, so the easiest way to set up your system to use SaQC
+for your needs is using the Python Package Index (PyPI). It might be a good idea
+to install SaQC into its own virtual environment.
+
+
+System Requirements
+-------------------
+SaQC is tested to run with Python version from 3.7 to 3.9 on 64-bit operating systems (Linux and Windows).
+
+
+Set up a virtual environment
+-----------------------------
+
+It is good practice to create new virtual environments for different projects. This
+helps keeping dependencies separated and avoids issues with conflicting versions of
+a single module. The exact process to setup such an environment depends on your operating
+system and python version/distribution. The following sections should get you started on
+UNIX-like Systems and Windows.
+
+
+On Unix/Mac-systems
+"""""""""""""""""""
+
+On Unix-like systems the process is usually rather easy. Open up a terminal window and
+copy-paste the following commands
+
+.. code-block:: sh
+
+ # create virtual environment called "saqc-env"
+ python -m venv saqc-env
+
+ # activate the virtual environment
+ source saqc-env/bin/activate
+
+On Windows-systems
+""""""""""""""""""
+
+On windows, things are a bit more evolved however. The first hurdle to take is usually an
+installation of Python itself. There are many options available, one popular solution is
+the `Conda package management system <https://docs.conda.io/en/latest/>`_. After its
+`installation <https://docs.conda.io/projects/conda/en/latest/user-guide/install/index.html>`_
+type in the following:
+
+.. code-block:: sh
+
+ # create virtual environment called "saqc-env"
+ conda create -n saqc-enc
+
+ # activate the virtual environment
+ conda activate saqc-env
+
+ # install pip, we will need it later
+ conda install pip
+
+
+Install SaQC
+------------
+
+We currently distribute SaQC via the `Python Package Index (PyPI) <https://pypi.org/>`_
+or through our `GitLab-repository <https://git.ufz.de/rdm-software/saqc>`_.
+
+The latest stable versions are available with
+
+.. code-block:: sh
+
+ python -m pip install saqc
+
+or
+
+.. code-block:: sh
+
+ pip install git+https://git.ufz.de/rdm-software/saqc@master
+
+
+If you feel more adventurous feel free to use the latest development version from our
+`GitLab-repository <https://git.ufz.de/rdm-software/saqc>`_. While we try to keep the
+develop branch in a workable state, we sill won't make any guarantees here.
+
+.. code-block:: sh
+
+ pip install git+https://git.ufz.de/rdm-software/saqc@develop
+
diff --git a/sphinx-doc/getting_started/ParameterDescriptions.rst b/sphinx-doc/getting_started/ParameterDescriptions.rst
new file mode 100644
index 0000000000000000000000000000000000000000..1f7386125dcaebc91576908adb11535429e2cc2e
--- /dev/null
+++ b/sphinx-doc/getting_started/ParameterDescriptions.rst
@@ -0,0 +1,64 @@
+
+Offset Strings
+--------------
+
+All the `pandas offset aliases <https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#offset-aliases>`_ ars supported by SaQC. The following table lists some of the more relevant options:
+
+.. list-table::
+ :header-rows: 1
+
+ * - Alias
+ - Description
+ * - ``"S"``\ , ``"s"``
+ - second
+ * - ``"T"``\ , ``"Min"``\ , ``"min"``
+ - minute
+ * - ``"H"``\ , ``"h"``
+ - hour
+ * - ``"D"``\ , ``"d"``
+ - calendar day
+ * - ``"W"``\ , ``"w"``
+ - week
+ * - ``"M"``\ , ``"m"``
+ - month
+ * - ``"Y"``\ , ``"y"``
+ - year
+
+
+Multiples are build by preceeding the alias with the desired multiply (e.g ``"5Min"``\ , ``"4W"``\ )
+
+Constants
+---------
+
+Flagging Constants
+^^^^^^^^^^^^^^^^^^
+
+The following flag constants are available and can be used to mark the quality of a data point:
+
+.. list-table::
+ :header-rows: 1
+
+ * - Alias
+ - Description
+ * - ``GOOD``
+ - A value did pass all the test and is therefore considered to be valid
+ * - ``BAD``
+ - At least on test failed on the values and is therefore considered to be invalid
+ * - ``UNFLAGGED``
+ - The value has not got a flag yet. This might mean, that all tests passed or that no tests ran
+
+
+How these aliases will be translated into 'real' flags (output of SaQC) dependes on the :doc:`flagging scheme <FlaggingSchemes>`
+and might range from numerical values to string constants.
+
+Numerical Constants
+^^^^^^^^^^^^^^^^^^^
+
+.. list-table::
+ :header-rows: 1
+
+ * - Alias
+ - Description
+ * - ``NAN``
+ - Not a number
+
diff --git a/sphinx-doc/getting_started/TutorialAPI.rst b/sphinx-doc/getting_started/TutorialAPI.rst
new file mode 100644
index 0000000000000000000000000000000000000000..52f456871475755984648371700b7dc1c9475e9e
--- /dev/null
+++ b/sphinx-doc/getting_started/TutorialAPI.rst
@@ -0,0 +1,149 @@
+Python API
+==========
+
+General structure
+-----------------
+
+The Python API of SaQC consists of three distinct components:
+
+1. the core class ``SaQC``
+2. a number of flagging schemes
+3. a collection of functions
+
+One and two are implemented as distinct classes, the core object is called ``SaQC`` and we currently
+provide three flagging schemes, namely:
+
+1. ``FloatScheme``: Provides the quality flags ``-np.inf`` and ``[0..255]``.
+ ``-np.inf`` denotes the absence of quality flags and ``255`` denotes, that
+ the associated data value is considered to be bad. the absence of a flags,
+ ``1`` the presence of flag (i.e. one of the tests provided a positive result)
+ and ``0`` acts as an indicator for an tested-to-be-not-bad value.
+2. ``SimpleScheme``: Provides three distinct quality labels, namely ``UNFLAGGED``, ``BAD``, ``OKAY``.
+3. ``DmpScheme``: Provides the four distinct flags ``NIL``, ``OK``, ``DOUBTFUL``, ``BAD``, whereas each
+ flag is extended by information about the generating function and optional comments.
+
+The third component, the actual test functions, appear as methods of ``SaQC``.
+
+
+Getting started - Put something in
+----------------------------------
+
+The definition of a ``SaQC`` test suite starts with some data as a ``pandas.DataFrame`` and the selection
+of an appropriate flagging scheme. For reasons of simplicity, we'll use the ``SimpleScheme`` throughout
+the following examples. However, as the flagging schemes are mostly interchangable, replacing the ``SimpleScheme``
+with something more elaborate, is in fact a one line change. So let's start with:
+
+.. code-block:: python
+
+ import numpy as np
+ import pandas as pd
+ from saqc import SaQC
+
+ # we need some dummy data
+ values = np.random.randint(low=0, high=100, size=100)
+ dates = pd.date_range(start="2020-01-01", periods=len(values), freq="D")
+ data = pd.DataFrame({"a": values}, index=dates)
+ # let's insert some constant values ...
+ data.iloc[30:40] = values.mean()
+ # ... and an outlier
+ data.iloc[70] = 175
+
+ # initialize saqc
+ qc = SaQC(data=data, scheme="simple")
+
+
+Moving on - Quality control your data
+-------------------------------------
+
+The ``qc`` variable now serves as the base for all further processing steps. As mentioned above, all
+available functions appear as methods of the ``SaQC`` class, so we can add a tests to our suite with:
+
+.. code-block:: python
+
+ qc.flagRange("a", min=20, max=80)
+
+:py:func:`flagRange <Functions.saqc.flagRange>` is the easiest of all functions and simply marks all values
+smaller than ``min`` and larger than ``max``. This feature by itself wouldn't be worth the trouble of getting
+into ``SaQC``, but it serves as a simple example. All functions expect the name of a column in the given
+``data`` as their first positional argument (called ``field``). The function ``flagRange`` (like all other
+functions for that matter) is then called on the given ``field`` (only).
+
+Each call to a ``SaQC`` method returns a new object (all itermediate objects share the main internal data
+structures, so we only create shallow copies). Setting up more complex quality control suites (here by calling
+the additional methods :py:func:`flagConstants <Functions.saqc.flagConstants>` and
+:py:func:`flagByGrubbs <Functions.saqc.flagByGrubbs>`) is therefore simply a matter of method chaining.
+
+.. code-block:: python
+
+ # execute some tests
+ qc = (qc
+ .flagConstants("a", thresh=0.1, window=4)
+ .flagByGrubbs("a", window=10)
+ .flagRange("a", min=20, max=80))
+
+
+Getting done - Pull something out
+---------------------------------
+
+``saqc`` is eagerly evaluated, i.e. the results of all method calls are available as soon as they return. As
+we have seen above, calling quality checks does however not immediately return the produces data and the
+associated flags, but rather an new ``SaQC`` object. The actual execution products are accessible through a
+number of different attributes, of which you likely might want to use the following:
+
+.. code-block:: python
+
+ # retrieve the data as a pandas.DataFrame
+ qc.data
+
+ # retrieve the flags as a pandas.DataFrame
+ qc.flags
+
+
+Putting it together - The complete workflow
+-------------------------------------------
+The snippet below provides you with a compete example from the things we have seen so far.
+
+.. code-block:: python
+
+ import numpy as np
+ import pandas as pd
+ from saqc import SaQC
+
+ # we need some dummy data
+ values = np.random.randint(low=0, high=100, size=100)
+ dates = pd.date_range(start="2020-01-01", periods=len(values), freq="D")
+ data = pd.DataFrame({"a": values}, index=dates)
+ # let's insert some constant values ...
+ data.iloc[30:40] = values.mean()
+ # ... and an outlier
+ data.iloc[70] = 175
+
+ # initialize saqc
+ qc = SaQC(data=data, scheme="simple")
+
+ # execute some tests
+ qc = (qc
+ .flagConstants("a", thresh=0.1, window="4D")
+ .flagByGrubbs("a", window="10D")
+ .flagRange("a", min=20, max=80))
+
+ # retrieve the data as a pandas.DataFrame
+ qc.data
+
+ # retrieve the flags as a pandas.DataFrame
+ qc.flags
+
+
+
+Can I get something visual, please?
+-----------------------------------
+
+We provide an elaborated plotting method to generate and show or write matplotlib figures. Building on
+the example :ref:`above <getting_started/TutorialAPI:putting it together - the complete workflow>` above
+simply call:
+
+.. code-block:: python
+
+ qc.plot("a")
+
+.. image:: /ressources/images/tutorial_api_1.png
diff --git a/sphinx-doc/getting_started/TutorialCLI.rst b/sphinx-doc/getting_started/TutorialCLI.rst
new file mode 100644
index 0000000000000000000000000000000000000000..dd15a5df37170bcc6f6a35e04fdf6ed624fd7b6c
--- /dev/null
+++ b/sphinx-doc/getting_started/TutorialCLI.rst
@@ -0,0 +1,285 @@
+Command Line Application
+========================
+
+.. note:
+
+ The content of this page is outdate and the provided code sample will very likely
+ not work as expected. A rewrite of this document is pending...
+
+
+Contents
+--------
+
+
+* :ref:`1 Get toy data and configuration <getting_started/GettingStarted:get toy data and configuration>`
+* :ref:`2 Run SaQC <getting_started/GettingStarted:run saqc>`
+* :ref:`3 Configure SaQC <getting_started/GettingStarted:configure saqc>`
+
+ * :ref:`Change test parameters <getting_started/GettingStarted:change test parameters>`
+
+* :ref:`4 Explore the functionality <getting_started/GettingStarted:explore the functionality>`
+
+ * :ref:`Process multiple variables <getting_started/GettingStarted:process multiple variables>`
+ * :ref:`Data harmonization and custom functions <getting_started/GettingStarted:data harmonization and custom functions>`
+
+
+The following passage guides you through the essentials of the usage of SaQC via
+a toy dataset and a toy configuration.
+
+1. Get toy data and configuration
+---------------------------------
+
+If you take a look into the folder ``saqc/ressources/data`` you will find a toy
+dataset ``data.csv`` which contains the following:
+
+.. code-block::
+
+ Date,Battery,SM1,SM2
+ 2016-04-01 00:05:48,3573,32.685,29.3157
+ 2016-04-01 00:20:42,3572,32.7428,29.3157
+ 2016-04-01 00:35:37,3572,32.6186,29.3679
+ 2016-04-01 00:50:32,3572,32.736999999999995,29.3679
+ ...
+
+
+These are two timeseries of soil moisture (SM1+2) and the battery voltage of the
+measuring device over time. Generally, this is the way that your data should
+look like to run saqc. Note, however, that you do not necessarily need a series
+of dates to reference to and that you are free to use more columns of any name
+that you like.
+
+Now create your our own configuration file ``saqc/ressources/data/myconfig.csv``
+and paste the following lines into it:
+
+.. code-block::
+
+ varname;test;plot
+ SM2;flagRange(min=10, max=60);False
+ SM2;flagMad(window="30d", z=3.5);True
+
+
+These lines illustrate how different quality control tests can be specified for
+different variables,
+
+In this case, we define a range-test that flags all values outside the range
+[10,60] and a test to detect spikes using the MAD-method. You can find an
+overview of all available quality control tests in the
+:doc:`documentation <FunctionIndex>`. Note that the tests are
+*executed in the order that you define in the configuration file*. The quality
+flags that are set during one test are always passed on to the subsequent one.
+
+2. Run SaQC
+-----------
+
+Remember to have your virtual environment activated:
+
+On Unix/Mac-systems
+"""""""""""""""""""
+
+.. code-block:: sh
+
+ source env_saqc/bin/activate
+
+On Windows
+""""""""""
+
+.. code-block:: sh
+
+ cd env_saqc/Scripts
+ ./activate
+
+Via your console, move into the folder you downloaded saqc into:
+
+.. code-block:: sh
+
+ cd saqc
+
+From here, you can run saqc and tell it to run the tests from the toy
+config-file on the toy dataset via the ``-c`` and ``-d`` options:
+
+On Unix/Mac-systems
+"""""""""""""""""""
+
+.. code-block:: sh
+
+ python3 -m saqc -c ressources/data/myconfig.csv -d ressources/data/data.csv
+
+On Windows
+""""""""""
+
+.. code-block:: sh
+
+ py -3 -m saqc -c ressources/data/myconfig.csv -d ressources/data/data.csv
+
+If you installed saqc via PYPi, you can omit ``sh python -m``.
+
+The command will output this plot:
+
+
+.. image:: ../ressources/images/example_plot_1.png
+ :target: ../ressources/images/example_plot_1.png
+ :alt: Toy Plot
+
+
+So, what do we see here?
+
+
+* The plot shows the data as well as the quality flags that were set by the
+ tests for the variable ``SM2``\ , as defined in the config-file
+* Following our definition in the config-file, first the ``flagRange``\ -test that flags
+ all values outside the range [10,60] was executed and after that,
+ the ``flagMad``\ -test to identify spikes in the data
+* In the config, we set the plotting option to ``True`` for ``flagMad``\ ,
+ only. Thus, the plot aggregates all preceeding tests (here: ``range``\ ) to black
+ points and highlights the flags of the selected test as red points.
+
+Save outputs to file
+^^^^^^^^^^^^^^^^^^^^
+
+If you want the final results to be saved to a csv-file, you can do so by the
+use of the ``-o`` option:
+
+.. code-block:: sh
+
+ saqc -c ressources/data/config.csv -d ressources/data/data.csv -o ressources/data/out.csv
+
+Which saves a dataframe that contains both the original data and the quality
+flags that were assigned by SaQC for each of the variables:
+
+.. code-block::
+
+ Date,SM1,SM1_flags,SM2,SM2_flags
+ 2016-04-01 00:05:48,32.685,OK,29.3157,OK
+ 2016-04-01 00:20:42,32.7428,OK,29.3157,OK
+ 2016-04-01 00:35:37,32.6186,OK,29.3679,OK
+ 2016-04-01 00:50:32,32.736999999999995,OK,29.3679,OK
+ ...
+
+
+
+3. Configure SaQC
+-----------------
+
+Change test parameters
+""""""""""""""""""""""
+
+Now you can start to change the settings in the config-file and investigate the
+effect that has on how many datapoints are flagged as "BAD". When using your
+own data, this is your way to configure the tests according to your needs. For
+example, you could modify your ``myconfig.csv`` and change the parameters of the
+range-test:
+
+.. code-block::
+
+ varname;test;plot
+ SM2;flagRange(min=-20, max=60);False
+ SM2;flagMad(window="30d", z=3.5);True
+
+Rerunning SaQC as above produces the following plot:
+
+
+.. image:: ../ressources/images/example_plot_2.png
+ :target: ../ressources/images/example_plot_2.png
+ :alt: Changing the config
+
+
+You can see that the changes that we made to the parameters of the range test
+take effect so that only the values > 60 are flagged by it (black points). This,
+in turn, leaves more erroneous data that is then identified by the proceeding
+spike-test (red points).
+
+4. Explore the functionality
+----------------------------
+
+Process multiple variables
+""""""""""""""""""""""""""
+
+You can also define multiple tests for multiple variables in your data. These
+are then executed sequentially and can be plotted seperately. E.g. you could do
+something like this:
+
+.. code-block::
+
+ varname;test;plot
+ SM1;flagRange(min=10, max=60);False
+ SM2;flagRange(min=10, max=60);False
+ SM1;flagMad(window="15d", z=3.5);True
+ SM2;flagMad(window="30d", z=3.5);True
+
+
+which gives you separate plots for each line where the plotting option is set to
+``True`` as well as one summary "data plot" that depicts the joint flags from all
+tests:
+
+.. list-table::
+ :header-rows: 1
+
+ * - SM1
+ - SM2
+ * - here
+ - there
+
+
+.. list-table::
+ :header-rows: 1
+
+ * - SM1
+ - SM2
+ * - .. image:: ../ressources/images/example_plot_31.png
+ :target: ../ressources/images/example_plot_31.png
+ :alt:
+
+ - .. image:: ../ressources/images/example_plot_32.png
+ :target: ../ressources/images/example_plot_32.png
+ :alt:
+
+ * - .. image:: ../ressources/images/example_plot_31.png
+ :target: ../ressources/images/example_plot_31.png
+ :alt:
+
+ -
+
+
+
+.. image:: ../ressources/images/example_plot_33.png
+ :target: ../ressources/images/example_plot_33.png
+ :alt:
+
+
+Data harmonization and custom functions
+"""""""""""""""""""""""""""""""""""""""
+
+SaQC includes functionality to harmonize the timestamps of one or more data
+series. Also, you can write your own tests using a python-based
+:doc:`extension language <GenericFunctions>`. This would look like this:
+
+.. code-block::
+
+ varname;test;plot
+ SM2;shiftToFreq(freq="15Min");False
+ SM2;generic(func=(SM2 < 30));True
+
+
+The above executes an internal framework that harmonizes the timestamps of SM2
+to a 15min-grid (see data below). Further information about this routine can be
+found in the :ref:`Flagging Functions Overview <flaggingFunctions>`.
+
+.. code-block::
+
+ Date,SM1,SM1_flags,SM2,SM2_flags
+ 2016-04-01 00:00:00,,,29.3157,OK
+ 2016-04-01 00:05:48,32.685,OK,,
+ 2016-04-01 00:15:00,,,29.3157,OK
+ 2016-04-01 00:20:42,32.7428,OK,,
+ ...
+
+
+Also, all values where SM2 is below 30 are flagged via the custom function (see
+plot below). You can learn more about the syntax of these custom functions
+:doc:`here <GenericFunctions>`.
+
+
+.. image:: ../ressources/images/example_plot_4.png
+ :target: ../ressources/images/example_plot_4.png
+ :alt: Example custom function
+
diff --git a/sphinx-doc/getting_started_md/ConfigurationFiles.md b/sphinx-doc/getting_started_md/ConfigurationFiles.md
deleted file mode 100644
index 64743bb40d211db2a51adfb5ad07c64bbcbdd7be..0000000000000000000000000000000000000000
--- a/sphinx-doc/getting_started_md/ConfigurationFiles.md
+++ /dev/null
@@ -1,98 +0,0 @@
-# Configuration Files
-The behaviour of SaQC can be completely controlled by a text based configuration file.
-
-## Format
-SaQC expects configuration files to be semicolon-separated text files with a
-fixed header. Each row of the configuration file lists
-one variable and one or several test functions that are applied on the given variable.
-
-
-### Header names
-
-The header names are basically fixed, but if you really insist in custom
-configuration headers have a look [here](saqc/core/config.py).
-
-| Name | Data Type | Description | Required |
-|---------|----------------------------------------------|------------------------|----------|
-| varname | string | name of a variable | yes |
-| test | [function notation](#test-function-notation) | test function | yes |
-| plot | boolean (`True`/`False`) | plot the test's result | no |
-
-
-### Test function notation
-The notation of test functions follows the function call notation of Python and
-many other programming languages and looks like this:
-```
-flagRange(min=0, max=100)
-```
-Here the function `flagRange` is called and the values `0` and `100` are passed
-to the parameters `min` and `max` respectively. As we value readablity
-of the configuration more than conciseness of the extrension language, only
-keyword arguments are supported. That means that the notation `flagRange(0, 100)`
-is not a valid replacement for the above example.
-
-## Examples
-### Single Test
-Every row lists one test per variable. If you want to call multiple tests on
-a specific variable (and you probably want to), list them in separate rows:
-```
-varname | test
-#-------|----------------------------------
-x | flagMissing()
-x | flagRange(min=0, max=100)
-x | constants_flagBasic(window="3h")
-y | flagRange(min=-10, max=40)
-```
-
-### Multiple Tests
-A row lists multiple tests for a specific variable in separate columns. All test
-columns need to share the common prefix `test`:
-
-```
-varname ; test_1 ; test_2 ; test_3
-#-------;----------------------------;---------------------------;---------------------------------
-x ; flagMissing() ; flagRange(min=0, max=100) ; constants_flagBasic(window="3h")
-y ; flagRange(min=-10, max=40) ; ;
-```
-The evaluation of such a configuration is in columns-major order, so the given
-example is identical to the following:
-
-```
-varname ; test_1
-#-------;---------------------------------
-x ; flagMissing()
-y ; flagRange(min=-10, max=40)
-x ; flagRange(min=0, max=100)
-x ; constants_flagBasic(window="3h")
-
-```
-
-### Plotting
-As the process of finding a good quality check setup is somewhat experimental, SaQC
-provides a possibility to plot the results of the test functions. To use this feature, add the optional column `plot` and set it
-to `True` for all results you want to plot. These plots are
-meant to provide a quick and easy visual evaluation of the test.
-```
-varname ; test ; plot
-#-------;----------------------------------;-----
-x ; flagMissing() ;
-x ; flagRange(min=0, max=100) ; False
-x ; constants_flagBasic(window="3h") ; True
-y ; flagRange(min=-10, max=40)` ;
-```
-
-### Regular Expressions in `varname` column
-Some of the tests (e.g. checks for missing values, range tests or interpolation
-functions) are very likely to be used on all or at least several variables of
-the processed dataset. As it becomes quite cumbersome to list all these
-variables seperately, only to call the same functions with the same
-parameters, SaQC supports regular expressions
-within the `varname` column. Please not that a `varname` needs to be quoted
-(with `'` or `"`) in order to be interpreted as a regular expression.
-
-```
-varname ; test
-#----------;------------------------------
-'.*' ; harm_shift2Grid(freq="15Min")
-'(x \| y)' ; flagMissing()
-```
\ No newline at end of file
diff --git a/sphinx-doc/getting_started_md/Customizations.md b/sphinx-doc/getting_started_md/Customizations.md
deleted file mode 100644
index b9d9ff359c7ff40e508b67935c536924dfd07aa5..0000000000000000000000000000000000000000
--- a/sphinx-doc/getting_started_md/Customizations.md
+++ /dev/null
@@ -1,69 +0,0 @@
-# Customizations
-SaQC comes with a continuously growing number of pre-implemented
-[quality check and processing routines](sphinx-doc/getting_started_md/FunctionIndex.md) and
-flagging schemes.
-For any sufficiently large use case however it is very likely that the
-functions provided won't fulfill all your needs and requirements.
-
-Acknowledging the impossibility to address all imaginable use cases, we
-designed the system to allow for extensions and costumizations. The main extensions options, namely
-[quality check routines](#custom-quality-check-routines)
-and the [flagging scheme](#custom-flagging-schemes)
-are described within this documents.
-
-## Custom quality check routines
-In case you are missing quality check routines, you are of course very
-welcome to file a feature request issue on the project's
-[gitlab repository](https://git.ufz.de/rdm-software/saqc). However, if
-you are more the "no-way-I-get-this-done-by-myself" type of person,
-SaQC provides two ways to integrate custom routines into the system:
-1. The [extension language](sphinx-doc/getting_started_md/GenericFunctions.md)
-2. An [interface](#interface) to the evaluation machinery
-
-### Interface
-In order to make a function usable within the evaluation framework of SaQC the following interface is needed:
-
-```python
-import pandas
-import dios
-import saqc
-
-def yourTestFunction(
- data: pandas.DataFrame,
- field: str,
- flags: saqc.Flags,
- *args,
- **kwargs
- ) -> (dios.DictOfSeries, saqc.Flags)
-```
-
-#### Argument Descriptions
-
-| Name | Description |
-|-----------|--------------------------------------------------------------------------------------------------|
-| `data` | The actual dataset. |
-| `field` | The field/column within `data`, that function is processing. |
-| `flags` | An instance of Flags, responsible for the translation of test results into quality attributes. |
-| `args` | Any other arguments needed to parameterize the function. |
-| `kwargs` | Any other keyword arguments needed to parameterize the function. |
-
-### Integrate into SaQC
-In order make your function available to the system it needs to be registered. We provide a decorator
-[`register`](saqc/functions/register.py) with saqc, to integrate your
-test functions into SaQC. Here is a complete dummy example:
-
-```python
-from saqc import register
-
-@register()
-def yourTestFunction(data, field, flags, *args, **kwargs):
- return data, flags
-```
-
-### Example
-The function [`flagRange`](saqc/funcs/functions.py) provides a simple, yet complete implementation of
-a quality check routine. You might want to look into its implementation as a reference for your own.
-
-
-## Custom flagging schemes
-Sorry for the inconvenience! Coming soon...
diff --git a/sphinx-doc/getting_started_md/FlaggingSchemes.md b/sphinx-doc/getting_started_md/FlaggingSchemes.md
deleted file mode 100644
index edca5253ca8747048e2c4f3738dc9da522726222..0000000000000000000000000000000000000000
--- a/sphinx-doc/getting_started_md/FlaggingSchemes.md
+++ /dev/null
@@ -1,10 +0,0 @@
-# DMP flagging scheme
-
-## Possible flags
-
-The DMP scheme produces the following flag constants:
-
-* "ok"
-* "doubtfull"
-* "bad"
-
diff --git a/sphinx-doc/getting_started_md/GenericFunctions.md b/sphinx-doc/getting_started_md/GenericFunctions.md
deleted file mode 100644
index c2fa6654488728f72b43eef3188ff07f5d9baab8..0000000000000000000000000000000000000000
--- a/sphinx-doc/getting_started_md/GenericFunctions.md
+++ /dev/null
@@ -1,201 +0,0 @@
-# Generic Functions
-
-## Generic Flagging Functions
-
-Generic flagging functions provide for cross-variable quality
-constraints and to implement simple quality checks directly within the configuration.
-
-### Why?
-In most real world datasets many errors
-can be explained by the dataset itself. Think of a an active, fan-cooled
-measurement device: no matter how precise the instrument may work, problems
-are to be expected when the fan stops working or the power supply
-drops below a certain threshold. While these dependencies are easy to
-[formalize](#a-real-world-example) on a per dataset basis, it is quite
-challenging to translate them into generic source code.
-
-### Specification
-Generic flagging functions are used in the same manner as their
-[non-generic counterparts](sphinx-doc/getting_started_md/FunctionIndex.md). The basic
-signature looks like that:
-```sh
-flagGeneric(func=<expression>, flag=<flagging_constant>)
-```
-where `<expression>` is composed of the [supported constructs](#supported-constructs)
-and `<flag_constant>` is one of the predefined
-[flagging constants](ParameterDescriptions.md#flagging-constants) (default: `BAD`).
-Generic flagging functions are expected to return a boolean value, i.e. `True` or `False`. All other expressions will
-fail during the runtime of `SaQC`.
-
-
-### Examples
-
-#### Simple comparisons
-
-##### Task
-Flag all values of `x` where `y` falls below 0.
-
-##### Configuration file
-```
-varname ; test
-#-------;------------------------
-x ; flagGeneric(func=y < 0)
-```
-
-#### Calculations
-
-##### Task
-Flag all values of `x` that exceed 3 standard deviations of `y`.
-
-##### Configuration file
-```
-varname ; test
-#-------;---------------------------------
-x ; flagGeneric(func=x > std(y) * 3)
-```
-#### Special functions
-
-##### Task
-Flag all values of `x` where: `y` is flagged and `z` has missing values.
-
-##### Configuration file
-```
-varname ; test
-#-------;----------------------------------------------
-x ; flagGeneric(func=isflagged(y) & ismissing(z))
-```
-
-#### A real world example
-Let's consider the following dataset:
-
-| date | meas | fan | volt |
-|------------------|------|-----|------|
-| 2018-06-01 12:00 | 3.56 | 1 | 12.1 |
-| 2018-06-01 12:10 | 4.7 | 0 | 12.0 |
-| 2018-06-01 12:20 | 0.1 | 1 | 11.5 |
-| 2018-06-01 12:30 | 3.62 | 1 | 12.1 |
-| ... | | | |
-
-##### Task
-Flag `meas` where `fan` equals 0 and `volt`
-is lower than `12.0`.
-
-##### Configuration file
-There are various options. We can directly implement the condition as follows:
-```
-varname ; test
-#-------;-----------------------------------------------
-meas ; flagGeneric(func=(fan == 0) \| (volt < 12.0))
-```
-But we could also quality check our independent variables first
-and than leverage this information later on:
-```
-varname ; test
-#-------;----------------------------------------------------
-'.*' ; flagMissing()
-fan ; flagGeneric(func=fan == 0)
-volt ; flagGeneric(func=volt < 12.0)
-meas ; flagGeneric(func=isflagged(fan) \| isflagged(volt))
-```
-
-## Generic Processing
-
-Generic processing functions provide a way to evaluate mathmetical operations
-and functions on the variables of a given dataset.
-
-### Why
-In many real-world use cases, quality control is embedded into a larger data
-processing pipeline and it is not unusual to even have certain processing
-requirements as a part of the quality control itself. Generic processing
-functions make it easy to enrich a dataset through the evaluation of a
-given expression.
-
-### Specification
-The basic signature looks like that:
-```sh
-procGeneric(func=<expression>)
-```
-where `<expression>` is composed of the [supported constructs](#supported-constructs).
-
-
-## Variable References
-All variables of the processed dataset are available within generic functions,
-so arbitrary cross references are possible. The variable of interest
-is furthermore available with the special reference `this`, so the second
-[example](#calculations) could be rewritten as:
-```
-varname ; test
-#-------;------------------------------------
-x ; flagGeneric(func=this > std(y) * 3)
-```
-
-When referencing other variables, their flags will be respected during evaluation
-of the generic expression. So, in the example above only values of `x` and `y`, that
-are not already flagged with `BAD` will be used the avaluation of `x > std(y)*3`.
-
-
-## Supported constructs
-
-### Operators
-
-#### Comparison
-
-The following comparison operators are available:
-
-| Operator | Description |
-|----------|----------------------------------------------------------------------------------------------------|
-| `==` | `True` if the values of the operands are equal |
-| `!=` | `True` if the values of the operands are not equal |
-| `>` | `True` if the values of the left operand are greater than the values of the right operand |
-| `<` | `True` if the values of the left operand are smaller than the values of the right operand |
-| `>=` | `True` if the values of the left operand are greater or equal than the values of the right operand |
-| `<=` | `True` if the values of the left operand are smaller or equal than the values of the right operand |
-
-#### Arithmetics
-The following arithmetic operators are supported:
-
-| Operator | Description |
-|----------|----------------|
-| `+` | addition |
-| `-` | subtraction |
-| `*` | multiplication |
-| `/` | division |
-| `**` | exponentiation |
-| `%` | modulus |
-
-#### Bitwise
-The bitwise operators also act as logical operators in comparison chains
-
-| Operator | Description |
-|----------|-------------------|
-| `&` | binary and |
-| `\|` | binary or |
-| `^` | binary xor |
-| `~` | binary complement |
-
-### Functions
-All functions expect a [variable reference](#variable-references)
-as the only non-keyword argument (see [here](#special-functions))
-
-#### Mathematical Functions
-
-| Name | Description |
-|-------------|-----------------------------------|
-| `abs` | absolute values of a variable |
-| `max` | maximum value of a variable |
-| `min` | minimum value of a variable |
-| `mean` | mean value of a variable |
-| `sum` | sum of a variable |
-| `std` | standard deviation of a variable |
-| `len` | the number of values for variable |
-
-#### Special Functions
-
-| Name | Description |
-|-------------|-----------------------------------|
-| `ismissing` | check for missing values |
-| `isflagged` | check for flags |
-
-### Constants
-Generic functions support the same constants as normal functions, a detailed
-list is available [here](ParameterDescriptions.md#constants).
diff --git a/sphinx-doc/getting_started_md/GettingStarted.md b/sphinx-doc/getting_started_md/GettingStarted.md
deleted file mode 100644
index e1474bfe042c37160a6f5499ab369e197e8f8277..0000000000000000000000000000000000000000
--- a/sphinx-doc/getting_started_md/GettingStarted.md
+++ /dev/null
@@ -1,295 +0,0 @@
-# Getting started with SaQC
-
-Requirements: this tutorial assumes that you have Python version 3.6.1 or newer
-installed, and that both your operating system and Python version are in 64-bit.
-
-## Contents
-
-1. [Set up your environment](#1-set-up-your-environment)
-2. [Get SaQC](#2-get-saqc)
-3. [Training tour](#3-training-tour)
- * [3.1 Get toy data and configuration](#get-toy-data-and-configuration)
- * [3.2 Run SaQC](#run-saqc)
- * [3.3 Configure SaQC](#configure-saqc)
- * [Change test parameters](#change-test-parameters)
- * [3.4 Explore the functionality](#explore-the-functionality)
- * [Process multiple variables](#process-multiple-variables)
- * [Data harmonization and custom functions](#data-harmonization-and-custom-functions)
- * [Save outputs to file](#save-outputs-to-file)
-
-
-## 1. Set up your environment
-
-SaQC is written in Python, so the easiest way to set up your system to use SaQC
-for your needs is using the Python Package Index (PyPI). Following good Python
-practice, you will first want to create a new virtual environment that you
-install SaQC into by typing the following in your console:
-
-
-##### On Unix/Mac-systems
-
-```sh
-# if you have not installed venv yet, do so:
-python3 -m pip install --user virtualenv
-
-# move to the directory where you want to create your virtual environment
-cd YOURDIR
-
-# create virtual environment called "env_saqc"
-python3 -m venv env_saqc
-
-# activate the virtual environment
-source env_saqc/bin/activate
-```
-
-##### On Windows-systems
-
-```sh
-# if you have not installed venv yet, do so:
-py -3 -m pip install --user virtualenv
-
-# move to the directory where you want to create your virtual environment
-cd YOURDIR
-
-# create virtual environment called "env_saqc"
-py -3 -m venv env_saqc
-
-# move to the Scripts directory in "env_saqc"
-cd env_saqc/Scripts
-
-# activate the virtual environment
-./activate
-```
-
-## 2. Get SaQC
-
-### Via PyPI
-
-Type the following:
-
-##### On Unix/Mac-systems
-
-
-```sh
-python3 -m pip install saqc
-```
-
-##### On Windows-systems
-
-
-```sh
-py -3 -m pip install saqc
-```
-
-
-### From Gitlab repository
-
-Download SaQC directly from the [GitLab-repository](https://git.ufz.de/rdm/saqc) to make sure you use the most recent version:
-
-```sh
-# clone gitlab - repository
-git clone https://git.ufz.de/rdm-software/saqc
-
-# switch to the folder where you installed saqc
-cd saqc
-
-# install all required packages
-pip install -r requirements.txt
-
-# install all required submodules
-git submodule update --init --recursive
-```
-
-
-## 3. Training tour
-
-The following passage guides you through the essentials of the usage of SaQC via
-a toy dataset and a toy configuration.
-
-### Get toy data and configuration
-
-If you take a look into the folder `saqc/ressources/data` you will find a toy
-dataset `data.csv` which contains the following:
-
- Date,Battery,SM1,SM2
- 2016-04-01 00:05:48,3573,32.685,29.3157
- 2016-04-01 00:20:42,3572,32.7428,29.3157
- 2016-04-01 00:35:37,3572,32.6186,29.3679
- 2016-04-01 00:50:32,3572,32.736999999999995,29.3679
- ...
-
-These are two timeseries of soil moisture (SM1+2) and the battery voltage of the
-measuring device over time. Generally, this is the way that your data should
-look like to run saqc. Note, however, that you do not necessarily need a series
-of dates to reference to and that you are free to use more columns of any name
-that you like.
-
-Now create your our own configuration file `saqc/ressources/data/myconfig.csv`
-and paste the following lines into it:
-
- varname;test;plot
- SM2;flagRange(min=10, max=60);False
- SM2;flagMad(window="30d", z=3.5);True
-
-These lines illustrate how different quality control tests can be specified for
-different variables by following the pattern:
-
-*varname*|;| *testname (testparameters)*|;| *plotting option*|
-:---------------|:------|:------|:----|:--|
-
-In this case, we define a range-test that flags all values outside the range
-[10,60] and a test to detect spikes using the MAD-method. You can find an
-overview of all available quality control tests in the
-[documentation](FunctionIndex.md). Note that the tests are
-_executed in the order that you define in the configuration file_. The quality
-flags that are set during one test are always passed on to the subsequent one.
-
-### Run SaQC
-
-Remember to have your virtual environment activated:
-
-##### On Unix/Mac-systems
-
-```sh
-source env_saqc/bin/activate
-```
-
-##### On Windows
-
-```sh
-cd env_saqc/Scripts
-./activate
-```
-
-
-Via your console, move into the folder you downloaded saqc into:
-```sh
-cd saqc
-```
-
-From here, you can run saqc and tell it to run the tests from the toy
-config-file on the toy dataset via the `-c` and `-d` options:
-##### On Unix/Mac-systems
-```sh
-python3 -m saqc -c ressources/data/myconfig.csv -d ressources/data/data.csv
-```
-##### On Windows
-```sh
-py -3 -m saqc -c ressources/data/myconfig.csv -d ressources/data/data.csv
-```
-
-If you installed saqc via PYPi, you can omit ```sh python -m```.
-
-The command will output this plot:
-
-
-
-So, what do we see here?
-
-* The plot shows the data as well as the quality flags that were set by the
- tests for the variable `SM2`, as defined in the config-file
-* Following our definition in the config-file, first the `flagRange`-test that flags
- all values outside the range [10,60] was executed and after that,
- the `flagMad`-test to identify spikes in the data
-* In the config, we set the plotting option to `True` for `flagMad`,
- only. Thus, the plot aggregates all preceeding tests (here: `range`) to black
- points and highlights the flags of the selected test as red points.
-
-
-#### Save outputs to file
-If you want the final results to be saved to a csv-file, you can do so by the
-use of the `-o` option:
-
-```sh
-saqc -c ressources/data/config.csv -d ressources/data/data.csv -o ressources/data/out.csv
-```
-
-Which saves a dataframe that contains both the original data and the quality
-flags that were assigned by SaQC for each of the variables:
-
- Date,SM1,SM1_flags,SM2,SM2_flags
- 2016-04-01 00:05:48,32.685,OK,29.3157,OK
- 2016-04-01 00:20:42,32.7428,OK,29.3157,OK
- 2016-04-01 00:35:37,32.6186,OK,29.3679,OK
- 2016-04-01 00:50:32,32.736999999999995,OK,29.3679,OK
- ...
-
-
-### Configure SaQC
-
-#### Change test parameters
-Now you can start to change the settings in the config-file and investigate the
-effect that has on how many datapoints are flagged as "BAD". When using your
-own data, this is your way to configure the tests according to your needs. For
-example, you could modify your `myconfig.csv` and change the parameters of the
-range-test:
-
- varname;test;plot
- SM2;flagRange(min=-20, max=60);False
- SM2;flagMad(window="30d", z=3.5);True
-Rerunning SaQC as above produces the following plot:
-
-
-
-You can see that the changes that we made to the parameters of the range test
-take effect so that only the values > 60 are flagged by it (black points). This,
-in turn, leaves more erroneous data that is then identified by the proceeding
-spike-test (red points).
-
-### Explore the functionality
-#### Process multiple variables
-You can also define multiple tests for multiple variables in your data. These
-are then executed sequentially and can be plotted seperately. E.g. you could do
-something like this:
-
- varname;test;plot
- SM1;flagRange(min=10, max=60);False
- SM2;flagRange(min=10, max=60);False
- SM1;flagMad(window="15d", z=3.5);True
- SM2;flagMad(window="30d", z=3.5);True
-
-which gives you separate plots for each line where the plotting option is set to
-`True` as well as one summary "data plot" that depicts the joint flags from all
-tests:
-
-|SM1 | SM2 |
-|:-------------------------:|:-------------------------:|
-|here | there|
-
-
-
-|SM1 | SM2 |
-|:-------------------------:|:-------------------------:|
-|  | |
-|  | bumm|
-
-
-
-#### Data harmonization and custom functions
-
-SaQC includes functionality to harmonize the timestamps of one or more data
-series. Also, you can write your own tests using a python-based
-[extension language](sphinx-doc/getting_started_md/GenericFunctions.md). This would look like this:
-
- varname;test;plot
- SM2;shiftToFreq(freq="15Min");False
- SM2;generic(func=(SM2 < 30));True
-
-The above executes an internal framework that harmonizes the timestamps of SM2
-to a 15min-grid (see data below). Further information about this routine can be
-found in the :ref:`Flagging Functions Overview <flaggingFunctions>`.
-
- Date,SM1,SM1_flags,SM2,SM2_flags
- 2016-04-01 00:00:00,,,29.3157,OK
- 2016-04-01 00:05:48,32.685,OK,,
- 2016-04-01 00:15:00,,,29.3157,OK
- 2016-04-01 00:20:42,32.7428,OK,,
- ...
-
-Also, all values where SM2 is below 30 are flagged via the custom function (see
-plot below). You can learn more about the syntax of these custom functions
-[here](sphinx-doc/getting_started_md/GenericFunctions.md).
-
-
-
-:py:func:`lala <docs.func_modules.outliers.flagRange>`
\ No newline at end of file
diff --git a/sphinx-doc/getting_started_md/ParameterDescriptions.md b/sphinx-doc/getting_started_md/ParameterDescriptions.md
deleted file mode 100644
index 0581f55c189f0c1b80c22296a188104d9b6a6716..0000000000000000000000000000000000000000
--- a/sphinx-doc/getting_started_md/ParameterDescriptions.md
+++ /dev/null
@@ -1,34 +0,0 @@
-## Offset Strings
-All the [pandas offset aliases](https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#offset-aliases) ars supported by SaQC. The following table lists some of the more relevant options:
-
-| Alias | Description |
-| ----- | ----------- |
-| `"S"`, `"s"` | second |
-| `"T"`, `"Min"`, `"min"` | minute |
-| `"H"`, `"h"` | hour |
-| `"D"`, `"d"` | calendar day |
-| `"W"`, `"w"` | week |
-| `"M"`, `"m"` | month |
-| `"Y"`, `"y"` | year |
-
-Multiples are build by preceeding the alias with the desired multiply (e.g `"5Min"`, `"4W"`)
-
-
-## Constants
-
-### Flagging Constants
-The following flag constants are available and can be used to mark the quality of a data point:
-
-| Alias | Description |
-| ---- | ---- |
-| `GOOD` | A value did pass all the test and is therefore considered to be valid |
-| `BAD` | At least on test failed on the values and is therefore considered to be invalid |
-| `UNFLAGGED` | The value has not got a flag yet. This might mean, that all tests passed or that no tests ran |
-
-How these aliases will be translated into 'real' flags (output of SaQC) dependes on the [flagging scheme](FlaggingSchemes.md)
-and might range from numerical values to string constants.
-
-### Numerical Constants
-| Alias | Description |
-| ---- | ---- |
-| `NAN` | Not a number |
diff --git a/sphinx-doc/how_to_doc/HowToDoc.rst b/sphinx-doc/how_to_doc/HowToDoc.rst
new file mode 100644
index 0000000000000000000000000000000000000000..273ef245705123cc957ee4d080d0a6034fe066a6
--- /dev/null
+++ b/sphinx-doc/how_to_doc/HowToDoc.rst
@@ -0,0 +1,237 @@
+
+Documentation Guide
+===================
+
+We document our code via docstrings in numpy-style.
+Features, install and usage instructions and other more text intense stuff,
+is written in extra documents.
+The documents and the docstrings then are collected and rendered using `sphinx <https://www.sphinx-doc.org/>`_.
+
+Documentation Strings
+---------------------
+
+
+*
+ Write docstrings for all public modules, functions, classes, and methods.
+ Docstrings are not necessary for non-public methods,
+ but you should have a comment that describes what the method does.
+ This comment should appear after the def line.
+ [\ `PEP8 <https://www.python.org/dev/peps/pep-0008/#documentation-strings>`_\ ]
+
+*
+ Note that most importantly, the ``"""`` that ends a multiline docstring should be on a line by itself [\ `PEP8 <https://www.python.org/dev/peps/pep-0008/#documentation-strings>`_\ ] :
+
+ .. code-block:: python
+
+ """Return a foobang
+
+ Optional plotz says to frobnicate the bizbaz first.
+ """
+
+*
+ For one liner docstrings, please keep the closing ``"""`` on the same line.
+ [\ `PEP8 <https://www.python.org/dev/peps/pep-0008/#documentation-strings>`_\ ]
+
+Pandas Style
+^^^^^^^^^^^^
+
+We use `Pandas-style <https://pandas.pydata.org/pandas-docs/stable/development/contributing_docstring.html>`_ docstrings:
+
+Flagger, data, field, etc.
+--------------------------
+
+use this:
+
+.. code-block:: py
+
+ def foo(data, field, flagger):
+ """
+ data : dios.DictOfSeries
+ A saqc-data object.
+
+ field : str
+ A field denoting a column in data.
+
+ flagger : saqc.flagger.BaseFlagger
+ A saqc-flagger object.
+ """
+
+IDE helper
+^^^^^^^^^^
+
+In pycharm one can activate autogeneration of numpy doc style like so:
+
+
+#. ``File->Settings...``
+#. ``Tools->Python Integrated Tools``
+#. ``Docstrings->Docstring format``
+#. Choose ``NumPy``
+
+Docstring formatting pitfalls
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+
+* Latex is included via
+ .. code-block::
+
+ :math:`<latex_code>`
+
+*
+ Latex commands need to be signified with **double** backlash! (\ ``\\mu`` instead of ``\mu``\ )
+
+*
+ Nested lists need to be all of the same kind (either numbered or marked - otherwise result is salad)
+
+* List items covering several lines in the docstring have to be all aligned - (so, not only the superfluent ones, but ALL, including the first one - otherwise result is salad)
+* Start of a list has to be seperated from preceding docstring code by *one blank line* - (otherwise list items get just chained in one line and result is salad)
+* Most formatting signifiers are not allowed to start or end with a space. (so no :math: `1+1 `, ` var2`, `` a=1 ``, ...)
+* Do not include lines *only* containing two or more ``-`` signs, except it is the underscore line of the section heading (otherwise resulting html representation could be messed up)
+
+hyperlinking docstrings
+-----------------------
+
+
+*
+ Link code content/modules via python roles.
+
+*
+ Cite/link via the py domain roles. Link content ``bar``\ , that is registered to the API with the adress ``foo.bar`` and
+ shall be represented by the name ``link_name``\ , via:
+
+ .. code-block::
+
+ :py:role:`link_name <foo.bar>`
+
+*
+ check out the *_api* folder in the `repository <https://git.ufz.de/rdm-software/saqc/-/tree/develop/sphinx-doc>`_ to get an
+ overview of already registered paths. Most important may be:
+
+*
+ constants are available via ``saqc.constants`` - for example:
+
+ .. code-block::
+
+ :py:const:`~saqc.constants.BAD`
+
+*
+ the ``~`` is a shorthand for hiding the module path and only displaying ``BAD``.
+
+*
+ Functions are available via the "simulated" module ``Functions.saqc`` - for example:
+
+.. code-block::
+
+ :py:func:`saqc.flagRange <saqc.Functions.flagRange>`
+
+
+* Modules are available via the "simulated" package ``Functions.`` - for example:
+
+.. code-block::
+
+ :py:mod:`generic <Functions.generic>`
+
+
+* The saqc object and/or its content is available via:
+
+.. code-block::
+
+ :py:class:`saqc.SaQC`
+ :py:meth:`saqc.SaQC.getResults`
+
+
+* The Flags object and/or its content is available via:
+
+.. code-block::
+
+ :py:class:`saqc.Flags`
+
+
+*
+ you can add .rst files containing ``automodapi`` directives to the modulesAPI folder to make available more modules via pyroles
+
+*
+ the Environment table, including variables available via config files is available as restfile located in the environment folder. (Use include directive to include, or linking syntax to link it.
+
+Adding Markdown content to the Documentation
+--------------------------------------------
+
+
+*
+ By linking the markdown file "foo/bar.md", or any folder that contains markdown files directly,
+ you can trigger sphinx - ``recommonmark``\ , which is fine for not-too complex markdown documents.
+
+*
+ Especially, if you have multiple markdown files that are mutually linked and/or, contain tables of certain fencieness (tables with figures),
+ you will have to take some minor extra steps:
+
+*
+ You will have to gather all markdown files in subfolders of "sphinx-doc" directory (you can have multiple subfolders).
+
+*
+ To include a folder named ``foo`` of markdown files in the documentation, or refer to content in ``foo``\ , you will have
+ to append the folder name to the MDLIST variable in the Makefile:
+
+*
+ The markdown files must be in one of the subfolders listed in MDLIST - they cant be gathered in nested subfolders.
+
+*
+ You can not link to sections in other markdown files, that contain the ``-`` character (sorry).
+
+*
+ The Section structure/ordering must be consistent in the ReST sence (otherwise they wont appear - thats also required if you use plain ``recommonmark``
+
+*
+ You can link to ressources - like pictures and include them in the markdown, if the pictures are in (possibly another) folder in ``\sphinx-doc`` and the paths to this ressources are given relatively!
+
+*
+ You can include a markdown file in a rest document, by appending '_m2r' to the folder name when linking it path_wise.
+ So, to include the markdown file 'foo/bar.md' in a toc tree for example - you would do something like:
+
+*
+ the Environment table, including variables availbe via config files is available as restfile located in the environment folder. (Use include directive to include, or linking syntax to link it.)
+
+.. code-block:: python
+
+ .. toctree::
+ :hidden:
+ :maxdepth: 1
+
+ foo_m2r/bar
+
+Linking ReST sources in markdown documentation
+----------------------------------------------
+
+
+*
+ If you want to hyperlink/include other sources from the sphinx documentation that are rest-files (and docstrings),
+ you will not be able to include them in a way, that they will appear in you markdown rendering. - however - there is
+ the posibillity to just include the respective rest directives (see directive/link :ref:`examples <how_to_doc/HowToDoc:hyperlinking docstrings>`\ ).
+
+*
+ This will mess up your markdown code - meaning that you will have
+ those rest snippets flying around, but when the markdown file gets converted to the rest file and build into the
+ sphinx html build, the linked sources will be integrated properly. The syntax for linking rest sources is as
+ follows as follows:
+
+*
+ to include the link to the rest source ``functions.rst`` in the folder ``foo``\ , under the name ``bar``\ , you would need to insert:
+
+ .. code-block::
+
+ :doc:`foo <rel_path/functions>`
+
+*
+ to link to a section with name ``foo`` in a rest source named ``bumm.rst``\ , under the name ``bar``\ , you would just insert:
+
+ .. code-block::
+
+ :ref:`bar <relative/path/from/sphinx/root/bumm:foo>`
+
+*
+ in that manner you might be able to smuggle most rest directives through into the resulting html build. Especially if you want to link to the docstrings of certain (domain specific) objects. Lets say you want to link to the *function* ``saqc.funcs.flagRange`` under the name ``ranger`` - you just include:
+
+.. code-block::
+
+ :py:func:`Ranger <saqc.funcs.flagRange>`
+
+whereas the ``:func:`` part determines the role, the object is documented as. See `this page <https://www.sphinx-doc.org/en/master/#ref-role>`_ for an overview of the available roles
diff --git a/sphinx-doc/how_to_doc_md/HowToDoc.md b/sphinx-doc/how_to_doc_md/HowToDoc.md
deleted file mode 100644
index 61a902aa080cac8016d2e3e44d5f205d105ac534..0000000000000000000000000000000000000000
--- a/sphinx-doc/how_to_doc_md/HowToDoc.md
+++ /dev/null
@@ -1,138 +0,0 @@
-# Documentation Guide
-
-We document our code via docstrings in numpy-style.
-Features, install and usage instructions and other more text intense stuff,
-is written in extra documents.
-The documents and the docstrings then are collected and rendered using [sphinx](https://www.sphinx-doc.org/).
-
-
-## Documentation Strings
-
-
-- Write docstrings for all public modules, functions, classes, and methods.
- Docstrings are not necessary for non-public methods,
- but you should have a comment that describes what the method does.
- This comment should appear after the def line.
- [[PEP8](https://www.python.org/dev/peps/pep-0008/#documentation-strings)]
-
-- Note that most importantly, the `"""` that ends a multiline docstring should be on a line by itself [[PEP8](https://www.python.org/dev/peps/pep-0008/#documentation-strings)] :
- ```python
- """Return a foobang
-
- Optional plotz says to frobnicate the bizbaz first.
- """
- ```
-
-- For one liner docstrings, please keep the closing `"""` on the same line.
- [[PEP8](https://www.python.org/dev/peps/pep-0008/#documentation-strings)]
-
-### Pandas Style
-
-We use [Pandas-style](https://pandas.pydata.org/pandas-docs/stable/development/contributing_docstring.html) docstrings:
-
-
-
-## Flagger, data, field, etc.
-
-use this:
-```py
-def foo(data, field, flagger):
- """
- data : dios.DictOfSeries
- A saqc-data object.
-
- field : str
- A field denoting a column in data.
-
- flagger : saqc.flagger.BaseFlagger
- A saqc-flagger object.
- """
-```
-
-
-### IDE helper
-
-In pycharm one can activate autogeneration of numpy doc style like so:
-1. `File->Settings...`
-2. `Tools->Python Integrated Tools`
-3. `Docstrings->Docstring format`
-4. Choose `NumPy`
-
-
-### Docstring formatting pitfalls
-
-* Latex is included via :math:\`latex_code\`
-
- * note, the backticks surrounding the actual code
- * Latex commands need to be signified with **double** backlash! (``\\mu`` instead of ``\mu``)
-
-* Nested lists need to be all of the same kind (either numbered or marked - otherwise result is salad)
-* List items covering several lines in the docstring have to be all aligned - (so, not only the superfluent ones, but ALL, including the first one - otherwise result is salad)
-* Start of a list has to be seperated from preceding docstring code by *one blank line* - (otherwise list items get just chained in one line and result is salad)
-* Most formatting signifiers are not allowed to start or end with a space. (so no :math: \`1+1 \`, \` var2\`, \`\` a=1 \`\`, ...)
-* Do not include lines *only* containing two or more `-` signs, except it is the underscore line of the section heading (otherwise resulting html representation could be messed up)
-
-## Adding Markdown content to the Documentation
-
-* If you generate cookbooks and/or tutorials in markdown and want them to be integrated in the sphinx doc - there are some obstaclish thingies to care for
-
-- You will have to gather all markdown files in subfolders of "sphinx-doc".
-
-- To include a folder named 'foo_md' of markdown files in the documentation, you will have to add the following line to the Makefile:
-
-```python
-python make_md_to_rst.py -p "sphinx-doc/gfoo_md"
-```
-
-- The markdown files must be in that subfolders - they cant be gathered in nested subfolders.
-
-- You can not link to sections in other markdown files, that contain the `-` character.
-
-- The Section structure/ordering must be consistent in the ReST sence (otherwise they wont appear)
-
-- You can link to ressources - like pictures and include them in the markdown, if the pictures are in (possibly another) folder in `\sphinx-doc` and the pathes to this ressources are given relatively!
-
-- You can include a markdown file in a rest document, by appending '_m2r' to the folder name. So to include the markdown file 'foo_md/bar.md' in a toc tree for example - you would do something like:
-```python
-.. toctree::
- :hidden:
- :maxdepth: 1
-
- foo_md_m2r/bar
-```
-
-- If you want to hyperlink/include other sources from the sphinx documentation that are rest-files, you will not be able to include them in a way, that they will appear in you markdown rendering. - however - there is the slightly hacky possibillity to just include the respective rest directives. This will mess up your markdown code - meaning that you will have those rest snippets flying around, but when the markdown file gets converted to the rest file and build into the sphinx html build, the linked sources will be integrated properly. The syntax is as follows:
-
-- to include the link to the rest source `functions.rst` in the folder `foo`, under the name `bar`, you would need to insert:
-```python
-:doc:`foo <rel_path/functions>`
-```
-
-- to link to a section with name `foo` in a rest source named `bumm.rst`, under the name `bar`, you would just insert:
-```python
-:ref:`bar <relative/path/from/sphinx/root/bumm:foo>`
-```
-
-- in that manner you might be able to smuggle most rest directives through into the resulting html build. Especially if you want to link to the docstrings of certain (domain specific) objects. Lets say you want to link to the *function* `saqc.funcs.flagRange` under the name `ranger` - you just include:
-
-```python
-:py:func:`Ranger <saqc.funcs.flagRange>`
-```
-
-whereas the `:func:` part determines the role, the object is documented as. See [this page](https://www.sphinx-doc.org/en/master/#ref-role) for an overview of the available roles
-
-## Refering to documented Functions
-
-* Since the documentation generates an own module structure to document the functions, linking to the documented functions is a bit hacky:
-
-- Functions: to link to any function in the 'saqc.funcs' module - you will have to link the rest file it is documented in. All functions from the function module can be linked via replacing the 'saqc.funcs' part of the module path by 'docs.func_modules':
-
-- For example, 'saqc.funcs.outliers.flagRange' is linked via:
-```python
-:py:func:`docs.func_modules.outliers.flagRange`
-```
-
-To hide the temporal module structure and/or make transparent the intended module structure, use named links, like so:
-```python
-:py:func:`saqc.outliers.flagRange <docs.func_modules.outliers.flagRange>`
-```
diff --git a/sphinx-doc/index.rst b/sphinx-doc/index.rst
index b5875e5e3ff1e2d5bbccf5afa0de8fdca830b0e1..3ee256b2d00b5afa2591beaba1db9d8e104d5b96 100644
--- a/sphinx-doc/index.rst
+++ b/sphinx-doc/index.rst
@@ -1,50 +1,88 @@
-==================
-SaQC documentation
-==================
-Saqc is a great tool to clean data from rubbish.
+.. include:: misc/title.rst
+
.. toctree::
:hidden:
:maxdepth: 1
Repository <https://git.ufz.de/rdm-software/saqc>
- Documentation Guide <how_to_doc_m2r/HowToDoc>
-
+ Package API <moduleAPIs/saqcInit>
Getting Started
===============
.. toctree::
- :maxdepth: 2
+ :caption: Getting started
+ :maxdepth: 1
+ :hidden:
- getting_started_md_m2r/GettingStarted
+ getting_started/InstallationGuide
+ getting_started/TutorialAPI
+ getting_started/TutorialCLI
-Flagging Functions
-==================
+.. toctree::
+ :caption: Cookbooks
+ :maxdepth: 1
+ :hidden:
-Flagging Functions
+ cook_books/DataRegularisation
+ cook_books/OutlierDetection
+ cook_books/MultivariateFlagging
.. toctree::
- :maxdepth: 2
+ :caption: Documentation
+ :maxdepth: 1
+ :hidden:
+
+ documentation/FlaggingTranslation
+ documentation/GenericFunctions
+ documentation/SourceTarget
+
+
+.. toctree::
+ :caption: Functions
+ :hidden:
+ :maxdepth: 1
:glob:
:titlesonly:
- intro_modules/*
+ all <moduleAPIs/Functionssaqc>
+ resampling <moduleAPIs/Functionsresampling>
+ generic <moduleAPIs/Functionsgeneric>
+ outliers <moduleAPIs/Functionsoutliers>
+ breaks <moduleAPIs/Functionsbreaks>
+ constants <moduleAPIs/Functionsconstants>
+ changepoints <moduleAPIs/Functionschangepoints>
+ drift <moduleAPIs/Functionsdrift>
+ curvefit <moduleAPIs/Functionscurvefit>
+ interpolation <moduleAPIs/Functionsinterpolation>
+ residues <moduleAPIs/Functionsresidues>
+ tools <moduleAPIs/Functionstools>
+ flagtools <moduleAPIs/Functionsflagtools>
+ rolling <moduleAPIs/Functionsrolling>
+ scores <moduleAPIs/Functionsscores>
+ transformation <moduleAPIs/Functionstransformation>
+ noise <moduleAPIs/Functionsnoise>
-Func Modules
-============
+.. toctree::
+ :caption: SaQC
+ :hidden:
+ :maxdepth: 1
+
+ SaQC Object <moduleAPIs/saqcClass>
+ Constants <moduleAPI/saqcConstants>
+ Config Environment <environment/configEnv>
-Function modules
.. toctree::
- :maxdepth: 3
- :glob:
- :titlesonly:
+ :caption: Developer Ressources
+ :hidden:
+ :maxdepth: 1
- func_modules/*
+ Documentation Guide <how_to_doc/HowToDoc>
Indices and tables
@@ -53,3 +91,4 @@ Indices and tables
* :ref:`genindex`
* :ref:`modindex`
* :ref:`search`
+
diff --git a/sphinx-doc/intro_modules/AdvancedFlagging.rst b/sphinx-doc/intro_modules/AdvancedFlagging.rst
deleted file mode 100644
index 3fc3e5f13968f542df5a449c0bac962014a95888..0000000000000000000000000000000000000000
--- a/sphinx-doc/intro_modules/AdvancedFlagging.rst
+++ /dev/null
@@ -1,6 +0,0 @@
-
-AdvancedFlagging
-================
-
-.. automodapi:: docs.intro_modules.AdvancedFlagging
- :no-heading:
diff --git a/sphinx-doc/intro_modules/BasicFlagging.rst b/sphinx-doc/intro_modules/BasicFlagging.rst
deleted file mode 100644
index 265498330e2f3881778dcf8d167adefaeb58eb81..0000000000000000000000000000000000000000
--- a/sphinx-doc/intro_modules/BasicFlagging.rst
+++ /dev/null
@@ -1,6 +0,0 @@
-
-BasicFlagging
-=============
-
-.. automodapi:: docs.intro_modules.BasicFlagging
- :no-heading:
diff --git a/sphinx-doc/make_html_headings_proppa.py b/sphinx-doc/make_html_headings_proppa.py
deleted file mode 100644
index 42627f24438fc08cc769b494e884c475f217ce34..0000000000000000000000000000000000000000
--- a/sphinx-doc/make_html_headings_proppa.py
+++ /dev/null
@@ -1,66 +0,0 @@
-import os
-import click
-import re
-import pickle
-
-
-@click.command()
-@click.option(
- "-b",
- "--buildpath",
- type=str,
- required=True,
- default="sphinx-doc/_build/html/_api",
- help="Relative path to the html api files to be manipulated (relative to sphinx root).",
-)
-@click.option(
- "-sr",
- "--sphinxroot",
- type=str,
- required=True,
- default="..",
- help="Relative path to the sphinx root.",
-)
-@click.option(
- "-p",
- "--pckpath",
- type=str,
- required=True,
- default="docs/doc_modules/func_modules",
- help="Relative path to the documented package (relative to sphinx root).",
-)
-def main(buildpath, sphinxroot, pckpath):
- root_path = os.path.abspath(sphinxroot)
- buildpath = os.path.join(root_path, buildpath)
- pckpath = os.path.join(root_path, pckpath)
- files = os.listdir(buildpath)
- # gather all files from the doc module
- files = [f for f in files if re.search("^docs\.", f)]
- with open(os.path.join(pckpath, "module_dict.pkl"), "rb") as file_:
- doc_mod_structure = pickle.load(file_)
-
- for key in doc_mod_structure.keys():
- # search for all function files assigned to the module
- mod_f = [f for f in files if re.search(f"(^|[.]){key}\.[^.]*\.html", f)]
- for file_ in mod_f:
- parts = file_.split(".")
- func = parts[-2]
- module_domain = ".".join(parts[:-2])
-
- with open(os.path.join(buildpath, file_), "r") as wf:
- code = wf.read()
-
- old_domain_str = f'<code class="sig-prename descclassname">{module_domain}'
- new_domain = [
- f.split(".")[0]
- for f in doc_mod_structure[key]
- if f.split(".")[1] == func
- ][0]
- new_domain_str = f'<code class="sig-prename descclassname">{new_domain}'
- code = code.replace(old_domain_str, new_domain_str)
- with open(os.path.join(buildpath, file_), "w+") as wf:
- wf.write(code)
-
-
-if __name__ == "__main__":
- main()
diff --git a/sphinx-doc/misc/ExponentialModel.rst b/sphinx-doc/misc/ExponentialModel.rst
new file mode 100644
index 0000000000000000000000000000000000000000..abd06681b914f680045e559cffdb2852c110e445
--- /dev/null
+++ b/sphinx-doc/misc/ExponentialModel.rst
@@ -0,0 +1,31 @@
+
+Exponential Drift Model and Correction
+======================================
+
+It is assumed, that, in between maintenance events, there is a drift effect shifting the measurements in a way, that the resulting value course can be described by the exponential model $\ ``M``\ $:
+
+$\ ``M(t, a, b, c) = a + b(e^{ct}-1)``\ $
+
+We consider the timespan in between maintenance events to be scaled to the $\ ``[0,1]``\ $ interval.
+To additionally make sure, the modeled curve can be used to calibrate the value course, we added the following two conditions.
+
+$\ ``M(0, a, b, c) = y_0``\ $
+
+$\ ``M(1, a, b, c) = y_1``\ $
+
+With $\ ``y_0``\ $ denoting the mean value obtained from the first 6 meassurements directly after the last maintenance event, and $\ ``y_1``\ $ denoting the mean over the 6 meassurements, directly preceeding the beginning of the next maintenance event.
+
+Solving the equation, one obtains the one-parameter Model:
+
+$\ ``M_{drift}(t, c) = y_0 + ( \frac{y_1 - y_0}{e^c - 1} ) (e^{ct} - 1)``\ $
+
+For every datachunk in between maintenance events.
+
+After having found the parameter $\ ``c^*``\ $, that minimizes the squared residues between data and drift model, the correction is performed by bending the fitted curve, $\ ``M_{drift}(t, c^*)``\ $, in a way, that it matches $\ ``y_2``\ $ at $\ ``t=1``\ $ (,with $\ ``y_2``\ $ being the mean value observed directly after the end of the next maintenance event).
+This bended curve is given by:
+
+$\ ``M_{shift}(t, c^{*}) = M(t, y_0, \frac{y_1 - y_0}{e^c - 1} , c^*)``\ $
+
+the new values $\ ``y_{shifted}``\ $ are computed via:
+
+$\ ``y_{shifted} = y + M_{shift} - M_{drift}``\ $
diff --git a/sphinx-doc/misc/title.rst b/sphinx-doc/misc/title.rst
new file mode 100644
index 0000000000000000000000000000000000000000..bb92d288f350908b1b0727c4c0ff03947f752e01
--- /dev/null
+++ b/sphinx-doc/misc/title.rst
@@ -0,0 +1,82 @@
+
+.. |sacRaw| image:: /ressources/images/Representative/RawData.png
+ :height: 150 px
+ :width: 288 px
+
+.. |sacFlagged| image:: /ressources/images/Representative/DifferentFlags.png
+ :height: 150 px
+ :width: 288 px
+
+.. |sacMV| image:: /ressources/images/Representative/MultivarSAC.png
+ :height: 150 px
+ :width: 288 px
+
+.. |sacProc| image:: /ressources/images/Representative/ProcessingDrift.png
+ :height: 150 px
+ :width: 288 px
+
+.. |pyLogo| image:: /ressources/images/Representative/pythonLogo.png
+ :height: 108 px
+ :width: 105 px
+
+.. |csvConfig| image:: /ressources/images/Representative/csvConfig.png
+ :height: 100 px
+ :width: 176 px
+
+
+.. |legendEXMPL| image:: /ressources/images/Representative/legendEXMPL.png
+ :height: 100 px
+ :width: 200
+
+
+
+===========================================
+SaQC - System for automated Quality Control
+===========================================
+
+Anomalies and errors are the rule not the exception when working with
+time series data. This is especially true, if such data originates
+from in-situ measurements of environmental properties.
+Almost all applications, however, implicitly rely on data, that complies
+with some definition of 'correct'.
+In order to infer reliable data products and tools, there is no alternative
+to quality control. SaQC provides all the building blocks to comfortably
+bridge the gap between 'usually faulty' and 'expected to be corrected' in
+a accessible, consistent, objective and reproducible way.
+
+SaQC is developed and maintained by the
+`Research Data Management <https://www.ufz.de/index.php?en=45348>`_ Team at the
+`Helmholtz-Centre for Environmental Research - UFZ <https://www.ufz.de/>`_.
+It manifests the requirements and experiences made from the implementation and
+operation of fully automated quality control pipelines for environmental sensor data.
+The diversity of communities involved in this process and the special needs within the
+realm of scientific data acquisition and its provisioning, have shaped SaQC into
+its current state. We define this state as: inherently consistent, yet externally
+extensible, traceable, approachable for non-programmers and usable in a wide range
+of applications, from exploratory interactive programming environments to large-scale
+fully automated, managed workflows.
+
+--------
+Features
+--------
+
+.. list-table::
+
+ * - |pyLogo| |csvConfig|
+ - * :ref:`get and install SaQC <getting_started/InstallationGuide:installation guide>`
+ * :ref:`use the SaQC python API, enabling integration into larger programs <getting_started/TutorialAPI:python api>`
+ * or use SaQC as a commandline application and configure your pipelines via plain text
+ * - |sacRaw|
+ - * easily load data from multiple sources, concatenating them in a SaQC object
+ * preprocess your data, by aligning it to shared frequency grids
+ * - |sacFlagged|
+ - * apply basic plausibility checks, as well as
+ * more complex, univariat flagging Functions
+ * - |legendEXMPL|
+ - * automatically keep track of flagging history and flags significance for every datapoint
+ * define and use custom schemes to translate your flags to and from SaQC
+ * - |sacProc|
+ - * modify your data by :ref:`interpolations <cook_books/DataRegularisation:Interpolation>`, corrections and :ref:`transformations <cook_books/DataRegularisation:Aggregation>`
+ * calculate data products, such as :ref:`residues or outlier scores <cook_books/OutlierDetection:Residues and Scores>`
+ * - |sacMV|
+ - * apply multivariate flagging function
diff --git a/sphinx-doc/moduleAPIs/Functionsbreaks.rst b/sphinx-doc/moduleAPIs/Functionsbreaks.rst
new file mode 100644
index 0000000000000000000000000000000000000000..102ff3d23ea480793e5def5820cdd7d8ac6550af
--- /dev/null
+++ b/sphinx-doc/moduleAPIs/Functionsbreaks.rst
@@ -0,0 +1,5 @@
+breaks
+======
+
+.. automodapi:: Functions.breaks
+ :no-heading:
diff --git a/sphinx-doc/moduleAPIs/Functionschangepoints.rst b/sphinx-doc/moduleAPIs/Functionschangepoints.rst
new file mode 100644
index 0000000000000000000000000000000000000000..d3b545582d5403d065de1ef64f1ed96f65de29b8
--- /dev/null
+++ b/sphinx-doc/moduleAPIs/Functionschangepoints.rst
@@ -0,0 +1,5 @@
+changepoints
+============
+
+.. automodapi:: Functions.changepoints
+ :no-heading:
diff --git a/sphinx-doc/moduleAPIs/Functionsconstants.rst b/sphinx-doc/moduleAPIs/Functionsconstants.rst
new file mode 100644
index 0000000000000000000000000000000000000000..3aeb8feda3132a3eba0799d62a610ae1f9be9edd
--- /dev/null
+++ b/sphinx-doc/moduleAPIs/Functionsconstants.rst
@@ -0,0 +1,5 @@
+constants
+=========
+
+.. automodapi:: Functions.constants
+ :no-heading:
diff --git a/sphinx-doc/moduleAPIs/Functionscurvefit.rst b/sphinx-doc/moduleAPIs/Functionscurvefit.rst
new file mode 100644
index 0000000000000000000000000000000000000000..4e64814b5da5bfb1dc3fa3376473d36675601ab0
--- /dev/null
+++ b/sphinx-doc/moduleAPIs/Functionscurvefit.rst
@@ -0,0 +1,5 @@
+curvefit
+========
+
+.. automodapi:: Functions.curvefit
+ :no-heading:
diff --git a/sphinx-doc/moduleAPIs/Functionsdrift.rst b/sphinx-doc/moduleAPIs/Functionsdrift.rst
new file mode 100644
index 0000000000000000000000000000000000000000..6fff728821955cf745be0fb42274ac696508c46b
--- /dev/null
+++ b/sphinx-doc/moduleAPIs/Functionsdrift.rst
@@ -0,0 +1,5 @@
+drift
+=====
+
+.. automodapi:: Functions.drift
+ :no-heading:
diff --git a/sphinx-doc/moduleAPIs/Functionsflagtools.rst b/sphinx-doc/moduleAPIs/Functionsflagtools.rst
new file mode 100644
index 0000000000000000000000000000000000000000..d07f78c06d045ee97241a41bcd44a76d2fa81746
--- /dev/null
+++ b/sphinx-doc/moduleAPIs/Functionsflagtools.rst
@@ -0,0 +1,5 @@
+flagtools
+=========
+
+.. automodapi:: Functions.flagtools
+ :no-heading:
diff --git a/sphinx-doc/moduleAPIs/Functionsgeneric.rst b/sphinx-doc/moduleAPIs/Functionsgeneric.rst
new file mode 100644
index 0000000000000000000000000000000000000000..fa0f435dc27fdccca1fc907630bd59054fe4ebfd
--- /dev/null
+++ b/sphinx-doc/moduleAPIs/Functionsgeneric.rst
@@ -0,0 +1,5 @@
+generic
+=======
+
+.. automodapi:: Functions.generic
+ :no-heading:
diff --git a/sphinx-doc/moduleAPIs/Functionsinterpolation.rst b/sphinx-doc/moduleAPIs/Functionsinterpolation.rst
new file mode 100644
index 0000000000000000000000000000000000000000..b30ebf19c0dde054fdc82c8b3d1307e1e5ef3a57
--- /dev/null
+++ b/sphinx-doc/moduleAPIs/Functionsinterpolation.rst
@@ -0,0 +1,5 @@
+interpolation
+=============
+
+.. automodapi:: Functions.interpolation
+ :no-heading:
diff --git a/sphinx-doc/moduleAPIs/Functionsnoise.rst b/sphinx-doc/moduleAPIs/Functionsnoise.rst
new file mode 100644
index 0000000000000000000000000000000000000000..6a8a74657cd3554a40498b6048de354a8c77c1ea
--- /dev/null
+++ b/sphinx-doc/moduleAPIs/Functionsnoise.rst
@@ -0,0 +1,5 @@
+noise
+=====
+
+.. automodapi:: Functions.noise
+ :no-heading:
diff --git a/sphinx-doc/moduleAPIs/Functionsoutliers.rst b/sphinx-doc/moduleAPIs/Functionsoutliers.rst
new file mode 100644
index 0000000000000000000000000000000000000000..13dc3d806da548c1e5bbe62460746e69e1fb2057
--- /dev/null
+++ b/sphinx-doc/moduleAPIs/Functionsoutliers.rst
@@ -0,0 +1,5 @@
+outliers
+========
+
+.. automodapi:: Functions.outliers
+ :no-heading:
diff --git a/sphinx-doc/moduleAPIs/Functionspattern.rst b/sphinx-doc/moduleAPIs/Functionspattern.rst
new file mode 100644
index 0000000000000000000000000000000000000000..bd010b4d1d8f23305a6f380da07fcecae40bddcb
--- /dev/null
+++ b/sphinx-doc/moduleAPIs/Functionspattern.rst
@@ -0,0 +1,5 @@
+pattern
+=======
+
+.. automodapi:: Functions.pattern
+ :no-heading:
diff --git a/sphinx-doc/moduleAPIs/Functionsresampling.rst b/sphinx-doc/moduleAPIs/Functionsresampling.rst
new file mode 100644
index 0000000000000000000000000000000000000000..5e7e22367ad71274b16519fde5cb3f794e4990c1
--- /dev/null
+++ b/sphinx-doc/moduleAPIs/Functionsresampling.rst
@@ -0,0 +1,5 @@
+resampling
+==========
+
+.. automodapi:: Functions.resampling
+ :no-heading:
diff --git a/sphinx-doc/moduleAPIs/Functionsresidues.rst b/sphinx-doc/moduleAPIs/Functionsresidues.rst
new file mode 100644
index 0000000000000000000000000000000000000000..c97bfed02a2614d47ce1f25e9c0ce45e55a25e7e
--- /dev/null
+++ b/sphinx-doc/moduleAPIs/Functionsresidues.rst
@@ -0,0 +1,5 @@
+residues
+========
+
+.. automodapi:: Functions.residues
+ :no-heading:
diff --git a/sphinx-doc/moduleAPIs/Functionsrolling.rst b/sphinx-doc/moduleAPIs/Functionsrolling.rst
new file mode 100644
index 0000000000000000000000000000000000000000..8cd668265f234bfc1e7fb8705e4e4d16038a9bc6
--- /dev/null
+++ b/sphinx-doc/moduleAPIs/Functionsrolling.rst
@@ -0,0 +1,5 @@
+rolling
+=======
+
+.. automodapi:: Functions.rolling
+ :no-heading:
diff --git a/sphinx-doc/moduleAPIs/Functionssaqc.rst b/sphinx-doc/moduleAPIs/Functionssaqc.rst
new file mode 100644
index 0000000000000000000000000000000000000000..54c41273bafb912a4b31b9a6f00c662dab00ae01
--- /dev/null
+++ b/sphinx-doc/moduleAPIs/Functionssaqc.rst
@@ -0,0 +1,5 @@
+saqc
+====
+
+.. automodapi:: Functions.saqc
+ :no-heading:
diff --git a/sphinx-doc/moduleAPIs/Functionsscores.rst b/sphinx-doc/moduleAPIs/Functionsscores.rst
new file mode 100644
index 0000000000000000000000000000000000000000..c3867522989e9abfff6872c9fd8b5da042d5f7af
--- /dev/null
+++ b/sphinx-doc/moduleAPIs/Functionsscores.rst
@@ -0,0 +1,5 @@
+scores
+======
+
+.. automodapi:: Functions.scores
+ :no-heading:
diff --git a/sphinx-doc/moduleAPIs/Functionstools.rst b/sphinx-doc/moduleAPIs/Functionstools.rst
new file mode 100644
index 0000000000000000000000000000000000000000..42989321f245e2f5328683e3e709e4f87bb2a5bb
--- /dev/null
+++ b/sphinx-doc/moduleAPIs/Functionstools.rst
@@ -0,0 +1,5 @@
+tools
+=====
+
+.. automodapi:: Functions.tools
+ :no-heading:
diff --git a/sphinx-doc/moduleAPIs/Functionstransformation.rst b/sphinx-doc/moduleAPIs/Functionstransformation.rst
new file mode 100644
index 0000000000000000000000000000000000000000..8b20b6ebc1676c2b4523f92add02135f8306e8e2
--- /dev/null
+++ b/sphinx-doc/moduleAPIs/Functionstransformation.rst
@@ -0,0 +1,5 @@
+transformation
+==============
+
+.. automodapi:: Functions.transformation
+ :no-heading:
diff --git a/sphinx-doc/moduleAPIs/saqcClass.rst b/sphinx-doc/moduleAPIs/saqcClass.rst
new file mode 100644
index 0000000000000000000000000000000000000000..97f70bd99dcf779343cc68156707cfb3f91598a7
--- /dev/null
+++ b/sphinx-doc/moduleAPIs/saqcClass.rst
@@ -0,0 +1,5 @@
+
+SaQC
+====
+
+* :py:class:`saqc.SaQC <Core.Core.SaQC>`
\ No newline at end of file
diff --git a/sphinx-doc/moduleAPIs/saqcConstants.rst b/sphinx-doc/moduleAPIs/saqcConstants.rst
new file mode 100644
index 0000000000000000000000000000000000000000..3c6636edbcd85673ae95ccc19e41728fa231034f
--- /dev/null
+++ b/sphinx-doc/moduleAPIs/saqcConstants.rst
@@ -0,0 +1,6 @@
+
+saqc
+====
+
+.. automodapi:: saqc.constants
+ :include-all-objects:
diff --git a/sphinx-doc/moduleAPIs/saqcInit.rst b/sphinx-doc/moduleAPIs/saqcInit.rst
new file mode 100644
index 0000000000000000000000000000000000000000..c0fc167dcc27967152d8793e1e30ebe8f0353fa8
--- /dev/null
+++ b/sphinx-doc/moduleAPIs/saqcInit.rst
@@ -0,0 +1,7 @@
+
+saqc
+====
+
+UFZ proudly presents:
+
+.. automodapi:: saqc
\ No newline at end of file
diff --git a/sphinx-doc/moduleAPIs/saqcVisitor.rst b/sphinx-doc/moduleAPIs/saqcVisitor.rst
new file mode 100644
index 0000000000000000000000000000000000000000..3b15895e0979ecfce96f57ed803bf4146d7a3ddb
--- /dev/null
+++ b/sphinx-doc/moduleAPIs/saqcVisitor.rst
@@ -0,0 +1,6 @@
+
+saqc
+====
+
+.. automodapi:: saqc.core.visitor
+ :include-all-objects:
\ No newline at end of file
diff --git a/sphinx-doc/requirements_sphinx.txt b/sphinx-doc/requirements_sphinx.txt
index eb84550313ea402f974e97ab1c7cb19b4f0bef06..3ebf7c5bdf83131dfeadb917893b5678820d50e2 100644
--- a/sphinx-doc/requirements_sphinx.txt
+++ b/sphinx-doc/requirements_sphinx.txt
@@ -4,3 +4,4 @@ sphinx-automodapi
sphinxcontrib-fulltoc
sphinx-markdown-tables
m2r
+jupyter-sphinx
diff --git a/sphinx-doc/ressources/data/SoilMoisture.csv b/sphinx-doc/ressources/data/SoilMoisture.csv
new file mode 100644
index 0000000000000000000000000000000000000000..1b8e9a3ee3d218b28cad78a3cbaa536f590e456c
--- /dev/null
+++ b/sphinx-doc/ressources/data/SoilMoisture.csv
@@ -0,0 +1,10608 @@
+,SoilMoisture
+2021-01-01 00:09:07,23.42970085144043
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diff --git a/sphinx-doc/ressources/data/hydro_config.csv b/sphinx-doc/ressources/data/hydro_config.csv
new file mode 100644
index 0000000000000000000000000000000000000000..3589f46f9ccb2d2d58defb85ed6da900ccd165f1
--- /dev/null
+++ b/sphinx-doc/ressources/data/hydro_config.csv
@@ -0,0 +1,26 @@
+varname ; test
+#---------------;---------------------------------------
+level_raw ; flagRange(min=0)
+water_temp_raw ; flagRange(min=-1)
+sak254_raw ; flagRange(min=0, max=70)
+level_raw ; linear(target='level', freq='15min')
+water_temp_raw ; linear(target='water_temp', freq='15min')
+sak254_raw ; linear(target='sac254, freq='15min')
+sak254 ; correctDrift(target='sak254_corr', maintenance_field='maint', model='exponential')
+sak254_corr ; flagManual(mdata='maint')
+level ; transform(target='level_norm', func='zNorm', freq='20D')
+sac254_corr ; transform(target='sac254_norm', func='zNorm', freq='20D')
+water_temp ; transform('water_temp_norm', func='zNorm', freq='20D')
+level_norm, sac_corr_norm, water_temp_norm ; scores.assignKNNScore(target='kNN_scores', freq='20D')
+kNN_scores ; flagByStray(freq='20D')
+level ; genericFlag(func=lambda kNNscores: isFlagged(kNNscores))
+sac25_corr ; genericFlag(func=lambda kNNscores: isFlagged(kNNscores))
+water_temp ; genericFlag(func=lambda kNNscores: isFlagged(kNNscores))
+level ; concatFlags(target='level_raw', 'inverse_interpolation')
+sac_corr ; concatFlags(target='sac254_raw', 'inverse_interpolation')
+water_temp ; concatFlags(target='water_temp_raw', 'inverse_interpolation')
+
+
+
+
+
diff --git a/sphinx-doc/ressources/data/hydro_data.csv b/sphinx-doc/ressources/data/hydro_data.csv
new file mode 100644
index 0000000000000000000000000000000000000000..c27a60f4493e06bcddbe1a10894691bdd1be7968
--- /dev/null
+++ b/sphinx-doc/ressources/data/hydro_data.csv
@@ -0,0 +1,70164 @@
+Timestamp,sac254_raw,level_raw,water_temp_raw
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diff --git a/sphinx-doc/ressources/data/hydro_maint.csv b/sphinx-doc/ressources/data/hydro_maint.csv
new file mode 100644
index 0000000000000000000000000000000000000000..57a0a889d75760387f03c9a4ba1ae8b6afe25d88
--- /dev/null
+++ b/sphinx-doc/ressources/data/hydro_maint.csv
@@ -0,0 +1,37 @@
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+2016-03-08 09:30:00,2016-03-08 10:30:00
+2016-03-15 12:00:00,2016-03-15 16:30:00
+2016-03-21 11:00:00,2016-03-21 14:00:00
+2016-04-05 07:00:00,2016-04-05 07:55:00
+2016-05-02 15:30:00,2016-05-02 16:30:00
+2016-05-03 15:25:00,2016-05-03 16:35:00
+2016-05-31 07:00:00,2016-05-31 08:25:00
+2016-06-02 13:00:00,2016-06-02 15:50:00
+2016-06-28 07:30:00,2016-06-28 08:45:00
+2016-06-28 09:30:00,2016-06-28 10:30:00
+2016-07-25 08:45:00,2016-07-25 10:00:00
+2016-08-22 08:15:00,2016-08-22 09:20:00
+2016-09-20 07:00:00,2016-09-20 09:15:00
+2016-10-25 08:15:00,2016-10-25 09:50:00
+2016-11-08 09:40:00,2016-11-08 11:50:00
+2016-11-14 09:35:00,2016-11-14 10:40:00
+2016-12-13 15:05:00,2016-12-13 16:50:00
+2017-01-10 08:05:00,2017-01-10 09:15:00
+2017-02-06 09:20:00,2017-02-06 11:00:00
+2017-03-07 08:40:00,2017-03-07 09:40:00
+2017-04-05 07:50:00,2017-04-05 09:20:00
+2017-04-12 07:30:00,2017-04-12 09:45:00
+2017-05-02 13:20:00,2017-05-02 13:30:00
+2017-05-30 07:20:00,2017-05-30 08:30:00
+2017-06-14 08:25:00,2017-06-14 09:30:00
+2017-06-27 07:30:00,2017-06-27 08:40:00
+2017-07-24 08:00:00,2017-07-24 09:00:00
+2017-09-20 14:00:00,2017-09-20 14:35:00
+2017-10-17 08:55:00,2017-10-17 10:20:00
+2017-11-14 15:30:00,2017-11-14 16:20:00
+2017-11-27 09:10:00,2017-11-27 10:10:00
+2017-12-12 14:10:00,2017-12-12 14:50:00
diff --git a/sphinx-doc/ressources/images/Representative/DifferentFlags.png b/sphinx-doc/ressources/images/Representative/DifferentFlags.png
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diff --git a/sphinx-doc/ressources/images/Representative/MultivarSAC.png b/sphinx-doc/ressources/images/Representative/MultivarSAC.png
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diff --git a/sphinx-doc/ressources/images/Representative/ProcessingDrift.png b/sphinx-doc/ressources/images/Representative/ProcessingDrift.png
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diff --git a/sphinx-doc/ressources/images/cbook_incidents_scoresUnflagged.png b/sphinx-doc/ressources/images/cbook_incidents_scoresUnflagged.png
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diff --git a/sphinx-doc/ressources/images/cbooks_SoilMoisture.png b/sphinx-doc/ressources/images/cbooks_SoilMoisture.png
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diff --git a/sphinx-doc/ressources/images/cbooks_incidentsOverflagged.png b/sphinx-doc/ressources/images/cbooks_incidentsOverflagged.png
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diff --git a/sphinx-doc/ressources/images/cbooks_incidents_correctFlagged.png b/sphinx-doc/ressources/images/cbooks_incidents_correctFlagged.png
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diff --git a/sphinx-doc/ressources/images/cbooks_incidents_residues.png b/sphinx-doc/ressources/images/cbooks_incidents_residues.png
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diff --git a/sphinx-doc/ressources/images/cbooks_incidents_scores.png b/sphinx-doc/ressources/images/cbooks_incidents_scores.png
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diff --git a/sphinx-doc/ressources/images/hydro_usecase/BeforeAfter_sac254.png b/sphinx-doc/ressources/images/hydro_usecase/BeforeAfter_sac254.png
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diff --git a/sphinx-doc/ressources/images/hydro_usecase/Before_After_sac254_onePlot.png b/sphinx-doc/ressources/images/hydro_usecase/Before_After_sac254_onePlot.png
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diff --git a/sphinx-doc/ressources/images/hydro_usecase/Step1_sac254_maintenanceFlags.png b/sphinx-doc/ressources/images/hydro_usecase/Step1_sac254_maintenanceFlags.png
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diff --git a/sphinx-doc/ressources/images/hydro_usecase/Step2_sac254_driftcorrected_FlagsCleared.png b/sphinx-doc/ressources/images/hydro_usecase/Step2_sac254_driftcorrected_FlagsCleared.png
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diff --git a/sphinx-doc/ressources/images/hydro_usecase/Step2_sac254_driftcorrected_withFlags.png b/sphinx-doc/ressources/images/hydro_usecase/Step2_sac254_driftcorrected_withFlags.png
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diff --git a/sphinx-doc/ressources/images/hydro_usecase/Step3_kNNscores.png b/sphinx-doc/ressources/images/hydro_usecase/Step3_kNNscores.png
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diff --git a/sphinx-doc/ressources/images/hydro_usecase/Step4_sac254_cleared.png b/sphinx-doc/ressources/images/hydro_usecase/Step4_sac254_cleared.png
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diff --git a/sphinx-doc/ressources/images/hydro_usecase/Step4_sac254_withSTRAYFalgs.png b/sphinx-doc/ressources/images/hydro_usecase/Step4_sac254_withSTRAYFalgs.png
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diff --git a/sphinx-doc/ressources/images/hydro_usecase/level_raw.png b/sphinx-doc/ressources/images/hydro_usecase/level_raw.png
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diff --git a/sphinx-doc/ressources/images/hydro_usecase/sac254_flagged_noMaintflags.png b/sphinx-doc/ressources/images/hydro_usecase/sac254_flagged_noMaintflags.png
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diff --git a/sphinx-doc/ressources/images/hydro_usecase/sac254_raw.png b/sphinx-doc/ressources/images/hydro_usecase/sac254_raw.png
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diff --git a/sphinx-doc/ressources/images/hydro_usecase/water_temp_raw.png b/sphinx-doc/ressources/images/hydro_usecase/water_temp_raw.png
new file mode 100644
index 0000000000000000000000000000000000000000..6db9141146cca24dd938a5b28dbf04ead2283f19
Binary files /dev/null and b/sphinx-doc/ressources/images/hydro_usecase/water_temp_raw.png differ
diff --git a/sphinx-doc/ressources/images/tutorial_api_1.png b/sphinx-doc/ressources/images/tutorial_api_1.png
new file mode 100644
index 0000000000000000000000000000000000000000..6089baa9abbb22170896a08f2fe2ac9ccada4bbb
Binary files /dev/null and b/sphinx-doc/ressources/images/tutorial_api_1.png differ
diff --git a/sphinx-doc/make_doc_module.py b/sphinx-doc/scripts/make_doc_module.py
similarity index 65%
rename from sphinx-doc/make_doc_module.py
rename to sphinx-doc/scripts/make_doc_module.py
index f85db54991fba0a370d01ddaa2a366d990d7e0c8..e68178771a1895ab4463e221eba82752dde8cd22 100644
--- a/sphinx-doc/make_doc_module.py
+++ b/sphinx-doc/scripts/make_doc_module.py
@@ -9,13 +9,6 @@ import pickle
new_line_re = "(\r\n|[\r\n])"
-doc_mod_structure = {
- "BasicFlagging": ["outliers.flagRange", "breaks.flagMissing"],
- "BasicFlagging_dcstring": "",
- "AdvancedFlagging": ["pattern.flagPatternByDTW", "outliers.flagOffset"],
- "AdvancedFlagging_dcstring": "",
-}
-
def rm_section(dcstring, section, _return_section=False):
"""
@@ -124,7 +117,9 @@ def make_doc_module(targetpath, func_dict, doc_mod_structure):
d for d in doc_mod_structure.keys() if not re.search("_dcstring$", d)
]:
with open(os.path.join(targetpath, f"{doc_mod}.py"), "w+") as f:
- mod_string = ['"""\n' + doc_mod_structure[doc_mod + "_dcstring"] + '\n"""']
+ mod_string = [
+ '"""\n' + doc_mod_structure.get(doc_mod + "_dcstring", "") + '\n"""'
+ ]
mod_funcs = doc_mod_structure[doc_mod]
for func in mod_funcs:
mod_string.append(func_dict[func][0])
@@ -140,12 +135,78 @@ def make_doc_module(targetpath, func_dict, doc_mod_structure):
mod_string.append("")
f.write("\n".join(mod_string))
- with open(os.path.join(targetpath, "module_dict.pkl"), "wb+") as file:
- pickle.dump(doc_mod_structure, file)
+ return 0
+
+
+def make_doc_core(sphinxroot, func_dict, doc_mod_structure):
+ targetfolder = os.path.join(sphinxroot, "Core")
+ coresource = os.path.join(sphinxroot, os.path.normpath("saqc/core/core.py"))
+
+ # parse real core.py
+ with open(coresource) as f:
+ corelines = f.readlines()
+
+ # find SaQC class def
+ coreast = ast.parse("".join(corelines))
+ startline = None
+ endline = None
+ for node in coreast.body:
+ if isinstance(node, ast.ClassDef):
+ if node.name == "SaQC":
+ startline = node.lineno
+ elif startline and (not endline):
+ endline = node.lineno
+
+ start = corelines[: endline - 1]
+ end = corelines[endline - 1 :]
+ tab = " "
+ for doc_mod in [
+ d for d in doc_mod_structure.keys() if not re.search("_dcstring$", d)
+ ]:
+ with open(os.path.join(targetfolder, f"Core.py"), "w+") as f:
+ mod_string = []
+ mod_funcs = doc_mod_structure[doc_mod]
+ for func in mod_funcs:
+ def_string = func_dict[func][0]
+ i_pos = re.match("def [^ ]*\(", def_string).span()[-1]
+ def_string = def_string[:i_pos] + "self, " + def_string[i_pos:]
+ def_string = tab + def_string
+ mod_string.append(def_string)
+ mod_string.append(2 * tab + '"""')
+ # indent the docstring:
+ indented_doc_string = "\n".join(
+ [2 * tab + f"{l}" for l in func_dict[func][1].splitlines()]
+ )
+ mod_string.append(indented_doc_string)
+ mod_string.append(2 * tab + '"""')
+ mod_string.append(2 * tab + "pass")
+ mod_string.append("")
+ mod_string.append("")
+
+ newcore = (
+ "".join(start) + "\n" + "\n".join(mod_string) + "\n" + "".join(end)
+ )
+ f.write(newcore)
return 0
+def makeModuleAPIs(modules, folder_path="moduleAPIs", pck_path="Functions"):
+ f_path = os.path.abspath(folder_path)
+ for m in modules:
+ lines = []
+ lines += [m]
+ lines += ["=" * len(m)]
+ lines += [""]
+ lines += [f".. automodapi:: {pck_path}.{m}"]
+ lines += [" " * 3 + ":no-heading:"]
+ with open(os.path.join(f_path, f"{pck_path}{m}.rst"), "w") as f:
+ for l in lines:
+ f.write(l + "\n")
+
+ pass
+
+
@click.command()
@click.option(
"-p",
@@ -176,13 +237,16 @@ def make_doc_module(targetpath, func_dict, doc_mod_structure):
"--mode",
type=str,
required=True,
- default="intro_doc",
- help="either 'intro_doc' or 'module_doc'.",
+ default="Functions",
+ help="either 'Functions' or 'module_doc' or 'registered_doc' (All but 'Functions' is deprecated and will be remove "
+ "soon).",
)
def main(pckpath, targetpath, sphinxroot, mode):
root_path = os.path.abspath(sphinxroot)
pkg_path = os.path.join(root_path, pckpath)
targetpath = os.path.join(root_path, targetpath)
+ coretrg = os.path.join(sphinxroot, "Core")
+
modules = []
# collect modules
for _, modname, _ in pkgutil.walk_packages(path=[pkg_path], onerror=lambda x: None):
@@ -193,30 +257,34 @@ def main(pckpath, targetpath, sphinxroot, mode):
shutil.rmtree(targetpath)
os.makedirs(targetpath, exist_ok=True)
+ if os.path.isdir(coretrg):
+ shutil.rmtree(coretrg)
+ os.makedirs(coretrg, exist_ok=True)
+
# parse all the functions
module_paths = [os.path.join(pkg_path, f"{m}.py") for m in modules]
mod_dict = parse_module_dcstrings(module_paths)
+ mod_dict = dict(
+ zip([k + "_dcstring" for k in mod_dict.keys()], list(mod_dict.values()))
+ )
func_dict = parse_func_dcstrings(module_paths)
- if mode == "intro_doc":
- make_doc_module(targetpath, func_dict, doc_mod_structure)
- if mode == "registered_doc":
- doc_struct = {}
- for dm in func_dict.keys():
- module = func_dict[dm][2]
- if module:
- if module in doc_struct.keys():
- doc_struct[module].append(dm)
- else:
- doc_struct[module] = [dm]
- doc_struct[module + "_dcstring"] = mod_dict[module]
- make_doc_module(targetpath, func_dict, doc_struct)
- if mode == "module_doc":
+ if mode == "Functions":
+ # module docs
doc_struct = {m: [] for m in modules}
for dm in func_dict.keys():
module = re.search("([^ .]*)\.[^ ]*$", dm).group(1)
doc_struct[module].append(dm)
+ makeModuleAPIs(list(doc_struct.keys()))
+ doc_struct.update(mod_dict)
make_doc_module(targetpath, func_dict, doc_struct)
+ # complete docs
+ doc_mod_structure = {"saqc": [f for f in func_dict.keys()], "saqc_dcstring": ""}
+ makeModuleAPIs(["saqc"])
+ make_doc_module(targetpath, func_dict, doc_mod_structure)
+
+ make_doc_core(root_path, func_dict, doc_mod_structure)
+
if __name__ == "__main__":
main()
diff --git a/sphinx-doc/make_doc_rst.py b/sphinx-doc/scripts/make_doc_rst.py
similarity index 100%
rename from sphinx-doc/make_doc_rst.py
rename to sphinx-doc/scripts/make_doc_rst.py
diff --git a/sphinx-doc/scripts/make_env_tab.py b/sphinx-doc/scripts/make_env_tab.py
new file mode 100644
index 0000000000000000000000000000000000000000..af92bcb06e0bbce22c70af284b8b02f7d4b5a69d
--- /dev/null
+++ b/sphinx-doc/scripts/make_env_tab.py
@@ -0,0 +1,96 @@
+import ast
+import os
+import click
+import pkgutil
+import shutil
+import re
+from collections import OrderedDict
+import pickle
+
+
+@click.command()
+@click.option(
+ "-p",
+ "--envpath",
+ type=str,
+ required=True,
+ default="saqc/constants.py",
+ help="Relative path to the module containing the env dict (relative to sphinx root).",
+)
+@click.option(
+ "-t",
+ "--targetpath",
+ type=str,
+ required=True,
+ default="sphinx-doc/environment",
+ help="Output path to contain configEnv.rst (relative to sphinx root).",
+)
+@click.option(
+ "-sr",
+ "--sphinxroot",
+ type=str,
+ required=True,
+ default="..",
+ help="Relative path to the sphinx root.",
+)
+def main(envpath, targetpath, sphinxroot):
+ root_path = os.path.abspath(sphinxroot)
+ source_path = os.path.join(root_path, envpath)
+ target_path = os.path.join(root_path, targetpath)
+
+ with open(source_path) as f:
+ lines = f.readlines()
+
+ # get ENV definition linenumber
+ lino_st = None
+ lino_end = None
+ nodes = ast.parse("".join(lines))
+ for node in nodes.body:
+ if lino_st:
+ lino_end = node.lineno
+ break
+ if isinstance(node, ast.Assign):
+ if isinstance(node.targets[0], ast.Name):
+ if node.targets[0].id == "ENVIRONMENT":
+ lino_st = node.lineno
+
+ env_dict = lines[slice(lino_st, lino_end)]
+ entry = 0
+ val_dict = {}
+ doc_dict = {}
+ for line in env_dict:
+ val = re.match(' *(?P<Key>".*"):', line)
+ doc = re.match(" *#(?P<Comment>.*)", line)
+ if val:
+ if len(val.groups()) > 0:
+ entry += 1
+ val_dict.update({entry: val["Key"]})
+ if doc:
+ if len(doc.groups()) > 0:
+ doc_dict.update({entry: doc_dict.get(entry, "") + doc["Comment"]})
+
+ # make heading
+ heading = "Config file Environment"
+ lines = []
+ lines += [heading, len(heading) * "=", ""]
+ # make table directive
+ tab = " " * 3
+ new_row = tab + "* - "
+ new_col = tab + " " + " - "
+ # directive:
+ lines += [".. list-table::", tab + ":header-rows: 1", ""]
+ # table header:
+ lines += [new_row + "Env-Value"]
+ lines += [new_col + "Env-Doc"]
+ # table body:
+ for k in val_dict.keys():
+ lines += [new_row + val_dict[k]]
+ lines += [new_col + doc_dict.get(k, "")]
+
+ with open(os.path.join(target_path, "configEnv.rst"), "w") as f:
+ for s in lines:
+ f.write(s + "\n")
+
+
+if __name__ == "__main__":
+ main()
diff --git a/sphinx-doc/make_md_to_rst.py b/sphinx-doc/scripts/make_md_to_rst.py
similarity index 100%
rename from sphinx-doc/make_md_to_rst.py
rename to sphinx-doc/scripts/make_md_to_rst.py
diff --git a/tests/core/test_core.py b/tests/core/test_core.py
index e64f6b9a5f0317d136f16e9328b2e921f339227d..5662ba2e5d5c973871f5574df46c01f8516d1fb3 100644
--- a/tests/core/test_core.py
+++ b/tests/core/test_core.py
@@ -5,18 +5,17 @@ import pytest
import numpy as np
import pandas as pd
-
-from saqc.constants import *
-from saqc.core import initFlagsLike
-from saqc import SaQC, flagging
+import saqc
+from saqc.core import initFlagsLike, SaQC, register
+from saqc.core.flags import Flags
+from saqc.core.register import processing
from tests.common import initData, flagAll
-
OPTIONAL = [False, True]
-flagging(masking="field")(flagAll)
+register(mask=["field"], demask=["field"], squeeze=["field"])(flagAll)
@pytest.fixture
@@ -31,21 +30,21 @@ def flags(data, optional):
def test_errorHandling(data):
- @flagging(masking="field")
+ @processing()
def raisingFunc(data, field, flags, **kwargs):
raise TypeError
var1 = data.columns[0]
with pytest.raises(TypeError):
- SaQC(data).raisingFunc(var1).getResult()
+ SaQC(data).raisingFunc(var1)
def test_duplicatedVariable():
data = initData(1)
var1 = data.columns[0]
- pdata, pflags = SaQC(data).flagDummy(var1).getResult()
+ pflags = SaQC(data).flagDummy(var1).result.flags
if isinstance(pflags.columns, pd.MultiIndex):
cols = pflags.columns.get_level_values(0).drop_duplicates()
@@ -54,33 +53,108 @@ def test_duplicatedVariable():
assert (pflags.columns == [var1]).all()
-def test_sourceTarget():
+@pytest.mark.parametrize("optional", OPTIONAL)
+def test_dtypes(data, flags):
+ """
+ Test if the categorical dtype is preserved through the core functionality
+ """
+ flags = initFlagsLike(data)
+ flags_raw = flags.toDios()
+ var1, var2 = data.columns[:2]
+
+ pflags = SaQC(data, flags=flags_raw).flagAll(var1).flagAll(var2).result.flags_raw
+
+ for c in pflags.columns:
+ assert pflags[c].dtype == flags[c].dtype
+
+
+def test_copy(data):
+ qc = saqc.SaQC(data)
+
+ qc = qc.flagRange("var1").flagRange("var1", min=0, max=0)
+
+ deep = qc.copy(deep=True)
+ shallow = qc.copy(deep=False)
+
+ for copy in [deep, shallow]:
+ assert copy is not qc
+ assert copy._called is not qc._called
+ assert copy._scheme is not qc._scheme
+ assert copy._attrs is not qc._attrs
+
+ assert copy._data is not qc._data
+ assert copy._flags is not qc._flags
+
+ assert copy._data._data is not qc._data._data
+ assert copy._flags._data is not qc._flags._data
+
+ # underling data copied
+ assert deep._data._data.iloc[0] is not qc._data._data.iloc[0]
+ assert (
+ deep._flags._data["var1"].hist.index is not qc._flags._data["var1"].hist.index
+ )
+
+ # underling data NOT copied
+ assert shallow._data._data.iloc[0] is qc._data._data.iloc[0]
+ assert shallow._flags._data["var1"].hist.index is qc._flags._data["var1"].hist.index
+
+
+def test_sourceTargetCopy():
"""
- test implicit assignments
+ test implicit copies
"""
data = initData(1)
var1 = data.columns[0]
target = "new"
- pdata, pflags = SaQC(data).flagAll(field=var1, target=target).getResult(raw=True)
+ @register(mask=["field"], demask=["field"], squeeze=["field"], handles_target=False)
+ def flagTarget(data, field, flags, **kwargs):
+ assert "target" not in kwargs
+ return data, flags
- assert (pdata[var1] == pdata[target]).all(axis=None)
- assert all(pflags[var1] == UNFLAGGED)
- assert all(pflags[target] > UNFLAGGED)
+ qc = SaQC(data, flags=Flags({var1: pd.Series(127.0, index=data[var1].index)}))
+ qc = qc.flagTarget(field=var1, target=target)
+ assert (qc.data[var1] == qc.data[target]).all(axis=None)
+ assert all(qc.flags[var1] == qc.flags[target])
-@pytest.mark.parametrize("optional", OPTIONAL)
-def test_dtypes(data, flags):
+
+def test_sourceTargetNoCopy():
"""
- Test if the categorical dtype is preserved through the core functionality
+ test bypassing of the imlpicit copy machiners
"""
- flags = initFlagsLike(data)
- flags_raw = flags.toDios()
- var1, var2 = data.columns[:2]
+ data = initData(1)
+ var1 = data.columns[0]
+ target = "new"
- pdata, pflags = (
- SaQC(data, flags=flags_raw).flagAll(var1).flagAll(var2).getResult(raw=True)
- )
+ @register(mask=["field"], demask=["field"], squeeze=["field"], handles_target=True)
+ def flagField(data, field, flags, **kwargs):
+ assert "target" in kwargs
+ assert "target" not in data
+ assert "target" not in flags
+ return data, flags
- for c in pflags.columns:
- assert pflags[c].dtype == flags[c].dtype
+ SaQC(data).flagField(field=var1, target=target)
+
+
+def test_sourceTargetMultivariate():
+ """
+ test bypassing of the imlpicit copy machiners
+ """
+ data = initData(3)
+
+ @register(
+ mask=["field"],
+ demask=["field"],
+ squeeze=["field"],
+ handles_target=True,
+ multivariate=True,
+ )
+ def flagMulti(data, field, flags, **kwargs):
+ assert "target" in kwargs
+ assert "target" not in data
+ assert "target" not in flags
+ assert field == kwargs["target"]
+ return data, flags
+
+ SaQC(data).flagMulti(field=data.columns, target=data.columns)
diff --git a/tests/core/test_flags.py b/tests/core/test_flags.py
index 7bc08c00740d3577b8bd4f3f1ed90948b4cb3f5a..1e66ce62f42ba99f0869d5c77859e7dc567d50c2 100644
--- a/tests/core/test_flags.py
+++ b/tests/core/test_flags.py
@@ -72,16 +72,20 @@ def test_copy(data: Union[pd.DataFrame, dios.DictOfSeries, Dict[str, pd.Series]]
assert isinstance(copy, Flags)
assert copy is not flags
assert copy._data is not flags._data
+ for c in copy.columns:
+ assert copy._data[c] is not flags._data[c]
is_equal(copy, flags)
assert deep is not shallow
is_equal(deep, shallow)
+ # the underling series data is the same
for c in shallow.columns:
- assert shallow._data[c] is flags._data[c]
+ assert shallow._data[c].index is flags._data[c].index
+ # the underling series data was copied
for c in deep.columns:
- assert deep._data[c] is not flags._data[c]
+ assert deep._data[c].index is not flags._data[c].index
@pytest.mark.parametrize("data", data)
diff --git a/tests/core/test_history.py b/tests/core/test_history.py
index 6ced94eb4f24040fcea733acf45bd1f03f5148a4..6cfcffc336dd968329e50f7a728351ad0781d7dd 100644
--- a/tests/core/test_history.py
+++ b/tests/core/test_history.py
@@ -124,54 +124,63 @@ def test_copy(data):
deep = hist.copy(deep=True)
# checks
-
for copy in [deep, shallow]:
check_invariants(copy)
assert copy is not hist
+ assert copy.hist is not hist.hist
+ assert copy.meta is not hist.meta
assert is_equal(copy, hist)
assert deep is not shallow
assert is_equal(deep, shallow)
- assert deep.hist is not hist.hist
- assert deep.meta is not hist.meta
-
- assert shallow.hist is hist.hist
- assert shallow.meta is hist.meta
+ # underling pandas data was only copied with deep=True
+ assert shallow.hist.index is hist.hist.index
+ assert deep.hist.index is not hist.hist.index
+@pytest.mark.parametrize("copy", [True, False])
@pytest.mark.parametrize("data", data + [None])
-def test_reindex_trivial_cases(data):
+def test_reindex_trivial_cases(data, copy):
df = pd.DataFrame(data, dtype=float)
orig = dummyHistory(hist=df)
# checks
for index in [df.index, pd.Index([])]:
- hist = orig.copy()
- ref = hist.reindex(index)
- assert ref is hist # check if working inplace
+ ref = orig.copy()
+ hist = ref.reindex(index, copy=copy)
+ if copy:
+ assert hist is not ref
+ else:
+ assert hist is ref
check_invariants(hist)
+@pytest.mark.parametrize("copy", [True, False])
@pytest.mark.parametrize("data", data + [None])
-def test_reindex_missing_indicees(data):
+def test_reindex_missing_indicees(data, copy):
df = pd.DataFrame(data, dtype=float)
- hist = dummyHistory(hist=df)
+ orig = dummyHistory(hist=df)
index = df.index[1:-1]
- # checks
- ref = hist.reindex(index)
- assert ref is hist # check if working inplace
+ hist = orig.reindex(index, copy=copy)
+ if copy:
+ assert hist is not orig
+ else:
+ assert hist is orig
check_invariants(hist)
+@pytest.mark.parametrize("copy", [True, False])
@pytest.mark.parametrize("data", data + [None])
-def test_reindex_extra_indicees(data):
+def test_reindex_extra_indicees(data, copy):
df = pd.DataFrame(data, dtype=float)
- hist = dummyHistory(hist=df)
+ orig = dummyHistory(hist=df)
index = df.index.append(pd.Index(range(len(df.index), len(df.index) + 5)))
- # checks
- ref = hist.reindex(index)
- assert ref is hist # check if working inplace
+ hist = orig.reindex(index, copy=copy)
+ if copy:
+ assert hist is not orig
+ else:
+ assert hist is orig
check_invariants(hist)
diff --git a/tests/core/test_reader.py b/tests/core/test_reader.py
index e13f2976f97876e957c8640ac4e5680543b857e7..a1ddc8c5784b96a7dcfe50b4d51f4fe849ee1ef4 100644
--- a/tests/core/test_reader.py
+++ b/tests/core/test_reader.py
@@ -8,8 +8,7 @@ import dios
from pathlib import Path
from saqc.core.reader import fromConfig, readFile
-from saqc.core.register import FUNC_MAP, flagging
-from saqc.constants import UNTOUCHED
+from saqc.core.register import FUNC_MAP, register, flagging
from tests.common import initData, writeIO
@@ -23,7 +22,7 @@ def test_packagedConfig():
path = Path(__file__).parents[2] / "ressources/data"
- config_path = path / "config_ci.csv"
+ config_path = path / "config.csv"
data_path = path / "data.csv"
data = pd.read_csv(
@@ -32,7 +31,7 @@ def test_packagedConfig():
parse_dates=True,
)
saqc = fromConfig(config_path, data)
- saqc.getResult()
+ saqc.result._validate()
def test_variableRegex(data):
@@ -42,17 +41,27 @@ def test_variableRegex(data):
("'.*'", data.columns),
("'var(1|2)'", [c for c in data.columns if c[-1] in ("1", "2")]),
("'var[12]'", [c for c in data.columns if c[-1] in ("1", "2")]),
- ("var[12]", ["var[12]"]), # not quoted -> not a regex
('".*3"', [c for c in data.columns if c[-1] == "3"]),
]
for regex, expected in tests:
fobj = writeIO(header + "\n" + f"{regex} ; flagDummy()")
saqc = fromConfig(fobj, data=data)
- result = [field for field, _ in saqc.called]
+ result = [field for field, _ in saqc._called]
+ assert np.all(result == expected)
+
+ tests = [
+ ("var[12]", ["var[12]"]), # not quoted -> not a regex
+ ]
+ for regex, expected in tests:
+ fobj = writeIO(header + "\n" + f"{regex} ; flagDummy()")
+ with pytest.warns(RuntimeWarning):
+ saqc = fromConfig(fobj, data=data)
+ result = [field for field, _ in saqc._called]
assert np.all(result == expected)
+@pytest.mark.filterwarnings("ignore::RuntimeWarning")
def test_inlineComments(data):
"""
adresses issue #3
@@ -63,11 +72,11 @@ def test_inlineComments(data):
"""
saqc = fromConfig(writeIO(config), data)
- _, func = saqc.called[0]
+ _, func = saqc._called[0]
assert func[0] == FUNC_MAP["flagDummy"]
-def test_configReaderLineNumbers(data):
+def test_configReaderLineNumbers():
config = f"""
varname ; test
#temp1 ; flagDummy()
@@ -84,6 +93,7 @@ def test_configReaderLineNumbers(data):
assert (planned.index == expected).all()
+@pytest.mark.filterwarnings("ignore::RuntimeWarning")
def test_configFile(data):
# check that the reader accepts different whitespace patterns
@@ -107,9 +117,9 @@ def test_configChecks(data):
var1, _, var3, *_ = data.columns
- @flagging(masking="none")
+ @flagging()
def flagFunc(data, field, flags, arg, opt_arg=None, **kwargs):
- flags[:, field] = UNTOUCHED
+ flags[:, field] = np.nan
return data, flags
header = f"varname;test"
@@ -123,7 +133,7 @@ def test_configChecks(data):
for test, expected in tests:
fobj = writeIO(header + "\n" + test)
with pytest.raises(expected):
- fromConfig(fobj, data=data).evaluate()
+ fromConfig(fobj, data=data)
def test_supportedArguments(data):
@@ -133,9 +143,9 @@ def test_supportedArguments(data):
# TODO: necessary?
- @flagging(masking="field")
+ @flagging()
def func(data, field, flags, kwarg, **kwargs):
- flags[:, field] = UNTOUCHED
+ flags[:, field] = np.nan
return data, flags
var1 = data.columns[0]
diff --git a/tests/core/test_translator.py b/tests/core/test_translator.py
index 3e06597e5a676dbc567e6a20470f6cda56968ad3..581aeceb4f2c7dfd271280d095aa3089e8642e0b 100644
--- a/tests/core/test_translator.py
+++ b/tests/core/test_translator.py
@@ -9,13 +9,11 @@ import pandas as pd
import pytest
-from dios import DictOfSeries
-
-from saqc.constants import UNFLAGGED, BAD, DOUBTFUL
-from saqc.core.translator import (
- PositionalTranslator,
- Translator,
- DmpTranslator,
+from saqc.constants import UNFLAGGED, BAD, DOUBTFUL, FILTER_NONE
+from saqc.core.translation import (
+ PositionalScheme,
+ TranslationScheme,
+ DmpScheme,
)
from saqc.core.flags import Flags
from saqc.core.core import SaQC
@@ -30,8 +28,8 @@ def _genTranslators():
dtype(-1): BAD,
**{dtype(f * 10): float(f) for f in range(10)},
}
- translator = Translator(flags, {v: k for k, v in flags.items()})
- yield flags, translator
+ scheme = TranslationScheme(flags, {v: k for k, v in flags.items()})
+ yield flags, scheme
def _genFlags(data: Dict[str, Union[Sequence, pd.Series]]) -> Flags:
@@ -48,58 +46,56 @@ def _genFlags(data: Dict[str, Union[Sequence, pd.Series]]) -> Flags:
def test_forwardTranslation():
- for flags, translator in _genTranslators():
+ for flags, scheme in _genTranslators():
for k, expected in flags.items():
- got = translator(k)
+ got = scheme(k)
assert expected == got or np.isnan([got, expected]).all()
for k in ["bad", 3.14, max]:
with pytest.raises(ValueError):
- translator(k)
+ scheme(k)
def test_backwardTranslation():
field = "var1"
- for _, translator in _genTranslators():
- keys = tuple(translator._backward.keys())
+ for _, scheme in _genTranslators():
+ keys = tuple(scheme._backward.keys())
flags = _genFlags({field: np.array(keys)})
- translated = translator.backward(flags)
- expected = set(translator._backward.values())
+ translated = scheme.backward(flags)
+ expected = set(scheme._backward.values())
assert not (set(translated[field]) - expected)
def test_backwardTranslationFail():
field = "var1"
- for _, translator in _genTranslators():
- keys = tuple(translator._backward.keys())
+ for _, scheme in _genTranslators():
+ keys = tuple(scheme._backward.keys())
# add an scheme invalid value to the flags
flags = _genFlags({field: np.array(keys + (max(keys) + 1,))})
with pytest.raises(ValueError):
- translator.backward(flags)
+ scheme.backward(flags)
def test_dmpTranslator():
- translator = DmpTranslator()
+ scheme = DmpScheme()
# generate a bunch of dummy flags
- keys = np.array(tuple(translator._backward.keys()) * 50)
+ keys = np.array(tuple(scheme._backward.keys()) * 50)
flags = _genFlags({"var1": keys, "var2": keys, "var3": keys})
flags[:, "var1"] = BAD
flags[:, "var1"] = DOUBTFUL
flags[:, "var2"] = BAD
history1 = flags.history["var1"]
- history1.meta[1].update({"func": "flagFoo", "keywords": {"cause": "AUTOFLAGGED"}})
- history1.meta[2].update({"func": "flagBar", "keywords": {"comment": "I did it"}})
- flags.history["var1"] = history1
+ history1.meta[1].update({"func": "flagFoo", "kwargs": {"cause": "AUTOFLAGGED"}})
+ history1.meta[2].update({"func": "flagBar", "kwargs": {"comment": "I did it"}})
history2 = flags.history["var2"]
history2.meta[-1].update(
- {"func": "flagFoo", "keywords": {"cause": "BELOW_OR_ABOVE_MIN_MAX"}}
+ {"func": "flagFoo", "kwargs": {"cause": "BELOW_OR_ABOVE_MIN_MAX"}}
)
- flags.history["var2"] = history2
- tflags = translator.backward(flags)
+ tflags = scheme.backward(flags)
assert set(tflags.columns.get_level_values(1)) == {
"quality_flag",
@@ -136,13 +132,13 @@ def test_dmpTranslator():
def test_positionalTranslator():
- translator = PositionalTranslator()
+ scheme = PositionalScheme()
flags = _genFlags({"var1": np.zeros(100), "var2": np.zeros(50)})
flags[1::3, "var1"] = BAD
flags[1::3, "var1"] = DOUBTFUL
flags[2::3, "var1"] = BAD
- tflags = translator.backward(flags)
+ tflags = scheme.backward(flags)
assert (tflags["var2"].replace(-9999, np.nan).dropna() == 90).all(axis=None)
assert (tflags["var1"].iloc[1::3] == 90210).all(axis=None)
assert (tflags["var1"].iloc[2::3] == 90002).all(axis=None)
@@ -153,15 +149,15 @@ def test_positionalTranslatorIntegration():
data = initData(3)
col: str = data.columns[0]
- translator = PositionalTranslator()
- saqc = SaQC(data=data, scheme=translator)
+ scheme = PositionalScheme()
+ saqc = SaQC(data=data, scheme=scheme)
saqc = saqc.flagMissing(col).flagRange(col, min=3, max=10, flag=DOUBTFUL)
- data, flags = saqc.getResult()
+ flags = saqc.result.flags
for field in flags.columns:
assert flags[field].astype(str).str.match("^9[012]*$").all()
- round_trip = translator.backward(translator.forward(flags))
+ round_trip = scheme.backward(scheme.forward(flags))
assert (flags.values == round_trip.values).all()
assert (flags.index == round_trip.index).all()
@@ -173,10 +169,10 @@ def test_dmpTranslatorIntegration():
data = initData(1)
col = data.columns[0]
- translator = DmpTranslator()
- saqc = SaQC(data=data, scheme=translator)
+ scheme = DmpScheme()
+ saqc = SaQC(data=data, scheme=scheme)
saqc = saqc.flagMissing(col).flagRange(col, min=3, max=10)
- data, flags = saqc.getResult()
+ flags = saqc.result.flags
qflags = flags.xs("quality_flag", axis="columns", level=1)
qfunc = flags.xs("quality_comment", axis="columns", level=1).applymap(
@@ -184,11 +180,11 @@ def test_dmpTranslatorIntegration():
)
qcause = flags.xs("quality_cause", axis="columns", level=1)
- assert qflags.isin(translator._forward.keys()).all(axis=None)
+ assert qflags.isin(scheme._forward.keys()).all(axis=None)
assert qfunc.isin({"", "flagMissing", "flagRange"}).all(axis=None)
assert (qcause[qflags[col] == "BAD"] == "OTHER").all(axis=None)
- round_trip = translator.backward(translator.forward(flags))
+ round_trip = scheme.backward(scheme.forward(flags))
assert round_trip.xs("quality_flag", axis="columns", level=1).equals(qflags)
@@ -205,14 +201,14 @@ def test_dmpValidCombinations():
data = initData(1)
col = data.columns[0]
- translator = DmpTranslator()
- saqc = SaQC(data=data, scheme=translator)
+ scheme = DmpScheme()
+ saqc = SaQC(data=data, scheme=scheme)
- with pytest.raises(ValueError):
- saqc.flagRange(col, min=3, max=10, cause="SOMETHING_STUPID").getResult()
+ with pytest.raises(RuntimeError):
+ saqc.flagRange(col, min=3, max=10, cause="SOMETHING_STUPID").result
- with pytest.raises(ValueError):
- saqc.flagRange(col, min=3, max=10, cause="").getResult()
+ with pytest.raises(RuntimeError):
+ saqc.flagRange(col, min=3, max=10, cause="").result
def _buildupSaQCObjects():
@@ -229,8 +225,8 @@ def _buildupSaQCObjects():
out = []
for _ in range(2):
saqc = SaQC(data=data, flags=flags)
- saqc = saqc.flagRange(field=col, min=5, max=6, to_mask=False).flagRange(
- col, min=3, max=10, to_mask=False
+ saqc = saqc.flagRange(field=col, min=5, max=6, dfilter=FILTER_NONE).flagRange(
+ col, min=3, max=10, dfilter=FILTER_NONE
)
flags = saqc._flags
out.append(saqc)
@@ -240,8 +236,8 @@ def _buildupSaQCObjects():
def test_translationPreservesFlags():
saqc1, saqc2 = _buildupSaQCObjects()
- _, flags1 = saqc1.getResult(raw=True)
- _, flags2 = saqc2.getResult(raw=True)
+ flags1 = saqc1.result.flags_raw
+ flags2 = saqc2.result.flags_raw
for k in flags2.columns:
got = flags2.history[k].hist
@@ -255,8 +251,8 @@ def test_translationPreservesFlags():
def test_multicallsPreserveHistory():
saqc1, saqc2 = _buildupSaQCObjects()
- _, flags1 = saqc1.getResult(raw=True)
- _, flags2 = saqc2.getResult(raw=True)
+ flags1 = saqc1.result.flags_raw
+ flags2 = saqc2.result.flags_raw
# check, that the `History` is duplicated
for col in flags2.columns:
@@ -278,11 +274,11 @@ def test_positionalMulitcallsPreserveState():
saqc1, saqc2 = _buildupSaQCObjects()
- translator = PositionalTranslator()
- _, flags1 = saqc1.getResult(raw=True)
- _, flags2 = saqc2.getResult(raw=True)
- tflags1 = translator.backward(flags1).astype(str)
- tflags2 = translator.backward(flags2).astype(str)
+ scheme = PositionalScheme()
+ flags1 = saqc1.result.flags_raw
+ flags2 = saqc2.result.flags_raw
+ tflags1 = scheme.backward(flags1).astype(str)
+ tflags2 = scheme.backward(flags2).astype(str)
for k in flags2.columns:
expected = tflags1[k].str.slice(start=1) * 2
diff --git a/tests/funcs/test_functions.py b/tests/funcs/test_functions.py
index 7b8885d7434998f7e6b37e87a05f43d3f80ec129..ab8fa8f4985586b4b3f55ed2c6152464282eeaae 100644
--- a/tests/funcs/test_functions.py
+++ b/tests/funcs/test_functions.py
@@ -16,7 +16,7 @@ from saqc.funcs.drift import (
from saqc.funcs.outliers import flagCrossStatistic, flagRange
from saqc.funcs.flagtools import flagManual, forceFlags, clearFlags
from saqc.funcs.tools import dropField, copyField, maskTime
-from saqc.funcs.resampling import reindexFlags
+from saqc.funcs.resampling import concatFlags
from saqc.funcs.breaks import flagIsolated
from tests.fixtures import *
@@ -107,8 +107,8 @@ def test_flagSesonalRange(data, field):
data, flags = flagRange(
data, newfield, flags, min=test["min"], max=test["max"], flag=BAD
)
- data, flags = reindexFlags(
- data, field, flags, method="match", source=newfield, flag=BAD
+ data, flags = concatFlags(
+ data, newfield, flags, method="match", target=field, flag=BAD
)
data, flags = dropField(data, newfield, flags)
flagged = flags[field] > UNFLAGGED
@@ -129,8 +129,8 @@ def test_forceFlags(data, field):
flags[:, field] = BAD
assert all(flags[field] == BAD)
- _, flags = forceFlags(data, field, flags, flag=DOUBT)
- assert all(flags[field] == DOUBT)
+ _, flags = forceFlags(data, field, flags, flag=DOUBTFUL)
+ assert all(flags[field] == DOUBTFUL)
def test_flagIsolated(data, field):
@@ -175,7 +175,6 @@ def test_flagIsolated(data, field):
def test_flagCrossScoring(dat):
data1, characteristics = dat(initial_level=0, final_level=0, out_val=0)
data2, characteristics = dat(initial_level=0, final_level=0, out_val=10)
- field = "dummy"
fields = ["data1", "data2"]
s1, s2 = data1.squeeze(), data2.squeeze()
s1 = pd.Series(data=s1.values, index=s1.index)
@@ -183,7 +182,7 @@ def test_flagCrossScoring(dat):
data = dios.DictOfSeries([s1, s2], columns=["data1", "data2"])
flags = initFlagsLike(data)
_, flags_result = flagCrossStatistic(
- data, field, flags, fields=fields, thresh=3, method=np.mean, flag=BAD
+ data, fields, flags, thresh=3, method=np.mean, flag=BAD
)
for field in fields:
isflagged = flags_result[field] > UNFLAGGED
@@ -192,7 +191,6 @@ def test_flagCrossScoring(dat):
def test_flagManual(data, field):
flags = initFlagsLike(data)
- args = data, field, flags
dat = data[field]
mdata = pd.Series("lala", index=dat.index)
@@ -202,25 +200,31 @@ def test_flagManual(data, field):
shrinked = mdata.loc[index_exp.union(mdata.iloc[[1, 2, 3, 4, 600, 601]].index)]
kwargs_list = [
- dict(mdata=mdata, mflag="a", method="plain", flag=BAD),
- dict(mdata=mdata.to_list(), mflag="a", method="plain", flag=BAD),
- dict(mdata=mdata, mflag="a", method="ontime", flag=BAD),
- dict(mdata=shrinked, mflag="a", method="ontime", flag=BAD),
+ dict(mdata=mdata, mflag="a", method="plain", mformat="mflag", flag=BAD),
+ dict(mdata=mdata, mflag="a", method="ontime", mformat="mflag", flag=BAD),
+ dict(mdata=shrinked, mflag="a", method="ontime", mformat="mflag", flag=BAD),
]
for kw in kwargs_list:
- _, fl = flagManual(*args, **kw)
+ _, fl = flagManual(data.copy(), field, flags.copy(), **kw)
isflagged = fl[field] > UNFLAGGED
assert isflagged[isflagged].index.equals(index_exp)
# flag not exist in mdata
_, fl = flagManual(
- *args, mdata=mdata, mflag="i do not exist", method="ontime", flag=BAD
+ data.copy(),
+ field,
+ flags.copy(),
+ mdata=mdata,
+ mflag="i do not exist",
+ method="ontime",
+ mformat="mflag",
+ flag=BAD,
)
isflagged = fl[field] > UNFLAGGED
assert isflagged[isflagged].index.equals(pd.DatetimeIndex([]))
- # check right-open / ffill
+ # check closure methods
index = pd.date_range(start="2016-01-01", end="2018-12-31", periods=11)
mdata = pd.Series(0, index=index)
mdata.loc[index[[1, 5, 6, 7, 9, 10]]] = 1
@@ -237,41 +241,34 @@ def test_flagManual(data, field):
# 2018-09-12 12:00:00 1
# 2018-12-31 00:00:00 1
# dtype: int64
-
- # add first and last index from data
- expected = mdata.copy()
- expected.loc[dat.index[0]] = 0
- expected.loc[dat.index[-1]] = 1
- expected = expected.astype(bool)
-
- _, fl = flagManual(*args, mdata=mdata, mflag=1, method="right-open", flag=BAD)
- isflagged = fl[field] > UNFLAGGED
- last = expected.index[0]
-
- for curr in expected.index[1:]:
- expected_value = mdata[last]
- # datetime slicing is inclusive !
- i = isflagged[last:curr].index[:-1]
- chunk = isflagged.loc[i]
- assert (chunk == expected_value).all()
- last = curr
- # check last value
- assert isflagged[curr] == expected[curr]
-
- # check left-open / bfill
- expected.loc[dat.index[-1]] = 0 # this time the last is False
- _, fl = flagManual(*args, mdata=mdata, mflag=1, method="left-open", flag=BAD)
- isflagged = fl[field] > UNFLAGGED
- last = expected.index[0]
- assert isflagged[last] == expected[last]
-
- for curr in expected.index[1:]:
- expected_value = mdata[curr]
- # datetime slicing is inclusive !
- i = isflagged[last:curr].index[1:]
- chunk = isflagged.loc[i]
- assert (chunk == expected_value).all()
- last = curr
+ m_index = mdata.index
+ flag_intervals = [
+ (m_index[1], m_index[2]),
+ (m_index[5], m_index[8]),
+ (m_index[9], dat.index.shift(freq="1h")[-1]),
+ ]
+ bound_drops = {"right-open": [1], "left-open": [0], "closed": []}
+ for method in ["right-open", "left-open", "closed"]:
+ _, fl = flagManual(
+ data.copy(),
+ field,
+ flags.copy(),
+ mdata=mdata,
+ mflag=1,
+ method=method,
+ mformat="mflag",
+ flag=BAD,
+ )
+ isflagged = fl[field] > UNFLAGGED
+ for flag_i in flag_intervals:
+ f_i = isflagged[slice(flag_i[0], flag_i[-1])].index
+ check_i = f_i.drop(
+ [flag_i[k] for k in bound_drops[method]], errors="ignore"
+ )
+ assert isflagged[check_i].all()
+ unflagged = isflagged[f_i.difference(check_i)]
+ if not unflagged.empty:
+ assert ~unflagged.all()
@pytest.mark.parametrize("dat", [pytest.lazy_fixture("course_1")])
@@ -283,30 +280,29 @@ def test_flagDriftFromNormal(dat):
data["d5"] = 3 + 4 * data["d1"]
flags = initFlagsLike(data)
- data_norm, flags_norm = flagDriftFromNorm(
- data,
- "dummy",
- flags,
- ["d1", "d2", "d3"],
+ _, flags_norm = flagDriftFromNorm(
+ data=data.copy(),
+ field=["d1", "d2", "d3"],
+ flags=flags.copy(),
freq="200min",
spread=5,
flag=BAD,
)
- data_ref, flags_ref = flagDriftFromReference(
- data,
- "d1",
- flags,
- ["d1", "d2", "d3"],
+ _, flags_ref = flagDriftFromReference(
+ data=data.copy(),
+ field=["d1", "d2", "d3"],
+ flags=flags.copy(),
+ reference="d1",
freq="3D",
thresh=20,
flag=BAD,
)
- data_scale, flags_scale = flagDriftFromScaledNorm(
- data,
+ _, flags_scale = flagDriftFromScaledNorm(
+ data.copy(),
"dummy",
- flags,
+ flags.copy(),
["d1", "d3"],
["d4", "d5"],
freq="3D",
diff --git a/tests/funcs/test_generic_api_functions.py b/tests/funcs/test_generic_api_functions.py
index 360418cbb4254c206784746577a4a23550b218db..c8eecb617f1ff4cf6e94647b20310fcb4b5ac3d5 100644
--- a/tests/funcs/test_generic_api_functions.py
+++ b/tests/funcs/test_generic_api_functions.py
@@ -3,16 +3,14 @@
import pytest
import pandas as pd
+from dios.dios.dios import DictOfSeries
-from saqc.constants import *
-from saqc.core.register import flagging
-from saqc.funcs.tools import maskTime
+from saqc.constants import BAD, UNFLAGGED, FILTER_ALL
+from saqc.core.flags import Flags
from saqc import SaQC
+from saqc.lib.tools import toSequence
-from tests.common import initData, flagAll
-
-
-flagging(masking="field")(flagAll)
+from tests.common import initData
@pytest.fixture
@@ -20,23 +18,158 @@ def data():
return initData()
-def test_addFieldFlagGeneric(data):
- saqc = SaQC(data=data)
+def test_writeTargetFlagGeneric(data):
+ params = [
+ (["tmp"], lambda x, y: pd.Series(True, index=x.index.union(y.index))),
+ (
+ ["tmp1", "tmp2"],
+ lambda x, y: [pd.Series(True, index=x.index.union(y.index))] * 2,
+ ),
+ ]
+ for targets, func in params:
+ expected_meta = {
+ "func": "flagGeneric",
+ "args": (),
+ "kwargs": {
+ "field": data.columns.tolist(),
+ "func": func.__name__,
+ "target": targets,
+ "flag": BAD,
+ "dfilter": FILTER_ALL,
+ },
+ }
+
+ saqc = SaQC(data=data)
+ saqc = saqc.flagGeneric(field=data.columns, target=targets, func=func, flag=BAD)
+ for target in targets:
+ assert saqc._flags.history[target].hist.iloc[0].tolist() == [BAD]
+ assert saqc._flags.history[target].hist.iloc[0].tolist() == [BAD]
+ assert saqc._flags.history[target].meta[0] == expected_meta
+
+
+def test_overwriteFieldFlagGeneric(data):
+ params = [
+ (["var1"], lambda x: pd.Series(True, index=x.index)),
+ (
+ ["var1", "var2"],
+ lambda x, y: [pd.Series(True, index=x.index.union(y.index))] * 2,
+ ),
+ ]
+
+ flag = 12
+
+ for fields, func in params:
+ expected_meta = {
+ "func": "flagGeneric",
+ "args": (),
+ "kwargs": {
+ "field": fields,
+ "target": fields,
+ "func": func.__name__,
+ "flag": flag,
+ "dfilter": FILTER_ALL,
+ },
+ }
+
+ saqc = SaQC(
+ data=data.copy(),
+ flags=Flags(
+ {
+ k: pd.Series(data[k] % 2, index=data[k].index).replace(
+ {0: UNFLAGGED, 1: 127}
+ )
+ for k in data.columns
+ }
+ ),
+ )
+
+ res = saqc.flagGeneric(field=fields, func=func, flag=flag)
+ for field in fields:
+ assert (data[field] == res.data[field]).all(axis=None)
+ histcol0 = res._flags.history[field].hist[0]
+ histcol1 = res._flags.history[field].hist[1]
+ assert (histcol1[histcol0 == 127.0].isna()).all()
+ assert (histcol1[histcol0 != 127.0] == flag).all()
+ assert res._flags.history[field].meta[0] == {}
+ assert res._flags.history[field].meta[1] == expected_meta
+
+
+def test_writeTargetProcGeneric(data):
+ fields = ["var1", "var2"]
+ params = [
+ (["tmp"], lambda x, y: x + y),
+ (["tmp1", "tmp2"], lambda x, y: (x + y, y * 2)),
+ ]
+ dfilter = 128
+ for targets, func in params:
+
+ expected_data = DictOfSeries(
+ func(*[data[f] for f in fields]), columns=toSequence(targets)
+ ).squeeze()
+
+ expected_meta = {
+ "func": "procGeneric",
+ "args": (),
+ "kwargs": {
+ "field": data.columns.tolist(),
+ "target": targets,
+ "func": func.__name__,
+ "flag": BAD,
+ "dfilter": dfilter,
+ },
+ }
+ saqc = SaQC(
+ data=data,
+ flags=Flags(
+ {k: pd.Series(127.0, index=data[k].index) for k in data.columns}
+ ),
+ )
+ res = saqc.processGeneric(
+ field=fields, target=targets, func=func, flag=BAD, dfilter=dfilter
+ )
+ assert (expected_data == res.data[targets].squeeze()).all(axis=None)
+ # check that new histories where created
+ for target in targets:
+ assert res._flags.history[target].hist.iloc[0].tolist() == [BAD]
+ assert res._flags.history[target].meta[0] == expected_meta
- func = lambda var1: pd.Series(False, index=data[var1.name].index)
- data, flags = saqc.genericFlag("tmp1", func, flag=BAD).getResult()
- assert "tmp1" in flags.columns and "tmp1" not in data
+def test_overwriteFieldProcGeneric(data):
+ params = [
+ (["var1"], lambda x: x * 2),
+ (["var1", "var2"], lambda x, y: (x + y, y * 2)),
+ ]
+ dfilter = 128
+ flag = 12
+ for fields, func in params:
+ expected_data = DictOfSeries(
+ func(*[data[f] for f in fields]), columns=fields
+ ).squeeze()
-def test_addFieldProcGeneric(data):
- saqc = SaQC(data=data)
+ expected_meta = {
+ "func": "procGeneric",
+ "args": (),
+ "kwargs": {
+ "field": fields,
+ "target": fields,
+ "func": func.__name__,
+ "flag": flag,
+ "dfilter": dfilter,
+ },
+ }
- func = lambda: pd.Series([])
- data, flags = saqc.genericProcess("tmp1", func, flag=BAD).getResult(raw=True)
- assert "tmp1" in data.columns and data["tmp1"].empty
+ saqc = SaQC(
+ data=data,
+ flags=Flags(
+ {k: pd.Series(127.0, index=data[k].index) for k in data.columns}
+ ),
+ )
- func = lambda var1, var2: var1 + var2
- data, flags = saqc.genericProcess("tmp2", func, flag=BAD).getResult()
- assert "tmp2" in data.columns and (data["tmp2"] == data["var1"] + data["var2"]).all(
- axis=None
- )
+ res = saqc.processGeneric(field=fields, func=func, flag=flag, dfilter=dfilter)
+ assert (expected_data == res.data[fields].squeeze()).all(axis=None)
+ # check that the histories got appended
+ for field in fields:
+ assert (res._flags.history[field].hist[0] == 127.0).all()
+ assert (res._flags.history[field].hist[1] == 12.0).all()
+ assert res._flags.history[field].meta[0] == {}
+ assert res._flags.history[field].meta[1] == expected_meta
diff --git a/tests/funcs/test_generic_config_functions.py b/tests/funcs/test_generic_config_functions.py
index 2046ff8e9b02114e1390eb658323068a78ebcba8..556aeba816c9ae38345a3ac5bab965791a2ca55a 100644
--- a/tests/funcs/test_generic_config_functions.py
+++ b/tests/funcs/test_generic_config_functions.py
@@ -2,6 +2,7 @@
# -*- coding: utf-8 -*-
import ast
+from saqc.core.flags import Flags
from saqc.core.reader import fromConfig
import pytest
import numpy as np
@@ -9,9 +10,9 @@ import pandas as pd
import dios
from saqc.constants import *
-from saqc.core import initFlagsLike, Flags
+from saqc.core import initFlagsLike
from saqc.core.visitor import ConfigFunctionParser
-from saqc.core.register import flagging
+from saqc.core.register import register
from saqc.funcs.generic import _execGeneric
from saqc import SaQC
@@ -38,30 +39,13 @@ def data_diff():
)
-def _compileGeneric(expr, flags):
+def _compileGeneric(expr):
tree = ast.parse(expr, mode="eval")
_, kwargs = ConfigFunctionParser().parse(tree.body)
return kwargs["func"]
-def test_missingIdentifier(data):
- flags = Flags()
-
- # NOTE:
- # - the error is only raised at runtime during parsing would be better
- tests = [
- "fff(var2) < 5",
- "var3 != 42",
- ]
-
- for test in tests:
- func = _compileGeneric(f"genericFlag(func={test})", flags)
- with pytest.raises(NameError):
- _execGeneric(flags, data, func, field="")
-
-
def test_syntaxError():
- flags = Flags()
tests = [
"range(x=5",
"rangex=5)",
@@ -70,212 +54,171 @@ def test_syntaxError():
for test in tests:
with pytest.raises(SyntaxError):
- _compileGeneric(f"flag(func={test})", flags)
+ _compileGeneric(f"flag(func={test})")
def test_typeError():
"""
test that forbidden constructs actually throw an error
- TODO: find a few more cases or get rid of the test
"""
- flags = Flags()
- # : think about cases that should be forbidden
+ # TODO: think about cases that should be forbidden
tests = ("lambda x: x * 2",)
for test in tests:
with pytest.raises(TypeError):
- _compileGeneric(f"genericFlag(func={test})", flags)
+ _compileGeneric(f"flagGeneric(func={test})")
def test_comparisonOperators(data):
- flags = initFlagsLike(data)
var1, var2, *_ = data.columns
- this = var1
+ flags = initFlagsLike(data)
tests = [
- ("this > 100", data[this] > 100),
- (f"10 >= {var2}", 10 >= data[var2]),
- (f"{var2} < 100", data[var2] < 100),
- (f"this <= {var2}", data[this] <= data[var2]),
- (f"{var1} == {var2}", data[this] == data[var2]),
- (f"{var1} != {var2}", data[this] != data[var2]),
+ (["var1"], "x > 100", data[var1] > 100),
+ (["var2"], "10 >= y", 10 >= data[var2]),
+ (["var2"], f"y < 100", data[var2] < 100),
+ (["var1", "var2"], "x <= y", data[var1] <= data[var2]),
+ (["var1", "var2"], "x == y", data[var1] == data[var2]),
+ (["var1", "var2"], "x != y", data[var1] != data[var2]),
]
- for test, expected in tests:
- func = _compileGeneric(f"genericFlag(func={test})", flags)
- result = _execGeneric(flags, data, func, field=var1)
- assert np.all(result == expected)
+ for field, test, expected in tests:
+ func = _compileGeneric(f"flagGeneric(func={test})")
+ result = _execGeneric(Flags({f: flags[f] for f in field}), data[field], func)
+ assert (result == expected).all(axis=None)
def test_arithmeticOperators(data):
- flags = initFlagsLike(data)
- var1, *_ = data.columns
- this = data[var1]
-
- tests = [
- ("var1 + 100 > 110", this + 100 > 110),
- ("var1 - 100 > 0", this - 100 > 0),
- ("var1 * 100 > 200", this * 100 > 200),
- ("var1 / 100 > .1", this / 100 > 0.1),
- ("var1 % 2 == 1", this % 2 == 1),
- ("var1 ** 2 == 0", this ** 2 == 0),
- ]
- for test, expected in tests:
- func = _compileGeneric(f"genericProcess(func={test})", flags)
- result = _execGeneric(flags, data, func, field=var1)
- assert np.all(result == expected)
-
-
-def test_nonReduncingBuiltins(data):
- flags = initFlagsLike(data)
var1, *_ = data.columns
- this = var1
- tests = [
- (f"abs({this})", np.abs(data[this])),
- (f"log({this})", np.log(data[this])),
- (f"exp({this})", np.exp(data[this])),
- ]
-
- for test, expected in tests:
- func = _compileGeneric(f"genericProcess(func={test})", flags)
- result = _execGeneric(flags, data, func, field=this)
- assert (result == expected).all()
-
-
-def test_reduncingBuiltins(data):
- data.loc[::4] = np.nan
- flags = initFlagsLike(data)
- var1 = data.columns[0]
- this = data.iloc[:, 0]
+ data = data[var1]
+ flags = Flags({var1: pd.Series(UNFLAGGED, index=data.index)})
tests = [
- ("min(this)", np.nanmin(this)),
- (f"max({var1})", np.nanmax(this)),
- (f"sum({var1})", np.nansum(this)),
- ("mean(this)", np.nanmean(this)),
- (f"std({this.name})", np.std(this)),
- (f"len({this.name})", len(this)),
+ ("var1 + 100 > 110", data + 100 > 110),
+ ("var1 - 100 > 0", data - 100 > 0),
+ ("var1 * 100 > 200", data * 100 > 200),
+ ("var1 / 100 > .1", data / 100 > 0.1),
+ ("var1 % 2 == 1", data % 2 == 1),
+ ("var1 ** 2 == 0", data ** 2 == 0),
]
for test, expected in tests:
- func = _compileGeneric(f"genericProcess(func={test})", flags)
- result = _execGeneric(flags, data, func, field=this.name)
- assert result == expected
-
+ func = _compileGeneric(f"processGeneric(func={test})")
+ result = _execGeneric(flags, data, func)
+ assert (result == expected).all(axis=None)
-def test_ismissing(data):
- flags = initFlagsLike(data)
- data.iloc[: len(data) // 2, 0] = np.nan
- data.iloc[(len(data) // 2) + 1 :, 0] = -9999
- this = data.iloc[:, 0]
+def test_nonReduncingBuiltins(data):
+ var1, *_ = data.columns
+ data = data.iloc[1:10, 0]
+ flags = Flags({var1: pd.Series(UNFLAGGED, index=data.index)})
tests = [
- (f"ismissing({this.name})", pd.isnull(this)),
- (f"~ismissing({this.name})", pd.notnull(this)),
+ ("abs(x)", np.abs(data)),
+ ("log(x)", np.log(data)),
+ ("exp(x)", np.exp(data)),
]
for test, expected in tests:
- func = _compileGeneric(f"genericFlag(func={test})", flags)
- result = _execGeneric(flags, data, func, this.name)
- assert np.all(result == expected)
+ func = _compileGeneric(f"processGeneric(func={test})")
+ result = _execGeneric(flags, data, func)
+ assert (result == expected).all(axis=None)
def test_bitOps(data):
var1, var2, *_ = data.columns
- this = var1
-
flags = initFlagsLike(data)
tests = [
- ("~(this > mean(this))", ~(data[this] > np.nanmean(data[this]))),
- (f"(this <= 0) | (0 < {var1})", (data[this] <= 0) | (0 < data[var1])),
- (f"({var2} >= 0) & (0 > this)", (data[var2] >= 0) & (0 > data[this])),
+ ([var1], "~(x > mean(x))", ~(data[var1] > np.nanmean(data[var1]))),
+ ([var1], "(x <= 0) | (0 < x)", (data[var1] <= 0) | (0 < data[var1])),
+ ([var1, var2], "(y>= 0) & (0 > x)", (data[var2] >= 0) & (0 > data[var1])),
]
- for test, expected in tests:
- func = _compileGeneric(f"genericFlag(func={test})", flags)
- result = _execGeneric(flags, data, func, this)
- assert np.all(result == expected)
+ for field, test, expected in tests:
+ func = _compileGeneric(f"flagGeneric(func={test})")
+ result = _execGeneric(Flags({f: flags[f] for f in field}), data[field], func)
+ assert (result == expected).all(axis=None)
-def test_isflagged(data):
+def test_variableAssignments(data):
- var1, var2, *_ = data.columns
- flags = initFlagsLike(data)
- flags[data[var1].index[::2], var1] = BAD
+ config = f"""
+ varname ; test
+ dummy1 ; processGeneric(field=["var1", "var2"], func=x + y)
+ dummy2 ; flagGeneric(field=["var1", "var2"], func=x + y > 0)
+ """
- tests = [
- (f"isflagged({var1})", flags[var1] > UNFLAGGED),
- (f"isflagged({var1}, flag=BAD)", flags[var1] >= BAD),
- (f"isflagged({var1}, UNFLAGGED, '==')", flags[var1] == UNFLAGGED),
- (f"~isflagged({var2})", flags[var2] == UNFLAGGED),
- (
- f"~({var2}>999) & (~isflagged({var2}))",
- ~(data[var2] > 999) & (flags[var2] == UNFLAGGED),
- ),
- ]
+ fobj = writeIO(config)
+ saqc = fromConfig(fobj, data)
- for i, (test, expected) in enumerate(tests):
- try:
- func = _compileGeneric(f"genericFlag(func={test}, flag=BAD)", flags)
- result = _execGeneric(flags, data, func, field=None)
- assert np.all(result == expected)
- except Exception:
- print(i, test)
- raise
+ expected_columns = set(data.columns) | {"dummy1", "dummy2"}
+ assert set(saqc.data.columns) == expected_columns
+ assert set(saqc.flags.columns) == expected_columns
- # test bad combination
- for comp in [">", ">=", "==", "!=", "<", "<="]:
- fails = f"isflagged({var1}, comparator='{comp}')"
- func = _compileGeneric(f"genericFlag(func={fails}, flag=BAD)", flags)
- with pytest.raises(ValueError):
- _execGeneric(flags, data, func, field=None)
+def test_processExistingTarget(data):
+ config = f"""
+ varname ; test
+ var2 ; flagMissing()
+ var2 ; processGeneric(func=y - 1, flag=DOUBTFUL)
+ """
+ fobj = writeIO(config)
+ saqc = fromConfig(fobj, data)
+ assert (saqc._data["var2"] == data["var2"] - 1).all()
+ assert len(saqc._flags.history["var2"]) == 2
+ assert saqc._flags.history["var2"].hist[0].isna().all()
+ assert (saqc._flags.history["var2"].hist[1] == DOUBTFUL).all()
-def test_variableAssignments(data):
- var1, var2, *_ = data.columns
+def test_flagTargetExisting(data):
config = f"""
varname ; test
- dummy1 ; genericProcess(func=var1 + var2)
- dummy2 ; genericFlag(func=var1 + var2 > 0)
+ dummy ; processGeneric(field="var1", func=x < 1)
+ dummy ; processGeneric(field="var2", func=y >1)
"""
fobj = writeIO(config)
saqc = fromConfig(fobj, data)
- result_data, result_flags = saqc.getResult(raw=True)
+ assert len(saqc.data["dummy"]) == len(saqc.flags["dummy"])
+
- assert set(result_data.columns) == set(data.columns) | {
- "dummy1",
- }
- assert set(result_flags.columns) == set(data.columns) | {"dummy1", "dummy2"}
+def test_processTargetExistingFail(data_diff):
+ config = f"""
+ varname ; test
+ dummy ; processGeneric(field="var1", func=x + 1)
+ dummy ; processGeneric(field="var2", func=y - 1)
+ """
+
+ fobj = writeIO(config)
+ with pytest.raises(ValueError):
+ fromConfig(fobj, data_diff)
-def test_processMultiple(data_diff):
+def test_flagTargetExistingFail(data_diff):
config = f"""
varname ; test
- dummy ; genericProcess(func=var1 + 1)
- dummy ; genericProcess(func=var2 - 1)
+ dummy ; flagGeneric(field="var1", func=x < 1)
+ dummy ; flagGeneric(field="var2", func=y > 1)
"""
fobj = writeIO(config)
- saqc = fromConfig(fobj, data_diff)
- result_data, result_flags = saqc.getResult()
- assert len(result_data["dummy"]) == len(result_flags["dummy"])
+ with pytest.raises(ValueError):
+ fromConfig(fobj, data_diff)
def test_callableArgumentsUnary(data):
window = 5
- @flagging(masking="field")
+ @register(mask=["field"], demask=["field"], squeeze=["field"])
def testFuncUnary(data, field, flags, func, **kwargs):
- data[field] = data[field].rolling(window=window).apply(func)
+ value = data[field].rolling(window=window).apply(func)
+ data[field] = value
return data, initFlagsLike(data)
var = data.columns[0]
@@ -286,14 +229,14 @@ def test_callableArgumentsUnary(data):
"""
tests = [
- ("sum", np.nansum),
+ # ("sum", np.nansum),
("std(exp(x))", lambda x: np.std(np.exp(x))),
]
for (name, func) in tests:
fobj = writeIO(config.format(name))
- result_config, _ = fromConfig(fobj, data).getResult()
- result_api, _ = SaQC(data).testFuncUnary(var, func=func).getResult()
+ result_config = fromConfig(fobj, data).result.data
+ result_api = SaQC(data).testFuncUnary(var, func=func).result.data
expected = data[var].rolling(window=window).apply(func)
assert (result_config[var].dropna() == expected.dropna()).all(axis=None)
assert (result_api[var].dropna() == expected.dropna()).all(axis=None)
@@ -302,7 +245,7 @@ def test_callableArgumentsUnary(data):
def test_callableArgumentsBinary(data):
var1, var2 = data.columns[:2]
- @flagging(masking="field")
+ @register(mask=["field"], demask=["field"], squeeze=["field"])
def testFuncBinary(data, field, flags, func, **kwargs):
data[field] = func(data[var1], data[var2])
return data, initFlagsLike(data)
@@ -319,8 +262,31 @@ def test_callableArgumentsBinary(data):
for (name, func) in tests:
fobj = writeIO(config.format(name))
- result_config, _ = fromConfig(fobj, data).getResult()
- result_api, _ = SaQC(data).testFuncBinary(var1, func=func).getResult()
+ result_config = fromConfig(fobj, data).result.data
+ result_api = SaQC(data).testFuncBinary(var1, func=func).result.data
expected = func(data[var1], data[var2])
assert (result_config[var1].dropna() == expected.dropna()).all(axis=None)
assert (result_api[var1].dropna() == expected.dropna()).all(axis=None)
+
+
+def test_isflagged(data):
+
+ var1, var2, *_ = data.columns
+ flags = initFlagsLike(data)
+ flags[data[var1].index[::2], var1] = BAD
+
+ tests = [
+ ([var1], f"isflagged(x)", flags[var1] > UNFLAGGED),
+ ([var1], f"isflagged(x)", flags[var1] >= BAD),
+ ([var2], f"~isflagged(x)", flags[var2] == UNFLAGGED),
+ (
+ [var1, var2],
+ f"~(x > 999) & (~isflagged(y))",
+ ~(data[var1] > 999) & (flags[var2] == UNFLAGGED),
+ ),
+ ]
+
+ for field, test, expected in tests:
+ func = _compileGeneric(f"flagGeneric(func={test}, flag=BAD)")
+ result = _execGeneric(Flags({f: flags[f] for f in field}), data[field], func)
+ assert (result == expected).all(axis=None)
diff --git a/tests/funcs/test_harm_funcs.py b/tests/funcs/test_harm_funcs.py
index d645e1bd9478321921afc4ab58ba7660510137f4..2819a884bc7c1532fd168e80f091743329b93874 100644
--- a/tests/funcs/test_harm_funcs.py
+++ b/tests/funcs/test_harm_funcs.py
@@ -8,7 +8,7 @@ import dios
from saqc.core import initFlagsLike, Flags
from saqc.constants import BAD, UNFLAGGED
-from saqc.funcs.resampling import linear, interpolate, shift, reindexFlags, resample
+from saqc.funcs.resampling import linear, interpolate, shift, concatFlags, resample
from saqc.funcs.tools import copyField, dropField
from tests.common import checkDataFlagsInvariants
@@ -37,7 +37,7 @@ def data():
("linear", dict()),
("shift", dict(method="nshift")),
("interpolate", dict(method="spline")),
- ("resample", dict(agg_func=np.nansum, method="nagg")),
+ ("resample", dict(func=np.nansum, method="nagg")),
],
)
def test_wrapper(data, func, kws):
@@ -67,23 +67,28 @@ def test_gridInterpolation(data, method):
# we are just testing if the interpolation gets passed to the series without
# causing an error:
res = interpolate(
- data, field, flags, freq, method=method, downcast_interpolation=True
+ data.copy(),
+ field,
+ flags.copy(),
+ freq,
+ method=method,
+ downcast_interpolation=True,
)
if method == "polynomial":
res = interpolate(
- data,
+ data.copy(),
field,
- flags,
+ flags.copy(),
freq,
order=2,
method=method,
downcast_interpolation=True,
)
res = interpolate(
- data,
+ data.copy(),
field,
- flags,
+ flags.copy(),
freq,
order=10,
method=method,
@@ -142,8 +147,8 @@ def test_harmSingleVarIntermediateFlagging(data, reshaper):
# flag something bad
flags[data[field + "_interpolated"].index[3:4], field + "_interpolated"] = BAD
- data, flags = reindexFlags(
- data, field, flags, method="inverse_" + reshaper, source=field + "_interpolated"
+ data, flags = concatFlags(
+ data, field + "_interpolated", flags, method="inverse_" + reshaper, target=field
)
data, flags = dropField(data, field + "_interpolated", flags)
@@ -243,8 +248,8 @@ def test_harmSingleVarInterpolationAgg(data, params, expected):
assert data_harm[h_field].index.freq == pd.Timedelta(freq)
assert data_harm[h_field].equals(expected)
- data_deharm, flags_deharm = reindexFlags(
- data_harm, field, flags_harm, source=h_field, method="inverse_" + method
+ data_deharm, flags_deharm = concatFlags(
+ data_harm, h_field, flags_harm, target=field, method="inverse_" + method
)
data_deharm, flags_deharm = dropField(data_deharm, h_field, flags_deharm)
checkDataFlagsInvariants(data_deharm, flags_deharm, field, identical=True)
@@ -324,8 +329,8 @@ def test_harmSingleVarInterpolationShift(data, params, expected):
assert data_harm[h_field].equals(expected)
checkDataFlagsInvariants(data_harm, flags_harm, field, identical=True)
- data_deharm, flags_deharm = reindexFlags(
- data_harm, field, flags_harm, source=h_field, method="inverse_" + method
+ data_deharm, flags_deharm = concatFlags(
+ data_harm, h_field, flags_harm, target=field, method="inverse_" + method
)
checkDataFlagsInvariants(data_deharm, flags_deharm, field, identical=True)
diff --git a/tests/funcs/test_modelling.py b/tests/funcs/test_modelling.py
index ff244da97d922915fba48ff905152809825b5b7c..33d4fee458db6a8c26e6c0cbbd447fa8e5c41a41 100644
--- a/tests/funcs/test_modelling.py
+++ b/tests/funcs/test_modelling.py
@@ -3,6 +3,7 @@
# see test/functs/fixtures.py for global fixtures "course_..."
+import pytest
import dios
@@ -14,6 +15,7 @@ from saqc.funcs.residues import calculatePolynomialResidues, calculateRollingRes
from tests.fixtures import *
+@pytest.mark.filterwarnings("ignore: The fit may be poorly conditioned")
@pytest.mark.parametrize("dat", [pytest.lazy_fixture("course_2")])
def test_modelling_polyFit_forRegular(dat):
data, _ = dat(
@@ -54,7 +56,6 @@ def test_modelling_rollingMean_forRegular(dat):
flags,
5,
func=np.mean,
- set_flags=True,
min_periods=0,
center=True,
)
@@ -64,7 +65,6 @@ def test_modelling_rollingMean_forRegular(dat):
flags,
5,
func=np.mean,
- set_flags=True,
min_periods=0,
center=False,
)
diff --git a/tests/funcs/test_pattern_rec.py b/tests/funcs/test_pattern_rec.py
index 2c35dda3addbb2a20af0d1a3e6ea797a6a836507..18bb3384087fc63d502d15d2bdfcd8dfe2da37c1 100644
--- a/tests/funcs/test_pattern_rec.py
+++ b/tests/funcs/test_pattern_rec.py
@@ -5,9 +5,9 @@ import pytest
import pandas as pd
import dios
-from saqc.constants import *
+from saqc.constants import BAD, UNFLAGGED
from saqc.core import initFlagsLike
-from saqc.funcs.pattern import *
+from saqc.funcs.pattern import flagPatternByDTW
from tests.common import initData
@@ -21,23 +21,6 @@ def field(data):
return data.columns[0]
-@pytest.mark.skip(reason="faulty implementation - wait for #GL216")
-def test_flagPattern_wavelet():
- data = pd.Series(0, index=pd.date_range(start="2000", end="2001", freq="1d"))
- data.iloc[10:18] = [0, 5, 6, 7, 6, 8, 5, 0]
- pattern = data.iloc[10:18]
-
- data = dios.DictOfSeries(dict(data=data, pattern_data=pattern))
- flags = initFlagsLike(data, name="data")
- data, flags = flagPatternByWavelet(
- data, "data", flags, reference="pattern_data", flag=BAD
- )
-
- assert all(flags["data"].iloc[10:18] == BAD)
- assert all(flags["data"].iloc[:9] == UNFLAGGED)
- assert all(flags["data"].iloc[18:] == UNFLAGGED)
-
-
def test_flagPattern_dtw():
data = pd.Series(0, index=pd.date_range(start="2000", end="2001", freq="1d"))
data.iloc[10:18] = [0, 5, 6, 7, 6, 8, 5, 0]
@@ -52,7 +35,3 @@ def test_flagPattern_dtw():
assert all(flags["data"].iloc[10:18] == BAD)
assert all(flags["data"].iloc[:9] == UNFLAGGED)
assert all(flags["data"].iloc[18:] == UNFLAGGED)
-
- # visualize:
- # data['data'].plot()
- # ((flags['data']>0) *5.).plot()
diff --git a/tests/funcs/test_proc_functions.py b/tests/funcs/test_proc_functions.py
index 62a33cf313b77252f4f2a27152b35948e39ad781..0d63371877ddef401d5643db9fd5ecb6998d55c6 100644
--- a/tests/funcs/test_proc_functions.py
+++ b/tests/funcs/test_proc_functions.py
@@ -27,9 +27,9 @@ def test_rollingInterpolateMissing(course_5):
data = dios.DictOfSeries(data)
flags = initFlagsLike(data)
dataInt, *_ = interpolateByRolling(
- data,
+ data.copy(),
field,
- flags,
+ flags.copy(),
3,
func=np.median,
center=True,
@@ -38,9 +38,9 @@ def test_rollingInterpolateMissing(course_5):
)
assert dataInt[field][characteristics["missing"]].notna().all()
dataInt, *_ = interpolateByRolling(
- data,
+ data.copy(),
field,
- flags,
+ flags.copy(),
3,
func=np.nanmean,
center=False,
diff --git a/tests/funcs/test_spikes_detection.py b/tests/funcs/test_spikes_detection.py
index f5cb61bdfab078c8aabb7dc30d1ce62b9abcc7a5..9c951fc4d849d2a06288b2099f4277bb4c6e8157 100644
--- a/tests/funcs/test_spikes_detection.py
+++ b/tests/funcs/test_spikes_detection.py
@@ -86,7 +86,6 @@ def test_flagMultivarScores(dat):
data2, characteristics = dat(
periods=1000, initial_level=20, final_level=1, out_val=30
)
- field = "dummy"
fields = ["data1", "data2"]
s1, s2 = data1.squeeze(), data2.squeeze()
s1 = pd.Series(data=s1.values, index=s1.index)
@@ -94,10 +93,9 @@ def test_flagMultivarScores(dat):
data = dios.DictOfSeries([s1, s2], columns=["data1", "data2"])
flags = initFlagsLike(data)
_, flags_result = flagMVScores(
- data,
- field,
- flags,
- fields=fields,
+ data=data,
+ field=fields,
+ flags=flags,
trafo=np.log,
iter_start=0.95,
n=10,
diff --git a/tests/funcs/test_tools.py b/tests/funcs/test_tools.py
index c181277cc01129b2921ef6e494014a8ceeacb748..eb3daefa524309aff04c45082bd1be98e4624289 100644
--- a/tests/funcs/test_tools.py
+++ b/tests/funcs/test_tools.py
@@ -27,10 +27,8 @@ def test_makeFig():
dummy_path = ""
d_saqc = d_saqc.plot(field="data", path="")
+ d_saqc = d_saqc.plot(field="data", path=dummy_path, history="valid", stats=True)
+ d_saqc = d_saqc.plot(field="data", path=dummy_path, history="complete")
d_saqc = d_saqc.plot(
- field="data", path=dummy_path, plot_kwargs={"history": "valid"}, stats=True
- )
- d_saqc = d_saqc.plot(field="data", path=dummy_path, plot_kwargs={"history": "all"})
- d_saqc = d_saqc.plot(
- field="data", path=dummy_path, plot_kwargs={"slice": "2000-10"}, stats=True
+ field="data", path=dummy_path, ax_kwargs={"ylim": "2000-10"}, stats=True
)
diff --git a/tests/fuzzy/lib.py b/tests/fuzzy/lib.py
index 4b77a8b06e411c26bff8a2d1948b1e3228a19010..69ab3c6df58091c3f6d4236d1614e0009eff5e0f 100644
--- a/tests/fuzzy/lib.py
+++ b/tests/fuzzy/lib.py
@@ -24,7 +24,6 @@ from hypothesis.strategies._internal.types import _global_type_lookup
from saqc.constants import *
from saqc.core.register import FUNC_MAP
-from saqc.lib.types import FreqString
from saqc.core import initFlagsLike
MAX_EXAMPLES = 50
diff --git a/tests/fuzzy/test_masking.py b/tests/fuzzy/test_masking.py
index 15051ff0604f890c3f867bd49e8a548125c55f26..5faaa466a99a98670c0729603f8d83d07f88cc45 100644
--- a/tests/fuzzy/test_masking.py
+++ b/tests/fuzzy/test_masking.py
@@ -6,7 +6,7 @@ import pandas as pd
from hypothesis import given, settings
from saqc.constants import UNFLAGGED, BAD
-from saqc.core.register import _maskData, _unmaskData, CallState
+from saqc.core.register import FunctionWrapper
from tests.fuzzy.lib import dataFieldFlags, MAX_EXAMPLES
@@ -18,7 +18,7 @@ def test_maskingMasksData(data_field_flags):
test if flagged values are replaced by np.nan
"""
data_in, field, flags = data_field_flags
- data_masked, mask = _maskData(
+ data_masked, mask = FunctionWrapper._maskData(
data_in, flags, columns=[field], thresh=UNFLAGGED
) # thresh UNFLAGGED | np.inf
assert data_masked[field].iloc[mask[field].index].isna().all()
@@ -35,21 +35,12 @@ def test_dataMutationPreventsUnmasking(data_field_flags):
filler = -9999
data_in, field, flags = data_field_flags
- data_masked, mask = _maskData(data_in, flags, columns=[field], thresh=UNFLAGGED)
- state = CallState(
- func=lambda x: x,
- func_name="",
- flags=flags,
- field=field,
- args=(),
- kwargs={},
- masking="field",
- mthresh=UNFLAGGED,
- mask=mask,
- )
+ data_masked, mask = FunctionWrapper._maskData(
+ data_in, flags, columns=[field], thresh=UNFLAGGED
+ )
data_masked[field] = filler
- data_out = _unmaskData(data_masked, state)
+ data_out = FunctionWrapper._unmaskData(data_masked, mask)
assert (data_out[field] == filler).all(axis=None)
@@ -61,20 +52,12 @@ def test_flagsMutationPreventsUnmasking(data_field_flags):
if `flags` is mutated after `_maskData`, `_unmaskData` should be a no-op
"""
data_in, field, flags = data_field_flags
- data_masked, mask = _maskData(data_in, flags, columns=[field], thresh=UNFLAGGED)
- state = CallState(
- func=lambda x: x,
- func_name="",
- flags=flags,
- field=field,
- args=(),
- kwargs={},
- masking="field",
- mthresh=UNFLAGGED,
- mask=mask,
+
+ data_masked, mask = FunctionWrapper._maskData(
+ data_in, flags, columns=[field], thresh=UNFLAGGED
)
flags[:, field] = UNFLAGGED
- data_out = _unmaskData(data_masked, state)
+ data_out = FunctionWrapper._unmaskData(data_masked, mask)
assert (data_out.loc[flags[field] == BAD, field].isna()).all(axis=None)
@@ -90,17 +73,9 @@ def test_reshapingPreventsUnmasking(data_field_flags):
filler = -1111
data_in, field, flags = data_field_flags
- data_masked, mask = _maskData(data_in, flags, columns=[field], thresh=UNFLAGGED)
- state = CallState(
- func=lambda x: x,
- func_name="",
- flags=flags,
- field=field,
- args=(),
- kwargs={},
- masking="field",
- mthresh=UNFLAGGED,
- mask=mask,
+
+ data_masked, mask = FunctionWrapper._maskData(
+ data_in, flags, columns=[field], thresh=UNFLAGGED
)
# mutate indexes of `data` and `flags`
index = data_masked[field].index.to_series()
@@ -111,7 +86,7 @@ def test_reshapingPreventsUnmasking(data_field_flags):
flags.drop(field)
flags[field] = pd.Series(data=fflags.values, index=index)
- data_out = _unmaskData(data_masked, state)
+ data_out = FunctionWrapper._unmaskData(data_masked, mask)
assert (data_out[field] == filler).all(axis=None)
@@ -122,19 +97,11 @@ def test_unmaskingInvertsMasking(data_field_flags):
unmasking data should invert the masking
"""
data_in, field, flags = data_field_flags
- data_masked, mask = _maskData(data_in, flags, columns=[field], thresh=UNFLAGGED)
- state = CallState(
- func=lambda x: x,
- func_name="",
- flags=flags,
- field=field,
- args=(),
- kwargs={},
- masking="field",
- mthresh=UNFLAGGED,
- mask=mask,
+
+ data_masked, mask = FunctionWrapper._maskData(
+ data_in, flags, columns=[field], thresh=UNFLAGGED
)
- data_out = _unmaskData(data_masked, state)
+ data_out = FunctionWrapper._unmaskData(data_masked, mask)
assert pd.DataFrame.equals(
data_out.to_df().astype(float), data_in.to_df().astype(float)
)
@@ -153,10 +120,10 @@ def test_unmaskingInvertsMasking(data_field_flags):
# data_in, field, flags = data_field_flags
-# data_masked, mask = _maskData(data_in, flags, columns=[field], to_mask=flags.BAD)
+# data_masked, mask = _maskData(data_in, flags, columns=[field], dfilter=flags.BAD)
# func, kwargs = func_kwargs
# data_masked, _ = func(data_masked, field, flags, **kwargs)
-# data_out = _unmaskData(data_in, mask, data_masked, flags, to_mask=flags.BAD)
+# data_out = _unmaskData(data_in, mask, data_masked, flags, dfilter=flags.BAD)
# flags_in = flags.isFlagged(flag=flags.BAD)
# assert data_in.aloc[flags_in].equals(data_out.aloc[flags_in])
@@ -176,7 +143,7 @@ def test_unmaskingInvertsMasking(data_field_flags):
# flagged_in = flags.isFlagged(flag=flags.BAD, comparator=">=")
# # mask and call
-# data_left, _ = _maskData(data, flags, columns=[field], to_mask=flags.BAD)
+# data_left, _ = _maskData(data, flags, columns=[field], dfilter=flags.BAD)
# data_left, _ = func(data_left, field, flags, **kwargs)
# # remove and call
diff --git a/tests/integration/test_integration.py b/tests/integration/test_integration.py
index 634e2352167cf0ca24df77d5ccf5d687343fab37..7e47dd1544134847f67844420b1da00a632d4ad6 100644
--- a/tests/integration/test_integration.py
+++ b/tests/integration/test_integration.py
@@ -1,23 +1,22 @@
#!/usr/bin/env python
from click.testing import CliRunner
-import os
+from pathlib import Path
-def test__main__py():
+def test__main__py(tmp_path):
import saqc.__main__
# if not run from project root
- projpath = os.path.dirname(saqc.__file__) + "/../"
+ projpath = Path(saqc.__file__).parents[1]
args = [
"--config",
- projpath + "ressources/data/config_ci.csv",
+ Path(projpath, "ressources/data/config.csv"),
"--data",
- projpath + "ressources/data/data.csv",
+ Path(projpath, "ressources/data/data.csv"),
"--outfile",
- "/tmp/test.csv", # the filesystem temp dir
+ Path(tmp_path, "test.csv"), # the filesystem temp dir
]
runner = CliRunner()
-
for scheme in ["float", "positional", "dmp", "simple"]:
result = runner.invoke(saqc.__main__.main, args + ["--scheme", scheme])
assert result.exit_code == 0, result.output
diff --git a/tests/lib/test_rolling.py b/tests/lib/test_rolling.py
index ef94c987210dddeeee32f82ab859dc093ac089d0..c78fb61d03149215691852cb49504a5349227e3c 100644
--- a/tests/lib/test_rolling.py
+++ b/tests/lib/test_rolling.py
@@ -9,7 +9,7 @@ n = np.nan
def test_rolling_existence_of_attrs():
r = pd.DataFrame().rolling(0).validate()
- c = customRoller(pd.DataFrame(), 0)
+ c = customRoller(pd.DataFrame(), 0, min_periods=0)
expected = [attr for attr in dir(r) if not attr.startswith("_")]
result = [attr for attr in dir(c) if not attr.startswith("_")]
diff = [attr for attr in expected if attr not in result]